OGGM_flowchart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook can be used to make flow chart of OGGM. I think there is better software out there to do this. \n",
    "Eitherway feel free to use/adjust this notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from schemdraw import flow\n",
    "import schemdraw\n",
    "from schemdraw import dsp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "   </g>\n",
       "   <g id=\"text_10\">\n",
       "    <!-- Baseline  -->\n",
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      ],
      "text/plain": [
       "<schemdraw.backends.mpl.Figure object at 0x7ff4040db730>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Both the schemdraw.Drawing() and the d.add(flow.Start()) commends are needed to start the flow chart.\n",
    "# From there on you can start drawing line and arrows and add boxes. The .draw() is needed to draw the flow chart.\n",
    "d = schemdraw.Drawing(fontsize=11, color='black')\n",
    "d.add(flow.Start(w=2, h=1.5, label='RGI \\n & \\nDEM'))\n",
    "d.add(flow.Arrow('down', l=d.unit/2))\n",
    "\n",
    "topo_pre = d.add(flow.Box(w=5, h=1.5,  label=\"topographical data \\n preprocessing\"))\n",
    "d.add(flow.Arrow('down', l=d.unit/2))\n",
    "flow_pre = d.add(flow.Box(w=5, h=1.5, label=\"computation of \\n the flow lines\"))\n",
    "d.add(flow.Arrow(l=d.unit/2))\n",
    "width_pre = d.add(flow.Box(w=5, h=1.5, label=\"geometrical glacier \\n width determination\"))\n",
    "d.add(flow.Arrow(l=d.unit/2))\n",
    "wadjust_pre = d.add(flow.Box(w=5, h=1.5, label='width correction according \\n to catchment areas and \\n altitude–area distribution'))\n",
    "d.add(flow.Arrow('right', xy=wadjust_pre.E, l=d.unit/2))\n",
    "inversion = d.add(flow.Box(w=5, h=1.5, label='ice thinkness inversion', anchor='W'))\n",
    "d.add(flow.Line('up', xy=inversion.N, l=d.unit/2))\n",
    "\n",
    "MBcal = d.add(flow.Box(w=5, h=1.5, label='mass balance \\n calibration', anchor='S'))\n",
    "d.add(flow.Arrow(l=d.unit/2))\n",
    "d.add(flow.Line('up', xy=MBcal.N, l=d.unit/2))\n",
    "clim_pre = d.add(flow.Box(w=5, h=1.5, label='Climate preprocessing', anchor='S'))\n",
    "d.add(flow.Arrow(l=d.unit/2))\n",
    "\n",
    "d.add(flow.Arrow('right', xy=inversion.E, l=d.unit/2))\n",
    "d7 = d.add(flow.Box(w=5, h=1.5, label='Glacier simulation', anchor='W'))\n",
    "\n",
    "\n",
    "d.add(flow.Line('right', xy=topo_pre.E, l=d.unit+1))\n",
    "d.add(flow.Arrow('down', l=d.unit/2 + 0.75))\n",
    "\n",
    "\n",
    "d.add(flow.Line('right', xy=clim_pre.E, l=d.unit/2))\n",
    "\n",
    "d8 = d.add(flow.Start(w=3, h=2, label='Baseline \\nclimate', anchor='W'))\n",
    "d.add(flow.Arrow('left', l=d.unit/2, xy=d8.W))\n",
    "\n",
    "d.draw()\n",
    "# d.save('your_path/filename');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K++RSR2s0X2Nmc6eVSraJiPHAmaS4/5ZLZq1njicl2v8FDnCibQW/IiXa75GqSDjR7kBEvB8RPya1MQ6pidBvlBmTmZk1n5Yq2QaQNB+p7e0tSB3ebBkRb5UbVWuRtAOp6+qBwE5l1je15iLpSFIvkDOAYRFxc8khtYTcLn+lic+vRsS5vXhslzpaQ/kaM5s7LVWyDRAR75La3H6a1IX7n3KVCOsGSduSeqYcCPzcibZV5Da0f50nD3Wi3X0RcQ5wVJ48K7fgYmZm1nrJNkBETCX1ePg68HngCknzlxpUC5D0GVInJPORSuGGlxuRNQtJqwF/IfWaempEXFJySC0nIn5Dqus+APizpMVKDsnMzJpAy1UjKZK0Bak6xGKkHt92jYhXy42qOeVSy6uBhUg9AB7metoGIEnAKGAr0jWyp6+NnpE0CLgX2BC4HNg7GvxH1j/xW6P5GjObOy1Zsl0REXeR6m4/l1/vlvThcqNqLpLmkXQicAMp0b4E+IqTKSvYi5Rov0Kqb+xro4ciYgbwZeAt0vv6pXIjMjOzsrV0sg2pO2VgM+ARYC1gnKTdy42qOeSfsa8BKj1Wngwc6Pa0rSJfI7/Mk9+JiFfKjKcviIgngGPy5I8ktUpPvWZm1gAtn2wDRMR/SCXbNwFLAFdKukTS4FIDK5GkocBDwC7Aa6SWJX7oUktr5zukjqJGkzqOsvo4H3gSWAXYu+RYzMysRH0i2QaIiP8BOwBHAu8A+wKTJO2U66T2C5KWk/Qn4DbSP/oJwIYRMbLcyKzZ5PrFlTaij6vXFzFJz0h6TNL4/PoHSfPWY9/tjrOSpKmF6fGSFqj3cXoiImbS9ovS91y6bWbWf/WZZBsgkrOAIcD9wIrA9cAoSZuWGVujSRoo6ZvAY8A+wHTgB6SeNp8uNThrVrsASwGTSCXb9bR7RAwBPpGH3eq8/zlExJCIeKfRx6nBpcBTwKrAriXHYmZmJelTyXZFRDwObA4cR6pCsRVwn6S/SlqzzNjqTdKCkr4OPEHq+W9RUgsta0XEjyJiepnxWVM7LL+e18AWM+bPw6sAkraRNFrSOEmTJO1VWVHSSYUS8XGVamCSNpV0m6Qxedi5owNJCkkL5/FnJJ2Sj/VMvkcq660u6QZJD0qaIOmgRpx4Lt2udG7zuUYcw8zMml9LN/3XHZIWJ3VN/i1gAeB94DpSO9M3terDgpKWAI4gdaSxZJ79BHBsRPy9tMCsJUhaCniJ1FPk8rkaVr32/Qzpl5XpwMqk++yLedniwBsRMUvSMsAYYB1ApI6qlouIdyQtQqoOtjCpStROEfGCpOWAB4G1gcHAQxGxZN53AItExLQcw1URcZyklYDJpLrp00m/eu0TEY/l4zxEajb0sXq9B4X34hP52P/N51b3ZybcLJs1mq8xs7nTJ0u2iyLi1Yg4gfRP//fALNLP5yOBpySdIGnZMmPsLknzSto518n+N3AKKdF+CNgDWNOJtnXThqQE94F6JtoFlWokSwHzS/pWnr8UcJWkycCNwIeA1UkdVD0JXCzpMGDhXDL8KeBjwA2SxpOasAzS8whduRwgIp4hlayvCKwGrAlcnvd3F6mTp0b94vUI8C9gadJ7bmZm/UyfT7YrIuKFiDgC+DDwXeCfwEeBU4H/SBol6Zjck17TyHWxh0o6B3iRVCq/D7AgqfWVbYBNIuKqVi2lt1Ksn1/HNfIguRrTdcB2edbvgduBdXIy/iwwf752Pwn8lpQUj5G0LukLwcRcH7syfDgiHurG4YtVqGYBA/P+prbb30oRcc3cn+2