powerSpectraTrial.ipynb

{
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   "cell_type": "markdown",
   "id": "be0e6726-9d43-4726-8a4b-5eb96d5394e3",
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   "source": [
    "# Prepping data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5828d1d-1c7b-493c-9318-46514004afe0",
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   "source": [
    "from xgcm import Grid\n",
    "import pop_tools\n",
    "import gcsfs\n",
    "import fsspec as fs\n",
    "import numpy as np\n",
    "import xesmf as xe\n",
    "import xarray as xr\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import importlib\n",
    "import preprocessing\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "importlib.reload(preprocessing)\n",
    "from preprocessing import preprocess_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c7a33b3-6f5b-4007-9c4b-ce4d68a4fb68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load Data\n",
      "Interpolating ocean velocities\n",
      "Modify units\n",
      "Load Data\n",
      "Align in time\n",
      "Interpolating ocean velocities\n",
      "Regrid Atmospheric Data\n",
      "Modify units\n",
      "Mask nans\n",
      "Calculate relative wind\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3TB\n",
       "Dimensions:       (time: 7305, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * time          (time) object 58kB 0181-01-01 12:00:00 ... 0200-12-31 12:00:00\n",
       "  * xt_ocean      (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.85 79.95\n",
       "  * yt_ocean      (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.94 89.98\n",
       "    area_t        (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    dxt           (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    dyt           (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    geolat_t      (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    geolon_t      (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    ht            (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    kmt           (yt_ocean, xt_ocean) float32 39MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "    wet           (yt_ocean, xt_ocean) float64 78MB dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    slp           (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    t_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    q_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    wind          (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    surface_temp  (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    u_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "    v_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    regrid_method:  bilinear</pre><div class='xr-wrap' style='display:none'><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-4b1baa4a-65d3-4e0b-869c-ee8479237dc1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b1baa4a-65d3-4e0b-869c-ee8479237dc1' 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'>time</span>: 7305</li><li><span class='xr-has-index'>yt_ocean</span>: 2700</li><li><span class='xr-has-index'>xt_ocean</span>: 3600</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-67c3cbd6-59b8-46c1-8880-e64b80b1efc7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67c3cbd6-59b8-46c1-8880-e64b80b1efc7' class='xr-section-summary' >Coordinates: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00 ... 0200-12-...</div><input id='attrs-dd558568-ef62-46fd-87a6-8abcf093d5a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd558568-ef62-46fd-87a6-8abcf093d5a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9660e67f-3642-4f59-af36-746a32a57fb2' class='xr-var-data-in' type='checkbox'><label for='data-9660e67f-3642-4f59-af36-746a32a57fb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(181, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 3, 12, 0, 0, 0, has_year_zero=False), ...,\n",
       "       cftime.DatetimeJulian(200, 12, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(200, 12, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(200, 12, 31, 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-279.9 -279.8 ... 79.85 79.95</div><input id='attrs-fbdf4e7f-b674-4b9d-ad35-220b79845410' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbdf4e7f-b674-4b9d-ad35-220b79845410' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f54afd99-4df5-4f27-9f4b-173338062b9b' class='xr-var-data-in' type='checkbox'><label for='data-f54afd99-4df5-4f27-9f4b-173338062b9b' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-279.95, -279.85, -279.75, ...,   79.75,   79.85,   79.95])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-81.11 -81.07 ... 89.94 89.98</div><input id='attrs-fd880bc6-20e5-408f-b173-0421281b82e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd880bc6-20e5-408f-b173-0421281b82e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11539a79-231a-44d8-8bba-c471e9f94035' class='xr-var-data-in' type='checkbox'><label for='data-11539a79-231a-44d8-8bba-c471e9f94035' 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>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([-81.108632, -81.066392, -81.024153, ...,  89.894417,  89.936657,\n",
       "        89.978896])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-7f2861a6-7c4c-41cd-bb7e-de6f150df2a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f2861a6-7c4c-41cd-bb7e-de6f150df2a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cc56e4a-7d02-4f3a-89af-66a210058905' class='xr-var-data-in' type='checkbox'><label for='data-8cc56e4a-7d02-4f3a-89af-66a210058905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>tracer cell area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>valid_range :</span></dt><dd>[0.0, 999999986991104.0]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 8 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxt</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-dd56c058-52ec-42f7-b2fa-e10d92e22b33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd56c058-52ec-42f7-b2fa-e10d92e22b33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71605d75-a838-459b-95c0-b6deda618b8e' class='xr-var-data-in' type='checkbox'><label for='data-71605d75-a838-459b-95c0-b6deda618b8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>width of T_cell</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyt</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-6433c995-6403-4595-9926-fbc5ba73b762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6433c995-6403-4595-9926-fbc5ba73b762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-784a3aa7-42f1-4c59-999d-4eacf9b50152' class='xr-var-data-in' type='checkbox'><label for='data-784a3aa7-42f1-4c59-999d-4eacf9b50152' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>height of T_cell</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"90\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,90.0 0.0,90.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"110.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3600</text>\n",
       "  <text x=\"140.000000\" y=\"45.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,45.000000)\">2700</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolat_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-66f08d3d-5fab-487d-9b51-1e92fb1603e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66f08d3d-5fab-487d-9b51-1e92fb1603e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef4f489c-1979-4258-a880-6086234d2046' class='xr-var-data-in' type='checkbox'><label for='data-ef4f489c-1979-4258-a880-6086234d2046' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>tracer latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd><dt><span>valid_range :</span></dt><dd>[-91.0, 91.0]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"90\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geolon_t</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-1ef013f3-1e5f-4605-b668-3fc73a3b85b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ef013f3-1e5f-4605-b668-3fc73a3b85b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91141bd5-828d-407b-8be2-278fc433d1d7' class='xr-var-data-in' type='checkbox'><label for='data-91141bd5-828d-407b-8be2-278fc433d1d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>tracer longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd><dt><span>valid_range :</span></dt><dd>[-281.0, 361.0]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"140\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ht</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-9119c4f4-1d91-4b0b-9830-bc0c0be81b34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9119c4f4-1d91-4b0b-9830-bc0c0be81b34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c876646b-037d-4107-acad-212540a0eb38' class='xr-var-data-in' type='checkbox'><label for='data-c876646b-037d-4107-acad-212540a0eb38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>ocean depth on t-cells</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_range :</span></dt><dd>[-1000000000.0, 1000000000.0]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kmt</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-04b7cae0-4dae-4d38-9d64-45de59a3a6b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04b7cae0-4dae-4d38-9d64-45de59a3a6b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-427ed9b0-f39c-450d-957c-6bb656505576' class='xr-var-data-in' type='checkbox'><label for='data-427ed9b0-f39c-450d-957c-6bb656505576' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>number of depth levels on t-grid</dd><dt><span>units :</span></dt><dd>dimensionless</dd><dt><span>valid_range :</span></dt><dd>[-10.0, 1000000000.0]</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                        <td> 37.08 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wet</span></div><div class='xr-var-dims'>(yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-0eb3a910-e543-4858-8f94-bcb8aa8d63b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eb3a910-e543-4858-8f94-bcb8aa8d63b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a1c3530-c087-475f-b9e2-ff9dfe23b45f' class='xr-var-data-in' type='checkbox'><label for='data-7a1c3530-c087-475f-b9e2-ff9dfe23b45f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>land/sea flag (0=land) for T-cell</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                        <td> 74.16 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                        <td> (2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (7305, 2700, 3600) </td>\n",
       "                        <td> (3, 2700, 3600) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2435 chunks in 19 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_ref</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-13982f31-e585-47d8-8059-81e7ba7840a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13982f31-e585-47d8-8059-81e7ba7840a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37acba7a-0073-4d09-be78-f7050a68de07' class='xr-var-data-in' type='checkbox'><label for='data-37acba7a-0073-4d09-be78-f7050a68de07' 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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       "        <td>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (7305, 2700, 3600) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t_ref</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-9377638a-2e0d-4bd1-811f-cc5d72b82a62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9377638a-2e0d-4bd1-811f-cc5d72b82a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7295d416-7d37-48af-9a51-963dc04ccba6' class='xr-var-data-in' type='checkbox'><label for='data-7295d416-7d37-48af-9a51-963dc04ccba6' 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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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_relative</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-d685cd2c-2b15-4746-9d9e-83a330131d1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d685cd2c-2b15-4746-9d9e-83a330131d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e18adbed-8308-40ea-a6b1-adb5a7a51c26' class='xr-var-data-in' type='checkbox'><label for='data-e18adbed-8308-40ea-a6b1-adb5a7a51c26' 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",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (7305, 2700, 3600) </td>\n",
       "                        <td> (3, 2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2435 chunks in 37 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_relative</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 2700, 3600), meta=np.ndarray&gt;</div><input id='attrs-2b98d4a5-01e9-421a-9692-bee1bf5dbcb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b98d4a5-01e9-421a-9692-bee1bf5dbcb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb93d48b-79a9-4ea4-a35d-cfcdd510cf9d' class='xr-var-data-in' type='checkbox'><label for='data-eb93d48b-79a9-4ea4-a35d-cfcdd510cf9d' 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",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 264.51 GiB </td>\n",
       "                        <td> 111.24 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (7305, 2700, 3600) </td>\n",
       "                        <td> (3, 2700, 3600) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2435 chunks in 37 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9490449-678e-4298-a7eb-eed3ec7cc552' class='xr-section-summary-in' type='checkbox'  ><label for='section-d9490449-678e-4298-a7eb-eed3ec7cc552' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a7cc052-cec1-410f-b798-1ee4e7b04485' class='xr-index-data-in' type='checkbox'/><label for='index-3a7cc052-cec1-410f-b798-1ee4e7b04485' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0181-01-01 12:00:00, 0181-01-02 12:00:00, 0181-01-03 12:00:00,\n",
       "             0181-01-04 12:00:00, 0181-01-05 12:00:00, 0181-01-06 12:00:00,\n",
       "             0181-01-07 12:00:00, 0181-01-08 12:00:00, 0181-01-09 12:00:00,\n",
       "             0181-01-10 12:00:00,\n",
       "             ...\n",
       "             0200-12-22 12:00:00, 0200-12-23 12:00:00, 0200-12-24 12:00:00,\n",
       "             0200-12-25 12:00:00, 0200-12-26 12:00:00, 0200-12-27 12:00:00,\n",
       "             0200-12-28 12:00:00, 0200-12-29 12:00:00, 0200-12-30 12:00:00,\n",
       "             0200-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=7305, calendar=&#x27;julian&#x27;, freq=&#x27;D&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ff0f669-bbc9-44f5-8955-7f503478ec6f' class='xr-index-data-in' type='checkbox'/><label for='index-5ff0f669-bbc9-44f5-8955-7f503478ec6f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([            -279.95, -279.84999999999997, -279.74999999999994,\n",
       "        -279.6499999999999,  -279.5499999999999,  -279.4499999999999,\n",
       "       -279.34999999999985, -279.24999999999983,  -279.1499999999998,\n",
       "        -279.0499999999998,\n",
       "       ...\n",
       "         79.04999999999492,   79.14999999999495,   79.24999999999491,\n",
       "         79.34999999999494,    79.4499999999949,   79.54999999999492,\n",
       "         79.64999999999489,   79.74999999999491,   79.84999999999488,\n",
       "          79.9499999999949],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=3600))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-910c21f9-db8a-45e3-a35f-00eeeeb65377' class='xr-index-data-in' type='checkbox'/><label for='index-910c21f9-db8a-45e3-a35f-00eeeeb65377' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-81.10863167835483, -81.06639232397434, -81.02415296959386,\n",
       "       -80.98191361521337, -80.93967426083289,  -80.8974349064524,\n",
       "       -80.85519555207192, -80.81295619769143, -80.77071684331095,\n",
       "       -80.72847748893047,\n",
       "       ...\n",
       "        89.59874190883218,   89.6409812632127,   89.6832206175932,\n",
       "        89.72545997197363,  89.76769932635409,  89.80993868073463,\n",
       "        89.85217803511516,  89.89441738949557,    89.936656743876,\n",
       "        89.97889609825653],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=2700))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8d58e084-eacf-43a5-97e6-a32009a931b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d58e084-eacf-43a5-97e6-a32009a931b8' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 3TB\n",
       "Dimensions:       (time: 7305, yt_ocean: 2700, xt_ocean: 3600)\n",
       "Coordinates:\n",
       "  * time          (time) object 58kB 0181-01-01 12:00:00 ... 0200-12-31 12:00:00\n",
       "  * xt_ocean      (xt_ocean) float64 29kB -279.9 -279.8 -279.7 ... 79.85 79.95\n",
       "  * yt_ocean      (yt_ocean) float64 22kB -81.11 -81.07 -81.02 ... 89.94 89.98\n",
       "    area_t        (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    dxt           (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    dyt           (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    geolat_t      (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    geolon_t      (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    ht            (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    kmt           (yt_ocean, xt_ocean) float32 39MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "    wet           (yt_ocean, xt_ocean) float64 78MB dask.array<chunksize=(2700, 3600), meta=np.ndarray>\n",
       "Data variables:\n",
       "    slp           (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    v_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    u_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    t_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    q_ref         (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    wind          (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    surface_temp  (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    u_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    v_ocean       (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    u_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "    v_relative    (time, yt_ocean, xt_ocean) float32 284GB dask.array<chunksize=(3, 2700, 3600), meta=np.ndarray>\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_merged = preprocess_data()\n",
    "ds_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdb21a8-66a9-4c71-969b-7cca49f1a9ce",
   "metadata": {},
   "source": [
    "# Demonstrate that xrft can return original data after forward/inverse Fourier transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07122fbb-b01a-45a9-a35a-187242fe59b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xrft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94292ffc-aa80-4769-acfd-ee9a55f0488c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7c4734155d90>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_test = ds_merged.isel(time=0, xt_ocean=slice(1500, 1600), yt_ocean=slice(1500, 1600))\n",
    "ds_test.surface_temp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31ebb31a-c5fa-46de-94f7-fc64d7c98bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  padding-bottom: 6px;\n",
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       "\n",
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       "  content: '►';\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;surface_temp&#x27; (yt_ocean: 100, xt_ocean: 100)&gt; Size: 40kB\n",
