examples/run_eddytools_example_MITgcm_OW.ipynb

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   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from matplotlib import pyplot as plt\n",
    "import xmitgcm as xmit\n",
    "import xgcm\n",
    "import pickle\n",
    "import eddytools as et\n",
    "from cmocean import cm\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=RuntimeWarning)\n",
    "# Obviously it is not a great idea to ignore warnings, however there are quite many\n",
    "# RuntimeWarnings because of division by 0 in some parts of this notebook. To keep\n",
    "# the instructive nature of this example notebook, these warnings are ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment the following cell to use `dask distributed` (i.e for use with `dask_bags` in the detection).  \n",
    "__This is experimental not thouroughly tested__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from dask.distributed import Client, LocalCluster\n",
    "#cluster = LocalCluster(n_workers=1, threads_per_worker=4)\n",
    "#client = Client(cluster)\n",
    "#client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paths\n",
    "datapath = '/home/jan/Data/MITgcm_test_data/'\n",
    "gridpath = '/home/jan/Data/MITgcm_test_data/grid/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading data\n",
    "In case the MITgcm output is in binary format (`*.meta` and `*.data`), use `xmitgcm` to load the data. If the data is in `netCDF` format, make sure that you define all the necessary grid information for `xgcm` to work properly!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "start = 1555200\n",
    "end = 1580760\n",
    "step = 360\n",
    "dt = 1200.\n",
    "data = xmit.open_mdsdataset(datapath, iters=list(np.arange(start, end + 1, step)), delta_t=dt,\n",
    "                            ignore_unknown_vars=True, grid_dir=gridpath, \n",
    "                            geometry=\"cartesian\", calendar=\"360_day\", ref_date=\"0000-12-28 12:00:00\", \n",
    "                            prefix=\"3D_diags_5d\")\n",
    "data = data.chunk({'time': 1, 'Z': 55, 'Zl': 55, 'Zu': 55, 'Zp1': 56, 'XC': 250, 'XG': 250, 'YC': 300, 'YG': 300})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define metrics for xgcm (not strictly necessary)\n",
    "data['drW'] = data.hFacW * data.drF #vertical cell size at u point\n",
    "data['drS'] = data.hFacS * data.drF #vertical cell size at v point\n",
    "data['drC'] = data.hFacC * data.drF #vertical cell size at tracer point\n",
    "data = data.set_coords([\"drW\", \"drS\", \"drC\"])\n",
    "\n",
    "metrics = {\n",
    "    ('X'): ['dxC', 'dxG', 'dxF', 'dxV'], # X distances\n",
    "    ('Y'): ['dyC', 'dyG', 'dyF', 'dyU'], # Y distances\n",
    "    ('Z'): ['drF', 'drW', 'drS', 'drC'], # Z distances\n",
    "    ('X', 'Y'): ['rAw', 'rAs', 'rA', 'rAz'] # Areas in x-y plane\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (XC: 250, XG: 250, YC: 300, YG: 300, Z: 55, Zl: 55, Zp1: 56, Zu: 55, time: 72)\n",
       "Coordinates: (12/36)\n",
       "  * XC        (XC) &gt;f4 5e+03 1.5e+04 2.5e+04 ... 2.475e+06 2.485e+06 2.495e+06\n",
       "  * YC        (YC) &gt;f4 5e+03 1.5e+04 2.5e+04 ... 2.975e+06 2.985e+06 2.995e+06\n",
       "  * XG        (XG) &gt;f4 0.0 1e+04 2e+04 3e+04 ... 2.47e+06 2.48e+06 2.49e+06\n",
       "  * YG        (YG) &gt;f4 0.0 1e+04 2e+04 3e+04 ... 2.97e+06 2.98e+06 2.99e+06\n",
       "  * Z         (Z) &gt;f4 -1.065 -3.345 -5.948 ... -3.728e+03 -3.877e+03 -4.027e+03\n",
       "  * Zp1       (Zp1) &gt;f4 0.0 -2.13 -4.56 ... -3.803e+03 -3.952e+03 -4.102e+03\n",
       "    ...        ...\n",
       "    dxV       (YG, XG) &gt;f4 dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;\n",
       "    dyU       (YG, XG) &gt;f4 dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;\n",
       "    iter      (time) int64 dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
       "  * time      (time) object 0060-12-28 12:00:00 ... 0061-12-23 12:00:00\n",
       "    drW       (Z, YC, XG) float32 dask.array&lt;chunksize=(55, 300, 250), meta=np.ndarray&gt;\n",
       "    drS       (Z, YG, XC) float32 dask.array&lt;chunksize=(55, 300, 250), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    THETA     (time, Z, YC, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    SALT      (time, Z, YC, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    RHOAnoma  (time, Z, YC, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    VVEL      (time, Z, YG, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    UVEL      (time, Z, YC, XG) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    WVEL      (time, Zl, YC, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    momHDiv   (time, Z, YC, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "    PHIHYD    (time, Z, YC, XC) float32 dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        netCDF wrapper of MITgcm MDS binary data\n",
       "    source:       MITgcm\n",
       "    history:      Created by calling `open_mdsdataset(grid_dir=&#x27;/home/jan/Dat...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3602cd11-4571-4b16-8154-f68b8cea1612' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3602cd11-4571-4b16-8154-f68b8cea1612' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>XC</span>: 250</li><li><span class='xr-has-index'>XG</span>: 250</li><li><span class='xr-has-index'>YC</span>: 300</li><li><span class='xr-has-index'>YG</span>: 300</li><li><span class='xr-has-index'>Z</span>: 55</li><li><span class='xr-has-index'>Zl</span>: 55</li><li><span class='xr-has-index'>Zp1</span>: 56</li><li><span class='xr-has-index'>Zu</span>: 55</li><li><span class='xr-has-index'>time</span>: 72</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-630a20d3-f030-4e6f-9509-d29ac1cab4dd' class='xr-section-summary-in' type='checkbox'  ><label for='section-630a20d3-f030-4e6f-9509-d29ac1cab4dd' class='xr-section-summary' >Coordinates: <span>(36)</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'>XC</span></div><div class='xr-var-dims'>(XC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>5e+03 1.5e+04 ... 2.495e+06</div><input id='attrs-ff9cb9b1-36ef-4a86-a7c3-de0f49233ca5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff9cb9b1-36ef-4a86-a7c3-de0f49233ca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6708b883-aaaf-454b-8c8e-0dd2c851f1c8' class='xr-var-data-in' type='checkbox'><label for='data-6708b883-aaaf-454b-8c8e-0dd2c851f1c8' 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>standard_name :</span></dt><dd>plane_x_coordinate</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([   5000.,   15000.,   25000., ..., 2475000., 2485000., 2495000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>YC</span></div><div class='xr-var-dims'>(YC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>5e+03 1.5e+04 ... 2.995e+06</div><input id='attrs-44893dca-3267-46d4-9420-8a1927c49387' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44893dca-3267-46d4-9420-8a1927c49387' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0f7f27d-1133-4cab-8bc6-f30d96a6a0e3' class='xr-var-data-in' type='checkbox'><label for='data-c0f7f27d-1133-4cab-8bc6-f30d96a6a0e3' 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>standard_name :</span></dt><dd>plane_y_coordinate</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([   5000.,   15000.,   25000., ..., 2975000., 2985000., 2995000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>XG</span></div><div class='xr-var-dims'>(XG)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>0.0 1e+04 ... 2.48e+06 2.49e+06</div><input id='attrs-71cb3560-e584-44f2-a525-a995e6dea616' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71cb3560-e584-44f2-a525-a995e6dea616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff371643-3804-4bc3-ab5c-fed2ee59675a' class='xr-var-data-in' type='checkbox'><label for='data-ff371643-3804-4bc3-ab5c-fed2ee59675a' 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>standard_name :</span></dt><dd>plane_x_coordinate_at_f_location</dd><dt><span>long_name :</span></dt><dd>x coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([      0.,   10000.,   20000., ..., 2470000., 2480000., 2490000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>YG</span></div><div class='xr-var-dims'>(YG)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>0.0 1e+04 ... 2.98e+06 2.99e+06</div><input id='attrs-e867f4e1-8562-4ace-940b-72e3a294807b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e867f4e1-8562-4ace-940b-72e3a294807b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cca36bbd-39e2-4e22-8287-e1db45ff7ffd' class='xr-var-data-in' type='checkbox'><label for='data-cca36bbd-39e2-4e22-8287-e1db45ff7ffd' 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>standard_name :</span></dt><dd>plane_y_coordinate_at_f_location</dd><dt><span>long_name :</span></dt><dd>y coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([      0.,   10000.,   20000., ..., 2970000., 2980000., 2990000.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Z</span></div><div class='xr-var-dims'>(Z)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>-1.065 -3.345 ... -4.027e+03</div><input id='attrs-6b2baea5-e11e-48b0-ac1c-843034aee108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b2baea5-e11e-48b0-ac1c-843034aee108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03cec693-9bb3-407e-8133-c959e64efbb7' class='xr-var-data-in' type='checkbox'><label for='data-03cec693-9bb3-407e-8133-c959e64efbb7' 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>standard_name :</span></dt><dd>depth</dd><dt><span>long_name :</span></dt><dd>vertical coordinate of cell center</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([-1.064752e+00, -3.344950e+00, -5.947847e+00, -8.919078e+00,\n",
       "       -1.231071e+01, -1.618217e+01, -2.060120e+01, -2.564509e+01,\n",
       "       -3.140196e+01, -3.797221e+01, -4.547022e+01, -5.402617e+01,\n",
       "       -6.378813e+01, -7.492429e+01, -8.762543e+01, -1.021076e+02,\n",
       "       -1.186146e+02, -1.374210e+02, -1.588345e+02, -1.831981e+02,\n",
       "       -2.108911e+02, -2.423291e+02, -2.779624e+02, -3.182697e+02,\n",
       "       -3.637502e+02, -4.149087e+02, -4.722364e+02, -5.361846e+02,\n",
       "       -6.071351e+02, -6.853671e+02, -7.710265e+02, -8.641033e+02,\n",
       "       -9.644203e+02, -1.071639e+03, -1.185282e+03, -1.304765e+03,\n",
       "       -1.429442e+03, -1.558643e+03, -1.691713e+03, -1.828039e+03,\n",
       "       -1.967068e+03, -2.108312e+03, -2.251357e+03, -2.395852e+03,\n",
       "       -2.541508e+03, -2.688088e+03, -2.835400e+03, -2.983292e+03,\n",
       "       -3.131639e+03, -3.280346e+03, -3.429334e+03, -3.578543e+03,\n",
       "       -3.727925e+03, -3.877442e+03, -4.027065e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Zp1</span></div><div class='xr-var-dims'>(Zp1)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>0.0 -2.13 ... -3.952e+03 -4.102e+03</div><input id='attrs-cf859bb9-86a2-4da4-bc1b-f2f60eb3d7a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf859bb9-86a2-4da4-bc1b-f2f60eb3d7a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf7d24ea-223e-454d-8684-bb6e2dc5b605' class='xr-var-data-in' type='checkbox'><label for='data-cf7d24ea-223e-454d-8684-bb6e2dc5b605' 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>standard_name :</span></dt><dd>depth_at_w_location</dd><dt><span>long_name :</span></dt><dd>vertical coordinate of cell interface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>(-0.5, 0.5)</dd></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -2.129505e+00, -4.560395e+00, -7.335299e+00,\n",
       "       -1.050286e+01, -1.411857e+01, -1.824576e+01, -2.295664e+01,\n",
       "       -2.833355e+01, -3.447037e+01, -4.147405e+01, -4.946638e+01,\n",
       "       -5.858596e+01, -6.899030e+01, -8.085827e+01, -9.439259e+01,\n",
       "       -1.098225e+02, -1.274066e+02, -1.474354e+02, -1.702337e+02,\n",
       "       -1.961625e+02, -2.256196e+02, -2.590387e+02, -2.968860e+02,\n",
       "       -3.396534e+02, -3.878470e+02, -4.419705e+02, -5.025022e+02,\n",
       "       -5.698669e+02, -6.444033e+02, -7.263309e+02, -8.157222e+02,\n",
       "       -9.124844e+02, -1.016356e+03, -1.126922e+03, -1.243642e+03,\n",
       "       -1.365889e+03, -1.492995e+03, -1.624290e+03, -1.759135e+03,\n",
       "       -1.896943e+03, -2.037192e+03, -2.179433e+03, -2.323282e+03,\n",
       "       -2.468423e+03, -2.614593e+03, -2.761582e+03, -2.909218e+03,\n",
       "       -3.057365e+03, -3.205914e+03, -3.354778e+03, -3.503890e+03,\n",
       "       -3.653196e+03, -3.802654e+03, -3.952231e+03, -4.101900e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Zu</span></div><div class='xr-var-dims'>(Zu)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>-2.13 -4.56 ... -4.102e+03</div><input id='attrs-10ab2a43-f672-4c1b-8abc-b396b430ba1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10ab2a43-f672-4c1b-8abc-b396b430ba1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd82f8d7-5d25-4bf7-8aa8-c05611368aae' class='xr-var-data-in' type='checkbox'><label for='data-cd82f8d7-5d25-4bf7-8aa8-c05611368aae' 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>standard_name :</span></dt><dd>depth_at_upper_w_location</dd><dt><span>long_name :</span></dt><dd>vertical coordinate of upper cell interface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd></dl></div><div class='xr-var-data'><pre>array([-2.129505e+00, -4.560395e+00, -7.335299e+00, -1.050286e+01,\n",
       "       -1.411857e+01, -1.824576e+01, -2.295664e+01, -2.833355e+01,\n",
       "       -3.447037e+01, -4.147405e+01, -4.946638e+01, -5.858596e+01,\n",
       "       -6.899030e+01, -8.085827e+01, -9.439259e+01, -1.098225e+02,\n",
       "       -1.274066e+02, -1.474354e+02, -1.702337e+02, -1.961625e+02,\n",
       "       -2.256196e+02, -2.590387e+02, -2.968860e+02, -3.396534e+02,\n",
       "       -3.878470e+02, -4.419705e+02, -5.025022e+02, -5.698669e+02,\n",
       "       -6.444033e+02, -7.263309e+02, -8.157222e+02, -9.124844e+02,\n",
       "       -1.016356e+03, -1.126922e+03, -1.243642e+03, -1.365889e+03,\n",
       "       -1.492995e+03, -1.624290e+03, -1.759135e+03, -1.896943e+03,\n",
       "       -2.037192e+03, -2.179433e+03, -2.323282e+03, -2.468423e+03,\n",
       "       -2.614593e+03, -2.761582e+03, -2.909218e+03, -3.057365e+03,\n",
       "       -3.205914e+03, -3.354778e+03, -3.503890e+03, -3.653196e+03,\n",
       "       -3.802654e+03, -3.952231e+03, -4.101900e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Zl</span></div><div class='xr-var-dims'>(Zl)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>0.0 -2.13 ... -3.803e+03 -3.952e+03</div><input id='attrs-f86737c5-560f-4a2e-bd81-794cfb34638b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f86737c5-560f-4a2e-bd81-794cfb34638b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6618e67c-0935-4f87-8600-5afbc5ba11ee' class='xr-var-data-in' type='checkbox'><label for='data-6618e67c-0935-4f87-8600-5afbc5ba11ee' 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>standard_name :</span></dt><dd>depth_at_lower_w_location</dd><dt><span>long_name :</span></dt><dd>vertical coordinate of lower cell interface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -2.129505e+00, -4.560395e+00, -7.335299e+00,\n",
       "       -1.050286e+01, -1.411857e+01, -1.824576e+01, -2.295664e+01,\n",
       "       -2.833355e+01, -3.447037e+01, -4.147405e+01, -4.946638e+01,\n",
       "       -5.858596e+01, -6.899030e+01, -8.085827e+01, -9.439259e+01,\n",
       "       -1.098225e+02, -1.274066e+02, -1.474354e+02, -1.702337e+02,\n",
       "       -1.961625e+02, -2.256196e+02, -2.590387e+02, -2.968860e+02,\n",
       "       -3.396534e+02, -3.878470e+02, -4.419705e+02, -5.025022e+02,\n",
       "       -5.698669e+02, -6.444033e+02, -7.263309e+02, -8.157222e+02,\n",
       "       -9.124844e+02, -1.016356e+03, -1.126922e+03, -1.243642e+03,\n",
       "       -1.365889e+03, -1.492995e+03, -1.624290e+03, -1.759135e+03,\n",
       "       -1.896943e+03, -2.037192e+03, -2.179433e+03, -2.323282e+03,\n",
       "       -2.468423e+03, -2.614593e+03, -2.761582e+03, -2.909218e+03,\n",
       "       -3.057365e+03, -3.205914e+03, -3.354778e+03, -3.503890e+03,\n",
       "       -3.653196e+03, -3.802654e+03, -3.952231e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rA</span></div><div class='xr-var-dims'>(YC, XC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-561c8f39-56f3-4506-ae4f-3f43718a85f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-561c8f39-56f3-4506-ae4f-3f43718a85f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3db37cb-42e1-437c-a415-13d7a87b38e8' class='xr-var-data-in' type='checkbox'><label for='data-a3db37cb-42e1-437c-a415-13d7a87b38e8' 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>standard_name :</span></dt><dd>cell_area</dd><dt><span>long_name :</span></dt><dd>cell area</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "  <polygon points=\"0.0,0.0 100.0,0.0 100.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">300</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxG</span></div><div class='xr-var-dims'>(YG, XC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-cb56bac8-3e92-4856-b2d4-0bc887858ff3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb56bac8-3e92-4856-b2d4-0bc887858ff3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19fc568a-0efe-4fd5-a130-1caa0a038e98' class='xr-var-data-in' type='checkbox'><label for='data-19fc568a-0efe-4fd5-a130-1caa0a038e98' 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>standard_name :</span></dt><dd>cell_x_size_at_v_location</dd><dt><span>long_name :</span></dt><dd>cell x size</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <polygon points=\"0.0,0.0 100.0,0.0 100.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">300</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyG</span></div><div class='xr-var-dims'>(YC, XG)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-a9448ff7-dd35-44f4-9ec8-723080ab4d37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9448ff7-dd35-44f4-9ec8-723080ab4d37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c11632d-bca6-4d10-8c68-a313805f0760' class='xr-var-data-in' type='checkbox'><label for='data-8c11632d-bca6-4d10-8c68-a313805f0760' 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>standard_name :</span></dt><dd>cell_y_size_at_u_location</dd><dt><span>long_name :</span></dt><dd>cell y size</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XG</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">300</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Depth</span></div><div class='xr-var-dims'>(YC, XC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-a67cd290-23b4-4a05-8980-e8c0479bd079' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a67cd290-23b4-4a05-8980-e8c0479bd079' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7a6c74c-cf03-4f9f-b9c0-efb58ac3d348' class='xr-var-data-in' type='checkbox'><label for='data-d7a6c74c-cf03-4f9f-b9c0-efb58ac3d348' 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>standard_name :</span></dt><dd>ocean_depth</dd><dt><span>long_name :</span></dt><dd>ocean depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>XC YC</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 100.0,0.0 100.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAz</span></div><div class='xr-var-dims'>(YG, XG)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-3b6b1138-89ef-4733-98c0-5af5538890fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b6b1138-89ef-4733-98c0-5af5538890fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53909daa-046a-40b8-8cb6-7fcaf256c4a2' class='xr-var-data-in' type='checkbox'><label for='data-53909daa-046a-40b8-8cb6-7fcaf256c4a2' 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>standard_name :</span></dt><dd>cell_area_at_f_location</dd><dt><span>long_name :</span></dt><dd>cell area</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 100.0,0.0 100.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">300</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxC</span></div><div class='xr-var-dims'>(YC, XG)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-2767ad85-5efc-4ad7-b6a3-5af8139c47d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2767ad85-5efc-4ad7-b6a3-5af8139c47d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-023ca9c5-b220-446a-b963-2338b3969912' class='xr-var-data-in' type='checkbox'><label for='data-023ca9c5-b220-446a-b963-2338b3969912' 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>standard_name :</span></dt><dd>cell_x_size_at_u_location</dd><dt><span>long_name :</span></dt><dd>cell x size</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XG</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
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       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\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>dyC</span></div><div class='xr-var-dims'>(YG, XC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-a7d0dd30-a483-4f9c-97b3-0407b6114a62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7d0dd30-a483-4f9c-97b3-0407b6114a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b0ca7e7-9dc1-4a18-8b3b-80dcc69d8e84' class='xr-var-data-in' type='checkbox'><label for='data-8b0ca7e7-9dc1-4a18-8b3b-80dcc69d8e84' 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>standard_name :</span></dt><dd>cell_y_size_at_v_location</dd><dt><span>long_name :</span></dt><dd>cell y size</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 250) </td> <td> (300, 250) </td></tr>\n",
       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"150\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"100\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
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       "  <text x=\"50.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >250</text>\n",
       "  <text x=\"120.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,120.000000,60.000000)\">300</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAw</span></div><div class='xr-var-dims'>(YC, XG)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-6b77d0b7-9db9-4ed3-bd6a-d410be055584' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b77d0b7-9db9-4ed3-bd6a-d410be055584' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45a44964-2caf-4d03-b89e-ae3eb3ed748d' class='xr-var-data-in' type='checkbox'><label for='data-45a44964-2caf-4d03-b89e-ae3eb3ed748d' 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>standard_name :</span></dt><dd>cell_area_at_u_location</dd><dt><span>long_name :</span></dt><dd>cell area</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "<tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxF</span></div><div class='xr-var-dims'>(YC, XC)</div><div class='xr-var-dtype'>&gt;f4</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 250), meta=np.ndarray&gt;</div><input id='attrs-b1de5800-09d3-40b3-a3a7-088b7eabe4ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1de5800-09d3-40b3-a3a7-088b7eabe4ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47aa5439-490d-4504-85ab-22bbce3947aa' class='xr-var-data-in' type='checkbox'><label for='data-47aa5439-490d-4504-85ab-22bbce3947aa' 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>standard_name :</span></dt><dd>cell_x_size_at_t_location</dd><dt><span>long_name :</span></dt><dd>cell x size</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
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       "    <tr><th> Count </th><td> 3 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> >f4 </td><td> numpy.ndarray </td></tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 292.97 kiB </td> <td> 292.97 kiB </td></tr>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iter</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-41c2b56d-4dca-4dd9-9970-84f6b145c85e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41c2b56d-4dca-4dd9-9970-84f6b145c85e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b2434ea-28b8-4e57-b39f-328ca908a4d5' class='xr-var-data-in' type='checkbox'><label for='data-9b2434ea-28b8-4e57-b39f-328ca908a4d5' 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>standard_name :</span></dt><dd>timestep</dd><dt><span>long_name :</span></dt><dd>model timestep number</dd></dl></div><div class='xr-var-data'><table>\n",
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       "       cftime.Datetime360Day(61, 1, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 1, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 1, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 1, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 1, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 1, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 2, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 2, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 2, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 2, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 2, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 2, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 3, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 3, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 3, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 3, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 3, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 3, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 4, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 4, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 4, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 4, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 4, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 4, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 5, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 5, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 5, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 5, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 5, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 5, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 6, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 6, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 6, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 6, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 6, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 6, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 7, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 7, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 7, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 7, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 7, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 7, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 8, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 8, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 8, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 8, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 8, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 8, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 9, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 9, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 9, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 9, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 9, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 9, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 10, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 10, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 10, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 10, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 10, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 10, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 11, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 11, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 11, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 11, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 11, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 11, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 12, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 12, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 12, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 12, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.Datetime360Day(61, 12, 23, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drW</span></div><div class='xr-var-dims'>(Z, YC, XG)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(55, 300, 250), meta=np.ndarray&gt;</div><input id='attrs-2155c674-b2ec-48fa-81dd-db7bd0e89872' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2155c674-b2ec-48fa-81dd-db7bd0e89872' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30f00cdc-3607-42eb-b2c2-603df1cc14a9' class='xr-var-data-in' type='checkbox'><label for='data-30f00cdc-3607-42eb-b2c2-603df1cc14a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SALT</span></div><div class='xr-var-dims'>(time, Z, YC, XC)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;</div><input id='attrs-2304e864-1104-47bd-9c13-02a8cfa8fa59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2304e864-1104-47bd-9c13-02a8cfa8fa59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caacce01-c998-4544-8a48-741489df9cc6' class='xr-var-data-in' type='checkbox'><label for='data-caacce01-c998-4544-8a48-741489df9cc6' 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>standard_name :</span></dt><dd>SALT</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>units :</span></dt><dd>g/kg</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOAnoma</span></div><div class='xr-var-dims'>(time, Z, YC, XC)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;</div><input id='attrs-4a664e2d-5b74-4781-b273-69234e6336ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a664e2d-5b74-4781-b273-69234e6336ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cdd28fb-035f-44bc-a951-29f790b0efe3' class='xr-var-data-in' type='checkbox'><label for='data-8cdd28fb-035f-44bc-a951-29f790b0efe3' 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>standard_name :</span></dt><dd>RHOAnoma</dd><dt><span>long_name :</span></dt><dd>Density Anomaly (=Rho-rhoConst)</dd><dt><span>units :</span></dt><dd>kg/m^3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVEL</span></div><div class='xr-var-dims'>(time, Z, YC, XG)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;</div><input id='attrs-270b0991-83eb-4802-9b5a-0b909f2a356e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-270b0991-83eb-4802-9b5a-0b909f2a356e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e32bca99-18b4-48b2-8f9e-5d2d57d60264' class='xr-var-data-in' type='checkbox'><label for='data-e32bca99-18b4-48b2-8f9e-5d2d57d60264' 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>standard_name :</span></dt><dd>UVEL</dd><dt><span>long_name :</span></dt><dd>Zonal Component of Velocity (m/s)</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>mate :</span></dt><dd>VVEL</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WVEL</span></div><div class='xr-var-dims'>(time, Zl, YC, XC)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;</div><input id='attrs-bfba5963-3d5c-4dd7-bbc7-e171f2116b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfba5963-3d5c-4dd7-bbc7-e171f2116b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-291e97ef-f523-4eae-9011-6c3653d81754' class='xr-var-data-in' type='checkbox'><label for='data-291e97ef-f523-4eae-9011-6c3653d81754' 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>standard_name :</span></dt><dd>WVEL</dd><dt><span>long_name :</span></dt><dd>Vertical Component of Velocity (r_units/s)</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>momHDiv</span></div><div class='xr-var-dims'>(time, Z, YC, XC)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 55, 300, 250), meta=np.ndarray&gt;</div><input id='attrs-c625a10a-25a4-4a6a-9163-204ded1d4d28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c625a10a-25a4-4a6a-9163-204ded1d4d28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09c3d407-8dc9-413b-9cf6-38c6dd7d5797' class='xr-var-data-in' type='checkbox'><label for='data-09c3d407-8dc9-413b-9cf6-38c6dd7d5797' 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>standard_name :</span></dt><dd>momHDiv</dd><dt><span>long_name :</span></dt><dd>Horizontal Divergence (in momentum Eq.)</dd><dt><span>units :</span></dt><dd>s^-1</dd></dl></div><div class='xr-var-data'><table>\n",
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      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:   (XC: 250, XG: 250, YC: 300, YG: 300, Z: 55, Zl: 55, Zp1: 56, Zu: 55, time: 72)\n",
       "Coordinates: (12/36)\n",
       "  * XC        (XC) >f4 5e+03 1.5e+04 2.5e+04 ... 2.475e+06 2.485e+06 2.495e+06\n",
       "  * YC        (YC) >f4 5e+03 1.5e+04 2.5e+04 ... 2.975e+06 2.985e+06 2.995e+06\n",
       "  * XG        (XG) >f4 0.0 1e+04 2e+04 3e+04 ... 2.47e+06 2.48e+06 2.49e+06\n",
       "  * YG        (YG) >f4 0.0 1e+04 2e+04 3e+04 ... 2.97e+06 2.98e+06 2.99e+06\n",
       "  * Z         (Z) >f4 -1.065 -3.345 -5.948 ... -3.728e+03 -3.877e+03 -4.027e+03\n",
       "  * Zp1       (Zp1) >f4 0.0 -2.13 -4.56 ... -3.803e+03 -3.952e+03 -4.102e+03\n",
       "    ...        ...\n",
       "    dxV       (YG, XG) >f4 dask.array<chunksize=(300, 250), meta=np.ndarray>\n",
       "    dyU       (YG, XG) >f4 dask.array<chunksize=(300, 250), meta=np.ndarray>\n",
       "    iter      (time) int64 dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time      (time) object 0060-12-28 12:00:00 ... 0061-12-23 12:00:00\n",
       "    drW       (Z, YC, XG) float32 dask.array<chunksize=(55, 300, 250), meta=np.ndarray>\n",
       "    drS       (Z, YG, XC) float32 dask.array<chunksize=(55, 300, 250), meta=np.ndarray>\n",
       "Data variables:\n",
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       "    SALT      (time, Z, YC, XC) float32 dask.array<chunksize=(1, 55, 300, 250), meta=np.ndarray>\n",
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       "    VVEL      (time, Z, YG, XC) float32 dask.array<chunksize=(1, 55, 300, 250), meta=np.ndarray>\n",
       "    UVEL      (time, Z, YC, XG) float32 dask.array<chunksize=(1, 55, 300, 250), meta=np.ndarray>\n",
       "    WVEL      (time, Zl, YC, XC) float32 dask.array<chunksize=(1, 55, 300, 250), meta=np.ndarray>\n",
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       "    PHIHYD    (time, Z, YC, XC) float32 dask.array<chunksize=(1, 55, 300, 250), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    title:        netCDF wrapper of MITgcm MDS binary data\n",
       "    source:       MITgcm\n",
       "    history:      Created by calling `open_mdsdataset(grid_dir='/home/jan/Dat..."
