{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c99ef67a",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "This notebook converts a shell-based AMS input for a dihedral scan into a PLAMS workflow.\n",
    "We will use the same molecule and the same F-C-C-F scan coordinate in two different PES scan modes:\n",
    "\n",
    "- `PESScan%Optimize = \"Yes\"`: optimize all other degrees of freedom at every scanned dihedral.\n",
    "- `PESScan%Optimize = \"No\"`: perform a rigid scan and run only single-point calculations at the sampled geometries.\n",
    "\n",
    "The goal is to compare the resulting potential energy curves and understand what changes when the rest of the molecule is allowed to relax."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a62786",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bea75375",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scm.base import ChemicalSystem\n",
    "from scm.plams import AMSJob, Settings, Units, view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c22667-8f89-4fc6-b0ab-d179083006ed",
   "metadata": {},
   "source": [
    "## Units conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "acc1c492-f455-4661-8edd-67c90ca9bccc",
   "metadata": {},
   "outputs": [],
   "source": [
    "hartree_to_kcalmol = Units.convert(1.0, \"hartree\", \"kcal/mol\")\n",
    "rad_to_deg = 180.0/np.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066510bf",
   "metadata": {},
   "source": [
    "## Starting structure\n",
    "\n",
    "We start the chemical system using a `System Block` string. It's important to note that we make initial guesses about the bonds. This is crucial for the setting `PESScan%Optimize = \"No\"` because rigid scans rely on the bond structure to determine which atoms will move when an internal coordinate changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11cafe66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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3fGAoKdbnySgKAUJeRlMylCQ+MJAwlsJQ6ppO43+mcoMpM5RkE40ZT7E+rWvrbfKI3vbuKdu1dq1RbtdIkfvLX9p0yab325Ca8zX4+AEI8XHR6tcWK4s/r50igbxYdXRJEh/0HnbFB8IDERLui3K3QnzQjpIIQXREe0LQIzwqYV+vddtT3qaibamUFPL2lLehtjoIaUfVP42yKhsI+vbm9us2PdZtJ/g5NQETODwC8gx1MAETMIHDIBDi4xWrXyvj6dpySZrZCneRRhhGuIgwODYMovSmtnozy7pYX69rGp7kFTUmQkJ8kOYhN5h65lFlJuXHxLZ6Q19i05SlKqYhEdKhZ6Z+Rv3a1Z9bXehDbO93T0hO1/kpIRDi42lqO8vUdpbKXXC47EigN6LNxKxwKY0xHAwi7xvXgYsUdZzxTimvXo8k6NWTiNjgXwiQvH3k9xnCI1LEB8eR8k/HNdSeOp1mp2ysbly7+v6q7ZR2x8o5Om8CJjB5AuP/zz7547ynCZjAXCYQBtRPy4A6rUbPB70e9WKpbBV6PvT2Nrld6c1tfb4MII3/4C0t+egBacxvJAGC8YT4CEMpFyIgDqOJfC4+wlgKoYGhRD7SyMd+jA1JHzDco8xI0dEMWY3OPZ3XpZ6Qz8qQ8uBaEDscawLRdp6ktrNK4uMUCffF3bajMR+M/Rhzu4qeD7Wd3sBzejuqGG0pCXnal3pFUszaFO0q2lakE7WpaEvRdrryg2FUrWhbzNtLI2u0N7kn5FhXFZdvArOZgHtAZvPT9b2ZwLEg0B0021p16arX1E+uX4vbiIyjuuLYtzyYApSeD72NJY03swyKZV3ERqMrPEKARNo1mcbcR/LbCBHSbyxxDAYTKQZThFjXqckVixmDdDkSI3XNmtWuP7Z+7arrV41svmTz37gnJIg5PWYEKvGx6rGrXqPv4nTbzpAqrL7lkQaLq61Ii49FZoEjVuvSeA7GSREZ20Hvh3oPU4rooD1pmX+0pWhPpIiO7pZuu1KpvRBtijRER74vx1ZtipeW9U6j026salx76t2njmysbfwbHeZJHXo0nTEBE5gMAX5MHEzABExgcgT4zoe+U7Dix1ec31zd/HKxWrJjqQ49QesXK9Xb29TzQQ8IYz7U85F6PegF0XLq9dBb2sYCmUTDMpbko97Uv9xQCsMnUoyfPGAkIT4iDYPpYGnsz3S95V5F3LH26jq31fa2722v2fyTm29WoVhnUioOJnDUCSAlOqtWrbqwvrx+Y7mqnK92U6j91DXovNvzwbiPfOxHjPsgpReEsR+0pawHJHo9GvPUnmhbTVrT/sI+1oUQydsVbYN/0fMRKaKj/1/VztQNoja4q9xbe6D2rPvOuu+bbjtHvb64QBOY1QTcAzKrH69vzgSOKoFacXtRW37O8iWNJY3r9J2PhRpb0ZZR1Eh+6tmb2/SGlilA420tPR/yUU+RHhDlNb9nMpQG/Q3xQYqhFMYSRhIB4yf+xb6xX+wbdx7HYDilbZiBGpvCR9f0rZBOcVKxsL67vv609addeF9x3yM6Bbt1TxSFODWBIyOQVPRpS09b1l7QXi/RsVB1sKNY7287WtPt9chTjlZMU+lWaer5YMY49YSkPCljQap/0a76BT6Ve1C7CgFCu0J05PukdjP+/uvavy3htLC9t33d8i8sf87DxcN73HbGQ/KSCZjAxAQsQCZm4y0mYAI5gXWySTYU7eEfG/7D+rL6+Z2hTktuI82eAcXgWAkOqYoUk2EkgyiJkMpISuM95CbSqI+9ocVQwkjqN5TCkML4yQ2gMJRIERX5fmE05ZfNfoRIMbDKhlSGBsOXC+SKtats1R9Tf7ymGX2vTnRlwYcKNdlPOsh/TODoEEBOtNuL2u+tL6o/Xh8YbKndUOW737yhxrFHJTQizQVHcrlSO0rTVCtln+RupV5E0uR+pXwIj2hX+TLtg+VoJ3m7Su0ia1Mh2PN9AgVtSeU0JFRajZMb5w/vHv5DXc8b02q3ncDk1ARM4AAE+MlzMAETMIEDE+iO++isOm/VGk0T+kv6unlL0+eOiY/o/dAvSggODCaMqmQk8WaWiH+60rz3I4RHGExDsswm868rWbiEriUXx0R5pBhaYXCRx5hK/3hzLAHCmBT14DQ77Q4iZN1jPv6Ydekr6V0RcmAm3moCkyOQBO1jVj1mnVyn1iXx0a28PbFOO1HF7IqQyLNMJERKlrrLP1JF2lTko66T5v/yttLfXqLdDKs7JvKR5uuijCi3altNiZFW/ZT6L51252lrdHn4RnK/DiZgAiZwQAL81DmYgAmYwIEJnCuzQp0ItaW1q9OYjyFZQAyYpQ+1Eh8hOMIYSm9sMZLo/eB7BKQhQiphEMZMv3GTL5MPg4g0liOljFgfgoNtkSfN/yXjDYuO6+b6u/fBQOBSX6B++7nrzx0u1ul+ccVyMIEjI0AdKs8999xh9TC8XTZ+qfqGKk5ig7aSahl7hfCozkc9JUR9TSntifXpP+VYrmJ88yOv69G++lPaS7SbaFvRnvLlfB15/uVlcS6uoL5QE0AsbF+thfQ7Ud2CExMwAROYkIAFyIRovMEETCARoPdDA89PffKpV8rne205JN/vIQ3g4NcjjxhFGFRaF4ZViBAMpmQgaXvqBdFO8Q+Dhnxu2EQ+hAXLYQBFGtsijX3i2EjjPKQYb5GSjzEq+OJrZqxOY2XjCVsXbv11bWIgun8f3QSOlAB1qLN169Zf17inJ6jtdFSRewIk1TC1DdW3Qwqp7lJ/q3+9tpVqd7fGU//zuh/tIVLaDflB7Ylt8S+2k8b+pNGOlDaYUU6uWGtP/fapV+qS3AtySE/TO5vA3CTg/8HOzefuuzaByRNYX3TWFGuach353fRFZibcrHo9woAKwaEtySRKKW9m+VeJjpTirz7gXxg0uYFDPv6F8TPRdsrM94njWB95roXl6qpS2ruPbm8IA2tLfYvhLavft/pkAcKQOkTTcPJYveesJ0Dd6axenerSWzp1Tb92hOKjVnarY16HJ8pHO4v6P5l0UBs62HGpTdXVyDVDVzm//N01V62hX9Ezyc366u0bNIEjI2ABcmT8fLQJzHYCyI3yrtPvWiM3iydq8HZHvQbYHN3+Aeyh+BVRvidEtDoZRiFCWCKfL2tdGEmDUgwf1g9KBxlFUUbsH8ukXEuk6brSmup65BpGT41e+cq+05vcUxrL9GX3N2qXsrghSZTZ/ox9f8eGALK2VJ1/oyZdWKYKqM/3pYqXejyohwcMuTOT8rF/EiFsywLb+Dewjmfb2D7o36D2NJEY4fi8jVXnTTPK1U+qP/GuK+9ao1N2x6ln1+isCZiACeQEwnTI1zlvAiZgAl0C67pJOVy+Rb0fNb3B7Q4xHWA7YYhEiHyekp/sv0FGEseyPtJB+wzaHueMbbEc16oCuyKKX0NpHnpB9Cb31af96WkLirVpNqyxG+sd5IwJHJAAdaZ92mmnLVBtenWHj8909XQSHxMeWYmOXk8HPR7qS0jL9CkgRFhXpeRrnbFlyo363Z/GtrwdRBti31gfYoTlPD9oOT9eIr6Ui2Yt/VZwMgcTMAETOAABC5ADwPEmE5jjBGpMu3vKwlNWaYzEc9X7UcrIQAF0jSjSPPQZTxhJyVDigMgPcCGhiNwACkOo34AKYyffnuejjP51UU6cJ5ZTqh6Zcfejg/V9kFJvcs8cPXX0+dpWalpe/04Cz+FQCFBnytHR0eerB+RM8kmA9LcZSqzaxn5pCI6+9iNB0xMhydGJ4yuREnWbYgksR5pvI5+3k8hHmm9nHf8QI/3ro8yU0jO6Tz0+82vPPeMrZ6zSzm3d06A7TtfkPyZgAnObgP/HOrefv+/eBCYmsEYWh0LjxMZl+sKyvtcsMwdLoz9gABHyNN7aZsYVb2uT8ZQK6ZouHBZGDD4bkw35ZcTxsS5fniifnyftgxDh15DY0IVoRiwNGn6DlmppRqz8AOdN4OAEqMw1TbhAHYpW0D0qb0GVyOjtEXvGetKIbOtfH/vHtu4ZDulv3m44MG8zIUgmSvN906H6mLrE+9LRRaOXVReRfkOqvBMTMAET6BGwAOmhcMYETGAcgRuTuaM+j9rLSs32L8tkLLIjRo9CuISk5TCQ+gyj5CYShhRpX0B8hDFzKEKkr5gJF8PIih3iXPn6Xl4vh9ULwjTDzznjz844QRfmwegBzulkCNBSOmecccYJEtzPUaRqdXsOOTpvG3k+bx8Trc/36cvj5YXAj/bTzXXbVeTzi8/3y9dPlO+1D+1APv/HMYiUJOB19yr7ZcVVaWlAa5/oDF5vAiYwlwhYgMylp+17NYHJE0hG1NnLzl6sb4U/UxGro/t70W8csYyZEesjT8r3xJViGKV9yGMoVf+0tZePdaR8lTm+zJyvjzzb8mMjn1b2/ckNp75NY9cc184OHKAT1BfXT9i7ZO9z0jEbqnvfrwCvMIH9CKR2snfv3ueoB+QEbaWy4sy3f31jHVuJsT2Wo+2ovaRt2XraUDqGfaoo0ZzytJEI0ZZiOdpUpNGeYr98OfJxLMshOmJdLEcbU1ovR3T+ZvHMsy8+e3FqSRzmYAImYAJ9BCxA+oB40QRMIBFIRsPu+u6z1QNygtZg1XSNKDazFDGMpzCQqjQZSezDsoyktBzGkg7u/4dR1G8Yxbr+lGPzffN8lKuzphDLYSTF+l4a99FboYzczeTLXtOMX5en1afYiMrxOH9AAqntNBqNy1WDaDO0gLH2EvWNtXk+xETVftJRkY92kwkPBEcS85XwSG2MfCo2an03jfbDUt5WIj/R9ryUKDedYOI/taKVxoGcMHLiyNnVbhYgE/PyFhOYswQsQObso/eNm8ABCaTfBvV8rNHXy5nXnwGlXYMpjKJBKYYS67M0vZmt1iWjCcMpM5Ry4weDp61/se5gab5vGEscQz5SXU0KrNsvxD2wiXzsore2GHe696cnV5K1aet+h3uFCQwgQE2qdzqdp6eev1C+g9pPtJOoe1EPo15WwiNvT/152lKnrTrf6qY6b6r/eXuIdpS3l8hHGm0m9o32E+VE+4k0v+90i9F21Po1ZXdT5a5J+9zg3sOclfMmYAJdAhYgrgkmYAITEpALyYrextw4yvMYS5kh1S842IZx1BMfVT4MHQyayIcxRDqZfxwXx0QZUR5p5LkH8vuFMPRyA5DdpD2SYVd2nnjug+cutCvJfuS8YjAB3vZ3zj1XdaYonpgECP1p3TrVFbl5ncvbToiNbF3qNdRy3tOR2lG0pypNbQxhP6qoddEW+ttGtJcDta3YZ6IyaEfxbxyC+E2ompleXnR/O9aO28sLJmACJpAI8GbTwQRMwAT6CWAGES6oxm8wLmLMgArBgbhQT0EaZB4GVJUmIdLS9qrHIxlHDRlHTZk2o4qNKqpg3qBiFPUHDB0MoTTAVRvZL4wf1uf/wtgijX0OlOrgccIpLcc94reOK8lwbenWx2w9XXvenk6PNHEwgYkJIEDKrVu3ni7vq6XkFbsuSFG3IqW65/ms/dBmam0dlq0jH+t7bYvJIdTGaE/qrSuYLIJ2RttqN9qpvUQb6W9f0TZoU9GudEWp7eRtKY4n5ZhIY98oJ91p937it+IC9lFgrYMJmIAJjCNgATIOhxdMwARyAjIu9GUxGUJhKJGG4VSlAw0lvYWttSQWKoNI3xBJwqPelpFUGUhJgNRlKNUoqCsuUib7g3GDcYTRE0ZSLIcxhLGU/2PfMKDCWIo0N5ZCGKX74RLivsbutaw1a/M78zsrtfV2fROla0hm1+esCfQRSHVEon2leg/nK6U2detN1LFIoy3ly7Eu0tgWKYJebUn1sivs6fHQMu0MEaIxS/J/kvBQqrFbGsjUbQu0mTzQDviuB+2kX9xHW8nbVN6e2B7/eu2JwuMauXbFMv1w5Gd13gRMwATGCFiAjLFwzgRMoEsAa6XkK87t0fZjKsOcLooxIz3yGEQhNvTWNvZBlISBH+5XxajsEr2ZxTiqj2qUN29pZSzpI4dJXGDk5AEjB+Mo/mHsEEKIsD/r2G+QsZQbTWEoRRnJtSXuIdLc6KuMKE6p6z8hvy7nTeBgBKrZr8bvFvUr0qh3ear2pBmkUq9GL9WXNBAZ6as8yqe2hfDQzHRJ2LfUpkYlNiTmSZPwIC8B0h5S4WrN0WaiHSA+on3FOi6W/aId9repWGZ/js2P02L6HUhNWPqHNq+emcec9qenLbivuG+vdu5eAidxMAETMAERsABxNTABExhEQBZEsUBvMR+DcSHDYkyAYDBhKOWGU74OUSIDCaGR3s7yhpZeD4yial16SysDiUmCiMlQUpEYNQQMnNxIwnrhHyEMn0jZt6V/pCE6+tPcYEqDdGUgcU3cQwil/e5JB+na6jLqcCX5x+K26gLSVfiPCQwkEF0NF1Cvqx6Q7ljLEB7RdiKNdsQy/0fOlxEffe5YSfCr7aS2FUIEMZLF1L6qtkVvCb2MtJcItIdoX/1ti20EUtoRbSvER6SUxXb+pbxEUO+6ESD7dC4JkGJpsUCt5lHtFFziEpyagAnMcQIWIHO8Avj2TWAiAnqLi8XSnf0qjCVS9WQkYyPWhcFEyrbqDW4ynHDDCiNJQgQ3EXVpdEUHrleZWYIbSaPGZ8i77iGk9H5g9PQbSTpLMn5I2R7GUn/abyil8Sy67uoNbVeEcB+sy3pwVGRPZOnc7OFgApMmIPGxf52hNbE2by/9y1Hv1IaS0JB46NVFZEwVERvJDQshrTziPrUrCZPUpjgsa1/RtriBaFe0lWhXpLGNlG39bSnW5SllpbZEu9e9JFFf5XXdbfV0jqkeCnYwARMwgYqABYirggmYwEACMqK6AoQXomNGRdeIYhljiRRjaUgGUTXmI63Tm1vSZCjJKEoGEuuwj+jxqIwj8hGSOBjSMTptGEkYRoiQPLAOI6i7V/dNLPl4OzvIcGL7uJ4Pri0ERxiB3Aux30Bku4MJHA0CUbcw1qNngzrH/4kz8ZG3oVT96QnBrYn9qI9aTgPPaxpTJRs/ImM+aFOkEaj7jVIHVG2L9kPvR7SrEB/5/tG+QmzkvSDRviiXe0gvGLIexRBZ6jls1/al35Ao2qkJmIAJ9AhYgPRQOGMCJlARQHjU77nnnu2rV6++TQbNGhk/HRlFjXEGOoaTjJqeEEFgRGSbdEMYRr287JEkRtAdlfZIwkNnJGVsRn1IppFm9OEfxlH80xHjAgZQGErRCxIGUy5GKBfxgcHWkasIhlxuNPXe2oZxyLUrsl/yqR+p+XdyHHkvHIyA6lwzF9e9/alj1C9EBCnth3wsU9NYTzuqhAYpdTSVR72k96Pq6Yj2hWhPoiPa1Ziu1wEqi/aVtS3aTrQr0jywLf7l7Sjyvbamb490RrrtKrUnXBq5vnbZUZkNtbnb7vmVe7YXv5Ia8pgiyk/mvAmYwJwl4P+xztlH7xs3gYMSwHTphspASoaQBromIwlDicivSAiPKsUwQnQQERxy5+q5iWDvYPTIdNEOCpxFMQQIH1VLIqSpg9N/FNQNHIdxRAgjiXL4x3Lke0YSwkOiIz7UlsQHAkSxdw9h/MX9xPKIvgXyqPZrF/+WTviU6sRpwX9MYCCBbuUsin+jPiuMVV6WqPJRz3LxkYtfjmAfpUlsaNxUiJEkihn/QccCukH7hPCo0xuCEEkn0TbtktoKqcQHcaK2FSIk7Z8OHWtLeZtK7UrvIlKbCkE/oqNws6xiunbuZ0Sx21y5UgcTMAETGEfAAmQcDi+YgAnkBGREbU1vXsOgCOOJNAx1fkUUk3FUjflgsHkyvTCmKkMKgykMJAyeMHqwl/rFBwZObUjuV+oJaeubygiYdLxMKo7DUMr/YSSxnIwlGUjtjoauyD0EgyuJD1xFMneR9FY5EyHjBEl+j/tk4LU62xKTDemv/5jAQQno7f+2RgM1PiDQbohqU6nN8L0P6hy7015oTyFISNlG26HnQ+5VSdxrude+lE11v3K7oueQdqCVUtAqUNmO2kS9oy3qXaQ3kJm0UtvSt0JoW0nQpNOMta3UlnQwZSfhgZhXbLc05qqvJzGJjWg3CA/ubW+5VTkHEzABExhIwAJkIBavNIE5TwBTqCPj5Osyel4i470cZ2SEsVGJj3G9IDpyPyMJg4kC9Q8DqV3K8ME4irezlZsURlIyyjT4FiMppu0lTWXqkGSE8Qa4Kg8DCQGDkZXSylDqiQ/e/EYvCCIkekBkBPLWNhl4cT+kGFBE5cs95WhtR223loriXK7WwQQOSCDVEdXR3aqL1Kb9VUiICrZG+6l6DtPerKf1VRFxkFyuaC6R1+YkSlhGkNMe1MaS8EiHSmhU7SG5XtGuNE6LPGO1EPcxtS8fLEzn0vGchzJSm8raFcIjxUrQp7YVIkQ9IGlyCdqMBLvaTZkEzr7617VUFDek0iWDHEzABExgjIAFyBgL50zABMYIJENKRsftqfcB8wbDSAZGOU9GjAyY5L/eZ0Clt7T4p2MshRuWFvOQRIjERzJ0JBwQIgiH9JFCei30pXTyGEoYaKk3RQZa7veOsUUIQ6lnbFVCJrmcKB+9IKn3I3pB+ntCECGV4OiJjxEVjQvWrnLHvAfm3ZVOdo3WOZjAgQmkOjJv3ry79u3bt1tC5ETtzrrxrYC2RIz2Qxo9I6zPBEi0JYR3Wq+Eek8+iXHyUbqytIW0ipEY1UdEk/DQMu2A9pSL+/xcIWSiXe0n7AcJEHoS1X5KXLEU9RuRpuHt7Ovcrjspige5GgcTMAETGE+Anz0HEzABE+gnkIwGiY9vyAgZkYEzlNxCeMNJxH+dGAZUvMGthEcyljCKwlhSNg+ID4yjmowiZbsfV9MbWnpAkvsGRo3KTm9rK3eu/C1wKpe3vgqURegZS+Hvrre2GF68uQ0B0iu7coFJRmAYg4iQKuo7BqUGoNfkRvK9k+87efc95T26WBtSCbT/HIgAlbJ28skn777//vu/p3bwI6qXad24g+gFoa6pjjNFLlPqpt4P2hHioz+lLbGe9qV/tIUkFrSKtpCCzpK2dRtdt32pjdEGaFeIjjRbXSVAOEe4SqbyeGFAWYo9AaJcaj+VsM/bUmpTlRtj6kkcaz/18pFypP5I/RvputapEAcTMAET6CNgAdIHxIsmYAKJAFZ97cQTT/z+1q1bvycD5UnJ0levQLztTL0gIUByo4l8ZTAllxH81vttEEySKmLUIESI4X6VG2VhJIUB1jOSKgHSM5bo8YhYiZC0rLe+SXhoXc/3nnW5C9aY8dQVWCMa27u3Vq8/Wv/8LbfcMlpcnaQWssXBBA5GoEGdOfXUUz9fCRDaEq1iLFD3Q/jShkLA58KjakO9+s4YkLSgppO7Xulw6jnb0j+JDnpBQuAjPtptfUtHAiRF3BvpXcwESN62Qtj0t6skPhD06gUJQZ/Sqgex6v3oqOx6e2f7e8tuXvb9jeVGLqn7hmDs7p0zARMwgfQ/VWMwARMwgUEEGrfffvuIDKnrZUg9SbPt8MnxerFXu/b3fmBe9QsPXLEUMGQwhkirFanXIxlNGEVy50oChHw19gPBcCAjSUZN7w1wlJsMJoQH/0gRI6S4XClNIoR8CJLq7W0yBBEg0bvD/T2qS96lffeUN6Zrvj0uPi35jwkciECq6Kp/1J03K3YbQv8RyFnqnYQA4zEGCu28XVHn9S96F3sipBIfFE89T+Kj6lmMut8TH7SxXHyE4BlwHs6V/ql8ZbpjSqI9Ve0oxlOlHhDaz14J97aE+9769bdvuH2kuMHCnefiYAImsD8B94Dsz8RrTMAEugSSIaW3p/+iGX0wpDBTuob6sGyScB1hbW7IVOZWMpYq2yvyiAKMmV7EFUXvRzGcwkjaT3yo7HC/CpGT3tLGefQ2OJVLsYiOKqZyyVdGU1quxMc4f3veRIcAkfiQ21UpQ6pRbit3NDc2v6KtRbHBb3ETB/+ZDIH0xr/ZbH6l1Wrt0AFLFan1+wuRSoCMEyF5W6JtcVQWQ3hobarrkhQqvNuuktBX9e21qUqIhPhO4kP/109ipzpPylO+zjWoXVF2akNZuUnYhJjP28+jGjelqavbu9v/wvV5/Eei4D8mYAIDCFiADIDiVSZgAolAMqRkRN2ot66bFFdpLXNy1tRDkPzX0/tNDBliZcSMM5a0OwZSso9kwISBlARHvKVFfJDHJSq+qK43wpSdhMcBDLJkMOnUhGQoYSTxXxhLSjlXMsCqNOXp/eANdLjBYAjS89Ht/ejoC86Nckf5r/f+y70bi6tkml1jASI6DpMjoMpV1O+9996N+pDnv6rdvED1kbZETR4fWEvd4//EiI0BgqPXnrQ5AnV9v3aF0I62RLlVRCyEuE8zX6ne9wRI3rZov1WMdsV5oj312hTnCfFBm41JHPZKv4zUap2HOptad7duTNe6zu0mnplTEzCB8QQsQMbz8JIJmMAYAQyp5sMPP7xThtTfa0D6W+QHjtneTEaTREhyHalmvUrGCwYUAUOmCslYkgsWIYyYNN4DQYCRRC+IYupRkYsIKaYab2uTwaQy87e0lN17U0seC6kKKa8ySZPwoHxtDhGSzkMvCHdBjLe3lQBJHx5EXG1X3F1cp79FcUMyCynJwQQmS4CWQJ2hDr3ggAdRD3Ff0hH7tSeaDZEQaXep1/uR6ncluEPgh/jota1KhKR2pUHoqV3RxqLtcrWcn3FV1XmiXfXaVIh7xAdtlzYb01jTfh4t2hI6TbWbv3/4Lx/eKeHeVFncnYMJmIAJ7EfAAmQ/JF5hAiaQEUiGt4TDX0h8vFHrGf2BTVJLvQX6BelNvdtvLMlGSUFpEh7YNn29HmHIjDOUKh/1ZIwxA5YMpXFT+mIsVedKxlEYTBhICnGunvBgte6Ct7Zxnp74CAEiA1CuV4iOstgjN5KHyy2t77Q+qiNrxY3pKIp2MIHJEkBW19R7+FG5Ym1RfoUiNbGqrcpFYC0GfFWvEQHqNenV8d4RHMm+EZSnriM6UsiWe+Kb1qsroQeE8Rr0tAxsV3mbQoQQVGxqS8pGm+qVi/igPUUP4j5d2V7Nnr2tvU+ui3+Rju8KsCrrxARMwATGE7AAGc/DSyZgAuMJYMI0Nm/efId6QT6kXpBXS4hgXDWSWU5vAcZLZsD0DKbKjklGE3lFDJhchCAMUk8HvR6SNr1eD36ZKuFB70dP5Og8YaDlb2u1t4rnBOSUVG9r401wv9BJhlPeA9J1vSokPjr13fVGZ0fnvQ/e/uCuYp2uYoMFSJeq/x4CAWpi48EHH9yldvNetZureu1mUCG0sqoXZGBbimOq+p0WySumuk47ysR9T3QgEhAdStNUv92W221PWbvqCXwKRs/0NA0nyM5DPsQHZYWAfzQNPm9IgHxoy3/fckdqN9e43YiQgwmYwAQELEAmAOPVJmACPQKYHXopW/t9GVEvU77bC4KZUr25TQIh3tpyWGW0jEsxshTjLWoymDBmNO4jCQUMmkp4pB4KCZCeCJGrSM9IknGkGbnGGUraU6fipCnTO386F6spm/MjOsjn4oPej33aqer9aD/Yfrj+QP2d2qvmweei4HC4BKhxtJt3aiKHX1a6TMvUxsq87yuWeokQ1tYQ2X17dBcpoT9W7SpEyDiRoHKTiEeMVG0K16tOQ1P1Rrviiipxn19dr01xJzo8iR2uM29D3d6PWmd7Z19jR+P3tbVWnBuNUUsOJmACJjCAgAXIACheZQImMI4A5kdj48aN39Hb3A/qbe5rJEQw4bu/H7y5xWjSvxgY2zM/wlCihDzKgEkuHFWvRypJ+eQmgpFEyfSqKJ+MJAalV0ZSSisjLeW1W0o5FyEMpfzcGEycPwRIuI5IQCXxQU/OrqJd31Vvdh7uvGvjTRsfdu+HmDgcCQFqIO3mYbWbd1W9IGPtZlDJbEWEKPTaU3chrUvtKqvn49oU9b4aV4XLVU9009ZoP7SBSoCktoXg6GtX43oVaW/ZuaLsXjuKNrRXYz9Ga832A+0Pbnnflu+496P7qPzXBEzgwAT4iXEwARMwgYMRQA4UZ5xxxoqRkZGvK3tydUBanwTAfK1ZKC2wQD8r5OdlUdP2FkT6TiJFZLBMrERHGmzOepYrAZLy/FKxjpT1pERCpBhgYTEhNjCeiOQxviJi5FXGUxIfGHy7tddulbSxeLD27dq5G1+7cVtxTa8E7eBgAodFINVUfUvnJPUe3K4STlGkVnbbzURF0kaqNlSbpyJoS6zLY7SdPI02RVup2tW4r6yzPiJXEJGrjHalLG0qtSWulBDtKBfx9H7qg530gpYPlQ+1vtd62oMXPfiA2g2BPR1MwARMYEIC/Fw5mIAJmMDBCGBQNO65557NMqbepPyHZFBhyncNKQwUDHmFMFzS4NjccAkhECm/PiEKyKu09EaWPEaSSk6CpMonoREGkzYnlyylKWBA5cYS+fzccR7SEB8j2oHemz0pdmrbNIPPw8WbeGNd3J6uwEaU0DgcEQFqYZ06lbUbamG33UxUNMY9QUcntyelyb2K0ojUzEGRkllPmyGvtpR6GkN0VGn/7FchPlIPiA7LRX06X5yTMonRhh4tO7W9kjjbam968J8e3Fws6p2ZUhxMwARMYEIC/G/bwQRMwAQmSyBJBbmUrJdLybpxrliUwC8Kb2sXKOoNbm2+VvDWNn+Dm7+xJU+JEcNQYhkTjeVIKZs8aR61OC4MMtLCcKp6P0rEB0Zet/ejlVyv7u9s2HTrpivtejWOpheODgFqcnvCdjPROWgfWU9ibbhqT9ETkrcl2kx/e2LdgdpUtKdIaVf9AUFDm6INkUd88LJgb9mqt+SyuFHt5kNqN2vUim9MTo7awcEETMAEDkyAnycHEzABE5gsAcwQfRi98QYNrH2SBtaerze0aV0qAEMFo756O4vfePJHZzkXAXmeX6EwnsJYylOMo4gYSP3GUhhNnJvAuQjVNfTOyzllPGUfTkuzXtX21JqdzZ3vN77beIM/OpjI+c/RJ0BtrFft5kK1m8er3aR1BzwVxj71uoqpPVWzXaX6He2IFPERKUKbZVLaEu2LPG0n2la0o0hpRxGVTeck7W9HtKF9ZRr3ofFS32zco3bj2eIg5WACJnAIBOJ/3YdwiHc1AROY4wQwWTqnnXba+RIhN8qYwr99f2MKo0e9ID1f9vztbf7mljxGUaRhMIWxRJrHMJLi14s0xEdmrPUbTmmAOwYdPR+4XmnqUI37KIqHiu3yYf+JzfduvllL6d6UOpjA0SaQ6taqVasQIJ9S4SdUJ2D9gQN7RE9I1fuRekP621Eu5PvbE+2KciIlHzHalFaNC7SnECAIGEWJj44GndfLR8ptjQcba+773H3ftHAfR80LJmACkyAQ/wufxK7exQRMwAR6BDBj2jKmLsKYUjxxXE9I7JYbTpUbVs+NBAMpDCbSiJScxzCSSPnFilTZtEwaIQRIGE16I5x84CvjqSc+9kqyaOC5fNcbeou7bvP9m//BLiQB0ekxJEAtb6ndvFQujBuqNpPX6IlPTd2nzYQQIS8xUhvShonaEmfL21LkKYs8KXHQFeTig3bEhwxHNePVSK1Rbpf82C7R/rnNN7n3Q/wcTMAEDpkAPz0OJmACJnA4BJIxJb/2n5YA+V8qYMlAEULJueHEG1wt997ghvDIU4wjjKIwmMJAijQMJ+3SCyE+KsMpTRuKS0qIj+j9oOdjjz618IgMqUfK123atOn9Fh89is4cewLRbl6rdnNt1WZCBhz87BwdY0CqtpTaE0Ikb0ORpw2RjzYV7SrOGGl+5mhLEvKpHdGGEB/7ao1iZ7GzfLh89aZbNv2j200OzXkTMIFDIcBPj4MJmIAJHC4BTJvWypUrn6k3up+UQYU7FuYK68cHDJ/qrW16i0ueqD3TW1yOIIboiJTj8qjFtEzKLxjGEkGpzt1dpgcE8UFEeIT40DcLJD4ate36sOEuiY8HJD66Z+WaHUxgqghQ01sS7yFCOC+1lVp/8EC9j7YUIoQStS5Nuxtt6WDtaZD44Ow0I74pMtaGWnzro9xRbuvs6Fy+5etbvmrxASgHEzCBwyVgAXK45HycCZhAEEjGVOWOdZ2EyBOq2bEwpvb/jcmFCMZTZTiltDKYkhHFfrkICWOJtCq1990PrUpCJFyvQoCQhgDZrTe4u9XrsbvcpjEgP7vpwU3Xa2u6dg53MIEpJpDqnkTIZTrv31fiffIihIuNthRiZEBbog2Na08h5vP2pKJ6bUm6Ixv3UaoHsV0f0WxXOzp3ldvKV27+vtyuPOMV9B1MwASOgED1v/EjKMGHmoAJmEBXKrT1rYPlEh/Xabafy5TCZWKDCkMoDKaqJyS91Q3REWkYTKRhNFFyHjCaiARS+jMYaL5L8dEUOxJG9c5o59Zaq/YLGx/aeKvWJgNQqYMJHC8CqQ6q3TxDvXf/U1X0Gaq/HfU+1KnH6bscUe+p18RBgX1oQ9GOKJVIG4qU/ERtiXYV5ZN2ez7aEu+N+t560d7Zvr6+uf7KjTv1jRzPeCVADiZgAkdKwALkSAn6eBMwgSCAiYPpUuit7puUvE0G1QkSIigRzBq2Dw5hKIXhFEYTBlMYTvxa9QuQWEep6ukoH9V73D2a+resFbVFiivlarVCw9Dn67OFt5T/u3Vv6+cffvjhndqbM9jtCm4Ox5tAqovLly9fogv5q9Fy9GeaS5udZr3ZaOxrFBp3MfYxQQQG7SEXC/nVR3vpb0ccE+2IffrbUZTR7Tlsq2XU6qMS7I92tkvI//am+za9s9qFUlIbj0OcmoAJmMDhEOBnysEETMAEjgYBDJMkCTSw+516q3u9tMc75FryAgrXG94QImECjZ2TI4n0WoQRFQZTpHFUbjxJQiTRMVoW9YX1Yuj0oWLowqGifq5eID9O3iNLWsXoyGhZ7ixrI+eNbNn2q9t26g3ucLEhzYc1dn7nTOD4EUAID1fCeEvjhEateXKzbK5uFs0lzWKoM1Q0tjeK+n1qAA+pvu+Q+lC2Nk8NAUGCGEmdjVVKHrdD2guR9hMx1qVWqvWkXTGjfhf9axc1fbenwfGdVuefNePVr6i38E7txZHsafEhCA4mYAJHToCfHwcTMAETONoEMHmSsaLekEslQn5dy6RF5ZqF0cXvD4bN/r9DmDqxljSPWkwGlkqonVgrGmc1inkXzysWrF1QNM/SO5UTtHlktBjZOVKMbh8tRh8ZLTu7OrWRO0c2N65vnL3l01t2VyVyFgcTON4EqN3UxUVyXby7sbqxqvm4ZtlY2ajVV9aLximNorG8UTTnSZDsbRa1u9Qj8u2yaH+vXXQelNpQla/NVxG0uJD4g+4ob0PkQ7p0B38063h9MYlDp/i0XMD+WBM0fLoqpteWq2UnJmACJnDEBNwDcsQIXYAJmMAAAoiP9NZUvSEYMp9GiMjA+WXlL5Nr1jyMnWTwdIUKBtiYidQ1kMbMJG3FrQpJUzZ13Cr1eDyrXgz/6HAx/4nzi+GTh4vGkNxV9JXocqu2yZhqjDY0erZd1IfrtXKo7DROapwyesHoecWni68U63VtV/ptrpg7HH8CtJO25MV5qtunyHWwo96NOtNU1xv1ot5RHFV9XiQhckajGHq6ekTmSRM8IqF982ix79P7ivadahg7VAqTOvCdkOgRUVaBtsVfejnId9uatLteCDTkqMhLgX2dWodJGd61aXNPeER7TC8SKMLBBEzABI4WAQuQo0XS5ZiACfQTCDMovZvNhMiTJDx+SkbPS3TA0yVGFtIzUomRXqpt3eNlBslIqrebck0/o1WUTy6LxhPlkrJab2wXyabaLs+RjoTGEhlqC+ppxh9sLZWrEbSNotOUb0mj02me2Gy2F7ZfqnK/UtyWxE7/9XrZBI4HAQx9qeH2S5vzmw25EraSAJGLFdNTEyWiiyQVWlreo8gYp+W1Yt5L5hXNy5vFyN0jxciNI0XnC2oy96uwIbUdZE03pJzamA5SW6Kt6V+n3dFoqfJr9Vr9I1r/cbXPb1f7cz1JFCntipdqgxMTMAETOFoE0g/f0SrM5ZiACZjAAQggRDBoQpgUGifyWAmPC7XuAqUXKT1dBtJKpUuVqktDO7c6xa59u5L4aJwpV5SVele8TL7xy4dSz8fwsuFi+ETFxeoFWahTDHVf9o625H61T/FRxV2jnc7OTn3kOyN3PvDXD5wvATKqsm1cCbTDcSfA/4eHJJi/2VzVfOLQGUOdxqpGvX5yPdXzxomq8yeozi9VXKy4qFk05qtpqO9C02Ul8d3SqPFU3zeNFp3/2ykan9H2vSpWMkLuVPRg7FD72qL0h2pXNym9RenNGzduvFf5CIgOrsU9HkHEqQmYwDEj4B6QY4bWBZuACfQRCMMGQye9Ya0MIIygj1T7Ns4444wl+/btWy0D6VS9su1s37H9qXtH9v7Z0C65nuyQ1bVIRpV6PkoNPC9HsqhlxobIeSWV3pB+6dRloMkNSz0hdU1p2tEA37OXXbbsR7bWtn5ZblgNu2H1PSEvTjUBRDnuVz+isRxnJ/er+ZX71ZB6PaoeEMSGxoekqB4LqQR0goISPhio2aoKjXOiTZTtp6mncGvrV4c+M/QN1fe65oDbOG/evE333HPPTh0RbTAd3i2hN3qk92IgNjo1ARMwgWNFwALkWJF1uSZgAhMRwNAJYyfeumJR0SPRlqH0iFLiHYqEL0tAXKWZrE7SjFdluU9SYp+Eh6I811OalufrcJlXmsWn52aCsZbcsBodXLO6bliL2ldQpt2wElv/Ob4EkpKQ+9UV6tWQM2LlfqXxH0l8VMKj3uy6YOFWSEwChNaiKIGR6r3SUnO71cqN5batja3XFtv1BZzt+90cgifaGiXQDpkQwsEETMAEppQA//N3MAETMIHjRQADiLeyGEGkGEdEfpswluRQVYxIdnyJ73voK+adNO3uXuWJlQjpjKS3v6lXJIkQDUZnIDr/6AlhMK/eINf5InRtQe1FxeUaqnt1Op+KdzCB40aAOs+Q8xcxjbTGMNWZXjcNQFcPSJ1ekKz3g9499QyOCRCOVixb0hLyvqIVdfZ0vpSmmb4gtR3aUC7yOSLaWrwE0CoHEzABE5haAhYgU8vbZzMBEzgwAd7KxptZjCXy+jpBsR53q85uCQ19bLDYqw3RC4L4CFcs3LB0VOoF0UDdJD6qwegy5Ooy3krcsE5+/MnnqcwyfdX5wNfjrSZwrAggDkp9ueY8CeOz64vkJSgpwoDzJDwYfM4sWOr9IEV89Ho/OBDXq7ZcrzRGqueOuFv1/1E5F9IKfqpqO91eDtoR0cEETMAEpgUBC5Bp8Rh8ESZgAhMQQIQUnfmaIrSlgbQ7y0Zyw8p6QMYJkRgH0t8DggiRD70MvbZmw6rrQ2+XpPOdm3pbJji1V5vAMSVAT5+UQvuS+jz1fCystZV2Z72qRAdCpNFUD56WcSVM4z/oAWFK6nCe0rgnCfBSPSCNztbOjpH7RphOtyiuQYo7mIAJmMD0JGABMj2fi6/KBEygS4C3tvViT7FZblhfkHsJLiad3P0quWFFLwgCBAcTRb4JkrthJQOuKROOKU2Ha0zHy++f3VAEweG4EKDuqTIWL5VbIDNb1ZLrFfWT3g9csOgBITLuQ65Yyf0qxIeOZvwH7lfqAem6X+3ufGHPP+3ZXFyV6rZ7PI7LY/VJTcAEJkPAAmQylLyPCZjA8STQ/Z2qFRtSb4fcTGIcSLFPRli4YjEgHTetUVlmuGGFANFAdN4cY8RJhGDGlfXF9Wec/MaTz9Zb4k5lrB3P+/O55x6BEL9nq1fuGcn9an6tET0gMQA9Bp+P6/2g007SIokPBIhEd3JB1BipYrdGf3SD/98+9+qU79gEZhQB/0jNqMflizWBOUmg64bV6XxSPRvb9T2PnhtWZ696RCQ8UlQvCIPRmYqXt8K9cSAayx5jQZIvvdywhk4cGm4sbry4ounfwTlZrY7rTac6J1n8YomOYQ1AH3O/ip6PKsV1MPWCxPS7iA+N/0gCpCs+SrWLRntbe3tje+OT6a7sfnVcH65PbgImcHAC/h/vwRl5DxMwgeNLQCZXcinBDevWcW5Y1UxY6Q0wPSHRAyIRQi9IvSPXFbmsxHS8uRtWY17jCu2FHz6uMA4mMJUEqHNMZ3UF7lea/arrfiXRwUxtacxHCA/cr5j9ikh1pTWobqeej8z9SuOjbn1ww4N2v5rKp+hzmYAJHDYBC5DDRucDTcAEppBA97eqVnySGbA0G1YZ40DSGBB6QaoekCRGmJaUfhOZeYwDUR/ImBtWTW5YzIa1uPGM5a9Zfo7dsKbwKfpUEKAud4aL4XOS+9VC1dB59QbjP2LsR+6Clfd+JAGiOj3O/aolWa6Z4TRFdbf3o1u+SZuACZjAtCZgATKtH48vzgRMoCKQeik6zc5H5F41oq8+N/j6cy5CYixIejNcDUbHDWu/gehMaYob1klDw8PLhi+vyvdvoavaVBFIda1VtC6X0EjuV3z7Iw06z9yumP0q9dipB4TxS/Tipd6PECDd3o9C46Aa7a3tkdrW2kfSDTCuycEETMAEpjkB/093mj8gX54JmEAigFFV15ee78YNSx8lrCm2kwDJ3LBSL0g1GB0h0huMrkNjHEjlU19jwK96QZ5f8bXR5oo2VQS6da1WPF+uV8n9qpqZrfv9j8oFK41X0pQJaQYsTb2bxIeqNOM/kgtWV4C01QpqEuS3PvSPD91dTajgujxVT9LnMQETOGwCFiCHjc4HmoAJTDEBfq86coP/MMIDN6wYhN7vhsVMWEmAVNPxpnEguQiRdYc7S31+fc0przllVXLD6o4HmeJb8unmGIEYc7RK0+quaSySa6C+AdL7+CCCIwahq6cuekB6Xz9HWsi1MOq36niZXBL3dj6sLWz1/9PnWIXy7ZrATCXgH6uZ+uR83SYw9wikN7tyw/pocsPardmwqt6PXs9HNRA9uWFV40B60/HKNusNRmcYSCO5YS3RjFhdN6yrNFDEwQSOLYFUx1QTcb9aoh4QZr+qJdGhjw0yAL0361X1/Q/qLP9y96vUA0IP30jRaG1rjdR21j6aLtvuV8f26bl0EzCBo0bAAuSooXRBJmACx5hA9w0vblid8hb1gOznhtWbjhc3rGocSO6GlUw5DDqMO94wz5ef/fzGuuq6k8A5xvfg4uc2gW4dqxXrcL9iBqx88Hmv9yO+fK46Su8HIX39nNmvxj4+qI+oa/Xu8paHPmT3q7ldrXz3JjDzCFiAzLxn5is2gblMgN8svoLwqeSGtUduWAxGj54PpuGtxoCkXpG+jxIyDiQbC5LcsBoLG889ZZ3dsOZypZqiex9zv6rXnqt6V2gq6HpjSG5YTL8r9yt6P6IXhGV6P+Lr5+nbH9R8CevKBatMroe7O5+iTSj6/+dT9CB9GhMwgSMn4B+sI2foEkzABKaOgPxOCgy1T8kNq9Pe3a7nA9ERHfEtkF4PiL4JghsWb5Bl8sUX0Smj64Z14tDSoRVDL0i3YDesqXuSc+9M4X71AvV6LFXvR1uzX6Xvf8T0u73vf2jWqzT+o3K/Sr0fkhjR+5E+tjla1vXxwU5rRwsBUhS3JyetuUfVd2wCJjAjCViAzMjH5os2gTlLgK971Fqt1s1653u33E/q+gZCJ76G3huMHr0gDEaXywpuWOmjhBp4nkQIhh1Gnt44NxY0iub85osTURtxc7ZiTcGNJ/GsKvhiTX6Q6l0MPkd4hFtgSqupd8d9fBABwsxXqs/qAeno6+f19s723dvu2HYzbaLYQC13MAETMIGZQcACZGY8J1+lCZjAGAHeJPPZwY8xC1Z7T7sTg9HHuWJVvSGpJ0SmWZoJSx8l5F9yxaqrN6RRl5dLrWgubF60/EXLl1RGXNfpfux8zpnAkRKgTiEQlqi+XYToTbNfVV8+x+1qPxFS9X6kE0u6pN4Pxn/IBYvePw1A5zs4H1PPx4im3/UECkf6hHy8CZjAlBKwAJlS3D6ZCZjAUSCQ3iSr9+LDMsY69IIgRNKYkOpr6Gn8BwIEY43B6Jh+iumr6IwDQXx0e0DkA1PrNJc2T130mEU/nq5tnX3pj8IzchHjCaT/16rm/bjcrU6tL1R/XMx+JRGSPjqoDw+miRHolWMsCCOUNAAd4TxOfFCnR8p665EWdf/D6TTuuRtP20smYALTnoAFyLR/RL5AEzCBPgLICdyw/k3y4271gNQ1ED25YeUuWD0RwnS8fA9EX0XPp+TNREhHPSBFbX4tzYa1bl1MitV3Vi+awBESUK/dOgkPvj/TSTNe8c0PIlPwMggdEYL7Fb0fimnsB3JbMURIp1W5X+1q3/3Atx74N221+9URPhcfbgImMPUELECmnrnPaAImcOQEkhuW3gX/Y/oY4Z5OcsPKZ8BKYiR6RKqxIGkwuuw1mXe9b4LorXMjuWHNa16GG9aGKzckgXPkl+gSTCAR6Llfqa5dVs1+1eiN/2Cq3WoGLHpAkgBBhERHnMZ+0HuXBEi3R69Dr15tX+0f7X7lGmYCJjBTCViAzNQn5+s2gblNgPfCGGv/jDHW2dPpumFpOt6eCJHoiC9GM2sQRlyMA+mbjremcjpDJwytWnSy3bDmdrU6Jnc/5n7VrK3S9z967ldp9iu+RyOXq574qL79kdyvJJaZYLfTHnMnxP2qs0Njn7a3/zldrd2vjslDc6EmYALHloAFyLHl69JNwASODQHeCxftdvtWCYvNlQApYzD6uFmx6AVpafc+EYKLS7hhyQUmuWE1FnU/Smg3rGPz0OZyqeF+pQ9fdhAeIT7CBQsBkmZlY3wS7leV+Ehf+BjrASmZ/aq1vbV55AcjtyaeG9Iecxmt790ETGAGErAAmYEPzZdsAiaQvnmAG9Z2GXbX8zFCxXbq/ag+SsgYkPZIu9sLog8U4sKSekH0TRDcWzTUd2w2rJrcsPjw23D90tUXrF5oNyzXsKNEINyvFqqX7dJq9qsGHx9M4z0Y/4HbVfSCZOM/0vlj7AfT79LT1+q0Gc8kt8Prt92ybXuxPs1+lXoDj9L1uhgTMAETmBICFiBTgtknMQETOFYEZNhtwNWKjxKm8SDVuA8+SBix54qFG1b1UcIY6KvjefOc3LCGlw6vnPfUeeena/VsWMfqkc2lctP/Y5tF83xNtbsS9yt9/bwW4z/C9SpS6mKa/Yr+D81+1Rv7EdPvyv2qvUui+tHOhgSx+3cu8fS9moAJzBICFiCz5EH6NkxgDhIIN6zPaTaszTLKECAlImTcAHQEST4eRCKE6XjTP9ywmJa364PfGVoy1Kgvqr8UlmvOXcPbawcTOBICqQ61i/ZLNftVI9yvktsVYz3odat6QBAfKYb7Ff0aVQ8ILoTqASklSJL7Vev+1ufSRdn96kiejY81ARM4jgQsQI4jfJ/aBEzgiAhgouGGtbMsy54bVv9UvLhhpZ6Q6ivSvFXGaz59E6Ryw6qMP3WKpKlQX1ScWwzfeM2N7OlgAkdCgDo0rHr1IomPQr0fddyv0kcHq+l3kysWLlga+5HGJGWzX8XUu7hfKd9Oblhyv3r4iw/vtPvVkTwWH2sCJnC8CViAHO8n4PObgAkcMQEJiPUa81Gmb4LsU4+Hej0QHXma8jLkeuNA1AtC70cajN79OKGK0WxYi4fOPuMZZ/yILqos1iWBc8TX5wLmJAHEcSn3qx+R4Dhb4z+Y/apO70f68nmM+6jEh7Z0PzxYfXwQkdwTIBLPqr/1zu5OWeyV9CDY/Sph8B8TMIGZScACZGY+N1+1CZhAl0C4YX1JBtsjSYDIDSt6QUKEtEerwegIED5KGONAECFjX0XHHQY3rPrQoqErKN5uWK5mR0Ag3K+u0JiPepp+l14PRXo9eu5XuF7hhkVauV+l8R8hQOj9wP2qO/vVI7vu2PWldE12vzqCR+NDTcAEjjcBC5Dj/QR8fhMwgSMhgBsWv2OPyA3rS9VsWN2votMDEr0hMTOWfOnTlLxyjMk/Shg9ITIC5aivXpF59RedffbZ8+yGdSSPZs4fi/vVPNyv9OXzojHcSL0fMf6j53ql+hYiOAkQBp9TqxEgldugxjB1yGu66S9t/+b2R4qrUp1nLwcTMAETmJEELEBm5GPzRZuACWQE+B3Tp6GL9fjIqxeEaUp74gPXqzQIvXLLwpCLXpB8HEjlg1/XB+DKoYVDZ5fPKM9L5doNK0Pt7CQJJPeroWLoPPVunN1c0CwZ/xHiI40Bodej2e39YOxR9H5QviZV6H58ULNfxQxu5aOq2/vauF+VxQ0xUGSSV+PdTMAETGCaEbAAmWYPxJdjAiZwyATSYPFOp3O9fOZ3SIA00mxYITiiJySbCQvfet4wpy+jy5bD+MMFJr2JbtTbmo63Xj+xfglXYjesQ34ePkBVCwia/eqS5H41v95OrlcagN77AGE19qMSvqn+8fFBeuaom4xVSmNAuu5XjdHtoztGHxq9PsG9MU2lkLL+YwImYAIzkYAFyEx8ar5mEzCBnACuKPyWbZYb1hfae2X27W0nNyxcsOJjhKknBFHS9akfG4yuQ9NUvAxIxw+/Xq/hHqPIdLz1G4sbMQcdTOBQCFBnVJkKpt9l9qsaAoTB58nFTz0fpOF6Rf1L4gPdQm3W0ZX4oL4m9ysJ6y88eOODm+1+dSiPwfuagAlMVwIWINP1yfi6TMAEDoVA97esVmxIA8/5Mno2/qPnhhW9INlX0aMXJIkQpkJV4DNwcsN6xuNf9vizi2vS+2j/Vh7K05jb+1JXECBny/3qGcn9arjRSO5XlctVGnSOC1bVC9JzwarGf6TxHtV4peQ+yLdt9pZp3qs1xRrXxbldv3z3JjArCPiHbFY8Rt+ECcx5AuGG9Um9Od7e2tNKbljjPkhYCZKeT33lhjXuo4TxLQa5YWk2rOHh+cMvhuyaq2z0zfkaNnkA6f+r6kl7sUTHsL7/0XW/6vaqpXEfee9HT3xUvR+M/0iRAeiV+1Vre2v73of3fpJL8MQIk38Q3tMETGD6ErAAmb7PxldmAiYweQK5G9atDELHDWuc+xW9HzEgXfk0GB3ZonfV+Mqkf9VYkOSGhb/+vBrT8dbshjX5B+E9U+9HTa5UV4T7FR8fjLEfiI/oAQkhktyvYvYrxn4gPrpRFVnegnvat9r9yjXLBExgNhGwAJlNT9P3