       "dask.array&lt;getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 100</li><li><span class='xr-has-index'>xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd2737ca-2fe9-44b3-9769-06d1ab95bdb5' class='xr-array-in' type='checkbox' checked><label for='section-bd2737ca-2fe9-44b3-9769-06d1ab95bdb5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 100) </td>\n",
       "                        <td> (100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"120\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-3797d940-ffc8-42b4-85b2-55276245d3ab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3797d940-ffc8-42b4-85b2-55276245d3ab' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-abf54ea4-9033-411a-b357-310c9fd970c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abf54ea4-9033-411a-b357-310c9fd970c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0624cace-b09e-4e02-bd75-a0a76735a065' class='xr-var-data-in' type='checkbox'><label for='data-0624cace-b09e-4e02-bd75-a0a76735a065' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.0 -129.9 ... -120.2 -120.1</div><input id='attrs-ae3deb82-7f96-40c5-8d64-1694a0daefc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae3deb82-7f96-40c5-8d64-1694a0daefc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a8e10a0-f647-41f2-878e-c2be864304f6' class='xr-var-data-in' type='checkbox'><label for='data-1a8e10a0-f647-41f2-878e-c2be864304f6' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-129.95, -129.85, -129.75, -129.65, -129.55, -129.45, -129.35, -129.25,\n",
       "       -129.15, -129.05, -128.95, -128.85, -128.75, -128.65, -128.55, -128.45,\n",
       "       -128.35, -128.25, -128.15, -128.05, -127.95, -127.85, -127.75, -127.65,\n",
       "       -127.55, -127.45, -127.35, -127.25, -127.15, -127.05, -126.95, -126.85,\n",
       "       -126.75, -126.65, -126.55, -126.45, -126.35, -126.25, -126.15, -126.05,\n",
       "       -125.95, -125.85, -125.75, -125.65, -125.55, -125.45, -125.35, -125.25,\n",
       "       -125.15, -125.05, -124.95, -124.85, -124.75, -124.65, -124.55, -124.45,\n",
       "       -124.35, -124.25, -124.15, -124.05, -123.95, -123.85, -123.75, -123.65,\n",
       "       -123.55, -123.45, -123.35, -123.25, -123.15, -123.05, -122.95, -122.85,\n",
       "       -122.75, -122.65, -122.55, -122.45, -122.35, -122.25, -122.15, -122.05,\n",
       "       -121.95, -121.85, -121.75, -121.65, -121.55, -121.45, -121.35, -121.25,\n",
       "       -121.15, -121.05, -120.95, -120.85, -120.75, -120.65, -120.55, -120.45,\n",
       "       -120.35, -120.25, -120.15, -120.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.74 24.83 24.92 ... 33.3 33.38</div><input id='attrs-25bd3705-a197-46b3-90c0-6ed8ce0a1446' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25bd3705-a197-46b3-90c0-6ed8ce0a1446' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeb510e2-4ef4-4207-b4fd-d16d02fd2501' class='xr-var-data-in' type='checkbox'><label for='data-eeb510e2-4ef4-4207-b4fd-d16d02fd2501' 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>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([24.743028, 24.833814, 24.924534, 25.015187, 25.105773, 25.196292,\n",
       "       25.286744, 25.377128, 25.467445, 25.557694, 25.647876, 25.737989,\n",
       "       25.828033, 25.91801 , 26.007918, 26.097756, 26.187526, 26.277227,\n",
       "       26.366859, 26.456421, 26.545914, 26.635336, 26.724689, 26.813972,\n",
       "       26.903184, 26.992326, 27.081398, 27.170399, 27.259328, 27.348187,\n",
       "       27.436975, 27.525691, 27.614335, 27.702908, 27.791409, 27.879838,\n",
       "       27.968195, 28.05648 , 28.144692, 28.232832, 28.320899, 28.408893,\n",
       "       28.496814, 28.584661, 28.672436, 28.760137, 28.847764, 28.935318,\n",
       "       29.022798, 29.110203, 29.197535, 29.284792, 29.371975, 29.459083,\n",
       "       29.546116, 29.633074, 29.719958, 29.806766, 29.893499, 29.980157,\n",
       "       30.066739, 30.153245, 30.239676, 30.326031, 30.412309, 30.498512,\n",
       "       30.584638, 30.670687, 30.75666 , 30.842557, 30.928376, 31.014119,\n",
       "       31.099784, 31.185373, 31.270884, 31.356317, 31.441673, 31.526951,\n",
       "       31.612152, 31.697275, 31.782319, 31.867286, 31.952174, 32.036984,\n",
       "       32.121715, 32.206368, 32.290942, 32.375437, 32.459853, 32.544191,\n",
       "       32.628449, 32.712627, 32.796727, 32.880747, 32.964687, 33.048548,\n",
       "       33.132329, 33.21603 , 33.299651, 33.383192])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd3a1b90-6c8f-4b87-8eb0-c989ebb6af5d' class='xr-section-summary-in' type='checkbox'  ><label for='section-fd3a1b90-6c8f-4b87-8eb0-c989ebb6af5d' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ae5a1e99-d08f-4126-bf1c-fd5e55f0764b' class='xr-index-data-in' type='checkbox'/><label for='index-ae5a1e99-d08f-4126-bf1c-fd5e55f0764b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-129.95000000000175, -129.85000000000173, -129.75000000000176,\n",
       "       -129.65000000000174, -129.55000000000177, -129.45000000000175,\n",
       "       -129.35000000000178, -129.25000000000176,  -129.1500000000018,\n",
       "       -129.05000000000177,  -128.9500000000018, -128.85000000000178,\n",
       "       -128.75000000000182,  -128.6500000000018, -128.55000000000183,\n",
       "        -128.4500000000018, -128.35000000000184, -128.25000000000182,\n",
       "       -128.15000000000185, -128.05000000000183, -127.95000000000186,\n",
       "       -127.85000000000184, -127.75000000000188, -127.65000000000185,\n",
       "       -127.55000000000189, -127.45000000000186,  -127.3500000000019,\n",
       "       -127.25000000000188, -127.15000000000191, -127.05000000000189,\n",
       "       -126.95000000000192,  -126.8500000000019, -126.75000000000193,\n",
       "       -126.65000000000191, -126.55000000000194, -126.45000000000192,\n",
       "       -126.35000000000196, -126.25000000000193, -126.15000000000197,\n",
       "       -126.05000000000194, -125.95000000000198, -125.85000000000196,\n",
       "       -125.75000000000199, -125.65000000000197,   -125.550000000002,\n",
       "       -125.45000000000198, -125.35000000000201, -125.25000000000199,\n",
       "       -125.15000000000202,   -125.050000000002, -124.95000000000203,\n",
       "       -124.85000000000201, -124.75000000000205, -124.65000000000202,\n",
       "       -124.55000000000206, -124.45000000000203, -124.35000000000207,\n",
       "       -124.25000000000205, -124.15000000000208, -124.05000000000206,\n",
       "       -123.95000000000209, -123.85000000000207,  -123.7500000000021,\n",
       "       -123.65000000000208, -123.55000000000211, -123.45000000000209,\n",
       "       -123.35000000000213,  -123.2500000000021, -123.15000000000214,\n",
       "       -123.05000000000211, -122.95000000000215, -122.85000000000213,\n",
       "       -122.75000000000216, -122.65000000000214, -122.55000000000217,\n",
       "       -122.45000000000215, -122.35000000000218, -122.25000000000216,\n",
       "        -122.1500000000022, -122.05000000000217,  -121.9500000000022,\n",
       "       -121.85000000000218, -121.75000000000222,  -121.6500000000022,\n",
       "       -121.55000000000223,  -121.4500000000022, -121.35000000000224,\n",
       "       -121.25000000000222, -121.15000000000225, -121.05000000000223,\n",
       "       -120.95000000000226, -120.85000000000224, -120.75000000000227,\n",
       "       -120.65000000000225, -120.55000000000229, -120.45000000000226,\n",
       "        -120.3500000000023, -120.25000000000227, -120.15000000000231,\n",
       "       -120.05000000000229],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c5a98415-31bb-4bfd-84d9-5ca015375ed0' class='xr-index-data-in' type='checkbox'/><label for='index-c5a98415-31bb-4bfd-84d9-5ca015375ed0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([24.743028005786304, 24.833814202883772, 24.924533960340074,\n",
       "       25.015186931740978, 25.105773107375004,  25.19629214217983,\n",
       "        25.28674402829334, 25.377128422005594, 25.467445317308336,\n",
       "       25.557694371844295, 25.647875581463406, 25.737988605161227,\n",
       "       25.828033440650042, 25.918009748278212, 26.007917527624393,\n",
       "       26.097756440389567,  26.18752648802262, 26.277227333576807,\n",
       "       26.366858980374957,  26.45642109282209, 26.545913676118523,\n",
       "       26.635336396020385,  26.72468925960888, 26.813971933990416,\n",
       "       26.903184428130338, 26.992326410484353,  27.08139789190504,\n",
       "       27.170398542196274, 27.259328374100818, 27.348187058769412,\n",
       "       27.436974610837808, 27.525690702802166, 27.614335351193873,\n",
       "        27.70290822985291,  27.79140935720881,  27.87983840844362,\n",
       "       27.968195403887382,   28.0564800200623, 28.144692279201134,\n",
       "         28.2328318591642, 28.320898784089056, 28.408892733171918,\n",
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       "<xarray.DataArray 'surface_temp' (yt_ocean: 100, xt_ocean: 100)> Size: 40kB\n",
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       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38"
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     "execution_count": 6,
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    "SST = ds_test.surface_temp\n",
    "ds_finalTest = SST.drop(['area_t', 'dxt', 'dyt', 'geolat_t', 'geolon_t', 'ht', 'kmt', 'wet'])\n",
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    "y_beg = ds_finalTest.yt_ocean[0]\n",
    "y_end = ds_finalTest.yt_ocean[-1]\n",
    "y_len = len(ds_finalTest.yt_ocean)\n",
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       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.21 33.3 33.38</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'surface_temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 100</li><li><span class='xr-has-index'>xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1df099da-9e2e-4b23-91a3-185f0d540b04' class='xr-array-in' type='checkbox' checked><label for='section-1df099da-9e2e-4b23-91a3-185f0d540b04' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 100) </td>\n",
       "                        <td> (100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-02b6dee5-1d61-47fa-9723-71db4a3bd1fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02b6dee5-1d61-47fa-9723-71db4a3bd1fe' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-217aaec7-a4e3-4d0b-8d53-cf0c4b97b9a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-217aaec7-a4e3-4d0b-8d53-cf0c4b97b9a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efa0c3d5-3116-4909-97d7-3b8ba0b3d110' class='xr-var-data-in' type='checkbox'><label for='data-efa0c3d5-3116-4909-97d7-3b8ba0b3d110' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.0 -129.9 ... -120.2 -120.1</div><input id='attrs-cd7867ae-f64f-4fff-a505-57886a3d504f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd7867ae-f64f-4fff-a505-57886a3d504f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-739ed2af-2fbc-4c03-b853-760d70542903' class='xr-var-data-in' type='checkbox'><label for='data-739ed2af-2fbc-4c03-b853-760d70542903' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-129.95, -129.85, -129.75, -129.65, -129.55, -129.45, -129.35, -129.25,\n",
       "       -129.15, -129.05, -128.95, -128.85, -128.75, -128.65, -128.55, -128.45,\n",
       "       -128.35, -128.25, -128.15, -128.05, -127.95, -127.85, -127.75, -127.65,\n",
       "       -127.55, -127.45, -127.35, -127.25, -127.15, -127.05, -126.95, -126.85,\n",
       "       -126.75, -126.65, -126.55, -126.45, -126.35, -126.25, -126.15, -126.05,\n",
       "       -125.95, -125.85, -125.75, -125.65, -125.55, -125.45, -125.35, -125.25,\n",
       "       -125.15, -125.05, -124.95, -124.85, -124.75, -124.65, -124.55, -124.45,\n",
       "       -124.35, -124.25, -124.15, -124.05, -123.95, -123.85, -123.75, -123.65,\n",
       "       -123.55, -123.45, -123.35, -123.25, -123.15, -123.05, -122.95, -122.85,\n",
       "       -122.75, -122.65, -122.55, -122.45, -122.35, -122.25, -122.15, -122.05,\n",
       "       -121.95, -121.85, -121.75, -121.65, -121.55, -121.45, -121.35, -121.25,\n",
       "       -121.15, -121.05, -120.95, -120.85, -120.75, -120.65, -120.55, -120.45,\n",
       "       -120.35, -120.25, -120.15, -120.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.74 24.83 24.92 ... 33.3 33.38</div><input id='attrs-07e10681-7037-40d7-8087-2e860e3b4dc0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07e10681-7037-40d7-8087-2e860e3b4dc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09925375-d1d4-4074-9973-85c3e822008c' class='xr-var-data-in' type='checkbox'><label for='data-09925375-d1d4-4074-9973-85c3e822008c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([24.743028, 24.830302, 24.917577, 25.004851, 25.092126, 25.1794  ,\n",
       "       25.266674, 25.353949, 25.441223, 25.528497, 25.615772, 25.703046,\n",
       "       25.790321, 25.877595, 25.964869, 26.052144, 26.139418, 26.226693,\n",
       "       26.313967, 26.401241, 26.488516, 26.57579 , 26.663065, 26.750339,\n",
       "       26.837613, 26.924888, 27.012162, 27.099436, 27.186711, 27.273985,\n",
       "       27.36126 , 27.448534, 27.535808, 27.623083, 27.710357, 27.797632,\n",
       "       27.884906, 27.97218 , 28.059455, 28.146729, 28.234004, 28.321278,\n",
       "       28.408552, 28.495827, 28.583101, 28.670375, 28.75765 , 28.844924,\n",
       "       28.932199, 29.019473, 29.106747, 29.194022, 29.281296, 29.368571,\n",
       "       29.455845, 29.543119, 29.630394, 29.717668, 29.804943, 29.892217,\n",
       "       29.979491, 30.066766, 30.15404 , 30.241314, 30.328589, 30.415863,\n",
       "       30.503138, 30.590412, 30.677686, 30.764961, 30.852235, 30.93951 ,\n",
       "       31.026784, 31.114058, 31.201333, 31.288607, 31.375882, 31.463156,\n",
       "       31.55043 , 31.637705, 31.724979, 31.812253, 31.899528, 31.986802,\n",
       "       32.074077, 32.161351, 32.248625, 32.3359  , 32.423174, 32.510449,\n",
       "       32.597723, 32.684997, 32.772272, 32.859546, 32.94682 , 33.034095,\n",
       "       33.121369, 33.208644, 33.295918, 33.383192])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7831edfc-a323-436d-b33a-040630c014df' class='xr-section-summary-in' type='checkbox'  ><label for='section-7831edfc-a323-436d-b33a-040630c014df' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c3072b2-d42f-4bd5-bd2e-ccc8eef61c2a' class='xr-index-data-in' type='checkbox'/><label for='index-2c3072b2-d42f-4bd5-bd2e-ccc8eef61c2a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-129.95000000000175, -129.85000000000173, -129.75000000000176,\n",
       "       -129.65000000000174, -129.55000000000177, -129.45000000000175,\n",
       "       -129.35000000000178, -129.25000000000176,  -129.1500000000018,\n",
       "       -129.05000000000177,  -128.9500000000018, -128.85000000000178,\n",
       "       -128.75000000000182,  -128.6500000000018, -128.55000000000183,\n",
       "        -128.4500000000018, -128.35000000000184, -128.25000000000182,\n",
       "       -128.15000000000185, -128.05000000000183, -127.95000000000186,\n",
       "       -127.85000000000184, -127.75000000000188, -127.65000000000185,\n",
       "       -127.55000000000189, -127.45000000000186,  -127.3500000000019,\n",
       "       -127.25000000000188, -127.15000000000191, -127.05000000000189,\n",
       "       -126.95000000000192,  -126.8500000000019, -126.75000000000193,\n",
       "       -126.65000000000191, -126.55000000000194, -126.45000000000192,\n",
       "       -126.35000000000196, -126.25000000000193, -126.15000000000197,\n",
       "       -126.05000000000194, -125.95000000000198, -125.85000000000196,\n",
       "       -125.75000000000199, -125.65000000000197,   -125.550000000002,\n",
       "       -125.45000000000198, -125.35000000000201, -125.25000000000199,\n",
       "       -125.15000000000202,   -125.050000000002, -124.95000000000203,\n",
       "       -124.85000000000201, -124.75000000000205, -124.65000000000202,\n",
       "       -124.55000000000206, -124.45000000000203, -124.35000000000207,\n",
       "       -124.25000000000205, -124.15000000000208, -124.05000000000206,\n",
       "       -123.95000000000209, -123.85000000000207,  -123.7500000000021,\n",
       "       -123.65000000000208, -123.55000000000211, -123.45000000000209,\n",
       "       -123.35000000000213,  -123.2500000000021, -123.15000000000214,\n",
       "       -123.05000000000211, -122.95000000000215, -122.85000000000213,\n",
       "       -122.75000000000216, -122.65000000000214, -122.55000000000217,\n",
       "       -122.45000000000215, -122.35000000000218, -122.25000000000216,\n",
       "        -122.1500000000022, -122.05000000000217,  -121.9500000000022,\n",
       "       -121.85000000000218, -121.75000000000222,  -121.6500000000022,\n",
       "       -121.55000000000223,  -121.4500000000022, -121.35000000000224,\n",
       "       -121.25000000000222, -121.15000000000225, -121.05000000000223,\n",
       "       -120.95000000000226, -120.85000000000224, -120.75000000000227,\n",
       "       -120.65000000000225, -120.55000000000229, -120.45000000000226,\n",
       "        -120.3500000000023, -120.25000000000227, -120.15000000000231,\n",
       "       -120.05000000000229],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7214d257-891d-4cb6-abf8-bc24daa28d5a' class='xr-index-data-in' type='checkbox'/><label for='index-7214d257-891d-4cb6-abf8-bc24daa28d5a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([24.743028005786304, 24.830302393971603, 24.917576782156903,\n",
       "       25.004851170342203, 25.092125558527503, 25.179399946712806,\n",
       "       25.266674334898106, 25.353948723083406, 25.441223111268705,\n",
       "       25.528497499454005, 25.615771887639305, 25.703046275824605,\n",
       "       25.790320664009904, 25.877595052195204, 25.964869440380504,\n",
       "       26.052143828565807, 26.139418216751107, 26.226692604936407,\n",
       "       26.313966993121706, 26.401241381307006, 26.488515769492306,\n",
       "       26.575790157677606, 26.663064545862905,  26.75033893404821,\n",
       "        26.83761332223351,  26.92488771041881, 27.012162098604108,\n",
       "       27.099436486789408, 27.186710874974708, 27.273985263160007,\n",
       "       27.361259651345307, 27.448534039530607, 27.535808427715907,\n",
       "        27.62308281590121,  27.71035720408651,  27.79763159227181,\n",
       "        27.88490598045711,  27.97218036864241,  28.05945475682771,\n",
       "        28.14672914501301, 28.234003533198308,  28.32127792138361,\n",
       "        28.40855230956891,  28.49582669775421,  28.58310108593951,\n",
       "        28.67037547412481,  28.75764986231011,  28.84492425049541,\n",
       "        28.93219863868071,  29.01947302686601,  29.10674741505131,\n",
       "        29.19402180323661, 29.281296191421912, 29.368570579607212,\n",
       "       29.455844967792512,  29.54311935597781,  29.63039374416311,\n",
       "        29.71766813234841,  29.80494252053371, 29.892216908719014,\n",
       "       29.979491296904314, 30.066765685089614, 30.154040073274913,\n",
       "       30.241314461460213, 30.328588849645513, 30.415863237830813,\n",