ccvWcG/Lk1j3dj6TF8gOtvSpXwVk7j5+XOxKr174HS/p2vfbX10k6XNLRZcdhZrXrN8l2RUS8FBGnkUrGdiC1Qy3SP8JfAFMkPS7pDEnD8s/WvSaXXm8mabikkcD/SD+jf4VUCvgI8D1glYjYPiJubXQPddYnbZBfxzbyIJLmAT4DPJ5nDQaeiYiQtB25hDpX51gqIu6IiJNIVS/WJvXGuKqkrQr73HguWhiaArwtab/C/taQtGgP99fdY0L6ot9TQ4HnJZ0paYMyWliKiENzR2L1MhhoeLItaUCd9lNqizIRcXZEnFFmDGbWM/0u2a6IiPcj4saI2I30E+8+wJ9JPzevSqrj/XfSP7gXJF0n6YeSPi9pXUlLzM0/PEkDJH1c0o6SviXp95JuIz3QeS/wU2BHYBHSz+unAesBa0fEqRHxVI9P3qyt2sSkBu3/qlxNYzLp78wpef5wYEReticwMc9fDPibpIm5ismLwF8j4lVSta+T8sOMj5I6Z+rRvZerpnwO2Csf62HgLKCRpcYv5Ne5ra62BPANUj33CfmXuGXmcp8A5C/4d+f3eIKkz3awzu2ShuXxCyWdI+lWSf/KhRNbS7orl4Z/s7DdiMLDqKMkfTQv+h0wWOmB2HvzustJukrSA0oP0J5QJd4DJd0s6VpJj+Q4Vigsu0XSNflaWkdVHrJVbj5S0i/y9TCpUnpfWDZC0ljgUEmr5HOYKGmspB26eg9V5YFcpYfbr8zxT5D0l8L6o/O8yZKOy/NPljSicI43SbpC0sOS7lGuDilpkKRzlQqN7pb0W0lX9fTaMLM6iAgPhYH0U/PmwE9IJcqvk+qIdjS8Q0qEbyfVD70IOI/0z/ufpAe+fgH8EbgauJVUkvhP0k/c1fb7KHAOqTOMFct+Tzz0vYHURGQAa5QdS18fSCX71e71ug1zEd+HSF9uPpWnBwCL5/FnSF/wyX/nhuXxC4G7SfXdFyQ9AHo+6YvVCsA0Ur17gCULxzoUuDyPr0Sq0lOM5WZgyzw+iFSnfrsOYj4w//1dPU+fRHogtrJsGrBynh5Mqi61XJ5ejlR9aXCOIYD987Khedl8hWVfKhz3fuCQPL4WMJX0HEKH7yHp/8mYyn1GKjyZAqwBfAG4sbDvynv+a+C7Hcw/GRhROMdXgQ/n6T8Ap+bxbwD/yMeeH7iv8t7MxTXS8OvXQ6nDf8v+O9nXB3e00E6kkq+781D5GfzjpJ/dNwTWJf0cvCKpNG7lPFRzTCfLnif94S0OYyPipbk7CzNrIs3+LMVmwCMRcS9ApPrzr3Zju79FxLsAkqYAIyO1tvKcpFdJfyMfA3aUdCSpZZmq/3MkLURKdpcq/Gi4COlXmJs72OTuiKhU0TmP2X+luTvafv0rPmRbWR6kKkxTSS3y/Cmf++2S3iE9tPsGqVCkUuK8CKkPhwvyuo/kX2g+SWrlao73UNJatD2QWzl25YHcCcCakn5H+iJzfV5+J/BzSQuSCnxuq/KW3ROp92RICXXluYitgEvy/7KZki4j9bBsVs1SZQfQ1znZ7kL+5/FkHv5SXKbUpu8KpH8qS5NKYgYB85J+IoVUJ/G1PLxaeP1vRLzZ6PjNrHSVOsNvR8RC3d1I0hDgEOCrpL8pHXmvk2WN1v4B1DkeSM1VRs4ANo6If0r6FKm6XkfmISXBG0fEe3MZ27TCeOUh2y3br6TULGRn3opctNtDHzyQ2+HC1DTkNqQqgz+RtE5EXC1pNPBZUrWrg0k9IrfX0QPADRVu+q/PUW7W0Rqr39bZroeImBYRUyJiVERcFhEXRcQfIvViWVnn9Dzvyoi4JSIeioinnGhbyWbm1wVLjYIP6p9elcc3knRpnff/eUmb1HOfNao8ZH1Dp2u1ExHjI+IbETEI+Hy7xWNI1QXq8QD3aGAtSZvBB8+TLF6H/ULqZGsG8GL+lfDwwrI3gAWVHzzMfxPvIiWY5Fg+rOpNs35a0qp5/CBSNb2OdPWQ7SBSlT1yfe0FSCXys8nxjQcOyOuuSXqO5j6qv4dVH8iVtCIwKyL+BhxNro4iaRXgxYi4EPghUOu1ezuwj1JLVvMDX6pxezOrMyfbZv3TI/l13VKjaCd/Gd2nzrv9PLUnLPVUSRZf6HStrr1EegZk3YjYKCJ+GxGvzOU+idTO+m7ALyVNJCXydWkTPCImAVeSrrf7Sc+rFI97KTBJ+QFJ0oPqa+UHFScBV5DqVnfkHtLDto+QWpP6ZpUYXqXzh2xfAYbkcz8L+HJEzKhyzH2AffO6lwL7RcTL1d7D6PyB3HWA0ZImAA8AP42I50kPDk+SNA74TbXz6sTZpGvtEdIXkEdJzx6ZWUn6fA+SZZF73LImJmk4qcWb30TEUb10zEGkB493IP3s/XREfEHSgaQH73aXNJT0ENhG+Sf+h0gPf+1AKnHch1Q6uinpAbldI+JFSeuQkpiFSA+FnRsRv5K0PXAZ8Dapfu4vI+JiSQeQemAdSEpEvlao/1vv8z6bVBVkeET8rIf7WBR4p6PqFf3xb03xmpnL/axEoRfSvkLSIhHxpqT5gGuBKyPivLnYX7+7xvoLf7a9w3W2zfqncfl1/U7Xqq/vkh82jogZkrqT4CxBetjtu5KOB0YBQyPiMElnAV8ntTv/DLBtRLybn6V4QNKNEXGjpGtJCdVv4YOqAnuSWr14V9KOpJY0Pl3n8yVXVdgpT47q6X4i4o36RGT9xC050Z4fuIXUeoyZlcTJtln/NIb0MNomkpaoR3WEbhgGHFv5iT4ipnZjm2kRUWmlYSzwbESMz9NjaGuBYUHg95LWI7UMsTypPu2jHezzc3nZ/bnarkjNtDXCJ0itF71EgzsQ6k9yfeYL67CfZ4A+VaoNEBGblh2DmbVxnW2zfignujeS6o7u18XqZXq3MN5hixd5/Cekdo7Xj4j1SHVg56+yTwHnR1t37etFxEfqHHfFzvn1htyykZmZ9TNOts36rz/k10MLLTM00nXAt3LdbbpZjaS7BgP/iYiZktZm9naF3yC1iV/xd2D/3BpEpeWIujwQWCRpXlJdbYD/q/f+zcysNTjZNuu/riP1/PcJGlBfuQOnkepWj8+dgZxdx33/GDgstwRxMqljkIpLgL2VugXfPyLuBE4Ers0tQUwGdq1jLBX7kTpTmUJK8M3MrB9yayQN4id8rRVI+jEp8XwA2MxVHeojl2o/RnogdN+IqGvb4e2O5b811lC+xvouf7a9wyXbZv3bz4DnSe1QH1pyLH3JoaRE+3Hg8pJjMTOzEjnZNuvHcq94R+fJ0yQtVWY8fYGkNYARefJ7ETGrzHjMzKxcTrYbQNKQsmMwq8GVpLZ4Fwf+kLvVth7IbRtfRmqK8E8RcWXJIZmZWcn8T7Uxyuwa2qwmkR7c+BqpJ8VdSc3oWc+cBgwBngaOLDcUMzNrBk62G8M/xVtLiYgngd1JbVd/R9JBJYfUciQdA3wLmAl82b0+mpkZuDWSupL0W1LPdbvS9kXmmvx6ZkTcXkZcZt0l6SvAOaSEcZeIuKHkkFqCpK8Dv8mTh0TE+b14bLcmYA3la6zv8mfbO1yyXV9vAV9g9vf1C6RuqieXEpFZDSLiXOCXpJ4Zr5W0f8khNT1JX6Ut0f5abybaZmbW/Jxs19dFVeb/PXePbdYKjic1CTgQuEjSd3uph8mWknuePIW2znm+GRH17KjHzMz6ACfbdRQRj5A6B2nvwl4OxazHIuL9iBgOHAUE6YHJsyTNX25kzUPSh0g9cH4feB/4VkScWW5UZmbWjJxs19+F7ab/C/yjhDjM5kpE/Ab4EjADOBwYI2mDcqMqn6T1gTHADsArwPYR8etyozIzs2blZLv+riAlJxWXRsR7ZQVjNjdyO9GbA1OAtYD7JX1P0sByI+t9khaS9FPgfmAl4CFgg4i4pdTAzMysqXWrNRJJ1wM7NT4c6+dGRsTOZQdhc5K0IPBTUtUSSCW7x/WHFnZyffXPA78CPkKqWvN74NiImF5eZEmlNQGzRnOLFX2PWyPpHd1Ntv3H3HqFb/jmJmkb4ALgw3nWjcB3I2JceVE1Tq4yciqwY541jtTiyP3lRTU7F4ZYL3FhSB/kZLt31JRs+8OwRvE11jokLQwcTWq1ZJE8+3Lg1Iho+SYuc0n21sB3gO3y7NeBE4GzI2JWWbHZ3PPfGrM2vh96h5Ntawq+xlqPpCWB75K6JZ8vz76H1CnOlc1QxaIWubWVXUlfIjbMs6cB5wI/j4iXyorN6sd/a8za+H7oHU62rSn4Gmtdkj4MDAf2o62k+3+kduf/BIyLJu2qNj/ouTXwZWA3YNG86GXg18BZEfFqSeFZA/hvjVkb3w+9w8m2NQVfY60vVy/5MvBV2kqGAV4Ars/DLRExrYTwPpDbyN4C+CywO7B0YfE44A/AhRHxTgnhWYP5b41ZG98PvcPJtjUFX2N9i6SNgIOBXYAVCotmkKqaPASMJbVq8lREvN+gOJSPvynwmTysAxSvsynAZcDlETGlEXFY8/DfGrM2vh96h5Ntawq+xvqmnOyuCwzLw6bMnugCvEEqUX4CeBZ4rvD6PPA2MKP9g4mS5iFV+1gcGJxflwFWLwyrAQu1O967pLay7wCuAcY3azUXqz//rTFr4/uhdzjZtqbga6x/kLQUsBmwAamqyYbAct3cPEgl4++