regrid_CESM2_seaice_to_1x1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example script to regrid CESM2 sea ice concentration to a regular 1$^\\circ$x1$^\\circ$ grid using xesmf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start by loading appropriate packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import xesmf as xe\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get historical sea ice concentration data from CESM2 for member r1i1p1f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:    (d2: 2, ni: 320, nj: 384, nvertices: 4, time: 1980)\n",
      "Coordinates:\n",
      "    lat        (nj, ni) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    lon        (nj, ni) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "  * ni         (ni) int32 1 2 3 4 5 6 7 8 9 ... 313 314 315 316 317 318 319 320\n",
      "  * nj         (nj) int32 1 2 3 4 5 6 7 8 9 ... 377 378 379 380 381 382 383 384\n",
      "  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
      "Dimensions without coordinates: d2, nvertices\n",
      "Data variables:\n",
      "    siconc     (time, nj, ni) float32 dask.array<chunksize=(1980, 384, 320), meta=np.ndarray>\n",
      "    time_bnds  (time, d2) object dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
      "    lat_bnds   (nj, ni, nvertices) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
      "    lon_bnds   (nj, ni, nvertices) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
      "Attributes: (12/45)\n",
      "    Conventions:            CF-1.7 CMIP-6.2\n",
      "    activity_id:            CMIP\n",
      "    case_id:                15\n",
      "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
      "    contact:                cesm_cmip6@ucar.edu\n",
      "    creation_date:          2019-01-17T00:39:18Z\n",
      "    ...                     ...\n",
      "    sub_experiment:         none\n",
      "    sub_experiment_id:      none\n",
      "    branch_time_in_parent:  219000.0\n",
      "    branch_time_in_child:   674885.0\n",
      "    branch_method:          standard\n",
      "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pade7652/opt/anaconda3/envs/regrid_env/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'siconc' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n"
     ]
    }
   ],
   "source": [
    "direc_i = '/Users/pade7652/Google Drive/CUBoulder/Projects/CESM2/Data/CESM2-CMIP6/' # Change to match the directory where your data is found\n",
    "ds_i    = xr.open_dataset(direc_i+'siconc_SImon_CESM2_historical_r1i1p1f1_gn_185001-201412.nc',chunks={}) # The 'chunks' option above tells xarray to make this a dask array\n",
    "print(ds_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename the coordinate variables to 'lat' and 'lon'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "xESMF can only recognize coordinates named 'lon' and 'lat', so if they are named something different 'lat' and 'lon', we need to rename them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds_i = ds_i.rename({'latitude':'lat','longitude':'lon'})\n",
    "# print(ds_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select the variable that you want to regrid\n",
    "\n",
    "For this example, we'll be regridding sea ice concentration ('siconc'). Can also subset for specific years and/or months at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'siconc' (time: 1980, nj: 384, ni: 320)>\n",
      "dask.array<open_dataset-49a09dc33c4112a84ad64670f8eb4236siconc, shape=(1980, 384, 320), dtype=float32, chunksize=(1980, 384, 320), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "    lat      (nj, ni) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    lon      (nj, ni) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "  * ni       (ni) int32 1 2 3 4 5 6 7 8 9 ... 313 314 315 316 317 318 319 320\n",
      "  * nj       (nj) int32 1 2 3 4 5 6 7 8 9 ... 377 378 379 380 381 382 383 384\n",
      "  * time     (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00\n",
      "Attributes: (12/19)\n",
      "    cell_measures:  area: areacello\n",
      "    cell_methods:   area: mean where sea time: mean\n",
      "    comment:        Area fraction of grid cell covered by sea ice\n",
      "    description:    Area fraction of grid cell covered by sea ice\n",
      "    frequency:      mon\n",
      "    id:             siconc\n",
      "    ...             ...\n",
      "    time_label:     time-mean\n",
      "    time_title:     Temporal mean\n",
      "    title:          Sea-ice Area Percentage (Ocean Grid)\n",
      "    type:           real\n",
      "    units:          %\n",
      "    variable_id:    siconc\n"
     ]
    }
   ],
   "source": [
    "sic = ds_i['siconc']\n",
    "# sic = ds_i['siconc'].sel({'time':slice('1980-01-01','2000-01-01')})\n",
    "print(sic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pade7652/opt/anaconda3/envs/regrid_env/lib/python3.7/site-packages/ipykernel_launcher.py:3: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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z+pYHVH3/veq5PbGnz9r47NFoTxqkU0Dyg0jiEuUhxHkR79oxaC9fd+dOu777vl3WwOPasrd/h2tZrNCyV8gFyildmIlpydu44JynuMs5b+M9u1Svavc2be0cH4JwrDaczkcPnpGURvv2TThhB60tKqZ0y6C6FE97w3udZOQr+1SKH+4MiJVZdGa2kGvHJ9mwVy/x/kXnPc7Z13NYPze36W6KScqeYhSK8Sk2TzNEZ/KJIoVNLd6r2PFqZQGNOoVRNTAHjrqDTt3xBsMTqu39yj07rJv/QMYUYttj+nm98xFVz01bm25YiDwEKc4gBrJKAtIcGb13VMUHXnbL49bNv3jIXp588lG149SoMdgkuvjBTTfs9b2Ir9pUuBdmorILksDcos9NzhFUD7Yyz6u4mgU9iIlvUMnruGZJp11vWKbE4u7JaWtVZ4e9PDyhXse96P7u1pzOQilB4zcTjbkt0MjG0rskCjScySdsvsQwxwsSpxylcDRjMOXaZW9FtQcAFfoMhSy69YLHHyx45t7l70m8c3frsQC/vXuHte0RJSCtPZtVPZNeFzaoYgzkn3aQ1Y646Dv32/Ujt/2JnvR71MYqiMZqsPElyn266YZ4n2diBOS0ChmYG1bhAgO7Oqz2dnXpyZSG8kvwQCuZdnFusd2dW1yvW9LlCsn8KQ/lv9zYxlLzz+ZtCxsCmWembBxKCXGIyvSl+6lU5Pm9vtrxk+YUgHETcvWyXNohOMb3Iu3rqB4o8/vNasDqziepXR7cWXgMgmHASZMlEiR4LmwAAAAAgKgwQ6NAxAKSB6T8xxf/aC8P/OkO110tg2FqAHpBDQZboymOxma/Gv0+tjBnbfMOipGpy4wBNc16XsJWqttm9Xzbra4L25zCUGontblpidQJxANb3XzOS2KJcqbcLOAejzuIZwRxGl0dxbGLxeqWY1Etdk5B0xeVSqpeLaEi6fTM6Qml/eKxAdtG1cDFx3epwTGTw8OuyzrAs7xQKBlIH6EE5M7RaWv2rj+oP6bGGsJdDWKEhDgM7bb01WNt14LMbPglh6fkb8wuyTqislXHQLaQqGxvlQTyCpWeQotJOVgmf2CLPStSEZ3nN5ODNMafvOxi69wLL8o9niE5G2UQTVARCbGZLOTarlacYDlirZIBMEFjMGt5TkHPIep0MefoFHuSeUQNoskVkLztkf1KQO7apVvhyRGVvq5OvORfzrQ+/OEP1+39c8mfqQdEJCDHRiat9hXjsRCOlVgi5bWgTvCUjzrv5+hONWBmV6u6FFtIIDrTETqzDrmzDZnJ4vssFQ/ZrkWlNljaSBPQZEi5rJkpvMi0huY6eXVrs5EyyBPzePklH3X2F3EZByD+QJBrIgoLYNipBKMQd37HLfSeUY/6jvLzREXG77dezM2ENzI5Zz2wW1kbh/fr9Dw8FW9IL2IUnsCek9+Y79WJAXBhVysGki1INXRXB6GUOR1iMaZ43NqDnO6HaG1rdQbYyIhrrlt1I9PizGzkpqPo6VD788huEXosHMWa6Qy2sfIH25gUy/nYrHOd2u+tG+MrL/1IoNHfQVLhoNECtaDYdVbOfNrlCic/N3Al1kvzuIXer5zjVZOovRQ8aYI5jTC3U2J5lBzIO8anrCf3KMvjzOSM26Gvg0HotKPX2vV9u9VgHpB0ARlTdzVEYgLc2qNqhPbI/iXWSL9K7dMhQpJUYasWcFKzS1ueheJeZvdMt35Nh46V5N5tzmwzBVP7+P/h18g3NWULTqkWdsS33z7FHvLl7AdANUZNJ9GKl4SZdCSP7gw9r6WNkM72kyNT1uDgZE5e3kqMQpVYImU2sj/riT3iAhzYVROQ+GqTgNzssRDeOsm4wLkiZ2W2ozk3MTxPmcmY81qLO1s8IFw5y0Fj/Ar+UWB/e3aacINywoL4RFAt5NqqpVgLKsoqcTnHKVVPvc9Twn1MREDun5i3JieVcFwUV0oEXsVyBtZIarY7NqvUgKDxQBqfhBHmJg6zb+RiU4Rji8rJaXWqObHbOtqsVu2Hltlq2IXdpS2K3W2qbm9uznFxywAbd8aawtbG3Jlj8lYZu8GMB9JJvQVZkPc3RWatZ6KJI/IdmHNhu96X/HAcEZrZiDyLYa2RnJOSefix/ZG8f2TAThYYCEgAAAAAAAKjsIMDAZkgqpmby3vsiiySbH0Uy2NXn1rVvdSue5b0WD09alufnnWmt7PV6u9Sl+rSdrWtq6XZGX1tWiq9I/q4F55njbRTQ+bGRdrGxiKDXeISU2haFUwLaVzODzQe1U7tU0tK5V6s13cr96xkaeDMDZJ2R2DLoXiVZf+BkRnrW1+4PNR7mseQNkLS+XS0ZqylS3XmC2pDmXlPGFGtELf69sf1/Nug4YCAbGDqmcyV3zu0iDTd1t1L1HLPMrvq7e+165Wreqz+PtXA9XUpsbiqp9Va26tFZZsSlV1tLY7rWubV5oEzXiFl50LzuHG4oeY8kjkrCdGeMr2iRSIzjm5sM22QwHMAMxCSIIq4yDiJsEbA7/vi79PttIq7OOukA/NrWaTpWdT7L6d2T0ZVW8agwc9c+fGC53DW+R/U7+X+ttLccVu6jtpYZkuPGqw41hyRDNDtu99zwXxWzTzzDXb94B6dRmjftmjeP6oOOlzYgYGAbDAacgYAEY6tam5ri62N3f1qcZmqDzx4hV2vXtFtrVyiBOT6flUfvrzTWtetGrulZI1kutqbHeHYpevcOV9VN5wtktIjF1hA/tcVFxc8XbEM2MfSlsq4CMkkWIkAqBZxEr5+g4Ik7pBbxEVtgRNLHG+TpjKr13FH192utnEts82IMOTBhdN68OGUHnyoMlAo2rW18bjlS6xH1qgUak8cuNyux57srXxiED7xJt0OZ7SsMCybm36oUvVsfNkS68WHqvd911fvdGezAw0JBGSD0HDC0bQ2tnXmuKvZ6igWx4MOUZ9r9cpuu16ztNM6dIXa/8gVXXa9tqvDyfXopukhF7YxraH9VtRgymhtEY2fuyq/p16KK3Q+NT/ikGrED1geQZQEHZTidz/USsTFSSyaBLXkiqfDHMgnyy16oAt7G8SiKLBfxGslY2eMdKhlEwtPEak6ysfq726zTlin2tqthyrvz5ZHDrBmRwZy06qVg4jPjBaSbNlsU224tUS1818dOsj62Q8esJefvEMLyDrOguMHDJDByYhJPQhNXauy7U95bfCjg0SLxjxXBYtGcYd0qZhGq4uEYqcSju2dygLZv7LfWr1GuVFW9KsGZt0yVR9PjdsRy1QDt5KEI9PR1mR16pHZMpWhHe+oVRNvZzjWsVpTm8WJNHxGEC/4MeG1fge91uIq9KolEKuVguhc7RlhTegd6JGbNNzNizujO9SSP5e37B5WFsifP6Ha72/+crO1+ZbfqgNNRJBSRyyRPDmEGA86ul2DgkwioWcj40Tm0/d85i7SIidW/uaVcfwJz8j+9BY9XXOErF3SFovPFzUQkDEk7sKxEJtuJtdJr3JPtPZpF3Vft9XXr8Rir45j7CQ39DId57isV4nK4w9Q+/zVqiXWyh61TnrUbGmUATPSbHbQ31ddVtgNnSYh5fcA4phIWCVBXEiCiCxHGAaJTa7kuzGtk6ZwZIYm5qzdo0qsHbm6x+lkj07N2cv3DyjX8bW3bbN+8YPb1UF2PqLqKFL75Li1jS9A8k4a+SdjJSB/XQUBuTSZAhIu7DrTqGLRj40vbra+Mf4Ue7m7W8cqdrVaHR16zmrtcu4iNzQXZoUecb1OC0n+u7fTnZrQfh2JRTGUf+oTEI3FwGAaAOqLn1PPbzrTKAZrSAYKtkjKSGuZbGHv2Ix1y7f/017+Kz0Qh8Vlm26HV3SoNveA5V1W9woVgz6xZ3PlrmyBRahMwt1AII1PcOozfh8AAAAAADQssEDWgSRZHb2sWqViXSQVT3dHi5OHUVJUdLa1UDB3q5NWgjlAj7LmQTLd7c05c7pydLkcQ1w2pvs6Te7qUq4vuK1Bta41732WBLd0GEwrYjHroTfno98xTCqZRcdME5TRGSMk3lG8O4ykPJMR3Yy0s8vJS9S3TMWpT0jMYhQWyAYFaXyCAwGZMNH4h1UvteuxCdUAtGtX8foV3dYhj3y/6u//kslf2vXMsSrXF9PWoge76EZsWVeLE9N4oM5FtlTnfOSpCN0cjZaTQkcayjTc3KXmBE7bgxvEA1x3wdqgYim/zEkA3GkGM1U5rwOXaTFo5I5951kXOoHk0kHnSRr6+9W+uzpVdgxrUqXdAaAYEJAJY2pGBSZL+gZvrrFa8byD1CjseYqDkYbtUD2YhlPyjEyqQO4+He+4RM86wwlyRTRxwlzGnFxGZprxGyRiJs6N4gEZtWWznAdw2NdEMSoUAIjFwvdPJZ1YEZe2eJSJDSpIHCO/k5m0XNpIv0GGn7vqEuvfzrwgJ36SJ2lYrsVmZskqdX5Du/IGuqSFFNgoIgMCsoEtj940Ou3HPNs6+mkqp9YyPV3VSbt/rDYOV+00fNnQ3+00ZjIY5qv/tcnZ/q6z35/TC5YBNmZjLbnQJD2Ft3fvTBOmF/j/Jr09rJD0e2D6ue3CHiMOmA8ZAEpdJ8CKTjD6dHLt9dxOOduyFU/DaI74lnazEDwJAzM+Pe8MppE0aktXqI7/0A71/LBmJso6n0YlmxIvV1RAQDYoftNFzc3OOcJxpY5BtHbX8qxcVi1pdyygn//kJXn5zJzpAiOwosnsDmYjajYCfmIyaEONh2p5hBXfwH+kbulp17SVvsj9VApc44XhazisoCj2+/mF4kQ1y1XQWaqu/sTH7PrN7z7frg9Y0mkdpC2QS5crL9FQe3cqBSQIBwRkg1Fs/unzjttKgpFKHYWj8FmfuVoF82FXrpArJE6CJDtO2wOzlkLO/G6Dfs9pF5phhaPA+5udJ1AdgsZflxs+I7GQn/h44RmwqsFyPchmgsKe1vS6addsOvQMMqmcZRAmyKBAQAZwOxcTbSA8UTzswgjPtAuUWoLvOjwiPCACU0rW9cxcXmQa1aiRGMnT3nqW1aVDiJzOvUxByInAUxgHCYIBAZmAFDuNeM61JO0PZgysiTdpvz6ttFuUDQvnOXq6witqKCTXUNjT+kklGLt0NoyWLuXCnuepB2enanYudccOC6n3STQOEJAAAAAAAAT0IwRkVSx8cGWDpOA3MruYJawS1zRGgZf+bkA68LNuOYNoOBF4HdSLjMpmjlinEoo/sXqZXQ/s7k2XBRKEAhbIOmAK0Urcz3Bdg0oJO4gpLTGOccwJCmqPGUNdajCNd33o0ducXFzPJiPZJIKOrK6EqzZdbL31vRvt5becoMTkfY/vt+uB+9MnEeDCDk76ro4yqMTyCKslSCtekVRKNMVJnMbpXEC88IttjEJ02KPqJdF4jS2RX/lPlaP3n//9PLv+qyNW2PV9Pxiv7YmAhgICskHwE6LOuoye+m/9sdZhTz3UXnxN6621OjWQIqppOUuDlROWx2Rao4OKSnO/YiKxXnPaf/Pzn7DrdbJiKn15fJwpJkFJICAbgJJWzOyi0+o065ldAEiLyCpHcCJHJag2LAKDiES4TEGjkmoBWcsYwnLeK6z7e+MJPH+pnsMUgBQS9UAg83hRWEa9x4BFsvEIE4pRriUxLtcFP7dSF4YFA2RgUiUgMegEgORRq2kpkU8ThLlOinU4/KZa5XX1cl0XI01ZSOy5sOt9Eg0E/J0AAAAAACAUqbJAlkuaemAAgOq7zOPiogTVI2zWAbY+4rqoP4hJDQ4EZAwJIlThjgcgWcnaAQCgkYCArLMwhHUTgGTiZ4GUdRCSoNg1g+ujnkl8EAUZlNQLSK+oq7VlD25xABrzIb9o+Louv+SjgV4rL5GZRvxSvdRi9hFQf9KQ97QhgX4MTOoFZC2JwtoI1zUA9UFE3/yCesLMzC9YU7ML9vK7z36/XXe2qangCo2m9Vsv60xBCtIDhCRoVFIpIMsVcKZ4K3aMUiKv1HG8r4eVEoD6I66t8Zl5u945OEUiUiXxX9rdatfLutvsuqeDmtYAKVlYNAa1XgIAqg+6ccFJlYCMQohFLeaCWBRhdQSg/ojlcQcJR+au3UOO1fDI+V67XnQnhbJ6O5SoLJrbL9s4nWaQTmDAAIVIlYAEAAAAACjUoUMkSXAgICMAvXQAks/e0Rm7/vwdW+360Z2jVnenakJfdJQyPT63RcVAdrQ1OfGQrUXmp1+I+dNKrE9o49JBvQeVxmMmmnjfk3ECAhIAAErQf8qbrNd8/jZ7ecuju+0605Sx+vr7cvZb26tiILtIPPZ1Khd2M+3HNPn4sjP07+yNF9nLV276aGxdj+ax0iYqakU1U/dEMXATrmzgBQIy5E2CxhOA5JPXDvzyvy1rdlItNynLotXWYU2OrM7Z7Zbl3Xa9pqtTDaQhWrSAZEukiEnRkvznxMxC9c67CsAq2ThEeT0EHUTa8MAAGRgIyDTcEACAytqC0YH8dVNjljU9YS/uXlAjs3+l3daryRL5sjbdvGqx2EuCsq2lKc+tLevEEpklt/b8onqKffryj8X2l4NVEoB0AwEJAGhIJAEzhxFGkXw7dCcyS3GP0+NqefdjdrVtfL9df2FwzHr0BUfYy393zHK7Pm7lEifNT4cTH5lxxKTEXk3PL1qD47M5b3XuBRdZ03OLOdbLOItLEA/8PGYwlhQHBsjgQEAWAe5qAOLHf5z7AbsWkdXR2mydtfGD9nKzVleXX/rRwIMEInmgzmvBN7zHrkbvHbJu2LrFXv7VwYfY9fEnrLdOOXqFWl6tYidXdrc7Fsi5efXo2js5bT08qITpqaefadcDY2oADzOrhaQfkX