docs/apps/van_der_waals/chemical_potential.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chemical potential of a van der Waals real gas\n",
    "\n",
    "\n",
    "**Code:** #117-000\n",
    "\n",
    "**File:** `apps/van_der_waals/chemical_potential.ipynb`\n",
    "\n",
    "**Run it online:** [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jongablop/MinervaLab/master?urlpath=%2Fnotebooks%2Fapps%2Fvan_der_waals%2Fchemical_potential.ipynb)\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this notebook is to show the contruction of the chemical potential based in van der Waals' isotherms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface\n",
    "\n",
    "The main interface (``main_block_117_000``) is divided in five VBox: ``block_1``, ``block_2``, ``block_3``, ``block_4`` and ``block_5``.\n",
    "``block_1`` contains two bqplot Figures: ``fig_117_001`` and ``fig_117_002``.\n",
    "``block_2`` contains the ``T_slider`` widget which controls the isotherms and chemical potentials shown in ``fig_117_001``, ``fig_117_002``, ``fig_117_003`` and ``fig_117_004``.\n",
    "``block_3`` contains two bqplot Figures: ``fig_117_003`` and ``fig_117_004``.\n",
    "``block_4`` contains two bqplot Figures: ``fig_117_005`` and ``fig_117_006``.\n",
    "``block_5`` contains two bqplot Figures: ``fig_117_007`` and ``fig_117_008``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='../../static/images/apps/117-000_1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h+kfEkwqJyL4YY5FVAQezuB2XBfyCHcpfefXjdrEZy0SAYXbMOuXyV4oEMYIc6/SjW0gxlumSnIJyXUALrB2Jg7QDQRop+dh6dhU4LcuYTngNCrg3SFzxbHnVqDESpCg+6Zy4pY9Sr0cnxmibQ2yzAoWyK7MgyLsKEKrXrr/IIIP0fY0PqQdQyreF8gbl9a3VNlE2d6LHkjuoH1TA00WNnTRdh42v9BsWBoX607T+jWylAZMXATI8suVP0/x7yaaHCjJO76cN3+bm36Wh+bLZ9N/LPDMjIiJyNMtH1iBNmmC+MTTth3VWO4IX61qmSBo7RpD4R3/yEGKsIP2ocv1hC0LdpgbZYLxQofZSAxd7N3ZI9mz1QqK/thCGW/uTJhFRWDDGIusCDme//SYOtKFMsQKPjAE7mf/lRQZCCRqu9jO2CG8O9BRHzGBmbemHSPP9Hmxva/kYbcLa38GCtANKIxUYYhmWxJKLZBmfLLB/KDXlpGH97Pd+qwPOKDPO25KNhclVDgPHaEJssyTZHYV2uAnL7WHoXntgdGdy6Zs3J0fkFjuaPKScCpVjsOGRLL1B2U3lHhFklGy5Aj/vDB03G78vAp/O8PfFMH6aVr+RAxu2ESbLRxnfq+GRrXyaZt9LNj2U+UcSOMy4r/JdGoovm03/vfSWMtT/p4mIiOzM8pE1SJMmmG8MXfthldWOQJ6nZ3J0Fo+sT3eyPmvL0lH9vfeUYuIwG/RPdCZC3aZa6QgMS2LJV2B8Mju2eiHRX1vww/W/nHEyFhHZGWMsCob81TyYP0MFFfgXsaAzrAIXDxKXf6nbsmXLunLRAZNwIij9EJkggbZn1Nh/VDLsFTVp82D20ujHzSDzvIP0B2aNVODscbMwQjYEcWuvDgyytncUlzgOWAZJvJFKchEi8zfywKN5wJXj9EH6mBgxAt5xyG2WTvYmynC5Ndi2wexdmQrVa3/v7RZwtR7TTzxqjBhioPEhZQskB+kPFDV3w9riBEDjK37vPaNUwDD9+QK/eiZ9k2GEfKDAbwvzK0iH7dO0+o38YHlV/ckCnztgYdcEpToGvI3AN2vl0zT/XrLpoXKLVjFGNsMHJwbOML7dIN+lofiyhf6/l9VvDiIiIsewfCgKcvgTlI2haz+sstoRvPceo8c44mK/2gMHHmrN2xGF/jbiJkigjTTtUCLEKOBmfS8htG2q2Qa5VoCxI9XIvweatK72a/VCoL+2YIfr8/K/amEEIqJgMMai4OiHZ1tSrMAjluXzBN/4uHcrniXwUmoxEmQp3s3dx/hnLwvk4Xu1394xVfRrx4hf00PaS7C87kGQ/sCkkQrsYIL8/czSZKC9UxrkS6r3BVbfSMDbNXnVqy+vVp8+WPLjV4bLrcG2DSbvKohQvXZ9kP6V0hq6BlMOHAj6kA9Ozmsjx2jvr8qgLZff609s/oq1YYYL/2hPV2XMXpMZQwHMvi+ixk6qPV+QMWH6NOVnZuEb+f3l1YYXF/DMYzzFh6FPWJLfPVY+TfV7KfQPpbVzl1cHfnQBH6/52w3yXaoJxZctdP+99M452G8fIiKicGX5yGrp8GdhYyjbD8usdwTKkdZaO2Iq8G2c9HEP5qhuUSjbVJMN8u9VQU5GsDC1yn6tXnD01xbs8IAeiCkWEdkdYyz6vlnsaEJHnxdmfSa4ExOLczKHoK+hp1j/yf8WREREDmU5jQud/3CbahCQYoV8wSciIlsxxqLvWxhiLPnnqWDWgXdSAcsXJOi2T94kCr2Av/6yoyQiIgq7sMRY/9k21UC/bDn/cEZE4YAxFn3fwhJjybMB/2u/sz/zaJ6t/JgDyoJHRCELaJmNFyIgIiKirxemGOs/2qYaBLw7w9V6iIjsiTEWfd/CFmNpDYZb7qj8QxCRENBRBnvpJCIiIgqtMMZY/+U2Vf/DWXAXFSci+nqMsej7FtYYS1+33eyaLkT/l55tap7A/CKcRERE9PXCHGP9Z9tUkc+x+yaicMMYi4iIiIiIiIiInABjLCIiIiIiIiIicgKMsYiIiIiIiIiIyAkwxiIiIiIiIiIiIifAGIuIiIiIiIiIiJwAYywiIiIiIiIiInICjLGIiIiIiIiIiMgJMMYiIiIiIiIiIiInwBiLiIiIiIiIiIicAGMsIiIiIiIiIiJyAoyxiIiIiIiIiIjICTDGIiIiIiIiIiIiJ8AYi4iIiIiIiIiInABjLCIiIiIiIiIicgKMsYiIiIiIiIiIyAkwxiIiIiIiIiIiIifAGIuIiIiIiIiIiJwAYywiIiIiIiIiInICjLGIiIiIiIiIiMgJMMYiIiIiIiIiIiInwBiLiIiIiIiIiIicAGMsG3Tp0qUJERE5D+3ntvwJTkROgu0WEZFzYbtFDsYYywYpU6bMmTOn/M9KRETfN+0ntvZzW/4EJyInwXaLiMiJsN0ix2OMZQPt/+f8+fPlDSIi+r5pP7HZVxE5HbZbREROhO0WOR5jLBuwryIiciLsq4icEdstIiInwnaLHI8xlg3YVxERORH2VUTOiO0WEZETYbtFjscYywbsq4iInAj7KiJnxHaLiMiJsN0ix2OMZQP2VUREToR9FZEzYrtFRORE2G6R4zHGsgH7KiIiJ8K+isgZsd0iInIibLfI8Rhj2YB9FRGRE2FfReSM2G4RETkRtlvkeIyxbMC+iojIibCvInJGbLeIiJwI2y1yPOeIsQ4vdwli1ubL8t6gHp/etmzWBFdtlOuEaYs3n7z/Qd6hC+Fuq9hXERE5EfZVRM6I7RYRkRNhu0WO5xQxlu/GGS4uU+e7m/E8clferfjgu3m2q4vr5EUbdh/YvXnZTDcXl4nLDz+W94Z0d3DYVxERORH2VUTOiO0WEZETYbtFjucMMdaHA0tdXCas8ZI3Q3Br21xt9NIDgcHUB9+Ns1xcJq7x0qdchXB3sNhXERE5EfZVRM6I7RYRkRNhu0WO5wwxVsBkrPk7rEy+UlzePMvFZYrHWXlTeL5nsSEGC+Hu4LGvIiJyIuyriJwR2y0iIifCdosczwliLJsmYwUMdll+WN7UBeRgAWtphXB3CNhXERE5EfZVRM6I7RYRkRNhu0WO5wQxVkDKNNdz/+bF08Sy7K4Tps1fe+CGlXMAA2ZbzdjoK2/qAja6uh8J8e4QsK8iInIi7KuInBHbLSIiJ8J2ixzv+4+x9AlULi5uM5dt3n3gwPa18yaLMGv2Zl9LSVZAJBVkZlXAlQ7FHKwQ7g4B+yoiIifCvorIGbHdIiJyImy3yPG+/xjr2o75E1zNriWor8ruMnfbLbnBhL1iLDcr2FdZ8OQJDh2S/yYi+m6wryJyRoyxiIicCNstcjxnWOI9qIBV2S2u+m4xpwrYaDyp0PrdIWBfZcGRI4gQQdThkKezERE5EvsqImfEdouIyDk8eoRNm05XqVIoaVK5hcghnCHG+vDi8QvlBMKA+VNBZlVpPnCJdwfz8ZEx1rx5cgsR0feBMRaRM2K7RUT0nfrwAQcPYvx41KuHdOnkr4ERIpRLkkQOIHKI7z7GurZltouLy9IDZjnWrW1zXVxclx+2sDpWwNSqKR5n5U0hYO6WvNRhCHcHj32VZZEji59fvXvLm0RE3wfGWETOiO0WEdF35OxZMV+hbVvkzm3IrZQqwdlY5Fjf/2ys8xumKSu6Pz68fKKLy4RVxy2t8a5HXCZraekraU1c46WPDuHuYLGvsixLFvHzq3JleZOI6PvAGIvIGbHdIiL6lm7fxpo16NMHJUogVizTuMpYSZKI3/6GD8fWrcumTGG7RQ7mBCcVfvDeMN018EqFuzevFFcqdJ3sbgiiAiZYmZwU+MFrbcCIeWu3a8OXzXQzi61Cujs47Kssq1ZN/CzLkEHeJCL6PjDGInJGbLeIiBzq5Uvs3IlRo1C9OpInN4urDPXzzyhaFD17YuVK3LghdwzAdosczzmWePe/un/l7MkTXF1cXFwnTJ69cv9Vf3mPRo2xNI9Pb1s2Sw6fNn/D0fvmU61CuNsq9lWW/fOP/On26ZPcQkT0HWBfReSM2G4REYU7Ly9Mn45mzZAtmzGrUur339GqFWbPxpkzci9L2G6R4zlHjPWdYF9l2eLF8ifdWdM1x4iIvjH2VUTOiO0WEZH9Xb2K5cvRrRsKFkTUqMasyrRSp0bt2hg3Dvv24e1buWNI2G6R4zHGsgH7KstOnJA/+FaulFuIiL4D7KuInBHbLSIiO3jyBFu2YOhQVKyIhAmNWZVp/fILSpdG//5Yvx737skdbcR2ixyPMZYN2FdZ9uaN/Dk4ZIjcQkT0HWBfReSM2G4REX2NL19w5AgmTULDhsiY0ZhVmVbkyMiXDx07YtEi+PjIHb/a7ZtYt/Jc1TJFkvNKheRQjLFswL7KKu0XRe3HYp068iYR0XeAMRaRM2K7RUQUWr6+YoGXjh1FOBU5slliZagMGUSwNWmSCLk+f5Y7fp03r7FvF8aPQsNqyJwMcSLoVe63JHIAkUMwxrIB+yqrypYVPyJz5pQ3iYi+A4yxiJwR2y0iIqsePsTGjRg4UPz+FT++WVxlqIQJUaGCOJ1wyxZxamEYnTuDhbPRuSUK5jDkVkoV42wscizGWDZgX2VVt27iJ+aPP8qbRETfAcZYRM6I7RYRkdGnTzh0CBMmoH59pEtnFlcZKmpUsXB7165Ytkws5R5G9+7Acy0G90Gl4kgWU0msZOVKg+Z1MdUNRw8unDOH7RY5GGMsG7Cvsmr2bPkz9NIluYWI6FtjjEXkjNhuEdH/Ox8fLFyIDh2QNy8iRjTGVaaVJQuaNsW0aeJyW2H07h0O78fkcWhaCzlSqYmVXinioFopDPsHm9fjgdli8Gy3yPEYY9mAfZVVBw/Kn6ceHnILEdG3xr6KyBmx3SKi/zsPH8LTU5wqWKYM4sUzZlWmlSQJqlTBiBHYvh0vXsgdv9qli1i2AN3boXgeNbEyVJHf0a0NFs/FxXNyL0vYbpHjMcayAfsqq54+lT9eR42SW4iIvjX2VUTOiO0WEf33ffwoThUcPx716iFtWmNWZVrRoqFwYfToAXd3XL8ud/xqjx9h+yaMGoQaZZE6nppY6ZU1ORpVx4QxOLAHb/3ljiFhu0WOxxjLBuyrgpMsmfhp27ChvElE9K2xryJyRmy3iOi/ydcXixaFcKpg1qxo1gzTp8PLS+4VFiePY/YUtGmEPzOqiZVeiaOjQlEM7In1q+F3S+5lI7Zb5HiMsWzAvio4JUuKn7y5c8ubRETfGvsqImfEdouI/iMeP8amTRg0KLirCiZJgsqV7Xaq4M3rWOuO/t1RthASRlVDK73yZUH7ppg3HWdOyr3C4uG9Xb07/pnyN3mTyCEYY9mAfVVwOnUSP4ijR5c3iYi+NcZYRM6I7RYROat//8XRo5g0CQ0aIH16s7jKUD/9hEKF0L27fU4V9H+D/bsxfjQaVkPmZGpipVe6X1G7AsYOxc6teP5M7vjVtPd46gjmTUSneiicBikiaFUuZRJ5L5FDMMayAfuq4MyYIX80h/0ir0RE9sAYi8gZsd0iImdy5QqWLkWXLihQAFGiGOMq0zJcVdAupwr6nMeSeejaRizBriRWesWNiL/zoldHuC/G1ctyr7C4cxOe7hjWDTUKIs0PenRlWoyxyMEYY9mAfVVwDhyQP6bXr5dbiIi+KcZYRM6I7RYRfdeePcO2bRg2DBUrImFCY1ZlWokSoVIlMUYbqY0Po8ePsNUTIwagemmkjKuGVnrlTI1mdTDVDUcP4tMnueNX+/gBx/Zjpgva1kC+35TQStafSdCyCqaN2jKwV2q2W+RYjLFswL4qOLxYIRF9ZxhjETkjtltE9N3x8hJzqZo2FfOqDFmVaf3wA/76C127Ytky+5yboq/O3roh8mRQEyu9kv6MSsUxuA82rsP9u3KvsLhxBeuWYFBHVPpTTaz0ShkRVfJhSGesX4abxve4fOrEbKmSyxtEDsEYywbsq0KQNKn4Ic6LFRLR94ExFpEzYrtFRN/erVtYtQo9e6JIEUSLZpZYGSpDBvGLz+TJOHZMrBgVRn63sG4lBvQIbnX2/FnRsTnmz8TZ03KvsPB/g0O7MHUkWlRG7kRqaKVXgRRoXwuzXXHiID59lDtqNi6G+1T0b4TKGZAjQrk0PKmQHIoxlg3YV4WgVCnxA/2PP+RNIqJvijEWkTNiu0VE34C/P/bswZgxqFEDyZObxVWGihdPXHNw0CBx/cHHj+WOX+39exzej0kuaFITWZOriZVe+ursLsOwaxtePJc7hsWVi1g1H/3aoPzvamKlV7qoqFkII3pg0yrcuy330jy+jz0emNgHLYohb3TkiGBajLHIwRhj2YB9VQi6dBE/33/6Sd4kIvqmGGMROSO2W0TkIOfPY948tGmD3383i6tMK08etG+PhQvh4yP3Cosrl8Sy6z07oMSfamJlKO0ubcCKRWJw2L16gf3bMHEompZHrvhqaKVXkbToXB/zJ+HMMbmX7sIJrJiCfg1QMZ2SW8kq9is6VTzZtloWnlRIjsUYywbsq0Iwe7b8cX/JHj9ziYjChjEWkTNiu0VE4eXhQ3h6YsAAlC6NuHGNWZVppUqF2rXh6iouYPXhg9zxq718gd3bxXSqOhWRPqGaWOmVLYWYkDV5nJic9f693DEsfM9hxRz0aYnS2dXESq9MMVC3OMb0xXYPPLov99I8vofd68SUq+ZF8Wc0NbTSq87vGNEWGxbihq++E9stcjzGWDZgXxWCQ4fkT/+1a+UWIqJvh30VkTNiu0VE9nTsmFi+qkEDpE9vzKpM6+efUbw4+vQRv8LcuSP3CosLZ7FwNjq3xF/Z1cRKr0Q/oVxhDOwJj1W4Y3Li3ld7/hR7NmP8IDQqg+xx1dBKrxKZ0L0JlkzH+VNyL93541g+WUy5qpBWTaz0Kp4QnSthzkgc24W3b+ReJthukeMxxrIB+6oQvHghDwbDhsktRETfDvsqImfEdouIwuT6daxYgW7dULCguICgIa4yrWzZ0Lw5Zs7EaXuslf74EbZtxIgBqF4aKeKooZVeeTKIyw7OniIuQWgXPt5YNgu9mqNUVjWx0itrbDQsBdcB2LURT01W8grNlKt6uTGyPTwX4WbIJ9mw3SLHY4xlA/ZVIdN+hGkHhjp15E0iom+HfRWRM2K7RUS2efMGu3dj9GhUrSqvnB60EiVCpUoYPhzbt4s/vYfdaS/MnYa2jZEno5pY6fVbLFQtieH9sWUDHj2Ue4XF86fYvQluA9GwtIiolNBKr5JZ0LMZls4UIZepEFe5KpEInSuLKVfHd+Odv9wrdNhukeMxxrIB+6qQlS8vjhPZssmbRETfDvsqImfEdouIQnbhQgirs0eKhHz50KkTli7FlStyr7C4fxeeazG4DyoWQ+LoamilV4Fs6NQCC2bhvHmK9NV8vEUm1bOZyKeUxEqvbHHEiYTjB4mTCp8/lXtpnjzAnvWY1Bctiwe9sKCsun9gRLtQTrkKBtstcjzGWDZgXxWy3r3FYSNiRHz5IrcQEX0j7KuInBHbLSKy4MkTbNqEQYNQpgx++cUssTJU6tTipJDx48WKvZ8+yR3D4sRRzJiIFvWQK42aWOmVNgFqV8DYodi1TazmHnaGKVcNSlmdclUqK3q3wPLZ8Dkr99L5nIL7NPRvhMoZ1MRKL32Vq9kjrK1y9TWunT3Ut0n+NMnkTSKHYIxlA/ZVIVu0SB5FvO30Jwgioq/FGIvIGbHdIiLJywtTp6JxY2TKZMyqTMuwOvu6dbh7V+4VFndui2XXB/RA2UL49Uc1tNKr6B/o3g7LFuCyj9wrjHzOylWurE25yh43YMrVYOzdghfP5F6a54+xzxNT+qPV3ygQUw2t9DJcWDBsU66M/F/hxHYsGIKeZVE+LgpH0KpcuiTyXiKHYIxlA/ZVITt1Sh5Uli2TW4iIvhHGWETOiO0W0f8vPz+sWYNevVC0KKJFM8ZVppU1K5o1w4wZ9lmd/fNnHDuEqW5oVhvZU6qJlV4ZEqN+FYwfjf278da2daMse/lcBFLjBwd3YcHS2eSUK99zci/d5bNYMwuDmqFqZjWx0qtoAnSsgNnDcWQH/F/LvcLI7zK2LoRrWzTPpedWSpVOyxiLHIoxlg3YV4Xs40d5gOnbV24hIvpGGGMROSO2W0T/Rz59EicAurmJkwFTpTJmVaYVP75YfnfIEGzdimcmc5G+2q0bWLMC/bqiVAHEi6yGVnqV+BO9OmLlElyzx7pamkvnsWIO+rREqWxqYqWXnHI1SJ1y9foFDm3F9MFoVwYF46ihlV41c2BoK3jMw/WLcq8w+vQR3vuxfCz6V0PVxEpoJathJoxqig0zPdyGsd0iB2OMZQP2VaGSObM43lSsKG8SEX0jjLGInBHbLaL/uGvXxHkbnTsjf36xELtpYmWo3LnRrh0WLoSPPU7c+/gRRw5g8jg0qYksv6mJlV6Zk6FRdUwci0P78P693DEsXr/Evm2YOBRNyiFnPDW00qtUVnEuYdBVrq77YP18kUzVyK4mVnoViov2ZTFjCA5vEzmXXTzyw55VmNIN7QqgaCQ1tNKqZDR0LoaZfXFoA54/knux3aJvgTGWDdhXhUry5OLYkyqVvElE9I2wryJyRmy3iP5r/P2xezdGj0bVqkiSxCyuMlSyZKheHWPGYO9evH0rdwyLm9exZjn6dsHf+RA3ohpaafVLJJTMLwasXi4G28VVH6xegL6tUTanmljplTU2GpYWK7grFxZ85y+WXZ89QizBXuxXNbTSq1oWDG6ONbNxxfw0w7Dw9cLaKRhWH3XSqKGVXrVTYWg9rJkEn+NylyDYbpHjMcayAfuqUBk2TB6NXr6UW4iIvgX2VUTOiO0W0X+Br6+YS9WuHf74w5hVmVaUKPjrL3TrhhUrcOOG3CssPn7E4f2Y5ILGNcTUKiW00itbCjSthSmuOHrQPpcyfOuPQ7swdSRaVMLvv6qhlV4lMqFHUyydiYtn5F6621exeSlGd0S9PGpipVeBmGLt9in9sX8jXjyRe4XRq2c4uhlzB6Dr3yj9sxpaaVUkItrmx+Su2LMSD2/LvYLFdoscjzGWDdhXhcq6dfLgdPCg3EJE9C2wryJyRmy3iJzSy5fYvl38PbtCBSRIYIyrTCtNGtStiwkTcPgwvnyRO4aFPuWqT2erU67iR0Hpv/BPN6x1x+2bcq8wunkV65ZgYAdUzK0mVnplioF6JeDyD3Z64qnx5DuxnPzpg1g4Dj1qoFQyNbTSq1J69G8E92m4eFLuFXa3fbF5Psa1RtPsamilV+WE+KcKlo7GmX34+EHuFWpst8jxGGPZgH1VqFy9Kg9U06fLLURE3wL7KiJnxHaLyGmcP4+5c9GqFXLkMGZVphU9OooVQ58+WLsWd+/KvcJCX+VKn3JlbZWr7CnFZQenuolLEH7+LHcMi0+fcPwAZrqgTXXkTaqGVnoVTYeuDbFoKs56yb10j+5ix2q49UCTgvg9ihpaaZX7RzQtjPG9sGstntyXe4WRvkD7sjH4pyqqJFJDK72aZhfB1ub5IuQKG7Zb5HiMsWzAviq0YsYUx6327eVNIqJvgX0VkTNiu0X0/Xr2DFu2YPBglC2LX34xS6wMlSEDGjXC1KnwMg90vtrtm/LCgiXziwWtlNBKK33KVf/uWLcSfrfkXmF07zY2rsSwbqhWAKkiqaGVVumiolYRjO6Nbevw8J7cS3fxJNynol8DVEirhlZ6lUmBXrWxZDy8j8hdwu7xXexdjand0f4vFLG0QHvpn9GtpDid8OhmcWqhvVw/c3hgowLpksmbRA7BGMsG7KtCq0ABcQwrVEjeJCL6FhhjETkjtltE3xdvb8yahebNkTWrWVxlqFixULIk+vfHhg14+FDuFRZfvoiJVFPdxDpW2VKooZVe+ipX+pQru5yfqDlzDPMmomNdFEylhlZ6/ZUSHepg7gScPip30b18hoObMXUAWpcUC1qZJlaGapAXYzpj6wrcs9O5jZqrZ+AxHSMaoV46NbTSq05qsXz7msliKXd7+fAW3rvgPgxDyqFeXFSKoFW5jEnkvUQOwRjLBuyrQqt1a3FIixtX3iQi+hYYYxE5I7ZbRN/YkyfYtAkDB6JUKcSObZZYGSpLFjRrhpkzccZ82fKvduc2PFaJGVVlCorZVUpopVW8yChVQMzJsuMqV08fYft6jOmLOsWQIZoaWmmVMiKq5sfQrvB0x13zeV7XfeAxD0NboWZ2NbHSq3hCdK6MuaPgtRcf3su9wujta5zYjgVD0KMMysZWQyu92uTDlK7Y7R7KBdpD5YkfDrhjThf0yKvnVkqVzphUjiRyCMZYNmBfFVpTpsgj3HU7XbyWiMh2jLGInBHbLaJv4MwZkUk1bYrMmY1ZlWnFjYsyZTBoEDZvxtOncq8wOnEUMyaieV3kSKWGVnplTY4mNe15YUGNz1ksm4WezcQFBJXQSq88idGqKmaMxbH9YpEpgw/vRSA1d5QIp4onVEMrvWpkx7DWWD9fhFz2cu86JnQQ1TK3mljpVSkB+lbC4pE4vQcf7ZSXaa6fweZpGN8QrdMqoZWs9pkwqRm2z143cTjbLXIwxlg2YF8VWvv2yQPe+vVyCxGRwzHGInJGbLeIHOHxY2zcKM4ELFlSnBVoiKtMK1s2tGiB2bNx7pzcK4we3sfGdRjcG+WLINFPamilV8l84sqDa5bj1g25Vxi9eY0DOzBxKJqUQ/a4amilV/nf0b8d1izC9ctyL93929i+Ei5d0Si/mljpVSCmOJFw2kAc2CxOLbQXn+NYPRFD6qBmCjW00qtRZoxpjo1zcOOC3CXsPr4TZwuuHG56tqBZVYmMPgUxvyeOrMUz42r0bLfI8Rhj2YB9VWg9fy4PfsOGyS1ERA7HvorIGbHdIgovZ85gxgwx5SpTJmNWZVq//CLWbh8yBFu3in7eLrxPYe40tGmE3OnVxEqvjEnQsBomjMGhffjwQe4VRjevYt0SDGgv8iklsdIre1w0Liuyrf3b4f9a7qW74IUVU9C3PsqnVkMrvSqkxT8NxSLuF0/KXcLu9XOx8vrcAej2N0rFUEMrrUr8iE5FMLMPDq7H80dyr7B7eheHVmFuN/TMp4ZWejVIgBGVsXo0LpjPTTPBdoscjzGWDdhX2SB1anEsrFVL3iQicjj2VUTOiO0Wkd3oq1wNGCBWubI25Sp7drRqhblzcf683CuMnj3F9k0YMQBVSyJZTDW00qvoH+jRHu6Lce2K3CuM/v0XXocw2xVtayBvUjW00qtEJvRoKs4o9Dkr99K9ei7mUk0bKOZV5f9ZDa20yhUJjf+Ca3dsX4WHd+ReYXf3KrYtgmtbNMuphlZ6VU+KgTXg7orzh+UudnHzHLbNwsQmaJNeDa30apdRP1sQt0M11YvtFjkeYywbsK+yQeXK4riYMaO8SUTkcOyriJwR2y2iMNEvLNismdVVrn75BeXKySlXL17IvcLI5zwWzUGnFsifVU2s9EqbAHUqwnUE9u3CW3+5Vxg9uo9t6zCqF2oVRpof1NBKq/Q/oXZRjO6DHevxxPwSikd3YMNCDG+DmjnU0EqvEonQtSrmj8FJq7OQvsaFo1g5HoNqoXoyNbTSq1kOuLbB1oXwMz+9MSy+fBZzqVaPxvBKaJhADa20qhwRvQpgfg8cXmN6tmAosd0ix2OMZQP2VTYYMEAeKf3tdKAiIrIR+yoiZ8R2i8g2z55hyxax+HqZMogTxxhXmVb27GjZEnPm2G3Klf8b7N2JccNRuwLSxFdDK70K5kDX1lgyD5cuyr3Czvcsls5E9yYonlENrfQqkALta2PeBJw+KnfRffmCM4ewcBy6V8ffSdTQSq+a2cWVB9fPxw1fuVfYvXqGI5sw+x90KY6S0dTQSqtS0dG1BOb0F8O0wfby4hGOemBhb/QpJNa0UnIrrer9gmEVsGokzu3FpzCdy8l2ixyPMZYN2FfZYNUqedQ8ckRuISJyLPZVRM6I7RZRyM6fF5lUy5ZiFXZDVmVaceOKVa4GD7bnKlc3rmHlEvTsgGK51cRKr+SxUb00Rg3Czi14YacnfeuPgzsxaVhwC7RXyoPBnbB+GfzMV4V/+hC7PTChN5oVxh8/qKGVVnmjo1UJTOmPA5vsuUC7fragW1s0zaGGVnrV+E1MyFo1ARePyV3s4u4l7FqAqa3QMasaWunVOi3GN8LWGbjhLXexh5UzXLOnTS5vEDkEYywbsK+ywaVL8iA6c6bcQkTkWIyxiJwR2y0iC169wvbt4upJ5csjXjxjXGVaWbKgeXN7XljwyxccO4QprmhcA5mTqaGVXnkyoG1jzJuOc2fkXmHnd0NkUoM6omJuNbHSK2c8NC2PycNxKMgpitcuYO0cDG6OqpnV0EqvsinRuw6WTsT543IXu7h4TMRSg2qJiEoJrfRqlhNu7bBtMe5clbvYxaVjWD8eY2qiWTI1tNKrex7M7owD7njiJ3exC79z2DMTMxuhZ1o0ilAuSxK5ncghGGPZgH2VbWLGFAfU9u3lTSIix2KMReSM2G4RSZcuYeFCtG2LXLnM4ipDxYol1m4fOFCs4/70qdwrjB7cg+daDOyJsoWQ4Ac1tNIqYVSUL4IhfbDJAw8fyL3C7sxxzJuIDnXEWYFKaKVX8YxygXZf85Du00d47cPcUehcCUUTqKGVXvXyYHRHbF6Gu+bTtcLC/xWObcXcgehq5dqChrMFj24WFyK0l3evcWoblg3CwJKoFUMNrbSq9TMGlcLywTi9He/eyL3s4vIhbBqLiVXQPgEaRTAtxljkYIyxbMC+yjZ//SWOrwULyptERI7FGIvIGbHd+u94/RoNGsh/U2i8f4+9ezF6NKpUQaJEZomVoTJmRJMmmDEDp0/LvcLu7GkxnapNI+ROr4ZWemVNjqa1MG08TthvtZCXz7FnM1wHoP7fyBRDDa20SvMDahbG6N7Y7oHH5nnZk/vYuQZuPVAglriMoBJaaVUwDtqXxcyhYin3t/aLcu7fxM5lmNgRLf9QQyu9qifDwJpiEXf7ni345A4OrsScLmJqlRJa6dXsN7jUhudEXDkhd7GLd6/gvQVrBmB0cTSLqkRXotrHx/hKJ4bUzJyaJxWSQzHGsgH7Ktu0ayeOtTFjyptERI7FGIvIGbHd+o9Il07GLh07yi1k0Y0bWL4cnTsjb175iSkVPTqKF0ffvli/Hg/NL7f31V69xK5tGD0Y1csgRRw1tNKreB707oQ1y3HLfjOYblzB6oXo1wZlcqihlV5/JkGb6pg1Dl6H8O+/ci/d1fNYMwsDm6JKRjW00qtCWvzTEO7T4Gu/gE9z5TTWTcWw+qidSg2t9GqaXV5b8M4VuYtd3L6A7bMxoQnapFdDK706ZsPU1ti9EPfs+rzP7+LYSizpjEG51dBKr+6pMbMhdk2H31l9D7Zb5HiMsWzAvso2M2fKo++lS3ILEZEDsa8ickZst5zep0+IGNGYwuTPj5cv5V2kO3IE48ejdm2kSGH8oEwrTRrUr4/Jk3HCfpNrrl+F+2J0b4civ6uJlV5p4ovLDrqOwP7dePdW7hV2Jw9jtiva1kDepGpopVeZHCLYWr0QNy7LXXSfP4mzBeeNDu5swQZ54dIV21figf0Wfvr4Hqd2Y9Fw9CqH8nHV0EqrEj+ic1HM7ItDnnhpp3M5dT6HsW4cRlZFo4RqaKVV5UjoU1BcfPDYerx8LHexi3u+2DcXc5qhdwY1tNKrf04sbIfDS/HkptzFBNstcjzGWDZgX2Wbo0flkXjlSrmFiMiB2FcROSO2W86tfHljFqNVxIhy+/+5+/exbh1690bhwvjxR7OPSK8oUcRCHD16YPVq3Lkj9wq744fFAu1NalpdoD1fFnRqgUVz4HNe7hJ2z55gpyfG9kOdYkgXVQ2ttMoUQ5xIOK4/dm/CC/PrAz55gN3r4NYTjf+yfLZgobjoUB6zh+PYLnx4J/cKu2cPsH8tpnZH2/xqaKVX5YT4pyqWu+DcIbmLXbx9hVNbsXQABpRA9Z/U3EqrenExrAJWjcS5vWIVMDu6fgLbJmBKLXROqoZWWjWJjOGFsLIPTnvidQhRHdstcjzGWDZgX2Wbt2/lgfmff+QWIiIHYl9F5IzYbjmrU6cQObIxl9EqXjx51/+nM2cwfToaN0aGDGYfi6GSJUONGhg3DocOiWsC2sXjR9i8HoP7iFXYE0ZVQyutEkVDxWIY2g9bNuCJ/Wb03LiM1QvQtzVKZVNDK73yJ0f72mIR9zNBrg94/aK8tmCVTGpopVeFtOjfCCun45K33MUubvti8zyMbYFGmdXQSq8GGTG6GTbOwc2Lche70Be6mt0Z3XKroZVeLVPBtT42TcUN+138UfPBHz57sH44xpVF61hqbqVVmzhwLQ/PkfDZiy+f5F6hwHaLHI8xlg3YV9ksSxZxkK5QQd4kInIg9lVEzojtllNKlcosoNGqSxd51/+Ply+xdSsGD0aZMogTR/1A9MqdWywWtnQprl2Te4Wd7wUxnapjc+TNrIZWemVNjma1MX0CThyVu9jFqSPybME/k6ihlV7lf8eA9li3BLeCvNkzh7DABV2rokQiNbTSy3C24EP7zU3TXDwm1l8fUAPVkqihlV5t84s5WXtX48k9uYtd+Plg+xxMbIq2GdTQSq/OOTC9LfYuwYPrche7ePNMTKda2UdMrWocSc2ttOryG6bWxvZJuHFS7mI7tlvkeIyxbMC+ymb164sDdrJk8iYRkQOxryJyRmy3nEyLFmYrYem1YYO89z/v8mUsWoS2bZErl/oh6BU/vviD7vDh2LUL/v5yrzD6+BEH92L8KNSthLQJ1NBKr2K50bMDVi3FTfvFIi+eYddGuPwjzhZM/5MaWmmV+Wc0KAm3gdi3Fa/NF0R78RT7PDG5H5oXRe4f1dBKq79ii2sLzhqGozvx3n4rc717gxPbMW8QupVEqRhqaKVV2VjoUQYLhsLLrs+ruXQMHm4YXQONE6uhlVZVIqNPISzsg2MbQjxrzzZP/XB0BRZ3xIBcamilV59MmNsC++fjgfliZF+L7RY5HmMsG7CvstnYsfIQfv++3EJE5Cjsq4icEdstp7F5M374wZjXGOqmhUWg/zu+fMHBg3BxQfXqSJJEfe96Zcki0r05c3DefgtO3b+L9avRvztKFUDciGpopVWKOKhRFmOHYvd2vHkt9wq7m1exdjH+aYuyOdXQSq/8ydGhjjhb0DvIavR+17BxMUa0Rc0camilV7lU6Fsf7lPhc0ruYheP72LPKkzugtZ/qqGVXjWTY0gdrJ4EXy+5i134v4T3LqwYikGlUetnNbfSql5cDC0vF7r6bMNZeyG774u9czC7KXqlV0MrvQblwdIuOL4Kz+06y+zLJ1zec3JUtcKZk8otRA7BGMsG7Ktstn27PJxv2SK3EBE5CmMsImfEdssJPH2KRImMqY1hNpb2jzdv5Jj/knv3sHYtevYUq7BHiWJ844aKHh0lSojVYD098eSJ3CvsLpzF/Jlo1wR5MqihlV6/p0Xrhpg7DWdPy13swvuEiKU61BERlRJa6VUuF/q3s3y24AUvLJuE3nVQOrkaWulV9w+M7ojNy3DPrnHnLR+xiNXoZqifQQ2t9GqSDeNaY9si3L0qd7GLl49x1APze6L3X2popVfz5BhXFxsn45pdozrNjZPiZEBra7Q3/QGjimF1f5zdgnev5C528f4VLmzGxn8wsQg6R0bHCFqVy55E3kvkEIyxbMC+ymaPHsmj+6hRcgsRkaMwxiJyRmy3vnf58hnjG61Sp5b/iBgRnz/LMf8B3t6YMSO4Bdq140vdupg4EceDrFn+1d6/x4E9cB2B2hWQOp4aWun1dz780w0eq3DPfotG+b/B/u0YPxgNSiFzTDW00ipDNNQrIa4tuGczXr2Qe+k+vBOnAc4cinZlUCCWGlpplTc6Wv+NaQNxaCve2DVPuXgM7m4YWANVE6uhlVZFI6F9QUzvhYPr8cJ+i9lrHt0Ui1hNb4tO2dXQSq8OWTC1FXYvxL0rchd7uXwQG0fDrQLaxlVzK63kGu2j4Lsf/9rpogG614/gvRZru2Hcn3pupVSJrJyNRQ7FGMsG7Ku+RooU4khfu7a8SUTkKIyxiJwR263vV4MGZstglS+Pv/+W/44USY5xXv7+2LkTw4ahXDnEjWt8m6b155/o3BkrVuDWLblX2OlnC/7TDSXzq4mVXqnjiVTLbaRIuN6/l3uF3f072LgSQ7qg0p9qaKVX3qRi+fY5bjgdZGH4x/exYzXGdUPDfGpopdffSdCjBha74axdF5V//1asYLVgCLqXsrzQVbk46FUOi0fg1B58+ij3sotb57F1JsY3Qqs0amilV4+8mNcdh9fg+QO5i118eItz27FmIEaXQLOoam6lVeekYkLWtom4+fVrtFv23A8nl8O9HUZlV0IrWWN/x+pOOLVyxazxbLfIwRhj2YB91deoXFkc9TNkkDeJiByFMRaRM2K79T3q0weRIxvTnOjR0asXGjWSNyNGlMOczs2bWL4cnTohTx7juzOtePHEAu0jRmD3bry13/rfF89hwazgzhbUtmv3zp8pRtqR7zksnYlujVEknRpa6VUqK/q2wqr5uH5J7mJwwxce8zCkBapkUkMrvapmFvdqY7SRdvT8EQ6sw7Qe4hqCSmill1joqi7WTMZle5+1d+kY1rthdHU0TqSGVlpV/wn9S2DpQJzejvd2Wrxf9+YpTq3Hip4Yml8NrfTqmQ6zmmDfHNzzkbvYy6MrODofw9JjeAY1tNJr/F9Y3xvnPfH2udyF7RZ9C4yxbMC+6msMHiz7gJfm1yshIgpn7KuInBHbre/L5Mn48UdjrJM8uZiTpcmcWW5xunMJvbzEm6pXT/tWM74v08qUCc2aYdYsnLNfhPTpEw7tk9cWTBNfDa30KplfrOC+fjUe2G8R7n//xYmDmD4GLSojVwI1tNIqdWTUKIhRvbDdA08fyb0Mzh/HkvHoUVPMrlJCK70a5odbD+xaiyd2nYJ095pYxGpcazTOqoZWemnbXVph60I7L3T13h/n9sB9GAaXQe2Yam6lVYP4GFEZa8fC55DcxV6e3cHiTljUAf1zqqGVXv1zYFF7HFmGJ/abCai7dx4HZ2BRAwxKoYZWWnWNisklsHkQfHfg4zu5i8Hbp/DxODeuTLFsPKmQHIoxlg3YV30NDw/ZE+zdK7cQETkEYywiZ8R263uxYAF+/tkY7sSKhZEjRSyiMcxdihjxK/9O+fEjenfCnKnyZrjSXuHWrRg4ECVLmr0jQ/3wAwoXRu/eomt9+FDuFXaPHmKTB/7OizIFES+yGlppleoXcbag6wg7ny348jl2bcTYfqhdFGl/VHMrrXLGQ/OKmDYKx/aJfM3U+7c4sgMzhqBNKeT/WQ2ttCoQC+3LYtYwHNuFD/Z7zZqr3vCYhmH1USulGlrp1SYfpnbH/rV4Zr+vkcb/Bbw2YVFf9CmEypHU3EqrFikwrh42TcUNb7mLvVzYhQMLMLcFemdUQyu9huTD8h446YFXdl3bS+N3CnsnYl5N/JNYza206hkL08th+0hcOyDHm3p9H+dXYVMnTMuFARH0KpeLS7yTQzHGsgH7qq9x65ZsESZMkFuIiByCMRaRM2K79e1t3YqYMY0pT6RI6NsXzwPPISpZUm6PGBGPv+q36xJ5jVHOeXtHA7pr17BkCdq3x++/G9+IaSVOjGrV4OKCQ4dkNmcXl32xZB46t0S+LMb3aFq/p0WbRpg3w85v/M5NrF+GAe3FNQSV0EqvwmnQtSGWTIdPkOd98kBMp3Ltjkb51dBKr9K/oVdtcf3BC15yF3s5dwjLxqJfZVSMr4ZWWpWKjm4lMX8wvHaIfM2Ont3DoVWY3RndcquhlV7tMmFyC+xaYP812v3OYfcMzGiAbinV0Eor/fKCawbg7Fa8t/dFP28exU4XzKqM3r+ouZVWfRNgTjXsdsMtS5cseHEb3kvh2RaTsxqiK9PibCxyMMZYNmBf9ZUSJhTtQuPG8iYRkUMwxiJyRmy3vqVt2xA7tlnc07kzTposHT19uvGuM2fkxtB78xpxI5rFOr4X5F1hd/y4uG5gnTry+kJBK1s2tGqlHRtwKcjCT2Fx4iimuqFxDWRMYvbWDFXiT/TtgrXuuOsnd7ELn7MiluraEIVSq6GVXuV/x8AO2LAcd4Ochnb7CtYvwNBWqJZFDa300rZr92pjtJF2JNZo34F5g9D1b5SMpuZWWlVJiP7VsGIcLhyRu9jLg+vYvUhcQLB9ZjW00qtbbhFsHVwpQi77unkS2yZgcg10TKTmVlq1joVxZbFhBHz32fnygv/+i6v7sW0EppdFj5/V3EqrAckwvw72T8VdS6Hqs2s4tQAeLTAxgxJaiRoUGXOLYGd/XN22eN5MtlvkYIyxbMC+6iuVKSNbByIiB2KMReSM2G59GwsXiuXMTUOfLFnEnCZTPXsa7125Um4MvSK/m4U7ccO8MHyIZwv+9BOKFUO/fti4EU+fyr3C7t4d7NyKkQNR5W8kjm72pvT6LRZqlMGYIdizA2/tuvj3iYOYMdbqQlfpoqJOMbj8gz2b8eqF3MXA9zRWTEGfuiidXA2t9GpUQCx0tXsdntr1rL0Xj3HAI7g12uukxvCG2DATN87LXezl9gVxeUG3hmiVWg2ttKocCX0LY3E/cVKhf5CPK4yuHMbGMRhfEW3jqrmVVh1+xaRq2OqG65amPoXFpw+4tAubB4spV12jqrmVVoNTY3FjHJ6Nh5ZW4n/sC6/ZWNsYbqnV3EqrodGwoCT2DMX13fhiPB2V7RY5HmMsG7Cv+kpa96A3E3a8wgsRUUjYVxE5I7ZbjrZ5M+LHN0Y/WmXKBN8gv+JOn44oUeSAwYPlxlCa5KJOwkoeR95lqxs3sGwZOnTAH38YX7BpJU2KGjXg5oYjdp3Oc+c21qwQ63kVy232RgyVLQWa1cHMSTht1zPv3rzC3i0Y11/kU+miqrmVVrkSoGUVkW15BVl0/MsXnDqAeaPRqSIK/6KGVlrl/xltS2PmULHQ1fsgq3eHxYNb2LkMbu3QLIcaWunVNDvc2mL7Ety/IXexl6sn4TkRY2qicWI1t9KqVgwMKi0WcT+7G58/yl3s4stnXNyD9cPgUhotoqu5lVZdfsO0utg1DbftfSLtB39c3ArPfzCxCDpFUnMrrYZnxPKWOLYQT67LXUw9uoATM7CqPsYlV3MrrYbHwuJy2D8KNy2tk/XxNW5s9p5avmhOnlRIDsUYywbsq77SqlWysThk74t6EBFZxxiLyBmx3XKcKVMQN64xANKqVSsLAZbm4EFxjUJ9TJ06cmNonDuDRNHUxKdzC3lvKJ06JV5q/fpIlcr4Uk0rWza0bi3WpL98We5iFxfPYf5MtG0sFrRS3oJef2VH1zZYsQjX7Xq9vAd3sXElBndGxdxqaKVX4TTo1ghLZsA3yLUUH93DwS2Y0h8tiuGPH9TcSqsSidGjhrj+4Hl7zwO67YuNczCqCeqlU0MrvdrmF3Oy9q8T87Psy+cQ1ozBsIqoF1fNrbRqEB8jq2CtC3ztfaLih7c4v10sZTWqGJpEUXMrrXqmxewm2DsH9+16Hqvm3Uuc34j1veFWQA2t9BqVDSvb4eRyvLgjdzF13xtHp8C9NsYmUXMrrUb+gqWVcdAFfkfleFMfXuKaJ/b3wvL8cI2gV7ncXOKdHIoxlg3YV32la9dkk6G1IEREjsIYi8gZsd1yhP79ES2aMQbSqm1bbN8u71V8/IgCBeSwHDnkxhB9+IDsKdXcR6tdW+WAYLx9i127MGwYypZFnDjGF2moH39E0aJi4XlPT3ueLag5dggTx6J+FaRNoL5yreJHQbnCGNwHWzbgmV2f95ov3OeiV3MUz6iGVnqVzSlWcF+/TKzmrnh8D9tXYWwX1MujhlZ6VUyHAU2wZjauX5S72MuV01gzGYNro1pSNbTS6u+f0LWEWAnrxHa8s+vJlZ8/ielUK4ZiYEnUiKbmVlo1TQaXOtg0BddtX8EteG9f4sxGrOyDYX+poZVefTJjfmscWownQb5SYeT/FGc9sK47xv2phlZ6jf0dqzvj9Gq8snRa6P3TODIRK2pgTEI1t9Jq9K9YXh1HJuCupemE757h6nrs64GlfxqiK9MqztlY5FiMsWzAvurr6ZPVmzWTN4mIwh9jLCJnxHYrfHXogB9+MIuEOnfGdUunGhnUqSNHJkggt4SofBE1ANIqbkS8fiUHBHXvHlavRrduyJfP+NpMK2FCVK0qry1oR69eYsdmDO+PisWQMKr6mrVKHQ91K2H8aBzej8+f5V52ce4k5k9C+9rIl0wNrbRKFQk1C2F0H+z0xItncheD21exYSGGtETVzGpopVftXBjVAVuW4/5tuYu9nD8sLi/YtxIqxFNzK63Kx0Wfilg2BmcPyvH28u4NTm3F4n/Qr4hY1krJrbRqnRYTm2D7HNyxNKMwLF4/wcl1WNYNg/9UQyu9+ufE4o445o7n9l4e/vUjnFmDNV0w9g81tNLLNS88euDcevgH+SbR3PXC4fFYVhWj4qu5lVZjE8O9Fo5Oxn1LYd+7p7iyDnu7YWkeJbSStSwf9vXE1Q1L501ju0UOxhjLBuyrvl6pUqL/yJlT3iQiCn+MsYicEdut8NK0KSJHNqZC0aOjT5+QrzbYt68cHzWq3BK8js3NlsFK+rP8R7zIcoCpjRsxZ474M2fGjMYXZlqZMqF5czHmol2nEd27I64b2LsTiv5hfKmmlTM1WjXAvOk4b9dljL58wbH9mDYKzSoge1w1t9Iqa2w0KYcpI3B0Lz4FWbnp8lmsnI6+9VE2pRpaaZUzIpoWwsQ+2OeJl5YSja+mvZJTu7FgKLqXQsnoam6lVbUkGFQLaybhkslFLe3iwXUcW4/5PdErvxpa6dUxK6a1wd4leGjvqU8vH+D4KizuhAG51NBKr8F/imDr5Dq8svc5ki/v49RKrOqIMTnV0Eqv8X+J0wnPbxSnFgZ15ziWVsKyKuLcQCW30mrcb1hVD8en46GlBfXfPsGVtdjbFUv+UEMrvZYXwIE+uL4JH4yRNNstcjzGWDZgX/X1tD5Jb0fev5dbiIjCGfsqImfEdsvOnj1DhQqIFMmYDSVMKM7X8/OTA4LRpIncJWLEkFu4wX1EVmVIgrImR6ak8t8JfpRjNEeOwNUV1aohUSLjSzKtAgXQowfWrsWDB3IXu7jsg0Vz0L4p8mQwvkjTKpwLPdpj9TLctmsg8ua1cY32ND+ouZVWeZOifS3Mm4izlk7mOncMi1zRtSqK/armVloZ1mg/vhsfP8hd7OLtaxzbgtn/oFMRFI2s5lZa1UmDkY3hORs37X2i4vMHOLwaszuj6+9qaKWXtn1WJxxahWf35S728uwOjizHwnbol00NrfQa9pc4nfDMRnFqoX29vIeTK7CyPUZnV0MrrTpFxITCYhH3i1vFgu5B+R3DoXEivRoZV82ttHJNgdUNcGImHvvI8abeBURXe7pg8e9qaKXXioI42A83tuDjG7mLqafeR+c1KJgzmbxJ5BCMsWzAvurrrVwpW5PDh+UWIqJwxhiLyBmx3bKbvXvFRHhDPKRV+vSYOBH//isHBG//fuOOwfdv40cjwQ/GPChDIrHwee708maSGNi6FQMHokQJdUEuveLEQblyGD4ce/aIFbXs6ORxTBuPxjWQMYnx5RkqXmSx0NWQvti2Ec/tOn3pyUNsWYNh3VDpTzW00qtoevRsJhbDuhbk9LfPn+C1F7OHo31ZFIip5lZaFfsV3aqJbOvcMbmLvbx8ggMemNodbfKqoZVejbNgXGtsW2z/yws+uYP9yzGjHTpmU0MrvXrlF3Oyjq3Ha7t+pTRXDuPQErGaVd/MamilVeNIGFkEq/vj3DbL+VFYvLgDr2Vwb4uRWdTcSqvOUTCpGDYNhO8OfLIUIvsdFauwL6mIEbHV3Eort1RY0wgn5+CJpdXlQ46uCuHgP7ixFZ8sXWj+qTcuTMaumliWCHMiaFUuL5d4J4dijGUD9lVf7+pV2alMniy3EBGFM8ZYRM6I7ZYd7NihXtQvb14sWiTvDQ1fX6RJI/ft2VNuDGrmJLP1pJL+jLFDscUTOVLJLb+YnMZoWtoP53r1xMV/Tp2SD2UXHz5g/264DEP1MkgW0/jCDJUiDmqVh9tIHNqHT5/kXnbhdwPrlqBfG5TKqoZWepXLhUEd4bkC94LMg/N/LS8v2Lwofo9iFlrpVTalOJdw5XRxXqF9PbmHPSsxoSOa51JDK71a/oFJnbF3FZ7ae+rTwxvYvQhTWqJdRjW00qpyJPQtLFbCOrlFrIplX49v4OBCzG2B3hnU3Eqr5lEx5m94DMHF3fhi128SzXM/nFgK9zYYkVnNrbTq8iMml8CWIbhs5an9juLAWCypYCW6So21TXByLp5ekeNNhRhduRfGof64uQ2f3sldTD07h4tTsasWlsroyrRK5uES7+RQjLFswL4qTBIkEF1L06byJhFROGOMReSM2G6FyejRiBHDLDAqXx6bNsl7Q69CBbn7n3/KLYqpbkgczRgPJfgBnVph0kRULIv4P8qNsU1ehlY5cojrIS5ZEsKK8rrTR+F7Tv47eC+eY6unOKWxTEGzZbkMleU3NKuDWZPhbdfITHPlIpbNQrfGKJxGDa30qlEQo3tjxwYLa7S/eII9HnDrgYb51NBKryqZxAru6xeI1dzt6/5NMaNqXGs0yqyGVnq1K4DpvXBoA17Ze+rTvSvYOQ8Tm6JNOjW30qp6VPQvgeWD4b0Ln+w6L0/z6Br2z8ecZuiZVs2ttGoRHS6lsX44fPeFdrpi6OnR1YrWlqOrbj9hSklsGYore/HvF7mLKUN0NTyWmltpNT4N1jbFqfl4aun7RC7Tbm2tq4giujrYHze347OlCV/PzuPiNOyug6WJldxK1LKk2FMXPtPXzR/BdoscjDGWDdhXhUnp0qKDyZ5d3iQiCmeMsYicEdutr/HmDapUUS9B2LQpjh+XA2zSu7d8hDhx5BZTU93wq8kMrLgRkSM9EiUU4yNFQMzA7bEjiBXlCxUSC6Ru2ICnT+XuoZEiosiA6v8tbwZlWKO9yO/GV2Jaf2ZCpxZYOh9XL8td7OX8qYDLC9bCn0nMEiu9MkZHg5KYMAQHd+JDkFzg4R1sXSEuIFgrpxpa6VXnd4zphO0r8eiu3MVe7lzBpnkY3Qz10qmhlVbFoqBTUcwdgOPb8M7ep87d8cX22RjfCC1TqbmVVrViYHAZrByBC/vtnx89uIJ9czG7CbqnVnMrrVrFhGs5eI7CZXtfVFHz3A9eenSVSc2ttOoWDVNLYdtwXNknxyv8jskTBodbmnU1IS3WNQuIrq7J8abeP8fV9eIKg0tyB8mtAir46OqFD3xmYE89kVIpuZVWy5KIVOviNJFwBWK7RY7nbDHWh7Me01xcZm0O/oj0+PS2ZbMmuLq4uLhOmLZ488n75nF+CHdbxb4qTPr1ky2Rv72PjkRElrCvInJGbLdsc/gw8uQRS7DrXZZWv/wiLi94+7YcYKulS+XjRIoktxiMGWIWYGkVLQIiBz5vpIDoSt+eODr27cPnz3LH0KtZ2CwVmjZabtdcvYwl88SVEK2t0V48D/p2gccqPLgnd7GXk4cxYyyaV0KOX8xenl4546FFZTFAGxbUyf3wXCTmVVXJpIZWejUqgPG9sHeDnS8vqLl5EZ6zMLwhaqVUcyutSkUXVx5cOAyn9uCL7V+p4PldxNaZcK2P5snV3EqrOrExrALWjIFPOKyZe267iK5mNUb3VGpupVWb2HCrgI1jcPWIHG9HhllXw61EV9NKY9sIXN0vxyvuHMfBgGXaR8RRcyutgo+uPrzEoYHY1wNL/1RDK72Cj65eXobvbOxpgBXJ1dxKq2WJxbmEF6aI8wotYbtFjudcMdYHrzUTXTTBxlgffDfPdnVxnbxow+4Duzcvm+nm4jJx+WHDhVBDuDs47KvCZM0a2eXst/Kzm4jIrthXETkjtluhNXYskiaVzZVe2bKJ1abCsuTThQuIHl0+2rZtcuPjx6hVEb9EMsuMYkZAxMDnTZECNWsiZ1p5V/LYckebbPdAyoBJWIZKGQnnvTFnKprXFScGGp7aUPGjoEJRDPsHOzbjtfHy/3agfYZH9mDSMDQsjUwxzF6VXuLygrWxYDIunJa7mLrhizWz0b8xKqRRQyut/vgBLYph6gAcDoepT9fPwWMahtRF9WRqbqVV2djoUwFLR+PcITnejm6dx+ZpGFcXTZOquZVW9X7ByCrwcMUle69Mr3lwGXvnYGYjK9FVHIyvhM0uuBYOT/3iLk4uxwora10ZoqtrB+R4xV0vHHLDsioY+YuaW2llPGHQYnT1GkeGYH8vLMunhlZ6rSiIA/2sLtP+6houzcO+xnBPpeZWWi35VazgfmGyWM09JGy3yPGcKcYKCLFcXV2Dj7FubZvr4jJh6YHAYOqD78ZZLi4T13jpU65CuDtY7KvC5NAh2euMHy+3EBGFJ/ZVRM6I7VYI3r1Dw4b46SfZVulVrhzWr5cDwuKvv+QD1q0rFrFq2xZJ45olR7Ej4KeAACtLFrRqhYULxWV8NDXLyQFJfw54IFu8eoHMMc1CopQRUbcS0sQ3Pq+hkscWz6Wv0f4Vs72C8dYf+7dhXH/ULoo0Ucxej16F04hlsJbPxlUfuYupK+fgPg196qL0b2pupVX+n9GujLj+4Ml9+GJp/aOwuHIaqydhYE1USaTmVlpVjI9/qmKlG3y+6gzT4N08i01TMbY2GidWcyutGibA6OrYMAFXT8rxdnQ/MLrqllLNrbRqGxcTKmPzOFwLh3f98j5OrsDKdhhh6QqDIUZX907jyESsqI5R8dXcSit9mfZT8/HMUnT16Z2IpQ72w/K/1NBKrxV/4UBf3NiCT5YS0te3cGURDrTAqnRqbqXV4njYWQ3nJ+CppXDWOrZb5HjOE2M9P+I+0WWi+xaPWcHGWJc3a/dP8TC9isfzPYtdXCas8RL/DuHu4LGvCqskSURjVL++vElEFJ7YVxE5I7ZbVp09q54/+MMPyJoVp237ndOqBg3kw0aJIoKqn83zo1gRkO43dGiHtWvx8KHcRZc9pRzza1S5JfSKplfTouTmz6tV+kRoXAPTJ+DUCbmXvbx8jp2eGNUL1QqoL0OvvzOjbyusWSQuRBiUzyksnYjuNVAikZpbaVX4F3SuhAUu8A6H89d8vbByPPpXQ6UEam6lVZVEItVaM0kkXHYnoqspGFsLjROpuZVW2sYxNbFxMq6fkePt6OHV4E4YbPsLJlbBFldct/f3iebVQ5xehVUdMCq7mltp1TWqXOvK2gmDF9fi2BSsqInRv6q5lVauKbGmkdUrDH75hFs7cXgg3IuooZVey/KJOVnXN4n5WUG9vYery3CoDVZnUnMrrRbHxfbKOOeGx6H4ZdgKtlvkeM4SYz0+vHxiwJypgBzKeoz14cBSFxeX5ebnWftunCH3CeHuELCvCquKFUVvlDGjvElEFJ7YVxE5I7ZbFkyYgIQBa6gbKkMGuLjg+XM54KudOCGmydeoIVZz1x/ZdJl2vVLFQ5lCVtc2/Su7HBY/itwSGv5vUO53NTPSKkngk+ZIhdYNMX8mfC+I8W9eYZt7wJ5h9uQRtq7F0K6omFt9dr3K5cKgjvB0x0NLa2ydP45FruhSBYXjqbmVViUSoUdNkW352PuqiBrfE3B3xT9VUCGemltpVS2pOJfQY5o4r9DuQoiuEosJWdoAbZjdPQy4wuDsppaXaW8XEF1tdcONr09hrPJ/Cu+1WN0ZY3KpuZVWXX/ElL/FFQavWlmm/dFFnJiBlXUxNomaW2k17jesbgCv2XhySY5X+O3FkaFYVQLjo6i5lVZL84iVsK5tEKtiBfXuMW6sxuGOWJtdza20WhgT28rj7Fg8ss9Zlmy3yPGcI8YyhFhyOpX1zCng7hkbfeVNXcBGV/cjId4dAvZVYTV4sOyQntl7DUsioiDYVxE5I7ZbRn5+4mzBH3+U7ZNe5ctj3To54Ct8+SIWXx85UjxyrFhmjxwlYMqVniLp9XderFgkd7RozBA58pdIeG9p6WhTz55i83oM7ImsCdTkSK+cv6FLKyxfiBvmp1P98aNIiPJExxtLv7GHxsN7IpYa2AFlcqhPqlflvBjRQ6zP9dzSFRW9j2DBWHSqiIJxzEIrvUolE+cSuk8T5xXa3cVjWDEOfSuhfFw1t9KqZnIMq48NM8Vq7nanR1fNfrMcXTVNCpc6YjGsm+Hwrp/cxMGFmNscPdOquZVW+gmD4TTr6t1LnNuAdd3hklvNrbTqHBmTi2PzYFzejX8tnRz69ApOzsHqhhiXXM2ttHJJKlKtEzNEwmXR3UM4NhJrymBiNDW30mpxLuzpgivr8M7Sd+mHF7i1Hke7weMPNbfSav5P2FIaZ0biof2XRdN+YmdMl1zeIHIIZ4ixHh9YOsGwelVoYqwgdx9eLudghXB3CNhXhdWmTbJP2r5dbiEiCjeMsYicEdstYf16pEplDJi0ih0bXbvivPEi9zbw9xfrtQ8YgGLFxHmIpg+r189RjBcZ1KtmOYwaJHe3Zt4M4/j7d+VGxcP7WLcSvTuhyO9iWOIg+ZFeKSPiqvmfmXVLxpsFRkNayu2hce82PJaiXxuUzKI+nV41C2FMX+zZLKZ6BXX6IOaMRPuyYlkr09egV7lU6N8Ia2fjuqV1ssLo4jEsdxHRVbk4am6lVa2UGNEInrNx28osnrC4eS64ta4M0dWtr/o+DN5TPxxegvmt0DuDmltpJZdpH4fr4bDW1Qd/XNiM9b3hmk/NrbTqFBETi2DTQFzaic8f5S6mnt/E+lbiMoLj06i5lVZjEsK9Fo5NxUMrH9qDE/AaB4+KmBxTza20WpgNuzrg8iq8fSTHm/r8Dn5bcKIPNuRXcyut5kbG5uI4NQT3rcwXC6MXZ3BlAg5Xfb8qRrkCSeRGIof4/mOsx3sWu7pM8zgrl2B3UIzlZgX7qjB58EB2SyNHyi1EROGGMRaRM/q/jrEeP0b16sZrBeqVNy9mzrT5+oNPnogsrGdP5M9v9miGypABTRqjWF71EoTtmsArFKcabV5v3GXnFrlRd+c2enZA1zbIn9U4Rgmw0v2EdFHlv9P8IHc09cAPv0cxD48iyruC4XdDrGPVpyWKZzQ+l6G0J6pbHG4DcWAHPliaO3ZyP+aMQNvSyBvd/KkDqkJaDGqG9fNx29ICRmHkc1zOurIYXdVJjVFNsGku7oTDU9++gC3TxRUGmyRRcyuttI3hF109v4cjy7GgLfpmVnMrrVrHglsFbBqLq0fleDv68knEUhv7Y0IhNbfSa3xBbPwHPtvwydK3yqt78F6G9a0xMaOaW2k1Kh6WVxPruN+3skDY47M4PRkbqmNafDW30mp+RuxoDZ9leG0pHf73X9zbg5ODsLGImlvptbEQvPrjzg58sRS6hdHrSzjeEMfqYmNirI5gKMZY5GDfe4x1a8d80xDr62KsgI3Gkwqt320kU6sgGGOFVZo0onOqVk3eJCIKN4yxiJzR/2mMtWwZMmUyi5kiRkSzZjhg5WJnFt27B3d3dOyIXLnMHspQOXOiQwesWIE17iiU05gxaRU7Agb3we2b8qGCd+YkksSQO7qOEFtuXMOyBejUAnkyGB9TLyXAyh5XZEw548mbWWIFPKK5EonVCGnaQHlXULeuYfUC9GyGoumMz2Ko9D+hQUlMHIojeyxf2dBrH2YNQ5tS+PMn9Um1qpxBTAHzXIS7lpZ4D6MQoqs0GNUUm+bhTsC1IO3rji+2zoRrfTRNpuZWWulrXYXTCYMPr+HEaizuiH+yq7mVVi1jYFxZbByNKyGfKPM1ru7DliHixMDOkdXcSivXfGJO1vlN+PBGjjfl/wQX1mBjR0zJruZWWg2PhSUVcXAc7lo51fHZJXjPxKZ6mJFEza20mpsG25rhwkK8tPJ/8OFheI/C1tKYH1XNrbRanxfHe8Fvs+ULFIaR/y3cXAivZtiS2jS6krU5+dUVf+XMzJMKyaG+8xgrIGGyxtJpgB+4xPt3rk4d0UX99pu8SUQUbhhjETmj/69268ED1K2rTr/Knh1ubnhqafmboG7cwOLFaN0amTObPYih8uVDjx7w8BDzszRzpiJ1PLOYKWYERI2AVasCHi4UrlxCnoxy34I5xOytXGmMj2aoRNGQK7E4W9CQKGWLI+KkTStFtKRvyR+kIayfVw2SckaSd5m6eRUr56FHUxROY3x8Q2X+GY3LYupIHLcUAv77L7z2YuZQtC6JPFHVp9OqSiYMa41NS3DvltzFjny9xDLt/SpbXutKj642z8dd89XB7OL+VeyYiwmN0SKlmltp1SihuMJgOC3T/vYFTq3Hsm4Y+IeaW2nV/CeMLYUNI3DJltA29G4cwY7RmFYG3aKpuZVWY//A2m44u168yKA+vMaljdjaAzPyqLmVVkOjYVEZ7B+F21ZCt5c3RTK1tSlmp1JzK61mJcPmBjg7G8+tTLJ7ehrnx2NHZSyKreZWWq3NiaNdcHMdPoT5Ug9BfXiMO6twqh22Z1FzK608f8XRWrg2DS/FFRjYbpHjfecx1nPfYwfMbV02xcVlyrKt2j/P3JajTAUEX1M8TH/+Pt+z2MVlwpqAy1eEcHfwGGPZwbhxsqm6benLR0RkP+yriJzR/0u7pXVE6dMbwya9GjbEjh1yQDAuXcKcOWjSBGnTqo+gVZQoKFpUrIS1dSveBE4qefMabRoiYVSzpClTcvwUsMvff8thIbp0EfmzmD2IaSWLKRbVGj8KVfKZBVi5EmDycLz1x471xo0ls8rH1HWuhJwR1USpTAp5r+bGFbjPRbfGKJTa+CCGyhILzSpg+hictBIo6LOurEVX1bJgRFtsXoaHd+R4O7p8Cqsm4J+qlq8wKE8YDJ9ZVw9vYPdCTGqGVmnU3Eqrhgkwujo8J+G6lXPfwuKDP7w3w703huRTcyutmkTG6OJYNwQ+e0WwaHd3TmPPeMyugl5x1NxKq1HZsLojzqzGm8dyvKkvn3B1B3b2x5yCam6l1cBImFcMe4bgxl45XuH/EJfcsbMt5mdScyutpv8Kz5o4PQVPrJyk+dwHPtOxuzaWJlJzK61WZcTBNri2Am8fyPF29Nkf9zbibA/syq3mVlp5xMShirjsiucn5fhAbLfI8ZxhiXczFk8LNHVr21wXl4nLDwf+UPrgu1HbI3CF+JDuDhZjLDvYu1f2WGvWyC1EROGDfRWRM/qPt1snTqB2bZE06e2QXjlyoFIlsTBWMM6fx4wZqF8fyZOb7atXjBgoXRrDholGSzl17tQJFMhmFjYljIoe7TFujNw3WjQ50hqf85g3Hc3rInMys8fRK2Vc1K2ESS44cRSvXqBWEbN0KV8yNC2PdwEnOo3sadxeu2jAQwfoU89CgKXVmE44tEtGVwVTGfc1VLY4aF4RM11w2sraSSf3Y/ZwqycMVs8qoqsty8Mlurp2FqsnYWANVEqg5lZa1U4loqvN83A3HKKrp3exbxmmtUa7jGpupVX9XzCiCtaPx1U1jLCDL59xcRfWDMCIwmpupdfwQljdH+d3WF4rPYzuX8TB6ZhfG/0SqrmVVsMyYEVreC3DCyuXI7h1EHuHY8HfGBRFja60mv0XdvTDlW34bOmXxo+vcc0Te7th8e9qbqXV5FjwqAQvVzy08pm/voXLC7G/KVakVHMrrdxTibsuL8Dr0J3za6tHe3BhEPYWVnMrrdZEwf6/4TMcTw7KwZaw3SLH+w/EWPqJh8ZNH7zWTnZ1cZ08b+32A7s3L5vpZhZbhXR3cBhj2YG/v+yZ+vaVW4iIwgf7KiJn9J9tt6ZNQ4IEsgvSK0oUNG2KPXvkgKC8vTFlioi9kiQx21GvuHFF+DV2LA5bmX/UrY2YHmUaOeXLgqlueP0Kjx4hYUL5OCct/Wp98Zw4/bB5HWRMYvYIhmpYDdPG43Tg6Qw3r6pJU7EM6FBb3qvZvNp4V/OKcmP/RmYBVq5Ixn+3KGd51lX2uGhRGbPG4YyVK9adPojZAcu05wk2unpkJcsIixsX4DEdQ+qgamI1t9KqZgpxhcGNc+AX8komNnv5GIdXY2YHdMym5lZa1Y6JYRWw1gWXw+Eyf5rLB7F+OMb8jaY/qLmVVkPyiTlZ3pvF/Cy7e3oDR+djcWMMSqHmVloNSinu0gY8uS7HK+6exCFXsabV8JhqbqXV9D+wtTt8N+C9pWtZ/vsZt3biUH8s/0vNrbSa8CNWl8Kxkbh7SI5XvHuMG6twqB1WZ1ZzK62WJsLuOvCZgReWruAZds9O4NJYHCiLddHU6EqrPX/h/D94uAP/hurKEmy3yPH+gzGW5vHpbctmTXDVNrtOmDZ/w9H75ql5CHdbxRjLPnLmFD1T6KevExF9FfZVRM7ov9ZuHTmCP/9E5MgyM9Irf35MnYpXln43PnUKEyaI6xX++qvZLnppG6tVw/jx8LK+HMaRAyhfBPEim6VODapiq6ccoKlfXz5g3bpyi8ZzrYiumtVGhsRm++qVIo78R9yIeHBP7qLZvh6//2qWNFXOi35t5L260b2N97YOuM5P9+pmAVahuMgf03jTMFgvPbqa7QpvK4tnex/BvNFoXw75YhgfxFD6CYPhNOvK7zI8Z2N4A9T4Tc2ttKqeDMPqw3MWbodDGPH2FY5vwLzu6PqHmltpVSMaBpfB6lHwtZJyhtENL7hVFFcSbBVTza20Gvg7lnbFSQ/LC06F0euHOLkC7m0wPKOaW2n1TyLMr4ODM/DQymf+2AfHp8O9NsYkVHMrrSZnwcb2OL8Kbx7J8Yp7R7CmtIioxv+gRldarSiEQwNwe5eYmBbU53diFfbjvbA+j5pbabUwFrZXEothPT0tx9vXy4tiNaujNbEhvppbabUjJ7y74t56fHwpx4ca2y1yPKeLsb4lxlj20aqVaJtiWbowDRGR/bCvInJG/5F26949sdBVzJgyLdIrXjx07izOK1RoW1xdUaWKGGA6Xq8kScSErClTxOSs4M2YiHQJjcGTVukTiusPXjWf/rNwoXzkOHHkrCtr0ZW2sWktzJ6CBbOMC8MvnC0fx20QMkQzy5sal8VOk7BMN66/cUDXhmhX1izA+jM6cv+M7IE3tX/oI7PFkdGVtVlXF05g4Th0qoi/YhsfzVBV9WXal4ZLdPXgJrYuxOhmYlkrJbfSqkoiDK4Nj2m4bmXxo7D49AGntmFxP/QqoOZWWlWJjAElsGIozu+T4+3rzgXsnIopNdE+gZpbadU3Cxa2w7GVePlQjrej969x3lOsxT4ml5pbadUrjlgGa894+FkJgF7cxplFWNsUbqnU3Eort9RY10wM0IZZ9PgsTk/C+qqYGlfNrbRa8gf2dsf1jfho6fqGmgf7cWoINhVVcyut5kXB5r9xZgQeWpm0FUZv/XBzUcBFBlOpuZVW29LjZCv4Lce7+3L8V2G7RY7HGMsGjLHsY/Zs2TydDYcroRARBWJfReSMnL7dcnW1sHxVhQpYvlwO0B0/LlZ5r1xZnBuoDNZKe4T69cViWBfEhcBCcOUSqvyNXyIZ4yetUsbFikVygKkHDxA/rrg0YYwIaualV4bEItWaM1UkXAZlCsp761QQK7W3qoqUkYzhlFaF01heoGrqSLnWe6oIyB/HLGnKFgHptacz2ZIjklzr6swxubtimzuWTEDXqigSz2SvwKqUHkNaYuNi3A+H6wg9vY+dy+HaBg0yqrmVVhXjY0B1rJmEqyFFjV/n/D4sH4J6cVE5khpdadWnIBb/gzM78CkcFpx6dB3752FmI3RNruZWWvVIg7nNcXARnobHtZv+xW5XbBqA8QXV3EqrrlExrTS2jxIXIrTo7TNcWINNHTE5q5pbaTU2MVbVxYmZeHJJjle8uI5zc8WVBGclU3MrreZnwq52uLQSb61M2npyCudcsa0CFv6sRldaeRbAiX64u0MsJ293H1/g7jqc6Ywd2dXcSquNiXGsHq7Pxhu7rcvGdoscjzGWDRhj2Ye3t2zR5syRW4iIwgH7KiJn5Kzt1v794uTBSJGMUZRWWbOiVCncDFyYWY+uKlWyHF1pP68aNhR/7fMN9QloU8YhbQJjAqVVijjo0xnep+QAg8s+mDcDLeohUXSz8XpZjK4MuraRwzL9imLpzdKrbHHERQn9bsiRigWTxXStNAGJlWnelDUgwNJ2z2iysVIGuZfi2gW4T0Ov2iiRyDjYUBXSYGBTbFiALQuxbKzcxV5eP8f+dZjUGU1zqLmVVmVjoU9FuLvCNxTXO/8KV05gnQuGlkfN6GpupVWPPzG/J05sxDsrM4DC4tUjHHNHn8zonVHNrbTqnBQz6mPPLDwIh0W+NDePYcdoTC0tgiolutJqQiFsHIDLu0XIFdSXj2IV9u19MCufmltpNTwWllbGITfctzJp6+1jkUyJiwxmVHMrrWYlx9bGOD8fL6x8w7+8At9Z2F3X8kUG12bHkc64uQ4fwuFES83DXTjfXyxrpeRWWomLDFbClfF4EQ6XpARWLnLNkTm5vEHkEIyxbMAYy270OfatW8ubREThgDEWkTNysnbr0iWUK4fo0c0Cqdix0aYN9gWc2KWfMFipkjiDz3SMXqlTo3FjzJ2Ly7YkAl7HULqAWKPKkENpVb4Ilswzu0zh1cviBMDWDZE1udlIvdInEicMWouuDLQB2uBfIyBFwKQqQ5XKivmT8NHKArMvn2OOGzL/aDxbUK+sEZApgsi2GpZGp+rG7R3KyR11t69i3Vz80xClkxvHGErbqN21dg5uXxGDj20zRkt3AraExcf3OLoFM3qjTT7jwxrq75/QozSWjML58Flw6vYFbJqC0TXQIL6aW2nVMRtmdcThNXj9VI63ow/+OLMRy7pj4B9qbqVV27iYWBXbJ+J2+Ew3u38B+6dibg30iafmVlqN/QPruuOcJz5YyexuH8a+EZYvMjgwEuaXEJcgvGXlWntfPuDGFuzvhaV51NxKq6m/YEN1nJ6CJ1ZOEX33CNdW4GBrrEqv5lZauafG/ua4ugT+4XBJAc3zk7jkgoPlLK/Uvq8YLg7FkwNysH19eolHHrjUCUezYWeEcoWSyO1EDsEYywaMseymRAnRuuXKJW8SEYUDxlhEzsg52q2nT8Xi6MqVB7WqVAnLlon1193cxFpXFmddpU6NJk0wbx6u2Ji5vH6Fvl3Uiw+mjic2GqZf3bqBZQvQvilypjYbplfcSOJcwqgRsGenHB+8iaOQxCS60qtpeWz3kANMvX+HvVswuo+Yn5UxYJUr0+xJu1kxByYOxbGAdK9vfeNdg1uILQ/vYNMScVZgxXTGuwxVIjF618HK6bh+UQzWvXmBYj+YxUxfPSFr9SQsGIJORc0ezVDa9nmDcNr6NSXD4uFN7FqACY3RIoWaW2nVOi0mt8DeJXgSDot8aXz3Ye0gjCyi5lZaNfsRLqXhOQpXLZ0uGnbP/XB8EZY2FdcTVHIrrYZlEIu4n1yBV1ZW2np4HkcnY3k1jIyrRldazcwr5mRd2SbmZ1l07zCODseqEnCNqEZX438Ui7gfH417Vt74lw/w24JjPeGRW82ttFqSALtqwWd6eF1k8PVV3JiNY3XhmVDNrbTa+QfO9sD9Tfj8Vo63r2d7cW0gvAphp/ZcxmKMRQ7GGMsGjLHspm9f2cm9CYeJ0EREARhjETmj77rd+vIF3bqJHMqQSen155/o3l1My6peHfHjq/dqZYiurn7VejQLZiJ3BjWTql4aK5eIe+/fxepl6NoaeYKM0Sp1PHGZwukTkD6NfDH16wc8aLA83fFnErP0KmN0lMmBLWvkAIMjezB+EGoXFWNSRkDmIAlUtewY2x2fTNYA6l3HeG+XyhjTCTWyGbcYqvAvYhmspRNxydKZUNWSWgibbOJ7Astd0Ls8SsVQH0qrNvkwsw+ObcWHd3K8Hb1+ikOrMaM9OmRRcyutmiaFa31sn417YZ5cZtHNU9jiCtfyaBFdja60Gl4Qq/vjwi7LZ+2F0ftXOOuBNZ0xKruaW2nVPykWNcDhOXh8TY5XvPQLWKm9CVxTqLmVVpOzwLM9LqzGWyuz1Z5cwOnJ2FANU+Ko0ZVWy/LjQD/c2ol/v8jxiodHxFrsm0tgbiQ1upofFVvL4OxYPLZyMc0w+vAEd1bhVFtszaDmVlptTSdWar+9HO8eyPH29dobt8bDuxL2/KykV6JOFju1tlrmDDypkByKMZYNGGPZzbp1spHavVtuISKyN8ZYRM7oO223Ro5EjhyyezFU0qRi6SutEiZU79JK+/nTqJFYCdTWWVcG2zehRhn8+qNZLJXgB4wYIM4rXL8avTqiQDaze/VKHht1K2GKK04F/lK9ZYt8VdGjyy0WvX+H3i2QLqpZgJU+ujh/8K2/HKM5fVRclLBRGaT/SY5JE3DCoGkIlT0C/oiJO9flLgY9apoNUypfDHQojwVjcc7KKu+axlnUyKloZHlXiG75YN00DKqFSgnUB9GqWQ6xEtaBdXj1TI63o88fxUUGF/ZBj7xqbqVV3TgYWRWeE3EjfM7ae3gVe2djej10TKTmVlr1z4mlXXBqPd69kuPt6/IebBpoeaX2nrEwqzL2TsBdK2/8w2v4rMemzpiaXc2ttBr3G9Y0wqn5eB64Ap3i9V1cXIy56TAnpZpbabUgC3Z3xJW1eP9cjlc898HFqdhVA4t/UaMrrTwLwOsf3N2Ff8Mh8sO/eLgT5/thdz41t9LK81ccq41rM/A6fFYoe+eHewtxoTEO/qbmVlody4XL3fFEzvliu0WOxxjLBoyx7ObUKdlLjRoltxAR2Rv7KiJn9H21W9OmoWBBRIwo+xa9okdH2rQWzijUKnlyNGgglmm/ZOXyZ6Hx6qU4KzBlXDWcalobowdjYE8Uz6PepVWiaKheBuNH44SlC7cZMridVk4nPH0Uf2c2Rld6JYmAZNHkAN9zIsxqXQ05fjEbkyHI+YP6Cu7Z48oddSumYuZQFE9oNlKvXJHQsri49+R+OdiaVrnV4Emr8e3kvdY8voNtizG6GWqnUvfVql46jG2JHUvxOHxWL/I5jJUjMOBvVImsRldVo2BgSXGvNiY8vHqMYyuxoA16Z1BzK63ERQZb4PBSPL8nx9vXnTPY7YYZFdA9hhpdaTWpGLYMxTXryzbd3I+5RTG3sJpbaTUiNpZVwZGJeGDlouef3uL6RuzthkU51dxKq1m/YUsjXFiAV7fkeMW7h7i2HAdaYmUaNbfSak0WHOmIm2vxwUryFUbPTwUsd1UWa6Oq0dXaH3GgNC6NwbPjcrB9fXmLx5643BVHc6i5lVaHUuFic9xfgvfqNwzbLXI8xlg2YIxlT2kCZrZXrSpvEhHZG/sqImf0XbRbW7agdGlEiSKjH0P9/LO6RavEiVGnDqZPx4ULcvevNm64heXY82dFrXIoVUDdrtUvkVCpOMYMwaGABaesGTRIvtRixeQWA/836NNSrLlumkzlSYKE2oNHQPyIWDAFXRsi/29mA7RKGQG5YyBnRLNAqlRKkWpp96aLKh7c9zQWu6FTRRSIaTZMr4b5MLEPDm+zuk68wedPaJZTjZ+0KhIJL62cPvb2NQ6ux+QuYoKVspdWVRNjaD1smGWHJeEt8rsYsFJ7ddT/RY2utOqVH4v74fR28b7sTnvMjaOxsg+G5FVzK63aJ8CUWtg9A/fDELMG49ktHJ2PRQ0xIKmaW2k15nd49MCFTfhoZdkmn3UinFpaGcNjqtGVVvOKYc9Q3LSefMnlroqruZVWk2NifWWcnIDHVpKvLx/htxXHe1le7mp5UuxtgEvz8MrKNQrDyP8WbszD8QbYlESNrrTa9SfO9cHD7fjX5AIOdvTyCG4Mx6niam6l1d7Y8K6K25PxxsoPN+0lvdh2dnO54vmTyi1EDsEYywaMseypXj3RTiXhcoBEFF4YYxE5o2/Zbp05g7Jl8dNPMvQJpuLHF8tgTZoEb3ucArZxHf7KLjIp04gqZVyxTLtyRqFepQpgaD/s3o4PIQVAmrNn5WuOEkVu0e3ejCLpzJIprfIkxtpF+D0FEkfAb+Z36ZXvN9TKi4JxzQKp/D9jTGcM7SgGpIqA9BHRu65YlN10jGn1a4BXoZvJ8uYF6qVXQyi9KsaXY0yd3ivWYu9QSB2sVemf0aci3N1w5bQcbF/P7omF2Cc3R8tUam6lVYcsmNkh4CKD4XC6oub6CWwcI1Zkb/ajGl01j4pxZcS92pjw8P514HJX2dTcSqvBqbGsBU4sxcv7crzi1V2cWYy1TS0vdzU1J7Z0w6WN+GhyTqupp744Mw2eNTAlrhpdaeVeGIcHw896yPvYC95jsLU05kZRo6sF0bG9As654amlpdnC7vMb3F2PM52xI5uaW2m1NR1OtYafOz48luPty/8y7szA2ZrYF0+NrrQ6WQTXh+C59cTQ/xQeuOByGXj9iBMRtCpXlL/TkUMxxrIBYyx7mjhRNlVft9YpEVFIGGMROaNv0G7t3InKlREjhuxMrFXMmKhYEa6u4kKEdnHGS8yliv+DWUT1SyQkjGq2Ra/CudCvKzavF9crtIn2mvXXr3+q9++gZRWk+9EsnCqUWizf/k9bVPrTbLteOX4R5xLOHIvWpfB7FLNAqu4f6FUbD+9iWHtxLqGyPJZe5VKhRnbjzcn/BLyskNzyRZVEZjlU8R+N/x5QTQ7TXPXGyvEiorK4UnunouIShN4hna74dT6+h9dmzO+Brr+ruZVWTZPBrSF2zsMjK8s2hdGj69g3B9PqokNCNbrSamg+rOyL8zvwJXym8Fw7iC1DMLGImltp1Tsu5lTH/ql44CMHKz5/wOUt2NoD035XcyutXFOIVOvMYpFwWfT2CXzdsaM15qVTcyutFmbF7k644oEPVv6nvLqBS3Oxpx6WJlSjK63Eclf9cS/cVu99chg+w7GvmJpbabXhFxypgWvT8Tp85sp9eo6Hq+HTFoczqLmVVkez4VInPF6Pz6/leMWH23g8H9ca4EwSPboyrWKcjUWOxRjLBoyx7OnoUdlULV0qtxAR2RVjLCJn5Lh2y9tbRDxRo8qGxGL98ANKlsTw4Th4UO4VdpcuomE1JImhBlVBK3d6cf3B1cvx8GsvQLZokXwjv/2GaaPwewI1oiqTHeVyIXUUdbtWDUuJXU4fxcbFKParMYfSq3xqLByHyf3EuYHKXVoViS+Wcnefhus+4rRBw/YxneQLC8bmeSgTyyyKapodDTMZb+5dhUd+2LoQIxqhxm/G7YZqngtTu+PwRryzMoUnjC4fw6qRYrmrypHU6KrWzxhWEevH43r4TOF59wpe67C4I/pmVnMrrXpnwPw2Yj2s10/kePt66CvCqTnV0CuOGl1pNbk4tg3HdevrfN05gf2jsbAUBkZWo6thMcW5hGK5q3NycFC3d+NQf3E9QSW30mpmEmxpiPPzrS539ekNbm3A4U5Yk1XNrbRanRGH2uHGarwPn7lyb67g+gwcqYkN8dToSqt9xUSw9SR81kfTPN+Pa4PgVVDNrbQ6kBjn6+PuXLyzkrR+eY8Xm3C7Gy7kUHIrUefS42YbPF25bNEUtlvkYIyxbMAYy85+/FH0VR07yptERHbFGIvIGYV7u3XqlDhz8IcfZL5jsf76C//8gx078Ml+qxc9e4qeHZA6nppVKZUxCVrWx6I5uB7m6epv3ogl56NGEGcIpoxoFlFli4NMMcy2yIqCRBEQPwKuXBIJVIfy6vSr3D+Kja1KIE9Us+16lUmNeaPNLjK4ZrY45VC/d0gLudGayV1RLIpZINWjDFxaoUTgPKwikeDaxvJyV7VTiUXcty/Bk/BZsPz+NWyfDZc6aJBAja606lMQSwfi3F452O4uH8S6IRhRWM2ttGqfAFNrY89MPAifdb78n+LUSqxojaHp1NxKq9E5sLYrznlaXe7qxS2cmodV9TEmkRpdaTWnIHYPwk3rJ/09OY9TE+FRCZNiqNHVhKhYWxYnXPDwlBwc1MMjOD0cm4qpuZVWS+JjVy34zsTL8PncPr7E3bU43R7bMqq5lVY7suNMF9zz1K/0Z3/+l+A3Fd7VxOJWSnS1KwpOl8bNMXhlfVapvxfuj8HlUvCKrEZXp+Liag08mo73xiskst0ix2OMZQPGWHZWqJDoFP/8U94kIrIr9lVEzii82q0NG5AnDyJFMourTCtHDnTpgvXr8fKl3MUuXr/CoF5IE1+Nq0wr6c+oUxHTxuOs/ZZt8ruB/GnUlCpVJHWLVsUzon87bF6NGmXk66maV0ynMoRTepVLhcLx1I1a/fmzWNY9TQQ0KSef2uC6D0omlcNal5Qbg3r2EB0KokhEs1hqeEN4zsLikWJNK9PtplUuDvpXw9opuHlRPlTwbnjjrvF375C9e42jHmJNq/aZ1dxKK22jdteRtWIBr/Dw4DJ2TcfkGmgbV42uGkfCmL/hORLXTBJD+7q6DxsHwK2Amltp1T8pFjfC0QV47icHKz6/F6tZbemKKdnV3EqrSRnh2Q4X1uCdlc/t7WP4rsD2FpiTSo2utFqaBwf64OZ2/PtFjle8ug7f2dhVWwRVSnSl1ebiIth6aOlqnnbx5BAuDsW+ImpupdXGxDhWXyzl7m9lylgYfXohzhn0bYvD6dXoSqtjv+NKTzzdKhazt+iDH54sCDhnMLEaXWnlUxh3h+C15UmpbLfI8Rhj2YAxlp317Ckbx3fv5BYiIvthX0XkjOzcbs2di1SpEDGiMa4yrWTJ0Lw5lizBXSur8Hw1/zcY2hdpfzVmVUGrXGGMHhzCRQZt9eIZBrRHllhqVqXUHwnRsS6Wz8ata3LHupXE1Qkzm0dUWv0ZTd2iVYW0GNYa29zRppp8wIKp5OMYPHuEGtnk+Ib55EbFmX2ontQsmSobG9N6YtcKbJqLeunM7jJUpyJiuauztpzm+fCGzJ5c6sgtwfA9Avdh6FfUmFgZquGvGFcX2+fgwXU52L7evoTXWixsJ04PVKIrrfrnxLJuOLMJH8Onc/bZJs4ZnF0VPWOp0VXXHzG9LHaNg5/1qU93vcQ5gwtKYmAkNboaFQ/utXB8Bp5Yn/p0ew8O9sdyS+cMzkmJbc3gsxT+Vs6u/fQWtz1xpDNWZ1FzK63WZBV33dqAT+Fzkumba7g+E0drWTpnMBL2l8SlMXhupwX1gnpxSCzH7lVIza20OpgMFxrj/iK8t/Lz7d9PeLkVfj1wIZeaW2l1LiNudcDzdfgcQrLPdoscjzGWDRhj2dmaNbKJ3LNHbiEish/2VUTOyA7t1vv3IpyKGdMYV5lWtGgoXRpTp8LHyvrTYfHqJbq1xW+x1MTKUPmzonenr1mpPUTTRyNnPGNQFbTSREGDUpg+Bt7mV6x7/hjNiyO7eVAVtArGQffqYrmrG75yR9cB8pEzRJdbTDUvKncsn1puMTWxE/6OahZONc4qThgc3x5Nsplt16txFkzoiAMeeGtl/Wlr3vujSmSzKOrYenmXqYc3sE0/ZzC+2WCtKkcUy2CtGolL4Tb16cphrB+GkUXU3EqrTkkwsyEOLMBTK1OfwujdS5xZA/e2GJZeja60GvsH1vcW8Za1deJf3cXphVjdEGMTq9GVVvOKYu8w3La+6tNTX5yegvVVMDmmGl1Nig6Pijg5AY+tr5b1+AS8R2Pz35gbUY2ulv6KPXVxaQ5e35CD7evzW3FK4JnOWBs1SHQVATtzwLsb7m8Sa0uFh7fXcGcmztXC3l/U6Gr3DzhdBjdd8Mp64PjWGw9ccbksvH5Qo6tTcXGtJh7NwDsbzrVku0WOxxjLBoyx7OzOHdlQjholtxAR2Q/7KiJn9PXtlocHMmdG5MjGxMq00qcX08APh886yk8eo1Z5JPxJDa30ShlXLHe1eC5uh8MV6+ZNxF8p1aWvTKtcLozogb1b8CnIyUQT+6CIpfMETatZEcwcitNBpj71ayMfP1UkvAuyvk/PWnL3wvHkFt2lkyKQUiKqMjFRNw2KRla369WxMO4GThmzyYd3qPeLmkk1SSbv1Xz+KK4zOLcbOmVXh2nVMRvmdMWJjeE19enJLeybK64z2D6BGl01iQyX0tg0FjdOysF2d+OIWI7d4nUGByTDkiY4vhgvra8ydm0HtvXC9D/U3EqriRmxsQMursN7K0Hth1e4shY722FeejW60mppHuzvg1s75eCg/O/i8kLsbYilidXoSqtNxXBmBB4elYPt7vlJ+I7G/r+xJqIaXW1MhOP1cXM+3t6Wg+3ryzs89sSlzjiSRY2utDqaE1d64MkWMcHKok+P8XQZbjbH2RRqdKWVbyHcHWztnMEQsd0ix2OMZQPGWPaXNq3oLCtXljeJiOyHfRWRM/qadmvgQLPEylARI4r0auhQ+IfPyUQ7tyJfFsSLrOZWehXOhfGjccp86pO9bFyJvEmtplcpo4hl2mNEgM95Od7U+gX4OwlyRjRmVUpVzoAR7bBrLV5bOZlozSLjxQ0P7JAbDcZ0ko+TN5rcopnVFyWjm+VTRSKirPkVCfWqlcL47+Vj5e42ef0M9eOpsVSVyPLem2fh4YohZVE1ijqmYQIxIWv7HDE5Kzz8+wVnt2JZd/TPoUZXWv2TXZwz6L0Znz7I8fb13A9H5mFBPfT7VY2uOkfBtNLY5YI71ldne3QRRydhSUUMiaZGVyPiYEUNHJ+Op9an8Nw/hmMjsLKYmltpNes3bGuKi0vw5r4cHNS9XTjRBx651dxKq9WZcKQjbnqIyxGGh3f3cWsxTjTGxiRqdKXVvuLwHYlnx+Vgu3t1CjfH4lQp7IqkRlcHEonrDN6bj3fWg7NX+3BnAHzyq7mVVmfTiusMPluNz8/l4K/FdoscjzGWDRhj2V+DBqLL/PVXeZOIyH7YVxE5o69pt4YNM0uv9NMGz1tKcOwl/g9qaKVX/CgokA3ui8Nr3c85bsiVwBhXmVbKiKhXQpwz2L2j/BwqVZJ76Y7sQKX0VtOrbBHQtSrcp+LmJTnemrf+KJ1NPungTnKjwbzR8gFzRcbH9zh3EA0zGWMpa1U/PdzaYf9aceagYeOu5fIxQ+/iQdSJpYZTWi0dgHndMbUVWqZS79KqXxGxGNalcJvCc/cCtk2AWwU0j6pGV+3jY2odMS3rcTjM1NP57sD6Xhj7uxpdaTU6O9Z2w4XNYmKaRR9e4+JasSL7hHRqdKXV7ALYNRA398vBQb25hwsLsaUhZiRSoyu3yFhTWlxn8JH14OzFJVyYgh1VsPBnNbpaFAe7asBnRnhdZ1DzaA/O9cPuP9XcSqttmXCmI+6GW3D24RHuL8XFpjj4mxpdaXWyOG6MxEvrwdn7a+KswKs1cCqOGl2djI4rlfBwEt6F7qoIocN2ixyPMZYNGGPZ39SpstMKj/UpiOj/G/sqImf0Ne3WiRNIlEjMvapbV24JV1mSqelVomioVR7Xr8oBdje6j9VV29P+iD4tsWODPLPv/n2R4mmdVZQoAXsC546hVDKr6VXun5FSe/0RsHC2HB+i/MnlU5fNKbcYbFoiH1Z7uj6VUOJHYyYVtEpGR7/K4jqDtwMX25pokmF5Ww9HLNoxF9WjqvmUVlUi4x9Li7W3TovpbXF4DfztemFKg/dv4LUOC9qiVzo1utJqRGF4DBVLYoWTh5ewfwpmVUb36Gp01fsXzK+Nw3Pw1PqMM7FY+yjML67mVlq5pYRHC5xbAf8ncnBQfntwsJ84PVCJrrRakBV7uuD6Jny2MuPs01uc+AeHO2B1RjW60mpDfpwcgPt2vSqCqddXcW06jlTH+lhqdOURC4eriXvfhFtw9nw/rg3AifxqbqXV4QzwbY9H6/DZytpw/37Ei0243RXns6rRlVYXc8OvD15aP1UzbFYtH5czW3J5g8ghGGPZgDGW/Z06JWOsefPkFiIiO2GMReSMnKDdevhAplfJY2NoP7kxPHRpYPW0wSyx0L8tvA7JkQbt2snOqnUdFP7FcnqVK5I4bXDDQgzpKd9I0T/k7iGaMsL4AhTLp4gHzxMBhUyyqqDVPBem98KJIKciNgpcM6tIRFw9IzeGxpQWqBxJTaksVvWoGFYBnhNx+4Lc1+5unRZrWo35W82ttOqeCvNb4dhK+If1HC7LPn3A+Y1Y3QnDM6nRlVbj/8LmwbhmffEj/8c4uwzrmsIlqRpdDYyMRWVwyBX3rX9dXlyH9wxsqI4psdXoakoceNaE90y8tH55x6dncNYFW0phTpDF2pf/hv3NcHUZ3j2Sg+3ry0exFvuZLtiRVY2utNqdF+f/ETOzwsm7m7g7O2Cx9rhqdLXnZ3hXgd9U+FufIPn2HB64BSzWHkWNrs4kxvWGeLIQH62vcRYmX+C/FY+743YOXIlQrkQSuZnIIRhj2YAxVrjQLyTUsqW8SURkJ4yxiJwR2y3Jfa4xtNIrZUTkTSauD/jlixyjOHFCTK3KFiS30ivPT+Lqgd6BM4BOe8kMK17golEh2ukpX0mqSOLUQlM73VHU+tyr8nExuDY2zsFDK4v4uLaRI4tExHXrV6Yz5f8CXXKKywiaBlVVLOVZHbOJddxPbhFLU4WH969xYjXmtUK3lGp01SQyxpXBFlf4nZWD7e7+BewZj+ll0TmyGl0NTI6lzXByOd5Ynzl1+zD2DMHsv9ToSqvJWbC5My5ttDpz6t9/cXMb9nbHouxqdKXVsnw42B9+1mdOfXiBG6twoCXcU6rRlVZbSsJ7DJ5Yv9xeGL08j8vjcbAc1vygRlcbk4iVsG4txvsHcrDd3RiFyz1wLKcaXWl1PDeu9MGzXXJkUJ9figWtbrbG2dRqdKWVbzHcH4k34bZW14eLeDEJ9yri6k+4EsFQjLHIwRhj2YB9VbgoVUrEWFmzyptERHbCGIvIGbHdkh4/MKZXRdOLNd1DVKWKGl1pVSAWhrfBkyC/kFcsJmOslUvkluCd9UKexPIlea6QG+9dR7sCKBLJmFiZVumfMasfTu+Vg60xZliR8OCW3BiMPYvVBbCCple1fsbIqtgyHfe/6iqHoXF8ldWJV70zYkknnPYUq4OFh0/vcW4DVnXAsPRqdKXV5BLYOQZ+1gOgNw9xZjHcUmNMQjW6GvYzllXFsWnBLdb+/ApOT4FHJUyMpkZXM5NgSyNcXIw31gOgx144MxKbiqq5lVar0uNQ+4DF2sPnmgyf/XHPA6fbYWs6NbrSal+xgMXaw+eaDBr/S/CbAu/K2B1Nja72J8T5Bri3AO/vysFB+Xvh3kiRUim5lVZn0wQs1r4GX6xcHTKM/n2LNx54pH1p0ptGV7LuFD61s2qmDDypkByKMZYN2FeFi8GD5ez3x4/lFiIie2CMReSM2G4Zea7Aqxfy3yHy9BTdlCG9KhgXs4bLu4Ka4iozrPyh/jtik3Iyw+pQG/6v0SoPilpKr4pERO1UmNQFj/zkjsGb0CFwx0h4EWwr+OUzBpdRp18FrSZJcCbIGYv28u4lTnqIFa+6p1ajq2Y/ikXct0/CvcClvuzuwibsnWB54tWQNFjRBt5rxdQwa24fxu7BmFVAja60mpYL23rhWjBrJ/2LG1uwpysWZFGjK63cC+PocHEtQms+PMe1FeLcwOXJ1OhqflRsq4ALk/A83NbJfeGNy+NwoJSaW2m1JTVOtcHdNfgYPkuk/fsJjzfhUmccyaxGV1p5FRLrtb84IgcH9fkpni7HjabwTqZGV14/4HI5PBiPt+F2LYvXK/HcBXdLqbmVVjdS4mFLMeDzM20g2y1yPMZYNmBfFS62bZMx1sKFcgsRkT2wryJyRmy3vlL+/LKhmj5UbrHm2hVxRcU4EfBLJLklRG4DA+aFRUChiMbESqmCEbHFxl5ucle5b5FIuGN9+s/eJagfT42rDGW6MNb68XKXr9Y3Gxa2w4v78qbuwWWRT7mWEycJKulV74xY3AlnNuHLJznYvv79Iq4kuKYzhmdUoyutppbCrnG4a/2MRf8n8F6KNY0xJpEaXY2IjRU14TUbL6zPgPPbgzPT4FHZwsSrWb9hW3Nccsc7EWRY9uSkmHi1sYgaXWm1JguOdoXfFvz7WQ62r09vcGetiKi2pFajq9WRcLAMLrviRbid6envg9sTcaY8dv2gRlcHfsPF5niwAh+fysFBvTmKe8PgW0iNrrQ6nwW3uuDFZhGQhYcvr/B6NR62ws1UanR1JaKItJ654L26RBrbLXI8xlg2YF8VLl6/ll1XgQJyCxGRPbCvInJGbLe+hvaJhb6balhNTsWaOFZuCZ5hSax8QTKsopHxVwTkCrg04QMr615ZM6uvfJAiEfHY0rlUj2+jUw5UsjL9qkY0tM2AFinkzcqR8ND6pfdC495FNI4ow6mF7cWWzeOwoif6ZTOGVno1joRxZbFtAu6F2wSipzdwYDpmV0G3aGp0NSQN3NvCex0+WD/z7uxS7B+JOYXV6Eqr6b9jex9c3y1HWnRrJ/b1xEJLK16tLIZjo/DwpBwZ1MdXuLFarHi1IrkaXS2Iju2VcXEqXobbxf5enMUlKxOvtqbH6Q64twGf38nB9vXlIx5vxKVOOJJRja60OlUCN0fjlfUzPT89xq32uNFYLM2uRFcnY+BqVTychvfhdi3UV8vwbDTuFA8SXUXArQx41AFv1uNfq58b2y1yPMZYNmBfFV5y5xaN16+/yptERPbAvorIGbHd+ho//ihaqYgR8TmkuS3ui2WGlTF0SzI/fYR8yWSM1a+2Mb1qlhM7VqJgHHkO49bA1bJCaeEwY4Z1J8gv5yOqoEpkY2JlWnVjY0Y7nNwM92GoG0durBld7vh1fPaIZMo0qOryG1rHMtuiVbeUWNBGnFT44a3c0e6u7MOGvhj7uxpdaSVWvBqLu95yZFAf3+LCWmxoDdcUanQ1PCaWV8eJWcFNvHp9B+fmiksKTo6lRlczk4mJV77ueG/9GotPz4pLDW7+W42utFqTFUe74c42OdLuPr8X4dTp9tiaVo2u1kQRi7hfmYBXF+Vgu/P3xe1JOFPBwsSrQ6ng0xqP1uCT9SWr3hwTE698CqrRlVbnc8CvJ16G2xmyX16KswIftsCN5Gp0dfVH3C2PFxPFgu6hwHaLHI8xlg3YV4WXTp3knxBD7L2IiEKNfRWRM2K7ZTM3N9lH1aght1jz1h+Zk4kMK25E3LsjNwavbQ2ZYbWvhWtncWST3K5pVEBmWAOayC2htHK8McM6Y3IluwmNLadXlSOiaVKsGWOcbzW5hfHelqnkxq+wfZIaYAWtEYXhORI3rM8/CqO3z+G1DIsboW8CNbrqnxTLmuPUSrEmlzVPLuPoJCwuh4GR1PRqSjZs7YGrweYg947g0EAsy6tGV1q5F8axEXhgfcnzL59weyPWZsfKdGp0NT8qtlfChSl4cVkOtrsH23FlIg6Vx9ooanq1TZ945YkvVq6xGFb/4skWXO5iecWrU3/jlgteWw8cP1yXK16dSaJGV6di4mp1PJqJD2GbWhiM96fExKu7FideZcbjLvC3+YxFtlvkeIyxbMC+KrysWCHbr13WLy5LRGQj9lVEzojtlm3evxfz2bUmKlIoFrrq01lOxWrbWG4J3mxXmWHliCe3GAxtJTOssjb+mN00V2ZYWu0OuPzily9Y0MsYS5lWy5QWlrsaWsE4oH9xudFWbhWNpxAGrTZxMK0uDi7Cq0dyvN09uCiWtZpcQo2utHLLjy1DcNP6iuma63vEouxTs6vR1aAoWFweRyeLeMuaD69waSW2NRMXFlSiq+m/YnNDXFyCt9aX2391HReniZRqflQ1vVqVAUc64fam8FrxSvNwO7y7YXsWNbpaEzFgxSs3vLwgR9rd2+u4Mx3eVbAnyKUGD6YUE68ersFn60vs+5/E/VHwLapGV1pdyAm/XngZzBL7YfPlEV6vCVjxKqUaXV2NinsV8WIyPlySg23HdoscjzGWDdhXhRc/PxljdewotxARhRn7KiJnxHbLNkOGyCaqc2e5xZojB2SGlTh0p+CdOSYzrFSR8M78NLrlk2WGlecnuSWU9q0xZlgbZsmNGrcGxmRKq+YpcMDKWYqL+hqHze0uN4be09vomFANrQzVJArGlsZRG0+QtMnl3VjXAyMyq9FVz5iYWxNH5+HlPTkyqHfPxXrtqxtgVDw1vXJLhQ1t4bMen63PP3rmi5PjsbqUGl1ptSQ3DvXHnQNypEUPDuBEP6z7XY2utNpSCudc8Tz88iM/XJ+NozXg8bOaXm1OhVNtcXcdPofbmZ7P9uBqHxzLpUZXWp0sjptj8Pq0HBnUF388X4ubreCdQo2uTsbAlap4NCMcJ159OCciqjsl1ehKK+PEKzsEjmy3yPEYY9mAfVU4SptWdGBJk8qbRERhxr6KyBmx3bLB1auIEUN0UD/8ILcEo1xhGWPt2Cy3BK/SnzLGWjRVbtGdOiAzLK2u27LG+em9KBpJZljzh8iNusNrxPpWVSJjV7DXOpze1phh7bTxm2RuCzSyMv2qcUTMbgLvLXKk3b16gFPuAacNxlfTq2EZsKYLfIJdN+qxLw65YsHfanSl1ZxC2DcCd4M94dFvT8B67VnV6GpiNKyvCu+ZeGl9wayPL3FtBfY1xpJf1ehqeTIcaCFWc//0Rg62u6dHcGEQdudVoyut9hWD72g8t54fhdGHB7i/EOfqYm9cNbo6mCzgUoPu+PRCDg7q7UU8GI/LZdToSqvzWeHXHS/CbaUwfMIbT7Eo+810anR19Qfcq4Dnk/Dx6ydeWcR2ixyPMZYN2FeFo8aNRRMWObK8SUQUZuyriJwR2y0b9O4tp2KNGCG3WDNnqsyw8meRW4I3vLvMsEpnl1t0b9+gSiaZYS2dKDeGxoF1qJRAZlgjGsmNBv/+K/8RDMOMrcoRcW6P3BiiE6vR4ic1tzKteS3lSLt7dgsHpmF6OTW60mpiEewYg7tn5UiLbu4XlxQMetrg8Fhwr43TC/D6gRwZ1IeX8F2OLQ0xLb6aXs1Ni90dcX0T/v0iBwf1whfnx4s5Vkp0pdX6fDg1BI+CPeExLD69gd8qeDXHpqRqdOWZECca4/YyfHgmB9vdKy9cHwavgmp0pdWJv3B9KF4E+8ZfbMXtrjiXSY2uvH7AlQp4OBnvwm2lsI9X8GIKrifEFe25zNOrm+nxuKPItv79KAfb2zbP7nnz/CZvEDkEYywbsK8KR7NmyT7M01NuISIKG8ZYRM6I7VZo3b0r1sPSeqdo0eQWa549Rep4IsOKG1FuCd5OT5lhpYsqtxj0rS8zrAZ55ZbQuHkRjTLLDKtLCbnRJiOrBmZYkXDvitwYjLcvsHe2mlgp1fSHcLnm4F1vbBsh1rdSoqtOETGvFo7Ox2vr6219/oiL6+DRAmMTq+nVhPTY1BlXtsqRFj2/ilMTsaa0Gl1pJdZrH4VHZ+RIi+7vw/HeWJNNja4WxMCuGvCdA/+7cqTdvb4s1ms/UEaNrrTalRvn++PJQTnS7r68x6N1YmWrgynU6GrvLzhfT0zL+hBMYuiHR7PFuuwnY6jp1dm0uNUeL8IxP4L/Lvjlxa3sanSl1d1SeO6KD+flSLv7cgtvZ+JlNTyOgUcRypUK3ZVPieyEMZYN2FeFowsXZIzVs6fcQkQUNoyxiJwR263Q6tpV9k6jR8st1vRoL6di9Q/FYlL+b5DmBxljHTRfc3rJeJlh/RVbbgmlzsVkhtUkm9wSem9fY3AZmWFViWJD8DQ4j1lo1TIG+mQ23hz4hxxmL9cPYUMfjMyiplddf4RrPuyfIodZ9OYRTs3D8moYElVNr+YUxL6RuB9s/HTvKA4NwJI/1Ohq0s/wrIULC+D/UI4M6pM/rq/C/qZYmlBNr1amFeu1+4XbuZaax/txrg/WRVOjq7VRcbgKrs+Ev/UTHsPo7TX4TcGZCtgVWU2vjuXEld5iSaxgvDmKu4NxMa8aXWnlWxz3x+Kt9SsVhtHnB3i5APfr4HpcNbq6kRyPWonV3L+E25meHw/gzT94nhuPIpgWYyxyMMZYNmBfFb4SJRKtWDbb+xsiIksYYxE5I7ZboXLrlsywooe0XvvxwzLDShZTbgle7xYyw2pcVm7RnTkkM6ycEfHyqdwYGrVSygyrckK5JfTevcY/xWSGVcPG5eS3uMrFsOY0x+VDaPajMcPaN0eOCYueMbGiNU4sxepOGJRSTa+6/YRppXHjiBxs0dOrODIB80uo0ZVWy6rAazZeWV/uXXNjC3a1x+yUano1Nw12dcD1YFdAe3MbF6djWwXMiaimV54FcWYkngYbnIXF5zfwW4kTTbAxoZpebUmN0+1xf2M4Xujw+QFc7Ydjv6vR1a4oItLym4K3V+XIoP59h2drcLMlvJOp0dWZRLjeGE+X4/NzOdju3nvh2XDcKahGV1r5FRR3vTshR9rdl6d4txQvG+LJr0p6hSeJ8arJ3m1tUqdOIQcTOQRjLBuwrwpf1auLbozLYxGRnTDGInJGbLdCpVMnGWMtDHZNdE3VkjLG2hqKdRs8V8gMK4t55vXlM2pmlzHWyulyY2hM6ykzrJIhnfkYlP8L1I4lM6yaobu6oqkngRN59s01BljNouKT9Wv5hdLihmpoZageP2NeDVwMdgHvB2exdzhm5lWjq0FRMC2XuCuYqw1+8ofvCmxugClx1PRq6Z84MgQPvORIi55648wIeBZQo6sF0bCzujht8O19OdLu3lzH1Sk4WE6NrrTa8xd8huN5sAvVh8XnN3i4CheaYn8iNb06mEKcTvjIQ5xaaM37a3g4BVfKwyuSml5dzI27/fE63E54xEe8WY+HbXAzpRpdXfsFD+rh1SJ8tn6Oahh99ob/aDwvqkZXWj3PhzeD8PGwPpDtFjkeYywbsK8KX25usiG7HG7LHxLR/xP2VUTOiO1WyG7elC1TjBhyizUrFskM68+Mckswnj9FnsQyxvK7ITfqhraSGVbzonJLaAysKTOsIpHw3sZVqN48R++/ZIZV18ZzGE259zZmWL1D8SEEr0+QSw3q1eNnLG6M535ymEV3vbCzv4Ul2wf/iFn5cX23HGaR/0OcmwOPynCLpKZXa8vi9BS8NP96KcSiVz2xOpOaXi3/DYfa4NaG4JZ7D6Onx3FxMHblUaOrddFwpDpuzMW7YGechcXba7g9Gaf/x95dgEdxrlEAZhOsQCkV6kZdboVSd6Vu1N1bKmiFlrZQoC2FNDS4uztFiyRIcALBCSEQhUDcPfnuTvYkK1mbZHeS2Zz32ec+zJmphU32u4d//nnGtroyvnYYv+B9JXM7rrQrZ5Mk/CwHbrOtrnY2liMvSNJIKXD6Na+JomjJGCEnnleeLWjTXsXdIik9JM/tRxyoViqFyyS7k6ReZVtdJZ8uma9J/ngptd0ijeMWaY81lgqcq7xr+3bMZJ957ZExRFSfcK4i0iOOW6516YKRacYMJI7ceoXSYZ1pkHw3WqRvP0SH1f0DJCaLJ6HDuttVa2Zp0+KKDssgESpvd8rNkB/uRYf1zlkI1SrKl2GvmTus6V2QV0N8uHT2s62ulJdBpn0g6Qm4zK5dE2X1TzL4Otv2qs9pMvUZObgIl9mVGSO7hsjcx22rq0CDTLxODk5RnkjoRNxS2fiFzLjQtr1acIvs6CknN+Eybzj5n+zupNwkaNNeLb9Mdn0pJ5Z4sTjL2CJHe8n2222rq5CmsreDHB8rBY5/v0y3DUZ/InsutG2v9l4usV9KxmIv7teev1lSf5X4222rqyN+cuJZyRiqPI7QS0rjJX+MpFwoyafZtlepN0h2dylchSvt4bhF2mONpQLnKq9r2VKZye68E4dERDXAuYpIjzhuubBtGzqsFi2QODKwL5ZiffoWEicWz0SH9T/rpU/bg5X2ylRjxbq9Xn79fHn6DNRY/45C6Ka8LHmz4l7Cd85GqNaJCOl9e0WHZZAD1nvVqxUZYltgdW0suY43CEs5LHumyeqeMuR62/bqt8Yy+SmJclYKSPoRCftbZt1vp72afods7o3L7CrOk2OzZe07MrmlbXu19CHZGyDph3ClxxVnS9xM2f6u/NvKtr0Kvl0O9JbUbbjS88okeYkc6igbL7VtrzZeLhFfKWedFGeFscrqqiPPy46qtw3eLcf7SK7Xtp0qy5ecBXLqU4m+yLa9ir5E2a/deLbUC8/TNCnaqtwbmH6XbXVlfKW3l7x/pMStdwvHLdIeaywVOFd53TPPKGOZy+dGExG5gXMVkR5x3HKhe3fUWGPHIrErPlbO9lc6rLMbInEiO1Puvhg1VqL1WpWvnkKHNcjtZ0nn58rHt6LD6v8hQjflZUnPh9FhvVvdDmv/avnmXHRYnzSVkmLk1RO/SwY/bC6wujSU/CycqmpQm/K6ymBVXRlfvRvJtOcl3mmPkx4p2wfIzHts26ugxjLzXtnjdEuywgw5MlnWdJAJDW3bq1mXSsRYL256lX9cjo2STc/ZVlfGV+iTcmSI5BzFlR5XlCwnJsm+12RtM9v2avudcqyPi9sGc3fK8b5y6G7b6iqssRx5SZLHSKHTu0RroigGtw0e8bdtr+LulNTfpMB7lV+hFC6SrM8l9VLb6krZr/1jKZgjZY7f4fZw3CLtscZSgXOV1/35JyazuDgkRETVxbmKSI84bjmzb5/yMBzjpORyV6wun2Ep1thhSJz46XN0WN9at07j/kSH9ewVSNzx6yvosD5U+fjpkhLp/SQ6rLfPRKjWxskVi7AaSNdLEFZbcIC5wDK+Zjre+GL9H7bVlfHVy0/GPShx2AnbvoyjsiNAKaps2qt/GsnsByVyAS6zKz9ZqahWPWdbXY0zyPwbZGs3Kc7BlR6XHSGRAbLuQdvqatHpsu0tiZ0mRRm40uNyD0vsINn1mG11tcYg4c9KwgjJj8GVdmWslLgusvcq2/Zq98US/bmkL/LibYMFOyStj1JU2VRXRxrJiRclY5QUe+3/AZXGSt5IyXxBkhvZtlfpt0vOL1JU/ZtMOW6R9lhjqcC5yutCQ1Fj9emDhIioujhXEekRxy1nfvoJk1L//kjs2rEFHVYbNzaWWr0YHdaNLZGY7NmMDqutHxJ3fHkPOqxnWiFx3x8vosN6s7p7ui8bYO6wBjyOsHpSjsrIZ8wF1ndOb+HcNlz6NrMqsP46T2KdPsAuJ1GWvCZzHrZtrwb5ycz75ZTTx/blJ8u69+S/p+20VwtulgNDcJk3pO9Ubg9cc5tte7X8EmXTq8RluMwbMrdL1K+y7Tbb9mr9OXLgAzk1R0pzcWVVpdmSOlOOvSvhZ9q2Vwdvk4RfJNtp1VhDucsl6WuJaWPbXim3DX4hOYtEvFacFW+XnN8k7U7b6iq5oWQ8L3kjpMQDG9Vz3CLtscZSgXOVFpo2VYazu+/GIRFRdXGuItIjjlsOHT8uzZopY5JxWHLu9WdRY4U43YPJ5NHrUGPtCEVi8vYdqLEWjkfi0opJ6LAeaSilJQjd9Pdb6LBeU7ORvKWZ35o7rCnfIKyebZOV3qqyw1r8E/Kqto+U8Q9aF1itccqu4kI5OE0WPm/bXgUaZNKNEuf0CXQFaRIxTlY+Y9tejfeTBf+TQ07vOqyhlE2y9wdZeZ1te7XqBtnXQ1KcFnY1lLZGDneRTVfYtlebr5HI7pIWjMvsKoxXNr2KfNa2ujK+Dj8hJ4OkwGubppekSOZkSXxDjrawba/i2klqL8nfgis9r0wKl0rWl5JymW17lXKxZH0mhQulrADXegLHLdIeaywVOFdp4YknlPnM5a6lRESucK4i0iOOWw7164elWH37IrFr2SJ0WLdfi8SJ/j+gw3rvSSQmQT3QYb3j9oN3VkxSHkpoqrG2qFyVM+wzdFgdGiNRa8wHKLA+MMjWWQirZ3ZHc4H1bTOHW4OXlsjIdlYF1pAbccquTb/IkGYytKVVezXIIGMulch5uMau0gKJmiarX7LXXt0skVNwmTckrZXdXWRFG9v2KuROOdRPMnbjMo8rK5GkhXLwY9nQ2ra9Uja96itZu3ClXXn7JfEvibjftrradbocfVNSpkpxGq70uKJISf9Hjj9uW10ZX8eflvShUuS1ncJKT0n+BMl8VZKb2bZXabdLzq9S7K3ijOMWaY81lgqcq7TQpw9GtESvbUVJRPUD5yoiPeK4ZV9pqZx7rjIgNWqExJHH7kSNFR+LxJHZ49FhXWVdHm1djQ7rdrdLpbxs+eBGdFjjf0XopgnfosN6uZGUqL+7qrhQOp1f0WH5yWHrNWWqxG6XgbeZO6wJryOvas3Pyq7tlQVWH6ePJzqyUMa1sWqvjK/BTWWb0ztDjcJ+kXXvysQm1gWWQeZeJ5GTcY03nFoj4d/Isott26v1D0lkoOR4bwVTliROk72vS8hptu3VrickbojkHcOVduVskYSesv8m2/ZqzyUS86VkePOGx4LtkvCQxN1mW10dO0tOvifZs6RU3abpKpQckNyBkvGwbXVlfGU8I3nDpMTpF80TOG6R9lhjqcC5SgshIaixhg5FQkRULZyriPSI45Z9QUEYkLp1Q2LX1PHosJ68D4kTrz+EGmvtCiQmr96EGmvLaiQu9X4dHdbX9yNx0+x+6LBe9JPU4wjdl5UsfzyIDuvDhlKQjbwaJr9jLrA6+0uSg7ImL00WdzQXWL8aZOc4nKrqxGaZcottgTXxepx1JGmrbO0q08+3bq8ayPQL5OAIXOMNp1ZL+Ney9ALb9mrZhcqu7Xlee2xf4Sk5PlZ2P2dbXYU0lr2vyPGJUpSCK+3KXC2xnWVvG9v2av9NkvCTZFd/53LXcldJcmeJrrLpVczVktxV8tz+9qmGog2S00PS/mdbXSW3lqwPyp826LXd/avguEXaY42lAucqLZSUSMOGypR2p9uL2ImI7OFcRaRHHLfsu+oqZTry98ehI7ddpXRYZxpw6MSEweiwnroZiUngt+iwPnd7i/TJ/dBhqd3Wfdmwig7LIHEHELovOVp+bYsO6/Oa7Ucxy+JGwt8cf3BELJGgq8wd1uDrkNu16HkZ0sKqwFryGk7ZlX1M9gfJona27dWk5rKtO67xhqSQ8rVXF9m2V0svkP09peAULvO4gniJG6oss7Jpr9adKQfel1PzpKwQV9qV/q9EfyK7z7Vtrw7dK4n9JW8fLvO4sgLJniunPpCjZ9m2V/F3SWpfKXB6w2ONlEjhv8rmVikX2rZXaTdIzndS5HRvNa/huEXaY42lAucqjTzyiDKonX46DomIqoVzFZEecdyyz7QU6623cGjXsEAsxfrQ8d1wJokJcn1zpcNqY/0Uwsl/o8O6w9Uu8pU2LUaH9ZCfFOYjdMe4ruiwjK89axC6L+GA/HANOqyvzkFYDaci5J/7zB3W4h+RV2W5E1YvP4l2XBls/k3ZBsuywApxuuV81DQJ/UzZ68qqwDLIf+2lRM2XVJWUTbKnuyy/zLa9WnahhHeWQqcLoGoi75jEDpKwh2zbq9ALJeJLSbFeGGijrEBSZ8uxd5Rdrmzaq8gn5dQwKXR1I221lSRJ5gRJfFmiGtq2V8eflIyhUuS1e/fKUiR/krLpVVJT2/Yq/QHJ/UtKvNbZuYfjFmmPNZYKnKs00rs3ZrVTXvvzHyKqBzhXEekRxy37jh1zcTthfp5ceY7SYZ3tasWWUfcPsBQrqA8Sk1dvRo0VuhyJc0nx8tL5qLF2uPFUxEp7Q8wd1nL1N8pF75SuF6PD6nYZwmpY/KP80AoFVmd/STyI3MbO8dKvubnDGtEWeVXJ+2TC1VYF1pyHcMqug6Mk+FXr9qqBTGompzbjAo9LD5cDv8jKa23bq6XnKs8cLEzFZR6XGyWxf0vY/bbt1aYr5HBXSXO6jKg0S1KnStSrEtbIqroKayhRHSR5opR47V+7KFrSh8jxJ2yrq6PNJPF1yZoiJV7bLb4kSvKCJP1x2+oqyV8yX5T8MVKq/iZc7+C4RdpjjaUC5yqNBAejxho9GgkRkXqcq4j0iONWNT33MJZi/ezqBrSQZeiwbrVexDTkJ3RYHz2AxKUXWqPD+v1dJO6IOyDvtUaHNV797XJRW+Sbc9Fh/XgDwmoY8RQKLOPrlwsRVrVpkLnA6uUnkf8hr2rvaBnWylxgjXC6Rixll8y61LbAmum1z6zcGDk8UIJvt22vFjaXsE8l32uFSN7R8vbqPtv2avO1EtVDMpxuXFV4TJInyJEXraor42tnCzn6tqTNltI8XOlxhQcl/S+Jv9e2vTp2rpz6RHIWiTh4fmXNFe+S3L6Sdodte5XSWrI+lIK5Uua1BXrVxXGLtMcaSwXOVRrJz1e2fmjQQO64AwkRkXqcq4j0iONWdaQky/mnKR1WazeeLfhMW9RYkRbbUe3ehA6rnasnIVYa/ys6rFcuRuKO7DTpdBM6rD9eROi+4BHy5ZnosHrfjrAapn9s7rDGvIDQRtoxmf2GucMKdLzsK/WQLHzOXGD94y8Jjh+YmJMg82+0aq/GG5Q93b2hMEViJkhoe9v2akET2fSi5BzFZR6XH6PcObijytqrtS0k/FnJ3I7L7CpJleRxcuQ52/Yq/GyJ/ljSFuEybyjYKam9Ja6tbXsVfUX5lu3BuMwblC3bv5fU62zbq9SrJLurFKq/61ZDHLdIe6yxVOBcpR1uj0VENca5ikiPOG5VR+8fsBRr8EAkjowcgA7rvfZITD64DzXWVvcer7bxX3RYD7txD6Ol355Ch/W1+oVUEevli5bosPrchVCt9HgZ/BAKrM4G2T4FuY2o1TLoCnOHtf4P5FXtDJJB/uYOa5GDUswk/HeZ0NiiwzJI5CSc8qyTK2XHh7KwiXWB5SfrHpQ0py1STRSckPhhsvMR2/ZKeebga5LuuNozKklT2qvIZ23bq93nScwXkuHeXa7Vk79ZUn6U2Ott26vYmyT1Z8nfisu8oXC5ZH8pKRfbtlfp7SS3txTvwGV1G8ct0h5rLBU4V2mncnusxEQkREQqca4i0iOOW6qdSFAeTdiqgbIgy7kT8XJ1E6XDamPdPU0cgA7rHfcWwqcmSocLUWNtX4nQHUM/QYf13rlI3LdqiHx+Ojqs/o8iVGvDcOl9GTqsLo0k18GeSqEDzQVW39OkMBu5jdg1suBZc4E1uJkUO77h6+hsmXutVYG172+c8qDcY3Kwj6y8xrq9aiDLL5VYB4VdzRVnyPFxEv50lfaqibL2KnkZLrOrJFPZ3OrI81Xaqwsk9ivJVLPhmlp5GyS5u8Rcadtexd0pqb9LwV5c5nlFUjBPsj6Q5DOrtFcPS16glBzGhTrBcYu0xxpLBc5V2gkJQY01eDASIiKVOFcR6RHHLdV+6oqlWFPGIXHku4+wFGvkACRG0RHosNq6va7q55fRYfV7B4k7ZvVFh/WK249BrHQ41LwOa9BzCNWa+42y/MrUYf3koEcrzpe575g7rLH3IK9q7xgZfJq5w1rr+K7A+OUy40KLAquBhLyBUx6093vZ9IJte7WwqeztgQs8rqxUTs6SPa/YtlfB/rLrMUl2evdfab6kTFM2aLdpr3Y2loiHJNO9JYHVkxciyV0k+jLb9irhQUn/WwojcZnHlWZK/lTJfE2Smti2V5nPSf5oKT2BK/WG4xZpjzWWCpyrtFNSIo0aKTXWXdVdMU5E9R7nKiI94rilTlwMOqwLmiFxZFMwOiybnd27vYwaa85IJM7NGIgO6+ULkLgjZDI6rBf9JDkWoZvWj5OOrdBh/fUYQrVGP4cCy/j628Gis+NhMvyWig7LINscPEIxK06Wv2susIa2lNJinKpq9x9WBdbsNsg9pSBJ2fpqxZVW7dV8g7Kbe24crvG4lP/k4MfKRlc27dXWm120V0Zp8+XoWxLW2LrA8pOIByRtLq7xhrxgSeok0ZdUaa8eU55FWKTyPem+smTJHycZz9tWV8mnSebrkj9NyrJwpW5x3CLtscZSgXOVph5/XKmxmjfHIRGRSpyriPSI45Y6992MGmveTCSO3NgSNdbW9UiMlkxBh/W0ez8t921Ch/WQnxQVIHRp2XB0WMbX3hCEbooOk69bo8Ma+CRCtRZ0N3dY8zojtLF7qvRuiA6rT1PJPoXcxrFlMvYyc4e18hPkVZ1YK//eaS6wJjaRzCM45RHxM2Xl9VbtlfG1oLEcHYYLPC5rp+y4UzZeat1eGWTzlZLg6vHimf9J9Cey6wzb9urAbXLKvf60evJCyturi23bq+NPSsYIKfbaAqiS45I/QjKerNJenVX+wMEFxitwpf5x3CLtscZSgXOVpvr1w32FCQlIiIjU4FxFpEcct1SIOYYO66IWSByZMQYd1ssWd8llpik3EppqrOwMhM590hY11kq3N1qK2imfXo4Oa+ZvCN2061/pejE6rP4PI1Rr8jvmDitsBkIba36pWITVQAZfh7CqWQ+YC6ygJpKXgryqbd+aC6zxBtnxE3KP2PWlLLvQtsBafrkUO9jDq4YKT0hsoGy/w6q9Mr7WnyPRv+MaR5LGSFw32XOxdXtlkH3Xebm9Wi9JXST60irt1dOSOVpKknCZx5XGSd4QSX/Utr1KuUCyOyobuvsijlukPdZYKnCu0tSGDaixfvkFCRGRGpyriPSI45YKP3RCjbVwDhK7igrl7ouVDutyg5SWIjQa2BUdVp9PkTg3uDM6rM4PIXHHr4+jw/rtKSRuSjom31+FDqvfvQhVSYuT4e1RYHX2k1MRyC2VFsmcN80d1rJOyG3Eh8qIc8wd1qwHkVcV/5/Mv9HcYc28FLlHRPwui8+2LrAMsl3NDmWqnJore162ba/WNZcD70tROq6xK/+wnOgn+/9n3V41kD2XSlx3KXN8D2YN5W+W5O8kuo299mqMlCTjMo8riZa8fyT9Idv2KvVyyekihcG4zEdx3CLtscZSgXOV1po1U2qs22/HIRGRGpyriPSI45a74mPRYV18OhJHAn7GUqyfOyIx2r0JHdbd7m3gsG4eOqwnXG3CZWnkl+iwProYiZsyT8kvt6DD+vlmhKocDZWB7dBhdWti/xmCKYdl1B0osHr5SfQ65DYi58vwsys6LIMcWYi8KqtFWH4SswB5zR3+S9kDy7LAmm/8F/bOd0p2uER+KxvOsy6w/GTH/ZKzH9fYVZIhSaMk4mHb9ir8HGVNVlE8LvO4gp2S+rPEXlelvXrSu2uvSqIld5CkP1ilvbpKsr+VolBc5us4bpH2WGOpwLlKa61bKzVWU/WPsyEi4lxFpE8ct9zVsxtqrDnTkNgVE4UO6xrrgerTR1Bj7duGxIn4SHnlItRY+zcjdGlxEDqsV5ogcVNOmvS7Dx3Wj45v8XMifpf8dgU6rB/PRmjjyH/S/xx0WH2b2e+5jCxvJBxhvTu+pcQNsrCtucNaUK3qza6UzbL0fOsCq5Gk78JZDyorkuPjJOxB6/aqgWy4UE5OxzWOpC+WPZcpdwtatldhjeTIC5K3D9d4XGGEpP4ucbdWaa8ek4zhUpKIyzyuNKZ87VXV9uoayekhRW5/g/gKjlukPdZYKnCu0tqAAbiv8OhRJEREbuNcRaRHHLfccvKEnGlQOiyXu2J9+yFqrLkWX9W5o9BhvX4rEuf6vYMO688PkLi0ayU6rBcNkqTyMXCVe7p3UvMwxErrhyjVlanD6nMFQhthY1FgGV9BVyK0kX5EFr1o7rAWvYS8qvB+5gJrvL/ELUVeQydXy44PrAqshU0l+zDOetDJmXK4k6xradVeBTeUAx9ImdOdyNPmSvwPsucSq/Zqh0HZuD1zNa7xuOIESQ+S+Pts26tjZ0v6P1LsteczlsZL3mDJeLhKe3Wd5PwoxW7UwT6K4xZpjzWWCpyrtLZtG2qszz9HQkTkNs5VRHrEccstv/XAUqxxI5DYtT0UHVa785AYJR2XR85VOqxbDVLqxuPSlk9Ah/VCayQupSTIRxehxgpW+bs54i10WF+ehUSVA8uka2N0WANuQ2gj5DdzhzX1OYQ2dg+T8VeiwBrkJzErkdvIipL/njR3WPOvR15Dhamyr4dVgbWgsaSH46wHnZwhux63aq+Mr01XSJrT7ZyKUyRpdJXHDjaQvZfKqX9wjceV5UrmRGWXK9v26gw59bHkWzx/07NKTyrPHEx/zLa9Sqvv7VUljlukPdZYKnCuqgVnnaXUWDfdhEMiIrdxriLSI45brsVGy3lNlQ7rAlfbVL3/FGqsvWFIjCp3djf+wqVTcfJ0S6XDesggJ91eVNWrPTqsAa8hcdPkr9Bhfebejl02FnRHgWV8DW+P0Ma/n5s7rHX9ENrYM8q8CGtYSykrQ24jaqpMPr2iwzLIoTHIa2jb21Z3Ec73lxNLcMpTCk/Jzsdk42VW7VVIQznoarP/7FDlIYM27VVYE4n+WEpzcY3H5fwrJ9+RqEZW7dXR05SbBwt34xqPK0uX/HGS8Yxte5V6dfmdg1txGXHcotrAGksFzlW1oEMHpcby88MhEZHbOFcR6RHHLdcG9MFSrIF9kdi1Yj46rPYWfxy4ZDI6rLvc26n911ewFGvE90hcmvAtOqzP2iBx04Je6LA+biwlRQjdt32KucOa+j5CS4VZMv1FFFi9/CTWwQ7ca7uZO6xZjp+QuPkb8yKsqY73zFLlyD+y7n5zgTXPIId+wylPiRkoh76waq+Mr/WtJXUNLrCr6KScGioH77AusAyy/xbJO4BrPC5zrCR3kWPnW7VXUQ0l/m7Jc7AZvwcUSsF0yXi5SnvVRnK+laJNuIoscNwi7bHGUoFzVS0YMgT3FW7YgISIyD2cq4j0iOOWC0VFcsXZSod1nqt9059rhxorIw2J0dfPoMba4sbWRYtHo8N65RIkLoVMRof1SmMkblo1BB3Wh36SqH77p2kfmjusOV8jtJQZL2PuQYfVu6HkJiO3lBkjC583d1hbf0du49gc+fcuc4cV6om9L7IOy8rrLQqsBrLxSZzylNgACb3Qqr0KNkjYAzjrSMok2XeDst7KssDa2VyO98YFHpcXKmn9Je4Wq/bK+Iq5UnI8vSrNUuEiyXpXkppYtVcpF0p2JynyXmvmCzhukfZYY6nAuaoW7N2LGuuHH5AQEbmHcxWRHnHccmH4ICzF6uV0edSMMeiw3rdoQwZ0Rof1jBvrpE7GypMtcDthbhZC52L2Kg8lNNVYxyMRumN6N3RYxtfe/xC6L3SkucNabq9eOTBPgq5Bh/V7S4Q2Tu2SSTdWdFh+kupgkVHkRBnfEAXWhIZyciPymtjxriw+y1xgLa3WxvZORP0kOx+xKrDW+MmxXjjrSOo0iXjEqr1S9m5vK3l7cIFnlZVI1lQ7W19FnyunPpUyB8+RrLnCYMnqKCnnWLVXyWdJ1mdS6GBDNLLGcYu0xxpLBc5VtePii5Ua69JLcUhE5B7OVUR6xHHLhVuvUDqscxri0JEHrlA6rMsNODR56Tqlw7rVIDmZSJzo9SqWYo3/FYlL39+NDmvaz0jcsWqIfGBAhxWq/rd+w3Bzh7VmIEJLMevlr9bosAIcLCs7skAGN0OHNfQMhFUtbGtehDXnKoQ1ETdLQtubC6z5DSVpLU55xOGusuMeqwIrpKmkOH2QYnGSnPhd9lxqXWA1loSeuMDj8kMlqaMcbWldYDWREy9L4T5c43EF05UtrlKvsGqvkpooC7IKFuIacg/HLdIeaywVOFfVjvfeU2osf38cEhG5h3MVkR5x3HJm6ngsxfr6IyR2jRqIpVjdP0BiNL4/lmJ1exmJE8sqnk74qtu3E476Gh1W99uRuONgiHS+EB3WzO8Qum/i6+YOK9TeQxtX/yy/NUKHNeJWhDZ2BlUswmogU25BaCM7Rqacae6wtn2LvCYO9jEXWMbXdnv7eVXb4a6ytoVVgbW2lRTE4axdubslpqN1e9VA9lwiOdtxgWcVJ0pagMTdat1eGSSureS6ccdr9ZTGSN4ASWtn3V41kMwXpWCalBXgMlKD4xZpjzWWCpyrasfHH+O+wlWrkBARuYFzFZEecdxy5shhufYCOdvpn+3l5sitZysd1pUWK7ZSTsqdpykdVrtGSJxIOyXPn4PbCbPTETq3ZgI6rNfVPGGwuEB5IqGpw/rnBYTus1yHtXkcQkt7pqPAMr6mPofQxvrvzR3Wyk8Q2ohbJtPPQ4E1vqGk1XiJUPIGWdjcXGAtPVdK1W9p70jSQtlxr1WBtfEiKcnGWbuy1kjUy9YFlkGOPIOzHpezRBJft26vGsixVpKsvsd0U1mu5E+QjCdt26uU8yTrHSlNwmVULRy3SHussVTgXFU7oqNRY73v0T+kIiJfx7mKSI84btXUoF5YitWnCxKjP7/GUqyJA5A48ecHWIo1tBsS52L3m7fESopF6I7KdVg9LZ6l6CbL/bDWD0FoKWyMucNa+DFCG+PaoMAaZJB99oowo32B5kVYMy9CWBP7e5oLrHkGibL3L189acGy63GrAiv0Eil1usIo7nuJeNiqwNrZyFv3DxbFSGofZad2y/YqqpGceFbyd+Eaj8v5TbLek+TGVu1VcjPJ/kyKw3EN1QzHLdIeaywVOFfVmuuvV2qsc8/FIRGRGzhXEekRx60aSU2Sa5oqHdbVFs8xPLgTHdb9rZA4sX4+Oqxnz0Ti0o8PoMOa7mrXcEvTu6LD+upsJO7bPNbcYRl/XdWWweYOa7W9Uib9iMx7vKLD8pcEB0/Enn+DucNa8yrCakvZKMG3mzus/65GXnPZu2Xva1YF1qaLpSQPZ+1KnSYH77AusFpIxgqc9aycJRJ9kVV7ZXxFXyjZC3CBxxWHl299dal1e9VI0h+QYj793MM4bpH29FFjFUaHLpg4fFCA0aDBY2esDLf3iFwLyeErZ4wJCjReHhg0YurynYmFOGHi4rRDnKtqzVdfYUFWcTESIiJXOFcR6RHHrRr583ssxRodgMSo28uosXa42j68uFDeugo11imn+yhVmvg9OqxvbkTijiEd0GF93FjKyhC6afsUc4e1cRRCS6EDzB3W5iCEllIPyZSb0WEFNZVie3VP8k5Z9igKrPEGiXCwVst9Vjth+UnCHOQ1lHNAIr6xKrDWnyOFTu+SS5ksB26xKrDCz5OCozjrQaXJkvaXxFxr1V5FNZKTb0tZDq7xrNJUyR8m6fdatVfGV8oVUjAP15Cncdwi7emhxopcMTYwIGDQ6DmrQkNDlk8faTwYPHuLwxv1CyOWG68PHDplcYjx8hmjBxkvn7m5svhycdoZzlW1Zt481Fhz5yIhInKFcxWRHnHcqr4T8eiwrrPYoGp0X3RYz7RB4sSI79Bh/ezGNvBGWxeiw+rQGIk7whejw3rfIMnRCN20a465w1r7D0JL6/qZO6xt9jZ9T9gooy9EhzXqfIQ2Tm6U2Zejw5rYpKabYR1fIOsfNndYq9T0fc6FPy0hzcwF1roWSqvlRExH2W9dYO29UkpzcdaD8jfJqY+s2ivjK/oSyZyKCzwut7dkvV2lvTpbcn6UMqf7glGNcdwi7dX9Git9/bTAgIAxSyMqlkylb54ZZAyWR+LYRuzK8QEBQdNDK4qpwoilYwICBs8PM/31Lk47xbmq1hw9ihqrfXskRESucK4i0iOOW9X3WxfUWPMmITH64D7UWEnHkTiybxM6rPbubdOekSQfXogaa28IQpeOH5COrVBjbZqG0E3Le5s7rBV9EVoK6Y0Cq5dBDi1CaGlTLwlqgg5r4nUIbWz6CgWW8TXjQoTVFjVc5vtXdFgGibH4ramJU3Nl643mAiu4oSQ6/WLGdbe9hXDfDSKlOOtBWVMk4UHrAstPjj8tpd7pkopWSc6vktLGqr1Sbh5sLyURuIa8jOMWaa/u11hhC0cMHjR1reXiq80zAwICZm7GkbXI5WMCAoYt2otDRfraqQEBQfPDlF+7OO0c56radM89So3VsiUOiYhc4VxFpEcct6opPhod1g0tkBgtmYIO6812SJx4+gzUWKELkTg38HV0WIHvInGpuFB6344Oa/ynCN0UuVa6NkKHtaA7QkvjHqhYh2WQOHv/R+HgVBRYxtc8B38yuvtPc4e19EGE1VOULssvNy/CWn4p8hrK2SehF5oLrDUGibT31ah04g/bTdz3q99Q36WiCEn7XWIutSqwok6XtIG4wOMKptl78uCVkue5LfPJPRy3SHt63OK9vIpy0DsVhk6vWnFFLB2F5VsuTrvAuao29eyJBVlJfCYuEbmFcxWRHnHcqqZe36DGWmaxA8PL1ysd1q0GKS5C4si8weiwOt6NxLllw9BhfXABEneMfBsdVu87kLhpxzTpcSY6rKn2Hl0d/Kt5HVZsKEJL4cPNHdZKBw8unP8/c4e1owfC6jm5Qla0MXdYB9Rsfu9E1M8WBVYD2X4XcrtydkjUy9YF1rUint5ntnCPJH0pRwwWBZbxt+B/UngIF3hWcbhkd5OUc6zaq+RmkvUBbx6sLRy3SHs6q7Fykw9umD0yMCBw7Ar7rVN5xTVqqfUa0vIwcPYWl6dd4FxVm9asQY31119IiIic4lxFpEcct6oj7hg6rP+dgcRo+mAsxfruNSSORO6SJ1soHdZDfkic2zxfXjQoHdaLfpKXhdClhX3QYX3p9jMQTQ4HS+/L0GGNfg6hpfEPmTusIysRWto+wNxhhdp7cGFJgax+qaLDMsjRWcirZ/2j5gJrUQtlWVbNnZorm642F1jrWkmR4z/ZLYyX/TdYFVh7LpESt3+n3JS7Rk50sGivjC8/SeyAsx6XP0kyHrFqr5IMknanUmxRreK4RdrTUY11dMXYAJOhs7c42pK9vJKqsrKq8i5EF6dd4FxVm8rKpGlTpca69VYkREROca4i0iOOW9VRuRRr5lgkxUXy2AVKh9XWH4kT/T/CUqzJ/ZA491YrLMVabO85gHZtm40O60N/KVSzp3hOsvS/GR1W4D0ILa3tgw7rV4NErkBoaXNvc4e1dwxCSyc3yeK70WFNbCx5icirIe+4LDnP3GGtfxR5TWRslX1vmQusYD85Phqn7DreV8IamQusXWdIwWGc8pTsuZLwkFWBFdXUW/cPluyT7O6SfJZVgZXcXLK/wQVU2zhukfZ0VGMdD1sbEhq6avFU5eGCg8atqNzz3ZKnaqxBDnCuqk3PPKPUWE2a4JCIyCnOVUR6xBpLtR0b0WHd1AqJ0eePYSnWwK5IHJn6Bzqs585G4tzMPuiwOt+MxKWE/fJZc9RY+1chdEdZqQx5GB3W7/Z2ZF//p7nDOhaM0NKMuys6LINE2yu5knfI7CvQYU2xWMtWDYlLZdmFKLDm+0ncdOQ1ET9EQk4zd1jhTyC3K+YL5cmDlQVWWBNJX4BTnpI1UeLaWhVYx1pL9mKc9ayC2ZLxlFV7lWSQ1NuleBMuoLqB4xZpT497Y5keLhgwfmUsAgt2e6ry0HxToePTZmitquBcVZsCA3Ff4SHv3GxPRL6FcxWRHrHGUq1PxQMK509BkpUu97RQOqx2jZA40b45aqx9brQDBzeiw3q5IRJ39LkLHdbUTkjcNP4VdFg/29uBa9rzFh2WvUclTrvN3GGd2IrQUsJ/Mvl0dFhzrkJYPYd+Ny/CWnIuwprIDpddT5gLrHWnS67jRVUpEyTySXOBtcMgx3vjlKckvikx11oVWNFXS1EUznpQ6XFJf1RSL7cqsJJPlxxXhSzVEo5bpD091lgix1dPdHAbYCG3ePdh4eGosT78EAkRkWOcq4j0iOOWOiePo8O60eJpzkN+wlKscX8icWR2IDqs7o8jce7bO1BjhUxG4tL4T9Bh/XILEjfN+gId1vf2HlQ963Vzh3XkP4SWJt+EDmuQn5y092ioqOkosIyvZQ8hrIbibNn6urnD2vgM8po49ru5wFpj/A/8AXlVRSckrqtFgdVA6bM8K32IRLexKrBi20pZPs56UNEGyXrfqr0yvtLaSvFOXEB1Esct0l6dr7GOrp48eJDtYwnLt8myvyl7+dKqYYv24lCRvnZq5ZMNXZx2jnNV7bvoIqXGatMGh0REjnGuItIjjlvq9P8BNdaiilvYkk/Ibf5Kh3VXMySOpCZiZ/eH3dg/y2jKT+iwfnoAiUsrB6PD+sJeFeXEsl/RYXWzt5vEwo8rOqwGctDefXOTbqjosPwlyd4W4AeHmzustW8hrIbo8bLqBhRY8/0kYRHyasvcLmEPmDusjZeKlOFUVcc+lN3nmwus8AukVM2+Yy5lDJXoyy0KLIMcfxqnPKtgiqTfb9VeJTeRrI9ESnEB1WEct0h7dX811v7FIwICgqaHmjd1T948c3BAQOC09Xaf+hG7cnxAwOCZmyuuN92COHh+mGkrLRenneJcVfvef1+psQwGHBIROca5ikiPOG6pEH0EHdb1zZEYBXTDUqzJfyNxJOBzLMWa1h+JE/vWocN6tSkSlyLWo8N63yBZjh+rV9XEN9BhdfaT5Cq3ra3oZu6wdtl7q0y83txhpexDaGn3n+YOa3sPhNUQM9G8CGtxKymp8QIly7sIla3cRyGvKnWWHH7KXGCFNZb0JTjlEZmjJOZKqwLrxLM45UHFhyX3D0m91KrASr1A8kbgAtIDjlukPR3cVJi+Zc7QwICAweMWrAoNDVk+Z0L5kbmIKl9gZXFTYGHYAuMVgUMnGK8PWT5D2RDe4mpXp53hXFX7Jk/GfYXjxiEhInKAcxWRHnHcUmFQL9RYIyp6qBMx6LDuc7X6ac8GdFjPnonEuW63ocY6ZGdbDzvys6THtaixNqj5Dd2zoKLDMkhklR2vIleYO6yNAQgtTarosP5pKBlHEVra0RMF1niDHLL34EI37eth7rDW3I6w2rL3yM5HzB3WFnv72Vc63suiwDLIsQ+Qe0TWZIm90arASnwZpzyoeK9kfyVJflYFVtq9UrIfF5B+cNwi7eljb6zcqA1zxg4eFGAUGDR07JwNURbrZW1rLKPk8JUzxgQFll8+YuLirYnWS61cnHaIc1XtS0hAjfWyFz5Qici3cK4i0iOOW+4qyJebz1Q6rGsslke9dgtqrNVzkTjS9VHUWNvsbSxlY2pPdFg/P4zEpRFvocMKeAqJO2K2KXcRmmqsLRMQVorbLH2bocP673uElibfaO6wsuMRWtrSpWIdlkESViNUqzjL/ERC4+tAL+TVtqeDucBSFmE5/sPajBVy4BZzh7X7Ik/uUZX8o8TdZV1gvYhTHpQXIJmvWLVXSQ0l6xOcJR3iuEXa0+cW77WEc1Wd0LatUmO1snikNBGRPZyriPSI45a7PnoWS7H++Q1JwjF0WPeejsSRFZPQYX1xJxInlo9Ah9XB3jZVdi3/Gx3W1+cgcUfGcel7FTqseZ0RVko6KAMvQIc17x2EloK/MXdYWXYeaC6hn1Wsw/KTjAiEamXsltU3o8Ba0FCS1iOvnqxdsudlc4e12fHTEkvzJPZrc4G1s6GkeO7bJH+DHH/KosBqIAlub3/mvoI5kv6wVYGV3FJy7S2pI13huEXaY42lAueqOuGOO7AgK93u5mhERMC5ikiPOG65655LlQ7rioY4NPrza9RYS6YgceSNNqixigqQOPHDPaixNs5B4lzlllgf+Em2Wxt3wJBH0GENfwJJpZP7ZMgN6LAmVTlrtLYrOqxB/pJm7/nj695DhzWxkeTYW6jljuMLZWFzdFiLz5aymm1AfnKWbGhdsQjLINF/Ia8qbZ7sudzcYR16EHnNFUbIyfesCqzY/4mU4Kyn5I+TtFusCqyUC6R4G86SznHcIu2xxlKBc1WdsGIFaqzevZEQEdnDuYpIjzhuuWXGGCzF+rkjkp0b0GE9cBYSRyb1QYfV/0MkTsz5HR2Wm08nLC6Qn25AjbVtNkJ3TPsQHdavlyCpVJwv4x5AhzXS3i5UG3qYO6zUAwgtBb+BDmtCYylIRajWkX9QYBlfwTXeDOtwZ/MirA3nSkkechuFxyX6U3OBFdZEsjfiVA2VZkvKD1YFVvQlUhSHs55RJpkdJPVKqwIr7RYp3oPz5BM4bpH2WGOpwLmqTigulkaNlBrrjjuQEBHZw7mKSI84brnluXZKh9XGD4dGlbtirZiBxK6keHmkodJhPebGHYKb56PDetlizZdzYz9Ch/X300jcUbkO63t7t0POeBkdVtDVSCxt/g0dVqCfpNjbIHzNK+iwJp0mxRb766qyp5u5w9r5GcLqyQqTHXeaO6z99m6QNEmfL3suNXdYUR2Q11zGMDnW2lxgHW2p3FfoQWUFkv6UsuTKXGAZJOMpJSefw3GLtMcaSwXOVXXFU08pNVZDtycqIqqXOFcR6RHHLbekp8r3H8vEIThMjEWHda+rBxQGfI6lWHODkDjx21OosZYOReJcyGh0WB3V7GG6fQo6rM7+kp+JsNLijuiw+p+NxNKOgRUdlkGS9yK0tPoldFiTm0tpMUJVSvNly6sVHZZBjo5AXj373jQXWMGNJS8KuY3SQon50lxg7WwmBQ6uVCu1n8TfaS6wjjSSrCpb6ddEWa7k/i4p51gUWH6S+RbOki/iuEXaY42lAuequuLvv3Ff4e7dSIiIquBcRaRHHLeqI6AbaqyVTu/jO7gNHdZz9iohG/+NQofV+WYkziXslw8bltdYBkk8jNClTaPRYRlf8TsRVgrpjQ6rj721Y0vfMHdYp3YhtLTqRXRYU1ztee9IbqysvRcd1vxGkl6Du+HKimXzNeYOa9ttyKvKXCn7rzN3WEffQF5DRVFy8h2LAsv4Rau4I9UjSjMkt5+y6sqywMr6HGfJd3HcIu2xxlKBc1VdER6OGuvTT5EQEVXBuYpIjzhuqRa5V9r6ubUU66cXUGNF2it9LKWflLdalddYBikuROhcx1ZYirVsABKXTkWYH024oi/CSnPfRofV21/SYxBWOjAFHdYgg8QFI7Q044KKdVjV7bCiJ8iKNuiwFp8pZTXY+Dxzm2y9xdxhxQ5EXlXCT+YCa1czyVqHvIaOP670VpUdVlxb5J5RLLl/SsrZ5gIr2V+yqzxrknwUxy3SHmssFThX1SGXXKLUWMb/JSJygHMVkR5x3FJtyE9YijV9MBK7Ni1Gh/XONUicGPwRlmJN+xmJc/N+QYf1041I3DHyGXRYQx9DUiliMTqsXgaJWoWwUtS/FeuwGsi+cQgtremADmtSCyRqHeglC5uhw1p5HcLqSRhtLrDWtnB4I2FuuOxqae6wIh5CXkM5/yoPH6wssI6dJcWxOOURuYGScr5FgdVQsrviFNUPHLdIe6yxVOBcVYd89JFSYxkMOCQiqoJzFfm0ld0uc6LbSlymPxy31MnOkLuaKR3W3c2QONLxbtRY0fae5Wdpx1J0WO+fh8S5JX+iw/rYjW3jKy3ohg6rd5Uf1Cd2Sb8WqLF2VmmpwofIPw3RYW2017IFv1axDqu6HVb0BBRYxlfoEwirJ+Irc4cV5vhpj0nDzQXWzkaSWaW5q4biODn5rrnAOuIv6YNwyiOy3pXUNuYCK8lfsrvgFNUnHLdIe6yxVOBcVYdMn477Cmc4fRwPEdVjnKvIp1WtsS46o0kDRfPWrLHqkTH9sBRrqtOGYvlEdFhdH0XiROebUWPFHUTiRHGB9LgWNdY+t993m8agw+pWpflKjpAh16PDWvwFwkrJe2VEa3RYazshtLT2nZquwzocYO6wavJQwrRgCXvQ3GHF/IXcRmmWHHvX3GHtuxZ5DaUHSdRp5g4r7j7kHpH9jaTdbC6wlFsIPbrNFukKxy3SHmssFThX1SGnTqHGetSNaYyI6iXOVVT/FMSv63l784s6LktDoEMct1QoKZFHzlU6rDtcLYN6rLHSYT3sJ2WlSByZ1RcdVr/nkTg38Qt0WEEvIXHp6EZ0WJ0NcrzK4wX7n4MOa/TdSCrlnJCJ16HDWvIqQktzr67osJq7/i+1a39Pc4d1OABhNUT9LOvPRoEV0lRyDiG3kbla9l1V0WEZ5ITb24o5kb9VEh42F1hHWynLsjyl8D/JeMhcYCUZJPNtnKL6iuMWaY81lgqcq+qWu+5SaqzTq7ttJxH5Os5VVD9t6XlJg/uGHcWR/nDcUmFaEJZi/fM9Ervm/IOlWH3eQuJIQgQ6rA6NkTg3rQs6rC/OQOJSXrr0uxY1VvDfCCst7ogOa1AbJJWK82TW/eiwZlZpuIw2fYUOa2JTKSlAqEr4VxUdlkGOz0dYDeFPmRdhbb4aYVWHnzQvwtp5hhR7on1O6WkusI4YJLU38por3iWZr1gUWA0k42mcovqN4xZpjzWWCpyr6paff8aCrMREJEREFjhXUf10csKTDRq8twhH+sNxS4XnrlI6rLb+OLSrqEBePK98KZbTy0z+ehU11ip7+6bbyEqSTucrHdYHBknYj9Clca+gwxr1DJJKU59Bh9XX3j5fizugwxp3BRJL275DhzWhkRRlIlSl8qGE8/wkeT3Cath0tbnDOvAhQhsFxySqg7nDOvw48prIXSlxN5s7rNgbkNdc8T7J/tqqwEprK5KHs1Tvcdwi7bHGUoFzVd3y99+osQY6fmgxEdVjnKuoPirYM+D2Jg0u6bkFx/rDcctdiydhKVYvB12JyaS+WIo18gckjkz4Dh3Wx5cicW7kO1iKNfkrJC6t6IMO68dzkFQ6MA8dVi8/yYhHWGnNl+iwRrRGYmnZw+iwxjeUrGqsRCyTzS+iw5rfSDJdbYHvSP4xCbsfBVawn5ycidxG5mrZ2wYFVpi/JI1GXm1lhZLcxVxgHWkoOUtwquYynpTkZuYCK/l8Kanyu0P1G8ct0h5rLBU4V9U5LVsqNdYV9v5QjojqPc5V5NPsP6mwfJv3JrcPcntpTN3DcUuFzx51sStWZqq0b650WI+58QzBV5ooHdaLBsk4hcSJ8Z+iw+p0PhKXlvREh9W1kRTlIzRJ3K2swDLVWAfmIay0pQ86rKCmSCztC6zosPwk6xhC9xVny4bH0WEtaCYl1v9i7ktdLaEXoMNa20IKTyK3cfJv8yKs8POktFo3P1rKWSqx15o7rHjHD0NUq2CmpF1nUWC1kIIa3GhJvovjFmnPGzXWoo5tHu0+PHhfWo1/LtcxnKvqnFdeUWqs5s1xSERkgXMV+TT7NdZlNz7afcJOB/8HWh84bnnSqB5YijX1TySOzO6HpVgDX0fiRNwe+fKs8hrLIBnu7e2QFis9z0WNFbMNoUlBugy/BR3Wiu4IK/33ITqsQf6SHoWwUsRYdFhjG0hiKEL3ZUfI2nvRYS0+C2E1HB+DAsv42uzoUYPFcux9c4d1xO0d8R0qleTO5gIr6jTJr8G9kJYKJin7XlUWWEn+kvs7ThFVwXGLtOeNGmv9L1ebnvjc5Ix27w6Yszk6Byf0jnNVnTNyJO4rDAtDQkRUgXMVkR5x3PKYpAR5yE/psNrb22rK0oFQedFP6bA6NELi3Ig3sRRrtqsbFSuNeBId1vSPkFSa/To6rNF3IqkUvaKiwzJIfJWOpnI/LOPr8HiE7kvbIQubocNadiHCaojqae6w9r6C0EbqLNnZDAVWmJ+kTkdebbkrld2vKjusEy8gr6GSBMnuYlFgNZDMDjhF5ADHLdKet24qzEnatWTA54+a+qwmZ1ztE8uzOFfVOUeOoMbq0QMJEVEFzlVUD+QkxdqVpN8/Q+S45TFDu2Ip1jJXFc+g97AUa3EQEidCJ6HD6noxEpcG3YsO6/frkVQK/hUd1sAqNyem7JdhZ6LGCh+OsNLxYHOHdXAkQvflHJNV/6vosC5CWA373zV3WNF/ILQR+42ENUGHFd5KStKRV1tyD3OBpSzC8tBWeJnvSHJrc4GVeqWUVWuzfKpnOG6R9ry+N5ZVn9W89d3vztDvbg2cq+qiG25QaqzHHsMhEVEFzlXk2wr2DHrItPzdDj6psN5LjEaH9UwrJI7M648O6+NLkDiRGCFfn6N0WB8YJDsFoXN7FqLD6lJlqdfemeiwejeUkkKEJkXZMuUWdFgr3kdYKXW3TD0LHdZ2t1eEVTq1WlZeiw5r5XUI1craJWEPocAKbijpG5HbSPgVBZbxdfAuhNWWv03i7zJ3WAmeeMShUdEmSX/EXGClNJfCZThF5ArHLdKeNlu85yTtCh7c9ZErT+dcRZ7WpQsWZOX4ys2rROQhnKvIp+0fcEuDBk2u/WT4XHs2qd/puq7guOUZH92MGit4FhJHetyHGivRjWf8fX8VlmLNqLKJlV0ZCdLzPNRYu603CE/cI31PQ411cAHCSoteRIc1814klXKPy9xr0WGtfQuh+7IOyX9XocNa0xahWpk7ZNNV6LDWnSGlucgtlebL0TfMHVb8d8irLT3AXGBFNZGcKl+0aijLk+yu5gIrySA5nXCKyD0ct0h7Xq2xbO4sbPdCrwmbo3R8ZyHnqrpoyRLUWAsXIiEiKse5inzaovcaNLhlgI6fSOgIxy0P2LUWHdZTLZE4snIMOqwfqrRFVW2fgw6rs9s7SY1+Hh3WrI5ITMrKZNQd6LDW9ERYaW1XdFgjWyOpVJIvS+5Hh7XsYYTuyzooK65Ah7X6JoRqJS2WkGbosLZcg9BG3j450BYFVpi/ZPyLvHqK45Tdryo7rPgar+oyyZ8qqZeYO6y0dsq28UQqcdwi7XmjxrJqr8pvJBywZJeO92ioxLmqLiooED8/pcb6+mskRETlOFeRT9vS85IGtw+KwJEP4bjlAYO+Qo21dBwSuwpy5aOLlA7rRT8kThTlS/fLUGOlJSB0buUf6LB+qbKL1sKP0WGNvA1JpV1D0GEFNUViaU0HdFiz2iBxX+Iyc4cVUt0m6MAHKLCMr50PIbSR0FN2tUSHFX62lNXsD/GzZ8mxcyoWYflLxgjkNVESKSkXmgus5OZStBaniFTiuEXa80aNpfzxoGlbd30vvqqCc1Ud9eSTSo11jYM/DSOi+opzFfm2o6MeadL6vXnxPjRpleO4VVMJUeiwnj8biSM/PYClWGPcuI9said0WKPfQ+JcZAg6rK4Npcx6jc/MDuiw/jgTSaVjy9BhDTJIZgzCSpu+Roc17Vwk7ss+LCuuRIe1zo2lZ3ZF/2nusCK+QGjj5D8osIyvg3cjrLa4tuZFWNGXSFk+8prIeFaS/MwdVtYnyImqheMWac8bNdb+1b6x+KoKzlV1VEAA7iuM8ME/lCaiauNcRb4ubWXHixo0OeOiy6rqthLX6A/HrZoK+gY11jan74IDG9BhvdYMiRP7V6PD6uhqw3iT4gLloYSmGmv/UoQm0evQYfXyk9xkhCaxa2REa9RYu6usOVr+BDqsCY2RuO9UsPx3DTqsau+HFdnd3GHFV3lyoklsF3OHFfslwuop2C3x95g7rFOfI6+J4i2S/oC5wEq5TErTcIqoujhukfa02eLdR3CuqqPCw1FjDR2KhIiIcxX5uILgTq2V3RsaNG+N6soSa6z66mQMOqznXC3FGvwRaqy1U5E40asdaqz4vUic+/06dFijnkNiUpApg69BjRU2BmGlmfeiw1pZZX1Q5CR0WOP9JPMIQjflxsrKG9Bhra3u8ijzvYQGSba30VVpnhx5GQVWmEFO1WwozRwrR/xRYEU1lcIw5DWR+4u5wEryl7xhyIlqhuMWaY81lgqcq+quSy9VaqwXX8QhERHnKvJxwZ3ObHDmG7N97p5Cjls180lb1FgrpyCx69AmdFjvnoPEiX9/R4cV8CQS57ZNRof1Q5WlW3PeRIc1/QUklVZ+jA5rcpWd108Eo8MyvhLXI3RT5j5Zcys6rFX/Q6hKab7sfgEdVnAjybb3XIX8SDlwe0WH1UQKY5FXT+wN5kVYcXcgrImiEEm7zdxhpXloh3iichy3SHussVTgXFV3ffKJUmM1c2NVPBHVG5yryKcpW5G+OtsHN3HguFV9keHykEHpsJ5sgcSRPs+gxtphfcdfVQkH0GF92BCJc6nR8n3L8hrLICetd3uY9hw6rIEXIKm0fSA6rGFVmq+MSJl+Pjqs3X8gdFNhurKVu6nDWnMLQlWKUpR93E0d1toWUpKN3FLWOtl9ATqs3echrB7lRsI7Kzosg6T9hbwmsn8wF1jJTSR/OnIiD+G4RdpjjaUC56q6a9Ys3Fe4ejUSIqr3OFeRT0ub+XKTM79c5XOLsThu1cCoHliKNd/pHW1bFqLD6nwzEicGv4Qa679BSJwb/TyWYi39BYmJ5ZZYRdb1q3lbd3/JT0FoUloki+9Ch7X2bYRuKs6S9Q+jw1pxBUJV0kNl663osNafhdBG6kwUWMbX/hsRVk/mJDnSCB3W0RZSuA95teUNkrRbzR1Wxv3IiTyK4xZpjzWWCpyr6q6UFNRY336LhIjqPc5V5ONOLnqvdfObOg4P3hVbhY6ftcNxq5qy0uTxpm4txfruTqXDetEgWdadUVWTOqLD6nEdEudCAtFh/WZdGxXnyeDrUGNtGYLQJDNGRp6HGit6OcJKIW+gw1qofl/2jc9UdFjV+iDI2Sebr0aHZfyFXZWbYRlfUa8irJ7k7hWLsBpI3O0IayKnp7nASm4qhSHIiTyN4xZpjzWWCpyr6rTrrlNqrNtuwyER1Xucq8inKTcVOvbeIlymPxy3qmnib1iKtbDKM/4sVe7s/ttTSBwpLpBulyod1gcGyctA6ET8LnRYXfyltBihyfwP0GFNq7Il1rzH0WEtfxdJpe0/osOa5sYGXja2vo4Oa7H6v9YoY6uEXoAOa4uDCi+hp7nDiu+BsBqK4+V4+4oOyyCpfZFXW/FWZferyg4rw70dzYiqi+MWaY81lgqcq+q03r2xICs+HgkR1W+cq8inHds014lNx3CZ/nDcqo7iIuXRhA82kCdOQ+LIF1cpHdZLfjh0YuZ3WIo1qSMS5wY/iBprg/Uj8LaPRIfVv0qjtK4bOqzJVTZfPzwOHdbExlJSiNBNYZ+gw/r3TCSqpK2V9a3QYe18CKGNmM8rOiyDpC9AWA25/0n0heiwoppI3jrk1Zbb31xgJTeRgnnIibyG4xZpjzWWCpyr6rQFC1Bj8feIiMpxriLSI45b1TE7EEuxJvZGYteiQCzFCvoAiSPHdqDD+qw5EudW9EGHFXg3EpPE3fKrobzGMkhuMkKTA5PQYQ2p8oiexHXosIyv9EMI3bTmNnRYC5pKWQlC90X/KSFN0WHtsreOqaxUojqgwwrzl5ytyKsh/Z+KRVgNJOYahNVWEikZT5k7rPR7kBN5Gcct0h5rLBU4V9V1BoNSY73zDg6JqH7jXEWkRxy3quONNkqH9YjThwkW5su7rZUO62U3njn491OosTa78WC7Y5vRYXVtjKTSuPuxFGvNr0hMknbLP42UDmuQQdKOIDTJPSGzLkOHtUflo/oO9qnosBpLUTpC9yUvljUGdFj73kJoqSRVws9Eh7WruRS72lzMidgbzB1Wosrd66vKHy9Jp1V0WA25CIu0xHGLtMcaSwXOVXXd+ecrNdZ5NXvUMRH5Cs5VRHrEcUu1/h9iKVb/j5DYNfM3LMWa+D0SRzZORofV8yYkzgXejRorfA4Sk1U90GGNsV6iZTT9TizF2vADkkor2qPDWvMKEjdFDUWHNc9PcmMRuu/IDyiwjK8DHyK0VHBU9t9a0WG1QlgNxXGS8EhFh+UnWdOQV09piqReY16EldoGOZFWOG6R9lhjqcC5qq7r2hX3FW7bhoSI6jHOVUR6xHFLtf4fKR3WQ063u8pKkdeaKR3Wa642zyrMle6XlddYBuXXLg1+CB3W39Zd1eGl6LD6VPknru6IDmvW/UgqbemMDmuee89GrLTr64oOyyApGxG679Rcc4cV2RWhpbzdsvcKdFj7HDy40B15IRJ9MTqsqOZSkoi8eoqCJfXaig7LILk13h6eSD2OW6Q91lgqcK6q644dQ431xx9IiKge41xFpEcct6pj+USJ2IFf2zXpByzFWjoUiSP97sVSrElfInGi8nbC7k2RmGQel0FtUGMl7kFo8t+H6LCGnoGk0qFR6LAmWv/dXDq1qqLDaiBHRyF038nZFh1WF4SWTg2T3a0rOqzrEVZD5tiKRVgNJKYGfx+T9MfNi7BSzpDSA8iJtMVxi7THGksFzlU60LChUmOdWa0H0xCRb+FcRaRHHLc8LzleXjQoHdYbLZE4smsxOiw3d3YfdC9qrH2LkZgs+BAd1jzrXZ+S9sg/DctrLINkWz9aOnEDOqxxBknbj9Ad8XPk31bosA79idB9kd3MHdZR6w28TDL/k52nocOKeAxhNaT+bO6wEl9FWD2lxyXjeXOHlfUycqLawHGLtMcaSwXOVTrQrh0WZGVnIyGi+opzFZEecdzyvLFdsBRrw0wkjgx7DTVW2EIkTqz8HR1W0INITMInocPqfw6SSjPvxVKszb8hMSlIkzlXo8ba/w9Cd2TslpXXosPa4erxi1WdmmPRYdl7yGPaAhRYxldUB4TVEHN5RYdlkMwRCKuncJGknI8CK7mhFExGTlRLOG6R9lhjqcC5SgdGj0aNtdCN8YuIfBrnKiI94rjlYQc2oMN6y9Wu5OFL0GF1d+MnZ0I4OiybpxNmxMrvZ5TXWAZJjkBosq4bOqwZdyGptKYDOqzVLyFx07oH0WFteBSJ+yK/t+iweiG0FNvJ3GHFfIpQrbytEn8/OqyoRpK7DHn15P5qXoSVeqVIKXKi2sNxi7THGksFzlU6UFCAGuubb5AQUX3FuYpIjzhuedior1FjrZ2CxJHet6PGSjyMxIlhj6PG2m79oL0ZL2MpVrD1DXoRs9FhDWmBpNKu39BhzbgIiZu2vYkOa9UNSNx39Bdzh3XMemmYScokc4cV3wOhWvmbJPoydFjHzpayPOTVoNxI+Ky5w8r6HDlRbeO4RdpjjaUC5yp9aNFCqbGuU/mAGyLyOZyriPSI45Yn7V7j7lKs1UPRYQ14HIkT64egw/rDuj/aNhwdVtA1SEyy4mXEOaix0qw7spiF6LAmNJLSYoTu2NMdHdZi9Tuipiw3d1iHuyG0lDzG3GEl/IhQrZTucsQfHVbsTQirp3CZpFxY0WE1VO4rJKozOG6R9lhjqcC5Sh9efBELsqKikBBRvcS5ikiPOG550sgvUWOtGInEroIc+eZcpcP60B+JE0c3Ks8l7NRAuvhLWRlCo5TD8ltjpcPq5SdFuQhNFr2ADmutdWeUHSPTWqPGOjYXoTsiB6HDWtBESgsRuiktWIIbocOK7I7QUtIIc4dl/HX1JL6CAsv4Ov4MwurJeNK8CCvVuh8kqgM4bpH2WGOpwLlKH5YuRY01Sv0Tl4nIh3CuItIjjlsekxhVsRTL1Xql2T2wFGvuT0icmPgGlmL99zsSk6nPYinWliAkJjsGosMa1wZJpRXt0WFt/BKJO/Z+iw5rnp+kbETopuOjZW0LdFgRXyO0dGqoRYc1GqFaKT+ZO6zUXxBWQ1meZL5l7rCyP0JOVJdw3CLtscZSgXOVbvj5KTXW66/jkIjqJc5VRHrEcctjRn2FGmv9dCR2JR5Gh/XF6UicmNsJHdZv1p3UlsEVtxNehcTkxBZ0WIP8pbQIocmOn9BhLfgfEnekbZeFTVBjJcxD6KbscNlwLjqs/e8gtHRqiEWHNQyhWqc+RoEV5SfZ1huHqVK8Q9Juquiw/CV/EnKiOobjFmmPNZYKnKt049JLlRrr7LNxSET1EucqIj3iuOUZJ4+hw3rH1Tg0+j3UWDtd7bhUUii9Ly+vsQySnYTQ6MhK+a2R0mH19pcS665q2u2osXb+g8QkZgE6rIlNkbij4JSsvAYd1v6fEbopN1I2XY4Oa8/LCC1FvWrusNIXI1SlJF1OPF+xDquh5K1HXg35k5XqytRhpZwnZTnIieoejlukPdZYKnCu0o1OnXBf4ZYtSIio/uFcRaRHHLc8Y0xn1Fib5yOxa/UwdFjd3fhpObAdlmLN+gKJybTnsRRrXT8kJuu+RYc191EkJqe2mbfESt2L0B2h7dFhhT6JxE3Zu2XL/9Bh7WqP0NKpYSiwwgySNhuhKsXxEn8POqyjLZRKq9pyfqpYhNVA0p9ASFRXcdwi7bHGUoFzlW7s2YMa648/kBBR/cO5ikiPOG55QEoCOqx3XS3FCngSNVai9QMEq9o0Bh3WD9YPPZzzFjqsIdYPiT66BB3WkBZIKq18Fh3W5m+QuGNXR3RYyy9D4qbCJNlxDzqs7XcitHTKYk/3k0MQqlK4V2KuQYcVfSnCaijLkswOFR2WQXL7ICeqwzhukfZYY6nAuUpPGjdWaqzHHsMhEdU/nKuI9IjjlgdM/L5iKdYCJHbN+h4dVu87kDgx5GHUWHE7kRilx0q/FuU1lp8U5yM0KsqRcVegxjq5A6FJeD90WLPUtFGH/0aH9W9LJO4LfxId1uarkVhKHmvusFJnIlQlb70ca40OK1bNPl82indK2v/QYSU3lsI1yInqNo5bpD3WWCpwrtKTtm2VGstgkHyLoYqI6hPOVUR6xHGrprJSpENjpcN68wwkjvRqp3RYHxgk19UdcBuGo8MadC8Sk9lvYClW6AAkJis/RYe14n0kJsdXo8Oa0AiJO44vQoc1z0+yjyB008ZL0WEZf1FV9GcWHdYchKrkLJaoRuiw4u9BWA0FsyW5SUWHdYFIKXKiOo/jFmmPNZYKnKv0ZMQI3Fe4fDkSIqpnOFcR6RHHrZqa3gtLsVaPR2LX6PexFGvQc0gcST6i3EjYqYF08UdismcaOqx/rkRiEjELHdaI1khMSvJk7jWosQ47/XezlLhM/m2FGitxCUI3HeqIDmv9OUgspc4yd1jVu5cw6SsUWMZX4psIqyG3Pwos4yuDm2GRznDcIu2xxlKBc5WeZGSgxvr+eyREVM9wriLSI45bNVKYL2+fqXRYrzdH4sh3V5QvxfLDoROzvsBSrOW/ITHKPSUDLyyvsQySm4zQKC9FRl2AGisrDqHJho/QYQW/isS1Mgm+Ex3W7i7I3BT1Mzqstc2krBhhpfRF5g7reLW2oMocZe6wkrsjrIbsz80dVs6vCIn0g+MWaY81lgqcq3SmaVOlxrr9dhwSUT3DuYpIjzhu1ciCgViKNa8/ErsGPYelWBM+Q+JI5Fp0WD+ehcRkyZdYirXcul1a8T46rJDOSEwOj0eHNf08JO5YcSU6rJC7kbgp7h90WGv8pSgJYaXMVeYOK/FPhKqkB5o7rPSBCNUqS5KM9hUdlr8UzEVOpCsct0h7rLFU4FylMw88gAVZyRZ/SEhE9QbnKiI94rhVI59cqnRYHRrj0K7sVPn6HKXD+siNDaqCHkCNdTQUidGGv9Bh9be+We/QdHRYI627qsxImXia0mGN95O8kwhdOvwXOqx/XW3yZSNxWkWHZZCscISVsjfJzubosOJ7IFQlrR8KrCiDpFW3wyoKk7Rr0WElny4lx5AT6Q3HLdIeaywVOFfpzOjRqLHmVGvPTiLSOc5VRHrEcav6VozCUqxxXZHY9edDWIo15yckjoSOQIcVaL15+Yi2qLGSI5AY5afIqPNRY+UkIjT570ksxdpjvRO8EycWo8Oa7y+Frnagt5S6qqLDaiDHxyGslDZPdp+HDivG1Uo0u1J/rliHZZDsWQjVKlwkyc3QYaXY23ueSD84bpH2WGOpwLlKZ0pKUGN9/TUSIqpPOFcR6RHHrerrdJPSYb1kvRG7jcQI+aSp0mEZ/9e5/Ez5qbXSYXW23j9rXT90WNOs94b/70N0WGutbzPc/Sc6rAW3IHEp55gsaY0aK24aQnecmi/rz0KHdbQXwkpZobL3KnRYR15CqErKd+YOK38HQrWy3kCBZXxltEdIpFsct0h7rLFU4FylP6efrtRYN96IQyKqTzhXEekRx63qm/i9vOgnA17DoV2/3YmlWEtc7Qm1oBuWYs3thMQocjk6rD5NkJgcnoMOa4T1bYanNqPDmuD0PkcbGx5FhxX2MRJ35B6WrTeiwzr4CcJKBdFy4DZ0WBEPI1Ql9paKDstfiiyWoamSfq+5w8rhY4jIF3DcIu2xxlKBc5X+tG+PBVkJCUiIqN7gXEWkRxy3vOjodnRYn7dA4kjcTnRY31lfOeMl1Fi7LRZJFeXImEuVDmuQQdKOIDT5907UWMdXIXFpT1d0WKtvQuKmXU+gw9r1KJJKZWUS8Sg6rAPtEKqS9FVFh9VIio4iVCvr44oOyyAFCxAS6RzHLdIeaywVOFfpz8iRqLGmT0dCRPUG5yoiPeK45UU/34Iaa+VgJI6MeQE11vYpSIz2TEeHFXQNEpPgr7AU678PkZhs74EOa8XjSFyKmYgOa9HpSNx04EN0WJuvRWJp90XosPZZ/5u7KekLdFhRTaTwEEJVSk9JxrMVHVYjKanW34SoTuK4RdpjjaUC5ypdMtVYX3yBQyKqNzhXEekRxy1vObSuYilWSySO7J6HDqufRelTUiiBlysdVi+D5FvsuX5sOTqsYdbPEzy+Gh3WpOZIXMoIl/kNy2ssg6RtR+iOHfejw1rXComlfdehw9p9PhJVTn1W0WGdJkWRCFUpXC/pd1R0WK2kTM2O9UR1Hsct0h5rLBU4V+lSq1ZKjXX99TgkonqDcxWRHnHc8pbJX6HGChmNxJG/blY6rM4GSbG4e25VDyzFWmT9gL/JN6HGil+HxGT+/1BjpYQjcSn4DizFivgLiTsSxqDDCm4kxZkIKyX8hA5rl8rlXSanPkWHdfQ0KUlBqEr+JEm9HB1WShuERD6E4xZpjzWWCpyrdOnJJ7Egi9tjEdUznKuI9IjjllfsX40Oq+OZSBwJCcRSrDEvIjHaNxsdVl/rpVUbf0aHtfB5JCZbu6HDWv0yEpd2foEOa91DSNyRuhId1hqD5OxHWOnkIHRYYY2kJBWh+yrXYR1tKqVpCFXJ/UOSW6DDSr8DIZFv4bhF2mONpQLnKl0aMQI11syZSIiofuBcRaRHHLe8YlJH1FgbnH5tU2Plh1ZKh9W1IRKTqc+ixopYjMQocTs6rMFNkZgk/IcOa7KruxcrRY9Fh7XkbCTuyD0s61ujxjoxDmGl1OnosHYYpOAwQvcldUSHFdVYSqrVYWV1QoFlfGVa13xEPoTjFmmPNZYKnKt0qawMNdbXXyMhovqBcxWRHnHc8rwDa9BhfeWqJFrQHUuxFn6LxGjpN+iwht2MxGTuY6ixDkxCYjLvBtRY6VWWR9mVsVvm+ysd1nw/yY1D6FJZsaxtjg7r4CcIK2WuqeiwGkjGSoTuS/q6osNqUp1lXEZ5I8wdVvaXCIl8Ecct0h5rLBU4V+lV8+ZKjXXrrTgkovqBcxWRHnHc8rxJX6LG2jQViV37l6LD+q4FEpPfGikdVi+DFFjsPLV7ODqsKdbd1rZv0WEFv4bEpZC7sRTryBAk7tj/Fjqsbe2QVMo/IGF+6LBO/o3QfcldLTqsZISq5PY3d1g5vRAS+SiOW6Q91lgqcK7SqzvuwIKslGrtzUlE+sS5ikiPOG55WNJRd5diTXoLNdZWi6//+IewFOvfT5EY5STK0JZKhzXIT8pKERodX4MOy/3bCXd3QYe1/kEk7jj6Czqs9WchqVSSLgduRocV/SFC96X8UNFhNZaSkwhVyelZ0WEZJG8wQiLfxXGLtMcaSwXOVXo1bBhqrMUWGzoQka/jXEWkRxy3PGxqJ9RYm6chsStkEDqsXy9GYnRkJTqsvs2QmPz3EZZibfwFicnCtqixUvcgcS5+Jjqsxa2QuOPEZHRYIY2kNB9hpcin0WEduh+J+1J7mTusoiiEqmRX7oflLwVOv+BEvoLjFmmPNZYKnKv0KisLNdZPPyEhonqAcxWRHnHc8qTUOHeXYvU4CzVWXBgSo8DLUWPtn4vEKHo5OqyR5yIxCfsFHdaqF5A4lxsji88sr7EMkuXeLlpGGVvQYQUblF/biP4UHdZuizLOTWl/VnRYDSV/G0JVsj5Gh5XcWArXICTydRy3SHussVTgXKVjjRsrNdbDD+OQiOoBzlVEesRxy5Nmfosay/muWLtmo8MKvAuJ0fo/0WENugKJydS2qLFSDyExOjwRHdak5khc2vQslmLt74nEpaI02XwtaqzoPxBWOt4XHdZO67Vj7kgPQod1xE9yVyBUJfPNig7rNCk5iJCoHuC4RdpjjaUC5yod+9//lBqrqfUDoYnIp3GuItIjjlsek5UkHzVUOqyOrm7ZG9BW6bA6G6QgC4nR2HtRY6XHIDGa/xQ6rEXWS66mtkKNlbgBiXMRf6LDWnUjEnfseREd1u5nkVRKmYwOa4e/FKvcCzVzbMU6LD/JnoFQlcyX0GGltJSydIRE9QPHLdIeaywVOFfpWI8euK9wW7VWiRORDnGuItIjjlseM+8XLMXaMAGJXZPfwVKscR2QGIWNQYc16QkkRnHrZJC/0mEFNUZisuoFdFhL3NuOKnkDOqwFxr9PGUKXjvyIDiu0yg2D2esrOqwGkh2K0E1JnSs6LINkjEaoQpFkPFXRYZ0tZUWIieoNjlukPdZYKnCu0rHQUNRYQ9Q8y5mI9IxzFZEecdzyjMJc+aKl0mF9fjoSR3pfrnRYXfxxaFRSJAEXKx1WL4vQaOzlWIq13+I36MRamdBI6bCM/+um1begxopx+zc6cSo6rGDrBs2oME72XIIO6+QghG5K7YMOy/hKC0DovrIcSX+0osM6HyFRPcNxi7SnjxorN2rDnLFDgwIDjAYNHjV1+c7EQpyyKzl85Ywx5ZcHBo2oerWL0w5xrtI3g0Gpsd59F4dE5Os4VxHpEcctz1jSH0ux/nPa7KwegKVYczshMQr+FUuxVnyLxGjjr+iwxllvlRX8GpZiHRqFxLndndBhbXwaiUtZuyS4EWqsnCrbTkU8hA7r6JtI3JS3UaKaosNK+Rmh+8oyJP3Big7rEoRE9Q/HLdKeDmqs9C2zBwcEBA6dMGd5SGjoqgUThgYGBAyetj4W520VRiwfG2i8fsrikNCQ5TNGDzJePXNzMs66Ou0M5yp9a9FCqbGuvRaHROTrOFcR6RHHLc/ofKHSYX3idFfQwhw8oLCrxRKn9Gh0WP1aIDGZehtqrKw4JEZRM9Bhzb0GiXMJ89Bh/XsGEpdK82XbbeiwYgYirBT9MTqs/TcjcVPuYok+Hx1W0lcI3VeWJun3osNKte71iOoZjlukvbpfY8WuHB8QEDQ91Fw0Fe5dNMIYzd1udxGV7fWFEUvHBAQMnh9mutrFaac4V+nbo4/ivsIUlRt/EpE+ca4i0iOOWx6wZjiWYs3ugcSuZb2wFGt1fyRGCz9BjbXLYketHX+jw/r3JSRGJQUy+/LyGssg+ScROlGYIssuQo2VshGhSwc+QIe18zEklU78iQ4r/Ewkbio5IbHXo8NKfA2h+0pTJP3uig7Lvf6OyHdx3CLt1fkaK3vTnMGDAmZuxqFJxNJRAQFjlkfi0FLk8jEBAcMW7cWhIn3t1ICAoPlhyq9dnHaOc5W+jR6NGmvVKiRE5NM4VxHpEcctD/jxeqXD+rAhDu3KTpJuTZQO6zuLVVdxW9BhDbDY6Sn3lAxpoXRYNju7b+2OpVgbP0Pi3PZ30WHtdO96o9hB6LA2nIekUto8dFhhDaU4FaE7ygok4T50WAkPIXRfabIkt6josK5HSFSPcdwi7elzi/ew+UEOaqzC0OkBAY5bLxenXeBcpW8REaix/voLCRH5NM5VRHrEcaum0hKwFGvM+0jsWvQdlmJttVh1Ne051FiRK5AYre6IpVg7g5AYndqMDmuS9b2HjkRPQIe19AIkLqWFoMMKbihF1kVV3j7Z1QI1VvYWhG468QI6rBj1JVRpikWHdQNCovqN4xZpT481VnLo9KCAgBGL9+PYUvlqq1FLI3BoUh4GzjZ+xrk47QLnKt0z7fL++us4JCKfxrmKSI84bnnA/tXy12NSVobDqtLj0GH90AqJUeXO7oMtNhI9sRkd1ijr9VDLHkGNFT0fiRN5cbKoZXmNZZBCt3akleJ02XQVaqykhQhNyorkQDt0WAm/IHTTqU/QYR2tsrzLpdI0Sau8l/BGhET1Hsct0p7uaizTDu0Od2Uvr6SqrKzaPBNrsFycdoFzle6ddZZSY13DXQyI6gXOVUR6xHFLC3M7ocbaMh6J0e+no8ZKPYrEaP6TqLFiViIxWvcOOqzF9yBxbnMHLMU69AcSl/a9jg5rX5XnDx57Fx1WxMNI3JTyY0WH5d4KMkulmZJ+P9dhEVXFcYu0p68aqzB67TTlqYVjl0c42JHdUzXWIAc4V+nbww/jvsLMTCRE5Ls4VxHpEWssr4sMsbMUa9Gn6LAmt0didGg6OqzJtyAxyjwiU89WOqzx/lJWitCJY6PQYa10+2nRMQPQYW1qg6RSYsW27rsvROKm9CB0WFENpTQdoZtK0yTj4YoO6zqERFSO4xZpT0c1Vu6B5eMGKR3W0gO5iKqy21OVh+abCh2fNkNrVQXnKn3r0wc1VmgoEiLyXZyriPSINZbX/XEjaqyNI5EYDb5W6bB6+Vk1UxOvK6+xDFKYgcRowU1YihXeD4kTOcdk4WlKhzXfX4qzETqXFowOK6SRFFn3TemL0GGFNZJi925ONEnugg7riEGKDiN0V5FkPFHRYV2NjIgqcNwi7emkxipMDJ09VLmXcNraaAfrsEwKucU7OREVhRprpMXcRkQ+inMVkR5x3PKuU4fRYf14NhKjLUFYirXwEyRG85/CUqwVFlvFnwxFhzXdvb2lNr+MpVhHLLaHd6IkSzZfjRorYQRCk4KjEn4Oaqxki23pXUofUtFhNZC8EITuy3i2osPiBwqRHRy3SHu6qLGSNzaybuMAAF4YSURBVM9UbiUcOX+76z92KV9aNWzRXhwq0tdODQgImh+m/NrFaec4V/kCU4319dc4JCLfxbmKSI84bnnXrC9QY+2ajaSkUAacr3RYvRshMUqLkKEtlA4rqAkSk8qd3VMtp2kHjo2uuJ3Q7Rvx9r+DDmvvq0gqHX4EHVa0RdfmUl6IRF+EDit9IEL3Zb6CDivF7QcsEtUzHLdIe3W/xjLt6R44dlG4e0uHY1eOt9oBvjBi6RhjMD/MtIrLxWmnOFf5gkaNlBrrkUdwSES+i3MVkR5x3PKipEh0WD9blDJr+2Ip1hqLp/6FdMZSrN3DkRhFjEWHtfQBJE7kxsmi08tvJ/STwhSEzsUNQYe14XwkleK6oMPadxMSd5QVSvxd6LBOvovQfVnvVnRYZyEhoio4bpH26nyNFbt6YmBAQMCwCbNm21iy5Xj5FeULrCxuCiwMWzA0MCBw6IQFq0JDls8YPciqtnJ12hnOVb7g0kuVGuv8KuMREfkczlVEesRxy4tmdUSNdTgYSV6q9GuhdFh9myExSt6DDmvEuUiMSvJkxoVKhzXeT0qLETqx9XUsxdr3AxLnsnagwwr2l2LrLbGSJ6DD2qnyCYMnXkGHFX83Evdlf44OK1n9Yw2J6hOOW6S9ul5jmTazsquiuLKtsYySw1fOGBOk1F+BQSMmLt6aaL3UysVphzhX+YLKhxWmuPcHg0SkW5yriPSI45a3JEehw/rF4hl/q37EUqyNFjfcLXkdNVaCxSNxtv+ApVibvkTiRMxkdFjLL0Pi0vY7UWOdmoXEJHeX7PAvr7EMUnAMoTuSu6HDir4YifvSbqvosJpJWT5CIrKH4xZpTydbvNcNnKt8wV9/ocbabL3TPxH5HM5VRHrEcctb5nyNGiuyYilW1nHp7a90WP2aIzGKXY0Oa8K1SIxiFqLDmmxxpSO5MbLkvPIayyAF7v2p4eGu6LB2Po6k0sHbsRQryXrHd+fSg9BhHW0mYvHsRXekP4gOK6mJlKUiJCIHOG6R9lhjqcC5yhesXYsaa/JkJETkozhXEekRxy2viN6MDuun1kiMlnfFUqxQi6VYcx5BjZWyD4nRzItRYx2bh8SJnZ9iKdb+nkicO/ItOqy1ZyCpFPM5OqyIx5C4I7UnOqwjBimKRuim3ICKDquRlMQiJCLHOG6R9lhjqcC5yhcUFqLG6tULCRH5KM5VRHrEccsrBrZDjbW14msbuwkd1p9nIjEK6YQOa9odSIxiF6PDmncDEidOLEaHtfQiJM4VnpQN56HGSl2N0CR5DDqs8LORuKNgjxw9HTVWzmKEbsofgw4r2V+K1iIkIqc4bpH2WGOpwLnKR/j5KTXWe+/hkIh8FOcqIj3iuOV5WSels5/SYX1/OhKjJV+hxtpvscBq8s1KhzXIIMW5SIwWtVM6rPEGyT2BxImV16PGyjqMxLl9r6HDivgCiUnuLgnzK6+xDJLv3t/KqDRHYm9Bh5X6E0I3FcypWIdlkLyxCInIFY5bpD3WWCpwrvIRTZsqNdYDbjwrmoj0jHMVkR5x3PK8JT2xFCskEElKJDqsAIvt3nePwFKsZW8iMTo4DEuxVr+ExIl9P6LD2mrxd3Aifig6rE1tkFQ6eCeWYiVW/Du7I7Hi0YQJVfbYcq5wdUWH1UByByMkIjdw3CLtscZSgXOVjzjrLKXGusztR+cQkT5xriLSI45bHpafJd+1sF2K9e8XqLFi1iMxGnNp+VKshjg0KkiT6ecpHdYEi9CRuCnosBaehsS57L2yxqB0WMH+UpKD0CS2Ezqsw48icUdKD3RY0Sp/8hfMlORWFR3WnwiJyD0ct0h7rLFU4FzlI267Tamx/P1xSEQ+inMVkR5x3PKw/37HUqwNw5Ec+Q8d1oDzkRht/wtLsdZ1R2K0+B4sxdrSBYkT/56JGitxORLnwh7GUqzEqUhMUqahw1K1JdapD9FhRTVV92jC4q2SehU6rKwvERKR2zhukfZYY6nAucpHvPaaUmMZX4mJSIjIF3GuItIjjlse1vM8pcPqbrFCasgNqLH2z0GSkyjDWikd1uCmSIwyj6DDmtQciRNRw9Bhrb0PiXPH+qLD2nE3EpOCo7LrjPIayyB5hxC6lDECHdYRgxTuReiWfEm/Gx1WxvPIiEgNjlukPdZYKnCu8hE//4waKzwcCRH5Is5VRHrEccuTxr+KpVjLKh7QXLkr1p8Wa51m3Y+lWBt/RmK0/kPUWEdnIXEkP1H+PUPpsOY3FClD6ET6RnRYIU2QVIp8Ckux4iwWhTlXkiwxV6HGypyA0E0Zz6PDSr8dCRGpxHGLtMcaSwXOVT5i3DjUWCtXIiEiX8S5ikiPOG55Ur9rlA6rq8XOVku+RI21r6Kcyj0pQY3Ll2JZrNg6uREd1uwqm69XFfYxlmJF9Efi3PY7UGOdnInE5HhfdFj7rkfijhPPocNKfAOJm1KvRYeVehESIlKP4xZpjzWWCpyrfMSuXaixpk9HQkS+iHMVkR5x3PKY7VOwFGvKe0jSjqHDGngBEqP132IpVvhQJEYrn0GNleHqzr7EFeiwllrstOVE2L3osHY9hcQkKwQd1s4qS7SciL8LHVbMtUjclPMLOqxki53viUg9jlukPdZYKnCu8hGnTqHGGjYMCRH5Is5VRHrEcctjDq+Rbk2ksx8OjZZ2Qo0VvQ5JZgw6rBEW9xjGLESHtciNW+3WtEONlb4LiRNxg9BhrW2JxKSsSPbdgBorZwtCl7Imo8OKcu/ZiJXyh6PDSmosZWkIiahaOG6R9lhjqcC5ykcUFqLG+uMPJETkizhXEekRxy1vSdiODmvAuUiMgr9BjRX9HxKjSc1RY2XFIHHk8EB0WJteQOLctltRY6WuQWIS0xEd1tHXkLhUsFOiGpbXWAYp3IfQHYULKzosPynejpCIqovjFmmPNZYKnKt8h6nG6tEDh0TkizhXEekRxy1vGXUHaqzwyUhi11QsxWqNxOjweHRYK59B4khurCxsonRYC9y7DfBID3RY4c8iMUmdgQ5r11lIXCorkthbsRQrQ83i+qLtknwaaqy8QIREVAMct0h7rLFU4FzlO0w1VqdOOCQiX8S5ikiPOG55RW6K/NZY6bD6NUdiNOlG1FgHKootozlXKx3WeH8pc/XMwR0fYCnW0ZFInIgbgg5rrcW/gFHRcQk/p7zGMkjBEYQunXwfHdZx6w22nCtNlNSr0GHlfI+QiGqG4xZpjzWWCpyrfIepxvrsMxwSkS/iXEWkRxy3vCK4F5ZibfobSWqEnaVY+wZhKdbGjkgcieiPDmuZe4/523Ybaqws6y20ol7FUqyEX5G4dOJFdFjHzkPipoxH0WFldkBCRDXGcYu0xxpLBc5VvsNgUGqsjz7CIRH5Is5VRHrEccvzivPlzzPLl2K1QGK0+nPUWEcXIynKlGnnKh3WhMZInFjUAjVWxm4kTkT9gg5rz4tITE4NR4e1z+3nDOYFo8M64iel6QjdkfUhOqy0W5AQkSdw3CLtscZSgXOV7zDVWO+/j0Mi8kWcq4j0iOOW54UOrFiKNQhJ5VKssZcgMVr2CJZibfsWiSNRQ9BhbbTe5cqujC3osGxuJ8w7JGGNlA4rzF9KshE6V5ImMVeixspZjtAd6Q+hw0qxeCAjEXkCxy3SHmssFThX+Q5TjfXBBzgkIl/EuYpIjzhued7flygdVh+LjdhXf4EaK2EDkqyjMqmZ0mFNOg2JI0VpsvhMpcOa3wiJc2H3o8ZKnIrEJPIpLMU60ReJS9EXo8M6+R4Sd+RPRIeV3FDKMhESkYdw3CLtscZSgXOV7zDVWB9/jEMi8kWcq4j0iOOWh+0YhaVYy7ogiVmFDmuUxd5S23tgKdbB4Ugc2d0ZS7EO9UPiRPjT6LB2PYLE5OTf6LAO3IbEpbT+6LBi2iBxR9F6dFhJBinZg5CIPIfjFmmPNZYKnKt8R4PyLd6//BKHROSLOFcR6RHHLQ8b+j+lw+rlh0OjNV+hxto7BklugowzKB3W1LOQOBI7BR3Wv2cgcSLnIDqskKZITPL2oMPa0VjE1fMQTfLWoMM64i9l+QhdKj0uqZehxsoPQkhEHsVxi7THGksFzlW+w1RjffcdDonIF3GuItIjjlseZlqKNfdtHGZE21mKtaUzlmJFTUPiyKbnUGOdXIXEid3Po8ZKGIvE5PBjqLFOjUTiXGmmxFyFGitvPUJ3ZDyGDivjJSRE5Gkct0h7rLFU4FzlI4qKUGP16YOEiHwR5yoiPeK45WFZJ2SFxZbta7uixor+D8mJdeiwprRC4kjCPHRYwe2QOHHgI3RYm62fQpg4EB2W+7cTJr6FDuukmofzZH+BDouPJiTyJo5bpD3WWCpwrvIRR4+ixho8GAkR+SLOVUR6xHHLi5LCZZBB6bBGtkZitOkr1FiHJyBxZPXN5TWWQQpTkTiSGynrz1E6rGA/KclBaJQ2Fx1WWGMkLqUHocM6diESd+QFosNKsfgvJSIv4LhF2mONpQLnKh+xdi1qrBkzkBCRL+JcRaRHHLe8KPRHLMXaV3GXX8ZhdFgzXJVEm1/EUqytryJx4tBnWIoV/TsSk8j2qLHS5iNxLn8rOqwof+XWQjfl9EaHleQvxQcQEpF3cNwi7bHGUoFzlY8YORI1VkgIEiLyRZyriPSI45a3FGTIkGZKhzW0BRKj0M9RY53chMSu3HhZfJbSYc1vhMSJlBXosEKtq7GT/6DDOngHEpfi26HGylmMxKWSKEk+BzVWwRyEROQ1HLdIe6yxVOBc5SM+/hg1VmQkEiLyRZyriPSI45a3bOmLpVg7K57ZF7cMHdb0C5A4svc7LMWK/BuJE9vaosZK34DEKHOlhDUsv52wobtPJ0zuhA7r+FNI3JF+PzqsbIsdwYjIazhukfZYY6nAucpHtGuHGquwEAkR+SLOVUR6xHHLK0oKZERrpcMaYrEUa2PFUqzD45HYlXUIHdbis5A4Ef07Oqy9ryAxOfIilmKlTkfiXPZMdFhHWyJxh3lb97uQEJGXcdwi7bHGUoFzlY844wylw+JPWyJfx7mKSI84bnnFvy9hKdbWP5CYd8W6CIkjK9qgxoqZhMSR3Ah0WGubITFJGosOa//NSJwrjpejZ5fXWAYpOorQpbzB6LBSzkBCRN7HcYu0xxpLBc5VPsLPT6mxnlKzQJ2IdIhzFZEecdzyivFXKh3WPw1xaLSxI2qspK1I7Epejw5ruRs/Tve+ghorYSQSo7yDEn5W+e2EflKcjNC5Ey9hKVZqLyQuFQajw0ryl9JEhETkfRy3SHussVTgXOULSktxR+F33yEhIh/FuYpIjzhued7+CViKtepTJJlR6LBmunpA4Yb2qLGyXe0oGtkNHdbma5CYHPsAS7GO90XiXHogOqyYK5G4VJokKZejxirgm4dIUxy3SHussVTgXOULFi9GjTXdva0ZiEi3OFcR6RHHLc+b1k7psAb549Bo8zeosY44vU9w3w/osNbei8SJzdeU11gGKUxCYpS2AB3W3jZInCsIQ4cV1UjKChC6lPECOqyst5AQkVY4bpH2WGOpwLnKFzzxBGqsw4eREJGP4lxFpEcctzzv5A6Zdb+s+hyHOXHosKafi8SR4HZKhzXfIAUWzZRdR3/FUqxDnyFRlMnea8prLOPfwb3HQ8ffixorew4Sl3J+QYeVdgMSItIQxy3SHmssFThX+YIWLZQO6yJXu5kSkf5xriLSI45bXrftW9RYccuQ2HVsLJZibXkZiSPJS9FhhTRFYhL/A5ZiHXsfiXMpPdFhJTyIxKXC+eiwkq03lScirXDcIu2xxlKBc5UvMC3Feotrzol8H+cqIj3iuOVdGYdkQiOlw5p6NhJHVlxZvhTLYld4R/a+ihrr5GwkRjlb0WHtOhOJc7mr0WEdbY7EpZKjknIWaqyiNQiJSFsct0h7rLFU4Fyle8bfPlONNW4cEiLyXZyriPSI45Z3hf2CpVj7BiGxa9PzWIoV/g0SR04tQIe19X9ITA4/ihorcxUSZ8ok9kbUWAVhyFxKfwwdVq7bDzQkIk/juEXaY42lAucq3bvuOtRY8fFIiMh3ca4i0iOOW15UlCOTWyod1uTTkdhVlClLWisd1oJGSJzYepPSYQUbpPA4EqOjb6DDOnQ3EueSu6DDSuyAxKWcH9BhpbVDQkS1geMWaY81lgqcq3TPYFA6rAcewCER+TTOVUR6xHHLi3b3x1Ksg8OR2HXwNyzFOvQ7EkdiBmAp1v43kRgVn5LwVuU1VkNlmZVLKb+gwzp6FhKXCmajw0px+w5EIvIOjlukPdZYKnCu0rfJk7EU659/kBCRT+NcRaRHHLe8aLy/0mFNtN6I3UZ2pCxqpnRYi5yu2DIqOC5rT1M6rJDTkJjEfIqlWKfcmLjK8iXmKtRYRREInSs5JslnltdYBik+gJCIagnHLdIeaywVOFfp23nnocY6brHonYh8F+cqIj3iuOUtB4djKdbmTkjs2vs9lmJFBiJx5NDnWIoVPwSJUeYqdFi7z0XiXNKX6LBOvo3EpYwnsRQrpwsSIqo9HLdIe6yxVOBcpWMnT6LDeuUVJETk6zhXEekRxy1vmXe90mFNcLrdVV48Oqx/z0DiSMIodFgbrOuqg3eixsqPROJEyk/osI6513kZ5fyKDiutLRIiqlUct0h7rLFU4FylY3fdhRpr6VIkROTrOFcR6RHHLa+Imo6lWKGfIrFrd2fUWAkLkDiyqz1qrJz9SIxOBqLDinwBiRNlBRJzJWqsoiMInStcig4ruQUSIqptHLdIe6yxVOBcpWOmzd3b8g/uiOoRzlVEesRxyyvyk2TOVcreWE72XE9aiw5riau1USdnoMPacS8So7xw2dmsvMZqiMS5pK/RYZ202B7eibJUSbkUNVbBXIREVNs4bpH2WGOpwLlKr558Ekuxxo9HQkT1AOcqIj3iuFVrdnVEjXVsNBJHtlxXXmMZpCgJiVH0J1iKdXIwEidylqLDOnYeEpey3kSHlf0NEiKqAzhukfZYY6nAuUqXiovRYd1wAxIiqh84VxHpEcet2pEVgQ5r2UVIHIkdiKVYhz5DYpQVjA5rz2VInIu9HjVW0WEkzuUNRoeVeiUSIqobOG6R9lhjqcC5SpfatkWNNWkSEiKqHzhXEekRx63asfMz1FipW5DYlXdE1rZQOqy1pyEx2dUSNVbWOiROxN2BDuuEew/eKQ5Dh5XkL2VZCImobuC4RdpjjaUC5yr9CQlBh3X//UiIqN7gXEWkRxy3akHiCnRYS89H4sjhLliKFfUzEqNTI9BhRbRH4kTeOnRYx85G4lL6PaixClxtPE9EmuO4RdpjjaUC5yr9adYMNVZwMBIiqjc4VxHpEcetWlC5FCthHhK7svehw1rfGolRUZLsPk/psMLc29k9/k7UWLkhSJzL+R4dVvpDSIioLuG4RdpjjaUC5yqdee45dFhffYWEiOoTzlVEesRxS2uJy9FhLXe1rdW+N1FjZWxEYnTwDizFiu2ExInUfuiwTjyNxLncXuiwks9AQkR1DMct0h5rLBU4V+nJxo3osIw/VXNyEBJRfcK5ikiPOG5pbefnqLFOrURiV2wgOqyNFj9X8/agwwp34w7Bgr3osI62QOJcWZaktkGNVbQVIRHVMRy3SHussVTgXKUnDRuixlq0CAkR1TOcq4j0iOOWprIj0WG53BVr52OosfKOITE60gE1ljs7ux9/BjVW5igkzmV9iA4r+xMkRFT3cNwi7bHGUoFzlW5ceCE6rB9+QEJE9Q/nKiI94rilqfCvK5ZiOd1FNLI7Oqwwiw2qEn5Bh7XvGiROZI5BhxV/BxLn8iehw0pxdasjEdUqjlukPdZYKnCu0oennkKH1d6Nx+UQke/iXEWkRxy3tJMbgw5r2QVIHNnWtrzGMkhxGhKjg7crHVaYQYoSkThSdFR5LqFSY/lLWT5CJ0rjJLlVeY1l/CfuQ0hEdRLHLdIeaywVOFfpQLdu6LAuu0yOH0dIRPUS5yoiPeK4pZ093VBjRQ1DYlfCaCzF2vsGEqOjb2ApVtQrSJyIuRZLsZK7InEuswOWYuX9jYSI6iqOW6Q91lgqcK6q6/75Bx2Wn5+EhiIkovqKcxWRHnHc0kjBSZnvp3RYS85F4simNkqHFdwQh0aleRJ+ntJh7bQIHcldgQ4r+lIkzuUNR4eVegMSIqrDOG6R9lhjqcC5qk4bMQIdlvE1Zw5CIqrHOFcR6RHHLY3s74mlWPFzkdgVMxBLsfa/j8QooSeWYp34HYkTcW1RYxW4cXtg4X+S1Ki8xvIXyUVIRHUYxy3SHmssFThX1V2V67CMr7FjERJR/ca5ikiPOG5poThLFrVQOqxFzZHYVZor689WOqyQ05AYZa1Fh7WrJRIn0v5Ah3XCjXsPjTKeqbidcAISIqrbOG6R9lhjqcC5qo767TdzhzV0KEIiqvc4VxHpEcctLRz6HUuxjgxGYtfRXliKFROAxCj6Y9RYyWOQOFIYiQ7rSDMkzuX+gw4r7SYkRFTncdwi7bHGUoFzVV30+efmDmv4cIRERJyriPSJ45b3lcmS81wvxSpKkpCmSoe11qKEyt6MDmvvFUiciL4ENVb6ACROFB+UJD+lw0puIlKCkIjqPI5bpD3WWCpwrqpzHn/c3GHxt4aIrHGuItIjjlted2QwlmLtdvrcwCPfYSlW4lQkRkeeQ42VtQ6JIznz0WHF3ojEuYynsRQr143Oi4jqDI5bpD3WWCpwrqpb2rRBgdW0qSxahJCIqALnKiI94rjldSuvUTqsBY1xaFfmNnRY685EYpS+GB3WgTuQOBFzfXmNZZCSk0icyBuMDivdjb8zEdUlHLdIe6yxVOBcVVeEh0uTJuiwrr5atm1DTkRkgXMVkR5x3PKu2MlYihX+NRK7DndCjRU3DInRwbvLayyDFJ1C4khaXyzFOvUREidKIiueTthIyooREpFOcNwi7bHGUoFzVZ3w7bcosIyvp5+WU64GKSKqrzhXEekRxy3vCn2ivMYy4NCu1DXosEIvRGKUMglLsQ4/jsSRvGB0WEdaIHEu88WK2wmDkBCRfnDcIu2xxlKBc1Xtu+EGc4f17bcIiYjs4VxFpEcct7yrNF/2dJVjI3Fo16EvUGOdmo3EaN91SocV5u96//WTb6HGShuIxIm80RW3E7ZFQkS6wnGLtMcaSwXOVbUpKEj8/VFgnX66TJ+OnIjIAc5VRHrEcauW5RxCh7XxYiRGJ4OwFCvmCySO5CxBhxV7PRInisMl5YzyGstPJBchEekKxy3SHmssFThX1Zp27cyLsJ56SqKikBMROca5ikiPOG7Vsi3XocbK3IqkrEh2X6B0WDud7gpvEtUMNVZxHBInst7BUqy8oUiISG84bpH2WGOpwLmqFgQEmBdhGV8D+AxmInIX5yoiPeK4VZtyD6LD2nA+EqOIx7EUK/EPJI6kD0KHdfJ1JE7kz0KHlXIJEiLSIY5bpD3WWCpwrtLU7t1y3nnmAuuxx5QHFBIRuY1zFZEecdyqTQc/QY2VFoykOE12tihfitUUiSNFEXK0pdJhRTVB4kRZnqRchtsJSxMREpEOcdwi7bHGUoFzlUZKS+W++8wFVvPmMngwThERuY1zFZEecdyqNbkVu2JtsvjJmfAjlmKdcnXfX/I3WIqV0hOJE2m3YClWlqvNtoiobuO4RdpjjaUC5yotfPSRGAzmDst4mJCAU0REanCuItIjjlu15uBnqLGOT0JSdFzC/JQOK/xMJI7kb0eHdexCJE4UBlfcTngeEiLSLY5bpD3WWCpwrvKuXr2kUSNzgXX//bJ6NU4REanHuYpIjzhu1Y6U/9BhbbwMiVFsFyzFOhmIxJETz6HGylmMxIm0O1BjFW9DQkS6xXGLtKezGit588zBATM348ih5PCVM8YEBQYEBAQGjZi6fGdiIU6YuDjtEOcqbxkxQrlzsLLAuuIKGT8ep4iIqotzFZEecdyqHRFfosZKXookez06rN0W273blT0XHVbcnUicyO2PDivjBSREpGcct0h7OqqxCqPXTh+qdE8uaqzCiOVjAwMCh05ZHBIasnzG6EEBAYNnbk7GWVenneFc5Xn//COnn24usM46i88iJCJP4VxFpEcct2pBfjQ6rFCLWwJjv0SNlTIFiSNRTctrLIOUpiJxpGi1JBnKa6zTkBCRznHcIu3po8bKjdq8eEp5haVwXmPFrhwfEBA0PbSimCqMWDomIGDw/DDTkisXp53iXOVJnTtL48bmAqt5c+ndW3JycJaIqMY4VxHpEcetWnC4C2qs9A1I8iPQYe25GIkj6f9gKdbJd5E4kfkKlmLlj0BCRDrHcYu0p4saa/PM8vpq0LjFCye5qrEil48JCBi2aC8OFelrpwYEBM0PU37t4rRznKs8ID9f3nvPag+sli2VXbHS03EBEZGHcK4i0iOOW1orPIEOa10rJEYxn6HGyq4otuwqOSHHzlY6rKgmSJzI7oIOK+1mJESkfxy3SHv6qLHmDJ+6eHNULgotZzVWYej0qldELB0VEDBmeaTL0y5wrqqRw4flrrvM7ZXxdeGF0r8/V2ARkZdwriLSI45bWjvSAzVW4kwk+QcrlmK5euxgyg9YipX8LRJHyool9YryGssgpXwINZHv4LhF2tPZFu8ua6zy1Vajlkbg0KQ8DJy9xeVpFzhXVdOAAXL++VYF1k03Kdu6ExF5E+cqIj3iuKWpguMScprtUqzoT1Bj5W5HYlfuKnRY0a2ROJHTA0uxclwVXkSkKxy3SHs+WWNVWVlV+Ze5OO0C5yp1EhLktdekSROrAuvJJ2X+fFxARORNnKuI9IjjlqaO9cVSrOjfkeQdqFiKdRESR06+jxory9UDpovD0GEln46EiHwFxy3SHmssIzt/10EOcK5yS9++0qaNVXvVqJF8+aWEubEDGRGRh3CuItIj1ljaKc2XDa3Ll2K1RGIU8ylqrKxVSOzKC0WHFXM1EicynkaNVbwVCRH5Co5bpL16UWOVh+abCh2fNkNrVQXnKmf275dHH7Xavt34uuUWCQqSzExcQ0SkFc5VRHrEGks7cf9gKVbUT0gyVlQsxboMiSMnnkaNVbgHiSP5k9BhpbdDQkQ+hOMWac/XaqxCbvGuvfR06d5dWrSwaq/8/eXDDyU4GNcQEWmOcxWRHnHc0s6mK5QOK6QpDo2iP0eNlTINiV3ZC9FhJdyLxJGyPEm9tLzG8pcyPtWHyAdx3CLt+VqNZVpaNWzRXhwq0tdODQgIml9+P5uL085xrrJSWip//y2XXWbVXhlf990nw4dLRgYuIyKqJZyriPSI45ZGjo/HUqzI7kiygt1dihXVvLzGMkhJChJHcn7AUqzsL5AQkW/huEXa87kaS2JXjg8IGDxzczKOCyOWjjEG88MKyw9dnHaKcxWMGCHXXCMGg1V7Zfzh1aOH7N6Na4iIahvnKiI94rilkW23KR1WsD8OjWK/Ro2VOhOJXZkTsBTrxItIHCnejQ4r5RwkRORzOG6R9nygxipfYGVxU2Bh2IKhgQGBQycsWBUasnzG6EFWtZWr087U97kqIEBuusm2vWrZUj75RFauxDVERHUG5yoiPWKNpZF9rys11v63cZizBR3WblcPKIy5urzG8peyMiSOZL6IGit/FhIi8jkct0h7PlhjGSWHr5wxJijQGAcGjZi4eGui9VIrF6cdqo9zVWGhTJmirL3y87Nqrxo1kjfeULbEIiKqqzhXEekRayzt5MfgF0Zx3VFjpS9EYlf6ICzFSqzovxwpmIMOK/12JETkizhukfZ0VmPVrno0V6WkSL9+cvHFtmuv/P3llVdk+nTJy8OVRER1FecqIj1ijVULik5JWEOlwwp3evdfabYcO0/psKJOQ+JE6vXlNZZBylKREJEv4rhF2mONpYLvz1WHDsnbb8s551hVV8ZX06bK2qsZMyQ3F1cSEdV5nKuI9Ig1Vi04dH/FUqzFSOxK64ulWMndkDiS+yeWYmV/joSIfBTHLdIeaywVfHauCgmRu+6SZs1s26vWreWjj2TBAikuxpVERPrBuYpIj1hjaa00R3a1VDqsnc2R2FV0FA8oPNoSiSPFOyWpSXmN1RQJEfkujlukPdZYKvjUXHXypPz9t7RpY7vplfF1zTXSrZusWYMriYj0iXMVkR6xxtJaYn8sxUoei8SulB5YipUWgMSR7M+wFCt/PBIi8l0ct0h7rLFU8IW56t9/pUMHadHCtroyvh58UPr3l927cSURkc5xriLSI9ZYWttzodJhhTXBoV3FCXLEoHRYx5xunmVUtBEdVuqlSIjIp3HcIu2xxlJBr3NVQoL07CnXXmtn4dVZZ8lbb8nkyXLqFC4mIvIVnKuI9Ig1lqZODcNSrIRfkNiV3AVLsbKmIXEkoz1qrOI9SIjIp3HcIu2xxlJBZ3PV5MnyxBP2F141ayY//STr1uFKIiJfxLmKSI9YY2lq33XlS7Ea4dCuoih0WNHnI3GkYAY6rLT7kRCRr+O4RdpjjaWCDuaqffvkgw/koovEYLCtrs46S3na4LhxsnUrLiYi8mmcq4j0iDWWdlKnYSnWsfeR2BV7I2qsvFVIHEm9vrzG8pOybCRE5Os4bpH2WGOpUEfnqqQkmTFDbrhBGje2ra6Mr3vvlYcektBQXExEVG9wriLSI9ZY2on7trzG8sOhXYWHKpZiXYTEkcznsRQr6zMkRFQPcNwi7bHGUqFuzVV//qnseHXXXba9lfHVpo189pl06SIpKbiYiKj+4VxFpEessTSVvlhyw/Fru2KvQ42VvwWJXaWpktyyvMY6DQkR1Q8ct0h7rLFUqP25avFiGTxYXnhBmje3ra6aNZPnn5egINm/HxcTEdVvnKuI9Ig1Vh1SeKBiKdbFSBzJ6Y6lWHl/IyGi+oHjFmmPNZYKtTNXZWTI3LnyxRdy5ZW21ZXxdeutypqs4GBcTEREFThXEekRa6w65NTHFUuxtiOxq/gAOqyU85AQUb3BcYu0xxpLBU3nqp07pX9/eeQR297K+GrYUK6/XkaNkrg4XExERFVwriLSI9ZYdUXhfnRYx1ztipX1DmqswiVIiKje4LhF2mONpYLX56rCQlm4UFl4ddllttWV8XXWWcqOV3zOIBGRezhXEekRa6y6IuaaiqVYO5DYVbQOHVbq9UiIqD7huEXaY42lgrfmqqQkGTdOXnpJGjWyra6aNJH771f2wyopwcVEROQezlVEesQaq04oPFixFOsCJI5ktEeNVXIECRHVJxy3SHussVTw8FyVkqLcGNi+vW11ZXy1aiUdO8oRTgNERNXHuYpIj1hj1QkxFQ8oLNiGxK6C+eiwMh5GQkT1DMct0h5rLBU8NlfNny8dOthWVwaDsol7QIAUFOAyIiKqAc5VRHrEGqv2FUWiw4q+EIkjae3KayyDlKUjIaJ6huMWaY81lgo1navS0+X33233vfL3l7ZtZQl3xCQi8jDOVUR6xBqr9iV9hRorz+mzsPPHYSlW5ltIiKj+4bhF2mONpUKN5qp+/aRZM6sCy/jdPns2zhIRkadxriLSI9ZYtawoGh3WsfOROJJ6ZXmN5YdDIqqXOG6R9lhjqVDNuWr/fmnXztxeNWwoH34oubk4S0RE3sG5ikiPWGPVsuSuqLFyFiKxKy8QS7FyvkNCRPUSxy3SHmssFaozV+XmylVXocDy85OffkJORERexrmKSI9YY9Wm4kSJ8lM6rKPnILGrLF+SW5fXWI2QEFF9xXGLtMcaS4XqzFUjRqDDOuMMJEREpAnOVUR6xBqrNqX8hKVYWROQ2JX7G5Zi5QYiIaL6iuMWaY81lgo1qrGaNpXduxESEZH3ca4i0iPWWLWmNFOimpcvxWqFxK6yFElupnRYxv8lonqP4xZpjzWWCtWZq3Jz5brr0GQZX82by+uvy6pVOEtERF7DuYpIj1hj1Zq037EUK93pGqv0u7AUq2AqEiKqxzhukfZYY6lQzbkqPl5ee83cZJlefn5ywQVKPnCgRETgSiIi8hzOVUR6xBqrdpSVyrHzypdiNUdiV2k8OqzklkiIqH7juEXaY42lQo3mqh07pGtXadPGts8yvRo3lv/9T15+Wb79VoYPl2XLlOcbZmXhryUiIvU4VxHpEWus2pExGEuxUpw+jyjtFtRYRcuREFH9xnGLtMcaSwXPzFV798qwYfLuu1Y3Gzp6nXmm3HSTPPWUfPSR/PyzDB0qc+fKhg1y+LCkp+NvSERE9nCuItIj1li1I+ZqpcOKaoxDu0qi0GGlnI2EiOo9jlukPdZYKnh+rsrKks2bZdw4+e47eeEFueEGadLEtsly8mrcWC66SG69VR5/XN58U77+Wnr1kqAgmTJFli6VTZvk0CFJTJSCAvzjiIjqE85VRHrEGqsWZE7CUqxTnyGxK+2GiqVYoUiIqN7juEXaY42lgkZzVUKCbNwo06fLX38pzdRLL8kddyh1lZ+fbY3l/uu005StuK6/Xu65R1nb9cYb8vnnSnfWr58MHmz82SPz5ikbz2/ZotzMGBsraWlSXIx/HyIifeJcRaRHrLFqQfwd5Uux/HFoV/HBiqVYFyAhIuK4RbWBNZYKtT9XnTwpu3fLypUydaoEBkqPHvLxx8oyrnvvlWuvlbPPFoPBtsCqyeu00+Tcc+WKK+Tmm5V/xBNPKLt3vfuudOyo7OHVq5cMGKDcIGn8msyeraz/CgmRbdtk3z45elRZBZaZyS6MiGoR5yoiPWKNVQvS+kpUE0nsgEO7Uq9FjVW8DQkREcctqg2ssVTQx1yVmiqRkUqdtGKFzJghI0bIH3/I99/LZ58pD0Zs317uukvZluuCC6RZM9veyhuvxo2lVSvlH3fllco29nfeKQ89pKwI69BB3nlHPvlEvvlG+dfr1Uv+/FP++UdGjZJJk2TWLFm0SCns1q9X/lv27FG2A4uNlVOnlHassBD/sUREjnGuItIj1lh1Ucl+dFiplyEhIirHcYu0xxpLBR+cq4qLldorOlrpiUJDZflyZV3VuHFKndS3r/zwg3z1lbz/vlI5tW+vLMi6+WZlcdZ550mLFh5e+aX25eenLBY780w5/3zjb4yyGM3473bnnfLAA8pOYc8+K6+8Im+/rWyN37GjdOmi/Lf88otyE+XAgcp9lKNGyYQJyp2b8+bJ4sXy33/KUrJNm5QHSu7dKxERcuyYHD8uyclKa5afL6Wl+IoRkX5wriLSI9ZYdVHWRxVLscKQEBGV47hF2mONpQLnKls5OcryqOhoZUetbdtk7VpZtkx5luLkycoqsL//Vmqjn35SWqTPP1fuRnzlFXn6aXn4YWVF2M03y9VXy8UXK/dCNmtWo52/tHk1aqT8e7ZqpdxoafzXvuIKZVGb8b/i9tuVgu+hh5SbLp95RtnL7LXXlIVmH36o/Fd//bV064YSrW9f6d9f+bIMGSIjRyp1ofELNWOGUqX9+6/SIa5eLevWKW3a9u0SHq58VQ8fVgq1+HjlftKUFKVTy8vjrZpEbuJcRaRHHLfqnOJ96LBS2iAhIqrAcYu0xxpLBc5V3lVQIOnpyqZax47JwYOyc6dS6AQHK9XY/PnKyqnx42X4cGVTsD//VG5C/OEH6dxZvvhCPvhAeVDjyy8rHdmjj8p99ynV0k03KTXZpZcqa8datVIaKH9/22ZKvy+DQblb0/gfdcYZcs45ypK0Sy5RmrVrr5Ubb1QeXmn8Ctxzj7I2zfgFad9eWZ724otKjWj8Qr37rrJI7bPP5MsvlS9g9+7KV7JnT+ndW6kdTUVbUJCy69moUUrXNmmSTJum3OY5b55yp+fSpcrNnsbfl/Xrld+gbduU36k9e5TSLSJCoqIkJkZ5TIGpd8vIULrO/HxWb1QrOFcR6RHHrTrHvBRrJxIiogoct0h7rLFU4Fyle8XFSquSmqqUZTExyiZi+/YpLczmzcoyqD//lCVLlLJmxgyluxkzRqlyBg1SHhnZt6/8/LOyh1eXLkr788knyr2Wb76p3G753HPy5JPyyCNKfXbHHXLLLXLDDXLVVXLZZcqOYOecozRNp50mDRvaVlH18GX8IjRtKs2bK1+Ts89W1rVdeKFSNbZpo3SO112n7J52663Srp2yXs/49XzwQaWGe+IJpaB8/nllpdurryrP2XznHeXr//HHShnXsaOyvZrx9+Xbb5U+7qef5Ndf5bffUMkNHKj0noMHK7+VI0cqv6cTJiiL4KZNk5kzlZWDCxYoS+GWLlX2klu9Wrm3dP165VGhW7Yoa+J27UJDd+iQ8m45elR528THy4kTyjrElBSleM3KktxcpYQ1vrvKyvBOo7qBcxWRHnHcqlsKF6PDSr0SCRGRBY5bpD3WWCpwrqIaKS1Vyo7sbKVHO3lS4uKUWiQiQqnSdu2SrVuV7clCQpS1TkuXysKFMmeOsgZt0iRlSdTIkcqtiIGBSqfWr5+ycqpnT6VW69pVKXG++ELpdN57T956Syl6XnpJWX7Vvr3SAT3wgLIs6447lHrof/9TqqIrr1QqtosuUlqks86Sli2Vlq1Ro1re7MxnXn5+yhfT1NadfrqyEtBU2F1wgXIvqvErf8UV6OxuvFG5KbVtW6W2u/NO5bfp/vuV5u6RR5T93Z58UinvnntOWUbXoQP6u7ffVn6XP/jAXOF99ZV06qS8Dbp3V94PPXoob4xff1XeIX37Ko93qOzy/vlHeQtV1nnGN5Xxp5mp0ZsxQ9kUz1TqLVqklLnLlikbxq1apSy7W7tWNmxQqr3Nm5XFdzt2KG/X3buV9+2BA8ob2FTwRUcrb+mEBKUjPnVK2VcuLU1Zjmeq+fLzlSczlJRo3PRxriLSI45bdUvWpxVLsXYgISKywHGLtMcaSwXOVeTjiouVuiE7WykgkpKUNUdxcco9npGRym2ee/cq/cX27UqdsWGD0ritWqX0HYsXK/XHnDlKGzJlirLcacwYpSsZOlSpTgIClCbFVL398ov8+KN8951Su3TqpKxr++wzpZF5/31lgdUbbyi3Pb70ktLdPP20sgjrkUeUWufee5WWp107ZaWbqYm7+mplAdellyqLuc47T1ny1qqV0hk1a6bca+lLd4/65MtgUNblGX+nKsu+M85QHtdg/H0891zlDtmLLlJukr3sMuV3+aqr5JprlN/0G25Q7hQ2vgdMxd8dd8jddyvvDVP39/DD8thjynvGVP89+6y88ILxvRTTrt39F1+MtzcR6QTHrTqk8L+KpVhXICEissYai7THGksFzlVE+lBWplRyBQXKPaSZmUorl5ysrBJKSJDYWGXpUGSkcp/g/v3KPYM7dyrd3JYtyoKj9euV9UerVin3GC5dqtxvuGCBskxp1ixl1dLkyUpJN3assmvY8OHK4qZ//lG2EhswQKnqfv9d+vRRdm37+Welrfv+e2WFVJcuynK5L79U9vv/5BNl4//33lMWVb35pvIogA4dlMVWptqufXtlEZapubv/fmVxlqm8a9tWWbR1441KlXPNNcpiOuOgcMklSoV3/vnSurWypM7U4jVvrhRDjRqxyLN8PWP8QhGRrnDcqkOyO6LGKgpBQkRkjTUWaY81lgqcq4hIN0pLpahIWV6Xm6vc2ZeertzNmpSk1HnHjyvr7GJilEbvyBHlxsCDB5WbBPfsURbchYUpNw+aer0NG5SbCk3V3n//Kc/TXLIE7d68ecqtiDNnKgXflCnKLYrjxysL8Uwd39ChypZkgwYpNd/AgUrN98cfyqK8335Tmr5fflF2MevRQyn7vv1WWZ3XuTP6vi++UNbomSq/999Xnkjw9tvKSr3XXlMW6738srLM6rnnlKeCPvWU0v099pjS/T30kHL/7H33KUu07rxTecTBbbfJrbemXXLJoxddhC8IEekEx626onBtxVKsy5AQEVXBGou0xxpLBc5VREQ6wrmKSI84btUVOV1QYxXOR0JEVAXHLdIeaywVOFcREekI5yoiPeK4VScU76pYisU1rUTkDMct0h5rLBU4VxER6QjnKiI94rhVJ+T0QI2VNxoJEZE9HLdIe6yxVOBcRUSkI5yriPSI41btK02XpNOUDiulNRIiIgc4bpH2WGOpwLmKiEhHOFcR6RHHrdqX0wdLsXL7IiEicoDjFmmPNZYKnKuIiHSEcxWRHnHcqm3FyiKspAaSfAYCIiLHOG6R9lhjqcC5iohIRzhXEekRx61aljcIS7GyuyEhInKM4xZpjzWWCpyriIh0hHMVkR5x3KplqVeUL8VqikMiIqc4bpH2WGOpwLmKiEhHOFcR6RHHrdqUPx5LsbI6IiEicorjFmmPNZYKnKuIiHSEcxWRHnHcqk1pbctrLH8cEhG5wnGLtMcaSwXOVUREOsK5ikiPOG7VniIsxcp+DwERkSsct0h7rLFU4FxFRKQjnKuI9IjjVm0q3iYZT4vk4ZCIyBWOW6Q91lgqcK4iItIRzlVEesRxi4hIRzhukfZYY6nAuYqISEc4VxHpEcctIiId4bhF2mONpQLnKiIiHeFcRaRHHLeIiHSE4xZpjzWWCpyriIh0hHMVkR5x3CIi0hGOW6Q91lgqcK4iItIRzlVEesRxi4hIRzhukfZYY6nAuYqISEc4VxHpEcctIiId4bhF2mONpQLnKiIiHeFcRaRHHLeIiHSE4xZpjzWWCpyriIh0hHMVkR5x3CIi0hGOW6Q91lgqcK4iItIRzlVEesRxi4hIRzhukfZYY6nAuYqISEc4VxHpEcctIiId4bhF2mONpYLx+/PWW2/9kIiI9MD4E5tzFZHucNwiItIRjlukPdZYKnTt2hXfrBUGlcMBkW7xbUw+wO4PZOPPbfwEJyKd4LhFvopvY/IBdn8gc9wijbHGqhHTtzEOiHSLb2PyAfyBTOSr+N1NvoFvY/IB/IFMdQFrrBrhtzH5Br6NyQfwBzKRr+J3N/kGvo3JB/AHMtUFrLFqhN/G5Bv4NiYfwB/IRL6K393kG/g2Jh/AH8hUF7DGqhF+G5Nv4NuYfAB/IBP5Kn53k2/g25h8AH8gU13AGqtG+G1MvoFvY/IB/IFM5Kv43U2+gW9j8gH8gUx1AWusGuG3MfkGvo3JB/AHMpGv4nc3+Qa+jckH8Acy1QWssYiIiIiIiIiISAdYYxERERERERERkQ6wxiIiIiIiIiIiIh1gjUVERERERERERDrAGsuV5PCVM8YEBQYEBAQGjZi6fGdiIU7Yp/JyIm0VRiwfGzhmeSQOHSmMDl0wcfgg4/s4YNDgsXM2ROXiBFFtyo3aMGfs0PKfsMZ35iiXP2L5A5mozvLseMVPLapdHhqv+KlFtcLD4xV/IJMGWGM5Vf6hFBA4dMrikNCQ5TNGG78fB8/cnIyzVai8nEhbyeGLxymfKa7mrOTNMwcbP3hGz1kVGrpqwYShxjf12OURHKWodqVvmW18XwYOnTBneUjFOzNg8LT1sThviz+QieosD49X/NSiWuWh8YqfWlQrPD1e8QcyaYI1ljOxK8cHBARND634xiyMWDrG+J06P8z+d6LKy4k0U5i4L3iO8jlTzvmcVbh9bpDxmqWVHzjJodODAgInrnb0cUakBdsfsMa36t5FI4zR3O12f8TyBzJRneXZ8YqfWlRrPDle8VOLaoWHxyv+QCaNsMZyInK58bty2KK9OFSkr51q/M6dH4ZDKyovJ9JM+XtT+YOW6Ytmj3IxZxVunhkYEDB1bTqOFXsXDQsImLj6OA6JtJe9ac7gQQEzN+PQJGKpw/czfyAT1VmeHa/4qUW1xpPjFT+1qFZ4eLziD2TSCmssxwpDpxs/mtz+tlZ5OZF2IldMGDMneF9ioenDx+k70t571u6bm6jWhc1X/siPP5CJdEXlt6ery/mpRbXGg+OV3fcsP7WodlR/vOIPZNIKayzHyj+RRi2NwKFJeRg4ewsOLai8nKg2uJ6zNs80ftRMD7VeR1wejl1xFIdEdUH5OvWAEYv349gSfyAT1Vkqvz1dXc5PLaoDyt+RNRmvVH5bEHlPTcYr/kAmrbDGcsz+J1L5N6K9Plnl5US1wf7b1JLdt6zrv4xIW6YdRh1uf8sfyER1lofHK35qUR3g+h3n4o2q8tuCyEtqOF7xBzJphTWWYyo/UPj5Qzrg+oOEnz9U9xVGr52mPFbH8aNv+AOZqM7y8HjFTy2qA1y/41y8UVV+WxB5Q83HK/5AJq2wxnLM7rdceeh41bv7lxPVBtcfJHY/f8pDrgamOiH3wHLlyeaBY5ceyEVUFX8gE9VZKr89XV3OTy2qA+y+Ta24eKOq/LYg8jiPjFf8gUxaYY3lWKG6zRZVXk5UG1zPWdybkeqwwsTQ2UOVxe7T1kY7+INCE/5AJqqzVH57urqcn1pUB9R4vLL7nuWnFmnEY+MVfyCTVlhjOVH+keSxJ0IT1QGu5yw+KZfqrOTNM5W17iPnb7e7YYMV/kAmqrM8O17xU4vqgJqPV/zUolrjwfGKP5BJK6yxnIldOd5qi7vCiKXG79zB88Ps19QqLyfSnus5Swq3zw2yuiu+/IElgRNXx+KYqDaYNh0NHLso3PWQpeAPZKI6y7PjFT+1qPZ5YLzipxbVCg+PV/yBTBphjeVUYdiCocZv7KETFqwKDVk+Y/Qgq29b052+FmskXVxOVOvszVnlmWUYGzzJ+N4dNHrG8pDQVQsmKG9qx3s9EmkidvXEQOP7dNiEWbNtLNli+gM+/kAm0g0X3562H0uuvpv5qUW1zW6NZfux5OKNyk8tqgWuxyv+QKY6iTWWK8nhK2eMCVK+vwODRkxcvDXR4rvQ9uPJyNnlRLXOrRrL+AkVHbpg4nDjh5DxY2jw2BnBTvZ6JNJCYfnGCnZVvHX5A5lIT5x9e9r5WHLx3cxPLapdbtVYrt+o/NQijRW6Hq/4A5nqJNZYRERERERERESkA6yxiIiIiIiIiIhIB1hjERERERERERGRDrDGIiIiIiIiIiIiHWCNRUREREREREREOsAai4iIiIiIiIiIdIA1FhERERERERER6QBrLCIiIiIiIiIi0gHWWEREREREREREpAOssYiIiIiIiIiISAdYYxERERERERERkQ6wxiIiIiIiIiIiIh1gjUVERERERERERDrAGouIiIiIiIiIiHSANRYREREREREREekAaywiIiIiIiIiItIB1lhERERERERERKQDrLGIiIiIiIiIiEgHWGMREREREREREZEOsMYiIiIiIiIiIiIdYI1FREREREREREQ6wBqLiIiIiIiIiIh0gDUWERERERERERHpAGssIiIiIiIiIiLSAdZYRERERERERESkA6yxiIiIiIiIiIhIB1hjERERERERERGRDrDGIiIiIiIiIiIiHWCNRUREREREREREOsAai4iIiIiIiIiIdIA1FlF9tX/ALQ0avDcvcl7Hdmc0adCgyRlXvzRgXXwBzhIRERFRDXHcIiLyNNZYRPVV+VzVunXrJhc923vi3IkDXr/WOF21/mRZGs4TERERUY1w3CIi8jTWWET1Vflc1aD1e4tOIijY0vPKBg2u7B2GYyIiIiKqCY5bRESexhqLqL4qn6tuGbAfh4ry6PZBETgkIiIiohrguEVE5GmssYjqq/Ih6r1FODJZ9F6DKhkRERERVQvHLSIiT2ONRVRfOZqrmnyyDEdEREREVAMct4iIPI01FlF9VT5XPTIqDocKOxERERERVRPHLSIiT2ONRVRflQ9RDW75Y0/FQ59zVn3ZukGD+4YdxTERERER1QTHLSIiT2ONRVRfmeaqBg2a3/TugIlzJ/Z+9qImDZrcPqByzCIiIiKiGuG4RUTkaayxiOqr8rnqlj/mjXrp6jOaGMer1nd3nH0oByeJiIiIqKY4bhEReRprLKL6yjRXWT0BmoiIiIg8h+MWEZGnscYiqq84VxERERF5FcctIiJPY41FVF9xriIiIiLyKo5bRESexhqLqL7iXEVERETkVRy3iIg8jTUWERERERERERHpAGssIiIiIiIiIiLSAdZYRERERERERESkA6yxiIiIiIiIiIhIB1hjERERERERERGRDrDGIiIiIiIiIiIiHWCNRUREREREREREOsAai4iIiIiIiIiIdIA1FhERERERERER6QBrLCIiIiIiIiIi0gHWWEREREREREREpAOssYj0LY3IPXjHEBERkUr4KCVyBe8YIvIm1lhE+obPTCJX8I4hIiIilfBRSuQK3jFE5E2ssYj0DZ+ZRK7gHUNEREQq4aOUyBW8Y4jIm1hjEekbPjOJXME7hoiIiFTCRymRK3jHEJE3scYi0jd8ZhK5gncMERERqYSPUiJX8I4hIm9ijUWkb/jMJHIF7xgiIiJSCR+lRK7gHUNE3sQai0jf8JlJ5AreMURERKQSPkqJXME7hoi8iTUWkb7hM5PIFbxjiIiISCV8lBK5gncMEXkTaywifcNnJpEreMcQERGRSvgoJXIF7xgi8ibWWET6hs9MIlfwjiEiIiKV8FFK5AreMUTkTayxiPQNn5lEruAdQ0RERCrho5TIFbxjiMibWGMR6Rs+M4lcwTuGiIiIVMJHKZEreMcQkTexxiLSN3xm+o5dI1651a7vl+AKLzP+C2j1j9IW3jFERESkEj5KfQfHLW/BO4aIvIk1FpG+4TPTd5TPVbU32NTyP96b8I4hIiIilfBR6js4bnkL3jFE5E2ssYj0DZ+ZvoNzlbfgHUNEREQq4aPUd3Dc8ha8Y4jIm1hjEekbPjN9h/PBpvysxekl3xsPXxmxy/Sr75eUH5crDy2Yz1j97Y3xKyNGmM698oryNwffm63wjiEiIiKV8FHqOzhueQveMUTkTayxiPQNn5m+w+WfzykDEoam8lkJ12JuwlH536VytLKexiz/Kvxl5n+ey3+8fuEdQ0RERCrho9R3cNzyFrxjiMibWGMR6Rs+M32HaQaqymLWwWRlNR9ZT0tGFgMSri+Py1kEtuc4VxEREZENfJT6Do5b3oJ3DBF5E2ssIn3DZ6bvcGewqZy9LK5TBiSrv0y5qHxisvN3NE9Ttn8Z5yoiIiKygY9S38Fxy1vwjiEib2KNRaRv+Mz0He4NNso4ZPXHei7mKqtLOVcRERGRCvgo9R0ct7wF7xgi8ibWWET6hs9M3+HOYFM+VSksxqUqc1Xl7GTn72i+mHMVERERuYCPUt/Bcctb8I4hIm9ijUWkb/jM9B2uB5vyqcp4RfmVlZNVRVpBOYuTlRNWBYuAcxURERG5gI9S38Fxy1vwjiEib2KNRaRv+Mz0Ha4GG8vzlpNV+Vxlfaby71J+VHloNYFxriIiIiIX8FHqO1zNO5bny3/NcctNeMcQkTexxiLSN3xm+g7TDGSPMu2Uz0QWf9RnnpiUM6+MGGGaroxsZyPMXQrLPyq0nasqL7S8yDfgHUNEREQq4aPUd3Dc8ha8Y4jIm1hjEekbPjPJNFf52izkSXjHEBERkUr4KCWOW67gHUNE3sQai0jf8JlJnKtcwTuGiIiIVMJHKXHccgXvGCLyJtZYRPqGz0ziXOUK3jFERESkEj5KieOWK3jHEJE3scYi0jd8ZhK5gncM/b+9+wexZDsPBN7ZCxQYBS8yQoFx5MAGOzDOVslmAjMLG73cGAxm2Q6sxGzgwBgMwjAsGzQsb4RRb7ANg8YIj1kYw0rBswaBBowbGXqTceAxhknUkfZW1XeqTp2qe6u7qu97t+r+foHePX+ruup7fc751N0PAB4pllKYEhEDHJM0FqxbrJkwJSIGAHikWEphSkQMcEzSWLBusWbClIgYAOCRYimFKRExwDFJY8G6xZoJUyJiAIBHiqUUpkTEAMckjQXrFmsmTImIAQAeKZZSmBIRAxyTNBasW6yZMCUiBgB4pFhKYUpEDHBM0liwbrFmwpSIGADgkWIphSkRMcAxSWPBusWaCVMiYgCAR4qlFKZExADHJI0FAAAAwApIYwEAAACwAtJYAAAAAKyANBYAAAAAKyCNBQAAAMAKSGMBAMC5i//QGkyJiEmiFqZExMBi0lgAAHDu4qAJUyJikqiFKRExsJg0FgAAnLs4aMKUiJgkamFKRAwsJo0FAADnLg6aMCUiJolamBIRA4tJYwEAwLmLgyZMiYhJohamRMTAYtJYAABw7uKgCVMiYpKohSkRMbCYNBYAAJy7OGjClIiYJGphSkQMLCaNBQAA5y4OmjAlIiaJWpgSEQOLSWMBAMC5i4MmTImISaIWpkTEwGLSWAAAcO7ioAlTImKSqIUpETGwmDQWAACcuzhowpSImCRqYUpEDCwmjQUAAOcuDpowJSImiVqYEhEDi0ljAQDAuYuD5kn6yfNnv9W5fBnVlZeXRcURtbdx+bL6+Oz5T6LhzETEJFH7dKp3mslf71f/3LM7ePKb2U24OJazOR52f0/+VRwQEQOLSWMBAMC5i4PmyalzGtkxuzp1Z6mNLy+NVV/4y7nUiYuISaL2adRPOc+q9Cu+zJzLuOwOnvhm6q90YYDNmOPLfKQRMbCYNBYAAJy7OGiemCpLNThj15mtOKtLY335ImKSqH0SYzmV/MF/mTmXcdkdPPHN5F/nXDPm+DIfaUQMLCaNBQAA5y4Omidl36H85cu2rkhj1RmuRnE0r+dqdUOaCbJxo0mArL2euH/2z+a+fJ7dc3Fz+ZdTfW76VoO6umT0Lk5EREwStU+iemKHcirNc3/ZPah+5/wJppb8qe/0i818o9fLX3nWJxuxb3BTn91k8TLzmVPTWF19qyGbYjB9uodyjv795bPl8/V65VOMfWkLRcTAYtJYAABw7uKgeVKqQ3V2fh+Td6nP4KOF+gjfzlSX+mf/Xmn8kr05srN/Xl9/7mbIb25n2DPLFNQVqdwrnJyImCRqn0bzOvZ+7fGA84fYdq2HjjzB6mPbqZmgNyZ7Q62qvu3UzJxdM1p6E2eaa7RN+ehoS6VhU3szdSlNMSz07qe7i94cVSFaevX9MV2v3hfdH/FEImJgMWksAAA4d3HQPCUPOUlXR++my6B3dyrvTuqNvGvv7H7omr2WbsbuBmp1r3zusi3K5XWK2xi0n5KImCRqn0z1KDK9h7D/sQ0eWFeRPduq8tmzZ6lj8YJaZX05w+BjT33lvKEbnk3UKGZur1p2zBp7/Xbyrr22qtA0dJ8aebe2rTf2OCJiYDFpLAAAOHdx0DwlDzlXV2f4ustI57atVXcK0VJ02n/NXktVqM/+Zbqh1+3A3O0EjcE0ZYdTEhGTRO2Tq55JMnjuoXtu40+wGdi9h92n5jdIi/p9qh4hJs/uYN8rGtanC3W31Kqams55Y1ebdJOW0+d9excoO+7U7SG69SfOGo4gIgYWk8YCAIBzFwfNk5JO/we0XfIDeqYZnjVWFXUxJi6ukTf19VqqQn32H+QbHjh3O0Gj6jlU5CBORURMErVHUz+beBTFY8sef/Gsd7LHnRp3/4x/VIOqDv0hrWpAo569Kg7voLyZZFifrj8yopu6akz3010/1/XLJ8lurjdH3rGub6T7SN360+UXzi/yNCJiYDFprGnv37//8Y9/fHNz86K2+/DmzZu7u7toBgCAlYuD5knpHcozWX1KEOztW8kP+pW8bztBY/80vZaqUM9YTv3QudsJGoNpTllETBK1x5M9t+KxZc9t+ASzvvFx94+6oilWfyS9PyKp2vMXl08+nLX62DOob+cbTJzHSN54KB7K6fO+vQt0Hcvp8m7ldKEaUt7rYhExsJg01iEfPnx4/fr1q1ev3r59u/t8X9t9ePfu3U3t48eP0RUAAFYrDponZvQ4X5+ws/N+83HQtT2flwf1+hA/nKCWH/D7ei3dlGPj87mzC1fF1NRNUBtetpj3lETEJFH7FIoHFrJnVTy2bMDgCfYq6nGXl00WK4Y960+VKZ99VR65g/JmkvrKeUM3X3e/IavIbzj/3OjmKC+bz9kb13YsR9S98kvlja29DfNFxMBi0lh73d7eXl9fv3v3LsoDu6arq6sDHQB4Qj/789+8CL/55z+LyhE3n0WvXr+sNjPe4eD0AJsUB82TUx3S89N07wien+6ja36GT4Vhw07MmU+wkw/r67VUhZggr4+50+3WxVSob6LXM/uqir51517zKYmISaL2STQPsP/88zdUPrb8OdUPOBX6jzNN3M7TvIx9T3j4LirN2OwOyptJ4lqpKb/HaOvdRv61tYWm1LuHVCgvW7b1JmwahtfZiTFtr97Y4rafSEQMLCaNNa75eavJH7a6v79/9eqVTBbAsdU5rM9uBp9Lg377UlJ11qqdJC8dHAawUXHQPEn1KbzVnbR3qqasIu+Z90tH98rubJ4d64sJirN8ptfSnv3bUmPwq2rdDcVlY4b+BI1umqdPIDyliJgkap9M/hgq+dsoH1v1RLNyPrR4idkr36k7HnrG3XurRmXdszsobyape18+r/63VvbJ5u43RUOqrOcJWcfysv1nkM2Rdyzmqns1T2Nfr8ETfAIRMbCYNNaIjx8/Xl9ff/jwIcoH3d/fX11dPbAzALNUaaYss1RlmkYTTY/p1yWxym57hwFsVhw0WahKEDz9+f+kRMQkUUtSJ4M2HgPzRMTAYtJYIx77A1Z3d3fX19dRAODJDfJKD0w0FVmtpBqd/TiXrBWAZMQ8dcqi+1mYs8hgRMQkUUsijbVPRAwsJo1Vuru7e/36dRQebDfk9vY2CgA8rSod1aWddopE1Lh9ncrkVsxe/4RWTU4LOD9x0OTR4te4whmkLyJikqglkcbaJyIGFtt+Guvff/w//9uf/Off/eY3P/3V//QX/+MPfvtXPrm4+Nrv/dXeE8qbN2/evn0bhQe7vb2dkfwC4EEencZKKal9P4qV19cVO2m6QQeAMxAHTZgSEZNELUyJiIHFzuOnsX70nW9UJ5Sv/d5/ffl/X1/Wx5W9h5+bm5sZf+jq/v7+888//zuAsxffFp/Wo9NYYaTbsGqYtnrg7AAbEgdNmBIRk0QtTImIgcXOIo31j3/5OxcXF5/+/l//c1WqTycXv/anX9RtA9fX15P/gcKh+/v7Fy9exBkO4IzFt8WnNTeN1Ywc/OjVVGJrMApg8+KgCVMiYpKohSkRMbDYOaSx/u3zb1d5q+/86Bd18ad/9hu74je+86O6NPDixYv7+/soPMbV1VWc4QDOWHxPfFpVpmnw81IPyjOVCamxBNUgSSaNBZyfOGjClIiYJGphSkQMLHYGaaxf3Hz2ycXFN/7wb5t/cZqk1tc/u9nz79G8Xyr8+PHj97///TjDAZyx+Lb4xIq80r4s1uAHq8qK8YFlHmswDcDmxUETpkTEJFELUyJiYLEzSGPVfxjrk9//6zyL9elnN/9SF4fevHnz7t27KDzY3d3dD37wgzjDAZyx+Lb41OrMUiSgDmWZqoxU25YPagx/8KrWm7GeYixJBrBhcdCEKRExSdTClIgYWGz7aaz6bJLOI7/40Xd+7eLi69+++qfmFwxH3N3d3dw8+v+Cf/369W5gFAA4gub7ee1gaqpOQ4UiZTWZAGvs6QGwYXHQhCkRMUnUwpSIGFhs82ms//ff/8PuRPKNy7//xS9/+S83n3168cnv/PHf7E9i1W5ubh6Vk7q9vd0NmfcXtQAA4CsXB02YEhGTRC1MiYiBxbaexmr+MNYnX/v0t7/1rV//1V//1n+5+od9v03Y+fjx44sXLx743yu8v7+/vr5+//59lAEAAAA4gq2nsf7uj75+cfHJtz+fzl31vXv37ubmZjKTdX9///r167dv30YZAAAAgOPYeBrrp3/2Gxdz/1Dvu3fvrq6ubm9vozzw/v376+vrXTe/TggAAABwbJtOYzV/0P3i4j9ePfaHscKHDx9ubm5ev359e3vb/GTW/f397sPd3d2ucte069D0BAAAAOCotpzG+vuX/yv533/zf342+49X3d7evnnz5vr6+urq6sWLF7sPr1692lX6ISwAAACAL83W/zYWAAAAAJsgjQUAAADACkhjAQAAALAC0lgAAAAArIA0FgAAAAArII0FAAAAwApIYwEAAACwAtJYAAAAAKyANBYAAAAAKyCNBQAAAMAKSGMBAAAAsALSWAAAAACsgDQWAAAAACsgjQUAAADACkhjAQAAALAC0lgAAAAArIA0FgAAAAArII0FAAAAwApIYwEAAACwAtJYAAAAAKyANBYAAAAAKyCNBQAAAMAKSGMBAAAAsALSWAAAAACsgDQWAAAAACtwommsK4CjiW80mX+Dh4mISaIWpkTELBDfvwCOI77XJPHNC6ZExCRRC1MiYmbx01jAeRlu1HbiuylMiYhJohamRMQsMPq9C+BJSGMxW0RMErUwJSJmFmks4LxIY7FEREwStTAlImYBaSzgeKSxmC0iJolamBIRM4s0FnBepLFYIiImiVqYEhGzgDQWcDzSWMwWEZNELUyJiJlFGgs4L9JYLBERk0QtTImIWUAaCzgeaSxmi4hJohamRMTMIo0FnBdpLJaIiEmiFqZExCwgjQUcjzQWs0XEJFELUyJiZpHGAs6LNBZLRMQkUQtTImIWkMYCjkcai9kiYpKohSkRMbNIYwHnRRqLJSJikqiFKRExC0hjAccjjcVsETFJ1MKUiJhZpLGA8yKNxRIRMUnUwpSImAWksYDjkcZitoiYJGphSkTMLNJYwHmRxmKJiJgkamFKRMwC0ljA8UhjMVtETBK1MCUiZhZpLOC8SGOxRERMErUwJSJmAWks4HiksZgtIiaJWpgSETOLNBZwXqSxWCIiJolamBIRs4A0FnA80ljMFhGTRC1MiYiZZbVprH/94nvf/e4Pfx6lB5o1qBn2vS/+NUrAqm0sjfXy8rd2nj3/SZT3qLrt6bS3qWq4fBmFfX7y/NkDeg1UwyZv+iRFxCRRu0l73u2BWDpk5rDtiIhZ4CFprJ//8LuP37DM3BxV13r8KOA0bSqNVa9f07uTA1uYvU0P278sWCkfv6X66kXEJFG7UePv9kAsHTJz2GZExMwijfUA0liwIdtKY+0W02eXl9Nr4IEN1d6mB+2mFqzbMzZ4JyAiJonaTdrzbg/E0iEzh21HRMwC0ljA8WwpjVUtX5eX06vOgS3M3qaH7V8WrJSP31J99SJikqjdqPF3eyCWDpk5bDMiYmaRxnoAaSzYkC2lsWL1e8AieGBDtbfpQbupBev2jA3eCYiISaJ2k/a82wOxdMjMYdsREbOANBZwPBtKY8V6M73sHNjC7G162P5lwUr5+C3VVy8iJonajRp/twdi6ZCZwzYjImaWjaax6tZW260Z9EXXWEyQD8uaquq0K8w/Ayu0oTRWu/iNr4LVQtt49vx5f9E90NTp76bqa7Ta+ubSz7vG4ja6K/UuUg2bscE7ARExSdRu0nhY9TZwzXt82b3+/ks9EGd5PK0zEh4rImaB5WmsqrXVdZvaHGXD8rnzNJaUFqzddtJY7SqVL1etbPVpNi+jG52yqdOse+2k2S4nv1hz6WrhaxS3kS+B+UWqYWMXPXURMUnUbtRoWDWvNL275j1msdF7qYfiLBuzykh4rIiYWbaYxuo31aXYd9Wfu11YvTEb7Tgs5Ds3YMW2k8bKlsze6lmrF8KoiQUzLboHmnqaVbj+2J+/LsWQA3MXpV6hGjZ60ZMXEZNE7Sb133qrepHp3cXrj055YPRf+FiY9OZYZzA8SkTMAgvTWP2mfAd0cHPUKw0KbTdg5TaTxsoWqXy9qvUWnHopGl/B+k09Vb+8Wzd/PSiGHJi7X+o3VcPGLnrqImKSqN2o/ltP6heZ3l3z+lOnPDD6L3wkTPpzrDEYHiUiZpYNprHKpFPWs/6Yt1V7sChnH2vFsLwJWLHNpLF6K2Z/H9Rv28maDzQVqhW06Vj1ybtkcwyGV6PGr5Q1DWZcj4iYJGo3afACG9lrHHTZ//br8p7A2HOhjYmIWWBZGqtMOmU9D2yOsr1QIxtWzgis2VbSWPkaVRey5aXXtpM1H2jqq1as6Fj1ybtkc5TDs3WuvFK+BJYzrkVETBK1G1W+wEZvJ1N0OfD2q/Kepj0X2paImFm2/Lex6i5hXz6q3YRlG7PQdR4OA1ZrK2mssZWwLVeF3kaoaz3QVBj0jJU4NE1VTX90GtZb0RvdjMNhaxERk0TtJo28wUoeMOV77NoG0dNv6r/8PVfaloiYBZalsVpVlxA992+O6o1Uf6/V7puyT8D6bSONVS4nvXK5ZGWtB5oKg5471bIWommwBrbDxpfAfcNWIiImidqNGr7BSi9givfYtQ2ip99UvPzxK21KRMwsm/2lwkaxCdu/U8u3dZmm83AYsFobSWNVa9tQrHbDtbBdOA80lbJVuB7VqGqySYaj07BsTG7fsLWIiEmidpOy15zLd2fle2y3XMOxXdd8gsaeK21LRMwCC9NY3T6n7pD13L85yjZUuWZPlToBW7CJNFa9mgzF+tIuUa12PTrQVMjXvapPo67JJhmMbod1Y3L7hq1EREwStRuVveZMHhfle+w2OcOxbdfeBI3xK21KRMwsG0xjlXu4rOdgp9a1laNyg2HAem0jjTW2tGV17aqYdG0Hmgpdz7JLtyAPl922Les0NLJar0RETBK12zSIlUrvvZbvsYuUwdh+U//lrzccHiEiZoFFaazBrinruX9zNBiVk8aCLdlCGmts45HVDdaaru1AU6HrOeiSrW7718DhEpgZDFuJiJgkajdqECu13nst3mMXKYOx/abi5a81HB4hImaW7aWxyt1Y3TF6DgZ1u7jhfN3+rJwSWLFNpLHGd0FV7fhiWJebAQeaCu3yWXXJe9RDYo5yuuzWBjeZzVNOuR4RMUnUbtPoW2rDolL26N55GRh1ebxpWLFJETELLEpjlTmnqpx6HtgcDebLdkTllMCabSCNNb6YZMtPuS+pyjHgQFNfNV03W96jHhJzlNN1tza8yWyecsq1iIhJonarxt5SFxaVokf2zsvAqMp7moYVGxQRM8tGfxqrbam7tVu1KKUdWa9n09hu1vKdW7ZpA9ZuA2msej0cW9mK1TCtoXX/bsSBpp6q33CyNCTGHJh7bFxeGL3oyYuISaJ2q/rvM15i9uLK91j1H4uFsTBJhXLOrYqIWWBRGmu4x6k0W6CDm6Neqe6ZClVL2w1YufWnscoVK8lWmd6CU/fvr1PjTT1Vv5HJ0pAYc2DukXG9wthFT11ETBK1m9V/gzv1685eXPEe6/5jsTASJu24cs5tioiZZd1prIHehqy225V1W7Cq4Xtf/LxtLrd6xcCojWFNMf8MrND601iD5bPTW/Riddx59vz57nM25EBTp+qU5mrW1sauc3edquHZ85dtczlTd6XeatwMG/sSTl1ETBK1G5a/+p3+lqp8j9XrzsoH4iyftj/nVkXELPDQNNZA7Fryth/+vN7xNC1Tm6P+wKjsp7Hyz8AarT6N1e1MSvWCkxagbPW5fF597oYcaOr01r3+Hie7TtVw+bJrLmbKrpSvjDEsCisSEZNE7ablr77/EneK91i/7ofFWTZtMec2RcTMsto0FsAs609j8VWKiEmiFqZExCzwkDQWwDyrT2Px1YmISaIWpkTEzCKNBZwXaSyWiIhJohamRMQsII0FHI80FrNFxCRRC1MiYmaRxgLOizQWS0TEJFELUyJiFpDGAo5HGovZImKSqIUpETGzSGMB50UaiyUiYpKohSkRMQtIYwHHI43FbBExSdTClIiYWaSxgPMijcUSETFJ1MKUiJgFpLGA45HGYraImCRqYUpEzCzSWMB5kcZiiYiYJGphSkTMAtJYwPFIYzFbREwStTAlImYWaSzgvEhjsURETBK1MCUiZgFpLOB4pLGYLSImiVqYEhEzizQWcF6ksVgiIiaJWpgSEbOANBZwPNJYzBYRk0QtTImImUUaCzgv0lgsERGTRC1MiYhZQBoLOB5pLGaLiEmiFqZExMwijQWcF2ksloiISaIWpkTELCCNBRyPNBazRcQkUQtTImJmOdE01u47KcCRxDeaTHw3hSkRMUnUwpSImAXi+xfAccT3miS+ecGUiJgkamFKRMwsfhoLAAAAgBWQxgIAAABgBaSxAAAAAFgBaSwAAAAAVkAaCwAAAIAVkMYCAAAAYAWksQAAAABYAWksAAAAAFZAGgsAAACAFZDGAgAAAGAFpLEAAAAAWAFpLAAAAABWQBoLAAAAgBWQxgIAAABgBaSxAAAAAFgBaSwAAAAAVkAaCwAAAIAVkMYCAAAA4OT98pf/H4xdpSZhAeb8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='../../static/images/apps/117-000_2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='../../static/images/apps/117-000_3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='../../static/images/apps/117-000_4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A custom `css` file is used to improve the interface of this application. It can be found [here](https://github.com/jongablop/MinervaLab/blob/master/static/custom.css)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<head><link rel='stylesheet' type='text/css' href='./../../static/custom.css'></head>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.widget-label { display: contents !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.slider-container { margin: 12px !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.jupyter-widgets { overflow: auto !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "display(HTML(\"<head><link rel='stylesheet' type='text/css' href='./../../static/custom.css'></head>\"))\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "display(HTML(\"<style>.widget-label { display: contents !important; }</style>\"))\n",
    "display(HTML(\"<style>.slider-container { margin: 12px !important; }</style>\"))\n",
    "display(HTML(\"<style>.jupyter-widgets { overflow: auto !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bqplot import *\n",
    "import bqplot as bq\n",
    "import bqplot.marks as bqm\n",
    "import bqplot.scales as bqs\n",
    "import bqplot.axes as bqa\n",
    "\n",
    "import ipywidgets as widgets\n",
    "\n",
    "import urllib.parse\n",
    "import webbrowser\n",
    "\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Physical functions\n",
    "\n",
    "This are the functions that have a physical meaning:\n",
    "\n",
    "- `get_relative_isotherms`\n",
    "- `experimetal_isotherms`\n",
    "- `get_roots`\n",
    "- `p_indefinite_integral`\n",
    "- `p_definite_integral`\n",
    "- `find_real_fixed_p`\n",
    "- `find_real_fixed_T`\n",
    "- `get_chemical_potential`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_relative_isotherms(v_range, T_range):\n",
    "    \"\"\"This function calculates the theoretical p(v, T) plane \n",
    "        (in reduced coordinates) according to van der Waals\n",
    "        equation of state from a given range of volumes\n",
    "        and tenperatures.\n",
    "\n",
    "    Args:\n",
    "        v_range: An array containing the values of v\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated.\\n\n",
    "        T_range: An array containing the values of v\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        isotherms: A list consisted of numpy arrays containing the\n",
    "        pressures of each isotherm.\n",
    "    \"\"\"\n",
    "\n",
    "    isotherms = []\n",
    "\n",
    "    for T in T_range:\n",
    "        p_R = []\n",
    "        for v in v_range:\n",
    "            val = (8.0/3.0*T/(v - 1.0/3.0) - 3.0/v**2)\n",
    "            p_R = np.append(p_R, val)\n",
    "\n",
    "        isotherms.append(p_R)\n",
    "\n",
    "    return isotherms\n",
    "\n",
    "    return isotherms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experimental_isotherms(p_range, v_range, T_range, fixed_p, fixed_T):\n",
    "    \"\"\"This function calculates the experimental p(v, T) plane \n",
    "        (in reduced coordinates) according to van der Waals\n",
    "        equation of state for a given range of volumes\n",
    "        and tenperatures or for a given range of volumes\n",
    "        and pressures.\n",
    "\n",
    "    Args:\n",
    "        p_range: An array containing the values of p\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated. Only used if fixed_p == True.\\n\n",
    "        v_range: An array containing the values of v\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated.\\n\n",
    "        T_range: An array containing the values of v\n",
    "        (in reduced coordinates)for which the isotherms must be \n",
    "        calculated. Only used if fixed_T == True.\\n\n",
    "        fixed_p: Boolean variable which represents if the isotherms \n",
    "        must be calculated for a given pressures.\\n\n",
    "        fixed_T: Boolean variable which represents if the isotherms \n",
    "        must be calculated for a given pressures.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        expe_data: A list consisted of numpy arrays containing the\n",
    "        pressures of each theoretical isotherm.\\n\n",
    "        theo_data: A list consisted of numpy arrays containing the\n",
    "        pressures of each theoretical isotherm.\\n\n",
    "        v_limits: A list consisted of arrays of the volume limits of\n",
    "        the phase-transition of each subcritical isotherm.\\n\n",
    "        p_limits: A list consisted of arrays of the pressure limits of\n",
    "        the phase-transition of each subcritical isotherm.\\n\n",
    "        tenperatures: A list consisted of the tenperatures of the\n",
    "        isotherms.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    if fixed_T:\n",
    "    \n",
    "        theo_data = get_relative_isotherms(v_range, T_range)\n",
    "        expe_data = []\n",
    "\n",
    "        v_limits = []\n",
    "        p_limits = []\n",
    "\n",
    "        p_range = np.linspace(0.001, 1.0, num=10000)\n",
    "        pressures, v_isobaric_limits = find_real_fixed_T(p_range, T_range)\n",
    "\n",
    "        for i in range(len(theo_data)):\n",
    "\n",
    "            p_expe = []\n",
    "\n",
    "            if i < len(v_isobaric_limits):\n",
    "\n",
    "                    v_lim = v_isobaric_limits[i]\n",
    "\n",
    "                    if len(v_lim) > 1: #check if there is only one point \n",
    "                        for j in range(len(v_range)):\n",
    "\n",
    "                            if v_range[j] > v_lim[0] and v_range[j] < v_lim[1]:\n",
    "                                p_expe.append(pressures[i])\n",
    "\n",
    "                            else:\n",
    "                                p_expe.append(theo_data[i][j])\n",
    "\n",
    "                        v_limits = np.append(v_limits, [v_lim[0], v_lim[1]])\n",
    "                        p_limits = np.append(p_limits, [pressures[i], pressures[i]])\n",
    "\n",
    "                    else:\n",
    "                        p_expe = theo_data[i]\n",
    "                        v_limits = np.append(v_limits, [1.0])\n",
    "                        p_limits = np.append(p_limits, [1.0])\n",
    "\n",
    "            else:\n",
    "\n",
    "                p_expe = theo_data[i]\n",
    "\n",
    "            expe_data.append(p_expe)      \n",
    "\n",
    "        tenperatures = T_range\n",
    "        \n",
    "        return expe_data, theo_data, p_limits, v_limits, tenperatures\n",
    "    \n",
    "    elif fixed_p:\n",
    "        \n",
    "        tenperatures, v_isobaric_limits = find_real_fixed_p(p_range, T_range)\n",
    "\n",
    "        theo_data = get_relative_isotherms(v_range, tenperatures)\n",
    "        expe_data = []\n",
    "\n",
    "        v_limits = []\n",
    "        p_limits = []\n",
    "        \n",
    "        for i in range(len(theo_data)):\n",
    "\n",
    "            p_expe = []\n",
    "\n",
    "            if i < len(v_isobaric_limits):\n",
    "\n",
    "                    v_lim = v_isobaric_limits[i]\n",
    "\n",
    "                    if len(v_lim) > 1: #check if there is only one point \n",
    "                        \n",
    "                        for j in range(len(v_range)):\n",
    "\n",
    "                            if v_range[j] > v_lim[0] and v_range[j] < v_lim[1]:\n",
    "                                p_expe.append(p_range[i])\n",
    "\n",
    "                            else:\n",
    "                                p_expe.append(theo_data[i][j])\n",
    "\n",
    "                        v_limits = np.append(v_limits, [v_lim[0], v_lim[1]])\n",
    "                        p_limits = np.append(p_limits, [p_range[i], p_range[i]])\n",
    "\n",
    "                    else:\n",
    "                        p_expe = theo_data[i]\n",
    "                        v_limits = np.append(v_limits, [1.0])\n",
    "                        p_limits = np.append(p_limits, [1.0])\n",
    "\n",
    "            else:\n",
    "\n",
    "                p_expe = theo_data[i]\n",
    "\n",
    "            expe_data.append(p_expe)      \n",
    "\n",
    "        return expe_data, theo_data, p_limits, v_limits, tenperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_roots(p, T):\n",
    "    \"\"\"This function finds the intersection between an isobaric curve\n",
    "       and Van der Waals equation of state for a given T.\\n\n",
    "       Values of v with no physical meaning are dismissed\n",
    "       (v < 0 or complex).\n",
    "\n",
    "    Args:\n",
    "        p: Pressure of the isobaric curve.\\n\n",
    "        T: Tenperature of the isotherm.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        roots_in_range: A sorted list of the volumes in which the\n",
    "        isobaric curve intersects the isotherm.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    roots = np.roots([1.0, - 1.0/3.0*(1.0 + 8.0*T/p), 3.0/p, -1.0/p])\n",
    "    roots_in_range = []\n",
    "    \n",
    "    for root in roots:\n",
    "        \n",
    "        # A third degree polynomial has 3 complex roots,\n",
    "        # but we are only interested in the ones which are\n",
    "        # purely real.\n",
    "        \n",
    "        if np.isreal(root):\n",
    "            \n",
    "            root = np.real(root)\n",
    "            \n",
    "            if root > 0:\n",
    "                \n",
    "                roots_in_range.append(root)\n",
    "\n",
    "    roots_in_range.sort()\n",
    "    \n",
    "    return roots_in_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def p_indefinite_integral(p_0, v_0, T):\n",
    "    \"\"\"This function calculates the indefinite integral between\n",
    "    a van der Waals isotherm and a isobaric line.\n",
    "    \n",
    "    Args:\n",
    "        p0: Isobaric line's pressure.\\n\n",
    "        v0: Value of the volume.\\n\n",
    "        T: Value of the tenperature.\\n\n",
    "\n",
    "    Returns:\n",
    "        integral: Value of the indefinite integral between a \n",
    "        van der Waals isotherm at T and a isobaric line of p0 at a\n",
    "        volume v0.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    integral = 8.0/3.0 * T *np.log(v_0 - 1.0/3.0) + 3.0/v_0 - p_0*v_0\n",
    "    \n",
    "    return integral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def definite_integral(p_0, v_range, T):\n",
    "    \"\"\"This function 'p_indefinite_integral' function to calculate\n",
    "    the definite integral between a van der Waals isotherm and a\n",
    "    isobaric line.\n",
    "    \n",
    "    Args:\n",
    "        p0: Isobaric line's pressure.\\n\n",
    "        v_range: Tuple or list consisted of volume limits.\\n\n",
    "        T: Value of the tenperature.\\n\n",
    "\n",
    "    Returns:\n",
    "        integral: Value of the definite integral between a \n",
    "        van der Waals isotherm at T and a isobaric line of p0 in a\n",
    "        volume range v_range.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    v_0, v_1 = v_range[0], v_range[1]\n",
    "    \n",
    "    integral = p_indefinite_integral(p_0, v_1, T) - p_indefinite_integral(p_0, v_0, T)\n",
    "    \n",
    "    return integral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_real_fixed_T(p_values, T_values):\n",
    "    \"\"\"This function uses Maxwell's construction to find the\n",
    "       pressures in which phase transition happens given some\n",
    "       fixed tenperatures.\\n\n",
    "\n",
    "    Args:\n",
    "        p_values: List of pressures in which the real isotherm is\n",
    "        searched.\\n\n",
    "        T_values: List of tenperatures of the isotherms.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        pressures: List of pressures in which phase transition\n",
    "        happens.\\n\n",
    "        v_range: Volume limits of phase transition zones.\n",
    "    \"\"\"\n",
    "    \n",
    "    eps = 1e-3\n",
    "    \n",
    "    pressures = []\n",
    "    v_ranges = []\n",
    "    \n",
    "    for T in T_values:\n",
    "        \n",
    "        if T < 1.0:\n",
    "        \n",
    "            for p in p_values:\n",
    "                \n",
    "                roots = get_roots(p, T)\n",
    "                \n",
    "                if len(roots) == 3:\n",
    "                    \n",
    "                    v_range = [roots[0], roots[2]]\n",
    "                    area = definite_integral(p, v_range, T)\n",
    "                    \n",
    "                    if abs(area) < eps:\n",
    "                        \n",
    "                        pressures.append(p)\n",
    "                        v_ranges.append(v_range)\n",
    "                        \n",
    "                        break\n",
    "                        \n",
    "        elif T == 1.0:\n",
    "            \n",
    "            pressures.append(1.0)\n",
    "            v_ranges.append([1.0])\n",
    "\n",
    "    return pressures, v_ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_chemical_potential(p_values, v_values):\n",
    "    \"\"\"This function calculates chemical potential by integrating\n",
    "    v(p) isotherms.\\n\n",
    "\n",
    "    Args:\n",
    "        p_values: List of numpy arrays containing the pressures of\n",
    "        the isotherms.\\n\n",
    "        v_values: List of numpy arrays containing the volumes of\n",
    "        the isotherms.\\n\n",
    "\n",
    "        \n",
    "    Returns:\n",
    "        mu: List of numpy arrays containing the chemical potentials of\n",
    "        the isotherms.\n",
    "    \"\"\"\n",
    "    \n",
    "    mu = []\n",
    "    \n",
    "    \n",
    "    for i in range(len(v_values)):\n",
    "\n",
    "        v = v_values[i]\n",
    "        p = p_values[i]\n",
    "        \n",
    "        pot = [10.0] #starting random value\n",
    "        \n",
    "        l = np.size(p)\n",
    "        \n",
    "        for j in range(1, l):\n",
    "            pot.append(pot[j-1] + v[j]*(p[j] - p[j-1]))\n",
    "        \n",
    "        mu.append(pot)\n",
    "        \n",
    "    return mu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions related with the interaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_nearest_index(array, value):\n",
    "    \"\"\"This function find index of the element in an array which\n",
    "    value is the nearest to the given one.\n",
    "    \n",
    "    Args:\n",
    "        array: A list or numpy array containing the elements.\\n\n",
    "        value: Float number.\\n\n",
    "\n",
    "    Returns:\n",
    "        idx: Index of the element in array which value is the nearest\n",
    "        to value.\n",
    "    \"\"\"\n",
    "    \n",
    "    array = np.asarray(array)\n",
    "    idx = (np.abs(array - value)).argmin()\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_tracer(change):\n",
    "    \"\"\"This function updates the tracer marks tracer_117_003\n",
    "    (fig_117_003) and tracer_117_004 (fig_117_004).\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    tracer_117_003.visible = True\n",
    "    tracer_117_004.visible = True\n",
    "    \n",
    "    if p_slider.value == p_slider.options[-1]:\n",
    "        p_slider.disabled = True\n",
    "    \n",
    "    v = theo_v_values_inverted[T_slider.index][p_slider.index]\n",
    "    p = theo_p_values_inverted[T_slider.index][p_slider.index]\n",
    "    \n",
    "    tracer_117_003.x, tracer_117_003.y = [p], [v]\n",
    "\n",
    "    area_117_003.x = np.append(area_117_003.x, p)\n",
    "    area_117_003.y = np.append(area_117_003.y, v)\n",
    "        \n",
    "    lines_117_004.x = np.append(lines_117_004.x, p)\n",
    "    lines_117_004.y = np.append(lines_117_004.y, mu[T_slider.index][p_slider.index])\n",
    "    \n",
    "    tracer_117_004.x, tracer_117_004.y = [p], [mu[T_slider.index][p_slider.index]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def restart_chemical_potential(a):\n",
    "    \"\"\"This function clear fig_117_004 and restarts\n",
    "    the tracers tracer_117_003 and tracer_117_004 and\n",
    "    deletes the labels labels_117_003 and labels_117_004.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    lines_117_004.x, lines_117_004.y = [], []\n",
    "    area_117_003.x, area_117_003.y = [], []\n",
    "    \n",
    "    p_slider.index = 0\n",
    "    p_slider.disabled = False\n",
    "    \n",
    "    axis_y_004.scale =  bqs.LinearScale(\n",
    "        min = min(mu[T_slider.index]),\n",
    "        max = max(mu[T_slider.index])\n",
    "    )\n",
    "    \n",
    "    lines_117_004.scales = {'x': scale_x_004, 'y': axis_y_004.scale}\n",
    "    \n",
    "    fig_117_004.axis = [\n",
    "        axis_x_004,\n",
    "        axis_y_004\n",
    "    ]\n",
    "    \n",
    "    labels_117_003.x, labels_117_003.y = [], []\n",
    "    labels_117_003.names = []\n",
    "    \n",
    "    labels_117_004.x, labels_117_004.y = [], []\n",
    "    labels_117_004.names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_tenperature(change):\n",
    "    \"\"\"This function changes the visible isotherm\n",
    "    in fig_117_001, fig_117_002 and fig_117_003.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    p_slider.options = theo_v_values_inverted[T_slider.index]\n",
    "    \n",
    "    #restart chemical potential plot and tracer\n",
    "    restart_chemical_potential(None)\n",
    "    update_tracer(None)\n",
    "    \n",
    "    obj = change.owner\n",
    "    \n",
    "    i = obj.index\n",
    "    \n",
    "    opacities = [0.0 for t in obj.options]\n",
    "    opacities[i] = 1.0\n",
    "    \n",
    "    lines_117_001.opacities = opacities\n",
    "    lines_117_002.opacities = opacities    \n",
    "    \n",
    "    v = theo_v_values_inverted[T_slider.index][i]\n",
    "    p = theo_p_values_inverted[T_slider.index][i]\n",
    "    \n",
    "    tracer_117_003.x, tracer_117_003.y = [p], [v]\n",
    "    \n",
    "    axis_y_004.scale =  bqs.LinearScale(\n",
    "        min = min(mu[T_slider.index]),\n",
    "        max = max(mu[T_slider.index])\n",
    "    )\n",
    "    \n",
    "    lines_117_004.scales = {'x': scale_x_004, 'y': axis_y_004.scale}\n",
    "    tracer_117_004.scales = {'x': scale_x_004, 'y': axis_y_004.scale}\n",
    "    labels_117_004.scales = {'x': scale_x_004, 'y': axis_y_004.scale}\n",
    "    \n",
    "    fig_117_004.axis = [\n",
    "        axis_x_004,\n",
    "        axis_y_004\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_all_potentials(change):\n",
    "    \"\"\"This function show all mu(p,T) lines in\n",
    "    fig_117_004.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    max_limit = 0.0\n",
    "    min_limit = 100.0\n",
    "    \n",
    "    for pot in mu:\n",
    "        max_pot = max(pot)\n",
    "        min_pot = min(pot)\n",
    "        \n",
    "        if max_pot > max_limit:\n",
    "            max_limit = max_pot\n",
    "            \n",
    "        if min_pot < min_limit:\n",
    "            min_limit = min_pot\n",
    "            \n",
    "    axis_y_004.scale =  bqs.LinearScale(min = min_limit, max = max_limit)\n",
    "\n",
    "    lines_117_004.scales = {'x': scale_x_004, 'y': axis_y_004.scale}\n",
    "    axis_y_004.tick_values = np.linspace(0.0, max_limit, 4)\n",
    "\n",
    "    p_values = [p.tolist() for p in theo_p_values_inverted]\n",
    "    mu_values = [m.tolist() for m in mu]\n",
    "\n",
    "    lines_117_004.x, lines_117_004.y = p_values, mu_values\n",
    "    \n",
    "    tracer_117_004.visible = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_text(change):\n",
    "    \"\"\"This function show all mu(p,T) lines in\n",
    "    fig_117_004.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    obj = change.owner\n",
    "    i = obj.index\n",
    "    \n",
    "    p_text.value = '<p> {:.3f} </p>'.format(theo_p_values_inverted[T_slider.index][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_label(mark, x, y, label):\n",
    "    \"\"\"This function adds a point and a name to the bqplot mark.\\n\n",
    "    \n",
    "    Args:\n",
    "        mark: A bqplot marks.\\n\n",
    "        x: x value of the new point.\\n\n",
    "        y: y value of the new point.\\n\n",
    "        label: String containing the new label.\\n\n",
    "\n",
    "    \"\"\"\n",
    "        \n",
    "    x_labels = [elem for elem in mark.x]\n",
    "    y_labels = [elem for elem in mark.y]\n",
    "    \n",
    "    x_labels.append(x)\n",
    "    y_labels.append(y)\n",
    "    \n",
    "    mark.x, mark.y = x_labels, y_labels\n",
    "    mark.names = np.append(mark.names, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_new_label(labels):\n",
    "    \"\"\"This function returns an alphabetical label different\n",
    "    from the ones given in the 'labels' list.\n",
    "    \n",
    "    Args:\n",
    "        labels: A list consisted of the current labels.\\n\n",
    "\n",
    "    Returns:\n",
    "        elem: A string with a new label which is not in 'labels'.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    \n",
    "    alpha = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n",
    "    alpha2 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n",
    "    count = 0\n",
    "    i = 0\n",
    "    while i < len(alpha):\n",
    "        \n",
    "        elem = alpha[i]\n",
    "        \n",
    "        if not np.isin(elem,labels):\n",
    "            break\n",
    "        \n",
    "        if i == (len(alpha) - 1):\n",
    "            alpha = [alpha2[count] + elem for elem in alpha2]\n",
    "            i = 0\n",
    "            count = count + 1\n",
    "            \n",
    "        i = i + 1\n",
    "    \n",
    "    return elem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_label_button_clicked(a):\n",
    "    \"\"\"This function is called when 'add_label_button_117_003' or\n",
    "    'add_label_button_117_004' are clicked and calls 'add_label'\n",
    "    function.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    if a is add_label_button_117_003:\n",
    "        \n",
    "        label = label_input_117_003.value\n",
    "\n",
    "        if label == '':\n",
    "            \n",
    "            label = get_new_label(labels_117_003.names)\n",
    "        \n",
    "        add_label(\n",
    "            labels_117_003,\n",
    "            tracer_117_003.x[0],\n",
    "            tracer_117_003.y[0],\n",
    "            label\n",
    "        )\n",
    "        \n",
    "        label_input_117_003.value = ''\n",
    "        \n",
    "    \n",
    "    elif a is add_label_button_117_004:\n",
    "        \n",
    "        label = label_input_117_004.value\n",
    "\n",
    "        if label == '':\n",
    "            \n",
    "            label = get_new_label(labels_117_004.names)\n",
    "        \n",
    "        add_label(\n",
    "            labels_117_004,\n",
    "            lines_117_004.x[-1],\n",
    "            lines_117_004.y[-1],\n",
    "            label\n",
    "        )\n",
    "    \n",
    "        label_input_117_004.value = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def undo_label_button_clicked(a):\n",
    "    \"\"\"This function is called when 'undo_label_button_117_003' or\n",
    "    'undo_label_button_117_004' are clicked  and removes the last\n",
    "    label added from the bqplot marks 'labels_117_003' or\n",
    "    'labels_117_004' of the bqplot figures 'fig_117_003'\n",
    "    and 'fig_117_004'.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    if a is undo_label_button_117_003 and len(labels_117_003.x) > 0:\n",
    "\n",
    "        labels_117_003.x, labels_117_003.y = labels_117_003.x[:-1], labels_117_003.y[:-1]\n",
    "        labels_117_003.names = labels_117_003.names[:-1]\n",
    "    \n",
    "    elif a is undo_label_button_117_004 and len(labels_117_004.x) > 0:\n",
    "    \n",
    "        labels_117_004.x, labels_117_004.y = labels_117_004.x[:-1], labels_117_004.y[:-1]\n",
    "        labels_117_004.names = labels_117_004.names[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_view(change):\n",
    "    \"\"\"This function changes the visualization of all the\n",
    "    components of the application so they are suitable for\n",
    "    a projection.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    obj = change.owner\n",
    "\n",
    "    if obj.value:\n",
    "        \n",
    "        obj.description = 'Presentation mode (ON)'\n",
    "        \n",
    "        display(HTML(\n",
    "            \"<style>\" \\\n",
    "            \".widget-readout { font-size: 30px ; }\" \\\n",
    "            \".widget-label-basic {font-size: 30px;}\" \\\n",
    "            \"option {font-size: 25px;}\" \\\n",
    "            \".p-Widget.jupyter-widgets.widget-slider.widget-vslider.widget-inline-vbox {width: auto}\" \\\n",
    "            \".p-Widget .jupyter-widgets .widgets-label {width: auto; height: auto; font-size: 30px;}\" \\\n",
    "            \".widget-label {font-size: 30px ; height: auto !important;}\" \\\n",
    "            \".p-Widget .bqplot .figure .jupyter-widgets {height: auto !important;}\" \\\n",
    "            \".widget-text input[type='number'] {font-size: 30px;height: auto;}\" \\\n",
    "            \".option { font-size: 30px ;}\" \\\n",
    "            \".p-Widget .jupyter-widgets .jupyter-button.widget-button {font-size: 30px ; width: auto; height: auto;}\" \\\n",
    "            \".p-Widget.jupyter-widgets.jupyter-button.widget-toggle-button{font-size: 30px ; width: auto; height: auto;}\" \\\n",
    "            \".p-Widget.p-Panel.jupyter-widgets.widget-container.widget-box.widget-vbox {padding-bottom: 30px}\" \\\n",
    "            \".bqplot > svg .axis text.axislabel, .bqplot > svg .axis tspan.axislabel {font-size: 30px;}\"\n",
    "            \"</style>\"\n",
    "            )\n",
    "        )\n",
    "\n",
    "        for figure in figures:\n",
    "            \n",
    "            figure.legend_text = {'font-size': '30px'}\n",
    "            figure.title_style = {'font-size': '30px'}\n",
    "\n",
    "            for axis in figure.axes:\n",
    "                axis.tick_style = {'font-size': '30px'}\n",
    "                axis.label_style = {'font-size': '30px'}\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        obj.description = 'Presentation mode (OFF)'\n",
    "        \n",
    "        display(HTML(\n",
    "            \"<style>\" \\\n",
    "            \".widget-readout { font-size: 14px ;}\" \\\n",
    "            \".widget-label-basic {font-size: 14px;}\" \\\n",
    "            \"option {font-size: 12px;}\" \\\n",
    "            \".p-Widget .jupyter-widgets .widgets-label {font-size: 14px;}\" \\\n",
    "            \".widget-label {font-size: 14px ;}\" \\\n",
    "            \".widget-text input[type='number'] {font-size: 14px;}\" \\\n",
    "            \".option { font-size: 14px ;}\" \\\n",
    "            \".p-Widget .jupyter-widgets .jupyter-button.widget-button {font-size: 14px;}\" \\\n",
    "            \".p-Widget.jupyter-widgets.jupyter-button.widget-toggle-button {font-size: 14px;}\" \\\n",
    "            \".bqplot > svg .axis text.axislabel, .bqplot > svg .axis tspan.axislabel {font-size: 14px;}\"\n",
    "            \"</style>\"\n",
    "            )\n",
    "        )\n",
    "        \n",
    "        for figure in figures:\n",
    "            \n",
    "            figure.legend_text = {'font-size': '15px'}\n",
    "            figure.title_style = {'font-size': '20px'}\n",
    "\n",
    "            for axis in figure.axes:\n",
    "                axis.tick_style = {'font-size': '15px'}\n",
    "                axis.label_style = {'font-size': '15px'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_export(button):\n",
    "    \"\"\"This function sends the selected plot to the 'export_plot'\n",
    "    function.\n",
    "    \"\"\"\n",
    "    \n",
    "    if button is prepare_export_fig_117_001_button:\n",
    "        \n",
    "        export_plot(fig_117_001)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_002_button:\n",
    "        \n",
    "        export_plot(fig_117_002)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_003_button:\n",
    "        \n",
    "        export_plot(fig_117_003)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_004_button:\n",
    "        \n",
    "        export_plot(fig_117_004)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_005_button:\n",
    "        \n",
    "        export_plot(fig_117_005)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_006_button:\n",
    "        \n",
    "        export_plot(fig_117_006)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_007_button:\n",
    "        \n",
    "        export_plot(fig_117_007)\n",
    "        \n",
    "    elif button is prepare_export_fig_117_008_button:\n",
    "        \n",
    "        export_plot(fig_117_008)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def export_plot(plot):\n",
    "    \"\"\"This function sends the selected plot to the export module.\n",
    "    \"\"\"\n",
    "    \n",
    "    global data\n",
    "    \n",
    "    text_lines = []\n",
    "    \n",
    "    np.set_printoptions(threshold=sys.maxsize)\n",
    "    data =  repr((plot, text_lines))\n",
    "    \n",
    "    %store data\n",
    "    \n",
    "    rel_url = \"../../../apps/modules/export_module.ipynb\"\n",
    "    abs_url = urllib.parse.urljoin(notebook_url, rel_url)\n",
    "\n",
    "    if not webbrowser.open(abs_url):\n",
    "        go_to_export_button.value = \"<form action=\" + abs_url + \" target='_blank'><button type=''submit''>Open in export module</button></form>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%javascript \n",
    "\n",
    "//Get the URL of the current notebook\n",
    "\n",
    "var kernel = Jupyter.notebook.kernel; \n",
    "var command = [\"notebook_url = \",\n",
    "               \"'\", window.location.href, \"'\" ].join('')\n",
    "\n",
    "kernel.execute(command)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions related to visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hex_to_rgb(number_hex):\n",
    "    \"\"\"This function converts a hexadecimal color to its rgb\n",
    "    equivalent.\n",
    "    \n",
    "    Args:\n",
    "        number_hex: String containing the hexadecimal representation\n",
    "        of the color.\\n\n",
    "        \n",
    "    Returns:\n",
    "        number_rgb: Tuple consisted of the 3 numbers of the rgb\n",
    "        representation of the color.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    if '#' in number_hex:\n",
    "        number_hex = number_hex[1:]\n",
    "    \n",
    "    number_rgb = (int(number_hex[0:2], 16), \\\n",
    "                  int(number_hex[2:4], 16), \\\n",
    "                  int(number_hex[4:], 16))\n",
    "    \n",
    "    return number_rgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rgb_to_hex(number_rgb):\n",
    "    \"\"\"This function converts a rgb color to its hexadecimal\n",
    "    equivalent.\n",
    "    \n",
    "    Args:\n",
    "        number_rgb: Tuple consisted of the 3 numbers of the rgb\n",
    "        representation of the color.\\n\n",
    "        \n",
    "    Returns:\n",
    "        number_hex: String containing the hexadecimal representation\n",
    "        of the color.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    number_rgb = '#' \\\n",
    "    + format(number_rgb[0], '02x') \\\n",
    "    + format(number_rgb[1], '02x') \\\n",
    "    + format(number_rgb[2], '02x')\n",
    "    \n",
    "    return number_rgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_gradient(initial, final, length):\n",
    "    \"\"\"This function generates a color gradient consisted of N\n",
    "    colors from the initial to the final.\n",
    "    \n",
    "    Args:\n",
    "        initial: String of the hexadecimal representation of the\n",
    "        initial color.\\n\n",
    "        final: String of the hexadecimal representation of the\n",
    "        final color.\\n\n",
    "        length: Number of colors.\\n\n",
    "        \n",
    "    Returns:\n",
    "        colors: List consisted of strings of the hexadecimal\n",
    "        colors.\\n\n",
    "    \"\"\"\n",
    "    \n",
    "    i_r, i_g, i_b = hex_to_rgb(initial)\n",
    "    f_r, f_g, f_b = hex_to_rgb(final)\n",
    "    \n",
    "    r_step = (f_r - i_r)/length\n",
    "    g_step = (f_g - i_g)/length\n",
    "    b_step = (f_b - i_b)/length\n",
    "    \n",
    "    r, g, b = i_r, i_g, i_b\n",
    "    colors = []\n",
    "    \n",
    "    for i in range(length):\n",
    "\n",
    "        h = rgb_to_hex((int(round(r)),int(round(g)),int(round(b))))\n",
    "        \n",
    "        colors.append(h)\n",
    "        \n",
    "        r = r + r_step\n",
    "        g = g + g_step\n",
    "        b = b + b_step\n",
    "\n",
    "    return colors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#v_values_1 = np.geomspace(0.48, 0.8, 1000)\n",
    "#v_values_2 = np.geomspace(0.8, 5.2, 500)\n",
    "#\n",
    "#v_values = np.concatenate((v_values_1, v_values_2))\n",
    "v_values = np.geomspace(0.48, 5.0, 1000)\n",
    "T_values = np.round(np.linspace(0.85, 1.1, 15), 2)\n",
    "\n",
    "if 1.0 not in T_values:\n",
    "    T_values = np.sort(np.append(T_values, 1.0))\n",
    "\n",
    "data = experimental_isotherms(\n",
    "        p_range=[],\n",
    "        v_range=v_values,\n",
    "        T_range=T_values,\n",
    "        fixed_T = True,\n",
    "        fixed_p = False\n",
    ")\n",
    "\n",
    "expe_p_values = data[0]\n",
    "theo_p_values = data[1]\n",
    "\n",
    "p_limits = data[2]\n",
    "v_limits = data[3]\n",
    "T_limits = data[4]\n",
    "\n",
    "colors = generate_gradient('#FF0000', '#FFfa00', len(T_limits)) \n",
    "opacities = [0.0 for t in T_values]\n",
    "opacities[0] = 1.0\n",
    "\n",
    "# change view button\n",
    "\n",
    "change_view_button = widgets.ToggleButton(\n",
    "    value=False,\n",
    "    description='Presentation mode (OFF)',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    icon='desktop',\n",
    "    layout=widgets.Layout(\n",
    "        width='auto'\n",
    "    )\n",
    ")\n",
    "\n",
    "change_view_button.observe(change_view, 'value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################\n",
    "######1ST BLOCK#################\n",
    "################################\n",
    "\n",
    "# This block shows the p(v,T) (fig_117_001) and\n",
    "# v(p,T) (fig_117_002) figures.\n",
    "\n",
    "\n",
    "block_1 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        align_items='center',\n",
    "        align_self= 'center'\n",
    "    )\n",
    ")\n",
    "\n",
    "scale_x = bqs.LinearScale(min = 0.4, max = 5.0)\n",
    "scale_y = bqs.LinearScale(min = 0.0, max = 2.0)\n",
    "\n",
    "axis_x_001 = bqa.Axis(\n",
    "    scale=scale_x,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [1.0, 2.0, 3.0, 4.0, 5.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='v',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_y_001 = bqa.Axis(\n",
    "    scale=scale_y,\n",
    "    tick_format='.1f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "fig_117_001 = Figure(\n",
    "    title='p vs v (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[axis_x_001, axis_y_001],\n",
    "    animation_duration=250, \n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True\n",
    ")\n",
    "\n",
    "lines_117_001 = bqm.Lines(\n",
    "    x = v_values, \n",
    "    y = np.array(theo_p_values), \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = opacities,\n",
    "    visible = True,\n",
    "    colors = colors,\n",
    ")\n",
    "\n",
    "fig_117_001.marks = [lines_117_001]\n",
    "\n",
    "axis_x_002 = bqa.Axis(\n",
    "    scale=scale_y,\n",
    "    tick_format='.1f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_y_002 = bqa.Axis(\n",