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OKUBO-WEISS\n",
    "We use xgcm to define the grid for the interpolation and differentiation necessary for the calculation of vorticity and the Okubo-Weiss parameter.  \n",
    "For more information on the Okubo-Weiss parameter and its calculation see [Okubo, 1970](https://doi.org/10.1016/0011-7471(70)90059-8); [Weiss, 1991](https://doi.org/10.1016/0167-2789(91)90088-Q); [Basdevant and Philipovitch, 1994](https://doi.org/10.1016/0167-2789(94)90222-4); [Chelton et al., 2007](https://doi.org/10.1029/2007GL030812)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = xgcm.Grid(data, metrics=metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate vorticity and Okubo-Weiss parameter\n",
    "data_OW = et.okuboweiss.calc(data, grid, 'UVEL', 'VVEL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge the new variables `OW` and `vort` to the dataset `data` and make sure the chunk sizes are as before.\n",
    "data = xr.merge([data, data_OW], compat='override').chunk({'XC': 250, 'XG': 250, 'YC': 300, 'YG': 300})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INTERPOLATION\n",
    "Now we interpolate the data. If the data is entirely on a grid, that has one-dimensional latitude and longitude dimensions (i.e. longitude at one x-index does not change with changing y-index and latitude at one y-index does not change with changing x-index), the fields are simply interpolated to the F-point of the grid (vorticity and OW paramter are calculated and stored already on the F-point). This behaviour can also be triggered/forced by setting `avoid_regrid = True` as an argument to `interp.horizontal()`, although this should only be done when absolutely necessary -> only when regridding does not work.\n",
    "If the grid is curvilinear, e.g. North of 20S or so on the ORCA grids, a regridding is performed (unless `avoid_regrid = True`). There are several methods available for this regridding which can be chosen by setting `regrid_method` in the `interpolation_parameters`. Please refer to [the xesmf documentation](https://xesmf.readthedocs.io/en/latest/notebooks/Compare_algorithms.html) for details on the regridding methods. More advanced regridding with masks and extrapolations can also be performed, again refer to  [the xesmf documentation](https://xesmf.readthedocs.io/en/latest/notebooks/Masking.html) and set the extrapolation method `ext_method` in `interpolation_parameters` if necessary.  \n",
    "\n",
    "Note that the regridding can take VERY long for large region and/or long time periods. If one has to deal with problems regarding the time it takes, one could split the regridding into several parts, let's say regrid one year at a time, store the results and later load the combined regridded data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The longitude in all the parameter definitions (`lon1` and `lon2`) needs to be in the range (-180, 180) for `latlon` grids. Longitudes larger then 180 will result in an error. For cartesian grids, longitudes must be positive with meter as units."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORTANT:  \n",
    "The region that gets interpolated needs to be larger than the region used later for detection and tracking. This is required to ensure that eddies close to the boundaries of the detection region can be detected, tracked and sampled, even if their surroundings extend beyond the limits of the detection region.  \n",
    "To handle this, the region is internally extended by 2 degrees (for a `latlon` grid) or 200 km (for a cartesian grid) in each longitude direction and 1 degree (`latlon`) or 100km (cartesian) in latitude direction. So you need to make sure that the region you specify is not closer to the limits of your data that that.  \n",
    "If you want to detect eddies in the region 140-170E and 50-60S, the interpolation will be carried out within 138-172E and 49-61S! However, you still need to define 140-170E and 50-60S in the `interpolation_parameters` (or something equivalent for the cartesian grids).  \n",
    "\n",
    "Before the interpolation, we need to define a ocean mask at the F-point, as the MITgcm does not write this as output by default. For that we interpolate the ocean mask at the V point (`maskS`) in X direction, to shift it onto the F-point, then we decide that the mask should define ocean everywhere, where it is not zero. I.e. if an ocean point (`1`) and a land point (`0`) are interpolated (=`0.5`), this is converted to ocean (`1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add maskZ for interpolation (mask at F point)\n",
    "data['maskZ'] = grid.interp(data['maskS'], \"X\", to=\"left\", metric_weighted=[\"X\", \"Y\"])\n",
    "data['maskZ'] = data['maskZ'].where(data['maskZ']==1, other=0)\n",
    "data = data.set_coords([\"maskZ\"]).chunk({'XC': 250, 'XG': 250, 'YC': 300, 'YG': 300})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are not 100% sure yet how big the region will be in which you want to detect the eddies, you can specify a larger region for the interpolation and then later define smaller regions for the detection, tracking, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the parameters for the interpolation\n",
    "interpolation_parameters = {'model': 'MITgcm',\n",
    "                            'grid': 'cartesian',\n",
    "                            'start_time': '0061-01-01', # time range start\n",
    "                            'end_time': '0061-12-30', # time range end\n",
    "                            'calendar': '360_day', # calendar, must be either 360_day or standard\n",
    "                            'lon1': 0.6e6, # minimum longitude of detection region\n",
    "                            'lon2': 1.3e6, # maximum longitude\n",
    "                            'lat1': 1.0e6, # minimum latitude\n",
    "                            'lat2': 1.3e6, # maximum latitude\n",
    "                            'res': 10., # resolution of the fields in km\n",
    "                            'vars_to_interpolate': ['OW', 'vort', 'THETA'], # variables to be interpolated \n",
    "                            'mask_to_interpolate': ['maskZ', 'maskC', 'Depth'], # masks to interpolate\n",
    "                            'regrid_method': 'bilinear', # method used for regridding (default is 'bilinear')\n",
    "                            'ext_method': None # method used for extrapolation (only used in special cases)\n",
    "                            } "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interpolating from model grid: MITgcm\n",
      "Interpolating OW\n",
      "Interpolating vort\n",
      "Interpolating THETA\n",
      "Interpolating maskZ\n",
      "Interpolating maskC\n",
      "Interpolating Depth\n",
      "Interpolating dxV\n",
      "Interpolating dyU\n"
     ]
    }
   ],
   "source": [
    "data_int, regridder = et.interp.horizontal(data, metrics, interpolation_parameters, weights=None, avoid_regrid=False)\n",
    "\n",
    "# The variable `regridder` returns the weights if a regridding with xESMF has been performed. \n",
    "# If yes, execute the following line to save this regridder to disk, so it can be reused\n",
    "#regridder.to_netcdf(\"test_regridder.nc\")\n",
    "\n",
    "# Then the weights can be reused for future interpolation with the sam grid (i.e. the `lon1`, `lon2`, `lat1`, `lat2` \n",
    "# in `interpolation_paramters` must be identical)\n",
    "#weights = xr.open_dataset(\"test_regridder.nc\")\n",
    "#data_int, regridder = et.interp.horizontal(data, metrics, interpolation_parameters, weights=weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't be confused that interpolation of `dxV` and `dyU` is done although we did not request that. `maskZ`, `dxV` and `dyU` are always interpolated, even if you do not request that because they are essential for the detection of the eddies!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold for the Okubo-Weiss parameter\n",
    "Some sort of threshold needs to be defined for the Okubo-Weiss parameter to distinguish regions within and outside of eddies. This is usually defined as `OW_thr = a * sigma` ([Chelton et al., 2007](https://doi.org/10.1029/2007GL030812)), where `sigma` is the spatial standard deviation of the Okubo-Weiss parameter and `a` is some factor (more on the factor later).  \n",
    "In regions with highly variable dynamic conditions, detection based on a single values of this standard deviation often fails to detect eddies in more \"quiet\" areas of the ocean ([Isern-Fontanet et al., 2003](https://doi.org/10.1175/1520-0426(2003)20<772:IOMEFA>2.0.CO;2)).Thus we here define a spatially variable threshold for the Okubo-Weiss parameter by calculating the (2D) spatial standard deviation in moving boxes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load `OW` into memory so the `.rolling` operation is faster\n",
    "OW_tmp = data_int['OW'].sel(z=100, method=\"nearest\").compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Convert all land values to `NaN` so we don't have a lot of zeros when calculating\n",
    "# the standard deviation\n",
    "OW_tmp = OW_tmp.where(OW_tmp != 0)\n",
    "lon_tmp = OW_tmp['lon'].where(OW_tmp['lon'] > 0, other=OW_tmp['lon'] + 360.)\n",
    "OW_tmp = OW_tmp.assign_coords({'lon': lon_tmp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_OW_spatial_std = OW_tmp.rolling(\n",
    "                          lon=200, center=True, min_periods=1\n",
    "                          ).std(skipna=True).rolling(\n",
    "                          lat=100, center=True, min_periods=1\n",
    "                          ).std(skipna=True).mean('time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to use the mean spatial standard deviation of the Okubo-Weiss parameter over the whole considered region. To do that execute the cell below instead of the one above! Please note that `'OW_thr'` in `detection_parameters` needs to be defined differently, depending on which method of calculating the standard deviation you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#mean_OW_spatial_std = OW_tmp.std(('lon', 'lat'), skipna=True).mean('time') # uncomment if necessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use if OW_std is 2D\n",
    "data_int = data_int.update({'OW_std': (['lat', 'lon'], mean_OW_spatial_std.values)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use if OW_std is 1D\n",
    "#data_int = data_int.update({'OW_std': mean_OW_spatial_std})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the interpolated field on disk so we do not have to do the interpolation again\n",
    "data_int.to_netcdf(datapath + 'test_data_OW.nc', mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If we already did the interpolation earlier and now start off with the interpolated fields,\n",
    "# we just uncomment the line below to load them\n",
    "data_int = xr.open_dataset(datapath + 'test_data_OW.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f34a9459460>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's have a look at the fields\n",
    "plt.pcolormesh(data_int['OW'].isel(time=0, z=20), vmin=-2e-10, vmax=2e-10, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see several eddy-like features that have a low OW-parameter, now let's see whether the algorithm detects them as eddies!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DETECTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note for the setting of `detection_parameters`:  \n",
    "1. `'lon1'` and `'lon2'` need to be positive and in meters. \n",
    "2. `'OW_thr': data_int` if the field `OW_std` in `data_int` is 2D, i.e. we have a spatially varying standard deviation of the Okubo-Weiss parameter. `'OW_thr': data_int['OW_std'].values` if the field `OW_std` in `data_int` is 1D.  \n",
    "3. `'OW_thr_factor'` is usually chosen in the range `(-0.5, -0.2)` ([Chelton et al., 2007](https://doi.org/10.1029/2007GL030812); [Isern-Fontanet et al., 2003](https://doi.org/10.1175/1520-0426(2003)20<772:IOMEFA>2.0.CO;2))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters for eddy detection\n",
    "detection_parameters = {'model': 'MITgcm',\n",
    "                        'grid': 'cartesian',\n",
    "                        'hemi': 'south',\n",
    "                        'start_time': '0061-01-01', # time range start\n",
    "                        'end_time': '0061-12-30', # time range end\n",
    "                        'calendar': '360_day', # calendar, must be either 360_day or standard\n",
    "                        'lon1': 0.6e6, # minimum longitude of detection region\n",
    "                        'lon2': 1.3e6, # maximum longitude\n",
    "                        'lat1': 1.0e6, # minimum latitude\n",
    "                        'lat2': 1.3e6, # maximum latitude\n",
    "                        'res': 10., # resolution of the fields in km\n",
    "                        'min_dep': 1000, # minimum ocean depth where to look for eddies in m\n",
    "                        'OW_thr': data_int, # \n",
    "                        'OW_thr_name': 'OW_std', # Okubo-Weiss threshold for eddy detection\n",
    "                        'OW_thr_factor': -0.3, # Okubo-Weiss parameter threshold\n",
    "                        'Npix_min': 20, # minimum number of pixels (grid cells) to be considered as eddy\n",
    "                        'Npix_max': 500, # maximum number of pixels (grid cells)\n",
    "                        'no_long': False, # If True, elongated shapes will not be considered\n",
    "                        'no_two': False # If True, eddies with two minima in the OW\n",
    "                                        # parameter and a OW > OW_thr in between  will not\n",
    "                                        # be considered\n",
    "                       }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that, when regridding was forced to be avoided in the interpolation (`avoid_regrid = True`), it will not work (yet) with `et.detection.detect_SSH`. Only detection based on the Okubo-Weiss parameter will work with the `avoid_regrid = True` option.  \n",
    "\n",
    "### Parallel detection\n",
    "There are two ways to parallelize the detection (if the necessary packages are installed.)  \n",
    "   1. [**dask_bags** ](https://docs.dask.org/en/stable/bag.html). To use `dask_bags`, simply set the argument `use_bags=True` in the call to `et.detection.detect_OW()`.  \n",
    "   2. [**multiprocessing**](https://docs.python.org/3/library/multiprocessing.html). To use `multiprocessing`, set argument `use_mp=True` in the call to `et.detection.detect_OW()` and specify how many CPUs to use by setting `mp_cpu=N`, with `N` being an integer.  \n",
    "   \n",
    "Note that `dask_bags` also uses `multiprocessing` under the hood. In most cases using `multiprocessing` directly will be faster, but tests should be conducted for each operating system and/or dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preparing data for eddy detection (masking and region extracting etc.)\n",
      "detection at time step  1  of  71\n",
      "detection at time step  9  of  71\n",
      "detection at time step  17  of  71\n",
      "detection at time step  25  of  71\n",
      "detection at time step  33  of  71\n",
      "detection at time step  40  of  71\n",
      "detection at time step  48  of  71\n",
      "detection at time step  56  of  71\n",
      "detection at time step  64  of  71\n"
     ]
    }
   ],
   "source": [
    "# We detect eddies at a depth of 100 m\n",
    "eddies = et.detection.detect_OW(data_int.sel(z=100, method='nearest'), detection_parameters, 'OW', 'vort', regrid_avoided=False, \n",
    "                                 use_bags=False, use_mp=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': array(cftime.Datetime360Day(61, 1, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "       dtype=object),\n",
       " 'lon': array([1095107.2974184]),\n",
       " 'lat': array([1042972.04615846]),\n",
       " 'amp': array([3.5489793e-06], dtype=float32),\n",
       " 'eddy_j': array([11, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15,\n",
       "        16, 16, 16, 16, 17]),\n",
       " 'eddy_i': array([69, 68, 69, 67, 68, 69, 70, 67, 68, 69, 70, 71, 68, 69, 70, 71, 72,\n",
       "        69, 70, 71, 72, 70]),\n",
       " 'area': array([2200.], dtype=float32),\n",
       " 'scale': array([[26.462837]], dtype=float32),\n",
       " 'type': 'cyclonic'}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this format, information for every detected eddy is stored\n",
    "# (eddies[t][ed], where t is the time step and ed the eddy number)\n",
    "eddies[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-68-1adeb0a55293>:5: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  plt.pcolormesh(plot_lon, data_int['lat'].values, data_int.OW.isel(time=t).sel(z=100, method='nearest').values,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a single time step to see what the detection method detected\n",
    "t=0\n",
    "d_p = detection_parameters\n",
    "plot_lon = data_int['lon'].where(data_int['lon'].values > 0, other=data_int['lon'].values + 360)\n",
    "plt.pcolormesh(plot_lon, data_int['lat'].values, data_int.OW.isel(time=t).sel(z=100, method='nearest').values,\n",
    "               vmin=-2e-10, vmax=2e-10, cmap=cm.balance)\n",
    "for c, l in zip(['whitesmoke', 'dimgray'], ['-', '--']):\n",
    "    plt.plot([d_p['lon1'], d_p['lon2'], d_p['lon2'], d_p['lon1'], d_p['lon1']],\n",
    "             [d_p['lat1'], d_p['lat1'], d_p['lat2'], d_p['lat2'], d_p['lat1']],\n",
    "             color=c, linestyle=l)\n",
    "\n",
    "for i in np.arange(0, len(eddies[t])-1):\n",
    "    if eddies[t][i]['lon'] < 0:\n",