2ekMAamSzw7ANBgIAEADQM7znn/WSJUzGDLWS9Y+YWFimOUImqJV1q4IpJMUFVNbHFgnLvk/biyP7tdv2bB3qs36w+1F5eu2GtXR900FJreV+7vTw2NWfXjzyyz9q3a5+9vHq9irF83jMOsE59yjJ7+YS1/dU//wpAjGSyKDbZRRyvv0qJeWKEWAEBqUGPC4D4wyOZW7TLd0En9h6amLUGqTA3/+ennX1j83Bb1INkJkcsa8u99uKubferur3bsto71fZ5FUdpTY06Fs2dm5Wr+3/v6LX+t09ZLzeeNBSvzwcSR5qvLaTxCU7hBGUAAAAAAAD4AAskAKAm8ChjmfpPBoLw1H+S2kZqcxSyuJP2jasYwD0jM1avTt49Passe90dLdb1X7yiJp+hYnjgjRkzyfWEsij6Qu55m/FBa+OLmxvKOgRXdnJplGuwLODCDgwEpAZB4ABUBxngwuJxQQvDiRnlrp0kESgJtqWenJ23JnVuxEXZX7t325qarf1aTMqAmZ9ed3Xif7p6DY6JAj5vhAgBkDwgIAEAVU2zMze/6FgTp7TVcL8eQf3E0IS1eVgl6J6YVfvtHWcBqQTjrLbAiZAcpljHu+9Rg1Levua+xP9ySRmsAGtkfCgm5hv9OosCGCCDAwEJAIiccy64yB4dzWS0pXCB/maLI7NvQlkR90/PWn/eqZNxDykhuW9wyprQg2LmdMqayfEpux4aGLKyu1TORWuNGr2cZPBAB6DGc2FDQQYGAtIH9JYBKA9OeM1kDReztMcL1DKLJXGQhCPz5NCMIxw3bxm26z3b91ozY2PqRbNKOFoTw87sLxtfvhQ/DwARgo4KKAcISABAdOjBMbqyTAXJg2NYRDIz2jq5Zf+UtX3nqL289cHNakfOnyiDTCQFjtQBwQMRAFAOSOMTHAhIAECk0wp6G2JZx4NkZKS1CMyZuQVreFBZIK09m3OtjiBxYLBifEAnC1QKBCQAAAAAgB0Eia8BAhIAUF88DTFPPSjzPq/p7rDr5b3t1uyMkROxQmBVaRwQa16771hAOqXSQD8GBxZIAEDFXPbxj+StO+t8lf+xWc9Z3dbkTnzVqpe725opvFEny8bwx1QCIVnd7zXoegDCAgEJAKgKV112cd66U08/0653Tag4x9HpeVdAyvQ02fCCAw9FAErfC7hPSlOPfmwmk3kfVW/jt6fyZypvpdJF5X+obKCyhcprs9lskWmrak+qBKSf+d7vhqqWmb+W7gS4LkAceXJU5Xx8bFAJyL0j09bc7Fw9TwkAAOpGJpM5gKr/oHIMCcQp+vt6Wn49/03lF7RuE63bSMtczo/TT5UaAVlIrFVDxMVxpCGmEwNx4L7dSkDuHFGJxHfuHbfmpiobdQ2rSjJAGwVSnManhUonCcU5bXncSeUCKifr7V+j8msqEJCNTDGLZakHmXe/ajSYeJiCOHPPVpUQfGxCWR337B63rEmdJBy5HgEAKSObze4g4XgFLW6lwr3pn9G6n9G61VTv0vvsor9X1fVEC6heUIOYk7hYIgGoFd+cfLZdy+wzp/fcbj348D57OSsz0gwMWtbECH4UAEA8qI4BcgUJwDuNv68hUXgNL9D6fqpeSeUQKtyb/l9ad3pVziJiICDrTJQCs5T1sdh2CFwQNVv/qNvLtk5VP8eydt17j1ruWa5qtj7OKLc2AKB84H2KdRrIfSQYTyyw7cVUnqDt9gOaxOP3qHoulT20vFZbH9fS3wPVObXycfNqAAAAAACAWrKVyrNJJHZR4VQUp1B5kMpNVN6s9+H6xrj9LLBAVgG25oXtDdbb1V3v9wcJZMgO36EutfRTyeo4tFstitt6YS507CMAIBdYHxs3jU82m72ddON3aPFPVOap3E2F3ds9VK6nbWdokfma2p5ZaWCBrMJN6refd2R2IaGGhgAkjuyiKuYyu625RDD7DAAANDLZbPZDVI6ichyVN1KZobKfyilUjtA1BYzHC1ggA1AtUVeOpbLaIJUGAAA0BnF7fiQjiQ8mMwwKBGQVbs5CwtDPTRw0BVAtgTsbRHUNAQBAQwH9GBgIyIQLtKCz7wAAAAAABAUCssEoNstNUEEaR6snSAaYhxeA+t5noDJggAwOBtE0SCPA+0bdaBSb3hGjsQEAAABQCFggAQAAAACytU/j08hAQKbcvQBLI4iCILMcNdJ9AUBcwX1U7ZlooCCDAhd2jdzFYaj3+wMQhqDXKjorAACQHFJjgSw2+KRehMkDCSsOSALeew8dJQBArIABMjCpEZBBqeYDDQ9PkCQquVcgHEGx6yIunXwAGpVMJnNQhYfYnc1mi04VlkoBWUmC8Ereo5LGEQ9cUE/Kuf6QgxSAdMxgliQSZIDcUuHH+Vsqvyy2QyoFZFAq6QUXczmHnVO71LzZ6K2DagErIwAANCw3ULkv5Gu6qZwdZMdUC8hCPbm4CTL0NgEAaQKdY1AvEpbG57vkhv5mSNf3cqrOCbJvqgUkAAAAAICQoDQ+X6DySBmvm9Kv3V5qx1QKSNPCGDdrY6W9da+1MgmfDzQmyBwAQPTAIwWCQJbHd5b5ukmqAr02lQIy7fMQQ1Smi7CdimpNmYkHHwgLXzNor9zvAtSAxBggqw8EZIPd7HgYg0qvwUaI+wUAAFA9KNbxQKr+hcp6Kjup/A9ZHx8Pc4xUCcgkPCTRCwXVJupOCq5ZAHD/NArZdIjHF1D1Eyr7dKzj66hcROtfRSLyx0GPkxoBmQTxWA54eIN6kNb7DUQPRmSDWorHxYQNwy7AJipXklj8IP9BwrGdqu9QuYwKBGS9gasZ1JtS+UO96yD6AIgXMACASiBh+J9UbSShOOHZdDCV78kftH2G9v2RFpaBSY0Fsl6YD2U0BiDpM15AhIJqgQwToBYkzP74PCoPkjh8N4nEHxjr76DyEVp/pnZhP5XKe6j8MczBEy8g4/RAC2qVrNac2XH6LkCy53kHoNok2Wq+CSOuQTScqGeV+RaJxf+j+r0kJAeo/g8qbHF8zNiXl0Ol/mmK5hwBAAAAABrb/MghkFGXun2cbJZCOrOX0+LTqPRTeYiE5Bm0bhstH0/l+VReT4UH1RxL6x8Nc/zEWyCTEB9ZjisRAKHcayfM9VnJ6wEAID7z0CTMiU2QMNxM1UtIPL6J6iupPp3qt9P6W6kuGwjIBgGDckAcCOoyhHgEAIB4QYLx63qwzKeo3EvLH6P6E7R+vpzjQUA22ACbUq+B0ATlUuwarPXsNQAAUJ80PolMGH4qlW4qt5FY5IEyp9N6Xvc5Kq+n5bfReh5YEwoIyAam2EM9qcHlIDzoVICkIh2XuLd3xTr7AFQLEoYvoupGPd5lmspSWncxicUPU/kpLR9L69gK+Vta/jzVF/qk/CkIBGRMQGMCGuW6hLURgMrx3kd4BsSDhMVAXk7l91R4hpkpEokX0PJHqf4M/b2X19HfZ9Pf36T6i1Qe0Dki0y0gcTMCED0QjwBU577gY+C5BSLmSCpf0EKR+QaVj1M5lIpz0dL2u0hEPpMWz7LSnsYHNyEA/vcF7g2QRFh8Jalzg3u1fiQpjQ/BaXlepacqZN5AZYEKj8rOgUTkgk75E5jEWiABAAAAAFLswj6fyg1U9pOI5BjIZVQ4BjKS3laiBCSsK7UFMTzJJ0lWHQCiAvcFaARIKP6chOPRtPhSKp16FHbo0dapcmGD+oBGFQBQT9AGgSjS+ERd6gkJxq1UrqFydZTiMXEWSAAAAIBB6hwAqksiBCRc1wAAAAColKTEQGYymZ9RdSlZHX8V8nW9VH2Xyrn02nsTLyBBuN54VILbL0F1oyT2BQAkm0ZMoI/2s/7UedR0lLyYylfLeF2bfu3yUjtCQAIAAEgMxUQYBBpIGW8niyKLwTBwyp9AMrrhBSQsXcmbVxlUh0ayxACQZnCv1on6522MmhfoUhUaXkCC8KAXDgBIUzsHQQbSRjabrXqWHQhIAAAAAKQeO41PQgbR1AIISFAxpks6SE8fc77WFlhfAEAbBIKRMBd2VYGATAlxc+OEPRfETQIAAADxoWEFJARF5dTCEhjWOgkAANVq59AGgdJZIGGCTLyABPGkmCCtpBHHwJ/o5irHQxSA+LUpxdIOARBHMBc2qDmVNNjcoKJRBQCkod2Ii7hNWwxk1CWpwAKZImCBSg+N+LAEIM2WyEJzd6PdBnGl4QRkvW9yEA1oFGv7EAIANHb85kaEoFSdNKTxyWQyXVRt0FMVZnzyR/4msQISAAAAAKAaJNXlnFHC8Soqby2g/VhM8qdvTqyAjIu7AYA4ETT/JgCg9nifV2HuRe8zj2vcy6AMrqZyBpUfUfkllf1lHKOxBSRIhiBHA1i77xDfNQD1Icp21Wyr4c6uHgk1QDKnUfkWuaj/hf+IAghI0JDUajabYuKrXlZwCEIAAAAh6aTy65CvKQoEZApB77UxrLemUMRvBkB8qOf9WOg9K3GTAxey0CX167iTyhFRHhACEtTclR1Vw1YoUTYAAKQdxEqWOwo7sWyk8oNMJvO/JJL/GMUBISABiBmwHAAAAIiYt1PZTuU2EpG3Ub2ZyoJnH9KWWR5oEwgISAAiGqQCCyiIEwh7qG9ctd+xat05xDUQkmyiXdhvMZafp4sX/vAQkAAAAAAAwGJhHPnU1bBAAhBjFzKsmiAJ13FS8bPwVeueDfpepWajwnVRnMTaH6sABCSoWXocNFwAgDhTbjhKvQYfIhds9CTYhW1D8Y8848wJVA61V6hYyLs5+FH/HZjITZqgcUiadatSgcqvh8gFAKAtAEkkk8m8lKrHqfAo7P/RhZcfo22nhj0eLJCg6qRFlFXDehvld4eAegDC339J62iDdKbxyWQyPGjmJioTVD5N5S9607F6gM1NtM8LyRB5a9BjQkACAAAAACSbi6jspvIsEom7zA0kHC+n6na9D1spAwEXNqgqjWJ9bJTzBADUFriz00TWjoGMusSEZ1G5xiseGb3ui1SeHeaAsECCquQ1hCArH7iaAYgfyPcKGpw2KmNFto/qfQIDCySIlDT31hErBUDyiap94/YCbUb8YINh1CUmPEjl9eSuzjMc6nWv0/sEBgISAAAAAIBgvRd1iQmf027sX5Bg/Acqh+jyMl6nt302zAHhwk45aXeXpvVzAwCq024Usyr6bUt7GwxqA8U5fonE4hG0eA6Vv/bZ5XLa59pEC0iY/EEUVKOxLjUDRFj4tVGcJ+4ZAAAImMYnRj7nqCGBeD6JSBaJr6RyCJWMzgt5E217JOzxGk5AgviCHjQAAAAQX7RQ5LQ9FQMBCSp2o0A4Fv9uYAEEAIDGILn2x+hpOAGJVArxIY7CEdcHAKBR59OOY5uaKuxR08mQkJlM5stU8Yd5O32mBf13KXhK7DMSKyABAOGAqAYg/kA8goh5ixaQ76SyoP8uBe8PAQnKI2ivGY1dbcAITQAak02GNRFhLI1BkubCzmazOWkavX9HAfJAAgAAAAAACEgAQGFgDQEAgHTNRJPJZDZTeUWR7S/jfcIcEzGQILQ7BiTHpYbfFYDa3nvm/QfiR4LzQG6g0lNkezeVg8McEAISgBQ/0CAkAQAAEKupTFIJDAQkKAmsVMkAYhEAAApjz12dIANkJpN5AVUnG6v+kdYd7rPrMiqvp3JPmONDQIKqCEeIFQAAAKCuvJDKh/QyS+N/1MWPx6i8L8zBISBBVYDVsvh3Uo8YKPwmAMQDvhejbgPQaW/cGMhMJrOUqi9ROY4Kn8C/UnmYyv/o2MUtVF6bzWaHQh76U1S+ym9BhQfI/D8qN3r24fcbp2MPhj3vlqTEcAHQSOD6BQCAmEFSarE+LuyrqfyERNyrSUy20XIXlQup/ILWbaJ1G2mZy/lhDkqvHaFqRItUtkY+QOsiGxGLPJAAAAAAAHUgk8n0UcWxitfy3yTwZqkM0+IrqXxN78b1aZW8Dx3zlijFY0NbIEF8iKPrpJiFL07nCQAAUWK2eWjrGmIQzaFU+KH0FRKTx1N9F5UzqawmwbfLPi+qaduqSt+IjtGiheizqPT7GBHprTAXNgAAAJAKCoVyxbFzn1JWkHi70/j7GhJq1+hlFnVPp/JeWnc77Xe1dldHCh2XR1r/SsdZckwkS2WuGVnGXNig+jRy7Cmfe6M0qNU4z0b+7QAALriXoyZrLdoaKnL2kTg8scC27VxYPOq/v6MF5B4SfWu19XEt/T1Q4Tl8jMpRVN5G5ddUHqdyKpWtVD5I5Qj9d3pc2NUYzQYK917xXSdjME2jCGgAQHVppA51Eslms7tJIG6j8hRa5pHXp1B5QJc3U9mka+/o6bD8A5Wv03uwq3y5Xreg3/N0Wsei8lIq70yNgATVB6KxPqBRBwBE0SajDQ9OnRKJv5fKdXoENqfbeauOT7ye1p2hrYSvqfA91lD5o16e13WHsf0GKudSgYAEoBBpF2am1T7t3wUAcQGWwPrD2rEeaXyy2SzPAOPn4mZrZFQM6vmumTEqc1TWG9vn9MCawMACCUAKRR3mwgagvsQpFyw6lKngESrHaMG6SJbNu2nxLVRzovFmKm/S1s905YHkGxGWFADKv3fi8BADAIA4zEQTdYkJP6PCicrb9d9X6XQ+g3qADltAPxnmgLBAAgAAAAAkm0uoXEHWxxn+g2qOr+RYyNOpLFD5Dq3jqRMDAwEJQMpArBUAjePKhnu5hpCxMD4Gw2ghccifbMaz7ntUcSmLRLiwASgEQhvwnQDQqCC0pD6DaKIu9YYsjT1UFqhwvsfIgAUSpE6kxdUCF8dzAgDEQzDWQkzC2plMstnsOInH4QiSkecAAQlSg19CdIg2AAAAhthK6pfxKyp/Q+ULUR0QAhKAFAMXGQDxwC8bQr1mAUMHO5GcS+UWskR+hOorSSiPVnpACEiQeOJiZYxTowzhCED8iFNuyLQSh5jFKvELPfPMB7iQkOSLbdKzD+nK7GFBDwgBCUDKHgpxOQ8AAIgT9ZqJpkbwdIiRfjoISAAAAACABJPNZk+O+pgQkAAAAECMiEOYS1rJRmukiw3ksn4BVQ+SkNxbYPsKqo6h7b9JZR5ITGkIQPD7BA8pAEAxEO6SuFHYf1tk+yl6n8DAAgnKBgIEAADSGUudSGKS+LtKZEpsb6ayGOaAibJAChA2IM7E8QGAewYAABJPtsi251LZF+ZgsEACkCDilCoIAAAajSTlEc9kMmdSxUX4FK37uM+u/VT6qHw5zPEhIAPg9zCOoxUJxBu4oAAAjUaaOqWsHReTpCAti6cvfFIvb6Cyn8oezz78gf9C5Q9UPhXm4IkVkFE8rNNww6SVev+2aWqUAQCNj/ksRbvVGNCI6q9R9TVtjXyCqo207qaojp9YAQlA2hv7II08HgQAgHLal6S2H0kdRJPNZg+J+pgQkAAAAAAAKSGTyXRRtZwXfYQmz1gTCAhIH5LYqwLJBjG5AABQOckKgcwRjZx15zwq76Wyxt3im84nEBCQACTUtROncwEAJI+ktTFkfUvaIBqTTVTOoXI/le/qATUVkXgB6TeYJikXez3Bdxj/hhW/EQAAAM3pVH5CIvnv9d8Vk3gBWa0HKlKygCDXRdDBLJUA9zUAAERDcg2QFud6vDHKA6ZGQFbrIY4ckaDRLZUAAFAJtegog4r5M5W1FR8ljQISgLR3bAAAABQn1GTQjcVHqFybyWSuJTf2tigOCAEJAAAAgNSTwJloTJ6hZ6V5gETk96nmxOIL5g4EacvsxZ51BYGABAAAAABINh/2DKjxg9UzBCSoDnCNhvueODaoWoNcMHgGAACiJbkGSAsz0QAAAAAAgOCQa5rd15ECFzYAAAAAQILnwjahGMjDqVpN5S8kLEfMbWHgqW0AAHWiHBd3Nd3iAAAAEiscX0blcVp8mMpv9MAaXr+KymNUXh3meLBAAgAAADEHncbaxD/ydIZJJJPJnEwVj76+h8rXzEE19JkHtLB8PZXvBD0mLJAg8KAQDKAJT9TfGR4iAABQXRd21CUmXETlXirPovIZn+23UXl6mANCQAIAAACgIhBaE3tOpHIdWRsL5UrfTmVNmAPChQ0AAAAAYMXKYhg1zVRmimxfQWU2zAFhgQQlgesaAAAAaGgepPL8Ittfpl3cgYEFEoAGEOfljNQuV/jLe6HjAABIE2x8TOogGuJaKp/OZDI3U30TFSZLf3dRvYnKc6i8Sa8PBAQkADEGg2YASDdoA2pLoQDBRiebzX6OxOLzaPGLVK7kVVS+RWW5dm9/hfa5LswxISBBQWCBin5aw3pivj9+WwAASBfZbPZ0EpHf1XNhH0UlQ+V2Kl+nbbw+FBCQACSQUgIRbmoA4k+9O51phIRUvU+h2p+Pc0FyqRgISABSKCqLPZhgnQQAgGSRyWRY73WRgBwtsL2PqknaPh/0mBCQANSRUpbAarm/IRIBAMCDPRNNYr+VK6n8HZUjC2z/I5UfUjk76AGRxgcAAAAAVtoTimfp3yIpyKhLTDiVSrE4x+9qgRkYCEgAAAAAgGSzngrPd12IzXqfwMCFDfKAexMAACoHg9Uaj/gYDCOHZ5lZW2T7mrBZjGCBBKCGwrzRxXmjuqYAALUFbUXsuJvKazOZTJt3g173Oir3hTkgLJARghsGNMp1GVbImseAVQUAEKbdaKSOc4LT+HyGyv9S+T8SjBcYYvFpVC6hcgyVfw5zQAjIBCaMBumlkRpqAEA6aEQhmTSy2ex3STheSosX6OTh9syN2hPNCcUvo33+J8wxISABSBloxAHAvQb8Sa4B0mIR+X4SkTfomWgO18LxYSrfpG2cxicUEJAgB4gLUIl1HZYGAECx9iHOzxgWjzFKu1MVtFAMLRb9gIAEIAZgnmoAAACNBAQkAAAAAACRZPtjJpM5iKp3UDmCynJelW+gzJ4S9HgQkAAklGoM5mL3k99xvevi7qoCAIA0kclkeJaZ71PhlD1jVAYrPSYEJLDBwz7eNOIIf8RDAgAarV0gC1y9T6Fa8AjsfVROo894ZxQHhICsAoWsNAAEuT7iHA/plw8SAFAdkvwsiaOQZOm4mFj9aB1F5QNRiUcGAhKAmFGsQQ3zQCnXahm1gI3jgwIAEA+4ffjwPfU+i1SwV09naNVFQK5Z2mINRfnuAIDQeIVYVBYKP6HnXVeJRQRxkQCUd0+C2pFgF/Z/U/knKp+O6oCwQAIACgKrIQAAJIKvUnlhJpO5keqrqTxBZcFHQG8NekAISAAafFBMVCIv6HEqOXe4swEAcSa5BkjrISpZnbrnZUX2aw56QAhIAAAAAABSjwl2YX+USqQfLrSAhAUhuWlXQDpALCIA8QbPDRA1JIw/HPUxYYEEAJRFLVzZ6LACAGpFwtP4RA4EZMrBIIn0UK3fOsm56gAAIClkMpkmqt5M5VVUDtWrN1P5HpWvk5VysSYCEpYBABoj1KEWnQSEbAAAkkBSYyAzmUwnVT+i8gIq/CF36U1/T+UfqLyJ9vl7+vzTQY/JarQiYHkAAAAAAIg1H6DyN1SupLKShOJ6LrS8gsoVVE6m8v4wB4QLG4CYgHACF3g4QJqBRb9+JNP+aPM6KteTaDzP/ktDfw9TdT5ZHw+m+g1UPmhuLwYEJAAxIK3iER4MAECcWEyoC5s4UFsaC3ELldOKbM8DArLKoCcJ0mR9w/UOQOWgYwWqAFsajyiy/XC9j1WzGEgAAAAAgEaHjY/VKDHh51TeRa7qU70baN1LqHonlZ+GOSAskADElLQl/E7TZwWgGLDkgyoNomHx+CMSjHdTfb9efyyVE6jso3JRmANCQAIAAAAAJDiNTzabfZKE44m0eCmVl1N5ut40RuVbVC6kfbbWXEA2csxW2sFv13ixUEm7z5L2eQCICiTprz0J1Y82WiD+CwnJDNX8cOF6gNaX9alhgQSgwUUlBBgAAICgaME4EHT/QkBAAtCgQDgCAEDUc2EnxwSZyWQOoOouKteRZjy7yH5XUfV6KifQfnuCHh8CEoCY0cgB9OWcM8IoAACgKvDI6jYqHymx34f1HNnv1MuBgIAEoAEsjH7CrBGFl/dcG1EkAwCSS4IMkMxLqXyXrIqj/EcheDtZIf9Xz4kdWEAiDyQAAAAAAKlHDg+MutSRI7ULOwj36P0DAwEJQINY7gpZGmHFAwCAxiaTyTRTuZvKD/Xfy6j8nMqjuu4v47Dsvp4NuC/v1x7m4JG4sBvJhQZAI+N3rxVzb5d6ba3d7n77QwADAOLCYv0MhmdSeZBKn/57I5VfkAVzE4lHXuZyfshjDpSYvtA7lWGokdmwQALQ4AQVhizUCom1aoi4YlZTAADuI6AggXigjj/8klpj80oqX9PLXJ9mbAvKH6i8jo5f1FhI21v1KOzbwhwcg2gASCCNKNwacVAQACA5sPGRohbr8dafonIelV5j3WqyPu6yz4tqEnmryjjuF6i8mspX6PVn0HFmC4jHa6kcTOVtYQ4OAQlAiqmFaGvktEQAABABK0io3Wn8fQ2JuWt4gda/TM8Gcxctnxzlt03H/AUdk8XhGVSeS8tfp/peKqNarPIc2G+ksoHKl2j/X4Y5PgQkAAkFFj0AAAhHlQZN7yNxxvNQ+/E8Kq8gcff3VHdQ6aPlb1C9h+q12vq4toKZY95OhS2Z51D5EBXzE/JUhtNUPhYmfY8AAQlAQvFa/vysjHFwF3vPk+s4nBcAIH3UOu1ONpu9gKoLtDWSLZDn0LrTaflyndx7k65vLPP4/IEuouP9p46zPE4P1GEr5F+o/B/tUlaDW7GAREMPQOMB6yQAAMSaTVSu59hFqrdSeU0lB9Mi8atRnJgACyQAAAAAQH3T+Fgk8n5N1a/18n6qTonzjwIBWSOQ7w7Ui7i6rr1gsA0AADQOEJDABi5NEBfiKG4BAMmHowVVyCAIAgQkAAAAAAAB/RgczEQDAAAAxBzM7AQSY4GEmylZ4PcEAACQdhZhggwMXNgAgIJ4Z49BRwMAABoXSgv0AqpeQmU1lSsp5vMhWtdDy0+nch/9PRz0WBCQwAaDaEDQJN/ebQAAkBSSaoDMZDLNVH1Tz43NM9DwJ/0WlYeozFO5gcoVVC4JekzEQAIAAAAg9WT5HynIqEtMOJ/KP1E5i8rRWkTa0DnydIbfp8LTKQYGAhIAUBZsjfS6uAEAAMSSN1H5OonFq6ne57P9QSqHhTkgBCQAoCIgIgEAicDOAxl9iQkbqNxWZDvHPvaHOWDoGMhNiHsqiyQ+ZBE3CQAAADQEY1SWFdl+OJVQge0YRAMAqGrHJ4mdJwDqBRtxcE9VjxjFLEbN76icnslkPuHdQOvY8vivVH4S5oBwYQMAKgJeCQAAiD0fp3IElV9SeZledzyJx3dQ/Scq3VQ2hTkgLJAAAAAAAERSDZDZbPZOEov/SIvXUvmKXn2FHo09QOVVtM8DYY4JAQnKAi4UAAAASYNEVL1PoZqf7UckInkwzd8aqXwepfJT2jYZ9ngQkAAAAAAAKSCbzc5Q9UNdKgIxkAAAG+R1BACkmawSWIlMJJ7JZE6g8u4i299N5a/CHBMCEgBQMyBSAQCgLnyIyj8U2f53VC4Kc0AISABAzWNaISQBALEj2YnEn0nlliLbedtJYQ6IGMgqg8EmoNHANQsASCtxcTlXgRVUBkvMRMP7BAYCEoQC4iJZ4PcEoHFzr+L+BSHgVD3HFtl+XAmBmQdc2ACAisBDDACQFBLswr6ZytsymUyeiKR1x1B1ht4nMLBAAhvMJpIuIPoAACBVfIwKJxL/IwnGL1N9DxWWtyfoaQxnqVwc5oAQkCAQEBwAABAv4MqOmvik3Yka+lyPk3A8hRa/SuVdns33U3kr7cNJxQMDAQkAAACA1KPyQCb3a8hms3dSdZzO93iEnonmYVp/bznHg4AEJYH1EQS9RhAKAQAA8YYEI7uvuVQEBtEAAAAADQw6bhGRTe5MNNUAFsiUg4YnfVTTogxLJAAAxANyVd8X8iWkd7PHB90ZAhKUDcQnAADEAwyoiYaEGQz7qFTtE0FAphgIQFAt0hA3y58R9xAAIK6QNXFDNY8PAQnKBu5KAAAASSLJMYt1FZC7h+et9tVRnwKoNVFZTWB9AWkG1z+II3BlVwb0Y3AwChsAAAAAAIQCAhIAUDfSECsJAGikROJI4xMUCMgauBPg6gIAAABAkmip9wmA2gEhCwAAABRLJI5vJygQkACkhLi6izGaHwAQFzAKOzhwYQMAAAAAgFDAApkS4L5OH3G1OBYClkgAQL1Jsgs7k8k8j6oLqDyLSj+v8pnKMLAuhAUSgATSaOIRAABAVcXjC6j6lRaPt2v9x3//kTdT+QuV/w5zTFgga2gBrOdDHdOupYMkCEfzM8ByDgCoHSqFT0J5P5VdVE6kwh9ygMol9Hl/SeLyJbT8HSrvCnNAWCABAAAAkHoSngfyJCpfovPhqegWTQ1I636mrY8XhzkgLJAg0dastJDU3yrquEjEWQIAUko7lR16eUbXvcb2e6icHuaAEJCgLOBaBI0IrlsAQErzQO6icqC2OE6Q23qYFo+j8n29nbfNhzkgBCQADU4SrY8QegAAECk8WIZHYQvstn4fCckntSv7PXpwTWAgIAEAAABQF+LWWYxRzGLUXEvlLSQYO+kzTtHyhVSeT+WrevtuKueFOSAEJAAgdiBWEYBkEzfhKCRVP2az2Z9T9XPj780kJo+kxVOoLFD5Ha0bCXNMCEgAAAAApFo4ppEsxUJSdVO5r4eABAAAAICVdvHIxsfFxYSaIDVkdTxEWx1XU7mOROQWWtdGy2uo7Ka/Z/WuJUEeSAAAAACAhJPJZC6j6hEq11D5KJVD9aYOKg+ETSQOCyQADUoSR18DAJJH3C2PaUjjk8lk3kHVuVQ+TeWHehS2DVkdR2k7u7JfTuVTQY8JAVmHm6hRHvyNdr5pIw2/DwbTANC4NIxwTAfvovJ9Eov/j8Ticp/t9+lUPoGBgEwJUd3IaBAAAAAkFRJY9T6FasEjrj9XZDuLhBVhDggBmXCiEHwQjaDewBIJQGPRqM+N5OpHa5pKd5HtB1Ph2WkCg0E0AAAAAADJ5g4qr/LbQC5tHkTzRiq/D3NACEgAAAAAACtru7CjLjHhcirPIbH431Q/Ta9bQ3+fSvWv9VzYV4Q5IFzYAAAAAAAJJpvN3kxi8Z20eDWVf9arWUwynPvx32if28IcEwISAAAAAKmHbYXxMRhGDwnEa3S6ntdQOYpKhsqjVK6nbTvCHg8CEgAAAADAzgOZYAVp2Z9vN1X/SaViICATmr+vUUfAAQAAKJ8k54UF0UGWSNZ/J1E5gMoDJCzvD3sMDKIBAAAAALCUBTLqUkeReDKVT1NZ4zMf9l1Ufkvl21Tuo3VfDnt8CEgAGhxYmwEAAPjwFiqv1G5rk69ReSqVW6l8Us+D/WYSkW/2OUZB4MKu84M/ancDxAQAAIB60fDPoGSFQD6Tyg/MFSQSefDMX1P5DQnLk/W6D1J1N5U3aXEZCFggAQAAAAAS5sIm1lJ5hIoJi0Y+qS/JCjrHKaq+aeSHDAQskAAAAABIt+UxmbRTYXHotUoyt3jWb6OyJMzBISATBG5gAAAAoHwSlsZnK5VjPevYfT1An5MFo0kXFcyFDQAAAACQcn5L5U0U4/hUHevIc2EfQeXHPvvyPqGSicMCmYKckABEZdGu13UK6zoA8SUp92cMYhaj5lIq/0LlHhKP+6lerqctvNLcibY1U/UKKt8Nc3AIyASQlJsXxP96qkbmAAAAANFDYvgJEod/Q4sfonI4lTuofMwnafgLqbDAvDHM8SEgAQChOiGwmgMAkkrCLJAWfZ47qXp5iX1upsp2c4cBArKBgeURJP0aivv5AQASRrL0Y1WBgGww8EAFcbmOYIkEIN3geZRuICBjgt/DGDcniPLaAgAAkC4XdjWBgIwZeNiDRuuAxGm0NgBppdr3HJ5NwAsEJAAAAAAAAQtkcCAgAUgQSbESxO1zsHUnbucEAIgY8l5DQAanKfiuAABQWGAhfhcAANIDLJAAgIpJunXOTKCe9M8KQKrBGJrAwAIJAAAANDAYtNa4ZDKZ9VR+ReVBKvdTOVOvX0bl51Qe1XV/vc/VCwQkAAAAAFJPlv/p+bCjLCWYp3I27Xc01c+m8m4Si8dQvZHKL2j9EVzrv2MFXNgAgKoQNtF4o7iGEesJ0kSj3JdRUetBNNlsdhdVu/TyGFsiafEAKq+kcrLe7WtUfk3l/JqeXAkgIAEAdXsAYXQzAPElbeKx3mQymQ1UnUDldiqrtbi0RSZtW1XXk/MBAhIAAAAAQIm1anwPK0gA3mn8fQ29zzXmDrS9h6rvUvl/tG2U/o797wEBCQCoG41i4fBzwzfKuQNQClzLVWcficITC20ksdiqxeN1tN/39Oo9tH6ttj6upb8Hqn+a4cAgGgAAKAEesACkJ5F41KUYGWVqvJbKg7TvVcamm6i8WS9zfWOx49QDWCABAAAAAOrD86i8kcqfSUveo9ddSGUTletp3RlUb6XymjqdX0EgIAEAIASwRgKQYGqcSDybzf6OqkIBj6fU8lzCAgEJAKg5jRJTiATNIKnE8X6LAyTo6n0KDQMEJAAg9jkia433vPCwBY0y1SYAtQICEgAAAEgZmNu98Ew0IBgQkAAAYABLDkgDsKqDSoGABAAAAAAgYIGEgAQANABxt4LE/fwAABHC3mt4sAMDCyQAAHhc1xCOAAAAAQkAAAVBzCNIE+gcFQcu7OBgKkMAQGqBeARJANcxqAdwYQMAgAEsNCCp4NouDSyQwYGABAAAABoQWB6jBwIyOBCQAIDUAosMAACUBwQkAAAAAFIPZqIJBwbRAAAAAA1qQQ9jRYfLG0QJLJAAAAAAAEgkDgEJAAAAJB1YFEE9gQUSAAAASAEYNFYajMIODgQkaNieNxpDAAAoDdrK4EBABgeDaEBDggYRAAAAqB+wQAIAAAAN3JEuFAuJjnZ4YIEMDiyQAAAAAAAgFLBAAgAAAABwKvEs5/IBEJAAAAAACIXpEk+VGzyrCwgELJAAAABAA1LtPJCpEo8gNBCQAAAAQAIH0VR63LRhGyDhwg4MBtEAAAAADUwhwcfCErPVgGoBCyQAAAAAAAELJASkS8YwsmYy+euyi/nfVqUmbHmfUpjv43ceAAAAQBlWSNPyKMtpdU2HAi7swCTXhc0ikUtTsyrNZGxtaVOFl53SmltkX7PIMQqVoPvLOTnFEJqyDgAAAKgCcGeDKIELGwAAAACArY/wBqZMQHotd2zZY4sfY9bFLHzmRVPMhO13cclxi7nDeZtYHM3je9fxsXABAwAAqAB2V8N1DapJS6LiGUUs+glI02XsJ/iCxCPa+zT7rNf7Z5qL92IQWwEAAKBGIOaxDPCcbmABKeKOxZ65LNv8hKOsM8WiuWxvM45nvs60EDKLhvhzarEOGut8hadxLO9xCwlUXKwAAAAAaDBqJyALiT9nu2ebDDwxt8nf3mOYItH7XsXe309A8t+LC2p5fk6/WMSlXi/7yev8jlFKOAIAAAAgXuA5HQMBaQpBRkY8m+v8fjDH9cz7FxGQ3vcJdE5FjiNCj0Wi45JuyhWOfsLQz5roJyr9gPUxnUQ92h4NHgAgjgQddxAn8FyusYD0XiSS2oZp7VB1G9Wt7e72QqLN1w1dxHJpH2cxmIDLc2H7DFjxtSguRmttDDtIBzQuxazt5bw269Nh8QvnCEuha9J8PwAAKCcMza/dAmkTkCXiEsXCyOKxvUstS93WZTW1tjmHYbIcb8j1wrxr5SsWA2liCjl57cKcMaGl58Hn59bm18lrzWOI69q7rZQL29mGh24qCSoWC3WEiu1rDtQyY3KLHSNM779Q50cGhpXaDwCQPvye1842I0TMT0gWy15SN5DGJwzIXA0AAAAAAKpogWTjBs/kwphpcmRZLIxtnbbF0d7cTsv8stYW2i1Xry4uqp7H4kKzs2xbD/mtmjLUgck3sch+jvXS/tOIUTRrdSBdG8eS7WxtZOsnY1odnZhHP9d0wNHVILkEsSCWciv79daLHT9bwjVdzDOQd6wCsbzOchGLf04mggaMbwIAVI5fnmXfdsbw2jntlNc76PPaeuHnvQRRCcgm1yXNg1wYHugiorLFFZBNra2OcLR3b2pyBKF3snJe74hFGedi7C84ItM8Bos9x3VtxCqWEo52PZ/vrva7eJxjGBe/+RHCXnB+CcWdbUgk3tAuab/R/37b/ARfsfcodn68f4uEjxgD1bwxxOY9Mz+rludm3HuhmKiUU7L3kfc113kpEb8JAGgsnAGxPO1vS/54Brm/zWepb3sYcGxDvUCMZpUEJF8sHd3ustTy0NICsokeZiwAbZxn1mK+5dGwOoogdERmJl9c8f7OccRyyGLQHCVdLGWQ9Ib8HrzZ5tJ5IG2xWmQUdljMGJGc9Z4bCg/egN+n+Vsvlj84pVTC+kIiMai49Bsgxh0yMw1VseP5xRzrwWp876lVzXkdMLnHFhYWrMVZEZDTbu1njXc6Z4Z10nvtm3GZznka2x0gKgFoKLi98Q6I5ee8N37Rfg4X8QD6eSzNtg+kwQKZUe7pnIdcftJuFnrOw0q7l3Osjl7BZ1gMszm5HnXt6DfDvSwPNnNgi5mmRy5KPwukIwzb88/FHBEu2+ZmjW0RjyALZG0KKIyA8b2G7NWWEnrmtmLuZ7/jed08bLX3cwGZPXxZ52OpzOjOGYtEsfKLpV9qDgFxkEtbd9gW5umunFcZEeZm1UNhfpYHj+nrfF5bJflvx1I56953Xlc3/+1njTRnZlJnYJxTtvKOAACgOkhbxGKxXRuNxHjESLvA7QZjDoR1OpaG+HS8lK3B3N91A4Noqisgvb0FXuczSjmbN8qzxMPDMYWLdXLB3/XmFXfmax23eotjiZG4S9si6hGktiVGWxSzYnWx4yLncm8Sv89QrfxXfg/PUqPZ0k5osZgJn7s06OhmLywWTUuhHN97zRqpr+SababXihAUiz7XznZDQLa25V7vZqdN4oVNy//C3ELOMeaonp9T7y/iMkdAtmhL5eyMKzD98qP6TedphoCEtQzjek8GQe4ZtGm1wa8DLOukjRJDUWevKyDl95mZcENfpH3wex5zu+eEtXW4x/fOEgdSIiD9Akz5oWQKPHu/oJYGPzezJ4l3oeOZ5m99kTYbVhh5MDo1P4z1a1g4MvNz83axl/WDenHOEAryeSSh+YKPq8506Zmfy88N7Ze3z3xN3mc0xKJXTJdyw4L877UYZiiG5CvlRs+vsSt2TN+cqJ4E+lSLFdEUiCISzXV5ApIEolgZRTS2tZOAbM193/n5LF3nWjDKqRjnlm3O5olVb2zyYqadLm8fd7pYN/V9lBNG4qTj4n0CdHbKuRdAfCn1exbtWBcZvOW7P66F0JheDdM1rQe7iljMsHAkuK2R5+XixKjaZ2bKFZDm/e7tgPNzWYSjd/BtnEEMZGBCBh9k3R6Hn6jzE5BFGxTZxuJKBJLstOD/Wo+bj3NLtnWoi7O1vdV5sMpDVgQki0exNs6J2d14P7HS8N+LheIb+SaQODGzB2b2vuQzewWfn9AsSLHYMZ9G009UpqFxLfYwChqPmOOq6coVkCL8Ch3XDOOQY3nfl8WiXmcOFPOuM+MV5VpcoJjdZh1wLjHBLCRdy6OqWTzK6xcda6MrIPMskLReOk5yT+RY450MBz7XkPmdaBFqLbbmP1Ds+8RnwJnzGwS8PvN+xxJWizRc941izSq2TtYzTrvYHC5nqV+bit/fH/O5Ke1c91K1qaef9KNq87yGF24zxobG1P5TWkDOkoAUTK9KnoBsNX5j8Rw2wG8U9/NrXAvkoht0H7iX6NND8XuIFxMDZqyE7smIQGTxKBe/U5NFpq3NFY4MP0ynp9VDc2GhKc/CIzeMQr3Hojd42H7xfP7gA4kPK2Y9NdMDFcN2/XkbVvOC9vuefFIKJd1iEzQZdyHRKL+ppJ7iv/16yb4J6X0Cw+1NxcWNY+Hjn1irqhzh6JOdINui1rVmXOHmZjNQf8/OujHHjoCk610sB14ByTGQIiB5WWpZdoSjGTIiGPdgjpiUsA/nvpjJHeFdLH4yDL6Dc4zjlHKLJfV+qCV+VulS64plHSiFX3YA+dsb0uQ3oCvNv7ljbdT3KonF5hUH2Iv9K/vtuqunw2k35nVoi7Qd/Fwc2zOgXjs1nj/TnLcTbW83RKPc+96xBiARYPgTAAAAAIDdKYHIrY6A5C9WrG2CX8+j0HbvOjP9TxEXR8YYXOCN/2KrY2e3sib19CjLCFsfpaO7sKAuhpmZbL7bkM7Xm9zcdBF6UwvZbnBtnZmbVTEji3NGMvJFXfPrnBHc2iIzP5+7XchLSG7k2XOj14wzLBJf6pcWqFHc2lEEVJeyfojFkAPEJebHGR3Y7hN24BOKYY7M9qSjyLEglpqXXQa4OH/nv1eWzs17fXotifbfbKl0LJDaDU1/O65oz2Aau3YybrjXuHvd6/fn1FbOCXbk3YOm1d4bHjI/M+O6upx6Oj/o3s/KaRJ0OtNiabX8LPONcE9UmzBeCr/E9IXSpXnXcfvN95dp8fez8ptZLpz0MMb14Rcq5Z0C1+9a8LNKlqIRrws/D5+0b2R5ZHoPPtw64ui19vLaFSrecWJ6ztq+U7mnF9vUMSYn1csOP2y59Zald+oDKtf3plu73ftY3tO8jyU2mska4WL239ny49RBAgbR5ATJe5J2L8qD2sdlYY48dUZmqQuypc3IG2ki6e70IBZ+YDkDB3TcY1d3m9VNhenokKTlGSf+y3Hj8QNVHqTmQ1Mf20xyLnFnck7yoOS/nfRE+uHND0znQa7fk92Di/ywzPksHCspg3HMBlBv92bsLxgv5tPoFxts43e8ejeOYZNmh2n88xpRQ/CZg2PMnGZ5rzOEe7GUPc6Dzedc/R5yfq4387OZo7/ViRbPhSovM+6dnE6S3JZaVJrbRJi2tOU3AblCWA6sqpYWd4CaOae93ANm+iDdXfLHzHDgHTxh/mbeUZx2Z1PvL98rH4sD+80Om5kY3W9Qjvle9b4f6j6wxZtuqdB+Pm5on5hf97fT91tHlzM4o6m9w7mOBLNdlmt7UQwVdoiQdMIllZTR+fC2aTki1CfNlPOmpX7zgB3aKK+dcjrRfr+P2d71rbAXVxx2qF2f/JwN1slHKDEpsf63PjFqbd4ybC+vWdNj1y8avVkdY797+E13rVIL/LyVTqETusIhK56BdCU6z/FNJJ7w9qCug2i85MSE+eTPMx+KkuxYNyLtHe3O4BcRcrnH1i/V2/hh19WlBGRnZ6sjGlv1YAIWjmJ1dJ7jMiBhwRhUYDSUTgykxLEZs+LIg7JVP2SbDWvQ/LxrcTHFpH0eM03WrDzjzF54zgADrjn3nl42H4rFRnrnJEOP4WCbIA8ZP4pNCVlOD9UnbtYRkFz7xfE57+8jwnMaQB/hKPubiXUL/Z78eeRe6FQP1o7OjpzsAExbe5vVs1Q16CtXqVGR69f2WoetVstret1z3zGiHq7bB5XpYOfecWtsTMUgzszoDo5jkXTvleZmtxZrvdS8vzn7kzp1w9opHSa6np2RmiIAzOwMgh17Kt+7z/1ujoLvUJ+xTcc1S8fRbzDc3Ax99lltMpked+O1RHjk5Jv1XEu2uGhqzIdJ0Dhgc99i91IxAelnbTRj4cx7SxsGZB1fx96Bjty2eo0GpoCU33Z2etYuzKJ0EuS3ZtHinUnM7BAUEy8lR3wX+d2LWbQrIUw755dyTDrFnarNaFu22jr0KBXv+PJnr7frF25YZnVp8b51bMKuNzz8PWuD/sksPU7Gj7WHH2zXPKhmfk6918yUamOykyNGh84YUOcXj+qIfr8Yf5DsPJBOSpISDUueBaHTbkiY9i7VsHR2KzdiZ1ebk4bEub9pQYScCEQWi2JlbNPrmg2Lx5yIOnp4OQJP94rm5gpYbuTU9XuZefbkfdvbJRVQk/MQluM6xzFqFrzywJufU591YaHLHV0rD1sWnuZgHLueMXrYRgCy3JxmvswmT45Cs/H0plWxv1i/G7WCh6f3N+5eamX6lXtk+drldt3b1+kImNFB1TqND+yh/4ZzP3fO+fok7/YLoPfil8/MTCUhuc3oAcej99VHKJI30UxcbzwoMjrHqDwMu/uUCFyzbqn1vKeqz//wzhH1WSdmrbt//2DO99W/apl11LFqv7NepCwDs3Rt/maLes22/aphb6OH7IHL1Dk/96Aldn3Esh6rW98D7S35v93UrDrf/eOz1p/3qeP99nH1Xe/YP+l8Tct61HW5Zqk6/vqlbVavvs7n9effPTZn7RyeyTmnsfEZZzDa5KR6eE9Nug98U3DK95QR0Wj/kXfKTgdRvk8OS3E6l3ownMD3tQiK5ulm5zec08dYbDJ+d9N1Lh0378w6hR5eQQflOPsv1tbaJMcolW0gL0m9Z/CDb1thPORNgSgi0fAgteo23RGI7E3S95TTpjYbmQOctGrs/cnkfeXSbs/P6+PS8Wfa1TU4O92aGz7EsynlWSeNlFKmpbLYNJ1+noGiVtn8VRVdE+XksfWIRatnudW1pM9eXLlumV0/86lrrJcfu9JePrxfi0pqM6Zn1XltWKLarQd83mLTj3RHbPWh1qFPPcxePOxQdfyR0S5rnNoXZmifGqE9Rr/xgnxXIurt77NIhpIgnZp6gBjIKgpIbwyL39RE9lyZuqep46X4gdDZo278pcvUhbtsmer2dHe2OLMgzTuuZ/fGbdUPynYSdHwD2G8reRvphTMyklRGktHfIvBMq2NOUnH7fE3RJ2KVYrycxi53mwhR86Pa5yEubh+3oCtM3WM4IpQeutKDk3qWa07Uqv7Qb8rub49DkB/OzgwBuhHh30EaUr9jeDHdqzkxaT69dK/bgR8iS5RLo/8Q1cCc8oLDrHOerwSR/J7T9DsM6gf+Z3+/xa7vf6Db2r9b+UbmRwf1+U4awtgzI4u9o8ySYlgc8tInZXJTSDg5zrT7rKPTuRZFrDhztRtCUpJs828yPTWdIyrZYi2vkXVDu/aq+uH7rVfqh4ZyHFnWGf+x0Xr+LtXIynW1f/egddtP7rCX7xr6mV2/++z3W+0STnG4egB00N89+lpqkmwC9J5yD4jQYyEpHSm5V/rIQi/i8/kHrXBEpX2eZLHr1XPVL9dxw3yMDi3WTAv9jH6gz+p6kI7xpLZcPDigrq3NeyesndryuV+L1ImJOfIStOR4C8xclSIUZmfNezXr3Hdy73n3t62n3kTqi+79JvlcF1jsyP3hXDum4DBm0Mibw9cQEsUeKkVn3SlB0FyiZgiQZ8rYnFk9zNAVOS/HldnhtBHSiefvUMS+N6UTH6PJR9TLa+X6b+9wPULye9lh8k6n3f1dpf0zazNsyK2bctpIvt8cD5Tcq7rNXiSxml3syktRZXf87A8m8bbz+aOBTe9PjregiNAMLQwDxl6WElB+Mdy67e1brQTi+g0rrOMoXpF5kXZRH728z+rt0Onu9CFm6PnV2qLe74Yvf7Lwe+qYybYe/WzRz1/mKQf3U9yk+u626fCx3fTbDOvP4TypJkf9M4/kCcg4ubB97n0QlYBscnNIGY2ZmYzYG2gvqXWW9HdbK1dqS40WjnJx8/XkPKik8aGGQO49aXRauSfrGajD+4lwtHNHasTd3KYfwFznu+3oeMay1N772bQ6uq5x1zLiuLwdy2WTnUpIva+2ntoDe/QNphvqKboJpSc3OqoE5NT4lDU1oR78juvGEH9iOevo6nAsudKw29+Ax53u5PYzLLpm7Jo8PGbZDcj7z0y7otPM+SmNl34o96xcaa3TwuTIQ1Rj838/ud86/LEbcr884p1nXWjXb+IBR8RnSFzI+41KTKk+bltXp9XVI7GxrlCT72JqQp3b/DSfpxadZiMl16U+XgdZuXuWqEawT1vbllLdp4VTp36PHhI565aqXv3rj1PWwT3j09YjQ6onfvc2VT+8bdj606/+pN5jYiQ3P5r9falGXLj205soCN3zhRyuiwELuPVL1Odu1dckX6+3bdtnLx+1TPX+33LtH63XtN6a89ozz/uAcw/Q1au+BjpGiz6OCM4ebblcNdtuTWirsFjtVy9pt+cEsN9fP2B4Hxaiprjjv9f1q+/xhLVyDHI96t9Rjnf2DX+xDlqlvnexdh7c32atFsGq75XR2Xnr0X1K1N375JBaR/eEdPxEOMo9yN+JtDOLLW5DL6JC2h1bSCy25+a6pHvDERemaPBOj2pOy1bsIW/PFe7pgJkWTr8HkV+IR96grCa3oy5WcxKAppVPPquZ51Y+qwgy2dZB7URvr/ouTMEn37F4CFjM28egC8FtP9UxuJYOkHRS2qid4zbZawEf1aETLvkiwpuySq1zr7MFsUQakz04nTj9Wfnze5Pvm2mxzN99XotFRyxzW+S1Xpo5fXPi1BdKi8qcD1JiIJc60WAx4bxNOgyGa3r9YWvs5ec8TbVVf3vkMusQbVGU56q0I2YHsIW+p+u/eGXhc9esOPpYu+4nY89BByjvx6E6dGZ1bws9r7NOu8nYz1H9diP6M8yx9VqeJaYnJ24WR1AjAUkNXHN3X8F5eKVh415rX596GC/TVpB1JBrlQdLXoYWmvpCm6eKe0I3XrG6Q2KXnHQXN4lEuPfN2XNC58jrF1WyITGnsOqj31KVFHS9LA9giDx4ZcJB1xewUPdxUrc6Ne12TbfM5vWs+R2lYu/QDmh+yS3TPbIm2wrTSOc3pBnBcN9gj5N4c6laNV3e3+u7Gx9vIeqOExIwWTeo93F6//VnpPbyzj6iYtdzvTEQzPzC842v4AeLk/3JcR4vGgySbF2cqloYJOndxYR735A9U7RFFwueuusSu/+3MC+z6l+RS3fzY3pyHQpO2iLHYW7VmSc61080zrej3Hdc938GhKWuICjM9OeM8KORaFLG4fHm3tW55V467dmVPC8UPqt/nWQcoa9/5P3zA+snND6nv9o/fyvsMKvrHsi5853nWU2/4Sa7VVIuGjacpa0A5XHXZxXnrDvmHf7XeeYmyUG78qx12/RqfkM2rP/ExZ/mcCy6yaxZ0GW1BlvvMtGJ++8/qeF/58aN2/cZeZRFl3nf+B+36M7dusdp9votivPbt59j1Yw/vsZ54fJ8jYJiDyA3/3CNUp+PUw1T9nIO7reccqL6/+9epB+Qd28esnfq3HaLrjBkl1zkzNTVvTXifscYDKbPohqJ48RtIx9efOXJdjpc3gI7aJVlnxoBKOIqZT9PeRsd0JicwztMrdMzjmrMNmVkmmC5qHzpEGOj7nu9FMy5bnZv7fG7X4q+X2opu/Vpp+7hzIZ0OERfTs2bn3RXs9vfAAw6dDrgWkPa5q3Wzzud2xaPbCXC/95zvwvM7sniUPIQz0+K2nnUnfhCDgnT2+XlkPIec2ErPd2ImznfjZrkTq9qABb1NCUiPpdLuJHgtlX4Wap84y2L45kVucmODJXSAOsKtPeqZu2y1aquOO2a19aoTVtvLx69UvdNueqaKmJf7nHPNiiCX3+nbX7i89LkRZ7/uaXbN14j87hLi0knXnXzEJfpZru4tK+c6HqdreHpyOtcIEvZ7qgdxPa8YEifbMQAAAAAAaAAyfq6EQrSuOCy76jVX5KbR6XJ7xpJOp5/cJSu1BXJ1n1q3rKvF6vRYzGZ172ia4jLYCsnM6XVsrZvVZnJPR95mwXCZcY/ZdA2brkmxAPZTLb2lTp3rqsV0d+jj8TmN65748FRuPT4951gjZX/u7XXqnr64+/izLu3U76U/M/fi+HOax9tHrtwhbcVja6S8x4zHAsg9fraWmp+LLQhOzLL+3NzLXKp/gw3LlbXtKSuV9W0J9QblnPfpeMutw7PkplXvL+/Jlj455x7d42wnl+aLNyjr2u+2q9jFz//wEev0ntud7y8ML3vz+6wXXXCjvTy78wn9IWbc1B/L1tmLqw9a46RqOvpw5