YgJzm0D396xWfBKh0X60XeKGRS9IfA8kZsNKU/GOCpaMOwb9ptmw+Bhh94OE+ObjIFOqB+QZZ/zUGefYDWtuV6xDuHvqYGe4GD4nuV/Nb5YahN5gtquYdje5XTH+AyFSffujNwNWjP2Q+KCOqr6WjGnS9NKp98PuV4fwJLyrCZjAtCZgATKtH48vzgRM4BAI4HdfdJqdj+jt8YgESCMNQt8nA46ej4j0fjAzlgy8JED05plxIDIJ02xYSYTIONSH4pgNa3jeCfMup1y7YUHB4SAE0v9TW0Xr8nC/ovcjjf9gvIfcsFLvR3eygyR4c/FB3wmDzztt1VE+mjlaNEZ3jo60drU+wnnlfpXq+EGuwZtNwARMYNoTsACZ9o/IF2gCJjBJAhhn9WKkuFuzYd0qAVLT2+M2YiO5YoX7Fb0j8WV0vWneT4RUb6XVC1JrDjcL9YI8n/OvLdba+Jvkg5jDu3XrSK14frhfIT56wgMRUvV+xAxY+u6MRn90Z79KYz8kQCr3q7bESK29u33rxs9svLua/cp1cA5XLt+6CcwmAhYgs+lp+l5MwAT4TevInvtwuGEhPnouWFUvSFrOxoEkNyxpl9T7MeaGpc/DqWdkXmPNGf/+jFXXXHMNxp8sRQcTGEiAukEdWaXeszXN+c309fPUA8LsVzEDVtX7kdyvyOsfPXBp+l3Gf1QCBPerJJ73tT9MuWtu8EQIA6l7pQmYwIwkYAEyIx+bL9oETGACAukNsdywPipDrueGldyvskHo9IDg4oIbFgZf9IJgDPbGgWg2Xs1Q1J63ZN6S+SfODzcsvnDtYAKDCKS6oTp0ucTGkmr2q1pyucp7PvrGf0TvB9IlXK/oASlaRWNk58hIa1/ro5zsxhvtfjUIuteZgAnMTAIWIDPzufmqTcAEBhNAgHTdsDrlLXLBSm5Y6Zsg1UcJezNj0QMSHyXUUbyFzr8Jwhtq3GTUA1I0m811nG6t3bAGU/daCCTxqz6yddSZxrCixn/0ekCY1oAeD8RINeEB4iP9owdEvR9EBLGEcXK/6uzp3LLxn+1+5eplAiYw+whYgMy+Z+o7MoG5TiC5YcmT/lOIDYmQMsaAhOBI7ln940CYDYseEP6NzYiVnGSG5g8994w1dsOa6xXrAPcvBVG5X9Vrz60ESH3CsR8hRjTtMwIkxn7QA5Jmvxqp3K/2tD9FuXa/OgB5bzIBE5iRBCxAZuRj80WbgAkcgID8VwoG+35KriwdfUOhnnpAwgWrfxxIuGBV0/EyK1Hq/eiOBakpjxvW0kWnLHoB5Wo2LLthAcIhJ9B1v6rXX6BB50slWNvq+ail2a+Ygjem3K161VIdo57pH+OPkC5JhNAj13W/qmv2q86+XfsQIMWNK25MdTo/ofMmYAImMJMJWIDM5KfnazcBExhEAGeWWqvVullG3d2tva16e1+7k0RINgNWzISVvg3CtKf6AnVywSq7hmFyv5ILFmJkaHio0KxGL+ZkK25fYWNwEPW5va5bJ8rixcntChcspt+txn5EmgsRej7SwHOORICMDT7Xxz/Kemt36+6NWzberK21YkNy0JrbhH33JmACs4qABcisepy+GRMwgYoAb6Q1AW/5seRupa+ip1QuWT33K3pC6BWpxoJo0G/3o4SZG1bqCZH1qMHoTMd70dnPPHvp+g3r8fXntxO3G4e5TYA6QF2gTixVPblIg88RH/XexwejByTECL0g6l3rff9DAqQnPhDC6rXDVVCi+WPF7cXImjXucZvbVcx3bwKzk0Bzdt6W78oETGCOE0hvpPXG+cPyqX+LvglST9Px4uKCLOFDhCE+5PKS3F74NZQpyXcZwkWm6gWpaSRJe3jh8KlDjx16Tu2rtX/Wnqn8q666Kr3EueGGG1K6YsWKcsOGDRijafscfwaz6fZr69atqz/wwANJdK5du5ZnXFRTM8ezfo6Ex6mV+1WjN/6jcruK736ktHK/opZ0Ot2Z2EKESHwk9yvVWabftfsVEBxMwARmHQG/wZt1j9Q3ZAImUBHg921IYuKbw0uGn7ho9aLOguUL6ppSt5h/guLS+cWCJQuKhUsXFvMXzaeHo2jOaxb0dmAUjo6OFqP7RouRfSPFvr372qOPjjZ2/mDnB0/cdOIvNk9qNnZv3r3ny1/+8qODaMtYbWCsVlOnJmN10H5eN60J1NX7UK9EJW59+4VnPetZC9rt9kKF9hc+94X3FPOKly9ZvaS9cNnCxoITFhTDi4e7cZFSxXkL5xUSKKmeIVDKmno6Ou1idKSqZ3v2dVTP6rs27brzO3d853z1gIzqpCFw9ju/V5iACZjATCVgATJTn5yv2wRM4GAE6NNoqUfjj/RF899YuGpha9HJi5rzT+oKEAzE+Uu6ImT+Yq1bOL871kMuM2VdhmHZLkb2jhQj20eK0QdHi5peftd21oqhztA2RIq+tr5LLlp8dX1U5/iqzvVdGaO3Kb3t4x//+J64OMQIefWMDDRiYz+n04PAoOd1wQUXLBweHn4KcWho6Cw982dqauYhPfezFRcztmPPnj0n7dqzq9je2l6MDI0UtUVy21syXETdQoAML1CU0GWKXn2sUMpC9aytejaieqa6tm/PvpYESHPnxp3/9a6P3fWfJICaErFyDnQwARMwgdlFwAJkdj1P340JmMAYAQz/tozDtUo/q96PzqKVi+oLTpLwoBeEHpClC4oFixXVE8Ky3KyS+1XnUfWAPDBatO6X7bdDnlmj8tvXgPRaU+5Z8uUnMD6ESJARmnpNNPCd9IeKtyj+n0cfffTTn/zkJ+9jHxmttSuvvLJuIQKNaRmoLz33ufPPP/+0xYsXX6r680KJjQsUT583bx7fhEnPG9FB4NlHXWAdkxmMtEeKPZ09xa76rmJkwUhRX6SJDBYPFfPmzyuG50mAqPcDd79OqXrWUk+bekD2Pbqv2Ld7X2ff9n317Ru3X3LPZ+65oVinOaE9AD1x9h8TMIHZRcACZHY9T9+NCZjAGAF+33BfOUEG4rfnLZ63Sm5YpdxjauGChRChJ2TBogXynplXNHY0is5DskF3yT5sdQVGvK3GyEwfjatpriwZnYqUnfISF+Q5X13rCUmQ7N27d4fWfVTuXB/4yEc+8mnlkxC5+uqra9X4AVY5HF8CqMj0LLmMpz/96ZdKaLxKQuPF8+fPX6oejxAZpepARyKDtIbY4EGT53mnmQpUA8jHOA/Ka5WtYqSp3o0F+4rWCa2itlh1Z7j7v17GHuHqJxe/YuTRkVKxtnPzzs1bvr7lSdu+t227DmdHrs3BBEzABGYVAVwUHEzABExgNhLAcOM19XZJhk9p2t1X66OE7da+VpM3zkOjMix3y1jcI0NynwSDej14/52mUeUr1ppKNQ1GR3hgYFYp1qZCMj4HQZMWSW/R2UdjA5bquFfJxeZVL3vZyz6t9I+1HiFS4urjAeuDCE7ZujSwPHqkEB4SHb8uN6tLJTyiV6PNc5TYQHDUFQmxrVcnom5EGnfAsQhb9bcVhZzyVP+K1iMSJEvkcrVIoqQpUSKBUn2AUHMdFE1NF/0pxEdVP+y2FzCdmoAJzCoC3dcws+qWfDMmYAIm0COQ/GRkNF4mX/uPLzxlYbn81OX1ZScuK06Yd0KxoLagGKoNJZcYejpwjUlvryU2MCYjYkhGzI1M8hHoBGEfAnkGsislKNvhTXkdX/99+/b9s956/4Z6RL7Fvsyk5d4QSExp4MEhFIsnPelJ56nH479KdLwAFys9w456PUrVGQRHDZerEB2R8tzzyHNnOepI1APWMc4jgmqRVIjqh9y02ppbbWR4pNg1rHEjte3Fzr07O3u276nt2bTnpy44+YLrN8j3yu5XQc6pCZjAbCNgATLbnqjvxwRMoEcgG8R70urVq7+/YuWKpScsPaGYPzy/a1hKcPC9hmaja2QeyMBkWxiYnADjMg9sk9hIq/KUPGJEURqonYSIXLOkRUb+4Hvf+97bb7nlllG/7c5JHvM8opSehaFzzjnnrQsWLPhNxWE9347ERqnYCNHBM4/I8yZP2h959qyL+hH5uBPWE0KMpPqhqoILFmJkVHPv7i52Fw/semDHLbff8vjt39y+Lau7UYxTEzABE5g1BCxAZs2j9I2YgAnkBMKAe/KTn/yMRYsWvUdG5oV6s53eatPTgW8/BiXiA4NxIkMT45FtYWRyjjAww7DMz0s+BAjCg3wmQpj1SF9F7OhLdfVi165d/7pz586f/8QnPnGXRUg/xaO/HIzPPPPMJ6i346/kIvccxIZiW/UhCY+DiY/JipC8vvD8WY56keoEciTqBh8gbGtBvTIjrZGbNaPWL37wgx+8Vb1jTfWOeRaso18VXKIJmMBxJmABcpwfgE9vAiZw9Alk4uNKGZnvVTxJBn85NCyHq6a+w1C51UR6IAHCNiIGZAgRrphlYuRJcwOTbdIaaTvryZMiSpQvFRlf0JSxuXX37t2v+OhHP3p9XHc6yH+ONgHGPLYe97jHXSZ3q79TnVim59+qhEfqEYv6EGmIDdLIR12JepGnPPOoK5HnmZOPkNcR8kTqBjNitVttXL9q6iHbpjrxBomQ9RYhQc6pCZjAbCIw9qs4m+7K92ICJjBnCYQRL9/+12oa1Wvx65dB2dbg4gnfcIeBGUZmf4oBGesAmxuZg0CHYUkavSCRYmyG0alpe1NvCNP3yuB83Yc//OH3x/UPKtfrDo9AMD399NNfK+FxLYPMo070esL6RGk8b8QIzzuWIw3hkS+H6Ij6QTooRP1gW9QH6kclTlOdIC9x+roPfOAD77cIGUTR60zABGYyAQuQmfz0fO0mYALjCIShifjA0JT4wLWmJvFRx5AkYjBGSr4/hrGZG5bkJzIu87fbXAzGJQEDMlKMTEJ6010ZmpWxyTSsHR1TSoQ05JKVREi4CqWD/OdICaQxH495zGNeK1c8xEeqE6oXdcTHwepF1IdIo77EMmnEqDNRV7hw8oPqCPWEOpCnCFGWo05oewNhahFypFXAx5uACUw3AhYg0+2J+HpMwAQOl0BysXnCE56QDM1KfNQlPnruNRicGIshQEjDkMzzuZHJegzIMC7ZP4zKSPMLxoCMiIGZG5mxHEKElCjDM02VlYuQEFN52c4fMoFUJzQBQfR80BOG8Kjl4iPqA8848lEvoi5Emq+PfKSTqSPUDQJp1A3qAMsIkKgjEiGpTmh9EqYWIYf87H2ACZjANCZgATKNH44vzQRMYNIE0ltu+fe/ZsmSJX9dveVO4iM3NMOIDCOT5Tyfb8eojG3kw8hEdJAP8UGaBwzJ3LjEoAzBwfrIk+YREaLlDiLkkUceufRjH/vYv7gnJCd7yPlUJ1asWPHv1Bv2aU1CQM/HfnWCZxzPOZ7/RGleJ2KfWJfXD7b115P86vvrR15HIo8YoU5omdDYsWPH/3Pdddf9jetETtJ5EzCBmUpgsIPqTL0bX7cJmMBcJMDvWPuxj33shRIefy4jk69V0+uRvlaNYTgo5gZkGI+sC2M0TxExIWT0Bj3lSSMf2/tTysjXxTkHpVyvroPr7mjsygd/8id/8kw+kif/f/9OH3qtTnVC4uNMPaMP6hl0KrYwHlcfciHZn2ffEBLkeW758Xl9iTzPu/+553Ug8hPVHY6N+qF9U53QNXQkoP785S9/+YWuE4deGXyECZjA9CNA97SDCZiACcxUAqn7Yfny5Utk0K2XAFko4w0BIjtxf0MzNyL7Dcsw+iLFECRyTBiFcXwcS0qMEL0f8YZbb66TSw37kCeQp+djglCX601bxuZypX93wQUX/Jj2Y2dO0vXdmeBAr+4RiAcypGf5d6oXy5W2FRuwz+sFy/2xfzvL/cepqJ5IiHxeVwbVk97VKUNdiLoSvWAcQ69HHtim/erav0Pd1vL6F73oRU/TPrsVXSdyWM6bgAnMKAJ+szajHpcv1gRMoI8Av2EdDS7+Qxntj5cR15JByLc+9jM0w5DMjcnIx/65MRmiI95os5y/veYN9kQx9suPjePjHP0p10fU+oaMztbSpUsv1qxNb9V3IDpyu/Fvdd+DP8BiqhMrV658q/hfXNUJsPbqRC468nrBekL/9tinej7xnHoipL+uxHPP60HUFdbl+ViO+hHHspyVW6dOqGfs8aoXf+g6cYCn700mYAIzgkC8KZoRF+uLNAETMIGMQPLx1wDjH5Nh9jn5+aepdjHu+o243JjDEI3lQ0k5LiKGaMTselI23m6TxtttUt5uD1rO10eeAcjat6PvQXS2b9/+FD5UiCsWhmf/+bw8jkASH5rx6gky2G9TXahrMoLeuI/+ehF1IZ7rgZYnqiscE+VGOewbYiVEbn6VurbUA0J9iHoSz540j6oLqd6Qan1bkUHpP67xIJ/3eJCcqvMmYAIziYBdsGbS0/K1moAJBIFwP5knI/MdiA4ZfMm/P397PSgfwmFQmhuQgwzO2J4bl5wjAoZlLkDiHPl15PuyP/vEcZHXeWqUI7eboZGRkbfrmCtvv/32sRNFIU77CSRGMuzfLlGAC1ZbTHvjPvLnEHkKiDxpvszzYB1pf4y6QD3J8/ky66OMKJvyo46wHREyaB/2C6FCWu0bs3e94/LLL3/2ueeeO6rduGi75wHMwQRMYMYQcLf+jHlUvlATMIGMQHrTvWrVqpdLfPyIjDsMTcJ+hiKGXxh/pLFPrI91YURGGoYkb7eJLEf+QCliKN8/jiONGOeIlGsiH9dWpbhiMR5k3Ytf/OK1DD7mjXfGwNnxBFKPmFyv1orfOm2iTjAYaKDA4NCoA3k+1kVaPYtxQiSeVTy/eK7xrKN+9C/H+qgjsdyfclyUnadajytWWz1+P6JxTy+3KxZPzsEETGAmEnAPyEx8ar5mE5jbBHjjiyvSsAy335SBVspIHNf7keMZZEiyLgxLDLzIk+YGH4YhyxiEEfv3p6wI8cY63mrjShPnYp/YTspb8Py8rGOZ9RzDecjrvKXyV+nwz+mNt990B+z9U9jUxe4qRZglnrCMyCEw7g9sjxD5OCbS/FmxLsonjRh1hH1Zx3IcF+VynqgHUU/YJ7ZH3Yh9Ypm6QJk6ht6xkrovQfr369evH9Wx7gWJB+jUBExgRhDY/5d4Rly2L9IETGAOE+B3q9Sb7lfICDtHxlua9Qob7FBjGH5hJJKGMRkpRmQYk/k6xMmgyP75eo6JcqOsKCc/b1x7rGNZ+QbGplyx1mpa3gv1xrt0L8jAmk/vB3XiQjFbSx52wTSOYJmQr491sX5QGuvYN55PpPEsI2U9zz+edV4X8nxeT/J87JOXF+fi/Fqfev805ukc7fsKraNO+P/lPCQHEzCBGUPAPSAz5lH5Qk3ABCoC6U23DLE3yjArMcrCMIw8+0W+P+3fFsYdKUZfLIcBSJobk7Ge/fLz5m+sebNNzENs5012vM2OfJyTlHX5NWtdRy47DKT+jyrvq3mZzo8jQF34j7BT6CilN2Qcy2rbuINiId8Wx7Et8nkaz35QfYm6EmnsQ1kcR4heDepIft6oD3n94HiW2TfyVT0pdY43qrj/5Z6xhNV/TMAEZhABC5AZ9LB8qSZgAkX4+V8sY4yxH7033bAJYy5S1oXhT57AtkExjErSMBpJ83xsi/WUw7oIYVjm52dbrCfFkIxzxfGDridbl3pBdM6XPO95z/ttFffQmjVrmmvXrvWMWIJxww031PXBwfLmm29e+eijj75ExjqGeer9gD0BlrDvD6yfKMQ20vx5xXKsIyVGncjz+TqOizK5lljO10U9CQESoiNPq+MaWtdRD8iP6OOEF6ln7Cv0jDFOaKL78XoTMAETmE4ELECm09PwtZiACRyMQFiML5VxJ1uv3pJBtt+bbgoJAw+DMIy8WB9p/z7sG/tHnjQMyf6U49keITcs83VRRm5YRvlRRlxLf6r9ajI223LDOlnfO3mWjMyPUvaNN94Yp5jraRJiGpT9LBiJcVsMEarjnvsgSINESezXvy1/LlE2z5D1kUY+nm2k1Js4nmMpm20Ii1imbrCefVkfdY1xRFF+lFelHU0x3NRUzS9VGV9RL0i0jVSm/5iACZjAdCZgATKdn46vzQRMoJ9AW4bW8NatW19YGXRJfOQ7YcSxLQ+58TfR+nyfMPhYF0Zfno91sV+UmZ87jMx839g/1sU5I6WcCfLMflTu27fvfzznOc95s/bhBvd/pR8XMrfSmoz1UnXirAcffJAesZ4gBQOoeBaHE/JnEcfHuu4jGHte8UxJ2ZYvx7o4hrIQHBG4PpaJ+XFRDmkeOU7LfCGd9IXq/eCDlUzJ62ACJmACM4KABciMeEy+SBMwARGgq6HzyCOPPE5G2uOVx+d/rPvhEBCFMReH5MsYgITcaGR7vtyfj3LCkIzy2C