{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a39005bc-f1d3-4363-a157-a8f0e6f08349",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/euroargodev/argopy/master/docs/_static/argopy_logo_long.png\" alt=\"argopy logo\" width=\"200\"/> \n",
    "\n",
    "# BGC-Argo data mode census\n",
    "\n",
    "This notebook shows how to:\n",
    "- download BGC-Argo index\n",
    "- search for profiles with a specific parameter\n",
    "- search for profile with a specific data mode for one parameter\n",
    "- export search results as a Pandas DataFrame\n",
    "- extract data mode values for this parameter\n",
    "- download info from Argo NVS Reference Tables about a parameter\n",
    "- make a pie plot color coded with parameter data mode census"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44a80811-1b3e-4d61-9ae8-40c9ee01d601",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Import and set-up"
   ]
  },
  {
   "cell_type": "code",
   "id": "a73e49bd-4447-403d-95ea-ad0e6ed08649",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:26.925667Z",
     "start_time": "2026-01-28T22:45:26.906906Z"
    }
   },
   "source": [
    "from argopy import ArgoIndex  #  This is the class to work with Argo index content\n",
    "from argopy import ArgoNVSReferenceTables  # This is the class to retrieve data from Argo reference tables\n",
    "from argopy import ArgoColors  # This is a class with usefull pre-defined colors\n",
    "from argopy.plot import scatter_map  # This is a function to easily make maps \n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "id": "9ae7b86f-7fcd-4220-a143-d997fbf7b76d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load the index of BGC profiles"
   ]
  },
  {
   "cell_type": "code",
   "id": "4e374d68-1c30-4b28-9dce-1cb527361efd",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:30.480760Z",
     "start_time": "2026-01-28T22:45:26.936854Z"
    }
   },
   "source": [
    "idx = ArgoIndex(index_file='bgc-b').load()  # 'bgc-b' is a shortcut for 'argo_bio-profile_index.txt'\n",
    "idx"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<argoindex.pyarrow>\n",
       "Host: https://data-argo.ifremer.fr\n",
       "Index: argo_bio-profile_index.txt.gz\n",
       "Convention: argo_bio-profile_index (Bio-Profile directory file of the Argo GDAC)\n",
       "In memory: True (379298 records)\n",
       "Searched: False"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "id": "c208048f-a81c-4a6e-a42a-4c1053a64a44",
   "metadata": {},
   "source": [
    "## Define a parameter to work with"
   ]
  },
  {
   "cell_type": "code",
   "id": "29106422-6a89-4160-b69d-2d7e8cef67e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:30.493917Z",
     "start_time": "2026-01-28T22:45:30.489073Z"
    }
   },
   "source": [
    "param = 'BBP700'"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "a290fc9f-b981-436e-9d4a-62e9e3e87d4e",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:31.219356Z",
     "start_time": "2026-01-28T22:45:30.495417Z"
    }
   },
   "source": [
    "# Get more verbose information about this parameter (useful for plot titles):\n",
    "reftbl = ArgoNVSReferenceTables().tbl('R03')\n",
    "param_info = reftbl[reftbl['altLabel']==param].iloc[0]\n",
    "param_info"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "altLabel                                                 BBP700\n",
       "prefLabel             Particle backscattering at 700 nanometers\n",
       "definition    Particle backscattering at 700 nm wavelength, ...\n",
       "deprecated                                                false\n",
       "id            http://vocab.nerc.ac.uk/collection/R03/current...\n",
       "Name: 44, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "3d61a51f-c476-4ae2-b858-a62edd009955",
   "metadata": {},
   "source": [
    "## Data mode census"
   ]
  },
  {
   "cell_type": "code",
   "id": "d8d47313-5577-4eae-b19a-476066e85f74",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:31.571351Z",
     "start_time": "2026-01-28T22:45:31.344510Z"
    }
   },
   "source": [
    "# List of all possible data mode values\n",
    "# a blank string is where no data mode is found (parameter exists but data mode is empty)\n",
    "# an empty string is for profiles without the parameter\n",
    "dm_values = ['R', 'A', 'D', ' ', '']  "
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "7b24cff6-f032-4a16-9438-dd5fa4598347",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:53.692655Z",
     "start_time": "2026-01-28T22:45:52.446678Z"
    }
   },
   "source": [
    "# Make a census of profiles in each of possible data modes for this parameter:\n",
    "n, N = [], {}\n",
    "for dm in dm_values:\n",
    "    n_match = idx.query.parameter_data_mode({param: dm}).N_MATCH\n",
    "    n.append(n_match), N.update({dm: n_match})\n",
    "\n",
    "# Census result\n",
    "N    "
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R': 35683, 'A': 119258, 'D': 17864, ' ': 0, '': 206493}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "7d29fa40-bd6f-483c-8e21-f1ed028f8733",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2026-01-28T22:45:59.696552Z",
     "start_time": "2026-01-28T22:45:59.592751Z"
    }
   },
   "source": [
    "# Check that the census is correct:\n",
    "\n",
    "# Number of profiles with this PARAMETER vs decomposition by data mode:\n",
    "assert idx.query.params(param).N_MATCH == np.sum(n[0:-1])\n",