Rp+xsGqx72hv92a1p/7ce0C3To45XxnYnHpot9GLC07dJzenx/aa21/aLO9vPFEZQkF9YHDG+T3kftjiFzY4gXYPKJCB7YMTzujzfcMK+vkgFgpqR7XblOxMIkl0Jv31bRWFsq6wFNESvymtG1s8RfruxnSIpZ7N9baPbYZ9+y+X+45qde4o+NlH3kvCYXp5Nm19LoWZ5CKsgx6P4+ELoi1kesOfU+zJ8T+rFQ7YQeOJVLV/N1Lm2KGETneHF2zi1R+uxGyDDM7BiesPRQTy4yNqTZtljw53gGM3t+BYWvugscCaadE0681rbZidXTiMqVdZI+YPj8zxtKNO593LJDOe+h1/P45A+jUgts2eeMoTXxHevukWnO/AP+R1Dr0RgaYLlm+xNqgQ4T+5mjVBv/tISvIm6fDGYxrwfXPyWnQs2xGnfMNX74q/5yL8HJqo8UTOCneNpk9i/7N6882Qt8jc//AhPWXHSrWe9vuMSceenJM3aPyXavQkvwsFqPfftNddL4nhjrJKtDUe0C2/cR/j/y407++KBafr64u7DZqxI54ijKdi4tWuUe0SNPxNctJQC3vVut6tWuBBwSYpnVzEMA83+D6ppvXFxdfuCIqRWhy3IUITHktN3bGeBv1XiR4xE2+TD8IurixcdzV+oFimY2oduNQwyEme6+25gazu13HZMk62rdLN87d7bom0SKNrOP2oc8uo2Z7O/QDg75+aexFhM7OtTviWJoDJeq0m1y/Vxt9RnkP+azrejqsNRQbySzRv4UTr2S4HZyMQVnXxSEPbx6QJI3TDhJkzEd+9rA1/7vrcr6L093Y6tD88GuftM46Sv8htZPzkM9HuVc3/XSz02A/cbtya9/+lGPs+u2vOs4645kH2cujB6hG7FYSt2M6PGBSjzTkkcT3bVEDdR5+YJddj+weoA2P6fdTxwX1gcMb/v19F+a4UNdR6IIsd+lOzPGr3UDSSb1t+5jqEPxiM4VE7FZxqINaaI6MTDsD3nLSdjm5MNXfLDZEpEmGB6579P3T16muS+6Y9er7TOI3WTzNSIdXYvck/tC+312xJjj7Ge2XN1SGO8XSLjkDCDkHrgijQl+mRgRmu3Hvi3A0dZu0x/o0Lc4br7c4baTZ9kl7w22PMDEr4lOd+xTdfxICw8JRBJq4+v0ywQksLLw5dXn/QvPGm1k0BFMsmlEMXvHJnQVxewvNFNfuhDPogVm2qBV3shMTbsQJuG/sur/NXLB+MY3qRJx4bTOuvbdfxRkeuF61SycdudJ60aFKQG7o1+KSngXe6zhjfLXO5c4dDL/JPQIgz3Ru5rv0fSTPZq7k+pUOzKqeWWvVEvXskc4Hx82O6WeJhDvNzbrxqGZIQpHsQbXHz2IFKheQbNV5ge4FsZVNxEun7rl36vgN/pvjYxhXtFG8jFzL3mua7z3ptIklkK7SGQny1o3PFDUus7JOhCYLSMm8ILE/PKLUaUR1o0/v3+oXF6WvYhGELEzFoinnJOetgpJz46DM10pg8WJ2wRFkXdrayY1vmxaLvVosdlFDsLRDC1ctlvkYck7S6LP1QKyBPbrRY4viMm0FlsERpsVjUgspaQi4UZXtshd/T02tMnpXvf+Nd++25n73jZzv6Hl531pt2HiqThtiIzMa3Gv/n731Xqvjue9Xy7qTwOJ5Zl7nQdSxkn/ZOmT95Z6t9vLkbiVMNz6XLSQQjnHh859UMbICC8qRqbnceF19T7A1bb2OX12rBz0d1t9j7Z/UeTDH1e//0MCUbeFnRKBxRoBZTxYFtuhzJ5gRgcbIkngrlna4XoU+LTj5/hQRJkirwOtND4d0lCVjxIS26gyT5W7vhDrusH7ITpLVSDrIjsjjQVEeMWAPOvHEdNrv41js8nN3uoKCO+1yHOmUu387HV9dt9P7i3B0Ytfpu5R47lE5d/odJM2TWPbYKpxndWKLmfe7Mz5DTrxp1l8Ems8RM6ZVT6SUEw8uyw4srjwDkHJiZCXlGs8R36zjW42Zl7yYOSwdS6U6uK4lvtWNqRXraVef8hKtXtNnHXOo8rS8+EglGo9bucQZFCPGhpxvzRgUIJ0O1wBCv5nPTFZBMH9/WZ7XE4Uor596r66sfh7RPbFcd7oWFt3Y9QktKqVzNE3XtowfkOdRJ30+1UKDRAtIDqJ9lh6RJaKRL1BpKB2BYrhWzHXS8Pj1ibyNWRu1Ap1O3kR1muyyFDHJlkKGRaaIStNyZ1rZHCundj2Ybh95zYKktKC/p7UQFFEnf7NY9VpATbe59PzZbbqsSx42ql7e0U7CUQfGiwuGGwT9uVkcM7smphyXkty4/P1K8tduLSDZyikuLekN9tENzO4/5jN/eNKu1z/0Xbs+//0fcj0rzjx27vc+PKmsjV7xGGfu265GQR+4VDXAT1nVax1LjTAjAmRgdMa6bXjUEI6g0QSlFxmMxb8tw/fB4XrE97ol6uF1zHJ6UOl7c1Tc2kagv7QBpoVmWt+DnOZon+S41ILT1G6y3EFtYLt2P5qeBrnHFj33mzkwUDrA9nmJe1WfLwtdGc3uZG6gh7eIAVnHr5P2zQypkXbW2Ub7iTiW8zPF24LfDF2ez6reT39P+txHSSgO6cFNwzoEh8Wj69Z3Bxo66OPZ+Xb9Eolncq3GTfQc8A6mNAcd+bnCRayaItQbqsUj+R3h2Oxu804JyiIzzypqiF9zQJWb8UJnF2nKH4wlVk8OAVuiB/UdepB2UT9lmfWstWpZvEBsAMmzexgfRS4jvnbkdxfNyAYCcyR2GESsqmUR7PL56DfRZpOWBbUfpxvjwYn2Z9MdjTnq6M2Lh0sGptL9JNe7hFisIG+lnvcmBnCHBWl8quPCpgtpVbeyCpkC0bnUHHM6N2J62TC1Fxq9b3sEHAEpK/P36zDG/DgxkEbPUoQkx/LIdtOKKOcsF3gX3Zzs2jaFGefi62ied1zhap0eNc1T+YrQ1I0jWwtkZLBYOvitJWfWAXoE8NueTsJbf355EPA+vdp6NqZjiHJ6jPrc2RorAnOEk+cS28fnrbt3KkF01+MqLvGFIz93XqrmHXC57OMfcZZZTNqwqNfrgo7OixO/+fZ/5a0790I1CnmFTlvy2Vc/1Trkke/X9LxAdfni1ZfmrTv9XefZ9cP7VGfh6JV91qi+p5brkcxm+yMPSPaMyHpxm7MYlDjooUndYaX7b0KHRcjDcEVXljqF6rWdWnJlm9x4Rrdzqh+i1GaM6RG/+ydUvZfqQS2+pPM3Rp0fr5WG46AdEejjljQtlc5++uHeRI1rkxNPLevc17pWXvmbvDCeZ6h0nJmpOdfqKKPk2fJoH4OFjD64WNgWWxfz4kxNb4kz7Sxb+4zYVff8ch8Gpivb69ZWL1CVdxYlc78mHsEuv5lxfCcG0sgr6U2Mbrq/ZUS6X5gEj5b3TkYh4wRWkGXu6LXKXf3XB6nwjHW9nU4uVvmNc56ZpnCU38x4/n3z85/I+7xhOWujysAgzzTOGykDw7P6AcaGyEWd4UHEZT8nfO9R31lPu/sclE6MnCf/1PL8lRy3Pca0lqCxCPXL8cUsVi/HisfCUF9YOamtPAvcwHmbPefaF/XoFZBOQ2A5YlEskOLe5oZ9VAcKiztlnBp6sRxwL9leR43yiOEiElbrXuBbn6mmfOrnlDHNal3Gc54sUPfrYODNw8pV9jC5yvaOqrirzU+oPHZ7d+yzZodVChNrXK1b92J/98d7zlFu2KEJHTdi5KY0xaTk7bt1hxKL1/12q3Xf7Q/byxtPULF9QTHFZNK4/JKP1vsUQB34xmdzH55Hvv0c6969agaeZ+mZOXaOTTlhLBIK0kJxyxIrKO1X82TGaT/EDc6WNhGE4lJcu6TdWr9U3ZfLdKykhOwwix5rIw9G2KsHrQ3oeu/YjDWorXic/9Len9onv3Rc3mTcLF6cwTaS2seINXc+Dye/lxy1zeYgGjnPXPHEXhVph6fEOmnGbc4Zgwp1++omgW+2enrEQihCqjknn6S8v1hZp7S3gFODcZomM4UZD6oRS2aeBZLTCHkmh7D38RgvvN+ZqjM+ac3M+EkdO09enbWrlXX7JB2L+OwDlpK7Vv/eTlyqGybgdBw4LZF8p4u5YUk8KFLCqyTvqu0u9uTqtY/rsabw8cWyJ5+QvV5R4nRS2EkmIQT63Zqok9Sk02U5Mfz0G6/UHbVuLQhloI39GnktfT55rsm9KB652AALZJUEJP3zumpMAZmzb+594Ou3lsvGHASTNW4S6fVO6qBbdkWNaSvfEDVeYiEYnJzPcVuOUm+Ye07McToYecvAuLV9h3J57t6mRNjkAAmvMbV81BPGdGsh4PHC6yRWsNc7MIQpfmP/1xUfL+t9/57LCWW9FIDEc/01KlsEc+y/nGnXV/zycafDeNQaNSDhmJU91nGrl+Q+IKnxGplWokVGfPPc4hLbJyJoD3kXpuZUyMSGZaqtWqJjjjnsRNoy9iAwI/T6QS2Q9mmxyOJxXAYYaGGWF6+n17mjtU2vjlgl3RHaEispopFrES6O5ZVezOE69vtK7mxjYI85oQPDgsWNldQWVbLSivVQOr3t7a1Wl44p7dMiq5+sbktlUJIeiMTnI5ZcEdO7hyhuld08hMzjzuJSBkN5LYrKhW0VzC9pThwhwtEU4Vlv2BT9riu1x+gZG7Rb+eB+a21fZ45I4+/T9bDlvr86nqr5azLDCGSdc/6ZfCOL9xjqDyt3pjWqZddvfT5az5EYKpx4W/YOOrHzWcPD6IZMyGhw6ZRJJ4pP16sDWDeYYW2yLjbYhisf9yeIYiaa/BgaX6uj8YefuPT2qBjvjca9N8dtq1MFDEzOWNuGVcO7Q4+23E0N/D6dnmVkxE1iu+0xNWDimU/bZtfP5f9EI6qZ93RdnnAEADQGP73uancwmHYMzOtB+PdROerfzslzB0qqrUEtZPbtm7TGhlVKIYl1G9jTbU1qUTm30O/MKc4spdAUGTTnWLjoZdNzOh5SVJuR8NucDlBwrFqGgHNmYjGmn3RS/BiWSrPlFT3gHfRjvoczQxZ10sVLI4OOzFhvZ9Yb+r7knEU0cnYOGY170FJlkVrbRwJST4koIoOtT/J1i4FgYHLa2jyovu/tQ6p930/fPxsEGBHwjsvb+E5ywixFmBii0Sta+DOIe3mtnhntmRv6rJN0DOIqmbmHXM8y2YQTZ+qXnsnnj2bD7es9z5I6RQs02w3s8fDxiPfvXVt6Nply+IKOP5bZw1S8Z84p+Z8uawPxnMlI82y+DuCv0jsWwi+OFTQGCD4AAAAAAMAgmuoJSLuf4Oks+KXEKpSozBz9mzvy2uylu2Z6GRUpoyh3js5aW/Yra+MWnax029Zha2hAxRnO6vhEe9o3NRMTAAAU5fovXuEkuGfYyyIZfTgujxkaGLYmh1VMpczvO76/05mjXZhfVAMiDlyatVP/5MYdqqT8jEyrygNenGwLRvykDNaTgT2cBkXcyQJbG2XAjLirzQE2TjgQz3mvX9pszCnuxkrmNths2RPLozupAQ+OkSkUtbua891qK16/HjW8jqyPB/Wr5TXdHU4GCslj6xvvZylrH88Ff9wKHbuuvU67J6esrcOqXd82or7rYR0zOkFWUonHlJhAsRKa2PH3nrhIzrt7qB65/+LDVIzsIcu6Kb5V/S7mKGsZnCluXfVeuS5c/ssNGzRCDPQ5ON+xGXfpCf3LHdiUcd3VRhwqw9/5Ua94m7380E1fyvu8USDhY35jF/wyDMi+OZ/R5/PzZ8zLYQkDZHoskO79aQx8sTw3ji72Nmd4tbuf3OROrAhdjaZwFNEoA1b26kD2ndSA7NTuanEjzEzNWLOTat3Gp+8O+3EAAMBJcM/84xlnO0mzR3WqoMkh6qQO7cpNKN3SZg3p1GCPaYEkbdrMXK8zG5TE/THdOq/knHb5cnMnuWJlGzdt4uqWxPijFAsoufRy5uz2PHztuDOPhuIHtAhiZxAHCTkn7Ys+xoLO6cfvw6mE1PHkYE1OZgkRjSyCZUKJdX2tjrt6lR5MwSNzGRaPInRl5hQW1a7otfLE79qsEp+HLnRbT1+76AhGZliL9gFq+/foWFIZ1c6xq+J+l89gut/b9Lmv7mu3nrNexa8epNOA8eeS54/M3DM3n7W2aaPFzd/8dO4XS5x3ocpoYWfvdQwj+W517zauveEE/Kd3P14nA9OdlDzZRevEA5XofSjvjKJBOitm7lHz0S/C0c8N7whEyz/eM298RNxADGR1BKQdj+GTkDXjBHXri96MfdDbit1g3KOTHveETnOxY2LKum+nunFlWrPdQxSHpGOS/mHuFrt+EQ+eVgOoAQCgYgbGp60hPdhldEjFPZK5kRTJeO5UdnPTzojNfSQm7TbNieF28/Id2K8EFYtSWScChdtCSRcms0yxUOhs1aN2jbQ7IuDMWE0nRlKvM5peJz6SJyHgHL7e95AH+dyCZ4Q2beAk0Hkz6+jjidDlqWF51jGmX49M51y3fTrfrTNC2ceKZeKmrHFn63Ly+C420/upc5CBOKsXdQJ5nrlGW8qcFG5kOZWZivgZwmwfnXaEuAhoFrorOzsckcg8OjpmffteZYQ4+OHvFTljl09c8hEnNdqiWCUl7pGLO4TZrJQI05vke180hJn8nvxZvEKTv1c/S2s14Pd2R+sbllDv2/vEoNrLngU1Wt2zDmNW0mOBlB5arlvbHZ1l1zmmeLnoOEWCJ7m3vvlZNEogteRJ2z4864yq3qBvZjUzMgAAVI/dE9PWIztVxobhfdptPTXuCkcnzQeJnRnVybUGd6qq2U0U3autczKPPc/U4eeuEwOZNK22C92YLpDJZFqsbmemq3zrj+ncFpHkzmnvzm8vKVR4FLY72YKql3aqo6zsXswRjiJ4O7W469RpWjj9ijcVi+nylNm45rPzbj5AYyS31zV84kHLHOEk4pPztItnv1lSxzjPFDfJuPM9GEaOtf1KIB5LIkzy7G7V018OkRXzkWEdBqVd5Lc8uNf6wy0P2ssbj885bFVSo73vfJVz0Zx1SJ6J4zoLAFtTv1ulATPFyPiIYDeXo+u4znVRF1aCvoNpcyPa4gPS+FTJAkn/nJ6uEQPhtSya7mppMOxJ2bVIFCuj5HLk3iHHN6r91esOWNJqZW8N1gsEAIBKee3b1WjsP+8ZsbZv17Pz7n3SjXv0Plj4b1mlrZPZkQG7Hh3sskbGVF6vKZ06aIasgCLunPA3Q8gZqxyrVKvO4szWS6/RiffzurDZmuek7JF8iTxC1pmlxBWQbkaN3Id8S7c7bSJPrODMOOZYQ903lTZfrHg80cHO8SlnHnqGYxgl9GjngPqehoenrUGKK2Um9qu56jeeuNeZCECEFNfemXL83abyGdzzE1c7u9AlBvOhQfW7fvuOndbQkDrPXdtVDP3wVvqt929XBzq++lOdfvKyi3OEJLuNJ3Saugt+pITsMVtuqvp5+CHPef59ncwoTpJ195kv8OUiI85LkTcjWqwwTMMgYgtk1rixtTmRRaCZp5HhWRwkx9eknr2B858N6nxf+8amnakBmb8+YrnVdse3ct5KNSkAABA9p731LLteYlsF1YPvru2q1bn0xoesrXfepXacGC5ulZD1C3qu9kklUCZGJ5y0M14rmSl4Wu0BO66bWjCnMWW6OGbRk4M3N2+g+x5+c0w7s6jIbFxG3j5nWlqxUtpT4LkWRbtuMvNkup9BBltIvOFjZNX7/ROjTgoe5si1vdbvtymL7hMPq/Rqc/t2Ojl4N/69GswS9UQAIkan6Rk0rafhk+fSrTffp8ISjN/M7iSIlbmGiJA0OabmZ5HLLp3/tKutxQhFMAa9OhOUS+U/2Mb4w6wKrwNJtkCyYNSjAp2p9easPROqodg6NOvMrsDTXNnbdQ6vwcEpa9+AchkMDqiGenFU3cDPwhzFAIAa8Devf69dS5zc5uEJ684dSkD88j4V//bQ7/7ouKQDI8JDj9C2RvaQhWutMzWhzGDT0p4/VZ3XlcwWN7H2iNWPBZ0kaHYFZybPNcwv81rs7AEb+jTbtEWxt7WV4hx1XKSeUk6EgoqPdAfbqGMZA4Tm3XjDFXoux/FpbbEkMfrL21Xu3Z0PqCEexz971PpHOYGjzS/NFY7VwE+MvvptyspszZJFdHiPxySGOZCFf//6nXZ96eueZq3vU4OMnE6I0dPxC1sztzlfrTmY1jPNcK3iOQNh++xxHQQlZnMIAQAAAACARFkguacwrS2Qk9oCOTg1a20ZVFbGx/coC+Oe/ZPWqJ4femRo0glGXxjSPb6xwdx0GJbKnQYAANVi6plvsL58l4px275vwq537x6zdm3day9PP/lwrtu6HMSVTW3c7ieVRfNRnSORB0SsXKLiIXt1Gh+ObZR4x1kdRzg9n6HYQ2UF6Za5o3kOYSemMdcdzThx6D7p1RgZ7CIzwvTR+7sjnvPNR516pLXA7ympXca1a/68H95vnbT7x3mvfZPKjmNZz87bVHe+8yWV83OjfW4qFQ7I59Utt9r1f9+1wnrnsw92cnzKoB8x0ollMVMg/tFxexsDaJ25wp2Yypj9AnGyiCZ1JhqJgZwkN8agTrY7OOpO+zWig5XHhpSotIZ2urEmC6oB2vhyCEcAQG24+lM3un9MjbmDX3R7FKnrijrH2e1qIMSjWpA+2b/SWrZGiZYDDlAqa1lfh5MexxSEIur6dOqag/rbrfVLVDogSZPDrmxnAgaZZs8eWGMk3eP9Mk1Wlx5EIm5qO/eufk2fTp4tAvHBPaPWbdd/tuRHPCnA1wAaGx7Ec8sWtXz6u86z63HqCMl1Z6ZdEkFoTvXoHWlthkJ4s7LEAz7BuCnahAhIbpikJ2vOmiAxOZ26IeqiRm98RCfPlcZ5fs6JE4JwBADUHE4EXg2x6AcfX+IhB9QTeJbiKnfvVMJx746D7HrpiqVWR1d7zoPXnOO5s1O1qStX9lhHHqBGBj9lVacz37YIwx6dWqe7ldbpQTEyNzFbh7yJn9lztGufOr9Hf3BtpB8dJJNvfPYTeeteq+eRH56cs+cNN3M/s5FJOjNmAnW5Bt3BWLFNKQ6qZYGUBo7Fo6RLkN7yssOWW319qlF8RHcz3mXPElP91AgAAOCLEzJTY0SscuJx3Yle2KZCe/bv7SazoBKEVnOru788ZXVeySe7+6zHV/bby/etV56b1cu7nSkE+zpVfcphS50OfZfkpDSmS5TMF3/ZNWoN3vz1yD8qSOc0oMw/ve1sZ9YkpocMSjIITGbTaaK/s9r25CaLj5MFkoiVRTRhMZCTOl2D9BokRse0Sh71xI3WUXrdi9dHcJYAAFAjNp620tp0g4qLjByJkZRaEpHnTeGR+5RdIHG5b5fqlO97XAnIpp5+q0WPoJ4dV/kVlz9D5aEEoNZ890u5Cc9f/45zrRmdTF46MHZOUi0qRad553gHCXZhs4uEmZ9VvfmO5ibryFUquHbFfddHfHoAABCdMIx634qFpulKzzF8LORbT8UlPqlyKi7u327NitAUQfoMDAwB8eDbX7jcWT73ApWTc/fIjDOnu8xRbk7NGQsQA1kdAdlJ1saTDlVulGokfgUAgKSK0nLIEajO0Ff93q+AWASNweWXNohOUHM31vxtM5nMS6m6mgq7dL9EIYKban4SZYA8kAAAAAAAdSCTybBo/AyVv9OTEL2B1tV7MqLoLZC7d+2ExREAABJi4QQA1D2Nz0lUHiOr42b+g8Tjt6l6JZUH4v7bwAIJAAAAAFAfDqCi5v9UbNfrYk9G0vEE2jmT4YCcJ6t3OgAAAABIGQeTFqm7uZ00zk+oWlGFQ3dQUdPzKa6hz3uNfs/XUHUq/f02/fcbqTqJ/n5vFc6jfi7sOPzAAAAAAABRQxqHB7PUmu1UzISHB1LZWYfzCA1c2AAAAAAA9eGPVI4gy+MhVHhGgNdTuSnRM9EAAAAAAICKrJ7zJBzfQ4s/pcIjsr9M6+4v/4gxjYEEAAAAAAAALmwAAAAAABAKCEgAAAAAAAABCQAAAAAAqgcskAAAAAAAAAISAAAAAABUD1ggAQAAAAAABCQAAAAAAKgesEACAAAAAIBQ/H+IFY+alzk84QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.set_facecolor('0.5') # Fill in model land by making the axis background whatever color you want\n",
    "im1 = ax.pcolormesh(ds_i.coords['ni'],ds_i.coords['nj'],sic[0],cmap='Blues_r',vmin=0,vmax=100)\n",
    "cbar = plt.colorbar(im1)\n",
    "cbar.set_label('Sea Ice Concentration [%]',fontsize=18)\n",
    "plt.xticks([]);\n",
