HFR_trial.ipynb

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    "# from erddapy import ERDDAP\n",
    "import cmocean as cmo\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import datetime\n",
    "from scipy import interpolate\n",
    "import scipy\n",
    "import pandas as pd\n",
    "import metpy.calc as mc\n",
    "import xarray as xr\n",
    "import pyproj\n"
   ]
  },
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   "cell_type": "code",
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    "def get_ticks(bounds, dirs, otherbounds):\n",
    "    dirs = dirs.lower()\n",
    "    l0 = np.float(bounds[0])\n",
    "    l1 = np.float(bounds[1])\n",
    "    r = np.max([l1 - l0, np.float(otherbounds[1]) - np.float(otherbounds[0])])\n",
    "    if r <= 1.5:\n",
    "        # <1.5 degrees: 15' major ticks, 5' minor ticks\n",
    "        minor_int = 1.0 / 12.0\n",
    "        major_int = 1.0 / 4.0\n",
    "    elif r <= 3.0:\n",
    "        # <3 degrees: 30' major ticks, 10' minor ticks\n",
    "        minor_int = 1.0 / 6.0\n",
    "        major_int = 0.5\n",
    "    elif r <= 7.0:\n",
    "        # <7 degrees: 1d major ticks, 15' minor ticks\n",
    "        minor_int = 0.25\n",
    "        major_int = np.float(1)\n",
    "    elif r <= 15:\n",
    "        # <15 degrees: 2d major ticks, 30' minor ticks\n",
    "        minor_int = 0.5\n",
    "        major_int = np.float(2)\n",
    "    elif r <= 30:\n",
    "        # <30 degrees: 3d major ticks, 1d minor ticks\n",
    "        minor_int = np.float(1)\n",
    "        major_int = np.float(3)\n",
    "    else:\n",
    "        # >=30 degrees: 5d major ticks, 1d minor ticks\n",
    "        minor_int = np.float(1)\n",
    "        major_int = np.float(5)\n",
    "\n",
    "    minor_ticks = np.arange(np.ceil(l0 / minor_int) * minor_int, np.ceil(l1 / minor_int) * minor_int + minor_int,\n",
    "                            minor_int)\n",
    "    minor_ticks = minor_ticks[minor_ticks <= l1]\n",
    "    major_ticks = np.arange(np.ceil(l0 / major_int) * major_int, np.ceil(l1 / major_int) * major_int + major_int,\n",
    "                            major_int)\n",
    "    major_ticks = major_ticks[major_ticks <= l1]\n",
    "\n",
    "    if major_int < 1:\n",
    "        d, m, s = dd2dms(np.array(major_ticks))\n",
    "        if dirs == 'we' or dirs == 'ew' or dirs == 'lon' or dirs == 'long' or dirs == 'longitude':\n",
    "            n = 'W' * sum(d < 0)\n",
    "            p = 'E' * sum(d >= 0)\n",
    "            dir = n + p\n",
    "            major_tick_labels = [str(np.abs(int(d[i]))) + u\"\\N{DEGREE SIGN}\" + str(int(m[i])) + \"'\" + dir[i] for i in\n",
    "                                 range(len(d))]\n",
    "        elif dirs == 'sn' or dirs == 'ns' or dirs == 'lat' or dirs == 'latitude':\n",
    "            n = 'S' * sum(d < 0)\n",
    "            p = 'N' * sum(d >= 0)\n",
    "            dir = n + p\n",
    "            major_tick_labels = [str(np.abs(int(d[i]))) + u\"\\N{DEGREE SIGN}\" + str(int(m[i])) + \"'\" + dir[i] for i in\n",
    "                                 range(len(d))]\n",
    "        else:\n",
    "            major_tick_labels = [str(int(d[i])) + u\"\\N{DEGREE SIGN}\" + str(int(m[i])) + \"'\" for i in range(len(d))]\n",
    "    else:\n",
    "        d = major_ticks\n",
    "        if dirs == 'we' or dirs == 'ew' or dirs == 'lon' or dirs == 'long' or dirs == 'longitude':\n",
    "            n = 'W' * sum(d < 0)\n",
    "            p = 'E' * sum(d >= 0)\n",
    "            dir = n + p\n",
    "            major_tick_labels = [str(np.abs(int(d[i]))) + u\"\\N{DEGREE SIGN}\" + dir[i] for i in range(len(d))]\n",
    "        elif dirs == 'sn' or dirs == 'ns' or dirs == 'lat' or dirs == 'latitude':\n",
    "            n = 'S' * sum(d < 0)\n",
    "            p = 'N' * sum(d >= 0)\n",
    "            dir = n + p\n",
    "            major_tick_labels = [str(np.abs(int(d[i]))) + u\"\\N{DEGREE SIGN}\" + dir[i] for i in range(len(d))]\n",
    "        else:\n",
    "            major_tick_labels = [str(int(d[i])) + u\"\\N{DEGREE SIGN}\" for i in range(len(d))]\n",
    "\n",
    "    return minor_ticks, major_ticks, major_tick_labels"
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    "def add_map_features(ax, extent):\n",
    "#     # Gridlines and grid labels\n",
    "#     gl = ax.gridlines(\n",
    "#         draw_labels=True,\n",
    "#         linewidth=.5,\n",
    "#         color='black',\n",
    "#         alpha=0.25,\n",
    "#         linestyle='--',\n",
    "#     )\n",
    "\n",
    "#     gl.xlabels_top = gl.ylabels_right = False\n",
    "#     gl.xlabel_style = {'size': 16, 'color': 'black'}\n",
    "#     gl.ylabel_style = {'size': 16, 'color': 'black'}\n",
    "\n",
    "#     gl.xformatter = LONGITUDE_FORMATTER\n",
    "#     gl.yformatter = LATITUDE_FORMATTER\n",
    "\n",
    "    xl = [extent[0], extent[1]]\n",
    "    yl = [extent[2], extent[3]]\n",
    "\n",
    "    tick0x, tick1, ticklab = get_ticks(xl, 'we', yl)\n",
    "    ax.set_xticks(tick0x, minor=True, crs=ccrs.PlateCarree())\n",
    "    ax.set_xticks(tick1, crs=ccrs.PlateCarree())\n",
    "    ax.set_xticklabels(ticklab, fontsize=14)\n",
    "\n",
    "    # get and add latitude ticks/labels\n",
    "    tick0y, tick1, ticklab = get_ticks(yl, 'sn', xl)\n",
    "    ax.set_yticks(tick0y, minor=True, crs=ccrs.PlateCarree())\n",
    "    ax.set_yticks(tick1, crs=ccrs.PlateCarree())\n",
    "    ax.set_yticklabels(ticklab, fontsize=14)\n",
    "\n",
    "    gl = ax.gridlines(draw_labels=False, linewidth=.5, color='gray', alpha=0.75, linestyle='--', crs=ccrs.PlateCarree())\n",
    "    gl.xlocator = mticker.FixedLocator(tick0x)\n",
    "    gl.ylocator = mticker.FixedLocator(tick0y)\n",
    "\n",
    "    ax.tick_params(which='major',\n",
    "                   direction='out',\n",
    "                   bottom=True, top=True,\n",
    "                   labelbottom=True, labeltop=False,\n",
    "                   left=True, right=True,\n",
    "                   labelleft=True, labelright=False,\n",
    "                   length=5, width=2)\n",
    "\n",
    "    ax.tick_params(which='minor',\n",
    "                   direction='out',\n",
    "                   bottom=True, top=True,\n",
    "                   labelbottom=True, labeltop=False,\n",
    "                   left=True, right=True,\n",
    "                   labelleft=True, labelright=False,\n",
    "                   width=1)\n",
    "\n",
    "    # Axes properties and features\n",
    "    ax.set_extent(extent)\n",
    "    ax.add_feature(LAND, zorder=0, edgecolor='black')\n",
    "    ax.add_feature(cfeature.LAKES)\n",
    "    ax.add_feature(cfeature.BORDERS)\n",
    "    ax.add_feature(state_lines, edgecolor='black')\n",
    "    return ax"
   ]
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (lat: 1380, lon: 2103, nv: 2, time: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2021-07-18T10:00:00\n",
       "  * lat                    (lat) float32 21.7 21.71798 ... 46.47644 46.49442\n",
       "  * lon                    (lon) float32 -97.88385 -97.864494 ... -57.192493\n",
       "    depth                  float32 0.55\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (time, nv) datetime64[ns] 2021-07-18T09:30:00 2021...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (time, lat, lon) float32 ...\n",
       "    v                      (time, lat, lon) float32 ...\n",
       "    dopx                   (time, lat, lon) float32 ...\n",
       "    dopy                   (time, lat, lon) float32 ...\n",
       "    hdop                   (time, lat, lon) float32 ...\n",
       "    number_of_sites        (time, lat, lon) float32 ...\n",
       "    number_of_radials      (time, lat, lon) float32 ...\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00</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-f80167b6-a743-4955-ae43-9da0fe5f43f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f80167b6-a743-4955-ae43-9da0fe5f43f7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 1380</li><li><span class='xr-has-index'>lon</span>: 2103</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-93504e2f-cf08-408f-a758-df649c4f48bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93504e2f-cf08-408f-a758-df649c4f48bf' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T10:00:00</div><input id='attrs-abaa14d0-6c7e-449c-93c9-8d14f21a7187' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abaa14d0-6c7e-449c-93c9-8d14f21a7187' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4410a576-3285-4ba6-ab52-9a1a91ef7e16' class='xr-var-data-in' type='checkbox'><label for='data-4410a576-3285-4ba6-ab52-9a1a91ef7e16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-18T10:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>21.7 21.71798 ... 46.47644 46.49442</div><input id='attrs-972995c4-66dd-4d35-b2ba-410bc90e851f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-972995c4-66dd-4d35-b2ba-410bc90e851f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4126c594-42b3-4a54-adcb-a94606b8c16f' class='xr-var-data-in' type='checkbox'><label for='data-4126c594-42b3-4a54-adcb-a94606b8c16f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([21.7    , 21.71798, 21.73596, ..., 46.45846, 46.47644, 46.49442],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-97.88385 -97.864494 ... -57.192493</div><input id='attrs-86acd1df-708b-4c01-8edc-c5ac750f6944' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86acd1df-708b-4c01-8edc-c5ac750f6944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71cc56d4-94f5-4062-972d-6499698b17d3' class='xr-var-data-in' type='checkbox'><label for='data-71cc56d4-94f5-4062-972d-6499698b17d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-97.88385 , -97.864494, -97.84513 , ..., -57.23121 , -57.211853,\n",
       "       -57.192493], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2bd3c4b3-6b13-4704-9808-025e711fcaff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bd3c4b3-6b13-4704-9808-025e711fcaff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d606df1c-bfd0-4237-bb53-7dc4cde6db26' class='xr-var-data-in' type='checkbox'><label for='data-d606df1c-bfd0-4237-bb53-7dc4cde6db26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd><dt><span>comment :</span></dt><dd>Nominal depth (and corresponding bounds) based on contributing radars</dd></dl></div><div class='xr-var-data'><pre>array(0.55, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-918a284c-db15-4299-8819-8a4769ae7cfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-918a284c-db15-4299-8819-8a4769ae7cfb' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8978697-d5e1-4841-8deb-735464f9645a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8978697-d5e1-4841-8deb-735464f9645a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7529a329-cd0c-4a48-8df7-df7661595b16' class='xr-var-data-in' type='checkbox'><label for='data-7529a329-cd0c-4a48-8df7-df7661595b16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2021-07-18T09:30:00.000000000&#x27;, &#x27;2021-07-18T10:30:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_bnds</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d19dba8e-871a-453b-85c8-1f7565f6df2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d19dba8e-871a-453b-85c8-1f7565f6df2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dd34a14-f23c-496e-865a-2d3c35afb160' class='xr-var-data-in' type='checkbox'><label for='data-3dd34a14-f23c-496e-865a-2d3c35afb160' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wgs84</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33d54919-fa46-4acc-91e5-e70c34c3a047' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33d54919-fa46-4acc-91e5-e70c34c3a047' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93521f5e-1478-49fc-b336-c134c00fa2ba' class='xr-var-data-in' type='checkbox'><label for='data-93521f5e-1478-49fc-b336-c134c00fa2ba' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-efc535b6-487b-40d6-8467-d0855ed9fcf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc535b6-487b-40d6-8467-d0855ed9fcf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3e0b5eb-9778-4aa1-b0e6-a07490cd06b0' class='xr-var-data-in' type='checkbox'><label for='data-a3e0b5eb-9778-4aa1-b0e6-a07490cd06b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopx</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ff0d0d1f-ce8c-40dd-a28d-00d732a52800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ff0d0d1f-ce8c-40dd-a28d-00d732a52800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfa8be3d-c72e-45da-974c-c14c04d7f285' class='xr-var-data-in' type='checkbox'><label for='data-dfa8be3d-c72e-45da-974c-c14c04d7f285' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopy</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopx</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c759952-78d5-4463-b24e-917bd44c7543' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c759952-78d5-4463-b24e-917bd44c7543' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d6c9648-d75f-4371-8010-afa3ca78a7a0' class='xr-var-data-in' type='checkbox'><label for='data-3d6c9648-d75f-4371-8010-afa3ca78a7a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The longitudinal dilution of precision (dopx) represents the \n",
       "contribution of radial geometry to uncertainty in the eastward \n",
       "velocity estimate (u).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopy</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4866cb6a-7f73-412f-bc15-65a6f31cdaed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4866cb6a-7f73-412f-bc15-65a6f31cdaed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bad4ee4b-95cc-4860-a0f0-a27747993bfc' class='xr-var-data-in' type='checkbox'><label for='data-bad4ee4b-95cc-4860-a0f0-a27747993bfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The latitudinal dilution of precision (dopy) represents the \n",
       "contribution of radial geometry to uncertainty in the northward \n",
       "velocity estimate (v).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-62774bd9-7772-4713-9154-eda393b0dd1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62774bd9-7772-4713-9154-eda393b0dd1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d29d7db-f769-487b-9879-0561e58316b2' class='xr-var-data-in' type='checkbox'><label for='data-4d29d7db-f769-487b-9879-0561e58316b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>horizontal dilution of precision</dd><dt><span>comment :</span></dt><dd>The horizontal dilution of precision (hdop) is the vector length \n",
       "(magnitude) of the eastward (dopx) and northward (dopy) dilution of \n",
       "precision.  It represents the contribution of radial geometry to the \n",
       "overall uncertainty in the total velocity (u and v) estimate.</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>ancillary_variables :</span></dt><dd>dopx dopy</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_sites</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e36ab92-08ce-4322-8a5f-14d949100b10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e36ab92-08ce-4322-8a5f-14d949100b10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e917410-ab31-4a86-a345-00a402d90063' class='xr-var-data-in' type='checkbox'><label for='data-6e917410-ab31-4a86-a345-00a402d90063' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radars</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radars contributing radials to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_radials</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe67482a-58e3-4e16-b840-67328291fb57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe67482a-58e3-4e16-b840-67328291fb57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4998d02-dd4a-48d9-96d2-5aaef8eedc03' class='xr-var-data-in' type='checkbox'><label for='data-e4998d02-dd4a-48d9-96d2-5aaef8eedc03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radials</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radials contributing to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>[2902140 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_parameters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b89586df-26f4-4b2a-b768-64faf5788637' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b89586df-26f4-4b2a-b768-64faf5788637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c5c62db-50af-4a44-a9b7-626bb8a9361c' class='xr-var-data-in' type='checkbox'><label for='data-5c5c62db-50af-4a44-a9b7-626bb8a9361c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Methods and parameters used to compute total solutions</dd><dt><span>combine_method_name :</span></dt><dd>Unweighted Least Squares</dd><dt><span>combine_method_description :</span></dt><dd>Method used to compute total solutions from radial velocities</dd><dt><span>grid_search_radius :</span></dt><dd>3.0</dd><dt><span>grid_search_radius_units :</span></dt><dd>km</dd><dt><span>grid_search_radius_description :</span></dt><dd>Search radius used for finding contributing radial velocities</dd><dt><span>max_radial_speed :</span></dt><dd>300</dd><dt><span>max_radial_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_radial_speed_description :</span></dt><dd>Maximum radial speed allowed to contribute to total solutions</dd><dt><span>max_rtv_speed :</span></dt><dd>300</dd><dt><span>max_rtv_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_rtv_speed_description :</span></dt><dd>Maximum allowed total speed</dd><dt><span>min_radar_sites :</span></dt><dd>2</dd><dt><span>min_radar_sites_description :</span></dt><dd>Minimum number of radar sites required to make a total solution</dd><dt><span>min_radials :</span></dt><dd>3</dd><dt><span>min_radials_description :</span></dt><dd>Minimum number of radials required to make a solution</dd><dt><span>max_hdop :</span></dt><dd>1.25</dd><dt><span>max_hdop_description :</span></dt><dd>Maximum allowed HDOP</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radial_metadata</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bcb6a442-d0c6-4bbd-a31d-fb7f75ae8b14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcb6a442-d0c6-4bbd-a31d-fb7f75ae8b14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c549b89b-f534-4858-849f-2d8ad6030d99' class='xr-var-data-in' type='checkbox'><label for='data-c549b89b-f534-4858-849f-2d8ad6030d99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Metadata on radial velocities used to compute total solutions</dd><dt><span>number_files_loaded :</span></dt><dd>30</dd><dt><span>number_files_loaded_description :</span></dt><dd>Number of radial files loaded</dd><dt><span>files_loaded :</span></dt><dd>RDL_m_URI_BISL_2021_07_18_1000.ruv\n",
       "RDL_WHOI_CPVN_2021_07_18_0945.ruv\n",
       "RDL_WHOI_LPWR_2021_07_18_0945.ruv\n",
       "RDL_i_URI_MISQ_2021_07_18_1000.ruv\n",
       "RDL_UMiami_CDN_2021_07_18_1000.ruv\n",
       "RDL_USF_FDS_2021_07_18_1000.ruv\n",
       "RDL_UMiami_NKL_2021_07_18_1000.ruv\n",
       "RDL_UMiami_STF_2021_07_18_1000.ruv\n",
       "RDL_USF_VEN_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRAD_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_BRMR_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRNT_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CAPE_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CMPT_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_FLND_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_GCAP_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_HLPN_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_LEWE_2021_07_18_1000.ruv\n",
       "RDL_WHOI_NWTP_2021_07_18_0945.ruv\n",
       "RDL_m_Rutgers_PORT_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_RATH_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SEAB_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SILD_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SPRK_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_STLI_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_SUNS_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_VIEW_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_WOOD_2021_07_18_1000.ruv\n",
       "RDL_SKIO_CAT_2021_07_18_1003.ruv\n",
       "RDL_SKIO_JEK_2021_07_18_1000.ruv</dd><dt><span>files_loaded_description :</span></dt><dd>Radial file names loaded</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d24ced05-db3d-4ba6-a169-53585107fa52' class='xr-section-summary-in' type='checkbox'  ><label for='section-d24ced05-db3d-4ba6-a169-53585107fa52' class='xr-section-summary' >Attributes: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>ACDD-1.3,CF-1.7</dd><dt><span>id :</span></dt><dd>202107181000ndbchfruwlsrtvusegc2km</dd><dt><span>date_created :</span></dt><dd>2021-07-18T12:51:48Z</dd><dt><span>source :</span></dt><dd>surface ocean velocity field from hf-radar</dd><dt><span>program :</span></dt><dd>Integrated Ocean Observing System (IOOS)</dd><dt><span>title :</span></dt><dd>Near-Real Time Surface Ocean Velocity, U.S. East and Gulf Coast, 2km Resolution</dd><dt><span>summary :</span></dt><dd>Surface ocean velocities estimated from HF-Radar are representative \n",
       "of the upper 1.0 meters of the ocean.  The main objective of \n",
       "near-real time processing is to produce the best product from \n",
       "available data at the time of processing.  Radial velocity \n",
       "measurements are obtained from individual radar sites through the \n",
       "U.S. HF-Radar Network. Hourly radial data are processed by unweighted \n",
       "least squares on a 2km resolution grid of the U.S. East and Gulf \n",
       "Coast to produce near real-time surface current maps.</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments, Active Remote Sensing, \n",
       "Profilers/Sounders, Radar Sounders, Doppler RADAR</dd><dt><span>keywords :</span></dt><dd>Earth Science, Oceans, Sea Surface, Coastal Processes, Marine \n",
       "Environment Monitoring, Ocean Circulation, Ocean Currents, \n",
       "Wind-Driven Circulation, Tides, Tidal Currents, Atlantic Ocean, North \n",
       "Atlantic Ocean, Gulf of Mexico</dd><dt><span>geospatial_lat_min :</span></dt><dd>21.7</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.49442</dd><dt><span>geospatial_lon_min :</span></dt><dd>-97.88385</dd><dt><span>geospatial_lon_max :</span></dt><dd>-57.192493</dd><dt><span>processing_level :</span></dt><dd>Near real-time dataset with automated data acquisition and processing \n",
       "quality control</dd><dt><span>history :</span></dt><dd>2021-07-18T10:51:28Z hfrnet rtvMergeData: Saving 2898 new solutions\n",
       "2021-07-18T11:21:35Z hfrnet rtvMergeData: Saving 8321 solutions; 5458 \n",
       "new or updated, 2863 unmodified from previous run(s)\n",
       "2021-07-18T12:21:56Z hfrnet rtvMergeData: Saving 9504 solutions; 1183 \n",
       "new or updated, 8321 unmodified from previous run(s)\n",
       "2021-07-18T12:51:47Z hfrnet rtvMergeData: Saving 9690 solutions; 233 \n",
       "new or updated, 9457 unmodified from previous run(s)\n",
       "2021-07-18T12:51:48Z hfrnet rtvSaveNetcdf: Removed 2648 solutions \n",
       "exceeding HDOP threshold of 1.25</dd><dt><span>references :</span></dt><dd>Terrill, E. et al., 2006. Data Management and Real-time Distribution \n",
       "in the HF-Radar National Network. Proceedings of the MTS/IEEE Oceans \n",
       "2006 Conference, Boston MA, September 2006.</dd><dt><span>institution :</span></dt><dd>National Data Buoy Center</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_name :</span></dt><dd>NDBC Webmaster</dd><dt><span>creator_email :</span></dt><dd>webmaster.ndbc@noaa.gov</dd><dt><span>creator_url :</span></dt><dd>https://hfradar.ndbc.noaa.gov</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ndbc</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table, Version 51</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>format_version :</span></dt><dd>1.1.00</dd><dt><span>product_version :</span></dt><dd>2.0.00</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (lat: 1380, lon: 2103, nv: 2, time: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2021-07-18T10:00:00\n",
       "  * lat                    (lat) float32 21.7 21.71798 ... 46.47644 46.49442\n",
       "  * lon                    (lon) float32 -97.88385 -97.864494 ... -57.192493\n",
       "    depth                  float32 ...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (time, nv) datetime64[ns] ...\n",
       "    depth_bnds             (nv) float32 ...\n",
       "    wgs84                  int8 ...\n",
       "    u                      (time, lat, lon) float32 ...\n",
       "    v                      (time, lat, lon) float32 ...\n",
       "    dopx                   (time, lat, lon) float32 ...\n",
       "    dopy                   (time, lat, lon) float32 ...\n",
       "    hdop                   (time, lat, lon) float32 ...\n",
       "    number_of_sites        (time, lat, lon) float32 ...\n",
       "    number_of_radials      (time, lat, lon) float32 ...\n",
       "    processing_parameters  int8 ...\n",
       "    radial_metadata        int8 ...\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = 'C:/users/fishp/software_bootcamp_2021/Thesis/HFR_Data/202107181000_hfr_usegc_2km_rtv_uwls_NDBC.nc'\n",
    "ds = xr.open_dataset(file)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27aceaa3-46ab-4a7f-983a-683bc4334866",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fef47c4a-b836-4e53-86b3-6ca9ab96f2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "lon = ds['lon']\n",
    "lat = ds['lat']\n",
    "u = ds['u']\n",
    "v= ds['v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d06472dd-4846-4fc5-ba6f-6da93cf7cded",
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (lat: 42, lon: 77, nv: 2, time: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2021-07-18T10:00:00\n",
       "  * lat                    (lat) float32 38.5113 38.52928 ... 39.2305 39.24848\n",
       "  * lon                    (lon) float32 -75.48618 -75.46682 ... -74.014946\n",
       "    depth                  float32 0.55\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (time, nv) datetime64[ns] 2021-07-18T09:30:00 2021...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (time, lat, lon) float32 nan nan nan ... 0.07 0.07\n",
       "    v                      (time, lat, lon) float32 nan nan nan ... -0.08 -0.07\n",
       "    dopx                   (time, lat, lon) float32 nan nan ... 0.59999996\n",
       "    dopy                   (time, lat, lon) float32 nan nan nan ... 0.64 0.57\n",
       "    hdop                   (time, lat, lon) float32 nan nan nan ... 0.84 0.83\n",
       "    number_of_sites        (time, lat, lon) float32 nan nan nan ... 3.0 3.0 3.0\n",
       "    number_of_radials      (time, lat, lon) float32 nan nan nan ... 6.0 7.0 7.0\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00</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-ed2a9aa8-64f6-48c1-8367-42281d9d1da2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ed2a9aa8-64f6-48c1-8367-42281d9d1da2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 42</li><li><span class='xr-has-index'>lon</span>: 77</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b4e057bb-035a-48c0-b3a2-6125151d3945' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4e057bb-035a-48c0-b3a2-6125151d3945' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T10:00:00</div><input id='attrs-a29eeba2-d8cb-46de-a58f-0aaaa853f11c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a29eeba2-d8cb-46de-a58f-0aaaa853f11c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64ea0529-16c5-4c88-810d-08213d3e47e0' class='xr-var-data-in' type='checkbox'><label for='data-64ea0529-16c5-4c88-810d-08213d3e47e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-18T10:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.5113 38.52928 ... 39.24848</div><input id='attrs-68d9675d-e442-4c97-9443-134dcfbc3d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68d9675d-e442-4c97-9443-134dcfbc3d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5efa9d9-f989-4d86-b37f-ccefa09aa01d' class='xr-var-data-in' type='checkbox'><label for='data-a5efa9d9-f989-4d86-b37f-ccefa09aa01d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([38.5113 , 38.52928, 38.54726, 38.56524, 38.58322, 38.6012 , 38.61918,\n",
       "       38.63716, 38.65514, 38.67312, 38.6911 , 38.70908, 38.72706, 38.74504,\n",
       "       38.76302, 38.781  , 38.79898, 38.81696, 38.83494, 38.85292, 38.8709 ,\n",
       "       38.88888, 38.90686, 38.92484, 38.94282, 38.9608 , 38.97878, 38.99676,\n",
       "       39.01474, 39.03272, 39.0507 , 39.06868, 39.08666, 39.10464, 39.12262,\n",
       "       39.1406 , 39.15858, 39.17656, 39.19454, 39.21252, 39.2305 , 39.24848],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-75.48618 -75.46682 ... -74.014946</div><input id='attrs-248a9639-a420-4566-aecf-40a4f66612f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-248a9639-a420-4566-aecf-40a4f66612f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6880f0b8-f608-4d31-b7f6-e4bd0cda7c71' class='xr-var-data-in' type='checkbox'><label for='data-6880f0b8-f608-4d31-b7f6-e4bd0cda7c71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-75.48618 , -75.46682 , -75.447464, -75.42811 , -75.408745, -75.38939 ,\n",
       "       -75.37003 , -75.35067 , -75.331314, -75.31196 , -75.292595, -75.27324 ,\n",
       "       -75.25388 , -75.23452 , -75.215164, -75.19581 , -75.176445, -75.15709 ,\n",
       "       -75.137726, -75.11837 , -75.099014, -75.07965 , -75.060295, -75.04094 ,\n",
       "       -75.021576, -75.00222 , -74.982864, -74.9635  , -74.944145, -74.92479 ,\n",
       "       -74.905426, -74.88607 , -74.866714, -74.84735 , -74.827995, -74.80864 ,\n",
       "       -74.789276, -74.76992 , -74.750565, -74.7312  , -74.711845, -74.69249 ,\n",
       "       -74.67313 , -74.65377 , -74.634415, -74.61505 , -74.595695, -74.57634 ,\n",
       "       -74.55698 , -74.53762 , -74.51826 , -74.4989  , -74.479546, -74.46018 ,\n",
       "       -74.44083 , -74.42147 , -74.40211 , -74.38275 , -74.363396, -74.34403 ,\n",
       "       -74.32468 , -74.30532 , -74.28596 , -74.2666  , -74.247246, -74.22788 ,\n",
       "       -74.20853 , -74.18917 , -74.16981 , -74.15045 , -74.131096, -74.11173 ,\n",
       "       -74.09238 , -74.07302 , -74.05366 , -74.0343  , -74.014946],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.55</div><input id='attrs-84818791-b228-411e-ad19-206cbb034ddf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84818791-b228-411e-ad19-206cbb034ddf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ef35672-1355-41f4-8bea-de157842ae40' class='xr-var-data-in' type='checkbox'><label for='data-2ef35672-1355-41f4-8bea-de157842ae40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd><dt><span>comment :</span></dt><dd>Nominal depth (and corresponding bounds) based on contributing radars</dd></dl></div><div class='xr-var-data'><pre>array(0.55, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-79b9520b-cc4a-4514-8962-a165d6c298ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-79b9520b-cc4a-4514-8962-a165d6c298ef' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T09:30:00 2021-07-18T1...</div><input id='attrs-c92533cf-0705-40a6-b052-c6c121c49c08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c92533cf-0705-40a6-b052-c6c121c49c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7072d833-7be9-49c7-a4e4-03e25d4d2e4a' class='xr-var-data-in' type='checkbox'><label for='data-7072d833-7be9-49c7-a4e4-03e25d4d2e4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2021-07-18T09:30:00.000000000&#x27;, &#x27;2021-07-18T10:30:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_bnds</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-938a442f-4faf-4de9-ac8f-448a2ff36b5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-938a442f-4faf-4de9-ac8f-448a2ff36b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6b1731a-443b-4d9f-83fb-c3bcbbfca383' class='xr-var-data-in' type='checkbox'><label for='data-e6b1731a-443b-4d9f-83fb-c3bcbbfca383' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wgs84</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-d7688a3d-0fad-4651-8aa1-a977472d7c81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7688a3d-0fad-4651-8aa1-a977472d7c81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa1f4d19-f47f-4256-93a7-bd19e9a3ef1f' class='xr-var-data-in' type='checkbox'><label for='data-aa1f4d19-f47f-4256-93a7-bd19e9a3ef1f' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0447dad7-dd7a-4336-9751-a1f4bc9c6660' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0447dad7-dd7a-4336-9751-a1f4bc9c6660' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3636f324-18c5-4538-8d0d-6c2b6459d6b6' class='xr-var-data-in' type='checkbox'><label for='data-3636f324-18c5-4538-8d0d-6c2b6459d6b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopx</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.04, 0.07],\n",
       "        [ nan,  nan, ..., 0.07, 0.07]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dae37068-1bf2-4cc0-8738-9cfc02d3bad3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dae37068-1bf2-4cc0-8738-9cfc02d3bad3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f3341db-0c29-4359-83a1-b9a16e264d13' class='xr-var-data-in' type='checkbox'><label for='data-4f3341db-0c29-4359-83a1-b9a16e264d13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopy</dd></dl></div><div class='xr-var-data'><pre>array([[[  nan,   nan, ...,   nan,   nan],\n",