RukhfhDkT9468DEwE7iQl2Pe32sObVj/+W2PWxvdD73CybU3B11j/JWk5YH3go6QqHyvmYQVgeVJLJ4PoOLF+A3gVeC2/TiWVkE+pDH7A0Yr8t8asje+H3uFk25qCrzHriqQBwLykxHse4E23eW218t8asza+H3rHwLIDMDPrjpxYzwJcBcTMzFrGPGUHYGZmZmbWVznZNjMzMzNrECfbZmZmZmYN0jTJtqSTJQ0qO465Jel2ScOqLDtP0haN2He79VaSNLUb6w2RtGdP4zEzMzOzzjVNsg2cRGploHSSGvLgaEQcGhF3NWLfPTQEcLJtZmZm1iBNkWxL+l0evVfSeEmDJS0j6RpJEyVNkrR/Yf1nJJ0maYykJyV9vbBsY0mj83ajJW1cWPZ1SU9IelDSDyulv5WSYEkjJI0FDpW0Td5+XD7+XoX93C7pV5IeyMf/SbtT+oykuyU9Lem0dtsNy+OLSTo/73uCpN/m+VWP28V7eGSOZSxwSGH+QEk3SnpI0sOSLpA0SNISwCnAtvk9PzOvf2led1J+/xfvzvHNzMzMbE5N0fRfRBwp6QjgUxExDUDSFcDkiPhC7vRijKSxETE5b7Z0RGwoaRlgnKQ7gceAq4GDImKUpG2BqyWtAqwBfBcYEhEvS/p1uzCWAB6MiOPy8RcHNo+IWfkYYyTdWOggYy3gU8D8wGhJ90bEdXnZR4AtST3cPSXpjxHxRLvj/QqYBqwXEe9LWjLPH9vFcecgaV3gRGD9iHhJ0lmFxbOAvSPildx19kXAwRFxtqQfAMMiYvfC+t+MiMqXkB8D3wGGVzu2mZmZmVXXFCXbVWwLnAMQES8AI4GtCsv/mJe9BFwPDAVWB2ZExKi87BZS986r5+UjI+LlvP357Y43HfhLYXop4CpJk4EbgQ/l/VRcFBEz85eDy4GtC8uujIj3I+J14FFg5Q7ObxhwekS8n2Ot1LHu6rgdGQpcn98LgHMLy+YBjpM0ntRl9dak6iPV7J9/MZgE7N3FumZmZmbWiWZOtnvbWzF7d5q/B24H1omIIcCzpFLs7ih2ujGL2n5BmJvjdmRvYHNgi4hYBzir2v7yw5tfA3bI635vLo9tZmZm1q81U7L9JrBYYfoW4DAAScsCOwG3FpYfmJctlZfdBkwBBknaKi/bmtS98xTgDmDHQnWNA7qIZzDwTESEpO2AVdot3zfXh16I9JDhre130IXrgONz1Q4KcXV13I7cDuwkaek8fUhh2WBgakS8KWkxUvJd8Qazv+eDgdeBVyTNBxxcywmZmZmZ2eyaKdn+BXBr5QFJ4ChgPUkTgZuB4RHxcGH9qZLGAKOBn0bEpIiYAXwR+Ene7lRg94iYERETgJ+T6lePAWaSEstqhgMjcvWLPUlVMIoeA+4FJpCqcFxHbY4m1emeLGkC8INuHncOETER+AlwTz631wqLLwYWkfQY8Heg2BrKKGCh/IDmmcA/gKeAx0lfTsbWeE5mZmZmVqDZa05UWUkKgIhQwyPqBknPkB7sm9zVuu22WyQi3szjJwOrRMS+PTj+7cCIHiTYVkXlGrM5jIyIncsOwqyvaLb/Z2Zl8v3QO5qiNZJedJqkT5Pa834a+ErJ8Zh1ZaeyAzAzM7Oea8mSbet7GnmNter126pxmzUz31dmbXw/9I5mqrNtNZJ0iqQvlR2HmZmZmXWs35VsS5oHiOjOiXe9rwERMasOYfV7LtmeU6vGbdbMfF+ZtfH90Dv6TMm2pJMl/UXSrZIek3R1buqusuxKSTcBjwCDJe0k6Z7cgctoSZ/M6w7NrXNcnLs3f0DSWoVlE3OX5+NJTQl21j38sNz1+QSl7tfXzfM3lXRbPvYYSTvn+UtLukWpq/RJks7I8z8laWxuqeVhSV/O8y9U7qo+n+Nlkkbm879e0oJ52WL5/XhM0qh8biN655MxMzMz67/62gOSW5C6Y39J0vnA94Hj8rJNgQ0iYqqklfOy7SPiDUmfAG4gdbMOsC5wVETsL+kAUvN5G+VlnwC+GhGjJQ0CnqTj7uFXAs4jdSbzRG63elBu1vBsYKeIeEGpK/oHJa0N7AM8FRHbwgddxkPqMv30iLgst8tdbBu7aCNgY1KThjfm/f2B1KzgqxGxhqQPAWNI3dqbmZmZWQP1tWT7ukKX5X8EflNYNrLQJfr2pC7U78x9ygAMlLRMHn8yIu7I45cA50paNE8/ERGj8/gc3cNLqnQPv2U+5hN52bvAu5J2Aj4G3FA4dpA6r7kPOFrS6aR2rm/My28Dvpe/JNwcEfdXOf8bI+I1AEn309ZN/FbAN3Ic/5P0tyrbm5mZmVkd9bVkuzPTCuMC/hER+7dfSdKaNeynJwRMjIgtO1worQ9sB+xH6uBm84j4laS/A9sCv5F0U0R8r4PN23cTv8BcxmpmZmZmc6HP1NnOdlbqvh3gIKp3oX4TsEOuPgJAsa41sLKkLfL43sCkiHijg/101j38TaQu1FfNy+aTtAip18lVK9tUjq3kY8AbEXE5cAywoaR5JK0WEU9FxDnAr4FNuv+WAKk79/3zsQYDu9a4vZmZmZn1QF8r2b4LuFzSCqQHIY/taKVch3pf4I+SFiB1cnMP8GBeZRJwqKTfA2+TE9UO9jND0heBMyUtBLxF7h4eeELSYcAVkgaQSpoPiIhJknYBTpf0K9o62PkcMBQ4RtIs0hehwyPifUlH5eR8BvAuuUpIDU4BLlDqsv0F4CE676rezMzMzOqgzzT9p9T9+sIRcVxX63axn6Gkrtg36mLVliFpXmBAREzPdc/vBo6JiFtKDu0DjbzGWuH67Uirxm3WzHxfmbXx/dA7+lrJtnVscdIDmQOA+YE/N1OibWZmZtZX9ZmSbWttLtmeU6vGbdbMfF+ZtfH90Dv62gOSZmZmXZL02bJjMLP+wdVIzKzpFDpvWo5U9WkQqaWfyusA0kO+rwGv5td3ojs/1ZklW5NajTIzaygn22ZWipxQfwTYAFif1OvqCsCKeViwxl3OkDQVeILU/GZx+GdEzKpP5NZHLF12AGbWP7jOtjUF19meU6vGXY2kBUm9mW5OSrA3BJboZJO3gOdJzW/OAN4rvAawKDCY9ADw4qRS72reJDXveUcexuQmOq0fkLQZcEae3LSw6H5gKvA5/ypi/VFf+z/TrJxsW1Nwsj2nVo27SNJHgJ3zsA2pSkjRK8AYYCzwOPAc8Gx+faOWBCi3mb8ssBqwemFYg1RiXvQ2MBq4BrgyIv5b04lZS5E0H+mL24c6WHxmRHyzl0Myawp94f9MK3CybU3ByfacWjjuwcB+wMHAkHaLx5DqyT5ISrD/3Rslirmjqy2Bz+RhjcLiWcAo4DLgmohwh099kKTfAF/vYNEGETGut+Mxawat+n+m1TjZtqbgZHtOrRR3rn+9CfBVYC9ggbxoGnAzcB1wQ0S8UE6Es5O0DLAdKdbtaXt+5V3gSuDnETGppPCsASRtSOo9t2giMMRVSKy/aqX/M63MybY1BSfbc2qFuHOS/TngZNJDjhWjgHOAayPi3RJC6zZJSwBfBL5MKvWuvN8jgdOAu52Mtb58rU4E1i7MPiYizqiyiVmf1wr/Z/oCJ9vWFJxsz6nZ45a0JSkZ3SzPegW4ADg3Ip4oLbC5IOljwDHAIbSVzo8GToiI28uKy+pD0rHAiDw5E1jB9fWtP2v2/zN9RU3JtlmjOdlu06xxS1oH+BmwY571MvBjUpI9vbTA6kjSUqT6vV+n7aG6S4HjIuLF0gLrgKTrgZ3KjsP6vJERsXPZQVh9Nev/mb6mu8m2/5hbb2jIH/NW/WPSbHFLGgh8m1RlZF5Sc3ojgDMi4s0SQ2sYSQuTSrq/S2pJ5Q3g+8BZETGzzNgqXBhivaVZ/hZZ/TTb/5m+qlvJttXOF3DzaNXPopnilrQqcDHwyTzr98BJEfFyeVH1HkkfB84kNWEIqTWVPSLiX+VFlTTTdWJ9k6+xvsufbe+Yp+wAzKy5SfoKMJ6UaD8HfDYijugviTZARDxNehB0V+DfwMbAGEnblRqYmZk1PSfbZtYhSfNIOp3UqsiCpDrL60TEzeVGVo5IriW1unIDqffLGyWdIMl/S83MrEP+B2Fmc5A0CLgEOI7UasOBEbFvRLxabmTli4j/AcOAU0jNBJ4K/CnXaTczM5uN62w3iOtBNY9W/SzKilvSIqRuzLchdUrzxYi4qTdjaBWShgF/BhYhfTk5KCJm9XIMLXl9W+vwNdZ3+bPtHS7ZNrMP5NLZq0mJ9n+BzzjRri4iriM1gfgWqYv6c12lxMzMivxPwcyAD3rYO5PUjfnLwKcjYmy5UTW/iLiH1ErJO8DBpPfQzMwMcLJtZm2+AXwNeBf4fEQ8WXI8LSMi7iDV454OHClp75JDMjOzJuFk28yQNBQ4I08eHBH3lhdNa4qIW4Gj8uTZuW1uMzPr55xsm/VzueWRs0l/D34SEX8uOaRWdh6pzvsiwJ8lzVtyPGZmVjIn22Z2LLA68DipOTvroUjNOx0G/AfYFDi63IjMzKxsbvqvQdycTvNo1c+iN+KW9FHgUWABYLuIuKVRx+pPJG0P/AN4BVgpIqY18FgteX1b6/A11nf5s+0dLtk2699OIiXaV7RPtCU9I+kFSQMK8w6UFJK+3sigJN2e27CudbuQtHAjYqrRTcC9