    "plt.yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the figure above that the data is not on a regular grid since the North Pole is over Greenland."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change the lat/lon coordinates to use the ones from the ocean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The lat/lon coordinates in CESM sea ice files have missing values over the land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X2Dd3N4k8VUqT7DSn8wmeccpD/nU1hI+tCF4NJAZdh/pRHXdXTdY+/Rxdzrd5W8UGHtTzsZn7W7d/m2L5lF0pcCNVl317T/M8/65cS6fIn98W+4XYv8iyf5Flt5DPWf+H4lO+79lvyToSkgXzOq7OcNNGU1oF4eh20HCT1N/bqj6aJnFZF4BLoknxK3O+2tjPsn61BupfWS9zq4Adh0CdzXR5gHEplwXibL2FGPMRoUvrlEE6lts2Xaya820reHUeweqPDlVeizVbF6uq20ikp+JQQjRp12l+bi+vUQtEf9HYRqDZeh21eSwJoM0fdK7buohB1xlrfMZ9XPX0OlbzatNkvXyM6m316RbzXCyfeZt6mZvK68tmHTt6HFuIKB8BON8mUu1o6CAShN3l+vl05ftWEYdbZ3vMsl+Rj8PEbiX2P2LvkWWP66IQ8QhJ1QVb++4yu4jVvNtZsxe0Kt60hVLtW7Gt0XPp0hxe21YrEDvqX1k8/IyistFbWfSbVK1f3FblrawO1eQeqFPZb4OonOWrEGOz68EoKjfTK8ukENxbCqFWJmsWlaZ8zee73r2hejz1dT7isprf4rKlAlb2oVraDbr8bQSjKY/6NraCr43XUbfO1PdPt62NGGzTR7JNwExjE2/92BTH3abJuknc1bfVp1vOt768vt3iNrV0DfmFYKPJ3+K93JTOViC2KXms3kcVHRV1m7wPD2xYd7DYFbL+h7P9Z9l58vH7YlfJ33uX3sgiqurqborWHgazBwAAANjwSYb9Z6Bo0r67iMWbiBWJ7y/2JbELxI4s0xSf5xvyGU8fSZsfVKqmZSMW6Rvn1bbNL4CXcv7nojNM5VVb3tY6CtwyUMd1rMpqs3fdm2ruS5lt1rXjoOfVfdlEfK/nWfXJzc9xvS6aPI6L3onCA2cf8V3fj9o7OUuVV8uXaz2I1bJspmvuQtHc9G32Um4ehSrsZtnjpW7argcrBe+BoqXp4Wubj42n0pQmVJ/HPqcvdG3uru7Dte/jRr61tEv7rZa/RfO11b7KL5ded1Ut10Xuu/Neyu3X959ZDc3zwJtsDr1TLZJtPR6qGFZHVxfaZQH6JZruLd27//Cb7zPqSO3Fe6hf8jz/hFwj75U/Pyt2g9jnxIpm718WO1fWHV2KzUf3XLTu+0ga05UJbfWkLnhmg4Z1ypsjUwS9zLPJAzeBa/o+LszGMy9TQ1lUzdi6QJ1qtwDbvpTzfK2bopV9KavirvzhYArQKRNqBayxL+Xifl36UtaF3JZKk//ydo0nt5aturnfZnzK5eXN4nLzWrFrAq+maRKY9f2q0Edwq9K7EeKBbdu0HarJ2pSfbcS1Sx5Zy8HCY+n72Cbi2r6J2e6qyiwDZrR5ZHrRqMpXLzjV+7DNr61YbAzAcayYEerHhcIX74a+yfP8ZfJRWJXrSu9k9LgJycz/orL2UOpugkKw5G77UHr+TNtaHONC1LNBIGoDTJp1RbP3URG40dTXdH272v4Nwwotir2aeDMMJ7T5A8JuOKHFaS+r+3LrS1kViiGnZtR7FTeFWvW4Fz2W83IuevfWvYWLYtBFXNaPpqhDG09jdb/Ne41z+B8XwWsSizGLxo38LPslNm0zX+ca0VzdR5spCl0jrkOP8ajDpk+lCZN3VCca6+tV33X5beRr2Ea3rc26pvx88hkLHQXbJM0OvXWazeyGDJq9OEO2kXUkLlWidkFYaQRiU1N46ECdpu2qAsh9Zp2mABzzcEKLAStu3sd1QZVrylXZr6X3cVnIqT2c8/SVAlf+XvZ+rgQQl9XclwXs8t6rQVXKUtaEcb3kqttK5eHsC9O+bIWiKr2LWGw7tqN6//4eRJNoVO2jzfzWNk3WTcdv02Td5RiPOjLLKQi1eUh5l8R3g3gcWjRqhWQAcTimYBod6z+8IIo+km0je3RDAilF3tKLW7HM0MStHK7HtN/McnaeMt2CKNrQRJtiZikKvBDfiv1rhxZSeCtNonJj/zXhZ55ZZ1Ps1UWjaUadZWFVEZdNQrv2Almf+nBRDNpGi7t6H23F6ILgbSEubftDLorQRRZP37KA1N3KKg9n39hEuDalsBGDqnSqNC6iUZefq2hUrXOdYcaln+OKZps281q7itqNtJmpLtTLbagOX1c/Htv31KqlaFQtcxGNtmKxcd/qpFbvcluBmIo3UvUDDQbsI6ncViGAVGwImtxz/Mk2HbgcxKWq9VV3bI0ey3peOo9bgzZqGiao6qnaFKN5Y/pqyuL466Jolk7XVF/xUmx6Iuf7NQ07VBF3c/GkEdqm/pW6JvqqQJv3iVkU5t2KS1Oz+MZRzRLNy67ySDZ7GjOHAJxlZ7T+hrFpCg/9LGkWjc052QpKnVBSbWPKdwjR2Ec/R1WZ15t4/cvn0lStElg+/Rz1fRXtcmlsqteIRuX+PURjo5C03L6+X9ttbPY3dlI8plGMI+mch22/ycxPbKpE5uxl7ij8jIItU4gwXfuibj7xBu+jzuP2vp+bIwvbcq+dNqMbddHg1fKtlGVUicbledQr/RwD9a+sC76m8qnE5Xy7rsVlfX/zfJcF3WLXgBVF/W3sRyM0N1MvX5DV44i5j6Ttfk1C0UUw1vdpWm/b3DyEaFRtt1CGevk8+jlW06nK92/X66Opbbjfjfdeyndjv5YX5oU/0z876xHVLh7HzFI0qtK7ejN122n3aagnUzWm6rSzHK4H2gpJAAAAgBQZImp77AzikbTeV/npGnwza5K19FS6Nk+ryrTQ91CRfrM/pHKXm/l4DjHUJXPPr0+gzvLMP03eR7dAnc10/n0kTQE7qu1UfWd8vZSmbVdMATi1hKZm8YX9Kvphrows2EbnafT1NrqkU10LPt5Hm22HmmGmvu95Ot/yhQrIUOUb+srUBgpZeh838sq69z66eh2ztk3aCWuthA9tvMP/aLNrEHxONG2vutlWuheXxn6V9ZWZR5/KEB3ULMk8A3Wq6YxR1kuicbnpVh3QYz8QuqoZu9r0PT8OlybzWVrHJvAVQ/N5Ux7VdIuXgiad4tpSCc6F9Yq+skNjboZrTuArFH0EnyofXZN1aNFY36a+ncvYjtU05nTL5fQZ27HNvM2hhc2SoLMUgzGJRl2VuATr+KQZK8Wh0bTdQ9T2au0p0loI2uw3a95XoxhtSFtfpeprGUJc6gJhiorcFGOb+6+Xr3psqijwPqiPt1YILG3UuGEgdFvRph4IvcynTKfqP6iKAvfpI2lTzqZtbQWiThhWm1faCM16vk0PSZ34tGE5Ul1Nm4e0aUtXQWkSirp8M8d8dEKsL9Fozm85XcixHUN7JOdF6WIu602hvbzMp79jCNEYQixqvZSWVde2imMXoswbPUDUdvWiyAOLxDZo81Uty9qLy2oe1f1XxeKcpajppnwHvunqTduLJ7za7LuyLBorHjn3KPCayFsQjdXd69L5iMv1PG3Epe228+1n6Q0i03ZQ8Xo/nqbbRyU467j8NmkSjO0Eov22WYvhO3xEoi5PneDb2K66voXg7EM0ho6yXk4Xhi31fA1e3xBi1XQ8OtGoKlco0RhSLBo9/y0qN3LtuECoHyNTImgfSZVgss/Ybt/a8SMt96tKryy7o7gskihm51OXoba/dZG1uHJdjC2mM3pCA6PyJqjm7t4s51ww+c+yY+1B7EBczo/HVSDqt1WUSZFHU1/KNl5IVcdx1+unOhh7180+rmO4ZR16J1XpTWLRRvi5Cs4hRaNS5KzYiMvmfNuimpUm9Lt/S+2kmvbVqt+kQTSGEItBPJF2ybzyjoWiuLquP9BjsI1P30drEZr15/EMLS6rTdfn/Nf3jPt/1K/so/VYPvxm6+urxasfZ1PZ5sv/6af6cvyLxRBDD7jJ3kaPpc0sO7p+ljGJyxVD83hdXM63r5Zpc/vFstUvb1V/zGp+rl5IW+9j0377JMSQQLYC0bRf1zmVVXm55hGjaPzIdT9YKnPf/LPhmRUCnRiMQTS69nG0cQA1rve8/ccmIueMtdzj6SOZufejcBaUBhGqE5z1PoZdEEJc2haweABVPZYbm5Z/V/tZNkWG99HPUuWRUPWHzGoey+q6+TZDi8v5vlTi0rXv5WZ6tUBU9cPcWK/oj2nKb46PF3JxCsjuPY9deCuNL0RLb6Kt0HPJzzZ6ej1tHKKxKd0U2Ogj6Sj8+hKNLoKx8QdT1o2oGrcQYxzJKIf/6aoPpJYGkZlbiFbbZQvrXcWloxhfwHLYoT77Wc5+YOia6kclLtf3VheX1f1pReZC+sVybJZlfR8b2yxns1Ibj90pv4XyaNfqm8Bd0d3ioS5BG4Fr6izv403U5W2Tn0kwmpu7hxGNo9YEIYTkSn+isZWwXF6kTa9b7prGJ22szJwiQxdihDAgOQAAAECWhiBOdkByl36TNl5MXZrGfamWdXQ8Ji+l1T7K/czyUey3uo8lb16PATuzIJFaoRbGXRyRl3Kex3J/yM2DM3krTc3hm9sslmmzXJv7WthfNZ0ivzqqfp0L611d5JWyqPB9/vp4AEwBHDZlCelxDOF1rKcf2vvYRTT0WKg35ZvGbhza+xg6wMa35TEFYuzWEztRz2wTGpX4VAk+VV9LW3Hr27+zKb8NNOVrEpc20eAhWBe8i2KwOvC4bjB1VQDOeqa1Elby61Zczo+oTK9pCl8s1qbIXCp6pc+l4nAq+9rsN7m5rT7IZ3H7RTLTfNmWN3O1qDphGoKs5+buUCLRVJZVT7HYl2h02ccUWIpC71A0DiEWEZIN9SLGFImRC0nX/pJtxFuQvpkGD2YIcWkagqKubRbElq23UyPeQqASvI1CreKxnKWbLbcQiJX8NvY7zzOguKwdhjmIppafTmTOt1nfV3Pfy8Vtq/tr9kKG9j5WRelQGmK1Ry9lG6GoFn7mbV0HJu/C0+i67ZT7SNbX1f9WfTelt9lHiGU262zWt00/BhI8pPFGbZvy6T0Ax7B/62WGJmtbcXnkXptD+9TT65rDF+YRV3gY69HbZ1sMM9SWf7ym+31AOO60ZbelZfXLtWvPow+2j56m+betXrQBhaKLWPy3669qLhT0TtaDaLQRgW/6Tvtn69P23ye4kExRQE7V+z76pm1bb51JnA0uTAOIy4X8KulNeddfVro+bAAFS7PiDHTf+GD9omvV3B22P+X69or988KKlvpUwCYhGUI0Ni1ri60DCBFZ1gM+SWeS7yOpE3EqIdtm2cJ6R3FpzE8jLtf77zWPYwmweG1mre9p27m1u35gm4f68S9Dq8AbSzE6ssfpZOhaNPYlIuf5IiQt66o0GJmQdPHWuYgt121DEJu4BLCZq9iE+hrs5gLzuW77HFsyhFDEEzmiPpI9isauntkq76rPfrsUujHB/ekO40gCAAAAWMygBS2FZFG/ql83ITx9tp7J0P0hWwXeWJTF1Cwe2ksJYKI+FJExajtLP2rb1dPYlF+TN0O12Hb4JYgjajuEp3EI72RTXkN4ImO/5GnaHrBpu76sjdAJIZSa8ugjKCeE4GwjLocOPIJx/+KuD3sUA1ngpilTU7hrc7StSKSZO41xJEMLxJiFZNtyxC4clyicZWMr8yibtm0vvh76I8bsmQwRad5WXAKoSGmomXvttFcr7+Ml1/7AmO6gnfey8iLaCkVVugf90t6b2yjWv+9n328uIATnjCvNw+489dfUw+rEIiDnnP6f+mN51m0Xh6ILVp6RvoNGWuxEg21aeCk39tHSs9m0X1cR6hO17Vomm/Wu4hIgdWwDh7LAHlyXJuzZcsUKnwHJIR6aoqFji9o2odyvR1lScV6kchwrU59re3Oj9Q9j5HJA72ebpnVbwWlqil6IxrbwdhrFJUCizPo0WT6UssBi1VYcujR3u+YJcY812XZZLxi8qVMSX8VhcL9FPrNNZ/33LLyPKrHp0pdyiKAcU5l061O5sQFsvYW2fZtcb41ZkKF2vV1/yGp+rnNxQ3yMpY9k2+Ow3TYNMqK2xzKOpE/Tskt6dSbuQrNLXD2N1TQE1gA0ewttn1GrHjMBtRGIunTKZcaSwZCkJiRd9pmegNwk5WOb9BSJtunbCE7VJiqRGX1QTldeX4AIaW52DvegUnk6XaZXbFqu7Jqm2hcvtuT6SDYtG+LXg2nA8ikJLZq2/WBAcgAAAIDEhfKohv9xys6xablNf0PfSO5Gb2VX83RbBuC8/uvmISoAUqbtsDiH3nR96BN9H0i35bN1lulthwey4TG77WMXvGNqkm0RgfzW70/nmfS6r7Y71mffbp/WIuZ5d/QfusdZME1BYLWMA5kqg8+1vZmxY7/JNrsyCEpb4demOdx3v22iygFA3zTeKBo9n4m2QjFEP7nqvlzEYlM6U5naBGmAXX2b0P6YMeVnub+pnduJHW4cUySGFjXWQsmyb6HO0+kiylz7NypFpkd+9HkE6A6l0HMUhhvbafbTJsDG9kW+RTUcjU8ZTCK0LoCsSgdN9dhGqPn2s8wGFJExC9Os4T6Cjj2SqosixBSHtvkYxaBJcDruv5UA1KRzCeiJ+UYEGBO6l0YWUXO3DVmLQA+joFTtD49kZ0Eu1s94R69m3wJyjO+qERY54SkSV7qdncY1XaPgtBCNLv03vZunG9LZ9MMEAD9sRFsvzd2W3klbYeLlfVwJJ0LBTIiobVuvZq8CcuTXA9dzjH0kK+nbzBiznoFdOp+hdLTbWoq8pnyCezEVRQQAdzZexJbpnJu7A3gobZ+51jOtGPYbqgkcDAQQ5DZD9/h6N11I5fw3efUhpJDMwvTn8PaoWXrjmkSe1S4s93HS56YTnQjgG0XcFT6PIZtnl8lD6SoMTdvZiraj9rGL8G0jTrMATeBPv9U+S8uqfzPSxCYnf779O0R1PjZXBsijw21jJcFD6hzGkQQAAAAQFUmwTQ9CMsQvkGoert7J+bam7bTxN459H132CwD9zsjiuh/TMyxrkYcub9d1ymVttu1wme02EBjP681mfejtxsBQTdtZlu0qH2eI3UGsUBlPEvuK2Dli+4t9U+zwPM9/1H/pOgm2CVvLm6LOTaGZhJ2V8GtoxtYJzZRvIoCQtGkm9sG5+3bLpr+QonFjnWV6lzKFaLJus4xnZnf4CnivczKREznQUZ4m9n4Rio8SUbmj/H0TsReKXSzLTpRlL5C/CztumOKFHEeysK5quczYy0Op2aiV4MQLCdCrRzLU86WtSOxSGJq2cxUHsXofJ6I7BsXmB4FbgiCbjJq+jzfLsl3k495iTyy+i3C8Xj6ul+WHyed9y2RniV2ShJCcBdt0PFhn9Rzma+FEaBvBWayjSRsgfERpbMEAbZq9fZoSQwhE1/QxiEwIw0bdGirZ+T6Y8KDcHV2vu4sw/HTl+1YRjFvLv28t9kOxt0ia35bPz4gdK3YLSTObA7b4lHV7dlKyQfpI2lxggfoRZlv8pk1sFKAtBCd9JAGGEZLBRGXAffg0d/chKGMQk5sLFcsgKJmF299FFAYXUWO7BgpnWTdl3iZi8ECNDruz2DMlzSdEMJ5WNmNPPNhGkaaNR881OKYQoFqx2EJw8usaYGXl6H27G9rHBZ/7MVSzd9u+aGNt2t5c6L7tc++wjzHdq/+dodVsecW/fdeY5uX32tcus5YCKoV3Y9bRD2ADVxZWiMjy+3tLIXmViMq9S2/k3vL9akM+YxpH0u9qqW4mFdPpOJIL+8s7FJwAE2WAh23vzdi26bryYPYpKBuXDyVSIRwBrt/mbdM7eX0fUZ7nP5B6/I7Yb8rfRaT2/cUuL+1IsRPLz/N7LtqwUyQas1hQla7bLn6XijdsME/XneAEmBoh3h9dvYO68FKGiIT1DtjpoBl7c2Xc3kxoj/qc9NBHZKQMdB0+U+ydZcT2N8SOEit+rp8ry46Wz2+LPXqQklnAgOQAAAAAAwnJPM8/Lx+qPpSFdzJ6hh/+Z8nD6Lj5LKTaJp3BexnAcwkwFbpu2g71nImxuTuU91G7rsc+lS5loGm7fxa8jx32KU6B4lindLyjntnGlL9zM7JBBG7mPU9o2r9Fx0mACTNEH8m2z55QItElXWjRuDJQE3gf/SehI1qIoymepykec+/BNis9zHu2tIe13COgZ6WF97HuJrXaPcBkGDrYpl2AQHz9J7sUiL7p23oU2/SXhIHu1b7mNY2YoZ9tk/BI2lZy0OCULZlznpmFCHURnvX1rzhov8rG+rLoPJzKVeWyl39EP7TDy+65OaxDU2S5Ov/Nha/8OENtwLgftn2ISut9eDZpm9IEbSrvW5i2rBNo5s/vX3kPtRSDoc/FWM/tWMs9IiFp7yNXJmupLq2Fn4cINQlPm6kUm9brmtQ3862s9Hi5NQ3enqlEJncKJCQkV0Jc3hE1e/uKRheBqFvXtzDlcdSCmmh0qcug9Z7ISaSP5Bj6SCo29h5SMnPLxFqEivC06iLZQnA2ik3XQKPyZb6gQecFzJdF5hyGMoK2hH0JBcyrnnWHwQUhmrR9+0P2IhAbyhC2+TwNATKaH3QB6jv1U5b68cXRR7Jr93fun4n7YOV6MbiiS1Z6OpvW6wRnY0CRoe/mcj5Zc4CQRjjPNqPfJ7Sgj7l4h25qi6nZO2jTtkdZ3L2UmX3eEXu3o6dNC6H1PlpsO0IQkr0M/9PxVbUggPIWXkq7DWx2sS68zH0tmwSnVmgqBKfPi0kXINSm+RwgpqbtkI8fp7wi8VL20bRtXKdYoc1Hc63w0m5HG4+2Pt/pviAmfOjeMCA5AAAATJ5CRMbc/zudPpI9VnJW+0nlNOe1ygvXpmndovm8yXNp21Tu2ndxfsHbNpO3mZoSQHXtRUErz0s36UN4JkP3kTSu02zk2iQdqkkdDCi7IfhVJF0MuA576yOZOQ4t4No8rd19NWjEth9h5U6bC9EX/NO3tNu86g9+1b/5vHYTqwNr1Cuc7//yXCxsVuuT2RhkVC5/5f320w5D9NJLrnQsFEyBmF86bQRJZ1GvbYJuDOt9glmCN0V7i1rUo44TH/Jr2vWt7seOqn7053TkxR9H07bzL3jNBnm/fSnr0ctNrFYCabSeUAuvZ2Ngi4XgNKGsWs1wR84BQNxQYOmRdA5064k+ggz69VLqE0QvEPFIOtF4zrKBRV7C74ax6+CRDP8Tsrd7GA9m8JloMoUAzd28nrr8VMfoE0m94B2u5akd7qgh4lwXAATQdH2pvrcmG6en0s1L6S8KWwexRCIQeWn30HSdTdzD6MjEDnccHknn7EP04/PwVjaWQZGnLt8Nr6dBeNoIThMLxdR4OJf7gebOghNg4dpLIGrbKz/HDdqIQetdtmoW161sk2/DCk3XKF7gqjrJjOdBuU1bJvron1X3RI+95z6SbXbXHqeAG09hOvf06ZusDfkaxOxSM7tP07amnBvFMzSr2wpOgHiDbbJBsnJ5FvoM6eWTj5WIyDrwHHoKRF7aeqxjElrcAkO/02MjqmdbsuNIWl7YXWmQZQHWwY4y89SGqvSNXkqbpnefPp+rFk3rhny1wUM85aHx2gsn3rqgzcvR+7K3fedbpzOpRv99WZWh4Ry3EoaOkeGTJ+voOu+hskd7Psda7gFhHEkAAACAMQvg8QjJzLqWrZKF8CY2NNO2ynLVr2nduhk9s2s2P/mw/TWZuDeZhwgKAihY1YyAoO0uMRRtmv48trXup9bCq2idzsJ73Gb4oVZBQ7FcH5GifBd5XJDhg+ESPXH2EgfiidpezquNeFlspm0hgnTNL1vc96ESbSGau62Ea0MeNn006wPCA6iuq+XrMkw9df1A93qWOW7iHGDboi9iDEMMmft4ap6t9E1TVrZ/V4swN9CUorZn3femc7jDBdusdHyzK8XLWr8idaP/lym95ViQrv06jcLTIDq9hKdHH02YLqtD95EMvHs/r6PjBpZ1FnSooZZDDNl5TBv6Utq8K3hp66vH+poJVJGIeX7QjM4jaYuiGc1rWB/Lp9bGMS40m+fhPKKG5nhXr2do4TmlX6DgR4zeo2CXradIDu59dEzYfpigrP15b7MPHjv29RXg/uM531Qv7et2anjMte1Xy6G9XMqbINQuVi1EqMlLailCjeLTwuvZi/AEWLjmxvG0HayZPfB4k5sJbfMzeCIDeAvDRJWP4zoajNVAddVDNSdxLukj2VPTtufFYrzIggTeqLINNCB5nS32+7AWoUuiMY9CeAIYLjMvBnvvBNixVxaBROBGOluvVAhBF8Cb2dlQNgnj47jpTNClIBQtmMhhDu2RDLr/as7BBiFv9rSti6bXPv4A53ysByff2KC9CF0QoGt+Xk/lTVFJ+9z3XNG4fxgnr3rwrzpvM4bfE50+4Ns4e3x/XAcUgk7lCCEKbfKx8nouZ3LKo2+tW73y3HO/YZFxGtifi0CBNYh5lGRMw/+EZClYxfvNl3mL1OUoa1MgTjVpixlyLMVnY/9PgwCFaXkxnH5wjISuPDBeL1XPooQcLqiT/Ix9I7Pe+4ROAkVdBBN71PNylVAtXjAgOQAAAECmHyMXAgjJ4geMTZ+NXprJlM3JeY9TLmbWXk1rb6YmklqfX5i+nJAGOo/F5MYGDRZs0zKjrMN99ehVdEhm6aU0pJl4U+vsnI24Q/IonZ5jLPPoPJI2syS45LcWUNwox45sn4cpE/emdzsRqu57qRw5XJHMMI4kJEvs82CvTF1cuupEFzE1gBhcT2aRzvI4eE5V68xdSXf+mE/8+cJrcozjSFoMI9JqtpsQ4tUw7qNpG91GvYhQPJGTIoSQjMF73augCOH0aeM962y4oIBCsAsxGLrPZ2IYDzuwqJtqPS+GgUy8DnrxSA4RbKPbp88Lb0uLWW98hKmrpzS4CPUPMoLxobwuHM/9WB6mnUaZDjZMkKuodNxJ7GJwJNdeL1SEYtfTE8O8bqiJjseRzOLpI2m6ITSFeOYb/6Ojwmxy+lNup12f2YrRNiJUs4FJbMCIMQ1GPaFzH/x9GURceuThKJhDi0D3fLPAQyBt5nfaUbc1pjv2zC9ZZjwcr/vj2zevHHG/yMiK4ARR22PtI9lFX8lQ/SXb7F7Vt0VXAAcvqbNX1GKQckgH+kha1lOot9xq3P0s3YN3HD2iHmIw6P5HJlaWCj6U59uVxPtGbjRtj2TWrpgYvo9kT30lC3q9PFT15Nhv0TrYRlN/6jEnIWWCPwj77AYRQZRukGdcNoS4zLrrEhCLCExIzDj/4Ov42MfSnaVrqAZ3GEcSAAAAQEBIdi0kB+5A0Mt83YEwVpNv07vj8EROXk1I39Phc+pj71OZjcVLmfUfVOSyz0i8iV03xUeDpj6CewfHWkfDhG0PXYj0p0jMtrg90fLQfR91hI7uboGqnkxN895DFYUIyIFk0L6EpnrqO7jmg0SMtymX57ZOmzk2p3YuAhN6dm3UVWLR2GPvox1BFY6Ozof/8eqv1Ynoy4afI9VH6GrqTzuHt2phn6IeBoPO4i6V1dFzoY33sU2RPF/iTgKwJ/HXpeAd62DjvQu1qamqWavr0IUYH3E2bbvsI9Jm2me87vPa9a8/5nfbz6jjug3RaNPA8wUV1b0V0QssWFECCACvJs8egne8N+na49khS8/wkEKv6+OMqB5joqiVVd6THc+1XVhsF6ChPDHM0OH/sFE0WXsMfh6r2IYePZI+3ujY7nUTPXpqourD1ofga1HHfUSht9rGE9duXn2VNbp39Nig+rrvI7nS9ubpWeRIr85O8/fGdZxJU7Ol5bSKkD6Z6kXCr+xqBXVZ+XFk1UJUK68f80a+O3PfJJZmbJfhlkJec0Mff8pCdRZrM8zxZdlsqohPi31XHGCHyveby9/niO0v9k2xw2X5jwYpXPRN2675JeJdk4tEtdDfsxpRoBEk0rQ9UTp7jwQUAMrnh/3G/Wwz33Q1UY+kal+hznGfYiZlYejDcNVxrFgxJdMu5fcXiF0s7/4T5Vor/i7suKEKN44Bye0LYJUs1ibtDeaeXes5vjXH7dPcDcniHGwzpiCsob0xXT0HI5gSr1fB12Lbwd9BcyJt2o6unkbGEPWWZdl+8vEQsRPEnlMuPkzsvuXfZ4ldkoyQBAAAAEiSbnTk7iIWi2brOVvF2bW18v21Ys8Xu2ll2S0kzfeLP4pP2X7PTko2TB9Jz9GJ+x7Hsde9eWAzhphtnek8ULF7ZiE48sBx2yDl/pN9exc6Ga8yG91YleubZkl4nxsxHF/r4x9b15UUPKBFH8lunofbRAweqNxllh0qH1fL+s/I33MP5Khw7iPpfXMEuMhsm6ufdsKlrffVNU8/6eO97Odvjru7eoVlXf7N8+/mve2cp7/6k07pIdEXzQjoNJAjwubwIGKnTZ11JHIbn3uWz983vOD3hrnXYp45bgL0NcJhjXuIPUzq/w/kc2exXeTvd8jnVfK5d+mN3Fu+X13bbjoDkoeECz1gndmeR8egoA3whA5G62FJXPA5z2N+YY2lX1sMYxm27bPZ2qPZ4j6obxvovPT6DhvzfTYYM29Zr3vM8/x4+Ti+vD4Kj+TzZNnj5O9Xy99Hip1Yfp7fa8F6bdpeWwtXGgiPqiuCw8s/U21vc855iA1GVnkJygNpci+rQX9wduXRjHAavTDezNVIuxO0KBeDiY+6xSQih9WJYudKeY6Wz2+LPXrg8nTYtO3bZ9KBzl+GCaO8KdpOh2Z7zjlvg4uZaMdRjYUhXhqxD0QdkbgLckyhhxFKuO9jRCJqGIrDH/A05HleRGZfUv79X/Jx/+FKk1jT9uQv7gEGPt+o+zbbT/2hNBQRvJBSpZdnUVfnLyZxt5lZHHnUCewgoUl7PEQz4P2I8BCSvKRGRduXUhuPJh7JQVB2R0i1O0qsonksfSkXM40vr8Dn17berOu31z6PkV7riZGa4yrLsnu3zOIL4h39sS4B40gCAAAADBS23TFFU3mb/oEPEPtgwD6SWb8RoXPwbHmz0DTtUY9PfcWHjWne+PKDWq238ZDZlANKTA/CHvo1j+jX+tAFGFf+A3kILTIKk089vw69gFE2oQ59P0RAoo7frWKuYw4Wg6Oflk7TNtd2i7qrnK+uAkqbXi7WA6ojbKKJOAXLSu75odTROe1MSAcXdqtxdQ+xymSgF1eiSijV4X964iPSPP0ux2fDbvJxejLBNhD5r96mB5ftrvOE+uzFgO4lmFL/yDkjEc69eD8793AG9kjGOlySNmq75+uNdy713Y5ijMrPeWz3s3LbrwYVksWDMMgvNR1rDPUTlNXuvX2N3R2sm9LxSPYmWPD+2lRgsHPRih4ES7RCbmgR1/FzMxuT1zDGJviV7i7bjqZIHAzxRJ7kud218nHSOJu20RRh6cULgkdyaj8eIML+b0Pe66OIdF/tXujFKLrGJFojYPB+0yPEeWYbKnlcqGemCev1zSrCJfdpps4QPsl34o+RWF8Yfb74e6iDzr1vXVzvqjwHFmS8e3ur6L72lAz0kUwd1cOvotvOOOWw5fUVMfjkP/0H4y6efNw/atef8eqHNq+0FLVnnHRoqzKkxJte9eChiwCRvoB6aTLt84dKR8dTfe4NJtAiuF5A1bad3nnJsuwa+cjdWrbzm9kmdh7+Z2XLgENPphgo0P0FNLyHUPcysN0VATmV6lyuNC9P8MQYTb+0oTzKPdaP13PJf2f97WthvyO53kYWtNY1CerIgs9YCsk9xX7LMu0GDEgOAAAAUJBg16A8z+9r+GH3y/LxPLHnlIsu6FRI9vpLsg4Rp+ECL3r0YAW5ZuhHqX3QZUSpxUFkXqjen9dTmDIwcs/doO/osVO0bEd+fgNfK4VAeJrYS8T2EPuY2HEiPD/qkk98UdsQlsabYrW/rgS2UcQ0z1o3bcuN7n8+YHwv3KHLOsRzf6AXenTXBe/cnus7svPf3XV+uHz8udhviH1Z7KnyXjnfJy/HPpKzvfvsBwai1UMxlAfY+oXg+OKYap/ZYjxX7sPxEZMgmKpIi+kcDF0XoECerQk2bdfuwYPK8SHvIvZ9sT8WO1NE5Fp/w//UxQUDiMdN4Q0c2HvV2ctjql0dYnoZToGRNHUNLtJmhRi4rmKog7EFd/VN7EItomso8PPhTqWAfKDYT8ReLPZaEZC/aJt3+6btPt7lNHm2e5hZ3BedRv2amrbzOJppz3zdEZtfPOvjScec264MpxetDf0ypmbyKMRSX8QoRAau/6jEWeyCyIeY6ncIEm11zbLsbfLxWLHrxV4jdoI8938UKn/n4X8GuZEt98kQKP4Puy6DNYzXjOV92/n5rT5AhgruGeD+SvC5GR+RVzICLW1hFdX5jV5Hxn2vevI4scJj8J9iB4idZThO0Zm5YpDpCQxIvh6ABIuVsprML/euI5OjeNhGfH9NlSiui1S8ZhHW5ajPL8+L0BUqrawjvh70FA+Donm7MBOMIwkAAADgRKJN23med6qOGf4ncaL4tR1DGUbivVHOjR6alALkIjhngxD5PRXFc6fOWAVCjHWZMIk2ba9EIySlh2ScDwhoJoKbQjWlXywBNlWy1R2G73/bx/1FD5BoGdXzNYJnSyNjqsexnv9UGckoDTHhPo7kVD0AI0UljnoPSlI9HLMIo/Q1L8a++t/yIkmMmMWWDSMQNqO/Z3inRja1zcjv2QGgaXvq0+kFaOY864yntM5jgYYXg04Av/VNR4ctw1DwEIubsYuWFMRYJMLryCdt1a4/68xinOcGxlTfU2JiUySGwn1Acm6AcWE6X1sCeAR7erBro7YT6fena15XwZBXHnU81WdYJALMiTGfq0rZJ3vNjRF+zDuT1PA/sIzXA6zcxlqkxPCQDCGIR/iyDz4kUoyCfIwCaGhiuCdbMnbxtVF+3pkjQp41eCS790im8ICaFC0eYrb9AqN94CvKFb0Hb+i6JBBndER7/7mSmuCaH08q52cyXSQTuw5jDLZJ5qE1Ffo4XyO68RbEcYyR49xfYHehpFdPqV37NG2PkxTvrQrZ+ktwpzzPf15Ztqt8FIEGNxc7W9Z9sbJJB03bAAAAACmS2g+aZd4odnexO5Qi8kbycanY7cr1z5Flvydi8vPLm6ohajtxevFwjfXGq//wjKHZO/FfwzCxe2zCXnjtcSV6zKNHnr9ZulMkzrmn2HnzL8KjShH5DLHPiZ0t9gKxIyppQkdt86IbE49//Gna9W9/x7Pt1scgsrrGNchIwdve9kzt+ic84XVWZQDYuCSmck2M7DhNz05beKdGxsiuQw/2Frui8v0hYpeJB/JvyuuxGNfqqS4Z4pGcOKaHmFxcU7m5Fl/cAwnnyYgGMDPRawFhVa2MaV4Dg5K+syyrhVXet+ah/L7Ynt0G2xAan/6DqiKipvhQn4nnoR7gvDigfklM8B7cgPvBspoQnEEut2mMlX2F2IPE3iDPlnuUHsoPVdbvI/bjjof/GfihFmGkbQoPoHyt4oFL/0ZSUwroIV/ckxYN0MxU78kaCKYuKpVnzmZdTGLc2reInSrvmv+Qz33Frha7sLL+bmJfTjtqm4u+m2qd4C/aBfEcy8s6hjJA9EzxftXCewGCXUurqbe4vVZE5E3lzz8sg2teOB8KSJbvVkZ0n+KSJ30kp85UHsAKT3aML+MYywQjYSr3si+JCwQIQTaJZ3Ce56+Uj1cqlv+Xa//IcXokAQAAAEKTTesHR5ZlO8nH7mI/FBF5vW8+OzjX8YQqeRKkdj6boq3H4q0ZSzlhPKR2j3cM77gpI8/fCXgksyy7c9l8fc8ygvsBYh+U5YU38t1irxJheZFtfsy1PXHG9NC0Gt9xRMejZOzlh1EypudAlFB/ySBiaugidH18vyMfHxHbJvY2saPm60Q8Xi3rbyx/HinWkZDM0q/kyRHDA9ByzMYpvOymcIwwYrg+u6ta3q2TvMazLLtlKej2EitehltF0J0my4t5r88R21/sm2KHy/IfBdjlK8S+J/a7YjuLPam2/uJiX9MKtpnCjCsdIhdrJ/nKBT/ea2pIqAtIhK6eLRAAnjMN9VJY79ftDWLPlXfmZ8to6s/I5wfk84liF8vyE+V7MWVhYccF2N+9yqbrn5Z9JOt8uxxLckLBNtwQ3dafp1DnJbIyWL05ifjE4TocITzTYbiLbyXbUp30pXvyPC9mkvl++fc18sz6Ujm+42HlrDMFZ4ldEkhI7mwYcHwX1ww95trGezQpON+jA/HUV0XzLIwV3lMwxvs6y7L9yybnT4jdohSZM7Ep65yH5Wng62J30ay/n9jlHTdt01yREjxwAwb6WPDu814eJB9IE+7HSOG9N5nz3NE9uLsIwU9Xvhf9ILcu7jr7Zfn4W7FnybqfyPcuylHwLrGXSP7nlgOSF8yasWTZc+XjELFjOxWSPOgSI+UHZMAmXq57GBUp39cJw3Mm2Xtnm4jDA5t3md2oFJHvlHTnlYuvkuV7l97IvcupDENwSjncz4XlVIjFi/I1so89yoCfon/mX7tkSNsMAAAAQEHhkQxtGkTAFcr1zWJfEtF4amXVBeUwPAXF5/mabKwpBx4vhOTzxH4hdq3YAeVwQM8XOzR3bIIbf7ANJPMLOFTz8QZ4ZQCSe04AdIU0bA9xrd9D7PFiXxRN+fly2QvFThQ7V5YdXUZSPzrUDkUoFpHirymtNY7jSBYDSfJASQrX89nhcEu8rAAmCO8UiIXCN9h/1Pal5Z5V3L/PsvhCsA24wUMfAMYILRRgvkgm4dDI1gdB/zOxB4oV0eCHiKD9YNlP8iSxv5Hvn7LNj2CbqcPDtR8Y2zF9uJeg8dJIX5wkQ+L3cZZlt5KPj5fjSRafRSDPDBGPP5T1RVDQk8W6E5KQFo95aDFYfjPn/uPJPZVkZP0vXUn84QTDg1iZHocfWsRGrEziGd4Pk+i+d4JY8UK8QxlsU48G/2exh3Y6IPkEKhmqhDzfA4o5XrIAXd5gvBeihXPjUFeTeFccLPY68T5+R7yPuynWf0tsv86E5Po0lHhWwJP0b1AAGADeS9RNoCtJXlP9BtsMQDEF4mzGnAZ2dNWGjlHbhSEGpgQPaACAwPT5HuWdTX0t8h2x2y8uWuDuYl/TrF8CVQgAAAAw6723Gtwio5g550niJCr6SK7Upkh8ZDleZTF9ojUE24Aefs3aMXQwD8QP9xLAGDpJDl2IPoJtDhX7hNi/liLyBSIi/0I+7ypWDIr+l50G2xT/YDoMdb7z9R9I4wGRAInBsz4NOI+uFRadBzEoeZ7/RETj78mfrxR7rFhWTpn4P+Uc2y+SNMW0iV0OSJ52JcMij3rws4xV8t73vTZ4tfHwAwC7hwXvpDbP8Pe+/3QutI33TtpR21mWFZFE+4r9VMTisfJ5bDkIeXHoP5RlXh4cBiSHAFdnujceAEAMdCVwUhZOzmTJR23fSOwbYseLvXo+CHnbTOkjCa3hQTQeBh+YvQO4/gAg3AMlXWGdS5O1eCC3yZ8/C5mvx4Dk9JGE+mUR0TXBVIRaEF0TJKb7E+KD66NaGVN4Rv5zGWxT9IcMAn0kIS14KAJMi/Rf/N1C/U3th9fzxT4gnsmz5PMUsa8Wnso2GdK0DQAAACBMwCN5tVgRVPPbYo8rFoiorKcRbZnv0NnMNooddoYcSG/7ghakf+MBAEyGPt/zUVEcd/rvs7eJBRVXO6R4MZ930Ru06x9x8NNabW+TRyrY1AUAAIyTyYrGBhKP2l4RB90TQ+fpISSncNFN4RhtoS7WwTsO3G+QIjzjF+oifY9kcDyitidQydkEjhEc4WELEBU8p6nH0JfUBDy0WZb9qoXX5Bdi/1V0lBx90zZEAA9rAACYjJJM3pH0TTEbgfhzEZ0Xy+fLRE9+IfDMNmmr9YIpHKMt1AUAQLrwjF+ojSlMz/sKsYeI/a7YhWJfKZffVuyBYp8V+3D5vUh3f7lG7i1i8nMBx5FMvpKncYy2UBfTIsaRErgGAbi/enveJO+RvFzsGWK/LeLwsuoKEYx3lI8PiZ0s6/5Uvt9J/v6o2EvFHt6U4SSDbf7uQ2d2nsfDD3pS630A9A6iDaJk/O+dWHn4QUd3/r4cD8nPtV3wQrHX10VkgSz7oojHYsabF4u9V77/u3x/k/z9hHraKvSRBAAAAJiGR/I3xX5oGLC8SDPnS2I31WWIkAQDeAIAAGAC9DzpykBcJfaHYq+vr8jWD/7hZZo5e4j9dzAhOeuGmr5ahwqcbwCA6TK9d0DyQvLNYn8mmvGf5PM0sf8slxdeyGPF7iv28kr6IuDm87oMJ9lHElzgfAMATJcpvQMmMVb2CWL7iD1V7BDFyd4q9uezL1m2czmlYhHJHW6ubTrjTwydmz/GCF8AiJ/0mw/TYWLnKvWm7TzP1+Tj6XKcr5PPh4rtL1Yc9BVi/yDrL6+kvVY+3mjKkz6S4E/iNxwAAEyM9D2SM0rBuCEa28CA5DDpX2cAANDMH95HO/LLjPP/9e1JVOGU4kCyLPsl+fg9sVuIXSTCshpg0/Fc25PqLwGcbwAA0JOSLkjpWBpF5NPl41Viu4gVfdQeIHaVLC8itL8jdowIy6KvpBXTkN4AAAAANnEgoS0iRCw+shz6p5jB5snFovk6EY/F+JLvFzvMJU+PKRKdt4AxE9P5JrYHIP37HMZHQtePCK2hi9A1fyr2IRGND5dj3U3+PqO2/tNiT3HJkGAbGA/J398AADAc8XkQO6CYT/s4zfrvi+3pkiHjSIKB5G8qAABoRUrviZSORcl2Q7fGYozJn2nWB4jaTr+SoQLnGwAApvKeSOlYGviC2IPETq+vkKbuQmA+WuxTDduG6iOZfCVDFc53dDzsnv9Xu/6CS9/dU0kAABJ7T6R0LGr+SuzdIhpfWc5aU7Aq34spEv9C7PaGpu8l6CMJAAAAMCNtIZnn+TkiGot+ki8SO75c/P7K+I4vkzTvc8mTPpIw6ZsqTThnAMAzx+/Zmf7zM8/zF4uYPE/+/COx25YH/VWxt8u6ImrbCQYkBwPp31TpwTkDAJ45Xk/P9Ju2Z4hg/Kx8FNYaBiQHAAAAWPBKhjSjeD1E7CtiXxN7gT51fHhEbcOU4HyPD84ZAPDMGQdZlm0pZ5oppim8UuxTsuwC8RheHij/Mz02k93nR9smJtgGAAAAYF169V0PdxX7mgi3b8z2nmVnl1MUBhGSwhM188RliuXFsuKzQyE5kf4DU+EfPnru0EWA0HCPAiTBQ+9x+NBFsINnjondRSBWg1i2inDcWv69r9h3KusKr+TdTBnaIvtZ6MIo5dijjNL+ltjJFcFaDPvzfLFbih3isg+iticPPwzSg3MKADxzfJi76gKzTQTdgQ4P7I6KMeMvxa6W8jyitvxjYsX824XIPFXsCbYZEmwDAAAAUCq40GbgytILOGc/se8ZtmnDQ8T+QbO+WPcHLhkiJAEAAACG4VNitxFP4K3EdpS/jxC7oMP97VSK1Sb2K9NYQ9P25KEZND04pwDAM8eHvNNWZcX+8vwGEZB/In9eKFZEcJ8pyy5bThmMS8WeWTRhy37+tbpClt2nWFemsYaobQAAAJg8lk3Rwcnz/J/lo7A+eE4pFD9UBgB9uSiC2G+JFf04fyL2XJcMEZIAAAAAEyDP88tFQN5Z/vwLsUPF/k+56qdi54i9eD4UUWdCcgi1Dt3B+RwfF3z0vdr1nFMA6JOUnjl9N20PgQjFb8rHY0VQFv2g9hTLykjuNZ/88EgCAAAATERIzhHhWBzsVfPvviAkAQAAAGbiKq1qyLJsH/n4b9GM1zpuV3gp9xb7oWz7v7q0NG1PnMTuGQAA6JmU3iMpHUtJMWvO48XeVX635ebltsUc4B/UJcQjCQAAAJOnaNZOsGk7E/M9qGJbIwhJAAAAACE5GbnOO6Sl+h3rf4avDo+m7USreaJwPgEAoA2H3eNR2vV//9H3jKaC1+NPkuKsltsbp2vEIwkAAAAgrCUmJPM8P6rrfewwhlHfoTs4nwAA0CVjec+gcfzAIwkAAAAg0N0LIQkAAADgRWIt271AsM3E4dcXAADwnuGd6AsDkk8cfnwBAADvmfWI7e1rvBV76CNJJdvwtx89R7v+kfd4jHvVdwLnEwAAuuOR9zh8NNVLK507BNsAAAAATKyPZJZlO8nH7uV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      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "im2 = plt.pcolormesh(ds_i.coords['lat'],cmap='twilight_shifted',vmin=-90,vmax=90);\n",
    "cbar = plt.colorbar(im2)\n",
    "cbar.set_label('Latitude [degrees N]',fontsize=18)\n",
    "plt.xticks([]);\n",
    "plt.yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The continents have values of 9.96920997e+36 (missing value)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the lat/lon coordinates from an ocean file, which has no missing values over land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pade7652/opt/anaconda3/envs/regrid_env/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'areacello' has multiple fill values {1e+20, 1e+20}, decoding all values to NaN.\n",
      "  decode_timedelta=decode_timedelta,\n"
     ]
    },
    {
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       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (ni: 320, nj: 384, vertices: 4)\n",
       "Coordinates:\n",
       "    lat        (nj, ni) float64 dask.array&lt;chunksize=(384, 320), meta=np.ndarray&gt;\n",
       "    lon        (nj, ni) float64 dask.array&lt;chunksize=(384, 320), meta=np.ndarray&gt;\n",
       "  * nj         (nj) int32 1 2 3 4 5 6 7 8 9 ... 377 378 379 380 381 382 383 384\n",
       "  * ni         (ni) int32 1 2 3 4 5 6 7 8 9 ... 313 314 315 316 317 318 319 320\n",
       "Dimensions without coordinates: vertices\n",
       "Data variables:\n",
       "    areacello  (nj, ni) float32 dask.array&lt;chunksize=(384, 320), meta=np.ndarray&gt;\n",
       "    lat_bnds   (nj, ni, vertices) float32 dask.array&lt;chunksize=(384, 320, 4), meta=np.ndarray&gt;\n",
       "    lon_bnds   (nj, ni, vertices) float32 dask.array&lt;chunksize=(384, 320, 4), meta=np.ndarray&gt;\n",
       "Attributes: (12/44)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                15\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-17T00:41:10Z\n",
       "    ...                     ...\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    branch_time_in_parent:  219000.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-627439a8-89a1-4eca-b914-63358c18467c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-627439a8-89a1-4eca-b914-63358c18467c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ni</span>: 320</li><li><span class='xr-has-index'>nj</span>: 384</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-04221845-af25-421f-9bd2-ffc8994614cf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04221845-af25-421f-9bd2-ffc8994614cf' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(384, 320), meta=np.ndarray&gt;</div><input id='attrs-82db352e-9358-4a73-acd7-66ec696b44af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82db352e-9358-4a73-acd7-66ec696b44af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f85855c0-5fe8-4867-b23b-52887017cb9a' class='xr-var-data-in' type='checkbox'><label for='data-f85855c0-5fe8-4867-b23b-52887017cb9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>title :</span></dt><dd>Latitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 0.94 MiB </td> <td> 0.94 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (384, 320) </td> <td> (384, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 100.0,0.0 100.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">384</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(384, 320), meta=np.ndarray&gt;</div><input id='attrs-f73223a5-591f-48f8-99b9-8a2f09d8607c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f73223a5-591f-48f8-99b9-8a2f09d8607c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-969dc03f-e5c2-4bac-8ff6-d16cb8eb8e01' class='xr-var-data-in' type='checkbox'><label for='data-969dc03f-e5c2-4bac-8ff6-d16cb8eb8e01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>title :</span></dt><dd>Longitude</dd><dt><span>type :</span></dt><dd>double</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>valid_min :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 0.94 MiB </td> <td> 0.94 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (384, 320) </td> <td> (384, 320) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 100.0,0.0 100.