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   "source": [
    "# Model Agnostic Analysis"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this tutorial model agnostic analysis means writing your notebook so that it can easily be used with any CF compliant data source.\n",
    "\n",
    "### What are the CF Conventions?\n",
    "\n",
    "From [CF Metadata conventions](https://cfconventions.org):\n",
    "\n",
    "> The CF metadata conventions are designed to promote the processing and sharing of files created with the NetCDF API. The conventions define metadata that provide a definitive description of what the data in each variable represents, and the spatial and temporal properties of the data. This enables users of data from different sources to decide which quantities are comparable, and facilitates building applications with powerful extraction, regridding, and display capabilities. The CF convention includes a standard name table, which defines strings that identify physical quantities.\n",
    "\n",
    "In most cases the model output data accessed through the COSIMA Cookbook complies with some version of the CF conventions, enough to be usable for model agnostic analysis.\n",
    "\n",
    "### Why bother?\n",
    "\n",
    "Model agnostic means the same code can work for multiple models. This makes your code more usable by **you** and by others. You no longer need to have different versions of code for different models. It makes you and any one who uses your code more productive. It allows for common tasks to be abstracted into general methods that can be more easily reused, meaning less code needs to be written and maintained. This is an enormous produtivity boost.\n",
    "\n",
    "### How is model agnostic analysis achieved?\n",
    "\n",
    "This can be achieved by using packages that enable this:\n",
    "- [cf_xarray](https://cf-xarray.readthedocs.io/en/latest/index.html) for generalised coordinate naming\n",
    "- [xgcm](https://xgcm.readthedocs.io) to make grid operations generic across data\n",
    "- [pint](https://pint.readthedocs.io/) and [pint-xarray](https://pint-xarray.readthedocs.io/) for handling units easily and robustly"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example uses an example analysis, shows how the this might be done in a traditional, model specific, manner, and then implements the same analysis in a model agnostic way.\n",
    "\n",
    "First step is to import necessary libaries."
   ]
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  {
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   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import cosima_cookbook as cc\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import cf_xarray as cfxr\n",
    "import pint_xarray\n",
    "from pint import application_registry as ureg\n",
    "import cf_xarray.units\n",
    "import cmocean as cm\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cf_xarray works best when xarray keeps attributes by default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.set_options(keep_attrs=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a dataset using COSIMA Cookbook, so first open a session to the default database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "session = cc.database.create_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now load surface temperature data from a 0.25$^\\circ$ global MOM5 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = '025deg_jra55v13_iaf_gmredi6'\n",
    "variable = 'surface_temp'\n",
    "SST = cc.querying.getvar(experiment, variable, session, frequency='1 monthly', n=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a 3D dataset in latitude, longitude and time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;surface_temp&#x27; (time: 288, yt_ocean: 1080, xt_ocean: 1440)&gt;\n",
       "dask.array&lt;concatenate, shape=(288, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
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       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "  * time      (time) datetime64[ns] 1958-01-14T12:00:00 ... 1981-12-14T12:00:00\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    units:          deg_C\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 288</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7c31ed8d-165a-4410-a9d0-071dd54eeb18' class='xr-array-in' type='checkbox' checked><label for='section-7c31ed8d-165a-4410-a9d0-071dd54eeb18' 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>dask.array&lt;chunksize=(1, 540, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-8b2c9a98-851d-4752-9c73-529cf5ab0f27' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8b2c9a98-851d-4752-9c73-529cf5ab0f27' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-915df0f5-c4c5-42e6-8af0-104d5276e3be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-915df0f5-c4c5-42e6-8af0-104d5276e3be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-120cbd6a-f492-4e4c-acba-1bfea48b0c0e' class='xr-var-data-in' type='checkbox'><label for='data-120cbd6a-f492-4e4c-acba-1bfea48b0c0e' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-9532aba3-3f87-40fa-9f32-8e992b6d6863' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9532aba3-3f87-40fa-9f32-8e992b6d6863' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0de71b16-fecc-4873-874e-0eb693b345f4' class='xr-var-data-in' type='checkbox'><label for='data-0de71b16-fecc-4873-874e-0eb693b345f4' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-01-14T12:00:00 ... 1981-12-...</div><input id='attrs-36160b70-18c6-4690-b36e-fc2d19873b27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36160b70-18c6-4690-b36e-fc2d19873b27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ceabdb95-a402-4143-b7d1-ac46edc68b75' class='xr-var-data-in' type='checkbox'><label for='data-ceabdb95-a402-4143-b7d1-ac46edc68b75' 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>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>GREGORIAN</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-01-14T12:00:00.000000000&#x27;, &#x27;1958-02-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-14T12:00:00.000000000&#x27;, ..., &#x27;1981-10-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-11-14T00:00:00.000000000&#x27;, &#x27;1981-12-14T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bc29e893-f49b-441b-96db-08957b72b0e5' class='xr-section-summary-in' type='checkbox'  ><label for='section-bc29e893-f49b-441b-96db-08957b72b0e5' class='xr-section-summary' >Indexes: <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-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-96bf09b7-10af-4075-969f-aabdd691e707' class='xr-index-data-in' type='checkbox'/><label for='index-96bf09b7-10af-4075-969f-aabdd691e707' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625,\n",
       "              -278.375, -278.125, -277.875, -277.625,\n",
       "              ...\n",
       "                77.625,   77.875,   78.125,   78.375,   78.625,   78.875,\n",
       "                79.125,   79.375,   79.625,   79.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56fb4a43-75bf-4d05-9613-ba39cb6ac68f' class='xr-index-data-in' type='checkbox'/><label for='index-56fb4a43-75bf-4d05-9613-ba39cb6ac68f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
       "               -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
       "               -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
       "              -80.12661353099249,\n",
       "              ...\n",
       "                88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
       "               89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
       "                 89.630486559435,  89.73608514863992,  89.84168373784476,\n",
       "               89.94728232704986],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=1080))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4226c14-e10d-4c30-b540-7693d296a8b1' class='xr-index-data-in' type='checkbox'/><label for='index-d4226c14-e10d-4c30-b540-7693d296a8b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1958-01-14 12:00:00&#x27;, &#x27;1958-02-13 00:00:00&#x27;,\n",
       "               &#x27;1958-03-14 12:00:00&#x27;, &#x27;1958-04-14 00:00:00&#x27;,\n",
       "               &#x27;1958-05-14 12:00:00&#x27;, &#x27;1958-06-14 00:00:00&#x27;,\n",
       "               &#x27;1958-07-14 12:00:00&#x27;, &#x27;1958-08-14 12:00:00&#x27;,\n",
       "               &#x27;1958-09-14 00:00:00&#x27;, &#x27;1958-10-14 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;1981-03-14 12:00:00&#x27;, &#x27;1981-04-14 00:00:00&#x27;,\n",
       "               &#x27;1981-05-14 12:00:00&#x27;, &#x27;1981-06-14 00:00:00&#x27;,\n",
       "               &#x27;1981-07-14 12:00:00&#x27;, &#x27;1981-08-14 12:00:00&#x27;,\n",
       "               &#x27;1981-09-14 00:00:00&#x27;, &#x27;1981-10-14 12:00:00&#x27;,\n",
       "               &#x27;1981-11-14 00:00:00&#x27;, &#x27;1981-12-14 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=288, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a28e976-7ef9-4e94-8ee9-71b29a0ed7af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a28e976-7ef9-4e94-8ee9-71b29a0ed7af' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Conservative temperature</dd><dt><span>units :</span></dt><dd>deg_C</dd><dt><span>valid_range :</span></dt><dd>[-10. 500.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>coordinates :</span></dt><dd>geolon_t geolat_t</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output001/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output002/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output003/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output004/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output005/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output006/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output007/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output008/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output009/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output010/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output011/ocean/ocean_month.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'surface_temp' (time: 288, yt_ocean: 1080, xt_ocean: 1440)>\n",
       "dask.array<concatenate, shape=(288, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "  * time      (time) datetime64[ns] 1958-01-14T12:00:00 ... 1981-12-14T12:00:00\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    units:          deg_C\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model specific"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First do this as it might usually be done, in a model specific manner:\n",
    "\n",
    "1. Use the time coordinate name in the mean function\n",
    "2. Subtract a hard-coded value to convert the temperature degrees celcius from degrees Kelvin (the meta-data says the units are `deg_C` but this is clearly incorrect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;surface_temp&#x27; (yt_ocean: 1080, xt_ocean: 1440)&gt;\n",
       "dask.array&lt;sub, shape=(1080, 1440), dtype=float32, chunksize=(540, 720), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    units:          deg_C\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3fab1b31-61b3-48c9-a995-e4cb94dedeb6' class='xr-array-in' type='checkbox' checked><label for='section-3fab1b31-61b3-48c9-a995-e4cb94dedeb6' 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>dask.array&lt;chunksize=(540, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 1.48 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (540, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 32 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"90\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">1080</text>\n",
       "</svg>\n",
       "        </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-3be4b1ea-e3dc-480d-93ff-650d6fb9830a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3be4b1ea-e3dc-480d-93ff-650d6fb9830a' 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-d74cc591-a092-4340-9516-a831a0ccd550' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d74cc591-a092-4340-9516-a831a0ccd550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb8f4041-2185-4a9a-a2bd-8ad8c697da15' class='xr-var-data-in' type='checkbox'><label for='data-eb8f4041-2185-4a9a-a2bd-8ad8c697da15' 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>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-3b1a752f-517c-4682-b6e5-e73c47ab3d15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b1a752f-517c-4682-b6e5-e73c47ab3d15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d96085e0-7441-4022-bff9-8949199487ec' class='xr-var-data-in' type='checkbox'><label for='data-d96085e0-7441-4022-bff9-8949199487ec' 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>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
       "        89.947282])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad28608d-d998-4b8a-ae71-fec82f157b78' class='xr-section-summary-in' type='checkbox'  ><label for='section-ad28608d-d998-4b8a-ae71-fec82f157b78' class='xr-section-summary' >Indexes: <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-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f0747147-3178-41aa-bd14-c947c3f19f06' class='xr-index-data-in' type='checkbox'/><label for='index-f0747147-3178-41aa-bd14-c947c3f19f06' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625,\n",
       "              -278.375, -278.125, -277.875, -277.625,\n",
       "              ...\n",
       "                77.625,   77.875,   78.125,   78.375,   78.625,   78.875,\n",
       "                79.125,   79.375,   79.625,   79.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d65e00ef-be27-43f1-b61e-3e6af3cb9056' class='xr-index-data-in' type='checkbox'/><label for='index-d65e00ef-be27-43f1-b61e-3e6af3cb9056' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
       "               -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
       "               -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
       "              -80.12661353099249,\n",
       "              ...\n",
       "                88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
       "               89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
       "                 89.630486559435,  89.73608514863992,  89.84168373784476,\n",
       "               89.94728232704986],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=1080))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-445b5a76-415f-4349-a7a0-a0f128fc6f28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-445b5a76-415f-4349-a7a0-a0f128fc6f28' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Conservative temperature</dd><dt><span>units :</span></dt><dd>deg_C</dd><dt><span>valid_range :</span></dt><dd>[-10. 500.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>coordinates :</span></dt><dd>geolon_t geolat_t</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output001/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output002/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output003/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output004/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output005/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output006/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output007/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output008/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output009/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output010/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output011/ocean/ocean_month.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'surface_temp' (yt_ocean: 1080, xt_ocean: 1440)>\n",
       "dask.array<sub, shape=(1080, 1440), dtype=float32, chunksize=(540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    units:          deg_C\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST_time_mean = SST.mean('time') - 273.15\n",
    "SST_time_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax = plt.axes(projection=ccrs.Robinson())\n",
    "\n",
    "SST_time_mean.plot(ax=ax,\n",
    "                   x='xt_ocean', y='yt_ocean', \n",
    "                   transform=ccrs.PlateCarree(),\n",
    "                   vmin=-2, vmax=30, extend='both',\n",
    "                   cmap=cm.cm.thermal)\n",
    "\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the arctic is not correctly respresented due to the 1D lat/lon coordinates not being correct in the tripole area. See the [Making Maps with cartopy Tutorial](https://cosima-recipes.readthedocs.io/en/latest/tutorials/Making_Maps_with_Cartopy.html#fixing-the-tripole) for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model agnostic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now do the same calculation in a model agnostic manner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this data it is necessary to correct the units attribute first. This shouldn't be necessary if the metadata is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SST.attrs['units'] = 'K'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use `pint` to ensure this is in degrees C. Note that if the data was originally in degrees celcius this would be fine, it would do nothing. So this is a way of catering for any temperature units that are understood by `pint` in a transparent way. Note the call to `quantify` which invokes `pint`'s machinery to parse the units and allow unit conversions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "SST = SST.pint.quantify().pint.to('C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;surface_temp&#x27; (time: 288, yt_ocean: 1080, xt_ocean: 1440)&gt;\n",
       "&lt;Quantity(dask.array&lt;truediv, shape=(288, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray&gt;, &#x27;degree_Celsius&#x27;)&gt;\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "  * time      (time) datetime64[ns] 1958-01-14T12:00:00 ... 1981-12-14T12:00:00\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 288</li><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d96be759-ecbb-480c-bcb1-2a422a8075db' class='xr-array-in' type='checkbox' checked><label for='section-d96be759-ecbb-480c-bcb1-2a422a8075db' 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>[°C] dask.array&lt;truediv, shape=(288, 1080, 1440), dtype=float32, ch...</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><table>\n",
       "    <tr>\n",
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       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.67 GiB </td>\n",
       "                        <td> 1.48 MiB </td>\n",
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       "</table></td></tr><tr><th>Units</th><td style='text-align:left;'>degree_Celsius</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-5ff1063a-c6a8-49d6-b7c0-c633597bd8c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ff1063a-c6a8-49d6-b7c0-c633597bd8c8' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-6d905cc1-b3c7-4a4a-a610-89ebe5a6316b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d905cc1-b3c7-4a4a-a610-89ebe5a6316b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5976b864-7c43-4051-be54-069dae761d7e' class='xr-var-data-in' type='checkbox'><label for='data-5976b864-7c43-4051-be54-069dae761d7e' 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>tcell longitude</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-f77254b8-1997-45b1-9ac8-501853947aa5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f77254b8-1997-45b1-9ac8-501853947aa5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7622111-8aed-4df1-8210-4ec052d29152' class='xr-var-data-in' type='checkbox'><label for='data-c7622111-8aed-4df1-8210-4ec052d29152' 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>tcell latitude</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
       "        89.947282])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-01-14T12:00:00 ... 1981-12-...</div><input id='attrs-839fe848-8aa5-444d-bd2e-700da9b62e6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-839fe848-8aa5-444d-bd2e-700da9b62e6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3977de57-dc8e-4d48-8925-94c9c950fbd2' class='xr-var-data-in' type='checkbox'><label for='data-3977de57-dc8e-4d48-8925-94c9c950fbd2' 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>time</dd><dt><span>cartesian_axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>GREGORIAN</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-01-14T12:00:00.000000000&#x27;, &#x27;1958-02-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-14T12:00:00.000000000&#x27;, ..., &#x27;1981-10-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-11-14T00:00:00.000000000&#x27;, &#x27;1981-12-14T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ea381fd-1876-41f0-a3f2-16e276803428' class='xr-section-summary-in' type='checkbox'  ><label for='section-6ea381fd-1876-41f0-a3f2-16e276803428' class='xr-section-summary' >Indexes: <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-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3b20a4e0-ba1b-486a-bbbd-3dd78b104ce8' class='xr-index-data-in' type='checkbox'/><label for='index-3b20a4e0-ba1b-486a-bbbd-3dd78b104ce8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625,\n",
       "              -278.375, -278.125, -277.875, -277.625,\n",
       "              ...\n",
       "                77.625,   77.875,   78.125,   78.375,   78.625,   78.875,\n",
       "                79.125,   79.375,   79.625,   79.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63ec751f-1f20-4552-ada2-7af862c5ee16' class='xr-index-data-in' type='checkbox'/><label for='index-63ec751f-1f20-4552-ada2-7af862c5ee16' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
       "               -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
       "               -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
       "              -80.12661353099249,\n",
       "              ...\n",
       "                88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
       "               89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
       "                 89.630486559435,  89.73608514863992,  89.84168373784476,\n",
       "               89.94728232704986],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=1080))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3bd96a9e-272c-42a2-94ec-973118c06538' class='xr-index-data-in' type='checkbox'/><label for='index-3bd96a9e-272c-42a2-94ec-973118c06538' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1958-01-14 12:00:00&#x27;, &#x27;1958-02-13 00:00:00&#x27;,\n",
       "               &#x27;1958-03-14 12:00:00&#x27;, &#x27;1958-04-14 00:00:00&#x27;,\n",
       "               &#x27;1958-05-14 12:00:00&#x27;, &#x27;1958-06-14 00:00:00&#x27;,\n",
       "               &#x27;1958-07-14 12:00:00&#x27;, &#x27;1958-08-14 12:00:00&#x27;,\n",
       "               &#x27;1958-09-14 00:00:00&#x27;, &#x27;1958-10-14 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;1981-03-14 12:00:00&#x27;, &#x27;1981-04-14 00:00:00&#x27;,\n",
       "               &#x27;1981-05-14 12:00:00&#x27;, &#x27;1981-06-14 00:00:00&#x27;,\n",
       "               &#x27;1981-07-14 12:00:00&#x27;, &#x27;1981-08-14 12:00:00&#x27;,\n",
       "               &#x27;1981-09-14 00:00:00&#x27;, &#x27;1981-10-14 12:00:00&#x27;,\n",
       "               &#x27;1981-11-14 00:00:00&#x27;, &#x27;1981-12-14 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=288, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-746c14eb-12c8-415c-9180-050d97ac0e1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-746c14eb-12c8-415c-9180-050d97ac0e1d' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Conservative temperature</dd><dt><span>valid_range :</span></dt><dd>[-10. 500.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>coordinates :</span></dt><dd>geolon_t geolat_t</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output001/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output002/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output003/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output004/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output005/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output006/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output007/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output008/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output009/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output010/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output011/ocean/ocean_month.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'surface_temp' (time: 288, yt_ocean: 1080, xt_ocean: 1440)>\n",
       "<Quantity(dask.array<truediv, shape=(288, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>, 'degree_Celsius')>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "  * time      (time) datetime64[ns] 1958-01-14T12:00:00 ... 1981-12-14T12:00:00\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now take the time mean, but this time use the `cf` accessor to automatically determine the name of the time dimension. `cf_xarray` checks the names of variables and coordinates, and associated metadata to try and infer information about the data based on the CF conventions.\n",
    "\n",
    "To see what `cf_xarray` information is available just evaluate the accessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes: * X: ['xt_ocean']\n",
       "           * Y: ['yt_ocean']\n",
       "           * T: ['time']\n",
       "             Z: n/a\n",
       "\n",
       "- CF Coordinates: * longitude: ['xt_ocean']\n",
       "                  * latitude: ['yt_ocean']\n",
       "                  * time: ['time']\n",
       "                    vertical: n/a\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   n/a\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST.cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case it has found `X`, `Y` and `T` axes, and `longitude`, `latitude` and `time` axes. These are now accessible like a `dict` using the `cf` accessor. Note that this returns the actual coordinate, and many functions just want a simple string argument, which is the name of the coordinate.\n",
    "\n",
    "`cf_xarray` also wraps many standard xarray functions allowing `cf` names to be used, which are [automatically converted to the name in the data](https://cf-xarray.readthedocs.io/en/latest/examples/introduction.html#feature-rewriting-arguments). \n",
    "\n",
    "The upshot: just use the `cf` accessor and then append the required function and use the standard CF coordinate name (in this case they are the same, `time`, but that is not guaranteed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  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",
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       "\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",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\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",
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       "\n",
       ".xr-array-data,\n",
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\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",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\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",
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       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
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       "\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",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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;surface_temp&#x27; (yt_ocean: 1080, xt_ocean: 1440)&gt;\n",
       "&lt;Quantity(dask.array&lt;mean_agg-aggregate, shape=(1080, 1440), dtype=float32, chunksize=(540, 720), chunktype=numpy.ndarray&gt;, &#x27;degree_Celsius&#x27;)&gt;\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 1080</li><li><span class='xr-has-index'>xt_ocean</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-29ca5789-f969-4f6c-ba21-402cf1900ec5' class='xr-array-in' type='checkbox' checked><label for='section-29ca5789-f969-4f6c-ba21-402cf1900ec5' 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>[°C] dask.array&lt;mean_agg-aggregate, shape=(1080, 1440), dtype=float...</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 1.48 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (540, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 34 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"90\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">1080</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></td></tr><tr><th>Units</th><td style='text-align:left;'>degree_Celsius</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-5ea948cd-1047-4801-80a1-9ad19eb4d8a4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ea948cd-1047-4801-80a1-9ad19eb4d8a4' 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.6 ... 79.62 79.88</div><input id='attrs-ccfcc0b4-e12f-4733-8290-9a7a189bb289' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccfcc0b4-e12f-4733-8290-9a7a189bb289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d729f418-9766-4b68-bbf1-502312c92312' class='xr-var-data-in' type='checkbox'><label for='data-d729f418-9766-4b68-bbf1-502312c92312' 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>tcell longitude</dd><dt><span>cartesian_axis :</span></dt><dd>X</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-279.875, -279.625, -279.375, ...,   79.375,   79.625,   79.875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.08 -80.97 ... 89.84 89.95</div><input id='attrs-ffcd4f9e-bf57-480b-8b80-68df6cc05cc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffcd4f9e-bf57-480b-8b80-68df6cc05cc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8d688b1-224a-41bd-8ebe-a1e1b4c72c3b' class='xr-var-data-in' type='checkbox'><label for='data-e8d688b1-224a-41bd-8ebe-a1e1b4c72c3b' 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>tcell latitude</dd><dt><span>cartesian_axis :</span></dt><dd>Y</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-81.077001, -80.971402, -80.865804, ...,  89.736085,  89.841684,\n",
       "        89.947282])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb7f6e1a-4b62-4dd3-bbe0-d4f7a2536d7f' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb7f6e1a-4b62-4dd3-bbe0-d4f7a2536d7f' class='xr-section-summary' >Indexes: <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-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-11aa9205-ad3b-45e7-9f5a-37a8bae91630' class='xr-index-data-in' type='checkbox'/><label for='index-11aa9205-ad3b-45e7-9f5a-37a8bae91630' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-279.875, -279.625, -279.375, -279.125, -278.875, -278.625,\n",
       "              -278.375, -278.125, -277.875, -277.625,\n",
       "              ...\n",
       "                77.625,   77.875,   78.125,   78.375,   78.625,   78.875,\n",
       "                79.125,   79.375,   79.625,   79.875],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b2c8b4f4-659e-433e-ac7f-3faf65922fcd' class='xr-index-data-in' type='checkbox'/><label for='index-b2c8b4f4-659e-433e-ac7f-3faf65922fcd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -81.0770008338366,  -80.9714022446317,  -80.8658036554268,\n",
       "               -80.7602050662219,   -80.654606477017,  -80.5490078878121,\n",
       "               -80.4434092986072, -80.33781070940229, -80.23221212019739,\n",
       "              -80.12661353099249,\n",
       "              ...\n",
       "                88.9968950242055,  89.10249361341039,  89.20809220261533,\n",
       "               89.31369079182024,  89.41928938102512,  89.52488797023008,\n",
       "                 89.630486559435,  89.73608514863992,  89.84168373784476,\n",