       "       30.503137626016112, 30.590412014201412, 30.677686402386712,\n",
       "        30.76496079057201, 30.852235178757315, 30.939509566942615,\n",
       "       31.026783955127915, 31.114058343313214, 31.201332731498514,\n",
       "       31.288607119683814, 31.375881507869114, 31.463155896054417,\n",
       "       31.550430284239717, 31.637704672425016, 31.724979060610316,\n",
       "       31.812253448795616, 31.899527836980916, 31.986802225166215,\n",
       "       32.074076613351515, 32.161351001536815, 32.248625389722115,\n",
       "       32.335899777907414, 32.423174166092714, 32.510448554278014,\n",
       "       32.597722942463314,  32.68499733064861,  32.77227171883392,\n",
       "        32.85954610701921,  32.94682049520452,  33.03409488338982,\n",
       "        33.12136927157512,  33.20864365976042,  33.29591804794572,\n",
       "        33.38319243613102],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17dbd2c4-0fa9-4479-9194-3f6528af2721' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17dbd2c4-0fa9-4479-9194-3f6528af2721' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'surface_temp' (yt_ocean: 100, xt_ocean: 100)> Size: 40kB\n",
       "dask.array<getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.21 33.3 33.38"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_finalTest['yt_ocean'] = yt_new\n",
    "ds_finalTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7702dd12-ad98-4f86-b557-bc0f0c9e4e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
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       "\n",
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       "\n",
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       "  display: inline-block;\n",
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       "  width: 15px;\n",
       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
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       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (freq_yt_ocean: 100, freq_xt_ocean: 100)&gt; Size: 80kB\n",
       "array([[ 0.0314615 , -0.01736799, -0.00048955, ..., -0.00813807,\n",
       "         0.00154147,  0.01641297],\n",
       "       [-0.00101467, -0.0031688 ,  0.01458142, ...,  0.00983546,\n",
       "         0.00297317, -0.00349447],\n",
       "       [ 0.02127895, -0.02337942, -0.00164586, ..., -0.00821584,\n",
       "         0.00579791, -0.00973802],\n",
       "       ...,\n",
       "       [ 0.0002379 , -0.00582676,  0.00355756, ..., -0.01420672,\n",
       "         0.01324685,  0.02244774],\n",
       "       [-0.02127895, -0.00973802,  0.00579791, ..., -0.01113551,\n",
       "        -0.00164586, -0.02337942],\n",
       "       [ 0.00101467, -0.00349447,  0.00297317, ..., -0.01344756,\n",
       "         0.01458142, -0.0031688 ]])\n",
       "Coordinates:\n",
       "    time           object 8B 0181-01-01 12:00:00\n",
       "  * freq_xt_ocean  (freq_xt_ocean) float64 800B -5.0 -4.9 -4.8 ... 4.7 4.8 4.9\n",
       "  * freq_yt_ocean  (freq_yt_ocean) float64 800B -5.729 -5.614 -5.5 ... 5.5 5.614</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>freq_yt_ocean</span>: 100</li><li><span class='xr-has-index'>freq_xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2c55125d-80f4-47fb-b168-6f89367f103b' class='xr-array-in' type='checkbox' checked><label for='section-2c55125d-80f4-47fb-b168-6f89367f103b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.03146 -0.01737 -0.0004896 0.007604 ... -0.01345 0.01458 -0.003169</span></div><div class='xr-array-data'><pre>array([[ 0.0314615 , -0.01736799, -0.00048955, ..., -0.00813807,\n",
       "         0.00154147,  0.01641297],\n",
       "       [-0.00101467, -0.0031688 ,  0.01458142, ...,  0.00983546,\n",
       "         0.00297317, -0.00349447],\n",
       "       [ 0.02127895, -0.02337942, -0.00164586, ..., -0.00821584,\n",
       "         0.00579791, -0.00973802],\n",
       "       ...,\n",
       "       [ 0.0002379 , -0.00582676,  0.00355756, ..., -0.01420672,\n",
       "         0.01324685,  0.02244774],\n",
       "       [-0.02127895, -0.00973802,  0.00579791, ..., -0.01113551,\n",
       "        -0.00164586, -0.02337942],\n",
       "       [ 0.00101467, -0.00349447,  0.00297317, ..., -0.01344756,\n",
       "         0.01458142, -0.0031688 ]])</pre></div></div></li><li class='xr-section-item'><input id='section-e9de39ac-2744-47cc-a5c3-7a823b3b634c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e9de39ac-2744-47cc-a5c3-7a823b3b634c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-06d734f9-0678-4680-a981-6eb09485ef2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06d734f9-0678-4680-a981-6eb09485ef2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4d63cae-e8ec-4088-baa1-05c47964d334' class='xr-var-data-in' type='checkbox'><label for='data-d4d63cae-e8ec-4088-baa1-05c47964d334' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>freq_xt_ocean</span></div><div class='xr-var-dims'>(freq_xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.0 -4.9 -4.8 -4.7 ... 4.7 4.8 4.9</div><input id='attrs-9e27ed14-dd51-40e9-93f7-84d0fd874fd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e27ed14-dd51-40e9-93f7-84d0fd874fd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b578ee0-e5cd-45c5-ba75-4ee8199f13e5' class='xr-var-data-in' type='checkbox'><label for='data-5b578ee0-e5cd-45c5-ba75-4ee8199f13e5' 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>spacing :</span></dt><dd>0.09999999999997744</dd><dt><span>direct_lag :</span></dt><dd>-124.95000000000203</dd></dl></div><div class='xr-var-data'><pre>array([-5. , -4.9, -4.8, -4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4. , -3.9,\n",
       "       -3.8, -3.7, -3.6, -3.5, -3.4, -3.3, -3.2, -3.1, -3. , -2.9, -2.8, -2.7,\n",
       "       -2.6, -2.5, -2.4, -2.3, -2.2, -2.1, -2. , -1.9, -1.8, -1.7, -1.6, -1.5,\n",
       "       -1.4, -1.3, -1.2, -1.1, -1. , -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3,\n",
       "       -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,\n",
       "        1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,\n",
       "        3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,\n",
       "        4.6,  4.7,  4.8,  4.9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>freq_yt_ocean</span></div><div class='xr-var-dims'>(freq_yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.729 -5.614 -5.5 ... 5.5 5.614</div><input id='attrs-08031bde-d1d1-49f5-97f2-eb26658a56bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08031bde-d1d1-49f5-97f2-eb26658a56bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3803370c-7723-43b2-bb69-530141daedca' class='xr-var-data-in' type='checkbox'><label for='data-3803370c-7723-43b2-bb69-530141daedca' 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>spacing :</span></dt><dd>0.11458115270619995</dd><dt><span>direct_lag :</span></dt><dd>29.10674741505131</dd></dl></div><div class='xr-var-data'><pre>array([-5.729058, -5.614476, -5.499895, -5.385314, -5.270733, -5.156152,\n",
       "       -5.041571, -4.92699 , -4.812408, -4.697827, -4.583246, -4.468665,\n",
       "       -4.354084, -4.239503, -4.124921, -4.01034 , -3.895759, -3.781178,\n",
       "       -3.666597, -3.552016, -3.437435, -3.322853, -3.208272, -3.093691,\n",
       "       -2.97911 , -2.864529, -2.749948, -2.635367, -2.520785, -2.406204,\n",
       "       -2.291623, -2.177042, -2.062461, -1.94788 , -1.833298, -1.718717,\n",
       "       -1.604136, -1.489555, -1.374974, -1.260393, -1.145812, -1.03123 ,\n",
       "       -0.916649, -0.802068, -0.687487, -0.572906, -0.458325, -0.343743,\n",
       "       -0.229162, -0.114581,  0.      ,  0.114581,  0.229162,  0.343743,\n",
       "        0.458325,  0.572906,  0.687487,  0.802068,  0.916649,  1.03123 ,\n",
       "        1.145812,  1.260393,  1.374974,  1.489555,  1.604136,  1.718717,\n",
       "        1.833298,  1.94788 ,  2.062461,  2.177042,  2.291623,  2.406204,\n",
       "        2.520785,  2.635367,  2.749948,  2.864529,  2.97911 ,  3.093691,\n",
       "        3.208272,  3.322853,  3.437435,  3.552016,  3.666597,  3.781178,\n",
       "        3.895759,  4.01034 ,  4.124921,  4.239503,  4.354084,  4.468665,\n",
       "        4.583246,  4.697827,  4.812408,  4.92699 ,  5.041571,  5.156152,\n",
       "        5.270733,  5.385314,  5.499895,  5.614476])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71aaa4b0-1831-4225-84a8-e24822a07c15' class='xr-section-summary-in' type='checkbox'  ><label for='section-71aaa4b0-1831-4225-84a8-e24822a07c15' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>freq_xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a070eda5-e1fd-4bd0-8139-0d94caf551b6' class='xr-index-data-in' type='checkbox'/><label for='index-a070eda5-e1fd-4bd0-8139-0d94caf551b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -4.999999999998863,   -4.899999999998886,   -4.799999999998908,\n",
       "         -4.699999999998931,   -4.599999999998954,   -4.499999999998977,\n",
       "         -4.399999999998999,   -4.299999999999022,  -4.1999999999990445,\n",
       "         -4.099999999999068,  -3.9999999999990905,   -3.899999999999113,\n",
       "         -3.799999999999136,  -3.6999999999991586,   -3.599999999999181,\n",
       "         -3.499999999999204,  -3.3999999999992268,  -3.2999999999992498,\n",
       "        -3.1999999999992723,   -3.099999999999295,   -2.999999999999318,\n",
       "        -2.8999999999993404,  -2.7999999999993634,   -2.699999999999386,\n",
       "        -2.5999999999994086,  -2.4999999999994316,   -2.399999999999454,\n",
       "         -2.299999999999477,  -2.1999999999994997,  -2.0999999999995222,\n",
       "        -1.9999999999995453,   -1.899999999999568,  -1.7999999999995906,\n",
       "        -1.6999999999996134,  -1.5999999999996362,   -1.499999999999659,\n",
       "        -1.3999999999996817,  -1.2999999999997043,   -1.199999999999727,\n",
       "        -1.0999999999997498,  -0.9999999999997726,  -0.8999999999997953,\n",
       "        -0.7999999999998181,  -0.6999999999998409,  -0.5999999999998635,\n",
       "        -0.4999999999998863, -0.39999999999990904, -0.29999999999993177,\n",
       "       -0.19999999999995452, -0.09999999999997726,                  0.0,\n",
       "        0.09999999999997726,  0.19999999999995452,  0.29999999999993177,\n",
       "        0.39999999999990904,   0.4999999999998863,   0.5999999999998635,\n",
       "         0.6999999999998409,   0.7999999999998181,   0.8999999999997953,\n",
       "         0.9999999999997726,   1.0999999999997498,    1.199999999999727,\n",
       "         1.2999999999997043,   1.3999999999996817,    1.499999999999659,\n",
       "         1.5999999999996362,   1.6999999999996134,   1.7999999999995906,\n",
       "          1.899999999999568,   1.9999999999995453,   2.0999999999995222,\n",
       "         2.1999999999994997,    2.299999999999477,    2.399999999999454,\n",
       "         2.4999999999994316,   2.5999999999994086,    2.699999999999386,\n",
       "         2.7999999999993634,   2.8999999999993404,    2.999999999999318,\n",
       "          3.099999999999295,   3.1999999999992723,   3.2999999999992498,\n",
       "         3.3999999999992268,    3.499999999999204,    3.599999999999181,\n",
       "         3.6999999999991586,    3.799999999999136,    3.899999999999113,\n",
       "         3.9999999999990905,    4.099999999999068,   4.1999999999990445,\n",
       "          4.299999999999022,    4.399999999998999,    4.499999999998977,\n",
       "          4.599999999998954,    4.699999999998931,    4.799999999998908,\n",
       "          4.899999999998886],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;freq_xt_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>freq_yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e64a1625-8424-4c5c-ac36-5fd5f535d377' class='xr-index-data-in' type='checkbox'/><label for='index-e64a1625-8424-4c5c-ac36-5fd5f535d377' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -5.729057635309996,   -5.614476482603796,   -5.499895329897596,\n",
       "         -5.385314177191396,   -5.270733024485196,   -5.156151871778996,\n",
       "         -5.041570719072796,   -4.926989566366596,   -4.812408413660396,\n",
       "         -4.697827260954196,   -4.583246108247996,   -4.468664955541796,\n",
       "         -4.354083802835596,   -4.239502650129396,  -4.1249214974231965,\n",
       "        -4.0103403447169965,   -3.895759192010797,   -3.781178039304597,\n",
       "         -3.666596886598397,   -3.552015733892197,   -3.437434581185997,\n",
       "        -3.3228534284797973,  -3.2082722757735973,  -3.0936911230673974,\n",
       "         -2.979109970361198,   -2.864528817654998,   -2.749947664948798,\n",
       "         -2.635366512242598,   -2.520785359536398,   -2.406204206830198,\n",
       "         -2.291623054123998,   -2.177041901417798,  -2.0624607487115982,\n",
       "        -1.9478795960053985,  -1.8332984432991986,  -1.7187172905929986,\n",
       "        -1.6041361378867987,   -1.489554985180599,   -1.374973832474399,\n",
       "         -1.260392679768199,   -1.145811527061999,  -1.0312303743557991,\n",
       "        -0.9166492216495993,  -0.8020680689433993,  -0.6874869162371995,\n",
       "        -0.5729057635309995, -0.45832461082479964, -0.34374345811859974,\n",
       "       -0.22916230541239982, -0.11458115270619991,                  0.0,\n",
       "        0.11458115270619991,  0.22916230541239982,  0.34374345811859974,\n",
       "        0.45832461082479964,   0.5729057635309995,   0.6874869162371995,\n",
       "         0.8020680689433993,   0.9166492216495993,   1.0312303743557991,\n",
       "          1.145811527061999,    1.260392679768199,    1.374973832474399,\n",
       "          1.489554985180599,   1.6041361378867987,   1.7187172905929986,\n",
       "         1.8332984432991986,   1.9478795960053985,   2.0624607487115982,\n",
       "          2.177041901417798,    2.291623054123998,    2.406204206830198,\n",
       "          2.520785359536398,    2.635366512242598,    2.749947664948798,\n",
       "          2.864528817654998,    2.979109970361198,   3.0936911230673974,\n",
       "         3.2082722757735973,   3.3228534284797973,    3.437434581185997,\n",
       "          3.552015733892197,    3.666596886598397,    3.781178039304597,\n",
       "          3.895759192010797,   4.0103403447169965,   4.1249214974231965,\n",
       "          4.239502650129396,    4.354083802835596,    4.468664955541796,\n",
       "          4.583246108247996,    4.697827260954196,    4.812408413660396,\n",
       "          4.926989566366596,    5.041570719072796,    5.156151871778996,\n",
       "          5.270733024485196,    5.385314177191396,    5.499895329897596,\n",
       "          5.614476482603796],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;freq_yt_ocean&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46c0bb6c-f62c-4515-8bca-8296c659554e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46c0bb6c-f62c-4515-8bca-8296c659554e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (freq_yt_ocean: 100, freq_xt_ocean: 100)> Size: 80kB\n",
       "array([[ 0.0314615 , -0.01736799, -0.00048955, ..., -0.00813807,\n",
       "         0.00154147,  0.01641297],\n",
       "       [-0.00101467, -0.0031688 ,  0.01458142, ...,  0.00983546,\n",
       "         0.00297317, -0.00349447],\n",
       "       [ 0.02127895, -0.02337942, -0.00164586, ..., -0.00821584,\n",
       "         0.00579791, -0.00973802],\n",
       "       ...,\n",
       "       [ 0.0002379 , -0.00582676,  0.00355756, ..., -0.01420672,\n",
       "         0.01324685,  0.02244774],\n",
       "       [-0.02127895, -0.00973802,  0.00579791, ..., -0.01113551,\n",
       "        -0.00164586, -0.02337942],\n",
       "       [ 0.00101467, -0.00349447,  0.00297317, ..., -0.01344756,\n",
       "         0.01458142, -0.0031688 ]])\n",
       "Coordinates:\n",
       "    time           object 8B 0181-01-01 12:00:00\n",
       "  * freq_xt_ocean  (freq_xt_ocean) float64 800B -5.0 -4.9 -4.8 ... 4.7 4.8 4.9\n",
       "  * freq_yt_ocean  (freq_yt_ocean) float64 800B -5.729 -5.614 -5.5 ... 5.5 5.614"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fda = xrft.dft(ds_finalTest, dim=['xt_ocean', 'yt_ocean'], true_phase=True, true_amplitude=True).compute()\n",
    "Fda.real"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e468ad25-edf9-4f74-b998-a3b6328bca0b",
   "metadata": {},
   "source": [
    "#### Question: Why does the below plot only work when there are not log scales on the axes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "698e84ec-1eb4-4c61-9f83-9cd6f43ae5c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7c471061f590>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fda.real.plot(robust=True)\n",
    "#plt.yscale('log')\n",
    "#plt.xscale('log') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f032535-6b72-4b9b-b132-d5515351f4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7c47106970d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Fda_1 = xrft.idft(Fda, dim=[\"freq_xt_ocean\",\"freq_yt_ocean\"], true_phase=True, true_amplitude=True)\n",
    "Fda_1.real.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c45b473-14bf-4db0-9ced-7a7ba26fa1dd",
   "metadata": {},
   "source": [
    "# Power Spectra for SST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07385fb3-1bba-4af1-9229-0dcd1710adeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SST Power Spectra')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps = xrft.power_spectrum(ds_finalTest.mean(dim='yt_ocean'), dim='xt_ocean', window=True)\n",
    "ps1 = xrft.power_spectrum(ds_finalTest, dim='xt_ocean', window=True).mean(dim='yt_ocean')\n",
    "ps.plot()\n",
    "ps1.plot()\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.title('SST Power Spectra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0a13c597-5c11-41aa-8b2c-15dbd6f7091a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "body[data-theme=dark],\n",
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       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;rechunk-merge-7f82a64a7e06a13d620e9c30ef631adf&#x27; (\n",
       "                                                                    freq_xt_ocean: 100)&gt; Size: 800B\n",
       "dask.array&lt;mul, shape=(100,), dtype=float64, chunksize=(100,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    time           object 8B 0181-01-01 12:00:00\n",
       "  * freq_xt_ocean  (freq_xt_ocean) float64 800B -5.0 -4.9 -4.8 ... 4.7 4.8 4.9</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'rechunk-merge-7f82a64a7e06a13d620e9c30ef631adf'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>freq_xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f9fc63af-5577-4ef7-bd05-c1394c073d0e' class='xr-array-in' type='checkbox' checked><label for='section-f9fc63af-5577-4ef7-bd05-c1394c073d0e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 800 B </td>\n",
       "                        <td> 800 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100,) </td>\n",