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">384</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>nj</span></div><div class='xr-var-dims'>(nj)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 380 381 382 383 384</div><input id='attrs-4e3665e1-6e07-4f18-b946-f90af03e9b81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e3665e1-6e07-4f18-b946-f90af03e9b81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5802828c-6a58-4f13-b156-699875d269ea' class='xr-var-data-in' type='checkbox'><label for='data-5802828c-6a58-4f13-b156-699875d269ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3, ..., 382, 383, 384], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ni</span></div><div class='xr-var-dims'>(ni)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 316 317 318 319 320</div><input id='attrs-6762358a-20bb-4a5f-bffd-f426e8ed2979' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6762358a-20bb-4a5f-bffd-f426e8ed2979' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-198f4960-aabe-4b1c-a801-5158a01173d9' class='xr-var-data-in' type='checkbox'><label for='data-198f4960-aabe-4b1c-a801-5158a01173d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  1,   2,   3, ..., 318, 319, 320], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e41d5e9b-a0db-420b-8bc3-523db46d49af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e41d5e9b-a0db-420b-8bc3-523db46d49af' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>areacello</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(384, 320), meta=np.ndarray&gt;</div><input id='attrs-64e2f8ff-14f6-4361-93e4-4fda2732353b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64e2f8ff-14f6-4361-93e4-4fda2732353b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b95c487e-a7ed-4244-a7db-64551ada722c' class='xr-var-data-in' type='checkbox'><label for='data-b95c487e-a7ed-4244-a7db-64551ada722c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>area: sum</dd><dt><span>comment :</span></dt><dd>Cell areas for any grid used to report ocean variables and variables which are requested as used on the model ocean grid (e.g. hfsso, which is a downward heat flux from the atmosphere interpolated onto the ocean grid). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>description :</span></dt><dd>Cell areas for any grid used to report ocean variables and variables which are requested as used on the model ocean grid (e.g. hfsso, which is a downward heat flux from the atmosphere interpolated onto the ocean grid). These cell areas should be defined to enable exact calculation of global integrals (e.g., of vertical fluxes of energy at the surface and top of the atmosphere).</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>id :</span></dt><dd>areacello</dd><dt><span>long_name :</span></dt><dd>Grid-Cell Area for Ocean Variables</dd><dt><span>mipTable :</span></dt><dd>Ofx</dd><dt><span>out_name :</span></dt><dd>areacello</dd><dt><span>prov :</span></dt><dd>Ofx ((isd.003))</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>time_label :</span></dt><dd>None</dd><dt><span>time_title :</span></dt><dd>No temporal dimensions ... fixed field</dd><dt><span>title :</span></dt><dd>Grid-Cell Area for Ocean Variables</dd><dt><span>type :</span></dt><dd>real</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>variable_id :</span></dt><dd>areacello</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(nj, ni, vertices)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(384, 320, 4), meta=np.ndarray&gt;</div><input id='attrs-a11a761d-6bb3-4199-a006-75ca366727d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a11a761d-6bb3-4199-a006-75ca366727d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ebed809-f9d8-4da4-9e03-c52f060d45f7' class='xr-var-data-in' type='checkbox'><label for='data-8ebed809-f9d8-4da4-9e03-c52f060d45f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(nj, ni, vertices)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(384, 320, 4), meta=np.ndarray&gt;</div><input id='attrs-7e4ad413-18df-4fa3-8b09-c36c63df1744' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e4ad413-18df-4fa3-8b09-c36c63df1744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1eb638c-3408-4722-910e-8c22a27a689c' class='xr-var-data-in' type='checkbox'><label for='data-c1eb638c-3408-4722-910e-8c22a27a689c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-23b730ca-fc5d-45b3-a433-8afbd604e11e' class='xr-section-summary-in' type='checkbox'  ><label for='section-23b730ca-fc5d-45b3-a433-8afbd604e11e' class='xr-section-summary' >Attributes: <span>(44)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>case_id :</span></dt><dd>15</dd><dt><span>cesm_casename :</span></dt><dd>b.e21.BHIST.f09_g17.CMIP6-historical.001</dd><dt><span>contact :</span></dt><dd>cesm_cmip6@ucar.edu</dd><dt><span>creation_date :</span></dt><dd>2019-01-17T00:41:10Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.29</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>fx</dd><dt><span>grid :</span></dt><dd>native gx1v7 displaced pole grid (384x320 latxlon)</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Center for Atmospheric Research, Climate and Global Dynamics Laboratory, 1850 Table Mesa Drive, Boulder, CO 80305, USA</dd><dt><span>institution_id :</span></dt><dd>NCAR</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by &lt;The National Center for Atmospheric Research&gt; is licensed under a Creative Commons Attribution-[]ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file)[]. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>CESM2</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-01-01 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>source :</span></dt><dd>CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m); sea_ice: CICE5.1 (same grid as ocean); land: CLM5 0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); aerosol: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); atmoschem: MAM4 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb); landIce: CISM2.1; ocnBgchem: MARBL (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m)</dd><dt><span>source_id :</span></dt><dd>CESM2</dd><dt><span>source_type :</span></dt><dd>AOGCM BGC</dd><dt><span>table_id :</span></dt><dd>Ofx</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/ce138a75-763c-4f4f-aa61-34dd1dd02d02</dd><dt><span>variable_id :</span></dt><dd>areacello</dd><dt><span>variant_info :</span></dt><dd>CMIP6 20th century experiments (1850-2014) with CAM6, interactive land (CLM5), coupled ocean (POP2) with biogeochemistry (MARBL), interactive sea ice (CICE5.1), and non-evolving land ice (CISM2.1)\r\n",
       "</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>branch_time_in_parent :</span></dt><dd>219000.0</dd><dt><span>branch_time_in_child :</span></dt><dd>674885.0</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.historical.none.r1i1p1f1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (ni: 320, nj: 384, vertices: 4)\n",
       "Coordinates:\n",
       "    lat        (nj, ni) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
       "    lon        (nj, ni) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
       "  * nj         (nj) int32 1 2 3 4 5 6 7 8 9 ... 377 378 379 380 381 382 383 384\n",
       "  * ni         (ni) int32 1 2 3 4 5 6 7 8 9 ... 313 314 315 316 317 318 319 320\n",
       "Dimensions without coordinates: vertices\n",
       "Data variables:\n",
       "    areacello  (nj, ni) float32 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
       "    lat_bnds   (nj, ni, vertices) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
       "    lon_bnds   (nj, ni, vertices) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
       "Attributes: (12/44)\n",
       "    Conventions:            CF-1.7 CMIP-6.2\n",
       "    activity_id:            CMIP\n",
       "    case_id:                15\n",
       "    cesm_casename:          b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
       "    contact:                cesm_cmip6@ucar.edu\n",
       "    creation_date:          2019-01-17T00:41:10Z\n",
       "    ...                     ...\n",
       "    sub_experiment:         none\n",
       "    sub_experiment_id:      none\n",
       "    branch_time_in_parent:  219000.0\n",
       "    branch_time_in_child:   674885.0\n",
       "    branch_method:          standard\n",
       "    further_info_url:       https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2.h..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_ocean = xr.open_dataset(direc_i+'areacello_Ofx_CESM2_historical_r1i1p1f1_gn.nc',chunks={}) # The 'chunks' option above tells xarray to make this a dask array\n",
    "# Rename the dimensions to match the dimensions of the sea ice file\n",
    "ds_ocean = ds_ocean.rename({'nlat':'nj','nlon':'ni'})\n",
    "ds_ocean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "im3 = plt.pcolormesh(ds_ocean.coords['lat'],cmap='twilight_shifted',vmin=-90,vmax=90);\n",
    "cbar = plt.colorbar(im3)\n",
    "cbar.set_label('Latitude [degrees N]',fontsize=18)\n",
    "plt.xticks([]);\n",
    "plt.yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the lat/lon fields are smooth and don't have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a new dataset with the sea ice concentration and the lat/lon coordinates from the ocean file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_siconc_ocean               = xr.Dataset()\n",
    "ds_siconc_ocean['siconc']     = (('time','nj','ni'),sic)\n",
    "ds_siconc_ocean.coords['lat'] = (('nj','ni'),ds_ocean['lat'])\n",
    "ds_siconc_ocean.coords['lon'] = (('nj','ni'),ds_ocean['lon'])\n",
    "sic_ocean                     = ds_siconc_ocean['siconc']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the regridder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the target grid (the new grid you want your output on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (x: 361, y: 179)\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    lat      (y, x) float64 -89.0 -89.0 -89.0 -89.0 ... 89.0 89.0 89.0 89.0\n",
       "    lon      (y, x) float64 0.0 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d3416b3f-11bb-499e-8143-b5a8cc36a474' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d3416b3f-11bb-499e-8143-b5a8cc36a474' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>x</span>: 361</li><li><span>y</span>: 179</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f1580862-3d1b-45ae-9b53-581fcbb01eeb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f1580862-3d1b-45ae-9b53-581fcbb01eeb' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a5156d77-82aa-4537-b135-9b44d38b9410' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5156d77-82aa-4537-b135-9b44d38b9410' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -89.0 -89.0 ... 89.0 89.0</div><input id='attrs-ff6f01b8-e219-4457-8cc1-747cb2fe4216' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff6f01b8-e219-4457-8cc1-747cb2fe4216' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48b1a936-357a-48a7-b747-f81d0a285909' class='xr-var-data-in' type='checkbox'><label for='data-48b1a936-357a-48a7-b747-f81d0a285909' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-89., -89., -89., ..., -89., -89., -89.],\n",
       "       [-88., -88., -88., ..., -88., -88., -88.],\n",
       "       [-87., -87., -87., ..., -87., -87., -87.],\n",
       "       ...,\n",
       "       [ 87.,  87.,  87., ...,  87.,  87.,  87.],\n",
       "       [ 88.,  88.,  88., ...,  88.,  88.,  88.],\n",
       "       [ 89.,  89.,  89., ...,  89.,  89.,  89.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 358.0 359.0 360.0</div><input id='attrs-db2d8481-5451-489d-b17f-143d3497f8c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db2d8481-5451-489d-b17f-143d3497f8c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59cc700-0516-4262-9f2f-39fdc87d6437' class='xr-var-data-in' type='checkbox'><label for='data-d59cc700-0516-4262-9f2f-39fdc87d6437' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       ...,\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a1cf493-ba21-4acd-ad41-fdce7968d537' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a1cf493-ba21-4acd-ad41-fdce7968d537' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
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       "<xarray.Dataset>\n",
       "Dimensions:  (x: 361, y: 179)\n",
       "Dimensions without coordinates: x, y\n",
       "Data variables:\n",
       "    lat      (y, x) float64 -89.0 -89.0 -89.0 -89.0 ... 89.0 89.0 89.0 89.0\n",