       "               89.94728232704986],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=1080))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eebab117-cb89-4deb-b914-c8d02ef17608' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eebab117-cb89-4deb-b914-c8d02ef17608' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Conservative temperature</dd><dt><span>valid_range :</span></dt><dd>[-10. 500.]</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>coordinates :</span></dt><dd>geolon_t geolat_t</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output001/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output002/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output003/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output004/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output005/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output006/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output007/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output008/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output009/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output010/ocean/ocean_month.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output011/ocean/ocean_month.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'surface_temp' (yt_ocean: 1080, xt_ocean: 1440)>\n",
       "<Quantity(dask.array<mean_agg-aggregate, shape=(1080, 1440), dtype=float32, chunksize=(540, 720), chunktype=numpy.ndarray>, 'degree_Celsius')>\n",
       "Coordinates:\n",
       "  * xt_ocean  (xt_ocean) float64 -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
       "  * yt_ocean  (yt_ocean) float64 -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
       "Attributes:\n",
       "    long_name:      Conservative temperature\n",
       "    valid_range:    [-10. 500.]\n",
       "    cell_methods:   time: mean\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    coordinates:    geolon_t geolat_t\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST_time_mean = SST.cf.mean('time')\n",
    "SST_time_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `cf_xarray` wrapped function makes the code more legible and easier to write, i.e.\n",
    "```python\n",
    "SST.cf.mean('time')\n",
    "```\n",
    "compared to\n",
    "```python\n",
    "SST.mean(SST.cf['time'].name)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same way the `cf` accessor can be used in the plot and the CF names for latitude and longitude used as `x` and `y` arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "ax = plt.axes(projection=ccrs.Robinson())\n",
    "\n",
    "SST_time_mean.cf.plot(ax=ax,\n",
    "                      x='longitude', y='latitude', \n",
    "                      transform=ccrs.PlateCarree(),\n",
    "                      vmin=-2, vmax=30, extend='both',\n",
    "                      cmap=cm.cm.thermal)\n",
    "\n",
    "ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting this into practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above a model agnostic version of some code was demonstrated, but that doesn't utilise the full power of what it is capable of. The model agnostic code can now be easily turned into a function that accepts an xarray DataArray:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_global_temp_in_degrees_celcius(da):\n",
    "    # Take the time mean of da and plot a global temperature field in a Robinson projection\n",
    "    # \n",
    "    # Input DataArray (da) should be a 3D array of latitude, longitude and time.\n",
    "    \n",
    "    da = da.pint.quantify().pint.to('C')\n",
    "    da_time_mean = da.cf.mean('time')\n",
    "    \n",
    "    plt.figure(figsize=(12, 6))\n",
    "    ax = plt.axes(projection=ccrs.Robinson())\n",
    "\n",
    "    da_time_mean.cf.plot(ax=ax,\n",
    "                         x='longitude', y='latitude', \n",
    "                         transform=ccrs.PlateCarree(),\n",
    "                         vmin=-2, vmax=30, extend='both',\n",
    "                         cmap=cm.cm.thermal)\n",
    "\n",
    "    ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try it out with the SST data used above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_global_temp_in_degrees_celcius(SST)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so now try on the output from a different experiment and model (MOM6):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = 'OM4_025.JRA_RYF'\n",
    "variable = 'tos'\n",
    "SST_mom6 = cc.querying.getvar(experiment, variable, session, frequency='1 monthly', n=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".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",
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       "\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",
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       "\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",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "\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",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-dims {\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
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       "\n",
       ".xr-index-preview {\n",
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       "\n",
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       ".xr-var-dims,\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
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       ".xr-attrs dt:hover {\n",
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       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\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",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tos&#x27; (time: 144, yh: 1080, xh: 1440)&gt;\n",
       "dask.array&lt;concatenate, shape=(144, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * xh       (xh) float64 -299.7 -299.5 -299.2 -299.0 ... 59.53 59.78 60.03\n",
       "  * yh       (yh) float64 -80.39 -80.31 -80.23 -80.15 ... 89.73 89.84 89.95\n",
       "  * time     (time) object 1900-01-16 12:00:00 ... 1911-12-16 12:00:00\n",
       "Attributes:\n",
       "    units:          degC\n",
       "    long_name:      Sea Surface Temperature\n",
       "    cell_methods:   area:mean yh:mean xh:mean time: mean\n",
       "    cell_measures:  area: areacello\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_surface_temperature\n",
       "    ncfiles:        [&#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/outpu...\n",
       "    contact:        Andy Hogg\n",
       "    email:          Andy.Hogg@anu.edu.au\n",
       "    created:        2021-11-01\n",
       "    description:    0.25 degree OM4 (MOM6+SIS2) global model configuration un...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tos'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 144</li><li><span class='xr-has-index'>yh</span>: 1080</li><li><span class='xr-has-index'>xh</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e0759127-5711-4a9b-b710-d52b7e99e588' class='xr-array-in' type='checkbox' checked><label for='section-e0759127-5711-4a9b-b710-d52b7e99e588' 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>dask.array&lt;chunksize=(1, 540, 720), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 854.30 MiB </td>\n",
       "                        <td> 1.48 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (144, 1080, 1440) </td>\n",
       "                        <td> (1, 540, 720) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 576 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-ff3d814c-b15e-4ae5-ac47-298625614820' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff3d814c-b15e-4ae5-ac47-298625614820' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>xh</span></div><div class='xr-var-dims'>(xh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-299.7 -299.5 ... 59.78 60.03</div><input id='attrs-98b2cc60-9e99-4ea3-838c-ee78584b404f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98b2cc60-9e99-4ea3-838c-ee78584b404f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3931d44-3506-4aa3-ad9d-9de5b7f4c932' class='xr-var-data-in' type='checkbox'><label for='data-d3931d44-3506-4aa3-ad9d-9de5b7f4c932' 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>domain_decomposition :</span></dt><dd>[   1 1440    1 1440]</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>h point nominal longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-299.724244, -299.476198, -299.22815 , ...,   59.531631,   59.77967 ,\n",
       "         60.027712])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yh</span></div><div class='xr-var-dims'>(yh)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-80.39 -80.31 ... 89.84 89.95</div><input id='attrs-3c374a9e-c98a-4372-b083-77c8ede71d9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c374a9e-c98a-4372-b083-77c8ede71d9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d7e36a3-9b66-45de-b925-83feba535cc2' class='xr-var-data-in' type='checkbox'><label for='data-6d7e36a3-9b66-45de-b925-83feba535cc2' 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>domain_decomposition :</span></dt><dd>[   1 1080    1 1080]</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>h point nominal latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-80.389238, -80.308075, -80.226911, ...,  89.729781,  89.837868,\n",
       "        89.945956])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1900-01-16 12:00:00 ... 1911-12-...</div><input id='attrs-73fc3e24-4a9b-45d7-9ef2-b9c6cc160d65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73fc3e24-4a9b-45d7-9ef2-b9c6cc160d65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc0707c-2b9b-47c7-ba92-aefeefed5b54' class='xr-var-data-in' type='checkbox'><label for='data-bcc0707c-2b9b-47c7-ba92-aefeefed5b54' 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>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>calendar_type :</span></dt><dd>NOLEAP</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1900, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1900, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1901, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1902, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1903, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1904, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1905, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1906, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1907, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1908, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1909, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1910, 12, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 4, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 5, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 6, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 7, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 8, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 9, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1911, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae4325d5-7640-44b9-a253-7650cf86e78d' class='xr-section-summary-in' type='checkbox'  ><label for='section-ae4325d5-7640-44b9-a253-7650cf86e78d' class='xr-section-summary' >Indexes: <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-index-name'><div>xh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa1e918f-f209-4212-b8d7-dba969c605af' class='xr-index-data-in' type='checkbox'/><label for='index-aa1e918f-f209-4212-b8d7-dba969c605af' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-299.72424385142534, -299.47619795417893, -299.22815003129284,\n",
       "               -298.9801000979502, -298.73204816972844, -298.48399426259493,\n",
       "               -298.2359383929031, -297.98788057738807,  -297.7398208331622,\n",
       "              -297.49175917771095,\n",
       "              ...\n",
       "                 57.7954116364209,    58.0434365318752,   58.29146358221654,\n",
       "                58.53949277642749,   58.78752410305645,    59.0355575502213,\n",
       "               59.283593105613505,   59.53163075650182,   59.77967048973631,\n",
       "                60.02771229175239],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;xh&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yh</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-641a8204-d998-4c59-b7b4-bb659e43e5aa' class='xr-index-data-in' type='checkbox'/><label for='index-641a8204-d998-4c59-b7b4-bb659e43e5aa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-80.38923793857394, -80.30807479101273, -80.22691082925455,\n",
       "              -80.14574767486899, -80.06458691047965,  -79.9834300812582,\n",
       "              -79.90227869634805, -79.82113423022128, -79.73999812397257,\n",
       "              -79.65887178655316,\n",
       "              ...\n",
       "                88.9731671559671,  89.08125482376003,  89.18934249155296,\n",
       "               89.29743015934592,  89.40551782713882,  89.51360549493178,\n",
       "               89.62169316272471,  89.72978083051767,  89.83786849831056,\n",
       "                89.9459561661037],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;yh&#x27;, length=1080))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d96fe5bf-5151-4e71-9ba8-735ae27ed6b5' class='xr-index-data-in' type='checkbox'/><label for='index-d96fe5bf-5151-4e71-9ba8-735ae27ed6b5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1900-01-16 12:00:00, 1900-02-15 00:00:00, 1900-03-16 12:00:00,\n",
       "             1900-04-16 00:00:00, 1900-05-16 12:00:00, 1900-06-16 00:00:00,\n",
       "             1900-07-16 12:00:00, 1900-08-16 12:00:00, 1900-09-16 00:00:00,\n",
       "             1900-10-16 12:00:00,\n",
       "             ...\n",
       "             1911-03-16 12:00:00, 1911-04-16 00:00:00, 1911-05-16 12:00:00,\n",
       "             1911-06-16 00:00:00, 1911-07-16 12:00:00, 1911-08-16 12:00:00,\n",
       "             1911-09-16 00:00:00, 1911-10-16 12:00:00, 1911-11-16 00:00:00,\n",
       "             1911-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=144, calendar=&#x27;noleap&#x27;, freq=&#x27;None&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b923dd82-c70c-41b2-8136-a4f219bd614f' class='xr-section-summary-in' type='checkbox'  ><label for='section-b923dd82-c70c-41b2-8136-a4f219bd614f' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degC</dd><dt><span>long_name :</span></dt><dd>Sea Surface Temperature</dd><dt><span>cell_methods :</span></dt><dd>area:mean yh:mean xh:mean time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacello</dd><dt><span>time_avg_info :</span></dt><dd>average_T1,average_T2,average_DT</dd><dt><span>standard_name :</span></dt><dd>sea_surface_temperature</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output000/19000101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output001/19010101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output002/19020101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output003/19030101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output004/19040101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output005/19050101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output006/19060101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output007/19070101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output008/19080101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output009/19090101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output010/19100101.ocean_month.nc&#x27;, &#x27;/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/output011/19110101.ocean_month.nc&#x27;]</dd><dt><span>contact :</span></dt><dd>Andy Hogg</dd><dt><span>email :</span></dt><dd>Andy.Hogg@anu.edu.au</dd><dt><span>created :</span></dt><dd>2021-11-01</dd><dt><span>description :</span></dt><dd>0.25 degree OM4 (MOM6+SIS2) global model configuration under 1990-1991 repeat year forcing. Aiming for long spinup. Run still ongoing.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tos' (time: 144, yh: 1080, xh: 1440)>\n",
       "dask.array<concatenate, shape=(144, 1080, 1440), dtype=float32, chunksize=(1, 540, 720), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * xh       (xh) float64 -299.7 -299.5 -299.2 -299.0 ... 59.53 59.78 60.03\n",
       "  * yh       (yh) float64 -80.39 -80.31 -80.23 -80.15 ... 89.73 89.84 89.95\n",
       "  * time     (time) object 1900-01-16 12:00:00 ... 1911-12-16 12:00:00\n",
       "Attributes:\n",
       "    units:          degC\n",
       "    long_name:      Sea Surface Temperature\n",
       "    cell_methods:   area:mean yh:mean xh:mean time: mean\n",
       "    cell_measures:  area: areacello\n",
       "    time_avg_info:  average_T1,average_T2,average_DT\n",
       "    standard_name:  sea_surface_temperature\n",
       "    ncfiles:        ['/g/data/ik11/outputs/mom6-om4-025/OM4_025.JRA_RYF/outpu...\n",
       "    contact:        Andy Hogg\n",
       "    email:          Andy.Hogg@anu.edu.au\n",
       "    created:        2021-11-01\n",
       "    description:    0.25 degree OM4 (MOM6+SIS2) global model configuration un..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST_mom6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check to see it has correctly parsed the CF information. It is not necessary to print this out, but interesting, and note it has quite different index and coordinate names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes: * X: ['xh']\n",
       "           * Y: ['yh']\n",
       "           * T: ['time']\n",
       "             Z: n/a\n",
       "\n",
       "- CF Coordinates: * longitude: ['xh']\n",
       "                  * latitude: ['yh']\n",
       "                  * time: ['time']\n",
       "                    vertical: n/a\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   n/a\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SST_mom6.cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function from above which also worked on MOM5 data with very different coordinates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fr0S37bXXXmPjxo1kZmZis9nYtWsXqqpy1VVX0apVq/0885pnT6Iwo9+dSAxK5j8DmJbDWOti5PPieU/z2Wef8Yer/4pRX4lQNG669gpe/nQqwpOCUNRw8nCsdREi51Jj0WiGIZifRayIto7H/+qtg3tDCAWRkAoJqchQUK2rLn4Ih/8hd2rWJl/5rj8Cs6zyNxYW+4dlMfwNIYTwuBOTbvB6XNeV+PR2hiddCHfSEXEVi9gbSNgdKFQPiupCFS40xU35ghcP+3Y0R6xlqjlh+Prrr3P77bdTFQDhTgKbE+FMQFaX0DsvnaVLlxIKNR+mpOR0QklIBuJjtnw+H96uQ5B6EFlXjlFZRKd2+axZs8acQbUhkrJRnIkIhxs0J0JRCayefkj3/UDw+Xxs3bqV9u3bozpcEAo0fOhMQDgTEA4Pwukxj5UQ4ThRDaG5UbVkHGoaSUouHpmAhkaIELowCOCnRF8XDR3w9r0OgYIibISMOmy4qKlahvRVIFQ7qjsDZ5Uf6oqpqGrwBCqKA0WoOB0Z+ANl6IYfKQ1at/4jiekDMDCwY8eFDZsQ+KVBLfUYGNSISoLSLMXpkxXo0k/VwrEk9rmKmsVvNXtMxo8fz+WXX37YjnmErH53N4lzTOp7fVS8AuT2f5CbznXwj3/8A4A333yT9u3bM/SCPyNrK/BQTd++fRk9ejR//OMfycrKanZd9k6Dmwi/wOofgNiY2gPJZv6lxeSeMTObqzEqixDBesPttD9WU1H2jJSy8pfeNotfD0II9wnHpdROfu/Ak/7OvHruUWkxtIThbwAhRPvs7Ox7anz+i+u0RJfwZiA0+2FaVzMX/UgZEkVDURPjxKAq7FHrx+GiuLgYKSWZmZlx0xcvXswTTzzBx9/NRNg9CJcXnB4wDOZ/9Dzp6enk5uZy22238e9//xsAJacjSkIawbUzMQyDH374gfvvv5+ffvoJgJkzZ1JVVYWUkgEDBpCamoqt4wlIfzX6luUAdO7cmW3btpGdnU1aWhp5eXl8MmU2GAZG+TZz4+wubG36mXF4MZm2AP4V3x7W47Wv2DsPQS8qwKgoBNWGmtcLodqiiSKxKDYvqpaEqiSgKk5UbDiVZDwk45UJeISGhkAREJKSWqmzXWylWt+BKhy0EJ0IihAbN/6HUNl6lORWEKxD1leRkXw8Sc4MCkt/pra+CAApdTTVicOWhGGE8AXKAEmn/KtIThuMjwAKCioKHqHhlwa7RClB6lEIu6EJEpC1+GU1ulFPRAQJQzDmjlMIBAIMHz78kFkK94XsfveiE6B4/lN7nK/lcQ+g15dw93Ud+fe//01qaionnHACffr0ISMjgzVr1jBt2jSWLVvGbbfdxueff05+fj69e/emX79+9OnTh8TExD2uA8IxoOE4zj3HMO5eDDZ7zdhPmneLHzxSDyKrijGqi3F7EpfXlRVdJKVccVhWZvGbIioM3x14wGOcefU8ps2yhKHFEUIIoWia9vuMjIwHSmr8x+kJGYrwpBwy6+BuL+aR6UKLZhMLxY6iJqAKFw41+bALQTATMCZPnsyZZ54ZnTZy5Ei+/vprAAoKCujQoQNgxu21b9+eKVOm8PLLLzN+/HgKCgooLS3lz3/+M+MmL0TfMA8AJa0VCAUj4IOqXQB4vV6q/DpKSguUxHRib4gVi6aQ1Pl4jNoyZPkO8GYgQkF6dshj48aNVNXUIexOsHsQNhfC7jKzv52J0exfIbRfjRiMYOt4POgGoU2LUHO7oLi8zZagidTXE5obRXGCMIWjUBzYFC82xYNLJOMiEY9040DDJRQcisAe7k7vNwQ2RVISMlgZ+IGa9RPBCOFpMQSZmMapzpOxKQaqAttK5jBrzVgcNi+a6iDBmU12an9SErvjdiShCoWAFPgMiYbAQKKEBelWWYmPamw4UVAJUE9A1qITxC+rw4koBgIlzkJ3pMnudy875z+2z/MHg0HmzJnDvHnzWLJkCVu3bmXr1q2Ul5eTlpZGfn4+u3bt4qSTTuJ///sfBQUFAMydO3eviU6NMcMjQruJY4zUWGwg3pPQNHM9nqZZ4+ZK9r8e4/4KSSklsq4CWbETReAXgbrLQ8HAZ1ZG87GLEMJ9Qt/k2u/fOQhh+Kf5TLWEocXhRgjhSkpK+lO9Lp8MqC6XmtzCdEXGEFfqI+ZiHFsOpPnBm7vxA4pmJgFEXsM3fiFsKEJDEU4cShJO4WXrvPsPdhcBKCwsjFo8AP773/8yYsQIXnnlFWbMmMHQoUPj5v/nP//JLbfcQmpqKgDOrqO45PgM3p74A3869zRefWM8CQ6N7777jmHDhuHzNTRMUFt1R+o6sr4a9CB/+sNo3n//fWprzVJowpuFmtUWIRQCa36ioqKCadOm8e233zJ23OtR17NISAMpMXasMge2OcPHSkW4U1AydpPdKxQESlSUGKGq/S5VcziwtepDqLgALa9vw3Y3Pn8i982YOoNgJo0IxYGmetGUBNxKGh6ScUknLuw4hIJLETgUA5siUcLD14cUCoMh/nFfGhdeehPBuh30yr+E9i2GUVS+kHnrxnN8p8vZWryIDbtmoakuQrr5XZ476HUMBIaEoCEwGlXh9BuSChnAJ+qxSY1aUUutLMXAFIQqNgKyFokRjYn8Jcjt/yC6DLJz/mP7LRKbY9q0aVxwwQVkZWXRokULnE4nW7duxev10qpVK0455RT+9Kc/7fe49s5D2W2h7QjR80WJf78vQq9RIfl9Zjdj7qtYlIF6jMqd4KuUCW7X/1WXlz4hpazev42w+K0TFYbjD0IYXjufqbMtYWhxmBBCpGdkZNzpC4Ru9NkSPSIpG6Hampmx8Y27uWK0jeaJdpNoyAKNTDOFoK2hzAwKsV0lUo776yGJHQyFQvz888988cUXfPPNN6xfv57U1FRSU1NZvXo1AG+88QZXXXUVdXV1PPDAAyxbtgyfz8ftt9/O6NGj48YLBAL4fD7mz5/P8Ev+DHqA+ZO/5Mwzz2THjh3hw6Caos7uQkltxXkDO/DRRx8B8NBDD/HwWxOQNWWgKFSsW0RiYiK1tbVxWb67du0iIyMDMK0pgVVTUZKykGFrY8Mh19Dan9BQI3APAj3yHTRX3uVIsGTJEl599VUmT55MQUEBalYn1NTW8dnD0NBxRNEaXOFht7gQNoTQUBQHqnCjCSeacGIXHlwk4pB2PDhJVFQcikQVEpsicaoGAUMhaAhCup+v592ElDot0/pSUbuFnnkjaZs1mPpAHZtLFlLrL8fjzCDD2wmvKw1Dgi4FAd20HAYNolbDENIUjRj4CVEpKqmVpQBxcX1px92KgX5Izuu6ujpKSkpYsmQJn332GVu2bEEIwe23387vf//7Zpdp1f8RdBlgx/xHgP23IO6OyspK1q9fz3HHHcfdd9/NwIEDOeccs5ZnTk4OQ4cOpX379vj9fhRFwel00qtXL/r160dubm6ThxqzLqNZZxEjptYimFn4YSIPPbHvzf/EF0qP6/gSudY0nh7L3srz7EUINn6AbvzgLPUQRmURRtUuPIlJU2pLCy+XUu7Y46AWRw1CCPcJfZJrp4zfz/qeMZxx3Xymzi6zhKHFoUUIkZeYnv1hva+un5GQqYrEjPhSM3uM31GanyfmvaDRWI0EYnMdQA419fX1uFyuuGlOp5OMjAzKy83fk9frpa6ujj/84Q+MGTOGIUOG0KJFCz799FMyMjK46KKLeOGFF5BS8uCDD/Lwww/jcDgIKA6wOZBVxSjpeZzSqz1TZvwIeojzzzuXz2YsxCjewKmnnsrcuXNRFIWqWh/oQfNYKAotc7IpKSkhJSWFwp1FdO3SmR49enDzzTdzwglNa1ytWbOGzj37hgt4a+GSItIMepfS/P4UDeFJRfXmgD1cM7DR9/VLuZe/+uorbrzxRrbuLMHW+jiwueLa40bLpMTcvIGGh4mIMFQcKEJDoIatyzZswo1TeHHgJlkmkSRsuFRTEKpC4tQihadBEaAIiS9QRWH5SkChVXpfhLChS4FuNGyUIkAVpoQIGQp+XRAMf24g8BsSv5QEzDxpFBTKRDF+WUdQ1qIIG4kikw3z7qRj/+col9tRUPe74PXGjRv54osv+PLLL5k7dy6GYZCeno6UktzcXCoqKli3bh39+/dn7ty5zY6R1/8xDIw4cdii3/3R/++JwsJCtm/fzoQJE5g9ezbBYBApJW3atGHChAnk5OSwYsUK1qxZQ8eOHQHz91dcXMxrr71GcXExeXl5SCmpqalh8eLFzJs3D5vNRnkwC0dWH6TdScWCMXh6X2p+7eHz1ZAhs96iNAB9N32hw3GcjUSfUV2Cvmstg/p2Y9biVeZ5ZHcjHAkIRyKKPcEsvi6UpuWNYtsNRqaZo8bPd4BIaSCrSzEqduBye9bXlRWdIaVcc8ADWvwmiArDN/cv3CKWM/68gKlzLGFocYgQQuRnZ2f/q7Sm/jwjqYUiPKl7iB/cczxgxJrT5Kk9xq1zpLuOxBIIBHjyySex2Wzk5uby3HPPsXDhQgAuueQS3G43P/30E2eeeSYLFixg2rRpAHz++ec8+uijLFiwgOOOO4758+ezadMm2rRpA4CS1hphc6KXbDZrCBoG6AEuvPBC1q1bx+ItZSgpLRhzxxU8/fTTFBYWUl9fT0jXQbExeNAAvF4viqLw448/csEFF/DCCy80EbGxRIL1JQJZV2FOFGrYqmZ2GZHSAD2IrCnDqNiGmt8PxZVkzhr+Xn7JmMNQKMStt97Kf/7zH2xdft+kd2/E5W1ucMw5JhRANWMMhQZCjROGkexju/CQKDJIlykkqyoOxcChSrSwW1lTTCewqkgilx9DCnQpou8jPwUpQSIYP/1aRh//FhNmXwnAOSe8GV4OpDRnDoWtiSEJPmlQSS1Vopx6WYUhgzhEYlR8te//DD6q0bCxed69ez1m3377Lffeey9bt25l9OjRnH322aSkpPDyyy8zdepUVFVl8ODBDBw4kIEDB9KvX79omaJYOvZ/DgOdIPVN1rs7cVhRUcG4ceNYsGABkyZNok2bNrjdbgYMGMAZZ5yBruusWLGCs88+m/z8/L3uS2OklKxbt46bb76ZSZMmEQqFUFXT8p3U93pzHiIxh2bxcUMGwx1ngvFWwYhoBCDSYUdHr9xBaOsClMRs/vXAHbRv354NGzbw888/U1BQwPr166nVbXjtBpWVZvKw4slAOJMRNjtKcktQlXihGLFo76clcU/HQdZVYpRtxel0bvKV7xphCcSjF0sY7h6rjuERJiIIbQnJ5xUrKYpokYYSJwhjBN1uB1EabtTS+FUlNkydOpVVq1ZRVVVFVVUVHo+Hrl27MmjQIJxOJ3369KG4uJjWrVtTXl7O++83FBxevXo1LpeLli1b4nK5+Pe//01eXh73338/o0aNAiA/P58lS5ZwyimnUF5bhlRsKInpGOXbAbDb7eTm5vLhhx+CzcnZQ/qyYsUKCkrrwJYEqgehmy6xjIwMkpOTWbNmDbquc9JJJ+1RFAIEVk/H3nkIAhCeNHNi4+LPkWkJmcigD6NwDWrb4+Mstr8UPp+PCRMmMGbMGISjaWHsOCthk21VY3obq2acYVgUAgihmq5kkUyiTCRRUbGLhvjCWBQhEUhUxRR3Des3248KIGQI3pp+Hf36jePk/m8xY96V0dn8usBvCKbMu4Lj+73B7PlXx43ftn9D1q+KHQQEqad9/2comHc7BfNup2P/51g779a9HjMpJc888wwLFy7E7/dTV1fH3/72N7755hvuuusuHn30UfLz8/cpMcwmNYIivE2N2J3F8IUXXmDcuHH87W9/Y+zYsc1mG//ud7/b67p3x9q1axkyZAgAr7zySlQUArtN0knqe31MK0KIiLPYguRgWhmROiKlDUJzoVdu4857H0KG6jG/5cgxkyD91CZ1RjGKMap3YtQWQ20xAA5vS4TdgzRCSOlHSAMUpfki4PubnxdeVgiB8CQj3EkEfJX5or5+tTs1a7OvfNfplkA8SpEcWMWmoxzLYniEEEJkpmVmv1ZWXTtaSctHeNIQyCbB2nGWvkbxg7Fi8Ei4gPeXgoICBgwYwDnnnENGRgZer5eKigpWrVpFXV0dRUVFbNq0iR07duD1etm6dStPPvkkubm5tG/fnh49etC+fXsWLFjAvHnzqK6u5i9/+QvJycnU19fz1Vdf8cknn0QLUK9Zs4Zvl2zh7IEd+fbbb6mtD5implCQ/Pw8nnjiCebNm8fq1av5etJk0EOIpEwwdKQe4qar/sjGjRsJhUK0bduWrl27cu211+JwOHa7j5Gi1nEZmrsp8SOEhpSS4Nrp2NoMNN1mKL/Id2cYBnV1dcyfP59TTjkFADWzI2pGu90u01QgqiiKPRxfaIpCEwVVceIQiSSKjDgXciS2MPKqKQaqIqMWQ1WYf0FDIBEIJBIzhnBHrYNJc684ZMcgu9+9qMKGAw8KCgXzbt+n5S644AI++eQTZs2axfHHH88NN9xAWVkZr776KklJSfu9HZ37v8DqeTc3+1llZSWPPPIIS5cupb6+HrvdzuzZs6mtraVt27asX79+v9e3N95//33++Mc/omka7dq1w+l00qZNG9q3b09NTQ2hUIjk5GSSk5Np3bo1F110ETab+TDw5ptvct0tD2Dz5vHCg+Z31bVrVwYMGED6wFuivaql1DGk2SFHSgMZrDeFmN0DUhDcsZBBXXKYtXIHiicD1ZsLqt1sNYkRjd2V4Sz5WFd2RBxGE4ybuJwPDCl1pK8So2QLTpdzva+saKiUcttBDWrxq0EI4T6hd3Lt1Nf7HfAYI29YeFRaDC1heJgRQrjT09PvrQ/J23zubKeS3MoUhI0uWmbGZ2wNOd20xISFoJQG9cs+/2V2Yi8sWLCAhx9+mJkzZ/LXv/6V+++/HyEEPp+P7777jvHjxzNr1ix27dqFYZj7ff/999OlSxf69OnDF198wciRI7n33nuZN28eVVVV0azi9957D13XefXVV1m/fj2PPPII06ZNi1oaU1NTue++++jUqROjrvwrsqaUluleOnbsyPz58+nXrx9TfpwJgXCWss1pZnnbnCTJOioqKjjppJNYvXo1xcXFpKenU1xcvMf9jc/WbMYKGE3iMf9C25YgXEloaW2BIxPXGUsoFOLRRx/loYceMi19djdaTk8UT3K827g5YtzHsclJEbexqpiJJy6RTKrMwIsTpyJwKA2CUBUyKgI1peG9QEZjB4WQSCmiMYTVAY33f9j/TNq9kdXvbrMGo/CyYd6de52/rq6Oli1bUl5eTigUYvHixYwcOZKVK1eSlpa2z+t99tlnWbx4Ma1btyYvL4/hw4fj8Xh49913ufrqq/F6vQCUl5fTq1cvTj31VK6++moCAbP4eEZGBnl5eSQnJx/Qfu8LgUCANWvWUFlZyc8//8xDDz3Ev/71LxwOBxUVFVRUVDB79mw2bNhAXl4eCQkJTJs2jbq6OgYPHkzHjh2RUrJo0SI2bdrE8OHDue+++zj1qrcJyXoMGcSQfnTpj7qgpTRAqPiXfdpke9xd/oBic4Fio+H31tBDW8pgWCQC6A3JMRFXMzQvDhtP20OMtjm7jqwpxSjdhCch4buaksILpJRV+32ALX5VmMIwqXbqawchDG9cxNS5ljC02EeEEGpiYuI1UrE9XedMS1RT26KoLiJPurHB1IrqMcvDxNygIxe2I9Hb9UBZtGgRDz74IAsWLOCee+7hqquuwuVy8fPPP/Pss88yceJEDMMgLy+Pk08+mdGjR0ezJO+8804++eQT6uvrycjIYOnSpYwcOZKXX345epN5/fXXefzxx3nttddo3bo1//nPfxg5ciRr1qyhR48etGnThh49evDZN1Np1yKNUaNG0bZtW1544QV69+7NZ/PXE9qwgIkTJzJq1CiUnI4ITzKhdXP4+uuvzRqJNheE/CAUHDaVgOKmdPt6UlJSdrvfjm4jwpmaAZor0xGt/4iCNHSCm+aAENjbDjni4r6srCxOwGi5fVFTWhOXPBAj/mTUrRafdCJQzJaHihtbOAtZxbQaJYg0smQyHlVE4wkjok8IiQJmXGFYBCrhP1UxPxNCRtpM88KkGw/7MYmIQ5tw7jW+8LPPPuOiiy6ipKQEr9fLySefzMUXX8z111+/z+szDAO73c5LL73Erl27KCgoYMqUKbzzzjsMGzYMMBOCvv/+e3bs2MGiRYsoKChgyJAhTJw4cZ8KVR8ppJQsXbqUsrIyampqqK2tpaamht///vfk5uZG5ysqKuL999/niSee4IknnuC+lzejyyA6AXQjQAgfIaMGKYPhrGaF+qUfAHDqqadSWVlJWVkZxcXFBINBgoYNobkRmgOhuRA2J6q3JbiTQcgYoRmME4jQqFd33M7sXiDGZlpHHvJql0zE2ao7Rvl2vB7HfZVlpf+SMmwStfjNIYRwn9ArqXbq2IMQhn+1hKHFPqJp2kCnJ2FibUCm21odh+pMibH8NVxHzKxO88/EfBpWhDPaUqwxUkqWL1/O6tWrOeGEE+IuxkcSXddp3bo1wWCQn376idTUVBYvXsyLL77IrFmzKCsrIzU1lfPPP5+nnnoKj8fTZIxgMIiiKCQmJuLz+Vi7di0lJSWcd955rFu3Do/HQ0lJCTfffDOLFi3ikUceYf78+fz3v/9F13WKi4sJBIJce+2fePnll1FVlaFDh9KyZUvefvvtaKyU2Sc2vM5w/cAJEybw0ksvMXX5doQrCWF3IkNBjF0FGFU7ufXWW2nXrh033XRTk+129boAQ69F6vXxxXmjGbsNpV38q74FPYiS3BotuzNCtR0xi2FZWRk//vgjJSUlvPjiiyxZsgR7+1PNVneRrOOYsjOCBstMbDKJKuxmX2Ps2IQz2lkkSSbjxYlbEThVI85lLMKxhRERKAjHFcZYCGNFoYIZU/jidzfs8/4ZhkFhYSFZWVlo2r6HS2f1uxtNmEWwbTj3aDm86qqr+Oqrr3jxxRcZPHgw7733Hp999hkvvfQSAwbsW5mLadOmcf7557N58+ZoKaTOnTszcuRI3nrrLbxeL5s3b6Zz5848+OCD2O12vvrqK958802EEFEr+2+Rn3/+mbPPPpt+/foxtyAZzZ1N8fIxnDBoAAvXB9H9lZzUtyU/LS0hWL0Vo74MgOXLl9OtWzfAjIstLi6O+9u8eTMPPvZvpJRoSa1RPBkonjRQNVMcRlzO4f/vyXIYazXfbQJfGP+Kb6mtrcWb3wNqSnQRrDs9FApNORzHzuLwIoRwn9grqXbqq8cd8Bi//+sips4rt4Shxe4RQmRkZma+XlJRM8oI1AFgzzsBNTnP/BwlGpsVKe8RwZBBFGGLFt6VUkbro82dO5f58+cTCoWYN28ebrebvLw8pk2bxumnn86kSZP48ssvadWqFRkZGbRs2fKQ71t5eTkOhwO3u6HYdkVFBf369WP9+vUkJSXRu3dvhgwZwsKFC6MdSgCeeuophg8fTpcuXaKxSbEUFxeTmpqKqqqcfvrppKam8vbbb0fnlVLyj3/8gxUrViClZPbs2Tz88MNcddVVzJs3jxtvvJGlS5eaZWQ6dwbMG0vvc66NriO4dmaT9UbiBYViR2KgKE70+gpG98pi0KBBPPvss4wcOZJTTjmFUaNGRd197t6XoAfLTGEIDWVcFHuTm4leWURoa0PZkvvvv5+HH34Ye6fBBNb8dCBfxT4TiR1TvC2YPvEDbr75Zm6++Wb+8uzn4f12gFBQhANNScCmeNAICz+homHDhpMQQRRUPNKDAzt2VGxC4BSmy9geIwojglBgXlc0Jd5t3JwgFEIS1BUChsKr3+/eEldTU8PVV1/N9OnTcTgclJaWkpCQQCgU4rTTTmP48OFNLFe7I1I7cG8JKKWlpbzwwgssXbqUn376iSuuuILx48dTUlICmDUhe/bsudvlpZQ4nU5effVVrrzyyuj0++67j8ceM2sX9uzZk6VLlwLEZQRH3u+P6P014vP5+Pjjjxk7diyLFy/G5/Nx4YUX0r59e1q1akXLli1ZvXo1mzZtol+/fixcuJDbb799r9cxKSVz5szh+++/Z/r06Uyd9gOKMwVFc3Nc12zKy8spLy+nuLwWNAfC7kZJzERJzCQuI2ovRbijwjH2ARAFf8GPoPtxJiTtqK/YNdCKP/xtERWG/+l7wGP8/pbFljC0aB4hhOpNy/qnoet31rtauLAnoDoSEY5EhGKLE4SErTCKsKGgUrrgueg4hmHw9ddf89FHH/H1118jpaRHjx4MGDCAfv36YbPZ6N27N+3atSMUCvHvf/+bDh06cM4555CcnEyrVq3YsWMHqamptG7dmuzsbLKzs9m8eTP9+vVj9OjRdOrUCUVRdrsvhmGwePFifvzxR1auXMnMmTM5++yzefnll0lJSWHGjBncdNNNHHfccZx22mkUFRXRqVMnOnXqFM3KbNu2LRs3bmwytsvloq6ubo/HsqCggJtvvplVq1bxxhtvMHDgQN58800KCgo47rjjuPnmm5k7dy7t27fH5/Px/PPPM27cuGgLvJycHEKhED169GBpwRZuuvpSOnToQLdu3cjOziYQCDDgrKsxqnYi3MkoidmgqqZQUp0I4Yi6ezdt2sTEiRP53//+R2FhIfPnz4/etB3dRpjCMDb2Lto5JhIrKglu+AHFncZrT93Dc889x5IlS7B1PoXAqqn7eZbtH6+99hrXXXddk+m2dkNRXBHXpIpQNFTFhSKc2JVEHCIxmpyhYcMmNRzYCRLChkaSsOFRZbSFXWycoKY0ipuNmd7YbRz5P5hJpM9809QyO3/+/KiLtbKyksrKSoYPH85jjz1GIBAgJSWFxMRECgsL+ec//8m4cePo1KkTzz//PEOGDNljhvm+1g2MZc6cOUydOpXq6moqKiqYMWMGH3zwAT169ABg586dbNmyhfz8fDIyMqK/h1deeYVHH32USZMmRa1gALNmzWL48OH07duXwYMHR4Xi0Ux1dTUzZ84kPz+fTp06HdKxA4EA8+bNo6KigpSUFFJSUqKJazt37mTZsmX85fZ/IoN12FoPRHF6zUQYGSKSCx/b07x+2eesXr2abv2HIFzJoNn57JXHOfevD5qlsaSEUNAsXVW5HW+C+8XKksLbLffybwNLGO4eSxgeJEKInpmZmR+UypQutuyeiEhHgHAZh9gMTiFsqMKBJpwAFM83S2ps3LiRb7/9ltdffx0w3VdnnXXWAVn+DMNgxYoV0ZtUSUkJLVu2ZPr06UyZMoWysjKGDRvGSy+9hBCCjz76iMmTJzN79mw8Hg81NTVkZGQwdOhQevTowbhx4+jVqxd33nkn5557LmvWNFRt6NixI4mJiZSWlnL99dczbNgwevfuTWZmJunp6Zx77rlccMEFBAIBcnJyaNWqFaqqIqWksLCQ1atXU1VVRY8ePWjb1mwnN3ToUNasWUNtbS39+vVj2rRp9O3bl6FDh/L222+TmJjIv/71L84//3zGjBkTdfW+/fbbnH766WRmZlJdXU1S646kKAGcTicVFRWEQiGysrJITU1l6brNCIcH6atCGjq2ticSWGV2IXH2GI2qeqld/G50P2tqamjXrh0///wzPc69y8ysNAIxZS60OIEYscQRDFK/6ku0tA4Y/kqMGrNbitr+eELrZu33d7s/lJWV8dxzz/Hmm2+ybds2UO3Y8gahuBIaYgoVLRzKYIu2tVNQMdBRUHHgxiEduHCioUTb3LliLISRRJJYlzE0dRub84aFYfhzQ5p2xWe/uYm/Dn856kauqKjg9ttv57vvvuOSSy5h+PDhZGZmYhgGPXv2bLYsTGVlJbfddhvjx4+nR48eLFtegDf1OJ545BJatWpFfn4+nTt3RghBbv8HkVLfb2G4Nzp37syaNWtITEwkFArRsWNHhgwZwogRI5gxYwZfffUV5557Lvfee29UtN5///08+uijDBo0iPfee4+WLVs2a1W3OHS8/fbb3HbbbSQkJJCWlsbixYsBUFWVnJwcWrRowdzFa5BSR3Emo1duAcxSWV27dqW2tha32019fX30GtaxY0cWr9qAImTQqK8+yTCMOb/gLlrsA0II94k9k2qnvnwQwvDWxUydbwlDizBCCFdGRsa//H7/dXUp3ey2pFbRem6Ng50bLIY2NMWNXrmN+l2LCNUV4XaqeDweRowYwTnnnMPo0aP3aNE7WHbt2sWLL77I008/jcPh4KyzzmLUqFEMGjSI+vp6nE7nbl1x27Ztw+l08sMPP9CpUye6dOmCoij88MMPfPLJJ3z//fds3rwZVVVxu934/X66dOnCkCFDKCsro7S0lM2bN7Nq1Sp0XcflcpGenk5tbS2hUIhWrVqxcOFCJk+eTP/+/fm///s/nnzySbxeL16vFyklOTk5DBs2jCeeMDtWJCcnU1lZScuWLRk4cCAff/wx77zzDn//+9+ZPXs2+fn51NXVceGFF5Kens57M9dFW2OFdhYgq4q44YYbuP/++2kz/MZoN4+aRW/j9/s555xzmD59Oscffzzvvvuu2Yu2xznIiDgkXhgqqieauasIDaOugsDORchADQ7hR+SeSu26Qx9jqOs6mzdvZvXq1dGyPh06dGDe5nrU5Nyw62z32cWakoBLJCOEioKKig0NGw7pwIMLDQWbEHgUcKhNs43VGCEYsRCqzVgH1YiVUBDtbCIx4wtfmHQjX3/9Nddffz1nnnlm9LvfH2pra/F4PBQUFDD45Nvw+0sIBMsJ1BchhcCR0glXancc7hZsn/cgYAr/xx57jEGDBtGhQwfatm3bpGSRruvRhJF3332XN954gzZt2lBVVcWWLVt48803sdlsrF27lj/96U/cf//9rFy5kunTp/PVV19FH7z8fj8JCQmMGTOGCy+8kMrKSrp3726Kd+C6667j1VdfPeDzwGLfkFJSUFDArl276NevHw6HA7/fz/bt2yksLCQhIQHDMJgwYQKvvfYa27ebdVJXrVoVDVeJUFFRwerVq1m8eDFff/01CxYs8BmG8XFRUdFNVg/mXy8RYThtTJ8DHuP3f1vCFEsYWgDY7fZTUlNTx991110ZH3/8sVPXdebMmYOn96XRbE4ZUzJBUxJQhQMFFakHKV/yMjZ3CzI7XMHKH+4mISHhsIrB5jAMg1Ao1Gx3hoPhiSee4J577gHg7LPPRtd1VFWlsrKSs88+m1tuuYWRI0dywQUXkJWVxe23305KSgpJSUmcfvrp5OfnU1NTQyAQ4O9//ztnnnkmb731FgC5ubmcd955PPvss1GX7tlnn81ll13GI488Qq9evQgGg3zw2UTUlj0JFpgxfO3bt2dDUSVKRj5Cs5kdDsq3Y5TvAAQiIQNZW4I9/wRsia2pWfQ2YN48xowZw1NPPUVubi5Dhgzhuuuuo23btji7n4mUIVMcRi2FdjRbatQ6bBhB/Ftnotfs5J47buLiiy+me/fuh+Q4FxcX89xzz/H1119TV1fH1q1bycjIYHt5ACUhCzW5JcLmjM4faWNnxrWGxWDYcm2gRxNMbJj9ju3hWENVKlFh6BIKLlXiCGcYR1zIEaugpkgcqo5dbchAjlgHzW2I3wfdENHP7vvv5fzlL39hzpw5jB07Npqteyjo3P8FdKnjr9tKZdl8asuWIDQnLzzzINdddx01NTXRMjDt27dn586djB8/nnPPPZelS5fyzjvv8P7779OiRQsuvfRSbr31VpKTk8nIyKBjx46kpqZyySWXMHXqVCZOnMh3333XxNq/bds2Pv30Uz7++GN+/vln7rjjDp56yvQY1NXV8fTTT/PAAw+Qnp7O2LFjo9n7Fr8Oli9fjq7r9OrVa6/zSil58803g/fee29JdXX1X2pqar48AptosZ8IIdwn9kiqnfbSQQjD25YwZYElDI9phBDu7Ozsl9u0aXPWBx98kNy6