       "        [  nan,   nan, ...,   nan,   nan],\n",
       "        ...,\n",
       "        [  nan,   nan, ..., -0.03, -0.02],\n",
       "        [  nan,   nan, ..., -0.08, -0.07]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopx</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5a61748c-1e60-4c1e-9f17-dd2eb656dc0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a61748c-1e60-4c1e-9f17-dd2eb656dc0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60438f0b-974f-430d-877e-0e342b2d3aad' class='xr-var-data-in' type='checkbox'><label for='data-60438f0b-974f-430d-877e-0e342b2d3aad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The longitudinal dilution of precision (dopx) represents the \n",
       "contribution of radial geometry to uncertainty in the eastward \n",
       "velocity estimate (u).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.53, 0.63],\n",
       "        [ nan,  nan, ..., 0.54, 0.6 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopy</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5548803a-c610-4b2a-a3e5-9cd7f8acde91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5548803a-c610-4b2a-a3e5-9cd7f8acde91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0348a8f5-2c63-4001-8de3-b8790daa1934' class='xr-var-data-in' type='checkbox'><label for='data-0348a8f5-2c63-4001-8de3-b8790daa1934' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The latitudinal dilution of precision (dopy) represents the \n",
       "contribution of radial geometry to uncertainty in the northward \n",
       "velocity estimate (v).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.72, 0.78],\n",
       "        [ nan,  nan, ..., 0.64, 0.57]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-28b26d61-9df6-4913-8e82-ab1d0e863823' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b26d61-9df6-4913-8e82-ab1d0e863823' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a0e51ee-b317-4044-8cae-5f040a5a5f26' class='xr-var-data-in' type='checkbox'><label for='data-3a0e51ee-b317-4044-8cae-5f040a5a5f26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>horizontal dilution of precision</dd><dt><span>comment :</span></dt><dd>The horizontal dilution of precision (hdop) is the vector length \n",
       "(magnitude) of the eastward (dopx) and northward (dopy) dilution of \n",
       "precision.  It represents the contribution of radial geometry to the \n",
       "overall uncertainty in the total velocity (u and v) estimate.</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>ancillary_variables :</span></dt><dd>dopx dopy</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.9 , 1.  ],\n",
       "        [ nan,  nan, ..., 0.84, 0.83]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_sites</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-856cc3d5-9bfb-4c42-9d5f-76deb12f0d00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-856cc3d5-9bfb-4c42-9d5f-76deb12f0d00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe89d20-6258-42fe-acc3-264a12542961' class='xr-var-data-in' type='checkbox'><label for='data-1fe89d20-6258-42fe-acc3-264a12542961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radars</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radars contributing radials to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, ...,  3.,  3.],\n",
       "        [nan, nan, ...,  3.,  3.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_radials</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e1753c47-9f13-44c9-ab27-afcf9180b600' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1753c47-9f13-44c9-ab27-afcf9180b600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d196d96-975a-48de-8630-55b8d671e227' class='xr-var-data-in' type='checkbox'><label for='data-0d196d96-975a-48de-8630-55b8d671e227' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radials</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radials contributing to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, ...,  6.,  5.],\n",
       "        [nan, nan, ...,  7.,  7.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_parameters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-abcae695-016f-4c9f-97f5-56c39720147f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abcae695-016f-4c9f-97f5-56c39720147f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03e615ef-3280-4c1e-b715-9974a96c6a70' class='xr-var-data-in' type='checkbox'><label for='data-03e615ef-3280-4c1e-b715-9974a96c6a70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Methods and parameters used to compute total solutions</dd><dt><span>combine_method_name :</span></dt><dd>Unweighted Least Squares</dd><dt><span>combine_method_description :</span></dt><dd>Method used to compute total solutions from radial velocities</dd><dt><span>grid_search_radius :</span></dt><dd>3.0</dd><dt><span>grid_search_radius_units :</span></dt><dd>km</dd><dt><span>grid_search_radius_description :</span></dt><dd>Search radius used for finding contributing radial velocities</dd><dt><span>max_radial_speed :</span></dt><dd>300</dd><dt><span>max_radial_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_radial_speed_description :</span></dt><dd>Maximum radial speed allowed to contribute to total solutions</dd><dt><span>max_rtv_speed :</span></dt><dd>300</dd><dt><span>max_rtv_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_rtv_speed_description :</span></dt><dd>Maximum allowed total speed</dd><dt><span>min_radar_sites :</span></dt><dd>2</dd><dt><span>min_radar_sites_description :</span></dt><dd>Minimum number of radar sites required to make a total solution</dd><dt><span>min_radials :</span></dt><dd>3</dd><dt><span>min_radials_description :</span></dt><dd>Minimum number of radials required to make a solution</dd><dt><span>max_hdop :</span></dt><dd>1.25</dd><dt><span>max_hdop_description :</span></dt><dd>Maximum allowed HDOP</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radial_metadata</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-7ea3bc7e-1e22-4668-840c-91c20913102f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ea3bc7e-1e22-4668-840c-91c20913102f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-980d17c1-a1d8-4948-bde3-324761907132' class='xr-var-data-in' type='checkbox'><label for='data-980d17c1-a1d8-4948-bde3-324761907132' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Metadata on radial velocities used to compute total solutions</dd><dt><span>number_files_loaded :</span></dt><dd>30</dd><dt><span>number_files_loaded_description :</span></dt><dd>Number of radial files loaded</dd><dt><span>files_loaded :</span></dt><dd>RDL_m_URI_BISL_2021_07_18_1000.ruv\n",
       "RDL_WHOI_CPVN_2021_07_18_0945.ruv\n",
       "RDL_WHOI_LPWR_2021_07_18_0945.ruv\n",
       "RDL_i_URI_MISQ_2021_07_18_1000.ruv\n",
       "RDL_UMiami_CDN_2021_07_18_1000.ruv\n",
       "RDL_USF_FDS_2021_07_18_1000.ruv\n",
       "RDL_UMiami_NKL_2021_07_18_1000.ruv\n",
       "RDL_UMiami_STF_2021_07_18_1000.ruv\n",
       "RDL_USF_VEN_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRAD_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_BRMR_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRNT_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CAPE_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CMPT_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_FLND_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_GCAP_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_HLPN_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_LEWE_2021_07_18_1000.ruv\n",
       "RDL_WHOI_NWTP_2021_07_18_0945.ruv\n",
       "RDL_m_Rutgers_PORT_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_RATH_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SEAB_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SILD_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SPRK_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_STLI_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_SUNS_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_VIEW_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_WOOD_2021_07_18_1000.ruv\n",
       "RDL_SKIO_CAT_2021_07_18_1003.ruv\n",
       "RDL_SKIO_JEK_2021_07_18_1000.ruv</dd><dt><span>files_loaded_description :</span></dt><dd>Radial file names loaded</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92a7123a-bb7c-4261-bbf4-d70d309c01cf' class='xr-section-summary-in' type='checkbox'  ><label for='section-92a7123a-bb7c-4261-bbf4-d70d309c01cf' class='xr-section-summary' >Attributes: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>ACDD-1.3,CF-1.7</dd><dt><span>id :</span></dt><dd>202107181000ndbchfruwlsrtvusegc2km</dd><dt><span>date_created :</span></dt><dd>2021-07-18T12:51:48Z</dd><dt><span>source :</span></dt><dd>surface ocean velocity field from hf-radar</dd><dt><span>program :</span></dt><dd>Integrated Ocean Observing System (IOOS)</dd><dt><span>title :</span></dt><dd>Near-Real Time Surface Ocean Velocity, U.S. East and Gulf Coast, 2km Resolution</dd><dt><span>summary :</span></dt><dd>Surface ocean velocities estimated from HF-Radar are representative \n",
       "of the upper 1.0 meters of the ocean.  The main objective of \n",
       "near-real time processing is to produce the best product from \n",
       "available data at the time of processing.  Radial velocity \n",
       "measurements are obtained from individual radar sites through the \n",
       "U.S. HF-Radar Network. Hourly radial data are processed by unweighted \n",
       "least squares on a 2km resolution grid of the U.S. East and Gulf \n",
       "Coast to produce near real-time surface current maps.</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments, Active Remote Sensing, \n",
       "Profilers/Sounders, Radar Sounders, Doppler RADAR</dd><dt><span>keywords :</span></dt><dd>Earth Science, Oceans, Sea Surface, Coastal Processes, Marine \n",
       "Environment Monitoring, Ocean Circulation, Ocean Currents, \n",
       "Wind-Driven Circulation, Tides, Tidal Currents, Atlantic Ocean, North \n",
       "Atlantic Ocean, Gulf of Mexico</dd><dt><span>geospatial_lat_min :</span></dt><dd>21.7</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.49442</dd><dt><span>geospatial_lon_min :</span></dt><dd>-97.88385</dd><dt><span>geospatial_lon_max :</span></dt><dd>-57.192493</dd><dt><span>processing_level :</span></dt><dd>Near real-time dataset with automated data acquisition and processing \n",
       "quality control</dd><dt><span>history :</span></dt><dd>2021-07-18T10:51:28Z hfrnet rtvMergeData: Saving 2898 new solutions\n",
       "2021-07-18T11:21:35Z hfrnet rtvMergeData: Saving 8321 solutions; 5458 \n",
       "new or updated, 2863 unmodified from previous run(s)\n",
       "2021-07-18T12:21:56Z hfrnet rtvMergeData: Saving 9504 solutions; 1183 \n",
       "new or updated, 8321 unmodified from previous run(s)\n",
       "2021-07-18T12:51:47Z hfrnet rtvMergeData: Saving 9690 solutions; 233 \n",
       "new or updated, 9457 unmodified from previous run(s)\n",
       "2021-07-18T12:51:48Z hfrnet rtvSaveNetcdf: Removed 2648 solutions \n",
       "exceeding HDOP threshold of 1.25</dd><dt><span>references :</span></dt><dd>Terrill, E. et al., 2006. Data Management and Real-time Distribution \n",
       "in the HF-Radar National Network. Proceedings of the MTS/IEEE Oceans \n",
       "2006 Conference, Boston MA, September 2006.</dd><dt><span>institution :</span></dt><dd>National Data Buoy Center</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_name :</span></dt><dd>NDBC Webmaster</dd><dt><span>creator_email :</span></dt><dd>webmaster.ndbc@noaa.gov</dd><dt><span>creator_url :</span></dt><dd>https://hfradar.ndbc.noaa.gov</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ndbc</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table, Version 51</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>format_version :</span></dt><dd>1.1.00</dd><dt><span>product_version :</span></dt><dd>2.0.00</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (lat: 42, lon: 77, nv: 2, time: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2021-07-18T10:00:00\n",
       "  * lat                    (lat) float32 38.5113 38.52928 ... 39.2305 39.24848\n",
       "  * lon                    (lon) float32 -75.48618 -75.46682 ... -74.014946\n",
       "    depth                  float32 0.55\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (time, nv) datetime64[ns] 2021-07-18T09:30:00 2021...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (time, lat, lon) float32 ...\n",
       "    v                      (time, lat, lon) float32 ...\n",
       "    dopx                   (time, lat, lon) float32 ...\n",
       "    dopy                   (time, lat, lon) float32 ...\n",
       "    hdop                   (time, lat, lon) float32 ...\n",
       "    number_of_sites        (time, lat, lon) float32 ...\n",
       "    number_of_radials      (time, lat, lon) float32 ...\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = ds.sel(lon=slice(-75.5, -74), lat=slice(38.5, 39.25))\n",
    "region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5b56fd7-cc8e-4168-b00c-6c101cc5b49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "lons = region['lon']\n",
    "lats = region['lat']\n",
    "us = region['u']\n",
    "vs = region['v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b332fb2a-5a9e-43dd-b281-812f992b8133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1dce70922c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "div=mc.divergence(us,vs)\n",
    "div.plot(cmap=cmo.cm.balance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cea3151-d4f6-4f20-a5cf-5a6fb3362299",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fed42451-2aee-42c2-9439-16f461eeaaef",
   "metadata": {},
   "outputs": [],
   "source": [
    "projection = ccrs.PlateCarree()\n",
    "\n",
    "extent = [\n",
    "    -75.5,\n",
    "    -74.75,\n",
    "    38.5,\n",
    "    39.25\n",
    "]\n",
    "\n",
    "LAND = cfeature.NaturalEarthFeature(\n",
    "    'physical', 'land', '10m',\n",
    "    edgecolor='face',\n",
    "    facecolor='tan'\n",
    ")\n",
    "\n",
    "state_lines = cfeature.NaturalEarthFeature(\n",
    "    category='cultural',\n",
    "    name='admin_1_states_provinces_lines',\n",
    "    scale='50m',\n",
    "    facecolor='none'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "49ff92c5-d15c-4421-96fa-a3785043872e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x1dce7acd1c8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:228: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(multi_line_string) > 1:\n",
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:280: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  for line in multi_line_string:\n",
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:347: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  if len(p_mline) > 0:\n",
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:239: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:239: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings = list(multi_line_string)\n",
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:385: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n",
      "C:\\Users\\Fishp\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\crs.py:385: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the  number of parts of a multi-part geometry.\n",
      "  line_strings.extend(multi_line_string)\n"
     ]
    },
    {
     "data": {
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bzvTZU/97GNmpqlhwecIGY329wVh+D2PaU9LzlTNViiQ9J6m73/xbME1Sx41mj3TU5g+H+6+fceX/l3uet0oVzak0Y8wuG53va6xN3F+bui6/zbKlkppv8PWGh1ItlFQqqd4Gj9VaG90XTTf6mbTQf9ZM2q7njTHm7C089ou2cNjX9vw8/s68uVlT3hbOAwAAoPkDAAlsuSp2a6oKYyW1NcacY4xJrzztY4zZbVPFnuddstFOQ9k+GyKDVXG4ygme5xVvdN5oSR2MMT2MMZmS7lTFejZ/SBW/dEt6UNKR3iZ2NqrMnKmK97Y0Y0zm34sAbyL/LEm/SLqrsu4UVTRNNl7Xp7ekYX5mq1Re99/bpmdUfv23SZIuMMaEK2cTXaT/Xv9lS+MGK8dKkxSozJteefYEVRxCdqUxJqNyVpQkfbGZ4YZJOt8Y075y7aHbJb0i/bNmz7uS7jXGZBljDpB0kipmaW3KFu+vyuu61hjT1BjTRNJ1f19X5e2aYIy5ezNjv1l5m5pV5rz57zM8z1sq6f8kDTDG1DLGBIwxrY0xh2xw+QaVl083xpyuisfcR5XnbdfzxvO8kVt47P+9Q9umbPHnsSFjzL7GmF0rb1tdSU+pYre5NZXnn1S5fpQxxnSVdKUqFkAHACBusNV7fKH5AwCJ6yFJt1ce+nL99gxUufDxUapYN2WJKg6BeUT/aWZsN2NMS1Vsbd1J0rINZkqcXZkhTxULIT8gqUAVi/ZuuI7L/arYln3SBpd9boPzX1TFoWxnqmLr9mJtfqcqVY7dpfK6HpZ0WmWGv/M2VcWuWcN83sRiVRzaI0l/VH79t76q2Fp9kaTFqmg+9PE57jmVYw1WxeLTxaq4rfI8L6KK7cPPlbS68npOrvz+P4eo/T2Q53mfSOqvijVtFlSe7trgui6TFFbFmjmvS7rU87wNZ179fdicn/vreUkfqGIXtamSPqz83t+aS/p2M7f5RUmfqqJBNkUVTakNnauKNZSmV17325Iab3D+j5J2UcXaOQ+o4r5dVXnek5JOMxW7ej21meuvDlv8eVQemnh25Zc7S/pEFYfQTVXFTKcN1/TpJWlO5fnDJD3ied5md6MDAAAw/g69BwAAqBrGmGaS3vI8r1s1jN1H0gWe5x24tVoAAFB9WqSFvetqtnIdw5erV8/4yfO8Lq5zVKc01wEAAEBqqVyvqcobPwAAIL4EAkw2iRcc9gUAAAAAAJDEaP4AAICk4XneKxzyBQAA8N9o/gAAAAAAACQx1vwBAAAAAABVLmBcJ8Dfttj8OaBzO2914bodlSXpTZ+zSLVr11br1q1dR4GFmTNnqqioSOHMkHZqWl/G8AoGADuC53ma+ecSeZL22msvXn8BAAnpp59++tTzvGNc50Bq22LzZ3XhOo0aePUOipL8juxznzJq1NLkyZNdR4Glp59+WldccYVyc7I1+N4LXccBgJRw0qX95UmaO3euWrVKjK1iAQDYmDGmnusMAGv+7EB77baTFi5cqLy8PNdRYKlfv346+OCDNXnqXNdRACAl3PjIcM1buELvvfcejR8AABKQMVIwQU6pgObPDvTQ9WcpPS2odu3auY6CbfD444+rNFKuRctWuY4CAEltwsRp+uTrX5WWlqbLLrtMrVu3Vvv27dW5c2cddNBBOuaYY3Taaaepb9++uvrqqzV16lTXkavcG2+8od1220133XWX6ygAACAJ0PzZgYLBoEY+fqXy8/N11FFHuY4DS507d1ZaWpoefXGM6ygAkNR2alJfu7dppuaNcqXyYhWtWallSxZq9qwZmvLTJH054Qt9MOZ9DR/2qgYNekp77LGHGjZsoDFjEvv1efTo0Wrfvr2CwYB69eqlP/74QyNHjnQdCwAAJAF2+9rBdm3VRFf3PlYDX/1YTz/9tPr16+c6Eix07NhRP/yafH9hBoB4slOzBnrdYs3B8T9M1T1Pv62TTjpJWVlZuvPOO3XjjTdWX8AqNGbMGN16662aMWOGYrGYsrMydfK/9tH1F56gky95zHU8AAC2g6dAwHMdApWY+eNA39OPUKfddtIVV1yhuXNZQyaR3HfffSouiWhNYZHrKACASoft10ETRtyttwZdq3q1a+imm25Senq6LrzwQkWjUdfx/senn36qPffcU8FgUCeddJLmz5ur4w/bS9+Muk/fvXG/7r7yDGWHw65jAgCAJELzx5Fhj/ZTVo1MdezYMS4/mGLTunfvrkAgoMdeHus6CgBgI7u2aqIPnr9JE0berT3aNtOQl15Senq6LrnkEtfR9MUXX2ivvfZSWlqajjnmGM2ZPVPHHrynvn7tbn3/5v26/5ozVSubhg8AAKgeNH8ceu/ZG1RcvF5du+7jOgos7LLLLvri+2muYwAANiM3J1uv9u+nSe8+qMP2ba/nn39etWrV1MSJE3dojgkTJqhLly5KS0vTEUccoVl/zNCR+3fQhJF364e3HtBD15+tnFrZm7285zFVHgAAVA2aPw41rJej+67upSlTftYdd9zhOg58uvnmm7V2XbGKiyOuowAAtiAUCmng7edpzHM3Kmg87bvvvjrqqKOqdcbtt99+q65duyo9PU2HHXaYpk/7XYfv217jht2hH95+QP1vOke5OZtv+EhSJBLRaVcMUF5+oQ4//PBqywoAQHUykgImMU6pgOaPYyce0UWH77e77r///h3+F0lsmz59+sgYo2de+9R1FACADzs1a6CvXrtHF5xxuD7//DOFw2G98847VTb+xIkT1a1bN6Wnp+vAAw/U77/9ooO7tNNnr9yhH99+UANu7a36uTm+xvr4y5+17+m3a86C5Ro6dKheeOGFKssJAABSl9nSlOLdd2nujbLYbQPb7tB/36O160pUVFSkUCjkOg62omXLlioqzNeEEXe7jgIAsFBYVKyzrn1Sfy1Zqd12203vvfee2rZtaz3OL7/8oiuuuEI//vijysrKlJmRrq4d2+i2S09R4wa51uNFo1Gdc/3Tmjp7odrtuqum/Pyzwiz6DABJwRjzk+d5XVzn2NF2Ss/0bq+7k+sYvly4fGbS30fM/IkTY567UdFoVO3atXMdBT5cfvnlyl9dxGLdAJBgamWHNfaFm3XvVWdo7pzZ2nXXXVWjRg2ddtppysvL2+Jlf/vtNx1yyCEKhULaa6+9NGnij+q6x8768IWbNfGdh/T0XedvU+Nn/A9Ttc+pt2r63EUaNGiQZvzxB40fAEDiM1Ig4CXEKRXQ/IkTtbLDevquvpo3b54uuOAC13GwFdddd50k6dV3J7gNAgDYJicf2VWTRz+skQOuVOtm9fTe6NFq0KCBcnJydNlll6m4uFiSNGPGDB1xxBHKyMjQnnvuqR++/06dd99JY567UZPefUiD771QzZvU26YM0WhU5930jK66/xU1b9FC+fkF6tevX1XeTAAAAEkc9hV37n/mHb358ff68MMP1b17d9dxsAWNGjWUiUX0f0Nvdx0FAFAFxv8wVQNf+UjzF+fJ8zylp6errKxM6elp2mu3lrr54pPVpmXjKrmuH3+drcvuHqLy8qgeeugh3XTTTVUyLgAg/qTsYV+hTO/Oei1dx/Dl/KWzkv4+SnMdAP/t9st76PtfZumEE05Qfn6+cnL8LRCJHe/cc3vrsccedR0DAFBFDtuvgw7br4Mk6fWx3+ibSX/oyj7dtWurJlV6PZfe+aK+nTJTzZo106+//qrcXPtDxQAAAGxw2FccGv3MdQoGjHbZZRfXUbAF999/vySjfU+7VTPmLnIdBwBQhc48/kA9c88FVdr4+fWP+ep66i36dspM3XnnnVq4cCGNHwBA0jKSAoHEOKWCFLmZiSUUCmn4Y/2Ul5enE0880XUcbEYoFNLcuXNVo0a2el41ULc/8brrSACAOHXNA6/onOufVk6dXC1dulT33HOP60gAACCF0PyJU+3bNNelZx6lDz74QF988YXrONiMVq1aaeWqVbrwwgs1ZtxPOrDXHVq4ZKXrWACAODFj7iLte9qtGvf9VF1zzTVatmyZGjVq5DoWAABIMTR/4tilZx+lrBqZuuKKK1xHwVa88MIL+v333xVTUMdd9LBOv/Jx/blwuetYAACHbn38NfW8aqBq1MjWn3/+qccff9x1JAAAdijXW7iz1ft/0PyJc8ce3EkzZsxwHQM+dOjQQWvXrtVVV12lv5bm6+RLH9WBve7UyDFfu44GANjBTrj4EY39YoouvvhirVy1Sq1atXIdCQAApDCaP3Hu5otOlOd5GjRokOso8GngwIFav75Yn3zyiWrn1tMjL7yvvU68Uf3uGaLComLX8QAA1ezh50drweI8ffLJJ3ruuedcxwEAAKD5E+9CoZDq1amphx56yHUUWDr66KO1YMECrV69WkcdfbS+nTJLB/a6Q8f0fUCTp851HQ8AUA1+n/mXXvvgW5111lk6+uijXccBAMAZY9zv4sVuX/+RIjczsfU59VAtXbpUkUjEdRRsg5ycHH300UcqLy/XgAEDtL40pr43D9ajL77vOhoAoApFo1Gdd/Ozql+/vkaOHOk6DgAAwD9o/iSAc085RMZI1113neso2E7XXnutClav1r///W8Nf/9r9bp6oKLRqOtYAIAqcOa1T6msPKpp06a5jgIAAPBfaP4kiJ2aNtCwYcNcx0AVGT58uF599VXNmLtYB515l/LXFLmOBADYDkPfGa8/5i7Wyy+/rPr167uOAwAA8F9o/iSIa/oep8LCQi1btsx1FFSRc889V3/88YfKY9Lh/75HX09iVzcASESLlq3SE0M/1KGHHqo+ffq4jgMAQNxwvZYPa/78R4rczMR3aNfdFQwGdOGFF7qOgirUtm1bFRUVqdXOO+vye4bosSEfuI4EALB0xpVPKDsrS+PHj3cdBQAAYJNo/iSQjru20Gef/Z/rGKhiwWBQc+bM0VlnnaVho7/Umdc8yTpAAJAgLrnjBRWtL9FPU6a4jgIAALBZaa4DwL+7rjhdJ1/6qCZOnKiuXbu6joMqNnLkSB155JHq27evDjrrLo194Wbl5mS7jgUA2IyPvpyi736epXvvvVdt27Z1HSclrVowz3dtrNxu19Ti9CzbOL5llhRa1afX8b+O1NLVa63Grlezhu/azHS7Xx3m5RVY1WdlhHzXtmnV0mrseFGwZJFV/bqYsaoPR/0/ziPpmXZjB/3XFlv+HXP2inyr+oM77W53BXDDSIGg5zoFKjHzJ4Hs3LyhMjPSdfnll7uOgmrSp0+finWAop4O//c9+uanP1xHAgBsQmFRsW557HXtvnt73XHHHa7jAAAAbBHNnwRzaNf2+pmp5Umtbdu2WrOmUDu1aqXL7npJj7881nUkAMBGTr70UaWnpenXX39zHQUAAGCraP4kmNsvP03RWEyvvfaa6yioRqFQSHPnzlWvXr30yrsTdNa1T7qOBACodMfAUVpZUKgvxo9XMGhxHAQAAIAjNH8STK3ssHJq1tDtt9/uOgp2gNdff11Dhw7VtNmLdEDPO5S/psh1JABIaZN+m6P3P5+siy++WAcccIDrOAAAxC0j91u4s9X7f6TIzUwupx2zn+bN87/AIRLb3+sARcpjOvwc1gECAFcikYguvuNFNW3aVM8995zrOAAAAL7R/ElAF53xL0nSsmXLHCfBjtK2bVsVFhaqZcudWAcIABw5/cqBinmepk2b5joKAACAFZo/CSgcrtgG88MPP3ScBDtSKBTSn3/++c86QGdf+5TrSACQMp4e/rHmLVqhN998Uzk5Oa7jAAAQ/4wUCCbGKRXQ/ElQwWBA3333nesYcOD111/XSy+9pN9n/cU6QACwA/y5cLleeGOcTjjhBPXo0cN1HAAAAGs0fxJUZihdM2bMcB0Djpx//vmaPn36P+sAff/zTNeRACApRaNRnXn1k6pdu7bGjBnjOg4AAMA2ofmToGplh7Vw4ULXMeDQbrvtVrkOUEtdfMeLemIo6wABQFXre8tglUTKNHXqVNdRAABIOCaQGKdUkCI3M/k0ql9bBQUFrmPAsYp1gOapZ8+eGvrOBN0z6C3XkQAgabz18ff6efp8Pfnkk2ratKnrOAAAANuM5k+Cat2ikUpKSlzHQJwYNWqULr/8cr3z6Y965d0JruMAQMLLy1+j+599R/vss4+uuOIK13EAAAC2S5rrANg2e7ffSW9/8oPrGIgjTz/9tGbPnq3HXx6rlk3q6bD9OriOBAAJq0e/x5WRkanvv//edZSUUrhqpVV9enYt37XRWMxu7LJy37WZ6ZYfqdP855akYMD4rq2TFbYaOyPo/2/BZUVrrcZunh2yqk8LZ/muLcjLsxq7Tv36VvXxolY4w6q+pMz/tkVL8wurLYtt7j0asIsiUN2Y+ZOgDurSTpJUVMROT/iPTz/9VO3atdPV97+imfOWuI4DAAnpuoeGaXXhOn3//fcKBlNk/1cAAKqYYav3uELzJ0Hl1MqWVPHLPrChqVOnKrduXfW6eiDbwAOApQkTp+mzb3/TDTfcoE6dOrmOAwAAUCVo/iSwQMDoq6++ch0DcSYYDGrhwoVKT0/XMX0fVCQScR0JABJCcXFE19z/qnbeeWf179/fdRwAAIAqQ/MngWWE0vX777+7joE4FA6HNWfOXJVGynTDIyNdxwGAhHBKv8dkjGFbdwAAqojrLdzZ6v0/WPA5gWXXyNT8+fNdx0Ccatq0qZo3b67pcxe5jgIAce/h50dryfJ8ffLJJwqH7RbMBQAAiHcp0uNKTg1ya2nVqlWuYyCOderUSQVr1rmOAQBx7feZf+m1D75Vr169dPTRR7uOAwAAUOVo/iSwnZrV1/r1613HQBw78cQTFbHYqhYAUk00GtV5Nz+r+vXr6fXXX3cdBwAAoFrQ/ElgHdu1VHk5v9hj88466yxJ0vxFKxwnAYD4dOa1T6msPKpp06a7jgIAQHIxUiBoEuKUCmj+JLCDuuwmSezmhM2KRqOuIwBA3Br6znj9MXexXn75ZdWvX991HAAAgGpD8yeBNWtUV5L0zTffOE6CePX8889LknZq1sBxEgCIL4uWrdITQz/UoYceqj59+riOAwAAUK1o/iQ4Y4zGjRvnOgbi1HvvvadwZsh1DACIO2dc+YSys7I0fvx411EAAEhKRu63cGer9/9gq/cE53keU9WxWdOnT1eT+nVcxwCAuBGJRHTWdYNUtL5EM2fOdB0HAABgh6D5k8AKi4olSWeffbbjJIhXq1ev1jEHdHMdAwDiwh0DR2nMuMkyJqBBgwapbdu2riMBAADsEDR/EtjHX/4sScz8wSYVFRUpFoup13EHuI4CAE69MOpzPff6/6k8GtMJJ5yg0aNHKxgMuo6VsBYtWuy7NivD7tDjgDyr+tJozHdtpNxuE4TMULrvWhOz2301WlZmVW8sHq+hshKrsSPF/jcOSQtn2Y1dWGBVH7P4uZiA7XM4Pj4vBzPCVvWFxXb356qi9b5rG9Syuz/Xlfp/rJRE7J4TGWGWKUhKRjIpspNWIqD5k8C+/3mmQhYfTJBaWOwZQKr7+Mufdfegt1RcElHnzp01btw45eTkuI4FAACww9H8SWBzFixXnTq5rmMgTrHYM4BU9fvMv3TFvS8rf02RWrRooc8++4xDvAAAQEqj+ZPAVq5eq7327uI6BuLUtGnT1LQBiz0DSB3LV67R+bcO1l9LVqpOndoaN26cDj/8cNexAAAAnKP5k8CKSyLaf//9XcdAnFq7tlD779nRdQwAqHbFxRFdctcL+nn6fGVmZuqll17S+eef7zoWAAApjzV/4gfNnwQVjUbleZ569uzpOgriVDhcQwsWr3Qdw5loNKop0+Zp/uI8LVlRoOWrViu/oEhr1q7X2vUlKi6JqCRSprKyqMqjUcWiMXmep6ysTJ18RBdd3ae7QiEOmwPi3Z1PvqH3P5ukQDCoG2+8UY888ojrSAAAAHGH5k+C+m7KLElS586dHSdBvGrWrJkWLf7LdYwdJhKJaOQH3+rDCVO0YPFKlUb+s2NIIBBQIBBQenqaMkIZCofDysmtrRY5Oapbt64aNGigJk2aqHbt2nrllaEa+cG3GjHmGzWqX1uXnXWUTj6yq8NbBmBznh7+sd77bJJOO+00jRo1ih28AAAANoPmT4L64oepfMjFFnXt2lUjRsx0HaPaFBUX69V3vtT/ffObFi3PV1lZxZaitWrV0v4HHKjLL79cPXr0sB73lltuUTQa1UMPPaQnn3xSdz75pu5+6i21a91Ud/brofZtmlf1TQGwDRYuWakX3hin7t2766233nIdBwAAbMwYBQIc9hUvaP4kqGmzF6lmzWzXMRDHTjvtNL366quuY1SZ/DVFevnt8Rr/w1QtzVut8vKojDGqXbu2jjjiX7r22mt15JFHVsl1BYNB3X777br99tu1Zs0aXXrppXrvvffU6+on1f2QvfTwDWdXyfUA2Ha9rnlS2dnZ+vDDD11HAQAAiHs0fxLU0rwCNW+5s+sYiGPHHnusJOnXP+Zrz3Y7uQ2zDZauyNeQt8br68kztCK/UNFoTMYY1atXT8cff4Juuukm7bffftWeIycnR6+99pok6fDDD9dnX39F8wdw7JoHXtHadcWaPn266ygAAAAJgeZPgipaX6K9997bdQzEsWAwqEAgoPc/n5wwzZ85C5bq8ruHaPmqQsViMQUCATVs0EA9e3bXLbfcog4dOjjNN2TIEO28886a9Nsc7dOxjdMsQKr6/ueZGvf9VN1www3abbfdXMcBAABICAHXAbBtotGYTjzxRNcxEOd23729Rv/fREUiEddRtuqOgaN06uUDtK40qr59+2rOnDmKRqNasnSpRo4c6bzxI0mtWrVSdlaWHn7hPddRgJQUiUR0+d0vq0WL5urfv7/rOAAAYAuMKrZ6T4RTKqD5k4D+XLhcknTCCSc4ToJ49+OPE+VJOvv6p11H2az5i1bogJ536P3PJ6tv375as6ZQL774olq3bu062iadfsYZmrNguesYQEo667pBinmepk3jcC8AAAAbNH8S0Mdf/ixjjEKhkOsoiHPhcFjPPfecZv65RB99OcV1nP/xwLPv6MRL+ssE0vT7779ryJAhriNt1TPPPCPP8/TGh9+5jgKklBHvfaVZ85Zq6NChys5mwwMAAAAbrPmTgKZMm6dwOOw6BhLEhRdeqMcff1y3PT5K/+rWIS6ahktX5Kvn1U9qdeE69erVS6+//rrrSL6Fw2E1bNhAL701Tj2P2991HCAl5K8p0qNDxqhbt24699xzXcdJOgV5edU29triUqv6zJDdR9OMoP+/Y4aMZzV2IBj0XbuuNGo1do0aWVb1pQUrfdc2aNveauxq1aSZVfnyP36vpiDS6uVLfdfWbtjYamyb3Jl1G1qNHUrz/ziUpBZ1a/uuLYvaPW4l/58hswJ2z7doabFlFiQEIxmL12lUL+6JBLRgSZ4aNGjgOgYSyJQpU+R50jk3POM6ih5/eayO7vugyqKeJk+enFCNn7+1bt1GRevtfqEBsO3OuPIJhUIZ+vrrr11HAQAASEg0fxLQmrXr1b59HP1VB3EvHA7r2Wef1Yy5i/Xxlz87yRCNRnXM+Q/qlXcn6OSTT1ZR0Tp17tzZSZbttWbNGoXSmTgJ7Ag/T5+nFavW6N1331XQYiYGAAAA/oPmTwIqjZTriCOOcB0DCebiiy9Wu1131S0DXtPP0+ft0OsuKi7WQWfepaUrCjRu3DiNHj16h15/VVu7dq31oQkAts2N/Ueqdk6Ounfv7joKAACwZAImIU6+bosxxxhjZhpj5hhjbt7E+cYY81Tl+b8ZY/be2mWNMbnGmM+MMbMr/61T+f0jjTE/GWN+r/z38O29L2j+JJjCoorjYc866yzHSZCIpvz8s+rXb6DeNz6jC297TlHrY73tLVq2SoecebdKy6KaPXu2Dj98u1+3nFu/fr2ywhmuYwBJ78+Fy7V85WoNejp+dywEAADJzxgTlPSMpGMltZd0pjFm48NxjpW0S+XpIkmDfVz2ZknjPM/bRdK4yq8laaWkEzzP20NSb0nDt/c20PxJMP/3zS+SpEaNGrkNgoQUDoe1bNky3XnnnZr421zt0+NWffbtr9V2fZOnztVxFz6szHAN5eXlxe327bZKS0qUlZXpOgaQ9K66/xXVqFFD//73v11HAQAAqa2rpDme5/3peV5E0ihJJ21Uc5KkYV6FHyTVNsY03splT5L0auX/X5V0siR5nvez53lLKr8/TVKmMWa7/vpM8yfBfD9llkKhdNcxkODuueceFRQUqGXLnXTdQ8N12hUDVFwcqdLrGDNusvrePFjNmjVVQUGBcnJyqnR8l8rKy1SrBjvuAdVp+co1WrA4Tw888IDrKAAAAE0lLdzg60WV3/NTs6XLNvQ8b6kkVf67qZ2dekj62fO87dpxhkUrEszM+UtVp04d1zGQBHJycjRnzhy9+OKLuvTSS9XtjNt0w4Un6uwTD9rusZ8Z8YmeH/W59tlnH02cOLEK0saXGjWytHTl6mobPy9/jcaOn6IffpmthUtXyUhKSwsoLS2oUFqa0tPTFEoPKjMjpIz0NIVCaQpnZiicka5wZkhZNTJVIzNDWeGQsrPDyq6RqZpZYdWumaVaWWGFw/63agVcueLel5WREdLVV1/tOgoAANgWxiTSVu/1jDGTN/j6Bc/zXtjg600tDORt9PXmavxcdpOMMbtLekTSUX7qt4TmT4JZWbBWe3bae+uFgE8XXnihevfurQMOOECPvPC+ho3+Sq8PvEq5OdnWY82Yu0iX3vmS8tcU6bTTTtNbb71VDYnda9WqlWbOmFZl4y1dka/+L47R97/M1vriioa+MUaZmRnKzc2VJ6P1ZeUqLy5TNFqsaDSmWCyqaDQmLxZTzPMUi8Ukz5MnT54neZ6v9xNJUmZGuvZq30o3XHCC2rRsXGW3C9hWhUXF+uPPxbr11ltdRwEAAKlhped5XbZw/iJJzTf4upmkJT5rQlu47HJjTGPP85ZWHiK24u8iY0wzSaMlnet53lybG7MpNH8STElJRPvvv7/rGEgyoVBIkyZN0kcffaTjjz9eV90/VMMfvcL35QuLitX3lmc1a95S1a5dW+PHj9ehhx5afYEdO+CAA/TzlCnbNcaMuYv02Esf6LeZC1QaKVdaWlB77NFRffv21TnnnFOth8kVFxcrLy9Pq1ev1uzZs/XYY4/ppyk/6dTLBygtGNSurRrr8nOO0YGd21VbBmBLrrrvZaWlpXHIFwAAiBeTJO1ijGklabGkXpI23oVpjKR+xphRkvaVtKayqZO3hcuOUcWCzg9X/vu+JBljakv6UNItnud9WxU3gOZPAolGo4p5ns444wzXUZCk/t5KeZ89/C3MHI1GdWP/kfrs29+Unp6uJ554IiUO0Tj77LP11FNP6aMvp6j7If5n4n3/80w9+erHmjVvicqjMWVkhLRftwM0YMAAde7cuRoT/7dwOKwWLVqoRYsW6tixo3r06CFJmjVrlq677jqNH/+FLrvrJQWMUbPGddXn1EN02jHddlg+pLbi4oh+mjZPF198sesoAABgexgl0mFfW+R5Xrkxpp+kTyUFJb3sed40Y8wllec/J+kjSd0lzZG0XtJ5W7ps5dAPS3rTGHO+pL8knV75/X6S2ki6wxhzR+X3jvI875+ZQbbMlg4N2H2X5t6ogVdv69ioYt//PFMX3/Gi1eEcgI1vv/1WBx54oL576z5lh/97QeNIJKLlKwu1bOVq5RUU6tcZC/Tmx98rFvN0zjnn6NVXX93MqMmpUaOGWr58hYyRGtTN0cn/2kcXnnG4QqGK9XT+XLhcE36Yql//+EtzFizT4hX5isU8ZWXV0L/+daSeeOIJtWrVyvGt2Lz8/HzdcMMNGj16tAoKCmSMdEmvo3Tp2dt9uDGwRVfe+7K+mvyHIpGIgsGg6zgpoSAvz6p+Xan/DQJiMbvPLJkhu79LZlj8UuHFolZjB9L8r49m8zORpBrpdo/t0oKVvmsbtN145+HEsfyP36tt7Iw69XzX1m5odwi0Te7Mug2txi6Nxqzq0y1eN8uids+JSLn/+qyA3XPf9vmZ26ylVb1rxpiftnJIUVJqW7uG9+yBu7qO4cuRH/6S9PcRM38SyLjvpvJBGNXqiSeekCQdeMad/6wdsznGGHXu3Fnjx49Xdrb9+kCJbtmy5SouLtZdd92l4cOH6/lRn+v5UZ8rYIxilT84Y4xCoXTVrl1bvXv30WOPPabc3FzHyf3Jzc3VkCFDNGTIEEUiEZ100kka/Pon+virn/X2oGv+aXIBVSkajerLSTPUo0cP3u8AAACqEM2fBPL7rL9S8pds7DgPPvigcnJyVLduXdWrV09NmjRRkyZN1LJlSzVv3pxf+DcSDofVv39/9e/fX5I0ZMgQzZgxQ927d9chhxySNL+8hkIhffzxx3rnnXfUs+cZ2vf02zXozr6sCYQqd8eTb0iSRo4c6TgJAADYXkZGJpAch30lA5o/CWRp3mo1TbApjkgsbdu21ZAhQ1zHSFjnn3++6wjVqkePHlq9eo322KODLrvrJR15QEcNuOVc17GQRD6a8IuOOOIIGs072Or1xdU2ts0hKJLdYSW249exPJTHRq1qG7lS/QbVfQ1xoWG7Papt7IIli3zXrlowz2rs8qJC37WRdLvXt/RadjOGywr8H8aZnm33yM3I8J89Wmr3uhLMCG+9CMB2oQ2XQIrWFWvvvdnmHYA72dnZmjdvvq677jp99u1vOujMO7V85RrXsZAE+r/wnmKxmEaPHu06CgAAQNKh+ZNAyqMxHX/88a5jAIAee+wx/frrr4qUezqqz30aNvpL15GQ4EZ99L26du3K4c0AAADVgMO+EsT8RRU7up144omOkwBAhY4dO2rt2rU67LBD9diQD/TB+J806omrkmatI+w4L745TuXlUY0dO9Z1FAAAUFWSaKv3ZMA9kSA+/eZXGWMUDnM8LID4EQwG9dVXX2vo0KGaNW+pTrr0UdeRkIBefONztW/fXvXr13cdBQAAICnR/EkQk36bo8zMTNcxAGCT+vTpoyeffFJ/LVmpvHzWAIJ/b3/yvUpKyzRmzBjXUQAAAJIWzZ8EsWDJSv4iCiCuXXHFFUpPT9fVD7zqOgoSyICXP9ROO7VU69atXUcBAABVzAQCCXFKBalxK5PA6sL1at++vesYALBFffv21e8z/1IkEnEdBQlg3He/a936Er399juuowAAACQ1mj8JIhIp02GHHeY6BgBs0TPPPCNjjG5/4g3XUZAA7nn6bTVs2FCdO3d2HQUAACCpsdtXAigqLpYn6eyzz3YdBQC2KBgM6rDDDtNnX05wHQVxbvLUuVpduE7/9/Zo11EAAACSHjN/EsDPU+dLkpo2beo2CAD48O677yoajen5UZ+5joI4dvOjr6lOndo68sgjXUcBAADVwRgpGEiMUwpIjVuZ4JasKHAdAQB8y8nJUbt27fTyW+NdR0GcmjlviVasWqPBg59zHQUAACAl0PxJAMtXrVYgYFzHAADf3nrrLRWXRvTZt7+6joI4dO2Dryo7K0s9e/Z0HQUAACAlsOZPAshfXaSAoU8HIHF06NBBDRs20APPjtaRB+zpOg7iyNIV+Vq4dJUGDRrkOgoAAKhmqbKNeiKg+ZMAVheuUzCNu8q1NXkrfNdGS4utxs5t1tI2DhD3Bg9+TqeeeqpmzluiXVs1cR0HceKK+4YqIyND/fr1cx0lqa1Ytsx3bd3sLLuxC4t81xZHSq3GDqUFreqbNWM9RPhTp0kz37WFq1ZZjR3MzPRdW7xiqdXYZUWFVvWhWnX8jx2w+/0iWhb1XxwIWY0dWr/Oqh6APdpwCWDN2vVKp/kDIMGccsopqlGjhm58ZITrKIgTawqLNGveUt10002uowAAAKQUmj8JoKi4VOkhu+45AMSDW265RfMWrdAai5kCSF5X3PeK0tLSdM8997iOAgAAqpsxMsFAQpxSQWrcygS3vrhUYYvppAAQL26//XalBYO67uHhrqPAseLiiH6ZMV+XXHKJ6ygAAAAph+ZPAigpLVONLLvj8QEgXpzRs6cm/f6nolGLtQKQdK5/eJgCgYAGDhzoOgoAAEDKofmTACJl5cqi+QMgQQ0dOlSe5+n+Z991HQWORKNRfTPlD/Xs2VPBoN2CvgAAANh+NH8SQFl5VDk5Oa5jAMA2CYVC6tatm94fN9l1FDhy64DXJRkNH87hfwAApAojyQSCCXFKBTR/EkB5eVR16vjfthEA4s27776r8vKoRrz3leso2MGi0ag+/eZXHX300cz6AQAAcITmTwKIxmKqW7eu6xgAsM0aNWqkVq1a6ZnX/s91FOxg/V8co1jM07vvctgfAACAKzR/EkAs5qlRo0auYwDAdhk1apTWrS/R9z/PdB0FO9BbH/+gbt26KRwOu44CAAB2JLZ6jyupcSsTnOd5aty4sesYALBdunbtqjp16ujOgW+6joIdZPDI/1N5NKqxY8e6jgIAAJDSaP4kiKZNm7qOAADbbeDAgVq+ao0WLVvlOgp2gJff+UJ77LGHcnNzXUcBAABIaTR/EsS6detcRwCA7XbuuecqIyND1zz4qusoqGavj/1GpZFyvf/++66jAAAApDyaPwnAGKOFCxe6jgEAVeLKK6/UzD+XqLg44joKqtGTr36snXfeWa1atXIdBQAAOGICgYQ4pYI01wGwdQFjtHjxYtcx4t702X9a1TeuXdOqvqQs6rs2K5xlNXbBkkW+a+s0aWY1dnX67fGbrOobdv+379pV33xoNXb7C262qoc7/fv314ABA3TToyP01J19XcdBNfj061+0vrhUo0ePdh0lZaUHg75ro7GY1dgNamX7rv1r1WqrsVcWFVvVZy1Z6ru2cRPWT0xlBXl5vmvLov4/80lSScR/fU4ju89xxXlLrOpDtWr7ri0pK7caOysj3Xft6nUlVmNPXFxgVX9SS/6wANhKjRZXggsEA1q+fLnrGABQZbp3766vJ//hOgaqyf3PvqvGjRurY8eOrqMAAABAzPxJCGnBgPLz813HAIAq07x5c3muQ6Ba/PjrbK1Zu17vjbGbvQcAAJKMMVLA/0xUVC9m/iSA9LQ0FRTYTYUEgHj2008/KZwZch0D1eCWx15X3dxcHXrooa6jAAAAoBIzfxJARihNa9eudR0DAKrMXwsWqG6O/3VDkBhmzF2klQWFevvtt11HAQAAwAZo/iSAcEaIrd4BJJWC1au1b8edXcdAFbv2wWGqWTNbPXr0cB0FAADEARPkYKN4QfMnAdQIZ2h5Ac0fAMkjEolo7/Y7uY6BKrRwyUotXp6v5557znUUAAAAbIQ2XAKolZ2pSCTiOgYAVIlIJCLP83R4tz1cR0EVuur+ocrMzNTFF1/sOgoAAAA2QvMnAeTUzFJZWZnrGABQJf7v//5PkrRTswaOk6Cq5K8p0py/luu2225zHQUAAACbwGFfCSA3J0vRaMx1DACoEp988okCAf72kEyuuPdlpaen6/bbb3cdBQAAxAtjZNjqPW7w6TsBNKhXW7EYzR8AyeGnn35SOCPddQxUkaLiYv0+8y9ddtllrqMAAABgM2j+JIDG9XLkeZ7rGABQJRbMn6/c2mzzniyue3C4gsGABg4c6DoKAAAANoPDvhJA00Z1XUcAgCqTX1Cgrh1auY6BSpOnztWipavUabedrNdhikQi+uGXWTr77H9XUzoAAJCojNjqPZ7Q/EkALZvWlyRFo1EFg/F3zGThqlW+ayPRqNXY60v873IWs5wdtba41Ko+M+T/6VJevM5q7JJVK6pt7GhpsVW9Z7G+VLMz7A7zKCsq9F1be58jrMZe8uskq/pocZHv2mDYbpZKqHFL37X1GqTeoseRSESddqf549qMuYt0+d0va2XB/z4vA4GA0tOCql2zhl5+6FI1b1Jvk2PcMmCUJKNXXnmlesPC6r1WkgKqvhnDJRabUNSukWk1dpblIaGFxSW+a2vk+X+vlaSc+qn3+rw1K5Yts6rPsPylz1isB1do+TnORklZuVV9m1b+3/cXLVpsNXZGRtiqPlbu//lZErH7XB4p919fO8su96G7NLOqB2CPNlwCyM2p+MVz0iS7X24BIN5Eo1F5nqcj92ebd1eWr1yjky7pr55XDVRZzOjDDz+U53kqLy/XzJkz9dJLL6lfv3467PAjVFRSruMvfliffv3L/4wTjUb1+Xe/67jjjovLP0wAAADgP5j5kyACAaORI0dqv/32cx0FALbZp59+Kolt3l0oLo7okrte0M/T5ysjI0PPPvusLr300n/ODwaDatu2rdq2bfvP96LRqDp12lM3PDJCP0+fp5svPuWf8x4YPFqe5+nNN9/cobcDAAAA9mj+JIiscKa+/fZb1zEAYLt8/PHHbPPuwPxFK3TSpY8qEDC64YYb1L9/f1+XCwaD+v33qTrvvPP0yiuv6PeZCzXy8SslSaM/m6iDDjpI4bDd1H4AAJAi2Oo9rtD8SRCNG9TW/PnzXMcAgO3y008/KZNt3neoaDSq0698QjVr1lR+fv42HaI1dOhQHXLIITrvvPN00Jl36qR/7aNoNKYPPvigGhIDAACgqvHn1wTRqd1OKixc6zoGAGyX+fPnq24O27zvSGdcNVCRsor1fLZnbZ4+ffro999/V3FpmYaN/lKdOnVSTk5OFSYFAABAdaH5kyCOOaSTotGoopa7ZQFAPCkoyFeLzewchar3+MtjNXv+Uo0YMUKNGjXa7vE6dOig1avX6LDDDtPYsWOrICEAAEhqgWBinFIAzZ8E0aVDa0nS559/7jgJAGy70tKI9mrPNu87ws/T5+mVdyfo9NNP11lnnVVl44bDYX3xxRdq2rRplY0JAACA6kXzJ4EEgwF2VQGQsP7e5v1fB7DNe3UrLo7o/FsGq3HjxrxvAAAAgAWfE0nNrLB+/PFH1zEAYJv8PXNx5+YNHSdJfidd1l+S0axZs1xHAQAAqcoYmSDzTeIF90QCadYwVwv/+st1DADYJh999JECAeM6RtJ7ZsQnWpa3WpnhsLp166Zhw4a5jgQAAADHaP4kkC57tFZRUZHrGACwTSZPnqzMjJDrGEnvxMO7aN8926heTljz5s5R79692SwAAAAgxdH8SSBnn3iQYp6nxYsXu44CANbmz5unXLZ5r3bNm9TTiw9cojHP36T3Bt+wyZqffvpJ9evVkzFGxhhlZGSoYcMG6tq1q6644gpFIpEdnBoAAADVieZPAmlYL0fGGA0YMMB1FACwll9QoBaN67qOkVKWrSyQpH+aOWvWrFHHjh3VpUsXRUrX68nb++imi07SgXvvonC6pxnTftfTTz+tnXfe2WVsAACQJEwgmBCnVMCCzwkmp2YNffzxx3r88cd9X2bFsmVW11ESKbeN5VtxWZlVfTTm+a5tXqem1dhrS+2yZKan+64tKcy3GjtUq47v2mBGptXYaeEsq/qygP+XhVix3WGIGXXq+c9RVGg19rp5f1jVB7Nr+a4tt7w/18+b4bt27V6HWo1t8zhs3KSx1djVrbS0VHvtzjbvO9Ke7XaSMUY33nijli1bprfffltpwaBuuugknX3iQf/Ubfj/yVPnqu/Ng9WjRw+98847LmJjE2rVtWucLpv+i+/atLDdjLxQDf/vK9GA3d8Zg5b1dbP9Z/Fidoc/FixZ5Ls2Zvn5xla6xXvWnOWrqi1Hi3q1reqjpcVW9YF0/4cGh9Lsflmz+XzbplVLq7FtNGvW1Kp+9fKl1ZREygzZ/SqYHvT/M19XajeD1LY+p75VOQDR/Ek47Vs31U/T57mOAQBW/t7m/YhuHVxHSTnNGubq6aefliQdd+heeuj6s7dY36VDa11wxuF66c13NWTIEJ1//vk7IiYAAACqEYd9JZgex+yr0tJSFu8EkFC++OILSVKblvE1GykV3HP1GerSYWd999Z9W238/O3Kc7urQ9vmuvDCCzV37txqTggAAJKTcX44F4d9/QfNnwRz+H4VfzV/++23HScBAP8+/PBDtnl3pEuH1nr54cuUHQ5bXW74o/1UI5yhPffsWE3JAAAAsKPQ/EkwwWBQGaF0vfTSS66jAIBvEydOZJv3BBMMBvXmwKu1bt16FRfbrdsBAACA+ELzJwE1aVBHU6ZMcR0DAHybOPFHdWpXfQtoono0b1KxSPsnn3ziOAkAAAC2B82fBHRI1/YqKChwHQMAfLn22msVjcb02K3nuI6CbRAIBDRu3DjXMQAAQKIxkoKBxDilgNS4lUnmnJMPkud5mjePXb8AxL9nnnlau7dpZr3mDOJDZka6fvnlF9cxAAAAsB1o/iSg+rk5MsZo0KBBrqMAwBYNGDBAkUiZnrrzPNdRsI1q16yhBQsWuI4BAACA7ZDmOgC2lafc3FzXIQBgi+666061aFJP9XNzXEfBNmraMFfT5i51HQMAACQYU7nVO+IDM38SUDQaledJxx57rOsoALBZb7zxhtatW6+Bt/V2HQXbod3OTVRSUuI6BgAAALYDzZ8ENGt+xV9gO3Xq5DYIAGzBZZddpvq5OWrTsrHrKNgO3fbeVbFYzHUMAAAAbAcO+0pAX0/6Q8YYBYNMoQMQn7766ivl5+frxQcudh0F26msrFySFIlEFAqFHKcBAAAJw3DYVzxh5k8CmjZ7oUKhdNcxAGCzzj77bNXKDmvfPXdxHQXbIRqN6oZHRqhFixY0fgAAABIYM38S0IIleapZs5bv+sx0u7s53XJGUVk06ru2RtRu3YhQ7dq+a1evsxu7sKTUqj4W83zX1sqpYzV2WVGh79qSVSusxs5q0sKqflXhOt+19WpkWY1tczvTs/0/xiUpaFlfsmiu/2LLv1iEm7X2Xyv/zx9JClj07FfMmm41doO27a3qN2fGjBlatGiRHry2V5WMB3euuv8VRcrKNXnyZNdRYKlR+06+a/PmzrQa27N4309Pq+6mof/3Ztu/PgfSqy97tLT61tGql13Dqj4QMNWURIqFwlb1xvjPkmmZO2rxOa5gySKrses0aea7tnDVSquxbR+3sbKI/+KA3e8IwYD/zyCZlp9vpuf7/4woSW1aWZUDEDN/ElL+mnVq3Jg1NADEp1NPPVWZGSEdf3gX11GwHWbMXaSvJs3Qddddp/r167uOAwAAgO3AzJ8EtG59idq2bes6BgD8j7y8PP3xxx+6uje7ESa68299TnXq1NFjjz3mOgoAAEhQxmLGGKoXzZ8EVF4eVdeuXV3HAID/ccIJJygtLai+px/hOgq2w3UPvqqidSWaPmmK6ygAAACoArThEkw0GpUnqXv37q6jAMB/KS4u1o8//qgzjt3PdRRsh6eGfaTPvvtdd999t3bbbTfXcQAAAFAFmPmTYH6b+ZckqUOHDo6TAMB/O+200xQIGN1wwYmuo2Abjf1isl568wv17NlTd911l+s4AAAgkRkjWW4mhOrDzJ8E892UmVY7IQDAjhCNRvXxxx/riG57KMibfEL69Y/5uvXxUerSpYtGjRrlOg4AAACqEM2fBDN99iJlZFT3tqkAYOeiiy6SPE8PXcf27olo+co16n3jM2rcuLEmTZrkOg4AAACqGM2fBLNw2Srl5OS4jgEA/2XYsFfVucPOCoVoTieaSCSi4y96WJmZmZo/f77rOAAAAKgGrPmTYPLXFGnnNru6jgEA/7jjjjtUXh7VwNvPcx0F2+Covg+pPBrTwkULaN4BAIAqZQIsBxAvaP4kmPUlEXZfARBXHnvsUbVt1Vi1ssOuo8DSaVc8rvzVazV58mTVr1/fdRwAAABUEw77SjDl5VF169bNdQwAkCQ9//zzKikp1cDb+riOAkvXPfiqZs1bolGjRqlz586u4wAAAKAaMfMngUSjUUlS9+7dHScBgAo33HCDmjTMVbNGdV1HgYWnhn2kz777XXfffbd69uzpOg4AAEhGxnDYVxxh5k8CWbK8QJLUpEkTx0kAQBozZozWrl2rx285x3UUWBj7xWS99OYX6tmzp+666y7XcQAAALADMPMngTRvUk9G0sMPP6x77rnH9+Uihautrse2O2vKI/6LLcdeO2+m79oaLXaxGrtmOMOqPiDPd60Xi1qNHS0t9l0bzMi0HLvEqj6raJXv2pJiu/5xWjjbd23xiqVWY+fssodVfY3GO/muLV290mrs8jqNfdcGLB+HkVXLfNeW5i2xGltt21uVX3DBBcqtna32bZrbXQ+c+fWP+br18VHq0qWLRo0a5ToO4kR5uJZVvVdU4Ls2o049q7FNoPr+LlleXGRVX51/rU6rkWVVH43FfNfmhO0Wbre5nSVlZVZj20oz/j9rKWB3O7My0n3XxsqthlbBkkW+a20/x9k+DoMZ/tffs/oMLylmUW/7+bNTPf+fEQFsG2b+JJicWll6/fXXXccAkOImTpyovLw83X3l6a6jwKflK9eo943PqHHjxpo0aZLrOAAAIBUEA4lxSgGpcSuTyD4ddta8efNcxwCQ4nr2PENZNTJ1aNfdXUeBD5FIRMdf9LAyMzM1f/5813EAAACwg9H8STAXnXmkysvLlZeX5zoKgBQ1b948zZ+/QNeed5zrKPDpqL4PqTwa0/z5CxQK2R0qAQAAgMRH8yfB7NqqiQLGWK35AwBV6eSTT1ZGKF2nH9vNdRT4cNoVjyt/9Vr9+OOPql+/vus4AAAAcIDmTwKql1tL77//vusYAFLQmjVr9Ntvv6n3KQe7jgIfnhr2kWbNW6JRo0apc+fOruMAAIAUYiq3ek+EUyqg+ZOADurSTkuWWO7iAwBV4MQTT1QwGFC/c451HQU+jHj/a+2xxx7q2bOn6ygAAABwiOZPArrkzKMUi8U0Y8YM11EApJBIJKKvvvpKJxzODJJE8M1Pf6iktEyvvfaa6ygAAABwjOZPAmpYL0fBYED33Xef6ygAUshZZ50lY4zu6nea6yjw4b6n31GdOrXVoUMH11EAAECKcn04F4d9/Uea6wDYNk3q19Hnn3/uOgaAFBGNRjV69Ggd1KWdgsHUeINMZGsKi7Q0r0BPPfWU6ygAAACIA8z8SVBHHbSnVq5c6ToGgBRx9dVXKxaL6dEbz3EdBT7c+OhrSgsGdcUVV7iOAgAAgDhA8ydBnX/GYfI8T1999ZXrKABSwPPPP6+O7VoqHA65jgIffvx1to4/4QTXMQAAABAnOOwrQWWHwwqlp+mhhx7SwQez5TKA6vPII4+orKxMT91xnuso8OGFUZ8rFvM0fPhw11EAAEBKM1KKrKeTCJj5k8BaNq2nH77/3nUMAEnu3nvvUatmDZSbk+06CnwY+u4EtWnTRtnZ3F8AAACowMyfBHbKv7qq/0tjtlpXr1Wbas2RN3em79pYWcRq7HCjZv7HthpZiq0vsqovKy32P3a53e3MrNvQd60XjVqNXbJqhVW9F/M/fsziZyJJsbIy37WhWnWsxv50+nyr+qXrSnzXHrKT//tHkuqvWuS71u4nKKu/nrQ46Gjb0f/HiBEjtH59sZ4Y0G+7x0L1+33mX1q3vkSvvPKK6ygpqXDVKt+1Nq+1kpRTv4FlFv/rAqaXrrMaW+nVd/hnJGr3bh6L+a8PWea2+cxiu1OMbX0gYHzXejHPauxIYYHv2rT0dKux08J2TWib54Xtc8jm/gxmZFqNvc4iSpb835eSFLV4jFuz/ExZp4n/z+UFS/x/FrIdG8C2YeZPAjvzhAMkSe+8847jJACS1RVXXKGG9XK0c3O7BhjcuO3x15Wdna0DDjjAdRQAAJDqjJEJBhPilApo/iSwYDCocGZId9xxh+soAJLQZ599ptWrV+uh689yHQU+FBdHNH9xnq6//nrXUQAAABBnaP4kuEvPOkozZsxQy5YtFYnYHWoEAFvSu3dv1a6VpS4dWruOAh963/SMAoGA7rrrLtdRAAAAEGdY8yfB9Tn1UO3eppkuvP0F1axZUz/99JM6dOjgOhaABDd16lQtXbpUj970b9dRsBXRaFQnX/aYFizO0wsvvOA6DgAAwD9s1zhD9WHmTxLYp2MbffXaPaqRma499thDTz/9tOtIABLcqaeeqnBmSEcf1Ml1FGxBJBLREefep7+WrNSHH36oCy+80HUkAAAAxCGaP0miVnZYX712j/bevZWuuOIKnXLKKa4jAUhQixcv1uzZs3X52du/Wxiqz5rCIh101t1aU7ReU6ZMUffu3V1HAgAAQJyi+ZNkXnnkcl165lF677331Lx5cxUXW28kDSDFnXzyyUpPT9O5pxziOgo2Y/qchTrs3PsU86QFC/5Sp06dXEcCAABAHGPNnyR06dlHaZ89W+vCW59X7ZwcTZo8WR07dnQdC0ACKCoq0uTJk2n8xKk1hUXqe+tzmj1/merXr68///xT2dnZrmMBAAD8L2OkAPNN4gX3RJLq0qG1vnztHmWFQ9pzzz01cOBA15EAJIAePXooEAjo+vNPcB0FG4hGo7ruoWE66Ky7NX/xKg0aNEgrVqyg8QMAAABfaP4ksVrZYX352j3aZ4/Wuuaaa3TCCfwyB2DzotGoPvvsMx19IDMF48nLb41Tl1Nv0eff/a5zzz1XkUhE/fr1cx0LAAAACYTDvlLAkIcu1fOjPtMzI8aqSZMmmjt3rsLhsOtYAOJMnz59JHl68LozXUdJadFoVK++O0GjP5+sRctWKRqNqXPnzpowYQIzfQAAQEIxQbZ6jxc0f1LExb2OVNeObdT3lsHKycnR999/r86dO7uOBSCOvP7669p3z10U5E16h/t5+jwNfu3/9NvMv7S+uFSSVDc3V6eddroeeughtWrVynFCAAAAJDIO+0ohe7Vvpa9H3aOaNTLUpUsXDRgwwHUkAHHixhtvVDQa1eO3nes6SkooLCpW/xfe05F97tOeJ9yg3jc+o59nLNBu7Tto5MiR8jxPK1et0qhRo2j8AAAAxAFjzDHGmJnGmDnGmJs3cb4xxjxVef5vxpi9t3ZZY0yuMeYzY8zsyn/rVH6/rjFmvDGmyBjzdFXkZ+ZPiskOhzVh5N268LbndP311+uXX37R8OHDt2vM+q139V278IfxVmOnZ+/iv9ZYDa1o0K73GSuP+K8tK7PLUlLiuzatRpbV2OEGja3qvYDFy0JZqdXYNj+XLxflW429ptTuZ96yVg2rehsmLeS7dnY0w2rsg/fc3TaOL089+aR2a91U2RwSWm0+/vJnDRv9pWYvWKZIWbmMMWrSuLEuv/wc3XPPPcrNzXUdEdspEo36rg1ZvgcVLFlkVR/M8P9ctn1f8SxuZ6zM/3unJJWp+mYeBjL8vzZbj23xui9J5cVFVvVpYf/3kQnY/QzTs2tZjG33uI0UFljV22QvW7fGauy0cPUdNpuVkem7tsTyM2Jmuu2va/4/ENdq0sxybP/qVOPYgAvGmKCkZyQdKWmRpEnGmDGe503foOxYSbtUnvaVNFjSvlu57M2Sxnme93BlU+hmSTdJKpF0h6QOlaftRvMnRb34wCV6YuhYDR0xQuvWrdO7777rOhIARwYNGqTSSERP3dHXdZSkdUCvO7S2qFhZWVk68KCDdccdd+jQQw91HQsAAKDaGGOsG9JxrKukOZ7n/SlJxphRkk6StGHz5yRJwzzP8yT9YIypbYxpLGmnLVz2JEmHVl7+VUkTJN3ked46Sd8YY9pU1Q2g+ZPCrjnveGWGQhr8+mgde+yx+vjjj11HAuDArbfeouaN66phvRzXUZLW2qJiPfvss7r00ktdRwEAAIC9ppIWbvD1IlXM7tlaTdOtXLah53lLJcnzvKXGmAZVGXpDNH9S3KVnH6VwOKTHXx6rQw45RF9++aXrSAB2oHfeeUdFRes09KGLXUdJWpFIxWEvJ554ouMkAAAA2Ix6xpjJG3z9gud5L2zw9aaOqfQ2+npzNX4uW+1o/kB9Tj1U4YyQHhj8rvbdd1/9+OOPriMB2EEuvvhi1atTU7u2auI6StL67pfZkqSmTZs6TgIAALBjJdBhXys9z+uyhfMXSWq+wdfNJC3xWRPawmWXG2MaV876aSxpxbaE94PdviBJ6nnc/rr/ml6aOHGiOnbcw3UcADvAt99+q1WrVuneq3u6jpLUJv4yWwHLRVIBAAAQVyZJ2sUY08oYE5LUS9KYjWrGSDq3ctev/SStqTyka0uXHSOpd+X/e0t6v7puADN/8I8Tj+iicGa6rntouNq23UWzZs12HQlANTrzzF6qmRXWgZ3buY6S1GbMXazMTP87wQAAACC+eJ5XbozpJ+lTSUFJL3ueN80Yc0nl+c9J+khSd0lzJK2XdN6WLls59MOS3jTGnC/pL0mn/32dxpj5kmpJChljTpZ01Ea7i1mh+YP/cuQBe+qZu0K6/J4hatmypf78808FgwkzVQ+AT7NmzdLChYt0/zW9XEdJenn5a5WVZbeVNgAAQMIzRkqcw762yvO8j1TR4Nnwe89t8H9P0uV+L1v5/VWSjtjMZXbajrj/g3no+B8H7bObhjx4iRYu/EvNmjVTNBp1HQlAFTvllFOUmZGuE4/Y0qHNqAqHdN1NK1eudB0DAAAAKYzmDzZpn45tNOKxK7V8+XI1atiQBhCQRPLy8jR9+nRdcPom/8iAKnZV72PleZ4ef/xx11EAAACQomj+YLP22LWF3nzqGuXn56te3br/bFcMILGdcsopSgsGdVGvf7mOkhJCoZDq1ampAY895joKAAAAUhTNH2zRrq2a6L3BN2jt2rWqm5uroqIi15EAbIfi4mJ9++23OvWorq6jpJRzTj5YS5YuZRYlAABIIUYmGEyIUyqg+YOt2qlZA3300s0qKSlWgwYNtGbNGteRAGyjc889V8YY3XLJya6jpJRzTz5YknTLLbc4TgIAAIBUxG5f8KVxg1x9OvQ2HdP3QTVs2FALFy5U/fr1rccJZoSt6iOFBb5rQ7Xq2MaxUmaRxfZ2rls013dt7XZ7WY09dYndQrPLi4p91+aGM6zGDgSM79pQ0K43vVv9HKv6dZEy37ULV9vNeLvvt1W+ayfN9f8zkaQFnXa3qt/YBx+M0Z7tWrKL3w4WDAbVrFFdDRkyRP3793cdBz4t/+N3q/pw/Sa+a72Y3SywVaV29ZmxEt+1WRkhq7HLPP+vW5nplh81S/2/NktSVka679pVReusxq6b7X+XvvJiu/eJ8jS7908V22W3YSx24omW+n9cSVIg3f/9I0kmo4bv2mC53XIE0VL/n29iZXZjl1vcPxmWn1fXLfnLqj7coLHv2jV5K6zGzqnfwKoeQHxh5g98q5+boy+G3SF5MTVt2lSLFy92HQmAha+++kqlpRHde9UZrqOkpMvOOlL5+fkc+gUAAFKDqWgwJ8IpFdD8gZWcWtkaP/JOpQWNdmrZUsuWLXMdCYBPl19+uWpkZminZvzlzoXjD+8iSRo+fLjjJAAAAEg1NH9gLTsc1oThd8sYqVu3/VzHAeDTtGnT1P1Qu8MGUbUyM9I1bNgw1zEAAACQYmj+YJuEwyH1v/Hfmj9/gd544w3XcQBsxSOPPCLP83TzRSe6jpLSmjSoo19//dV1DAAAAKQYmj/YZkfsv4eaN6qr3r3PdR0FwFYMGPCYGtWvrVDIbmFXVK0D9t5Vqwv8Lx4PAACQuIwUCCbGKQXQ/MF2GT7gCpWWRnTmmWe6jgJgM/Lz85WXt1L9/n2M6ygp798nHaSY57FeGgAAAHYomj/YLrk52Tr+8L01atQofpkB4tRFF12kQCCgE4/o4jpKyntm5KeSpOzsbMdJAAAAkEpo/mC7PXjtWQqlp2n//bu5jgJgE8aOHas9dm3uOkbKKyou1gfjftKpp55K8wcAACQ/I5lgICFOqSA1biWq3QPX9tK8efP11ltvuY4CYAPffvutSktLdc+VZ7iOkvIuuOV5BYNBvfnmm66jAAAAIMXQ/EGVOPqgTmrWqK7OOecc11EAbOCyyy5TODOknZs3dB0lpc1ZsFTT5yzS/Q88oGAwNRYVBAAAQPyg+YMq82r/y1RaWkoDCIgjv//+u7ofspfrGCnvottfUK2aNXXTTTe5jgIAALBDGBmZQDAhTqmA5g+qTP3cHB136F4aMWKE8vLyXMcBUt6gQYPkeZ5uufgk11FS2kdfTtHKgrV6+513XEcBAABAiqL5gyr10PVnK5Sepv322891FCDlvfnmmwpnhhQKhVxHSWl3PfmWdt55Zx155JGuowAAACBFpbkOgOTzwLW9dMMjI/TOO++oR48e/3Vek73smkKLJn7lu9bUrm81dmZGpl19/Sa+awNpdr9sZ+T6X49l6epCq7GXFK63ql9TWua7Nm99qdXY9Wtk+K5tW7+21dhFFrkl6dtFBb5rI1HPauygMb5rFzzyL6uxbcyaNVP1c2tV2/jYuv4vvKfSSJm++sr/axkS17olC3zXhhs0tho7GPD/umJrbbHda3lRacR3bdPadjvblZTZvZZHYzHftbk17N73I9Go/+JQ2GrstEixVb2NaGlJtY2dnl297ylemf/HYqhWHauxY2X+H7e2P0ObQ0Zy6jewGrusyO5zX+2Gdq8tNpYuWeq7tnGT6ssBYNsw8wdV7uiDOqlpw1ydffbZrqMAKa2goEDtWzd1HSNlRSIRjfzgWx155JFq2pT7AQAApBgjKRBMjFMKoPmDajHs0ctVWlqqPn36uI4CpKyysnIdtl8H1zFS1iV3viRjjD7++GPXUQAAAJDiaP6gWtTPzdGxB3fSq6++yuLPgANTp06VJB3RbXfHSVLTomWrNHnqn7r++uvZ2h0AAADO0fxBtXnkxn8rlJ6mbt26uY4CpJzhw4fLGMNiz45ccOvzqlEjrEceecR1FAAAAEfY6j2e0PxBtbrv6p6aO3euRo8e7ToKkFK+/vprhTNp/Ljw9aQZWrIiX8OGDXcdBQAAAJBE8wfV7NhD9lKTBrk666yzXEcBUsrs2bPVsC47fblwQ/8RatKkyf/sdggAAAC4QvMH1e7V/perpKREffv2dR0FSBmrV6/Wbq2buY6RcgaP/D+tLy7V+PHjXUcBAAAA/kHzB9WuYb0cHXPQnho6dCiLPwM7SHl5uY48oKPrGCklGo3q+Tc+13777ae2bdu6jgMAAOCWMTLBYEKcUgHNH+wQ/W86R+lpQR1wwAGuowBJ75dffpEkHbpve7dBUsw1D7wqz/P0xRdfuI4CAAAA/BeaP9hh7rump2bPns3iz0A1GzZsmALGsMX4DpS/pkgTJk7XBRdcoHA47DoOAAAA8F9o/mCH6X7I3mpcvw6LPwPV7Ouvv1Y4nOE6Rko576ZnFQqF9MILL7iOAgAAED8CwcQ4pQCaP9ihhj3aTyUlJbrwwgtdRwGS1rx5f6pR3RzXMVLG5KlzNW/RCj377LOuowAAAACbRPMHO1TDejk66sCOeumll5Sfn+86DpCUVq9eo93bstPXjnLVfa+oXr16Ov/8811HAQAAADYpzXUApJ7Hbj5XnU++Sd32208zZ83aYm2zrgf7Hrdw1SqrHIvy11jVh9IyfdfWtlzzY9Xadb5rozHPauzmtbOt6htGo75r5xcUWY2dt77Uf+2C5VZjB42xqj+4RV3ftXs3rmM19oriMqv6qhSJRBSNRtXj6P2cZUgV0WhUx5z/kNauK9b4L792Hed/rFi2zKo+PVbuu9Z2V4zaDRtb1ccLYz0N3P9zP5CWbjVy3eyQVX00FvNdu7bY/2uzrbl5q6ttbElqXLum79qY7N4nYjH/74clEf/PH0nKtvzzq8mo4bs2Pd3usRJI819fWFxiNXaZxWcKScrK8J8lUmr3XlvL4rNZMMP/Zz5JqlW3nlW9jXqt2lTb2LYaN0nM13K4ZLbhvRTVhZk/cOKeK0/XrNmzNXbsWNdRgKQydOhQSdJe7Vs5TpLcIpGIDjvnXq1YtUbffPONOnfu7DoSAAAAsFk0f+DE8Yd3UaP6tXVmr16uowBJ5Y033lBmht2MAtgpKi7WIWffo7XrivXLL7/ogAMOcB0JAAAA2CKaP3Dmur7Hq2id/8OdAGzdb7/9qsb17Q5Tg39rCot02Nn3qrQsqtmz56hjx46uIwEAAABbxZo/cOZf++8hSZo4caK6du3qOA2QHAoKVuvo/bu5jpGUlq7I13EXPaJgME1LlixU/fr1XUcCAACIX8ZYrxOI6sPMHzgTDAYVCAQ0bNgw11GApJCfn69YLKazTzjIdZSk8+fC5Tr2goeVmZGplStX0vgBAABAQqH5A6dqZmXq66/jb5ccIBE988wzMkZq3qT6dh1JRb/+MV+nXPaYcnJytCo/X9nZdjvoAQAAAK5x2Becat6orubPm+c6BpAU3n//fdUI221Piy375qc/dPldL6lR48ZauHChgkxdBgAA8MVIbPUeR5j5A6f26dhaRUVFrmMASWHWrFnaiVk/VebjL3/WZXe9pNZtWmvJkiU0fgAAAJCwaP7AqVOO7KqY56m4uNh1FCDhFRUV6dD9dncdIym89fH3uunRkdprr06aPXuO6zgAAADAdqH5A6d2atZAkjRixAjHSYDENmvWLHmep17d2elre7345jjd98w7OvjggzVlys+u4wAAAADbjeYPnAulp+n99993HQNIaIMGDVLAGOXUYjHi7fHYkA80aNjHOvHEE/Xll1+6jgMAAJC4jJECgcQ4pYDUuJWIa7m1s/Xrr7+6jgEktM8//1y1atZwHSOh3fnkGxo2+kv17t2bhjQAAACSCs0fONeuVROtWLHCdQwgoS1YMF+7tGzkOkbCuvr+oXrvs0m65ppr9Morr7iOAwAAAFQptnqHc4d366AJE6fv8OsNBoxVfWa6/6fLstVrrcaOxmK+a2uFM6zGtq0vLC71XVsW9Z9bkj79c6Xv2jmryq3GPqhF2Kq+S4uGvmttt6ismbfUorqV1dibU1xcomMP6VQlY6WaC24drIm/zdV9992n22+/3XWczVq6xOZxJZWVR63qM7P8P4fWlUasxq5tVV29Fi1a7Lu2JD3LauxatXJ915auXmU1tq2M2v53/ot6dq/ldSweKzOX2d3OOjXs3rNsHosrCu12F61Tw//tDFh+pliy3u49rkXYYvxAutXYq4rW+a7NTLcbOxbzrOojFq9bZVG717jC4hLftTUz7G6nzetz4yaNrcaOJ/mLFviuzW3WshqTIJEYdkuNG8z8gXOHd9vDdQQgoU2YMEGSdOLhnd0GSUA9rxqoib/N1aBBg+K68QMAAABsD2b+wLl0usHAdhk8eLDSggGFQiHXURLK8Rc9rL+WrNTIkSN11llnuY4DAAAAVBuaP3CO3g+wfb777jvVrVPTdYyEEY1GdXTfB7Vi1Rp9+OGH6t69u+tIAAAAyccY6+UTUH047AsAEtyyZcu0ZzuOrfdj0m9z1O2M25WXX6hvvvmGxg8AAABSAjN/4FyQqT/ANotEIiovL1fP7vu7jhLXIpGI+tw0WFNnL1SjRo303XffqVWrqllsGwAAAIh3zPyBczR/gG03fPhwSdI+Hds4ThK/Xnl3grqedpumz12sAQMGaOnSpTR+AAAAkFKY+YO4EY1GaQQBlkaOHKlMyy1pU8WiZat07g1Pa2XBWnXt2lVff/01i2IDAADsSKz5Ezdo/gBAAvvtt1/VuH4d1zHizo2PDNcnX/+qrKwaGj9+vA499FDXkQAAAABnOOwLcSMajbqOACSc/PwCHbB3W9cx4saEidPU5dSb9cnXv+qCCy5QUdE6Gj8AAABIecz8Qdyg+QPY+eyzz+R5ni4562jXUZwrKi7Wudc/ozkLlqlFi+b68ceJatSoketYAAAAqcsYGZb1iBvM/AGABHXXXXcpMyNdtbLDrqM4NXjk/+mAM+7UvEV5eu6557RgwV80fgAAAIANMPMHcYOZP4CdKVOmqGPbFq5jODNnwVL1veU5rS5cp0MOOUTjxo1j0XgAAABgE2j+IG7Q/AH8y8vLU2lpqa7sfazrKDtcNBrVVfe/oq8mzVCtWrX0448/qmvXrq5jAQAAAHGL5g+SxrrSiFV9ON1ue+wyi+ZUNBazGrtODf+H7dTOyrQauzhvqVV9o+xavmtLyjKsxraxUx27GRy75GZZ1ZdG/d9HmQG7I2Sb7LmPVf22uOmmmxQIGO3Zbqdqv6548vGXP+u2J0YpGo3pqquu0sCBA6v9On/9Y3a1jd00N8d3babsGuRZlq8Vkud/7Ay718/8RQt81+Y2a2k19qw3n7eqz+m4v+/aWA27nfRWrl3vu7ZOlv/XWsn+faV4XbHv2toW70GStKrI/+1s08DuZ2jzXitJxZFy37U2zzdJiln+zG3Uza5hVR+xeM+yVTvd/3tc0PK5X6tJY6v6FcuW+c9i+d5s8xyKldl9pmzcpJlVfaKyfX0GJCPDVu9xg+YP4kZxcbFycuw+mAGpasyYMWraMNd1jB2msKhYZ137pP5aslJt2rTRjz/+qNzc1Ln9AAAAwPZgwWfEjYKCAtcRgIQQjUa1atUqnX5sN9dRdojHhnygg3rdoaV5azRy5EjNnj2bxg8AAABggZk/iBv5+fmuIwAJYfDgwZKkc046yHGS6jV9zkJdcNvzKlpXomOPPVYffPABCzoDAAAkCGPEYV9xhOYP4gYzfwB/5syZo4AxSdsIyV9TpGseeEU/T5+v3Nxc/f7DJHXo0MF1LAAAACBhcdgX4kZhYaHrCEBC+Ne//qWY539x3kTx3mcTdfg59+jQs+/W77MW6vbbb9eqVato/AAAAADbiZk/iBtr1qxxHQFICEcddZQkaemKfDVukNhr36wpLNKtA0bpu19mKRqNqWHDhnrppSd0/vnnu44GAACA7WJkknSmeiKi+YO4YAwzfwC/QqGQJGnCxOk68/gDHafZNmO/mKwnXvlIefmFCgaDOvzwIzRy5EjVr1/fdTQAAAAg6dD8QZwwNH8AC+npafpl+vyEav4UFhXr1gGv6bspM1UejalBg/p64YUXdOGFF7qOBgAAACQ1mj+IC8YYFRUVuY4BJIysrGz9uWiF6xi+fPzlzxrw8litWLVGwWBQhxxyqEaOHKlGjRq5jgYAAACkBJo/iAvGiOYPYKFhw4bKW77YdYzNKiwq1h1PjNLXP/2h8vKo6tevp2effVaXXnqp62gAAADYEYwktnqPGzR/EBcCxmjdunWuYwAJY5dddtGc2bMViUT+WQMoHnz27a/q/+IYLV+5RsFgQAcddLBGjBihpk2buo4GAAAApCy2ekdcCAQCKi4udh0DSBgDBw6UjNFBZ92twiK3z52i4mJd9+Cr2vvkm3TdQ8MV9dL01FNPqbw8qvHjx9P4AQAAABxj5g/igjFGJSUl2zVGYbHd5etn2PU+Y6Gw79rM9HSrsTNiEd+1patXWY0dzPCfW5IC6f5nkWSmR63G3rthtu/aaMyzGjsSjVnVL1291ndtMGD3WOlQt55V/bZo3bq1/vzzT+2ySxsd9u+7NfaFm3b4tu/jvvtdj7z4vpblrVYgENCBBx6o4cOHq0WLFjs0x8y586zq69bMsqvPruG71vaxstbidatmONNq7HLL14q0sP+fi83rhCSZDP8/w9XLl1qNXXffo6zq1y+d77s2vazMauw6dRv7rrV9zyqzfI2rlZnhu7akrNxq7KwMm/cJu4+aUc/utb+m3VuclYDF8zlojOXYdrfTRiho9zpUVuz/cV6nSTPbOFYaWKwFV7hqZbXlqLUD3seB1GBkOOwrbjDzB3EhEDDM/AEstWjRQitW5CmUkaljL3hYM+ctqfbrLCou1vUPD1Pnk2/SNQ++qrJoQAMGDFA0GtWXX365wxs/AAAAALaOmT+IC8FAYLtn/gCpKCcnR/n5+WrSpInOuPJxvfTAJdqnY5sqv54JE6fpocHvaWlegQKBgA444ACNGDGCZg8AAACQAJj5g7gQDAZUWlrqOgaQkEKhkJYvX64WLVrq/Fuf06df/1Il4xYXR3RT/xHqfMrNuvLeoSoplx555BFFo1F99dVXNH4AAACABMHMH8SFQCCg9evXu44BJKxgMKj58+erU6dOuuGREVpZsFZnn3jQNo319aQZevC50Vq8PF+BQEDdunXT8OHD1apVqypODQAAgKRlJGO5DhmqD80fxIUj9++gNz76Xt9++60OOOAA13GAhPXLL7/o8MMP1yMvvK/Pvv1NfXocqkO77r7Vy309aYZGjf1WP/42R5GyctWuXVsPP/ywbrrpph2QGgAAAEB1ovmDuHDbZT30xQ/Tdfjhh2n9+mIFg6wKD2yrL774QhdccIFGjhyhK+8dKknKrpGp9m2a6t8nH6y92++s4e99qS9+mKqFS/NVUlqx21wolK699u6skSNHqnXr1i5vAgAAAIAqxBwsxI3Rz16vsrJy7b333q6jAAnvpZdeUnFxiUpLS/XEE0+oTdt2+m3WYl1571Ad2OsOvfDGOOWtLlHXfffT0KFDVV5ertLSiH744QcaPwAAAKgCFVu9J8IpFdD8QdyolR3WA9f01G+//aZHHnnEdRwgKYRCIV199dX6+eefVVxcrNLSUi1YsECxWEwFBQX68ssv1adPH2bbAQAAAEmM5g/iyvGHd9F+nXbRzTffrHnz5rmOAySdUCjELl0AAABAiqH5g7jzwv0Xq0Y4Q506dXIdBQAAAACwDQyHfcUVmj+IS6OeuEqFhYU69thjXUcBAAAAACCh0fxBXNqpWQNd/u+j9cknn+itt95yHQcAAAAAgIRF8wdx6+JeR2rn5g3Vq1cvFRUVuY4DAAAAAEBCSnMdANiSN5+8Svudfod22203LVy4cIu1u7ZuZTV2wZJFVvVBY/zXKmo1drSszP/YGZlWYwczwlb168v8Zy8siViNnR3y/5Lz24pCq7H3bZprVR+Neb5rg7TJN2n+5+/7rq2zZzersRcWrPVd26JujtXYJWXlVvUxi8dKSZndcyIz3f9zoqzc7nUlvWYdq3ovUuy7tnz9Oquxg5n+X7eWFPrPsS2ySv2Pb2rZ/QzT0vyvGRCyqJWkxrVrWtXb8f/+JknRWKyackhpnt3j3Iva1dsoXb3Sd22oYXOrsVcV2v1Ry+q1ImD3ppWRoG9yterWs6ovXOX//gRQRYxk2FE2biTmqz1SRigU0vP3XahFixbpsssucx0HAAAAAICEQ/MHcW+fjm100r+6aPDgwfriiy9cxwEAAAAAIKFw2BcSwn1X99KPv87R0UcdpYLVq5Wdne06EgAAAABgs0zKbKOeCJj5g4Tx/rM3SvLUrl0711EAAAAAAEgYNH+QMMLhkIY8dKkWL16sXr16uY4DAAAAAEBCoPmDhLJX+1bqc+qheuONN/TGG2+4jgMAAAAAQNyj+YOEc23f49WmZSOdeeaZysvLcx0HAAAAALAxI5lAMCFOqYDmDxLSW09do1B6mtq2bes6CgAAAAAAcY3mDxJSMBjUG09erdWrV+vwww93HQcAAAAAgLhF8wcJa+fmDXX9+Sdo/PjxGjRokOs4AAAAAIB/GJlgICFOqSA1biWS1rmnHKLOu7fSlVdeqVmzZrmOAwAAAABA3KH5g4Q39JHLlZ2VqU6dOikajbqOAwAAAABAXKH5g6Qw+pkbVFJSrC5dOruOAgAAAAApz7DbV1xJcx0AqAoN6+XogWt66dbHR+m2227TAw88sNXL1GnSzOo65i34y3dtNOZZjV0rnO27NpRu97SNlZdZ1ZeV+589VadGhtXY4XT/L6w7182xGjuUZveiPW3pKt+19V69zWrsVz7803dtn0Uzrca2sfyP363qgxlhq/rae3T1XRstLbYau252DYuxS6zGzszItKpPD/p/bK0tLrUaOxjw/zeYdMvHeNAYq3pZfPCx/ZmXFxT5ri0otbudy4vsHlutG+7iu7aR7G6nKV7nu7ZmOMtq7JJVK+yyWDxug5bPCZPh//m5rjRiNXZ2ut3fJW2yRwpXW40ds3ic277GZVq+l68qWu+7tmmu3ftnboOWVvXxomDJIqt62899AJBsmPmDpHH84V105P576MEHH9QPP/zgOg4AAAAAAHGB5g+SyoBbe6tenZo6+OCDFYnY/bURAAAAAIBkRPMHSWfMCzcqFo2qXbtdXUcBAAAAgBRlZILBhDilApo/SDrZ4bCeved8zZs3X3379nUdBwAAAAAAp2j+ICl122tX9ezeTUOHDtXYsWNdxwEAAAAAwBl2+0LSuu2yHvr+l9k66aSTlJ+fr5wcu90vAAAAAADbyJiU2UY9ETDzB0nt3aevVTBgtMsubVxHAQAAAADACZo/SGqhUEgjBlyhvLyVOuGEE1zHAQAAAABgh6P5g6S3W+tmuvzfR2vs2LEaMmSI6zgAAAAAgARjjDnGGDPTGDPHGHPzJs43xpinKs//zRiz99Yua4zJNcZ8ZoyZXflvnQ3Ou6WyfqYx5ujtzU/zBynh4l5HqsMuzXXhhRfqr7/+ch0HAAAAAJKeCQQS4rTV22FMUNIzko6V1F7SmcaY9huVHStpl8rTRZIG+7jszZLGeZ63i6RxlV+r8vxeknaXdIykZyvH2WY0f5Ayhj/WT5kZ6dp9990VjUZdxwEAAAAAJIaukuZ4nven53kRSaMknbRRzUmShnkVfpBU2xjTeCuXPUnSq5X/f1XSyRt8f5TneaWe582TNKdynG1G8wcpIxgM6p2nr9O6oiIdeOCBruMAAAAAABJDU0kLN/h6UeX3/NRs6bINPc9bKkmV/zawuD4rNH+QUpo1qqs7+vXQDz/8oIceesh1HAAAAABIYiZBTqpnjJm8wemiTdyQjXk+a/xcdmPbcpktStueCwOJ6LRjumncd1N166236rjjjlPHjh19Xa5Vyxa+r+OH3//Y1nhblZlu97Rdv2yRVX2wLOK7tkGTllZjR2tm+c9R7j+HJL05zW4tp64TX/Nde+XwiVZjP3yO/xmZeXNnWo3tWRyyGEgPWY0dC2db1S8pXO+7tl7NGlZjZ1k8zr2Y3eHP5cXrrOpNhv/stbPCVmOXlJVZ1dsIWH4+iMb8P7bK19v9DNfPn+G7tsN+R1qNPePXuVb1axav8l27W4PaVmPvWtf/cygY2NRnui2o22DrNRsoXe3/dtqyeWwFfayjsKGIZ/dzydzkZ+NNKysqtBo7kJHpv9b29bbY7j2uce2avmvXl9iNPWfeAt+1bVrZve+vWjDPqj6thv/PCXWaNLMaGwC2YqXneV22cP4iSc03+LqZpCU+a0JbuOxyY0xjz/OWVh4itsLi+qww8wcpafC9F6p2rSx17boP6/8AAAAAALZkkqRdjDGtjDEhVSzGPGajmjGSzq3c9Ws/SWsqD+Xa0mXHSOpd+f/ekt7f4Pu9jDEZxphWqlhE2u4v0hth5g9S1gfP3aBDzr5HHXbfXTP+qL6ZOgAAAACQiqKxmOsIVcLzvHJjTD9Jn0oKSnrZ87xpxphLKs9/TtJHkrqrYnHm9ZLO29JlK4d+WNKbxpjzJf0l6fTKy0wzxrwpabqkckmXe563XbMWaP4gZeXUytaAW87VNQ++qquuukpPPvmk60gAAAAAgDjked5HqmjwbPi95zb4vyfpcr+Xrfz+KklHbOYyD0h6YDsi/xcO+0JKO2L/PXTiEZ311FNP6YsvvnAdBwAAAACAKkfzBynv/mvOVOP6dXTUUUepqKjIdRwAAAAAAKoUzR9A0nvP3iAjT23btnUdBQAAAAASnicp6nkJcUoFNH8ASeFwSEMeulRLly7VGWec4ToOAAAAAABVhuYPUGmv9q10Xo9D9dZbb+mNN95wHQcAAAAAgCrBbl/ABq4573h989NMnXnmmTrkkEPUqFEj15EAAAAAIPF4nmJJstV7MmDmD7CRN5+8Whnpadptt91cRwEAAAAAYLvR/AE2EgwGNerJq7V69WoddthhruMAAAAAALBdaP4Am7Bz84a6/vwTNGHCBA0cONB1HAAAAAAAthlr/gCbce4ph2jCj9N0zTXXqHv37lbbwO+3Rzur65q34C/ftWVFa63GTs+uZVUfK4tYDJ5hNXZJqf+xM8tKrMbe79d3rerHPfaF79o7DmxiNXbbGwf6ri3NyLYau6SsvFpqJWn54jyr+p3r5fiuLVhXbDV2OD3dd23NsN3jMBYKW9WHAv7/TlJSVmY1dsBi7KAxVmPbHmFv83Op0aiZ1diR/GW+axe+/ZzV2D1O6mtVX6eJ/+wTpky1GntFYZHv2qyMkNXYNTP8PyckKZDmf/xAul2W8vXrfNdmWb4H/bVqtVV9i7q1fdeGGzS2GtuLRn3XRkvtXuPSLe4fScoI+n+tiKYFrcbODPn/dWDpkqVWY2dl17SqjxSu9l9cv4HV2AB2PE9SNJYa26gnAmb+AFvw8sOXqWZWWJ067amoxYdAAAAAAADiBc0fYCveG3y9SkpK1bp1a9dRAAAAAACwRvMH2Ir6uTl66YGLtWDBAhaABgAAAAAfPE+KxmIJcUoFNH8AH/bp2Ea3XHKyJkyYoH79+rmOAwAAAACAbzR/AJ/OPP5A9Th6Xz3zzDN6/vnnXccBAAAAAMAXdvsCLNx1xemat2iFLrnkEu222246+OCDXUcCAAAAgDjkKcZuX3GDmT+ApVceuVwN69XWYYcdpsWLF7uOAwAAAADAFtH8AbbBJ0NuUUYoTW3atFEkEnEdBwAAAACAzaL5A2yDYDCoj4fcqkgkopYtW7qOAwAAAADAZtH8AbZRbk62hj/aT8uWLdO+++7rOg4AAAAAxA1PUtSLJcQpFdD8AbbDHru20P3X9NLEiRN17rnnuo4DAAAAAMD/YLcvYDudeEQXzZq/VMOGD9eee+6p6667znUkAAAAAAD+QfMHqALXn3+C5i5Ypuuvv14dOnTQ0Ucf7ToSAAAAALjjSVG2eo8bNH+AKjL43gt1/EUP69hjj9Xs2bPVunVr35dt1bKF79qCJYuscpWuXmlVn1m3ge/aOSvyrcZuWTfHd+2y90dYjb1u7h9W9ScNu8137aSctlZjvzhtme/axlkZVmNHYv6PSV4biVqNvUudLKv64ki579pQWtBqbBuritZb1UctfoaSVCuc6bs23fJ2phv/tSZgUSyppMz//SNJJRb3Z8AyS2iXvXzXNt5pN6ux6zRpZlVv49C9O1Tb2Lav5bEyu50lvbD/53MsUmw1dtm6Nb5rg5n+nz+S/fMzUljgu7asqNBq7Kwm/jd0WFW0zm7sNLvn0Poy/6/nmel2H+/Lov7HDlo+92vVrWdVL9t6AIBvrPkDVKH3B9+grHCG2rdvr+Jiuw/TAAAAAABUB5o/QBUKBoP65OXbFIuWq3mzZopa/DUNAAAAAJKFJ0/RWCwhTqmA5g9QxWplh/XmU9coPz9fnTrt6ToOAAAAACDF0fwBqkGblo312C3naOrUaTr11FNdxwEAAAAApDCaP0A1OfKAPXXpmUdp9OjRuuuuu1zHAQAAAACkKHb7AqrRpWcfpTkLluree+9Vx44d1aNHD9eRAAAAAGCHYKv3+MHMH6CaDbi1t3Zp2Uinn366pk6d6joOAAAAACDF0PwBdoA3n7pGOTVraK+99tKaNWtcxwEAAAAApBCaP8AOEAwG9emQ22TkqUXz5mwBDwAAACCpeZ4U87yEOKUCmj/ADhIOh/TeszdobVGRdt11V9dxAAAAAAApguYPsAM1b1JPz9x9vubOnaujjjrKdRwAAAAAQAqg+QPsYAd2bqdr+x6vzz77TNdee63rOAAAAACAJMdW74ADfU49VHMWLNUTTzyhTp066dxzz3UdCQAAAACqVDQWcx0BlWj+AI7cf82ZmrcwT71791a7du3UtWtXX5er06SZ1fWUF6+zqo8UFviubVOnntXYdRo18l1btPPuVmOvOfAMq/p+Xy32XfvrfLsd2vbe2f+icV2bWg2tumH/L9u1M+xe4rMz0q3qyywWLm+Wm2M19qoi/4/bkrJyq7Ez0+1+LutKI75rcy0/35RH/V8gkG53/2RmhK3qbW5ncandovVZGSHftQUxu0nJ3ooVVvX1GjSwqq8utq/lBUsWWdWHgkHftdGA/1pJSgtn+64tLVhpNXaTdP+PFUlaH8zyXVurgf9aW0Fj97htYPF+CABAVeCwL8ChkY9fqXp1amn//ffXsmXLXMcBAAAAACQhmj+AY58MuVlpwYBat26tSMT/X94BAAAAIF558hSNJcYpFdD8ARwLhUIa+8LNKikuVuvWrV3HAQAAAAAkGZo/QBxoWC9HLz10iRYtWqSDDjzQdRwAAAAAQBKh+QPEiS4dWuuOy3vom2+/1UUXXeQ6DgAAAABsM8+Top6XEKdUQPMHiCOnH9tNZx5/gF588UU9/fTTruMAAAAAAJIAzR8gztxyySnq2rG1rrjiCn300Ueu4wAAAAAAEhzNHyAOvfTgpWrVvIGOO+44DRs2zHUcAAAAAEACo/kDxKn3B9+oPdq2UO/evdW/f3/XcQAAAADAiust3Nnq/T9o/gBxbOTjV+rgfXbTTTfdpGuvvdZ1HAAAAABAAqL5A8S5p+86XycfuY+eeOIJnX322a7jAAAAAAASTJrrAAC27t6reqpu7Zoa8tprWrZsmcaNG+c6EgAAAABslicpGou5joFKzPwBEsRVvbvrpotO0hdffKG99urkOk61GDt2rE4++WTXMQAAAAAgqTDzB0ggZ594kOrVqakbHhmhnXbaSXPnzlUwGNziZeq33nUHpds2RUVFOv/88/Xee6MViZRJki666CK98MILVuOMeO9Lq/rWdf33vo9vm2k1drOaYd+12RnpVmMHjfFdG4lGrcZuVjvbqr5RrSz/WQoLrMbOCvvPYrtIX2grz5ntkRb2f99LUnlxke9aL2Z3f9qy+TkWlkSsxg4G/D/f6mbXqLaxJWnlihW+a+s1aGA1dnWq06SZ6wjYglp1XScAAGDLmPkDJJijD+qkIQ9eor/+WqCGDRsqErH7JSye/PXXX6pZs6befust7d6mqT566RYd1LmdXnrppYS+XQAAAAAQT2j+AAlon45t9OZT16qgoEC5ublas2aN60jbpGnTppKky885Rq/276dmjerqqTvPkyQdd9xxLqMBAAAA2B6ep2iCnFIBzR8gQe3aqok+fulmRUpL1KBBfS1evNh1JGvBYFCNGzfWqLHf/tf3zjx+f33++efKy8tzmA4AAAAAkgPNHyCBNW6Qq/Ej7lYwYNSyZUv99ttvriNZu+yyy5SXX6joBuvT3HzxKQqlp+nQQw91FwwAAAAAkgTNHyDB1coO6+vX7lbNrEx16tRJEyZMcB3Jyi233CJJGvLW+P/6/g0XnKjp06frl19+cZAKAAAAwPbwJMViXkKcUgHNHyAJhEIhjR9+pxrXr6PDDjtMb731lutIvgWDQTVp3FhvfPTdf32/53H7q2ZWWMccc4yjZAAAAACQHGj+AEkiGAzqk5dvVdtWTXTGGWdo0KBBriP51ue885SXX/g/33/s5n9r+fLlCdXMAgAAAIB4Q/MHSDJvD7pW++7ZRldeeaVuu+0213F8+eGHH5SenvY/3++2165qXL+O+vY9z0EqAAAAANvD9S5e7Pb1HzR/gCT04gOX6NiDO+nBB/+/vTsPs7Os78f/fuYkk0wyJBACgSTshE1W2UG/UigFkYriAlYFXKtC0fanBXEDVERcq0IrltYFFKiK0qosAqJgWZRNILJIWAKEBCKBCUOGnHl+fxAokSxzwmSes7xe13Uu5jznvu/zTsrFZT/zue/7lBx66KFLHabcjK6+6qrsuNVGy/zsw+8+OH19C0c4EQAAQPtQ/IE29fl/flvefsgrc8EFF2T06NHZaaedcuutt1Yd60VuvfXWLBoYyEff97plfr7ny7dIksyfP38EUwEAALQPxR9oYx95zyG58aefz5tfvUf+OPO2bLfddllrzTVz2mmnVR3teccee2xGjx6VzTdaf5mf9/b0JEkuuuiikYwFAADQNhR/oM3VarV87ANvyHU/+ly++en3ZPzYWo477rjUarXst99+mTdvXqX5rr766uy09bK3fD2n1tWVq666aoQSAQAAL1VZJvXBwZZ4dQLFH+gge+60ZX5x1gn57X99OvvstnV+feWvsu6662bq1Kk577zzRjzPrbfemoGBgZzw/kNXOG7MmNFNuWUNAACgFbz4eh2g7fX29OSrH3/2Bq0fX3JtvvadX+Twww/PEUe8PW9845vy7//+7+lZst1qdRgYGMhZZ52Vk08+Od2jR2XTDaascPwa43ty//33r3DM0Ttt2FCGrtGjhzx20eOPNrT2wj/dNuSxPRtu0dDag2tPHfLYCT1jGlq77/67Gxr/TNfkIY9t5O87SRr5/Uujv63pWsbNcivSPaq22rI0oqw3tnajWWpdRUPjG7Fw0cCQx647YXxDa9cX9Tc0fvLU6Q2NBwBoBzp/oMMd+je751fnnJiL/+OEbLPZtPzgBz/IuHHjsuWWW+Tqq68elu+45ZZbctRRR2XjjTdOd/fojBkzJh/4wAey4PH5OfptB6x0/pTJEx34DAAALaRM9Ve4u+r9/+j8AZIk6687Kd/9wjFJktPPvihn//Q3ecUrXpHx48fn6KOPzimnnJJabeUdEAMDAznjjDNy/vnn57Zbb82TfX0pyzK1rq5MWrM3++y2TQ5/zV7ZdfvNh5xtk+nr5Pa7Zq/ynw0AAKCTKf4AL3L02w7M0W87MLff/UA+9qVzc9ppp+ULX/hCdt1113z/+9/PZptt9vzY3//+9/nqV7+aK6+8Mg8//HAWL16cJBnXMyYbT1sn+73+FXnL3+71/K1dq2KHrTbKT3/5u5f85wIAAOhEij/Acm2z+Qa54F8/koGBgXz6jB/nF1fekM033zxrrrlm6osX58m+viRJrdaVyWuukb/e82V56yGvzA5bbTysOV65yzZJfpSBgYF0d3cP69oAAADtTvEHWKnu7u58+kOH59MfOjxXXHNrvnTW/2RC7xrZ/xWvyuEH7Z2entVbkJkyeWKS5IorrsgBB6z8jCAAAKB69cHOOE+nFSj+AA35qz22zV/tse2If29RFLn88ssVfwAAABrkti+gJXSPHpWbb7656hgAAAAtR+cP0BLGjxubWbNmVR0DAAAYgrK07auZ6PwBWsLktXozb97cqmMAAAC0HMUfoCVsNHWd9PUtrDoGAABAy7HtC2gJ226xQX752z9UHQMAABiiemnbV7PQ+QO0hL1evmXKsky9Xq86CgAAQEvR+QO0hC03mZokuemmm7Lzzju/6PO1N9qkofUeuOaKIY+t9fQ2tHbvjO2HPPaJ23/X0Nq5a+g3nvV31Rpaesw6Uxsa3//QvUMeu9YOeza09hP9i4Y8tmf06IbWHtvd2PjBwcEhjy0GFze09kDfE0MeWw42VvjsrjX2+53xY8YMeezA4say1LqGnmX2/KH/nSTJNjM2bWg8AEAn0vkDtIyiSC655JKqYwAAALQUnT9Ayxg1alR+97sGO2UAAIARV8ZV781E5w/QMsaN7c5dd91VdQwAAICWovgDtIxJE8dnzpw5VccAAABoKbZ9AS1j+pS1c/1ts6qOAQAArESZ0lXvTUTnD9AyttxsahYtGqg6BgAAQEtR/AFaxu47zEjptwcAAAANUfwBWsZOW2+UJJk1y9YvAACAoVL8AVpGd3d3kuSSSy6pOAkAALBC5bNXvbfCqxMo/gAtZVStK//7v/9bdQwAAICWofgDtJSxY7ozc+bMqmMAAAC0DFe9Ay1l4hrj8uCDD1YdAwAAWIEycdV7E9H5A7SU9ddZM48//ueqYwAAALQMnT9AS5mx8fq5cea9L3mdnk1fNuSxg4ODDa39RP+iIY9de7d9G1r70d9eNOSxC274dUNrj990m4bG9265U0PjGzGhZ8yQxzZ6SN8zi+sNja91FUMe2zWqu7G1x/QMeezAE40VPQefeaah8Y38D4LxYxr7cy5cNDDksd2jag2tDQDAyin+AC3l5S/bJD/4n6urjgEAAKxAmcZ/ScfqY9sX0FL23GnLJMmCBQsqTgIAANAaFH+AljKh99ltMpdccknFSQAAAFqD4g/Qcrq6uvLrXzd2ng0AAECncuYP0HLGdI/KH/7wh6pjAAAAy1O66r2Z6PwBWs4a48fm/vvvrzoGAABAS1D8AVrOupMm5rHHHqs6BgAAQEuw7QtoORtvsE7+OOvhqmMAAADLUaa07auJ6PwBWs4OW26cxYsXVx0DAACgJSj+AC1n7523TJIMDAxUnAQAAKD5Kf4ALWf6emsnSa666qqKkwAAADQ/Z/4ALakoilx22WXZd999q44CAAD8hTLJ4kFn/jQLnT9AS+oePSo33XRT1TEAAACans4foCWN7xmTe+65Z5XnT1533SGPvf62O1b5e1ame43xDY2v9fQOfXB9sKG1Fz/x54bGj+qdOOSxg88809DaY3uG/vdSb/A3Sl1pbPzipxYOeezCBrM08n/P8b0TGlq76Ko1NL4+OPR/X2rPNHbgeq1r6L9r2nyTjRpaGwCAlVP8AVrS2mv1Zu7cuVXHAAAAlsNV783Dti+gJW24/uT09T1ZdQwAAICmp/gDtKRtZmyQZxrcegIAANCJFH+AlrT3y7dIqY0UAACaUlk+ewRlK7w6geIP0JK23GRqkuSWW26pOAkAANCpiqKYVBTFpUVR3LXkn2stZ9yBRVHcURTF3UVRHD+U+UVRfHTJ+DuKojjgBc8/WxTFA0VR9A01p+IP0JJqtVqKJBdddFHVUQAAgM51fJLLyrKckeSyJe+XUhRFLcnpSV6dZJskbymKYpsVzV/y+eFJXpbkwCRnLFknSf47yW6NhFT8AVrWqFG1XH/99VXHAAAAOtchSb6z5OfvJHndMsbsluTusizvKctyIMm5S+ataP4hSc4ty3JRWZazkty9ZJ2UZXlNWZYPNxLSVe9Ay+oZOyZ33XVX1TEAAIBl6JCr3qc8V4gpy/LhoijWXcaYaUkeeMH72Ul2X8n8aUmu+Ys501Y1pOIP0LLWmjguc+bMqToGAADQ2iYXRfG7F7w/syzLM597UxTFL5Ost4x5Hxvi+sUynq2sMrYqc5ZL8QdoWdPWnZQbZt5fdQwAAKC1PVqW5S7L+7Asy79e3mdFUTxSFMX6S7p21k8ydxnDZifZ4AXvpyd5aMnPy5u/ojkNc+YP0LK23HRqBgYWVR0DAAD4C2WS+mDZEq+X6MIkRy75+cgkP13GmOuTzCiKYpOiKLrz7EHOF65k/oVJDi+KYkxRFJskmZHkulUNqfgDtKxdt988gy/9P9YAAACr6tQk+xdFcVeS/Ze8T1EUU4ui+HmSlGW5OMkxSS5OMjPJ+WVZ3rai+Us+Pz/J7UkuSnJ0WZb1JWufVhTF7CTjiqKYXRTFiSsLadsX0LJ2227TJMmcOXOy3nrL2oILAACw+pRl+ViS/Zbx/KEkB73g/c+T/Hyo85d89tkkn13G839O8s+N5FT8AVpWd3d3kuRnP/tZ3vWud6227xlYXG9ofK1r6E2V9XKwobXX2euAoQ/uqjW09jPzH2lo/JhJU4Y89ok0lmWNJx4f8tiBxx9taO1nnpjf0PiNXnXQygctMffO2xtau2vNyUMeu6je2L8rT/cPNDT+nkcfH/LYfV6+bUNrAwBQLdu+gJZWq3Xlt7/9bdUxAACAFyjLMvUWeXUCxR+gpY3tHp2ZM2dWHQMAAKBpKf4ALW1Cb08eeOCBqmMAAAA0LWf+AC1tvXXWzF33zas6BgAA8BcWN3ZkIauRzh+gpW224Xrpf/rpqmMAAAA0LcUfoKW9fJuNU683dhsXAABAJ7HtC2hpr9xlqyRJX19fent7K04DAAAkSZl0zE1arUDnD9DSJk54tuBz6aWXVpwEAACgOSn+AC2vq6vIlVdeWXUMAACApqT4A7S87tGj84c//KHqGAAAAE3JmT9Ay1tj/Njce++9VccAAACWKMuk7qr3pqHzB2h560yakMcee6zqGAAAAE1J5w/Q8jaetk7uvHfOalt/7x22aWj8L667achj1xo3pqG1F/cvHPLYnqkbN7T2Wjvs2dD4B/oGhjy2b1F/Q2t3j6oNeewma05uaO2pO+3R0PhGrLtFY/+u/Pmh2UMeO9j3RENrjx/T09D4fV6+bUPjAQBoHYo/QMvbbssN8/Mrb6w6BgAA8AKuem8etn0BLe//7bJ1kqRer1ecBAAAoPko/gAtb4Opz277ufrqqytOAgAA0HwUf4CmVa/Xh9zNUxRFLrvsstWcCAAAoPU48wdoOmf/5Nf59gVXZu5jC9LVVeSHX/+nbL7R+iucM3pULTfccMMIJQQAAFakTFJ35E/T0PkDNIWLf3NTDn7vqdnhbz+c0/79wnSN7smpp56atdZcK2845su5Y9ZDK5w/vmdM/vSnP41QWgAAgNah+ANU5sbbZ+WwD34lO772I/nI58/OE08tzrHHfjCLFi3Kww8/nOOOOy6PzJ2bSZMm5c3HfmWFBaC11+zN3LlzRzA9AABAa7DtCxhR986em8+c8aPccPu9Wby4nnHjxuWwww7PN77xjUyaNOlF42u1Wh555JFMmTIlbz72Kzn/a/+YLTeZ+qJxG6w/OQ/ceNdI/BEAAICVKJMsHrTvq1ko/gArtP3BH86oWlc23XBKjnr9q3Lwvrs0vMaCJ/py8uk/zm9+NzNPL3om3d2js+++++Xf/u3fsskmm6x0/nMFoPVWUADaZsb0/Oq62xvOBgAA0O5s+wJWaM0J4zNYJnMe7csJXz432x/84ezztpPy2TN+lCf6+pc7b2BgIJ8940fZ+7BP5JV/d2Iuv+a2bLf9jrn22muzaNFALr744iEVfp5Tq9Uy55FHsvakSXnLh/7lRZ/vseOMlKXfLAAAAPwlxR9ghb5w3NsyODiY7519dhYtWpQTTzwxvRPWyn/94pq84vBPZLc3fDTvPP6M3Hj7rNTr9Zx57i+zz9tOyi6HnpDzf3FNpm2wYX784x9n8eLFue6667LbbrutcpZarZaf/PSnWbyM69+3nbFBkmTmzJmrvD4AADBMymdv+2qFVydQ/AFWaPcdZmSdSRNzxBFHpLu7O5/61Kdy3333pT44mEsuuSQ777Jbbr37wRz5z6dnp0OOyzfOvijjeifm61//egYHBzNz5h/z+te/ftjy7LrrrkmS+Qv6lnpeq9WSJL/4xS+G7bsAAADageIPsFJf/+RRWbBgQb797W8v9Xz//ffPVVddlf7+pzN37tyceeaZWbx4cR544IEcc8wxqyVLd3d3kuTmmfe+6LNRo2q57rrrVsv3AgAAtCoHPgMrtc3mG2T6emvn6KOPzlFHHbXMMeuss07e8573jEieoihy+92z81d7bLvU856x3bnzzjtHJMOKvHq3HYc89pLrb25o7Z4paw95bKP/gZ80faPGxje4Pi+21tTpVUcAAKAD6PwBhuSME9+Vp556Kl/+8perjpJRo0Zl1uy5L3q+1hrj8/BDD1WQCAAAeKEySX2wbIlXJ1D8AYZk4+nrZvMNp+SEE06oOkp6enry8LzHX/R8/XXXzIInnhj5QAAAAE1M8QcYsm9+5r1ZtGhRPvaxj1WaY8KECZn/eN+Lnk+bMikDAwMVJAIAAGheij/AkK0zaWK222LDnHbaaZXmmDx5cvqeevpFz8eN7c6zDaYAAEDVqr7C3VXv/0fxB2jIv578nixevDgf+MAHKsswffr0PL3omRc9Hzu2O2WH/McbAABgqBR/gIZM6O3J7jtsnm9+85up1+uVZJgxY0aeWbz4Rc97xo5JqfoDAACwFMUfoGFf+/g7Uw4O5q1vfWsl37/jjjtmcBmn8j+77QsAAIAXUvwBGtbT051999w2559/fiUHLO+9995J8qLOo56x3Tp/AACgCZRlUh9sjVcnUPwBVskXj397kuSQQw4Z8e/ebLPNkiT3PfToUs/H94wZ8SwAAADNTvEHWCW1Wi2H/PUuueiii9LX9+Jr10fCTTPvXer9+HFjK8kBAADQzBR/gFX2qWPemFpXVw488MAR/+5aV1funPXQUs/WGK/4AwAAzaBMmfpga7w6geIPsMpqtVreesgrc/XVV2fevHkj+t2ju0fngYceW+rZGuN7RjQDAABAK1D8AV6SD7/rbzN6VC3777//iH7vuHHj88j8BUs90/kDAADwYqOqDgA0p3q9nlmz5+bJhU9np202WeHYo9/6N/nqd36RWbNmZZNNVjx2uEycODGPP7F0509vz7OdP/V6PbVabURyvFR/s+sOq2/xDaevvrUBAGAl6p2xo6olKP4Az7v97gfy9o+cnmeeWbzU81G1ruy+w4x89v97SyZN7H3RvHe+ab/827m/zIEHHJA77rxzRLKut956ueWRh5d61tPTnSTp7+9Pb++LcwIAAHQixR/ocH39/TnlXy/IDbfNypx5j2eNNSbkc5/7XPbZZ59sscUWmTdvXo488shcfvnl2eetJ6bW1ZWxY7szacL4bLD+2tl2iw3yqt1elo+8+7X59Ok/yi233JLtt99+tefeeOON87vrr1vmZwsWLFD8AQAAWELxBzrYyd/4YX540TXp6urK+uuvnz322Do/+clPss466zw/Zr311svFF1+cJLn44otzwQUX5Oabb859992XG/94f/73prty5nmXPT/+sMMOy8yZM1d79q233jqL64PL/OzPf/5zpk2bttozAAAAtALFH+hg/2+3rfPDi67Jlltumdtvv32l4w844IAccMABL3per9fz+9//Pj/72c/yqle9anVEfZGdd945ZbnsTcQLFixY5nMAAGBklGWynN/VUgHFH+hg++z2snztk+/IsSf/Z7bacsv88Y47VmmdWq2W3XbbLbvtttswJ1y+vffeO0nS3z/w/Fk/z1H8AQAA+D+ueocOt89uL8vpn3pX7rjzzmyxxYyq4wzZxIkTkyS3/emBF3325JNPjnQcAACApqX4A+SVu26dM056d+666+5sttlmVccZsqIo8oc77n/R876+vgrSAAAAL1QvW+PVCRR/gCTJK3beKt/89Htyzz33ZNNNN6k6zpDUarX86f45Sz0rovMHAADghRR/gOftudOW+dZn/z6zZt2bjTbaMPV6vepIKzRmzJg8OGf+0g+LQucPAADACyj+AEvZfYcZOeuU9+WBB2ZnjTXWyKxZs6qOtFy9vb159PGlu3yKJAsXLqwmEAAAQBNS/AFeZNftN88l//nxdGUwm222Wb797W9XHWmZJk2alCf6+pd+WBR56qmnqgkEAAAkSco8e9V7K7w6geIPsExTJk/MVeeenG02n553vOMdOfzww6uO9CJTp05N/9PPLPWsKKL4AwAA8AKKP8By1Wq1/OArH8w73rBPzjvvvGy00YYZGBioOtbzNt100zyzePFSz4qiyNNPP11RIgAAgOaj+AOs1D++4+Ccdcr78uDsB7NGb29uuummqiMlSbbbbrvU/6JPs6so0t/fv5wZAADASCjLpF6WLfHqBIo/wJDsuv3mufL7J6V33JjstNNOOeCAAzJ//vyVT1yN9tprrxc90/kDAACwNMUfYMgm9PbkV+ecmINetVMuu+yXWXvttbPBBhvkggsuqCTPjjvumCSZN3/B88+6uhR/AAAAXkjxB2jYqR95a2786Wn52PsPTd8Tf86hhx6asWPG5KijjhrRM4FqtVqS5KaZ9z7/rKsosmjRohHLAAAALNvgYGu8OsGoqgMAreuw1+yVw16zV2bPeSzHf/H7+e53v5vvfOc72WqrrXLKKadk3XXXXeka48ePf76DZ1V0dRWZ+acHs//eOzz7vtbVVIdSAwAAVE3xB3jJpq+3ds7+4j+kXq/nG2dfnB/8z9U59NBDhzz/qquuyt57771K3z12bE9+fPF1OfqtB6RWq6WrKBR/AAAAXkDxBxg2tVotHzzyoHzwyINWOvb0sy/KN8/9ZQ466KBVLvwkyWWXXZa99torB7/38/nFWSek1tWVZ555ZpXXAwAAaDeKP8CI+8K3fprv/fQ3edOb3pTzzz//Ja21xx575Je//GX222+/HPL+01Kz7QsAACpXJql3yHk6rcCBz8CIOvFr5+d7P/1N3vGOd7zkws9z9t133/z3f/93Zj0wN/PmP6HzBwAA4AUUf4ARc9xpZ+fHl1yXY489Nv/xH/8xrGsffPDBzxeT6vX6sK4NAADQymz7AkbEMSedlV9fPzMf//jH8+lPf3q1fMeb3vSmXHLJJatlbQAAoAFlUi+rDsFzFH+A1e7dJ/xrrrvlTzn11FNz3HHHrdbv2n///Vfr+gAAAK1G8QdYrd76T1/LH+68P1//+tdzzDHHVB0HAACg4yj+AKvNG475Uu669+F85zvfyRFHHFF1HAAAgI6k+AOsFq95z6l54OFH88Mf/jBveMMbqo4DAACMIFe9NxfFH2BY1ev1HPiuU/LIowty0UUX5YADDqg6EgAAQEdT/AGGTb1ez75HfDqPP7EwV111Vfbee++qIwEAAHQ8xR9gWAwMDOSv3v7pLOxflBtuuCE77rhj1ZEAAIAKDdr21TQUf4BVdvMf782/nnNJbr7j/ix86unUarXceuut2XrrrauOBgAAwBKKP8CQzV/QlzPOvjhXXHtbHv3zkynLMt3d3dl6661z9NFH553vfGdqtVrVMQEAAHgBxR9guer1es792W/zo4uvzb0PzsvixfV0dXVl6tSpOfbt78gnP/nJTJo0qeqYAABAkynLpF5WnYLnKP4AS/ndrX/Kmef+MrfccX+e6l+UJJk4cWIOPPDVOfHEE7PzzjtXnBAAAIBGKP5Ah5s3f0HOOOeSXHnd7Xns8b6UZZkxY7qzzTYvy7HHHpu3v/3tLbWV67kzh1opMwAAwOqk+AMdpl6v55wLr8oFl1yX+x5+9PmtXNOnT8+R73xvPvGJT2TixIlVx1wl8+fPz3bbbZeurq588pOfzKc+9amqIwEAAFRO8Qc6wLU335VvnffL3HrX7Oe3cq255pp5zWsOzsknn5ztt9++4oTD43Wve11G1brysi02yIknnpgvf+lLuebaa90+BgAAI6xMUnfVe9NQ/IE29MijC3L6ORflN9f/MfMXPJmyTMaMGZPtttsuH/zgB/O2t72t6ojDrl6v56qrrsohf71LTv7gYbn7vofzpmO/kqOOOirXXntt1fEAAAAqo/gDbaBer+e7P/l1fnLp9Xng4UezuD6YWldXpm+wQd713vfnE5/4RHp7e6uOuVodccQRSZJPHfPGJMnmG62faetOyn333VdlLAAAgMop/kCL+t8b78i3zrsst909O/1PDyRJ1lprrbz2kNflpJNOyrbbbltxwpF13nnnZfcdNl/qoOeNp6+Ta2/5U4WpAACgcw3a9tU0FH+gRTw8d35OP+fiXPX7O/LnBX0py2Ts2LHZfvsd80//9E857LDDqo5Ymcsvvzz1ej1fPP6IpZ7vvO2m+c3v/lhRKgAAgOag+AMt4KB3n5LZc+anVqtlww03zPs+cGw+/vGPp6enp+poTWHOnDlJkgm9S/99/PWe2+Ur//mzDAwMpLu7u4poAAAAlVP8gSb3zuPPyOw583PFFVdkn332qTpOU1q4cOEyn28wdXKS5KKLLsprX/vakYwEAADQNLqqDgAs32ln/iS/u/WefP3rX1f4WYGFCxemWM5nRVG47QsAAEZYWT571XsrvDqB4g80qZ9feUPOvvCqHHnkkTnmmGOqjtPU+vv7k2LZ5Z9Rta7cfffdI5wIAACgeSj+QBO6+76Hc/wXvp+Xv3ynfPvb3646TtN76qmnlvtZ9+hRefDBB0cwDQAAQHNx5g80mb7+/rz52K9m7UmT8vvf31B1nJbQ39+/vMaf9Iztzty5c0c2EAAAkHpZdQKeo/MHmsyr3/m5FF1dufe++6qO0jKefvrpFMs59WdCb08ef/zxkQ0EAADQRBR/oIm86dgv54m+/txwww3p7e2tOk7LePbMn2V/tvaaa+Spp5Z9GxgAAEAnsO0LmsQJX/5+7rjnoZxzzjnZdtttq47TUp5++ukUy9n3tf66a+bGmbqoAABgJJVJBjvkJq1WoPMHmsA5F/4m/3P5DfnHf/zH/N3f/V3VcVrOokWL0rWc4s+G66+Ter0+wokAAACah+IPVOzG22fl82f+NPvss0++/OUvVx2nJc2ePTtdXcsu/myx6fopSyfNAQAAnUvxByo0f0Ff3nH8GZk2bVquuOKKquO0pDvvvDPXXnttDtlvl2V+vv2WGyWJ7h8AAKBjOfMHKlKv13PQu05JUuQTn/hEzjrrrJXOmTJlSg4++ODVH24IFixYkBtvvDFrrLFGdt5558py7L333hnfMyYf+8Ablvn5pInPHpz9hz/8ITvuuOMIJgMAgA5WJnVn/jQNxR+oyB2zHkr/0wMpk7zvfe8b8rzHH388EydOHNYsfX19ueGGG3LbbbfljjvuyH333Zc5c+bkscceyxNPLMjChU9lYGBRFi+uZ3AZp7adcsop+ehHPzqsmYbipJNOyqOPPppzvnTsSsdef/31ij8AAEBHUvyBimyz+Qa5+X++OOTxf/v3n89jC54a1sLPP/zDP+Qb3/jGUs+6uroyqtaV7tGjMnbM6Kwxfmymbjg560yakGlTJmWjaetkq02nZvMNp6S7uzvHnvwfOeGEE7LFFlvkDW9YdvfN6tDX15eTTjopu2y7abbbcsMVju3q6sptt902QskAAACai+IPtIAn+vpz34Pzcuqppw7LenPmzMmOO+6QRx6ZmwNfuUNOOvaw9PR0r9JaX/vkO/OGo7+YN77xjbnxxhtHrLtm9913T1dXkW999u9XOnb0qFpmzZo1AqkAAIDk2ave6+5daRoOfIYW8JHPfy+1Wi3HHXfcS17r85//fKZOnZrH//znfO+Lx+S0496+yoWf5/zo9A9nrYm92WWXXTJv3ryXnHFljjvuuNx+++056dg3p1arrXR8URRZtGjRas8FAADQjBR/oMnV6/Vcc9Nded3rXveS1unv78+MGTNy/PHHZ5dtN801//WZ7LDVxsOSMUku/c8TMqrWlc0222zY1lzm91x6aU477bT8zSu2z2uXc8PXXyrLMmPHjl2tuQAAAJqV4g80uS+d9d8pyzLf+973VnmNs88+O729vZk165585YQjc9bn3j+kjplGdHd354IzPpwnn3yyoQOsGzF//vy8+tUHZuq6k/LF448Y8rzBQcUfAACgcyn+QBNb8ERfzvnvq7Lvvvump6en4fn1ej177LFH3v72t2fT6evm2v/6TPbba7vVkPRZ09dbO/vvtV2++c1vrpbtXzNmzEhXV5EL/+3DDc0ry0HFHwAAGGGDg63x6gSKP9DE3vzBf8no0d255JJLGp57+eWXZ9y4cbnuumtz3HsPyY/P+HC6u1/a2T5D8aUTjszo0aOy++67D+u6r3zlKzJ//vyc99UPNfznKMtk3Lhxw5oHAACgVSj+QJP6j/+6LA/P+3MuuOCChrdoHXLIIdlvv/2y1ho9+fU5J+atr33lakq5bKd++C2ZNWtWvv/97w/Lep/61Kdy1VVX52PvPzSbb7R+w/PLlIo/AABAx3LVOzShJ/r68y/f/UVe8Yq9c9BBBw153q233po999wjfX0Lc9Sh++Sf3nnwaky5fPvvvUM2mnZRjjrqqBx22GEv6XyhX//61zn55JPzV7u/LIe9Zq9VWqMsk/Hjx69yBgAAoDFlmdQ7ZEtVK9D5A01mYGAgB7zzsxk9anR+9asrhzzvmGOOyXbbbZeiHMzPzjy+ssLPc87+wtFZ/MwzL+mWsgULFmTffffNlMkT8y+feMdLSFMq/gAAAB1L5w80kYGBgfzV2z+dpxc9kzvuuGNIHTNz5szJDjtsn7lz5+WgV+2UUz/y1hFIunITJ/TmLX+7d77/3/+TO++8M1tssUXDa8yYsXmKIrnwX497SVnKMlljjTVe0hoAAACtSucPNIl6vZ59j/h0FvYvyu23357NNttspXM+97nPZerUqVnw+OP53hePaZrCz3OO//vXZ/y4sdlll51Tr9cbmrvvvvtm3rxHc86X/iE9PS/9oGrFHwAAGFlV3+Lltq//o/gDTWDmn2Znr8M+kb6nFuXWW29daZdMX19fNt9885xwwgnZZdtNc81/fSY7bLXxyIRt0Plf/VD6+vqy6667DHnO5z73uVxxxRX553e/NltvNn1YckyYMGFY1gEAAGg1ij9QoXq9nvd94swc9sGvZmzP+MycOTNbb731Cud897vfzcSJE3PvvbPylROOzFmfe/9LOlB5ddtg6uR89h8Pz4033pQPfvCDKx1/zTXX5IQTTsgrd94qb3vd/xu2HL29vcO2FgAAQCtR/IGKzF/Ql13fcEJ+e+Odectb3pL58+evsOOnXq9n9913z5FHHplNN1g31/7XZ7LfXtuNYOJVd/C+u+S1++2cr33ta7nwwguXO66vry+vfOUrM3mtCTn9pHcPa4aJEycO63oAAABFUUwqiuLSoijuWvLPtZYz7sCiKO4oiuLuoiiOH8r8oig+umT8HUVRHLDk2biiKH5WFMUfi6K4rSiKU4eSU/EHKvLMM/WsNWF8al1d+cEPfpCiKDJuXE+23HLLvPe9781NN930/NjLL78848b15Prrr89H3/e6/Pj0D6e7+6WfgzOSPvOPb8lG09bJ61//+syZM2eZYzbffPNkyW1lw23NNdcc9jUBAIBle+6q91Z4vUTHJ7msLMsZSS5b8n4pRVHUkpye5NVJtknylqIotlnR/CWfH57kZUkOTHLGknWS5ItlWW6VZKckexdF8eqVhXTbF1RkyuSJuey7n0zy7C1fF17++1z8m5tz570P5ayzzsq3vvWtJElPz9j09z+dKZPXzA+/9qFMnNC625d+csaHs+ebP54ttpiRP//58ee3q82bNy9vfvOb88gjj+ScLx07LAc8/6W11lpmAR4AAOClOCTJPkt+/k6SXyX5y+uKd0tyd1mW9yRJURTnLpl3+wrmH5Lk3LIsFyWZVRTF3Ul2K8vyf5NckSRlWQ4URXFDkpUelKr4A02gu7s7bzxwz7zxwD2ff9bfP5CfXHZdLr3qluy63eZ5/1v/psKEw6NWq+XHp384r3nP5zJ69OiUZbnU5x868tXZbssNh/U7n7tlzLYvAABgNZhSluXDSVKW5cNFUay7jDHTkjzwgvezk+y+kvnTklzzF3OmvXDRoijWTPK3Sf5lZSEVf6BJ9fR05y0HvyJvOfgVVUcZVtPXWzvnfvVDuejXN2WzDdfLNptPyybT111th1b3LexPkqY+FBsAANpRvVz5mCYxuSiK373g/ZllWZ753JuiKH6ZZL1lzPvYENcvlvFsZX87K5xTFMWoJD9I8rXnOopWRPEHGHFbbzZ92K5wX5kn+p4eke8BAABa1qNlWe6yvA/Lsvzr5X1WFMUjRVGsv6RrZ/0kc5cxbHaSDV7wfnqSh5b8vLz5K5qTJGcmuassy68uL9sLOfAZaGtPPtVfdQQAAKB9XZjkyCU/H5nkp8sYc32SGUVRbFIURXeePcj5wpXMvzDJ4UVRjCmKYpMkM5JclyRFUXwmycQkHxpqSMUfoK09uVDnDwAAsNqcmmT/oijuSrL/kvcpimJqURQ/T5KyLBcnOSbJxUlmJjm/LMvbVjR/yefn59lDoS9KcnRZlvWiKKbn2e1m2yS5oSiKm4qiePfKQtr2BbS1PsUfAAAYcWWSwcFlHVvTXsqyfCzJfst4/lCSg17w/udJfj7U+Us++2ySz/7Fs9lZ9nlAK6TzB2hrC/sXVR0BAACgUoo/QFtb2K/zBwAA6Gy2fQFtrb9/UYqi/dtNAQCgqZRJfbDqEDxH5w/Q1p56eqDxDbEAAABtRPEHaGtPDyxOdP4AAAAdzLYvoK1tsfH6GRzUbwoAACOpjG1fzUTnD9DW9t3rZUmSBx98sOIkAAAA1VD8Adpab09PkuS8886rOAkAAEA1FH+Attc9elSuvPLKqmMAAABUwpk/QNub0NuT2267reoYAADQURy92Tx0/gBtb9p6k/LII3OqjgEAAFAJxR+g7W2/xYZ56qn+qmMAAABUwrYvoO391Z7b5ns//U3q9XpqtVrVcQAAoO2VZZH6YFF1DJbQ+QO0vZ223jhJcv3111cbBAAAoAKKP0Dbq9Vq6SqK/PCHP6w6CgAAwIhT/AE6wtix3bnmmmuqjgEAADDinPkDdIS1J/bmnnvuqToGAAB0hDJJ3VXvTUPnD9ARNtlg3cyfP7/qGAAAACNO8QfoCLtst1kWLVpUdQwAAIARp/gDdIRX/7+dkiT9/f0VJwEAgA5QJoODrfHqBIo/QEeYMnlikuRHP/pRxUkAAABGluIP0DFG1bpy8cUXVx0DAABgRLntC+gYveN7ctNNN1UdAwAAOsLgYFF1BJbQ+QN0jCmTJ2b27NlVxwAAABhRij9Ax9h0+rpZuHBh1TEAAABGlOIP0DE2WH/t1OuLq44BAAAwopz5A3SMTTecksHBsuoYAADQ9sp0zjXqrUDnD9Axttp0WtURAAAARpziD9AxNpo6OUkyb968ipMAAACMHMUfoGPUarUkyQ033FBxEgAAaHPls9u+WuHVCRR/gI5SFMnNN99cdQwAAIARo/gDdJSurq7cfffdVccAAAAYMYo/QEcZPaqWBx54oOoYAAAAI8ZV70BH6R49Oo888kjVMQAAoO2Vg0XVEVhC5w/QUcb1dGf+/PlVxwAAABgxij9AR5nQ25O+J5+sOgYAAMCIse0L6CiTJvZm9iOPVx0DAADaW5mkQ65RbwU6f4COMmXyxAwMPFN1DAAAgBGj+AN0lA3Wm5x6vV51DAAAgBFj2xfQUWZsvF7Ksqw6BgAAtD/bvpqGzh+go2y16dQk0f0DAAB0DMUfoKOsv+6kJMn9999fcRIAAICRofgDdKQbbrih6ggAAAAjQvEH6DhFUeT222+vOgYAALS1rsHWeHUCxR+g49S6unL33XdXHQMAAGBEKP4AHWf06FoeeOCBqmMAAACMCFe9Ax1n7JjuzJ07t+oYAADQvsqkcMFu09D5A3ScNcaPzWOPPlp1DAAAgBGh+AN0nLXX7E3fwr6qYwAAAIwIxR+g40xdd60sWrSo6hgAAAAjwpk/QMfZZPqULF58U9UxAACgbRVJah1yjXor0Pk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      "text/plain": [