       "    lon      (y, x) float64 0.0 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0"
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     "execution_count": 10,
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    "# In this case, we want to regrid to a 1 deg lat x 1 deg lon grid\n",
    "lat = np.arange(-89.0,90.0,1.0) # Do not include 90S and 90N, it won't work because of the singularity at the poles\n",
    "lon = np.arange(0.0,361.0,1.0) # cartopy gets rage-y (i.e. it doesn't work) when plotting\n",
    "lon2d,lat2d = np.meshgrid(lon,lat)\n",
    "target_grid = xr.Dataset({'lat': (['y', 'x'], lat2d),'lon': (['y', 'x'], lon2d)})\n",
    "target_grid\n",
    "\n",
    "# This is another way to set up the target grid using built-in functions from xesmf\n",
    "# target_grid_new = xe.util.grid_global(0.5,0.5)\n",
    "# target_grid_new"
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   "metadata": {},
   "source": [
    "### Create regridding function"
   ]
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pade7652/opt/anaconda3/envs/regrid_env/lib/python3.7/site-packages/xarray/core/dataarray.py:745: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return key in self.data\n"
     ]
    }
   ],
   "source": [
    "# Possible regridding algorithms. For a comparison of the different algorithms, check out: https://xesmf.readthedocs.io/en/latest/notebooks/Compare_algorithms.html\n",
    "method_list = ['bilinear', 'conservative', 'nearest_s2d', 'nearest_d2s', 'patch']\n",
    "# NOTE: Once you have the output of this (the weight file), you can set reuse_weights=True so that you don't need to recreate the weight file\n",
    "regridder = xe.Regridder(sic_ocean, target_grid, method_list[2], periodic=True)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform regridding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pade7652/opt/anaconda3/envs/regrid_env/lib/python3.7/site-packages/xesmf/frontend.py:478: FutureWarning: ``output_sizes`` should be given in the ``dask_gufunc_kwargs`` parameter. It will be removed as direct parameter in a future version.\n",
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       "\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       ".xr-var-dtype:hover,\n",
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       "\n",
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       ".xr-var-data {\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
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       "}\n",
       "\n",
       "dl.xr-attrs {\n",
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       "  margin: 0;\n",
       "  display: grid;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;siconc&#x27; (time: 1980, y: 179, x: 361)&gt;\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [99.65435028, 99.65435028, 99.65435028, ..., 99.63287354,\n",
       "         99.65435028, 99.65435028],\n",
       "        [99.75475311, 99.75475311, 99.75475311, ..., 99.75475311,\n",
       "         99.75475311, 99.75475311],\n",
       "        [99.78426361, 99.77059937, 99.77059937, ..., 99.78426361,\n",
       "         99.78426361, 99.78426361]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [99.88413239, 99.88413239, 99.88413239, ..., 99.8852005 ,\n",
       "         99.88413239, 99.88413239],\n",
       "        [99.8714447 , 99.8714447 , 99.8714447 , ..., 99.8714447 ,\n",
       "         99.8714447 , 99.8714447 ],\n",
       "        [99.84216309, 99.83854675, 99.83854675, ..., 99.84216309,\n",
       "         99.84216309, 99.84216309]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [99.93615723, 99.93615723, 99.93615723, ..., 99.94313812,\n",
       "         99.93615723, 99.93615723],\n",
       "        [99.9491806 , 99.9491806 , 99.9491806 , ..., 99.9491806 ,\n",
       "         99.9491806 , 99.9491806 ],\n",
       "        [99.93619537, 99.93518829, 99.93518829, ..., 99.93619537,\n",
       "         99.93619537, 99.93619537]]])\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 0.0 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "    lat      (y, x) float64 -89.0 -89.0 -89.0 -89.0 ... 89.0 89.0 89.0 89.0\n",
       "Dimensions without coordinates: time, y, x\n",
       "Attributes:\n",
       "    regrid_method:  nearest_s2d</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'siconc'</div><ul class='xr-dim-list'><li><span>time</span>: 1980</li><li><span>y</span>: 179</li><li><span>x</span>: 361</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cd4e2d6f-40ec-47fc-a0b6-fda2b58434ea' class='xr-array-in' type='checkbox' checked><label for='section-cd4e2d6f-40ec-47fc-a0b6-fda2b58434ea' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan ... 99.94 99.94 99.94 99.94 99.94 99.94</span></div><div class='xr-array-data'><pre>array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [99.65435028, 99.65435028, 99.65435028, ..., 99.63287354,\n",
       "         99.65435028, 99.65435028],\n",
       "        [99.75475311, 99.75475311, 99.75475311, ..., 99.75475311,\n",
       "         99.75475311, 99.75475311],\n",
       "        [99.78426361, 99.77059937, 99.77059937, ..., 99.78426361,\n",
       "         99.78426361, 99.78426361]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [99.88413239, 99.88413239, 99.88413239, ..., 99.8852005 ,\n",
       "         99.88413239, 99.88413239],\n",
       "        [99.8714447 , 99.8714447 , 99.8714447 , ..., 99.8714447 ,\n",
       "         99.8714447 , 99.8714447 ],\n",
       "        [99.84216309, 99.83854675, 99.83854675, ..., 99.84216309,\n",
       "         99.84216309, 99.84216309]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [99.93615723, 99.93615723, 99.93615723, ..., 99.94313812,\n",
       "         99.93615723, 99.93615723],\n",
       "        [99.9491806 , 99.9491806 , 99.9491806 , ..., 99.9491806 ,\n",
       "         99.9491806 , 99.9491806 ],\n",
       "        [99.93619537, 99.93518829, 99.93518829, ..., 99.93619537,\n",
       "         99.93619537, 99.93619537]]])</pre></div></div></li><li class='xr-section-item'><input id='section-b18633e1-ede6-41d2-8110-85e39b0bf183' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b18633e1-ede6-41d2-8110-85e39b0bf183' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 358.0 359.0 360.0</div><input id='attrs-3a60e58f-1ee5-4444-9bd4-980d45aa91aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a60e58f-1ee5-4444-9bd4-980d45aa91aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea39484-6183-4684-a423-83c824b74229' class='xr-var-data-in' type='checkbox'><label for='data-6ea39484-6183-4684-a423-83c824b74229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       ...,\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.],\n",
       "       [  0.,   1.,   2., ..., 358., 359., 360.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -89.0 -89.0 ... 89.0 89.0</div><input id='attrs-8a7bc458-adee-4426-b8cb-b85c8974d85c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a7bc458-adee-4426-b8cb-b85c8974d85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44ffe62e-4806-4e7b-82d6-6497302bc388' class='xr-var-data-in' type='checkbox'><label for='data-44ffe62e-4806-4e7b-82d6-6497302bc388' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-89., -89., -89., ..., -89., -89., -89.],\n",
       "       [-88., -88., -88., ..., -88., -88., -88.],\n",
       "       [-87., -87., -87., ..., -87., -87., -87.],\n",
       "       ...,\n",
       "       [ 87.,  87.,  87., ...,  87.,  87.,  87.],\n",
       "       [ 88.,  88.,  88., ...,  88.,  88.,  88.],\n",
       "       [ 89.,  89.,  89., ...,  89.,  89.,  89.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49963e88-af01-4cdf-8d95-20665f39002e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49963e88-af01-4cdf-8d95-20665f39002e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>nearest_s2d</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'siconc' (time: 1980, y: 179, x: 361)>\n",
       "array([[[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [99.65435028, 99.65435028, 99.65435028, ..., 99.63287354,\n",
       "         99.65435028, 99.65435028],\n",
       "        [99.75475311, 99.75475311, 99.75475311, ..., 99.75475311,\n",
       "         99.75475311, 99.75475311],\n",
       "        [99.78426361, 99.77059937, 99.77059937, ..., 99.78426361,\n",
       "         99.78426361, 99.78426361]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "...\n",
       "        [99.88413239, 99.88413239, 99.88413239, ..., 99.8852005 ,\n",
       "         99.88413239, 99.88413239],\n",
       "        [99.8714447 , 99.8714447 , 99.8714447 , ..., 99.8714447 ,\n",
       "         99.8714447 , 99.8714447 ],\n",
       "        [99.84216309, 99.83854675, 99.83854675, ..., 99.84216309,\n",
       "         99.84216309, 99.84216309]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        [        nan,         nan,         nan, ...,         nan,\n",
       "                 nan,         nan],\n",
       "        ...,\n",
       "        [99.93615723, 99.93615723, 99.93615723, ..., 99.94313812,\n",
       "         99.93615723, 99.93615723],\n",
       "        [99.9491806 , 99.9491806 , 99.9491806 , ..., 99.9491806 ,\n",
       "         99.9491806 , 99.9491806 ],\n",
       "        [99.93619537, 99.93518829, 99.93518829, ..., 99.93619537,\n",
       "         99.93619537, 99.93619537]]])\n",
       "Coordinates:\n",
       "    lon      (y, x) float64 0.0 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "    lat      (y, x) float64 -89.0 -89.0 -89.0 -89.0 ... 89.0 89.0 89.0 89.0\n",
       "Dimensions without coordinates: time, y, x\n",
       "Attributes:\n",
       "    regrid_method:  nearest_s2d"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sic_rg = regridder(sic_ocean)\n",
    "sic_rg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot regridded data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pade7652/opt/anaconda3/envs/regrid_env/lib/python3.7/site-packages/ipykernel_launcher.py:3: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "ax.set_facecolor('0.5') # Fill in model land by making the axis background whatever color you want\n",
    "im4 = ax.pcolormesh(lon,lat,sic_rg[0],cmap='Blues_r',vmin=0,vmax=100)\n",
    "cbar = plt.colorbar(im4)\n",
    "cbar.set_label('Sea Ice Concentration [%]',fontsize=18)\n",
    "# plt.xticks([]);\n",
    "# plt.yticks([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save regridded data as a netCDF file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create output dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_o = xr.Dataset()\n",
    "ds_o['siconc']      = (('time','y','x'),sic_rg)\n",
    "ds_o.coords['lat']  = (('y','x'),sic_rg['lat'])\n",
    "ds_o.coords['lon']  = (('y','x'),sic_rg['lon'])\n",
    "ds_o.coords['time'] = (('time'),sic_rg['time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add attributes to document the output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_o['siconc'].attrs['long_name'] = 'Sea Ice Concentration'\n",
    "ds_o['siconc'].attrs['units']     = '%'\n",
    "ds_o.attrs['model']               = 'CESM2'\n",
    "ds_o.attrs['Description']         = 'historical siconc r1i1p1f1 regridded onto a rectilinear 1.0deg lat x 1.0deg lon grid'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the missing and fill values to whatever they are in the original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_o['siconc'].encoding['_FillValue']    = ds_i['siconc'].encoding['_FillValue']\n",
    "ds_o['siconc'].encoding['missing_value'] = ds_i['siconc'].encoding['missing_value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save as netCDF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "svdirec = '../Data/'\n",
    "ds_o.to_netcdf(path=svdirec+'CESM2_siconc_hist_r1i1p1f1_xesmf_rg_1x1.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}