    "\n",
    "# Number of index profiles vs all expected data modes:\n",
    "assert idx.N_RECORDS == np.sum(n)"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "cf3b8f43-78de-47bc-9a81-118bad4666d2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Make a pie plot to visualise census"
   ]
  },
  {
   "cell_type": "code",
   "id": "8ce7f548-8d3a-4304-8a04-742bd5ee3d3c",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2026-01-28T22:46:14.343706Z",
     "start_time": "2026-01-28T22:46:14.188882Z"
    }
   },
   "source": [
    "pf = lambda n: \"{:,}\".format(n)\n",
    "autopct = lambda v: \"%0.0f%%\" % v\n",
    "\n",
    "x = N.copy()\n",
    "x.pop('')\n",
    "if x[' '] == 0:\n",
    "    x.pop(' ')\n",
    "labels = [\"%s (%s profiles)\" % (k, pf(x[k])) for k in x.keys()]\n",
    "\n",
    "fix, ax = plt.subplots()\n",
    "ax.pie(x.values(), \n",
    "       labels=labels, \n",
    "       autopct=autopct, \n",
    "       colors=ArgoColors('data_mode').lookup.values(), \n",
    "       textprops=dict(weight=\"bold\")\n",
    "      );\n",
    "ax.set_title(\"Data mode for '%s' (%s)\\n%s profiles from the %s\" % (param_info['prefLabel'], \n",
    "                                                                   param, \n",
    "                                                                   pf(idx.query.params(param).N_MATCH),\n",
    "                                                                   idx.convention_title)\n",
    "            );"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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eGSw3POfYiuLMf7YIHH744fj777+3aZWrSFlh9zGHilhstWI5cXYD8RgzH3nHb7FVg+dvLOJxnYsF85vdnv/3f/8X9vfRZgU7fxfr56Jh61K4VhBnvrJVjOcBW7HZm8Myw641Cs0Xfi+HyIRu07k9Xud4PWM3vJO97oVe51iunb0Rtozz3Hbup33ffldlymQoturxb9jq59wGyx/zwNldG6lcszyxxYxliy3Izpb2irLd5OzSDYdd1dwvvoYPH25aIHmesMXV2WNiseXVfp4twhyGxJZR2zUduroBjz1fbH0cNGiQ6UVgr0g4bEljGlifMr9CVaT8VuSzNm+cXbRI9a5YFnRWNM4mTJ7UPNnYLcNuCXbLMDNZWNh1UB6eSBxzwELJbXDbPFH5PsccxbINYveIE4ORaO+zyZ8XZu4PL9KhTcbcB16s+Huy//I9J/4tL8ROHLf05ZdfRuxCjmXsYKhY01led2ZFsNLjSczvYPc1u5R48a7scatqmpYuXVqmAo9VPI5H6HF3vmfznhdK5hmDbeYbf89jxqZ/55gv7gfzMdw2Q02ZMsV0l3IYALuuQ5111lmmMnn11VdNpcH853nIC6odZ8Pv49jCaGJNe2XYcXyhlbUTK1lnBV2RshJ6/hGHJTjLII8Ru7lChXsvnKpe52LB7mKO5WMXv3NYhXMfQ89zm3dMC8c12c/y+rZ+/XrT5Rz62Z122qlS+WrTZPeXwS67wVgxs7wxSOD3sduWXemh+cLfhVbi3CbTanH/WPbCVcgsj6H7X5nrfmXLZCi7DZaHcEK7Q7n/zvGRxG5o3nTcf//9pguf5zrLPs93du3GMgzJsvkdLlCy6eEYRCd2O/PGnF3VocEdxzY6P88baqaPY+3Zne4c+sIhMk8++aQ5btzPDh06lOZ5pBsYCu2GrWj55WcjnRPhyoHNm3ids74I7Nh/X1xcXLrmGccusDWE47w4ZsCy43Viwb5ujpHhOC/ePVnhBromAgsGK0ZWfs6giS2THOtgT1p7keN7zj57/m1owWKrAO9EebKGw5ajRKXTisd6X/ye0AtBVY5bVdPE9FRmQHE8jgfzONJ7tmxwzA7vVjmY2rmvoYO+uR88j/j35QUwHMPIYIsXUmKgFVphcHwPX7zx4rglno9swWZQyBbUWPIt1rRXhm0l4zglBv3hhAZY8V6vjsfIVsTlHddQ8bjOxSJaZceKki0uHLsaiu+x5cdWWvbGm+9zHKpzX3kTE+4GoTLYusYbO7ZM2VY6qsp6bzxObMnhdc1ZBtg7wOufs8W1ustkpG1wTKTtqYgmUpnm8eJ4M+4zg3teU9kzwuPtLG/lsempSJm0QRiv5bHgddSm0xnYMUCNVFeE4ljDd9991wRo4dZorUj55WcjnRMUWtZt3sSrHEWTFF2xc+bMMQOreQA5+NMWVB7k0LtLDl5mxRXtbi+0sIduwznzNpFs11boIOqPP/7YVJT29zaY5aB5JzY5h86sZKXKwIcnDAt76KsygV2s6awu8TpukcpFOOxeYbASOii7PPE4Huw6CL34cVA5WzLtIHnmCe9SnRdwXoxCZ5baLmAGabFg4MwLv53EEHpuWWxd4LY5g5AXT6bZfh+HEkyePDnid8Sa9nCtYc73KfR37N5laxK7mcLlP1/R7u7jgYEHy42zdZatm+x+Lk88rnPlYZDIc5td6+ECL7ZWsRuTLavOQfW8LvPYsoXMYlcig7zQSVF2tisnYcVDIq7dvI6xJTB05q5d7DZe17mKlMlIx5StXTwunOgRaRsVzU8O92DLF9PmHOIRCwaXDAaZnlgxr3kzHqn7NhQnn1Csnw+HM/95Htqb1VAVKb8cssAJO7wZsFgf81xiUBh6bbeLeTsntaRMix0rQTtegHdKnJXESoXdR5xebFuM2KzMGTicBcQImEsAsFmZd562W8CyFyvOqGFlyAPHFgJG/axweWfCiyebTtltxj796sDuKp6gHOPA8VI84e1sUy4Vwq4uO0aDM8K4cjWbx9kszXziTMTQ5nXebTH9e+65J/r162dm07FpmTObOBaBYynK6xqrbDqrS7yOm70743gOBiAsY7wrDFfRc+YdW5Q4i43fy0qQF1uWOQZvHG8TTjyOBy8QHHPEWbFsZeOFg9tkum13gV1ihmMQ2cXDLinO+uPnOXvQYvcVjxe7r9iKxL9j5cHZfdwWx6aE4vb4O/7LfeYsb+YRu2x4MWd54PcwGOMMQt6A2VZc7j/H2fFcZXcP85ytKlxWgF0wPJaxpt0eM7bS8Hjz9zyfmaeRznG2wrBlhAEq75i5fVYMbH1msMx/Yw1yK4vBLtPLwIA/M8943O04qkizCON1nQvXXezEQIZ5E661zuIsUR5THiuWf5ZhziZlmjjL1OK5yG48dhvzZw6Z4Dgpll1uP16VGs8njg/jOFteh3hd5I1vrK0/4fDGhbNDWVZ4frKscWwpZ5+y27ui42sj4TCcWMukvUY9/fTT5vPcT5Z3lgOeWyxPDBgYkDA/eE5zTCxvtMpbZootwRzLzGCFXZ+8lvI85PkZ65IlFq8HbOEPfVqJ80bG/o4/c5wZZ9mzezXcUy143tvPs9Was+RZ5hmwhpuJGqvXX3/dnH8c3hBORcovlwRieWGvBsf/8RgyP3kTy/SG4v6wjrGz5RMq4BF2NpR9caXnxo0bm8VrOfMqdHVz4orVnLHHFZ25ePHhhx8eGDduXNinMXBmUrt27cyMTfuEC+JsPc4w5N9zO5zBxhlHsSw0G2kxxUgzfO3MOufsOM7KufHGG02aOcuGM4Uuu+wys8hi6AKH1113nckTzsTbfffdzerZ4faV2+eMXO4vt8kZWDvttJOZVeScxRVOpMUkY01nPJ5YEG6B4lCxHrdwee7MUy7I3KhRIzPz1Dn7LFy+cl+vuuqqQOvWrU0e8FhwXydNmlQm7aHlpirHw+Ynn8LClf55XnChzieeeGKbz3JGGn/HWV2cPf7qq6+Gnc3J2XdPPvmkWTWd2+OMVy78yRXeQ783tLxzhi/PM86WffvttwMHHHCAma3O7fApBJyNF/q0Ej61gbNjOVuP+28/x5l0FU07Zwdzpfn8/HzzO+fi1pHOceJsSe4P855p4JMV+P/OczdaWYk0KzZcWWeaQhfd5mr7nInH/WM+cMaenW25atWqQDTxuM5Fw/OI5/yaNWuifo6zKA866CCT95yRzVn+kZ7Owtm6nEXIcsHzhfnHGe7liXTttNda54xfzj5kuWV6eA7zXOYTY0L3O9I1LdwxXb58eeDSSy811zfOOmUe8wkEfMpAedcoXjv4PmeyxlJPxFImid/Pc4YzQ0PzgDNaeQ7yeLBsMb18ao9zBnuk/ed167TTTjNPgOCi9LwOcEYun5JQGa+//ropd3aGtPP7Q2e52rr9008/DZtXzhfTzhnkPF7OBbIrWt/MmTPH5OHZZ59d7mdjLb98YAK3x2No6+VIKyfwqS2hM6ETJY3/SXz4KCIiTmwNYMsQu/hFkh1bcTnBjC247N2RrdhFzTVYOZu5oq2hlaHATkQkwdjtzGELnAXI7jd2G7Lbi11Dzqc8iCQzdiGz25Ldw9Fm9aaa8847z0wiq65hXp4bYyci4jec6MAxaRyHyEHYHKvD2XkcOyviF3x0IMfoMbBL+NMVkkRRUZEZE86nNlUXtdiJiIiI+ERSLHciIiIiIkkc2HG9JD5GhAOMucQJuy/CTYsm/i7Sy7mQIQcpcz08Lk7IpQI4nZlLNXCRx1ixK+WKK64w08M5bZrr93BNHK7pFIrLtfBxNFwSgMtFcDp46GNp7Dp14dLOKexewzXKuMQAl5rg1G27yCOn3zsfOcZB4dyH0PWAEo3LdnDdMC65we/nEjFexQVWWabt+kxOzEsui5Bo/H5nmePSGzy2XN7hjz/+KPPZRB1Tlh1nGrjfXAfKrh8WT1yyho9o47nL72Le2zwIPSft+pGJxOPM/XeKdq1LJD7qit9blUV+E4nLUHBwPtdvs0u9hDtObuVfpDSGw6WOotVnrGOqS2FhYekTNypSF7qBy21xaRFeH5i/XP6FizmzruTans5FzWdtuV7ZFz/LpX+4bM8111xTutZmtHGx/LvQR4aG4tJfHEPHpYW4xBCvX1xf9JFHHonrIuK+GGPHJypwPSYumsh1drggZyThnjXHRQO59pjzuYc//PCDeYIF1/HiwWXfNy/0XIeGa/5wDEw0LDRcg4Zr7/DzHCfDNWu4jhJnu/D5hFw/yn6W61bxIsk1iLjGDde8YQHkGjfO1dKJgWLoAsTRLgxuDo7lIqBch4kBsg0+uMZg6Jp6buBAdK4PxkV1ua5TaKXptcCO5YhpDLcKenXiunIMhrnGFG9SeFFihcnzyC6CzICP5xrHi8Qbb7C4LiNxkDF/5rpdPJaXXXZZXL6D64Px3Oc5yPWmuH4fH0XF9am8dBPFPK7oWpPxCuxYHhlseu3aw8Wq+fQWrtvGNSftwr08jl5PY6TAjvWBWwFo6Hp29skonMwT7VFnbuLadjxP2WDCsXzMZ17jFy5caOpfXvtZB997771l/o5rc3LdOl7bWB/z5p+PKmQdxnU3wz1/lsGuXZCf10auu+d86pPFxyly/U2uLcjtMCbg3/LxfFyrkucy68ZqF/CokpIS8yKuKxXLunJO5557rlmbbOrUqaXvcTt2m05cB4drIYWuVRSK69MwHa+99lqZ9wcMGGDe/+STT0rfe/755817XGvJKiwsNOvxcK0gJ67nwzXK3LBu3boKfZ5rRXHNo/LYNZ1iWUcrnrj2FNfXKw/XYQtXFqoT1+qKlEeR1p6Kt0hrt02fPt28zzW0Ei3cWlRcM5Brc3Xs2DHi3xUVFZV7zjr9/vvvZp8++OCDcj8bbi26ROBx5v5XVCLKL9dfc67l6NY1Jpz77rvPpM259mEkFa0r4qUiaeQaeJGq31jW8Ywnnntcr43rGXKdN647WV3HNVa27qxbt65ZwzScWbNmlVkXb2aEdQXt+cP1IPn7b775Zpvfcy1B/o55w3/vv//+bT7Dup3r9nE74a5DXCf1888/D7jBs12xtvm0st24fFwPW8X4HEOLXaLhtsmnCPChv+U1m9qHIrNlw8ne3TofgMwonVE8u18tNs9zFhxXBucdQDzY5ma2sPBukd0ATAdX6A7t9rVdTnxcDO/KeLdjW1+4BhFn7bA5mauI8+7k8ssvL9Mtw79lyymfPmCPj+2WC+2KjXbXxbsntmDyjpZP1eCdqxPvrPhkBOYfu8yYv3wiBFs+I7GPfGErLFsVneXH/o4ttryrY9c+u8bZqsrvYt6xy57pYbq4An3o803ZesWV/XkHxlXvmS7uM5+KQmwJZssWt8vZYLzLi4ZPT7BPZ2Azvk1v6B08H7nDblG2jHKpDK4RFfoMVXaPM7/sPnD/uE22UFWWLePOB4FH6orl6vxsnWZrNfef+cP8qAoecx7/2bNnl/luHivuK8sp95WPtLKPCuK5xu9nOrhWlLMln2WTDwunU045xWzLduGF64oNp6r5zHzjPtlyH6mrObQcRCu/xF4H7juXl2A54VNi2CoRiq2vfDQYu6N4jeC5z14Nmwe25YJ5a8sjyylV9Dzhc4NZDphOppnDVTj0hdfZUAceeKDpHo+E5xm7OIndbs78ibXLnENo+DhKtoTy+sZ9ZOtk6CMZw4ll36OlMRTLor3mObsKWcadOGua5YR5yJ4rtqxV5npaXq8Br1UsFzz+3NdwQy3s0BA+B5XDo3iO2UessY6wx5efOfLII82s2HB5UNlrBetTPoKNrXTcx3A4LCrWR9bl5eWZ1kle3/hUl1D8HcsJr++87vLf0CV/+UQS7iN7FsO1zvLv+cQgVwSSQEVb7F555RXz+ffeey+mz++///5m5XK2AJR318AnBrB1bcSIEYGCgoLAqFGjAr169QrsuOOOZVam5uryfBpCqK+++sqk7fvvvy99jy0DXLWaK8vzDqB9+/aBW265xdxVlMfelbRq1Sqw9957Bz7++GNzt7HLLruY1cydLYa2dYYtBHyKBFsguXI57/wPO+ww09p1++23B3744YfAY489ZlqMevfuXXo3widd9OnTx7TY8We+7BNBQlfBD9diN378eLO6ec+ePQPvvPOO+R4+TYN3iXfddVfp5x588EGTD0zvkCFDAt99951ZUd/5mVBMB9PD7+TK6zZ9ztXsubL7xRdfHPj222/Nkxx4vPn//N0VV1xhvuell14yZYH56WzF4jFq0KBBoEuXLmaFdR6/o446yvzt3XffbfZp4MCB5u6Pq49zFfj58+dHTC9XULfpuu2220rTa++WmZe8i+ZTGHgsuJL8HXfcYVqh+X3Op0jwjpHHiu/zmLJFmfvKO9zyypAtE1xBneWbd5ls5T7llFPMPjifIhHumP7yyy+mnPG8YEsYy9Ohhx5q0vn+++8HKttix3OJq9NzxX3nd3O/uNI+jx/LD9/v37+/+R2/l9/PdDA9zD8+8YH4hATbis4n2TCvWR6deRCtxa6q+WyP9bHHHmue8MFrE1sjWc5CW+xCr3XRyi9bEpjXfLIHry3sNeDTGJhOu3/Ess3jtP3225snC/z000+BN954I3Dqqaea37PcXXnllaU9D7Y82pX+K3KecBV+vv/ss8+aJwnwCQujR482f88nijgxjXyfxyYSPk3iggsuMJ/jdzvPk3Atq6H5t3DhwtJ8fvnll825dO+995ryzZ6d8sSy79HSGIplkdcoftbmM1/2Osv3+RQW9uwMGjTIXFNYR/H6zJb0il5Po2H54fd9/fXXph5gHvHJJaGtwbwesfwwXbw+87rMayDPC9Y7rL/49BimgedHp06dtjkOVblWXHTRRWZ7kydPDsRqZpQWO8teq3nts3jcmIe2/ub1mdth+i2ee+zl49NkvMiXgR0zm022fAxWeXih4bb5CJFY8LE7fCyI85EnPOn4KBonFuBLLrlkm79noMW/YfetxUdLvfDCC+ZiyxOMFxCexPvuu685cWIpvKwAnfvLdPICe/DBB5e+ZyswBghOvBDx/UceeaTM+zz5+D4D5fK6CGMJ7Bg8tmzZcpvHwnB/eWFYsWKF+X8GTAyWKyNcN4atGEMfJTNx4kTzft++fcu8P3z4cPM+g2uLlQff42OVLB5zBqAMdJ1BHB97xc8+88wzVeqK5e94YXdiYM3g0mIwyc8xoA+3bZaraGyZCH2xG9Q5tCDSMeWFkQEYb3KcFz0+rozHOpbuQpYd7hcvrnzxe+z+89Fbzu/m44+cN1A8P1j2Wbk5zxWmh+nac889y320UyyBXVXy2aaRN3/O/GDXEa8TsQZ2oeWXj0jidYIBmRP3nTeWfGybxXzjK9o1MVJXbGXOE1b8ofi70POawyZY1pzlpyJDBmIJ7Hgd5qPwZs+eXeZzvGHiZ50BcKiK7Hu0R9JVtCuWj+lzPuKNN14MNhhUVfR6GgnLIm8ueMNgGzXsPoQeP3s+8mbAifUV33/xxRfLvM90hh6HqlwrbLdpaJcn/8ZeN/hyNs7MjCGw4w1saPf5PffcUxqg04wZM0zwedZZZ5U5HvyMvTHyGs92xVYWZ7qwy+GMM84o0zUaDh9Ozu5GdkuGe/h5KA6KZDcOZ9Jx0CS7G95++23TrcquHz6s2Cla947zd+ze4QBxPkie3W4c1MmZP9w+B+TG4vjjjy+zv2zqZvM6t8HFUZ1OOOGEMv//008/mX9Du1I5qYTdO+Fm8lYUu3q5HU5mYRM8u0Dsi/vM39uHPrNrnA/D5qBUDorlTKh4CN1v240Xut/8fjb3h+43Jw9wwojFrgd2gXDiQ/PmzUvft10FthuxslhGeAyd2CXt3C67Z9htyc8585Rp4kw325VWHk7o4cOuOUyA2+QDz0899dSoA385sYHnGs8f5wxezpbmJAV2VXFyEbEMOtPHLp/QweTsFuGL3WSDBg0y5yTPDSd2bTi7h7l9difx+zij12J6eLxZpsJ1/1VUVfLZppFdZs7znl1H7IqqbPnlucE0sFvQmSZeBzgMxaaJqwHwkUbsLivvmhhORc8TDvFg92qoq666ylw77WxrntfsbuQkmUTOAOex47WV56gznzjBgYYOHRq3fY8XptdOxLPdu7zW2HO/ItfTSLjfHOrB/Oc56xwWwskFsZRBm3cnn3xymfdPO+20Sl8rKoLDc+x1gy92WVdEaPcq/992v9pHf/F6xO7+jz/+OG51UaL5LrBj3zhxpls0vCgyGOLB42zUWMbYcNsMBvkoIG5/n332MRdVjlHguDXn0hocx8KZvaHsOD4GBdHYFenLOzktVi7h3uO4oLVr124ToDgxnRz/x7E7TswTbiPcflQUt8GLDoNW54nIFy9EtGzZMvMvx/pxViT3nRdf5iXHZXCmUVWE2+9w7xMrgdD9DnfMOI4i9H2+R7y4VgUv2KEVMcdyOLfL2Wwc48LvDM1XjiuyeVoeXhA5LpPj/jhGhmNUOT6VNz6RcHY4L4SR8o9sHvL4OdMW+hgtjn9jYMljzLE03KdnnnmmNC8rewwZQDKdVVWVfLZpjHSOxip0H+1MRh6z0DRx3J1Nkx0DWNmZthU9T8J9jo499lgzFs2OAeNYLlb40cpYPDCfvvzyy23yyI7ri+XYxbrv8cJrXiie+xzfXNHraXl1JYNDlm2+OLaW5yKDmNBlb3g9Cl35wNYdoddABqKVvVaEw7Hj4W6WebPE6wZfdvZ+RXB7zFebfjZyzJw50zRqMIiz+cLAlTeIAwcONJ+zS5jxs17k2eVOKoNBDO8A2aoSbfkIBnUcZMm7Whbg0MojEt5t8g4jtABxqRKeiOPGjSt9jwPoOdA0lH2PA4xj4WyFiIaVS7j3uG+hd8OhQSzTzosEKwBncMcTkduwg/yrgnfx9u4s0oWcd0bECwXXEOKLJxVbk2655RYzKHzu3LnmhKqMcPtNnC4fWumxhYUnr9cxjdyPSJM1nHf9FcFyx4qPAR6XF2BrQbhjys8x/0Ix/2z6iEvkcFKTM91OrFAYWFblGIZLA9PHdLqZzzaNkc7RWIXuu81Drj3G1r9I7DkdOtEhVhU9TyLdJPNY8Nznufz444+bpUoY8HNCSSIxfWzp5uSycJyt7aG8eo2oyPU0HPYuse6jSNd3rgvHXpNox9XWHWywcAZ3oeW6IteKcNgAw0kKnCTFtWgtXpfstYnnYOjEsmjY0zZq1CgTB7DOcQa7TzzxhHmF4u85CYd5z7LLhh6eV24sT5QyLXY86LxLYZdDJJxZxqCOdyWfffZZ1LWGwl0A2KXEuwMndnXwbsN5cHkXNGnSJNP8bPEE4No4XFwx2sWE2MVLu+++e0xpYyuisyWHlSjvUtmqaJvZI7Gzm+y6PRZPfN5R299XBYMxdi9wth4vsqzEQ1/h7lLZ/cXme168ePEInTlWFba7KHS/eXy5HlI89jsaW/bsXXhlcPFMlj2Wy3B5WtlKk9vjTQjTGGl9QnbTsyyz7Dn3ga1kzFOeD1wnjpgOZ7ritb4gt8sZ3KyEnN0qLLcsv3ambFVVJZ/5O7ZU8G7fmUa2FnDtuMrijQ4rJHazhkuTDZR5DDgDlt1r0Sq+SOUxnucJezp4s8mhMux6q46FeHnseNPNPAiXR9GuxYm6RlT13K/s9dTi+cLv5ppv7G4OfTHIitQd62TXY2ULsRPXEa3stSIc1qdcI44zUVmvVtWGDRtMWWSdzAch2FZFDj3hmprh8oRllsfdNuCwZ4nn80UXXWQalcIN3WId7AZPt9gxGuYF2t7ps4vGrozN5ubQCzajaU5jZvNsOJxqzaCO3R+8awxd8Z8Fx1ZiDCB4x8PxB3b6N8cfPPnkk2acAae284LNad0sbCy4fCKDxa4mdjmwSZfj5XhXwTtUXszYAmX99ttv5k6SBZctfwzOuN+8O+FFJXSMVSQM3nhXw1YuniwPP/ywaUrmlP7y8O9YSdx4443mb1iwuZo2F17u3bu3uSuMB46HYEDNYJNjClm589hynAdPADvWj/vMFk1enNjawAqQ3dxslejUqRPihcePC12yO4N3k+z25XG//fbbzRgLrk6eSKxoWF45FIDjddiyykqmvKDfiePg+Pc8HziGiWN/2B3Du0hejNj95VykOxLeudolTth1xYs6L6DMg2jjsrjAJ8sPKxneSbPSZjnnxY+BTGWXLIoVjxuXouBFlxU476YZvHAJA7b28tyLh6rkM9PICpQVCT/DioBp41IQFemKDcXz55577jFLQPA6xMVb2TLC48exkrwm2fOf1yKeV7xR5DFl1xYXombvhV0Y3T60necpr3vcP54j8TxPeKPG4Stckojnc6zXt6pgHg0ePNiMZ+zXr5/ZH15nuQ8c28mFZCO1uCTqGmHzmtdpbpPXbwZosfYeVeR6Gg7rSpYVnrPhzm8eI7ZYcaxztHFrLHOsL7gME+sO9pZxmSG7lI+zx6kq1wrmDxtiWE/x3OM5xHFv3AeeS2xAYVrDLYUyZ84cM6yH9SJbKu0CxaxX2HLM5VuI5wHLBctIuCV0GCjzM8w7xgG8aWQ5Zqsm95vHgL0cDOj4HazDWY9VRxnfRsDDOFss3Iy9cDO3OEOMs4ZCZ47FMgPQvjhrzho7dqx576abbiqzDS4FwdkxnPbNadKtW7c2M2vCzazizBmmh7NTOUuJs4K4TELo9jgjkDOTuD1+jjP8OA09lsVX7cyfhx9+2Ewz5+wiLvPAZUqcS6qUN2uLs+W4BArznDP1mjVrZmascaFYp6rMirXvc2kG7i+/h8sGcOYiF/e0Hn/8cfNew4YNzb4wj7mUAGcRVmVWLGcwhpuxyLzr3LmzSQ+/88wzz9xmqYJIi0iHW6ojUjrC4WzLrl27mu92ziKLlM/hZnByNhhn+O2www6m/HAGILfJ2YDOBbpjPSdYXjmznDPgnDNNIx1TLily4IEHmvRyhjDLOZf0iFWkPHQqb4Ybl05gmrn/TMdBBx0U+OOPP8p8piqzYquaz8TlUbgMBMs0yxvzN9wCxZFmxYYrv3bfuQQMZ5fyGsLtcTkNLuvhxCU1jjjiCLNEBj/HWbLXXHNNmc9wQWrO4OW11HlNrOp54sRlI7htLo8Rq6rMiiX+Xb9+/cxSHkw/yziX3eCKBGvXro363bHue0VmxXJZIS72zusfZ1w667RI147Qa2ys19NQdumZq6++OuJnJk2aZD5jZ1xHWzCds2/PO+88sxIFlwDhQsd//fVX2NUmqnqt4AxgLlfE5bxY3jkrnDNt+Z1cMse5aPLMLdcM++IKBlxSjMed+x5aZ3PGNrfFYxMJ08vj7/wMV0Fg/rCe4rltlwnj6hN2ObDqlsb/VH846X28k2ATLbs5QgeCeoltWWQLhXPsgYiIF7F1hy0dHC8brbtQkhe7etmKzhnQFZn5LSnQFesmdq+wSdbLQZ2ISLJgdxjHI/OmmV3mCur8gd2onIjA7mV2vfI4s6GBz1VXUOcOBXYRcDagiIjEh53IwrGQoesTSvLibFROluAx5Zh4ThTiun86xu5RV6yIiIiIT/hquRMRERGRVKbATkRERMQnFNiJiIiI+IQCOxERERGfUGAnIiIi4hMK7ERERER8QoGdiIiIiE8osBMRERHxCQV2IiIiIj6hwE5ERETEJxTYiYiIiPiEAjsRERERn1BgJyIiIuITCuxEREREfEKBnYiIiIhPKLATERER8QkFdiIiIiI+ocBORERExCcU2ImIiIj4hAI7EREREZ9QYCciIiLiEwrsRERERHxCgZ2IiIiITyiwExEREfEJBXYiIiIiPqHATkRERMQnFNiJiIiI+IQCOxERERGfUGAnIiIi4hMK7ERERER8QoGdiIiIiE8osBMRERHxCQV2IiIiIj6hwE5ERETEJxTYiYiIiPiEAjsRERERn1BgJyIiIuITCuxEREREfEKBnYiIiIhPKLATERER8QkFdiIiIiI+kel2AkREqqx4I7B55ZbXqm1/LlwFFK3f+vm0NPvDlpfjvYxcILsekF0/+MqpX/b/s+vogImIZymwExHvKywA1k4H1s4ACrb8a18b5gUDu+qSlglk1wXymgM1OwC1OgA1O279N78VkJ5RfekREXFICwQCAecbIiKuYQvbilHAipHAytFbg7lNy5LnoKRnAzXabgn6OgJ1twPq7wLU7Qmk615aRBJLgZ2IuNcKt+KfYBDH1/KRwUAOPr3XzMgD6vUKBnkNdgUa7ALU6uToFhYRqToFdiJSPdbNBRYPARb/DCwfARRMAQIlqZ37WXWBBjsHg73G+wGN9wUy89xOlYgkMQV2IpIYhWuART9ufRVMVU6XJz0HaLQn0PRgoPFhQMMd1aInIhWiwE5E4mfVWGDBt8CCb4BlfwIlhcrdStsP+HwSsMsRwK5HAjsfBuTXUn6KSFQK7ESkalaNA2YNAGa/D6ybqdyMl0k9gb/Gbv3/zCyg577A7scA+58K1GusvBaRbSiwE5GKWzsLmD0wGNCtHqccjLfM2sBb64Ci4vC/z8gEdj4cOOQcYPejgewcHQMRMRTYiUhsNi4BZn8QDOiWDVOuJVLebsCLw2P7bK16wH6nBoO8brvpuIikOAV2IhJZSTEw/wtg2svBCRCBCC1IEl9r9gE++a3if9eqK3Dw2cDBZwGNWuqoiKQgBXYisq1NK4DprwFTXwDWzVYOVbefmgNzFlT+79PTgR0OAA45F9jvZCArO56pExEPU2AnImVntU5+BpjVHyjeoJxxQ1574MUZ8dteg+bA8dcAR16iWbUiKUCBnUiqM92tnwcDuiVD3U6NpO8LvPFr/POhZl3gqMuA/7sKqNdE+SziUwrsRFJV8WZg+qvAxEfV3eolM3cEhv6TuO1n5wKHnguc+D+geYfEfY+IuEKBnUiq4aLBM94Ext0HrJ/rdmok9Hmy/UuAjZsSny/pGcA+JwCn3AR07K3jIOITCuxEUqnLdda7wNh7tJCwV+XvBLwwqvq/d8dDgFNvBnodUP3fLSJxpcBOxO8CJcCsgcC4u/W8Vq9bvy8wKAHj62K1ax/gkieAVl3cS4OIVIkCOxE/m/MhMPYuYPUEt1MisfizLTBllrt5xadaHN0XOPNOoHZ9d9MiIhWmwE7Ej1aOAUZeDiz93e2USKxyWwIvzfNOfvGJFgzujrk8GOyJSFJIdzsBIhJHm1cDI/sB3+2ooC7ZFLeDpxSsBF68Gri4JzD8a7dTIyIxUoudiB8EAsDMd4D/bgQ2LnY7NVIZ83cFBo/wbt7tdGhw/F3bHm6nRESiUGAnkuxWjt7S7fqH2ymRykrPAj7KAdas9XYecomUPhcB5z0Q7KoVEc9RV6xIUne7Xgl8t5OCumSX2937QZ1dMuerl4CLtwNGfOt2akQkDAV2Islo4Q/A192BKc8BgWK3UyNVtbpucuXh8gXAbX2AJy8C1he4nRoRcVBgJ5JMijYAf18B/Hw4sGGB26mReBm7MDnz8tvXgEu2B8boGcMiXqExdiLJYvnfwLCzgDWT3U6JxFNOY+CVJUAgibM1LQ047irgggeDz6IVEdeoxU7E60qKgLF3Az/sqaDOlzold1BnZ2V/+hRwWW9gkodn9oqkAAV2Il62ZioweO/g0yMCRW6nRhJhXol/8nXuJODqPYE3bwOKCt1OjUhKUlesiFdNfRn451qgeL3bKZFESUsHvqoLLF3hvzxuvwNw+0dAi45up0QkpajFTsRrijcCw84F/r5UQZ3f5XX1Z1BHM0YDV+4C/P2d2ykRSSkK7ES8ZP08YPA+wMy33U6JVId1jfydz2tXAbcfCbz/kNspEUkZCuxEvGLJr8HFhleMdDslUl0mLvd/XpeUAG/cDNx/KrBRwwpEEk2BnYgXTH4WGHIQsHGJ2ymR6pJVBxg1IXXye+gHwYkVi2a5nRIRX1NgJ+Km4k3AX+cBo/pp1muqyewGFPtoRmxFxt3997PbKRHxLQV2Im6Pp5vxlo5BKlqYiZS0ehlw86HAJ0+5nRIRX9JyJyJuWDUe+PlQPRYslf3YFJi3CCnt4LOBq1/W0ypE4kgtdiLVbekw4Md9FNSlsryOCurox3eAW/sAG9a6fUREfEOBnUh1WvAd8NPBwOaVyvdUtqm52ynwjtE/AzcdElwaRUSqTIGdSHWZNQD49RgtOizA1ALlgtPEv4D/7Q+s1KxwkapSYCdSXcuZ/HkmUKLnZ6a8jBrAiPEpnw1hZ8xety+wbL7yRqQKFNiJJNqYO4LLmSCgvBYgpxuwabNyIpx5k4Fr9wEWzlD+iFSSAjuRRAkEgL/7AuPuVR7LVsvylRvRLJoZDO7mTFQ+iVSCAjuRRPn7MmDqi8pfKesfPXmhXMsXBLtlp/2bNKXnlVdeQVpaGs455xy3k+Jr06ZNw8EHH4zatWub/N5///3xyy+/mJ/5mjUreH6de+65pb+Pp9WrV5vv5mv5cm8+ElCBnUgijOwHTHtZeStl5bYGps1RrsS6kPH1BwDj/0x4frHyt4EBX5mZmWjatClOOukkzJw5s9y/LywsxH333Wd+vuaaa0rff+qpp7DDDjugbt26yMnJQcuWLc02x4wZU+bv27ZtW+b77evMM8+MKf2fffYZ9t13X9SqVQt5eXno1KkTHnroodLfFxcX44EHHsB2221nApIaNWqgc+fOuPHGG7Fhw4bSz51//vnmb2vWrGk+06FDB/Tr1w8rVqyAV1x33XUYMmSIyfNddtkF3bt3N/u02267mVdOTk5Cv79OnTq44IILUFBQgMceewxepMBOJN7+vR6Y8qzyVbZV3Ea5UhHrVgefUjH572rJt+zsbBMctG/fHosXL8ZHH32Eo48+uty/+/LLLzF37lz06NEDvXr1Kn1/6NChWLp0Kdq1a2eCpIULF5ptHnDAAVi3bt022+nWrVtpgMJXx44dy/3uxx9/HP/3f/+H3377zQRk3AaDNQY/1r333otbb70V48ePR5MmTdCiRQtMnToVjzzyCP73v/+Vfu7zzz83QWDXrl3RsGFDzJgxA88++yxOP/10JMrmzRUbb8p9oMsvvxwjRozACy+8gB133BF//fWXeTVr1gyJdsYZZ5h/X3vtNRNgeo0CO5F4GnMnMNGbd3HiATM3up2C5LNxHXDbkcD8qQn/KgYFDA6mTJmCs846qzSQKK/F6v333zf/hgaBAwcOxIIFC/Dvv/9iwoQJuOWWW8z73N6kSZO22Q6DFBug8HXXXXdF/V4GkzfddJP5+ZlnnjHf9c8//2DevHn45JNPSj/3+++/m3+7dOliAjruH3+m2bNnl35u/vz5JpgbOXKkeX/vvfc27//xxx9R0+Hs9mQg2Lp1a+Tm5uKII44waQxtGWXeXn/99WjcuHFpOhhQsgWMLXBsdWPL2KGHHlqadnax8m+nT59eGtDy//nd4bpiw9m0aRPuvPNO0yrJ7+D3s5Vy2bJlpZ9ZtGiRCdxYFhjoN2rUyKT766+/Lv3MzjvvbFp0+Xc//vgjvEaBnUi8THoKGHeP8lMiXG2zgeETlDuVsXopcMvhwMrF1ZZ/AU5+Akw3H7s4o7HBx6677lrmfQY3X3zxBXbffXcTsLA7lBgssCs01AknnGD+hr+74YYbsGbNmqjfy+CtqKgI+fn5JhBkKxsDEgZOzhbBffbZx/w7efJkE9Rw+/yZLYw2TTa9DCbZWsjuYbtfNsArD9PA7l22HLIl67vvvsNxxx1XmpfWoEGD8PTTT5vWQ3ZT0yWXXGKCvYkTJ5rAkN3hgwcPNq2bbPlkIMZ0Mdgitjry/9kSGqvjjz8e99xzj+leZ6skA70333wT++23X2mXdN++fTFgwACsXbsWPXv2NF3bv/76K/7+u2yrsT3WbCn1GgV2IvEw423gn2uVlxJZbnegYNvuN4kRl0BJ8OPH2FXKIIytSO+99x7q1auH119/HVlZWRH/hgEA/44YDIVasmQJhg8fbgKWkpIS0y37888/bxMssoWKY/D4L1vVHn30URx22GHmbyJhcEbr16/Hhx9+aII6Duhn2vv06VPaTXj77bfj5ptvLp18wO2zdYuBS6tWrcpsk79nF6dtyeNEBQZisWCQyQCIrZNsfSS2IH7//fdlPsdAj3kyduxY0zrIVsI33njD/O6qq64y6eN7bdq0Mdu84447SltTbVfrhRdeaP6f+xaLoUOH4ptvvjE///TTTxg9erRpNWXgxvQymCN+N7HlcdSoUZgzZ45pyTz11FPLbI9pc37eSxTYiVTVvM+B4RdonTqJblVt5VBVTfsHuPt4oKgwYeO9GHCwq5IY+LA1p7xZkla4lj0GIAzOGCidcsopprWI/3LwvcVxdwzIGGwwiLDdwAxc/vwz8uQRBj0WA6Nx48aZQJTY/Wu7UBm0sJuTrXXsqmRa+DO7kM8777wy22RQyHzg33OyBbsaOZ4tFttvv71pBaTTTjut9H0GcE5shevdu7f5OSMjwwR3tlXPjudjgMvglPj7qhoxYkTpzzymDGybN29e2lLHvHZ2p3OCBMc4HnXUUSZP+FkntuSGHn+vUGAnUhXLRgB/nAoEipWPEt2YBcqhePhnMPD4+cF1IuOMrTAc68UuRHb9sQuOFXw0toK3rXfhMIhg96IdY8dxexx/5xyzxQCH2AV58sknl/6OLUaRsDvS4gzR0O5gO96M3aNsveOYN+4j03L44Yeb34UbI8YWSk4Cueiii8z/v/vuu6XBbjTcz1hwfFpVt1FRAUd5cU5QsS+bpvvvvx9fffWVCWaZTywD7BZ3Bqpku8mdx98rFNiJVNb6+cBvxwHFGhAv5chpCoybpmyKlyHvAa8HJw3EW3p6uukCta1UnPFqW3PCYSsdx4qFTkRgCxwDIuesT9sVSHYMHIM8trJxvBcxsGQLnmW7d996661tJgiwm9SyrVrO1i22yjlblf777z+zfbYg8mfisibELlROQrCYbmfQF24Wbygu42JnrX7wwQel77PlM1rwttNOO5W+179//9I02/xi4FtVuzoCXnZL2wkqHEfIcYU2gGcrJ1v0OBmFXbbPP/+8eZ8BnpM91rHMXK5uCuxEKqNoA/DrscCG4NgakehiH+AtMRr0CPDpMwldL80O1H/wwQejftZOLnAGVexqPfvss83kAAY2bP2x49wYDHIgP3E5FHbXsuuRXZ9shXv77bfN7w488EDsscceEb93r732wrHHHmt+Zpcqv8d2rR500EHm93ZShg1OOMaPLxuo2AWVGZCxi7R+/fqmtY5j2RjUEv+f6/GVh62cbDlkd+yll15a+rcMlKPhBAjOTiVOqmBAyiVnGDyxBfPuu+9GVe2///6l6eCEDk6eYDp5fNiSaYNlzjJu0KCBCdgYcHJSh+1mdrKTKezEFC9RYCdSUWzS/+tcYMUo5Z3EZq666hPi5WuAobEN7K8ojqmyY90Y4HD8WiR2YL0NhIgBA99ngMQlOjjBghMVuOgwx/HZwfdcd46LGnPCBpcpYcsYAzQGk+wStC1ZK1euNP+yy5Czai2Ok2NXK9/nQH4GbZxQ4EzLyy+/bLoYGcisWrXKvBiosVXKBq0MKtk9y5mxnEzACRlMG9e5Y8sVWzLLw5Y1LsrMLml253K5Ei6eHEv3KtPIdfX4nex+ZtcxWyQ50SReT4/47LPPzEQMBo6cnMGlTfh9t912m9l/4vhHBqfsauXYQHscnV3nnFTB48kA8JBDDoHXpAVC5yGLSHRj7wbGRl9fSmTrVTYD+LwWsGKVMiURsnKAJ34DugTHmLmB3ZZsdWJgxu7I0K7HeGAL36effmoCOQYfXsK15NjKyC5MZ3euX1177bV48sknTUDtfMKHV6jFTqQi5nwUDOxEYpXXTUFdIhVuAu49MfgIMpewy9Yuu5GIx0yx/YXrpbFFzWtBXapZvXq1eeIEu9O57p4XZbqdAJGkseJfYBjHo6iRWypgbQNlV6ItmQM8eDrwwHec/eBKfl988cXmlQjsyuRYPHFfnTp1yl042m3qihWJxYbFwPc7A+vnKb+kYsZ1B0bqiRPV4rRbgPPur57vEvEodcWKlIfDUIedraBOKi6rHvDPROVcdXn/QWDYF8pvSWkK7ETKM+kJYNEPyiepuIyuQIm67qv1Juyxc4HFW9eTE0k1CuxEolnxDzA6uFq8SIUt1CW22hWsDI63K976uC2RVKKrjkgkReuAP04DSrauHC9SISO894DwlDDhT+DtO9xOhYgrFNiJRDKyH1BQ/vMRRcLK7wwsXKLMccsHDwGjBiv/JeUosBMJZ86HwIw3lDdSeRuaKffcHm/3yFnASgXXkloU2ImEWjcHGJ6Y9agkhUwJPnhdXLRyMfDClToEklIU2Ik4BUqAP88ACvX4J6mCzJrAyPHKQi/gs2T/+srtVIhUGwV2Ik6TnwWW/q48karJ7gZsKlQuesWzfYH1BW6nQqRaKLATsdbPB8YEn/coUiVLcpWBXrJ0LvDmrW6nQqRaKLATsUb1A4p0Vy9xMGqmstFrvnwemPiX26kQSTgFdiI0/ytg7ifKC6m6vLbATD1T2HNKSoAnLwKK1EUu/qbAToQLEY+8Qvkg8VHYWjnpVbPGAYMecTsVIgmlwE5k7F3AOj1bUuJkxnplpZf1vxeYp4XHxb8U2ElqWzkGmPSU26kQv0jPAf7SMieeVrgp2CXLBYxFfEiBnaT2mnUjLgECeli4xEluD2D9BmWn1439Ffj2NbdTIZIQCuwkdc14E1iuWXISRytrKjuTBZc/2bDW7VSIxJ0CO0lNRRuAMXe6nQrxm9Hz3U6BxGr1UuDjJ5Rf4jsK7CQ1TXkW2KBKWOIopxkwYbqyNJl89DiwZrnbqRCJKwV2kno2rwImPOR2KsRvAh3cToFU1Po1wPsPKt/EVxTYSeqZ8AiweaXbqRC/maNJOEnpi+eBpVpQWvxDgZ2klg0LgclPu50K8Zu0DGD4BLdTIZWxeSPw3t3KO/ENBXaSWsbeAxRrAVmJs7zuwMo1ytZk9f2bWrRYfEOBnaSOgmnAdK1dJQmwpr6yNZmVFANv3+52KkTiQoGdpI4xt2sxYkmMCUuUs8nu1w+Baf+6nQqRKlNgJ6lh9URg9gdup0L8KLsB8N8kt1MhVcVHjL1xs/JRkp4CO0kNk7gQqZ4NKQmQ3hkoUdnyhZHfA+N+dzsVIlWiwE78b+MSYOa7bqdC/GpBmtspkHj65CnlpyQ1BXbif1OeB0o2uZ0K8aU0YMQUtxMh8fTnZ8CSucpTSVoK7MT/z4Sd+oLbqRC/yu8CLFrmdiok3jNkv3xeeSpJS4Gd+NvMd4BNqnglQdY3Udb60bevBRcuFklCCuzE37PcJj/pdirEzyZ799F0j88C9h8JNBsK5PwItPkNOGccMMOxPvd9M4Bdhwd/nzY4+NpYXHY73y0Deg4D8ocE/+X/O/WdCPT4EygsgX+sWQ4M6e92KkQqRYGd+NeCr4E1k91OhfhVZm1gpHcfI/bsXGDoSiA7HWiRC8zZCLyzENjrb2DNlsfafrQYmLIeaJQdfhurCoETxwANs4CF+wL1M4P/z/dp2CrglfnAy92ALL/VJp8/63YKRCrFb6eiyFYTH1duSOJkdQUKt0RIHnRRC2D23sDsfYAZewNXtw6+v2gzMGRF8OevegMr9wcubBF+G1PXA+uKgT3qAHWygD3rBv9/2vpgC91FE4ALmgN714P/zBgNjBnqdipEKkyBnfjTqrHAkl/cToX42ZIceNmt7YHWeVv/f5+6W3/O2bJCS8tcIC3Kai2d8oEaGcCw1cDqQuDPVUDNDKBjPvDQLGBZIfBwJ/jXZ2q1k+SjwE78acbbbqdA/G7UDCSLohLguS0reLTPAw5qENvf1c0CPto+GMA1/RVYUQR8uD2wZDNw/0zg6S7Ay/OA1r8Fx/JdNzn4Xb6hpU8kCWW6nQCRhCxXMEsDnyWB8toBs2YmRRaz6/S0McDPK4Gm2cCXvYCcCtzSH94w+HLOSTpgFHBAPaB2JnDTNKBvy+A4vlunBVv5Lm0Ffy19csFDbqdEJGZqsRP/WfQDsHGR26kQP9ucHJHLok3Afn8DXy4DOucDf+wCdK9ZtW2+Ph/4ezXwQjfgx+XB9y5tCVy5JUsGbxm/56ulTwo3u50KkZgpsBP/UTesJNr0dZ7P4/Frgd1HAKMKguPrhu0KtM+v2jYXbwJumArc1QFol7f16cuceZvl1yercemTfwa7nQqRmCmwE3/ZvBqY/7nbqRA/S88FRoyH1x0/Gpi9ZY3dgmKgz7/BQI+v1