dWsCgQAul4uHHnqIs846i6ysLPKH3wpSR1Ec2JQkVGGPlvYQqLz9+Mmcee7VtO7yt30usvtbYfz48fz973+nZcuWJCUl4fV6SUlJYdu2bcyYMYNevXrxzjvvRJ+6t27dyqpVq9iwYQPjxo3D4/HQokULwBR9mqZx/vnnc9ttt3HxxRfTr1/T0gI7duygTZs2JCQkcPXVV/PC18sBHUP3g7+a0NalqK17IWymFUhWFaMXFSA8qcj6arR2J2LsXMsT993KnXc2zVANBoNMmTKFiRMnMnPmTKZMmYLX60VV1Wjpmkhwuqol4dAyzV60BZ8Dgoodq6PZqIeCn376iYsuuohRo0bx5vfrcNlbY3OkoAudgFGNEa4TFwmaVxV31GWsoJo9jXFGrYO2sAg0MPBTiw0nGuF5UfBKD4lCQxHgUBqEYdSNDFF3sV01cGo6mhKfidzY+RsRhIYU7KzRKHR8Tm5uLmPGjIlLcDoUdO7/AobZnRgDs3aer3o9nbLXkJGRwSeffMLMmTO56KKLeP755zn//PO54IILOPnkk3nooYe4+uqrueyyy+jSpQtgWoi2bNlChw4d9toppzkKCwtJSkqK28/vv/+eO++8k9NPP50lS5bwzTffHLL9t/hl2LVrF5dddlnFsmXLphcWFl4ppaz8pbfJogFTGHprp71wEMLwjiVMWVBhCcNjFYfDcUJaWtqH9957b9YNN9xgi8Q5derUibVr15Kenk5WVhbr169HtjwRxZNJotYaFRtrpv2dhQsXMn/+fMaPH891113Hfffdx4oVK36xcjNHmpqaGsrLy2nVqtV+Lbd9+3ZKSkr2+qQ+fvx4rrnzcaZ8PBbDMDjtD1djVO0CI4SS2hIlKSs6r/RVY1QWmclBrmSM4g38bsgAxo8fT3Z2tjmPlKxatYqdO3cy8uYXqV/+OVJKLr74Yr744gtat27N2rVrcXQdbg4adiM7bNk4RTKGDFG86FneenMcV1xxaLp3LF26lNdff50PPviASk9X7MkdECgkqNkkkkqFLMIvqwgalVF3eYLMxukLYJcqiYmdCYggeqgOv2qQQhK1FUvx1ReiaYmkZ5wMQIWoIUQQDRuqVNDQcGEnSVGjWciRzOOIMIzEGdoUA4dqoMSUromIwMYIIGgI6tutZd26dUycOHGvGbgXn/Q69brC5zOv2uvx+uqrrxg9ejSnn346C5fZEJ4MnJ48VM1t1tOrKWLHmpf45JNPKCws5J133qG6utq0KIfjYG+55RazMPhh5owzziAnJ4cff/yRa6+9ljvuuOOwr9Pi8COl5N1339Xvuuuu4pqamiuqqqq++6W3ycIkKgyfPwhheKclDI9JhBCO5MwuP1WWbuinCp1gMD7h7C9/+QvDhg3jjDPO4Oeff2b4qPOwpXfjk5fvYfLkyUyfPp3169fTo0ePaOYsmJ0MNm/efEAWB4t4Nm/eTJsufejWLpcV6zaBoiDsbkRiOsLlBSKuzHDJiXBZGb2qCL1oNY7cQfg2TotLarjnnnt455132FFcieLNQcvuRt2ST3nzzTe55557KHe2NMteSAMUDUVxRUWhKmzUV6yhYvPX1FQWHdB37Pf7Wbp0KXPmzGHevHksXbqUkpISioKp2NI64XK1ilqhE0UGHulhO+sIyXo8SgaZsgVJ/k3MXP0i/Qf0pLy8nNzcXJYsrKO0YiFJSS7sdjudO3fmxBNP5PHHH6d3lwdxuE3h7iNAiBB27NhQ0VDwqgr2sFWwOWthJK7QphrYw4Iw0ulkd8xc/xO7xHJmzZq11/ZyBQUFnHHKXZTVbMTrbk2n7JP434LdW9zXr1/PiSeeSFFRER5Xa3RVQ2MX2dnZbNxSjB6sxenQyM7OZujQoXz77bf07t2biRMnMnfuXNq0aRN9UDjcDB8+nMmTJ/OPf/yDBx544JCHeFj8smzfvp12XYficjlWVxSt6CelrP2lt+lYRwjhPrG7t3b6c70PeIwRf1/KlIW7F4ZCiFbA20A2ZpHYsVLK54UQDwLXApE2W/+QUv7vgDfkEGMJwz2gqmofp9s7o66uJpFo6yXTtahpGtu3byevy0D0mp2ARDiTcKR0Iil1AEXLX+DRRx/ld7/7Hb1798Zut+P3+9m6dStOpzPa79fiwJFSMnbsWK6/4SYi389DDz3EP/7xD9zdT20oYCsNZF0lRnkhQwf2JhAIUFpayurVq/noo4+44IILmox90kknMXjwYG6//XYyWrRG9ebSv0M6UkoWltgRzgSzxI3QQNiwaaloihuH8OIQbkK6j+KVr+JsOZiqgn3vdhIMBnnwwQd57rnnqAuB4k7B4e0AdjdaQi4uNZVs8vHiJEkDW0zruUR7EF0KdEMgBPy85mPWFX4PmBbViooK/H4/11xzDT6fj7q6Ojp06ADApZdeyvbt2/n22285+cT3CIUdr46wU1kR4FLMziaRdUYEYeNC1pHkE6dqRIVh4/hCKWFV4WK+W/4ZS1cuoF273fdsBrN0UOeOfchI6U+Cpy2V1asoLPmBsrKyPXapAfjggw+4/k/30rnzvVTWbuS88ySjR4/m5ZdfZv78+cyaNYvExEQCgQB+v/9X1WnE4uhBSklSmzPw7VqAYlSf7K+v++GX3qZjmagw/HfvAx5jxF17FYY5QI6UcqEQIhFYAJwN/AGokVI+fcArP4z8tiunHiaEELasrKzHVFfaHUZCG5HS7jikESJQsZ66rVP55JNPqKmp4drrb0JNzcPR6gxUmxenmoZDJOIMmokR1dXVfPzxx2Zm2ys/sKv0RwB+/PFHBg8e/Evu4m+e+fPn8+CDD/L1d9NAKMyaNYtVq1bx4Ycf0r17dz5/5hnOue3/AJBVu9B3beTOO25H0zRWrFiBz+dj7ty59O/fv9nx//vf/3LBBRcwY8YM1q1cSqeBw5m/pQ4lpTXCqTd0PBEKNi0Vu5KIW0nDgRsNGyHVidvTBluwaWmVPXH//fczb9489LxTSXd3JlFkkiSTcKBhIGnjBLdNx6bUh2P3GgpHA2hSoCsCKSHLm8m6QnPcoqKiZuMnpZQsW7aMoqIi/H4/Zw1+i1TVgU0R1Os26gyJUxG4FIkQBpqIL1StRLehIaZQYq4/8sgZEYXREjUSNhSv5LvlH/PT7B/2KgrB7B5yyrDjqSu5kNq6zWwt/IqTTz55n47peeedx0033UR99WrWrR/DmDF2XnjhBXJzc1m9ejXLly9n0KBB2O12y1JncdjQdR1n3Xyq68tQbO4ZSdkdf64qWnealLL+l942i8ODlLIQKAz/v1oIsQr41cePWRbDRgghOtgSsleoiW1s7pyB8T1jgdriBQSKFiM0J2pyHra0LqiqM9o6zCWSsUsXeuVG6ms2oggbwUAZNTVrqffv4ssvv2TUqFHN1mKz2D3V1dW89NJL3Pvwv8hJdbNjVxnCnoCsK2HGjBmkpKRw77338vWMhZx92kA+++wz1OyOKElZGHUV6EXrzexhVQMlXFbIV8G9d/+dRx55BCEEPp+PyZMnc+aZZ6IoSrTA+Lr6TIQrCTBrU0brFyoObFoaCUoWTuFFQSFRJkZF3PJVD+NpfRrFq17fp33cuHEjxx9/vNnR5opP8ZCEQ9pxYKOdC9y2EIIYwQVRd23EegdErYYAG4rmMXn5m9hUN2+9/RqdOnWidevWLFhg9pCeONEsnZMs+tIn7zQcmsChGdSHVEKGoC6oUR7Q0MLxgnEdTmiILYy4kiNxh5oi0URDPCLhbdWNENNW/Y8VOxbwv8kT9+kBafHixVx44YWcf/75fPLOJjYWTuCNN//DH//4x33+Hb355ps888wzrFixAoBhx73KX+/Pwul08rvf/e6IVwWwOPaQUnLbbbfx3HPPAaAltUEGawxZX9ZH10NLf9mtO/YQQrhP7Oatnf5s7wMeY8TdS5myaN9iDIUQ+cAPQHfgNuBKoAqYD9z+a4pTtIRhDGlpaddU1mvjElsPR/VkAiCjhapVpNQx0Gkjf2bRokXR5RI6nocjMQ+vyIZALXUlC5GhOtD9+EPLqKurY/DgwTzwwAP7bOWwMAmFQmzbto123Y9HaHbU9PagCvTqYvTCZUC4pdiKNSjeLIy6MvDXAKBktkdNyyOw8nvsnYcSWD0de9fTotY+vXQzxq4CCgsLyc7OZteuXWRlmUkq3377LSeccALdunVjwoQJHH/ZQ0CDpdCmpeFU03GLZBJkEom4saGgCTOQRAGWbXkbv6yjetfsPe5jIBBg3LhxPPLII9x3333884119FX7kOUMRt2yACHDFC+RZI6mdQQb6gYamO7aQMjH2sL51AUqKK3ZRXntLqrryxg4qB/6zhTaZ3YnIzELm2pa/wwJdSEVX1DFr6sYElyaji4FvpAajSls7D6ObIemmO7jSFZyxI3sD9azdNtcFmz6gQRXNgtWTSU9PX2Px6W+vp7HHnuMV155hfzkcwjq9awv/I5lq8z6lAfC2rVrKSgoYOTIkQe0vIXFoWD16tX0PGEUwaptKJoDb1LasxW7NtwhrRvyESMqDJ/eewmi3THiH8uYsqjib8ClMZPHSinHNlpXAjADeExK+ZkQIgsowXzOfwTT3Xz1AW/IIcYShphfWosWLd4v8yePcrc6DU11YaBH6xBGBGEE3V9BqGYHwYoNqKqbnNYXoYVCVOz6GSO0iEsuuYSMjAwcDgdXXHEFGRkZcf1PLfZOMBjktdde46Zb/g4ClIRM1BY9EcKIFpaW0kAvXAmqDaN0U3RZtf2JyPoqlIRsZM0upK8KxZmIcCYiDR0ZqEEkZiIk6EVrSNX8XHXVVVx66aUsW7aMSy+9FDQn6AGUpJaoOZ1R1EjBYxWHLQunkoxHpOGSLtJFAhrCFEsx++AL1jJrxb1887+POe2005rdz0g3j9zcXB5//HHOu34y/eyZJNhCUWEGMTUDY7qMqJHXcBKIEhaQ0dZzELbixdQSJL6eYGy3EjALTevhjiRSmtbHiCUyZCgYkXkMETMG2CIJKWEhqIVfpZTMLPieORum0jqtE+M+ep7Bgwfvk6Vv2LBheL1efFtPItnp5fM5t7Jg0exDVgvSwuJgkFLy4osvsmXLFv72t7/td4WJ+vp6brzxRt544w1QbLi9OaV1FVs6/JosR0czUWH41EEKw8V7thgKs/PFRGCSlPLZZj7PByZKKX81F7ZjXhg6nO6+Uk2a78zsJ9xpPZHoUVEYsRbqmJnIUuoIEe4KIZw4RSL2+iDF2ydiUzdz1VVXcdNNN9G6detfcpd+8/z4449cc801bCoKomR1QrgSzWb20iBqtQsjMQisMGu+Kal5KCktCa3/GQA1sxMdUgUXXXQR8+fPZ+nSpaiqSlpaGomJify43UBR7chgPUb5NvTqXRCqB0Nn7Nix3Pivd0BzoGhmVrEQNlTVg0vNIklkkygTcWHDIQSaEGFBFr8v64qmU1S1CFfb30VbIMYSDAZxOp34fD7anPg43UUbMpxBDCCgmzKzsWUwtqtIbMu5iLUwIhxjXbjQUEtQiOanAdHYQEM27EjIENTrCs9/eyM3Dn85KjylFCjhJJPo+sNCUw8vP3v9NJZvn8+8ZT/v1++ipKSEdu3aMazrU9hUFUXAtGX/ok1mP2atfW+fx7GwOFQUFxczZ84cEhISGDp0KABOdy5pKWbx/R9++AGn07nnQTB/81988QUXXX49qiMZxeElWLkJzZmOEajCoenDaqpKDm8zdQtTGHb11k7/10EIw/v2LAyF+QQ8HiiTUt4aMz0nHH+IEOJvwEAp5UUHvCGHmGM2+UQIIbKzs+9CS/u/9DbnI5xmqQxdAujoMhAVh0q41Z2mOHGIRDRsJMgkvIaL1dvu4G+3moWRD6Qw7/bt23nwwQcZMGAA1157rbkN4W4hxxp+v59//vOfPP3vl9Fy+6K1yUXKYExPYiUq1mOxd/u9+TkK0jBQvNkongz0im1scHfngQceiJtf13WuvvpqQjM/Q03NQ3EloWZ3RsvuigwFMOoruPGZDwis+wFnj9Fmv+twqzxNScQj0vBIz15FIUCKJ5+tO75k/dd3EAwGUVU1Lp7twQcf5He/+x2tBj1IC5FPpjtAyFAI6CK8zw1jmUkfDVa/xqIw1mLXeFMiyzQWhJF1KJGSPoopDo0Yoaio5me3jBiDgkALWyZtmo5N2U1JGsMUh7X+Kjrn9Nnvh6U5c+bQoUMHVKXhd9C15TDW7JixX+NYWBwKPGm98VWuwe5uQShQzhmnn8Szzz5LQmofKuo2c2J+PrfeeiuvvPLKXsfKaHsmdaVLyMk5i6yUQahCwd8qSLWopta/g4qNn0/JaNF7YknhkrNlcxc8i0OLsfdZdsve7WonApcBy4QQi8PT/gFcLIToHR5hE/Dng9iKQ84xKQyFEGnZ2dmf+2Wnk1p2+j26IpHohAiioFAv61CxoWOgCWecldCmq6j11QTxsWrnJAL+RL6d0JIHHtg/UThnzhyOP/746Pvc3FzatbmG4qJvqAvs4u677zbLrhziLhC/ViorKxk1ahSzlm7B2en3oNqb9Jw2LYZqg6qRRrTjSPS9omBrfTzSX02opIC6gmlN1qWqKg888ABer5dPP/2UK/4wimcnLDI7hWh21MRsZLAeZ/czUVRPtKewKtwkKS1w4saFHYcQKOGNMSDuIqGGxVaaJ4/MtBPIzskFKRGKxsSvPmfkyJFMnTqVt956Cz3jErK11nRwaoQMSUAX4Z4lptUvaiEkxn0cE1eoCuIEYuTwRKx3jQVhYwGrEF9ORoQzXCLOhICuRGsX1obUqPvaoRrhAtYQ0EWcwIxsS4onne3lW/bjTDA56aSTCIVCLN/5AT3zz8Ouqmiqg8q6nXz99decccYZ5nE3DBYtWsTSpUupqqoiNTWViy66CJvNtt/rtLDYHSF/Kcntz0dJyCSk+5i2YC0DBw6krKKGtI6XMWNuJX06b2p2WZ/Px4svvkiHDh049dRTqd01jw5trseb0L6hFBQqyTgJ2tOp6dienYUTz0xO9pULITpLKXcc2b09thCH0WsqpfyJps/pAL+amoXNccy5kh1252C7LfOHFrlnC7vXbMvmFwECmBUDqo2dQHz/WBU7DuHGRSJr5t8SHatHjx60bt2aRbMr+OzrZxg4cOAe111fX89XX33FlVfdRV3tRgD+7//+j4SEBJYuXcq74z9keI9r8bpS+GH1R9SJQrp3705FRQUDBgzgsssuY9CgQUddBmUgEGDIkCEsXF+Do+UgJH4MGQIZjvOMupEj7OYhOlJCBhW9dD2yvppQ2cbdrresrIy2bdtSl9UXxZ6ElH5kKIC+cyVGZSG2jqei2FwgbAihYNcyyFI745UJuNDQdhMmp4TjDdVwrJ0hISgFtYZBWc1aCta/RO9enSgoKOCjjz7ib/9YSzstAY9NJ6ALgoYSTfBQRaQeoBEViBFhpkViCyNJIDH9iaFBECqiqfgDU/g1tkgaMv69iFnOkIKgYQpWmyqj70OGwJCEaxvGr2N98VpmrJnEBQNu5dlvbtrtd9EcxcXF/PGPf8TtdpNZPQKBTkHRfOat/4pzLzyTiooKZs+eTXW5n/SkDjg1LyXVGwjqdZzb/z7emrF/67OwALOVnaqqpKamRmNh7Qm5uHIHgycFQ/oBMAK1GL5SHCmd6eFeyr333suIESN49913KS4u5pGHxiOFQtBfgsOeiq77qaxZDcDJfceiKmZpJCX2N0iDAausahVrt7wPomZ0dW3ZV0dq/48VIq7kGf/X84DHOP2fy5myxOp88ptFCCEyMjJuDfmSn+3W9nqwpRLEwE8In6iP9okNESQga1Gxo6AghIoDDwoKDumgumwhddXryUgbhCoF2wu/pKJ6Fffddx/3339/tA6alDIuwP6jjz7ikkuuxulqQW3NOgDsmhu3I5mcpLZkJObQOr0zqQlZ0QSGyrpdVPpKsWtOtpWuZcWOufz+rGG8997RFWN111138ewrH+Fpdwa6rMUI9/mNFYYRGgRirDhUo9OkIdCLV6OXb2HmD1MZNGjQbtf73nvv8dFHHzFpvY5QHBiBKoKb5yJcKZzU03QNXXT/G4hIKIGaTJbWlVSZjEuocRfxxoJKwRSIWlisRUSe31DY6SumNliCZvOSaM8iW1NxaTohwxSFYFoAtXApGptidhOxKUacKIyIwKj4i3kF4rKCY28+sQktscSKwNhHDxEznpmUIghJgW5AwFCicYmRTORYquureGX6/1FdVxXX7m7ChAk8/fTT/Pe//91j0P5VV11Ffn4+O340s8VVIan0lTOnYAKbSxYT0v2oih27Q0UYCQRDNTz86D+5++67dzumxbHNmjVrWLRoERs2bGDDhg34fD5SUlJITU3lhddnU130IxLwuDReeuklrrjiCpJyT6V658+oaW1w5ByPUFQiv363moVj1/v88MMP5Ofn405sj69mPTnpQ/C6W6OpDlqkDUYKQZ2/lKKKheSmD8Wu2uJCUJRGfklFgD9YxaKCV3G7tbe37lx6pZW1fOgQQrhP7OKtnfH4QQjDBy1h+JtFCOFs0aLFf22htmd3y7scXdjRJYSkJCShBjPLtVbUUSNLUYUdFRsyLD5EWHg4ceOQpmAE072wfPk/4taVk5NDYaFZVdgwDKSUTJgwgT9e9Gfyc0bSrUU/5q17k1ZpnWmZ2oH0hHSkjMSTRVq30cQSBBAIBXh9xqN8OuFDhg8ffliP2ZFC13XsziQkBo6WJ6Alt2oQhtAgDiGuk0mDMGyIQRNCIbBlLif1bsdHH31EZmbmHtf95JNP8thjj+FP7o4RqiVUso4nHnmAv//977z00kvMmzePjxfXIoSGxEBVXGRrvUiXKdhipJMec0FXw9ZCAC0sDCNZupG6fxEiVjwpIWgohMIfaWH3sRm/Z2APl4CJbTkXEYWxWccR8Rlx6UYEoYhZTwSJQNBwvjVsU7wQjE4Pv1m7ayPLd6xAUey4bB4mr/iUu854saGeYswykdW9NuMpnnrp/7j00ksxDINHH32UcePGka7ks7NyCzvKNzXJUi4qKmLkyJEUFxdzaptb8TodqBHLJdD1HI2//+1+JnzzMSeddBI+n48dO3aQkpKy1zI4Fscu5eXlpKe3IDGpGw5HBjZ7GigqUvehh+oI6XV4k3tj9+ZTVLcIT+VkCourMIJ1KHYPiisNV+tTEKoNQwbRFDcpSmt8255l4cKF5OTk4PP58CQkUVVZxnmnfgrE/8akbHigjAjDiChsbD0UQmJInZWbP8YX2lFUWLK8g5Sy+ogcrKOcqDB87CCF4VJLGP7mEEK0Sktst6Fl+glaftbQJjegqqB5gw9ISRX1+ERDEfoA9SiokaZgqNKcV0PDhobUA1SUzyPR1Yqi0pmUlM0mpNeSl5fH888/z/z583n6X2NIz/Rw7bXXsvibFqS49OjNHUzrS+RGHC0zEiMM1ZibrQQKilby3fIPcCVqVFYG6dAhl1dfffU33UlFSsngwYNZtNOLltQKKYNmdrgMtyEMx18351ZuXIBcr9xBaOdy9PqqfSqJ8s0333DuuedywQUX8Je//CVqYdy5cyddunTB32oEQlGRMoiiOMhTB0RrFuqNnvDV8DcVsRg2TkqJxgzGFKIOhTuBxM4TKwwdqh62Gja0l1PCxaNjRWFEJEbcxqrS1AIYySTWZYMwjK17CPGu6IilEyBoCM7711n07NqbPq1PZNGOHxk+fDgtqvqT4ExEYgpJhxpJFGrIcN5YvJZJyz9DlzqGFqBv3750U05FUWy8P+s5Tuo0is/mx5X94vvvv+eBBx6gp+si7JoS93sIhODGl0YwbNA5bC1dQSgUOiaTtSz2n8y2l1BftpxW7a8DQAn/ixD7fwODYKiGeqOakE1DV3QUVHQZwC+r0QliMxyI0k34yiezbNmyaH3NxMSOJHnak595EonOrCbXKXP8ht9+c8IwFgXYXjqLDTu/M6p92zqHQsF1h+aIHLsIIdwndvbWznj0IIThw5Yw/M2RlJR0KiHvlB75l5KR1LGJqT4kzZptQUNQbRjU4seGhoaCjwAGBgoKIUyBEnsRUVAwIrfTmq2UlEynvG4RWVlZ7NxRSUj3gwgQCpnLts4YyGndr4reOJvEcoUtSrHCMFKLrrGL8JEvbmD0oDFU1lezZOMTrFy58jddIkfXdRISElAyTwBATWoNmi2cgWwg0c1+x3GWxEg8YTgbOSZRpX75l5QW7yQ1NXWf1h8MBptNVjj++ON5/PHHGXXbG+Z6hEK61pUUmYoDLZp4Ak1FITTEGja+JUQKYEeXFTJu2ahlMEYYRmIMI+IwrlwNsVnK8dnJsecRmEJNl6LJNoGZkW+O37BdSlhMVgZUJiz+iLOuGcEJJ5zA7373O1JSUigvL6eoqIhnrvoQoNneyFKaVt4dFVtIdqdRF6hlxY5F/Lh2Ep2ye1JeV8qOss1x4u6TTz4x3cw1p0bHUoWkPlDOR3P/Q22onCEdr2Dy0rEEgoE4N7WFxe5wp/UkOaEbKRknAqALI/rA3/j6DqBG38cmmkl8+ClkPfZ6g/L1r3D77bfz8MMPR+O/CwsLueOOO5g1axZFO+oY2OUO3I70uOv+njBiri2x963qui0s3fA6Tpe4tKhk09EVU3SEiQrDhw9CGD6ynCnLjj5heHRlMYQRQoisrKx7bCL7+/6dbiPN2zHms3izviok9VISwiBVOEkUGg6h4MGBBycKCnbseMx0A2xoOLBh+MsoK5xMddm/WLrmcXaUzsTn8xGqyaZXuxsI6XWEQiEyve0Z2vVyTu9+MTbFMNuVSWH2k202Wal5IhYZXQo0TSPBkcjzb44gJyfnNy0KwUw+Qc3mjIFpjD4hk+qV71O/eQbokfqRMRbCyCVaaOHXRoIu6AfBfmWlNjevlJLU1FQWL15sWguFDSkNamVpOCa16RU+NrYwIgqbI2JFjPwpgmgsok0x4lrPQYMlUI08PDQ3ZowojJS1iVrZYqcTdmtHO6XIqMU6Igoj2c8RhDAtkAXFq9g6pY5hw4ZRW1vLV199RcuWLXn6yg+iYQ9NElowxaxDE7RJz6OsppDxP79IUdVOUlJSaJ3WEafNzWndz4/bnw0bNpCdnR03TVMkCzfNoGVqB2bMmMGy4gl4XWmWKLTYJ+rr6/FXFpCQ3B0FBV0YplVQmA/vRjP/goTQww+dNhRsKDhQ8eDEK7LJcHbC4e5Bq1at4pICc3JyeO+999iwYQMtM4cxe+UT1NYX7faaEEusKIwUmTcw/zzuPI7r9HeMUOK7rVv0/kQ0Z4q02D/kQfwdpRx1V1QhhCs7o8s8u8jq1rnDJdhUmxlb1ajOmhBgC9/IkjVIFwIwCBoKASmwSzPT0hWJJzRC+KoLqAoUM2BQPR9//DE2m436+noykrrTIrUPWSk9SHWnYlcNvNqNZHpb4nYkY0gRLmci49YvkPtkLYy1Bum64PrT/kOqE/Lz89m8eTPV1dUkJiYemQN8GHC5XPhqGrKHx46t5rbbbuPND7/B1WoIit2FIf1xIjDWQiiEjbrF7+LqOgr/xp+wpXc+6OMxffp0Jk2axLQCEO5kJLoZw2hU41OrceGMWhAiJSeiorBJnFB4mxu5jKMFqKHZAtYRF3JEqEWXjYnni808bs6SqMScZ2AKPyPsSo61XtjVhtuRiPmtqGGXcIKm0za9I0n9zZtofX09F110Ebn2zqiKKdoiRbFt4Z7JzbGhZB198wbz07pvEULwo28ifVsO5ed131BSUsLChQt55plnWL58OSfknkvL1JhSPIA/5OeaO/7AgAED2LHDquJhse988803KK40sHsIhr1AEXzChx0Hfvxo2LBJ89YYFCFCBPFIDwAOzGLrDhTyyMCHQWrq8Tz88MOcddZZTR5mAJIyT2Xzzm+ZvuRuRvR/FVWxN/EYQUxSWOxvR8QLRQC7LYG+HW9j3bZPzstI9RcKITpIKasO8vAcm0gOdx3D3yRHlTAUQmSlJuWvSfMMSGqdObRpJqYQqI1jOQSoSPTwyWFXDdSwe7lel1TUrKe8fD6biyZFx9qw2VwwK6kT+W1+T5v0duGYMANFmFau1uk9wm676GpitnP/RWFsS7RXvv8LAH/961+58MILf9OisDkSExMZM2YMTuftjHllHLbktiiJuaCoKDY3ONzEOkOloWNv0ZfgrpXYM3tQv33uQW/D0KFDGTt2LPfccw9V9EW4M1CEhiH91MkKbMJJQDrQ0DCwoUkFW7gAoIKIumMh5kIv4l3N0djSSFxg2H0cEUKR7z/2PNYUIxxWEBNbKOJjCiNWxYgojGxLg+ATGFKiCBH9PDbO1Zy3IYHFFwpSHfCRkZjDM888g9Pp5MYbb2T48OFMnDiRjmXdaJXW1vwupFnTUBWgKubvLjYMwmVzU1xTzIQJE8jOzmbnzp1sLllNUPdzYo9TMRJ8ZCk96ZNxOu0y2iGEHhWcipDkJufy4mNv8Ne//vWgv2OLY4tLrr4XJaUlhaFlpGkdcOKOCkAHdgwMStlJvV6CQ00llw4kSHf0AVBBoId/35GHwCRFRUnqw+baTVx44YV8++23uFyuuPXOnX8NrTPfpbJ2E5FbbqQ0VNxDpGgaYmTIptnKAAhBx1Z/YGfp3MxAoL5UCNFeSrn5EB4ui2OYo8YMbbPZuqeltFjTPueCpPysoTEWnHgLiB4O9teN+B+hmfVp3th2lK9h1qqnmTL/ahavfozNRZNQhMbf//53Bne9nd/3uZ8LT3iR4T1voEt2Pk5NR1OMaPxWpNcshAsKE1NCJHKzjrEQKWEXXnOiMLJ84+LFfx3+MkVzdCZMmEAwGDysx/aXwG638+KLL1JespMbLh3O77o7GZBbR93qz5BVu5AY6NWFBDbPxLfyM0YOaM2alUvx75i3T0kne0MIwTXXXMOYMWNIrlqMb+VnhHatRhEO/EYl5cZWSthGhSilQlRTix+/NAjJhvqFdiFxKAaaIBwvaL63q+ZfrMtYVcJWQ0ViV424WMKGcyQ+hrDhYaHBZRyfVdxglVSjLmWiZXCcqoFLNbOeI5/FrVPAvC1LeWz6Mzz300vY25hWlrdefo+NGzfy+uuv8+yzz7Iy8BO+gJm0ZW5/xKLZsH4pzTqIXVv0ZNWOxbz54MdkZ2fz1FNPUVq7i5YtW+J1ptPK1pvj257MxMVvUOUrY2bBJH5aO4HK2k0s3fIDP6z9Fl33ce+oFw76O7Y4dqiqqqK+ci1j/nkNP711M6WhdfioRhdGNLYwKEJk04ZcrQ85tANAC7uOXULFhmJWHQiPGQkn8aoKXXLPoVWrVpx88snRqhSxrFg/AUUozFzxEHqoLvoQOHH2ldF5zIQ1og9+EP//xmhC0jK9P11bX6olulttUlV1z4V0LZrHciU34ahIPvF4vGfaRMqEXu3+LBJdLZq1yED8k1djSwyAXxe8+OEw8vLyAEh0tSA/cxD5GcfhdWWEY71kVOTtjthhGwRe/E16dxbCyLZFxSHmzVYTkrqgn5kFP6IIaJfZhcLKLVR4tjBz5sxDIoZ+7YwfP54xY8awMtANhI5v7SSuuPAMHnvsMXJycg7beqWUrF27luOOOw57+8vRFYNINxyb8OAVGXhlAnZUHELgUU0h2Nz51rh1XOOMdFWYwjDWPRzJQtaaST6JWONis4lj3cdK2CKtNjq3zPka3L+Nz+hgyMf7C75iffFGrhp0KQ9++TgdOnTgxhtv5KvXv2XFjqW8+MrzXH755Vx55ZV8/fXXXNr3NrxOD07NCBf2FlGLubkO03097scX6d9mCNvUlcydO5eO2b3YWlpAi5Q83v78NZ64cSyLt/xEaU0RV193JU6nk6effhpVUZk1exb9+/c/NF+sxTFDdXU19957L1u3bmXi19PxJHZE5PbEVzQPu+Eg2dOZ1LTjWVf2Bf7ts+nU9X48WgoKIhoqosWEiURCRKSMrTIAS7Z/hl1dzJdffkmfPn2i6+/V9yVWL7+XzOSe9G5zOZqqhVPmzAENSbRsGcSXs2k8jUafGUCdv4SF615Gswf+VFK27fVDddyOZoQQ7hM7emtn/LPXAY9x+hPLmLL86Es++c0Lw7S0tD8TzHilX4cbcNi8exWDsZ+BaTn8dObV/GHwGw03T/zYVS0av7WvNGcZbM5dbG5X89ZBJWb7Y12FQsBq91ymTJnCrl27GNXrYq579BIeeOABFixYsH8H7TfKiBEjmLnOgZLcEt2ox791Fs888Jcj5lZs3bo1xdUeXPmnIRQ1Kg49Io10mYoDFbcicGtGXMkYtZGYk43KxUDDdx2NH4xa7hrcx2aZowZrXsQNHBGR0FQQQkzGMjHnJw2uKglx7lpVwMeLvyHUQuWLL74gISGBTZs20b17d24++R6Wb59PSXUpW8q2sK18EwBXX301HTt2JDTH2XAOY94sa/01LN+xhMKK7Wwu20hxdRHn9LmE4/L6YfStoaioiEsuuYQVK1Zw991307FjR26//XZGjx4dLRjv9/sxDKOJm87CYn+pqanhvvvu46Uxr+OwG/z73//mtjuewQhUkp02mI07zNqD3bs+jMdlFl9XENiEiIrDSMvLJsYFQ7CtdC4lVR/x6quvcu655wLmw2Ve1kkYhk6/DtcihGjWRbw7oQgN5aYaE7nnBYL1LCj4DzanPmb7zhVW25+9EBWG9x+kMFxhCcNfDeFOJo94bHl3d829RrVrDqBBCDZnETR/bKYQPP/EN6LuvOYK/TZmdwIxXtjJmPmbtww2XaZ5MRiJ8YqIC4ng3z88QllZGaN6X0KXFsczq+A7kjrCV18dG92SUlNTcba+ljq1BkMGqVv1KXN/nkzfvn2PyPpLSkoYMGAApfYBOBPysQsPEQdTokwkWThJ1mS0f7CmGNEkktikkgixHUgiDw8Rl7GUIlq3T4t7bRB9SiQmkfibRkSERmhsgY4VkUA09EELjxc0BEt2FPDF0klsLjP7HM+dO5c//OEPDEw/kelrv2f4WcMZOnQo11xzDQBLlixhxIgRHJ89hA5ZnUj3JKMImLVhFv9bPpEu2V1JdiXRKrUthtR5Z/abzR7jYDCIpmnHhAXc4pdFSklVVRVJSUkATJkyhX/84x8sW7aMvn37MmvOIvLa/Il0r1nORIu4kkVD8frIQ1rs9V2Xgu2VmyipeZ0bbriBu+++m+rqajrkDWVXxSJO7fUIye4Wcduyp9aUejNisHHZm+i9zggyf/1bSLVq0vaiFSNlfC9RixgiwnD6vQcuDEf86+gUhr/JGEMhhJqZmfmWR+l6b+/W16lOzR6N14rECiqiIY7Qr5u/GrtqkGDXuXLoWBJsIezhFmOxJWykjO8QAfFWltiYP61RPJgiGuLENMUwXYAx/W0jy8WWH4ksFzuGFrMvDQkIkrKyMgCOb9MPVUjanZDO5s2bKS0tPSLH/ZfGMEwXriacUFuBEayjZ88Dr0G1v+i6TklJCZo7C4GKEe6+omHDLwIEwyeOeW4Y0e/QLE5t/jlUA5emh/+M8PljRM8lR/hhxanpOMLvnZqBRzNwaYbZN1mYY9rD50kkRlAL/1+LFLqOcV83xBjGxxBG/rSoyDSnF1YW4rI7ASgoKOD666/nnnvuQcogQT1Al+quUVEI0KtXL5599lmKPdspztmKBAKhEJNXTuLPQ27k8oGXcEaPMxg/a9xuRSGYpYMsUWhxJBBCREUhwLBhw5g9ezYZnp7MmTOH5559gs0bX6eqpgAFEVeiKoTZNSsYLlIfdf0KM4ykdXIeaSm38Ngjr3D11VcDUFe/k66t/0BC2AoZW7KqMbG/W3tMCatIiIom4v+iyWuaxqCOV5OgtT69RUanpUII+2E7gEcDkazkA/37bdrV9spvThgKIWw5Ge3mZzgGXt6/3aXYVSX6w4qU4RCYN2aXZt6EvfYQLk1vIgSjbr3Y6gAxP9TY2K2IuIuIvkjRYRFzo43e4MOCQGskBiM3bE00EpJCNrqBy7hkgsi0s3ufz4huI1EAj02nS5cuKIqC0+k8sl/CL0S3bt2oKV2IXleCu/IHvv3fl0ekhp3P5+PZZ5+lU6dO1Gs5KIodVdjwkEyi9OKSTmxSw0eQOl0xk0vCQjAi9qKiLmZa5NWhGrg1HbdNxxkWgi7NfIhJtOl4ND2aKBJ5dagSR1gkRgWhiAhF833khhIRhJowoskuDZU0ZbQcTuT3EwzW8vnSSbTt254RI8yC1ln+dIq/LuLjhR+T2SqLMx4a2eQ4nXXWWWzdupXaZXWoAuZtnkt2Ug5tUrO59ePbuf2T2w/7d2VhcTAIIdhYNBO7msQjjzzC/ffeQcGGl6n2bYsraB8bbxhBShHnBWjtTqdv53/w+ReLGTVqFO99+AoFhd+a94VGFsLYP2je+xQVimrEKNHwfy32dy0EfdqcR1ZCn25ZqR22CyHch+t4HRVIceB/Rym/KVeyEMKRmZk5Kcc99OQuLYfHxe5BvOtYxLmUzf8be/giG7uK4+KxRNN4QWhaYqbxcg3LxruIafS+cV/bSPmQiNsv9iKhS4EhBSFp8MaSMXz55Zf06NFj3w/ib5j169fTsVNPhFB4953XuOiiiw7buvx+Py+99BJfffUVc+bMob4+ABhcdtllfLeyFQ7hxkUiLmmKchUFHYME7GTaDRLtIVRFRi2EDtWInosC07oH4eD1GPcwNLiItZjzr3E7PXO++KSmyHnSpEwTTc+j2OLquhRx49iEzvKdm1hfXorL5qRfq244NBsBQ/D+/M/5af0sAJ48+yH+/vk/0XWdqqoqKisr6dmzJ+eeey4zJk6nvK6C60+8gicmP38IvhELiyNHfvYIaut3Iezb6Ny5MzNnLaZDm+tI8XaLupPtQkSz/BsnlUXjd6UgaBis2fYFu6qm4lRbcXK3O+NCnRrX/GwuC1mI/b9PSylYv/MnNpf+WLmrfH1LKWXNfg9yFCOEcJ/YwVs7/a7eBzzGiGeWMmXV0edK/s3UMRRCuDMyMqbme08b2Dl3KOFOk43mMV8jN1+It/Q2FyfY+DeoxDzNxd5o91RvsPFykXFjtyNWoO6PIIx0q2jYF0lt90oeeeQRbIqNUaNGcc899/DnP/95zwfwKKBdu3Zs2riajIyMg7KSrly5kh07dhAIBFBVlaysLNq0aYPX66WsrIxFixZx66230qZNGwrWJhEMqKSmJjNs2DAmr2iFQ2kQhY5wJ20/OnZsaMI8M0OGglMLogpTHEaSRjRFNvMgEf/9R6zEsTGn0ZjV6DnRNPA9Mh40nPeqiD/HI9PNOobmzUuNOd8jVoseOW3olNWOoNHQoWdL2VZmbphDp8z2XHzDpXz01QQmDP6OxYsXo6oqQV+Alsm5/Py/H3nlvVc55ZRToskjFha/JX5e8Dpt23SjY+qFbN78LTfdeDUvjXmVVjln0SpzGAqCkDQz72X4hhD5XceXmjGwKdC99blsK++KQ1UxMOvpNrYKxhJbraC5DOV9Qkg65pyIXbUlIY2dQoiWUsqK/T8aFscavwlhKIRwZ6W039E+dXBSu+wT4n5QzWV1NrYMxr5rVhw2ujGa0/Ytkzh2/MbCr/H6IoWMY8Vg82PHi8Lo06I0n1Rff+ENBg8ezCmnnMLYsWO57bbb6NGjByeccMJej+VvnVatWh3wsvX19dxyyy289eZHeJw5BEJV+ANlKIqDUKgOiYGq2HE60mmZPZJdG1qwq/gpdKOWsrJabDYbqrAhUAngR0UxeyYL0KQZmK4JgSoMPPYQLs0IhxsQjg8y4wIhJuGI+IcENeYcio0FhIbMYlUYcaUqmiPWIiiijxTmNEM2FOA2X2PP0Ujyi2lJDEkRHem1me9hSHN/Pn71fU5qN4jCyiLuHnozQgh+XD+Ln9bP4evv/sfJJ598wN+ThcUvTW5uLhO++pCzRl3Cce2v5f333+CrCR9x7rl/oqZuM13yLsehmg89BhA0BEJEbqgN1n7zc0F5CFK8XUjQzF9srHiMsDurYOSaEEkuE6KpoIx4BmKzliPztcnqj6ZqHim/KRRC5FjiMAbJURsneDD86l3JQghnZmbmjA4pZwxolz2oSQZx5AYbWwpk92M1et/IIggNT2VKs5ad+FpwsUKwuW2KsxY2Iwh3ZzmKtRLGuq4NzB9+YVUJ7614h9NPP51TTz2Vm669GVuCypQpU44Zt/L+sGPHDv7zn//w8ssvU1ZWRl72mSiKWap2w47Po/Md3/1J7M5MFCAUrGD+qkfp2up8rruzD+PHj2fWrFmAQmJyT3LbXI6uCpJlIolCw8Bs0udRINkRIsUZwKka0Wxip2bg1gzcqpmwYiAaStjQYBGEho4ImjDizgVFSFQaZTaHX6UUTW4sjUtbxIrIiEhsLBoFEpuQ6FIQkIr5aigIJHXBIDX+GjISUgkZEgOF+yf+H7pNEqwLMCCvL69MfJ2OHTtiYXE0kJk6kPSEXK6/YyifffYZl156Kddeey0edxv6dLiZREdytMRMbPkaVTSIQwOBT1eQEhzhB0RVaahaEWF3FsHdCcbGrujYh8FYIteTrSWLWbH9m7pdFRuzpZTV+3kojjqEEO4T23trp9/Z54DHGPHcEsuVfKQRQtgzMjK+a5f8uwEdco5HEUacIDPCJT3irICiIV6quX6UDbM1jRUEomVA9tciGDetUcmcxkKwYbyY7YmLFWu8fWFhidmSKcebztW9ruWRNx4kNzeXLr07sXV1Idddd11YvFhE+OGHH+KsV2eccQbdunVl1qxZGIZBRspjFJXNprZuCzbVjlMIKqqWsrDgP3TIHc2CglcAuO6661iwYAHff/8999xzDwH/77G5s/Hhxy+DOLDhEWq0y4kC+EIqRbUOhIAcTz3tknw4VQM9fN4KZEwySLx1WaXpTaNxTK2MsebR6AECmj4IR93MUkCj8SMPVJFkKsMcEjAFKkCC3cbOyiremP0hm8u2k+jwUBesZ0S7Ibz986ckJCTs13djYfFrZ+bc9+japQ/T3j+Rosoi7rz1/3DYktA0N1tKfqZL7pk4FCPOmtf4XhO5H9RLiTN6v2j4/TVuyNAcu4tHNJdr+lnsfUli3ivzMnphyJBbiO+3CSFaSClr9+NQHLUclG3s121XO2B+tcJQCKG2zOywonVy7/bdWw1GhEuDQMyPpBmXWuRHaNAQO9W4LV5EpEHzcYKNrYF7sgRG1hlbPDtWCMbWKNxdTElz1sFYK2NkHiUch6ZLSHUnMKzL781YQ9VBh07tosVULcwaZWPGjIkWv77rrrv4y1/+Ql5eHmPHjmX8+PG08F5IRnIuSe7zgLAl2Khn7ppnyfB2ZOXmD6LjCSHo3bs3JSUlAOj1pbhdLXFgx45KAB2fNEBX8NXZ2VlnllDyaDoSQVXAhhB12JUGV5IatgA2SaIici6YnRFiX2Ot3jL8sGAu02CdUKOfN2BAdDrN3IBkzPqFMNv52RUj3EJS4DNUDCkprCoiqAdZsXolUkoWL17M4MGDLVFocVTSvn17kj2tqKjZSAfP+RT4nyAUCtGvy4MYtiT8hsShgC38u26ufi6ACwO7ENTpCl7NvJfFWvljBWVz4i+W+IzmpvH0Te9nMmpN7JjdB2TAqwixNexW9u/vMTmqkAKM5i2t+8bBLPvr5VcpDIUQolVWu+9zPJ3a980bSuNIqtgEEMKfxprh92aSjwi/xu3BGovAPQlAc56GH3HT9na7iRNstJ1x4zVyk0e3k/hYEgH4pSCzdzL/ufk/PPLII6xcuZIrr7xyN3t+bFFdXc0555zDlClTyM/PZ/bs2WRlZUU///TTTxk3bhxjHy1BVYyoZVmXgvK6rQBIexledw66HkBRbJx13mm89957XHbZZZx77rmMGjWIl1/SSRRa2EooqAxBSAJIPCok20O4bSEMKagLqQQNJZzNaMR0QolvndfYvdScizgSkC7DQewRjEbvIwJQShB7u4DFWScbzkNb2FpoV3R8uka/lh1YWbianl270ye3M5NXzMTtPnqqYUgp2b59O5s3b+bEE0/8pTfH4ldAKFRNusdLy5RshnS6mqkrxjJ35f10b/tnvMk98BsCh02PC02CmLi/cNiIokjU8IcyastroLkwqd1V0lAa3csi3Y8iJaekJBqzbF4/GrwMnVsMQDd8KZpNXSaE6HzMF8E+isvOHCi/SmGYmZn5VKojd+jA9qc3e1MUjZ6S1JjPG7vkGrO38jGReRqLtMYWv1jXM+xZEDYWgOZy8fGKcZ9F4ybjXcxmrIj5/GcYfr755hueeeYZXn75Zf785z+TkZGx2/0+VigpKYkeh7vuuovHHnsMVVXj5tm2bRsXnHcZJ3b5G6kJLdENWF84hcLyhQzqcidn9HsKlQAhqRMI+vh+6f/x3nvvAWYCy08//cSmgsF47SnhmCHzO0rSBL5wMfU0ZwCHqkcvzjZFkqDpCMyOKLawOIx1C0Mk4cS8TpsZj5FzID58QoQ/i2BaBJuxBJoDNCsLm3OhxJ/7DRZsTRg4FJ3kJDv3D/sD9aEAz/88kePb92LR1tVNjvGvkU2bNjFr1ix+97vfkZ6eHp1eX1/PpEmTmD9/Po8++mh0el1d3X634Nu5cyfPP/88b7/9NoMGDSIhIYGrrrrKSsb5DeMLVOOxu7GpBsfldSe92x9YtWoVi5Y9Q5usofRvf2lc5YoIpndHYEgZfsADWyNXciReuMELFi/6GpfBaUxEPAoBmmJebwxDRLulRNYRjTcOG0R6th5Cnb+sg6OlfQpwyqE6Vr85JByMLP6Vp2gcML86YZiX0+7BFLXl7ad0OS/8Y4nP4owVdo2fpuLqB0ZeRdPPzPdNYwHN5Xbvco4dt3Fv2shn8e5Ac7oaIzobu6Mbj2XusRkn2bQ2lkCX5sXmp/WzGDZsGDk5OSxduhQLE4/Hw5gxYxg5ciT5+fnNzlNfX099oIIpSx4A4Prrr+ezWe+TlpaGUzUw1FQcqo6mSAIhweCut+F0tKSsahnTvltITsYltPYkRwVcBEOY35FLkSTYg4BZr9BrD5HjCpBiD5KoBXEqoah7x5DhcjBhQWgmnMSUwSD+vFBjbiwRJAIRFowQX69TNnNexy7XmOiDTIwYjUvSQmJHx6MK7jv5TO785n3O7z2Mz5dN391X8qvA5/PRv39/6qrqSPekcPmNVzF69Gg6d+5Mnz59UKslOd5MTmzTj1mbFrJy1cr9FoVPPvkkTzzxBBdccAHffPMNixcvZtOmTVx//fWsWrXqMO2ZxeEmw9uS0pqNZHl7Y1cNUot7s3btlwBsLJpOiieHHq2GxiU/RpO5wqJQlyKuBav522+mOoVoaHAQmxTZXI9kaPq5JiSGYt7bItuiCjCaSXI5seNoJi97e2jHvG4vrN284uZDdbwsfvv8qrKSM1KyL3SrSR+c1+/PaKqtWetfxMq3p97FROZp5PqN0NgFHJ0eXTbWTB8ZN/bmGC8AY9cXO2ZzruDm6s/tLqYksp0NZUYgJBUq6oM8NfNFJk+ebGUh7yNSSgKBAH6/nxkzZnDttddSUlyKYRioqoMTTuzPnDlzGNHrEVz2VNPKpxpICQFDIaArqOFsXTXmoh3pdhAyBH5DISQFnVJq8NpDZLmCpNmDuNQQHjWEXdGbCDojevE2heHuirHHirjIZ7HnRZP93cP8e6LhPI+vmRiZFjuPRLCpopIbJrzJfy++izPefGCv4++JL774gieffJL09HS+/PJLFOXQNWa69957WbNmDUNFb1buXMN/fnoXMC3MeXl5/LHX2aiKgi9Qx+Q1P7OpZOs+d9UpKyvjuuuu4/vvv2f27Nl07twZgMrKSlatWsUpp5xCUVERXq/3kO2PxZEjPbElp3W7kLy0fByqaelfsL2Un9e8R4vUfqR525Gf1gZVkeiN4tXqQlr0Gq6FuxHFtl8Fs/tVrOHAtP41tFqV4ZZ7ZpeuBktgY6+VgSkMlfB1SpeiSXw9xBslpAzy+YKxCId+44Zta18+RIfsN4EQwn1i26Taqbced8Bj/H7MIqauKbeykg8Xmqr1ykjM+eC8467EY1chWgSggYhbLlZkxZbZaM7CF7d8o/e7E2h7yw6O/D9WuMZaEht3o9ifCvfNI6OuAUPCmvTNjBw50hKF+4CUklNOOYVZs2YRCAQA6N+/P4sWLWLMmDHMnz+f2bNns32Nj1F9HiDFnYgQQUKGac1zajqBgGJ2MglfdIOGgkPVMaQgaCjUBVX8hkKyPUSfzEpS7CFcqoFXC+BWg9gVPU5URS2CUcuwERViUSuiiHzfMda/6LkeKTrdEHLQdN4G9lbOornzsnmLYeMbjIFCEK/DddCiUNd1zj/3PM7o1IsJsyfSoUMHPvjgA/r3739Q4wK89957PP7447x83j2kuQRqRhoA6Z5kfD4fl112Ga+88kp0/smTJ+9Xq8VHH30Up9PJihUryM01++BWVFSQnZ1Nly5dSEhIoK6uzhKGv0F27dpFdX0ZrVJa4bHpUbE2Yf4j9OkzkcWLP6ZH3lnkJOXhsQtUpeF+MG7qnxl9/FsEpCDFHor2MQei4i1yD4l0O4qUSYvMY84vkYBuxN7xTCLrA1M4RtzKznDiWMgQ0barkXtIrAcLVM7peyUfzP3PmAR3wsqauprph+tY/mo5mBjDozQ+8VdhMRRC5GR5W+z4Q78rSUvICE9r+Hx3xaCjy8fMZ76PF3ixRMz3scs1V0vqQCyATS0szcd27SkG0tzGxtYis4BqwFCYs2U1Uwuns3jxYutGswd8Ph8TJkzg2WefZcuWLYRqFAJ6gOtuvJItW7YwadIkUu355Gf0JC+jG4mOBEKGEnXVBgwF3RB4HaZLWGB+L3r4q4vE8gQNQU3ARr2ucHJuGVlOP07FwK7oYVEYirMExpapiBCbUQymVSC2GDWw2/cRmhOQzU1rjt1ZBKPbtweBuKhwGy/N/pFlRVt3O35jgsEgb731Funp6Zx11lkoikIoFMJms/G/y27gjYVz8IVCLKouZsuWLQcVvxgZ98r+Z3JW1wHRpB9fMMRfPn+J1p3bUVJSwikZXeiS2ZqpBYuZUjCfHr168eqrr+6TMBVC8P333zNs2DAAtmzZQl5eHgBVVVUkJiYe8PZb/LKc1v0KNpcs4eIBV5m9ycPWPZsiKSjZwis/vESL5M5U+Urp2vJU2mb2ZeOuubTOOBmbZqfSbwvXMA1hV83rgBpth2l6DCLCLdLzOE74xcwf+3tWhAwLxUZesvB0NRz3HClUH7muRUplNb43VtdX8v7sVympLeoWCoVWHp6j+etCCOE+oU1S7dSb+x3wGCP/s4ipa8ssi+GhRgjhaZWWt2l4lzPI8qY3W7sNmlrkiHkfNx5Ng4DjPlciYzd1JceO0Zwl0HzftCVZ46LUEetPY2K3aU8dKxoLx4hboLSumq83fMMnn3xiicLdMH36dF577TW++eYbvEoWffMG0bvjYN6dPZabb7uJcePG0S3tBK4ddBcJdhcBwyw8G5IGUpoFpR1qw7eTYNNRBAQM8+nbrysEDWFelFWzyLMuBTZF4tYMXKqOU9GxKzo2RTfjBhU9HNYgo6/QkHFsuorMjGIzWcQsTyNjLugRS2Lse2i4WcRPl83Mu7vQi6aiMDL21spKpm9cQ+vkNNLdLqSU5HqTSHO5MKSBKgSri3eQtR+lalasWMHdd99NZWUlZatXc9MVlzN9wUI6dOjA9ddfz/3z5nBblx7kJXkZ/PoLvPjii9x66637PH5jqqurSUxM5JyuA7ArelR8u2waj51+GSuLtjBkyAjcNlN89skewbC2HXl9/lQGDhjArX/7G88+++we1/H+++9z2WWXMXXqVDp37kxOTg5Dhw5lxowZ2Gy2A952i1+ebaUraJvRJcbaRtRe3zmjFaeeeioXXnghbdq0YezYsbz/0Z04NA9byzYwqNOf8dh0nJqOS9Oj7uHIrzHiIlZocB037oceHxIVL+gMRYaTW2JCrJDYtIZ7lBDmNaIhntGI87KZowrS3Ilc1P9SPpz/7rJwjcOiw390fw2Io9bqdzD8ohZDIYTSNqft6v4t+nfon28+mcee+LuzrB1Ilm+sla/ZbdmNm3f3buH4m2njOnQN+xg/bmwQcXPiMPJ55DNdmnFrxXUB3tn4Oeeddx733Xff7nfkGObdd9/lzjvvpH/WYHq06EaSy0uCprO5Yif/nf8lCc4kRnYfTYrbS4o9RMBQzKd0Ydbssynmq0vRCYbFuC7NM8sIxxrW6So1QZXakEJ9SMGvK1T67bRM9NEnpZpEWwBP2FKoCVMYqsIIZwca4XplDYlTspFVzwjXDYwNpGg4J+Kn7c4yuLsSF5HPYc9xg1JK3lw4h/8um8+JeV2QbTMpKytDSsm6deuoq6sjFAphGAZJSUk8f9pFXPHJK3Hr8fl8PP3000yaNIn1S5aSn5xG7zNH8Mknn3BR27b8qW8fdlRX8d6yZaS5XDw/ey5SSsaNG8eDDz7If0ecTyBUyp++nEiiw8Ez77zD8OHD9++EAG655RaKioq43N0lmiwU+X2qwoj7TTcuiP/t2kU8P3Myf7zyCm699Va6deu22/Xcd999+Hw+nnnmmei0888/n6SkJB566CFatmy539tuceT58ccfefvtt3nggQfYvn07J590KlcNvoPMxNRo33Onara2XLBlCR8u/ILe/fvw448/AuD3+5k2bRpnnjGavm3OpU/+UJyaHr2+xLp+I2LOrhg41PikFGgQj9AQz9y4lFU09jDm2hDpu67FJLpEPR00n+wYYW3RGiat/rZiS8mW7KO9xqFpMUyunXLjgYernPHqAqauO/oshr+oMGzRosV/emf0uH5k599Fp8XWdGvawq6B5jKQG1sTI9PMeXbvXo79vPllm1oBG3eiaGyh3FMV+92Jw6aiUBCSCvW6whSxFK/Xy8svv4xozsx5jLNx40b69+/PJb3+RLu0dBRMd49TNQgYgvKAhiakGftnC5FkCxIwFDxqiETNjyqMaMyfLpWwIFcISiV6QfbpGtUh021cGdQo92sEdNNa2DrBTytXHal2Hx6tHpuio4jGHREkiogUt204Y2LFXETwxYm93QjDiFUxMkaEPQnDxjR++DKkwBcMMPq91xj/4X8566yz4j6XUuLz+XC73RiGgWEYzcbjffjhhzz55JNcmdOO1t5ENlWWsqWijCF5remSkcz/1q7ltknf8/zzz/PIPXczZdZsevbsCcDFF1+M3W7ndo8HRQhmbl3HP6fOYuCwU7nvvvv2Oe5QSklGRgbPnHop2YlJ0ZqMERr/5iPHMJIIpAqDar+PNxfO5tt1y3n6pRe45pprml3Xjz/+yIUXXhi1GoIZn3bXXXcxYcIEbrvtNu6999592m6LX4Z169bRq/txtMnoyZbKJQTrdUb2upTuLbrh1IxobJ5DNXCpBj8UzKauFYwfP55bb72VU045hZNPPpm2bTrQN284vVofR3ZiMpoisSvm77+xpTDSPz0Se6iHw1XiHxibZ09JZwJzzNj7UmwJm9iQKYgvu/LDup//n73zjpOa+Pv4e7K71wt3x3EcvfcuvSOIoAgIKmIFK3ax994LFqz4A7EhYgELooiIWBAbIEUQlCa9t+PKbub5I2WTbLK7dweIPnxer4HbZDIzSSYzn/m24bdtS3/7Y8PKVvJosDU7TDCJ4eXty1zGyeN+/k8Sw39MlVyvau2b8/0VRw1s3IvwVnfhzm71yLWqb50rIyOv8dtpP+iEmxQygkxaz9nCz7jZIkpXMqt4fM4qmqrQ3IIM+wCgAoourdJWeoKCoOTz2Z+zaNGiY6TQAz/99BM7duxg3Y4/aJKbQ0CRJCr6bjmKQr20Amql7KZC4j6TBEK4T4VUH0HVp0kIpaBY9ROUPrNvhKRCggiQpIQoVn1kBIJUTRY6gZBk+ovJTjhAir+IgK/EIRVUEA4HE0R4Jx9VCvR9TbSt6AjvaGKEslFlOHi1lAJVaHELbYTQvKf4JYaRHouCtAQ/ratU47exEyOIoRDCDGitKIqn5/CePXto2bIlJ+hq5oYVU1FENV1qpzLul1+pVasWxcXF3NerPcd37MDjfXszctonjBs3juHDhzPii1l0rJbP+a2aM2fkUKYsXclJPXvw0NPPcPHFF3veo7WtDRo0YMnmNVRJbwmE1XQHS4r5du0qftm4hoKSEqSUtKtWj951m5Hs10N8SEF6YjKjO/fg5IbNuOrKqxk+fLhrQO9u3brx0EMP0adPH+bMmUO9evWoVKkSr776KrNnz2bgwIEsXLiQs846i1NPPTVm24/hyCIUCtGgQQOqVqjJkDanEwwOwq8IEgN+EpQQybo3smGv5xNwXLUG3Dn9eW4eeBfvzf+AX375hcsuu4z0xAxObdmLZH+IZF+R6RxWotskG1DR9k5OUFRz7tPGfffFnRsRNODmDGnMhSHLdUa5hhbNaqNoZDuhUSe27tvcwl9DGQfE/tCO4T+Hf4QY+v3+djUrVHnk0t6nkehDJ4aRdoN2kmgg0jDeJIjYV0NOVW08e1Ja81nzuoW+cZI/20rMwxPUejxsV6a3V2DaO6iEbUN+WLOApk2bmh6PxwCqqvLdd98xf/581q1bx4wZMwBIa5ZCsEQQQGoOJPrz7FDxT1KT94MiQRWoqoIqfabqNhgMEJI+PeaYQqJ+DjCPJfuKdUmiZo9meO4KJIm+EhJ9JQR8JWG1j/HudRUyuJsWCARSJ39C70FOr2TbAkJocQvBqUKKPBbx3PRzigcxRBhG8SplERbs27eP7777jvvvv1/brnHBfEJSwa8Ebd9qx2r5TFy4lFatWtFo01IqpSVx+SezWXbjjZxyyilMmzaNiRMncvHFFzNr917e7d2Jc1s25bj8ygy45BLOP/98EhISYrbngQceYNiwYfy+dT096zQjKymF3YUFPPntx1ROz+GCW68hOzubkpIS3nzzTV5791kGN2lL7zoNqJGZxf7iEgKKZGfBXopCQVNa6oYRI0ZQUlJCr169mD17NvXr1wegcePGHDhwgAULFvD111+Tn59Px44dS/1sj+HwYceOHQR8CVzSbRR+v4r0+bWwMUKTDib6VPwivBDzCUleWiYXdTiFl759gZJQELkjwBltrmZf4QH8iiTNr5LmD+IXqmmWEtQX/ebOR2CaOGiaCnsYK5tNoQxL/dzgtE+0XmcVsFgdMI3z1jIARrQfwOOzJ1yUmpL67oGCAzPL9FD/DXBIZ0uP/6ag5ogTQyFExWq5lb+4vutp5CQpKARjEjUDbrIJq8QuFnFzb4+M6BiukkGkq9rNrtKO9PbS8oX/Docj0SQ+xtZmKgKkVl+JfkwCOwv289WGb3n33Xdj3sv/F3zyySeccsop5u/2NZpzRv0upDc6lQrBXBJ8IS3+F4J6qXtomruKxOSDKPoepWrQRyjkM9W5UgoS/MWoOuEzVbdSoKraMVVVTOII4BMhW9/R4orZ7X8MFbRX/zbVxrpHlCIFxu5UEV7IUrEdM/qZVZXsM/J7jFVSCpz+vREe0kISUhVa5FXm920b3AvywPTp0xkwYACtK+dyV7tmnLBkDtKvBbVRMPaSlSzbupOJC5eiAL///juNhaRzjUp8OeIknpj1Eef972U6VM3nkY8/Y8WKFTz00EMcP+VjLm1ajzObNSA9IRBBClVVZdy4cXz00Ufs37+f2rVrc9ddd3HzzTezfft2pm/fzt9JIYqKiigqKmLsq68wdOhQWxnnn38+CxYsYNy4cVw3bRrBYJADBw6gqioVAim8O/UDcnJyoj6Diy++mFAoxIABA/j000+pW7cu+fn5nHnmmUyePJm+ffvSo0cPVq9eTZUqVUr1fI+hdDhw4AC33noroVCIxx57jNTUVM+8lSpVIi0xnZ/WfEufRj0tCzJtXtHIHKT5gxibDyQpIfrVa0B2wlAe/WYaJSW7mffHNC7vdi7pgWKyE4pJ82khskJSoUgfS7Swa6pN4q9iEMOweYhTsOE8bqikIVKjZT0H9k0TpBSEdKGEpyDFB9d1P40HZr3+mRCippQy/rAD/yqI8u2V/B9VtB9RYiiE8NWtXO3nc5p1z6xVIRMIRSVvTu9NJ4wdGiKOW/JaRefeFTl/RtopugahdhJRjzqskhxVSpO0AAgpCOmSQgVJicVuLCQFP6es5rzzzqNz587e7f9/BCmlSQpPbtSRttXq0KtmddICQfYHE0hQdlAh4QCV0rfiTyjG5wuh+EMInRSiChS/UVZ4KJQ+oZM5i+2fFMiQYiGKPlcVT2Qbw+UIi7TQed6ULEqLFFmEySqAGbNQhCyTlWLmN/Y4DvdzbNfbYDlkTBRuqmWhSFbv2kHTSvETl40bN3LZZZcxbdo02n4zFZ8viBAlEc8CoGqFRADm//QT/fr147QLTkIIlbz0BB7vdxw7DxbxxLdL6dm6BR99/S0dO3bktdde4+FvfmRFbjUk8PPPP9O2rRZmYtq0aaZ6NjclhcdP7M1dH07j9ddfN9tXs2ZN5s2bF3M3k9atW/Piiy/y/PPPs2HDBqpUqWKab8QbcHvUqFFMmTKFevXq8e6773LaaadxzTXXMH36dIQQTJo0ifz8/Lif7TGUHqFQiHb12uFTElDVQq4puob//e9/SCnZuXNnBMGfOnUqB4sP0LpaM5J16aCxlaUhcTMW+EmK9k37FY3cNamUQ3J6CtPmTuf0XoN4bf673Nh9MJn+IpJ8JfiESkgqlOimKob9KoQFBQYpDMnIPmZ8o2YsW2M+saqIdWMU62/rolXLL0xBikpYcmnVhhnnAJJSU7iy00nipZ8/XyKEqPRfdUYpj8TwP8oLjywxrF2l2pMdcmvW7F6zDhAEPAiXA27jsVfQXSsM6UmsuIFu5Ua0wSWch1Nd7NzVwoBhr6RNxgZJDIv0NcmRpAQlopP6/f5jHo1oq/+lS5fy+++/06BBAx7peCb5SYXUzvqbzIoLEHqIGakKhBEnTF8JCkVqdp0hBYkCUiJ89gDqUhW6Gj9kSvuk1FS8xjtRVNU87oTVzFs7H5aQ2fJJw2bQKNdCEIW0kUQAVShmnWb4GQcZlFKJVCE5SGBpsOPgAeatX8slx3WI+5pnnnmGIUOG0O7bD/D5tW/bCOKrWAJ8A+SmBqiQlMDusQ/g9/vN89p+4yoVU/w80rcltbPSOLFLZyrWqs1J9WtxsKSYtT98wxMntqdbp46sXv83lStXNif5ymkp9KlTg+41K3Nvr16MnPYhX331Fd27dy/1LiqKolC9evVSXWNFu3btyMzMpGvXrgDUqlWLxo0bk5ycHCGpPIZDjylTplBQfIBLu49kvvyOhg0b8uyzz3Lfbfew5+A+eh7fk3POOYfzzz+fefPmcebpw7i0yzk0yM7EJ7T+mKiEUED3/A07jGghqDRSGFBU8tPSOK1uKy644AK+W/ETTWs04qu/FnNO07qk+Irw67bOxao/ghiads0Y5FCJ0EQZc0f4/xjmItISsQD7HuoSYd+L3VQxu897bfKrcGLtphk/pCTPAI4v6/s4aiE5Fq7GBUeMGPr9/o7NqtW8/JK2XfArwYjzXnsfx0UcXe0qBIpSPj5vt3uMryyvQMOGlNBJEkMyHF5ARZqTvkBSKBW+/fZbrrnmmnLdx78d/fr1o2bNmowbNw6Al04dReWkIprXXEog/SBKYkizHQRQhbaSDina5ujG+5BCj+ugkUThM7yDdUKoYG6mrqmPpE7IwqttKRSQAuGqelDD5Rmmha6qZY1EmltdWcrX/g97QWuq35AuGQQVxVaWKXm2OLJ4wZhsYkk8FSGZvGQZ3WvWZuCkiTHLNTB//nwuvfRSfIs+CS+ihMTvs7dNi6OmkJuSxOD3Z5GYmKjdr6JqBhWWay9tV5fv121h196dfLp+E79edipVMhJRhOCbtZu4sncn3l3yF127diUnJ4eeJ5zAw1WSCEmVgC9ETk4O6enph3RrvXixZs0aevXqReXKlQGoXLkyDz30EMOGDWPChAlccMEFR7xN/x9w8OBB6ufXZvPebYzsdBYVEoFlRdz04U1kp2RxadfzyUrNY/mmxVxyySUMGTKEOnXqUBwqYc3O9bSrVpNUv4JfUU0v9hKpmNI4w2PdHs9WMqJVa86dPY1Vq1bxwhsvc/V5F3Nl68qkJx4wzSiCqp+iUIJejooQKqrUnN6sYaqcEQcABzGMjFwA4TnKkDraHU08zFksQg03x06A4c1bs2Djn70qZmQO2r53z4dlezNHJyTltDH8j5LKIzJiCiEyqmbnzLin8/GBFL9Kkq8kIiUowYhjiYp23CuZBv9KMCIl+4pJVNzPxZv8Ssisyy9CZlKEaibnMb8Sjl1nnAtYjmkkULUkbdVpXqPoq1F90i8sLCStFAGE/2uYMWMGn3/+ORUqVABg6vCLOb66oE3jn0nI2Y+SFoQEFfwSfIBPInwS4Ve1/30aETRUrhopVDUJo6Lb/xnSKkWa0kWERCgqiiX5fEF8viCK3z0Z5VoTijTrURQjjmEIgYoiQvgU1ZYUJaTVYf6v4vOF8PlC+JWgadtY66lX9LJUfErIlqz900jufc/ocyEzKUKlOFjMwWARhYWFcb2jUChEQkIC8+fP1x6x/jwVEYpMinbfLw3qxIPdW/LDuVpsQmNw3n4gSGGJyoEilRXb9xCSku/Xb2LEiBHkpSXiU0ARIR7u25I/duzm+eefRwjBvffey4cffsjfp1+KlIKuNfKo4hP8dM89h7I7xv08pk6dypAhQ2zHe/fuzXvvvceDDz54xNv0/wX/G347Ow7s5sbel9K+RnN8QtIwrx6Dmp/I7SdeRY2sKqQl+GherQ2KovD111+Tl5fHypUr2bhnE3fNeJF9hbsJ6ON3ghLS45KGSFSCJCpBkn0lJPmCtrnBJzQno9WrV6MoCllJCST4giT4i0hMKCIQKCYxUERyoJBEXzFJfu3/RH8Ryf5CkvxFJPmLSfEXkRIo0v73F5HsL9bmOaWEZF8xSb5i2//a3+E5NMEy9znnUbd5N1EJn3fOt2YeX4i7evYlKyFxqhCi0j/9jg8tBFItezpirRQiWQjR8EjVd0Qkhm1r1vp6cL2GFepkpQIlEbYPTli3CHOzQfS8zsU5JF64rRqcEh8Dbht0GcdU7Mb/xurPVBkL4aJWFqhS0Y2RtVhXxfqqr1+/frz++us88MADcd/LfwknnXQSAI899hg3de1Fl5r7yay6GSU5BAFp7mQjNTduPeYRGiGTgApCEcgQYZIotY9aAFIBoephG0xplWWlbrx3Qy0N2vWmhM+yynZI7kwJoVTAp/cli1G7VqZqK8eHYVpgSAfDdpCGM4yQko03no9fsbfB7K8OO0NVV1e7QXGYuAshufi4Rpz1/heMHTSYGz//zPU6Kz777DM2bNjAK81zUIQwJX+udSoSoULLypm0qJxlU7tvPVBM87Efogjte8lMSmBnkaZduCfnIIl+jVwDpCX6eOGUtgy77UauuOIKWrduzcGDB7niiiv45ZdfWHvdBRwMFrN022qklEc0zJPP56Nfv3689dZb3HTTTba627Zty6ZNmygpKTm2K8phwFXTxlDzo56cddow8jIqcnGnM6mcnsX8NftYv/Nv6lWqj0/xkeoP0aFDB4YPH07r1q156qmn+GntYh599FFuePARnuw/jNpZWWa5VumbdZEPmvTvQEmQ5cuXk56ezvcPPw+ymLTEPSQGikHRzViUIH5fkGDIb7M9DoX8WpQEhzmKEQ1B+63YpITOeKdeUi+7wYw7vK61fr9V0hO4pkMnMXHxwsVCiMr/mfiG5VUlH4GnIIQ4BXgCSABqCyFaAfdJKQcerjoPOzFMSUk5qUt+fqtTG9VDEUFXezwr4vFQ9nbysNtnWVEakmiF+dF4XO6syySIxgerE0DrVmdCtydRBJptoaroXsgaiTSCKicoKrt+Xk9yt+gG8/9ljB8/ngsvvJDWlfO5uXtF0mtsRCSqiAAa+ROA1MicBN0F3CBKIBWJHhxQO6YIpKoRSqkSJodGf1TDDh2m05MUGqnE6A8yTBSj9KvwgKtGDL5SV19rfyt2e0cpQLUTRq0titn3DdWTcb12v6rtt3Gd4iCfTji/ucoZCVTPTGPTvl3MnDmT5ORkOnbs6Epkrr/+eiZNmsSdbWuTmgiCsIreILiayjzE56s2snFvATUqpFEvO523Fv3Fj39vp1/9qrTKr0idrFSqZKRQMSWJK+5/lKKiIqZMmcJzzSqRnqhLYS1tbZqfTmZSAh+c3Z0ZKdpium9aCZtvPgefCPDxOX0YPGkWH3zwwRG363vkkUcYPHgwY8aM4fHHH+e8884DIC0tjUaNGvHRRx8dszU8TBg4cCDb9uzkwk4n8/jsV8ivXY0W7Vrw4eczWLPrVYa26MvJTXtxduXe1GjgZ/2GHfTr148JEyZwyy23kJuby7VXX8trQ4ZTKS289ahmEqSaDhw+oTLrr79YtXMHG/fto127dvTp04eePXvyeqWKfLtuPf2aZCGEJKT4UFUfiqri002pTHMRRRsfQqr1u1VQVQW/FBEmJFYTJTOygkO17DQbcds1JRoM20Mr+tSpztdrVlXalZd3GfBCad7JMZQL9wDtgTkAUsqFQohah7PCw0oMhRAZtSrlvn1vry4k+ktKdW00m7547A7jNbh3M/Q1y9AYQ9zlW68Ne5NKhAyTvmirE2N7I9BsWRJQSQsk4PO5ySj/f2D79u0AfHjWCaTlbUBJUyGgE0JrnIWQ/py9NqEGk/BFkEPj/UsBPksIGjV8XdhLWD9kI4oeUK39gXAdFlglZsZvgzRKqYQljA7pomIuPBQg5Fqe2+4p0sXrUYhIqeH6PXv54s91fHTm6QghOKFeFZ75dhFjxozhxx9/xO/3U1BQwLJly/jq9B7kpCrsKyrhw9/XM2/9Ntbt3s/GfQXsKSzmQEkJWcmJVM9Io3leDpO3hti+/U+GDBnCaafV5IYbbgBg261n8tOok5m5aiPvPHY3X/61mV4n9CU/Q+BPiHSIlFIwumt9rp/xKzkpS7n88su5LmMXqpQEAsVUTEzgiX6tueiKK5h4x9V0OPsybrjhBsaMGcN3EyfQZcQF3HjjjQQCAb7//nuuvfZa6tatS+3atXnooYfc32mcaNKkCX/88YcZ1Pq6666jbt26bN68mW3bth3b6/wwIzk5mbcWfMmvv/7K4sWLOe+88xg7dixPPvkk89Ys5MyWXTko/JzQuA8+IRm76iMGDhxIMBjkwgsvZNu2bdw99jn+N/g0G6myb2YgWbR5PdP/WElqpUoseP55QHMYPK9NDb5e9zcnt9LMgIRPRYZCqKr9+1NVHyIkUVUFxReyOLGFTLJoDatlJBXFFEJYF49OQuc1D3rZJzqPG8H5DdzevT3D3/v4OSHE+/+V/ZTLF8cwOoQQ1YHXgcpoA/M4KeUzQohs4B2gFrAGOCPK7ilBKeWeI6n1OKxb4nWuV2v+0Ho125/etN4hKc+p9ooGtZTmk64SHWeeOD8yq9jf+FhDqmIaFFu90EpUHxJBiaoFTtakhZoBsQrs7lmdzz//nLfffrtU9/NfwcyZM3nkkUf4qFcayfV3QSJhlmV03SAaNwppKmNCQvvtdEQB3QlFH0itxyBsM+KmnlGj9w+3eJiucCFrXnV7ekDLSO91o41OFbdzs0ZNehdWi3lBk5RqhHPN7kK6vjKdytWqU7duXYYkCVRC+ISPtlWy8SmSnzdt4p7Zv9I8L4fTb76LunXrUqNGDbKyskhOTmb16tU0adIkQqUbDAbp0aMHgzL2cvFxjcKTsCJZvmUfIGiUl0bAIIaO5yelYPryzbz0w18cVyWb27q3sNUhhOSbdZspHngVb95zNd9tKSEhIYGHuucxeeEGVm0/QFLl2qxfv55HTqzKw1/8xT6S2bXLa4wuPaSUbN26lVWrVlGpUiXq1at3bAejI4xVq1bRqklz7jnhPPKzalIlqQCAItXPrpJE5q3+lU83L+Cnn34iKyuLUChErexsHurdl9b5VfApqumgIdCiChwsKWHsjz/R/JxzuPbaa20L+EmnncRdc75m3ugOpCcGwmGvHN+0DCmEQj4zXqoJ1f79hyXvkWOCkziC1Xkudj8z5kpjfnXOnVZiLKXgy7/W8b9Fyzd+v3rdv3rHBSFESqca2Qc+u6BbmcsY9Pr3zPlrm+eWeEKIfCBfSvmrECId+AUYDIwAdkopHxFC3AJkSSlv9ihjPPAlcAswFLgaCEgpR5W54TFw2CSGfr+/Y9fqldqf2awmQti9kGOJtK2d0Jk/XviI9NT0+kisnqRueb1IphnGxGLTZXiJhlSftoUSCkjwKSqoir6VmRbXynAGCEqfFrUeVfs0dW1mUFU4+PEyPv/qcwoKCjx3XPgv48svv6Rx48Yk5S1Fpuv9oSRsP2hCAaQuNXS+eiF1KSFhJxRA+NCJhtQkh+ZrlpoNokOSaJ6V4VW5tW8K4lh9WgmmVcIsHGRUFa5qaqtEMIIECqeEESLU2AJT2hi+W8VyOvxQhaLdX80KiRSFVNauXUujRo1YtXMzuamJtM6vxNgfF/HJijW0rJzDq+9+QL9+/Vxvu2nTpq7H/X4/I0aM4NVXX2VUoESToOgSiib5KUipoChBMwSRsRowbEQBBjTO46SG+Xy4dDO5j7zLua1rkZWUQPWMVC5sW5futSvhW/YW/Ye35a9d+6mWmUhyouD01pX4dOl25q/bzZNL93L5u3tZdXcXmj80j1AodMgk9UII8vLyyMvLOyTlHUPpUFhYSPsWrbioXW+a5FUhWSlAEeAXIZJ8QQpVH13qtGb1jjXcc889PPPMM/h8PkbffTfT/vcKnapXNCNpvLHoDz5asYrft20n5FMgGKSVzxfRV85671OerlmB+et30bdJDjKkmuTQGtVAKmqENNFK6pxmIW6LP7B/w6ZtsdsC1wXWSAhW6WBE3Tr61c/noxUrq+RmpA/ZtnffB1ELP+oRH3kuK6SUm4BN+t/7hBC/A1WBQUBPPdtraGpiV2IIXAXcDhQBk4DPgcPqdHBYiKEQwp+Xl/fGI33bkBCIDE0TD5z2EuVFpFQvusjcugrztNESlo9Q/zAFht2IFpBY0VmeqqsHrWplH9qevT5b/HpVd07RAqhWTsugWrVqLFq0iE6dOpX7OfxbsHLlSiZMmMA7455j1lVtoJaiq4zRuIHRrbwEwwLd0UGYdobCcNZw2XRUO2c5bu1zzvdujY/oUq0TNu81qzQLOxG0HhOKvV6rKlu6OMJoA3soXJ5iGdgdkk/nwkw4FlHmef3+pi1dR9sqOXz71wYef/xxiouLWbl+PZN++onOnXuw5usfyczMdLnz+JCXl0dCQgL+xGLUEr95P4oR6kYKMz6lUFQMm081pJg2oIqikpms3ecbC9aYZXeokU2zyhVMiUydCun6DjgqG/Ye5Iw3fjXzTjy7GSmJPgK+wzdRHMORx9KlS1GlpE1+NZKVEn17yyLTiSTDX8yekkTumPgc559/PqFQiGeeeYZH77qHYc0akRw4CMCa3QU8tXAxb7/9Nm3btiUjI4PZs2eb4/KOHTtYvnw5O8aPZn7+CazafoDGVZNB0dwoESHd3MUyBkiBDGlhyoTDDsaIreqmzXKahjjHEesxp8TPPO9QWwNmLFF3ExW9Cim4r3drhr07Z7IQIlNKeTD+t3F0QYtgVo7gLNpzOV8IcY7l6Dgp5ThnVt0usDUwH8jTSSNSyk1e3t5CCB/wkZSyDxo5PCI4LMSweY38+/tWr1CvQcUUwjN4bFg7nyGFc0Ztt+aJR5LolOpB+IOwf3D2j1KV7lJNg0A6P0ghpU74jMnYqDOkST2EUYcW4V5BatsSWYKNhs2KtXsNoIVgMYLiXnrppVx66aVmbLT/IoLBIDfccANvv/02p9RM5ss7m1KzhU4KJYiQtL8q/bi2ubTRaezeyUIVuu2g5jQSlhRCWAJl70s2VZ+jmwlfKRcqzn7qVFE7JJIR57GTSCtpNKQIQrpNCOFvxxz8HLaRwmmdodgloXsKS7jvq6W8dEp7EhMTueOOO+K+7Xiwe/duLrroIl566SXUZa9ihg4CFJ+K0A3zpaqEPZ31cUDxWRZsquD4ernsvXcgq3bsZ/fBYnLTEqmVlYKUJbbJEV1VXSMrmZt61+brVbu4tFN1hh2Xy5aCQlSpxcP7/xwm6r+EZs2acU6L1lz76Ruc0rAFl7XvTkgqJChBfEIlzV+ET0j+fv5zcnJyqFixIpX9ibw6qA+NcnOAEv7eu4/hM3/irrvuIhgM0rlzZ1atWoXf7+fuu+/m+uuvp2OT6mSn+ElP8lO34kZ+ua0d1bIT0SYDVTNxkUSMB5rEUDH7sQFpqqIsRNAiKbcG5Af9O3fsyepGesLzWShyOzhzsRt5zrjOB1TL9HNei5qBKatSXgbOi/b8j3aUR2KoP63XpJRPR8snhEgD3geulVLujdeUREoZEkIU6AR8T5kbWkocchtDIUSlZnmZW2Ze0JPkQOlVMW7EL9qLc91Zwg1RbMTc7QndxehgF9Fb7b2shsGmKsC0NdSi2ps2hlJQIn2oeqR7bYN1nx75HnOz85BUKAmF2LJvA7fO/AB/hUwWLVpEdna25zP5N0JKyZNPPsk999xD1+rJvDayETmNBaQYgyNQIjU1sibw0WAQQ92mEIkeugZtIDauBcdq2lK5h3rXPBS/aavHzQnX3zYbR13yFa4zsk1OqYCrzZK5g4uLqtmxwLEei/iOCDF16Sbu+mIxgxpX4/kf/ijVLceLwsJCqlWrxgnVUvjfGc0073ILiRN+bYKSqqJJC61SWstzkKpi3+nGcU/SIqlRfCrCH9KCaht5fdrk/fAXq9m4q5iX55Zun+hjOPrx66gRDJw0jdGde9CnTh0zlIvhYVys+ikJFaOqe6leQZKcUISiqBQUC+7fm83LL78MQHpCAjIhgZo1a7J17UoKZICedRPx+QTvX90k/B3rY5CUaH04FP42zW9e/1uqIEMWEudY+HnOa8Y847BRNq91uyROIuS0V9SOhdsYVFVOnDCXXzfurCOlXB1XoUcRhBApHavnHPj0vF5lLuPUSXP5evVWTxtDvZ4A8AnwuZRyjH5sBdBTlxbmA3OklK5xCoUQU4COwBfAAeO4lPLqMjc8Bg65xLBy5cpP39CtKmlJVn2fhlK5yx8Cvb+tM3vYiAGuO1nYCLOwlIXi2HTc2CgpDEWq5p63Wj0KqhZNTpMWCqlvVRRWIUskfkKaKlpfTWj7dZagBCR5yTl8OPxsHpj7Jffffz9PPfVU2R7KUYji4mIefvhh3nrrLX69oQV1G/mQFRWkRaUntC1ikCIcOiaqL5ICGksU4XxmLEGLylcVFimaftIB4dPzee2kY+0/LsTFWqTVzjG8apQR+ayTBobNo5FbsZRtjReomyuY+Y1QOyK85V74uzK27Iu8nZKQyplvz2f7gRLe/PBTevbs6X7fhwBJSUksXryYk046iWHfKwxL30iltAQa5KZTKS1R+zYVTRZvBiQ34JMcLAmxfEsBNTJTyUpKsBNdyy4qqIrZb4Si2qSHQsHM+9mSndw59v+no9d/HVnJCTzetw+XT59BXtpQmlfKIcEXnqMqJu3GrwQJ+EtITCpACM1b+MGd+Uyf/jFz587l/pHns3DzRvLr1GH1H0spKA7x2oV1mLV8F7cPqqFbA1kWeRZzFgHhhatTaqhou/84HeK08Fkiqv2yVn78xBCIWEQ5j3tHzwirl31ScM8JDXnomzVfAnU8K/t/DKEN8uOB3w1SqOMj4HzgEf3/D6MUM11PRwyHlBgKIZp2qFdl6JDmlSNCYIDWCc0OGU0NLEX08zHg/CCMwMPhScMhYneQRnBRsWEQSs1LQVoInmbgbw8irOhB8kLSp5NDjYz6FFWPVxVerYYr0wiiJjGUtp0q/EqI1IDCyfXr8smqVWV+NkcbfvnlF84++2zq16/P9POyqd3aj0wSGik0xKba49N++yU4Ix9Z35XADEOj/TZ1+u7XWAZG963udERTH3ud88kw2TT6vUWNHWHraJEa2uwhpdC/HT0bmIG5DUhdAmFMIOa3ZpAj7Hndvi+j7z/69QoECr/8vfWIhErKz8/n22+/5Z133uGtKVPY8c18Vu6XNKvg46JO1RjWJg/hMlJ9umwbF09eSqW0RDYcFFSvXp3XT6hIo0rp+reKKWW0OtWg725jmxSFpKAkxML1++ndu/dhv+djOPKo/dQ4agPr+w7i2hkf8OKAITSumGV6HBcHE0hOOUhiUgE+3TZeqCqjK/zOdEXhrbfeYtIPP9K4dj65ubnU8ldg/CX1qZAcYFjX3PC4YzfPs0N3hLNCqtL89t0d4hzOcOaFejk+CxF1hMjyJJMeZnXO4P5RNXKqoFe9HF6a/1dtv8/XJRgKfede6lEMGUUaG9f1MXN0Ac4FFgshFurHbkMjhFOEEBcC64DTPauQ8rWyN7BsOKTEsEvd3G8e6lszwRfwFuVEdMhSvJR4XqBTUuJ9vbtDic3D0/JbqBaRusWFVQHducSQymCql6XU9rpVhWYjJ4XUvJUVFVSN/IVUBYvViEYiRdjhxtjOzCdUFCEpVhUUv1b3H3/8wbp16+jevTsJCQnxPcSjCF988QVnnXUWTw+oxJl99iArJ2qE0K/tECOkzggdBEoKq8ew5YRiIWAKlv2PHV+vJq41Cgsf98lIKaTbAOr0ho4iuTS7oLEQt0glTUmkAcuEYE4UimWSMCcAaUoStDJ1CbhBBHUVs/BZJYR6Q0KKq+TemARCIYX3l2xg7EnHH9H4mampqVxwwQXmHsLFxcV8/PHH3HbbbQT8WzmtTa7WTv15bdpXyMWTl/Lp7G/o0KEDqqry0EMP0fuJJ2iQIWhfowKju9cGIcnPTEDx2UlgiCCo4Pcp5ktasaWA2hWTSExMPGL3fQxHHjfN/BD69mPUx+/xTP+TOa5KZdMj90BhKoGEIvyiBCWhBOFTqZUt+O7SGlw2bwMvv/wyuekJjDlxJ02rN9ZLlKbZikbsdLJn2Dn7pNbFhLtWwiRjxpCmGpJtwgvFaCYuUuA8a3NQs51wmUMtC1KbKZfZvsg4rnp4DaQUPHhSA7YcKJ4lhEj5N+6IUj7tZCzNp/w2Sqa4VqBCiNW4UFAp5WGT0h4yYuj3+zuf0iQnq33tdKwGsTFRiv0GzSkuqs2hm1rY3qmdXmHWfGG1m3UHBwVDwBgmiLr6EW13B59+nSrD3qFmGAApdS9RTZITkoom0dGliCF9k/aQTcdp3I80JYYAaQkJ7N65E4DzzjuPVatW4fP5+PLLL2nWrFlcz/FowMaNGznrrLOYcl41undNI1TJbyeEQav+Vf9fX4kLKcPHBHZypngMutayrKY8TqmdCxeySSDd8oT3Q7QULOzHDDWmtR2WA1bJoiE1iCpJ0EmidSFk7aNm8U7vasNhwyq51/u9qirMW7MTRfg46Y1pkQ/iCCIhIYGhQ4eSnJzMqFGjWL5lPwNb5tC8ShoChU17i6iYGqBDhw4AKIrCHXfcwejRo1mwYAFPPPEEXV+dTygUIqlkH4OaV+L0VpXJTQtw2bvL+H71bgI+QZ2KyVx7QjW61svk2TnraFM9/R+972M4Mrhp5mdknNKbe+fM5tNzhul7eWumCnv3V6CCsoPEpGKU5CDCL6mcpzAkZxXPT93EgeIQB4OqfRwwdljS7QmFH2RQ6ntcWsw+IHKKN8cAtwWjZTFogS28lst8aDviFH7YQnM5jhvXO8Y84VqORkgbVUmiZZXUpJU7C08F/lXhayRExHo9CtHW8ncSmnTxsDoZHDJimJub+/xd/WtqxuKOFUzUzabLEui1NAzfaptlqunCZdi8vQy1m0W8bJBApyQR3SgYQOhEWAgQuprZKkWUUtG8lqXQVMtSIqUWy9AkNcJqc4gtCLFAogiJIkLUzMxkxbx5SClZtvAXVt7biSum/M633377jxNDKSX79u0jKSmJQCDAzp07ycrKQlEiP7wJEyZwWis/3Vuno1ZQkMk+CEpEiaoRPxVTjSwMdXLQItEzXqrx/Ly+bTcpIJjjrIhTKBaRz61POwmnVTJpnrCqYvT2ybAaW1ri9Qkh3KWIFuIYIUXUCWLE7kCqbtBglmHp02q4XWt2H6Bpbs5RE4C5f//+TJgwgRkzZnDalPdIS0vjmhbQOD+Fv/cUsvuBflS4I7yfc2pqKl27dqVr166A1ieXLl3Ke++9x0WTJrFlyxZuueUWvrjhBnbs2EF+fj5vrKnM/XPWULNmIyZNmvRP3eoxHGFUGnkF62acQUHJQXJTw/E+FUUlWJxAgloQ3n7TJxneK4dVO9ZTUKkix9VPC49B+vgdHosM21YjpqpjLHAbA8BOGn3STg4tkNKFPEaD07TKI5vzeFiQ4lKWFLboDLf1r8mvG/dNFkIkSVlud70jiPKpko+EeFRKucNx6GkhxLfAXYerzkNCDH0+pdfprXNbNa2WisHBTTg6kBVxdR+3l6bHNIsHVmPaCImjGjbqNbwh9Rz2eHEWohgmiYrNtsOc2PWwNRAmiW4EUUqBgoKqqvgV+24UqhSoMswoNNtETWJYJT2FvTt38dtvv5GSoJCVnECC4juiwa8LCgpITk62kYdff/2VXr16ESzcT0iVKIpGlBtXSWHq18v48MMPycvL4/TTNVOKdevW0bpmKjIL1MwAqDopDMmwXSEWUmisxq1QHP979SdflHMOyZ0nnHU7Caf1vCpMVbZ5WrVkNKWIYDrHGARRvx4MkqhLEV1iMDqPuW7xZ22vpd9Ly/dgqNwVRaVuxWT+/nlPjIdx5CCEoE+fPvTp04cnnniCuXPncuedd7Jw+kLeeud9KgweHPP6Zs2a0axZM+6++26Ki4tNVXHlypVRVZWdO3eSk5NzyNs+Y8YMnr3hHBQfVK6QwGNTlxyWeo6hbPD5fOSmJLNs6y761LePn8Knx8tMlJqMRkAgSXDfWTW0WdOYOY3P0UoOjd8+GTYTsi1cw/bCtoWjk4BZ502n9A4MNYMdrkKY8tGX8LzpKN8yvtTITaBT7bTApn3Bs4E3ylXhEYYzxnGpUC41dHwQQrSx/FTQJIiHVbVxSIhhuzrZ7912Uq3w/rE2ZwCXSco45SWtseWNLDPsXWjJZRjmO46ZpNRhNKtl1idIXfXmVLsZUhVzsnWRIprmIToJFOheaFILHCwREQQRQEpF815Gidj3UiAignqHJUAKrdoex9y5cxHAz2v20qRKKj/99BPnnXf4wknt37+fe+65h/fff5+/16+lfoNG+PevpefgC3nqqaeYMmUKtSuE+PnBzpSgUqSqpGT6ufbFP6lRowZDOuQw74991KxZk/bt25ORkcHezSHUbL+mPkbqNoUuMKSHxt/aY7D/Blc1sE0N7SzTimjaBGdeG7FzHJPYbB2NY7Z+aS3HGnNR2K/X7FUJq6mIJIOaHSLmsfDfwmLgqF+LRWoupNYOU4ouUUOSdxdvID8jOcrD+OcghKBHjx58/fXXlJSUlNquVggRYT+4fPlymjRpwpYtW6hUqRLff/89QohDEkz+rLPOIkUWcHP/GsxYvoPbb7+dl156qdzlHsOhwaBBgzjw0stcMnIEv1x2Crlp4Y9UUUIaORQg/cKUBopUGXYssTqZWDQ/dnMVi9kLOJzQHA1y0zgYiGuuJEwmD4PMTkhskkynOeFN/Wowf+3+8UKIN/8ttoYSyiUxPEJ40vJ3EFgNnHE4Kyw3MRRCtO7fpnp646rJmCEwnEb1oAX4xOW4ARcyCO6Ez7UdLuftu0dYJC062Qv/LWw2FLZ4ccbE66J6NtqtHQurIqQSDiisxaFSdUcU7be276W2w4mihImiGfvQ8iys2/FJKfArIUZXq8Y1Dz/MKWeezzXvT+bB02px9/c/R39A5cCiRYsYPnw47XJ2MO3SajSpUZX5q/fhpz43T3qNxISxhFS4uG9lqCgI+P0E9GufuqYet59Vg8rpASZ8sZm7RvXjs193UrduXeYtKDDVwKI4chwRBvmx2hD6sZsmRJMGGuW4jVGl8atwdk1re5xE1Tjmc58QINxXwwRRhj2nrTaJVgmiw8M57JRiWbC4Oqpg7hpiXCtRwmpnvf8XFIW49uNlLNq4m+9Xri3FwznyEEIcMmerhg210GFDOjTi29U76dKlC7169WL27NnlLvuaa65h0zcvc8WAfM46pSLNrpzAhRdeSLt27cpd9jEcGpx11lk8et01rN69i8pZGQghUfwhfInF+NKLNWmhZZbUtjzV/hZS/0B9aATRa7FqHQe8TFscMNd0sWzwy8G/bMIWJfKYDdYwOvo92FomoVpugGZVkgIL1+89Hm1v32M4NLhQSvmX9YAQovbhrLDcxLBdw7yJN/XNDgi/IcHQT0QJ4WFCdc64kbDliPYNeH5AFhUamLZaYbInTVJoJYta3jBhDHsnh8mfcdxQOxsqZ6dE0dzD1mqTaJEkAjZ1c/h+7dvxBaUfvxKka60krtpdiZ+2b+fndftYu6eQP//czMKFC2nVqlWUh1Q6rFmzhssvv5xf583igbNrMrJvAy3GogId22QC8GWblhQHVQJJCvj12IOWAVLxSypVT0IWqbRvmsG9764DoG/fvtx3333IgAKK0P5XpRnaB1UiFWEhh5b36wzbEGPRYKj2y7yKtk4ERjnGgB+LJJr5I4miuZhRLBJAcx1jabNhs6RfF6lilo5B3r4YEj5p2hEmXT+Tg49r+xlLKdhXIFm44QAPfvU7uSlJzFu1joyMjDgfzL8fiqJwyy238Mgjj7Bo0SIAmjRpckjKvuyyy2j87LPcvr2IqtUSeOLiOpw7pAe/LN9GamrqIanjGMqHXbt2URIKciBUiD9Bk5T7kopRkoKIJIkMiLAUELQA+wpIoTnK4bOMC27aCbcxx21R6ox0YBw7VII3l3Zoc5vlh2G76FmGsLXHRiL1v687uSp/7Qx+AGSWp7lHDI45t9SXH8KmRMF7QBuXY8cdrgrL5Y4jhKjiDx1s0aVRujbx+aTW6eNOMv6kaIb2EUlo/itu51D05FM1MiH0cwrhoLaWZJ7z6b8VPdaZsU2XvpWWdu/h4+EHEv4tnOfADNBrJlQUEdJWqfoxRQlpagyhWv7WyvGJEIqicv/XP/DYN0s588wzOeWUU9hVVMLjZ2fRo0cPJk+eXJ5XamL16tV06tSJbjm/8+cr7RgxIB+ZpGg7kSQJSBDIJAWZpBBI90OiD5mgIAMayZM+LRahDCgQ0PJO/WE7gzrlGH2HQCBgl+YpwiSeKNqALPX/USzJL7Sk6MnnkQT2FK0vGmU7fytoyyfrZGB8NQb/sto6GiEqnfUaZZvXyPB5CPdJs2y9X1qvE+h9SOvzRjw+Z5BmZ782+jZA4RMnknyj5qyxZsdBOr3wNbd89htdh4/k3cV//r8ihQbuu+8+AHNR9fzzzzNr1qxyl5uXl8fIkSO56e3VHCgMcWbXSrRrmE6vXr0YN24cwWDZ9pE/hkOHO++8k9y0RFpWTUFJ0EPUBEKI5BCk6fOZMQZ5LCzNMcH53Vu/fa9kjDcBwraLxjXG327lxhrP4klKKfIKtHnYH56ThY9w8mupWc1U0hPVDCHEoVldHQFYNXWlTQ656SGFEKKREGIokCmEGGJJI9Bk2YcN5ZIYVqxY8cqLemeh+GNL/qKdiim+jrggivQIl1Nuu1sYYUH08sISG0lE/DgF/W9dOugICyLAnNANl397cFA1HFRbt1s0bBG1NmkOK4oAt32cVSnNgOFSKqzaUULFarXo168fWVlZXDVyEAvGtmHNziK+++47zjzzTM9nEi/uvfdeRh2fyI3n1tAIoOmMbSFuHr81r2KLdC2kghDkVExk1eZCAKpXr862bds4oNZn19ZCCg8GoUSlbtVUPb/+3IMyvAOK81X7LOpmN7gFtnYiHimiKsMDuaqrsq2SAqsEEdwlBk67RKsU0almNkJFGDaIznsRuprZoWLWvJjdbyFsb6tS+MSJSNXPDdOXcGazWjw8d0kcD+G/i0AgwIwZM+jfvz+Lxx3Hhl3FjDjrZBYs/Zvc3NxylX3HHXdw3nl/cM6z3/PWHY0Zd10DPv15J8++eBPvvvsukydPJicnh4KCAiZNmsTSpUu55JJLaNy4cezCj6FckFLy4RsT+eC8dlSvKhEJxQCIZBWRLJFJ+gIUTFtCm0bEWpYWVsJzPDHGCmlx2IspljE1WOWQS7k5ucRTnPM+nCG5JJ7OMZecUJmDJL0I9Ii/of8cDnOA6/KgITAAqACcYjm+D7j4cFZcZomhEMKXmRi64vQuFSEg3ZNT6ud3JGPlEbAk6yokoCe/PcUnYSRSMmlIHXUJoylRNKSFPhkhSbRKYtykh1bJoTB2UxAOyaBFGmiTBPpUMyl+TRroU4IoIoRPCZop4CshwV9MwFdCYqCQlwa1oIFSQGZmJoFAgNrNe/DURxsYfnwlprz1MuPHjy93x/j283c4tXsOaqoPmahoUkAj+RRTGuiZEn2atNAfzjegVy6fzt9JYWEhfr+ftLQ0vl+2h7p95tL0lO9pOuQH5i7ajfQpZkxDowz8AgKRUkF7u4Q9+UXkMUuKuuq2rewtEkgfYQmiT2+jMVk4V9hWiYHfUp71uLESN6Xb0iTFwnqNzyIBNOuK7NPCr2oSciNZ+7GiamplEeK57/5i+dYDXNO5Qbn7yn8BJ554Im3btmVF9q30Pi6LM0+ozCWXXFLucrOyspg8eTKJNY6n/y2LCSiCgd1zmfFca5pVXUnt2rUZPHgwTZs2ZerUqXw9cwLDBrePMOw/hkOP5cuXI1FpXkdB+FWUVBUlI4RIkMgUfbwxJIVWM5Fokjar5M+SNE2K0M4byYdWh1vyhfPJBOGeAi6/rcnn8tsYFz2S7T6s7TSScU/Oc5bf/dtms33X3m5CiKPTi80CSXklhoexbVJ+KKUcCQyQUo60pKullN8fzrrLTAwVIU7o2zwpIyXFF0X07EheYnTbRBomcc7J0kiG2s2YPE3CaCvbMZFaRf2KQeosKjnr1mgOtZyVLBrED4ggh8YxZx6vZCWKQkgbUXRLil8jk7mZ8Pa5rXj1zBacccYZNGnShKXrC6iTn8w1Z1Rn+vTyb6u4/WAiObmJNhVqWDpIhNrXSpDUgIU4Glvb+QVVKifTsn4abz92HGvXrsXn81GnegrXjazFQ9fWB7RVPDqhQ/cGNAmYsJA647jP0ianitlQAXkkqQjPZMtrJYjGOUd/tj4j+3MirG2wHsNSNpY8YFMzW/t6uDwZLs/Rp2392ijLsJ/V/x/79d+89csmZnz/M5Uf+aA83eQ/AyEEd911F3fddRfFJSr3XlSLhT9+xpdflt+GPjU1lTfeeIN5y/dz4yt/cenjK/AHJQ9f35ClM9szsMtqXrq3Eu0brmTBsr3s3FPCO++8w049kP0xHB7MnDmTExrm4E8NIRJUSJXIDAEpQKIIayqsTnDGN6cI7+Rl2mIZC83kNUf6RewUEPYxL6I+R7kuhNU5J9sWzy5jbsw5PgD+RMHgjtmiYoWUsw/j6ztEEGZkkLIkaRu4DxsWCCGuEEK8IISYYKTDWWGZVcntm+Q9es7xlTDdT8HbvV5azisueY38zmPWRbPVm8ttMW1VDRt5DJWcNcCo1ajf4fkpI8KCWPantYQJcVMt23abwLhPxx6XNvWyE+GdVrzyGOeET0UJhDirc0WmLd9AKBRi8V8HwAd+BZYtW8Zvv/1GixYtPOqKjoULF5KkFJCc7tekd9blg0EIjXu0EESNUOnPySc0vb1P0chJUEJAcMeoOgwZvYjT1z3AwO4BalVL4a6RtVi+5gD/u7cJXY/L1o269aW6Xl7EZu+WDzLCFEE/pe04E+XDNdW8kc9ba79ejnVi0J9BONC2DDuhWMqVCEtfsqicXfK6fhPW+3WEuNCVUu59GsL9WhVhW3FVQYYEarGfV77/m2f7d6dRo0YR9/3/GQMGDGDcuHE8/d4SbjynJs/e2JDhp/dn2R8bqVixYrnKTkxMpG7dujz9wUqSk5O5/qyD1E/ykZvoY3j/fGb/vJMHx2p7oD96a2Meue8SLr2ogIp5NenSpQstW7akZcuWZGRksH79enbu3EkwGKSwsJD169ezdu1aunXrxrXXXnsInsT/D7z99tvc1DoLklRmLNpJXuMkWjXOMAPsixJV9zYWlm/fUoD+2cloY4zbKWk5buwH73XeDW7Tg8/+U6hSH0NdrokWBsfaFqt5DPYx1zV4v6Xss3vn8eWygkeA/7nkOnogy6lKPjJ4A1gOnAjcB5wN/H44KywTMRRCJDeomV2/XeN0u82Ey/O12UdYO5Lbu3DKL73ss9zy+Gw9WD8nzMnUkySaKgK92+t8xkoQbVuTGb+lyT7C5NDYfcIW89B+o9Y4clHhPG+GJNGlhwkhlMQQi/7ez/VDhzJp0iTWbS/i2mHVSUrfzBlnnMHSpUvLtN/tnXfeye0ja5GalWAjVvHYx9gHSf1ZhFTzdXdpn8N5g6uy5LepvPJoc6SU1DzpW/bsD1I4v7emzjDKQjFJVdRg6JZ2GdsPRr3G0p8iyKPN005G3JNJEs2+KCLy2yYR3SbRbFdUz2UsfddyzurRbPFmNnuIrU0OkmiBDCls2a2yZV8x3V6cEpnh/zmEEDz11FN06tSJgd0LOLFDNp3bZPHOO+9wxRVXlLvsDz74gObNm2vB9P0CNcEHCixbtZ8R1y9i5hezueKKK8hM9fH91K6EgOV/7ufHBQtZunAu0ybvZd+BIDXyk8nJDOD3QUJAoVpeEq3aJfHQM59RWFjILbfccmgeyH8Y8+bNY+uqRZw8qi2/bz3AyNc2U1Swmz+ndSIrya+RQmMRJ1zIn5XYudkcRtk5KMLe0Ccolx2hAcs3HyHttAlZPNoVjUw6yreV4UIm69VIRoZKcoQQOS47dxw1MFTJ5bn+CKCelPJ0IcQgKeVrQohJwOdemYUQH8VR5k4p5Qivk2UihkkJvpMHd0hJFn5hfzIuRvcRqwwDPtw/Bje3fa/zejl6RZF5TEmNQaosWQ3Dfmf8OEUPUG0EKRZSJ4NYbA2FI5hweJywOqY4mwV2QhiVHLrs7BJWN0tEIAQByagb7uXMM89k27ZtXPecZMoDTbl0UD7Tvv6N66+/nqeeeqpU25tt2bKF777+jNdv7BqbFEYMliLimNQu1n6oQEDw2G2NzYFWBlUqZPjZsz/Itv1BKgYSwmpbQ+IaS/KnECZxLvECTTiJmxq+L7Mv2q5xDK6EJYlClWFVirGQMFb/ZmcIt8/cRccqPYyoz+O4qjdFgjV4diRBJLwosRBJqYIMCXbtD9Fv/HxGtKlPWloaxxCJevXq8eCDD3LmY8/y7fMpjBxSlSvuu578/HyGDBlSrrKbNWvG3LlzOfXUUznz9iW0aZJJUpLCrHk7eGLMOHr27MnDDz/MVddcw6QxiTRvmkmT+uk0qa9tciBCqtbvVJexU5V0a5tF26F3ctppp1GvXr1ytfW/jFWrVnH2Kcdz5/Dq+DPh0VmNueWW4Txw762oIUtGgxBahx5jHDK+PWuUAut5L6gSN/WjLaRWWQy8HBK88HhmEX5Ec9TDQia1BoXLsi48Xc5HzKE6Tu2Sw4bdnA+Mid74Y4iBEv3/3UKIZsBmoFaU/I2Bi6KcF8Dz0Sosk41hp+a5jw7pkevpEOBp1O+wbYhwCHCzZzDKMm0vIsux2T5Y81vd7K2G/T6LTaJhi2izH3E4qIjIEDdmSByLbaCWTzWletFsBQ17QeFTEf6QLSkJQS1PIGgmX2IQJTGIkhjSwikkqIgEyQ21P+GxYekoisK3v+1m5s87ET6FSY+1YNaM8aV2RPnuu+/o2DyTlDS/t80cmIOBjTCaK+EwFwSQPgU14ENN9OmOKwpClShFIRRFcO2IWgBkZvgdA67w7icRNn0ibL/jaffnTOF7civbZu/jsFuUPoc9os8lr7PvWtpk67fCnsd23HrO/G30S2x2iM6vWUr9ZagKUhXc8NFyutXI49l5S0vVJ/6/4eKLL2bZsmUsWLWfE7tU5KV7mzL6qrN5++23y112t27deP/99+lx4vl0H3gvrXreysNPvMr5558PwMCBA7nzzjs59bI/adB1Ng8+tQKCIRsptMLat/Pzkrjtwtq0bd2YK8+oTlFRUbnb+1/DTz/9RI92Tbnp5Oqcd0IeoUT49NNPGThwIEUlKllZAZtjndUu0HRwC4hIZzw3BzinQ571mM89GeOH13nPZHXSs7bVoz0x7RedDn4xHPvc5vwhvXKpnptw+z/7xmNDi55bthRd53/IME4IkQXcAXwELAMejZL/dinl11HSHODeaBWWWmIohPA3q51ap3WjBmFpi20rOSJXTU4pnyrdVx9WSY5TwuJVlotdoU0F7VTPWaSIdgmiY4VlsdPSQoJIm2ratD3U1XdSDceKMwJnG/VYYbsfUwJp/207bjoayLCjjA+E4fUdgGG9KvHJgp1MnrONH5fto2/XXLLS/Ix/oBkDr7ydIUOGkJ2dTTyYP38+7Zpl2lfCcYR9Me0LLeoIKWDP/iATJ6/jk1lb+OHXXVTLTyItyUfXNhUYfWEd6lRL5uJBVTjjhMr4k/zaU7OpXN3qjuwTNsmfI8Bz+Cph6z+uu+lYpY/W/DYJt12SaEg/zX2djfZbJYhO0qZa22u93/A9RdgeWn9bpYeW7maDKpAhTWK4aVeQT3/fytot210yHoMVQggaN27M1rQbgfH07pLLhMdbcsGNFzJ06NBy77rSo0cPevTo4Xn+ggsuYOTIkaxcuZJhw4aRk76GK86piW2HHA9cMbI2Z5+Yx6hHVtCtWzemTp1K1apVy9XesmDu9B4U+G6lQ4cOZGVlHfH6rQgGg6xcuZI5c+Zw983X8NKI+gw6PlsjNKkK6enpLFq0iGp5iZqRtpBExDx2hudyHvOCzSYxnC9iXtNhM3+JJ9yW17Uq5jjsVldU0xyI4DuR2hdHeS5NrVk1mYOFhdlCiGQp5cEYNf5DEOXcK/nQtcQNQggF2Cul3AXMBerEbJKUEXZCejlpUsq9XnmsKIvEsGOHphmaM4ZOmG0endYVhJGcHsmxPEcNyaBFwvLVb7spDkkiJCvW5JSwuB23Sl0sEpdw3rCNmtOD2RbeRsGUGDoDZ9uP26WFttA4RngRRUX4LSmgJ+NvXTpIggoJ0kYKDUnsxQPyuensGtx8Tg1Ae24tG2VwYrcEXnjhhbhf7hdffEH3NhVKRQpRDFKkE1p9cbBmfQE1233BHY8t54dfdwFQv3EmL074hMqNR3L8yD/ofd6P3PLsKjJTFHtcQOwDrikZMdpkbZdFWuaaL0Zem5TQWb4jv3nM+mz0/2zezB55nEbsEWU683md83otht2sseVdSCCDPhasO0D1zNT/lwGsy4Lx48czevRoXnxrLSjQ6bhsalVP4aOPPjoi9QshaNCgAe+//z4PjNvCy2+t1ezeglIjiKrxrRlS+jBJqVApmSn3NOG4ups5d2jZHNDKilAohJSSi0Zvon///pxwwgn/mORy586d9OzZk7Zt23JyjzZ8NO52Zt3XjEG9spFJArViAJnq56IBAa666irq10glHGFBMccB6VPCURf8LseMOc0tWccgqzbDSwtilQI6x6cYWhNrm13rEh71WK9zud4sw6Uttjnd5Z67t8pEUUT3f6QDxAltO9oypsPeNqkCV5blWiHEJCFEhhAiFU3KuEIIcWM815aaGHY/Lv+mfp2yvd3kDbJodEYvwmhTMYvI8ow8fth5oIQT7lzC+/O2893KfWzbH/T4MIRdDeckpNZjgrCKWejqZVONJy3JaKcjBqIeJ84gd+b/pU0+qZM/Qz2skUCRqEKSikiSWkpWESkSkSwhWWpxz326XYhP0L1VBR64qDaBBJ8tnMtJx1di7ty5pXrHPsPWxLIijiBP+uQkQioH95awdWshGzccZM8ubRLYsq2Q5j2/IhSSdO9akV9n9uDqC2uzY2shA085gT+WvsqFF17I8AvvZ8XWhpxz+xKKitUI2ynnbiNxqYbd2hxFjRyTWGI/b/3tSg6N39Y8TtIYTb3szGetzwpjseKEztE120KFjbuKGfXBIm7rdSxocrzo2LEj33//PU+/U8h3C3YjFUGtGins2HFk7ejr1KnD8ccfj1qkaraFIRUR8pA2GeQwoKBmJ3LwYAhSWqKqYfFO4aK+h7yN+/fv58svv+Tpp5/G7/ejKAorV67ku3c7kZuylpdeeumQ12ngu+++46WXXtJiEkpNs7NmzRqeeOIJWrVqRVWWcVrrPSx7vyPTXm5Dgx4VKamXQkm1FEKpAWSij9EX1KJhtWL6dqkYJn+W+KvWv815zMhjJXFRYqaGTaVc1MfOEDbxkkyX5FVX1Dq8iGqs692uc6S+nStyas+qTx+2DlBOSP4VquQvhBA3CCGqCyGyjRTHdU10CeFg4FOgBnBuPBWK0gZSbd0o6+AXzzZPykj1GwXYM7ipjZ1wHIrYvcKq+tWxbmshib0+oFq1ajSrlcKpnSty/ZBqpCY5XKwMFa+02ONYy3M7Zvw21L4WVXH4vAj/thj2A0R9hFZVsjVWorU/KRbVupCa/aOwHDdIgk5qI0hEIDxYSZ8wg0tLn2BHgUqdzrPYt+8giYmJURqq4fbbbye44S3uHd3ARgytECGVUIlk4ocbeHP6Jhav2EdKajb79++natWqLPysIaoCn3+1lYrZCbRtlaWVo2h7H+/eW8L4t9byxdfb+GtdAX/9tZOzzjqLP5fNYvgpVbjsrBokBBQiVKxR1B+2fmjtT6pHHre8jmsiynQry2nYbVgDGGplRxuwHrdd71FurL5r9Fujz4YEhDRnE1mioBb5+XzJbp7+ei1f/bUl8l6PISoefPBB1ix+macfbMGzr61m8dK9THp//RGr//PPP+ecc85h+ZuNSMtKNAO/a9+/iFyoWLBvTwlDrlxA3WanMH78eH766Sfuv3kg54x6hrPOOuuQtfGiiy7i268m07NDNhkVEhjUK5egCm3bZDPny83cPzHADz/8cMjqA5BScs899zDh+Ufo0bICb83eysUXX8y8efPYsfEPejbP5OrBVWnbPEMjykl6oH6f/qAs44mQElXVF/0S+/dqk9w7xkPLuaghaxyImO9iqXVLAc9xMEZdcY2NLtd6elPr1xYWheh68cLgbyv3BNwz/nMQQqQcl59/4K3TTi1zGRdM+4jv1/+drat6DwuEEKtdDkspZVS1shBiKdAKmAQ8J6X8WgixSErZMladpZIYCiGyU5JkUkZmwFtMbTVI9RIxe0kXI4xlw6l61WTylp/F4vFt6XTC2dw/aR0XPLUiMjCoRQrjauBv3Y/Seq2xk4R1NxbdSSVitxW/Tth0la7w68mmItaTKQVU9d/SohbWk74LjCYp1MtPkJCAlhJBJglkokAmCEjUk7FfsWNnEgNSEYwZ9yfNm7eOixQC1K9fn9XrC9wHEENq5lMY//FGxk9P4q4HJ7F12162bt3KI488Qm7mDqRPIISg3/F5JimE8ICYWSGB666oz6fvdKJafhIdO3bkhBNO4Pb7/setT6ygUd+v+eSrrbY6I3Ye8JC62aSD1vx4SD691M04CLGLRNLWvlhqZWs+60LTKhFUXMoV9vO2Y1FgSLaVgEqlTD9Ltuzhh2u6xr7wGGwIBALsLGmH9AnOOr0GX323jW+++eaI1D1//nyuueYaJoyuTHqFhMjRWpWeE7NUBGlZCUx7sQ3b1s4gIyODYUOPJzcrwBtvvHFI25mZmcnAEyrz5L3NuXt0Q1q1yqZdqyxESKVLl1x2blnMO++8c0jrvPLKK/n8naf44dnWTLy1ES+Nrs/vv//O78uWsHZyB157oCltOmQRSg8QSg8gk30YMVmlX9NiWb854VNAcTiSWJ1ErA4Yxk4hFicRM6p8HMks09CmRduJxGhjjPOu87GbmthF6hdL3ex6bZwq8cSUANXyEvxCiNqHtAMcQpRn55PDrUrW2idru6SopFDHy8AaIBWYK4SoCeyNp85SEcPEBF+fnm2z41bNeap6rXYJEeVYiKNV9Yw22DWqkcILw1bwwIhafPDdDl75dFOkmg57/eadunl6uqmerd7MVk9mBbs3c7Qt/9zOByyk07rNX4Cw3aCDrBoE2dw+SY+WHybf+j36w/dqJTQzv09m8ODBcb/j/v37M2cxLFm+J+qKcda327npppvo378/KSkpLFy4kIceeoiXn2hluyTaSloIwSdvd2LRokVcfvnl/P7779x2221UrtaEM65ewA+L9/DS5PXM+GYbL01ay1vT/mb//qB7YdbVewxbwYh8ZhkxyKFLXbbfTntC80bdm+xK+nAhh9HqjgUhaVMzleu712PM13+W8uJjqFmzpqaiVARZ2Yk880QbTjmlN88/HzXaQ7lRXFxMr1696NRoH30758R9nekpDyAEKRkBPhjbmrWfdWDpR1144LqGh0x6FwqFGDduHBNfHcuZp1Wzj0l+BRnwEUjxM/Hp1lx11VVMmTLlkNgbzp07l4+m/I8ZjzQnr2IiCMirlMjSpUvJSvfzyU87w9tyJio2Kav12diImUEEDRWpk4RZ7OhtJE8pWzLaYEsGLH/b8rvAs/xo83C0hbFzHieOBXWM6/t2yqZx3ezzyvKuj0GTbAoh7hBCjNN/1xdCDIh1nZTyWSllVSnlSVLDWqBXPHWWyiu5e7v8O3t3zNY6mzXuEtgDY4It0LAJtxhOMaQfpjeU6ZAAKII/1O40qbGVy8eu4ry+lUlMUMw6TE9kXV1ga6szfqJTcqM6jkswg2ebqgfrPcUhvjFgqJKddToJgs8i8dRtME37TOOjxyoVdZBDi+Tp7JNDfDNvHiUlJQQCsaX5eXl5XHbZZUya8RoPNcnU71faCNLfmw7y7S+7eGVSuI9NmjSJvj0SqF0rNWIgs3rtOs/5FEHn9tk0qJvGgR1vsWNHEX06J3DvNe2555mVzJ23g9atW1OrVmNCoRAX3/kRN11ej+suqEW6Yc7gjE8IUeIZOmwYrfmsHs1quP9KR9xA45y9DqNcYXoi27yVjcWI4a1slGPtZ4722zyWLf3ZhPO3E0rY/jU3RyL/LvNGR/9v0a5dO6655hpCoXYoPujXO4+5s4/npIE3UqtWLU4++eTDUu/OnTtJSkri5WvqQbJuGuIhpRZSG1dt35YwfgtUIC07EVGikpPt42DBXgoKCkhJSSlz+3bt2kWfPn1ICqzlozc60qBeunnO/BJCEimgRfMKTHyqDqeeM5yHHn6M66+/vsz1Ajxz92ncfm5N0jIDSODzBbu48rld3Hnnnaxdu5ZnP3iN/idXCY+LYJ+LjAW0z3LQ8uwkxIz557zGvNb6CmTkbzOfIlzqELa/I1TOpYDNq1r/21meM0qD6cVsnR+tY57XdTGu790ph2lf77mMGCFS/gkYNoZlR3mujRuvAr8AnfXffwPvAp9Eu0gIcZfHqftiVVgq+cOmrXuatG+eGVNSaIqYo3lNOcXTLisN8FbZFa+fwbJ1BYy9uh4JicJ+Poqhv7NtnlJDv+W4c19IqyralPTFSAGLNFDfU9K2+bixYbpBAo06/NZ7EeH22zzk7KTQOmBdfE5NQgd+pmPHjqxZsyau9zx06FAmzSzk9akb2LW7GICDBUG+m7+De55aQZfhP3Dn3Y+Sl5cHQEFBAb/99hudjsuKWq7byjekSt6b2IGxD7Xgrusa8syDLbhiZG1CUvLMg80BGDt2LB988AEffvghX331FY+9sIr2A79j644iDhQE2bytiJISx0jlsuLVjgvb8/H8AqJJDxVHn7Sec17rvG/bgO0yqHi1x9mfjd+A6UCloM0+Vmcp3VGqWk6AtJRDaMz0/wR16tQhLy+PH37eaS5satRI5e57W/Dkkxccljp37NjB6NGj2bVrF7v3B83xCoiUTuMi1Xb8Nre19GsmHvVqpLBo0aIyt09KyWWXXUbrpjuZ8U4nWjSvoB13Sq7M/ino3iWXV59uxUsvvcS+ffvKXDfAgcQ2BEEjIgHB7+sKGDhwINdeey0L577GJWfW8CaFbtBJWjCosn13MSrSFEBETWjEz5psz8nlty2fU9rmwKGWRrrmK7OE0OVaj+vr1Upl+64DlfSQKUcdVCnKnA7FhjVxoK6U8jH0QNd66J94GOkBSwoB/YkeGNtE3C9KCFGneuVExZ/gc7VD8LRniIMwai2JMWFbjPxFicqL1zVg49TOjBpYJRw6xxYIWy/PqpJ2CbYtAyLSTkJX15qEzYXA2VKiJbkdS3S/TiYYagwRth8MoKmM9WQEJrV6nGlkULN5URN85t9hLzn9HoQgOcXPexPacWLXfQwePJj333+frVu3Rn3XLVq04KWXXmL6gnp0P3s+HU77nqrdv+LGsatQKp3Jl1/9zOjRo838qampfP7555w2MHa8NOuq9a+1B8hr+Cm1W8+mVa/ZfP3tNgBenbyOIRf8ROvj5wBw00038fLLL5OUlMS5555LZrqfjVsL+WXxHiodN4s6PeeQ2eoLfl6+z3vgIwpBdKhcTDjzEJ0c2n4bfVKHbQcFxZHPupBxa481v3OxY5oeGGYKullCQDdpCGi2q7lZPn76ew8lL/eMeCfHEB1XX301F167hrnfbDX7b/9++SxfsY9vv/32kNYVCoV48MEHkZu/ZOWkDmTmJdsnYrCQEkv/MCCc/V/rX2rAh6qrSvv3yC1X2J3x48ez8JfpPHRHE3NXJVd1p5VAKYJTBlSlc5si29hRFjz++OPcO2ENi/7aD0D347KZNGkSeXl5bNtTQq8uFSO/GytsEi2NwEqf4PaxdWjYZS4Z9T6lz5nzIkhfRPLFII5R0qEsK6JsvxJJGF1sISNsHRUR224xmpDHmPMdZQifoFn9NAE0L9eLPywQ5bIxPEIoFkIkowuzhRB1gZg2GVLKJy3pQaAnEFdQ07h1S9kVkvv1aJ/tTSUdsedsW/KYeSwqvziFFzbvYt2jUyqC1DQ/qRDB2G3icltbXV6ic99bizrZJl72eP+uht8OJ+moNmq2yV7/mISFROjHbIO/sRJWLNdYJgoU4/rw5CB8Cjdf3YCaNf7muadGce45u3jzrSlRt/gaPHgwgwYNYuLEidSpU4euXbt67rucnJzMmHvqk6yr891Uxtb3YvxdMTNAl/bZ/LX2AH+uKWDC1Kr06FzEecOqs37DQarlJyGBt6cuYtSoUdxxVT06tM6iZtU6rFx9gH49K9GwbhoBv6BCbksq5yZog57be7G+WyHs/dOpYnHrw1ZVirBs02eonJ3lmjeLqVszvLK1H7h/B5b8Uct1Bm73oa0J9aDXQjFOa/+WSImigCw6pk4uLS688EJq1KjBsGHD+HlOBbKrpJCa7OPBh1py1lknMmPGjzRt2rTc9axYsYIePXqwb98+vnioATWrJoe7hxHw3PqtQ3jB4iRmlt9SoC2OFR8iqHJynzyufOBjHn744VK38e233+aO265k+judSU7W+5LNNEeYx9zI4kO3N6Fx1yk8+eSTZGZmlrp+gObNm/PMCxM586YL+f5/bWndII0lbzdn+boCjmuTRVKqex932hcaBA2dFC1dupSPPvqIH3/8kccevVd75B7SPNdy40DY7CVyXnTjGc5dU2NxETO/lxTSrS3WOcrlvbmZ6oDdlCZcv8v9AT3aZbFmsxgJXOvd+iMPCahHRh1cHtwNfAZUF0K8BXQBRpShnBTiCJANxB+u5sTu1VfeMapqvXbNXT7mGG7tpQol4nI+ZkgctzZEI4xueeIpsyyIGLAd551EEGP1iJ0EOsqLsC9UwgOUsZqzDnqmOsGv8MuCXYy49Ee69RjCXXfdRYMGDcp8e7t27SI3N5etv51AYqIvrILRYbOzs8IYDKVk6Yp91KmeQooZAsmedcvWQpau2EvvThXjGoTD5Et61G3J69Y3o/XLOM7FFcrGgJXcueT1DGdjvZeQ5W+Ja19+ddYWZizYxZTrGuM7e25khmOIiVGjRpGdnc1N1y7XpHA+hWeeXcGcuduYM2tjqfYkt2Lnzp089dRTLFiwgIzin5h4b1PUJL/9u7fuhOMYFyIIj5nHcr1xXJWoQUndjl/wy8JV1KhRI+52Pvvsszz52C1MHt+eZk3iIHUec8vJZ83j1ttfp3///gDMmTOH1q1bl5ooXnXVVaz9bTJTxrQExaJViTZGuJFC3dbwpDO+494Hp9CzZ0/atc7m2lH1GHxyFe0y68KwFEQwFjztCN2Ox6o3jvnKbh8fmd99nowzn1lu5KG/1hdw2T1/7Jg7f3PFmI08QhBCpLSuXOXAq6eeUeYyLv34A+b/ve6whqsBEELkAB3RmMIPUsqYW1gJIRYTnkl8QC5wn5TyuVjXxq1K3rx1b52WjTMiVcBgUxm4leppf+VqS2gpy8NmIcK7WbFcZyTH7isRAbcVEXboELiHvYkn6KhbsqiC8WELuxOx36SxH6cxqAWETSVs/u3Yb9OpNjalhBZSaKgUTPWSX0H1CVq3zeabOb2pWfVXOnVqTqNGjXjzzTexLhJKSkpYsWIFhYWFUfvF1KlTad++PYkBRQ94rSVzZwZV2gcRy64NAEIImjXKIDXFb2wWE5E3r2Iix3fJddjqich+Z/QlJxm2vHOzX3mply39zgaXc6VSK0O4L1rbIix5rf3Y6Jdeah2rs5ebatmR3vlxG0Pa5B4jheXAjTfeyLhx4wjq/VcJqVx0YV02bzrI559/Xuryli1bRq9evahSpQoblr5Kh9orePzOJoTSE7Rv3KmiiwYHKbRBsXwHfgXFL+jduSIzZsyIu61//vknzz//PI/f17z0pNBh73awqBY5OZqn9QsvvECfPsdz8803x90WA08++STb9pRwzi2L2XlA3/RAsddlg5MUEiZ5RUHJL4sK2LBhA4qi0KzVIPbuC0dAiBhDHOXGnZyPyatcNzVtLHiolm31ORcLjjyu7XFpv6u9olmuIwG1qyWza1dBthDCXe30D8GQGJY1HUH0AHqjeRV3i/OaAcApeuoLVImHFEKcxFAIUbFChk8JJNqzx7TJimXfZapCteRqw+ASO8nV7sFhJ+hJ7Hz25GZfGDOCvTWWlTMlWEhcopZsNoMJSjglKrp9oaLZCjo3XY+yUbsaEWfLpxFBs32aPYl53K+g6pONGvCRlp3E9bc0ZfmKU3jysTyefOIqatWqxe23386YMWOoW7cuJ/ZsRdXKGYwZMwY3yfLy5cu58MIL2bnlNy65aREPP7eSXqd/T//h8xg68kfNZtCqVnZboTrJoJEv2oo0nkFUnwRtdjU4VvuO/mkr29InI+qNcZ1t8eQkfGCSwTCBJZIIOm1kA8J9wWHYkxZid5zyY2vnnoIQFdOOuhiz/yrUrVuXGjVq8PHHGwgmB0CVpCcI+vatzPz580td3uOPP06rmqvZ/GMPnnu4BddfWZ+s3CTvUdkkNfYJ2o0URjg+WKWKAR8nHl+JyZMnu37XVqxevZo+ffrQoUMHTj9F0LtHJbM8T8eHGNix/Xeee+45zj33XJ59+hZmTe/Bu+++y4YNG+K63kBCQgKz522iapMz6XH2fHbtK4lcDBq2dQasklQLYd60pZCDBw+yefNmpk+fzoxPJ9OxXXYkKYqD7EVFlGvL6mwS8z04CKCrs4wLmXQt14PwRm2HbmfYsnG6AFqW7oEdgxDiBWAUsBhYAlwqhPCMlyWEMPY93WdJB4GMOHdMic/GUFFE5w6ts/Qf+kGLuNjTrtBpm6DaJ1qv8CAx89rgPrC52T8AkRuKW8ssrdNmNMmoy/nI1ZuwSZVsdkPO39gHd/O3dSKwfNDWyUK1SJdUM/K/dsynCDr1yGPW7N4s/30vr7/+DuvWhZjwSl06NG7Fyg2FnDPyHoQQEUbj1atXZ9SoUSiKQu3atdmxYwc33t6GpKQkduzYwYU33MKV5+3mmisb2O3roiFWnjgnH2v+lPrTKVhxkml/GKHedtryKcLdptCoP9Z1Lv1dKDIyVI1RvuWevG1kLbCWI6Rmd5shQHWomvUsMxfuYvveEo5/7OeYj+sYouOFF15g4MCB7N4fZMQ5tVBU+GP5XgJN9sR1vZSSBQsWMH78eN555x1mv96KhMRIIYpn4GrHmGCDGym09HVrPxs0qBpP/+8bJk6cyMiRIz3b+84775CVsYKF33YgI11bWJTKwMaFOM14rwvvfvgru3aX8OX07qRlJNC5UxJff/11qXdkSU5O5plnniG0YxqX3r6Ed15oYyeCoI2livaM3cZJgBrVkqlQoQLDhg1j3NMncP6ZNW0heLzupdxwK9Pt3cdTt3QfU4BI20YXu0bbAt05zhr5vcYqZ/tkZP72rSrw1Y/7TgB+jXEnRxCifE4kh8DaLA70AJpJfRUnhHgNjSR6YRKatPAXtBZab1ASh51hXMTwhG7Vr+vYJss+oToHpriInP0peseaQ590LR3dGTfRgFv8RON6J9ziKNrKcinfiXjIXhx5vcJPOK93JYO2/EQMdDY7I8ffZkxC1f5+hE+hcbMKPPxYG0RIxVcUgqBK3TppTHqnKyf2vZWmTZvSt294r9XU1FRefPHFyGeko3HjxnTs2JGrLq2n7b/sIFyukkIveBDCaN+0Uf6BlSeH37pN2mLJa7hqWGMWOhxLtDLdFzNesQ61v3XyaFuUWBckln5u9GfnozCySxDCOujqhLtEq8/Zv79fvJeTHljKxzc2jSuO5TFER8eOHZk7dy6nnXYaCxak8cSdO+ncMYfJ74xnzJgxUa+dNm0aN910EyUH/+aMAVVYOrszlXISdac6aZEiyfBiw81ZAew2h16wjjcWEgTgT/Lz0uMtGTTyZk488USqVKniWsSBAweoVT3FJIWxYC4APYiMVAT5lZO55uK6+gGJsr+Ybu2zmTVrVpm36hszYSVV8tJYv/Eg1aummHXZ6o4yVgqfwkUXXUTNmjVp3rw5NSpvRQ2pCEXEbTtaVtvDiAVzjPq86hFec5fDoUSoLnOzKj3HUiFxzx/lft3a0q5VBRrW3XMJ8Kjnhf8AyhPH8MjwQlag7XO8Vv9dHfjNK7OUcoD+f+2yVhiXKnn7rgPt2reqEK7YTWTtoTqOK8q6I3+4TOGSiJoi7BHd1NPRUhwbmdtC9DhU2TYVtuLI75bPOO9QPZp2g1AqUmh9dlIRrh+7q6OPcS6kagOH5VyNGqlMeLUjZ589kMcff5wZM2ZQUlISWbADu3fvplmzZijGNn1lJYUucIsb5grLStfVdsYoz9onMaS5DvKOh0mE87oo5+xSYSO59EOHyYP1uGlnatjIGvU5rxHQoEYy9fKT2LS3/DtOHIOGRo0aMX/+fA4ePMglN/o5/7Tq/L2+gP3790fklVIyefJkbrvtNi677DLG3pnFb3OP545bm5BbWQ9FI2V80nQvuHrhh8eFCIKk/27WvAInH5/Em2++6Vl0p06dmP/zTu/x0lJXzGYa46Fu2iL9CmqiHzXBxynHV+LDDz+kuLg4rrKcSEhI4NTTzueDGZsiTzrItZf6/ebLVwCaPeX3P+6kUsNPadtrNm9OWRcZJ9Xt3sqIWGr4eNX1nvk87ANtcHuvRn4Rn+rZqy0GmjZIZ+v2/dU9b+AfgKR8cQw5MnaGOcDvQog5Qog5wDIgVwjxkRDiI6+LhBBdhBCp+t/nCCHGCCHi8jaLSQyFEEpJSUlKdoWEuGwLzFJjELZoZM81bpKF3HnGTXQSO7d2xGM/GC3pjh/WZLP18yme+b3ymUTQsvWSW3BS7dlGJ4Xm4Gsj7drftpAxVnKoSo0QhlREUEUpCpmqJym0vJ26VOL9qd1ZvORF7r77HFq1asV9993H2rVrefXVV9m7d29E35kwYQKXnBmHCtkRysaZnHlKBZfBy2vQ9CSHSmR/cu3LxnZaPu9zXvax8fRVu/2h4z7dnE78gorZCYwZVZdXZm+meH7vsj3DY4hAamoqb731FgcOHOCJl+rQsU2W6cwhpWTq1Kk8+eSTnHzyydx120Xs2zyZT99qRKfj85FJATN+nNWj3fgGbSYBHhOv7f1bJ31LVtvC0jJWm+d9CiedmB/VcaZDhw4s/G03MuRBjKz34Kw3yjHVJ1B92vgSSvRRrUYqjesLXn/9dQBmzpzJPffcw7x58zzb5sQZZ5zBezM2u9ZnczbxkMglJChccG4tgsFC9u4PIgT8vUnloadXMGXa3zHvsbyI224zmmDD0l8iyvAgiO5zuEeZgviIoks7fAk+0lJEwCArRwskoszpCOEutODUd+vpJOB+4Ek9eeFFoEAI0RK4CU3iGNdG6fGokqtVr5LkbbNg/cjiiYHkFu/Q8yOLTQa8bAkB8LnEf4tWVmltSKLY+USU57RHdLtnNwmWS9mug7/jb5tNkUV1YD1ufTaGlFAJSQeBRFczSJo1zeTpp49DSsmsWZv55ONXadXqQXbvLmbu3Lm8+uqrtub+8ccfXDE8g6iweC+Hb8TRhpA2iaFKzalGb1csiaFxPq3OJxz4c4BtEouwebTYH5qmEm4qZJdYnK42iE4VcwzTioh+7NK3rFtRiSj2RFYIJMInSEv2kdDhS898x1B6JCQk8N5779GlSxeWLt1G87bzqFSpEk899RTLli3jhO5BBvbJZNAzPUlN8dsWbAIQJfriJyg1Ew9rX/FSIVvg+d6dZMCZ1/J3p/Y5jLxqLkVFRSQmJkYUlZ2dTUhNYPeeErKyI89Hg2v73NSYOp68qwl9ho3mwgsv5OmnRrBw0W6WL19O1apV4wqr0759e1auORB5wmUB7QpFkJLqJxiE3IoJXHpxXe57cBl//w0PjVnOqQOqkJTijx7UOxqs+cuz0C1DXTYzMOMZeNmxes7hke13jsERmiBHWQ3qpDF/we6GHDV2hobkr2w4EqpkKeXXYDqV+C3Hd8a4NCillEKIQcAzUsrxQojz46kzpsRQUUSTBnXTI1YCXtHVIySKLpHVpZv0xE1C6HNK2GJICKN5L8eRIqQ2MdTKbu1zlhexCbsjAr3tWuuqLcqKTXsxkZ3ZKZkTqgx7/RrqYadELqhJCUVIopRof0eUEZIoJSGUkIoSUvGpkhOPz2Psk61ZvXIAQ4dU47PPJ7Nr1y7zuuLiYtavX0+yi2F9NGhSSz3kTWGI+8es4KTzf2THhgK+nbuV0N7i8H3o9+ZMTuz/a4D9uRl92Lkitwz42jsgUkJo64PY+1mUPF67B7h637v0rYjyDLMDZ3gkm+ey1vdqVktm1aZCQqEQx3BokZmZyezZs8nNzeWpp57iqssHkp60gC8+rM9DD7Zi+Nm1SclIQA0ophNYUVGI1av3hxdFUpLU/HOElHZSaEGEqYliH19RhKlFiGqC4hg3Miok0Om4lIhFnYHFixdTMQcqZCXYy4qBqKpKIr9TGfDRpFkFioqKKC4uJj2jG+2Py2Lpks9o06Y+l1xyCQcPHoxaZ0pKCgeL1Ij6TO2Kq+mI/Zu/86bGZGZmsmVrEddf0YAt6wdx1RX12bYDKjf8lBGX/RzTk9sV0Z5HaZIDXlI/r4WAqwQxRoqYw/X53619sXaLaVg/jYo5qa1L/wAPH452iaEQ4hIhxBY0u8Kf0ZxK4vEk3CeEuBU4B5iuhwqKy1g4JjHs3aPmsEb10w65i7trvhj2gzFtCF0IZZk/QCVa+R5EUnGx83Deu46Ydh4OROyxSZRB2mIjKMzJB3eSKNGkhEHdtlCGk7ASSQlKiWpPOpn83/NtOb5HJUaPHm0Omr/88gs5OTk0amTx7LMOUNZ7cTwzISXbthdx9g2LeOX9Dcz5eRcvf7SJEy/5hfFz+rN720H7fRrtDKqIkpBGcF3Gbtf+63wXDtVyhHkE1kna8b5c+mlkvyKyz7gsjrxsdq3leC5yrGX4BQ1qplAxI8CNo+rHjEt5DKVHYWEh27Zt497bm/LN7N6MffY4MnPs0rWC/UFW/7Wf199YTe9+c2jVZRYbNxdq35oqKVzQF4IyLFGzjqlWu1QspNCN/BiIQ6plXH/PLY2555572LFjR0SeX375hY5ts+N2wHBFvGTSp1C9enV++OEHTdswqh7fzOnNz9+fwNq1axk7dmzU60OhEKGQdLUHlM5v3TFnAaiqJNkvmD+zHc8+3orMKtMYN/5PhpxajUmvHwfAtE828uui3fHeuYY477+0iEXSXVXDbufigcs8FrcK2lJPo3rpdGmfNzL+io8BuBFoKqWsJaWsI6WsLaWsE8d1w9C2zrtQSrkZbTu8x+OpMCYxTE5UT2zUIN0xUHnYGngRrHKQxciVSnwE0lWSExepjE783MhfVImp1/3j8sxcEM3Jws12LyKYNJF2e0oonEyJoUEK3WCVLLokX4nKY3c25fMZk7n//vsBmDt3Li1atIg6oTj3Cv3tj32cf/0iMo/7ghaDvqNOlWSq124GwDsfbaBypUTefPNNOp25inWr9yFCKkpJKGwfGVLx7S/Bd6AEpShI8cEgC37bTVFhyLVewHVRoz20GATRy87QkS+6bW004uhCOLGShChlWG0dfQLFL3jt1kZ8/9NOevXqxcaNGz3fyTGUHrm5ufTr14+p0wOEjO4jYc/eEj6Yup7zLviBJq1nMPiM7/hqzhZuu/MlcnNz8ZuOXriavEQ4K+GuDo2wN3ZDFFLQvEUWp/dP5pZbbom4LCMjg4MHgxHH44IL+bK12QWnnZbIxIkTWfnHYlq2zEIqgsyKSdx3t8oTTzzhuc/77t27GTRoEKedmIffEgw8gjADbt+7UCX1m33Ky6/+ReW8JM46TfOReHuKym23/8a5Ixeal3/06eH/fuKZC0tTlgkXUlzaBMRPFB31NmyQzoGCgy3L9XAOIbR1WdmdT46Q1PBPoKC0F0kpN0spx0gpv9F/r5NSvh7PtTGJ4d+bDmTVr59uEyG7iZGlbkjsmmxkS89v8Uwz1dJWSV8s0bYzxSCTcadYH4bXRuSlELe7kUGvZxf5xjw6ogwTO1dVsVcKqZp00JikHGUahMskYCUhW1KKtVQhUaFbhxzTw7Fnz578/PPPhJyrd7f2K4LVGws55bz5tOp8BbO/+obFy1bzxJtradZMI4YTXpvBhk0F/PLLLwwfPpzjBv/MGaN+ptvp87jr8RWo+0q46cHfqdF3LknHzSKl6efkNPuc7qd+x8c/DguHXPB45p4LGava36VfuaqKvVTEXlI+N2l3FHVzvE5SRlITfDRsks639zajS5MtnHPGUTMu/yeQnJzMp59+SlZWFqOu9HPxpT9Rp/F0mraZw1vvZTJ02CMs+30ta9cd4INPNnL66adTWFhI4p5ilP1B3eHE4cBhJYSWxajZD41g9nrfdS64I6Q4uEuJDOnZdVfU570pr7Fnzx7b+Z07d7J7d4ln3E2z/Ghjm/53Rv5UT0JjHF+9uhlFRUW0bpVFIMVv9v/6jStwzrk5dOnShdGjR7N7927z2q+++ooWLVpQJ3MZEx5qHo7sZHk2Ngmr4x6EKtm0+SA7dhbz3XfbECGVgG4Cs2zZMubN38GBA2HbxZNPzI+8dydcnkMslIX0laXsmNK9GMk2Z1vGyahzo56qV09h05aDSYf8BsuBo12VDNwKfC+EeFkI8ayRvDILIfYJIfa6pH1CiL3xVBjT+aSkJJiY5rJjgnRQStfYSE54xEpyi5NUWisOzxhOZoEeJZZSRRLPijzq9WXpS1HKdgau9bzPKNdH+23CopKOsIFStAmqcaMMZnz1Nzk5OWzZsoU6depw75MruO+WxpbmSfbvD5KeYe9TX8zZSr++lbn99tvNY7t37+add94BoE6dOiiK1uluvvlmOnbsiBCC3Nxc7r77bip2+5aa+T7e+/BL/H4/nTt3NsupWrWq1mYrAbTcE4Tfi60vOh1VnH3FNT6Y5bTFWQSIjNVpyRkLNucUX5RO5HDy0vKjEcpMP7cNq0qDYfN59pGWXH3Lopj1HkN8EEIwdepUXnjhBSpWPIGnnzuJKlWquErM582bR926damU5kdKiVSUMBHEQ3WsVWKSG6vE2ybFwTFGuRAhZ1BjqQhyqqbSvVMO7777LhdddBGgkaKbbryCYYOrsmXTQfL0+IBGeaUNsbNny5DoGRRBdvYCJkxYy+ir65rPQSoCAdx2W1NO6L2dKe9/RIcOnzJjfmv+XrmH00+bzYSHm3NC54raYi3amO6M36qboYy8Qpsvzz27FmqiNi2+/nI7Hn+ukMWLtVjCp59+Or/++iu1aqSY7bW2vaxwm1PinSficcJzNatx1On2LqMRVK/5PlqPEKpE8SkkJAifECJRSvmPx9DSprNyvLsj4X0CLwOz0YJax9yGQ0qZHitPLESVGAohMjLS/cJLchaPE0U8ErRoUsZSr2LcVjOKcF29xJQ0OtoeVQJofTFu9yOifMCx7i8G3EK8xJtsNnohe7Idl5jHnPsfSwHSr3DhubVo3CDAzp076d4pj6effpq/Njdj3MTVhEpUFi3azRkX/USVlp+T3eBzXnx1NaqA5WsLePKFlXTvdbvtvkpKSkhKSiKvUiKblp9jHs/OzubUU09l8ODBdOnShVmzZrFt2zaWrtxDt27d6NSpE9988w1