    "        eddy_lon = eddies[t][i]['lon'] + 360\n",
    "    else:\n",
    "        eddy_lon = eddies[t][i]['lon']\n",
    "    if eddies[t][i]['type'] == 'cyclonic':\n",
    "        for s, c in zip([7, 6], ['k', 'gold']):\n",
    "            plt.plot(eddy_lon, eddies[t][i]['lat'], marker='o', color=c, markersize=s)\n",
    "    elif eddies[t][i]['type'] == 'anticyclonic':\n",
    "        for s, c in zip([7, 6], ['k', 'silver']):\n",
    "            plt.plot(eddy_lon, eddies[t][i]['lat'], marker='o', color=c, markersize=s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that all features below a certain OW threshold have been detected. Some of them, we might not consider as an eddy. If you have the feeling, that two many features are detected, that are not eddies, change parameters like `OW_thr_factor`, `Npix_min`, `Npix_max`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For every time step, we store one file on disk with all the information of the detected\n",
    "# eddies at this time step\n",
    "for i in np.arange(0, len(eddies)):\n",
    "    datestring = str(eddies[i][0]['time'])[0:10]\n",
    "    with open(datapath + 'test_'\n",
    "          + str(datestring) + '_eddies_OW0.3_test.pickle', 'wb') as f:\n",
    "        pickle.dump(eddies[i], f, pickle.HIGHEST_PROTOCOL)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRACKING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note for the setting of `tracking_parameters`:  \n",
    "1. `'start_time'` is required to be no earlier than the earliest actual date of the detected eddies. In our case here, for the year 0002 and a 5-day temporal resolution of the data, this is `'0002-01-05'` (The `MITgcm` stores the 5-daily averages at the end of the 5-day period).\n",
    "2. `'lon1'` and `'lon2'` need to be identical to `'lon1'` and `'lon2'` in `detection_parameters`.  \n",
    "3. If you stored the detected eddies in files and want to track these, set `'dict': 0`, make sure `'data_path'`, `'file_root'` and `'file_spec'` are set accordingly and use `in_file=True` as an argument to `et.tracking.track()`.. The method will look for files `datapath + file_root + 'YYYYMMDD' + file_spec + '.pickle'`, the date `'YYYYMMDD'` is automatically calculated from `'start_time'`, `'dt'`, and `'end_time'`. You have to make sure that the stored, detected eddies contain that date in their filename (e.g. as defined in the cell above)!  \n",
    "\n",
    "Some notes on the search distance `search_dist` that is used to determine in what radius to look for a similar eddy at the next time step.  \n",
    "    - If `search_circle: True`, the algorithm simply searches for similar eddies within a radius of `search_dist` kilometers around the center of the current eddy. This is the simplest method.\n",
    "    - If `search_circle: False`, the algorithm will determine where to look for similar eddies based on an ellipse with a minor axis of `search_dist` kilometers. If `search_dist: 0`, the minor and major axes of the ellipse are calculated based on the propagation of Rossby waves to account for the fact that eddies will move towards the West (see [Chelton et al., 2011](https://www.sciencedirect.com/science/article/pii/S0079661111000036) for details). In regions with strong currents this might lead to a loss of a lot of tracks, but no sensitivity studies have been conducted... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify parameters for eddy tracking\n",
    "tracking_parameters = {'model': 'MITgcm',\n",
    "                       'grid': 'cartesian',\n",
    "                       'start_time': '0061-01-05', # time range start\n",
    "                       'end_time': '0061-12-30', # time range end\n",
    "                       'calendar': '360_day', # calendar, must be either 360_day or standard\n",
    "                       'dt': 5, # temporal resolution of the data in days\n",
    "                       'lon1': 0.6e6, # minimum longitude of detection region\n",
    "                       'lon2': 1.3e6, # maximum longitude\n",
    "                       'lat1': 1.0e6, # minimum latitude\n",
    "                       'lat2': 1.3e6, # maximum latitude\n",
    "                       'search_dist': 0., # maximum distance of search ellipse/circle from eddy center in km\n",
    "                                          # if ellipse: towards the east (if set to 0, it\n",
    "                                          # will be calculated as (150. / (7. / dt)))\n",
    "                       'search_circle': False, # if True, search in a circle. otherwise use ellipse\n",
    "                       'eddy_scale_min': 0.5, # minimum factor by which eddy amplitude and area are allowed to change in one timestep\n",
    "                       'eddy_scale_max': 1.5, # maximum factor by which eddy amplitude and area are allowed to change in one timestep\n",
    "                       'dict': eddies, # dictionary containing detected eddies to be used when not stored in files (set to 0 otherwise)\n",
    "                       'data_path': datapath, # path to the detected eddies pickle files\n",
    "                       'file_root': 'test',\n",
    "                       'file_spec': 'eddies_OW0.3_test',\n",
    "                       'ross_path': datapath} # path to rossrad.dat containing Chelton et a1. 1998 Rossby radii"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tracking at time step  9  of  72\n",
      "tracking at time step  17  of  72\n",
      "tracking at time step  25  of  72\n",
      "tracking at time step  33  of  72\n",
      "tracking at time step  41  of  72\n",
      "tracking at time step  49  of  72\n",
      "tracking at time step  57  of  72\n",
      "tracking at time step  65  of  72\n"
     ]
    }
   ],
   "source": [
    "# Now we track the eddies, all information needed has to be added to `tracking_parameters`\n",
    "tracks = et.tracking.track(tracking_parameters, in_file=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have tracked all eddies that met the criteria specified in `tracking_parameters`. Every entry `i` in `tracks[i]` corresponds to one complete track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time': array([cftime.Datetime360Day(61, 1, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 1, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 1, 13, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 1, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 1, 23, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 1, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 2, 3, 12, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.Datetime360Day(61, 2, 8, 12, 0, 0, 0, has_year_zero=False)],\n",
       "       dtype=object),\n",
       " 'lon': array([1095107.2974184 , 1107735.8908555 , 1122593.30079433,\n",
       "        1142358.81783928, 1180019.30462993, 1139098.7496963 ,\n",
       "        1188100.82775412, 1217309.07945502]),\n",
       " 'lat': array([1042972.04615846, 1066194.40129733, 1100653.32566718,\n",
       "        1143740.89213039, 1179893.14737822, 1164799.99082927,\n",
       "        1207181.24884419, 1122604.56662288]),\n",
       " 'amp': array([3.5489793e-06, 6.1373075e-06, 9.5184041e-06, 1.4848842e-05,\n",
       "        1.2410406e-05, 9.0903322e-06, 8.0220007e-06, 4.0140876e-06],\n",
       "       dtype=float32),\n",
       " 'eddy_j': {0: array([11, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15,\n",
       "         16, 16, 16, 16, 17]),\n",
       "  1: array([12, 13, 13, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17,\n",
       "         17, 17, 18, 18, 18, 18, 19, 19, 19]),\n",
       "  2: array([17, 17, 18, 18, 18, 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21,\n",
       "         22, 22, 22, 22]),\n",
       "  3: array([21, 21, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 25, 25, 25, 25,\n",
       "         26, 26, 26, 26, 27, 27]),\n",
       "  4: array([25, 26, 26, 26, 26, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 29, 29,\n",
       "         29, 29, 30, 30, 30, 30]),\n",
       "  5: array([22, 22, 23, 23, 24, 24, 24, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27,\n",
       "         28, 28, 28, 28, 29, 29, 29, 30]),\n",
       "  6: array([28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 31,\n",
       "         31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 32]),\n",
       "  7: array([17, 18, 18, 18, 19, 19, 20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23,\n",
       "         23, 23, 23, 24, 24, 24, 24, 25, 25, 26])},\n",
       " 'eddy_i': {0: array([69, 68, 69, 67, 68, 69, 70, 67, 68, 69, 70, 71, 68, 69, 70, 71, 72,\n",
       "         69, 70, 71, 72, 70]),\n",
       "  1: array([69, 69, 70, 69, 70, 71, 69, 70, 71, 72, 69, 70, 71, 72, 69, 70, 71,\n",
       "         72, 73, 70, 71, 72, 73, 70, 71, 72]),\n",
       "  2: array([72, 73, 71, 72, 73, 71, 72, 73, 74, 71, 72, 73, 74, 71, 72, 73, 74,\n",
       "         71, 72, 73, 74]),\n",
       "  3: array([73, 74, 73, 74, 75, 73, 74, 75, 76, 73, 74, 75, 76, 73, 74, 75, 76,\n",
       "         73, 74, 75, 76, 74, 75]),\n",
       "  4: array([76, 76, 77, 78, 79, 76, 77, 78, 79, 80, 76, 77, 78, 79, 80, 77, 78,\n",
       "         79, 80, 78, 79, 80, 81]),\n",
       "  5: array([72, 73, 72, 73, 72, 73, 74, 72, 73, 74, 72, 73, 74, 75, 73, 74, 75,\n",
       "         73, 74, 75, 76, 74, 75, 76, 76]),\n",
       "  6: array([76, 77, 76, 77, 78, 79, 80, 81, 82, 76, 77, 78, 79, 80, 81, 82, 77,\n",
       "         78, 79, 80, 81, 82, 77, 78, 79, 80, 81, 82]),\n",
       "  7: array([78, 78, 79, 80, 79, 80, 79, 80, 80, 81, 82, 83, 81, 82, 83, 84, 81,\n",
       "         82, 83, 84, 81, 82, 83, 84, 82, 83, 82])},\n",
       " 'area': array([2200., 2600., 2100., 2300., 2300., 2500., 2800., 2700.],\n",
       "       dtype=float32),\n",
       " 'scale': array([26.462837, 28.768137, 25.854414, 27.05758 , 27.05758 , 28.209478,\n",
       "        29.854105, 29.31615 ], dtype=float32),\n",
       " 'type': 'cyclonic',\n",
       " 'exist_at_start': True,\n",
       " 'terminated': True}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The entries in `track` look like this\n",
    "tracks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WTH/b/d4HysZ9YiOv6f5F71Ueh+LwKGsX6+MLHufA+YhZJwPvt4RfYf2ESY4P2OYTyyPYjwaZBLFHOcm55n55MNa/31Nfb1YXc7/nGUfhuhqPG01pw9VGK8tFxk+b+fzidh7VMTAxLzyZibrkgwnliZC5Qg/zmPJd2I9Jqr7fsUm5YI5vO/hMTZvSw8oxHPYPH6HvL86Rya69mXlttnfb7CldMR5EiuOvtboctcmoMhCe07Jsnb8ctQW9lCxT0Xjm7VhgH/SMl5LH/JnVMhKufSWFgu5zPdyHWwjzh7zOYyDk7dlt9w23647MtxyPvXBdNcunu9hOe+1hntVtuId4cO0Tj7m95oe91lZ7KY9vYPNUUizmlrGXM5eF3KJ8IR4pjgUe7fqNR/p1tzSP8Lr1spSn0+NaSPqHY6ZjGBgdXeP1lFtgfcNh//j+rrVa3ppogNNFToZyNLimMnyPgfmgl61BAaCj3mbyG8J6sTVx1evm5f+r8X0Oi1JIDDA+mN/7XeUJheEZtwnUhm699IP5AjwRwJC1mxnaNndbnkG/297V37FXfpCNRM5KP3xXXsfnFcodPcZsNbtmz/LBXvmfa3LlOuhT1H65A/tZb+SzU+R1Xptk63opC8e7fVYis4LPQsVEx154DqkPvY/NmXz+sDwzrldq+j4Zbv1+GsZVxi6IWWVcr9nxzeeOium3Xj9+tqlMDN+pKJvDlDPTkpzWVQZL4PWQpaL5wrJYeF6vJ+Xfaxbxv0RwuVwul8vlcrlcLpfLtS/5jwgul8vlcrlcLpfL5XK59iX/EcHlcrlcLpfL5XK5XC7XvnRgTIQkSVAol3f564xnmR5l42Wyvh+rAa/jgH9Ivhc89u14n89R5RgEb2S1t8ubpF4R9c1Zv055YkaP4lmhJ6ZLjxLvPRHvN06vPIznyPoJO1vipW2tLMnp3Xif0bpel54Zuw915smJ9xHdzXjIfDrxXqaD/rrYr7Ot3vO1HfH7XNEjvUTTI/Q2lrXsnej6bPdsX28pR2AimP1bw/67jBN938aF3Wc26Xb1/sqH2G4O7Ne609F767Xvu2EWANBeF2/RztJZ0yaxT5sKXrawR3HMgyA/ImMjxPvyDnqReN7V9wsv7tv7dq1q0PdsvWtltnl5eAoLex/TG73Pvc8Zl+E6Ob7mpFAc8OUH5oXJZYzdwgArwcRLDiMhMDbIWClXo/NCb2u8WU8k48v6klPjtWO8sA2Khbhtd+8XHPYeVz8+83c3JzeSmTI9KbmJ3lGyUyi2FT/v9uXzsJe65cwMMC3i+YLoBo6R1YZcj/vFTyqPYHpExv7UqHje015v13jWOu7iqAC7PKrGT2lz5uixRQDA05clf6w35fMZ5TIE33fF8n3oZbVshLhts33gY+/qoB/9sOaD3YqZBlRgdBT357W1DI+8sUfWDjXgrTbX5dzaazUzD6rJE5ZdMlBD870XKrs/+eB9mCNjlg7LZ/cHrxhWw+7r2vag9oo9xih5I4G5ovM+ve6BWaL8ooWJ0ej9la0dAMDqjqxDGjrPb7VjVsK0jjUyNMI6YONKVK6CMhGYbwKTZVd9bXtleTbOpzaPW696HgthIKaUZRUYVlzXdeNYJsMl3E/XG3nrlMOsJE1RSnvot6UNOiamB3hUZs2VrTfj2LZcopJ+PqXzRB7XyvLH6H/ffb3sWcbGA/S7HfM6fv6w+STvmcjmyGFrmKspMWM3cB5yWRo58XwVJsxgGXQN0onn1v1yvvi8saXriu123N9kpXC+H9H5v2L6gLmP6xOym9rd4XVhHO3OmWTPpf2N6NyecuLYn0VlIYTxORI/a9kcmjGxYhZLFjdyfZhn4MrsEQDA1N33AwAeu7gCANi4IvmlVpK6zo1J28yOS7nKwzFEQf6XCC6Xy+VyuVwul8vlcrn2Jf8RweVyuVwul8vlcrlcLte+5D8iuFwul8vlcrlcLpfL5dqXDoyJACRIisVdnpfYtzVw9oCvMPaJUtbTlLd/eIW+EvWh0tvL67E8QDns30l/VWAdqLeFXuXy9Lx8nnLPeuPP1e+ThdBRO1Vfy1YL95QP6Asu6Ovty7LvZ/vKUlRH+oVYrnA/9fHRJ1ZTn59tk94ur1yiZQ57h+awEOj94T7s9CQub4sHjPs204NEXw/3a25cvqBlbkZtUtFjLezHbf0+ol6nF30/6y9E59s4oHb7mHnvsA+rlpF+7M6W9rPGQam+oG3APWnpLde9ynO8ozYG2d/0mHWMBzVvj23LRuiFPWrjvXMZ04dNab+3p0/U9ic9kPl+w9g/Zjkhg0yWuM3tdePP1CuoZaJvjv2mQyDXG2i90n3Efkz6/MlC2NQ91WsVSd/kQ9g2oQZZCLGHf5ifG8i8d1Pq3W90uiGm+B46Uha7hzm5MaEO9Kaqv7M6NQcAmB2TMWVj3ZYx7IGubdG0/RXaOq4rkRnTWt6wr7S2FfMUvazjffVMD+nnsFc9fa45scjcyXyxpjmR31+YkDw8Ujb7w6c6FxYtG2G4bF5hrFvvKnM1+85yYg6Lrr6vfczosT5eG+Nh33bjfQ5XK8Z+42z9EftTbR4JnIFCMYyfks67a9tyb/p1+6n49stF+c6EznmcI+0clnE/9hcfoS57ep/jujFeOJ8UbJ3NPJUUi1fZGz5uJ54X5irjTa9re0/UZByyTchasXl6hOfPCzNlthGzmdb1NeOAYzCMBR2rYO6lX5nryMCPamr5dY/1+lj4b/qm7bjiPW2etW1jZTlhlr1CPkNFcyiUxbW8KWuwus4Ls2MjADKWFq9nmRyHmZXQ73XRWl3eNd/H61e7xi6EOLBrNGVl5TCQqGIYe+QcxWstMjvoh2e+yXJ0Zc/xmHHJ4v6qTgmb68Ka9OfSuvR3syP34rqT/DH2P5k7oSw56wSb66i9yptbjyFtaNc6dt7PGBKduEymDBxrRfPv4B1de/V1nmd+yThBcY4rd2P+mR2TvcBYkPKQnRee8XQMje3iGbHe3W1dC4V2j3OKZZrYcZk9gw1/1qWSwvC1b0KWluaL8VvuAQD88Wlhun1yScrX6kldZuuSNxbHpHzHlC9DRkKe/C8RXC6Xy+VyuVwul8vlcu1L/iOCy+VyuVwul8vlcrlcrn3Jf0RwuVwul8vlcrlcLpfLtS/5jwgul8vlcrlcLpfL5XK59qUDBCumSHu9AYBVPoQG0ft54DV7nex68e8lhBZVDKQoVZDGbpAjwRWFMQVX1QT2EwCJBWnGSxsC/yEQcUzPIyiDAEbCOSyALUChFFZGdQkTsm0SwF567MSADsIONwhiU6jQ3LgAV6oKLekqwGN3fxAKFaCNfQLJpOyECa0qNGxV77HeVMiQgmYISDo6NQ4A2FkSqEfr8vmo7KxLuG8pjgvCpTLwSgwbKvZZhxgaaeOE1yWwpVDOQDe8JkFEBKOt96Quy+sCHNm+JJ8XFO5CqAoBNux/HlPbT3oMoCy9f638/IBZAR6mbU3sCqFVV8OQXbNK++h32gMALYoQGqsCYohppucHVAyAToUYckyv7kjfE+gzPVLD1KjEHCOVcJ5ilQC7GOpGaNMAAMfEXyh5Nc4b2wr3YdwxXjpbCv9i2zS1xuV4TO0Foe0oFJFwRKoyIdCy2xfmMoikjsP2xqocVwX62lmTY19BaAS1lvQapdHJ6N55ADwLk0tNjl3WOjOXMrcdmxSI3dSo1LmiuXdB3x/TMdkw/c6xyv7td7u7YimGu9qckgf17GvsEYDJMhHI29mycEitaznOD3ZOtJBRiu+HHM7yG+Dq4UwMokGAnwUqiooDAMR47CUFBf0xvnrD1xtWFhCYARvjeak6PYdPX7wCAPjIIzLnnV6TObKtIKvxilzjlimJi7vmZWzcdkRAeWUD2EtM/2f54/n9W1De2optaYGKzGsW8BevsSys04wZU2auHzZ0vcD+G9W5tFyyMR/nbbY3xdw1omuy0Wk5TtTitRTHIsd5deaIlEtzpAXDpjnzUFIohjowJ1l4I9ttpCZ1tmvNDCo5HOILxGC9ktatPn8MAPDwecm1f/TY4wCAMxty/uKo3P9NJySOXnmDAL+LLTNPSOoMa6IkNy6uXRVKZdRmj4TXFgBeISjPwAIzoF+cY1GK1wmDEOIYBFjkmLEgb/M9Ku33snG0E7e3jW1eozYrIO9nVwRe/InzKwCA02vSn42ulKlSlLIcG5P+vGlK1g03z00BAOZG5P32zqqWOX5e4Bqaa6AAHTUgb6usLYd/nvZ6A2Bam68HIMG71ue7v5edH3+fY61T4lpaxkBB18YVfZ/nBSi1gaCzD9hrCxOj0fX4LMV1B0UY7kS9ir55fuNzGeGs3S3px35b15Qh1wxf27IfBtYdJrYygL8CfXWtxfHd0+N6U2KV/XWsLmU/Oibfm6jxmU/amGutPPlfIrhcLpfL5XK5XC6Xy+Xal/xHBJfL5XK5XC6Xy+VyuVz7kv+I4HK5XC6Xy+VyuVwul2tfOjAmQooEnUIJ7Y74POhZKdN3U4iLlnlVc3ykev6aepcb7W50XXpeg1dGr2e9T/uRved28EurJ059fWrHCZ7mtvr6rBeOdQtsBHM/MhgqU3PR+71W7FWhd4Ze/mX1ZC5tChthTH07vN/ChPiE6Qvqd9rBg1RQ/x69P5aBsLwt96a/pqF8B3qQj00Id+Hm+SkAQEdZCDvPin+vq15qep9KY+LfYVutbIlvuJjI9Sa0/1qr4geznrPgRcvxC2eeN+h9K+F79FuyLI2ifPbUucta16aWJYnacaIi55GFwNiinzP4Qo3/3pbd8icyv+1w35j1QhXN6462AePscClBUiyiaLzKwRe8BzMlz3tnfaf2++UxYXZkvkNhdnx6RcZOW9vylinp67vmJ4O/rqJshOA1Zfvnli0uIxX635yX5QuyENSzpuPf5gHGePD5dYf7etm2veDVEz9wZ0PyBkxuLJQrgY/AXFMe60Xf7TIWyQfgeRPq81Zfb+a3jJkVrAv7m7mMef3RJSnbM+s7UV1um5Fcdqv6ydsba1pm9Vjr/SbrOmar8ZjhGG1vyH2SQnGAdUANvB7o15hFQHaF9aCWRphrYw9imAfYv83YI5vHRsjySTwPWP9unrf1WlcfCWBymp07M8U+3tDGykIoG5+pjbvU8Agsa6erPlayQFLt2/riKQDAc6ubeHhJ5rgLhn1BFsLCiMTF/IjklEmdR8giILuCcWg1OMfFfvA85cUvxXi1bUPZNcKwsuQxjBibUyMyXsfrMbuEPKLelrRd03I/GPtdXl/KRj8x8xD5ASN6v8Ar0Tbt9eX1Ulvi5BnpTqw3mSfk+7edlHw3q/mCPJNirYZOS8rS0DVsuxd7mrleYL+SC1XW7hmMWVFgJRlGEl9f2ZE2PbMqhV7akTXaVkvqslKU8jytn88pi+WmealLa03WUHb+sByqw6KkUAy5s9cbDnxhX4TXufkiVhbjhr1i85C2HZ8JSlVZ/yZ96ZvAG9rZzngeJo8HVoLhhzAHTY1IbN4yK7E5WdN4aMj3uAav6/qTDByu4dOClInzQq9l+VGxOGYtu+n5ynLvgPz1mL3X4D2VWZfTf2QT8JjPzImf/+wanX3BfqvqmFwc1Tm1OhlddzeDJ9VcVptfBAB0R5Wdp888AznM8qPCs9BYVGbGon0usGIbhn4ukm0jR8bNisbNmPIhpjVPzY0p60vjiOy+3Ptd9VOXy+VyuVwul8vlcrlcLpX/iOByuVwul8vlcrlcLpdrX/IfEVwul8vlcrlcLpfL5XLtSwfGROin4rWgl6RgPC52n2e7dyp9Hy31QD11eQ0A8PiyeJrXdE/1Wd3z8tS0+EtO6P7B3OPd7g9K7fbkWA8K/Tgds1coPfGlVK7Z2d7WsqrvX8tcMX4dytato7arHd1LGUXxtpWP3AgAmNTr0KcX9lI2PiB6o/i60+NR96HV8iWFYqj3RkPae039d+fXxbdzTj3DF7ZlL9SW7nk9VZW63zYT703bXXoOALB9+mE5X19T1SMnpE7qtb6wJp7IZ66IF4m+89fcuBh9jxwBeixt/+X5uIKndWc7fE7v0MUNee/Cuhzb2j5H1CNEr9CE+jgz9kEs+u8Cb4MsDOO7pdeVZQpsCvaf9n8lZy9bKngoC9wjtxyV/zCpl6bYaPXQS2M/+ajuYWv3Hc/z4to26pg+4Pfo8T+r8fbRc+JxfnhZPPcrupfz4pjEN8dcvVwKXBXrp7Qeeau8PY/zPM70stPHx1xIvx5fF4yX2Xqpre+P6tn9w5nveH/15hXHJvHkkvhpG5r7jug+yjNHj8u1uOexlqFIXz6POm4z7kfsu2ZdgodZfYIb9KBrPy5qPNAfekNV2mbnsY/J9zQvjB6/GUA2TzCeWuplZ+6EbftSZSDHsH33YltQ1vdpffmrynlY2pA5a6MpOXVUmTo3q4e53FdmhbYBFXz63TjW7VzJGG9qv9u59jDJrgss+4A5r9ONj1vK9Nlux7HPPbFnx8mPiL3vNl/0dN7obute39rWRfW9MjZm6yN4zbFZAMDtc3JP5qBy8KbGTB3LB0iTq3vUB+Muzke5eaU4PC9x/OethYJHG/H81lpdDu2VN99aXgf7sdmK78VYbuo6gWWhT7g6e0SOyoeydbEMHdt/ha6uC3QsPqHrxv/6SclrH3xC6jZelzb8/Lvk/a++cwEA8Mob5uU6pRJGErn3dEf6b3Un3kO+o3PYGH3TCfN3zHPJ84FbL3l43ZO60ct8alLePyHIBYxX5PtkbZDZsLIl951hPlihJ1v6jrF8GJVxX4bL8mR6nXhM2D7gfNFucewq90Lzic3tfJ99Tz85ORjRuNiDhRDWc4bbMlaWe95xVPLKceY0PXYMD4J5pq7zFp8DKsodY53TnHXiYJ6I2UzhXXIFuvHzUDw/Wk4Ln0nIbWC7KvdpIM+Hq+r3YhYB25Dres6VnCPJYuKR/37OXMa5nnmD1wm8AvKpcvghPT7bTUzh3Jp85ynla3HNcmxC+u2OV98OAGicPyOfr1yUe4R+l7LwuWQnxCqf10pR3QfnQkR1opijb9N1Bds+j53G5xFy6fJ0eFcTLpfL5XK5XC6Xy+VyuV5W+Y8ILpfL5XK5XC6Xy+VyufYl/xHB5XK5XC6Xy+VyuVwu1750YEyEYiIen+DH6cZ+IHqW7L7N4fvqF1pW/8mnL60BAD6yRD+7nHdqUv1E6h+jr30KsZfe7kNP9TvtAV9f2CdZPSn0MpKF0F43+4EGD5R6h4o5/AV633Wv2bVt8bAtqVd/aUv8OPTWHZscjeukey/TK2P3+6yrl2ZqRMpr94NOCoXgKd1oiL+PLIRndV/25zbl/c221IV7Xh8fl2veqfu0V3bED7RNb2ODfAhtO+2/6vwx+XxCPI5/+tizAIBPr8j9bpmS6959TD4vGr8Qr9drxnsr8zy7D6ztS5SruKxeQXoa6SVbGJd9def0SC+SFe+R+bzN3rPd+N7he734PO4HT9/W5rb096Z6K0OccRv6zvDrsm7lIfvzXutqdXt44vJqiPFp9XXOjkkfkEfRh/HS5rTtgOee3l31tHHM0YtH5sGNE/L58XHxiy2Oxh78xanxsBdx1v9m//Vc9sFwr3L2veLQ86zfl7HPeBm2H3P8/dgfSObKyMQUAKCh9+2YPFdfEB7JU5dXcWZVmCX0bZOJQD4D9Ltjt9wjZdVxfUb9gRvndI9y9WfS/39Ex9jChOQPdGQsst9OzkoZb5qXz5srSwCA1pWzAIBN3YM50b2Ua8paWdG93M+vy/2PTUqOnGJbBZ6M7Ys2UrNP+94shNj3afemDvt2qy9/dVvG+3M6h11R9s2MeuXHNL5OzYof3OYTq6wO6g9VPydzZbMd8wEOldI+0GmhT99oidwXaWNygxqG90GP8tKm5Pj1pnxeL8djdbQqbT6a888qNp9QjDfrLx4pF3FSuUD9vvUqM+fEntZB9tJe+7JbL+vw62R73V9d5M0MvM+90w3Lp6Fjrr26jL6O03BPbQ/yVGrKTNnqS51WL1+R93VOnS1Iu61+5Pfl2s8+IffSPD1y6k45X9cLHNcbDenXac3P07oHek/zUTf4/aV8bNOaXpfzy3Rdxu7MOPebl3rM1ni+ZT30UdbhvjAxpt+Ra9KjTq8xmRdZTlOGDX3VpnuYVywjgbE3Oybj+Z5FuT7XgRTXeQWdQ/uad7imo8d5RNdg3L/e9uGhUNpHr9UY4PzYcZqtC5VvUxo+d+dxpKx/vNePGQhbhrUyR986+Vi71qV9HT8sc5HsgYJdw9bDd3aLaxWuhXi0DDTmHZaZfBiqwvgrDWeoDPJN+uZ1vC7JW4MlxcLg2kjr3EmlLuT1MLa5nijWdB2vz0RkZXHscR3QXhPGUUeP3a316H58zhg5fovUXbkglqkQjoalwvKTG8Jj4FjsWoutKkfug+ekDH++JHWbHpFx9oYbJPf9hVNHAQBHZ4W3Qg5DZVLKdlmf93i9sCbWXJcxdOL8MMihieNnXp8La2W5L2PYsi7IAcljJlD+lwgul8vlcrlcLpfL5XK59iX/EcHlcrlcLpfL5XK5XC7XvuQ/IrhcLpfL5XK5XC6Xy+Xal/xHBJfL5XK5XC6Xy+VyuVz70oGBFdO0H0Byu5VYCKGCLnoG+kJYBKExhIlUigKHUHZJAIBVSgV9P/7dJIPXxNCQYecQFkTwTalL4JCCrAhKUSAKAUQWysQjASr2uoRFEdDz+LJARgg1nKrFkBKCLwhKLLcE0DGrVT26MB2dz/K2FahDFcplVBSERBjjRE2gQDdo+9ZLMYju2IQA0W5WiNQIBNLRUfBRVQFnxboCUxR8QrDiuALYPnDmAgDgzy4IDKynLI+ZeiWqW0PbjhCSpCgxlPXRaNQmxZy+C0CuRiuARQjbGtWyTY0qmLI4/Lc2C6JhjOZBuBgXVhlYk2BG+V5Z27qtbbnZkLoSqMK6DUCFcu5/GNTp93F5u4lJBWRN9KXf+gFoJHUrKhjJjtoAATN5o6ugHsY++4IQ1EWF2zA/nJqJ4XOEY2Uwm8IAUDNTYl6nQ8/r63kWhFTQ2K8UY4BfugsGCgCbCsrSIYeREr83PMc9o6C9Dz7+JABgrSlfvGd+HABw/0kB/dQ1BzAetzpSro1mO1z7xJScMzci/dPSopRvkvH8a586AwD4zScvAgBOX5LPlX+HeWE74r7jcr3Pv3FK7q3jfExBa61VATGGWA7jX+qSaj9XZo4AAEZP3AwAeEJz5qfPXYzaYlKhhXPTUv4AKyRkdhcMKkBg9bNBoOJwkCL7k2K/thVsxbzeMHnnaFFii0BFAvgYJwQk9loc99qY5eHgPJaX82xNY3515xAC1FR5QEUCtzhvZflf+mpaYcKczxjHhKPWFPCZKByZ0DsLt+OY4PzFeIQBh/VaDVR0zkMx/iwDIFog7n7/TYfxZfMMrzMcyGhBnxTbKADSesMBa4w/xmNhRtqyNnMUlUShoQo66+n8XNTvlDSntDdk3BKUW94SyNjG04/KtW4Q8NnRv/jVAICnFcC4rWWtlaXdZzVJjmwIvKxzRnLaFb0v8wL7heuP0phASjkv3Hl0FgDwnRofeIMcGCdTCjEjDDHRevaRZO2mebWq59S45tDY6nfkXp2OAZpqvg5wtpx+Y1yEdduOzFlT+vnMpK4HNCbZP5wfljalTZhvODZuPSJ1b14+L+832MqHR2k/Ra/VDM8DAXCas/bhunEQlq45V7/fCWNRzuN6l/mFudzmekIt6wpyD+A+vW5pJMvfAaypsvNNeB3AqMPXFVyHsMTk4yV6HTvuLdCVskDPvcTzewb4bAHASaE4CKXXstV0XJb7Eps7CmJn2zDndMPzndyztSbrAgIaawpevbS8FpWRcNvelaXo/bNXJG88cVnO57qAzzFTN81FdbAAfkKLCVjErucSzt9chhHyv9WWOmzqG9sK4+wr7Lmq47el+ZdAxfPrmksL8XqRmhpRWKi+DvmE60cz/zD2x8q6MYDJG3weIuwyb96g/C8RXC6Xy+VyuVwul8vlcu1L/iOCy+VyuVwul8vlcrlcrn1pzx8RkiR5V5Ikl5IkeTjn8y9LkuSTSZJ8PEmSDydJ8rkvfjFdLte1JM8LLpfLyvOCy+Wy8rzgcl2f2g8T4UEAPwbgZ3I+/x0Av5qmaZokyb0AfgHAnXteNU3R77YH/Bv09/J9etF66kFJDTPh5LGTADKP0vyInLfVlguNqTdpOniZc3xnOT7iYrUWPGQdelF69F/GXlTrSetoHXJbmWyF4EGKvS6rO+Jp21SPynhFzptSzw29tPTg1NS/ta0et776/orqRyzksBpCnYuF4PmZUc/S/NEZrbuc0+nGHrGqXoseQ7Zbff4YgMxXM3L0OACgcUnYB/REbWp/0+dz/xHxQN0yKz7tu4/NS13Um1SbFe8z44Cedut1K4+J6TpF7GOml7apfqRe2g++2GIi59QqcYfxO/Q2Mx4otgX9Xd1GXBb6u4q1WtRGe6mm/jp6khhP7IMKuSCGA8Drv8RMhAfxEuSFcqGAo2P1UPfgcU7jNigW4rFneSOU9eJxTHS3xRPHuGXbzWl8kYcROAUaP7vzh/Uwp8Y7Zn2XPLZNP1r19Hv23nzd1NxGZgbjd1et5TyNiwtrmwCA//npcwCA9z4q7yt2Ioy9YxMSvzfNC0OloPmo32iH+yyMi5d5cVTac+fiWfns2I0AgF9/5AwA4LefljFwLsauYEpt4idnpYz3Lcj17lRuy7gOvfaGMA2Yq2y/UVXNM90JyRN/9LQwEB5dkXlgR3kOp+gbDnwJuVFNWRjsu93sHetJDeM+jP84D1i2BcW5iV7D7Za0f0fzCn20Ne3PSeVv0FfL65bo97V5e9dctbvcVMZY0blwZDib5UXSg3gp1guqvnKIwtxbqww9z44t9gH/2cR6nIuJGom1SwuBMyFHenMpuz6h2Ae71w28l81Rdu2z33zN+YTjux3mZKkc80IoUxJ7qa0nmrmPYl13OnLd1W0ZE6vKM3pOPbpnN7O6k6vy2lMyHtMlyQvt1WWtq8TsuLZr81nxPne0/VZO3gsA+Pr//qzU4e//OACg1xImQv+GvyTff4OMjb/+gMTwX3nN7XKdtct61PtpnUrjkleKmtfLE9NRHUs69k7OTmpbqOdZ1xOtNTmyzXuaN85ezhJbxhaQtU13S3NUGJdSVsuHCKyVnDVpJuV8aWzZ8y0nZLMh/bW0sQMAOL8Rsw44h3EeynhR+1uXvEA9iJfqOaLTDm2ZN/9TXINZ8bzgC29L/JBfw9zd0DGx0ZS1OZ8zKM6hXEuyjZmvkkJxoL3t80KH/Cedu9CRe3Hccm1k8wp9+8PW9cCueSGsnYYzVTKlw981cyXXnywH451r8N25kGJdArPK8Dj4mrmJTKt1fbbhvP9URcbev/mtTwAAfu8haZu5dckr5VfJ6299o5Tpa+6V55nFqpRnblyuwxxnNcDEKfSj18NYONMjUu/bp0f0XRmHU1Upy51zMv5HK/G6jevJsvYLPyfPr93N2FRAtm4YrUq/VnPYbZapYeOFKhpGRy9wyIbHQbj+VT8FkKbpBwBcucrnW2kaSA+jyIs8l8t13cjzgsvlsvK84HK5rDwvuFzXp14UJkKSJF+RJMmjAH4DwDdf5bx36p8qfXjlymreaS6X6zrQC8kL66ueF1yu61kvJC8sr+Q+f7hcrutAL+g5YtXzgst1kHpRfkRI0/Q9aZreCeDLAfzzq5z302maPpCm6QOzM9N5p7lcrutALyQvTE57XnC5rme9kLwwNzvzspXP5XK9/HpBzxHTnhdcroPUfpgI+1aaph9IkuSWJEnm0jRdvvrJ4gkMfjy1FtF7Zr1NfN3T/cF76pGhR/aEemRuuu0EgMzLRM8jfUSdLfEHN1c2outm/lf1r6uvp9dPg1dp0BedRt+huG9qz+xtT7FMFeOFDF6VNInKfKfyAXg+PdBHxsV/NVGV9+lPpn94wINJj7bxzuw+v7Mh/xJMzw89hNVp2Te1UqX3TDkBndiHz3Zb2RJP0+p2M6rzgvYT/TtF/aO1B07EXvRN3Te6eUb2g524Rfaf//hz4nmmn/g29W+PGc+q7RPGWbHHPVLV41QtD+z7Th8buQmdffYnxf1erR8/cCU6cWyXjS+XfrGixhc9T/TlWYX+1I9Dn+zptXx59HzyQpIkKBcLoQ8aHd2TPOGYMCwEs7e53ffZ5pMC9/vVmGffMG7pHytqX1fIpVDvbqeR+f+stznUoRjvA52xMLRf6VE2/W73/7XxwI2HB71vMQOBeYrxSh9ntSjnnRAbISr6mvdpmPhiHHFM1iolFJRNw/HJ/ZGhTIS7FqbkHlPxeOxrWZjTpkfE27wwIf7ARPeJZlvW5xej77P925qfwn7go+JlfmbpSlRnerTJwjkyIf7E8Xrsic1864N7ZGf/zX4Yvk93+JzXNP1JxErG89CY1mNb+6fT09fqcWdbkbGQN54Z64xhzn0U80tFy8n+PGg9r/UCEiTF4oDHmXMic/JmQ/zDz63K/E7/PsfApIJA6F2e0KZgnzC1Z77leCyFPq0yb0g80Y++ot7a5tY6yjpOJzTmOG/neYnzxDwSOA0lXXdwPaLxVbD5BXHdwpxtGAiB1aLrjuU1GeOcb1i38+qxf/yKHC9sy+dT1UJoX8Zq67LwV9rL4mHu6rgNaw0tw5G3fgUA4MEP/DkAYFTSAs59zpvke10p00hNrntihDlNXl9U3suR47dEdWHuIgcqcKEML4R5JcvZOpbGJK9wLF1SrsD5C2sAxAs/P1qLzknMmgOmXzk+yWMoGKaW9ahbdo7lfJAT1O5KHdhfW8pcWdWxwP5j7DMXFtrqA+dab2oO14KeT15IkSLt98JYsrwByy3ia7IvApdEz2cbru1Im17R47auBbdaykboxmvCeQ3cCWW0jOjRPiskhUJgIHC+tWwTK8tCCHU3a3DG8oDXnWNAj6yjZbaUtaw2X9h1js1XZB9wfbutnv2GrpU6W62Qn1mXgslR06PKMNPnuub5MwCA1gU5bj0p+YG5cOTmuwEAx4/I896DX/4GAMAH7rsEAPjtM3K/Kw0p1Jr221PLawCAe5RX0Dwr65gqWUjaRlsjui7R+1mOXHhWMyy4tFrDZF2+8/ob5ZnmllmdE8z6imyDSkk+nx3Tcamxsjilz306f2/oeOYcxDYMzAINtew5z64Xshjc/TnL3gvMN/aVXL9eufrPBJ/xXyIkSXJrkkhtkiR5DYAKgJXP9Loul+vwyvOCy+Wy8rzgcrmsPC+4XIdTe/4lQpIkPwfgLQDmkiQ5C+D7ocjiNE1/CsBXAfhrSZJ0ADQA/OVdgBSXy3UdyvOCy+Wy8rzgcrmsPC+4XNen9vwRIU3Tr9/j8x8C8EMvWolcLtc1L88LLpfLyvOCy+Wy8rzgcl2felHAii6Xy+VyuVwul8vlcrmuf72oYMXPRBk8KAaj9MsxJIRwDwu0sCA9wugI7Gqtib2quyOAHIJ9LAzHgnmK1RqKBQJwUnNE9JogklIqZSwT3lcwwB1+i7BIwoYU2lFV8M6tR4Q8S1hRBnaUIyGRjUsCEyHIp6RQoKQUw58IVqG6CkljW+wGLhGoWJk9CgDYIqgylfYk4C4P9lVMFEilf5G2vCV1XFc4yLEpAZfMjEjZ6h0FMT78STnvY38UXb+uAJU/eU768aMXpTy3zcjrt54SWtzdiwIHKmmfWIAOlQH5svcZe2xvgtC2FVRkgYoBgJgD77GvO3odgnQoXpcAFV6P8cSrMaZtOTKIUDGqkwXsHAalaRqgmcAg/CeD23HMWaih9onG+sDnAcwm8U2A39krAvTZOC8cJ46xOYXdEJZDeFGv1Ri4NnNQ6A8DQEwNXKhoQFp8nYb+ZQwP78e0IOl7U2Fu5QCR1TjS7925IPmA0KfbpgVCeGlHxuK4gnMm6wKH6m0JrJb5oazwwkK/h+ZlAaV11qSd2M7rj8u4ndV7LijIrKYgVcKXKMZm2hJIW6Jjr6dteEmBaYQPMQ6OTEhZZsdGta2kD+6aldddTXHMG6X6qJYzBipaaFRX89tucBJhXezHrF/j8WaV9a8cbe8RQreq7X+lobnVxAtBfARedRUqzH6x97cgtoFyKYipcBi3YE8SFErlXWODUCl5Tbjd+XVpG0IA1zXnVrRPxjTWORYI2sybJ2wuDxBUM0es6hjkPNdLU4xVpP+Yt/esounPAIc1ayIqQNsMvI3KQIoxtDTURcfEmsKPGxrv0wreZD5Z2VSooMIFS9ok9ZKCOqvF0K5FXcsQqEigYVHXNCXNA3x/7dGPAQD+WmkJAPC3vlQAiaXFUwCAC5oHCAdkv80qVJpwwJ6C00YUsLhX21lZgCb7lTC0hoFM9vppAGdSBCamJl8XwvpLjhaomJr5nLL5gDEXgJfduIxcazH/TGs+PzI+okcp37iu+luaw9k2pYnDuzNSBlJWeClhc2b9wHFu10acBxjzAYKa0zdjVQUEa/44NinxPKWwzW5DxsrutQygkGWdtzOQXRod+wacVynxGIMPey2ZPwah0nHMM+7CGOjF4LxC4er/lmzzBq/fTeR66wqfDG0+RBw322HOk+OkjudjSrjd2JC1SQBfa56ozsk6jYBWjjHGLNv7804tAADedOtxAMC5K7KWYe5a1lz3iJbr2LHbAAALCtrlM1XPwOmpLI8QZhy3baFcCd8hNJrAxJBT2swl8pp5nOOY80VfIZNTkPPmFLDPdV+A/Or3c9eJBDObfs7ykMYTy0MQsx7LewDa/S8RXC6Xy+VyuVwul8vlcu1L/iOCy+VyuVwul8vlcrlcrn3Jf0RwuVwul8vlcrlcLpfLtS8dHBMhEW9pATELIfjG9HXaN34eFT1QdfXcPnZR/F0byxcBAKPqR5wbE6/NwtRcdP2+8aWHYhnf625Zz0+ezyrjNMTet8yzSO+z9RqpD4t+HPVfl7TMJfUH2fPpxQNi/6/1RiXGa0+vbyhHoYiKeow6VbnWpQ3xmFqfzID/koyBghxH1Os0l6rfX++1pR7VDfVETY1I/9i2I5Nh+6lPAQAuf+BXAQCvvvMtAID//jHhQPzOw3K/Dz4n/f5Ft60BAO5dEO/l8SnxEc2pL7BWLkVt0EeCtR1p76UV8WMtq791vaksBPUe1bXuMyPiNaTncKJW1WtLfwTWhPrA2A9W5EYMSj1yhpVhfZ6pice9fJ+HQaVCAXNj9eAHHFN/GOOJ/i/GrK2zjfVwHKFnLfay0XN7QT1z9L7Sz8x4qannt6qe3kK5HPqFKhhOyIDnjP5K028F0997KamORGWnFusS6ztLzwEAmhflWByRvHH0+M0AgPpJ8Q2uqj9wQuN4uiDla+j3C4ZT0Gs10dPYpljmrnIUKrNH5B7KMCnovTv062q/sv0zv560WacV+/qYczeaki/oIwxMljXxUjfOPgUAaGnZi8pCGL/7ASnHjLBd6EfM+BHSr6U6x+jevADrYc68hklUxr7x67O9lzbFr39Z/aHMLzNahgXNVYuau5orkuu62+KRZK4tKI+C+T8wDzgvDcxX6vveZ5xde0oGGQVaV/pIj0xI23G+OaVty/UA/f7B86xz7ADlSNuYPlHLwQicG5067dqgUiwOMG6oPJ++XXPYe+36xJYWwCDXwa4vKHJCVrZ2orIv1KVNts8+ISfqeqMyIWsnxuvSTnzdqVoJN85IXty5cAYAMHbbqwAA1Zkj0bmMva3T4komW6XO3HREvMzkPNw0r+sR9Sp3tiXPtDdk3MP4vsle6VeU1dJQ3oOyE+qax8nCqBTjNRFbuqrv09fM+CLLqd3tZZ5h9rO2a95a1TKprNhfqYm5CueRSryeZOxnazJRNmeVozqyDbsN8r7qUXmzdeThUaFYQnV6Du1U2qKl60vr97f8obCGMmwsNiXXpZxnJupyXfrD2bZlvWzGN5N44/MF14J8XazWUNM5kvnZcjco9lulP3xdF9YZ4Z04PrI8E+cP1mlQMdvNciN4vc3A9pLPuX7gdcn82s2JeeSyjNuHl6V9FnQ99pdfcRIAsHPxLICM51bT9QN9/r3b7pUaKvPkPX8u8/2fPyP3uGX9MgDgbbdK7C9A5srpy7IeIO2D1y9p/3Josd+KNX3WMmPBzvHUMGaGnUsynpeOS9NOfBZa1nzM8bs4Ku3dWJJcRx5VmPc137TNs3M2rq8+b/QMe4vrh4qWL+SjfhyXVv6XCC6Xy+VyuVwul8vlcrn2Jf8RweVyuVwul8vlcrlcLte+5D8iuFwul8vlcrlcLpfL5dqXDoyJkBSK0d7hg/6/JHpNXxm/Q8/KWd0HlD527sFNjXVi/0lV/SLWp273l6WvtdfK2An0vtGXTX/fwH7iYd/OTlS3zEskHpPgJVLvIa/X1r1Q6W1pFaSs51fWAGR+Tu6VTu3lxe7ofekTKwdvjZZztIievreqvt1K2DdXykA/D71Kdg9Z+mjo4xvwlmlb9YwfjF5zepPqp+6UspHRoN7Jm899AgDw3r/xNgDArz7yNADgsSvyvZWGXO+JK+JFYzxwv3nuM02f0upOC2dWxSv4zLq0/1pLvkOP0HhF2uv4uHiOatom3Ft2rCyfN1eWojpkDAP666tad+7TG/+GVw5xFfvqqL7BeFiOSOYLHY+uc5hUKRVxw3SWF7I2Ss1xvxruheyF/aPl+vRMs2/HdIzRQ1sIjAX1j9ZHh+y/G4+3dm/QLwfs8q7nsC+S4vDfdumBsywE8j66usdye1XGSnX+Brn+tHgw13WsTmm81noyRrpL5wAAmzvymgyF0ij5DxkTIew9XY9zT0W9ivVjpwBkLBXosa5jgGyLouHC8MjcRl/ghHqXO7onOveyZz6f1PzQ1vIUtcx9jf3m+WcAANPKznlqXcqzql5p5nL2e13vWykVM2/xrveAbJwyh1hGAr2sPe3nzFcr/X1Czzuh/mrOA/Rfc//2rbOS23qBsaIsBHpdc+KE5WSMs/zM2Xk8oMMkO/9w7psZqUVHxhX9vWlH+r3TiXM0v2/nMypvnmPfLug+8cwXvX4aYqscPOtc0+QxkyzrYC8xfzSjMmVljvOJZSG0dUzNV+Xz9U99RK8n+Yi+5G1dt6w04rn7limJ29ccmw08hc6puwAAl5QxczKs8XRdxbWNrt8m7ny1nN+VMiytyvcqRSkDx8asXod1tC5dtinrzDHIdQvFsUsvO3kjdq4Ma7lC7Dsf07FXKFWQFNVDrnvUMwtkXC314xv2ANeWzGmZf1/Xl6bMnE/IWOH4zpgHMScoHOkn18YKfu/C/vzeh0JJgkKpgp5yZwZYCAnrpO+X6PeOczbzA3Mj25AjtMQ2bfGoOdb40Hn9xPQJlfZ7aOn8PK4xXdN1JL3xXBOXNX6Yv8u5zJQ8dko4k3c3ZelHZczLfZxv1pWlQT7R8UlZd7RWhUfQ1fps6PTy/ifFw/+rj+7guRV57w23SFnIQpjpCLsg0XasTgt/pVCKGUXMJ+975AwA4M8vx1yfqaq00ZTm/fZ5WdOQkdTTMZrodbl+4fNFeWJG3te8RP6cXVtz7Dbb8bNce9dYtmsd5hr2D9cuZCWx37faMYOtOCP8BnJfQj8xVg0jLeP2XP1vAyzHwa6x8+bWPPlfIrhcLpfL5XK5XC6Xy+Xal/xHBJfL5XK5XC6Xy+VyuVz7kv+I4HK5XC6Xy+VyuVwul2tfOjAmApIEaaGEDtkDHXpKxK+Ree+593G8nzi9R2NV+d4x9RXRh0xv60RNPHXW02z38g3cAJYneKaT8N1KYB3E+6eSz5C3zy9ZCM+srGuZlRege1bTg0gvbPD3jYon5pnLa6EsADA7NhLdj34dljnbszrmDtCLU0wK0f35fZSrWFW/pN1/u2b2DqWsl5RHum7o8wu+LvXz0d+XedekLBX1KAUGhnqV2OZd9VTufOR3AQBfqB7od9wt/u/6wqKcl+i+8132p/HAsX/HCqiX5dxT0+PROfRVZr5Mafe+esfbq8JA2KJnWdsm80SqP1vbnQyKWsV6YUX0yHdzPMtZ2+vYUK/bivrFlrcaUV2PTBy+fZ+BrB2A3W06/PdOyzbJvjfc78fX/B7HQOCcpPK9ctijW/3vyaBfNC+HUPY71nuWMgY1LvK8iRxj9M51NMcxt5W66qXUsVE/Kl7m51py/8efEm8i43t6QfJKV/dc7xkWAvd2r0xOR+XptRphn2XsYkMAQGFKvIxkIeSxVOx+2jaf0JvK/iEjAXqd8X41Op+xUtG9t1kuMG70OtyH+k7ND8s70uYcM8yRnH8qpeLAPuy2Dv3u/vZPHtH8Ui9KWab1Hsz7vS1ps60N8WfbOao8wX6wHnfpL7YVcyk9leWiYW+QC2Da/LCo1++HPrFjL5xj+CJ5dbUshMAnMJ7pgfxhxiZHvGUA9dI0jH877vfavzvUJde7iqiM4So5bcL4YP7g8Qb13G4/9Yicp+uA+vGbAWT8KUUh4IHFKQAZP+a2IzI3l/tdXNKTHjoj4+y1J+ZZyqisbPcJ3fd9RT3mzY6UybYZ1yp5dWQ/9xHHNtcLRfVAT5U550p5OlvixWYe6ihPhnkj5DfLhyll842NMctCyJubqIyVAa3r8DgIbaGxzzVxvy2x3jF5IeQDeqX3YB7Y/eIPk1KITz3jTMXzdcateX45L2/dYHNz1sbk0Gg5NE64nu21lNGyi5PEdWGpqGySsZidEddyd06K+2m//Tvgfc+ZD1hGjlWyEM4of+6+EzLXrj36cbmurl8qs0cBAL/2yccAAD/zUYnPYgL8wBdKrnjrTfLdxuULci/Ocfqsk9U9fpZhHW6alvE4PyrrgDHNRTfOyvis96SsTV5X1zJl5h/NcQUyLAIjT/kC2m92ncK+KpC1otcf1z7jWrzT64V1QlefC1o7ymPQuaaqMdHU3Mb1xYweyXXgXBbmqoK0EZ/X+NyZMVeGx4Ed34G5YPJNiK8cNkae/C8RXC6Xy+VyuVwul8vlcu1L/iOCy+VyuVwul8vlcrlcrn3Jf0RwuVwul8vlcrlcLpfLtS/5jwgul8vlcrlcLpfL5XK59qUDAyv20xTNTjfA/igCzWrlGD5nQUjUuELvRmoCxBgE9g2HG2Wyn7Mc2PV+DCrMIEyV6P0BQElBmvfCukB7mgr5mxuLYW7tbYWMKdCkqpCyp68I/KetoIsTEwJGI2yM0JGmghsJkWEbEhLJNm7r/evluC0I6ljZ2gmAMYIQqUE4C4bWObzfi0E0AQ7EE4oxQIWwoABUVIgIgStUf/4YAKCrsBICkVqXz8nrtctyHYWNFBU+ViFQxcCO6v0exhXKpLzCXVAdBZIo+Ky5tRLXOQe2Q5gbIS0EHvF7BDZaAEoG/SPca/hvfBwDqzsCXnlqeQ0A8Oz6TnR9xtthFmExFnTGtu7o0Gy2OQYUfFONATn2e1RZu2BqtMY7mhLYfJHmfsbxt5cCLMhcmnA2woZCPGgcNRWAyPE/UZXzGpcEyEdoUKMswJ3VNQEt3qLQoZvmBejT3Za8UtOxRPE+5bHxqJyEoxar9QGQVGViCgCwrTloWkGVzEF5edfmcTvmej0FGhXb0f3s+V09P8CdpueG1onl7jXlutNarJnpelyuXW0fAEMKOOrocQBgpvdgjmwbMFEA87J/y72h1wnnB5ASQW0KoyUIy4CvbNtUBnJrNzqvMJzrek0rhQKhNCXbeYjKgwvmnce5nEBmgngJvWOfBTRVDnQqgAP1taSC4euCQbDi/tYkA2OIc6qOe5aBcRJiWdchG41NAFluhOYBzpGlUwJkffSizHM3j8n3JvuSd+4dlfu3VwXU2tySY/X2e/GHn3oOAFDXdrtheiIqK8tE0FxHy1TR7poO+VfE/MGyEoRIWJmFjOaB7kKeaHDsxPNBBqdWQJoCGcPcrUd+XlDQ4narHdZXxaJC2QoGCpkq2FS7raj9UynaGL3aHDM499k8kKe9gHvXgxLIGp9wumzeiSGUdtzmrVsZT8ViDEK064cMxMf8oX1jAO18jrHw5GH6zIG3jJur93cekJXwwFCOssT6c5dlTX1sUl5vnP6UnEd4qc65T1+W9ca4Dup/93aBGL/6xEKIRYIKs7LE49CClVkX5oFFfRzg2qRekTwyozBC5gnCiLnGKdZ0/V+KJz87tvYrxhOfQ5g3+q0GtrSOAfLLfKx14nfqCkYcH5Mj53muUdr63EExzxN4y5jiuqNn8gFjsde3+UVFaLh5267N9ppT/S8RXC6Xy+VyuVwul8vlcu1L/iOCy+VyuVwul8vlcrlcrn3Jf0RwuVwul8vlcrlcLpfLtS8dGBOhkCSolUsDPuKy8XMO8gf0U3qU9MiK0M+xt+9w+PuDvuYU9OX0jN+JZS3k+H35LrkM08pvmKiLt4U+Gvr8qJ7Wdau9DgAYq0gbTI3E/t3tVjt6XVPPDT155BtQ9I2RH0E/EH3ljXY389f0Y48oFbyipp3zPKN5nvTMA00vVMzGoIL3LHgdxa9Nb2VXj/QfsU0HfMMDPsRM1nuc+3k4rxDdw9bVlj3fU6TeeOORr1Tr0efZ9dUHGmI7brNJ9crXFejBuDtMSvt99FqNIXES9yd9vs229Hfwp9J7R4ZHKfYuDrsfkHknrWzfxXllL35CfB59k3ZMWf6IzV0946Xl+CXbgN7E2uxxAMBzq+K5Y74hC4F5KtExkyfr76SvszQyOuBZZOwyLzDmeqm839FxHbgAxbhfrQ9zwJuoKSwvb+TlF5avVB+NXod8pf3N+tCrOczzHtgHJhflxVQhx68fvIll6ZcSz6tfPW7CK9MGWV6y3AnOV8q84dxZyrvPtS96nwfeN8yUq+X56HvhvPiadv7P2pDrkKsDJazfeHcZ96/nl0dY5pqJT2pH1wGc/xcmxNvcXFkCAIweuxEA8PD5ZQDA6k4r/v7Z0wCAxrNPRO/XT94GADi3thU4PA+cmJcPO3KNlOOOayVtP+vfJa+JZazpmidcJ/CCYkbBgEfdjHO7dqPYZr3gX459zOE8vU9J/cutkBvTbN6G9kNqYynOiflcrhempBjH1SA3xq4XwpkAhrXJ4f03xTAPaRMEr7vJ5+H5gPMQ2V17+MD38syH3JQzdxcHcvUL17AcY86IyxZyWhqVwbJULJPr0saWfl/On9L1JqkGVeUNcC1+St+fuSh5JNkURsLjl8r49KU1AMDb77kJgLADgIxdZtfgoYwaq8wLnR7HlJZpNGaZkBfQ0vyyvK08I+WFlYsxMyvkmWT4vGG5Asy5vZR9oPmqLvcdmZhGa1Xqz9jqcW2qda1Oz0avmXO6DeUrkHmSs6axscq1FsvKXNrJ4fcEbkhf88BAfkJ0/71i//BmDZfL5XK5XC6Xy+VyuVwvq/xHBJfL5XK5XC6Xy+VyuVz7kv+I4HK5XC6Xy+VyuVwul2tfOjAmAlVVj0rY2zhRP0/YzDr2meX5NAb9ofvV3r7xPA9insct+Ia1THNjI9F16B9OjX+fHjfueU9xD+W0Jb6eXoF7KFejcmRtEvuUKb4ua5WDL1m9fP00qwc9QHbPcyrzNg/3nlEDjIR97tdrP8/8QfG+0NzTnp5F6yeirsY9yGMW5O2zTJ9byfT/Xl61Af+u3nezofvCatvQrxX28x4oD6LPj09JG8yp95UeqUrx8P1GmBQKKNXHMraF6Ud66FmzMTVBJtXYq5wEH2De+B7uS706A2F/19j1bQBZvPRzfLk2j1iPIj3ttQr3VpfzG/TvapnJUml3hZVwQvdqT/oSFy0d5xtbDb2Oeu24p7YZ89YfXi0Wd/EAKtG5rFuz24uuxZxT034K7BK7J7Uq7M9u9n/P87oPsBFy9k63Y5lMjVI99ljvZi7k+WTzYiLsDz/006Glj18NeJhjxsHec1zsmR+Mt8OXD3ZLyj98jOTNPwPjOXhuY/ZSyJXF4Tk/fw/24X00XPubJ6zCHus5eSTv/EzxvEIGE8fyZkvG4pjm0LkxGQvj+npDx0ZxVPOJ1pGe6G0Arz+5AACYr3I914nukfFDmGvUa6zeYjvnMWf1mQPVv83rkeMU/L96LPSVdRI4Q9YnHDOfOCbIk5iakToyHuxcDbJ5CgkqiPMnr2X5MC+UhbDfGM/Ov/rnNi4Oez6gioVCmKvI/enuiL+c/n7OjTtNwxHTObUW4jNvXXD1tto79w+77l7PHvuLl/2uO6ng77dsNzO3Nzdkrj4+LevLzqowVMZuvC06r7UmDICVh94rr5eeAwCc/MbvBgB85y99HLfOSjt/aRLnSfIUijUd33swZzhOR6txf7V1bbO8Jrltw/TzhHKkyIPhs1TakPPC84Nl5GjbkLXBOGu2JT9tKZduW3PoRrGA6YkZAMCI8hnIW2EO21TO03PKoOF6/ci4PCcuzM5EZWQuDUwkrnkKypHRMmY1VhZDP44fu54ju8k+P2a8qrgN83R9ZBGXy+VyuVwul8vlcrlcL7n8RwSXy+VyuVwul8vlcrlc+5L/iOByuVwul8vlcrlcLpdrXzowJkKapuj0etmeleoXGXKm/P8evp+8/cL38o/a++R/nnlKysXYTxn2mjd+fL4e1/3amx36/OT8MvcyN35+alJ9PLPKVNg6+3RUN/qJoD6hgT1W6cnTfejz/GL0NpWLBRSCj8/6Z7gXdSmq454+vD3236XsdfJYCoyCXI5BcbiH+urcg+H7Jtu9rIftUz54rV1lHvA4J9HrNd3DdnlLvE/0eVmWhRWvS3/YnHqp6NPK41gcJllvHP1fYXwbb7Pt38zzevVxbhkb+do7D9l+tiyV4NtlHjE+zL1yXMHEl90jnSwV7qnMvNNXb12hIt5meujoF260pFzMT22N+9EK/YfZ3uu8Z1N9enn7J1vvIlkIWf8VorJn+7urv8+0XbMp97PeZuarsL98OfZWs//73dhXmLVkvHd26MM9vJmxru7TH8xtOdwGw4EY3PN+4BvRK8Y824wqFDTmn6cX/1qWnWNtm+YxNfbPTsrzIw9fJ9i5Axj0wjMGw5Vy9rAPr0NOkntst1pyHR1zmRf/6rmL8wl9/q01YaaU6rru6Mr16BfmmA3zjLIPinX5nGO2NnsEALBYKKG/I17kzpZcm2uTQT+2slN0ruJayrIQ2DYsI3PSpnqYG5041/E63Pe9YtqS+WpbPcwbDWnLdT0ubzWi4/SI5M5p3Ye+FtgJpXC/Xjo8Rp4vC2GvvMHYHsgjOWsvOy/k5xPmj+fHarimlKbod9thvmlcvgAg86EzDpt9qev5dYlTxuGkzpFcYzMO91rXWj1/FhswyFkZ3g82PgY+3yOPhPc1Djo9ZWcFz7x8n23YC+tLiXWyUdr6nLK0pW1b0jG4Kt7+5tmnAAClqVkAwC9/8jQA4INPJJiux2uMquaO8AyT1YallXuautW1amyTnY58vqJr6C3Dd2G/8nudjSsABp+1yODicygZSZZjVtb8VKnKBUdqkhO5lm92uljV/56oy7VGbR278jnzx/K2tCc5DjweGZf2npoY1a9ta9k70ZHjnM+TRY1h5kb2s31+5fOgXZtZXtVea2P/SwSXy+VyuVwul8vlcrlc+5L/iOByuVwul8vlcrlcLpdrX/IfEVwul8vlcrlcLpfL5XLtS3v+iJAkybuSJLmUJMnDOZ//1SRJPqn/eyhJkle9+MV0uVzXkjwvuFwuK88LLpfLyvOCy3V9aj9gxQcB/BiAn8n5/GkAb07TdDVJki8G8NMAXr/XRZMkQa1czmAO3e6+CpwHUnv+YJM8mMxwKBqQAYosrGsAbGLKFoAW+n0C0AgJq40LeKOayuu6gg4J5tg+/4ycv3IRAFBQ6EcAahQn9HVl6H1Zzm6zqa9jqEhFrzdaraCjdSkmChKqSIhkQMXhECArwl0IyCPco9Eh1MVAC7X/AgTKAG4IQskDaWX3HQ7UGjhvAC60G4SkrweAaTH0iTW3wBvem+8TDkWxLTaaLb2vlIEQp3ISX9fCXez7WdtKPxNeU86p+4ukB/ES5AVA6pcXXxbAt+tb0atBoKIogwnpa4XbWGimhWLtD6jIeyhoVY98HeCkCv9iPxMCxTrbcWzrxjoQyNprabxpOSYUyIqOgtg0Lso6zkf0c5uHCC9jW9TD2M8ggwSTWZAiy0QwFeFsFiZn+8+CGpsKTmP+ZSwzb2wrOGmLZdcy18tynQm9/9yY1JVjqkqYJCFC+wDx5UH4bCwwJ7EfeY/Qrwa4a8U2KRioU8l8vhfgiLEc5qnnCXh7kfQgXqK8AKQhnrL5II4vG1cWqPhSyc4d0tYW4jocnJg3f2T5nflErsexUUxjqGi4s51bk3gdEN4nzLorccqxy/VBN5HrliZm5D4DsESFSrYa6DYEWBdiucz5m3DheJwzRu09me85BphveGSuGtPvER5LIGO/LWOPq0mWI4PG6dyrQMVtzSPrzbhtmHeoxSlZY7U3VkOdbYwRUJ1qLunlxGAeQDEDpsX5wn6OPOifASoW+9KG7Ge7bsgDg79EehAvQV7odztoXLoQXrevyBq5r7m3r7l44pa7AQB1BfCtN3X+MjDadkmOBHPa9Sdl17d5kFurwTwRXdW8jqG/4d08wKLNIwa0zrzRNuDdsoEIs4ycOzmfBUjlynp0fpVAvzEZI1UFsba1DcbrKRZG4+cIQGKS+cA+X3TMVEWgId9uaBUIVGSdjk0K6HBKy27bpKxlbK/LOG6vCRSS/VuZnpM6TMXAf7ahnYvXNiTvEdi53e6gXpK6EP7MtWhV88KEQhlfecOCXGOnEdUhQKIr+9v3IA+EGGJYn1RSrtkacn6Hc6h5HrRro0L56pDpPUuZpukHkiQ5dZXPH9r18oMAju91TZfLdbjlecHlcll5XnC5XFaeF1yu61MvNhPhWwD8r7wPkyR5Z5IkH06S5MMrKysv8q1dLtc1Ks8LLpfLyvOCy+Wy2n9eWF19GYvlcrmsXrQfEZIkeStk8H933jlpmv50mqYPpGn6wOzs7It1a5fLdY3K84LL5bLyvOByuayed16Ynn75CudyuQa0P9PFHkqS5F4A/wnAF6dpuq9/Mkj7PXS2NrJrFOPfM/K8s9SApz7HT3SVUgMYZB8M8zLRw1zRMuayEHL8l/T50KPShHyfPj/6eugPnKgrQ0F9QJ2NK3Jf9XmRicD70D/Y1zqxLayfay8vVa1SRg2sa1HrGvt9qQEvajH2DNFPubaxDQBY3hLfDz2IbGf6r2rqA5oeEQ8S/Vj0VltfoPX/2GMWP5Z9YOMiHfAEBm9iqHvso071/UQ9kKHupdgPmnkg45iiV4o+sNmxETmvJXHQbamHXr1IlutAH96FtU05rksb0yde1+seGR/BQeqF5AWkKdJ+b4B9Yfs73MPkjUGfao5nMR2eH/byj8f+xOHcBXrgQmwHT5lck/mj147jLcRLoRfVwcYP88ZoVfqXLVLSfi+XhJXQod/PeN7oC4Tmtb7hRGR5qBaVe7vVCR7GdsiX8tlYDgthgGmh/UGWQbPRjMpAH+B4UbzOZLSsbssYbARGgry/1e5Gx3Y3bqvgL+QY0/uGMW3zyC5/cx5fhXXqNmTcdXe2o2vleZaDx9nk474yLYp63ULIK/I+/aEd7XfGV8F4ZSshT1iOh7xmX3VyODYvl15YXuij12qGNYOdj+jXDZ7XwPYZ3lZDbrDH7XMYPAPXVU5AP83PMZa/Uro6A4V5hP5/+z7jyMZ0YKsU4mVelkPZNsPnRq6JVnSMVgJrI+YX1YvFsAYJnIVwLzl328Qu5z5bZvKDeP5GI+5nuy6weSafhcGcrGsrMlK0j0L+6Mc+ZvJlemaN0NlYzXzybG+2QV182WTWFEz/5jEwON9bDzLnh15BeQ9hDGjbmfzOcqSj/ej+iXqxmQesP9+u6V5uvZC80O+00bxwJutnrVNxVPIA+6KzJWulm+aF77Fh5h2OgcAxYt6wZTTrCs5j/cA90vsP8GjyZZ858vLGrkLE9yhxjZy3VpLzyEAi7yOsU7o6ns1yhh5+rkd5PY7dusYpc+/IqTsBACV9/Zo5+eH36+5v4/NOyn93Vi/LOTo2ahqrYW0E9oPUgc9AYQzo+aubslYuaBscn5mIysbrhblTcxnXNpw/uso4YdxwfWCfM+wajeV4ankNAPDEFWEiNLp9zNbIqpNrcn3faci4ba0saRlk/JY0pqrMG5oDk67O/+Y5o1iTHGvXtjaOGNM9MtxYZ12v9JRjk8/7iO+bp8/4LxGSJDkJ4H8C+IY0TR//TK/ncrkOvzwvuFwuK88LLpfLyvOCy3U4tedfIiRJ8nMA3gJgLkmSswC+H5B/rk7T9KcA/F8AZgH8RCK/CnXTNH3gpSqwy+U6eHlecLlcVp4XXC6XlecFl+v61H52Z/j6PT7/GwD+xotWIpfLdc3L84LL5bLyvOByuaw8L7hc16deFCbCZyK7Fy49J32ITyPf2557RfM69ovQb5THQKDvj565YiHZ5TXK3yt+d9nz9hWnN5mMBbsXMfcafeaKeGeOT+mep7NHovPKum8z/Z+Z70euR69ixXp6C7G3yXrvyknWD92G4QAYvoL1zdDHs7Ytvptl9aAtq5f5iu7/Tu8h+6GibTVZi8sSvMzqccr8XcP3BQ97I6snk76/Xi/mUVC7/WO9pnqF1CNEz2Hu/t3lytD3s2vH36MHid6iin5/WvujdfmcfK9nWAit+Lr0kZ3fkDb98Fnxl13a0T1ui+oPGxe/1uLEKA6dkgSFUjnbQ9vE315shPx94ekDjbkhu64QvbK+0ex6WX7JY4zk5aq98sdesl5q7i88Xpf+rmjd0obunbytcWz8woyvio7Zonqt6bku0EOvnj2UlU/Q7QUWArke9CbbfZntHuj0TDPnNTUPkIFA/2W4Z1/Hf5UeaLnOpH7f7sGc9dfwfuSRcTLgdSxqG+l9+532Lp+3+iA1T1jGRG1W9nvuaS5c05xHXzfvzVxGX3ddQ4p7VvO61ne7ozm1bVgG1t9drNLHG+8vzjYnV4J8msOkfq+L1toyWpfPA8hydIl7k0+L5/bCmry/0ZS2JB+Gccq2Iu8jxJtqcL2Boa93fQIg65v+Vca45TIM+qWv7oVmf5MD0GV8GT6E5QKFubY03HPP9QivS3Edsd2S95fUhzxZIzNF8sJodRQlg9/JWAjxNbNxrmNJxx99vvSY57EQOIZsWa3y1mLk0/B65IOQpbJuyhvWi2bsdbfWA3OGDILKbLwuYGxxzcT+oId8R7kuZGJd2ZEjxyvvzX3nJ2rS3ovHbgYAVFuyrmjqmKDH2TJX2N8tcmja8TqRPvG9vM/XpPo99Ha2UJk/BgAo3HgXAODJS5JTp3XuOjkia2n2yQg035fIHdK1uK6hU33Z34OxFbgE4Xkhnl8G15B7M5UGqpiTU/iuhlE2DwQGR5xPOM5ZRjt/9Dut6Pyw1uKY1XKQhTCieaCobTV+x6uj79+hMNzFyTFUWxKbjNXy2G1RGTnuqUoi73c1T/BZZ2MnzgsLk9KvlWLcL5Z1xyMUK0OOCOtYmZqTj6djgG/z8gX5vnLpyEEbu1HKT+7YbjYTY4LjlzmrsST5on1FmAhdsu7CGoQ8j0mpM8ukz3/VaXndTuX6G8qb22hIvzXM8ySfoch1mR2f1vfldUvboM88pW2d8PmDsT/A7Il1CLOGy+VyuVwul8vlcrlcroOQ/4jgcrlcLpfL5XK5XC6Xa1/yHxFcLpfL5XK5XC6Xy+Vy7UsHyERIkBQLA54h68MJ3lrjcbPe171YCFb0qVifIa+ThL220yGMg3gf98x/N3yfbnri6AOkR437h1Ln1ePy1Kocr+yI1+XNtx2XMhfofa1Hx23jHywj9oPTz1UoX93bkvZ7A1704OszPuBSWbxIBfWa0dfXDvuxGsaBttW6elUbZp956/feS9l+0NI3wf9JD3Rqr2v3Thf1mk10ttflMzIJzJ7X9GM1tV3p16QvmzFWK8b751qPOfc4b65cApB5kSjLm6C413FBPVJQ7+MrjojHKexprfebGZFyb59/BodNaa+L1tpK5uM37I1sL215zX2F6T9HRxkYJd2PPHgU43633kabJ6xfOc/jGF/TchgQXdteM/Mux69t/wdvK/dpJlNlWzxw9OAznzDPWL/3YK7VeNfXjL+uevSsd3q3mD9nx0aja4drNCSHcQyR28J+GldPeimN23UwR8kX6du1959TH+CAp7IYzxNs+7bZ/5nK9oHW+JmYDvW351Z1HD65JFuZnz5/EQBwQdkFa03NbZqDqsoqqatHcaoqZT86Lm1wYmocAHDD/LzcryW5lG04qcwLu+c1xbZhfmFLlMfkusyJ9EjuuQ/5NakESaGIkvpFKeYJcmc6anp9bk1yJOt+QvlCtx4RnlBT9+pm3zLHh33Ac/PB8Lbj/NYbMo/lsQ/yfdPD/fzMVfw87eXzF6L7JzE3JBtj8pr7w3fNOqKibcK46aj3dku5AWHP9X4/5CaWmT5d+vsXJqT97TrCMm5WdA3EsmYshOFjMTWsAps7ua6ze6FzLXVydkqvL2NyVddczBuL6rlOdI2wc+GM3LfbQe3YKQAIMRlyoOZdspPq85IvVlry+VNnxGf97LqM8/NbXBPpPEGfven+kZK8sTAiXuq7FqTsd99yNwBg+/yzADI2gp0rt9VPzjVaoRCvjbiePExKSmVUZo9g4tZ7AAD/7Pc/DQD41U9KHe89Ief9wzfJf9wxK/3JscOcabkiGWNL14I585ONe5sfhj3f2HWCzQ9F82+7w3LK1TS43tD8TyZXMV4TkYXQz5n3bXnJ0mCcsk3o2e8b1kNxdQnbyv2qTMscx3HKeZvch8BMabeje/PI2GVeyGMhsGx9MpNMrDNv1BZPAQC2x4SFcHpZxs4rbxDOUXtVuGPtZWE5lMZl/mitytx/6qg8m90wMxnumyhfg3MS2QfkLpCrsKM5qbN2Rsqqr0tkE2idS2OTUZl7iNuCbbjdlv5rdOL1B9uITBtyHBZPCtdhZ+k5uY6ZE9mvZLHlyf8SweVyuVwul8vlcrlcLte+5D8iuFwul8vlcrlcLpfL5dqX/EcEl8vlcrlcLpfL5XK5XPuS/4jgcrlcLpfL5XK5XC6Xa186OLBimgaQB7AbMkagiUCnCNjpEAbVs/CZ4UCTAPDpxfCiJMDwFIJj7mvBKbvhRoNwNgP90HsRsmSBioS4WJjTggI3lhQqBMiR8MFVBacdmZyO7s/79bWNLPBksA7DYZS2XkDWfnnQp8qElIVQN0KWZsfikCL0Y1OhTdsKZSKciRCYuoKNCGScMFDC7q5YAbL+KtVHo/tkEJnhkEuK0Lm03wvwHPbHWQUePX55TV4/JWCUC1taZg015aVhqir3WhiRNpisSdlmtA7HpwVwNmPK3NfX7dVlqePGalTGsoJqarMCeelvS/wcret1FObCvmpcEmDT8sOPAwCa507jsCntdUN7AEBRoS4hHyiUZmVL+m91J46r6RFp8xunBaDUWluJvp/Bowz8ZyCPxHGzF9BrmDhWBsFq+jnBrAEeSXhYDGS1qmgbMMZXdxrR6xEDQiKUknW3OY736Q9AB2PAUqGQhHHOowWlMceF7+i9thSoSkBaBncaWsUMUqZgNAKsmBcm6pwfNJ/r/EBAU5ltyvyhsLFQLm2TXk/ih2OScCggAyMRLHRa88HpZ58GkIEUCYNaHJUy3Twl92ZOo1hW5qq2gtSeW9sEADQUQnfTvOTW/pqMA4JYQ9m1Ddlf61o3AliZmxn7LMWE1vm+E0dw2JSUyijOHMFKUfqiPi+gtKmytD0hlAuj8vl6g4A8+fzm+SkA2RhZq0muH6kpsDVlzKfR96g8oOrA/DjsHOSBEocrgw1e/XxChZN+PN4z8KKWrSAR0CFMTMck7xMAYBpHIW/VJJ4najLmCPIiaJE5QNpK2m1NxwRz0vRIPboXx117ZzV6v6VjgiBGfo/3yGv/gqlLr9mM6hJyGvOUWbNxLM3p2Dl6dCa6HgGcTZ2byxPyeWVqLoPGas5jDFZnj0TXpgi+JVyWMXbrjERNOcBgFQKr/bWhufNKQ46bbWmj0yty33pZ6nLjwmJUZor5nW05XpfrE2obQLylq4O3r0UVylWMHL8FS1tSh9MrUqcL5yXmK0VpK0LlCFZkTg8A9wDFlOukvXgMEcTYRzzfWcBzBrMrRtfdvV7IWztYyGhYF+SCVvm9PGh0eIdnShm1jna+zwM7s468bgalj+/DuG52YvBqUiygqAB2wgW51ukESKvOaZzvy/H8TBFaXsuZWyum7KE/9HRen9DTzqSsrf/7R2Wt3NbF/TEFqlZ0zVSZOwYAqM7fIOU9diMAYGVLxnwGMz8TwKplnYcrs0e1XSQWq1qH7dPaXmefkrK2pa71E7dG9wz5XPtpVEHLrHNf68jrb7XjI58HM4ilPlPr65NHBA7JeOiZtVLfPHtZ+V8iuFwul8vlcrlcLpfL5dqX/EcEl8vlcrlcLpfL5XK5XPuS/4jgcrlcLpfL5XK5XC6Xa186YCZCO3hf6FHaVI/M2so6gMw7XyyIr2NavSdTo+rvpZfJ+IczL5L6QNQTZ71wifEPZ9698q738lgI8htMbwhTYNg16TXpbq1HR5b95nnxCzW7sWeKHsSsTNIWHfUgFbRtAhOhMMyZCVhuxLB60a+VtVMlKiO5AfQ/1dRsVEnoPd6OvlfW69APqJY0WJ+W9YHTV9Qxfi3ev6NVoReedafX2nq1ra+IPp/y2ETwOn/oKfE6f+QiPeby3TefEPbAgvrt6S2q6z1n9P1aKY4l64m+3FLfVlUaYbysnqiLzwHI2qx65ERcV/Vc0q/dTYzvS32g9H3S+15dOIHDpwRJsRjigOOXbXN5U+JrWZkI1K0L4j+rtmSMkQ/BvELt5TcexgeR84f7juNrxn5begPpJaaHMMSqYSBwTNiyhVxm7sOxB7F7YqMpPvDJaYmb9obEGXMd4ylVj3RB7xM8kFregbGv5aiUioFpYMcXfdV8n95n3qvda0d1H+4ez2RzLDWQJ+jXY9sw1+r3+8HnWdA6xT5ltklgbuzyi24ncu4nT58DkHmS71qYAgC89Y6TALKcY72mwbNeldy3siUdtaF+fTIQ6Nsk/4f8mEmN3dYV8TiTmcLcyFxdUu8lxz3bhv03yAM5fGq2O/jU+cv4nacvAwDObkjbveao1OlL7xKPam1D+DW3j0m/k33TflY4MYVTdwIAnrgsbXmb8ifGdSile/iP8zgl1DDvvvUUs5+L5l6MbctYarYlnrgOqJm1S1LQ7xWHl5nrgY4Wjdct6xBkGzF+M+aG1HW8LvHLsWvnWiANMU0WAkXmhJ1/eU+2DdcTZJmMK/uE6wPLderE6JqwkG3rGOluy9oq5TzC/FDielMXIuVy9Dmvu9mUNp8KHm5dz4R8UQ9lHtF5+cqf/jaAzOM8esd9crzlFQAyVsnJCYnZm+Znorp3dJ7opTHXhfMGzyM7a1XbnGtkrgvY1j1di7FNgs+crC5tM5bLsrQOhYpFYHQC0HXBW05J29bKEst3zkn/3rM4C2CX71v7s1PQNZe2wSB/RpkpHHPF4euAeGU9+Plu5TER8taoZJPYvMD+4siwucnmnYFymHWFfSba/Qy0+zzOsVy2MO7IbqNHv5jIfcen5sL44T24hmYuCSyEUsxayvKpXJP5hDmNOXOQaZVoGeVz5qf5sZh/9omLwh36X4/J/U4qn4RjbWJeuAScO1f7cp8/e+wZAEClJK9fd0T5ZK1GaL/AnGjJvF/W15s6F3WUeVRRJgEbNG+eDuParFHJUpkg30fbZqwic2RgMOlxS+eTwo6cN6XP1FVtk36YD3TuNKw2K/9LBJfL5XK5XC6Xy+VyuVz7kv+I4HK5XC6Xy+VyuVwul2tf8h8RXC6Xy+VyuVwul8vlcu1LB8dEKCQoVuuDLATd9/0K939X/8aJqXEAwBTkdfOMeFK66uMo6T6/9H+GvdDV1lNEbV/Fsh6mZqc74GkO+6TqOXx/l0s6KgvK4u+jF4re1q7xqlX1+Crdx5seObuHKn3Gne5wb9V+NcxvTM9X8E+bvaSDX6fDPaelbPTP8POsHYfvTRs8T73Yl5WVrRhdj3HC/XiLat8rl2MfWbcRezKDD7AVv9+pyvXe/+ln8J7HxD9555y06w+//b7oOywzvUHlsemoTNYLT88a+4deaMb0ielxva60AfeR5V71lkPB2G6vy/07Wo6dsBe28cLpdSZO3obDpqRURm1+MbQ12zT47nbi/d/vPqacCPV/ntZthUdrUwCAec0XjN/K5P72ws7bl3y3V59+PHoH6XVuqp+Ovrqe8e2NQn196mm0fj7LZcjbzzn4dHXMMldyz3WOYcsn4H7CweNsOTHF2Ktd5JgrJAPtYsevzZ/cm545tFZm3fJcpLEyn7jZz9vkkSxfmPyfI/t5a20FAFBVD/QHzy6H/nzTyXk9Rz2ME3KPVGOOuyizvZ9ek/H++LJyfXS/5jFlWMyPyvdHKzo5hfiIPcnMfTx2enJ/ehZh2BUUc21en1hf72FQL02x3e7giSvSlh95WvrmmVVp64WRiwCAtxyT3LrxqQ8DyNqKXnju3X3HEfGjT6j3nvmDYtzZfd7z5qmrsxDi9rb7q9trkaXC/NHQ+CETgXE5otfP2+c9K3OcTzgv2bnTKniyC5Jz67wu5+5E7r/T6YV1G8V2zfZtV2bSTtzOFP3T/F42FuRo+TJW/F5q1hXF+lh0Huta1DmSnKGtjrTRY88tRefff1w+r0xK3Jxdl3hburCKuwpSl4/90P8JAChPyviffu2bAWT9Ty4Dc9OFbXn9xBnx3zdMnehdnh+VOh1RntS05o0jE1L2imEw9QMvRuc8w96ycVKekrwW4gWHT2maotPtYaIubfP2O24AALz5lLTBgrZVvRevkcgZubAua6gj43JeTePCevLZpoHMkZND2Qe9MLcPZ67tVsYeUh+/MgU4dgJHRK/N8dvXMVLL4TVY5bHdBnNazB8JddM4Zq5kPJMrsdHUONdy8bmlWB0J9+Aal4yC0ar6+IuGv9CLOQ189iFHiq8Hyxxr26zFFiYkH3CuvvzsGgBAESg4Oh4/41UnhHWwrjykRy7ImH1mQ/LA555cAJA9K1Sm5kJdm0vCOtt67KMAgPrJ2wEAK/O3AgAe7gun4/V6jcrpjwEAusr1qR07Jcejwjbjc0RTGWmMB+YLzgtjuq6YVEYCP2f/tHOYHGzzfnju0TGwBwfI/xLB5XK5XC6Xy+VyuVwu177kPyK4XC6Xy+VyuVwul8vl2pf8RwSXy+VyuVwul8vlcrlc+9KBMRGSpIBirRb25aVPN/iH1VN0TD1NR4ryuW6hi/oR8Yl0dD/grvrD6fMpDfgR5UbWS0MfiPUBb7cyXyK9SZXScA+zdZPxHvRxP3ZhOfrezepV3zl7GsCgBz6h91a9sD3EHpaC2Sc+7DudxD5Cu2fqXq63pFAY3Ms67O8a+2KsfzfP/2k/Dz7BvvqKjdc07Rnflu6Bavc+pyyzIXAEyFAIfkRpy4u6x/K/eehTAICtdoqf+pI7o2sH7xm9R8pA+JsfEN/0U0ty/MpXiffoG+6TPcrnq7oXvZatXJF7ch9nxtGsehxbXfEejRyVfWIDU0F5APQ2Ny6ckfdXxK/ZXj6vbdWL6lZUnxe9jr2JGRw2tXp9nF5v4tYF8Tb2d2Rccy/cIxPqD61J228+/RgA4NFE6v7gJ6SNvvV+2d/36KzEwVZf/cYtabMpcgyMf9zK+ozjz2LvIMcEj+zvnhkrecr2SpayZTwPLWMv9mcWy1V7CQBAUz3U44axsaM5thMYL1oubdssB9q6DrIagp8yxGC8l3UYz6xbOjz35LV7+Nzkh8BCYK4sxjyHkGcG9riOPe7Wm325L+d/+JNPAgDefs9NWPmdXwIArO/I+CyNcS9o6Y+67u+8pn7qD3/qaQDAp1fk2o2uvF9RgMvCiLwmG2Ga/u9y7HXnMTAptB+LmtOst5n7wXc1X9l+LGjfsPwV5T4cJpUKBcyN1vHaRbKNpA/Ixrmifs90VOpY1joyZxY0R7LtLAuB8RDalPuVp+o71jbMWz8MlLc+GryofTJKwH3d41i0TB0bBxw7nJd6gRMSz6l23Gb5QtqmrPmiHTgi8RixrBV7vTDmuA+64RUAmZeYXme7F322J318j7D/exLv/27bhLJcCev3rquPuKRzIM8n14T5qqHdR2/8usbRPUdnorb4LR3bZzcl7r5qsomn/vMPAgAmX/lqKavmIO7/Xl08BQCoKHeBbKwxTdv3LM5q3aTsXAMvbYr3mSyVJfVCM19Pj0gsc1/4jsYXvfGb9DRrnurp2pgMFXJBKK5zAsPrECmBtAu5QPWi1KGnTza9HXk+SMivqsr6oaltynX+mY6cd++i9Hvj0oX4PjpHZnFLRk/Ms+ml8r7lDlV2PV+kZo4MzxzKCSAvgNecVV4D5xGOrZ6Jectmove9oEOnUoy5Qnn5h+sZy3Li3Ev/fxib5fgxkvcpB6ZDP6xRWbc+c5zmNnKX7HqA3+sjfrYZWGcM8B6kDGSoZNyA/q6rZHPxa07IO597g3JNRmO220ZTyr2u8TKudV5UVl9N++Sxi8vo9eXeN+kzaknnpH9zWq7xy//3v9QyS4zc+Xe/EQDw394heaR5Wdb3o8fkueLixpaWQZ9lkuHrydBfup4I7L5CP2oLijHJeGpcljzQN2viuGUH5X+J4HK5XC6Xy+VyuVwul2tf8h8RXC6Xy+VyuVwul8vlcu1L/iOCy+VyuVwul8vlcrlcrn3pwJgILpfL5XK5XC6Xy+VyXe96/fpN+KpL92O2O4qV0hYe+lCCT7/26ry6a1kH9yNCkiApFANQg6Cj+TEBWjQULkF+zp9eFijFB88JAOXvvuEOAEBn5XEAQHVeQGrlsXHeAEAGAel3DBDJQEVKAdgn3yMQZapUR99AOwgsIeCCcBACy0arAjip6LXmtE5PXF7TK8jrEwr9GTt5K4AM8kTQTQYhioFahECVFGBSrRICYoGPyHl9NbFOCunguznAxO2WwD4CxMNA2FimDJCk11VgEUrx+bZfCFTZVCBLoa9gE23j1ipBSdIGtdmFoeX8+HMC2Po/fl3i6se+VMB9Nxea4buMuY6CDauzRwAA//HPJMb++I+ljqfuFiDKK+alXybqCpVDDI1a3xFACSE7N84KtIWgvIoCjljHtCN1ZP+2lp6Tcj37uL4+G9WprHUtKUSM4DSC2NJuDMM8DNpqd/EHZy4F4NGrTkgftNYEZlnX8bz2xDPyBY2vc4n0zcyIwkvnpW1bet2NHQEpnZydApDBK228UQOvDXg1VgwwLeg5BcKWlGeTAdNikBZzTVFPbCuQdbMhcTI7IWVurlyS72sbMNdlIMf4voQNDgOgxXW9+h+kWWhR9N0ANoxBaYzxUtFCfuJ7WahT24DqKgr9YU5sK7QsgFZ17FIZHDUGNzGHE0pbVgjp5S1py4eekbH/Na+6BQBw+qe+P8D5OA7rx28GAEzf/xYAQBPMaVLm2+dl/BHKRm23O1GZxjR3sU1sPPA1YVABnFofA7ALNqmQv7bmLaq6KECmyuxRbZMYoEcI8WFSrVzCHUfnAkzunnmJhyuNOMctK5RuTqFWNj6KAbBoAb46BhUut6bgtXKIX2nDvHmQYvxtNFpY0/xPKCxhXVUCzkys22szDgq6zqgQ0laKz8uDiGav5T6lAcAr80QMaGQbFE3b2VzZ2QVLtUBFviaIMgMjE4CqcxThbwnvIWOgb4CtzJFlxHXnuA4AZZ37rkDK8cjjzwLYBVC7cVHaQvtga1v6iNCxSY2vxSnJD5cUasbc+vWnZAxeet9v4s5/8lMAgCe3pMzHpyUfFy5LvmivXo7qyDwd5oVOPO6p0YquHzX2GD/MnVybMZ9wvWnXjWwTAhVDf3JtpfNIR9cJnT1i+1pUkghUMcD/NPQZZ5wjCfTbWNsEAHTM2GMfpIX4kSiA10PMy/t9PZ9xzjHXD7k8nmsrAcyc7JrzYjAzYYMsW0eL2NT5o1wlDDoGG6YEs5u18pauzZkvpjT/PP8/PyeUXuG1vXitzutPj8gzHHMA18VAmrW/HseqMSCdCjHL11onPmdQNY4RzX2pyQNJMc6VnDcKWpeenndKx+xX3CGtcv9JmTPLfc7F8Zio6/UmFezNurJtFifHQr23z8sa9Xxd1hHv/YnfwwNL4/imC29ANZXvzXXH8cU/n2K82kf3bZJravosu65z27KuUWz+Z85iHqhrLPJZupgQki/XCRBLnU8IKe9uXJGjQswJiC0UFH6/B/za7Qwul8vlcrlcLpfL5XK9BPqyMwvhBwSq3E7wwHsO76P44S25y+VyuVwul8vlcrlc17BmWsP/+H9s5WUuyIso/xHB5XK5XC6Xy+VyuVyul0Cr1e7Q97dmX+aCvIg6OCZCmiLt90CXXkF5ABvqP+2pP2Pr+N0AgH/6O+cAAKfm5fw6rSq33AsA+MQ58QsfL4ov7EhFrmz9n/SFlUbEO2f9hBS9Te1uL3iVKHrSwjXVe0LfHr2s9NEtTonvhp7IRy/Kz07l4+K/pd/78S3xntyl3nl64+kL6qRypJ+PnyfW8wbrfcxjItjzsnNZdvvdwGnQezbbsVdppExPdCO+E31g6lWid23AFxq853od/Zx+rswPJuXbqYsPmf6gCfXSsxzve1Q8kT/8R+KBf/83CH+idWVJa1fE1rNPyHeOiZe4pN7jRNvgG159EwDgVQvixz41I/15anYyapOkoCwE9TI9ckH69cSUXG9mhB4xEfuVfr62+sg76lFqGUZDQf2e5H+QhUDvdmVajowD6y87DNru9PGh8y0AUueFcfFtzXSk33eekb7qbQsbpare51fMCgPh9jnpk8LGcnTdY+qBp88Yo/K6pF42MlPY5zZe6ZXc7V+lTzrrf/WuGg8zpSEZ3qeXjbmGZ5Ot8szKRnTedDUeA/Qb05fHOOL1WZ5O8NqrJy7HG70XG2HY53wvXNsyCjR/cPwyr9DDSI/pmnqPe8bPzTrN6djpbkm/c0yE+2kfkHWwop52+gnZb7cuSJxU1Df4m4/JfPNX7xJGyqM/+G1aryK2Hv80AKB2VHzUZCIEJoXGUFM90VuaeybrUubZMYld5n/b7iwTfaKWF8G4KGmclbRuHNfbZx6VcswIN2TqVW+UtqmORm3Q0PvQa70wOYZDpzRFv9vO/LY16T/OtTyyTavzc9HX6VktlGJ2R0lzavAXa5uz7+wcaccAX6eG7dHpNcOcRKYBxwgHup3rKMZJXfvdckIyDecPMR/t5WWlQpvo2GSbZPlieF5gnhutVgIHht5vy0/gkdyFlvZXTxtjlD59je2sDv3oXhT9vSxjVa9LztAz52Tu/dAFyaGzdcnfN89NAQAWJkbjumj5j09JPmmuyPpgblKu9+qe8A2W/+BPAQCbb/3f8aa//QkAwMYjPynHv/R9cs/vlXXEiF6DdbYxSu4PffXWJ76kfA/WFZoneD7XkxwT5PyEOo3EsV1SBhfbiGtgzm2D88LhERkEzbbETUNfb6xu6+v44Y3si7LlCIUxFY8d9uGmru14fuCbhPPjNuQzgV0jDBPPZf9SRTuue8MZRWQpsO6Ms75httXCWIy/nxRtHlLOhGFrJcW4DizvwsSYfo9tIPfdaLTCXGTFcR1ymMmJrGnggRj2BEz7h3Wcvh6vc92hz2ZmjByfmYiO0LU3iRbl6fnovmQhMI+QQ1KsyfqkVimH9mI7tRVu8cytd+DcY8D0UopkV5wk9QR3/fNj6GxclGuR0aZVJOtgTuck9i/nF8quKzd0ncI8kuU46aepsrxubmxH901GYvbSXvK/RHC5XC6Xy+VyuVwul+tF1uueBV65lOAj9dNYLm2gjxSXxlPc8G8WMfVVUwddvBcs3+LR5XK5XC6Xy+VyuVyuF1HpWh9/74+Bp6ZT/NjUb6KX9LH99m8HADx+iH9AAPbxlwhJkrwrSZJLSZI8nPP5nUmS/EmSJK0kSb7rxS+iy+W61uR5weVyWXlecLlcVp4XXJ/N6v3gJsZawL/9PKCX5G+ZfRi1n79EeBDAjwH4mZzPrwD4ewC+/PncOO330NnaCL6L7rb6f5WFUFEvyull8cD21Bb21XdOyfm6l/p7P/0UAOCWWfGdLpSkg7oN8YOEPbZ1v2F6m6yyfcXjDq6UisHvRx9OzzASwj7vyXCvMb1B9A7dNS7Hqr5uq//n7nnxqD22Im1w9zHxuq5tS13Or8v7x9TTOq4e2cxzZzwyxuM0sDe22ad22LnhnLJ4UOkFYyv2K9zr3LAT1LfF/XrZ/m3j5yrS/9URv197XTxLXWVkpOqR7Gld18pS90fPCAODe9PefUziJdHv/ek5iaePLsm+wO95+xQAYPvZx+V6el6/1UBt8ZT8t94reAXpq6rE++Cu7khZR6saY9omz62Kn+r8hniQjuk+rDfNixfR+nLZO+R20F/VWVsOZQMyBkL95G3yPWU2kJ3A8xkHI0eO42XQg3gJ8kICsbld2hE/3yX1c81Pcn933fNc/Z1sm1vnhUzDtmxcjHG3dT3v2ZU1AJknln1AT1vmjVY/s/bVypbEC31oo9VKYCJYZft567iv5P1Wq2NEz080p5EqwL2Nec/iRNwGzJ0T6vurVXTf5jDu4zGZsRMkXss551nFPs6r81a4n3NRfbZVfb/Q1j3JTV7ogD4+5cgYXx/LGvaZ13xenVe2jeYF5vmMcSPnsw2ZH3aUufOhJbnPN9x/BwDgyR/+TgBAeXxK7tftYPQm4adUOZ5MbHSM551l3wpeZykD5wcaPOldbbbJqpAP7H7w7Peajnf6OVsXzki5NG/VjgoXpKkMFWqBzJQFqfu5Vckvl9ZjTtCLrAfxEuQFIEXa66FI/k/gBkicVYr96P3gA9dc3muRcaI+UOMr5d7ena3N6Hs7nXhOHQ2+dcZ9fD+qVi5jok6+kHxWNqwSyr4O41JvVdNlGsvKo/VjB+4A907nHvf0A/foJyZPIB6L1rsfPNTaxoGSpPcr6n2KhXJoD+slZ9147STwf6T9mS94L8s+suoZfze/Xy6p99kwWSpF5Q4l5A+RlbWt9xvV7+se7B3lEul1N58W7sjGw8JCmHvzOwAAX/4PnsK2shCo+Yd+GgCwOPWjAICn3/1euZdyXMgsmdDxTM86WQise1jH6dxHlgn3dR+vx2ylPGZGaVT4QNx3nryYjYbUcWND2TrapoWB9euLqgfxUjxHpCk6vR6Wta2224wrOW7pa8Z0XcFEc9rWc2MS2xzXZGFw3ZlWuS6IxyhzfZiX+Fyh9+H1ytqkPM9yB4BsvGYshLh/+X6iY4zjNeMH6HpDcxXnTpYxm1eU+1CMn4EylkpfjzG/gXmEa6GQV8gtU5ZX4EfpnE7e0UajFdqfsVyrxByOVMuczd86XrXOo1XyYcib0/YPTKuYpUIV9fOd5eWozFwvlPsyBtl/Tf0+61RRJsr0iJxP7kO1r88KdXnuPHtF5tZ6pYTRlvKdlFl35GMjOPveFs79lQRf+zUJ/viZvwsA+HdvF9ba9qc/LHXQe7IslRkZt7Pjo1pn6Y+NFXn+YGxzDuT4ZU5cb3INJKswPjdO1mOGSmDhaH6g7Byapz3/EiFN0w9ABnje55fSNP0QMhaFy+W6zuV5weVyWXlecLlcVp4XXJ+N6q6kuPDdS6jdV8OzX3nQpXlp9LIyEZIkeSeAdwLA8WPHXs5bu1yua1S788LIzMIBl8blcl0LitYLN9xwwKVxuWK9cfsefN3GWzHbm8RKcR2/NP9hfHDi9EEX67qX5wXXtay1X2vg0r/dRvdCH0kFSLvAqf/3JHDlzEEX7SXRy7o7Q5qmP52m6QNpmj4wOzP9ct7a5XJdo9qdF2rjkwddHJfLdQ1od16Ym5056OK4XEGvP1fCO9e+BPO9KRSQYL43hb++9GZ8zsYtB120617Rc8Ts7EEXx+UK2vrlTVz4x5vonu8DKZC2ABSAxsPNgy7aSybf4tHlcrlcLpfL5dqHvuKxCqpp7C2vpmV89fLrD6hELpfroLX2r1eR2t8LOsClf3X5QMrzcujAtnhM0z56rUYA8BFaRaBieUL+UuGY5unv+lwBYLx1IYaOfPm98stv2hKwSr8iAIy6fp+gtXBfA0wjlIbgnQAl2wVAIYAIAa5CEBYhT/a3mDQ6WqAh7719/lkAwOixkwCA5orAAm9TgNqFNYF1EOR3ZlWgTxtNef3KG+RPv0d5/wBOUgCkgQlZOMxgeTP1FWyy3ZB7beiREKCJqkI3FFxSDpCVGE54ZUdG1Oqa2OEIe7EwsrkR7Q+FBPa03whSIxSouSXXv/OI9O98VUFZF56R8h2/Wa67IlCT73iFwMW2FZDU3ZRy7JyR1/2dbbSOC2wtuf8tUnYFlhHuUivJPdjef35O+uljZyUxEHhC3T4n/5p+65EZbRMBGAXwDMF4euwr5IugxN62gJgIUnx4RMBu//A3pW02FBr6T75AfoX/grJ8f/3jfxhdt37zPThsqhQT3DhZDOOR8JjymNSV0JliXWEzE/yLJolhtiXzh4WOzo4JnIpQMhQJ/Lo6ZJBjPQP+ZX2eN64GQatG5nsBiKbj96Z5qQNBOb2mjCWOLcKECOQsKnApTeI6EczU79JuqhCjAss3HJaYD2LNciJzC9uDwLNLOxKjbO+a9ksGmJLzyxUpK0FUzAeEx4US9eR7hITtFGUeOKOQwIm+1P241mVKrbXNVYENrl18AgAweeerAQC3NeR6Z98tELTacYEcTb/+bVL+saP43acFsnXPvMCTPue45JLG0lkpa0tyzOKUjPdNzZEE4D5yQeCeR8alDQj1othmZZM/eqbdOYc1z0uOK88IcLc0Jvcl9K2tYEV+PqZ1ZbycnJXzCR07jKpB4uLWBcmthFRyXBKQF+Z5Hf/MCxwThFoyL4xXY0hqS+dSQugIXiTIi9fJG/u1cikDmek5Fnh6NbAxsCvnpASlIbpemEcUYsqy1hReVjYww6bGJ+vM65YCoE2BaMw3XEcE8N7QYgJIB3JFJgWjBlCaiOOdkMcG87YCMEcMAJttSTjpjuYHrkvYthUC8/R4akLy0FglBuh11mTu5pqLsLpUoYelOckzBLB1b3kFAOBjupycaQ5vjNnuGJ772R8GAKw89JBcQ+eqks5J06+UHxqWFAbItRCBZ4S3MW+zPzmfEMjI/ub5DQNYrihY1YLReL2wFjPg8EOlJEGhUAhwyA2FyV3clH5vmHnkZEVy8eKU5HQCvQlUbGlccN2ZrQt0LahxxL5J+9J2zBdsSwIVbR5K+71sDiSotxA/R2TwViqGuFpIaYAQEjZbiR/rCIDOQNDx9QJgVd/Ng0AWDEiac3hi8ofNI8VCEoCKE3WB/JXDWtjGXDyu7OfZmorrPYIV4zmNZSNAta39WztyIiojAYoEx9dmZe7cPivA/q1nntA6aHm13KWTAl3+6DMX4+Kf72KYuue6eIWuI776HskDY5fkHuN3vxYAcOn9vwgAqOszDNuolOpzQoB6WxDncHHdwblrRNcfXQU+UkUDo83gk1ffjIDa80eEJEl+DsBbAMwlSXIWwPcDKANAmqY/lSTJUQAfBjABoJ8kyXcAuDtN0429ru1yuQ6nPC+4XC4rzwuuzwatFNcx35safL8O+B/YD8rzguuzQeUbyuicHfyRvrR4/f7R/54/IqRp+vV7fH4RwMuyp5zL5bo25HnB5XJZeV5wfTbo3RO/h29d/VKUdy2hW8UUv3hXF997gOW6VuV5wfXZoGP/+Aie+fbngE72FxVJDVj4jjouHWC5Xkpdvz+PuFwul8vlcrlcL6IeGn0Ep8vn0UcffaS4XFzDf3lVFx88fggtAS6X60XR7FdPoXpHD0hSIElRnO9i8Z+NYvJLa3t/+ZDqwJgISFPxsdAzpP6LYlW87/TA3zElfowTiXicOhurep74ODrqidkoi++r2BOPy2Qx9jZnPiJ6m/dK9rFvaLf2YiHQxxM8i73Yl0WPGj1G7Y01AECzJnWeGpW2WFC/Du9DD916U/5cZmVLXo9WpwDs8uYbL1OeB4peR6qYJOE9+uOC/069ZvSejZfEQ9RaFd8vPUgl9RSSTbG6zTokWtbYV0WfFn067J/KzFEAQHVa/jiQ/X2j9iN94M0V/Ws3beumeqNefUL4BSsf/gM5X/2Cm4/8mRw//Sm9bxP1Z8WbxP6a/dwvkddTR6Kykrvxijmp44lp8TjRj0tvYltjtLO1GbUNRR8XeSCsS1e/lygz48yU+LT/5pd9B4bpB39N2uhr3v8vAADLv/M/AQClUfE+jylT4TBprFLE552cxpWdtr6W+CAvIPhL1Q9u/VyZj0s9rj3yS4b7kjMNZyFYZQyAZMh34vFlFbzNWpZiyC1yZPyFq/Vl7NHD2rognvf6/KJ8X+tOP2eifuLqtHhh6X2mP5NxxjHGHNs3Oa6Yb34eOGeA96Lvt7XdmaNOKuclY9RovjZslMyfG3sLizXtV42Dpy9K3lnelnmBbb4wKWOQHJK+HumFvLgheWZMWQmpjsUbvupbAQBf9vNPAgBO//u/E+49On07AOBHfvpvAgDuXhWGCn2WnLvKU8Lzueuo/IPamOY6MhIqRWmD4JVPbLtL3el1rCsHYuusbBtXUb/m5oQcRyGfb2t/9unvVJEL0tVcXdJ+Hinb+eHaV9rro7O1kXlYVWTn8NjX3JcoO4NxQwVGisYF+4LxUh6TnL6qcWJ9ymEM6Ri26wqqWCigWIjnukxxv++VLzgXW781PbK9RDkPnXjMVNQLy+tn/AjLaNJ1iH7eMT7yUOpcplIm5rBh7RF9rsdx9Ugzb1imBefKcB2T79lGvTRec9HzTqYOxbVbQ+OIcVMek3hZ6cvnjz4p3JP7Typ/RMcS89XCsS/AjeeO4PdHPoH/OPMbAIB3/eQP4xsBPPf3f0Wu3ZQybZ8W/hIZR1wfTGvdt9vkdWjuU+7GWFnbTO+5ppwZciBYx9blc/J9XTcwNhnTWdtpniInRHNsT9kKpXJevF67SiBzOhk667pO3dKxwP46OiZ1v++E9GdDcyo5VF1yhXR9wVxemZR+59iomTbaVL4M2R5TyuLIY69VSuUB5sBebJTsfctSEfVsfjDrfzum7PeLJu6YY7mGCqwDevRzmAwhrxhe0mi1kvF/OM+HZyWbL+I1VWDY6Lqe/Uyff+BK9PU5Q2M8qQoPINXnip6yEUK+LsZ1ts9ypVFZG3W3YzfNyLEbAQCXdH44rs8As8qIW/nQn6D91Ajqn