    "    scale=scale_x,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [1.0, 2.0, 3.0, 4.0, 5.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='v',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "fig_117_002 = Figure(\n",
    "    title='v vs p (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[axis_x_002, axis_y_002],\n",
    "    animation_duration=250,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "lines_117_002 = bqm.Lines(\n",
    "    x = np.array(theo_p_values),\n",
    "    y = np.array([v_values for p in theo_p_values]),   \n",
    "    scales = {'x': scale_y, 'y': scale_x}, \n",
    "    opacities = opacities,\n",
    "    visible = True,\n",
    "    colors = colors\n",
    ")\n",
    "\n",
    "fig_117_002.marks = [lines_117_002]\n",
    "\n",
    "# Export buttons\n",
    "\n",
    "prepare_export_fig_117_001_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_001_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_117_002_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_002_button.on_click(prepare_export)\n",
    "\n",
    "\n",
    "block_1.children = [\n",
    "    widgets.HBox([\n",
    "        widgets.VBox([\n",
    "            fig_117_001,\n",
    "            prepare_export_fig_117_001_button\n",
    "        ],\n",
    "        layout=widgets.Layout(\n",
    "            align_items='center'\n",
    "        )),\n",
    "        widgets.VBox([\n",
    "            fig_117_002,\n",
    "            prepare_export_fig_117_002_button\n",
    "        ],\n",
    "        layout=widgets.Layout(\n",
    "            align_items='center'\n",
    "        )),\n",
    "    ], \n",
    "        layout=widgets.Layout(\n",
    "            width='100%',\n",
    "            align_items='center'\n",
    "        )\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################\n",
    "######2ND BLOCK#################\n",
    "################################\n",
    "\n",
    "# This block shows the slider to control the tenperature \n",
    "# (T_slider).\n",
    "\n",
    "block_2 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "T_slider = widgets.SelectionSlider(\n",
    "    options= T_values,\n",
    "    value=T_values[0],\n",
    "    description=r'\\( T \\)',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=True,\n",
    "    layout = widgets.Layout(\n",
    "        width = '300px',\n",
    "        height = 'auto',\n",
    "        margin='0 0 0 50px'\n",
    "    )\n",
    ")\n",
    "\n",
    "T_slider.observe(change_tenperature, 'value')\n",
    "\n",
    "block_2.children = [T_slider]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################\n",
    "######3RD BLOCK#################\n",
    "################################\n",
    "\n",
    "# This block shows the v(p,T) (fig_117_003) and\n",
    "# mu(p,T) (fig_117_004) figures.\n",
    "\n",
    "fig_117_003 = Figure(\n",
    "    title='v vs p (fixed T, reduced variables)',\n",
    "    marks=[],\n",
    "    axes=[axis_x_002, axis_y_002],\n",
    "    animation_duration=0, #500,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "area_117_003 = bqm.Lines(\n",
    "    x = [], \n",
    "    y = [], \n",
    "    scales = {'x': scale_y, 'y': scale_x}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = ['#39362d'],\n",
    "    fill = 'bottom',\n",
    "    fill_colors = ['#ffd429'],\n",
    "    fill_opacities = [0.4]\n",
    ")\n",
    "\n",
    "tracer_117_003 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = [0.0],\n",
    "    y = [0.0],\n",
    "    scales = {'x': scale_y, 'y': scale_x}, \n",
    "    opacity = [1.0, 0.0],\n",
    "    visible = False,\n",
    "    colors = ['#2807a3'],\n",
    ")\n",
    "\n",
    "tt_003 = bq.Tooltip(\n",
    "    fields = ['x', 'y'],\n",
    "    formats = ['.3f', '.3f'],\n",
    "    labels = ['v', 'p']\n",
    ")\n",
    "\n",
    "labels_117_003 = bqm.Scatter(\n",
    "    name = 'labels',\n",
    "    x = [],\n",
    "    y = [],\n",
    "    scales = {'x': scale_y, 'y': scale_x}, \n",
    "    #opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = ['black'],\n",
    "    names = [],\n",
    "    labels=['labels'],\n",
    "    tooltip = tt_003,\n",
    ") \n",
    "\n",
    "fig_117_003.marks = [\n",
    "    lines_117_002,\n",
    "    area_117_003,\n",
    "    tracer_117_003,\n",
    "    labels_117_003\n",
    "]\n",
    "\n",
    "theo_p_values_inverted = []\n",
    "theo_v_values_inverted = []\n",
    "\n",
    "for p_values in theo_p_values:\n",
    "    \n",
    "    indexes = np.where(p_values < 2.2)\n",
    "    \n",
    "    theo_p_values_inverted.append(\n",
    "        np.flip(np.take(p_values, indexes[0]))\n",
    "    )\n",
    "    \n",
    "    theo_v_values_inverted.append(\n",
    "        np.flip(np.take(v_values, indexes[0]))\n",
    "    )\n",
    "\n",
    "mu = get_chemical_potential(\n",
    "    theo_p_values_inverted,\n",
    "    theo_v_values_inverted\n",
    ")\n",
    "\n",
    "for i in range(len(mu)):\n",
    "    mu[i] = np.array(mu[i]) - 10.0*T_slider.options[i]\n",
    "\n",
    "p_text = widgets.HTML(\n",
    "    value='<p> {:.3f} </p>'.format(theo_p_values_inverted[T_slider.index][i]),\n",
    "    layout = widgets.Layout(height='auto', margin='10px 0 0 10px')\n",
    ")\n",
    "\n",
    "p_slider = widgets.SelectionSlider(\n",
    "    options= theo_v_values_inverted[0],\n",
    "    value=theo_v_values_inverted[0][0],\n",
    "    description=r'\\( p \\)',\n",
    "    disabled=False,\n",
    "    continuous_update=True,\n",
    "    orientation='horizontal',\n",
    "    readout=False,\n",
    "    layout = widgets.Layout(\n",
    "        width = '60%',\n",
    "        height = 'auto',\n",
    "        margin='0 0 0 50px'\n",
    "    )\n",
    ")\n",
    "\n",
    "p_slider.observe(update_tracer, 'value')\n",
    "p_slider.observe(update_text, 'value')\n",
    "\n",
    "\n",
    "# Figure fig_117_004\n",
    "\n",
    "scale_x_004 = bqs.LinearScale(min = 0.0, max = 2.0)\n",
    "scale_y_004 = bqs.LinearScale(\n",
    "    min = min(mu[T_slider.index]),\n",
    "    max = max(mu[T_slider.index])\n",
    ")\n",
    "\n",
    "axis_x_004 = bqa.Axis(\n",
    "    scale=scale_x_004,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_y_004 = bqa.Axis(\n",
    "    scale=scale_y_004,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    num_ticks = 0,\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='mu',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "fig_117_004 = Figure(\n",
    "    title='mu vs p (fixed T)',\n",
    "    marks=[],\n",
    "    axes=[axis_x_004, axis_y_004],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "lines_117_004 = bqm.Lines(\n",
    "    x = [], \n",
    "    y = [], \n",
    "    scales = {'x': scale_x_004, 'y': scale_y_004}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = colors,\n",
    ")\n",
    "\n",
    "tracer_117_004 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = [0.0],\n",
    "    y = [0.0],\n",
    "    scales = {'x': scale_x_004, 'y': scale_y_004}, \n",
    "    opacity = [1.0, 0.0],\n",
    "    visible = True,\n",
    "    colors = ['#2807a3'],\n",
    ")\n",
    "\n",
    "labels_117_004 = bqm.Scatter(\n",
    "    name = 'labels',\n",
    "    x = [],\n",
    "    y = [],\n",
    "    scales = {'x': scale_x_004, 'y': scale_y_004}, \n",
    "    #opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = ['black'],\n",
    "    names = [],\n",
    "    labels=['labels'],\n",
    ")\n",
    "\n",
    "fig_117_004.marks = [\n",
    "    lines_117_004,\n",
    "    tracer_117_004,\n",
    "    labels_117_004,\n",
    "]\n",
    "\n",
    "restart_button = widgets.Button(\n",
    "    description='Clean figure',\n",
    "    disabled=False,\n",
    "    button_style='', \n",
    "    tooltip=\"\",\n",
    "    layout = widgets.Layout(height='auto')\n",
    ")\n",
    "\n",
    "restart_button.on_click(restart_chemical_potential)\n",
    "\n",
    "\n",
    "show_all_button = widgets.Button(\n",
    "    description='Show all potentials',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip=\"\",\n",
    "    layout = widgets.Layout(height='auto')\n",
    ")\n",
    "\n",
    "show_all_button.on_click(show_all_potentials)\n",
    "\n",
    "\n",
    "# Label buttons\n",
    "\n",
    "label_input_117_003 = widgets.Text(\n",
    "    value='',\n",
    "    placeholder=\"Label:\",\n",
    "    disabled = False,\n",
    ")\n",
    "\n",
    "add_label_button_117_003 = widgets.Button(\n",
    "    description='Add label',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip=\"Add label in tracer's position\",\n",
    ")\n",
    "\n",
    "add_label_button_117_003.on_click(add_label_button_clicked)\n",
    "\n",
    "undo_label_button_117_003 = widgets.Button(\n",
    "    description='Undo',\n",
    "    disabled=False,\n",
    "    button_style='', \n",
    "    tooltip=\"Remove last added label\",\n",
    ")\n",
    "\n",
    "undo_label_button_117_003.on_click(undo_label_button_clicked)\n",
    "\n",
    "label_input_117_004 = widgets.Text(\n",
    "    value='',\n",
    "    placeholder=\"Label:\",\n",
    "    disabled = False,\n",
    ")\n",
    "\n",
    "add_label_button_117_004 = widgets.Button(\n",
    "    description='Add label',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip=\"Add label in tracer's position\",\n",
    ")\n",
    "\n",
    "add_label_button_117_004.on_click(add_label_button_clicked)\n",
    "\n",
    "undo_label_button_117_004 = widgets.Button(\n",
    "    description='Undo',\n",
    "    disabled=False,\n",
    "    button_style='', \n",
    "    tooltip=\"Remove last added label\",\n",
    ")\n",
    "\n",
    "undo_label_button_117_004.on_click(undo_label_button_clicked)\n",
    "\n",
    "# Export buttons\n",
    "\n",
    "prepare_export_fig_117_003_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    layout=widgets.Layout(align_self='center')\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_003_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_117_004_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_004_button.on_click(prepare_export)\n",
    "\n",
    "\n",
    "block_3 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "block_3.children = [\n",
    "    widgets.HBox([\n",
    "        widgets.VBox([\n",
    "            fig_117_003,\n",
    "            prepare_export_fig_117_003_button,\n",
    "            widgets.HBox([p_slider, p_text])],\n",
    "            layout=widgets.Layout(\n",
    "                height='auto',\n",
    "            )),\n",
    "        widgets.VBox([\n",
    "            fig_117_004,\n",
    "            prepare_export_fig_117_004_button,\n",
    "            widgets.HBox([restart_button, show_all_button]),\n",
    "        ],\n",
    "            layout=widgets.Layout(align_items='center')\n",
    "        )    \n",
    "    ]),\n",
    "    widgets.HBox([\n",
    "        label_input_117_003,\n",
    "        add_label_button_117_003,\n",
    "        undo_label_button_117_003,\n",
    "        widgets.HTML('<div style=\"width:10px\"></div>'),\n",
    "        label_input_117_004,\n",
    "        add_label_button_117_004,\n",
    "        undo_label_button_117_004\n",
    "    ]),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################\n",
    "######4TH BLOCK#################\n",
    "################################\n",
    "\n",
    "block_4 = widgets.HBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(\n",
    "        align_items='center'\n",
    "    )\n",
    ")\n",
    "\n",
    "tt_005 = bq.Tooltip(\n",
    "    fields = ['y', 'x'],\n",
    "    formats = ['.3f', '.3f'],\n",
    "    labels = ['p', 'v']\n",
    ")\n",
    "\n",
    "lines_117_005 = bqm.Lines(\n",
    "    x = v_values,\n",
    "    y = theo_p_values, \n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = colors,\n",
    ")\n",
    "\n",
    "scatter_117_005 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = v_limits,\n",
    "    y = p_limits,\n",
    "    scales = {'x': scale_x, 'y': scale_y}, \n",
    "    visible = True,\n",
    "    colors = ['black'],\n",
    "    default_size = 15,\n",
    "    tooltip = tt_005\n",
    ")   \n",
    "\n",
    "fig_117_005 = Figure(\n",
    "    title='v vs p (fixed T, reduced variables)',\n",
    "    marks=[lines_117_005, scatter_117_005],\n",
    "    axes=[axis_x_001, axis_y_001],\n",
    "    animation_duration=0, #500,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "# fig_117_006\n",
    "\n",
    "# Calculate the limits of the plot\n",
    "max_limit = 0.0\n",
    "min_limit = 100.0\n",
    "\n",
    "for pot in mu:\n",
    "    max_pot = max(pot)\n",
    "    min_pot = min(pot)\n",
    "\n",
    "    if max_pot > max_limit:\n",
    "        max_limit = max_pot\n",
    "\n",
    "    if min_pot < min_limit:\n",
    "        min_limit = min_pot\n",
    "\n",
    "# Calculate the phase transition points in the mu(p, T) plane\n",
    "trans_mu = []\n",
    "\n",
    "trans_p = np.unique(p_limits)\n",
    "\n",
    "for i in range(len(trans_p)):\n",
    "    \n",
    "    p = trans_p[i]\n",
    "    m = mu[i]\n",
    "    \n",
    "    j = find_nearest_index(theo_p_values_inverted[i], p)\n",
    "    \n",
    "    trans_mu.append(mu[i][j])\n",
    "\n",
    "scale_x_006 = bqs.LinearScale(min = 0.0, max = 2.0)\n",
    "scale_y_006 = bqs.LinearScale(min = min_limit, max = max_limit)\n",
    "\n",
    "axis_x_006 = bqa.Axis(\n",
    "    scale=scale_x_006,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "axis_y_006 = bqa.Axis(\n",
    "    scale=scale_y_006,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    num_ticks = 5,\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='mu',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px'\n",
    ")\n",
    "\n",
    "\n",
    "lines_117_006 = bqm.Lines(\n",
    "    x = [p.tolist() for p in theo_p_values_inverted],\n",
    "    y = [m.tolist() for m in mu], \n",
    "    scales = {'x': scale_x_006, 'y': scale_y_006}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = colors,\n",
    ")\n",
    "\n",
    "scatter_117_006 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = trans_p,\n",
    "    y = trans_mu,\n",
    "    scales = {'x': scale_x_006, 'y': scale_y_006}, \n",
    "    visible = True,\n",
    "    colors = ['black'],\n",
    "    default_size = 15,\n",
    ") \n",
    "\n",
    "fig_117_006 = Figure(\n",
    "    title='mu vs p (fixed T)',\n",
    "    marks=[lines_117_006, scatter_117_006],\n",
    "    axes=[axis_x_006, axis_y_006],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "# Export buttons\n",
    "\n",
    "prepare_export_fig_117_005_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    layout=widgets.Layout(align_self='center')\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_005_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_117_006_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_006_button.on_click(prepare_export)\n",
    "\n",
    "\n",
    "block_4.children = [\n",
    "    widgets.HBox([\n",
    "        widgets.VBox([\n",
    "            fig_117_005,\n",
    "            prepare_export_fig_117_005_button\n",
    "        ],\n",
    "        layout=widgets.Layout(\n",
    "            align_items='center'\n",
    "        )),\n",
    "        widgets.VBox([\n",
    "            fig_117_006,\n",
    "            prepare_export_fig_117_006_button\n",
    "        ],\n",
    "        layout=widgets.Layout(\n",
    "            align_items='center'\n",
    "        )),\n",
    "    ], \n",
    "        layout=widgets.Layout(\n",
    "            width='100%',\n",
    "            align_items='center'\n",
    "        )\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################\n",
    "######5TH BLOCK#################\n",
    "################################\n",
    "\n",
    "# This block contains the p(T) and T(p) figures\n",
    "\n",
    "block_5 = widgets.HBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "scale_y_007 = bqs.LinearScale(\n",
    "    min = min(T_values),\n",
    "    max = max(T_values)\n",
    ")\n",
    "          \n",
    "axis_y_007 = bqa.Axis(\n",
    "    scale=scale_y_007,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='T',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px',\n",
    ")\n",
    "\n",
    "tt_007 = bq.Tooltip(\n",
    "    fields = ['y', 'x'],\n",
    "    formats = ['.3f', '.3f'],\n",
    "    labels = ['T', 'p']\n",
    ")\n",
    "\n",
    "lines_117_007 = bqm.Lines(\n",
    "    x = trans_p,\n",
    "    y = T_values, \n",
    "    scales = {'x': scale_x_006, 'y': scale_y_007}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = ['red'],\n",
    ")\n",
    "\n",
    "scatter_117_007 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = trans_p,\n",
    "    y = T_values,\n",
    "    scales = {'x': scale_x_006, 'y': scale_y_007}, \n",
    "    visible = True,\n",
    "    colors = ['black'],\n",
    "    default_size = 15,\n",
    "    tooltip = tt_007\n",
    ")   \n",
    "\n",
    "fig_117_007 = Figure(\n",
    "    title='T vs p (fixed v, reduced variables)',\n",
    "    marks=[lines_117_007, scatter_117_007],\n",
    "    axes=[axis_x_006, axis_y_007],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=80, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "# fig_117_008\n",
    "\n",
    "scale_y_008 = bqs.LinearScale(\n",
    "    min = min(T_values),\n",
    "    max = max(T_values),\n",
    "    reverse = True\n",
    ")\n",
    "      \n",
    "axis_x_008 = bqa.Axis(\n",
    "    scale=scale_x_006,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    label='p',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'black', 'default-size': 35},\n",
    "    label_offset='30px',\n",
    "    side='top'\n",
    ")    \n",
    "\n",
    "axis_y_008 = bqa.Axis(\n",
    "    scale=scale_y_008,\n",
    "    tick_format='.2f',\n",
    "    tick_style={'font-size': '15px'},\n",
    "    tick_values = [0.0, 1.0, 2.0],\n",
    "    grid_lines = 'none',\n",
    "    grid_color = '#8e8e8e', \n",
    "    orientation='vertical',\n",
    "    label='T',\n",
    "    label_location='middle',\n",
    "    label_style={'stroke': 'red', 'default_size': 35},\n",
    "    label_offset='50px',\n",
    ")\n",
    "\n",
    "tt_008 = bq.Tooltip(\n",
    "    fields = ['y', 'x'],\n",
    "    formats = ['.3f', '.3f'],\n",
    "    labels = ['T', 'p']\n",
    ")\n",
    "\n",
    "lines_117_008 = bqm.Lines(\n",
    "    x = trans_p,\n",
    "    y = T_values, \n",
    "    scales = {'x': scale_x_006, 'y': scale_y_008}, \n",
    "    opacities = [1.0],\n",
    "    visible = True,\n",
    "    colors = ['red'],\n",
    ")\n",
    "\n",
    "scatter_117_008 = bqm.Scatter(\n",
    "    name = '',\n",
    "    x = trans_p,\n",
    "    y = T_values,\n",
    "    scales = {'x': scale_x_006, 'y': scale_y_008}, \n",
    "    visible = True,\n",
    "    colors = ['black'],\n",
    "    default_size = 15,\n",
    "    tooltip = tt_008\n",
    ")   \n",
    "\n",
    "fig_117_008 = Figure(\n",
    "    title='T vs p (fixed v, reduced variables)',\n",
    "    marks=[lines_117_008, scatter_117_008],\n",
    "    axes=[axis_x_008, axis_y_008],\n",
    "    animation_duration=0,\n",
    "    legend_location='top-right',\n",
    "    background_style= {'fill': 'white',  'stroke': 'black'},\n",
    "    min_aspect_ratio=1.0,\n",
    "    max_aspect_ratio=1.0,\n",
    "    fig_margin=dict(top=160, bottom=60, left=70, right=20),\n",
    "    toolbar = True,\n",
    ")\n",
    "\n",
    "# Export buttons\n",
    "\n",
    "prepare_export_fig_117_007_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    "    layout=widgets.Layout(align_self='center')\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_007_button.on_click(prepare_export)\n",
    "\n",
    "prepare_export_fig_117_008_button = widgets.Button(\n",
    "    description='Export',\n",
    "    disabled=False,\n",
    "    button_style='',\n",
    "    tooltip='',\n",
    ")\n",
    "\n",
    "prepare_export_fig_117_008_button.on_click(prepare_export)\n",
    "\n",
    "block_5.children = [\n",
    "    widgets.HBox([\n",
    "        widgets.VBox([\n",
    "            fig_117_007,\n",
    "            prepare_export_fig_117_007_button\n",
    "        ],\n",
    "        layout=widgets.Layout(\n",
    "            align_items='center'\n",
    "        )),\n",
    "        widgets.VBox([\n",
    "            fig_117_008,\n",
    "            prepare_export_fig_117_008_button\n",
    "        ],\n",
    "        layout=widgets.Layout(\n",
    "            align_items='center'\n",
    "        )),\n",
    "    ], \n",
    "        layout=widgets.Layout(\n",
    "            width='100%',\n",
    "            align_items='center'\n",
    "        )\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "###################################\n",
    "#############MAIN BLOCK############\n",
    "###################################\n",
    "\n",
    "main_block_117_000 = widgets.VBox(\n",
    "    [],\n",
    "    layout=widgets.Layout(align_items='center')\n",
    ")\n",
    "\n",
    "main_block_117_000.children = [\n",
    "    change_view_button,\n",
    "    block_1,\n",
    "    block_2,\n",
    "    block_3,\n",
    "    block_4,\n",
    "    block_5\n",
    "]\n",
    "\n",
    "figures = [\n",
    "    fig_117_001,\n",
    "    fig_117_002,\n",
    "    fig_117_003,\n",
    "    fig_117_004,\n",
    "    fig_117_005,\n",
    "    fig_117_006,\n",
    "    fig_117_007,\n",
    "    fig_117_008,\n",
    "]\n",
    "\n",
    "main_block_117_000"
   ]
  }
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