{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples: Loading data from python into IDV\n",
    "\n",
    "## 1. A random array, created from scratch\n",
    "## 2. xarray open a 2D field, send .to_IDV()\n",
    "## 3. Compute 2D vertical integral from 3D (value added)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0050cea6f19b44bcacf9810ddcbe32cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<h3>ipython_IDV Control Panel</h3>'), HBox(children=(HTML(value='<b>Resources:</b>'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19694cb68e3e44ce80741849952fce72",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<b>Search Results:</b> annual <br>'), HBox(children=(HTML(value='&nbsp;&nbsp;&nbsp;…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "%load_bundle http://weather.rsmas.miami.edu/repository/entry/get/AnnualCycleGlobeMonthly.xidv?entryid=e3880649-f98a-4126-a437-509bce201d16\n"
     ]
    }
   ],
   "source": [
    "%load_ext ipython_IDV  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: Load a random array into IDV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a random data and make a xarray dataarray\n",
    "data=np.random.random((180,360))\n",
    "data_xr=xr.DataArray(data,dims=['lat','lon'],coords={'lat':range(-90,90),'lon':range(0,360)},name='random') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'random' (lat: 180, lon: 360)>\n",
       "array([[0.670093, 0.784377, 0.409137, ..., 0.726175, 0.466415, 0.173591],\n",
       "       [0.29685 , 0.557347, 0.890125, ..., 0.022452, 0.130703, 0.087429],\n",
       "       [0.097889, 0.178755, 0.834005, ..., 0.934752, 0.901992, 0.949533],\n",
       "       ...,\n",
       "       [0.770955, 0.309456, 0.414089, ..., 0.270197, 0.195103, 0.559949],\n",
       "       [0.857394, 0.1685  , 0.959192, ..., 0.563705, 0.27302 , 0.550274],\n",
       "       [0.653033, 0.152847, 0.378471, ..., 0.593272, 0.78419 , 0.521551]])\n",
       "Coordinates:\n",
       "  * lat      (lat) int64 -90 -89 -88 -87 -86 -85 -84 -83 -82 -81 -80 -79 -78 ...\n",
       "  * lon      (lon) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loaded\n"
     ]
    }
   ],
   "source": [
    "data_xr.to_IDV() # this data now is located in your datasources of IDV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now go to IDV Dashboard, Field Selector tab, and make a display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xkAq+4AuyIAufQAU/4ALWeAAEsESwZxDg0AWomZqp+Vgt9C23JE2QUlL/EWvsgSv3pyQUNS+JeIKa1jl/dyy9IWxh2SwghyviCIizd1wt9ZUz1Zi7iT2c8yPfUTPDNBpUBjK3iDuVBTQQM10gdyxvFnY6/0JucNMrbiZFqlM7s7U06+ExNUNBQFNj2OIhUSRke0cx4tQhSbgzb5ZePxRfUKUi2jkcMqI66NFa8dZKsIhhZclKdRR2nNItxgYyxyV0MmdWxrM8l7J9eRhrReNG3iYs2QcmGzQzI4kt4qY1t7g9DJc3UhRGH9ZCYINPe/VAvZNUNqJt8dRIFNWB50R/ZkJUZAcAQqKXNAAANJCkfklRh0kD/UADKwk2RPVXQkcDmtYPmyEQEkoDV0ID8ZGkYAqmXzAQYNoPAqEFAGAAFkAEoZAKsyALpKAJU+AD1cgAXBAdZwoOYIoFTgqmRChO8DZcl2NmglVHerM1ASNF5LkdRP/1N64YheDgXOn3WEsUVZEFJhKXMX/DHfOiI8klXIcJAHeTNNCEfArEP5wjZl6Qc4EXQv7EgvOVPTfEHEeEe14Yhjn2Kr60IJ4qbE01fHgSjw4EakDThaD4cmvyV4P6o/8DZnfUUJ0aKz5VOWoCe+pyIjvDa/EzVtszLbWzR/YUSQ25dzbWIm8mc1Q2NJqCgaDhcRTDRru5ISonbQvqRrP3cYkTSZAUpFhKV2jVL2+SMF4VRv3SK+wCLziZO9BViFd0QaSyNFT4K2GocpaDLmhqEHVEA4WZMQLRXP5AA4I5EFP1BV66gbn2JwNhKx7mGWQapi77BUunMCBLAwHXsiT/ywVcwA9HKhAD8AEkAANPUArO6AlCYAEGQAADYBBgiqbZImCPxVSR6m8BlSvB8yw7I042UDQT2W+rVFnplUw0wiPxxDbBwyu3d4gdN1gyNx99SaTDJWYqBaOfZmSERCDd4j3FpjIAZ0pIYo/ek2cIA3v/BXLgl2Eg6UbetUdGllPIl0GCg0l+pzLbI46DA3KFWFPP5HmpJ0EX1G9uNC/zwVLsQXzT0ildRH8T9C0PJkFhCJDT92nBh1YHqDNcV1NQJUW9wnfrSliI9Hd9gylQFXVRZC7BklM9qW4j5Bvxk3qzBrkiKIUM5xvZFHWRxIKwWEAyciQR+C+TMjpdlD0p/wgp8SGXVOgCHUBBtnGmEKOYAAC5AuGksRFp2rGzBeEFNJAeWipEoWq/xpcQumm/dFQDOHsfNGAiZFoaYUoQSeoPBtE6CvAAFpABGJABLtADHMAALCh785JJfcUjXCIeLhg/4jEvK7YhHnJHQvI0a5SBPOI5YdiQ/wM8aEZHy6RqKiMQ79NcSpS1RUJQ/cY6RGUhSpkxy3Vxm9hkMsVlAqZWQvewC5Yobfg4GdI3UDWeaPJAQiMeZ/Q3fVM9ePYtXkxImQRheUY4H5Y33iElnld+pkVEuVM1ZwVrd1MwsysleVQdYjZzPIS3yBd0NrRx3iNu0NFLaVhiUmMpESiOlv/aNrZCN0IGR9/Stf1wf9lHQ4Q0MrcItsSGa92SSZm0NTD6Ih8nVNOSJIvSL6i1TZCTH8pxAR1QBEfgAYMJjAAwHoF5JfkLBjSAobgSHfR7po+yy5Cry/EUavWRs8GCwC67zMzMpVyzp/AzELyiIQ3gAqigCnNgAfaUQFFVkmcSUKjYUQ8KQt9hTQ2yNw7lNl5YM5Bzh41WYh6aG1LiZpUnPOohZGL2PN1zHlraRYLytUcjJTGbYiNYzm8bYrsGbjuUXkITgUQlNPPSHSm5iZJEWTLlZRhDRHtkhaEBMbaDVIESODZCxVkpdF6mcvPM0diTvSinSTA5MWSyM8VFWgX/jZW6gVaNNnAqKGAq51wjJCeUtDOcsTUz2ToNmUDSIy5n5TykVnLAw4jZkmSQw1J8l0thGSgeVkV41rXwJnEPDXuT4l0SOhze1iqhph2A4k971C7+RHGa5HCHWQEQAAEVcAEacNcacAF6nQF2rQEdoAEJgMM0YL+4YaWalhwgqxkKYtgbgrNgs6SfpadboszMrMvLbE0NySIXt1Oe4QR9QAiEwAYZcMocxC9kok6iYnxhx0HLpDRCskaKhUjmAVamtRoJxGBYg2ulzDkBbB4gA1AJWGrGN6S3BT/XdX+ftXdsc2UYIzLRylshIlzatm8hFh02FIF5ZloI6Jt3hVga/2pBc0tQPdgwhXNO4pFnHyQt5mIuIDof18JnwvUi6TVMddQi09Fdf1a54qQ+fwVjpjEpa5OGyQWAnFxXpZiE3WJaw1JHj2U7PRSB/bFDoYJnMLMfHxSRq+rF07IaJrm5NuYxZ4KwwCKdHsbDNZPJTyM1RYVi1zIs5jE0EJlrT2OSWNTT2hKKpSVVyrANzZAMxTAMmHAJljDkklDka3Dka4AGYwADcpAQSUoPADAP98AFhsCnQwoAH+sF0lwDYDoQJRY7YDAQWKR522OSSdpxiX0aEWABMoAGrGAN1tAN13AN07AMwXAJlVAJMTABBDIpDzZ7mwdJmpQv9qgsSVJJDf+yPOnF4tiCPuwUcENDO9pxMn+XO/wzGugjMrnULz30IgIBZgryJkklK8k0Jg4nI8XGMIHzZw061CKYKe16ZeWTbWFnhR+HH8AzInX0KBe4MIILcia3HxjoInj7V86lTyiDJ5aYc6C2XW52QfPlWPkBo/kT3gQFPgJ7JpN3Yx4jSWv0IKknJd71Zy6U3ffDkj3yf+3xXxK3SWDyLqtlPQXkyY3TV43GOVpwKyGTTR7jfbEzHRRz29WzRrABUN6Da3oijixVYmRiVpP3Nx1EKhjTQWt0VqJKN3IFo56Bsxzf8R7PuF9F2CDrcNrShbrMQWITLJNID2E6N0mqpwucARz/4AIcAAEM0AAPgEnK7bICeH4ldigl425XY0F7dF0qIh774THUI0TE5jt0VDOBQkbiaDPwNvFzk0rc029ebcTvg2eOIhBYVChAo3LEt0y/8k2VA062oydadE74tTFCEjH3k1yk9gUYSDb+lEJ9YinfM1PK8htAU1IqlXrSIrsNolUThyA5JygbdiLnFLWYhnIJI5yDo/Sy0z/y9lhBAqMeEmA8thoABaNjVblrF0JFojR/VyeWqCg18y7BNDSJ4lViZImUlfrGZpIZ7nkjc70rFjNecycQw+lE5UtBdEtalDGwCB6BtFc9FZbAcUGJ4/ejsXluVCct1CfxVUl9wi7I/+Q36RaGjco3SkNlziFKwzLU3jZIVKicfNW1bR2tqMU5vhg7QqaCy7ZS3bUzcsTrquQxmTTeAFGjBhgw/Php6dePYEJw4PxhweLPn0B58vzRo3dF4xWJGOlBnDGjYcMrIRvemJGwX0OJBiUOBCMxIcSDCPvNAADgIj2JNiRq0eKRXg2IQCtivJGUJj+MNYCGFGjDhkIwX6x+MQixH8SlBHlK9BcSI5imNZKelQiRYEMwUitKTQrOJLisWAiC4YolYVaDBkNqlCfQoMAbGQlu1HgjKD2rGtlWterFy15+X7zcaGgQoxekYG74OxsyJEGjkvndSKhRoBeBAiFKvik6qf9EcEkFAjUoT2NHekDBYZQqsDFGoLw7Egz5dIbujRUle2lotWLFK7e1GAQKpmJCqQ1vh2zJr6LcGZKtAr3SEGJavRKtusYy430Nq1LRF/cnXR49qZIlVvxCNPm+0Ei0yLzILaGF+gEKC9z4CSkh4RqqSCCCbPiLI3+OwijDtGTq56ov6ElIsormclALEU0zDcIZgOonOKeA0mI5eSCirjChTjNIKgKvgAgjiV7UQjIY+6lBvYgmqsGGiiCSyr2QpHLLIn/A4M4G+PrRiLTr+Lkrr/X0S0giL6JkUqoZDGLJnyptgEi4LyQCg60bdrtIOf04Y+yLhmaSjLScVPyCpvn/YEuIRi16soG/qUIsFAsvKvJSLCip1Og8xCQcyELt5JEKC4E2Y20gIf3h9AYjtYizhq306key9TZUCckkwbmCIIMC1c69AGfw8YqzILqBH9C8aFDUGa947kyf/KGxVYKgtY0eVc8EKio4IzosPXBaBAM+5FDzDZzztEApqUVRS1SLzMSTZ8ByTQppqbKIwgIjCkFdUKoOERVQC68yKimlKh8T6KH4bOyPIXDE7RFTIDEqUCRwFvWnSzAk608qjYLsTTKMKjLoi/68+BgLbzEyKLUWBYpwtcleZUvOhsoFRyqDNPqS14owfqtRemqyakELD9TPHzZxLXgkOVmGCt+W/6yi8yzFzCosqcvuVCvcGjCSdOaGJGvSBkVPSoogq0I6qsKy2ZKI5y8tNkoehipOSKyEglZrPrblMQoxECMNFQv7zKJ6J7O/BKq7/3TLiUnYwM6XnoriwqjWnRQs6CCK4p1BLaCsIg5omVvVQqOdq3NKMo1ktKHkG15DdnWYwQsLWCpD+qLESeUR+VzrsMgtMJgwtJE2c0f3kzyukgVwztVhSyoklE6alFh+HANHJa1sHanYP/tBKtMvnjS8tRqSQ2g8h66MSUwspuOe7K9JXeoqSvGqHKYDC0yIVQZTNiolDjAB/MKoPlK8oNHjbwqE1OMEAg6INAQjRQPDDORmFf+27G06JZLRBU02GizZiiuq4wd3IsYooJRKTaETlQV7wyosUDBsGkMQPwSiEe4sZVcou0JNXIKqfshDbT8yiQB1WAODdI4kG8FKZSK1lCAaC1Wf8QefhgTD+RlPRrmDCAcdciQ6gaGF5bIKZmhIu5LVR0vrmwGTLtVEUjWEd9HTz2r2VTyF+e4qPgPdFUhHIqrIw47yyYnJ3ujAGUwLLIrRgsyA0xd+jK88OfyZivRFF35cSkOQ1IINPjNBcCBnTWq6FtmAcqrWyApWY/scwmqgsML9jFdiJKLrvPUqMVIkJIiq204o+ZxNVswLILtMhBJSrOhdoSlJsdJ/CKKty3z/SV1FugzIRlKbDs3gBqIMzeVsYsYaESluuZKhDG2EJ6/w7kfRSUgxOTIrwIAuJeTzyJBIxhR6kARHwJtZbB6HHYJwbHczcBSp3GM+gaTmiY2R28niiR4NUQyDFbThp4QSQIcd8SoeTIuSOLQfR4FlQ/Gi3TaX6MFVysyVX0jKWDY2qYT10Vbooh+zzvS9magsL14S2pH+dBfIaERIYYyUUm7gmbhMs2Wx8kKEqOQkI4ZrVT/0giPPtZc2HZJ1BwpO3VpEwx2lED/8yAk/oXQzLVSETpqx1pcWZp8vEWxPk5FKrQiirm5aZW8iY+RnROM26hQFY4KJzheEmjeEzjAt/3jyT8b2lZor2CBlbiSLR1bFHF61KTZrsSHyTjU20KKpbKhJjJNIqaQh1RRPNrQN1lDS2BYxTUYVWhBQkuItt0IrdZVN4xuNNCWrXQGf78rMhWxwxJVcjIbRy9nirEOjhkyVnAeK0D7zGqOu6cyKG4GPRpzFx4zdC4795GS7iERR3gmmL+WyrCATeCsvgIggFTqRaHqyv3cNiG9YeI+iRGhcfPqVc0sDzz8dFt3CqUdVreLVUGYkoNYoaSZSOaJkZqBAt21EmfipIoWJktiBtcZ6Z1GJd1oDuFAu7oUG6h1U6ROpxyjyl/XNmNx4qJC6yCMns/QHS2SaFysuSDGGJP8PmuK4E5nNSZjw0hnF5CsYxkamWFkyKZjqNJKFLIhRVizcpQiJx/O5zbt2IWdZQIcm8SJNf7rp6TRdxT0RXSVD23FnfomXQkgdMXZaVs0s+3GWzEHqBjAtjITaBdF+9NhuMaquDdKYlEYfKCkGiSlBaKc0StYENKFpZFv2aSYgbllbO1waorxgYMlEByRLJmFRrxA0JHenlAWR8gDvwp7+lJGKnENUjBEYT8bICXfIAy1Eqjgd5vyo0NHqooq3E7RsfS2x/vrIyEiqFhmBbCOUlekMifXbJWvTXOLN6pbh12pOwg+FpjSXk+voUme5ikE0VGU5eaaYnvKqcwPraHj/4Pqp+Rhli3OBDFy7gygs5OQ3hIRMYtkSaHRFx2Osa+h8QOsuG2Yngy+alWmUguaUWQsMRnVsJik9aNBSZ9a1Aux65tIsCc/HPq16zExBS7Vj2yDSH5dpX7Z7Mjc9ClHd49Ks5bGrWU22iBh0ZVsf3VuCtVFrJ97nFSiqKo/ZT7eKne2AzgWWHVaRHprmyns45d58Bjm6m2kjiI4ZtiM1NmUuq++wDOcwElpwquYJUAtnFhIp/lDT3cpQZX3uvBrOx5icmVVDyQeyV1dOxRimjORdlLg2gY7dRpLb9xSLMU5d0stZfNmMj2LAXHkGotsc0OdGNjZM3ui+LKYreol9/99AM/sjlXuJUzzTRK/1qcSzzMu7AOOpTSKd3R0BmfIUuSBY1gBAOZkpQ6LHPLfvFSwDUqYAu6ZEw0m6eVJuy+KEcusbGI9qnNqrIP2iO3QxkZ9PVFbwp0pgUj/FpNz7CgS6L16RqOhxpS5pE/PRIKVzOftjqw7xoMRylsJxpQ07L0Jikvk7DWWBsDORsjZZDI0pKPZRlLR4j9kim5/jH6/5iRb7FGJjCMqpJ7axirxIDinTGrx6I48iPH7yGMEzKGxjJFe6N0b5GZZZoh6BK8pBM6TKGwzCjkhyisKhpMMCsiBRsW6JIkbZDSSMQjbhFe6ppHjbMRhSntcRIU6xp/810TK16Rbvkq9Is48bsL+uiarGeqqHsbenEo2uCT4JY7ey0ymxGJrCMJ8yKaiZiZPxkbK3+CcRwSbWuhI/+oLDSERjaxV6wL7vmanV4Q712apicSeScK+60LfWAxFY2R/H+KaB65Ucg6nqijTF4J3EIr4/66nxIScFMg8FUq7n6MFpUSLKMZ7IOiKmIbWiqBLiO6maAhu3IcKIcSX/OLR9siDlQDsbUZQK8p1myR0Riac2Spmf6KnwuBEsOLSBcS7J85dM+h7fIixQwZDtgsTxwZNfoabJOBWTajR4eTUjeSE0KSe3GQ7LASjsWB08OUc3Gos0vCmBCY8s0bJNkon/XrI+u3mcbmIx5bgUPbwxjhEKxIAVbVG2srC1LUsZhVOudnkflwsgOow6VmEPZcsV++Get9Awugkl4NjBEjI084uuKTE/frg4XSohyVOOnUAMC3KbWqER58AxotwWXRnDbcqJGvqWYyqL+SgbJFwLa5EfGzARKyGZMjmpc4ovwhiJEaEHm2Snu0iQfuSNIjpHyuI4mvu9UIM9DVKeFiQnDpmuNrGf1ig0CeGVO8sLJSIPc5SH0ePJjuCNuskca0EgC0I5wMMmzlkL97k4fDrErYAP/diId9wc0KoaZcGdtaFJUrMiGaI+olJMZdqfodkepWuZpcFG9MmkxjinOkK5/31ZDJ4UjMIBkXgjyYigjOiJp1bJpD4hJc14jH/CwJPyu/h6DDr6vX+yGB9hS7BgnjDqlqVpHuURoXSTPOLgzr/5puU6OQJ7M/UBmoVkyxTJkFqsDRrarqQ7lXIioZnQNgFRJOahKIMsFw1Dt9s5v7PjH66ASpdAKrQCgHRzK3fCIKpAjORgrMQgEATTHeRBOTNZudXJliwRofyYryicOxH6JflZjQGSk0xBOzkBnQdJDRBRnvs5J1cDMMtYNScrm3MiPMErNHnkTqpBKkdaFqUjybFUlfNwpLH0Hx7tkE9Di/yIGhIyv6hZkMwaoJ9hzdNAoAcSl5liG5MSECopHf/47Ck8CTHdqcHUqxeHmb+W2K7xgZRLURRscicgirOqgL/0owrJVNA+GaQLEaGusSFPWSV30hYt26Yn0knKmhEEcg6eZKulk0qQKaEyvCQYEpETOZXMScf0C71tXJL5WiUF1YpLlYfZycrS0RlmIzXR8CwuEQqozD5IiayzhMURkSFbu6RtUhODMksTHY2coEsKHaSZcJvGMZuliTSLYbfNOJU8xBWhpL6KGaJzlKEdQqreqk3OWY+U2S6a8DgRuguDco+Yq5zGoiy2oT2apIgaVBbNMEv0aayT4aCcJD6SihgxAj82lA4PybFHYx+1kaHtCBHlkYxhocMKi7c3isL/jjgNEfIueHE5/qHXj0mZbWohBNKtb2I3GhVH1lGWumkQWTXR/DQw9CDEB6m1xUlXIKopKawnlsAd+Mi4SmTBIPtWJOO41YkMdzLLH/Ed5XqbW9MXwTPFiXW7TkqRbhIN+3miN5oqKOkWdDQJ/IiaGbm9pmCsy7GRq/iekUlVpIIOpDtPoSQ2Hm3O3vCdHuQZN2pZREm2uF0yE5UpxzAwf3C4H4q3LVqlWXnQuTAfFnvOJWoNomqoM0RFZ42e8kuWHdoiBFI8PYuPQlvG7WGeEtuiYUqWKpmxk0kOg1LYHslJ7tzUyzWk/YEwNyK+xTAsqPwVUyqo8BgkToqsvGJX/+/IkNJSM+YIox48kOfgHgZtzPlSkLWBIQWRm4iBEdqpKR0pDO/qx8r8DXqFD4aJL3plGrmhDYp6HEx11sHwElL7kRYpGu7spuDQNjSxEubtDqjUlj55EK7oqc/JK0bxspp6vZxsl4FM3vfAJU5LGCXBsLZ4ifXUigzJ3TYijDlpEe8SGVtjq6y0NfEaotWJLuvAqqeCGTEysGmqF6xsoUspu9HDneKYKuZhHRMZlHYZL35qos7sB/c4XX4gm4hMVsRjwwACPDxlPvutp3/Cpm454fIzLBxTLorqHJDZFSQ8Uj25CmwKPu8wLRy9krESmIIgoRI2Pq/5k/o1yPl6Iv/AWiKM0hUloSh5dC/S4Mn548Au4oqM/L2TSTcYnL836a0DeR+IyjeWuDQjsRZCNKqlAJl7YzeqocAcUrojip6qShnBoJ0MwZcF6ZBUNag85RwF5Q1TYku28pi/6cEvOZfBKOEXQrLwcCeDLB3e+Ao+wr+wyTGKarWukDUdziEbnTRVkRDQShmlKGIHmZWpWg3fuZQSMub1QcLCkauFmYk2OiKwGCK9MlUNy0lCxLGlpL0epIgs0YJEOicgqZwdmxGnQ5ZIfDh7g0qR0aWI8ae0czIp/Bs7Oj1Hgh/oqqc4U542sZDcU1Dm2Dfb0I99iTimAQlH0i4nYWWLcbs6jTf/tqQrGOrcDDon5ehFOKobpqkbkpmuLs4hzLwOTiEV0MlGi7EYRuQJbek79lDY9OvB4LgxBcqc+TOwHxkgQMExulq8FvyL4mG3n7FT8OmtdiEiWM6IAzElU+7OsKC4Lm4h7jmSzvmcojCpz6sbbVHbGXmh6OK87tknmcGn23GMUyGhKvK4c0Ey91kPxfAu5MgSzxCNs0E6qUtcRimgGNsys9SsnRbL+kIfxDioGxONbukp80tgPGES9zG99NsZ+8lJHBOZq1QeKWyVwp4pFMLU10EeYYuNhtqsqtIUZyKkqPCgiFE6pAqOkym0tpyZBA41hxGhW1OxorGfEiZtkCma//+pIrqMrGhtJq0LJbRzJGwqC5bIC+XAF3rk2RpwpdWB5n5ET/OTVSSkV0V6KvOIwqgBQhR6jYFJ4B592zTZlpeQoR3Dkblo3zOeGftLZZ/b2BfepPzqDv3oGiLZJ4bwrgv2uNt2H1KpgUDT5mPyWOqyDrCKrLNuo7yQMJTDm5j+69/zIMEwpZThH22pQZj7HdLwkm5yu8s4DaupnpXDF8JVGZdeQ4txwlipm6hxv0gBEMRwJZggJeW4pGaxEA+ai0tSH5eADxVTPDLWRlbJifgCEGct20253n0ilY+yFTY5kE0rTqZREJYciXRmIzAJbZdQ2FXhtG6+J/orJzecKf+oVLxymYloDTXHlkoOSboKbS49DEOZgiiIu4tM6Tsu2xgdI2cXwajUICpi02Xe0Bkw1r35IK9IUaiJSOyCmc+waRl1PWqKkiGDyx0Zzi90gyp3Mp8y8yDaLCN6je3Y4Ao4FO6C8biFuB3KU58XnT/8EIwyMiidklT6iLUL55y5+Gu6oigaIw+O0ZbkZN6x9EwC05lWwaB+Nh9/AWF88UdluY0vG0Me5Y03nmbfirdWUZOT0RbSJshKZBQlURmdEkStMyk268F4bRFEia/wKI42CmcAKDPcKMGdkEcY5JJELC3Bc1Yc42vaNMhDmo6pCiFHIm7AKmJOaZXW0HDfkBv/wJoOpC46xCgojVkdqhKKVNWdvQ0ibIOrJWqWCoka7jxHKaMM0kZC50gcRVnuZ1ENuMId/gng6+DpxWFh5LkxyyOPqDCa+4EVnhyPJc4hZetSN2Y3hAFynfoS94obzUTJ6vCS1AUZ5hEQNswKzgmhTZKyFiuXMiJnQtyLFsa3c8GgUBMhDhI8tCMZ5emxB2HG/JrYBC6jf+ucVblh3wpiFy9G6fQISKkbTLOafu4edqcSctom7miXpYivB8ENf/s3wJKTlmGP4kk/2lulI9rTc6JKg6SOq5SRHKKsMfmR4CsVu1eIV0OMJZKhxNnRoGEIbQERqawSM4nA6FpPd0IO/6QI/O2gHWfFpzWdpyXxuNx5l/+gHBFH/rPwFnBQObk4tKUBCypqvdhQl/ZDsi2rklZNfqOAJiuBi+JqGuUz9idJfuufKulYGgqGKNzkyUCbqe85JNTjTtdRoBKWuhZ8v3v7wG6vov+4KK4ACDAzZvjzp0XLlSs15MkDA6YgPXpXCnrxEvGgFxs2+vW7qMUivYoDE2LBwu/Ll4MFUYLhxy/hQJcfK3pxiVKeyxo1HDL055KhTp0usRwsWbIguIMuCx6cwbDkTXk2wIFz6ZJjDZlaCjqMWZIqR4b9qFJlaLPklRtq13pZG3Gkw7cDZ2j8wrEiSp0E/SXUmHAhQ3k6Nf/2K+lSI82KOh1ydUjS5MsrAmdEjHi14lPIX4ZiQQrO31y6NhjO5dgPAAC1Xhxi4ajxYEJwep3i5AfO8RWUYVG+1uJQC9QvhvnpzGgDZd4aWCJWZGzQeVx6pgdqjPilIMemM7QwTPhby8iDDnWSPEiackiOJReDkWpDJV+FCZM71MgvYkGGEZNqQRlxsE3CYeHQF0DV0FhQHDlEFjh29dNdbpst5U9FLtEzEEpUVUQPgQIO1OAXGnkGjlyhRSSPf/ToVNJB3AmWFT8zTVhhRLHJVoMNCXHWz1pqwSjSDOAk1BhmVt1HD0QDlVRRQlpQhVJFGpVkQ0n4+YPYikaZJyL/hcY5uF4NF2lElUsxDTZabYppVJwXT3I2XD8dDoRdZRfOYBRrWDzGFWxXHFVQQmbxY5pLzYHBET+N7ZdUYhXGuJVDE8rT4w02dDWDF42pxdNAGHnBUKXvaeHaVLdpxNBBdy1a4xVI+oOqi5jSZJ5UCZG6GJMK7QRGSS0lapAWVmEmD1VDukQpPTfodMOklIZ6A2oFymOUgg/5dF+1vRUG2ZNfuJplRVho+GlDYESEpk6LrgYGg1/phFUNYIllWrEZgsiQDawyRN95BVkV0YJV8TMDvH16ERVVA4VJT0lhuddiuFigpIVlVi3ml2TW1mQVxv7o1CdFBzs0HowpBWsb/zgVtahea/0U5A9aV3BEz7zwzRCgoI4l1hOFFtJDq5+d+RPRzBrV9cUNXuh5BcbzzsAmosQFNzOHHPGG5rUcjeskiSF58Zg8eH2hJFE6UXUQShP5E/V+AznkrLNe9BPgVf1UxN9AcGfJ0XZbxXdQDUCGNtZt5trJd37yhMYuOAwxGKINUW7Ujw0DsZqQRGozVJ8N/gQcYNDz8nQ2QlfYRPYNd2LhBa0lDcTz2nMTV4OsNj+tWLxCXhG2Fxg6CBwWvotY5ww98kOs7p5mTLVGYOi0ORgwaYeSYTzPgBpRXM/Akzx83+wobuBwSXQ/BG8LpReAZUaVX3cXDhFY/XgHRv9/X1DFpq8xWbW7PAnlWoOnrCSTilgNci4bmnQeJBWFAXAmpoGev0YSITI9aiZZQplW1tOX4yCHH71qT2BcdDTm4K5bNNHfx7SAIxuU6Wk7cV2QBFYbQF3hdWtqIJduVCV6uOQKudLIQAjnLw+2ZlInetHkUsil2hQFMuLqGrz8MTHwoCdyctvWShjov3VFz1k8RJ+VsCA6uAlNLJnJEsNaRpUOOa+B6LNKwyhHpX0JiEx/qUGTELJD/S0lYhhL2boy04/ZPOYLQVlYp7TjGZ7IxV6BKQibTtZEBc0PcxxiE8a8cxAuue6GPJudJc/2GPsMjHwokUlvdNOPq4nqO3H/OhTh/vQqxd3sJUCiynAGdifUwHJDLkkYesRnxU3maDbYWeUdySaqq6HkdYGRBzFHdUBayYqGSuEeUyhmFCzMJnBXZBmTCiJB+6TKbjSZ3JQ6RyEDQU85q9uY40gUsqsFZVjFcUiuMjQQlXENNHMZF6C0MkV6ADOFGdugf6iWyyYWBDFx1E8Ca6m/KgmFiBS0yjFpEzCquIpsEVlSPMVJl9lYCSTFgxtKKRUonMznC7Ya5Lu2pRd8ptScXJnTy/jGJmD6hTy6gsgNScXCR7U0eOhJUbnimM1W0ax/TF3h4tqYO6HqLzRaIGVvHDMbrSVFJ8d8S53GRS3TXIR8pDwl/90eBY7h4HM18xrTWu9Ck8PElGccAkMzzecXMH3lM0ohIudsoJO8Bi1dd5XVk1CDg8Uu9jrX8p3HcGgpvEKIVUYTm1CaeCZbkWxRuUvcmHK1JC5a8l1Gm+yHXmq5NH7hUo1x0RSbySIq+m+1aPtCpqToLa+11H7CHKyBKubbFFnug/dbSNN4VyGqrRavkItjAo+LL5wOcWnOdM1LqYcFEeU1OTI6iSHxyFF6oLUyKKHhujZ4JsJlhiHrOhODGkfLd3XoVCjiKkzQsxweRqYnI3Ouc+LDpbkgx0Hlso7JZjA/IxUHX6W6n0cTiLIhxmRckgOH72hFq+4RbmYF/A7Thv96yMpMKGEhm63rYmYkdeIOVn5zWKxciJ3VrhEMdUkcBUFy2go1z1qM+RBZusMRHRFxV+4jG/LQR5i8PvNguzXUjSBipwjNBTX/uDKWs6zlLXO5y17+MpjDLOYxkxnLWSgzmc+MZjCrec1uvnKb4bzlOL+5y3Gmc53drGY671nLfEYznuVc5kBnmdBzZrOf/2zmRRe60Ipm9KPt7OdJNzrPlr40pjPdZStrutNrzgKoDe3pRI/a0aI+dKnhDOpUazrUqyZ1qU/NalUHmtCynnWaQ51nV79azLYGtKQtzes76xrXxuYyp489619X+st3DnazPc1sRlNb0JAWtrUF/ez/bP+jzdPudphP/ehtV9vLvaZ0uc3s7VvXutreRnWaoR1vdA/a3HPG87upzedzgzvb+XY2vfsdbWUT/MrJLriyb61nc7uay/sudr8hDnCHd5vfDTe1wCtu8VXLmtkS53Wi8c1veYf83CBXt8Sd/fGL19vf5Y60r0kebkTH3N6wdjfOHU3vf2ec5DBXOMJZffCgE73oQB/10cf8b3LPvOgqLzjPwY3vnld80LU++siZznBRK/zMUwe61w/N7oBPndtOP/uXh472tWe87Dlvu857zfRvN3vkl176wCce97mrW9XXjjq8rW1ymdOc4l+neMsDX/O8K53w9j584yHfdKq7/z3pbC+42i+v+c1zXuyBj/rSef7uPrOZ5YvXNupd3veeizvgbd922M3O97d3vva2vz2yAYD73fO+975nteWlnerg/774xq9z5o+v/OUzv/nOfz70o5923Uu/+ta/Pvazr/3trzn53P8++MMv/vGT/9jeLz/606/+9bOf++dvP9upb3BkY1n+8tfy/fNf//1nWff297L96d/8bVmy3d8/COABDiD/EeACKiD9zd//NWAEKiD1GWACOmAAbpoFTqD/SWD9IaD+/d8E4t8H9t8CbuAJbloKwt/uvR8LviDBWaCbySDmwaAN3mCeuSAO7iAP9qAP/qDB0SAQDiERFqERfv+fDh6hEi4hEzZh5yWhE0ahFE4hFXYaFFYhFmahFm5hEHKhF34hGGLhFYYhGZahGarfGJ6hGq4hG1ZfGrYhHMahHLagEM6hHd4hHm4eL4WGFUVFQUDGZuWGdKGI/fyS7lxEctiXiAQRTJHEbDDQAQEZUwxHt/xM/UiZPJzLZBmIgiWErqgMLhnVyGAMdUSOJ/qQcWDYXkzRglxNIumWZVHJaWnHTCBYyBiHoTiEAgHRXshXYwTRdcmOtezQKcWTV3lOjyVOXOQX52SiiqwQ93AY6eTLXQ2He8RTZmRMdETJEPFTblVHAlEMPfBimYAjdV3JarWQP/mUOJHMNl2V0Xj/D3+NEmKwzslkEzXGFGzgxh11jZSNBGHI0ZOconj4iq9oRzj5EyNOy9KMlu6oUoUMUbWkTXIkxoBEj8zIDn9Q0Lmwh42ISsWEolHk1/0YSF4ZDql0BdW0lDOWBS0BGEZijLSMlq2cSZ1YRVRgREv5jp3MSbQcUoRkI9MMFuRgjpxsT3uoUnJw065oVg3E0hNdRZbMVlDSUsPwE9lc5HF1Ckcgy2B9SI8QS4egT2R5xFLMVp3AlPeAo+zAi5LUyVr0x2m9YvwckYD0VV8d0NGEBYNYSZccjX+42MlcAYNsEGQchCbCj3zdUAyVBIRAJZu0TAE5Zp78YRntEMvwx/ww/4lHuJehQI9ZgKNjMuTxQFP9YIgulg9SFk634ItpyI+uABLGjGUKVVQabdN/bIglipOUXORXgKN2SROCrNBgqg1VxEce2YSDYUGPoMQNbAuBeI/bOFeXwApXQA+RIaVcQFJNyY7kAKYysRDZkE5/lQvm0FAc8cp+8cyLQBLtzEVK9UgbrRE4uAV/weY+ORlZDAlqoOLEsOSLABJ8FEUNrAU/MAtEnZduxRfpiARX4ZeNxI/RtAjXECYgBQhuOVkc4RH/9FANbQ/VXI0XFZBv4ZQ5ueST8Mxxvgy5sAQsSdjseAQV5Y2y2ATctAVbXJE8NEYotpZUcSi3gIhiiA0J5f+WdvVEfmKSpTTUUH1IRYgUYzDIYXQOkGWJrwjmueTEe3qiloai51jLvOSOISpJjRFZjfxjJwpYjoTYKkFO3QCGvVDEBrGKhKTImRhNivCGi2ZT7zQnV1REpdzW7MjVDYADVhyEW3RHW4CjWDjL6/xJjyBQarrotjCnWrhOiXSifcIKjIAJciQODVEHbggKehgW3zzptZBPR6TRJmmpdtDGXxkWPL4NpTiZKx4RKYmj2agiucAEajjVu6yoVeFOxaTRD00WLB3E/fxEbXAVnjDOk7AlWe1QcDjIlAYGMCXNYBWqPPKX0YRGuECUWhhNfaZOSajF9lDKsowrmtQJuUz/D27V4388ZU6WTogBjjtR0YqOV2RVC4u4BPeoBDhapsQgzXap0ziuaXCIY3yilG4W0ExgVb5EDcDiFVcdzT4d12l5E6reBYhl5GS0VcJ4KMvsh4v6TBuREGmgT7EoBPHE0DMeEuAw5l40VLX4FH+YR6EKCVqBylrAyHg9pxxdxabMgHO2yWew68u8zIFqhLOoxA2kzUTcwIGSF3J0ZcPewCzNCYU2ElrxVEBqhVowVTktCUcMUZNskY28iP6gjSF6BvCASZPEJXPy6bwQ6XjNq7QwKarYy/UAgFMBDjCdoqscU7P0CMu0CET5j7O+U5NERyiui4iqBXyc6w30h1rI/5Gljq3pJErmtumJIGjhiM0a+cUNfJTEKEXSUK1tEFm18MOwqNLM5CcFZahbEgyQ5cpaMWVmSNN4JVItsUkQWdIpggGqMuvBnq0/kIn7CErlyIpunsp30Nh/eeaVTNbSXkETMas0+ZEVhVg96gbBGkV8JMtaSEwLiVbMOKdaIMl0yiiS0RIhuuipFIpxmMdLOcrygggDWch5BNWwsFG3hG3QqsVJ0Cq6Zu6NOO3PZgd3RC3SYO1jFKxHJM1oXGpDCe2aPtOBrsVJ0QYhmo2zBGTbyApn/IbT8gM2qRJRXoshlkiuFKRzwY1LnsmsGq/SDAiFKqMlZYYYmWZlDQloRP+LXLQIJPKIWohkZ6RQYboFosYLUfbJ5nwQl5TOWuyuOx2SWEQFexhFQM5UG5HSyDiMfXUJQ80S8nhK7oxL8/SwNHbVUxKOwgSkoxjH+IKQ+zRPPY4WU4Qi9CBtyLBPlowSg7nTr7iPTdjXMR5G3bTVgehWS/XDbblkYMjVmgQkrZRFfmHMmh6N1spOPJ3T5IBBjxTkhDjGk6zFyzzPjs7S2JRGm/pj7JgGH8eHU5VUbmmvrsaxDLWy0eQXmEAvTWhIaEzpYpTiuMgRUY5JCwEMXjltZSAw+5arWwQMhixKQkStkK5FT1iqLgOOJvLjUMKI0yIEe4TMTJzHpHyN2nj/qNEckTjZSCk6hvnGJ4NIcv0cl2yiiY4kxmkAwA9bjhasBcaExNj6DD/0mM8WrA8jLhJfrv+EzGyoUBzPFtLmYvTYF3HtUDIV0NKSiXYYkya2Ew3lafzozxXx06rMD8+40x0RBs/Qij+lE6AQEy/WCHysFBwZVXbAyrBkz8Jyxw2x8ofaC442bEQQNKW0VV40Sw04Z4GO5h4ThaGwiXEMx5DUiU2yCl4eDR0ZRgbD0DMOmSdqLYWo8qu85mNcUTnVM3jNk10MxzGdDAVxBSuTNQhdzdnA5UmpBfL4cUMsrURyZ2IsDHxsyIXpTl84x6ak5ne8DFUUqrYoi/m2cu+4/xAjHkWKfBBM+QxYbBBZ805v3sRE0/WUoiqq0JGyGioNRQfjSJmyypVPEQQEewFq8OpqbJNpaK2zQBKT9ghtC4qt1BgdiZNniI2vtNDHuJakSq1n1A4VRUdl9IZh9Ji+nEdF+QpXuU0pbjL6EBle+BQ/JfTzzosy5856sIpWeA2znC6zEtQ+FwonI6sQE2mBNcbDqEWBqMUxSS0oGqo52UTilNfb1JLjkAa61kVlCO0xAfgQ7UvAEBmaAIlMRIcz8U50uE6PEFm6+Ew6yQ56rcZ4OQpMZQmUHCONgQNq9IhtowaFogaMw7hv4BXgVssp6k5M1UCM7ziP9zgJfQGP5/+EAVSAD8TBKODCLwADLqRCKmxCFPBAB1xABDCAAVSKQ8D4Sd9MOaFPm94QTbDE+f7wmkYHdvQY+1SO/cqt8qBighCOf9xRCy0Fs/JhteBOJxriXVwuAp9v3vxqpUBJOGPko6xYvrRt6cDMwSaOCqvneJmHh+IGUBvVf/IPOAJGN65GxRCTjzpzaIjGhJBopOjSlzYRk5ijC+lKVJyiLkuO40jOMSJQy/aQR5BNHe0uPxkNzihOxRDZowQMU6yUp/DOvFbHSKRO8bjM6aaIzyBswupFWiTxZ/iEM49YC23TYpCKM4vQmprNFB0X7BqHZ4JIY+SVOekmV6CziwRFC5X/RWoiWPbEE4a00CbFOJBFRIyrBWpEBmocB2qAxD/3+JT+LYyL6a/Yh3Dr96ag7sQEMI3zeAFAgAu4QSrIAsWLwiZQQQ9AgAIUQABkD+f0AxdwwWhyRI/DuAez78f8iaE8FXFe1YTUTak4M6B0S5bUEucYSQINkZz/yhBFO8ngEuewL6X4Q78f0v5WLOBMSIHVkuRkSAE9hq6sdsBcqg04y/wkTd861sCGBrRLyymebss481rwyFzuxUXmkWN4Mrp2DU5JyXXjq9Wq+u5I0DHiM4IdJwkBc8zY15OGOpFljKFskpRxxvXqc7c4TFfORmSb0ys9E3k0Nly98rDcBbxU/5SEEKVwZUYx5xdR+krMrMRsUPdxObNIeEYe5dYRoYphJcZMfEeq5ErxEtABlTyPoypq0AANuLhL4P7t4z5G4D6vtCyQA8CRkK0W4H7uAwDyJ//yEz3t8/jyMz/vA8AXKAAEcAAPcEEVaAInaAIV+AAGNIAB8DgY0EBgHu7Y12dBZpM0wYq9qGLiZA6ej8VXM+VT+9bS9k23kA0RBUfC+C1AzLBhQ6CNfv38+cOCRcuVKwtnzJAnDws/fjVq3AAAQOKNGxbphaQHBkzEGxNDyrtxEAw4cB4jktRywx9GLVpcglk4kWS/Gxi9mLzx5YtLeh5tavGy9ItHeQmJgrlp9P+oPHouJ954edJlwoE3Bzq8YjHhUi1ebSQVORBjVKBeLC60YfFgSJcDCc5wOHGgvJsHby6VB5Kew68WLWJ0iYVkyJsY+zok6nDhlZTyooLhF3Lh0p1LbywUTQ9qUXCCE87weGNpxKVeSJJM7bD1jKVXIk48OBEuv5skF/IbuDDkwIP8GtNLyu/gwYgHa7i0aNgGbDAHb19pCS6iw5uPv2sx3s8yvaUHy5IkmrBsSIdda060mBNM2hqUsbhcuhEAvY3+5OGPnokWomEjujaqoTMsNqLnIBrAoUFCG2gg6bx+xMuLhte8SIq/Dxdi7z8JwRBpIwM34oILCVH8T6EPvzD/cIELMnDBhR7aACWVWlRBBZU54CCCiCBcsECqs0jiK6/3EPNNC+feAyczcG5KKLkZFsKIJ826K++5vuSBLjopv2oIN4GIumkhovjJbCD3TopoKikD88KGjbwAywaPvlBttT8dimjPlU6S5y4b5BnIpRtm8qefkJbaCguMEPKHpNzkqc++vw6CSCKr6EGMU4uQqsujzZI7SD8vvmiuH6LYTEgkem76oiwvxPQnJAvzPMu5GQ4aDot+FOVuht+WSmqxkBLSM8OJ5NQCC8m+kI+fQLMjySIt/MRwptBEw6IpRlsCdyGWaHvtz34wuoGeMq8A7LuI7ruCqIOIquFCqrSI/4sxMDxz6dCIioPMhjptgPULXsWiNqctIwUHi4kwIgmjLDmkNFgbkpQnInDGS7W2LGuYYT6Jp00XrYnUpOcCF2CAoQaiPC5ooo1i3AgncPirkCQAapDww6FZLNroo5E+aIult+CCaRpkJSrZmWkImoZOGez53H4iGhoADFq4kYg23nBBW354i9a7Jx/Frle+MEpIVXAAZvWLkCLyp0p/BmptIun4yZLeegmb+YubOAs30ILQlufCdv9MbQbDm+rnP2vBoI6ovi7806OxNlNu5pA8F+g45K7rB9qBCDNJYHDI8scLuQ7WAqg5xYSdn4R2yjQs7WJ7D6/aJ/oisRpOpv/1u0slcqktWx2lzqHSRJJ6dixsw7UfkgS+bCCZbmpY7tMFtWGx8VnS6e+XwJgsoeIXzpyk1fy8Acx09b3eocLDc0g9iwZCFJGEBysTWd9NXMMr91yheGsLCb7wc7rTdKgGW/LWQLr0hb0MBjEWq0y8HkWPw5kpJGyqS2FAByuMRGQhZ3FUP14HBow4hluTqwF7SOIDITEAADaQAAhKoIKYDRFmRTCCEZCgBCekAQJFCxae7EShVxnPIQfxGgDWpyLHecEhNPDLWbxAg5OJkYIQYmFFvCghfPVpcovT07WuUAMAtKANbSCCC7ZUxe0xj0ATC5NL1ritZvULjg+pyO7//BEXgg2oZlP8XWpYVkNomaxLdoHdlrY0gwKipCryUIzuMiVDBW2EWHgB0BYXgwWDqA5UPAkgrOiSHZd854ThAx4HMcKPG/hpkq+Tx+IsIh82kSpJVwDXrab4Oo3d7SpU+g5zSvnAfvSGZOshSrR2VZy58CMiILFIRNJkOwtOEy7yWkpK8qWgSVUQbxHBUn7o9qvp1YRkR2nOvbg2g3xNEpxf0B5vvFAci9AsTKVkiO3G1JpUrgp4CYmdkzh0nDLFjT3mAwd7+Max9PmySX3DFac+GD9ANsedK5wB+KTUqkrNDRyXOU6tVBobqmzOIHpa5EIyd4VZagEGDSABDJSg/4QnQKEOeeDDJJA6CUlUohKscCorFkEIRXhNHmQM4xW5cAUaOMYlQ9NqhfjBH8Q1rJsXadJC+BNGGrAxO066wgFeAIMlQGENdlhqMCohiSXY4EzvSxSihkcth9SAJGsTYFJ+Z7zQCSZRsqlJDWoFvsuUzGT8SKVIPgaOt4QKbau5CWtm55FmslJLW/QHfHjWn4thQXnD3Ng2mcc2Pb7FCztllmdYKzo5MTakoQEJTXTFWMS9qzNRDA5QCvWT6nRoNS6JSltRt0etYMoGweHH1JrjV4zUYDegGqax1gjOFmqBVxS0iFlsh9vncI2T5xQdXmg4k+v5Yyg36IktQ3Yoi/+QxKInq9hAYWUlGe43dTSjHgpLUllqYcY0E5FMOwFYOxGaJpcXKeEXQuSPGVgSP9vUY7A+ON6J6A2h0iRv3YrVG8mByZOELREjQ5kQQHKKKOO14VtucgQlLGENZjDDGIIKg1SmK2FfoOg3saAiGlAonUteLg3rBbh+0EAs2jxIeSQXmytuBAt6a4ABXBAOc9SjHu8gBywKwQYzkIEMYpDBCEQgAgUQwIWPZQt3twemx0WHdpZkVwX/WqXaZCaRcDwZ6vR0zXa+pWJzg1/82mLfEl2pIPCkT/EW6Z/+yHK1pQ20UrxgpW2dGKcjzijBYmexGwIoSkqCXlNA2pRV4RT/PujhGycvvM6sqHJJiWOWP6q1xXmaxwuMCucXvgeGPqIwXFhw17ukaeou0xd53BzcGReiu1HTpG5IyYj2EHm2r2S4Npqt9vgom05AalPG4PiiYZkJWWkdFL0uPJkNZjVMaJapPYx0qLW5aC+F+bPdrmEVYx1ilzUhGy+UjdjadCKaSRYaJR6Ryp1NWrelcA92Hi3guQu9HoX4q2LKfpODpvivdGGnYwC1TzAZjMGvDAVWFNygbOzHKTAAgLyM6teWAbDYC+F2IJbaDp+KQt+VKKnZ8KubbppTPAP+lT3DrLCm0heiMkXSOs4NMPgmY0gNXuHfubQlazhzKz2CAS2l/7H5QzYywff4L6e8WRzW3umSfho9wncZ3O/+8htlc1Cn4Nhf4Jw9FNAg1HArU4lH5rbCwl7BloEZr8ZOyvZCS4c4I7EfvmXVTPul9C0EPa2516YFgDqWm4XqST8QmZnfREUrE5QNfvjLV2jR5POanCAnSyksugDYeTMTsMgNuLU1BnQkR+LvcFXNQIqs0zO/MxhkbBkbzRdaT/0g2arYhrX35E1XzYxJUpYiq9XSEJ4bu3JH8SLwaA30zko6Xr2AM6xwokUzoesuI4kCcKCnY14J35qpxrAnPLSpIDyq2HKLdurCltol1PjGl3Bp/0hM9X7pCjDIdEopkoItIUoLcf8GxoYKzcqI5YRej7KKjsT6LyQqrHYWDqAyyrEgAzQKJVmmLveCwoamZsIACWfm5vTO5n04RVgQBVR+Z51KkJmK63UyKsWCQk1Spi36iXQ8Z/DwZSZCgjAQjZC65F8i7C8maeHGTgIz4iYmyV30guwK7ZN6xSVWbrAArVZw0Lo0BfKm6FRMTQRLJv1OyUIST3Q+509WMFqSrXbUkMHIyQ8FT3kI7E0mb73iyHBeqdD8RXB8b6QmI9uMRaFairOSJP88xdzCTjRSydM8ZphaUFxUKH+w551u4psiZin2a5F4BdjGbvWkQnIASp9Mo7ty6rx65V2WBIRkaoJSqf8UplP/VgvqUGeeWGIR3QlLig1fPm8yIsmvis2FKq7uHm81HMJzkG59osSirulTUMdTQMKWdiXBPmVuPoWg8oVkBmegIubf/Ah3WAPAOI7BtqteDEgwNgLy1GX68Imc9IQyjsdTevCEFqg85qSKTgZ+2jB9BE+UqOLFXs++KDB2qmiG6GHv+CYAQ3GWLouRRvJTvO6xwqnYLO5XaKzzrCWjahE2TFIlSGdqus8PF844MuwljEnzKObcvkUna0zwKHE84EN2KNARpWXqdqr89sZ5LsRVFGLaPMUVeSe3lsIALQklXid2EKx9MEyVUqkDjazahicw5NAN12MRLywJB8ksFujC/wznLXJiFpsEmkpJYYKJds6mIN5SVuQFfFYpKUrjz/wqNTAq9CiwtJxH5OiQYzRvVZKkSYTyITIjSgSmn+qiKDVmA8/mJ44CtFzlN6zFkg5pVURCglJqmzLsezIr2daDn97JMqEugFbrW8jjJQNOsTqFlOYynSAlT1gDMTWqUmqDA5OQLq7MOR2Le2DuzgSmAyNljebm87JjsIrvbOytTRiuO6KTDrWnYWollfzFBv4Nvc5kNZvvhQbH7BBKsgjlCtxLKz5il47lLOjFczYn96bDmDrvk/BMl/pmNY5kkphpfWwRoKbmKxKONDaPOuCD8jJkjWoo64bFU6yFLUQuvP/wEMWYR4/8YYMWLmVwi6I0LuAoI52mrjcWcV5mwPEKT/vmIymsBTLORptQTjunCYEKAjK+iQE5JiYGbzEFjxiBZYqmpkk3piswU5TOcnD25ov2pGOUx+sQCA1VhRrd6SwXSndurAbwBib6U7jWZ27gqUiJY9Rs6bE6rwObyZJmMZwuI9hS6S9+jWQ2T0o2AhjFpPFe6Fv8ysFOx0QX8S1izOgWSAuIglH08J0CklTQbsLUJDqLD44mKffyAgqBbYHaDY7SZy8iSiGvMoQgVbFi5zx600z68zihhRhbgya8Liv+BAb/JCgSwjBorjBoTkHZo0lkpbmEwiNmoimeoib/rGc1Ck1VzcRi8uUjxJOCTgglPonp7qxOWAhMRiqfioU2PNFEAdHr7AZJBcy8wu06MGKeEOOT0vGgDE5W2ov5EKrdqEJgTgdvYKp/aq2UVvUtvgJMTJI9aAe9UMYk96eP5lVPBOzFPIoOT/MtA0Z3SpV5FIYZgw0GMcVDDaWdBgmekPSayNEnnu0tFaNdDWk+Jsnj4KOfBCPsSqvI3jEn5saWvEUyDmR4QOWMzELV2oVjbCk74GlrnqPCehU/YbKZrEU0zsYnRIKTBKjzFkdpJWdz8EOPKIOximUtBYjY1rLkaIYvy2edupJDOMixBospVEo65unw5m3BVkM47KMr/1ZDt2q0NHbKgMajIQfUJ9bkKAwGp9zF0QzINLzC51LOj/irWc8yfxZxfeRO4KqoWDyFbdDthgTvhKKIbPXihI5nkDRQZd1ti07nxUyNpD6lTqDpHamjyDip6Nop2dyJMBBI++TFDYeLMjAqnbTE1iy3YWoDh+rGMQa09O7sjCbjKNLpyubTcNbJeviiTjjlhaauK2vIFp3EGK935NbxoECldw4CuMgvMQ7P1hwqPETS305QhLRvbWrHYLgn2bKm0vKDk9bn09CrULoSbzYGny7qsQzuYqgQuai1+Nr1BgpvnuaNZIDjWjnoUy7jeGJv7Apr6K4nQ3wWLhiLplYS5v/6CWUETwSzg1Py1uWqK5KohVRhyFiK7lCSbVZWkl7wA3cMWD8V2JC2RBRjY5cYJeD0rg0XGHiYBWtyb4TqJDXaid0AxLr85fcYTE3K6idV1hOzUkwean3Gi9hEUlbyj4YwdGNEwyRJpqBa7q+gh/wmozYuRAPBRFB+jcCEbm7mgxXto8XYVnSHKeHckdj46kyEFD2m7rPwyaMCo0gpqjzWxqG6EJrAh7J2CiSQdIpndpUiblE2MGVGqkt3prRiwg/7CEOLxS7cRklYCbegoqy04HKWNQkPxS4MjlESaPCUqcKKyxWV5TM9ylMUiwD3pF9xgn6YJyFAizUZ42tPV4b/rq0/6yazBAbqGmYDCcMuVPbkQsRB7WYlwQc8Tmx33YiQvFakUur0LuyBeHb68GKX/AV4OGQ+pMeY2DA3P6+wiDlhkQ1+xvUdYRCybqWcPwgMwEUSNY9h0rW26Cb/Fsk02JZewsPWAiVK0Ba9DK5xHOs21ideoQ4BL9NYcOr13GY+MoygLCulCAqEhDQu8MNgJMOBc4pS5Y0K/SjCym94wgJ8IgklrOvXGAxZT2zVxisAT4bjgPH4xo6CXAshwsW9VlWP+oILuZcijgdJRWe86Dd91utfii87vWWYzFJ70UMB8SSzjO6MMrODrmMoQq+0sM77YhF4Tsdgpoj8fMJu/5ZwYhqGVMQFuwoFhKzYUbbLKfSEkfhNctAHQw4v+hLnhBgOEMvkurxgkgITC3Ar25qE7lzIQM2Uee6MNlqQmMWxY7KETf9lO17V2l7rztjWz+guMXiaebRlhUd6Ox4DzwYl//oZZh/ywPgCdBunXoYrN6hDMEtEjulWlCjKiaGuSspKehRGeD0xfSyE66xDJZsvnMJj3lTqUMNkn1twOJSkkVeJQ6wkPE4ufHPrmiYU7zbMbRandh07JejLVUL7L5lpZ4qUplZ1kG4TVGY7SbPS2SoFo5w5O4yDCgGqLaCTWlrY4KQmXX7x5JK2TAMEAFq0SYWUYGBK+IbiwgIlOf9MSgOx+WL0+TRzGAFHA4UWRXYEBbiSZL0ynAqpRevY0ykh49farcXozji60zmnTeTIj8Qo0V4qjA7HRJvEgo4b5z03MCmc7UhmRWD+MVMWsD8R7ythq+QA6l+6lpkcWVOSRJqMKTXUg+HOBQEvbIKeR8Aw9FDCR1jglqL2VUiFlL9PTEyOpDOlr2bGPLfKh2A2aFRfKfdIzA95xYUyY1uk0plWyety45MWMbc+rFIIaW/yZuNiA6fub9XWEhBzabXwaRZXhUUvZLXtx9RelzB2Y07aCTt0bjuAZyZgmowPKnQU5nwe61by1mDcVHJuNcdnfFUzMVwmyYAORY54bnz/dJHA/sZT9Ch+5ifB7CZKngPA5IWrXoJtbqOj8eOUPsk54NRMtEfAsm21NnUz9oYOa6jZKuJ58PDuyMlbZqV3uBwxcmJ4RK5OjfE8WAeaAAnqQvkhSKf8YnYtMQ4iOUtpG5m/VqvVojw7fPCxt0g8M8wnKrGfJsPgiu/P1qgFx660rmxrOAnQgNTobPu0MD4U+SLIWYfTA3J4nkc8CQgcWlhypqIDi0VVHPI8zwhlawPv+qjCdOdWwvWDfAlwBoYsD/Bk/o2thdSx/oxaZog+aEYrKgwBnUmbZGJ8SG50lMdVfGLeWi461yd1kuNSTmeNakaPlL5JzgbQXkdRsuNW/6AnYtjCRDfiOdFGwMYnpyKGQLSXMlhDT4qdYJy1/DbMGC/jhWpjoMIjkvpTJM+p1Tjo17VgdglClhSdWE+U7NhWqTEKPhy7WDYrfwsKnsKOMx6KblfpHbdleFK7MAyIPz2DL8iRJsIDpw4PD+0iWEULhfZmYKvLjyimU2huPVauhZc1XRpcLAAYdqhDTGLxbPiLPgJZJMsn4IYr0MHEEzWvnWzRYBSGsrakSrzujXunrDjvoM720zF3bQxOeS5DPpLC1iLleQbHW6yFQMDE0TUGwgRLaQECHLgv/Ph58YIFiw0b4A6CAdOv34wbX/z5OzhjxpcvWhZeuRJxYT+B8v/kLdyYkF7EG/4ilqS38KHFiFg+bqRHr6SWGjUORnzoZaFPiTPoFSwp8KDFhFi0aMkoUAtOlFg28sO5tOnTjCW5yvNXcuMMAADAWZzpNGNGnlhw8syYcEbBh04LctzK0+FBniUPCpyRkp5TgTUy0tXyUZ7fgRu/gDn5JWHjhVj9FbySkOdGniedhrTBk19Gi3CbPsSJU+nGhQstImxL76NTnFcsbnQpr6cXz/0S7qzhlCdqgbSvbAS3kLONknwzRhQ7o9/Dhzq19Hs7g7o8fhvDfvkI7uHGhwLBbK5xIz3FL17Sn1Qvb+PHhAIrni0YsYZvqYK1gEurkX1hRbRWDQv/3UDgDe1ldIVw9ASGBU1MiUVeQwd5IVN3JoFWwxeQAVjDT2AIBF5BvH3kBYkNhtiPFxY5lZBFAllYEINXREeUQEudBVZJ8mDx1ww8kSbfRzfOYNGRB5mohUI2QAUOZyr25QU/L9HDU1JRNYlFRgXZ8FGWjNHjJT8IPnSFUF6MBwZmVX3hlX9mgXUDTgJhoZ2RN0I3pD9XLCladmAAVtMVBeHUT0EqHlfaQwsJxM9CzvVDm3KQ1VCffRlpsdFiFilWZaRkAecUl1gweVxmhSHpDz1DHbTQm8GxqIVFDopoX0IxPZTQZVfMdpBbPIUYo2X8WASGjOBkRJlhg8rFD1uo/z2IBXMlMWlXY18QOIMW+PXjFHlHxfdFVzPktipQXsiT2I+FghFapDYktBt/h9IDzr19TjWaP1bxA1E/rRl7LroROXXRhbZ9V5Ka010Z0XxNTcTbDYaqW+awvEaYqK36YeGar75mRs8NcZmMRb3ggUMTjxtZZIOyPNbmT0ap1YshGOOiGJeQBtpgUKiICnzQrA91y2VEB6G7EFLgIGVueWDQ81BBew37sUU4sUszzDZct6psW8FLaqlaTPeZbxreXO52/LTbWERe9+X1wBum195H/tA351RmpheYj0r5G5lCevsj0MENs8YTGE536oVxX/BkdooWxtoUTgVZfWGL6//KU2CrVD/rlnfTwcva1xFd1ys/4JAlumQq1sUPUyTJk9+uCZXkz66U4evUcf+d7fhNMMXcKoou+lM1wi0leuxHONk4w5ZUGunU4k55QXmtxvaNb5Q9Bc+eF6gdxtNz5w0lUmVy914ZrGA6y5x+BfKVG+8JF22d0jm/uLqRgMM3Q8OIXPJTg49EjCl4Mk9FcCKetvmmZ5uL1LQkZCV0Uck+0MEJkGb0kPTgxAYOedadtuSsrUxrhJmqEI6W9pXezQsn4dLZ27xDPt0QJyfyeBq5NsUp/x3kPB+bVPrqV6ZTRatDheuHxBYCo+2FKmJSsxNyGCK1ksyncWDYz+oylST/EY3QBmCAIecepTl+RGRz6nHM1GKzRn44RT3uA5dUCPMzSSGphL4q36fkAcGeJUQmQboa4fromAstT5Gkkkz5wuNAHfGoTGfBH2T6wzuyUOp48nhIAJnTrkxpYXCzAl2A9NS2g3xkIfiZSvnWyMpAJQZyCdniz2wEG56skoSQ2WVGlhfLn4ikH5OsHM48pqbhQHJwhZMYYSa1JSzo0kgvsskXpOYh1pDPVgAq0hWsuDXFAGhxPZyThPbGMWXZgHaA8kwFPcadydVAWIFykOZUeSSB2EBbVIlRtn7HIXz+xJqfodnNknKwjpAxWTXTmquCYoPRYS40EquObnbno2+S/wlpHgQimozEIQ+mLFixcdMa4/UocIRPjy+7WkofUoOogUM2H9lYfQLzMmXpbzW0Sx/0eChSI6nnNTTM0g3YohBzZetv6rnBt55XoxkYiYaIMZSifIgdL0QEUmrsTWBcs6aZEAWBJLyQTPxlRQYyZZirTKPDPkoPsgRMPLMpaX4AhcceHYRekdtPBWFzG+gxR0K42RD+YHQ1kv7KW65j2XVKcprwBWgoiZOIUxS4uueRtFdSyxapJOQWKJVEQqO0HOeqN6da1uZEkjPf6OLlxjHmFJ230yXnrpbAxXrFlJq5pkqztZp9FoRvZiur9joCJZ8ypoSaW9heUwY5R6XOWf8k7Y9H0jQv2jUNdFrMZ74cGViuvu1P3IupdkpDkED9sq9O4qrPpNa0gTErouEMUw0KUoOzijRQWx1e0IznI+hAiVkVbGddt0QzQNGnWCtbKGXntCKfiqdPwuNhQY6KH/32x3P7Cl8ZIWpNxtxJK6pTFXnk96UnjTjFH7vq4OQFuSQpa74Coy9u0utTZuEmrgAo2JBy6jyk9VZpqaNqBq+EKPuajTgMVEt2elugJc7oisvBXY2ZAsE3vmWHI5YRF1sXsCADxmz6u6LN6KE1ZR1uSc5SckbO46MyOa8f11riWlQ0RDidS7WX9FE5s6S84JKrwSSE6c8Gw6RhngSGSMX/rrmIzBo8VllZnZqWdxJix2/FRNPIa9vpggPF0npSfldT1PTUchJhFQudh2uNsHgkp3shTH2BXeVHkmugNlt1RMPzTtAsgt8aoNMrYnoTZFKCzy+v91vjTU5+zUPSgoh1vN1lTQmV8yYN7ZUqKxMgVVXUxTcZeqX0TY+7hoKswQwPSjP6F7o0VKQJNcZWVKlMj/zHlYgdySWKBIMmAZSvq5SUPIeTrjz/9RfS+Ewpt11TetGMOPHFappv5ogk9VTSyqBIpPXDVMDflyTtGO/DNvpIY51tlVsLSjb9qrhQnH0YHN9SqpUV0ZySojIJk02LUWXgdHS1zo449DOey0qM/yK4y33KEzseiayzLyRFKAJdbA53JJoiMsaoQhRykurTfohaUtlwtTrVApXCH7VeP7tp1f6QkxSP4sl+UKWrgeFKsLdLmns5LeLk20ymvsUfyGBETZhdGpS+rseVLbJeSZlmQhkzsjeZUljBw2PJo1pZJTZ187WWnEpqHK+fiY1AYqXKoYlXNK6ylSoJZYv8vALJlDKnZ2de1e2Y4mQvkEWrXv2O5No1z+Otr1/WdJzbDMiqSVGVzlzTwg1KAp9OMvSoHqffYQ7n+6uhxtmfoU4c1a061KgnRz7cXVPHBT1434XXTp6Nav+olTp78krnYX6QIDsir71oJ4Yu9mlZNf88VDUsG0E+aHI6vnIw9tU9vJcX5dUmmxd93nESu1IcUFQaQHc82IEiPJJQqOVQzJEVl2VVUoVdUjNSwiMUC8NANSY2dnEv6sNr17UxZYdZR+MVNvIxkKFPJncsEXcFdSY8mCYi8dJYfXdN18YiNcZOeYY51SYizwUtHdRUcYc38lOFTqI8ETRoi0UbjsQbwsYj6KQF5gJlVvQt0JI9MdQwt+M8nzMX4bZEehQsEpMVj/EkM6B7AKAyv9IuyiMP2nWHYEJe/QVIRtFVP1Fv9AAdGgUgzIJz0uYPebRQEORnDNaAmMOI3hNjdZVVzuJs43VqRiJd8vMbU1YZXLIYKJH/UirnJt1mcxIHJgSoUBJDKFYUFXHkbCtCZoCEG3/EKucxQgDyXLZGXlRhH/YFK6znOaBSEgyEXUpzAzuEGswEOXiEaPKiPD2iPMSRULhhEylmTSwyOAPlaG4ziZ+Hf8anR97TWwJmTJ5hI2uEIP6QHsgBgTfwRjMyEfhTGR6nbhEDgSWjHhLTUq+RUqhRLFSCPDyoQBtxj2l0JQupUWCgHgEiPFwBHQo5K+9DM91FSBYiVqhRQ+4UT04za9JhQwLZRgHkW4czLL5yimdHFj7jGgVSceXFLWwBI11CKJEYfAPjcSVEJajlPLRRP9ITGyIIiTykWigSR2xiR4EhJv2j/3LUEWr78UGKNBfGM1wpo4OQpE+ZUiCs8S2wMji7tCrdIxtAx0BOIYj6lj1X+UE1Q4JWtEnb1RoMxEVuJHLZxGtioihmox3lZDwMUhpuaQPqsTEVMlNGUm1xZ00rQhUxkVOO5Fqt01PCE4TgcI9O1CD3yCXKsUnYVi0w2CZWZTlRdiwI5FAj1TOU8YpQNC3q8iJcRYqzAkPGZyP1kVJlQhQLoy5W9hnhMkwS4mMxJX0gwzzhxhTkU0FztHk9mEHXZTVY93ndoW8zoph9UiYZFFnZYzZwKVt3wUPrtRa/81zI45ZkMS8jRTCs4hn/kkYa8yyH0jNVc4g7hEDk1YCcAf85/XIXKmcif9RdY7gh/7IiflYdDuc8JOEsSYKUIlIq3JJT7YlR/VU+uiNNytGGTFdlQcM55LGT+zcXJSRWVzA7WxFw8RIc+oYbSsQmXKGaY4SShwFZUhQYvKZvUkIjkdJUdEg4LCJdmeFNGuUzWWYbNuI120IUIjha0DFixxN3cxRVw+QFjcJQPqkhlPM7ZuMblgQ0ViJSseSAz6I0BRIkV/BR2KE0YkQt9xU0qrI8fjZcp5Nc/8IjxFFnQYKciFk122WLBaIndkhSBvSH/BF38lQsavFtFvIxGxMWZlNXVeY/PJiVn5MWXMRHu4KUv9EnkGViM6k+myQ1eaFarvT/MfuBPQYaWf0RWfzzbm3TGPtxVs8Wmb9inYASPlejRWnDk4MRWdfDktnXLhsTprvZEFLEJtLjRWAzA5gTd37iJmXyBeE0LkHjknBKFK60Sv/yhrkCG36TQcFVPrikEO7FKntlaB+FIwj2J+aiLhxjPIP0XA3aEBCiRAOkFS5zZgQ1ZbtEXl6Dga7UjE2xMGdnnkhTPmU2U3l2ZuhSQ8A2LshymkHJe5JhIy9VgVVmiHUmIeQiLLyiheXEex13X4Y2Yl7ljWpqHoNqfAb1UHXWg1kWpnXGFvFSGl6SdGGKouJzVSjpqwwlX9VjT1hiIN+iHI1TU89CGTnVgB8EfLmG/4dQxykIRxb/gLVZq7Vby7Vd67VfC7ZhK7ZjS7Zla7Zni7Zpq7Zry7Zt67ZvC7dxK7dzS7d1a7d3i7d5e7ZXq7d967demwV4G7hdO7hZO7iFi7WI+w+Ky7iJq7VZcLhnq7iEu7WFi7iXS7aQC7iVu7mau7hjO7mN+7mPy7lmO7mOa7qbm7pfG7qii7qja7iP67qoO7uUS7q3G7t/q7u767V8y7tzK7qQK7yny7XDy7jCa7t0S7yLO7zFW7zNS7ueC7bEe7jSy7zIa7jIa7nQm72Bu7y5+7rg272Xy71ha7mgy73fC7rhO7q1q77Om7xl+73vK77wm72lm7vui7+0u/+/8Qu77Pu/v6u3vivAdWu81Fu+8mu9Yku/a5vABcy62AvB3TvBbPvAC1y/FXy6Eqy2B5y5gNvAibvBIQzCCczBFWy3BIzCckvCq2u/AIy5t9vC9hvDsFvDNqy6APzCuNu/1JvDHxzAGdy/PPy/5yvEOwy+NRy6kvvD64vEDMy6hOu6Svy8+0vF5tvDQ7zCKQwAW7zCN8y+V8y84ru9Rhy8pUu+5mu8pmvER4zEYozDSYy7JyzCU0zE9UvCbcy/RHy8F9zE14vB8+vCT7zDkXvHWRzA6qu/h+zFXqzCjQzJghvJKDzDd1vJBszIcHvJk8zJnezJWPvInyzKowy8Ufz/uYYsx6n8umDMwojcvkqsx1AsxMc7xiI8y2icyN5LyrvMy7obyr0MzMEszMNMzMVszMcMtr+MzMvMzM3szM8MzdEctsoszdVszdeMzdmszQPcxdvszd8MzuEszt5MzeOMwt0MymShzlqLzuystu28tfBctu0sz2Zbz3S7zlx7z/q8u/tct/48twBtzuFczgMNwfTctQjtz12MzgztzqAczxGd0BIN0fxc0VmL0Bf9Dwr9tfCc0b2L0RS90SE90RZN0iat0Q+t0iOd0g1N0R590i1d0if90TAd0zQN0gbNyQWt0z3t0z8N1EH9zgIt1EVt1EeN1OLM00nN1E3t1E8t/8xLDdVTTdVVbdW/K9VXrdVbzdVdvbdE7dVhLdZjzdVZTdZnjdZpHdRmrdZt7dZvnc1sDddzTdd1Tcxybdd5rdd7Dcl4zdd/DdiB3bd+LdiFbdiHPdSIrdiLzdhvS9iNDdmRDdiPLdmVbdlwTdmXrdmbLdaZzdmfDdpU7dmhTdqlbdTqjNqprdqrzdqt7dqvDduxLduzTdu1bdu3jdu5rdu7zdu97du/DdzBLdzDTdzFbdzHzdvjtSmj5ZnPoSzFYWXMzTtntqzBhTWpMlLjcocVhCKzcoNv+HpNiV1qUVfhVCo2OFoF8pofWIjUoiEn1JhCqDVuyk3Ek6tOJibucv8p+ZVQEdQdQCQ8jfWwuSGxrcN8NZQciuTdXIRzk6F0leUmvwNoq6o2hVMc2TQrKdWeDrIX2gFDblIdMkE+w6SvDlJZblRxnoQd3qGv63UbKpdGAgSFSLMVn/gti0Mc4LdQkjNIduiT1ikWfKWQJKQqeKYuaWQsobYXHvMvG9pbkrShodatvoU1yCk2QiE2PMJEXalwFoKUl4E/TzQwvpJbfPMyuySCnsk3TFJeQ3MczE2wVLIxEPeU/zIk7FlZnyNJW7Mw+vMb/IMaSIlUqlNCehJpkKQZ/YJAGWSdQMQlqcMUXGQYcaQn+pQ6H+VANaQj1WE28VQhiLIx7VIXrmT/mXV2OKDSLliBf3wzkcYHmapqVVb1LbUkIqpSO5J3qEUBcb3lWusVS7O6obIxbYEiOpYOIzczKahFq8/CItiBEgLaVYgefOkji921PL3lNORWcbDR43qjPOs1p1yjMMXuQz07X4F1hzdzIVx0opIEObn1YlbiZFYEFf1FVk4rpGpi6aOjdqOXafIyLL6YHdWOLoboUKFhQCKyOfgDVg10Zj5hTHvhNIsTcT2uRVCiPjQDJcHWI63qKqFIXry2H0KBf8UBcaxEFj9oSmcx52iGtMGnFaWkGJVVcUyahmIDLfr3R1dlK+8D5ZSWTVPrFbemc/OqWqoFKvxDa9slN8HG/z8MNS0tRD7MrWfvd/AsEhcOBfQYXlVZonW8Z0czmDJAREHCeKXZEU60VCGB5UFpSKPeCjA6cx6pg5L/aihAxCQIM0bTQjz8xBGHkWJbsTgjFHfb5RN+Fkf1ElV9IYRJBEN15XtvOBO0By3MUlccuUtNlkZAp3SeFVMiIjYxQjuTWSQ75HAez1jidRXQB4GR4z87tCKZIhQikjrpxZmbZ0XasnlcdAX3eANuGUGzwrLaciEQI3fVmhGbJw++nx4VBnHAFlgQ+mzXU4NtQhZLpBOP3/X68myFAyDM7/ur8ksDNrVVhuNPgxswBP51Uh8/uv5O1YMeQT45WnHXqiL7Qf9lSqcqmwREy58eRAEQYMDw43fD4A0wNmx8+XLFnz8sWK5cEcgv4owZXrwIfOhFnjyNGh9eYahFS8SHGGdENAkOnECYDz9qEdjPZr+P/CbaNEmPHsMZJmdqITiQX096BGswrFHD5UOGXxTaVDiD4UeGBCd+wfjxoUuYTQW6tOGyaUSb8mxinNoPy1CfOfn5+9jU5kSJDiFi6WqSoc+UTevWUFjDpEaFPr9oZGsjIsOFXx5WnOtTY1OwLm/2+8p4Jd8Z8iK69NlV3pemPptqzIvRIFSBWm7YJOvy4O0Zt3Xf8ElQ4VilNWJrsXHw4W6MHT1+1EjWxsN+BF1qHDv8cg3/L00Jfowob+SViD77MaQOZrrJiRoJZtV4ePkV3wfxEqQNjqlLv5L9gbOLEwCAL/KDj5+PVDOMIJMi0u2h1by44TiDarjNH5MIWk+qiILjjjkvbPrrOoI0Aq+1jLzYD5yiErKhH+34cWkigeiZaCKk+nlxwJoEwgwcrxL7qaErgtIivY3AoFGhoXxDUh7N+jkJi352000en8DBiKCUZrCJniv5wWg6vPLSiB6brqvSPfrcShCLqCTjyJ8Zr/CJnsFc8q2pNlnD6osCf/LJpLuuiKw94mxoSk56KrQICwq1II0eEX8zysOPBPKMH4GI/GgicKrkr4abfKqpH9YA80ex/y/I7KexiWScsSmTbNCxBoxYw6KGx7jSkjOMLpoBVrQ424ukLwSSckGbXARHoWObddYgcG5zaToT/fl1KYZ0q4Gg3CYUqE0sqiqRIXDQowgmOzGC1SQtVC1NyBlSzDTIJsWLLqIxf6yBQeHA+IiqiCbCKEosTGpKLDCkskHgz+RZsjvvLOSnn//cwqI9DtnVQp51Y13IzrkavYInLYCKF6mIWNLCn6wWjXFgFmv46Fp8vVDpQob9SbE8HpnkUbe3qKRHytTolMeo4Bis7yGTPmMIJqO8MFBBKX+UVqO6RAxyBrwe+o7k4p6jCySb15rhzREZ/s0lhnIFRyR/2juouP9mBbUBo4ls0GprpaBzUgvt8IzqN5Nj/DFVLkP7aCimDnYUHJseAkygiJqa7NArXEru1MLBQCzObxfDEk5iZ/BpohC9GNniJfvxqWawPG+VnpTDhTFhqZ4F44bbBTPI2Bvqus2mCCWcrenc1Kqd02jHena3kEJiiGXE/bbbJ+hVtk23XfEimTwvehaozj0fUujuXZsqjDKGbNqWn7OwQNBRbj+Tzb4Ah9RLalV3TBxVmHgFTv9BH+Uw0jjAaO0ho4nIrP4UHHFdQWPgy5Tl9vKRl4DBJIYDR4aUhZLynM8GhTIQ78BQOYPQjlE6Q9W6viCx9r3GH2/CiP6UI5tn1YD/SMrhh0IiUhjs2MteeYmczgryvGbhrjdHRM7WFEItspWEKD0cym5OCKW/YWZ6/ohgn574KZmRDW97sowXULKu+Oklbwu7AqgsljoxVSsxkNEb/GpwuqfEzQsEoYekJuIVxNhAI2tBHYF8JiyFKOdWhLFBXOSRuvChyJAXM2NebmAzSW3QcwQ5yLUiSSTM7QZTCTHITvpBN2P5QzAOc9BssvU8HE7pfVcRDV9QhRHxmEQm8mhan+hokEfqhFhV8pvKnrSmw3TJJaMZpLDa5pwyhgQrt6GHKt24o6NowX24glLtRHhKNlIMAAfLlEKEohYbATE8SZEL8spVKIa9kzfl//lj0ZqETpUJJk5bWcztbIISqt0gMnx6EOkMt0JRgQFW0fodGHpVHAkp5Au7WSabbqCsNvXqhyNsl4wEopJ48SNxZcqODpcUu9phgVrJIhf+JKcR8D0tTRojjEG+kJZLIhEjo1oOVRpJj6boxje+ypUXvrg4KlmKfxN5iCFNxa3mdBALUpofGAL1nVPRA2qVM6kgI4kS1UxoQAYqCdt0pZQluoeNIGvSk8BHFa95LUpX0E0/difQOgLKLYYjWWf2WFDmLYtuEerW9jYmOZvK6wZ3TMqi2LhTFf2tVbcBH8jk0Zh/nRJz15HHbjYlwfhlzGMW5JrdBIS5XDVKkyYxiP/8jsidiwnQUxLs6KNg4sOgeQF5NfiPFrD2SFPSUlcHYyz1hreqd40uZZMBw5z66MjB3maP/GhOXGtIHFv5wyCt1GZ0CgKzWd2gg9ACB4SMYy2EeM6Mromulbp3LtSEUaLxVV0b3xemcBlqX1l1rqr4R1KJNkmjWamoS+oSLAyWd1qfyhvEKCe//iwwXHv705OINRGEqeSgWAAqdmxWWv7So6J/Y234DCLIG+inwi+krm5v05JpdWdHzlGcQoITpdfY1jz168/51hQwkDWFLb6q8IunKl4HcfGn6nsOU7V7RCyE93kaLFTAmoXj4hkPJTK8qIgk9BFg8lJB+p2oTW3/8iYFffRsRmrjOrHshb9oVaGEGU6aOjK0ThZEWLtsml4ZspXvqIxUkawf/+J3ZN5Vyl/egdNY8Ie3xskrdAFziKRs8J8OuY5LHrNVfn01kUtmWkn9YF4ZxZaskF2GXAfhI0jnZdEqzgZ6um2MhP3hocVMN0S63Muf6Pg6RJc5mF6awVc+Vd6xwe3MZpWtUq97GsS1VEgk84nY1shkNm70j7Manzz4ZsizXGeXjZJiylYJRMr9kWNuNCpHldRRwx0qrktkpKk+yw86YphWjm4fqdrGJiB1Vje/9TaimIxn8PWpjzaY2a+atMYveHBvK9J0Zl1mFIYV90QWytVwpFrX/47iGjPqGeWbMl006tiblyvrNtymPZ5cX1GjYBZvigxskFq18kr2lOQMbjRYYfK8KlM1V4wyrLjVUSsrGttOLafq2g1Gj1rVbss5V2iuM9KqRE80Z6GmB7Ir/Idsm2qSkDPileDVEne4HqqCbG0QiUH8s+JZLPa6dRuhJVgl10aYWavd8Ef16sGOVmKHGTOY0fDngFFZC9WLfsLQ5UfJ6jLMSdaUr4hTckfsnu4SC2ZDa8pKRbuslvn0Czlw5mdidp1zeATI68Ojc76ka1l7PINgW/07v5v5Dv/Al57RcU9x4TmIv0oZ11uvSkp3vQHgDHMdqP2I8nE5c3NF/DZ9O//aWiH1IFq6LR0eGZhsoLbpDdb5pOjNJE6oDolCbFVtWvI6Lw2HVeP+chBoAy/FV6AO7861HJjZKDaSlO2aqDhTkd+ijDVzDd8KH/BpER0aEd+hOB9xn+qTCxUCoi/CP6HJkLRYuWwhvdKjsRlIDM/gLQCwCj4BibswCJhTlZZSCpByDUGJHN6oJp0hLgNpGilyv4NQFa4CA7/5kk4pJF77gtj5CB+6kf7YktsLGoWxoI9yvw+bDwsxtr34DryRoL5SMv6BMZhqukdhPbgZiQproCsqFvMAB7XJkqgKo9DpDaT6jvgajtPJOb+jkz+zr+0QocYxnQ4LHeoxC+w4iH7/QTz5QQpga6wJuSTJkTOm2ZsbGTQlyRZSW5O+u67qWzRU+TPm2kKvaQzSWD9S0wtrcjNSUw7YcpVHJA+qYzWZ2Y34AqfbCQ/KgzE1tCDtihBUaar+cA1e3EUdug3gIUKB4zLx8ql8ORo2SzCvuZD8WCxGMrvuCqoPQ72marFsAikfoqPG+JJdSpNFU7dN6bp3ybwoOSfGKpP/SJzpmao+yRSuQjUb+RTWkkatk49jlJKfMcQ5AZlyGxXIWrn+opNsZDL3Ehd7g6n4Io8tLCRkK6O8ObT8QpRtqsU9UbQ1e0SmGY5dYsUZaj3kaZM/w4+OOiCYSRMV5LVYaZkg6zdF/8s1CWsclcqgL7Km6iuJKIEKJCwXdTuxRtK/Y+GXodCCLRQRiaQtnIA224o1F3yQD+y2adQ6qACzKCm2W7uBXWGoUKsWCxIpOcuj8GgLzTmbG4Gb/WIurRmRRzE0p/iU14GuyBDCRqFC+fI/mbiQJekhhvOZhAoJqEi7kmq6RxyavmyOKKGMCsIWpOCIZqK8m1g8gcOS2ajHkOq7ZeEXqBGWLcHB8+m2f8uf1IGRLlmTnzJJ28u0G/gPPfILIVsQRxOoTQEf83ONbDTEUtJIIVEYGJsRk5EY90KV5jCNB3OMoKkwh9EvszMwD8qSt6mk0ClFU0oLRZFIhnywkoiepv/7MNMojYf5vL1InWlBtXdJpDexMQKhOuJiI02CjraMJDVMQo7pN/OLMCWZOuWIk2nhEIRTQ/Fim/KgNPFAKnHpFKPRNwP1lLRoqYTiRBwEvwaEGKiwjfjajaxqlXxyo0R5HIHUCqGirm3aouTkkD/yG0Iktmb6l3squJ3oj+BLkc5clL8ogAWwAB+IA1oABlkYBU/YhDiIAyqIgh/YAbghlzQpCAZ5EwESFo4wiCFRM4HLCI3CNsdQQuNUl23yMwdaOKwkOd1DnlYxm4Zzj5JSDtZTN98wHCmqvDJzkFeinP8Io1oJE42oTCw7CBNzwZZZuIIYFWZcJWTqtm4zziX/QR2m0R8++ifXqgpJmZUT4SqPgcIV1JHPIjmYKCLNGRWl46fRObM4Yy7qsdI/gRqTERbBQQuefJQKqTC90os9bJunWbSm6rd+47AfuUe6fLdCWY4vypu9WRcsQgiSChr92xYJKyODSbh1S8PM06X5cSTv8hvLSBojap+g2o1AQZiDQYmxKTjnuAmSA79Gua6W4A0XXMo/A4BBA4z/SMG2sIl2bVewtKMGiZPpgi3NaSaVubR4/Y/m0ggCUIALIAJPuAV20Ad2wAVZIIVNoIIf6IALqIAGMAADcL7/iAqYqQtF3SbSaRA8IzmtUZfrojz8GRWqcA6DARWC8CC8IZnY/+kX5WgR1hOIrgM4jaohqLkuXbnHxYqlU/LXFPUMPHsQ7MEr4uENhgmRG2kSA+UQ8cO4GekOQhFLj8lGhxKSwWgxR1IM09PL50guZXGVKb0jnzIrnFzK7YifLVyPoeCXRXGPz9KLpPlAOqKbaGkL+hDL49wiQmTGdfqoffWq5BOg4aEbR0khNTSbVpm5gtsVrcQlj7rB4JiRtnI+ibGQRNpDATzUWC1Br1I0usyp12ibWUE1r6IMvLDS+SmSxfsoR6qdlRmbiYjX5kux1QSAAIvX/CqS/wgsfdMk7+jXfj0d/jM0MGjXgzBK4R0nfu3XAYAAH5iCOLgFYNgFXEgFUv+IAyLYAQ6wAAY4AAJIEJ/4j5qZE+IajValrZBUmRQ6HMExGUBUlPP5jmXKEquLvjmBM8TJl8d4RIH0GyZ72405jUvyGIEqCrrajv8QTCTLtHzpqOB4mtmhjFXxXJ9aR4tRlCJJpBPZGpD6px70qr/5ol0zSp8z0Jpck20Slo26i+hBng4zFUKaEeUAEhopmcyzVR9pn5TlYNTbqEzh2QghG57UqNXpNlGpn3/DnwZJlnxBGBxuksegNNuNjovym10yClNZpzk5iIIhmdpZw3NqnOlCirWELUtpmc76FskUGsXhtc/KoOv0nM144S/SK+4ExLYRIufCgv9gwxlxv97/ehwUjFf3AABgQ8HmjdN+Pd52xYp4zRLdjVe6AiaNEF6XiNcZ6K0ZSAAbjYNUoAVbSIVPiIMp8IEMYAADGIAU3E1dQREA6AIu4IIuqGVbvuEfQgrPAjYivIlTZRQ1M020yRweyY/S7Mz4kY6qsr7PexcGgZgEuQ0CpKZE+Q/P1aSrVTf9zRfEIN499DpdKZMEI94ZgRvYCik1ZhKT6bQbudyW22Z+Ukg68eGeSc4uCQmUijKH0isvqo9/O50447XIxMpY4RCSoq02+aOmI5KBKZptgjbnUkLJU7K9qJmdyBPdOokECx6E8GLXstKfiiuJipPN0TrmkStwzWjNULpm/y0PW/OR8tDfD0wNIlHHO4qrfwPENeoN8SvLSpLnRoHApQgUcRI9wYLcP17g26UBAKABp2Zq3BUKM3VqDbquK6CBPdyvIiIXGsgqs1mUpwZP9YnXhhCIdt2XAriAKZCFWZiFhS3lKeiBC2iAAwiAdp3lE26Kp37q5vPZDgMzeKurBMsmkEkLe+FIIjRbH1FQWkq86/AwOIq01aM4JcGbc0Kq4m2ldvytFbouHWkTGJuTbHwdPoKUjM4fNKRCr3KkoiwzVkuRHqyPzajkH/yN4KA07rAXkFKYR9GMwbO+SNoTzEAeJRmT66go0NgcfBkwIMkqAaohKtQktqCj+eE4gP9Tn099xjS54Xp8FxFxJf8cGi76rE/NF4QAqw7LGdPjJ9CgJSpUGMh9SIgRC+LNklJM2WlR4uZmmV2CX3ttN7LRPlW6kM41Nlr6MArWtKB5iHZd5HalAX7NNQvpXRqICBqoJDVUqpWVCK2hAf15ZHn4Y3CgAbtw6qjY6xl4ajrRgnj1j3Y9AAvYgSnwhFH4hE+gAi7ggQyAAAUgAADwAqcG8qcmCxq4KL78pxTjqpHAuY6el3NJPBhRlMTrSRirCG7tEqOptRMpkYPmv8XLRq+Ri4pIZmFiPN75j9UTj0DiF3ah2Yu6DQ/6C7PCnJ9iHmYJntj5Lgvdi/rCNZEoMeT/WRG8KqNaAov0Ah7T7jpGI5TkMBeEORcLEonnLhFYMTZNyqb2VCWL4zji9REsilVz8WAQsxbBESCjoFnjBA0J2xTT0y5w3AtgnJZ+a6Vica5YJeIToY4dURk5i1WTJBZe8yE8lHUpGkgoHuCQjJ4h6o93Sc5z2qfEE+exQ6eYqb4Kwa3FCxHipY6LbVcLd+Q5I6Dn+IILz+h+gOp4derlderHMeSYHWSUoAFKZyQbUHcCEgh0t3cuYIAM2IEfiIIq0ARN0F7vNQABaFd/+A+n9oci3x4z0zTiLQ1H0re67EskGZ3UYZdVyuWOikc03EIh85vqdI/qS77GQZ1A2pEr//is2zUuAiG56oM789sm89Nn1sK3FdMPNdQrrWQbK7UgHeYologWkzmPQumy8EGsFAMMOq2vv2AukwTCjwYJieGQvoRtruBgTYqLQOK1CFywGQG/NGlWE/aUDKLSdRO/ZKk+H4kMEYIOkmst4widG5GTF9vFXFkiaINM15k9a7qsEll1qNFX18omQdSrB7uW1EkOs/UadKxgj8Gdp9WgFOGhZV48FvPz0r6dm/obqsO0fo0kfg0PDyffiPDXqUhxayZo8aCB0PmN2iHx5VX4p+ZDQ5p9ANAuEmckAJjlp6bksPuPCHCBNgCFH00FWUAFFyiAg19eYnP8/DGlpyjt+f+KjNQpDfBztH7JrhPJIHNZDrWsFpWorHJpEwl7zsq9QXX7FnsSu/UrGqDIKYBDQwepyYN4oa5sJQdRDORAHJBJGYDQooUfP3DgwCAkSBALln79ZvjzR4+ewytXbnz5YvCGlhv+sCC0YcNhjRoI/RmMyHDiF4cK+Tm04cXLxIny5I3sd5NhSXozBd50GNELQoQWBdaYMSPjzZAWlYo0WIOhRZEW+wk06HDmTXAiZ04UiOXmxBvy6N0oWWOmUoMdPfq7mZFfRIRz+dkQiHbGDZEOZzAkePPnQoZYIn6xekUky5IZDQr+O8Og3S/0bpKll5WrPKVXEX+ZydimvJIO6WX/tDizJMJ+K+lFFHglY8mnSyM6PCnRoUCCkG++nAkuY8bYWliKBKAcQMTlamncUB5WuQ2jykvepDGbRo2ME2lIpLd8vHIaE7GQJ09jPY30zmtw4dIeAMsvWmiAG894PA1+AAg4YIELQ4ShiiqpgAIKEUL4QARCDMlD1RXGqTYRQRa5pRttNWhhm0E3XXGTQKCBYZBIhYERkUHeaTZQSS8RVINixfkDBosqgtNXdWBMVBI4Anmm3BUO3VTDSyGJhBBrYNzQpJM3rIadPF88WUOTWFh5wxU1aQahPETKM1toXqTkT04WEcRYkTLacINXDD2Wozz+zCShcfzQ+CI/9IiU/9VBYBzmj0C98WNYRCUJpBE4M7EI4ktl8kiPiTakFpVB4ASGJFsz2FASQyJJeYVBnImE0RdNgZFmWSkKqkWbnSY1A0ubatXPcFRiwVeiX5gEhlIRXTiRcUOdyI9FjmXUGlZa5DrDoFrgCOtRx9EzZD8EzWQXT2qpxJBLMPXTK6Nj1gXiFQjVFJGFdEW05ppXlDTTTARpQVlRMQrrj0V1gSGPCzAoUeKt/X55rX8ArHfwd8ohmZx7ywXG1XJKccehFiUpJW8/NGTE0BYfg8fFDOvJs96oXlzR38EQY8HefO/ZuhwDLghBRBtwvPEgFgJ58a1F3goVnlK+YSZPnVgG1f/PRA5hQVDGXtxkA7ZeiFS1QDfs7KoNwPHsxdB4HS3QVz1OFWiTD0EU15T15QcAyld86tkVM6mm1A0Ex6usl1p0hWmgV4hd9Q22nu2aky9SiSRrXx9aAz3bxliD31+Dta+ZO2bUz7wKGTUpb1p4AVSEgVosHDiOIQRVTlXL9cW0i3qxbUY2SOU4RdeKlJFSMaUuKz2ClSaSYViA9evZj6EraY5TlTT4abBFVCr0BEeUllsdMqRUZZOdXrZLS2YLRg0VnSt+UkGzGNhEXzv0hbIEpT6i2sda6pBLMdmgGk2kJaQQFudlzUQIAY6x4JW0os1AfxH5leqcNgOCuAYLHID/AQxIAIAURU45gyoPYJRTGqVokD3dOZU8BEKDpbgve5yyAX6wYJGUEaQ4SpHHxz7GhRri8GM+CR212OMP36XJBv54GQCwUKyCgCMCLoBDKtBhC1QQIQNbIQoYluWpQPVpaf34GUi0WDTbGeRTUvsW0IgjkqJpbWpKyYi8hgcn0dggMHjS3QwcYhBRgQNNHBkIQ9yiHCxcChyo+QKO6pQj6+URXgyJiOXkEcamzZFqUlOIiLRgH9Dx8CZ8EdgBozSTrVhKScyT3KUYQjC5ic6Rt5IMPwQStK8QZIEzwAySZOmPyAWnZ9czSFv6p78XJstGrqPbluiBLqYB0itx3Bm//4D2p4sQUydG65nBAAWSJ+EKC03yB1zOUhMaNUU25SuaPJCUkGIhZFA/odZp7FUUMFiEkP4gzpgAx7fSkJJOXkCWXMr0mpw86yquSY21dEdACGruQvAi5fjCFasVDYaHdhwNWG6nBaXQo0FEKIIESqg1gVzHAgyggAhkoISTkoABpTMP2ciDzz0tByE0yIxIIsKe+LBnhxyqmEVoABITuhBe67kMcdYTEX3F8wsMSc8FehAGKDoARyyqiUCUhSwSlkRdSpFjVh+JUS2aUpjg2N2yEnI9ZCUEIbxM4BX4oVZwRG6sKzSIheyWllgl0zvKIRM4SNK12l1sLW+LyUy0RP8ivkzxL/zAZkS5uU3vQKk+vRvT+gBzGDSBS5O+gSs/uBIvHo6mlfXqHmauKJnykTWrz9tVDZKGP6U8EFzJy0nrHPPVntBDLQhFFP4I2D6dRa1T6+JLXxoizdnZYAYYuwFaJtJW0RKzRuYbnkVGF72qZa1xEDLdUFDmyn5khGf2CtWQLDeu8DROXbWDTKHs1490agEMxfKL/dSCTIZQs6b++Oz+RjNN00WlY/+7jCPha5wZym85EGDABTSAghU8uAMb0ACFNbCBDeRABz/QwAOIuByXgZg9yvTTpQAQHwCIpSj9oUFNteMS8JhJNdpZzNZK9pK2CFKgiAKdiDCwCiX/WCAMbSCJxQ6VOSvWwCKRCqZbPrtjJL4zq6/ZakPy9d1pypFs+0xyiKZkn88qRS/HaqgXPDK1bSXyjh7c1u/4EVFhzaRdquwreJWKpRrcUiG54dGH5BG5o/bOLIX1Ugzts6l3YiYjTcLW4O7CFkZaC79EWRMbvYBU3fyqNhOap52bRT750u5Sz1MlvzRJKDseBLn0NFiZYmOpF3YZbWpRy0JH4yb3gUGcWUFInC9HvP5SSiNMwUxYHdrrQ6lOzkL84SxxIsR3qhALApblDFWdGCG2TjeXKprwwjJaqEgW1CHRZNrEIkYC7pdNjzRuUj/J3rUqk8qimyRYlmOAMgzC/w9pUAIFAXZSflPQpEaAgQgu4IX1gIE9SGwXjBVlkPXEBzY0kN9N+EODcIWYBluu47K+c6mKjWaAa9LKBETwAoCt4Q5rUAISivk80eAWYyhU5kQCyaechDV2hkr3wjk9kaKYTim0LuaYwSVQ1nk0lj/EiNJvMBnnLtRR7NKCBl8DaqXXaVewnYGlw6MnS7bEflEBknJH6JLGcpMoN+AHsna3Lv/uqjdO/2qTQuJYy+K2nHizHbv6NNpbITQ3NldeAYN7F7VJqE6fSxrcshbcMDU+M0Gtqq9WmOfwDqqh/cCatC0T1klZa3Mr0pnSOwPbr0iSiqKby7jILcjHGTEvx/+ZKuy9bO7K/ElCJAkaXS+z48puscuTCuz1hjUpo0xIdPXhh9wI6rtHyqW6+MzNFZUsMK/3Sh4wSIIVJhGMYDxDGtoY//ilwQxhVOIQhygEG5TgAvfgBxz4IQ8XllMyh1h8ZQDAc7qXE7huOVtmLMcQsVjqsJSrSAAJ4EElOIM3UAM1RIM0SEM2XMISwMCeENSaBJulDM/b9BlTEFTvWdNRhQe0fARIVJFOrNWmlB1BTAQctZaXtc57sUpYWUkx5U5omBuOpArbtZZyaA4PHVVlpROvUVN9fIG8XVRltA8hBc4nKY0xwUnYbQRiwIUrCViS9EvgbEvnMRfwmIU8aMn/r6Tbk2wEc4UOlECNR8mT3MhFM0FS1/GLZJygA7kgHW2K/QRS9PSNFi6UnEkI2A1bOl2JNm0TNSEFKTkUQY0IPdFXUF3RTQALjsHXnySNSHAcFH4TCQkP8fSNbrDFRGAiCs2R/NSc4pTNtcmLzplemZBbeHhbxxQGFOJTIH2KJF4OZ9Ti67XZI5HIswDPJEyCJERCJKzBGixBMi7Bv5HBGLgfs8QesLTSetQAfnCBP7RCO6jDchCTy6THTOXd6OTEF6QH0HAGepAHA8DAIsACLJCDNSxDJqxfIVACJRAjMr6AAoDI5nTN21TUFZWOJOWg61gLJmYVUoUXMe2KwGRh/w4yjj6loKVsVesQRKMhEzFBUu8ExWtcgXLI1bG84JapxPX0WnJx3dvsBDK9xKYdzSp23l2oRpNU0t0wG6Qxxju9FZ0xRNcg0Rp9wWLFyNTUXF991pPkVq8sEkp01z8Rk0KNXYe8kBhKE/xECp2ti2CACpdBEL/oRqlwxJNEDp55BHPBRkXdxbLpj2gg3ww5EqpUFk/0EeB11pp0DQlN0WMcmRjpE6pgCFzVSr5EIY0cheWYxkN4W9Jg0Y7AUeXd0UIhkf4MzU50HmMAj9ItmaJU1bSEVqEQkN/xA0lhQKvYBS5OypjABxfQRHxEVDWSz2LQAHzNhbIcinl8ViciYv9gJclagdgXFAAE9EAHeEIsmMNwjgMk9EEPWIABKGcBKIACHEAOxgtVVpY/3OW1vWDgfNadSUnmyA/Z0aCkeQvFmR6NkQ3ogUYn9gMJYoQkbtZ3zQB8mc6e9OJ1PBKB0YhKeFtFcY8cMUonlonBdA0yjdZ17cRT3E0V9cW4CKWPvF5I9M7JBAojNQn0OMugfI1JKsvVNA8KnVHpeZRg5SDy6Ygo5s9sMBdZMQWXfF1jkkRaBM2iuVlE+R1b2AZZ2YpcSQnsfVqq2IukydV1BQ6rlIn2SJ9teNSeeYvcoCiReZuSxMkV0dY0UUZhRg7kVQacsBcd5Q6RJdXc8JBH1Jz/HLHlsiGUKHLGXdgZvwRU5PCPPzyM26yVulRFfIBBnBWaufBawoFHIppgfnjba7KKgKVHECLcklCZonyBy+yMiNER7H3Nz0mI6zGopH2ShAiYNUUU3YRSnXoN1KkQKWkRccTdaM0L1NFXwTSPYFFL/6BL38CRTGwZOjZbavTNtA2fOq2IZdBDWySNV/mk9XnLFeVqutmVUTRPYMGJFpGh31hKpWoTZ4lSiYqgfP0cqM0OS7gJZ+lcYVQF0DTJ11XUa7weN+GYFxQGQd2obWjJCDmW7ciRfNaOWtzEk3iTFjaZkczRg1yXKP0ItagQnK3hhsTgs4CF34lcD85azWUl/4v0BD0dmTw9i4msU3d1ls49IuWRRtXUx+Z4mbrURNn4JLec4PV8JyTVRn2Aho8c5BQum8dKIqFkZQ8iU7s0YVLuRL5QybcezWicoCEWjpacTJNwnOjsytswxJOs0JNghNL2xZMwm45JX2WMZL/Wzpy0SlZWqKbpIZ2NWcEAjWMQkGQ+JRmNBWg43V+si7mgpEe9ENBQnduEFQpuy2B8ztG8Z+XZqxy507/eTp8gU5jFl14Yk404HRS6SU/Qy2hZiJKQJQQtSe8sDY5QGZtwRjrljpZ4aew1ihh6xMmET6hxiVvsDuYOjhgS2BXFV6ToT2eRZZyiRNDBlvk0iUU43f8IAU/fyBVroYpLDltJjAbYASsgCW4QEUdhvlBFFcuWkdtoyB4JjgaBBdFRyQ8xNV5O9p448ZKrUpOfVWzyfhW5/VpfCsdnBdeu3q0L9iJp0hlSoYqPSMhmrdBbFcdd7NgdXQ8dmZ7qOEn1OEl1jeLmbR7zOcmesYVbEtjq1NgaxSXqSdesMQkeYRZZuRWrrEkyfUu79JmyBGGO+oTv7oiIXE/f+EgQIan+Xg6ZreEtml6xpaxyoNDL3hP+dM91bZ6pmIvC2gDUhRGqDI+KjGJI8h/PmklFwsvaudkoOokRzWF60gkAZQZxjAusmZ650dVriOFRhOSK6EiJ7tgidY3/bshndyEGN9ETlRlG/7QKNHlBMP2h2vgFVTItp0qjPrFlTRAZGRZF0fTPmPTZUAwPmFjTaxwKbkThh+zKXZzHKOaqPKUlmWihGG1aE8oGfO1k5uUgkYWaA1kI2RihaMmc03iJcVTN5nRXqMFa6rCXE1Op/2xIYYxs9wBNrcEqowSOShZm3O2v1R3b3IVZZa3Tr1Uy8jWevXAWLnpH/8waIyFLlBCMuQkdCX4NqyzrrRBkYdxTRUUUpnqUsMCJjhzVKCIJJB1JqoxiR+6fe2lNtS5QvgSSs22us6lodwCPfrFRtxBTp2XNUcyaTHbGZl2Rk1QNbR2Zt4Fbi0CdbJmP/0It1LwcaKRUz37dxQy+V6+Cg4iQDwfaDV88V7+Q5TvJVeIIk+4QGl7khU8ebZOcYV9UFQCxCB8HkiAljYDNC5joz5hEjmh0bccGjemMVi1R57I8i9ZsHGzhotrQ0QrZ4Z48ZEBLE32tEZU1ITy90FuxyRWdxBTaLfoS8laF8x/fqFZzUc7tmSgfEGbUyeZ4m+zl4CHDWlo2YZG4iWyEYbdVSzGJxno1bc7eWhIuhbmNYo1pZolu2kF6hr2QldYkJSOF5CjiE2vh1rfQl7mSNBdt2YO07jo9S68Q8guiGjmiWLcIjEcvioBZnfZIdWuQIbKUz8nYBqjlHF91D4MOhP9CkM1z2QtYuElVlChfB45P3igd7UtWVrKzaSFtcITdmGvhrmHynMd1yU2s4IiI0G7dwaUXHtIOPQsZzrafeGX5nIYZNokWCEVke2B3kcZPFlC30EZmfM1YeBOY1Fmt/IXOEEp46HazrosKybF0VVcwaRopKgQG8+LpWox432hYGIfMBRb+CIcseVuhBAoyyd5bWR8mFQtJFEsgYRLuKhNg21PZhZVv2cuaxNaRHYsYkY83kVWYPQ3WLFZVZck2nSHiEU+TIBeysAtrhzc0Cl2siInM5Y7gNiY09vEt0aIqmSABPcaYlckZ0ZcWBluUQEVf9A9XqE6PV2xdKMeYyMX/jglUna0iqPRZtdLg714UFGraJ5HNGZkLCWtOBPvO/rDIA/NeIqVgLUvTRTUbYooSQgkM0GjPV5WKTFjJ85Bg2CmbEIVPnJjkdLPEnildXXciilYUANFg+CTxXRViBLGXiAjYS1itHAXTjmxWn8iZnqxSPwS0tbwEfkV1Cv5M68QJ2uDYV5ix5t6XcTFUrLRm74QVqH3Vz9WW5oHZ1unGFe0QYeIrqziQ36JNMEGlEeJXME2NKn1LHhKUA02T0jlNDhYLcYcaUDzSrE33C/VFzYGJFkLejqmLYogc5BVWpPTG9QKvHqcgm8Bs/kRJEInW1rEGiQZNWNCXbqBEKRlK//M4TzwBLr+AA2gcjFpohTZZREhSERcVR/g8DXkeZVrlGjRykUOZS/Ssmv3cGXJ9hc4wiWEQEPJJthihnp2x+R29XdVwln9LzndV1qCgj50pk0D9ip+I4QKhNFxlq9cYcXwS8qG7zxnqCXE1jljoNl0GDvpO8OdsBcoLjAK7t/MmlRbOdrWmBINWBq9knzu7zrZ4LJdJiF88DaTFaEBTRqUfpf2QWPdwlnGYknvJ8BT5iWhaU8Z4rLLIC6wNl2Q0kKSJDqi22QJBYeDQhgIVPL56yUoIvmRbC+VllUDZi+7QyOZFxf/+tpc8j6Jc8hbJz22b67HoCM/0pTT2YGNqj/807lg6387sVFTjCMdXHQ0J3To+Udmgh9ohMW10AABJy8vuvg1fxJZCSVSeg8472QasXJd+SRKDYssrtgqdhc+LMMm3rE/viTEudk2SkA9C5RmHF1CtpAT69jgTHyQASVLtnB6szuuXMB1eaInTlYU+xUb/TDl1AwQWMGDAgbNxwwY9ejNm9Ovnz1+Ngl++1KihRQs4ixht2MD40aE8efz4+etokaTFjmAsWqRIUYtIkl68QLxCUaRDLFi8iLxy88tOcBRP1hAKzh/JghYZfum4syPNGSj5KVQZ1UvBnf0GFu1I0SgWjBAV2mA6o6PImhFbqqU4c+0MpeAwWvT3M63/vLPyilKEOINmQZoKaa6FuJFiWRsMbRx1aFGklo4/GdIE8/VLXap/p9b4UtnLwJ3+dAoEQ5YeOIYKb9zw65DiQIhadjIsSPJn7o4k/RUciBGqx51SZ7hWDdjLzp0j+YlcerGrjYK7FQ606BDMz4Fgazikp9khQ3+B6WqB6AVzR50iB5LcWZXedQAAQoNhrP20P783ZogMXuMnkigqiJ6BZPMHo35ceim8GbAgqbSdwAiuo69g++KKG3ZSKDVwFPrLQDBIwogfrBKjBzuGfsqpn8/QsoEi8Siiaat+rrCuBoUi+6m03fjprp/ItGAouytKE2kGMG4A54YLr9CitYd6/wOHH4yYPPGnL4QkiZ7WbvDHyzC95AtGL70IsESzBrLsMhv8sUoicLyjxyQbBlLIPSLzU6gfwXqSp7aXvqDJPY4ae5AfmuKskSF5frJoIIdsNLIfrK5o1NEraPRrSox+yui487xATlHPvlCwBpHU8gLInwpSSzPI5DHwI1CvI5SfAZGy6L1cnfLpCotouvRFYbOakyaZchWpo4IKMg/BIRmagR6aZtPCofFYbQkins68oiCHMEryBpomu4JM9sAI70CFmpIVMy1IGmjVcVWVB6KDvBhR2tkUWk4ejORhCNmaHPIxJ0+vGM4LirCAFAyFZsQo0n4UFRfUw1RcWCwt0P+V59n5dLPhSS3gtOgGWR0q6E16SNITUCwwJpSoCrvFApyBbgCDItdMnRG9xjpasj1+GuKqSC4ZSrAfT8edIcsvuOQMU35CwjepKpsd6gt3P5XX6n7CXhpajOjBMrdUQabSV4KYLNeLL72w8u0mp2XKta5RJIlLDR+O26EboCy3IP6Iam2jIVuLKEPQ+vFyWpZJ42o1eiiDmsv4cjw73H52EpY7kaq7k86WIeOxH7Jn4FQrgXbCCCe9MBZpRtQsIiy5ln7+wsMC28SNbLuS+gl3GrErMjFMIbrtR+4KajRGahdyUDyIWs4KnM9r6IgehLEmdFWRzmPx5tIVqvWuKxT/Ew/JziC+YS6Foe18NNRXBJlTPfnBLzf1OdeoBsu7QYcIV7gvsUV8dwENprA1Kdxc6jGb69wV1CMlZ/1GCyFKlBfgNCNePSwsHyOKyNBEQRidSlJgAxPfmnOhnVAkdUd7Cz86BqydtCZnEwLhatqluSZZLDlYEEkNDqQ9pyxmBhgZlvYmMqiavIpMvilMEGVzIiF9hzaIChCOmggW1BFnergbEL1mpcMM1UBAX+CPR0DVOeeQxEvgogtNWgOlDGHRWouLWPdac0TIQe0KJLGTgUTnOxx2hDPyII4afSa8o+hHb5zSAq4KCREwkKg5qgqkr8y1GJtskkNesYFOXIgF/86g0VfO89PrtHDEgkTtRmBoX74gAhOTLWRNo3pRLQnWIi3J45fbqQgRnzMnIcbsfv0hk/WyBsRz5Ws6o/QlgIASonUBsZYYosnKwOGqt9lgVc6aUwKrta/9aYpVJBni9sAJrOKdiXYYMqEul8c1qRltBtmkUmsst8mwRec6MOwTOIaFl0G+UGo+6pWqGqSVJnKPHmR6ynwkBUM4fSgi8TxIozLHzW/FbmnGwiSYdpaflbDkfiLhjxZu10/nnA9sNHIOAAfkkJ+MZko0RBSbgiSPbPlDKFE7DBqpwp2gKeR5nYmJPGAqMItE8ykKgxNL4wTHMAErNhG7kLqCWCOIqP/KWtqCJNNY9rgbMEchGVrbs5YqJ6SJKUzKEWKGltQRFeEOR6bSXidBNTreTCty2YsahJrGJoa0xCFp/AlZopfYqnTyKE6sTWdQ1T+fVExXR+PX1rJGNw9e5CMWMSHEGIKF+33NQR37GlTTuklK0YQ7SWxr79byFBC2k3QVTFmccDQ3yZywfUez7Tmfo6LgbFNsqYwdjSTUpo1RSjGlawrvmJS4+10SVMiVEGGzijXJJQZ1CXzJYQNIGodhwQbz0R2A4jfJholUXgOcrvZswC9BUfNlq/vCnepIVa3oSSiCI6xd++MuQC4NP7b8AjONNRpV4qR1fuKZPAlEpwOtk5r/xGEeFk50FGl27qvO4lpkGINE31Bsj3BV8YrNatYwcfJPtfToWsQUtRrcwH9YwPFPBHevgaxmnfJYEhTPWrO1cchlYUthFoVSNFu2cr/YvQ7ukKqap23tqUklHj1w1k1iqfO8V2snaNzTQ6RuOV/SyRnutDepovwkaJJCmg003DtgLtgfwgwXbIdZGINy00UVbgpozsuP58BlRktDiDQ9h4XKveyVHyMYVnSkF+byWUVsSnPA3IvBiA4stX/hs6x2yxB+mZZS96MYMyONlDpVTM6+bBTurpUm0c7H1bcrLaIMVGWK8QdmMctk5WBzv61JysA6c01HMImdlDH7t2Ag/w7pmNohbiapSIaKim28/GOzla6WZ5nLNSc7Jw79tDZTTJxb1zksEfsJzyfa5nOMVctIX+hQ8QxJa3gDyekx7zF8QtW/OGOowQyVX3Gt56cxalfJtcZcwpzLgEwNn6fOsH3VJjCEUAOaQVY5gQQNInFakhJ2IpFuapmX28SDUFXTBHbxHtTGsuoxdHKKUuQ1EEQyp6romfyFlnQbxXxjH+mBdrJNOVdBN/njFy2tKAOcTgLJCxSucjA9anYWcZhlp42VWOQH8qIcdZXFbLWGJRD5qUO84NhVwRCRdWIMK3t17kp9PDpoHmo/5sM3lISzdwDkTNysJ8yXRA2KHFvQhv++U/GmtGadM1Tjx1ZlGuGh/SqMr7Zj04xt/JS2tU/r8gc/J7F7kmideZUIQk9Uuq+5pH+jyydbfqqtreHnR0QskqmjuTFp+5a8k2PIAC3ZUtaYBFgqXciX5Bz3c6r97pqT1XQq91V8yVVPK+NOSi8EsQ4F3x8vgvO3gIrqD9pGib5NTJtRFbSbmqZkCJ1X9d7vu+NBW1WTrU0E8cO2gaAph7YMQsiDRZ7qgfIugTAGVKLHeiroKBpGntaJRHxkNjREYWYoLEJki4xOZVStVsxDtNKmBurjkVqGy7rsdYaGwzKjlbCCm5Isv5ws9dzml1BjJ64gm4SOZDYJYlColub/w+d0BacEA8V0bCW2rLmuyeRg52rSiEO47WpQKMtmZGdiR3TcozUqRMhqoMRkQqo0Jzw2S5r0K4KE5QYpRXxKg5mQ46I+zbcGovcE5c0yD73ACVM6J8oKqVeKBE7UDqeihrr4ob8Gp49sCl2w4ksIpmIggmi64qzIZDgkR85c7QXpZLqgjW8C54AgY7LyS29CMDYgitt+qcoCwz+Uw1B6qnN2I5e+pkoyyG22Q87WEMlqrVaupikSqJAOylsepaNiKJYqbpuKIta4IqvkL5qiJyE4RGH45gx3Yw9F5JySjComo5Fm6GxQiGn6odKe6wwR6iOETgZjpkIWw1BEDFv8/0by8oJ0cu+DZqhpoCWNZEZKpuj3wC9mlotVLucK5sM0hugocMpXbsiESsJ6tqy+XhFjuoeNqOJ83ANREKnovOSS4q5wRimaPgd+oMjkDO2CJoxPYCqNwOFemsiXnkPtWkp7ogUzxAtgqA2TKii5Mod4rqJ1IKliUKwwtu9SgEaQ4qjSrAOisKyheicjditDMocLiwPU3uIPc65B+OWaGgy98CRNAAYSeW1dZmiAEOKX+OVVOsdPsKdmUKOBIKpbjOpP9MJUIMtYPPFgljEqtEdZOCqTgCVa2me9uAlSngaK7kmQpGkyMIibJkedQA2LfkqQcEQqnU5QYGIwTpBARP+jYxan6IKjp0qFe+6lQbbjXGbHWEqsJchIVboEYLjHlrZET0bKJFpKJc8QYybH6WwiROiG8hxlPoxOdE4q7iglep5N3HQqWFDJ6ejx/SZLzQBtwXYGYnhlVRJL6kiSOfijjMJLkY6n4p7lAPMjlijCm/LsmsYiz4ri26REVFqHQZIrPPgMaKQn0jgiOI6xxE7qA/EtIfDyh+iRPLSiVBoMTo5H9/4vN/zQWyAPUGSvMzrkg6TCmrDL5sjDoHTJUPDEttonaESMR8SzKP7ET0YHklaxMVDsOlKnwp6jSKhtJOQqqaaH2ypuUlqUWsaP8U7wWhzmXnaNHmHKWKoNdYj/wvv66QSF5y1myvrCJll8ypdOyJbco3Ma5HFsruJUBcUcqjxkhTDkCle0xeS+wqDICEqQNKlO6Hi2ope8iUTAEVQSEjnprML4j27iTocape+ssjByaUPeL+Z2bLH8gVkqDqIkqp1q74EqraSy7FVUcrfcZHLuCEUEheesZoCAZbrcxklu4tuKQnoCEyauhe1qomg4Y0OckOEgMuYyqSh4Sb+YKZOEx/iWYrdusNDgI/eiKVsIS5hQKatCzK16xfi4IzzOqokoxvTgIlMMqpfMhIrWRUi+LRvpxiUUQ/4a5CzU7DtxMr8kkae4EUUYprnwrFe0ous+KbTYSXeORttI/2bXxDP8Zo9sRE7guoIz4DB7ECrNBuPbyAOVCsk3oK21lrK1OvVQjrQlokauTOuA7mcpH6W86qNUZvFRhWk4WxFdyIY1mqZTE4cZqcrpsE1UBAlXxvLbuG1bRPW3hgoO5ZXvACCGZEPgsIytbOtxcMttTEVAM02XrBJSimmgbgy0PhZoUIYfDASOoIVDZrRaEkmRfKt0KLA3YOqk/qRLZWmmBEaROBI+ag/6asmr+BSYNNWJ0CmsBGlBLkcwcExP2qN/rsJtHtXUfoo4ci/9uKdinM5dQodMqOtxnERZ+AyIIg03gozT3uNCBvV+TC7bwGZGoGi9FAZN/KIcsSJpvf9zk9jGEBPkA2+p2qI045CUwG5pTmwKoZ4Di7TnqZDsg3qK5vDsJGkqKGzJm6gJRZOrkMQHNB5VOfKiU6OsuZ7HNTSDRoSOPJLrTRUVyhwLM1BIE0+wPwEkeozMUGqFVL5FmKSFuWCnnhTW2lZXnsYPNTjkC+ZDmrbC8ILC0YylLKEkqchUeiaLmkqDbHyFx6bEy9jKO0kPUdLEd+fmvLJqkMQTTe5sT/ev0dpHFef1sNhEUOqDrURXcmCOpbZsSmPOEPXrvFSk645HXFpDTw5DYXEFu9JXZsgmzZylLxYsBKOmLKYowSJzaE5l5Q6zJcgKKSQnnmALOFANVzwSd3X/JYJaBiuShYUwyCbaC7biqZBAa2uUg8+4i5WaaHL+Um9QRErOkFLq7myybCFjBUhWkHkImLCOB8+Q7P/SbLJgUTNwgjw+ydQWMVp4y0CIR0OiRkWaaLr0xgG77PfYpDZuKH1Qohyrp0J0xyfaBy4YTVKCh2kwZXsphW8Kqf50RV3uKUvUS3fSp2aebKiEtUsa0E5Qx8BGawTNqS6OImXKgq0G97wYRor5RXT0xFiGMzL31NVQrFvGL/vEE2OkM42sQ0BrJSoyp2jsT1K+qsQcmGvHaVamCy5GRBDnpFkgxrbqUFkihTL9JDcirSz6J5oShDz4N6lipEmgqByTtoL4/5OD+onkyhCxbFA5OM3YpEMxCIwtbKnoGJJT3CWHw4ZMwOOnAAtS2rA9qud4xiVsfQVISgONC6OYfAQ/lrGgxBMWhSetAlNh7izuCIbbqAILPhkHfRhgwAg1SmOA+qnCwPN6wYVFlBJA1GUu6Khy/iW/ylGKP6grBGWypFZyAQaVTG8jOvWUIbKTMiU4yiKNtMAfbWmLlWMjtAtRXMRdHnGTagQO+01z7WjXxOdrWC1zGM/RZIsxoU+gAKiXWERqVwU3QgRTpgW5pKJ/bGsGkgqok2NPTyrKvq0u8BDPTGzE+ALJCExYFusRaSSaVgUoFOtSpg278KyXugJgtmww6P9S2CR3OlLmaawPu5hLJSqNRSzDsty5ZkklMKmMmSJzoIIjtyQ3cRCl6JRol3VowjzOTQRuikwDTjDlTyZL4GKIeV7xOZAEIhNoLVoLoXwHNGCOKqQyghrEd9R2QoqClSQFg8gIln3rIPJNVf6vtTLnid2KTYJ2Zrcnb47I5D4N2qwj/VjkzrqOKKgWsLyTbgwFnS5HKQgL+k4IM2q5o+VhPqQFP6SRbhoNaG4DRxhxSQR2KcEzrKBEVJIVLK4FP04Q2mqpe6cnU5iLMjrVwHbEfyLorcvDiqCPbuaVm4CDzLy7NlADV1jwOQZDoASSsL4tD+9sLpAjcDgEAmdIUTD/wktiqF+spzoyBIGDgjL/1FGyKXOkiTJ2jC1xJUHuBWBmL5eECSSxCMWwCy6w54Es6V+8RXzOq4O0JMsGz8BE1kcUq2AIpqUcOIgSZ0xHp3MT6JVx23c6+lxwA5NIUy9CpOgY9pytw5sYiIyPySCL7iyCCMXEZ4Fz5kvSr7WOynfYo2bKHD5KZ9En6Xg+KU66bjjUZUGQZtcghNCGya5ymXLT7P2Sa4YcLodaJ3A3Gnzp5i7RistGNMFre/Z0rKRQm1FkyzvPCyv2aEQJo3K+BtXOS4nQ8ZYmDGCiUEB9RDJ7Z16aCJZK5YYtT20IsGhGdPfWF71+Sl6RtIxjgtB1/+9RWASuYgm5JuWTwqW1X+lA4Kc4ChFpbLmy60RvBCcg9VJtlKPEbMJPdEdHYMpLfuQlDQQ5oO0lNrs+PHINp6WnYodHLHw4sYg71KNiwHFwDXouIBJ0WJDAcmOdomLLHbinMCNLCqkwEeRCIjN9xNU8rsYqvWVBkkUx0+RlTE5QWCKUVSo7/NZXyom5ZqRTEUdMpNPNIxmF2KjjAAkoLKVGIFJbGpQwwBiFSkdA1S7NPmhtApPnkAwL+g5i8qMa3SSrArNItsYvTM+BzQJISHWSmqhopBRaNuRqROzoTOVVKhTUgCguR5FTgr5BjoIhezvPdSgy42TLx0+O6Mbhgf8oes65evJlis5lc2TFyUXlpDJFPFVCclpnc04TnHAqkXqGa9xcVgBGP34INXJoY15GYYXO1TDoKqxnuuI9SpDGU4SO/z5o5R5TmL5inUqabLnmFdOsWxDUO77tQHTvXOAEPibcQK4CzfErEyHmyqnig5itU4eKukzvBieu+7O2meJTbwjlKExuXoemndhCe6wkMkN7tehCcCZJVupJzc7CO3DEKhW1scHlgd4XIMCAmTFD4JUrNmzUOMiPnzx5DfnVqOHFy5cv/vzNmHixIZaP9Og9HCkP3ESCVwAA0NKw4oyQH8GBIwgmIcOIWj4mrJhxZEgtWvr1qyEz5EGXLzP/Tvwo1IvAkAJl1mj40OYVeRcf6rTh7yNBoTK9JIQKJuNBLRUFbgXKkt/Hif0E8vQndAZQghdlzhAqsC+YpXK9NBU7lp5QjgcJfjSYMeFbogmjzpxRsd/BpVgS8hN48ePFKwLxfqnIFqhMjxMFTjwIpipXfwkzEsZyIyPehhNlApVXUebFiQprEHwYUt7NkD1LElRMG5wN4jdqh5xYQ6DQj5vBXMcyXCBQoQmBerk5EacW5niL0rM9QyY4vn27YgFanl+/kDCxTK8hL6FMx5lF5NZHmYU0V0VXYfWFTI199JBQ4Nlw0BVezcDUQcl1ptdyCX0Rml1ahNRPS16YNpk8/xllxNmCsnlxwwyaSfaQe0BldFF0NwAlkFbc7UUff/LURVBFn11REBhnacGehVg8NJE81AFp4EMCWZVQP6UllFpiTF5kA3bZCaWkPz+FuN5+M6hEz0F8FUbQlQkRFFJYD82lJVXGTQijDUI1JOJ9NIEBVGRKTZUdPRephUVlDx10kZhp1WQDQVrkBk5FHH2xEEISXpHoSXs+NENDd43KzwyPWuappkMKFtdAe231YaU15OWcDV8IdVBh7qWmXT983mdYPxdVNKBBoA0kphYCkYppDQZ6YahvF2Fk1Kr02fgFfvRMlNMN/IDLDzg3EAXOQw3RNJGx/ExXWT8VVQYUgf8EXmSmi7TheEOQ/Ui10Gnt0vNuP1QiecW58iDrb0LBqWauSTWASY9MQkVLsDzDJVzWbzUMagM/qdpKWkYySXxRRkwR69dRTtXpBZ4zkLSTF/g9VOqu2la8nl+9gePPhgmJ2pgNOho8Gs30fGTsQeRG95Wc9BBU8gw3gBGdfkkTBNyXTcqDBZsjjutzQxk1FOdlFLk6H1olBgUswI8B6SvAWjjqYVlDsycTvyHhNpWvDTUlkI0q3c1RnBvVcBhZJF/q5WsTNvTfx/z4wzJQl/d56gxcvrSePxVxvRgYuh1UsGYkVkQvFoNBDU5D1WrKX42Wryq2d7TK5/KcUYq652v/YAr1hX/gINf4m8/JowWAkr/nMdn+1Di5ZwvOeakXb/34bH+vha7QVrppMZlo87KuWHT0GESk7A2R7l66LxUbb+DWXTzdDcF+EV0/lKJ0xbtXgE/TpAUWn/mjJTdL0FdIIplkMcgfclGRpKhklSf1J1OlUZWfJmZAadWgRb8RTfsGgri+bWZZQnGS6jLTKaMoilSVAuEXsJOho10hSvS6mNIIk6gIWWVYV7iBi+YkIK1R5Ab8C5XyrgQv7GFBcgVLFRh2WBgsrMsLD5xMCjWWk69xSkTUgprwoFclCQGMIKRiGjhAQg8bxMuDu1GjDVRynsTxA0Og88JIwEYSFpZs/1HYQ6NDSuIztAWKNML7wk2Wlzwj+sNJHdNCogiCMizYq1VkoUf5ZHKQkNjAYTNao6JW5CqbMQsMbWQYj4LDrjJB7mDu+Z8XKJkifzyOOALzy+BsaZWKeNIG4OhIwOIUPn/lSliNA8d3XnURENXHhCA7GiRvxcThmChCYvuRDwUpGwfJo1wZ6dMNVoWUBK0POWosjtdiU7vysOZXsNtWq0jDLF1xyjPhc9kxmzUg2HSKOWtknbC2UzvmMI1eJjzaaq