       "                        <td> (100,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 14 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-d63cfefb-b7c1-475a-8eae-e92cdafac554' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d63cfefb-b7c1-475a-8eae-e92cdafac554' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-61cf46f3-9943-462b-89ca-fbf665a6385e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61cf46f3-9943-462b-89ca-fbf665a6385e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83a3b822-c41c-400d-88a2-3c6d17fa7039' class='xr-var-data-in' type='checkbox'><label for='data-83a3b822-c41c-400d-88a2-3c6d17fa7039' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>freq_xt_ocean</span></div><div class='xr-var-dims'>(freq_xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.0 -4.9 -4.8 -4.7 ... 4.7 4.8 4.9</div><input id='attrs-625c85d6-2eb3-40dc-acf1-2e65672706dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-625c85d6-2eb3-40dc-acf1-2e65672706dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8164c28a-230d-43b7-b1f4-1dc2efd40b44' class='xr-var-data-in' type='checkbox'><label for='data-8164c28a-230d-43b7-b1f4-1dc2efd40b44' 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>spacing :</span></dt><dd>0.09999999999997744</dd></dl></div><div class='xr-var-data'><pre>array([-5. , -4.9, -4.8, -4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4. , -3.9,\n",
       "       -3.8, -3.7, -3.6, -3.5, -3.4, -3.3, -3.2, -3.1, -3. , -2.9, -2.8, -2.7,\n",
       "       -2.6, -2.5, -2.4, -2.3, -2.2, -2.1, -2. , -1.9, -1.8, -1.7, -1.6, -1.5,\n",
       "       -1.4, -1.3, -1.2, -1.1, -1. , -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3,\n",
       "       -0.2, -0.1,  0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,\n",
       "        1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,  3.3,\n",
       "        3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,  4.4,  4.5,\n",
       "        4.6,  4.7,  4.8,  4.9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-35dd0244-6121-4d86-bf1d-6741cf9253ff' class='xr-section-summary-in' type='checkbox'  ><label for='section-35dd0244-6121-4d86-bf1d-6741cf9253ff' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>freq_xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-298e1e55-de4e-47e0-873d-66df6ad8557f' class='xr-index-data-in' type='checkbox'/><label for='index-298e1e55-de4e-47e0-873d-66df6ad8557f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -4.999999999998863,   -4.899999999998886,   -4.799999999998908,\n",
       "         -4.699999999998931,   -4.599999999998954,   -4.499999999998977,\n",
       "         -4.399999999998999,   -4.299999999999022,  -4.1999999999990445,\n",
       "         -4.099999999999068,  -3.9999999999990905,   -3.899999999999113,\n",
       "         -3.799999999999136,  -3.6999999999991586,   -3.599999999999181,\n",
       "         -3.499999999999204,  -3.3999999999992268,  -3.2999999999992498,\n",
       "        -3.1999999999992723,   -3.099999999999295,   -2.999999999999318,\n",
       "        -2.8999999999993404,  -2.7999999999993634,   -2.699999999999386,\n",
       "        -2.5999999999994086,  -2.4999999999994316,   -2.399999999999454,\n",
       "         -2.299999999999477,  -2.1999999999994997,  -2.0999999999995222,\n",
       "        -1.9999999999995453,   -1.899999999999568,  -1.7999999999995906,\n",
       "        -1.6999999999996134,  -1.5999999999996362,   -1.499999999999659,\n",
       "        -1.3999999999996817,  -1.2999999999997043,   -1.199999999999727,\n",
       "        -1.0999999999997498,  -0.9999999999997726,  -0.8999999999997953,\n",
       "        -0.7999999999998181,  -0.6999999999998409,  -0.5999999999998635,\n",
       "        -0.4999999999998863, -0.39999999999990904, -0.29999999999993177,\n",
       "       -0.19999999999995452, -0.09999999999997726,                  0.0,\n",
       "        0.09999999999997726,  0.19999999999995452,  0.29999999999993177,\n",
       "        0.39999999999990904,   0.4999999999998863,   0.5999999999998635,\n",
       "         0.6999999999998409,   0.7999999999998181,   0.8999999999997953,\n",
       "         0.9999999999997726,   1.0999999999997498,    1.199999999999727,\n",
       "         1.2999999999997043,   1.3999999999996817,    1.499999999999659,\n",
       "         1.5999999999996362,   1.6999999999996134,   1.7999999999995906,\n",
       "          1.899999999999568,   1.9999999999995453,   2.0999999999995222,\n",
       "         2.1999999999994997,    2.299999999999477,    2.399999999999454,\n",
       "         2.4999999999994316,   2.5999999999994086,    2.699999999999386,\n",
       "         2.7999999999993634,   2.8999999999993404,    2.999999999999318,\n",
       "          3.099999999999295,   3.1999999999992723,   3.2999999999992498,\n",
       "         3.3999999999992268,    3.499999999999204,    3.599999999999181,\n",
       "         3.6999999999991586,    3.799999999999136,    3.899999999999113,\n",
       "         3.9999999999990905,    4.099999999999068,   4.1999999999990445,\n",
       "          4.299999999999022,    4.399999999998999,    4.499999999998977,\n",
       "          4.599999999998954,    4.699999999998931,    4.799999999998908,\n",
       "          4.899999999998886],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;freq_xt_ocean&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95d03d45-de68-41fe-b3cb-dfb72c2ce178' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-95d03d45-de68-41fe-b3cb-dfb72c2ce178' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'rechunk-merge-7f82a64a7e06a13d620e9c30ef631adf' (\n",
       "                                                                    freq_xt_ocean: 100)> Size: 800B\n",
       "dask.array<mul, shape=(100,), dtype=float64, chunksize=(100,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    time           object 8B 0181-01-01 12:00:00\n",
       "  * freq_xt_ocean  (freq_xt_ocean) float64 800B -5.0 -4.9 -4.8 ... 4.7 4.8 4.9"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "448a803c-debc-4b0b-90a9-6cb96c3abe9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform curve fitting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_data = np.log(ps.freq_xt_ocean)\n",
    "y_data = np.log(ps)\n",
    "x_data_np = x_data.values[52:-1] #log made first 51 x values nans or infs\n",
    "y_data_np = y_data.values[52:-1] #51st point is an outlier\n",
    "\n",
    "X = np.vstack((np.ones_like(x_data_np), x_data_np)).T\n",
    "beta = np.linalg.inv(X.T @ X) @ X.T @ y_data_np\n",
    "beta_0, beta_1 = beta\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(x_data_np, y_data_np, label='Data')\n",
    "plt.plot(x_data_np, beta_0 + beta_1 * x_data_np, 'r-', label='Best Fit Line')\n",
    "plt.legend()\n",
    "plt.title('Best Fit Line using Linear Algebra')\n",
    "plt.grid(True)  # Add grid for better visualization\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d07ec1-4d18-4546-a606-b8c2ee885c50",
   "metadata": {},
   "source": [
    "#### Final fit equation:\n",
    "\n",
    "$Y = \\beta_0 + \\beta_1  X\\\\$\n",
    "$Y = -8.558836190400873 + -3.599407652706688  X$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12e19557-18c9-4c6f-9dd4-476999387d48",
   "metadata": {},
   "source": [
    "**HW: We want to find the equation that matches**\n",
    "\n",
    "$log(\\mathcal{F}(SST)) = \\beta_0 + \\beta_1 log(\\mathcal{F}(x))$, where $x$ is a spatial coordinate. $\\\\$\n",
    "We let $Y = log(\\mathcal{F}(SST))$ and $X = log(\\mathcal{F}(x))$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3b1f61-b389-4af4-9ef4-c940daee36ed",
   "metadata": {},
   "source": [
    "# Power Spectra for u_relative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb815230-7744-4ac7-b9b3-217321c34451",
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;u_relative&#x27; (yt_ocean: 100, xt_ocean: 100)&gt; Size: 40kB\n",
       "dask.array&lt;getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'u_relative'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 100</li><li><span class='xr-has-index'>xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-359a41d5-8475-4f65-8eac-2420fc9db052' class='xr-array-in' type='checkbox' checked><label for='section-359a41d5-8475-4f65-8eac-2420fc9db052' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 100) </td>\n",
       "                        <td> (100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 38 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"120\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-7a9a35c9-4d2a-47e8-83aa-205a38c9e558' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a9a35c9-4d2a-47e8-83aa-205a38c9e558' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-6217e4de-4f1d-442f-876e-16a9a3cfdab2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6217e4de-4f1d-442f-876e-16a9a3cfdab2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ba728ac-f6e9-4d33-89cf-2af30a1ba15b' class='xr-var-data-in' type='checkbox'><label for='data-3ba728ac-f6e9-4d33-89cf-2af30a1ba15b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.0 -129.9 ... -120.2 -120.1</div><input id='attrs-40855c6c-6b5f-4643-932c-ef473f18fa10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40855c6c-6b5f-4643-932c-ef473f18fa10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-542f726b-4028-4853-9fd5-f80b87a2bc87' class='xr-var-data-in' type='checkbox'><label for='data-542f726b-4028-4853-9fd5-f80b87a2bc87' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-129.95, -129.85, -129.75, -129.65, -129.55, -129.45, -129.35, -129.25,\n",
       "       -129.15, -129.05, -128.95, -128.85, -128.75, -128.65, -128.55, -128.45,\n",
       "       -128.35, -128.25, -128.15, -128.05, -127.95, -127.85, -127.75, -127.65,\n",
       "       -127.55, -127.45, -127.35, -127.25, -127.15, -127.05, -126.95, -126.85,\n",
       "       -126.75, -126.65, -126.55, -126.45, -126.35, -126.25, -126.15, -126.05,\n",
       "       -125.95, -125.85, -125.75, -125.65, -125.55, -125.45, -125.35, -125.25,\n",
       "       -125.15, -125.05, -124.95, -124.85, -124.75, -124.65, -124.55, -124.45,\n",
       "       -124.35, -124.25, -124.15, -124.05, -123.95, -123.85, -123.75, -123.65,\n",
       "       -123.55, -123.45, -123.35, -123.25, -123.15, -123.05, -122.95, -122.85,\n",
       "       -122.75, -122.65, -122.55, -122.45, -122.35, -122.25, -122.15, -122.05,\n",
       "       -121.95, -121.85, -121.75, -121.65, -121.55, -121.45, -121.35, -121.25,\n",
       "       -121.15, -121.05, -120.95, -120.85, -120.75, -120.65, -120.55, -120.45,\n",
       "       -120.35, -120.25, -120.15, -120.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.74 24.83 24.92 ... 33.3 33.38</div><input id='attrs-b874fd7f-df98-403a-b0b6-9ef0fd609d7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b874fd7f-df98-403a-b0b6-9ef0fd609d7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-902dccf3-a60b-42d3-94a3-9428776aa90e' class='xr-var-data-in' type='checkbox'><label for='data-902dccf3-a60b-42d3-94a3-9428776aa90e' 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>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([24.743028, 24.833814, 24.924534, 25.015187, 25.105773, 25.196292,\n",
       "       25.286744, 25.377128, 25.467445, 25.557694, 25.647876, 25.737989,\n",
       "       25.828033, 25.91801 , 26.007918, 26.097756, 26.187526, 26.277227,\n",
       "       26.366859, 26.456421, 26.545914, 26.635336, 26.724689, 26.813972,\n",
       "       26.903184, 26.992326, 27.081398, 27.170399, 27.259328, 27.348187,\n",
       "       27.436975, 27.525691, 27.614335, 27.702908, 27.791409, 27.879838,\n",
       "       27.968195, 28.05648 , 28.144692, 28.232832, 28.320899, 28.408893,\n",
       "       28.496814, 28.584661, 28.672436, 28.760137, 28.847764, 28.935318,\n",
       "       29.022798, 29.110203, 29.197535, 29.284792, 29.371975, 29.459083,\n",
       "       29.546116, 29.633074, 29.719958, 29.806766, 29.893499, 29.980157,\n",
       "       30.066739, 30.153245, 30.239676, 30.326031, 30.412309, 30.498512,\n",
       "       30.584638, 30.670687, 30.75666 , 30.842557, 30.928376, 31.014119,\n",
       "       31.099784, 31.185373, 31.270884, 31.356317, 31.441673, 31.526951,\n",
       "       31.612152, 31.697275, 31.782319, 31.867286, 31.952174, 32.036984,\n",
       "       32.121715, 32.206368, 32.290942, 32.375437, 32.459853, 32.544191,\n",
       "       32.628449, 32.712627, 32.796727, 32.880747, 32.964687, 33.048548,\n",
       "       33.132329, 33.21603 , 33.299651, 33.383192])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54454e9b-1ee5-4506-8f6b-fe8c2c425eb7' class='xr-section-summary-in' type='checkbox'  ><label for='section-54454e9b-1ee5-4506-8f6b-fe8c2c425eb7' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd450fd0-1e69-4ae9-8f6b-d95f03b985e0' class='xr-index-data-in' type='checkbox'/><label for='index-fd450fd0-1e69-4ae9-8f6b-d95f03b985e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-129.95000000000175, -129.85000000000173, -129.75000000000176,\n",
       "       -129.65000000000174, -129.55000000000177, -129.45000000000175,\n",
       "       -129.35000000000178, -129.25000000000176,  -129.1500000000018,\n",
       "       -129.05000000000177,  -128.9500000000018, -128.85000000000178,\n",
       "       -128.75000000000182,  -128.6500000000018, -128.55000000000183,\n",
       "        -128.4500000000018, -128.35000000000184, -128.25000000000182,\n",
       "       -128.15000000000185, -128.05000000000183, -127.95000000000186,\n",
       "       -127.85000000000184, -127.75000000000188, -127.65000000000185,\n",
       "       -127.55000000000189, -127.45000000000186,  -127.3500000000019,\n",
       "       -127.25000000000188, -127.15000000000191, -127.05000000000189,\n",
       "       -126.95000000000192,  -126.8500000000019, -126.75000000000193,\n",
       "       -126.65000000000191, -126.55000000000194, -126.45000000000192,\n",
       "       -126.35000000000196, -126.25000000000193, -126.15000000000197,\n",
       "       -126.05000000000194, -125.95000000000198, -125.85000000000196,\n",
       "       -125.75000000000199, -125.65000000000197,   -125.550000000002,\n",
       "       -125.45000000000198, -125.35000000000201, -125.25000000000199,\n",
       "       -125.15000000000202,   -125.050000000002, -124.95000000000203,\n",
       "       -124.85000000000201, -124.75000000000205, -124.65000000000202,\n",
       "       -124.55000000000206, -124.45000000000203, -124.35000000000207,\n",
       "       -124.25000000000205, -124.15000000000208, -124.05000000000206,\n",
       "       -123.95000000000209, -123.85000000000207,  -123.7500000000021,\n",
       "       -123.65000000000208, -123.55000000000211, -123.45000000000209,\n",
       "       -123.35000000000213,  -123.2500000000021, -123.15000000000214,\n",
       "       -123.05000000000211, -122.95000000000215, -122.85000000000213,\n",
       "       -122.75000000000216, -122.65000000000214, -122.55000000000217,\n",
       "       -122.45000000000215, -122.35000000000218, -122.25000000000216,\n",
       "        -122.1500000000022, -122.05000000000217,  -121.9500000000022,\n",
       "       -121.85000000000218, -121.75000000000222,  -121.6500000000022,\n",
       "       -121.55000000000223,  -121.4500000000022, -121.35000000000224,\n",
       "       -121.25000000000222, -121.15000000000225, -121.05000000000223,\n",
       "       -120.95000000000226, -120.85000000000224, -120.75000000000227,\n",
       "       -120.65000000000225, -120.55000000000229, -120.45000000000226,\n",
       "        -120.3500000000023, -120.25000000000227, -120.15000000000231,\n",
       "       -120.05000000000229],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8bfdea53-0ce5-493b-aaf7-8120c3412483' class='xr-index-data-in' type='checkbox'/><label for='index-8bfdea53-0ce5-493b-aaf7-8120c3412483' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([24.743028005786304, 24.833814202883772, 24.924533960340074,\n",
       "       25.015186931740978, 25.105773107375004,  25.19629214217983,\n",
       "        25.28674402829334, 25.377128422005594, 25.467445317308336,\n",
       "       25.557694371844295, 25.647875581463406, 25.737988605161227,\n",
       "       25.828033440650042, 25.918009748278212, 26.007917527624393,\n",
       "       26.097756440389567,  26.18752648802262, 26.277227333576807,\n",
       "       26.366858980374957,  26.45642109282209, 26.545913676118523,\n",
       "       26.635336396020385,  26.72468925960888, 26.813971933990416,\n",
       "       26.903184428130338, 26.992326410484353,  27.08139789190504,\n",
       "       27.170398542196274, 27.259328374100818, 27.348187058769412,\n",
       "       27.436974610837808, 27.525690702802166, 27.614335351193873,\n",
       "        27.70290822985291,  27.79140935720881,  27.87983840844362,\n",
       "       27.968195403887382,   28.0564800200623, 28.144692279201134,\n",
       "         28.2328318591642, 28.320898784089056, 28.408892733171918,\n",
       "        28.49681373245707,  28.58466146247428, 28.672435951176354,\n",
       "       28.760136880424106, 28.847764280080515, 28.935317833334803,\n",
       "       29.022797571961636, 29.110203180475846, 29.197534692565167,\n",
       "        29.28479179406711,  29.37197452058372,  29.45908255927211,\n",
       "        29.54611594764974,  29.63307437419011, 29.719957878327065,\n",
       "       29.806766149847146, 29.893499230101426,  29.98015681018598,\n",
       "       30.066738933369823, 30.153245292054937,  30.23967593142886,\n",
       "        30.32603054519571, 30.412309180462007,   30.4985115322301,\n",
       "        30.58463764952582,  30.67068722864571,  30.75666032053511,\n",
       "       30.842556622780595,  30.92837618824709, 31.014118715806898,\n",
       "        31.09978426024449,  31.18537252171347, 31.270883556917696,\n",
       "       31.356317067287502, 31.441673111445844,  31.52695139209512,\n",
       "       31.612151969776978,  31.69727454846106,  31.78231919060719,\n",
       "       31.867285601447325, 31.952173845358825,   32.0369836288309,\n",
       "        32.12171501815771,  32.20636772108053,  32.29094180580946,\n",
       "       32.375436981332555,  32.45985331777485, 32.544190525365785,\n",
       "        32.62844867614426,  32.71262748157553,  32.79672701561119,\n",
       "       32.880746990946676, 32.964687483444955,  33.04854820702588,\n",
       "         33.1323292394624,   33.2160302958929,  33.29965145599879,\n",
       "        33.38319243613102],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5774d55b-ddb5-40b4-92c8-a7b975c4b8bc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5774d55b-ddb5-40b4-92c8-a7b975c4b8bc' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'u_relative' (yt_ocean: 100, xt_ocean: 100)> Size: 40kB\n",
       "dask.array<getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "urel = ds_test.u_relative\n",
    "ds_urel = urel.drop(['area_t', 'dxt', 'dyt', 'geolat_t', 'geolon_t', 'ht', 'kmt', 'wet'])\n",