pF4mjyw5ln5J0smSRvRgux5de2Zm1TjZNuunJC0G7JUnv19lteeB7QvTBwJuDrALuTrJyXny+Cb5AmBmZiVwsm3Wf32ZVKp9W0Q8UWWdC0kJNrl1jYWASZWFkraRNFrSOEmTJO1VWHaSpMckjc/LB0taUNKVkh6RNEHSXzqJb1tJD0h6UtJPCvs9VtKDeZ+jJQ3paGNJI/J6EySNylVmkLSSpKmSTs37mCJp88J2wyQ9lLcbJ2ndPH9TSbdJGpOHnTt7c4FbgNGk0u29uli3z8m/MpyYP4On87Xy0/yeTpa0Zl5v2cL7+rCknxf2sWu+rsbnbYbm+XNcW1XC+IikW/O6V+cvmJ1et+3OYW9J9+f1xknaprDsGUmn5P08U/y1R9Kakm6SNDHv/4A8fzlJV+XrepKkE+buXTazlhARHhowAEEu4PLgz6IZ4wbG5GPsXWX5M8A6pDrdiwM/JLXFfSHw9bzO4sCAPL4M8Gye9yHgNWCBvGwRYCDwBeDGwjEWr3Ls20lVMQYCC5MS/GF52VKF9bYF7mv3ni2cx5cszD8UuDyPr5TXq+xvH+CePL4a8CKwap6eL8c+GBgHLJfnL5fPdXAX7/FX87GuadXrZC7jOjKP7wFMK7zn3wb+lMfnL3xm8wK3Ajvk6QnAZnl8ALBotWurg+OfDLwALJOnzwdGdHbdFq69SpxL0PZs0+rAs+3uj8r+Vsrnt3C+Zh8H9iisu0R+vRnYMo8PAu4iPStR+ufViteYB3+2rTIMxMz6nVzKuwHwOvDXTlYN4C+kktm9gE8BGxaWLwWcL2lVYCYpEVodeBB4ErhY0k3AdRHxpqQJwJqSfkdKaq7v5NgXRcRMYJqky4GtgeuADXOJ4IeA90kJckd2lHQkbQlQ0bRIXa0D3Af8Io9vB4yMXNIfEe8C70raCfgYcIP0wXNEAawCPNTJOYys7FfSfHl//ckV+XUs6R965T0fA+yWxwcAp0v6FCBgWWAI6QHTW4EzJF0N3BARk/MzBHNcW1WOf11EvJTH/wj8Jo9Xu27va7f9ysBlklYA3gOWlbRsRLyYl19OOrFnJL0KrJjPZ2BEXFnZSUS8ImkhYCiwVOEaWgRYk5SEm1kf5WTbrH+qJMz3RcT0Lta9CLgfuDMnDcVlvweuBXaLiJD0ODB/RMyS9Eng06QkeYykHSJioqRPANsAOwI/kbRON2IAPmgT/CpS6eBYScsDz3Ww3kdJnfRsHBH/zIlcsf3wYtI7i67/FgqYGBFbdifOioj4j6SJwLrAFqSqJf1J5XOdRfX3/BhSSfOmETFd0rmk0m4i4mhJ65CuoSsl/TIi/lDt2qohrg6v2w7Wuww4NiL+Jmke4O126xWv28o5VWvia568bOOIeK+GWM2sxbnOtln/tH5+HdfVihHxNHAi8KMOFg8GnskJy3akkl4kLUKq7nFHRJwETAbWlrQiMCsi/kZqg3opUqliR/aVNDCXCO5JKuWcn5TQ/Cevc0SVbRcFZgAv5iTp8K7OM7sJ2CmXeCJpvnwu9wKrStqqsqKkjdXum0cVlVLLT3czhjnkeuar93T7JjcYeCEn2isAu1YWSFo9IiZFxK+BPwEbV7u2qux7Z0lL5fGDSNdQ5ZhzXLdVYvtnHj+YVK2oK1OAmZL2KJzHErn0/S5geGH+hyUt2419mlkLc7Jt1j9tkF+7TLYBIuLciBjfwaLhwAhJ40kJcaV0cTHgb/kBscmketB/JdUBH52rkzwA/DQinq9y2MdISe4E4PqIuC4i3gB+ADwoaQzwVpV4JwFXAo+QSuX/2dF6HWz3BKlTmityjKNJ7WS/CuwCnKT04OSjpDrB3Um2K8devjsxVLEe8JikeyUdVnnQr484E/h0vk7+CIwqLDstPxg5nlTF52dUv7Y6chdwuaTHSF/qKl8Yq1237X0rH2ss8HFSu+mdylWfdgUOzw9BTgB2yov3AdbK8yeRqtkM7mqfZtba3KlNg8gNxTeNVv0sGhl3ThbXANbNiak1iKTdSF24XxsRu3a1fpV97Ar8rTBrOinBvJBUGt9y17e1jlb9G2pd82fbO1xn26x/qvxhdd3Rxqs8TLdL5R9bHcwP7J0Hs15Rx+vXrF9xNRIzs8Ya0PUqZmbWV7lk28yssSoP1U2IiCHd3UjSh0mtbWwMbAps1MFqj1O96UOzunJVg77Hv1b0Dpdsm/VPb+fXai2BlEqp98GqXZzn1jm+0psxzYXl8uujtWwUEf+JiIsi4uvAjwuL3gDOJbV5vkZ9QmwOkk6WNKIb611Y7LFxLo+5kqSphenxkhbI489IqtbSSS3HGCrps4Xp5SXdNrf7NbPW4GTbrH+qtL4wpMwg5sJKQKsk25Wm3V6Yi328T3oQcm9g2Yj4akSMDj/hXncRMSQi3qllG0ld/Uo8FPgg2Y6I5yNiq+qrm1lf4mTbrH8am1836HStXiJpN0mP5VLF7xfmXyrpodxU2jWSFs+LfkdqQm28pKvyuiMkPZib5huVO7ZpBpU2zZ+ei31cFxHbR8RltSaCjZJ/fTgxv+dPS9pG0k8ljcvN9a1ZWPc7ed5kSRdUfrWQtJikq/Jnfzupx8bKNoMknS7pgfyZXtLZrx2F7TaTdHfeZkKlRLm710cHv6rsK2mMpCeLpem51Ps0SQ8A50haVtJted2HJf08r7cOqZ33/fP1OryD0vQd8vs2McdWaa9+aN7mnLxsQvF9NbPW4GTbrH+qtK9derItaRngD8CuuU5zsafBb0bERhGxDvAw8J08/0jgkVwKuXued1pEbBwR65F6/vtZ75xBdbnEc4c8eWNP99PEJdivRcTGpM/l/4B7ImJ94GJSR0hI2hHYj1TtZR3SA6OVL1Q/AN6IiDWA3YHPFPb9beD1iNgkf6bPA9/tLBhJHwKuAb6dt9kAeDAv7un1sXREbEjqlOgESesWli2a4zsEeA34XF53CLCRUs+Wk4CzgYvz9Xpau5iXBi4B9omIdUk9nV5aWOUTwNl52V+A73UzbjNrEn5A0qx/Gkdqq3l9SR+LiG51+tIgmwJjI2JKnj6XtkRof0n7AIOAhUgPBFazo6QjgYVpnr9tm5K6In8id5jT11yRX8eSvhNcl6fHALvl8W2By3OHRCh1x/7rvGwr4BukjadKKnZOswuwqKTKl6n5SB0cdWYz0pewe/M+ZwGv5mU9vT7+mPf1kqTrSVVCKtWwLi6sNwA4XdKnSE1rLktKuv/Rxf43JT08+0ievgA4S6mnTIApEVH5cnwf8LkaYjezJuCSbbN+KCKmAVflyUPKjKUT6wNfA3bIJdvfI7UvPYdcJeAM4MsRsTapa+0O1+1llcRoZKlRNM70/DqL2X+RmMXcf+ERcEQuDR4SEWtGxF492lHjro9phfFjSF+sNs2l0H+r0zGmF8br8b6aWS9zsm3Wf/0hvx7UjQe8Guk+Ugn7qnn60Pw6GHgdeEXSfKQEqeINUrfdFYsCM4AXJc1DqiNbKkmL0vYQ51WdrdvH3QJ8SdIikkT6fG/Oy24FDgKQtATwhcJ21wLHFFoGWaQb9ZVHk+ryb5a3GZDr+c/N9XFg3tdSpG7Xq7UiMhh4ISKmS1qB1GV7Rfvrteg+YD1JlZZlDgDGRcSbNcRoZk3MybZZ/3UXMAVYntmTnF4VEf8lJaV/lzSOttLA24GnSFVH7qDtoU5IP+NPyQ/cXZXrxV4JPALcD5RZLabiG6SSzjsj4u6ygylLRNwA/ImUCE/KsytNGf4IWFzSY6Qu7e8sbHoaqdrIg5ImAncDnSbbEfE/UvWVX+ZtxgAbzuX1MVXSmBz/T/O+OnIm8GlJk0lVT0YVll0DbFx5QLJdzC+T6rT/Oce8bx7MrI9Q8z5309qUG4p3JwDla9XPojfilnQEqWWPfwFrRcTbXWxi3ZBLtZ8hJdvbRMStDTxWS17f1jp8jfVd/mx7h0u2zfq3c4HxwEfJrUdYXXyPlGjfRfVqB2Zm1g+4ZLtB/G2xebTqZ9Fbcef6rfcC7wHrFFoFsR6QtA2pTnIAn46I+xp8vJa8vq11+Brru/zZ9g6XbDeApC3LjsGsuyJiNKmO6bzAFd3pOMQ6lh+iu4TUksYPG51om5lZ83MTQo2xUdkBWGNJWh1Yj0LTXhFxcfUtmt7xwJakc7pU0m65jWLrJkkDSG0kL0eqPnJquRGZmVkzcMl2YyxQdgDWOJKOAv5K6hVun/z65VKDmksR8SowjNQByC40Qe+LrSQn2ucDO5N6EtzXX1bMzAycbNeVpBsk/Yu2Zq2Q9K887FxiaFZfXwE2Af4dEdvn8ZZvEzciHge+CMwEjpX0ndwusnUit9t8DrA/8Bapy+5/lxuVmZk1Cyfb9XUb8JF28z5C6uzALRL0HdMj4i1gHkmKiMnAamUHVQ8RcRttHbGcBpyRk0nrQH5vfkvqhfMdYFh/blPbzMzm5H+i9fUn4P0O5l/h9ov7lLclzUvqcONnkr4BDCg5prqJiAtI1WJmAN8ELpfUDF2fNxVJg4H/I3Up/y6wS0TcXmZMZmbWfJxs11FEPA/c2MGiC3s5FGusI4BBwLHAh4DPkHqA6zMi4nJgB1I303sAt0j6aLlRNQ9J6wIP0VbPfVhE3FJuVGZm1oycbNffhe2mnyB182t9x9IR8VZE/DciDo2I3UlJd5+Sq5RsATwPfBqYJOnA/lyPW8n+wH3AysA4UnfgTrTNzKxDTrbr71pSawQVF4Z7DuprRnRzXsuLiInAEOAaYBFS03bXSFq6zLjKIGk10i9XF5FaHLqA1GnNP0sNzMzMmlq3epCUdD2wU+PDsX5uZETUvdWWevWQJWkV0oOQZwJHFRYtBpwUEWvMzf47OF7T9OyVS7P3JT0MuCjwP+AnwO8iYnqZsTWapAWBE0htkQ8iVRs5lib5Il25TswarRn+Fll9NdP/mb6su8m2/5hbr2jEDV/HZPsA4EBSp0UPFRa9AZwbEdfPzf47OF7T/RGU9BHgPGC7POtZ4GTgooiYWVZcjSBpPlJd/O8Blfrq5wPDI+Ll0gJrx4Uh1ksaUhhi5WrG/zN9UU3Jtj8Ma5RGXmP13rekAyPiwnrsq4vjNOV9l0u5dwB+SupxEuAxUlOBf4mId8qKrR4kLQocDnyL1BskpJZnjoiIe8uKy+qjWe8rszL4fugdTratKbRSsp33uRiwOrN3135nvfafj9HU911uY/pLpE6cPp5nv0aq03xORDxaUmg9IukTwAHAV0lVZQAmknrT/EtfK7nvr5r9vjLrTb4feoeTbWsKrZRsS9oT+AWwOPAcsAowISI2qMf+C8dpiftO0iBSt/WHk3rTrLiL1Pb89RHxXBmxdUXSx4C9SO2Kr1NYdDspyb6xGeplW/20yn1l1ht8P/QOJ9vWFFos2Z5AqrN8Y0SsL2k7YPeI+Go99l84Tsvdd5I2IJUM7wMsVFg0HrgOuB54MCJm9X50HzzsuBmpbfTPApsWFr8KXA2cFxH3lxCe9YJWvK/MGsX3Q+9wsm1NocWS7TERsaGkSRGxTp43tr+WbHck13veA/gc6YvJgoXFrwFj8zAmvz4ZER31vjo3MQwiVW9ZnZRUfwbYGJi3sNrbpF4gLyN9eZpRzxis+bTyfWVWb74feoeTbWsKLZZs30vq5OVq4DbgGeAXEbFaPfZfOE6fuO9yV++fIfW2uDPwsQ5WexN4ktS6yXOF1+dJCfEM4L3C6/ukJhcHk6rzVF6XISXXq5MS7QHtjvM+qZT9jjzcEhFv1eE0rUX0lfvKrB58P/QOJ9vWFFos2d6aVCK7NPB7UtL33Xr3ItgX77vcksnywAbAhoVhuc6266EgfRGaQnrQ8U7g7oh4vQHHshbRF+8rs57y/dA7nGxbU2ilZLu3tGrcPSFpGVJb1isAKxZelwPmI3UmM2/hdQDwOqlKyqv59TXgZeAJUoL9ZF/vcMdq15/uK7Ou+H7oHQPLDsCsVUj6eWfLI+LbvRVLXxMRLwEvlR2HmZlZvc1TdgBmLeStPCxLal963jzsSaorbGZmZjYbVyOxptBK1Ugk3QrsERGv5OklgCsjYut67L9wHN93ZnXm+8qsje+H3uGSbbPaLVtJtAHy+LIlxmNmZmZNysl2HUgaKumz3Vx3SO6BsDhvvKQFGhPdHMf/lKTJksZJ2qo3jtkHPSzpPEmb5eFc4JGygzIzM7Pm42S7PoaSeqPrjiGkOr4fiIghEfFOnWOqZj/goohYPyJu66Vj9jWHkFq++G0eXgcOLjMgMzMza04tV2db0mbA6cAiedbxEXGTpI2BM0ldRL8FHBURD0paCXgI+AOwA7AAqSvpw0m9yr0D7BoRL0o6MC97B1gFeBHYLyKek3QysHBEHJfjOBlYGLgIuJn0xeV54HJgBKlb6iXy8R4gdWG9CDAOWJTU/u+dEXFUfn8XiYhp3TiPc4CdSD3yHRIRd3fwHi0DnA2sDAg4PSIulnQ88F1SJyFTgc16McnvVCvV2e4trRq3WTPzfWXWxvdD72ipkm1JHwKuAb4dEeuROsZ4MHfLfDXwvYhYF/g+cHWeDynpvTsi1gf+CIwCfpfXHQN8vXCYzUkJ/FqkHuZ+3VlMETGJlNhenEuoTwNmAXtHxEbA2qQ2gQ/OdXt/QOq1bkhEHNXu/LpzHqPzeZwC/KxKWGcCk/M+PgucJmntiDgduBY4rZdL083MzMz6pZZKtoHNgEci4l6AiJgVEa+SumaeERGj8vxbSN06r563mxYR1+fxscCzETE+T48hlWJX3B0RU/L4eUBPWpiYBzhO0nhSz3Vbk6qPdKU753FdHr+PVHLdkW1JJeBExAvASMD1s83MzMx6Wasl2z31bmF8FjC93XR3OveZyezv1/ydrLs3qYR8i4hYBziri/W7q/15uFMiMzMzsybWasn2aGCtXG8bSQMkLU7qmnlQpXUNSVuTOhuZUnVP1X1a0qp5/CDg1jz+JLChpHkkLQIMK2zzBrBYYXowMDUi3pS0GCn5rrZuUb3O4xbgsLyPZUl1vG/tdAvrNknDc9vaZmZmZp1qqZLRiPifpN2AX0paCHgfOC4ibpH0ReDMPP8tYPeImCHVXOf/HmBETrhfJLXeAfBXUq+BjwL/JlU/qbgG2D9XG7kc+D2wq6THgP8Cd5EelIRUX/w4SROAO4r1tnO89TiPo4BzJE0kPSA5PCIermUH1qnlgEck3Uiq+39/2QGZmZlZc2q51kgaKbdGMiwidi87lv6m1VojyV+G9gOOINWr/x1wWURM73TD2o7RL+47s97k+8qsje+H3tFq1UjMmkJEvAWcC5wMLE1qUvHJ9h0WmZmZWf/mkm1rCq1Usp3bMf8qcCCp7fPfRsSdkj5Gqhr0kTodx/edWZ35vjJr4/uhd7RUnW2zJjEOuBDYMiKercyMiH9KuqC0qMzMzKzp9LlqJJKGSvpsYXolSVPrsN9VJY3Lwz6Sbpc0rOstaz7OgZKuyuMbSbq03sewnpM0APhhRJxQTLQrIuKkEsIyMzOzJtXnkm1gKKnXxHrbDbg3ItaPiF5JgCPioYjYpzeOZd0TEbPIzSqamZmZdaVPJduS1gEOJzfDJ2l4YdmpuVR6iqTNC/N3knSPpDGSRkv6ZAf73Qc4Gtgj73fldsuXkXSNpImSJknaP8/fXtL1eXxpSe9L2iNPf1vST7o4n6GSHsrjK0maWut5SFo9T0+QNFnScbW+rzaH2yS5xRozMzPrUp+qsx0RkySdDSwcEcdBSlKBJYDREXFiTpx/Ruq8ZmXg+8D2EfGGpE8ANwAfabffS3O728X9Flc5E5gcEV+QtBwwRtJYUvvaf5Y0L7ANqYv1bYAr8+vPazzFnpzHEcC1EfHTHPfiNR7T5nQgcKykd0htoQuIiFi61KjMzMys6fSpZLsT0yLiujx+H/CLPL49sDJwZyF5HihpmYh4qYb9bwscCxARL0gaCWwVEZMlTQY2zeucApwuaT5gY1IHOg09D+BO4OeSFgRuy4PNnY3KDsDMzMxaQ5+qRtKJdwvjs2j7kiHgHxExpDAsX2Oi3ZVbSaXYn8zjLwF7AeN70AFKzecREVcDWwBPAcOBS+biXCz5UkT8qziQehc1MzMzm01fTLbfABbr5ro3ATvkahcASNq4B8e8hfzQnKRlgZ1IiTWk7tkPAv4TETPy9A/za71UPQ9JqwAvRsSF+bib1PG4/dVe3ZxnZmZm/VxfrEZyDfkBSeDyPHQoIp6QtC/wR0kLAINIVTserPGYRwHnSJpIKmUeHhEP52X3A0vSllyPAn5CWzI+17o4jz2BfSTNAAL4Zr2O299I2o7U0s3ykor17Rcjfe5mZmZms3EPktYUWqEHSUmfITUteThwdmHRG8A1EfHM3Oy/g+P5vjOrM99XZm18P/QOJ9vWFFoh2S7sb+2ImFyPfXVxHN93ZnXm+8qsje+H3tEX62ybNdp/Jf1J0p0AktaVdHjZQZlZ90nqi9UozXpEkvPBBvKba1a7PwB3A4Pz9GOk9szNrHV8vuwAzJrImmUH0Jc52Tar3QoRcTap+UVyKzPvlxuSmdVo67IDMGsiK5UdQF/mZNusdjOLE5IG49ZIzFrN/GUHYNZE/D+sgZxsm9Xur5LOARaRdCCpnfPzyw3JzKqRtKWk9yS9V5h9UJ73mApd75r1dZK+0sH98Pc8729lxdWXOdk2q1FE/By4ExhD6sDozIj4dblRmVkn7gaeZc6+JQYCl0Z3muUy6zuuJFV97Oh++FPvh9P3uek/awqt1PRfb2nVuM2akaSTgZM6WPSxereRb9bsJF1B6vSu6FVguYh4t4SQ+jQ3fWRWI0lLA18HVqFwD0VE+z9cZtY8LmbOZPs2J9rWT13InMn2ZU60G8PJtlnt/g8YC9xCbpHEzJpbRDwt6Q7gM4XZF5YUjlnZbgZeAJYrzLuwnFD6PifbZrVbMCKOLDsIM6vZhbQl29OAq8sLxaw8ETFT0iXAt/OsR4CHSgypT6sp2a7UITXr5+6XtE5ETCo7EDNJ15Me1LXaLAxMc0Mk3TYyInYuO4ha+N6oyVrA+74feqTLe6O7D0j6grXe0JA/5vV+0FDSBsAo4D/A9Mr8iNikHvsvHMcPSFqXXAhivaXV/hb53rDe0tW90a1k22rnRKl5NCDZfoTUrvZYCnW2I+KOeuy/cBxfQ9YlXyfWaK16jbVq3NY6unuNuc62We2mR8SIsoMwMzOz5udObcxq9w9JO5QdhJmZmTU/VyNpEP981TwaUI3kZWAJ4E3gXUBp97F0PfZfOI6vIeuSrxNrtFa9xlo1bmsdrkZi1jgblR2AmZmZtQYn22Y1ioh/SRoIrJ5nTYmImWXGZGZmZs3JybZZjSRtROoMo1KFZKCkL0bE2HIjMzMzs2bjByTNavdr4OCIWC0iVgUOAX5TckxmZmbWhJxsm9VuoYgYVZmIiFuBhUqMx8zMzJqUk22z2r0taWhlQtJngLdLi8bMzMyalutsm9Xum8BVkt7N04OAL5YYj5mZmTUpt7PdIG7fs3k04rOQNC+zt0byXr32XTiGryHrkq8Ta7RWvcZaNW5rHd29xlyNxKxntgSGRsRkYHFJq5UdkJmZmTUfJ9tmNZI0HDiJVJ0EUjWS88uLyMzMzJqVk22z2n0Z2AaYBhARzwKLlhqRmZmZNSUn22a1e6eDOtp++MHMzMzm4NZIzGr3H0mbAyFpHuAE4OGSYzIzM7Mm5GTbrHbfAC4G1ia1r30XsE+pEZmZmVlTcjUSsxpFxIsR8VlgMLBkRGwXEf8tOSzrwyStJ+nLkhao837H12ufkoZI2rPdvJC08NwcW9IzktauR4xzS9Ipkr7UBHGcJ2mLsuNoBpK2kLRrbo61qXV2P3Rz+7rdrx3se3lJt9V5nwdKuqob6w2V9NlGxlI2l2yb1UjSTu2mAV4HJkfE66UEZX3dSsCfgdclXQFcCNwXc9lRQkQMmevI2gwBhgF/KeHYvSIiftBbx5I0MCJmVonj0N6KowWsDZwFvCzpUuDCiJhQckwN0ZN7prPrqN2+nwe26klcdTAUWBi4qQliaQiXbJvV7vvAtcCP8/B/wJnAFEnDygzM+rzFgK8A9wKPSfru3OysWNImaU1JN0maKGmSpAPy/OUkXSXpgTz/hA72swRwCrBtLn07s7D4KEkPSnpa0hcL2xSP/UwuNR6dx79eJd5jJY2StJikkyVdJmmkpMckXS9pwbzeIEmn55gnSLqkcKyvSHo0xzlR0hqS5pF0Vt7PBEn3VDn+hZXYqh0/D1MlLVnYboSkk/L4ppJukzQmDzvn+Svl7UZIGgscmktsJ+VYJ0samte9vfK3RtIykq4pfG77F47brfe1j1gK+BYwXtI4Sd/sYv2GkPTFfD2Mk3RCtdLs/Dk/mK+3UZI+Wlg2TNJDedk4Sevm+cV7ZnVJNxT2cVBh+8jX54OkZmqLx+3wWq9cf+32cWLh3t1G0k9zPJMlrZnXm630uv10Yf6yhev+YUk/z/PXAQ4H9s/X+fAOYtkhH3difq9WyfOH5m3OycsmVOJqOhHhoQEDqXWKKDsOD/X/LIBLgA0L0xsAfwLWBMbXO24PHrozzMU1tjDpV87HgT0Ky5bIrzcDW+bxQaRnFLbrYF8HAld1sP+v5/FPA8+1P3YefwYYkcdXIjWrWVy2LukL7aXAoDz/ZOAJUnUukUrFDsvLvgd8r3CsnwGn5vHXgeXy+HzAgsD6wKPAPHn+4lXerwsL59PZ8c8DjsrjA4Hn83kNBsYVjr8c8Gyev1J+T75UON4EYLM8PgBYNI/fDgzL41cAPyrs73lg7a7e1/70t6gR/1eqvE/LAK8Aq+bpo5n9Oi+OL1nY7lDg8jy+GvBiYR/zAYt0cL+OAdbI8xcBphSmA/hOlRg7vNbz9TG13Wd+ZB7fI187lWvu28CfOrrvi9PtxucvnPu8wK3ADoV7aURhHx/EAiwNvAyslacPAe7P40OB94D18/SJwKW99XkX742u1nPJtlnt1ouIMZWJiBgLrBMRj5L+6Zq1mtWBgRFxZWVGRLwiaSHSP7QzJY0HHgCWJ32x7K7L8+t9wPKS5u9svYh4BngVWLGwrNJp1L4RMaMw/8aIeC3Sf737gZXz/F2AfXOp1/g8XVl2K3CRpG8AK0TE28DTpATgj5L2q+Hcqh3/QlKiAbAj8Fg+r08BHwNuyHHdQPpnvUpedzqzV8O5FThD0vHAmhHxRgcxbAucAxARLwAjmf0n+M7eV6uvTYGxEfFEnu6ss7MdJd0naTJwHKkaFsB2wMjKPiLi3Yh4s922q5HuwcvzdXQXKSkv3pcXVTluLdf6Ffl1bAolrsvTY2i7ZrtrAHC6pAl5+7VpO+fObApMiIhH8vQFwBBJi+TpKRExLo/fR9s92FRcZ9usdm9L+nJEXAYg6cvAO3lZ1PtgEeEEvh/Kie4ypJKs7YGft1tlOnANqZOlRpmHdE1vHHO2Ld9d0wEiYpbS8w3V/u9ML4zParfenaSkfyngv51sU3l4TMAREXFrB8fZDdgY2Bq4TdLhEXGDpE/kY2wL/EzSBhHxYnfOrf3xI+JuSYvkn8gPJCUIlbgmRsSW7XckaSXgrZy4k/dzdN7H1sCVkn4ZEX/oIqauYuzx//1m+VuUE61lSL9K7A8c226VaaRE8ZBeDq1bcpWRM0j31T8lfYr0TEa3d0Eq+R3SyTrTOpoZEa93dK1X2Ufl2pkFvFuYX7yOZjJ7leRqX6aPARYHNo2I6ZLO7WTdWtTt+m4kl2yb1e4g4FhJ70h6h/SH/uCcHB1fbmjWV0TEWxHxdERMJFXxqBgNfJVUFWHvOh1uCjBT0h6VGZKWyCVqdwHDC/M/LGnZDvbxBqlOeSOcD/wCGCVp+W6sfy1wjHLLDTnxXVPSQODjEfFARJxGqvqxvqSlgAUj4kbSub4OfHwuY76I9LdhS+DqPO9eYFVJH5Q8S9pY+VtIe5JWj4hJEfFrUlW1jTtY7RbgsLz+ssBOpBLxPisi3oyIJ/O98VRlNjAK2A9YNsp5iPR+YANJldLVA6qstygwA3hRqa+GwwvLbgJ2krQqgKT5CqW4FVNIhT4flEwrPXvQZU/GDbjWnwTWzXEOAnavst5g4IWcaK8A7FpY1tnfjvuA9SStkacPAMZ1UNrf1JryG4BZM8vVRTaq/AFsd9PfXE5U1sf9D/gpcFFETKn3ziNipqRdgd9K+gHwPjCC9HzCPqSqDJPy6m8CB5PqlRaNAo7LPxPfERFH1TnGSyVNJyXcO3ax+mmkeqAPSnqflIj9kPQT+oWSBpPO8T+khOOjwB9yMj6QVL3jvrkM+WLgn8AFuaoKEfGqpF1IP6f/ilQH/mngc9XOIyddM4HX6Lik9ijgHEkTSSWewyOiP3Wy9TzpofVLIuJfZQYSES9JOhwYKelt4DpSneK32603SdKVwCPAVFLVny3zsickHQZcIWkAqbT2AGBSYfuZkj4H/CpXMRoAvATM1vRmFR+m42v9Iz085/sk3ULq2O150nMGy3Ww6pmkX2cmk55TGFVYdg35AUlStadK1TMi4uX8peLPOeaXgX17EmuZVPjFyupIUqo53yQ/u/VnrfpZtGrc1rt8nVijteo1VkbckhapFMAotRBySERs3lvHt97V3WvMJdtmZmZm9XFUro41kPSL1GElx2NNwCXbDdKqJQF9Uat+Fq0at/UuXyfWaK16jbVq3NY6unuN+QFJMzMzM7MGcTUSs25S6o2r6k9BEbFJL4ZjZmZmLcDJtln3HZdfdwbWAP6Ypw8iNcVkZmZmNhvX2W4Q1xVrHvX+LCTdD3yy0vlEbp7p3ojYtB77LxzH15B1ydeJNVqrXmOtGre1DtfZNmucDzF7z1fz5XlmZmZms3E1kgaQtGDZMVhDXQGMlnRFnt6TQiP8ZmZmZhWuRtIAkrYl9yTon6/K14ifEnPvXUPz5K0RcX299l04hn8CtS75OrFGa9VrrFXjttbhTm3KVde6u9Z8IuLvku6LiJfLjsXMzMyal+ts15GkJSUtCSzWfp6rlvQdkjaV9C9gbJ7eSNK5JYdlZmZmTcjJdn1dA7wMHF+Y93IetiglImuEXwI7AlMBIuIh4NOlRmRmZmZNycl2fV1YZf7zwC29GIc11qCIeKTdvBmlRGJmZmZNzcl2fV0JvNPB/IsjYlZvB2MN866khcm9SUpaC5hebkhmZmbWjJxs11FEvAFc3cGii3o7FmuoU4GbgOUlXQjcCny/1IjMzMysKbnpvzqTtA2zVxm5LyI2Kysea1jTfx8HtgcE3BQRT9Zr34VjuNkq65KvE2u0Vr3GWjVuax3uQbI8twH/KUxfWFIc1kAR8XRE/B64Cliy7HjMzMysOXWrZFvS9cBOjQ/H+rmREbFzvXda79INSXcBw0il2g8Dr5FiP76z7XpwHJfKWJcq14lZo7Xa3yLfG9Zb6lWy7UTbekOrXGcLR8TrpIT7UmAdYIdyQ7J+bGTZAVi/0IrXWSvGbK2ny+usph4kW+1brbWOFiuBmC+/bgVcHhHvS5pZZkDWfzXi1yCzvsD3hjULd9duVrvbJT1Cun8OlzQYcNOOZmZmNofu1tl23VFrqEZeYw2osy1gPeDpiHhD0hLARyJiXD32XziO7zszM7MW59ZIzGq3KvBYTrS3Bw4Dnik3JDMzM2tGTrbNavcXYJakjwHnAB/HHReZmZlZB5xsm9Xu/Yh4D9gZOCsivgJ8pOSYzMzMrAk52Tar3fySlgE+R+qqHVKb22ZmZmazcbJtVrtfAVOAaRHxUO66/fVyQzIzM7Nm5NZIrCm0UmskHex/ADAgImbUeb++78zMzFqcS7bNaiRpQUk/knRpnrUqrdP7pZmZmfUiJ9tmtfs9MC8wJE8/C5xUWjRmZmbWtJxsm9Vu3YgYDswAiIhp+F4yMzOzDjhBqELSgZJW62Kd8yRtMRfHuF3SsBq3WUnS1Lk45vKSbuvp9gbAu8UJSfPje8nMzMw6MLDsAJrYgcBU4PGOFkoaEBGH9mpEdRARzwNb1bqdpIERMbMBIbWiOyWdAMwnaShwDPB/pUZkZmZmTan00jhJX5T0mKRxkk6QFJIWzss2lXSbpDF52Lmw3f6SJkmaKOkaSUvn+QdKuknSX/J+R0laS9JISY9LulSS8rqL5tLpB/J+fi1pgKSDgI2AMyWNl7Rt3u8t+ViTgXWKJdOSFpN0fo5pgqTf5vnbSBqdz2+SpL26+b58XdITkh6U9MNqpdn5fB7K+75G0uKFZQfnWCbk/SzTvmS82ntcWU/SCEljgZb7YtFAJ5La1X4T+DnwAHBymQGZmZlZk4qILgcg0qpdr1vLACwDvAKsmqePzsdaGBgMjAOWy8uWIz2INhhYG3i+sOxHwBV5/EDgVWDFPH0dMD5vNxCYCGybl50H7JfH5wEuAw7L07cDwwqxHghMA1YuzPtgHeAC4DfAPHl6yfy6OKlZuMr5Pgss3tExCvtdF3gOWCpP/xqYmsdXqowXj5PHfwyclseHAk8Cy+bphYH5i9t38R6vlD+LL9X7c+/Na6zR+27V98SDBw8ePHjw0DtD2dVINgXGRsQTefp84Jd5/FPAx4AbckE0pORjFWAzYGREvJDnnwNMKOz3noh4No+PA56JiNcAJE3I+7gF2AXYRNKxed0FSclmNXdHxFNVlg0DNoyI9wEiolJ6vBRwvqRVgZnAh4DVgfs6Oc7QfH4v5+nzgX2qrLu/pH2AQcBCtFV72Rm4OCJezPFMAyi8l9D5ezwVmA78pZM4+yVJZwInR8T/8vQSwPcj4lulBmZmZmZNp+xkuzMCJkbElnMskDbrYtvphfFZHUxXzlvA5yPi6W7GNK2b6xX9HrgW2C0iQtLjpBLmuZYfzvwa8KmIeFnS3sBXatkF1d/jlYC3IqLrXo/6ny0qiTZARLwi6TNlBmRmZmbNqew62/cDG0haOU8fUFh2L7CqpA8e5pO0ca5vfRuwk6Rl86LDgJt7cPxrgeG5B0AkLSnpY3nZG8BiNezrOuD4Qn3wJfP8waSS9ZC0HanUuCt3ADsW9nFAlfUGk7oJf0XSfMDBhWXXk0q9l8nxLJxbzSjq7D226gZ0MG/eXo/CzMzMml6pyXZEvAQcDoyUNI5U5eI94O2IeJVUzeOk/IDfo6SH0BQRk4HhwM2SJgLrAd/sQQjfIpV0T5A0CfgHsEJedi7wg8oDkt3Y19HAIsDkXFXlB3n+cGCEpPHAnqQ6452KiAmkB+9GSxpDqn7yeger/gN4ilR15A5gbGEftwM/BW7J8dxKuy8Pnb3H3Tjf/uzB/DDtCpJWlPRr4MGygzIzM7Pmo+7UEpCUntaKqHsSJmmRiHgzjx8EHBIRm9f7OK2m3ftyMrBKROxbblSN0+BrrK77lrQo6aHVSus4fweOiYiOvhDNzXEa9p6YmZlZ72iGZPtEYA9SPer/AV+NiEfrfZxWI+l3wKdJDz4+DXwlUhvZfVIrJdu9pVXjNjMzszalJ9tm0FrJdq7T/hWgUr3oJuC8ej9M6vvOzMys9TVzayRmzernwPqkttUhPcC6KvDt0iIyMzOzplR2ayTdJulwSUdXWXagpKvy+BBJe7Zb/kGvlDUe82RJI7qx3uclbVLr/nsQz0aSLp3LfXT0/oyXtMDcRdevbA/sEBGXRsSlpLrbO5Qck5mZmTWhlkm2I+LsiDijG6sOIbX60Zs+D/Qo2a40O9gdEfFQRFTr3Ka7htDu/YmIIRHxzlzutz8RqfOfisAtuJiZmVkHmiLZlvScpKXz+EhJ1+fxpSU9m8c/KGWWNEjSOZKekDSanOjmnvxOAbbNpbVnFg5zlKQHJT0t6YtV4lhM0lWSHpN0O7ByYdkgSadLeiA3k3dJbrt6e1LzecPzMffP6x8g6X5JYyTdKmn1PP9ASbdIukbSZGCdXPJ+YiG+bST9VNI4SZMlrZm3HSrpoTy+kqSpkk7N602RtHleNlDSjZIekvSwpAty/B2+P8WSf6V2tkdLmphfN+7qeP3QjaReN/dW6kjoelIzjGZmZmazaYpkm9RJzdaS5iV1H/6xPL5NXtbeV/N6a+V1NoHUkx+pfetbcmntUYVt3oiIjYH9gDPp2A/yemsAuwPFXgG/DbweEZtExHrA88B3I+JGUuc4p+VjXqzUs+OewJYRsSFwOqnL9YpPAsdFxNoRMT7Pey3H9x3g/0hdzq8PXAycWCXeJYDReb1TgJ/l+bOAvSNiI2BtUicsB3fx/iBpEHA18L2IWBf4PnB1nt/Z8fqbbwN/BXbLwzWkz83MzMxsNs3ygOQoUssOzwH3kX6S3zTPu7WD9bcCLoqI94D3JP0J6KqU9fL8eh+wvKT5I2J6u3W2Ar4BEBFTJf21sGwXYFFJu+fp+YAJVY71OVJHO/crdygJLF5YfndEPNVumyvy69h0+LguT48hJXQdmVZY7z7gF3l8HuA4STuSEu3Fgber7KNodWBGRIwiBXGLpBl5/pudHK/fyNV+/i8ihgFnlx2PmZmZNbdmSbZvJZW4PktKvEUqsd4G+GGdjjEdICJm5QS41nMXcEREdJT8d7Tu+RHxgyrLp1WLj1Qq/W5h/iyqx1ptvb1JXz62iIg3JZ0ArNaNuLvS3bj6rHz9LCFJ9W7qz8zMzPqepqhGEhH/IiVvB5CS7VHAgcB7EfHvDja5Fdgv101egJRcVrxBu27Ja3ArcBB8UP/7C4Vl1wLHVFrtkLRIpS51B8f8O7C/pBXzugMkbdjDmHpiMDA1J9qL0f33ZwowSNJWAJK2BubN863NfcA1kvaUtFNlKDsoMzMzaz5NkWxno4C3I+KF3FPiO3RchQTgXODfwKN5nQfb7Weh/BBjtbrZ1fwIWFzSY6S6y3cWlp1GqjbyoKSJwN1AJdm+BNi78oBkRNxJqmd9raQJwGRg1xpjmRsXA4vk8/g7cFdhWdX3JyJmAF8EfpLP8VRg9zzf2gwhfWH5GnB8Ho4rMyAzMzNrTu5B0ppCI6+xVr1+WzVuMzMza9Pv6tya1UOuNrJ1nhwVETeUGY+ZmZk1p2aqRmLWEiSdSqpW9L88/FTSj8qNyszMzJqRq5FYU2ilaiSSHgfWj4i38vRCwLiIqEeLL8Xj+L4zMzNrcS7ZNqvdq8zebvn0PM/MzMxsNq6zbVa7e0ndtV+Up/cF7q40/xcRI0uLzMzMzJqKq5FYU2ixaiS3dbI4ImLrTpbXchzfd2ZmZi3OybY1hVZKtntLq8ZtZmZmbVxn26xGSg6RdFqeXknSp8qOy8zMzJqPk22z2v0S2Ab4fJ5+E/hVWcGYmZlZ83KybVa7rYB9gHcAIuIVYP5SIzIzM7Om5GTbrHbTo/Cwg6R5ANerNjMzsznU1PRf5YEts35ukqR9SNW3VwK+C9xVbkhmZmbWjLpbsu12g603tMp1dgwwFFgOuJ90Hx1fZkBmZmbWnLrV9J/Vzs22NY96fhaSBgA/iIiT5jqwro/la8jMzKzFuc62WQ0iYhawY9lxmJmZWWtwsm1Wu+slHSdpaUkLVoaygzIzM7Pm42okDeIqAM2jAd21v9/B7IiIAfXYf+E4vobMzMxaXE2tkZgZRIR/ETIzM7NucbJt1gOSlgQ+mSdH545tzMzMzGbjEjqzGknaDXgMOCoPj0r6fKlBmZmZWVNyne0GcX3b5tGAOtuPArtGxON5elXg2ohYsx77LxzH15CZmVmLc8m2We2mVxJtgIh4AninxHjMzMysSTnZNqvd/0k6UdKykpaTdALwN0kLuAlAMzMzK3I1kgZxFYDm0UtN/1XUrQlAX0NmZmatz62RmNXITf+ZmZlZdzlpMDMzMzNrECfbZmZmZmYN4mTbzMzMzKxBnGybmZmZmTWIk20zMzMzswZxsm1mZmZm1iBOts3MzMzMGsTJtpmZmZlZgzjZNjMzMzNrECfbZmZmZmYN4mTbzMzMzKxBnGybmZmZmTWIk20zMzMzswZxsm1mZmZm1iBOts3MzMzMGsTJtpmZmZlZgzjZNjMzMzNrECfbZmZmZmYN4mTbzMzMzKxBnGybmZmZmTWIk20zMzMzswZxsm1mZmZm1iBOthtA0jJlx2BmZmZm5XOy3RifLDsAMzMzMyufk+3GWLvsAMzMzMysfAPLDqAvkbQNMC+wSWHeDnn0sYh4poy4zMzMzKwcioiyY+gzJP0N2LXK4o0j4qFeDMcySQEQESo7llq0atxmZmbWxtVI6uvCKvMfBsb0YhxmZmZm1gScbNfXSGBqB/MvDP+EYGZmZtbvONmuo4iYAVzabvasDuaZmZmZWT/gZLv+Lmw3/Y+IeKGMQMzMzMysXE626ywixgMTCrMuLCcSMzMzMytbt1ojkXQ9sFPjw7F+bmRE7FzvnbZqqx6tGreZmZm16W6y7Yf7rFc0IrFs1aS1VeM2MzOzNjV1auN/+tYo/kJnZmZmfZHrbJuZmZmZNYiTbTMzMzOzBnGybWZmZmbWIE62zczMzMwaxMm2mZmZmVmDNCTZljRe0gJVlj0jae08/i1JSxeWnSxpRCNiahRJQyTt2W5e1fNvZcXPR9Lhko4uOyYzMzOzZtaQZDsihkTEO91Y9VvA0l2t1AiSBnY2XYMhwGzJdg3n37S6ej8i4uyIOKO34jEzMzNrRTUl25K+Kul3eXwTSSFp4zx9lqSv5PGQtHAe30LSpDz8FlCefyKwPHBVLgleKx9mBUkjJT0m6XpJC1aJZTNJd0uakIfP5vkbSxotaWJ+rcS3kqSpkkZIGgscKul2Sb+SdB9wbV7vO5IekDRW0t8lLZvnD8rbTs7Hu0bSEsApwLb5HM7s4Py7iudUSeMkTZG0eZVz3VvS/Xm9cZK2KSxbU9JNef+TJB2Q568g6eo8f6Kk7+b5y+TYK+vvX9jXM5JOk/QAcI6kxSRdlT+L24GVC+sWS7kPzDFcIelhSfe0e9/OlfR4/rx+K+mqTi80MzMzs74iIrocgMj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       "Q 53.46875 37.75 57.5 32.78125 \n",