XXnklWVlZKDvvNu0kYz13TyliHM5Wrn0qWpgaF5tYT7vYaE5QblsoJtqTmuQjlJlAesDHt/e04IkxK3hvYoeYfesY4kdmZia33norF198MVWrVvU0o/j111/p1KkTMtmHmuSL2PoSiJQS+sLjjykJs+yFbpMSWvutlTgSOQaZ/V0RnD6wCpMnTzbPjRkzhhZNMvj6++30HvI7f662x2ksj3QrwvlEaCFsioq6UrFiRX5ZsocVK/fZ6lL8Ch07VuSBu5qxYcMG1izfzXmnzeXBWxrTp0cl1EQfMuDTtgv1he/fRhSdY7UieGbCccybN48br2lAnxPC0sC+x+exdOlScism8OAtjZjw0AF69OhBu56z+fzLzRHPoawp4l1YmxhjToiH08RyCDGefUTbYuQ3Qw65zJOuWkX9eNUqSQLN3u0oQNmlhdLcxzRGDUJMEEJsFUIssRy7RwixQQixUE8nRSkiKKW8Tkr5qpTyNSPFdXdCdBVCjNT/riiEqB3PdbFUyVWrVEmOTsjcOrmXGphSiLR1xNOp3e8sBtnyGtDKSM6sbT2q4CB/4bhp0iYFtA3UXsd1uE1ACEFWhQS+nNaNnX8NoHq1ZJ5//nmuvPJKrr93KXXaf8Hpl/6EL7kdQgi6du3KG1MVrr1zCQPP/J6bb3k0Ymuu3NxcLrzwQgafOoLjjp8d9TbT09Px+8MC8K5duzJ27Fguu+wyXpiSz7qtRfb794LlfUe8S5c+4ar+idLntXKip9Law0Z4v3stghIVQtkJ1OqZzUt3NOa2e5ZwDEceM2fOpGPe19r+6LrZgJsds5XwWRctUaWEjn5pRayxqW+fyixYsIB58+Yxbtw43nzjVY5rVYG16wu44ooruGL0AmRQjRjnDonqU7/HRYsW8eijj1Ktcl8uu2Q+UtoXolIIFEWQkZHBmafM4brL6nH20Gr2e7R+by5SVOvfM2dv5q677qJT+2xuvq5RuD2qJCVRYfqkDmRnBsjPTeLxN9ry2muvcVzb7ox96c9y37vnPBbPvBPP/BZtLnMpuzR+AzYO4GVG5kGEq1RNhqOGGLpPkfGmODER6Ody/CkpZSs9fRrl+q90z+R8IUS2kWJVKoS4G7gZTRUNkAB4R7O3IJYquWrl/OToImXjD4vazTVfHKo4W95oH0WEPY53VjdiY6KcH7VXXUcFOYzWay3n3AhhxN8eg0nEJKXD71c449RqnHb+y7z88sucdtppnHHGGcyaNYsPPviA+dM6037QV9x3333k5+fTu/9Ezh5+tWtTY20zFguDBw9mzJgxdBvyG/ffWIVzh1ZHxmP2YEywTlW7jMzj1vcj4ny67B3qBeG1/7ejfCc5cC3fOSEGfAhV0q13HutuWMS+fftITy+35uEY4sT27dv59ttvef3G1q6Byq1mC06yZ/ztSgothNAsq5TjUFJqgFcer8vgwYNJSkpi2usdSE7yMfv7NG644QYmT57M57M2c2J/9+3zDgUWLFjAeeedx+7du2nb7gN++mE77Tvl2saj5GQfzz9ZG7WkBn17VoKgPURNLNJsxYTXM6lVM4Xp73fTdmiyakJUla+/286OPclcecdiMitsYcGCBdSoUYOqVXIoKVHxW8JxlXvcdyNr0ebTKOe98tvqKcX8F7MeJdK0QILrPJRfOZnEBN9RsQOKhMO+JZ6Ucq4QolaZKwFjn8hbLccksfc8PhVojR4zUkq5UQgR12AflRhmZCTVqVIlGWm1aXK+fJ+IsBH0tFeIOC5c84f3i/Xwuo2nQ5srzNhZITqpi0X4nOQzGhl1LcONxJWGtMZYukQlx/GU4fLMY0ofeuXx27e9WfDbbuZ8PZ8zzngPgKFDh9Ks3n4GnZBHdna2bs90URwNLBvatWvH22+/zZIlSzj55JOpVO9uTmw5AUrUsKmCBRHkztFvnfcd/z6i8dtkyWg2hEYVAQejiEYIDNKgXyNUiQgonNg9lztvOY6nn/8jrnYdQ/nx5ptvcnLHZFKzEjRJixsZsMQmBItZAxZJIpGER1rzO+HSR93y9DmxCs8VhVi/roCurSuwfJ221Z/P5+P666/nlQnXl4oYllai1qd3HrO+3MLQoUPp1a8Kc2Zvpn2HivZg/CFJ7+65tkgKEd9DjGcE2nfwzoQkoG/42en/C+CXpft44bUtDBs2jFdeeYVnnr2P5s2bs3v3bqpWSWb5H/to1ryCdp3XN2eUF8e3H+tZ2exQ4xgjbNeWcrMHr3ptcI6NljbZAv5bIFRJlWrJdOpU7XhgUpkadIhRHhtDHecLIc6x/B4npRwXx3VXCiHOQ4tJeL2UcpdbpnLseVysB7iWAKXZcSaqKvm44/KOz6+aYleFuYmSHeJnLxuKeNXRNjVAaVzibXcWOelHQzQ18KFUEccKxmziEMizPetwXuNShtOOMKbc3BL/0Phdq3oKQ/rn88y9zbhqRC1qVElmaDdNBXP9xXWYNvke/vrrr+jlHiI0a9aMZ599lgsuuIBt+4Nhm0OnnaV1oLNOKh59yXMS9lj9lzt52e8Y36CLXa6hXrbZQvoVnn64BTM+28iQk/JZ9GWvQ/CUjyEapJSMGzeOC06vFiZ/BpwhrizquVik0DoWeqoAnYgyNvY7uSqXXlQXEnzUrpXG33//TVFREW3atOHPv1x2FrHeY7zjs0db3p3che0bB1NwYCFp/pP434TVvP7aX+zZFwxHWXCORdZFkdvfHqRw585i7nuyLj//qm0g4ZzDfvhlJ3v37mXblk8ZcW4txo0bx7Zt22jcuDF/rNrPho0eAbdd6ovn27bCzXSkvChV2DZnvW5zt9dxJUp5iiC/SgqJSUrLct/Q0YPXpJRtLSkeUvgiUBdoBWwiytZ2QogUIcQdQohx+u/6QogBcdQxRQjxMlBBCHExMAt4JY7roksMU1JE28rVUlxXKrbVhxKWhtg+WeOYbSXhUpHHqsopZbR5Hnuo5qINGtY2xYu4pG1eiFaXcc+xyKEj/yFriwcZdDtmPuM4pbXC2FdVL09RBI/c3IhHb2yo+VSp0LZpBi0bpzP2oT489b8jQw47d+5M7969GTJyNVPfqUKOTyBLVIJBSYIR+ywk9R1DtDWTbcVvvXeHFDFav45AvFJvS/mucE4mbmW6SU5UCQhyqqTyzWc9GfP8SvqdNY/PX+9BixO/jrtdrm2VkiVLlvDLL7+wY8cOpJQExFtcNfoX06v8/ytmzJiBUryOLu07R3jP2kiE1XHCChf1sishjBeW8dUgoAKQKKh62xJ80Lp5KhMnTmTHjh1Uq5Jk6+ul9Uw24NanpU4ohE/h9jubMXLEmzz55Mu8/fbbvPHWN0ya2JHKFS2Bwx0SMOObjRaaxoonnvuDZSsO8tpr8/nqs55Uq56iPXdAKVG57Kq51Kh9EiedVIVPP9nAH38qrFy5ks2bNceTVq2z7PfiuXgsw9jtVZbHXOkFT1OtUiAawfecfz2OSwSVq6UQDJXULVUjDiMOtyrZ9Toptxh/CyFeAT6Jkv1VtN1OjFAbfwPvxrgGKeUTQogTgL1AQ+AuKeUX8bQv6ki9e09xZn5lF+cTiFhRxJQQGo0Vkck85zYYOj5wm/2IRZJoLSP6HcffCUpFCmNI8myewF75jRWxNeh0tPIPoZVs3INFFOmi8/5Myah5zL6gqFklmb+3HLmIBbm5ubz33nv07NmTNh3n0bb/XDIbfkrV4+bQ79JfGDdpLTv3ltjbrCNavyw1PCcQj+/DY1VutCviu3GRstvqtZSRlOLnthsbc8f1jehz9g+882LbMt3S9u3beeSRR6hTpw6DTu7IZ1NvZ/XvL7D2jxd58621XDTiqJkH/hFIKbnvvvu45eI64FciNCCARkqcpNAhSXRqWSA6MYlHOmXmFdik0kbe+29tzOhrL+erGU/x5H3NzbGpNATDa8zftauYnTuL7NvMKYJOXSpxyqAMNm3axIwZM+jb5xL6DfiaLVsL3W0IFff5wAuffr6J96bu48033+Skk4ZxyZU/89nMTYRCWjsy8qci1Yvof3JVpBD0PaEyqCu4/PLL8fsFD9/bjLxKSWE7a8vzMtuEhRR6fcNRvm2jHLf3Fq+GwfVdxHltWcqPp72Vq6awfXvhURHLUGLKKsqUygohhCUYJqcC0TwB60opHwNKAKSUB8GbzQoh6gkhuuh5v5BS3iilvAEoEkLEMQ+ttgAA9LNJREFUNRBHlRgWFZdkVKiYiFQc9lHGCzdXm/oqxkWKF20DbgNOiUtEfcb1FoNb23khbKtH1+s96nYiJhmMprqNVrbDRsbplOBdpkdxZSUlEeW71C/tz9ZNvWrGz/K4Z/M56ufd6vl91X5adby8TO0uK4QQjBkzhuuvv55FixbRpk0b0tLS+PLLL3n77bd54NkveGVsQ/p2zNakJ47BDTxsaC19+JCYHUQZ1D1RCicU23epSs4bXpPGdVM594pf2b69OVfcudjMf+DAAT7++GOWLFnChg0byM/Pp127drRr146MjAyee+45Hn/8cU499VTeeDyfVi0aI3wKRhijfQdCdOr7FV/N7EWvvl+V/ln8B/DRRx+xd9sSTu3fJVKN7CB4ripjPZ+tP1pfseKSPwrMPhAxlmPGNhT68Q7tcti8tD+KMbk7yolaT5Q2SiEYMGQ1q1evwO8T1K6dys03N6HfSVVBEaSnDqCwsBAhBA8//DCKonDVdeOZ8mY4Rqkx9pthflzu09oOKSX/e3U1j4/ZyPTp06lSpQqvvPIKL774IsPPvZZrrqzPvXc0Y9fO02ySPn+ij0+mdWP6JxtZsqQBl11a37wnQ9JpzlHOpriMIaXBoRrry4qyqrGFh3NpclqAUCiU6HLJPwCBLNeAHftaIcTbQE+gohDib+BuoKcQohUaN10DXBqliGIhRLKeF53cRZOoPA3c5nK8QD93Ssw2R9sQvFnznJKv5vXxKy5ZIiZHq8rXy2nECadUzUtF4SJ9MxFPXVHq8coTtTwnZJT22eqIXkwEPOS5h2ugiCklg+jkw608y24stvtX4MW31rLkrwImvL22zG0+1Pj+++859dRTGftsPU5um2WaQURVyZRRneYKj1W4J1wGbTfS4Tzv9o0IVfLXn/sYfN4PtGySySVXj+ebb75h/PjxtG4kaduiAtWqJvPn2gKW/LGPH3/dRTAkOb5LRR65vQnVqiYjAz7X9o59eRUzv9rC+1N/Z+bMmSxevJhHHnmExYsXm7vb/Fexe/dumjdvzvgH8unasaJNzQkWqTBhUuhKHBXhSrTM6yyINZnbnDk8xktjnCxt8ONYZNA4LhXBTTf8yp4dLXnppZf46aefOPfcwbz3bldatMlhyfK9DDn5ZzZv3ozf76e4uJi6devy3lsNaVw/3dZWq+DAU80OPDduFZMnq0ybNo169eqZx59+ojX3PriU8ePa07NPvqsJkqLvUGMuihWB6gtLVw3YYsR6EPbSvi/n83S2zTNPHOZMZYGnCtkNLud6dfxcXfLbTp9L7iMGIURK07waBx476YIyl3H756+zcONf2V6OI+WBEGKmlLKvEKIvcDvQBJgJdAFGSCnneFy3RErpOqgKIRZLKZvHqjuqxFDxSZ8Q4cHIKTny6gxSiLgGHluD4yCF0QhhdKKK5znP6z3qiZo/CvErk0dYyH3AsEtvS1+sJxztF64WFOFVoAg5pLa2bFHev45V6wpIrBAtrueRR+fOnRk/fjyDBg3i+++/p2nu/bEHUOukVF642B9GDU1hPOdSrOq9vhWpCOrUSeOHL3oybsJqHrrnXFo2yWDWG42pWydNy2shJ4VFIQpLJBX0XWykY5K01nP5JfX44aed1KubT4umFWjTIpNTTqzM1aN6MXX6KjIzM+Nu/78JUkquuuoq+nURdOmc6/4OrZLAUtoR2iWI8fcBW17LGGKVIkoH0bK1IRZcCJEpVbMcv//Blpx+6lzuv/9+nn76acaNe5vhw8/m3Q+607R5BSpUqMDSpUtp2bIlCQkJ1K9fn02bC2nUMMM9QkUUqepvv+/l6ac38uuvv1KjRg3z+Pz587nvoWWMvqYhvXpXjlAHW78/oUq7ZNcqMTTbYH8GZlvKQgbjWSgq7lodiEee5Y3Y0mDhqiW0wss2W4gjt59cNFj9JctWwCFrihtyAaSUM4UQvwAd0V7pNVLK7VGui6amT46n4qjEUAiE28t3fihA/JNjFMIWkxBGI4NxEsGISTFu6WbkoXB9saWhscrwLNtNXGtFSP+/rKu/aO8s5F6uSQgdH73rQKe4/K0baOzbH2Tnxg/QHLSOHgwYMID777+fsWPH8vyTCfgOlriSs6gmC+WBm9kG3gRbKsJBxC3nSlu3EKSkBLj2ivpce0V9s1439WGiXyFRaMsH6yTpzAeaA9JbE/SdVnSJSsG+Yi65biFnD23AJ7O28F/ExIkT+WXeVOZM7eoeYsR4Zh5xC22k0ItsWcoqFfTvVzok+dbynO8+Xri20Sr51P8OpASYOLkbZw56k+HDt/DWW29RXPwqpw4ewf9e68w5I9O45JJL+PbbbwkEAhQVFSGMxYl1PnJb7AdVZKImwd61L8gll/7N008/bSOFALfddhv33tucs8+p7W2bZzwnwylNWOwKLYQXQKpEhIeJ9z3FQ8Zcj3uEr3F1Fo2BMi8wHHCqkq0kMSnZJ4QQybq93D8K9R9wPokTmUIIt30kuwshkFJ+4HHdT0KIi6WUNg9kIcSFaE4sMeFJDIUQCW3a5QCREkDrhOhFDp3XAJ7kzVOV4SEhjIsQllv97Djv9WHFQQCjfpTR1AAQJn4xIOMknaWWXIbC79O1DMt5k8TaBklhJ4SAqkrembGJGXO2sXDxkfFILi2GDx9Ox44dUZUuKIrQJhm/YvfSLgVsNrSluTwOVVCkCYCl3rJYSMe0ey3bM3AS6ZS0AOOfakW1VjNZv2IA1RtGdbL712H+/PncdNNNfPr6caSk6kOtixQpvA2eByG0nCsvKXSqqJ0SnYhxpJwaCTdS6DxXISeRaTN7M2zAOiZOnMiFF15IpUqVGDZsGO981IaP3t/G9OnTGTx4MC1btuSHH7+iR888U6JplqiTOqN/qok+3vl4I888+Ttr/ipm1KhRnHOONdychh07duDzJ0S23UUyZ5WuW9+dTWhi3KsL4Yjnu3GSLSFlucPVHIpwN2Wp04s3ZFZIAKgA/OPE8ChGJjAAd8GvBLyI4bXAVCHE2YSJYFu0nU9OjafiaBLDChWywh+Lm4OJKzksD+JQG7uRQi9CGE06GEsqGI3UOvPGfU2Ua2NeEwPual+XfGWVm1vfv0OiZZJCaaigMCcUoejk0IL3P9vEoy/9xauvv0+1atU4GlG7dm3S09NZv+keauXcZTsXb1/3cmQqVwgkK0LSlWTaJ3vvyqLaTVrhIsH0IofW486/3bDvQIikRIVqDT72bOe/ETNnzuTss8/m5Ydq0aRBuvYW3KSpLupVM088pNCDaJTGCQWwL+6t5+MZ55xtcsJFUuhEUoqf+x5PZ/jgW8jLy2PAgAGMHTuWkWddRM8T8hkxYgR33303l19+Od27j+fM02tQs7Zm3iAlpqOYGlA4uL+EvzccpE6LbO665S9ef/0devXqRUJCJPkDeO211+jRox0DBlQlVSMstnaqFscWqwZH+hR2bC9iz+5iajfIiLgu7ucTA/8EqTtU8OINOrfIQovh94+ifM4nhxVrpZSlNoDUQ+F0FkL0Agxbw+lSStu+skKILC/byGjEMD0tze/qgexGDk3EoVJ2lRbGIIUxpYQxpIz2uqxtiTIRlpEo2vKWVRIZ0ab4sh0uCNDuRToO+vXYZ+ZEha7q0SWFuprMiuseXs5Z547ilFNiOkf9ozjnnHM48cQTWbBgASmM0o9qoYS8JuRSk744SGY0Iuq6lFSEKcmNgFW145UnCqx2ZxrxM9oI6ETQ2t6I9jnu5ZufdtKhbTbiXzz5WaGqKg899BDPPfsAbz3Tmi4dclydS8y/LWQpHseSaFLC8jileToTOMtUo0ivYpDUWPaQLY/LYeKUVlx89iW8/fbbnHnmmRQWFmrfX9orPPb47SxatIihQ8/jjbe/4fY7wvb10qc5ggQTfFx1seTTT7+heq0EMjIq0bdv36j9q3HjxhQUBFnx+x6O66A5B6klIVQh8AXs/hEff/Q3e3eXcNbIukhFcNvVkqlTP2HshE6cfk6sHcqOwUBauh8g459uh+Qfn1qjoVyDopTyK+CrKFm+BNq4nYhGDBP8zm2GygAn8YsVHDVeUugmJXSzRbRfa6lH2ttV2vO2PB75IslkZJtNOH4eFtu18sDtC1LQpFaK0CSWVgmHz0EMFdi8vYgnXlvLjt0lXHPNNUe0+WXB3XffzZQpU1i3bh0Na4a3kwONVJV2W6oykcAySHijEj6raUIcZCyCMBiTvPWbUwRgkELH9THK/2XBLmpWT4nZjn8D9u3bx7Bhw9i77Qe+e68zlfPtdt4R9pfxqBVjkCm3fIcCnlqgsqisiZPcqpL2nXK5/q4aPPXUU/To0YMRI0YwYsQIbr75Zjp0qs/S3z/h5x93cMtNjc2yzMgBfoXFv+1mwYI/2LBhA3/88Qdt2rSJuegIBAJcf/3NDBnyBFPe70btOmkMHfwnu/espv+AqlSpmkJ6ZgIrl+/hlRdXcly7HIZfWA8pBCtWrODee+/lxsvv47Sza/9nFjiHE1IRBBIUgMA/3RbKGa6mPMGx48C5h7NwohDPaMQwoL8871KjSTJKM6G5leMkhfEQQtfrXNoUi8Q5ro1FAO1lR7ZPaxcubbXmxQ7XZxJ5qFyIp08bdQYtxxTL/0JXY5uG82gvSdUnPkVqJDEED72ymr8LW7Jly89UqlTpUN3FYcNjjz1GamoqNao8RElaEooeeBy/ojlQWMmhLin33AXFkscLsexgI/JAbOm3A3Zy4ZT2O/IqIkwyjesMqSBEeK/a7KsMFaXzerNwzQElEFBQDjGp+Sewa9cuTjzxRJrV2szkx9vjS/C5kyjh7qDjJS30xFH6zMpFUBVBbtIkBp8xjHtvmsG+fftIT9fC0lSpUoUJ/5vGiBEj+PiznnRplxPp9KEI/t5wkCZNmpCdnU3Hjh3jqvbjjz9m4mtP0++kKhQVBune5QeCwSAtW3Zg/MtzzHwG6Xvp9c7md5SWlsbqTW8SCsljpDAKnCrlQODoIIZHs8RQShkt6PUhqcLrRDRi6Pf5LTOFy6QWT8gaM08cKuayIkJ95UXsohHCWNd4qoqx53MSQC/y5zwfQQyj1OWF0j7eaOOYtV0qUCQgWT9o1a4o4Qts6mSf/s59gCIIqpLZP+7k5deu/1eQQoDZs2dTUlLCjbemk11lAdfc0pQcc9stiVQkSon2Umw7+AgzS1yIJR32IoNx91FrWR51uIXbEG5SHgVEyFBX6/msqlGdCNqvxTP+2nfzd3DTFfUiz/2LEAwGGTZsGG0bbuHx25ogFMX8fEotjPBQIUdDachYvPHnyqKxcGtHaYni1uKzSUsP0OekbK666iomTJhgbqV4wgkn8OCDDzJs6MXs21vE7j3DbPVIIUhND7B///5S1fn25Ku57rrGXHhhXX74YTvbtm3jpAFV6dhlNwMGtsHnEyxdsoegKhl2bh1q1Ek332+TNruY+NJKLruusVme8e1t21rIzh1F1G+ceYw06jB4gf8oIYZQPhvDcoW6OYoRjRiGVKtKKpbaN064SlQc3l5ClZgR7R2ec9JKAC32j9brTLsn/fM1nB+ETSLidS76ces57bxxPNwW4z7AQRCt3rnGPQvLudIuXZzP0YNclhnGckqCDIJ/6DeEJncLn1ewqSZNBxiBHoMR8IPwwdyFu0hKUOjVq1c5G3Xk8P777/P999+zadMm7rprLt98sZLk5PWcclIVTh9YldzsBFS0dyxcHEFcCWK8CyzLzjixHDhiIpbjlGJ5dxbCZzoeGOct5+zByo0+blULhsmiAK0POZ7PipX7WLn6AF27/jsWCl64/fbbKdn3K4+MOQ7hDE4NEWF84kVU72/LmOgVw86rTGu+cvetKHWWVXoopOTJFztwRv/pPProo9x6663mud27d9OidRZFRe4bPzRskslvv33F3r17yciIz4St4ECQxAQFFMFnn2m+EL36VKZDp4p8OXMTk95Yzco/9rFxzxn4E3zmsCuk5JzTXmTiS31o2jwLgKKiEO9PWs3Cn3bw2Ud7CSTsRwg449w63Hx3C+25HCOJhIIS7HqofwxHq8TQCn3nkxpSyhWHslivE9F0xSUlRZEyqDJ7tbrA5qXm5bHnls+ifrHuJWvmM/YW1beekn4BimagbO7x7NMGAvxakj4jKVoKKNrxgKIdDyj63+Fz9vNG0s8HBFh+ywQF6dfaYuQlagICWMp1JJ8wy8OHlgKE/1bQaL+RFJcU9eXYf4oAhKZ0CxNYFe2zDqLt4FiCtklPEVoAggMgDkjEfokoULl27CrOueTOf9XKOS0tjb59+3L++efTsGFDmjZtyi23TOD3P1rRtvs8Lr85jYVrCvSwFd73FbEgNfqwo8+7JedeuZptGlp/FpYYapbjtnNGfdFg2wBUmkmEVITUSK8ISQiGkwip4VSiIkKa9FQEpZZKVHueoKqdD4XLW7VqP43rpJJb/98bpmb9+vWMGzeO159ogT/gC48r8ZAiNbzfsM2Jzkhg7qIhVKm9Cz3Z8lpgy29JTsQ6XxpSVxpSGUGKHPdglJWSFuC5cR0YM2aMTQI4ePBggoU1ad2msm2RZTyfvLwk+vfvzw033EBJSUn0tkjJNddcw9Kle+h5fGUAZn2h8Oqrr/LoA0sYOuBX5n9XhZV/7CM1zY/P8Y1LIWjW/VVuu78lTVtqxHDn1kJuu2YRvy/K4t1332X9uv18/tlPvDZuJRv/LgBACakoIZXgwRIK9xWD+m+gJocGRt8t0TQt0V/QMQAghDgFWAh8pv9uJYT4KM5ruwohRup/5wohaltO9/a6Lho9KC4JBrUv4RCrgD0ltx42OeZ11vNOVZXlmI1IOsmly+Rpm1z9lonXIJLWwd5KMi3nbUTTet6SDDKJT0vmOYOYBiwkz5FXS1jOh5NbXTbC6EzGM/AijmBxJNH/9snw0kpaUgjteAgICQgKZIlAFglkodBU0IVQOzeJ2rVrc/DgvzNs1fvvv89rr73GKaecwmuvvcaff/5J69atGXrab2wLPq1JxiSeyRMxSIStz3sFt7USQKutp/OcWSe4Lg7cRgMrUbQQExGSdkIZdJBJqZ03iYe+xYAIqeaxvt0qsm7jQWZNis8W7GjDd999R48ePRg9IpecnMQIhxKvcc5GxmzEz9FfXEiiWYbT7tmFJHrWeZTDIHl1GmTQqWsSr7wSjtNbq1YtfvjhBzZuPMiBvU8SSvRHPJ9bHyti3rx5fPbZZ1Hr2b17N+PHP8+sL3tTrXoKe/aVsG7dOs4991w+++w7BgwYwPz5sxnz9HGs+/tUKqVODrfR8vyvvrkZjZtVQAmptKozlVcntSc/P59Ro0bx22+/EQwGqVEzldV/7qNSwlskB8dx/12LqJ//Pg3y3+fkbjOZNf3v/zxBtD6z4FFCDLVpTJQ5HSHcA7QHdgNIKRcCtWJdJIS4G7gZMETuAeBN47yUcqfXtdGI4YGDBbqkN07POC+4SQXjviba8Sjk0JbXkLrglD5im0AjJTAuJNF53oNIOomi/bx+zCB/ik78hJ5HsZBJmwQwklwSkQeT6NnJq4UU+9DInjW5SRSt5NCA6vjbIIiqQIY0lTMlGkGkWEEWKMgDCtv2lDBs2DA6d+7s+l6PdqSnp5u2TgDZ2dlcf/311KlTh7p169KuzxyuuHcpM+ZsoeBgiIIDQQ7sL9GcVeKBixQxAvFI613sDW11RByzpHjKdpATuwTLSBYiGXIQROM+pCQxQeGea+oz+p6lFBYWxtGAowdz5sxh6NChPHhNJjdeXAdrPEKIJIUxIybgJH7uCwrrc4x4/hCTJMZDEMtCIstNOj2exbU3NeWxxx5j79695jmfz8foa2+lRct6PHFvDdQEn3mNryhI5Qp+2rVrx5Yt0XfT2bNnD5mZCWToWzq+M2kNPXr0QFEUtmzZQq1atahWLYXhI+qg+hW2FQw3r7XOg3t2F7Ny+R7WrjvA1oPD6d2/Ki+/m0YoFKJVq1a0bNmSBb/sZP3q/azZrY2BOze2ZtXKdRQUFHPrzS9z360LOL7dDO4a/RPzv9mCLKVmbs/uYi4791t+/THaTmlHDw7sDwKUzhj0cECCKkWZ0xEih0Ep5Z4yXHcqMBA4ACCl3Aikx3NhtOlg9+5dxVEvdvOus5133SJNJ2giPHh6kj2H1M+1Li9y6CU9jEEQjXZHSGE8SKIXkbRJJK0SR1eiaVxjJ4muRNEgcjbJX+R1tvOuElLHfRvXge1ZxNXvVWGxR9T+liGBWqwQKvAT2hfgo8tbkJbkIy8vL44C/z249NJLeeyxx3jjrS9p3vJKnn0tmSqtZ1Gjw9fU7DiXG56pxd5dRTElOoCnahDAy9s+QrVoOWectyFeNWc87XSqnd1Uz2pYehhBEIFhJ+dTv1Yql59dr9ST4T+JMWPGcNHQNAadWBk14ENaHPWiSQq1DNL+Pj1Ux+AiPbSU5UUSI1TNzmS9Xve0F0E1OiF0KcPz/jx+W9sZVx2qpHnLLE7on8IVV1xh6x/33HMPixau5K23n2PBTzuQiiA7c4pZb8UqP3HrrbfSvFUWl19+OW+++SajR4/mr7/+IhTSDGW//OZsmjWvYJb55RcVOfvss3nonuacfPLJZGdnk5WdwOsT/rQ1M1Qc4u4bfmb0JfOYN2cznRp9SbcWn9Cu4UfUqfQeH0xeQ+GeYjZv3ky3bt2YPXs2Tz31FA/etYgLhn1D8+bNeeONN6hSpQo+n4+hQ4eydPEOXnlpOjXyz+PaS35gQLfP+fidNRQdjM8Mb/2a/Ux9Zy0ndf2cR+5aeNR/S3t2F4MuAfunIcuRjhCWCCHOAnxCiPpCiLHA93FcVyy1jiABhBCp8VboSQyllAeLCt33Y7MSPldy6EYSY0xGXmTPRGnJofO4h0rOXg4RBNETXvliTbrRCLTikc/yp/Q4HhfivTdnfifRjMgnw/mFDLdLCmTQh1riZ85v+0kOKIwdO7aUjT66MXLkSG688UbatWvH9ddfz9y5cykpKeHgwYNs3LiRYDBI20HLWLDlVjsJ8JiwDThVsMYxwMNj3X2YOiKG7i4qZRshDDqkilb4FF56sBkLlu3l7sv/Hd7JS5YsYf78+VwyvIa56CuTY6PlvToJonEcHJJDj3xxEcUoZUQcj0UEPY7HKzmMShAdeODR1ixevJgJEyaErxeCGjVqkJLiJ8GnOW9tKzzLPH/DzU2Y+VUHxjzdlnnzJ/PGpJvZd/AT2rdvQufOnZk0aRIXnfc9HbvkmmPt0CG7Ofvss/nu/9j77jg5avP9R5rda+7dxr1hY4rpYMBg00JvoUNCT/gmkARIAoH8aKEmIYHQa0IzNY1m000xHWOMwcYY997t8/nudmf0/v6QNKPRaGb3zhWH15/17c5oJI1GIz163qKP5V7Kq1atws47nYoZ0w1iS933vX/7Gk/8Yzru/dsUbNXFx6DBrbHdDm1Rt8bH+We9jxbVHP99aQ9MmDABt9xyC+bNm4cnn3gJXTuMwN13352wtc7lcthrr71wxRVX4JuvV+LyS+/DqIcqMLTPv3Hxue/hm69WZrbR9kPbYcaKE7HH3p1w601f4k/XflFW224qUaTTik1dD0J2ty/12Ujg8EIA20Ja8I8CsApy27tS8jRj7F4AbRlj5wF4DcD9Ja4BALCslcUOO7Wn198/JDpgqZHC747BzEzvTGupTdKuTRxP280kVh4Sx7LyaVJsuFh5KemymJuyrneUZXxNhMLJCoOTOJ9Rnn2dfc4+RojUyYo1JAGpSg4YRKOHoCGPuUt97HH723h93IfYaaedHBlu2TJ69GicccYZ+McdAzFiaJuyPUedfbQZO+w0aZ/vlDyaJXoRwRVLnWOxRZDZDosWNmDfUz7Anf9vGxz1s/HrWPCGlUsuuQSe5+Gq876MOR3pcccJEq1nXt5CNenNnAlAs8pogjR5MVGi3PURfHvixJU4+ejxeO6558L4hKtWrcJW3TtixqyjkcvxBEkRgmRjziEGHHf0YvhsCt57azHe+eAHGDJYei/zYoCzzhOYMuUNDBnUCm+924Crr74aF154IT7/+khs1b0mzG/2rDpMnVaLXXbtgHZtoqgrc+fX47F/TMdll28L8gX22HU8unVbjQ4d9sLcuXNx6aWXYrfddkPfvn3Luu+5c+fi3gcOwz13TsUDo/bBsP2kxsXeu54JwtzZdThi/1dRuzqH2tpafLP0RLRqvVlEhAEQH4cO3GM0vpiwgtMmpDYZYzVbd+pT9/uDf9rsPG5+/UF8uXBa+7St5Ta1MMYOAnAwJF3zMhG9Ws51mZZFImBxHjvF1tBUy7qYvCyVsjyfzCeT7SsxcK0Tawg0nVlriqzjIFkWKCxDEnHrTPtD0wYxzWmF2+cJ8EiG9+GQLKIKdP3GN8uxW48O/5OgEAAOPfRQPPXUUzjj51Nx0+O74o23F2Phwnqn6s0FCmNSLlPtMI1I9OUstXIKKIw7oIj0j+mgoryYQ29m2zEFQJeuVbj3D9vhZ9d+hZUrV7oL3wzkvffew1NPPYWzfvBhats57QOz1KzWAoAFIv7bUi2nOjdZdEaah3Jav3N5P2d9YvdmMIh2GVnez4m2S6nv0O3a4Ja/bY2jjz4at956KwBg3rx56N6jBjmlxjeBIHepxjkDYwz/eqErnv/vfli28gQJCo26L1y4EC1bbo1ttm+Lfv364cILLwQAPHDnVHBfgCub4V69W+CAg7qhXZt8TBXfs2sVfnfZEFkHAJ+8twNe/OdwPHIncNYJPh576JfYfffdceaZZ2L69Okl26NHjx74w9UT8eSTL+LcU9/FvKmrwItB7PPlhOX4cNxivPziPCxZ3AjfV97PHsOnHy7FiuXu0D4bU+zFqRBMbEpQaMq6MIYbQxhjrzLG2hq/2zHGXi7z8qmQgPDXAMYxxtbZxhC5vOfXF6wZogxHlCx7w1TwV2KV3WxnlKx8ssCho+52XiUN9ksBzHIM/oFsvroM9XM8TbxO6aDB+KTlWTLkDQPzBA7o3xkfzVmOhQsXlrhgy5WRI0fi3XffxYoVK3DrqNbY58TJWNnqgbIny7IkAxxqKbVIc5ZvggF7VDTVyKZocGJ6KAcitD0Mv4vo/kbu1QG7b98GT9y8+XopP/roozjvxBr07r7u2/ilPlfGUlXLqeCuDKCYZaNo1yurrKjMFDW1lU+pvMsFrIcd3h0vv7Eb7rzzTjzwwANYtGgR2rbNJ8BwqZk8BN/mMSI0rPHx0UcfweMzUFHp4fnnn8cOO+yAFi1zcm9fC3Br8Bm7d7MuRGCefAnvum8aBvZrgcce3AOfvrM7unb6ELvvvjtGjRrlbFtbDjjgAOy3X2f89Y9fYdm8OlB9EWee+DZ+96uPsf8eY3Dkga/jdxd/ipoWHk47sz/m1J6EljUeDh/+Mo4YXi6G2DDi0lgQbR7hAwlYJ6/kjYQNOxLRyrDOkp0sGfxVqY+fBXCvOtQdwH/KKTBzam/VOu+vdK02LHDoAn3uuIMp9oiWQ0osjpvxKRnfLSW/RJ3LtTcsRyyvZrsOTnDoKCdzwnbN26nAr0xw6AB2ToCYJk4VM5NeyUI6n4AYiBjaVOWR4xzffvut46L/HRk0aBBuv/12vP766xg5ciRGjx4dO9+cRUumOJ5Rqr2ZTq+dRFxg0Kc4ELTShU4naWFsApLxDYtJgKhjHB45ojP+M3ZJ8+53I8hLL72EIw/uFjtWMiSRljSQIiiyJbXZw1IAJ+2TwizqZ2THlCxFjZjOKfaHGyGKEouHJoI/psvyRSIfXgzQa6tqPPbEAPz+9xfi0EMPxFeTGjCg/3MYud+rePG/c1BUzhqMKIwXaH7nxQC80Zf3r2081d/K1nncfscu2GOvjjjmmJ7IV12Iu+/rhssv3xYXXrB1jClPAEDjE5ar7mHanLW4/A9f4W8PzgA4Q+v2lbjsN9vgoVFDcdVVV0GUGabmllveQVXVgdhj95dx7eUT8NKL8/Hg/d/iwAMPRE+13/ivfj0EV/xhKCqUp/aRRx6Jb7+pxej/zimrjExmuEQa82/W9Y2NAfJ5njzxvaSJYIz10j8YY71RnnnjzwHsDWA1ABDRNygDUAIl4E9FvmL+4vkpcecSti/JiayUWrkcti+Rxsi7KYGwS15fZjgbO58wnaMdygaHiXOJUyHIWyd7nXLBriku+0Nz4ifjLwHaKxmCgQTAGCHHgSMGbYUTDz0In3/+efPrvwXJyJEj8eabb4I3xK011un5xtiL5Gmn+k+nTQODwgB6KDHo63w04DAAYwwkEkUOKTq8jarTrtu2xnsTV2HBggXNb4cNKJ06dcI/nplT1qicFdeSEVJBXgwkUgqwygCMifzseil1aJaKmgdxcJlaplWezMvqHxmA1pVvluqaEWHrfi3w1eQjsPe+nXHggQdiypQ5uOHGx3HRr6fhnHM+wAP3fYNHHp2Jflu/gKOOeRt33zUVCxbWx+og8qYXeRQ94uRT++Cqq3dADwW0Bg9ujfPPH4jKymgfUA2G9SfRBkL3f9l2CxZeAAA4/AddY89h+NDWaGhowIwZM9K6UEx69+6Na6+9FsOH/wD3PDgd33x5GG6+YQcIfyIOOKArPvn8MPz8om3QokUubNuf/U6GCjzrhLdx3IGvYta01c68s5yBSgE9ncb8myZMEBbPX4tWLatWlnPPG0NKrImyCeiNA2+vAPAuY+xRxtijAN5GFJswSxqJKAwtwxjLocwaZ0KFqipvzoL5azOuXj/gsBTbF0tjp7MZRFeeVn7NAoiOe2wyOEwLOpylcs4CCmmMYLlSEjwYJ2xQqI8Zzidk5ceUl3JFJeG2o7fHtQfsiAP32hOzZs1qRmW3LNHAsGroK5kqxfUhzkkaSAC+2PlU9hDJj94RxR41DYYxfn1UVqy/cYaBfVvgx0d0w21XbJ7xLp977jnc/tAMLC0RysspNkCyAaKDYQOQYBMzVbAO4FYKWKblVw4DWaru5YTQSYDAlDrF6y/Qu3s1Jk6ciOuuuw4DBw7EzJkzscuu52HW7L3x8ceDcdNNt+OS3zyAKVN3xPEnzUJNWxWgOsP2PbEwM9o25vBhMK0mw8kCkqBa19cXGNr/bvz5uu1x+g97gDf6IdBeU+ujdevWJW0NgyAIw8/M/epUvP3WGDz3r33QoUMFzvvpQDz7z+G45dZd0Lu/YT6myt9lp3b4yz174Ke/HIz33l6MPYY8jxf+OQtP/H0aIEQm2Fvfop/dgnn1aNu+ctVGKbQMoc1clUxEYwDsDOApAE8D2IWIyrEPeIsxdjmAauWE8gyA58spMxNOfP7ZspcWzFubukosJeQCX8ZL2RSAmFAx2+myVMymOtrIr0kAMTUeoPkBUhlGq8WzwGHit6VSXi+qRctrNT54G+f1qkhAbn8nrGMKFIa/Y/cgwDwBnveRqyjihB0744Tt+uDaYw9vXv23IKmsrAzjqdmS+XzTPN3TwJyp9jKBWhCdD9M4gFwMDGrwF8Q/LOMTplN5m/aGUTBsROMKZzjigM5486Pl69jCG0amTp2KHXfcEZ3aV4bHnPbRJvCx4gRqwBCet9m5LFBHjo9OFzjUvWacQlc9bGBjpS2lSraZxQRgiqUVzk8pFbJLrc0Dwl9u3hFPPtwPyxb+B/uPGIo+ffrg+uv/gDfffBPbbLMNRo4ciaOOOgoPP/wwevbsiVatWmHobi/j6j9MwqpaPxy3Fy1pxKrV0SYcRCSBmCDU1weYOTMZh7lNl3+BWMSucgMQtu72b3kvxQC52kZ0KhTx86O6IV8ny+CNPogIp5y5AkOGDEFFRQVqa2sTZcyZMwdnntkP7du3x9ChQ3HnnXdi6PCXsdtuHXD7nd+g4FsLAvu7+nv6Gf1wz62T8eWsY/F/vxyMKRNX4KKffoi9tnsBa2uLJYF45kIkQ9KuWTi/HnVrxCdlZbKBxWH50DTGcONJAGAxZKiaIYyxfcu45lIASwB8AeCnAF4C8PtyCstlnZw3d+WUBfMcquR1bRHOwjw0QNKTnBlmIAREYVqotNHkyQTF0sWOh+AO7rTqxS51PYiseiGcoImxaILW9+U6b9xzervI66JronxKSlNMeTPD8RjfyTgmHMds4ZD3yBiYpxJqcMIZuBfggj0H4ICH3sDYsWMxYsSIJlR6y5LPPvsMO+20E9ZOOTTBDMaYIvO3i93Tso7nYuet42F643RscjC7kL5Ov3MBAWpRJ/NhYCAQGBgnkIj+AvI9GbZjW3w1o07tTNEGm5OsWbMGnTt3lgtG47GFY4klWZMoM8YbmGOTFscxOz/iLFQ36+/NFSYQv55FYx5ljF82LtbbQwIANXGuMNvQHO/TZPCAVrj7rzuDiDB/Xj06tK/AF1+uwqhn78cBB9yL1atXo1u3bsjn8xg6dCj23XdfPPbYY3j66ZfxyfhD8JdbpuD226ejUCig21bVuPKq7XH1VRORz3Fsu20bvPzyArRunceMmUejfbtnMOrRYXj5lYUY/1lbTPpyFd5+bSSGDlF9VAGg2jlHy3oLAnkcQq0hyItW++98vALjxo3DkCFtcPwPn0N9Qx6///3vcdlll4Vp9tl7MI47vBs+GrMbps2ow+U334e2bdvipdGzcdABPTDqiVk468x+Km/1rFJs1pc0nIohvf6Fa244BUSELu3PxG9+8xucevRYvD9uCT744gj0G1CWw2p4b+Fzcixknf1eHZs/tw7jP17wWtmFbWDZuPiu6cIYOxfALwH0gNwzeU8A7wPYP+MaDmAiEW2HMmMXmpIJDAHMXTB/bbOp5syVhTXQmADL7Gg2mJNprXx0Wv0lDSB6xsCpgB4Z+TITsJpAkLHywWETpEmgsVyxJ/EMljBx3jXBZwHCtCpzeZKpCzyPIAoCRAw9O+Tx/0bsgEtPPxUfzp1f6m62WHnnnXew0047gYnx8Vh45YDCNKAIpJ/LvMaomAPwlQSBrmvNc9x4P5jxHhGTe4aDS3AIDgjCxClr0LZlDvn85hODTcuQIUPw6aefonbNLmjZJl6/NHAYJTDGEyUucBiLh1hiXDAX0eUyORkVjL5ZIFMC/BQm2wSPiI/Z5hVpTLir3ia4TEtjls0YQ/ce0i5wt906YLfdOgAAlq8oYPGSBhR9CRz/8cTj2HevPE47fVd8+OEy3HLLlKgMBrz7ziIsWii3Z5w7dy1atsrh7nt3x9dfr8Zf/9obzzw9G/l8HsViESNHdEa/fi3j8wIQtZtuB6HmDy9qn2EDajB61J7I5zmGbN0KawqEg46/B7Nnz0bPnj3Rq1cvLF3aiAt/3Add21egR8dK/L9zGnDNTTfhuOOOw28vHowzz/sQa2uLuOBnW4OLAAJyDiOuFl+cAQGF88zSupMl2GcMZ/7fJ+jYfS+sWXYK3h93IUb941v8/tqh7jYuIea861zcmGmJsGD+Wqxa1TCjWYWtZyEAolnR6fX168fcp4T8EsBuAD4gopGMscEArsmsF5FgjH3OGOtFRLObWmApYDhvwbxkvDWg6erMdR+0UD6ASgN4QAjyYufMfJsCDjPrgJIsXtqkHUoIzuJgj1m/09IlwB0cgDALDLryiqVnyWOmcJLnAjUgMgJjhB9u2wM3vj0J77//PoYNG5Zy8ZYrn3/+Of7xj3/gk08+Aeqi4KplM4WuY00FhHafAUozgmkA0nF9TLTziQYLRJJFBMLFQwgOGUOhKNC2VQ41NeseEmZ9S79+/TBs2DC8+PocnHxsj4QaORUcZiwcY+DQPE4pamqXNHd8LQOshXVLuwcWAZ4wvaM+TZkxshgnp6TcR7tOVWjXqQoAsN0O7XDwoVuFZkSrVhfxyMN7onvPFujRowZHHD4Fjz/2DQ4+uCuuu35HdO/TApWVHhbNX4v99/sY3bp1A+ezcf5P++CcM/uhd+8WEjC76i6UiYAxfzCKXpHqKg/7DOsY3ldrAp57oA/+Pfp1zJtaxOsv1eK+G7dH13Z5pXUhHHZAZ/zz/fb48Y9/jCeeeAMnHDMIv7/2S9QVj8Klv5wCRiQd/hCfu0KQCPnuEWPo2OJJXPSzdli58kJ06lSJv90yGb+/evvEjixlRamILQjdz8gcu5QWcl7pjDeOrCdKZkNKAxE1MAnqK4loCmNsUBnXdQPwJWPsI6j9kgGAiI4qdWEpYLh8+TJ3cMx1BnqOlcRGExc4BOKq5TRgaUoW+MtiU0pVz9UWTQGFTQGELkaxHDBoHSdXPpYwT9ocVlQQfr7HYPz+tFPw+vSZ7sRbqMyfPx9HHnkk7rjjDnSu/SkoZyOLZjCFgpoPCLNYwRLq5Ohcid9ag6YAIQTk/t6qrzIQwNWkxoG9dmyLVXVBqG7f3OTUU0/FtVf+BAeN6Iz27SvcibIWsVSafYtAtHFZBiPZbNKjDLCVNda71NfM8f5nqaLXSRyAsORuMep867YVOOKI7hL8CIH/O78lrrm2DV55ZSGOP3EF+g9sBQjCffdOw6JFi+D7KzD2tf2xvd5fWcTf1dDOU9tYKo/lkiYDnIEEoX/flvj1+QNCW1KmbHpDUMcY7r5kCU66wsPOO++M3XYbAWA6brzxRlSyIfjlRYOlDbzIBuEMhGW1J+GaGwfi3jumYtfdO2DypJVoqPNRU2NBguY4NaaVq9ppwby1ADB3/eW8xctcFeD6PwBeZYytAFCOui2TVcwSVir4+NChHRvHvLpfhemub0sWe1jOyi/VBb6sa1PS2+nsvEw1STiJUjxP68VfLzZf5Rwvhyk00yRAol0GUtOY+SeBYQkQGMuDxY+RvJ4IQMBBPgcFDML3EDTmUbvWw863v4r7jhyJI0e9gP8Fqaurw/Dhw3H88cfjoqPeloO4Z428yl6sbPBX6lhTWWdH2pLmCC5JAYchA5ED4DHAUyYeOQbyZMgQ5BjueHwWXv9gOV4atzS9jE0kRITjjjsOB+76Lc48uZcTlGknktBhxCXleMVqyQJWzXVGK0PKBpwpdVjn7fDKaSMbCBq/S8axtWTRogZ8MWkV9hneCVUVstNO/Gw5PvxoGQ75QTf06tUi8Rx4QFEIoGIAFhBWLG9ETcscKquS3ItpOhKKkE48FAicdsF4bD+oFa74aX/orSPN+atYFPho0mp89U0t3nhvGf776iKcd2Zf/PHmHWPOlPL+M9pKgdSnnpiJn/3fx+g/oCU++uiQZLoSbVZKYuwzEYbv/Vrw1ZcrSpFSG1wYYzX9O/apu3j/nzU7j9vfuh9TFn2zQbbEY4z1JaIZ1rH9ALQBMMYMRbO+peTD6dy55dLp39RutY02sNVi2wGWkjIo5i1G0tjCEsedtmLmRO5iCc1jaeyQi0UsBQbLBYIZeZAGhwJqpmRgjMBzAVpUerhkrx1w/TsTcARRQoWxJcptt92Gfv364VdHvgUdsD0mFPfes1nC7MWEKx2i9KUWF2EdEL/WztM+V+7rqxywmVp1Sdsc1SdAkVpZSLXyAXu0x33PbjbappgwxtCpUycIf2rypAJwxJS9FxBXKcJSzTocj5yTsGOhG2Oh1qeYY3sZWWfZQq77W51sD81IlgIrzWFRu3SpQpcuVUZZhKFD22Ho0HbQrqg8kHUij0WMntqrmQN4+/1FOPz0DwEAz9y7Cw49IIpfGLaVC9wGhC9XXIXnXt0TU6atkcAQSbV+rtLDXru2x7DdO+DsH/dFwBm4x+UjCFTf02XYjJ+h+ibGwAKBk07oiY4dKtC6dT56z5VtotkOYT1TJGZj6hJBWLSwHu3aVmfEwNu4QmAQ69BLNzB6eRbALoyx14noAAAgorfKvZgxVotkFVcB+ATAJUQ0Pe3aksCwVcv8F19/vXqrIdu0Do9J6t0qrwyj0yxpjmq6rN0GlJRtG9hccdnUpDGhaWrBtIm8KYDQBfxcx1KAYAIEZgFA43pyHAMpUKjiA5FgoICDBIcQHk7eoS/u/ngyXnnlFfzgBz/Aliz19fX429/+htdeew0Mv46fNJloGwCmAMJs1tBIm9aPUk0SrLz0sdjzTfmeJua1qmvIUDYAqT24mUdSvewxsIAwe16DtK/aTKWurg41LWT9EraAmq0BpHNNnoMFhIf+MR0H7NEefZXDgkzkBoflyAYbzxz5ZmqF0k4osLMuEoIcnR9gRJlQaczyEIHmNCeesmFAbJGmy4zszF2q/SlTa7G2IcDOu7ZHRW5rXHDlDPS6dzoG9qpBy5Y5jH5jMS4+vz/OOaNvQsPFfIG77roLP/nJT3Dfffdh5aoC2raLTBXMcGphGDbIreqJCLwQyLpxY0zOmKd1FD4G4KCRXWRaXxiOQxQvF0gARlMyPfDVeDJ1ymp061azWUWvXyduasMiQ84YuwrA1oyxixNFE/2lxPV/gVQ5j4J8zCcD6ArgawAPARiRWnCpmr340sz7vp6yKslk2KJWU+FHpUsLoNmcuEiuF7VcadYgatHy5aQtZXPobAsXU4hmgkJCxAbpj3ks0N8ZdPxBCiQg1NvZQW1nB2LRNfq6gIXXUsCiUGo6v0CqjCmwQaFuJ9U3mEBNhY8r9t0ZPzvlZDQ0NKS17BYhl112GUaOHIkBdEmoHgJQEhSGon/rNOG7Fr1nEIhiDapnLu2eKHr+1vlYP7HzivUXx0efMz+B42Nfoz9FgPkEViSgCKAovzfUB7jyrm9x+oFd1t8DWM/Svn17LFuZrcmRrBIHeRwTptbiF9dNxk9vZoC9x67TppiSv9fXx1nZ7PTlxreLfcqISVjq44rvGM4ZVt1ic48xr5QKyp36UcL0e6RjPSpwyE3QKwgPPTgNh58yCXc82gLbDDoU999/P2bOnImrrn0U/bY7Cy07VOLgQ0/FfY/ODFXPpjT4Ao888ghuvPFGbDegJT6fvDp1PjHvzTRZYHYbWPeeuf2hcS+xZ2B/KP0TtZn7+NSvV2PChFVNDp+yIcU1tJX7SXmb1pecDKABksBr5fiUkkOI6F4iqiWi1UR0H4DDiOgpAO2yLizJGBYK/qSpX8vgm6bXWbkq4PXtpAI0HRQmxGVfaJ5OM/I2nFZSpbmxB7PUcxmgMNWO0FYjh+cihjCVHcxSDSN+TF9Hdh7GdxMg6us4F6BcgCO36YqnJrXFRcOH4+6PP8aWKDfccANGjRqFqVOnAnNPTpwviym01cblMM1A9Pyt85n2qPo6R7omhS3SIhwLK+2IAkjGEDK2IRjhsru/xaCtqnDun6Ykr9tMpKKiAr7vuHGL3dLy/sfLcfgxPfHF+NmYNHkrbLdtm5i3aFngcH1JE8buRJzEJkrCa5dl3K8rnVEuA0JHjfC36XFr9POEWjPGlEXitEdMq5KZr8msCcI339bi4iu+wPTp09G3b9/YdYcffjgOP/xwjBmzN84/9zhcebHlUKoA3LjpP8eQIXPQrl07HLV/Z7zxwXLst2fHZEU0yyzIqdplgsJX0nVLzLrvkqLU0+b1LrFxgWtu/frr1fj226Xjyi98w4pc+2yeZkxE9DVj7E8AZhPRE83IQjDGToRUSQPA8Wb2WReWYwA6fcbMOjCjc6TZG7Tt+E+sXPpDR/XWzwBng7VNpXJpklraxbTax7JUyLbaz5ioE6pjcxI3fxPSAaEJBtOAoKEaBhD3QKaUc+Hv+PnwvhmBqZH8omHb4/inXsbd2DLlH//4B1588UVUzz05YXgeA4X6OZf9W2Xi6jcqXVrfAdYBDNr9wjzmEHurRABAwOSWiQEki+wBLE9YtLKIUa8vxrezF23WdqfFYhG5asfk7JhwT/vJR3h+zEJMmDABt99+O96fOA7bD2ktg3sbDZeI3+qSkHlfH3dh5JciMU3RehlHy8yDGySEBpcKDIXARh+2gAs4AzM2FZLx/JBQ2cfClanrgGioMtXHmuErFASeH7MAEyaswAXnD0CXrtVoWOvj9J/Ox9lnn50Ahab8v0tPxkHDO+Kko7snW0EQxowZg7PPPhuMMaxYWUCvHtWAEAC4XDwZxIyuFwHSllVQDCiHbeZYlJUfci3q3zEwaYH9VFxglcEI+PrrNQAwuXThG082MOu3TqLiEf4fgOYAw9MA3AbgLsjb/ADA6YyxagAXZF1YcnghIh/E/Pr6oCQlv3LxcUkQmML4xTZnd4hzX07EVRpOsWlxWKuj9bkKb+LgnBkTKm1yDy8201rq53JAISEChaa62KFeTqiHDXVwnP3j8rhSG5PKU9oRyr/htcYndEAgDs4C9GnXFlW5HCZMmNC0Bv2OSENDA7p0idSimUba6xsUqueeqS52pIlMB6zfsX4hf4emCK6Pb+fLwg/5ABUBKjKgyIAi8Pz7y3Dw0LZo37598xt8IwhjDMVi6bGk0Bhg3IfLcP/992Po0KHo0qULVqwsxp5nmKc9tjl0V4lnt46fLLVg7KO2Ttygn1h5asHkGypMvX2jro+5fV/aloyCjDQCtnpctnP8t97mTl/H64sAEd4atwSd+j2PG/6yEkXvYOx/xFtYurAe947aHdtssw0eeOCB1H7w7LPPYtmKIm74zaCQIYRRHgAsXboUnTp1AgB0aV+BW/8+E8NP/RDCF9Ez0/W0zI9iqnZzK8LAum/ddi4VsbUIYL7ArJlrQIUATpVyqFpO4gJ7m0dGAPkCK1cWBBGtTG2o78UlrzLGfs0Y68kYa68/pS4ioulEdCQRdSSiTur7NCKqJ6J3s64ty2W8Q/uWk7/4fMX2e+zeIX4iLUSB7qiWOCfCMgKsxqSJq9ZMg1jbrkIfp+Qxd13KrIStBlTH0mwvY9clfjvUxy5QqCdwwM0SWgxhJjvoYgWNJbrNCobUvLFaJSMP/Z3zAJRj6NDCx+X77oTDhu+Ll955GzvuuGOiXb7L0rFjRyxduhRdlB154hkDxiShF1rI+B2l13aE+ntJhlmXaQJI86+9qAAiEAiUNj8oJbEub6nxAuCjybXYe0CbMjPbdNKmTRssrzsMgKHutsayZYsbcNrPPsWBBx2Lc845BwDQrVs3fDhxrXwemhVzsDqppjq6n6wrY5iyKEmTdYoeYZaVpcK0PWZ1Z7HulcXUv4aa2Uhrxv4Lfwck2TWGMCpAzIFFM2mCwIsBeGOAoCYPUZnD2HFLce4vpuHqq6/GL37xC7Rr1w41hTEYdtCbqG94Ddfd8JdMhvuKS8/ElRf0R4uaXPwV0OU1BpgzZw569+4NAPjxb19D663/g+dG/QF/fmA6LlUeyrqvSDMERDvS2My19bhcrGqY1GSqDXa2ZY//YIdeHGef1hu33rADbPYxTV0fS2U8+8lTVqNzp1YLUxtpE8m6EOHrkWbKkrPV359bRffLuogxtjWAuwF0IaLtGGM7ADiKiK4rVWBZwPCDjxfd8cn41vfusasBUjPo6EwFkN2JSoKv0k1ftoojzf7BBQozrm+ySqUpdofOch3Xu367quUChbHrLFCYohKOeRjDmswcgJAsMOkSvROKx338aKdeqPAqMGLYMFx21VX49a9/jVxuk4e6Wi/SsWNHLFmyBOimBnF7orNVuuZxl9lBc0GhDf6yVMWWmtgJCO3rYV3HKXkMFmmvz3FC4BNeHL8cvx7RG5uzNDQ04O9//zue+lsvAG0T54Uv8Pb7y3DFTZNx8GE/wc033wzGGKZPn44LL7wQW2+9dawPpDrzuURpFtdpNnOMRaWAaNa1TSu7vHpr0AytBjbENE2QwdHNk0yCSqGPG6pVxhKqV+KAac8IALwoGTKR56Acx0efLMe5F0zFU089hZEjR4ZF/en+b3H+Zd9i0aJF8plmyG8vvw2XX/5zMI/jh0duBSYQbsHKAoF5C+oxZcoUbLPNNgCAvn374qKLLsLcj/+KQiEaB/SCgETKPQIRUNRNYgM2w1s85vVtngdh3peHAQA+/GS5sq+MJ0sEMi/xbD/4aBlW1/qPZibaBELl+6lvEiGidPuEbLkfwG8A3KvymcgYGwVg/QDDtWsL7z706Eycc1pvVFepQNcug/KmiGUvkSaZICxjFesyljZDDdj1ACxQaKkXyqoPEGd2rGNlS1oRdj6udCZbmMYUIkpjg8IYIHQwhGSCR4sZtMEguUCkJZwHQE6uVE/Yvht273EoLrn7Djzyl1vx+MujN8udL5oqxWIR+XwerCiU8byxQrcYwTQmJzFxp4FCE+TZLKMFFmNpE4yx+uNaVKSxzeb1QGSuYFbb7MMUP//l/Fq0qcpjm6s+xOYs1157LXYcWMCOQ9rEXsHGeh9X/3EKXn5rCWpaD8DJp1+Gyy67LGSSJk+WplWH7dqgnkMG+LNEP38ZKqy8emYHIM8GfGUDxSxZFxCpQYgdaJnF7TIR2qlGY1UIKi2QSFyCGQJkcPUwU4pAjnofKe9h0eIGfPT6Ypz2k4/xl7/8JQYKAclGDhgwAAMGDCh5O+eccw769++PX55/FF57ewnuuX47cMhFIisK3P/anjjzzF3QuXPn2HV+QaBNC08xfhHgM+8RQHQeiBZkup4KWEc2gvF7MIUM0Niu2sMpx2yFZ55fgKAxgJe3kKFj7nbZyRYKAmPfW4pHnpyFzyaueiqjmTa6EDZ/xpAx9mNn2USPlLi0hog+sp6xX06Z5VIyk4tFIioEjFWE+1s5E6bZT6VuoG6uctKkBABMiH55HMdTWc5SoNCugw0AM+oRS2dcm/jeHDGZn6w0pthVFdYE7gCFpro45sXlAIUJQFiCNeRcbgBfgQL6dajE0yfth8cmzMHB++yNw44/AX/4wx/Qq1evEje5+cq8efPQvXt3YDVkn1CTkj35pjGHodimBUD64qAck4O0ssoBhVYQc/nDNC9A4pj+bZ7TCw0ihm8WNmBgh5aOCm0eMmfOHNx888145ZVXMObBbWOx5ADgvQ+X4bUPluMfj72CYcOGJSbdiooK7L777rjpolaWOjFZVgKYKVVh2SrdrNnOZNxc5WSkd15TRjlNEg32YC0klPd6TBT4gwJ/jJsLUZK/HXMLIxisIYP29OVFAfIYZs+oxQHHv4dFSxpx5pln4rTTTmvmzUQyYsQIvD9+IQ7epzuO++mnuO6SQdi+fwssXdqIhx56CO+9917imlYtc5g6s07OXSYuCxC7b0CzpxHDat93bD4L8yILzKmjis08YmQXPPGf+Zgztx59exl7l1uso7MMAJTj6DDoJQBAB7l95BcZTbRJZGOAu3WU3YzvVQAOADAeQClguJQx1h/qFhljxwMoK4ZkWcCQiOjg/bZaMXdWXfvBW7eOTrhc5VMCmrqOp+1h6ZRyVRDhhEdNf+KW8S0AONXPLlYwVof10NVSBtXUvPXEb07UsfMZKmSdRAOAlMkbSGEJ0xhC+7hRh8gBRR7jPAAxAUYCXo7h3D0748SdDsLt4z7G0EFb40+334Fzzz3Xfe+bucyfPx/tV/xcTVoAg4h77qUBg5RzIVsYYwfJYA5LgEJY3222EGUyzWne6rb5AWCli8CgDnhOgqGGarB67eY3TM+aNQvXXnst/vOf/+BHR7XGy3/vhy4dK2PDCyPgz3d/i1/96o/Ya6+9nPlcc801OOOA1QBagRFF9mJlg7OU/pAiZamoS4G+LHVhiUe1rl7MxBU7qLtlYrcQSHCizTNUf2OcDGaeQrAY2uUp1jDuzStvSHofc5xx8WoM2/tQ/Oc//8EJJ5yQYPKaKzU1NXjhjRn461//ioPPvAGP3bQ9Hh47ACedtD0GDhyYSH/MBc/i2MP3cS4KQ9bQYEzNANQJNbsp+l2Mqd0RBQ9X8/UxB3dB7Zc/AOcMUE4rZIBPWQ5CYifciq8Y4J4n5uD1d5ZgxJ7tMWzndnjzw1UNS5ctKYux2piyTuFqNkKoGyK60PzNGGsDoByV/M8B3AdgMGNsHoAZAE4vp8yyjbhmzCn85e33l103pG8L+fBdu580QUi5wAOIderwvLkKaUoxBtvXnNq5AGHseBZT2NxB2+mU486PWBNYA1tc9TNVyE52xw0K7ZcpCxSaabVXsutl5DxOf7bPAVcdPBBHbtsZx/3iArRq1QonnnjiZh3GxJZ58+S2bp32fA3F9w4AYwKU57GVPpANBBO2habYoLAccbKMkQo51SbVBIXhMUcMSwdQdDHOISgMOIgY2lRVYXl9scyb2Dgybdo0jBgxAqcdUYmJzw9Fu/aVqdqNSV/X4oc/dITrAiCEwFdffYUjrtg2sq9O0zakLQrTdhFx2Yg1lfmLLRgc1xqHnIAvbUhaB41IWD/ddWJIXOXNGRinBAhM+54GDgGAF2VlFy9pwLRp0/Dhhx9i+fLloafw+pK2bdvimmuuQY8ePXDFnXdizz3b47rr3GZfnTt3RkOjcI/5mjUEYD6AkDlUaUwh057SZGATtplKhNxZxUmamGKptl+cdDb+9vCvwYJa7D60LbYf3Brjxje86LzJTShNGTbTri8ljLGHABwBYDERbaeOtQfwFIA+AGYCOLEJ+y2vBZBcRdh1k1veHcgYawGAE1FtmfmXDwynzVj2z7c/9K77v5N6xu0ZbDEBjdXBzDQxuwdHjKnMqT8cMFzMmNGBy1FTp+Tv9ByFYzBPU8s4zseKMAFe2kBuS1OcWNKyM5keW4UsK+YEiS5P43KYQhdbaINCZiD/8DujcEW8S4/WePLUXfF/PzsXK565Huc/OzHl5jY/efjhh/GjH/0IxQ8OiFQ+gXyIBF4yiHUMFOqmyQKC5ZgpyLkyW1LUxzZTmGqTGp5PAsJwYaHOEzEIwdGvTVusbChizJgxOOSQQ0pUcMPLhAkTcPTRR+N357bB2Sf2dNssA+EkXFXlYc2aNejYMRmUeNy4cdhqq63QuWNFJhAsadtXzvsvHLtNNYH5S0z85s+sfMpJ31TRcwizAA+Xx6D2LWYewkUN4xRnD0k7ncBw2pDHmQo5xBsCBC3yWLE2wI9/04jTTjsNnuetd1BoynnnnYfzzjsvM82nn36KnQe2TJ0fQmBnsoZA6tZ1CfvM8DuLmFcTTBrah8w5X5WvQwR1rf8z2tWsROuW1bjsJ/3xwNNz8PYH8+7MvNlNJOszgl2K/APAHYirfi8D8DoR3cQYu0z9vtR1MWPseURvlQdgGwBPpxXm2j5PHQcAlLGVXvnAEMDXU2esJeELxjlL2noASQ+yIJnEPG57jwFIt0V0vhjlPlGDiSwj4ntT2cFUVVA5dojOCiB+awzlgULzpS4x+YdVTmz0WuI43NR7asia2HVpeg0RYxGZRREzT2BY7/b45bDBePnL5Tg/JZfNUZYtW4b+/fuDBVNkcGv9HHkEEAFY6l83KCy5m4mSaMGBpk3MTiYx5bhtk6q/GoDSaWNqgkLzL4CWFTnccfgeOPP4H2L28hWoqIj2id3Y8uabb+LEE0/Ejb/uhtMP6xZvAmWPZst2g1vho48+Qp8+fRLnHn30UZy6X0Oc7S61qLTSuVXDJW8lmV8W+2cXYfaztDSuejTXLtGSmOMaRzRwKbYQKmQLqWOMIwKIOYMxVNU27fBYQIAvQbSo8iACgd9dPQn9+x+E22+/vVn1Xd8ydOhQfDylFk++uAAnHdLVqS1hFHdMidkcpok1V+v2cs7Zoc2+g5kG4vaOqj57bN8GHz61Z5j0/c9WEoCkEeX/gBDR24yxPtbhoxHtVfwwgLFIAYYA/mx89wHMIqK5GUXq7fIGQdonPqd+Hwng7XLqXDYwJCL6wT7dFn85eXWXHba2tukzIqQDFhuWlp8rTpXpcr+BxG3raDEBGUCwnAE8NV0CKKbkY0q5bGLsGkLZXuNpNhIm25NxHVmskD4Wngu/89i5WFYkPd3N3VA4F2oCpvC6PXu3xx/f2ayC5peUadOmYbvttlOgkIVse6IfGn3EFcA6MyyNui5TmhsySV/nIoZMVtFimksyhQZbKBlD2QeG9+mIri2r8e6772L//fdvRoXXXZ577jmcffbZePyGPhixc1sIbbNlRlIwwaE6vuvQtnh33JU48cQTY/nV1dXh2WefxaePbhsdLGPRmcr6lRoPHKdZFrBLUyOn9a0SNtax9FlllSGhHR2DYqWM8ZpTyBwytdgCI5CndtUhAjwGkogyvuDnADjD6rUBPpi0CnPn1eOp0QvRmB+E0Q/fCs7X19Yy6yb9+vXD6NfexTkn7Y/3JqzCbRcOAK/gQD6+wIiBNhPc6fc3kG0RzrVBvL8FKm3C8xgwAKc1dpt5uACrum7Owga0blFRT0SN2Xe7aWQThavpQkQLAICIFjDGUo1YiegtAGCMdQCwL+T+yanAkIiuUelfAbCzViEzxq4G8Ew5lWtS758wpfa6Nz5cjtATVn98Mv7KuEwQcjXm/Agjkj4ZUerT0m/gjx2BP6y7uj8ZxV3vOECJ83aaWDph5EOUDgrNAdT83hQnmjBPVv41gFN1HJ10sD36VAlQGKWzQKHeOcP6aIBAxCEEjxxb1HUDO1Zh2doGrFmzpvyb24Ty5ptvYsKECTi244MxMJfsH/E+EqY1QSGhtPq4BGsTSlpfg6EGboqU2Xfi5STNGHT/OGJQb9xzYeaOTRtU7r//fvzl/E4YObSNtAcFoncdcTtkvfsDEeGNd5di772uSeT3z3/+E3vttRe26lQZyychZhlEyO/zZvy40SfSPixw7EwRWNckxm71MdPpHW+MnWvCHUUo2pkk9gkQ3zHHzMM38gqa8NHX6d9FyN1QwjlDn1NjsmYCzXcqEGH9IYAVq4v4dn4Drr1zGnY78X1c/4883vl2R/zk4jswduxYtG3bNuUBbRrZY4898N7EBZgwbQ3OvHFKfFFpg3r741ME4Mw5OfbsCIf+agJqho/FRxNWOObH0vOzcxcbdd0bHy5HQ9H7+8ZrsfLFORQ34aNa9gzG2CfG5yfro26MsRcYY9omsRuASZDBrh9ljP2qjCx6ASgYvwuQNo0lpUkRhBcvq/vPq+8vu/2iE3umJ0qzK0xLA0QrkfXpVNAUyKtXWCl7PJal8nHca0njbuM0s4+ZrJAr/zKAX5Mm+aaoji2HkjRQKNkgAxS6dkIxf+tJkREY4xBchrLRqmXPI/Ru2xLTpk37TuyO8otf/AK33XYbWrS6FSgKIGes6119yqEeTngf6/OApVJ25ONI4xSbGRbWdwJi9oUlPPEo5Tm7Fg1mHYgYBHk4dkgfHPD3FzHh/87Djnffn1nWhpA2bdpgUZuzQd7LABSwsNRmdnDhZ1+Yj4bGAMcff3wiv0ceeQTnjFwIRnJbxFIaFb04KL49Uh6wxobEWGFL6nO2VwGOa9K0GnZZpRYhdp915lFivLfZQiBhw87UPtskmKFqVu3LSJaRY2BBgAVz63HZfdPx/Mf1aGhowHnnnYe/P34CRowYsdk7tLVu3RrXn9MXR185CcFaAc8nUI0XJbDHDC3m+2/OiYa5DuU5hh9+Ab749ib88HeT8NbfdkL/7tUqHaJ8TQ9xWxIe41H+b360HB9PXJS+X+AmlnWyMZTXPkxEtzbxykWMsW6KLewGYLEjTV8imqS+nwXgVSL6MWOsFYBxAEqV+SiAjxhj/1Y1PRZSbV1SmsQYEtHcFat8sXZ10b03ZVGAFfV3x3mi2ErCvA6+vn497btZTN8/072nJlJWUzDqLeIrMVca4x7tVVn4AWIvcSYoTJv8GZKDsSVh3Kv14VJfpmo61dkkAywQSXWP/gjyJGMYcAjhKRZRpuvXoQbTp09f9/vZwFIoFPD111/j8IpbjAUHIubF2U8oOcBngUJY52xxDXiuCTstbYbYi44E61wOi+gQQQzdWrbE8N498NBnkzLTbii55JJLcNddd8UnO+P9NRkRCGD2nLX4xVVf4rdX3APP82J5zZs3D+PHj8fhe0fbiSYWl4ZNacXeb6SOF+FYQbDGCuNjMoDmx2YNyXGtqdXQzB4hYv5M9s9m/shIa++vrffH1toB/duqDwXRJ3admd4sT9cnZAn1bzU+E0CC8PX0Ojz29Dzs89v56LPnuZg3bx6KxSLuuusujBw5crMHhVr2//UEDOhSjXGTVsl7rg3A1gYxwB+bS8LnZsxZ+mO0IysKXLT3WLRu3wODetbguCsmYcWyghyn/DLmToJsc/OjyqGiwBffrCEAm6XHIIFBrMNnHTDlcwDOUN/PAPBfRxozRMMBAF4CAKUaLkn7ENH1kAzjCgArAZxFRDeWU7km7znWtWOb99/6dMXehw/r4E5Qho2gvftIIl