5gouJp0kxdrKXMYMgthvklJKhaEMQugqhqfQQoAwLKn8r2wBbxwwZXY6IkjxkusJgteazxHVLA88xh6RFK0P9KHxhuiry+1YdL7xmKoZ6CR9e05Je2Yti7VKee8lFHPE7kzXgYqhIsfAhCqAtPeNxmE+Sd7IAwdBkW/JUyF/4qK/0pTcG6RR2OaaxQRPKYaHIqJKBU61kPrM4Uu3aSl67lnTcx0pHomaLYoZWawQFO2bJTmWjx6AuQpGdfQtJMymAxLLPkHEO3g9H4De+TBpMPfCpGL3uJ7z0zK1pQjWeTmSGFchu6zAMnxKmAqlSSpLQIR9m6LDJxkCyn7AwWMjKvXcLwL9S5lsBexo+5NHeqYLMBZb8QqMLKTkgGe4pIsPKVfilTkkJKGSHzWZKUaQlIp4WQUP3pEYFqFLOLWaH/pX51RSG5x5S9wdEXoOrd0R1EiE6M1KSO9Lh0sWcwffFfUbhUwB6ZZUJgKJJVfubQH/Xto6eczmJNyMECzqt5MkXXSVVCIRZ+AXq+nFiKe1YtPIoIeSBtV6CsUhcutQg/jVNRzywlVAE9BKy4bMgV/rRKCcmMleuDqlTZ46TA8MiyC+MasIIjqwIWpgbnDRpWJmK8eKEVcVeymwm92Lg5+YUkimsi9GS5WKY96GLJGfJDcScPE8rEOr7yVk7Y5ePHdUh2wkncmyFk2poRkkde3GXJDhy2GkUqZY9UXt0E20TvfcZ71kMu3J5YJOt1BlQxeZ0pz0uYMn00SaYzzt4I//mRTromr5XcGlu+2pv1Qe26TVysfPyHl+ggri4wQZ+jAicsR8EFQvW7lXqOQim2DaoiD7EVoOWmV0llEUAZe0ugFkqYWtLHzteT6lwa8zhwqCSPoamPlmlVJINMV8SvExtmtpU0rGkLIncUbFoKdZgPlimc/egakRPmGuYEWs/NdorBrBfM/dJqOMRrnMJtVZQVvfQgLCxMVVIVoc9ZKjY+VlDEsNZvTqpHu91G94cKlq43OrZQfXzwdpaC1omUbWjvNJR68YJhpOiVY2Nmo8O6+Grn/BisRHrsZ/cyZfakC8N+Y5uuNXJs1MV7t1QZTKg9CL2igdZ3aAxMqLXgGv9e9SwwyC2V3xC20JF4DJIKgppSLC075F4Ll7J0mLesFp13UQ1HRW2gLGuAv+g0akIIs+d3SmPXBS0LeggTMqeeG23IBgmPoW6UtrQrnyuSiloj3C7tIvI4eqgkO+lKZP0w4lupmQTjgGyUlhNFOtPCvk86W2ZyH9pJLWtLI5ZFnCVnKFDrCDJJqLOPUDmyajUq8oabElOzTUl2r1kxkrpqNeLV82D8cLyJF2teOKerTq3Id6jqYaf131hj6EPbaztCUe3uKfB6nxaPHXLZhk4bLdXZLVAOpV3phDixpjPyUyvVskzHpB6cxDHwky67UWdREzOj80P+ghRP9Fygsir/BfQosRM+nVMqnkESkEQXFxNKD/UuWRc2ESIegdJeEDJ6zWU8W0QsxyRMR3Jez8QciuIFyZEQ+/NLUBUdFAZVlWOCc/VBaANwcgZVE3KAhtI2HREdkhRFR1Nv7CIksmRCUUE7y/Q5Q6MVBWNEA4IhKsE1ZmM9bMQWAFMs7WUitQMwyMFPK9Rq+zE0Y+VQu8InwlU+Y0JLJ8NJ0GdXrUJck2YwKYYcxFMk8hdrH1Qq00VdM2CIrodT2nJf3NNvXSRMdQE/LeI+9hRK/cAe+3FSbnRTItV/PtgV/aaGOxZJncIeaJNm7/Qdz2IVhqNXBFJJbMQpqah9+BFPUPFg6yMo/6fEHNzjF6thiI2HEltUjKACQ0IlFJV0LO6TTBkDKqbyLmVTJN7DUGbiO4MUIXujaBAzcgV0LUmUH0oRai9VR6WiK8+THTEjgQsFHPmhhXZDLWtzeFbhi5MmVJizKlnBMsLUFyM0QmAAMxYCMLWEN63yFVGmg1uzQSVWNDdXS761JnImYjcVJyoBIl6RirYiWFCjE6lWHxDDHB6hTmjVPi0CSQSSe8wiUYQ0IKliNkIiPyMRGZLyfvg2QkxHexilXfKTII1nOj3IXPNSSUYUd3sSE8tUNMvxGI4SSuziFdVkR9p3iTvERseUMWrBLbs1irZEb7YyfIXBGjrjPdxDEv9QpVs7BH1/NHs/goNl0kkmhBkgZSuS13CDsYfyQYCgc3SB0ogQhFxE4zaFsn1+tx3ygxcDKWLRtEusIT+0F0nyszfN8ydQgWG+kyD5ERWVtD74xjKLATa1GCLHhmHPNxikojlMBI6JdCW2xykcxWU6ZRS6KUzuUUtLYUHqcX2dJjB/ojPNVn3LMoEmNC8FpInspDngoEY1QEe0lVMvFSPIZXMagSfucS4vJYf8dEhi0V62kiL50SiuUWALlTJnpVDV4Rq+ghKGWHmTCHzsUi2osxg7xE8B1zXLsScrtUuSOWuykyK99UgOmG/DcniSUS+s1xOgok0840RYsyLVopv/uBNJFNNEkYFdqCQwWyFitII4Yohv9QMrDGdA/EI9WgND8hNppZIRE1ghbGGQzxcTLvNmxBd/PxGadJmcpxWW9UgpydZwivFVmbKYidSS+LE+EINHpgFAomkYilIawyUfUDWdggUgbvM4cFE/bwRtdpNqk5Z+k1I+whUwApIpksgthwFDWmaM6qUUZRQtR/h+ILUi3Lkn2sKdaUY9kMJc/nNo+DaTwvJmEXZiIwYAZtkRc1F54OAXfxY2z+IZP7RdFwor7eVMaeNq8mcx3eWEbHR4XiY70QY12pUb3pSK5hJKqYGBWGMmWxUVoSQgNGonDMM2sQOhAGMTyTGmkrci/yClRo03LQ2HO10UoO0RVIfHpX7ZLmWEOpjjMjPlqmnTcMLlSyKUb2ZiWqnSKRiGHATlD6thT9npMXpWfd21IT1SNCPxLgvlUIZYLAzTKaFTgQM2crizVQ7EpT8xXXpTKFCVUd4kItz5Zj0RivIXYExZTzYYKaekfVGxNSkGHq3iTciKJ0Byb/0hqQGlU/oyst3FTsBHbJwBcGThOUyEG47kW+YifBm3YZ/DpbTzY0LISHlELTSFgOa2EvuBNJWnGUKHXDx0NDrxUsyRh/lhItThL+1TJgvVGpKGq76ilzrVNCOLNybSN2glSPkWGHgigaODN29kWD+YPMnjK4HoHv/IIhHU9l8jK13p86xaO7LPh48cKadySmUmxQ/zsrTECSbwozq/ZqkkoigNFy1VAj2JxD0IUmIGsh1l1Db1g0ZD4zf/2Srtk4r21EyYVUbPlWLEdXTscUoSRCkSZEIaU0DfyVa3hhHqMV2SskHowk+6Cbgr1WxDoi2cKJTL4UF58zMuw09mEi0fYRZjqjbGIyvCJVb0cDUEAS4OB3nhBiAf0laf5R20FSHJUjCpMXDeQk+mtTfUMheSuKoLMRjn+UsPdjeNoRI35RW0R2cyqCM9hR+wdFykwyN+UaCOOyIpw52Lmi8jWy6R1BD74na0MmmCFLnuQxKHccBwMRcxBn3/E6wvD3t02wV7b0KNiRkepuQaAvUjVJIzU9YUACd3tCQZt4NcVfEseLKrdRQ052WIXtGh0hG9SeIrXkRxUbZQecQyfkNvrsEX3pRicBtKHPWQC3mEwLc6ViRcPWwipAY22pWBy0IfBvdBlpMyMGQa0ga/hBU9aCE/55QxI+REseMybvNA8shIJqRbfrE6viIefrNVHiEaz0JhirIa9nIDMnED3bI/ZtkhEEy1fbFDWxxeFxpqfaiXbDQZ6uQSQaZ8j4JZIPxQRmRz96p9IHLD0cMtWqASCFgRB5zKqrzKOHKfqpwsvtUibDI9TGs3iJOnTsQ0C1UY1Uct+/GkbWNz/2gDhIDnkLAROxEobqu4VayXYo8CpbcCKzMKQVcWsx9Ht3lRP/2mfFOLSrsVSj0iSA4Co9VCYZWXJLnmT8YSMs+XIDsEO2yRI4BXKDTKVlgjYooBH/uxV9RjFsi8UuB4LaVyJPBzZ8/yubbCcpgRasBHngp3LcfmWxxMDwFjmqoWb9bzKq/1SfMaUNT3lRmnHnucOKlyfX5BMaOjpLVSP6nYd7RRITmiBVVTLh0qVCGxL6nUKqs2aZEbLzR5r8EhQa+yWLFhRPT0XUOLK5pyTUkEMYXRFGVplvd5uB+EI15QOOJ8XpKBN/YkFxZ5BTZirF1EGGrao0liiAnyZg5SsP+mcoS7VTIbCNa2xDhgxRZslLyJuahdPX0KMiyJFFe0Yjz4QV4fpTN3NkXGI3lYslIzAyuTQzvDd9jSR2CsZ5apSxmKcq9efC0IwpL55iQi1kD5EaBwa1qiAmagFRFaxi7R4VB/Yln5mStb8Se6CyBxUj4p8xXT5bSDoTkUnW/epqxjkofVzHRmOmmSeK9oFEo3YYpWZmSZqCqtq9HMRUjl82MvMr3RAVkoNbLEctYy2ods8x/xN7IZwcrigkSL3Wz/9UYbtHdPHGNZiju4MZC9uEV4jTXvslscWSrlAUlqGj6pGDX6IoEGo16J8oHn5itP7BpGQYJSBUvqSzm48xX/GFYo1VJA01o/jHukP4ZfTmQm6SQSySyBl6GwkUa3LqM1fCR/JDeNQ1FM3wUnDBNqO+VEh0TLIPVLkXjJt9Q4yvqdGBk+Bug/6gTFtRMao5My/1EoVrGogfIbhQIqaruY2H0DX9VZXgSo1ZEp8sUlj0NvEcIT/XZ0QSNZ7pNNp5QuPskebfNnFBNjRmTPBkyyYag8NoIjWhUc8hO9U1VG5IIRgqy1iiNkrCzfuKR4bOSTGsUlh+c5yWE2eMcpcdhwmCOm0hIdeslfBEPbedo8PcN07PSZL04aSX0yFEtfC6HnL9U2f+Kmrrcj9VNLWEMx8psqmoU2rbY+j2PSRvsy/1dbYkpdJsL1W96nKcxMb8BXSZVlWZXVQp9THJ1FFnnROAu1XYhTGbSlRqboTTuxiA73KPnRMw9VMGQ+Zdbz2RhjKnkoGmjVRdFiI5DIQbCkTzNkT0i1TIL0K9rSmVulfVfIQUHTOG68NQQiRHhULvCzVx5jHSkjPzYYgRDyQIVKuhRWlMbILlU9shFTMEAxsv9lfbpoVZxqPbp5Z531ZyASrRPyGMD2UCDsOcA1aZ0iiEMTLH4jtG+JItqcRBKYuIr5Ie8CTLfiPewVEfrlIaucYEfyKzOzHKGEPFCPb7aBtb8dUHNh3uYaSZgDjJ8jKdhkHwQ/Xmi1FeDYTKfHeP83D1qvAj1o3F3NVD6iwUIVziQR8erxyT2tYnnCpSumZeR/pDmaZYiYFW9Z/DWY4T1yPCwZPmWYEVpmPDm6dUX9o1mc2DM7VI+Hd+kMv1J8BkPCWtvANJB4xKVFvF+BgmBD43LrcXg4t6B7sx1CAiw4Aiqb8mcrHiX5kYHL9CBv5E23Aq+8wXhjMjiGbMhj0t04cuPjtvWwCFrsc4RmQyJbTHa7pV3PVSg0Qe3PtznOF66LCer86ygDeWU947qdCiL4xiNvhIC0NC/fybli5F15ops2gjx4/SBlSpuuhx3o/WYAceVKv35fCNqwgQWLPHlavnyZMeMhGIpg/NGj50//oz+GX/jxq1EDHDiGWrx4ofhxZD+EHyOO9HfyYz8AALRoZIhFIMOIEeVFpPgS3BeFWrSMjHhlZr+IRsGdRIhxZNCHCjUSpDfyodGH4BCeZHiV4UeKCK8ojBiS4kaMGhU+5IcwJxa1DxGG1Hpy5Ei9asGEDGkj5MmIV/uJJMlQXkh6CB3b0Ajm48eHOxUK1HhyY0SXMwR6WarZH5i3EGdQlPcRrLyR/Djzw0gRHEWCiuV5wUpPIUWBRxO35AdOIUPa/U7iZjqDssIbN/z6C/kxZMSDAov6vpK2hlssWgRSNIrQYD+jBMlGpfeRIcYaAgV+xDjDKEWM/Jrfv59Sofgv/3pj/23vCjBOIkgggijCYqOL6DmuNdjoMYquGoziqj+BRgrJrfnAsK47LeTBiCCCFPIipIcewggLyohaSJ4rjDrJKPgEiohELzADrIaTTMTooZPeCko5foy6zYvXFMoKnI8qvEqhpbpCyEmQamDQi6+stGFK9178cMMVk3woIvZqyHIyqGzw7gqEcJLHn+se0ugKhkIyCiMtgAJDnpqw6Iqk2uRBiCAvQuznOi0CLejEGq4TSCqCIoqyRfK0IDSkxbbzBzgsDrKh0fQqAiO+GWzgNLcbryCsp5Eg82dDhlD1IkI+v8CoSqRGzQ1GK0eqz7UZFENopUIVYoweAwu6EP8cXQH7k6Av2BT1rsxi1WK6JbW4doYDwehpBn/UU0wzB8/UdlLgJlPtODgrOwsLSBmKVKNqAWMTjBFVBAk/fffVl89xkZOHIMNqFLEfhgiiE7WGDrVBnodCEhSl5tDy9tsev7iVn+9CpYcg7AQEcCujmpq3SJ7K9a0kh764wq8/Mf2zODclJI7DGrFAlJ+CR3ovxjRTWkrUoOJUasibHBwuNWE/si48NFcOTkmdDb7UK8FqYPqKWtELNDAbtAPw1u8E8jozjF51Dy9hz3YxyB3bu9dT47zQqFgsLqYJACwUruHhvvvzYjy+sGzMhiAxMpxjTo3l8jgv+MrQnz/pUXv/KX9GMkojlU4iu4axnP1iLmW1kLLDw2qgKDqdI+MwuxncHdWGXgNUGPPpZtBrqC9kh/BDxTieNjYpH8X9StZXlsrqOYuDyR/T9iuTWqA5jzzpKcMsN67Ec3fyVhvnzXGjzhTaC93iyqxKp5YXNf5b2E4cT6DEFdxNLtb2uvP1u3WDPUDywYnPQ/g1wBtQJFWI+whCkjIQCs2LaIAiVT/aEhOEBQhz0vFcwA7jIyPNgDFsWsp87EShOXFOTkV6nNRkM5ua5QZr/NDIlcjmqRjKTiFS6hpiRhKWSKWuPTKCoT8iwjnq9EN+DROVVoiSOfcRSYGxIw4G50QlHy4QISub/2Jg6JS/R/kQYv3wx3v48T/emacmOqLWyoqFoRXxh1bp0ZjWOlbDkAzHUAxzCRC3eJNMRXAtUloNgNYSkxsVx2s1eA1BdtgQRAmMIxORzBjpUjBRrc9pM7HU/SB1RZMADFDrYdxd4GUDXsExjkxMDT/AM6+RobJNbgLiCBsmD97AqpN+QQgYRLZAW2qhllwBGnx0Y8BBwZEe0/MCsSimMl8l6yP7SdH+4IIhxDSqOB0q0p92qCugpegkY4IJ8YqILX74bSXAIdJtfAJBzNGHcWJi0BCxlCE8ZdJqkOSJ/SqYzARJToK9A5x26ATKDoFDIBwkUoVq1RivKcQ8Y9ykp//K+YUNEctBwrocOOSZGeiJxqEPAR1lcrehMi10ItMSyqUKKhwlpsuAZOGWfMrzz5N5pmi9yZ+QNnQaMMynJj8FalCFOlSiFtWoR0VqUpW6VKY21alPhWpUpTpVqlbVqlfFala1ulWudtWrX5XqP8Q6VrKW1axnRWta1bpWtrbVrW8laxbg+la5irWucy3rXfG613/oVa9j/Stfz+pXwe71rn+Va2ATa9YsBFatjR2sY9MK2bzGta2Unexl7crYvtK1s5WVbGUtO1rShrawp0VtagVbE9W21rWvZatp49pYzD4WrYjdLGA5S9rc4razsp0tbR3r28/m1ri2LS5vk3v/WNFqVrejJe5x+ypcxd42r9TdrXXdalrgLve539Xuc6M72OZKt7uwRW96T8ta9bbXvXA9b2GZ+935gje5xiXsb6UbW/5Ct7j5ja1kxwve2pI3s9Ud7lwFrODswjeznkUwhDl73vra97/vxXCG28peDad3wO+NL4MN62HRhtjAnl0rgC2sWhV3+MLHrfB+UxxeFNP4wMgFbHVhbF/aNhfAwB2vjl3cYQ4P2bUL/iyQk5xjHL8WyTwub5JNXOL8/tjHwuWtkpv84h1rNsHB7XFvZUxj3wZZxA3ero37e2ImK7euZnazeWcc5ygb2b1FtnOeDTvl1IY5uHQt8J7jG+jp/yoWyzkmdJ8TzeJAH5qv2K2zjP0c4AI7OsWTpi6FF+1gNjsXzZce9KT1/Fo8j9rJJH40qj/N6Ro/2NRQlq+r21thK+vWxE9O85Zl3elKE9jN0aX1muWs3Fe3t9TF7rOqoyzk+76Zz/Qtra/pa+kWd3rFzbZrozc950xf29vAlranu4xtYs951br2trWJ3Wto8xjc4941nJF9ZwDM296gZuy2/3xaSz9W25Wmtqj9Heovn/u2/0Zsvw8OcH0PXOC4RjSgDU3w6wp84cmOLHwhju/tNvzecz32x0Xeahc/G8UfVveoTc7iYcs71wZXc6Rj7u5lDzvbNSd3vHE+8nmHnP/n7k3sYnfO5Wb7ucwzR3lr3yzzczu6xU6P9paDnXJhk9vl4i43uu+L9XXDGdheZ7rNxf5zU/uc7GdnNdrVvna2t93tbx+y2eE+d7qjd+WnfvWA757uuvfd7+ut998F33Zcx3jq3t3s0pVt9XCLOe1bH26Y9Z5lyg/e8oKX++U1v3nO753fi+d86NGeedGX3vSnR33qVa9a0q/e9a+Hfexl3/fWz972t8d97nX/3trv3ve/B37wg9974Rff+MdH/uaJn3zmN9/5z//48qE/fepX3/qkDvz1tb997nefr9L3fvjFP37jg5/8Y83+P9Iv1uyvf/3fL+v73Sp/+N9Zz/T/Zyv+0at/Uud//+dHLfMDwPSTv/ZDP7QyQANkvwMkK/dDwPhrwAiEQAlcwLNSQPV7QArEwLS6wAr0QAJkwAkMwQ0kQQ8UwQwsQRJ0QBNUwRNMQRakvw40KxA8QRkEQLwSwPOjwRHkQQt0QRlcQR9kvx2kQA4LwvYDQf0DQrVawhHsQAV8vydEvxicwSnkwR0sMinUwBf8wAs0QhE8NizswRuEqxwkQ9Tjvw3Ls586wzZENjN0wziUwzk8O7CywzvEwzzUwz3kwz70wz8ExEAUxEEUKjo0xENERLiDw0RkxEZ0RNdaxEeUxEmkxLWKxErExEyUxEvUxE70xDnkxE8U/8VRJL9QJMVTREXrM8VUZMVWRL5VdMVYlEXdg8VZtMVbdL1axMVd5EXO08VeBMZgrLtfFMZiNMbRS8NjVMZlHDxiZMZnhEYjc8ZopMZqVK9ptMZs1EbA28Zu9EZ7w8ZvFMdxbMBkJMdzRMfCCsd0ZMdqXMd2hEdmfMd4pMdinMd6xEdevMd85MdZ3Md+BEhW/MeAJMhRHMiCREhNPMiEZMhJXMiGhEhGfMiIpEg6nMiKxMg2vMiM5EgdNMeOBEmB/MiQJEmDBAAySZyVYZ29IBARIRLQIRSP8JXmcQ+hcJB5+R8RmSCFSSGN+I/ZIA1pohWKgST1ySSQkiCZ4IeTeP8po4mcFEqVkfkidOkHcLEKQioaldiLmFKZ3fDJWoGTBLIBpMSZCFqVWcIC7+knimIl38CXNkKTwTgVxkASynmhU+mVuikWnNKce7kSAKKHdcoQRDmZqTkotmTKZPkP3EEOQiEktdkRx1GYhUkWkrmaOglM5RgLfjiYfgADJcKTASmmKfKNDTmbltQOClmRr5SjZ9qU5IgdNkGODjEgR5opiIkI+OELzESU0bCIjDIK1ImpVImjq9yQYbmStPgPr5kUV9GgfxqkK6Cf14yPdAGXOPoI7miVeemcDMKptSiOQaIbIeoJRgmLjagTsXQKcACjBTGRL5iQNOmc8dyhmoj/o6bozGN5lozCiJ1oFqohz4vhDbu4mvcUDixQSnh5jayhltvhCZgil8twjyeSqYVSGV/iyoDRDophFbapldJgiJFIS4NqF0fqGM8EHug4zn/qIO3YIejxG3uZFIqAn5+QqT6JGniaE82RpJlQmbIQDMcJTkTyi8E5Ef4QlnZBD7zYEllZmbo8KE7ymlUhFiqpEuFkiNVolx+1iApBlANdI/YsiCCBpNNZp66gmJMAKRqZgV7ph8GhGArZJNWZmlHilfXMJQ5JHyCFjhrIjTCxkSzFEn5Sm88gprxUz1vSS7gYoxzpE7fwGqOQnz7JEe/kkq/gy434G9fUiJcwoV5Z/x+0gR5WyRr/GVE4OSJJHYiVupYSsVLQSYw/DR1BRZC7MYwVkckMqYmYghQ1nVW3wCCzuAKMAYejxBit9BQ1QslRIs8Kso7b0Y72FAp5kRIlAqj9PBvESZwpsqn5jBURhZ2KENZNOpXPwBKNqhiRkQ0CJRnneaMjKktOaopvogc4IaRBRYmKoJon0ojEYB3qWKeH+s26hIsQLVZMAYM8rZdQYVMgBRG7zFPFUKTR4Z1aVQ/7WaTLABWMgAoe9ZnxRA+P0dbXiB1RUZCvaVM4KgztwAwh8qEb6o7HIB446hDH8Jns3E7e+SJJraXOAcyvTApECZahaFIMJYtsYUzpJP/PIpIHCbUREkVKmeDRbakMv3GcvWmdhxodB0Ecv0iWp+kVcL0hNsWQKcIexWGJxCmX/hzW0Emek9GlL6glYZ1VGzEQr1moyXgIlTkV7EGRbvGHmiAlMfUCg/3KV9rbV5oXn6kVT6kjnImYnlWTofUh0zCM6OhMUaGQUU2IKg0SGc2ar32MWorbDYGJC1VNSColFJVQZrqY0ijcVGrZItmJh+UIWgpKnvmPFjIiRrkpqhyWgWBdCZGkxB2ZCXrPYpIn4DiRKu2NW/WVBTnLCOHYDnoUcN0pD6qg3CQKNl0KoCwWiBEZtBnP9zTYJHmiqoimWgojWJGn8SRf0XhbF7n/Ff44CHhyHFcdpAJpHg4KENFQj25JnpeY2Aoay/38ooS1gdyJisl0EZzyGHK6i8iB2/G9FDj6yuhIlW2ZjEyaFqzFX9xoIMfJoEVaE3BVGNcRlsro1BlwkAypmYVSXptiSuxlmNh1p0apIX6oiWPqKYeoIdswVdccliptjYtJnqV01QmSDnn6IqkxJwBaIUYl0Q6BGCMeHcSQ0gxymN0Z3ARypNw5oVrVCJZp29I44D95DEtNFtxVVd4AlUzKILbxGaOVpEeJYOzRnSspiu8FhzhRGyBVHjc+is1Ej709S79IEnjaTXIqIis9lusdHdvIzSDhDlctmNqQGyNyHZsS/0vFIKMDbp2ZmpF2WaWZWSgnIRrsKZwnEp4bWpPbfc31FBhGPRZ/sg2zeFycNQ3mRUxGdSQbvZ2dqCHoYZzBYY/arSAF6SO/QU9KscuD1RUSSZ+CiSkFmSBoFhqLEJlIOWDssYoKchCJABwlqogVMVjcXSHEMSF5opARytKrfBsK0VUAOKHCYJ26dJ8KSpEGyqQUIp4TiWC0GRmcjeXb1cq6caTm6SclUqBUQRsP8lMURRH+UZ/WFVItAB0v7kz3kcv5+MrkmYgK6cznUR+2sMt7qZgJUiIvVlW86IyWnNGgJBGSoRijkSegYBsOwlolyg0PAaVAYVSYmiivCZQV4v8ZpSCZa8kcbV0oDIWfrVgl9VwnrjmfWSIQ23goaumMeH7NI1kNzDRjmGoS2CHZUcFMR5rQzkWLXhlVRvUbDfol0kkQIEqfA1ZMBoGkp9ZKdKGHk6ldLyCgwSbs5vjjPpKjlpZbBb4a0qWYkSCgkAAZ+DkOUIJJobAffjlMYp0gzUFKosFS2j0W3qiJExpqrVzQ2h2M2lVV2NkYJ/lYahGT68ikG2JbtUFXzJGV28kl4nHNwAGcL7gBRi4lmB1PBcnkCK6PKZqWPlUZe/1MMe1MeukjQXkTLybnbkER9CiOtChTTvadFRqMSGmplpVjQZ7kgtmS1kDvE1mNirbR567/aFmJIWJakbe4Fph6inwtDaztbolIak6JbtvI3FtJHo1OFO9R6zEm4UVREeI5Fur04Y0h51GSjTBGjsOuIb4ooqh9z+wtJlGJERCSJMQQSxTJnAEqjuRZn2iyCra9mNiNnHfumMJujvzVFxcW1rfxTeiR7Mi5l6ZdS7asARtvjqypkhngFwqhplqZ65khkZqAT8Bo27mQn/cUVh8+2zxBSr+RkFWq0jc9UAc5HZiV1MS9mcKmJiXnF4wYbNagUfemm2IBopnBzBD5ok0KkaW4nbu5j964D8do17X5a4eBWXBdiRpCyvsppY04E/gp9AZimr0g0Si5Fm31SRO+Ghc+/1C0EfMq/ZGCSR/cvcqrFHJCIZ83Fk05SqfjUBiF0Z5omgvOXBi//W/ZmOJZjaACWZ/9FtY4n/IhhQ+YrUs0GqFO7aHcPWQWQXDWidSs6dc/LlrKGKXmiJLm6AcjJ6DIqBkx4il6IKAqb5OtxhI3upmWuBndjqNu1xqicSjPrMvzvBS97Al9mdw1BZmQ0Bdhmgk29dW9wAz7zKROUhmygQ8bF+7mcOdDoZHmQKSe6KBbgYpcHlwNEtSOWBFOsVJr/eva/R/ieKga4pL0iQ3uvc6kvg6r4Y8w19wPyXGgSRwwuA8WnpmNkSR5Ek9BFY22pe+lTXmgrAg2wdCCGYtjEf9riEgnIIokzJSOKC5jAuWUCu/irDGN85wKH1qhwX3c2iAeRGkRzNzM2/0i62jZZVKojBAiOUbJ+DyTHkmVSLEL6I6kBXmbqRCWhU6VZfofUemI8EUj9YkI/FhsLbiPVWmeTtUXG/XZXslTqSlye1faaaEIhZ+WcEmhWK7KqGHKz1ylt3FVq1wId9+fRNn8RjbXFVFiXXkV+7gBVrEOP34Y3bbXg62NmvCnoO3cUpoljWgO93BrfLmPbG+OE4kmw/iOGtJLCoGkGqF4Zu7zhYimt1kYaupfDIqYLn53XuEP8pVvzugIBA4QFO0MO3kMX+UXzXkN53hWNNoinBikaZH/1tgZjg6hGPTtXEHnqVF6eEFNpx8e2o4FCH8CBfbrh+XKFSxY6NETaEOLFhsSbXz5Iq9GjRkzMHrxInBGQYEcvWDpWBEhP35eKmJkWYNeRYkFEXpB2PILGDAaIWoRCFHgFaBXOhasIREjRI0e/UlMWdGfQokaU8pLibBgxZT+pvIrWBAMxqg25MkDWwMhxIplwVQUe+Mt3LdgpKZ9eyWlQSxfbmiMy3dGxxtX+mksKNHvW3DgBKYEJxEiw5wzbAi+Ag6xwIqKN9ZwKRALxJwVNWos6SVnZosVI4Nh2JOpDYQazSpUSBaLRLJfwRY0+UVxShsdMTLEcoOfy4pJSc9Q/0ycrFCNDOmVBgDgS9ovd/mRZYg0JcTaxm9gEVkjsUAviG/4g5jS6RcbClNivE2WaL/Vw2uk7Jjw4BVKeQQcPznJhFFj4CCUEz0IoaUFQ/0ohlKBYPSTE0aKQScRQycFCBFNp4GBVm7yuMfPRGQpVpAWCu2G0Ho30AMOVx+B1A91M6SkkWI1/RciPb15QdyMEHWEl0Hh6VXRF/6g5o+DV0xHVobgBHdTRR1hQZZa8nTomhYzXUHRFx1BdNRZYKakUHtaKMaQfxLlhKRFa3U0VU4hkoUQOAw5V4NXrAm5UEP+dPhFkIRNFpuUUSIVEYYZAUYSFjUoNNqNW82gkEZIuf/Yjz8xnhcjGAT1w49ib9loQ0Hy3KCYQDHeAEaCCsElkGI9LsXZXnZdgRF//GgEXahvMfkFbjYIqBmIV+SUE3Cd1sAThAUxtJMWOlaYZ4Ar4pgoPwp1dGgN5uWkWz/yAVhYP29pAWwNokkk26ZblmibPP6815V1eZl5nxfUXjQtTxL9ZiU/7IHIohaY8gMZPeL9lFW4WDDIbn3ycCauF6TBR6++FXYErYrgoJtqjP/BRQ9ZtL6HkUgFwdpmvRU967IX3llqcWubzUBPXLnGdZF/Ac5wRVxC+SWRQADfOFupUCpEr5yKtbUkOB2lF/CZr9I4g2QzaDemskePaZXR9Jn/ChGCWMZnA1tfQH0oojmSdXd28LoJTkiGNpSTQjA7iRGfXwsEk9xgg9ERowvG7YVWUF4xGkMU+leQWLoVZ5piOfm4eVSOVxSXcEOOKit5FsPl6g0Zubheu0MbhDrtta/U5GqFznRYZfqtV+hyYmLBcJZemLwWlTUc/xRsZRdllA3xulxRUfTtDJ6bO8blpnJaIJRehP1UmeOhzxJ7hXXTrRacDWDKYy09KTGIbmoPawE4ofWCWXlCGlFtQ58KJR4vcIgeHdFQ56KGIQIxhCxhShpVWHcrf+gGIWTBljyYBhXTVKUr6XpImA4XsUrVADoJi4t3pEOP7ABIImKpwX6O/4QqjXwGCzy6zKyeZDP45U5RTbsbOATXHZatyTRgkFFqDgiO0b2Fd4ipUNNuoiF5KMhBDCGIzcAwMxfKDGETwUikxKUYiGDlCzDMWY4KYr8daesk85LSdNoXP1RVMWQqZIzgdoMFxSjkXPLYI98uBAZq+QOMtClPq96Xnyb5JEyxUw9ccHgclCUma17I13EQc7pbaQExowuf/4YytaF4oYudvIFXviUpOPmIlF3Myokg8iZuuatFB2GIBhmTkkN9D2D7kdyxOoQ5G5qNfCzT2Ax+4rnbFesvNdPOcrgkN/41kCxFxIJ1ygY4jbjkikWrITj6aBbXWE8qR6NW8fIkOP98gSMnoWnNjOZDRzaNbC4TiYgcCfeWwL1kgvwgF8G+sC/qpUsgT9rJ9/xhJrZBj5+dPAspt4YidaEJjfDD3FVAFbJCdmZocLEJ9BBCD9HUzSku3BIyjSYRXcnDNycai4nggh8wfIqhS2SkOa8wxJUGMWN3Opo0WcmmR4XlIEJ5I6Dy0h+ShMgh7YuJDaxXuZnJkB5yGpFO88W40hXEbBFckVO3RsICWlMlSoygaUZZu1p2KpIhItxdIiW/ucTKLzgpVYzK+Dt6YGQnYuoIleBzGxseSoX9+AkFTZStIcYkhx1ESO4W449h8kQhSjISV/i4R/ykBpAF7AhopZk0jDz/DzhkFN/O8iIWmbyxH9aRB0glJFnmaGE/DNklxxByVQsddm/BKmsXawOtXL0tfNZSyOaYpMaJAvFrcOkMDhV124mWTUAone4NDvO18diQpJE6DnO9NJ1LKpabZZLIuPgKqC41i2wMHU8kcRVAjuoRNLIq4Gf8kqzDvfFS2LliM+HytiJ6J71lrC0BozpRcL1HNF00WOKM19OJRA+0S+mNO703mAtBTYnayihyZ5TFXY6USIDVWPEm9Kvp5OhN8CIqmmg4EFYJEq7gEJirFESx5Mlqk3C5EF+y5LUAs6eu2aWMumx2g9f4KsDHatcRYZfQC86gTRAzr0DpqJGJLJcn/4IB0zBLqVG0abhoGuqrttKSIPnakp8POuw+G0leIa3mTdNpCbFK1LCDzcA65ZKcDQaiqfSc1kYT7ZvninooBWPrefZjc4uUmJpfJRViI2yZPfmBkbc8z1W9zSJyZcigGT2JLylyVZTXQ8nYJXPP6RnWwM5ErAy3VML0uhuVYSbLAIopKH5DrKYG25JYvqs+rTtjsXLnR0YtOUo0jFSGZ/CWgZqRYhiRyYnli9qlAuym+JKJCItoTwTxwyHStPMMyrpUxYCQxFvOiZDWVE0BqTeBBBLW03Q0WExjBd4f7Jwub9Vq0dxgL/jkVJUpaCTQJhBYe9GZX6qkKvjeZjpHJv8jXMpG0ILXicJ3pcwNBiZhDYLnUDP72pyQg5NZda94sP0PscgbtnGB9NvLXCYyhXRtAGacwvLoXjXpuC+iUFjGqJ2Nnw3pXi2cDE0h5k4Hl3NjfCJtrcf2UyAVadlivzxKWXLcMDfoEFk5C57l8Vuk3MTxsCWrddnNkrQreakEHas0YoFY2ZmzS3bth2wlw19tdKVGzdz0cL7xjcsYxjaDDU1EbHzMVaHWG3QVEFi2sToAj9edZfKPMUPU5aQ0ux7s8Tah32MN/giHXgnD9y07I7J6lWO1xNFnugtnFIG8ZcviGUbQplqyhncjK56FD7GWPGFlcrV6+vAlVzdWld//erUtIhWRUVCTSbN01noCIbNEhoHrflqGGLQUFWAFi96fzfaYPVMaIf618v0G6TXwdnKw5BXhhCBmZ7mc9D7LqYF1/EyDDEWVJFCZcE2JSNZNvAXirNtiOB+BrQfZfBCLgcdwEUtepAbQbIdhyNGNCEyMQE1O7JMF5QtDhBwS+YMZEQz+9IOF7cuKjBorJQ4/hYUMbVVt2B8BdQnF6Jb1iBNKeUYhnZQ4qdcPGVSXhE0GqctP9RFp9QPACJJ8EQff3Q4k0U6vRJLVTQ52aFbugIaGQdV2TJEflVLI/REY1stp5UryuATcuExfhBwlwYWmpVa6cFpcPMtbiE2vFIsE/zWRHNGUcWgTGNRaU9CRwWyZHDkR1glYz8XOrNhZV6XEWoEceQyRWFSRsbXMwoWIFkRQpKCMCn5B7MDNGNnSQCwT8xhNUVnQEw5Tbz2PEtHhP82IyYiHt0hY/XBHiTBP3XSKdSCYNWUUD0oLkSTbjxHGtWhLDZ2dDFpVNa2fFmWNTBmPoMWdTLFJM0GEX8ShX+CQZUhWKp2OtC0OSWxZHn6NmIhGxkAYR4AU2tQGmogQiyWKRblggh1MVz3TPAESlXxKyfiD88GVumDafDxhDSmYSK2QLXEhSIWN4myOev3U6U2HZvXWwrGbgImQmplVs2BjrthGATqGspDFIf5Y1P/YGbSw0RPeU5sdBIGgmbqoUFo40D7pCmzoDNMkFInxjN60iHvAhTeuVbucBLzARXlA33H4x6YdWcjZUnYtYMHtRg3FheLYRIihUnoom1m84Yn0DRpZ4nFo1A8lxWl5CCO5HxjiI4EoxQnGxfD0Q1b5EVAMF6ZwIbndTNyMEmwYyGs9iPJ8TZQUiqncTXahy9YxnXjRRHCZBlqMmmaJCFTtEvTZSqqhEsjxDFZFTlLlDq61kzkSnj0t5KVw4w1AEqpQyG6YlTSlUo6cFXLgB5opiq7wCT8NliOhW6tpy0gUxkJGiTnq0m3IhEYBhxKVVdlYGC6NRbQJIGiWoUdclaD/XYGSRJAjaUmyxMlE+IZGiQebLGRxTBu+FU1tQBZ2QMgeRiRDZku9UNnPMYmBjMVSrd/LxVu5zZ607BKVoQk+YprNzB5x0tFb+gXE/NiEwZSteNJvQJ9aLgmYDI02OlEJksVxFAtpnOF7VolsDOi3fI0MiVNEgBddEtRNhEyCLOOkPMiz7IWphN7x7Bw+LVMfEVJiYVU15aBi7Umj+FqfAUBW4QXaRIpATh1BhJyQZBSZkJiDUJnjzFjmycowCaVfzFHkgI9FBhCNCYk0FeISKmI4mUZS+MXAFMc7pdye2Rmm+QgbgRaFSNNdmcpRCNpyHSCR/AlMZRSGeAj+DIye/2SQQhDIfHQRiXXQ1sCLaTBPYzDMSZES+xCHmFyVLXYJ1nFUk7ZHBD3keZwYahAn76DRR+ZhlF1VrsDLZ1AYvmTZrVERnvKWIEWONv7HaWmNCvnY/BGoPFmWolkVjWnKzgHOVmXHsxRObmiN72FjN4pNGMWl9OwTFjjFT74FpTgVki0UrPFJQRTcp5jVUkXGQiWPpGALjKQnRFCZ2WAnY8SdpmzVQ8TFiqmXf72neTiFksjDa51YOD6PUhALs8ZNGQVHbUxceRjHvugWRehohLTOtRSZrQxRyZAGwQKLXdYNTN3QMvKMOHVO7vRm8vxpEGEZKSqjrjYL4JBUdsCGtP+Eh5KkF7KYRpUkVVXNERgm1LXF5w8thVNlSAdmhwcl6zE10takmyJBGDBlmZk0YVzWmn/JY5ZqFE0cStFATrltEA3xxZRWCIVCX5FG0nT4G0MhmCExCKYQiSJBZtY85DRqShEVkSCVFQIdT9FwXMGtjC1ZmANFyXKB1uqFGJk40GsASK5NS3LUDQoykW+Q0giux42sFsEmxhUy4EitHqbtSNgs2QEVD7lB1G2SjhwVxW6U1QihCU95VOp4EN8yUWIR65CaE4f8FABexzLyhLT0BiV+6VARjPOFI9Cwjet1xnmWoCDSbDNV3ZfuDLzoRpcYktLia6Qc4rMkUokoFNf/aGNzLVxg6oqcLOTJ3NgT+gz02GeCNaPFdBtJ2hN2Euve4IvnUNXIteJ/KNgPNuvWEMjbJI8uKdhuDMekceGnHQyuKdJWdW9vlczLKaJuLW6RnQrU6QoBwcWogVDKGdSIXMboJBxHEQuXMEcZzddkpRLHCMXLARixUieEqJfMRJC00gs/TYqF9YjeTRQYeaklfdBNrBQbsUZxpSPLHWCtsgYW/kbxPK9xPAjbLCe36Ba51RKlPUnRktewnCZP8gPByhGxmI3PXBUc7a6DxGdc7O+fWFKJoISFuRYA8OF9iAkkJgrh0Qdz+A+naRT01SGRPAofnknpwNMwgVGLNkef/1AIYh3MssmHir0ijhgtl8yjk+jE6oVSs76HfImd5ISSIyEWeoGJrmRZyUTIMk0nmAChXgwX1diIEpXQWuQO1J3P5BgS3gnI7DWQh5DbErpjRugMSY3in4yMIW1ss1raCM2R8CmUCD3JJ0KxZTzRRFGPfhnS15yW+YjXsfha2CSUpvDUAGYUNOfqTGzNYA2gCGXJcsqTKO+HS3yQ4yDXye7NXQmOeTAgopIjaRCHeXhdpPCIeDYlYpROA3FLQOpSy8nIzqGZI3HEHgaSLh3Jcijikqhu2UgWcF2IjuLa0i3jyjIMlYSPF7zW1iXEW4iXO3EsHW+YTRKH+zkWFeHHb/8S4G9RkVAmEHrJVzgzTIh8ad8My80oYn10oB9BlAqdSyqRskmC0IJYmC4xRioNk0ZHDUgRzFKJML6wyac8oXFxpWJyCy7VMqWEY0mHY5dQiRJxLdPRi7eMjWYNhEHb0D6hUl4smYhi1SGuZ7ENqLSEnB6L5ogAWHaMcAKeJuSYk4wd33uIy1IvSYdO3bzAIGh2TMIZMpf60ladG41IE/YozrmgdR6iiWsAzAxaD6zV2u+1ThmFkgdBBqdF9l4Yx92ckJGJz9V8YwCFDT5mzJppkJgkTItJVkJfhmxt10FHz/plVPxN6MAkTuEAmyFVznGiIZcay8oOjEhYx3nuCWX/SMtjqGXYGTTwkY1te8XtLKPS8AUlQtSo9HRYcMvz2htJIrCdOSvviomnEBO7wIbgyM9uANgoloiW+BtBEYVTEau6LK/7uqG6mIYIpc1EZVRgUgvTMIxYcE16yQ+8+O6BtTUrSRSqFF3CrVHUeie0gtaWrAzQCAaS2Ah8Rln//lT96qj1MAmslQaVoQ17cIbdMQ0rEezAiKWT+IUIk5cMvcm41towTR2RrFTy+JFZEeucSJJMydFC5hTf2t1L2CL7LCbS6OqQ7viSrsUpeg6PVZEIK2IWuotLXNdICSLUkqwtmYV/IsocvsrYSBuuCeAygRF8zF6udOAp+l1RA62j/8RLldCxWYFJ0/ibDaSPeW1QyLHGfzwhXvwJHR9btTIrcgeYq9gW1sFHePgb1ChWPbKMLu3c15gTgNVa4Bp2lDSGCBUN+onHvIQPXAGONGGa4oiQT2TYD2nzIYfFchzLSZwiRLMLkszxa5wJxcQkQfknsfpXWKj0k+IhyWolLd+YBRWZWFKGrnCKzIoy7JZrTcS4CEPhe6ok357OtYIqlcRYwvVKu3gjuxCIrNTicNWQrnbRbKgYsf4ZBBfNf9gZGmWI6xVHNJJJ1NDxcJhkZTQNfJRIeKmkOS7FXtyHK+eL3wANTB0QKs3KXFEa0IU59O7jK5rXBd0KvFvPcAUaR//djEbBj7T60dWcZ1I7C1coiRyd4hIbhnWglHRw44BXXU2A121dla8syW0gJbuA61AUqqQUG2XVlq7cjJ3njv4MYF+VkaCZFnuimd+UCIYIRe7oo6L0h5Do3x6XHbHsDEjJBLHsuKaIBVDQsac3CGKVYF8TE4vw7YmBoX+NK5pkjNFoBrSWRrMUkAA2E8eIiG+cx2/DJUrlLWjU00gkmDxVpE7K0QBybLLw4MpGrVj6hVw6iHpAr/SmklqK2WlNxwkx0mlSjzytTYJEjpkr0oJIE0+NMLv6GhWFJEpFjaDhRTpDa5Mj1rhslYrpFmqEtr/Junzh0LpeHdA4vGHPZgb/fiHPbFstrgjBiokhJplF+HMwe1HplKU07RrTll1kzAwlC0nrVB17ZIl1QG9aoBmJ1firHCZsFQ95oGvIPNdezKVOtNrb7JqDOMx7fA1A0KP35YsXL+BmzJAnDwwYLFhsPFwoUIsWeTZs1NBYIyE4cPIe8uPnz19Hgw8bEux3heWVfi/7yasoUqDBKwYJNnxIUItBgyIXekmYcCK9GRgXPqzoLyQ/gx7BvaRp9KgNfw1JYgQqEOPGGjZYVsTqr5/HmgfBfWnoseHCGgRv3Hio8aU/kRg9GqRH0mZLqAvj3qCHJScYcHHTGtSJhaXAmAsv2ugocCFLjeAECvT4Uh5B/7P0GoIpSlKkx55eJMoj+eUw3BuHb8yI609gY6Va6PajV5EtmJczRLIMfIPfDS8vNRIkGTmoF5aqyZYFp5FfQoOc5ZUFsL1yy34at4cX3/ClQH47vwicoYADkTi4Wt0q5SaOKE5xpADh0YGDhQYGwBFJquL8qWGshUjqh6SxKrqiIaLkkU2oGeb6CqOIFsLooYRqQK6Gil5SCwyNuJKMLy9EIughLCpKqLHfFIpwhop80ujEfhp6Tp67GhKIJ9wqGs61wLDYroaFrGPpIYNugAmxhIwDh6V+biCIJq1uIC+mhjRaqCKl2mJItIseQkoelkbyp8EraHqpKYyqCm3EGv/88ajCijRciKCFuOzuJ35Cq6tOhLTMy4svWLIyzZfAIEigBcHwZ8/IKJKToIQULPCtLx7K1DPMdtOCH4oq0uKlK1ZD9AoVC0PQLpX6gRULH7+oSKMbSOMHIzxt8CJDGzxiyYbgrtDoy5eyfOkkPsFgkjiSHjrLVoIMcstA9G6zi5+KKMtOpAR1owdFfmzUViWNNpQty7gsM3DOGh5arjuPMILpz9LAUVOLhIJErEmY9ERUWS8eLRCAqmDqRwt6xGt4o75YGjcpLAIzqOGLAbChRy8IaMCFOEzpxZ55dJEllVQ2oeKHHTLwzwADbtAivCovnQG7Dvvp9Ds629Xrhpv/xu2LQ373neGywhIGFpxdcetQpKZcfIk3cLgcyyDq+MExNKYJ+yKhdLWQk2ivZ9QisA+F68eG7RCaQbEVsXDSn+HozsqGbmcg6YYd+Zmyt4BJyntQnNQSSSQOP3xQcIEaVZUkj9z2osUZesyMHq6x0FVjMGq7AjLERQJDQOkSAiMhjFg6nVrn2HTqUI0illxUOGOEqasa7MSihs5F4rbauOgBSh56DNXKKdSvwCg0lk50lCDlrhKN6S/2lofO6AiKXYvN+vnzC3kVBQ2M2IT/SMbYMEovM0FfiipT4UUMtnUbmkuo98ohlEcxzgfDYii6b0CiqZTJBv34lj9iZxhQ//1pZ+jqmhfKlRDNwOYGGOnLdkqFMQCErVHh0VpLriClYuHMgTnTIAAG1R3+4YRtvgEAPwCQAAu4ZxXq2IUvcDGKUXhCZS7IQAQYUAAF2CBL23mK++wHolgV5DgEuZ1PAuObnGEhQyypXIKUp0A4ieQkuqvBFWjEuqvlxiOOatfhgDMS4Q3lKIxqFgQ5giZ+cE9ygZGHXIizoZht542cMYgELwe8uIwvivQIjP2K8xqWcA9UzfEIn4ilJCoaZGrQ4lWuTFc2a7muWz6yW4q+gBHaUAVf9EgdhbxIj5fgJS3fy81CgOJAF7XuC17SAkmoc5tqnYl52rIRdpxYLyodpv+ABsyaR6BFxeFBzyNdWgzjTHcbC3ohOoAJzBeoFBfeAE9EMGrM15SZOmQNpTaENCS5vrhI99mqBoZzpyizdwUCWkVfhanKLkNzIldRDwtcwtld9kcsZRqPRoqzZ7fgdkt/dAcj4qGZcbbDGnCEByK9ash21ClCY3EQOl84oSmBJhADnZCkGySJASCwAzeEAhfAAIYvZDEKTURhBx24wH8KIBMtFGk7qNSLSDfyEHAoMSy3XExcSDQQgiTneeQM1V1EicAQggMLgdMkSORkuUOp76c7IQk0fwMujWjlNnoKjBYuRBxAxQUoEU0qE+2IoPZRNXPbul08z9O1jgyVJMn/JAlviMXO4alSbcXs61R8JLypiY9WFWGiR56GBbYEE6r+wFxQ0JjFpUWEMfJUZmRyl6tt7bRr9JPRGNN0Osm4LanYCWAN5FIRZikNl7h7JN/+lJlkPq932wsQP3QqtaaJa6vU+xlxOJu+71l2IXMdikGI+CWKxQUjJeJHYZqJM0nBVX3AlYehnhOmAUHlT8nxwiBf8xDm3Q6ZalJthcDAzuepRSCpeUq0lCrK1IhnT2zdTpYcMoOS+hZdJSWpF8xEkIYJpjhq204/DAwAAkDABVPYRC588VJZhCIORGAZBBRgAAFIxlbhaZFHupBiFas4drCjZNP0RjabhSskuyRa/2Zsmc9Rwg2kZpJabzZKGmYt5JEORGyXvouQ2Qxqb4Y85FH+uzcmXad7BNuNedCZk8aIT0CqXWVGHDPFmTilIVENFhsxlbMfTVNhaFUaFyf3GckOlbQeyhQBT+UT2UkwqeCaAQibB0LHcgq/1VVqm8dKT/cRMEeXtKNBUgVOl2itKKKrzgxUJNml1oDKnKSHRhqihU+5iTDqXVVcqHRAQUFyVHthyqwyUz2KmarNcFOgbDvzBY2Mi1vEbcnEGrJV4tyxIXHBdC0HG6OlLJSXVHtjYY5jL5+42TS0nhAgVRRmyw0mPCC0oEc32MwbNCyMNPaigS9KUquSNC5BKekVBP+8nQAw4AI/iEMqciELWqQiDlPwAQcioAAFEOBgZVOweFTMhRV3oahl6vUR211f/82AezvLdPSIVqaxqFOnNetdeT4Nr7r+Viz8Y9HCqILL1PQrlEREL3Xvtpsoc7PXOQphukrXrMgmldO+ys5jpjqlKgeb1D8a7E/19OVTgbHMNsA1ZEj1FD3bfKwaUW1Klp4uBCsNv3GTDtO2esTKRWYtdK5V0W6Xzrd+T3i/0hThRNKlJU1olVh+3HQWA6mgt84yfYFer1j3mgAaMnrlWg2W5SdFwEixylu2TVVLQjn+2SCeze3HUCqEnGUHm5pRFd4IETmpD1mEb2Jp8fUMV9v/+rpRuOCgcjDjq9Ay1qrbowODQ42U7tizzfXbIcnFqHjCY3NRcr8Pj2vEZOjoSFCDKPVBHEBhslSIImU7ADEBBCBJcA6WC1KMNwCoYqZNwZZJ1Ivv0iGUr0GFyGtTUxRppwa7qXTHQQ45eUL6KvGiDyzu8LLlXFEOkOYmMDBi3ACggiivIErE5kbrne4Gkdbk1aIKlhbjM2SLWGzu5NKFJ3TE0I4EOoiPf3orfSpHcYbCeMDleRoCjaJHumzC0BBnhCYFhMDoNMzE9bbO8CZIjq6AVMCJ0HLnxS4ElJAEy+RotMrur3QkjPTHyhKNKoRDNjKFZrCMK77qcjIijtSq/71azZ8E6NLgBtS+DJpID3mO5uyyy3XM5CmgJyXIpsp+akFOBNc2Ao0eSUto5eSIrG2+AI1kJjy+Yq8EwqEMkAZoYDtsgAbAzcqWiG1oQLTghAaaw7YOSCskkViopSEMkQa65jTkYTsMUdcoKmMI4AGIQBRkYRZmIaZSxgcsgAFELDzAACO4QEOUiQb25vsaZgPXRZvixR/2pwdDjmmGJU1GR2mgiSSmatM2JHRmsRjb5H1S74ByRf38yU6aDi4Gg1rioqeYJjRmKShW5yxiLlFUxc1qRln2sHNaAtKYrRmvqw+9q5WsAzWESoTcCVDGhHBYJ5gAKTLuDjJoh/jQYv8xHkSJiqKYZg+/VkV9lg0m8E9fTIvKbGLQKu/zgAZTWmtncs1yMmTQ7ObV5m5K5uvFlOkwFi827E94LqRg7EZVTgmXZqm9omNgpkY6CEcp9sevDG1/tupmdkd7SI9E+mq7NqRzDG2OzAdy3IaABJCd+g+bVuLBNIt2GOZgJirWQNEQw2MTvfIrwTIsxXIsx1JTUEc8aKCDfA8ADKADqEAW4PIUqK/DOsACHuAAvIALuMAQKQ4cvhI0boWDyqNLYgblVnAAHwOypmZ0BktQHmWvZs+yYm6ClmhiDOVhggY1Nu0YB4R2ahAyrIlutgOk8ES4PuXPWgJ5rGd3iMd99KX/c8yiOSQpQZpio8QEaxKFcJ4OlBRLuXhl4gImIvsykuDNvuyR9GZJLJQI1MBghCywaEJKfNpCTlAiUipEPSgHNukKpNwQ1ziIcD5S9lop2vAFTqDSu3CmHuuCmAAPMagKqHRmOiwEhHoOlvKq5aZCWYQF8I5oI/bwaZDEaDbCfHrteVIjYdxs12QHdugF8JCp7SBjZ/DraZjydiCvW5DE8rbMfubqEwGgIBaMBrzgEAGgvcwDAGigH2jg6hp0rhIN8CQRJhrm8maABp4mFB+FHrzSL7+nK1+CO+ThACrg+WQBHWiBFDrMB/QyxEzMEDWCE1P0BgzR0HhjSOiGggoT/2D4ph7dTDdFZa5OCSxaouwKq0RiZ7USpoymToqa5pR6B27McTh2ZDR3CfKIhkvma7EKZoCm4sXOrjqnyeXaDTLMJi6MS1H0cc3mCllgToIQ0NIcSDgCg7DYqOU+YzMMEoCG4zImB6r0YoxyZ+Iickl+0HG6xaq6VHYUJwEJSlQia9MS0CW1JWtWogalAzttokkKYzhMyxwf5afyDqhi0m7MceImZZpaDpBmybOshX96CVOK6jIg69LiaTXAR8ZACLroZ3/aq53i0Z7ublImZAVhaDSVyfjCA5y2MqL+hDuugBOxYEQ5UUQ4L7KAhiVoQAuuAINIj/doIDdKSkq9Mv88FoKiFAACOOAHpqAK9DIOqODfHmABCE48joQG6OWO6CabCK1cvkk2lseK7C/xxuweJWpqVvAXEYkeAtTMEIIixSghoIca7XMFjaxvGhKbuvGFQK7KomqjOEhQYwOVQKk1N01YtBMv9qY3iANKhiOZWIf9II1GCvN0GLViQGLOig0cBUNbZWsOi0ZfJDVH/OlEmM2qxkg8S260Sg6XLM+dYG0/V2+JQk1fT2mWAoepXMXSmm5FRjWcQnIA1S9d5AIqYMVZgIdW2sIdDWVOcodb3LDsGuRH7Gbijmj2zi7nVnNuEYTzfspNcgM7IwPXaPAe1bEJLW9n3GgmbBFm+7D/pMBtO7AAF7dSEhFxu0S0Igwxg05o9PJ1okYzPIARgkgUAOJlRceooShqYFGUBvYVALxAAfTyB6KgCjShEzrMBRpAAQRgO8oVRalnE19DNkQC1SxnQ7ulXSTStJDigIivVJDM4VCu0qpzlqwOXMYsVKgMK9gPdthEW6XVc3jJAOFIaiSOQwIjOszGgsanRbvnfEcoXrfuNnIRbqCkTibobDYiVfZEUpklMDAykyTitQSierg1QLDjp5rGKi9HQ4VRp/7KVVcJTkeoJUqERNhpbkVkf7ikYNyR/VTlVyB4DE2JV5jTVmkUMhRHMEzDn+ACtowjuszGtnSNNWdTW7sj/1Nmz4JeWGeCg53cVLjcaDVL5KvIFXHgCMuC6WozSzIMBcsUqlxIDYJ7iWz8KXWYUlsvJCH4lb+I5l8xCDwypoAMMd0a5gsUUVVgtgbEowYJw3nP+Cr5hoAOmXEsNi2Hd3khDBT1cjsegARgAAmaQA9QcQ6IIOAMYAAaRl5HOGg5I56OiDeUbopUFjrMJHoqB08iqygrarwwYzHciCcuZLVQR1BuR3NiUJ5CsHHSaHx9z0qHQ2sCRC68DrbyipDUyiFFbblERIncE9luB1pKRLoQo2byAk/U6ikGUKkeRZL8Lj0gpdoIqykwy3Caw7Sc8VKiquQog2vfOKNEqHWmNf9EbgOQoMKfgnk1b0sq3MjQWPVCEuRPCnBvBrBx5KcHz0qh1KVKsCzXEKc8lKu2TouaXk1MOI/UDKfklEOJFkNAiQ//IrOvCA1GeIWizSfUnPFETpqDWFceoXYqQFYqe20Nfbd56UWRiVdHDXBF+KheatRDBQflVNQNlQduLhlFL8YvR0ixdNSoUZSaHnGstpILuJort2MAXEAJwgAOUiEU2qA/RCwAAOD2MCYit2tN5kpbSsSJFOWn0qg3monzfrGgYy9i8vaNrOrYXnm+kgkHuadfQIpPkCowfDdG1MpyHoQ4mqzdGCUu/ohylmyj2CI1im1nri2jDXWKw0jYOkr/LRarelrzjmCDitqLGMW0debQbjYqM+5KLRQ0sooR5Bp0tTqQfpSICX06eZzOnaS1n/iHnZDCHCWphXNtBSWui5pwulbE1T6lWgKn3c6Ls3U4UkoiduoR3mrnMXiNg0LCFktEKipno2gVRpBjWvfTC7SkLxQoOd23VNzsVM5CUOy7cYrSytSJbfmmN7JQkjzihDwU/CaGRDdxdsmaLEtaYWhHRGtkgxSqYGEX8tA1rq+SePOVf7kALcl6XQGgATjABXqACKYA+lJBCHagB4aACMJACHwIAhiAAWIven5lJt1JiVBz9tZi8/gHlZBsBWcJOwvL8qS15dZEdf60a+Kx/+lGOEDdL4zW5gCrRCcmKFgCY4qYlmZuRZrvqC/1T7KNC4BSEJucOYqUyya0NUGqBL+oyz0FJQLxVkbOjr93JXYmBeqIKyvCbIwiJoym9WeDeVhq63YUqltIxOoEVLXyipU2Y4wI6Tj2E2u0pUmMR/F4SSXohUhOrn/2SoHnJGA6S5KUorccBTSmtbe45YVx2kwKZsbkCLqYZdnSaBwHfSwU1NIOWyGoVDAhJNG0hXYgq11sKSj+8IVcR3Q0fCFItCOM5JEwCoDzdRP9kizBcnV2l066MovC4ypL3JFVQjzwwsQBgAEaAAIywAXgQBXSAR1qQRVQYQ7cIAzagAiIYP/VE+aEEay9H0NbirImolJ9pJL4mmrGFOfSrGquLqTpvARfJElGBAWyYoZcgeKF5fpM2GVnaEVC5CLTgm26DiOREknPIm4gcIXZYHaCIuvk8CU03NbrFoewruBnPmXJmFuYmHNQkslHBuQYf6pbimliSOTFLI8v5CRaWXe2wlRpRG4/p+Qmq4zIpolUlGlQBcOJjaMj4uIXAyZN3PAuuCVoK+g35LkJXRVuVgN5DG2KPwUoYKQ3HAU0SQUHA3RKUkVbcWw1y8OV4Yg88OuFG5J/YRZcxoKwxgyy7JP4Wgl6CpTcG0ZTnnQ0ZS5jcsRIPpB5p7jcH/HAlFfCCbbALub/X8W6qT2UIL7SxKua4jg5AlABFeAgAzJgxTmgBxbjantpTayMWJZYvDeNbveqMmwpVbSoGFEiXLEZnfKRXJCnDV/97h6VfzgVOiPqV+RTQQ0p2hAJln6DrVxEG3M1aD0iS44oVBxIJwLrNOqPVv4caJAlO8YCSmTCTFwlWOYLydQ4TQCiRg1/Nmx4OehFIL2D/vxp0cJQIBYsBwv26/el4MSJ/Do2lCdvxoyH/C5euQJSoI2HBU/So1dD5MGHIif6EwgOnEhwN2xcbEjvxg1/M8CAkXfjpNGeBr28FKmlYMcvX07aEHiD6o2LVMEZfXkxp7+nMyaKBPOwYUewRhtO/3wo7+DULxz5fWk4E6KXo/Ia5uy6EQvIqSDdYhF55eFJgX7BYWmoFEzLjm39dayh+MpFkPJYFrQxkZ5RkSKpwqxBlapRgfJ6KAn8UsvJiSABAOjrz3Y/GlRtXzH60Lbw4QC0nP0p/CCN0l+I2/ZHA7ZyMDO4cOHdkIZ27WCGe9EuUvvwsTCd28jgYgiRMG+EyL6i8GBgyL87frYB8svX0zm14N0LxkuBYfGQTSel5ZAWXyDkxUQCUVXDRPfl1E9OIhUk0BUvNdQQVqPNcNFBZWFxUnMAaNZPSh1RJllBq9UAhlAz2MCPUAsmxJo8XzAl1A0O1nDFDS/FRps8m8lzBf9VB4lFkElX2GcDPSnVUJBQoGHRFU99yUfiFQ2RRFWHNaxV0EM5GfVYgiTxs5GYD32R00GmSYnkivwwGSA9ORVE1RUF5ZQTR0YVJOKMEmGBYQ0gXRGnFyBJlRVSPfITpWg9enaDQCK9dONO4IQl1EQ3jKTgFzBSN4Na/GhIz5f++eOFVKvCh5pqYFxUIFQPvbpnholp8RKKHR1kFF1YCMSYPxtd1NFFp8p5Y0N8RTYkUPR49JE8OWYoEIMdaXGmUWBctqE/J0l7FBguuLDDX/qBxCw/tslDw7wb2jboZ/UCsNa1w2HBz0Eg0TQDDQI9pOkMCO1GFRZbPLyFdcttt1z/oyfRwA89+wqnsXby2OAchQI84IIQRLjxBhFoEghRvLNhcVE/1orUETicxdXgsfD2A1Y/APMjk6MfDxvrfQWybMMNgz1UqH1ceubUaTb5s1U/ImX7Bac72tajUBjJyGSFPF1xIX5CgbQnfn/yJJQ/PC/kRaBC1dBPj6h2nSlmWsRs1gwgvbTiQ/H2U0PXQWW8X1jgyBkzRqnxY21sCv7n7EAvGZWX21aD2I9GMK+tKxgHHQyuY+V6FhV+IZHaJVWDzaqwF5F9K9JFXvsc2I4PgZGTjxNdBMZJCPmOIlb0lCkbrruKBGSSX3Bl6kGKx9bRRJg3CCg4BW16vEor6WVD/2r6iS77SSXC6RVI1wc65YsOMihQzC7ytZNF/SRe6I8z1OxYl1cwRBDVFYgeFuAADGAAANeVTR62CZ8XbFM6AHhBC2DYWA0M5hwA0AA38iiScihGAzCICSEag1jEuGDCLYBwhSwUj3BgtjmW2IZkRJhDKEDRhja4wCgdAYkXdHWgvfEsRBPUwqOSpTjyzelqCCLUD9EGjuodi3nN61etHiKgwJykbPzwoRcqt5GlUA1vz7NNziwioc+syHYz8kk/vEAVxeWkM0JEGN6yYiymSbFGXlNali7SlHP5IzXqC56f3Pg2wJWkZ2QpUaFutqv9yGmLM+iZCP1xvd01hGaQM//XFbBwvYnESXAxQ9uaAPhFvh1mRo3Kl/UaN7oaRLFGv+qIl2DlKKbYjnQPueP3BtI1ZC2KWJdk2knwdCsW5YhzjctVqjQXv378r4j8SxEdrWIDspGGf7+yJBiq8iR+EOwizxTkSeRxmRoUqh9HY1I2UYIbHiKrBsHSEPeyOKicXKADB5zXo5LzQAB0zDbVM9FuaBCoIdHjYq0CAMs2WTCbSWs5bVzdRWhgHXq88Ts08IzjJqId6UmTKlCi2L8EUhKfZYAIqFAFKHrAAAOUS1PQk5X0JiK+mukqmjqZQSuPgk1S+hQc9XTJW14VJlgJT2j2WVMPjdQQPn3hapXrCd7/vCAU22Ahe/RIaqO2mrecLGZqgpRHoH6mJFk99U1Mw6oRQ3ID3tnMSCmKpUiLFi6J4ARQ1gMJYn7FGXeRc3+kjKpBsCUSzuQLopbhB0pXJL3+lG4nPJzVQcxnqz5dllVfuYgotXelV60sLEu5wWatKT2h8AMM1otRVu7YkB4pNKnqy4xRbiaPfPFQVsBpKxY1yrNwicuQdxlkai4LF23JEhy4fNAX8sOk0BxPeZnxbJ9QRBW/oXONb7QlrXJCN8LFZKjPxRkpQfuSg/TMOJXcARKQEAIFDOd7AQ2OBrWTGwDoTTkvEYhRiFOD4Xilgn87HsVEcxGJwQ0z2mledNCi/4XoXIwGL9HRF7BDA+hgli7C+aRg5DEERJxjDg9ozJyGtLtbiZeQygVKTX7GmLPe07Og/CacasdOCo5Lss7loVEsNIOTrFau6ezp2gC3qbzVxF30eM7rDCnVguCsbloDh5Azdpqs0aNvF4qXUY7Uxn6J5AbZw9YZf6lOM31KvLM0jPpa8htxHdNob/1COvlHj13pOLHgrVVDpHlIQZ5YSTmLHfDkSlQ6zW9X5rtlzDpStkD9OWZogmJc+4S18cVZRjPAG0iSMjouXbm/eo6MnSkVPi3neVdzvJlkZXK9euYniD0D5+/6UbNYFjLH2wtyks65XWVO5CQX4VRmtJbSev/OxVTjfcukswcGhayaihwCAxGmgO0GtBEcCJjAB15wwHAbQQnkLkMalCDTjtIAkCYCQEHWTVJiAzhbjeLCZlnIP4zhCKEv9lh4HMc8SnpmOBxwARGE4IIIKCC3hjw0qkBSJLFB71uu8q6HsVU97vkVRFSB8yYHxt6NA83XL8lJFxleyKHeTEBzW24P3WdGChGurX3WQrfKB0isClo/nSZi3VT7pvIyhEmmMa3WPnQjshgonGDmH9GUm5eCAE7NOVLurRHzocppTnIQ2hkgbbDNlO7Hjflh9lQreZr+PrZyQpTydrmyskddRZGd1siROg52TQlJSAv9ll7O5b6NnHP/avd56E0iJJglMReXv4Ie0/KqKCRpRpDQIo+YgNJnk7Opcb1tEZQahywaTySVBRmheo9XKZwp/k8Nw0KFzdpbVyUWQRWIQAQKEIAFhOAELOg9C1aQguB3oAM5yAEPfMAEJSxcRwDu7XJ+x0JwAJgLBRouxkp/hXXjOsMEER4NsAkS7azIUwst3n5B7g8G9KANRGD7q2qlPF/lyVb9uTnp7jTgFynL9fR48dtctRlcYkXq5CtlZ3Pq9EOkUn7RdBA3QDQ/YmWMYmUQNDXFVSTbMxdcUn5j5DWxMhQBtBbF8jy+lDRdsz9pVBiuUnr+8BfZIjmd805Fol2uoiqpIz61/3NM/cMhxXYal9FbBJMnVZZoHMI/Jvc69QQohuUgO2capAQ9YfIT/lBgCFF2WTFy6XQWPRQqgpM29NEq+zdpuyNZc5Jq4fJXKEJjUbNxm5EwrZcfk0ZEi0djIdJrYKZnPWNcmSFdLBiBOZFeQRR0iHdUopQtVPQ8YsNVvyV1ohFtEBJgwqEAFnABGqABG2CJlHgBk1iJOfADObAAAzAcFNVRBzh62ncY4ndoq8QFV9BhdPRB5EQx0HFhB+aH10IDACJdl/U6v5VoFSADj8AISgABRBBlNmAhi3JIZSc0jtgQ5eVcHHFdagcoUZghpLE/qDZeXMWICUJoF2ItzUI4Yv9lPkgxJFeDS3OVXbrhGcw2WlazJpfDWlmSWLDHF4/VXKdyVFpAGV4jWVoQGHDhQV7wLVRhJntHZqBFME3Bg/cheYGBIS9CUpSiSIy0iE5kVotTNE71EnT2TroVPPlyVrUSGZjDZF2FPrtyH7eVVFsxKpxBaBYhVcP2PyI4XG/ldzhlZ4niJhuZatZjjoZFFM+UaWfXOEaVJsj4Pbd1kbVCaBWBLkVoK43Tj6RzIMS2OaHRL+GyOj9oc68nUpzxetgSZ7YkHDBgAeQGAxyglikQbi9AAiRgAXEJAQDwRrcoftBBV6IUQmiBUxvUQRjVUWnDQADwETRwAxTzQdpRJh3/QTHOUjGgdSgjgjC1Ag41IANjoAZ+QAdmAAOOhSyNIpK/sjLj5YiXIXN5cVw4YhrQk15OMWmt5QVi8iG1xV7Apjkpxjyt+RNbkZDGSFTMMZgddCz09FSnh0v0JnOtxxh2cmh6MlfpVG13Y1qh8mFzoRdnIS4uSRcOGDB1E5EiQh9puBdTQmieVBlX4yvNaCyr5CQtkj08eJHo4ldrkkR4Jz64hn8c4hBr02jwQkXzRCtSd5Djkxlmcj1KeHpSBQ6uwxmtJV4A1CFlky02AFFyh0ni0jdYYiwQlVj2mREUWnaVASg3oxGSA2SVYivzozrmeDSIojoe4T1nBUPnE1mz/wRkoPViyUUjARM1NSABHeAEggAIfmAGT6AEUAAFa7AGSJqkd3AIgEAHapAGg9AxFMMk9NKM2SMxAZIdr8IZxCFe2oQxt0hTOVZyCAUOBkMllaKUygUOFBBuMbAEdpAHkmAHS6AErMKDFSFtbQgni8hV9gOb83F4+Wlc8Eg+QIYsLrGAY+IkXydAWuCAsWWFfLQT9ZQj4fQlunEfUogzZ0V28vB6LZJTJbFZ/ciDtnUUd9YjO7I62SN5nDQYEdkRcSV5bYNLjpM90aRl/aiPX9czAskmwxkm2aWAosGTkHaN1zJyBGlzu7ZRKdVWFeaIPyKq/Ucan6Vif9UhPyIkMf8TR5vTdavDHKnkRRs5FzujEZPZKEP1WZdVGRjXViKFapOpn755rzagYj82onbiRDlGYyXCr41DGgnTWwt6iM9WizsjTqXicLWId7wGdmhnPTn4EBEgAuAGBXbAB8HgscRADNAgsswQDMuwDKzACodACH1ABM6BTrcYUMNhHRrEKlqQQfgyLitSHOVHSZdhJ1jgHRV0dpvVsBNAAkqAB5XgscFADNIgDcQABTKQTeYodx/zGeezGEYRRKZBOnB2aq35KJVBOgQKLHQyXvwjXdjaZ+r1VEjBEljlIyX3KVPiRXzoFxTIf57XSfIRdNYYm+TxWA4rP8ZIMM7FLHVLNXv/USPOVTuKtIh05Ky1yj220yNE4TvShnLKBTiGpWYIC47Ao40mB2xS5REQ1yXyoTw742E6ih8vU2Bdgptt1FjOijC2gzum0VppUzVygX/jUxcfYY1ORCi9VXY8yqCXumUuikUnlrwWdjQiOGAy6SxRuLCkQxaABkVlslcIkhZpo7VeklzI9lfXhXZnNWvwFE3RhniRIRZqd1t7EAzGwA3RILLQgA33Cw3FIAyVQAl4gAdrgAZjAANxkEHakRO2MA+NEAgzKxzacRG2MTEYgwX7MhCHF7PuRqH7Z7UFdlHQYQMhZFIQIAJQgAfL4Aze8A3fUA3PQAzHgAyRYAIikDHX/zUlqbY2gVE+reY611WLrJUm1vIqDpoiQFYoM/BYrYkhZVd293NJrSUUrCJVRTQ1Z5JOwSlFmuN2aVFIVvkFqKMX/SOTe4WwIghxjaI+cXUSN6CPQ0Y+ARtE4SItPQJDLBcUWOBL8CIUAdIjwiptt4RxdqFWnrk6zrWGKwNnpDKPkKZn0SScknNZ3qOU6nVsGyipfrco44SfKnZ65CSepJEZXFImqjKbsWQYF3okB4KFQwUg9FFIwoo8DHFes8SjdbEpWfKSFtdNGsUlTGKj/cMahdKaOoaTpSGAqHa6RVM5Vhuu/GAMzRwMkyAJkSDN0oykS7AEa2AG5OYCPXCzGv/EDxRGD9aBD+pAHNAhPd28WQZ7EsOxIqXKJ4dpl90RAUrABonACvtgDc5gspXAz5UgCdEcAxKwNO/xYn/riL4SZ0vkU6aLLsZCTL+zMiG5cbU4IpJTGfoKYVPMrwyhNHvVmuCQYtL2KrVjG1LjekEHjmo0ZFDxqA+qeL+FS9KVMHphumnMEULxhXADchs5ekXDEYUznZnSEw0Yee5CPlf2ZTECZI9SF7EUM9kSetaEbHeCY8aaRzqGEI+VFwjYN+Q0hT26ppWiZ6ESWztyK5lhP3LFW5KheX2Ws+KDLmMyZNCDG3OiFJx0PhbWaiVCFlCyvOjzWPRkklNTvhEoId7/1ThX8jLJlSDShivXszbCClFup1z7oViXRCGk1Ho0MU95waRKis1mQAZkoAQwIAIUQAJqGSvM2FhloR0SQxWhkAoF0xRVCiQIhTEl8X2Maj+sAlHEITg3qwAQkAGDEA7wsA7kAAuOUAigDdrW/AITUAAc4j3p63YihRNpJLfiJXdpUSi20lhXZnK9JVVoR0WxpGI6bCvOFVeMSBYaAW18cXZDYhs9tRitmSxbBdJ6EVVBA2wwVBe29B89LR+lKjxJAXJxFZT0IXXDxa9rdlRyAWRasSJyfXoU0i2+syn+5cN+MeD8F0jhhE373Z9G8mR5smaK1EOJcmUyc6EX7ROk//QSDogsVEM1G3J6EZnBS1UR3zuPc7UfL4Z47jIsU9JW5UVEXRGuaYRLWyyB+MeDf6Y19PEeYkJOkrMzaTI/FaGvnrIYYyKOTWO6eMesryMtQrg7QbSI1fM6yMMP4CYD5FYExZKCaXMjN1dhXIAzhrlu5kMDGI3SX+biF3QaXD568bemciUcU0UxC6AADQAK5dAO8AAP7iAOixAIZ4AEHGABD8AABDAAA6AAjiisQZ4qSf6h6eUZNzecORJH3MteTNzTv4MgG3dI0ghat4R3v6OfkdIX/CNUvaV4WFYz83I0Hyom75NKgn5qjcZDzOGv0RQv/QFFSbWmWdFL2vMj/f/yGUcjLfmqyCM9VGTWxaT4M0bcPJKaKXBF42sKg3A8SMgY346RFCghFJV8crPOIJFxFQjoOYnWa40xKcZ7iGJDZgNpdjdCX9QkTl/+qNczWD+2gmPYWoLeIvv+Sbqinj/sSYbx1sVFXXsIK3U+S6iSMJC2V7IpQs71Jlshe9eovinoQ1grgRrqJDtBH5wLLmLSeuRjjmFif3JdKjZgHeDABfcDjxCVXt9cWcTG4b1jMG6dJwCHdmEyGrGoHQ/AAQg3BaXwCq7gCo/wB52+ANrBEoUkWeVTtbmloqojICL1dXmyHweiedjp4i1OOB/SaPA36JEngXhnoNQxoFOV1ir/hjttZRPfm1/LFRplp5/8N639d1ZE86Gf9Wd+J1Jv4U6q0yWdthSdAy1Ewx+JNigf8lX6mcZfSCoEYx92/3OSMip4t13ZgjBcbitpXJHvxHdV/VwKFbjiWFUuP0Zty+Vt5WvWGDOKGnnJ2547Fr0UETfmiDwu0hgQZUjIOHtCNFp/VdWVY9GNCNiIF6qKp77xwhqmZizoMxdlc4i+olldQx79o6yPoioLKTAWNhVTw5XhhLm5RVCoBhDgAAz0Ag7cjBlcFC5kyPDLF3nyrlwBA8ZLRRr+tGipUUOLP38C6dHTQuNLRZAPsWDRWJIGjX5evNCo2LEiwpUPImR4oYCG/xYsL2kYfPkFIb2VCPnxGwlm5cSICMF1NLhSpsx+T6+oxOIUS8SrXq7YsGGwoswZIycmnVHD4Mh+/R5+2fhwo0GZTOlNJBtXXtwaNzrK1NIUzNKK9CLi9ULWhsyOWAZ+ISt1Zd+YhLUsfRgXDGaQFQvTk0kWZL+KFSNOxGpXC9mJWgz6u8H4xg0sCMku9TJyaVyZYEaa9hfRRsSVgsmudAvO9dIZIDtCjRgX98qNbWtE3Lh7Nz+DZG+Difhw7Up+tzdqQY4w8Uh6NpjXmHi7H0KEt2/svcKv49KNUErpi6ViM6ijuHbbaDFwNhrLhrgQA8Mf1hpz7CGJrkAOi43AA/9nKRCZei+ujbAYaSWDultqpNP6IauG0o7LEAuZUtyMn5CmqgEkkLwoEYu5jLJJOHoeMuiLiSL0b7u4HkpvvxkeWo5GL76IcTgIZVrpoR4NCnEpkPipEZzqUFtqIDQJjAyLiR6ihyqFalhJnorMo4g8ebwA6QZ+Hsprt4FmiAucG/Ab6Qs0E1UUTYSSdLHNk/TTj4b0aJD0Cx5zHG2kNaP8YqVMzUNosCq/cNGGiqCLrsM+kdyqwK1cBQekuuh6La6VVhrJy//4iWukGaKa4QYvRp1IQoTkAdMfqqZ6sQYwBiJNrCt0nC8iecjaSKbQANSiopXwBKmytJatacfiJjL/yDgbMPXnixs2whVexsKdzZ+4xo0IviI/7Uie27Ag94oTTaQnJbCoBWnaeF2jDFU8dT0vOYNtgKufiEBiiTY++cGvI5xy/RDEjW7wxyIv9gsMq35wPPkwfrQF1R+0ZtBxWcAQeqjZfrAEaV0Hr6iV0x+39XGjMD20GD4nfzsVaZpHytgfVakN0s+fD5pBJiu9WI1gesDZ8uEXERoztm9z/UoehKjmZ98bRHsW5BlQDg4ML8kaM6IaluXMXSyWmk81si5WqaIKgVMN29J6Le/hGbTV4oa5vJhOQzontEE9zyRlNWWDwKkIAL2uAs6Ly2x4OXTKv5iVY3bVXvshlMv0/0seiLAl7l0Cl0QMM3BZkpw7LS7C27m5Xq71PFeT/i9oYk/skVrPxlWdd8vpu0IyAILFHUkGucpR8m2P/AJYm8usKjedXQ2+nzXNZ7aG9uO+oqN41zyfnrjaDil1xvTjNl7ix7lsoC5wGGZpFSGLlA5Vm5GhLnD80Ip8sCBA6fSNH8XrDVIMlsCDFcpm4PCC4IDkqn4YZCKNEgyX5lcwEO1KRx3Zzg0iIhh5YKkiKeoV7ZxivXM1aDRsYZFMHkdD4hktPjYIEJ5gRsMa7OpZbnpTFK9mKrnQaj3h0cz0xCRBzHDoW0Ri19L8VMK3uZBfcmLfzjqiMSzYCDVeWcp0Dv/lLjfqCI5SypTU5NGSpTwLZrja2F/k8qlCysNgA7IOsVLHIrVhph8IlIkIScQeedSKWrrST+QOCDZ4gU03m7sNSBp1BcBQp06As9GBtqPFZ81gI5eLSwrBEZeJ/IuQgKlB7payIbIIUUlzGlCh+uebt2WIWsSbyPYu2A8tDIRNzsRcWHZTy8ys8I5YoocJv0MkkJQpV9j6npYQdJ9h4aiB8GpOWhSTSS08RFCH1E8//IG+DdEuYb8KW0eUuBfVEMt4S/MMzE7Wul795ixeM+Qt7XkDsoBBP06MGAFPSQ8bquUKd/sCqdC2Nome5Qbwg5Ge8IUxrawEOE6rlejAcMr/0IGjP/xoZjO7QqQmguFYHtuanmJ6F7O8tKeBfMxCs3aFrGGHPYXrl1/O05GRaGFQrzsjSRd2MI28MUcIuZELlyJTzAXVgyxbUbLk0RE3Qm4GuaRPkxCJVpUekizZkhEuN2oQhEx1IiSJ5/6OBJIrdFKWEVnREusGhmbZsIAwe8s7KZirmvIPmpi8DN146b0E2eCN6xkj4u5nyhGGiYCAvcIM6ggtmEl1jlP1x8z+9UeNTuSqHpRK6Eo7QmOy1m3SERAhN6jaGRAkXE4J3mu9cEkt5eYmntwhejyUGiJKVluE5Oq3NmIYeAnUOjrq5m8bu9jbIJOkqREqXLIWshro/42hcmtXKLW0TqmVMkdhYWBjZaiFvApwpBjbD9/YOEaN5qWSYdPaz8aEn7rhD0ki7GXvyBm66u4sIgvVEobOmt6l9XUiwRMiMtfZ0RrYdaMd/E72hMVUJn6ER0x1bMyu15Lp2fSmMQVh0PDKIJYp64slPso7cSITShaEVOyih3tghkwy/TFH+VIlYt2ila8eJKLH41LT/CkbmdpInsFCsHqxYJ+eYVRD5LKOfnEbN5R57z3GyaywEBmex+jpXEOqFlhVFSEB+TeODl2WPNaVuVpCZjs5JE1HpswdGzGIKkQtKPs2Z70QZwaYwhquPXlXg0BZuCVlCqZW+xI3C+81bP9MRSBIvTIzcJBrJJCxHov8YYP01EeDdB3lba5XkXxFzYP76lfX7ASV6qIogbpSzA1is+qCOAeisLmfR4DCEqMJSC/TSwltghSyXKUKRw4SqA+bHU66tqyAth7pnbi125rxWrJ0C83iJgyzrv25R2q73akXyawWyRCeQSqswGxQ2+RRaMQ1mnLqkHpS19ypaOvBcGdIWpaR9VgvQ8rLLOlnWo/NScndUe+ySqPCZt6EgAW7sJIE50YTtnbPa8KloePMNY779py1sqGSiY2yTIlQlujTDJWU6llnBnVkU3OTtqEIWLR2zSbEYwu6QIsY0GZtLjvFTsL8GByNX/dgyVn/9or/fdjlfGmxLm7UQO6J1etpnVNKhRspnUs0/GqnL6tsmkrVJiGsgrYpnKLcfjE60eN9RkaS0xoBh2vvYYbolBc+7X7uVNPAjsZOQa3cccVtJQgWtoNx0w+bECwa9uRlv2zN3pqwgh/9cAw3V/6lbM9+FywlHWxQczU9lpudYNU0Ycm28GpY/1s8ZdaVcU/aYsxWKgetD46WdmNS0vowB0HNwks8d3kQbCf8nPGXBb2kGO9MUtcleUhfd726T6mxFgVJskZk2wjFGCQBo7G6p7pydpAjqf3wKz1EdRWxTWk0eYEf5ZsfGUmdlBiluVIyMMgsltswMGAV6JId3imT/x4hDpfDuHtJQAtKutAwC2M7OL5qH7C4I4PAlbuJJgCINBJhKgFxp6xZF/h6J7pao176JboaEJOyCqtRm6DhCBranKiIsjXBMsfQj+/hJ9zLsNE4loLqlWYiFT5JDVczmQazQYcLPlvijnJ7kKrIj2GZKYkiNPLSGnihO6PQOKKKO5W4jvCztRQjkrE5lDGJqXdyKx30FYxRpirJiyUpDbZ6nJOanhQJKuK4JM56jcegG7PikcfBj+9QCqaAROvpC3bpnygJGbfSqZA7HgxbE5QYm+TCOFezxJuaklyZqsISOLU5i4KCpTIhJy3hw6h6DY5zmF9rJtM5pBuykcCKHf+NQjaIS6zbSA/z0ZkfIRcZQRtmOzitapSgyi9EnBnAAsavu7BaoaGLYbSYIKrzyB6Jwo1d0QzOQq18I42pQhQAwKLZCKo+C8ApkREsWYtmIZf90cGYejXHYAxZeja2MgpPwShT/Eb9oIv8K6zFYpVTuqk0lCpKOyFXCRr8WZD2gxLXOy28AKrE+cN9q6Qvm6DPiBfV4pInUbIM6ilwO4vkqKJ4KRRJWUD4qa4pSzGJ7Ixzu7LEgEDBsz1NzK9mk5L2chsTYo7aKsFDCjqtQYvvWqzMURupkD1n2rNrwRZPISqNCRJSYR+lgo4x4cPy0klusZOEEQ3QShhPIcoJ2yn/zTCstUKlOTqeMQmXv3E4LFiTMlks1/MMiUQwGtkveFknzIEasJgcI/w7YwMYY+MU6qg/CwO58ChG71kstliRoDEM/FCfg3icI5qPUdGgeHsdwSEegxgpNPKhdXGjy2EcrxmIG+mRKLuY9kIgnWEXrjEV4vGWCXEbloMfXlquE7ok04CqaYGOUTykL5skzEERwvIQE6IpzMGm5lCbdUEbohKhNYwXgwkZO2GoQ/wWSNEo7VCPZysWd6IcibqRS9ohsbmYCHPDJAkY/TAc/9I9WGm3fdufu7SrJHRArZup83gLOGI/mFEVOBskcXsKYfOvB8Qy0iI0VXEQl6OiukhA/wBdoRFqLvYxGhdblg2jTE/SrItrInWZGemMKddTLZLLIaK0kQ66Mr6wGFb5G03cKcCYsjw7D24algEilGZKtethkMkxzpEQDJCUEL25