9ivi7ykcbxLIexnG9TGTXW6w3/6XrrfTrbHLoE4MJ4C5g88MADk8ISjPOdB63Lt+f54J8/I7bHctSP/NnHOvbjXCyT5sfE/rFvvhzrYn+lCBDczR6vjxQ+TsXeqZjaiFIHEzABE5jWBCxApvXj8cWZgAlkBJJxJfeUZ8v3fVgGGP4rk3K1yco4YBbjjhBp7BzLpBPl2TcMSozF6u10z8DsP5b9B5WVHxvlxb4aiM7g6sN7nU8hszDAGQGiOoFI20+QBkNYk48QedI8nz+TfFscNyiNY/Jt+TryEfv3Yf0AcdHbv78cjs/KYoKCYY19ebZW33nVVVfV+TZIfg7nTcAETGA6ErAAmY5PxddkAiYwIQEZXxdWG9MMWOTDgIyDwnAkxUAl5vvE9th/ojQ3/ibaJ1/P/nGePJ/vcyR52ZqyVfezsY+kyBl/LLwlPlLkZvJnFs8if979+QAQ+0ZdIWUcRn/Ij+/fli9HebGO5fzaWM/yoHWxLY7N98v3Vz7aAG3ir2N/pyZgAiYw3QlYgEz3J+TrMwETCALxZvdcVsj42s8Pqd/oq/ZLRl5si5RtkSedKOb75fnYn8vIy4l9WJdvi/Wk/SGOZ30umPpvUQIkGcX5/v1lzcVlekD0xfjUkwA/Qs4o8qR5/kD7xbY4JtKJ1rM9Fy+xf6SD6kJso8zDDMk1T2WnNqEyoo0cZnE+zARMwASmhoAFyNRw9llMwASOjABiozznnHOWbN++/ayqqGR85cXmBl3kI2U/8pHm+Xxd7B9pCIJI+12kWB9CgWPCCM3z+bp8PfkI5GM5Ly/Wc16N/+idK45z2u1JOOmkkwoE2sjIyDgkwTRSNkY+2PansU//epYHPct8XZ7Pj2c9z5WYr+/Ps1+cP2X0J/bpX69lvopOmWe96EUvWiL3q12s4xD2dTABEzCB6UrAAmS6PhlflwmYQD+BUr7u/GadwAYZZakHJIwz0gixrtqvZ8DF+kjDWByUsi4i+0eeNAxJyo88+xDYzjSqxDhPHJ8vR579I58K0J9YJo1APgZbxzqnXQI8AzhGyPkdSp7jJ7M/54oY+8dypDz/qBuUi4CM5YmOifX9KccTWJ8HLUcPyAk6H22DHfbrGcyPcd4ETMAEpgMBC5Dp8BR8DSZgApMioNl+cMqX3dU1xCLlYPJ5HLSO7RiI+X6xPCjNjUkMyDAqKZtAOblRybo4hpT9ifk68nH+PB/rJkop22FyBCZiGOspZVA+1vFceN75cjzDQet4xowXiX0ijfoSx+R1hXXsl9eRQXUlyiKNciLN7oOxIPsPWJkcLu9lAiZgAlNOwAJkypH7hCZgAodBgJHXbRle58owPEFpRzGNxlaaDDnKJH8oMYy6PA2DEIOSPIZoxDAg41yxnuUIYTBGOf1pvj2uNT9/5NlGyFO2cQ0O4wnABDaE4JXng/PhpPE8SIk8zxAb8WypB+SjPvQ/I9bHgHa2xTVGeVFOfo7Ikx7gunHB6qjsE3QOxoF8Tt8EqfuL6Dx9BxMwgelMwAJkOj8dX5sJmMA4AjLEeMubfNwxynLjjDyGXm6sDVoXx8WxYVCGARnCI75AnRuVYTxSRhihbM8D5VIW+1BGRNbFtsiznMe4dtZFnjSWGd/Qb9zm556reZjE2I94RsEs5zgoz3799Sbfj+1RVuR5fvH8yXN81BfOH88oymFf8rGe58RyXh7lRF1hfX8dyfePcmOdUtqEe0DmagPwfZvADCRgATIDH5ov2QTmKgEZWr1ej9wIg0e+TB4jLgzLMPQije2xPMig5NgwLnPDMYxJjmU9++WB9ZQfBmV+DgxM1hNZH2kcE2ncS75Mfu/eveOM2Py8cznPc0CAwI0Q/EjhFvUgX5/nYx9SyiKNyLHkeVaRz5dZF1Eugr3HQPkR2J998noU54/6MFEa52J7HMM6YiyTanl8RYyTOzUBEzCBaUjAAmQaPhRfkgmYwH4EkjWnQdh3ytjaqa2LZXApW6ZZgAYYYz2jk20YfxhpRJYj5kZl5MNQJEUwEDgu0ryc2DdtrP7EtVAeedJ4sx150jzGfnF9/SlFs0+85c/P53x3IoB4VvCIZzRRCssQj7EP66KekJ8o8tzYL2KUw3nzfFxHlNtfV+K8lMc+uThFyPTXD/aJfclzvFIqZk3H7pQwvpNzyv1qTPmwwsEETMAEpiEBC5Bp+FB8SSZgAvsRSEbV/Pnzd+zZs6clQ4/ZfwgHNDYx1PqNSpZZn8cwKjEgI+Zvs+Nq4nzsT08I+1JeHiiX/cJYJA3jMlLWxXb2z/P5dUVZsU6zgKWy8/M5PyZA4E6I59SfwjHqA9sGLcczDeZ5ynPieNKoJ6Q8V0LUmThvHMsxUW7aUX/yfSiPSDn9dSS2RVmkce1VGTXt09q9e/eOqmwLkIDs1ARMYNoSsACZto/GF2YCJtBPQIaW7LhasvbCCMsNs8hj8JGPlDwGYGzHqItt5MM4jJRtBJYJubGI8CBGGbFP2rHaN85Dyn7EMCzDyIxltsX+eT7WRco2ia90LXEup10CPAP48JwiBLc8jWdOmj9Tjo8Y+7DMsZTLOlLWkc/3Jd8f+utmfly+b35tlB91ghQxE8tsI8b+5DlHrFPa0v77X0h+MudNwARMYBoRsACZRg/Dl2ICJjAhASzL+j333LN99erVd8mgO0XGGKGRG5Ja7hlp/YYkRmBsj20sY8TlBiLbIrCeMOgc+TH9++UGKOVHDIOyP43riOsj7T8ny4wBoSyH/QnABUb5s8g5xjOJZ89+8QyDdewT6zmePGWTshx5lol5iHLYj2+2sC9ilXNGvYpj4lyk7Mcxeb3I82yLc8e+LOtYpv5qKH/XjTfeuP2qq66q62OEYx9EyS/OeRMwAROYRgQsQKbRw/ClmIAJHJAA1h7G1d0y4p4to6vECIsYxl8YdqzH2MsNTow3QuxLnn3YF4MvAtsJsR/bo9zcqORYYoQoK/aNa+OYMBw5D5FtsT625fvH9kg5B2/F5WrTM2bjvHM9hTtsYBXPo/8ZsC2vC+RjH47pjzyTWBfPIMqOdBD3KJNj+nvK4pz5cewX5Ud9iDrSvxz7ZqlOl1zJ7laZ5e233z6mnvOTOG8CJmAC04yABcg0eyC+HBMwgQkJhKV/i/Z4NXtlhti4fBiaYfBhyJEfHh4uFixYkPJh3FFGhEGGZb9BSTkYluxLnjQ/LsrLr41zsRznJB0kQmJ7fs4oh20SH51du3aVnNdhPAEx02OojQMDx4jBEXbBN57dRCnM2UZKIJ+naaH603+eeN5RV/JzcA2USR1gYgH2jXLjOtmOqCLtj1E266t7oU0UDzzwQLSRVJ7/mIAJmMB0JWABMl2fjK/LBEygn0Cy0mRwfVEGGINBcD1JxhlGXhhukWLkkafHYNGiRcXSpUtTXLx4caHB7MlFBuPtkUceKXbs2JH2xUhkXYTKuBtXdhiPpLlRmR/HeeNYUgxF1oUheaD8gbbpOutcL+49+fnieudqCguYLFy4cByCYBnPKp5J/tzYlq9nmeNiH54ZgeXIjzuJFuL4SOO81EtERpyfZfbZuXNnqpeUyTqunZRltvfXF8qYoO409u3b15aI+SLXtHbt2o5csfovz8smYAImMO0IWIBMu0fiCzIBE5iAQFIGMtDu0PYtMtZOVYobVpqKFwMtjMcwANVbUJx00knFKaecksQHQgTxMW/evGJoaCj1iCxfvjyNrfjhD3+YjEK2RaCc3KhkmXOEQYnBGDGOYf+I+fFhVLKOOIFBOU7sxL5KS+2vU9W+p5mw7pHByj2PKaU4+dxM04xoEh9n6LmcWXHp9QQEw6gb8bxYjmcLNtazL88llmNf0lifNlZ/4jmTEqI8yqF8jiHleOobQle9FGk/6hkxhAf7kCeQZ7xPuJVRTh6r60x1X/tsUZm0iULjP1wnAOFgAiYw7QlYgEz7R+QLNAETqAhgXDW2bNmyWwPRPy3D7tUyxNqKzTD08pSejxUrVhSnnnpqT3zgfhXiA4MwjEN6Rc4999zi+9//fvHggw8mkYIxiUFIyn6V0ZfyLMexGIt5CGOU/ckTyeeR6xy0HOtJ+2Kpt9zlE57whJdt3LjxZrbFefJzz+X8smXLLhSXr1QMeg8FzvDiOeWRdYR4fvly7FeVddAknjP1Jc5HPuoIrn/33XdfsWnTplT/6KmJ/dgn9uVEscw6rkk9HOlZk++LbZ23od6RT3/jG9/YvW7duoa+AdK9qYNesXcwARMwgeNLwALk+PL32U3ABA6DgIy3DTISX6ND6xh/GGYYbkQMO1xWTj755J74WLJkSRr7gfjAGAyXF4y8PJx55pnp+IceeiiJEMoish9pnAMDlfxEhmoYpJFGOXGt/WlsJ+0zMrkXre7U9Ub8brlffUPXm2b+Uuq33d2Hl8SGnvE3tm7d+j09k7O1Gne93niQnC/5eH6xnmWeSWzrFlsVXgmXfF2ej+OiLOoK5bFMoM7Ru/aDH/ygQOiynnEf1B/2JY36S1kEjkcgn3jiiWkbPSesi7pB/Vasq6yaytqQDvIfEzABE5hBBCxAZtDD8qWagAkUHd703n///Z/TlLz3iMcZMto6MszqGHMYaBhxq1atKh7zmMcUJ5xwQoH44I1ziA8MuzD8MOqIHBPHnn766QWuW7x5Zl+2YTSG+MiFB3kCZeSBYwZFyjlY5DpiHwxNLaf7k6vNdZrlaGTNmjVN+fmPTdmVn3ju5puwUc/YdXomV4nffgIErjwv0jDmwcVyHuJZkkY+304+ni2nQcx2Tzc2KUKch/qDmL3tttuSAMalim3EyHMO6iORckhZxz7kcR+kHjD2h/Vcr5bT/UmU3iPR/Llf+qVfalx55ZVdxdN/sV42ARMwgWlIYPz/NafhBfqSTMAETEAEahIe9fXr13dkhKXXxHKn+lWN7/hvMgYxxpsYbBiDp512WvHYxz42vT0O8cG4D3o+QnyEkYdBhzGJ4VcZdimvN+nF3XffnfanXPaLY1iOdawnRJoW9IcyCbmhGufpTzl3HrkOljE6FUsMVY37GNm+fftTZMh+V8WiemxsAngsJCYy1s9SHbhNq4erTb3/x/GMqB8ReZ6DIs821kf+QGls4xjynIc01t98881pbBGTIFAP+yPCOMRx1FGuMS+POkEPCvWS8lUnWlrXlBB/y6233vpn3KvqFfda13bqRrcCssHBBEzABKYhAfeATMOH4ksyARMYIxC+7fi3Y3y9/vWvP1cDx39CRtpl1113XaneigbGHkY74z3owcB1BYOPno9cfIRhRznEEANxNpYx+BkrwrH0hCBaonyOCeMyyuBY8v2BsiKyjeuL5RAcLEc+UozNiLoWre405GbzIcRHsOg/l5eTIGto/M531QvyIT2PV4stXRs9HztYw5XnR0qIZ5insT7t0PeHMgh5Sj5i1A3KR1Tce++9KVIfGVTOedgnBE6IjEEp54n9Oebxj3986pVDhKj8hupy+eu//uuXaWKFUr11n9K+t+uQdGO6noaWuzdJQQ4mYAImMM0IWIBMswfiyzEBE+gS4KvOV199NR93wJAafsMb3vBz6vH4jzLmLtKb7iZuVhh4EiZJcNDzccYZZ6RZr+j5yGe8inEfYSBi2GE0YvSTz5cxHul94Phwe+E49iGNY8gTWB8pZUboz7Mc54yUsvI852YdqcRHmvlKvR8tGZi/r3JrGig/doI4kdMgABs9jtrvi+krlOcBpXWxA2x5tjwzIpwJ5A8WeE4RIh/PDgFB2aRRNvVDbmH0XiUxwrGsGxRDgMQ2rot8Xh6C5pxzzik04JzJEmovfOELi2c+85mX7dmz5zKNEWnJLe8mCdW/uPbaa/9W1zCia6tdffXVNX8ZPZ6aUxMwgelE4OC/utPpan0tJmACc4JA/qb/5S9/+X/QW+1fVTxPMx2l3gmJg5aibLR6/c1vfnNyr3rSk56UhAjjPgaJjzDyAiAGY2bsp54Pxn0QMRr5VgMzF2FQciwhUtZhIBLC4Iw8aRiokec8rOuPcQ2sz8UHRrIECDN8NXQd7/7a1772yzkTynUYSIAH1ZY4fZeezxvFFYXR6wXhiHie0RsWadQP0jzPc2Y5jmN5ohj1gv15hp/4xCdSHYveOHrViPSw5ZFeOgRGpAjmfnesOL9c8YrvfOc7hXo/6OXr6B5xuWrSU0fvnUTztxT/7PnPf/5fcr/a7t4QQDiYgAlMKwLuAZlWj8MXYwImEIb2c5/73CdqgO3/kEvVZQzE1QxCbfVs1JTWZJw1Wcc4Db7jsXLlygJxgqGH+MC4w5jDKItxHxiNETD4iYQQARiMRGYoCiHCPiEMOJ5lDEFivKUmH2XFOSgz1sc20jyyD8tx/kgpF9crpTW93X5ALje/TW+Q32TH0ztgijFeV/34bTFcp/zJimldHAVzGOfPMbaRsj4P7E+IZxvLpHnk+bEPKWUwcxWCgH30HNM6tucxhE6sy5cjT5qfH4F90UUXpbEs6g2s6xw6vM43QUrV9VLt4zwJsP+psSFXqqfk/9W13KlrsAhJFP3HBExguhCwAJkuT8LXYQImUDDDk1yqWmvXrr1M7lR/J7/3ZTKyWhIWdYmPBq5VvClGbGDcve9970szXeGOxRSnbEd8sE+8QcaACyMOxBiEGKCRx2Bkuep1SAKEHhCMRs6R7xdGa6QYjuRjH5Yjz3nYRkrgPASWyecp54/rIK/I162HdB1v0hSu2+TKgxXaLSCV4j8TEAB2HWYaD/Qm8f+QuPIQx9SnFmAP53h2/WXFM4uU7eRjmZRnHet4drFMmeQRsaxnOR//wbY8sj3ERqzPl7lO1rMu9qXsz33uc8UrX/nKVO+1jzZp9LnOp3rcUb3tSLxfprbwJblmvUKbrv/sZz/bvOSSSzx7Wv/D9rIJmMBxIWABclyw+6QmYAL9BKqej5ZEyGslPq5lJisJirbERxNhgcCgV4MeD3op/uAP/iAtMzg3xEcMOo+eD9xrMN5kgKXTYTCGURh5DLwQH+F+hfjA1YXzYPixL2X0xzAOYztlE1ieKGWf2E6eMlgmJcp4ZIajIX1I8a/uuOOO9dEjlAr0n8kQSG5X+mDjegnTy/X8fl6c00xp+cGw57kfKMRzIs0jdSqWKSeWyVNH2EZPHEKYOsW6XISwzDFE6hcxz8cy9Zc6wXKch+ulnlPeRz/60UL1I7UDzlmdu65j6jovrmjL1HY++aUvfel1P/qjP/p+7eOekAM9cG8zAROYMgIWIFOG2icyAROYiED0fFx88cWv1fc7rpXhhPCoKTYw5DC4MObUG5JEwdve9rZk2D3xiU8cJz7o/ejv+cAoI2CgYcQRSIkYd0R6OhAbGHUhPrZt25bKiv0pJwxH1sVylBnnYVsEzkkg7Y9xDayP65BBrGy7KfFxs74f8SYdytet3fORKB7SH5jxwcY3ifP5ejYXKr/feBCewUQihOcSgfxEkefONspCRLBMHhGs+lts2bIluUuxDz1rsQ/pwWIIkTyNa6JdPPzww8U//MM/FFdccUXq+eMcnB/hotigJ0RpedZZZ137la98hW3vd09IEHRqAiZwPAlYgBxP+j63CZgAb3Axslvnn3/+v5PwuHbFihVtGVd1xRqCIkQFxhwG1tVXX50EAzMC0TNCxBiLAbwYfhhgYdyBOAzEyLOM0Z/3fIT4wHd/06ZNPSOSfTDqIlIugZR1YfSllQP+xHbSiBio5Ekz8cG4j4aM1G0aeH6lDNfdnEZxzBIeUL5XDSQAsxKGGh90pZ7VLXpWJ4k5wmScOxbPIBch8Yww+slHGuvzlGOpB6yjLsQyV8Qys7IhQCKwHwKXYwZFztUf2Y91pNSVOI6yGA/CtLz/+3//79QTQt2Pa+GcWtbu9VKxrZ7Ca7/4xS9+/9nPfva/aB/3hMRDcWoCJnBcCFiAHBfsPqkJmEBFoC7x0daA8rPkWvVBiY+OejvSQHOEB6KCHg3yCI13vetd6S3yeeed1xMf4ZpFD0mIjzDYOAcGGYZhng+jn54PXGQQH+p1SDNfIT5YRtSwHUOSiOEXRiYpZcQ20tzwi3PFerYRCVwLMa6LcmQAK2k3dN5tEiCXaQzD97VrmtEpHeQ/h0Mg9YJIAHxfwvYyFXC9ngciZMKeEJ4JdYcQ+Twln8d4viE8WCYfqaaNLjR1cpqOF1HMsWynrlGfJhtDlFBXyFMOgbLoFUSEaKxHcemll/a2pR30R/vX1DZqOldHvYsf/MxnPnOx8t9VGfHRwtjVqQmYgAlMGQELkClD7ROZgAmIQC2MJ2hceeWVNQmQugYMv09G4nIZ/W0JkAZiIlypECH0frz73e9OhlyIj5huF3ES4gMBglGXG5EYaRHIVwZ/EhchPngrzbS7mzdvTm4tiI/o+aC8MCjJE1gmEljHPeXrWM7vM/KcnzxpXAvKQyGJD13PZZq56CYVa/GR6B7xnyQ29FxvmowIiedEmtchlqNexfNjmchzzNOoB6R6roWM/jQjFlM6hwhhPR+5zPeN8vpTriMi28hHyvGcHxFy5513pm3Pe97zevUx6Gn/us7d1rUsVz1/3+/8zu/8hLbRFruVmIVufe4qmzjQqQmYgAkcIwIWIMcIrIs1ARMoCqaPFYdktV999dUdGTnEHA1G0avVA/J89WS0JCaaufhAhCBA3vOe9xTf/va3i6c+9anJ7QTxQc9HiA/2w7gL44xzYChG5ISZwd8THwiQEB/6inbPXx8DkUh5pBh8xMhTfpwj7ifWca44L+s4b76O5bgWiZyW9m2q12Or4uUyUhEf/C4feHR0KtF/JkkAEdLMRMgn9VyWift+A9N5bghPnjWBZeoAacSoCzxDtvGM2RbPmjSOJ6/nWjzhCU9I5SJwQ4Rwnol6QqIeUw75iLFMGnWRPOdHhNBGCJdcckmqY7Gtur4G9U2zYz3/pS996c/p2v4X18e2CMrXb7jhBiKrOp76Ocg4NQETONoELECONlGXZwJznECIjquvvpq5QbG+kwUuYwYyjV/8xV9cSkbCoqYPqg3L5em3JCZKGVm8pU1uVOFKdfLJJ6dBtt/85jeLCy+8cD/xES5aHEfE4AqjCsMKI400DH6W8wHn4XbFYF6+qs5AdMRMPvsV10q5UTZlcY4IrCdwnnx9rGN9RI6tYilbkA8NNuV2dZOE0Cvvv//+u3QMPR8WH8A7ugGmDUSIBO+zxP06PbeLlKb12tZ7oDyrqDc8W5ZJiTxf0hAIPMt8PXlC7EfK8ezHbG1R30KEUM8mEiFxnjgXy7EuUrZRfgSmp0aE4K7It0LYxjUQCarbzI5F3fsttdNPabzTiAR/KuAP//APd2g/xFpXMSuj42sSIw1FixHxcDABEzh6BHo/ukevSJdkAiYwFwkgPPS9ClyqMGJSeP3rX3+u3sw+Uy5NT5Px81QZXqtl8KxCYGA8KfBWdimiAKOPwDZmvdJ4kOKmm24qNIVo8eQnP7nAn563vPR8sD3ER5RVlZfKwPCiPJXdExz0djC2gx4PjD4Gm+uL0QU9H3fddVfKU1YYehhteT4Muf40nVB/WJ8bg6xnOSJGqCIfjNOltZtcmwzQv5BLzK/Jh3+HdrfbFdCObUiMZagvVf35Uz2z/1g9n/2ECJfBM4060C8EYj1p1Ik8z7oog3NwPHVSUyunOsgy6wn05OFmSN3G/S9S6nlE9iFS74n0FBKps8QQ4JRHXf+xH/uxQrNfFQ888EChMUVpYgXqOnW/Gtu0Q22yHcfrsM26pk0q5xu6rq+rjK/+wi/8wu2UR1i/fn3jtttuK90r0uXhvyZgAkdGwALkyPj5aBOY8wT6hcfP/uzPPmn16tU/JcPpJYoXqxcjTaUbxhKGF0YPKQabAlZYGhuCYYTxxJthxMLnP//5tC9fPUeAhPjAEKOnIsqhrDD4MOow9IiUE2XiCkPE957xHkyzS88HvvP451MW1xPXFcZkpGGMhrHJhcc5I2VdGJWRIjyUR3ikWa5Y1nVt1HX9mr5V8UGOUQBE781zWuM/x4pAj7XGHr1cj+ZP9YxP5bkooILZPu7/jfHs8/qRr4s8aX+k0AjULQTw3Xffneon+xJIQ4TErG6TESEhRKIdUAbtjEHp99xzT+p5oa5TPkImRI7ypWIt2hDihTwp1wgLtZO22HxF8SNqKx/XRw+Tf5eWG1dffbWFSDxUpyZgAodFYNyP7GGV4INMwATmLIH8I3kaUH6epjx9i4ycVyidx5tcjBoZRW0ZRaViTQZOTcZSDSMnDH2lvd8hGTfJ+AnxgCHEW1sMKow/xn5gdOWGE+uJBI4P8YHwQIBM1POB+Pjud79baNB3TzRgqEV5ecr62MZ54nysGxS4jiqmHg/dR5NlXc+Iru99+sjh7+lN9AMUpYgA674KH1SY1x0LAjw4Ykd1dYXq4H/R83m9nutwJUQO2iNCHYg6EfmoJ3kaF886ysbIpxfiBz/4QaojrCeQ0mYQCf0ihPWIC0QEkXz0hNAWyHMcbYX6rG/IJEHNOj7cSRrHsD8iRWmJcAnxousqibqX9EJA19NgX4J6CvepPf2dxMx/e+ELX/gt1omXp/IFhIMJmMBhERj8f8/DKsoHmYAJzCECNfV81HDHuOSSS86Rf/t/klvLK/VGeRhjSG9vW7xhlaFT13J604oxQ8QAI4YACeMNA51AiqFWuSillLfG9FggKjCmOAfHUQaGG5HjcvHBvoiP6PnI3a4QHxhqvIlmPwKGGOelHGJcV38a5yMdEHRoCh39rau8ulLuYUT56xT/m6aFTQacjrXL1QCAU7yq9wwkRM7Ts36Lzv9KpcM8N0W6RYjRK9J76P31IpYnqh9RX6jX1H8Z9UkoxHrumzIQHxGjFwQBQgwBQkobQEjQHiiDsR+IGvbTjF/JXTGEC+IjBAjHVAIk1fk+EZKugetQKFUu9RgR3aTt6kUAGvo6uXT9kV44fEebkoDWfhbQEHMwAROYNIHej+mkj/COJmACc5pA3uvxkpe85E0y3N6mgb0nYOzIYGor1hEfYfBguGDwhKETb1z7BQhQMaRk7PTEBMYaAkFGT3LJYtyGeg+SgUZviAyg3rMI8RHCJXo+crcrjL4YcP5v//ZvyRWLcrgmfOW5Vgw7DESuhaDrSYNTtBwhtuUuU+mtsXbHoE2B+1C8TwsfVPo3fcKDY8cuvnuI/x4fAjxoDOn0nCsh8hotv1wP/DTFVM+qusY+8ayVTYHvaUSdwGgnstyrCxxLfaBuUseok4hq6nVehymNdoEAQVggQCLSviLSthAhjImi3n7jG99Ix+kFQMFA9NiPlP2iLdIOqd9cQ0Tqfv5SIG+XXA/3omsv1b6SCyHHqYdFzXD7bz/3uc99J/swPkSCJPFj2cEETMAEDkbAAuRghLzdBEygRyDEx2mnnbbsWc961nuUXonBIyO+JUOpoVjrN3gwWHIBkhs7vGnN3xxzIgwyYggKUgw1IqICn3ZcTCiXWbLyfUN8sG/e88ExIT407qJAfDAlKgbaP/3TP6UB7x//+MeLz372s2mQMK4sCB+MMe4nritlqj8YZhHIV9eNEXan8tfLaLtB1/PZaoA5u2KQYrzmwoX1DtODQPRyJEOageqqt5foWa7V8+VDhk9UXU2igmcdIc+zLupFvp51iADq69lnn53q9oc//OEkSqLu5OUhChAgIUJylyzqY4gSevEY6yHRlHo92C+25eIjFyC0RSLth/PkkfoekeuqekKibnPdpdqFknaT7RLV6zVRxC++9rWv3ar7tUtWPESnJmACByUw9n/Qg+7qHUzABOYygRAfT3/605+g2XWue9zjHneRhMeojKSmYi0MHgwk3rKGi0i/sZMLkHjbijGTB94WY8AhPhAVISyiV4O3xzJ+kgGnAe9pO4KBGPsgQMLtil4Tej74yvmtt96axAeD0T/wgQ8UP/dzP9c7NcczIxbT/hI1Y1Hri1/84pd0fXyv42yli7mu6nqZNne3lh+RofZ1pcwe9G0Jmzu1Pn8bzHTniA4LD0GYAQEhQmQcSISG3JqeqOf+JMWnqn4+TemJ2rhI8QlRJ5Tu0vq7lWKg/6i2JUOdCRQQCYzHQCBQZ7/1rW8VH/vYx5IA4HhiHhAG9M4NEiGs0xTWqTeQ6X0pn3UhQGiLRIRHiI+8TSI+IuYChLYZAgTxEQKE6+L6aJdEtTGESEtte4hv12gs1Ss1NuQu94TkT9B5EzCBAxEY/3/9A+3pbSZgAnOWQIiPpzzlKRfpQ2afVFwmo6clA6mZGz0hPjB64k1rvwDB4AkjR0baAZli9IQIQRxEL0i4sCBCECcYdrGN2bNCfETPB+NH6NX4+te/nr6mTllvfvObi3e84x3peAwthAvChlTHlxIoNY0R2aqPH576xCc+cd8FF1ywUGXM0zXrssqa/OC3HeDiER3R2zHesjzAQd40rQhQOaNXJBcj4y7y9NNPPynqhOrVPoncPerlmKf6s1Hrl2nnUm2lRh3FZYpIm2GcBr1vX/7yl5OgpV1Q3yNyEtpOvwhBaDBrG/VcLwHS9tiHcvMXAf0CpL9N9ouQeDmQC49xN1stRJtUO2lp36ba4Vb1xFx+6aWX3qTrd0/IIGheZwImMI4A/5N0MAETMIEJCTDNrgabt2WEP14uV9c/9rGPPUlvcdsydJoIDmK8ZcXAiZ6PSDFyWB9vWhEfMswmPF++gf3irWykea8JRhwiBHcqjLC8pwQRQi8HET959WSkWa80XqV461vfWuiDiEncUB4Bw696u9v7Roh6Tmpve9vbFmvzqNy29mifPWnn7h9uIo8IjYgTGqzZ8c5ObwI8y+jJyp8z+XjO5QAhWpeQpc6o+nbHNCF4MdqjB4GUevnTP/3ThdpU8alPfSrVOXBwDHWS+ogYZtwTgiDaAnWd8iR8UlvL2160scmkIT5IY3/a2GQC10fU/k21s7Z6d5bp+q5XuEDp93XtjItxj99kYHofE5ijBCb3azNH4fi2TcAEut9D4C2vDKX1iidJeCA+GiE88reqeT6ER6QYOQd7szoR7zC+OD6MMdYR+GAh4zro5eAcVQ9GT3zgdkXky9BvetObile84hUF3xXBwAvxEW5ekYaxqLSU2EruU7/zO7+DEMPwjBBGaCw7nb0EDvSsuxWxe+/kO7LLO+qR69UV6hOiIWIs0+N2ySWXFBdffHHqnWMWK1wEWR8ihJ4ORAjL1G3KQETnwiPERC4kEBMsk3IsMbbnaQgQth9q4Bh+CxAhGgB/ko5fr7FUP6F0u9qXmqhnxzpUpt7fBOYKAQuQufKkfZ8mcBgE1qxZ05DR3XrmM5/5fvU2XChjo6XYzI0fREcYQHka6+kJweAJwXAYl9E7BPFBuVEWIoK3ySFCMNQw0vKej/vvv7/4kz/5k+JpT3tarxwMwDC4KAP3LWIYiKSVS1dTA96TgXl19+NrPaOyV5gzc53AfnVCdZI60/v/a16vIh9iF+HMGI6f/MmfTCljO/7zf/7Pqf5Rz6mfCBKEBG2JSR8Q/yH2aVu0uxAYg4RH/zqWYx3HR1s4nAfJNep6ECEtuYRdqPb3fq17qa6b+3dP4OFA9TEmMAcI4N/qYAImYAL7EWDcx4033th60pOe9EYZ+FeoJ2BUhkYTIyh3rwoDqD/FKMJIYn0Ihv1OchgrKCuEDuUjhjDI8LFHoCBCECAYdrfffntytUJ8YPhh9GHQ5QZXiI++lEG2jAXZeO+99z6qY3TapEMO44p9yBwjQEV5VHEj9616wxS24wRuLnbZpnqWPrZJT955551X/PIv/3Jy2aLORUQwUN+p61H/aVtE6jPbSQfFfFscE2neFrjeww1qh021mVENir/i5ptvfqOum4kbDr1b5XAvwMeZgAnMKAIWIDPqcfliTWDKCNQ3bNjQkfA4U4Nm/0DjK9oSHUl8hKgIAyZP87eqYSRhQB3tQJlRPteDIFLPTOoJQWAw4xXfRmCg+c/8zM8k4cF1cn1xPeyHIYiLC70mfbFkWYbhDzRIGGOSm9jvTffRvi+XN+MJUEdqGiT+qOrZD6q74UN+qa71idzeOsQuQoQ6ibvgT/3UTxW/+qu/mkQz6xAd1F/qPPWdNOpzLi5CfISrIinrIo3tkXLs0Qy6tqbOxZiQP/j85z9/pspOH+Q8mudwWSZgArODgAXI7HiOvgsTOKoE1PuRDG65e/yRvl2wpOr1SF80D8ERRkwYQrE+T8PYP6oXVxXG+eNcGGRcI1OcnnHGGcX3vve9Qu5jxUtf+tIkPvoNLYy6SmAkAYIIicjbaMUStxdN43szp5P7lX8rj8VDnJ1lRl25uar/SbjmAhchEsuRD3GCEGGg+RVXXFFoatskTKjXlEUdj/YW7S9PERrsR5pH9onl2J9yjnb71DlqlC8BskQvLv5I5SdBNjsfs+/KBEzgSAjED+WRlOFjTcAEZheBhno/eIv5PE35+VK9fW3rzWYj3rqGAYRRn799jeUQBf1G/7FAxDniukKE0BMiAVVo9q40PiS/DoQHbliIC2IlNlKKAIllbatr7EdbHy/8J65brlzu/TgWD3B2lpnqioTEP6nng1m00pfSqXfUr+hxC8GBEAkRkm9jdrff+I3fKJ73vOel46jfROpziAhEBSIijyE08pTtLMdxUcaxwK/239C9tjVm7KXqPXyezt1Wu7Mr1rGA7TJNYAYTsACZwQ/Pl24Cx4IAxjtBwuPXJEDKfpePMGImSjFuECFTEcKoinNioOGuwhen+QghhhdvlDH+MPQQHXwbhG0Yg/0ihGXFjmJd3/zY/H/+z//5OveBO9pU3I/PMSsIpLqi9sHHKTfrjvj/LK5IvV626G0LwRHL1NHI45al8Udpmmnqd4iGaHe5wCCfi5CgGOv69439Y7+jndL++e3Qy4BfO9pluzwTMIHZQcACZHY8R9+FCRwtAoz9aMt4OEuG/CWVy0cdgyIMn4lSjJowkshPVeB6wkAjRYAgMJhNCIOOQemIjohsO1DUAPYOg9j1DZD1fFROPSk4yrsHZKoe6Mw/D3WlSd2RAFhftYUOYgBREb1s0RsSgiNPEcKEL33pS0mE8BKAcqjrpCEsSCPEOpYH5WMdx1fXFIce9VTtsI7w1+/IJZ/5zGfO0rnpBZm6H4Wjfkcu0ARM4GgTOLoj0I721bk8EzCBqSaQ3tbqzeWr5He+QIZPS8YEA0uT0RIpxkx/PowjUrZPVeA68nOT56OEfFOB6Xl580zEIGIwML0huLxgDBLD8MPok0gpFeuaunef/PDfX92Dez+m6mHOnvOkOqN6937VuV/SbdElqMWyRj2jpw5RT0Q0U2ejDkfbkftf+ko6+1B/2c62iWKgi+NZ7s/HsbSZYxxquq+WvrezYOvWra/Sua6+4YYb0m/LMT6vizcBE5ghBCxAZsiD8mWawBQRwGd9WEbPK3jrKqOnnhtHGC4YMZFGPpZJiVMd8uvgerl2xAV+9IwJCfER7liIELaH7z1vo+kVkfhoq+ejKfHxoX/8x3+8g6mI+Qr8VN+PzzfjCSBAGqpHd6xevfpDahOvlhBpqZ42qXvUtVx8RPuhHhMRJUzJy2B09iNE22J7f0CgRBiUj2P60zjmWKS63jrXont5hXoRf3/t2rWjx+I8LtMETGBmEph6S2FmcvJVm8BcIMDvQblkyZLHyeg5Q4ZDiQDB8MFw6TdeYl2k7BdxKmHF+Unj/Bht5B944IH0TZDcDSs+Ukgabllsl/Ao9da59sMf/nCn4ttkPNXOPffcMctuKm/K55oNBKg7NQmOt0l87CSvyAdlUq8b9Y/eEMQIaUSWcR285557krsW9ZhjBgUM/BAc/fl8/9iHdROVle9/NPK67rruu1RP6hnPf/7zH6fzlhIitjmOBlyXYQKzgIB/DGbBQ/QtmMBRIpB+DzTu49lyERmW+OiE8YPREhFjZqqMmMO5L66N60aESFT0PkyI2AjxQT5ESSU+ComPlgaeN/Tm+Tf1Aca7r7zyyrp6P+x+dTgPwcdAgLrDZAZ3yxD/TdVJZoLqfRkcwUE9RHDkIiTycgNMLoMUlIdcTIToyFOdK4mSfF2epyyWj3Wofi86GgcyrB7JZ3O+tWvX2uY41uBdvgnMEAJ2wZohD8qXaQJTRUDC40JcQJQmK6VfbLAcBs1UXdPhnAcBIv/zJEK4XgwzXLCI+fgPDEEJk1EJkSF9P+TDH//4x9+tb4g0NRi/Zywezvl9jAmIAO57TbkCvluuWJdIhFyheogr0hD1EUEcIh9a1FPqJ3VX00CncR+xPtJoe7nQyPP92/u3sTxVQfdW8lui9EKd868lQKbq1D6PCZjANCdgATLNH5AvzwSmkEBYJudWomOc30cYNgdLp/B606n6r4eVrMOww5hTz0bqscHwYjnGf+DmUo39aMkQHNKUpzd9/etffy1uIh73MdVPcVafDxHCTHKvlfA9XW3rItVPxG2T+kgPXNRh6iiR9kfdDLEfab4f+dg/z0+0LvaJMqaIeI3z6frPrc4XvzFTdHqfxgRMYLoSsACZrk/G12UCU0sg+acvX758iU57VnXqJEByg6bfiIltpGHYkFfvyZRd/aBrqoye1NMRb5kx9kKAMPic2a/U+zGK+JC7y0133HHHZRr7sU3iAzeRY++jMmWEfKLjTCCNBaFunX766ZdJhFwvgxwRknpCqJO4YtFuyFN3qZ+4YiGiCVHH8/Z2sDxl5fvEMimRnokpCDV6GxXO+qM/+iN+W3bpXnT76QvpU3B6n8IETGC6ErA/5nR9Mr4uE5h6AuWCBQuYcudETi3jpYYBg/GTG0Dkw5jpN3BiPcdPVcivof96uQbGgdALQty2bVvx8MMP495Syjd/VLMMDd1555098VENkvVb2ql6eHPnPNSpOiJEPSGXqQ3dJHFBW8M6L2k3iJCop/SK5CHaXLQv6jn5WI58vhz7RJrvQ54yj3XQeWqcS+HEZcuWDVl4HGviLt8EZg6BKXkFMnNw+EpNYG4TkFGEVdLqN+QxInJDJs+zrT9i3MjYmBKY/eeO5bhGRAfXU7lelXq73FbPR1PG3pCEyPqvfe1rv6gL3aboQedT8sTm7El6IuS00067XPX0PWpvV1I3FVuKDdVLZs0q9CIg1dm8DVGvo0731/FYzlPKYTnS/m2UdSx7KimfyP0ptPSB0GOveOZs1fKNm8DMI2ABMvOema/YBI41gfTWMjdY+g2ffFt/HpeL6hsix/o69zPIBl0LvR0ywnjL3NG4j6ZcW5oSJQ9IgPyePlb4zuoi6Q12z8cxf2Jz/gRJhNx3331bReJlGpj+BYmM/yIhsiKEiOpoXW2IsRM9Bc826vYgMdFf5yda5tiItFHisRYg2bX07mXO1wADMAETSATsguWKYAImMI5AGDuZ8dAzXPrXsdxv1LCMcTMVIc4d54w0DCwZc6VmIKppjEdd31Vo3n333Vs009U16vV4KuKjcrnCOLL4mIoH5nNAgLpGnatv2rTpnaqzT5XAv0btbouESFORD/ixfVyPQbS9qNssk6fOx7ZYx/rIR5uINI6JfXSeYxI4Xx5xhXQwARMwgSDgHpAg4dQETIAehWQY9RsruYET2/oNHAwaIgNoGeAa8VhhjfPHeeO6IqXXRuM8at/5znfaest8m/b7gMZ8fEDXs4Vr8lfOj9WTcbmTIIC4IDb0sUzq49UrVqx4j+rpqyQ+XqXlpyjf4GUAQfmeoIi2SD3PI+2A5f7trGdaX1LaJCkx2qm++3PU3SVpe3EeUnp1NKsXvy0OJmACJpAIuAfEFcEETAACWDq1s846i9Gvd2HEyEApcyMiz4eRn6+LPIYNUUZHcpGi8KMdMHA4R5wzT6vzd7SOL7nfJAPv/DPPPPMCiY8/0XVs4RsfSmv6zkcaHXu0r83lmcAhEKAOYpg3ESL6COafqFfkAgmP8xVvkvCgXabeOcRIXs/J006jLZKP7bGONPKxjTRvoywf7RDlq2xddrruuzSwfofyuiXPgHW0ebs8E5iJBCxAZuJT8zWbwLEhUNMXwFvyAHnoYAJkkDGD0YHoCOODaW6PhQgJ8RHniWuJ5Srt6Pw13cv1Wr7jjW98I4ZcEh7co/LdV8vHhqNLNYFDIUBdpE4mIaK0lFi+QynT9bJuQgESdT9PBwmOfDttMo+0U7YfrUBZ2e9Aeomh3o+HNL113OPROpXLMQETmMEE7II1gx+eL90EjjIBXkh0ZPN8TUb+5TJMmDGqJygwLFgeHh5OBgt5XDtIw92KQa1EGf7JrYOUwKD0yKcVh/kH8ZEZN+ncYUzFtbKM+GBaUw04/5pOVX/3u9+NIYcB5GAC05VACBH+v8w4kK/Re6BA3U3tCXFBPc8FBflYHy5W0R4jjXYZbTOWo512dY4+z672fCSB6+hrn/Eb8jXKveGGG9JvzJGcw8eagAnMDgIWILPjOfouTOBoEOhaO7XazZWhX+PtaG5QhAjBUAnxgTEThg4GDcsYNGHUYEQRES7sdziB46temWSA5WIj8qSV+Ch13Y2tW7fukEvLF3W+jno90r0dzrl9jAlMMQHcskq1ly+qve1QfinLiumr4nl9J0+7IqVtRfuMNpkLDfK0z4h5G1XZqY3S7ikv2i7rJxs4d9X+0kc++e3gRQAfVFS8mXI0JsvtcLJAvZ8JzHIC3deTs/wmfXsmYAKTIpBcPWSoYLTv0NvMhgyIEqMij2EAxTqWw/CItDI+CrlepKjvG/DV8bQfQmKyAeERb1UpK8qN80Qa66vzdXRNpb5w/vmPfOQjm6qZrmz4TBa69zveBKirfLRwk9LPSwywnNomFxbtLW+H5BEAeYztkdJWyJNGjHZDStuhjZLS5mh7kwm0Z8rL22dVHr8dDU2DvUPTXvObwsQPvfuYTNnexwRMYPYSOLzXkbOXh+/MBOYygZ7ho+8T/KsMkBfozWVn0aJFCJHkRsXbUXo+oscjf8Pa784BSN6oEjFSMGqI0XsSb2QnehPbf1xuXMXbVgyqMKIq8ZGMKE35WVP8h+ph2u1jLtfqmXnvqc6qDfyD2tpP5mKAdhECItpj3qaiTUb7ih6PSPPeDcqNSBuNdkr7inZOORO10WjTtMNok1xb1SY72t7Q2PN/fctb3rJJ56mrHAuQmVkffdUmcNQJWIAcdaQu0ARmNIEw1q/TXbwAY4K3okzVyTiOMHQQIP0GziDDBmMpFxL9hk2UkR/L/hhFYRCFUUTK8cR4qxsCJMSHBFOp663r2x8PyP3qEzwJDX6dfJfLjH50vvhZRCDVWbWLT6g9PKD7OkWRFwRpPEjUewRIvAyINkkaYiNP8zYWnEJ8RBuljdHGo53SPqONUlYE9o9jECHRLkMY0R753SBKgPBbUtzg8R+Bz6kJmIAIWIC4GpiACeQEMHxqMio+KkNmiwyUFRj1EiC1eNsahk4YNxg2uXEShWHc5EYKhkq8VY0yMG76j4/j4liMIvIYOf3GThhiGDy6TmJbsfnwww//7Sc/+ckH5X7VrGbfictyagIzgQBio6nZsB5Ub+Tfqn29RW2ASRTS/7NpD/QyDBIgIRgipX31RwDk4iNva7QxIm2rX8z0H0fb7H8xwHVJeDAOiw8tbtF3eD6qcyGc/CIAgA4mYAKJgAWIK4IJmEBOAMOnocGiu2T4vFeGz1Uy7lsY9WHsYNiEcYPwCOMmCukXEGHQxFtVjJoQIBzfX8Ygw4gyMHRIKQfjiBjuHrxp1ZgPxn3U1fOxXYbP+zB6rr76art8xINxOtMIUHfVvGrvk0D4BeWXKPZ6QWgHCO9oS3m7jDaZp3Hz0b7ydooAibbV38tJuf3lsD/HR5uMY6MHhBcBap9NuUG+98///M93rV27tnHllVdagMRDcGoCJuAeENcBEzCB/QiE4fNOGRr/r7aeoCltmZWn1i8e+g2TMG4wUMKoCSMlDBsMmtxoogxESIQoIwyc/nIwvIhh7ETvh66xLXcPPuj2js985jN3yuBp+GODQdXpDCRAO2xIUN+plwHvUBu5Sm2h1wtC+6C3IW9LIRZyYd9/39G+oo2SRhtFSISYCEGTlxllcUy0T47N26TaI4PPeRGw7Qc/+ME7tR+9H9yLgwmYgAn0CLgHpIfCGRMwgYpA6gWRAfHwqaee+tsSCO/S28wWxn0YO5PptcAwiRiuV5H2GzchZOIJYNyEgUQZ5HPjaEDvR4fr08xB35XR82eabQfxYaMngDqdqQSSCJHQ+DO5Qf6c2slZahusS4qdtkHvH+0pFx3RPvtvmnYVMW9ftK14QdDfRqMs0gjRPqN95wJE19PW9TY1+9Vv6/s7D69Zs8a9HwHOqQmYQI+ABUgPhTMmYAIZAdwlePv6br19fYmMj+djWEiANDBE+o0RjgujBMMmDBNSjJPcuMnFR5SVl5eXRZlRVr8A4e1v9H5IfHQ08Lwm16vX33LLLdvPPPPMBpdEWQ4mMIMJpDqsaWy362XA63Ufn1bsCRDui/Yll6dxAgRB3x/y9tnfRqNtkSLu40VDv7CJMqOsaJtcA8fxG6Eymvfee+9n3v72t7+bFwF2vQpqTk3ABHICFiA5DedNwARyAsnfXAbF62WQ3KQNuGJ1JBaYTrO3H8ZICIXcsMGYwTCJwev5m1UMHEQHBk70fvSXSVlRLoYO5RH7ez90TaOPPPLI0H333XfNDTfc8Bm9cW2q9wNXFQcTmA0EeBnQ1MuAz+hlwO+q3eCKNap1Q3FztAlESLSlWE8a7ZM02lR/O+1/QRACpL+NRrlRVrTLqk125HpVU8/HNg2ef732YQyWXwIENKcmYALjCFiAjMPhBRMwgYxAcv/QgPTvrlq16nUSCxtkcDDOIgkT9gtDJAwa0hAKkeY9IGHYRC8IBg6x33DKy42yKSfetNL7geuJ4qgGug7J7erD//f//t+rER/66rnFR/YQnZ0VBNLYD/XwXS0Rcp7azBVqFz0RQnuhTURgmTCoHfW3UV4Q5G002makg9pntEkECMdKeDDug0kgGnoR8Lq/+Iu/+K56bTwGKx6IUxMwgf0IWIDsh8QrTMAEMgLp7aveaP6DDB9EyLUyPhAhdRk3tTBEIo03oogPDBOMm7wHBKMmFyEID9blvR+cG8OJSHkRQ3zI0AnXq1H1fgxp3MdNX/va117L9Sh4pp3s4Tk7qwhQt2vqrXit2sLpEgYXqd0NFCHRfmiXeYy2RPuMtkq7ih6QaJuRhvggjUDZUWbVJkv1gHTUA9PQ78Tr/vIv//IfPP110HJqAiYwEYExP4qJ9vB6EzABE+h+f6AlEfJaGSNJhGDt6yvp9aVLlxZKiwULFhQLFy7sfbSQDxceTICEgaOieiKk33jCUMLQIVbig48NtiU+mnrb+tXbb7/9cvWCbNNDwkqi18bBBGYrgVTHTz/99JPUHj6p9vNMiQF6Rxjz1Pv/OW2PNkl7pF3yIdH4mCjbom0iPGijCA7yITxIaZsRaZ8RQnxULxk6Eh+p50Pf3nndBz7wgfdbfAQppyZgAgciMParcqC9vM0ETMAEMhEig+RahIJCS4ZNc8mSJT0REgZPGDl5D0gYOIPcO8LIyd+w5m9p8TOXmwkfGqxLcNQ03e5fasD5r+gadipafAiCw5wgkOr68uXLl6htvUMi4T9IFNAYk8tkEEBQhACJNhkihDZJ+4yejxAfpHnbRID0vxxAgNAu1R5bEkFNvQgo5G71ug9/+MMWHwHfqQmYwEEJWIAcFJF3MAETyAjgtklPyGVK/0oGymoZI8yOVZOxU1+8eHF644rB0/+mNYycECHxdjWMHM6R935g5BDp+eAtq3o/kpuHxqS0NOj817/97W+/g2MULD66HPx37hDo1Xm1xV/Rbf+xhEJT7Qc3Lbal/7fTthAdtMcQIXm77H85EG0TERLtkpQQLwbU89FRuyzVLhsSHpvUFn/+X/7lX673+KuEyX9MwAQmScACZJKgvJsJmECPAO4ebb2BPVUGzJ/KQHl5JRzaEhlJiMSb17wXBOMmf8OaGzmDej/CxUOGTkc9H/Gm9SsyeN58//33f0WuHvVrrrmGN7+pK6Z3dc6YwNwgwP+/iZ2VK1derHb43xUvrtoiblkoh6QeaHshQPJekAMJkOj5iLYp0aHOj06pdtlg+msNOP+gXgb82q233rqx+u6Ox18JuIMJmMDkCFiATI6T9zIBExhPIIkQVukN7M/KSPktxfMq46eU0GjL0Kkr1oi5oRMuHqQYN/GGlbJw78DKwdhRr0dNscFsVxr0vkVGz9s14Py92m3UBg+0HEwgEYi2OKS2+Aa1qbcqroyXAtpD2ZKps+u8AOClQC5CWEcc0PvBrA5E2mNDzbLGGCy5QH5L4uP3NfHD33N2t8X0DPzHBEzgEAlYgBwiMO9uAibQI9B7A3vuuecOq2fi52To/IIMlmfHHspj3NAzUkqEMIPPuF4QthMVSgwcxbresNZwu2JaUYmObyn/Nxrz8QEZPluqcnmr68HmFQwnJiACvTaxYsWKlRITr1JbfI3a1nm0L+WTClFKuym1vdcW4+VAJkDSdHI6DmGTXgrQFtUOvygB8j+/853v/K1Wj7gHEjoOJmACh0vAAuRwyfk4EzCBIBBvYNOyvtj8bBk6L9HCTyo+MQwZDCF6O/LIugh6yxpT7t6h/Jdl8HxUM+v8s7aPVPtwnmRAxTFOTcAEegRoTAiR5ArFSwGN0XiBll+sdvYspU+O9oYgiZC3x+iVZJvaYFv73ankExIgH5G71RfjGPd6BAmnJmACh0tg7P/+h1uCjzMBEzCBri96vIUN66ahDxgiQJ6k+FQZM8STBIvt/PakVOv4lsFXFb+r/W7TtwRuVT7/mCAD3zGqolxlHUzABCYgQNtCrI9rQ2qLz5DYeIra2FkSFc9UypfU1fzKvC1u0/pvqCfyG+qJ/PbWrVvv1D5J0LDflVdeWd+wYYNfAgiKgwmYgAmYgAmYwPQigPFzpB85pQwixpGDCZjAoRMIIUI7OuzA7FY6+IjKOOyT+0ATMIFZS8D/c5+1j9Y3ZgLHnQC/L3mkB2NQL0bswwWz3W9YIeFgAkePAG2MHkpSwsHaYmyPtHuU/5qACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZhARqBWlmUtW3bWBEzABEzABEzABEzABEzABI4ygbKoFXWVaelxlMG6OBMwARMwARMwARMwARMwgfEE1heNtOLvit9b9P5F737H29+xsuiusxwZT8pLJmACJmACJmACJmACJmACR0TgKvV7qPdj3s/Ne3zxD8X2S6+6tPzqF756BT0h69ev7wqTIzqBDzYBEzABEzABEzABEzABEzCBIFD1fjTe3fj74ttFedmnLmud/Ncn/1XaXCanrNjTqQmYgAmYgAmYgAmYgAmYgAkcAYF1levVC4ufKf5c/SDri3L4I8NlyrOOEPscwWl8qAmYgAmYgAmYgAmYgAmYgAlAoDvG45nFs4plxW8VHyi+XPyNInnWdYPHgVQgnJiACZiACZiACZiACZiACRwNAsx+RSiLP06RfKwj72ACJmACJmACJmACJmACJjCQgM3mgVgOsvKDxXCxpmgWI8XCFMmzzsEETMAETMAETMAETMAETMAEjjqBUoKDUBbvTLGb765LG/zHBEzABEzABEzABEzABExgEAH3gAyi4nUmYAImYAImYAImYAImYALHhIAFyDHB6kJNwARMwARMwARMwARMwAQGEbAAGUTF60zABEzABEzABEzABEzABI4JAQuQY4LVhZqACZiACZiACZiACZiACQwiYAEyiIrXmYAJmIAJmIAJmIAJmIAJHBMCFiDHBKsLNQETMAETMAETMAETMAETGETAAmQQFa8zARMwARMwARMwARMwARM4JgQsQI4JVhdqAiZgAiZgAiZgAiZgAiYwiIA/njeIymTXdSa7o/czARMwARMwARMwARMwAROAwBztAVnX0L3XDrsK3FLU1hdFI9ETwZTXusMuzweagAmYgAmYgAmYgAmYgAnMRgJXSS6URy66yq5wW/hQ8S5iIlWtm43UfE8mYAImYAImYAImYAImcLQIzKG39p+Vu9klrS64F72kKDZvWrfu/7vpttvObdx++1Mm5Ux1QXFL7dHie7UXFFcOnVgUvze6sHg15Q3tKf6XCn6rsnuvlsIR1JL1DiZgAiZgAiZgAiZgAiZgAnOawI8/oSje+7dF8a6yXi9+41BRRNfJ7xbFn75DIuNtim9X/O+KVxfFn1Fecsci42ACJmACJmACJmACJmACJrAfgTnQA0KHxIaholj6e0Wx6JeK4vRFRfEVCZAdN69de8fDW7b85j233XbSaI1uiwN4Z9WKjro16sXCYkvjLcWZr55f7Jn/aK1W29uoF4vnzS8bu3dvfbAozn5nUexQ90cqbj/aXmECJmACJmACJmACJmACc5xAvNSfMxhQBtINncWL73/6M55x8dN27Vo+UhQdrZ08CvyrVEYxWpbFHeedX6585jOLttYtW7ZsznD0jZqACZiACZiACZiACZiACRyUwI8/oVZ7z3VF8Y7ysY9d/Pef+9yGn5Ur1qRD7Pq7teJP/7s6TH7rhBPKt15xRfme004rfwcXLIkSu2BNGqd3NAETMAETMAETMAETmIME9OZ/roR8EPpLXlIU93+vKL76dY3aGC6KdYc0CH1d8eZmo9j09qHh4XXzhoYK+W9t2LNrlwehz5Wq5Ps0ARMwARMwARMwARMwgckRGD8N7/r16/keyGGHX9Ogkp9TPOwCfKAJmIAJmIAJmIAJmIAJmMBcIHCEHyLUEJDc1arKz6HepLlQR3yPJmACJmACJmACJmACJjD9CCA6LDym33PxFZmACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACUwpgf8fI5ANSzqdZ2UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=800x400>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system = ChemicalSystem(\n",
    "\"\"\"\n",
    "    System\n",
    "        Atoms\n",
    "            C 0.0 0.0 0.7613406700000001\n",