6VLaUcRmx4G66QknVJ86xyO9Mg0fxNCvEQFmpcX2rVngIIY2UL4zrzu12sHswz2MAocenB0YxZKPMvxQzxkowRIa5mFuE5Do8LMF+Gt2EqKHQF91Asbl4hTWwJggB//OMf0atXL/ACQEUR7dXq6ieulb4+ngYI0/qGLVlsISEO2E01p2ZwrTTx65W9oJG3s084whslhEtAwhkhAPCbvXfGMU88j59NmYLBgwdnX7ueZccdd8TMmTPx6VedsfO2bZxtSwJhuI9X316CY394Gk455ZREuieffBJHDatEVaUnJ0uHRiUCYoTimyOicdDuF2Z/AOJjTjkLBcf5VGbQ7jeuBYXdLNY2iuFXm4E2JU1LETrRqYw4JDiEWvAyyPFdM1pM3QuTk71mc397/3Tc9q95GD58OB5++JrE7iXfJWGMoVPrPM7661S8c9MO2KpzlbQ1tBeStrbJ7jdARJMFABjQmnM8c0k7HHFdPdasqsUv75iGR3+j3rsY06j+uvZX1mlFdM0X09egc/uWcyZNX9MU/dVGlQ3tlcwYewLS0aQjY2wugKsA3ATgacbYOQBmAzjBcekcxtiFkPaEOwMYo/KrBpDPKG88Ee0MAET0KYBPs9K4pMnAcMwH867s3SF4/fBd25cGbCliG96zwBgwHVmWfHCpqhNH2eVwpKGBbok8S6l04JjszfQJdUwTQWFYSEpdM4QxgLiakOSoWh6zaKdLCUMT80BOA5ApoFD/DgQHZwwcARBwMEYoBgxzHnoEOOmkcm91o0ljYyO++OILjBs3Dk8++STG/LRDuHLXk1aquPqQvVDQx4zzqQsVRx8q21FlXSXNLrWM/iUUM9yrTRv8YEA/PP7Tn+APb5XlSLfehDGGNm3aYMWqQmQPaqdREQCIMdSvDVLt0p566ilcfWoXJ0CPvdMpIK8s1jgLeBllJVTYLpBXqs+Ev23zEuuna0FiP/8skBlWnCXOk1LjM2IKGJIM4uEj3F6TwAAPuO1fMo7omDFjUFNTY+f+nZOzLrsT559/Pnqf+zH8f++DmOOECQrN5+bqE7Z6OSDs2Lcl3rqmBy79exEH7NDG2IbPSqsDi9tiarHU39HvL8dnU1de2/Q73XiygXEhiCi5YpRyQIlLzwFwLYADAZxERCvV8T0B/D3jum0YY1kMLQPQJqvgJgNDIejdcZNWgwokY0etD7FjJ1njR+S93ETm0CnZE7Qd1b2UlLT3AVJeTMdE4BjgYx6qaawAGX9djidOT2CEgzvjAAVlgnzbvhButtD1PWQMjQcl7crigDCySwSICQjGwBmBMYIvOPr99Jzy6rqR5OOPP8b111+PN19+Ab07VaEqx3H5oVuh71aVoFasPOBe7gLCtVAw06akd07wRl9JZQv1oTB9OnuYYAtTYl46g6bDfP4MAXFU5yuxehP4MRIRWrdujR4dKsNwJgnR3uUgbNO7Bv9+8GEAt8WSzJ07F99++y3222n7aDFsayGybMPCNEhngLLYP0tKxza0yk4Dg2afSVzLksfMPMJz5nN311cLM98fTmpVq1hFRmA51V891ZaeBIeLVxTQokULrFy5Erlck6e6zVJOPfVUbPXNTfjNozNAlZIZZYKU2thkDDMWjaZY49KAztX456VDwnMxTZ5p52lIanxDAKM/XkEraxtdatLvpYQQ0WIgGZ2NiN4E8GbGpeWoWDJnpCa/LURUOHCnTiunzalvu/VW1Qm1R7M9v1NAXbzTOUaQpjCAptj11OoIu4ymsKJlqpPj18A9uAPpA7V9zqHySzieGGmydq7Q3zND1TjOZ7GF5bKErpA2RJ6McajC2BCJzcYm6OWXX8aoUaMwevRoXHNwW9zz593QqUMOqAEor3qSvYK3JYu9S1MTZzE9pc6bkzw5mB3DCUins4t39ZfoZNJzPS22pb0QsEUIwKf1tfosX4QQmDNnDqqre8q292UD2IF+9ZZj++7QGsd+uTqRz7PPPotDd8mjgjG1dRucQJ7Z/cNmBnX6LC2ClWfseNrvNJbaPGcvHsx0LhAYu48U8JdgG61+IeKanfBSRlKdzEmCRS4HbelvQhGDGBBQAVz2+mCce+6wLQYUaulzxnOYectAfDV3LYb0bRF5ZZuA0FzYNUVDkJXW1KSZuzaFKxvEnv/S1UUUiygQ0dIm1GCjCgGG+VLzrt/chIhmrWsezXpjJs9ruPI/7y3722+P6RF1EMOuoFmSshrJtD3U17k8obmVBtkrm1DsezBX+vGKRVKqd5RQdad6k7rycKkLTVBoTzBpnb6MGIW2xIJQc4qBw7Rt7sIsHcyQMECECQrTYh3KMjhm3vcQcOyx6fXfwCKEwGWXXYZ//vOf+OU+DNdd2RfdWlSBtRCgaibfKvMWdN9uwgCdyf44QUTKefNcqX5qgkJroZFgC63viYVEysIhtk+y+awT6Tk6tWyJGSuSgGtDyowZM3DNNddgxx13RM9OBmNoLt5gjAkBkGMMRIQgCELnEyLCPffcg7v+ryugQ9rorfCytAWZ51T5aWYlpVTKpYCjC7yZiwfzGruvWNelAkEHCHSdI5vPYGrlDqlCJhMkQrKIpIA68wC/nvDkk0+GW1JuSdKnTx/c9Nd7cMrVv8DH9+yMCk8+i9ABx2ANAcRJm3KRTMY8mur4aLHhz32wDGuK+bvKLHHTCJVmrP8XpVlQbv6SNY89/VFdA3xIv5kiwhhTG+RTMD6u843GxzxmpWMF5WWV9tHe1PbHcTyWt5/9Kcsj2vbsE8bHiCWWSGOBQhKIqwURT+fc0qwpKyal0o0dcmxm7QR34KHqWITAgIdex0JE8QulOjF+vBjkQSD4YtMyAJdeeik++OADvHdJZ/z8oK2wVdtKsDxJUBgGlIVsc5d3flCiH2qvQf1epcV285HsO+akYHuMmnEjsxYQJjZxMdFpoDCFLcwChdpBKfRMJxYGOu/Xrj1mrlzW1MfTLJk1axbOOecc7LrrrujatSteua4FPEIUv832ugwIKMpzFQTsOLAlLrvsMtTW1mLMmDE4+uijUV2Yi322bxM+z7gXp/oYsV1lvkg858R4YaYRUbomxQcMkPT6NT6hh3DYl+w0UPch+xMF8Q8CHn7sc+HH54CIPubx2Mc4BhE/joCDijyqF2SdvEYP/fv3x6xZ60yebJZyzjnnoGf7Stz7/PzIK9ueg3UfKrLoe1P7RtH62OOP+SnEfz89bim+mrE0bl+xGQqtw2dLlWbNsES0YvdBXZZ8Pbe+56Ctapq200bpzEunKbcoFwtprnpckoixaFwTq4LBZJa7q0TarTVF5ZNh95MAhLZaENa5rN9px1xZZbCSaedMUBD9ZhDWAwgUUximAUNBbHz1opZPPvkEjz76KCb8tj861CjHME/3BaO9VZ+IreK1lGJ2TImIkvL7T6bazvia1e8c6j7nTifGefPahNo4a3wQcaCopX+7Tpi2fAmEEBt0J4qvv/4a++67L8477zxMeWAQ2rb9AMjn5VikPcnttjexMWd4+upt8f/uexjdut2NHj164NcHBjjmRztIbadvUDZpUQnKVRMnVP9GXmmscbmOIqakjSPWuEOJcSijX5iLBkcacjGLJlPIKbQrBBgYl0wt8yRDy9SiiACMnbAGkydPxquvvopddtnFcYPffTn/modw73Vn48Iju0egMKZCZuG8wOxxOO11TOkboWq/HFt0BixaVUBtAxrXh1pzQ8s6qZLX4dpyhTFWBemIsi2AqqhsOntDldls6mXm4jVXj3p38YNXH9snHqdtXcQISWCK3Py8TI/iUuJ6jnZYG2eaJuaZJVnYN6sNU1Q9gAEKyXoWxiCRyv6EAzNgqwVNJwI7XE2sao6JPXbOYAsTTJEChOlqZAkIBTFUehUoBptuZ6VRo0bhggsuQMeWrwFcSFBYhdDgHUC0mMhS6wCl35dSa6RSi7FyQKDZX+y+klaGy7YQ8b4SprPZxQy2UPeNQNkVdm3VAjX5CkyePBnbbrstNpRMnz4dy5cvx+/3fhu5fF42iYAEhaHtlvUwrHAdvdtX4pHfDsa8+Q3o0rESOY/JUCIFdT2HZBhtMYGgy0zETGeDQzu7csLFmNeHCdIWdmZdUoCgup7s/mSOKVYZrj2000wKAITjMwlDO8GkDpB5ktlk0gtD1rWB4aA7xgMAxo0b5763LUDq37kMlXkOViCpIQsiIBiK+l7WZgd2PzCuSd3m3hRDc/Tku0uwssH7YxlXbVL5jjB/jwKYAuAHkF7KpwHYoHvDNhsYLlm5dtTTn1bd/vtDWE0uj6bZL7iEIdVPhsy/6wUcZiz/TaPatLJS2UYj34wBLvV8uWKCwTA/JFf2tvF32qpe56VBoWX7ZYcbIVc6My8FENKAnplOg0IhOHToBWEBTA0KBXG0rqzGsvpCWpYbXIIgiIzZBaI3yLQjNPuxCQ7TWJyM96ZZW9SZkgUC0hYP6rtzcrdAgKsflFIhx9I53gMi6YWe5wG27tAZkyZN2qDAcPDgwcjlcvA45J7WgAy6X6QkQIsqmTwmgO5d1IJeAUG9WCDh2O0kjUl2sYQODUFYlSwG2l4ouiQLYAJl9YnEsbBuybHDLqNkOCOBcMwmKHDIowfDPMgQNgQwaw75+9//7s5zC5AXxy/Hvtu1UWpjBirC/bzXdYEJpPuwxkgV/YwJj7+3GF/PWXNr6Yw3vXwHbAwHENEJjLGjiehhxtgoAC9vyAKbDQyJqKFbt25PjZ64/KwjdujoDCZdShiTD6VJTqYBItV1M8pUJZdFpccMoB12dAlHmTRVieO8mcYGoC52NBUgmINxGuujfut0rkHdnXdStROCQrNuJeh0zRaG1zsCX2tQGAgvBhDN84HgaFVZjQ4/GJZZ3oaUfffdF3//+99xyWEcrErEzQhcg7FtB2qeg/Fc1xUAmlKKAbLqEGOD7Ik8TX2csogIs88AhWnMMiAnfY/JbRCrco0Y2qUL7v/9H3HSBoxb+dRTT+GMM84A8yZHzGCRItYXSY/kUFKeW8yEgBkOKzYQTOsz5rG0MDGpqnxHnuG5NMY/I10GGAyP2+pgGwgaedossnkszeSAqbE+dHLTFZadFgxcAiNBADHcddz2eNMbjI4dOzrz+65L8aF98ey4pbjlzH5AkandYpJzgJZU8LMuBAWnhIaPMeD9aavRoXWL2US1y5uf+fdiiN7RYaXaO3khytzzuLmyTlb8Cxcu/PP97xTPOnxI5/RELkClRe9Pbw2uJVXGdpBlXYY+ps9nlp0NKpNg1QFErTSlYGp43gZ9jtVYZEuDEuDNKiBjkgcyVvoOwCfzYXCCwpTJ3mQLE1W1gTHFbQo1KIxUitxIJ3+vKfiY9cI7wNXpTbIhhYhQWVkJ8hvAPcQZQSAJuGzVDsy0ZbA5YcHqbxkLmoSkMUCu8ylqwcTkb07o9oSfAgpND3S7fzAIcMYAHoAxASIOjwU4dft+OPjRpzFhwoQNtj/222+/jXN2nQmITghDC/kO9S6iEDUmaJQ3ZWVqgz5T0lTCWWygy0zELjcLCKSYBMR+OplRx0JB/S7FBpYNAF1MspLQvlipkQmQDnBcjfFcgIiBCaHi6gpAcIAHqK6udtzQliH5s99Gm4sr0FgvQDWIHIIcDoZAyrM1zjdbrDmWANw3dgHe+HLx6euW8caT70C4mvsYY+0A/D/IrfRaArhyQxa4TsCQiL7ao3+n5V/NqW8/uEvLcFWXkBSAltaoMXK6XPvCtJ07ssBhxjmCVaYOj5AmWUAzhd3MZEpVjK7MPC3JNAS3Bmn7uGuyT7B6Gd6nNgMUTgAZtoV22lh+mkEkjkBdExDHhIXzcc+zf0lvlw0sH3/8Mfr27QtggqospLceDIDvavMmTOZ2v2iWqqMJ4YgSjgApqsFyAGHa4sEJCi1zA8YoJGClJoHQpWUFrhoxHIftOxIzlixEZWVlk5siS7799lt8+OGHuP/0gZHq1wZ9triYPVjHbBV0CTOCRIBxV37Gtc0KB+M4X276VCCXYn8ce7+F+1ypMcQUpuKYMh0VgQikYhcyo9I6WP92Xdrg/z35Ymp+33WZfMPuIALaV+RDUEh6P2p7fHcRA7akgaOS82q8X8xb2YDJixoLgaB3m3pPm0IIGw3cNVuI6AH19S0A/TZGmesc9+OTmctPufWtuS/fffyQuHFwmtjnTWbPZPz06SD+F0AKAE3r2I5DevwpsVKg2OhrpHXdY8DCfBMBWkX8fHg8q60CN5iMiQk4m8IOGumcYE+DwiYwhfq7CShNFbKz+o7zIjFZRACRwLCyoR4dOqTs072B5fnnn8fjjz+OV08dKHdbANSADLeNp5amMHZo5kDlaGfbVKFckGCHpHHuVFLC9jT8nQEKY0WnGA9zLnDkoP54+sspuOrgk3DTW/9xpmuOvPvuuzjhhBNw/Unt0aVdhQxDpcNxpD0EhhSbQ+N7hvmAO/A8i76beWWBwKzJ3sHUxU6nqaDD8+7rs8BgGhMYO5f4awW0z1jMhDEgVSijECBCBbwXyoGpKOeHIV1aYu3atWhsbFzvi4lNLUSEq16YgfMO6IoqlgOEAxRmjPlhPjFHRJ6cuw2P8KQ4jhPDnW/PxcI1wc+IvgOWe0B83ttMhTHWBcANALYiokMZY0MADCOiBzdUmesMDIUQr46ft6Zh/vJiVbc2ldH4aAK+dRBnjDz9pQwbw9j1Wi1R5pZ3qQxoIiHF62V9D8VmIGEN8q72chzTLCoAN4tZDjuoz6VM9LaNGFDeRG96mWqmMDrOE3kACNXH5u9AcAhIFbJQoFAQw07deuMvZ12KBz96IXnfG1huuOEG3DKiK3q3rwZYvaxrIwN8dY/ms3BM4KZsMNWOI5+0slKdBewJpAQYtK9L7ScWw2guIPRvYVyrY1oCwKk7DMVdH32MG4nWy843M2fOxA9/+EM8dFYn/GDv9mCNJOOw2Q4hLrGLN4HcujKAaWp945idZynAnzARSXu+YR0dYDDlebvYfjvPtAVBWj+whUEAhiqZMa52PyFwNZhzmZHM3xMQAsjlcqirq9vigOGos7fDpNl1ePCMQRFTGPDIJCiInlmmzScQH7cR2XCWJHgcsqrex5gpy8X85bUPN/PWNomsi3n3RkK//4DcG/kK9XsqgKcAbL7AkIioQ5tW5//1rZn/uOmwIY4EFkAsBzA2AVRmgTfTJsU8likGA9cUAGqX65LwqLaRSV5ZIuMkAE2VtMnBAoNAcuXvmuSj7+lgUNYtPsnb14WTvhGeRnsha5WxsMrWAbED4ujfvgumLl9cTgusV5k4cSLmzJmDQw7bATzvg1UIucAImAywa0/iKRN4gkneEFKKBUpJmzV5O9lj63h43u4nKs+0fgIg0VcAxQ6BkOdF7N+nJ/707jsYN24c9tlnn5JNkCVEhIsvvhg/G1GFQ/ZoJwNLN8Ad7sOSVKewMhhjO1xUGkObCvrshZ0jvSzHsUBxnXNca6exFwpCmDsUuccCOx/9rBlE4rkDiO2AZIscS+U1er90ECQgZCQdlvVfToACNp/NrsU222yD9u3bO/P9rsrq1atxyb+/wb9+sS2qkAMVFRBUY7gzuoTrfQacYJGsv02Ru9+dDspV3UC0ym/G5ZtECMairLkZbHjpSERPM8Z+BwBE5DNm+9+vX1kvW0gsX73m0Went7j6ghXFPt3byNVZc+lZ87qyVi1W82TuyqG+ZzKBruYOmlAfIAFGbQkBq5aSjjDGVnTNWMm5VomlVMXmda5JwLXaT5vkbdBggsLQwQSaadRp5Iesz6rGerTaBAzArbfeitMH1iCfY/BqlJNYwECNXrTbA5AJyAGrbzgm0czn63JGshddZtlZ72CGg4B93MU6OAGAZVJg5yPUTjbRbze7ZAqHADGG6ryPs3baCb866cf4ZN709PsqQ+655x5MmzYNj1zWXQK5IkAFpnbxQHKwN5vIlWHA4oyfubA1wZ/+7TiWpr4ND6WAvbTn5zpfUkVsXZfF+Ga991Ga+DaXZe+QZKQ1/0oQKNTe6QycBcoPKAdOATgJaXfICX7At7g9kgHg3vN3xF4D2mD3Hm3k7i8CgOCpgNAe54H0hYWL/dUq+1Lz+cr6Ip6dtFBMXrT6+vVyoxtRmotVpKzLtWVLHWOsA/T0wdieAFZtyALXy5tDRKKmpubSW9755qk/H7aDcZyHHobNy7fU+XTK2zRWjo4pxs2yhXRNyq7O4rbBQDTIZoFBngSoRmGp19npydURy9h5JmulaKuFkn+N52eBQhfz45oU9HcTFOp7IVLBjS2Vsi2CGLbt3B0Pfrpx7Zoff/xxjP3vkxj7k/2ig9rgWw3OFGhDPscgW0ZICCrVh7QXZuLCJOhPzTvjWuekkdofHEDQKj8BHlMWDmY+WfX1mMAJ226Nhz77DGPHjsWIESPc91RCHnnkEVx33XV4/ZJuqK70JChsYNIcQBgArynisu2yAHuWOQdg3HMWw5cB/F1Bo515IPkco+NJllenM991fcxm+mKxRx2LwVLsinmNBiTcdCxh0oxAxjEMwBiHEPGIUZwLkAC6t6nAvHmzsgssQ4rFIsaNG4eXXnoJEydOxMqVK1FXV4df//rX+NGPfrRBd+SxZcqUKbj5+Tl46zc7Wepi/dcNCu1x3hTXu62BoJ6/s4ChPnfHe9NR5BW/J6KGDXP3G06+A6rkiyG9kfszxsYB6ATg+A1Z4HpbUtXX1z/7ybyVhRlLGyt6t6+JBhULOJSz+sgS+9p0hsWLn+NUIv36EQ1I48colXUs11mnXJYzc+sxIHtysViC2PmUSSJtgkgDhGEeYdBqL1aOZgtjzLF6/Tgj7NK9D64f+xxmzpyJPn36ZN/repAXXngBl1xyCf514h5oU1kFlqsHiEE0SvWx0GrkwJoUM9rZFv3MGKf0gaYJrHU5ZUUHHOAwNqmngDaH1zqARL9Iq5vdv9KYIyAaM6pzDDt364HnrrgJI8aNSL3HNHnwwQdx1VVX4ZVfdcPAHtVAAwMV1Ec9x1Qbz5TQUamAD9Zxx3vkPG/noUFmxnNKHueJtotJCvhMW+gl6ueIQ+qqj0B8cVhq4Wf3NcaEVB8jGrt1nEuZZwCPM8AAh0QMnAvMWxE0y0ltyZIlePzxxzFlyhSsWrUKo//9b/Rq0woj+vbCSV07oU3vzqgPCNdc/Cuce+65+NWvfoXrr78eFRUVTS6rKbJ48WIcvu9OuPG4fti6Y8sobqEGf4EacwMegkGXN7jr3dZzltP8w7b7tEkITlhS14AXv14UfLNo5S0b5Ob/x4WIxjPG9gMwCJKi/JqIiiUuWydZb8CQiERVZcXhV73x5SsPHbMncxkSZzF8rnNpE1zW4MJTgBezgiWWrRbOKKsckBt6zznD1ZhsZlQ/vVKLpQvWAyhIOZc1+evfaRO/HmDSJgp7q7soTqHMJ8xDA0VDlWxeoyXHPQzp0gPjx4/fKMDw3nvvxfV798Kgdp0hRADGBShgEAVPAkPfS67KAedEbn9PSAmziFLHS+afks7F+pQDAG3wEDuv+oS5z7Wr/Kz6CgukNPoBnv96Em4+dLey7lFLsVjEH/7wBzzyyCN45RddMWirGtBaAAUuzQB8rpjfOMMCIFb/NJBFwtwuya3ad/WP9GeQZPJMzYuL1YuVYb2r8bzSx1QXaE+rl7DYQyewt3YyMoFh5jgFJg1HGA/Hc/lbAkViDJ4ysSLi4JwhB1/WyAP8Qg53fzgdJ5/789QybBkzZgwee+wxvPDss9ivT0/s2LUT+uRz+OWPfoiuLWsS6V/+8QlYXFfEnn/+MwqFAm677bbw3Kuvvoq+fftiwIABZZefJc888wx+ff6P8KNhXXHmHt1Dm0IKJPgjwSMbXsHDY+ZzMVnAEEAqYcxa1MLqj4aNaLjFnj4mBG56cwoW1gdnEtGm25KqmSJtDJtPVG1IYYztBmAOES1UdoW7APghgFmMsauJaIMFEF+vRhgNjYXXunXrNu7dWSv22aNHRtDrJko5qiYtem5Ngi4vdUI1waQeuPSxLNajlOpZp+GMwIQe4NIAqmdd6SXKkBVzO9KU3bmzQkKkAAD7WJq6WANBM73NFJgThD4HIPRCNkFhYIDHsPokl0wbQ4Uzffp0vPvuu7jqpENQW98SlflGVNaslYCwKF8doVboplG+vk+XOJ+TGXaoDGku620DHhfASAMXaQxSGvNXqj/a/aSUMEZoWUnYtnMX7P7bH5dMD8jnN3r0aNx+++3oRUvx9oUDsFWnClADA/mSJZSg0IsYF/0cBUuw9E42Xj17ITwJXHhS9ZZsG+ve02wGYxK3z0x7L9PKycrfrl8W6DPfZc4otshzmbnEAKdjR6M0kTEtSb3vmjHk8JikB4kYOARy3IcQHgQjMCIIATw+fineXrgCo84/PzV/U9566y386IfH4Rd77Iafn34SOrWoiS/UIe9Ts5f6PtpW1eDCPXbHN998o+6HcPPNN+N3v/sdnnzyyfUCDO84YRv85c2ZePD0bbDvgPay3yrTlRAQigjsEWlvfsMu1HSoDMddwyHIWuyZ/dd8fswRTHjKktX4fEnt2mWrax9f55vdFEJIbIPexMs3pNwL4EAAYIztC+AmABcC2BHAfdiA6uT1bp27cOHCc68bG0z59ymHyI3NlawLy9GUSYY7LAZcHdqsjwtMBlkr2iaci+Jtxctw2UAC2SAVQCrABOKThXne5RVYTv2zJhpbLeFSFes8NBiMfrMYOLQBYWh7qNOoXU90fYqC8O5t/8YxxxyTeS/lyGWXXYahQ4filFNOSZz7yU9+giuvvBLV387A6gJHz1YrwHMBKOAIijkwRggCT95fUFo1Grad6xmIiJXKEtdzT02bwtbZdXB5i5YDAF0Mkuv+s+oh65KyqDJUiFpmrVyOe395GUZ8/gEAoK6uDi+//DLuvPNOzJ8/H4sXL8a2226LKVOmYMmSJTh+h264/eCuGLH9EDDOQPVMhvZQDkPkexC+JyfXgIcAH4C0IY0BvBSwr65jjCBs7UCGt6689+xYns5rUgBfVt+zn4+7LlbYKGu/ckAu4LSYAMJ8hi4GWSBZrn1MC1euZjoUnl5cg4AAXIWuEQoIcnheRLU/PWER7vx2CZ555hm0atUqPB4EAf785z/jjTfewOQPPkT7vn3Qs2dPLFiwANO//BJ//sFBGN67h7oXgChaqGvgQEQxtSsAnLDtdjj0qacwfvx4nH3YYfh80SK0aNECxx57rPPemiL/OGUobnhlOl67cBf071gTqo8p4CEwtBemQngxkCgPRnkSeOwZwugXQngQgiOXi2spbfY/bBcBXDt2Aj6fv3TkdyZuoUPSxp/NQDyDFTwJwH1E9E8A/2SMTdiQBa93YEhEX+89oM8rj3/+7cGn7DAYgARrAXkhaBMp4CRtYrTp8LTzOu+ESlpkT7hNZWCaAgxdZcjVcJI5ZIyynKJLOtpEdeCJAczFcpaSNBCgxWVDBCDBIth7Hpu/NSsYmEyikY8+LoEh4JOHFfVrcOLN5auJsuSxxx7DzTffjIsuuggnnngiDj74YHTt2hWjR4/GlClTcH2/7bASLVDBfQgFIoJCPmSJINQKPcO5whaXkX6aJG1S48/VPG6bILiuL8Vo6jQuIFOKSQLMey/N6NrmE2nnPUSLqMuH747LXx8HPnRntNhjVzz++OPYrl1rnLnTQGyzVx90a9sPXy1Zjj67bAOWH4AhPWrA8gEgSDKEGhBqW6yAh8AwwbQ4283Finm6EaQE9nn3tS7VcNr3WH7W+Jlked3MnVmmmc5pCmDlYTJHLpAXr18aCHaDRy16AUwaGKoG9SAQqLmBh8cAQZGDiiAGTnL8WLZsGXSsy/r6evz1r3/F7TfegN5tW+MnuwxF96MOwcrGApbU1aNLvz7oNnR7dGpRI3dDNMYfU6Qqm8CVmluORxyfLliKmpoaHDJ8OLbtLLVkdXV162xz+O+zdsPvXvwaL/5kV/Rr1yLa6EH3VQUMQ02NGouIWMz7P83+2xYihga/EqsaW+KWcaPR4Dfi9sP3Cc8BiC+aAIz+ZibqyPvCD4KP1ulmN6HIRcA6ZrDhxGOM5YjIB3AAgJ8Y5zaoy/0Gyfy9b2f9cH7t2vkH9e/Tqn11DfTG84GlLi0F8kqdS0P6Tc1XP1xW5lMutcJI7gkcTdhcqzxsAEtu0OZmQG0GxqUmb5qqLktcbIOLabBVSgnWyWIJtdig0PVXXx8Qw5I1q9C7d+91uict48ePxwknnIBPPvkEM15+CTf+519YuGYNhuw9HA/ufwg4z8MPPPiBBy+vwKEChRRwCPIQCJMtzbbXcbUfUJr1jsQ2OZDCmF7VZ7P09jN0sXkuNa/r3XOxSy6wkSa8xHtE5IGzILQ5A4DThg7CAf22wnVvfYqKj9/DR2cdh3bVHJwHqKqqRy5fQM+O7cFyAXg+D3AhAWCRhWE9YqAw8ELGRTO/rntOi7+nj7kM+eP34gBfKYDb9T3RNg7g5izXAcayWL54P8hm/rLGYztgvauuyXuK7Am5AoYybiFXAFCSXwwMxGT/Y4KrsVSqk3+4bW+0r+6Ik046CZWVlSgUCtitXWvcesgBGNq1K3KcJ/quvhdzfDJFMpYAVPncOL1Pzx4YNr0d3q9bi7EzZwIAevXq5by/cuXzzz/HT5+diFGn7YRtOrUGacpSMIjACxlq3e9CMwhiMUYwPG/1Kftd5xAQkGNZTa4RT06aAkGE3bp3xek7bB1Lq6+tKxTx1w8m0uQlyw9Yp5vd5JLtEFVKNjBN+gSAtxhjSwHUA3gHABhjA/BdCFdjCxGt6dimzfk3vvPp4zcdtB9ASacQwF6prh+bMT0I2SCvKXRxGkAsteLVom1vwuuIwQRvpkF1rFyDQdFpbDBt19G+r3LAbXOocxP4mfmY9SjHnshUG+vj+to09lCQ2jMZDA1+gLpiIzp3Xj82rJ07d8bYsWNx7LHH4uijj8aw8Z+CMYIvPDQGeTQKD6v9CnSsWAvGhByY1WAsSAJGrb4x71Peq1t95zpmPpO0Z94UaU4/cKswswFfKVWiK60JWD2HF78Wmc4DEcHjQXhP3VvX4J6jIjbD84qoqlobqRRN8w3BIXzA3gVCq+Io4Aj0M9QTbBmhdRIsnbFIC9uhBOBzM3ZJRs8lLpbPdV1aOvP9M7/bkniP0/pBxoIgrZ8l5gTDnlDb8+m/ngqbwkk6nxAYOGMgJkBg8EUOBb8CHgswvFdbvHvqkahtLKIogK4t20DvpuSL5KLGvHd7fNJq7cCoKgfB47LfVuc9XDXyCHwy7xt8NG8BHhz/CTp06IDa2tqYKrtc+eSTT3DoiL3xl8O3x549OkGovbtJgcJwrLHYwbS96ANykzEmMxrAgyCGQpADEUPPXr2wV+sWeGfWApyy/TbGc4v65m0fTICfy/+eiJY0+SY3M9lcdeBEdD1j7HUA3QC8YqjrOaSt4QaTDUZHLlu9+olpq1v9edzsRd326NE9ds4eELJDGKwb2xXmsx7sCMxJIstuKsu72jxnhmKQ4iXO63JdzjAusJ0lZlnltEfa4JmVh0vl5PptM4EuljAwv6sBu7bQiJp8JdbHtmhaGGM45phjcPnll+P2fffDoA6dUSQPPnloCHJY4+dRzSvgFypC1jAQuch+khgCcoPDdEDlsBNzTKAum9MNJWnPtxS4MI+bE6ornco4TKO1Ca5+bzI0ghgYjPh2PIDHBQLB4SmHDyAK60TElC2hLtN4NnpStUCha5K175li/TvdrjIdnHHnuaaoXM26hOky1LtpwM9lupEow6pLKlMZ5pF+vXmNthl0iQSE+m8EED0m4DGBgDg8kt99RvBE5Kin01d4QIcaKNBo7KpktEXMyQJxc5io7RCrp1YpBwEPFzUeE9i1+0Ds2n0gjh2yE656Ywyef/55nHrqqe4bTJHFixfjmAP2xV8P3wlHDO6OwI+bWghf90/PCQTl/blDBLnGEXMMMhflW2+9NZ549VWcvN2QEAyatuJfLVmK9+ctXvvNwkU3N+kGv5cmCxF94Dg2dUOXu8GAIRERY2yP6wIx9YmjjqqqyecBZKwWMyRrdRymcUxI5nHzXNrgkHadna8AAygbtIbsIChRnplfbEI0yig3NE2pdCZ7mdXmtqdhWnmxaxyDfhoojNkfZnw3WUIbFBaJ48tFc+DxdWfUbDnzzDMxfvx4jPvkQ/Rr3w2+8BAQx7JCNVYWc9iqSqChUI0aXheCB22srUFhXB0VZ36yHAo081quyi0tnyxxvQeuc1lgw66zXcdy2Gszjc0ERX1Zm10gBIehlz5FYTcAgYpcAcwTEWATDIwpG0Lb21s7ggjpPBQEuXCy1SxhYNgZ2mo3WTOeOB63m0166JZS4ZYD4uzzYdklAGWmvWEZwNDMI3TCKAUOHXVzZJoqWm0MRH3EBIoeE8ixIMYm6us802QH0hZQfxLAT5WhHduyFstmX9YA1X4WAXG0qe4Ir0c3dOvWLf0GU+TM4TvhuCG9cEj/vgiKkXe7Vg/rfm/uIR4YcWBdJj1pY79rTtXttOLLyQCAJyd9hdN22BaDOnaU4x1xFIMAV77xNr5YtGg3Itqg27JtDCGs24J7fRBOm6NsUANGIprTrl273976wUd/+83e+8aAWVA+LrTyzAZxaT01dZCi9IdbTodJ7Rhp92epS2AMgLbNoRYX+xICzhLtaLY5N8ow1XrlTkJh1TIAddbkZE9I9jlzAgrVyaq8QE2gY6d/gX7bDMy+6WYIEeGNN97AHXfcgYYnxkgj9kINFjdWIMcJ9UEOuVwRQnD4QoIKzRQKxUjIfHjyfjMm31JgO+vZ2AudrPRpoM0GBy4TBc04uMBGFgBJ1NkCMXqC1R+mwCERUxNvHBwycAgi+CKHPCtCOxwwwSAUcOSqHOYJGVNQx13T9VQquaCYRyByIcjXacyJ17SpMtvXZV+ZZjqRxrpmsemu9myKzV45oF2fC8ooL5ancQ9h2rDu9u/mTZrm4pkxAORJ9a0Cfgwcec4N0Ehh/wkYj70X+nlp0GPfW1r/1xKLCGG8G76hopV1kgs7P2jE+PHjMWzYsMR9+b4PznkYaouI8Pnnn2Pp0qV4+eWX8dXi1bj7iBEo+l7oTGazga4FqMs20uwDnBF8kQ4QdX/0hYe6YhEzGuvRuUULLK6rQ/vqVnj088lY1dCAc3feDfd9+hmCisp7iOircp/nZi0U9dvmXr8lygbfTHLlypV3fFa9+MqP583vuPNWPcqazLLDbDR9sMkaIM00aWxZubY+TRENCs2BqCksqskwmQOW63uiXKPeLkax1D1RCUCaBiDS7ZVg/WYJUKj7yz59t8OYrz/NrF9TJQgCPPDAA/B9Hw2PvAHiORAYlhQqUR9wbFVRQJeq1RDCgw/A9/MxllCyEckAvmnMULlenWkgIUqXDdpiaVMYkXIYo7CesftJO+4GFLYwEALG4KnYdOEHAmACIK76rwjBoXY44AzRZEkeIKBClzCQJ2Q8wRjYi5h1IaRnuXYaMu1DGaMQFMZMAxygqdwFQJZZRSqD2ARgl6qydjyrzEWbvs4B+OTxLGY5fsy0Gm0KOAwXxmQGOovOSWAo+0sQcAUII1Wz+VeLGdVA3jdi9y2PxevIrQV6mr2udkLRANVjAlW5HHbYYQf87Gc/w0knnYSRI0ciCAJcc801uPnmmzFq1CicfPLJeOyxx3DlLy5BURTRo3UrDO5Yg6dOOBygahQDCvutrqvJSJvsZ1o/08cYk/aRAcVbVDOrUbvIfJ+oqMdhhx2GO+64A8fuuAtGPvoYCoUCKj0Pw3oOwJuzZjV+tWjBL5wN8h2VLZX1WxfZ4MBQqZR3vvm9t6ffd8TxuVaVVYk0iQmphJ1KqQksMzh1xsTpYjHLKbtZHStlwLFXqOXmnTZ4lSMaWDjtUBwraVsd6DqezmLEjzknOJjqDaYYZvnZaavBePCjlzB+/HjsvPPOTb7XadOm4ZlnnkHnzp2xePFizJgxA2PHjkXHjh3xs4F7IcfkYOmr8DiNIrIlImIoFitQFHkEoV2TCAdrYQ2+9ipe3pv7WNgmKUxTJjBwPPosFikNNKSCx2T2qSBXnovuyzSlAEwTCoCTnuwlQMyxAIGa+PVkK3e5EPC4SIJC9QwEY9LOkAQYMQDSQzUQOfiBtNvNcR9cOQzYoFCH4QjBI9LNANJ28LBZmyz7Syfr6HgXymFo0wC6M61Rn+TiBGEdXaAvui7+O5ZvmQuMVNG2p3aIJrkqUH1F9hfNJppaEI6IvdNiah7MOkf3l+yboHRTHt0zpJpaq6wBghcuxq8Zsjf+9fkEXPziOViVB6qrq5FbVQ8AGDt2LP50ye9QW2jA74YPx/BeXVGVl3EDfZFDMYgz+WadzT5mO8m47k3fi923GCIHnqJ6h5bVN+DtblV464W3MG7cOLRu3RqvfTsVF+w5Av+d8jnuOPxEXPHa8/hqycKdNvR2bBtTpCp53a7fEmWDA0MAIKI5rVu3/vEN77w+6g/7HxFzHMgCWra6yz5vp9H5OQcWx7VNYUhk+vRzWfml2Xc4GULL0DmrPrY0BRxKg/70c7Ke8XJdQNWlekwT1zNMsg1JlsJcPedzeRw+ZF/87Liz8MHMz0uWacvSpUtx+eWX44D+26JDTWt0btkOFwzaB9t23gqkfBCLxFHr57GqmAMR4DFCUXk9AnIA99WAypkIWUO9ktfqUAFpl2ObCSRsnVLaKo1JykpjSlr/tScKE4jL9k4DNnFJV3Nni55cPe1IAq4mfC+c2DmTk3wF95HnvmQFOUGQB85I2lapgqLtvvLRJEoMhaACgfCQ94rIe0VU5Aswd3oAACE4iiIvy+TRAsBkggPiiUWTm81xT9hhnhb4TgOG5u8sAJj1Puky5TFdP/fCRF+XAHouVjABWo08Um0QE8U5JQwDoxncMHe5IAhI9gkOHQs2+gtEXuzl2tDa4jLlsfPSU1e4wIEs34OAIA7OgNZVrXD2LvvgRzvtiylLFmBNoQG7dO+HlQ0N+NPo/2Lv3tvgxO22R/vKBnAeoNGvcCxAkosJF8tcysbXteDwmEBB5MIx65tli/Hbt/6FI488EmPGjEFFRQWOO+44vPDf59CnXXvcdeRpeHTCewgqclcT0eTUBvyOSnNNHrZk2SjAEABWr179xIAePc967uuvDjpi0Papk1gaS5a2UjZjSuljabuW2IBHH3MNXFmr38TAZ5+n5AvqkqzdVdIkzTawqUG6w12SUs6tszrIklJ2jLZ9knmNQDRxyYGOUJlvUXadTNlzzz3RvXt3nL/bSHRq2QZFvWOAAZCkFyNDYyDLX+Pn4DGBosgjNMLWwFBNS0XyDLAobyYImUQWU3WFqnLjeKK9sib/BIAob8FjMifhMYqr6jUrZLJD5nMoB9CmCQHh1aYpBVd5e9Zkn+MCLTzlGMBlHfMIIIgpj1QPXETtF5C0/9Seq41BHr6QYLNlxVoAgOf5sp7KccgXORQC6RiXIz9ihyHV1DqEh8dFCPBtlbFtM+jycDWfuQ0CovaxPfXTF8WlAGBYbsZzjZXteMbJMToJ/tyqWUf+Gf1Gj/nMqLgcq805Qf9V7C6lmOPYeZc5LnIAsO5XbjjArDTx45r1llE5KTZ7ERj6dZS7qRQJaFVZg2sPOgUCQKVXHzq4AUj0IX09kL0ATDueWMgYbRmQjKjgq/FpVWMDOOc49dRT0aVLFxw/dHcsX1uLl358ISpzlXhv9jR8vXL57K/nzb22rMb8jkn5e0kl5XvGcD3It/PmHvdUo79kQPutqvp16AKg/NVkqjrFwbClAjdKDhyuJ5sF9GxGyyVp4TmaK+UCs6aCMiC6V1cUyXIZIrN8057LzrPcspLqnmhCk2BKxrZriixatAgdOnRALpfDwIEDUX/Y7hBvfQ0CQ8GI6K8B4spCDnVFD+2rfFR7AQoih7XFyiiNDmispoKCyKEgchCKYdQAMLAGdbuPanYsyXwj9tssC4i/AyaAs1XvWjT4IpJe3gyEai8IVb2+kGG1gxgwMScgGEAlUkmliYvpss/Z5yVLKIGexwhVXIAIKHKOCp5D3ohlGDqsqL7mCy/mEBAQR0F4aBCS9a1obI32FXVokWtA3iuqsj3U+xXwhVQ3V3AvBIa+8NAo8igIOUTmWIAK7sfsn+1+YN6fsNrRxRbai2ET4Jt2trq8sM3AUsGf/I3U3y7g52L9gPS+mJbGftQuu207DTP+l8ygoc5VRzljMQBokgFxG+3ofUot0Fl+uvraFL1o0TaQGiAKEAJtFgFSCxOgSBzfLluCFQ0N2KFrT3DGsKiuFh0rBUSuAmuDyrC/ucwISmkMMkUvtI13VbKuDLW+h9enTcC0pQtw/p5HoEvr7ujVqxcmTpyIi350NtpWVuGKEUfC81pg9qoVuP/Td8W3yxbt8l3e9u57aZpsVGBIRGsYYztd9/7oz2498OSqFpXVJRgGB3MYy88+n81KAeley7YtlMzfnYe77GT90qSp1HXZKxqVbxrwSlMhmdfYKs8EOMwanCj661rFC6tuTQGyErRIBU73Nl3w4exJ6fWwZObMmejbty861rTG3geNxKRJkzDlof+ifZ/tIcBknELPB4ccOFf5ecytq0SlJ9AyF4ADKAoPjSxvTOLxVXtB5FAX5BEQMzwoNZOV3i+1jVTWM0vzKDcBgmapAmIohmyABbAJKCg2lADUeAFa5yTYEUB4jQkAbSEFOuU51d8ymHgJKG0HGZWXpbKW98jAAOS5bOcAUrVfRRyeyBngIPJS1exHQXgKEPNYGxQFQ7UnQWVReCHA1DZWAUm7Rl94YT0l0PdQIA8eCBVcLg4iwB+1L2cSOJrButPU/jYjKMUBHI1nr9sq3jeStoBZwDAOOKLnlfWczWeaBuajvFjsuZr3bdY5TUKnxFAjIo8zILTlC8d3is6ZY74TGDrEBpMyT2ads0AhU0fICL5tLIJl+0SjKQNhRf1aXP3es6gpSi1HYyXD4sWLAQBvnHMFPCa9qM37zm73+GIiXr/kQ9TpdZs0CI7VjQL3LHgHb4x7AwAw+uuPkM/n0SpfhWt/fyVOGzoMJ26/Jwgca4s+bhj7X8xcsWQPIlqa3arfTSHrPfpepGxUYAgARDSldevWZ9zw9vNPXXPAidKYHG71hkyfPbCUs4ZxeWq58k+Paeiuj30OGenc9SpfmrRaTCvPwRDI44aHHTl2bkF59kP2gOx2rtHnEA6yTal3QAxbtemCBasWgYjw0Gl/wjmjfpt5fYcOHdCuXTtcsMthWL50LQ7b9zR0bdUBSwrSZtBjhLVBDpU8QEAMSxvz8ImhpSfBR7Xnh+DPpa4RxFAM1c9emKdpBwWkM6Ew0kSqVDLs1aJ+y61r7b4ZQAKhguDhpK/bvCjkuUbB4QuGBk+maen5IVtYFMwCf5HofAKKAwHzMev3kamJVD+zKMB0vP6xHSUUKGSMoEMWFQRHHTzkOaGCC8XIyE+OC4Ph4wrIcTQEHI2Cx/oNZ0CtXwGfOHKKbUx7pySw5CGI9hghLzwwJuvfIDwUlNlAngnkuEAFF6hQsfXMCVkym3E2EapNXOY0gTXemeA5zRM4zSHExQzGWclsIBI/bh6z620+1ySQLWecC2B5Ihv9SP6OwjPFjuvfgOpLcRtz9xNmBiC0z7jHLEYRsy+fLQMxAqPo3RTgECAI9dw7nHUwtl80Hq+++irefPNN9OvXD0fsuS9+MHAofPJAEAA5TEP0d8fcRxS/d71gzLIbZQxoDDx8tmAWHp/1PnbffXeMHj0ahx56KLq37oR5q5ega7uu+OnuB2K7Lt0REINPwG3vj0Yhz6/wg+ATZzNuIbIuzielGOnvqmx0YAgAq1evfnpIr/4n/H38O8f/aOeRqbskmC9BVrgEU7IGofCaNNBmrbjTrk8972I4ywCIzelbpRxhgGyv5sREQNE1LibIZhbSVIVpah7zfHzFnrYCjtdTlykIqK6QW021a9cOW7ftjx8XL0JeBVB3SatWrfDTn/4UDz7+OBbMm4/fjDgF7Vt0Cid9rXZdVcxhRSGH2oKc9Cu4BCACTAGBSFWp7QV1G+uVZwAdlJvkZAJY6se4aBbUVPl4DCGgiKn/bNbIAvo6D62+lmEq5HVEQFEB2MaAoTHgqPMVQ1gBVHkibAfNKGrwZ4M9XVYEXOS9eYzgEwvTeIqJJIo/3bhKOtkuHiMUGcAMD00GQo4TKjihUgGxKvUXkHUokgTEawMPDQFXan2ZX0PAAeRQII4K1bY6BAoRQpbRVKU3BLJ9zL7YEEjAKFXP+qPyU/X3VT45JutYk/NDpwld1zQm2KUmNhcHsefvaLs4K+l+f+zfSXBog5W4uBbjZl/Q95J2vUsYpDYnwdeFfc+w64sdT+bFUwqMpY2xjvGaJNTKqh/pkVGf9QAwMARQu/gQgZj8gAMfvPceBg8eDM45hg0bhv0H7wKPc+w3YFelao5Pv65FgPlsQ+BstYkHadsY3SeF2gMAKPgM07avwZ1jRuP+++9Hz549ceSIw7Bj98H42bBjUAzq0bNNe3AQGlSB//nyAyxqWPPptPlzbnS35pYj60K6rA/CZnOUTQIMAWDynOkn1xUaPu02rcvQvftun/AwA7Q9R/RbBznWUmolWi5F7DTGTgVUbtYtOl86n3QHm6xaJiXLjkuX5KpH3FbNVTvT9kb+1SyRM0AqkpOFHIRZ7C65WmFzc4SOAVLEVvIuYBmQ3NmiS6vOmL5sJj5eNR7ffPMNhgwZ4rj/SG688Ubk83n84Q9/QPtDh6L4eRGCovssCI4lDXk0BhyNAUeHqiKqPIEWuQBEUp1penVGcdVEOAgXBA8ZNxayF9E9CgXUTPs8rXI1AZkEMzxkxszrYm2MCDjFPY5lkb7QNocRO1dQbGJDwBEICSIYCK3zgQRJSIJC015Rs18m4NPgoiBYjFXxSbNjbqY+MABiqJYFwWdMlROBNAYCE3In+TwXqPQINV6Aai9iDRuFh9qih9XFnGpbCvurZhFzjJDnhEoeIM+lTVgAhjrfQ6Pg4WJUt11BRAysuYD1OJBjEqzmWATuNPDWgLLKE2id91HtCeRUGtOzNs+i3Tpku1psnHp+euwzzTH0giFtgZB0SkpbvCK83vU+u+yqbRMUGxCadQFKT6D2u55QmZoAHW7gqEWzj4lxneJ1jpUfA40RO2ky33rs0uyhZuND8xIwAELttc7x4IP346WXXsKYMWNw6KGHYnifbfGrfc8CwQsBGJAEga55S4LAuGhVdsIJUb1PvgDGz/8Wo5d9jlYzW+HVV1/FE088gVOPPxkn7fQDjOy/CzwOAK1QHyjvakb4aM7XGDv7q2XfLJy915ZuV0i0jozhFiqbDBgSUcAYG/5M8O7cmurOrXu37wmf5KozzwktcgEqeRBjTeR1xvdmMnTO+sTycJ1PU7GwxLFS15Yr5XTYeL3Ty7HWwMnzjokgydjKa9OcSmS6JHBm0KCfhbHHbNG2ZUAUGyym8rFkaI9tMXvFXOR4Dt9++21JYLhy5UrcdddduOaQ/0P3z4vwFcMl7cwY6gMJxNYUPVR5AlXGRA4gdM7Q9xio35qhM9Wwpm2dfaeBBkQhuJQAQ6sg9TvAFNNlMlGBIz9TIxZnHCLmSvcjQQy+YCgoxhAAGnyO5cijKDha5oOQgbNFs48a1JvOBr6Q+QowpeIlZa8ZgUpdB7O+guLsGCD7AWcE3wbBLJqsC8JDQ0Bo8DgquUCea1DGsLrooTHgErBxgARQVOBJgyrOCHnuhSU0Ctke+j4iUBPVwbRx4wygIP6MAyGfndA7TPDIs3qFl0N1TqmbeQQmNUDVjKO9KI6BQkTg3GxDfcwej3Q/s4FexFLGRwE7ja3yN9OlOUbptsl2VkkTlqiPfTRzOKS4M5QdbcJcjJlp7Mz1oTAsTQg8ZbtxplXLJkiUf0PvZCIUSWD+/Pm4+OKL8c6bb6FX2y44Y48TQKwCa4PkCGzfW8y8ByljJgimXbkEpB4KfoAPZk/Ce3VTUSwWceWVV2KfffbB0EFD0a9DT1x1yC/Qurot6gUgFNrUC77ZKxbgic/fCGYtn781ERVcTb2lyZYNfZsnmwwYAgAR1TLGhj780T9nnL/PmWjfoh0AYG0gmZd2FUAFD2IqOqB8tk6mbRooa4oXpUsFE6WL2LWmgNVo+knmmXmdMainnSslrknArICLDTUHsER+RnoNKCTQURMcizWmGtzMwTiyeYznyzC8/zDMWbEQXjcJDEtJPp/H2rVrsVXbHljjezHnCAKw1pcsmtw9IwJhRcFQweIrcwHJSmjA0yi8SG2LCKBpABhvMxhASamyOZA3mFlSgKmo1Fp5RqF9m62+1RKFUYkzRr4BvmwbtmLAAU+gwVfBvAOG1hUBqjwdz0+W54eA1QCZ6jkIikCoL6TtVc5QwQuKQFMgbKAVdzLQZZpggCugaYJeDb4aA7kThrl4aQw4fGLIE6Eo1ESOdMeyQNXbFxF4BKKJMgl94tcFIgpvBEg2kzNCHgA4hcxNvc8RcKDIJSD0lCo6UH1NHzMZMungEmeEZZuoZ0BuRlAzsOFf2As22cKe6ld2m7jYPxcjbabRQNO9SUA8rSkm8+gapgjROFF6G9Wo55hpXSDTTOMav5h1jlE0NnnMHJtMVbPkDjlxABwX7n0SVq9ajbt+eCmY1wIMhIJwg1RZz/ie6Tp8jy7Drqt5Pz6AOSuW4fkv38DkVbOw44474g+X/QELFizART/5JVbWr0bX1l1xxh6nIMcJRRGPtZljhNqGVXjog2ewoHbxNkS0PK2VtzTJcooqJVsqptykwBAAiGhmdWX1XveNe+y9n+93LlpWVCOv3oKVxRw6VIiEHVaWrAs4LKUGTvPG078TrJtjIFyfq5NS6hyznmXl52jfUqt9ImmonBWChhv5+JAgy2QETbZBD8CAHIRtoBgK4xg/5zOMHDgSbz/yIX71q+x7W7RoEdq1a4eFtSvRoWWncKAVJG3TanJCsV6SRav3Oao9gYLgyHNCDiIW8iUERepOtOpR37d93mxHE4BIRofgQ7KHQMS06XSaXYqzbRrohQ1isYfynN4jVYM0yRhyFBVD5jEG8ggFxSCKAkO1JxTAkcBYgz79jDQYDNXfChhq4BeQtr+MgLAuT8cr9BTYZla7yOccHfSY23wjECy8T1kvCsviAHwhy+cG+MkZaXzBYkBfGM+PsQhIctX5hHA4+qh7Doz2ATjyXKAolEWq6jSy7T14jFDpCeQ9GeLEJwafR8DQZD814yufrsmkJ211TfCoQaEZm1MvcmBcbwIj12LUBQYje0fEJbZIiRisNLts+3pbTWqrdssVu1xddhrwBABynmEh0A29nVV7CnmRKovFzB88xkLb5d16bQcGuXtSUcTZdlsbEi2wmVUHXTv5PaAkkF1Zvwb//eJ1TFj+DS6++GI8dcYZ6NSpE4YP2g+zV8zG4dsfi/6d+qHaYygSoRiwMH4rIN/HNX4B9737GNai8aCiX/wmu5W3HDEXPc29fkuUTQ4MAaC+sf79Xl16/eiB9x599Px9zkFFTlaLBLCsUIFqL9qkvCkPsZTjRTytnSbJ9MR+h6trZv3W593spgt8ptnXpNU7LTxIWnq7DvYkkO7RlsyzXNtI23vOrKcwQAYDhTvh6EGdGYOmdPKIM0hSRZdHl1ZdcPPNN2OXXXZJFmTILbfcghtvvBHVQRXyuZZoDHhoz6pVbnoCznFCg+9hrS9QnYs8Sis4iwE339h71cmiOSZTs21CQEkIbfNMWzUJpkywouMjJm0LdVvHmCQNDCiqk69s5YrKjlIQi0A4J1DA4QvC2iJPMHlAkmEKbSMV26bZzkKgnqPB5BUCDl9w5LgA54BgMtxHoNvDYLZMD23fcMwwJQLoph2sBlEEj8cdfzwGBOq7r4BqCK5FFE5Hd2Udi5JT1IZpYYd0/aWqXaAx8GL2Z9Gzk5/KnEClMlXIKdVylSeP5RUobVSTtwY1Guz4qj41OYEaL0DO8LSIOZ0Y6mDdNvq8QLToAiLmK9a+DiAYs2W1FsG6jQBI1TiX+ZrXmPZ4ZjnuRnUfLhWKhgz1qvzNouOpFzHnWAVEi1qmVjkSoEf9TKuWtVuatgRmUKYbivUN3w9E72baripA3MY7YgsjcKjv7ePZn+NfU1/Fqaeeiid//zw6duyI+fPnY2C3wajKV+O8fS5AZb4KgoC1QbTIzHPDGYoEnvj4CXjVucuWzln6WnpDfS//K7JZAEMAmL1o9mM79tthl8c/fvJXP97jVHAV3ykg6R2oJ0UT7MiXNMqjHMBnSppqOjt+oXnOPG4MykaacuxsiFx8m6O+jgxMj1EgyR7addaiQY4NAJ31dVyf1i4mGHA1vnk+YhZU3U3GkPS9mAyjrLccjAmtqlph+vTpJYHhX//6V5yz85no0bZr6FDAGQsZJO0ooZkxIv0co+DX0ibRuH/H89RAxQUKNTgz2yoxeTNtYxeBLtPmzVQdeZbrpaWVD+33CBF484mh0ecoCgnSAKDR52gEh6cmCzNfDUg81d4i/sjVPbEY46ZVskA8nI+uEwQHZwIQkN6bRhuF7We9D5qdjN2v8d0MJg5A2Y5SuKiQ4JDC9vYFRyHgMbBkA5ZAAbo8F2AsulcNkMzFnGlHGYjI1jSqqxy7clxItTiYYmvlJF2hPMJ9Yqjg0tmoXpk26HsKBMOqxjzqlGNN++pGdKwuoiYXhP0yxyi0ETU9hOXCJ/6uCwboQM1glFAPp4FBeyFMqm76/uW7zOGRXPBpwBjayxrop3wTF71wlKAoDcTp1m7acai8XVdEDh8srIMGitokJlIAgyEcT7QJQFGY5hdxz233ripmnXRoHCnxX8C8QWtx7xOj8P7772PPPffE2rVrcffdd+PSi3+H3fsOww+22V/No7JEPW7a9/v8F6Phe8Fz38yadnNmI22hsk5xDMvow4yxmQBqIYlxn4h2bX6BG0c2G2AIABOmT7xoaL8dhvxn4osHHzP0SAAIt2cj/dYp0cDCtkFzbzVl/bYOZHndyWPmCtS4LgNQmelc6hTT7suVn6ueaVKOjUQSQMYZHTsvctxHpnMLRWBdl2Pm6/otM49AhJ6ik/WMVM5aZbN8zVJMWzINrVu3RkNDA6qqqlLr1qNHD7w17X0cPfQoVHiyyzcGDD5j4UQjFHjyGKGBGOqKXqjW84nBE5Lt8Vi87fS9FxX40ROpqS7Wnm8BxUGn2c5Md2/125xszbYPQaKasVJt9RABqkitLAFhoGzj5OTPwzI8rlXWegoixUTKZ5Pj0QRjismo+YLHWDwzjT5WCHio4jXZz6itIoeOSF0dsYOxfXJVHmbEAp8RcqF9FqlAwtHOD1qdbkc5AEw1tgq/o9S/HpfPX4NsE2iEjkfGOx1nbbWqWNmk+hwFJlXOgSckYA8IDZyH9pSNAUdd0UNj4MFXoFCyoLLdlqytwoqGiojR9gQ6VBdCb/qIDZLMo1T1R30Nqi31kza9eMnoq+bixdVv9WLKFyxkRkm91yYDqx1uvIRJRHwRASQhnD4jNCyyxjJTtD2paVcaBs/OGCdj8TSNpZgux1xwa6cPHcJG7uEs2yynnhEgA82bDLhpA+wSHcHAHENNMxo7DM9rj70CBoZLLrkEYiHDF3M/R/d2fXH6Hj9Cnw59VftEAb8jkC4RCmOEt6a+i1mr50yetuDbY1MbZwsWQtLMYQPJyO9SkPDNChgCwMQZXxzSrVu3/46d+s6R+w3cF4LJl9D5SqsXRatFWPxwJgCEI02Wd60LBJrHXUBQ51kuI5fGwpWJD0sCN1O096d5hTlQ29e46uBStzPzh33S+m0G0tZ2Tsx6iKYqhSlQKBhDzqvGHn2H4cRjTkaHll0xfUn63u6jR4/G+eefj3s+uAvdvO44fPvDUZGrxpqipwCPBidR+Y2+h3pPRDZhHMirZX183Y5IFepoJM26EMUnXLNJBKmJzGhPDUAkwIsWFgkVveOZx+PlReDQN4BWFMImAjGyzIiZ13XVKs0qT6DCk17AZiiYkDmkiNlzOiGQrJwsKwoercX8rkGmWT9Aq8xVvEceBTXWIFJvq0dEIdvJOFQImijfsE0pzkbmOcLjpBqaGCm7yQisaolsPXXw8IhphfHsiGRMRyhQ6SnHFAQcxUC2YYUng2UTSS/pel8CwzVFjpwqu1GBzByTu7gEQgKPSi/yYG9VESCvQLwvmIrpGDnDMbMtIUPvmGNfIJLqYdcCRfeZYsBD9TQxIGBGHycm7TSFPOdTsr+q2oT114sRXZ696IzqnjRs0SycycYFVip7kRqHgTK9HfNQR23RYbg4k2YgWsOhAaJvtZf0No+HkzLPm/dl2g9q8GnuuGICVMaAd6e9hxuO/RM+mP4h6ioLOH2vX2KrNh1D1lkvpOw5jFT+E2ZPwOcLJi6fvnjGjkS0kfDRZiaUYc5Q3uVbpGx2wJCIiDF2/KfB59/UVLTptWvvodpYxSkcynszZRWpJ1IXAMxy0rBf5PgxM/9o0tblRXlaE0isDvGVtwkS7Prr867VtfMaZEsELphSx0dXuLYty5JyPa5tD217d5XQ3k0/x/C6+IQguPxdXdkah29/DL6YNxHDtz4gs+x27drhySefxMSJE3H88cdj4erF6NKmrwxrYtx7IeDKfpHQ6HOsKeRQDDgqPIGafIAWOemU4RlMFxAxAfI+oOzA4io4U93ocliKJuAo7InteayBmmbaIs9qFgNOsv002GUG6IvYNxvICjAEAQtZuEAw1AuGAslJJsfktoBVgqHKi1TOgaE2LWXzCgBMAc9CIHdgASIgpR0wABUCJ4XR42qCjDm+qHQeI+RYtIio8ORuKRpkB0I7ZkgG1VT16TbW96I9mk3GWtokIlY3Afmbm51X93NEINFkUz2iENTpZ9Lgy+9r/Qi4NooIPAv1nNb48pwPQiMJCBDaBDkEogL1vqcApmR+NSCsVOFybDs2jxE8BXpNNjv2Hcl+qhcsvsFs+9aYJp9X9Mz18zPbxdz5R9tbeqreZtkJdpDk6GUubl0aEBNkhpda92IKDwG9XVx8fDKdvjgIgrPYOyfPyEWsaQoTjavJYPdAnCFkSDq/gJFcqBFw7TG3whcV2K7nviDIdL6g0PxEO0RVeSJkc3X9vln0NcZOe6d+7vLZff5XwtK4hMAymeTSwgDgDMbY6cbB+4jovlgxwCtMdv57rXObpWx2wBAAiKjAGNvuLf+txZX5qqptuw2K2XcBxgsGhIOrPpDY8i582axVvEqTxgaa57LAoDwWvz7NSDueVpfhPg77vPrtMoLPGuwSaRG3SXM7rrgCKqTkZ5Vpe0u76uXKO16naBCLLd6F8qhUSLu+sBYPj7ujZB0ZY1i6dCmWL18O0aMGRaVO1SY4ZptWeoFkZwIv3OIu1yhQlQvQMh+glQrpohkOOYFGKkUdE0+Wq+8/zhaG4WfCyU8xHRQBCN1upmpV9y+bsTMnTzPmnmkXJ5vMPSEBCG0FZT1k+iIRPDDkFNDS6tggiFSNWi0dgh4WAYGkGk/lKxgaBYMAIccYKrlinNR9S/UnYtdyNTkSRSyQBmWm7RwxFgJXX/DQacYE6L4RiDoCgZEtHqCej3Fct6neWtDs54whUmGSfg5QTBqDYIAQURsJJpk1QDJ0jAEFYqgLPBAh3JGnQIRKxuCT2oEnIATkg/OcAriEIgKsEoBf9LDG90Lbxbx2askFgA8IzkIva/1ucqV2d4XW0qDMHAti9odkqvqjdMLq5zDKYoBSJxO0uhnQrL1sUf1s9Xtheq/reuhsPR791qy7ObZoxxqGdKYwTAuHLav5TqWNz2AIAjnGxJhtAMTl3zTbb3u+MX2QNXgODBBc73thtAEdCUC3eUCSJDGfhcdIer2zyHlz1tLpeGXy8/7ClfP6EFEt/selXJMt57Xyz8NEdGtGsr2JaD5jrDOAVxljU4jo7eaXuuFlswSGAKBiHPZ4aeoLXxLLdxncpb8T15uqRkC9aBRtHC7Ml886HrsGbhAo65IEgvJ4yvXWhJbFCqbZ8pF1PnEdkpK1K4xtXK2HYW7la4qLBc2SNIAIxMG6TJz0BDTrGE4A4SRrrsZlmI8cy6EyX4UpU6Zg8ODBmXUTQuCoo47CUdufiXYtuqEQqEDKgkH7Oug2IWKozgXICaFW53IAXtVQgRUNsp4dahrDgNCMxePhFbVaExErUjDDoxgASgDKQzbuGasBpwaDGtSZdoemvV28nePbuNnXRW0cqX8jJi0ykBeQoLAePoqCow33pJoxZGZkqKIiSeDCwZBToEe/Y9razY4lWBRAHQVhCBCPJIrSvzXY04BTGD2eh+BYMbQKjHEG5IhJJx5iqFAqYK2yDu+VWAgKZZu51UkaiGp2SF9fILkfLtd1YRHTE1NhGmAdgQ0ajPtRIKJADPXGIFQkgTxjKAogxxgKQRHvfv4r+MFadOs0Et26H4sG5Q4rkAMnCY8qwJAjlQmixUW9L5+Fp2MmKjWnryqm66edjdLAoOkopG1L7X7KDCSn+72205RKy+ga2e/l8/FFfKzgALgCV6ZZji5DkMnIxRdZsm2VHSoDTGVptgOLobFQI06araUuQwNPLSQkUKyAXMmaXtFp3t265BBUqmWxXmgV1GJkre+F1+rLY8BeDR4BsTBQP5Fkn+csn4Mxk/4lFq2a15eIFme3wv+GbGgdOhHNV38XM8b+DWB3AN8Dw+YKES1jjA19iT/7AXByn74d+8bOa4rd0+EDYqqJaLVoAj4iHqP248FidbnpjKDMxwBgTQSD5stsHo/dN6KVunksqksJoJoCEMNwMCxK75uDOLKvLyWZ3l3GOXPSiIHBGMKPBkjNCHvG4CcIEIxjj37DccrBZ+Cz2R9m1m3VqlXI5/Po1WEwAAoH28ZA2kjZbaDt1XKcQtVh3hMK/HEsXFMFX0hj8zyXk2yFYhG1t6serHUQZADh9mqBYCioNqlg0iNY5wXIdtdxAc0+KoEnD9tRGvRH5+24kPq4BkRAnMXTE7lvg8IQ6BCK8LGGFbCSgNZ+C1QzL5yoi0QQEOBgqFSsGYihSIBPJAEji1gT/T7Wkh8CPNkuEYMWV3FTWJ8ApH6r+yOmAJr8K/sTUwAJyDOGXMBDu8Ho2UZgkytmxgaG0jaLhQyiTu8TIVCAWTKCBJCOb+dAl+R4h4xkumo5FoEFrR6WbcHAmXQeWbDiI3To2BI77TQcY8aMQbutDkXAKuCjiEY0oh4NqKQKtKJqAPK+pcqcwRdeAqCZali9GNGg0bT3NE0a0pjs0EFIpQ3zNRYkRSGZXPNYuEDiFHNgMZ2Toh3Ko/y0c5RrcQQY4xxZYXqSTyhxnS4HAPKeGtFjc4h8nxgidp4r8GWqv3PajlTINOa4a0ckiNUBgLSRlfsg+8SwVvAwYkKamB7sRdWW0jRBOjAtrp2Hl794kpasntefiOaWaIrvZT0IY6wFAK6IrhYADgZw7SauVknZrIEhABDRIsbYPi82/HPWgUNO8AZ06iOPq/MM0WACA5RpGyPbo4wIIUjx1DubUD0jyZaVAwaRuCYOCM16p7GCMM7r9GlALQsIml6cur7aBkjHeYsysuufvNc0SYu56LqnyLEkqkcAQ5Udgno98cv7kc9XetJ5iNSCgoBdeg/HHW9cj5kzZ6JPnz7Ouvi+j9NOOw177723nMgM0Maggusqhk/XO8dF6MFrg3uoOld4cgeB+oIXqj21YwAg+0Vo10PxLeoCYmjQhXEGITgaRaSG1epZIK6alWFnDAAGQk64FwDmrh+x4MaGGtYVSiMCQYAPAQEBH0UICKxkAnWUQw45cHAIVXIeOYC8MH9t/+ZDICABDxwV8JAHQz0FMRBVJKCogF0M+IEZdSWZl6oPN6Z4of7p71z9y5OspY45klfXBCp/T7NVqvHM47p6Goya9ZB1kUxpuWJebx/Ps4i1CwGTSu9DYCX5yMODl28HCgbjs88EBgy+FMhVQcBX6YooohZ1DKhFFTqKjvCLebQQ0kY2LI9Jmz8d59EU6RDjBox26CEgnc3WWhtznNSLEAQR26dBYcBYaIdo2iBqlbMdmkkvurR61QRdGiwqTk3FyXSbT+j6u7QynBHynoSSGkRrMKyZ4MhmD2GYJp1VjkdmJtohJa7ZSpapywUBgjFwIhSIoxDEg9fbbaHBuR7bzODt2qRlee1cvP7lo7Skdt4gIprpaI7/SZFz9DpcX/raLgD+rcI05QCMIqIxzS9x48hmDwwBgIjmMcZ6vz591LsN/kl9+ncaGAvsqm12Yi8P6V02tAG5zguhLVDA4qpol+NKuWrisFwlpp1NVt9x2QZGgNENUmGctx1pbKcZGXTYqBMkmBYUDxeSqJcB+JrrgJK4zDigoSFnkU2WCRxN0Migwb5klaRnshwE87mWGD7wIOyy3R6Y9M0EdOvWLVGPCRMm4IsvvsCx2/wGlJM7EWjw1BjwkJnTzJ9mAwp+PRp9IJdrqe4zaudA1VE7ODC1Sm8IoumWMckGhu0U/lXgjoBGBMiJHMC18wILGa0iMdQJqU4M1ZZhHpJJy4GhyDQ7F6klc4zHAnLHGRrVzBQHlBoQaiawgABF+GhkBRTRIPNCAfVMII8qVFO1UR+BIjgKJIeUInz48BEwCdrylEMROeQpFwN3Hjg4GIoI4KuyCqqsClShkipCEKjzAwBP6f91/rIOEgFyeMirejSqcznk4IPHys7Di/VJE7QFECggssnPIYc8ZN1N0WXrOnopnJR5nZkmB2lGIAAUjTQagBYVNC+gAN6qNzq36h2Wq9vCRxGEAESBfE5Mtn89eRABQ0548FRflIwgQFZQSh3yx2QKE+YfDgn3cxaGraExZkZxPKNrOIvYXkHa7EHv4czCNCFYFG5G0CQDTFtAPTcwXT81jptbMupFu+l1resqHdDk1oYBo3ARRiRtVhmj8L0OiIFCR7FIDR+CQjv4p0PiToUsBIc+WNg+tve8OaZrsKpV+hpg62e5aNUsvPHVKLG0dt5gIvqf2dWkPCE4mf4mXJ95lmg6gKHrUMAmke8EMARCcLjnuODZzwNxTJd+nbYNVcJyMIg7UcSNn80XT52HseURzLTxfFyAMMtm0EyrBx+769hDRdITOIW1tMpxqXjseHDR/cYHIB0ixWb8bANt5/2hPCnXESYsOwQvUb0kyI9CWeiQGKTC7RAHdum7Pxauno9TRv4fxk75T6KMHXbYAW3atMHURROwTbddpD2XYCAVMiWaMARyTODDac9j6oIPwHkO9YVa7DnwBPTvuk84AUR1jzOIepuxkHUjUvsdx/tYQECBCPXwkQOXNnIqY45oIigQoRb1qEc9BARaUAvkkAvBlLRv4+AkQ2cUIQCSzJgAIacnX0T9IwfEJht93hQNChtRQD2rRxENMfAVUBGCBeCMIyd3BUYRPjg4fPLRyBpDIAkADB4Ey4NTBMwkC+mHgKrIfMV6NcCXEBI+itJ+z2AmNfhTjrmx44QADJ4a1HISrDLJqHGKwKUGhibzKKxWqGcNEAhC6AryE2nCZx+rXzJP8zozLVfPsVHdk8lMAkiA0CLz4REP7ynK00MRxeg6KqKRFVBNFdDOPhxq8UzyiSTDCcnxwOMGODRAVcyu1RoBbE/tcAGkQKJemOj3QIKv6F4Do35cnZOsHFNeufGA4tFi0R67JOjVtrsIx0bNZsaBlan+NkGjNhgVxEIgqD2wASDvKaZQg14RAUqh1Np2tICETaKxODOfAQMBLAKBoabBArXRfUQxSmUcRRED9wtWfou3pjwjltbO60dEs/C9xITgMPVo4vVbonxngCEArVbe9kPx4oyC39hqcLedpaoVgAhYGDw1FgqFzIcX2YUwmAxhtEpzQRkKU8WPmaygnd60NXF59YX2fol7TIJCFzNoiulgYwJCEwzaKnE7XIx9s64OXwroleOkkvT6jg/6zBgUTU/AQD0j+WEI1CQgiIG4wMAuQ/H6V//GV199hSFDhsTKqKiowBVXXIFTTz0Vy9cswh79D1dlm4Gk5d/VheUYP3M0ACCfz6NVm1bosxuAOREQFwYDZ953uItLuIiQTB7IVJcrNhBCgak8GkkDCOlh6hGP7OagJ/4GrGQNqEYrybQRRw45gCqQB4/Z30lQyMM+o88Bsm09VQ/zGhOQ+BBoRAGNrBCqkDUgI5IALCCgyBrCc4ECJpxx+CgioCI8llfACgDyaGQFNBosnM5TlilZr0Zaq/LxABSichO76SrWjAoxoMWYJ9uLRYpnbtQiBIqqDV2iQaouQ99XCBIhGUubvdRiA0M7nQlMbbCpSzFBoQkmG1ljDHzqenJwgFWAwUODWIklNB0F3h0dqb1UpxOQJ4YKxSRzWIGP1fPPCUKl4VClTRfCurBo7ALijKDLHEGygpol1OYByknHWpRzQsx5KXz3FZjVmh+9WDTDw0gVMgMp0KXBURRUXjGLxmJY113b/6pAB+qZqS0J1QFfqZ0rvSD8DSC2y43H5M37HOE7Zu8tzZg8p4c2MsY6TVyE+5urscYX0btsByIninbayalt7rSqfvbSyXh/2nPBktVzexPRPHwvTlkXVfKWKt8pYAgAyiGlx+fea5OKqOu5bbfh4BqEKRstMyYcYLFSQOil5hlndNoEUDPOu2Jm6TQuVs1elcI+RrouFLOHzFQdm4DEUve6GEKX57QgGf3eZbuTJmnON7Y07yUzVtOO+ugJwAzKrdU8eU4Q4OjTaSj2HtiAHXfYGfsfOAJ/+9vfsPXWWwMA7r77blx88cVg4Phk+hgM7X0YKnIRGCQFEMGBfK4Ddul3HPJeHkFQQNuW3YFZgyVLSSy2kg/riSg2nm4DM8acajSVlhAotgyABIIKCPjwUc/qw3w1GwdIANBItQhQQA1rC59Jlk6qnORrHBhAo2iAIX0OgPOYzXKZgMUEZEQSAEomDWigWjB4CFCA3m/CQ0V4jdwWLACUgwQU+GMxC7Do/nxqQCA5VhAFqr75sF5hPRAohjC51vcor1SpkcsCB4dgJhhTTKAF9nQZvrrHqCyh6lEMn4vJWOZYPpaHbf8YfmdxsOdRBPgCJtSCgKPgAItcgTufQQHeeP3kvUl1Mmd5+NSA1bQQjawuBOmt0B4tRQ1asBwqGJPhc8JWVHuHMwafPES2qUkmU4LD6HecIZQmDQHiA0FAkU2nPmW+6zlogJX0BLfLNHen0ecAxXRygAkGwVjMTjcCs+mgUEcTaBQIHcvMXWxq8kF4bZHiTKQ2dclzgTwEwA2QSZHNX44LBJZdozkWa1tgxuSYVFCOcXq+0fuR67S+4KHDEJHce9xjhCnzP8IX895au3j1nD5EtATfi1MIyb7a1Ou3RPnOAUMAIKLVjLGBAW/499pC7SG79DyUMcbAEG3NFKWNs2x6cNCrSr2ytFWoOsBonPWzVBel6on4itH02ot2lYirODxGUh3gMA6P8nUDQhug6pVynN1SqhNi4EIOVPreswAfkA76zCC15Xg0x8CfdSycEGLH488v9EbnBF9EnpT9u+6JKQsn4uWXX8agQYNw+OGH484778QVV1wBBBX4dPx7uPDCC/HWlKcwcpuTw0FdgzohGDzmYZseh4QTBmNyoiMRMYWmMb3J8IbqWqZUZ2DwQ6AWt7mT12gop4CUCUgoAGNeCA4DKiKgAgqqU3pMunKAAY2WelLmHQcoJujQIMQlun4CgQRrKIYgTR4tqroKEEUARZbtgQzwwxAgUHXXQE6DS32Pso09mRcVwZjpgyp5In1M7eybAIR6AzR5LgiBnwagHBw+K8au0YDQQwXioDgIwZb5LAQzALICt2Y6rsrS15ig3sxbl51DHoLFj5nA0SU+ighQMICqdY40e+iBINAYrEQjVoJQRJXXCS1YW2V7yFBP0SJFLxY4GHIkWw6Iq7Jl/RW7TNKMIW/0ryJE6BwEpNta5hz9Tps+cCD0KJdlRoDUNMeIjwcqX2V3LMdUL8bQRepvPU7GHWO0U1hRSIcwDqBReOECr4IxVHNCQDJ8VUAUxho1Q/VUKLtELqKFvwaPutYFZYOsbSHz4W43hpMKovmCqy0ShaFKDkgCRkK0a1MUcUDg01mvYc6KSUsWrpjVl4jqnA/iewllXWwMt1RgyMimpb5DwhjjnTt3vq+d1++cEUNOgcclzk3Y4hmsmRnKw9xz1RVTT6t6bYCH2G8WC5dg52WvGk3bE9vDT9uJ6F0g9D6vpfaVBdwMoWlQneZprPfHjbVTCZWxPX1xx7Ho/kvB5/JEg0575wS9Mbzez9ZjhFlLP8NLE+6PXd+qqjMO3/UG3PfSiTjooIPQo0cPiNlD0KqmZ4JZMEO7mGJ7WZqBne39thtIxc2D9mIVTnAIQMGtuB2fzCdQDJoHHw3wqQEe8sixKnjIR+AQiFS5CuyYwhExY9rDGEiCF7Ns6UzSgEBtiiAUjNDl6PrZzB1TZZkSKHApqAgNR/QOXIy5AYQEdRqcx4GhSzRjyZlsFzOtCRLNNvFYBArjbSBC9s1lV6ivlWnjKm59jcfyyCEflqvbzUM+ASLjeVtMp1F+gEL47BKg2gDGBapDY7AMHtPOQQKM5dHO642W1AY5ZX9pluNynjE9qTWjbXp9R20VOcIAUN7gcizWzLi5cNHnTFtZXZaH5Htn2nzbTlj6PEekPtbOhTkWjRFazDFR/9ZxOBspMrkITTLAkQdHmxxQo3aPkef1mC13zvAYydinPPJg1ovtQDDU+dH2hVVqJ6WWeV95PicX1+Zco89rj2Mdrsq0JZRq6AAffvMvrCzMmzB70de7E1F8NfS9xIQxVtOq5cC6IYMua3Yek6fegtW1X7UnohXrsWqbXL6TjKEWIhKMsfMqu7Za+cJnd19y0HbnojJfE/M2syP6p7F+NvhyxceynT3M1aBWNXsOkBnVF1Zd4rtZ6HwDwdHoRyERzO2kKhSbaBaRYAqNFbFpF2efAyQTRhQFvE0L7ZAGtgHEot2Uq0o27ZvKktBmlIV/9MpaMEAwBl+F4ejRYWecue+N+Hj6C/hy7jhUVlbirvtvwemny12LXn31Vdx55524+fWbsc0222DF7BYY0HVftKrpBgHFKhuqfrvOYAAjAucU7iMMXRdIp5GiteCSDIt83ThFThF6wvRQARayVQHmfXw1uu92NTwCClgLnxoUcyVVhgweQAXpBAIvAjLMi7GF+juDF6WzAIkLoOg0GnikpQvTh2mKskSWV/kICCqCqBgCQhNIEgmAAsAAfxFYVG4Kij11qY5NgBQ7Zjy3QMJ460rZ3jbjqe9Tgy2TgZT1k+0LMm0l42puBtlmPpMMTpQ/j/JHHHRG5yKx7Stt9laejAPHIq1FwV+KnNcGeVYT5uOxChSpAQFrBZAfA3KaTU4Di7JsP2a3addRt08OeWjHIg0Y7cWIIK36jpYsMo/k9mSx+JRgCjhGYzQA5WQV34eZMx1WRvPJxmIapg0klJNYnPHUHunSmz2HiiCPSi53FpHPhKHBl0+3Ujl8EDEUg/hCvCjkFodywQ9UqkV/dS4IQWHMPCVhehTZGZo7J8nnHc05vmjEm1/+HcgVnp296OsT6bvM+GxEIawbY7ilcobfacbQlFatWh1X7XV4esTgc7zWNZ3ddn+WutQVqkXvrWkCRpcI0sbJcXUikIw7p+1GtHpYB5jVddIMlRnywdyL1Vz1Ss/BSOXs3s5O369SCTlsE01DaxuAmnmkybraGabtpVuuJLwUDfbXZA/rC4vx+aw3MHXBx9h5t6FYOrMFnn7pTxgwYAB69eqFFSvkQu/uu+/GuCcUu2O0vet+zB1D6gMe2mGZtlU28wBENoCNrBCyUlq084UpJju0lpZLVasCXRweGPOczJg5yYcAT/MzzAvTJr1n3cBPgyRtS6iP6Wu0ilj/1YCOs7xxrghBypuWNOBRrCE4oO5Fg8I4w5cVlliE5znLJ9pE36sWfV63lW4fD/mwbcz7deVjs49kAUxTPW22d1TjeDvrc2nspXmNCVjj6WT7F0Qtiv4ycF6NvNcmYpeRRxVrrYBbBC41NEurl61mN9Xt5nFTNZ9DZHMZmiNAe4zno7QUB582GDVZRs0uyt8GAAzBolHvcKyMs56muQcQd74yQwMVmY8CGsP7bElt0B4t0T4vY5QKyPc+IKDGE6j0KDYemdqGVUUJe/U+4DU5gVYVRVTlAuk8h7i5k55DdAxV3yAO9Fxja0rqCyvw2hf3I1clfjVv0Yzb8L2UJYyxmpYtB9QNGnRps/OYOvUvqK2dvMUxhlsMMASAfD6/U+vqLh/v3u8Ur0vbgQCQeKmA+KrcFR8rPMfiXmwJgEDR9mfyd7RqNXekkPvnytVwQNJTtYLJ7YpqciIMr6CBYbhFmSonEQMMhhpVAUVXCAlAHnexc/ZOGNoWx5SS4DDjfGLlT1H9w3vJWG2V2rLKlJBRRZxdjcoBArEWdfWzMG/5ZHyz6BOsqV+GI448HMcccwwe+uPb6Np+TzSIKmnQ7WzveJmaedDxA4tqujYnIh2bz1ZHaubFBIONtDbmxAEAFaxFCAR8NKAo6hQD15Bg3iRgrEQFbxVj60w7QOlwkQ+/Mwc4tCUCAOqvshcUSktlq4dj2isDpMm6FGMMl0yjuCCWL6FWNsVkFCOWjrF8WJ7LVjG8b4tdZMwLwZNLZe5SYbvYy0Q5RvvK2oqwrjJNHKhrD2vzuFlPE+xrIKj5MKJAgcLlAADPawGP1yDPakJwmGdVMdtIzSQDbvtWDi+m+pbpCihSQ6y/6Hvi4Mizqpgpg7aJ1PerQbNtbqDPA3Evbxss2uljoZvC45FHv2z3lMU9IitR/U+CQukEVaA6MHhoy7qgJdWgGjkEINSjiDw8tOM5VHkCREwt+ilc0NcLGYO0tcfD+JF5TqjJ+ajKBWHMVNvuvKC2xzQX93puAOR4lFO7JHFGWFY7G+9MeQRrGpeMaCw0vOW80e/FKYyxmhYtB9QNHPSbZucxbeqtWPM9MNz8hTHWvVObvt/06bhX9cBu+4UBU4G4KjgyCLZUfg5gYYq9JZOO4aWdETRQFIgCCWswqIfSIpGCCxJItGAeWuciWz/bhs3cN1aDFL27hg0STdHTBhABm3D/aIrqrI/bgcB1HmHbhCyYG7VlAcnY/qmWZLGHPDbAs8QxU1xxKMNziAbnSi8AEUEbCgTEsLwhj5V+8kIe/nUzD6YNoWnIb7KDUUiRuJ2ftCvUHrzFEBgGFDFzcWClWbmioZ4VAAkQCXBeAY/XgLPKkFXU9n2mnZ4NjqI2ck/AJsAUqkzNDBICyQTquglfPQsOmEBP1TEEf4xH5TlYvli9jHxcbJ3Olxmsn51flp2iCZgBhKDXvM52tHHlYV/jGTaGNqtoO8dolb0JSE2xbTlN+85ANCCgeohgjQTejIPzauS8NvBYZWhzaTOZMRMEA3TKdooYZq0eDkgGOrf7ppmvabdpO9zYDjO2PaV5zAwn5Ar/o9lG/c/l7GJ63dvl2N735mKtqOx5fZLvZwfeJwzmLtMJtKAaVCMPD3J/cL2loQChngTq0IBWqEZrj0PbO1blhLJDjGINurYVlPWLzzXm2KrZxxmLPsSUBa8Ey2tnDyaiaYkG+F4yhTFWU9Oyf92AQb9udh7Tp96GNbVTtjhg+J22MXSJCoTdPlfT8MSSb2Yds3O/0+HxPGzHDVvdKqzjNlAyWam4Y0fcyUOLDlCsY9Zp5wNzYNKgYSU81BVboZp5qOYMOR0kFRGI8yla+XJioBjgZaG6AoizoEHCZkfb18QBnw4HYaqtbaAX2enEwbbL+cQJEh0OHToodFQXKRpEukBorO62sSWi/OIAUqplGgGsgQePI4z7VRAcRcHUvr8ErZ4y1cJpwM80vnd5qJqsoLbzK6IhZCNkPR0qRKU21tdGYEyBIcQZQyCACOpA5IPzaqVWzYdqXBBC4CTCO4x7+WYBQwlEIzAIIGQITXAKBaoIHHKLPBNMeRE4JKHsRLkClSqdA7ASRSyq3gKOs1zEmpLcvdhjkTdqotuENrYOe0omIGKqcINxpEidbzO04XfmKTtGCVE0EA9UiB59vU7LwGPnGDjyVBUDUrrdk20RebD7ohZCNIbMIfdaQgRrQEIGvPEBwGujFgJcMaFBeF8myLaBq1zyeAAV4asFjGaL9cIiYiwN/2UqQPaSiB31WOQgZaqdA6O8ABr0G4solmQW9fV26B8ACdW0DSRNMYOEmyryAtXJBRsVUaS1qORt4KOIOiadsTzkUYEqFFCIArQTAAKqUYF6FLCSLYOHChThozbIoS1q0MqTpAHzpYLb3KrUpamx5yltDsQZEPgBJs16FnXF2TOX187egYhq8b00W9K0JeXIlkWrRbLFAUMAIKIGxthxXbvSJWMn/flPuw48Hy0r2yXSmYyZHXbExZSZtivxfOLXyEGZYtuKadhQz+phh8TIowqrGQeoBUTgJcrRQWKNO4Rv1UMAaAjksRwz6y7FjIOlV7ZRKAimDLwjY23zfl1i1kcgnUUsRUjbOzDoLQw1iHTVIQZ8E9cnGcZknWSsMqG3VSOGtSLO/AmI0FmkCB91rA6NWIuACvBYBWy1m8mqyHrE1ZWmqs6eYIHIW1UCbQ8yzMva2OQtbCdDm4UDQKJRhofhleCsUpVfVIDLB0dO5kkRS6VbC7CP2RJ3GiEqgsiXgBCBrI8GmSTTh6pxbTtos4jhgksd144oFhCTvUJ7MudDQKR/26A2Umlr8JvO9uklXey+w+tdPvgIgacZc5EQgJgIwa5LOMXropk73T9McGqKzVYWghVhGUyznRSA82rJNokCgkCxex6QRw2IRcxvCCYVaxjdXfK3Ll9fIxDIsuABTDNzkde1WV9PAWVzZxYwtz2jMEBhgGLEbsaYRS91IueMG2kiFlSGK0oeN+sZoCidcxRrXxC1IBThUyXq8f/bO/M4K6oz7/9O3aWX2wu9QkNDQ6MsooALxpWIimgQcRtcMBqNed8xiVnGMb7KJ290Mpo3ZjLGTxKVkGjcxkhm8rpEB+woonlFQTYRAaHZmqaB3m/fvarO8/5Re926t/cGus/3n+57b9U5p6rurfqdZzsd5u81xMrAoUJm1sTPWLqxhbUjiRiMmpIcHDkIIqhXvdeSSLQkFJWse5t9yUoDu8fGcCMzRoinwti053cIFeS80tSyc6lIMukfpE17+9XCcGRYCkMA0H8w/zaqqHLTxzuffPe0mltRUTQtbTu7W9Arcw3QhRNgfgfc2apA+g9ba49MUWG4E1XIZiyRgoT+0NTWhY0jCRl+c+ULQLPiGTEz9lgZo96Xl3VLImdAtn3VAWeSBMHuxJP0YzSWZTPEIuCMsbNn+Rnnywt3jGS2RBlDqErQs+30z40F641+jXa9rJramNNFcaYxGSs1pFxZiXGWQBfa4EcAOQhBggQZCcR4q/aAIKegY2RZ0cwHPHHTlWgkMRgPfYW0B7YKQEJAz2C1roThJla5Vuw6U4ybUfLF/GscHXFA5eA+Q7goACkAk8DtYoLsbclmrJ+zLZcFy2UZdI8p7X/DMkgAmA9pZWp016c9ttIUdeRszxDBmgWUO93RritsxfJ53fTtUy7V7Mdu2XWIN/21w8JmnBcPy6Rb+NlRbccJaMLJLgiN74bWtAS31ZBBgqy2a9cU0AS3O27TdF9zcLkTMnGQT4bEctKsnhyyoz+v187DsyyfKjgYOUWkaoaiGFbTlGZ5hGXtI3LW3dSs2JZL2xCFqnn+jHLtTiujG25eH2cil+rYJr0AOYdqFldXKaVN3ChpJk8ldYNcLhsFLWbSB1WfJOayIm0dcKaY5Z0klgs/Arq72a99y4gZK+1BsxlaV8KeHGiF+jjj4SUA7ZED2Lr3WeSF/DfvO7T91YwnQtAr1G7qh2aDhDA8OekIH3uPMTZuf/vzde2RS0+bMHp+WtyhYUGzq3+HW5jDYfoHrE3NBc5dws0QcgH4kUd5CLOEWW5ChWzehAAggFykWAJg+moWgCO+xqtumEGmGmR27CLSGJv9ryEG7dsY+xii0wdrzV9TOBrHmuW34TgnWQSk9pmzPJApWEnrL5NgtLfvXCPbaCfdimhPHjFigoz4oSSiSFEUMdLW+/WzXBA4ZH25NsMVC2jB9UYMlz0WT+ExEGRILBdGkWYGCSol05JHJOYHcQ5OSWdMIVcAXWRqFje7yHA9IO3igCsgya8LVg7iSV1AwHxIw8y4dVr6MpImPlyfeSWOEAckv/2qmcfgsHTaxKFDDBqWSACOY88wFoeLG9y0BDImpZ1vBp8pwE1MEedci9iYlrnbYx6izDZ1099Ij1e02nVZuPTYUcN6a4kb47UMRXF5DR39W+fXGg8H53HNwyDlmeN3bOOyOmtj87l+jc54S+jFyO0FyFVYEwYGybSOKkjAiOM0Vm0xroUPAcBWAN3Y1zhyiazsce4uSWQbk2SbBHhZQO1WWHs7ql5GyRCGRCoUHjHHonLtNx+UigEAMiXMdcCNLO84iyNBYTDmQ5CFEKIChJCnLUWojyql19XizFr2Dx4hSHKGn9mB5r+jofk9NRxrmNIZpb3eWwkEA8OwF4YAQESHGWNnsqo1z2/bu+/mqTXfgM+XYxNI3uJO+8yK63O/pzXu3NYdewZo8SwyJRCjNnNbBskM5I+hTZu3Mm2mzHQXjZXt54MRYm1f/cBdJgKAuZ19VuwO1HYen3eAtltsSjDczcz8X/vcqc4yuZ+9RKF9KSL30nH2grfGMlpO0WiNwTMm0tEfMwW8XQIZ8Z9diCHO4mYyiBF4rlISRDJkdNpa8um2TZs7Sn9Iul2AnCc116IuyuxFm7UxqiBSoCKpvSRZG7ZdaJlxdNwUKJ4YAsEQRUaGsG1/DcW5j10UZhN/jtPpdAdrGG4/S9gySJpINRJR9GMw9tfOlzOWzxI2qnM8rqQo4y3DGqklocDsW/trWEwta6dhNdVc4DKgx9+BSXo7HnF9rr/atdTatddftPctMaPYvtNC514OkJPPjAXllLS2dSXcEGQochvAAnBMCuzue+a37e/XrbuKaeU1Jh7m5EB312v9c4d12H1c6SLYstYa8af2UkFaPGPSfJ9sFmp7QpSCRFr8pV0gapMuK94RsISfpL/HjCQrl5XX6MsYjxnTa3optJJEnGRYRdiT2nliAf13rSU1caiQKWr27We5UCmFKOtAiqIIshAKUYo8yoUP2kouKX3bIPmQw/Q7BmmVKex2Y3vCnzYFsN5XVQU7G16BrB5t6Izsmy5WMhlY+utKHp72whEiDAGAiFIAbhldMfmzTV/+/LGpE7+J/LyxAJyi0F7k1CDTF8frfaPkgfXaWAkhCM5lqDzmsDgYQkNGVLM+wbkaQ6aMOsM9aZXFSDgsEo6MP70fP7TVGNxB2gDSCi7b64y5a415WSbNchEe1kfn+UkX3/bPPNv1aNM4OsNN7mzHAz2OJwnVIeCNwr0JCiOlu4eMTE/7g9pqRndnmpYRfcVt0l2cxhhIAZECzlNIs+65rDpW43ZrmWKLu3O5ig3cD2uHCOSWu9FL8Nm3dXzeg5skAVbKkcv6ZNvfTC4xxmC4jO3Xi1ljMcIq0o9F68/xOXSRaLrxbYLSdszOuYKVp2+JKcMC5rrJZ7KCAlpfpnBX0voBk8zl6Rxvk5QhftI4FZIpyIyEG8Yk07WpXW85fV+uAJJf/854XL+0SYbiEKxg3ClgmWQdl1lXMgBA+04aGdycknCsYmMKX+27YQgrEIdKKjiTrWQo/aQZZYa0a5A0BRxjAUgs4MjQt4tPHwua92mVoua1NWtN2q2b5msjocty3xvtG6JQ4XpmN1Tt+0ocPilgygdjMq9SEil0QWLaSkRG7KMCxVx2EjA8P7ngJDm8MH7mvGc6S11pRBPN2LFvBUKhwDNHW7d/W8QTDg7Zqg10z/C8JCNGGBocba7/WSAQeOtQ66/fyM25sqa07ALTteyVaQpkFoaesSo2N7FdZHFwBKVCJEnVS0vENWHI/OBMBmdJM4jefuO3l9GwY9Xzt8qPMHJZa+AUiO4SFEYxWm38zoK1Wt+WpdItJN1Zfla5Ce1Y7a+BdDe3ex1Wr//dbbvbyywajeuZbv01RKECxcwKN1aG8JNlQTD3sYtDI65Nf8BrMVhWqRYz/g6qKexIj+0D7LGCNisP0uPI0oRgmrvSeN91otwCkDtdoo5+TEHoLRoz4liVxPbXZTk0mzLiC23bmMNmkhanof9vWf+4Zmm0jY2RpIsUu7Uy1WMrp9WWW8g6z5HTGpqhMSZljKv1vJ7G+2lC0+co36MJebtgc1vyfPr3zub+N64xVyx3PGBaCi1RKGccs/t9zRJq7KdnNBvCFJLuMlatiYc+flP4mg3plj7jHPN0QWyKYXvsq+5mtYtrUzDCukfaQwGM2FhuTkTcB2n84abAtI7XVv7JOJ+Mg3gcjPm1JDAKwPAJMQTgk7TakBLT7qOG9TCiP0Uk0ms66gI2QH7kIGi6l824cUofpApCc/t6NB15i2Sl+aLOrvhHEAwKwmLozYgThgAgy/JnjLEZY8e+8+LefduvG1dzC5hPy+B0lx4x/trri9lXljBeu+uVqZDNOljaNtosVQtolkG61YlIBmOKJgzhLPRLZM1lDOuiN1YMm/dMGeZ7jPngI2u9Xa96dsZ47ZmBABzu6hSzRK+99lgmYedlpfTa1qvsi1dbxnqr9j7dKyS4sVsKzf709WeNOoLGA8KwDFhiT9uaIaBbbizXnz0Wjrgeu0dWP6Zg1ycCgF0kutyQ5BZaHpZCtxjyEEdkLNnm6fbVzkb6Tj2xFmazpBntOvvM7P62/mXwW+Io0z7EnWKkRwLWuR2xbo6BnMkhmbaxDsES+Npr5/UyMrKNfp1ta3Yic0f7X/gAZog8DjhEktPS6zmBMD43SgPBLhLt4tP7WJkpDi0XvWmddZw/vSQR57rwtVy+TMrRNZ5trB4xmU5xnNQmC0Zikd2lbcTlAeDcmSRjLHnodY+0WzXtkyorLlh2TOpAHKTK2jEySV+xx7Lgc8ShKlocol8qgE/SioiDtHusNpkPgkNbitHwIkkkQdIn4klKn7QCgMKTaGh4FZy3t0VjDZOJqMPzAgkGDGExTGdECkMA0GM1rh9TNfX79bue+FVVzU3IC9V4ikIz45RSZuKIu+Ct/SZlWPAMdwUnGYptrVhtQ0l/inBwitssCtYyXGkrQdiyMO1WQe0z55Ji9huh3brImAQVASgsod3AXHFZ9vEbWbXZllUDrFIrgPePjKCmCVD79m6LpHt5OHe5ColJjpqQBu6CuPb/jbqRbstokmJmnJNCCcg8DK7GnePXBaAk5ZnihUhGYtsbyD39auv86dYGu3hhzA9IfsdD3H7d0qx4jvOWRQD21P3bndjraVxhpn08hYW3pdMb4/usAJxbVlZ3l3bRbMeVwOI5Rq+x23+HXn11h/tasfT3jPftrvP0a5q5Xcp0PMy6B2Qcl0MU6qLG67plOE9OIeshhI37hmG1Ne5n9rZ5EoQk3OLZ0Q5c1lddXJqC0rh/MUMgJ9O+C1wPy3FY5s06lM7lF937ucM8jO+eNlb9HqXGkVaOCRKYlAOVx7Sah+g04xGNbGYGCUGpEDILIYRR2j3IHm7imqSm4kfQuP8ljBpV8kTT4W33CdfxUECOZ01f9h+OjFhhaHCkadeTjLG/th55daO/YGbxqNFf1eJ64Cw2a8cQEZbL0Vn0V/srm3EypsvDtObpLgvbjUazlOjN6QLQnRUI8lmbMKfLinjKM9sQju2NWbx2w3XWxbOOwRJiWjyRfRk1iQUcmYKAZnUz6rFpLXnVRTMKJBvj8dk+g0McKqbw5uniT19ZwR6LaVoMmVWXzSgP5DfrA6qIUYeZUWhYRFO8ywqQJ1l/kKiWK84csB5XyK1rFpxxFaDKYFJ6Rq0j01a3IroTFDLFCJruyKzipu/uD6uNASCLwOpLW3bXe48tmF7/u8liIRwQ0mI1YQmzDKKx23G4nzmu8+LptmZSWghBpv7SrZ22SYvd7Z9JPNvbcARz2vZ1H477HgXn90ezVMK6V5AWz0mesbr2vixhaiTXuCfJ2TL50zLiGbSkF1VOd8vDEM72jHOjzJPmfTD65JQEl2T4pQA4c3pBDOMDEaGr5RNEWzZQMn545uHo/s/TD1QwGPTflSyE4bCFiOoZY5VVecpvmup3fauo5lpI/lzYixIDMONTVGhZqKCkzeWoOm4umvjrdHZkm9F6uQ2tm6ytwIpjJmy7cXl8H83sS/MNyyVjZmzqmZjcKCPidiW7MmtV5jPLewAwl1vzs1zHbVYreOy0DLrjHB0FfAmOLEb3dgaKKZxVPRhdhg8BKCw9uN++f5K6tJpiuvVToQRUHjNjhADtJm6U/2C6VY+4Zh1kzK9bsSyhYrdkUSIMuWELkIriG9/4Bl5et0+zDHKXq9Px8DFOjGQ9aDzFYTZ6aB3sifVsIOmzQLT/BnohCAeTge6/B67/NDxK4aQ16xJVju+bzT3r2Najf8cEM5O1V7I9Kryslx5WXq/vQoZVMZ3922McoaTd67LF5Rr7aGPK1pH3cTrvwYqnWHYk81ASxLRwILdHB9CEocpjSEmdkFgO/CzXWQNVUdF58G3kBvgXiWjDHCK9JpZAcBwZdmsl95dQYdn1SVX6r/yqi5BTcopZysBA5QlYcVTcWjuWJ3UxmPK+ScLmSrHH/bjxuOnaV46w758VD4uAuw2nK9pynZhv6cLFy2WtWRElzxhFI9bRawUHz+BzR58+uJcfs8dLasHelhXT7QIHYFoCjRpkZh07jzgt4sk0ke4VI6Z9pOJPj96DG264wfy8oKAA8dzRkMon2A5yACx69rZ6Y0E73ri/m32yJBr72MTisKS762l34/bxPHid/x59j1zn3uue4/5eZrrWPfgOZIzr7KslOsPvptswAXdIgut9x73YnVRmuMHtlkW9b2Mdc+OcSiwANXIEiUMfIT9HujvcfuQPvT5GQb9gjOUHCyZEy6fd2ec2Wna9iFTXXrFW8nAn2tX6F8ZYJQtv+KCzc9u04NivgPmC5udGnKBhJTQtbvYMS8DTneMuaZEVY/ZvuoxtN6GeWhLcAdeOpABrPGnC0W5lNFzQhhDT3aLctS1s1j+jbp/7PNk/Tw9C52mfp1li9b6Y6d62hKn9wWKU9+CU9DxXaUu42SwtmcS6Gj6KgsgB3HDD3wAAzz//PO780aNIhEogBfMd23rS0xi4TPueLGSyjtmtpN3CM/zfG+z98Azve/Wbab+e7O9uy9i+r8cwANfdvB4ZRLb7elCGcZPHOcx2rT3bzNAnnL9/47U2bJuVHpZ72B43nXmCLaV//9xkmnR5uva5eS8278PG/Vlvn4GnWyr1mEPVSFZTk5CbPoNPjkbUePP0cIwOpXcmGAoI5GnA6E0LwxEhDD0gombG2GllZWV3dOx561lpzBnMVzjG4QI21ol17sgz3Ii6sQxmHYwh8nSrVU9n5V6ukkwxUOb/1l+3aCSWcsbYODJK7RbNLKtz6J87Y39UpD9Q3FZIvR1uuVlVpi/fliX5wFHgOdM5yPa5cdNPJaA2fYEPNm/EnDlz8Pnnn2PGom/BN6rKtU8WUejVdw9wC+juSEtYOlEYcnGbqb/uxtH950QEyElQKqodVyAXLJgPJnll+Pfw2nd73Qbi/NnFIUdq5wcAgOD0S80tiAgU7wDvPAopNApS0ZhuWyVV1mJt3ROkXlgKtUJSGdy7XIHauB0U60BgyoXWPQsZHsvmPcHeCOApjDOJ2Z7+jtLCgbza4HqpniR4tBVK03aMKir4l/bWpodFgsnxpn8xhkIYjjD0H+wfGWPvSK1f7k51HMoPjJ0JJvmdD2uj3lrGh38/RKFXO+7MPwCwzV7T+8nSl/mdNsafPqtO+9q73CjubTxdQu6Ac3csTpbYKOexOAdGTEm3MhjnoZsYojQyWQiSMSgHN8FXMRnjx4/H6aefjilTpqC1tRWlpaUITpsLT6tQj61jA0smIdldYtKQcZzOy0DA42HwjiaU5xCCwSCmTZuG/Px8vPnOe4CSgn/8GWDBvKxt2NeKdqwb3Zd7gsd55OFjoFQCPntoAwDYJ29kiUIASO14D7FYDH/+85/x9NNPox0J/M8f/Aj3/a+HAOaHVFgOR2ayooBHWvCV2nLU19cjHo+jKxqHr3IyWGEFmC/dMk6qCkqEtWHnFWmTPDkBJvnBckIgLoMSEVAqBqgyAAJUFSTHQYkuQEmCFY3Wj9sPZAjXIdu9JL2qg+1cZKMHoRDdhgHp/RiWRc5TUFv2giXCCiU6p7bFO/ZmH4RgqOhPVrJIPhmh6MvpFVRWVn6nff/aJ1FxmiQVlGvWwqyisBcCxNFf7yyJ2s3P7v4xXvfCZedl8eoui9P8PThFEWV1xdni9TK6cDL07zWzd69WYXvf4WLqrXXWtGgSlEOfwVd5CuIHtmL+/PmorKzE8uXLUVJSAgBI7fxAE4cZhOWJAmV6WOqf2a24btHifs+gz4Iyk3s5m6uvP3QXA+eJ9l3lsQ5QvBOQEygLyHjokYdw3XXXoaamxrH1vHnzsHbTDvjKxgP+ILqzJNjPZXci3X3tSJHx1tM/xdfuvh9QZTx+393w+Xz44IMP8Je/fKhtG8jDC7/8Mdrb21FRUYFUKoVwOIzzzjsPsixjzZo1aGxshCzLaGpqwm9+8xuceeaZeOCBB3DNNdfA5/Ph3HPPxfXXX48Lpp0CIsKb76xBSb4WUrJw4ULccsstOOuss1BSUoINGzbghhtuwNE9u8GKKiEVVAA+P5CKYf7sSVj1zt/A8opwybmz8NlnnyE3Nxe1tbX4cP1mIBVDyahizJ49G9OmnY+ysjIEAgGEQiFMmDABU6ZMwRVXXIFx48aioABIJuNYv2WbHrpDmvuCSYDkg1RYDql0nPd5zXify+JmNgtedzNR9YK02o6U6IJ6ZBeKi0et6Ii2/iP11g0gGDS0rOT+uJKHJyL5pBcwxmrGjBnzf5tTgTNZxWTLdWS3eAHoj5Wwr/cMr5qHfel/0DHH1ccH/kBam7KdEyaBlBRyj2zFunXrsHnzZqxYsQJr1qyBz+d0GQanXjRwY8rAQDxLslkN+9umXTweV3d2d8kWXiLUNhGgRBTU1YxrvnoOmpqa8Mmmrfine7+NiooKLFmyBLW1tZ7ddnZ24vvf/z5WrVqF6upqbDmSACQJPNwM6moGywkBOSGwnHzAFwTz+cFyC3p+XJyDYp3g7Y0oyQ9g5syZ6OzsxLhx41BbWwtVVTFjxgwsXboUK1euRF1dHYgIZWVlaGlpgd/vR2FhIVavXo3y8nIUFhZizJgx8Pl8CIVC+O53v4uZM2emdbtx40YcPHgQAFBVVYXa2lpUVFSYq0W5aWlpwe9+9zt8/PHHCIfDGDduHBYuXIjFixcjFAplODQOxljGNgGgqakJGzduRGFhIXJychAKhVBYWAjGGAKBAFKpFCZNmgSWVwRf9QzPNry+//bi2mmYkxb7ZLvnvxkigLceBIt3yGqscyYR7ezxzoJBhzGW7y8YFy2eelOf2wh/+Z+Quw4Ou+QTIQx7CWNMKi4f/a/hruiDUuUkSPmjunePDZSVsMdjHNoHc7/EQI/262P8jxdZz7Xd8qq/E26G2rIPf19Th+uuuw6PP/44brnlFuTk5JjbBKZcMCTnvL/fE7eI622bJ2z8ohvdOsRjHQBXwUKlnvF/RASKhUGJLtx46RysXbsWlZWVuPHGGzF16lSEQiFcddVVaROBbBARnn32WaxYsQKKomDBggX45je/iR07dmDr1q344osv0NTUhI8++gjK+DPTXcru8XW1YM7EUnz++ec477zzcM899+Caa66B3y+cPW6+973v4de/fRqssBwsr1AT3v4cAASKd4HlhMB83uctTSBmcif3IJEGAHi8C/xYPUIFBSsjLU1LKVOpCsFxQwjDzAhh2EcYYzWjR49+pSXJzmdlE8B83msad8dgehUG80Geyb1o9NunGLaM20mZt+mrZdRzO80qwGOdOKMM2Fp/GFL5RKj7N0CRZWzatAkPPPAANm3ahCVLlmDZsmWmSzEw5YL0wxmE8z8Q4rCnbQyJEOxrpnYWCAy8eR8mleZizJgx+GjTNrCCcrBADqTCcpAsQ23ei8p8hpqaGlx44YU488wzceGFF2a0CA4kTU1NmDhxIpJJax3u4NSLbNYpgFIxqEd24aJzZuHBBx/EvHnzkJubO+hjO9nZv38/XnnlFXz88cdYv349UqkUFEVBYWEhGls74RtzKuDPyWqdNOjLfYtUGbzlACQ5HleiHWcS0a6+HotgcGGM5ftDY6NFU/+hz22Ed/8FSleDEIYCC8YYKyoqWsqZ75l4XkWIFZbDiC3KdFM5EcJLeuJS7Kso6FdbWbfL4vLpCb047z+45lwcPHgQ1dXVWLlyJRoaD6OzvQ1FRUUAtAf7U089heXLl+O1117DBRdcMGTC0GAwvkf9Hm+WWEFSUqBYp+ZKZT7tLySQkgTFOgFJwoFPVqNmznyQkoJWaJ2B+XMAf1CzAEm29h0Z8gyUjGDZHVdj06ZN2Lx5M6ZPn46VK1ciPz8fy5cvx+7du1FXV4cvDzSBUjH85MfLcPvttw+JEDRoaWnBCy+8gOeeew6LFi3CY489BgDYvHkzzp73NUBVAFXGmFH5SCQSWLZsGX74wx9CkoZAoA9DiAjHjh2Dz+dDSUkJvvOd7+Ctt97CoUOHIJVNMGMRu8PL0m59aPMudLVCbd2PglDof0famh8VsYQnNoyxfF+oKlo49YbuN85AZPfrQhgKvGGMleaXjv4gJcunobSGITg8ZvZD5jrsiSDszT5ugdJtUpCWaMCYH8kv3kFjYyNmz56NDz/8EFu2bMGdd96J6upqrF27FmPHjjX3Wr16NW677TY88sgj+N4TL4KZyzo4x3a8LIfZrIO9HhOTQETplhbiSO36e9rmiqJg+/btqKurw4YNG1BXV4dZs2ahtbUVR44cwaOPPorGxkY8+eSTmDt3Lvbt24dt27bh2muvRW1tLfLz85FKpXDw4EHs3bsXsixjyZIlqK+vxx9efQM3LbwUsizjwIED2LlzJ0aPHo0bb7wRX/nKVzB9+nRMmzYtbUyJRALvvvsuZs+ejXHjeiYKBorPPvsMs2bNwvTp0/Hzn/8cCxcuhKIoeOKJJ/DLX/4SP/zhD1FbW4uysjKceuqpmDBhQo+sWoLe84tf/AI/+tGP8NJLL+H2+38KSBIApv0mAjlg/qDnfp6/GSaBUgmox+oRDPiOJDuazySiI4N7BIKBwBCGBVOv63Mb0d1vQOk6JIShIDN+v//c8vLyF1pk/1RWWp1e2uYkY8AFTW+zQu376ft6lcPp0WoG3ZURYn6kdryHLVu24OxLF4O3H8TDDz+Mn/zkJ9iyZQvmzp0Lv9+P1157DXPnzjV3++KLL3DHHXfgwgsvxG/fXu9qd3AFYm+ss30RiDweBkXa8KtHHsCtt96KSy65BA0NDbj33nsxe/Zs7Ny5E/X19QiHw5g5cyZqa2uxatUqbNmyBfX19Rg/fjyuuOIKzJkzB5dffrkpxrZs2YJ7770XtbW1eOSRRzBx4kTE43G0traiurraY+yEp59+Grt378a0adMQCoUgSRICgQCqq6sxbdo0M0v8RGXlypW46aabcPPNN+Pll1/GM888g0cffRSnn346fv/732P8+PHHe4gjiueeew5vvPEGOOfo6uoCESEej+OTDZ8CwTzNophfnLafPRaRuAre2gDE2jlTklcocurdIT4MQT/QhOGYaGjK4j63Ed3zV6hdjUIYCrLDGJNGjRr1jzGZ/0otrAqwogow3b18MorEvliWstOD9vR1nAmAvKNOe51XrAWOB/IAIrCcEKTiseh28VUD+7n3KlsBCcR8UHa+h69+9au46qqr4Pf7sWDBApx22ml45pln8PDDDwPQHvKXXHKJuWc4HMasWbPw+OOP49Zlv+zxsfZHKGYrP+O1TY+Fof6ah5tRQR245557sG7dOqxatQpnn302XnjhBTz22GOIRCKYMmUKpkyZgqKiIjz33HMAgBtvvBHnnHMOpkyZgry87DX9Rgoffvgh5s6diyVLlmDx4sVYvnw5kskknnrqKZx11lnHe3gCGzt27MBpp2v1KH3jzzDfdwtCirZDbT2IwoKCJ7taj95P9vU2BScFmjAcHc2fck2f24jteVsIQ0HPYYyVllWOWR6OJa/nJeMlKVSmW66UE14gujNWM4qPPgsbKfO+RvwYk6Ae3YMCpR3hcNhz05qaGhzOP8VT9KlHvgSC+ZCKKgBfUBOTkssKqW+b2vk+gqddru0eaYPaXK+t5iDHAQClpaUIhUKmGMzJycGKFSscY/n0009x9dVXY8WKFbj+vkedx9oDuhOJ2ZJ9+oJXfwfe/zPq6urwyiuvYPv27Vi5ciXOO+88AFpJESLqVYauQKO9vR333nsvdu/ejXHjxuFrX/sa7rzzTnEuTxBisRhWrlyJO++5F5CTYEUV8JXW6C5l5++Nx8PgzfuRl5e7I9Z29FLhNj55YYzlS6HR0fxTF/a5jfieVVAjh4UwFPQOxtjkMWPGLG+JqZeibBJjAX3N4RNcHHrhEBN6BqXa0QR+9Euw3EKwwkpIhRVQm3ZoZUKKKsH8QZCSglTqjJkiJQVIEpiUuewGDx+Deng7AOD2229HaWkp9u3bh48//hglJSW45ZZbUF1djf3792P9+vXYtm0bcnJyMHPmTKxevRpz587Fp59+ikgkohWrDoTAcgs1C2QgFyyYB+XAJlx33XV4/f1PAQb86ZlfYtGiRfD5fMirnQPe0Ygp48pw6NAh5OXl4fzzz8cvfvELTJ06NW2869atw+LFi7F69Wqce9M9tk96J6AHUgBmah+AGRtIRLjrrrvw2muvYd68ebj22muxZMkSkQUrGJZwzrF3717s27cPr7/+On779HKw3AJIo6rACsr0xCfo9yYjSzwOtWUf/OBdclfr+US0/TgegmAA0IRhZTT/1K/1uY34ntVQI01CGAr6ht/vP6+guOTVsOqf4CubCOb3thacaIKRiIBkBJSMgfmCAJi2GkAgD/D5QYkuFKda0N5u/S4WL16M119/3dGOVFajZZ/6fGC+IEoDMlra2iEVVkIqm6C16csB8+tlf4y4QkWBemw3SgMpXHLJJZgwYQLuvvtuHDhwAG+++SYikQjGjx+POXPmYPbs2YhGo/jiiy9QUVFhxgLG43FIkoTXXnsNe/bsQUdHB/bv34+tW7fijDPOwNVXX43W1lYQEf72t79h3bp1SKVSOOuss3DXXXdhzpw5mDRpEoqL02OO7DQ0NODrX/867rjjDvyPny332OJ4Z5daK+LIX35kvnvw4EHU1NTg1ltvxcsvv3ycxiYQDA5EhPXr1+OCq28GpWKgaDvAGBDIhZRXBKl4NFgg17Y910QhcXA5Bt7WBJaKqUyOX6nIqb8dx0MRDCBCGGZGCMMhhDHG/H7/lWVlZU+0JH1TWclYsEBO9zv2gr4IS1IVQEkCwXxAVUDRNs3Nqirg0XaAq/jm7UvNFRSKi4uxb98+RKNRFBcXo7q6Gm1tbWhpaUFbWxuCwSAkSUIkEkEymUR+fj5mzZqFu+66C+3t7Th69Cjmz5+PsrIyPPHEE2Z8WjweRzihgAVycf937sa/vfAaGGOgVFxbkSIV1dZR5SqmTZuGLVu2OApNDxRtbW0IhUK9avvtt9/GokWLsGjRIvx1W4NWUDnNXXt8haFdDBq0trbiP/7jP3D//fdj6dKl+MMf/nAcRiYQDCyKouCTTz7BxYv+ARTRntksvxgsmA8pVKqXS0JaUpp2/+QgJQXedhgsGVODTL0tEY+tFOVnhheGMMw7dUGf24jvqQOPHBHCUNB/DIFYXl7+780JTENxFST9RjXQS4p1l7lKRFB2a4KhuLgYneEusFAJmC+An/zTPbjoootwySWXDGotNUXRFgXgnGPr1q1oaGjA559/jo0bN8Lv92PKlCkYPXo0IpEIGhsbsWvXLnz00UdoaGhARUXFoI0rG7Is49NPP8WaNWuwfv16rF27Fi+99BIWLkyPVwmcem6GmMr+ntNMzymrXcMtbYjCeDyOzz77DO+//z7++7//G5s3b8b8+fPx4IMP4uyzz+7neASC48uGDRtw3vxrwLtagEAOpFCJtvJNboFzCVMXZgyvnITa1gAkozwA9dZUIv5nIQiHJ5owrIjmnnJFn9tI1L8rhKFgYGGMsUAgsEDKLVipMn8BFY9hUp5WRDmTQHTfo7pbI5mIAOLeNdGYhNTOD7B27VozsWLjxo0iU1Jn27ZtqK+vB2MM7e3t5rqsnZ2d+PGPf4xx48bhsssuw9lnn42FCxdi1KhRPWrXEoqZhGF/nkN6TFQiAoqHQak4kIpj0tgKRCIRdHR0YPr06bj44ouxYMECXHrppcjPz+9HfwLB8YVzjtdffx033HoHSJUhFY+GVDQ63RuTRd9RMgq1rRFMSSgBkm9NJhL/JQTh8MYQhjmnXN7nNpL174FHjmYVhoyxKwE8CcAH4PdE9H/63OEQIRbcPI6QpspXASjy+/0XjC4IPNbc1HgxLxwtsZB3XbZurYmOVSEk8PaD4M37tH2LxmiB1XIC8OdACpXi2WefxapVqzB58mRceeWVnnXkRio/+MEP8N5775mvJ02ahDlz5iA3NxfPP/+8p3WwJ8i71zteZ7Yo9gYJxDko3g4KN6My5MfS25dixowZOOWUUzB27FiEQiFUVlaKbFjBsMFfexb4kXoQ5/CV1YAVlGjlwTKtwmP3lnAVFO8EbzsMn4Qooh3XcKI1CWEtGTkQmclGgwFjzAfgtwDmAzgEYANj7A0i+mLQOh0AhMXwBIMxduqYMWMeOdYZuZkVjmaseLTlAnHTzYLuPB4GJeOAmoTU0QhFUVBdXY27774bxcXF2LBhAwKBAM455xzccccdKCwsHKSjOjkhInzwwQd49NFHUVdXBwBgjEFV1UFZlYJzjqeeegrf+9EykJICC+ZqS8EVlmdcjYEUGbz9MCjehTyJY/bs2Vi6dCluvvlmlJWVDfgYBYIThR07duC0004Dcgvhq54BZfe6bvcJTDlPm0BFWkGdx5CXH6qPtR29log+H4IhC04gGGP5LL8smnPKvD63kdr7AXjkWEaLIWPsfAAPE9EC/fWDAEBEP+tzp0OAsBieYBDRbgC3Msa+X5af8z1/OPLN66+/PnTfffcVTZ48uVdtffvb38bTTz8NQHNOFhcXo7y8HK+++qoZn3fZZZehoKAA7777LkpKSlBeXo6SkhLk5eUhFAohGPQWJCMBxhhaW1tRV1eH2267DaNGjcK8efMGbamyZ599Fg8//DDeWPkyampqsGvXLrz11lt488038eKLL+Kqq65K2+fFF1/EsmXL8OrbdZg9ezZ8Ph+am5vR0dGBQCBgru8sEJysqKqKjRs3oqWlBfF4HAcPHsT+/fvx7rvaQiNVJQU43ANRuH//fnx34fmRP/3pT1GVqy81JyL/Ho13HR7s8QtOcPofMXAHY+w22+vfEdHv9P/HAWiwfXYIwFf62+FgIyyGJziMMX8wGFxcVlb249ra2nHLli0rX7BgQY+SQYgI27dvxzvvvIO//vWvWLNmjePzLVu2YM2aNdi0aRM6OzvR3t6O1tZWtLe3Ix6PIxaLYeLEiZgxYwZOP/10nH/++bj88ssR0GsxjgSICD/96U/x61//GlVVVXjooYfMjOru9rMLyH/+539GZWUlvvWtb6Ut33bs2DFs3boV99xzD5YvX47LLrvM8fm6detwwQUXYOfOnWn1E1taWnDOOecgEolAVVVEo1GUlZWBc45AIID3338fp5xySj/PgkAwtBAR1q5diz/+8Y946623UFVVherqauTm5qK6uhoTJ05EVVUVqqqqcNFFF8Hv97ZxcM5RV1eHn/3sZ227d+9uam9vfywej/8nEaWG+JAEJximxXDyV/vcRmrf37uzGP4DgAVEdLf++usAziWie/vc6RAghOFJBGNsRlVV1UOMsSsmTJjQ68AIIkIqlfKpqsoCgQAPBAJpUyUiYoao4ZyzWCzmUxRFUhSFdXZ2BhljmDx5svdSJMOcrq6uQEdHRzAWiwUYY+Tz+SgQCPCcnBw1JydHzcvLU4PBICcifPnll2lFDxljCIVCcnV1ddR4T1EUac+ePUUAUFRUlCotLU0CYLIss2Qy6Usmk75EIuGTZVmqrq6OFhQUpC29RUQsmUxKsixLnHOmqipTFEVqa2vLAYDJkyeHva61QHCiEY1G/V1dXYFIJBKQJAmjRo1KFhYWyn39/h4+fNjHOX//0KFD/0JE2wZ6vIKTF0MYBmsv7nMbqX3/DxRtHnauZCEMT0IYYwEAIoBMIBAIshMmotjxHoTgxEMThqXRYO2FfW4jtW8dKNqSTRj6AXwJ4DIAjQA2ALj1RF85R8QYnoToC7aLNToFAoFAIOgzNBAxhplbJ1IYY98FsBpauZpnT3RRCAhhKBAIBAKBYIRC/aob273HlYjeBvB2PzoZcoQwFAgEAoFAMPKgwbUYnqwIYSgQCAQCgWBk0i9hODxzNAZvAVyBQCAQCAQCwUmFsBgKBAKBQCAYgVA/YwyHJ0IYCgQCgUAgGJn0x5U8TMv9CWEoEAgEAoFgBNLf5BMhDAUCgUAgEAiGEcKV7EYIQ4FAIBAIBCMTYTFMQwhDgUAgEAgEIw4iAvUrxnDgxnIiIYShQCAQCASCEYqwGLoRdQwFAoFAIBAIBACExVAgEAgEAsGIRCyJ54UQhgKBQCAQCEYgolyNF8KVLBAIBAKBYKQRBwEkJ/u0M8kpEFcBoHNAR3UCIIShQCAQCASCEQURkVQ6FrytsU/787ZGSCVjQf1Kaz4xEcJQIBAIBALBiEM9/CUDV0GJaK/2o2QMUGXwI3uGpYYalgclEAgEAoFA0B0UC8/krYdAPVz3mIjAWxtB8a6zqac7nWQIYSgQCAQCgWBEQkTbEMgBxXoWKkjxMODzg4g2DfLQjhtCGAoEAoFAIBixUFdrNW9r6tZqSETgbYdBkbaJQzOy44MQhgKBQCAQCEYsRNTIQkWgcEv27bpawfIKQUQHhmhoxwUhDAUCgUAgEIxoqP1IEQ83GyVo0j/nHLzzGKjjaMkQD23IEcJQIBAIBALBiIaIuqSiCvCOo96fdx6FVFgGIuoY2pENPWLlE4FAIBAIBCMe3nIwiGB+iorKwfxB831SZPBIO5CK5xzH4Q0ZwmIoEAgEAoFgxENEslRaBd7W5HiftzdBKhkDIkodp6ENKUIYCgQCgUAgEADgR+olyAlQKg4AoJT2Pz+6b8TopRFzoAKBQCAQCATZICKiROQ83qotlcfbGoFE5OLhWszaCzaCjlUgEAgEAoGgW6SCUmLBPFAqBh5pZ8d7PEOJEIYCgUAgEAgENhhjtWBSPYhPI6Jdx3s8Q4kQhgKBQCAQCAQCACLGUCAQCAQCgUCgI4ShQCAQCAQCgQCAEIYCgUAgEAgEAh0hDAUCgUAgEAgEAIQwFAgEAoFAIBDoCGEoEAgEAoFAIAAA/H/R0qlJ44i7qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_global_temp_in_degrees_celcius(SST_mom6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What to do when it goes wrong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model agnostic function worked flawlessly with two different ocean data sets, after the units were corrected in the MOM5 data. What about some ice data?\n",
    "\n",
    "Using the same experiment from which the first `SST` data was obtained, load the ice air temperature variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment = '025deg_jra55v13_iaf_gmredi6'\n",
    "variable = 'Tair_m'\n",
    "ice_air_temp = cc.querying.getvar(experiment, variable, session, frequency='1 monthly', n=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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       "  <text x=\"7.474299\" y=\"117.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,117.474299)\">12</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-a3e9c919-d04b-4441-919a-450e19f93fef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3e9c919-d04b-4441-919a-450e19f93fef' class='xr-section-summary' >Coordinates: <span>(5)</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 class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-02-01 ... 1959-01-01</div><input id='attrs-f0240545-5723-4c6d-8f2f-dc07b1edfb06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0240545-5723-4c6d-8f2f-dc07b1edfb06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1251e1f-6586-4390-be2b-d20322b65b91' class='xr-var-data-in' type='checkbox'><label for='data-f1251e1f-6586-4390-be2b-d20322b65b91' 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>model time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-02-01T00:00:00.000000000&#x27;, &#x27;1958-03-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-04-01T00:00:00.000000000&#x27;, &#x27;1958-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-06-01T00:00:00.000000000&#x27;, &#x27;1958-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-08-01T00:00:00.000000000&#x27;, &#x27;1958-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-10-01T00:00:00.000000000&#x27;, &#x27;1958-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-12-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLON</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=(1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-24c3f451-9263-454e-a281-177f934dc5ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24c3f451-9263-454e-a281-177f934dc5ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fd4a0ee-32c3-4820-ab55-8481a903a5da' class='xr-var-data-in' type='checkbox'><label for='data-7fd4a0ee-32c3-4820-ab55-8481a903a5da' 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>T grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 55 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</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=(1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-ec1a88fd-b1c9-41fa-b10b-df96c79135dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec1a88fd-b1c9-41fa-b10b-df96c79135dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22e9af5a-ca2d-48c1-a2d9-b758a5da7f94' class='xr-var-data-in' type='checkbox'><label for='data-22e9af5a-ca2d-48c1-a2d9-b758a5da7f94' 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>T grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 55 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ULON</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=(1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-d1e8447f-5a5f-4d50-b8f3-f9dd451b7629' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1e8447f-5a5f-4d50-b8f3-f9dd451b7629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e2107b-4487-4f52-995c-fd2f08229976' class='xr-var-data-in' type='checkbox'><label for='data-68e2107b-4487-4f52-995c-fd2f08229976' 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>U grid center longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 55 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">1080</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ULAT</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=(1080, 1440), meta=np.ndarray&gt;</div><input id='attrs-dbe1e4e8-6407-471c-a604-bc93036894a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbe1e4e8-6407-471c-a604-bc93036894a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac7e2903-3a8c-429d-8ec9-169f3e0a4114' class='xr-var-data-in' type='checkbox'><label for='data-ac7e2903-3a8c-429d-8ec9-169f3e0a4114' 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>U grid center latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>Latitude of NE corner of T grid cell</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                        <td> (1080, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 55 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">1080</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b5241788-b105-4a6d-8c57-c4adedbe2205' class='xr-section-summary-in' type='checkbox'  ><label for='section-b5241788-b105-4a6d-8c57-c4adedbe2205' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aac8f638-47c8-4715-b18e-42af13acc0f2' class='xr-index-data-in' type='checkbox'/><label for='index-aac8f638-47c8-4715-b18e-42af13acc0f2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1958-02-01&#x27;, &#x27;1958-03-01&#x27;, &#x27;1958-04-01&#x27;, &#x27;1958-05-01&#x27;,\n",
       "               &#x27;1958-06-01&#x27;, &#x27;1958-07-01&#x27;, &#x27;1958-08-01&#x27;, &#x27;1958-09-01&#x27;,\n",
       "               &#x27;1958-10-01&#x27;, &#x27;1958-11-01&#x27;, &#x27;1958-12-01&#x27;, &#x27;1959-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e64a29de-9857-42b2-97ea-e00c9e3fc3df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e64a29de-9857-42b2-97ea-e00c9e3fc3df' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd><dt><span>long_name :</span></dt><dd>air temperature</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-01.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-02.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-03.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-04.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-05.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-06.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-07.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-08.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-09.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-10.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-11.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-12.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Tair_m' (time: 12, nj: 1080, ni: 1440)>\n",
       "dask.array<concatenate, shape=(12, 1080, 1440), dtype=float32, chunksize=(1, 1080, 1440), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1958-02-01 1958-03-01 ... 1959-01-01\n",
       "    TLON     (nj, ni) float32 dask.array<chunksize=(1080, 1440), meta=np.ndarray>\n",
       "    TLAT     (nj, ni) float32 dask.array<chunksize=(1080, 1440), meta=np.ndarray>\n",
       "    ULON     (nj, ni) float32 dask.array<chunksize=(1080, 1440), meta=np.ndarray>\n",
       "    ULAT     (nj, ni) float32 dask.array<chunksize=(1080, 1440), meta=np.ndarray>\n",
       "Dimensions without coordinates: nj, ni\n",
       "Attributes:\n",
       "    units:          C\n",
       "    long_name:      air temperature\n",
       "    cell_measures:  area: tarea\n",
       "    cell_methods:   time: mean\n",
       "    time_rep:       averaged\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice_air_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And try the generic routine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "skip-execution"
    ]