jWaSFAfUH6MAPcKbRaJcm9qSieDEww2rvgtDGd1TsiLLvv1RMofDn15ZrvYDOQxxFHRUMXtxKfQsFbVpCiNzQHTKIPNYU2pxjylZqNCBohQJEU6hjyhbKIwCK2bzR45rH1vaMtl6FrZzDIjZmXgZlkpKDwAlEunBx1O6uKzhjsB6iLhhjrG5jX0RR+LTCG8JDyO9E34qJfXYL3w8HXWZHj/qHdcbm0FDn88ktNs5JKLsn//gYZWza9FgIiFkYgucexmo8KHLmUoQCSf7eqf6m6qo0iK68cHMqhG946vHapHLaCKQE40YcRoMi7IUYb+YcoyvyQ5PxJPfCCr/wjiHy6XUsStCyy+uOr4vWwpJCQyTqQ2IAjerIBafTDqXYYz6+JHNbBQ3GiVbkaHYOFAl+6LbSRFw+xuwahbrCbdTYaAzopvNgRje8JpR2swvCg3V5IpWIZVa4qxWwwyuWK8EPYrUGVS6qKndHJLYAU5BA6eTwp+em8sQi6vkCifnbI47+VjVGBCFpY7kwBFi+REWtaa0c1pynKX9EKgUczlQ8SzGQIx0zSug4tROQpr9miwCigr/18ArmwmZwqIhYvsziMksYGEZBAMw/sirBAyqN2IL2ZwqTyQqZjMYCpVYgVsovflbg+EgP+IM9TqPOhOvSeJDy0LZrNgzJzMSIQqd74gNo8UTFSOybqoVXBuvM9K0KAtcV6IinlqTMHGh0BVMiApSyzG24QJOKpkqyCAfLJOs29iyffGvYL1N6fSYRGsoCrVEyYqdrkhY71mQmkq/1Ekdjmidq0oxbwkmcLuCyUgZ/WBRtjwkqG0OXsM1BlLCnuKlxOnTBMwQbaQWVVm1bbI9Y8u1bvVNCMnDuCMnXDkXpzmeuHKPHhOPxxAMfgqVXGMXmjEeyyW/0hrIgloW/piS//zqGn6xj4XisC8LDNqwuz2zwb5yrl+CIxj9DG2Ds85lPPBwOEZlyxCxikx0ybV4HMtFp9rSG2zzNyyiNwHUEZ50wJBJDVWDDzxxo1oiFQQ7rAzSFSBjRg26TVWZLnTyIS1wwOQqFW0zIqL82LyyE1XJEzRKGk/yyV9i1ERbHyhqk5ranF25nUNjK9pjNnerkPDC1sZaHnIKXQxjFdgQNKL6LQSzCSzJtagCntvMkfUBFa3aQkJVsR0aEH5QzdPxGjbEoszguLU4LbYQT36KLScFK3H7M3lyFNBLjWbZO520DotIGtIiKfRxofZqrEEUG4PZF7OSn6QzmpuyxABEpP/EVac4I5SBDD+AeSDBcLa+OOIT6ouZqY36MBn/ApMFyT+VaCKexIx7OeBMubK18dGPmMWsKSlsypeZYYsTgiNg5Jc/I7DRuicH5AqISx2d6RVGLR+wGpBNIzQpmRoA/MV+aTBSMYsXBZFa+jAHSx1t8ySQW5jzTND6vTJG0qqpiY5MOTuTeq0DcSG2gpisuRsd+6oPPCGtjSlD7Rn/OqcMMbrwUr2U/ZbklKEJYgz4UBLzgbVsxUehGlAw4BJhCRmI4bR2GUKpMjbXSOLewRWzQ9kgxT8Iuph8uRht88GGTFAI7dyr9Nko5s9m46AWNbS/wQ3CgBVFBC1G1ZnTsTr/18BmGxy/RcwRW7FcbPsRQISVSkqhYaktYlGke0WgWoqrZvkQCEpVoM6dyqKh+WOQdlM5tcGWIAPOaXGuuXg3YkOtM1ohtzotRv1JgTMkNzoRGyQyswLOQwI3qhC2O7oJtLFcl8TfmHiWO/o6O2ajaXIamSYOyckecmGOOX42WE7iszgtR7GgEEni38IZjFmlwLWyJS7dZuodrnCumWoRk+kg0BqnC3sWf8DO8GuJXAO9owlNCHq2O2yVh2G3NfkmZEPb3AkS/0q25MwYoC2VrlkcFpKUOfIzMhwIF7uKdwOyTgoR1ghA4jEez9INT3OMCvRQAmIo/7mL09Hfihip/8IaIVfjFnzmoIiyK6dxGwbxEry2JbdBHwTJoKuyimBVRPUip9Z5kgMqlCC1Zo0oaE/Kj+Ey1aXJL+kwnyvTEY47xNsBk2ehbFSBoiDBpI8FLAsjJIaCFBQJWDaaDSG8uIdJHbToFfRVr/WZo2dZp/9SseHgWBmZaCTKk6Y5NwwUojdq0XYNlilyl55y6WkxqQzqKyzRDWHjJe5gtuMTvLLuFdBCqzv3ipMarqvSGriQ3hamFbYgIMj7jhwBPejVwfxwHXsdJxscvvGFM704F89YZxD7FYuzxAO2kQL3SVx5t+Xs034YiDCTksyelskxHo5zyelplAR0GuOcsnYbOv+62mtyqRG3mipfTuJccqWmThqfbBQRazbLq6m6WB9vShrNuK2V1smT2szn8UHQ458PrCT94Ovqqm7tMKLWgZjMDazHbo5II6pKAj0386Ot3T3aZiJRSxpBhTjAirlDirtGuyC91bEXcSG3mgHnItzlcpTLtmuVqJD5CI2wqKkTei2w8JYk8p8ErDPy+qorS05D0zZuwiaHO5pR0mqeYlShWhxGaiIsditBmyfhmxXGOBdE2kzA7A4025evtZV9ZKtBycvsEzZhgw4oAhYbqi72w6QUFRuLAngT8ZagmR9iUzVXFqJW0bryYBClCjM4eqMoQ0fj0Q0HLKZCASbN2in/y9VJAZGXpHM4nHCaVYzZC3GrZYaPxjKlig9u9aIiz1g8zFZYf8QeCcKlc/Gjqiak74g04nEPR9E4HCUUt5eUrPGu1tGo3GEVoH8ZScFRN8wuYtmpF6lgwTs7nrH6KRtWI7sqW2I/56s8weE454KMKkenDBmSWurtOIN2PRxnTalqLPMftAiLpCMpWLQcu/qzo4Gid0tQoEIQPrRcpp+ge379krqtZ+H1MGM/zmCXn9IiBJsWLcoXQdMqKtnw7GjwZYOKBAxdIz67dx60dbqRxdEM75ka8fCWBD/RBckvZDup76iOoNFBlRiSOzoagAADpl8/L15qENSiBRw4f/5q/8yYceUKFiwKr8iTx1ALAABfbNioUYNeRIEOHYK5oUWkQXr0bPCLyY8hwRkGY2ZU+MUlw5gVQV5xqOUG0Rs2HtZgKDAiP5ARbxg8qNClQ5E1DFYkyPBgjYxKwYj099NGxZwGBX75wtAfQYMTaVZMy1CkvIgGE2rJ6M+lTH5ReYKbaLeiS6gRCfa7wS9j2isua9zQe1KeQJEgvaiMSVShQXCE6TEER9TlUxsZ+0XMqYUgGIURFTqcCIYhYNIZJ6IEQ3JG4ysMvxAEjLtibIx1D/dzSdXfjIwxhZeVd7fmDIfO+WkRaBCk0xkvbbg06PDLxLQiCfoOfKX3FYWXvQS/GP90L731XwRWZFgRTEbl9AgC189XaRFIkGRY6IdFf/TI4xJIJ/mT0QwVdRYaP+f1oxo9LEUVl1EuaeeFPGGJhQVBIuUVmTwTbZcVYhVRVBEWArnnkkJagdOPQGiR5xg9JaZVUWQ3pOTQVzXE1JYXh3FnGmVgGaRQTFGZ5E9aN/YzU0P+MBTRFSKltV1ICmV0E3af8cVPP7D5w09HIsoDkl9ehOmFZgxNhOMMMTkUEV8uVRRRn1PRo1BFhmo2X2RXgFSmF0ZGZMMN5aXlkIxYhGWZDQJNKdJjNTgEUl5x2pBWTOodqkVEWKQm3ZJ7YvgalqTN0J0XE03kEm4bjaoQGEL/KnYDhQYRhddC4Dz1ZQ3EySeSYm3yA1VRRTUnD4BpTRrTDCqpuq2zN1RK14rpTYRFuSb2w2J4r1o6EU7jYnqVnVoi1A9x/rg2AzgiCYRYQWnZqIVg103Un1zgaCuoPyzeegVOIIIBEl/u2cAQSD3G9YVPGWnaWXFy8rObg6VqjJ1BGV38BXMzUFyeS2llFGiS9pq7nmTX+QcaOApZ5hMWPttgKBYgR3tDVTXImR84aa2KxQ0iBepdobl1l5E8EP5FT2+sJRyVF4/FjGlR/DgdGWREpddSoTzXoNMXN06EJYCAWqTFcmrtO5Jy/TgEmIEgifQVPc/RE+IVTc/QEa5A/xFqFURPnyZgQ2mWxJ+LnNmZVpOWIigQUV98XgOBX4g59A1facsaGO6GLCte7XVL6XN9RlStmhleDa0WIP22E0lxazHz7bIKZTfA9eHalUYI85PWbDtrQaw/XijE0LS1i7Tt8VnKRI+wmGKG1w30eNElbx/Z0A+K3U57g13FPkXUWmzp6Kf3h0tEuKda00QzS+YpD2K6+RF96JIn1LBMVZXikmdmlCqYjQgvjCFd3zakN6w4bnTggBjI8Mc2h/AjV+TrkOootRvJ9ORCADSfPJgmtVUprILla6CmvvMuh9zEUZKBE0E4R8CAwUaE3YnQcRznuVNBL25GyhHNFnYF9P8YZ2kqWxmnZLM6XL1sLqBioM4qRBOjiSs2dnlVTqAYE8GcSG/9Y1T6+MZAcNAoOzua06fCBLV4wWiCNujI+dIouikmRDNEvA1BzvhEvpFpRPJ6Um50pZIrKWQ1qKmI4zSjNkii6y15C6FxKsediIRmS77DDQM5WUKMeE1J0YMPYmxkKpngED53lE8C1yWhKUXlKb3LiLBINz4LAqsoE5EUwK4Ars0UCYRFM1r73CeaSIIuMlWp00mKkpy1WfGP37JjvVzSn8CBA2VkAVzbVrmqRi0PMZGqmHq8oLQDTu9RDAyTC6ukHZHwyR/9EgkpZQJOF9ZtjH1bkNpWssVQAob/iYRLICQvMj+CiAqAYtqdkw52krFEaSEZw5PDwiZQiuVmgO+ZiKdAx1GspLCESzmf82ICwce0KmpeoVXxEISsGfBoBj2E1g3SNT6OBo6fMTqVhGplAy/Srk4t7JG4SAYxpeBIPmsS2HoUxzo2psVfweOHPxQjE2v1I0hYyCpyXlm328RIfviJl5+q9BhhKSk6NagSJUXXHczF6J+kexRDxKkUr+0HDFbynYaEZkh+VCYpUjTPAQ3iJ/sZRC4Cychav+RVbNWqeqExkEaQqMlVbmYoxsQchERKMqB4bmxN2RF/GlS4iInmQStzVfzAZRXOreV4C1zlu5bTmq1abUUu/92rU4pDuO9oiiDoEivtkNQ8AoETHFipG/o42pjJHidU43mb3UgSonWOp6xfYAxlwQDA52EIUGErWF3ECaEkiYmU7ATJtWpAKdYOpCDybMx0OCoa9Dlqs9eUzAgNaRDncgxpThrXutwywWEeVWG9K5nC5PKcSTlsZgIhZ1gDtJWGsXGtpgJZRd4FVlB6pncSY1CTsNCRkNHjJ7ySDBgINxSfrFNKIaOUo/pBX8T0azS7yWqG1PaUlVVLXDFiEz57hyUm2o9NNYtlbPoK1mGeS3Ob8k9Gx3WW5zkEgQJJkA3st0QvjBkcslVwYopimug2sE8kImUDiYJPokB2XuohJ//HPDdVXlq5eMPE1tOu0j5J2feJ8mNeTza8KfMCh1fRkYuoDBpJzTbpt3U6zHV+TJYZRQwmXHWismB0BS+ATDI+cpyfUjrVBNKSN/iUCaYpBU55qo5jRVWYk9KsLQiCJGPlJFRjgqO1AOmoxloSVHRwpGbuKE1Idf2yBfMVEaskSLkg4ZFj95dgXCGHTRmu3Qyg1juBfAoiCUwJYrRIF4pRjNx91O8flYYWW8kTV1LVkc/k8eImubZpUUosZLYtH7dQJNsIVJ9zi4OZ0VjZZ6DWzDUZ5FqAfUYwEhHPyjR3rgOW62Sk+g2YfBdZI0JaHjpeZ7w45zV5xKRKJI2shxH/RkrWacHU2GqQZprJnE/F8GCJJFUhL7QsQyp3rN57WrBnND4cBRdvodpMh6Oz4WiC6wuU7LLVQoPwrfGuga/ZaRrxJUdjozpoW/WSsWNCokh+2zc95t4AJaPOamHudhCDMMkW9CBabRsMtbOxqlj7uuLpiiIQAiBddhr2mjEshiGykSE5hL7sTWis5BkfANFzbasEqeQiV3tGe8jZA1lkjDGZCjoPFPNfnZVukTPaypSZ6Jm999L7s7gyX8Za4KldU2MOvIsB4Dj7IhFzX+CQQhQzZK3HS9RuY+y4z4duo7FNP5XLT2ZuVzVaaZyqPNIJhDhEnBVKcXlKO0tvaPOj/yolONGO1RfzNE83i+Fop8vbpcrXieLWtA/FFAV2mUSCERn9AMtNKBXt1RzH/ZIhAdC0mAvajEp5+UmCeQVRRB2AuV+a+MtUXQocKVtDOc9vnce6AEYCrZMWhYaX+IhNaRxOiBVnOYnqkY1bKYd6LEfYDdB0nQeECVXa+cuGzZ/7LVVuHIx+SRTC/dhvoBitfMEYiRW9OBfNvYpK/BjN2I+2cFRR+dhRfY3FycuwuNCpoZlg+UOjYE4cWVzT9AnNiUuhMQV25BzIGYevQIh/hcqjfeC1pMa6FE9ThIR9dcRBTc5rXcqo9cuaRIb5yFZ1FJC8TFGaiFPR2BqSUEeYKP8GRB3FSSDW83xFsjkeTOzhpUgMZ42O1WiKT7ChBR2HbLFInaTiiiRdYqVLeowOgbjgaUQEBK3KzJSg5kRLSjRTMRLIaBRLvbhNnvQbWM2XpPxSYjxX+GSJkK2VePTF6mnBYCmGJ11HLMmGFpFThn1YWxBZhUCfwlQOpLFXo1gMO3FK4rndXNHV0BxVbpRYCKnOdCBbhxnb8v0Rr1VOgmRYcJWHCBlLdGjRaYgT2+iMlgGKN7nWfpBTxtkhxKyN09CSDz0kw6SZPoFBhfCJQLDOSkmRDJUPh4iJMuYcFhxYvgBPT9HMlykbuv2H5KBhX4DIBK1P7pCbUrzSyEgXKxX/35Z4StO83gDxSkrUDnaVWFG5BIC0Izu10m6kFvroGEEUWqVt2/zZpGv1T5eQJOw0yatw1FIQyuJhHhLlV9QY2964UaB4zfowUYK5RSh92BQdz124m7yMB0ddC1lMS4DIw5CMGsCg2MzoTRoBCu1QTEsczTKVjbAskcXtYJkVBUhuB48gkVzM4adI0bv84XfMoRvRymYqZTjlzlAOy+c5l6aUWQJZ2bykGbNpHnutktts16vcV2swzAId0uftSO9Z3FLN4R1NUZr5ylfYTzlFTS8ChRniyuv5H8uQ00+pmWR8nn2UV0ukTNh5HRmRiuBJBP7wzpdtV6L1zka4iEMQ/8kWkV1BFJ2jZJVsdcenGRaM3WTgvVZ0LgdS9ENHSBKArFSldVfKCInbVIiCGdKGzSFricqX+QlcTVUsbUt+RaeosU4xzUfR5U1ijUyASGTMwFm5NAlexCYuUQUO5kYYKpOhiJCLgEU5QVgYOlq3KU9MNAl43GQvqgUpjaimDKQLKgyo/RhatKZEEMVMpAqOKE9veArtvEwUfhnoKJc2rpSUYBfwjNfy+MrqVNolbpE2GtVYlGB6jNFhHI9E4g1yWAahvEsU7gnZKMiKAJBnfYnbWJlpRSXfyFZ5KY9qsM5z7aaIhJKWJdsCZeF0RErCZIxOiSK9QcSUeo+gpNDuuf+g2uiNIYGQvyQejYTNixadMoFV79BcjzzLXnAWhgwlx70LGK5TD+VTVOWj0nTYGGmF0FwBHzWdakpkX1ValPQU6pEGJxKZUarbgS2L0DgLZiCR4xgTucxHS9YVUaCLRGrLglwHPm2XFZlhXFlEdwzLnOgReMxhUclFqlSJvABPTViHqwSboNqHKUkOeL0jf/oHuSVaqtAEh0zL0rWPbiQlU6pEw5gPrpzlACGgkZbKpXHcC0nNn+ajzwzlBBnKHGbMIJ7LWrjIrIGQVzTQVCjrciAiFgQM7HCijBxMXYEVdonTFFUYeczfdIwKOkXKcrwejPUXo70Sn1neFEFTyeT/inysiw2EX7f0UHA1H5G9B2ckzJwlHpvAmIaZIIrpmFWSp080VRvBnmigVc30j2tolriUxNGsF8W4HxONXvo4yr3I6eq92DwuhqukLYk8hTyBZC5SipXlVb2ZbKHMYS9qoCK9EugYR9smlnt2laZ8hmuNahqW2Viu1N8Cz6po0aXozStJSYIRXFJkzJoWB9v0iAgtxdHsVDyFnRUZ3VgyxTu6YEhST7eERU38EkrsHlGkIVrxEkkYjb/IFieF2RdakLhYjJkRUYlEzSJKkVBZTX3Ro858Xp8ABVE0TImQ1LKwir5gjtvEWLykUdqdC0kKzSuRZ3EQxiI62tBFTaCk/5EVMc9YnspYemCFRE7gNGioGUfwRlLvxNCPrV9vJJiUgIxQCIt8vBL2/p7DrJS43KJqXBpS4IqynKHSVFD3JsjufA8koc/ODo5MVI5Lce7zdGkFwch2UQ3r8It57dQXdMQG3SSkrSccXe9Q/EZ0Ok6HpfBGXLAcvca9mIm1jiVhEIkIwYZqbGJipJnu+ovaEAeKeY0FAZCo1NVXwG6aVQqvPSN5QoxMeBoYQq6SaAUUXYc7YcESRc5ShcihnHHuII9Luc+2PEVrnZF8QAVQKOvSDgWejQ6bQHCGlayw+ZFzKccSrdi8LFBbDAuWSM25oAfHhU0bmU+afcd7MA3mUP/Hc+ESfrBuebKR+eQHAGlcS9xLen3G+gKG8BUdhvCDjy6RdYzRh1GV0AAIm6SRTZzhTkXFPL4UcF6B57RfCwKQtn7Yu4CaalSazhjbZF1vcHXvcqBISrxeSd3HAIVFCDJPwaRwQinJ/DQzAdUwH0oOVzBGR1gNgSTImX2p02xbjIJafs2jTnDWcCkHiqFiGpkqkUBQfxSNPTbK06yTcxCtBdWENv7YvDkOGR+QXiQdhNjyp0hk7KaZU4zOODKuPR4HyBJoa9FNzIkQiTSRE5mzaNChjkVLgBTMe3TVYSyie4pgxKhI081Q8GpF8Lpfx2HlVt0O7iKFPERNwviOUDn/z4qhWJcCRmeETarQF0Z/4ScinJR86YD4C0P7RyBeGmf14s241qMNC0kRDnX0ctbOyRdiyBCWYMZoScrcrHLIh0yYWtL8zCcaUgfPGlCATsk9B5HJjUS6YIxkGXdtI0hySvM88iehbWisRgaXDIA9CV84jlmridQ+F7rE0lnMH4nwtOY5SqJ5SUcU84hEWxm3iVhNipxAiBBpI158B9vYmM/o6FF8xX5gYSdaTE+9Y7VGnNFpyYD4zjXpmFwEV+90mBaF2f7UldmljGRAX3l5k45loU4ijwU9XhmXIF6ECJ0ApGDUSWsXhURWxoqFXb084dLljTHpz1Mch6ncywFB/+oc+sr3kjGP6qi1Wk3TvV6HXVhRXIdmpVM4oyP6VJAkrZilLKEq/lHpcIhPc+LIkLG4JAzMzS1amV1RHZ+TzlW+WFnJtDYnfvWTNcR8Hsfa7JSOJhaWrlQuvg0T21TJ6jQDrVVSu0oNBRd13JvRHhKZcMg6WYXL9sf3iNOxJZKUVI6Ohcr+ELep4QX9rFRssJZmHe6KdATsaoXFakGlfAXtMYcLXQudcibZpIz7ycYx5+BkFVqlpJHnrEfCiMrTEA5HtRxSNMkhxZJrlHZ6ENC1TNaXvQfu1ImpgRlIRyF7oZVz8Uqx4o6ptphLknG5qVufIkXCRIvTYC+H8KgmCf/Fp+7IFYbWX2seZIyRYhHfjLwdRbWWsWkF3bBXaohV1HSXMZUNfJATj7wtjDHOKonJiu3GdFUo5OZEmtyZV/mHjIhYgFifsrENRU0sdhyND2nRWJbMHuJGmmjjqXyZJmnJ1kwSaHMUjxIZXZDbwE4zhJ3RwTRJofmMdmzh7eRN0VFH5q3rSHCIFuHK/P0Jhh1giCSdYhlbO04agazSOeuTkpxpiJRc5nnKk7fKdzhabPjhVq4lnSoLdU4IFF1KpITlSaZECkPN3EWLzlyH+zxJQkuH6szhRtyvkrNjeoDkkesTORGKG8VSghQI99TEqe6V1HCUe5jpNGdvpv1WJAX/JnhAeFfZyzaHBqIftDt9iurQ41iS0kzSXsxAzOfpzB05Fn71PMNke4Ql0IoJYnTYBXiqR66TE5x8GMQMVv8E25fGStp+qBVhqzU/Vu2YT5JonPkgz3Qpx724TZ0WlYwYG9ucTOIFZjY/d3EP1tzOj3MoSeRozh5vlU35y2GoRAw++FOnGPSIS3hcipnE0zxeDIRh11CU6k2RjOpkBXJ8VvBkDKb5zDrNUJ/yE6Z1IuylxpugGEg2zWT1hZ9NoitShv9m71enMpu5UPBmXXSWRQT7BLgoq1bSIs08lOMYM9CTCh8rjFaV5Iy+4LVqDUpDrpSI3LkOSwj2hRX9NTh5/0qBauypyRapGhN+UxKOBBZ6YS+QQ+qO/qzfqG60LCsP41SNAcSVK1++8OMHDhwYhFq01KjRr58NiWDAYMHiz58XL/RmzCCosWM/jATp0ZMnzwbEkgZP1hDIsGRHeQIlWpwIhp5BigYnOnSI0aJBfichMvTX0SBNGwMJfhEo0ItFhBApUvRykqFGiibllaRIkGFYhha/QMR4kmhJhxIx1jwpcSW/lP00asQIRixckyUtliQ4Q2BJiBY70oOoUWBHxQY1lpTn7wbEwVgeRrRBkp4WhAY7EpSohSBGhqFPlmToEMxJoBax3MD4+ugMhOAQX5HYbyyWkxodFuQHUSDBfv8IO2r8CPWqPJB6MSb2WUMeAABPBcZ2WNLxc4w1bvBzmBtjTn6BM2uxUVKpQIryDCJ0iPAGXyxT+/Gr64XgDS9hJfLTv5+hl7Sw6IYbFNMIIom+oMihk9RzSCCETuIHvSu2Mqyj5wgSqiOMFtKiH4sokihCgVADoyiEQJrBIi0Y8wLCK+TJyov0rvBHIALjo6cG1iirQSiJTuyLMMVuAoMgFb3oZ0N+MDIIJg/B8cyGGxh0yTYb8NIiQnBgUmyGn/wBx0DXBIJoqpG+eOsmKEEcji1/4lQInLPkWUss07RojMQrZEKJog5fo82LC4UkbgYpa2BoJ6FQWkrGR0/6Qqv/inRDiT4Fv0gUxysgOsmjLzCCiKAa7qsJC4hqkJA9fhia4dUwa/hMo9LoUS/DHrFgrLiN6LmNKiS/cKgxNz/TYoYbNp0BLXm+YAgh5GwwCCKW7oSupOauYMgGB5kilTzEbtjTi84o9UJOG3hbFaH1nBU1XS/6k+s8erygtp/ehOtnN18tSxKck8AQUwvpKBSvLquiwuLWK1oEbdgbnrLTBqTodUwiiYgS6cnxrnDsJCvfm+1avm5Ya9R+JBasKXipBTmxUOkk1amBYPrVBodmiEtTLUR7065GMeqoL4MwK82ghzdkU8xlQ3ttqGu3qwE7Mk92yCKmgjrYO3aB9Qff/6+GNXpBMM6ib+vr/Bq2smRvQAgsLXCip8laqZMopE8LXJgifmcMC7u43avhi5JQ3RCiXDuyIdorEFITDM5kq9MfugpFsR/BLNJIRCRXaw+cS+MUsKFZc/ZphqLMy9hPFrEwamhOO4K2McP6GTF2ihBi7WmJGrtZI/esLandYyusobCGD5N38+R8Cu4LH1u7sdOWOjKxBowuX1cyjsHmR+wrTqxNntm1oKdA3Jb/7nw9T1SMIu3vlUsj0Ou6uSMVHSLbtDhPTGhm0rEUVernsQRp6Fkg6o65orU/obToehYyW+VC9Cl5UCRCN5jWwdajrS+8DRyMC2GVkOURuPlmRP/4kgo4qHIaqtkLP2obkbpWpBOzNUk+17KLP8QGjupkZGGrmxD4zNaS7PmDL0bkXKp4QsPmQWZNrUJIeTpCEcJgJSxSWd59YtMylZAtRxfxx5ss0jF5EKhsxHNhgs6DRYuYhSFGZNMMgNUyzbCwHz9c1lpomDl6wGZU1iqSRla3OBuI5k/6+hhXCugij/3wjFigklfWpqpFya1jbCQi8SjHpC8Mjih2igmzxEI0LEgEHDqbwamw07AcWRJVi+Rjuxq3NcPNUHWU8xapTrbEsawmaxZaV5fesqIP1UtINaBIgZipwSThjY6XKUifaoM5h6iuQxXqEleuKZmw9OtJqGr/1GxmI51+VNFsgrGMe45pgzdqUGB0IlzZYnaFFcUFNYNDUypPRqP2fARG3CFi5MblMyTKp5Xoik2/opQug3hITzdJjlLcJY8P7YZVBlEnIM3ij+ilimOTaiJA7VmXjp2OIDFTkcTQuRPq8JFOHUNKR2vIPo3ApW+3A0PyQtWgR30Sj5FJ6RCNU5bg+fAKSZHj34JCs6R8TCBDRI2AyOYFVj3nktQhW7oyRkPl4Kdm7cGXgHAjlvZ1hSMzsFb5mCUPNs5Onep0mCSBep3cPAccrcRaqkAFGKaMCmD6omuAHJdK9cyNKMPjSmx2mDSbdMutlitb54ijLBdy7i5gOGp9/1x1xzhBrFr0agpECgST5j2HKebSGLdsUMr7HBYsvVKalLBHD/oArGbICU8rvyDA+6SurGAqD5WCoqy1JeZCqtRCs5SENa+AoUCggp70KvS4Zmows2xVnWdyM92LxGld9YKNhGKEy9ooRYxPE9QYSwicgTgQe+wyHKQ8NRxwWKt13mIfFqB7M04CkFVzgyZD+NG8eQ1Mman5Kn/BgMopFS4nwboJWt3rmhmIDVWe7AvdPrmup2EQWiMMUCyT6ioxhZaKsCohWhllTWWOhp6RYqvYGJZWaDZMNWO6r6ZmyN6pKcygBGnUctFyzH3+hVkji+MZK9Q90bgpdkCsiqUi1/+1phFLofvb674I3NwjanZJeZSgfyCrrYxp1HZ/m2JaD8OQYEVrmVkkC1AfdrCW7W5q8uMUHyUnxvw9eCYS/GyTpGeRvhZ5hDWQznwIVWN9+UQvNszrDQrt1cf4Y0IdzTFNsAfCKZVkmD+cMktCu1u/vVHMd7yPfC5MJ0RR73Hs3ZKt2HdH+WFVnaqqVAQjOef2CMpaCZxveHa7ZMGkz5NTDBdYyRUs3Z0EIX3yI/QGs+UoPgXVptYd5az0I0H9JZjyc5KOn4umKTcJHHmLp3ii2irOfqiKJzH031KqLVBblTysat9894eqlD2Yj2UM7aTKkjmL+BQmpWKXRttlqdL/aNqiee1Tw3Uin3lPTlPZ+hlByILBEsMt1mv+yJVUkhlN/Ql9WgLDmTwZxecYVN505UusYgbUcT1WMuRBJPwwV+O+duZYcQsMFpXk13V1Zs1W9FG/Hupr6WC1U1SKm0HZNzfXtEVLLB9llrDsJl3B54UidLMn+cFx1jQmtMZGJ5bFZypf/ZByLQYKctBZ1pRBa4hCHpjH86IuNDeKHjnVtCEJ6bONgWynoXoRlPxTclSeCFWnhKwNdbrC8zbOcNSJdmtBdFS9i9BcybIvmzR207nsrnZ0D5Mln8Mxi+uGsGzKK86g6Q99hXgrmtrtKueHr0etsaNgQo1aXrgTQQXr/1WNLfuwujfnrx71mvM6bL1qAzu5IXi3JT5WVKkUu3NOzoE1juBu3Xmpe5o6RK2p7ux5OMHGBSyyaIZQPO2nEQO1clf8MCRcLNk5aZo93t4ljFoCzRQOaKzKS0DEsUwv0B7LcMimYADAoMyiSdwldLAggkilKgok8cCqS0KCbFTHNBYiMixnrjyujIyIPtSqswgrzDJLKvZqcDQKdrCnhAqta8xOJ2JmecoK/y4FwoTiIBKiq57GtmbPd5ZkXvbJbywomGrMNe7tXfjIJ0ouLkoqLkZkr4hp2/rFOdyO6cxuVCRGlVqmdaDvIbqp+zwKezZmrixILTZmX9pP6TykV//W7C3yRinsI8ueo6v0ZfnEYipizXxGyJDo4wJ7BThMqXHcLm+uKJIc6L7GzfSiJZjIKeJAB28e6qDgBnQy6WHQaVRiROGOUGzk4W0cyOwScL4oYgI56+AIR0+6BzcojcHQwr2gLSGC5bmAC18Igq1Y7hOFsFGgI+I2j1B+yDjkZ/OwxCwWLW4qw0e+A0HSz2h46gYcxLHejBMtBiuuwEBcRzUEZLzQbMMwUMWuZNxoBk1UyYKoolkKSIICpa0OJMxA4m7Kb3Ispnmw4Iduomxu5jRuC14qZnKAESVaCZqIpTqyccE2zwubJKX8ZEVcETS8T6fKz1vujR5iMeaWr0v/quSSBEVgaCQW66VZlmflngKLJChm6qKJOBI2ase+HstQJnK/QMci50fvRATJTO+gtigqkgt52io5iiTX/kwoQsynWHDetCf75ook6PE+SkcIGcWSEmdnruQmJG+vxk6nWsxBsIy1bLFZMKhC0IyC6KNSiGhwJEJZvmBeroNNWuySaiWy3oVNuIvIwCSlQoy1SipmxOo3OCZBpA5e/JF6ogMALkc1zEXvdIo6ZkcrwTGlQGf/IkjjtCIynuo02GhtPIpt3GRh1qY4Co1YDAKBgkPp9IWtCKwwqaej3EPjuuSjAOppTEdbkIJG1OlRNCU05ix+bC/2rqUmauU/wGI4//EywQAqJmiLevaoKEcEyU6EKvDJGA2qgEoQyYxIVxRGJmADTDLGfXyCjBSzOSRHU7CggFQJpDZD/ziFXpiQZh7LrFbkOOqrcwoO8lLCIm1lK1rmuerF7HQnFi0yrRSuP7aNnBLjQ9zrkmhmN+broppowzCH5XrFdVoGVDTu2MqmT8ZCLMrIo9jnud7oos7obWqHtaImx1ZkclJmkUBGK6oRoF5j05jiCCk03Y5q83jva9SkiqTDHy7xsFrpghCsccLMR1QmitzwRh6GRlzjXFakOvTJcr5qNWIu/2Lnqzwu2swHy0bFhfbFr0iSPZbIYhKsPJvihADQXFpT45wQoP9OJF7qYnG2NJJm41jiY3Zgo2di54OaCakMopl0RUCwhHTa54hKkUdWBC75i01+KPAa7JC6qAaz5FO2CtPcyoj0DsziQkSGSEligxObpcw6hUHmRX7+RmFqxV3UcM50b8lIVHfQjBN1SmwOg5OKBGo8BjUUZ3KkwmKW6HXIhUNHyePK5j9R4hBTqShFSGywJrSewkoE4qn6Az4FxXbMLm7QMWT+A7I2DyzjqTEOK1a+KUlp4kV6hS0ark9+xDY/KZm0siNrrBrR4jako4QOq8pCLP0AajOuBCP0g1CMqEMUpmC1c4mm9WeO1D6DbLdGKdCWKDGWh2JnhDwsaXJgzDH/7u6OLnCfxqJXbIgwdquAEmhwGmNfSFQoRJBu3AXdejTMdsReWuZFFuU/tLKEYMMrwFE/voIDLUUvHERN0GwuBMJAoqrrOstvYg5UMqtShAwzxKIqYLMu4FPU5AMvRXPDXHNkYsdXwERF5gWMSm5F8uWFCm9h2CdYamyGZifH2A4pK5V6+iQpYLOi7mTKGm57fIVBPrCtDApVJkRKruQ0j9VddAz3XKM3G0Ze6sVdhKWrhBA7IONnwmw4TYRVUCU5rkQ46HFrvq+o+Ai4QoxasC6MOKtJ8Kbhdgf2BoRhpGPyhiKzUmYlj0re5NFhSksLruu6/ug1YmK+QIaCaqjs/8QCi1RMbc61sIboUvEnmnivS7hFCLUFiJwzM8aLcvjQwbCMK1hlME6IPLr0ezKm5Iasq4oKPdCiQHhPNwrkUsniZ3JjJR2r4mxLWd6mQCoEdj6jcNFCGuMyNJBjny4NOfKT2uzLY+BSbdGxUUak4WJFbUfJdwRkvoRMY/Y0IXaP97oGOIflTyxSx6gEVC6OBTfjYVYDrFTHLASkV8oDZCbEbTkmePQlV8pqiORtg5Wsm0bJYybS817ksQglpwwEXvDzWaTNMkYPFQvkowzj6uDwJlJGMY9mRrAsWCK3GgvDWkq1SEOxY9fG1zROjOuxrTr1L2gXdZvFHLtphxi1KP+BKXzEB9XMJ6XMLgnn5oftJXkE5Io/KivI6WaEZd4sKMbm5V7XTCfIlylKar86x6d0txgrYyWTxyY6qgsnw3wi7ko8zhg5NoIUJueop0tYt2zgbIIMNTKUJVjoLlawDsJujtEuJVe41JCaw8vUhsWQpTtOZt6mDEg5ryk2pi1jLXZCguB2yWLbbrjY5jZyas7eRCLHZggdi1jiBppY0jjVhWsBxG80JThsZZ9Eg2Zqp1O0xSjgUDzYM6AAKrJOaoNYUF+EDMtQEYMah3/uY87usVySxe1IBq1GJJ72hfM8+CmoBDFQzbGkbMrmRnOFRSUy64c4JpkMDT7j5GD4wpz/+qplIGwwjIi1HImcXJdQ1sxXMsm+Wucm0GQwx42DiU8qo+hJWK4OwwhYgqLF1qYqgNKjamfDdsZ2YibimrlBzM6BbMdOCGO+iopQLGY9eEaaiAPJyCYYTVlGePb6gApkSEVKIJVAfOKETgfKkMg/1sZsm0U95uwJw4iQ94RmGOKXDTYBqQWM2u5HlmwsmkVNgMWTVKd19IQlyPX6wAx7RCNGdDE2piYWPYlgx8T5Iu6OnuojzKWjakYlLdV0Gk6NyMWxHAl2VPhYR8keR4Ym+AhVVC5HwjEBY8c8r4/WtDJcCe6jSu5v5OMj6OOHDankzgWLTGN5LOYlELMoqnHb/0BqRV4DM8B0DqWDcsi0hHLF0YqSMGwINhMoV2o0oPqFODaMKdEp9jyvKSzVWydJeJMiFhX0MsYumeIzdlrkQu5NNLtKezYMg29FTSzI7cauRdhHbXa0YZ7pdHoYOvIGyXRVwdxMldxl+QguQsRm24SFO1gOoCKXSpjJracENvBFVwRSM2LlaSZFK/molcskHJ35XvliMuas1ZSkUkSTaQinZ0rIkukkQtzEvRAQew7jZ0h0NgoogoDrz4ACNpqF0XJlaCYyZpqjZDtL8v5sLRqH6WiNXRAwNywGUkaGJJCiBDEoCfVupv/E1jo1PnbHXSazOo+opCQnUIIJzd4Nqf+SxJRmaDOgqeTargiH5YRiB2KO6iwKyAb4tV3lA1EsFVuX8rF2CC1mMV/WTPWEd68O40K2DUiipk/0MpLYokj2KXKfS50rJWIdqbEVloJUjIHSzdmqAmtI2XLI03Z6I2Y6527L7z1uAlEQBHsei4jKW4LMpfDCAj5/5ErUg02wjpki1cI8QjXZJY7Eey1UyVYCTS07Nd0UQyrgCa0gg/gSbDKIiJOZxSLZKCsO3YE6y5SQjv1iYzZSPYxd6GWw42uhw8FSWwh9hPoUCjEILtY0MrLYCNvQ8Y0g6V2O3Egugx5vhrMKD1EuLbOSRH4MZEHpRBdbWzSX9roiKSRcaGP/7HEiW40cY0Mcr+VafArU4qlPbqPvpqp1ZnVrcsqg9CRbiG8GnPtFHozPz4dEfe3PoOdvRmbZfWLKrgk644e1CE7y7solMDKlNvjc1WmD8xZikit2hBBAHqslFPqfyWWDQ4uGDEnI3GuD4a81TYVKrFs+lsw40AmdFIp/bOtFsM5yCD2VRplO6sWnwmhZquq1Cos1CK6bhMWUqIKq6MXNTit7+rckNGiDOItmpgipdaWt+mQVqSO0CctOWqVFvYOEsxc5yIa8IcyQXqjYMU1Iown+wus84saxQMojH8WiVc4smqZmnvp0Y4W2hwVgOuYnYPcmLpU7MiyCMGMSFU5Z/4CoiV4EZ96aUBwMKjjxqRLtkl5kfjgWtwqOQRgxARcmmYIjln2sq2Cs7WpFOpTndvSCt2/sCJNjh4QEv54imlDMdtLKIidiamyIw5QOQbyFyyplwEbGw4YFIK5csWFD4JcvYGrUmDGDoDx5M/jx06KFocQZB/v1M/hl4QwvXjSCAcOwHxYsBEcy1KLwIDhwBL9oFEiQHr2X4BSO1GhDo02NB0HyOzlSoEqNFEdK1AJSoc2mEl8ePOjPJkeKNfz5O2jTpkItN8LyCxu2xsuHF2ckrCGUn1eQGkHCpXiyLpaDWp6GxXJDXt8bGl8qvPJS4tOQGh8WJawYjEDBNQja0P+5FqfCk4Zf+qQH9yHDxls71qBIkF/Gfi7BgdSi9SVmt1T9nQQ6cjFIMA/ben74kCDIrg8pMqSp9bRNf7XBgHzp7yVDhxC1Ekzq+MqX3/ROCn18Q2FJhjYZup4RFgzfG9kZKiR48iEWgRqjgvMnUatEgjMEKoRrkuJqLTwVNhQWCtXwEGUCasHRSFr5Q5BW4oEjz2YSKcQPZRLFxeBWAAAww0sCzWBTbl7UR9lILUFXUj81GEVSQzaYdp0XNlFk30MCadVea+B4xRZFj4EjEBYgkZjjSAddhgU/0tkA5BUMeVHgaEx5cYVGWPaDlU0bFscfRaRBqBYY01EEGUgppWT/3ntQkkhQYmjaoJV7QUnJlhdcUfRTP2jZlNpI9Cx2mBdmSijPSVB+ZCd8D810xUPO2XADiAN1x6NxEmnnxUng6BkoGBLxg98MmxFF0Q1gvNTXF5DO5w9D/oAk0ENPJVnDUlr01I+YkAl5hVx4fvGrF3PKE9RgV9gEokoYfcHcYi8lJZB8J2lFrD8UHfSQsz3SI5FA9OhXw0kt1SkRb/D1U1N2KNm12RfFweuPQaXx4x4WirJHpBfy0OSkFi8JpZBzEbnFEFk3JBUniXzewNds/czQnjwSLQdOxB+CYxg9MzSK5XLxtgoGaZKJx6xFF4JEbg3vlosWP4gJBKYWKNkQ/6Fisl4x0lkQX9EhlXS9lq2zK8NaFX9x3vUFVixrqZVT9IgEhrwnqbnmt1foid2UJmFhH5PI8ficDRyPifJlXbF74MsUSTTVaStlqKUWO59rLKsQdezdmEXOeiiba91ng3aebqyxW/QopJWfRh8kERg8yUkfPx3LoxBSrAEb76Py+OZFbbRGvdEVJ1VEHoBhXcHPSCCdTRnEYCtpLdZYnxQWu3sR+aRmJjUYmnQ8+n6FmIf5p5S9/f56knqRxdjWnjb8R9kMQcfF73o2tAqpZxkDenxTU1ZbFT3J+/7m3jKbvtbM1EGu7uBYaKGtPIfhjfW2pzWZW5MP+tO5pDfwwv8NcsQpV/lpLLkbEYniVbUcNediWuqNZHTSqjpZRzQz80LAvKCvvHgrVMDqR5Hypjguvcgr+EtO896EpXEtiR/g6BBC1vKFUV2ORSbkUbD6JxDVwRAmMWKSQP4zRELFh3L5SZSI6GEtDtplZ53y20jqBTO52AUL1gPPTdw2FYXIw0bzuuAPR8I02WCBRMqaEvcko6MXAoUgLzmO2GAUHH3JZ2710oqNXqIvlyhLNZsiENPA978mEsSFcOwUwBjCELzQDIsMOYkJh2Wrx+HkcOCpU1v6sRM+0a8surqBTDBmuiLORGrgCKCUAuOqigknidzzZGOWlSpXsU4rU8yePMj/VJAreLFz5ZrZ6AznKjs1MUmN/MLjSDIqmzwOc4mxnMpqBq5wjURoLDnmFzBTLAuGry6e1FRMEBI5goBLI2Sh0RZDxbyQ3GBsddTVNKPSmNOsKU42IdCdmNOcFTXmkf76iVS+oEWg1Ac5vCQMJgumIIVKkh7Rk8tXtJA8PnYIQHPjYBCduTpzXmlXW/FYxaT0tWiOKUQFAwpIDlI1J1HGT0vz28SqtBj/tLR0TMElL58EqgFxVJ2ZUtrITPe2Kj3NLDnpH9Rw2RbqIJFZkjwIipDKouYpUDSUws6FXPgSpbgmfg39n4VWNzKb1CuSSrqX1/xHP3Z57nPu+0laoKPL/3iBLDRFpJKqJrcaerxzieB6zUMzqkFwUEVfbeEckvCmrcWsbECtS6ILOxkeT8WJfVL71OI+1cg/kixR3FsKF9t2oUjW7DP8TAtXQgfGrnztIQ8c0r7C+KTlvGax02qLVjQSmrDwpwZQFEhYAMq50olPMpNhi/Va6j7keoejHQzqil4o1anJa3K7xdJQg9a6yGrxWj1UKCBdN6H3aeGizOOHA0eYvGi6J5GawlfBWhXGvbHOiKgRlg9zpUvmsXWiUjXaSfASJXUCKmiUMutUW4WFazbYuogJDt3AAFHJ3Cu5z6NHmMJ4yWEZrUEHgc5TkrOhGvYSO3B0HOLwEy2A5f+kBqOUI04oIrWDUI8lt0JcqOp0ILMgVyOfKVTkfsi0cqLMWhOrAcJSd8HIcm6lXzhOkBRYM62IS706cx8XywaspDiqjy0m0SiPVy2PmhYcENtTtVCKKMLYhaMzQlahuuWpR/qON53DVdokk5zwpKZc9HgbTgfkKd7Sh59NRGBLkXKasQRGcDjSGfciy5Ox7olflquBCTE9NmeOFaUQHCugHCjBipnVBqmRDNI2FUcNP7JA/gmkPHJ2XgA8eEXvExx/afy+RI3zaxOh6KxZ/eI0W9N98/sXRRqTuALZapdgOOpKSZZMTYtuJfKr08BcBdw19bhg+pqOi6alXobcF4z/oXkbpJxbIMktEy6HuRfe9kOoCVM40ARVFE6cnDWNPDSTJl6MGaXmE7s4UjOeow4E5WjOt3rhOfIZji/5GV44LnpuM6zTVHyEufG1moyLFR9dNoSrtUANXG0BiX1yli6y4dvc0Y7XDc3toquuqMWjNG1iLrlseaAZi+/7pYuzpLh6QYxbGyTKNLftPnoDOWOGirWEwrK0KvXjq4vpRwA1NUWYhziIMxCT9Woj8TiilJXytXIPreuvHLWaac7EKaAS9zWO3gSCLX7hWfKG6fvIUO2kw4JVPqpXQ8VF6t1qGol+kzPBgcjg8sLxZ3wOjjEZuksjwZ+vPEYUHvFTJ2Uy/2HyhGs6GONt79al1PI29DKHfC1CD2ThQKNcYeHgtLphp63pVrq3nvuq52iZKHVKVEVH8kMjUCsSzJeXMagvnInYg7mrJiaSUQnOI+BLWz9UO0r9Vqhps3XQv2zAz1f6kER7H3oLzQJbf3Tc0GlrMlzjJiM5SpZKZGRKkHjd01+WzncWU289BkfIRyWjAkSxoRkv43Q+Unb2d1IlZ13Ltndf5Q8JUy9H8T4jERY3JmQRlGkCwj/jJBK8s1mccS2ew1Zt5i82w0jelXVdg2oBVx0N8kvOcRu89C3gE1nX8hsdEngxcVDYAUiHMXsO9BNjQ1plk0vpkkRGgx1M9EBMVf9Y21ZVoYI8kxM637InF1YQeHZIdxIs6TRsYnIg93Uv+vZi+hJ0iGQD7sNHLtc82LMtSxUrUCZBYzM6XPFBbMIjypIceNY0ujUvKjc5j+Ex7YcjNTJhy8ZRXQU5lDFR+3KF5/J141QgzOMxWOJIwEF5auUiPLJWK4IrduhgvCVhZwcs4zQs69JV2xZUMlJNA6VPZ6YxEgaGGrWIpZM+fqNQDMYm5PM/n9dG2AMmsbEcUKZFomE9X2NGEtdIfTEqQJZPgjdA1iE3g7eM2vWGiTR4D3dj/xFRmmdYapRptZFIraEvk0cuMIcsGuU7W/JBGzRshSgdXxdDANAPpwZtWXL/IH4zUEaFLTiWNwcRXniTM/Wij/jGH6ICdrBUJ22mT6MyJD7iLxpVkAPRgigYRNWnK77TgVXiH0flOV3xiIW4TWxSQB7BSnSBLA7UUH8yNcg1NE/zShiZLHenQPU2eQ7BT/7mNRDDI/8yUriyfaFyVDICNRbCPGlTRaAGE2/DUnUxOV9nVvSWLtVxGua0WQGIOWoCIVyZi7n0WLIlYXcyMYpYNp4iX9RRcVCjKeokW/ExVt0Ih8lkh9jziAaSN5TSUA4mFQ8VGw/UfiFGWjdkGKGzgun0UDg2KUrSXwXjOHVyPLx1RBKHk08SKnu3N4ghID8peFG4Utm1IusBLCX2/0dXdHE0AV0RVRvg0iGicy4OVHeTh2dZBjUoEjrhdYIMiWVpGDmxiTx1wVESVktvE0mZ9z+8BWC0d03b1lInCBl+VnWdxDkHNWAF1FIWATsNWVXRlE8qxhGvIjEpiS+CA4aw0mbZ9T6U4m9tBDHq9zVA1GaM44LqJBcYGSFlI2YEtRgwlzGUARRcY5SlQSlXiBOv8RhvIy4ZuXeNxEXV1Si+B0TDIj47gywo80ISRCsj6Ek3lksz4w839FU105dMUWieJIObZWMq9h9NwT1N8o3xYmO40mEL9kUoSonqxTvWszTD5TEm9gWipEz2MjTJQRYHIRYNOSbh0Szg4GBFOf8ziiMbpKWO7RQsFzQxjCQiX3c8BZNd1jJOPgk+T3hMHVKKJEGXuoRjGFkUMPUyhwMZA0aNcqQtWFNgMXdDJeRq2XVGbRVNuPE/puURE4VtcCWLACl5HYOODwRXqRFHiyU4Q4JcCiSDWVITRwWHk2M3STJYVQUiH3dv74imL9IYFwEs8XZUeLZW6bIyStNj9tEkLpJjWdJIOBVZ3WYrHDWcn/IVE5NIdcVXeCJvJkYq7yMmFzFoWxWQBzUDN1Qvh7OH57JcWBAwY6VHE+aGgbqlZDVN0SJBrWgx97U+pvN9hoIsvEEyPXY3WvQ0cWIrazF4uomDUzJh6bRktjM8/hP/XMNiVcEYXoY3OjTGPtkGQaTVGi/jL1WEP43iQAWEI/Eyd2ZEb0sxUMsUjGC0YyZGDx2yVtGiJHvDN1ZyflgzKqP0dJfzH/UUUzhZgLrSel74N1/XPNjhE/yhJpZ3dd3iElBULsphJ6aFk4GJEBbkQ55UiHaJP+mjrUQRc1XiI8xhF6/UhkAxQ2f5QkMyiojDX/MDW3CGkiYmNyuFK8wTayMbVHB1aqwzP54ios0jPkg1eD/Jl93yHFmFGDlmWimGkuKiTg4KI4aFN017Yy8TGNCqSIp0X97RKofRIr7kEYdRrTMGJi82NGhSiDDnQ1IpScgyeN8BbhHFLtFkIcwi/1trgj0slSUjBHJD1aSNKkBL9lfC1R120Ub6qqVb+lWnIYAkZCDlMjH6dQUIQyBT1WL2CSw6YVLNBz7XqiSQkZHYEx4ShgUdQlYXUbWdJC9NKhiyNUIZmUKp4khyuX2bwWsFsojgAh3xMp5xEklSSXAos7RBdHxV1RJ7FyT6hSxIgW0SJmwrw2uBB7CtpnZdcRrW40nysVswpUDusXQ5VkunNidwiRj3N0IP1n49pC+JGT+U0iQHWVgr0WrAwiY2NibdMndTNirE+C5xVlQYNUqtBiKVhEQX0XNN+jdMmCxn8o0vRKb5cRoDlV+p8SSM9EoDhmew6l6U52q9WV5yU/8m30hmQDc3X6NO2dUop6FiYwKGlmZaDnYtEmQTsUsWINViQjGazaI44pJdmFklPaRtOVZvKpYtK/sbmiI3w6FVZKVI2OoT5qNLfsosFqKeHeI+SOIp6XIn8qE4JKIpgUdamQJTR3QgP+leYDi9OxiXpMU7ezl3zlEnrARkT5Il7idC6oQZj8hFVPKTXnEz5VU6FjNSGWO7wwPATWZCYzVDpyaYD5tpPoS8cmY6vME8qfFBbxOEA9JipDFCZdKCkBEYk3lvYAORRBLNyNWC99UVn5WRYAVpEBFpETIgb8NKoZJXBpKcpkWkWyorLSUgzopUuVRI47ku//q4FbIh1Ij/RCypBcVHe7xRwhECcSBEfGDiWQqSaMksPkGirfxBcQOKJFNazAtIq/jsaqY8pX/0sx7lEqmBU7qyMjsTJ4KTMa9xSK+xQfYxToWorwTzJusSTUQ6cw3xnR0CP/eBmqBJudF6HUFVKBx2OMfxPYGEKD7SJJI7eG8ilUHDGuO5vpQCYAWGrUZRRGkjfgM1K76zMgKpjCWMrTcT0UgnQQX4SCoGZGnRRLXkKK0CUlpkIZ4jYV2hObJRgKhpMRPVS2IiLk3jIjQGPhs9VQ62Vm8WLqHHLtVkzM2pdlA2VFB1RlazFvo6MyX8Ufy5FihDYzfURgMCRZ43eSE7UoYWi6xY/xCeEloek0jQxiMFAjGtBh9aBLcFpGvYtiJZw1/LVDkV+bguBzGXmRos1XNt5y+jPFEcoS1FpGK1sUzL1FPcI7kHhUtiQmmQwW7ttlHhIncxBza+0xXV9DKYhh8dcmPL4ympoURL1ECcM1YvZlqRHIzz8zhN1GPgpJBJcWeUJ86jdB9Px2MSlEvsdYUZWF3hMXcWJGEYCW13HGyCLV9eVML6okiYczIrYxCt0hbLw0LppZ6h8XPT5CdP1xigucBREy8w5RpVGS46sxkLAx0Shp8oKSvPWTPbwjRajL44+UFK0japgTWBaz3+YkYqHCvtB3Py4DtT9S+G5pIbBGQ/Wf8njfQkLyMjG2IUGme1lltYFPNDe8IlOMU9nEONO9NGd4g101QdjpJ/zfaT2ZIuTXVELFTbVZvUVecvXIGb9/a4MCUfrnJU+sS42VEu1aROWdKkYOIxeqJFuAJ0nDwa/XNEviIY4Q2rsQFGN1YucUGproY8ofMT4jIzcWQXW0pD+WYh2waHuhQ5vDp33hHDP9car/NC7kPpEjMz7QdE7VoVPLJeJZK5j0tUojHMDMXUCLxV6nG3Jfw3dB5leNYrx21id7PQ0XPk/3I4JBM5MyLf76EixqpRibQetR646nRVKDk3T+yG5V4cjZF/zRNrOxEn4BMVTTo2tCc7tNwk4zP/pXokIItYyuM8Or0UIevB65akSPhTRV+jOMzXYyYepCh5guJyTQUmN1gGhkiix9mFZZGmanCFPUozPyojlUGis4tIKWtSXU4Bh9T4N/yBS4XySpcUuOfiMs8eeB0vL6b3UA3iMZFVXmfHJ6Z5UeFlbecZI1M1pfNFeWfheEDtpSIbRAPGLQ2S8v0iQV+3VeOLbHEGXBI0PfmBrfiTexEvI2G72VzXU13DPUlYXWJSdUOD7x3nPzN8Tp7kIiQqmP7TezjGSbUxTaC5WF1kberugXHiPjD3FC3FNtFXOX66TCKhRZc0J00+6rhSJ2z1SotjWtjWUFAmW4YyQkDRtKzl/+WPW3kgKRUfnjcTxkolrDRHVZgvTKQhfoIugSzYCkhq/acONjMLLnUo1UFxnlx6bz3AkjwH/kVTVmHirTSYwVHfNxVatCc5pq67xGvoVB68dBhgM0Jts1hG3z+28jSRjxRtIXGemOXzrUAioWI2IEMSFmytRT4stCdZhoN8FGskcy3w90HBSDC8cc3JpVkA4Q8MGCxYwHjxQo/elSvy5GHp169GjYITLUaMiFCLFnAMNyJU6FCeP38KFfqzYYMhw4FeWILhx4+hDYcm6W1UOCOlwoEM+e2kF1Mhv4kIETqsMWPGxJRfvjzEopBhxJhXbMZ06MUpyY0bKWJJSZJhDf+h9JSSJAixX1ctP21sTMkWpQ2lShFGpQcOnFKmAyNOVKhWYUqH4Ap+9BKz4JWYEx2mrIFQr96BNfROZKixMVl+YiMOHKh37EDESm0UVLjRaV2lMRV/BgO5hkm9Uq9oicsRHFowVwoqVUg1Ze6CWr+o/jJwxkCFTp2CQzywoMPO/vQqxiJvo2t+A/05bM1v41zvAgfGPM9Po5Z+XLUgdIqFbw2kkafLs9nP6Ubq/OiR9OcGAScDpx+EIhrOBjA2IhDBiDYqKCaVbmtLvLYQAg2m7kCiZyKnYmrPnwj5cSg2G/Raj56BVpvBKS/EuiI9ekSS5woAAFiJoY4orO+LppL/W1CLkfz5UD2XqroiIqlIUkgvkg5jKLwInfQnJpwmYitCp/7ibEb86MFIohEXs8os04ZzqganevKtIssmI21LA1/0p5+CruNHL+oQmigiynAy0088nQqMocDUWkpN5wp6cM207tQLMQgnUio99vqx7TkQ+wFPKYYK4s0oj7Qgjb2UKktqBnCYoyelmMBRSDJwXCUpIkLpKcgGhfrE6IsDQ2RIHk8Z40epL36bIUznXPRCti9eSg8LJunhk76YSOo0MQsjAvDMiSaL6LHT1NJCR3Acis+psPzhC64JscrIi6wSY3ROVmuo1U4sxrMSNxtkhTWvvWbAq6iE6DlOi1sj/5rJUIS/qBU5Lq9wVF0bqiRRHg+T24ghp24AQ0DrwIHvi5D0SpA5Scu94lx5IhWyU0CNoipjW/fTYldFFf5i4ivMO0qeiHS62Dy79Ar3xhA/RAhbeZ7L8Md+Yqq2IB8nDOkhnsCQUj6vInPJKoemPVCpB99zCTUvMyvZsaeBUxEMnQ3LTrucO9TzZXAmOsgLpdS+O7fQCgOHOyabniuukuOz+gs8U/r5O3mMQgjJFROWqWXoeOZML0e7QrYupDmtsWWPHS/cv4ES7LehyY0yG1CDwVm2uISM/QLJu3GWc8LD7q5TX5IwY5jY4dzFkj9hk+1Hr7+V2mggkX7FrOWT/P9Brtnh2B53dbqWKqjU3GIEQ7Qyd4eYXVWHr6E0xmsCcyKnuf2u+LH98cL2fsG4GcMFpcPCS/ZWg5RwCEF2yV/dOpc6pNzPKY9B1w3AISAKVlBAdSsW89alvk2tKF9bm8FgbFCzVMmvJHib1fdyta6ykGRH45HcCXMTFln1bmPgUNpGlJI624SGIbxpysZetReSzKdpVDNKkf4zMqacpnq40w2MDOUwGB3sPhbBE/3EIr2nmUxFCBmWT1BmAyFGaTm9uc1Z1Gcx7fEtSLkjCgG/p7kwhcaFJPNCx3iFKocQ628FMQgAZzgtBI0QcYCU1uVm8iOLpApyrJKNURATlxX/jso4jeuJklplg5jY5UcISUminrSv96gtR7rBit3qhrgzDSxofUQkgLAHs8qRyC/6ORYW+BERtSlFHpshzJBmUBbCjQw5afIcHB8kQmYRDSFTmdPy9PI77Q0LRSRZD4qqdzbawfFTWHgg3JKHOkrNYCWdVFWB7NSYCwZIHlLrx+nA1A/ZNAZyFBpavhgnx+j1DZVuYRjjzjYRLIDye4XxXt6+wppd7dBlgGSNF250t6JkzWL8UBuqtMesXElIhZA6FIMG+KaJoCc1WqgeGByyHdf4w40OowcF8RickAlIpEN54PJc85JaiuRQoMFmEpfltGDSRGM1WJEFKXgFAdVA/6k36NCbbKBUhkjwBsZ0TgUJaJuYULCrFpQW/p46Vq+i8y6hZB7VkAjAbsbKC6u7VvAeWMCwqc0LvAxkAfs5tGGSxFVx1BcC0QOo4q0GRW81kZE4xJX7FXEGpAIDb6DHm6HYkn/NIxCsytkciNXGJGv6a9a6oyHABAU50NOCXUiyLBChh2t18YIJ1+Uy/1nJJncJ6xUo9yu8jPJU73sdFqAqXAHFBHPVOew7u/dai1iMNDkqJPlKBAa+BYd0WFoPzvSUTMR9oVJyi2qS5vSc3YgQRkDy643AQsbIbOqbRHpW5IpisI18Blk5iqNntyKinKbET03Ml1uCic2IKFUeN//4nqNCGFXvkLWC1hXuD0GFPciqD0/YiukF1SI9JBVvMivVTYvgSCUR6TJzfoGb8igzRhpRF6kqtRuNhrQTvppxMh2mp0PqxaIZVPBHv8xTCqnmFJ8EOXAT4htuBXUWgzLrCqX9Y8zcs5Wz2WaA94xrFwEYPjV27MnTnVbkOPQR/2LqJu9x67C0lLDVJgdJ4UFbLR+4GS5irD4lxSiEAOlClrpnBjoG51PeiadNcQaGyhkmpMIlNAL1J5c3YOoNhMuPGxwsJ+ZkFWuaEq80JQVG96SPSJqoL4/u1o28QeOIWAK3ykYTKsbFzh3Vd6ZOMa8fN7qrvuJLZNfNZpOd9tv/DM6bnP6AUY1mM3N9+3ibpS66PBUcmPRK9UMisa20m+TVqVRaM5GO0MwKuQF7HAroIX2huA6ZGj9yirPyDQy1uApg5HSsPNeSjrN9Ipwv5WHHubjnxY2cJhLVuLmTNjdhjiJfeqDJMotQ0AsOptB9ZGyxq7FqkaklGCB3Ipth0SlfOqRS1vBVYi93cpGUIUjJxkgnxjHqniyFo3XXNMaXAHLZsKlLp8ayQvK+C7kdcTTq6tUPVLkGZsvCI6owkhskGicl95kJbHA7THV7d8HMu2WlEVLWs07Ifw/kNaDahLO/yHKUMZvPapqUuqH1636PyRsM5QVkAMYRrSB5b4lN/2SXGynqLoftV4VL7BPHXvqofM2XvjoG8STpRakLodjVWhNUMg1wkakh3K2uvR75jUWONxB2pJW6LOW9DnG5SR+HtqTyFL5Wh8IWCFC+NavZAwVql+XSLiVSg5c8LGvTZA12gHzMzros7Ow5/b8SIydeu05/6q6gu+Q7cp3hpT9/g43OHhVosZCeZxSyzevPiE7KVKuw5J1uHBHpJNa0xH+24dUJ7zcUt+ZUc2VBmvvP5GJz3Qd0BU2g4fAsxUi7nrETNwqNAgkfMNgvD7klQAkQCvoNSdsIkBkQddo9cMOPu/OCuGMbtsm1k0oQ9uINoossjAkLI8ojjlAeivuC9v/RDb66Ml2ZJ+OwriLyq+8pO6Dpu4e7oELysgHSt1dhEznporigIC1onBEpih/pNLzwH64oD1bBiDNpjo/DuJh5idejEFT5FOWBm1xzE+nJDRjhvchzI1e5HvjZHM0jIOPKpRWpD964CBMkEANctiKpjloyE/ICictBlm6aLc9iDbjpouojpZjBM7SJq8qRigFxqrHCiDgSOFWhNvcDshYqiT96ptvgLu3JFbXJwdj6it9RrqDaNH0pLX/Bk9ZoomKLrUjqQNrBjhExQRapFZv4o0AikwkLkrzzh2ABskk0kluqDlaJlN3SlvFID6dxMLSANJBRKdQ6inDLjUKijGD/qi8DWTvbWCIQAw/KUZ4JKaQkCxs3Cp+4YikjIZwf6pedCLeJqCB0caqUuIEbsTrFkI0EPMHiaYmyoLb4gpniII5AC59RcQ/XQTd9sRtjayn4WjssKKeA3BqSCDy18cXL8UfUQheTURZf0YqvuR7a2BxAu5S1k47r6TDkoKvXGiMYCjSUe5Dv2q8QkawZQKwVgoww0y2X+CMkaSC6a7cyCZ1h2hgwRCttc41gqiUKyo9+wEmjgEN9AQqqcJcVeiwlBCS+MjPt2pt4qZmgyBxZgiy5EJEx0hnmEUYvs7oEaY12JBaAwizIERf7uSX3w4qBrBt2cTBNshWElByeoaZZ/1QQGEOcpSqT1Mg6Sjsb1Io0yikLLLGUCgmrUmkMLzOlRZswKMy1W4G81yup+tAz1GELk6gLAfmSmrqBXkkOpLuRIMsVaMq+amGbuSgy0ygPL/uSaumnNinNS3MYx0ASzjKJIosVx1pAt7icBLxFA3QM54lK9FiPUbIbvNqJBYy/72CVr+kiJAoJukqQ9bqMN8EC82EWv3IY10qdUZIfZGubteMN5yiP3aqZdXkLtMEdehoXzXStFaobzxseKukHCxIVCgGQjHKxFNKzUNIdtCkxbROUIYEfYjwbHaERLoExmLGILnPQM9GkIYMjGCKWxsmwSltIY+kXIzKXoHmQTv9hlKfywSTUDS1wsBol0FnsMQqqjxh1uLuKq/lsqheLF1JMECapSf1UrhGBDGrzvBOhm+QRSyMcEjMZxPSJRAtaFfCqjEDcnBOkGlzzMpZZtqNgqaRkmwhtNYxgwFziLE85KU0DvOqQI23jCRqZsCcDm6cLoJNqs6aRnVQJNu8pKYcJI+8xKJR6E8/iCttJJvHcEU+pPAxMwCaKPOlrk4xbu0ipT/bCU7aoiUVzGtjrtMF4PdxqmJ5knPTxPFASqGgKGkS6AnsUkFm0CWphOWYZTwbxqNocU1o9m0XzmF1Fw3jJFzQKJgmhQtIBDXGZLJdSFB5ji/WASnMSLwEJkQn/QrCmoCDwmgzQuwH6qCCXgpnR0a1s9awu+qatwK2U8Cx4cxpQgpEOIy9IOaFUGgwY2RrQEzHy8iUWmZf7zE55Kx3vOKwPOqlSSbecgpngUU2oGjsDtBXzYR/OoCLscSwbuBFnBY1UAgqFnB6XQDmd4aTRej3F0wL2cQ5B8Z7C1CT/uay8CUy0OQwrC5gY8ynICzX6eYysMbYzBFURsS5eeRORchf91NPyS6DvIsxx2Q4R4ixBqyyLGRKU6woC6SdjOkmEGRbRkqWXyDgAEpSiodQYYZHLGblGMg1xqUABGaoT+hsA1diNwa0zxJ2msC66Ch63ycHX6afrgJn0IJry/7ALdyGvtagQvlAqfQOVHM3WlSKJGxC0uHAWkKQWskofrVIqOOwbCqTMCY2n21AbbUMdM1q2BXMaCYGfRQpGzcEZ8ikwHXUYxAgX62rEIZIVDSoanlCbeIkZFxw815UIATmTUiOs3XMOh5K2y5IlvEAafAS0DfNS/4CNaxyRZFoiggLA8sSj6RqM+lg7xCKQOxHaNyGdMBEN8uov2+hIvukUiyyLBOwkLvEy48i3pTiURcsNmQ20u+EOvrmn+eSOe3JWLwsqICk6dWOpw+oKvNIiMlGShYQhqssp1+rJoMGmslCeNiEkTEmQj8mfRRLGoTEYEmocTbosqR2JTVFgz//qxOkomSibPeSInJpRqpCJKoyQOx+tuk2RDPHLOFzC3qERUQoBm0JMlpANFKgM1bpoN8LxHPD0tu+xjSkSWO8xj0F5lvuADb0pOeijIE5UHmRpLBJKiulUH7CZvWAxNjnRyrq9GHAT3r/SG0tyiVsSIRebrih5PcbJmCLCNTmhVs9bjMW5Gon8N7CJIzbJlVtBDtRo14fhJ545CTVqHNgyDtOo2JC4tK15jjuqHi/hwM/NkevQU3HRnmSSmEORlTObrqYppLXLnE5RDOhclpWyK5Z7vbXTnHmJj+Jhpm0MD+WFryHaJD3zGA5hsTuaF0TVU+EKm8e1oLRwF7/YMZD/xCXlwQgqGZbXuR5CLJVacir14ZNo1rOomMeKACBE8w+BKS3KuLQfyUryQpI24ZUF28Yisolpi1VzobbhIOODEZexuSW++pK1crHCRCAEWTntmYsAfottHafoearLoquc05szsei4otaryBPikqlZobbk6RgyuR4TKSQQUWa96SZAg5zTek22ONnEYdlmhC/YYZ7V0DPcWa9S+cmywFBmjUpBa9VKxThDmSa1MLnIESnJcK1DwVPbkVwavBkDlI2DVGrdKCBgC7aijjwQkRXb+TORGBXy+R1m4Q4L2a13PBEvo7Yd2Yz9+K54OUG6Wqxly0E0XCCh0ReuTBDLFTQw/2qkQzGxbUQ3yrkPBYWsrIDpmB4IC0K2k51oypEli5hmBsQz9x3ZX/HUW2EVI2GfSuHk+Mogc0lZ/UxgktkfNEqYy+sQWRGU1EEW/kgl2+ETXKQw7YXOyoGbCPW862nLcZmf0xG9sSIKhVPIp9KPQvI/FnEU77gep4Gj2Vup2goy/SS/3ailWwMAUnIeLuIrI6HgllkX62KexuETO9VeyJGV8hAYf1wQ8jIYDQmPqujVsUAMlRSPX7GYELGux5gP2XU2Lx0WebYQtMHeIOuXilgk1lpAGomZH3ErUvzEYNsMr/ir+UgLh8KKuCJd2XAXF8LeyurJRTIbKsEOToEqL/9hCvn1F2xJcBPT3qDFF5yBG+XDq9WaD7YBDUhjDaiqusWKwApKi7iZMHQDtBCvF5Rbk3ECSdeol6frSEjJnOoZabyY785Y1wlJOhhjlIkcu8A2k64YuCz9EtzJmyGhrxf/jJdwGBczqBwJknX5Ddm4nzhymEj5pguXuRBFPQO0mPtE2Fs6agCRZZkwtqN2jb+ZKKeJt1r7s+9yqFwSKU/ZFNTCI6BAORGbMCVBZZ5JIQuuk/7wnktNlPI4oVH6kGAaQ4uZPZAkEMlwo0ZiL2PLlzehEcAYwaLREDgMFjIJqm6huo7jYTpJi7JVKSQinAx63/K5JRoJQXhjE90Ql1f/uY9boplpNS7hGk/vwLNyQpBvEqkrmI+z20J/McBtRA+gsNcOwa91zsrOVo9RxlYY7cCtSalfgSbpkKUIx5OTMmS2cBFzMSFSUmRS+pIwKY6NuRUTAy4KWQ+hMCPb+aFGqhYv4ihwUmcLeZ2uEZh+qUlbxKUUYsLZMCiDsvOV4I9+gkOBYRGR5Y5DVd0YKmtVFc27wTjpuZFxOaIMcg9cKovNeLHwgLfv2hTMkvGeNBimaCZR9TI4c5ple3AbKBI8lbGGxBll/i4psq5SuRTc8StEWkjPMSgAsogyxJ9yOtZa8p/NmTCW6pvyYBjzeb3UWUSUKh7+rDpEWx48EyET/yyVEkGMjCJXPHXSrRMQTuK1CUuT6R0XRcmRnCit8rC6fpZ6vtJNoWGRo96kB6RRdCqzYFtAANkY0QSKLNkXt5HjQ/krUkS0jJgP6CQfz3PP7IM3zpjbyHh6IAskRLuMIpGl2ugP6Ay1ynYLNhT+M2QJukqzPwOUW3rBr3Ase9YNPlnA+0iY3zSqeCky7Q0aHXKj2tK22G0kp0kLvlEv9MNfbzsYvOqKMluWr/CyJuGrUhaa4nUM2RpZtbinociQdy6klgWIK1e8eJlhcAY4cDb8+dOiBQwYefIGenGIBcuXL/Q2Ekxo0Ea/fhsdErSRcCM/fgYlyrNhElzIi1hq1P/YKDDkF4EJSXqhWSOkRJwMJW78aKMGQ4IOac5I6dCplhkhIR6c4S8lS3kE/SVMWdKG1H4OXaYs65Wgl4z+Mgq8+FGiy4sMtfhE6k8imJA39u6tIRHcVq7gLl4JKXDjxS9d+REEGpJhSINYQgKVF5Lmw4jyxtqQl5FmSII3Mrq1enVjRo9NBULcGFJLQtZgaGZ0GHXGUr8EJRp8nVCiQ7Y0IVa2W5l4P38XaeIFc0UyFpch+VHuF/sK44paZJcNWXQGVYYCoQpOeVEiv4TT+bXlHPxLenApi9dQeLRGysMbXe4HSdOlQcPRpBprAv23kD9ebCSRQAbWp55I9DBI1Qz/BNFUWD+EXeFaYDQhhtoX/QAAQHw+IWcQZBA65JFLLo3HT030rMefQJxBFeNFtmlBz0UbXdfSSxel9MV58kDWT179GAQighC5NNd2TwpGHESySXajX5Zl5FJCv2XEkRdw2dBTlj3S4w+FZtlFkE369cegYVe0aMNTZtWohZK4acEQaEc+dhWE3vEY2QwXnXTiDBIxZENKEGVkoaHgEEXPFbQRiQVE8iQE4Az8QcRQRiheJV1ya4XIkIY18LVXPzQxxF6lNRBk0BUSpQaORViwNGROzwnJj0MbqcpqShc2eIVaypHaKBgvNWfrFzvyOJOsCtamxbX0OCRQRin501Z0/zZcRKcWBHH7RVzX/eZZSj6CIxBD31KkaJtK4TkpQye1KZdyWND1U57uggMRhQUhJOlEyLpE00UQYQbmtVes++la/FG6IT1eSAkakgk1DAZhFxH0JT1OQdQlYGFK5CE9BoFTl5SefYEqFrMapNSISbKZkmo+umSvi0yZhpOD0JG7pQ2hJpqVe386xFtgd/KTqZEN7SgTtQE2RpnIXlzBEK1yMupQaGiBlGG4Sa2cpcNdY6UpOGcmmBa691W7thcQmYusYjdrl5A/VSUUNVTZqobZdlWK/aWgvwKrBb8/X1SZa0qmZJB5WNBDU+aLlXXk0id3KTd0iNX8Fzhgdyr0Rv9JfedwDZ2xpJ9pArm0Ku653xBT1nprVyzuXjAEhqig09QROEbF6iBD9CwMcItQnYvFkvSQLA9Ek5e67YIJC8upQbA/GFKLmKt0kBZk8eOSQ1UKHdJOrvoj6AwbuUuQTNWNVLn63fIK1WCwwCyCaIZ8QSobQVJyn8o0z1Ff6E0/XLScGkTKaplz0vOUdoUJboQqyPvCiDClmRp8hoRfGBn8UsYfl1BoP62RUQRtcDGeII9MRyLKgORDneTsZE9XSc/emCOPHWZEIobKVX42OJnhzAY/1Bmec55TKKOgBEayQd2//JaQLY1EC1kBQ+vm1sFfEUpjYxIemsCALY3QY1P/sqsBcgC3FqMxijHtA8NZYkUz9FgKguuzge88JkDVdO1fdYnX5QqFhS6ZBTl1JA0WHBSuwLQrIxmJEyDBEK9+NUt2KLuNmUpTA3DpawbXSswXAGYQpA0MDDbbjwLBMoOb6Eh+AgGSoJJymVFKDFfFytJlHmUwDw1liDtsXxefJo9q3VIryGNWQjSDHGF5yx8/81ql9LMoGWItlq160LqsKS4RXqhOqjMXBG3Xme9skUgOedXjgGQUvPxybfSAXdjYV8Rl+uQ+DoEgeJrIMXl1TiGV48eIwnJJd8HOCxoCEQVlsq07Ncd+dwTT0ywFrUvdkU+KY8sV8BShBPKjjSkT/yKG4kI9PbWye+aqDkix2ZxxJVIngOFJRhQ3LbJ9s1PjRI68SjOk7r0tO/DyB+eoUz2QJg0+ZXEUzexnvbch7YQGA9dLEpkUh8JOcWHpoUBUFj5XOhQnbGwY/vzlw8GFUoDpyZEPQVW9c1mnS+qkyDbHJUMQGdFfoJpZ4DrlN4MEZzdVi83IHlg/CSGOYmVRVBoNNDgv8E6x6oxmm1xClKLu8DwkY6II9zk4+nAOdoJEW2Vk06srTqR5GVPZ5/zRwWZxlmFJhQ7m1Km+XiZVPY786TZV97IYCSt28/nm86JIIf4k63gO/RE/ZYkW7ViSZyPCAXaxu0aWJcSJVSVZEv9ds02G6QquNlJJnRDnQ1J96leGbSLFzIWcA9XAfCkEDqn+JDOIZA5g9Jsl4tT3TdQZhVxbg8uk1JklLsWtZEl8512YlZt/Qhiyam2XttLXnLBWxEJw7CQYrhU2itzxTl/gb1kQ47sq3hOP2VlOaYCjhfPZNHN7NVO3uvugwyAHXM6szG94gmLEsGlzD5pfniA4Y36UUD3qU6bBxLSTFwqkegb7iqvGKkT75FQzPg3KNx2KhSN10aoB8okX5bGvRZ7LXptZo1Tvo9L1rNRFGGNytDKznMVASDB+S4peAzO5S/bDf1btoZMwDCJNzs1lkjJWpBYJDv8R0JXMIixPu3f/kBfd6SBQoQpE2nfd7OLgfHBFT9i6tthfboll/JK0jsDUj6PSbI288amD28k59qX5eN1b0cv6a5fxuBFEBjsuqtb1z0K5UY5I/tjeTMrEuXgYnpZBEhgQXZ2wLVl7xgIjDCGZJZJC5iuJxBXkJhMht6K4XSgrDUkECZVMvqh9WkRYuOCKEzv3MEDxpaySXpTLYL0WcGOV0mKheCt0e5hnmn3zY67nzH4lliPZc2tHJuU5fry5yhVKy52mCxPKSPUL9rSeya2MRtfE0tzOvaYJTeeTSDWHpSNf5QnP8h4pYu2a2HwhmV6TbpQFsJ0y27ajr/Wp50Xa0QTDo+KYyA/X/zKolwQ7J2WfR14ji8l+nAqMtAw06uz+o+xmPzva0672tbO97W5/O9zjLve5073udr873vOu973zve9+/zvgAy/4wRO+8IZ/+4gOr/jFrz0Ljn/84CHP+L8/Pgtqt/zkGS95tGM+7Z3nfObdXvm8V97xcTf95en++bOv3u6l5zzqQy/7xSd+9rYnfOv5nnvSZ371rd/93j//+8iDvuydF77Zhz/33QO/8aI//fNZ73vWU9/4sPd88qv/j+ZbX/vdz/7tw2/42ou//HrnPt6B/3rjr5/9sXd/7Ke/ffe/Xf7e3/7ok5//+n9f9Juf//1FHwCCn/31H9wxn+oJoAJi3//1ed/xFR/7QWD3FWDq3R8Fmh8G6h35ZSAHHiDuOV/1UaAIGiDmvR//LeADgp8BgmDu+d7+AWAJQp8FkqAEemAFyp36LV/b/V4BpqAK4l8NxmAABiERdqAR2t0GHqES/mDgIWAIFiETCmEEDqAM7iAEIh8Tsp0JRmHjxSD6OSEV+mAWLuAYWiED6iAINqD2YSEVAuEVruEJcuEQLiEdol0S1mEHlh763d3/0d/5beEWKl4g+t0L4l8fLl/7lWELDuLlASIjep4J6uEBHqLrpWEV1qD/7aEf4iEn3iEnSqAmAl4o1p0YkiEhquAo3uAlWqLyNeEcEh8NDh8PImIjImL/D55e82mi5ckiDrbh/O3hCILiJ8qdJw4jDfYeLJJiMmJiAqKhGa5gKjpjKxqeFE4gFK5iGT6jKppiFAbjLbKhNb7iMWZhNBpjMRqjJIbjF8LgD07jDhaiJc6gPBKgL6Yj/D1gCrKh8ImhDz7iPW4jP4ojPZ7hMV7gM8pfMErfIYKh/lFiDvZiPMZjNfpiERrkPEKjMdbdOQ6jE05k6k0kPJajDRJkG3rj+RUkHKYkQa6jNg4gOFIk9rUgM/4iTY4kSl6kTQrkNq5gHJKkG8rhN8qjRcohOWYk3W3kJzIkTDqg/k2hTqZfOxblG/KkAJZiPrpkU1LlS+4kFMbfTPok/0ZCYla2JPXxIliSJVW2pEgq5fOZpQe6ZVX65FYapR0CAF3eJV7mpV7uJV/2ZV8iJV6K5EiOomAqYCk2I0RqY2GeYlompuYxZT3WI2EuIi1OZf1xHzBG5lIWH0Lm5AUepl/+A2De5WJqIVQKHls6Izaepe2V5lcuXkCOY2Ny5WbSZja+5ixCZhiqYf/FplpeY2iq3WjSpS4mYkTKZm9ypmQW4gt65ffpo28eH0tCojuWoHQ6pT8aonMOpGlKpErWpjCiInDaoyLCXkii5XHeZlQmp2Gu52/iZHDWZXzO5mmS5DR2Ju+xJ1a6J0NO50yKYB/CY/Sl5iveon6SoT4CZ/9PPuUcpuJD8uZuHuhP6udcjudrBudwGqVrpmGC2qd4IuZ+PudYduhKDmY28uMu7uOEdmeEsuZSGmiLsuhHxqRtpqeDuqiMXmhZ0qgMwuVByqV66mWGzieRUmMkUmIjCujtbSgOBiKBcqCS4mKUnuWN1qiU/h+SFulR2qWWimIlzh6TsqCO0mfohemXhiiakmmDgqiViqMLniGJOuWHiigKWmiX3t2Q3qmDMqfp7aOTvl8iBirkmSkDDiUz8umRAmpKOil1uiCWTqmYPiF8cmOV5ih6kqBJOmCmqmdCqumdIh6XfqpNVud3XqeHamf8tZ+h6h5yWiqjoqJMzql3Smj/Q2apKuInUa4meH5lpcrlpvbmr9Zmp+6qqLpdnnYp6gFieLZonLIjba5q8LVqesoqGJqqsFomTJZjrDIrjlIpm3pqgYoltlplKzYrQCposW5pupKerSrktv6jeTqqo+JeohYncyZpbipk8P1pvU7iQh5nu+qreTYpoBpnF2ZnTgbpf9oiwa6rujpsHkIsHpJnawZsfkosxg7jsWbsB3Jsmz5m+X1m3xGqx5as322syaasdfppqjbnoLqru5JsN7psv0pnwEKrdhLgq9YqOMpsyqYsyv6sx+5smkYnHHokYw6kFL7kyt4sJvqpuIblgVao0FYtEoaq1Watt8oqmhqt/8LyYUW6Zzg6pmZGqlRKqHVqrdpe7dq27WXaorxOHsVq4SLuIsxepiO+6y8irJx+rNu2bdD+reCmq8/qarQOLuKeXeAmLuP6JaQKImo2LuMuruRWruVeLubuHeVmLud2rud27uZ+ruiOLumubeiWLuqmruo67Omuruu+LuzyZevGLu3Wru0q4ezeru7uLu9OXu72LvAGr/BqINYOr/EeL/IS3u8mL/M2L/Aur/NGr/TCLvROr/Ver+hWL/ZuL/dKrvZ2L/iGr+kWr/iWr/mO7veer/qub7qmL/u+L/zGp/s2LtYWb/3aId6Fqv3iqeLmHfkG3v+aXQCn3QAbK/8yXv8BA14Ci6bgLfDuzu/k9i8Bl93+4q8ECzAFU/CI6K8GJ/AGMzAGC/AHZ/AIX3AHhzAKE3ASbnAAc7BornBdku8Is3AMq7BdtvAL1+8GcmkJi/AN17AEl3AP2zAI+7AP2+8KD7EQD7HwQnDi3u8EZ7AURzEDu3ARI7EJZzEUF7EWZzEKW7EFp3AB6+8WX/EF/28FT3Eah/HakfEZh3Ea73AZpzAXvzHbgbEY07HxOjHizvEUV3Hb/TAJ2zEd47AJY3EhHzINq3Ebs/EYJ/IXszAe/3EOCzEbe3El47EmQ3IdrzEnZ7IeS7J8HjH5ObDu8vHg+vH+PjIHb7EnG3IeU/GZGoPx/Q7wLAfyJ08yJVMyLO/yLrsyHAczIfOycAozIx9yMftyHe+xKdeuDi8yFd/xJevwJxOxK/fwDd/hM78wJgNxNx9zLBuxN3Oz4mpz4vWyEdfeK5/zOHfyJZezIJ9wKW+zOjOxEjcz7aJy/PIdPkvz7fXzPmcvQAc0QRe0luqzQSe0QosfQi+0Qz807Q00RE80RRshAAQEADs=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%make_image -caption 'IDV with display of injected random dataset'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------\n",
    "# Example 2\n",