    "ds_urel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0aae4859-5b8a-4cac-81ff-e4ffff8af9d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relative Zonal Velocity (u_rel) Power Spectra')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ubm4FOmvRxcWFAQMGsHDhQiZMmMClS5c4dOgQCxcufO65jx8/xsTEJEOCXFjKlCmT4XXa/7H+/ftne05kZCRWVlY5Xtfc3DzX/y5atGjB3LlzUalUWFpaUqlSpfT/J2FhYSiKkilOyPz32KxZMywtLdm9ezflypXjzp07dOzYkaCgIH766SdiY2PZvXs3FStWTJ/sosv7zSqWnHh5eeHl5QVoJ918//33fPzxx0yaNCnDWFcXF5dM57q6umZ4j7n9HkkbP1a2bNk8xfpfWb3XdevW8corr/Dyyy/zySef4OrqirGxMQsWLMh2hrR4SpIxUeR5eXmlD9pv27YtqampLF68mDVr1tC/f//0loKJEydmO8usWrVqWW6Piopiy5YtTJkyhQkTJqRvT0xMzPcU886dO+Pm5sbSpUvp3LkzS5cupXHjxhmSJWdnZ5ycnNixY0eW13h2tlV2Zs6cyZo1a/j0009z/OHh5OTE/fv3M21PGzSe1xaXwvzs3nzzTSZMmMDJkyf5+++/UavVGZJYZ2dnLCwssv2Sz+m95PZzKFWqFIcPH0aj0RRoQvbRRx/xxx9/sHHjRnbs2IG9vX16q25OnJ2dSUpKIi4uLkMSYG5uDmg/92flNpHPyrODyNPuDfDTTz9lOYMXsk4a/isyMjLX9fjs7OzS/9//l4ODA2q1Old/j6amprRo0YLdu3dTtmxZXF1dqVWrFhUrVgS0kxb27NlDjx490q+hy/v972eWFyqVijFjxjBt2jQuXryYYd9/JyoBhISEAE9/Ycrt90ipUqUACAoKoly5cvmK97/+/PNPPD09WbVqVYb9//13KbImyZgodr799lvWrl3L5MmT6du3L9WqVaNKlSqcP3+er7/+Ok/XUqlUKIqSaVr24sWLM81WyqplKCdpXSbff/89hw4dws/PL1MLSI8ePVi5ciWpqak0btw4T7ED7Ny5ky+++IIOHTo89723b9+emTNncubMmQx1upYvX45KpaJt27Z5undePru8GjJkCF988QULFy5k06ZNtG/fHg8Pj/T9PXr04Ouvv8bJySnPpTty+zl07dqVFStWsGzZsjx1VT7v34mPjw/NmjVj1qxZXLx4kbfffvu5LUoA1atXB+DmzZvpXcZAenJz4cIFOnfunL5906ZNuY75eZo3b469vT3+/v588MEHOl0jJSWFu3fv0q1bt3zHY2VlRePGjVm3bh3fffcdFhYWgLYW3Z9//knZsmWpWrVq+vEdOnRg4sSJ2NjYpHdFWllZ0aRJE3766SeCg4PTt0PBvN/s3L9/P8uWpeDgYKKjo/Hx8cmwPSYmhk2bNmXoqkz7BSWtazu33yOdOnXCyMiIBQsW0LRp02yPMzMzy3PLtkqlwtTUNEMiFhISIrMpc0mSMVHsODg4MHHiRD799FP+/vtvXn/9dRYuXEjXrl3p3LkzQ4cOxd3dncjISC5fvsyZM2dYvXp1lteytbWlVatWzJ49G2dnZypUqMCBAwdYsmQJ9vb2GY719vYGYNGiRdjY2GBubo6np2em7rxnvfnmm8yaNYtBgwZhYWHBgAEDMux/9dVX+euvv+jWrRsfffQRjRo1wsTEhKCgIPbt28dLL71Enz59srz27du3GThwIBYWFowePZpTp05leVzZsmUpW7YsY8aMYfny5XTv3p1p06bh4eHB1q1bmT9/PiNHjszwwys38vLZ5ZWrqyvdunVj6dKlKIqSXhA2zejRo1m7di2tWrVizJgx1K5dG41GQ2BgILt27WLs2LHZ/lDK7ecwcOBAli5dyrvvvsvVq1dp27YtGo2GEydO4OXlxauvvprl9StVqoSFhQV//fUXXl5eWFtb4+bmlt59BtrWsQEDBqBSqXjvvfdy9Zm0adMGgOPHj2dIxlxdXenQoQMzZ87EwcEBDw8P9uzZw7p163J13dywtrbmp59+YsiQIURGRtK/f39Kly7NgwcPOH/+PA8ePHhuMdYLFy4QHx+f56Q/OzNnzqRjx460bduWcePGYWpqyvz587l48SIrVqzIkBS0b9+e1NRU9uzZw++//56+vUOHDkyZMgWVSpWh/llBvN/svP322zx69Ih+/frh7e2NkZERV65cYe7cuajV6kx19JycnBg5ciSBgYFUrVqVbdu28euvvzJy5EjKly8P5P57pEKFCnz22Wd89dVXPH78mIEDB2JnZ4e/vz/h4eFMnToVgFq1arFu3ToWLFiAj48ParU621bKND169GDdunW899579O/fn7t37/LVV19RpkwZnVdMeaEYcPKAEDnKrrSFoijK48ePlfLlyytVqlRJn/F3/vx55ZVXXlFKly6tmJiYKK6urkq7du2UX375Jf28rGaeBQUFKf369VMcHBwUGxsbpUuXLsrFixczzZJTFEX5/vvvFU9PT8XIyCjDTLX/zqZ8VrNmzRQg2xlMycnJynfffafUqVNHMTc3V6ytrZXq1asr77zzjnL9+vXnfj7Pe0yZMiX9nICAAGXQoEGKk5OTYmJiolSrVk2ZPXt2+mxTRXk6Y2r27NmZ7vnf6+X2s8vLbMo0GzduVADF0dExfSbcs2JjY5UvvvhCqVatmmJqaqrY2dkptWrVUsaMGZNhxmFWf4+5+RwURfvvbPLkyUqVKlUUU1NTxcnJSWnXrp1y9OjRHK+/YsUKpXr16oqJiUmmz0xRFCUxMVExMzNTunTpkuvPQ1EUpWXLlkq3bt0ybb9//77Sv39/xdHRUbGzs1Nef/11xc/PT+fZlKtXr85y/4EDB5Tu3bsrjo6OiomJieLu7q507949w/HZzaacNGmS4uzsnOXf5X9lVdoiK4cOHVLatWunWFlZKRYWFkqTJk2UzZs3ZzpOo9Eozs7OCqDcu3cvffuRI0cUQKlfv77O7zdtNuWzpVxysnPnTuXNN99UatSoodjZ2SnGxsZKmTJllL59+6aXkEjTunVrpWbNmsr+/fuVBg0aKGZmZkqZMmWUzz77LNPM8bx8jyxfvlxp2LBh+nH16tXL8O8kMjJS6d+/v2Jvb6+oVColLVXI6btBURTlm2++USpUqKCYmZkpXl5eyq+//pr++YicqRSlmJX7FUKIYm7z5s306tWLrVu35qnbbu3atQwYMICAgADc3d0LMcKClZqaSuXKlRk0aBAzZswwdDjFRps2bQgPD880jkyUPFLaQggh9MTf35/t27enVyrv2rVrns7v27cvDRs2ZObMmYUUYeH4888/iY2N5ZNPPjF0KEIUSZKMCSGEnrz33nv06tULBweHTOOackOlUvHrr7/i5uaWaemmnCiKkqH+VFaPwuwk0Wg0/PXXX/keSyhESSXdlEIIUcLt37//uQPnly5dmqF8iBBCfyQZE0KIEi4mJibHVSiA584MFkIUHknGhBBCCCEMSOqMFXEajYbg4GBsbGzyVeFZCCGEEPqjKAoxMTG5WlZNkrEiLjg4OF/LVgghhBDCcO7evfvc9UAlGSvi0tYUu3v3Lra2tgaORgghhBC5ER0dTbly5XK1xrAkY0VcWtekra2tJGNCCCFEMZObIUZSZ0wIIYQQwoAkGRNCCCGEMCBJxoQQQgghDEiSMSGEEEIIA5JkTAghhBDCgCQZE0IIIYQwIEnGhBBCCCEMSJIxIYQQQggDkmRMCCGEEMKAJBkTQgghhDAgScb0oE+fPjg4ONC/f39DhyKEEEKIIkaSMT0YNWoUy5cvN3QYQgghhCiCJBnTg7Zt2+Zq1XYhhBBCvHgkGXuOgwcP0rNnT9zc3FCpVGzYsCHTMfPnz8fT0xNzc3N8fHw4dOiQ/gMVQgghRLEkydhzxMXFUadOHebNm5fl/lWrVjF69Gg+//xzzp49S8uWLenatSuBgYE63S8xMZHo6OgMDyGEEEKUXJKMPUfXrl2ZPn06ffv2zXL/nDlzGD58OCNGjMDLy4vvv/+ecuXKsWDBAp3uN3PmTOzs7NIf5cqVy0/4QgghhCjiJBnLh6SkJE6fPk2nTp0ybO/UqRNHjx7V6ZoTJ04kKioq/XH37t2CCFUIIYQQRZSxoQMozsLDw0lNTcXFxSXDdhcXF0JCQtJfd+7cmTNnzhAXF0fZsmVZv349DRs2zPKaZmZmmJmZFWrcQgghhCg6JBkrACqVKsNrRVEybNu5c6e+QxJCCCFEMSHdlPng7OyMkZFRhlYwgLCwsEytZUIIIYQQWZFkLB9MTU3x8fHB19c3w3ZfX1+aNWtmoKiEEEIIUZxIN+VzxMbGcuPGjfTXt2/f5ty5czg6OlK+fHk+/vhjBg8eTIMGDWjatCmLFi0iMDCQd99914BRCyGEEKK4kGTsOfz8/Gjbtm36648//hiAIUOGsGzZMgYMGEBERATTpk3j/v37eHt7s23bNjw8PAwVshBCCCGKEZWiKIqhgxDZi46Oxs7OjqioKGxtbQ0djhBCCCFyIS8/v2XMmBBCCCGEAUkyJoQQQghhQJKMCSGEEEIYkAzgLy7WvgWWJgV7TSMzqD8YKrYp2OsKIYQQItckGSsurm0HM9Xzj8uri2ug/hDo9BWY2xX89YUQQgiRI0nGiotOX4O1RcFeM+QCnF4GZ36HG7uh549QpUPB3kMIIYQQOZLSFkVcoZe2uHMYNn4AD29rX9d9DTrPAAuHgr+XEEII8YKQ0hYi9yq0gJFHoMl7gArO/QU/N4Gr2w0dmRBCCPFCkGRMgKkVdJkJb+4Ap8oQGwIrXtVOGoiPNHR0QgghRIkmyZh4qnwTePcwNPsQVGr49x/4uTH4bzJ0ZEIIIUSJJcmYyMjEAjpNh+G+4FwN4sLgn8GweijEhRs6OiGEEKLEkQH8RVzaAMD6X2zAyNyqQK9tYqSmTz13RraphJVZFhNrkxPgwCw48gMoqWDpBN2+g5p9QFUIZTaEEEKIEiIvA/glGSvi0v4yy43+B7WZZaHcw8XWjPFdqtO7rjtqdRZJVvBZ2PAehPlrX3v1hO5zwLp0ocQjhBBCFHeSjJUgaX+Zx68EYm1TsKUtbj2IY/bOqwRGxgNQp5w9U3rWoH75LMpapCTBoe/g0P9Ak6ItfdH1W6j1srSSCSGEEP8hyVgJUth1xhKSU/ntyG1+3nuDuKRUAPrUc2d8l+q42plnPuH+Bdj4HoT8q31dtSv0mAu2ZQo8NiGEEKK4kmSsBCn0oq9PhEUn8O3Oq6w5HQSAhYkRI9tU4u1WFTE3Mcp4cGoyHJ4LB74FTbJ2GaXOM6HuIGklE0IIIZBkrETRVzKW5kLQI6Zu9ud0wEMA3O0t+KybF91quaL6b6IVegk2vq8dUwZQuSP0/B7syhZ6nEIIIURRJslYCaLvZAxAURQ2nQ/mm+1XuB+VAECjCo5M7lkDb/f/LCaemgJHf4T9MyE1CcxstYuO1x8irWRCCCFeWJKMlSCGSMbSPE5K5ZcDN1l48CYJyRpUKhjQoBxjO1WjlI1ZxoMfXNW2kgWd0r6u2BZ6/Qj25fUasxBCCFEUSDJWghgyGUsT/Ogx32y/wqbzwQBYmxkzqn1lhjbzxNT4mbrBmlQ4Ph/2ToeUBDC1ho5TwedNUEt9YSGEEC8OScZKkKKQjKXxuxPJ1M3+/HsvCoAKTpZ80b0G7b1KZxxPFn4DNn0Agce0ryu0hF4/gaOnAaIWQggh9E+SsRKkKCVjABqNwpozQXy74yrhsYkAtKzizKQeNajqYvPsgXByEez+ElIeg4kltJ8Cjd6WVjIhhBAlniRjJUhRS8bSxCQk8/O+m/x2+DZJqRqM1Cpea1yeMR2q4mBl+vTAyFuw8UMIOKx9Xb4pvPQzOFUyTOBCCCGEHkgyVoIU1WQsTUBEHF9vu8zOS6EA2FmYMKZDFV5r4oGJ0ZMWMI0G/JaA7xRIjgNjc2g3CZqMBLVRDlcXQgghiidJxkqQop6MpTl6I5xpW/y5EhIDQJXS1kzqUYNWVUs9PehhAGweBbf2a1+XbahtJStVTf8BCyGEEIVIkrESpLgkYwApqRpWnLrLnF1XeRifDEAHr9J83r0Gns5W2oMUBc78Dju/gKQYMDKDthOh6YdgZGzA6IUQQoiCI8lYCVKckrE0UfHJ/LDnOsuP3SFFo2BipGJoswp82L4KtuYmTw4Kgs0fwY3d2tdu9bWtZC41DBe4EEIIUUAkGStBimMyluZGWCzTt/qz/+oDAJysTPmkczVeblAOI7VK20p27m/YMRESo0BtAq3HQ4vRYGRi2OCFEEKIfJBkrAQpzslYmn1Xwvhqqz+3HsQBUKOMLVN61qBxRSftAdHBsGUMXNuhfe1aG3rPB9daBopYCCGEyB9JxkqQkpCMASSlaFh+7A4/7LlOTEIKAN1rlWFC1+qUc7TUtpJd+Ae2fwoJj0BtDC3HQcuxYGya88WFEEKIIkaSsRKkpCRjaSJiE5nje40VJwPRKGBqrOadVhV5t3UlrMyMISYUtn4MV7ZoT3DxhpfmgVs9wwYuhBBC5IEkYyVISUvG0ly+H820zf4cuxUBgIutGeO7VKd3XXfUKuDSOtj2CcRHgMoImn8EbSaAsVnOFxZCCCGKAEnGSpCSmowBKIrCzkshzNh2mbuRjwGoW86eKT1rUK+8A8SFw7ZxcGm99oRS1eGl+VDWx4BRCyGEEM8nyVgJUpKTsTQJyan8duQ28/beID4pFYA+9dwZ36U6rnbm4L8Rto6FuAegUkPTD6DtZ2BiYeDIhRBCiKxJMlaCvAjJWJqw6AS+3XmVNaeDALAwMeK9NpV4q1VFzJOjtIP7/12tPdipsraVrHxjA0YshBBCZE2SsRLkRUrG0py/+4ipmy9xJvARAO72FnzWzYtutVxRXd2uLYMRGwKotOtbtpsEppYGjVkIIYR4liRjJciLmIyBdjzZpvPBfLP9CvejEgBo5OnIlJ41qOmggR2fwfm/tQc7eGqr91dobsCIhRBCiKckGStBXtRkLE18UgoLD9xi4cGbJCRrUKng1YblGNupGs7BB7RLKsUEaw9u9Da0nwJm1oYNWgghxAtPkrES5EVPxtLce/SYb7ZfYfN5beJlY2bMqPZVGFLfAdO9k+HMcu2B9uWh1zyo2NqA0QohhHjRSTJWgkgyltGpO5FM2+zPv/eiAPB0tuLzbl60N72IavNHEHVXe6DPMOg4DczlMxNCCKF/koyVIJKMZabRKKw5E8S3O64SHpsIQMsqzkzuVJ4q52eD3xLtgbZlodcPULmDAaMVQgjxIirSydjly5c5fvw4TZs2pXr16ly5coUffviBxMREXn/9ddq1a6fPcIo8ScayF5OQzM/7bvLb4dskpWowUqt4vXF5PqkahvXO0fAoQHtgvdeh0wywsDdkuEIIIV4gRTYZ27FjBy+99BLW1tbEx8ezfv163njjDerUqYOiKBw4cICdO3dKQvYMScaeLyAijhlbL7PLPxQAe0sTPmlTloExS1GfWqQ9yMYNen4PVTsbLlAhhBAvjCKbjDVr1ox27doxffp0Vq5cyXvvvcfIkSOZMWMGAJ9//jmnTp1i165d+gqpyJNkLPeO3Ahn2mZ/robGAFDVxZrZjeKoc/pziLylPaj2q9BlJlg6GjBSIYQQJV2RTcbs7Ow4ffo0lStXRqPRYGZmxokTJ6hfvz4AFy9epEOHDoSEhOgrpCJPkrG8SUnVsOLUXebsusrD+GQAulWzY6bDJuzOLQIUsHaBHnOhenfDBiuEEKLEysvPb7WeYsp8Y7Uac3Nz7O3t07fZ2NgQFRVlqJBECWBspGZwEw/2j2vLsOYVMFar2HY1igan2rDcaxGpjpUhNhRWDoI1wyEuwtAhCyGEeMHpNRmrUKECN27cSH997Ngxypcvn/767t27lClTRp8hiRLKztKEKT1rsmN0S1pXLUVyqsLks1a0iprK5YrDUFRquLgG5jeGSxsMHa4QQogXmF6TsZEjR5Kampr+2tvbG2Nj4/TX27dvl8H7okBVLm3D7282YunQhlR0tuJenIqu/h352GY28XZVIO4BrB4C/7wBsQ8MHa4QQogXkNQZK+JkzFjBSUrRsPzYHX7Yc52YhBRMSeYHN1+6PFyBSkkFC0foNhu8+4FKZehwhRBCFGNFdgC/yDtJxgpeRGwi//O9xsqTgWgUqGccwELbJZSOf9KFXq079JgDNq6GDVQIIUSxVWQH8Pfs2ZM//viDx48f6/O2QmTgZG3G131qseXDljSp6MjZFA+aR07mV6MBaFTGcHUr/NwYzq8E+V1FCCFEIdNry5harcbIyAgrKysGDhzIiBEj8PHx0dftiyVpGStciqKw81IIM7Zd5m7kY7xUAcyzWkyllJvaA6p01haLtXUzaJxCCCGKlyLbMgZw/vx5vvzyS44cOUKjRo2oU6cO8+bN4+HDh/oORQhUKhVdvMvgO6Y1n3SuRoBJRTrHTmF28iukqEzg+k74uQmc+UNayYQQQhQKvbeMhYSEULp0aQBOnjzJkiVLWLVqFUlJSfTu3ZsRI0bIjMpnSMuYfoVGJ/DtjqusPRNEFVUQ/zNdSG3Vk1aySu2h5w9gX86wQQohhCjyiuwA/v8mY2keP37MqlWrWLJkCUePHs1Q/uJFJ8mYYZy/+4ipmy9xPjCCEUbb+NhkDWYko5jaoOo0DXyGyYxLIYQQ2Sp2ydizrl+/TpUqVfQVUpEnyZjhKIrCpvPBfLP9CpbRN/nWZBE+6uvanZ6toNdP4FDBoDEKIYQomorsmLHWrVtjamqa4zGSiImiQqVS8VJdd/aMbU33dm14XTOVr5Jf57FiCrcPosxvqh1LJoQQQuSDXpOxffv2ZViL8kXRp08fHBwc6N+/v6FDETqwNDXm445V2T2uHWHeI+iaNJMTmuqokuNh0wdwfIGhQxRCCFGMGWyh8BfJqFGjWL58uaHDEPnkbm/BTwPrMfudvkx3/pYFKT21O3ZMgGM/GzY4IYQQxVaRSsYuX75MxYoVDR1GgWvbti02NjaGDkMUkIYVHNn4QStOVvyQn1J6azfu/AyOzjNoXEIIIYqnIpWMJSUlERAQoNd7Hjx4kJ49e+Lm5oZKpWLDhg2Zjpk/fz6enp6Ym5vj4+PDoUOH9BqjKHrUahVf9KzJD5pX+DEtIdv1ORz9yaBxCSGEKH6M9Xmzjz/+OMf9Dx480FMkT8XFxVGnTh2GDRtGv379Mu1ftWoVo0ePZv78+TRv3pyFCxfStWtX/P39KV++PAA+Pj4kJiZmOnfXrl24ueWtcntiYmKGa0VHR+fxHQl9qVTKmiHNPJlz+GUcLc14PWkV7PpCWxy2+ShDhyeEEKKY0GtpCyMjI+rWrZvtFM/Y2FjOnDljsDpjKpWK9evX07t37/RtjRs3pn79+ixY8HSQtpeXF71792bmzJm5vvb+/fuZN28ea9asyfG4L7/8kqlTp2baLqUtiqaox8m0/W4/kXFJbKhxkLq3ftHu6DAVWow2aGxCCCEMJy+lLfTaMlalShXGjBnD66+/nuX+c+fOFam1KpOSkjh9+jQTJkzIsL1Tp04cPXq0UO45ceLEDC2I0dHRlCsnFd+LKjsLEz7uWJUvNlxk6J32HGtui8WRb2H3FECBFmMMHaIQQogiTq9jxnx8fDh9+nS2+1UqFXpsqHuu8PBwUlNTcXFxybDdxcWFkJCQXF+nc+fOvPzyy2zbto2yZcty6tSpbI81MzPD1tY2w0MUba82LEc1FxsexScz63FvaPOZdsfuL+HQ/wwZmhBCiGJAry1j//vf/7IcW5WmTp06aDQaPUaUO6r/LHujKEqmbTnZuXNnQYckihBjIzWTe9bgtcUn+ON4AK999B5VVCrYNwP2TNOOIWs1ztBhCiGEKKL02jLm6uqKh4eHPm+ZL87OzhgZGWVqBQsLC8vUWiZebM0rO9OxhgupGoWvtl5GafUJtP1Cu3PvV3BwtmEDFEIIUWQVqdIWRY2pqSk+Pj74+vpm2O7r60uzZs0MFJUoqj7v5oWJkYqD1x6w/+oDaP0JtJuk3bl3Ohz41rABCiGEKJKKVDJ2/vx5jIyM9HrP2NhYzp07x7lz5wC4ffs2586dIzAwENCW41i8eDG//fYbly9fZsyYMQQGBvLuu+/qNU5R9FVwtuLN5p4AfLXVn+RUjbZ7sv1k7QH7ZsD+WQaMUAghRFGk1zFjuaHvAfx+fn60bds2/XXaTMYhQ4awbNkyBgwYQEREBNOmTeP+/ft4e3uzbdu2YtXdKvTng3aVWXsmiFsP4lh+LIDhLTyh5VhQqbUD+vd/DSjQZsLzLiWEEOIFodc6Y3379s1xf1RUFPv37zdYnbGiKC91SkTRsPJkIBPW/YutuTH7P2mLo5Wpdsfh75+UvABaT4C2Ew0WoxBCiMKVl5/feu2m3Lx5MwkJCdjZ2WX5sLa21mc4QhSKlxuUw6uMLdEJKczxvfp0R4vR0HGa9vmBb2Df19qZlkIIIV5oeu2m9PLyol+/fgwfPjzL/efOnWPLli36DEmIAmekVjGlZw1eXXScv08E8noTD6q7PvmtqPlH2i7LXV/AgVmgaKDt55CHUilCCCFKFr0XfT1z5ky2+83MzNLXexSiOGtS0Ymu3q5oFPhqi3/GsZDNPoROM7TPD87WzrSUFjIhhHhh6XXMWGJiIqmpqVhaWurrlsWejBkrvu5GxtN+zgGSUjQsGuxDp5quGQ849jPsfFKtv+VYbRkMaSETQogSociOGTMzM5NETLwwyjlaMqKFttTFjG2XSUz5z8SUpu9D5yeLzR/6H+yZKi1kQgjxAjJYnbGbN2/yxRdfMHDgQMLCwgDYsWMHly5dMlRIQhS499pWppSNGQER8fx+9E7mA5q+B12e1B47PFdb/kISMiGEeKEYJBk7cOAAtWrV4sSJE6xbt47Y2FgALly4wJQpUwwRkhCFwtrMmE87VwPgpz03CI/NYm3WJu9C1yfV+Y98ry1/IQmZEEK8MAySjE2YMIHp06fj6+uLqalp+va2bdty7NgxQ4QkRKHpV78stdztiElM4X+7rmZ9UON3oOuT9SuP/AC+kyQhE0KIF4RBkrF///2XPn36ZNpeqlQpIiIiDBCREIVHrVYxuWcNAFaeusul4KisD2z8NnT7Tvv86E/a8heSkAkhRIlnkGTM3t6e+/fvZ9p+9uxZ3N3dDRCREIWrYQVHetQug6LAtM3+2S/71egt6P4/7fNj82Dn55KQCSFECWeQZGzQoEGMHz+ekJAQVCoVGo2GI0eOMG7cON544w1DhCREoZvYzQszYzUnbkey42JI9gc2HAHd52ifH39S/kISMiGEKLEMkozNmDGD8uXL4+7uTmxsLDVq1KBVq1Y0a9aML774whAhCVHo3O0teKdVRQC+3n6ZhOQc1mBtOBx6fK99fnw+7JgoCZkQQpRQei36+l+3bt3izJkzaDQa6tWrR5UqVQwVSpElRV9LlvikFNp9d4CQ6AQ+7VKN99pUzvkEv6WwZbT2eeN3ocs3UhhWCCGKgbz8/DZoMiaeT5Kxkmf92SDGrDqPlakR+8a1obStec4nnP4dNo/SPm/0DnSdJQmZEEIUcUW2Aj/A48ePOXz4MP7+/pn2JSQksHz5cn2HJIRevVTHnbrl7IlLSmX2zmxKXTzLZwj0+glQwcmFsO0T6bIUQogSRK/J2LVr1/Dy8qJVq1bUqlWLNm3aEBwcnL4/KiqKYcOG6TMkIfTu2VIXa84E8W9QNqUunlX/jacJ2alfYds4SciEEKKE0GsyNn78eGrVqkVYWBhXr17F1taWFi1aEBgYqM8whDC4+uUd6F3XDUWBqZsvZV/qIsNJg+Gln9EmZIth61jQaAo9ViGEEIVLr8nY0aNH+frrr3F2dqZy5cps2rSJrl270rJlS27duqXPUIQwuPFdq2NhYoRfwEO2XMhcdy9L9V6D3vMBFfgtgW2SkAkhRHGn12Ts8ePHGBsbZ9j2888/06tXL1q3bs21a9f0GY4QBlXGzoJ3W1cC4JvtV3IudfGsuoOg9wK0CdlvsHWMJGRCCFGM6TUZq169On5+fpm2//TTT7z00kv06tVLn+EIYXBvt6qIm5059x495teDeWgdrjsQ+vwCqOD0Mm35C0nIhBCiWNJrMtanTx9WrFiR5b558+YxcODA3I2dEaKEsDA1YkI3LwDm779JSFRC7k+u8yr0WQgqNZz5HbZ8JAmZEEIUQ1JnrIiTOmMln6Io9P/lGKcDHtK3njtzBtTN2wUurIb1b4OigXqDoeePoDbI4hpCCCGeKNJ1xoQQGalUKib30Ja6WHf2HmcDH+btArVfhj6LtC1kZ/+ATR9KC5kQQhQjxs8/pHCcOnWK1atXExgYSFJSUoZ969atM1BUQhhGnXL29KtflrVngpi2xZ91I5uhykuV/dova6vyr3sLzv0JKNq6ZGqjQotZCCFEwTBIy9jKlStp3rw5/v7+rF+/nuTkZPz9/dm7dy92dnaGCEkIg/u0SzUsTY04G/iIjeeCn3/Cf9XqD/0Wg8oIzv0FGz8ATS5naAohhDAYgyRjX3/9NXPnzmXLli2Ympryww8/cPnyZV555RXKly9viJCEMDgXW3Peb6tdOPyb7VeIT0rJ+0W8+z1NyM7/DRvfl4RMCCGKOIMkYzdv3qR79+4AmJmZERcXh0qlYsyYMSxatMgQIQlRJAxv4UlZBwtCohNYeEDHQsjefaH/kicJ2QrY8J4kZEIIUYQZJBlzdHQkJiYGAHd3dy5evAjAo0ePiI+PN0RIQhQJ5iZGfPak1MXCgzcJfvRYtwvV7AP9f9MmZBdWwvp3JSETQogiyiDJWMuWLfH19QXglVde4aOPPuKtt95i4MCBtG/f3hAhCVFkdPV2pZGnIwnJGr7ZfkX3C9XsDS8vBbUx/PsPrH8HUnXo+hRCCFGoDFJnLDIykoSEBNzc3NBoNHz33XccPnyYypUrM2nSJBwcHPQdUpEldcZeTBfvRdFz3mEUBdaObIqPh6PuF7u8GVYPBU0KePfXFoo1MthEaiGEeCHk5ee3FH0t4iQZe3GNX3OBVX53qV3Wjg3vNUetzkOpi//KkJD109Ylk4RMCCEKTZEv+rpt2zZ27tyZafuuXbvYvn27ASISougZ17ka1mbGXAiKYt3Ze/m7mFdPeGU5qE3g4lptPTLpshRCiCLBIMnYhAkTSE3NPJhYo9EwYcIEA0QkRNFTysaMD9ppS118u+MKcYn5TJ6qd3+akF1aBysGwMOAAohUCCFEfhgkGbt+/To1atTItL169ercuHHDABEJUTQNa14BDydLwmISWbD/Zv4vWL0bDPgDjEzhxm74uTEcnA0pifm/thBCCJ0YJBmzs7Pj1q3MNZRu3LiBlZWVASISomgyM35a6mLRoVvcjSyA0i/VusI7B6FCS0h5DHunw/wmcH13/q8thBAizwySjPXq1YvRo0dz8+bT3/Rv3LjB2LFj6dWrlyFCEqLI6lTDhWaVnEhKyWepi2eV9oIhm6HfErB2hchb8Fc/WPmadF0KIYSe5TsZs7W1zbKVKyezZ8/GysqK6tWr4+npiaenJ15eXjg5OfHdd9/lNyQhShSVSsWkHjVQq2Drv/c5cSuioC6sXc/yg1PQ9ANtgdgrW6TrUggh9CzfpS1sbGw4f/48FStWzNN5iqLg6+vL+fPnsbCwoHbt2rRq1So/oZRIUtpCpPls/b/8fSKQmm62bPqgBUb5KXWRlbDLsO0TuHNI+9qxInSdDVU6FOx9hBDiBaDXOmO6JmMidyQZE2kiYhNp891+YhJSmNWvFgMali/4myiKtvTFzs8hNkS7rXoP6Pw1OHgU/P2EEKKE0mudsddffz1PScLmzZuZMmUKx44dA2Dv3r1069aNLl26yCLhQuTAydqMj9pXAWD2zqvEJCQX/E2k61IIIfQu38nY3LlzcXZ2ztWxv/zyC3379mXr1q106dKFv/76i969e+Pu7k6FChUYPXo0P/zwQ35DEqLEeqNpBSo6WxEem8TP+wqg1EV2zG2h8wwYeURmXQohRCHTKRnTaDR89dVXuLu7Y21tnT6Af9KkSSxZsiTb83788Ufmz5+Pn58fGzZsYMSIEXzzzTf8+uuv/PLLL8yfP5+FCxfq9k6EeAGYGqv5vLu21MVvh28TEBFXuDeUWZdCCFHodErGpk+fzrJly/j2228xNTVN316rVi0WL16c7Xl37tyhc+fOALRt25bU1NQMg/bbtGlDQIB8wQuRk3bVS9OyijNJqRq+3na58G8oXZdCCFGodErGli9fzqJFi3jttdcwMjJK3167dm2uXMm+DpKTk1N6shUcHExKSgqBgYHp+wMCAnB0dNQlJCFeGGmlLozUKnZeCuXozXD93Fi6LoUQolDolIzdu3ePypUrZ9qu0WhITs5+UPFLL73E8OHDmTFjBn369OGNN95g7Nix7Nixg507d/Lhhx/SqVMnXUIS4oVS1cWG1xprZ1NO2+xPqiZfk6LzRrouhRCiQOmUjNWsWZNDhw5l2r569Wrq1auX7XmzZs2idevWrFy5kvr16/Prr78yfPhwXnrpJbp27YqTkxMzZ87UJSQhXjhjOlTFzsKEKyExrDwV+PwTCpJ0XQohRIHRqc7Y5s2bGTx4MBMnTmTatGlMnTqVq1evsnz5crZs2ULHjh3zdL2EhASSk5OxsbHJayglntQZEzlZeuQ2Uzf742hlyr5xbbCzMDFMIKH+2oKxAYe1r6VgrBDiBVfodcZ69uzJqlWr2LZtGyqVismTJ3P58mU2b96c50QMwNzcXBIxIXTwehMPKpe2JjIuiXl7rxsuEJcaMHSLdF0KIYQO8l2BX1dBQUFs2rSJwMBAkpKSMuybM2eOIUIqkqRlTDzP/qthDF16ChMjFbvGtMbT2cqwASVEw4FZcHwBKKlgbAGtxkKzUWBsZtjYhBBCT/S6HJIu9uzZQ69evfD09OTq1at4e3tz584dFEWhfv367N27V98hFVmSjIncGLb0JPuuPqCDV2kWD2lo6HC0pOtSCPECK/RuSgcHBxwdHTM9nJyccHd3p3Xr1ixdujTb8ydOnMjYsWO5ePEi5ubmrF27lrt379K6dWtefvllXUIS4oX2efcaGKtV7L4cxqHrDwwdjpZ0XQohRK7olIxNnjwZtVpN9+7dmTp1Kl9++SXdu3dHrVbz/vvvU7VqVUaOHMmvv/6a5fmXL19myJAhABgbG/P48WOsra2ZNm0as2bN0v3dCPGCqlzamsFNtQt5f7XFn5RUjYEjekJmXQohxHMZ63LS4cOHmT59Ou+++26G7QsXLmTXrl2sXbuW2rVr8+OPP/LWW29lOt/KyorERO2XsJubGzdv3qRmzZoAhIfrqYClECXM6PZV2XD2HtdCY/n7ZCBvNK1g6JCeSisYW/e1p12Xe6fDiUXaumWOnuDgCQ4Vnj43l255IcSLQacxY9bW1pw7dy5T4dcbN25Qt25dYmNjuXnzJrVr1yYuLvPaeb1796Z79+689dZbfPrpp6xfv56hQ4eybt06HBwc2L1bqnmnkTFjIi/+OHaHSRsvYW9pwv5xbbC3NH3+SfqmKHBxLez8HGJDsj/O0kmblDk+SdLSn3uCjau21U0IIYqovPz81qllzNHRkc2bNzNmzJgM2zdv3py+nFFcXFy25SrmzJlDbGwsAF9++SWxsbGsWrWKypUrM3fuXF1CEkIAAxuV58/jgVwNjeGHPdeZ0rOmoUPKLK3rsnp3CD4HD29D5O1n/rwD8eEQH6F93PPLfA1jiycJWoWnCVran/blwbgIJqFCCJENnZKxSZMmMXLkSPbt20ejRo1QqVScPHmSbdu28csvvwDg6+tL69at08/58ccfefvttzE3N8fY2JhatWoBYGlpyfz58wvgrQghjI3UTOpRg9eXnOCPYwG81lhbh6xIMrEAj6bax38lRGuTsmcTtYd3tM+j7mrXxXxwWfv4L5UabN0zJmqVO0CZ2oX8hoQQQjc6l7Y4cuQI8+bN4+rVqyiKQvXq1fnwww9p1qxZlscbGxsTHBxM6dKlMTIy4v79+5QuXTpfwRcHMTExtGvXjuTkZFJTUxk1alSW4+iyI92UQhcjfvdj9+VQ2lQrxbJhjQwdTsFKTYZHgRlb0tIStYe3ITk+8zkqI+3MTo+sv5+EEKKgFWqdseTkZN5++20mTZpExYoVc31e+fLlmThxIt26dcPT0xM/Pz+cnZ2zPbakSE1NJTExEUtLS+Lj4/H29ubUqVM4OTnl6nxJxoQubofH0WnuAZJTFZYOa0jbaiX/Fx9AOx4tNixjonZrP9w9ri2v8c5BsHExdJRCiBdAoRd9tbe358yZM3lKxhYtWsSHH35ISkpKtscoioJKpSI1NTWvIRULkZGR1KtXj9OnT2ebiP6XJGNCVzO2+vProdtUKmXFjtGtMDHSqZJN8ZcYC4vbw4MrUKElDN4ARjqN0BBCiFwr9KKvffr0YcOGDXk65+233yY8PJzz58+jKAq+vr6cOXMmw+Ps2bOcOXNGl5B0dvDgQXr27ImbmxsqlSrL9zV//nw8PT0xNzfHx8eHQ4cO5ekejx49ok6dOpQtW5ZPP/0014mYEPnxYfsqOFmZcvNBHH8ce4GLrJpZwyvLwcQK7hyCfTMMHZEQQmSg06+HlStX5quvvuLo0aP4+PhgZZVxLbxRo0ZlOidtAL+3tzdLly6ladOmWFhY6BZ1AYqLi6NOnToMGzaMfv36Zdq/atUqRo8ezfz582nevDkLFy6ka9eu+Pv7p3en+vj4pNdNe9auXbtwc3PD3t6e8+fPExoaSt++fenfvz8uLtJVIgqXrbkJYztV47P1//L97mv0rueOo9ULOsuwVDXo9SOsHQ6H50C5RlCtq6GjEkIIQMduSk9Pz+wvqFJx69atTNuLwwB+lUrF+vXr6d27d/q2xo0bU79+fRYsWJC+zcvLi969ezNz5sw832PkyJG0a9cu22WfEhMTMyR20dHRlCtXTrophU5SNQo9fjrM5fvRvNHUg2kveRs6JMPa9gmcXATmdtrxYw4VDB2REKKEKvRuytu3b2f7yCoRA22l/bVr1xIQEICiKAQFBREYGJjlo6hISkri9OnTdOrUKcP2Tp06cfTo0VxdIzQ0lOjoaED7F3Pw4EGqVauW7fEzZ87Ezs4u/VGuXDnd34B44RmpVUzuUQOAv04Eci00xsARGVinGeDeABKi4J83IDnB0BEJIYRuyViapKQkrl69muOg/DRffPEFo0ePpmLFiqhUKho2bIinp2eGR4UKFXJsddO38PBwUlNTM3Upuri4EBKSQ+XwZwQFBdGqVSvq1KlDixYt+OCDD6hdO/t6RxMnTiQqKir9cffu3Xy9ByGaVnKiS01XUjUKX23xR8dqNiWDsSm8vAwsHOH+edgxwdARCSGEbmPG4uPj+fDDD/n9998BuHbtGhUrVmTUqFG4ubkxYULmL7i3336bgQMHEhAQQO3atdm9e3euyzsYmuo/y66kzfrMDR8fH86dO5fre5mZmWFmZpaX8IR4rs+6ebH3ShiHroez53IYHWq8wGMW7ctBv1/hz/5weimUbwJ1XjV0VEKIF5hOLWMTJ07k/Pnz7N+/H3Nz8/TtHTp0YNWqVdmeZ2Njkz6Av3nz5tSpUyfLR1Hh7OyMkZFRplawsLAwGYAvipXyTpa82ULb6jxj22WSUjQGjkh/FEXJ3BpYuQO0Hq99vnk0hF7Se1xCCJFGp2Rsw4YNzJs3jxYtWmRoIapRowY3b9587vlDhgwhNDSUEydO4OfnR3h4uC5hFDpTU1N8fHzw9fXNsN3X1zfblQaEKKo+aFcZZ2szbofH8fvRO4YORy8CI+J56ecj9J5/lOTU/ySgrT+FSu20SyutGqxdgkkIIQxAp2TswYMHWc6EjIuLe2733fz58/Hw8MDT05NmzZrRuHFjXFxcaNGiBadPn9YlnHyJjY3l3Llz6V2Jt2/f5ty5c+kTCT7++GMWL17Mb7/9xuXLlxkzZgyBgYG8++67eo9ViPywNjPm087aySM/7rlOeGzmciwlycnbkfSef4QLQVGcv/uI47ciMh6gNoK+i7XrWEbehE0faCv4CyGEnumUjDVs2JCtW7emv05LwH799VeaNs1i0d8nvvvuO6ZPn87HH3/M/PnzqVatGl9++SVbt26lYsWKtGrVCj8/P11C0pmfnx/16tWjXr16gDb5qlevHpMnTwZgwIABfP/990ybNo26dety8OBBtm3bhoeHh17jFKIg9Pcpi7e7LTGJKXyx/iIxCcmGDqlQrDkdxGuLjxMZl4SxWvv9tONiFpNurJzg5d9BbQz+G+HEL3qOVAghdKwzdvToUbp06cJrr73GsmXLeOedd7h06RLHjh3jwIED+Pj4ZHmep6cn8+fPp2tXbbHFa9eu0axZM0JCQjA2Nuajjz7i8uXL7Nq1K3/vqgSR5ZBEQTt1J5JXFh5DUcDZ2ozxXarRr35Z1OrcTUopyjQahdm7rrJgv3a4RLdarvSq48a7f56hlI0ZJya2z/p9Hv8FdozXJmVDt0H5xnqOXAhR0hR6nbFmzZpx5MgR4uPjqVSpErt27cLFxYVjx45lm4iBduC7l5dX+usqVaoQFRXFgwcPAHjzzTc5duyYLiEJIXKpYQVHlg1rhKezFeGxiXyy5gJ9Fhzl3N1Hhg4tX+KTUhj51+n0ROzDdpWZN7A+7aq7YGNmzIOYRM7efZj1yY3fgZp9QJMCq4dCXNEcxyqEKJl0rjNWq1Ytfv/9dy5evIi/vz9//vkntWrVyvGcqlWrZhgMv2/fPkxNTXF1dQXA3Nw81yUjhBC6a121FDtHt2Ji1+pYmRpx/u4jev98hHGrzxMWU/wKod6Pekz/BcfYeSkUUyM1cwfUYWynaqjVKkyN1bT30o5xzbKrEkClgl4/gVMViAnWLpukSdXjOxBCvMjyVfQ1LCyMixcvcuHChQyP7EycOJH333+fAQMGMGTIEF566SVGjRqVnoDt378fb+8XfLkWIfTE1FjNO60rsW9cG/rVLwtox1q1++4Aiw7eLDblL87ffUSveUfwvx+Ns7UpK95uQp96ZTMc08Vb+wvfjksh2Re9NbOBAX+AiSXc2g/7vynkyIUQQkunMWOnT59myJAhXL58OdMXm0qlIjU1+98ot2/fzp9//kliYiKdO3fmrbfeSt8XEaGd7VRcisHqg4wZE/pyNvAhX266xPmgKAAqOlsxqWcN2lYrWmvIPmvLhWDG/nOexBQN1VxsWDK0AWUdLDMdF5+UQv2vfElI1rB1VAtqutllf9EL/8C6J99Lr62BKh0LKXohREmWl5/fOiVjtWvXpnLlyowfPx4XF5dMXYsy07DgSDIm9EmjUVh7JohZO66ml75oV700k3rUwNPZysDRPaUoCj/tvcEc32uANsYfB9bD2iz7RUXe+cOPnZdCGdWuMh93yn59WAC2jAG/38DCQbuguH35ggxfCPECKPRkzMbGhrNnz1K5cmWdgxS5I8mYMISYhGR+2nuDpUduk5yqYGKk4s0WnnzYrkqOCY8+JCSnMn7tBTaeCwZgRAtPJnbzwug5s0HXnw1izKrzVHWxZteY1jnfJCURfusMwWfB3QeGbQdjWaZMCJF7hT6bsn379pw/f16n4ObPn0+HDh145ZVX2Lt3b4Z94eHhVKxYUafrCiEKjo25CZ9182LH6Fa0rlqK5FSFhQdu0fa7/aw9HYRGY5jiqGExCby66DgbzwVjrFYxs28tvuhR47mJGEC76i4Yq1VcC43l5oPYnA82NtPWHzO3h3unYefnBfMGhBAiCzq1jIWHhzNkyBAaNWqEt7c3JiYmGfb36tUry/N+/PFHJk6cyLBhw4iKimL16tVMmTKFiRMnAhAaGoqbm1uOY85eNNIyJgxNURT2Xgnjqy3+3ImIB6BuOXum9qpJnXL2eovj8v1ohi87RXBUAnYWJix4vT7NKjnn6RqDl5zg0PVwPu1Sjffa5KJl/9pO+PsV7fN+S6BWfx0iF0K8iAq9m3LTpk0MHjyYmJiYzBfMYQB/zZo1+fzzzxk0aBAAx44do3fv3rzzzjtMmzZNkrEsSDImiorElFSWHrnDT3uuE5ek/T/6SoOyfNK5OqVsCrcLb7d/KKNWniU+KZWKzlYsGdpQpzFsf50I4PP1F6lTzp6N7zfP3Ul7voJD34GJFby1F0pXz/N9hRAvnkLvphw1ahSDBw/m/v37aDSaDI+cEqnbt29nWGC7adOm7N27l0WLFqW3jgkhiiYzYyPebV2JvePa0Le+OwD/+AXR7rv9/HrwVqGUwlAUhUUHb/LWH37EJ6XSvLIT699rrvNkgo41XFCptOUwgh89zt1JbT8Dz1aQHAf/vAGJz+niFEKIPNIpGYuIiGDMmDG4uLjk6TxnZ2fu3r2bYVvNmjXZu3cvS5cu5ZNPPtElHCGEHrnYmjPnlbqse68ZtcvaEZOYwoxtl+nyw0H2Xw0rsPskpWiYsPZfvt52BUWB1xqXZ9mwRthZmjz/5GyUtjGngYcDALsuZVMA9r/URtDvN7ApA+FXYfMoWVBcCFGgdErG+vbty759+/J8XosWLVi7dm2m7TVq1GDPnj3s2LFDl3CEEAZQv7wDG95rzrf9auNsbcqtB3EMXXqKEb+f4k54XL6u/TAuicFLTrDK7y5qFUzpWYPpvb0xMcpXnWoAOtd8WgA216xLQf+loDKCi2vh1OJ8xyGEEGl0mqNetWpVJk6cyOHDh6lVq1amAfyjRo3K8rwJEyZw+vTpLPfVrFmTffv2sWbNmufePygoCHt7e6ytrTNsT05O5tixY7Rq1SqX70QIkR9qtYpXGpajSy1Xftx9nWVH77D7chgHr4UzvKUnH7StjFUeS2HcCItl+O+nCIiIx9rMmJ8G1SvQwrOda7oyfetlTt6OJCI2ESfrXI5382gKHafBrs9hx0Rwqw9ls1+LVwghckunAfyenp7ZX1Cl4tatW/kKKjv379/npZde4vTp06hUKl577TV+/vnn9KSsJE4AkAH8oji5ERbLtC3+HLz2AAAXWzMmdK1O77ruuVp39tD1B7z31xliElIo62DBb0MbUtXFpsDj7PHTIS7ei2ZWv1oMaJiHgq6KAv8MhsubtQVhyzcDp4rgVFn7cKwENq7atS6FEC+0Qp9NWVji4uI4ffp0ti1bQ4YM4dq1a/z00088evSIiRMnoigKvr6+ODg4EBoaSpkyZdBoiseaerkhyZgobhRFYc/lMKZt8ScwUlsKo355e77sVZPaZe2zPe+PY3f4crM/qRqFBh4OLBzsk/tWqzyat/c63+26RttqpVg6rFHeTk6IgsUdtePHsmJiBU6VnjyeSdKcKoGlY/6DF0IUC3pNxo4cOUKDBg0wM8v/l+b58+epX79+ti1b7u7urF+/nkaNtF+eiYmJDBgwgICAAPbs2UNycrK0jAlRRCQkp7Lk8G1+3neD+KRUVCp4xaccn3SphvMzSVZKqobpWy+z7OgdAPrWd2dm31qYGRsVWmzXQ2PoOPcgpkZqTk/qgI15HicFJD+GgKMQeQsibjx53IRHAaDk8MugheMzSVolbZLm0Rxs8jYZSghR9Ok1GbO1teXcuXMFUjn/ecmYtbU1Z8+epUqVKunbUlJSePnll7l16xZ//vkndevWlWRMiCIkJCqBWTuusP7sPQBszI35qH0VhjSrwOPkVD74+2x6t+anXaoxsnWlXHVp5oeiKLSfc4BbD+L4cWA9etVxK5gLpyTBwzsQeTNjkhZxE2KCsz7H1AaGbYUydQomBiFEkZCXn9/5XmQuL7mco2POTfTPS6IqVqzIhQsXMiRjxsbGrF69mpdffpkePXrkOhYhhH642pkzd0BdXm9Sni83+fPvvSimb73MylPaMjc3wmKxMDFi7oA6dPEuo5eYVCoVXWq6Mn//TXZeDCm4ZMzYFEpV1T7+KzFW25KWnqjdhLsnta//ehlG7JYFyYV4QeW7ZczGxobz58/nqmXMysqKkSNHUqtWrSz3BwQEMHXq1GyTsvHjx3Pu3Dl27tyZaV9KSgr9+vVjy5Yt0jImRBGVqlFY7XeX2TuvEhGXBICrrTmLhzTA291Or7FcCHpEr3lHsDQ14sykjpibFF63aLYSouC3rhB2CZyrwfCd2okBQohiT6/dlH///TcvvfQSVlbPr4jdvHlzXnnlFT766KMs9z+vmzIlJYX4+Phs31RqaipBQUF4eHjk/g0UcZKMiZIo6nEy8/fdIOjRYyb3qIGLrbneY1AUhebf7CU4KoFf32hAxxoGGrcVdQ8Wd9B2Y3o0h9fXgYn+Pw8hRMEq9OWQ9uzZk/580KBBGRKxefPmZXte9+7defToUbb7HR0deeONN7Ldb2xsnOMbMjIyKlGJmBAllZ2FCRO7efHzoPoGScRA21XZ2ftJAdiLeSgAW9Ds3OH1NWBmCwFHYMO7UIJmhAshnk+nZKxfv36cOnUq0/bvv/+ezz77LNvzPvvsM6ZMmZLt/nLlyrF06dJs9589e5bbt2+nv/7zzz9p3rw55cqVo0WLFqxcuTKX70AIIaDLk2r8uy+HkpxqwATIpSYM+BPUJnBpPfhOMlwsQgi90ykZmzt3Lt26dcPf3z9923fffceUKVPYunVrgQX3X8OHD+fOnTsALF68mLfffpsGDRrw+eef07BhQ9566y1+++23Qru/EKJkaVDBEScrU6IeJ3PiVqRhg6nYGnov0D4/Ng+O/2LYeIQQeqPTbMphw4YRERFBp06dOHz4MKtWreLrr79m+/btNGvW7Lnn7927l8OHD3P//n2MjIzw9PSkV69eGWZJZuXq1atUqlQJgPnz5/P999/z9ttvp+9v2LAhM2bM4M0339TlbQkhXjBGahUda7iw8tRddly6T4sqzoYNqPbLEHUX9kyFHRPA1g1q9DJsTEKIQqdzaYtx48YRERFBgwYNSE1NZdeuXTRu3DjHc8LCwujZsyenTp1CrVajKAr16tVj7dq1jB8/no8//phvv/022/MtLCx48OAB5cuX5969e5nu17hx4wzdmEII8TydvV1Zeeouuy6FMq2XN2q1gZcyajEGooLAbwmsewusS0P5JoaNSQhRqHKdjP3444+ZtpUpUwZLS0tatWrFiRMnOHHiBJD9QuGjRo3Czc2NyMhIzMzM+OSTT4iJicHPz4+9e/fyyiuv4O7unu1sy65du7JgwQIWL15M69atWbNmDXXqPC2U+M8//1C5cuXcviUhhKBZJSdszIwJi0nk7N1H+HgYuLSESgXdZkPMfbi6DVa8CsN9wTnnngMhRPGV69IWOS0OnuGCOSwUbmdnx9GjR6lZsyagXYvSwcGB8PBwbG1t+fPPP5k+fTpXrlzJ8vzg4GCaN29O+fLladCgAQsWLMDHxwcvLy+uXr3K8ePHWb9+Pd26dctVrMWBlLYQovB9tPIsG88F83arinzWzcvQ4WglxcPvPeGen7YY7PDdsmySEMVIoVTgL4juPzMzswzLnKjValJTU0lJSQGgWbNm6QP0s+Lm5sbZs2f55ptv2Lx5M4qicPLkSe7evUvz5s3T18kUQoi86FLTlY3ngtlxMYSJXasX+nJMuWJqCYNWwZKO2sr9f78CQ7eCmbWhIxNCFDCdZlPqqkWLFkyePJm4uDiSk5P57LPPqFixYvoySQ8ePMDBIecuAnt7e7755hsuXbrE48ePSUxM5M6dO/z111+SiAkhdNK6WinMjNUERsZz+X6M3u57JSSay/ejsz/AyhleWwOWTnD/HKwZBqkpeotPCKEfOg3gT01NZdmyZezZs4ewsDA0/ylQuHfv3izP++677+jUqRP29vaoVCqsrKxYvXp1+v7Lly8zdOhQXUISQgidWZoa07pqKXb5h7LjUgg13Ap/SEDQw3hemncEY7WKY5+1x9bcJOsDnSrBoH9gWQ+4vgu2fgw9f9COLRNClAg6JWMfffQRy5Yto3v37nh7e+e6ST9toe8jR46QmJhIkyZNcHZ+OpVcEjEhhKF08XZll38oOy+G8HHHLBb6LmBzfK+RmKIhETgb+IjWVUtlf3DZBtD/N1j1Gpz5HezKQetPCj1GIYR+6JSMrVy5kn/++UengfKWlpZ07NhRl9sKIUShaV/dBWO1iquhMdwOj8PT+fnr7erqakgM68/eS3/tdycy52QMoHo37SzLrWNh33RtDbJ6rxVajEII/dFpzJipqalOJST+97//ERAQoMsthRCiUNlZmtC0khMAOy8V7lqVs3deQVHAxlz7+7DfnYe5O7HhCG0dMoDNo+DGnpyPF0IUCzolY2PHjuWHH34gl1Ux0n3yySdUqlSJjh07smrVKpKSknS5vRBCFIrONQt/4fBTdyLZfTkMI7WK2f1rA3Du7qPcr43ZbjLUegU0KfDPG7D+XdjxGRz8DvyWgv8muHMEwq5AbJgM+BeiGNCpm/Lw4cPs27eP7du3U7NmTUxMMg48XbduXbbnLl68mA0bNjB48GBsbW15/fXXGTFiBN7e3rqEIoQQBaZTDRcmbbzIubuPuB/1mDJ2FgV6fUVRmLVdW0fxlQbl6FTDFVtzY6ITUrh8P5raZe2ffxG1Gl76WVsU9s4hOL/i+eeY22lnZFo6gYUjuNaClh+DaeF1xQohck+nZMze3p4+ffrodMNu3boxdOhQwsLCWLZsGUuXLuWnn37Cx8eHt956i1dffRUbGxudri2EEPlR2tYcn/IO+AU8ZNelUIY0q1Cg1997JQy/gIeYGasZ3aEKarWK+h4O7L/6AL87D3OXjAEYm2pLXlzeDNH3ID4C4iOf/BkBj588f/yk+zMhSvuIfFKQ+/pObXX/V5ZLZX8hioBcV+AvCGq1mpCQEEqXLp1h+6FDh1iyZAlr1qwBIDY2Vl8hFXlSgV8I/Vp86BbTt16maUUnVrxdcGtCpmoUuv1wiKuhMbzbuhITulYH4Od9N5i98yrda5Xh59fqF9j9tDdNgYRHGRO1mPtwcDbEhoKpNfT6Cbz7Fux9hRB5+vmt16Kv2ZXAaNmyJcuWLSM4OJi5c+fqMyQhhMggbdzYidsRRMYV3LjWDWfvcTU0BltzY0a2rpS+PW0tTL+AyDyPw30uI2Nt4dhSVcGjKXj1gEZvwTuHoEJLSIrVFpLdPh5SZAyvEIaiUzelp6dnjrXFslub8nlfNLa2trz11lu6hCSEEAWinKMlNd1suRQczW7/UF5pWC7f10xMSWWO7zUA3mtbGTvLp+Ns65S1x1itIjQ6kaCHjynnaJnv+z2XjQsM3qAtkXF4Lpz4Be6dhpeXgV3Zwr+/ECIDnZKx0aNHZ3idnJzM2bNn2bFjB598knUhwujo6EyV+oUQoijqXNOVS8HR7LgUUiDJ2F/HA7n36DEutmYMaVohwz4LUyNquttx/u4j/AIi9ZOMgbbVrMOXUK4xrH8Hgk7BLy2h369QuUPerqVJ1a4OcGIhBJ+FTtOh/uBCCVuIkkjnCvxZ+fnnn/Hz88tyn4ODAyEhIZQqVYp27dqxbt067O3tdbm9EEIUqi7erszxvcbh6+HEJqZgbabTVyUAMQnJzNt3A4DRHapiYWqU6ZgGHg7aZOzOQ/rU03PLVLWu8M5BbZmM++fhz/7Qejy0/hTUmWPN4PEjOPsnnPoVHt55un3TB9rJAu0maWd/CiFyVKD/S7p27cratWuz3GdtbU14eDgA+/fvJzk5uSBvLYQQBaZKaWsqOluRlKph35WwfF3r10O3iYxLomIpK172yTrRavBk3NjpgFwWfy1oDhXgzV3Q4E1AgQPfwF/9IS486+PDrsCWMTDHC3Z9rk3EzO2h2Sho/uSX9cNzYO2bkPxYP+9BiGJM91/3srBmzRocHR2z3NehQwfatm2Ll5cXAH369MHU1DTLY7NbaFwIIfRBpVLR2duVBftvsuNSCD3ruOl0nfDYRBYf0o6h/aRTNYyNsv7916eCNhm7GhpD1ONk7CyyWTS8MJmYQ4+52m7LzaPh5l5Y2Eo7jqxcI21X5LWd2vFltw88Pa90DWj8jrYQremTLlbnarD5I7i0HqLuwcAV2okEQogs6ZSM1atXL8MAfkVRCAkJ4cGDB8yfPz/Lc/78809+//13bt68yYEDB6hZsyaWlnoaGyGEEHnUpaY2Gdt3JYyE5FTMTZ7TZZeFeXtvEJ+USp2ydnTxds32uNI25ng4WRIQEc/ZwIe0qVY622MLXZ1XwbU2/DMYIm7A0q5Qb7A2OXv0ZDk7lRqqdYPG70KFFvDfCV31XgP7crDqdQg6Cb+2g9dWQ6lq+n8/QhQDOiVjvXv3zvBarVZTqlQp2rRpQ/Xq1bM8x8LCgnfffRcAPz8/Zs2aJWPGhBBFVu2ydpSxM+d+VAKHr4fToYZLns4PjIjnrxPa5GV8l+o5zkAHbYmLgIh4/O4YOBkDcKkBb++HTR9qW7dOL9Vut3CA+m9o18i0L5/zNTxbwfDd8PfL2m7MJR1hwJ/a7UKIDHRKxqZMmZKvm+7bty9f5wshRGFTqVR0runKsqN32HEpJM/J2BzfqySnKrSs4kyzys/vomvg4ci6M/fwC4jUNeSCZWYD/ZeCR3O4sgVq9oVaLz/tisyNUlVhxB5YMVDbQvZHH+j5o7blTAiRTucxYxqNhhs3bhAWFpapZEWrVjn/5tO/f38aNGjAhAkTMmyfPXs2J0+eZPXq1bqGJYQQBaaLtzYZ2305lORUDSbZjPn6L//gaDaeDwa0rWK50eDJuLG0RcNze69CpVJpi8Q2ykf9RytnGLIZNoyES+tg43vamZZtP5eZlkI8oVMydvz4cQYNGkRAQECmQq4qlYrU1NQczz9w4ECWrWtdunThu+++0yUkIYQocA0rOOJoZUpkXBInb0fSPBctXACzd15BUaBH7TJ4u9vl6pzKpazTFw33D46mTjn7fERexJiYQ78l4FgRDn2nfUTehLqvg5UTWDprFzHPS6ubECWITsnYu+++S4MGDdi6dStlypR57liI/4qNjc1yJqWJiQnR0dG6hCSEEAXOSK2io5cLq/zusvNSSK6SsRO3Ith39QHGahXjOuV+wLparaJBBcf0xcRLVDIG2law9pPA0fPpTMtL6zMeY2KpTcysnLTJmaWzdrUAGzewLQO27mBTBqxdtEVrhSghdPrXfP36ddasWUPlypV1uqm3tzerVq1i8uTJGbavXLmSGjVq6HRNIYQoDF28XdOTsS971kStzv6XT0VRmLXjCgCvNipHBWerPN3Lx8OBvVfCOB0QyfAWnvmKu8iq97q2rtmRH7SLlsdFQHw4pCZBcjxEBWofOVGpwaq0NkFzrgrtvnj+hAIhijCdkrHGjRtz48YNnZOxSZMm0a9fP27evEm7du0A2LNnDytWrJDxYkKIIqVZZSeszYwJjU7kXNAj6pd3yPZYX/9QzgQ+wsLEiFHtquT5XmnFX0/deYiiKHnudSg2KrTQPtIoCiTGaJOyuAiIf5KgxT2AmFCICYboYIi+D7EhoEnR/hkbol1+6eZeePVvbT00IYqhXCdjFy5cSH/+4YcfMnbsWEJCQqhVqxYmJhkLFNauXTvHa/Xq1YsNGzbw9ddfs2bNGiwsLKhduza7d++mdevWeXwLQghReMyMjWhXvTSbzgez82JItslYqkZh9s6rALzZogKlbc3zfK865ewxMVLxICaRu5GPKe/0goyhUqnA3Fb7cKyY87EazZMk7UmCtm8mhP4Ly7pDr3lQZ4B+YhaiAKmU/47Az4ZarUalUmUasJ9+oSf7cjOAX+RedHQ0dnZ2REVFYWtra+hwhHghbfv3Pu/9dQYPJ0v2j2uTZYvVP353+XTNBewtTTj4aVtszXWrot/75yOcu/uIOa/UoW99Pa9TWRwlxmoXOr+yRfu6xceyJqYoEvLy8zvXLWO3b9/Od2DPCggIICQkBJVKhaurK+XLS3+/EKJoal21FGbGagIi4rkSEoNXmYxfrAnJqXzvew2A99tU1jkRA2hYwYFzdx/hF/BQkrHcMLOGV/6AvV9p18M8PAfCr0HfRWCatzF7QhhKrpMxDw+PArnh3LlzmTNnDsHBwemtbCqVCjc3N8aOHcvo0aML5D5CCFFQrMyMaVW1FL7+oey4GJIpGfvzeADBUQmUsTNncNP8fVf6eDjy66HbnL5joEXDiyO1GjpM0S63tOlDbSvZb51h4Eqwk4RWFH06tePOnDmT3377LdP23377jVmzZmV73ldffcWXX37JBx98wOnTp7l37x5BQUGcPn2aDz74gC+//JLp06frEpIQQhSqzjW1a0vuvBSSYXt0QjLz9t0AYEyHqjqtYfksnyeD+K+FaRcNF3lQ51UYskVbEiPkX+2amEGnDR2VEM+lUzK2cOHCLNegrFmzJr/88ku25y1atIjff/+d8ePHU7duXcqUKYObmxt169Zl/PjxLF26lAULFugSkhBCFKoOXqUxUqu4EhLDnfC49O2LDtziUXwylUtb07e+e77vU8rGjApOligKnAks+NaxpUdu89n6f0lK0Tz/4DxKSdUQ9DC+wK+bJ+Ubw9v7oHRNiA2FZd3g9O/aGZtCFFE6JWMhISGUKVMm0/ZSpUpx//79bM+LiIigWrXsiyBWrVqVhw9LXtO8sbExdevWpW7duowYMcLQ4QghdGBvaUrTik7A09axsJgElhzWjqf9pHM1jAtoCSMfD0cA/O4U7DqVEbGJTN96mb9PBOLrH1qg1wb4ZvsVWszaVyjXzhP78jB8J1TtCikJsHkUrHtLWz5DiCJIp2+OcuXKceTIkUzbjxw5gpubW7bnNWrUiBkzZpCSkpJpX0pKCl9//TWNGpW8OjH29vacO3eOc+fOsXjxYkOHI4TQUWdvbVfljifJ2E97bvA4OZV65e3plMeFxHOStk6lXwGPG9t2MYRUjbaFaO2ZoAK9dmJKKv/43QVI/9OgzGy0tcc6fAkqI/h3NSxqA/cvPO9MIfROp6KvI0aMYPTo0SQnJ2co2vrpp58yduzYbM/76aef6NSpE6VLl6Z169a4uLigUqkICQnh4MGDmJmZ4evrq9s7EUKIQta5hguTN17kbOAjTtyKYMVJbaX48V2qF2iB1oZPkrHzQQW7aPjmc8Hpzw9ce0BYTAKlbfJeDy0rh66FE52g/UX74LUHxCelYGlq4CWL1GpoMQbKN4U1b0LEDVjcAbp8DQ2Ga+ubCVEE6PQ//NNPP2X48OG89957VKxYkYoVK/Lhhx8yatQoJk6cmO15tWrV4tq1a8yYMQNbW1tu377NrVu3sLW1ZcaMGVy5coWaNWvq/GZ0cfDgQXr27ImbmxsqlYoNGzZkOmb+/Pl4enpibm6Oj48Phw4dytM9oqOj8fHxoUWLFhw4cKCAIhdC6FtpW/P0oq/v/HmaFI1Cm2qlaPKk+7KgVHS2xt7ShIRkDZeCC2a93vtRjzn5pNuzorMVqRqFjWeDn3NW7m2+8PRaiSkaDl57UGDXzrfyTeDdw1C1C6QmwtaxsGYYJEQZOjIhAB1bxlQqFbNmzWLSpElcvnwZCwsLqlSpgpmZ2XPPtbGxYeTIkYwcOVKXWxe4uLg46tSpw7Bhw+jXr1+m/atWrWL06NHMnz+f5s2bs3DhQrp27Yq/v396bTQfHx8SExMznbtr1y7c3Ny4c+cObm5uXLx4ke7du/Pvv/9mWwAuMTExw7Vk4XQhipYuNV05HfCQR/HamY6fds48mSm/1GoVPuUd2HMlDL87kdQtgEXDt5zXjudtVMGRXnXd+GLDRdaeCWJES898t+o9Tkpl95NxYo0qOHLyTiQ7L4XSxTvz2GKDsXTUlro49jPsnqJdpDz4LPT9VZZREgaXr7bvkJAQIiMjqVSpEmZmZtlW589OSkoKvr6+LFmyhD179hikcn/Xrl2ZPn06ffv2zXL/nDlzGD58OCNGjMDLy4vvv/+ecuXKZZj1efr0aS5evJjpkTZ+Lu1Pb29vatSowbVr17KNZ+bMmdjZ2aU/ypUrV4DvVgiRX2klLgBequtGDbfCWRnD50lX5emAghk3tum8tuWqZ103etZ2w9RYzZWQmAJpedt3NYy4pFTKOlgwrrN2ktaey6Ekpxb8jM18Uamg2Qfw5k6wKw8P78CSjrBiEIReMnR04gWmUzIWERFB+/btqVq1Kt26dUufQTlixIgcx4yNGjWKrVu3AhAUFEStWrXo2rUrn3/+OZ07d6ZevXrcu3dPl5AKRVJSEqdPn6ZTp04Ztnfq1ImjR4/m6hoPHz5Mb+kKCgrC39+fihWzX3tt4sSJREVFpT/u3i0CA2GFEOnKO1nSqmopHK1MGdsx+9nh+dXgyYzKtEXD8+N2eBz/3ovCSK2im7crdpYmdHwy4WDN6fwP5N/8JNHrUdsNHw8HnK1NiU5I4fitiHxfu1CUbQDvHoR6r4NKDVe3woLmsGY4RNzM+pzkx3B1O2x8X1u/bPeXEHZFr2GLkkunZGzMmDGYmJgQGBiIpeXThWwHDBjAjh07sj1vzZo16YnI2LFjKVu2LCEhIYSEhBAWFoaHh0eRqsAfHh5OamoqLi4ZZ0m5uLgQEhKSzVkZXb58mQYNGlCnTh169OjBDz/8gKOjY7bHm5mZYWtrm+EhhChalg1tyNEJ7Qp1Ie/aZe0wMVIRHptIYGT+andtejJwv3llZ5ystcNJ+j9ZamnT+eB81RyLTUxh75UwAHrWKYORWkXHGk9mnV7M3fekQVg4wEs/w3snoGYfQIGLa2BeQ9j4ATy6C/GRcG4FrHwNvq0IK16Fs3/CvdNweC7Mb6ydoXliIcSFG/odiWJMpzFju3btYufOnZQtm3GZiSpVqhAQEJDteQ8fPsTcXDtz5+jRo6xduxZnZ2cAHB0dmTlzJm3bttUlpEL13/EUaQui50azZs34999/CyMsIYSBqNUqzNX5q7T/POYmRtRyt+NM4CP87jzEw0m3dRYVRWHTeW2PQ686T0sPtaziTCkbMx7EJLL/ahidnul+zYvd/qEkpmioWMqKGk+Wiepc04UVJ7W1zL56yRu1ugjPWixVFV5epl1gfN8MuLYDzv4B51eCogHlmeEztmWhejdwrQ1Xt8H1XdpxZ8FnYednUKUTtBoH7j4GezuieNKpZSwuLi5Di1ia8PDwHAfxV61alZMnTwLagfz/HZweExODRlN0xhg4OztjZGSUqRUsLCwsU2uZEEIUtAYVnhR/zce4scv3Y7j5IA5TYzWdaz793jI2UtOnnnbFgPzUHHu2izLtl9RmlZyxMTMmLCaRs3cf6XxtvSpTGwatguG+4NkKNMnaRMylFrQeD28fgDEXodtsqD8YBq6AsVeh67fgVg80KdoEbWk3uLzZ0O9GFDM6JWOtWrVi+fLl6a9VKhUajYbZs2fn2LI1ZswYxo0bx/79+5k4cSKjRo1iz549BAcHs2/fPt55551sB9IbgqmpKT4+Pplqn/n6+tKsWTMDRSWEeFGkrVN5OkD3SvxpA/fbVSuNjblJhn39nnRV7r0SRmRcUp6vHRWfzMHr2hIWPWs/nTlpaqymbfXSAOy6VIS7KrNSrhEM2aztvvzoAow8DG0/A7e6meuSWTlD43fg7f3w/klt6YyUBFg1GE4sMkT0opjSqZvyu+++o3Xr1vj5+ZGUlMSnn37KpUuXiIyMzLIyf5qhQ4cSGRlJ9+7dURSF1NTUDIPje/Xqxdy5c3UJSWexsbHcuHEj/fXt27c5d+4cjo6OlC9fno8//pjBgwfToEEDmjZtyqJFiwgMDOTdd9/Va5xCiBdP+qLhobFExSdjZ2nynDMyUhQlveWqV93Mq6NUc7XB292Wi/ei2XTuHkObe+bp+jsvhZCcqlDd1YYqLjYZ9nWu6cqm88HsvBTChK4FWxRXL0rnsWRJqWow4C/YNg5OL4Xtn0B0ELT/Ult8Vogc5DkZS05O5r333mPTpk1s374dIyMj4uLi6Nu3L++//36Wa1Y+6+OPP+bNN9/E19eXW7duodFoKFOmDM2bN6dKlSo6vxFd+fn5ZWjN+/jjjwEYMmQIy5YtY8CAAURERDBt2jTu37+Pt7c327Ztw8PDQ++xCiFeLM7WZng6W3E7PI4zgQ/TW5ty60zgQ+49eoyVqRHtsjm3X/2yXLznz9ozeU/G0gq99qyTOdFrU60UpsZq7kTEcy00lmquNpmOKXGMjKHHXLAvB3umwZEfIOoe9J4Pxs+vwyleXHlOxkxMTLh48SJOTk5MnTpVp5va29vz8ssv63RuQWvTps1zp42/9957vPfee3qKSAghnvLxcOB2eByn7kTmORnb/KTQa6earpibZD3hoFcdN2Zsvcy/96K4GhKT66QpPDaRoze1pSt61M78S7iVmTEtKzuz50oYOy+FvBjJGGi7MluOBVt3bRmMi2sgNhT6/AJ2ZZ9/vngh6dR2+sYbb7BkyZKCjoW4uDgOHjxY4NcVQojiKm2dyrwO4k9J1bDlgjYZ65VFy1UaJ2uz9FazvAzk3/5k0fE6Ze2ynemZViB3ZyGNG7sdHscfx+6QkKz/guHPVedVeG01mNrAnUMwtyb8WA82jYILqyGmmI2lE4VKpzFjSUlJLF68GF9fXxo0aICVVcb/iHPmzNEpmBs3btC2bVuDVOIXQoiiyOdJ8dfzdx+RlKLB1Dh3v0MfvxVJeGwiDpYmtKjinOOx/XzKsss/lPVn7/Fp52oY52Jh8mdnUWanQw0X1OvgUnA0dyPjKedYsHXZPl//L0dvRnD8diTzBtYreuPSKrWDYdu048iCTkHkLe3jzO/a/aW8oOl7UGcgGOVtPKAoWXRKxi5evEj9+vUBMi3tU+T+MwghRDFWqZQVDpYmPIxP5lJwFPWeLFT+PGm1xbrWKoPJc5KrttVK42BpwoOYRA7dCKdttZy7Q0OiEjj1ZNHx7ll0UaZxtDKlkacjx29Fsss/lOEt8jYmLScJyan43dG2Fm69cJ8qpa0Z3aFqgV2/wJSpDcN3aRclDzimbSW7cwjuX4AHl2HTh3BwtrZrs84gMDY1dMTCAHRKxvbt26fTzXKqPA9Ii5gQQvyHSqXCx8OB3ZfDOB3wMFfJWGJKKtufVL/PqYsyjamxmpfqurPs6B3Wng56bjK29d/7KIq2C9XN3iLHYzvXdOX4rUh2Xgop0GTsTOBDklI1mBqpSUrV8P3u61QubZ1jS51BmdtBtS7aB8Djh3D2L+0g/0eBsPkjOPg/aDkG6r4uSdkLRqdkTFeJiYmMHDmSWrVqZbk/ICBA50kBQghRUvl4OLL7chh+dx4youXzjz94LZyYhBRcbM1oWCHnX4LT9Pcpy7Kjd9jlH/rcMhq56aJM06mmK1M3+3Pqjrbb1Nm6YGYVHr+lbZnrWssVZ2szlhy+zdh/zlPe0ZLaZe0L5B6FysJBu2h5gzfh9DI48j1EBcKWMRB4HPpKnbIXiV6Tsbp161KuXDmGDBmS5f7z589LMiaEEP/RIH0Qf2SulmPb9EyyZJTLpYhqutlSzcWGq6ExbPk3mNcaZ12+525kPOfuPkKt0iZCz+Nub0Etdzv+vRfFbv9QXm1UPlfxPE/aIuRNKjrxSoNy3HoQy76rD3hruR8b32+Bq515gdyn0JlaaseNNRgGfku1yypdWAVNRmor+4sXgl4r0XXv3p1Hjx5lu9/R0ZE33nhDfwEJIUQxUMvdDlMjNeGxSQRE5LxoeHxSCrv9Q4HcdVGmUalU9PN5sjzS6exnVabN0GxayYnSNrlLeNKWYSqoWZUJyamcC3wEaJMxI7WKHwfWo0ppa0KjE3n7Dz8eJxWzYS8mFtqkrPYA7eu90w0bj9ArvSZjn332GVOmTMl2f7ly5Vi6dKkeIxJCiKLP3MSIWmXtgOeXuPD1D+VxcioeTpbUfnJObvWu645aBWcCH3HrQWyWx+SlizJNWomLIzciiElIzlNMWTkb+IikVA0utmZUcNLO0LQxN2HJkIY4WJpwISiKT9acf24NySKpzQRQG8ON3RBw1NDRCD3RWzJWv359Hj7UfolMmzaN+Picf7sTQgjxVINcrlOZvvxRHbc8z24vbWtO66qlgKxrjt18EIv//WiM1Sq61Hx+F2WayqWtqehsRVKqhv1XH+Qppqw820X57Hss72TJgtd9MFar2HLhPj/uuZHdJYouR0+oN1j7fM9XUBwTSpFnekvGLl++TFxcHABTp04lNjbr37qEEEJklrZOZVo5h6xExSdz4NqThbvz0EX5rH4+2irx68/cQ6PJmAhseVLRv2UVZxyscj/bT6VS0dm74ArAPpuM/VeTik7M6OMNwNzd19j6pFu1WGn9KRiZQeBRuLnH0NEIPdDbAP66desybNgwWrRogaIofPfdd1hbW2d57OTJk/UVlhBCFAtpydj1sFgexSdhb5k5Gdpx6X76wt1VXXRbfqiDlwu25sYERyVw7FYEzStrC8YqipK+FqUu5SM613Rlwf6b7L/6gMSUVMyMs16e6XkSklM5e/cRAI09s54pOqBhea6FxmpnWK4+RzVXayqXLkbLMdm6QaO34Ng8betYpfbaZZZEiaW3lrFly5bh5OTEli1bUKlUbN++nfXr12d6bNiwQV8hCSFEseFkbUZFZ+1qJ2cCs24dS5tFqWurGGjHp/V4cv6aZwbyXw2N4UZYLKbGajo+GZCfF7Xd7XC1NSc2MYWjNyJ0ju9soHYlgtI22kXUs/NZNy9aVHYmIVnDLwdu6Xw/g2kxBkyt4f45uLzZ0NGIQqa3ZKxatWqsXLmSU6dOoSgKe/bs4ezZs5keZ86c0VdIQghRrKSVuDiVRVdlWEwCx54s3J2XWZRZ6f+kq3LHxRBiE1OAp2PR2lYrha153pfuUatVdCqAWZXZjRf7LyO1io87aSvybzoXTFhMgs73NAgrZ2jynvb53umgKWazQ0We6HU2ZRqNRkPp0jlXeBZCCJFRgyfrVJ7OIhnbeuE+GgXqlbfP9xqQ9crZU9HZisfJqWz79762i/LJeLH8VLhPm1Xp6x9Kqka3gek5jRf7r/rlHahX3p6kVA1/Hg/U6X4G1ewDMLeH8KvaLsvI25BczJJKkSsGScay8/DhQ5YvX27oMIQQokjyedIydj5I21X3rGdnUeaXtuaYtnVs7ekg/r0XRWBkPBYmRrT30v0X6UaejthZmBARl8Tp55ToyMqz48WaVMzdygJpSzD9dTyAhORi1rpkbgfNP9I+950MP9aFGS4wqwIsaA6H54JGk9MVRDFRpJKxwMBAhg0bZugwhBCiSKrobIWjlSmJKRouBkelb78bGc+ZQG1V/O61sl+4Oy/61HNHpYITtyOZv+8mAB1quGBpqvu8LxMjdXoyt+Ni3rsqczte7Fldarribm9BRFwSG8/dy/M9Da7xu1CjNzh4gvGTIruPH0LoRdj9JfzVD+LCDRmhKAB6Tcaio6NzfMTExOgzHCGEKFZUKhX1nywU/mxXZdosxyYVnShtWzDLALnZW9CskrYrcMeTMV49auc/0Uvrqtx5KSTPRVlzO17sWcZGaoY00y7ttOTw7eJXCNbUEl75HT46B5+HwKe3YeRR6P4/MLaAm3vhl5YQeMLQkYp80GsyZm9vj4ODQ7aPVq1a6TMcIYQodp5dpzLNpnMF10X5rLSB/AA2ZsbpBWHzo1WVUpibqLn36DGXgqPzdG5exos9a0DD8liaGnEtNJbDN4pxK5JKBZaO4FITGo6At/aCUxWICYZl3eDYz1IktpjS60LhNjY2fP755zRu3DjL/devX+edd97RZ0hCCFGsNKzwtPiroijcCIvlSkgMJkYqunoXTBdlms41XbEyvUhcUiqdarpibqJbbbBnWZga0aZqaXZcCmG131283XO3ZJMu48XS2FmY8EqDciw7eoffDt+mZZX8J5VFgksNeHsfbBoFl9ZpFxmPuQ+dZF3L4kavyVj9+vUBaN26dZb77e3ti18TshBC6JG3ux2mxmoi4pK4ExGfPnC/ddVS2FnmveRETixNjRnavAK/HrzN4KYeBXbdwU092HEphL9PBjKiZcVczf7UZbzYs4Y2q8Dvx+6w7+oDboTFUrl01kXHix0zG+j/G5RrDDvGw9GfoJQX1Hst87HJjyHkX3BvAOoiNWT8hafXv41BgwZhbp79eAZXV9ccFxIXQogXnZmxEbWftCb53YkskEKvORnXqRr+0zpTt5x9gV2zeWVnWlZxJjlV4X+7rubqHF3Giz2rgrMV7atr65wtPXI7z+cXaSoVNHkXWo/Xvt4yGgKPZzzm4R34tR0s6Qjr3oLUFH1HKXKg12TsrbfeYtSoUdnud3FxkWRMCCGeI63Exe/H7nAnIh5zEzUdvPJeFT83VCoVxkYF/6NifJfqAGw4F8ylZ2aGZkfX8WLPSitzsfZMEA/jknS+TpHVegJ49YLUJFj5Gjx6Ulst4Kg2EQvz176+uAY2vieFZIsQvSVjjo6OhIdrB06++eabMnNSCCF0lFb89eI97QD4Dl4uWJnpddRJvnm726VPOPh2R86tY/kZL/asJhUdqVHGloRkDX+fLIZFYJ9HrYY+v4BrLYgPhxUD4eSv8HsviI+AMnWhx1xQG8OFVbDxA0nIigi9JWNJSUlER2u/OH7//XcSEqSKsBBC6CJt0fA0BT2LUl/GdaqGiZGKA9cecDSHWY5p48VK6TheLI1KpUpvHVt+7E6mwrmgXRB90/lguv1wiM5zD/LRyrPM33+DfVfCuB/1uOiPaza1gldXgFUpbS2ybeNAkww1+8Cw7dDgTei3BFRGcP5v2DxKCscWAXr7Vapp06b07t0bHx8fFEVh1KhRWFhYZHnsb7/9pq+whBCi2HG0MqVSKStuPojD1tyY1tWK5+zA8k6WDGpUnt+PBTBrxxU2vN88y/Fg+R0v9qyeddz4ZscVQqMT2fbvfXrXc0/fdybwIV9t8eds4KP0bVdDM/bi2JobU6usHV+95E3FUkV0EoB9ORjwF/zeQ9tl2WaidjxZ2mdXszcoqbB2BJz9E6xdoP1kg4b8otNby9iff/5Jt27diI2NRaVSERUVxcOHD7N8CCGEyFkjT213XRdvV8yM819ywlA+bF8FK1MjzgdFsT2bqvxPkzHduyjTmBqreaNJxiKwQQ/j+XDFWfrOP8rZwEdYmBjxcceqLBnSgE86V6NnHTequlhjpFYRnZDCkRsR/LDner5jKVTlG8M7B+GtfdBmwtNELI13P+i9QPv8yA8QXsTfTwmnUgzQ5urp6Ymfnx9OTroPxHxRREdHY2dnR1RUFLa2toYORwhRRIREJbD06G3ealkRZ2szQ4eTL9/vvsb3u6/j6WzFrjGtMHlmwkBCciq1p+4iKUXDnrGtqVQArVGRcUk0nbmHxBQNfeq5s/Xf+ySlaFCpoH/9sozrXA2XLFYySExJ5dC1cEYs98PUSM3xz9rjaGWa73gM6q9X4PpOqNIJXltt6GhKlLz8/DZIoZHbt29LIiaEEPngamfOxK5exT4RAxjRsiJOVqbcDo9j1am7GfY9O16sYj7Giz3L0cqUvvW13ZPrz94jKUVDk4qObP6gBbNfrpNlIgbasiIdarhQy92OpFQNa07fzfK4YqXz16A2geu74NouQ0fzwipSVd9CQ0OZNm2aocMQQgihR9ZmxoxqXwWAH/ZcJz7paQ2sE7cLbrzYs95qWRFbc2M8na1YNNiHFW81yfVqAIMalwdgxcm7aDRFfED/8zhX1tYoA9g5EVJKYMmPYqBIJWMhISFMnTrV0GEIIYTQs4GNylPe0ZIHMYn8dvhpUdaCHC/2rIqlrPH7oiN7x7amU03XPCV6veq4YW1mzO3wOI49ia9Ya/WJdvZlxA04udDQ0byQ9JqMXbhwIcfH1au5q8QshBCiZDE1VjO2U1UAfjlwi8i4JBKSUznzZGZjfoq95nRPXVrbrMyM6V1PW07k7xMloF6ZuR20f1Jw/cC3EBtm2HheQHqtEli3bl1UKlWWdVrSthdkM7QQQojio2dtNxYdvMWl4Gh+3neDjjVcCny8WEEZ1MiDP48HsvNSCGExCZS2yXqcmaIonLv7CAdLUzycLIvuz7i6r8GpxXD/nLZYbNdZULaBoaN6Yeg1GXNycmLWrFm0b98+y/2XLl2iZ8+e+gxJCCFEEaFWqxjfpTpv/HaSP44FEPU4GSj48WIFoYabLfXL23Mm8BGr/YJ4v23lLI9bePAW32y/AmhrlNUua0+tsnbUKWtHvfIO2U4W0Du1GrrPgWXd4Z4fLG4PNV6CdpO148pEodJrMubj40NwcDAeHh5Z7n/06FHRr24shBCi0LSs4kzzyk4cuRHBmtNBQMGPFysogxp7cCbwEStOBjKydSXU6owJ4+mASGbv1A6/MTHS1ig7fCOcw09WG1CrtDNJP+5YFXOTIlArrqwPfHAK9s+Ec3+D/0a4vAV6/Qj1Xjd0dCWaXseMvfPOO1SoUCHb/eXLl2fp0qX6C0gIIUSRolKp0hcRT1MY48UKQo/aZbA1Nybo4WMOXn+QYd+j+CRGrThHqkbhpbpuXJrahS0ftuDrPrUY2KgcXmVs0Siw6OAtevx0mPNP1t40OPty0Hs+jDyqrT2mpMLOz+DxI0NHVqIZpOiryD0p+iqEeBG9//cZtl64TykbM05+1r7IdVOmmbr5EkuP3KFjDRd+fUM7xkpRFN7+4zS+/qFUcLJky6iWWGexkLuvfygT1/1LeGwiRmoVI1tX4sP2lYvOigqaVPilBYT5Q8tx0H5S7s89+hM8uKLt+jQu/rXwdFHki74KIYQQOZnQpTq1y9rxbutKRTYRA3jtSc2xvU8WEgdYdvQOvv6hmBqpmTeofpaJGEDHGi74jmlFrzpupGoU5u27wUvzjnA7PE5v8edIbQRtP9c+P74g97Ms4yPBd4p23cvTvxdefCWI3lrGPv7441wfO2fOnEKMpHiRljEhhCjaXll4jJO3IxndoQrtq7vQb8FRklI1fNmzBkObe+bqGtv/vc/nGy4SGZdEBy8XFg8pIjMZFQV+bQfBZ6DxSOj6zfPPOfsnbHxf+9ymDIw6ByZFZKKCHuXl57feBvCfPXs2w+vTp0+TmppKtWrVALh27RpGRkb4+PjoKyQhhBAi315rXJ6TtyNZefKudnmlVA2dargwpFmFXF+ja60y2FqY8NriE9wIiym8YPNKpYL2k+GP3uC3BJq+rx1XlpPLm58+j7kPp5c9rfIvsqS3bsp9+/alP3r27EmbNm0ICgrizJkznDlzhrt379K2bVu6d++ur5CEEEKIfOvi7YqjlSkh0QkERMTjbm/Bt/1r57l71cPJEoB7jx4XrWWWKraBCi0hNQkOfpvzsQnRcHOv9nnjJwnY4TmQ/LhQQyzuDDJm7H//+x8zZ87EwcEhfZuDgwPTp0/nf//7nyFCEkIIIXRiZmzEyz5lATBSq/hxYF3sLU3zfB1XW3OM1CqSUxXCYhILOkzdpbWOAZz9S1ul/8o2iLwNGk3GY6/v0iZtTlWg41dgVx5iQ8HvN/3HXYwYJBmLjo4mNDQ00/awsDBiYopQ86wQQgiRC8NbetKqaim+6VsLHw/d6qIZG6kpY6cdWxX0ML4gw8u/co2gWjdtqYt9M2DlQPixLixomnFg/+VN