crop_AVISO_to_edward.ipynb

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    "### Crop AVISO dataset to Gulf Stream Eddies\n",
    "\n",
    "Data file from AVISO during Hackweek: 'Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc'\n",
    "\n",
    "Updated files downloaded 02/03/2025 (allsat): \n",
    "- 'Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111.nc'\n",
    "- 'Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111.nc'\n",
    "\n",
    "\n",
    "Lexi Jones-Kellett\n",
    "\n",
    "Date created: 08/06/24\n",
    "\n",
    "Last edited: 02/04/25"
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   "source": [
    "import math, pylab, csv\n",
    "import xarray as xr\n",
    "import netCDF4\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "from itertools import groupby\n",
    "from matplotlib.path import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import Normalize\n",
    "import matplotlib.dates as mdates\n",
    "import cartopy.crs as ccrs\n"
   ]
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    "fontsize = 18\n",
    "\n",
    "plt.rc('font', size=fontsize)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=fontsize)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=fontsize)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=fontsize)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=fontsize)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=fontsize)    # legend fontsize\n",
    "plt.rc('figure', titlesize=fontsize)  # fontsize of the figure title"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Hackweek Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                        (obs: 8408689, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float32 ...\n",
       "    effective_area                 (obs) float32 ...\n",
       "    effective_contour_height       (obs) float32 ...\n",
       "    effective_contour_latitude     (obs, NbSample) float32 ...\n",
       "    effective_contour_longitude    (obs, NbSample) float32 ...\n",
       "    effective_contour_shape_error  (obs) float32 ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float32 ...\n",
       "    speed_contour_shape_error      (obs) float32 ...\n",
       "    speed_radius                   (obs) float32 ...\n",
       "    time                           (obs) datetime64[ns] ...\n",
       "    track                          (obs) uint32 ...\n",
       "    uavg_profile                   (obs, NbSample) float32 ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2024-08-06T09:38:06Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2396D\n",
       "    time_coverage_end:         2024-07-23T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Cyclonic Eddies in Altimeter Observa...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ec2abfe4-60d6-4321-a256-f5bb9f999cc3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec2abfe4-60d6-4321-a256-f5bb9f999cc3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 8408689</li><li><span>NbSample</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-07860b00-663d-4125-a963-58a5f6cb1dc9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-07860b00-663d-4125-a963-58a5f6cb1dc9' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d178ba08-d0e9-4931-bb62-1be941aae1a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-d178ba08-d0e9-4931-bb62-1be941aae1a1' class='xr-section-summary' >Data variables: <span>(27)</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>amplitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d31c6c3-9a06-42f6-8036-ba0d84ce6365' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d31c6c3-9a06-42f6-8036-ba0d84ce6365' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c6a43bf-7b08-407c-acca-dcb7ecef8ded' class='xr-var-data-in' type='checkbox'><label for='data-3c6a43bf-7b08-407c-acca-dcb7ecef8ded' 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>comment :</span></dt><dd>Magnitude of the height difference between the extremum of SSH within the eddy and the SSH around the effective contour defining the eddy edge</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>0.004</dd><dt><span>max :</span></dt><dd>1.405</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41af8bf4-0da4-4fcf-918d-eaf78da03738' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41af8bf4-0da4-4fcf-918d-eaf78da03738' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a736e701-278b-497a-832d-420403f3f1d4' class='xr-var-data-in' type='checkbox'><label for='data-a736e701-278b-497a-832d-420403f3f1d4' 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>comment :</span></dt><dd>Area enclosed by the effective contour in m^2</dd><dt><span>long_name :</span></dt><dd>Effective area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>128509144.0</dd><dt><span>max :</span></dt><dd>325393650000.0</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25ab6b12-307c-4590-bd1a-46c2aab1c18e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25ab6b12-307c-4590-bd1a-46c2aab1c18e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07862bb1-cc58-4bb8-b1c5-24d9e10022e9' class='xr-var-data-in' type='checkbox'><label for='data-07862bb1-cc58-4bb8-b1c5-24d9e10022e9' 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>comment :</span></dt><dd>SSH filtered height for effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.918</dd><dt><span>max :</span></dt><dd>0.446</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cfaea4c2-7ac0-43f6-a1b8-bdf5ad81b0c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfaea4c2-7ac0-43f6-a1b8-bdf5ad81b0c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee00eecc-4671-4461-a38a-b90ca4bd5112' class='xr-var-data-in' type='checkbox'><label for='data-ee00eecc-4671-4461-a38a-b90ca4bd5112' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[168173780 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e1ac806-a956-4bed-87fd-c1bc2aa56b83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e1ac806-a956-4bed-87fd-c1bc2aa56b83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bedffd72-400f-431f-9da4-b9e2e87ed21b' class='xr-var-data-in' type='checkbox'><label for='data-bedffd72-400f-431f-9da4-b9e2e87ed21b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[168173780 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8884e13-119f-4fad-9a72-dd0647b9e422' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8884e13-119f-4fad-9a72-dd0647b9e422' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be9e68e7-4fd6-43fe-9799-a796b6570bd6' class='xr-var-data-in' type='checkbox'><label for='data-be9e68e7-4fd6-43fe-9799-a796b6570bd6' 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>comment :</span></dt><dd>Error criterion between the effective contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Effective Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>2.5</dd><dt><span>max :</span></dt><dd>75.5</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-679f6875-f2ea-47bd-b9da-4b0eeab55590' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-679f6875-f2ea-47bd-b9da-4b0eeab55590' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc43ec82-e367-44df-836c-56d3e7b9e19a' class='xr-var-data-in' type='checkbox'><label for='data-dc43ec82-e367-44df-836c-56d3e7b9e19a' 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>comment :</span></dt><dd>Radius of the best fit circle corresponding to the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7050.0</dd><dt><span>max :</span></dt><dd>378400.0</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inner_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d56bc8a1-12ca-4d97-9568-7d050fc6de7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d56bc8a1-12ca-4d97-9568-7d050fc6de7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1003d3a8-01c8-4cc9-a0f4-d4e03ac15f50' class='xr-var-data-in' type='checkbox'><label for='data-1003d3a8-01c8-4cc9-a0f4-d4e03ac15f50' 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>comment :</span></dt><dd>SSH filtered height for the smallest detected contour</dd><dt><span>long_name :</span></dt><dd>Inner Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-1.462</dd><dt><span>max :</span></dt><dd>0.406</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2af92820-94e8-4e05-a4fa-ff789f086df1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2af92820-94e8-4e05-a4fa-ff789f086df1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50a1035b-7aa6-4883-b5b9-8634c6188da3' class='xr-var-data-in' type='checkbox'><label for='data-50a1035b-7aa6-4883-b5b9-8634c6188da3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-78.05134</dd><dt><span>max :</span></dt><dd>84.03748</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21c8ce18-6180-4788-a2e8-3f82ad2f8417' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21c8ce18-6180-4788-a2e8-3f82ad2f8417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b06cef3-1ceb-4635-9fc7-74b74b010825' class='xr-var-data-in' type='checkbox'><label for='data-2b06cef3-1ceb-4635-9fc7-74b74b010825' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Latitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-90.0</dd><dt><span>max :</span></dt><dd>84.12464</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bf8b9c9f-935f-4871-966a-47c9cd4be3e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf8b9c9f-935f-4871-966a-47c9cd4be3e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c2f16ff-406c-436a-97a6-e22326c475d7' class='xr-var-data-in' type='checkbox'><label for='data-0c2f16ff-406c-436a-97a6-e22326c475d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-28.260529</dd><dt><span>max :</span></dt><dd>367.2987</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-32ce00c6-d6cd-456b-94a8-936f454491fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32ce00c6-d6cd-456b-94a8-936f454491fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2066b87-6cb0-4aed-901f-0da607a9d8fd' class='xr-var-data-in' type='checkbox'><label for='data-d2066b87-6cb0-4aed-901f-0da607a9d8fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Longitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-28.13443</dd><dt><span>max :</span></dt><dd>367.44983</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_contours</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-55428018-7dfc-4d9e-8863-bf5edb6c7f96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55428018-7dfc-4d9e-8863-bf5edb6c7f96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-958aabc3-bf6b-41cc-a365-583b99a731d7' class='xr-var-data-in' type='checkbox'><label for='data-958aabc3-bf6b-41cc-a365-583b99a731d7' 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>comment :</span></dt><dd>Number of contours selected for this eddy</dd><dt><span>long_name :</span></dt><dd>Number of contours</dd><dt><span>min :</span></dt><dd>1</dd><dt><span>max :</span></dt><dd>703</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_e</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bc503238-1ed2-4071-97a6-23da372ad131' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc503238-1ed2-4071-97a6-23da372ad131' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a48712-44d6-4992-9712-e9dc4324efb4' class='xr-var-data-in' type='checkbox'><label for='data-58a48712-44d6-4992-9712-e9dc4324efb4' 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>description :</span></dt><dd>Number of points for effective contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for effective contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>145</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_s</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5aa4fbe8-0a74-48f7-a829-461f63934b9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5aa4fbe8-0a74-48f7-a829-461f63934b9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c19b0d81-b451-46c8-8991-0e91f9769794' class='xr-var-data-in' type='checkbox'><label for='data-c19b0d81-b451-46c8-8991-0e91f9769794' 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>description :</span></dt><dd>Number of points for speed contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for speed contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>135</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bd7383f1-39b7-4f3a-9bc8-f2d33e3a2f30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd7383f1-39b7-4f3a-9bc8-f2d33e3a2f30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b97770e1-a889-4a86-a1aa-174393eba7a3' class='xr-var-data-in' type='checkbox'><label for='data-b97770e1-a889-4a86-a1aa-174393eba7a3' 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>comment :</span></dt><dd>Flag indicating if the value is interpolated between two observations or not (0: observed eddy, 1: interpolated eddy)</dd><dt><span>long_name :</span></dt><dd>Virtual Eddy Position</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_number</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-534b1c8d-9b70-4d69-b919-a68ca4e01a7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-534b1c8d-9b70-4d69-b919-a68ca4e01a7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1a60d0d-42dc-46f0-85bd-a8dd42ec92c0' class='xr-var-data-in' type='checkbox'><label for='data-a1a60d0d-42dc-46f0-85bd-a8dd42ec92c0' 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>comment :</span></dt><dd>Observation sequence number, days starting at the eddy first detection</dd><dt><span>long_name :</span></dt><dd>Eddy temporal index in a trajectory</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>1961</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-29fefe31-c1d8-4aa0-8c5b-cd03f05330ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29fefe31-c1d8-4aa0-8c5b-cd03f05330ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6ff52fc-d3ea-4fd5-881c-c8c392e5f930' class='xr-var-data-in' type='checkbox'><label for='data-d6ff52fc-d3ea-4fd5-881c-c8c392e5f930' 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>comment :</span></dt><dd>Area enclosed by the speed contour in m^2</dd><dt><span>long_name :</span></dt><dd>Speed area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>128509144.0</dd><dt><span>max :</span></dt><dd>258590110000.0</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_average</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc96ca97-4d86-48b8-ba32-a3ce27a414e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc96ca97-4d86-48b8-ba32-a3ce27a414e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-165497a8-614a-486a-ac94-193ed3e525ce' class='xr-var-data-in' type='checkbox'><label for='data-165497a8-614a-486a-ac94-193ed3e525ce' 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>comment :</span></dt><dd>Average speed of the contour defining the radius scale “speed_radius”</dd><dt><span>long_name :</span></dt><dd>Maximum circum-averaged Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>min :</span></dt><dd>0.0</dd><dt><span>max :</span></dt><dd>5.6524</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aa23e25e-3ab5-4034-b34a-9afac7de5750' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa23e25e-3ab5-4034-b34a-9afac7de5750' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b82ab23-bf88-4f1a-851c-7b6abbe9542c' class='xr-var-data-in' type='checkbox'><label for='data-9b82ab23-bf88-4f1a-851c-7b6abbe9542c' 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>comment :</span></dt><dd>SSH filtered height for speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.918</dd><dt><span>max :</span></dt><dd>0.446</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9d0c251-88ba-475d-9bd1-93e702c06238' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d0c251-88ba-475d-9bd1-93e702c06238' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7096f103-35a1-4efd-89e5-9d9886bbaff2' class='xr-var-data-in' type='checkbox'><label for='data-7096f103-35a1-4efd-89e5-9d9886bbaff2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[168173780 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d702fef-8c77-44d5-bb50-8776b1ef0293' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d702fef-8c77-44d5-bb50-8776b1ef0293' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee284630-ea8c-4bfd-baa1-beda5fb7d989' class='xr-var-data-in' type='checkbox'><label for='data-ee284630-ea8c-4bfd-baa1-beda5fb7d989' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[168173780 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98172659-2991-4b26-b0e0-49d4f4506765' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98172659-2991-4b26-b0e0-49d4f4506765' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-712cdc2a-e884-4e6c-a65f-c4505476ad3f' class='xr-var-data-in' type='checkbox'><label for='data-712cdc2a-e884-4e6c-a65f-c4505476ad3f' 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>comment :</span></dt><dd>Error criterion between the speed contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Speed Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>1.5</dd><dt><span>max :</span></dt><dd>93.0</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0b64de8c-b001-4dfc-8d4c-cdbca371e277' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b64de8c-b001-4dfc-8d4c-cdbca371e277' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a258612-92d9-4c16-a142-eef606c0d631' class='xr-var-data-in' type='checkbox'><label for='data-7a258612-92d9-4c16-a142-eef606c0d631' 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>comment :</span></dt><dd>Radius of the best fit circle corresponding to the contour of maximum circum-average speed</dd><dt><span>long_name :</span></dt><dd>Speed Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7050.0</dd><dt><span>max :</span></dt><dd>342200.0</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ee7b8b1a-5ecc-4293-87ff-924d5f02d570' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee7b8b1a-5ecc-4293-87ff-924d5f02d570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbecdc6f-da1a-4da9-9b49-d0bfaeaca3d5' class='xr-var-data-in' type='checkbox'><label for='data-cbecdc6f-da1a-4da9-9b49-d0bfaeaca3d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Date of this observation</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>min :</span></dt><dd>24837.0</dd><dt><span>max :</span></dt><dd>27232.0</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>track</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba847bf6-c83f-469d-b2f5-e1e9493f22cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba847bf6-c83f-469d-b2f5-e1e9493f22cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8c2a47d-6bf6-4041-a354-0521290b84ec' class='xr-var-data-in' type='checkbox'><label for='data-c8c2a47d-6bf6-4041-a354-0521290b84ec' 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>comment :</span></dt><dd>Trajectory identification number</dd><dt><span>long_name :</span></dt><dd>Trajectory number</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>191385</dd></dl></div><div class='xr-var-data'><pre>[8408689 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uavg_profile</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b315d9b8-cf0f-4c61-9ecb-f08522783241' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b315d9b8-cf0f-4c61-9ecb-f08522783241' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6f47cf5-ee9d-4abf-95b4-918756539a66' class='xr-var-data-in' type='checkbox'><label for='data-c6f47cf5-ee9d-4abf-95b4-918756539a66' 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>comment :</span></dt><dd>Speed averaged values from the effective contour inwards to the smallest contour, evenly spaced points</dd><dt><span>long_name :</span></dt><dd>Radial Speed Profile</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[168173780 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-012c0d83-265a-4252-a493-6013adb169a8' class='xr-section-summary-in' type='checkbox'  ><label for='section-012c0d83-265a-4252-a493-6013adb169a8' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>comment :</span></dt><dd>Surface product; mesoscale eddies</dd><dt><span>creator_email :</span></dt><dd>aviso@altimetry.fr</dd><dt><span>creator_url :</span></dt><dd>https://www.aviso.altimetry.fr</dd><dt><span>date_created :</span></dt><dd>2024-08-06T09:38:06Z</dd><dt><span>framework_used :</span></dt><dd>https://github.com/AntSimi/py-eddy-tracker</dd><dt><span>framework_version :</span></dt><dd>v3.6.1</dd><dt><span>input_maps_processing :</span></dt><dd>DUACS DT2018 until 2021-12-07, DUACS DT2021 since 2021-12-08</dd><dt><span>input_product_reference :</span></dt><dd>https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product.html</dd><dt><span>institution :</span></dt><dd>CLS/CNES in collaboration with IMEDEA</dd><dt><span>license :</span></dt><dd>https://www.aviso.altimetry.fr/fileadmin/documents/data/License_Aviso.pdf</dd><dt><span>product_version :</span></dt><dd>3.2exp</dd><dt><span>project :</span></dt><dd>SSALTO/DUACS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table</dd><dt><span>summary :</span></dt><dd>This dataset contains eddy atlas from all-satellite NRT CMEMS maps over global ocean (SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS*)</dd><dt><span>time_coverage_duration :</span></dt><dd>P2396D</dd><dt><span>time_coverage_end :</span></dt><dd>2024-07-23T00:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2018-01-01T00:00:00Z</dd><dt><span>title :</span></dt><dd>Mesoscale Cyclonic Eddies in Altimeter Observations of ADT</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                        (obs: 8408689, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float32 ...\n",
       "    effective_area                 (obs) float32 ...\n",
       "    effective_contour_height       (obs) float32 ...\n",
       "    effective_contour_latitude     (obs, NbSample) float32 ...\n",
       "    effective_contour_longitude    (obs, NbSample) float32 ...\n",
       "    effective_contour_shape_error  (obs) float32 ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float32 ...\n",
       "    speed_contour_shape_error      (obs) float32 ...\n",
       "    speed_radius                   (obs) float32 ...\n",
       "    time                           (obs) datetime64[ns] ...\n",
       "    track                          (obs) uint32 ...\n",
       "    uavg_profile                   (obs, NbSample) float32 ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2024-08-06T09:38:06Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2396D\n",
       "    time_coverage_end:         2024-07-23T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Cyclonic Eddies in Altimeter Observa..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset('../Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc', engine=\"netcdf4\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149\n"
     ]
    }
   ],
   "source": [
    "lat_min,lat_max = 34.5,37\n",
    "lon_min,lon_max = -73+360,-68+360\n",
    "\n",
    "subset_inds = np.where(((ds.latitude > lat_min) & (ds.latitude < lat_max)) & \n",
    "                       ((ds.longitude > lon_min) & (ds.longitude < lon_max)) & \n",
    "                       ((ds.time) >= np.datetime64('2024-04-01')))[0]\n",
    "print(len(subset_inds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat,lon,time,track = [],[],[],[]\n",
    "for i in subset_inds: \n",
    "    this_track_id = int(ds.track[i].values)\n",
    "    if ds.longitude[i] > 360:\n",
    "        lon.append(float((ds.longitude[i] - 360).values))\n",
    "    else:\n",
    "        lon.append(float(ds.longitude[i].values))\n",
    "    lat.append(float(ds.latitude[i].values))\n",
    "    time.append(np.datetime64(ds.time[i].values))\n",
    "    track.append(this_track_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(8,6))\n",
    "\n",
    "ax.scatter(lon,lat,s=3,c=time)\n",
    "scat = ax.set_title('Eddy Centers in Box (N = %s)'%(len(track)))\n",
    "ax.set_xlabel('Longitude ($^{\\circ}$E)');\n",
    "ax.set_ylabel('Latitude ($^{\\circ}$N)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([154367, 174440, 184611])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(track)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refine down to the one track ID = 174440"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset_inds = np.where((ds.track==174440))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat,lon,time,track = [],[],[],[]\n",
    "for i in subset_inds: \n",
    "    this_track_id = int(ds.track[i].values)\n",
    "    if ds.longitude[i] > 360:\n",
    "        lon.append(float((ds.longitude[i] - 360).values))\n",
    "    else:\n",
    "        lon.append(float(ds.longitude[i].values))\n",
    "    lat.append(float(ds.latitude[i].values))\n",
    "    time.append(np.datetime64(ds.time[i].values))\n",
    "    track.append(this_track_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(10,5))\n",
    "\n",
    "ax.scatter(lon,lat,s=3,c=time)\n",
    "scat = ax.set_title('Edward Eddy Centers')\n",
    "ax.set_xlabel('Longitude ($^{\\circ}$E)');\n",
    "ax.set_ylabel('Latitude ($^{\\circ}$N)');\n",
    "\n",
    "# Create a colorbar\n",
    "norm = Normalize(vmin=0, vmax=len(time))\n",
    "sm = plt.cm.ScalarMappable(cmap='viridis', norm=norm)\n",
    "sm.set_array([])  # Dummy array for the colorbar\n",
    "cbar = plt.colorbar(sm, ax=ax)\n",
    "cbar.set_label('Days', rotation=270,labelpad=20)\n",
    "\n",
    "# Format colorbar ticks as dates\n",
    "#cb.ax.yaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_eddy_by_ID_date(ds,track_id,eddy_date):\n",
    "    \"\"\"\n",
    "    ds: netCDF AVISO format\n",
    "    track_id: id of eddy to extract\n",
    "    eddy_date: date in format 'YYYY-MM-DD'\n",
    "\n",
    "    Returns contour lons, contour lats, center lon, center lat\n",
    "    \"\"\"\n",
    "    try:\n",
    "        ind = np.where((ds.track == track_id) & (ds.time == np.datetime64(eddy_date)))[0][0]\n",
    "        contour_lons = np.array(ds.effective_contour_longitude[ind])\n",
    "        contour_lats = np.array(ds.effective_contour_latitude[ind])\n",
    "    except:\n",
    "        print('No eddy data available with that request ... :(')        \n",
    "    \n",
    "    return contour_lons,contour_lats,ds.longitude[ind],ds.latitude[ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_to_plot = '2024-05-08'\n",
    "\n",
    "############################\n",
    "contour_lons,contour_lats,center_lon,center_lat = get_eddy_by_ID_date(ds,174440,date_to_plot)\n",
    "\n",
    "# Plot\n",
    "plt.plot(contour_lons,contour_lats)\n",
    "plt.scatter(center_lon,center_lat,marker='*',s=100)\n",
    "\n",
    "# Labels\n",
    "plt.title('Edward on %s'%(date_to_plot))\n",
    "plt.xlabel('Longitude ($^{\\circ}$E)');\n",
    "plt.ylabel('Latitude ($^{\\circ}$N)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test plotting first 10 days of the contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(8,6))\n",
    "\n",
    "for t in time[0:10]:\n",
    "    ind = np.where((ds.track == 174440) & (ds.time == np.datetime64(t)))[0][0]\n",
    "    contour_lons = np.array(ds.effective_contour_longitude[ind])\n",
    "    contour_lats = np.array(ds.effective_contour_latitude[ind])\n",
    "    ax.plot(contour_lons,contour_lats)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "223\n",
      "2023-12-14T00:00:00.000000000\n",
      "2024-07-23T00:00:00.000000000\n"
     ]
    }
   ],
   "source": [
    "print(len(time))\n",
    "print(min(time))\n",
    "print(max(time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How old is Edward on May 8th, 2024?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.datetime64('2024-05-08') - time[0]).astype('timedelta64[D]')/ np.timedelta64(1, 'D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Edward Subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ds_Edward = ds.isel(obs=subset_inds)\n",
    "ds_Edward.to_netcdf('Edward_Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test opening Edward subset of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('Edward_Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20240723.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test checking if a float is in or out of the eddy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def in_eddy(ds,float_lat,float_lon,float_time):\n",
    "    \"\"\"\n",
    "    float_lat: degrees north\n",
    "    float_lon: degrees east\n",
    "    float_time: should be in format 'YYYY-MM-DD'\n",
    "    \"\"\"\n",
    "\n",
    "    def all_equal(iterable):\n",
    "        g = groupby(iterable)\n",
    "        return next(g, True) and not next(g, False)\n",
    "\n",
    "    in_eddy_flag = False \n",
    "\n",
    "    float_time = np.datetime64(float_time)\n",
    "    if float_time in ds.time: # some dates not in dateset        \n",
    "        for i in np.where(ds.time == float_time)[0]: # usually will only be 1 eddy, but sometimes there are 2 after a split\n",
    "            contour_lons = np.array(ds.effective_contour_longitude[i]) # eddy lons\n",
    "            contour_lats = np.array(ds.effective_contour_latitude[i]) # eddy lats\n",
    "            \n",
    "            if all_equal(contour_lons): # eddy break\n",
    "                pass\n",
    "            else:\n",
    "                poly = Path([(contour_lats[j],contour_lons[j]) for j in np.arange(0,len(contour_lats))]) # set up the polygon\n",
    "                if poly.contains_points([(float_lat,float_lon)]): #find if point is inside the polygon\n",
    "                    in_eddy_flag = True \n",
    "\n",
    "    return in_eddy_flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_eddy(ds,35,289.5,'2024-05-09')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BGC-Argo Co-locate\n",
    "\n",
    "Marin subsetted the BGC-Argo floats located in the Gulf Stream region \n",
    "\n",
    " --------60--------\n",
    " \n",
    "-10  Gulf Stream -76\n",
    "\n",
    " --------30--------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "          WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3  \\\n",
       "0     1902304  1902304_150  2024-01-09 -16.197  54.261  YES  YES  YES  YES   \n",
       "1     1902304  1902304_151  2024-01-19 -16.829  54.248  YES  YES  YES  YES   \n",
       "2     1902304  1902304_152  2024-01-30 -17.855  54.507  YES  YES  YES  YES   \n",
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       "4     1902304  1902304_155  2024-03-01 -19.243  54.658  YES  YES  YES  YES   \n",
       "...       ...          ...         ...     ...     ...  ...  ...  ...  ...   \n",
       "1871  4902688  4902688_056  2024-12-29 -52.153  59.486  YES  YES  YES   NO   \n",
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       "1875  4902688  4902688_060  2025-01-22 -51.155  59.655  YES  YES  YES   NO   \n",
       "\n",
       "       PH  PAR  \n",
       "0     YES   NO  \n",
       "1     YES   NO  \n",
       "2     YES   NO  \n",
       "3     YES   NO  \n",
       "4     YES   NO  \n",
       "...   ...  ...  \n",
       "1871   NO  YES  \n",
       "1872   NO  YES  \n",
       "1873   NO  YES  \n",
       "1874   NO  YES  \n",
       "1875   NO  YES  \n",
       "\n",
       "[1876 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo_df = pd.read_csv('../BGC_floats_Cornec_20250125.csv')\n",
    "argo_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add columns to add eddy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "          WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3  \\\n",
       "0     1902304  1902304_150  2024-01-09 -16.197  54.261  YES  YES  YES  YES   \n",
       "1     1902304  1902304_151  2024-01-19 -16.829  54.248  YES  YES  YES  YES   \n",
       "2     1902304  1902304_152  2024-01-30 -17.855  54.507  YES  YES  YES  YES   \n",
       "3     1902304  1902304_154  2024-02-20 -19.162  54.789  YES  YES  YES  YES   \n",
       "4     1902304  1902304_155  2024-03-01 -19.243  54.658  YES  YES  YES  YES   \n",
       "...       ...          ...         ...     ...     ...  ...  ...  ...  ...   \n",
       "1871  4902688  4902688_056  2024-12-29 -52.153  59.486  YES  YES  YES   NO   \n",
       "1872  4902688  4902688_057  2025-01-04 -52.065  59.398  YES  YES  YES   NO   \n",
       "1873  4902688  4902688_058  2025-01-10 -52.066  59.233  YES  YES  YES   NO   \n",
       "1874  4902688  4902688_059  2025-01-16 -52.164  59.497  YES  YES  YES   NO   \n",
       "1875  4902688  4902688_060  2025-01-22 -51.155  59.655  YES  YES  YES   NO   \n",
       "\n",
       "       PH  PAR POLARITY  ID  \n",
       "0     YES   NO     None NaN  \n",
       "1     YES   NO     None NaN  \n",
       "2     YES   NO     None NaN  \n",
       "3     YES   NO     None NaN  \n",
       "4     YES   NO     None NaN  \n",
       "...   ...  ...      ...  ..  \n",
       "1871   NO  YES     None NaN  \n",
       "1872   NO  YES     None NaN  \n",
       "1873   NO  YES     None NaN  \n",
       "1874   NO  YES     None NaN  \n",
       "1875   NO  YES     None NaN  \n",
       "\n",
       "[1876 rows x 13 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo_df[\"POLARITY\"] = 'None'\n",
    "argo_df[\"ID\"] = np.nan\n",
    "argo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-01-01\n",
      "-76.07\n",
      "-10.004\n",
      "30.107\n",
      "59.989\n"
     ]
    }
   ],
   "source": [
    "print(min(argo_df.DATE))\n",
    "print(min(argo_df.LON))\n",
    "print(max(argo_df.LON))\n",
    "print(min(argo_df.LAT))\n",
    "print(max(argo_df.LAT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_min,lat_max = min(argo_df.LAT),max(argo_df.LAT)\n",
    "lon_min,lon_max = min(argo_df.LON)+360,max(argo_df.LON)+360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "anti_ds = xr.open_dataset('../Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111.nc', engine=\"netcdf4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_ds(ds):\n",
    "    subset_inds = np.where(((ds.latitude >= lat_min) & (ds.latitude <= lat_max)) & \n",
    "                            ((ds.longitude >= lon_min) & (ds.longitude <= lon_max)) & \n",
    "                            ((ds.time) >= np.datetime64('2024-01-01')))[0]\n",
    "    return ds.isel(obs=subset_inds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 62MB\n",
       "Dimensions:                        (obs: 68573, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float64 549kB ...\n",
       "    effective_area                 (obs) float32 274kB ...\n",
       "    effective_contour_height       (obs) float32 274kB ...\n",
       "    effective_contour_latitude     (obs, NbSample) float64 11MB ...\n",
       "    effective_contour_longitude    (obs, NbSample) float64 11MB ...\n",
       "    effective_contour_shape_error  (obs) float64 549kB ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float64 11MB ...\n",
       "    speed_contour_shape_error      (obs) float64 549kB ...\n",
       "    speed_radius                   (obs) float64 549kB ...\n",
       "    time                           (obs) datetime64[ns] 549kB 2024-01-01 ... ...\n",
       "    track                          (obs) uint32 274kB ...\n",
       "    uavg_profile                   (obs, NbSample) float64 11MB ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2025-02-03T08:34:25Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2507D\n",
       "    time_coverage_end:         2024-11-11T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Anticyclonic Eddies in Altimeter Obs...</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-7b2c0d5c-eee9-4590-a703-56c3009768d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7b2c0d5c-eee9-4590-a703-56c3009768d0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 68573</li><li><span>NbSample</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9c652cd4-1e38-4f73-8c6c-1e0e7b31cc5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9c652cd4-1e38-4f73-8c6c-1e0e7b31cc5f' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d1d3d3c9-794e-4e92-a816-f145c0838151' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1d3d3c9-794e-4e92-a816-f145c0838151' class='xr-section-summary' >Data variables: <span>(27)</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>amplitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-16165142-5040-47aa-b86a-d2bdc0cce7c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16165142-5040-47aa-b86a-d2bdc0cce7c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94a87387-45ae-4c9a-b5b0-b1d4a4ead653' class='xr-var-data-in' type='checkbox'><label for='data-94a87387-45ae-4c9a-b5b0-b1d4a4ead653' 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>comment :</span></dt><dd>Magnitude of the height difference between the extremum of SSH within the eddy and the SSH around the effective contour defining the eddy edge</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>0.004</dd><dt><span>max :</span></dt><dd>1.1294</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a12720e3-bd0c-4ae2-9b69-c41d0a2e73f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a12720e3-bd0c-4ae2-9b69-c41d0a2e73f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e57699-60a8-4447-b75c-40fbd639010f' class='xr-var-data-in' type='checkbox'><label for='data-27e57699-60a8-4447-b75c-40fbd639010f' 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>comment :</span></dt><dd>Area enclosed by the effective contour in m^2</dd><dt><span>long_name :</span></dt><dd>Effective area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>140961710.0</dd><dt><span>max :</span></dt><dd>335498020000.0</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6198c4e4-49e8-499a-8ec8-600b3be966c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6198c4e4-49e8-499a-8ec8-600b3be966c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2711dae7-7d95-40a2-8569-d6f8895a5087' class='xr-var-data-in' type='checkbox'><label for='data-2711dae7-7d95-40a2-8569-d6f8895a5087' 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>comment :</span></dt><dd>SSH filtered height for effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.342</dd><dt><span>max :</span></dt><dd>0.938</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fabad791-41a8-4c96-aa44-327e7acd217a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fabad791-41a8-4c96-aa44-327e7acd217a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a69cca3-810d-4004-9c27-8af9016fc7ff' class='xr-var-data-in' type='checkbox'><label for='data-4a69cca3-810d-4004-9c27-8af9016fc7ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[1371460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa35f63e-96e3-45e7-92fc-86df200c5c22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa35f63e-96e3-45e7-92fc-86df200c5c22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b90787b3-7384-4f66-8a79-6536f54380d3' class='xr-var-data-in' type='checkbox'><label for='data-b90787b3-7384-4f66-8a79-6536f54380d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[1371460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b553289-589f-410c-bf45-c8629b0f8cb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b553289-589f-410c-bf45-c8629b0f8cb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-055d9ecb-6235-4e70-9222-3746d631ac2f' class='xr-var-data-in' type='checkbox'><label for='data-055d9ecb-6235-4e70-9222-3746d631ac2f' 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>comment :</span></dt><dd>Error criterion between the effective contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Effective Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>2.0</dd><dt><span>max :</span></dt><dd>77.0</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1999817-4b9f-475d-90cf-20a5730c5d46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1999817-4b9f-475d-90cf-20a5730c5d46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9319a382-e9ee-4772-9a9a-54e32e433327' class='xr-var-data-in' type='checkbox'><label for='data-9319a382-e9ee-4772-9a9a-54e32e433327' 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>comment :</span></dt><dd>Radius of the best fit circle corresponding to the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7400.0</dd><dt><span>max :</span></dt><dd>381650.0</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inner_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-931a5477-7fbf-42a7-a36e-1e69e94deb7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-931a5477-7fbf-42a7-a36e-1e69e94deb7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3bfb1fe-46f0-49c9-82f4-e7709a29e3d6' class='xr-var-data-in' type='checkbox'><label for='data-b3bfb1fe-46f0-49c9-82f4-e7709a29e3d6' 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>comment :</span></dt><dd>SSH filtered height for the smallest detected contour</dd><dt><span>long_name :</span></dt><dd>Inner Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.31</dd><dt><span>max :</span></dt><dd>1.114</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>55.01 55.0 54.99 ... 51.82 51.84</div><input id='attrs-13835e0f-a310-4562-9064-e894b09609b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13835e0f-a310-4562-9064-e894b09609b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58fbb583-e1c6-4417-bf65-0371539bd283' class='xr-var-data-in' type='checkbox'><label for='data-58fbb583-e1c6-4417-bf65-0371539bd283' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-77.85374</dd><dt><span>max :</span></dt><dd>84.63258</dd></dl></div><div class='xr-var-data'><pre>array([55.007133, 54.995876, 54.992573, ..., 51.80967 , 51.823143, 51.839848],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-799a8f26-7320-45df-a8f0-d2ef84a8c556' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-799a8f26-7320-45df-a8f0-d2ef84a8c556' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3326661-39b5-42ad-885e-5ebcae177e2a' class='xr-var-data-in' type='checkbox'><label for='data-f3326661-39b5-42ad-885e-5ebcae177e2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Latitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-90.0</dd><dt><span>max :</span></dt><dd>84.62542</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>347.7 347.7 347.6 ... 314.1 314.1</div><input id='attrs-5a309517-d8c9-4d15-b27f-7d795c2b7d5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a309517-d8c9-4d15-b27f-7d795c2b7d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b65a534-a717-4988-8eb3-e602cc68d2e3' class='xr-var-data-in' type='checkbox'><label for='data-6b65a534-a717-4988-8eb3-e602cc68d2e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-46.457214</dd><dt><span>max :</span></dt><dd>367.75803</dd></dl></div><div class='xr-var-data'><pre>array([347.66916, 347.65192, 347.60446, ..., 314.0947 , 314.107  , 314.09622],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f4cf2d8b-9fd5-420d-a678-85ae8245abdd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4cf2d8b-9fd5-420d-a678-85ae8245abdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128e165d-279b-43a0-a9e3-d3223ebe8531' class='xr-var-data-in' type='checkbox'><label for='data-128e165d-279b-43a0-a9e3-d3223ebe8531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Longitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-46.493805</dd><dt><span>max :</span></dt><dd>367.8839</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_contours</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a2535bb0-e53c-43cf-81c1-091e3211ac7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2535bb0-e53c-43cf-81c1-091e3211ac7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43e336d6-52d3-4252-bb94-4f16314dc1c8' class='xr-var-data-in' type='checkbox'><label for='data-43e336d6-52d3-4252-bb94-4f16314dc1c8' 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>comment :</span></dt><dd>Number of contours selected for this eddy</dd><dt><span>long_name :</span></dt><dd>Number of contours</dd><dt><span>min :</span></dt><dd>1</dd><dt><span>max :</span></dt><dd>565</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_e</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c066a0c8-e13c-4849-8217-624416992a3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c066a0c8-e13c-4849-8217-624416992a3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab8319bb-5307-4996-a2ea-cbace082eeed' class='xr-var-data-in' type='checkbox'><label for='data-ab8319bb-5307-4996-a2ea-cbace082eeed' 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>description :</span></dt><dd>Number of points for effective contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for effective contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>223</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_s</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b351cc59-1147-464d-a8b3-3d6bd84d8ce5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b351cc59-1147-464d-a8b3-3d6bd84d8ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5be159a-5352-4e62-98d1-c69b92257e45' class='xr-var-data-in' type='checkbox'><label for='data-f5be159a-5352-4e62-98d1-c69b92257e45' 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>description :</span></dt><dd>Number of points for speed contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for speed contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>197</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff87bea2-dc84-476a-8c3b-098e7134ed6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff87bea2-dc84-476a-8c3b-098e7134ed6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e46d0771-3857-4ff7-ae05-d18f410530bd' class='xr-var-data-in' type='checkbox'><label for='data-e46d0771-3857-4ff7-ae05-d18f410530bd' 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>comment :</span></dt><dd>Flag indicating if the value is interpolated between two observations or not (0: observed eddy, 1: interpolated eddy)</dd><dt><span>long_name :</span></dt><dd>Virtual Eddy Position</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_number</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-703447ec-30f9-43cf-be5d-19614184e759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-703447ec-30f9-43cf-be5d-19614184e759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38e02dd6-0d4e-42e6-b063-cb00a4e24cb8' class='xr-var-data-in' type='checkbox'><label for='data-38e02dd6-0d4e-42e6-b063-cb00a4e24cb8' 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>comment :</span></dt><dd>Observation sequence number, days starting at the eddy first detection</dd><dt><span>long_name :</span></dt><dd>Eddy temporal index in a trajectory</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>2228</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c31df7ee-a186-4613-9120-93a8a65efd26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c31df7ee-a186-4613-9120-93a8a65efd26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ff0731-983c-4e78-93ac-50c0965d4ef0' class='xr-var-data-in' type='checkbox'><label for='data-f5ff0731-983c-4e78-93ac-50c0965d4ef0' 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>comment :</span></dt><dd>Area enclosed by the speed contour in m^2</dd><dt><span>long_name :</span></dt><dd>Speed area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>68983310.0</dd><dt><span>max :</span></dt><dd>312962220000.0</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_average</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f98ecf9a-919c-4b30-8c1b-df59342e675e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f98ecf9a-919c-4b30-8c1b-df59342e675e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecb793f2-2c5a-4cb3-bebe-b15d082190fa' class='xr-var-data-in' type='checkbox'><label for='data-ecb793f2-2c5a-4cb3-bebe-b15d082190fa' 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>comment :</span></dt><dd>Average speed of the contour defining the radius scale “speed_radius”</dd><dt><span>long_name :</span></dt><dd>Maximum circum-averaged Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>min :</span></dt><dd>0.0</dd><dt><span>max :</span></dt><dd>5.926900000000001</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d69ae6d-2669-44a3-87bc-67898a0fcc26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d69ae6d-2669-44a3-87bc-67898a0fcc26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd08f380-cf12-4868-b4e8-c3aae38a1ed4' class='xr-var-data-in' type='checkbox'><label for='data-fd08f380-cf12-4868-b4e8-c3aae38a1ed4' 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>comment :</span></dt><dd>SSH filtered height for speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.342</dd><dt><span>max :</span></dt><dd>0.938</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cb94dac9-ac1e-48cd-b879-50c3328a18cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb94dac9-ac1e-48cd-b879-50c3328a18cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98c0237b-4a37-44cb-8fc9-5466e7f1dd78' class='xr-var-data-in' type='checkbox'><label for='data-98c0237b-4a37-44cb-8fc9-5466e7f1dd78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[1371460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ded9b47-3bd3-4788-a46e-50807fcd05e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ded9b47-3bd3-4788-a46e-50807fcd05e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-163f121d-51b1-4b71-8337-563a1c396892' class='xr-var-data-in' type='checkbox'><label for='data-163f121d-51b1-4b71-8337-563a1c396892' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[1371460 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-83669380-0aba-442d-8903-ef050870f509' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83669380-0aba-442d-8903-ef050870f509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a78491d1-85b2-4115-ab8d-15d7cd2adabb' class='xr-var-data-in' type='checkbox'><label for='data-a78491d1-85b2-4115-ab8d-15d7cd2adabb' 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>comment :</span></dt><dd>Error criterion between the speed contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Speed Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>1.5</dd><dt><span>max :</span></dt><dd>94.0</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-201176d7-e5dc-40c4-8977-f0b934907ca8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-201176d7-e5dc-40c4-8977-f0b934907ca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d0d9b4a-21e3-4261-9d00-85aeda4c3504' class='xr-var-data-in' type='checkbox'><label for='data-2d0d9b4a-21e3-4261-9d00-85aeda4c3504' 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>comment :</span></dt><dd>Radius of the best fit circle corresponding to the contour of maximum circum-average speed</dd><dt><span>long_name :</span></dt><dd>Speed Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7300.0</dd><dt><span>max :</span></dt><dd>373250.0</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2024-01-01 ... 2024-11-11</div><input id='attrs-e4c2b3f9-8403-4a2f-a808-f3317146cb17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4c2b3f9-8403-4a2f-a808-f3317146cb17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f6553de-c485-4d6a-ae35-4a6f06e2c127' class='xr-var-data-in' type='checkbox'><label for='data-5f6553de-c485-4d6a-ae35-4a6f06e2c127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Date of this observation</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>min :</span></dt><dd>24837.0</dd><dt><span>max :</span></dt><dd>27343.0</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2024-01-01T00:00:00.000000000&#x27;, &#x27;2024-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-01-03T00:00:00.000000000&#x27;, ..., &#x27;2024-11-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-11-10T00:00:00.000000000&#x27;, &#x27;2024-11-11T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>track</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc7789e5-096f-49df-8241-e6e835162b5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc7789e5-096f-49df-8241-e6e835162b5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b8a64cd-aec8-4960-ac72-0741754410ad' class='xr-var-data-in' type='checkbox'><label for='data-8b8a64cd-aec8-4960-ac72-0741754410ad' 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>comment :</span></dt><dd>Trajectory identification number</dd><dt><span>long_name :</span></dt><dd>Trajectory number</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>191800</dd></dl></div><div class='xr-var-data'><pre>[68573 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uavg_profile</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-252c312b-92d0-49ef-ae23-0b183cbea715' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-252c312b-92d0-49ef-ae23-0b183cbea715' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba706e0-c52b-4140-84b9-f5d32a86450d' class='xr-var-data-in' type='checkbox'><label for='data-0ba706e0-c52b-4140-84b9-f5d32a86450d' 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>comment :</span></dt><dd>Speed averaged values from the effective contour inwards to the smallest contour, evenly spaced points</dd><dt><span>long_name :</span></dt><dd>Radial Speed Profile</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[1371460 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8fb09048-b94a-4f25-a439-8734d81df044' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8fb09048-b94a-4f25-a439-8734d81df044' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5b0f0acf-3785-4b1b-8b28-72ffc1d6c74d' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b0f0acf-3785-4b1b-8b28-72ffc1d6c74d' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>comment :</span></dt><dd>Surface product; mesoscale eddies</dd><dt><span>creator_email :</span></dt><dd>aviso@altimetry.fr</dd><dt><span>creator_url :</span></dt><dd>https://www.aviso.altimetry.fr</dd><dt><span>date_created :</span></dt><dd>2025-02-03T08:34:25Z</dd><dt><span>framework_used :</span></dt><dd>https://github.com/AntSimi/py-eddy-tracker</dd><dt><span>framework_version :</span></dt><dd>v3.6.1</dd><dt><span>input_maps_processing :</span></dt><dd>DUACS DT2018 until 2021-12-07, DUACS DT2021 since 2021-12-08</dd><dt><span>input_product_reference :</span></dt><dd>https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product.html</dd><dt><span>institution :</span></dt><dd>CLS/CNES in collaboration with IMEDEA</dd><dt><span>license :</span></dt><dd>https://www.aviso.altimetry.fr/fileadmin/documents/data/License_Aviso.pdf</dd><dt><span>product_version :</span></dt><dd>3.2exp</dd><dt><span>project :</span></dt><dd>SSALTO/DUACS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table</dd><dt><span>summary :</span></dt><dd>This dataset contains eddy atlas from all-satellite NRT CMEMS maps over global ocean (SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS*)</dd><dt><span>time_coverage_duration :</span></dt><dd>P2507D</dd><dt><span>time_coverage_end :</span></dt><dd>2024-11-11T00:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2018-01-01T00:00:00Z</dd><dt><span>title :</span></dt><dd>Mesoscale Anticyclonic Eddies in Altimeter Observations of ADT</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 62MB\n",
       "Dimensions:                        (obs: 68573, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float64 549kB ...\n",
       "    effective_area                 (obs) float32 274kB ...\n",
       "    effective_contour_height       (obs) float32 274kB ...\n",
       "    effective_contour_latitude     (obs, NbSample) float64 11MB ...\n",
       "    effective_contour_longitude    (obs, NbSample) float64 11MB ...\n",
       "    effective_contour_shape_error  (obs) float64 549kB ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float64 11MB ...\n",
       "    speed_contour_shape_error      (obs) float64 549kB ...\n",
       "    speed_radius                   (obs) float64 549kB ...\n",
       "    time                           (obs) datetime64[ns] 549kB 2024-01-01 ... ...\n",
       "    track                          (obs) uint32 274kB ...\n",
       "    uavg_profile                   (obs, NbSample) float64 11MB ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2025-02-03T08:34:25Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2507D\n",
       "    time_coverage_end:         2024-11-11T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Anticyclonic Eddies in Altimeter Obs..."