7eB2e8Qttn4nRJznzuvz6zrFwAA/+oZaZ/vPyoxyGeYus5dHW2KfrsRlZ2cKdvulg/EdUQ2xzWjuhdwddYBz7dzrpX/JYLL5XK5XC6Xy7UP3bM2hXpaxQdHPnXQRXG5XNeIGh8uIt0pov6GzYMuyssm/xHB5XK5XC6Xy+Xah+5fnsV6YRuPVM8cdFFcLtc1ou3fLyMZ66H6yu29T75O5D8iuFwul8vlcrlce6mR4hVrU/iz+qfRT/Zng3O5XNe3+o0+dh4qof66TSQHBwp42XXgVaWPi94h+nT5mh5EshDo96bfi/6R1TU5b34s3kOdHhkreuno/+ub/c6Hye77HnxOxoNkfXxWwUuke5MW9VjQfVW59+jcmHhouL8391h9ZmU9uh79QtnetWaP9eDPEo8TvTP0F9GPXCmXUaQnqUh/bjmqIz1N3Ya0Nz2F3FcXeqCPM/h7w/66cj27zzxVUT93Vfc43tY2GTUey+CF0ram/5f7f/e3lqPyda4s6fnSVtMPfI5c9477QtnJJNh89GMAgIm7H5D3NSY3Tz8i91K/XFH9dCzZpvIgWBbrv7L+L+5t292W/qR/m37/H/jABVxNzW2JE/InGufFx1k/KXXn2DlMGquU8DnH53BhO2Zw9LvqG9U2LeSMa453nt9TRgo9+hzvjONK0Y53iUfr6eUYKUa+w6t7m61HORuf+4NvBXYBy3BcGBmbTz8GABi7MWZecE/k2qzwQDYDM0XarMS26dCDq35DHWNsk6L+tjzogU4G/tuew3sxd9Gfl/Fh4rqHPej1Osyx9Eiz3XmfbfWgNrRt2a9N5nHTtuQIkJnDuhaefRwAMHLzPQCAdz8qjITT//6HYLW9+nhUl6Xf+FkAQP3kLQCAsdtfLWV6VvaU7rfk9aLOTTfcIFyXns5prKP1vDM3knGxqb5d6qNFuc57/kjef9UR6c9v+IK/DABY/sNfB7CLK6N56LJyIybq6pXEcM/7Na1SCYWpOdR0vG6ffxYAsHPm0wCApu7rTd95iesI4+ulz5RjdXZMcjb5I2vbsf+THmeuN/L4Nrz+sD21rWeVnmW7BuFcGJhLXG+YsdHXve05pgJXJIfVVAzfVy5MzqqvH1hJ/eh8Xp95a9DDfbUH6eGfcS3Ca4wrP4Tt3FEfbuAHaV3Lo1KGEfWuT2guY5vx/BHtB3KLWBf2c0m/z3XGhs7ij5y9DEDmIQCotmT9sPLEJwEA99z1ADb/qI+L/SI+/bX3YeHUq/A9b5kCANx2Xs5JvuefAgAammO2n3xY6qzzNBlZcxqjZ67I/M/5oqZlZ9xwDUTdNC/jmsyloq6JyVpor8r8T34EOVWcGy2zi7K5+TAo7ffQ2doIfAjOF6d0TE0rX4yMtK3T0hf093PNxz6pHJecPqJcmxaZSoU41jkPre1InFrWF8eI5YwMrjcy9QyHjMq87cNZCJZtxjYIr5lvQg7TXFW6OgOD8RByW4lrd+WQ6OeW2WLzUKFQCDmEOY7rs3zJeXy2WdU5jHULvCeusZkvdIxdXJW174yuiZKS4Tvo0T4fBvaFrqFSfR4J65eC9MXChNyXjKed889g83f7SBsJxt5WQHlqPqzn1z72R/Kdv/TVAIA3fOWPAADe8g++FgCw9L6fBwDMv/UrAGQxF1qC7Yw4D4Q6mLVqrcw4iRl9yQArLxbXhfb5ddictlsH/iOCy+VyuVzXit64fQ++buOtmO1NYqW4jndP/B4eGn3koIvlcrkOUDsPTWH5H3bQvQj0kxRT63t/x+VyXd9afc8mLvxLyQtIUnSvfHb9gb//iOByuVwuF+QHhHeufQmqqfz6Pt+bwjvXvuSAS+VyuQ5SOw9NYeNdJ8B/DCykCb7u14oAesBbDrJkLpfroLT6nk2c/e4VpNwIJU2w+qOjSArAyJsaV/3u9aLPrp9MXC6Xy+XK0ddtvDX8gEBV0wq+buOtB1Qil8t10Nr6H4tAO14uVzoJ/rffOXzbpbpcrhdHF39oFWkjtpOkrQRr/6We843rTwf3lwhJgkK5HNgH9G/Z/UKD74/+rhHxstHDtLIlnpfZcXmfvh963gZ9ey8FCEc9QmF/1eH7qIbz1FNE7xO9TRd0P/FzG+Jl/tNz4rm/94h43153RLxvN5bp25nT64iXhT4teqgKpq70xtA/mHmY5NjZ2kCH/ny9JpkDJXoOtd0brXjvUnqLgsdIrz2mHumy8WnR51csyPncozipSh0vbXBvdfneBveBpSddr0OfDz1MZW3b7YviiUq5D6zu+3rTt/5TAMDN/494akd/4l+GMm180fcBAD76j04BALY+9RAAYOreN8pRGQmrn/gTAMCI+m/ZBoxVxh7bMM9TZPey76wtR9c5f2V86PesdtSvWRyRMVJdlL1s9/JAXYvqd9rYOf8MFm+8HUAWJ70m9wGWOpbVB0pZHgHjlvvErzZirzPHiPXmWa8uxyz96vTM7fYn2nvTo7gXdyHfUxyzFriXMXNjVX3+OxeFgUHvO+ON31/eakTH49MyBuojjMf4voEnEOIm3oe4l/YHmAM2h/S2JH+MGf5Hc0W8p4z1nubxqvp1Kd6L+25TWS7VvZTJWinL9SdqUif2a4c+YO3/KxtyX/qJL5E3oZ7Hs5elje/qxXFFzfUmceKS+Gl/9mefBAC88bXiwx9VrsK45gfuQV5XXy37pX1ZGCfk/gSvqd5j64KM/51nxEvdvHAGAHDsS78RAPAdPynv93/53QCA32pcAgD8+F/5ewCAP/y2L5bvf+pDAICu7pE9OiJ1CvyZw2d9xupOE7/0iScxPyJcoLfefhIAsPah3wEA7JyRtinpWGCeCOsHjYNl462F8gW6YT93iQPyh0qpxrGZ0wNzpZm/h3aex7lQ0Pyg3VEu2/3iYxXt/vGG+0J+SGHXvuzA4L7wNQNDsCwnrkvIDxjXNrDrCJsXdnONMs/x8D3ke7yU+nxZBvJ76DGm7DqEPu2y1m1xSvqbebmvHmjWfVYZWZYDUJnU76VSvqXLawCAE1OSN25fkM8vvv+X5Lord2KYZjaAG5YlL/zn/ikAwK/8O2HW/PHPfTMA4Llf+AkAwNSr3hh9d7Iu/TSpucuyD7hOYyy2N6SMXX1oSbnO0HUD8wVzbKJMluDnNm2Qrd1krOzF8roW1W+3sH32dOBIHdXxD/WFd7dlDZ3qeuGJmsyd9x+XvH9JuRXVIycAZCyEzVa8LqgW43je0PUEuWJTI+RNxJwJO1+m/f6AL58iM4VxwLmMsmwVyxHK4wvZMuUpMJh0fhr8Hu8T86WocnH4/XavfzKuSt7aVK7B8UzGGXMo112BMaEsCj53PLYk/c313tiGsI7Kyj4Jd2HuUx5dsxP3d9Nwimxu5pqsc3542/YuF8N4erAsuePn3/wPAADf+6N/CwDw9nnp37HjfxdA1m7Prcrz3kRNysYnWYrPvqEu7G/Dl7OfW1lOFfuEz9j5fRTL/xLB5XK5XC4AK8XhRueV8s7LXBKXy3WtKJkcPv6L8747g8v12ary4vBH6NKRl7kgByj/EcHlcrlcLgDvnvg9tJL4X+RaSRfvOfrwAZXI5XIdtGpvexgox39RklRTTH9z/l+iuFyu61sL3zWOxGwUltSAmb99MOU5CPmPCC6Xy+VyAXho9BH89NRvYCuRPwleKazjZ45/BH82ffaAS+ZyuQ5KlfueQ/3LPoJkRP7suXS0gNnvbGDsL+61VZ3L5bpeNfWOERz7l5MoTIqVoLSQYP57gIkv+ux5tPbdGVwul8vlUj00+gjG+yP4pvUvxHcf/Y9Ipm866CK5XK4DVuW+51AozmD752/DLb8yh9bG6kEXyeVyHbCm3jGC3nobF//vBk79wiRQ3zjoIr2sOrAfEZKkiFJ9DIUywRgEK8bgIoJ2qjNiMkkspEwZE4R3pFVSMYdDJihCRz6T35EJRgqAo1ygYqxCgLVJ819YE5DGH5wRUNYvPyJ/IndZY/HNd6wAAG5TKFhJwV2tK0sAgPGbBNyxrECWVYVHTY9KWxC0RJBaQaEh5HB0d8GICKwjXINqXBZASeOswMS6ClCrKOSNsLcAVlRI09RoTCkdgAcp4KpUlPutbMXew4YCTwhWuWFGypc2FC45v6hlj8FNARqk9andcAsA4Fv+19PSJr/+Q7CaeO+/AAC8pquAxX8sIKRzv/RTAIC5t34FAKB1071yflWBJgbiRfAJY5sitC8dgAAqpEtjvbctHf99ny/Atu/6LwNFBQC880f/MQBg7UPvAwBM3vt6qdtNdwHYPxjlWlKaSjsWDSSssyrxVlSYFEFpFOOD57cUctqvSN802jImCPrkGOwqGI15JQ96xLGzG0ZmgYqUBSmybIT0WODZXrAhvs9cSJAirxNy5PSs3k/iaWlTxtJ6U143tBx3HpXz2IJZfkB0XYowo14v8/8S+pX05ZrtDYlZwkLttRjbBKQRpMa6hbyhoCOqkAeb0/x9ZGwEAHB8hjAoaUsLB1qcikGaBR2znVWBkn3L/W8GAPz7t3+vfP7z7wcAVEYW8H3/7zcAAM7/538IADgNqeOJh6WsJ7UuJYW3Tr5CxmFrVfI2YV2E1rLuFEForaXnAADbTwootad5+eFlabvuL75Lztu5HH1/479KLvvdL/hXAIDXax3bClwbVVBWMeSjw/dvBys7fTz40W287kYZr6+7UeKorXMg42zsLoFbBtioArCWd+S4tCFt+srjAuIlDHlTQVwc34NQM4UiEwioIMZ+cf8wujDXEdZYJKy1z5tEZbDjkK8JA2x35fzxghw5ju0Y5FxYDTmOeUdhhGadQqBi2pL80SXMUOMogyIneiznghQ7PQuVHA59I4QswEa1zARv8yqdDQGnta9clPcVHljXY9/MuVlujevO/mxrXY5PC8R4XEFtG6cfketvXomv15bzz73nP2Ds7psBAHNf+PUAgAfv/ysAgJHJm/XeAjqd/py3AQAqE1NShy1Z7xF8ujAhMEe2K9uovyPjvqExzrmvr9DYqnbbqq4Hm3pkzLNNAxxb4YPMU1WFyrL/i9VDSpTv90JsNstSh2dWJNf++IclB/7qn0k//tBXSmzfdOlRAEBt8RQAYEzX0FudGIY6pxBi9lWjSAim5NhRjZeJusxfnIszmLa0Lcd+2u/tCbye1TmNcyv0mEGfdc40c+PAisWMrez5RM4cBELLkdDCPEA7YZEZWLUQHW3+KVZrGbQ1Z12VJwIWs+8nURl7+gC40pLjJy5KP98wLnHAPEFwJteNbIMtHXPMnbZNbfk4X/CZKeSr0UkADaSTs5g8IfN95aTAwd/6nIzfb/q17wcALE5JrF5Yk3XAqPYTYZ5XduQeCxO6hunGkOp8xbmXsnViPBBaG9Zg2jbMuRkM9+p99NnzNxcul9Ebt+/Bj174Nvx/Z78PP3rh2/DG7XsOukgul+uA9eYngK/c+AsAgH9+5itRf7//ybLL9dmu9sdPoPFe2ZFp+ye+ADsPTR1sgVwu14Fr7Ve2cfFfyj8AP/P2Z7Dyi2sHW6CXWYfvnyRcrhdBb9y+B+9c+5KwJ/x8bwrvXPsSAMB7D7JgLpfrwPTmJ4Bv/8MEtVT+JWO2O4b+D/uPCC7XZ7PaHz+Bxq/cD3RkyZxujGLjXfov9194gAVzuVwHprVf2ca5711Dqtuudi908ex3ngMAjH7Z2NW+et3I/xLB9Vmpr9t4a/gBgaqmFXzdxlsPqEQul+ug9Y0fSlDrxn/eXWgBE/+pm/MNl8t1vav5268IPyAEtQvY+h+LB1Mgl8t14Fr61xvhBwQqbaQ4/wNLB1Sil18Hx0QoFFAaGQ3+C3pfKurroAeJPq3CiPyq07osv/KkU+LBHy3RB2I9N1f3cdAnQq/Sfs7NPED08+mvT4lco2J4DvTGZ94k9b4bz9Bx9fWvNZ8FAHz8tFzv2LPinXtiRjxS9GlNalv01FvbU193uyvlWVVPTd94WehxKppyBq9UdQTnVuWaZy5IO9NPfWpa7nnbKfGObT72MbmGeoRYp3DNQuzrpBeRfm2K3kj6QouJ/q6lPs+Nhpw/PSp+naL6/bikL6lPkG1DHsQUvfOMH63jpc0WAGC2N4lhmutN4ru/TMpAnxve9pcBAM/9zL8GAMy8SbyOiR5b2s+r29IWHa3LDdMT2hZ8KJFj8Cr24/ig6PP9nM0nAAC/8Vs/IfdfE7/dW24Tz9WF9/6cXE/9gLOf+yVRnQ+jCqUyavPHwnhGpxV9Tu8j/YHNwMxQanZjIzpvWeOhXpHYJ6PDjkEqi1fmhcS8P8g7KRqPMT+7pP5r61WmJ54xye8xli1bJXiYNa7IJ6nMHgWQeWeptnromKfW1GtX2ZT7z4xIuei9ZPmsh9K2RblcQaKeY+u7pdjurAvV0Gu0ViS2yVSB3qMyJf5c6HywV16mF5XcF+Y264HmvLKyGfcFtf7RD8j9nhIP9ML2Fw+9X+lSitv+4b8FAPzANwq74Kkf/4GoDvS60o9PzgP7vaRe5k5P+j/VNiQvoqMMg9aS5N6SeqhPr8g8YFkIVo2OeuY1Pqiu8moYV61e7Jk8DGq2E3z62SJed6O8rrakzTj/HP/fvxMA0F0U9g1j9/yGtP2jlwSC97obZczQl76sEwnzzdz4SHRfvt+x3lydtwJDxfic034/rEUYy4GhlEiskv9DVgpj2jIz6FXleqKmcdVsd6MywOQ0+ncZh4F9pP1PZk+vFI8JeqA7mlPt/BT8yJpfysXirjURcw/zpJSN83NTmQI1zceVRNtT5yyyTBijzGV1/bx5/ox8rvyQkjKPBn3fMfuAvcPXLA/LybzQVlgiOQJcn6brcVxQ/Svl0B4T98i+bq/8SsmBXMOSWcV5gfwdrgdr6uuuaT7uaW7c0Tr2NL+PKxesTZ7Ddgx2rCgXgvmkG/KJXKc8MQMAqJ8US0bgxlS5vjx8f3G1jRL+FLP4D78ma+ez/+33AQDNGz4PAPCT3ydt9pPvkLbJWDzymmNrQz31DNsZxQ8wrshCWNO19YRyQ8izIAshjK26ji2de2tlicB+tzfE257oOSX9jtyD3B7OYUXDBdjrGceK983rZzI5GppXMqaa1iWwEOS+/VAeaLn1GUCvXx4TzshGoxXO5bjP+FB9PcZrDXIXNhqy/uPzAe89WtdHV+VFfPg5sRM8dkXa7iteKVySzZV4nZ9xJchziddKtk0Dm0/HMvuZ6lyIc2N4/1wHBT331TMSY+UxOV7akPcZO0kh/keLY5PyPtuo25U2CAyDIllsur7gWhnsj3g9ypgPTCbNcXx+ZNCTmcK1GNdyMPODlf8lguuzUivF9eHvl3eGvu9yua5/lReHT4mlIy9zQVwu1zWjZHL4uqA4//we5Fwu1/Wj0g3D/x2+fOzwQc1fqPxHBNdnpd498XtoJfG/oraSLt5z9OEDKpHL5TpoLXzXOJJ44w8kNWD27/hU6XJ9tqr2toeBcmxpSqoppr+5eUAlcrlcB62F75lHUo//kiCpJzj63dMHVKKXXw5WdH1W6qFR+fPlr9t4K2Z7k1gpruOXjz2GP5s+izsOuGwul+tgNPUO+dPec//PJrDUR+lYCbN/q4+JL/7s+ZcFl8sVq3KfWAJav3sf+lcqKB0rYuqvbWHsLx4+C4DL5XpxNPWVYgE4/wNLwMU+iotFHPtH05j+ivFgP7jedWA/IqRJgm5SRMfsOQyzXzv3fS2pN5q+0kvqdaZvp1CIvW+p2Stzr33f8/Z83y3rNVlvSNlHarqXrHoO6Z+h6HGiuupNoe+O+3n/lXvF8PnElacAAB+aEc/abfPSNtw3dOv0clQOel0mJuYBZH6/QjJ831Aq+I/UH7SytRP2lj+n/umlbRkIl3Sf7bk7bgAAjOn+uqzL4LVjT+Km8gK4v+5ETTxlYd9X9fPSP9po0EsmIUr/dmtN9l6nH4heqfd++hm5j/q5vuEB8SGufeyPAAAbD/8pAOC/f923AgDe/tvfjE8B+HuP/Vgo8y3f9t04gtfg/3jTXQAy5gDbaeLez5F2+sNfB5B5mhfe/A75XBkKF9bEb0sfbbHAYTb8Tx8T9WmVp+b1ulL39Y9L2du//Yty/Yuy5+0nqtJ206+TbehOfO3fkfN0//C+7qUdPE2HSEmxiOLYZNgjua2+reCZNQyMZY3X4zNS1/aG9MlqM44f9g3HTPDO5u7ZbGA5xhd8tT17yUIIe84j9qZRjGHrXQu+YsO24PnlotSV+aij59FfPKEetjnNjZe3Y18h9yFmXqAfmLyIit6HfsDg+96137Ddz51lZU586pmLek+py81zU3KvCbkm2Tb8tz368Nhm9BnztW1/9if7l2WldjSfPHFJxsJ2Wz5/0y3CE3lWGQgbj8qx35Ry75x5XNriO09h7PZXAwDOnnwNlgC844eESVBvi9fz4//sPwAAtp74hJRVWQjkRbC/qC4kLjgu6SHt7tBPO5yRcnJKYr5clQVLpzU8537RXTJ/XP7EewAApYnh/xpCL/1h0uJkgr//JRV8y+slNy//2e8CAG78xn8AAHh8Vdrybp0nnlySfv+jZ8Ur+/k3C3doVNcJT67Hf4W2GHyow5dEhTCfmTxQ5NiwTIRemFczfoJcY7sh429cY7iia572xprcQ+PCXjN46TXWx9WX3TO+Yo5J+rm5/qBfm77eEpTRoaum7bAWk+tWNU7DvuIa18wTu+vaa7WiMoS9xzUvcDwzH4wUNP/qsq9dlrZ49LysbR5f1ryv64MvfYV4nMkN6W6Kr5e+cfZDaHMM9zhz3qAylopyHsitIRtB359+/duA1wNnvkfWP6cWptF/+mEARVT70i7v+iGJzbuPyTxOtgT5DuYPnEIb2XmB68HWxeeiOlp169I/Z6ZuAgC855LE+ohywt75F4UP0vqA5INtzXnsT3qgR49L22be6sOj9VYX731qDX/jNVMAgDd/7bcByOZcMi7YrxXlzFBcjzKmw9jgc8YGPfASNxlHiMyDmENCxgLnWq5nB1lLg5ywqo5vMkqYJzjPV3Se5jjkfMHYt35/zoGjGj/83m6eE5DlNorrVo5ZsK7KWLPPE5YnQtbKbu7IRD0uu+UnMLeRz8E19HldS80Z9hF5A5tFae9PXJLXU1Vpix1d74+cvB1Axv+w/IC8fM8c21qVsdjTZzuWu6xza7VYwJGvmcbMV8vrQruBtNcPuRbI5nuuSW0O4rMqORtc22TcqXhtw7q0DduIzB3GMPujqXmkceZROZ6T58uuzjdUZV54QfUbJG9UF2U9UT92CleT/42my+VyuVwul8vlcrlcrn3Jf0RwuVwul8vlcrlcLpfLtS/5jwgul8vlcrlcLpfL5XK59qWDYyKkKTrdXvC8Fcz+6xvq0+PeqDsXxfO+NS37u9JjUzR7bNq9zq1Xjr6eSvAXqg8w+Imu5gvT/ZP1lIZ6Vca1Fenfpje2qXUYnZVr7yydlXupR4nuypru71vcEE/bT32J+Pm3+1LmsbKU9cpH/xAA0LpwRs6nt1YZDBNHjkelrXB/am2DbC/n4YyEXr8f/E51/e54JT53dUe8j/NH5V7tjdGozt1EfX7qNeP1ttRvSU4DORIUy6jb6WK7JXVenBJvGr1ImZdKrvdkS+pOZsPrbpC9kFc+8gcAgMu/88sAgPVHJX7mzz0NAHjo534UAPDv//gGHBmV/vmau8Tv+Nwv/ASAzDtUP3GrlPnUXXoU9OLWYx8HACTqYRu7/V4AwC3an/S/MSZTs588fXeVWfEiFUfkdVn94Rt//pDc56kzUp5L6g97ncTH4v/2jQCAJ9Q/evuCeqCn5fuHcT94pCmSfhftdfFzsa3oNaSfjJ45+vE4thqjM9H7VTPOC2bP28yjqHsfd4fDcIL/cNf5ReMlDH5cHWfMC/QkV7VfzqyIp3314hoAYELHwknNaTtnT8v1DEtl4s77AAAfW5L+/thF2Rv7zlmJm3sWZwEAs2MSz3ccnYuuv7ylY0hzYEM9k41OI/p8TD2QUyM1rY/Wo98JOYV7GLM96B3902eWAAB/cl76Z6st93rDDRK7f/le8e+2rwgzIclhHmS+yzT6nB5T+v/sPs70vK6sSxvTS3nfCdmjce3Rj0f3qR+VsdfZkPOLurf17Oe9I+zvvnRRYrGudZl59k8AAI91vgwAcPOREwCy8VbSWLPMGfI80mo//lw5EdznvTq3qHWT67xqRuqw+C3Cc7n8394tZS2Jf/RN3ynl2Pi9X5K2UC8kAstikGlx2DSVdPHlpUvYfFra8uzi3QCAX/mk+D2/6bWSkz+lnvr3nZY4fNMJyQc3jInf9INn5XPGxY2z4gsf9MYO99RzbAc+EuK23c0RaWiuon+a3md6X5kXOsr/oZe4qF515royffst4b9w7kt78n16ZKHrisbl83KdKn3e2/pavbZajn5ZrtNJ5L6rykppqxea5RxVBg9bgvXgmq2cDPF8l+U7F9Y2orpMjyh3xbContGc+L6nZA3050tyj7vntU203XvKYKrqWod1Z14f4EOAku8zf3D9R3W2JH81lySeOIaqyn4av03m9kW1M0/Ua8DdDwDI2vtUR+q6/bTE2Mb0cT1X2iLwF3Lm5dYVidn2ih6X5bpcD5B9tVKS77/7z6Ws//PffRwAsPrMr0fX+6XZVwAA/tODfxMAMHZamAibj3xI2mBWciJ5NEVdjxwmHasC339zD+O3SM58clX64NYFGfc2Lukj7/fJqdDnBc21LWV1bClzLXCCisNzJ/kjFHN9X9cLnCezeayfsVF0jcyYDGNHv2vnWnrdmTcy7oiylXI4TWtalwnlBVQNd44io4WqVeKcyDYjM4Fr9r6Z79gmVa1nUi6EcVnUcdqH8hk0l3BuPHtF+u/0ihzr+kBwfFrm5ZquVdgWH9P8stmW63/Dq8THX7iouXVNOEbVaVkbbTZaWteYyUbx2Yjrz5aOReaD1PAoOJb5/FMamwD7mv1EVkWjLWVnHuAcwrqEMjBGzb0CX0YTMfujGpg6Usa2chxampd6ev36KVnPTH/O2+S++ry6qfyIrceE7dS+LGuzEWWr7aXDu6pwuVwul8vlcrlcLpfL9bLKf0RwuVwul8vlcrlcLpfLtS/5jwgul8vlcrlcLpfL5XK59iX/EcHlcrlcLpfL5XK5XC7XvnRgYMVCkqBWLgW4BGE9zyowbUuhIzdCYELF47fIUcEYBKdRxUIMULMQO4owChAKopCiUK4AWKRSEMpD6AZBJQsTAqJprwiIoj4vcJc/Pq2QNwWXjSmg5LWnBNxHQBJhYMm4QDlaJQFnJCsCF2o9+zgAYFMBGX0FcxQqcl5xdDIqaXtjTe6nsKEMpEVoXDeumQI7CEKZqNdwbFLqOFET8MhGsxV9Z3pEgSQK+ajPy3FH67qtbRPKpJAmgpUIGcrAlgqi0vMJSOJ5HYV/sD8JIyTE5eYJgcHcfWxe7v/0pwEAywpW3D5zTo6XFXTzmLTpuV/4cQDAN937BlTnBcJD4NTUaz8fAFCeEfBQS2FLhDrNfu6XAAC6W/KaABN+n4ATtm9ZAZgWwkOQVlXBRtBjT+9LiEtX+3VCQS3zb/saAMAa5PuLk/WoTQizsmPkMCjt9QLUBsjigcdLK2sAsrF1pK8wmTFpu1Eda6WuxG2/H8OHCPghXJAi6MvChkK5DBwzPleBq9rfk0V5TaBZdUqgPoQGEU56bkPywOVt6ddb7jwpZdUY7zCONEc1NS98ckm+/4M/K2OquCgAnH/0Nrnf1yi8sLIj8cm4WZhfAABsahpY3pL7r2pOJQhuaVPen6zJWOaYnx6pB5hSANjqyCWUbUuv0dPmJUOsqEDVAIPSOhY0jxCstLKlELhEwXXaX8zTZcKFNC9kMCfmbbnPydkpuX8Askr5yhMy1iuaq6trAiEaf4VA0o4prPTptR3U9B6vPC7t9i/eKe3558tvAADccVT6Nb2s91AIcFEr3VLAUQDfmdzF9zlnEXRWO36zfF/zzspD7wUA/PY7vx4A8JtvkrpxXnkV1gAAF9/zQamjxltZYU+MfbZNwcCkDoMKpTKq88fw6b602W88JsCr73jD7QCAtR3pg19/XObiV8zLeffNSZv/3tNy/r3HJE/MKmQsSRSCpX3HvMB45bqix7nSwCnzYJWdKL3ItQjA6mzJGocxyzhYb0gcLF2S8bysc+loRc67Z1pAmi2NWeYF9nNlUo4ELfPIMcYcGkBsWqeegvrOXNmM2uDEpJTrGOHG2kYB+Kj1majXUNY1CddGS5cFaNbU+X9uVO5JoBljvpMmUV0vbGmZNG8sKiS2cfmCtJXWdfb1X4DdCuC2AttYxm4Ye6pBoKLUeUdh1c2zAlMmjIz345w+q3M5kIZ24JqEoDL2x5jOvwTkca1jQXRcS4W66Dqjp/MHReDilXFpk3c9JPdPDVCR2lx5GADwT94v65OfOnkbAGDpfb8MABhRgC+B0eXRCRw2JZoXfvaTZ6L3mZvrCqu9uCFtOTsufcL1Aeehto6FEZ1GUp3aNQ0EIONOUwHNmkO5xrJ5olyM1wa7YeaMG4oxGeDBTM/6PucwinPlwLONgcmzbM0CYa/x9bge5TMPy0HAYiUXeswcmer35TrJiCmP5r3N7WZoX67rCR/mWKAIOrxlVmLxhhl5xukpdLShuW/sFoGGPnpa1vtvOCbgxamdFQBATaHIK3/2u/L6tlfJ9zXPMA9ZyDyfydp6n5YBrXJN3tHPCT1N9fkUAMoKZ2Y/cO2xMBmDS8OashfDYO2ahfMEnyM6hIIqoJHrQkJh+TzC9cTHJ2Q98V0/KOc1Pv5dAIDqiDwzveV7vgUA8H+95RQA4OKv/lcAwNZjnwSwN3DV/xLB5XK5XC6Xy+VyuVwu177kPyK4XC6Xy+VyuVwul8vl2pf2/BEhSZJ3JUlyKUmSh3M+T5Ik+dEkSZ5MkuSTSZK85sUvpsvlupbkecHlcll5XnC5XFaeF1yu61P7YSI8CODHAPxMzudfDOA2/d/rAfykHq+uNEXS7yKF+EaW1Yf7nDIRPue4+sNbUkR6mhar8rtH0hdfEf081rdjvc30+dHPQ9HHzvPTHE/07nPovyMLgZ64xy6KH2erJWWjx4j+7XOr4pk7OTsZfY++KfIDytPiZZmZFS8u/YD04NNrQ69K8EiZsts2sfWgh4oenNH6KIoFudaUlqXTEx8mPcr0EgUfuDbnRkP8O9bvFfxT6nWiJ4p+cfqBQtnUV1WhV1Xfp7exm6gHWr1JNT2jWFCvm/qG6ur/m7lf+mTyFVK+yXslNGfe+EUAgJ36JD66JB7UTy6Jl2hxTMr45V8g7IGVj3wAANBWrxH1UCr+6sak9Oc71B/VLounbKQsZd25KIyM0WM3AgAubYjfcmpUvJDWp1msqX/0jlcDyNgM7PcxrVtPvWilVNqAftQN9e1N1OmFfkn0IF6KvFBIUKzWB3yAjK/z69J29y9IP/eqU/I1jVdsSF/21e9XHqPfVOKvl6rH3njhesYDnVE64nxBH3FSKO7y4eq1mVvUV0ePMsdEXT3sc1om+gTpt6SHcPSEeNgKx+T4zIqM+1vH5PtfNS39+p/vFD9g//fPAAD+v7lTAIC33SptNadjpKl+3/YVyVejJ2/Xcoxom0i5a+rdZRuMVSW3To3I/Sbq1TBuOS7p511Uv9+rtA1maltR3e5amJKyas6p6Fihz4+chlXlQ7BsWY5q6utOfB1tY+YF9hfPZ/m66nHurIlXm/5i5sjqwgkAmU/01iN1WD/rV94p/tqv1nOaeg2WpdaXI3MQrx3YN8EDXYtesw1CXRdPyfc1Pjoa0xfe+3MAgNfSh6nnr555VO47MQUgy330RKIs+Yzcob6ZA19kPYiXIi+UK0iO3Ij2WWnzb/+cWwEA9Y607c89If1KD/3nH5X54j1PSMy/4xXCCenqWOhrTm3r+qPTEo90TcdosTh8HUBWQpYX5DW9vVyPNButMNdtt5Rd0JKylsdi7zl5QswH28qDurIj3yMTgfegX7+jzA3GByC+3PGb7pDrLsm8E+Z7/X7ZxGexr15pzQOfXpFyriijwXINKDIRev0+mg0p8/JWnLePK09hcUp8wuQ28d41jeW6+nsXx6Sss3W59htOiG+315IcOH3/W6SNEmmTybr6sQtxPwQmVmG4N52fk4Ww9ejHAACtS+IrLimnqq9t3bosbKVE61xR7sFucdxXp6VOifIegj8+rFkrUVmZD5gTmZsA6T/6rxvKazjxRmEYjNdlPbIxUJJY8xpugT8zpjwazWOsUxbbL4kexEuQFwqlMiqzR7H2qIyBr7z7OACgtaZrceWMMf44L6Aq8wtZF2RogGNT+RCX1qT/t9sS34zTWZ2fAguBa4GwjhjelrtZKaNVPrvE3B+u85j/OYcxj2RrbTKL5POxgnKKNM/3IOdVapxvpKzs93A/89zA9WSvFfMKeporuZ5hnWsdaRPGOfNIs635Ku0HvgK5MG2d58Nzl16LnLmCjlfOsS09lkbk8z8/J/7+G5UH9vm3y/xNTsDW2aflfsrEaLZ1DaZ8gaLhQzAueB+Owb6+TzYKn7ksj4Lnd3e2QhmrOsdwPW/X+YEHF/hwcXv3tWyc58lMo1hm8hu4zqPWjsg64Hu/4WcBAI2LD0Wft3Zkzvyz/yzrh8n/Jqw3MhHIZOltXz3D7PmXCGmafgDAlauc8mUAfiYVfRDAVJIki3td1+VyHV55XnC5XFaeF1wul5XnBZfr+tSLwUS4AcBzu16f1fcGlCTJO5Mk+XCSJB9evrLyItza5XJdo3pBeWHlytXWGS6X65DrheWFFV8vuFzXsV7Yc4TnBZfrQPVi/Igw7O92hv69ZJqmP52m6QNpmj4wNzP7Itza5XJdo3pBeWF2ZuYlLpbL5TpAvbC8MOvrBZfrOtYLe47wvOByHaj2w0TYS2cBnNj1+jiA8znnBqVpH71mEz31+zxxeQ0AcP9J9Sqqz+/JlnhPblNrc2c79o1AbXr01lh/T3idYwOlLyjsnTzgpdvln1PvIvdfhVhaUFAPTHFb/FWvPi4+viXlOCypT3Bd92MP/myzXyt9Wzaf0ksf9mbXMg/wBLjfbC9mIQTv28Ae15ah0M98Teqb5R7HfJ9epdBu6rflefxeR8vA9ydqcp71RAWPeZGcBvEs2T1S2+ovDPt1m36id4q+wcazT0TXmbr/LwAA1u77QgDAzzwsnInPO9nDtLIDFsd072D1tT2rXvR59RhTjfNn5P2j9wAAfvIj4rudrKkf91bxYYV9f3ftLQ0Az1wRj9HHzoonifEyNxLv27talKB/BOrH6ku57tdjdVPira+xu9HsRuXvGd//y6wXlBeABEmxkI1DjZfHlFtx3wn1mSkz4+yqMFRm1sQ/2tV8UpuXv4RMjGfRshDsvu8U/clFep11DO1mIhTNubxHxezLDvUo0ke50ZB+o39vekSOHFstjfVPXZD4+MVHxf/3zfeJ3/OWSbnfH/x18YX/wdvkh5cLWxLrRzWPbD38Kbm9cgAKupd7a1QYHmNH5XpTR7kQi/3e9CkyT7XUHwpk+ZJ5gPyN4/r59IiMd3oPWXeybMrTEvPkg5DjQS4Mveld3TM9cAU0Hti2ZCGQBzKvvImwR7PWYetp8f1d+q13AwC2n34SADB+m/iLuf/z6if+RMo3NYcRzbsU8217R/qPvuiuloW+2jMbkkfoDb19IfYy0vNeN7b74FXnfbXtWupt724q74PzhvozR07JntiJxmjtiPIdZo4CAC4oZ4hMDvJtDkgvKC8UkgQj5SJef0zafPO08NmuHJf+47zyZTdInP3gxyQn/19vkc+X/+R9AICx2+8FADSK0lbb6i+mV9Zq0Osct51dL9AvXCwUwlzIcd7vcI0i12AcZEwVuRdZA0fHJZZvW5jR97UtNN6Kuhd60/A9Pv6cvD6zKnH4xlMSB7NVvT/ncnJjdF0zr8yDC1vyvUs7Ur7Slc2ofHPK8tntL2a7HJ+W3FNTjgP91dzLPGNHVKIyHxmXfnvtYje6xy3zU9pmM1Hdxio7eh/NnTr2mI9YN5vHuR7Y0Tl856lH5H1lH4zeLHM6+QFhLaV17+p9dvBcxrQqxO3JWKgZFsVWRz3kyrAqtpXvRb+4shR6x07JvYzXmXvW188+DgD4R2+Vtv7BD/9tAMDGIz+J3eq86fsAAP/6C4WVsfme35M63iTrmdoN8n5Z5wMb2y+zXth6Qdlqf0c5EVuflPyd6pptosS5WT32Omu3PhH7w6de+ToAwBPL0r/PnZe2HqtwjpaxQY5BuRTPL1TWhvEcSt5Rv98L+YCyzwFUpyDnregcydxCLtR6U8bnm2+TOOisybxEL3uiY6yi68eC8mJ6zWZ0HztmGPMDTDWNZ87RDS1PR+dFtg3nly1lKnR6vcBtsBwGru8Dr4G8uY24LiwbeWCdMxIar75BxkxNr/P+R88AAP7CUZlLa8qVK9XJxmK/xHXk3N2+Ivmlp+sOMhWmX/UGAMBpfU7leoX5bryleeHZJ9DXMrO9WIawHlQlJm9QbH/LriCPqqC8llT7IWMnSDxUtMzvflTaaN2wEKx6o4t6fYmrgq7tiiOSk0tD+C+79WJkjV8F8NeUrvo5ANbTNL3wIlzX5XIdXnlecLlcVp4XXC6XlecFl+sQas+/REiS5OcAvAXAXJIkZwF8P4AyAKRp+lMAfhPA2wE8CWAHwF9/qQrrcrmuDXlecLlcVp4XXC6XlecFl+v61J4/IqRp+vV7fJ4C+LsvWolcLtc1L88LLpfLyvOCy+Wy8rzgcl2fejGYCC9ISaGI0sgoHjknft0Tup9weUt8n481xWlxp9gysHP2NACgdlRsU4nZc3PQ72+cGr14T3d6Yeg7CX4g4wNKCpk/m/4n+n/bHfHZ0PtKpgG9rsHrqJ/TVxX82WHP846WoTy0LNajZFkIWVljjw19WB29P8z3KoED0Q91hXrHWHZ6xun1or8vlFmvNTsmPj/uZcs9yclSYJt1jHcxlD0wETpRXZNCvDd1paN7o6pnaVu9zvQ2Lv/h+wEAVz4lfsKJExJA9eOnAAA3V6Wu/+fnifdx996ttx4R32VHvUbZXr9St0n1LG88/kkAwCvHpcz/4i+KN538B3pU6Z9kf7Dur1kUj9Ql3VubjITlLel/+skvrIsH6nefWdOySnnuPCIepQr9+brncW9rLSrHAXscX5DSfg+drY3ghdupi19zoqYxfUW8zr+7KnW/v6ttzf18F+V8trlldFAhrnO8zmXt+qQax9//3965R8l1VWf+O/Wu7upWP6RutSRL8kNGMsIy2Bhsg8EYgg1JgMDkAUkI8WSSTCCZZMgigZmEPBiSmazEk5kQwsp4DJNJmBACSRjHJDFv8HtsC8kvybIsS7JaUj/Vj+p6nflj732r7+kuddG4qqvN91vLq1RVt87d59xz9jn3ur/v2AgqVatR3zGfhZ6s+XlUomOAul7PNGc2JmzvZKub6f7TevxlI6L3e7fqi0c2qPeKnmf6Kenz+8ZF+7ZPY5u6X/ScpuurarmWNxbOyPGRZ4dq3iKNrxLmQl+tLtH91vV80mDmF2PjvV6GXIc5fXt6YhIAMDFnPjGC6UXtdVZjN92f6f5t72xre/NCMH2pteW5QzJWJ++VvFDRfeo37LtK4lefCPNSMdKL9mQ2/wcbv+ZdYJ9bW5hmsT8lnz9wTHTg1sdM3x3l1pT1l/n4uU2jvENiywyKpt28LUz7aFrF7OCwlqdzmXr0HD4tc2kpymdSXnc2MGNYD3gPX6uieEb+wrl/r2wh/82npK+/9QKZez/2mLi1mxfCs3/9MQDAhiteBQBIbRS96Ljqihvt575iOMHYsLnW8k13No1p9UDKzKm+1jTveoytA7qzku9N87whL+936HrB1hPTC3LOh06IXvj+56Q/vmW3aFp39Uu/rJ2QNvjgHeJlcN2l8vrbrxMN/HDB5tqSnl/Os2PAvHsE0zznzbtF4+rVV9OF2ysAdKvXhPXpko4Z8yKIPE2CNY75uJh3iumrLa/cc0R8QQ6ekbq8dHOflKN52/ZKtzrlVBecCTyYbB/4Wc0L0PMP3fgOAHUfoqzm4F4dK7PPSn4wj5bF2HjNBNrh0MMqk5J2O619r9+8JfR483fJaO7p3nV5rLz5E7IGnrj7TgDA9Xskh73hz8Xv6UuHxJHGdPyv2SbrmVNflH3izfelsOdKKV89NVJdsr5ZYw+lVeGdQ8Ul4TT3ZjQXWi6OrkFa+vjZz0o+MB+Zhd2idf/7h6Rtu7Qvby7I8ebNYX0+F3inLF1jxc3Xorzg4/4oi2NMqUeGrcPNe61ckfFn12V8TvrwvH5/+RbxMkqrz1BZ56GklmfzkrVBSec+8xuonzfuS1bvt/G1eFo9WiwvnNUxVdJ1RT5t65r4PUN3Jg0bGaH+3+Zxq6PTc0Vzq44FuzcyD6WRXol9s75++YlnAAD/97DUzebc3cNyvr4ZWfPYmjyjudjurcrqwVfStZHT8w28VOaN3/2S5ItPfkt9op6Tuu16qcxHv3GDeDzt27QFs08/pu0X93+w9q5G91jxvpOMfFjkHOH9n61p8uaxpm2Z6pVxntK2SnbL98NO6rBhWObKqdF7sRxvfo9cnRnNiXavlFVvpXzgCRey/u4yCCGEEEIIIYQQsibwIQIhhBBCCCGEEEKagg8RCCGEEEIIIYQQ0hR8iEAIIYQQQgghhJCmWDNjxVKliqNjU5G52Pa8PM94RPwtsGNAzKFmHr8PAJBWIwwzjzLMtAKRsZEZKNrzETWxUKMNs44x0wszlTEjxtAwafE5amZ0oa/2W2/mbDBDEnk1s0EzUsupQVpCY3I5NR8LjBPNlKhet3gc0WsyueznZuhm8ZqxlmHGe0iGz5Acql6NQ2pmEKMtZqYrVraa9FisZpzUpaYfZrBm2PF1w8Tksu/tuBAztDmnBptzR8W85NwB6R8LZ8Vwr+dSMcwZev0lsd9X1VDlzFc+J+WN7AQApAeGI2MyM0zLR+ZxapynfSuZlutZuFBMu6AGJoNWRzV3OqdmjxU1sEmoSYyZtVmdN+r7kZ1iAmWmMWdntE3VjOeaLWI6NdAlcY30yXlL01KOmVlan19s3rPu8DVUF4rIDsg1eXpS2uSihPSvb01Lm5ihohlk5bdfCqBuyGN5wtrGMBMwywfLBKBhqOGODgPLU1H+qVZhOcf6po2FyHzQxU1cjciAdU6O71ETQqjRkhkfpdVoZ/eQGHdFY0RzV5f2YWuropoCVWelr7seMdxJ94npT9QP1YjHDJgiI9ZK3Aiobp4o9Utmc1HOMVPApXnXxcqK8qyaNo7NiDHaVFHzhxlO6XXZqAaJZgJlhopRngjyRVb7enHsdOy4uWOHANQNFc1M7OxlrwEA7B+XOPaNSFuMJMqx31VmpzB3UsyazITJjI+i66P5woyQrO/1a3u9dJu0u/WdKK9rrqwV4kZ2Vic7T9nL9allpe0ym8Q4zXJrPXdaHpe2n56X8q2PW76334W5eV3gHFwiGfVZy5XXXqhGiWo69s7Lpa0e+52fBQAMXPN9AICCmsiNz8o1MKPVmpe2y6TipnLWLxvliXCuNRO0usmgi+Z7V83rb+Rcs2q4aEastYr0vfD6ZGs6Z1bj6wCL7WWbJY8M92g/0bH2sh1itPjKXWL69Y9flTFy3QUnAAA/9XKZv2ZOHor9zixk92j/ywyI4Zb1c5sHjcVrLKtDZX5ey5T31pftNREY09m5k5ZrojWWvH/qzCQAYP9pyWnnSmqIrXkm5eX9OTNEszWTGitaXHae2SMHAAALp8WQc+gNPwIA+KP9Mp/c+bhcx6lZKf869RR7/7U7AAD9OoaLJ48io+cwQrPvRGAC6zSGQjY+N3X36vi2H6pBY93gbnes3Lmjsr4Y/9oX5Liv/B0A4MWWzzUPHddXM1ozM8H8NjHYtJxsc9ypyWmsN+ZLZTzy7Ci6daxdqG0w+dA3AAA9u18KADj8kfcBAAZe+VoAwIFBGQP3PCI5fqhLf98vbWLGfXatQhPRxusH+by+Bpe8snhNFhmZqyG2YYaKhp1rXsfSBjVg3r15MFamGbKWujboOXVOVYPNpBov2ho9v0nygxkFh/cZ0XrAjBW1Lhmtss0n83pfMbWgY39OzpvSsTmg8XZn0tF4tRxYTkhskzOy5rGcl07K57WMmjQGZoSZmuROM1I2bntYzGTveiSpMcn79/dIG2dHxRh1Xu8fei5TY2UzdtQ1k603zATRDBs/LkMM3V/9qPxOz/vM3XK+2wd+FQDwibdejrlnnpQ66v2AGSCXNI9G940I7++k1HShJ/Z5aHRZmoqvP7KLTKAX89rN8vmDv/Q6AMAXHr5B6p6XtvyV10gcZko8c0hNi1/xBilX81touB3Cv0QghBBCCCGEEEJIU/AhAiGEEEIIIYQQQpqCDxEIIYQQQgghhBDSFGsmnq55j/lyFS8aFL3FE2OicTx4ZgoAsCcl+p2q6lHTqiuJ9MaBjqdZTEdUq4guxDTOpuk1pVOoM11MeO7IPcB0gOp1UFKtbL6qWuOSaJNgGrkGdTDdcUioQ66fOa7DKjfwQEhonTJR+T52XLVWw1wx8CRIxY+1OprG0N5HZaimyHSAhh1f11nHdbmmlayV48cbpguqzEr/mHr4m/K5nn/o5ncCAP5sTvThX3xM2vqqHdI2f/C2fQCA0//0fwAApTHRQKb7NkZaMSOjfa1aLMbqVNIYTFN2fFx1VKpRHSzI77pVf1tUTbWR7CrE6hppnVQ/PqQ6vKFe0yBpm6uuc2FCNFYzqtu2MWF6slotfj1Nn76ecIkUMr39mE+q/q9PPh8tyZjamVTN6p1/DQCRLtW00qY3M134En1goGU0fXHVdH/m/aGavZp+XtXfLdZKh/4rYZ+OX436mKiVRdfXn1OPFNXSVs17oBbPD+Grae+tPNPGZTZIf6jMzep5dMw1yCfG+TwQ9IhFNYp7IESeM4GnTDh+jbTW1fSdliN79f1gQTSMC2dOxMpLBJrqMHfaGJp96iAAYEz1wrmR7QCAD50Tj5SH3nWrlivHj77i3QCAP3xnFgDwg5svACCeK0nVAkZeFzpXWHubN4JdB58VTWq1Km1i4zjKH9PT+l76pl0n01KH18HOY3px8wGYUQ1qXucyG+cTc9KvQp+JYc0rpsH3qpFdT/hqBaXpiag/DGalbuY/c+sRqdPNf/ZeAEDfvssAALltFwEAFhJy7Ubtmml/7c1pWyfjc2SYJ+r6f6HhXKvXcnJ2Hhvymg80J9nYsZwUeqX0dedj567N2fwjrz2a416/e0fsd+YHUlRPFZcQPfDH3ypz3v8YkTbqiXTZcv7ytBhQmXdPVFfV0CPwgLJ8YP2zsij+pTlqeZ116IVkYyNcO51RX6ADoxKbeSEM5qUOWzYUtO4yj5uW2fKElW96bqN4/IjWeRIA8HRC6vr5j8vnk0/cFjv+Kxk5z/wHfgEA8Ik3iffO6BMPRXk1p3OQXScbtza+bU6yPLEhLTEtaO4rat3TOi7DvF5TzyYjYTrx2biHgXku2avlryj/B3kloeuRR0+ekTiCvrweSCYc+vNZXDIsPgGn7vobAEDxhGjfH/zQHwEAMjnp84Vfl3Xi5+47BgC4dEBy4uXDkrvNC6Ffx2LogRDmAcPmw9CfqjubDo7zka9PUucTu/6Wx7OaQ6x/9Kpn0rAuC239MKn3E6Pn1EtJr98F/XGvHSNZkDrecfBpOa5P6voS9YFw5tWWiM9HkT+UeiiYB8uE5qd59XJIOm1jXWtt6pa4Nxby0dxj8/20+sLYestin1DPGrtnMV85a5tezStzp44DAKr9MjYOiSUathwTr6PZvbKOsDwxf0A8U4onZJxnh2Wez23ZKXVNxa+T5b4zOqduPfR1AMBk/Kiobb79rOZUDzjzlFBPBBundc+s+Bqm7m0X71uRX5/2h2hdp2sdW3+E67aqHleYkDr/1hWyLvztK7VfqX9MZVr6QXFUvu95kfiH5AaHpJxafG3XCP4lAiGEEEIIIYQQQpqCDxEIIYQQQgghhBDSFHyIQAghhBBCCCGEkKZYM0+EXDqFPZsHcE71nR/9uuzb+wOXioYlb1oV2+NU9emRfjTQ+dY1ustjmhrbo9X8BUzDZNqneoPUNZGmVbG9TkMtdFKPTUTaR6GsWiHTONl+4vVyw31Cl983tJEOuU6wp3UqXs5SPVf893bearUaacfr/gmqx1G9ZTXYt72un47vBx1FFuzrHurGQ92V/d70/3adI3236sgqut98717Z7/WPpmRP9s9+4COx8u/Q17s/cTMA4G9uf5t88FXR1M8dfRw9e3TPWNUKmrYx9C4wzKNgo+5BmzMtkmmftU5d22WT6XS/xHb4tGhQT06qZkn31B6pJLQ8p+XJNSipbnPu5FGps2qgUra/vLWptkmXaiVNp5tYQcvUibhkEsnCBvTqWCvPyHX/X7qf809M3isHqh9EqkdfVe8X7X2t+w8nXbwNTC9u2D7O6TCf6HGm1bPv7fhMOh2NR9NHp3X4ReNWNWW2v7rlIOvTptf1gTYu0h4v8TJYftwbltMyvX3yrcZnYyfsx9HvdExaP7L6VDVf1T1UXKTfrOvl4trxKMPob02/Z5hPwLl50+/LcT2q+/QLc3Im01br9TQdb5RPkvHcGOWNUZlHJh+R/eBf8pOyf/M9b/lNOV95PhZP779Ivrh99wcAAD/8E+KhMHHvP0Xnzqom2TSI+SHxRMnp55arzKPg9LT0PRuHpnXP9sevu+nAK/PidRB6s1jbpbRuOwelr9ucOTYjbTV6Tl7H5+V406LuGJQxkdM6l2fkd5bX1hPe11BdmI/0npOqbT85LP4zP1/+ZwDAs+ozkhveCgDIj+wEABwZEy+d2ZK0QX+XtFFe57vMCvu/h+uLYinuBWS+NNVFHizRHKnj0sroyYv/hgvGb/2qWN4IYtD+YeVarsuqb5SNY8sftk/5uy/ujR0/e1L04D7YB96oRZ4dM/Hza7mWJyw/JVKZaPzZuJ1UbbOtmVSSjqR5T0WeBfpex3PJy/GjOobOqe57S0HOecmA7Gm+sUven1PPBBur5hdgRDGaJ4PplbWOT54VvXLohWBUStIGXzko8Z25UeKtzs+i+NxRAEDXzj0A6l5Klm/t/UK0X/yA/FbbwtYNNs6rdj2CtZBdX2vblHoZ2HUzHwg7X32feRcrz7D56MSEarV1rtu1qX/ZNuhk0qhhC+Yw+lXxyDKvq+kD/w8A8KenxB/kjz/0YwCA2/eLll4tNnBRn6zhdgzIGOnVsdlo7RSu/UMvBMPygY3lYln6cTaZWKK/t5yRD6b7XLD+tHnGvI+woB5dOu9sUy8E82GwfGH9r2t4GwDgGydk7Tz6uOTEX7xazv+yHSOx80fzhE6Ztr7t29Qn5+vr0TjVP0rr0d8lbbh1QOYfzE6jKtM9ain5ztYNttYoWV00H/d1xb1NbEzZHGweKNbuO2SJjSNXyvW8eZf8Plec1uPUa0H9zBKayyJ/EK2rrbGrer/z8m0y9o5d9moAQO/Jr2E5rrlY13bzs/X1lL7aOG3kgRf6xdn60cap5W2jMqe/07xR1pgrM3I9y+MyBhZGta+Pm2+M9IfMgHgedKsHQvd28XkppqXNpjX32jXKrbBeWH93GYQQQgghhBBCCFkT+BCBEEIIIYQQQgghTcGHCIQQQgghhBBCCGmKNfNEqFXKKI6N4iMPyh7H//KghDLco4IPHAYAvPrCzfJ5QTWzpiOuxvWGpj+uY+9d7H2oS7H95E2ru0R2DB9piyMdlGrIErXl91U1urP2jOb8mpJwf9DFfgzAUh3W0iDjngmZhvvCL/87I5NMAqpNNN1/RXX7oV7XdDqmJYr0P8H+zKHO2/aJNe2paaLquhvdIzvQUpt+OPRU6LroxQCAL/32keUqHDF14i4AwP7nrgcAXKl7KftaFUXVUZu2sGvztlhd0oFXgmmY0nq9S7p/u31ux1l/yAbtfvCsaNIeOCnH9+akztdsFT3X3qE+AMBlWzbF6hzVXcs1XZhRi/qJlNe4H3Q2yUQCM8cOAQD+4EnJBy8aUA1zr+i3TFdo1yyle5ubZt9IB/4g1kameQz1XvZ728fYNJJGYpkxWNdRx6+zjVfr45F3isbug73VLQ/44H04vi3/rOx1ol4uwZgJv182DyxDtVZbVP+4l4TFYPp+ew31uBm9XqYLt99FumDND+Y3YGPK8keoC7e2NT2w9YdkTups/gWhF0LI/sMy/xShY3p8tL7fevaKWCylKdEgQjXOmUGZo8rq07KpEHjj6P7alivLCfWdyauG3XJm4B9jmuhov2htK+uThbS8396bj52nZPtTPyV5baFbtKlJLSeTXn/aZ1+poDRxFiXd49r2955WTezB9/8pAOBFr5ecmVX9b7UgdZ2YFF2o9e3ujHon2PwVzO2WJ6rBuiL0RshpOZZz63kj19CDpNF49UHZ5hOQSst1i3T9wXrB3psXis2xCe2/VdXGzus8F62d1D8go2MLgTeLeR9E8Zmnk64abUym8oV6u0T5NZ6b6rlKvW7m4uM4mdP1QTmu8941IOsM87Aw3beNQfNC6FLvCyvHxqrlWhtDNqbPPfoQAOAV21Uf3C/zyuzEk1iOXVtqGpdqp9Ppup+TeZfoOWwczibk85zmCfN7KSekXc0zxTTspWzgL6NtY+U16k92ftOsR/0mH88bhs1D29UzZTgpev3yxHPLlt/JVKYnceauz2L+mNwvDL3xRwEAz/zvv4wdN/jatwIA9t8rbfjyC6R/XT4yKN8XumLHWx6wa1RcsgZH7HujJ1ovLPVCqBNf+9r1D/HBvGDzgfXtXvVd2JAXb4Jo/aCxhr8zj603Xyz98fb9Mk8cHpd5yzwRQt8zw+beOc0nwzqvbO3bFDvevBvmj8hxyXwBhW0XSexaZ/M8ME8D81UwL4nIC68cvw+IfEF0TV7WnHTjRTLu375Hyvm+Sy+QWE7KOrLrQvHOyQ3L53b/YvnC1g/mLzDz6AMAgP4zcl/xX98l5/ul1IcAADuefAIAkL7xYgDAr18v/mdzz9XvQyy/Rnk79GcJ7hfDtU7dbyp+7xr6ztXzkPpVmX+drsHs+9zIDgBAQb0Q8npNrO6Teh9j1yTy90lXcD74lwiEEEIIIYQQQghpCj5EIIQQQgghhBBCSFPwIQIhhBBCCCGEEEKagg8RCCGEEEIIIYQQ0hRrZqzoEymUCwP4zD+ICceWx/4WAPCZ+R8CAJy9Xkwe9g6LUdFIn5iHVKuhuUzcfCI0PomMt5JxM6PT07Ox4yKDr7i3kZxBv0s32VrVwOAuNEZLRgYoZqS1/LOc0MSpMVb35Q3eVvpd9K5Wi2Iykw8zNHSBCZyZhIXmIEZozmTXwYxoahprsSSmHaHBnRmfRAZJ6biRXu/eq6S8cTHMKl23U3741PI1rVWlnEJGLuKCGnTlt10cmS2ZEYkZy1gMkXGJmriEr2aoNFGUuo4uyPXMp+V3Q2oWdemQGJiYOdTIY88AAD77uBjbfOmoGC+dK8nxBTXc2aImc4Zdo4zGHRk/6rWqt2Wz/aBzqJUXMHP8CMa7xaznC7cfAABkb5H+cu+QGOO8cc9OAEBp7BSAutmMZYe64V/crMoIzQPNTMaMVs3gJzQOTC7TpuF4bnyuuNlro3EfjqkwD1h59nlpSU4Mz7sSjfpJvO2SiURUf69f1cqSn8va9yJzrxXMARNmXBeYg6YLkufHZmQs9OTV9E1Nfsx0yEwMw7bIqcFa1/atAIDJg2KQ1Lf1Bnl/4svL1vSGK9QAq6Z5b6G4xOjQ8oGZRpr5m80lgwX9fi5uKhm1yUJS6yjj1Tk1cSzXzymNE2+7s3Py/TPjMleaSd1FG/sk5jkxg5pTM1IzXDXTOctrljtrmbgx07rAe/hKKcr3d+TEFOr07CQA4Mf/4OcBAHNHHwcAZLUfTMzG+1dOjVYLNr71vbVpWceSzUtVb0bAidjx9mrrCjOhWmya2Gh8L8Xygc4zgSFaI+NlM4E1U0iLJZOMH59Wo+BaeSj2eX2uj49de2+GfU7ncOu3NpYjU2ssMp3Wcdhr47Z+Nj2HmhNr2WbyWC2q4WWX9NmNBa+vYnhnhnU1HVvJDTKXpvtlnrB8kSxK7INqbhoZY6qZac8eWTcUnzsqdfv6ZwEAP/27PwIA+Ivfl3wxd1ZeKz94CwDg114tZnTdXsqfTWXgzCAtFTeVthyV0fn72NgUAOCCfslt+SCvWC6bVVPX0WmpyyXDA/q9GqbqXGcGapG5cGCca/1mdsFMgqXNM9pn+9SQdfa4mMBZvkhk119eqM7NYPLBbyLdKyZ/f18Ro8SbfvGXAQC3/MofAgDym2Xd0Js7BgDYt0n6x8Ye6V9L1urWL7U/W14oB3OtrQ9svWDlmLmprcWsvy8eM2beimi8Lr9WiYxQgzVyuCYP53HrF2awaWPvVRdq3bvj19vGf0nNBcP8YP3E5pOK9svoXsHyh54npYZ9md7+qP52C5VISl3CPGExlNUcMJp7Nedk++M5sXdWzvXWy7ZreWrYrOXMbxPD1KdPS53Gx2S9sntI2mprV9zE2gxaS2Myz0zvvxsA8Gpdax/6t5I/sgNvBAAs6Hy0cOhBqXs2h6QZKmoeSKuxanTvZMbaev0XtK9Eq3b9vIa4GW1osB72h2o0jtWYUc3GMxu3SMxqKpnZJO8tn9eNFJe/38xnzn/jy79EIIQQQgghhBBCSFPwIQIhhBBCCCGEEEKagg8RCCGEEEIIIYQQ0hRr5okwUyrhG0dOou/Rz8j7iScBAP0PfxIAMPuKdwGo6/tMJ2I6nSU6sED7bDoP0yqZXsh0YsWyaB4HVXNn35v+CKnF+mUfi8WoqTbNiHTAi3SRi6l61U2iEXHvhOh4F6+bD7QrK2kvw+OXfl/XeflA8xXqsyMvhGSox4rjAo1kiGkVE4GPg+m2MqqnmlGdb35YdJ2mxzKdT60kOqDPv1s0sq+a/CAAoP+u2+R3G1XDdNOLAQCv2CTX+8TkmJx2+6XI9IveybRckW5TtcPW/qbLMs2YaR/ntckeHx0HAByZiPtt7BoQjePV20WTWjspWsQ3bpS++ea3vwQA8OAx0TzmUzIsrW+6sno0mCZVtc7WRpEefB17IRi+XEbpzEm8/SuTAIBzB/4bAODvbv1+AMBfXrcXAFDIHAcAXLddNLEV1dClVVObiDxO4v0v3cALIcwXdY1jfCzbmE+k0g3HefNIvwq19yGhz0v4eejTUB/vXstd7bPixp4plici/XSg4448TNJxvXA0tsxrRXOqy0pfn54XzeK8atKTTq5LX3dcO1menYr93s7Ts+tyAMDCdTcDAIonZKx9+vYfBgD8+zuvBgA8cEja+vX7JK5ffoV4KEw//rCUl8og1TMQK9swbwNrZ+srp6clNw3l09oWMl6L5UKsbmfHZvRzqeOQ6nK3bpM8VJmb1fNKObma9JOnJ+R3dx6R10JG8sU7dosO+PorXwMAmDq0X9poQjwUMuqpklQt5HrE16qoTE+gcJnk81tv+Z8AgGyP5P3PvF6u6zfecxMAYEE1reoygV69Rv1d0o+6cjq+3fI65Mj7AHEPBDve1hs+0kzXYsf5Wm3V487yQDR3LvFbiFMsqWZZz91oHkgu0bzrnJuQVmqouU7GtbxL8UCk3437w5hOtz7uS8vGlMzlYhH3qV478mlQLwTT8T5xSvp2dyYek2miLU8XdW42L6aczpldO3cDAOaeehQA8AOzou/+8f8sY6j3knfG6hz5FhwX0yWXziA9IOOq7vcSz98+mpdlPj8zI3UZ0JDNU2l+9Fmpi+quL9okeSflJWZrMRfMXamC+ACY94nFMevkBP948GkAwBPj0vbDXRLH6y6SuHdqXpg/eRTrlWqxhKknTuDin5X1wUd/5sMAgI9cI+vAb3/pcwDq+eDGi6RfXTwo/cjGSjgH18f58mMup/0uvCewnJ4IxsHisWMeRuFaJPRMM8L5ZykyaibVHyCnGva0nrtSNv8RvYfSsbh7aEOslNKUrGttrCKY6yOPBB2T3rwSIt8yiTOr80xW+5dLJKMybM0c3etoWbZ+C/NDtH6wGNTbpKj3cVbXHkuVWt4zs/L9I8+NxsobyEt5hWy8TcN7JKtDRdf6xRMy7uf11XzUIr8BPT5V2LCkvdKac+p11xbQ61zSukDX/RlbS6Xi967WNyfV5yeao4L+YdfZ8kJuy04A9bWR3TeYT0VO2zSdkvyRjvyu1CvB+kMD+JcIhBBCCCGEEEIIaQo+RCCEEEIIIYQQQkhT8CECIYQQQgghhBBCmmLNPBGqNY+phTISibieIzdwBQDguh3y+VCv6DRqga4nkV9e92d6o2SotVddoe0b3ZsT7Zzp1Yzl9UdSVri3cd0XYCVtdPj58seFe9JGe09rjI32oQ/9IhoR+h2EOjCXTC7R3UV7m9qeyIFe02rSSB8efW56r6C9Qw2laaWPTct1yvWKrgqjooFPdYuOawHPap2k3MEF0Uj/1btFT3jnq38aALCzV+L+gT2yR+roneK5Ydcyme+ONEILE2NapsSSCvSytm9rQrVKFuu01u2ExvzYmGiITs2o5rEir1eN9MnxB+7VAvU8ev59F4veP7dpRM8o+jqobi+bFR1WXVulvhLm5xFdz/X7bNCl0sgMDmP6k/8h9vnEM18AAPSrbuvUG+J7H0d7KWtbJLUfm8Iu1DJHviPRvuaSD0ynGPbn8/mKmP62kadBSCPto2E5zPwc6jHFddhGeL1dNKbix62UH+q/D3NAYsl5LB9Hngb2Guioo7wd6Ss1f6fNN0D6su2rbXuZ2/Uw7WnYxuYLUjzzHIC67tDO133xi2Pn3TggeePfvVLG0Pg+Oe7anZvlPGeeAQBMHTkgcWwaibwEwvYIteX1PuVjdTfuOyox3v+c5Kjj0xWtq/zu6s3Sl29SHbhTvwfLM12qdX6leqo8NSl9fmze+pGUszApOnHThXbt2CXlDEgdw360rqjVUFuYxx3npF/MTz8Te8UnvwwAuPf7/wsA4PKqXN+hDYVYMdavMsnzj9W0ft0oD9Qqqn1WrXNCp73Qw0mIr0WWfo7g+0Z67Pjvbe2SS4deBfHjGq1PLMZGmuuV55F6Lq3nprgXgo13G0PmaRDtdR74LJg2ujgX9xXKDUrfPz4uY8PyeXQ9NW9ktf3nz5wEAHSrl9JxXff19IuHTm7rxVJOt/kKyBgzn4FID1yUsWY+SGUdY8nuXuR033XLYdZekUZc62bX6fiE+CpsVC16QvXZqS7xZyiqbj9Tk741UZa2m83Lmqa3X9YFvbqEsvVHutCjrSRt8uyorGOOTi3E2vCaC8Q7ZXtS4ps/pefTPNelbbWucA7JdAJ93/dj8v4/3gEASNz9nwAAr/nJGwAAn/sFyQM/9SbxWjNvtEZrc+vP3dl4/wzX2l6HeVmLsTkgZwnkPGM+zC3hPUu41g7XyobVZb4c9wmw/mFrXcPWuxZz6PllRFr4hK2tEIvLdeu9gc6H9nubt2xNkMp3R+1SLzvulRJ5UQRzq+ULi7WsnhN2Xeb0vqCoeeZITcbiyWnxIds3ImPH/OUsP/dm4/du5ifRo55a2WDuz2/XudTyltU5v0ybaXulLafoa+iJYNgax7wO7J438oPR62jta9d5Rn28hjPxNZf5MyT0fF3bNNcVzAND2qIS5NhyVuuiubQ8dlqOXmHduH7vNgghhBBCCCGEENJW+BCBEEIIIYQQQgghTcGHCIQQQgghhBBCCGmKph4iOOducs494Zw77Jz7tWW+73fOfc45t985d59zbu/zHyohpJNgXiCEhDAvEEJCmBcIeeGxorGicy4J4E8AvAHAcQD3O+f+3nv/6KLDPgjgYe/925xzu/X4G89Xbj6dwt6hPlRu/n4AQO2pHwQA7H6dmDj80GVi8tKjBhpmbGMmFaHBSWR2p59nA4OjyTkxzggNu0Jzk+XMhMzYJLI9qcWNFZslGRj1hUZ4Zv6R0PfpZNwMztdCY6TAGDERNwtpZLhoRh3GYuOwyOgoMDwy47OymnlYTHVTp7gpVEgj40V7b2ZEZgKzTU2HkjkxByku6OtJMdJK94tBmhkhzRx6BABwidb1vWo2UpuS6z73uX8AACycFZOz3MgOKad3IOpT5ZlpaQM1TwkNyMzApluNjIpqPDKiJizAKQDAVFHqNDYjn56eEzMYM4PzWse5o0/EYxmQcjJ9cQPFsJ+ZyVxo8BmaVLWSVuUFl0ohOzDc8Pu5DdsBANs3dAEAFtSMKqUGPGZGY21tNDKTszyw1GgtbqQWjrXFn0Wmjkva//xjIhwLkbFqRY15Uuc3VKzHsTpj1frvl/+8bqaq+anmkQwMkOr5cnmTOB+YEobnstjN8MgsoNQbKroupWkZm2a0dKos59uqpkEzTx2I1dHGhNe8Mn/kIADgCo2n4sWgrXT/wwCASe1HVueunXsis8Zq0O6JwMjO8nVeg57XKianpMwtapQ0dmQcAPDIcWmborpF5VNiAnXtvOSqnJrClSfOahtJgXlt81++/HI5TvNOaFLVu0u+t2tU77txA8hW0Kq84H0NtYUi3v8XYhZXaHCc1a08Lm1fUyO8jb1isGXzVSMT2qV5Yvk8sKjG8jt3/jHaHIHpa2QGljxv2d+pkWqjPLCk3Abrm3C9Ua5WlxiXRWaNDaYki6FiBobad83sy0zZzOzPrqsZZdo6r26MLW13elom3b6NYi42e/IYAKBfj6rNyXH5zWKKmNZ+YeezMWNxVGYl75Q0P1g+yQ5fEOWH0AwuMpWtmNGdNEI+HW83uz4PPSvriJmSrBNmTssa5b7nzunnUvc9g7J2ffvenRLDvNS1oq8l7evDuhb+wKvFsNHmpdL0ZOz4nBqwZTZYnlt/eSHVncfAy1+CTz14aNnvJ0+I4eq5A2Iue0Kv36Yb3gYA8GmZTyJTzEo5LEKOq5qRos71gdG4rYttzl7JPH3xv+s5Z6W1M2LntHN0Z3WsaP+KDPmCeyObt6yuyeB6h/3Y6hjmkbrZupQXmgvW6yflT88vRHXozprpoq0jZI1r9ySLzRgXl5F2apA/LX3c5uuCmgfP6a3srjmZ1y/JqlF2QV5rGanbOZ1jrU3MgDVv+SplbSFxmcHicuu/xURmquk0UnnNC7nljRQNy9c2d9T7htO20DyhudHuT0b64uuSaH24Rdri1JSM740FqXNBc3B1RtpmXu+lzWDzX56V92Pz0qY/cdWLAAAlrbMd14hm/hLhagCHvfdHvPclAJ8G8JbgmMsA3AUA3vvHAex0zjW+EyCErHeYFwghIcwLhJAQ5gVCXoA08xBhK6D76QnH9bPFPALghwDAOXc1gB0AluwX45z7N865B5xzD0xOjK8uYkJIJ9CSvDA2zrxAyDqmJXlhQv8PMyFkXdKa+4i5hfBrQkgbaeYhwnJ/YxP+rc7vAeh3zj0M4H0AHkK0yf2iH3n/Ce/9Vd77q/r6B77TWAkhnUNL8sLgAPMCIeuYluSF/t5GAgZCyDqgNfcRXdnnPVBCSPM478+vg3LOXQPgw977N+r7XwcA7/1HGxzvADwN4HLv/fR5yj0DYBbA2dWF3hY2onPjY2yrp5Pja0VsO7z3m57PApkXOjY+xrZ6Ojk+5gXmhe8GxrZ6Ojk+5gXmhe8GxrZ6Ojm+tuWFFY0VAdwPYJdz7kIAJwD8KIB3Lj7AOdcHYE61Tv8awNfON/ABwHu/yTn3gPf+qiYr0HY6OT7Gtno6Ob5Oji2AeaEDYWyrp5Pj6+TYApgXOhDGtno6Ob5Oji2AeaEDYWyrp5Pja2dsKz5E8N5XnHPvBfBFAEkAt3nvDzrnfk6//ziAPQA+5ZyrAngUwC0tjJkQssYwLxBCQpgXCCEhzAuEvDBp5i8R4L2/A8AdwWcfX/TvuwHsen5DI4R0MswLhJAQ5gVCSAjzAiEvPJoxVmwln1jj869EJ8fH2FZPJ8fXybG1i05vg06Oj7Gtnk6Or5Njaxed3gadHB9jWz2dHF8nx9YuOr0NOjk+xrZ6Ojm+tsW2orEiIYQQQgghhBBCCLD2f4lACCGEEEIIIYSQdQIfIhBCCCGEEEIIIaQp2vIQwTl3k3PuCefcYefcr53nuJc756rOuXe0I65mY3POvdY597Bz7qBz7qudEptzboNz7h+cc49obO9pY2y3OedOO+cONPjeOef+WGPf75x7WbtiazK+d2lc+51z33LO7euU2BYd1/bx0E6YF1oX31rlBuaF1sW26DjmBTAvrCY+5oVVx8e8sMYwL7QuPuaFVcfHvOC9b+l/kO1cngJwEYAMgEcAXNbguC9B3Fvf0eq4mo0NQB9ku5nt+n6og2L7IIDf139vAjAOINOm+K4H8DIABxp8/yYA/wjAAXglgHvbEdd3EN+1APr13ze3M76VYlt0/ds6Htp8fZgXWhvfmuQG5oXWxbbo2jMvMC+sNj7mhdXFx7ywhv8xL7Q8PuaF1cX3PZ8X2vGXCFcDOOy9P+K9LwH4NIC3LHPc+wB8FsDpNsT0ncT2TgB/670/BgDe+3bF10xsHkCPc84BKEAGfqUdwXnvv6bna8RbAHzKC/cA6HPOjbQjNmDl+Lz33/LeT+jbewBsa0tgaKrtgLUZD+2EeaG18a1JbmBeWD3MCwCYF1odH/PCMjAvdDzMC62Nj3lhGZgXVqYdDxG2Anh20fvj+lmEc24rgLcB+Djay4qxAbgUQL9z7ivOuQedcz/ZQbH9dwB7AJwE8G0Av+S9r7UnvBVpJv5O4RbI086OYA3HQzthXlg96zk3MC+sEuYFgXmhIcwL7YF5of0wL6we5oX28D2ZF1KtLFxxy3wW7it5K4APeO+r8iCsbTQTWwrAlQBuBJAHcLdz7h7v/ZMdENsbATwM4HUALgbwz865r3vvp1scWzM0E/+a45y7ATL4X7XWsSziVqzNeGgnzAurZz3nBuaF1XMrmBcA5oVGMC+0GOaFNYN5YfUwL7SY7+W80I6HCMcBXLDo/TbI067FXAXg01rRjQDe5JyreO8/3wGxHQdw1ns/C2DWOfc1APsAtHrwNxPbewD8nhfxy2Hn3NMAdgO4r8WxNUMz8a8pzrnLAfw5gJu992NrHc8i1mo8tBPmhdbG16m5gXlh9TAvCMwLq4+PeWGVMC+sKcwLrY2PeWGVfM/nhZVME77b/yAPKo4AuBB1U48Xn+f429E+Q5QVY4P8ic9demwXgAMA9nZIbH8K4MP672EAJwBsbEfb6Tl3orHhyJsRN0S5r11xNRnfdgCHAVzb7rhWii04rm3joc31Z15obXxrlhuYF1oTW3Ac80Kb24F54buOj3mhBbEFxzEvtLkdmBe+6/iYF1oQW3Bcy8ZDy/8SwXtfcc69F8AXIU6Rt3nvDzrnfk6/XzMdVzOxee8fc87dCWA/gBqAP/fen3dLjXbFBuB3ANzunPs2ZJB9wHt/ttWxAYBz7q8AvBbARufccQC/CSC9KLY7IM6qhwHMQZ50to0m4vsNAIMAPqZP6ire+6s6JLYXPMwLrY0Pa5QbmBdaGtsLHuaF1sYH5oXVxse8sIYwL7Q2PjAvrDa+7/m84PQpBSGEEEIIIYQQQsh5acfuDIQQQgghhBBCCHkBwIcIhBBCCCGEEEIIaQo+RCCEEEIIIYQQQkhT8CECIYQQQgghhBBCmoIPEQjpUJxztznnTjvnmnLxdc79sHPuUefcQefcX7Y6PkJI+2FeIISEMC8QQkJanRe4OwMhHYpz7noAMwA+5b3fu8KxuwD8NYDXee8nnHND3vvT7YiTENI+mBcIISHMC4SQkFbnBf4lAiEdivf+awDGF3/mnLvYOXenc+5B59zXnXO79aufAfAn3vsJ/S0XBIS8AGFeIISEMC8QQkJanRf4EIGQ9cUnALzPe38lgPcD+Jh+fimAS51z33TO3eOcu2nNIiSEtBvmBUJICPMCISTkecsLqRYGSQh5HnHOFQBcC+Azzjn7OKuvKQC7ALwWwDYAX3fO7fXeT7Y5TEJIG2FeIISEMC8QQkKe77zAhwiErB8SACa991cs891xAPd478sAnnbOPQFJBve3MT5CSPthXiCEhDAvEEJCnte8QDkDIesE7/00ZGD/KwBwwj79+vMAbtDPN0L+LOnIWsRJCGkfzAuEkBDmBUJIyPOdF/gQgZAOxTn3VwDuBvAi59xx59wtAN4F4Bbn3CMADgJ4ix7+RQBjzrlHAXwZwK9678fWIm5CSOtgXiCEhDAvEEJCWp0XuMUjIYQQQgghhBBCmoJ/iUAIIYQQQgghhJCm4EMEQgghhBBCCCGENAUfIhBCCCGEEEIIIaQp+BCBEEIIIYQQQgghTcGHCIQQQgghhBBCCGkKPkQghBBCCCGEEEJIU/AhAiGEEEIIIYQQQpri/wM3AejS1gbUPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To have a look at how the tracking performs, just pick an eddy and see whether it is tracked.\n",
    "ed1 = 1\n",
    "t = 0\n",
    "j = 4\n",
    "\n",
    "plt.figure(figsize=(18, 4))\n",
    "\n",
    "for i in np.arange(0, j):\n",
    "    plt.subplot(1, j, i + 1)\n",
    "    plt.pcolormesh(data_int.lon, data_int.lat, data_int.OW.sel(time=tracks[ed1]['time'][i]).sel(z=100, method='nearest').values,\n",
    "                   vmin=-1e-9, vmax=1e-9, cmap=cm.balance, shading='auto')\n",
    "    plt.plot(tracks[ed1]['lon'][t:t + i+1], tracks[ed1]['lat'][t:t + i+1], marker='o', color='m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We save the tracks for later use\n",
    "with open(datapath\n",
    "          + 'test_00610101_00611230_tracks_OW0.3'\n",
    "          + '_test.pickle', 'wb') as f:\n",
    "    pickle.dump(tracks, f, pickle.HIGHEST_PROTOCOL)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is how to open the tracks-file again (no need to do that if we just saved it)\n",
    "with open(datapath\n",
    "          + 'test_00610101_00611230_tracks_OW0.3'\n",
    "          + '_test.pickle', 'rb') as f:\n",
    "    tracks = pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SAMPLING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things to note for the setting of `sample_parameters`:  \n",