       "<Figure size 1584x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(22, 16),\n",
    "    subplot_kw=dict(projection=ccrs.Mercator())\n",
    ")\n",
    "\n",
    "div.plot(cmap=cmo.cm.balance, zorder=10,transform=ccrs.PlateCarree())\n",
    "# add_map_features(ax, extent)\n",
    "ax.coastlines()\n",
    "ax.add_feature(cfeature.STATES)\n",
    "ax.add_feature(LAND, zorder=0, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd431cbc-0e06-4558-8a7c-0491b1118618",
   "metadata": {},
   "outputs": [],
   "source": [
    "dss=region\n",
    "lonz = dss['lon']\n",
    "latz = dss['lat']\n",
    "uz = dss['u']\n",
    "vz = dss['v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b2e4784-70f9-4586-870c-e7748e66d7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (lat: 42, lon: 77, nv: 2, time: 1)\n",
       "Coordinates:\n",
       "  * time                   (time) datetime64[ns] 2021-07-18T10:00:00\n",
       "  * lat                    (lat) float32 38.5113 38.52928 ... 39.2305 39.24848\n",
       "  * lon                    (lon) float32 -75.48618 -75.46682 ... -74.014946\n",
       "    depth                  float32 0.55\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (time, nv) datetime64[ns] 2021-07-18T09:30:00 2021...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (time, lat, lon) float32 nan nan nan ... 0.07 0.07\n",
       "    v                      (time, lat, lon) float32 nan nan nan ... -0.08 -0.07\n",
       "    dopx                   (time, lat, lon) float32 nan nan ... 0.59999996\n",
       "    dopy                   (time, lat, lon) float32 nan nan nan ... 0.64 0.57\n",
       "    hdop                   (time, lat, lon) float32 nan nan nan ... 0.84 0.83\n",
       "    number_of_sites        (time, lat, lon) float32 nan nan nan ... 3.0 3.0 3.0\n",
       "    number_of_radials      (time, lat, lon) float32 nan nan nan ... 6.0 7.0 7.0\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00</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-dbdc4f6f-60ea-46ca-927a-47bef649c3ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dbdc4f6f-60ea-46ca-927a-47bef649c3ff' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 42</li><li><span class='xr-has-index'>lon</span>: 77</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4218abca-9989-44ca-9b82-aa052b321c0d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4218abca-9989-44ca-9b82-aa052b321c0d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T10:00:00</div><input id='attrs-06640eef-7765-4d4d-af23-76006808ea3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06640eef-7765-4d4d-af23-76006808ea3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77005b43-62b3-452b-b244-5ce469776e3c' class='xr-var-data-in' type='checkbox'><label for='data-77005b43-62b3-452b-b244-5ce469776e3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>_metpy_axis :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-18T10:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.5113 38.52928 ... 39.24848</div><input id='attrs-e3c240fd-49b4-43fa-a41f-e28732e6f38f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3c240fd-49b4-43fa-a41f-e28732e6f38f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54483433-3504-4404-83c5-1f2334c5c499' class='xr-var-data-in' type='checkbox'><label for='data-54483433-3504-4404-83c5-1f2334c5c499' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_metpy_axis :</span></dt><dd>y,latitude</dd></dl></div><div class='xr-var-data'><pre>array([38.5113 , 38.52928, 38.54726, 38.56524, 38.58322, 38.6012 , 38.61918,\n",
       "       38.63716, 38.65514, 38.67312, 38.6911 , 38.70908, 38.72706, 38.74504,\n",
       "       38.76302, 38.781  , 38.79898, 38.81696, 38.83494, 38.85292, 38.8709 ,\n",
       "       38.88888, 38.90686, 38.92484, 38.94282, 38.9608 , 38.97878, 38.99676,\n",
       "       39.01474, 39.03272, 39.0507 , 39.06868, 39.08666, 39.10464, 39.12262,\n",
       "       39.1406 , 39.15858, 39.17656, 39.19454, 39.21252, 39.2305 , 39.24848],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-75.48618 -75.46682 ... -74.014946</div><input id='attrs-92fd49e0-7475-462d-bd55-f159ce13a399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92fd49e0-7475-462d-bd55-f159ce13a399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e03bbba-2580-493b-ac5d-b90e94d54b70' class='xr-var-data-in' type='checkbox'><label for='data-7e03bbba-2580-493b-ac5d-b90e94d54b70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_metpy_axis :</span></dt><dd>x,longitude</dd></dl></div><div class='xr-var-data'><pre>array([-75.48618 , -75.46682 , -75.447464, -75.42811 , -75.408745, -75.38939 ,\n",
       "       -75.37003 , -75.35067 , -75.331314, -75.31196 , -75.292595, -75.27324 ,\n",
       "       -75.25388 , -75.23452 , -75.215164, -75.19581 , -75.176445, -75.15709 ,\n",
       "       -75.137726, -75.11837 , -75.099014, -75.07965 , -75.060295, -75.04094 ,\n",
       "       -75.021576, -75.00222 , -74.982864, -74.9635  , -74.944145, -74.92479 ,\n",
       "       -74.905426, -74.88607 , -74.866714, -74.84735 , -74.827995, -74.80864 ,\n",
       "       -74.789276, -74.76992 , -74.750565, -74.7312  , -74.711845, -74.69249 ,\n",
       "       -74.67313 , -74.65377 , -74.634415, -74.61505 , -74.595695, -74.57634 ,\n",
       "       -74.55698 , -74.53762 , -74.51826 , -74.4989  , -74.479546, -74.46018 ,\n",
       "       -74.44083 , -74.42147 , -74.40211 , -74.38275 , -74.363396, -74.34403 ,\n",
       "       -74.32468 , -74.30532 , -74.28596 , -74.2666  , -74.247246, -74.22788 ,\n",
       "       -74.20853 , -74.18917 , -74.16981 , -74.15045 , -74.131096, -74.11173 ,\n",
       "       -74.09238 , -74.07302 , -74.05366 , -74.0343  , -74.014946],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.55</div><input id='attrs-633ee29b-3c2c-4fd1-9485-cc9a28a4f050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-633ee29b-3c2c-4fd1-9485-cc9a28a4f050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f55fe52-045a-4509-8a79-6682e843ea0d' class='xr-var-data-in' type='checkbox'><label for='data-5f55fe52-045a-4509-8a79-6682e843ea0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd><dt><span>comment :</span></dt><dd>Nominal depth (and corresponding bounds) based on contributing radars</dd><dt><span>_metpy_axis :</span></dt><dd>vertical</dd></dl></div><div class='xr-var-data'><pre>array(0.55, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ef9a6fd-174c-45fb-98da-f9e30361561f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ef9a6fd-174c-45fb-98da-f9e30361561f' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T09:30:00 2021-07-18T1...</div><input id='attrs-59447ec9-b691-4266-b9bd-a7ebfb7db1fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59447ec9-b691-4266-b9bd-a7ebfb7db1fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6108afa-fd43-4590-8a5d-0d30a6176885' class='xr-var-data-in' type='checkbox'><label for='data-a6108afa-fd43-4590-8a5d-0d30a6176885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[&#x27;2021-07-18T09:30:00.000000000&#x27;, &#x27;2021-07-18T10:30:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_bnds</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-6da32e18-79b9-4fec-8f6e-026e17140eb5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6da32e18-79b9-4fec-8f6e-026e17140eb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4090b24e-9cb4-42ee-823d-d6e67bb5466a' class='xr-var-data-in' type='checkbox'><label for='data-4090b24e-9cb4-42ee-823d-d6e67bb5466a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wgs84</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-9235d90a-1b45-40e0-a56c-46ffc9b284f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9235d90a-1b45-40e0-a56c-46ffc9b284f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c737e6d1-870d-469f-9bf1-a5b0d1ff1d0a' class='xr-var-data-in' type='checkbox'><label for='data-c737e6d1-870d-469f-9bf1-a5b0d1ff1d0a' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.01 0.07 0.07</div><input id='attrs-94b054f9-2e7f-4fc9-b37b-dd645f76e932' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94b054f9-2e7f-4fc9-b37b-dd645f76e932' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b014ec-4ca6-4412-b64f-b68309bff9e3' class='xr-var-data-in' type='checkbox'><label for='data-60b014ec-4ca6-4412-b64f-b68309bff9e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopx</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.04, 0.07],\n",
       "        [ nan,  nan, ..., 0.07, 0.07]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.08 -0.07</div><input id='attrs-f448f3c6-b1cf-4d44-b74e-9a389acb76cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f448f3c6-b1cf-4d44-b74e-9a389acb76cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58ce93f3-8b3a-45ae-a367-5ad4f3d153c5' class='xr-var-data-in' type='checkbox'><label for='data-58ce93f3-8b3a-45ae-a367-5ad4f3d153c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopy</dd></dl></div><div class='xr-var-data'><pre>array([[[  nan,   nan, ...,   nan,   nan],\n",
       "        [  nan,   nan, ...,   nan,   nan],\n",
       "        ...,\n",
       "        [  nan,   nan, ..., -0.03, -0.02],\n",
       "        [  nan,   nan, ..., -0.08, -0.07]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopx</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan ... 0.53999996 0.59999996</div><input id='attrs-9ab01e38-ee20-4aca-a605-c8e5fd954b5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ab01e38-ee20-4aca-a605-c8e5fd954b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2502065b-f643-46e9-b142-4640e01265f5' class='xr-var-data-in' type='checkbox'><label for='data-2502065b-f643-46e9-b142-4640e01265f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The longitudinal dilution of precision (dopx) represents the \n",
       "contribution of radial geometry to uncertainty in the eastward \n",
       "velocity estimate (u).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.53, 0.63],\n",
       "        [ nan,  nan, ..., 0.54, 0.6 ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopy</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.64 0.57</div><input id='attrs-e3df0f4f-023e-4ed3-87a6-413225ccad1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3df0f4f-023e-4ed3-87a6-413225ccad1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-790be705-c628-4d18-aab7-41b2fdc7284c' class='xr-var-data-in' type='checkbox'><label for='data-790be705-c628-4d18-aab7-41b2fdc7284c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The latitudinal dilution of precision (dopy) represents the \n",
       "contribution of radial geometry to uncertainty in the northward \n",
       "velocity estimate (v).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.72, 0.78],\n",
       "        [ nan,  nan, ..., 0.64, 0.57]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.84 0.83</div><input id='attrs-ec5cde6e-169e-4958-b0da-5cdefa1c7d0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec5cde6e-169e-4958-b0da-5cdefa1c7d0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c4e9c01-9636-45de-9f1a-e85e1b135347' class='xr-var-data-in' type='checkbox'><label for='data-4c4e9c01-9636-45de-9f1a-e85e1b135347' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>horizontal dilution of precision</dd><dt><span>comment :</span></dt><dd>The horizontal dilution of precision (hdop) is the vector length \n",
       "(magnitude) of the eastward (dopx) and northward (dopy) dilution of \n",
       "precision.  It represents the contribution of radial geometry to the \n",
       "overall uncertainty in the total velocity (u and v) estimate.</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>ancillary_variables :</span></dt><dd>dopx dopy</dd></dl></div><div class='xr-var-data'><pre>array([[[ nan,  nan, ...,  nan,  nan],\n",
       "        [ nan,  nan, ...,  nan,  nan],\n",
       "        ...,\n",
       "        [ nan,  nan, ..., 0.9 , 1.  ],\n",
       "        [ nan,  nan, ..., 0.84, 0.83]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_sites</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 3.0 3.0 3.0 3.0</div><input id='attrs-cf0e7560-16a3-457a-984a-f8369f3370cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf0e7560-16a3-457a-984a-f8369f3370cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84eb2bc0-4b56-45d3-8e7e-cafc5d6dd8fe' class='xr-var-data-in' type='checkbox'><label for='data-84eb2bc0-4b56-45d3-8e7e-cafc5d6dd8fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radars</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radars contributing radials to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, ...,  3.,  3.],\n",
       "        [nan, nan, ...,  3.,  3.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_radials</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 7.0 6.0 7.0 7.0</div><input id='attrs-db7f8677-0189-4d8a-88f6-498e295084fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db7f8677-0189-4d8a-88f6-498e295084fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9cf9af9-ba24-4058-b705-e3b4358621c8' class='xr-var-data-in' type='checkbox'><label for='data-a9cf9af9-ba24-4058-b705-e3b4358621c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radials</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radials contributing to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, ..., nan, nan],\n",
       "        [nan, nan, ..., nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, ...,  6.,  5.],\n",
       "        [nan, nan, ...,  7.,  7.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_parameters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-cdcef3d8-97b4-46a8-80c4-6c02da4c852e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdcef3d8-97b4-46a8-80c4-6c02da4c852e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-720266e6-d18b-4761-9152-a6ea140633d6' class='xr-var-data-in' type='checkbox'><label for='data-720266e6-d18b-4761-9152-a6ea140633d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Methods and parameters used to compute total solutions</dd><dt><span>combine_method_name :</span></dt><dd>Unweighted Least Squares</dd><dt><span>combine_method_description :</span></dt><dd>Method used to compute total solutions from radial velocities</dd><dt><span>grid_search_radius :</span></dt><dd>3.0</dd><dt><span>grid_search_radius_units :</span></dt><dd>km</dd><dt><span>grid_search_radius_description :</span></dt><dd>Search radius used for finding contributing radial velocities</dd><dt><span>max_radial_speed :</span></dt><dd>300</dd><dt><span>max_radial_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_radial_speed_description :</span></dt><dd>Maximum radial speed allowed to contribute to total solutions</dd><dt><span>max_rtv_speed :</span></dt><dd>300</dd><dt><span>max_rtv_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_rtv_speed_description :</span></dt><dd>Maximum allowed total speed</dd><dt><span>min_radar_sites :</span></dt><dd>2</dd><dt><span>min_radar_sites_description :</span></dt><dd>Minimum number of radar sites required to make a total solution</dd><dt><span>min_radials :</span></dt><dd>3</dd><dt><span>min_radials_description :</span></dt><dd>Minimum number of radials required to make a solution</dd><dt><span>max_hdop :</span></dt><dd>1.25</dd><dt><span>max_hdop_description :</span></dt><dd>Maximum allowed HDOP</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radial_metadata</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-240faffb-f363-45a1-9040-e0aaa25d4864' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-240faffb-f363-45a1-9040-e0aaa25d4864' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e3e3564-1f71-4e5c-9185-322364a5ddbe' class='xr-var-data-in' type='checkbox'><label for='data-3e3e3564-1f71-4e5c-9185-322364a5ddbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Metadata on radial velocities used to compute total solutions</dd><dt><span>number_files_loaded :</span></dt><dd>30</dd><dt><span>number_files_loaded_description :</span></dt><dd>Number of radial files loaded</dd><dt><span>files_loaded :</span></dt><dd>RDL_m_URI_BISL_2021_07_18_1000.ruv\n",
       "RDL_WHOI_CPVN_2021_07_18_0945.ruv\n",
       "RDL_WHOI_LPWR_2021_07_18_0945.ruv\n",
       "RDL_i_URI_MISQ_2021_07_18_1000.ruv\n",
       "RDL_UMiami_CDN_2021_07_18_1000.ruv\n",
       "RDL_USF_FDS_2021_07_18_1000.ruv\n",
       "RDL_UMiami_NKL_2021_07_18_1000.ruv\n",
       "RDL_UMiami_STF_2021_07_18_1000.ruv\n",
       "RDL_USF_VEN_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRAD_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_BRMR_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRNT_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CAPE_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CMPT_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_FLND_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_GCAP_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_HLPN_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_LEWE_2021_07_18_1000.ruv\n",
       "RDL_WHOI_NWTP_2021_07_18_0945.ruv\n",
       "RDL_m_Rutgers_PORT_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_RATH_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SEAB_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SILD_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SPRK_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_STLI_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_SUNS_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_VIEW_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_WOOD_2021_07_18_1000.ruv\n",
       "RDL_SKIO_CAT_2021_07_18_1003.ruv\n",
       "RDL_SKIO_JEK_2021_07_18_1000.ruv</dd><dt><span>files_loaded_description :</span></dt><dd>Radial file names loaded</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a248a60a-e640-41e2-b310-50667a69bdf6' class='xr-section-summary-in' type='checkbox'  ><label for='section-a248a60a-e640-41e2-b310-50667a69bdf6' class='xr-section-summary' >Attributes: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>ACDD-1.3,CF-1.7</dd><dt><span>id :</span></dt><dd>202107181000ndbchfruwlsrtvusegc2km</dd><dt><span>date_created :</span></dt><dd>2021-07-18T12:51:48Z</dd><dt><span>source :</span></dt><dd>surface ocean velocity field from hf-radar</dd><dt><span>program :</span></dt><dd>Integrated Ocean Observing System (IOOS)</dd><dt><span>title :</span></dt><dd>Near-Real Time Surface Ocean Velocity, U.S. East and Gulf Coast, 2km Resolution</dd><dt><span>summary :</span></dt><dd>Surface ocean velocities estimated from HF-Radar are representative \n",
       "of the upper 1.0 meters of the ocean.  The main objective of \n",
       "near-real time processing is to produce the best product from \n",
       "available data at the time of processing.  Radial velocity \n",
       "measurements are obtained from individual radar sites through the \n",
       "U.S. HF-Radar Network. Hourly radial data are processed by unweighted \n",
       "least squares on a 2km resolution grid of the U.S. East and Gulf \n",
       "Coast to produce near real-time surface current maps.</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments, Active Remote Sensing, \n",
       "Profilers/Sounders, Radar Sounders, Doppler RADAR</dd><dt><span>keywords :</span></dt><dd>Earth Science, Oceans, Sea Surface, Coastal Processes, Marine \n",
       "Environment Monitoring, Ocean Circulation, Ocean Currents, \n",
       "Wind-Driven Circulation, Tides, Tidal Currents, Atlantic Ocean, North \n",
       "Atlantic Ocean, Gulf of Mexico</dd><dt><span>geospatial_lat_min :</span></dt><dd>21.7</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.49442</dd><dt><span>geospatial_lon_min :</span></dt><dd>-97.88385</dd><dt><span>geospatial_lon_max :</span></dt><dd>-57.192493</dd><dt><span>processing_level :</span></dt><dd>Near real-time dataset with automated data acquisition and processing \n",
       "quality control</dd><dt><span>history :</span></dt><dd>2021-07-18T10:51:28Z hfrnet rtvMergeData: Saving 2898 new solutions\n",
       "2021-07-18T11:21:35Z hfrnet rtvMergeData: Saving 8321 solutions; 5458 \n",
       "new or updated, 2863 unmodified from previous run(s)\n",
       "2021-07-18T12:21:56Z hfrnet rtvMergeData: Saving 9504 solutions; 1183 \n",
       "new or updated, 8321 unmodified from previous run(s)\n",
       "2021-07-18T12:51:47Z hfrnet rtvMergeData: Saving 9690 solutions; 233 \n",
       "new or updated, 9457 unmodified from previous run(s)\n",
       "2021-07-18T12:51:48Z hfrnet rtvSaveNetcdf: Removed 2648 solutions \n",
       "exceeding HDOP threshold of 1.25</dd><dt><span>references :</span></dt><dd>Terrill, E. et al., 2006. Data Management and Real-time Distribution \n",
       "in the HF-Radar National Network. Proceedings of the MTS/IEEE Oceans \n",
       "2006 Conference, Boston MA, September 2006.</dd><dt><span>institution :</span></dt><dd>National Data Buoy Center</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_name :</span></dt><dd>NDBC Webmaster</dd><dt><span>creator_email :</span></dt><dd>webmaster.ndbc@noaa.gov</dd><dt><span>creator_url :</span></dt><dd>https://hfradar.ndbc.noaa.gov</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ndbc</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table, Version 51</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>format_version :</span></dt><dd>1.1.00</dd><dt><span>product_version :</span></dt><dd>2.0.00</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
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       "  * lat                    (lat) float32 38.5113 38.52928 ... 39.2305 39.24848\n",
       "  * lon                    (lon) float32 -75.48618 -75.46682 ... -74.014946\n",
       "    depth                  float32 0.55\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (time, nv) datetime64[ns] 2021-07-18T09:30:00 2021...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (time, lat, lon) float32 nan nan nan ... 0.07 0.07\n",
       "    v                      (time, lat, lon) float32 nan nan nan ... -0.08 -0.07\n",
       "    dopx                   (time, lat, lon) float32 nan nan ... 0.59999996\n",
       "    dopy                   (time, lat, lon) float32 nan nan nan ... 0.64 0.57\n",
       "    hdop                   (time, lat, lon) float32 nan nan nan ... 0.84 0.83\n",
       "    number_of_sites        (time, lat, lon) float32 nan nan nan ... 3.0 3.0 3.0\n",
       "    number_of_radials      (time, lat, lon) float32 nan nan nan ... 6.0 7.0 7.0\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                (lat: 42, lon: 77, nv: 2)\n",
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       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (nv) datetime64[ns] 2021-07-18T09:30:00 2021-07-18...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (lat, lon) float32 nan nan nan nan ... 0.01 0.07 0.07\n",
       "    v                      (lat, lon) float32 nan nan nan ... -0.08 -0.07\n",
       "    dopx                   (lat, lon) float32 nan nan ... 0.53999996 0.59999996\n",
       "    dopy                   (lat, lon) float32 nan nan nan ... 0.64 0.57\n",
       "    hdop                   (lat, lon) float32 nan nan nan ... 0.84 0.83\n",
       "    number_of_sites        (lat, lon) float32 nan nan nan nan ... 3.0 3.0 3.0\n",
       "    number_of_radials      (lat, lon) float32 nan nan nan nan ... 6.0 7.0 7.0\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00</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-c05447db-6eaa-41e4-a933-16085786fc45' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c05447db-6eaa-41e4-a933-16085786fc45' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 42</li><li><span class='xr-has-index'>lon</span>: 77</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e09ceb38-9f69-4188-9c14-9e095913b194' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e09ceb38-9f69-4188-9c14-9e095913b194' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T10:00:00</div><input id='attrs-70ed07bd-5d91-4755-9759-7c5bf1746e85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70ed07bd-5d91-4755-9759-7c5bf1746e85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-976873fd-c191-4ba7-8960-237ce0448e6e' class='xr-var-data-in' type='checkbox'><label for='data-976873fd-c191-4ba7-8960-237ce0448e6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>_metpy_axis :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2021-07-18T10:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>38.5113 38.52928 ... 39.24848</div><input id='attrs-f55f881f-7ab1-414e-8463-a012ceef2d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f55f881f-7ab1-414e-8463-a012ceef2d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e2078c6-2b49-4189-9da0-e894ad4ef182' class='xr-var-data-in' type='checkbox'><label for='data-1e2078c6-2b49-4189-9da0-e894ad4ef182' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>_metpy_axis :</span></dt><dd>y,latitude</dd></dl></div><div class='xr-var-data'><pre>array([38.5113 , 38.52928, 38.54726, 38.56524, 38.58322, 38.6012 , 38.61918,\n",
       "       38.63716, 38.65514, 38.67312, 38.6911 , 38.70908, 38.72706, 38.74504,\n",
       "       38.76302, 38.781  , 38.79898, 38.81696, 38.83494, 38.85292, 38.8709 ,\n",
       "       38.88888, 38.90686, 38.92484, 38.94282, 38.9608 , 38.97878, 38.99676,\n",
       "       39.01474, 39.03272, 39.0507 , 39.06868, 39.08666, 39.10464, 39.12262,\n",
       "       39.1406 , 39.15858, 39.17656, 39.19454, 39.21252, 39.2305 , 39.24848],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-75.48618 -75.46682 ... -74.014946</div><input id='attrs-105a4a2d-d11d-42ba-acf9-ab757679cd3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-105a4a2d-d11d-42ba-acf9-ab757679cd3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fa4d348-edca-4f20-91ff-a36716ed0470' class='xr-var-data-in' type='checkbox'><label for='data-9fa4d348-edca-4f20-91ff-a36716ed0470' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>_metpy_axis :</span></dt><dd>x,longitude</dd></dl></div><div class='xr-var-data'><pre>array([-75.48618 , -75.46682 , -75.447464, -75.42811 , -75.408745, -75.38939 ,\n",
       "       -75.37003 , -75.35067 , -75.331314, -75.31196 , -75.292595, -75.27324 ,\n",
       "       -75.25388 , -75.23452 , -75.215164, -75.19581 , -75.176445, -75.15709 ,\n",
       "       -75.137726, -75.11837 , -75.099014, -75.07965 , -75.060295, -75.04094 ,\n",
       "       -75.021576, -75.00222 , -74.982864, -74.9635  , -74.944145, -74.92479 ,\n",
       "       -74.905426, -74.88607 , -74.866714, -74.84735 , -74.827995, -74.80864 ,\n",
       "       -74.789276, -74.76992 , -74.750565, -74.7312  , -74.711845, -74.69249 ,\n",
       "       -74.67313 , -74.65377 , -74.634415, -74.61505 , -74.595695, -74.57634 ,\n",
       "       -74.55698 , -74.53762 , -74.51826 , -74.4989  , -74.479546, -74.46018 ,\n",
       "       -74.44083 , -74.42147 , -74.40211 , -74.38275 , -74.363396, -74.34403 ,\n",
       "       -74.32468 , -74.30532 , -74.28596 , -74.2666  , -74.247246, -74.22788 ,\n",
       "       -74.20853 , -74.18917 , -74.16981 , -74.15045 , -74.131096, -74.11173 ,\n",
       "       -74.09238 , -74.07302 , -74.05366 , -74.0343  , -74.014946],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.55</div><input id='attrs-7d4a248f-01a5-4a4c-b005-a778a830781b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d4a248f-01a5-4a4c-b005-a778a830781b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1373dc2-0797-4880-81ef-4caaf098a920' class='xr-var-data-in' type='checkbox'><label for='data-a1373dc2-0797-4880-81ef-4caaf098a920' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd><dt><span>comment :</span></dt><dd>Nominal depth (and corresponding bounds) based on contributing radars</dd><dt><span>_metpy_axis :</span></dt><dd>vertical</dd></dl></div><div class='xr-var-data'><pre>array(0.55, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2641ccc-74d3-4a09-9580-4a795ef6d2e8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2641ccc-74d3-4a09-9580-4a795ef6d2e8' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-18T09:30:00 2021-07-18T1...</div><input id='attrs-5074176c-f714-47e6-a8f5-9d15c57f4346' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5074176c-f714-47e6-a8f5-9d15c57f4346' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-083df12a-eab1-4d69-8d95-4cedf9068cd2' class='xr-var-data-in' type='checkbox'><label for='data-083df12a-eab1-4d69-8d95-4cedf9068cd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-18T09:30:00.000000000&#x27;, &#x27;2021-07-18T10:30:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_bnds</span></div><div class='xr-var-dims'>(nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-7b78a36f-ca92-4692-94ab-09701bbbdb98' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b78a36f-ca92-4692-94ab-09701bbbdb98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0985b928-528e-484f-b391-4681f1ead005' class='xr-var-data-in' type='checkbox'><label for='data-0985b928-528e-484f-b391-4681f1ead005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wgs84</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-d4adcbfd-bd6c-4e70-9619-6faea517baa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4adcbfd-bd6c-4e70-9619-6faea517baa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-054449ee-fbc2-4fbf-8087-5b9ffc95df29' class='xr-var-data-in' type='checkbox'><label for='data-054449ee-fbc2-4fbf-8087-5b9ffc95df29' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.01 0.07 0.07</div><input id='attrs-215102c8-ebba-4144-aa6d-03293aa012a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-215102c8-ebba-4144-aa6d-03293aa012a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb9382bb-d334-410d-81ef-71320ae14414' class='xr-var-data-in' type='checkbox'><label for='data-bb9382bb-d334-410d-81ef-71320ae14414' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopx</dd></dl></div><div class='xr-var-data'><pre>array([[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       ...,\n",
       "       [ nan,  nan,  nan, ..., 0.09, 0.05, 0.05],\n",
       "       [ nan,  nan,  nan, ..., 0.04, 0.04, 0.07],\n",