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cropped_ds = crop_ds(anti_ds)\n",
    "cropped_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TEST: Iterate through floats & check 2 degree box around them for eddies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def all_equal(iterable):\n",
    "    g = groupby(iterable)\n",
    "    return next(g, True) and not next(g, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "5\n",
      "4\n",
      "4\n",
      "4\n",
      "5\n",
      "4\n",
      "4\n",
      "4\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for index, row in argo_df[0:10].iterrows():\n",
    "    argo_lon,argo_lat,argo_time = row['LON']+360,row['LAT'],np.datetime64(row['DATE'])\n",
    "\n",
    "    box_size = 2\n",
    "    subset_inds = np.where(((cropped_ds.latitude >= argo_lat-box_size) & (cropped_ds.latitude <= argo_lat+box_size)) & \n",
    "                            ((cropped_ds.longitude >= argo_lon-box_size) & (cropped_ds.longitude <= argo_lon+box_size)) & \n",
    "                            ((cropped_ds.time) == argo_time))[0]\n",
    "\n",
    "    print(len(subset_inds))\n",
    "    \n",
    "    check_ds = cropped_ds.isel(obs=subset_inds)\n",
    "    \n",
    "    for obs in np.arange(len(check_ds.obs)):    \n",
    "        contour_lons = np.array(check_ds.effective_contour_longitude[obs])\n",
    "        contour_lats = np.array(check_ds.effective_contour_latitude[obs])\n",
    "\n",
    "        if all_equal(contour_lons): # eddy break\n",
    "            pass\n",
    "        else:\n",
    "            poly = Path([(contour_lats[j],contour_lons[j]) for j in np.arange(0,len(contour_lats))]) # set up the polygon\n",
    "            if poly.contains_points([(argo_lat,argo_lon)]): #find if point is inside the polygon\n",
    "                argo_df.at[index,'POLARITY']='ANTI'\n",
    "                argo_df.at[index,'ID']=check_ds.track[obs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ANTI', 'None'], dtype=object)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(argo_df['POLARITY'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMO</th>\n",
       "      <th>WMO_CYCLE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>CHLA</th>\n",
       "      <th>BBP</th>\n",
       "      <th>DOXY</th>\n",
       "      <th>NO3</th>\n",
       "      <th>PH</th>\n",
       "      <th>PAR</th>\n",
       "      <th>POLARITY</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_150</td>\n",
       "      <td>2024-01-09</td>\n",
       "      <td>-16.197</td>\n",
       "      <td>54.261</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_151</td>\n",
       "      <td>2024-01-19</td>\n",
       "      <td>-16.829</td>\n",
       "      <td>54.248</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_152</td>\n",
       "      <td>2024-01-30</td>\n",
       "      <td>-17.855</td>\n",
       "      <td>54.507</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_154</td>\n",
       "      <td>2024-02-20</td>\n",
       "      <td>-19.162</td>\n",
       "      <td>54.789</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_155</td>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>-19.243</td>\n",
       "      <td>54.658</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_056</td>\n",
       "      <td>2024-12-29</td>\n",
       "      <td>-52.153</td>\n",
       "      <td>59.486</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_057</td>\n",
       "      <td>2025-01-04</td>\n",
       "      <td>-52.065</td>\n",
       "      <td>59.398</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_058</td>\n",
       "      <td>2025-01-10</td>\n",
       "      <td>-52.066</td>\n",
       "      <td>59.233</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_059</td>\n",
       "      <td>2025-01-16</td>\n",
       "      <td>-52.164</td>\n",
       "      <td>59.497</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_060</td>\n",
       "      <td>2025-01-22</td>\n",
       "      <td>-51.155</td>\n",
       "      <td>59.655</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1876 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3  \\\n",
       "0     1902304  1902304_150  2024-01-09 -16.197  54.261  YES  YES  YES  YES   \n",
       "1     1902304  1902304_151  2024-01-19 -16.829  54.248  YES  YES  YES  YES   \n",
       "2     1902304  1902304_152  2024-01-30 -17.855  54.507  YES  YES  YES  YES   \n",
       "3     1902304  1902304_154  2024-02-20 -19.162  54.789  YES  YES  YES  YES   \n",
       "4     1902304  1902304_155  2024-03-01 -19.243  54.658  YES  YES  YES  YES   \n",
       "...       ...          ...         ...     ...     ...  ...  ...  ...  ...   \n",
       "1871  4902688  4902688_056  2024-12-29 -52.153  59.486  YES  YES  YES   NO   \n",
       "1872  4902688  4902688_057  2025-01-04 -52.065  59.398  YES  YES  YES   NO   \n",
       "1873  4902688  4902688_058  2025-01-10 -52.066  59.233  YES  YES  YES   NO   \n",
       "1874  4902688  4902688_059  2025-01-16 -52.164  59.497  YES  YES  YES   NO   \n",
       "1875  4902688  4902688_060  2025-01-22 -51.155  59.655  YES  YES  YES   NO   \n",
       "\n",
       "       PH  PAR POLARITY  ID  \n",
       "0     YES   NO     None NaN  \n",
       "1     YES   NO     None NaN  \n",
       "2     YES   NO     None NaN  \n",
       "3     YES   NO     None NaN  \n",
       "4     YES   NO     None NaN  \n",
       "...   ...  ...      ...  ..  \n",
       "1871   NO  YES     None NaN  \n",
       "1872   NO  YES     None NaN  \n",
       "1873   NO  YES     None NaN  \n",
       "1874   NO  YES     None NaN  \n",
       "1875   NO  YES     None NaN  \n",
       "\n",
       "[1876 rows x 13 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMO</th>\n",
       "      <th>WMO_CYCLE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>CHLA</th>\n",
       "      <th>BBP</th>\n",
       "      <th>DOXY</th>\n",
       "      <th>NO3</th>\n",
       "      <th>PH</th>\n",
       "      <th>PAR</th>\n",
       "      <th>POLARITY</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_158</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>-18.865</td>\n",
       "      <td>55.727</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_159</td>\n",
       "      <td>2024-04-10</td>\n",
       "      <td>-18.987</td>\n",
       "      <td>56.325</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_160</td>\n",
       "      <td>2024-04-20</td>\n",
       "      <td>-18.758</td>\n",
       "      <td>56.290</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3   PH  \\\n",
       "7  1902304  1902304_158  2024-03-31 -18.865  55.727  YES  YES  YES  YES  YES   \n",
       "8  1902304  1902304_159  2024-04-10 -18.987  56.325  YES  YES  YES  YES  YES   \n",
       "9  1902304  1902304_160  2024-04-20 -18.758  56.290  YES  YES  YES  YES  YES   \n",
       "\n",
       "  PAR POLARITY        ID  \n",
       "7  NO     ANTI  168940.0  \n",
       "8  NO     ANTI  168940.0  \n",
       "9  NO     ANTI  168940.0  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_eddy_argo = argo_df[argo_df['POLARITY'] != 'None']\n",
    "in_eddy_argo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,1,figsize=(8,6))\n",
    "\n",
    "for obs in np.arange(len(check_ds.obs)):    \n",
    "    contour_lons = np.array(check_ds.effective_contour_longitude[obs])\n",
    "    contour_lats = np.array(check_ds.effective_contour_latitude[obs])\n",
    "\n",
    "    is_all_same = len(np.unique(contour_lons)) == 1\n",
    "    if is_all_same:\n",
    "        pass\n",
    "\n",
    "    else:    \n",
    "        ax.plot(contour_lons,contour_lats)\n",
    "        ax.scatter(check_ds.longitude[obs],check_ds.latitude[obs])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date_to_plot = '2024-03-31'\n",
    "\n",
    "############################\n",
    "contour_lons,contour_lats,center_lon,center_lat = get_eddy_by_ID_date(anti_ds,168940,date_to_plot)\n",
    "\n",
    "# Plot\n",
    "plt.plot(contour_lons,contour_lats)\n",
    "plt.scatter(center_lon,center_lat,label='Eddy Center')\n",
    "plt.scatter(-18.865+360,55.727,label='Argo Loc')\n",
    "\n",
    "# Labels\n",
    "plt.xlabel('Longitude ($^{\\circ}$E)');\n",
    "plt.ylabel('Latitude ($^{\\circ}$N)');\n",
    "plt.legend(bbox_to_anchor=(1,1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results from `colocate_Argo_AVISO.py`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMO</th>\n",
       "      <th>WMO_CYCLE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>CHLA</th>\n",
       "      <th>BBP</th>\n",
       "      <th>DOXY</th>\n",
       "      <th>NO3</th>\n",
       "      <th>PH</th>\n",
       "      <th>PAR</th>\n",
       "      <th>POLARITY</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_158</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>-18.865</td>\n",
       "      <td>55.727</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_159</td>\n",
       "      <td>2024-04-10</td>\n",
       "      <td>-18.987</td>\n",
       "      <td>56.325</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_160</td>\n",
       "      <td>2024-04-20</td>\n",
       "      <td>-18.758</td>\n",
       "      <td>56.290</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_161</td>\n",
       "      <td>2024-04-30</td>\n",
       "      <td>-18.912</td>\n",
       "      <td>56.509</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>168940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1902304</td>\n",
       "      <td>1902304_165</td>\n",
       "      <td>2024-06-09</td>\n",
       "      <td>-19.128</td>\n",
       "      <td>57.992</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>186937.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_025</td>\n",
       "      <td>2024-06-26</td>\n",
       "      <td>-48.154</td>\n",
       "      <td>57.821</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>CYC</td>\n",
       "      <td>188597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_029</td>\n",
       "      <td>2024-07-20</td>\n",
       "      <td>-50.088</td>\n",
       "      <td>58.088</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>CYC</td>\n",
       "      <td>188597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_031</td>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>-51.095</td>\n",
       "      <td>58.095</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>CYC</td>\n",
       "      <td>188597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_034</td>\n",
       "      <td>2024-08-19</td>\n",
       "      <td>-51.002</td>\n",
       "      <td>58.502</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>181464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>4902688</td>\n",
       "      <td>4902688_035</td>\n",
       "      <td>2024-08-25</td>\n",
       "      <td>-51.084</td>\n",
       "      <td>59.084</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>NO</td>\n",
       "      <td>YES</td>\n",
       "      <td>ANTI</td>\n",
       "      <td>181464.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>437 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3  \\\n",
       "0    1902304  1902304_158  2024-03-31 -18.865  55.727  YES  YES  YES  YES   \n",
       "1    1902304  1902304_159  2024-04-10 -18.987  56.325  YES  YES  YES  YES   \n",
       "2    1902304  1902304_160  2024-04-20 -18.758  56.290  YES  YES  YES  YES   \n",
       "3    1902304  1902304_161  2024-04-30 -18.912  56.509  YES  YES  YES  YES   \n",
       "4    1902304  1902304_165  2024-06-09 -19.128  57.992  YES  YES  YES  YES   \n",
       "..       ...          ...         ...     ...     ...  ...  ...  ...  ...   \n",
       "432  4902688  4902688_025  2024-06-26 -48.154  57.821  YES  YES  YES   NO   \n",
       "433  4902688  4902688_029  2024-07-20 -50.088  58.088  YES  YES  YES   NO   \n",
       "434  4902688  4902688_031  2024-08-01 -51.095  58.095  YES  YES  YES   NO   \n",
       "435  4902688  4902688_034  2024-08-19 -51.002  58.502  YES  YES  YES   NO   \n",
       "436  4902688  4902688_035  2024-08-25 -51.084  59.084  YES  YES  YES   NO   \n",
       "\n",
       "      PH  PAR POLARITY        ID  \n",
       "0    YES   NO     ANTI  168940.0  \n",
       "1    YES   NO     ANTI  168940.0  \n",
       "2    YES   NO     ANTI  168940.0  \n",
       "3    YES   NO     ANTI  168940.0  \n",
       "4    YES   NO      CYC  186937.0  \n",
       "..   ...  ...      ...       ...  \n",
       "432   NO  YES      CYC  188597.0  \n",
       "433   NO  YES      CYC  188597.0  \n",
       "434   NO  YES      CYC  188597.0  \n",
       "435   NO  YES     ANTI  181464.0  \n",
       "436   NO  YES     ANTI  181464.0  \n",
       "\n",
       "[437 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "argo_df = pd.read_csv('../BGC_floats_Cornec_20250125_in_eddy_LJK.csv')\n",
    "argo_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-75.206\n",
      "-10.189\n",
      "30.148\n",
      "59.944\n"
     ]
    }
   ],
   "source": [
    "print(min(argo_df.LON))\n",
    "print(max(argo_df.LON))\n",
    "print(min(argo_df.LAT))\n",
    "print(max(argo_df.LAT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(argo_df['ID']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-12.374 55.771 ANTI 93157.0\n",
      "-11.45 55.494 CYC 152635.0\n",
      "-44.16 40.936 ANTI 163201.0\n",
      "-10.994 35.775 ANTI 164852.0\n",
      "-51.37 36.743 CYC 164996.0\n",
      "-20.808 50.624 CYC 165543.0\n",
      "-52.122 56.955 ANTI 167427.0\n",
      "-24.499 48.102 ANTI 168319.0\n",
      "-18.865 55.727 ANTI 168940.0\n",
      "-28.816 57.533 ANTI 169206.0\n",
      "-50.358 32.575 CYC 171334.0\n",
      "-74.677 34.574 ANTI 171347.0\n",
      "-12.719 48.947 ANTI 174927.0\n",
      "-56.925 36.396 CYC 175098.0\n",
      "-16.555 55.087 ANTI 175587.0\n",
      "-51.087 58.087 ANTI 177495.0\n",
      "-22.716 42.727 ANTI 177667.0\n",
      "-51.106 57.606 ANTI 178406.0\n",
      "-52.485 58.167 ANTI 179520.0\n",
      "-20.247 59.917 ANTI 179703.0\n",
      "-15.51 55.477 CYC 180103.0\n",
      "-31.929 57.683 ANTI 180171.0\n",
      "-29.639 36.154 CYC 180396.0\n",
      "-31.0 36.112 ANTI 180977.0\n",
      "-40.457 43.968 ANTI 181195.0\n",
      "-36.367 45.658 CYC 181272.0\n",
      "-51.828 58.726 ANTI 181464.0\n",
      "-11.85 51.269 CYC 181901.0\n",
      "-52.833 35.353 ANTI 182188.0\n",
      "-30.792 49.374 CYC 182869.0\n",
      "-57.807 35.872 ANTI 183223.0\n",
      "-68.061 38.496 CYC 183620.0\n",
      "-43.277 51.988 ANTI 184581.0\n",
      "-50.1 56.1 ANTI 184625.0\n",
      "-61.859 38.394 CYC 185338.0\n",
      "-28.346 40.642 CYC 185698.0\n",
      "-16.004 48.897 CYC 187487.0\n",
      "-37.43 55.776 CYC 188029.0\n",
      "-48.144 57.644 CYC 188597.0\n",
      "-60.774 35.058 ANTI 188598.0\n",
      "-59.778 34.028 CYC 189921.0\n",
      "-33.719 48.595 CYC 190961.0\n",
      "-37.346 44.308 ANTI 191061.0\n",
      "-56.423 58.479 CYC 192271.0\n",
      "-64.857 35.726 CYC 192365.0\n",
      "-65.641 33.239 CYC 192629.0\n",
      "-66.82 37.392 CYC 194009.0\n",
      "-51.997 59.199 CYC 194395.0\n",
      "-69.932 34.029 CYC 195148.0\n",
      "-64.197 39.654 CYC 196238.0\n",
      "-15.439 54.974 CYC 197464.0\n",
      "-26.437 57.117 CYC 198227.0\n"
     ]
    }
   ],
   "source": [
    "for i in np.unique(argo_df['ID']):\n",
    "    if len(argo_df[argo_df['ID']==i]) > 2:\n",
    "        eddy_df = argo_df[argo_df['ID']==i]\n",
    "        print(eddy_df.iloc[0]['LON'],eddy_df.iloc[0]['LAT'],eddy_df.iloc[0]['POLARITY'],eddy_df.iloc[0]['ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5b80554690>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W1taYMWMG1qxZw69bGWe7du2gr6/Pb8/hcODg4IBVq1YhPT0dGhoaNZ6nsBnu7OxsoTPiTY2Sb0IIIYR81c6cOYOhQ4eiU6dOWLt2Lfr164cDBw7g3Llz8Pf3B1CxuE95eTmKioqE9iEhIVElIReWpKupqaF169YwNDQUuv0OIeTzaTSTadB6n0NcXBx2dna4dOkS3rx5g5YtW352n3379oWkpCROnz6NiRMnNkCUNTMzM8ORI0eqPV458xwfHw/GGNzc3KrUef36NVRUVLBx40b88ccfAICuXbsiPj4eSUlJKCgogJGREX+7s169egm0nz17Nv744w88f/4czZo1g76+PiZMmAB5eXlYWFgAANq0aVPtgpaMMYFYhTE1NQVQcQ/5gAEDBI49evSIf1yUKPkmhBBCyFfr/PnzcHd3h5ubGw4fPoy0tDTY2NggMzMTo0ePxuzZs2FjY4M2bdqAw+GgpKQEOTk5yMnJwYcPHwT+/G9ZdnY2Xr58KVBeeVkkAGhoaMDIyAiGhoYwMTGBj48PFBQURPhsEPJtsDRQRQslGaTmFgu975sDQEtJBpYGTTOTOXfuXFy8eBHjxo3DmTNnqsxkl5WVISgoCK6urnXqT0tLC15eXtixYwcOHDggdMXzly9foqCgQOjiYfXl5uaG+fPn49KlSwKJ9aVLl8AY489cOzo6IiwsrEr74cOHw8DAAKtWrRJ6606rVq0AVCTIlQu4ubu7V6knLS3NT4BTUlJw9OhRjBs3DrKysgAqvgQdNGgQTpw4gaSkJIF+g4KC0KZNG6ipqVV7njo6OrC0tMTBgwcxY8YM/mJxUVFRePbsGf9LA1Gi5JsQQgghX6VLly7hxx9/hIuLCw4dOgQJCQncu3cPKSkp+PPPP+Ht7Y3k5GQ8evQI58+fR3FxMeTk5CAvLw9FRUUYGxujc+fOQlfhFYYxhg8fPuDly5d4/vw5nj9/jhcvXuDp06c4evQorly5gvPnz9e5P0KIcOJiHCx2NYH3wRhwAIEEvPLmkMWuJo2+2Fola2tr7NixAz4+PrCwsIC3tzc6dOiAsrIy3L9/H7t374apqWmdk28A2LBhA169eoUxY8bg8uXLcHNzg6amJjIzM3HlyhX4+fnhyJEjNSbfycnJiI6OBlCRrAPAiRMnAFQkxF26dAFQcRn3pEmTsH37djRr1gxOTk5ISEjAggUL0LlzZwwbNgxAxZcCWlpaVcaRkZFB8+bN0adPH4Hy+fPno2PHjmjRogVSUlKwb98+3L59GxcuXOAn1EDFKuQnT55Ely5dIC0tjYcPH2L16tUwMjLCsmXLBPpctmwZLl26BEdHRyxZsgSKiorYu3cvHj58iGPHjgnUlZCQQO/evRESEsIvW7NmDfr16wd3d3f4+PggPT0dc+bMgampKcaOHVvjv0mT+KSdxr9A9dncnBBCCCFft8DAQCYtLc0GDhzISkpK+OVlZWXMw8ODoeLzOv8hKyvLmjdvzsTFxascA8B69OjBUlJSPjmea9euMWlpafbTTz+x8vLyhjhFQr4aRUVFLD4+nhUVFTVov5cevWNWK68y/dnn+Q+rlVfZpUfvGnScunrw4AHz9PRkenp6TEpKisnLy7POnTuzRYsWsfT09Cr1/fz8GAAWHR0ttL/y8nLm7+/PbG1tmaqqKpOQkGDq6urMycmJHT58mHG53Brjqexf2MPT07PKWKtXr2aGhoZMUlKStWjRgnl7e7MPHz7Uet76+vrM2dm5Srm3tzf/uVBTU2M//vgji42NrVLv2bNnrFevXkxVVZVJSUkxQ0NDtmDBApafny90vEePHjFnZ2fWrFkzJiMjw6ysrNi5c+eq1APAevfuXaU8ODiYWVlZMRkZGaaqqso8PDxYWlparedZndpe3/XJQzn/H/hXLy8vD0pKSsjNzaV7sAghhJBvVGFhIWbMmIEdO3ZgyJAhOHz4cJWZZh6Ph/379+Pp06coLCxEUVERMjIy8PTpU7x69QpcLldo3/fu3YO5ufknxxYYGIihQ4fC29sbW7ZsoQXcyHejuLgYiYmJMDAwgIxMw96HzeUx3EnMRvrHYmg0q7jUvKlmvAkBan991ycPpcvOCSGEECJypaWluHDhAqKiovDixQu8ffsW0tLS/MvEy8vL8fr1a7x69QrFxcXYvn07Jk6cCA6HAx6Ph/j4eERGRvIfz58/5/etr6+Pdu3awdnZGe3bt0fbtm2hpKQEcXFxiIuLQ0xMDCoqKtDU1Pysc3Bzc8POnTsxfvx4aGpqYuHChZ/7tBDy3RMX48C6TXNRh0FIg6DkmxBCCCEi5e/vj1mzZiE9PR36+vowNjaGiYkJysrKUFBQgI8fP0JMTAxdunTBkCFD4OjoiJycHCxfvhyRkZG4desWcnNzISYmhh9++AEODg5YtGgROnToAGNjY8jLyzfZuYwbNw7p6elYsGABNDQ0MGHChCYbmxBCyJeNkm9CCCGENDnGGF68eIGAgAAsW7YMI0aMwLx586rdCiYlJQX+/v44ceIEFi1aBB6PB2VlZf5esTY2NrC0tPwiVhufN28e0tLS4O3tDTU1Nfz444+iDokQQsgXgJJvQgghhDS6jx8/4saNG7h9+zbu3LmDO3fu4MOHD5CWlsb8+fOxbNmyKvdIFxcX48yZM9i3bx+uXLkCOTk5uLm5YdKkSbCxsUG7du1q3PNVVDgcDjZt2oSMjAyMHDkSQUFB6Nu3r6jDIoQQImKUfBNCCCGkUWRlZeHs2bM4deoUgoODUVpaCjU1NXTr1g1//PEHunXrBktLS6ioqAi0u3//Pvbt24dDhw7hw4cP6NGjB3x9feHu7v5FzGzXhZiYGPz9/ZGdnY3BgwcjLi4Ourq6og6LEEKICFHyTQghhJAG8/79ewQGBuLUqVO4du0aeDweevTogTVr1sDV1RWtW7cWugo4j8dDQEAANm3ahAcPHkBLSwvjx4/H2LFj0bZtWxGcyeeTkpLC0aNH0aFDB0yYMAEXLlygFdAJIeQ7Rsk3IYQQQoR6+fIlbt++jbdv36KgoAA6OjrQ09ODmpoaysvLUV5ejrKyMpSVlSE2NhanTp3CrVu3IC4uDltbW2zbtg2DBg2ClpZWjeM8efIEEyZMwM2bN+Hq6oply5bB0dEREhJf/8cUZWVl7NixA4MGDcKxY8fw008/iTokQgghIvL1/1YjhBBCSINhjCEmJgY7d+6En58fuFwumjVrBjk5OaSnp4MxJrSdjIwMHBwcsH//fri4uEBVVbXWsYqLi7Fy5UqsXr0arVq1QkhICGxtbRv6lERu4MCB6NmzJw4dOkTJNyGEfMco+SaEEEK+czweD3fu3MGJEydw8uRJJCUlQUNDA+vXr8fYsWOhpKQEoGIv7nfv3iE7OxsSEhKQlJSEpKQkJCQkoK6uXq8tvUJCQuDt7Y2kpCTMnTsXc+fOhYyMTGOdosh16dIFhw8f/uT2jDEUFhbi48eP+PjxI0pKSiAlJYWWLVtCTk6uASMlhBDSWCj5JoQQQr5jjx49wtixY3Hv3j1oaGhgyJAhGDp0KHr37l3lsm8pKSm0atUKrVq1+uTx3r59i3nz5uHAgQPo1asXzpw5g/bt23/mWXy5CgsLsXfvXvj6+sLNza3GutnZ2bh06RJevXqFpKQk/iMzMxP5+fng8XhV2lhbWyMiIoLuJSeEkK/Al7c/ByGEEEKaRFxcHKysrFBUVITg4GC8e/cOO3bsgJ2dXYPfb52RkYHp06fD0NAQFy5cgK+vL8LCwr7ZxDs+Ph6LFi2Cvr4+pk2bhkGDBmHjxo1V6vF4PISGhmLkyJHQ1tbG6NGjsWXLFsTFxfH3CF+4cCG2b9+OQ4cO4ezZs7h27RoiIyOxdetW3Lp1C1euXBHBGRLy/YmNjcXYsWNhYGAAGRkZKCgowNzcHGvXrkV2djaOHz8ODoeDLVu2CG0/fvx4SEtLIzY2FsD/Fpq0t7eHmpoaJCUloaGhARcXF5w7d07oF27/dv78eXh4eKBjx46QlJSs9ku4JUuWgMPhVPs4cuQIv+7jx4/h4+MDa2tryMvLg8Ph4Nq1a0L79fLygqmpKZSVlSErKwtjY2PMnDkTmZmZAvUePHgAZ2dn6OnpQVZWFqqqqrC2tsbBgwdrPD/GGHr16gUOh4PJkyfXWPffrl69Cmtra8jJyUFNTQ1jxoxBenp6nds3Jpr5JoQQQr5Tv/32GwwMDHD79u16XTJeHzk5Ofjrr7+wadMmcDgczJkzB1OnToWiomKjjCdKT548wfHjx3Hs2DE8fvwYioqKGDVqFGbOnAkDAwOBuhkZGdizZw98fX3x6tUrtGvXDitWrMDo0aOhqalZp/G6deuGEydOwN3dHaGhobCwsGiM0yKEANizZw98fHzQtm1bzJw5EyYmJigrK8Pdu3exc+dO3Lp1C4GBgRg5ciTmzJkDJycnGBoa8tsHBwdjz549WLVqFczMzFBcXIzBgwcjODgYw4cPx44dO6ClpYWMjAwEBQXB3d0dR48exaBBg6qNKTAwEFFRUejcuTOkpaVx7949ofW8vLzg6OhYpXzcuHF4+fKlwLG7d+/i9OnT6Ny5M+zs7HDu3Llqxy8oKMD48eNhaGgIGRkZ3L17FytWrMDFixdx//59SElJAaj4PaCrq4sRI0ZAR0cHBQUFOHToEH7++WckJSVhwYIFQvvftm0bXrx4Ue34wly/fh1OTk5wdnbGmTNnkJ6ejtmzZ8POzg53796FtLR0vfprcOwbkZubywCw3NxcUYdCCCGEiNS9e/fYiBEj2IULF1hJSYnQOtnZ2UxMTIzt2rWrUWLIzMxkK1asYMrKykxWVpbNmjWLZWZmNspYovT06VP2559/MlNTUwaANWvWjI0ePZqdPXuWFRcXC21z8uRJpqamxuTk5NiYMWNYeHg44/F4nzR+bm4us7S0ZKqqquzRo0efcyqEfLKioiIWHx/PioqKGr5zbjljr24wFnu84k9uecOPUYvIyEgmLi7OHB0dhf6/LikpYWfOnGGMVby3amtrs+7duzMul8sYq/h/qqury6ytrVl5eUX83t7eDADz9/cXOmZCQgJ7+PBhjXFV9s8YY5MmTWL1Se0SExMZh8Nho0ePrrbP48ePMwAsLCyszv1u376dAWAhISG11u3WrRvT1dWtNj4FBQV26tQpBoBNmjSpTuN37dqVmZiYsLKyMn5ZREQEA8C2b99et5P4j9pe3/XJQ2nmmxBCCPnGpKam4p9//sE///wDJSUlDBw4EC4uLjA0NISenh6aN2+Offv2gcfjwcnJqcHG5fF4uHbtGvbs2YNTp04BqLjMct68eWjRokWDjfMlyMrKwoQJE3Dy5EkoKChg4MCBWL58ORwcHKpdOC4nJwe///47AgIC4Obmhl27dkFdXf2z4lBUVERQUBBsbW1ha2uL8ePHw9nZGZaWlhAXF/+svgkRufizQNBsIO/d/8oUtQHHNYDJwCYLY+XKleBwONi9e7fQmVMpKSkMHFgRj4qKCnx9feHk5ISNGzdi+vTpmDp1KrKyshASEgJxcXGkpqZi7969cHBwgIeHh9AxjYyMao1LTOzT7yDet28fGGPw8vJqsD4B8N/T6nLrkpqaWrWXg48fPx79+vWrda2Mf3v79i2io6OxatUqgfFtbGxgbGyMwMBAeHt717m/xkDJNyGEEPKNcXJygq2tLUJDQ5Gbm4uAgAAEBATwj8vIyKC4uBiTJ0+Grq7uZ4/35s0bBAQEwNfXFy9fvkTbtm2xYsUKeHh4QEND47P7/xJt374dJ0+eBADY2dnB1NQUffr0qTbxDgkJwdixY5Gbm4sDBw5g9OjRDbZImoqKCoKDgzFjxgzs3LkTK1asgJiYGJo3bw41NTX+w9bWFpMmTaLF2cjXIf4scMwDwH+2N8x7X1E+7ECTJOBcLpd/W0dd3y8dHR0xYcIELFiwAGJiYti3bx+2bt3KT6jDwsJQVlaGwYMHN2Lk1ePxeNi/fz8MDQ3Ru3fvz+6vvLwcJSUlePDgARYuXIgePXqge/fuQsfl8Xj48OEDjh8/jsuXL2Pr1q1V6u3duxd37txBfHx8veKIi4sDAJiZmVU5ZmZmhoiIiHr11xgo+SaEEEK+MRwOB+fOncOJEydw4MABhIaGgjGGdu3aoU+fPjA0NISxsTFcXFw+qX/GGGJjY3HmzBmcOXMGMTExkJWVhbu7O/bv34/u3bt/8wmel5cXFBQU8OzZMzx9+hTLly/Hzp07ER4eLvABvbCwEHPnzsXmzZtha2sLPz8/6OnpNXg86urq8Pf3B5fLxZ07d/Dw4UNkZWUhMzMTmZmZeP/+PX777TfcuHEDfn5+jXaPPyENgsetmPH+b+IN/H8ZBwiaA7RzBsQa9wqPzMxMFBYWVlm3oTbr16/HlStXMG3aNNjb28PHx4d/LCUlBQDq3WdDCQ4OxuvXr7Fq1arP7isqKgrW1tb8nwcMGIAjR44IvfLGx8cHu3btAlBxtcDmzZsxYcIEgTpv377FjBkzsHbtWmhra9crlqysLACAqqpqlWOqqqr846JEyTchhBDyDZKTk4OHhwc8PDyQkpKCgwcPwt/fHzt37oSuri62b99ea4JcXFzMTy4r/3z69CkSEhJQUFCAZs2awcnJCdOnT4ezszN/P/DvQYsWLTB16lT+z8nJybCyssL06dNx7NgxMMZw/vx5zJgxAykpKfj7778xefLkz76kszbi4uKwtrYW+DBc6dSpU/Dw8ICNjQ1Onz4tsg/+hNQqOVLwUvMqGJD3tqKeQc8mC6s+FBQUMGvWLEycOBFLly79or6Q9PX1hYSEBMaMGfPZfXXs2BHR0dEoLCzEgwcPsHr1avTr1w+hoaGQk5MTqDtv3jx4eXkhPT0d586dw+TJk1FQUIAZM2bw60ycOBGdOnXCuHHjPjmm6p7rL+HfgJJvQggh5Bunp6eHefPmYe7cubh9+zaWLVsGV1dXTJ8+HevXr69SPyMjA+vWrcPOnTvx8eNHABUzq23btoWFhQVGjRqFTp06oWfPnqJfOfYLoa+vj3Xr1uHnn3/Gxo0bcerUKYSHh6Nv3744ffr0F7Gl2pAhQ2BsbIxBgwaha9euOHbsGGxtbUUdFiFV5ac1bL3PoKamBjk5OSQmJta7beX7Y+Wq35Uqr375lD4/V2ZmJs6ePQtnZ2doaWl9dn/y8vLo0qULAKBXr17o1q0brKyssGvXLoEvKIGK86489wEDBgAA5s6dC09PT6irq+PEiRMICgpCeHg4cnNzBdqWlpYiJycH8vLykJSUFBpL8+bNAUDoDHd2drbQGfGmRvt8E0IIId8JDoeDH374Afb29hATE8PFixer7CMbExMDc3Nz7Nq1Cz4+PoiIiEBmZibS09Nx8+ZN7NmzB9OnT4e9vT0l3v8xatQo9OjRA9OmTcPHjx8RFBSEkJCQLyLxrmRqaoro6GiYm5ujf//+CAsLE3VIhFSlULft9upc7zOIi4vDzs4O9+7dw5s3bxqkz759+0JSUhKnT59ukP7qIyAgAKWlpVUWWmsoXbp0gZiYGBISEmqta2lpifLycrx69QpAxT3b5eXlsLKygoqKCv8BVGz1pqKiggsXLlTbn6mpKQDg0aNHVY49evSIf1yUKPkmhBBCvgOpqalYvHgx9PT0MH36dEyaNAl37twRuAz6+PHj6NGjB7S0tBAfH4/Vq1fDxsaGP5tAasbhcHDkyBGcPXsWMTExcHBw+CIuc/wvVVVVXLx4ET169MDYsWORl5cn6pAIEaRvU7GqOar7/8MBFHUq6jWBuXPngjGGcePGobS0tMrxsrKyGvfD/i8tLS14eXnh8uXLOHDggNA6L1++RGxs7CfHXB1fX19oa2s36E4X/3b9+nXweDyBPc6rExYWBjExMbRu3RoAMGbMGISFhVV5AMDgwYMRFhaGHj16VNufjo4OLC0tcfDgQXC5XH55VFQUnj17hiFDhnzm2X0+uuycEEII+YYVFBRg8uTJOHToEKSkpPDLL7/gt99+E9jGJj8/H8uXL8eaNWswfPhw7Nu3D7KysiKM+uulo6MDHR0dUYdRKwkJCfj5+cHMzAy//fYb9u/f/0V+UUC+U2LiFduJHfNARQL+74XX/v916ri60Rdbq2RtbY0dO3bAx8cHFhYW8Pb2RocOHVBWVob79+9j9+7dMDU1haura5373LBhA169eoUxY8bg8uXLcHNzg6amJjIzM3HlyhX4+fnhyJEjQlfurpScnIzo6GgAFck6AJw4cQIA0KpVK/7l4JVu376Nx48fY968edVuRVhYWIiLFy8CqEhagYqEOjMzE/Ly8vyk/fz589izZw8GDhwIfX19lJWV4e7du9i0aRMMDQ0FZtbHjx8PRUVFWFpa8s/x+PHjOHr0KGbOnMnfnqxVq1Zo1aqV0Lh0dHTQp08fgTIJCQn07t0bISEh/LI1a9agX79+cHd3h4+PD9LT0zFnzhyYmppi7Nix1T6XTeaTdhr/AtVnc3NCCCHke7Fq1SoGgI0bN459+PBB4FhJSQnbsmUL09DQYNLS0mzlypWMx+OJJlAiEgcOHGAA2NixY1lZWZmowyFfqaKiIhYfH8+KiooatuPHZxj7qx1jixX/9/irfUW5CDx48IB5enoyPT09JiUlxeTl5Vnnzp3ZokWLWHp6epX6fn5+DACLjo4W2l95eTnz9/dntra2TFVVlUlISDB1dXXm5OTEDh8+zLhcbo3xVPYv7OHp6Vml/rhx4xiHw2EvX76sts/ExMRq+9TX1+fXe/LkCRs6dCjT19dnMjIyTEZGhrVr147NnDmTZWVlCfS5b98+1rNnT6ampsYkJCSYsrIy6927NwsICKjx/CoBYJMmTRJa3rt37yrlwcHBzMrKisnIyDBVVVXm4eHB0tLS6jSWMLW9vuuTh3L+P/CvXl5eHpSUlJCbmwtFRUVRh0MIIYR8EbZu3YrffvsNABAeHo7u3buDx+PhyJEjWLhwIZKSkuDp6YklS5Y0yhZY5Mt36NAheHh48FdkJ6S+iouLkZiYCAMDg2r3uv9kPG7Fqub5aRX3eOvbNNmMNyFA7a/v+uShdNk5IYQQ8o0KCQnB+fPn+T9HRkZi165duHnzJpKSkjBw4ECcPXsWHTp0EGGURNRGjRqFCRMmID8/X9ShEFKVmPgXu50YIfVFC64RQggh36ipU6fi8uXL/J/nzZuHJ0+ewNXVFREREThz5gwl3gRAxRZB27ZtQ3JysqhDIYSQbxbNfBNCCCHfqG3btmHLli2wsLCAjY0NLCwsICcnJ+qwyBdo79696NWrF5ydnXH//v1q99ElhBDy6Sj5JoQQQr5RPXv2RM+edLkmqZ22tjZOnDiBzp07IzAwEMOGDRN1SIQQ8s2hy84JIYQQQgh0dXUBgLYcI4SQRkLJNyGEEEIIwatXrwAAbdq0EXEkhBDybaLkmxBCCCGEYMOGDVBTU0O7du1EHQohhHyT6J5vQgghhJDvXFBQEI4cOYIDBw7QonyEENJIaOabEEIIIeQ7VlhYCB8fH9jZ2WH06NGiDocQQr5ZNPNNCCGEEPId+/PPP/Hu3TtcvnyZFlsjhJBGRDPfhBBCCCHfscjISHTt2hVGRkaiDoUQQr5plHwTQgghhHzHfHx8EB4ejlu3bok6FEK+eJs3bwaHw4GpqWm1dTgcDjgcDlavXl3l2P79+8HhcHD37l0kJSXx69b2SEpKwrVr18DhcHDixIla49y0aROGDBkCAwMDcDgc9OnTp9q6YWFh6NevHzQ0NKCgoAAzMzNs3rwZXC6XXycvLw8rVqxAnz59oKWlBQUFBXTs2BFr1qxBcXFxlT4XLFgAFxcX6OjogMPhYMyYMdWOf/LkSXTv3h2qqqpQVlaGpaUlAgICqtTLy8vD/PnzYWxsDDk5Oejo6MDd3R2PHz+u9fmotGXLFrRr1w7S0tIwMDDA0qVLUVZWVuf2n4uSb0IIIYSQ79iwYcNgamqKhQsXijoUQr54+/btAwA8fvwYt2/frrHu6tWrkZ2dXe3xFi1a4NatWwKPzp07o3Xr1lXKW7RoUa84d+7cieTkZNja2kJdXb3aelevXoW9vT3Ky8uxZ88enD59Gn369MGUKVMwbdo0fr2UlBRs2rQJ5ubm2L17N86ePYuhQ4diyZIlcHFxAWNMoN+NGzciKysLAwcOhJSUVLXj79u3D0OHDkWLFi1w6NAhHDlyBG3atIGHhwc2btwoUNfV1RWbNm3CuHHjcOHCBaxevRoPHjyAtbU1kpOTa31OVqxYgSlTpmDIkCG4fPkyfHx8sHLlSkyaNKnWtg2GfSNyc3MZAJabmyvqUAghhBBCvionT55kAFh4eLioQyFfoaKiIhYfH8+KiooavO9ybjm78/4Ou/DyArvz/g4r55Y3+Bh1FR0dzQAwZ2dnBoCNGzdOaD0AzN7enklISLBp06YJHPPz82MAWHR0tNC2vXv3Zh06dBB6LCwsjAFgx48frzVWLpfL/3uHDh1Y7969hdYbNWoUk5aWZvn5+QLl/fv3Z4qKivyf8/Pzq9RhjLF169YxAOzmzZvVji8vL888PT2Fjt+9e3emr68vUJ/H47F27doxMzMzftnz588ZALZgwQKB9pGRkQwA27Bhg9D+K2VmZjIZGRk2fvx4gfIVK1YwDofDHj9+XG3b2l7f9clDaeabEEIIIeQ716xZMwAQevkoIaJyNfkqHE464JfLv2D2zdn45fIvcDjpgKvJV0USj6+vL4CKGW0bGxscOXIEhYWFQuu2bdsWv/76K7Zt21anWdmGJiZWtzRPUlISUlJSkJWVFShXVlaGjIwM/2d5eXnIy8tXaW9paQkAeP369SePr6CgIFCfw+FAUVFRYHxJSUkAgJKSUpU4AQjUFSYoKAjFxcUYO3asQPnYsWPBGMPp06frFO/nouSbEEIIIeQ7t3r1anTr1g22traiDoUQABWJ97Rr05BWmCZQnl6YjmnXpjV5Al5UVIR//vkHXbt2hampKX755Rd8/PgRx48fr7bNkiVLIC4u/kXf0jFx4kSUlpbi999/x7t375CTk4OAgAAEBgZi1qxZtbYPDQ0FAHTo0OGTxv/tt9/w5MkTrFixAhkZGcjMzMT69etx7949zJgxg19PX18fgwYNwsaNGxEWFob8/Hw8ffoUv//+O/T09DB8+PAax4mLiwMAdOzYUaC8RYsWUFNT4x9vbJR8E0IIIYR8x3g8Hm7fvo2hQ4fSVmPki8DlcbH6zmowsCrHKsvW3FkDLo9b5XhjOXHiBHJzc/Hrr78CAH766ScoKCjwZ8OF0dLSwtSpU3Ho0CHExsY2Vaj10q1bN4SGhiIwMBA6OjpQUVHB2LFjsWLFCkyfPr3GtrGxsVi7di3c3NxgZmb2SeMPGTIEp06dwrp166ChoQF1dXUsWrQI/v7+cHd3F6h7/PhxODs7w9bWFs2aNUP79u2Rnp6O69evQ0VFpcZxsrKyIC0tLXT2XlVVFVlZWZ8Uf31R8k0IIYQQ8h17/fo1CgoKYGJiIupQCAEAxKTHVJnx/jcGhtTCVMSkxzRZTL6+vpCVleXPsCooKMDd3R03b97E8+fPq203a9YsqKqqYvbs2U0Var3cu3cPbm5usLCwwLlz5xAaGoq5c+diwYIFWLZsWbXtkpKS4OLiAl1dXezdu/eTxw8KCsLo0aMxZMgQXLp0CVeuXIGXlxfGjBkDPz8/gbre3t44efIkNm7ciOvXr+Po0aOQkpKCra1tnS7tr+nLxab64lGiSUYhhBBCCCFfpMrVmF+8eCHiSAipkFGY0aD1PteLFy9w48YN/Pjjj2CMIScnBwAwdOhQ+Pn5Yd++fVi1apXQtoqKiliwYAH++OMPhIWFNUm89TFp0iRoamoiMDAQ4uLiAIC+fftCTEwMS5YswahRo9C6dWuBNsnJyejbty8kJCQQEhICVVXVTxqbMYZffvkFvXr14q8iDwD29vbIzc3Fb7/9hmHDhkFeXh5BQUHw9fXF8ePHMXToUH7d/v37o1WrVliyZEmVZP3fmjdvjuLiYhQWFkJOTk7gWHZ2NiwsLD7pHOqLZr4JIYQQQr5jP/zwAyZPnoypU6fi5MmTog6HEKjLVb811qfU+1z79u0DYwwnTpyAiooK/+Hs7AwA8Pf3F9gT+7+8vb1hYGCA2bNnV9mSS9QePHgACwsLfuJdqWvXruDxeHjy5IlAeXJyMvr06QPGGMLCwtCyZctPHjstLQ3v37/nL9r23/ELCgqQlJTEj7Oy/N+UlZVhaGhY6z3blfd6P3r0SKA8NTUVmZmZNe7b3pAo+SaEEEII+Y5xOBz8/fffcHNzw4QJE1BQUCDqkMh3zlzDHJpymuBA+KXAHHCgJacFcw3zRo+Fy+XC398fbdq0QVhYWJXH9OnT8f79e1y6dKnaPqSkpLB8+XJER0fXuECbKGhra+Pu3btVvjy4desWAAgk1ykpKejTpw+4XC5CQ0Ohr6//WWOrqKhARkYGUVFRVY7dunULYmJi/P3NtbW1AaBK3aysLCQkJNT6JYCjoyNkZGSwf/9+gfL9+/eDw+Fg8ODBn34i9UCXnRNCCCGEfGcKCgqQkJCAZ8+e8R/Xr1/Hhw8fkJKSgvbt24s6RPIdExcTxxzLOZh2bRo44AgsvFaZkM+2nA1xMfHqumgwly5dwrt377BmzRr06dOnynFTU1Ns3boVvr6+cHFxqbafESNGYP369TUm6Q3p7t27/FnjvLw8/sw9UDF7XJk4T506Fb///jtcXV0xYcIEyMnJISQkBH/99Rfs7e3RqVMnAEB6ejr69u2L9+/fw9fXF+np6UhPT+eP17JlS4EE+Pr168jIqLgtgMvlIjk5mT9+7969oa6uDmlpafj4+GDDhg3w8PDATz/9BHFxcZw+fRqHDx/Gr7/+yr+kfciQIVi0aBG8vb3x5s0bmJub4/3791i3bh0KCwsxZcoUgbHt7OywaNEiLFq0CEDFomoLFizAwoULoaqqiv79+yM6OhpLliyBl5dXk615Qck3IYQQQsg3gjGGuLg4FBYWgsfjgcfj4ePHj1US7Tdv3vDbaGhooG3btvD09MTEiRNhaGgowjMgpIK9vj029NmA1XdWCyy+pimnidmWs2Gvb98kcfj6+kJKSqrK/tCV1NTU4ObmhhMnTiAtLQ2amppC63E4HKxZswb9+/dvzHD5tm7dCn9/f4GyytXD/fz8MGbMGAAVW33p6Ohg48aN8PLyQlFREVq1aoXFixdj6tSp/Lbx8fF49eoVAGD06NFVxlu8eDGWLFki8PP169f5P1+7dg3Xrl0DAISFhfG/yFi3bh3at2+PXbt2YfTo0eDxeGjTpg22bt2K8ePH89srKCggKioKK1aswM6dO/HmzRuoqqqic+fO2LFjB6ysrPh1GWPgcrng8XgCMc6fPx/NmjXDtm3bsH79emhpaWHOnDmYP39+HZ/Vz8dhX9qNB58oLy8PSkpKyM3NhaKioqjDIYQQQghpUuXl5bC1tcXNmzeFHtfW1oaxsTEsLS3RoUMHtG3bFsbGxrVu0UNIbYqLi5GYmAgDAwPIyMg0aN9cHhcx6THIKMyAupw6zDXMm2TGm5BKtb2+65OH0sw3IYQQQsg34PHjx9Um3gDw7t07vHv3DuHh4TAwMIChoaHAo127dlVWNSZE1MTFxNFVq2vtFQn5ClDyTQghhBDyDejUqRNSU1Px7t07pKWlITs7G2JiYgIPoGKF4RcvXuDFixcIDQ3Fnj17UFxcDAAwMzPDyJEjMXLkSOjq6orydAgh5JtDyTchhBBCyDdCU1Oz2ntOq8Pj8fDu3TvcvXsXR44cwdKlS7FkyRJcvXoV3bt3b6RICSHk+0PJNyGEEELId0xMTIy/UvHgwYPx8eNHuLq6YuDAgXj69CnU1ZtmL2VCCPnW0T7fhBBCCCHfkaioKPz2228ICAhAQkIC/rv2roKCAtzd3ZGdnY3MzEwRRUkIId8emvkmhBBCCPmOXLt2DVu3buX/rKysDC0tLcjLy0NBQQGpqal49uwZ+vfvj3bt2okwUkII+bbQzDchhBBCyHfE29sbEydO5C/AlpOTg6dPn0JFRQUtW7aElZUVrl27hqCgIHA4HBFHSwgh3w5KvgkhhBBCviNKSkrYsWMHiouLceHCBYwaNQry8vK4evUqxMXFsWbNGvTu3ZsSb0IIaWCUfBNCCCGEfIckJSUxYMAAHDx4EGlpadi5cyfOnz8PAwMDjBs3Dvfv3xd1iIQQ8k2h5JsQQggh5DsnLy+PCRMmICEhAfPmzUNQUBDMzc0xefJkUYdGCCHfDEq+CSGEEEIIAKB58+ZYsGABEhMTMXfuXOzZswcfPnwQdViEfBFiY2MxduxYGBgYQEZGBgoKCjA3N8fatWuRnZ2N48ePg8PhYMuWLULbjx8/HtLS0oiNjQUA8Hg8BAQEwN7eHmpqapCUlISGhgZcXFxw7tw58Hi8GuM5f/48PDw80LFjR0hKStZ4q8iLFy/w888/Q09PD7KysmjTpg2mTZuGrKwsgXr//PMPevXqBU1NTUhLS0NbWxuurq6IjIwU2m9mZiamTJmCVq1aQVpaGpqamnByckJ2dja/TmhoKH755Re0a9cO8vLy0NHRwaBBg3Dv3j2hfcbExMDe3h4KCgpQVlbGkCFD8OrVqxqfi3+7evUqrK2tIScnBzU1NYwZMwbp6el1bt+YKPkmhBBCCCECJCQk4OjoiNLSUjx+/FjU4RAicnv27IGFhQWio6Mxc+ZMBAUFITAwEO7u7ti5cyd+/fVXuLu7Y+TIkZgzZw5evHgh0D44OBh79uzB0qVLYWZmhuLiYgwYMACenp7Q0NDAjh07EBoaip07d0JbWxvu7u44d+5cjTEFBgYiKioKJiYm6NSpU7X1MjIyYGVlhYiICCxbtgwXL17EpEmTsGfPHtjb2wsk+VlZWejevTu2b9+O4OBgbNiwAWlpaejVqxeuX78u0O+7d+/QrVs3BAUFYeHChbhy5Qp27NgBQ0NDlJaW8uvt2LEDSUlJmDJlCi5evIi///4b6enpsLKyQmhoqECfT58+RZ8+fVBaWopjx45h3759SEhIQM+ePZGRkVHrv9P169fh5OQETU1NnDlzBn///TeuXr0KOzs7lJSU1Nq+0bFvRG5uLgPAcnNzRR0KIYQQQshXLT8/n7m4uDA9PT1WVlYm6nDIF66oqIjFx8ezoqKiBu+bV17O8qNus5xz51l+1G3GKy9v8DFqExkZycTFxZmjoyMrLi6ucrykpISdOXOGMcZYdnY209bWZt27d2dcLpcxVpGn6OrqMmtra1b+//F7e3szAMzf31/omAkJCezhw4c1xlXZP2OMTZo0iVWX2u3Zs4cBYFevXhUoX7lyJQPAYmJiahwnJyeHSUpKsp9//lmgfNCgQUxHR4dlZ2fX2D4tLa1K2cePH5mmpiazs7MTKHd3d2dqamoCOV1SUhKTlJRks2bNqnEcxhjr2rUrMzExEXjfioiIYADY9u3ba20vTG2v7/rkoTTzTQghhBBC+EJCQtCxY0dcvXoV69atg4SEhKhDIt+pvOBgvLCzR4qnJ97NmIEUT0+8sLNHXnBwk8axcuVKcDgc7N69G9LS0lWOS0lJYeDAgQAAFRUV+Pr6IiIiAhs3bgQATJ06FVlZWfD394e4uDhSU1Oxd+9eODg4wMPDQ+iYRkZGMDMzqzGuyu0CayMpKQmgYqeDf1NWVgYAyMjI1Ni+WbNmkJGREXgvSEpKwtmzZzFu3DioqKjU2F5DQ6NKmYKCAkxMTPD69Wt+WXl5Oc6fP48ff/wRioqK/HJ9fX307dsXgYGBNY7z9u1bREdH4+effxaI1cbGBsbGxrW2bwqUfBNCCCGEEOTk5MDLywv29vbQ19fHo0ePMGzYMFGHRb5TecHBeDvlD5SnpgqUl6el4e2UP5osAedyuQgNDYWFhQV0dXXr1MbR0RETJkzAggULsHHjRuzbtw9r166FkZERACAsLAxlZWUYPHhwI0b+P4MHD4aenh6mT5+Ox48fIz8/Hzdu3MDq1avh6uqK9u3bV2nD5XJRVlaGpKQkeHt7gzGGSZMm8Y/fvHkTjDFoa2tjxIgRUFBQgIyMDPr06YNbt27VGlNubi5iYmLQoUMHftnLly9RVFQk9EsHMzMzvHjxAsXFxdX2GRcXx68rrH3lcVGi5JsQQggh5DsXGBgIExMTHD9+HLt370ZISAgMDQ1FHRb5TjEuF2krVwGMCTlYUZa2chUYl9vosWRmZqKwsBAGBgb1ard+/Xpoa2tj2rRpsLe3h4+PD/9YSkoKANS7z0+lpKSEqKgolJWVwdTUFM2aNUPv3r3RrVs3HD9+XGibDh06QEpKCgYGBjh37hyCgoJgYWHBP/727VsAwIwZM1BUVISTJ0/i8OHD+PDhA2xtbfmLylVn0qRJKCgowPz58/lllYu/qaqqVqmvqqoKxliNC0DW1v6/i8uJAiXfhBBCCCHfqdTUVLi7u2PIkCHo2rUr4uPjMW7cuDpfzvpvhYWFtDgbaRCFd+9VmfEWwBjKU1NReFf4atlfAgUFBcyaNQsAsHTp0hpXIm9sHz58wKBBg5CXl4dDhw7hxo0b2L59O8LDwzFw4ECUl5dXaXPy5Encvn0bx48fh4mJCZycnHDt2jX+8cpF2lq2bImTJ0/CwcEBQ4YMQVBQEMTExLB27dpq41m4cCEOHTqEjRs3CiT0lWp6ruryPFZXR5T/BpXoJh5CCCGEkO8MYwz+/v6YNm0aJCQkcPToUbi7u3/Sh9Pnz59j1KhRuHv3LhhjOHfuHFxcXBohavK9KK/Dqtb1qfc51NTUICcnh8TExHq3rbw/XEpKSqBcT08PAD6pz0+xZs0aPHjwAMnJyWjRogUAoGfPnmjXrh1sbW1x6NAheHp6CrSpvBzc0tISgwcPRufOnTFlyhQ8fPgQQMW2hABgb28PcXFxfrsWLVqgU6dOiImJERrL0qVLsXz5cqxYsQKTJ08WOFbZp7AZ6uzsbHA4HP596sLU1l7YjHhTo5lvQgghhJDvSGJiIhwcHDB27Fi4urriyZMnGDZs2Ccl3hEREejWrRvy8vKwevVqABWz6YR8Dgl19Qat9znExcVhZ2eHe/fu4c2bNw3SZ9++fSEpKYnTp083SH+1efDgAXR0dPiJd6WuXbsCQK33QktISMDc3BwJCQn8spoWg2OMCb16ZunSpViyZAmWLFmCefPmVTnepk0byMrK4tGjR1WOPXr0CIaGhjUuDmdqasqvK6x95XFRouSbEEIIIeQ7kJycjIkTJ6Jt27Z49uwZLl26BH9/f/5sUX2Vl5djzJgxaNeuHdasWYOAgADIyMjA1dW1gSMn3xu5LhaQ0NICqvtCiMOBhJYW5LpUvWS5McydOxeMMYwbN05g/+pKZWVlte7J/W9aWlrw8vLC5cuXceDAAaF1Xr58Wet903Wlra2NN2/e8O/TrlS5MFrLli1rbF9cXIyoqCiBdSC6deuGli1bIjg4GNx/3Xv/7t07PHz4EFZWVgJ9LFu2DEuWLMGCBQuwePFioeNISEjA1dUVp06dwsePH/nlKSkpCAsLw5AhQ2qMU0dHB5aWljh48KBATFFRUXj27Fmt7ZsCXXZOCCGEEPINS05OxsqVK+Hn5wclJSWsWLECPj4+kJeX/6x+k5KS8OLFC7x48QKDBw+GtbU1IiMjoamp2UCRk+8VR1wcmvPm4u2UPyoS8H8vvPb/CbnmvLng/Oty58ZkbW2NHTt2wMfHBxYWFvD29kaHDh1QVlaG+/fvY/fu3TA1Na3XF08bNmzAq1evMGbMGFy+fBlubm7Q1NREZmYmrly5Aj8/Pxw5cqTGGebk5GRER0cDqEjWAeDEiRMAgFatWqFLly4AKhY3O3ToEPr164c5c+ZAV1cXcXFxWL58OTQ1NTFq1Ch+nzY2Nhg4cCDat28PJSUlJCUlYceOHXj58qXAVl1iYmLYuHEjhg0bhkGDBsHb2xsFBQVYtmwZpKSkMHfuXH7dv/76C4sWLYKjoyOcnZ0RFRUlcB7/TtSXLl2Krl27wsXFBXPmzEFxcTEWLVoENTU1TJ8+XaCdhIQEevfujZCQEH7ZmjVr0K9fP7i7u8PHxwfp6emYM2cOTE1NMXbs2Lr94zSmT9pp/AtUn83NCSGEEEK+dYmJiWzcuHFMQkKCqampsbVr17KPHz826BhXr15lO3bsYGfPnmVcLrdB+yZfj6KiIhYfH8+KiooatN/cy5dZQu8+LL5tO/4joXcflnv5coOOU1cPHjxgnp6eTE9Pj0lJSTF5eXnWuXNntmjRIpaenl6lvp+fHwPAoqOjhfZXXl7O/P39ma2tLVNVVWUSEhJMXV2dOTk5scOHD9f6f6qyf2EPT09PgboxMTHMzc2NtWzZkklLS7PWrVszLy8vlpKSIlBv+vTprFOnTkxJSYlJSEgwLS0t5ubmxiIiIoTGcPr0ada1a1cmIyPDlJSU2MCBA9njx48F6vTu3bvaOIWlo3fv3mV2dnZMTk6OKSoqssGDB7MXL15UqQeA9e7du0p5cHAws7KyYjIyMkxVVZV5eHiwtLS0Gp/LmtT2+q5PHsr5/8C/enl5eVBSUkJubq7ApuyEEEIIId+TxMRErFy5Evv374eKigpmzZoFb2/vz57pJqQ6xcXFSExMhIGBQY335H4KxuVWrH6ekQEJdXXIdbFoshlvQoDaX9/1yUPpsnNCCCGEkK8cYwyRkZH4+++/cerUKaiqqmLVqlWUdJOvHkdcHPLdLEUdBiENgpJvQgghhJCvVGlpKY4fP45Nmzbh7t27MDIywt9//40xY8ZQ0k0IIV8YSr4JIYQQQr4yGRkZ2L17N7Zt24b379+jX79+uHDhAhwdHYVu8UMIIUT0KPkmhBBCCPlKvHz5EqtWrcLBgwfB4XDg4eGB33//HR06dBB1aIQQQmpByTchhBBCyBfu7du3WLZsGXx9faGuro7Fixdj/Pjxn7xHNyGEkKZHyTchhBBCyBcqMzMTq1evxrZt2yAvL49Vq1Zh0qRJkJWVFXVohBBC6omSb0IIIYSQL0xubi42bNiADRs2gMPhYM6cOZg6dWqDbKdaXl4ODocD8Vq2a2KM4dq1a7h79y5SUlKQnJyM5ORkFBUVYdCgQVi0aBGaNWv22fEQQsj3glbkIIQQQgj5QhQWFmLdunVo3bo11q5di4kTJ+LVq1dYvHjxZyfez549g4+PD5SUlCAhIQFpaWkoKyujXbt2ePz4cZX6Hh4esLW1xZ9//omwsDBwuVx0794d/fr1w4YNG2BmZobCwsLPiokQQr4nNPNNCCGEECJipaWl2Lt3L5YvX46MjAx4eXlhwYIF0NHR+ax+GWO4evUqNm3ahIsXL0JTUxMzZsyAvr4+ioqKUFRUhD179uCXX37BtWvXBC5nZ4xBUlISN27cQOfOnQX6zcnJwcmTJyEhQR8lCSGkrugdkxBCCCHkMzDGUFRUhLy8POTm5iIvLw9iYmJo37495OTkamzL5XJx6NAhLF68GMnJyRg9ejSWLFmC1q1bf1Y8CQkJuHjxInx9ffH48WN06tQJ+/fvx/DhwyEtLS1Qv3v37ujRowcUFRXRqVMnWFpawtLSEhMmTMDRo0exadMm+Pv78+uHhobin3/+wbJlyyAlJfXJcRJCyPeGLjsnhBBCCKlFYWEhHj16hMDAQKxfvx4TJ06Evb09WrVqBSkpKcjLy6NFixZo164dLC0t0aVLFygoKMDIyAhDhgzB4sWLceLECcTFxeH+/fu4du0aNmzYgI4dO8LT0xOdO3fGo0ePcODAgU9KvAsLC3Hx4kVMnjwZhoaGaNeuHebOnQsjIyOEhYXh/v378PT0rJJ4A4C1tTXi4uKwefNmdOzYEdevX8cvv/yCXr16QV5eHgsXLuTXTUtLw6hRo2BnZ4c5c+Z81nNKyNcmNjYWY8eOhYGBAWRkZKCgoABzc3OsXbsW2dnZOH78ODgcDrZs2SK0/fjx4yEtLY3Y2FgAAI/HQ0BAAOzt7aGmpgZJSUloaGjAxcUF586dA4/HqzGe8+fPw8PDAx07doSkpCQ4HE61dV+8eIGff/4Zenp6kJWVRZs2bTBt2jRkZWVVqXvo0CF07twZMjIyUFNTw8iRI/H69esq9T5+/Ijff/8dOjo6kJaWhrGxMdauXQsulytQLzQ0FL/88gvatWsHeXl56OjoYNCgQbh3716VPsPDw+Hl5QULCwtIS0uDw+EgKSmpxufhv65evQpra2vIyclBTU0NY8aMQXp6er36aCw0800IIYQQ8i9FRUUIDg7GxYsX8fTpU7x48QLv3r3jH1dQUIChoSEMDQ0xYsQI6OrqQllZGYqKilBUVISSkhJKSkoQFxeHR48e4dGjR9i5c2eVD38yMjKwt7eHv78/unbtWu84X7x4gUuXLuHixYu4du0aiouL0apVKwwYMAADBgxAnz59IC8vX6e+2rdvj/bt28Pb2xtAxYJv9+7dQ6tWrfhfBuTl5WHo0KH8hKG2BdsI+Zbs2bMHPj4+aNu2LWbOnAkTExOUlZXh7t272LlzJ27duoXAwECMHDkSc+bMgZOTEwwNDfntg4ODsWfPHqxatQpmZmYoLi7G4MGDERwcjOHDh2PHjh3Q0tJCRkYGgoKC4O7ujqNHj2LQoEHVxhQYGIioqCh07twZ0tLSQpNZAMjIyICVlRUUFRWxbNky6Onp4f79+1i8eDHCwsJw7949iIlVzMlu2bIFv//+O7y8vLB69Wq8efMGCxcuRM+ePXH//n2oqKgAqFi4sV+/fkhISMCyZctgbGyMoKAgzJkzB2/evMHmzZv54+/YsQNZWVmYMmUKTExMkJGRgb/++gtWVla4fPkybG1t+XVDQkJw9epVdO7cGYqKirh27Vq9/p2uX78OJycnODs748yZM0hPT8fs2bNhZ2eHu3fvCv0Cskmxb0Rubi4DwHJzc0UdCiGEEEK+Qvfv32ejRo1icnJyDABr27YtGzFiBFu4cCHz9/dnERERLC0tjfF4vE/qPy0tjYWHh7O7d++yZ8+esaKionr38ezZM7ZgwQJmbGzMADBJSUlmb2/P/vrrL/bkyZNPjq0mPB6P3b59m3Xt2pUpKSmxyMjIBh+DfN2KiopYfHz8J72ma8Pl8tibp9ns2Z337M3TbMblNvxrvDaRkZFMXFycOTo6suLi4irHS0pK2JkzZxhjjGVnZzNtbW3WvXt3xuVyGWMVeYquri6ztrZm5eXljDHGvL29GQDm7+8vdMyEhAT28OHDGuOq7J8xxiZNmsSqS+327NnDALCrV68KlK9cuZIBYDExMYwxxoqLi5mSkhJzdXWtcv4A2Lx58/hl//zzDwPATp48KVB3/PjxTExMjD19+pRflpaWViWmjx8/Mk1NTWZnZ1ftOa1bt44BYImJiULPS5iuXbsyExMTVlZWxi+LiIhgANj27dvr3M+/1fb6rk8eSjPfhBBCCPluMcYQEhKCtWvX4sqVK9DX18eCBQswZMgQtG3btkHH0tDQgIaGRr3blZWV4ciRI9i2bRtu374NJSUl/Pjjj1i7di3s7OygoKDwSfEwxvDhwwdkZWVVeWRmZiI5ORnPnz/H8+fP8eHDB+jo6CA0NBTm5uafNB4h9fXyfjpuHn2OgpwSfpm8sjR6/mSENp3r/3/pU61cuRIcDge7d+8WOnMqJSWFgQMHAgBUVFTg6+sLJycnbNy4EdOnT8fUqVORlZWFkJAQiIuLIzU1FXv37oWDgwM8PDyEjmlkZFRrXJWz1bWRlJQEACgpKQmUKysrA6i4CgcA4uLikJubiwEDBgjUs7a2hqqqKk6ePIkVK1YAACIiIsDhcODk5CRQ18XFBbt370ZgYCD/1hRh73sKCgowMTGpcjl7Xc9JmLdv3yI6OhqrVq0SWAzSxsYGxsbGCAwM5F/dIyqUfBNCCCHku/Lx40ckJCQgJiYG27dvx4MHD9C5c2f8888/GDp06Be1gveVK1cwbtw4JCcnw8HBAUePHoWrq6vAquT1UVRUhJkzZ+L+/fuIj49HTk5OlTry8vJo3rw59PT00KFDBwwaNAhWVlbo1avXF/XckG/by/vpCNoVV6W8IKcEQbvi4DjBtEkScC6Xi9DQUFhYWEBXV7dObRwdHTFhwgQsWLAAYmJi2LdvH7Zu3cpPqMPCwlBWVobBgwc3YuT/M3jwYOjp6WH69OnYvn079PX1ERMTg9WrV8PV1RXt27cHULHrAgChXzBIS0vj+fPnKC4uhoyMDEpLSyEmJsZP7P9dDwD/vvbq5ObmIiYmRuCS888VF1fxejEzM6tyzMzMDBEREQ021qeid1BCCCGEfJWSkpKwZ88e3L9/HwkJCeBwOFBSUoKysjL/AQDp6elIT09HWloa0tPTBfam7t+/P65evQpbW9saFysShZ07d2Ly5MmwtbXF2bNnhX6grK/r169j27ZtGDZsGJydnWFoaAgNDQ00b96c/xD5PZHku8fjMdw8+rzGOuHHnsOgkzrExBr3/21mZiYKCwthYGBQr3br16/HlStXMG3aNNjb28PHx4d/LCUlBQDq3eenUlJSQlRUFH788UeYmpryy93d3REQEMD/uW3bthATE0NERATGjh3LL3/58iXev38PAPjw4QNatGgBExMTcLlcREVFoUePHvy64eHhACB0Ibd/mzRpEgoKCjB//vwGOcd/j6mqqlrlmKqqaq0xNQVKvgkhhBDyVUlISMDixYtx/PhxKCoqonv37nBzc4OYmBhycnL4j8rLGTU0NGBqagpbW1v+pd+GhoZo3749mjVrJuKzEc7X1xfe3t747bffsGHDhgabcb5//z6UlJRw5MiRL+7LBkIqvX+eI3CpuTD5H0rw/nkOdNqqNFFU9aOgoIBZs2Zh4sSJWLp0qUj/v3348AGDBg1CYWEhDh06BF1dXcTFxWHZsmUYOHAgLly4AAkJCaiqqmLUqFE4cOAAunbtCnd3d7x58wbjx4+HuLg4uFwu/7LwUaNG4c8//8T48ePh5+eHtm3b4tKlS/yF1mq6fHzhwoU4dOgQtmzZAgsLiwY/3+qe6y/hPY+Sb0IIIYR8NV69esVftffvv//GmDFj6ryi99eCx+NhzZo1GDp0qMCKwQ3VN4/HQ35+/hf7xQMhBXk1J971rfc51NTUICcnh8TExHq3rbyKREpKSqBcT08PAD6pz0+xZs0aPHjwAMnJyWjRogUAoGfPnmjXrh1sbW1x6NAheHp6AqhYmZwxBh8fH0ycOBFiYmL4+eefoampicuXL6N58+YAKp6XoKAgeHp6wsrKCgDQvHlzbNiwAb/++it0dHSExrJ06VIsX74cK1aswOTJkxv0PCtjEzbDnZ2dLXRGvKnRPt+EEEII+Wrs3bsXAPDgwQNMmjTpm0u8gYotiZ4/f44//vijwfseNWoUeDwe7OzssHXrVsyfPx8DBgxA+/bt0a9fP4SGhjb4mITUl7xi3W59qGu9zyEuLg47Ozvcu3cPb968aZA++/btC0lJSZw+fbpB+qvNgwcPoKOjw0+8K1VucVh5rzRQseZDQEAAMjMz8fDhQ6SlpWH//v149uwZbGxsBK7C6dq1K+Lj45GYmIi4uDi8e/eOf/94r169qsSxdOlSLFmyBEuWLMG8efMa/DwrL6l/9OhRlWOPHj0SuOReVCj5JoQQQshX4fXr1wgICMDQoUP593N/i7Zs2YLOnTvDxsamwftu1aoVwsLCwOPxMHXqVOzfvx8cDgcODg7Iz8+Hg4MDbt++3eDjElIfLYyUIa9cc2KtoCKNFkbKTRLP3LlzwRjDuHHj+IuS/VtZWRnOnTtX5/60tLTg5eWFy5cv48CBA0LrvHz5stZFy+pKW1sbb968wdu3bwXKb926BQBo2bJllTYqKiowMzODmpoazp49i2fPnmHKlClC+2/VqhU6dOgASUlJ/PXXX9DW1oa7u7tAnWXLlmHJkiVYsGABFi9e3CDn9V86OjqwtLTEwYMHweVy+eVRUVF49uwZhgwZ0ijj1gdddk4IIYSQL97hw4fh4+ODZs2aYdq0aaIOp9GEhobi0qVL2Lt3b6Pdn9i1a1fcvXsXXC4X4uLi/PKysjJ06tQJmzdvxqFDhxplbELqQkyMg54/GQld7bxSj2FGjb7YWiVra2vs2LEDPj4+sLCwgLe3Nzp06ICysjLcv38fu3fvhqmpKVxdXevc54YNG/Dq1SuMGTMGly9fhpubGzQ1NZGZmYkrV67Az88PR44cqXGhxeTkZERHRwOoSNYB4MSJEwAqEuIuXboAqFjc7NChQ+jXrx/mzJnDv+d7+fLl0NTUxKhRo/h9njx5kj+DXVxcjGvXruHvv//GxIkTMWjQIIHx58+fj44dO6JFixZISUnBvn37cPv2bVy4cEFgR4a//voLixYtgqOjI5ydnREVFSXQT+Vl6wCQkZGB69evA/jfDPalS5egrq4OdXV19O7dm19XQkICvXv3RkhICL9szZo16NevH9zd3eHj44P09HTMmTMHpqamAovIicwn7TT+BarP5uaEEEII+TpkZ2ezESNGMABsxIgRLDs7W9QhNYr8/Hw2bdo0JiYmxuzt7VlxcbFI4pg0aRIzNTUVydjk61VUVMTi4+NZUVFRg/b7IiaN+c0OZ1snhPAf++eEsxcxaQ06Tl09ePCAeXp6Mj09PSYlJcXk5eVZ586d2aJFi1h6enqV+n5+fgwAi46OFtpfeXk58/f3Z7a2tkxVVZVJSEgwdXV15uTkxA4fPsy4XG6N8VT2L+zh6ekpUDcmJoa5ubmxli1bMmlpada6dWvm5eXFUlJSBOoFBgayH374gcnLyzNZWVnWpUsX5uvry3g8XpXxvb29+c+Fmpoa+/HHH1lsbGyVer179642zv+mo2FhYdXW6927t0BdYWWMMRYcHMysrKyYjIwMU1VVZR4eHiwt7dNfM7W9vuuTh3L+P/CvXl5eHpSUlJCbmwtFRUVRh0MIIYSQzxQWFgYPDw98/PgRO3bswIgRI0QdUqMICQnBuHHj8P79eyxduhTTpk0T2X7aEydORGRkZINd7kq+D8XFxUhMTISBgQFkZGQatG8ej1Wsfp5XAnnFikvNm2rGmxCg9td3ffJQuuebEEIIIV+UkpISzJgxA3Z2djA0NERsbOw3mXinpKTg119/hb29PfT09BAbG4tZs2aJLPEGKvYB79atm8jGJ+S/xMQ40GmrAuOuWtBpq0KJN/mqUfJNCCGEkC/Go0ePYGlpiS1btmDdunUICQnhb8vzrUhISICZmRn09fVx4sQJ7Nq1C6GhoTAyMhJpXMHBwXj69Cnc3NxEGgchhHyrKPkmhBBCiMjxeDxs3LgRXbt2BZfLxZ07dzB9+nSIiX17H1WePXuGR48eYejQoUhOTsb48eNFfp7l5eWYNm0aevbsCScnJ5HGQggh3ypa7ZwQQgghIvXmzRuMGTMGISEhmDp1KlauXNng941+SZydneHq6orY2NgvZsu0vXv3Ij4+HtHR0Y22yjohhHzvKPkmhBBCiMgcPXoUEydOhLy8PK5cuQJ7e3tRh9ToxMTEUF5e/sUkue/fv8eiRYvg4eEBCwsLUYdDCCHfrG/vWi5CCCGEfPGKi4sxZswYDB8+HP3790dsbOx3kXgDwMWLF3Hp0iWsWrVK1KHgxo0bMDc3h4SEBFauXCnqcAgh5JtGyTchhBBCmlRubi6cnJxw5MgR+Pv748iRI1BVVRV1WE3ixYsX8Pb2hq2tLQYPHiyyOEpLS7F69WrY2tqibdu2iImJgba2tsjiIYSQ7wFddk4IIYSQJvPu3Ts4OTkhJSUFV69eRY8ePUQdUpO5c+cOXFxcoKqqCj8/P5Fcdl5cXAxfX1+sWbMGb968waxZs7B8+fI6b2/2+vVrvH79Gurq6mjdujXExcUbOWJCCPl20Mw3IYQQQhodYwwhISGwsbFBdnY2wsPDv5vE++nTp5g9ezb69u0LQ0NDRERENPn2aYwx7N+/H61bt8bvv/+Onj17Ii4uDqtXr6418WaM4eDBg2jbti309PTQvXt3GBsbY9GiRU0UPSGEfBso+SaEEEJIo2GM4fLly+jRowfs7e2hqamJyMhIdOjQQdShNSrGGI4dO4bu3bujffv22Lt3LyZMmICrV6+iefPmTR7P2rVrMXbsWPTt2xdPnz7FoUOHYGJiUqe2mzdvxs8//4wOHTrgxIkTmDx5MgCgdevWjRkyIYR8cyj5JoQQQkiDY4zh/Pnz6NatGxwdHcHlcnHhwgVERUVBV1dX1OE1qvLycgwdOhQ//fQT5OTkcPToUbx9+xYbNmyAnJycSOJZtmwZpkyZgkOHDsHIyKhe7f39/SEvL4/+/fsjICAAW7duxaRJk/DLL780UsSEEPJtouSbEEIIIQ3qwoULsLCwgKurK6SlpREcHIxbt25hwIABX8z2Wo3p8OHDOHXqFE6ePIkrV65g2LBhIt23PCEhAQUFBZ+8wNvcuXOhoqICb29vJCYmIiAgAFu2bPku/i0J+bfY2FiMHTsWBgYGkJGRgYKCAszNzbF27VpkZ2fj+PHj4HA42LJli9D248ePh7S0NGJjYwEAPB4PAQEBsLe3h5qaGiQlJaGhoQEXFxecO3cOPB6vxnjOnz8PDw8PdOzYEZKSkjX+n0xISMCPP/4IFRUVyMnJoVu3bjh79myVenv37sXgwYPRqlUryMrKwtDQEN7e3nj//n2Vul5eXjA1NYWysjJkZWVhbGyMmTNnIjMzU2gM4eHhGDBgAFRUVCArKwsjIyMsW7ZMoM6YMWPA4XCqPNq1a1fjc/FvV69ehbW1NeTk5KCmpoYxY8YgPT29zu0bEy24RgghhJAGUVZWhhkzZmDz5s3o06cPwsLC0Lt37+8uSVNTUwMABAcHw8LCAvr6+iKN5/79+wAAU1PTT2rv7u4Od3d3MMa+u39LQirt2bMHPj4+aNu2LWbOnAkTExOUlZXh7t272LlzJ27duoXAwECMHDkSc+bMgZOTEwwNDfntg4ODsWfPHqxatQpmZmYoLi7G4MGDERwcjOHDh2PHjh3Q0tJCRkYGgoKC4O7ujqNHj2LQoEHVxhQYGIioqCh07twZ0tLSuHfvntB6SUlJsLa2RosWLbBz504oKChgx44dGDx4MI4fP44ff/yRX3fx4sXo27cvVq5cCR0dHTx79gzLli3DmTNncP/+fWhqavLrFhQUYPz48TA0NISMjAzu3r2LFStW4OLFi7h//z6kpKT4dQ8fPoyff/4Zw4YNw4EDB6CgoICXL1/i3bt3VeKVlZVFaGholbK6uH79OpycnODs7IwzZ84gPT0ds2fPhp2dHe7evQtpaek69dNo2DciNzeXAWC5ubmiDoUQQgj57rx79451796dSUhIsK1btzIejyfqkESirKyMPXr0iE2YMIEB4D8eP34sknhevHjBNDU1Wffu3UUyPvk+FBUVsfj4eFZUVNTgfXO55Swl7iGLD7/GUuIeMi63vMHHqE1kZCQTFxdnjo6OrLi4uMrxkpISdubMGcYYY9nZ2UxbW5t1796dcblcxlhFnqKrq8usra1ZeXlF/N7e3gwA8/f3FzpmQkICe/jwYY1xVfbPGGOTJk1i1aV2EyZMYDIyMuzNmzf8svLycta+fXumq6sr0E9aWlqV9tHR0QwAW7ZsWY3xMMbY9u3bGQAWEhLCL3vz5g2Tl5dn3t7etbb39PRk8vLytdarTteuXZmJiQkrKyvjl0VERDAAbPv27Z/UZ22v7/rkoTTzTQghhJDPEh4eDnd3d4iJieH69euwsbERdUgikZSUhB9//BExMTFVjkVERNR5gbOG8u7dOzg4OEBRURGBgYFNOjYhDeH57UiE7t+N/Oz/XcasoKoG2zHjYdSt6d5nVq5cCQ6Hg927dwudOZWSksLAgQMBACoqKvD19YWTkxM2btyI6dOnY+rUqcjKykJISAjExcWRmpqKvXv3wsHBAR4eHkLHrMvaDGJidbuDOCIiAp06dYKOjg6/TFxcHE5OTtiwYQPu3LkDKysrAICGhkaV9hYWFhAXF8fr169rHUtdXR0ABHZR2Lt3LwoKCjB79uw6xfup3r59i+joaKxatUpgfBsbGxgbGyMwMBDe3t6NGkNt6J5vQgghhHwSxhg2b96Mvn37wtjYGPfu3ftuE+/U1FTY29vjw4cPuHTpEu7cuYM7d+7g2bNnKCsrw7hx45o0nmfPnsHGxgYlJSUIDg7mfyAm5Gvx/HYkzm5YKZB4A0B+dibObliJ57cjmyQOLpeL0NBQWFhY1HmxSEdHR0yYMAELFizAxo0bsW/fPqxdu5afUIeFhaGsrOyT12Gor9LSUqFfGlSWVd6DXp3r16+Dy+VWu0tFeXk5CgoKEBERgYULF6JHjx7o3r07//iNGzegqqqKp0+f4ocffoCEhAQ0NDQwceJE5OXlVemvqKgIWlpaEBcXR8uWLTF58mRkZ2fXep5xcXEAADMzsyrHzMzM+MdFiWa+CSGEEFJvlff6HT58GNOmTcPq1ashKSkp6rBEIicnB46OjigqKkJERARatWol0niioqLg4uICTU1NBAUFffOry5NvD4/HRej+3TXWCfPfjTZdu0FMTLxRY8nMzERhYSEMDAzq1W79+vW4cuUKpk2bBnt7e/j4+PCPpaSkAEC9+/xUJiYmuHbtGvLz86GgoMAvDw8PBwBkZWVV2/bjx4/w8fGBrq6u0B0OoqKiYG1tzf95wIABOHLkCMTF//fv8vbtWxQWFsLd3R1z587Fpk2bEB0djcWLFyMuLg43b97kryfRqVMndOrUib9GxfXr17Fx40aEhIQgOjpaIP7/qjwPVVXVKsdUVVVrPM+mQsk3IYQQQuqsoKAAgYGBWL16NZKSknDkyBH89NNPog5LZCq3FUtJScHNmzdFnnifP38ew4YNg4WFBc6ePQsVFRWRxkPIp3j75HGVGe//+piVibdPHkO3Q9VZzi+BgoICZs2ahYkTJ2Lp0qUiXaxw8uTJOHPmDDw8PLB+/XrIy8tj69atiIysuHqgusvXi4uLMWTIECQnJyM0NFRo4tuxY0dER0ejsLAQDx48wOrVq9GvXz+Ehobyt1bk8XgoLi7G4sWLMWfOHABAnz59ICUlhT/++AMhISGwt7cHAEydOlWg/379+qFz584YOnQo9uzZU+W4MNU911/CgpF02TkhhBBCasTlcnHlyhV4eHhAU1MTP//8M1RVVXH79u3vOvEGgOXLl+PatWs4depUtZdkNpV9+/Zh8ODBcHR0RHBwMCXe5KuVn/OhQet9DjU1NcjJySExMbHebSsv6/73qt8AoKenBwCf1OensLOzg5+fH27cuIE2bdpAS0sLp06d4m/z9e97wSuVlJTAzc0N4eHhOHv2LLp16ya0b3l5eXTp0gW9evXC77//jsDAQNy+fRu7du3i12nevDkAwMHBQaCtk5MTAAhdJ+Pf3NzcIC8vj6ioqBrrVY4jbIY7Oztb6Ix4U6PkmxBCCCFCpaWlYdasWdDT00P//v0RFRWF2bNn49WrV7hx44bIk01Re/PmDdasWYPZs2ejT58+IoujvLwcf/75J3799Vd4eXnh+PHjdd6Wh5AvkYJy3b44qmu9zyEuLg47Ozvcu3cPb968aZA++/btC0lJSZw+fbpB+qsLT09PpKamIj4+Hs+fP8fjx48BVMwG9+zZU6BuSUkJBg8ejLCwMJw+fRp2dnZ1HqdLly4QExNDQkICv0zYPdhAxbohQN0WjmOM1Vqv8lL1R48eVTn26NGjT95usSFR8k0IIYSQKsrLy+Hm5oY9e/bAzc0NUVFRePbsGRYuXNhk9yl+6ZYsWQIFBYVGX8G3OowxHDlyBB06dMDixYuxdOlS7NixQ+BeS0K+RjrtO0BBVa3GOs2aq0GnfdN8ATh37lwwxjBu3DiUlpZWOV5WVoZz587VuT8tLS14eXnh8uXLOHDggNA6L1++rHUhtPqSkJBA+/btYWhoiNzcXOzevRuDBg2Cvr4+v07ljHdoaChOnjxZZba6NtevXwePxxPY47xyH/FLly4J1L148SIA8Fdar86JEydQWFhYaz0dHR1YWlri4MGD4HK5/PLK319Dhgyp17k0BrrnmxBCCCFVrFmzBrdv38aNGzcEVq0lFZ48eQI/Pz9s2LABioqKIokhPDwcI0aMgJOTE/755x+Ym5uLJA5CGpqYmDhsx4zH2Q0rq63T13N8oy+2Vsna2ho7duyAj48PLCws4O3tjQ4dOqCsrAz379/H7t27YWpqCldX1zr3uWHDBrx69QpjxozB5cuX4ebmBk1NTWRmZuLKlSvw8/PDkSNHqp01BoDk5GRER0cDqEjWgYpEFQBatWqFLl26AADS09Px119/oXv37mjWrBmePn2KtWvXQkxMDNu2bRPoc+jQobh06RLmz5+P5s2bC1zqraioyN8y8fz589izZw8GDhwIfX19lJWV4e7du9i0aRMMDQ3h5eXFb9e/f3+4urrizz//BI/Hg5WVFe7evYulS5fCxcUFPXr04J/PyJEjMXz4cBgaGoLD4eD69evYtGkTOnToINAnUPFlQu/evRESEsIvW7NmDfr16wd3d3f4+PggPT0dc+bMgampKcaOHVvnf59G80k7jX+B6rO5OSGEiFJxcTFLSEhgV65cYVevXmX5+fmiDqlG5eXlLCsri718+ZK9evWKffz4kfF4PFGHRRpRVFQUk5CQYPPmzRN1KF+swYMHs1atWrHi4mKRxbBt2zYmJib2xb+HkG9fUVERi4+PZ0VFRQ3ab0JUBNs50ZOtH+bMf+zy9mQJURENOk5dPXjwgHl6ejI9PT0mJSXF5OXlWefOndmiRYtYenp6lfp+fn4MAIuOjhbaX3l5OfP392e2trZMVVWVSUhIMHV1debk5MQOHz7MuFxujfFU9i/s4enpya+XlZXF+vfvz9TV1ZmkpCTT09Njv/32G8vIyKjSZ3X9AWC9e/fm13vy5AkbOnQo09fXZzIyMkxGRoa1a9eOzZw5k2VlZVXpt7CwkM2ePZvp6uoyCQkJpqenx+bOnSvwHpqdnc3c3NxYq1atmKysLJOSkmJGRkZs1qxZLCcnR2is/46pUnBwMLOysmIyMjJMVVWVeXh4sLS0tBqfy5rU9vquTx7K+f/Av3p5eXlQUlJCbm6uyL6BJoR8m0pKSnDgwAEUFhaiU6dOMDMzq3HRjsLCQiQnJyM5ORlJSUlV/nz//r1AfQkJCVhaWqJ9+/ZQVVWFqqoqmjdvLvTvsrKyyM/PR2pqKv+RlpYm8HPlIyMjAyoqKtDT0xN46Orq8v+uoaHBX/0zIyMDt27dQmRkJKKiopCUlIQPHz4I3YNTRkYG6urqsLGxgZubGwYMGIBmzZp90vMbERGBS5cugcPhQEJCAuLi4vxHcXEx0tPTkZ6ejszMTJSWlvLvEZOUlISWlhZatGiBFi1aQFtbGy1atICioiLk5OQgLy8POTk5/qMu95R9zxISEnDq1CkEBgbizp07sLCwQGRkZJWFgghw69Yt2NjYICAgAKNHjxZZHD169ECzZs2qXMpJSFMrLi5GYmIiDAwMICMj06B983jcitXPcz5AQVkFOu07NNmMNyFA7a/v+uShlHwTQkg1eDwejh49innz5iElJQWSkpIoKSkBUHFfUadOndCxY0dwuVyB5DojI4Pfh7i4OFq2bIlWrVpBX1+/yp8lJSW4du0arl+/juTkZGRlZSE7OxsfPnyAsLdncXFxgfuYgIpVVLW0tKCpqQktLS3+Q11dHR8+fEBKSgpSUlLw+vVrpKSkoLCwkN9WWloaurq64PF4ePXqFQBAW1sb1tbWaNu2LVRUVKCiogJlZWUoKyuDx+MhIyMDmZmZePfuHYKDg3H//n1ISUnB3t4e/fr1g4qKCuTl5aGgoCD0T3l5eUhISCA8PBxLlixBSEgINDQ0ICMjg/LycnC5XP6fMjIy0NDQgIaGBtTU1AR+6ZWUlCA1NRXv37/Hu3fvhH5JUElGRgZmZmYwNzfnP0xNTfkr0X7JeDweUlJS0KJFi0+KlzGG0tJS5Ofno6CggP+o/DkqKgqBgYF4/Pgx5OTk4OTkhCFDhmDgwIE17qf6vWKMoXfv3sjLy0NMTIzIvtS5f/8+zM3NcfjwYYwYMUIkMRBSqTGTb0JEjZJvISj5JoTUJC8vD8+fP0diYiK6du0KfX198Hg8pKen482bN5CRkYGxsTF/li8sLAwzZ87EvXv3MHDgQKxevRpGRkZ4/vw5YmNj8fDhQ8TGxuLRo0eQlJSsNrnW0dGBhET9l9fg8XjIyclBdnY2srOz+Ul5bm4uVFRUBJJsZWXlOu9dyRhDdnY2PyGvTMrLy8thaWkJGxsb6Orq1msvzKSkJJw+fRqBgYG4desWysrKam0jLS2NkpISdOzYEYsXL4abm9tnJzGFhYVITU3Fx48fUVBQgMLCQhQWFqKgoACpqam4f/8+YmJi8OTJE/B4PEhISMDIyAhycnKQkpKCtLQ0pKSkBB4//PADfv31V6ip1bzwT0PLz8/HlStXcP78eVy4cAFpaWngcDjQ09ODkZERjIyMYGhoCAMDA+Tl5eHt27d49+4d/88PHz4IJNv//cLm35SVlTFw4EAMGTIE/fr14+/LSoQ7f/48XF1dcfHiRf42OU3t3bt36NatG9TU1HDr1i1KdojIUfJNvmUiT773799fpxvWr1y5wt8w/b/S0tKwdu1anD9/HikpKZCVlUWHDh3g6emJX3/9td6boFPyTQgRprS0FL/88gsOHTrELxMTE4OOjg7ev3+P8vJyfrm4uDiMjIygpKSE27dvw9LSEuvWrUOvXr1EEfpX69+zrDX9aWBgAGdn5yafOSwsLERsbCxiYmLw7NkzlJSUoLS0lP9n5aOoqIi/0MzIkSOxevVqaGho1GusgoICREdHIzIyEnFxcVBSUuJfJg8AOTk5VR7Z2dmIiYlBaWkp2rdvDxcXF/Tq1QtpaWl4/vw5//HixQsUFRUBAFRVVaGjowMdHR1oa2tDVVVV4EqDf//9vz+rq6tDUlKyYZ/kbxSXy8UPP/wANTU1hIaG1vuzSkPIysqCvb09MjMzERUVJXR/XkKaGiXf5FvWkMn3Z612LiYmBnV19WqPV3d53L179+Dg4MDfAF1BQQEfP35EeHg4wsPDcfz4cZw9e/aruByQEPLlKSwsxK1bt5CRkYF9+/bh+vXr2Lx5M6ysrNCyZUtcuHABiYmJaNmyJT9hKSwsRHx8PB4/foyUlBQcO3YMQ4cOFcmH66+dlJQU/x71L5GcnBysrKxq3bIEADIzM7F3715s2LABoaGhOH/+fLX7hDLGkJyczL9vPjIyEg8fPgSXy4WioiLMzMzw5MkTvH//Hunp6QAAJSUl/iX9lY+2bdti+PDhcHFxQZs2baqNjTGGjIwMKCoq0ofdJrJr1y48fvwYt2/fFsl7w/v379GvXz+kp6cjNDSUEm9CCPnKfNbMt76+PpKSkurVNjc3F+3atUNqairatWuHgIAAdOnSBaWlpdizZw+mTp2KsrIyeHt7Y/v27XXul2a+CSEAEBcXB3d3dzx9+hQAoKamhqNHj8LW1lbEkZGvWUpKCgYOHIhXr17hyJEjGDBgAEpKShATE4PIyEh+wl25mJ6RkRFsbGxgbW0NGxsbmJiYCOy9zOVyweFwaBG4r0h6ejratm0Ld3d37N69u8nHT05Ohr29PYqKihASEoK2bds2eQyEVIdmvsm37IuZ+f4U69evR2pqKmRlZXHx4kUYGBgAqJgpmTRpEvLy8jBv3jzs3r0bf/zxB4yNjZs6RELIV8rPzw+TJk2CoaEhoqOj0bZtWygoKNDsNflsenp6CA8Px6hRo+Dq6gpzc3PExsaitLQUsrKysLS0xJgxY2BtbQ0rK6sarwoDIJCIk6/DnDlzICYmhpUrq993uLE8f/4cdnZ2kJSUxM2bN/mfnWqSmJgIPz8//PPPP9DQ0MDmzZthYWHRBNESQgipTpN/5X7gwAEAwPDhw4X+8vjtt9+goKAALpcrcI8mIYRU5+XLl/D09MQvv/yCkSNHIioqCl26dEGzZs0o8SYNRkFBAadOncKff/4JIyMjrF+/Hnfv3kVubi6uXbuGlStXwtXVtdbEm3x9IiMj4efnh1WrVjX54ntxcXHo2bMn5OXlcePGjTol3pGRkejYsSP+/vtv9O7dG/n5+Rg0aBA+fPjQBBETQgipTpPOfD979gwpKSkAUO0KoQoKCujZsycuXbqE4OBgLF26tClDJIR8JTIzM3Hs2DEcPHgQt27dgqKiIvz9/eHh4SHq0Mg3TFxcHPPnzxd1GKQJlZeXY9KkSejatSt+/fXXJh07KysLTk5O0NLSwpUrV+r0xc7t27fh6OiILl264Pz581BQUMCLFy9gZGSEK1euYNiwYU0QOSGEEGE+a+Y7IyMDFhYWUFBQgKysLFq3bo3Ro0fj2rVrQuvHxcXx/17dgjX/PhYfH19tnZKSEuTl5Qk8CCHftvLychw9ehSurq5o0aIFpkyZgubNm+PIkSN4//49Jd6EkAa3efNmPHz4ENu2bWvy2wW8vLxQVFSE8+fP1ynxTkhIgIODA8zMzPiJNwD+ZyR1dXWUlpbi1q1bWLt2Lf9KDS0tLXTr1g3+/v41bhVYWFiIiIgIPHnypGFOkBBCvjOfNfNdWFiImJgYqKiooKCgAImJiUhMTMShQ4cwduxY7N69W2B/23fv3vH/XtMKnZXH8vLykJ+fz//l8W+rVq2iWXFCvjOTJ0/Grl27YG1tjU2bNmHYsGF0iS8hpNE8f/4c8+fPx5QpU9C1a9cmHTs2NhanT5/GwYMH0bJly1rrl5eXw9PTE+rq6rh48SL/sxNjDDNmzAAAbNu2Dc7OzigqKoKcnBysra3h4+MDCQkJ3L59G2PGjMHSpUsxe/ZstGvXDh8+fEB6ejpiYmJw+/ZtPHr0iL9n/L59++q07SwhhJD/+aSZb21tbSxevBgPHz5EcXExsrOz+d+GVu7r7efnh6lTpwq0+/jxI//vcnJy1fb/72P/bvNvc+fORW5uLv/x+vXrTzkVQshX4siRI9i1axd27dqFyMhITJo0iRLvesrPz8ft27dx8OBBLF68GKNGjcKQIUMwfPhweHh4wMfHB1u2bMH169eRnZ0t6nAJESkej4dff/0V2traWL58eZOPHxISAmlp6TpfJr5mzRrcuXMH/v7+AqvtRkREICwsDEDFJemLFy/GnTt3kJOTg6tXr2Lp0qVYuHAhzp8/jwcPHqBr167w9vZGnz594ObmhokTJyIiIgLm5ubYvn07f8E5ev8l36PY2FiMHTuWv+q1goICzM3NsXbtWmRnZ+P48ePgcDjYsmWL0Pbjx4+HtLQ0YmNjAVS8zwQEBMDe3h5qamqQlJSEhoYGXFxccO7cOfB4vBrjOX/+PDw8PNCxY0dISkrWuM5NQkICfvzxR6ioqEBOTg7dunXD2bNnhdZljMHPzw+WlpaQl5eHoqIizM3NcebMGX6d9+/fY8GCBbC2toaamhoUFRVhYWGB3bt387+kq/TgwQM4OztDT08PsrKyUFVVhbW1NQ4ePCh07D179sDCwgKKiopo3rw5evfujQsXLtT4XPzb1atXYW1tDTk5OaipqWHMmDH8LT5FjjUwLpfLBg0axAAwMTExlpCQwD+2YsUKBoABYGVlZdX2sXv3bn69d+/e1Wnc3NxcBoDl5uZ+9jkQQr4sCQkJTEFBgY0cOZLxeDxRh/NVKSwsZMePH2dDhgxh0tLS/PdWTU1N1r17d+bk5MTs7OxYz549WceOHZmUlBS/TsuWLZmTkxObPXs2O3ToEHv06BErLS0V9SkR0iQ2b97MALCwsDCRjL9t2zYmISFRa72cnBw2fvx4BoDNmzevyvHU1FTWr18/tnPnTlZcXFynsV+/fs2ePXvG0tPTBf7P+/v7MxkZGWZra0vvxURAUVERi4+PZ0VFRaIOpdHs3r2bSUhIsA4dOrBt27axsLAwFhwczFauXMkMDAzY4MGDGWOMjRw5ksnJybHnz58LtL98+TIDwFatWsUYq3jOHBwcGIfDYSNGjGDHjh1jN27cYCdPnmTjxo1j0tLS7PTp0zXG9MsvvzAjIyM2bNgwZmFhwapL7RITE5mqqirr0KEDO3LkCDt//jxzdnZmHA6HnThxokr9CRMmMGlpaTZnzhx29epVFhQUxNatW8cOHz7Mr3Pu3Dmmq6vL5s+fzy5cuMCCg4PZ1KlTmZiYGBs7dqxAf2FhYWzChAksICCAhYaGsnPnzrHhw4czAGzZsmUCdRcuXMgAsIkTJ7Lg4GB29uxZ1q9fPwaAnTx5ssbngzHGrl27xiQkJNigQYNYcHAwO3jwINPR0WGmpqZ1fg/8r9pe3/XJQxs8+WaMsefPn/M/vP3111/88spfZLUFt2nTJn69jx8/1mlMSr4J+TYVFRWxTp06MWNjY5aXlyfqcL4qKSkpTFlZmQFg5ubmbN26dezevXs1vk+WlpayuLg4dvjwYTZnzhzm7OzMdHV1+e/JkpKSzMzMjI0ePZotW7aMBQQEsJs3b7LXr1+z8vLyJjw7QhrPy5cvmZycHPPx8RFZDNOmTWNqamrVJrl37txho0aNYrKyskxOTo7t3r27URJiHo/HgoODWd++fRkANnbsWFZYWNjg45CvW2Mm3zwujxW9+MAK7qexohcfGI/b9F/8REZGMnFxcebo6Cg0gSspKWFnzpxhjDGWnZ3NtLW1Wffu3RmXy2WMVeQpurq6zNramv+70tvbmwFg/v7+QsdMSEhgDx8+rDGuyv4ZY2zSpEnVJt8TJkxgMjIy7M2bN/yy8vJy1r59e6arqyvQT2BgIAPAjh49WuPY2dnZQr+Qr4wjJSWlxvaMMdatWzemq6srUKajo8N69OghUFZUVMSUlJTYwIEDa+2za9euzMTERGCiNyIiggFg27dvr7W9MA2ZfDfKaueGhoZQU1NDZmYmXr16xS/X1tbm//3t27fVbkL+9u1bAICioqLQ+70JId+PadOm4enTp7h9+zaaNWsm6nC+Ks2bN4eGhgY0NDQQHh4OWVnZWttISkqiQ4cO6NChA0aMGMEv//DhA+Li4hAbG8t/BAUFITMzU6Ctnp4eWrVqJfTRokUL2t+afPF4PB68vLygrq6O1atXiySGO3fuYPPmzZg9e3aVy0h5PB5Wr16NRYsWoXXr1li4cCE8PT0FPmM1BB6Ph9OnT2PlypW4d+8eLCwscOrUKQwePJi2cCRNpiguEznnXoKbW8ovE1eSgrJrG8iaNt22fytXrgSHw8Hu3bshLS1d5biUlBQGDhwIAFBRUYGvry+cnJywceNGTJ8+HVOnTkVWVhZCQkIgLi6O1NRU7N27Fw4ODtUuFmtkZFRrXGJidbuDOCIiAp06dRJYc0tcXBxOTk7YsGED7ty5AysrKwDA33//jVatWtV6y4uKiorQcktLS2zbtg1v3ryBrq5ujX2oqalVuRxcUlISSkpKAmUyMjL8R03evn2L6OhorFq1SmDdMRsbGxgbGyMwMBDe3t419tHYmnSrsX+vcB4XF4f27dsLrVe5KrqJiUmTxEUI+TIdO3YMO3bswM6dO9GpUydRh/PVkZOTw4kTJ9CtWze0adMGrq6uGDhwIOzs7Gr9BfZfKioq6NmzJ3r27ClQnp+fj+TkZCQlJSEpKYn/99jYWJw9exYZGRn8uhISEtDT00ObNm3QpUsXWFlZoVu3btDU1GyQ8yWkIezevRthYWEIDg4WyRd+9+/fh4uLC8zNzbF48WKBY+np6fj5559x5coVzJs3D0uWLBH4gNkQkpKSsH//fvj5+SElJQV9+vRBcHAw7O3tKekmTaooLhNZB6uurM/NLUXWwSdoPrp9kyTgXC4XoaGhsLCwqDWZrOTo6IgJEyZgwYIFEBMTw759+7B161Z+Qh0WFoaysjIMHjy4ESP/n9LSUqiqqlYpr/wiITY2FlZWVigvL8etW7cwYMAAbNiwAX///TfevHkDfX19+Pj4YPr06bW+D4SGhkJCQgLGxsZVjvF4PPB4PHz48AHHjx/H5cuXsXXrVoE6U6ZMwYwZM+Dr64shQ4aguLgY69atQ25uLn7//fcax67MIc3MzKocMzMzQ0RERI3tm0KjJN8vX77kz4YYGBjwy9u2bQs9PT2kpKQgKCgI7u7uVdoWFBTg5s2bAID+/fs3RniEkK/Aixcv4OXlheHDh2P8+PGiDuer1bFjR9y6dQsBAQE4c+YMdu/eDTk5OTg4OGDQoEFwdnaGmtqnf3hRUFDgz5QLU1BQIJCcJyUlISEhAX5+fli1ahUAoFWrVrCysoKVlRXs7OzQoUMH+pBPRCIlJQUzZ86El5cX+vXr1+Tjv3r1Cn379oWRkREuXLgASUlJ/rHKBYvKysoQFBTUoJ+RiouLcfr0afj6+iIkJATy8vIYMWIEvLy8YGlp2WDjEFJXjMeQc+5ljXVyzr2CjElzcMQa9/dFZmYmCgsLBXKauli/fj2uXLmCadOmwd7eHj4+PvxjKSkpAFDvPj+ViYkJrl27VmUXqfDwcABAVlYWgIpzLSkpQUhICKKjo7FixQq0bNkSx48fx8yZM/HhwwesWLGi2nGCg4MREBDA3wr2v3x8fLBr1y4AFVcLbN68GRMmTBCo88cff0BWVhaTJk2Cl5cXAEBVVRXnzp1D9+7dazzPyvMQ9kWDqqoq/7go1Tv5ZozV+KGIMYaZM2cCqLgUwsXFReC4h4cHli9fjiNHjmDhwoVo1aqVwPFt27YhPz8f4uLiGDVqVH3DI4R8A4qLizFs2DBoampi165dlIh9pk6dOqFTp05Yt24dnj59irNnz+LMmTMYO3YsOBwObGxsMGjQIAwcOFDoN9WfQ15eHiYmJlWuZGKM4fXr17h9+zaioqIQFRWF06dPo7i4GHp6ehgwYACcnZ1ha2tb4+4YhDQUxhjGjRsHZWVlrF+/XiQxBAYGIjc3F5cuXRL4Uiw8PBwuLi4wNDTE2bNnG+wS8wcPHsDX1xeHDh3Chw8f0KNHD+zbtw/u7u6Ql5dvkDFEKSgoCAEBATA2NsZvv/0m9AM5+TKVJOYKXGouDDe3BCWJuZBpo9w0QdWTgoICZs2ahYkTJ2Lp0qUi/SwzefJknDlzBh4eHli/fj3k5eWxdetWREZGAvjf5euVq6vn5eXh8uXL/EvRbW1tkZqaig0bNmDu3LlCbwuOiYnBsGHDYGVlxf9y/b/mzZsHLy8vpKen49y5c5g8eTIKCgr42yECFTtmTZkyBZMnT4aTkxNKS0tx4MABDBo0CKdOnYKDg0Ot51vdc/1FfJ6s7w3niYmJrGvXrmznzp3s5cuX/MU9uFwuu3XrFnNwcOAvzOPt7V2lfU5ODtPS0mIAmImJCbt79y5jrGKhgu3bt/NX2hXWtia04Boh345JkyYxaWlpFhMTI+pQvmmpqals7969bODAgUxWVpYBYO3atWOzZs1i4eHhTb6AWlFREbt8+TL7/fffWZs2bRgAJi0tzRwdHdnmzZvZ69evmzQe8n3x9fVlANiFCxdEFkN8fDzjcDhs5syZ/LKLFy8yWVlZ1rt37wb5jJOfn8+2bt3KOnfuzAAwLS0tNnv2bPbs2bPP7lvULl++zMaMGcNCQ0PZ2bNn/4+9uw6Lqvv+h/+mSzqUVtrCAkGw9cZuxRbFQkW97a7b7sAOROxAQULFRpSSUlTAIlVKShhqZj1/+HOeLx8DkIEB3a/rOpc4s8/e6xAzs84ukpCQIDMzM5KXlyczMzN6//69sEP8Ywl6wbWCyDRKXhJQ4VEQmSaQ9n6lrKyMZGVlycrKqsrnnjx5kgBQWFhYucfPnTtHAOjQoUOCCvOXC64REbm5uZGqqio/T2vWrBlt2rSJANDp06eJ6OsOKSIiIqSgoPDd+UeOHCEAFBIS8t1zERERpKKiQhYWFpSTk1PpmJ2cnEhcXJzS09OJ6OsibjIyMjRr1qzvynbp0oUaN278y/pu3rz509fx4cOHk6amZqVj+78EueDab+3zHRYWBicnJxgaGkJGRgbq6uqQlZVFhw4dcOvWLQDApEmTsG/fvu/OVVRUhI+PD1RVVfHy5UtYWFjwF1abOXMmSkpKYGdnh927d/9OaAzD1HNXrlzBgQMHsHv3brRp00bY4fzRGjZsiMmTJ8PLywuZmZnw8vKCra0t3Nzc0LFjR2hqasLR0RFeXl4oLCys8XikpaVhZ2eHvXv34vXr14iNjcWmTZtQUlKC+fPnQ09PDz169MDJkyeRl5dX4/Ewf5eVK1di7Nix6Nu3r9BiaNq0KebMmYPt27cjLS0NFy5cwMCBA9GzZ0/cuHHjpwvVVlZKSgpsbW3h7OyM58+fo3Pnzpg6dSpkZGTw+fNnAV2FcBARJkyYADc3N3Tv3h2DBg3CgAED8OzZMzx9+hQlJSXo0KED/P39QUTCDpepgKi8pEDLVYeYmBh69OiB8PBwpKSkCKTObt26QUJCAp6engKprzIcHBzw6dMnvHz5Eq9fv8aLFy8AfO0N/raei4yMzE8Xevv2d/O/i7xFRkaiZ8+e0NfXh7+//3eLpf1K+/btUVZWxl+gOy4uDhwOB5aWlt+VtbCwQEJCAr58+fLT+r6tL/b8+fPvnnv+/Hm59ceEpqqZf2FhIbm4uNCYMWOoWbNmpK6uTuLi4tSgQQMyMzMjR0dHCgwMrLCeT58+0bx588jY2JikpaVJSUmJOnbsSMeOHSu33H1lsZ5vhqn/3rx5QwoKCmRvb8/2kBWisrIyevLkCS1dupSaNm1KAEhaWpqmTJlCr169EkpMOTk5dOLECerWrRuJiIiQtLQ0jRo1inx8fNje40y18Xg8EhcXpy1btgg7FPLy8iIANGfOHBIREaFx48YJ5Hf86dOn/B6vb4e8vDx/NCKActsQ1TeJiYkEgK5fv06enp60evVqKi4uJqKvvVbfehrxg15IpvoE3fPN4/Low6bgX/Z6f9gUUmvbjv3frca+/V79XyUlJXT9+vXvHv9ZzzdRxVuNvXnzpsKtxv6vinq+/1dOTg41btyYvz/5N8uWLSMA9Pjx43KPDxw4kBo0aFBuq8HIyEhSUVEhc3NzyszMrHTb34wfP55ERUX5Pd/f/o6dnJzKlePxeGRra0vKysoVfj5s3749tWjRotzovaCgoGqNNKjz+3wLA0u+GaZ+Kyoqonbt2pGhoSH7O65j4uPjae3ataSrq0uioqI0YcIEevv2rdDiSUpKos2bN1OzZs0IAKmrq9OiRYsoMTFRaDEx9Z+9vT0ZGBiU2xtWGBYuXEgA+MPPf6dD4n8FBATwE09lZWVycXGh+Ph4/odYBwcHMjY2rnM3PUtLS8nV1ZW6detGjRo1Ih0dHQoPD/9h2bi4OAJADx8+JB6PR8+fP6edO3dSr169+NNqGjVqRM2aNaOPHz/W8pX8+Wpin+/C5xm/TL4Ln2cIrK3KOHr0KImLi1OLFi3owIED9ODBA7p9+zZt27aNjIyMvktiiX6dfHM4HOrVqxeJiIjQmDFj6PLlyxQQEEBXr16lGTNmkLS0NHl6ev4ypoSEBLp8+TJdvnyZevfuTQD4//+/baalpdHixYvJy8uL7t27RwcPHqTGjRuTgYEBpaamlqszKyuL9PT0SEtLi06cOEG3bt2iqVOnEgDasWMHv1xsbCypqqqSiooKeXt7U1BQULnjW0JNRDR16lRasGABXbx4kR48eEBXrlyhkSNHEoBy02yIiIYOHUqioqI0d+5cunXrFl2/fp2GDRtGAGj9+vXlyoqJiVH37t3LPXb//n0SFxenIUOG0O3bt+ns2bOkq6tLLVq0+OEe7ZXBku8fYMk3w9Rvu3btIgkJiZ9+sGKEr6ioiFxcXKhRo0YkISFBGzZsEGqiwuPxKCIigubOnUuKiookKipKw4YNo4CAgDqXRDB1X3h4OAGglStXCvX3Z8mSJdS9e3eBrnnh7e1NoqKidPDgwe/WcuDxeGRlZUXjxo0TWHtVNWPGDJKTkyNlZWU6ePAgcblcunTpEpmamhIAat++PT+BvnLlyg/r+PDhAwEgAwMD0tTU5I/YsbOzox07dtCzZ8/Y60INqonkm+hrAv6/PeAfNoXUeuL9TVRUFDk4OJCenh5JSkqSnJwctWnThlavXl0u2fzmV8k30deRZqdOnaLu3buTiooKiYuLk7q6OvXp04fOnTtX4c23b/X/6HBwcOCXy8rKIjs7O1JXVycJCQnS09Oj2bNnU0bGj7+PSUlJNGrUKFJWViZJSUkyNzcnV1fXSrcNgE6ePMkv6+rqSp06dSI1NTUSFxcnJSUl6tKlC3+u+f/F4XBo+/btZG5uTvLy8qSiokLW1tZ05syZ7/6GAVCXLl2+q8Pf35+sra1JWlqaVFRUaMKECZSW9vvrAwgy+Rb5f4HXe3l5eVBUVERubm6150QxDFP77O3tkZ6ejgcPHgg7FKYChYWF2LBhA7Zu3Yq2bdvi1KlT361mXtu+fPmCU6dOYd++fYiPj0ebNm0wd+5cjBo1ir+PKcP8r7S0NOTn58PAwACioqLYvHkzli9fjrlz52LXrl3fzW3803z69AkzZ87EtWvXcPToUUydOlUocejq6iIlJQU9e/bEnTt3oK2tjdTUVABft2JKSUkBEWHs2LFwdXX96c9l7dq1CA4Ohrm5Oezs7NCxY0dIS0vX5qX8tYqKivD+/Xs0adJE4N9z4hGK3+eCl18CUXlJSDVRrPHtxRjm/6ro97sqeeif/a7CMEy9ER0djVatWgk7DKYSZGVlsWnTJjx58gT5+flo27YtgoODhRpTgwYNMGvWLLx69Qo3btxAw4YNMXHiROjp6WHNmjX49OmTUONj6pZXr17B0dERurq6MDY2hoqKChYtWgQnJyccPHgQ+/btg6OjI8rKyoQdao0gIri7u6NZs2YIDAzE5cuX4eDggMDAQNy7d69aC5J5eXlh8eLF/MWcKmPDhg0AgJ49e2Lnzp3lnpOXl8e2bdvw4cMHzJ8/H5MmTYKJiQnc3d2/q2ft2rW4efMmtm3bhp49e7LE+w8hIioCaUMlyLbWgLShEku8mfrtt/vf6xg27Jxh6q+CggISERGh48ePCzsUpooKCwvJ0tKSTE1Nyy3CUhe8evWKZs6cSXJyciQhIUHjxo1jCy395UpLS2n27NkEgLS0tGj79u108+ZNWrJkCX/Y886dO2nSpEkEgHbv3i3skAUuOTmZ+vbtW25rwd69e5OcnBx/uGiPHj0oJiamwrqysrLIy8uLVqxYQS9fviQiIllZWQJAYmJi5O/vX6mYeDweLVmyhACQgoICAaARI0bwh97Hx8dT//79CQDp6+vzpwcwdUdNDTtnmLqAzfn+AZZ8M0z9FRISwlagrcdevnxJUlJSNGPGjDo5pzI7O5t27NhBjRs3JgBkY2NDFy9eFPrCWkztys3Npd69e5O4uDjt3r37u4V3Pnz4QNOnTycxMTH+fOGzZ88KKdqasW3btu/mZcrKylLv3r1p27Zt9PTpU/L19SVjY2MSExOjf//9l7KzsyknJ4fCwsLo7NmztGbNGho9ejQ1b968XD3Tpk0jIiIpKSnauXMn2draUufOnSsdG5fLpXPnztG6desoODiYiL7eLNmyZQtJS0tTkyZNyN3dnT5+/EgA6MKFC7/1PSgqKqKPHz/+9sJLzI+x5Jv5k7E53z/A5nwzTP2Unp6OXr16ISUlBUlJSZCRkRF2SMxvOHLkCJycnLBs2TJs3LgRIiJ1b1ggl8vF9evXsXfvXjx8+BA6OjqYNWsWpk6dClVVVWGHx9Sg0tJS2NjY4PXr17hy5Qp69uz507IJCQngcDgwMzOrk7/H1dG5c2c8evQIrVu3xpAhQ9C9e3e0b98ekpLl90ouLi7Gnj17sH79epSUlKC0tJT/XKNGjWBsbIxmzZrB1tYWnTt3xrFjx7B161YkJSWhadOmWLlyJdLT03Ht2jW8fv36t2LNzs6Gvb097t27h3nz5uG///6DrKwsysrKoKioiHXr1mHhwoUoKyuDj48PQkNDISsrCxkZGWRlZeHz588//LewsBDA172bDQ0N0axZM/Tr1w/29vbs82M11OScb4YRNkHO+RavqSAZhmEqkpSUhH/++Qd5eXm4d+8eS7zrsenTp6OwsBDz589HbGwsJkyYgF69etWpn6mYmBiGDBmCIUOGICoqCvv27cPatWuxYsUKmJmZoU2bNujWrRvGjh3LPjz+YXbu3InIyEgEBQXB0tLyl2UbN25cO0EJQUBAQKXKSUlJYcmSJRg/fjwuXLgATU1NmJiYwNjY+IcfLPv27YuNGzfi4sWLyM/Ph5iYGOTk5PD58+ffijMqKgojR45ERkYG7ty5g27duvGfExcXh6WlJYKDg/Hx40cMHToUwcHB0NLSQmlpKYqKiqCqqgoVFRWoqqpCXV0dZmZm/P+rqKhASUkJnz59wsuXLxEdHY3p06dj7ty5GD58OKZPnw4bG5vfipthGKYirOebYZhaV1RUhKioKNjb20NcXBy3b9+GoaGhsMNiBODkyZPYvXs3nj9/jgYNGmDAgAEYMWIEevfuXacS8W8yMjLg5eWFiIgIREREIDQ0FBoaGpg3bx5mzJjB3k/+EKampujYsSNOnDgh7FD+SC9fvkTz5s0BAE2bNsXdu3cxfPhwqKiowNvbu8LziQivXr3Cy5cvcenSJVy+fBnNmjWDp6cnjI2Nvys/YcIEPH36FCIiIsjNzcXFixdha2v72/GnpKTA3d0drq6uePv2LTp27IilS5eib9++f9zoh5ryrWewcePGdfK1nmGqg8PhICEhQSA93yz5ZhhGoMrKypCQkID4+Hj+8f79exQWFqK4uBgfPnzgbxvTrFkz+Pv7Q1tbW9hhMwIWGxuLy5cv4/Lly/xEvH///hgxYgT69OlTZz+cvX79Gtu3b8epU6cgIyODmTNnYu7cuWjYsKGwQ2OqQVVVFQsXLsSyZcuEHcof68GDB5CVlYWlpSWSkpLQuHFjuLu7Y/z48T89p6ioCO7u7ti7dy9evnwJ4OvWYitXrsSECRMgLv7jAZqzZs3CwYMHoa6ujkePHsHU1FQg18Dj8eDj44PNmzcjODgYLVu2xJo1azBs2DCB1P8n43K5eP36NdTU1KCmpibscBhGoLKyspCeng4TExOIiYl99zxLvlnyzTA1iojw6dMnxMXFlUuy4+Pj8fbtW/72PNLS0jA2NoaBgQEaNGgAKSkpaGhowNjYGMbGxrCwsKizSRgjOHFxcfxE/NmzZ5CTk0P//v1hb29fZxPxDx8+YPfu3Th8+DDKysrg6OiIxYsXQ19fX9ihMVVERJCQkMDWrVuxYMECYYfzV5g5cyYuXryIhIQEyMvLAwACAwNx+PBhxMTEYOHChTA2NsbEiRMRFxeHQYMGYfr06WjXrh3U1dUrrP/du3dYtmwZli1bhtatWws8fiJCQEAANm7ciNu3b+P06dMYN26cwNv503z8+BE5OTlQUFCAgoICxMXF2cgBpl4jIhQWFiI9PR1KSkrQ1NT8YTmWfLPkm2EEJj09HQ8ePMCLFy/KJdlfvnwBAIiKiqJx48YwNTWFiYlJuUNHRweioqJCvoLaUVRUhJSUFCQnJ5c7srOzIS0tzT8UFRXRuHFj/qGrqwsJCQlhh19r4uPjcfnyZVy6dKlcIv6tR1xWVlbYIZaTnZ2NAwcOYO/evSgtLYW/vz/at28v7LCYKurbty9evnyJ69evo2XLliwhqEGvX79G8+bNsX79eixatAh+fn7YsmULHj9+DDMzM8TGxgL4+t7Rtm1bnDx5Ei1atBBy1D9GRHB0dMTZs2fx8OFDdOjQQdgh1WlEhNzcXKSnp4PL5Qo7HIYRGCUlJTRq1Oin7x0s+WbJN8P8ti9fvuDRo0e4c+cO7ty5g2fPngH4usLt/ybXJiYmMDAwgJSUlJCjFgwej4eCgoLvjsLCwnL/z8rKKpdgp6SkICMjo1xdqqqq0NXVhYqKCkpKSlBUVISioiJ8/vwZHz9+xLeXXlFRUWhra6NJkyblkvJvh46Ozh+bnH9LxC9fvozo6GjIycmhX79+GDx4MGxtbaGrq1tnkqTc3Fz07dsXMTExuHnzJvsQXs+8efMGNjY2yMjIgL6+Pnr16gV5eXmIiIiAy+WiT58++Oeff4QdZr2Xm5uLDh06oLS0FGfOnMGcOXMQGhoKGxsbLF26FP369cPWrVtx4sQJODk54d9///3p0PK6orS0FCYmJhgwYAD27dsn7HDqBSICl8vlj4JjmPpMQkLih0PN/y+WfLPkm2GqzM3NDa6urggKCkJZWRl0dHTQo0cP9OzZE927d4eWlpawQxQoIkJSUhLCwsL4R3h4OPLy8io8V0lJCbq6utDR0YGuru53h7a29i97cIuLi5GUlISEhITvjvfv3+Pjx4/8smJiYtDR0SmXkBsYGKBnz55/1M8kPj4eV65cweXLlxEVFQUA0NbWRocOHWBjY4MOHTqgTZs2Qr3Rk5+fj379+iEyMhIBAQFo06aN0GJhqq6kpAQPHz7E9evX8fDhQ3A4HLx58wYAMG7cOJw+fVrIEdZvZWVl6NevH8LCwjB+/HgcOnQIRkZGOHjwILp27Srs8Kqlf//+EBERqdTicQzD/H1Y8s2Sb4apsh49euDevXuws7PDvn37YGJiUmd6HQUhLS2tXKL99OlTfm+1jo4OLC0tYWlpiSZNmkBOTu6nh4yMTI0PpS8qKvouOX///j3/60+fPkFERAQdOnTAsGHDMHTo0D9qe6T09HQEBQXhyZMnCAoKQlhYGIqKiiAlJYW2bdvCzMwM+vr60NPTg76+PvT19aGrq/vdXsU1oaCgADY2NpCQkEBISEiFd8OZumvIkCHw9PTEwIEDsX//fujq6go7pHptyZIl2LVrFzw9PTF06FDY29vj+PHjf8TIqOHDhyMjIwMPHz4UdigMw9RBLPlmyTfDVFlMTAxsbW2Rl5eHyMjIGlnEprZwuVwEBwcjICAAT58+RVhYGJKTkwF8HQ7+LdH+djRq1EjIEVfN58+f4e3tDQ8PD/j7+6O4uBjt2rXDsGHDMGzYMJiYmAg7RIEqKSlBdHQ0goKCEBwcjLdv3yIxMRFpaWn8MiIiItDU1ISBgQFatmyJ1q1bo1WrVmjZsqXA55Hfvn0bdnZ2OH78OCZPnizQupnaERISAmtra5w6dQoTJkwQdjh/hO7du0NaWhre3t6QkZHBjh07MGfOHGGHVW3Pnz9H165d4eTkhI0bNwo7HIZh6iCWfLPkm2F+6dvCQ/Hx8UhISEBiYiISEhKgqqqK+fPnY9GiRfWuR4/D4eDu3bvw9PTE9evXkZGRAXl5ebRr165coq2vr/9H9ejn5+fD19cXHh4e8PPzQ2FhIVq0aAFnZ2dMmTKl3v0cq+LbCIGkpCQkJiYiMTERb968QXR0NOLi4sDlciEqKgpjY2N+Mv7tX01NzXK/BzweDxwO55dHXl4eoqKicO7cOWRnZ8PDwwN2dnZC/A4w/4vL5SIiIgLv3r1Dr169oKSk9MNyAwYMwNu3bxETE/PXLApZ04YNG4Zbt26hX79+uHTpEs6cOYOxY8cKO6zfVlJSgo0bN2LTpk0wMTGBn58f2+2AYZgfYsk3S74Z5jtxcXG4cOECLl++jBcvXqBBgwZo1qwZfx5x06ZNYW9vX+dWm/6V7Oxs+Pr6wtPTEzdv3kRBQQFMTEwwePBgDB48GO3bt/+jk8//VVhYCH9/f5w7dw6XL19G27ZtceDAAVhbWws7tFrH4XDw4sULREdHIyoqCtHR0YiOjubP6VdVVYWEhAQ/sS4pKalUvVpaWujSpQs2b97MPojXMZ8+fUK/fv0QEREBAJCUlISZmRlMTExgbGwMU1NTDBw4EJmZmTAxMYGbmxscHByEHPWfITc3l3+jQ1dXF6tXr4ajo2O9vLHB5XLh4eGBdevWIT4+HsuWLcOKFSv+iOHzDMPUDJZ8s+SbYfg+ffqEZcuWwc3NDfLy8hg0aBDs7e1hZ2dXLz9MJCUlwcvLC15eXnjw4AG4XC6srKz4CbeZmZmwQ6wTgoKCMGvWLERGRsLR0RFbtmyp1P65fzIiQkJCAqKiovDixQvweDzIyMj88pCVlS339c96Uhnhio+PR69evVBSUoJTp07BxMQEXl5eePnyJV6/fo34+HgkJydDSUkJU6ZMwc6dO+Ho6IgjR478VTfoakpZWRn27NkDWVlZODo6QlpaWtgh/RYvLy8sWbIEcXFx+Oeff7B9+3a0atVK2GExDFPHseSbJd8MAwA4d+4cnJycICkpifXr12PSpEn17kMRESEmJgaenp7w9PREREQEJCQk0L17dwwePBgDBw78o1b9FiQul4ujR49ixYoVICJs2LABTk5OLNlg/ijR0dHo2bMn1NTUcOvWLejp6f2wXFpaGv777z8cOXKEvwdxz5498d9//8Ha2vqPmo7CVA2Px8Pq1auxceNG9O7dG+vWrUP79u2FHRbDMPUES75Z8s0wSExMRNOmTTFgwAAcPnwYysrKwg6p0jgcDoKDg+Hj4wNPT0+8e/cO8vLy6Nu3LwYPHow+ffpAUVFR2GHWG5mZmVi2bBlOnDiB1q1b48CBA2yfauaPQETo1KkTcnNz8eDBA6iqqlZ4Tnx8PJYvXw4PDw/+Y5cuXcKIESNqMlSmDjtz5gzGjx+PrVu3YtGiRexGDMMwVcKSb5Z8M3+5Dx8+YOLEiXjx4gViY2MhLy8v7JB+6cuXL3jy5AkCAgLw8OFDhIaGoqSkBI0aNcKgQYMwePBgdOvWrV4Ok69LQkNDMWvWLDx9+hQTJ07E1q1boaGhIeywGOa33b9/H927d4ePjw/69etXpXOfPn2KBw8eICUlBf/99x/77PCXKisrg42NDeTk5HD//n1hh8MwTD3Ekm/2Bsr8hYgIT548gYuLCzw8PCAlJYULFy6gf//+wg7tOzk5OQgMDOQn2+Hh4eByuVBXV0fnzp3RpUsXdO7cGS1btqyXC/bUZVwuFydOnMCyZcsAAHfv3q3X28oxf7fu3bsjJycH4eHhrLeSqbLY2Fg4OTkhMDAQ9+/fR6dOnYQdEsMw9RBLvlnyzfxFOBwOzp8/DxcXF0RFRcHY2BjOzs5wcHCoM0OzMzMzERAQwE+2o6OjQUTQ1tbmJ9qdO3eGmZkZ+wBdSzIzM9GnTx8kJiYiKCgIhoaGwg6JYarEz88P/fr1w9WrVzFkyBBhh8PUEzweD/fv38fx48fh4eEBfX19HD58GD169BB2aAzD1FMs+WbJN/MXSEpKwsGDB3H8+HFkZWWhb9++mD17Nuzs7OpEb3Fubi4uXrwIV1dXhISEAACaNGnCT7a7dOmCJk2asGS7hvB4PKxatQpv3rxB27ZtIS4uDnFxcWhqasLCwgJNmjTB58+fYWxsjDlz5mDt2rXCDplhKi0vLw/NmzdHixYt4Ofnx15HmAp9/PgRbm5uOH78ON69ewczMzNMmzYNTk5OkJGREXZ4DMPUYyz5Zsk384e7cOECJk6cCCkpKTg6OmLWrFkwMjISdljg8Xh48OABTp48CQ8PDxQXF6N3794YNWoUunbtCl1dXWGH+NcoKChAgwYNfvq8srIyLCws8PTpUwwZMgQnTpyoxegYpnpmzJiBM2fOICYmhu23zvwUl8vFzZs3cezYMfj4+IDL5cLMzAxdunSBoaEhCgoK8OXLF3Tr1q3KawYwDMN8U5U8VLyWYmIYRgCICP/99x/Wrl2L8ePH4+DBg79MsGpLYmIi3Nzc4ObmhoSEBBgbG2P16tUYP348tLW1hR3eX0lOTg4lJSW4ceMGTp48CR8fHxgYGGDr1q2QkpLC06dPER4eDjk5OWhqago7XIaptAcPHuDw4cPYv38/S7yZn8rMzIS6uvp3j8fGxiI2NhYKCgrIy8sDANy+fZsl3wzD1ArW880w9URRUREcHR1x/vx5bNiwAcuXLxfqUEsOh4Nr167B1dUV9+7dg5ycHOzt7eHo6AgbGxs2DLSOiY2NxaRJkxASEoJ58+Zhw4YNbKglU2dFRkZi/fr1cHR0LLdoZHFxMVq0aAFNTU08ePCgTkyxYeomDocDR0dHxMfHo1WrVjAwMICxsTFMTEzw/v17nDx5Ejdu3ICmpiYuXLgAW1tbYYfMMEw9xXq+GeYPk56ejsGDByMyMlKo+9F++vQJvr6+8Pb2xu3bt1FYWIjOnTvD1dUVw4cPrxO98MyPmZmZITAwELt27cKqVavg4+MDNzc3tt83U6e8ffsWK1euxIULFwAA+vr65ZJvX19fvHnzBlevXmWJN/NLMjIyOH/+/HePx8bGYtiwYVBVVcWBAwcwevRo1mnDMEytYT3fDFPHxcTEYMCAAeBwOLh+/Trat29fa20TEaKjo+Ht7Q1vb2+EhYVBVFQUHTp0wIABAzB06FAYGxvXWjyMYLx69QqTJk1CaGgorl69isGDBws7JOYvl5aWhvXr1+PIkSMoKysDAKxevRpr1qwpl2QPHToUycnJCAsLE1aoTD2XlJSEZs2aoaCgAK1bt4a2tjY+fPiA1NRUcLlctGzZEubm5pg7dy4MDAyEHS7DMPVAVfJQdtuYYeqwmzdvwsbGBgoKCggNDa2VxJvD4cDPzw8zZsyAnp4e2rRpg+3bt6Nx48Zwd3dHWloaAgMDsWTJEpZ411NNmzbF48ePoaamhqioKGGHw/zF8vLysGbNGhgaGuLs2bOYNm0aAGDLli1Yt25ducT78+fP8PX1xbhx44QVLvMH0NPTw7t373D69Gm0bNkSIiIiaN++PZydnTF37lzweDzs27cPvr6+1W4rPT0du3btgo2NDebOnYs/pL+LYZhqYMPOGaaOOnDgAObMmYM+ffrg/PnzkJeXr9H2wsLCsGfPHnh6eqKwsBAGBgYYNmwYBgwYgE6dOkFSUrJG22dql5iYGIqKithIIUYoiAgXLlzA3LlzkZeXhzlz5mDp0qVIS0vjLxBoa2sLPT09KCoqQl5eHocPHwaXy8WoUaOEHT5Tz2loaGDcuHHf3cjhcDjw9fVFo0aNqjW9Kz8/H+vXr8eePXsgIiICS0tL7Nu3DwMHDmT7iTPMX44l3wxTx5SVlWHevHnYv38/5s2bh+3bt0NMTKzG2rp27Rr27NmDJ0+ewMDAAKtWrcKgQYNgZmbGFk37g/F4POTn59f4TR2G+V+fPn3CjBkz4OnpCXt7e+zYsYO/DaGKigr8/f0xadIkdOrU6btz//33XzRs2LC2Q2b+AjweDxMmTMCzZ88QEBCARo0aVXhOYWEhPnz4AE1NTcjJySE7OxvXrl3DihUrkJubi5UrV8LZ2RlKSkqQkpJCXFwcS74Z5i/Hkm+GqWMWLlyIQ4cO4dChQ3BycqqRNvLy8nD06FG4uLggKSkJXbp0gaenJ/r3719jiT5Tt3z58gUAWM83U6tiYmLQuXNnSEhI4MqVKxg2bNh3ZTp27IhXr14hIiIC2dnZ+PDhA968eQMlJSU4ODigtLQUEhISQoie+ZNdu3YNV65cwdWrV2FhYfHLsvfu3cPKlSsRGhoKLpcLAFBUVEReXh6ICCNGjMCOHTugp6cH4OtID3l5eWRmZtb4dTAMU7ex5Jth6pCLFy9i7969cHFxqbHE+969e3BwcEB6ejpGjx6NuXPnok2bNjXSFlN37d+/HyIiIjAzMxN2KMxf5NGjR8jOzsbw4cNx584d3Llzp9zzRUVFyMjIQHp6Ov/gcDj85xcvXgwAaNCgAYyMjNCyZUu0bNkSOjo6yM/PR15eHvLy8hAXF4eePXti6tSptXp9TP0UERGB9evXo0OHDhgyZAiICCkpKUhNTUXr1q0hLS0NAHjz5g0WLVoET09PdOjQAQcPHoSBgQHS0tKQkpICdXV19OjR47v952NiYpCdnQ1ra2thXB7DMHUIS74Zpo6IjY3FlClTMGrUKMyaNUvg9RcVFWHFihXYtWsXunXrhsDAwO8+IDB/h4CAAKxatQorVqxAq1athB0O8xexsrKClZUV3r9/j/fv33/3vKSkJDQ0NNC6dWtoaGiUOyQkJJCdnY3Pnz8jMzMTcXFxeP78Oa5evYqCgoLv6kpKSmLJN/NTpaWluHnzJvbt24c7d+6gSZMmmDp1Krp06YKIiAj+6KAGDRqgb9++0NDQwJEjR9CoUSOcP38eI0eOrPTUrE2bNkFNTQ1dunSpyUtiGKYeYFuNMfWSv78/lixZAmdnZ7Rr1w6Ghob1eu7qly9fYGVlBSJCaGioQPfL5nK5uHHjBpYvX464uDhs2rQJ8+bNY3vk/qUyMjLQunVrGBkZ4e7duxAXZ/dgmfrt8+fPcHFxgZubGxISEmBlZYU5c+Zg+PDhbKFIppxv77FnzpzBuXPn8PnzZwDAsGHDYGFhgXXr1sHU1BRjx45Fs2bNoKGhgdu3b+Pq1at4/fo1Fi5ciAULFkBWVrbSbW7evBnLly/HmTNnMHbs2Jq6NIZhhKgqeShLvpl6afr06Th69Gi5xxo2bAgjI6MfHkpKSsIJtBKICGPHjsX169cRFhaGpk2bCqTezMxMnDhxAocPH+Z/ID169CjMzc0FUj9Tv2RkZCA4OBg7d+7Ey5cvERUVBS0tLWGHxTC/JTExEZ6envD29sbDhw8hIiKCkSNHYvbs2bWyJSNTv2RkZODQoUNYu3btL7f7mjJlClxcXPjDzP8vIqryIqR79uzBvHnzsGbNGqxdu7aqYTMMU0+w5Jsl33+F1NRUHDp0CBs3buQ/NmjQIKSlpeHNmzflFjZRVVUtl4wbGhryv1ZTUxPqqt4uLi6YM2cOLly4gJEjR1arLh6Ph7CwMBw8eBAXL14EAIwcORKzZs1iH0j/IqWlpXj+/DmCgoIQHByMoKAgvH37FgCgqamJ06dPsxV3mXrL1dUVM2bMAAB0794dAwYMwNChQyu1OjXz9zl//jzGjBlT7rHu3bujd+/eaN68OYyNjfnTGqrSo/0r+fn5+O+//7Bjxw4sXrwYW7ZsYbuHMMwfjCXfLPn+q/B4PBw4cABLly6FgoICzM3Noa6uDklJSeTl5SE3NxcJCQl48+bND89XUFCAiYkJ7OzsMGzYMLRp06ZW3iTLysqwfPlybN++Hf/++y927979W/Xk5+dj9+7duHLlCl6/fo2ioiI0btwYM2bMgKOjI9TU1AQcOVMXlZaWYs+ePfDx8UFYWBg4HA4kJCTQpk0bdOjQAdbW1ujQoQP09PTYh0CmXiorK8PChQuxd+9eTJ06FTt37qzX042Y2rF9+3YsXrwYw4YNw/jx49GjRw+BTu169uwZtm7dipcvX0JeXh4iIiKIiIgAl8vF6tWrsWTJEvaayzB/OJZ8s+T7r/Tu3Tvs3bsXqampSE9P56+Y+21O169ISUmBx+OhtLQUTZo0wdChQzFs2DBYWVnVyNzoT58+YeTIkXj8+DE/+a7qm3NxcTEOHz6MDRs2ID8/H6NHj0arVq3QsmVLdO3alW0Z9hd5+fIlxo8fj2fPnmHQoEGwsbGBtbU12rZt+8PhkwxT36SlpWHcuHG4f/8+9u7di5kzZ7KEhhE6b29vjBo1ClpaWujRowcKCwtRXFyMtm3bYvTo0fytxhiG+bOx5Jsl38z/weVyQUQQFRWFiIgIREREQESIioqCp6cnPD098ezZM4iKikJMTAxcLhc8Hg8AoK2tjSFDhmDYsGHo2LGjQBanCgwMhL29PYgIly5dQqdOnap8PWfOnMGaNWuQnJyMSZMmYc2aNdDV1a12bEz9ExUVBRsbGzRu3Bju7u4V7k/LMPUJEeHChQuYPXs2REREcOnSJXTr1k3YYTF/OQ6Hgz179mDlypUYNGgQzpw5I7Ah6wzD1D8s+WbJN1NF79+/h5eXFzw9PfHo0SN+8v2/5OXloaSkBE1NTYwdOxYTJ06s9O8bj8fD3r17sWjRItja2uLixYtVnqP48uVLjBs3DpGRkRg2bBjWr18vsAXamPqnsLAQFhYWkJCQQFBQEPvwx/xRPn78iBkzZsDLywv29vbYv38/1NXVhR0W8xfjcDg4evQotm7dirS0NCxYsACbN29mI80Y5i9XlTyU7TXEMACaNGmCf//9Fw8ePEBaWhrc3NwwePBgyMjIlCunpKQEU1NTaGhoYP78+dDW1oazszNiY2N/Wvfnz5+xY8cOGBsbY/78+Zg/fz7u3r1bpcSbiHDo0CG0a9cORUVFCA4OxpUrV1ji/RcrLi7G7Nmz8f79e5w7d44l3swfg4hw+vRpNG/eHEFBQbhy5QouXrzIEm9GKIgIz549w3///QcDAwMsWLAAvXr1QlxcHLZt28YSb4ZhqoT1fDPMLxQWFuL27dsICAhAcHAwwsPDUVxcDAkJCWhqauLDhw8oKysDAFhaWqJ169YwNzeHuLg4Pn36hNevX+Pq1avg8Xiwt7eHs7MzrKysqhRDRkYGpkyZguvXr2PmzJnYvn07S7SEJDY2FpqamlBUVBRK+0SEd+/e4datW9ixYwcSExNx5MgRTJkyRSjxMIygpaamwsnJCT4+PhgzZgz27t3LFo1khObb6KJXr16hQYMGsLe3x/Lly2FoaCjs0BiGqUOqkodWfwIrw/zBZGVlMWjQIAwaNAgAUFJSgmfPniE4OBgPHjzAjRs3+Ml3cnIySkpK4ObmBh6PBw0NDWhpaWH16tWYPHkyNDQ0qty+v78/HBwcUFZWBi8vLwwcOFCg18dUjIjw8OFD/Pfff7h//z7k5eUxZcoUzJ07F/r6+rXSvo+PD65fv447d+4gISEBYmJi6NevH3x8fNCsWbMaj4FhahqPx4Obmxvmz58PGRkZeHp68l93GUZYpKSkYGVlhVevXmHcuHE4dOiQsENiGKaeY8POGaYKJCUlYWFhAWdnZ1y5cgWZmZnw8vJCjx49kJ6ejokTJ6KwsBAlJSX48OEDnj59imXLllU58S4uLsb8+fPRq1cvmJub49mzZyzxFoCsrCxwudzvHi8tLf3hPP83b96gd+/e6NatG7Kzs3Hu3Dk4OzvDzc0NhoaGGDVqFMLCwmo03uHDh2PgwIF48uQJBg4ciOvXr+Pz58/w8vJiiTdT7xERvL290bp1a0yePBkDBw7EixcvWOLN1AliYmI4efIkVq1aBTc3N3A4nGrVV1paCjc3N5ibm8PKygo+Pj4CipRhmHqD/hC5ubkEgHJzc4UdCvMXKisro0WLFhEAcnR0JC6X+9t1vXjxglq1akWSkpK0a9euatXFfH1tOHr0KHXo0IEAkLS0NLVt25YcHBxoyZIl1LlzZ5KQkCBRUVFq2LAhmZubU8+ePWn48OEkLS1N+vr6dO3aNeLxePw68/PzycXFhQwMDAgAWVhY0Jo1aygkJEQgP6/09HTavXs3aWpqkoqKCnl4eFS7Toapa+7fv8//u+zatSs9efJE2CExzHd4PB55eXkRAPL39/+tOgoLC8nFxYX09PQIAA0cOJDatWtHAOjly5cCjphhmNpWlTyUzflmGAFycnLCiRMnkJGRASUlpSqdS0Q4fPgw5s+fjyZNmuDcuXNo3bp1jcT5p+PxeHj48CFOnjyJK1euoLi4GJaWllBTU0NERATS0tLA4/GgpaUFKysrdOnSBdLS0khLS0N6ejr/X1tbW6xYsQJycnI/bIfL5eL69eu4cOEC/P39kZOTA3V1dfTq1Qt9+/aFnZ0dVFVVKxVzWVkZbt68iZMnT8Lb2xsAMHToUOzatQtaWloC+94wjLCFh4dj+fLl8Pf3R7t27bB582b07NmT7dvN1AllZWXYt28fQkJCEBcXh9evX6OwsBBGRka4c+dOlaYbJSYm4vTp03BxcUFmZiZGjx6NpUuXQkVFBUOHDkVYWBjevXtXK1OYGIapOWzON8MIAZfLxa1bt2Bvb1/lxPvRo0dYu3Yt7t27xxZVq4a3b9/C3d0dp0+fxvv37wGAvzheaGgoiAgKCgr8IeYNGzbEmDFjMHToUIiKVn0WjpiYGIYMGYIhQ4agrKwMwcHB8PPzw40bN3DmzBmIiorCysoKffr0gbW1NVq2bImGDRvykwwej4fY2FicOnUK7u7u+PTpE1q1aoXt27dj7NixbKEp5o8SGxuLlStXwsPDA2ZmZrhy5QqGDh3Kkm5GqPLz8xEREYGnT58iLCwMt2/fxufPn6GmpgZra2tMmDABJiYm6Nat209vxP5f2dnZuHz5Ms6ePYuAgADIyMhg3LhxWLJkCQwNDREQEICePXtCXFwcgYGBLPFmmL9NDffC1xo27JwRtitXrhAAevr0aaXK83g88vf3p86dOxMAMjc3J19f3xqO8s/F4/EIwHeHsrIy2dvb0/HjxykpKYmIiIqLi+nu3bvUo0cPAkBNmzYld3d3KikpEVg8KSkpdPz4cRo2bBgpKCjw41FTU6M2bdqQjo4OiYuL82N0dnamiIgIgbX//v17GjJkCA0fPpy+fPkisHoZpqpSUlJo0qRJJCoqSnp6enTy5EkqLS0VdlgMQ5mZmT983/i/R3BwcKXqunHjBg0ZMoQkJSVJVFSU7OzsyN3dnfLy8vhloqOjSVZWlrp27UppaWk1dVkMw9QyNuycDTtnhMDGxgaSkpJ48OBBhWXv37+PpUuXIjQ0FJaWlli1ahX69+/PeoCq6cmTJ3j06BFSUlKgoaGBXr16oV27dr/chzUoKAgbN26Er68v5OTkYGFhASsrK/6hra1d7bi4XC7evXuH58+f4/nz50hNTUWjRo2gpaUFfX19dOvWDdLS0tVu55vs7Gy0atUKnz9/RkFBAby9vdG/f3+B1c8wlRUZGYk+ffqAiLBixQpMnz4dUlJSwg6LYQB8ne7l4uKCR48eQVpaGtLS0tDV1YWuri4ePnyIU6dO4eXLl2jatOkv6zlz5gzGjx+PNm3aYMKECRg5ciQ0NTXLlcnOzkbbtm2hrKyMwMBANrqNYf4gVclDWfLNMAIQGBiITp06VWo7sPDwcNjY2KBdu3ZYt24dm+tYRzx79gw3b95ESEgIQkJCkJqaCgDQ1tYul4y3a9cODRo0EHK0vzZ58mS4urqiffv2CA0Nhbu7O8aPHy/ssJi/zP379zFo0CCYmZnB19cX6urqwg6JYSrl48ePaNu2Ldq2bQtfX99fln3w4AHs7OwwduxYuLq6/vT9fOzYsfDz80NUVBQbas4wfxiWfLPkm6lFRISOHTuisLAQ4eHhv5w7/O3Ot5qaGgIDA1kPUB2WmprKT8RDQkLw9OlTFBQUQFRUFC1atEDv3r2xePHiSi+oVlsePXqEzp07AwCMjY0xb948TJkyBRISEkKOjPmbcDgcNG7cGC1btoSnp2edv2HFMN/Ex8dj+PDhyMrKQnh4OBo1avTTsq9eveLfTPfz84OkpOQPy126dAkjR47EmTNnMHbs2JoKnWEYIalKHsr2+WaYavL09MSTJ0+wbdu2XybePB4PEyZMQG5uLi5fvswS7zpOW1sbQ4cOxdatW/HgwQPk5OQgOjoahw8fhoWFBQ4dOgRDQ0Ns3bq12nu/CkJubi7u3buHuLg4NGrUCIMHD4aUlBTMzc1Z4s3UusuXLyMjIwNHjhxhiTcjFFwut8rnuLu7o23btiguLsbNmzd/mXh/+vQJffr0gY6ODjw8PH6aeH/8+BEzZszA8OHDMWbMmCrHxDDMn4Ul3wxTDaWlpVi6dCns7Ozwzz///LLstm3b4OPjg9OnT6Nx48a1EyAjMOLi4jA3N8fUqVNx4sQJvHnzBuPHj8fKlSv5