       "Q 61.53125 27.828125 61.53125 20.40625 \n",
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       "     </defs>\n",
       "     <use xlink:href=\"#DejaVuSans-66\"/>\n",
       "     <use x=\"68.603516\" xlink:href=\"#DejaVuSans-97\"/>\n",
       "     <use x=\"129.882812\" xlink:href=\"#DejaVuSans-115\"/>\n",
       "     <use x=\"181.982422\" xlink:href=\"#DejaVuSans-101\"/>\n",
       "     <use x=\"243.505859\" xlink:href=\"#DejaVuSans-108\"/>\n",
       "     <use x=\"271.289062\" xlink:href=\"#DejaVuSans-105\"/>\n",
       "     <use x=\"299.072266\" xlink:href=\"#DejaVuSans-110\"/>\n",
       "     <use x=\"362.451172\" xlink:href=\"#DejaVuSans-101\"/>\n",
       "     <use x=\"423.974609\" xlink:href=\"#DejaVuSans-32\"/>\n",
       "    </g>\n",
       "    <!--  climate -->\n",
       "    <g transform=\"translate(278.910903 80.638388)scale(0.11 -0.11)\">\n",
       "     <use xlink:href=\"#DejaVuSans-32\"/>\n",
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       "    </g>\n",
       "   </g>\n",
       "   <g id=\"text_12\">\n",
       "    <!-- glacier simulation -->\n",
       "    <g transform=\"translate(596.205 258.079591)scale(0.11 -0.11)\">\n",
       "     <use xlink:href=\"#DejaVuSans-103\"/>\n",
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       "    </g>\n",
       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"pf58d9743fb\">\n",
       "   <rect height=\"411.554278\" width=\"717.57\" x=\"7.2\" y=\"7.2\"/>\n",
       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<schemdraw.backends.mpl.Figure object at 0x7ff3e8131250>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = schemdraw.Drawing(fontsize=11, color='black')\n",
    "\n",
    "\n",
    "d.add(flow.Start(w=3, h=2, label='Glacier outlines \\n & DEM'))\n",
    "d.add(flow.Arrow('down', l=d.unit/4))\n",
    "\n",
    "topo_pre = d.add(flow.Box(w=5, h=1.5,  label=\"topographical data \\n preprocessing\"))\n",
    "d.add(flow.Arrow('down', l=d.unit/2))\n",
    "flow_pre = d.add(flow.Box(w=5, h=1.5, label=\"computation of \\n the flow lines\"))\n",
    "d.add(flow.Arrow(l=d.unit/2))\n",
    "width_pre = d.add(flow.Box(w=5, h=1.5, label=\"geometrical glacier \\n width determination\"))\n",
    "d.add(flow.Arrow(l=d.unit/2))\n",
    "wadjust_pre = d.add(flow.Box(w=5, h=1.5, label='width correction according \\n to catchment areas and \\n altitude–area distribution'))\n",
    "d.add(flow.Line('right', xy=wadjust_pre.E, l=d.unit/2))\n",
    "\n",
    "d.add(flow.Line('right', xy=topo_pre.E, l=d.unit/2))\n",
    "pre_pro= d.add(flow.Box(w=1.5, h=9, rotation=90, label='preprocessed geometry data'))\n",
    "d.add(flow.Arrow('right', xy=pre_pro.E, l=d.unit*1.5))\n",
    "\n",
    "inversion = d.add(flow.Box(w=5, h=1.5, label='ice thinkness inversion', anchor='W'))\n",
    "\n",
    "d.add(flow.Line('up', l=d.unit/2, xy=inversion.N))\n",
    "MBcal =  d.add(flow.Box(w=5, h=1.5, label='mass balance \\nmodel calibration', anchor='S'))\n",
    "d.add(flow.Line('up', l=d.unit/2, xy=MBcal.N))\n",
    "clim_prepro = d.add(flow.Box(w=5, h=1.5, label='climate preprocessing', anchor='S'))\n",
    "d.add(flow.Line('left', l=d.unit/4, xy=MBcal.W))\n",
    "wgms = d.add(flow.Start(w=3, h=2, label='Mass balance \\ndata', anchor='E'))\n",
    "d.add(flow.Arrow('right', l=d.unit/4, xy=wgms.E))\n",
    "\n",
    "d.add(flow.Line('left', l=d.unit/4, xy=clim_prepro.W))\n",
    "clim = d.add(flow.Start(w=3, h=2, label='Baseline \\n climate', anchor='E'))\n",
    "d.add(flow.Arrow('right', l=d.unit/4, xy=clim.E))\n",
    "\n",
    "\n",
    "d.add(flow.Arrow('down', l=d.unit/2, xy=clim_prepro.S))\n",
    "d.add(flow.Arrow('down', l=d.unit/2, xy=MBcal.S))\n",
    "\n",
    "d.add(flow.Arrow('right', l=d.unit/2, xy=inversion.E))\n",
    "Dynamics = d.add(flow.Box(w=5, h=1.5, label='glacier simulation', anchor='W'))\n",
    "d.add(flow.Line('right', l=d.unit/2))\n",
    "\n",
    "d.add(flow.Line('right', l=d.unit*1.3, xy=MBcal.E))\n",
    "d.add(flow.Line('right', l=d.unit*1.3, xy=clim_prepro.E))\n",
    "d.add(flow.Arrow('down', l=d.unit*1.75))\n",
    "\n",
    "d.draw()\n",
    "# d.save('your_path/filename');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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