2j+9eoKxKbQap319eC4kFZGJCUWQQZKxPn36MGzYMNasWUNQUBBBQUGsWbOG4cOH07dvX0OEJIQQQuistI05y99sxMsNnjOe6jnc7S0ACHpYcN16Kakajt+KYPoWf0b87sfdSB0TvX5LoNMMqD0AXGqBkam2fMWGkdoWsuTHcN1Xe2yNXto/6w4Cew+IewA7JmjLZYhM9FqBP80vv/zCuHHjeP3110lO1i53YWxszPDhw5k9e7YhQhJCCCEMrqyDJSduR3LvUf6TsbOBD1l+LIC9V8LSl5YCKGVjxsy+tfJ+QVNLaPbB09dhl2FRG7ixG078AvblITle2zVZpq72GCMT6DQd/nkDziyHxFjos1DbaibSGaRlzNLSkvnz5xMREcHZs2c5c+YMkZGRzJ8/HyurorUYrBBCCKEvZR3SWsby100ZGp3Aq4uOs/7sPaIeJ+NgaULrqqUA2H7xPkkpmudcIRdKe0HnGdrnu6fAkR+0z716aseZpanRC/ovAbUJXFqn7eJMKmLdsAZm0KKv9+/f5/79+1StWhUrKytZl1IIIcQL7Wkylr+WsV8P3iIxRUNNN1tWv9sUvy868tvQhpSyMeNRfDKH/rN8k84aDIfqPbSD9oNOard59cx8nHc/GLQSTCy1LWnLusGjuwUTQwlgkGQsIiKC9u3bU7VqVbp168b9+/cBGDFiBGPHjjVESEIIIYTBlXXQlrfITzIWGZfEXycCAfi0S3UaVnDESK3CSK2ie60yAGw6H5z/YEHbAtbrJ21xVwBrFyjXOOtjK3eAwRvAwgGCz8Ki1nD7YMHEUcwZJBkbM2YMJiYmBAYGYmlpmb59wIAB7NixwxAhCSGEEAaX1jJ276HutcZ+O3ybx8mp1HK3o1UV5wz7etV1A7TrYj5OKqDB9JaO2sH9VqW1RWHVOaQW5RvD2wfAtTbER8Dyl+DkrwUTRzFmkGRs165dzJo1i7Jly2bYXqVKFQICAgwRkhBCCGFwZey0tcaSUjU8iM17rbGox8n8fvQOAO+3rZyp8Gy9cvaUc7QgPimV3Zczl5jSWYXm8Ml1aP7R84918IDhu6DOQFA0sH08RBdQS10xZZBkLC4uLkOLWJrw8HDMzF7M1d2FEEIIYyM1rra61xr749gdYhJTqOpiTacaLpn2q1QqetbWto4VWFelLkwsoPcCKN9UW7vs9DLDxVIEGCQZa9WqFcuXL09/rVKp0Gg0zJ49m7Zt2xoiJCGEEKJIcNdxEH98UgpLDt8G4L02lVGrs16OKa2r8sDVBxlKXhQkjUYhIfk53aAqFTR6S/vcbymkJBVKLMWBQZKx2bNns3DhQrp27UpSUhKffvop3t7eHDx4kFmzZhkiJCGEEKJI0HVG5d8nAnkYn0x5R0t61C6T7XHVXW2p6mJNUqqGnRdD8hVrdl5fcoJm3+x9frJXvad20H9c2NPq/S8ggyRjNWrU4MKFCzRq1IiOHTsSFxdH3759OXv2LJUqVTJESEIIIUSRoMuMyoTkVH49dAuAkW0qYWyU84/3XnUKr6syJiGZozcjiIxL4lroc5Y4NDYFn2Ha56cWF3gsxYVBKvADuLq6MnXqVEPdXgghhCiSdCn8uv7sPUKjEyljZ07f+u7PPb5nHTe+23WNozfDCYtJoLSNuc7x/teVkKcJ2P2ohOef4DMUDn0Hgf9v786jo6jTvYF/q9PpbJ3OCtkXtoSBKEiCDpF9SQzIdoaLjopBUIYRHZBXvXcueFQuI46yDXLkyMtF0NFXRwfBJRq2RDCgQCDsBA2EQDbIvpJOuuv9o9KVdNJJOiFJdZLv55wcuquqq56OWjw+v189v+NA3nnAtwOrA/RwijZ9JSIiInON21tY67tzUr/Op8eEwkFt1+bxIV4uGBHkDqMIJNR/trNcyimTX+eVWvEddH5S41igz7a56NZkrLa2Fq+++ioGDx6MBx98EB9++KHZ/vz8fNjZtf0vERERUW8VZBqmLLGu11j53Vr8cr0QABA7vPkTlC2ZWT+v7EBntrhA02TMyvYcDy6R/jz/RZ9cKqlbk7G//e1v+Oijj7B06VLExMTgpZdewp/+9CezY7gkEhER9WW+bo5QCYC+zogCK3qNHf21ALUGEQO9XTCwn9bq60wMl9aqPJVZbPHJR1EUkXqjGDV17WsOeym3UTJWZmV1LyQa0AVIC43f/KVd1+sNujUZ++STT7Bjxw68/PLLWLt2LVJTU5GUlIRnnnlGTsKaNqjr6dLT0zFy5Ej5x8nJCXv37lU6LCIislH2dir4udXPGytpO5k5dPk2AGDy0P7tus6gflr0c3VATZ0RZ7JKmu3fl5aDP2w7hhWfpVl9zlqDEen57ZwzBkhtLgaMl173wSWSujUZy87ORkREhPx+0KBBSE5OxvHjx7FgwQIYDJ20NIMNCQ8PR1paGtLS0vDTTz/BxcUF06ZNUzosIiKyYQHu1rW3MBhFJKVLydiU31k/RAlIxY/oQV4AgOMZBc32f3tOetLy+wt5+PlaoVXnvHanEvo6o/w+r41k7NqdioZjQsdJf2YetepavUm3JmO+vr7IyMgw2+bv74/Dhw/j5MmTiI+P785wut3XX3+NKVOmwMXFRelQiIjIhln7RGXazRIUVerh6qhGVKhHu69jSsaOZZgnW3drDUj5rWHbWwmXrZq/dim3FAAQ7CnNe7tdXoM6g9HiscWVeszY8hOmbzmKwooaYEB9MpZ9GqhpoyVGL9OtydjkyZPx6aefNttuSsgyMzO7MxwAwJEjRzBz5kz4+/tDEASLQ4jvv/8+BgwYAEdHR0RGRuLo0Y5l7f/617/w2GOP3WPERETU21nb+PVQ/eT7CWH9YN9GbzFLogdJC4mn3SxBlb5O3v7ztUJU1xrgrdVA66DGuVulVvUkM03eHx/mDTuVAINRREGF5c76l3PLUF1rQFGlHm9/fwVwDwY8QqXlkW4cb/d36cm6NRl77bXXMH/+fIv7AgICcOTIEezcubM7Q0JlZSVGjBiBrVu3Wtz/+eefY8WKFVi1ahXOnDmDcePGIS4uDllZWfIxkZGRiIiIaPaTk9PwL25ZWRlSUlIwffr0VuOpqalBWVmZ2Q8REfUt1jZ+PXxFGqKc2s4hSpMgT2cEejihzijiZGaxvD05/Q4AYNowH/x5otSM/d3EdHmif53BiH+n3sLnJ7PMHrwzTd6/L8ANPq7SWtN5ZZaHKjMKKuXXX6TewsnMokbzxn5s/5cRReDoBumn8cOAJVlA5k/m22xMtzZ9DQkJQUhIiPz+0qVLyMrKgl7fkDV7eLS/zHov4uLiEBcX1+L+jRs3YvHixXj22WcBAJs3b0ZiYiK2bduGdevWAQBSU1PbvM6+ffsQGxsLR8fWG+utW7eOzXCJiPo4a4YpbxVX4UpeOVSCVBnrqOhBXvjXqVs4llGACWH9IIqinORNDO+PCWH98M+fbyC7pBo7U65jRKA71nxzSZ6oH+DujLFDvCGKIi7nStuG+bnB180ROaV3pV5jQe7NrnvtTgUAQKNWQV9nxOqvLuC7SWOhPv1Rx+aNZR4FDq2RXvtEAGGxQHUx8H+nSMstBY4GYtYCwb9v/7m7mCJNX69du4YRI0YgIiICM2bMwJw5czBnzhzMnTsXc+fOVSIki/R6PVJTUxETE2O2PSYmBseOHWvXuawdovzrX/+K0tJS+efmzZvtug4REfV8pspYdnF1iy2fTAlTVIgnPFw0Hb6WaajyeP28sYw7lcgqqoLGToWxg73haG+HV2LDAQAb9l/Fkzt+MXti0rQMU35ZDYoq9bBTCRjio4Wvm1R8aOmJymt3pMrYiqlD4OmiQXp+OT67EyrtzD0HVBW174v8+E7D68T/lhYeP7xWSsQA4NZJYGcs8M1yoM7K/mfdRJFkbPny5RgwYADy8/Ph7OyMixcv4siRI4iKikJycrISIVlUUFAAg8EAHx/z8q+Pjw/y8qxfXLW0tBQnTpxAbGxsm8c6ODhAp9OZ/RARUd9i6jVWU2fEnRZ6jcktLX7XvpYWTY2pn8R/IbsUpVW1SKpP8h4a6AkXB2kAbc7IAEQE6GAwirBTCVgYHYqvX3gYggD8ePUOruaXy5P3B/fTwtHeDr46qbrX0hOV1wqkylhksAf+K24oAGDd0WIYvYYAEIEb7Sh63DguVcZU9oCzF1D4G/DtS8DJ/5X2/+F/gVHxAAQgdRfw4XSgrPPX5ewoRZKx48ePY82aNejXrx9UKhVUKhXGjh2LdevW4S9/+YsSIbWqae8zURTb1Q/Nzc0N+fn50Gg6/n8uRETUd2jUKvjqpMqSpWWRKmvq5ErW1HtMxnx0jhjUzwVGEfjleqHcKmNSeMN5VSoB256MxIuTB+OH5ePwxqzhuD/QHbHDfAEAO45ekyfvD/OXigh+rVTG7tYa5PlwA/tpMW9UINyd7VGpN6DEZ4x0UHv6jR2pr4qNfAKY8rr0Ou2fAEQgYh5w3zxg1hbgqS8BR3cg+xSwfSJQ8Jv11+hCiiRjBoMBWq3UJdjb21ue6B4SEoL09HQlQrLI29sbdnZ2zapgt2/fblYtIyIi6kwBrTxR+dNvBdAbjAj2dMagdnTdb4lpqHL/pXycuC4NDzZtIhvk6Yz/ExOOIT6u8rbnxg8EAOw9k4Mfr0qT/of5ScmYaZjSUmXsRmEVRBFwdVTDW6uBSiUgrP68v7qMlA6ydt7YrVQg4zAg2AHjVgIPPNWw2LjGFWLM/zSsIjB4KrAkGeg/DKjIBz6aDZQoPx1IkWQsIiIC586dAwA89NBDeOedd5CSkoI1a9Zg4MCBSoRkkUajQWRkJA4cOGC2/cCBA4iOjlYoKiIi6gtae6LS1NJi8tD+nbJyjanf2J7Tt1BnlJZWCvVuuydmZIgHRgW7Q28wyk9jNq2MWXqa0jR5f2A/rRx/eH0y9rNhuHTQ7UtAuRVTglI2S3+OeFxqjaGyA2ZuAfoNBR7diKX7chC97jCu5NV3J/AcADz9NeA1BCi7JSVkFbfbvk4XUiQZW716NYxGqQnc2rVrcePGDYwbNw4JCQnYsmVLt8ZSUVEhd8gHgOvXryMtLU1uXbFy5Urs2LEDO3fuxOXLl/HSSy8hKysLS5cu7dY4iYiob2npiUqjUcThK1IVqqMtLZr6/UApGTP1dZ0Ybv3Q53PjzIsov6uvjPnoGipjTR9CuFbf1mJQo4QvzFdKxs4W2QEBkdLGq4mtX7yqCEj/Xno9ZlnD9oBRwLJfgPvnI+W3QhRW6vGnj1NRWl0r7df2A57eB7gFA0UZwDcrrP26XaJbW1uYNJ7IPnDgQFy6dAlFRUXw8PDo9rUpT506hUmTJsnvV65cCQCIj4/Hrl278Nhjj6GwsBBr1qxBbm4uIiIikJCQYNaig4iIqLO11Pj1ZGYRCipqoHVQ48EBnp1yLQ8XDYb56eQ+Ye1Z5zJmuC+CPZ2RVVQFPzdHeNY/2WlKxvQGI4oq9fDSOsifyZArYw3JmKkylp5XDoyJA7JTgas/AJGtrM5zcQ9grJWGJX2GN9tdU2dARY3UzPZGYRVe+jwNO56OgkolAG4BwNN7gW9XADPWW/19u4IilTFLPD09FVkkfOLEiRBFsdnPrl275GOef/55ZGZmoqamBqmpqRg/fny3x0lERH1LwzBlQ2VMFEWs3y/NrZ5xnx806s77a9w0VOmisWtXkmenEuS5Y1GhDZ/TqFXwrk/Amk7iN7W1GNhovluYj/Q6u6QaVQPq13DOSAJqW2l8e/Yz6c/7H7e4u6hS6mOqEgAHtQqHr9zGzpTrDQd4DQLivwF0/m18y65lM8kYERERNTBVxrJLGnqNHbx8Gyczi+GgVmHFtCGder0Z9/vBTiVg9gMB7U7ynnooGB8uHI03Zg4z2+5nYRK/KIqN5ow1VMbcnTXoX9+1P10MBnSBQF01cK2FbvyFGVLvMEElPS1pgSkZ89I6yL3SDlzKb9d36w5MxoiIiGyQn5sTBAG4W2tEYaUedQYj3v7+MgBg0dgB8HNz6tTrPRDsgRP/PQVvzmo+3NcWQRAwaWh/s6FIoOGJytxGk/gLK/Uou1sHQQBCvcwfEgivnzd29XYFEP6ItPHq95Yveu5z6c9BkwFXX4uHmJIxT2eNXLW73mgZJlvBZIyIiMgGNe41dqu4Gl+k3kLGnUp4ONvL60V2Ni+tQ4cWHG+JKf78RpUx0xBlgLsTHO3tzI4Pk+eNVQBh9UsVXk1svq6kKLY5RAk0SsZcNBhQn/jdLq9BZU1di59RApMxIiIiGxXgLlW/ruaXY+OBqwCAFycPgc7RXsmwrGZpSaSMRm0tmjJN4r+aXw6EjgXsXYDyXCA3zfzArONAyQ1AowWGzmjx+oUV9cmYVgM3Z3t41T9cYGvVMSZjRERENso0b2zTgau4U16DIE8nPPn7YIWjsl5Dr7GGSfjyfDELfcxM7S3S88sBe0dgUH23g/QfGg4yGoD9r0mvh88BNM4tXr+4qmGYEoDcO43JGBEREVnF9ESlqbL0SuxQOKjtWvuITbFUGTMNUw7q1zwZG9JfqpbdKa9BcaUeCK8fqrz8jbTwNwCc2iktZ6RxBSatavX6hY2GKQFgQH0ylslkjIiIiKxhqowBwP2Bbnj0Pj8Fo2k/00MGjRu/mhq+WhqmdHFQI8izYWgWYY8Aakfg9kXg47lA3gXg4JvSwVNfb7MlRVGF6WlK82SMlTEiIiKyiqkyBgD/FTdUalbag5gm8FfpDSi7Wwd9nRFZRVLftIEWKmMAENa/0bwxF2/gsX9KVbAbPwEfjAP05UBAFBC1qM3rF7VQGbvGZIyIiIisMSrEHSOC3PH0mBB5Me+exEljBzcn6WGD/LK7yCqqhMEowlljJydqTZnNGwOAIdOAZw9K606KRkClBmb+Q1qDsg1FTeaMycOUhbaVjCmyHBIRERG1zVmjxr5lDysdxj3xc3NEaXUt9l/Mw57T2QCkfmItrbojP1GZV9Gwsf9Q4NnDwI9/B4IfAnwjrLq2XBmrH6Y09TUrqapFcaUeHvUVM6WxMkZERERdxjSJf/3+q7hWUAlfnSNen9lyY1m511h+ufkC4y5ewPR3gIg/mB1/KacM6xPTcbfWYLbdYBQbnqasT7qcNHbwr4/HloYqmYwRERFRlzG1twCkJZd+WDEOI4PcWzx+UH8XCAJQWl0rV7Za89c957A16Td8ceqm2faSKr3cK9bDuaECFmqDT1QyGSMiIqIu84dRgYgK8cCG/xiBrX98AO7OrQ8NOqjt4OVieYHxpnJLq3H2VikA4GRmsdk+U1VM56g2W1XAFp+o5JwxIiIi6jJRoZ748s/R7fqMn5sjCipqkF92FxEBbi0e13jR79Qb5slYYUXDIuGN2WIyxsoYERER2RRLzWItSbyYJ7/OLqlGbmlDp/+mbS1MmIwRERERtUFeRqmVZKy0qhY/XysCAPRzlapfjatjpu77Hs4tJ2MFFTWdF/Q9YDJGRERENsVH13Zl7NCVfBiMIob6uiIuwheAeTJWXJ+MeTWpjAV5OsPeTkB1rQFRaw9i4rtJ2PnT9c7+Cu3CZIyIiIhsiqkyll/WcjK2/6I0XyxmmA8iQzwAWK6MmXqMmdjbqbB6xjCE+UjLMWUWVuFunXlbjO7GCfxERERkUxrmjFVb3H+31oAfr94BAMQM94W7s9Tl/2JOGar0dXDWqOU5Y00rYwAQHx2K+OhQlFbV4szNYgz0br5OZndiZYyIiIhsimmB8dxGC4w3dvTXAlTXGhDg7oTh/joEuDvBV+cIg1HE2ZtSqwtTa4umc8Yac3O2x8Tw/gj2cm7xmO7AZIyIiIhsSuMFxstr6prtv5AtJVwPD/aCIAgQBAGRodJQ5eksaajS1Nqi6TClLWIyRkRERDal8QLjlp6ozC6Rhi+DPBoqWpHB5vPGWhumtDVMxoiIiMjmtNbeIrtYSsYCPJzkbVGhDclYncHYYp8xW8RkjIiIiGyOqb1Fa5WxAPeGZOx3fjp4uWhQWl2L/3ciC3qDEQCTMSIiIqIO8WuhC7/RKMpPWTaujNnbqbB43AAAwMYDVwEAjvYqOGtsv3EEkzEiIiKyOab2Fnll5u0t7lTUoNYgwk4lyBP9TRb8PgRuTvYorqoFAHnBcVvHZIyIiIhsTktzxm7Vzxfz1TlCbWeexrg62uOZh0Pl9z1hiBJgMkZEREQ2qKUlkSzNF2vsmegB0DpIQ5MeTMaIiIiIOsbU+DWvyZJIlp6kbMzN2R7x0SEAgFCFm7lay/ZntREREVGfY5ozVlJVi7u1Bjja2wEAskuqALRcGQOAl6aGIczHFWMHe3d9oJ2AlTEiIiKyOTpHNZw1UgLWeN5YW5UxAFDbqTB7ZAC8tJzAT0RERNQhgtDwtGTjeWNtzRnriZiMERERkU1q2t5CFEWrKmM9DZMxIiIiskm+TRq/llbXolJvAMDKGBEREVGXM/Uay69Pxkw9xry1GnlCf2/AZIyIiIhsUtM5Y6b5Yv69qCoGMBkjIiIiG+XbpNdYTi+cvA8wGSMiIiIb1XSxcHnyPpMxIiIioq4X6OEEQQDulNcg9UZxQ1uLXvQkJcBkjIiIiGyUu7MG/xEZCAB4be8FZBW13X2/J2IyRkRERDbrPx8ZCp2jGpdyy3AxpwwAK2NERERE3cZL64BXYsPNtgW694wFwK3FZIyIiIhs2hMPhWC4vw4AoHVQQ+ekVjiizsVkjIiIiGyanUrA2jkRcFCrEBXqAUEQlA6pU/Wu1JKIiIh6pQeCPXD0PydB52ivdCidjskYERER9Qj9XR2VDqFLcJiSiIiISEFMxoiIiIgUxGSMiIiISEFMxoiIiIgUxGSMiIiISEFMxoiIiIgUxGSMiIiISEFMxoiIiIgUxGSMiIiISEFMxoiIiIgUxGSMiIiISEFMxoiIiIgUxGSMiIiISEFqpQOg1omiCAAoKytTOBIiIiKylunvbdPf461hMmbjysvLAQBBQUEKR0JERETtVV5eDjc3t1aPEURrUjZSjNFoRE5ODlxdXSEIQqvHjh49GidPnrT63GVlZQgKCsLNmzeh0+nuNdQ+qb2/c1tiC7F3VwxdcZ3OOue9nKejn23P53if6By28N9bR9lC7D3xXiGKIsrLy+Hv7w+VqvVZYayM2TiVSoXAwECrjrWzs+vQzVKn0/Em20Ed/Z3bAluIvbti6IrrdNY57+U8Hf1sRz7H+8S9sYX/3jrKFmLvqfeKtipiJpzA34ssW7ZM6RD6nJ78O7eF2Lsrhq64Tmed817O09HP2sI/+76mJ//ObSH2nnyvsAaHKfuwsrIyuLm5obS0VPH/6yEi28T7BFHXY2WsD3NwcMDrr78OBwcHpUMhIhvF+wRR12NljIiIiEhBrIwRERERKYjJGBEREZGCmIwRERERKYjJGBEREZGCmIwRERERKYjJGFll7ty58PDwwLx585QOhYhsyLfffovw8HAMGTIEO3bsUDocoh6JrS3IKklJSaioqMDu3bvx5ZdfKh0OEdmAuro6DBs2DElJSdDpdBg1ahR++eUXeHp6Kh0aUY/CyhhZZdKkSXB1dVU6DCKyISdOnMDw4cMREBAAV1dXTJ8+HYmJiUqHRdTjMBnrBY4cOYKZM2fC398fgiBg7969zY55//33MWDAADg6OiIyMhJHjx7t/kCJyKbc670jJycHAQEB8vvAwEBkZ2d3R+hEvQqTsV6gsrISI0aMwNatWy3u//zzz7FixQqsWrUKZ86cwbhx4xAXF4esrCz5mMjISERERDT7ycnJ6a6vQUTd7F7vHZZmuQiC0KUxE/VGaqUDoHsXFxeHuLi4Fvdv3LgRixcvxrPPPgsA2Lx5MxITE7Ft2zasW7cOAJCamtotsRKR7bjXe0dAQIBZJezWrVt46KGHujxuot6GlbFeTq/XIzU1FTExMWbbY2JicOzYMYWiIiJbZ82948EHH8SFCxeQnZ2N8vJyJCQkIDY2VolwiXo0VsZ6uYKCAhgMBvj4+Jht9/HxQV5entXniY2NxenTp1FZWYnAwEB89dVXGD16dGeHS0Q2wpp7h1qtxoYNGzBp0iQYjUa8+uqr8PLyUiJcoh6NyVgf0XQehyiK7ZrbwSekiPqmtu4ds2bNwqxZs7o7LKJehcOUvZy3tzfs7OyaVcFu377d7P94iYhMeO8g6j5Mxno5jUaDyMhIHDhwwGz7gQMHEB0drVBURGTreO8g6j4cpuwFKioq8Ntvv8nvr1+/jrS0NHh6eiI4OBgrV67EggULEBUVhTFjxmD79u3IysrC0qVLFYyaiJTGeweRbeBySL1AcnIyJk2a1Gx7fHw8du3aBUBq3PjOO+8gNzcXERER2LRpE8aPH9/NkRKRLeG9g8g2MBkjIiIiUhDnjBEREREpiMkYERERkYKYjBEREREpiMkYERERkYKYjBEREREpiMkYERERkYKYjBEREREpiMkYERERkYKYjBEREREpiMkYEfV4oihiyZIl8PT0hCAISEtLUzokIiKrcTkkIurxvv/+e8yePRvJyckYOHAgvL29oVarlQ6rmYULF6KkpAR79+5VOhQisiG2d7ciImqnjIwM+Pn5ITo62uJ+vV4PjUbTzVEREVmHw5RE1KMtXLgQL774IrKysiAIAkJDQzFx4kS88MILWLlyJby9vTFt2jQAwKVLlzB9+nRotVr4+PhgwYIFKCgokM9VWVmJp59+GlqtFn5+ftiwYQMmTpyIFStWtBnHlStX4OzsjE8//VTetmfPHjg6OuL8+fN44403sHv3buzbtw+CIEAQBCQnJ7d53vPnz2Py5MlwcnKCl5cXlixZgoqKCrNjdu7cieHDh8PBwQF+fn544YUX5H2lpaVYsmQJ+vfvD51Oh8mTJ+Ps2bPy/oyMDMyePRs+Pj7QarUYPXo0Dh48aHb+0NBQvPXWW1i0aBFcXV0RHByM7du3txk7EVmHyRgR9Wj/+Mc/sGbNGgQGBiI3NxcnT54EAOzevRtqtRopKSn44IMPkJubiwkTJmDkyJE4deoUfvjhB+Tn52P+/PnyuV555RUkJSXhq6++wv79+5GcnIzU1FSr4hg6dCjWr1+P559/Hjdu3EBOTg6ee+45vP3227jvvvvw8ssvY/78+XjkkUeQm5uL3NzcFit5JlVVVXjkkUfg4eGBkydP4osvvsDBgwfNkq1t27Zh2bJlWLJkCc6fP4+vv/4agwcPBiDNpZsxYwby8vKQkJCA1NRUjBo1ClOmTEFRUREAoKKiAtOnT8fBgwdx5swZxMbGYubMmcjKyjKLZcOGDYiKisKZM2fw/PPP489//jOuXLli1e+GiNogEhH1cJs2bRJDQkLk9xMmTBBHjhxpdsxrr70mxsTEmG27efOmCEBMT08Xy8vLRY1GI3722Wfy/sLCQtHJyUlcvny51bHMmDFDHDdunDhlyhRx2rRpotFolPfFx8eLs2fPtvpc27dvFz08PMSKigp523fffSeqVCoxLy9PFEVR9Pf3F1etWmXx84cOHRJ1Op149+5ds+2DBg0SP/jggxavO2zYMPG9996T34eEhIhPPfWU/N5oNIr9+/cXt23bZvV3IaKWcc4YEfVKUVFRZu9TU1ORlJQErVbb7NiMjAxUV1dDr9djzJgx8nZPT0+Eh4e367o7d+5EWFgYVCoVLly4AEEQOvYFAFy+fBkjRoyAi4uLvO3hhx+G0WhEeno6BEFATk4OpkyZYvHzqampqKiogJeXl9n26upqZGRkAJCGZt988018++23yMnJQV1dHaqrq5tVxu6//375tSAI8PX1xe3btzv83YioAZMxIuqVGicwAGA0GjFz5kz8/e9/b3asn58ffv3110657tmzZ1FZWQmVSoW8vDz4+/t3+FyiKLaYzAmCACcnp1Y/bzQa4efnZ3Fumru7OwBpaDYxMRHr16/H4MGD4eTkhHnz5kGv15sdb29v3+z6RqPR+i9DRC1iMkZEfcKoUaPw73//G6GhoRbbXgwePBj29vb4+eefERwcDAAoLi7G1atXMWHCBKuuUVRUhIULF2LVqlXIy8vDk08+idOnT8tJk0ajgcFgsDrmYcOGYffu3aisrJSTy5SUFKhUKoSFhcHV1RWhoaE4dOgQJk2aZPE75+XlQa1WIzQ01OI1jh49ioULF2Lu3LkApDlkmZmZVsdIRPeOE/iJqE9YtmwZioqK8Mc//hEnTpzAtWvXsH//fixatAgGgwFarRaLFy/GK6+8gkOHDuHChQtYuHAhVCrrb5NLly5FUFAQVq9ejY0bN0IURbz88svy/tDQUJw7dw7p6ekoKChAbW1tq+d78skn4ejoiPj4eFy4cAFJSUl48cUXsWDBAvj4+AAA3njjDWzYsAFbtmzBr7/+itOnT+O9994DAEydOhVjxozBnDlzkJiYiMzMTBw7dgyrV6/GqVOnAEhJ6J49e5CWloazZ8/iiSeeYMWLqJsxGSOiPsHf3x8pKSkwGAyIjY1FREQEli9fDjc3NznhevfddzF+/HjMmjULU6dOxdixYxEZGWnV+T/66CMkJCTg448/hlqthrOzMz755BPs2LEDCQkJAIDnnnsO4eHhiIqKQr9+/ZCSktLqOZ2dnZGYmIiioiKMHj0a8+bNw5QpU7B161b5mPj4eGzevBnvv/8+hg8fjkcffVQechUEAQkJCRg/fjwWLVqEsLAwPP7448jMzJSTuU2bNsHDwwPR0dGYOXMmYmNjMWrUqHb/fomo49iBn4ioFRMnTsTIkSOxefNmpUMhol6KlTEiIiIiBTEZIyKywtGjR6HValv86Yi33nqrxfPFxcV18jcgIlvFYUoiIitUV1cjOzu7xf2mrvftUVRUJHfCb8rJyQkBAQHtPicR9TxMxoiIiIgUxGFKIiIiIgUxGSMiIiJSEJMxIiIiIgUxGSMiIiJSEJMxIiIiIgUxGSMiIiJSEJMxIiIiIgX9f0dfdCUrMZK9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps = xrft.power_spectrum(ds_urel.mean(dim='yt_ocean'), dim='xt_ocean', window=True)\n",
    "ps1 = xrft.power_spectrum(ds_urel, dim='xt_ocean', window=True).mean(dim='yt_ocean')\n",
    "ps.plot()\n",
    "ps1.plot()\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.title('Relative Zonal Velocity (u_rel) Power Spectra')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4004cf2a-48c5-436c-86f0-26f6da32fe28",
   "metadata": {},
   "source": [
    "# Power Spectra for v_relative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b16b0510-f3a3-411d-a32d-826450d6cb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;v_relative&#x27; (yt_ocean: 100, xt_ocean: 100)&gt; Size: 40kB\n",
       "dask.array&lt;getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'v_relative'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 100</li><li><span class='xr-has-index'>xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9082134e-442f-4fb7-86d4-83274335d862' class='xr-array-in' type='checkbox' checked><label for='section-9082134e-442f-4fb7-86d4-83274335d862' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 100) </td>\n",
       "                        <td> (100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 38 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"120\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-483aac61-cdb8-4c61-bdf1-97e1219e2c31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-483aac61-cdb8-4c61-bdf1-97e1219e2c31' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-1c8b7b3b-1784-4a3d-a486-cca0ee267e7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c8b7b3b-1784-4a3d-a486-cca0ee267e7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89d4da7e-e3e1-403a-99f3-23959aee4dde' class='xr-var-data-in' type='checkbox'><label for='data-89d4da7e-e3e1-403a-99f3-23959aee4dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.0 -129.9 ... -120.2 -120.1</div><input id='attrs-dae66f4b-d3f0-4e08-baaa-f3c985fb5680' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dae66f4b-d3f0-4e08-baaa-f3c985fb5680' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-178134d1-6ef3-4818-816a-e121afa131c9' class='xr-var-data-in' type='checkbox'><label for='data-178134d1-6ef3-4818-816a-e121afa131c9' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-129.95, -129.85, -129.75, -129.65, -129.55, -129.45, -129.35, -129.25,\n",
       "       -129.15, -129.05, -128.95, -128.85, -128.75, -128.65, -128.55, -128.45,\n",
       "       -128.35, -128.25, -128.15, -128.05, -127.95, -127.85, -127.75, -127.65,\n",
       "       -127.55, -127.45, -127.35, -127.25, -127.15, -127.05, -126.95, -126.85,\n",
       "       -126.75, -126.65, -126.55, -126.45, -126.35, -126.25, -126.15, -126.05,\n",
       "       -125.95, -125.85, -125.75, -125.65, -125.55, -125.45, -125.35, -125.25,\n",
       "       -125.15, -125.05, -124.95, -124.85, -124.75, -124.65, -124.55, -124.45,\n",
       "       -124.35, -124.25, -124.15, -124.05, -123.95, -123.85, -123.75, -123.65,\n",
       "       -123.55, -123.45, -123.35, -123.25, -123.15, -123.05, -122.95, -122.85,\n",
       "       -122.75, -122.65, -122.55, -122.45, -122.35, -122.25, -122.15, -122.05,\n",
       "       -121.95, -121.85, -121.75, -121.65, -121.55, -121.45, -121.35, -121.25,\n",
       "       -121.15, -121.05, -120.95, -120.85, -120.75, -120.65, -120.55, -120.45,\n",
       "       -120.35, -120.25, -120.15, -120.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.74 24.83 24.92 ... 33.3 33.38</div><input id='attrs-05cfd24d-4928-441c-b446-9626b1921bb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05cfd24d-4928-441c-b446-9626b1921bb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88dad1fd-189c-40be-9312-5938b027055e' class='xr-var-data-in' type='checkbox'><label for='data-88dad1fd-189c-40be-9312-5938b027055e' 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>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([24.743028, 24.833814, 24.924534, 25.015187, 25.105773, 25.196292,\n",
       "       25.286744, 25.377128, 25.467445, 25.557694, 25.647876, 25.737989,\n",
       "       25.828033, 25.91801 , 26.007918, 26.097756, 26.187526, 26.277227,\n",
       "       26.366859, 26.456421, 26.545914, 26.635336, 26.724689, 26.813972,\n",
       "       26.903184, 26.992326, 27.081398, 27.170399, 27.259328, 27.348187,\n",
       "       27.436975, 27.525691, 27.614335, 27.702908, 27.791409, 27.879838,\n",
       "       27.968195, 28.05648 , 28.144692, 28.232832, 28.320899, 28.408893,\n",
       "       28.496814, 28.584661, 28.672436, 28.760137, 28.847764, 28.935318,\n",
       "       29.022798, 29.110203, 29.197535, 29.284792, 29.371975, 29.459083,\n",
       "       29.546116, 29.633074, 29.719958, 29.806766, 29.893499, 29.980157,\n",
       "       30.066739, 30.153245, 30.239676, 30.326031, 30.412309, 30.498512,\n",
       "       30.584638, 30.670687, 30.75666 , 30.842557, 30.928376, 31.014119,\n",
       "       31.099784, 31.185373, 31.270884, 31.356317, 31.441673, 31.526951,\n",
       "       31.612152, 31.697275, 31.782319, 31.867286, 31.952174, 32.036984,\n",
       "       32.121715, 32.206368, 32.290942, 32.375437, 32.459853, 32.544191,\n",
       "       32.628449, 32.712627, 32.796727, 32.880747, 32.964687, 33.048548,\n",
       "       33.132329, 33.21603 , 33.299651, 33.383192])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf962e1c-b2e8-4824-88c8-6dda19bdec5b' class='xr-section-summary-in' type='checkbox'  ><label for='section-bf962e1c-b2e8-4824-88c8-6dda19bdec5b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a97addf2-01fd-4401-8825-ca33ee2a98c3' class='xr-index-data-in' type='checkbox'/><label for='index-a97addf2-01fd-4401-8825-ca33ee2a98c3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-129.95000000000175, -129.85000000000173, -129.75000000000176,\n",
       "       -129.65000000000174, -129.55000000000177, -129.45000000000175,\n",
       "       -129.35000000000178, -129.25000000000176,  -129.1500000000018,\n",
       "       -129.05000000000177,  -128.9500000000018, -128.85000000000178,\n",
       "       -128.75000000000182,  -128.6500000000018, -128.55000000000183,\n",
       "        -128.4500000000018, -128.35000000000184, -128.25000000000182,\n",
       "       -128.15000000000185, -128.05000000000183, -127.95000000000186,\n",
       "       -127.85000000000184, -127.75000000000188, -127.65000000000185,\n",
       "       -127.55000000000189, -127.45000000000186,  -127.3500000000019,\n",
       "       -127.25000000000188, -127.15000000000191, -127.05000000000189,\n",