+YF3z9jLNDxd+CZOVv/rsew4HufLN52m/0mA61zgWu2zLA9uH7w32+XAV9vWd/uoC3v+crQQW6nQCRmGmMn/jJnEFCsFeMlgXK7A+v/8XwWb3JMYvivoOzvDt8yeWL+JmD6hrK/m7Hl/9eELFT8zdLgunds+cvc0iTQpxFwXwfg4VlAYQDo1wq4OMLSKUlt2OfB7tisCAv+iXhIWiDAobAiPjF4b2DpH26nQvxsw77AB7+6nQqpbnd/AexxtPJdPE9dseIfBdMV1Eni/bdl3RBJLb+qO1aSgwI78Y9Z77mdAvG73BbA5ORY5kTibNgXwOZNylbxPAV24h9zP3U7BeJ3xe3cToG4Zf0aYOR3yn/xPAV24g/r5wGrRrudCvG72VrPLKVpdqwkAQV24g8LvnE7BeJ3aZnAXxPdToW4afiXwGbNuhdvU2An/jD/a7dTIH6X1x1YE7JuiKSW9QXAiG/dToVIVArsJPlx3brFQ9xOhfjdmnpup0C8QLNjxeMU2EnyW/wLUOT9RzxJkhsb5lEMknr+/RHQ8q/iYQrsJPktUDesJFh2Q2DMJGWzAKuXAbPGKSfEsxTYSfLT+DpJtLTOW594LzJmqPJAPEuBnSS31ROAdVowVhJsvnJYHEb/ouwQz1JgJ8lt4fdup0B8Lw0YPtntRIiXjP1V4+zEsxTYSXJb8pvbKRC/y+8KLF3udirES1YvBWZPcDsVImEpsJPktuwPt1MgfreusdspEC8ao+5Y8SYFdpK81kwBNi5xOxXid5NXuJ0C8SKNsxOPUmAnyWvp726nQPwuszYwUl1uEmGcnYgHKbCT5LVsmNspEL/L6gYUFbudCvGiVUs0zk48SYGdJK/lw91Ogfjd4iy3UyBepu5Y8SAFdpKcitYH17ATSaS/pyt/JbLxmrwl3qPATpLTilFAQF1kkkB5HYC5C5XFEtns8cod8RwFdpKclo9wOwXid5tauJ0C8bp5k4GSErdTIVKGAjtJTiv/czsF4nfT1rqdAvG6zRuBhTPcToVIGQrsJDmt0SOeJIEy8oHh45TFUr45Gusr3qLATpJTwRS3UyB+ltMd2LTZ7VRIMtCjxcRjFNhJ8tmwGChc7XYqxM+W5budAkkWarETj1FgJ8mnQN2wkmD/zVEWS2zUYiceo8BOkvMZsSKJktsKmDJL+SuxmTsJCASUW+IZCuwk+ajFThKpuK3yV2K3cR2weLZyTDxDgZ0kH82IlUSatUn5KxWjcXbiIQrsJPkosJNESc8Chmv5CqkgjbMTD1FgJ8mlpAhYN9PtVIhf5XYH1mhhYqmghXqmsHiHAjtJLhsWACWFbqdC/Gp1XbdTIMlo1VK3UyBSSoGdJJdNuoBKAo1dqOyVilut65J4hwI7SS4bdQGVBMlpDIzVUjpSCQrsxEMU2ElyUYudJEwnQMuRSWUosBMPUWAnyUUtdpIo80qUt1I5BSuAEpUf8QYFdpJc1GIniZCWDgyfpLyVymFQx+BOxAMU2ElyUWAniZDXDVi2UnkrlafuWPEIBXaSXNQVK4mwrqHyVapGS56IRyiwk+SycYnbKRA/mrjc7RRIslOLnXiEAjtJLuqKlXjLqguM0mPEpIoU2IlHKLCT5FKkxz1JnGV2BYo1o1GqSF2x4hEK7CS56HFiEm8LM5WnUnXrVysXxRMU2ElyCRS5nQLxm5HT3E6B+EGxrk3iDQrsJLmU6OIpcZTXEZi3SFkqVVdSrFwUT1BgJ8kloIunxNHG5spOiQ+12IlHKLCT5KKuWImnaQXKT4mPYt10ijcosJPkohY7iZeMGsCI8cpPiQ8NExGP0HQwSR66cEocFaWl44W7TsfwjFXKV6my/Wp0xcXKR/EABXaSPNQNK3GUWVSAK6a8g5yuJ+O1wDSUQGvZSeV1ye6t7BNPUFesJA91w0qcpQdKcMnE9/Hc2hqon1FX+SuVlpmmdhLxBgV2kjzSstxOgfjUbvOGYsDsKeid3d7tpEiSSld1Kh6hwE6SR0Y2kJ7tdirEpxqtnYeXxn6GczK6IA1pbidHkkxGWobbSRAxFNhJcsmq5XYKxMcyA0XoN34gnlhfD7UzVNYkdgrsxCsU2ElyyaztdgokBew750cMmDsHPbLbuJ0USRI5aTluJ0HEUGAnyUUtdlJNmq2ZidfHfYNTMrsqz6Vc9bLqKZfEExTYSXLJUoudVGNxK9mEG8YNwIObmqBGer6yXiKqm6lZ1eINCuwkuWRq3JNUv0Nnfov3FixBp+xWyn4JS4GdeIUCO0kuarETl7ReNQVvTRiMY7PUNSvbqpeprljxBgV2klw0xk5clFu0HneMHYC7ClsiNy1Xx0JKKbATr1BgJ8lFLXbiAUdP/wJvLylA26zmbidFPEKBnXiFAjtJLrmN3U6BiNFx+Vi8O2koDstW12yqy0/PR066ljsRb1BgJ8klX+uKiXfkFxbggTEDcFNxG2Sn6akoqUoTJ8RLFNhJcqmhwE6856Spn+KN5ZvRIkstyqlI3bDiJQrsJLnUbOt2CkTC6rbkH/SfMgL7Z3dRDqUYtdiJlyiwk+SS2xTQWBbxqFqbVuDxMQNxTaA9MtMy3U6OVBO12ImXKLCT5JKWBuRrkVjxtjMnf4RXVqajSWYDt5Mi1aBJdhPls3iGAjtJPhpnJ0lgh0V/of/00dgzu5PbSZEEa5urISLiHQrsJPkosJMkUW/DEjwzZhAuS+uEDGS4nRxJkPa57ZW34hkK7CT5KLCTJJKGAC6c+AGeL8hFgww9dspv0pGuFjvxFAV2knwU2EkS2mX+bxgwayJ2yu7gdlIkjpplN0Nuuh4vJ96haVuSfGp3Q7Jbuga451Pgi1HAwlVA7TxghzbAqxcC7RsDY+cAd30C/DUNWL4W6NwUuOYI4Lz9tm7ju9HA9QOA6UuADo2BR08HDt9h6+/7vgkMnQj89wCQpTPdExquW4AXx36CF7ufjLeKpyCAALym4J8CLH5rMdaPX4+iVUXmvVY3t0KjExuVfqZ4XTEWvLgAq4asQtGKImQ1yUL9PvXR7IJmSMtKM59Z/edqzH9qPjbN24ScljlocXUL1NmzTuk25jw4B2v/WYtuA7qV/k0yUjeseI1a7CT51O0JpCXveKVlBcBudwDP/QAsWg10bgY0qQMMmwosWAlMmAfsfhfwyd/Ahs1ApybA2LnA+a8AT30b3MaqdcCJTwMNawELnwfq1wz+P98nbuuVn4CXL1BQ5zUZgWJcMX4gnlpfF3UyasFrNkzagDV/rUFGnfDnWKA4gGn9pmHpwKUmqMtukY3NCzdj0auLMOuuWeYzRQVFmHnDTGTWzcT232+PzDqZ5v/5Pq0dsxbLPl2G1re0TuqgjtrnaXydeIsCO0k+mXlA7eRdBPa2QcDMpUCPlsCsp4BxDwPjHwFWvQrs0h5461dg/SYgOxOY+jgw9mHg1mODf8tWPAZ7UxcB6zYBe3QC6uQDe3YK/v+0xUBhEXDRa8AF+wN7J282+d7ec4ag/5zZ6JndDl7Clrdev/ZCx+c6hv39ql9WYd1/wTuI9o+2R49PeqDldS3N/6/8biXWT1yPTXM2oWRDCWpsXwMZtTLMv/z/TXM3IVAYwJz75qDhsQ1Rs3dNJLt2ud46fiIK7CQ51euNZBQIAIOGB39uVR845CGgxvnADjcDH48AcrKAksDWJfv4sj/T6vXA39OBTk2BGjnBljm+9+dUoGYu0LEJ8NCXwVbBh091aSclZs0KZuHVcV/gtMyunsk1trKl50auGtb8ucb8m5aThtp71zY/1z2o7tbfD1uDnNY5SM9Lx7ox61BcUGz+Tc9PR06rHCx6a5Hp4m3erzn8QF2x4jUK7CQ5JWlgx7F1K7d0l343JvhzvXxgzBzg9OeBj4YDJ+4KZKQDmwqBTtcB298E3P/51m3MXwnUrQF8dFUwgGvaF1ixFviwH7BkTfCzT58FvPwT0Lof0Oxy4Lr3gKJi13ZbosgqKcT/xg3AIxsbo0Z6Dc/nVeHiwtIAMC09eMeRVT+r9PebF21GZq1MtHuknQngxhw2BkWri9Du4Xam63bRG4vQ8n8tsezjZRjbZyzGHDoG856Yh0CR98YblicNaWqxE8/RkGpJTvV6IRkVlWz9uVtz4L8Hgz/3uhmYuAB4bjDwy23Al9cFJ1eMmxecPHH23sDbvwU/m7Vl6BMnSjgnS7A18ID7gQO6BSdj3PQ+0PdgoEV94NZBwVa+Sw+uzr2Vijho1nfoXKcTbmzTHZM3z/Vs5gVY0LZ5c9u3OFHCOVmCfzf1kqmotXMtZNTIwIJnF6DhSQ2R3TgbC55fYFr5nBM0kkHjrMbIz8h3OxkiZajFTpJTkrbYNaoVHDtHnAXLn/nizzRrafDfI3oBw+4GCl4H5j8HHLb91m10aRZ+26//Avw9A3jhPODHccH3Lj0IuPLQ4M+Dt7wn3tVq9VS8OeEHHJ/l3Znf2U2zzb9sjQtsGTdQuLJwm9+HWv7ZcjPTttVNrVAwvMC81+iERmh0SjCYs+8lE02cEC9SYCfJKac+kN8ayYbLjuy7ZTgVu1850YEv/kxsVSMuU2LNXR6cNEGccLFdmEflLl4N3DAQuOt4oF3jrQ0oDBptC58kh5yiDbh1bH/cu7k58tLz4DW19wyOqwtsCmD1b6vNz6sGr9r6+z2Cv3cqXF6I+c/MR7NLmiGnRU7p+5wRm5aZvLNiu+d3dzsJIttQV6wkr/q9gfVbIqIkct9JwK+TgAnzgfbXBLtQOW6O4+puOSb4mSMfBfJzgCa1gamLg+Pt+P9c585OpHDq9zbQukFwrTs6eDvgyW+Bb0cDrbY8h/6gHtW4k1JlfWZ8ha4NeuDG5u0xo3B+teXoyp9WYsHTC8yyJtbClxZiybtLkL9dPtre0xY1etUwM2O5hAmXO+FsV6p3eD3kd9u2a3Luo3NNS17j0xub/6+1ay0s6b/ETMTgGnjmvV28t/RLeXauubPbSRDZhlrsJHklaXfsbh2Bn24B9u8GrFgHbCwMBmJ/3AkcsCX4OnpHIDMdmLwwOPv1+F2AYXcFlzcJ9c1/wEcjgFcuBDK3tM716RUMIB/+CrjkdaDfYcDFB1bvfkrVtV8+Hu9M+hl9sqtv1mzJ2hKzqDDXprOKVhaZ9wqXFiItIw0dn+6IRqc2Qma9TGyev9kEbU0vaoq2d7XdZnurf19tFjJufWvr0ta5OnvXQbO+zcwM2Tn3zzHbanh8QySTrLQs9KzZ0+1kiGwjLRB2JKxIElj8MzBE0Yqkhk86HofHshZhU2BrwCXu2aHGDnijyxs6BOI5arGT5NVwTyDDe2OQRBLh+Gmf4c1lG9Eqa8tATHHVzrXUDSvepMBOkldGDtBoL7dTIVJtuiz9D+9N/gMHZeuRIm7bseaObidBJCwFdpLcmqgrVlJLzc2r8ciYgbiupB0y0zT/zQ3M9x1qOhaRFPEQBXaS3Joc5HYKRFxx+pSP8dqKAJpmJteivn7QI7+HJ5eiESEFdpLc6u8EZG1d3V4klfRc/DcGTBuFvbI7u52UlKJuWPEyBXaS3NIzgCb7u50KEdfU2bgMT4/5AJendUQGtBp1ddip1k7V8j0ilaHATpKfumMlxaUhgPMnDsKLa7LRMLO+28nxNQbPvWok57OqJTUosJPk11Tj7ERopwV/YMCM8dglu6MyJEF61OiBPC2zJB6mwE6SX53uQF4Lt1Mh4gkN1i/EC2M/wgXpnZGG5H0Oq1ftW2dft5MgEpUCO/GHlse5nQIRz0gPlKDvhPfxzLraqJuhyUXxdFBd9RCItymwE39ofYLbKRDxnD3n/owBc6Zjh+x2bifFFzrkdkDr3NZuJ0PEW4HdK6+8grS0NJxzzjnV/dUSxrvvvovOnTsjKyvLHJe33noL5557rvl5//23zjbl/9vfx9Mbb7xhtnvGGWdUbUON9gVykush4iLVoUnBHLwy9nOcmdlVGV5FB9Z1f0H0008/3Vwz33zzTbeT4mvff/89tt9+e+Tm5pr8vuuuu8yLP7dt27b0c/zZ/j6efvrpJ7Pdvfbaq3oCOxYoW9Gnp6dj1qxZMf1dYWEh7rvvPvPzNddcU/r+r7/+ij59+qBRo0al233ppZe2+Xv+7a677oqcnJzSz23cuLHMZwKBgAk+dt55Z9SuXRt169bFMcccgwkTJpSbPv7d4YcfjpYtW5qD2aJFCxx//PEYPXp0mc8x4LHf73ztvffeZT734YcfmvcaNmxotteqVSucddZZmDlzJrxgyZIluOCCCzB16lQ0adIEu+22mzkGHTp0MD9379494Wk488wzzXe///77MR2jqMueqDtWJKzMQBGuGTcAj29oiFrpNZVLLnTDOuuNjIwM1KpVC126dMF5552Hf/75J6Zt8Br5wQcfoHHjxibAs15++WVT1+Tn55d+x6RJkyJ+f+jLGahEwjp5p512Qr169ZCXl4d27drhsssuw/Lly8t8jvUbGweaNWuG7Oxsc30/8sgjsXr16m22+e+//5apz0PT7JaSkhKceuqpGDt2rDlOrA8ZF/DFn3v37p3wNBx44IHme/78808TZCY8sHO22jCQevvtt2P6uy+//BJz585Fjx490KvX1uniLNSDBw9G/frRp+l/9NFHmDJligk+Irn77rvNiTJq1Cg0bdrUFHR+L6Pe8gJQ7hczsLi42BT0BQsW4NNPPzV/Gy4Ya9++vTnI9sX9sn7++Weccsop+OOPP5CZmYmuXbua7b333ns46qijkCibN2+O+bPMSwbb9N133+Gvv/4yJ+Dtt99ufn7hhReQaDzxTzzxRHMi8eJUJa1PileyRHxp/9k/oP+8+eiW3cbtpCSd1jmt0Sm/U1yuebvssotpdOBNNesd1h/svYgluOK18qSTTjIBkfXtt9+aIIkBXyS8UXfWV3yxbiIGYdEwjQziWFcz0OHNP+tTpufss88uU6dw3xgTrFmzBt26dTP1Ouv3goKCMtvcsGGDCU4rUmdVxeYKfA/r6lWrVpU2ZLE+vPDCC82LPzMuqA42eK9oXVzhwI4Bzm+//WZ+ZqsY8SAywCsPW2Xo6KOPLvM+W7FYCMqLSr/66iusXLnSZG4kNgNOOOEEU8hmz55t7ix4kB544IGo2z/uuOPMHdHChQvNncNTTz1l3l+3bh0+++yzbT5vAyD7cgYmDOhsnvCE+++//0rTPWfOnKj5Ze9eHn/8cZx22mmoWbOmOWHvuOOO0r/jSWU/99prr+Gggw4yrYJ2H/kdPOEY3PJCwtbCvn37YsWKFeb3bDbeZ599Sr+zZ8+eZlu//PJL2K7YcJhHvMAw0OZFhifxiy++WOYz33zzDfbYYw9zEbN3efwbHkfLlgfeiVZ5PbvcyBc2EQFarJmON8Z/ixPVNVshh9U7LC7Fh0EU6ws2cowYMQJt2rRBUVERLrnkknJbrOw1MrQOZb3HOjRadyA/46yvnn/+efO9dOWVV0b93t9//938y6Bu2rRpGDduHA499FDzHutYq1+/fqYF74ADDsD8+fNNb9fEiRNNax3rIqdrr73W7C9v7GNluz1vuukmU58xaKxTp475edOmTaWfS9tSNz7yyCOm161GjRq4+OKLze9YB15++eWmTuQQJLYoMgZhnWmDWP7OYl7bYUjhumIjBYbnn38+mjdvbupfNgLde++9pflNPAastxs0aGDqT7YGsndx+vTpZb7b1qM8vgkL7LhzDC54kF5//fXSYI/dqeWxhYPdqU7cMVb65eGOM1Oj4d0Mhfvcjz/+GPVvr776ahOcWM7Ax3l3ZLE7me/zoLHQLF68uPR3bBa3aWBzKlsoGYAxCHr11VfL3Q+69dZbTb4yKFq6dKkpGM8+++w2n2MhZeDYqVMn08TPLlYGUxw/x4CW7y9atMgEXfvtt5/pvmZeOveV6eMdHLuvY8E7zd133920ojLPOU5v8uTJ5gS75557zGeY5v/7v/8zBZgnHz/D9PBvnM3yvMMj5h+D8Sp1x7ZSq51IebKLN+LmcQNw/+ZmyNczT2NyeP3D416w2Djy9NNPm59Z6UdrteP1ldd25zXTYgDBa39FPProo+bf1q1b4+STT476WVsXstWtY8eO2G677fDDDz+YoNTWSbxZ53vE7lruGwNB1hOs+23rILEXja19DCjZS1RRbHQZNGiQqRsZ8LBuu/nmm8M2vvz4448mzayrWfexDmSQyzqRdRL/nj1prDNZZ7GOdvYosp60w5RisWzZMrPPbOlbu3at+XsG8WyYscEl60z23HEcHYNLtqayRdH2alpMH+tOlg3WowkJ7BjQMViwTYQcWGj7mssbVM8dZEsYxdKfX1nsFycGDxy/wO+y3ai8g6gI22LHuwK2ADrxDoBRPQ82t89gjQWDrXvE1i6OsePnGLDwzoUHk0GgM6CKhgEwW+a4fXtihWt1ZKGbN2+eGQ9wyy23mDsx3jFw/CP758ePH2/SQrzTGjhwoGk9dDbvsmmZBWfHHXeMKW1MB4MznuAsiPzuJ5980vzuoYceMhcA3gGxsLI7nHdtzAPeLf39999lThLmLy8ANmCskranVe3vRVLI4TO+xrsLl6NDVku3k+JpXfO6om1uYuotZwMCr9WR2Gsjb77LG7ZUHtYrrCNtg4Yz6AqHkx0ZwPFzvN7bdHKIEbtlbfpsj9Inn3xi6jv2Ig0fPhxHHHGE+ZcYUHFsN+sOtqhVBoNR1oszZswwvVrEei90HF/btm3NvrLuYX3Huo91ILFO5H6wd411JevM5557zgSazq5W29IZawDKdDCP2BLI1jd+t81rxkls8WQQbMcmctvs1WPQzrQ5x7azAYj7avM3IYEdu+lskMSmS+e/TLgNasJxZritxBOB3Ze33XabCaCYuWz25iBEYmQcC0bHjKwZxLIblAeZB8kZ8DFAGTNmjPkOe6fAvLEFgoEOW68Y2LDVjfvPJmcWbk7Q4PiC8vDzTDNftrmaQSLvKpw49sG2ePKujYETMbC1gRq7mRlg0ciRI1FV7EIgFkQGryyAvEAQ9415wzGHPA7r1683XclMC7t5eQLxb5xsS2G4AbYV0nBPIF/LEYjEqu3KSXh74hAclRXbDWcqSkRrXWgvU3nstTEe9SdvwjmWnC1e0YY2WUOGDDH1HK/TvOazDmKvFIdPHXvsseYzzm7Ggw8+2AQ1DGIYhPK77DAddjnzxp9BFgO/ymBrl80H25jDuja0x+fcc88tDYKddSPrQtaJxHqJdWW860bW1az3WDfa72LgyxiAvZRsCLLBMYdCMUBlgMfJllWtGysU2Dlb5dgixULB5kXbImej0nCcXXz8bKKwuZVdlixUDCh4oOzdiD140bDAsV+bLXAM5jgJYt99y640zmZa9psTD5pzdpLtp2erFSNwts7xjoz7bz/HO5Zod2ZWLN21FDp2IdLfxzIOMlZ2WyyEoQNy+eJJxJOWk1ieeeaZ0oLNYJkXAtuCaNnxA7F2BUfEfW5/XtW2IZJi8orW4e6x/XFHUUvkpG077CSVpSM9buPrwrFj1inaSgT22ljV+pOtRbbL99JLL40pUGQ9z+9lMMcbdl73OXaNOAyI3Y9cRcJiNyzrHzsEh+zkRbZgMQhjdyUbTpgGi7Nub7zxxoTXjYlk60Y7mzb0ZRtYGCxzeBbjAjZ0sE5kQxkbp6paN8Yc2PGgfvzxx6X/z+iRL2chi9Ydy520rV7OwZbxxlYz5/Y52NT2+9vI3jn12zlBgF21DMI4Q5QnGCNrO0HEYrD2xBNPlJnh4xz0b7uZbXTNtLDQh94NhLZYhcMDzVmrvBNi0zYxD0P7+kMLuR1/wYGpdho9J3/YVsLQfaoMO06SJy4HdtoBuZzgwrGHPGlZIJmGK664woxhYFo4qJacYzLZ+mnzk2MhqqzjRUBa9K4FEdnWsdO+wNtL16FNVvRZkqlkj9p7oHF2YiZlsU6wS3+xAYID7iPhWGlbt9gZm5XBljPW22yc4GSHUHbynHPIlK3P2CBhlxjjTTuxG5M38RxvZ9PI3zHAYR1gW9Hs72wrJXv4+HJOemBjjPP/I+FYNFtncKwdcX9sEFle3cjvsRMiWS9x/GK860YeT04YtXUjZwazF4/jzpk3HCbFvGaQzd/btX2ddSM/ZxuLKlI3xhzYMciwXa3sZuMX2pcdPDl06NCoS4rYdd5CmzsZtDDRziCLdwh8z7lwLX/me2wBsnj3wPds4MMCxe4/HmDOwLTBHCPlcIXYiSeVXbOO+8XlShig8MVWQFsgrrvuOtO8y9Y49n/btfn4//Yuxo7J4wnBAs102vFxPPCxjLNjgePJxRfzljgbqDycTMEuaJ48e+65pxnLwJmoxJ/tmISqsM3ybBnlWEOOteSJzTske8dlJ3Gw2ZnjMdnkzMGixP+3bPM4A9ZYWlXLld8CaHlM1bcjkoI6LRuDdyf9hkOytaAxndpoa4NAPHCsOesU1h2sC3jzzyCAqypEqxd4bbTddDaosnjNZT3obO067LDDtqkv2VLGcWS2Pi1vmRPL1me83tsArn///qW/Y8ub7aliMMUght/NF2/c2ZDBWbDEGMEZPzgXWuZYbDu2PRoO52H9zvF9Nh0cksSGhmhY99llyVgn8mcuZ8a6khNQ2AhRVax/2XrJllEeM/bwMZ2sB23wxq5pdldzkgnTwK5Y2zDmrBsZFDOGYA+Y7bqNa2Bn16rjAWUinBiB8mCWt6adDbIYbTsxqmeBcba0sQ+f7zknPPBnvudcKoODJ/meba5kUMeThUEFP88M5YQCNns6Z7babTgz0XmnwALGFjv7slOQGXxwtioPFr+DrXEMWBhwcRCmHTPAtfRY4FhoeNKyJZF5x8LNFq5Y3H///WZ8IA8sCwW/t7zglNivzzsALv7Lgs67Ebb0seAzQKzsuAYnFthhw4aZk4NNy7yT48nB8YM2CGaaeUfCYI/7z/GIzCsGuM5xHWzlIwbSsTaxl6tT3/hsRyQF1Shcg4fGDMANJW2RlRbb2GQ/apPTxrTYxRODK47DYh3E+onLUrGOidZaR7w28hoZrg7leC7WUXbWLLGlh+/ZJa6IPScMLLktNlCEE65uvPPOO01AyPFoTD8DKwahXAHB2VPHhg22hLFlzE7g4zAcNubEOmkwFqwHGZgyrewN5Lg9BpXlyc3NNS1ibDljvcTAiQ0UrCtZn8U68zUaboP1L2MA1oGsG9lbxt5AO8GQgRq7oBmcMk7hWEQ24Pzvf/8rHd7mrBs5+aS8oNUpLRDPgVflYIFgQeYMTrb6hQaI1YXN2MxwBkAM4DhW0EtscMM7GQZGfsYywTtXBvJ2wkVcsFh/3Q1YE2xiF5HKGd94Z9zUuCEWFG0NGlLFDS1vwCmNg8GUFzBIYMDFuosNIXasd7wwHGBgwp4pfhcDDy9h8MP9ZqAZ70d4eREDaU6o4ALUbDTx5LNiWQi5rgw99thjcAsHq7J1iU2+XgvqUg3vIHm3yTvRuAV1xOC449ZBuSJSOT2WjET/aX9j3+yy45f8rmZGTRzdoOxCwG7jNZLXSk7As12Q8cQZr1yGg4GT14K6VPPTTz+ZoI5dsBUJ6qq9xU5ik0otdgm1eRXwaQugeL3bKRFJegGk4Z2uJ+F5zEQxiuF3pzc+Hde1DN9dKe5ItRa7ylJgJ/42/EJgevAJKSJSdf822xM3N8jH0qKtY7f8uMTJJz0+QaucrY+WEkkW1doVK1LtOl2uTBeJo94L/8TA6WOxW/bW5Sv8Zq/aeymok6SlwE78rX5voNHWR/aISNXV27AYz439EJekdzatW35zauP4LnEiUp38d0aKhNouOGFHROInPVCCiye8j+fW1kD9DP9MQmuX2w67197d7WSIVJoCO/G/ZocADeO7FpWIBO02bygGzJ6C3tntfZElpzTyzvImIpWhwE5Sw3Z3up0CEd9qtHYeXhr7Gc7J6II0xGmRcRc0zGrouSVORCpKgZ2khuaHAQ12czsVIr6VGShCv/ED8cT6eqidUf6D5b3ooqYXITe96k/mEXGTAjtJHdttfVSLiCTGvnN+xIC5c9Aju01SZTGXNjmu4XFuJ0OkyhTYSepo0Qeov4vbqRDxvWZrZuL1cd/glMyuSBaXNrsUmWmZbidDpMoU2Elq6alWO5HqkFWyCTeMG4AHNzVBjfR8T2d6l7wuOKzeYW4nQyQuFNhJamlxFFB/J7dTIZIyDp35Ld5bsASdsr37FIe+zfuWPspRJNkpsJPUs/19bqdAJKW0XjUFb00YjGOzvNc127tmb+xdZ2+3kyESNwrsJPU0PxxofqTbqRBJKblF63HH2AG4q7AlctO8M/P0iuZXuJ0EkbhSYCepaccngfRst1MhknKOnv4F3l5SgLZZzd1OCvapvQ961ezldjJE4kqBnaSm2p2ALle5nQqRlNRx+Vi8O2koDst2r2uWz7i9vMXlrn2/SKIosJPUfoZsblO3UyGSkvILC/DAmAG4qbgNstOqv/X8iPpHoFNep2r/XpFEU2AnqSurFtDrQbdTIZLSTpr6Kd5YthktsppU23fWyaiDq1tcXW3fJ1KdFNhJamt3DtBgV7dTIZLSui39B/2nDMf+2V2q5fuubnk16mfVr5bvEqluCuwktXHtqp2e4Q9up0QkpdXatAKPjxmIawLtE/oEiF1r7YpjGhyTsO2LuE2BnUjD3YB2ZysfRDzgzMkf4ZWV6WiS2SDu285Jy8EtrW+J+3ZFvESBnQj1fgzIbay8EPGAHRb9hf7TR2PP7PhObri42cVolePdJ2CIxIMCOxHKbQjs8rLyQsQj6m1YgmfGDMJlaZ2QgYy4PA/2zCZnxiVtIl6mwE7EanUc0FYXfhGvSEMAF078AM8X5KJBRr1Kb4eB4e1tbk/o2D0Rr1BgJ+K087NAXgvliYiH7DL/NwyYNRE7ZXeo1N+f1vg0dMvvFvd0iXiRAjsRp+y6wG6vKU9EPKbhugV4cewnOC+jC9IqMIu9RXYLXNr80oSmTcRLFNiJhGp+ONDhQuWLiMdkBIpxxfiBeGp9XdTJqBXT39zc+mbkpeclPG0iXqHATiScHZ8AarRR3oh40N5zhqD/nNnomd0u6udOb3w69qi9R7WlS8QLFNiJRHrc2O5vauFiEY9qVjALr477Aqdldg37e46p69e8X7WnS8RtaYFAIOB2IkQ8698bgYmPuJ0KEYliSNvDcXf+OqwrWWf+v0Z6DfTv1l9r1klKUoudSDQ7PAA0OUB5JOJhB836Dv3nL0KX7ODiw7e2vlVBnaQstdiJlGfjEuDbHYEN85VXIh62KTMPQ3a5D33aXet2UkRco8BOJBZLhwFD9gNKCpVfIl5Vb0fg0D+AjFy3UyLiGnXFisSi0R5A7yeUVyJelV0f2OdjBXWS8hTYicSqyxV65JiIF6WlA3u+B9Rs63ZKRFynwE6kInZ9GajbU3km4iXb3Qk0P8LtVIh4ggI7kYrIzAf2+QTIqqN8E/GCtmcBPe9wOxUinqHATqSianUE9vkISM9S3om4qcmBwO6v6xiIOCiwE6mMpgcDu+nJFCKuqdMj2HquGyyRMhTYiVRWuzOAXg8r/0SqW14zYP9vgGwNiRAJpcBOpCq6Xw90uUp5KFJdMmsC+30F1GitPBcJQ4GdSFXt+CTQ+hTlo0iipWUAew8C6u+ovBaJQIGdSFWlpQF7vKNnyook2s7Pa1kTkXIosBOJh4xsYN/PgLrbKz9FEmH7e4FOlyhvRcqhwE4kXrJqA/t/C9TsoDwViaee9wDb3aY8FYmBAjuReMpvDhw8FKjVWfkqEq+gruftykuRGCmwE4m3/BbB4K5Od+WtSFUoqBOpMAV2IomQ1xQ46BeNuROpLAV1IpWiwE4kUXIbAQf9BNTrrTwWqQgFdSKVpsBOJJFyGgSDuwa7Kp9FYqGgTqRK0gKBQKBqmxCRchWuAX4+Alj2pzJLJHx1BPR+FOh2nfJHpAoU2IlUl8K1wK/HAot/Up6LOGXkBhf5bn2S8kWkihTYiVSnkkJg+IXAzHeU7yJ2uMK+nwON9lJ+iMSBxtiJVKf0LGCPt4Ht7lC+i9RsDxzyp4I6kThSi52IW2a8BYy4ONiKJ5JqGuwG7PdlcPa4iMSNAjsRNy0eCvx+ArBpuY6DpI6W/wfs2R/IzHM7JSK+o8BOxG1rZwBDjwFWj3c7JSKJ1+UqYMcngDSNBBJJBAV2Il5QWAD8cRqw4Gu3UyKSGJk1gF1eBtqdoRwWSSAFdiJeESgBxt0PjLsbCBS7nRqR+OFzk/f+CKjTTbkqkmAK7ES8ZsmvwB+nAxvmu50Skapreyaw60vBFjsRSTgFdiJetHEZ8Ne56pqV5F50eKdngI4XuZ0SkZSiwE7Eq/i0v0lPAKNv1pIoklxqdgD2/hCo39vtlIikHAV2Il63bATwx6nAuplup0SkfK1OAHZ/A8iqrdwScYECO5FksHk1MOIiYM6HbqdEJLzs+sGuV816FXGVAjuRZDJrADDqamDTUrdTIrJVq+OBnV8A8pooV0RcpsBOJNnwKRX/XAvMfMftlEiqy2kE7Pwc0OZkt1MiIlsosBNJVgsHAyMu0dg7cUebU4GdngVyG+oIiHiIAjuRZFa0HhhzOzD5aS1qLNUjtymwy4tAq+OU4yIepMBOxA+WjwSGXwisGu12SsSv0jKADhcBO9wP5NR3OzUiEoECOxG/KCkCJj8VfCxZ4Sq3UyN+0vRQYMfHgbrbuZ0SESmHAjsRv9m0Ahh3HzD1eaBks9upkWRWuyvQ+3GgRR+3UyIiMVJgJ+JXa2cCo28BZn/Ax1i4nRpJJjkNgO3uAjpdCqRnup0aEakABXYiqTD+7t/rgSW/uJ0S8br0LKDzlcB2twPZdd1OjYhUggI7kVQx/yvgvxuB1RPcTol4TVom0O5MoMetQK2ObqdGRKpAgZ1IKikpBmYPBCY+Aqwa63ZqxG0ZuUD784HuNwA12ridGhGJAwV2IqlqwbfAhIeBJUPdTolUt8yaQKfLgK7XAnlNlf8iPqLATiTVLRsBTHwYmPcZEChxOzWSSNn1gM79gC79tBadiE8psBORoDVTgImPBZ9BW7JJueIn+a2AzlcEW+myarmdGhFJIAV2IlLWhkXA1BeBGW8B6+cod5JWGtDsUKBTX6D5kUB6htsJEpFqoMBORMJjt+yiH4HpbwS7adWKlxxymwDtzgY6XgLU6uB2akSkmimwE5HYnmYxqz8w4w1g5X/KMS8+x7V5n+AM1xZHaVFhkRSmwE5EKmbFv8EAb9YAYPMK5Z5r0oCGewCtjgfang7kNdOxEBEFdiJSSSWFwaVS5n0efK2fq6ysjoWEG+8XDOZa/Z+CORHZhlrsRCR+LXkM8OZ/ru7aeErPAZoeArQ+AWhxjJYpEZGoFNiJSPytmwPM+yIY5C35FSjZrFyuiJodgSb7A00PDo6d0xIlIhIjBXYiklhFG4DlI4Clvwdfy4YBhauV6061Oge7WBnMNd4fyG+u/BGRSlFgJyLVv4wKn1NrAz2+1s9LrRmstbsAjfbZGsxp4oOIxIkCOxFx37rZwIpRwYDPvtZOS/5HnOU2Ber2BOpuv/XfOt2AjFy3UyYiPqXATkS824VbMAVYMzn4KtjyL2ffblrqnaAvuz6Q33LLqxVQu+vWQC63kdupE5EUo8BORJJPSTGwaUnw8WcbFgIbFwVfzv8vWgsUbwo+McP8u9nxc8izcNPSgYx8IDMfyKix5V/+/5af+W9OY0cAt+WV1wLIzHMrF0REtqHATkRSU/GWQC89S12jIuIbCuxEREREfCLd7QSIiIiISHwosBMRERHxCQV2IiIiIj6hwE5ERETEJxTYiYiIiPiEAjsRERERn1BgJyIiIuITCuxEREREfEKBnYiIiIhPKLATERER8QkFdiIiIiI+ocBORERExCcU2ImIiIj4hAI7EREREZ9QYCciIiLiEwrsRERERHxCgZ2IiIiITyiwExEREfEJBXYiIiIiPqHATkRERMQnFNiJiIiI+IQCOxERERGfUGAnIiIi4hMK7ERERER8QoGdiIiIiE8osBMRERHxCQV2IiIiIj6hwE5ERETEJxTYiYiIiPiEAjsRERERn1BgJyIiIuITCuxEREREfEKBnYiIiIhPKLATERER8QkFdiIiIiI+ocBORERExCcU2ImIiIj4hAI7EREREZ9QYCciIiLiEwrsRERERHxCgZ2IiIiITyiwExEREfEJBXYiIiIiPqHATkRERMQnFNiJiIiIwB/+Hw+ZgPPOOBM5AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 14
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.17"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}