    "            C 0.0 0.0 -0.7613406700000001\n",
    "            H -0.8995903700000005 -0.4807776699999992 -1.16014003\n",
    "            H 0.8995878199999997 -0.4807824600000009 -1.16014003\n",
    "            F 3.450000001154741e-06 1.30012313 -1.17926967\n",
    "            H 0.89958909 -0.48078006 1.16014003\n",
    "            H -0.89958909 -0.48078006 1.16014003\n",
    "            F 0.0 1.30012315 1.17926966\n",
    "        End\n",
    "    End\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "system.guess_bonds()\n",
    "view(system, direction=\"along_pca3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3317272",
   "metadata": {},
   "source": [
    "## Common PLAMS settings\n",
    "\n",
    "The engine part of the original `.run` file is very small: it just uses the DFTB engine with default settings. We first create a `Settings` object with the common AMS and DFTB setup, and then copy it for the two scan modes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ada6c4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task PESScan\n",
      "\n",
      "System\n",
      "   Atoms\n",
      "      C 0 0 0.7613406699999999\n",
      "      C 0 0 -0.7613406699999999\n",
      "      H -0.8995903700000005 -0.4807776699999992 -1.16014003\n",
      "      H 0.8995878199999997 -0.4807824600000009 -1.16014003\n",
      "      F 3.450000001154741e-06 1.30012313 -1.17926967\n",
      "      H 0.89958909 -0.48078006 1.16014003\n",
      "      H -0.89958909 -0.48078006 1.16014003\n",
      "      F 0 1.30012315 1.17926966\n",
      "   End\n",
      "   BondOrders\n",
      "      1 2 1\n",
      "      1 6 1\n",
      "      1 7 1\n",
      "      1 8 1\n",
      "      2 3 1\n",
      "      2 4 1\n",
      "      2 5 1\n",
      "   End\n",
      "End\n",
      "\n",
      "Engine DFTB\n",
      "EndEngine\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "common_settings = Settings()\n",
    "common_settings.input.ams.Task = \"PESScan\"\n",
    "common_settings.input.DFTB = Settings()\n",
    "\n",
    "print(AMSJob(settings=common_settings, molecule=system, name=\"template\").get_input())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fca607a",
   "metadata": {},
   "source": [
    "## Add the PES scan block\n",
    "\n",
    "The original text input scans the dihedral defined by atoms `8 1 2 5` from `0` to `360` degrees using `41` points.\n",
    "In PLAMS, the same input is expressed through nested `Settings` keys. The only difference between the two settings objects below is the value of `PESScan.Optimize`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f976b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings_optimize_yes = common_settings.copy()\n",
    "settings_optimize_yes.input.ams.PESScan.Optimize = \"Yes\"\n",
    "settings_optimize_yes.input.ams.PESScan.ScanCoordinate.nPoints = 41\n",
    "settings_optimize_yes.input.ams.PESScan.ScanCoordinate.Dihedral = \"8 1 2 5 0 360\"\n",
    "\n",
    "settings_optimize_no = common_settings.copy()\n",
    "settings_optimize_no.input.ams.PESScan.Optimize = \"No\"\n",
    "settings_optimize_no.input.ams.PESScan.ScanCoordinate.nPoints = 41\n",
    "settings_optimize_no.input.ams.PESScan.ScanCoordinate.Dihedral = \"8 1 2 5 0 360\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fa5cb8",
   "metadata": {},
   "source": [
    "## Create and run the two jobs\n",
    "\n",
    "We now create the two jobs directly from the two settings objects. Running both jobs gives us two PES curves that differ only by the treatment of the remaining degrees of freedom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a36d52e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.06|11:46:14] JOB dihedral_scan_optimize_yes STARTED\n",
      "[10.06|11:46:14] JOB dihedral_scan_optimize_yes RUNNING\n",
      "[10.06|11:46:18] JOB dihedral_scan_optimize_yes FINISHED\n",
      "[10.06|11:46:18] JOB dihedral_scan_optimize_yes SUCCESSFUL\n",
      "[10.06|11:46:18] JOB dihedral_scan_optimize_no STARTED\n",
      "[10.06|11:46:18] JOB dihedral_scan_optimize_no RUNNING\n",
      "[10.06|11:46:19] JOB dihedral_scan_optimize_no FINISHED\n",
      "[10.06|11:46:19] JOB dihedral_scan_optimize_no SUCCESSFUL\n"
     ]
    }
   ],
   "source": [
    "job_optimize_yes = AMSJob(\n",
    "    molecule=system.copy(),\n",
    "    settings=settings_optimize_yes,\n",
    "    name=\"dihedral_scan_optimize_yes\",\n",
    ")\n",
    "job_optimize_no = AMSJob(\n",
    "    molecule=system.copy(),\n",
    "    settings=settings_optimize_no,\n",
    "    name=\"dihedral_scan_optimize_no\",\n",
    ")\n",
    "\n",
    "job_optimize_yes.run();\n",
    "job_optimize_no.run();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5546aa70",
   "metadata": {},
   "source": [
    "## Extract the scan coordinates and relative energies\n",
    "\n",
    "AMS stores PES scan data in the `PESScan` section of the results file. The `get_pesscan_results()` method returns the scanned coordinates and the energies in a convenient dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0bf1d691",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimize = Yes: maximum relative energy = 5.44 kcal/mol\n",
      "Optimize = No: maximum relative energy = 5.91 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "results_optimize_yes = job_optimize_yes.results.get_pesscan_results()\n",
    "dihedrals_optimize_yes = np.array(results_optimize_yes[\"RaveledPESCoords\"][0]) * rad_to_deg\n",
    "energies_optimize_yes = np.array(results_optimize_yes[\"PES\"]) * hartree_to_kcalmol\n",
    "relative_energies_optimize_yes = energies_optimize_yes - min(energies_optimize_yes)\n",
    "\n",
    "results_optimize_no = job_optimize_no.results.get_pesscan_results()\n",
    "dihedrals_optimize_no = np.array(results_optimize_no[\"RaveledPESCoords\"][0]) * rad_to_deg\n",
    "energies_optimize_no = np.array(results_optimize_no[\"PES\"]) * hartree_to_kcalmol\n",
    "relative_energies_optimize_no = energies_optimize_no - min(energies_optimize_no)\n",
    "\n",
    "print(f\"Optimize = Yes: maximum relative energy = {max(relative_energies_optimize_yes):.2f} kcal/mol\")\n",
    "print(f\"Optimize = No: maximum relative energy = {max(relative_energies_optimize_no):.2f} kcal/mol\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22179f48",
   "metadata": {},
   "source": [
    "## Plot the potential energy curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19fa7dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(\n",
    "    dihedrals_optimize_yes,\n",
    "    relative_energies_optimize_yes,\n",
    "    marker=\"o\",\n",
    "    linestyle=\"-\",\n",
    "    color=\"tab:blue\",\n",
    "    linewidth=1.8,\n",
    "    markersize=4,\n",
    "    label=\"Optimize = Yes\",\n",
    ")\n",
    "ax.plot(\n",
    "    dihedrals_optimize_no,\n",
    "    relative_energies_optimize_no,\n",
    "    marker=\"s\",\n",
    "    linestyle=\"--\",\n",
    "    color=\"tab:orange\",\n",
    "    linewidth=1.8,\n",
    "    markersize=4,\n",
    "    label=\"Optimize = No\",\n",
    ")\n",
    "ax.set_xlabel(\"F-C-C-F dihedral angle (degree)\")\n",
    "ax.set_ylabel(\"Relative energy (kcal/mol)\")\n",
    "ax.legend()\n",
    "ax;"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50426bdd",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "When `PESScan%Optimize = \"Yes\"`, every sampled dihedral is treated as a constrained geometry optimization. The scanned F-C-C-F angle is fixed, but the rest of the molecule can relax. This usually lowers the energy at each point and often smooths the curve.\n",
    "\n",
    "When `PESScan%Optimize = \"No\"`, AMS directly changes the dihedral and evaluates the energy of that geometry without any further relaxation. This rigid scan is cheaper and can be useful for fast exploration, but it generally keeps more strain in the structure.\n",
    "\n",
    "After running the notebook, compare the barrier heights and the detailed shape of the two curves. The rigid scan is expected to sit higher in energy at many points, because nearby bond lengths and angles are not allowed to adapt to the imposed dihedral."
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "executable": "/usr/bin/env plams",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}