       "       -126.95000000000192,  -126.8500000000019, -126.75000000000193,\n",
       "       -126.65000000000191, -126.55000000000194, -126.45000000000192,\n",
       "       -126.35000000000196, -126.25000000000193, -126.15000000000197,\n",
       "       -126.05000000000194, -125.95000000000198, -125.85000000000196,\n",
       "       -125.75000000000199, -125.65000000000197,   -125.550000000002,\n",
       "       -125.45000000000198, -125.35000000000201, -125.25000000000199,\n",
       "       -125.15000000000202,   -125.050000000002, -124.95000000000203,\n",
       "       -124.85000000000201, -124.75000000000205, -124.65000000000202,\n",
       "       -124.55000000000206, -124.45000000000203, -124.35000000000207,\n",
       "       -124.25000000000205, -124.15000000000208, -124.05000000000206,\n",
       "       -123.95000000000209, -123.85000000000207,  -123.7500000000021,\n",
       "       -123.65000000000208, -123.55000000000211, -123.45000000000209,\n",
       "       -123.35000000000213,  -123.2500000000021, -123.15000000000214,\n",
       "       -123.05000000000211, -122.95000000000215, -122.85000000000213,\n",
       "       -122.75000000000216, -122.65000000000214, -122.55000000000217,\n",
       "       -122.45000000000215, -122.35000000000218, -122.25000000000216,\n",
       "        -122.1500000000022, -122.05000000000217,  -121.9500000000022,\n",
       "       -121.85000000000218, -121.75000000000222,  -121.6500000000022,\n",
       "       -121.55000000000223,  -121.4500000000022, -121.35000000000224,\n",
       "       -121.25000000000222, -121.15000000000225, -121.05000000000223,\n",
       "       -120.95000000000226, -120.85000000000224, -120.75000000000227,\n",
       "       -120.65000000000225, -120.55000000000229, -120.45000000000226,\n",
       "        -120.3500000000023, -120.25000000000227, -120.15000000000231,\n",
       "       -120.05000000000229],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f649a4e-cd57-44a0-9f23-31e689084e03' class='xr-index-data-in' type='checkbox'/><label for='index-1f649a4e-cd57-44a0-9f23-31e689084e03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([24.743028005786304, 24.833814202883772, 24.924533960340074,\n",
       "       25.015186931740978, 25.105773107375004,  25.19629214217983,\n",
       "        25.28674402829334, 25.377128422005594, 25.467445317308336,\n",
       "       25.557694371844295, 25.647875581463406, 25.737988605161227,\n",
       "       25.828033440650042, 25.918009748278212, 26.007917527624393,\n",
       "       26.097756440389567,  26.18752648802262, 26.277227333576807,\n",
       "       26.366858980374957,  26.45642109282209, 26.545913676118523,\n",
       "       26.635336396020385,  26.72468925960888, 26.813971933990416,\n",
       "       26.903184428130338, 26.992326410484353,  27.08139789190504,\n",
       "       27.170398542196274, 27.259328374100818, 27.348187058769412,\n",
       "       27.436974610837808, 27.525690702802166, 27.614335351193873,\n",
       "        27.70290822985291,  27.79140935720881,  27.87983840844362,\n",
       "       27.968195403887382,   28.0564800200623, 28.144692279201134,\n",
       "         28.2328318591642, 28.320898784089056, 28.408892733171918,\n",
       "        28.49681373245707,  28.58466146247428, 28.672435951176354,\n",
       "       28.760136880424106, 28.847764280080515, 28.935317833334803,\n",
       "       29.022797571961636, 29.110203180475846, 29.197534692565167,\n",
       "        29.28479179406711,  29.37197452058372,  29.45908255927211,\n",
       "        29.54611594764974,  29.63307437419011, 29.719957878327065,\n",
       "       29.806766149847146, 29.893499230101426,  29.98015681018598,\n",
       "       30.066738933369823, 30.153245292054937,  30.23967593142886,\n",
       "        30.32603054519571, 30.412309180462007,   30.4985115322301,\n",
       "        30.58463764952582,  30.67068722864571,  30.75666032053511,\n",
       "       30.842556622780595,  30.92837618824709, 31.014118715806898,\n",
       "        31.09978426024449,  31.18537252171347, 31.270883556917696,\n",
       "       31.356317067287502, 31.441673111445844,  31.52695139209512,\n",
       "       31.612151969776978,  31.69727454846106,  31.78231919060719,\n",
       "       31.867285601447325, 31.952173845358825,   32.0369836288309,\n",
       "        32.12171501815771,  32.20636772108053,  32.29094180580946,\n",
       "       32.375436981332555,  32.45985331777485, 32.544190525365785,\n",
       "        32.62844867614426,  32.71262748157553,  32.79672701561119,\n",
       "       32.880746990946676, 32.964687483444955,  33.04854820702588,\n",
       "         33.1323292394624,   33.2160302958929,  33.29965145599879,\n",
       "        33.38319243613102],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de8d68ca-449a-4eb0-bd1d-13095d56cef3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de8d68ca-449a-4eb0-bd1d-13095d56cef3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'v_relative' (yt_ocean: 100, xt_ocean: 100)> Size: 40kB\n",
       "dask.array<getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vrel = ds_test.v_relative\n",
    "ds_vrel = vrel.drop(['area_t', 'dxt', 'dyt', 'geolat_t', 'geolon_t', 'ht', 'kmt', 'wet'])\n",
    "ds_vrel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "906d121a-72b0-49cb-98d4-14455ef259dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relative Meridional Velocity (v_rel) Power Spectra')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps = xrft.power_spectrum(ds_vrel.mean(dim='yt_ocean'), dim='xt_ocean', window=True)\n",
    "ps1 = xrft.power_spectrum(ds_vrel, dim='xt_ocean', window=True).mean(dim='yt_ocean')\n",
    "ps.plot()\n",
    "ps1.plot()\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.title('Relative Meridional Velocity (v_rel) Power Spectra')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab88ac8-8c91-46f1-a551-9de43a51b979",
   "metadata": {},
   "source": [
    "# Power Spectra for q_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "09f3cc57-4849-4316-a654-3683e29c3942",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;q_ref&#x27; (yt_ocean: 100, xt_ocean: 100)&gt; Size: 40kB\n",
       "dask.array&lt;getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'q_ref'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>yt_ocean</span>: 100</li><li><span class='xr-has-index'>xt_ocean</span>: 100</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-75549a2e-ed51-46b6-b08f-5a9d3a89746d' class='xr-array-in' type='checkbox' checked><label for='section-75549a2e-ed51-46b6-b08f-5a9d3a89746d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                        <td> 39.06 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (100, 100) </td>\n",
       "                        <td> (100, 100) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 19 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"120\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >100</text>\n",
       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,60.000000)\">100</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-6244a9a8-2f38-4d83-a715-33ecf1aa1250' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6244a9a8-2f38-4d83-a715-33ecf1aa1250' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00</div><input id='attrs-0ddbc1ab-eae3-4253-a03b-e6cac2241428' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ddbc1ab-eae3-4253-a03b-e6cac2241428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dacf808d-7670-4a69-826d-d4cc3233fdf4' class='xr-var-data-in' type='checkbox'><label for='data-dacf808d-7670-4a69-826d-d4cc3233fdf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeJulian(181, 1, 1, 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 class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.0 -129.9 ... -120.2 -120.1</div><input id='attrs-4163c8de-e799-4f40-a7eb-c93f6f88dcb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4163c8de-e799-4f40-a7eb-c93f6f88dcb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3818f301-eabb-4a5d-bdde-59fd4f15b152' class='xr-var-data-in' type='checkbox'><label for='data-3818f301-eabb-4a5d-bdde-59fd4f15b152' 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>cartesian_axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>tcell longitude</dd><dt><span>units :</span></dt><dd>degrees_E</dd></dl></div><div class='xr-var-data'><pre>array([-129.95, -129.85, -129.75, -129.65, -129.55, -129.45, -129.35, -129.25,\n",
       "       -129.15, -129.05, -128.95, -128.85, -128.75, -128.65, -128.55, -128.45,\n",
       "       -128.35, -128.25, -128.15, -128.05, -127.95, -127.85, -127.75, -127.65,\n",
       "       -127.55, -127.45, -127.35, -127.25, -127.15, -127.05, -126.95, -126.85,\n",
       "       -126.75, -126.65, -126.55, -126.45, -126.35, -126.25, -126.15, -126.05,\n",
       "       -125.95, -125.85, -125.75, -125.65, -125.55, -125.45, -125.35, -125.25,\n",
       "       -125.15, -125.05, -124.95, -124.85, -124.75, -124.65, -124.55, -124.45,\n",
       "       -124.35, -124.25, -124.15, -124.05, -123.95, -123.85, -123.75, -123.65,\n",
       "       -123.55, -123.45, -123.35, -123.25, -123.15, -123.05, -122.95, -122.85,\n",
       "       -122.75, -122.65, -122.55, -122.45, -122.35, -122.25, -122.15, -122.05,\n",
       "       -121.95, -121.85, -121.75, -121.65, -121.55, -121.45, -121.35, -121.25,\n",
       "       -121.15, -121.05, -120.95, -120.85, -120.75, -120.65, -120.55, -120.45,\n",
       "       -120.35, -120.25, -120.15, -120.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>24.74 24.83 24.92 ... 33.3 33.38</div><input id='attrs-48d3846d-58ac-43a1-9ec7-19058cd30bfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48d3846d-58ac-43a1-9ec7-19058cd30bfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57905ed0-759c-471f-859f-46a23c3e75a3' class='xr-var-data-in' type='checkbox'><label for='data-57905ed0-759c-471f-859f-46a23c3e75a3' 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>cartesian_axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>tcell latitude</dd><dt><span>units :</span></dt><dd>degrees_N</dd></dl></div><div class='xr-var-data'><pre>array([24.743028, 24.833814, 24.924534, 25.015187, 25.105773, 25.196292,\n",
       "       25.286744, 25.377128, 25.467445, 25.557694, 25.647876, 25.737989,\n",
       "       25.828033, 25.91801 , 26.007918, 26.097756, 26.187526, 26.277227,\n",
       "       26.366859, 26.456421, 26.545914, 26.635336, 26.724689, 26.813972,\n",
       "       26.903184, 26.992326, 27.081398, 27.170399, 27.259328, 27.348187,\n",
       "       27.436975, 27.525691, 27.614335, 27.702908, 27.791409, 27.879838,\n",
       "       27.968195, 28.05648 , 28.144692, 28.232832, 28.320899, 28.408893,\n",
       "       28.496814, 28.584661, 28.672436, 28.760137, 28.847764, 28.935318,\n",
       "       29.022798, 29.110203, 29.197535, 29.284792, 29.371975, 29.459083,\n",
       "       29.546116, 29.633074, 29.719958, 29.806766, 29.893499, 29.980157,\n",
       "       30.066739, 30.153245, 30.239676, 30.326031, 30.412309, 30.498512,\n",
       "       30.584638, 30.670687, 30.75666 , 30.842557, 30.928376, 31.014119,\n",
       "       31.099784, 31.185373, 31.270884, 31.356317, 31.441673, 31.526951,\n",
       "       31.612152, 31.697275, 31.782319, 31.867286, 31.952174, 32.036984,\n",
       "       32.121715, 32.206368, 32.290942, 32.375437, 32.459853, 32.544191,\n",
       "       32.628449, 32.712627, 32.796727, 32.880747, 32.964687, 33.048548,\n",
       "       33.132329, 33.21603 , 33.299651, 33.383192])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d3ed72e-e3db-4dd7-9e3b-742b9c33f58c' class='xr-section-summary-in' type='checkbox'  ><label for='section-2d3ed72e-e3db-4dd7-9e3b-742b9c33f58c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a5c1e14-fb23-4a27-ab53-8d86a39de96f' class='xr-index-data-in' type='checkbox'/><label for='index-3a5c1e14-fb23-4a27-ab53-8d86a39de96f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-129.95000000000175, -129.85000000000173, -129.75000000000176,\n",
       "       -129.65000000000174, -129.55000000000177, -129.45000000000175,\n",
       "       -129.35000000000178, -129.25000000000176,  -129.1500000000018,\n",
       "       -129.05000000000177,  -128.9500000000018, -128.85000000000178,\n",
       "       -128.75000000000182,  -128.6500000000018, -128.55000000000183,\n",
       "        -128.4500000000018, -128.35000000000184, -128.25000000000182,\n",
       "       -128.15000000000185, -128.05000000000183, -127.95000000000186,\n",
       "       -127.85000000000184, -127.75000000000188, -127.65000000000185,\n",
       "       -127.55000000000189, -127.45000000000186,  -127.3500000000019,\n",
       "       -127.25000000000188, -127.15000000000191, -127.05000000000189,\n",
       "       -126.95000000000192,  -126.8500000000019, -126.75000000000193,\n",
       "       -126.65000000000191, -126.55000000000194, -126.45000000000192,\n",
       "       -126.35000000000196, -126.25000000000193, -126.15000000000197,\n",
       "       -126.05000000000194, -125.95000000000198, -125.85000000000196,\n",
       "       -125.75000000000199, -125.65000000000197,   -125.550000000002,\n",
       "       -125.45000000000198, -125.35000000000201, -125.25000000000199,\n",
       "       -125.15000000000202,   -125.050000000002, -124.95000000000203,\n",
       "       -124.85000000000201, -124.75000000000205, -124.65000000000202,\n",
       "       -124.55000000000206, -124.45000000000203, -124.35000000000207,\n",
       "       -124.25000000000205, -124.15000000000208, -124.05000000000206,\n",
       "       -123.95000000000209, -123.85000000000207,  -123.7500000000021,\n",
       "       -123.65000000000208, -123.55000000000211, -123.45000000000209,\n",
       "       -123.35000000000213,  -123.2500000000021, -123.15000000000214,\n",
       "       -123.05000000000211, -122.95000000000215, -122.85000000000213,\n",
       "       -122.75000000000216, -122.65000000000214, -122.55000000000217,\n",
       "       -122.45000000000215, -122.35000000000218, -122.25000000000216,\n",
       "        -122.1500000000022, -122.05000000000217,  -121.9500000000022,\n",
       "       -121.85000000000218, -121.75000000000222,  -121.6500000000022,\n",
       "       -121.55000000000223,  -121.4500000000022, -121.35000000000224,\n",
       "       -121.25000000000222, -121.15000000000225, -121.05000000000223,\n",
       "       -120.95000000000226, -120.85000000000224, -120.75000000000227,\n",
       "       -120.65000000000225, -120.55000000000229, -120.45000000000226,\n",
       "        -120.3500000000023, -120.25000000000227, -120.15000000000231,\n",
       "       -120.05000000000229],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-30a7aef9-fb14-4607-8cda-987d797cb3fa' class='xr-index-data-in' type='checkbox'/><label for='index-30a7aef9-fb14-4607-8cda-987d797cb3fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([24.743028005786304, 24.833814202883772, 24.924533960340074,\n",
       "       25.015186931740978, 25.105773107375004,  25.19629214217983,\n",
       "        25.28674402829334, 25.377128422005594, 25.467445317308336,\n",
       "       25.557694371844295, 25.647875581463406, 25.737988605161227,\n",
       "       25.828033440650042, 25.918009748278212, 26.007917527624393,\n",
       "       26.097756440389567,  26.18752648802262, 26.277227333576807,\n",
       "       26.366858980374957,  26.45642109282209, 26.545913676118523,\n",
       "       26.635336396020385,  26.72468925960888, 26.813971933990416,\n",
       "       26.903184428130338, 26.992326410484353,  27.08139789190504,\n",
       "       27.170398542196274, 27.259328374100818, 27.348187058769412,\n",
       "       27.436974610837808, 27.525690702802166, 27.614335351193873,\n",
       "        27.70290822985291,  27.79140935720881,  27.87983840844362,\n",
       "       27.968195403887382,   28.0564800200623, 28.144692279201134,\n",
       "         28.2328318591642, 28.320898784089056, 28.408892733171918,\n",
       "        28.49681373245707,  28.58466146247428, 28.672435951176354,\n",
       "       28.760136880424106, 28.847764280080515, 28.935317833334803,\n",
       "       29.022797571961636, 29.110203180475846, 29.197534692565167,\n",
       "        29.28479179406711,  29.37197452058372,  29.45908255927211,\n",
       "        29.54611594764974,  29.63307437419011, 29.719957878327065,\n",
       "       29.806766149847146, 29.893499230101426,  29.98015681018598,\n",
       "       30.066738933369823, 30.153245292054937,  30.23967593142886,\n",
       "        30.32603054519571, 30.412309180462007,   30.4985115322301,\n",
       "        30.58463764952582,  30.67068722864571,  30.75666032053511,\n",
       "       30.842556622780595,  30.92837618824709, 31.014118715806898,\n",
       "        31.09978426024449,  31.18537252171347, 31.270883556917696,\n",
       "       31.356317067287502, 31.441673111445844,  31.52695139209512,\n",
       "       31.612151969776978,  31.69727454846106,  31.78231919060719,\n",
       "       31.867285601447325, 31.952173845358825,   32.0369836288309,\n",
       "        32.12171501815771,  32.20636772108053,  32.29094180580946,\n",
       "       32.375436981332555,  32.45985331777485, 32.544190525365785,\n",
       "        32.62844867614426,  32.71262748157553,  32.79672701561119,\n",
       "       32.880746990946676, 32.964687483444955,  33.04854820702588,\n",
       "         33.1323292394624,   33.2160302958929,  33.29965145599879,\n",
       "        33.38319243613102],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7372abd-7b86-4574-a42d-0ba68ee611ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7372abd-7b86-4574-a42d-0ba68ee611ff' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'q_ref' (yt_ocean: 100, xt_ocean: 100)> Size: 40kB\n",
       "dask.array<getitem, shape=(100, 100), dtype=float32, chunksize=(100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    time      object 8B 0181-01-01 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 800B -130.0 -129.9 -129.8 ... -120.2 -120.1\n",
       "  * yt_ocean  (yt_ocean) float64 800B 24.74 24.83 24.92 ... 33.22 33.3 33.38"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qref = ds_test.q_ref\n",
    "ds_qref = qref.drop(['area_t', 'dxt', 'dyt', 'geolat_t', 'geolon_t', 'ht', 'kmt', 'wet'])\n",
    "ds_qref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0e3bb300-f0a7-48b1-b165-6b20631d507f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Reference Potential Vorticity (q_ref) Power Spectra')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ps = xrft.power_spectrum(ds_qref.mean(dim='yt_ocean'), dim='xt_ocean', window=True)\n",
    "ps1 = xrft.power_spectrum(ds_qref, dim='xt_ocean', window=True).mean(dim='yt_ocean')\n",
    "ps.plot()\n",
    "ps1.plot()\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')\n",
    "plt.title('Reference Potential Vorticity (q_ref) Power Spectra')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0653e1fd-36e3-463c-b9d4-0f92efc6bbf5",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "It does look like smoother variables have steeper power spectra. Could this be because they are more evenly spread across scales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cea2f1bc-eb97-4d60-a1a8-7574595dd185",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}