    "## Open 2D dataset in xarrray, load the data into IDV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno -68] NetCDF: I/O failure: b'https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface/pr_wtr.mon.ltm.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-930a7d1a2285>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Open NCEP monthly climatology of column water vapor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mda_2dPW\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface/pr_wtr.mon.ltm.nc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#                   decode_times=False)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs)\u001b[0m\n\u001b[1;32m    318\u001b[0m                                                    \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m                                                    \u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 320\u001b[0;31m                                                    **backend_kwargs)\n\u001b[0m\u001b[1;32m    321\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'scipy'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m             store = backends.ScipyDataStore(filename_or_obj,\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(cls, filename, mode, format, group, writer, clobber, diskless, persist, autoclose, lock)\u001b[0m\n\u001b[1;32m    330\u001b[0m                                    \u001b[0mdiskless\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdiskless\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpersist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpersist\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m                                    format=format)\n\u001b[0;32m--> 332\u001b[0;31m         \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m         return cls(ds, mode=mode, writer=writer, opener=opener,\n\u001b[1;32m    334\u001b[0m                    autoclose=autoclose, lock=lock)\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m_open_netcdf4_group\u001b[0;34m(filename, mode, group, **kwargs)\u001b[0m\n\u001b[1;32m    229\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mnetCDF4\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnc4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnc4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mclose_on_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno -68] NetCDF: I/O failure: b'https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface/pr_wtr.mon.ltm.nc'"
     ]
    }
   ],
   "source": [
    "# Open NCEP monthly climatology of column water vapor\n",
    "da_2dPW=xr.open_dataset('https://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/surface/pr_wtr.mon.ltm.nc')\n",
    "#                   decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "da_2dPW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Send dataset into IDV\n",
    "da_2dPW.to_IDV()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------\n",
    "----------------\n",
    "\n",
    "# Example 3\n",
    "## Compute  mass weighted vertical integral of specific humidity \n",
    "## and load the data into IDV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open MERRA2 Reanalysis dataset\n",
    "#da=xr.open_dataset('https://geodesystems.com/repository/opendap/038361f9-fb9a-484c-9f1d-3623a12a47ca/entry.das') \n",
    "\n",
    "# Open NCEP monthly climatology from Mapes IDV collection .ncml aggregation\n",
    "da=xr.open_dataset('http://weather.rsmas.miami.edu/repository/opendap/0a9bbf2e-458d-4b7c-ad3a-b450988a0587/entry.das',\n",
    "                   decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (lat: 73, level: 17, lon: 144, nbnds: 2, time: 12)\n",
       "Coordinates:\n",
       "  * level               (level) float32 1000.0 925.0 850.0 700.0 600.0 500.0 ...\n",
       "  * lon                 (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 ...\n",
       "  * time                (time) float64 -6.571e+05 -6.57e+05 -6.57e+05 ...\n",
       "  * lat                 (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 ...\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    climatology_bounds  (time, nbnds) float64 ...\n",
       "    air                 (time, level, lat, lon) float32 ...\n",
       "    valid_yr_count      (time, level, lat, lon) float32 ...\n",
       "    hgt                 (time, level, lat, lon) float32 ...\n",
       "    uwnd                (time, level, lat, lon) float32 ...\n",
       "    vwnd                (time, level, lat, lon) float32 ...\n",
       "    omega               (time, level, lat, lon) float32 ...\n",
       "    shum                (time, level, lat, lon) float32 ...\n",
       "    rhum                (time, level, lat, lon) float32 ...\n",
       "    pottmp              (time, level, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    description:                     Data from NCEP initialized reanalysis (4...\n",
       "    platform:                       Model\n",
       "    Conventions:                    COARDS\n",
       "    not_missing_threshold_percent:  minimum 3% values input to have non-missi...\n",
       "    history:                        Created 2011/07/12 by doMonthLTM\\nConvert...\n",
       "    title:                          monthly ltm air from the NCEP Reanalysis\n",
       "    References:                     http://www.esrl.noaa.gov/psd/data/gridded...\n",
       "    dataset_title:                  NCEP-NCAR Reanalysis 1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get 3D specific humidity, subset for just 2 time levels. Call it q\n",
    "q=da.shum.isel(time=slice(0,2)) # just take a small slice of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "NetCDF: file not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-fe8554d81463>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Send whole 3D dataset into IDV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_IDV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/ipython_IDV.py\u001b[0m in \u001b[0;36mto_IDV\u001b[0;34m(data, filename)\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mNamedTemporaryFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuffix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'.nc'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m                 \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m                 \u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mto_netcdf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1588\u001b[0m             \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1589\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1590\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1591\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1592\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mto_netcdf\u001b[0;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute)\u001b[0m\n\u001b[1;32m   1148\u001b[0m                          \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m                          \u001b[0munlimited_dims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0munlimited_dims\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m                          compute=compute)\n\u001b[0m\u001b[1;32m   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m     def to_zarr(self, store=None, mode='w-', synchronizer=None, group=None,\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, writer, encoding, unlimited_dims, compute)\u001b[0m\n\u001b[1;32m    721\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m         dataset.dump_to_store(store, sync=sync, encoding=encoding,\n\u001b[0;32m--> 723\u001b[0;31m                               unlimited_dims=unlimited_dims, compute=compute)\n\u001b[0m\u001b[1;32m    724\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath_or_file\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36mdump_to_store\u001b[0;34m(self, store, encoder, sync, encoding, unlimited_dims, compute)\u001b[0m\n\u001b[1;32m   1073\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1074\u001b[0m         store.store(variables, attrs, check_encoding,\n\u001b[0;32m-> 1075\u001b[0;31m                     unlimited_dims=unlimited_dims)\n\u001b[0m\u001b[1;32m   1076\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1077\u001b[0m             \u001b[0mstore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/common.py\u001b[0m in \u001b[0;36mstore\u001b[0;34m(self, variables, attributes, check_encoding_set, unlimited_dims)\u001b[0m\n\u001b[1;32m    361\u001b[0m         \"\"\"\n\u001b[1;32m    362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 363\u001b[0;31m         \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_attributes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattributes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/common.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, variables, attributes)\u001b[0m\n\u001b[1;32m    446\u001b[0m         \u001b[0;31m# All NetCDF files get CF encoded by default, without this attempting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \u001b[0;31m# to write times, for example, would fail.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m         \u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcf_encoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m         variables = OrderedDict([(k, self.encode_variable(v))\n\u001b[1;32m    450\u001b[0m                                  for k, v in variables.items()])\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/conventions.py\u001b[0m in \u001b[0;36mcf_encoder\u001b[0;34m(variables, attributes)\u001b[0m\n\u001b[1;32m    571\u001b[0m     \"\"\"\n\u001b[1;32m    572\u001b[0m     new_vars = OrderedDict((k, encode_cf_variable(v, name=k))\n\u001b[0;32m--> 573\u001b[0;31m                            for k, v in iteritems(variables))\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/conventions.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    571\u001b[0m     \"\"\"\n\u001b[1;32m    572\u001b[0m     new_vars = OrderedDict((k, encode_cf_variable(v, name=k))\n\u001b[0;32m--> 573\u001b[0;31m                            for k, v in iteritems(variables))\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/conventions.py\u001b[0m in \u001b[0;36mencode_cf_variable\u001b[0;34m(var, needs_copy, name)\u001b[0m\n\u001b[1;32m    233\u001b[0m                   \u001b[0mvariables\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCFMaskCoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m                   variables.UnsignedIntegerCoder()]:\n\u001b[0;32m--> 235\u001b[0;31m         \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;31m# TODO(shoyer): convert all of these to use coders, too:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/times.py\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, variable, name)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m         \u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpack_for_encoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         if (np.issubdtype(data.dtype, np.datetime64) or\n\u001b[1;32m    386\u001b[0m            contains_cftime_datetimes(variable)):\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36munpack_for_encoding\u001b[0;34m(var)\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0munpack_for_encoding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mdata\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    293\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;34m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_as_array_or_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0mTODO\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mremove\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mreplace\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0monce\u001b[0m \u001b[0mthese\u001b[0m \u001b[0missues\u001b[0m \u001b[0mare\u001b[0m \u001b[0mfixed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \"\"\"\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'M'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_cached\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m_ensure_cached\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    619\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_ensure_cached\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNumpyIndexingAdapter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNumpyIndexingAdapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m_apply_mask\u001b[0;34m(data, encoded_fill_values, decoded_fill_value, dtype)\u001b[0m\n\u001b[1;32m    130\u001b[0m                 ):  # type: np.ndarray\n\u001b[1;32m    131\u001b[0m     \u001b[0;34m\"\"\"Mask all matching values in a NumPy arrays.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0mcondition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mencoded_fill_values\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatastore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0;31m# Catch IndexError in netCDF4 and return a more informative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/common.py\u001b[0m in \u001b[0;36mrobust_getitem\u001b[0;34m(array, key, catch, max_retries, initial_delay)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_retries\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmax_retries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Variable.__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Variable._get\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: NetCDF: file not found"
     ]
    }
   ],
   "source": [
    "# Send whole 3D dataset into IDV\n",
    "q.to_IDV()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vertical integral -- not working "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For a vertical mass integral, get dp/g \n",
    "dpbyg=da.level.copy() # make a copy of values\n",
    "dpbyg.values=-1*np.gradient(da.level*100.0)/9.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "NetCDF: file not found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-adbabca4d553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mq\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdpbyg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/dataarray.py\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1808\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m             variable = (f(self.variable, other_variable)\n\u001b[0;32m-> 1810\u001b[0;31m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreflexive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m                         else f(other_variable, self.variable))\n\u001b[1;32m   1812\u001b[0m             \u001b[0mcoords\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_merge_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother_coords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1575\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataArray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1576\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1577\u001b[0;31m             \u001b[0mself_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_compat_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1578\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1579\u001b[0m                 new_data = (f(self_data, other_data)\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36m_broadcast_compat_data\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1818\u001b[0m         \u001b[0;31m# `other` satisfies the necessary Variable API for broadcast_variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1819\u001b[0m         \u001b[0mnew_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_other\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_broadcast_compat_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1820\u001b[0;31m         \u001b[0mself_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_self\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1821\u001b[0m         \u001b[0mother_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_other\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1822\u001b[0m         \u001b[0mdims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_self\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mdata\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    293\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36mvalues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;34m\"\"\"The variable's data as a numpy.ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_as_array_or_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/variable.py\u001b[0m in \u001b[0;36m_as_array_or_item\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0mTODO\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mremove\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mreplace\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0monce\u001b[0m \u001b[0mthese\u001b[0m \u001b[0missues\u001b[0m \u001b[0mare\u001b[0m \u001b[0mfixed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \"\"\"\n\u001b[0;32m--> 211\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'M'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_cached\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    625\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m_ensure_cached\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    619\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_ensure_cached\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNumpyIndexingAdapter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 621\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNumpyIndexingAdapter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    622\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    623\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/coding/variables.py\u001b[0m in \u001b[0;36m_apply_mask\u001b[0;34m(data, encoded_fill_values, decoded_fill_value, dtype)\u001b[0m\n\u001b[1;32m    130\u001b[0m                 ):  # type: np.ndarray\n\u001b[1;32m    131\u001b[0m     \u001b[0;34m\"\"\"Mask all matching values in a NumPy arrays.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0mcondition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mencoded_fill_values\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/core/indexing.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mas_indexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 508\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    509\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/netCDF4_.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatastore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautoclose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m                 \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m                 \u001b[0;31m# Catch IndexError in netCDF4 and return a more informative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/envs/DRILSDOWN/lib/python3.6/site-packages/xarray/backends/common.py\u001b[0m in \u001b[0;36mrobust_getitem\u001b[0;34m(array, key, catch, max_retries, initial_delay)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_retries\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mcatch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmax_retries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Variable.__getitem__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Variable._get\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: NetCDF: file not found"
     ]
    }
   ],
   "source": [
    "q*dpbyg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "CWV=(q*dpbyg).sum(dim='level')  #should give Column Water Vapor in mm \n",
    "CWV.name='CWV'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loaded\n"
     ]
    }
   ],
   "source": [
    "CWV.to_IDV() #this data is in data sources, manually create a display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Operate the IDV to create a display of the new data, then %make_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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