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"Receive multiple variables for key 'longitude': ['ULON', 'TLON']. Expected only one. Please pass a list ['longitude'] instead to get all variables matching 'longitude'.\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_global_temp_in_degrees_celcius\u001b[49m\u001b[43m(\u001b[49m\u001b[43mice_air_temp\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[14], line 12\u001b[0m, in \u001b[0;36mplot_global_temp_in_degrees_celcius\u001b[0;34m(da)\u001b[0m\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m     10\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39maxes(projection\u001b[38;5;241m=\u001b[39mccrs\u001b[38;5;241m.\u001b[39mRobinson())\n\u001b[0;32m---> 12\u001b[0m \u001b[43mda_time_mean\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m     \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m     \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mccrs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPlateCarree\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m     \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mboth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m     \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthermal\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m ax\u001b[38;5;241m.\u001b[39mcoastlines()\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:907\u001b[0m, in \u001b[0;36m_CFWrappedPlotMethods.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    898\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    899\u001b[0m \u001b[38;5;124;03mAllows .plot()\u001b[39;00m\n\u001b[1;32m    900\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    901\u001b[0m plot \u001b[38;5;241m=\u001b[39m _getattr(\n\u001b[1;32m    902\u001b[0m     obj\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj,\n\u001b[1;32m    903\u001b[0m     attr\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplot\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    904\u001b[0m     accessor\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccessor,\n\u001b[1;32m    905\u001b[0m     key_mappers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m\u001b[38;5;241m.\u001b[39mfromkeys(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_keys, (_single(_get_all),)),\n\u001b[1;32m    906\u001b[0m )\n\u001b[0;32m--> 907\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_decorator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:890\u001b[0m, in \u001b[0;36m_CFWrappedPlotMethods._plot_decorator.<locals>._plot_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_x_or_y(kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, skip\u001b[38;5;241m=\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m    889\u001b[0m \u001b[38;5;66;03m# Now set some nice properties\u001b[39;00m\n\u001b[0;32m--> 890\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_axis_props\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    891\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_axis_props(kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:854\u001b[0m, in \u001b[0;36m_CFWrappedPlotMethods._set_axis_props\u001b[0;34m(self, kwargs, key)\u001b[0m\n\u001b[1;32m    852\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value:\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccessor\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m--> 854\u001b[0m         var \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccessor\u001b[49m\u001b[43m[\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    856\u001b[0m         var \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj[value]\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:2516\u001b[0m, in \u001b[0;36mCFDataArrayAccessor.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2511\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m   2512\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[1;32m   2513\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot use a list of keys with DataArrays. Expected a single string. Received \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2514\u001b[0m     )\n\u001b[0;32m-> 2516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_getitem\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:668\u001b[0m, in \u001b[0;36m_getitem\u001b[0;34m(accessor, key, skip)\u001b[0m\n\u001b[1;32m    666\u001b[0m names \u001b[38;5;241m=\u001b[39m _get_all(obj, k)\n\u001b[1;32m    667\u001b[0m names \u001b[38;5;241m=\u001b[39m drop_bounds(names)\n\u001b[0;32m--> 668\u001b[0m \u001b[43mcheck_results\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    669\u001b[0m successful[k] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mbool\u001b[39m(names)\n\u001b[1;32m    670\u001b[0m coords\u001b[38;5;241m.\u001b[39mextend(names)\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:647\u001b[0m, in \u001b[0;36m_getitem.<locals>.check_results\u001b[0;34m(names, key)\u001b[0m\n\u001b[1;32m    645\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcheck_results\u001b[39m(names, key):\n\u001b[1;32m    646\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m scalar_key \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 647\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[1;32m    648\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceive multiple variables for key \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnames\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    649\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected only one. Please pass a list [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m] \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    650\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minstead to get all variables matching \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    651\u001b[0m         )\n",
      "\u001b[0;31mKeyError\u001b[0m: \"Receive multiple variables for key 'longitude': ['ULON', 'TLON']. Expected only one. Please pass a list ['longitude'] instead to get all variables matching 'longitude'.\""
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_global_temp_in_degrees_celcius(ice_air_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error message is\n",
    "```\n",
    "\"Receive multiple variables for key 'longitude': ['TLON', 'ULON']. Expected only one. Please pass a list ['longitude'] instead to get all variables matching 'longitude'.\"\n",
    "```\n",
    "This suggests that `cf_xarray` has found multiple longitude coordinates `TLON` and `ULON` and doesn't know how to resolve this automatically. \n",
    "\n",
    "Inspecting the `cf` information doesn't show multiple axes like it [does in the documentation](https://cf-xarray.readthedocs.io/en/latest/examples/introduction.html#what-attributes-have-been-discovered):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes:   X, Y, Z, T: n/a\n",
       "\n",
       "- CF Coordinates:   longitude: ['TLON']\n",
       "                    latitude: ['TLAT']\n",
       "                    vertical, time: n/a\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   n/a\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice_air_temp.cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[This is a bug](https://github.com/xarray-contrib/cf-xarray/issues/396), taking the mean alters the coordinates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "- CF Axes:   X, Y, Z, T: n/a\n",
       "\n",
       "- CF Coordinates:   longitude: ['TLON', 'ULON']\n",
       "                    latitude: ['TLAT', 'ULAT']\n",
       "                    vertical, time: n/a\n",
       "\n",
       "- Cell Measures:   area, volume: n/a\n",
       "\n",
       "- Standard Names:   n/a\n",
       "\n",
       "- Bounds:   n/a"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice_air_temp.cf.mean('time').cf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the solution is to drop the redundant velocity grid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ice_air_temp = ice_air_temp.drop_vars(['ULON', 'ULAT'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now trying to plot again using the generic function and there is another error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "skip-execution"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y arguments to pcolormesh cannot have non-finite values or be of type numpy.ma.core.MaskedArray with masked values",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_global_temp_in_degrees_celcius\u001b[49m\u001b[43m(\u001b[49m\u001b[43mice_air_temp\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[14], line 12\u001b[0m, in \u001b[0;36mplot_global_temp_in_degrees_celcius\u001b[0;34m(da)\u001b[0m\n\u001b[1;32m      9\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m     10\u001b[0m ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39maxes(projection\u001b[38;5;241m=\u001b[39mccrs\u001b[38;5;241m.\u001b[39mRobinson())\n\u001b[0;32m---> 12\u001b[0m \u001b[43mda_time_mean\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m     \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m     \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mccrs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPlateCarree\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m     \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m30\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mboth\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m     \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthermal\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m ax\u001b[38;5;241m.\u001b[39mcoastlines()\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:907\u001b[0m, in \u001b[0;36m_CFWrappedPlotMethods.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    898\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    899\u001b[0m \u001b[38;5;124;03mAllows .plot()\u001b[39;00m\n\u001b[1;32m    900\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    901\u001b[0m plot \u001b[38;5;241m=\u001b[39m _getattr(\n\u001b[1;32m    902\u001b[0m     obj\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_obj,\n\u001b[1;32m    903\u001b[0m     attr\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplot\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    904\u001b[0m     accessor\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccessor,\n\u001b[1;32m    905\u001b[0m     key_mappers\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m\u001b[38;5;241m.\u001b[39mfromkeys(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_keys, (_single(_get_all),)),\n\u001b[1;32m    906\u001b[0m )\n\u001b[0;32m--> 907\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_decorator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mplot\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:893\u001b[0m, in \u001b[0;36m_CFWrappedPlotMethods._plot_decorator.<locals>._plot_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    890\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_axis_props(kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    891\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_axis_props(kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 893\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cf_xarray/accessor.py:598\u001b[0m, in \u001b[0;36m_getattr.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    594\u001b[0m posargs, arguments \u001b[38;5;241m=\u001b[39m accessor\u001b[38;5;241m.\u001b[39m_process_signature(\n\u001b[1;32m    595\u001b[0m     func, args, kwargs, key_mappers\n\u001b[1;32m    596\u001b[0m )\n\u001b[1;32m    597\u001b[0m final_func \u001b[38;5;241m=\u001b[39m extra_decorator(func) \u001b[38;5;28;01mif\u001b[39;00m extra_decorator \u001b[38;5;28;01melse\u001b[39;00m func\n\u001b[0;32m--> 598\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfinal_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mposargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    599\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wrap_classes \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, _WRAPPED_CLASSES):\n\u001b[1;32m    600\u001b[0m     result \u001b[38;5;241m=\u001b[39m _CFWrappedClass(result, accessor)\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/xarray/plot/accessor.py:46\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(dataarray_plot\u001b[38;5;241m.\u001b[39mplot, assigned\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__doc__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__annotations__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m---> 46\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdataarray_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_da\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/xarray/plot/dataarray_plot.py:312\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m    308\u001b[0m     plotfunc \u001b[38;5;241m=\u001b[39m hist\n\u001b[1;32m    310\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124max\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m ax\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplotfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdarray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/xarray/plot/dataarray_plot.py:1613\u001b[0m, in \u001b[0;36m_plot2d.<locals>.newplotfunc\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1609\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mplt.imshow\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms `aspect` kwarg is not available in xarray\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1611\u001b[0m ax \u001b[38;5;241m=\u001b[39m get_axis(figsize, size, aspect, ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msubplot_kws)\n\u001b[0;32m-> 1613\u001b[0m primitive \u001b[38;5;241m=\u001b[39m \u001b[43mplotfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1614\u001b[0m \u001b[43m    \u001b[49m\u001b[43mxplt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1615\u001b[0m \u001b[43m    \u001b[49m\u001b[43myplt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1616\u001b[0m \u001b[43m    \u001b[49m\u001b[43mzval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1617\u001b[0m \u001b[43m    \u001b[49m\u001b[43max\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1618\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcmap\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1619\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvmin\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1620\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mvmax\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1621\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap_params\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnorm\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1622\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1623\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1625\u001b[0m \u001b[38;5;66;03m# Label the plot with metadata\u001b[39;00m\n\u001b[1;32m   1626\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m add_labels:\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/xarray/plot/dataarray_plot.py:2340\u001b[0m, in \u001b[0;36mpcolormesh\u001b[0;34m(x, y, z, ax, xscale, yscale, infer_intervals, **kwargs)\u001b[0m\n\u001b[1;32m   2337\u001b[0m         y \u001b[38;5;241m=\u001b[39m _infer_interval_breaks(y, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, scale\u001b[38;5;241m=\u001b[39myscale)\n\u001b[1;32m   2339\u001b[0m ax\u001b[38;5;241m.\u001b[39mgrid(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m-> 2340\u001b[0m primitive \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2342\u001b[0m \u001b[38;5;66;03m# by default, pcolormesh picks \"round\" values for bounds\u001b[39;00m\n\u001b[1;32m   2343\u001b[0m \u001b[38;5;66;03m# this results in ugly looking plots with lots of surrounding whitespace\u001b[39;00m\n\u001b[1;32m   2344\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(ax, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprojection\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   2345\u001b[0m     \u001b[38;5;66;03m# not a cartopy geoaxis\u001b[39;00m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:318\u001b[0m, in \u001b[0;36m_add_transform.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    313\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid transform: Spherical \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    314\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not supported - consider using \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    315\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlateCarree/RotatedPole.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    317\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 318\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/cartopy/mpl/geoaxes.py:1797\u001b[0m, in \u001b[0;36mGeoAxes.pcolormesh\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1794\u001b[0m \u001b[38;5;66;03m# Add in an argument checker to handle Matplotlib's potential\u001b[39;00m\n\u001b[1;32m   1795\u001b[0m \u001b[38;5;66;03m# interpolation when coordinate wraps are involved\u001b[39;00m\n\u001b[1;32m   1796\u001b[0m args, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_args(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1797\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mmatplotlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpcolormesh\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1798\u001b[0m \u001b[38;5;66;03m# Wrap the quadrilaterals if necessary\u001b[39;00m\n\u001b[1;32m   1799\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wrap_quadmesh(result, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/matplotlib/__init__.py:1414\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1411\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1412\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1413\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1414\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1416\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1417\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1418\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/matplotlib/axes/_axes.py:6064\u001b[0m, in \u001b[0;36mAxes.pcolormesh\u001b[0;34m(self, alpha, norm, cmap, vmin, vmax, shading, antialiased, *args, **kwargs)\u001b[0m\n\u001b[1;32m   6061\u001b[0m shading \u001b[38;5;241m=\u001b[39m shading\u001b[38;5;241m.\u001b[39mlower()\n\u001b[1;32m   6062\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medgecolors\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 6064\u001b[0m X, Y, C, shading \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pcolorargs\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpcolormesh\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   6065\u001b[0m \u001b[43m                                    \u001b[49m\u001b[43mshading\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshading\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6066\u001b[0m coords \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mstack([X, Y], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m   6067\u001b[0m \u001b[38;5;66;03m# convert to one dimensional array\u001b[39;00m\n",
      "File \u001b[0;32m/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/matplotlib/axes/_axes.py:5539\u001b[0m, in \u001b[0;36mAxes._pcolorargs\u001b[0;34m(self, funcname, shading, *args, **kwargs)\u001b[0m\n\u001b[1;32m   5537\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m funcname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpcolormesh\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m   5538\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mis_masked(X) \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mis_masked(Y):\n\u001b[0;32m-> 5539\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   5540\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx and y arguments to pcolormesh cannot have \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   5541\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnon-finite values or be of type \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   5542\u001b[0m             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumpy.ma.core.MaskedArray with masked values\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   5543\u001b[0m     \u001b[38;5;66;03m# safe_masked_invalid() returns an ndarray for dtypes other\u001b[39;00m\n\u001b[1;32m   5544\u001b[0m     \u001b[38;5;66;03m# than floating point.\u001b[39;00m\n\u001b[1;32m   5545\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(X, np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mcore\u001b[38;5;241m.\u001b[39mMaskedArray):\n",
      "\u001b[0;31mValueError\u001b[0m: x and y arguments to pcolormesh cannot have non-finite values or be of type numpy.ma.core.MaskedArray with masked values"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_global_temp_in_degrees_celcius(ice_air_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This error:\n",
    "```\n",
    "ValueError: x and y arguments to pcolormesh cannot have non-finite values or be of type numpy.ma.core.MaskedArray with masked values\n",
    "```\n",
    "is because there are `NaN`'s in the coordinate variables, as [explained in the plotting with cartopy tutorial](https://cosima-recipes.readthedocs.io/en/latest/tutorials/Making_Maps_with_Cartopy.html#fixing-the-tripole).\n",
    "\n",
    "By following the instructions in that tutorial and the [Spatial selection with tripolar ACCESS-OM2 grid notebook](https://cosima-recipes.readthedocs.io/en/latest/documented_examples/Spatial_selection.html#gallery-documented-examples-spatial-selection-ipynb) the coordinates can be fixed by replacing them with coordinates from the ice grid input file. It requires some work, the dimensions must be renamed to match, and coordinates converted from radians to degrees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "  color: var(--xr-disabled-color);\n",
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       "\n",
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       "\n",
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       ".xr-section-inline-details {\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-dim-list li {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;Tair_m&#x27; (time: 12, nj: 1080, ni: 1440)&gt;\n",
       "dask.array&lt;concatenate, shape=(12, 1080, 1440), dtype=float32, chunksize=(1, 1080, 1440), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1958-02-01 1958-03-01 ... 1959-01-01\n",
       "    TLON     (nj, ni) float64 [degrees_east] -279.9 -279.6 -279.4 ... 80.0 80.0\n",
       "    TLAT     (nj, ni) float64 [degrees_north] -81.08 -81.08 ... 65.13 65.03\n",
       "Dimensions without coordinates: nj, ni\n",
       "Attributes:\n",
       "    units:          C\n",
       "    long_name:      air temperature\n",
       "    cell_measures:  area: tarea\n",
       "    cell_methods:   time: mean\n",
       "    time_rep:       averaged\n",
       "    ncfiles:        [&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'Tair_m'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 12</li><li><span>nj</span>: 1080</li><li><span>ni</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6fef4a11-0f28-442b-8bb8-49ead13504a5' class='xr-array-in' type='checkbox' checked><label for='section-6fef4a11-0f28-442b-8bb8-49ead13504a5' 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>dask.array&lt;chunksize=(1, 1080, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 71.19 MiB </td>\n",
       "                        <td> 5.93 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (1, 1080, 1440) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "       &#x27;1958-04-01T00:00:00.000000000&#x27;, &#x27;1958-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-06-01T00:00:00.000000000&#x27;, &#x27;1958-07-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-08-01T00:00:00.000000000&#x27;, &#x27;1958-09-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-10-01T00:00:00.000000000&#x27;, &#x27;1958-11-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-12-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLON</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>[degrees_east] -279.9 ... 80.0</div><input id='attrs-bb1e3cd5-3e47-4972-a7d8-4ea5e7d9c0d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb1e3cd5-3e47-4972-a7d8-4ea5e7d9c0d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-231fc97c-92a8-45aa-91ac-db1ee760f104' class='xr-var-data-in' type='checkbox'><label for='data-231fc97c-92a8-45aa-91ac-db1ee760f104' 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>title :</span></dt><dd>Longitude of T points</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[-279.875 -279.625 -279.375 ... 79.375 79.625 79.875]<br> [-279.875 -279.625 -279.375 ... 79.375 79.625 79.875]<br> [-279.875 -279.625 -279.375 ... 79.375 79.625 79.875]<br> ...<br> [-279.99740600545 -279.9922179176187 -279.98702953357895 ...<br>  79.98702953357898 79.99221791761875 79.99740600544997]<br> [-279.9984438186694 -279.9953313967614 -279.9922187971038 ...<br>  79.99221879710382 79.99533139676142 79.99844381866941]<br> [-279.9994817902001 -279.9984453508684 -279.99740885233774 ...<br>  79.99740885233776 79.99844535086841 79.99948179020012]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>degrees_east</td></tr></tbody></table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TLAT</span></div><div class='xr-var-dims'>(nj, ni)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>[degrees_north] -81.08 ... 65.03</div><input id='attrs-735b77f2-cad3-4b4f-bf14-b8d0952562e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-735b77f2-cad3-4b4f-bf14-b8d0952562e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-404d4fbd-61ff-49fa-9e27-12f1348c9156' class='xr-var-data-in' type='checkbox'><label for='data-404d4fbd-61ff-49fa-9e27-12f1348c9156' 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>title :</span></dt><dd>Latitude of T points</dd></dl></div><div class='xr-var-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[-81.0770008338366 -81.0770008338366 -81.0770008338366 ...<br>  -81.0770008338366 -81.0770008338366 -81.0770008338366]<br> [-80.9714022446317 -80.9714022446317 -80.9714022446317 ...<br>  -80.9714022446317 -80.9714022446317 -80.9714022446317]<br> [-80.8658036554268 -80.8658036554268 -80.8658036554268 ...<br>  -80.8658036554268 -80.8658036554268 -80.8658036554268]<br> ...<br> [65.02595119174076 65.1312730330055 65.23618183089523 ...<br>  65.23618183089523 65.1312730330055 65.02595119174076]<br> [65.02595846678675 65.13129477265242 65.23621792273246 ...<br>  65.23621792273246 65.13129477265242 65.02595846678675]<br> [65.02596210326624 65.13130563935326 65.23623596345988 ...<br>  65.23623596345988 65.13130563935326 65.02596210326624]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>degrees_north</td></tr></tbody></table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ef0ef56-c5ad-406a-9c06-c500bc94604d' class='xr-section-summary-in' type='checkbox'  ><label for='section-7ef0ef56-c5ad-406a-9c06-c500bc94604d' class='xr-section-summary' >Indexes: <span>(1)</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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a7cbb91b-9609-4af8-a313-94c3a1db584a' class='xr-index-data-in' type='checkbox'/><label for='index-a7cbb91b-9609-4af8-a313-94c3a1db584a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1958-02-01&#x27;, &#x27;1958-03-01&#x27;, &#x27;1958-04-01&#x27;, &#x27;1958-05-01&#x27;,\n",
       "               &#x27;1958-06-01&#x27;, &#x27;1958-07-01&#x27;, &#x27;1958-08-01&#x27;, &#x27;1958-09-01&#x27;,\n",
       "               &#x27;1958-10-01&#x27;, &#x27;1958-11-01&#x27;, &#x27;1958-12-01&#x27;, &#x27;1959-01-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1a50924a-f5f6-4b89-8d66-8236a1274383' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a50924a-f5f6-4b89-8d66-8236a1274383' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>C</dd><dt><span>long_name :</span></dt><dd>air temperature</dd><dt><span>cell_measures :</span></dt><dd>area: tarea</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>time_rep :</span></dt><dd>averaged</dd><dt><span>ncfiles :</span></dt><dd>[&#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-01.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-02.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-03.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-04.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-05.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-06.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-07.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-08.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-09.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-10.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-11.nc&#x27;, &#x27;/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_iaf_gmredi6/output000/ice/OUTPUT/iceh.1958-12.nc&#x27;]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'Tair_m' (time: 12, nj: 1080, ni: 1440)>\n",
       "dask.array<concatenate, shape=(12, 1080, 1440), dtype=float32, chunksize=(1, 1080, 1440), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1958-02-01 1958-03-01 ... 1959-01-01\n",
       "    TLON     (nj, ni) float64 [degrees_east] -279.9 -279.6 -279.4 ... 80.0 80.0\n",
       "    TLAT     (nj, ni) float64 [degrees_north] -81.08 -81.08 ... 65.13 65.03\n",
       "Dimensions without coordinates: nj, ni\n",
       "Attributes:\n",
       "    units:          C\n",
       "    long_name:      air temperature\n",
       "    cell_measures:  area: tarea\n",
       "    cell_methods:   time: mean\n",
       "    time_rep:       averaged\n",
       "    ncfiles:        ['/g/data/hh5/tmp/cosima/access-om2-025/025deg_jra55v13_i..."
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice_grid = xr.open_dataset('/g/data/ik11/inputs/access-om2/input_eee21b65/cice_025deg/grid.nc').rename({'ny': 'nj', 'nx': 'ni'})\n",
    "ice_grid = ice_grid.pint.quantify()\n",
    "\n",
    "ice_air_temp = ice_air_temp.assign_coords({'TLON': ice_grid.tlon.pint.to('degrees_E'), 'TLAT': ice_grid.tlat.pint.to('degrees_N')})\n",
    "ice_air_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, the generic plotting routine works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_global_temp_in_degrees_celcius(ice_air_temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One more step is to modify the original routine to take the vertical range as an argument, so it is more generally useful:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_global_temp_in_degrees_celcius(da, vmin=-2, vmax=30):\n",
    "    # Take the time mean of da and plot a global temperature field in a Robinson projection\n",
    "    # \n",
    "    # Input DataArray (da) should be a 3D array of latitude, longitude and time.\n",
    "    \n",
    "    da = da.pint.quantify().pint.to('C')\n",
    "    da_time_mean = da.cf.mean('time')\n",
    "    \n",
    "    plt.figure(figsize=(12, 6))\n",
    "    ax = plt.axes(projection=ccrs.Robinson())\n",
    "\n",
    "    da_time_mean.cf.plot(ax=ax,\n",
    "                         x='longitude', y='latitude', \n",
    "                         transform=ccrs.PlateCarree(),\n",
    "                         vmin=vmin, vmax=vmax, extend='both',\n",
    "                         cmap=cm.cm.thermal)\n",
    "\n",
    "    ax.coastlines();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By specifying default values for the arguments it is completely backwards compatible, we have lost no functionality, but the ice air temperature can now be plotted with a range that better suits the range of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_global_temp_in_degrees_celcius(ice_air_temp, vmin=-30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model specific code to take a time mean and plot the data was converted to a model agnostic function with no loss of functionality.\n",
    "\n",
    "The same function can be used with a wide range CF compliant data.\n",
    "\n",
    "In some cases the input data will need to be modified if it is not sufficiently compliant, or non-conforming in some way, such as the ice data above with `NaN`'s in the coordinate. It is better to modify the data to be more compliant and higher quality, and use generic tools, than have multiple code versions to account for the vagaries or problems with individual datasets. \n",
    "\n",
    "Ideally those improvements can be incorporated into future versions of the data at source, in post-processing, or in some utility functions for transforming a class of non-conforming data."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python [conda env:analysis3]",
   "language": "python",
   "name": "conda-env-analysis3-py"
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   "codemirror_mode": {
    "name": "ipython",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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