    "1. `'start_time'` is required to be no earlier than the earliest actual date of the tracked eddies. In our case here, for the year 0002 and a 5-day temporal resolution of the data, this is `'0002-01-05'` (The `MITgcm` stores the 5-daily averages at the end of the 5-day period).\n",
    "2. `'lon1'` and `'lon2'` need to be identical to `'lon1'` and `'lon2'` in `detection_parameters`.  \n",
    "3. Right now, the usage of `'range'` and `'split'` has not been thouroughly tested! It seems to work for most cases though.  \n",
    "\n",
    "`'range'`: Set to `True` if you only want to sample eddies within a certain range `'values_range'` of a property `'var_range'` in the dataset `'ds_range'`. `'var_range'` needs to be 2D (thus the `.isel(z=9)` in the example below) and interpolated to the same grid as `OW` used above. It is most likely that, if you follow this example, `'var_range'` is stored in the same dataset as `OW`. In the example below, only eddies that have a center temperature between 4 and 7 degrees C at depth level 10 (`z=9`) will be sampled and stored.  \n",
    "\n",
    "`'split'`: Set to `True` if you want to split the sampled eddies into two categories, above and below a certain threshold value `'value_split'` of a variable `'var_split'` in the dataset `'ds_split'`. As for `'range'`, `'var_split'` needs to be 2D and interpolated to the same grid as `OW` used above. In the example below the eddies will be put into two categories: In the first category, the eddies must have a center salinity above 34.0 and in the second category, below 34.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For the sampling we again have to specify some parameters, defining when, where and which \n",
    "# eddies to sample.\n",
    "# Because the dataset containing the sampled eddies can grow huge for larger regions and/or\n",
    "# longer time periods, `eddytools.sample.sample()` writes the samples for each individual\n",
    "# eddy to individual netcdf-files on disk!\n",
    "sample_parameters = {'model': 'MITgcm',\n",
    "                     'grid': 'cartesian',\n",
    "                     'start_time': '0061-01-05', # time range start\n",
    "                     'end_time': '0061-12-30', # time range end\n",
    "                     'max_time': 146, # maximum length of tracks to consider\n",
    "                                     # (model time steps)\n",
    "                     'calendar': '360_day', # calendar, must be either 360_day or standard\n",
    "                     'dt': 5, # temporal resolution of the data in days\n",
    "                     'lon1': 0.6e6, # minimum longitude of detection region\n",
    "                     'lon2': 1.3e6, # maximum longitude\n",
    "                     'lat1': 1.0e6, # minimum latitude\n",
    "                     'lat2': 1.3e6, # maximum latitude\n",
    "                     'res': 10.,\n",
    "                     'type': 'anticyclonic', # type of eddy\n",
    "                     'lifetime': 5, # length of the eddy's track in days\n",
    "                     'size': 25, # eddy size (radius in km)\n",
    "                     'd_surr': 1, # surroundings size\n",
    "                     'range': False, # sample eddy within a range of `var_range`\n",
    "                     'ds_range': data_int.isel(z=9), # dataset of `var_range`\n",
    "                     'var_range': ['THETA'], # variable to base the range on\n",
    "                     'value_range': [[4, 7],], # range of `var_range`\n",
    "                     'split': False, # split eddies at a threshold in below and above\n",
    "                     'ds_split': data_int.isel(z=9), # dataset of `var_split`\n",
    "                     'var_split': ['SALT'], # variable to base split on\n",
    "                     'value_split': [34.0,], # split eddies at this value\n",
    "                     'sample_vars': ['THETA'], # variables to sample\n",
    "                     'save_location': datapath, # where to store the netcdf files \n",
    "                     'save_name': 'test'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sampling can take quite long as for every eddy that fits the criteria we need to read data from disk at every time step  \n",
    "The names of the files will be defined by `save_name`, the criteria you specify and the eddy number  \n",
    "For this sampling parameters, the file name of the first eddy will be  \n",
    "`test.anticyclonic.larger_25.longer_5.0000001.nc`  \n",
    "\n",
    "If you set `'split'` to `True`, the file names will differ for the two categories.  \n",
    "`test.anticyclonic.larger_25.longer_5.0000001.above_thr.nc` for eddies that are above `'value_split'`  \n",
    "`test.anticyclonic.larger_25.longer_5.0000001.below_thr.nc`\n",
    "for eddies that are below `'value_split'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data prepared, now sampling\n",
      "sampling eddy number 0 of 130\n",
      "sampling eddy number 14 of 130\n",
      "sampling eddy number 29 of 130\n",
      "sampling eddy number 43 of 130\n",
      "sampling eddy number 58 of 130\n",
      "sampling eddy number 72 of 130\n",
      "sampling eddy number 87 of 130\n",
      "sampling eddy number 101 of 130\n",
      "sampling eddy number 116 of 130\n"
     ]
    }
   ],
   "source": [
    "et.sample.sample(tracks, data_int, sample_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AVERAGING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now average over the sampled eddies.  \n",
    "As each eddy has its own file, we first need to find out how many files/samples there are, so we can loop over them and then store the datasets in a dictionary.  \n",
    "Note that for large region, i.e. a lot of samples the resulting dataset could be too large to fit into memory. The `chunks={}` argument to `xr.open_dataset()` is an attempt to overcome this, however I do not know yet whether this has a lot of effect! One could also split the samples into several parts and then work on each part seperately (the number of eddies going into each average is stored, so one could later do a weighted average over the different parts!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find out number of samples\n",
    "samples = {}\n",
    "num_samples = len(glob(sample_parameters['save_location']\n",
    "                       + sample_parameters['save_name'] + '.anticyclonic.larger_25.longer_5.*.nc'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we load each stored sample\n",
    "for i in np.arange(1, num_samples + 1):\n",
    "    snum = \"%07d\" % (i,)\n",
    "    samples[i] = xr.open_dataset(sample_parameters['save_location'] + sample_parameters['save_name'] +\n",
    "                                 '.anticyclonic.larger_25.longer_5.' + str(snum) + '.nc', chunks={})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First a \"preparation\" is performed: Basically, all eddies are interpolated onto a normalized (in length) section crossing them through the eddy center. This section's orientation can be defined by the argument `section`, which can be either `'zonal'` (default) or `'meridional'`. The length of this section can be defined with argument `interp_vec`. A larger number gives a finer resolution of the interpolated data, but might not necessarily be useful (if input data is coarser resolution for example).    \n",
    "For every variable specified, at every time step, the values and anomalies (with respect to surroundings) are interpolated (with method `method`) onto the normalized section and stored according to the month the eddy was first detected. The depth profile of the surroundings is stored as well.  \n",
    "For available interpolation methods please have a look in the documentation of the underlying function [`scipy.interpolate.interp1d`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THETA\n"
     ]
    }
   ],
   "source": [
    "normalized_eddies = et.average.prepare(samples, ['THETA'], interp_vec=41, interp_method='nearest', section='meridional')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 12, 55, 41)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_eddies['THETA_anom']['12'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here for example, 6 eddies originating in December have been stored with a maximum length of 8 time steps. 55 is the length of the depth dimension, 41 the length of the normalized section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can average these samples in different ways.  \n",
    "Three possibilities are given with `average`  \n",
    "1. seasonal -> bins the samples (of all available years) into four seasons (DJF, MAM, JJA, SON) and averages them into a seasonal climatology  \n",
    "2. monthly  -> does the same for each month (so you end up with a monthly climatology)  \n",
    "3. total    -> averages over all available eddies  \n",
    "The three methods all return the means, standard deviations (across eddies), and the number of eddies that went into the derived quantities for a) the variables specified, b) their anomalies to the surroundings, c) the surroundings. These results are stored in the output dictionary under `output['ave']['mean'][period]['variable']`, `output['ave']['mean'][period]['variable_anom']`, `output['ave']['mean'][period]['variable_around']`, respectively. `period` refers to 1. `DJF`, `MAM`, `JJA` or `SON`, 2. `01`, `02`, `03`, etc., representing the month, or in case of the total average thie layer in the dictionary does not exist.  \n",
    "Additionally, the averaged temporal evolution of the eddy centers will be stored under `output['evo'][...]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "season_eddies = et.average.seasonal(normalized_eddies, ['THETA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f34a88ce160>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hZwPP9c3vKZaNss0o+5bW6LCJmdkwS6OFRF+IiK2Z9RqwLEbcZpR9S2tl4+34dTVNjdtadW3/zANYirHbSej1ljf3zW8C9o64zdoR9i3NYRMza62IYGFxaeg0gvuBLZLOlbQWuJreXeb9dgLvL7JOLgReLAbvG2Xf0lrZ8zYzA4gYOWwypJxYlHQ9cDcwB9waEY9J+kCx/mZgF3AF8BRwCPiZ3L7j1smNt5m1WNDt1hI2ISJ20Wug+5fd3Pc6gA+Ouu+4Jt54TyIeXXccz2M1z7a2x22noS35372YdzuvgbnnbWbtFbBUU8971rjxNrPWCqKWmPcscuNtZq0VAQtLbrzHF1FrfLLOWHSuXtHSD78u0445T/v4bdSWmDfUk20yi9zzNrPW6oVNHPM2M2uUiNrusJw5brzNrNXc865BRNBdOFpfeYlYdJUYaG6fOuvcRpOKOfvaw2ybxSfWBI55m5k1TkQ428TMrIkc8zYza5i6BqaaRRPP8+4uLJTbJZt/PXhdlRhsrl6xODjm7TFPemrN3a9yvWIWPoc64/4zkGOtufJ1mMXc8CDc8zYzayJnm5iZNUwEHPUFSzOzZumlCrrnPbaILktHXi21TzYWnYgzRoW87G4irg3QTdR5YrHW3AWXTiK3dgYu0kwqfl17nnmqvFxMt6njq1SIU6di27k4+bTi4Y55m5k1UYvH8569W6LMzGpy7Onxw6ZxSTpd0j2Sniz+njZgm82S/kbS45Iek/ShvnW/LOkfJT1UTFcMO6YbbzNrrWMx72FTDW4AdkfEFmB3Mb/cIvCRiHgzcCHwQUlv6Vv/2xFxfjENfd7lhPO8uywdOVxql1S8GTJjjlSIPy69+kp63aGDpcurNQ6bKysVS5xgDLbWPO+S9wFUPX7t1yuc543WrM2UN51+YkRwdDLXf64ELi5e3wbcC3x0WV32AfuK1y9Lehw4G/hGlQMOPaOprv4oPxPMzKYpgMVuDJ2ADZIe6Ju2lzzUWUXjfKyRPjO3saRzgLcCX+lbfL2kRyTdOkp7OkrP+1hX/2uSTgEelHQP8B/p/Uy4UdIN9H4mfDRTjpnZxI0Y034hIrbmNpD0ReANA1Z9rEx9JJ0MfAb4cES8VCz+BPBr9P6/+TXgN4H/lCtnaOOd6eoP/ZlgZjZNvbFN6ior3pFaJ+l5SRsjYp+kjcD+xHbz9BruP4uIz/aV/XzfNn8EfG5YfUrFvJd19V/zM0HSwJ8Jxc+P7QBnn3UmiwdfLHPI5LgiAN1E/LzK+NtLr6TrtXQoEQ/Pjbuymsb7qDGmmP3sUnn9Ncf3K5U3qbv4qsSOc3VLlNfJxK9TNJ+JeU8xjj+hPO+dwLXAjcXfu5ZvIEnAHwOPR8RvLVu38Vh7CrwXeHTYAUf+JiS6+kNFxI6I2BoRW08/9ftG3c3MbGwTzDa5EbhM0pPAZcU8kt4o6VjmyEXA+4C3D0gJ/HVJX5f0CHAJ8HPDDjhSzzvR1R/pZ4KZ2bREBEeWVr7nHREHgEsHLN8LXFG8/hKgxP7vK3vMUbJNUl39Yz8TIPEzwcxsmiZ1k840jNLzPtbV/7qkh4plv0jvZ8Edkq4DngWuGlpSt0s3k089yNKr6RzrVA542THDARZf/la6DgcHR4ly46HUGQPNHScVS5zUcyUBSI6rXv4cpK5jZPfJfQ4VVHpWZoV4fKU4cN3jqyTK68zPly/qhHXpdWvKl1eHOi9YzppRsk2SXX0G/EwwM5slTe1ZD+OBqcystXphk2nXYmW48Taz1opgIhcsp2Gy43l3l0rneefGHOkm4uFVYqALB9LJMgsvf3vg8tw4HNVyswcH53K57rMQ807Wu8qzRI8cKX2cKtc4svH4VJA0k2MdC4uDl1cIuObGAVFq/HaqXWPQ/OAmoJNYnjN34knp40xrbBPaOySse95m1mot7Xi78Taz9lrV2SZmZk0V1HYH5cyZbMx7aYnFRPw4JZfnnRpzpHu03HMyARa+cyC57siBfx64PBbTsdZKcdhUTPdIOubdOWHweBKpGOxKSMVaq8R7Fw+Vj3kvLZQ/TmR+S3cXBq9TJmy7lNgnMlVLldeZS2Xm5nUT7ylXXrIOa9NvttMZXN6a9emxTarE0OvisImZWcNEwNGGPht6GDfeZtZavWyTaddiZbjxNrPWivAdlrWokuedGwtl8ZXEmCMVxsc4mol5Hz0weNyTbiauvHS4vpj34uH077416wbneVeJA1eVih/n4sopRw6W3+fo4fIx4lxK9OJi+fIWF+vLY+50qjU23W5ibO5Meam08TVr0icotc+JpxxK7pO7XrDSJvMIy8lzz9vMWsthEzOzBvIFy7p0l1hKhDpScrfHLx18efDyCmGThRfT9TpyYHC6Yi40sXCownCoiS/ZQjp7jvkTUulzpQ+fHW00J1XvxYXy4YfDh9O/r7vdweUdqRDmyDmyVL68IzX27hKZeEOl0plz5aU+8vnMPqkwzPrD5UMtk+CwiZlZw/QuWE67FivDjbeZtdakYt6STgf+HDgHeAb4yYg47o5ESc8ALwNLwGJEbC2zf78p/pgxM1t53e7wqQY3ALsjYguwu5hPuSQizj/WcFfYH5h4qmA3G8MeJBvzTqyrEvNeOpTe58jLg1MCFzOx6MOH6ktfO3wk/X/sukTMu0q6W9W4ZKreqRh1ziuZ95q67lRnvBkg9Y3LXRI4zODf5kuJ5TlzkT5vuY8odRpymYdziYdkzef2SXyuC930Gap4OWVsExyY6krg4uL1bcC9wEdXcn+HTcystQJYGC3bZIOkB/rmd0TEjhKHOisi9gFExD5JZ2aq9AVJAfxh3zFG3f+73HibWXuN3vN+YVkY4ziSvgi8YcCqj5Wo0UURsbdonO+R9M2IuK/E/t/lxtvMWqvOZ1hGxDtS6yQ9L2lj0WveCAx8NFdE7C3+7pd0J3ABcB8w0v79Jpzn3S1963ruUWOp2HZun5Tcre5HE3f95vKYjx4tH+89ujA4olklj7nKPiesqfYtX0jkRVcZRjmXHp/69ftKlbhyItbbK29wJbIxbyXy7bN1GKyTiXnn6p2Kr+f2mUvG6svXYW5G86knlOe9E7gWuLH4e9fyDSStBzoR8XLx+p3Ar466/3LONjGz1jp2wXLYVIMbgcskPQlcVswj6Y2SdhXbnAV8SdLDwFeBv4yIz+f2z3HYxMxabRI974g4AFw6YPle4Iri9dPAeWX2z3HjbWat5bFNahIEkRoIIyH3OLFUbDsXv04fJ12vVGx7MXOYxSrjYyTi1IczId1OYp8q43NUlcrnTjwZLKvcXQA9qRzrvPQ+qfj1fCYWfTRRXjczlnTqmz2fGT+1m6l36huc24cYfKx8Xvbg8o5m4+TTEVS736AJ3PM2s/by0+PNzJrH43mbmTWUh4StgRAqO2h0JkYeNX4qqUeQQTq2nRs/JJX7nJOKEedixws1xptPrBoaTAyeMV8h1ph73mAuX7n0cTJx4NQ3NJ8vnfgckiOOpM1n1h2O8uXlrgmsS7yl3J0S6Tz0dKy+zs+ujG6IozWP9z4rhuZ5S7pV0n5Jj/YtO13SPZKeLP6etrLVNDOrICY2quDEjXKTzieBbcuWlR6+0MxsGrpdDZ2aaGjjXQyasvzx6VfSG7aQ4u976q2Wmdn4eqmC7ex5V415jzx8oaTtwHaAN7zupErjjiTLTgxAnVqeM3/Kicl1J508+PmWi5nAcpX/zVO52XMV4tdVnoO4fl21b3Gd43l3X03vs650aZnjKH1S18fgqPd8Jm67LhEHfrnC57AukXsNcLhCea9E+rpRKr6fylsHmEvU4XDmesVhTelOmWhu4zzMio9tEhE7ImJrRGw99cQ6//mZmQ0XXQ2dmqhqz7v08IVmZhMXQPkbrhuhas/72PCFMOLwhWZm09DpDp+aaGjPW9Kn6T1bbYOkPcDH6Q1XeIek64BngatGO1zQXTxaqoJzJ65P121+cEZsbp/kcU46KX2cucH/x+XGUDnpnyqM0rFn8OJXM+OGp8bgrjKedyf3sMPsfvWVt+7V9FdyfSonORMjTnkpE5+tIvXvPxe/Tlmfy5fOjK+Syl2fz5SXitUvZPPgB++zLpcHX+/pHl3AtMLtK21o4x0R1yRWlRq+0Mxs0sTsPiRiXL493szaK9x4m5k1Tq/nPa2Yzcqa7NgmnTnWrH9dqX1y8eulVwfHlVOx8Jw1rzs1ve7FwXneOZ358qdWnRcHLl88Uv7Lt1QhnX7tSdVSpubWDY6prllX/hy87jvpayKp43SPlu9aLWVy9A8fHFxe7vaBOnOJ50/IjeeT3m8x8ZmvyfxzmEusW8g8ajb1XnPn59ArFXIjyv+zO44iWJsZq7/J/AxLM2stBcx1u0OnsY8zwnhPkv61pIf6ppckfbhY98uS/rFv3RXDjunG28xaLOh0u0OnGgwd7ykinoiI8yPifOCHgUPAnX2b/Pax9RGxa/n+y7nxNrPWUkCnuzR0qkHZ8Z4uBf5vRPxD1QNO9oJlp1M+BzuRYw3peHj3yKvljgHZx22sOfnkgctT+d8Aa08rP0ruCWduGLg8N9Z4JxHfzz37MyX3fnJS9atS3imZ8d5T72nhpXqvSRz55+8MXJ57P6lzEBUuls2tK3/NJif3XjsnrB24vMpY+fMnZ65PHTlSujw+9ffl9zlO1NU4DzPyeE+Fq4FPL1t2vaT3Aw8AH4mIb+cKcM/bzNorgs7SkaETvZsQH+ibti8vStIXJT06YLqyTJUkrQXeDfyPvsWfAP4lcD6wD/jNYeU4VdDMWksE6o40uMkLEbE1t0FEvCN5HKnMeE+XA1+LiOf7yv7ua0l/BHxuWIXd8zazFgsUS0OnGpQZ7+kaloVMigb/mPcCjzLERHvenTXznHDWplL75MZCSY0Nrvlh4abjLR1Mx01zOeAppZ/VCUQiNlclb73SI7MrxrxTqozdnnuvqfJiodx4OQCdE9Ljty++Mvi7kPtMq4whnyxrfnAcemgdEucu9b3KlZc7P7E0uLzOCZkhn6tkdHzqS+X3WS4CRut5j2vgeE+S3gjcEhFXFPMnAZcB/3nZ/r8u6Xx64yA+M2D9cRw2MbN2i5VvvCPiAAPGe4qIvcAVffOHgDMGbPe+ssd0421mLRZQT1hk5rjxNrP2ii4sZe71b7DJjm0yv5a1r39jqX2q5GxrTTpmmLK0Ph3zXnr14ODj5OLaFWLeyaIy8cekCrmtlWLrZHKcS47dDvnPrlL+fkLunHYTn3e+wMTnXSXHOPPd6WTi4SndzDUBzaVi3un4derzTt1zMHXueZuZNc3ELlhOnBtvM2uxICZwwXIa3HibWXsFDpvUQ+Xj0RVyeFNxvJxcLuzE1FmHGmPuE5U7B6l1TX2vMyCVs13lPoXs5zClf19BEA6bmJk1TZfoVhgUqwHceJtZe8WM/KpeAW68zazFYiJ3WE7DZBtvqXQ8Ohd7a+v/qNZAdX4X/b2uURC+YGlm1jxuvM3MGiaiS3fJFyzNzJrHPe8Gcd5vtbhpzeN5m01dOM/bzKyBfMHSzKyR3HibmTVM7/Z4N95mpaTGzcipNKaGAZn7HnKNV+J8V2rwZrGRjKDbLT8+UhOMdYVK0jZJT0h6StINdVXKzKwevZ73sGlckq6S9JikrqStme0GtpmSTpd0j6Qni7+nDTtm5cZb0hxwE3A58BbgGklvqVqemdlKiFgaOtXgUeDfAfelNhjSZt4A7I6ILcDuYj5rnJ73BcBTEfF0RBwFbgeuHKM8M7NaRUym5x0Rj0fEE0M2y7WZVwK3Fa9vA94z7JjjxLzPBp7rm98DvG35RpK2A9uL2SP/4t/++0fHOGYbbABemHYlZoDPg88B5M/B949b+OLCy3f/057dG0bYdJ2kB/rmd0TEjnGPv0yuzTwrIvYBRMQ+SWcOK2ycxlsDlsVxC3onYAeApAciIhkPWg18Dnp8HnwOYOXPQURsq6ssSV8E3jBg1cci4q5Rihiw7Lg2c1TjNN57gM1985uAvWOUZ2Y2syLiHWMWkWszn5e0seh1bwT2DytsnJj3/cAWSedKWgtcDewcozwzszbLtZk7gWuL19cCQ3vylRvv6D2S+XrgbuBx4I6IeGzIbnXHkJrI56DH58HnAFpyDiS9V9Ie4EeAv5R0d7H8jZJ2wdA280bgMklPApcV8/ljRlQOuZiZ2ZR4GDkzswZy421m1kATabxX6230km6VtF/So33LSt8G22SSNkv6G0mPF7cPf6hYvmrOg6R1kr4q6eHiHPxKsXzVnINjJM1J+jtJnyvmV905qMuKN96r/Db6TwLL80xL3wbbcIvARyLizcCFwAeLz381nYcjwNsj4jzgfGCbpAtZXefgmA/Ru1h3zGo8B7WYRM971d5GHxH3Ad9atrj0bbBNFhH7IuJrxeuX6f3DPZtVdB6i52AxO19MwSo6BwCSNgE/DtzSt3hVnYM6TaLxHnRL6NkTOO6ses1tsMDQ22DbQtI5wFuBr7DKzkMRLniI3s0X90TEqjsHwO8APw90+5attnNQm0k03rXeEmrNJOlk4DPAhyPipWnXZ9IiYikizqd3V90Fkn5gylWaKEnvAvZHxIPTrktbTKLx9m30r/V8cfsro94G23SS5uk13H8WEZ8tFq+68wAQEd8B7qV3LWQ1nYOLgHdLeoZe6PTtkv6U1XUOajWJxtu30b9W6dtgm0ySgD8GHo+I3+pbtWrOg6TXSzq1eH0i8A7gm6yicxARvxARmyLiHHptwF9HxE+zis5B3SZyh6WkK+jFu+aAWyPiv6/4QWeApE8DF9Mb9vJ54OPAXwB3AG8CngWuiojlFzVbQ9KPAn8LfJ3vxTp/kV7ce1WcB0k/SO9i3By9DtMdEfGrks5glZyDfpIuBv5rRLxrtZ6DOvj2eDOzBvIdlmZmDeTG28ysgdx4m5k1kBtvM7MGcuNtZtZAbrzNzBrIjbeZWQP9f1cLh3NFw06mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(season_eddies['ave']['mean']['MAM']['THETA_anom'], vmin=-1, vmax=1, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_eddies = et.average.monthly(normalized_eddies, ['THETA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f34a2b50760>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(monthly_eddies['ave']['mean']['03']['THETA_anom'], vmin=-1, vmax=1, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_eddies = et.average.total(normalized_eddies, ['THETA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f34a2c2bd90>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(total_eddies['ave']['mean']['THETA_anom'], vmin=-1, vmax=1, cmap=cm.balance)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:py3_eddy_new]",
   "language": "python",
   "name": "conda-env-py3_eddy_new-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}