c1oFITs7G0FBQTh58iSWLFmC2bNno7S0tMLzRowYgR49emDatGnIzs7GzZs3ERMTAxkZGYHExTBVkZeXBwkJCbZNHlPriAgzZ86ElJQUunTpgpMnTyI/P/+X5+Tn52P8+PFwcHDA8OHDER4ejpYtW/60fEFBAQYMGICSkhL4+flBUVHxp7FMnToVEhISOHToEJtixjAMW+2cYX4Xj8ejGTNmkKioKEVGRv6y7IULF0hUVJSWL19eO8ExtcbPz48A0LVr16p8bllZGd24cYOcnJyoS5cu1LBhw+9W2tXT06OMjAxKS0uj5ORkSk9P/2FdXbp0ocGDB1NRUREVFBRQ//79ycTEhHg8XjWvkGGqLjY29rf/LpjaxePx6M6dOzR06FA6c+aMsMOptuTkZAJAU6dOpZ49e5KIiAjJysrShAkTfrijRHh4OBkZGVGDBg3o9OnTFdZfVlZGAwYMIDk5uQp3qDh+/DgBIG9v79++HoZh6r6q5KEs+WaY38Dj8Wj+/PkEgE6cOPHLsvv27SMREREaO3Ys217nD/XPP/+QqalppX++8fHxtGTJEtLS0iIAZGRkRCNHjqQ1a9bQ+fPnKSoqimbNmvXTrW969+5NPj4+xOVyiejr76OFhQUNHz6cXr9+TVZWViQmJkanTp2qyctmmF9q3rw5jRs3TthhMER07do1srGxofnz59Pdu3cpLi6Orl+/TrNmzSJDQ0MCQHJycqSvry/sUKvt6tWrBIBSUlKIiCgpKYk2btxIhoaGJCUlRefOnStXvmHDhmRkZETx8fEV1s3j8cjZ2ZnExMQq3Bo0JyeHVFVVacKECRXWy+Fw6O7duxXeyGcYpm5iyTfD1CAej0crVqwgALR///5fllu+fDkBoAULFvATJebPExERQSIiInT48OFflsvKyuJ/cFNWVqZZs2ZRaGjoD3un37x5Q66urnT27Fm6cuUKXb9+nW7evEknTpwgCwsLAkAGBga0c+dOunbtGgEgKSkpkpOTI0NDQwoKCqqpy2WYSlm9ejUpKipScXFxrbfN5XKprKys1tuti4KCgkhUVJRsbW2pQYMG5W7kNWnShJycnOjOnTu0fv16UlNTE3a41VJcXEwdO3YkAwOD715Xi4qKaPz48QSA3N3d+Y9bWlqSnZ1dperftm0bAajwtZ6IaPHixSQrK0upqam/LFdSUkI9evTg/0wmT57MRiwxTD3Dkm+GqSH5+fk0atQoAkDbtm37abnS0lJydHQkALR9+/ZajJARlnHjxlHDhg0pPz//u+dKS0vpwIEDpKKiQvLy8rR9+3bicDi/3RaPx6Pg4GAaO3YsSUhIkIyMDFlaWlL37t1p4cKFlJeXV51LYRiBeP78eaVGBwlSWVkZubm5kb6+PtnY2Pz1SUxBQQGZmJhQ+/btqbS0lIqKiuj169d09+5dio+PL/f9mTZtGrVt21aI0Vafs7MzSUhI0JMnT374PI/HoyFDhpChoSH/5szp06cJAAUHB/+03pKSEv5opMpMH/vy5QtJS0vTypUrKyy7atUqEhcXp+vXr9OOHTsIAD1//rzC8xiGqTuqkoeyrcYYppJiY2MxbNgwJCUlwdXVFSNGjPhhucLCQowaNQo3btyAq6sr21/5L5GYmAgTExO0adMG8+fPh6SkJN6+fYu3b9/iwYMHiI2NxaRJk7Bp0yY0bNhQIG16eHhgwoQJMDU1xd27d6GsrCyQehlGUMaMGYO7d+8iLi4OSkpKNdZOfn4+Ll26hJ07d+LVq1f8x318fNCwYUPIysqitLT0lweXy4WFhQWaNGlSY3HWJi6Xizlz5sDV1RVRUVEwNTX9Zfm+fftCSkoK165dq6UIBcvd3R0ODg44dOgQnJycflouJCQE1tbW8PT0xKBBg1BaWoqOHTsiKysLQUFB3+1Hn5mZCXt7ezx69AguLi6/rPubmzdvok+fPnj16hXMzMx+Wu7Tp08wNDTEnDlzsHnzZmRmZkJdXR1nz55lK6MzTD3C9vlmyTcjYJcvX4ajoyN0dXXh4eGBpk2b/rDc58+fMXDgQERGRuLKlSvo06dPLUfKCNOdO3fw33//4dGjRwAAOTk5GBoawszMDIsWLYKFhYXA2rpx4wb69++PESNGwNXVFbKysgKrm/k9hYWFiIqKQmRkJKSkpKCnp4dWrVoJ7GZLfZSamgpTU1NMmTIFe/bsEWjdSUlJuH//Pm7fvo1r166Bw+Ggb9++WLt2LSIiIjB9+vTfqrd58+Zo2rQpzMzMYGpqyv9XXl5eoPELEpfLRXh4OG7duoXg4GB8/vwZb9++RUZGBlxcXODs7FxhHS1atED37t2xb9++KrVdXFwMZ2dn3Lx5E0pKSlBSUoKysjKUlZUhKyuL/Px85OTkICcnB5qamti4cSNMTEz45xMRoqOjcfnyZWzatAn9+vWDj49PlWKIiIiAra0tRo8ejRMnTvBXFedwOLh69SpGjhwJcXFxfnkbGxtISkriwYMHAIA3b97A1tYWUlJSGDZsGH97xhcvXiAoKAhiYmLw8PBA586dKxXPwoULceHCBSQnJ/9yhfN///0Xbm5ueP/+Pf/mqbW1NRo0aIA7d+5U6XvAMIzwsOSbJd+MgJSWlmLJkiXYvXs3Ro4ciePHj/90z9pXr15h+PDhSEtLg6+vL6ysrGo5WqauePfuHeTk5KChoVEjW8u8evUK1tbW6NKlC65duwYxMTGBt8FUXnx8PLZu3YrTp0+jtLQUEhISKCsrAxGhTZs2iIiIEHaIQrVt2zYsX74ce/fuxcyZM6v0N1FSUoKEhAS8ffsWb9684f/78uVLvH//HgBgbm6OESNGwMHBAbq6uvxzi4uLkZmZibS0NHA4HEhISPzyAIBbt24hMDAQsbGxiIuLw4cPH/j1aWlpQVVVFUVFReWO4uJiKCkpQUdHBzo6OtDW1uZ//X8fk5OTq9b3saysDOnp6fj06VO5IyYmBv7+/sjKyoKioiI6duyIRo0aQVNTE/369YO1tXWFdRMRFBQUsGbNGixcuLBKcZ04cQLTpk3DvHnzUFJSgpycHGRnZyM7OxsFBQVQVFSEkpISFBUVERAQgISEBKirq8PY2BgyMjJ49epVue+zg4MD3NzcKt1+ZmYmLCwsoK6ujkePHkFaWhoAkJWVBTU1NQBAcnIydHR0+OecP38eY8aMwfv37/lbf6akpMDJyQnv3r1DaWkpeDweTE1NYWFhgSlTpkBPT6/SMY0ZMwYfPnzgJ/c/Eh8fj5YtW2LVqlVYuXIl//ELFy5g7NixiI2NhbGxcaXbZBhGeFjyzZJvRgA+fvwIe3t7BAcHY+fOnZg9e/YPPzQSEQ4dOoQFCxagcePGuHbt2i+HmTFMdWRlZcHKygoyMjJ48uRJne6N+9NlZGRg0aJFcHd3R6NGjTB37lzY2dlBXV0dAwYMQEJCAnx8fGBrayvsUIWqrKwMCxcuxN69e6Gvr4/Bgwdj0KBB0NbWRl5eHvLy8pCbm4u8vDxkZGSUS7QTExPB4/EAAJKSkjA0NIShoSGMjY3RsWNHdOnSBaqqqjUWe15eHuLj4xEbG4vY2Fjk5uZCRkYG0tLSkJaWhpSUFKSkpJCdnY2UlBSkpqYiJSUFKSkpyMrKKleXsrLyDxNzHR0dqKmpQUFBAfLy8lBQUICMjAwyMjKwefNm3L17F58+fUJmZib+9yOburo6mjRpAjs7O/Tu3RtWVlblengrQkSIjY2Fh4cHVq1ahQsXLmDkyJFV+h45ODggJiYG4eHhFZblcDjw9vZGXFwc4uPjUVhYCFNTUyQlJeHs2bOwt7fH6dOnISkpWam2uVwuevfujaioKISHh/MT5IMHD2L58uXIzc0FAPB4vHLv37m5uVBTU8O+ffswY8aMKl1vZUyZMgXPnz9HSEjID59/9uwZevXqBQUFBTx9+rTc63hJSQnk5OSwdu1arFixQuCxMQwjeCz5Zsk3U00PHz7EyJEjISYmhkuXLv30w3NaWhomT54MX19fzJw5E9u3b2fDf5kaNXnyZHh5eSEsLOyPmZta3/B4PJw8eRKLFy8GALRs2RKFhYUoKipCYWEh3r59C01NTdy6dQstW7YUcrR1R0BAAC5evAgvLy+kpqb+sMy3qRpGRkYwMjIq97W2tna9GuXB4XCQmppaLiH/dnx77NOnT98l1AAgKioKERERNGjQAKNHj4auri4aNWpU7lBXV+f32FcFj8dDaGgorl27Bk9PT8THx0NOTg59+/bFgQMHvpvzXBEDAwMMHDjwt6YVxMTEYPXq1bh27RqmTJmCw4cPV+pn/OTJE5iYmGDfvn3YuHEjbt++je7duwP42qNsamqKwYMHw9PTE46Ojjhx4sR3dXTt2hUKCgq4fv16leOuyNy5c3Hv3j08f/78u+du374Ne3t7NGnSBDdv3oSGhsZ3Zfr374+ioiI29Jxh6omq5KGVvz3KMH8BIsLOnTuxdOlSdO7cGefPn//pfE0fHx84OjpCREQEPj4+6NevXy1Hy/xtysrK4OnpiZkzZ7LEW0hevXqF6dOn49GjR2jfvj2Kiorw8OFD2NnZwdTUFDIyMjA1NUW/fv3+6rneP9K5c2d07twZLi4uiIqKQn5+PhQUFKCoqMjv9ZWSkhJ2mAIjIyPDv3HwM6Wlpfj48SOysrKQl5eH/Px8/mgAHo+HUaNGQUVFRSDxpKamYseOHThz5gwyMzP5j5uYmKBx48aIj4/HjBkz0K9fP4wePZo/fPtXQkND8f79e/zzzz+VjoPH4yE4OBj79+/HhQsX0LhxY5w6dQrjx4+vcEoCEWHz5s1YsWIFunfvjoiICMyfP5+feAPgzxdXVVWFpKQkli5d+sO6+vbti3Xr1qGoqKhS11oVsrKyKCwsLPdYeno6FixYgDNnzqBnz564cuUKFBUVf3i+oaEhbty4IdCYGIapIwS91LqwsK3GmOoKCQmhLl26EABasmQJlZaW/rBcQUEBzZgxgwBQv379KC0trZYjZf5Wjx49qnBLHKZmcDgcWrVqFUlISJCxsTGNGjWKxMXFydDQkPz8/IQdHsP8UnZ2drn9vb8d4uLiZGRkRL1796Zp06aRra0tiYqKkrq6OvXr148WL15Mbm5uFBYWRl++fCGir9t1ffr0iXx8fKhNmzZkZGRU4Z7qHA6HfH19aerUqdSwYUMCQDo6OnT48GEqKSmp1DWUlZWRk5MTP/Zve3ZHRETwyzx8+JCkpKSoV69eJCkpSatWrfppfTExMQSALl68WKn2q2LXrl0kLS1NqampxOFw6NChQ6SsrEwqKirk6upa4RZ4PXr0oCFDhgg8LoZhagbbaowNO2eqIC4uDitWrICHhwdatGiBHTt2oFevXj8sS0To0aMHfx64k5NTjSyoxTA/snTpUpw8eRIfP36EqKiosMP5K+Tn58PV1RW7d+/Ghw8fsGzZMmhoaMDZ2RkzZszArl27BN5rxjCCxuPx4ObmhqSkJDRq1Ig/nF9XV/e7OeLx8fFwdXXF8+fP8eLFCyQmJvKf09bWxufPn8HhcAAATZs2hZubG9q3b/9dm0SEK1eu4PLly7hx4wa+fPkCQ0NDDBkyBIMHD4a1tXWlpxFkZmbC0dER3t7eAIDRo0dDTk4Od+7cwbt37yAiIoLbt29j0KBB6NChA7hcLpKSkvDixQvIyMj8tF47OzskJiYiICBAoCNVsrKyYG5uDnFxcXA4HGRkZGDChAnYsWNHhcP6uVwu1NXV8e+//2L16tUCi4lhmJpTpTy0hm8E1BrW881UVVFREc2YMYPExMRIT0+P3NzcKrx7f+XKFQJAN2/erKUoGeb/16xZM5o0aZKww/hr7Nq1i5SUlEhMTIzGjBlDjx49oqVLl5KIiAjNnDmzwt4rhvkT5OfnU2hoKLm5udGKFSto165ddO3aNXr9+vUv/wbWrFlDAMjS0pI2btxIMTExVf6b4XK5dP78eVJTUyMlJSUCQHp6enTt2jUSExOjzZs3ExHRhw8fCAB16NCB3yPu7e1dYf0xMTGkpqZGPXr0qFJclfHu3Tvq1KkTOTs7U2xsbKXPu3XrFgGg0NBQgcfEMEzNYD3frOebqQCXy8WoUaPg4+ODDRs2YNasWRX2XhUXF6NZs2YwMzODr69vLUXKMF+9e/cOhoaG8PDwwNChQ4Udzh/vzZs3MDExwcSJE7FixQocO3YMLi4uEBERwYIFC7BmzRo2+oBhfuLo0aOYPn06tmzZgiVLllRYPjo6GpcuXUJwcDBERUUhLi6OhIQEvH//HsXFxRgxYgQGDx6MsWPHQkFBAV++fEGXLl1w8+ZNSEpK4u7du+jZsyeAr/Ot9+7diylTplTYbmlpKXR0dDB27Fjs2rWr2tddXQUFBWjXrh3k5eURGhrKRtYxTD3BFlxjmF8gIsyePRtXr17F1atXMWjQoArPefbsGZydnZGYmMgf9sYwtYXH42HXrl2QlJSs0sJGzO9zcXGBqqoqNmzYgHHjxuHx48dYvHgx/v333xrd2oph6rvr169jxowZcHZ25u8I8CtXrlzB2LFjoaioCFtbW0hISKCkpAR2dnYwMDBA27Zt0alTJ0ybNg0AoKurCycnJ8yYMYM/bL1z587w9vZGbm4uOnToAAMDg0rFunz5cmRlZWHSpEm/f8ECtGDBAiQlJSEiIoIl3gzzp6rpbvjawoadM5W1bt06AkDHjh2rsOznz5/J2dmZREVFyczMjO7evVsLETLM/y8rK4v69OlDIiIitH37dmGH81fIycmhBg0a0MqVK8nOzo5UVFTo4cOHwg6LYeq8J0+ekIyMDA0fPrzCaVxERPv37ycREREaPXo0FRcX/7JscnIyhYeHC2y6x+nTpwkA7dmzRyD1VZeXlxcBoEOHDgk7FIZhqqgqeShLvpm/yqFDhwgAbdy4scKyp06dIjU1NZKXl6cdO3ZU+MGAYQTt6dOnpK+vTyoqKmydgVq0a9cukpCQIB8fnxpbDZlh/jSvXr0iFRUV6tSpE3E4nF+W5fF4tGLFCgJA//77L3G53FqK8quwsDCSkpIiBweHOrF2w8ePH0lNTY0GDBhQJ+JhGKZq2JxvNueb+QEPDw+MGDECs2fPxp49e345pGvr1q1YunQpxo4di+3bt0NTU7MWI2X+dkSE48ePw9nZGa1atcLly5ehr68v7LD+ClwuF0ZGRujUqROaN2+OpUuXonfv3pCUlERRURHatWuHtWvXQlJSUtihMkyd8eHDB9jY2KBBgwZ49OgRlJWVf1q2rKwMTk5OOHHiBLZt24aFCxfW6hDr9PR0tGvXDlpaWnj48KHQdysgIvTt2xeRkZF4/vx5hauhMwxT97A53wzzP+7fv48xY8Zg5MiR2L179y/f6Hfu3ImlS5di9erVWLduXS1GyTBfPxguXLgQp0+fhpOTE/bs2QMpKSlhh/XX8PX1RUJCAkaPHg0rKysMGDCg3DoP/v7+mDt3rkC3JWKY+uz169fo1asXeDwebty48cvEu7CwEKNHj4afnx/c3d0xfvz4Woz0a6I7efJklJSU4Nq1a0JPvAFg//79uHnzJvz8/FjizTB/g5rthK89bNg58zMREREkLy9P//zzT4VDx/fs2UMAaPny5WzoF1OrioqKaOvWrSQvL0/Kysrk7u4u7JD+SmFhYdSqVSsC8N0xcuRISklJEXaIDFMn8Hg8OnLkCDVo0IBMTU0pMTHxl+WzsrLIxsaGZGVl6caNG7UUZXlHjx4lAHT9+nWhtP+/YmJiSEpKimbPni3sUBiGqQY27JwNO2f+n7dv38LW1ha6urq4d+8e5OXlf1p2//79mD17NhYvXowtW7awlUaZWkFEuHr1KhYvXozExETMmjULa9asgYqKirBD+6tlZGQgLS0NMjIykJaWhpycHJSUlIQdFsMIHREhOjoaS5cuxa1btzB58mTs2rXrl5+9eDwe2rZti9TUVPj6+qJ9+/a1GPFXb968QevWrTFmzBgcPXq01tv/X8XFxWjfvj24XC7CwsIgIyMj7JAYhvlNbNg589cjIvj5+WH27NlQUFCAn5/fLxPvQ4cOYfbs2Zg/fz5LvJlaExERgXnz5iEgIAB9+/aFj48PmjZtKuywGADq6upsCCjD/D88Hg9xcXG4fPkyzp8/j9jYWGhra8PX1xd9+/at8PwPHz4gOjoaV69eFUriXVZWhvHjx6Nhw4aV3s87JycHS5cuxd27d9G8eXNcunRJoGs9LF++HLGxsQgNDWWJN8P8RVjyzfxRviXda9euxdOnT2Fra4szZ8789EN0fn4+VqxYARcXF8yZMwc7duxgiTdT44gIzs7OOHToEJo2bYqbN2+iV69e5cp8+fIFycnJkJSUhKSkJJSUlH55A4lhmL/b8+fPERERgf79+1drL3oOh4PHjx/j2bNniImJQUxMDF6+fImCggI0aNAAQ4YMwa5du9CzZ09ISEhUqs63b98CAExNTX87rurYsmULQkND8ejRIzRo0KDC8pGRkRg+fDiysrJgaWkJLy8vREZGwsrKSiDxBAYGYteuXdi5cydatWolkDoZhqkfWPLN/BGICL6+vli3bh2ePn2Kjh074s6dO+jevftPk2k/Pz84OTkhKysLu3btwr///ssSb6ZWlJaW4uHDhyAitGnTBsDXIUvR0dG4e/cu7ty5g5CQEJSVlfHPERERQfPmzWFjY4OOHTti5MiRNbriNhEhMjISLVu2rPQHbIZhhCM/Px+dOnVCbm4uJCQkMHDgQLi6ulZpGl5sbCxWrlyJGzduoLCwEDIyMmjevDmaN28Oe3t7tGzZEp07d65yLy2Xy8WaNWugp6cHQ0PDql5atcXHx2PdunVYunQpbGxsKiwfEhKCf/75B8bGxrh9+zaCg4Nx584dge56kpCQAAAYNGiQwOpkGKaeqLGZ57WMLbj2d+LxeOTt7U3t2rUjANSxY0e6e/fuLxdLi4uLozFjxhAAsrOzo3fv3tVixAzzVXFxMe3du5f09PTKLeqlpKREQ4YMoQMHDlBAQADdu3ePbt68SSdPnqSpU6eSsbExAaCrV6/WWGwpKSnUv3//Gm+HYRjBSE5OJgC0fv162rNnD4mIiNCJEycqdW5ZWRnt3LmTpKWlydDQkDZt2kTPnz8XyN7bPB6PlixZQiIiIvTgwYNq1/c77O3tSVdXt8K9x4mIwsPDSVFRkWxtbSk/P5+IiHr37k22trYCjamgoICUlZVp6dKlAq2XYRjhqEoeynq+mXrrzp07WLp0KcLDw2FlZYWNGzeiVatWsLCw+K4HOz8/H5cuXYKrqyuePHkCNTU1nDp1CuPHj2e93YxQSEpKYs6cOZg9ezZevnyJ4OBgtGrVCm3atIGYmNgPz5k4cSIOHz4MZ2dndOrUSeAx0f/bX3zhwoX8GNjfR932bXunyMhIvHv3DhkZGVBWVoa6ujqcnZ3RpEkTYYfI1AIdHR1069YN/v7+uHTpEhYtWoSCgoIKz+NyuRg+fDi8vLwwd+5cbNy4EbKysgKJqaysDLNmzcLRo0exc+dOdOnSRSD1VkVERAQuXbqE48ePV7itWEhICPr27QszMzP4+fmhQYMGyMrKgr+/Pw4cOCDQuGRlZWFnZ4cnT54ItF6GYeqBmr8XUDtYz/ffIz4+ngYMGEAAyNTUlKytrUlUVJTfc7ht2zYi+nrH/eHDh+Tg4ECysrIkIiJCdnZ2dOHChUrdAWeYuqhbt270zz//CLzet2/fUvfu3QkAOTo6UmRkJAGgW7duCbwtRjBCQkLI3NycAJCamhq1b9+e+vfvTyYmJgSAzpw5I+wQmVp04cIFAkDDhg0jOTk5+vz5c4XnrF69mkRFRQW+9VZhYSENHjyYxMTEyM3NTaB1V0Xv3r3J1NSUSktLf1nu/PnzJCUlRba2tpSdnc1/PD8/n1RUVMjR0VHgsQ0YMKBGXssZhql9VclDWfLN1BvZ2dk0f/58kpCQIFVVVWrYsCEBIEtLSzI0NCQA1LVrV4qPj6f169fzHzMwMKD169dXuAcpw9R1cXFxJCoqSseOHRNovZcvXyY5OTnS19fnJ9uFhYUkISFB+/fvF2hbTPV9219ZUlKS2rdvT4GBgeWeX7BgAWloaFBJSYmQImSEoaioiNTU1Pjvi5Whp6cn8MTy3bt31LFjR5KRkSEfHx+B1l0Vr169IgB07ty5n5YpLS2lVatWEQAaN24cFRUVfVdm586dJCIiQhkZGQKL7ePHjyQlJUVbtmwRWJ0MwwgPG3bO/HGio6PRv39/ZGZmQl5eHllZWRgwYAAsLCzw+PFjhIWFYdCgQSgsLISpqSlkZGQwYsQInDhxAp06dYKoqKiwL4Fhqm3hwoXQ1dXF2LFjBVIfEWHVqlXYuHEjRo0ahaNHj/JXVJeRkYG5uTmCgoIwa9YsgbTHVF9RURFmzZoFV1dXzJw5E7t37/5u4T0dHR3k5eUhLy+vWqteM/WLlJQU7OzscO7cOeTn54PH41X43te2bVt4eHigdevWmDFjBsTFf/6xMDU1FXfu3MGdO3cQHx+PL1++oGnTprC1tYW2tjaysrIQHx+Pw4cPQ01NDffu3YO1tbWgL7PSLly4AEVFRQwZMuSHz7948QITJ05EREQENm7ciGXLlv1wmo2IiAikpKSgpKQksNj27t0LSUlJTJ8+XWB1MgxTT9T8vYDawXq+/1zXr18nOTm5cgtTDRw4kPT19QkAqaurk5ycHAEgW1tbOn78OOXl5Qk7bIYRqKioKIEPJf7W47Nly5bvFim8d+8eiYqK0qZNmwTWHlM9CQkJ1K5dO5KWlqZTp079tFxaWhopKirS1KlTazE6Rtjev39PcnJyZGlpSQBoxYoVFZ6TkZFBU6dOJRERETIyMqIVK1aQm5sbnTlzhrZu3Upz5syh4cOHU9OmTQkAiYiIUNu2bcnR0ZFmzZpFHTt2JCkpKQJAYmJi1LBhQ1q2bBl9+fKlFq7416ZMmULNmzf/7rWttLSUNm3aRJKSktS0aVMKCQn5ZT3jxo0jKysrgcWVk5NDCgoKtGjRIoHVyTCMcLFh58wfgcfj0a5du0hERIQGDx5MPXv25CffUlJSpKioSABIU1OTli5dSrGxscIOmWFqzMePH0lUVJSOHDkikPpcXV0JAG3duvW751JSUkhDQ4N69uxJZWVlAmmP+X0lJSW0a9cukpeXp8aNG1NERESF5+zfv59EREQoODi4FiJk6oI+ffqQnp4e5eXl0YgRIwgAvXz5slLnhoeH07hx40hDQ4P/PquoqEhNmzalnj170vTp0+nixYs/HHpdVFRE2dnZv9xlRBguX75MAGjWrFn05csX4nA4dOPGDbK0tCRRUVFasmRJpdZ/6dy5M40dO1ZgcW3ZsoUkJSUpNTVVYHUyDCNcVclDRYiIarWrvYbk5eVBUVERubm5VdrXkqmbSktLMWfOHBw+fBjdu3eHqKgoAgMDUVRUBDExMbRo0QKWlpYYMmQI7OzsfjlUjmH+FL169UJeXh6ePHlSrVXIDx06BGdnZ0ydOhWHDh0qV1dJSQm6du2K5ORkREREQF1dXRChM78pICAAs2bNwsuXLzFjxgxs2LChUsNfuVwuLC0tISoqipCQkJ+uoM/8GT5//gxVVVVMmzYNubm5uHTpElq3bo07d+7Ay8sLb968weDBg2FpaVlhXRwOB1wuFw0aNKiFyGvWt90hpKSkAACFhYVo3rw5Tpw4ASsrq0rVYWxsjCFDhmDbtm3VjicnJwdmZmYYOHAgjh49Wu36GIapG6qUh9b4rYBawnq+67/ExEQ6duwYjRgxgpSVlfl330VERKhFixa0a9cuCgwMpIKCAmGHyjBC4evrSwDIz8/vt87n8Xi0fPlyAkD//vvvD/fxnTdvHklISFBQUFB1w2Wq6cSJEwSArK2tKTw8vMrnBwUFEQA6dOhQDUTH1CXFxcXUu3dvAkB6enp0+PBhiomJIRkZGf5uIE2bNhV2mELx/v172rFjB23bto2eP39epR56Ho9HsrKytHv37mrHwePxaOTIkaSgoEAJCQnVro9hmLqD9Xyznu964cuXL3jw4AH8/f1x69YtxMfHf1dGVVUV69evx9SpU1nvNvPXIyJ06dIFeXl5iIiIqPJCgq6urpg8eTJ27NiBBQsW/LCMtbU1YmJicPLkSYwYMUIQYTO/wdPTE8OGDcPUqVNx8ODB3140csyYMYiIiEBsbKyAI2TqGi6Xi8ePH8Pa2hqSkpIICgqCjY0NAEBaWhr//PMPrl+/LuQo65fc3FwoKSnh4sWLsLe3r1Zd315/L1y4gJEjRwooQoZh6oKq5KEsm2FqDZfLRWRkJPz9/eHv74+HDx/+sJyamhratWsHGxsbODs7Q0VFpZYjZZi6SUREBJs3b0bHjh1x8eJFjB49utLnpqenY+HChZgwYcJPE28AuHXrFqZPnw57e3tMnz4du3fvhoyMjCDCZyrpxYsXGDVqFIYOHYoDBw5Ue7cG9vP7O4iJiaFz5878/7dq1QoeHh4IDw+HqKgo/v33X+EFV099+PABAKClpfXdc3l5eXB3d0dsbCw+ffoEZWVlODs7o1WrVt+VjY6OxuzZszF58mSWeDPMX471fDMCUVxcjKNHj+Lhw4fQ1NSEjo4OtLW18eXLF0RFRSEqKgoREREoLS397tyOHTuiS5cusLCwQLt27aCjo1Ot+awM86cbMGAAbt++jR49emDAgAEYPXo0FBUVf3nO3Llzcfr0acTFxVU4j5uIcPz4ccyZMwdGRka4ePEimjVrJshLYH6Cy+WiY8eOyMnJQWRkJKSlpX+7rszMTGhra2PDhg1YtGiRAKNkmL/D3bt30bNnT7x9+xYGBgYAvo7a279/P7Zv344vX77A1NQUmpqaiI+PR2JiIrZv317uBmd4eDj69+8PLS0tBAQEQE5OTliXwzBMDWE930yt4XK5OHfuHFavXo2EhAQYGhrCw8Pjl+coKytj4MCB6Nu3L/755x8oKyvXUrQM82dwd3eHq6srfHx84OzsjPv37+PixYu/PEdMTAwyMjJ48+YNMjMzoaSkBCUlJUhLS393s0tERARTp05Fhw4dMHLkSFhYWGD//v1wdHSsyctiABw8eBDBwcF49OhRtRJvADh58iREREQwadIkAUXHMH+Xb4sb3r17F5qamjh06BC2bNmCnJwcTJkyBcuXL4eOjg6ArwvFrl69GgsXLoSvry8sLS0RExODGzduoGXLlrhx4wZLvBmGYT3fzO8pKSnBjRs3sGLFCrx48eKn5RQUFNC4cWM0atQItra26Nu3L9q2bVvtYZQMw3y1evVqHDp0COnp6b8cMfL48WN07Njxu8c1NDQwcOBADB06FN27d+evCvxNYWEh5syZgxMnTuDYsWOYMmWKwK+B+erZs2ewsrLC5MmTsX///mrVxePxYGRkhI4dO8Ld3V1AETLM38fR0RFubm4QFxcHj8fDpEmTsHLlSujr6/+w/IULF3Dy5Em8fPkSTZs2xdChQzFlyhS2bg3D/MGqkoey5JupFCJCfHw8f3E0X1/fcs83bdoUDg4OaNGiBRo1aoRGjRpBQ0MDEhISQoqYYf4ON2/eRJ8+fRAXFwcTE5Nflk1LS0NGRgZycnKQm5uLnJwcREdH4+rVq3j79i309PQQEhKCRo0afXfu1KlTcf78eaSmplY4xJ2pui9fvsDS0hKSkpIIDg6u9jxtDw8PDB8+HMHBwZXeUolhmO+VlpbiwoULyMvLQ+/evWFoaCjskBiGqWNY8s2Sb4G6dOkSFi9ejMTExO+emzx5MiZPngxra2s2T5thhCA3NxfKyso4ceLEbw8vJiJERUWhX79+aNeuHby9vb8rExcXBzMzM3h4eGDo0KH8xx8/fowlS5ZAQ0MDxsbG/MPCwoINsayCiRMn4sqVK3j69CnMzMyqVRePx0ObNm2goaGB27dvCyhChmEYhmF+hM35ZgQmLi4OEydOxP+9R2NlZYVp06bB3t4eDRo0EGJ0DMMoKiqiRYsWePLkyW8n3yIiImjTpg1Gjx793agW4Gtyvn79egBftyL7Jjc3F2PGjEGDBg0gIyODixcvIikpCUQEFRUVzJ49G+PGjUNycjKkpKTQrFkz/hxK5v/n7u6OU6dO4dSpU9VOvAHA29sbz549Q0BAgACiYxiGYRhGUFjPN/NTaWlp6NevH8LDwwF8/dC9fv169OjRg/VyM0wdMmfOHFy8eBFv376t1g0xZ2dnBAYGIioqqtzjsbGxaNq0KQDA1tYWLi4uaNWqFRwdHXH16lU8e/YMjRs3BgAUFRUhPj6eP0ecw+Hw61FUVMTatWsxa9asGpuSQkTgcDjIyspCVlYWcnJywOFwvjuKiorA4XCgrKwMExMT2NjYQF5evkZi+pXY2FhYWFhg+PDhcHNzq3Z9RAQLCwsoKCjg/v371Q+QYRiGYZhfYsPOWfJdLe/evcOOHTvg6uqK4uJiAECfPn3g4+PDFkpjmDooMTERpqamWLJkCdatW/fb9UyZMgUxMTEIDg7+7rmQkBA8efIEbm5uSElJgb6+PiIjI+Hq6vrTHveMjAwEBwfD2NgYJSUlOHToEI4cOQJTU1OMGTMGnTt3Rvv27Ss9v5nD4SAtLQ3p6elIS0tDfHw8goODkZGRgc+fP/MT7m+vWz8jJSUFGRkZyMjIIC0tDTweD6KioujatSt2794Nc3PzSsVTXRwOB9bW1igpKUFYWJhARhL5+vqif//+uHfvHrp16yaAKBmGYRiG+RWWfLPk+7dERUVh69atuHDhQrnHdXV1ERkZCVVVVSFFxjBMRZYuXYp9+/bh3bt3P1wwrTLGjRuHoKAg7Nu3D9bW1lBVVQWPx0NpaSn/KCkpwbJly5CSkoIVK1agS5cuVWojKioK69atw/3795GbmwtJSUm0b98e7du3h6ioaLle6cLCQmRkZCAtLQ1paWnIz88vV5eUlBQ6dOgALS0tqKioQFVVFaqqqvyvVVRUoKysDFlZWX6yLSUlVe4m4rfFJB8+fIj169cjJSUF7969Q5MmTX7re1gVM2bMgJubG0JCQgSS8BMRrK2tISkpiYCAADZCiWEYhmFqAZvzzVQaEeHBgwdYs2YNHj16VO65rl27ol+/fhg9ejRLvBmmjhs/fjy2bt2KuLi4306+u3Xrhjt37qB///4Avu4NzuVyy5URFRVFw4YNoampiYSEhCon361bt8a1a9fA5XIRExODgIAABAQEwMvLC2JiYpCWluYnyjIyMjAwMECHDh3QsGFDNGzYEBoaGvyv1dXVq719j4iICKSkpBAbG4uUlBQAQHJyco0n35cuXcLhw4dx5MgRgfW0R0REIDQ0FH5+fizxZhiGYZg6iPV8/6V4PB48PT3h5OSEjIwM/uPdu3fHlClT0KdPH7YwEsPUIwEBAejSpQsiIyPRunXr366HiJCQkICgoCB+z7SEhAQkJCQgLi6OnJwcTJs2DQDQrl07PH36VEBXUPsiIyOxZcsWXLlyBYqKipgxYwZmzZoFLS2tGm337du3aNu2Lfr06YPz588LLFHes2cPli1bxv+5MQzDMAxT81jPN/NTxcXFOHPmDLZv3464uDj+40uWLMG0adNgYGAgxOgYhvldSkpKkJKSQrdu3TB37lzMnTsXysrKVa5HREQETZo0+WXP77FjxxAWFobly5dXJ+RfSk9PBwBoaGgIvO5Xr15h9erVuHLlCgwNDeHi4gIHB4da2RqtpKQEo0aNgpqaGo4ePSrQHurHjx/z9wr/v77dY2e94QzDMAwjXGz1rL9Afn4+wsPDsX37dhgYGGDKlCn8xHvixInIy8vDli1bWOLNMPWYubk53r59CwcHB2zduhUdO3assbbu3r2L1q1bY86cOUhISBBYvYWFhVi9ejXU1dXRsGHDcvuJC8K7d+/g4OCAFi1aICwsDK6uroiNjcXMmTNrJfHm8XhwdnZGdHQ0Ll68KPBRWsHBwbCysuL//9WrV1ixYgVMTEzQqFEj+Pn5CbQ9hmEYhmGqhiXffxgul4tbt25h1qxZ6NGjB7S1taGgoAALCwusWLGC/2FPQUEB58+fx8mTJ4WyvQ7DMIKnra2NPXv24OzZs3j58iV/DrOgycvLw9fXFxISEjA3N8fhw4fB4/F+u76SkhI8ePAA7dq1w/r165GZmQkAWLBgQbVj5XA4uHjxIvr16wcTExP4+/tj3759iIuLw6RJk6o9Z7yyuFwupkyZguPHj+Pw4cOwsLAQeBtaWlqIi4tDUlISxo0bh+bNm+PQoUPo0qULLCws0K9fP7i6ugq8XYZhGIZhKofN+f5DvHnzBm5ubjh16hRSUlJgZGSEVq1awczMDKampjAyMsLVq1exe/duWFlZ4ezZs/x9eRmG+bN8/PgRWlpaMDIygqamJhQVFaGkpMT/9/9+raioiDZt2kBdXb3K7eTk5GDx4sU4duwYOnXqhDlz5sDOzq5Sr8ElJSU4c+YMjh49isjISJSUlKBNmzaIjIzEuHHjsH//figqKv7O5YPH4yEwMBDu7u64fPky8vLy0KFDB0yYMAETJkyArKzsb9X7uz5+/AhHR0fcvn0bbm5uGDdunMDbeP78OSwtLaGkpITCwkI0aNAAa9aswaRJkyApKQkej4f+/fsjLy8PgYGBAm+fYRiGYf5WbKuxvyz5XrduHdauXQtFRUWMHj0akyZNgqWlJX9+37dekMePH2PVqlVYuXJlrfX2MAwjHIcPH8bLly+Rk5OD3Nxc5OTklPs6Ly+PPxdYQkICw4cPx8CBA6Gvrw89PT1oamqW25LrV+7fv4958+YhOjoa4uLiaNOmDVq3bo02bdqgTZs2MDc3L5fw3r9/HxMnTkRSUhIkJSVha2uLdevWoUOHDmjWrBl4PB5WrlyJvLw8dO/eHS1atKj0dfv5+WHWrFlISEhA48aNMWHCBIwbNw7GxsZV+wYKyOXLl+Hk5AQJCQmcOnUKvXr1Engbqamp6NixI38KwIQJE7Bv377vbl7s27cPixYtgpOTEzIzM5GZmYmcnByoqalBV1cXenp60NPTQ9euXaGjoyPwOBmGYRjmT8SS778o+c7MzISenh4mT56Mbdu2QUZGBsDXD2Pe3t64fv067t27Bw0NDZw9exadOnUScsQMw9QFPB4P+fn5yMrKgqenJ44cOYL4+Hj+8xISEtDR0eEnZO3bt4eDg8Mvp6kkJCTgxo0bCAkJQWRkJF6+fImysjKIiorC1NSUn4y/e/cOhw4dKnfutWvXMHjwYLx//x6DBg3C8+fPISYmBkVFRYSFhVVqTYqPHz+iadOmsLCwwJo1a2Bra1vpGwiCRkRwcnLC0aNHMXz4cBw6dAhqamoCbYPH48HLywsLFizA+/fvAQDnz5/HqFGjflj+zZs36NWrF2RkZKCmpgY1NTUoKioiMzMTSUlJSE5ORlZWFgDA2toaw4cPx4wZM2p9pADDMAzD1Ccs+f6Lku9169Zh69atSExMxIcPH3D9+nVcv34dT58+hZiYGDp37oyBAwdi4sSJbOswhmF+KTc3F0lJSeWOxMREJCYmIjQ0FIqKivDy8oKtrW2l6isqKsKLFy8QGRnJP6Kjo1FYWMgv06ZNG8yZMwcTJkzgJ8o8Hg8cDgclJSWwtLSEjIwMgoKC0KBBg1+2d/v2bdjZ2eHZs2do2bLl738jBODs2bMYN24cjhw5gqlTpwp0pfHS0lKcO3cOW7duxatXr/iP/yrxrqzs7Gz4+vrCw8MDfn5+GDVqFE6dOlXdkBmGYRjmj8WS778g+Q4NDcXevXtx7tw5AICenh6SkpKgoKCAvn37YuDAgejdu/dvbTXEMAzzv968eYOuXbsiOzsbERERMDU1/a16uFwu3rx5Ay6XC21t7Z/O6+ZyufD29sbatWsRHR3N7xn/lU+fPkFTUxMeHh4CXym9KtLT09GsWTP8888/OH/+vEDqJCIkJyfDy8sLO3bsQFJSEgYMGAArKyusXLkSLi4ucHZ2Fkhb3zg4OCAsLAwvX74UaL0MwzAM8ydh+3z/we7fv4+VK1fiyZMn/MckJSUxcOBADBo0CJ07d/5uj1eGYZjfFRkZCVdXV5w9exbZ2dmwsbGp1jBkMTGxChP3c+fOYezYsfz/W1tbo2/fvhXW3ahRI0hLSyM5Ofm34xOEOXPmAPg6x7o6iouL4efnh/Pnz+PRo0f49OkTxMTEMGrUKCxZsgQtW7ZEaGgoVq5cCUtLS0GEzhcfH4+LFy9i/vz5Aq2XYRiGYf5mLPmuR0JDQ9G7d2+UlJQAAERFRTF79mysXLlS4HMJGYb5e2VlZeHs2bM4efIkoqKi0KhRI0ybNg2TJk367R7vqrhw4QL/a2lpaQQHB+P69esYPnz4L88rLS1FUVER8vLyUFZWJpSFJRMSEnDx4kWcOHHit1aQB75+/7dv346jR48iOzsbbdu2haOjI6ysrGBtbQ0NDQ0AX3v6t27dCuBrb7ugPHnyBMOHD4euri5WrlwpsHoZhmEY5m/Hhp3XE6mpqWjSpAlKS0sBAMOHD8fmzZthZGQk5MgYhvnT6OjoIDU1FQCwefNmLFy4sNYT2ZSUFMjKykJZWRk2NjaQlpbGtWvXfrl2RXFxMdTV1ZGfnw8DAwNER0dXOE9c0O7evYuePXvizZs3MDQ0rNK5eXl52L17N3bt2gUul4sZM2Zg0qRJaNasWblyRIQzZ85g7ty5EBcXx4EDBzB8+HCBzCvPyclBw4YNAQCPHj1C+/btq10nwzAMw/zJ2JzvPyz5LiwsROfOnREeHg4AePz4MWxsbIQcFcMwf6rHjx9jy5YtCAwMRE5ODpo1a4ZRo0Zh9OjRQrnh5+HhgZEjR0JCQgIDBw5E8+bNoa6uDg6Hg/z8fHz58gX5+fnIz89HSkoKAgICAABRUVFo1apVrcZ64sQJTJ06FUVFRZWeAlRYWIj9+/fjv//+Q0FBAf9xBQUFyMvLw9vbG23atAEAJCcnw8nJCX5+fhg7diz27NkDNTU15OTk4PXr14iLi4O+vv5v72xBRHBxccH69euRl5eHqVOnYsOGDWzBToZhGIb5CZZ8/0HJNxFh1KhRuHTpEgDAzc0NDg4OQo6KYZi6aMOGDVBVVYWTk5NAekFLSkrg7++PCxcuwMvLCwUFBZg2bRo2btwIVVVVAURceZ8+fYK7uzuuXLmCpKQkZGZmQlZWFvLy8mjQoEG5f83NzTF9+nTo6enVaowAsGrVKpw8eRIpKSmVKh8SEgJra+tyj7Vt2xZNmjTBjRs3UFhYiBs3bqB3794AAHNzczx//hzW1tYwMzNDfHw8Xr9+jYyMDP755ubmiI6OrtZ1pKenw9LSEklJSThy5AimTZtWrfoYhmEY5k/Fku8/KPn+77//sGbNGgBAz5494e/vL9AtaxiG+TMUFBTwh1hPmTIFBw4cEOjiixwOB0ePHsWaNWsgKiqKDRs2YNq0aUKZV12XrV69GkeOHMGnT58q9VodHh4OCwsLSEpKYvr06cjMzERISAjevXsHZWVlDB06FLt37+bvr+7h4YFRo0ZBXl4eJiYmMDExgbGxMb58+YJt27ZBTEwM9+/f/62e79LSUty+fRvnzp2Dp6cnCgoK4OTkhB07dkBOTq7K9TEMwzDM34Al339A8s3lcrFs2TJs374dACAjI4OYmBgYGBgIOTKGYeqikpIS6OrqIicnB8DXFcI9PDwEvhhjeno6li9fDldXV5ibm2PPnj3Q1tZGfHw8vxe2rKwMSkpK6NOnD7p27Vovbxg+e/YMqqqq0NbWrtJ5Xl5eGDx4MJKTk6Gjo1Pp854/f44ePXogIyMD48aNw/jx49GtWzdISEh8V5bL5UJMTAzA19FRx44dw5w5c9CiRQtcvnwZTZo0qVLMBQUF2LhxI44ePYqsrCw0bdoUY8eOxejRo9l7DsMwDMNUoCp5qGgtxcRUQX5+PoYOHYqdO3fCxMQEIiIiWLduHfsQxDDMT0lKSmLBggUgIri7u+PVq1ews7NDXl4eAKCsrAzJyckIDQ3lD2f+HRoaGjh+/DhCQkIgJSWFbt26wcTEBP3798eKFSsQGBiIqKgonD9/Ht27d0eLFi1w8OBB5OfnC/Jya0xERAT69OmDVq1awdDQEJ6enlU638LCAgDw9OnTKp2noKDAX1jt7t276NKlyw8TbwD8xLugoAATJkzA9OnT4ejoiMDAwCon3n5+fmjevDl2794NBwcHREVF4cWLF1ixYgV7z2EYhmEYAWPJdx2TmJiIjh074t69e7CwsEB8fDzmzJmDBQsWCDs0hmHquBkzZkBOTg5BQUG4e/cu3r17h169emHAgAFQUVGBnp4erKys0LdvX/j7+1erLUtLSwQFBeHGjRu4e/cukpKS8OXLFzx79gyhoaFISkrCvXv3YGZmhjlz5kBbWxv//vtvnU3CX716hREjRqBdu3ZISEjA+fPnYW5ujpMnT1apHi0tLTRs2JC/QGZlqaiooGvXrvz/VzRaIDY2FlZWVrh27RrOnj2LgwcPQlpautLtcTgcjB8/Hv369YOJiQmeP3+OnTt3olWrVvVypALDMAzD1Adssl4dEhQUhMGDB0NaWhrt2rXDw4cPsWnTJixdupR9GGIYpkKnTp1CTk4O5OTk0LJlS3h5eWHixImQkZHB0qVLISoqio0bN8Lc3BwDBgyodnuioqL8hcD+l4iICLp164Zu3bohJSUFR44cwZ49e+Dn5wdfX18YGxsDAD58+IAnT54gKCgIycnJkJaWLndISUlBWVkZzZo1Q4sWLaCtrS3Q18PExESsXbsW7u7u0NHRwcmTJzFu3DiIi4sjLi4Oe/bsAYfDgYyMTKXqExERgYWFRZV6vjMyMmBjY4Pk5GQsXLgQS5cu/el8fSLC2bNn4eTkBD09PYSFhaFp06aVbgv4+j0fPHgwYmJicOrUKYwfP569xzAMwzBMLWBzvoUsLy8PSUlJePToEebNmwdLS0uoqanh+vXrOHLkCKZMmSLsEBmGqQc2b96M5cuXY8GCBdi+fXu5ZIrL5WLdunXYuHEjTE1N4evrW+XhyYLw+vVr9O/fH7m5udDQ0EB6ejrS0tIAAPr6+jAyMkJJSQmKiopQVFSE4uJiFBUVITMzkz9MXlFREc2bN0eLFi3QsmVLTJgw4bde8z99+oRNmzbh8OHDUFFRwcqVKzF16lRISUmVi9fExATu7u4YP358peteu3YtDhw4gPT09AqTWh6Ph06dOuHNmzcIDAyEgYEBHj16BG9vb+Tl5UFKSgoGBgZo2LAhYmNjcfv2bYSEhGDs2LE4fPhwlfcxf/jwIcaOHQsAuH79Otq2bVul8xmGYRiGKY8tuFYHk++0tDScPHkSCQkJSEpKQnJyMpKSkvjzMQFgwoQJsLKywqxZs+Dq6opJkyYJMWKGYeoDIsLy5cuxZcsWrF27FqtXry6X8OXk5GDs2LG4efMm1q1bh8WLFwt0FfSqevfuHTZv3gxpaWmoqamhZcuW6NChAzQ1NX96Do/HQ2JiImJiYvDixQvExMQgIiICr169wunTpzFu3LhKtZ2RkQEvLy94eHjg7t27kJOTw+LFizFnzpyfrubds2dP5OTk4N69e5V+b/Hx8cGAAQPw+vXrCvdFLysrg7GxMdTU1GBra4tLly7h48eP0NHRgZaWFgoLC/H27VtwOBw0bNgQFhYWmDNnDuzs7CoVC/D1d+T+/ftYv349Hjx4ACsrK1y9ehVaWlqVroNhGIZhmB+rUh5Kf4jc3FwCQLm5ucIOpRwej0dnzpwhZWVlkpaWJi0tLdLV1aVGjRqRgoICGRkZEQACQDt27CApKSmaNm2asMNmGKYe4HK55OzsTABo586d3z0fExNDRkZGpKysTLdu3RJChDXn2LFjBIBSUlJ+WS41NZVcXFyoa9euJCoqSqKiotS1a1dycXGhz58/V9jO3bt3SUZGhho2bEgnT54kLpf7w3IFBQV08+ZNmj9/Pjk6OpK6ujpZWlrSly9fKmwjKCiIJCQkqFGjRjR37lwKCgoiHo/Hf57H41FOTk6F9fwvHo9HN27cIBsbGwJAbdq0oatXr/70GhiGYRiGqbqq5KGs57sG5efnY/z48fDy8oKEhARKS0uhpKQE4GtvlJSUFCQkJNC+fXsEBgZCU1MTqqqqePz4cZUWzmEY5u9SXFyMDx8+4L///sOpU6dw+PBhTJs2rVwZDw8PODg4wMDAANeuXYOhoaGQohW82NhY/jzngQMHIisrC1lZWRAVFcWIESPg4OCAxo0bY/fu3Vi0aBFERUXRo0cPDBs2DIMGDYKGhkaV2ktOTsbixYtx4cIFtGzZEm3btoWuri50dHSQm5sLf39/BAYGori4GNra2igtLUV+fj64XC7atm0LT09PNGzY8JdtpKenQ1VVlb+S+e/Kzs5GcHAwgoKC4Ofnh/DwcFhZWWHVqlXo27cvm9vNMAzDMALGhp3XgeQ7IyMDffr0wbNnz1BaWopBgwbhwIEDCAoKwtixY0FEaNSoEfz8/DBkyBAQEbKyshARESGUuZgMw9RdPB4PV69exa5du/D69WtkZmYC+LrllLu7O8aMGcMvm5WVhTVr1uDAgQOwt7eHq6vrT4dU11c3b95Enz590Lx5c+jr60NNTQ2qqqrIzs7GlStX8OXLF+jr6yMxMRHz58/HypUroaysXO12Hz16hIMHDyIxMRHJycn48OEDpKWl0bVrV/zzzz+ws7ND06ZNkZeXh1WrVsHFxQWysrJQVVWFt7c3WrVqVWEb6enpePPmDQoKClBQUAAulwszMzOYmppCXLz8Gqk8Hg8vX75EUFAQ/4iNjQUA/jD2WbNmoWfPnizpZhiGYZgawpJvISffCQkJ6N69OxITE8Hj8TB9+nR07doV586dg4+PD0RERNC+fXt4eXnhxYsX6N69OyQkJLBhwwYsXrxYqLEzDFN3EBG8vb2xevVqREdHo2fPnujatSu0tbWhra0NU1NT6OnpAfjaG75//35s2LABPB4P//33H+bMmfPXJV0FBQXw8PDAgwcPMHbsWPTo0aPG2iorKwMR/XQ/bhcXF8yZMwdqamooKCjAhg0b0L17d6SlpeHx48coLi7mj4CKjY1FQEAAUlJSfliXlJQUmjdvDhUVFQBff97R0dHIy8uDqKgozM3N0aFDB/5haGj41/3sGYZhGEYYWPItpOQ7Ozsbx44dw86dO5Gfnw8Oh1PueUlJSZSUlGD06NFwdXWFtLQ0xowZg/v37yMtLQ2fPn2q8nBIhmH+PKWlpfD398e6desQFhaGLl26YP369ejUqdN3ZYkIHh4eWLJkCRITEzFt2jSsXbuWvZbUEceOHcP06dNhZWWFkJAQfHvLVVdXh6KiIoqLi5GdnY3CwkLweLxy5/bp0wenTp3Cy5cvER0djWfPnuHLly8Avm7z1qJFC3To0AGWlpZVXvWcYRiGYRjBqEoeyvb5roCtrS2ePHmCLl26oGnTpvwep2+HlpYWsrKysHfvXpw8eRKlpaUYN24c+vTpA1dXV0RERCAtLQ0iIiJo27YtFixYgGHDhkFERAQlJSXw9PSEqakptLW12YdlhvmLFRYW4tatW7h27Rp8fHyQnZ2NDh064M6dO+jevfsPezFDQ0Mxf/58PH78GH379oW3tzeaNWsmhOiZn5k6dSpSUlKwYcMGXLhwAU2aNIGCggJMTEz4P1MHBwe4u7tj5cqVGD9+PA4fPozdu3ejV69eUFdXR5cuXdClSxchXwnDMAzDMNXFer4r4OTkhCNHjlRYTl1dHTNnzsSUKVNw/PhxbNiwAZqamujduzd69eqFHj16fDfnMCgoCDY2NlBWVsb06dOxefNmgcXNMEzdl52dDW9vb1y7dg23bt0Ch8NB8+bNMXjwYAwZMgRt27b9YdKdlJSEZcuW4dy5c2jZsiV27tyJf/75RwhXwFQGl8tFz549ERcXh6ioqO9utH769AlTpkyBr68vOnXqhLdv36J9+/a4evUqGzrOMAzDMHUcG3Yu4GHnqampWL9+Pfz8/JCcnAwAkJeXh4GBAQwMDNCxY0fMmDEDcXFx6N27N9LS0tCoUSMMGDAAr169QmJiInR0dGBkZAQjIyNoaGggJCQEN2/eBBEhLS0Nd+/eRffu3QUaN8MwdVNmZia2bduG/fv3g8PhwNraGkOGDMGQIUNgbGz8y3PfvHkDa2triIuLY8OGDZg0aVK1V8hmat7Hjx9hamqKf//9F//99993zxMRDh06hLlz56JZs2a4cOECf0V3hmEYhmHqLpZ81+Cc78TERAQEBODRo0cICAhAXFwcAKBFixaIiYnhlxMTE4OZmRlatmyJJk2aIDU1Fa9fv+avVKyrqwtNTU28evUKhoaGCA4OhpSUVI3FzTBM1RUXF+Phw4fo2rUrJCUlq11fTk4Odu3ahd27dwMA5s2bBycnJ2hpaVXq/OzsbFhbW4OIEBQUBFVV1e/K5OXlobi4GCoqKlVKyokIX758QUZGBjQ0NNgcYgFJS0vDtWvX4OHhgbt378LFxQWzZs36aXkejwdRUdFajJBhGIZhmOpgyXctLriWnp6Ohw8fYvTo0Vi+fDksLCygr68PMzMzSElJITExEVu3bsXTp0/x+vVr5OTk8M/V1NREt27dcOjQIaGv0M4wTHlPnjyBra0tACAwMJD/9e/48uULXFxcsH37dhQVFcHZ2RmLFy+GmppapesoLS1F7969ERUVheDg4HI95CUlJTh79iwuXbqEO3fuoKysDCIiIlBWVoaqqirU1NTKHcDX166MjAykp6fzj6KiIgCAiIgIDA0NYW5ujlatWsHc3BxGtgGddwAAbN5JREFURkbgcDjIy8tDXl4e8vPz+V9/O4qLi6GlpQU9PT3o6+tDT08Penp6kJWV/e3vXX2Vnp6OLVu24ODBgygrK0O3bt0wdOhQTJky5aerozMMwzAMU/+w5FsIiWznzp2hrKwMLy8vAF/32t20aRP2798PZWVl9OvXD8bGxjAyMoKxsTEMDQ1ZzxLD1EHPnz/H3r17ceLECQCAqakpXr169VtzbzkcDg4fPozNmzcjJycH06dPx/Lly6GpqVnluo4ePYrp06fD3t4e48ePR4cOHaCqqorc3FwMGTIEDx48QKdOnTBixAjo6uoiMzPzh0dGRgaICA0bNoSGhsZ3h6qqKlJTU/mra0dHRyMrK+uHMUlJSUFBQYF/SEhI4MOHD/jw4UO5lbvV1NSgr6+PqVOnYvr06VW+9vrm9u3bGDJkCAoKCiAqKop+/fqhcePGUFBQgLy8/HeHkZER9PX1hR02wzAMwzC/ga12XgsSEhJw7tw5PHv2DDExMYiLi4OamhoKCwuxb98+bNmyBVwuFytXrsS8efNYos0w9cC3BRa/JdoqKioICAioUuJNRAgNDcWlS5dw7tw5ZGRkYNKkSVi5cmW1Eqxhw4YhLCwMp06dwqVLlyAqKoqOHTsiKysLKSkpePDgATp37vzdecXFxWjSpAk+fvyIXbt2oVOnTmjdujXExSv38k9E+PjxI96/f48GDRqUSyA5HA4KCwvB4XDKHXl5eXjz5g3i4uLg5eWFjx8/ori4uNJt1neampoYMWIEf4RARkYG3r17h/z8fP5RVlbGLy8qKorJkyfDxcWFTT9iGIZhmD/Y3/FJSIDCwsKwY8cOXLlyBXJycmjTpg06d+6MadOm4f379zAxMUF6ejqcnJywcuVKtn0Yw9QjKioqAAAtLS2kpqZi5MiRUFRUrPA8IkJ4eDguXbqES5cuITExEQ0bNsTw4cMxd+7cChdRqwxVVVUcO3YMhw8fRkpKCvz9/eHt7Q0Oh4PAwEC0aNHih+dJSkrybx7Mnz8fAGBjY4PHjx9Xql0RERFoaWlBS0sLnz9/xr179+Dv7w9/f38kJiZWeL66ujo2btyImTNnQklJqXIXW8+1aNECJ0+e/OnzRITi4mJ+Iu7j44PFixcjNTUV169fZwvoMQzDMMwfig07rwQejwdfX1/s2LEDAQEBMDQ0xPz58+Hg4ABZWVlcv34dy5Ytw6tXrzBq1Chs2LABhoaGAo2BYZiaR0SYPn06jh07hiZNmiA5ORmGhoY4ePDgd7sREBGioqL4Cfe7d++grq6OYcOGwd7eHp07d64TSVRycjIWLVoELy8v3Lt3DwsXLoS0tDTu3r1b4bmlpaUIDg7mJ9tPnz4Fj8eDmZkZ/vnnH3To0AEKCgqQkZH56SErK8sWEPsf+fn5ePHiBbKysviL+fXs2ROhoaFITk6u0loADMMwDMMIFxt2LiAcDgenT5/Grl27EBcXBxsbG1y9ehXt27fH48ePsWTJEty7dw+vXr1Cjx49cPr0abRr107YYTMM85uKiorQqVMnnDhxAs2a/X/t3Xd4FFX//vE7vZIEEkgg0nvvvXcRAUURpYooCMKjgPiAIChSrSgiSBGkSBEeld4JvTdBkN5LICGFEFJ3fn/wZX8gLSGZbMr7dV17uTtzZvYzYRy4c2bOKaUlS5aod+/eaty4sTp06KBXXnlFhmHowIEDWrhwoU6ePClfX1+1bdtWU6ZMUf369W1+a/W9W9DvvU6fPi1HR0fNnTtXzs7O2r59u6ZMmfLY7U+fPq2VK1dqzZo12rhxo6KiouTr66smTZqoZ8+eatq0qfLmzZuGR5Q5bN++XePHj9fu3bsfuGPAw8NDOXPm1KVLl/Tnn38SvAEAyMRSred77NixGjx4sPXzo3b76aef6rPPPnvqvk6ePKkiRYok6/tTs+c7Pj5ekyZN0siRIxUSEqL69eurRIkSio+P1+bNm3Xy5ElJUtGiRVWvXj21b99eTZo0eaYBmQDYlmEY2rJli2bOnKnFixcrMjJSrVq10i+//KLs2bPLMAzNmjVLAwcO1I0bNyRJ2bNn18svv6z27durYcOGNh29+lFhW5LKli2rBg0aqEGDBqpXr578/Pw0fvx4DR48WFFRUQ/1ylssFo0aNUrDhw+Xo6Oj6tSpo6ZNm6pZs2aqWLEivdfPwGKxaNOmTRo5cqQ2bNigUqVKqWXLlipbtqzKlCkjd3d3LVq0SMePH9eAAQNUvnx5W5cMAACSKc17vo8fP56kUH2Pk5OT9dnKRxZlw56jNWvW6IMPPtA///yjF198Uc7Ozlq1apWCgoJUtmxZNWvWTCNHjlTdunWfacRiAE+XkJCgTz75RC+88ILq1Kmj69ev6/z584qLi1O5cuVS7dGSQ4cOaeDAgVq7dq0KFy6sfv36qWPHjg88o21nZ6euXbvqjTfeUFRUlOzs7JQtWzabXqciIyM1f/58TZs2TXv27JF09znjFi1aWMN2zpw5H9ouODhYvr6+DwXvsLAwde7cWStWrNCwYcM0cOBAeXh4pMmxZBaXL1/WxYsXFRUVpWPHjmnjxo3atGmTbt68qQoVKmjx4sV66aWXHvolxpAhQ2xUMQAASHNGCiUmJhq1a9c2JBk1a9Y0JBmP2+3w4cMNSUb9+vVT+rUPiYiIMCQZERERz7T9yZMnjdatWxuSjGLFihmNGjUy3N3dDU9PT2Pw4MHGtWvXUrliAI+zf/9+67XEzc3N+v7e66OPPnrqPuLj442goCCjf//+RvXq1Y3vv//eSExMNAzDMP7++2+jW7duhp2dnVGsWDHj999/NywWi9mHlSIWi8XYvn270a1bN8Pd3d2wt7c3WrZsacyfP98IDg5O0j7mzZtnSDIGDx5s/O9//zMuXLhgWCwWo169eoaPj4+xfPlyk48iczl37pwxePBgo2zZsg+cn87Ozka9evWM4cOHG5s3b0735xYAAHh2ycmhKe66mTBhgrZt26aOHTuqSJEi2rFjR0p3maYiIyM1atQoffvtt5LujnJ84cIFXbt2Tf3799cHH3wgX19fG1cJZC25c+eWs7OzAgMD9Z///EcFChRQ/vz55eDgoM8++0wLFy7U2LFjH3rU49atW1q9erWWLFmi5cuX6+bNm8qdO7fKly+v//znP1q0aJGcnJy0fv16+fv7a8KECerRo4dNbxt/GsMwtGLFCo0aNUo7duxQ/vz5NWjQIHXr1k3PPfdcsvb1/PPP66WXXtKMGTM0ZswYSVK5cuXk7e2tXLlyqUWLFmYcQqaRmJioCxcu6PTp01q0aJF+/vlneXp6qlWrVho6dKhKliwpT09PBQQEyM3NzdblAgCAdCZF4fvs2bMaMmSIfH199e2332rixImpVZfpLBaLZs6cqY8//ljBwcHW5dHR0Ro0aJD+85//KHv27DasEMi6AgIC1K9fP02YMEHvvvuuXF1dret69eqlpk2bqmXLlsqdO7euXr1qfV2/fl2GYahs2bLq1auX2rRpo8qVK8ve3l4bN25U3759lS1bNs2ZM0ft2rWTs7OzDY/yyRITE7V48WKNHj1ahw4dUs2aNbV06VK98MILz/z8tY+Pj37//XcZhqErV65oz5496tevn/766y9JdwcFq127dmoeRqZw+PBhTZ8+XXPmzFFoaKgkyc/PTyNHjlTv3r3l6elp4woBAEBGkKLw/c477+j27dv68ccfH/l8YXq1bds2tW/fXpcvX7Yuy549u/r376++ffsmaV5fAOZ69dVXNW7cOB04cEA1a9a0Lm/SpIl+++03DR8+XKGhocqdO7dq1Kih3Llz67nnnlPDhg1VsGDBh/bXsGFDHTlyJC0P4ZnExcVpzpw5Gjt2rE6ePKmmTZtq48aNql+/fqoN6mhnZ6fAwEAFBgaqWrVqat68uY4cOaIZM2YQvv9PRESE5s+fr+nTp2vPnj3KmTOn3nzzTTVt2lSFCxdW/vz50/UdEwAAIP155vA9depUrV+/Xk2aNFGXLl2Ste3ff/+tMmXK6PTp03JwcFBgYKDq1aun3r17q2LFiknaR2xsrGJjY62fIyMjn7pNQkKCnJ2dHxiJvVy5cnrjjTfUu3fvVJ8fHMCzK1eunFxdXbVz584Hwrd0N5i/+uqrNqrMHHfu3NG0adP05Zdf6uLFi3rppZc0Z84cVatWzdTvzZMnjzZv3qxWrVpp7dq1pn5XehcbG6tt27Zp5syZWrRokWJjY9WiRQv973//U8uWLdP1nRIAACD9e6bwffnyZQ0cOFBubm766aefkr19SEiIbt68KR8fH0VGRurEiRM6ceKEpk+fro8//lgjR4586j7GjBmTrBHWJSkqKkqSZG9vr9mzZ6tJkybKlStXsusHkDb8/Px07NgxW5dhqsjISP3444/69ttvFRISojfeeEODBw9W6dKl06yG7Nmza/369bp27VqafWd6EB0drZ07d2rTpk3avHmzdu7cqZiYGBUuXFhDhw5V165dFRgYaOsyAQBAJvFM4btnz56KiIjQuHHjVKhQoSRvV7RoUX3xxRdq06aNChYsKCcnJ8XFxSkoKEgff/yx9u3bp1GjRil79uwaMGDAE/c1ePBg9e/f3/o5MjJSefPmfeI2Pj4+slgsSa4XgO2MGDFCwcHBeuedd2xdiilCQkL03XffacKECbpz5466deumgQMHqnDhwjapx8XFRfnz57fJd6cVwzC0e/du/fHHH9q8ebP27Nmj+Ph45ciRQ/Xq1dPo0aNVr149VapUKdVu8QcAALjHzrj/HuwkmDNnjjp37qwKFSpoz549D8x1++mnn1p7o5O5W8XExKhevXras2ePPD09denSpWQ9e52cyc0BpH+tWrVSfHy8Vq1aZetSUtXly5f19ddfW+8aevfdd9W/f396WE106dIlzZkzR7/88ov++ecf+fv7W+dDr1+/vkqWLPnMg9gBAICsLTk5NFk939evX9cHH3wgBwcHTZ069YHgnVKurq4aPXq0mjZtqqioKK1fv15t27ZNtf0DyFjy5cunOXPmqFu3bqpYsaJatGihokWL2rqsZ3b69Gl98cUXmjlzptzd3TVgwAD95z//kZ+fn61Ly5Sio6P1xx9/aObMmVq3bp1cXV3Vtm1bTZgwQQ0bNpSDg4OtSwQAAFlMstLzf//7X4WGhqpXr14qUaKE9Rnqe+Li4qzv761zdnZO8iA19w+qdObMmeSUBiCT6d27t2JiYnTo0CHNmzdPgwYN0oYNG1SjRg1bl5YsR44c0dixYzVv3jz5+flpxIgR6tWrF3fopDLDMHT+/Hlt27ZN69ev16JFi3Tr1i3VrVtXU6dOVbt27fiZAwAAm0pW+D579qwkadKkSZo0adIT22bLlk2S9P7772v8+PHPVh2ALKt06dKaPn26JGn16tV6/vnntWzZsgfCt2EYWrt2rXLlyqVy5cqlq1uH9+zZo9GjR+uPP/5Q3rx59d1336l79+5yc3OzdWmZQkJCgg4dOqStW7dq27Zt2rZtm65cuSJJKlGihPr166cuXbrY7Bl6AACAf0u9+8ZTwc6dO63vHzVPL4CsxWKxaMyYMRo2bJjq16+vPn36PLCuT58+1l8E5s+fX6dOnUrVx2GSyzAMbdq0SaNHj9batWtVrFgx/fzzz+rYsSPTVKVQYmKi9u/fr3Xr1mn9+vXauXOnbt++LWdnZ1WtWlWdOnVS7dq1VatWLW7lBwAA6VKy/pUaFBT0xPVPGnDNMIwnjh4bGxurIUOGSJI8PDzUuHHj5JQGIJMJDg5W586dtW7dOn3yySf65JNPHgjWP/30kyZPnqxJkybp7Nmz+uabb2za833mzBm9++67Wrt2rcqXL6/Ro0crIiJCb731liIjI/X+++/brLaMyDAMnT59WuvWrdO6deu0YcMGhYWFydPTU/Xr19fw4cNVu3ZtVa5cWS4uLrYuFwAA4KnSrIto8+bN+vzzz/Xmm2+qQYMGeu655yRJ8fHx2rx5swYPHqw9e/ZIkoYNGyYfH5+0Kg2AjTzul3IbNmxQx44drbeV//uXcYZhaPLkyXrppZf07rvvatiwYfL397dZ+D537pz19mYfHx9FR0fr448/tq6vUKGCTerKCMLCwrRgwQLFxMTIzc1NN2/e1MmTJ7VhwwadP39eDg4OqlGjht5//301adJE1apVk5OTk63LBgAASLY0C9+GYWj9+vVav369JMnNzU0eHh6KiIhQfHy8JMne3l6DBg3SRx99lFZlAUhjsbGx+u233/T999/r4MGDeu6551SgQAEVKFBA+fPnV0REhMaPH69GjRppzpw5CggIeGgf+/bt019//aUxY8bo6tWrCgoKsumzvffPRR4VFaXw8HBJUrFixTRx4kTVr1/fRpWlrUmTJsnNzU1dunR56i9Czp8/r2+//VbTpk1TbGysnJ2ddefOHeXIkUP58uVTmzZt1LRpU9WrV4+B0gAAQKaQZuG7bNmy+uqrr7Rjxw4dPnxYISEhCg8Pl7u7u0qVKqW6deuqR48eKlu2bFqVBCAN3Lx5U7NmzdLZs2d14cIF7dixQ8HBwWrWrJm++OILXb16VefPn9fRo0e1YsUKhYeHa8SIERo8ePBjp4OaMmWKpLthb+XKlXJ2dtasWbPS8rAUHh6uXbt26fDhw/L29lb58uXl6uqqXbt2KX/+/BoxYoQ6duyYYae0io+P19KlS9WsWTN5enomaZvevXtLkkaOHKnmzZurTJkyyp07t06dOqVTp07Jx8dH+fPn15YtW7Rw4UJ5e3urf//+eu+99+Tv7//Ux5MAAAAyMjvj3w9nZ1DJmdwcQNowDEPPP/+8goKCVKhQIeXPn1+lS5fW22+/rZIlSz52m6cFsHvrK1SooHfeeUcdOnQw/VEVwzC0YsUK/f7779qxY4eOHTsmwzDk4eGh7NmzK1u2bMqTJ4/eeecdvfLKKzYd+C019OnTRxMnTlSuXLk0bNgw9ejR46m3e+/Zs0fVqlWTJJUqVUrHjx9XYmKiPDw8VKRIEUVEROjixYvKly+f+vfvr27dusnDwyMtDgcAAMAUycmhGftfhwAekJiYKHt7+zTrPTQMQ2vWrNGuXbuULVs2vfjiiypatKiku1NBTZs2TWvWrNHy5cv1wgsvJGmfSak9JCRE586dU+XKlVNUf1LcO8aPPvpIf/31lxwdHdWuXTsNHDhQNWvWVLFixTJ0b+3Vq1e1bNky3bhxQ4MHD5adnZ0WLFigiRMnauTIkTp58qT69u2rI0eOPHWKyapVq6pnz576888/deTIEcXFxenmzZsKCAiw/owSEhLk4OCQoX9mAAAAz4KebyATMAxD48ePV//+/TVhwoQHpuQy04wZM/TWW2/J19dXUVFRio2NlYeHh7y9vRUVFaXIyEh17NhRc+bMSZN6UltwcLA6dOigDRs2PLDc0dFRf//9t4oVK2ajylLGYrFowoQJ+vXXX7V7927Z29vLYrFo8uTJ6tmzp7p37659+/bpwIEDsrOz03fffaf+/ftr165dqlKlyhP3/ccff+jll1/WpUuXFBgYmEZHBAAAYBv0fANZzIwZM9S/f39Jd3uFn+b27dsP3e4bHh6uzZs368aNG/Lw8FDDhg3l7+//xH0MHTpU7du317x58xQdHa3169fr9OnTioyMlJOTkxo2bGi9DTmjOXjwoFq0aKFr165Jutur6+fnp5UrV6pTp042HeAtpf7880998MEHevnllzVr1iy98MILGjRokD788EOVKFFCa9asUcWKFa29071799a0adNUs2ZNdevWTYMHD1bBggUfue/r169Lkm7cuEH4BgAAuA8930AGt3XrVjVv3lz169fX2rVr9cMPP6hnz55PbN+oUSP9+OOPevvttyVJ27Zt00svvfRQcK9Zs6bGjx//yAD9+eefa+TIkfrnn38eG8Qyqi1btqhevXrWz8OHD9eZM2c0e/ZsjRgxQkOHDs2wt01bLBZVqlRJvr6+1tknpLvX0DJlyujixYsqXLiwNm3a9EB4vn37tiZNmqRx48YpNDRUL7zwgmrXri0PDw95eHjI3d1dJ06c0IgRI/Tmm29q2rRpGfZnBAAAkFTJyqFGJhEREWFIMiIiImxdCmC6hIQE448//jDq1q1rSDIqVapkdOnSxZBkfPLJJ8b+/fsfu22LFi0MJycnw8HBwRg4cKDRrVs3w97e3ihZsqTx+++/GwkJCUZwcLDxyy+/GKVKlTIqV65sWCwWwzAMIyoqymjRooXx2muvGR4eHsaHH36YVoecZo4fP25IMiQZhQsXNoKCgowmTZoYTk5OxuzZs21dXor9/vvvhiRj06ZND63buHGj0bhxY+P8+fOP3T4qKsqYNm2aUb16dcPPz89wc3Oz/rwkGR999JGRkJBg5iEAAACkG8nJofR8AxnMvZ7LQ4cOqXbt2howYIBat26tzp07a968edZ2UVFRD91avmnTJjVo0EC//PKLzpw5oy+//FIJCQmKi4uztqlXr542bNggBwcHrV69Ws8//7z+/PNPtW7dWiNHjtSIESNUo0YNRUVFaf369cqePXuaHXtaqFatmvbs2aP33ntPH3zwgdq2basLFy7o999/V8OGDW1dXop16tRJS5cu1dmzZ5UjR45U2afFYlF0dLQSExPl7e2dKvsEAADICJKTQwnfQAZjGIYcHR316aef6pNPPrEuP3TokBo1aqSoqCh99913evfddx/Y7s6dOypfvrz8/f21adMmxcbGqnnz5jp48KDmzJmjF154QUuWLNGrr76qb7/9Vu+//74Mw1DLli114MABValSRcuWLVP//v319ddfp/Vhm+r8+fOaNWuWGjZsKBcXF1ksFrm4uKhly5ZycnLSihUrVKpUKVuXmSpOnTqlatWqqX379k8dvRwAAABPlpwcap9GNQFIJXZ2dvLx8ZGzs7N1WVxcnF544QUFBgZq//79j3zme+nSpTp58qQmT54se3t7devWTXv37tWKFSvUunVrOTo6qm3bturVq5eGDBmiq1evys7OTlOnTpXFYtGVK1c0e/ZsffXVV2l5uKY7duyYChQooGHDhmndunWqWrWqPDw8VLduXeXOnVs7d+7MNMFbkooUKaIBAwZo5syZunHjhq3LAQAAyDII30AGExoaqtjYWCUmJlqXbdy4UVeuXNGRI0dUpkwZ5cyZ0zrF172A5ebmJkny9fWVJK1evVqDBg1SnTp19Ndff+nPP//UoUOHVKRIEd2+fVu7d++WJAUGBurKlSvat2+fOnXqlKkG0YqLi1OHDh0kSTlz5tRHH30kSfrqq6/k6+urTZs2KSAgwJYlmqJjx46KiYnRtGnTbF0KAABAlsFUY0AGM3jwYDk6Oqp79+7WZdWrV9eECRNkb28vNzc3nT59WitXrtSvv/4qJycn/fLLLypQoICku3NXBwQEKCYmxhrIe/TooV27dkm627Ner149lS9f3rp/BweHtDvANDR8+HAdPHhQkjR37ly5u7srJCRE8+fP12efffbQM/MZ2Z07d/TGG2/Izc1Nu3fvVrZs2VSpUiVblwUAAJBlEL6BDGTHjh2aOnWqJk6c+MAc3D4+PurTp4/18507d9SkSRMVKlRIQ4cOVadOnVSpUiW5u7tbQ3jVqlX18ccfa9euXdq1a5fatWunfv36KX/+/MqTJ09aH1qaCw8P19ixYyVJPXv2VNOmTSVJP//8syQ98MuNzKBPnz5as2aNypYtq8KFC2vt2rUqVKiQrcsCAADIMgjfQAaRkJCgXr16qUqVKk+cx1uS3n33Xc2aNUt169bVmjVrVLRoUc2bN099+vSxjka9du1a/fTTT9Zbj2vUqKGaNWuafhzphZubmxo1aqT69etbB65LTEzUjz/+qPbt28vPz8/GFaaen3/+WT///LNmzpyprl272rocAACALInRzoEMYvz48erfv792796tKlWqPLadxWKRg4ODmjdvrrVr12rYsGEaPnz4Y9sbhqHTp0+rYMGCmfb28qRaunSpWrdurV27dqlatWq2LidVHDp0SDVq1FCnTp00depUW5cDAACQqTDaOZDJhIaGatiwYerdu/cTg7ck2dvby93dXUFBQbJYLDp16tQT29vZ2alIkSJZPnhL0sSJE1WlSpUMH7wtFos2btyoDh06qHr16ipRooS+//57W5cFAACQpXHbOZABHDt2TLdu3VKvXr2S1D4oKEjbtm1TwYIF1bp1a5OryxwuXLig1atXa8aMGbYuJUX++ecfvfTSSzp+/LiKFSumzz//XG+//bZ1cD0AAADYBuEbyADCwsIkSTly5EhS+6pVq6pq1apmlpTpXL9+XZJUoUIF2xaSAufPn1fTpk3l5eWlTZs2qW7duplqajgAAICMjPANpHMnT55U//79lSdPnkw1CFh6Y29/9ymc+Ph4G1fybIKDg9W0aVM5OTlp7dq1WWLEegAAgIyEZ76BdGz58uWqWbOmHBwctGXLFjk5Odm6pEyrSJEisre3t877nZGEhYWpefPmioqK0rp16wjeAAAA6RDhGzBRXFycgoODn9gmPj5eCxYs0KxZs7Rp0yadP39eR48eVcuWLfXiiy+qcuXK2r59O3Mym8zLy0uVK1dWUFCQrUtJln379ql27dq6ePEic3fDNFFRUfr+++91+PBhW5cCAECGRfgGTHLlyhXVrl1bpUqVUnR0tJYtW6aXXnpJd+7ckXQ3mE+ZMkVFixbV66+/rq5du6pBgwYqUKCASpcuraNHj2rx4sVatWpVkp/1Rso0bNhQQUFByggzMMbHx2vEiBGqUaOG3NzctGXLFpUuXdrWZSGTOXDggHr16qVs2bLp/fff15IlS2xdEgAAGRbPfAMmMAxDTZs2VWhoqG7evKnly5dr6NChOnHihKZOnaru3burcePG2r17t9q1a6elS5eqcOHCunDhgs6dO6dbt26pVatWcnV1tfWhZCkNGjTQF198oZMnT6pYsWK2Luexjh8/rs6dO2v//v0aMmSIhg4dyiMJSLZ27dpp0aJFKlCggHLnzi1fX1/5+fnJ09NTwcHB2rFjhy5dumRtX79+fQ0ePNiGFQMAkLERvoFUNHnyZLm6uqpSpUo6evSoVq5cqT59+mjBggU6ceKEJGnGjBlaunSp/v77b23fvl01atSwbl+iRAmVKFHCVuVneXXq1JGDg4OCgoLSbfiOjIxUw4YNlS1bNm3fvj3Dz0kO28mdO7ck6dy5czp37txj29nb26tbt26aPHmydWBCAACQfHZGRri/MgkiIyPl7e2tiIgIeXl52bocZEGJiYlydLz7+6zGjRtr586dCg0NVadOnbRo0SK5urrqq6++Up8+feTr66t58+apadOmNq4a/1ajRg0VKlRIv/76q61LeaR+/fppypQp+ueff5Q3b15bl4MM7ujRo/rhhx+0atUqxcbGKi4uTnFxcSpYsKCaNm2qJk2aqE6dOvLw8LB1qQAApEvJyaH0fANJZLFYtGbNGm3fvl2rV69WcHCwtm3bpsDAQElSaGiote369ev18ssvy8XFRe+99558fHzUtm1bPf/882rbtq0CAgKYfzmdatCggWbNmiXDMNLVn9GVK1f0n//8R4sXL9a4ceMI3kgVpUqV0o8//mjrMgAAyBII30AS7Nq1S3379tWePXuUM2dO3bhxQ5J0/fp1TZ8+XZs2bdKECROUPXt2tWnTRsWLF9cLL7wg6W6Ya9CggXVf9271RPrUoEEDjRs3Tnv27LHe0r169WotWrRIVapUUbVq1VSuXDk5ODikST2JiYmaPHmyBg8eLHd3d82fP1+vvfZamnw3AAAAUg8PbwFPEBwcrG7duqlGjRqKj4/Xpk2bFBwcrO+//16SVKlSJY0bN06nT59WnTp1ZLFYlDNnTg0aNEjlypWzcfV4FvXq1VPp0qXVtGlTLV++XFevXlXHjh21ZMkSvffee6pUqZKaNGmi2NhY02s5dOiQateurT59+qhDhw46duyY2rdvn6565AEAAJA0hG/gMc6ePavKlStr6dKlmjx5svbu3at69erJzs5OL7/8sj7//HMtXLhQly5d0oEDB9S6dWs5OTmpV69eti4dKeDu7q7t27erQYMGatWqlapWrSpnZ2cdPnxYkZGR+v3337Vjxw716NEj1aYkMwxDt27dUmhoqGJjY3X79m199NFHqly5sqKiorR161ZNnjxZ2bNnT5XvAwAAQNpjwDXgES5duqR69erJwcFBmzZtUp48eWxdEtKYxWLRp59+qt9++01//PGHihcvbl03b948dejQQb/88ou6dOnyzN9x7do1ffjhh/rtt98UFxdnXW5nZycXFxd98skn+vDDD+Xs7JyiYwEAAIA5kpNDCd/Av1y/fl3Vq1eXxWLRli1blC9fPluXhHSoXbt22rFjh06cOCF3d/dkbWuxWDRlyhQNGjRITk5OGjBggPLnzy8XFxdFRUUpOjpaTZs2VeHChU2qHgAAAKmB0c6BFJg7d67OnTunxo0by9/f39blIJ0aO3asSpYsqW+++UZDhw5N1ra//PKLevXqpe7du2vcuHHy9fU1qUoAAACkFzzzDfzLe++9p++++05BQUHq1KmTrctBOlW4cGH17dtXY8eO1bVr15K17aFDh1SyZElNmzaN4A0AAJBFEL6Bf3FyctKNGzeUmJio8uXL27ocpGNDhw6Vi4uLhg0blqztzp07pwIFCphTFAAAANIlbjsH7mOxWNS3b1/9+OOP+uKLLzRw4EBbl4R0LHv27Bo+fLj69esnX19fdejQQX5+ftq/f7/279+vgIAAdejQQR4eHvrf//6nwYMHKzIyUrdv31bHjh1tXT4AAADSEAOuAf8nPj5eb775pubPn6+ffvpJb7/9tq1LQgYQHx+vAQMGaM6cOQoLC7Muz549uyIiIpQtWza99tprWrx4scqVK6dq1appxYoVGjZsmNq1a2fDygEAAJBSjHZO+EYyRUdH67XXXtPatWv166+/6pVXXrF1Schg4uLitHbtWsXGxqpy5crKly+fLly4oMmTJ2vRokUqXry4pk+fziB+AAAAmQjhm/CNZLh48aJeeuklHT9+XL///ruaNm1q65IAAAAAZABMNQYk0bZt29S2bVu5ublp69atqlChgq1LAgAAAJAJMdo5sqxp06apYcOGKlmypPbs2UPwBgAAAGAawjeynISEBPXt21fvvPOO3n77ba1du1Y5c+a0dVkAAAAAMjFuO0eWM2bMGE2ePFmTJ09Wz549bV0OAAAAgCyAnm9kKZcvX9bYsWPVr18/gjcAAACANEP4RpYyZMgQeXh4aMiQIbYuBQAAAEAWwm3nyDL27t2rX375RZMmTZK3t7etywEAAACQhTDPN7IEwzBUr149hYWF6eDBg3J05PdOAAAAAFKGeb6Bf1m8eLG2bt2q1atXE7wBAAAApDme+Uamd/LkSQ0cOFAtW7ZUs2bNbF0OAAAAgCyI8I1MyzAMDR48WCVLllRCQoK++eYbW5cEAAAAIIsifCPT6t27t8aOHasRI0bo5MmTKlasmK1LAgAAAJBF8fArMqWzZ89q8uTJ+vbbb/XBBx/YuhwAAAAAWRw938iUFixYIHd3d73zzju2LgUAAAAACN/InObPn69WrVrJw8PD1qUAAAAAAOEbmc+xY8d06NAhvf7667YuBQAAAAAkEb6RCU2ePFm+vr56/vnnbV0KAAAAAEgifCOTCQ8P1/Tp09W7d2+5urrauhwAAAAAkET4RiYzZcoUxcfHq3fv3rYuBQAAAACsCN/IVObMmaOWLVsqICDA1qUAAAAAgBXhG5lK/fr1tX79et24ccPWpQAAAACAFeEbmcrw4cOVkJCgKVOm2LoUAAAAALAifCNT8fPz08svv6yZM2fqzp07ti4HAAAAACQRvpEJDRw4UFeuXFHHjh0VGhqqv/76SytWrNDly5dtXRoAAACALMrOMAzD1kWkhsjISHl7eysiIkJeXl62Lgc2tnTpUr300kuyWCzWZT4+Ptq7d68KFy5sw8oAAAAAZBbJyaGOaVQTkKZatWqlY8eO6cCBA1qzZo3OnDmjoKAgHTp0iPANAAAAIM0RvpEpJSQkaPfu3frss8906tQpVa5cWe+++65atGhh69IAAAAAZEGEb2QahmHo22+/1dKlS7V7925FR0erdevW+u2331ShQgVblwcAAAAgC2PANWQaU6dO1YABA+Tt7a1PP/1UBw8e1J9//knwBgAAAGBz9HwjUzh06JA++OAD9ezZU5MnT7Z1OQAAAADwAHq+keGdPn1azZs3V6lSpfT111/buhwAAAAAeAjhGxnaqVOn1LRpU/n4+GjlypXy8PCwdUkAAAAA8BDCNzKsHTt2qGbNmnJyctKaNWuUM2dOW5cEAAAAAI9E+EaGtHjxYjVq1EglSpTQ9u3blS9fPluXBAAAAACPRfhGhjNv3jy1a9dOrVq10tq1a+Xr62vrkgAAAADgiQjfyHDs7e3l5OSkffv2aciQIZo7d66OHTumxMREW5cGAAAAAI9kZxiGYesiUkNkZKS8vb0VEREhLy8vW5cDk/3zzz8aMWKEdu7cqbNnz0qSPDw8VKFCBTVs2FC9e/dW7ty5bVwlAAAAgMwsOTmU8I0MLywsTAcOHND+/fu1b98+LV++XDExMerUqZO+/fZbeXt727pEAAAAAJlQcnKoYxrVBJgme/bsatSokRo1aiRJioiI0NSpUzVixAjZ2dlp+vTpNq4QAAAAQFZH+Eam4+3trQ8//FBeXl7q2bOnOnTooMaNG9u6LAAAAABZGLedI9OyWCxq2LChLl++rMOHD8vNzc3WJQEAAADIRJKTQxntHJmWvb29pkyZokuXLumzzz5L1rYhISH66aefNGTIEE2cOFHnz583qUoAAAAAWQE938j0Ro0apeHDh2vPnj2qWLFikrbp2rWrZs2aJXt7e1ksFklSu3btNGPGDHl4eJhZLgAAAIAMgp5v4D4DBw5UoUKF1LNnzyRv07JlS0mSi4uLddlvv/2mhg0b6saNG6leIwAAAIDMjfCNTO/w4cM6efKkcubMqaTe6PHaa69pwYIFqlixoqZNm6ZixYpJks6dO6datWrpzJkzZpYMAAAAIJMhfCPTW7FihSSpVq1aCg8PT/J2r732mrZt26bu3btbpysbMmSIJKlmzZrat29fqtcKAAAAIHMifCPTe+utt9S9e3eNGDFCefPm1UcffaTExMRk7aN27dpq2rSpRo0apYULFypfvnxq3769SRUDAAAAyGwI38j0AgMDNW3aNJ0/f169e/fWl19+qc2bNydrH3Z2dvr111/l5uamd999VwMGDNDp06d18eJFk6oGAAAAkJkQvpFlBAQEaOzYscqRI4eCgoKSvb2fn58WLVqkgwcPavHixZKkrVu3pnKVAAAAADIjwjeyFHt7e9WvX18bN258pu2rVq2q4cOH63//+58KFSpE+AYAAACQJIRvpBv35tM2W8OGDbVz505FRUUlazuLxaLVq1drzZo1slgsKl26tLZs2WJSlQAAAAAyE8I30oUDBw7IwcFBFSpU0Pnz5039rhYtWkiSypYtq19++eWpg69dunRJ48ePV4kSJfT8888rIiJCM2fO1AsvvKAjR44ke/A2AAAAAFkP4Rvpgr+/vyTp0KFDql+/vmJjY63rli9fro8//lgnTpxIle8qUqSIDh48qMqVK+vNN99UoUKF1KlTJ/3www/as2ePLl++rAULFqhnz54qVqyY8ubNq4EDB6pKlSraunWr9u/fr65du8rf31+GYSgsLCxV6gIAAACQeTnaugBAkvLkyaMKFSrI0dFR+/bt0w8//KA333xTffr00fz58yVJ+fPnV7FixZK8z8jISE2cOFEWi0VvvvmmAgMDretKlSqlRYsWae/evZo9e7Z27dql3377TXFxcdY2JUuWVNOmTTV69Gg1aNBAfn5+D+z/3ueQkJCH1gEAAADA/QjfSBfi4uLk7OysxMRE9e3bVx9++KHGjBkji8Uie3t7+fv7q3Pnzknal2EY+u2339SvXz/dvHlTiYmJunXrlsaOHftQ2ypVqqhKlSqSpNjYWB08eFCXL19WzZo1lTt3bkl3n/W+fPmy1q9fr1OnTunq1asKDg629ng7Ozun0k8BAAAAQGZF+IbN3euZPnjwoFatWqX69evLyclJQUFBKlWqlObMmaNhw4bJ3d39qfs6ceKE+vTpo7Vr1+rll1/W+PHj1blz56fOx20YhsLDwxUXF6fw8HB99913OnnypPUVExMjSXJwcJC/v79y5cqlGzdu6LXXXlOhQoVS5ecAAAAAIPMifMPmZs+erfnz52vhwoVq2LChJOnGjRvat2+f9u3bp+zZs6t79+5P3MedO3c0ZswYjRs3ToGBgVq2bJlatmwpSXruued06dKlR273zz//6L///a82btyoW7duSZLs7OyUL18+FS1aVHXr1tVbb72lYsWKqWjRoipQoICcnJxS8egBAAAAZAWEb9jc0aNHVbBgQTVq1EiRkZE6c+aMZs2apTx58ig6Olq3b9+Wg4PDY7dfsWKF+vTpo8uXL+u///2vBg8eLDc3N+t6d3d3RUREWD8nJiZq7969+vXXX/Xjjz8qX758+vjjj1W8eHEVK1ZMhQsXlqurq6nHDAAAACBrIXzD5q5duyYfHx/5+voqX758ql+/vvLnz6+WLVtq6dKlCg8P19WrVxUYGKi4uDidPXtWV65c0dWrV7Vo0SL9/vvvatKkiVatWvXIAdl27Nih5557TtOmTdOaNWu0bt06hYWFycfHR59//rk++OADwjYAAAAAUxG+YXNXr161PlN94cIFrV27Vl27dtW1a9eUI0cOXbx4UadPn9batWs1dOhQXb582bptvnz5tGDBArVr1052dnaP3L+vr69Wr16ttWvXqlq1aurbt6+aN2+uatWqydGR/wUAAAAAmI/kAZu7P3w7Ojrq2rVrKlWqlDZv3qxcuXLJ29tbLVu2VFRUlF577TW9++67eu6555Q7d255eno+df8zZ860zh+ePXt2sw8HAAAAAB5ib+sCkLUZhqGrV6+qSJEiku72UkvSe++9p/3796tz586aO3euGjdurJ07d2rBggVq2LChihYtmqTgbRiGbt68qX/++UdffvmlTpw4YerxAAAAAMCj2BmGYdi6iNQQGRkpb29vRUREyMvLy9blIAkSExPVu3dvTZkyRWvXrlWBAgWUkJCguXPnyjAMvf322ypQoMAz7z8+Pl69evXS9OnT5e3tLQcHB4WFhalt27YaNmyYypUrl3oHAwAAACDLSU4OJXzDJm7fvq0ePXpo/vz5mjZtmrp165aq+4+IiFC7du0UFBSkn376SW+++aZiY2M1e/ZsjRs3Tjdv3tSlS5eSNHf4/bZs2aLly5drzJgxj33GHAAAAEDWkJwcyjPfMF14eLgOHjyo/fv3W1///POPHB0dNX/+fLVr1y7VvmvatGkaO3asQkNDJUnLli1TQECA5s6dq8OHD+vw4cOKiopSWFiYtm7dqmbNmiVr/4MGDdL27dtVp04dvfjii6lWNwAAAIDMjZ5vPCAuLk6GYcjFxeWhdRERETp8+LAOHTqkEydO6MqVK7p8+bKuXLmia9euydXVVT4+PtaXu7u7jh8/rjNnzkiS3NzcVKFCBVWqVEmVKlVS3bp1VbRo0VStv0mTJlq/fr0kydXVVQkJCUpISJB0d2T0smXLqly5cqpQoYLatm2b5NHOY2JitG3bNjVp0kSSVLx4cR0+fFhOTk6pWj8AAACAjIPbzrNo+D537pycnJwUGBiYrO1iY2O1evVqLVy4UEuWLNGdO3dUunRpVapUSf7+/vr777/1119/6fz585IkJycnFSlSRM8995zy5MmjwMBA+fv7Ky4uTuHh4QoLC1NERIRu3bqlwoULW8N2sWLFTJ/aKy4uTiNGjNCWLVtUqlQplStXTmXLllWZMmXk4+OT5P3ExsZq4cKF2rVrl3bt2qVDhw4pPj5ezZs314cffqhmzZrphx9+UO/evc07GAAAAADpGuHbRuE7PDw8WQEvtcTHx+vzzz/X6NGjlZiYqMKFC6tcuXJKSEhQbGzsI19xcXHW99HR0YqPj1eZMmX02muvyc/PTwcOHND+/ft1/fp1lS5dWuXKlbO+ihcvLmdn5zQ/zrT0+++/q23bttbPzs7Oat++vd5++22FhIRowIABKliwoDZs2GDDKgEAAADYEs9828DBgwdVsWJFtWrVSosWLUqzcHr06FF16dJFBw8e1NChQ1W2bFkFBQXp5MmTcnZ2lpeXl1xcXKwvZ2fnBz67uLjI3d1dDRo0UKlSpdKk5ozg5Zdf1tGjR7V7927t2bNHe/bs0YIFCzR79mxJUmBgoOrXr2/jKgEAAABkFPR8p5LY2Fi5urpKklq1aqX//e9/ptxiHRoaqqCgIG3cuFEbNmzQsWPHVLx4cc2ePVtVq1ZN9e/D/xcXF6djx44pZ86cypMnj63LAQAAAGBjycmh9mlUU6bn4uKicePGSZKWLl2qDz/8MNX2HR4erkmTJql69erKmTOnXn31Va1evVp16tTRr7/+qv3795sSvMPDwxUXF5fq+82onJ2dVb58eYI3AAAAgGQjfKeinj17Klu2bJKkJUuWpGhfFotFGzZsUMeOHZU7d2717dtXAQEBmjFjhi5cuKCTJ09qypQpeuONN5I9V/WT/P333/r0009lZ2en7Nmz6+OPP061fQMAAABAVsUz36nI29tbLVq00MKFC3X9+vVn2seFCxc0c+ZMzZgxQ+fOnVPx4sU1YsQIde7cWQEBAalc8f9348YNDR06VFOmTHlged++fU37TgAAAADIKuj5TmW+vr6SpNu3b+v27dtJ2ubetFbNmzdXgQIF9MUXX6hRo0batm2bjh07poEDB5oWvOPi4vTNN9+oaNGiDwTv4cOHKyEhQfnz57cue9zwAAkJCTpw4IDWrVtnSo0AAAAAkNHR853KvL29re/DwsLk4eHx2LaHDh3S9OnTNXfuXN28eVO1a9fW9OnT1a5dO3l6eppap2EYWrZsmT788EOdOnVKBQoU0O3bt+Xr66tff/1VjRo10j///KNt27Zp586d2rVrl06dOqWXX35Z3333nfbv36+lS5fqhx9+eGC/WXmedQAAAAB4HMJ3KrsXvnPkyPHYgbmuXbumDz74QAsWLJC/v7/efvttvfXWWypevLiptcXHx+vatWtasWKFvv/+ex09elTlypVT6dKldfjwYTVr1kzTpk3T1q1bVatWLe3YsUP29vYqU6aMatSooVdeeUXffPONfv3114f2XatWLY0fP57gDQAAAACPQPg2Sb169WRv/+Bd/YZhaMaMGRowYIAcHR31yy+/6I033pCTk1Oy9p2QkKBz587pwoULunLliu7cuaOYmBjFxMQoNjZWMTExunPnjkJCQnT16lVdu3ZNV69eVUhIiCTJ3t5ebdq0UdeuXfXll1/KYrHo559/1vXr11WtWjVdu3ZNjRo10uLFi9W0aVNly5ZN8fHxWrlypQoWLKhDhw5Jkl577TV1795djRs3loODQ+r84AAAAAAgEyJ8p7Lt27dLuhu+7xcTE6NevXpp5syZ6tq1q77++mvr8+HS3dHNz507p19//VXHjh2Tr6+vqlatqqpVq8rT01Ph4eEKDg7WH3/8oXnz5ik0NPSB/bu6usrV1VUuLi7W//r5+Sl37twqVqyYcufObX2VKVNGR48eVdu2bVWxYkV9+umn+uyzz7Rjxw51795d//nPf1S6dGlJ0rFjxzRjxgzNmjVLwcHBqlixoiZMmKAOHTooR44cJv80AQAAACBzsDMeN4pWBpOcyc3NkpCQYO3F3rt3rypXrixJunLlitq2bauDBw9q2rRpevnllzV69GjNmzdPYWFhMgzD2mvt6empypUr6+rVqzpx4sRD35EnTx516NBBzz//vPLnz688efLIzc1NdnZ2Sa7z4sWLKlSokBISEpQ7d25dvXpV+fLl09y5c1WnTh2FhYVp8eLF+vnnn7Vjxw7lyJFDnTp1Urdu3VShQoVU+VkBAAAAQEaXnBxKz3cqmjBhgvX9vZC6ceNGdezYUfb29po4caJKly6t6tWr6+zZs+rSpYsKFiwoOzs7ubi4qHDhwqpTp471ufHw8HDt27dP8fHx8vHxkY+Pj4oWLZriW7wDAgL0008/6cyZM4qMjFSDBg0UHR2tKVOm6O2339bx48dlb2+v5s2b67ffflOrVq3k4uKSou8EAAAAgKyMnu9UcvHiReXLl0+S9Oeff6pmzZrWebOrVaumnDlzavny5db2u3btUrVq1dK8zvsdOXJE06dP1+zZsxUaGqpKlSqpVq1aqlatmho1aqTAwECb1gcAAAAA6Rk93zbQt29f6/vhw4fr0KFDcnZ2Vv78+bV7927lyZNHM2fO1Pbt2/XXX3+patWqaV5jfHy8tm/fruXLl2vFihX6+++/lTNnTr355pvq3r27SpYsmeY1AQAAAEBWQPhOBX/88Yf+/PNP6+fr16/LMAxZLBZVrFhRX3zxhV588UW5u7ura9euaVKTYRj666+/tHr1av311186efKkjh07plu3bsnf318vvPCCPv/8c7Vs2VLOzs5pUhMAAAAAZFWE7xQ6deqU3nvvPetnLy8v3bhxQ998843eeust6/PbaSEiIkLr1q3TypUrtWrVKl2+fFkeHh6qUKGCSpcurbZt26px48aqVKnSQ9OgAQAAAADMQ/hOgVWrVqlz587y9fVVjx49NGfOHPn4+Gj9+vWqUqWK6d9vGIaOHDmilStXasWKFdq2bZsSEhJUokQJtW/fXi1atFDdunUZLA0AAAAAbIzw/Qy2bdum77//XgsXLlTz5s31xRdf6Pnnn1exYsW0bt26B+bvTm23bt2y9m6vXLlSly5dkru7uxo1aqTvv/9eLVq0UIECBUz7fgAAAABA8hG+k8Fisei///2vvvrqK/n6+qpo0aKqU6eOPvroIzk4OGjVqlXy9fXVP//8o3Hjxunq1auqXLmyhg8fnqTnqhMTE3Xq1Cn99ddf1tfhw4cVFRWlevXqKSQkRNu3b1d8fLyKFy+uV199VS+88ILq1q0rV1fXNPgJAAAAAACeBeE7iSwWi95++23NnDlTbm5uunXrlgoWLKhPPvlEkjRz5kz5+/vr8OHDaty4sdzd3RUTE6PVq1erSpUqevnll637MgxDISEhOnz4sDVg//XXXzpy5IhiYmIk3Z2Lu1y5cnrllVfk4OCgXbt2yc/PT99++61atGihQoUK2eTnAAAAAABIPub5TqI5c+aoc+fOkqT27dvr66+/1tdff60pU6bI0dFRpUuXloODg/bs2WMN0PcULlxYLi4uunXrlvWVmJgoSXJ1dVXp0qVVrlw5lStXTmXLllXZsmWVK1euVD8GAAAAAEDqSU4OJXwngcViUdmyZXXlyhWVKFFCq1at0nvvvae5c+dq5MiRCgsL0//+9z9VqVJFFSpU0JAhQ6zb3h+ms2XLJk9PT2XLlk1+fn4qW7asihQpIkdHbkAAAAAAgIwmOTmU1JcE69at09GjRyXd/eHmyZNHdnZ2mjt3rjp06CBJ+uqrr6ztmzRpIg8PD5UoUUIODg42qRkAAAAAkH4QvpPAyclJkvTcc8/J29tbH3/8sd58800FBgY+sn21atXSsjwAAAAAQDpnb+sCMoK6desqe/bsqlChgnr37q1GjRrJyclJ9+7Yv379uubNm6f4+HgbVwoAAAAASI/o+U4CR0dHDRgwQD/88IOWLVtmXZ4tWzYVKFBAx48fV1xcnG7evKn33nvPhpUCAAAAANIjBlxLpqioKJ05c0anT5/WqVOndPr0aRUuXFi7d+/W9u3bdeHCBZ7zBgAAAIAsgNHOTQzfjxMUFKSGDRvqyJEjKl26dJp/PwAAAAAgbSUnh/LMdyqpVKmSJGnv3r02rgQAAAAAkN4QvlOJq6urHB0ddfv2bVuXAgAAAABIZwjfqeTYsWNKSEhQuXLlbF0KAAAAACCdIXynkq1bt8re3p7wDQAAAAB4SKqF77Fjx8rOzs76epLg4GANGDBAxYsXl5ubm3LkyKG6detq2rRpyojjvxmGoalTp+rFF1+0yWBvAAAAAID0LVVGOz9+/LgqVKigmJgY67LH7Xbfvn1q3ry5QkNDJUmenp6KiYlRQkKCJKlZs2ZasmSJXFxcklWDLUc73717t6pXr64VK1aoRYsWafrdAAAAAADbSNPRzi0Wi7p3766YmBjVrFnziW0jIiL04osvKjQ0VCVKlNCePXt069Yt3b59Wz/88IOcnJy0Zs0a9evXL6VlpakVK1YoR44catasma1LAQAAAACkQykO3xMmTNC2bdvUsWPHp4bPr776SteuXZObm5tWrFihKlWqSJKcnZ313nvv6bPPPpMkTZkyRSdOnEhpaWlm06ZNqlevnhwcHGxdCgAAAAAgHUpR+D579qyGDBkiX19fffvtt09tP2vWLEnS66+/roIFCz60vm/fvvL09FRiYqLmzp2bktLSTGxsrHbs2KH69evbuhQAAAAAQDqVovD9zjvv6Pbt2/rmm2+UM2fOJ7Y9fvy4Lly4IEmPfS7a09NTdevWlSStWbMmJaWlmRMnTig2NlZVq1a1dSkAAAAAgHTqmcP31KlTtX79ejVp0kRdunR5avsjR45Y35cpU+ax7e6tO3r06BP3Fxsbq8jIyAdeac0wDO3YsUOSVKRIkTT/fgAAAABAxvBM4fvy5csaOHCg3Nzc9NNPPyVpmytXrljfBwYGPrbdvXWRkZGKiop6bLsxY8bI29vb+sqbN28Sq08dN2/eVGBgoHr27ClJypUrV5p+PwAAAAAg43im8N2zZ09FRETo008/VaFChZK0za1bt6zv3d3dH9vu/nX3b/NvgwcPVkREhPV18eLFJNWRWoYNG6arV69KkgoWLPjUuc0BAAAAAFmXY3I3mDNnjpYvX64KFSqof//+ZtSUJC4uLsmeCzy1xMbGasaMGXJ1dVVMTIwqVKhgkzoAAAAAABlDsnq+r1+/rg8++EAODg6aOnWqHB2Tnt2zZctmfR8dHf3Ydvevu3+b9GTbtm2Kjo5W+fLlJck6ZRoAAAAAAI+SrJ7v//73vwoNDVWvXr1UokSJh57JjouLs76/t87Z2VnOzs7KkyePdd3ly5fl5eX1yO+4fPmyJMnLy0uenp7JKS/NrF69Wv7+/tZb3WvXrm3jigAAAAAA6Vmyer7Pnj0rSZo0aZKyZcv20GvMmDHWtveWffTRR5IeHOH8/pHP/+3eulKlSiWntDS1evVqNWvWzDqIHNOMAQAAAACeJEXzfCdH8eLFlS9fPknSqlWrHtnm9u3b2rJliySpWbNmaVVasly7dk2HDh1S8+bNrcueNIAcAKRXhsVQzOlwRR+8rpjT4TIshq1LAgAAyLSSFb6DgoJkGMZjX8OHD7e2vbds/Pjx1mX35gOfP3++zp0799D+J06cqKioKDk4OKhjx47PdkQmW7NmjSTJ399fI0eO1LZt22xcEQAk350jIbo2brdCph7WzfnHFTL1sK6N2607R0JsXRoAAECmlGY935L04YcfKiAgQNHR0WrZsqX27dsn6e6z4pMmTdInn3wiSerRo4eKFSuWlqUliWEYWrBggcqUKaOmTZtq6NChqlWrlq3LAoBkuXMkRKFzjikxIu6B5YkRcQqdc4wADgAAYIJkTzWWEt7e3lq2bJmaN2+uo0ePqkqVKsqWLZtiYmIUHx8v6e7t5t9++21alpVkM2fO1IoVK6yDxX355Zc2rggAksewGApfevqJbcKXnpFrKV/Z2dulUVUAAACZX5r2fEtS5cqV9ffff6tfv34qWrSo4uPj5eHhoTp16mjq1KlauXKlzebvfpJTp06pb9++KlGihCIjIyVJ7733no2rAoDkiT0b8VCP978lRsQq9mxEGlUEAACQNdgZhpEpRtiJjIyUt7e3IiIiHjuN2bOKi4tTnTp1dOHCBQUHB0uSduzYoRo1aqTq9wCA2aIPXtfN+cef2i7H68XlXiFXGlQEAACQcSUnh6bpbecZUUJCgjp06KCDBw9ab42fMGECwRtAhmSfzTlV2wEAACBpCN+PsGvXLl24cEGlS5fWuHHj9McffygxMVGS1KZNG243B5BhuRT0loO38xNvPXfwdpFLQe80rAoAACDzI3w/wkcffaTNmzdLkuzs7DRz5kx17dpVkjR37lzZ2TEIEYCMyc7eTj6tCit0zrHHtvFpVYjB1gAAAFJZmg+4lhHUqlVLXl5e2rBhg3bv3q0uXbooMTFRwcHB8vDwsHV5AJAibmX85NuppBy8H7y13MHbRb6dSsqtjJ+NKgMAAMi86Pl+hPPnz8vJyUn16tWTg4ODJMne3l65cjH4EIDMwa2Mn1xL+Sr2bIQst+Jkn81ZLgW96fEGAAAwCeH7X0JCQjR//nz98MMP1uANAJmRnb2dXAv72LoMAACALIHbzv9lw4YNMgxDbdq0sXUpAAAAAIBMgp5vSYZh6OzZswoLC9MHH3yg6tWrKzAw0NZlAQAAAAAyiSzf871q1SrVqFFDhQsXVpUqVZQjRw79+eefti4LAAAAAJCJZOnwPXbsWLVo0UKOjo767bff9Mcff2jr1q3y9/e3dWkAAAAAgEwkS992/tVXX6lnz56aNGkSc3cDAAAAAEyTZXu+t23bprCwMFWqVIngDQAAAAAwVaYL30eOHHlqm/3796tZs2aqV6+eOnTokAZVAQAAAACyskwXvmvXrq327dvr9OnTj1wfFRWl119/XSVKlNDy5cvl6emZxhUCAAAAALKaTBe+f/zxR23btk0lS5bUrl27Hlr//vvv68qVK5o3b57c3d1tUCEAAAAAIKvJdOG7Y8eO+vXXXxUfHy8HB4cH1i1cuFA///yzJkyYoGLFitmoQgAAAABAVpPpwrck7dixQx4eHqpQoYJ12fnz59WjRw+1b99eb775ps1qAwAAAABkPZkyfG/evFk1a9aUo+PdmdQSEhLUsWNH+fj4aPLkyYxuDgAAAABIU5kufIeFhWndunVq2bKlJCk2NlaDBg3Sjh07NHfuXPn4+Ni2QAAAAABAluNo6wJS29KlSxUfH6+WLVvq66+/1jfffKOrV69q7Nixql27tq3LAwAAAABkQZkufC9btkyGYah69eq6deuWOnfurIEDB6pkyZK2Lg0AAAAAkEVluvDdrVs3OTg4qFixYurfv7/y5s1r65IAAAAAAFmcnWEYhq2LSA2RkZHy9vZWRESEvLy8bF0OAAAAACCTS04OzXQDrgEAAAAAkN4QvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJM52roAAOmbkZio6L37lHDjhhxz5pR7lcqyc3CwdVkAAABAhkL4BvBYkWvWKHj0GCVcu2Zd5hgQIP+PB8urWTMbVgYAAABkLNx2DuCRItes0eX3P3ggeEtSQnCwLr//gSLXrLFRZQAAAEDGQ/gG8BAjMVHBo8dIhvGIlXeXBY8eIyMxMY0rAwAAADImwjeAh0Tv3fdQj/cDDEMJ164peu++tCsKAAAAyMAI3wAeknDjRqq2AwAAALI6wjeAhzjmzJmq7QAAAICsjvAN4CHuVSrLMSBAsrN7dAM7OzkGBMi9SuW0LQwAAADIoAjfAB5i5+Ag/48H/9+HfwXw//vs//Fg5vsGAAAAkojwDeCRvJo1U+B34+Xo7//Ackd/fwV+N555vgEAAIBkcLR1AQDSL69mzZStceO7o5/fuCHHnDnlXqUyPd4AAABAMhG+ATyRnYODPKpXs3UZAAAAQIbGbecAAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJHG1dAAAAAID0L9FiaPfZm7p+K0a5srmqWsEccrC3s3VZQIZB+AYAAADwRKuOXNVnS4/qakSMdVlub1cNb1VKz5fJbcPKgIyD284BAAAAPNaqI1fVa87+B4K3JF2LiFGvOfu16shVG1UGZCyEbwAAAACPlGgx9NnSozIese7ess+WHlWi5VEtANyP8A0AAADgkXafvflQj/f9DElXI2K0++zNtCsKyKAI3wAAAAAe6fqtxwfvZ2kHZGWEbwAAAACPlCuba6q2A7IywjcAAACAR6pWMIdye7vqcROK2enuqOfVCuZIy7KADInwDQAAAOCRHOztNLxVKUl6KIDf+zy8VSnm+waSgPANAAAA4LGeL5NbkzpVUoD3g7eWB3i7alKnSszzDSSRo60LAAAAAJC+PV8mt5qWCtDuszd1/VaMcmW7e6s5Pd5A0hG+AQAAADyVg72dahb2tXUZQIbFbecAAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkcbV0AACBpLJZEXT72t6LCw+Tpk12BJUvL3t7B1mUBAAAgCQjfAJABnNy1XRtmTlHUzRDrMs8cfmr0Zg8VrV7LhpUBAAAgKbjtHADSuZO7tmvJN6MfCN6SFHUzREu+Ga2Tu7bbqDIAAAAkFeEbANIxiyVRG2ZOeWKbjb9MkcWSmEYVAQAA4FkQvgEgHbt87O+Herz/7VZoiC4f+zuNKgIAAMCzIHwDQDoWFR6Wqu0AAABgG4RvAEjHPH2yp2o7AAAA2AbhGwDSscCSpeWZw++JbbL5+imwZOk0qggAAADPgvANAOmYvb2DGr3Z44ltGnbtwXzfAAAA6RzhGwDSuaLVa6l1/48f6gHP5uun1v0/Zp5vAACADMDR1gUAAJ6uaPVaKly1+t3Rz8PD5OmTXYElS9PjDQAAkEEQvgEgg7C3d1De0uVsXQYAIBUkWhK1//p+3Yi+oZzuOVUpVyU58AtVIFMjfAMAAABpaN35dRq7e6yCo4Oty/zd/TWo2iA1yd/EhpUBMBPPfAMAAABpZN35deof1P+B4C1J16Ovq39Qf607v85GlQEwG+EbAAAASAOJlkSN3T1WhoyH1t1bNm73OCVaEtO6NABpgPANAAAApIH91/c/1ON9P0OGrkVf0/7r+9OwKgBphfANAAAApIEb0TdStR2AjIXwDQAAAKSBnO45U7UdgIyF8A0AAACkgUq5Ksnf3V92snvkejvZKcA9QJVyVUrjygCkBcI3AAAAkAYc7B00qNogSXoogN/7/N9q/2W+byCTeqbwvX//fn322Wdq3bq1SpQoIV9fXzk5OcnX11e1a9fWqFGjdPPmzUdu++mnn8rOzu6pr1OnTqXowAAAAID0pkn+JvqmwTfK5Z7rgeX+7v76psE3zPMNZGKOz7LRzz//rIkTJ1o/u7q6ys3NTTdv3tT27du1fft2jR8/XkuWLFHNmjUfuQ8nJyflyJHj8YU5PlNpAAAAQLrWJH8TNczbUPuv79eN6BvK6Z5TlXJVoscbyOSeKeFWq1ZNBQoUUJ06dVSiRAn5+PhIkqKiorR48WINHDhQN27c0EsvvaQTJ07I29v7oX3UqlVLQUFBKakdAAAAyJAc7B1UNaCqrcsAkIaeKXx36dLlkcs9PT3VtWtX5c6dW82bN9f169e1bNkydezYMUVFAgAAAACQkZky4FqNGjWs7y9dumTGVwAAAAAAkGGYEr63bNlifV+4cGEzvgIAAAAAgAwj1cJ3bGyszp07px9++EGdO3eWJBUpUkStWrV6ZPu///5bZcqUkZubmzw9PVW8eHG98847OnDgQJK/LzIy8oEXAAAAAADpUYrDt6urq+zs7OTq6qqCBQuqb9++CgsLU+3atbV+/Xq5uLg8cruQkBAdO3ZM7u7uio2N1YkTJzRt2jRVrlxZQ4cOfer3jhkzRt7e3tZX3rx5U3ooAAAAAACYIsXhOyAgQP7+/vLw8LAua9iwocaPH698+fI91L5o0aL64osvdPz4ccXExCg0NFS3b9/W6tWrVblyZRmGoVGjRunrr79+4vcOHjxYERER1tfFixdTeigAAAAAAJjCzjAMI7V2dv36dc2ePVujRo1SeHi4hg4dqhEjRiR5+5iYGNWrV0979uyRp6enLl269Mhpyh4lMjJS3t7eioiIkJeX17MeAgAAAAAASZKcHJqqA67lypVLAwYM0KpVq2RnZ6fPP/9cy5YtS/L2rq6uGj16tKS7c4avX78+NcsDAAAAAMAmTBntvFq1aqpTp44kacqUKcnatmbNmtb3Z86cSdW6AAAAAACwBVPCtyQFBgZKkk6dOmXWVwAAAAAAkCGYFr7v9Vpny5YtWdvt3LnT+r5gwYKpWhMAAAAAALaQ7PCdmJiop43Rtn79eu3evVuS1KBBA+vyp20XGxurIUOGSJI8PDzUuHHj5JYHAAAAAEC6k+zwffHiRVWsWFE//fSTzpw580CgvnjxosaOHas2bdrIMAzlyJFD/fr1s67fvHmzmjRpojlz5ujSpUvW5fHx8Vq/fr3q1q2rXbt2SZKGDRsmHx+fFBwaAAAAAADpg+OzbHTo0CG9++67kiRnZ2d5eXnpzp07un37trVNwYIFtXjxYgUEBFiXGYah9evXW0cxd3Nzk4eHhyIiIhQfHy9Jsre316BBg/TRRx8980EBAAAAAJCeJDt858mTRwsXLlRQUJB27dqlq1evKiQkRA4ODsqXL5/Kly+vNm3aqEOHDnJzc3tg27Jly+qrr77Sjh07dPjwYYWEhCg8PFzu7u4qVaqU6tatqx49eqhs2bKpdoAAAAAAANianfG0B7EziORMbg4AAAAAQEolJ4eaNto5AAAAAAC4i/ANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYzNHWBQAAgPTJYjF09WS4bkfGysPLRbmL+sje3s7WZQEAkCERvgEAwENOH7iuLQtO6nZ4rHWZh4+L6rYvqsIVc9mwMgAAMiZuOwcAAA84feC6Vv105IHgLUm3w2O16qcjOn3guo0qAwAg4yJ8AwAAK4vF0JYFJ5/YZuvCk7JYjDSqCACAzIHwDQAArK6eDH+ox/vfosJidfVkeNoUBABAJkH4BgAAVrcjnxy8k9sOAADcRfgGAABWHl4uqdoOAADcRfgGAABWuYv6yMPnycHaM/vdaccAAEDSEb4BAICVvb2d6rYv+sQ2dV4rynzfAAAkE+EbAAA8oHDFXHq+Z5mHesA9s7vo+Z5lmOcbAIBn4GjrAgAAQPpTuGIuFSyf8+7o55Gx8vC6e6s5Pd4AADwbwjcAAHgke3s7BRbPbusyAADIFLjtHAAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTEb4BAAAAADAZ4RsAAAAAAJMRvgEAAAAAMBnhGwAAAAAAkxG+AQAAAAAwGeEbAAAAAACTOdq6AAAAAAB4IkuidH67FBUsefpL+WtJ9g62rgpIFsI3AAAAgPTr6BJp1X+lyCv/f5lXHun5cVKp1rarC0gmbjsHAAAAkD4dXSIt7PJg8JakyKt3lx9dYpu6gGdA+AYAAACQ/lgS7/Z4y3jEyv9btmrQ3XZABkD4BgAAAJD+nN/+cI/3Awwp8vLddkAGQPgGAAAAkP5EBaduO8DGCN8AAAAA0h9P/9RtB9gY4RsAAABA+pO/1t1RzWX3mAZ2klfg3XZABkD4BgAAAJD+2DvcnU5M0sMB/P8+Pz+W+b6RYRC+AQAAAKRPpVpLr82SvHI/uNwrz93lzPONDMTR1gUAAFLm0KFDOnv2rF588UU5OnJZBwBkMqVaSyVa3h3VPCr47jPe+WvR440Mh3+lAUAGtmbNGjVv3lyStGDBAr322ms2rggAABPYO0gF69q6CiBFuO0cADKoTZs2qUWLFpKkvHnzqnVrbr0DAABIrwjfAJCBWCwWbd68Wd27d1eDBg1ksVgkSUuXLpWrq6uNqwMAAMDjcNs5AGQgzZs317p16x5YNnHiRJUvX95GFQEAACAp6PkGgAykTZs2kqTKlStrwoQJCgkJUe/evW1cFQAAAJ7GzjAMw9ZFpIbIyEh5e3srIiJCXl5eti4HAAAAAJDJJSeH0vMNAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJCN8AAAAAAJiM8A0AAAAAgMkI3wAAAAAAmIzwDQAAAACAyQjfAAAAAACYjPANAAAAAIDJnil879+/X5999plat26tEiVKyNfXV05OTvL19VXt2rU1atQo3bx584n7CA4O1oABA1S8eHG5ubkpR44cqlu3rqZNmybDMJ7pYAAAAAAASI/sjGdIun369NHEiROtn11dXeXk5KRbt25Zl/n5+WnJkiWqWbPmQ9vv27dPzZs3V2hoqCTJ09NTMTExSkhIkCQ1a9ZMS5YskYuLS5JrioyMlLe3tyIiIuTl5ZXcQwIAAAAAIFmSk0Ofqee7WrVq+vLLL7Vjxw6FhYXpzp07ioyM1K1btzRz5kzlzJlTISEheumllxQREfHAthEREXrxxRcVGhqqEiVKaM+ePbp165Zu376tH374QU5OTlqzZo369ev3LKUBAAAAAJDuPFPP99OsWbNGzZs3lyTNmTNHHTt2tK775JNPNHLkSLm5uenvv/9WwYIFH9h2zJgx+vjjj+Xg4KCjR4+qWLFiSfpOer4BAAAAAGnJ9J7vp6lRo4b1/aVLlx5YN2vWLEnS66+//lDwlqS+ffvK09NTiYmJmjt3rhnlAQAAAACQpkwJ31u2bLG+L1y4sPX98ePHdeHCBUlSixYtHrmtp6en6tatK+luDzoAAAAAABmdY2rtKDY2VlevXtWyZcs0bNgwSVKRIkXUqlUra5sjR45Y35cpU+ax+ypTpoxWrlypo0ePPvH7YmNjrZ8jIyMf+C8AAAAAAGZKTv5Mcfh2dXV9IATfU7t2bf36668PjFh+5coV6/vAwMDH7vPeusjISEVFRcnT0/OhNmPGjNFnn332wDJnZ2flzZs32ccAAAAAAMCzCAgIkLOz81PbpTh8BwQEKCYmRlFRUbp9+7YkqWHDhvriiy+UL1++B9rePxWZu7v7Y/d5/7pbt249MnwPHjxY/fv3t36OjIxU3rx5dfHiRQZcg6k415BWONeQVjjXkFY415BWONeQlpydneXq6vrUdikO3+fOnbO+v379umbPnq1Ro0apWrVqGjp0qEaMGJHSr3gkFxeXR84D7uXlxf9gSBOca0grnGtIK5xrSCuca0grnGtIT1J1wLVcuXJpwIABWrVqlezs7PT5559r2bJl1vXZsmWzvo+Ojn7sfu5fd/82AAAAAABkRKaMdl6tWjXVqVNHkjRlyhTr8jx58ljfX758+bHb31vn5eX1yFvOAQAAAADISEwJ39L/HzTt1KlT1mX3j3B+/8jn/3ZvXalSpZL8fS4uLho+fPgjb0UHUhPnGtIK5xrSCuca0grnGtIK5xrSIzvDMAwzdlyjRg3t2rVL1apV065du6zL8+fPrwsXLuitt97S9OnTH9ru9u3bCggIUFRUlIYNG/bQiOYAAAAAAGQ0ye75TkxM1NPy+vr167V7925JUoMGDR5Y16VLF0nS/PnzHxis7Z6JEycqKipKDg4O6tixY3LLAwAAAAAg3Ul2+L548aIqVqyon376SWfOnHkgiF+8eFFjx45VmzZtZBiGcuTIoX79+j2w/YcffqiAgABFR0erZcuW2rdvnyQpLi5OkyZN0ieffCJJ6tGjh4oVK5aSYwMAAAAAIF1I9m3n586dU8GCBa2fnZ2d5eXlpTt37ljn+ZakggULavHixapYseJD+9i3b5+aN2+u0NBQSXdHNI+JiVF8fLwkqVmzZlqyZAnPaAAAAAAAMoVkh++4uDj9+eefCgoK0q5du3T16lWFhITIwcFBOXPmVPny5dWmTRt16NBBbm5uj91PcHCwxo0bp2XLlunixYtydXVVmTJl1LVrV7311luytzdtLDgAAAAAANKUaQOuAQAAAACAuzJ897KdnV2SXw0bNnxo+08//TRJ294/ZRqyppSea/cEBwdrwIABKl68uNzc3JQjRw7VrVtX06ZNe+pghsh6IiMjNW7cONWqVUs5c+aUi4uLnnvuOTVs2FCffvqpwsPDH9qG6xqexbOca/dwXcPTzJw5M0nXpXXr1j1ye65rSKqUnmv3cF2DGRxtXUBK+fv7P3F9fHy8bt68KUmqWrXqY9s5OTkpR44cj13v6Jjhf1RIodQ41/493oGnp6du3bqlrVu3auvWrfrtt98Y7wBWGzdu1BtvvKHg4GBJd69Dnp6eunz5si5fvqygoCC99NJLqlChwiO357qGpErJucZ1Dclhb2+vnDlzPnb9084TrmtIqpSca1zXYBojk/vqq68MSYYk459//nlo/fDhww1JRv369dO+OGQqTzvXwsPDjYCAAEOSUaJECWPPnj2GYRhGbGys8cMPPxhOTk6GJKNXr15pXTrSoa1btxpubm6GJKNJkybG1q1bjcTERMMwDCM6OtrYu3evMWTIEOPMmTMPbct1DcmRknON6xqSasaMGYYkI3/+/M+0Pdc1JFVKzzWuazBThr/t/GmmT58uSapTp46KFy9u42qQmT3tXPvqq6907do1ubm5acWKFapSpYqkuzMGvPfee/rss88kSVOmTNGJEyfSrnCkO9HR0erSpYvu3LmjV155RatXr1bt2rWtA1G6ubmpcuXKGjly5AOzTwDJldJzjesagMyG6xrMlKnD9/bt23Xs2DFJ0ttvv23japCZJeVcmzVrliTp9ddff+Q/Yvv27StPT08lJiZq7ty55hWLdG/27Nk6c+aM3NzcNHnyZGZ/gGlSeq5xXQOQ2XBdg5ky9b/o7vVEenl5qV27djauBpnZ086148eP68KFC5KkFi1aPHIfnp6eqlu3riRpzZo1JlWKjODeX/xt2rSRn5+fjatBZpaSc43rGoDMhusazJZpw3dUVJQWLlwoSerQoYPc3d2f2P7vv/9WmTJl5ObmJk9PTxUvXlzvvPOODhw4kBblIgNLyrl25MgR6/syZco8dl/31h09ejSVq0RGERsbq71790qS6tevrzNnzqh79+567rnn5OLiooCAALVp00YrV6586r64ruFJUnqucV3Ds7hx44YqV64sT09Pubm5qVChQurUqZOCgoKStD3XNSTVs5xrXNdgtkwbvufPn6+oqChJSbvlPCQkRMeOHZO7u7tiY2N14sQJTZs2TZUrV9bQoUPNLhcZWFLOtStXrljfBwYGPnZf99ZFRkZa94ms5dy5c4qLi5MkXbp0SeXKldPPP/+sGzduyN3dXcHBwVqyZIleeOEF9erV64n74rqGJ0npucZ1Dc8iOjpa+/fvl7OzsywWi86ePau5c+eqYcOGeuutt5SQkPDE7bmuIame5VzjugazZdrwPW3aNElS+fLlVbly5ce2K1q0qL744gsdP35cMTExCg0N1e3bt7V69WpVrlxZhmFo1KhR+vrrr9OqdGQwSTnXbt26ZX3/pLsw7l93/zbIOsLCwqzvx4wZIycnJ82bN09RUVEKCwvThQsX9Prrr0uSJk+erO++++6hfXBdQ1Kk9FzjuobkyJMnj4YPH65Dhw4pJiZGN2/eVHR0tLZt26YmTZpIkmbMmKF+/fo9cnuua0iqlJxrXNdgurQeXv3e8P/P+lq5cuVTv+PIkSPW9hMmTHjmWu/cuWNUrVrVkGR4enoa4eHhz7wvpL30dK6NGjXK2i4+Pv6x7aZMmWJtd+XKlWc6bqS91DzXtm3b9sC6+fPnP/R9iYmJRsWKFQ1Jhp+f3xPPqX/jupaxpadzjeta5pYWf4fek5iYaLRp08aQZNjb2xsnTpxIVq1c1zK29HSucV2D2TJlz/e9nkhXV1d17Njxmffj6uqq0aNHS7r7XO/69etTpT5kHkk917Jly2Z9Hx0d/dh296+7fxtkHff/uefNm1ft27d/qI29vb0GDBgg6e4tmPv27Uvy/rmu4Z6Unmtc15Ba7O3t9dVXX0mSLBaLli5dmqztua4hqZ52rnFdg9kc0/oL33jjDb344ovPvL23t/cT18fFxWnOnDmSpFdeeUXZs2d/5u+SpJo1a1rfnzlzJkX7QtpKT+danjx5rO8vX74sLy+vR7a7fPmypLujpnt6eia3ZNhIap5r9z9jVqJEicduU7JkSev78+fPq3r16kn+Pq5rGVd6Ote4rmVuZv8d+m9FihSRn5+fQkJCnum6xHUt40pP5xrXNZgtzcO3i4uLXFxcTNv/n3/+qZCQEEnM7Z3Vpadz7f4RM48cOfLAP2bvd2+UzVKlSqVSlUgLqXmu5ciRQ4GBgbp8+bLs7Owe284wDOv7J7VD5pKezjWua5mb2X+HAvekp3ON6xrMluluO793G3CRIkVUv379FO9v586d1vcFCxZM8f6QeSTnXCtevLjy5csnSVq1atUj29y+fVtbtmyRJDVr1iwVK0VGc+/P/9ixYw8En/sdO3bM+j651yaua7gnJeca1zWkptOnT1t/of0s1yWua0iqJ51rXNdgtkwVvi9cuKB169ZJkt56662n9gY97h8a98TGxmrIkCGSJA8PDzVu3Dh1CkWGl9xzTZK6dOki6e7UZOfOnXto/cSJExUVFSUHB4cUjVWAjK9bt26SpIsXL2rBggUPrbdYLPrmm28k3b11uFKlStZ1XNeQHCk51ySua0iap12XDMPQwIEDJd19JvfftyBzXUNSpfRck7iuwWQ2GujNFMOHDzckGY6OjkkaeTAoKMho3LixMXv2bOPixYvW5XFxcca6deusI2dKMsaNG2dm6chgknuuGYZhhIeHGwEBAYYko1SpUsbevXsNwzCM2NhY48cffzScnZ0NSUavXr3MLB0ZxKuvvmpIMnx8fIz58+cbcXFxhmEYxoULF4zXX3/dem2aOXPmA9txXUNyPeu5Zhhc15A0Z8+eNapWrWpMnjzZOH36tGGxWAzDuDvy9I4dO4zmzZtbz7NHnStc15BUKT3XDIPrGsyVacJ3YmKikT9/fkOS0bp16yRts3HjxgemKnBzczP8/PwMJycn6zJ7e3vj448/Nrl6ZCTPcq7ds3fvXsPX19d6fmXLlu2B861Zs2ZGTEyMSZUjI4mKijLq1atnPTdcXFyM7NmzP3DNGjZs2EPbcV1Dcj3ruXYP1zU8zdmzZx84n1xcXAw/Pz/DxcXlgeXdunV75PROXNeQVCk91+7hugaz2BnGU+7PyCDWrFmj5s2bS5KWLFmiVq1aPXWb0NBQzZw5Uzt27NDhw4cVEhKiyMhIubu7q2DBgqpbt6569OihsmXLml0+MpBnOdfuFxwcrHHjxmnZsmW6ePGiXF1dVaZMGXXt2lVvvfWW7O0z1dMgSAGLxaKff/5Zs2fP1pEjR3Tr1i3lypVLdevWVd++fVWrVq2HtuG6hmfxLOfa/biu4Unu3Lmj6dOna8eOHTp48KBu3LihsLAwubq66rnnnlOtWrX01ltvqXbt2o/cnusakiql59r9uK7BDJkmfAMAAAAAkF7xKxsAAAAAAExG+AYAAAAAwGSEbwAAAAAATEb4BgAAAADAZIRvAAAAAABMRvgGAAAAAMBkhG8AAAAAAExG+AYAAAAAwGSEbwAAAAAATEb4BgAAAADAZIRvAAAAAABMRvgGAAAAAMBkhG8AAAAAAEz2/wDdb+XmBtuvuQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "gs = fig.add_gridspec(nrows=1, ncols=1)\n",
    "\n",
    "ax = fig.add_subplot(gs[:, :],projection=ccrs.PlateCarree())\n",
    "\n",
    "for i in np.unique(argo_df['ID']):\n",
    "    if len(argo_df[argo_df['ID']==i]) > 3:\n",
    "        eddy_df = argo_df[argo_df['ID']==i]\n",
    "        if (eddy_df.iloc[0]['LAT'] < 55) & (eddy_df.iloc[0]['LON'] < -45):\n",
    "            ax.scatter(eddy_df.iloc[0]['LON'],eddy_df.iloc[0]['LAT'],label='%s %s'%(eddy_df.iloc[0]['POLARITY'],eddy_df.iloc[0]['ID']))\n",
    "            \n",
    "ymin,ymax = round(min(argo_df.LAT)),55\n",
    "xmin,xmax = round(min(argo_df.LON)),-45#round(max(argo_df.LON))\n",
    "\n",
    "ax.coastlines()\n",
    "ax.set_xlim([xmin,xmax])\n",
    "ax.set_ylim([ymin,ymax])\n",
    "ax.set_xticks(range(xmin,xmax, 5)) \n",
    "ax.set_yticks(range(ymin,ymax, 5)) \n",
    "plt.legend(bbox_to_anchor=(1,1),fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMO</th>\n",
       "      <th>WMO_CYCLE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>CHLA</th>\n",
       "      <th>BBP</th>\n",
       "      <th>DOXY</th>\n",
       "      <th>NO3</th>\n",
       "      <th>PH</th>\n",
       "      <th>PAR</th>\n",
       "      <th>POLARITY</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>4903456</td>\n",
       "      <td>4903456_062</td>\n",
       "      <td>2024-05-13</td>\n",
       "      <td>-51.370</td>\n",
       "      <td>36.743</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>164996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4903456</td>\n",
       "      <td>4903456_063</td>\n",
       "      <td>2024-05-23</td>\n",
       "      <td>-50.611</td>\n",
       "      <td>36.775</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>164996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4903456</td>\n",
       "      <td>4903456_064</td>\n",
       "      <td>2024-06-02</td>\n",
       "      <td>-50.685</td>\n",
       "      <td>37.642</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>164996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4903456</td>\n",
       "      <td>4903456_069</td>\n",
       "      <td>2024-07-22</td>\n",
       "      <td>-52.558</td>\n",
       "      <td>37.508</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>164996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4903456</td>\n",
       "      <td>4903456_070</td>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>-53.036</td>\n",
       "      <td>37.169</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>164996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4903456</td>\n",
       "      <td>4903456_071</td>\n",
       "      <td>2024-08-11</td>\n",
       "      <td>-52.937</td>\n",
       "      <td>37.234</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>164996.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3   PH  \\\n",
       "45  4903456  4903456_062  2024-05-13 -51.370  36.743  YES  YES  YES  YES  YES   \n",
       "46  4903456  4903456_063  2024-05-23 -50.611  36.775  YES  YES  YES  YES  YES   \n",
       "47  4903456  4903456_064  2024-06-02 -50.685  37.642  YES  YES  YES  YES  YES   \n",
       "48  4903456  4903456_069  2024-07-22 -52.558  37.508  YES  YES  YES  YES  YES   \n",
       "49  4903456  4903456_070  2024-08-01 -53.036  37.169  YES  YES  YES  YES  YES   \n",
       "50  4903456  4903456_071  2024-08-11 -52.937  37.234  YES  YES  YES  YES  YES   \n",
       "\n",
       "   PAR POLARITY        ID  \n",
       "45  NO      CYC  164996.0  \n",
       "46  NO      CYC  164996.0  \n",
       "47  NO      CYC  164996.0  \n",
       "48  NO      CYC  164996.0  \n",
       "49  NO      CYC  164996.0  \n",
       "50  NO      CYC  164996.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyc1_df = argo_df[argo_df['ID']==164996]\n",
    "cyc1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WMO</th>\n",
       "      <th>WMO_CYCLE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>CHLA</th>\n",
       "      <th>BBP</th>\n",
       "      <th>DOXY</th>\n",
       "      <th>NO3</th>\n",
       "      <th>PH</th>\n",
       "      <th>PAR</th>\n",
       "      <th>POLARITY</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_102</td>\n",
       "      <td>2024-01-09</td>\n",
       "      <td>-50.358</td>\n",
       "      <td>32.575</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_103</td>\n",
       "      <td>2024-01-19</td>\n",
       "      <td>-50.315</td>\n",
       "      <td>32.136</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_104</td>\n",
       "      <td>2024-01-29</td>\n",
       "      <td>-50.005</td>\n",
       "      <td>32.165</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_105</td>\n",
       "      <td>2024-02-08</td>\n",
       "      <td>-50.000</td>\n",
       "      <td>32.559</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_106</td>\n",
       "      <td>2024-02-18</td>\n",
       "      <td>-50.381</td>\n",
       "      <td>33.153</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_107</td>\n",
       "      <td>2024-02-28</td>\n",
       "      <td>-50.823</td>\n",
       "      <td>33.688</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_108</td>\n",
       "      <td>2024-03-09</td>\n",
       "      <td>-51.515</td>\n",
       "      <td>33.979</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_109</td>\n",
       "      <td>2024-03-19</td>\n",
       "      <td>-52.722</td>\n",
       "      <td>33.990</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_110</td>\n",
       "      <td>2024-03-29</td>\n",
       "      <td>-53.829</td>\n",
       "      <td>33.720</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_111</td>\n",
       "      <td>2024-04-08</td>\n",
       "      <td>-54.339</td>\n",
       "      <td>33.372</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_112</td>\n",
       "      <td>2024-04-18</td>\n",
       "      <td>-54.259</td>\n",
       "      <td>33.071</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_113</td>\n",
       "      <td>2024-04-28</td>\n",
       "      <td>-54.093</td>\n",
       "      <td>33.234</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_114</td>\n",
       "      <td>2024-05-08</td>\n",
       "      <td>-54.141</td>\n",
       "      <td>33.408</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_115</td>\n",
       "      <td>2024-05-18</td>\n",
       "      <td>-54.395</td>\n",
       "      <td>33.245</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_116</td>\n",
       "      <td>2024-05-29</td>\n",
       "      <td>-54.442</td>\n",
       "      <td>33.081</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_117</td>\n",
       "      <td>2024-06-08</td>\n",
       "      <td>-54.300</td>\n",
       "      <td>33.103</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>5906435</td>\n",
       "      <td>5906435_118</td>\n",
       "      <td>2024-06-18</td>\n",
       "      <td>-54.244</td>\n",
       "      <td>33.426</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>YES</td>\n",
       "      <td>NO</td>\n",
       "      <td>CYC</td>\n",
       "      <td>171334.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         WMO    WMO_CYCLE        DATE     LON     LAT CHLA  BBP DOXY  NO3  \\\n",
       "101  5906435  5906435_102  2024-01-09 -50.358  32.575  YES  YES  YES  YES   \n",
       "102  5906435  5906435_103  2024-01-19 -50.315  32.136  YES  YES  YES  YES   \n",
       "103  5906435  5906435_104  2024-01-29 -50.005  32.165  YES  YES  YES  YES   \n",
       "104  5906435  5906435_105  2024-02-08 -50.000  32.559  YES  YES  YES  YES   \n",
       "105  5906435  5906435_106  2024-02-18 -50.381  33.153  YES  YES  YES  YES   \n",
       "106  5906435  5906435_107  2024-02-28 -50.823  33.688  YES  YES  YES  YES   \n",
       "107  5906435  5906435_108  2024-03-09 -51.515  33.979  YES  YES  YES  YES   \n",
       "108  5906435  5906435_109  2024-03-19 -52.722  33.990  YES  YES  YES  YES   \n",
       "109  5906435  5906435_110  2024-03-29 -53.829  33.720  YES  YES  YES  YES   \n",
       "110  5906435  5906435_111  2024-04-08 -54.339  33.372  YES  YES  YES  YES   \n",
       "111  5906435  5906435_112  2024-04-18 -54.259  33.071  YES  YES  YES  YES   \n",
       "112  5906435  5906435_113  2024-04-28 -54.093  33.234  YES  YES  YES  YES   \n",
       "113  5906435  5906435_114  2024-05-08 -54.141  33.408  YES  YES  YES  YES   \n",
       "114  5906435  5906435_115  2024-05-18 -54.395  33.245  YES  YES  YES  YES   \n",
       "115  5906435  5906435_116  2024-05-29 -54.442  33.081  YES  YES  YES  YES   \n",
       "116  5906435  5906435_117  2024-06-08 -54.300  33.103  YES  YES  YES  YES   \n",
       "117  5906435  5906435_118  2024-06-18 -54.244  33.426  YES  YES  YES  YES   \n",
       "\n",
       "      PH PAR POLARITY        ID  \n",
       "101  YES  NO      CYC  171334.0  \n",
       "102  YES  NO      CYC  171334.0  \n",
       "103  YES  NO      CYC  171334.0  \n",
       "104  YES  NO      CYC  171334.0  \n",
       "105  YES  NO      CYC  171334.0  \n",
       "106  YES  NO      CYC  171334.0  \n",
       "107  YES  NO      CYC  171334.0  \n",
       "108  YES  NO      CYC  171334.0  \n",
       "109  YES  NO      CYC  171334.0  \n",
       "110  YES  NO      CYC  171334.0  \n",
       "111  YES  NO      CYC  171334.0  \n",
       "112  YES  NO      CYC  171334.0  \n",
       "113  YES  NO      CYC  171334.0  \n",
       "114  YES  NO      CYC  171334.0  \n",
       "115  YES  NO      CYC  171334.0  \n",
       "116  YES  NO      CYC  171334.0  \n",
       "117  YES  NO      CYC  171334.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cyc2_df = argo_df[argo_df['ID']==171334]\n",
    "cyc2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cyc_ds = xr.open_dataset('../Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111.nc', engine=\"netcdf4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff90b524410>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 12))\n",
    "gs = fig.add_gridspec(nrows=1, ncols=1)\n",
    "\n",
    "ax = fig.add_subplot(gs[:, :],projection=ccrs.PlateCarree())\n",
    "\n",
    "for eddy_id in [164996,171334]:\n",
    "    cyc_inds = np.where((cyc_ds.track==eddy_id) & (cyc_ds.time >= np.datetime64('2024-02-01')))[0]\n",
    "    center_lats = [i for i in cyc_ds.isel(obs=cyc_inds).latitude.values]\n",
    "    center_lons = [i for i in cyc_ds.isel(obs=cyc_inds).longitude.values]\n",
    "    ax.scatter(center_lons,center_lats,s=1,label='CYC %s'%(eddy_id))\n",
    "\n",
    "ymin,ymax = round(min(argo_df.LAT)),45\n",
    "xmin,xmax = round(min(argo_df.LON)),-40\n",
    "\n",
    "ax.coastlines()\n",
    "ax.set_xlim([xmin,xmax])\n",
    "ax.set_ylim([ymin,ymax])\n",
    "ax.set_xticks(range(xmin,xmax, 5)) \n",
    "ax.set_yticks(range(ymin,ymax, 5)) \n",
    "ax.grid()\n",
    "plt.legend(fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save study candidate eddy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_130/3852210267.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n"
     ]
    }
   ],
   "source": [
    "for eddy_id in [164996,171334]:\n",
    "    subset_inds = np.where((cyc_ds.track==eddy_id))[0]\n",
    "    ds_eddy = cyc_ds.isel(obs=subset_inds)\n",
    "    ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/1964693339.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n"
     ]
    }
   ],
   "source": [
    "for eddy_id in [183620,189921,192365,196238]:\n",
    "    subset_inds = np.where((cyc_ds.track==eddy_id))[0]\n",
    "    ds_eddy = cyc_ds.isel(obs=subset_inds)\n",
    "    ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "anti_ds = xr.open_dataset('../Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111.nc', engine=\"netcdf4\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable amplitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable effective_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable effective_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable effective_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable effective_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable speed_average with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable speed_contour_latitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable speed_contour_longitude with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable speed_contour_shape_error with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable speed_radius with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable time with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n",
      "/tmp/ipykernel_129/4091044048.py:4: SerializationWarning: saving variable uavg_profile with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))\n"
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   "source": [
    "for eddy_id in [182188]:\n",
    "    subset_inds = np.where((anti_ds.track==eddy_id))[0]\n",
    "    ds_eddy = anti_ds.isel(obs=subset_inds)\n",
    "    ds_eddy.to_netcdf('Eddy_trajectory_nrt_3.2exp_anticyclonic_20180101_20241111_ID_%s.nc'%(eddy_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CHECK FILE LOOKS GUCCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 346kB\n",
       "Dimensions:                        (obs: 382, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float64 3kB ...\n",
       "    effective_area                 (obs) float32 2kB ...\n",
       "    effective_contour_height       (obs) float32 2kB ...\n",
       "    effective_contour_latitude     (obs, NbSample) float64 61kB ...\n",
       "    effective_contour_longitude    (obs, NbSample) float64 61kB ...\n",
       "    effective_contour_shape_error  (obs) float64 3kB ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float64 61kB ...\n",
       "    speed_contour_shape_error      (obs) float64 3kB ...\n",
       "    speed_radius                   (obs) float64 3kB ...\n",
       "    time                           (obs) datetime64[ns] 3kB ...\n",
       "    track                          (obs) uint32 2kB ...\n",
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       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2025-02-03T08:39:10Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2507D\n",
       "    time_coverage_end:         2024-11-11T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Cyclonic Eddies in Altimeter Observa...</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-942fe9b8-f24a-4c91-9bf0-ba708783d418' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-942fe9b8-f24a-4c91-9bf0-ba708783d418' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>obs</span>: 382</li><li><span>NbSample</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-71ff6320-bc68-4a33-8103-c241098f1309' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71ff6320-bc68-4a33-8103-c241098f1309' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-482ba687-76a5-4f72-968b-e5cdcf0b0a73' class='xr-section-summary-in' type='checkbox'  ><label for='section-482ba687-76a5-4f72-968b-e5cdcf0b0a73' class='xr-section-summary' >Data variables: <span>(27)</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>amplitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b2d959f-839b-4b50-9ef9-03a431c08e70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b2d959f-839b-4b50-9ef9-03a431c08e70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d707d16c-371f-43aa-97a2-6481d451b5de' class='xr-var-data-in' type='checkbox'><label for='data-d707d16c-371f-43aa-97a2-6481d451b5de' 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>comment :</span></dt><dd>Magnitude of the height difference between the extremum of SSH within the eddy and the SSH around the effective contour defining the eddy edge</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>0.004</dd><dt><span>max :</span></dt><dd>1.405</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3173a676-3a7c-426a-84c1-162423e90363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3173a676-3a7c-426a-84c1-162423e90363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f667504a-c77b-465e-9636-44215a85bb52' class='xr-var-data-in' type='checkbox'><label for='data-f667504a-c77b-465e-9636-44215a85bb52' 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>comment :</span></dt><dd>Area enclosed by the effective contour in m^2</dd><dt><span>long_name :</span></dt><dd>Effective area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>128509144.0</dd><dt><span>max :</span></dt><dd>325393650000.0</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7143e3fe-a5a6-401d-a69c-35f74d2ea5b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7143e3fe-a5a6-401d-a69c-35f74d2ea5b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ef67e54-8730-46d8-9a6a-db331c36b18a' class='xr-var-data-in' type='checkbox'><label for='data-0ef67e54-8730-46d8-9a6a-db331c36b18a' 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>comment :</span></dt><dd>SSH filtered height for effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.918</dd><dt><span>max :</span></dt><dd>0.446</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94469da8-7f67-4b84-ad62-901e66a081a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94469da8-7f67-4b84-ad62-901e66a081a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98bab9a0-eec2-42fe-ba04-d3fc810e0589' class='xr-var-data-in' type='checkbox'><label for='data-98bab9a0-eec2-42fe-ba04-d3fc810e0589' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[7640 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d63ea023-150a-4113-9b2b-250d6c40f1de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d63ea023-150a-4113-9b2b-250d6c40f1de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f9b2d69-b769-46fb-a81c-c3aacf8ba482' class='xr-var-data-in' type='checkbox'><label for='data-7f9b2d69-b769-46fb-a81c-c3aacf8ba482' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[7640 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-97916d0e-0ebf-470a-b2b6-ddf4dc135441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97916d0e-0ebf-470a-b2b6-ddf4dc135441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cc683e8-c88d-4deb-95e6-ccf87e5cdc4d' class='xr-var-data-in' type='checkbox'><label for='data-2cc683e8-c88d-4deb-95e6-ccf87e5cdc4d' 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>comment :</span></dt><dd>Error criterion between the effective contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Effective Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>2.5</dd><dt><span>max :</span></dt><dd>75.5</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>effective_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd536e06-1f88-4f9e-92fe-d37b20aa1637' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd536e06-1f88-4f9e-92fe-d37b20aa1637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0aa96809-2394-42e1-b5bb-e7ee908e3217' class='xr-var-data-in' type='checkbox'><label for='data-0aa96809-2394-42e1-b5bb-e7ee908e3217' 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>comment :</span></dt><dd>Radius of the best fit circle corresponding to the effective contour</dd><dt><span>long_name :</span></dt><dd>Effective Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>7050.0</dd><dt><span>max :</span></dt><dd>378400.0</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inner_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c082d2c8-79d6-4de7-b253-65a0ebe18ae6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c082d2c8-79d6-4de7-b253-65a0ebe18ae6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0797fe59-7e9b-49e8-8c0b-a128aa4608da' class='xr-var-data-in' type='checkbox'><label for='data-0797fe59-7e9b-49e8-8c0b-a128aa4608da' 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>comment :</span></dt><dd>SSH filtered height for the smallest detected contour</dd><dt><span>long_name :</span></dt><dd>Inner Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-1.462</dd><dt><span>max :</span></dt><dd>0.406</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49249ce7-e1e9-432e-bc90-4891dd9992f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49249ce7-e1e9-432e-bc90-4891dd9992f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b542ec7-4494-4743-9e65-9b304d094eb0' class='xr-var-data-in' type='checkbox'><label for='data-7b542ec7-4494-4743-9e65-9b304d094eb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-78.05134</dd><dt><span>max :</span></dt><dd>84.158295</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01be336a-735f-4d75-a413-fe916cde57ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01be336a-735f-4d75-a413-fe916cde57ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e8f8346-509e-4099-b005-2344f6b34c11' class='xr-var-data-in' type='checkbox'><label for='data-5e8f8346-509e-4099-b005-2344f6b34c11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>Latitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Latitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>min :</span></dt><dd>-90.0</dd><dt><span>max :</span></dt><dd>84.15227</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3857c607-57cd-4b0f-9274-227891a297a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3857c607-57cd-4b0f-9274-227891a297a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aec8ece5-416f-4ad0-8e64-084216cca1c5' class='xr-var-data-in' type='checkbox'><label for='data-aec8ece5-416f-4ad0-8e64-084216cca1c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude center of the best fit circle</dd><dt><span>long_name :</span></dt><dd>Eddy Center Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-28.260529</dd><dt><span>max :</span></dt><dd>367.2987</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_max</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-063604f0-300a-4747-96c6-e4cbf3574459' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-063604f0-300a-4747-96c6-e4cbf3574459' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1a8e985-0374-4964-9863-be0a279b42f1' class='xr-var-data-in' type='checkbox'><label for='data-c1a8e985-0374-4964-9863-be0a279b42f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitude of the inner contour</dd><dt><span>long_name :</span></dt><dd>Longitude of the SSH maximum</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>min :</span></dt><dd>-28.13443</dd><dt><span>max :</span></dt><dd>367.44983</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_contours</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3bbf0d0c-7e20-4599-bf7e-381327930c80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bbf0d0c-7e20-4599-bf7e-381327930c80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a337c57d-7a7c-4a0e-922b-9438a40bd93b' class='xr-var-data-in' type='checkbox'><label for='data-a337c57d-7a7c-4a0e-922b-9438a40bd93b' 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>comment :</span></dt><dd>Number of contours selected for this eddy</dd><dt><span>long_name :</span></dt><dd>Number of contours</dd><dt><span>min :</span></dt><dd>1</dd><dt><span>max :</span></dt><dd>703</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_e</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f09ee327-6123-4207-ac1f-9764951b9f5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f09ee327-6123-4207-ac1f-9764951b9f5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-331f6f21-c39c-48a7-9e30-6e0e3e567eaf' class='xr-var-data-in' type='checkbox'><label for='data-331f6f21-c39c-48a7-9e30-6e0e3e567eaf' 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>description :</span></dt><dd>Number of points for effective contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for effective contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>145</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_point_s</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-167d93e5-d056-4a9c-805f-4864261fb735' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-167d93e5-d056-4a9c-805f-4864261fb735' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dfecc3e-afed-43d9-955b-d5be4c16d87a' class='xr-var-data-in' type='checkbox'><label for='data-3dfecc3e-afed-43d9-955b-d5be4c16d87a' 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>description :</span></dt><dd>Number of points for speed contour before resampling</dd><dt><span>long_name :</span></dt><dd>number of points for speed contour</dd><dt><span>units :</span></dt><dd>ordinal</dd><dt><span>min :</span></dt><dd>10</dd><dt><span>max :</span></dt><dd>135</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_flag</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0a62e10-ef84-489e-b40b-2273254c9d01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0a62e10-ef84-489e-b40b-2273254c9d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3549c4f0-c30c-4f7a-b926-4319843ca6ea' class='xr-var-data-in' type='checkbox'><label for='data-3549c4f0-c30c-4f7a-b926-4319843ca6ea' 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>comment :</span></dt><dd>Flag indicating if the value is interpolated between two observations or not (0: observed eddy, 1: interpolated eddy)</dd><dt><span>long_name :</span></dt><dd>Virtual Eddy Position</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>observation_number</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36276d86-da63-491f-b245-7f7701232f0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36276d86-da63-491f-b245-7f7701232f0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3853aba-27ee-47ef-b8af-5dd725f37ff4' class='xr-var-data-in' type='checkbox'><label for='data-b3853aba-27ee-47ef-b8af-5dd725f37ff4' 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>comment :</span></dt><dd>Observation sequence number, days starting at the eddy first detection</dd><dt><span>long_name :</span></dt><dd>Eddy temporal index in a trajectory</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>2072</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=uint16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_area</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b33fc249-bb31-44bd-af01-666918226e9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b33fc249-bb31-44bd-af01-666918226e9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5c47637-3430-4dcc-9c46-498f3742d738' class='xr-var-data-in' type='checkbox'><label for='data-b5c47637-3430-4dcc-9c46-498f3742d738' 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>comment :</span></dt><dd>Area enclosed by the speed contour in m^2</dd><dt><span>long_name :</span></dt><dd>Speed area</dd><dt><span>units :</span></dt><dd>m^2</dd><dt><span>min :</span></dt><dd>39760428.0</dd><dt><span>max :</span></dt><dd>258590110000.0</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_average</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3ecff85-75c3-499e-bdb2-4e10838b93b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3ecff85-75c3-499e-bdb2-4e10838b93b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a9a5a76-1a72-420b-b225-d5b2b56c8696' class='xr-var-data-in' type='checkbox'><label for='data-4a9a5a76-1a72-420b-b225-d5b2b56c8696' 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>comment :</span></dt><dd>Average speed of the contour defining the radius scale “speed_radius”</dd><dt><span>long_name :</span></dt><dd>Maximum circum-averaged Speed</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>min :</span></dt><dd>0.0</dd><dt><span>max :</span></dt><dd>5.6524</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_height</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5df245bb-5938-414b-90b1-9d701d6ec324' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5df245bb-5938-414b-90b1-9d701d6ec324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63516f07-e1e0-4df4-b837-06ca32885db5' class='xr-var-data-in' type='checkbox'><label for='data-63516f07-e1e0-4df4-b837-06ca32885db5' 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>comment :</span></dt><dd>SSH filtered height for speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>-0.918</dd><dt><span>max :</span></dt><dd>0.446</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_latitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce1a4763-7ba8-4a04-8865-d19faab94e17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce1a4763-7ba8-4a04-8865-d19faab94e17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02b08efb-de22-43c9-a32e-51546d52b4c2' class='xr-var-data-in' type='checkbox'><label for='data-02b08efb-de22-43c9-a32e-51546d52b4c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Latitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Latitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[7640 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_longitude</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8d088fd3-39c5-46fb-8ed3-0f4da16f8a59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d088fd3-39c5-46fb-8ed3-0f4da16f8a59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-133dafc5-9ac9-4f0d-9692-db08ca1ae7d8' class='xr-var-data-in' type='checkbox'><label for='data-133dafc5-9ac9-4f0d-9692-db08ca1ae7d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>Longitudes of speed contour</dd><dt><span>long_name :</span></dt><dd>Speed Contour Longitudes</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[7640 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_contour_shape_error</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-64892319-eea2-4ecd-bbc5-5ae9ea4da2a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64892319-eea2-4ecd-bbc5-5ae9ea4da2a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cadc0391-c960-4316-ad65-f9a40d72de5b' class='xr-var-data-in' type='checkbox'><label for='data-cadc0391-c960-4316-ad65-f9a40d72de5b' 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>comment :</span></dt><dd>Error criterion between the speed contour and its best fit circle</dd><dt><span>long_name :</span></dt><dd>Speed Contour Shape Error</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>min :</span></dt><dd>1.5</dd><dt><span>max :</span></dt><dd>96.5</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_radius</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-27b5498c-7169-4d78-aa2c-046de75c0f05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27b5498c-7169-4d78-aa2c-046de75c0f05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e45419e-01f9-4274-9833-5f76980f9dc0' class='xr-var-data-in' type='checkbox'><label for='data-2e45419e-01f9-4274-9833-5f76980f9dc0' 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>comment :</span></dt><dd>Radius of the best fit circle corresponding to the contour of maximum circum-average speed</dd><dt><span>long_name :</span></dt><dd>Speed Radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>min :</span></dt><dd>5500.0</dd><dt><span>max :</span></dt><dd>342200.0</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6a44ae55-5b1b-4761-b211-07622b630d0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a44ae55-5b1b-4761-b211-07622b630d0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ac4a313-a66d-4acc-bca0-648d7384eb36' class='xr-var-data-in' type='checkbox'><label for='data-0ac4a313-a66d-4acc-bca0-648d7384eb36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>comment :</span></dt><dd>Date of this observation</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>min :</span></dt><dd>24837.0</dd><dt><span>max :</span></dt><dd>27343.0</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>track</span></div><div class='xr-var-dims'>(obs)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8a4c32e-443e-49ac-8765-896253470788' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8a4c32e-443e-49ac-8765-896253470788' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9f268d6-4c02-487a-8ca5-b3077eb39423' class='xr-var-data-in' type='checkbox'><label for='data-a9f268d6-4c02-487a-8ca5-b3077eb39423' 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>comment :</span></dt><dd>Trajectory identification number</dd><dt><span>long_name :</span></dt><dd>Trajectory number</dd><dt><span>min :</span></dt><dd>0</dd><dt><span>max :</span></dt><dd>200457</dd></dl></div><div class='xr-var-data'><pre>[382 values with dtype=uint32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uavg_profile</span></div><div class='xr-var-dims'>(obs, NbSample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9eb6a017-2ed5-4d90-ae18-36d78e3de31c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eb6a017-2ed5-4d90-ae18-36d78e3de31c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4b2f7aa-b218-4d64-bb21-1df56e9e4062' class='xr-var-data-in' type='checkbox'><label for='data-b4b2f7aa-b218-4d64-bb21-1df56e9e4062' 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>comment :</span></dt><dd>Speed averaged values from the effective contour inwards to the smallest contour, evenly spaced points</dd><dt><span>long_name :</span></dt><dd>Radial Speed Profile</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>[7640 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-327d8956-e5b8-436b-b30c-6d39e18a71c7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-327d8956-e5b8-436b-b30c-6d39e18a71c7' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-99521271-da46-4f41-93a3-9ebfbbff57a9' class='xr-section-summary-in' type='checkbox'  ><label for='section-99521271-da46-4f41-93a3-9ebfbbff57a9' class='xr-section-summary' >Attributes: <span>(19)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>comment :</span></dt><dd>Surface product; mesoscale eddies</dd><dt><span>creator_email :</span></dt><dd>aviso@altimetry.fr</dd><dt><span>creator_url :</span></dt><dd>https://www.aviso.altimetry.fr</dd><dt><span>date_created :</span></dt><dd>2025-02-03T08:39:10Z</dd><dt><span>framework_used :</span></dt><dd>https://github.com/AntSimi/py-eddy-tracker</dd><dt><span>framework_version :</span></dt><dd>v3.6.1</dd><dt><span>input_maps_processing :</span></dt><dd>DUACS DT2018 until 2021-12-07, DUACS DT2021 since 2021-12-08</dd><dt><span>input_product_reference :</span></dt><dd>https://www.aviso.altimetry.fr/en/data/products/value-added-products/global-mesoscale-eddy-trajectory-product.html</dd><dt><span>institution :</span></dt><dd>CLS/CNES in collaboration with IMEDEA</dd><dt><span>license :</span></dt><dd>https://www.aviso.altimetry.fr/fileadmin/documents/data/License_Aviso.pdf</dd><dt><span>product_version :</span></dt><dd>3.2exp</dd><dt><span>project :</span></dt><dd>SSALTO/DUACS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table</dd><dt><span>summary :</span></dt><dd>This dataset contains eddy atlas from all-satellite NRT CMEMS maps over global ocean (SEALEVEL_GLO_PHY_L4_NRT_OBSERVATIONS*)</dd><dt><span>time_coverage_duration :</span></dt><dd>P2507D</dd><dt><span>time_coverage_end :</span></dt><dd>2024-11-11T00:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2018-01-01T00:00:00Z</dd><dt><span>title :</span></dt><dd>Mesoscale Cyclonic Eddies in Altimeter Observations of ADT</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 346kB\n",
       "Dimensions:                        (obs: 382, NbSample: 20)\n",
       "Dimensions without coordinates: obs, NbSample\n",
       "Data variables: (12/27)\n",
       "    amplitude                      (obs) float64 3kB ...\n",
       "    effective_area                 (obs) float32 2kB ...\n",
       "    effective_contour_height       (obs) float32 2kB ...\n",
       "    effective_contour_latitude     (obs, NbSample) float64 61kB ...\n",
       "    effective_contour_longitude    (obs, NbSample) float64 61kB ...\n",
       "    effective_contour_shape_error  (obs) float64 3kB ...\n",
       "    ...                             ...\n",
       "    speed_contour_longitude        (obs, NbSample) float64 61kB ...\n",
       "    speed_contour_shape_error      (obs) float64 3kB ...\n",
       "    speed_radius                   (obs) float64 3kB ...\n",
       "    time                           (obs) datetime64[ns] 3kB ...\n",
       "    track                          (obs) uint32 2kB ...\n",
       "    uavg_profile                   (obs, NbSample) float64 61kB ...\n",
       "Attributes: (12/19)\n",
       "    Metadata_Conventions:      Unidata Dataset Discovery v1.0\n",
       "    comment:                   Surface product; mesoscale eddies\n",
       "    creator_email:             aviso@altimetry.fr\n",
       "    creator_url:               https://www.aviso.altimetry.fr\n",
       "    date_created:              2025-02-03T08:39:10Z\n",
       "    framework_used:            https://github.com/AntSimi/py-eddy-tracker\n",
       "    ...                        ...\n",
       "    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...\n",
       "    summary:                   This dataset contains eddy atlas from all-sate...\n",
       "    time_coverage_duration:    P2507D\n",
       "    time_coverage_end:         2024-11-11T00:00:00Z\n",
       "    time_coverage_start:       2018-01-01T00:00:00Z\n",
       "    title:                     Mesoscale Cyclonic Eddies in Altimeter Observa..."
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset('Eddy_trajectory_nrt_3.2exp_cyclonic_20180101_20241111_ID_164996.nc')\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[164996]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(ds.track.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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