       "       [ nan,  nan,  nan, ..., 0.01, 0.07, 0.07]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... -0.08 -0.07</div><input id='attrs-ae079407-1eca-45f9-bef5-9b991d52c4b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae079407-1eca-45f9-bef5-9b991d52c4b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5587d9e2-4a5d-427f-96a4-3d2769f5d5f5' class='xr-var-data-in' type='checkbox'><label for='data-5587d9e2-4a5d-427f-96a4-3d2769f5d5f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>surface_northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>cell_methods :</span></dt><dd>depth: mean time: mean</dd><dt><span>ancillary_variables :</span></dt><dd>dopy</dd></dl></div><div class='xr-var-data'><pre>array([[  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "       [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "       [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
       "       ...,\n",
       "       [  nan,   nan,   nan, ...,  0.  ,  0.  ,  0.  ],\n",
       "       [  nan,   nan,   nan, ..., -0.03, -0.03, -0.02],\n",
       "       [  nan,   nan,   nan, ..., -0.05, -0.08, -0.07]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopx</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan ... 0.53999996 0.59999996</div><input id='attrs-7fc3819d-a134-47a9-89c8-b74b6fae1ed4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7fc3819d-a134-47a9-89c8-b74b6fae1ed4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6e39559-9ff3-49f7-8344-b0beb756cfcc' class='xr-var-data-in' type='checkbox'><label for='data-c6e39559-9ff3-49f7-8344-b0beb756cfcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The longitudinal dilution of precision (dopx) represents the \n",
       "contribution of radial geometry to uncertainty in the eastward \n",
       "velocity estimate (u).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       ...,\n",
       "       [ nan,  nan,  nan, ..., 0.56, 0.6 , 0.56],\n",
       "       [ nan,  nan,  nan, ..., 0.58, 0.53, 0.63],\n",
       "       [ nan,  nan,  nan, ..., 0.55, 0.54, 0.6 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dopy</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.64 0.57</div><input id='attrs-df826aa7-460a-4e97-badd-6844700923da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df826aa7-460a-4e97-badd-6844700923da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c6e8710-4ba9-48cd-a820-2ee42341660a' class='xr-var-data-in' type='checkbox'><label for='data-8c6e8710-4ba9-48cd-a820-2ee42341660a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitudinal dilution of precision</dd><dt><span>comment :</span></dt><dd>The latitudinal dilution of precision (dopy) represents the \n",
       "contribution of radial geometry to uncertainty in the northward \n",
       "velocity estimate (v).</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       ...,\n",
       "       [ nan,  nan,  nan, ..., 0.54, 0.75, 0.74],\n",
       "       [ nan,  nan,  nan, ..., 0.61, 0.72, 0.78],\n",
       "       [ nan,  nan,  nan, ..., 0.73, 0.64, 0.57]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.84 0.83</div><input id='attrs-3ec090a2-6f8c-42e2-a76b-b4e052e2b4c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ec090a2-6f8c-42e2-a76b-b4e052e2b4c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b2f11e8-fbb2-4bc1-a2ec-17b7ce06ad34' class='xr-var-data-in' type='checkbox'><label for='data-7b2f11e8-fbb2-4bc1-a2ec-17b7ce06ad34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>horizontal dilution of precision</dd><dt><span>comment :</span></dt><dd>The horizontal dilution of precision (hdop) is the vector length \n",
       "(magnitude) of the eastward (dopx) and northward (dopy) dilution of \n",
       "precision.  It represents the contribution of radial geometry to the \n",
       "overall uncertainty in the total velocity (u and v) estimate.</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd><dt><span>ancillary_variables :</span></dt><dd>dopx dopy</dd></dl></div><div class='xr-var-data'><pre>array([[ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       [ nan,  nan,  nan, ...,  nan,  nan,  nan],\n",
       "       ...,\n",
       "       [ nan,  nan,  nan, ..., 0.78, 0.96, 0.93],\n",
       "       [ nan,  nan,  nan, ..., 0.84, 0.9 , 1.  ],\n",
       "       [ nan,  nan,  nan, ..., 0.92, 0.84, 0.83]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_sites</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 3.0 3.0 3.0 3.0</div><input id='attrs-267a3dda-ebd3-488c-ae9a-9f293afe60b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-267a3dda-ebd3-488c-ae9a-9f293afe60b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36ddb396-fce5-4d99-82a5-b41bd6ed0b9b' class='xr-var-data-in' type='checkbox'><label for='data-36ddb396-fce5-4d99-82a5-b41bd6ed0b9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radars</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radars contributing radials to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  3.,  3.,  3.],\n",
       "       [nan, nan, nan, ...,  3.,  3.,  3.],\n",
       "       [nan, nan, nan, ...,  3.,  3.,  3.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>number_of_radials</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 7.0 6.0 7.0 7.0</div><input id='attrs-7523c272-6685-4417-a49d-a7d52517e4a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7523c272-6685-4417-a49d-a7d52517e4a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-498c8e91-25de-4172-b758-2557597ff0cc' class='xr-var-data-in' type='checkbox'><label for='data-498c8e91-25de-4172-b758-2557597ff0cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>number of contributing radials</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>Number of radials contributing to the total solution</dd><dt><span>grid_mapping :</span></dt><dd>wgs84</dd></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ...,  9.,  6.,  7.],\n",
       "       [nan, nan, nan, ...,  7.,  6.,  5.],\n",
       "       [nan, nan, nan, ...,  6.,  7.,  7.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>processing_parameters</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-0773db0f-7557-4b3f-9315-c7d2fbbd17a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0773db0f-7557-4b3f-9315-c7d2fbbd17a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a11a1c0-f257-44ec-9fce-516963566e83' class='xr-var-data-in' type='checkbox'><label for='data-9a11a1c0-f257-44ec-9fce-516963566e83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Methods and parameters used to compute total solutions</dd><dt><span>combine_method_name :</span></dt><dd>Unweighted Least Squares</dd><dt><span>combine_method_description :</span></dt><dd>Method used to compute total solutions from radial velocities</dd><dt><span>grid_search_radius :</span></dt><dd>3.0</dd><dt><span>grid_search_radius_units :</span></dt><dd>km</dd><dt><span>grid_search_radius_description :</span></dt><dd>Search radius used for finding contributing radial velocities</dd><dt><span>max_radial_speed :</span></dt><dd>300</dd><dt><span>max_radial_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_radial_speed_description :</span></dt><dd>Maximum radial speed allowed to contribute to total solutions</dd><dt><span>max_rtv_speed :</span></dt><dd>300</dd><dt><span>max_rtv_speed_units :</span></dt><dd>cm s-1</dd><dt><span>max_rtv_speed_description :</span></dt><dd>Maximum allowed total speed</dd><dt><span>min_radar_sites :</span></dt><dd>2</dd><dt><span>min_radar_sites_description :</span></dt><dd>Minimum number of radar sites required to make a total solution</dd><dt><span>min_radials :</span></dt><dd>3</dd><dt><span>min_radials_description :</span></dt><dd>Minimum number of radials required to make a solution</dd><dt><span>max_hdop :</span></dt><dd>1.25</dd><dt><span>max_hdop_description :</span></dt><dd>Maximum allowed HDOP</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radial_metadata</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>-127</div><input id='attrs-006535c2-c6bb-456a-888a-f2e00d724fbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-006535c2-c6bb-456a-888a-f2e00d724fbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af2b8f08-81ab-473f-bd28-26006e41f9d7' class='xr-var-data-in' type='checkbox'><label for='data-af2b8f08-81ab-473f-bd28-26006e41f9d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Metadata on radial velocities used to compute total solutions</dd><dt><span>number_files_loaded :</span></dt><dd>30</dd><dt><span>number_files_loaded_description :</span></dt><dd>Number of radial files loaded</dd><dt><span>files_loaded :</span></dt><dd>RDL_m_URI_BISL_2021_07_18_1000.ruv\n",
       "RDL_WHOI_CPVN_2021_07_18_0945.ruv\n",
       "RDL_WHOI_LPWR_2021_07_18_0945.ruv\n",
       "RDL_i_URI_MISQ_2021_07_18_1000.ruv\n",
       "RDL_UMiami_CDN_2021_07_18_1000.ruv\n",
       "RDL_USF_FDS_2021_07_18_1000.ruv\n",
       "RDL_UMiami_NKL_2021_07_18_1000.ruv\n",
       "RDL_UMiami_STF_2021_07_18_1000.ruv\n",
       "RDL_USF_VEN_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRAD_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_BRMR_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_BRNT_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CAPE_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_CMPT_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_FLND_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_GCAP_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_HLPN_2021_07_18_1000.ruv\n",
       "RDL_i_Rutgers_LEWE_2021_07_18_1000.ruv\n",
       "RDL_WHOI_NWTP_2021_07_18_0945.ruv\n",
       "RDL_m_Rutgers_PORT_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_RATH_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SEAB_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SILD_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_SPRK_2021_07_18_1000.ruv\n",
       "RDL_m_UConn_STLI_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_SUNS_2021_07_18_1000.ruv\n",
       "RDL_i_ODU_VIEW_2021_07_18_1000.ruv\n",
       "RDL_m_Rutgers_WOOD_2021_07_18_1000.ruv\n",
       "RDL_SKIO_CAT_2021_07_18_1003.ruv\n",
       "RDL_SKIO_JEK_2021_07_18_1000.ruv</dd><dt><span>files_loaded_description :</span></dt><dd>Radial file names loaded</dd></dl></div><div class='xr-var-data'><pre>array(-127, dtype=int8)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e21d2ae1-10ea-48a8-b5f9-bfe290f594ef' class='xr-section-summary-in' type='checkbox'  ><label for='section-e21d2ae1-10ea-48a8-b5f9-bfe290f594ef' class='xr-section-summary' >Attributes: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>ACDD-1.3,CF-1.7</dd><dt><span>id :</span></dt><dd>202107181000ndbchfruwlsrtvusegc2km</dd><dt><span>date_created :</span></dt><dd>2021-07-18T12:51:48Z</dd><dt><span>source :</span></dt><dd>surface ocean velocity field from hf-radar</dd><dt><span>program :</span></dt><dd>Integrated Ocean Observing System (IOOS)</dd><dt><span>title :</span></dt><dd>Near-Real Time Surface Ocean Velocity, U.S. East and Gulf Coast, 2km Resolution</dd><dt><span>summary :</span></dt><dd>Surface ocean velocities estimated from HF-Radar are representative \n",
       "of the upper 1.0 meters of the ocean.  The main objective of \n",
       "near-real time processing is to produce the best product from \n",
       "available data at the time of processing.  Radial velocity \n",
       "measurements are obtained from individual radar sites through the \n",
       "U.S. HF-Radar Network. Hourly radial data are processed by unweighted \n",
       "least squares on a 2km resolution grid of the U.S. East and Gulf \n",
       "Coast to produce near real-time surface current maps.</dd><dt><span>instrument :</span></dt><dd>Earth Remote Sensing Instruments, Active Remote Sensing, \n",
       "Profilers/Sounders, Radar Sounders, Doppler RADAR</dd><dt><span>keywords :</span></dt><dd>Earth Science, Oceans, Sea Surface, Coastal Processes, Marine \n",
       "Environment Monitoring, Ocean Circulation, Ocean Currents, \n",
       "Wind-Driven Circulation, Tides, Tidal Currents, Atlantic Ocean, North \n",
       "Atlantic Ocean, Gulf of Mexico</dd><dt><span>geospatial_lat_min :</span></dt><dd>21.7</dd><dt><span>geospatial_lat_max :</span></dt><dd>46.49442</dd><dt><span>geospatial_lon_min :</span></dt><dd>-97.88385</dd><dt><span>geospatial_lon_max :</span></dt><dd>-57.192493</dd><dt><span>processing_level :</span></dt><dd>Near real-time dataset with automated data acquisition and processing \n",
       "quality control</dd><dt><span>history :</span></dt><dd>2021-07-18T10:51:28Z hfrnet rtvMergeData: Saving 2898 new solutions\n",
       "2021-07-18T11:21:35Z hfrnet rtvMergeData: Saving 8321 solutions; 5458 \n",
       "new or updated, 2863 unmodified from previous run(s)\n",
       "2021-07-18T12:21:56Z hfrnet rtvMergeData: Saving 9504 solutions; 1183 \n",
       "new or updated, 8321 unmodified from previous run(s)\n",
       "2021-07-18T12:51:47Z hfrnet rtvMergeData: Saving 9690 solutions; 233 \n",
       "new or updated, 9457 unmodified from previous run(s)\n",
       "2021-07-18T12:51:48Z hfrnet rtvSaveNetcdf: Removed 2648 solutions \n",
       "exceeding HDOP threshold of 1.25</dd><dt><span>references :</span></dt><dd>Terrill, E. et al., 2006. Data Management and Real-time Distribution \n",
       "in the HF-Radar National Network. Proceedings of the MTS/IEEE Oceans \n",
       "2006 Conference, Boston MA, September 2006.</dd><dt><span>institution :</span></dt><dd>National Data Buoy Center</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_name :</span></dt><dd>NDBC Webmaster</dd><dt><span>creator_email :</span></dt><dd>webmaster.ndbc@noaa.gov</dd><dt><span>creator_url :</span></dt><dd>https://hfradar.ndbc.noaa.gov</dd><dt><span>naming_authority :</span></dt><dd>gov.noaa.ndbc</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table, Version 51</dd><dt><span>keywords_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>instrument_vocabulary :</span></dt><dd>Global Change Master Directory (GCMD) Keywords, Version 8.6</dd><dt><span>format_version :</span></dt><dd>1.1.00</dd><dt><span>product_version :</span></dt><dd>2.0.00</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                (lat: 42, lon: 77, nv: 2)\n",
       "Coordinates:\n",
       "    time                   datetime64[ns] 2021-07-18T10:00:00\n",
       "  * lat                    (lat) float32 38.5113 38.52928 ... 39.2305 39.24848\n",
       "  * lon                    (lon) float32 -75.48618 -75.46682 ... -74.014946\n",
       "    depth                  float32 0.55\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    time_bnds              (nv) datetime64[ns] 2021-07-18T09:30:00 2021-07-18...\n",
       "    depth_bnds             (nv) float32 0.0 1.0\n",
       "    wgs84                  int8 -127\n",
       "    u                      (lat, lon) float32 nan nan nan nan ... 0.01 0.07 0.07\n",
       "    v                      (lat, lon) float32 nan nan nan ... -0.08 -0.07\n",
       "    dopx                   (lat, lon) float32 nan nan ... 0.53999996 0.59999996\n",
       "    dopy                   (lat, lon) float32 nan nan nan ... 0.64 0.57\n",
       "    hdop                   (lat, lon) float32 nan nan nan ... 0.84 0.83\n",
       "    number_of_sites        (lat, lon) float32 nan nan nan nan ... 3.0 3.0 3.0\n",
       "    number_of_radials      (lat, lon) float32 nan nan nan nan ... 6.0 7.0 7.0\n",
       "    processing_parameters  int8 -127\n",
       "    radial_metadata        int8 -127\n",
       "Attributes:\n",
       "    Conventions:               ACDD-1.3,CF-1.7\n",
       "    id:                        202107181000ndbchfruwlsrtvusegc2km\n",
       "    date_created:              2021-07-18T12:51:48Z\n",
       "    source:                    surface ocean velocity field from hf-radar\n",
       "    program:                   Integrated Ocean Observing System (IOOS)\n",
       "    title:                     Near-Real Time Surface Ocean Velocity, U.S. Ea...\n",
       "    summary:                   Surface ocean velocities estimated from HF-Rad...\n",
       "    instrument:                Earth Remote Sensing Instruments, Active Remot...\n",
       "    keywords:                  Earth Science, Oceans, Sea Surface, Coastal Pr...\n",
       "    geospatial_lat_min:        21.7\n",
       "    geospatial_lat_max:        46.49442\n",
       "    geospatial_lon_min:        -97.88385\n",
       "    geospatial_lon_max:        -57.192493\n",
       "    processing_level:          Near real-time dataset with automated data acq...\n",
       "    history:                   2021-07-18T10:51:28Z hfrnet rtvMergeData: Savi...\n",
       "    references:                Terrill, E. et al., 2006. Data Management and ...\n",
       "    institution:               National Data Buoy Center\n",
       "    creator_type:              group\n",
       "    creator_name:              NDBC Webmaster\n",
       "    creator_email:             webmaster.ndbc@noaa.gov\n",
       "    creator_url:               https://hfradar.ndbc.noaa.gov\n",
       "    naming_authority:          gov.noaa.ndbc\n",
       "    standard_name_vocabulary:  CF Standard Name Table, Version 51\n",
       "    keywords_vocabulary:       Global Change Master Directory (GCMD) Keywords...\n",
       "    instrument_vocabulary:     Global Change Master Directory (GCMD) Keywords...\n",
       "    format_version:            1.1.00\n",
       "    product_version:           2.0.00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dz=dss.squeeze()\n",
    "dz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2974ada4-ef94-4cc6-9114-a586e746fe55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.quiver.Quiver at 0x1dceaa79608>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.quiver(dz['lon'], dz['lat'], dz['u'], dz['v'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9fc73d75-6cc1-41fc-bbb9-605f2b7cb272",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, ax = plt.subplots(figsize=(22, 16))\n",
    "# plt.quiver(dz['lon'], dz['lat'], dz['u'], dz['v'],transform=ccrs.Mercator())\n",
    "# ax.coastlines()\n",
    "# ax.add_feature(cfeature.STATES)\n",
    "# ax.add_feature(LAND, zorder=0, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8ab1fa2-e8d4-4f2d-9ac3-0fab276dcaba",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Argument 'x' has incorrect type (expected numpy.ndarray, got DataArray)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_20672\\1805739016.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m111\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprojection\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMercator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'lon'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'lat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'u'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdz\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'v'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mccrs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlateCarree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoastlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_feature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcfeature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSTATES\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_feature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLAND\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    319\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'transform'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    322\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Thesis\\lib\\site-packages\\cartopy\\mpl\\geoaxes.py\u001b[0m in \u001b[0;36mquiver\u001b[1;34m(self, x, y, u, v, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1963\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1964\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1965\u001b[1;33m             \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprojection\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform_vectors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1966\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquiver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mlib/cartopy/_crs.pyx\u001b[0m in \u001b[0;36mcartopy._crs.CRS.transform_vectors\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Argument 'x' has incorrect type (expected numpy.ndarray, got DataArray)"
     ]
    }
   ],
   "source": [
    "ax = fig.add_subplot(111,projection=ccrs.Mercator())\n",
    "ax.quiver(dz['lon'], dz['lat'], dz['u'], dz['v'],transform=ccrs.PlateCarree(),ax=ax)\n",
    "ax.coastlines()\n",
    "ax.add_feature(cfeature.STATES)\n",
    "ax.add_feature(LAND, zorder=0, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b775ba78-7ac1-4fe9-adb8-6ed13ce33dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x1dcea9121c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4IIQAIE2aNCxdtpyWrdvx7dD53H/0xNwpCSGEEEKIZESKBxasYO4sUjwQQryglGL8+AkMHzmG74b/jZPLPXOnJIQQQgghkglZqtGCFcidGedrV82dhhAimenWvTt58ublm05fM6rXZ3xUo6y5UxJCCCGEBbJSljEdIKWQkQcWrFDe7Lg4XzN3GkKIZKhFixbs2rOXUX+tlVUYhBBCCCGEFA8sWaG82blx86a50xBCJFPVqlUjZ44c3L7/2NypCCGEEEIIM5PigQXLlS0jT589x99fOqsLId6sZq1aXLp+z9xpCCGEEMLCqGSwgoKstvAyKR5YMIPBQKF8ubgpow+EEG9Ru049Lt98ZO40hBBCCCGEmUnxwMIVyptNVlwQQrxVzZo1cbp+39xpCCGEEEIIM5PigYXLnysTLs7O5k5DCJFMlSxZEl+/QLyf+Zk7FSGEEEIIYUayVKOFK5QnG6edZblGIcSbGQwGqlevhpPLfRrWLGfudIQQQghhQayUHOtOTuTVsHCF8mXnhkxbEELEw9HBkYDgEHOnIYQQQgghzEiKBxauQO5s3HW9T2RkpLlTEUIkQ5qmceToUaqVK2LuVIQQQgghhBnJtAULZ2ebhqyZM3Dv3j2KFJGdAyHEy5ydnbFNa0Pu7JnNnYoQQgghLIgCrCxkCcSUQkYeCArlyyErLggh3ujAgQN8IKMOhBBCCCEsnhQPBNkzO/DgwQNzpyGESIb279tD1bIFzZ2GEEIIIYQwMykeCFwfeVOyZElzpyGESGYiIyM5duw4H5Qvau5UhBBCCCGEmUnPAwsXERnJ9dsPqFy5srlTEUIkM5cuXSJrZkeyZXY0dypCCCGEsEAG6XmQrMjIAwt3182L3LlykiFDBnOnIoRIZg4cOEC1soXMnYYQQgghhEgGpHhg4ZxvuVGlShVzpyGESIYO7N9DtXKFzZ2GEEIIIYRIBmTagoVzufuYavU+NXcaQohkJjw8nJMnzzCySz1zpyKEEEIIC6RQWCmZtpCcyMgDC+dy14Nq1aqZOw0hRDJz9uxZCuTNTkbH9OZORQghhBBCJANSPLBg4RGR3LjrRqVKlcydihAimTlwYD9VyxQ0dxpCCCGEECKZkGkLFuzOg8fkz5sHBwcHc6cihEhm9u/dQ7uGpcydhhBCCCEsmKy2kLzIyAMLdu3WQ6pWlSkLQoiXBQcHc+7CRSqXkZUWhBBCCCFENCkeWLDrrh5Uq/6hudMQQiQzN2/eJGe2zKRPZ2vuVIQQQgghRDIh0xYsmPMdD/oOrWruNIQQyUyxYsXw8PIhJDQc27Q25k5HpCARkZEEBYcRFBxKUEgogUHRP4OCQ7GyMlC5TGHspSglhBBCpEhSPLBQYeER3HJ9SMWKFc2dihAimUmXLh3FixXh+t1HVCxV0NzpiPfALyAYd6+nBAWHERgcQnBI2Isd/8DgUIJDwggOjSAoJJzgkHACQ0IJjtk2MDiUwKBggoKCCQuPIH06O+zt02OfPj329vbY29vj4OBAYFAgP09ZTZliBahZsTC1q5SgWMFcKFmGSwghxBsowEp6HiQrUjywULfvP6ZwwQKkTy/LsAkhXlejRi2u3HggxYNURtM0PH18uXHXnet33Ln5wIvrdx/h88yPggXy4+jgEL2z7+iAg70j9g6ZccyTgVwOjjjEXveWn/b29tjZ2cVbDAgMDOTw4cPs2L6NQVPWERwURK0qxalZsSgfViyOo73de3w2hBBCCJEYUjywUNduuUmzRCHEW9WsVZtVi2aYOw1hhMjIKO67P+H6XXdu3PXgxn0vrt9xw2AwUKF8eapUrcb3n1SlYsWKFC1aFCsrK5PnlD59elq0aEGLFi3QNI3bt2+zc+dOdmzfwi8zN1C6aH4+rFCI2lVKUrJwbhmVIIQQQiQjUjywUC53H1OveXtzpyGESKaqV6/OsCH3zZ2GSKCQ0HBu3fPghqs7N1wfc/O+JzfuuJEjezYqVKxIlaoNaPtdZSpWrEiuXMljqoBSimLFivHjjz/y448/EhwczJEjR9i5YxvD/9qIv58fNSuXpFalItSoVBxH+3TmTlkIIcR7JNMWkh8pHlgol7vuDKomIw+EEG9WrFgxgkLCePLUj2yZHc2djojD1z8IlzuPuHH3ETfvP+GGqwdu7p4ULVqYShUrU+PjtvSqVIkKFSqQIUMGc6ebYHZ2djRt2pSmTZsyY+Zs7ty5EzMqYSu/ztrI15/VpVeHRuZOUwghhLBYUjywQKFh4dy970758uXNnYoQIplSSlGtWhWu3HjARzXKmjsdi6RpGh5PnnHjrjsud925df8J1+88xDcgiPJly1C5ShVaf/U5FStWpHTp0qRNm9bcKeuqSJEi9O3bl759+/L48WNKlSxOu2bVyZLRwdypCSGEEBZJigcW6KarB0WLFMbOThpTCSHerkbN2lxxPiHFg/cgIjISVzcvbri6c93Vg5v3vHC57YatrS0VypejStUP6NG6ChUrVqRw4cIYDAZzp/xe5cyZky+/bMeq7afo83Vjc6cjhBBCWCQpHliga7fdqCpTFoQQ71CjRk1Gb11n7jRSJVc3L/67cpsbrp7cuPeY2/cekSdXTipWrEjlqo3p2C26P0GOHDnMnWqyMeTnoVSrWpkun9clfTpbc6cjhBDiPTBIz4NkRYoHFsjlrieNP5MjN0KI+NWoUYMHj7w58p8z9T4obe50UgU3D2/mrj7ImUu3aflJS+o1b0D/SpUoX748Dg4yHD8+RYoUoWHDhmzY+x+dP6tr7nSEEEIIiyPFAwvkfOcRw6tWNXcaQohkztHRkc1bt9GieVPm/JKBUkXymDulFMvLx5cFaw+x7+QV+vX7kRUbf8LRURpRJtaw4SNp2bwJHVrUxMZGvsIIIYQQ75NlTZoUBIeE8eDhY8qVK2fuVIQQKUD16tWZO+9v+k9Yhqe3r7nTSXF8/YOYvmQXX/T7i9zFq3Lj5m1+GfOrFA6SqHLlypQqVZqdRy6aOxUhhBAmplBYpaCTJZDigYW56epO8eJFU11XbiGE6XzxxRf07defHycsIyg41NzppAhBwaH8veYAn/acgiFDAS5fvca0aX+SNWtWc6eW4g0bMYp/N58gKirK3KkIIYQQFkWKBxbm2u2HVKv2gbnTEEKkMEOHDad6zXoMm7aGyEjZaXubsPAIlm85xic9pvAkND1n/jvHgoX/kDdvXnOnlmo0bNgQe8eMHDnrYu5UhBBCCIsixQML43L3MR9Ur2HuNIQQKYxSivl/L0BLk4Gpi3eaO51kJyIykk17/6NVz6lceRDMvgOHWL1mHcWKFTN3aqmOUoqhw0fy7+bj5k5FCCGEKSkwKJViTpZAigcWxvn2I6pKs0QhRBLY2NiwcdMW/rvmxuodJ82dTrKgaRp7jzvxxY8z2HfuAWvWb2Ln7j1UqFDB3Kmlap9//jkXr96WUTBCCCHEeyStii1IUHAoDz28KFOmjLlTEUKkUJkyZWLX7r3U/LA6eXJkok7VUuZOySw0TePkhZvMXrkf67T2zJ73D40bN0ZZyJEHc/Pz88PRIT1WVnIMRAghhHhfpHhgQa7fdad06ZKkSZPG3KkIIVKwwoULs3HzFj79pAXzf/2O4oVymzul98r1oRcT5m3laUAY4yf8Tps2bTAYZCf2ffLx8SGjo4O50xBCCCEsinzbsSDXbrtRtao0SxRCGK9mzZrMnDWHH8cv48lTP3On815ERUWxavsJugydz1ff9cTZ5QZffPGFFA7MwMfHh0wZ7M2dhhBCCBNSYPblF2WpxpfJyAMLcv2uJ591bGXuNIQQqUSHDh24fesm34+YT68ODWlcu3yq3ZH29PZlzKwNhGppOXXmP4oXL27ulCyaj48PGRzSmTsNIYQQwqKkzm954o2u3X4ozRKFELoaOWo0c+b/w7oD1/jixxnsOXaJqKjU1cRu55GLtB84k8Ytv+DUaSkcJAfRxQM7c6chhBBCWBQZeWAhAoJCeOzlQ+nSpc2dihAiFVFK0axZM5o2bcqePXsYPXI4f689TLcv6/NxrZQ9EsHXP4gJ87dy59Ezdu/ZJ8XXZMTb25uM9rbmTkMIIYSJWZk7AfGSlPutTiSKy+2HlC1bGmtrqRcJIfSnlKJp06acOXueGXMWsHZ/yh6JcOL8Db748S+KlPmAS05XpHCQzHh7P8FRigdCCCHEeyXFAwvhfPshH3xQ3dxpCCFSuZReRAgOCWPi/C2Mm7+dZStWM3PWbOzsZHh8cuP9xIuM0vNACCGEeK+keGAhXFy9qFpNigdCiPcjJRYRrtx4QPuBM9HS5eTK1Ws0atTI3CmJt8iaNRvezwPMnYYQQghhUWQMu4Vwvu0mw26FEO9dbBGhSZMmybYnQnh4BAvXHWL9nrPMnD2Hdu3amTsl8Q41atZiyvhd5k4jSfwCgnj4+CkPH/vwyPMp7k/8ePLUHwBrayusrQxYWRmwtjJgbWWFlZWK+f3/l9tYWWFtbcDKYIi+jbUV1jGXWVtZYfPKeauYWNbWsdcZXrtN7H3H/T05fD6FEJZLaWDQLGMJxJRCigcWwC8gCC+f55QsWdLcqQghLFRyLCI89wtk/Z4zrN11hkqVqnDR6TJ58uR573mIxKtevTqXXVyJiopKtju4AUEhXLzmygXnezx4/IxHns946OFFlAYFC+SjUKFCFC5SjFqVipIvXz6UUkRERBAeHh7vKSwsjPDwMCLCwwkLDyc4LIzw2FN4OGHhIYSHhxEe9v/bvBQ3IpyI8NjzEYRHRP+M3SYiIoKIyEjCwyMwGAzYWFvHFB2sYwoV1v8vPlhbk9ExHRWK56VymYJUKl0I+3TSi0IIIVIrKR5YAOfbj6hQvixWVtKvVAhhXm8rIkwZ0pFC+bK/lxzuPHjMyu2n2HvciVatWrFrz34qVqz4Xu5b6CNHjhxkyJiB+4+839v75l38AoK56OzKuauuXHB5wJ377lSuVIEGDRrR5IvyFCpUiEKFCpE5c2aUSv5H0jRNIzIy8p3FjMePH3Ps6FFW79/P4MkrKZw/F5VL5adKTDEho2N6cz8UIYQQOpHigQVwvu1GtQ8+NHcaQgjxQtwiQv58eQkMDjHp/UVFRXHiwg1W7TjDzXse9OzZi5mLNpAjRw6T3q8wnerVq3P55n2zFQ98/YO4cO3ui2LBvYeeVK1SiY8afsx3Pzbggw8+wNY25R6FV0pFjzKwto63aWjZsmVp1KgRv/IboaGhnDt3jsOHD7Pt0H5GTF9HnhxZqFy6IJVL56dK2cJkzeT4Hh+FECKlsyL5F1stiRQPLMB1Vy++7trR3GkIIcRrLl++DFoEZYrlM0n8oJBQth08z6odZ7B3yMDAQUNo3749adOmNcn9ifenZq06nN6/gVYNq72X+3vmG8j5a3c5f+0e56/d46GHN9WrV6XBRx/TfVB9qlWrRpo0ad5LLslV2rRpqVWrFrVq1WLEiBFERERw8eJFjhw5wqGD+xk/byuZMzhQuUxMMaFMYXJlz2TutIUQQiSQFA8swLXbblSpUsXcaQghxGvWrlnNxzXL6j6M+/GT56zecYpN+89Sp3ZtFv27grp166aI4eIiYRo1asTIkSO47faEiiWi59xXLFUQh/T6LK3p89yf81fvcv7afc5fc8XD6yk1alSnwUdN6TO0PlWqVMHGxkaX+0qtrK2tqVatGtWqVWPQoEFERUVx5coVjhw5wuFDB/jz37mkTWNDlTKFqFQqHzUrlSBntozmTlsIIUxCKdUU+AuwAhZqmjbpletVzPXNgSDgW03TLsR3W6VUZmANUBC4B3ypadozpdQHwN+xoYExmqZtMvoxaJr21ivLFMunrZ7e39j7EGb03C+Q5t0m4+vrl2ybSgkhLJOmaRQvWpjf+nxK2eL5E33b8IhIQkLDCQ2LPoWEhuP9zJ+N+89x+uItOn/TmR9/HEDhwoVN9AiEuQUEBHD69GmOHjnC4cMHOH/hEgXz5qRSqfxUKhV9ZDtLJoc33jYqKopnfoF4+fjx5KkvXj5+ePr44vU0kCs3H/DEx5eaNT+kwUcfU79+fSpVqoS1tRxz0ZOmady4cSO6mHDwAHv37aVl/Up0a/cRjvbpzJ2eEMlK+ZaDzmuaZlFLp+W0stW+ti9o7jQSbKrfjbe+RkopK+Am8DHwEDgLdNA0zTnONs2BvkQXD6oDf2maVj2+2yqlJgNPNU2bpJQaCmTSNO1npVQ6IEzTtAilVC7ACcitaVqEMY9R/gumcs63H1KxQjkpHAghkp2rV69y74Ebe09cYfvhS4SGRxIaFhFziikIhIUTGhpbHAh7cQoNDcPKyoBt2rTY2dpia2eLbdq0OGZw5Kuvv2HNlu9wdJS51amdvb09jRo1olGjRsBYwsLCOHfuHEePHmXfof38NnsTWTM5Uql0QexsbXjyLJAnT/3x8n6Gl/cz7O3TkytXDvLkzkOevHnJW7QUlfPmZWTVqlSoUEEaDZuYUoqSJUtSsmRJunfvjqenJ6NGjqBVr2l837Ye7ZrVwMZGvqoKIVKFD4DbmqbdBVBKrQZaAc5xtmkFLNWij+6fVkpljNnxLxjPbVsB9WNu/y9wGPhZ07SgOHFtgbePGEgE+Yucyl27/VCaJQohkqXs2bMzatRobG1tsbOze/HzbadXr5cdO/GqNGnSULNmTWrWrMnQoUOJjIzkypUrHDt2jPDwcPLkyUPu3Llf/EzJDQ1Toxw5cvD3goX0+7E/Pw3oz9p+f9G/cxMafFhGphwJIVK6PIBbnPMPiR5d8K5t8rzjtjk0TfMA0DTNQyn1oouwUqo6sAgoAHQydtQBSPEg1bvu6sl3zTqbOw0hhHhNjhw5GD16tLnTEKmYlZUVFStWlKU4U5iyZcuyZ99+9uzZw8ABP7Ji+yl+6tKM0kXzmjs1IYSIT1al1Lk45//WNC1u34FXvToa4G3bJOS2r2+gaWeAMkqpUsC/SqldmqYZtbyVFA9SOedbblStalHTo4QQQgiRCjRp0gSny1dZtOgffhw5kuoVitDnq4+lqaIQFkOltKUavePpS/EQiLu0VF7APYHbpInntp5KqVwxow5yAV6v3rGmaS5KqUCgLHDu1esTQybCp2I+z/3xDwymSJEi5k5FCCGEECLRrK2t6datO7fu3KVMtQa0GzCDWcv3Ehhk1MEzIYR4384CxZRShZRSaYD2wNZXttkKdFbRPgR8Y6YkxHfbrcA3Mb9/A2wBiNnWOub3AkAJoldjMIoUD1Ix59sPqVSxgswTFEIIIUSK5uDgwIQJE3G6fJUAlYlWvaexfvdpIiOjzJ2aEEK8U0y/gT7AHsAFWKtp2jWlVA+lVI+YzXYCd4HbwAKgV3y3jbnNJOBjpdQtoldjiF3+sTbgpJS6BGwCemma5m3s45BpC6mY861HfFBdmiUKIYQQInXIly8fK1au5ty5c/Tv15c1u/5jwDdNqFm5hLlTE0LoTJG6jnRrmraT6AJB3MvmxfldA3on9LYxl/sADd9w+TJgmZEpvyY1vR7iFdfveVLtg1ebeAohhBBCpGxVq1bl2ImTTJj8J5MX76XP2H+5ff+xudMSQohUTYoHqdjVmw+kWaIQQgghUiWlFJ9//jku12/yWbtv6Tb6H8bP3YzPc39zpyaEEKmSFA9SqSdP/QgLj6BgwYLmTkUIIYQQwmTSpEnDgAEDuXHzNjmKVOTzPn9y4+6rTcyFEEIYS3oepFLSLFEIIYQQliRz5sz89ddMSpYsxR9/z2TB2O/le5AQKVwKW6ox1ZORB6nUtdsPpVmiEEIIISxOt27dCQpX7Dtx2dypCCFEqiLFg1Tquqs0SxTiTSIjI82dghBCCBOysrJi5qw5/PnvHkJCw82djhBCpBpSPEiFNE3jmjRLFAJN09i/fz+TJ0/mo48+okuXLri76zMP9uzZs6xatYqqVavy+++/s2XLFl3iCiGEMF79+vWp/mENlmw6Yu5UhBBJpACrFHSyBNLzIBXy8vFDI3otZCEsmVKKtWvXsmDBAgBsbW35559/dImdL18+PvzwQ6Kiojh//jxnz57VJa4QQgh9TJ02ncoVK/BZw6rkzJbR3OkIIUSKJyMPUiHn225UrlRJmgQJAQwePBiDIfpPXZEiRV78bqycOXPSqFEjABwcHKhYsaIucYUQQuijYMGC9O7Th+lLd5s7FSGESBWkeJAKXbv9iGofSLNEIQCKFStG27ZtX/yup6+//hqAWrVqYW0tA7mEECK5GTpsOJeuP+T81bvmTkUIkQSGFHSyBJbyOC1KdLPED8ydhhDJxtChQwH9iwefffYZdnZ21K1bV9e4Qggh9JE+fXr+mDKVPxbtJDIyytzpCCFEiibFg1TI9aEnZcqUMXcaQiQblSpVokmTJroXDxwcHPjss8+oV6+ernGFEELop0OHDmTInJ3N+6U3jRBCGEOKB6mQr18AWbJkMXcaQiQrw4YNo2jRorrH7dq1q6xsIoQQyZhSipmz5jBn5X78AoLNnY4QQqRYUjxIZSIjowgIDCZDhgzmTkWIZKVu3brUqFFD97gNGjQgTZo0uscVQm9RUTJkW1iuKlWq0PKTT1mw9qC5UxFCJJAs1Zj8SPEglfEPDMbBPr1uHeWFSC2UUtja2po7DZEKPH/+nMDAQHOnkWDu7u4MGDCAGzdumDsVIcxq4qTf2XboAq5uXuZORQghUiTZw0xl/AKCyZQxo7nTEEKIVMfb25sRI0bQpUsX0qVLZ+503unx48cMGDCAIkWK4O/vT6lSpcydkhBmlSNHDoYNH8HUJbvMnYoQQqRIsrZYKuMXEETGjDJlQQgh9PL48WOmTJnC3LlzCQ0NxcnJCaWUudN6K09PTyZPnszcuXMJDg7GwcGBcePGmTstIZKFfv1+ZP68uRw960zdaqXNnY4QIh6x0xZE8iHFg1TGLyCYTJkymTsNId4L3yemGXoaGWq6hlqZ8xYwWWyhLzc3N/744w8WLFhASEgIAP3790+2q9k8efKEyZMnM2fOHIKCgl5cPmLECHLmzGnGzIRIPtKkScNfM2bRu8cP1KhYHBsb+SoshBAJJdMWUhlf/yAyZc5s7jSEECLFcnV1pXv37hQpUoSZM2e+KBxkz56dMWPGmDe5NwgPD2fEiBEUKlSIKVOmvFQ4KFSoED/++KMZsxMi+WnevDklS5Vl5faT5k5FCCFSFCkepDJ+gcFkluKBEEIkiaurK1999RV///034eHhL103efLkZLmSjY2NDb1796ZPnz6vXTdlyhRpFCrEG0yfMZPFG4/g88zf3KkIIUSKIcWDVMYvIJgsWbKZOw0hhEiRChUqxJEjR2jYsOFLl9eoUYNOnTqZKat30zSNrVu3vnRZ/fr1ad26tZkyEiJ5K1GiBN9824VZK/aZOxUhxNsoMKSgkyWQ4kEq4x8YQuYsWcydhhBCpEhBQUG0bt2aAwcOYGVlhbW1NUopZs2alWyXwHV1daVu3bq4uLiQJ08evvvuO5RS/Pnnn8m6saMQ5vbLL2M4fv4mzrcfmjsVIYRIEaRLTCrjFxgqDROFECIJnj9/TsuWLTlx4gS2trasX7+e6dOnU6RIESpXrmzu9N7o+vXrNGrUiEePHlGoUCEOHDhAUFAQVlZWVKxY0dzpCZGsZciQgXETJjD5rz9YPLGbFNuEEOIdpHiQyvgHhkjxQAghEsnDw4OmTZty+fJlMmTIwPbt26lduzbe3t60bNnS3Om90aVLl2jcuDFPnjyhVKlS7Nu3jzx58qBpGpMnTzZ3ekKkCF26fMfsWTPZeeQiLeonzyKhEJZKlmpMfpLnGEyRZH6BIdIwUQghEuHOnTvUrl2by5cvkzNnTo4ePUrt2rUB6Ny5M1mSMBUsKiqK3bt3c/fuXSIjI/VOmVOnTtGgQQOePHlCpUqVOHLkCHny5AFAKUXGjBl1v08hUiMrKytmzZ7LjGV7CQoJNXc6QgiRrMnIg1TGzz9IRh4IkYpERUUl27n2qYGTkxNNmjTB09OTwoULs3fvXooUKfLi+qQOYzYYDJw6dYpmzZpha2tLyZIlKV269ItTqVKlKFq0KNbWif83fPDgQT799FMCAwOpWbMmO3bskGKBEEaoVasWdevVZ9H6I/T5urG50xFCiGRLvpGmMn4BUjwQpuft7c3jx485ffq0rnGjoqIICgrCyclJ17gAfv7+/Hf2rO5HgcPCwggIDOT0f2d1jQtw4cIFpk2bpntc8X/jx4/H09OTcuXKcfz48ZcKB8Y6d+4cACEhIVy6dImVK1cycuRIRo4cyYkTJ4iIiEh0TE3TGD16NIGBgTRs2JC9e/dK4UAIHUyZ+ifrdp/mkedTc6cihIjD3CsoyGoLL5PiQSrz3NdfigfCZAICAli3bh25cuWibt26nD9/Xpe4jx494ubNmxQsWJA6deowYMAAXeICPHBzY/7ChZStVJkvv/qaZ8+e6RL36bNn3H/gRvnqNfmo+SesXLtOl7ixTp48SZUqVRg3bhxPn8qXWVNZtGgRffr04ciRI+TKlUvX2Dlz5nxpZEGjRo3YtWsXV69e5fvvv8fW1jbRMZVSbN68mQEDBrB9+3bSp0+vZ8pCWKy8efPSv/8A/lyy29ypCCFEsiXFg1QkPCKSkNAwHB0dzZ2KSKWmTZtG+/btiYiI4NatW3h6euoSN23atNSpUwc3NzcuXLiApmm6xAW4cvUqPw8fgZ+fH8+fP8dHpx3xyMhImrVuwxNvb+7cvYuvn58ucQHCw8NZtWoVBoMBX19fJk6cqFts8TJ7e3tmzpxpkqLrqFGjMBgMfPPNN1y6dIl9+/bRtGlTozu6Z82alWnTpiWp+CCEeLvBQ37GxdWT/y7fNncqQgiRLEnxIBXxDwwmg6ODLDUkTGbkyJEvdZ738vLSJW7WrFmZPXu2LrFe1aJZMwb+2O/FeW8fH13iZsualQWzZrzoR6Bn8cDGxoaZM2dy5coVWrVqxcyZM3nw4IFu8cX7ER4ejqurK0uWLKFChQrmTkcI8Q52dnZMnfYnf/yzkwgTNDoVQoiUTooHqYhfQDAZM8ioA2E6BoOBpUuXUrx4cQDdRh4AtG3blrZt2+oWL64RQ4dSv25dAHx89JsCUKvGhwz9aSAAfjoWD2KVLl2azZs3c/DgQfbt26d7fGFaxYoVI3fu3OZOQwiRCG3btiVH7rxs2POfuVMRwuLFLtWYUk6WQIoHqYivf5A0zhImlyFDBjZv3oy9vb2uxQOAWbNmJWlZvHexsrJi4by55M2TBx+dRh7EGtCnFw3q1sHPz1/XuHHVrFmT77//3mTxhRBCRFNKMWPmHOavPoCvf5C50xFCiGRFigepiKy0IN6XUqVKsXTpUt2mLcTKkSMHM2bM0DVmrKxZs/LvPwsJCAjQNa7BYGDuX3+SLp2drnGFEEKYR4UKFfi8TVvmrjpg7lSEECJZSfwC0yLZ8gsIJrMJjtoK8SatW7fm5s2busft0KGD7kWJWFUqV6ZwoUK6x82WNStTJ45H0zTpOSKEEKnA+AkTKVmiGG0aV6VYQX1XYhFCJJzBoF8TbWE8GXmQigQEhci0BfFeDRo0SNeVESB6yGjfvn11jRmXqUbnVKtSRQoHQgiRSmTNmpVRo39hyqJduv+fE0KIlEqKB6mIXdo0BAUGmjsNYUGsrKxMssNsZWUpbWeEEEIkV7169eZZYDiHz1wzdypCCJEsSPEgFcngkE73ZnBCCCGEEJbIxsaGGTNnM3XxLkLDws2djhBCmJ30PEhFMkrxQBjB+dZdk8TNldHBJHEBQsJNsw53erv0JokL8Mz9oUniZsqd1yRxU6rL0342Sdwczb82SVyf4ztMEheg9A9DTRZbiNTu448/pkKlKizfepzv2zYwdzpCWByDzAhNVmTkQSri6JCOZ8+emTsNIYQQQohU48/pM1i6+ThePr7mTkUIIcxKigepSEaH9Dx99tzcaQghhBBCpBpFihSha7duzFy+19ypCCGEWcm0hVTEwd4WP39/oqKiMBikLiSEEEIIoYeRI0dRtEghXN28KJQvu7nTEcIiKAVWMm0hWZE9zFTE2sqK9Ons8PWVYXVCCCFSlrCwMHOnIMRbOTg4YGVlhY2NrAYkhLBcUjxIZTI42vP06VNzpyGEEELES9M0bty4wdSpU2nUqBHHjh0zd0pCvJWPjw8BAYHkyZHZ3KkIIYTZyLSFVCajoz0+Pj4UKVLE3KkIIYQQLwkLC+Po0aNs376d7du3c+fOHQBmzpxJw4YNzZydEG/n5OREiSJ5UUrGUAvx/mgYDJq5kxBxSPEglclgn05GHgghhEg2PD092blzJ9u3b2fv3r0EBAS8dH2vXr3o06eP0fcTFhaGh4cH7u7uPHr0iEePHuHt7U2PHj3IkyeP0fGFZXNycqJYful1IISwbFI8SGUyONjh4+Nj7jSEEMmUv78/Z8+eJSAggMDAQAICAl78Hh4eTs+ePcmZM2eSYl+/fh0XFxfy5s1Lvnz5yJ49u27NW8PCwjAYDFhb6/tvKyIyCpdHnpTNl1PXI4q+fn5MmzOfDI6OZM2SmayZM7/008HePsn3FxkVhQLdG+PeuHGDlStXYmdnh52dHenSpXvp93Tp0vHhhx+SNm3aRMWNiIjg4MGDbNy48bXrGjduzF9//ZWkfDVNY+zYsWzatIlHjx7x5MmTl67Pli0bGzduTFLh4NmzZxw/fhxPT8/XTo6OjsyaNYv8+fMnKW+RMl26cJ5iBXKYOw0hhDArKR6kMo7p08rIAxOJiooC9P/CDtFfgk01FDIkJARbW1uTxDaV4OBgbG1tTfKcmOr5iIyMxMpK30Zas+f/TZlSpahTq6ZusdOnT8/69euZO3fuS5cXL16cVatWJblwAJA/f37atm3LtWvXAEiTJg158uQhX758FClShGHDhlGsWLEkx69ZsyZ//PEH9erVS3KMV1lbGZi35wT3njylWaVSNKtUiiI5sxodN4OjIzmyZ+O3P6a98fpqlSoyecwoShVP2vPRdfoi6pYryWc1q5AhvZ0xqb5QvHhxbt26xapVq167rkSJEsyZMyfRhQOAPHny0K5dO3bu3PnS/6dSpUqxZs2aJBeElFIMGDCANWvWvFY4qFixIlu2bEnyDr6joyO7du167XPSvHlzli5dSsaMGZMUV6Rcl5wu0vhbmVojhLBs0jAxlXFMnxZvb29zp5HqBAUFMXv2bLZv36577MuXLzNhwgTd4/r6+rJz506GDRuma1xN07h65TIBAf4s+3exrrG9vb05f+ECQ4YNw8vLS7e4mqZx+Mhhtm3bxrZt23SLC+D28CGXr1zhl9/G6ho3IiKCFWvW8HmHjlSoXoPfJk7ixq1bRsc1GAyvjU7q0qUL58+fp3LlykbFvn37NtevX39xPiwsDDc3N0qWLMmYMWOMKhzMmDGD69evU79+fTp27Ii7u7tRucZyeejJNTcPHvo8Z8H+U3z+xyK+mLKYRQfP4P7UuJVr/l299rXL8ufNw9wpk9iyfEmSCweX77px292LyWt30HDIRH5ZupHrbh5G5QoQHh7Oli1bXrrMzs6OCRMmcPnyZT766KMkxdU0jblz5/L06dMXBcEsWbKwfft2o3fC3d3dcXZ2fumyL774guPHjxs1MsDKyuq1lYtGjRrFtm3bpHBggcLDw7l16w5FCyS9uCqESDwFGFTKOVkCKR6kMhkc0uHj/eTdG4oECw0NZezYsfTr14/x48ejafo0bomKiuLAgQNUrlyZkSNHvrTTZayIiAh69uxJixYtWLp0qS5LoPn5+eHh4c70qVNo16Y17dq0ZveunTpkGy04OJgPa9emddu2rFi5Ehcdn4/FSxbTsWNH+v3YjwsXL+gSMyIiAh8fH2rVq0+jps3YtmOHLnHjxo8d5eLt48Pp//5j9br1PHBzMzp29+7dKV++PI6OjqxcuZJFixZhb29vdNyiRYtSv359IHrnq0uXLty4cYP58+cbPcT72rVr+Pv7A7Bq1SpKlCjBH3/8YfR729rKgF0am5cuu/3YmwNXbrL25CW8fP2THLt968/ImMERgMwZMzJ22BCO7dhC6xbNjR7BFBLzuIPDwll/7CwdJsxh0prtBASHJDlmmjRpGDx4MOnSpQOgVatWODs7M2zYMNKkSZPkuEop5s2bx9ChQ5k4cSI2NjZs2rSJwoULJzlmrBIlSjB8+PAXUxPGjh3LmjVrSJ8+vdGx+/TpQ506dXBwcGDTpk389ttvJhl5JpK/GzdukCtHVtLZJn7kjRBCpCYybSGVyeiQnntuUjzQk6ZppEuXDltbW/777z8OHjyoS1dwg8HAo0ePsLGxITIykg0bNjBixAgdMob169e/OIL49OlT9uzZwyeffGJUzDRp0vBVuy7cvXMbgHuurpSO2ckw1v6DB+ndpw++vr5ERkYC4OziQn0jh6drmsboX0azZMkSILoQdOnSJSOzjebu4UGLT1u9ODp57949nj1/TiadjkpaW1vzzVdfUa5sGSqWK6frVIuPPvqIRo0a0bt3b1124GKlS5eOBg0akCtXLkaPHm3USIO4wsPDefjwIXZ2dtjb2784bdq0CTc3N8aOHUuGDBmSFDtvloygFGXy5aRakfxULZqPSoXyYq/DTkKPLp35+99l9O/elV7ff4ujg4PRMSG64FG2YF5K5stNyXy5KJUvNwVzZsVah6kt/fr1Y+nSpfz1119G/82IK1++fEycOJHNmzezYMEC6tSpo1vs8ePHs2vXLmbNmsVnn32mW9waNWrQuHFj/v77b0qWLKlbXJHyXL58meIFc5k7DSGEMDsV31HUMsXyaaun939/2QijHTvnwuZjd9m7/6C5U0l1XF1d6d+/PwEBARw4cEC3uOfPn6d169ZkzZqVCxf0OSoO4OzsTNu2bXFxcaFdu3asXr06/u1v3X1nTBfna7T/og0R4eEAFC1WjC07dsd7m1wZE7bDtGTpUgYOGvTi/FcdOzJz+vR4bxMSHvHOuFFRUQweMvjFPG4bGxtuXL8R7854+rQ2b70urnv379Pi01Y8fPQIgG2bNlG3Tu14bxMZmvQjw/HJlDtvorY3VZ8NPz8/HB0ddY0ZFRWFpmmJ6vtwedrPCdouICQUTQMHu4QVC3I0/zrBOTx99pyw8DByZn93h3af4/qOXImr9A9DE7ytr68vNjY2L0Yf6C0sLMyoUQxv4+rqSqFChXSPa8p+NCLlGDJ4EMGPr9GtfSNzpyIsWPmWg85rmlbV3Hm8TwXT2GqjsxYwdxoJ9r3HzVT/Gsn4u1Qmg0M6WW3BRAoVKsSWLVsYOHCgbvOtAapUqcL58+fJmDEjd+++ewc+oUqXLs3Zs2fp1KkTW7dufTHk2xilSpehX/8BL86HhoYaHTPWt5078+svv7w47+Lioktcg8HAH5P/oNPXnYDoo9hXr17VJXbBAgXYumkjuWKaDDpdvqxL3PfBVDtEehcOIPo11LsZZSx727QJLhwkVuZMGRNUOEhOMmTIYLLCAWCSwgFgksIBmO5zIlKWixfPU7yQjDwQQggpHqQyGR3S8+zZM3Onkaq1aNGC3Llz6xozW7Zs7N27V/dl6NKnT8+///7LzJkz2bNnjy4xv/3uB6p9UB2AsFDjeynE1bd3b34aOBCA6zduvFjhwlgGg4FJkybx7bffAnDx4kVd4gIUKVyY7Zs3kyN7di45OekWVwghRPJw5fJVShTS9/++EEKkRFI8SGUyOKTj6bPn5k5DJIG1tbVJ1g1XSvH999/TsmVLXeJZWVkxYfIUHBwcdB15EGv4zz/T7YcfCAoK4v79+7rFVUoxftx4fvj+B92aJsYqWrQI2zZvwsPD+I73Qgghkg8vLy9CQkLImS2juVMRQgizk+JBKuOQ3paAwKAXTeeEiKVnw73cuXMzcsxvhIbpXzxQSjFh3Dg6tGun64oLsbF//fVXPqz+oa5xAUoUL86fU6fIZ08IIVKRy5cvU7xIXpnCIoQZKMBgSDknS2AhD9NyGAwGHOzTydQFYXItP/mUjxs3MUlsg8HAX3/+Sb68iWsCmBBKKTp37qx7XIguIJhqbr4QQoj379KlSxTLn8PcaQghRLIgxYNUKFMGB54+fWruNIQFGDZyNPGt2GIMa2trypUrZ5LYcgRJCCFEQly6cI7iBVNW41MhhDAVfbuziWQhg0N6WXFBvBem6KwvhBBCJBdOTk607GqaUXZCiHczGExzkEokjRQPUqEMDulk5EEC+ZmoyBJmonnvQSH6rm4QV5SJRhD4B+vfFyGWbRrT/AmLCA40SVyAEB8vk8Q1Zc6RocEmiatF6rOaxpvk/bKXSeKGB/iZJG7Gag1NEhfA3emsSeJGBgeYJC6AlZ29SeKmyWW69cKzprBlOcW7hYWFcfvOXYrkz2nuVIQQIlmQaQupUKYM6Xj06JG50xBCCCGESLGuX79OnlzZsU1rY+5UhBAiWZDiQSpUqmAOzpw+Ze40hBBCCCFSLCcnJ0oUymXuNISwWEqZfwUFWW3hZRbyMC1L+ZIFOH3qpLnTEEIIIYRIsS5evEDRfFnNnYYQQiQbUjxIhYoXys29B274+Zlmbq4QQgghRGp36cJ5ShTKbe40hBAi2ZDiQSpkY21F6WL5OXvWNE2yhBBCCCFSM03TuHL1KsWleCCEEC9I8SCVKls0N6dOSd8DIYQQQm+hoaGEhZlu9Rthfp6enkRERJA9iyxJLIQ5mbuPgfQ8eJks1ZhKlSuej/3Hj5o7DSGEECLFioyM5N79+1y/fh0XFxdcYn7Wr1ePcWPHmjs9YUJOTk6ULJIPpZS5UxFCiGRDigepVPkSBZgwfxuapsk/PiFSKLdH7uTNnUv3z7DLzZtYW1lTtHAhXWNrmsbW3XupU6M6mTNm1C1uLLdH7uTLo/8Q4nv3H5A3T26srfX/l2iqv8H+AQE42NvrGtPPP4AzFy+SPUsWsmXNQrbMWbCx0ec58QsIwD5dOgw6H5rRNA1A9+f49JkzjBw1ips3bxIcHPzSdf369mXkiBHyvzWVu3TpEsXyZzN3GkIIkaxYyAALy5MjawbS2Fhx584dc6fyVj4+PiaLHR4ebpK4sV9U9WaqfH19fYmIiDBJ7G2bN5kkblRUFPv37zNJ7F27d5vkNfR47Mn5i5d0j7vn8FGaf/UN2/cdIDIyUre49unTU79lKz5s1JRR4ydy/NRp3d6DF69cpUK9j+nc60c27dhFUFDwu2+UQLMWLWHmP4t1fw3PX7xIy7Zfcu/+A13jAkyft4DAwCDd4w7+bQJT5/5NQGCgbjEd7NOzYfsumn/1LdWafELBajUp16AxDdt2YN7SFUb9Lbl+x5XyTVrxefd+jPzjL5Zv2saFq84EBRv//uj60890/eln/l62Eqdrzrr8zfuwenVq1az5WuFg5IgRjBo50ujCwYMHD7h165ZRMd7GVP9PLM3FC+coXjCnudMQwrIpMFhpKeZkCWTkQSpWoWRBTp8+TdGiRc2dykt8fX1ZsmQJhw8fZtMm/XZANU1j69atPHnyhEyZMtGmTRvdYj9yd+fCxYvcu3ePvr176xY3KiqKrdu2cfLUKUYOH46jo35zK+/fv8eff/5JjRo1aNeuvW5xAWb9NZ2/584mIjKS1m3a6hb3ypUrrFy5gv3791GrVm3s7Ox0iRsREcEfU6awZetWQkJCaP3ZZ7rEBTh09BhLV67i4aNH7NmySbcjq+HhEcxYuBifZ8/oPngYhQvkp9e3nfi8RTPSpkljVOy5/ywmPDwc1/v3mb/4X+Yv/hdHBwca1qvDd52+pnqVykmK+9Ddg/n/Lgdg35Gj7DtyFDs7W5o2qE/rFs2oV7MGadLYJCn2hL9msv/IMTy8vHji7cOYwQN1ea5v373LqLHj8PR6Qt0mzZj02xg6fNFWl6PKk6bPZO2WrVy8coXFM6djZWVldEyA4/+d5diZ/9i2dz9L121kYI8f6Nj6M11GCRw7/d9L5wvkzcuwfr2oVa2qUXGdb97GLyCQc5evcu7yVaysDDSpW5vv2rXhgwrljIp95uIlnj57zs4DhwDImT07v/z0I580bmTU6+j28OGL35VS/D5pEl2+/daoXAECAgIYOHAgTk5OnD59mixZshgdM9auXbvYvHkz8+fP1y2mpbrs5ETrmi3MnYYQQiQrMvIgFStbNBcnTxw3dxov8fX1ZeLEifTv35/NmzfrduTF09OTZcuW0bp1a7p27cr58+d1iQtw8tQpunbvztfffMPe/ft1i+vv78+ixYvp2q0bixcv1rXBpa+vL1OmTGHTpo1MmzaVkBD9jv6GhYWyacM6NE3jlxHD2LhurW6xZ8yYzsqVK/Dy8mLRon90i3v5yhWm//UXrq6u/DZ2LIFB+hwJ1jSNqX/NZOuOnVy45MSaDRt1iQsQEhpC7pw5Xpy/e/8Bg34dT82Wn7Npp3EjKOrVqomNTfROvFKKqpUq0rfbD/Tv1YMPKldKclw7W1sqlSv70mXp7OyIiIzE/fFjvLy9kxRX0zTOO13Bw8sLgIUrVtFn2EjCdDjC6uX1hICA6KP3gYGB9P1pMF169OLps2dGxQ0OCWHtlq24P/Zk76Ej/PbHNKNzjWWbNu2LER3eT58yfMJkPmrbnp0HDhn1vlBKMaxfdHG0WOFCLJz2O9uW/mN04QCgbvWqZMucicwZM9Dnm684sWEl8yaMMbpwoJSi9gfVsLKyomGdWsz9fTzHt67n0yYfG10AGjFsGN26dsXKyorZs2bpUjgAOHv2LBs2bOD27du0bt2a0NBQXeK6u7vTrVs3/v77b/btM83oLUsRGhqK6737FMmf490bCyGEBZGRB6lY+ZIF+HP5YXOn8RJra2tCQkJQSqFpGjNnzmTGjBlGx82SJQu7du168cX5woULRseMVaJ4cZ77+gLRDZT0msNsb29PQEDAi/PHT5ygSZMmRscFSJs2LeXLl+fo0SM8fvyYJUuW0KNHT11i37pxk7r1G+B89So3b97gl5HDidKiaPulcaMblixZwv44xZl58+bSseNXZMqUyai4Pj4+/Prrry/Ou7u7M3PmTIb+/LNRcQEWLV3OuYsXX5z/beIkWjRtgqODg9Gxw8LCyZo5E1980oJC+fNRKH8+ChfIT8F8ebFPn96o2DbWNjT5qAGNGzagYb26ZNPpyKe9fXo0TaNl40bU+qAaNapVoXiRwkZ/Xs5fvoLrg5enFGzetQefZ8/5588/jHo+Tv139rVi0raduzh7/gKzpk2hQd06SYq7dvNWnj33fXF+/r/LKFQgP992aJfkXGN5PvGmYZ1aZMmciayZM5MlU0ayZM6Mo4M9AYGBRvVCKFKwANPGjKJNy2a69oDImjkTw3p3p2XD+timNW7kzKvatGjG2J9/ImvmzLrGLVKkCNWqVaN2rVo0a9ZMl5iapuHm5sYnn3zCgQMHOHbsGD/88ANLly41+nOSO3dutm/fzuTJk+nXrx8XL17E1tZWl7wtjbOzM/nz5CRtEkdKCSFEaqXiO0pRplg+bfX0/u8vG6GrkNBw6n39K97ePqRLl87c6bzk1KlTfP/997i5ufHw4UMyZMhgdMyIiAg6duzIunXryJo1K15eXu/8MuaXwL4L3t7efNK6Nc4uLlz47z+KFC4c7/ZhiZifvmz5cgYNHkzp0qU5dOBAvNsGhSRuaTA/Pz/mzZvL+vXr2Lt3Hxkzvn1H3C8k8Ue/wsJCuXXzFi7OV6lX/yOyZc/+2jaOtmkTHO/GjetMnvz7iyJC167dGDly1Fu3t02TsJ0bTdPYvWcP06ZN4/KVK6RNm5ZjR49SIH/+N25vHZHw5+KRuzvTZ89l+eo1hIWF0bdHd8aMGPbW7UN8vBIcOzFs7E23nFhkaMJGrkRERGBlZZXgnSAtMipB2wUFBXP3wQPu3LvPbdd73Ha9x51797hz7z7FChdi+ewZZM3y8o5j2kwJK4hERkYSGhZGaEgoIaGhhIaGRp8PDSE0LIyK5cq9GKUBEB7gl6C4ED364OR/Zzlw9Dj7jxzlkcdjls+bRYPatV7PIzQkwXGTi8jggHdvlERWdvo2g4yVJleBRG0fFRWV4OkxWd/w9y8+ISEhHDt2jJ07d1K9enXat9dvetm9e/cICQmhZMmSusW0JEuWLGHD8rlMGPCluVMR4oXyLQed1zTN+KFgKUgRW1ttYoF85k4jwdrdvJ3qXyMpHqRyXw+Zy+z5S6hdu7a5U3lNaGgo48aNI0eOHPTp00eXmOHh4bRr145NmzZx//598r9l5zBWQosHAE+ePKHlZ58xZNAg2rRuHe+2iSkeAOzcuZMevXrhdPFivEfaE1s8iOXp+RgvryeUK/f2IcJJKR4kRGKKB7HOnv2PiRMncuXKZQ4dOkLevHnfuF1CiwexNE1j3/79TJ06ldy5c7N40aI3bpeY4kGsRx4ezJw7n1Xr1nNgx1aKvqXAlJqLB4mV0OLB20RFReHh5UVoaBiFC7z8WU9o8SCxElM8iEvTNG673uO/8xdo82lLbNO+/LmQ4sHLkkvxIDESWzwQydePP/bD4HeX79o2MHcqQrwgxYPkzxKKB9LzIJUrVywvJ0+eNHcab5Q2bVrGjh2r69EWGxsbVq9ezaeffqrr1AWAbNmysW3TJkJC9P+S37x5c1atXMm1a9d0jw2QI0fOeAsHyU21ah+wYcNGZs+ew/bt23SLq5Si8ccfs3vXLr7+6is8PT11i50nVy4m/TaG04cOcO/+fd3iirczGAzkyZnztcJBcqSUoljhQnz1RZvXCgdCiOTl0oXzFC+Uy9xpCCFEsiM9D1K5csXzcuL4URgyxNypvFXWrFl1jZcmTRrWrl2ra9PEWNmzZ6ejjsWOuGrVrGmypSBTIqUUjRs3ISrKuKPTb4vdsGFD3eMC5MqZg1w5pcmWEEKkRJqmcfWaMyW6NTJ3KkIIBQZ9FioSOpGRB6lc5TKFOH78hK5rxKcEadOmpWbNmiaJrUezRHPETqn0WvpQCCGEeBd3d3cUGlkzGd/4VgghUhv5Vp7K5cyakWyZHTl79qy5UxFCCCGESNacnJwoUSSfFPOFEOINpHhgAWpWKsrOHTvMnYYQQgghRLJ26dIliuXPZu40hBAxlCHlnCyBhTxMy1a7cnG2b99q7jSEEEIIIZK1SxfOUayA9K0RQog3kYaJFqBi6ULcubMMT09PcuQw3T9Er8ePTRI3JCzCJHFNKTg83CRxI6NM11Axn4nmd/qHmua5ALC1sTFJ3BC/pyaJC5DG8e1LcRrDKq2tSeICWNulN0nccIPp/gVFmWgZwbSZ9G3wGiupS0AmRKDrdZPEtTLh8qARJvoMBrm6mCQugH+l+iaJa6q/cwC5csuKAq+6fPkyX9ZrZe40hBAiWZKRBxbAxtqK6hVLsGfPHnOnIoQQQgiRLAUHB3P/gRuF82U3dypCCJEsSfHAQtSqVITtWzebOw0hhBBCiGTp2rVrFMyXCxsbGZgrRHKgYpZqTCknSyDFAwtRq3JJ9h84SEREypsCIIQQQghhak5OThQrmNPcaQghRLIlxQMLkSNrBnJmy8SZM2fMnYoQQgghRLJz6eJ5WWlBCCHiIcUDC1KzYhF27thu7jSEEEIIIZKdSxcvUqJQbnOnIYSIw9zLL8pSjS+zkIcpAGpXKcGO7dvMnYYQQgghRLKiaRpXrl6jeCFZgUIIId5GigcWpEKpAty7fx8PDw9zpyKEEEIIkWysW7eO3DkykyWjaZYtFkKI1ECKBxbE2sqKGpVKsnv3bnOnIkS8wsPDTRI3MDDQJHEBfJ4+0z3mY09PZsybT2RkpK5xt+7Yyeix49h34CD+AQG6xr5+8yb7Dx3SNWZcPj4+uscMCQ3Fz89f97gAfv6miQsQFRVlstiWLjQ8nGt377PvzHkWb9vD+EUr6DVpBvM37pDnPRUKCAhgQP8fGfJ9C3OnIoQQyZoUDyxMrUpF2LFtS4K3f/z4scly8fLyMknckJBgfH19dY/r5+dnkh2XyMhI7t1z1T2ur68vPX74jkUL/+bO7dtomqZbbFfXe8yeN0/XmLHCwsIYOXKE7nE1TWPxv/+y/+BB3WNHRUXxfb/+uu+IR0VF8fv0v2j9VSfcHj3SLW6RQoVYuGQJ7Tp/Q+HSZfm45af8OmEiBw4fJsDIAktkRCRfft2ZEWN+JTQ0VKeMo1+/0NBQvu3SRfdiiteTJ3T8oRtBwcG6xtU0jfadOnPr9m1d4wI88fFhzj+LdY+raRozV6zVPS7A/BVrCAnR7z0B0Z+RYX/8xaR5i9h3/BQ+z5/rEtfKYGDJtt30mjSDCYtWsmTbXormzU3Xz5phMBj/1Unv97Awzq9jxlC1TAGqlC1s7lSEEHEpMFipFHOyBFI8sDC1qpRI8JKNCxcupEWLFrpPc3B1dWXs+PH0HzBA17jh4eHMmTObkSNHcPToEV1j37x5k759+zBr1kxd4wL8OfUPun//Hc+ePtU1rovzNU6eOM7Uyb/zafMmtGz6MXt279Jlh3/Y6FGMGP0L3Xr2IigoSIdso+3du5c2bT5n5cqVHDmi32sYEBDAJ61bs/jffxn0888E67iTuHzdej7p2IlTZ88xfe583eIGBgXxYcPGhIWFc+q/s9Rr1pL1mxNe+IvP4mXLXuzERUZGcv7iRf6aPYcu3Xow/JcxuCfxM+/u4UH9ps0AmLtgIU0+/Yxbt+8Yna+maYwYNYp27dvz39mzrFu/3uiYsa66uNDii/acOXeOb3v2JiwsTLfYHTp/w/kLF2nc8lNOnDylW9yjJ0/RvktXJk2bzvWbt3SLGxEZSc9ff2fakpUs3qRvc92Nu/cxfdFSWnXtza1793WLq5Ri28EjzF25lh+G/0rH/kM5cNL4VYWsraxIb2sHgI21Fb/37crAr9vqUjh49OgRAwf056Gbm9Gx4vL392fnzp24uupbjI6IiOCff/7B29tb17gA27Zt48qVK7rHTQwXFxcWLVpI/2+amDUPIYRICaR4YGGyZnIkX65snDoV/xfZ69evM3HiRC5cuMBvv/2m2/3fvXuXcePHM2vWLA4dPozXkye6xPX392f58mVMnTqFdevWcfToUV3ixsaeNWsmhw8fYtmypTx48EC32J6enmzfuo379+/Tq0d3XY/UlixVmmzZs/Nxk6aMnzSZpStX06RpM5QyrjIaEhLC7ZgdwnUbN9K4RUvu3ddnZ2DTpo1cunQJgOHDh+u2k3/N2ZkTJ09y+84dXF1dmTZ9ui5xIyIiWLR8JecuXgLg73+XcU+n98cTb29y5fz/euP+AQH0HDiI7j8O4LmRI2vaft6azJkyAZA2bVpaNm/G4vnzuOF0kRlT/iB3rqQ1DHv27DlZs2R5cf7y1as0aNqMZatWG1W0cnv4kLVr13Lq9GkAfp88Wbf3xh3Xezz29ATg0NFj9BjwU4KKq+8SFRWFl9cTwsLCeP78Oa3btWf1OuOLHpqmsW33Hq44OxMWHk6fwT/rNs3n8RNvTjlF78j9NmchWw7qV8B79NiTwKBgXG7fpWWXnqzdrk8hUylFiwZ16dz6E7YvmMnuxXNpWLO6DhnDkG/a0fjDqiwZM4TPP6qtS0xN01i+bBnbtm5l3bp1usSMdfPmTX7o+gOeMe9nvVhZWdGtW7cXf5v1NGXKFJPETShN0+jVoztdv2xA1kyOZstDCCFSCikeWKAaCViysWTJksyfP58qVaqwYMECbt68qct9FyhQgBIlSgDRX663b9fn6JaDgwM2NjYv5qIeO3ZUtyH1Dg4OfPXVV1SqVJnw8HCmTp2iS1yAHDlysHn7Dqb9NYOChQoybcofus2ntbW1Zde+A0yfOZvPPm9DlixZdYnr7+/P+N9+Zeb0Pxk9Yjh1atdi9rx5Rk9DWbJkCbt27Xpx/sGD+8ycafxIj2vOzvzQvftLz+ufM2Zw65ZxR2yfPX9O94GDuBHnyHpYeDhjftfn/fHA7eEbRwBs3Lad+s0/4fip00mObWNtTcUK5Zn95zRuXLrA0gV/06plC+zs7IxJmVP/ncHzlfdBUHAwPw4azPc9eyVpOtG58+fp1bs3/nF6B7i7u7N4yRKjcgV4+uwZi5eveOmybbt2M3D4SKM/h1euXiVf3rxULF+eTJkyER4eTs++/Zg4+Q+j/jadu3iJsxcuvjh/+ZqzbiNeHns/5cumjShbrDBKKQZNnsGRsxfffcME+Lzpx+xdtpAN82Ywf8IY0tnZ4eWjz2irCT/1ZeyA3pQrUczo4mhc6WzTMqnP93xQpqRuMZVSDB4yhGHDh7N1y2Zdpy9UqVKFTz/9lIwZM+oWE6Jz7tKlC40aNdI1bnBwMJMnT6ZTp066xk2MNWvW8Nj9Pu2a1zBbDkKIt1OYf/lFWarxZSq+LzFliuXTVk/v//6yEe/FP+sOYpW5BFOnTXvntlFRUaxbt45Lly4xceLEeLf1SkR/hP0HDtC7Tx9KlijBls2b4902JCzhRwEPHNhP7969CA4OZv/+gxQrVizBt30XTdPYs2c3f/zxBzNmzKRMmTJv3TY4iUcCNU0jKioKKyurN14fGaV/j4FYeTLamySuf2jCn4uHDx+ycOECVq5cSXBwMDY2NuzZs/etr6OjnW2C4np5eTFvwQIWLlr0Yge2bp06bN248Y07GyE+CTty98jDg7/mLWDlhg2Eh///fbp+yT/UqfHhG29jsEmToNh37rri5+9PSGgooaGhhIWFRf8eFkZoaBjh4WF80qzpiyP9VmkT9lxA9Odaj+HXr/J5+pSgoCACAgMJCgoiKCiYoKAgAoOCCAoOImvmLDT5uNGL5zzcYP3OmJqmcejQIcaOH4+zs/OLyzNmzMh/p0+TIUOGN97OEJzw/hNeT7w5c+4cp8+e4/S5c1x1duGHzp0YN2rEa+8P63TpExw3Lj9/fx64ufHgwQNKlypFwQIFXro+PMAvwbE0TePsxYssWbGarTt3EaVp7Fq/mgply75x+0DX64nO19c/gDOXr+J04zbfff4JWTK+/jxb2ZvuSK0Wrt/0kbjCEvjZTgqrSvUTtf21q1dJb29PwYIF493O1sYmwTHd3NxIly4dWeKMAIpPrtwJG2X07NkzMsWMVkot/P39KVG8GBMHtKVymULmTkeIdyrfctB5TdOqmjuP96lYelttWpn85k4jwT49eyvVv0ZSPLBAI/9aT6v2Xfnuu+8SfJvw8HCsra3jPaqTmOIBwP0HD+jatStLFi8md+7cb90uMcUDACenS3z3XRd69+7Dd999n6jbJkRERAR3796lePHib90mqcWDd0ntxYNYz549Y+nSpSxevIiiRYuybt36N773Elo8eJGLvz9Lli5lzrx5uHt4sGDuXL784ovXtkto8SCW2yN3/pr/N6s2bCIiIoJSxYuzf9M6rK1f3zlOaPEgsRJTPEguElI8iBUVFcXGjRuZNHkybjFzxfv26cPIEW9urpmY4sGrAgICOHvxIiWKFn1tCkdSiwfvkpjiQVzePk9ZtWEj/507z4KZ07FNm/a1bZJSPEgIKR68LLHFg4RKTPEgsRJaPEiNBg4cgOvV0/z2Y1tzpyJEgkjxIPmzhOKBhQywEHG5PnxC6dKlE3UbGxsbXYeDAhTIn5+tW7YQpvOOdoUKFdm0aTMPHz7UNW4sa2vreAsHwniZMmXixx9/5NSp07Rq9RlnzhjfAA2ip6D07d0bp/PnmT1jBkuXL+eZDt3Z8+XJzZTfxnBqz06++qINt+7eZcW6DUbHFf9nMBho27YtJ44dY+xvv5E5UyYWLFyoe0NXAHt7exrUqZPk3g/vU9Ysmenb7Qf+nTdblhAUIoGuXr3Kv0sW82NnaZIoRLKmQFmpFHOyBFI8sDBRUVHcufeIUqVKmTsVIHpe/qvDd/WQP38BBg8eontc8X7Z2dnRqVMnPvzwzVMAkipNmjR83bEjWzdt0nX4fv68eZg27jdO7N7Og0ePCDRy2UPxurRp09Kta1fOnD5Nzx49mD1njrlTShYMBgPpjOxZIYQl0DSNXj270/3Lj8iSycHc6QghRIqS8DGjIlV47P0cR0fHt84TTk2Mbf4mUj+DwUAGR/2HXhfMl49RgwbqHlf8n6OjI0N//hlvb280TdN9ZJQQInVauXIl3p6P+GJwK3OnIoQQKY6MPLAwd928KFWyhLnTEEIIXWTNmlUKB0KIBPHz82PQwAEM6/YJ1m9pTCyEEOLtZOSBhbn7wJMyb+nILYQQQgiRWo0ePZIalYpSsVRBc6cihEggS+klkFLIyAMLc8/9KWXKljd3GkIIIYQQ782VK1dYvmwZ/To1NncqQgiRYsnIAwtz96E3fUzULNHWxjRvJxsTDi0Mj4w0Sdx0kSEmiZsmY0aTxAV4HmianP1CQk0SFyDKREtXOmYw3XrmSV2S711CfLxMEhcgfW7TLJPk42e6hpJZk9mSiu9iY8JlD021pGLIwzsmiQuAwTR/9+3yFjFJXAA7TPP/xGDC4zxeN51NEjd78cSt6GRqmqbRs3s3erRvSJaM0iRRCCGSSooHFkTTNO7ce5ToZRqFEEIIIVKq5cuX8/zpY9o2+dzcqQghEkMpDAaZtpCcyLQFC+Lz3B9rG2uyZctm7lSEEEIIIUzO19eXwYN+Yli3T7Cykq+9QghhDPkrakHuPPCkRPHi5k5DCCGEEOK9GD1qBLWrFKN8iQLmTkUIIVI8KR5YkOiVFsqZOw0hhBBCCJNzcnJixYoV9Pu6iblTEUKIVEF6HlgQV3cfqn9Uz9xpCCGEEEKYlKZp9OzRjV4dGpIpg2kaqAohTEshSzUmNzLywIK4PvShTJky5k5DCCGEEMKk/v33XwKee/N54+rmTkUIIVINKR5YkLsPPGSlBSFSkJBQ0yxzqWkaUVFRJokthBDm9vz5c4YOGcywbi2lSaIQQuhIpi1YiOd+gYSEhpE7d25zpyJe8cTbh6xZMqOUvsOyVq1Zw7nz54mIiCQiMoKoyEgiIiLJnDkTPw8aRObMmZMcOzIyEoPBoHvOAMHBwdjZ2ekeF8DT8zE5cuQ0Sew16zfQrm0bXWNeunKVeYuW8OuwnymQL6+usb8fMIjvO7andvUPdI0bGBjIhk2b6Pz117rGBbh9+zZFihQxyfvOz98fRwf913+PiooiIiKCNGnS6B5b0zSTPBdCpHQjhg+jbrUSlC2e39ypCCGMoUBJATBZkVfDQtx186JEiWKJ/qLp4uKCpmm653Pr1m1u3rqle1yAhf/8Y5Kc169fz6NHj3SPu3nHDqrUbcDQMb9x6NgxQnU62ly8WHGWLF3GkqVLWb5iJStXryEkNITBAwcaVTiA6KM6P/88hIiICF1yjRUeHs7o4UPZs2unrnEBQkKC6dGjBzdv3tA1bkREBG4PH9J3wED2Hzyka2xvHx927dtP7SbNmThtOgGBgbrEvX3vHn7+AXzZtSdf9eyL8039Povu7h707T+AcRMn6vo5DAgIYMWypcyeNVO3mLHCwsLo0acvfv7+usaNjIzk9NlzrNm4Wde4EF04mDV3nu5xY2PvPXbCJLGf+QeYJO5zE8UVEBQcbJK4165dM0ncixcvsnbNavp+3dgk8YUQwpJJ8cBC3HXzpEzpsom6zbBhw2jdujVnzpzRNZc7d+7wRccOdPmhK8E6fyn5ceBARo0Zwx9Tp+oa98qVKwwZOpQWn3zCNWdnXWPv2X8At0eP+GfpMr785jvqt/iUQ8eOGR3Xz9+PyMhIADJkyMDCefNYumgRWbNmNSpuVFQUX3/9FWvWrOaHH74nKCjI6Fxjbd64gT27djKofz8GD/iR58+e6RLXz8+PBg3qc/myE507d8LDw0OXuAC/T51Gjbr1CQ8Pp3ufPjzUqcDk5+/P931+RNM0QsPC+HPOPGo1bs76LduM3in/Z/kqTp07D8ChEyf5+IsO/DhiNA/djXtebty8SbWaNQH4Y+o0evTuQ1hYmFExYw34sR9r1qzmrz+n8e+SxbrEBNizbz/V69Rj1569TPlzum5xff38+Oyrzgwa9QvT587T7XkAWLpiBQ2aNGXcpN957OmpW1wnlxt06DuIsTPmMvVv/Z7jqKgopq/ezIQla5ixZqtucQEu3bzD6PlL+WH8n7pMxYktiDq5XGfcjLnsPnKMNdt2cv7KVaNjA1x1dmHtps1s3LqNwzr8nY/19Okzps+azbyF/+Dz9KlucSMjI/muz48MHv0rDx7qVzx/9uwZzZo1o1u3bsybp18RLCoqip7du9GrYyMyOkqTRCGE0JsUDyyE6yNvSidimcadO3eyaNEibty4wYwZM3TLQ9M0Nm/bhqvrPa5eu8aI0b/oFtvj8WPOnjtHaGgokyZPZtmKFbrFvnf/PkFBQXh4eNDqs884quOXvuaNG1OhbFkG9+vL/i2bOLlvNw3q1DE6bt3atRny00983KghZ44f54u2bXQZ4vzkiRd+fn4AHDp0kPbtv8Tb29vouAC2adO+2DHevXMHn7VsxqED+42O+/SpDwEBAURERODh4cE333TC1/e50XE1TePqtWsExhRQnj59xnfdexAeHm507LCwcD6sWuWlyzw8Pen102A+af8Vl68m/ajdB5UrvXRe0zTWbdtBnU8/59cpf/LM1zdJccPDw0mfLt2L86vXrqVNu/Y8T2K8WJGRkfj6Pic8Zgd83G+/smH9OqNiQvSOxr4DB7h3/z4A8xYs5M7du0bH1TSNK9ecOX/Jidt3XXF75K7L6ANN07h85Qor16zF6fIVwsLC+PufRUbH9Q8M5OiZsyzfuJUT5y6wcPV6brreIyTU+ILHM/8ARsxbyuLt+1iyYz8PHnsZHTPW7lPn+G3hClbvPczlW654PXtudMyHjz1p3rkrv8/5m/krVjPw14kMGvc72bJkMT5hYNW6dfQe+BPd+v3Itev6jYKyt0/PbxMnMWLMryxftVq3uJ5PnnDo+AmWrllL94GDuH3XVZe4V69exc3NjQULFjB58mQmTpzIkydPjI67ZMkSggOe8XljfadjCSHMRxlUijm987Eo1VQpdUMpdVspNfQN1yul1IyY6y8rpSq/67ZKqcxKqX1KqVsxPzPFXP6xUuq8UupKzM+P9Hg9pHhgIVwfJW6lhWbNmjF+/HgcHBxYt26dbkdqlVL07dWLVcuW8UnLFixbsYIt27bpEjtXzpysW72a2TNm8EWbNkyaPJm9+/bpEvuTli25c/s2hw4eZMb06bi4uPBUp6M77dq0Zv/WTQzp348K5crqNofZysqKtm0+Z92qVeTKpd88fzu7dMycOYs//5xO3779yJ8/P7/8MgovL+N3CspXrMT6LdtYs3Ezq9Zt5K/Zc8mYMZNRUzmioqLYvXs3+fLlf/Hc3rhxg65dfyAkJCTJcSMiIvh1/AQOHTn60uVnz51nzLjxSY4by93DA6eYAoF9+vTkypGD4kWKUKViBdLZ2THz74X8d+FCouNGRUWx++Ah8uTKSYUypWlYpxZftvqEXl2+YWi/3pQtWYJ7D9wSHTc8PJzpM2a+NrXi6LFjNG3REreHDxMdM9b+ffsICQl56bMxfOjP7N61K8kxAVavXcf6TZtfnA8PD2fkmF+Niglw+64rv02e8tK0Hj1GHyileOzphXucESKL/l2Kf4BxQ/bt06Vj1+HjrNn+/+czMjKKGzrsKGZysKdW+VIExxQiHnrpU2gEaFitIpkc7V+cd3U3fhRGwbx5KJgvL8f+ix6Z4x8YSBobG/LkyG50bIAv23z+okD6YdWqusQESJMmDY6OjuTOlYtu33XRLa5bnNEGbT9tSdHChXSJe+XKlRe/u7q6omka2bJlMyrms2fPGDZ0CMO6tcRgkK+3QojkRSllBcwGmgGlgQ5KqVc72TcDisWcugFzE3DbocABTdOKAQdizgN4A59omlYO+AZYpsvjiG/4a5li+bTV0/vrcT/CzJr+8DvHTpymcOHCibqdm5sb3bp1o3r16owZMybebf18Ev+l8OmzZxw8eIhWn36CjY3NG7eJjEraEG1N03jw4AEFChR46zbhMcP69WYINs382zSOGU0SF+B5YNJ3pOPjF2KaFQMAHG3TJmr7Z8+ecfLkSU6cOM6JE8cpVao0s2fPwcrK6uW4aa3eEuFlT58+ZeuOnazfuImTp0+/NJ1g6T8L+aRF89duEx7gl6DYz319MRgMpE+X7rX83iQiKGH9EGJzTEyRKn3uhDcdCwwM5N79+7jeu4er6z3u3b+H6717hIaEMmXy75QqWfLFtl5+ievh4O/nx4UL5zl39iznzp7F5boLM2fNpk7deq9tmzVdwhoU+vr6Mn/hP8z5ewG+MSMk1q9aScMG9d+4fUJfv6ioKNZs3MS4KX/yJGZkztRxv9Gp/Zdv3N7G3jFBcQECA4OY9McfzF2wkMjISCb89is9u3V96/b+rtcTlO+QCVNYu2P3i8smDR1Ix1Yt33qbkId3Epzz3A07+HP1ZtLYWHN5+ex379wZEvYZDAwOodMvk7l65x5junaiY9MG8W5vl7fIO2NevXGTZp3//3wWK1iAg2uWvvN2djnyvXMbTdOo3bgpbg8fcufypbf+34vLkIBtAKrUqs2QAQMS1bj1Xe/n9Vu303vwz3z3dUcmjhqR4LjZi8e/slOvXr2YO3cuAEOGDGHSpElGF8579ujOs0cujOjRyqg4QiRX5VsOOq9pmn5VxxSguKOdNuvDouZOI8Ga7Lv61tdIKVUDGKNpWpOY88MANE2bGGeb+cBhTdNWxZy/AdQHCr7ttrHbaJrmoZTKFXP7Eq/ctyK6mJBb0zSjvphLadYCBASF8Oy5X7w70W+TL18+du7cyYcffmiSJoSZM2WibZvPE/QFKrGUUkl6zCL1ypQpEy1atGDChIkcOXKMkSNH8fz58yTHy5w5M992+prtmzZw5fxZxv4ymgrlo6cH9e4/gLuuST9ymzFDBhwdHBJUOEgMpZRJO/SnT5+eMqVL07J5c/r27sXUyZPZuHYtO7ZuoWSJEu8OEA8HR0fq1W/AT4OHsGrtOs6cPUfmzFmM+tuUIUMGhvw0EKf/TjNs8CAyZMjAiNG/GD31xGAw0KFtG07v202fbj9gY2OjW++D9OnTMXbMLxzcvZNKFSowZ/7fRjcvNRgM/D7sJ1o3bfTisms3bxub6gs9Pm9O63o1CAuP4MnzhBVgEiK9nS0LRvSnQM7s3PN4rEvMsiWKU+/D/w97L6jjSidKKb5s/RlVKlXU/f/eR/Xq8cXnrXWN+dDdnYZ16zB22M+6xr16NbqHRI8ePXQpHJw/f54N69fR56uP9UhPCCFMIQ8Qd2jnw5jLErJNfLfNoWmaB0DMzzcNlWsDXDS2cABSPLAIrm5eFCtWJMk7IUopmjZtKkuCiVQnb968ZNFpLnOe3Lnp07MHh/fu4cyxI/Ts+gPjJk4yampEaqP335C0aW0pU1afqT5xiwiff9aKDZu36JAhODjYM3rIII7t2kaZkiV1XXmhfLly7Nu5nT49u3Pg0GGj41lZWTF1xM+0bFgf0Ld4oJRibPfOVC9TQtepCwBZMjjyz6iB+Afp14C3d+eOL34vpPMyqW0/a0WND/Sfkz/y5yG6D9d3dLBn/p9TsLbWb2VvTdO4cuUKHTt2ZPbs2UZ/fqOioujZoxt9vvqYDA7p3n0DIUTKoRTKypBiTkBWpdS5OKducR/NGx7hq0c/3rZNQm77lqdQlQF+B7onZPt30e+/gUi27j70pHSp+IcQCiH0U7xYMYYNGYymabo0TxTvT2wRQe9lSAsXLMjS+XO4dcf4hoxxWVlZ0f2HH3TL19rair/GDCciIoIjZ84RGRmp2+iXNDbWzBrUkweexjfGe1X+nNkZ2DHhw/Xf5cPKFalUpjQXrznrXjzIkzs333fupGtMiH7v6u3bDu11L0i4u7tTp04dlixZokvsRYv+ITzYj1aNLGo0txAiefKOZ2rJQyDu/La8gHsCt0kTz209lVK54kxbeNGETCmVF9gEdNY0LeFzDeMhIw8swF23J5QpV8HcaQhhcZRSpEmTsLn3InnR80hrXMWKJK7vTELpma+NtTWzxo6iRuWK3HVLeqPLN8lgn55yRQrqGjNWtkz67Twrpej1TfTog0L59S0eAGTJnFn3mKZgisaDVlZWrF27VpdpG0+fPmX4sKEM7SpNEoUQyd5ZoJhSqpBSKg3QHnh1/eKtQOeYVRc+BHxjpiLEd9utRDdEJObnFgClVEZgBzBM07QTej0IGXlgAe67P+WTjglfaUEIIYRlS2Njw7wJY/Dy8TF3KmbTuE4tihbMr/vIA0uXM6d+q/8MG/ozjWqUpXRReY2ESJUUsdMBUjxN0yKUUn2APYAVsEjTtGtKqR4x188DdgLNgdtAENAlvtvGhJ4ErFVKfQ88AL6IubwPUBQYpZQaFXNZY03TjFoeTYoHFuDOA09KlSpl7jSEEEKkILZp05A/dy5zp2E2BoOBgV27kDNbVnOnIt7g7NmzbNq4gU2zB5g7FSGESBBN03YSXSCIe9m8OL9rQO+E3jbmch+g4RsuHweMMzLl10jxwAJomkZoqOmWyxNCCCFSo5YNG0iz4GQoMjKSHt270q9TYxztpUmiEEK8L1I8sAANqpdi3do1VKhg2r4HYX7PTRJXJXC97yTFjjB+2bQ3MlHO/q43TBIXIF3+YiaJ62CX1iRxAQwJazSbaFpUpEniAkSG6tcRPi6rtLYmiQsQGWqaFSPSB5huSHxIsGmGOVrb2ZskbrCXh0niAmQoVs4kcdPlKmiSuAChz/VdkSFWRKbEj6RI6F8Zg4n+1oX56LME5ZuEPnm1V5dOipu2SfPChQsgPIhPPqpi0vsRQpiXQqGkn0myIq+GBWhcqxxr1qw2ai10IYQQQghz8/b2ZuSI4QztJk0ShRDifZO/uhagbPF8hAQFcvnyZXOnIoQQQgiRZEN/HkLjWuUpWTiPuVMRQgiLI8UDC6CU4uNaZVm7ZrW5UxFCCCGESJIzZ86wbetmenVsZO5UhBDCIknxwEJ8XLOsTF0QQgghRIoUGRlJz+7d6NepCY72duZORwjxPsQs1ZhSTpbAMh6loEyxfISGBOHk5GTuVIQQQgghEuXv+fMxaCHSJFEIIcxIigcWQinFxzVl6oIQQgghUpYnT54wevRIhnVtKUtnCiGEGUnxwILIqgtCmEZISIjJPldeT0yzZB1AULBplo0UQgg9/TxkMM3qVqB4odzmTkUI8Z4pgyHFnCyBZTxKAUDponkJDwvh0qVLCb6NKQsNpoodERFhkrhXXa6bLOdd+/brHjMqKorFazfg/fSZ7rHP/Pcfd11ddY977vx5Fi1ezJn//sM/IEDX2Dt27eJiIt77ieFy/QZDho8gKipK99h/L13B4pVrdI8L0G3ATwQGBuoed8nyFURGRuoe1/2xJ26PTLMu/XNfP5PEBdP9TZJCsLAEp0+fZsf2bfRo39DcqQghhMWT4oEFSezUhbCwMBYvXsy9e/d0z+WJtw9z/1mke9zgkBAWLVuO8/XrusYNCwtj1oKFtPiyAxcvX9E19q07dxg25jd+GjGS4JAQ3eI+8/Vl8+59fNDyc/qO+o3zl6/qtrNx9epVPqxRgw5ffcWBgwd122lOmyYNw0eNolnLlhQoXJgqH3xA5y5d2LFzp9G5O12+QoPGTWjYtCmr1qwlRKfn2uPxY46fPMnCxUvo1a+/rjuKG7fv4pGHB8PHT9K1gPDIw4Pjp89w4Mgxug0crOuO/oVLl1iweDFjJ/2uW0wAzydPOHbmDP1GjNa9MPHc149+w0bh80zfQltwSAhPvH34Y8ZMXeMChIaFsXD5Kp75+uoeW7wsuRdpNE3jjus93eNGRESy9cBhoqKidH8Oli9fjre3N+Hh4fFuFxkZSY/uXRnwTVMc0kuTRCGEMDcpHliYxExd+Oabb/jxxx8ZOHCgrjlccHKi+keNmDj1T+7c1e/odVRUFF90/obxU6YybMxvun7ZuepynY1bt3Pu4iWafP4FfYcMxdPLS5fYPQb8xCMPD5auWkOzNl/o9pxcvOrM+StXCQsPZ9PuvbT6vgfNOn3P6q3bjS5SLPznHzRNY//+/bTv0IEatWrx94IF+Pv7GxX35u3bL363srKiXLly9OjWjebNmhk9z3XOvHkAnL9wkZ59+1KmUmXGjB3L/QcPjIr754yZjP5tLABr1q/n267dCA0NNSomwK27rvQbPorNu/YA6FZA0DSNn38dS5tvviMqKor9h48waqI+O/qHjx6jUYtPcLl+gxlz5rJ63Xpd4oaEhvJN3wEM/nUcZy5cZO6/y3SJC/DPilXUbPYp+44cZf6/y3WL63LjJvWbf8LnX3di5boNuhXYoqKiGDZ+Es07dOL3WXM4ceasLnEB5v39N7UbNODg4cO6xQQ4f/ES3/boyZBRo3WNq2kao3+fSsuvu/DI47Fucd3c3Lh16xbff9+F+fPnsX37Nl3irlq9mnPnz9OuY0emTJuGu4cHrjqM4AoOCeGL77rSa/BQeg8ZplsR2vvZM36aMJUxM+bRtEtPdh89oUtcgD179tChQwdKlSpF375931rMnTdvHmlUOM3rV9LtvoUQQiSdFA8sTKkieYgMD+PixYvxbrdnzx5OnTpFQEAAmzZtYu/evbrcf2hoKMvXrMM/IICQ0FB+HDpcly/Vmqaxcds2XO/dJzAwkBOnT7N15y4dMo7m6GCPffr0KKVwdHDg5Jn/+L5vf06fO29U3KioKPLkyvXi/DWX6zRs1ZpN27YbmzJVK5QjS6aMpLOzo2r5cnzTtjWd2nxGqaJFjd4Rr1e3Lvb29nzwwQd06dKF3j17UrlSJQxGzvf67NNPKVG8OAP79+fiuXMs+ecfataooUuDrJYtWmAwGChRvDhtP2/Nj717U69OXdKnS2dU3OzZsr10fseu3bTv1JnAwCCj4oaGhpEjW9aXLtOjgBAeHo6d7ctH8P5ZtoK/ddghDw0Nfem16j/kZ/4z8jMC4Ofvz3NfPyIiokcc/DFrLldcjB9ddOfefU7+d+7F0ftFK1brMvrg3MWLbNu1m7v37nH95i0ee3py9kL8f3MT4p6bGys2bGL/0WO43LpNUHAwR0+fMTpurGvOLly5cpXtO3YC+k23CAoOZvvuPezef0CXeLGUUpw6f4GLV6/h5OwCoMv/k7CwMJo2bcz+/fuZMGE89vYORscEsLGxoXGzZuzbv58ZM2dS5YMPsLUz/mi6v38A7o892bh9J1t376HbgEHsOXjI6LgeT7wJCw/n341buX73Hht27+fv1RtwuXPX6NjXr19n//793Llzh1WrVrF8+XK2b3/5/56Xlxe/jB7JUGmSKITlUgqsDCnnZAGszZ2AeL+ipy6UYe2a1VSuXPmt2zVq1IjJkyczbtw4rly5Qr9+/bh8+TJp0qQx6v7Tpk3LqCGDKFQgP/8sXc6Zc+dYsmIV33X6yqi4Sinq1KhJm1afsmT5CoKCgxk9fjyNGtQ3eucQoncQLx0/jH369EbvIMf12NOTjxvU54MqlfH188fP3x8/Pz/Wbd5CZGQkbVp9muQvTUHBIWxaMIeC+fLqmrOmafTu1YsJ48frGheid1b27NyJra2t7nG7fteFaZN/J50O74dYh44cYcacua9dfvjoMVq3a8e6FcvJkCFDouMGBQXz75p1uD/2fO264eMnAdClY7tEx42MjGTl+o0cPXnytetGT/ydfHny0KzRR4mOC+B8/TrDx4x5acRPWFgYnX/oyv4d28mbJ2mNzoJDQvhj9jw8nzx5cVl4RAR9ho1k9+rl2CXxvaJpGs43buJ0zfnFZYFBQcz/dznD+/dNUsxYdrZ2rNuy5aXLtu7cRfWqxi0xlztnTk6fv/DSUfajp/QrHrRs3oyly5ezc9cuxowexU+Dh7Bg3uvv78SqWL4cSincPTzw8PQkV44cOmQbrXzpkjjfvMXlmOKBl7cP37Zra1TMIkWK0KRJU3bsiN6ZLVCggNF5AlSu9P+j5wGBgTRr2pRcOXMaHdc/8P/9YcLDI4iMjOLj+vWMjuvh9XKz1r3HTxEcEsp3bT8zKq6maVyPM7XQx8eHIUOGcPz48Ze2GzxoIC3rV6JYwVyvhhBCCGEmKr6h3WWK5dNWT+///rIR74XLnUf8PG09d13vv3PHNCoqiq1btzJ27Fg6dOjAoEGD3rqtt+vtt173JhEREezYu48Va9cxdfxY8uXJ88btlMEqUXG9fXyYu/AfFi5dRo/vv2PYwAFv3TYqIixRsRMqsTknVKjP6zuTekmbv5hJ4lqZ8IiRAdPMRdaiEj6n3uvJE1yuX8fZ5fqLn9dv3CAwKIiyZUqzcfVqssUZQRCSgNfQ18+fsxcvcenqNZyuOXPp6jW8fZ6+tM2EEUNfKiAYrN9d2IuKiuKKswsHjh7j4NFjnHe6/NKRWjs7O7Ys/5cKZcu8dDsbe8d3xobonQKnK1fYtXcfu/fu48q1awCUL1uWHZs2vFbIC/FJ+NSfp8+fs2HbTlZs3MTNmCOf33dsz9ihg9+4vUrgEYDgkBDmLl7KzIWLCQkJIX26dJzZu50smTK9cXtrO/sExX3u60vPAT+x//ARAHLnysnFY0feWnALD0hYw8aoqCgmzpjF7EX/vrjs5I7NFMyX7623SZ87/ztjOru4kDdvXsqUr0BAYCCFCxUiKCiIG9euvvV24QEJn6ZUp0lTXG7c5N95c2nRtMk7tw99Hv8qIwePn+TAsRP4+vmxadceMjo68tzPj2m/jqZdq5ZvvV1EpoTtiF67do3mzZuilOLmzdsJKpw72KWN93pN0yhasiTPYka3rFy2jKZN3v1chPnEPyXjwuUrNG8XXYDPmT07+zetJWvmzO+MCxD65O0NSBet28yvM+e9OF84Xx42z5tOBod3j8TIX+ftj+vhw4fki/N+tbOz48CBA9SoUePFZSdOnKBN61Zsmj0A+3T6FpOFSKnKtxx0XtO0qubO430qkdlem9eonLnTSLCP1p1O9a+RZYyvEC8pWTg3WmQ4Fy5ceOe2BoOBzz77jHPnzvHhhx/q2k3e2tqaVs2bsXbJIjK/5Yt6UmTNkoVRPw/h4vGjpE2Thqc6N0ETIq7s2bJRr04denbryoxpU9m/awcPbt/k4plTDBsymJOnTye6/0YGRwca1avDoN49WDZnBpeP7Ofsvp0snD6FPj90oW6N6kyeOSfRUxgMBgMVypZhYK8ebF+9AudTx5j/5xTatW5FtqxZCA4O5usevXjonrQVDZRSVCxfnmGDfuLI3t04nTnF7+PGkjlzJgYM+dmovx+ZM2aka6eOHNq4li1LF/Hlpy1ZuXEzh0+eSnJMADtbWwb27Mbx7Zto3aLZi9EHxsqYIQMrFv7N4H7RoxjcPR5z/pKT0XENBgMj+vdj/PCfXxR/jR19YDAYWLp8OQWLFnuxfOddV1cidGxMWbliRQDOX7pEZGQk/503bjpL7erVOHTiJJtieoI894suvmTPmsWouLHKlCnDRx81JHfu3EaPuIullHox+iBXzpw0aqjP6gH+/tEjDwwGA3OnTEpw4eBdPOKM9nG0t+efSb8mqHDwLnFHHVhZWbF+/fqXCgcRERH07NGNAd82lcKBEMLsyy/KUo0vs4xHKV4Su+rC6tWrEnWb2rVr6z5MPZYeUwtelTlTJgb26a1rYUKIhDAYDBQsUIDmTZrQ6hPj5+sqpcibOxctPm7IiAH9WLNwHs4nD9Ogdk2j5qVnypiRz5o3Y8akCVw+dph9G9fx3Vcdmfn3whc7kcbIlzcvXbt8y8ZVK5kyYbwujdyUUlSrWIHp437lwv7dBIeE6NIcNU+unMyZPIEtyxZz/tJlXXofGAwGhvTvx/IF83Cwt9e1D0uX9l+ycNpkbNOm1WXqwphRoyhcqNBLBR49VrXw8PTE5+lTqsQUD46dOkX7b79j5959RsVNY2PD4F7dX7tcr+IBQJ8+fcmfX58pC7FiiwcdO3bE2lqfmaOx0xaG9O1FjWr6HfB6/CR69IeVlYE5vw6ncL68usSNWzxYvHgxzZs3f+n6OXNmY2cdRbO6FXW5PyGEEPqR4oGFaly7HOvWrkn2S1AJId5MKUXB/Pl02wExGAyUL1OaAT278/uY0aTToZFbXI6OjroXCTM4OtDsowa6NlP7oHJF1i2aj0Hp9++xScOG7Nuyidt37+r6N7dZw49Ys2AuLrduGb2jny5dOubOmvlSgThSh4aJVgYDVerWZ0rMcpUXnS5z6NgxXYq6rZo2pnTxl6db6Vk8qFKlCh06dNAtHkCVmF5DX+sY188/gLo1PqRv1+91iwnRDRMBRvXpTp1qb++RlFixxYMpU6bQqVOnl67z9PTkt1/HSJNEIYRIpqR4YKFKFMqNgSjOGzl0VAghUhuDwUCmjIlvdBmfIoUKsmDmX4TosIxnXNUqVuDfmdPx8vYxOlb1Dz6gb+/eL85HJqL3x9tkz5aNPt264u7h8dLlegytNxgMDOvX+6XzmTNmNDpuXJ9+2krXeJUqVqR+vXq6NWEESGdry+zJE7Gy0rfXjoeXNx0+aca3n3+qa1wXFxeGDBnCTz/99Np1g34awCcNKlO0gPGNJIUQqYBSKCtDijlZAst4lOI1Sika1SiTqKkLQgghks4+ffokrw4Rn6KFCpIrR3ZdYg3/eQilSpYEIDJSnx43PX/4nhzZX84vs07z8hvUqsGHVaKnAmTNnEn3HWi9j35ny5aN4UOH6hrz02ZNyKbjiAuIbqJZKG8efuvfS/fnoEaNGkyaNOm1y48dO8aB/fvo3i5pK74IIYQwPSkeWLDGtWTqghBCiP+ztbVl7qxZWFtbG9VPI6706dIx9JVVb/Rq6qeUejH6IFsWfXegTaVqFeOW7HyVKXoRhYVHMGP0z6SxsdE99pgxY14rSMQ2SRz4bVPSS5NEIYRItqR4YMGKF8qFtdI4d+6cuVMRQgiRTFSqWIHBAwfq0jAxVoe2bShZvPiL81my6FM8AKhaoTxN6tfVtd+BpbNNm4bMOk/difWmPi2zZs3E0dZA49oVTHKfQggh9KFPpy2RIimlaBgzdaFatWpGx8taqKgOWb1fT+7cMEncqPAwk8S1y6lPt+s30W8RzlfiBgWYKDKEhxq/IsCbREWY5vUDsM2SwyRxNR139F4V4uNlkriaDnPq3ybKVO+N8HCTxE3jaLpVYfY430v0bco1bU3BzVvZdfXuW49sewQmbvWMul99z/VffgbgwJMQ7IIevXXbegUT9znpOvBnVvy7mCdpM8a7XTafh4mKmximeccBBn2nYsSVv04Tk8VOKA8PD8b99huLJnSTJolCiJcoQJnwb6BIPBl5YOGayKoLQgghXmFtbU3v4b+gRelXVixdrQbFKlTGytoa23TpdYsLULRYMb7q/K2uMcX7MeinAXzWqAqF8unTt0MIIYTpSPHAwhUrmAsbK8XZs2fNnYoQQohkJH/holjptBQoRI92a/V9L+wzZDLJEeaicaZFiJTh6NGjHD54gK5fSpNEIYRICWTagoVTSvFxzeipCx988IG50xFCCJGK5StWgnqftTV3GiIZCA8Pp0f3rgzs0ox0dmnNnY4QIjmKWapRJB/yagia1inPv4sXs3LlSpm+IIQQwqTqf/aluVMQycCMGX+R2d6Gj2uVN3cqQgghEkiKB4Ii+XPy18jOjP1lGI0bNeTOnTvmTkkIIUQqpedUCJEyubu7M2H8OIb80EKaJAohRAoixQMBQIWSBVgxpTcVCtpTrWoVxo39jbAw03WcF0IIIYRlGjjgRz5vXI1CeaVJohBCpCRSPBAv2Fhb8c3n9Vg1rTcHdm6gfLkyHD161NxppXphYWFE6djRPC7/gACu37xlktji/3yePSPSREslRkZGSiFPCJFqHDp0iONHj/DDFw3MnYoQIgVQBkOKOVkCy3iUIlFyZ8/MXyM60b1tLdp/2YYu336Dj4+P7vdz6dIl3WMCnD592iRxz19y4oLTZd3jhoWF0+qrzpy9eEn32NZW1jRp1Zp9Bw/pGvfmzZv8MWUKwcH6rmweHBLCqHHjuXFL/4LH7buunLvkpHtcAKerznQbOITgkMSte58Qbg8fMmzUL7rHBXji7c2t2/pPU/L2ecqOfQd0jyuESNnCw8Pp2aMbP33XnHS20iRRiITy9PY1dwpCAFI8EG+hlKJRzXJsmNmf8Of3KV2yBEuWLNGtoeL58+fp3r07x44d0yVeLGdnZ4YMGcKvv/6qa9yIiAh27t1Pqw5fs2HrNl1j7z54iNt3XWnZ/iu6DRiE2yN3XeL6+fszauw4AgMD6fDtd8yev0C318/p8mUm//EHderWZffu3brFvXf/Prv27qPWx01o1b4jW3fuJDw8XJfYp8+f59OOnZgyaw4RERG6xAS4fus2O/btZ+f+g7T7vgc+z57pFnvh4iUcP3GSRf/+y+Kly3SL6+7hwcEjRzh4+AhDR4/WtVHq/qPH2LRzN5Nnz9V1NMZzPz8ePHqkW7y4Hj1+bJK4T58/5+mz5wA8dPfQNbaHiXIOCgggLDQET/eHeHno87coVkTMCJrn3k90jZsShIWH8+ixJ5qmmawxsalGP+mZ7/Q//yRbBlsa1iirW0whUrv9J6/QYeBMc6chBCDFA/EO9uls+bnrJ8wY2YkpE3+lfr06XL9+3aiY69evp2rVqvz333/06NFD1yHZffv25dixY4wZM4bROu4UHTlxkpl/LyA0LIyeAwcz7o+puk01mLd4CU+fPwdgy85d1GragvHTphMQEGhU3DUbNrJ42fIXX1ZHjxtP358GExoaalRcTdMYP2ECAPcfPKDTN9/Q4auvuHP3rlFxAa66uPDQPXqH5cTp03zXqw+Vatdl2qzZBAYFJTlucEgIv0ycTGRkJH/MnM2nX3Xm3oMHRucLMOufJazcsBmAs5ec+PSrb7n3wM3ouAcPH2bwsOH0HfgTAEOGj+Dk6TNGx/X19aXtV1/TvvO3TJs5i0NHjrJt5y6j4wLMWLCIzn36M+7Pv7h115Utu/fqEvec02UatulAj8HDiYiIIDIyktv37hsdNzgklG8GDKF26/a46bxzv23vfmp/0oYJM2Zx8ux5xv45Q5e4Xk+8afllB+o0bUFAQAAAIUZ+pmNtXLaYHm1asHvjOib9PJBH9111iXvn6mUm9fyG+b8M4fj2Texc9o8ucTVNo/t339Kg1oc8emj8Zw7g1PkLeD99yvSFi6nfpgOLVq/Dw8uLs0aOOrO2suKTzj+wYMVqyn7UlJ9+HQ9EF8X08vl33Wnc7msuXXXWLSbA9u3byZs3L7179zYqzsOHD5k0aQI/S5NEIRIkKDiUX2dtZObKQ2zboc//6RRHKTBYpZyTBZDigUiQ0kXzsuyPnnxYKis1a1Rn9KiRhCRhiLabmxsnTpx4cd7Z2ZmpU6fqkuOdO3fw9f3/sK6xY8cyYsQIXQoImqaRxsbmxfkZ8xfwTY/eL768G6NF449fOh8aFsaM+Qv4sEkz1m7ekuT8q1SsiJ2t7UuXrVq3ns/ad8TrSdKP/GmaRoXyLy+tdeDAAerWq8cfU6YYVQwqVbwEtra2VKpQnk4d2jN57G8snjOb7t91IX26dEmO6+3zlEwZM744f/6SEw1afc7K9RuNfn9ULvfyEbS79x/Q8qtvuHj5qlFx8+fLR+FChV6cj4iI4Jvvf8Dt4UOj4trZ2VGmVCkiIiK4dfs2ACPG/GpUcQai37dPYqY3hceM7Jg2b4Euozzy5MxJUHAwTs4uzFu6gtWbt7Fk9Vqj49rZpiUsLIzwiAhmLl4KRI9C0ONvRs5s2fD192f15m30GT6K/y5e0iVulsyZ8PbxwdfPj6Wr13D2wkVm/73AqJhPHnvgevMGAb7PCQkOYvncGTy674qPl+f/2LvrsKi2LoDDv0OXlN3Ygd3YXdfOa3d3d3d77Ra7u7AFBRRb7BZEUKQ75/sD5ROTmTnjgO73eXh0zpxZs8iZvc7ea6sVN9D3A3uWL+TBNWe8X7/k2d1b7Fu5GB2Z1oVKksRHX18C/P15JUPxEuCjfwAVGrVkz5HjPHv1mo279lKxcSsiI9Ur0Ojo6BAbF8e0xcsIDArmrJMz1Vr8y/odu9WKGxERSVxcHBGRkdx98JAHT55ibWWhVsyvPXjwAC8vLwLUnFU1fNgQWtUvT86s6WXKTBD+XO5PPGg7bDlmGfJw9959KlSooO2UBAEAsV+SkGx6urp0bFKF2hWLsmDjCYrs3MHadRuoVatWsmMYGBhgamqKpaUlgZ+utk+fPp02bdqQJ08etfLLkSMHGzZs4ObNm9y6dYubN2+yZMkSYmNjmTdvnspXOhQKBWnSpOG/ebPxfv8Bbx8fvH3e4+3znp6Dh7Fw5jSyZcmiUuyw8HCMjY1YPHM6VpYWWJibY2lhgaVFwr8mxsYq5e3z/gODR35/Db7bjZvUadyMHZvWU6RwYaVjBwcH07tXL4YNHYqpqSmmpqaYmJhgYmKCgYGB0vG+lC1rFp7fuYWezFu5WVlYcGTHFqKioomKjiYqKirhIyaGwKCgJIUFZdy5/4AN23ciSVKSgaGffwAtuvVi7cK51K1RTem4Hp5vmTF7Li9fJb3y+9HPj07de3Dy8CFMVCimREVFsefAQV6/STrrwuvdO5YsW87EsWOUjgkQFBxCrxGjuXLNLcnxl2/ecOikA62bNFIp7meZM2Zg6shhDJ86g0Wr12FqaoKJsTEzxoxU+wrmsF7duOx2g73HTtK3YzsGTZrO+gWzyZJR9S7wh0468NrzLemsrfno7897348AeHi9I2e2rGrlq6ury4DePRk+bgKrNmwCNlGhbBm1Yurp6TNv3HD8fT8kOf5RzeKBZfoMoFBwfv9O4P/T3+XcqjFX7ty8eP6MVy9fULlqNWJjY9X6+1HdrgKxsbF4fFo+9trzLWnMTClfqoTauaa1skxc1uQXEIAkQb/OHdSK6fX+PTMW/0enVi2IiY0lQ7q0ZFfx9ehHHjx4AICtra3KMc6fP4+ryxUOLBsqU1aC8OdRKBRcd3/BjuOu3H/qycpVa2jdurW20xKEJETxQFBapnSWLBrTAUe3h3Tp1J5q1WuwZOkyMmT49ZvtjBkzMnPmTMaOHcvGjRtZvHgxHh4eDBgwgFOnTqk1ENDX16dEiRKUKFGCHj16AAnNmR49ekRgYCBWVlYqxZUkifKlS1G+dKnv3q/O1URTExP6du2i8uN/JFPGDJw5dhhvHx/eeXvzzjvhX2+f94m3u/Tqy4zJE2lYr65SsS0tLalUqZLsOQNYWsh7xewzMzNTzMxMZY9boogtLqeOEhUdjcdbL157ePLa8y2vPTx47fmWGYv/46O/P+1bNlcqbpbMmejVvRuZM2fixCkH3n6x1v/uPXeGjBzFupUrlP59MTAwIE8uG8zMzL65b/matfzbujV58+RWKiaAhXkadq9dyaGTDsxeuhzvD/8fhC5Zu55mDeqh/8XMHWU9fPoMMzNT0qe1xtfPn+jAIAICg3jw5BlFCuZXOS5AuRLFqVy2NFeu36RZz/74Bwby+PkLtYoHRQsVZOaS5Xz0909y3O32HbWKB2Hh4Sz4bxnhEQlFwfefvs4vXqm3vMAqXTrGzFnM5IE9ifqi4KjuzAOAxt364n71CiEB//9ayFE8iIiIwNjYGJtcCT+vr16+5OTxY7z39qZbr94qx01jZkqF0iW5fO164rHqdhWSzDxTlbVl0r9vw/v0xMxUvb9LVubmnHO6gpNrwpImKwsLZixZhnkaM4b26qFW7M8+Fw8Kq1BshoQdhfr368Oo7g0xNlKvwCwIf6LomFhOOd5m54mrxKPPsBEjOd6pk0oXCf5Ekq6YKJ+SiO+GoLJq5QpzYPkQDKJ9sS1ckHXr1ia7D4CZmRlDhgzh+fPnbN++HW9vb/buVX8a8tf09fUpVqyYyoWD5EipazdNjI3JkysXVSpWpG3LFgwbOID5M6ezfeN6Lpw8xk1nJ+rUFFtlycHQwIB8uXNRp3pVenVqz6wJY9mxZgWXjx2kTdPGSheY9PT0qFypInNnzuDeDTcunnZg5LChFCxQAID9Bw+xfNVqpfOUJIlKdnYc2r2Ts8eP0uCLJTMxMTFqNU/U0dGhZaOGXD56kOF9e2P0qZP6a8+37D9+UqWYn/l+9GPk1Jn4+iUdjJ91VG8r2Wu379J12Gh8/ROuBn/uPfLkpXqD8by5bDi4eR05sia9Anz9jnrr5k1NTKhQtixbdu5Kcvzlq9dqL4nIlb8AAyckbTQrR/HAJE0aWvQelOSYnp76A/H79+7SolFD7t65DcCJo0cYP2oEFirOIvpS7SpJi6N1qlZWOyaA9RevQ3lsctC+WRO1Y5qnSQMkNGQEePLiJZt27qHJV8vhlBUfH8++ffuIi4vj0aNHgOozD5YsXkxmaxOql1d95oIg/In8AkNYs/scDXvNw/HeexYvW8PDx0/o06ePKBwIKZYoHghqMTEyZHi3hqye0o1V/y2gciU77t9P/lpvfX19OnTowJ07dyhYsKAGMxW+R52rwULy6OnpqVVgkiSJEsWLMWHMaFwdL3LD5QpTJ07g5OnTnL94SeW4pUuWZMfmTVw+e4YWTZugo6PDhUuOnHBwUDkmgImJMSP79+Hy0YO0+KcBAEvXbkgc3KiiWsUKHNu+mVw5sic5fkbN4kG5EsXIm8uGJy+Srpd/LMP2lTmzZeXAprXkscmZeEyOrULr167FghlJB/nhERH4vFd/oF++Wk3+7dU/8fZHGWIClKxWi0Jlyife1pXh706ZcuUxS5OG69cStub93Ag2e44casf+sligo6NDjUp2aseEpDMPxg/qj76++jMw9PX1vukH061dG3LnVO/roKOjw7Bhw2jRogWRkZEYGBjg7+/P3Llzlepj4unpyfx5cxktmiQKQqJnr72ZtuIgzfovJsogExcuXebs+YvUr19ftp4wgqAp4idUkEWB3FnYMrcPtUplp3q1KowZM5pwJRqwSZJE8eLFNZihIPwZ8uTOzZCBA3A4eoTy5cqqHc+2cCE2rFrJNcdLdPi3LVNmziI8IkLtuFkzZWLFnJkc22ZP+rTW7D2i3haneW1ycnz7ZqqUL5d47N7Dx7xTY4ArSRITBvWjY4umSY4/eSHPLgNZMmbk4Ka1FM6f71Pcl7J01+/c7l9GDhqY5NhLGXafAGjesStV6iYUffx8P8jS5FGSJFoPGI6+YcJsFF1d9TtSS5JEzz59vzmeLbv6xYOc2bKSP3dCs9KyxYt9s9xAVWmtLIGEpTL1qleVJSYkLBv6zMrSQrblCrly5eLo0aNAwtIDOzs7/P39leopMXTIINo0rED2zOlkyUkQUqv4+HguX39E3ymb6T99K8Xt6vHs+Qs2bNysVj8RQfjdRPFAkI2Ojg5tGtqx77/B3L9+kcKFCnDypHrTlQVB+DF110t/KU/uXCxftJAje/fg4SnPtncApYsX5dh2e/LlzvXrk3/B0tyc7SuX0qNd28Rj55yuqBVTkiRmjR5Os3r/n+b97NVrWXaJAEhnbc2+Dasp+WlXjhtqLl34bPTQwXRs2ybxtrp9Dz6TJIk+oyaQ37YoMdFRhAQFyhI3baYsNOjQDZBn5gFAlWrVKVCoUOJtPX19MmTMKEvsz7MPvl7CoI60n5YtTBo2SNar8F8WD0b265PktjpsbGyS3DY2NmbEiBHJfvyZM2e4fu0q3VtWlyUfQUhNYmLjePj8LXtOujDpv/007reIdQev0mfwaN54eDJp8mTSpxc7j/ySJCHp6Kaaj7+BKB4IsktnZc7cEf8ytkcD+vXuTutWLfD2lncPdUEQNCNb1qwUzK9eE8KvSZJE+VIlZYmlp6fH9DEjmDdxHHp6upy5pN7SBUi4Er5k6gTqfBooRkVH8+btO7XjfmZpbs7uNcuxK1Na7b4Hn0mSxIIZ06hXqyYAL169liUugIGhISNnLSBdxkyyLV0AqN68LVly5ZFtNxVJkujR+/+zD7JmzSbLrAaAOlUrffpXnn4HkLBsoVGdmpQqKu9VRss05gDkz52Lji2byRY3V66kBb++ffuSMZnFmaioKAb078uoHg0xMhTL44Q/n8/HQM5cucvizSfoPmE9VdpNZdrqE3iFmfJP6+6cOHWW23fd6dy5M4afZmEJQmokigeCxlQqXYD9y4ZipR9KUdvCrFixnLi4OG2nJQjCH6Bjq+bsXrOSR0+fExoWpnY8fT09Vs+ZTsUyCbuqPHqhft+DL5mZmrJtxRIs0ny704Wq9PT0WLdsKWVKlpBt5sFnltZpGTN3MeGhIbLF1NXTo+3gUegZyPfGuVaduth8GuRmy579F2cnX6miRShVtAh5c+X89cnJlClDesYN7CdbvM8+zzSYPGKorNvcfjnzwNDQkFGjRiX7sYsWLiRb+jSiSaLwRwqPjOLG/Rds2n+RkfN3UafbHNqPWMmFu+/JU6ImcxetxNvHh4ePn7LZfiu9evWiaNGiou+H8EcQWzUKGmVkqM+gjvVoWLU4s9asYMvmzazfuIkSJUpoOzVBEFI5uzKlOLh5HV7ePhTIm0fteEaGhmxaOJd2A4fy+PlLGtWSdzcSYyMj+nbpKGtME2Njtq9fy9Cx42WNC5AzT75k76CTXDYFbUmfVb5Bvq6uLt1792HyuLGyNEv8Mu6sMSNkfbNfoVRJjQweLMzNqVHJTrbGjp99OfOgV69eZM6cOVmP8/DwYOHC+exYOEDWfARBG+Lj43nt5Yv7Ew/cn77l/vN3vPL0xrZwQezsKtK1bwcqVKiAjY2NKA5ogITYqjGlEcUD4bfIkyMTG2b25Mi5G9SpVYOOnTszY8as7+43/zulz1NAI3E9r17USFx9s3waiQugr6HXvDgN/tGPj43WTFw1dgb4lbjISI3E1TORr//B14wzJG/QoCyFjvovQQWz2Hz/jpgoleKZAPt372bdZntMsnx71VlTPxuOb/1/fdIP6dJiwAic3gV9996gKHVz/v6MsZzmKm4lpurjfqDBP41Zs2IFWbP9uigh6RkkO27xYsWUyuNZ3C9mVKj4N7Zq8Z9fvbcpWJjJnTuTpXBh1Z7gR3E/zTwwMDBgzJgxyX7ckMEDafdPRbJlSitrPoLwOwQEhXH/qQf3nnhw/4U395+8xtLSkvLly2NXpxXDJ9tRokQJsfRA+GuJ4oHw2+jo6NC8bjmqlS/Mok0nKV2qBAcOHqZIkSLaTk0QBCGRlZUVwwamrqumadNn0HYKWqOvr0/XHj1la5aY2gwYMIDsMi7Z+Cx79uwJMzu6dydbtmzJeoyDgwO3brgxftlQ2fMRBLnFxMTy5NU73J94cP+5N+5PPfALCKZ06ZLY2VVmTPuKlC9fngwZ/t6/r4LwNVE8EH47awszZg1rw9HzN6hWtQoLFi6ke3d5tpYSBEGQg4FB8q9QC9rXtEVLAgMDtJ2GVmiicAAJPTVy5crF2LFjk3X+5yaJo3v+I5okCineqp3n2HLoErltclLBzo6m7Roxp0IFChYsKFvjVUH4E4nigaA1TWqVwTZfNkbPnMqlixdYvWYdpjJuPScIgiD8HQwNDcmYMZO20/jjTJ06lZw5k9c0csH8eeTKbEXVsvIunxAETTjrcp8rV5wpVaqUtlMRfubTVo1CyiE6UAhalSdHJrYt6Efw+xeUKV2Shw8fajslQRAEQRCA9u3bJ+u8169fs2TxYkb2aKjhjARBfdExsbx99wFbW7EbiCAoSxQPBK0zMTJkxpDWtK9fkiqVK7Fli722UxIEQRCEv15yu8cPGTSQ9o0rkjWjtYYzEgT1vfHyJUf2rKLpoSCoQCxbEFKMZnXKYZsvO6OnTOTSxYusXLUaExN5u3ILgiAIgiCfEydOcO/OTSYtG6LtVAQhWV54vKewrVhek2qIZQspiph5IKQo+Wwys2Nhfz6+fUzZMqV4/PixtlP6LQKDQzQS18PTkyPHT2gk9oWLlzQSN1JDWxnGxX1/uzm5+Pr5aSx2VJRq2w4KgiBoUmRkJIMG9md0r0YYGogmiULq8MLzA0WKFtd2GoKQKonigZDimBgbMmtoa1rXLkblinZs375d2ykBsH//fm7fvq2R2IOnzOLMZWfZ41qYm9OtT1/GTpos+wB06syZ9BkwkMCg7+8tr6r/Vq1mg/1WFAqFrHGfPH/B6GkzCQkNlTXuZ50GDuOjv79GYvcbNpzY2FiNxBYEQVDVvLlzyJMtLZVLF9R2KoKQbK+9/ChSpKi20xCEVEkUD4QUSZIkWtYrz9rp3Zk8YTQ9e3QnIiJCa/mEhoZib29P+fLlmTdvnqxXsbfsP8wLD096jp7EtKUriY6JkSWut48PrTt2BmDdps00bN6CNx4essQeP2kyvr6+7Nm3j0rVqnPxkqMscd0fPODS5SuMmTSZtp278v7DB1niAhxxOMO2vQeo3rQ1F6+4yBb36YuXzFi8DPdHj+k7eoJsg3yFQsGU2XO44OjE0ZOnmL1wkSxxAZ48fcruvfsSbweHyDPzJT4+np279xAj08/wl169esU9d3fZ48bFxeF0Rf7CHcCzFy956+Ule9yYmBgeu9+VPS5AcIA/ty5fkjVmfHy87MVASPgdiY+Plz3ul/E1PWNJTs+ePdPI796PvHz5kv+WLmVk9wa/7TkFQQ4vPN9TuLBYtpAqSBKSrk6q+fgb/B2fpZBq5c+VhZ0LB+D90p1yZUvz9OlTreTx8OFDzp07R0xMDGPHjqVmzZq8efNG7bgKhYLN+w7i+c4bgI17DtCyz2A8Pt1Wx75Dh7lx61bi7dt371G9fkNOnj6tVtzo6Gi2bt+Ot48PAF7v3tG8TRtGjR1HWFiYWrEvXb6C242bAJy/5EiVOvU5efqMWjEBwiMi2HngEADvfHxo32cAwyZOJUjN5SIKhYJhk6ezZmvC7BjXGzeZ9d8KtfMF2HvwECvXradNl64ALFuzllNnz6kd19vHh8bNW9B34EC27tgBQK9+/dT+3gH0HTSYAUOHMW3mbCBh0CjH4PnchQtUqlaNXn37yjqDJjIykqp169Gs7b/cunNHtrgAG7ZspXLdekyfN1/WuAF+HxnYthlTh/Qj4ONHAFkG5uGhIVy7cIZxnVqycf50WWe6nDywh66N63L22GFZ4sVER7Pyv6V8+PCeUrYFKVvMluCgIE4cPUJ0dLQszzFzyTKylyzH3OUrZYn32bu3ntQsU5wGlcvLGjc+Pp6aNWuSKVMm7sj8s/wjgwcNoFOzymTJIJokCqlHTEwsnl4fKFCggLZTEYRUSRQPhBTP1MSIOSPa0ry6LRUrlGfXrl2/PYcXL14kue3k5ESxYsXY8WnwpaqY2FgK5Mmd5NjdR09o2KU3Jy86qRW7ScMGWFpaJDkWFBREx+49mTB1mspvsvX19WnerOk3x9dv2kSVmrVwu35dpbgA1pZWSW77+fvTqWdvho4eS6gag9vgkBBMv2q+ufvQEao3bcnZS6p/naOio8mZPVuSY+u27eTQSQeVY35Wu0Z1ypQsmeTYgOEjeK3m7JFMGTPStEljAAYPG87MuXM5feYsa9avVysuQJN//gFg5dq1HDtxkqPHT7B6nfpxS5csSRozM549e8bipUsBuOLsrPag2cjIiKKFE7bqminzIL9c6dLExcVx8Ogx7j98JFtcS+u0pM2QkZjoaI7u3k5MTAyHtm+RJfbZA7uJjooiOjIS7zevZIkJkClrdoIC/MlXSJ5t0fQNDLh53Y0W/yRc8Y6JiaFlk384uH8fBgYGsjyHnq4u8fHxRGigB0tcXBzxcfLOmLh+/Tpv374lKirqtwyKjh49ygP3u3RuVlXjzyUIcnrr40+GDOnETguCoCJRPBBSBUmSaN2gAqumdmPc6OH06d1LY431vqddu3ZERkYSFxdHeHg4AQEBPHnyhEqVKql1hc7znTd2JYszuk8P+nb8lw7NGtO0Tk3KFCvCht372XH4uEoDpODgYPoOHkpg4Pf7Eaxev4F/WrRS6aqws6srH3x9qVWjBlWrVKainR3lypShVMkSmJqaMnz0GHbt2at03nFxcWTNmoUdmzawafVKVi1dzJJ5c5gzfSp5c+fm7PkLKg8WDQwM2LFmBVdOHE7ysX/zenLb5FRpanJsbCyzli7H4cKlb+4bOX0WD56oPkvm/Qdfxkyeyo2vemwEh4TQrd8AIiNVv/ouSRLzZ8+mV4/uACxcvASA/5avwE/Npo+NGjZgYN8+AAwYMpSZc+exd/8BtadSW1lZMXfOHACWLlvGjZs36dOvH4+fPFErLsDYkcPR09PjktNlLjsnLGeRY+p3sSK2NGvUCIBZCxcCCbNJ1C14SJJEyy4J37vTh/ezdsFszh8/rFZMABOzNAybu5Qc+RIGnm+eqf+1/SxvwUIUsC2KTd58ssUsZ2eXZLaM74cP1GvQULb4xkZGAIRH/L7XGVWcOnWK+Ph4Dhw4AECjRo0wNjbW6HNGREQweNAAxvRshIG+2LRLSF2yZrImKjKCBw8eaDsVQUiVxF99IVUplCcruxYPZNqKg5QvV4YDBw+TN2/e3/b8Ojo6GBsby/bmLE/OHOTJmUOWWF8yNzfH4cghYmNjCQkJITAoiKDgYIKCghP+DQ4mKCiIoydO0q1TR6U+n8oVK1K5YkXZc9bV1aV6lcqyxwWwtrTE2tJS1ph6enrMGDOSMQP7cf6yM8fPXeDCFWciI6OIjIyi5/DRnNy5BSsLi18H+0qG9OkYNqAfObNn49Cx43i8fZt4n/uDB4ydOpWlc+eonLufnx+FChTE1MSEsPBwIKEwsWjpf8yeMV3luAATx43l/CVHHj1+nNic8sy5c/zTQL110c2aNGHvvn2cPnOGJs2bExkZybnz5ylUUL1GbTY5c9KlQ3s2btnK9DlzOX30MENHjWbFksXJ3uP+R8aNGMaxU6c4e+Eim7ZtZ9KMmZQrUxqbHKr9zisUCu7fvomOpINl2rQE+vlx8eQxdHR0iIqKxNDQSK18TdOYM3zufyweM5g3Tx9RuX4jteJ9Zmmdlg59+ssS67PydhVZs2J54m0dHR1q1akrW3yTT38Tw7XYayc51q1bx8GDB7lw4QIALVu21Phzzp0zmwI5M1CptJj2LaQ+Bvp6tGlQgYUL5rPZXp5ZW4JmSWKrxhRFzDwQUh0zEyPmj2pHo8oFsCtfjr1792o7pRRLT08PKysrctnYUKJYMapVqUyTfxrSqd2/DOzbh/69e2n8KtWfzszUlKb167J+4VzcL55h7YI5NKlXh4/+AQwYO1GlWQ2SJGFbqBCTxozm5mVHHA4eoE+3rmTMkAGA7bv3sGvffpVzdrtxg9Xr1iUWDj7bsHmzWk01Hc6cJZ9tUR59tcXqjt17VI4JCX015i9cmDiY/zzr6POASV0jhgzG2MiIm7dv07lnL3bt25/Y00MduW1s+LdlCwBGT5pMVHQ09+7fVzmeJEmEh4Yye/RQAr+YJRIfH4+3TM1QTc3NGTbvP9kbERYrXU7WeEWKFsPExDTxdply5UibLp1s8U2MEwoxESl85kFMTAwbNmzg5cuXAFy8eJEOHToQGBioked78eIFy5cvZ2R3+WZ5CMLv1qZBBQ4fPoSPDH/nBeFvI4oHQqokSRL//lORFZO7MGr4YPr3k7eRmiCowsTYmEZ1arF63izuXTxNlzatcH+k3vRvSZIoU6oks6ZM5p6rM4d27qBzu3bMWbxE5bX0DevXx9XJkf8WLSRTxoyJx6Ojo5mtxtr/+nXrsGvbVrJkyZzk+Jlz5/ng66ty3CyZM2NmZsbpM0mbZ7peu0aomltvPnj4iLv33ClXpgxAYoPOe+6qD/IBXK65kaOQLTu+2NUC4N599abKlq9anX5jJn5z/K2MPQrMzC1o3XugbPE0QV9fn9Jlyyberltf3sGscWLxIGXPPPi6d83q1aupVq0aljLPtIKEmS8D+/ejS/MqZEovf3xB+F0szU2pV6UEK5Yv03YqgpDqiOKBkKoVzpuNXYsH8uLBdSqUL/tNY0NB0BZjIyPq1ahGiSLybQelq6tLlYp2LJ4zi5tOlzBUozmcnp4eXTp14ta1q0yeMB7zNGkA2Lt/P+5qDHAr2VXgyvlzNP7n/4O5uLg49u4/oHJMSZIY0K8f9hs3YmT0/6n50dHROLuot+2mmZkpI8dPwPHKlSTH76oxQwCgYvlybFu/9pvv0V01ixIANf9pQqf+g5Mce/vmtdpxv2T0xVX9lKq8XcLyKR0dHWrKtGQhNCyM+Pj4xBlZ4RERKBQK7qnZ9PJHvS4UCoVafTC+Lh78888/9OrVS+V4P3P06FGePnlApyaaWV4mCL9Th8YVWbt2DeFfzcATUhoJSUc31Xz8DUTxQEj10pgas2B0O+pXyEuFcmXZv1/16dyCkFro6+uTL28eteOYmJgwfMgQ7lx3Y0Dfvujr6zN91iy1YlpZWbFlw3qWLlyQeAV3x+49ajcLbNyoEUcPHSLdF9PTz50/r1bMnDlycGTvbjJnypTk+D13d7XiAlSvUoUta9ck2QHg3oMHsmyt2Kx9Z5q275R4++2rl2rHTG3K29kBULZ8Bayt5dku8KO/PzVatOG8U0Ix6d3797Tu2Qf7Pft+8cifCwkOZtrYUbx6/hwABQpuXXdj1qRxasX9srln2rRp2bBhg9q9Or4nPDycwYMGMrZXI/RFk0ThD2CTNT3FCuTE3t5e26kIQqoiigfCH0GSJNo3rsSyiZ0YPmQggwYOEMsYBEEJ1tbWzJo+jRuuLqRLlw5nV1e14kmSRJeOHbh4+jRFbAvz+MkTbt+5q3aeZUqX5szJk+TLl9C5/9z582oPxnPnysWRvXvIlDFD4rE799QvHkDClpubV69EX18fSNh+9J23tyyxO/UbTM2GCdtuyrlsIbXImy8/1mnTyrrLgk327JiamLDz4GEAfD744nL9JpXKllErrrmFBb7vfRg/bBAAUZGRDOvdHRMTU7UG+1/OPFi7di2ZviqCyWXO7FkUzp2RCiXyayS+IGhDxyZ2LF60QPYeL4LwJxPFA+GPUiR/DnYtHsCjOy5UqliBV6/+vjfUgqCOHNmzs3r5MkoUKyZLvAL583H2xHH69uqpduPEz2xsbDh94gSVK1XijYcHL2X4Pc+bJzeH9+4h/adZDd4+Pmr1afhSvVq12LhyBXp6CVds5Vi6AAkFmr6jJ1C2SjW8PT2IU2Pb2NRIkiQqVq5Czdp1ZI3brEG9b45VVLN4AFCxavVkHVPG5+JBp06dNLbTgo+PDytWLGd4N/V2TBGElKa0bW5MDHQ4fvy4tlMRhFRDzD0T/jjmZiYsHtuBHUevUK5sadat30jz5s1/aw66hprZwSA6OEAjcQEMzK00FltTYjT09dDU9w8g7K1m+nJYFiwpazyzNOaJ/7//7qPa8ToOGIb7ndvc9/ZPvNL6PlS9ZnQDZy5Ed+4M1uzeT8NW/ya5z9rYUPmAZumYt2o9w3p3JygwgCOXr1GuYtL13To6ql0lzlm2ElPmLmDqmJGcu3GHnOWSxjXQVbGWr2vAmOlzmDJsIH4+78iW0+abUwqlV3670OQIi4759Ukq8gxMXiPMJh27ESLpE5LM82fc8/vlORFpy4AkwacZLWYZszH7pS68/Pljr7/4+c9GTKhtktuSvhErXuVgpefPZ6K8KWH7w/uio6PJnj07y5cv/+E56lq0aCENqpYkUzpLjT2HIGiDJEl0bGzHgnlzaNKkibbTEb5HAlR9fRQ0Qnw3hD+SJEl0bFqFpeM7MmhAH4YOHfxNYylBEH6voiVKyroeW19fn0ETp5ErX0HZYubKk5dFa9ZjbmHB08fqNcn7WtVadZg0Zz4vnz2VNa6hkRGTFizRyFr3lC5ztuyyxzS2Ske6fEUTb6cvUFyWuHppc6Br8f9lBUY2pZH0VG96ChAbG4u9vT0WFpopEPn7+7Nxw3q6Nq+ikfiCoG11Khfj1csX3LhxQ9upCEKqIIoHwh+tWIGc7F48iHtujlSuZMebN2+0nZIgCDKSJIlCxUvIGjNPvgIsXLUeHy8vWeMC1KhTj1YdOsoe19QsDVlz5JQ97t8qe5nqif9Pn1+e4oEkSRjnrZB42zhPebVjDhgwgJo1a6od50eW/beU6uVtyZwh9c1ME4Tk0NPVpXWDcqxZvVLbqQhCqiCKB8IfzyKNCUvHd6Ja8WyULV2Ko0ePajslQRBSuHwFC9G1b3+NxC5ZppxG4gryyVq6SuK2W3LNPAAwyWOX+H/j3OoXDwYPHvzrk1QUEhLCihXL6daiqsaeQxBSghrlbHFwcJBlJxxBXpLYqjHFEcUD4a8gSRKdm1dl8dj29Ovdgy1b7LWdkiAIKVy69Bl+fZLwRzI0syBD4VKYZ8mJkYz9YAyzF0UyMMEgc0F0zdTfXlJHR3Nv41avXkXZonmwyZpeY88hCCmBTbb0EB/H48ePtZ2KIKR4ongg/FWKF7Jh+aQuDB82DHcZ9nEXBEEQ/kzZy1Qnff4SssaUdPUxylUa4zwVfn2yFkVGRrJ44UJ6tKym7VQEQeMkScKuZH4cHBy0nYogpHiieCD8dfLZZGZEtwa0aN6U4OBgbacjCIIgpEBZSlQkU9Gyssc1zmOXpPdBSrRx40YK5c1KgdxZtJ2KIPwWFYrn5tTJY9pOQ/iaJJYtpDSieCD8lRrXLE2pAlno1rWzWOMmCIIgfEPf2JSMtvIXD0zyVUI/fW7Z48olJiaGeXNn06Ol6HUg/D0qlMiHi+s1IiMjtZ2KIKRoongg/LVG9WzE04f3WLpksbZT4d37D8THx8seNzAomK179skeFyA4JASXq1dljxsfH09QUJDscQGNFoqCQkI0FjtKbDMqCFqhie0vdQxNUvS2mtu3bydrBkuKFxS7dwh/D3MzE/LnysaVK1e0nYogpGiieCD8tQwN9Jk/6l9mz5qJi4vLL88PCgrC3t5eI7mcueTE9CXLZR/cGhsbMXrqDCbMmktsbKxscePj44mKiqJ1h05ccrosW1wA/4AAOvXsTVRUlKxxAXYeOYGP70fZ4wKMmTkPL28f2eMqFAqGz5yvkeKSkLrFxsZoOwWtefPiOaFi2VkScrx+xMXFMWf2THq0rCJDRoKQutgVz8WpUye1nYYgpGiieCD81bJlSsuUgS1o3aolHz58+OF5QUFBDB8+nN69e+Po6ChrDss22rPv+EnW79jN8k1bZIv72sOTSg2aIEkSG7fvpH3v/gQGyfNme9DwEQwfM5aIyEjade3G2fMXZIn74NEjBgwbgfPVawwYNkK2AbNCoWDpxq3sO+HA+PlLZC3S3H34iInzFnHusjNTFy+TLW5sbBzDZ87n0OlzHL/gyI4jJ2SLfebsWYaPGkVMTMLgU6FQECzDzInIiAgWz5lJgL+f2rG+dvuaK64Xz8keNyQ4iKMH9speuFMoFLi5OPPy+TNZ4wIE+H3k3s3rbF7xn6xxY2NieOf1VtacFQoF504e5/WL57LFBDh99BAHtm/h6N5dxMlYGP0s2MeDsI/eKGT4GxQT8I7Y4ITXl8g3twh03Kj2z5ufnx8BAQGJt69fv46dnR0PHz5UK+6BAwcwMZAoVyyvWnEEITUyNjTg+dMn2k5D+Iqko5NqPv4Gf8dnKQg/Ua1cYRpWLUK7f9sQFxf33XMuXLjApk2biImJoXnz5jx9+lSW5w4LD2ft9l3cefAIgHkr17J1/0FZYh88fpK3794lvkl1cr1Kw7YdeP7qlVpx33p5sefAQU44nAYgKiqKjj16cvL0abVzXrfJnnMXLwFw6Nhxps6ao3ZMgNsPHrF4gz237j/k3BVXDp85L0vcqOhoeo4cx+Y9+4mIjOTk+YtccpFnKcf63fs44HCWYTPnAzBvzQbef1R/1kRgUBA9+/Zl4+bNNG/dGn9/f16+fMm4CRPUjj1n2iQO7tnFqIH9CA8PAyA8LEztuHevX2PmiEGsmD0NL4/Xasf7LDoqiq6tmrFk9gyuu/569pEyNq1eyagBfdi4arlsMePi4vjg4830UUOZOKgvV86fka3o8dHXl/HDh9CxeWMO7N4pS0wAP98PLJg6kWE9u+DieFGWmLGxMTieOcX+bZtZv2QBe7dskiXul9z3r8dhQhdeOh1XO1ag4wYinrsQ8eIaH/ZPJPjabsLuq/73MjIykmbNmvHs2TNCQkIYPHgw5cuX59q1a4wdO1bluAqFgpnTp9GjZdUUvaxCEDThhYcPmw9dZt6CRdpORRBSNFE8EASgf/s6hAW+Z8qUyd/cFxQUxMqVKxNvBwQE0KhRI/z9/dV+3mevXpPOKuke4uPnLOToGfWvsFarWAFjI6Mkx16+eUPDth255Kz6QClb1qxMHDM6ybGYmBi69u7LoaOqdyp+6+XFR7+kV6xXrlvP2o3qDQw8vX1Yt3Nvkv3QpyxewQc/9b5/sbGxXL52HUtz8yTHJ8xbRKQMSy7+qVENa0uLxNshYeFMWbpK7biWFhasWbECM1NTLl+5Qs169di6Ywfbd+7k3Hn1iio9+g7AwtKSxw8fMHHEUGJiotmyYS1PHql3NbRIqTLYlixNZEQECyeNJSoqEo+Xz7l7/ZpacQ0MDalZvwEAG1b+J+vsg1r1GyBJElcuXuCFTFeydHR0cDi0n+ePHqJQKPD/+BHvt56yxE6XPj1p06cnOjo6ye+Kup4+fACAoaER2XLayBLzlqsLwYGBibcvnjqO/aplPyz+qiLY+w0A5pnVW/cf6XmPiKeXCb5+AN9DUyEuBuO8dpgWqqlSvPj4eLp06cKVK1fYsmULhQoVYvnyhCVv7du3Z8OGDSrnevLkSWKiwqhWrrDKMQQhNYqKjmHc4r3MmzefggULajsdQUjRRPFAEAA9XV3mDm/LxvVrOXky6Xo3CwsLTp8+zdWrV5k8eTKlS5fm2bNntGjRgmg1GtnFx8fz2vMtZUsUwyZ7tsTjCoWCwROn4eiq+sDo/QdflqxZT5ZMGb8pIASHhNC+d382bNuh0mBpo/0WZsyd983x2NhYeg0YyN4Dqs2ciIyKorJdBWrXqI6JsXHi8QnTZnD0hOprELNlykivdq1pVKsaeroJ2+gEBgczccFStQaLHwMCuOjsyovXb5Icf+35ljXb1Lty63jtOg269cU/MGnjyFOXLnPmsvpXyBs2aMDpkyfJni0br169YumyhOUWg4cNI0iNdeQ5bHIxf/lqjIyMcXN1YfbkiZw8cpjtm1Qf0ADo6uoybMosLKys8XjxnE1LF7F38wYuOah/VbhDt54Ym5jw7PEjnM6fVTveZza581Ctdl0Atm9cn3hcnZ85SZLo3G8Q3QYOTTx2//YtleN9rf/QEaRNlw5dXfm2m3ry8AFW1mmZv2YDOWxyyRLzwqmkS3hs8uajVceusuUdGxVB+MeE/iVpMudQOY5CEU/ghTUAxAX5QHwsJgWqka7pZCQ9A5Vijh07lr179wKwatUqvLy8yJUrFw4ODuzYsYMMGTKomKuCGdOm0L1FFTHrQPjrLLF3wLZYKXr07KntVISvSRLo6qaej7+A9LM3Mrb5sit2Lx36+7IRBC27ef8lYxbuwe3GTXLm/PEVJx8fH06dOkW6dOlo3LjxN/e/u6381HXvDx+4evM2rjdvc/XWbbzf+7J79TJKFyuSeI6usanScRUKBUHBIXi/9+Gdz3u8fd7z7v17vH0+ULNqJRrXSxjgGJhb/SLS/33w9eX+w4e433+A+4MHuD94yPMXL1AoFEiSxJL58+jcvl3C88crfzUwKiqK67duccnpCpcuX+HJ06fs3bYFu/LlkpwX9valUnF9fD+y4/Bxdh4+hq9/ACtmTKJJ7RrfnKdraPydR3/fh49+bNi5hy37DhAaFg6AkaEBF/fvIkfWb/dIjw0PTVbc9x/92HLgCNsPH0+yk0PmDOk5t30DZiYmSc63LFgy2Tl/dsnJiZZt2iRpptmlUyeWLVmS5Dz3t++Viut62YmxQwcmXgmWJIkdh4+T46srz+9DI5SKe/f6VWYMH5Q4ADc0MmLj0dMYm3z7e2FtbJjsuJvXrGTr+jXksMnFpj0HCQ8PIzwsnIyZM39zro5O8gdWz588pse/rZAkiS0HjhAUGEhUZARl7Sp9c25wpHKFyDNHD7Ny3kyq1W3A8CkzfnqulRJfC8fzZ7l3+xaDRo755blh0b9u2Lhg6kTadu2hdOEgOu77vQZCg4Pp1KgOsTEx6Onr03PwcBq2bJPsAa/9vR8v/fn89yvgzVMuzB6IgZkFjRclf7ea6y+S5hB6/yz+J+cnOaZrlhaTQjWwsOuAjpFZsuK+mVcbSCgWDBgwIMl9xYoVw9nZGTOz5MX6kUuXLtG9SwcOLh+Krq64riT8PS5de8CCzWe4534fS0tLbafzU5Ik3VQoFGW0ncfvVDhrBsX2vi21nUaylZ685o//HonigSB8ZctBRxzvvuWKsyuGhsl/0/0lVYoHX/vw0Y/HL15QpVzZxDfGqhQPkkuZ4sH3hIWH8/DRY9zv3+f+w0e0bdWS8mXLqFQ8+FpAQCDuDx9SpaJdkkGCssWDz6Kiozl50YmjZy+wYPwo0lkn/dyVKR58FhgcjP3eA2zYuYeAwCDqVK2M/dIF35yX3OLBZ2HhEew9eZqNew7g+Wk3h26tmjF1aNJBhLLFA/utWxkxevR3d+E4fOAANapVS7ytTPHgofs9Ll+8wLFDBwgM+P/SkEbNWzJ2yvQk5ypTPLh7/RqP7t3h7JGDBH7RkHHghKnUaNDom/OVKR6EhoTQoWkDgoOCGD1lOlevXKZStRrU/efbwqAyxQOA8UMH4ex4kYpVq/P00UNad+zEv527fXOessUDAJdL59myajlr9x7+6XnKFA8A7t2+RbGSpX55XnKKB/4fP2KdLp1Szw8/Lh6cOrSfVfNnkzVHTkbPmEvu/AWUivuz4oHPfTciAv3Q0dXjhv0C0uUvRrURCxOLCr/yZfEgPjoC7w3diQtN+nxGNqWwqNwFwyzJXxrwZl5tjh07RrNmzb7bRNbOzo5Dhw6RMWPGZMf8Wq0a1ahePBPN6pT79cmC8If44BdEu+ErOHTkGJUqfVvUTWlE8SDl+xuKB6K8LAhf6dy8KhZGCoYNHaLVPDKkS0vV8uVSzRRSUxMTypYuRfcunVk8bw7ly8r3t9PKypKqlSrK9rUwNDCgeb3abF44GzNTeQoylubmDO3ZDbcTh5g2cijuj59wxlH9bSxNTYzp1qoZjrvtWTVjEiULF8T+wBHuPHysVtyunTvjfvs2o0aMIN1Xg7tBQ4YQouLuC9bp0vHm9askhQMAh2NH+PBe9a0sM2bJyu2rzkkKBwCODurvQmGWJg3tuyZMV102bw5O58/y7LF6fRoC/P24etmJQkWLAuDidImPvh94/fKF2vl+VrF6LfqNHEdQYMCvT1ZCcgoHyaVK4eBnLp46QY36/7Bk8w6lCwe/Ymhmya1tS3A/sA4ARVwcN7cu4tm5A0rHCrm+P0nhwDBbUTK0W0SGNvOUKhxAwk4K//77b5LCQe7cuenXrx+HDx/GwcFBrcKBm5sbTx4/4p/q8n3fBSGli4uLZ+J/+xk4aHCqKBwIQkohigeC8BVJkpg2qCUnjx9h5075uo4LKZORoWprj3/ExNiYnu3b4nJ0f5LeDerS1dXlnxpVObR2GftWLuaiq5vaDeKyZM7MxHHjeHj3LmtXraJkiRIAeL59y5Tp03/+4B/IlDkLsxf/x/xlq8ic9f+9PGJjY9mzTfWtSDNlzcbMVRtp8m/HJMfv37qBr4/qRYkAfz8mDBvEpXMJ3e8jIxNmQzx7ol5xJo25BccPH2DDiqTbd755qdpsmR8pUa48FpbqzRpKLfw/+lK/WUuGT5mB8VfLduRgki4TAFEhCb1G/F48wNPtEjnK11IqTmzIR4LdEvoSGGQpRIY288jQbhFG2YspnVNsoDeNGjVCR0eHJk2asHLlSp49e8aLFy9YtWoVTZs2xfyrpq3KmjFtCp2bVkJfX0+tOIKQmtgfckTP2JIJEydpOxXhFyQd3VTz8TcQxQNB+A5zM2MWjG7H4IED1N43W/g7GRoYULmc/DPXJEmibLEiDOvRWbYGcYaGhvzbpg0Xz57lnIMDrVu2ZNuOHThdVn3mRMWq1dh+4AjdevdDX18fgKMH9hH0RZd8Zenr69Nl4FDGz19KGouEnSgUCgWXz55SOaaVdVpad+jMq+fPkxx//vjxd6eIJ5eenh6TZs+nZNmk08Bfv3wh664OfxPrdOmp2fDbJSpyMTBNg95XS5ZyVf0HIyWXdAVd3oyeVTbSt5xJxg7/YWRTSqVZUwqFgiivB+zduxc/Pz+OHDlC//79yZs3r9KxfsTd3R03t2s0r1tetpiCkNLdffyGncdc2blrj6wNYgXhbyCKB4LwAwVzZ2Vwp7q0aN6U0FDl1qkLQmokSRJly5Rhw9q1uN+6hde7d2oNdA2NjOjRfyBb9x+hnF1FIiIi2L9rh9p5lq5YmUX2u7AtkTDN+pLDCbXyLFGmLLOWLMPgix4nYWGheHu9VStPQ0NDZi1eRoFC/5+mHh4Whu8H5RpQCr+HJEmJsw8AdPT0yV+vtVIx4iNDMM5XkUxdVmGcp7xaS60kScLUtjbVqlXDwEDeGVKfbd+2laa1SmNkqK+R+IKQ0oSERTB+8V7WrFtP9uzZtZ2OIKQ6onggCD/RvG45CudKR88e3cTVQuGvkilTJtq1bStLn4nsOXOyaNU6ZixYjNPFc4SHh6kdM236DEz5bzVtuvXC29ODF2r2KChd3o4ZC5cmzpIAePb4kbppYmpmxvwVa8j+xU4Tb17Ju3RBkI9p2v/3DshVpSHGFmmVeryOURpM8lVKNb1qXF2dKVVYni00BSGlUygUzF5zlAb/NKZFixbaTkdIDknS+lIEsWwhKbHATRB+YVzvpnQes5pVq1YyYMDAZD0mS8kKGsnlrZuTRuICSJbpNRLXyNBII3EBjNJ/uxWiHHRU3IM9OQytVW9s9jPegcEaiQvwLjhcljgFyldhctFSvHj3gbQZMhIU9etu/b9Sp303ctgW59pVV6xt8iUe9w2PUjqWRYFi9Jo0i7XTxxMXG8vV23fIXMouyTnpTVTYgcXQhAmLVjChfw/8PrznwaMn5CqatDld/vSWysdNplAZvs7f4/xW3kaNX4qO00yxVvcXg3qzTzMPdPT0KVS/7S/P/9LnLRVTi7i4OG7dvsucgQ20nYog/BZHL9zgpXcQ+04s+/XJgiB8l5h5IAi/YGSoz8LR7ZkyeRJubm7aTkcQUjVjE1PSZpC3gFKgeClqt/hXlljFKlSi14QZ6Ojq4vn8qSwxAdJnysTkxStIY2HB29di5kFKZZI2oXiQq1I9TKw0U1BNKR48eEDGtFaYm8nffFIQUprXXr4ssXdgz979GMvYzFgQ/jaieCAIyZAjSzom9m1Kq5Yt8PPz+/UDBEH4rfRlXBNeolJVeoybxttX8jY3zGaTiwkL/uPje9HzIKUyS5cJSUeXgvXlKUalZNeuXaNoAbHmW/jzxcTEMm7RHqbPmEnRT9vnCqmIrk7q+fgLiGULgpBMtSoW5c4TTzq0/5eTp06jo/N3/JEQhL9R6ao1iI+LJdjfD4u06WSLm69wEdr37i9bPEFepukyYVOxbpLeB38qF+fL2ObNrO00BEHjlm0/Q+78henff4C2UxGEVE+MfgRBCYM71eOjtwezZs7QdiqCIGhY2Rp1MLdWrmFecuQuUFD2mII8TNNlolCDdtpO47e4dvUqxQrk1HYagqBRzjefcNblIZvtt6aaRqaCkJKJ4oEgKEFfT5d5I9uyYsUyzp49q+10BEHQMPFm8++ib2yKWfo//2p8cHAwbzzfks/mz/9chb+XX0AIU1YcYPuOnaRNK38h+HcSO34JKYVYtiAISsqQ1oLZw9rQqUN7bty6TbZs2bSdkiAIgiAk2/Xr1ymUNwf6en/H1mLC3yc+Pp7Jyw/Qo2dvatSooe101PL+/Xs6dOig7TS0Qvq0VaOQcoiZB4KggnLF8tK2YTlat2pBTIxmtkETBEEQvi8+Lk62WJEhgcRGK7+155fi4+NwcXEhTsa8NOnq1asUEf0OhD/Y9qNXiFIYMm3adG2noraMGTNib2+v7TQEARDFA0FQWfeW1TGUIhk1aoTasV68kLer+5eOnDlPSGiYRmKHh4eLqXTCb3PX9QoeMm6f+LW42FiNxRbkER4UiNPuTVzasV6tOBGBH3l24TAXF47EackYdPX0lY4RHR6G5w1Hrm2ax9ERbVi+fDm6uqnjCpmryxWK5hc7LQh/pofP32J/8DK79+xDX1/53+2USOz0JaQUYtmCIKhIR0eHmUNa027ECipXrkqrVq1UiqNQKLC3t0ehUDBz5kxZcwyPiMD5xk22HTzCtqXzMTYykiVuVFQUhoaGOLu48OTJEwYOkK+DsUKhQJIk3K5fp1zZsrLF/VJYeDimJvLvbR4TE4Oenp5s6+Q/fy3kFh8fT1xcXOKbqoiICNn2vY6Jjk7cNvHSqeNUb9BIlrhRkZGEBAWyce40BkyfR4HipWSJ+5mvtxfXL56jYfsussbVtLi4uBQ/YPX39sI6c1a1YgR+8MHt6B7uXXQgNjqK1uNmE+TrQ4jfR7IVLJLsGE8uOfL21mX8XjxMPF6wwb/4v35MfGxswkdcwod55hykyZh0WVroh3e8c7/Ku7tX8X16D0X8/2caFC5cmP379/8yD0NDQxo3bpzMz1x+CoUCNzc3Brfqp7UcBEFTwiOiGLtoD8tXriJXrlzaTkcWb968oUGDBtpOQ2vEsoWURRQPBEENFmlMWDCqHf369KZYsWLkz59f6Rj9+vXj1KlTeHh4YGZmxtixY2XJ7fGLl7TqMwgzU1O8fN7TZ+xkNiyYhYGaVXiFQkGvPn0wNjYmX968LFy0iHz58lGvbl21c77m5sa0GTPZvsWeMeMnMGnCeGpWr6523MioKIaNm0jLJo2oUKY0w8ZPYv2yJWrHBThz4QI79uxl/fJlHD11ivRp01KtcmW144aEhNC9/0CGDuhPpQrlAQgMCsLSwkLt2Nu22HP0yGHmzl9Ivvz5WbV8GXXr16doseJqxQ0ODGBMz07Ua96aBi3bsmb+TExMTSlXVf31pgfWr+TahTNEhIexdNxweo2bSqkq1dWO+8HrLQc3rETfwJAXD+5Rv21HdGQYjEdFRXL+2GFy5s1PwaLFeXjnFkVLq18Mi4uL4+7t2/j6fiCNuTlvPTxo06692nF9vN8RGRlJVEQENnnyYmhoqHZMgNf373Bu61rsmrTGtnJNpR//4c1Lrh3ZzUPnCyji4xOPH5g/mfi4WHR09Ri549QPv2f+7zx5cu0yT6464fPy+7NWHp/azeNTu785XqxlLwrWa0NcTDRPzu7H49p5gr09fpjr5MmTk/U5ZciQgffv3yfrXE148+YNkgSZ0ltqLQdB0JS5649RrUZt/v33X22nIhsdHR2sra3x9vbWdiqCIIoHgqAu23zZ6duuJi2aN8Xt+k1MlLiiHRkZiZOTEx4eCW9Ix40bh5mZGQMHDlQ7r91HThAUEkpQSCgAF12vMWTKTFbMmKzWlcqHDx9y9uxZ4r94I9+vf39OHD9OoYKqb0EXGxtL3wEDefnqFfX+acTTZ8+YOn0G1atWRUdHvRVW6+y3cuDoMU6dO0/b5s04cvIUwwb0pXCBAmrFDQwKos+QoYSEhDJgxEg++vkRFxcvS/Fg0YqVXLx8Gbdbt9i/dQtZMmdi8qw5bFy5XK24UVFRbNywng/v39OiaROGDBvG06dPOHLkMPsPHSFDhgwqx75w/Ah+H96zc+0KHE8dJz4ujuUzJzN9xQZy5Vf9ax0XF8uj29cJDw0BIDYmmjUzJ9JxyCiqNmyqclyFQsH6WZPw/GIpxOPbNyhcprzKMT/bu3Edh3dupXCJUhQpWRrPVy/VKh7Ex8dz6cJ5DA0N6duzO6ampkRHRzN4mPrLpgBOHj3M7m1b0dPVxa5KVSbNnKN2zLjYWBw2LOODxysu7bEne8GimKdLn6zHej5y5+rhXby4dfW79ycUDnSxzJCJiJAgTC2tvzlHER9PSIAfUeFhP11eZWCaBn0jU3T09NDR1UNHTx8dPT2MzK0A0NU3oEDd1qTNVZB39xJmHYR9/PZNfNmyZTFKxuwuKyurX56jSdeuXaNYARuxk4jwxznpeJv7L95z+8CftRtW9uzZuXz5MtbW3/6dE4TfTRQPBEEGretX4O5jT/r26c2WrduS9aZMoVCwfft23r59m+T4oEGDMDMzo2vXrmrlVL9GVXYfO0FYeETisePnL2Fmasr88aNUfuNoa2vLiePH6dChA/4BAQCEhYXRqXNnTp86pfJ2SHp6euzctpVGzZrz9NkzAO7eu8fBw0do1aK5SjE/69utC04urjg5u7B5x04AVm3YxIoF89SKa2lhwfply+jYqzeHjh1PPO7q5oZduXJqxR4zbCh33d257OJK267dqF+nNkdOnGBo/34UtS2sclxDQ0P2HTzM5AnjcLx0iYXz//81GNS/H9t27MRAxavOTTt0wcLami0rluDl8RpIWG4wb9xwZq+1xzqZA8evhQUHY5E2HT6e/7/qq4iPZ9uSeQQH+PNP+65K/zy/efoIp+OHCQsOSnL8isNxtYoH8fHxxMfHU79FG47v3cnDO7d4eOcW2XPlVjkmJFx5evH8GcuWLAYSfudAvu27SpQqw/pPhamqNWvJEvOGw2E+eLxCV0+fXEVKEh//62aCCoWCh84XuXXegQCft0iSDgpF/Dfn5S5ZjlZjZv10loiko0NO2xLktC1B9Q69CAsKwM31Bj4Pb/D+wU0ig/0ByFGuJqXa/bxgq6unT8ZCpchYqBQl2vQj2NsD73tXeXfvKn4vHqJQxNO+fXuGDh36y89R21xcnLHNk0nbaQiCrN76+DF/w3HOnruAmZmZttORnbaLjoLwmWiYKAgykCSJCf2actXZkQ3rk9fIS5IkmjVrxvbt2xk9ejSVKlXC4NNa8R49erBv3z6V87n/5CnTliwnPCLym/t2Hz3BtKUrVR50PHr8mBkzZyYWDj7z9PSkZ69eREdHqxT3wqVLjJs4iYCv4s6YPVvlmADBISHs3H8Qva8GGQePneCdt4/KcQFCQkOJjo6mYL58SY4vXr5SrbgAxkZGbN+wngplyxAcEsLeg4cAmLtE/eUWGTNmZM36jUycPDXJ8bt3bjN1yiSVfzYkSaJwiVLkzp90Boq/7wfmjxtBVOS3P4/J8fjOTTJkzkpe22KYpjFPct8R+/XsWrkkyUyY5MieJz/ps2Qj0O9jkuN3XZwICQz4waN+TREfz+LJ4+jbqhGxXzRg9Pb0ULshY7eevbEtUjTJMWU/7x8pXKQoenr6mFtYULFKVbXjhQb443xoN+Ubt2LQ6h007DMMywy/HrBKkoRt5Zq0nTiPvit2MGLHSXottafl6BnU6NyXErUbkcO2BL5vXvLU7YpSOZlaWJGzfE3KdxtN4wW7qTt5LcVa9iLM15uIwOQ3I5MkCYssOSlYvy01Ry+hycK9lOs2mlevXqWK3RauujpTrEAObachCLKJiY1j/JK9TJg4iVKl5O2HI2ibBDq6qefjLyBmHgiCTEyMDFk4pj3dxo6mdJkyyXoBS5cuHU2aNKFJkyZAwjKGmzdv4uzszJ49e8iRIwflyyt/FbRIgfyc2rqB4NBQbro/wO3OPa7fdefOg0dERUezcfc+zM1MGd6rm9Kxc+fKxeCBAymQPz+nz5zh3bt3ife5uLoybvx4Fi5YoPSV4MoVK/L8+XPc79/n48f/D+hev36N/dat9O7ZU+lcAdKYmWFibMxd9/tJjsfGxrLWfgvTxo1RKS6At48P67ds4f6jR0mOX7x8mVt37lKqhOo9BKKiorji6krGDBmTHD997jw3b9+hdMkSKscGOHXyBBvWrf3m+MH9+ylYsBCduyr/sxEVGcmpA3vx9vx2XfjLJ49YOXsqQ6fOVnoZSrkadShXo07i7ZCgQHw83+Dt8Rofjzf4eL7hiP06mnTpia5u8l7WdHR1qf9vJwqVKsumudN4/zYh57jYWK6dO03tVqqtl9XV02PolJnMHDmYB7dvJh6PjY3l/TsvsuTIqVJcSJidM2vefFo3a5K4Rez3rsqrwsjYmIKFC5O/UGH09Q3Ujhfs50ufResxtVTvapmevgFps+YgbdZvB7vqbNcoSRKW2XJjmS03Beu1USdFDNNYYGNXh/961VYrzu8QHR2N+/2H2I5uqe1UBEE2a3adI0MWG4YOHabtVAThjyf97AqTbb7sit1Lh/6+bAThD3D68h1W7nbk1u27sk8ze+vmpNbjo6KjcX/89FMx4R4Na1an9T/1ATDIoXyzR4VCwf3793E4fZrTp0/jfj9hgD5r5kx69ugBgJG+cjXKkJBQVqxaxfJVKwkLCwcgffr03HZzI02apFMRo5S4WugfEMCM+YvYse//3dDNTE25ffkiFuZJr2br6CV/8KRQKDh26hSTZs7C693/10HXq12LHRtU304uNjaWbbv3MG/JUj5+tUVTjSpV2LdtS5JjH5W8qB0QEMClixc4f/YsVy47EfnFrABdXV3Wb9pMxUoJvRvuvVNuiyiFQsGbF8+45XKFm65XeP7wfuJshhaduvNvr4Qu70FRMcolrYQ4JWZPREdGsn/dcpyOHwYgUw4bpqzf/t0CWHqT5C3pCA8LZfKgPrx6+iTx2Jg5Cyn3gyaP+ZVoXrdh3Rr+W7QQgEFDh9O7X/+fnh+azK/zisULqF6rDkWKl0jW+aeeqzdz52ei4zSzBezLAM3NDNiTCooH169fp1P71uxbOkjbqQiCLK7dfcak/w5y5547GTNm/PUDUjFJkm4qFIoy2s7jd7LNnlmxa4TyFzO0pfiwOX/890gsWxAEmdWrUoJKJXLTuVMH2aYUy8XQwIAyxYrQv3N7Ni+aS6uG9dSKJ0kSRYsWZdTIkZw7e5ZbN24wd84cLl68iIuLi0ox06QxY9yY0dy5fp1ePbqjp6eHr68vK1evVitXaysrlsyZyZGd28iXJ2H9eWhYGFt27VErriRJNGnYEJdzZxk+cEDi0pPT587j/uDhLx79Y3p6enTr2AG3i+cZ3LdPYlxImNngcs1NrbytrKxo3qIlK1avwfX6TVasXkOzFi2xsLQkLi6OYYMH4fHmjUqxJUnCJm9+WnTuzqzVm1h32IH+46ZQoXotTh3Yg9OZk2rlLjcDIyPaDx5F/2nzSGNhiY/Ha14+vP/rB/6EiakZExcuI1PW/2/15/XmtZqZJujavWfizhhy9TwAaNikGbZq7rghpGxXr16lWL5svz5REFKBgKAwJi87gP3WbX984eCvJUlIurqp5uNvIIoHgqABw7s24O3rpyyYP1/bqfyU3N22s2bNSreuXdmxfTvl1GwYmCFDBhbOm4ebizMtmzdnxapVfPjwQe0c7cqV5cLRw4wdNgRDAwPWb9lKVJTqPRU+MzUxYfzIEVw540DtGtUBWLpqldpxzc3NmTx2DK7nz9KsUaPE43MWLZZt4GhsbEztOnWZO38BzlfdsN+2gybNmjNrxjRCQ0PVjm9hZU31Bo0YPn0uG4+dJUOmLGqv/9eEYnaVmbRuG0XK2eHscEzteJbWaZm0eAWWn5qIvpWpeKCnp8fMufMwMDCQtUCZO28+0YH/D+fqfAXbfFm0nYYgqE2hUDB1xQHadehEvXrqXQgRBCH5RPFAEDRAX1+P+SPbsWjhfBwdHbWdjlbo6cnTUiVP7txsWr+OY4cPc+36dVliGhoaMHxAPxxPHqNAvnwcPKb+QPGz3DY27N68iR0b13PvwQOePn8uS9yc2bOzYcUyTh7YR+kSJXB1c8PJ2VmW2F/S09Ojgp0dEyZNZs36jbJ9HxPj6+tTsFgJdGWOKxdzK2sGzFhA3qLFiVGjUednmbJmY9KiFZiYmeH1+pUMGSbInScvAwYPla3ngfB3uHbtKkXzi2aJQuq356QLgREwZ85cbaciCH8VUTwQBA3JlN6S6YNb0a5tG7y9v90TXFBOyRLFafzPP7LGzG2Tk332GylUQPl+D79Sr1YtLjs4EB0t77r+cqVL43DoAOuW/cfWXXtknbb+NUmSkrVv/Z9GkiQq1v0HfQP1GwcC2OTNx7h5S/B97yPr96tL9x4ULVZCtnjCn83Pzw/fj37kzi6mdwup29NX71i7+yJ79u5PsqRP+DNJOrqp5uNvIIoHgqBBlUoXoFmtkrRp3TLJtm1CyiFJEiWKFv31iSowMjKkSOFCsseVJIkWTRqzYtECtbaxFH6fwsVL0nf0BIIC/GWLqaurS5Vq1WSLJ/zZrl69SpECNujqird+QuoVERnNuMV7WbRkCfm+2iZZEATNS5nzRgXhD9K7bS0GztjC+HHjmL9ggVqxspVTf//1Hwn2U66jfnK99Q/SSFwAAz3NXBW3NDbWSFwAv5Aw+YJ9+vxDYiEuXnMzELJbmv36JBVkVGOrvV95HaB+r4bv8Q2PUvmxNqXKE61QfDeG75v36qT1U7oa6mNQNUdajcQFKJVZ3p1qPvsQobkdPlK68+fPUapQdm2nIQgq83j3kbGL9lC5Wk06d+6i7XQE4a8kys+CoGG6ujrMHtaGHdvsOXLkiLbTEQRBi0RDQkFbzp52oELxvNpOQxBUctLxNp3HrKb3gKFs3GQv/pYKgpaImQeC8BtYWZgyb1Q7evboRtGiN8idO7e2UxIEQRD+Eu/fv8fD0xPb/GLmgZC6hEdGsWDDCe48fce58xcpWbKktlMSfidJAh1xrTslEd8NQfhNihfMSY9W1WjRvCmRkZHaTkcQBEH4S5w/f56yxfKj95fsQy78GZ699qbjyFUYWuXg9p17onAgCCmAKB4Iwm/UvlEl0prpsmbNGm2nIgiCIPwlzpw+RfmiNtpOQxCSRaFQsN/hKr0mbWTClBns2LmLNGnSaDstQRAQyxYE4beSJIk6FW25dPE8Q4cO1XY6giAIwh9OoVBw9uxZVk8RDeaElC8kLIIZqw7z9mM4zi6uFCxYUNsppQh/84xVScyYSlHEzANB+M1KFrbB1dVV1v3eBUEQBOF7njx5Aop4bLKm13YqgvBT7k88+HfYCmwKlsbt+k1ROPiCkZFmdpcSBGWJ4oEg/GaZ01uhI8HLly+1nYpaYmNjtZ2CoCZNFbBiY2OJj4/XSGxRdBM+09TPmBxiYmJ4/15z228q4+zZs5Qvnld0pxdSrPj4eLYecmLI7G0s/m8Fq9esxViDWyYLgrZIklRfkqQnkiQ9lyRp7HfulyRJWvbp/nuSJJX61WMlSbKWJOmsJEnPPv1r9el4WkmSLkqSFCpJ0gq5PgdRPBCE30ySJEoWzoWzs7O2U1HLqdOn2bl7t8bip8ZBYkyMZvaQf/L4kUbibli3lrt3bsseV6GIZ1Cvbjx++ED22BtWLuPVi+eyx7171YXQ4CDZ4wLEREdrJG5qEBcXxy23q7LH/ejnx6jxE2SNGRkZyZxZMwkNDVXp8fHx8Vy6dIm+ffuSJUsWPn78KGt+qjrjcJLyxcQOP0LK5B8UyuCZW3G658X1G7do1aqVtlNKkdzc3LSdgqAmSZJ0gZVAA6Aw0E6SpMJfndYAyPfpozewOhmPHQucVygU+YDzn24DRAKTgJFyfh6ieCAIWlAsf1acHC9q/Hn8/f0B8PX15eJF+Z7Pw9OTwMBA+g8aJHsBIeBTzkcOHeT1q1eyxf28XlChUHDtmryDmdCwMOLi4hgxUta/z/h4e6NQKJg/dy7HjhyWLa639ztevXyJIj6ezu3/xX7jBtmKNZfOnSEwIJDo6Gi6t2vNvBlTCQoMUDvu2zdveO/tTVhoGH06/supo4fVT/aTsNAQbrk4sWXpfFmLVgqFAoVCwY6l84j7C2fqBAb4M25QPy6dPU1ERLhscZ88e0adRk148Ogxr16/liXmndu3adb4HxxOnsTExIRbt27y8aPvLx+nUChwv3eXObNmkj17dmrUqMHatWvJkSMH4eHhXLp0iRMnTrBv3z7s7e1ZuXIla9eulSXn5IiNjcXp8hXKF8/7255TEJLr+r3n/DtsOeWr1ueKsys2NjbaTilFevjwIfXq1dN2GlohSRKSjm6q+fiFcsBzhULxUqFQRAO7gaZfndMU2KpIcBWwlCQp8y8e2xTY8un/W4BmAAqFIkyhUFwhoYggG9EwURC0oGThXExddVyjz+Hk5ESjRo1Yvnw5GTNmpGfPnty7dw9ra2u14ioUCjp368b9Bw9QKBT0HzQIgPb//qt2zg4nTzBl4nimzZzNndu32Gq/mV37DmBoaKhW3ODgIDp1aE/NmrVo+E8jRgwbyplzFzAxMVE75/UbNrBmzRrGjR3Lrt27qV+vHg0bNlQ7boC/P82aNKJkyVK8fv2K8WPHkCFjJspXqKB27K2bN7PVfhPW1tbExsayYN4crl93Y9bc+VhaWqoc1/udF+OGDwFAT08fhULB4X17uHDGgb6Dh9GkRSt0VWx8tHHVci6cccDC0pKoyEjmTpnInRvXGTpuAsbGqn8fXzx6wILRg9HT0yckKJDiFSpRua7637+IsDB2LluAnr4+V885ULp6LYqWq6hWTIVCQWxMDK5nTpI2U2a8X7+iVsu2ak9HVygUuLu5ABATFUVxuyrEx8djoMbv3cN7d5k+dgQfP3xAR0cHM7M09B4yXK08ARwvX6FL7z4EBwfzxsOD6vUbcv+GG2nMzFSKFx0VxfL/lrJxw3ri4+MxMzOjasUKfPz4kbHjJ9C1e4/vPu75s2ecOH6Mk8eP8ebNm2/uv3XrFuXKlfvuYzNkyECfPn1UyldZbm5uZM2UjrSWolO9kHLExsWxbs8FDp29if3W7dSvX1/bKaVoNjY2lClThnPnzmk7FUE9WQHPL26/Bcon45ysv3hsRoVC4Q2gUCi8JUnKIGfSXxPFA0HQgvy5MuP51gt/f3+1B/M/smnTJkJCQujatSu5c+fGy8uLPn36sHfvXrUGGx6envj4+CRO0ZergKBQKDhy6CChoaGMGDoYY2NjIiIimDd7JpOnzVA5LsCF8+d5+OABDx884OSJ47z38WHj+nUMGjJUrbiRkZFs3LQJD09P+g0YAMDY8eOpUqWK2ttK3bhxnbDQUC5eOJ94bFD/vuzcs4+8+fKpFTsoKBAdHZ3EmSkAly6cp1Wzxixc8h8lSpb6yaN/LDAggAKFCvP08SNiY/+/hCM4KIj5M6ZyZP9eRoyfSNHiyu3VrVAoCAsLS8g9MDDxuMOxIzy67860BYvJlUe1K6suZ08R8Sk2wLZlCylQtATpM2dRKd5nD2+6cf3i2cTbbufPqF08cLtwhjvOjty+4oievgGW6dJRu5X6RTtJkji6dSPvXr9E38AQiy0bmLR6s0qxFAoFh3bvYO3SxcTFJcy2iI+PR9/AQO08t+7cxchx45P0WzEzNcHDwxPbwoWUjnf/vjvjRo3i2bOnicdCQ0MJDQ3F1NSMiIiI7z7u8aNH7Ni2lWtXr+Lh8W3hAMDAwIAMGTJgamqa5MPExIR06dIpnauqzp49I5YsCCmKz8dAJizZh5lVRm7fvUfmzJm1nVKKZ2JiwrFjx0QfiNQhnSRJN764vU6hUKz79P/vvfn+errjj85JzmN/C7FsQRC0QE9Xl2KFcuPq6qqx59i0aRNTpkxBkqTE5oz79+9ny5Ytv3jkz/n6+n7zAva5gKDOEgZJkli2ag09e/cFSHzjvmfXTk6dUG+WRrPmLZg4eQrw/0aV69etxcfHR624RkZG2G/alGQGg7e3N7PnzFErLkCduvWYOXtukmPBwcH07tkdX99fT6f+mdnzFtBvwKBvjnu/e0eXDu1UXsZQyLYI9nsO0KPvgO/e/+TRQ3p3as/MyRPw9/NLdlxJkpi3fBWNW7b+5r43r17Sp+O/nDxySKlcFQoFd6468+b50yTHI8PDWTN7SuLAV1kx0dFsWTiLnf/NT3L8josTkeFhP3hU8pSqUoPXTx4DEBsTjVX6jGrF+1LOfAWIiY4mPDSEYhUqoW+g/KyD8LAwZo4fzapF85N8/UxMTbl9/RoB/sn/nn8pPj6eqbNmM3TU6G8atQYEBvL42dMfPPL7oqOjWbZ0CW1btkhSOPisfAU7XN2u02/AwO8+vmChQsyYPYczFy5y0ekKcxcspHnLluTMmTPxHHNzc+7fv8/jx4+5efMmTk5OnDp1igMHDvzWZQunHU5Svlie3/Z8gvAzjm4P6TBiJU1atufsuQuicKCEv3m3BW0vRVBy2cJHhUJR5ouPdV98Km+B7F/czga8++rT/dE5P3vs+09LG/j07wd1v+Y/I4oHgqAlxfJl4cplJ43Ff/fuHUFBQRh8dcVv0KBBPH+uWsO5iIgILly8SJ48eUibNm2S+9QtICgUCi47Xvru12TKxAm8efNapbjx8fFs27qFgwf2JzkeERHB4oXzf/Co5Dl85Ah16tUjPDzpeu6NmzZx+7Z6jQh379rJuDGjvjn+zsuLPj17JF6JV8WZ0w7s27P7u1cxPi9jGNC3N4EByvcquHXDDYcTR8mbvwBFS5SkfMVKVK9VhwaNm9KibTs6dutBlqxZcbnsSKwSDSaP7NuDs+PF706lj4qMZN7UScyeND7Za+sVCgV6enpYpk2Prl7SSXjPH7hzfOfWZOf2JX0DA1r3HUz+r2ZXxERFccdZvd93fQMDGrbvknjbOr18MxOz58kPJBRqqjVqplKMl8+eUrNeQ5ZssGfj3kPsO30Rh6s3OeroyrJN27CyTvvrIF8Jj4iga5++LFu1GkgYlBcpXJiG9erSu0d3Jo4Zg6GBQbKblT558pi2LVuwasVy4uLivnvOtauurFi+LFnxMmfJQrPmLZgzbwGvX7/m5cuXbNy4kfr167Nt27bkfZIaEhISwt179yllm0ureQhCTEwsCzceZ95GBw4ePsrESZNVXsImCKnYdSCfJEm5JEkyAP4Fjn51zlGg86ddFyoAQZ+WJPzssUeBz28OugBHNPlJSD+7umSbL7ti99Khmnx+QfhrOd98wvbT97jiLH8nckhY67p27Vr27NnzzUCzfPnyXL58GX19/cRjwUpcCYaEwZfn27fcun2b23fucOv2be7cuUNIaCirli9PXMLw1j/5HeyfPX3CsSNHOHHsyDezAgoVtmXHnn1J+h8Y6CXvzcc7Ly9WrVrBgX37vrlyefDwUYoWK5bkmKVp8qcGvnjxgjVr17J7z57EpowARYoU4ezp0+h9NTD1C0n+oP/9+/c4X7nMlctOuDg7JxnMV6tRg5Wr1ybGj4tXbfZaTEwMoSEhBIcEExoSQkhwMCEhoYSEBJMmjTm169YlJEozu0hE/2Dw9svHRUcTFhpCWEgoIaHBhIWEEhoSQlhoCGnTZ6BC5Sq8Dkh+x/zgwABczp7C8eRR3n0qUuno6DJx+VryFCqS5NyI2OTlrFAouHRkP/vXrUhsllioVFmGzF2a7Ly+JzYmhind2+H33pt6bTvSvEe/Xz5GNxnLlF49fsjsQT0pUtaOIbMXJSuXXFaq9RlIjlKZrQC4ev06/v7+5MiWnezZsmJhYaFW3Ace74iPi0NHVxddHZ2Ef3V10NHRRVdXFx0dncR/lV3eVSBPyhqkHz9+nFlTxrBuendtpyL8xTy9PzJm4R5s8hZky9btGluq+TeQJOmmQqEoo+08fqciNtkU+yYP0XYayVa4x+iffo8kSWoILAV0gU0KhWKWJEl9ARQKxRop4YVnBVAfCAe6KRSKGz967KfjaYG9QA7AA2itUCj8P933GjAHDIBAoK5CoXiozucoigeCoCUhYRHU6TabgIDAb2YHyPo8ISHs2bOHjRs3cvXq/wsVkyZNYvr06Ym3lS0efE98fDwvX77kzr171KpZEytLS6WKB1/Gue52jeNHj3DG4VTi1mntOnRi4pSpieclt3jwmYeHByuXL+PwoYOJe8SXKVOWnXuS9oFQpnjwma+vL5s2b2bT5s2JvQSmTpnCgP79k5ynTPHgS3FxcTx8cJ8rlxOKCXdu36ZN23+ZPG06kiSpXDxIjuBIzWw1qGrxIDmUKR58plAoeP7AHceTR7l26RxWadMxfd1WjL5oyJjc4kFiHk8esWHWZD76vEPS0WHOjoNYpk2vdG5fcjl9gq2LZvPvwBFUb9Lil+cnp3gQHRXFoCa16T91DsXtKicrj99RPJDbhwjNFMIg5RUPhgweCEGv6NmmprZTEf5SpxxvM2/DcaZMncagQYPVbu76t/sriwe5siv2TRmm7TSSrXC3EX/890gUDwRBi9oMXY799j2UL/91s1XNePDgAZs2bWLr1q34+/vj6OhI5coJAwU5igffo0rx4EuRkZFcunCeY0ePcMXJkfmLl1KvfgNA+eLBZ69evmTF8mUcO3oEhULBshWraPDFDgmqFA8+Cw8PZ/eePaxevZoPHz5w2cmJHDlyJN6vavHgayEhwVy7epUCBQqSPUcOUTz4iirFgy9FhIVx9eJZUCio0bj5/48rWTwACA8NYdviOdy+4kir3oPUbnIYFxfL1B4daN1nEMWSMdBPTvEAYPmkUQyYOhedZE4nFsWDpFJa8aBQgXxM7tuQIvlz/PpkQZBRRGQ08zce586Td+zdd4BSpVRrwiskJYoHKd/fUDwQPQ8EQYtKFMrBlStXftvz2drasmjRIry8vNizZw87duwgJCTktz2/KoyMjKjf8B9WrlnHpSuuREdFER2t3mA2V+7cLFqylBMOZ2jQ8B8WLphHVJQ82+CamJjQvVs3rrq6snz5cvbt369S88FfSZPGnNp16pI9hxgYaIKxqSk1GjVLUjhQlYlZGnpPmkXbAcO4efmC2vF0dfVo1LE7Vhnk3Y2pbb8hyS4cCCnbu3fv8Hn/nkJ5smk7FeEv8+y1Nx1HrcLAIju379wThQNB+MOIrRoFQYuKF8jOZadLjBgx4rc+r4GBAa1ataJVq1YaGdhqipW1NY2bNpMtXr58+Vi2YiWPHj3k+bPn2BYp8usHJZOuri5NGjdOVV9fQXMkSaJG01bkLmRLWEgwpmnM1YpXtkbtb/p3qCtDFjHQ/FOcO3eOcsXzo6srrhEJv4dCoeDAaTdW7DjLwkWL6Nq1m1imIAh/IFE8EAQtKlnYhiVb1qJQKLT2Iite3KFQocIaiy2+vsKXcuYvJEscHV1dDMQsAeEHzjiconxRG22nIfwlQsIimLn6CB4fQrni7EKhQvL8nRMEkJDEa12KIkrSgqBFmdNboSMldOwXBEEQBHUpFArOnT9PhRL5tJ2K8Be4/9SDdsNXkCN/Sa7fuCUKB0qKi4v7ZrtnQUjJxMwDQdAiSZIoWTgXzs7O5M2bV9vpaISujuauvBvpa+ZPmE+g5vpAxH3a5UFu5saGvz4phcUOjojSSFyAmDjNfJ1Pv/yokbjP/eRdgvClKjlUbwD6M2VyZNRIXABJRzNXmtL4emskboKU0TDx4cOH6OtJZM+cTtupCH+w+Ph4th+5gv2hy6xas5bWrVtrO6VUxcPDg6VLl+Lu7s76deuTbEMtCCmZKB4IgpYVy5+Vy06X6NKli7ZTEQRBEFK506dPYydmHQga5B8UypRlBwiP0+f6zVvY2NhoOyWNioyMxNXVlQsXLtC8eXOVm0DGxMRw/Phx1q9fj4ODAwYGBpw6eUoUDn5G0lwxWVCNWLYgCFqWK1sGXoplC4IgCIIMHE4dp0LxPNpOQ/hDXb/3nH+HLads5bo4u1xN8YWDeBVm+8XGxuLq6sqsWbOoVasWlpaW1KxZE319fZUKBy9fvmT8+PHkyJGDFi1acOrUKRQKBePHjadgwYJKxxMEbRIzDwRByyRJEh35BUEQBLVFRUXh6nqNKT1raDsV4Q8TFxfPuj3nOXj2Jpu3bKNBgwZazUehUBAcHIyXlxdv377Fy8sr8ePzbX19fTZs2EDRokV/Ge/+/fucOXOGCxcu4OTk9M021v369WPSpElK5ejo6MiMGTM4f/78N/fVrl2bHj16KBVPEFICUTwQBC2TJETxQBAEQVCbs7MzeW2yYG5mou1UhD/I+49BjF+yF1PLDNy+e4/MmTNrNZ/4+Hj69u3L+vXrf3hOlSpV2L9/PxkyZEh23P379+Pq6vrN8ZYtW7J8+XKld0+ys7OjQ4cO3Lx5k8DAwMTjVlZW2NvboyOJCeBC6iN+agVB68TMA0EQBEF9px1OUb5oymjcKPwZnK4/pP2IFTRu0Y5z5y9qvXAAoKOjw6pVq6hZs+Z37+/Zsyfnzp1TqnCQL18+6tev/83xatWqsX37dnRV3C7w68IBwNq1a8maNatK8f4+Eujopp6Pv4CYeSAIWqZkIVsQBEEQvuu0wymGdayu7TSEP0B0TCzLt53m3NXHHDh0hCpVqmg7JQA+fvzIxo0bWb16NW/evElyn66uLkuWLGHgwIFKzRI4ffo0AwcO5Pnz50mOFy9enCNHjmBkZKR0nu/evaN169a4uLgACUUIR0dHOnXqJHamEFI1MfNAEFIAMfMgqbi4OG2nkCLExMTg/e6d7HH9/f1xu3ZNIz93hw8f5vLly8TGyr/1oNOli3h6eMgeNzQkmKePHsoeFyA+Pk78fgu/ha+vLy9fvaZogRzaTkVIxSKjYth17ApN+i0kKM6Mu/fcU0Th4MaNG3Tt2pVs2bIxduxY3rx5g5WVFfnyJewsYmlpiYODA4MGDUp24cDT05NWrVpRv359nj9/TqZMmdixYwfly5fHxsaGU6dOYWFhoXSuV65coXTp0ri4uGBubs7Ro0dZu3YtOXPmZPny5UrHE4SURBQPBEHLfkfDRE3F3713r0YG+hPGjdXI4BMgIiKCiIgIjcT2evtW1nh6enp079ieUyeOyxrX0tKS0aNG0qB+PXbu2EFYWJhssdOlS0f79u0oU6Y048aNw8XFWbafkcioKBrUqUmfHt24cO6sbD8jJqZmTBw6kOljR/L2zWtZYn7m/86Tmyf2yxozNdHU3x5Nxb15z52g4JBfn5gCnTt3jrLF86Gv93dMnRXkFR4RxZZDjjTquwB3z0gOHz3JocNHSZs2rdZyioyMZNu2bZQvX56yZcuyZcsWoqKiKFmyJBs3buTt27dUqVKFggUL4ubmRu3atZMVNzo6mvnz51OoUCEOHDiArq4uQ4cO5cmTJ7Rv3x5bW1tOnz6t9BINhULBypUrqVGjBj4+Ptja2nLjxg0aN25Mvnz52LNnj0rFCIBbt26p9LhUTwJJVyfVfPwN/o7PUhBSMAnNvRH28vICYPPmzQQEBMgW9/GTJwQGBbFuwwamzpghW9yYmBjc793lyKGDTBg7RqUtln7k8ePHREZG8vjxI8aNGyvb1zwmOprnz54SHR1F7+5dCPpqbaM6Tp04jo6uDuNGDmfcqBEEBwfLEveykxMWlpY8ffKEiRPGY1ehPNOnTePly5dqxQ0ICODIkcMYGBjg5+fH9u3baNu2LWXLlmHChAm4urqqXEg4evgQ9+7cRqFQcOWyE4P696VezWqsWrGcD+/fq5xzWGgo29avJm369DidO0P3Ns1ZMms6Hz+oHvOzAO+3+L5+waVta3l5+5ra8b6kiI8jXsbCnY/HK+I0ULDbtmMHb728ZP8bt37jRnx9fWX9G/HKw5OuQ0bg8+EDt90fEBEZKVvszxQKhcZmVjmcOkEF0e9AUFJIWATr917gn94LeBOox5mzFzh24hRly5bVal6urq7kyJGDzp074+bmhr6+Ph06dMDFxYWbN2/SvXt3TExMKF++PFevXk2cgfArMTExlC1bljFjxhAWFkalSpW4efMmS5YswdzcHIDly5eTP39+pXOeNm0aAwcOJDY2ljZt2iTJS0dHh/LlyysdE+DZs2e0biOWOggpgygeCIKWaWrmwZYtW8iTJw9Hjhxh7dq1zJs3T5a4ERERtO/UifIVK/L06VOWrVjB1u3bZYm9a8d2WrdojkKh4MjhQ8yeOUOWr42fnx/t27ejUaN/OHXqFAcOHGDLFnv1EwaWLVlMm+ZNWbJgAR5v3jBh7ChZBjT+/v5MHDMqcTbDqePHaNOsMdfd1B+Enj17Fvd79xJvh4aEYL95E7Vr1qBLp06cP3dOpQFOYGAgu3fvJjo6OslxX19ftm7dQps2rSlXrhxTp07Fz89PuZxPO7B544Ykx3x8fFi5bCm1q1dh6KABuDo7K/21f/b4IdvWr+XZ40cAxMfFceLQfjo3b8T6ZUsIDgpSKt5ncbExHF40lRPL56BQxHNs6Uz83r759QOT4ZXTca6umUqYr5cs8QDO7trCspH9ePXQXbaYcXFx/LdiJc1atabvwEHExMTIFnv/wYP07t+fchUrcuHSJbXj+QcE0mngUPwDApk4byGNO3dn2YbN6if6lf3791OmTBkuyZDzlxQKBWfPnMWupPIDHuHvFBgcxortZ2jUZwEBcRZccXFl3/6DFC9eXNupAVC4cGHCw8PJmjUrM2fOxNPTk+3bt2NnZ5dkWULv3r2Vupqvr69P3bp1SZ8+PZs3b8bJyembz9nERLXdSjp06IC1tTULFy5k9+7dmJmZqRTna3nz5qVx48ayxBIEdYnigSBomwYaJioUCk6fPk1UVBStWrXCzc2N//77j7cyTKt/92kNvs/794SEhgIwbORILl+5onbsr/dV3r51C8v/W6p23DdvXhMfH8+TJ09YvXo1kHCF4Pr162rFjYuL4/XrV8TExLB9qz0AjhcvsmXTRnVTJjwslPwFCiY55uPtTe+unVm6cAExXw3QlVGjRg2srKy+e5+z8xXmz5vL0iWLCQ8PVyquhYUFgwcP+e560/z589OtWzfmzJnD8OHDlZ4KW7NOHWrXrffd++Li4jh35jSzpk9lzcoVRCpxxdjUzIzqdb7tsB0dFcWerZvp1LQhOzdvICJCua9FWGAAsVGRKBQJxYzoiDAOzptARIhqxYjPfO674b5/LT7u1wj1lacfRpDfR+46X8Lz2WNO79hEWIg8M1xOnT7Ni5cvef7iBRcvXeLCpUtERUWpHdc/IIDrN25w4eJFnj1/zs5du7j/4IHKy5EiIiPpNnQkrzw8AXC5fhOFQsFHP39ZipePHz/G3d2dyMhIxowZw507d3B0dFQ77pcePXqErg7kyJJO1rjCn+djQDBL7E/StN8i4kyycOPmbbZt30nBggV//eDfyMLCgsuXL/P69WsmTJhAxowZZYs9ZcoUnjx5QteuXdHRkW8olC9fPl68eMGIESOU3tbxZyRJYuGChbLFS00kJCQd3VTz8TcQuy0IQgog98wDSZJYu3Ytd+/e5eHDhEZwkZGRTJ06lQ0bNvzi0T+XPkMGatWsyYsvprjHxsbSsWtXLpw+TZ48eVSKe+XyZQ7s2/vN8VUrlmNhYUGXbt1VzrlUqdIcP36CRo3+SbziHRsbS9++fTl16pRS2zl9SVdXl5lz59Ojc0eefLpyDfDf4oUUL1GSUmXKqJzze5/3vH796pvjCoUC+43rcXW5wuz5i8iTN6/SsWNiYwgICMDU1JSChQpRuHBhChcuTKHCtuTPn1+lztIA1tbWmJubo1AoyJQpE5UrV6FKlcpUrFiJTJkyqRTzs+YtWnHp/HkgYfpnrtx5sLW1pXCRIhQuXISChQph+uVVnujk9XHIV7Bw0sd9wcLSCqu0ablz3Y3Q4GDa9+iVrJjhwYFcPbSDoA8+SY4Hvn/H0SXTaTV+Hrp6qr38ZipSjsJNu+K+fy1hH+SZeeBy8jDxcXHkK16aNoNHY5rGXJa4y1euSvx/VHQ079+/R19fX+24lxwdk8ww2XfgAPHx8axSoRFZfHw8QyZO5cbde0mOlylejHFDBsgyAHBycmLNmjW0bNmSV69ekSVLFkaOHKl23C+dPn2aCiXyyTpgEf4sPr6B2B+6zEnHW3To0JG77vZkz55d22n9VMmSJTUSV64ZAd9jaWmpkbh6Kr5mCILcxE+iIGiZhPzLFq5evUqTJk3w9fVNcnzz5s0MHz6cwoULqxRXoVCwc9cunD9tPfSlwMBA2rRvz7nTp7FS4cXTtkgRJkyawo0b17lx3Y2HDx4kTp2fM2smZmnS0LKVamv+7t69S+/evb6ZKv/hw3v69evH7t27VRrUvPPyYkDfXjx/+jTJ8bi4OEYNG8K+I0extlat2VRMTDTTZs4mKjqK6OhooqOiiYqKIjoqiuiYaKKjojhycD8du3YjQwblrsikTZuOC5ccyZEjh6xXXT4XDS5dciR37tyyDmR8fX0pV8GOrj16UqBgIZWnlX7tg48P6TNmYvjEKVinTYdV2nRYp02LVdq06Ol952fiw6+vnBsYm5CvbGUkSYcXN10I8fv/76HH/dtcsF9JnZ5DVM45b62WhPt/IPSD+jMPYmOiueN0ntYDR1GhfmPZvmfX3K5z7dPMntYtWjBr+jSVi3RfO3/hQpLbI4YNY+K4cSr9LM9cupwT5y58c/zmPXeGTprGslnTsDRXr5ji6urK7du3uX37NgCzZs3C1NRUrZhfczh1nLqlc8saU/gzvPXxY9MBJ867utO9e08erd2tdjFXEIS/l/SzQYttvuyK3UuH/r5sBOEvdOvBK1bvv8pVtxuyxvXy8mLq1Kls2rQpyVW6pk2bcvjw4W/OD1ZyDbrn27ecPnOG02fO4Hj5cuJ05KpVqnBw797Ewbh3oGpToENDQ7lz+zY3b1znupsbD+67M2/hIurW+/8Uc1NDg2TH8/Ly4sSJE5w4cfybrsU9evRg6tRpibcDw5M/7T0uLo5bN65z4vgxzjicIuSLpoYVK1dm1bqN6Or+fypbnIwN3r5kbmyokbgA+rqamYoXHKH+FPYf8Q6WbweJL51++VGp8xUKBR9ePeP5DRee33Thw6uEfcRr9xxCybpNE8977qdcs0JFfDxvXE5jU7nBL8+tksP4h/d99PZCV1cPKyULUADdy/54fX3Hrt1wv3+fxfPnU6tmDaVj/2j6p0KhoGCRIvi8f4+enh5LFy2iU4cOyY4b+vb/M6Y2797LxLn/nwqcKX16qlWsQLWKFahSvizWShZBsxT/foO5AgUK8PSLAmP27NnJnz8/e/fuxdraWqnn+J6IiAgypE/HqQ1jsUgjT1FNSP1eeX5g4wFHrtx8TP/+Axg6TPnlYoJmeb/zVur8LFmz3FQoFKpPaUyFiuaxURycN1HbaSRb/ta9/vjvkSgeCIKW3X74ipV7Xbl2/aZG4j969IgJEyZw6NChxGNXrlyhUqVKSc5TtnjwpbCwMJwuX+b02bOcPnOGunXqsHTRIiRJUrl48LWoqCiePnmMbZGiiVcYlSkefOl7hYTly1fQrFkzQLniwZeio6O44uTEiWNHcbx4gaioKAYMHkrfAQMTzxHFg//7G4oHXwv++J7nN1x5dfsa5Zt3IFvBIoDyxQNIGEgnZ6bAz4oH6vhR8eD1mzds3b6dkcOGqTxD5EfFA/f796lcvTrmadKw1d6eGtWqKRX3c/HgzCUn+o+dSNkSxalWsQLVK1agQB71Zst8r3jw8eNH0qdPn+RYtmzZOH/+vErd3L9n165drFw8i9VTu8kST0jdnr56x4YDjtxwf8mQIcMYOGiQxqbSC+oRxYNfK5rXRnFw/hRtp5Fs+Vt2/+O/R2LZgiD84QoVKsTBgwdxdXVl9OjRXLlyhTFjxnD58mXZpiibmprSoH59GtSvj0KhwP3+fQICAmS5qvaZoaEhRYvJ0wU6a9as9O7dm969eycWEnbv3kWBAgUoVKiQynENDAypWbsONWvXITQ0hAvnznHqxHGuu12jbDnVtmgS/izm6TJSqn4zStVvpvZWiyl1fXuO7NmZPGGCRmKfu3CB7NmysXfXLgqr+LsaHx+PhXka3C+ewdhYtf4eyXX16tUkt21sbLhw4QK5csm3peLG9WtpXKOEbPGE1Mn9iQcbDzjx4LkXI0eNYt+xfhpd2y8Iwt9JFA8EQcs0tVXj1+zs7HBycuLEiROMHTuWY8eO0aRJE9mfR5IkihUtKntcTfmykKBMl/5fMTNLQ5NmzWnSrLksHeaFP4+OhmZ0aJucfTS+5ufnxzkHB7XWbOvo6FC+lGYasX3N1dU18f/58uXj/Pnzsjap8/Dw4OatW8wZ+O2OIcLf4eb9l2w84MgrLz/GjpvA8R49MDbWzGwjQRAEUTwQBC2TkH+3hR8+lyTRqFEjGjRooPY2hX8iVXcZ+BVDQ80tKRCEv8nkCRMwMFBtuZI2uHxqLmtra8u5c+dkb1S3xd6eepWLY2So/i4WQuqhUCi4dvcZ6/c58jEogvETJtG5c+dU9bshCMkj/TVbIKYWonggCNr2m2YefElXV5cKFSr81ucUBEFQV2oaHMXGxuLm5kaJEiU4e/Ys6dKlkzW+QqFg8+aNzBrcTNa4QsqlUChwuv6IDfsdiYiBiZOm0K5dO7GNnyAIv434ayMIWpZCly0LgiAIarh37x5FihTBwcEBKysr2eNfvnwZfR0FtvnkWwYhpEzx8fGcc3Fn4wEn9AxMmTR1Di1bttToEiFBEITvEcUDQUgBfvfMg+8Ji4rWSFxjfc1Np41Rs+Hcj2hqRwQAKxPNrEW1NNVc47cIX+U6QidXJjNzjcQFiIxJXUtFbKw0Ny0zn7WpRuJGxWnu98RIQ4OiH22nqAkKhYKzZ89ibq6Zn/MN69fSuHqJFNs4U1BfbFwcDk532HTgMpbW6Zi/eAX/MPIGXgABAABJREFU/PNPqv6e3338TNspKC2rtYVG4hqhmfcwgqBJonggCFom8fuXLQiCIAiaVbp0aY3FDgkJ4ciRoxxZNUJjzyFoT0xMLMcu3mTzwctkz2HD6vWbqVWrVqouGqRWcXFx3L59G0cnJ969e8fkyZMxM1WvIOvv78+Bgwcx0tOla8cOMmX6h5IkpD+0uXBqJYoHgqBlOjoS8QrNXcETBEEQ/ix79+6lbLG8pLVKo+1UBBlFRsVw+Kwb9ocvU7hwEbbu2EPVqlW1nZbGRUZGEuDvj5W1tcqNiyMjI3nr6Ymnx5uEjzce+H78QN8BgyhYqLBSsbzeeuLq4sxVFxeuX71KUFAg6dOn59SJEyoXDmJjY7l46RK7du/m9OnT5MqVi/MnjqkUSxC0SRQPBEHL9PX1iNbQkgFBEAThz7Nxwzr+rV1C22kIMgmPiGKfw1W2HXWmXNlyHDpygnLlymk7LVl4enri7OzMhw8f+PDhA4+ePsPf3w9/P38C/P3w9/MjIiKCQUOH07Vnr2TFVCgUHNi7hwf33fH08MDT4w3vfXySnGNlbc3yNeuSVTiIiYnB8eIFrro4c9XFmbeenknuNzU1ZdfOneTMmTP5n/gnz549Y9fu3ezdt4/3798DCTswrV29GhOxpaaQConigSBomZGBPhERkdpOQxAEQUgFnj17xvNnz6g8tqW2UxHUFBIWwe4Truw85kKNmjU4c/YCxYsX11o+ERER6Ovry7p7Q4YMGTh06BB79+797v0WFpasXLcCu0qVkh1TkiTKlCvHjq32vHr58pv7c+S0YeW6DWTPkSNZ8fT19dHX18f58mW833kluU9PT49NGzdSvFixZOcXGRnJnr172b17Nzdu3vzm/imTJ2Nrawux4sJRsoitGlMUUTwQBC0zNNAnIlIUDwRBEIRf27xpIw2rlUBfT7yhTq0Cg8PYccyZvaeu0rBhQ664uFKwYEFtp8Xr16+xs7NDoVBgZWWV+GFtbZ3kdqZMmWjXrl2ylhhERERQuXJlDh8+THR00sFywUKFWbR8BVmzZlMqz5iYGB4+eICe3rcNmYsVL8F/q9dgZWWtVEybXLnJlj37N8WDSdNnUrNGDaViGRoaksvGBt3vFGHq1qlDzx49lIonCCmJKB4IgpYZGeoTKYoHgiAIQjIcPLCfKf3+0XYaggo+BgSz7cgVDp+9QYuWLbh+4xZ58uTRdlqJ8ufPz7hx4xg7dizBwcG8efPmm3NatmzJvHnzflo4UCgUuLi4sH79evbu3UtERMQ35zRq2oyJU6cr1eMgPCyMQwf2s32L/TeDfIAatWoze8EijJVYDhAdHc3m9evYuG4N0dHR6OjoEP9px6UBg4fStHmLZMf6TJIkcuTIQXh4eJLjGTNmZNl//4nGl0KqJooHgqBlBvp6REZGaTsNpYWFhWGqZsfh3y0mJgZ9DWwdqVAoNPJmID4+PlXt4+0XEMAbD09sCxXE0MBA1tg7du/F+/17qlWuSMnixWWdVnvrxnU++vpSs05dWeMKgiakS5eOsIjU95rxN/N678+2I86cdLxFhw4duetuT/bs2bWdFkFBQVy7dg1nZ2dcXFy4evUqoaGh3z23VKlSLFmy5KcNHP38/Ni2bRvr16/n4cOHicdtbW3p0KED48ePR09Pj1HjJtCmXftkv276+/mxa/s29uzaQXBQEJDQ06B9x84ArFy2lH87dGTUuAnoKtGZ3+2qK7OmTeXN61cAVKhYicHDR9C+VQtatmlLz779kh3rSw4ODgwYNIjg4GCsra2Jjo4mLCyMVStWkDZtWpVi/r0kJLFsIUUR75IEQcsMDfSIio7W2ADU2dmZ3LlzkzlzZlnjLlu2jBIlStCgQQNZ4x48eIA8efJQvHgJWeMC2G/eRNmy5ShRsqS8cTduoGix4pSRucHVpEkT0dXVpUmTppQuXVq2n4/DR4/i7OJCpYoVqWRnR/r06WWJa2luTrsZs3n89Bm2hQpQqlgxShUrSqliRbHJkV2t/KtXq0K5KtWZs3ARZmZmVK5QgaqVK1K1UiUKFsivVmybXLnp060zGTNlpn2nzjRt2QpTUzOV43324fULHrtcpFyTthiZia74gjxsixTlhcd7yhfPp+1UhB9QKBQ8e+PDxasPuOT2BJ+PgXTp2pVHa3eTKVMmreX06tWrxEKBi4sL7u7u32wVnSZNGnR1dQkMDAQgc+bMzJkzh06dOn23mK1QKHB0dGTdunUcOHAgcWmCsbExbdu2pXfv3lSoUIFXr16xbNky5ixaQslSydvG9M3r12yz38TRQwcT42bPkZPO3brTuFlzjIyM2L7FnmGjxtC5W3elihGL5s3hxLGjAKRNl45RY8dTr2HCjJ7a9eozbtIUpV9XYmNjmT1nDstXrACgTOnSbFi/nqHDh1OsaFG1ds64ceu2yo8VBDmJ4oEgaJmOjg6GBgZERkYqNdXuVxQKBbdv32bLli3o6emxatUq2WJfvHiRly9fsmr1KhYvWkzr1q1lievr68uNGzcYM2Y0kydPoWPHTrINmC87OfHgwQPWrF7Nvv0HyC3TVNE3r1/z5PEjli9ZRJ/+A+nZt59SVz5+JD4+Hn9/f06ePMGWLfZky5adpk2b0KRJUwoWLKRWbF1dXdZv3MS6DRsBKJA/P5UrVaJSRTtqVK+OtZWVSnF9PviCQkF0TAy3793n9r37bPx0n7WlJRXKlGb04AEUzJdX6dhrN2xKWD8aFUVoaCgO587hcO4cABkzpKdKxYRCQu2a1cmYIUPyc/b2Zv7sGZiameH9zotF8+awdtUKWrZuy78dO5MhY0alc/0s+KMPbkd3c+fMEco2bkPphi0xMDZROd5nkWGhvH/5hBxFSslacDx/4hheHq/p3G+QbDEh4Q21mNEhnyJFi+F85vvN5wTtiYuL5+7jN1y89pBLbo9QSLo0a96cVevHUbFiRa3+DgQEBFCoUKHEbv9fypUrFxUrVqRixYpUqlSJIkWKUKZMGZ48ecLIkSMZPXo0ZmY/Lqa2adOG/fv3J94uUaIEvXr1okOHDlhYWCQeNzEx4datW3wI+v7Mhq+5XLnMgN49E4sbtkWL0q1nb2rUqp3kNbZZy1Y/ze9rMdHRtG/dAh9vbyRJovW/7Rg4dDjm5uaJ58xZsEil79eXhYM+vXszedIkDAwM6NypE/Xr1VM63mdvvd7RqGUrlR8vCHISr+aCkAIYGRkQEREha/FgypQpzJw5EyMjI6Kjoxk6dCj58+dXO254eDhDhg7h48ePAAwZOoTQsFC6de2mduy9e/ewY8d2ACZOnMCNGzeYM2cuJibqDbi8vLzo26d34rrLLp07s//gQTKqMTD8bPeObTicOA7A6hXLuHXjOrPmLySdmlfzIyIiOHnyROLtt289WblyJStXrqRs2bJMnz6TwoWV27v6s6tXryW57eXlRVhYGBYWFpinUf0KuUKh4N7DR98cL5Q/Hx1ataBFo3+wtrJUKbbH27eEhYV9977wiEh09XTJkCE9VpbKxX/z+hWOF84nORYaEsKWTRvYvtWe+g0b0alrN/IVUL6ZmdPODSji44kKD+PKns3cPHmQ8s3bU6JuE/QNDJWO95nPs4ccWzKNAnbVqdy+FybmlirH+tL5k8d4/vgRbbr1xMhIvr9Fffr2JSg4mMkTJ1K0aFFZCh7nLlygWJEiTJwyFT9/P+bOnMVlZ2e6d+2iVtz9+/eTOXNm3NzcOHHiBD179qREiRIEBwenmK3zbG1t2bbxo7bTEICo6Biu3X3GJbcnXLr2gMyZM9O8RSuOTF5CsWLFUszadisrq8QdBUqVKkWlSpWoWLEidnZ2ZMmSJcm5QUFBFCtWjKNHjyZraUXt2rVxcHCgXbt29O7d+4ez5D7PuPgQ9CxZOZcuWw7rtGkpVNiWrj16Ubps2e/GVaZwAKBvYED7Tp05cewoE6dMp+h3drdQdXlj/379OHP2LKNHjaJJ48aJxxs3aqRSvM+yZc3CgN69WLJipVpxBEEOonggCCmAoaHBdxsKqSouLo6nT5+iUCgS406cOPGHWyUpQ6FQkCN7jsTiAcCECRMICQ5h0KBBar1Zyps36RXpw4cP8fDhA9asWad2UylTU7PEr4WX11u6denC7r17k1xtUEUhW9skt69ddaVti6bMnr+Q8nYVVY5rYmJCly5d2bLFnjRp0lCuXHns7OyoUMGOwoULqzW7YUD/fmzasoVqVarQulVLGtSrp3aBBiBzxgxMHTOSqfMWYmZqSvN/GtC+VQtKFLFV+030iMEDefTkCc9fJGzLZWpqSoM6tWnWuBE1q1XF0FC1wXiOnDZ07dkb+w3rvrkvLjaWE0cPc/rkCVr/246+g4YoFTtf2cr4vf1/w7GIkCDcjuzG8/5tanQdgFWmrCrlnK1wcQZuPoYkcz+MMbPmERMdLWvhAOCamxu+vr4cPnKEYkpsd/Yzvr6+2JYoiUKhICYmhvKXKjNj6lS146ZJk4bKlStjbm5OcHAwjx8/xsvLC3d3d/WTlknhwoV59tpLY0vdhJ8LDo3g8o1HOF5/isutRxQtWoQWLdswf+U2cuXKpe30fujcuXPkyJHjlxcpzM3N2bJlS7Ljdu7cmfbt25NGjcLz9xgaGnLo+CnMv5i9IJf2nbrQvlMX2WeDpEuXDseLF2WZffi18aNH/Z3FA0lC0sDXU1CdKB4IQgpgaKAvW/HAz8+Ppk2b4uzsnOT4vn37uH79OmXLllU5dnx8PHPnzeXW7Vvf3Dd33lyCQ4KZMH6CSm9oIyMjuXfvHi1btiIgIICAgACCggLx9fWlWbMmzJkzl0aNGv860Ff8/PwYNGAAHz/6Jjn++PEj+vbuxWb7LRgq0e35SyEhIdy6foOKlasQGhpKaEgIoaEhhIWG0r9XD3r07kvv/gNUeoMSFxdH3rx5OX78BIUL28r6ZiQuLo4Hd25jba3cVla/oqurS0BgIEtnz6BxvTqYylCQ+NK7d940a/QPzZo0pnb16hgbq/Z9+1KmzJl5/Kmxl7GxCTa5c5Erdx5y5c6DTe485Mqdm2zZc3xxJSooWXFjY6IxS5uOJsOnYmadljTW6TC1tEb3O1uLKUtPjVkLP2NhqdpylV/R1dUlZ86cjBwxQraYDRs0YMjwEYnroOPi4qhapbLacStXroy+vj7BwcFAwqycQoUKJezJnkJkzJgRSdLBPzCUtFail8bv8P5jEJfcHuB4/Sl3Hr6kWtUqtOrUl637msjWM0bTChQokKzzlH39lnPG5Nc0UTgANLqERBOFA03GFQRlieKBIKQARjLOPEibNi0XLlxg7dq1zJgxA1/f/w+ax4wZw/nz51W+WqWjo8P0adMZ0H8Ajx8//v/Hk8c8ffqUVatWERIcwuzZs5V+oTMyMmLUqNHfvS8+Pp6QkGCVrrSlTZuWvfv3437vHi4uzrg4O3Pz5i2io6O4evUqw4cPY9nyFSq9MKdJk4aps2Z/9764uDjCwsISt3xSlp6eHp07qzcF+0eyZ1NuT21ljBs6WCNxLS0seHznpuwFiaCgQDp06crEaTPImCmTbLtb6OkbULJuU1lipXZ6urosXLBAltktn1mYm1OnVi2OnzwJgLW1NbYqLuP5kqmpKRUrVsTR0THxWNu2bVPUFX5JkihYID8vPN+L4oEGvfL8wIWr97l0/SlvvD7QsGEDho2dRt26dZWeKi8IgvCnEMUDQUgBjD41TJSLgYEBgwYNomvXrixatIiFCxcSFhbGxYsXOXPmDPXUaNwjSRKZMmUiU6ZMVK9ePfF4XFwcb9684fHjhEJCoULqNfX7ko6ODhYWlio/Xk9Pj5KlSlGyVCkGDBxEZGQkN2/ewMXZBRdnZ2bNnMmkyZNlHSDo6uqqvSRC+D9NFTwsLa2oWLmKRmILCTp06EDVKvJ/jVs0b5ZYPKhSuZJshZ9atWolKR60adNGlrhyKlK0KC/e+FCumPLNR4Xvi4+P5/4zTy5cfcglt8dERMXSrFkzFi0bTtWqVTWyza8gCD8ngdiqMYURxQNBSAEMDeVbtvClNGnSMHXqVPr168eMGTNYu3YtY8aMoU6dOrK90f5MV1eX3Llz/4+9u45rav/jOP46jO7GDlQsELG7W+zuwu7uzmt3d131Gte+1+5ObMHEAgWUrm2/P1B+Iigb2y6o3+fjsQewc/bZFxjjnPf5Bs7OzlqtqwvGxsaULVuOsmXjuzmHfPpETExMqsfNC4LwfX379NFJ3Vo1amBqakpERAQVy6d+CbRvVa1alXHjxgHg5uam1SBUWwq6FuLisV1p3YyfXmxsHFfvPOHk5QecvHIfWxtbGjVuwvZhM7S6PK4gCMKvQrtnD4IgpIqRob5OwoMvnJycWLx4MQ8ePCBfvnxs27ZNZ8/1M7K0shLBgSDoiK6u2JqZmVGrRg0AKmqxZ0Px4sUTJn9Lj70OAFxdXXn66n3KOwpJhEdEceTcbUbO3U6VDlNYu/8GHuU9OXvuIg8e+TBt+gyKFSsmggNBEIRkiJ4HgpAOGBnoNjz4Infu3Gzbto13797p/LkEQRB0rUnjRly6fJlcubTX48nAwICKFSty4MCBdBseFChQgCcv3qZ1M34agcGhnLpyn1NXH3H9ji+lS5ekcbPOrNrcgIwZM6Z18wRBEH4aIjwQhHRAm6stqOLLesuCIAg/s2pVquBZt47WrxJXrVqVV69e4eLiotW62pIhQwYsLCxYsuUI3VtWRV/MxJ7EyzcfOHHpLqev+eDz7DU1a1SnR/+R1KlTR8xHIwg/C0kCLQ+zFTQjwgNBSAd0PWxBEAThV2RsbMyIoUO1Xrdq1apancRW2yRJ4uKlK7Rr2xqv0auYOrA5mZ20u/Tqz0apVPLgyWtOXLrH6auPCA4Jp379Bkz5ozeVK1cWQ9MEQRC0QIQHgpAOGBnK0vWBqiAIQnplZ2en9Zqurq44ODhova42ZcyYkSNHjzNn9mzaDp3OiK6e1CxfOK2b9Z+Kk8u5fvcpJy8/5NSV+xibmNKocRPW9ZtIyZIltT4xsCAIwu9OhAeCkA78V3Me/EhIpG7CCwcj3R28KQxNdFLXWIdLchkpYnRSN/pjoE7qAsiMdPNz1jMw1EldAGMDuU7qFnHSzfrucoVSJ3UBYuQKndR9+zFUJ3UBZDo66XO1s1dpvy9L0qZ3enp6DB02jMpVqtCieVMu3nrCsK6emBr/ulfZI6NiuHDjEaeuPuLM1fvkyJGDJk2bMXr6UvLnz/9bTXT46MkzndS1szDTSV0AO3NTndTV1XsGQKiOjo8sTIx1UvdXI4lhWemKCA8EIR0wMJCleXggCIIg/JyKFSvGrdt36N2rB60GLWb6oBYUyJ0lrZulNR9Dwjl95T6nrj7m8q1HFCtWhCZN27BoTQOyZs2a1s0TBEH4bYjwQBDSAWMDfSIiItK6GYIgCMJPysLCgo2btrB161Z69+lNpyYVaFu/3E/bdf+1fxAnL93j9LXH3Pd5SZUqlenYYxB/1a2LjY1NWjdPSGeUSiVBwUE8ffKUp0+f8vz5cxo3aUye3Hm0Ul8ul+Pn54dMpkfWrNk0rufn95LrV65QtEgRChQooIUWCsJ/Q4QHgpAOGBkZEBERntbNEARBEH5yrVu3pnTp0rRs0YzL3huZ1LcJdjYWad2sFCmVSnyev42f8PDaY969/0i9ep6MnvgH1apVw8REN8OnhKQUCgWRkZFERUUl3CIiI4mKjMTMzEyjk12FQhFfLyKCiIgIIiMjiYiIQF9fHzc3txQfL5fLuX//Ps+ePePp06c8e/b/sODjx48AGBoaMn/+/FQFB7GxsTx/8Ryfxz48fvyYxz6PefToMU+ePKF4ieIsX7FS7ZoAgYGBXLxwgQsXznP+3Dn8/Pzo0rkzbdu0SVW934YkIemJYQvpiQgPBCEdMDY04IPoeSAIgiBoQc6cOTl3/iLjx4+jxaBFTOzThLJF86Z1s5KQyxXcevCcU1fuc/LyQ9CT0ahxE5auHEWZMmXQ1/+5D1OVyvi5TNL7PAxKpZIF8+aydfMmoqKiiIlJfm6eqlWrsnD+fLVqr1q9mmXLlycKC75VpEgRFi1arFI9PT09bt26xfjx44iNjU2y3cLCgrVr1lK2bFmV26hUKpk7by4HDhzg6dOnydatXacOc+fNV3nVjvDwcK5cucyF8+c5f+48Dx8+SFJv6pQp6f61IQjf+rnflQXhF2FkaEBUkJjzQBAEQdAOAwMDpk2bTvXqNWjbpjXVS/vSt11NDA3S9tAvOiaWS7d8OHXlIacu3yNTpkw0btKMfePmU6hQoV/qZEqSJHr37s3x48fJmjXrd28WFur1DImIiGDhwoXoGxmTPXsOsmXPjrW1tUbt7Nt/AHfv3OHc2TNJthsZGTF+3Di8unRR+/fj1aULN2/e5K+dO5N/3r79GDBgAAYqTlQsSRJVqlThn3/+4cyZ04m2OTk5sXnTZgoWLKhWGyVJomePnjx48IBHjx4l2d6yVSsmT5mKTI2J+86dPcu4sWN4//59km0lSpRk7tx5atUThPRChAeCkA6YmRrx6OID4uLifvorLYIg/HeUSuUvdbIlaF/lypXxvnOXTh3b0374cqYPak7OLI7/aRtCwiI5e/UBp64+4sKNhxQq5EqTpi2YtXQzOXPm/E/bkhxd/h1NnTqVffv2cezYse/uU6RIEZYtW0aJEiVUqmlqaookSQwdNDDhPisrK7Jlz0627NkTAoVs2bOTJ08eLCwsv1vL5/FjDh86yKGDB3j+LOnqDQXy52f5smXkz59fpbZ9ERgYyP4DB9i9Zw+XLl1Ksj1jxowsWLCQ0qVLq1zz0aNHLF++jL///pu4uLhE23Lnzs3WLVvJkkX9iUIDAwNZsXIFZ84kDU769OnDwMFD1H591KhZk4+fPjJy+PBE97vkzcvKVaswMlZvpYXk2iYIaUGcpQhCOlC5ZEH2HLuBV5fOrFu/QZwM6EhUVBTGav7D/hXp6kBZqVQSGhqGpaX2x1ffe/CAt2/fUbliBa1erQkPD+fvXTupUKkyWbNpPgmWKrTx84+Li+PfXdsoULgoOfOqd1Av/H7s7OzYu+8AS5cuoeOIMfRtW53aFQpjZqqb90OlUsnb98GcufqA09d8uH3/KRUrlKd5h15s2lkPBwcHnTxvavn4+NCnTx/q1q2Lp6cnuXLlSlWdgIAAvL29E93u3bv33WEAWbNmZezYsXTo0AFDwx8vXRsXF8fz58959OgRjx49wtfXF0mSEoZGfPr0iTve3tzx9gbiT867dO1O/vxJ5yd4+vQphw8e4J9Dh/DxeZxwv6GhIYaGhoSFhQHQrWtXxo4Zo/L/zdDQUA4dPszuPXs4ffo0cvn/l8w1NzdPqFu7dm3++GOmyhNfXr9+nSVLlnD06JGE+8qUKYODgwN79+6lWLFibFi/Qe2JNP39/Vm+fDkbN21MGE5hY21D8MdgAMaOHUvPHj2JSmYYw488f/6ciePHcfp04p4RGTNlYt36DVhaWalVD6BChQpqP+aXIeY8SFdEeCAI6YCBgT5zRrSm5/h1DBo0kLlz52nl5O7BgwcolUqdzOT7xNeXXLlza73upStX8XAvpPK4QnW069CBubNna31pr6XLltKieQvs7Oy0WvfM+QvkyZWLjBmctFr3xJmzbPhzOzWrVKZa5Yo4aelAXpIk+g4bjlKppHH9etSoUhlTLU1yli1rVmp51sfK2orWzZvTpmVLsmfX/GTfzMyMG9evMW3KJHLlzkOlylWoVKUK7oU9NAopggM/MGNoPwL9/VEoFMjlccjlciysrOkyaDjFylVMde3Hd71ZN+8PDI2MqNNcTLYlqCa+C30fKlasRK8e3Zi15gAZHezIlysTeXM4kj9XFvLnyoyVhaladUPDI3ny0h+f52/xfRmAr997fJ69Rl9fnxo1ajBoxERq1KiBubm5jr4zzbm4uGBtbc2AAQMYMGAA+fPnp169enh6elK6dOkkPQKjo6N5+PBhQkBw+/ZtvL298ff3T7a+vr5+oivlmTNnZvTo0XTu3DnJ/7rAwMCEgODrm6+vb7Jj8b+V09mZrt174FmvfqJAwu/lSw4dPMDhQwd5+OD/4+8NDAwoW648dep6UqVqVTp1aMe7t29ZtHAhVatUSfH5IiMjOXrsGLv37OHYsWNER0cnbHNzc6NRw4Y0bNCAiZMmceToUSZMmECrVq1TPMZRKpWcPHmCpUuXcvnyZSD+NVyrVi169uyFh4cHy5cvIyoqikWLFmNuZpZiW794/fo1S5Yu4c8//0xob6FChRgwYAAKuYJu3bsxe/ZsWrZoqXJNgOioKJYvX86ypUuJiYnG0NCQ7j168O8//+Dv78/6DRvImDGjWjW/OPqDniuC8F8S4YEgpBOmxkYsHNOeLqNXYW9nx+gxYzWqFx4ezubNm1m7di2nT5/GxcVFSy2Fu3fvMG70aEaPHUvRYsW1VvftO39WrltPUHAwG1etwFLNcaA/smbtWu7dvUv1mjVZs3o1ZcuU0Urdu3fvsm/fPpYvX87MmTOpVbOWVuoqlUp27NrNvoOHGNCnN726dtFar4lPISEcPXmKf44dB8CjkBs1qlSmRpXKuObPl+rgKjAoiLfv/Lnp7c3ho8cwMzOjTvVqNK5fj4ply6g8pjU5S5YtR08m4/XrN8yaN59Z8+ZTsXw52rRqSb06dVL9s3lw/z7e3rcBeOLrwxNfH9asWoG1tQ0VKlakUpWqlC1fHnNz9V6LNnb2mFlY8vKJb8J9JStWofOgEVhqMDY5OiqKvZvX4vfUl/6T/tBqD5Kd61fz1OcRgyZO18nwKYVCoZNlA+Pi4gjw98fBwQGDFK7eqkOpVBIWFoZCoeDOnTuUK1dOa7XTkqurK2fOXSA2NpaHDx9y8+ZNrl+7wvoDV/G+uxlrCzPy585C3uyO5M+Vmfy5M2NvY0lsbBzPXgXg8+Idvi/8efLqAz7P3hD8KZS8efNQyM2dYpUa0rlQIdzc3HB0dEzTXnTHjh2jZUvVT/6ioqISPn/w4AEPHjxg5syZ2NraUqdOHerVq0fNmjWxtLQkU6ZMBAUFJVsnd+7cFCpUKNHt8OHD9O3blwwZMjBq1Ci6du2a7HvWH3/8wYgRI77bRmNjY/LkyUPevHnJmzcvu3bvTggCCrq60q1HT6pVr5Ek+Hz16hXVq1RK+FpfX5/SZcpSq04dqlWvgdVXV8Jz5c7D9q1bVe4d0rNXLw4eOpTo+2/cqBENGzQgT57/r3ZgaWnJsSNHyJZDtWEqx48fp1OnjkB8wNG4cWN69OhJ7q8uWlSqVJmuXbupFfQqlUpatGzB06dPAShatCgDBwykcuXKSJLEsWPHWLVyFbVr11a55hfr169nwfx5AJQtV46Jkybj7OzMo0ePmDJtGnnypO44LCAggM5duqTqsYKgbSI8EIR0xMrClGUTOtFp5BJsbGzp1bt3qmutW7eOadOmAVClShVOnz6d6q6YXwsLDaVTu7aEhobSv09vdu7ZS4ZUJunf2r5rF3sPHASgXtPmbN+4gQxOmo/NDQgIYPSYMQldKJs2a8aUyZPp3KmTxge3e/ftxftzN9HOnTvTrGkzJk2alOhgLDU+fvzE4SNHCY+IYOrMWWz6808mjhlNvdq1NG7z85d+CV1dAe4+eIilhQVWlhZkypABO9vUraEeEhrKIx+fhK/Dw8P56++97DlwkGqVKjJx1Eicc2RPVe3rN28REhKS6L7TZ89x+uw5rKysaN6kMYMH9MfJUb3XS2RkBG9ev05y/8ePwVy/fg0LS0ts7ewoXqKk2m3OX8iDx3e9MTYxpfPAYZSuUl3j352RsTFeQ8dw9t+DFC2r3W6sTx495PH9O1o/4QsJCWFg7540bdmK2nU9tVJTqVTi9/IFx48eZfvWLSiUCnbvP6hxePBlSMm+ffvo3bs3BQsWxN/fn44dO/4y4cEXBgYGuLm54ebmRvv27YH4gMfX1/dzoHCVnaeucGvBTiQJIiIiyZolM65ubrgXLk/dVoVwdXXF2dk5XU78FhsbS2BgoMZ1goKCOHPmDFZWVjg4OFCpUiXy5s3L/fv3k4QErq6uyfawuHHjBnPnzqVHjx4/XHIyR44cQHzPhC8BwZdbvnz5yJYtW0IAFxcXx+IlSyhRshTdevSkbLly3/3bzZIlCwVdXbG0tKR2HU+q16z53S7+0/+Yib2l6r1E6tSpw21v7/geBg0b4ubqmmw7Zs+ahZ6eHlExqg0BqFy5MgULFqRs2XJ4eXUhY8ZMSfbJly+fyu38QpIkunXtxt59exnQfwDlvvm5Va1aNdXvge07dODY0SN06NiJup6eCXUmT5mq0VAdR0dHxo4Zw+gxY1Jd46clSUjp8P3ldyZ9fQD5rYJ5siq3zR/w37VGEAQAXr0LpPOolcyZt5DWrVur/fioqChGjx7N3LlzE+7Lli0bZ86cIXv25E/eHj1JOlFScrZt3cKsP/4gPDx+7KJboUJs/nP7d4cZOBipdqUxOjqafkOGsfPvvf9vc9Ys7Ni0gTzfCT0Uhqp1iQ8JCaFe/frcf5B4qaQ2rVvzx4wZSdoeGydHVbPnzE70cwbImCEjc+bOoVLFSkn2N1IkP/b1Wy/9/GjbpSuSJGFpYYGlpSWWlpbUrVkDz9pJezfIo6OSqZK8G7e9aePVnaqVKlCzSmUqlSuHhcX3DxbVWWO5dZeunDx7Do9CbpQpWZKypUpQomhRzEyTdoXWN1W9m+nDR4+pUK16Qrdda2trinp4ULxoEYoVLUpRj8KJZht/E6La6iVyuZw/pk1hy6aNSJKEu4cHlSpXpVLlyuTO45LkINL7reonI5ER4SyeNBavoaOwsbP/cTsU3/9frCkTA9V+f7Gxsejp6al8IpjL7vuTsH1r8vixlChZipp16qq0v0yFHgpLFs5n+ZL/L+927vI1rFLo1eHq8uMA9datW7Rr1w5zc/NEk7wtXryY3hqEuT8zpVLJmzdvsLW1/eGJb3oTGhrKy5cvVd5/8uTJbN++HYg/wSxZsiT16tWjXr16uH5zQvzx40esrKxUPsmMjY1VqedVZGQkcrlcpSEeb9++5fS5C3gUKaJSG2JiYlKcV+ELOwvV35vj4uLQ09NTuVeRquEBqNdbSZX3jNTUBdSa80CdOW0sTNTrMefg5HRdqVQWU+tBP7lC+fMqD65bntbNUFm20lV++d+RCA8EIZ3yef6W7uPWsH7jZurWVe2A+4vo6Ghu3LjBpUuXEm4vX74kZ86cnDlzJtnZiFUND6Kiojh/9ixHjvzLyePHCAkJoWHjJkz/Y2ay/zBVDQ8AwsLDefrsGb5PnvLk2TN8nz7j48ePTBo7hrx5ks6voGp4sG//fk6fPo2/vz/+AQH4+/sTEBCAXC6neLFirF27lgxO/59XQNXwQKFQ8PTpU+RyOXFxccgVcuRx8Z8rFAo8PDySHKypGh6oS53w4ENgEDbWViqfJKoaHoSFh3Pl+g1KFC2i0vhTdcKDeYsW8erVm89hQRFyOTv/8ABN1fAgPDycqZMmUKJkKSpUqoSt7Y/nrVAnPJDHxaEnk6l0IJkewgN1qRMeBAcH8fL5C9w9PFTaX5UTgdiYGNq0aMqD+/cBOHXhcorzjqQUHgAMHTqU2bNnJ7pv2bJl9OjRI8XHCj+ngIAA3NzcKFeuHJ6entStWxdHNXsxpQVV/2+rS53wQF3qhAfqUCc8UJe6EyaqSoQHKRPhQfojwgNBSMduP3xB/6mb+HvvPsqXL69RrTdv3nD58mWCgoLo1KlTktQ9NQchsbGxXLl8iSP//kPp0vHjJ7+lTnigLlXDg2Qfq1AQGBiIf0AAEiRaF1qdngfqSg/hgbrU6XmgDnXCA3WpGh6oS53wQB2/enigLlVPBB4/ekTLJg2JjY3lxNkLOKRwwqdKeBAREYGbm1vCmGiAVatW4eXlpVKbhJ9PcHAwpqamOpmoV5dEePB/Ijz4NYnwIP0Rcx4IQjrmni870wc1p3HDBhw9foLChQunulamTJlo1KiR9hrH/2dpLluuPD8KItMjPT09HBwc0t2SYYIgqM4lb1569xvA/DmzkMvjUn6ACkxNTVmxYgXVq1dPuC89jukXtEfdJf4EQfivSDq7gCGkju5iOkEQtKK0hwsjunlSu2YNfL6ajC69SctZtQVB+H117OKFu4cHcVrsMVStWjU6duyY8LUuVp8QBEEQhJ+NCA8E4SdQo5w7PVpWplrVKrx69SqtmyMIgpBuyGQyps6Yhb6Bdk/wZ8+enTDuXfQ8EARBEAQxbEEQfhqNa5QgNDyKalUrc+78Reztfzx7uyAIwu8i++cl7rTJzs6OhQsX0rJlSxEeCIIgpAFJ0t28S0LqiJ4HgvAT6dCoAuU9clCzRjVCQ0PTujmCIAi/tObNm+Pp6SmGLQiCIAgCIjwQhJ9O37Y1yZ3RnHqedYiK0t0M+4IgCL87SZJYunSpmFBPEARBEBDhgSD8dCRJYmT3BpjqRdG8WRPi4rQzw7ggCIKQVNasWalcuXJaN0MQBOE3JCHJZD/N7Xcg+uEJwk9IJtNjSv+mDJi+mc6dOrJ+w0b0NFzjOG+unFpqXWLBb3Q3waNMRys8yNDerO3fkutovWiZkXrrRatX20QndSNidfdzDomK0Uldc0Pd/Nv0DgjRSV2AkpltdVJXrtDd8qyydHZpQ6wmkz48P7ZXZ7Vt3EvrpK5fsO6GGGazs9JJ3ahY3V2UUOjofSMqVjfv+QDGWp6M9YtYLa4QIwj/lXT271kQBFUZGOgze1hr7t2+wsAB/VAqdXcgLwiCIAiCIAjC702EB4LwEzMxNmTh6PYcPXyACePHpXVzBEEQBEEQBEH4RYlhC4Lwk7M0N2Hp+I54jVmNoaEho8eMTesmCYIgCIIgpEipVBIdHQ2AsbF6w+/kcjmBgYH4+/vz5t07AgL8CfD3xz/AH1sbW3r37YuRhkP6YmJiePvKj5cvX5IjRw6yZ8+uUb0v3r9/z+NHjyhRooTa3/dvRQLEUo3piggPBOEXYGdjwcrJXegyehkGBoYMGz48rZskCMJP4O6tmxR0LyzG9AuCDvj4+vIq6BN2dvZYWVtrPDeRrgQFBbFx8xb09PSQyWQoAJlMhp6eXsJ9MpkMPUmiUqVKODo6qlQ3IiKCAYMG8eLFC6Kjo5O9xcTEULFiRWbNmqPSSfSTJ08YMmQwr1+/4sOHD8jlSecNqF6jJiNHjVEpOFAqlQQFBvLS7yWv/Pzw8/PD7+VL/Pxe4vfSj3fv3qJQKOjevTvly5dX6fv+WlxcHE+fPuX+/fvcv3/v88f7BAUFsXDhQhEcCD8dER4Iwi/CwdaSVZO98BozH319GYMGD0nrJgmCkM49f+LL7j83M3T8ZExMTdO6OYLwS4mOjqZV08ZERUWhr6+Pra0ddvZ22Nk7YGdvj52dHfb2DmTLnp0KlSqrFOJ9+viR8WNGYWFhSb5cOcmcORNZsmQhS+bMZM6UCTMzM7XbaWtrS2xcLFOmTf/uPhkyZGDG9OkqBwcApqamDB86lPoNG/Lhw4ck242MjJgwYRIdO3ZUOcDMlSsXgwcPoVOnDskGB/0GDKR3n74qBzVyuZwlSxazacP6ZLfr6ekxZcpUOnTooFI9gA8fPjB79izu3r3Lw4cPE3pWfGFgYMCyZcvwrOupck1BSC9EeCAIvxAneytWTuqC15jZGBga0rdvv7RukiAIOiCPi0Omr/m/8DIVK7NgxhRePn/GpDkLyJQlqxZaJwhCTEwMcXI5pcuW4+TxY8TFxcV3qw/wT9hH38CA5i1bUadefZVPnq2sralesxYjhgxKdruNjQ1ZsmSmRLHijB4xHDs7ux/Wi46O5uLlywQHBWNoaEhMTNJVC9q1bcu4sWOxtLRUqY0AL16+ZP/+/ezfvz/Z4KBAgYIsWLAAF5e8KtcMDg7mr792sHnzpiTtNDMzY9acuVSvUVPlegD6+vr0HzCQB/fvc+3qlUTbTE1NmbdgEXVqqVfT3t4eT896HDlyJElwYGRkxMqVK6lSpapaNR8/fqzW/r8OCUkMW0hXRHggCL+YjI42rJzcBa8xUzDQN6BHz55p3SQhlZRKpU66k0dHR2NoaKj12u/fv+fq9esU8fAgg5OTVmtPnDSRwA+BFChQIOFmb2+vcd2IiHBOHTtK6XIVsLHV3pKG8rg4FAoF+gYGWv85y+VxnN2zjUrN2mpcy9benoLuhbl76ya927di9LSZFCtVRgutjBcXF4dMJhPDIoR0KyQ8nFfvAiirwVKNcXFxPHz0iJu3b3Pr1m1u3r7F3Xv3kz0R/6KOZ336DBhA1qzZflhboVDwyu8ljx894vGjh/g8fszjRw+/u7+hgQHtWremQ7t23+0S/+r1a44eO87RY8c4feYMYeHhye7n7OzMnFmzKFNGtfeEL4HBvn37uO3tnXC/np4eCoUCiF/2tHv3HgwaNBgjI6MUayqVSm7fvsWmTRvZv39/wsm4ubk5YWFhAGTNmo3lK1fhklf1IALAz8+P9evWsHPHDiIiIhJtc3R0ZOXqtRR0dVWrJkBAQADHjh0lJCTxErwmJiasXbuOcuXKqV3TxcVF7ccIgi6I8EAQfkGZnWxZOakLXceMQ19fH6+uXbVa//379zg4OGi15hebt22jXu3aWFlpf/3qbdu307JFC63XPXPuPKVLlsDAwECrdf8+eAh//wC8OrRDXwtXmb/wDwigU4/edGrXhsYN6mNqYqKVuvb29ixdsYJz5y+QKWNGPAoXpmgRD4oULkzhwoWx1uB32rpVa6pVr8bOXTsT7nN0dPx/mJC/AIUKFSJ37txq1TU1NePUsaNMGTsKV/fClKtYmXIVK5EzV26NTnhjY2MZ2cuLJ48eYmhkhKGhEYZGRlhYWtKue29KlK+Y6tpndv3JmyfauwpVvko17t66SWhICOMHD2DklOmUq6zeVbHvUSgUzJw6ia69+uDgoHp3Z0HQVJxcjkKhwDCF92ULU1P6TZ2D6eI1tGnVkmaNG//war1SqeTR48fc/BwS3Lp1G++7d4mKikqyr7W1NfkLFuTq5cvExcUBULZ8BQYMHkK+/AWS7B8SEsKjB/d5/PgxPo8e8vjRI3x9HhMZGZni9+vo6MDAfv3o1KEDJt+8p8fFxXH5yhWOHDvG0WPHuXf/fpLHVqtSBRtrG5YsX45MJqNXz54MHjQoSa1v/SgwKF26NPXr1aNC+fKULluWzJkyMWfufEqXTjmoiYyMZO/evWzevJE7d+4k3F+gQAHatWtP1arVKFGiGGXLlmPJkqWYqdEr4tatm6xZtYoj//6TEGq4uOTF3MKcG9ev4+KSl1Vr1pIpc2aVa0J8z4hly5aybt26hNeDkZER0dHRmJubs2HDRkqUKKFWTYh/ze3atUvtxwmCLojwQBB+UVkz2rNiUhe6jhmJvoE+HTt20lrtcePGce/ePcaNG0fVqlW1dlXx/oMHrF63gdnzF7Jy8SJKFCuqlboAGzZtYtCQIURFR9OxfXut1fW+e5c5Cxbi4GDPikULkcm0071OqVTy94GDHDpylG27djNz8kRKFC2ildqnzp7jsY8P/YcOZ+zkKbRq1pSObdvgouaJ97eePnuG9527ALx5+5Y3b99y8PBhAHLmyMHEceOoV7dOql4vo8eMJjY2NtF9AQEBBAQEcO/ePdq2bavy1bGvPbh3l1s3rqNUKrlz6yZ3bt1k2YK5ZMqchXIVK1O2UiU8ihbDwMBQrbrGJiYYGBrFzyQeFUV0VBTFy5an26BhZMik3gHp16Iiwnlw+RwZcuRKdY1vlatclWVzZ6Gvr0+nXn21FhwAGBoaYmNrx7AB/Vi5fpPWA7Y3r1+TMVMmnfRsiIiIQCaTERoaqpVeLj+zmJgYZs+ezaBBg7Q+wdvMmTPx9/dn1KhRKXaxV8fVO/dpPXQspsbGWFuYY2VhjrWlBdYWFlhbmmNtYRF/n4U5+Zyzc/D0eUaMHsPYCROpXbMmrVu2oFqVKskGtzXqeia5qmxhbo67eyE83AvjUdgdD/fC5MiRnYt37lOnWhXcCrkzYPAQSpT6/onz9q2bWThvbpL7DQ0NyZUnDy5585HHxQWXvPlYsXQx169exd7engF9+9KlU0dMvzNvSXBwMHUbNESpVALxV/+LFS1KjWrVqF6tGu6F3NDT02Px0mW4FyrEnNmzcXNzU+nnPH3GDPbs2QP8PzBoUK8etevUwfHzRQZvb28aNWrEH9OnY2Si2pwM3t63GT58aML3X7euJ+3atadIkSJIkoSfnx+dO3dh9Ogx6OvrE5PM/AfJUSqVTBg3lnt34/9XlS1Xni5eXpQrX4HJkyZibGzC4iVLsVAjjPji33//ZdmyZQAULlyYoUOH8c8//7Bv3142bdqMh4eH2jUBQkNDmThpYqoeKwjaJsIDQfiFZc/swIqJXeg2bCj6+vq0bdtOK3Xv37/P2bNnadGiBa1bt2bKlCla6Slwy/sO3p//oddt3IRRQ4fQv3cvjWeoDgoKYsKkSSiVSgYNGYJCLqdzJ+2EKVt3/MXZCxeA+CsMi+bM1sqM2u8/BHL1xk0A7j18SN1mLWjbojljhw3B1sZGo9ohIaFEfL6SFRISyoo161ixZh2tmzdj/KgR2KfyAN7ayorMmTImHFQbGBhQt3ZtOrZrS4Xy5TX6ueTLm4+bN28S/lX32qJFitKpUyc8PT0xNFTv5P4LPT09DA2TntCaWVhgZmGOubkFMlnq/lVWrlmHh3duY+/kRLeBwyhZvqLGJ7nGpmb0mLmU2JjolHdWkVPGTDRu1YYGzVvpZM6D5q3a0KGzl9aDA4VCQevmTZg5Z94PT8jU9ddff/HkyRNGjhxJzZo1yZcvH/Pnz9da/Z9RmzZt2LlzJ9evX2fHjh1aC0mDg4OZMmUKoaGhrF69mmHDhjFgwICESf/evXuHsbEx1tbWatf+GBoKQERUFBFRUbx5n3TcfXJiY2PZd+AA+w4cwMnRkRbNmtGmVUvyfu42LkkSZcuUJjgomMKF3SlSuDAehQuTy9k52fe4169eMXfhYqrVqJni33/efPnJnDkLeVxcyJM3Hy558+KSNx/ZsmdPFGJERUXx5vVrJo4bR9cunVOcJNHBwYFqVatgbWVNjWrVqFqlcrJBTflyZenRrStxSlV+UvEaNmhAQEBAksDgay4uLixfujS+7TFxKtUtUaLk594FJWjWrHmS9mbOnJnx4yeo3tDPJEmiW/cenDp5gk5dvMj/VQ+QKlWqMnLU6FS/VzVp0oQTJ47TvHmLhAsrN27cYPv2HRQsWDBVNQEsLS2ZOGEiffr2SXWNn5YEkix9rlLyu5K+pJDJKZgnq3Lb/AH/XWsEQdAJ3xfv6DF+DQsWLaVly5Ya1VIqldy/f5+sWbOqNHlS8JtXKtedPnsOe/buIyw8nPDwcMLCw6lYrhzLFs5Pfgy9QcrjJQG279hB3wEDErqNAvwxfTpdu3RJ/gGxqp2YBbx/T+tOnblx81bCfZ3bt2PWtKnfPUiURyft2pqcew8e0qxDJ+Li4jA2NsLYyBgTY2MyZsjAxFHDyZsnT6L99dQ42Fm7cRPL16yjUMECFHJzxa1gQQq5FsTuO2P+ZUaqD2sYOXYs/x45Ssd27WjVovkPh7dExKp2pQjiXx9lypbh7du3NKjfgM6dO+Pu7v7d/V8EflK59oAeXbl57QrFSpaibMXKlK1QEacMGZPd90N4yt2Hv3j3+hX//L2Llp27YZxC11/vgJAfbtdEycwpz+Ugl8vVPiG0MVHt7y81DPVTbsuD+/d44uuLZ/0GKtd1dUm5x8bly5cpVapUwtcLFy6kb9++Kj/Hr+jUqVPUrFmTmJgYevfuzaJFi7TS20OpVPLvv/8yYsQIbt++DcTP6j9u3Di8vLy4cuUKAwYM4MiRI9jY2PD82F6Va8vlckLDI/gYGsbH0FA+ff74MSTs/5+Hxn9+z/cp/oFBydaxtLSkRLFi9OzWjapVKqv9PfoFh6r9mJSEfx7rnz976nsx/UhUrGon+KmqrWJ4oC5Vex6khrFB6q+1/ui9VaZmoJ4pc6brSqWyWKob8xNydy2g/HfH1rRuhsoyFvT45X9HIjwQhN/E4+dv6TVhHUuWraBp06b/2fOqGh4kR6FQEBkVhUKhwMLcPOkOKoYHAJ8+fcL7zh1u3b7NbW9vvL296dK5M92Tmw9CxfDgi1ev33D1+nWuXLvO1RvXKV+mDONGjkj24FrV8EBd6oQHMTExal2pVyc8uHP3Hq4FC6h0YqFOePD06VP2799PmzZtVOpCrmp4EBERzrXLlyhesrRKSxWqEx6oI63Dg9RI6/BAoVCo3ZtFlfAAoFKlSpw+fRqI74pco0YNtZ7nV7Rjxw5atmyJUqlk2rRpjBw5Umu1FQoF27ZtY8yYMTx79gyA3Llz4+npyfz58ylatChHjx7l0/UzWnvOLyIio6jepU9Cz4Q8uXNTongxShYvTvFixcjr4qJRryldhAdfZLPT/txAIMKDb2kSHvyICA9SJsKD9EeEB4LwG3n49DW9Jqxj1Zp1NGzY8D95Tk3CgxSpER4kJyQ0FHMzs6QHhmqGB9+KjIzEwMAg2bGy6SE8UJc64YE61AkP1KVOzwN1iPDg/9I6PEgNVcODQ4cOUbduXQCeP39O9uzZddKen82CBQsYMGAAAOvWraNjx45A/NAwWy2sVhITE8OKFSuYPHky79+/T7StSJEirBk9EGtLC42f52vHLl7h5oPHVPRsSPGiRbXyfXxNhAff1BbhQQIRHqTM3bWg8sjObWndDJVlyF/ol/8diUEkgvAbyeecmcXjOtK1S2f279+f1s1Jc5YWFlqZn+BbJiYmWl0dQRCE/1bt2rVxdXXF2NiYrFm1Pw/Ez6p///4MHRo/iZ2XlxeHDx/m5cuXdO/eXSv1DQ0N6du3L0+ePKFJkyaJtt24cYO2w8fzMUS7J+PVSpdgaOe21KxeXevBgSAIwq9GhAeC8JspkDsL80e3pVPH9hz+PBO+IAiC8H+SJDFs2DDy5Mmjk4DxZzZjxgzatGmDXC6nadOmDBo0iJ07d/LgwQOt1JfL5cyfP5+///47ybZ7vk91EiAIgiAIqhH/EQXhN1Qob3bmjWxLu7atOXr0aFo3RxAEId1p2bIl1apVS+tmpDt6enqsXbuW6tWrExERkbD+/KxZs7RWv1OnTuzatYsxY8ZQq1atRPOc3PN9Spth4wgO0d1QH0EQ0gcJCUlP9tPcfgciPBCE31Th/DmYPaw1LVs059SpU2ndHEEQhHTFwMCA8ePHp3Uz0p3AwEBmz56dsCTrF5s3b8bPz0/j+pIkkSVLFho0aMDkyZM5fPgwAQEBvHjxgmXjhtOrZRNsrSzpM3kWQZ9EgCAIgvBfEoNyBeE3VtTVmVlDW9K0SSP2/L2P8uXLp3WTBEEQ0g0rK91MSPczs7W1xcXFJUlQEBsby7x585g7d67Wn1OSJLJly0at8qWpVb40EL/MY3RMjNafSxAEQfg+0fNAEH5zxQvlZvqg5jRq2IALFy6kdXMEQRCEdEySJJo2bcrDhw8ZOHBgojXsV65cSWBg4H/WDmMj3a34IQiCICQleh4IgkCpwi5M6d+UBvU8OXDoMCVLltRabZtMWbRW61vPXrzUSV254vtL2GrK0sRcJ3UNdbSUFIAiLlYndWPjdLe0lo2pbk4qTAx0M6bRWUdLroHulj2891Z3J4n2G0brpO76g091Uheg46tHOqutK/4P72j0+OHdOuFZoQzDJ07h2q3bhIeH88eEcQwbNFBLLUzM2q2ETuoCyKN1swyrnbmpTuqC7pb+NTYy1kldAAOZbt6PQiM1W2L5R9RdUlFVBjp6b/6lSCDp6DUjpI7oeSAIAgBli+ZlQt9GeNatzbVr19K6OYIgCMJPoGC+vOzbsoE5k8ZjY2XFms1bCY+ISOtmCYIgCDogwgNBEBJUKF6AsT0bUKdWTW7evJnWzREEQRB+Anp6erRp1oSzh/ZSo0oltuz4K62bJAiCIOiAGLYgCEIilUu5EidXUKtmdY4dP4mbm1taN0kQBEH4Cdjb2jJ/6iReBXxI66YIgvBLkH6bJRB/FiI8EAQhieplCyGXK6herSonTp6iQIECad0kQRAE4SeRPWvWtG6CIGhNRGQEQUFBZMmsuzmcBOFnIcIDQRCSVatCYeRyBdWqVObEqdPky5cvrZskCL+9G9ev4VGkKJIkpXVTEkRFRnL35nXyF3LHzNwirZsjCL+k69evExoaip6eHvr6+shkMkxkEjKZHjKZDH2ZPnoyPYwMjXDOmSNN3yOUSiXnL1wgMjKSmJhYYmJjiP38MTo6hpjYWGJiYoiNjaV0yZKUKlVKpbphYWFcvHyZ0NBQQkJCCAkJITQsjNCQEAKDPxIaGkJoaBhWVlaMGzeezJkzq1Q3Li6Ot2/f4ufnx8uXL/Hze5nw+atXfujr67Nxw0aNwoOwsDCePn3Kk6dPePLkCU+fPqV+vXrU8/RMdU1BSAsiPBAE4bvqVi5CnEJB1cqVOHXmLHny5EnrJgnCb+3EsaN8/PiRKlWraa2mUqnU6ETD2MSEwAB/2tWugrNLPlyLFKNQ0eLkL1QYE1PdzTQvCOnV8xcvOXD4H3I55yRrLheyZcuGkYbLSkqShFdXLyJ+MBmlu5sbC2bPVOvv+e27dzRo1oLQsDDMzcywsDDHwsISc3PzRDdbWxs6tm+PrY2NSm19/+EDXbp1Ry5PflUdY2NjJowbR4kSqq+gYWZmxsWLF5m/cOF396lRowYzZ87EyspapZp//vknY8aMIi4uLtntBQoUZNPGjWTMmFHldl69epXr16/z5Gl8SPD06VP8/f0TtkuSxOTJk0VwIPyURHggCMIPNahaDLlcQeVKFTl95iy5cuVK6yYJgk4plfFLdaanq/tf+Pv7s2j+PCpVroKelpYPe/zoEb6+PtT1rJfqGjUbNuFjUCB/rlmB78P7/L11IzKZjNz5C+JWpBhuRYqRz60QRsYmWmmzIKRn2bNl5eKVK4yfOg2In1Ayc+bM5MyZE2dnZ5xzOpMzZ05y5sxJtmzZ0Nf/8eF4XFwcsXGxFC9enNOnTyfZbmJszIihg+np5ZVirW9lzJCB6ZMn0qxNO/y/s0+1qlWYMWWqSsEBgEKhIIOTE4Xd3bl+40aS7YXd3Vm2ZAl5XVzUamtsbCzFihbFydER/4CARNsMDAwYNWo0nTp1Vuu9u1WrVvj7v2PevLlJtlWuXIXFi5dgY63eUrqZMmViwsQJyU48bWhoyOJFi/FUMzi4cPGiWvv/MiTEnAfpjAgPBEFIUeMaJYiTy6lcqSJnzp4jR44cad0ktSkUCiRJSpcnhP+1TyEhWFpYaP1ncdvbm9dv3iS7zdbWllJqXGH6mkKhYOvWLVhYWJApUyYyZsyEk5MTBgYGmjQ3QUBAAHfv3OHe3TvcvXuHYsWK49Wtu1ZqA0RGRBAUFEjgh0AyZc6MvYNDqmv5v3vH40cP+ffwIWrX1c5VqzwuLnRo1xa/ly/p3rNXql8XzTt15VNwMId27wBALpfz6K43j+56s2vTOuq1aE2HXv2RaWHN7ug4OYER0QRFRpPRwhQ7U82u6gq/n6CgIB77+lKyeHGtvBdGRkbx7PlzfJ8948nTp4m2KRQK/Pz88PPz48yZMwDkyZOH3r16kyVL8l3h/f39OXnqJCdPnOTM2TN8+vQp2f0qlivH3D+mk1ON/8vv/P05f+kSFy5e4sLlKzx6/DjZ/XLkyMGMKVOoVbNGij+juLg4Lly8xN79+zhw8BDv/JNGETKZjEEDBjBk0CCV37+VSiXXrl9nx19/sWfvXoKCgpLskz1bNhYtXoK7e2GVan6pe/78eVatWsmpUyeTbG/bth0TJ05SO4yB+N93/vz5k4QHFhYWrFu7jjJlyqhds0zp0mo/RhB0QYQHgiCopHnt0sjlCipVLM+Zs+fJli2bTp5n2rRptGjRQus9HCIiIpg2dQoTJ03W2knnF+vWrqFOXU+cnJy0WvfWrVtERkZSWssHDffv36drj57UrFGd2jVrUq5sWYyNjTWua2NtQ7PWbXn/IfFM640a1Gfm1Kmprqunp4eRkRH9+vVNuE+SJBwcHMiUKRMtW7aiRYuWal+JVyqVzJ01k1UrVyTc16FjJ7y6ddf4ZCLwwwcG9u2Nz+NHRH7uZuzVvSc9+/bTqG7A5wPyJQsXUL1mrVQd2H5LT0+PsuXKMWf2LF6+fMnEyVNS9TciSRJdBgzh08dgzp84mmhb5dqeqQ4OlEol//i84eCjVwRFxBAUGU1YTHwX43wOliytr9p4aSF9ioyKwtjISOthZnh4BK/fvMbJySnZsNTGxoZe/QZgYmJC5w7tadqgHhbm5irVPn7yFL5Pn+L79BlPnj3jydNnvHr9WqXHFvEoQp++fahRvUaS96y7d++yf/9+Tpw8wb179xK319qGChUrcPnyZd69e4e1tTWTx42hdfPmKf7sIiIj2X/wEOcvXeLipcs8efYsyT5WVlYJAYWpqSmDBw6gT8+eKf5vOHf+PDt27uLAoUMEBgYm3G9pYUGtmjU5c/Ys7/z9cc6Zk5XLllKkSNEf1vsiICCA9Rs38tfOnYnCGEcHB5o0bsyGTZuIiIigfr16LJw3D8lAtQBRqVSyZ89uVq1ayf3794H49678+fNz//59JEli1KjRdO3aTe3XpK+vLwsWLODvvX8nGa6RIUMGNm/anKoJqKOjo1m+YkXKOwrCf0CEB4IgqKyVZ1ni4uIDhLPnLqg8GZGqzp49y9q1a5kxYwbLly+ndevWWqmrVCqZPm0a2/78k1evXrF4yVIsLS21UvvkiRNs+3Mr69euZe2GDeTKlVsrdePi4pg3fz7HT5xg7uzZNG/eXCt1AU6fOcur169ZvXYdq9euw8zMlMoVK9GgXj2aNG6U6ivD7z98SBQcODo4MOePGXjWqa1xm/fs2ZPoa6VSSa5cuRk8eDDFi6euR4MkSRT2KJLwdcfOXRg+cpRWTmLs7O2JiY4mMiICU1Mzpsz4gyrVa2hUU6lUkitPHoyMjejs1Y3ADx9wypBB47YCVKhYkdOnTuLhUSRh2EZqyGQyBoydRGjIJ7yvXUm43//N61QPs5AkidoumclgbsLyK494+Sk8YZvPh1Ci4uQYyrQzhONrT2MieRcXSxlT7bxXfBEeHs6oUaMYNWqU1gPHvXv34uvry+DBg7VaN/jjJ7oPGsrEEUPIr2ZX85QMHzcB73v3aN+yJU0b1MfSUjuTbl6/eYMGTePfN01NTHBycsLJyZEMjvEfnZwcyZ4tK6fOnGXIiJGMnzyFZo0a0rl9Wwrmz//D2gOGj+TN27dJ7re0tCBXTmdyOeckV84cLF6xivDw+Ndr5cqV6dO7D6VKlfrue8zRo0dZtHhRwtfu7u5UrlyZKpWr4OHhgZ6eHvny56NevXpMnjSZbI52Kv0slEolfQcPSRjXL0kSrgUKUKZUKcqUKknpkiWYNW8+q9atp3GD+kyeNIksKv5//2vXbjZs2gTEBzJ1a9eivqcnlSpWxNDQkBx5XOjSqROTJ4zHzMwMuUK195eg4GBmzJwJxIcZdWvXplmzZlSqUIHY2FjWrl/PrBkz6NypE5IkERoZrVJdSZLYuXMn9+/fx9jYmGbNmtOlixfPnj2jR49uzJ+/kDp16qhU61vPnj1j1+5dAJQvX5769eszdOhQcufOzdatW1M94aKenh5b//wzVY/9+UlIOnh/F1JPhAeCIKilXcPyyBX/74GgziRCKfHz8+PJkycAtGnTJv5AatEizFW8GvQ9oSEhnDh+DIBzZ8/SvGkT1qxdR+bvdBdVx82bN3j6uc0tmzVl5eo1eKh4ZeVHAgICOHvuHLGxsfTt358nT58yfNgwrYxzd3UtmPC5g4MDNatXo2aNGlSuWEmjLuWF3QvRqX171m3cSPOmTZg+aSK2trYatxfiXw8PHtwnKCiI4sWLM2jQkFR1/fxWhUqV8OrWDYVCydDhI7R69XP4mLFMGD2KuYuWkCu3dkKlhUuWaaXb/7cqVKzE0ROnsFFxTPOPGBgaMnLabMb07c6TRw9o5dWDkhUqafyz9chky/IGpTj3IoAVV314HhxGwwJZsTTSbk8igCcxkUx574ehJJHX0AQ7fe09R8eOHdm5cyd3797lyJEjWvt9ent706hRI5RKJZkyZaJVq1ZaqQswadZczly8hGer9iyfM5PqlSpopW5MTAz/HDtO8MePDB8/gQnTZ1C/bm3at2xJ8SIeiV4zL/z8yJnHReX3wLDwcCzMzQkNCyMiMpJnz5/z7Pnz7+4fHh7O+s1bWL95CyWKFaVTu7bUr1M72SvvVStVJCg4mFzOOcn9JSxwzom9nV1Cmx88fMSs+Qtp0KABvXr1ws3VLcU216hRg8c+j6lapSqVKlXC3t4+0faPHz+ycOFCatao+fme5Ccj/JaZqSmtmjfDytKSMiVLUqpEcaytrRPtExYezv6dOyhbujQyI9V7ojVv2gRJkqhfz5PyZcsm6rUUERHB6uXLqF5N/Qle8+XNS5dOnShapAh169ZN1CskJCSEfw8dopBbyj/T5PTp05cyZcrQpk3bhPc8f/93bN/+Fx4eHqmqCVCtWjW6de1Gw4YNKVy4MD4+PhQtWpQN6zdo9L/QwMCAKZMn07pNm1TXEARtkX50haFgnqzKbfMH/HetEQThp7H6r5P8c+EhZ86e18rVM6VSycyZMzl79iyhoaGEhYURFhZGlixZWL58ebIrPTx78VKl2idOHGfUiBEEfDXBkr29A6vWrMHd3T3J/qpeGQkNDWXUiOFcu3qFD5+vuBsbGzN/0WKqfmc2fEsT1bpWHj9+nE2bN/Pq1StevX5NcHAw9evXZ+H8+ZiYJJ10zthA9Sz41JkznDl7lto1a1K0SJEUD8YVcbEq1z5w6DD6+vrUqlE9xX1DolU78AUIDg6mS5fODBw4kHLlyqd4IhoZq3qbP3x4j52dvUont1Gxyc/InZyI8HDkCgUWFilfSTXQQSDwhaG+bmrfexv4w+0fg4MY1bML89ZvVXuiRPsNo3+4Xa5QcsT3Dfp6EtVzZ1K5rs/BpynvxOfeSh/88ImJoqSJBT1sUw5JO756pFLt+/fvU6JEiYQeCFM1GNLzrUGDBjFv3jwMDQ05evQoFSr8+CTf/+EdleoGBX/Ea8BgLly5ip6eHuOHDqZbh7bf/ZuRGan++w4KDmbHnr/ZvH0Hj3x8E+7Pmyc37Vq2oHmjhthYW7Np+w7+OX6SlUsWYWWl+gR2YeHh+Pv7884/AH9/f/wDAnjn74+/fwDHT57kQ+D3X8fFingwfeIEihRO+n8iJbe872BpYUHmXNrtqfE1YxXDA1UoFIqE/wXqhAfqUvX/q7pU7XmQGgapfA/99OkTBoYGmJokv+qMunUdnJyuK5XKYqlqzE+qcKFCyhOH9qd1M1RmlzXHL/87EuGBIAiptvzPY5y8/pTTZ87hoMEkcKmlanjwRVRUFK9fveLVq1f4+fkRGBhIu/btk1wRSM3BTWhICM+fP+fZs2e8fPmCxo2bkCmZbp+qhgffCgsL49Xr15ibmSU7wZY64YG61AkP1KFOeBATE4OBgYHKV6/VCQ/UoU54oI5fMTwAeP/uLQ4Z1O+dlFJ4kFqqhgcAz2OimPT+JUpgpH0WshgYcT0yjPJmyZ+4qhoeQPzycF+GZe3fv1/tmde/R6FQ0KxZM3bv3o21tTUXLlwg/w+64KsaHgDExMQycvJUtuzcDUDbZk2YPnZUsvNjqBMefKFUKrly/Qabtm9n38HDREZFAWBkaIhnrZpkzZKF+UuXkTNHDjatW5Pi0IKUPPbxoXzV6sTExCBJEnly56KQqyvurq64u7niVrCAVoa3ResZalzje7QZHnxNhAeJpTY80Hbd3zE88HAvpDxx+FBaN0Nltpmz/vK/IxEeCIKQakqlkiVbjnLhjh+nTp/Fzk618Zfaom54oCpdHdxA6sODlPzq4YG6RHjwf2kZHqRWeggPANYH+3M64hPZDIzIZmBEsDyOIfbJD3dSJzwA6NOnD0uWLMHa2pobN26QM2dOtR7/PZGRkVSpUoVLly6RI0cOLl269N3eYeqEBxD/nr9iwyYmzpyDUqmkbMnirJ4/N2EpO7lcjkwmS1V48LVPISHs/Hsfm7Zt597Dh0m2m5qYsHDeHJo0bJjq59i89U8iIiJwdy9EwQIFMDXU/vAXEOHBt0R4kPq6IjxI/36H8EDMQCEIQqpJkkTvNtUpkT8TVatUIjg4OK2bJAiCoDWNLe0wkfR4GRvNuYgQfGMikWswoeTX5syZQ4kSJfj48SNNmzYl6vOVdk2ZmJiwb98+cuXKxfPnz/H09EyYtE9TkiTRo2N7NixZgJmpKecvX6Vuy7Y8efYciJ8bISIyUuPnsbK0pEv7tpw8uI9/9+ykaYP6ibZHREbi1aMXYydMTJgEUF1tW7eim1cXShYvjrmZmcZtFgRB+B2I8EAQBI1IkkT/DrUonNuBalUr8/Hjx7RukiAIgkaUSiU7Pr1nhP9zIpWKhPujlUpexGrnCqeRkRF//fUXdnZ23Lhxg/79+2ulLsRPhHro0CFsbW25du0arVq1SrJ0nCZqVK7E/q0byZwxI09fvKBuq7acuXCJbXv+ZtbipVp7HkmSyJk9Oy9evUp2++LlK2jUvCXv339IdrsgCIKgXSI8EARBY5IkMahTHfJns6ZG9aqEhISkdZMEQRBSTZIkmlraU9wk6Uovj6MjtPY82bJlY/PmzUiSxMqVK9m4caPWaru4uLBv3z6MjIzYv38//fv3T1iG88MHzU+2C+R14fD2LRR1L8THTyG08OrOp5BQVqzfxPVbtzSu/8Vj3yfUrVGdPt28aNW8OdWqVKFwoUJkzpwJIyMjzl24QKWaNbl+46bWnlMQhPRCQpLJfprb70CEB4IgaIUkSQzz8sQ5gxm1alYnLCwsrZskCIKQanqSREdrJ2qbJ16+8lGM5t3yv1arVi3Gjh0LQI8ePbhzR715CH6kbNmybNq0CYAlS5Ywd+5cIiMjady4MQqFIoVHp8zRwZ6d61fj7lowIZhQKBT0Hz6S6Gjt9NAoWawovbt6MX7EcJYunM9fWzdz8sg/3L1+jbfPn/LC5xH7d+1EJpPxo3m8BEEQBM2J8EAQBK2RJImR3eqRyVqf2rVqaG2crSAIQlqQJIlmlvY0sbRPuM8nOhKFlk9Sx40bR40aNYiMjKRJkyZ8+vSJsLAw9u7dq3HtZs2aMWvWLACGDBlCp06dOHv2LP/884/GtZ+9eEm91u25ffdeovsf+fgyd4n2hi98jyRJWFpY4JwzJ4XdC6m8GosgCIKQOiI8EARBq/T09BjbqyH2Zkrq1qlFRIT2uvgKgiD81yRJwtPClnZWjkhAuFLBm7gYrT6HTCZjy5YtZMmSBR8fHzp37szy5cuZPXu2VuoPHjyYXr16AbB9+3YArdTOmT0b6xcvoI9XZ2ysEi9huWDZCu7cv6/xcwiC8BuTJCQ92U9z+x2IpRoFQdAJuVzBuIU7iVCacuDgYYyNdbf0k7ZdupN0aTBtsTfXbAmz73G01N1s4WGvnumkrjJWuydgXzPNlF0ndeX6ull2Tablk9Gv7binmyVNS1zZqpO6AP2m/qmTujPaldDo8Ud83zD15B36l8lP44LZEm1zGTZfo9oA127eokHrdsTGxmJgYEBsbCwX/jlIrpw5Ul0zPCKCpWvWcfLcea7fup1o29G/d+Lu6qphq+NFREaya+9+Vm/cxIPHPgC4ubpy+NAhDAy0swzih1DdhdH2FqY6qavLZXSVWhh6khx5tHaH5nxNMtLNz1mmp7vroVE6WvpXT802/5ZLNRYurDx19EhaN0Nl1o6//u9I9DwQBEEnZDI9JvZrgpEijAb1PbU2/lUQBCGt1MidiWk1PHj4/pPWa795+453AQEUzJcXgNjPJyzb9vytUV0zU1Mqly9HRETSE8Klq9dqVPtrpiYmtGvZnFMH97Hzr7+oWbMmd+/dY8lS3Q9fEARBEP4bIjwQBEFn9GUypgxshhQdTONGDYiJ0d3VVUEQhP9C2eyOtC2cU+t1o6KjWbh8Jbfu3E10/449e4mLi9OodrHC7hzdvYMxgwdi8lUvsH2H/+Xld5ZBTC1Jkihfrhwb16/n0sWLhIWF8fbtW60+hyAIws9IkqRakiQ9kiTJV5KkEclslyRJWvh5u7ckSUVSeqwkSbaSJB2VJMnn80ebr7aN/Lz/I0mSamrjexDhgSAIOqUvkzFtUAuiQ/xp3qxJwtU0QRCEn1U266RLOGrKOUd2DmzfSr/uXRNN/PcuIIBT5y9oXN/AwIC+3bw4deBvKpUrA4BcLmfV+k0a1/6eHNmzM2b0aDJmzKiz5xAE4dcm6en9NLcffh+SJAOWALWBAkArSZIKfLNbbSDP51s3YJkKjx0BHFcqlXmA45+/5vP2lkBBoBaw9HMdjYjwQBAEnTPQlzFzSEs+BbykZYtmGl9FEwQhbb164sPLwI/IdTTm+ndlaGjImKGD2blxHRmcHBPu37Zrj9aeI0fWrGxbvZIls/7A3taWTTv+4uMn7Q/DEARBEBIpAfgqlcqnSqUyBtgGNPhmnwbARmW8S4C1JEkZU3hsA2DD5883AA2/un+bUqmMViqVzwDfz3U0IsIDQRD+EwYG+swe3pr3r5/QpnVLESAIwk/MzMqawVsOU3bSSlos3saI7f+y8sRVjt715Yl/ILFx8rRu4k+tfOlSnDqwl1rVqgLwz4kTBAYFa62+JEk0re/J+SOHaFinNpu2/6W12oLwK5DL5bx//56Hjx5x5coVrR2zKJVKgoODuXf/Pu/evdNKTeGnkRnw++rrV5/vU2WfHz3WSalUvgX4/PFL8qzK86lNd1PACoIgfMPQQJ85w9swYNomOrRvx8ZNm5HJfo+lbQThV2Jj78Bqr0b023SAO37++LwLTLRdpieRxdaKzhWKUq9IvjRq5c/N1saGDcsWs2HLn4yb/ge7Dxyka/u2Wn0OG2tr5s+YypNnullR5XcTFRWl9ZWFwsPDWb12HXZ2tjg5OuLo6IiToyMODg7ofTW8RV1+r17x+vUbMmXKSAYnJwwNtbOSjEKhYPnqtcTJ47CytPz/zcoKKytLrO2dsLS0VOv5FAoFO3ftIjomBn2ZDAMDA2T6+ujLZOjr66Ovr4+hoSH6MhkZM2QgT548KtW9fuMG+w8eJDAwkKCgID4EBhIYGMiHwEA+fvyIUqnE3t6e5cuWoa+v2ilTdHQ0Pj4+vHn7ljdv3vDmzRvevn2b8PXbN2+IjIqifbt2zJg+XeWfwe8t9a/zNGAvSdK1r75eqVQqV37+PLlv5NtlD7+3jyqP/VZqHpMiER4IgvCfMjYyYN7ItvSbupHOnTqybv0GtZcrEn4vCoWC2w8ekiNLFmysLHXyHH6vXnHpylVKlyxBlswaB/O/BStTY5Z3asDQP//hgk/i5SDlCiU13fLg6ZE3jVr3a5AkiQ6tWlCqeFEWr1qDUqlMNB+CtuTKqf0JIH9HK1etYuuff+JasCAFCxbE1dWVooXdyZghQ6p/b2Zm8cvw9u7XP9H9enp62NvZJYQJObJnZ9CA/mTOlEmlurY2NjRr1ZqHjx4hSRIO9vZkypSJTBkzksHRgUwZM5ApY0YyZshA8SJFMDFRLRTR09PD3c2Vxq3b/vBqfWF3d+bPnUshNzeVatrY2tKmXbsf1uzYvj0Tx49XqZ0AhdzcWL12LVu3bUt2e7GiRdmwdi32jo7Jbk+Ovr4+O3ftYtny5clul8lkTJs6lc6dOql18eTAwYMq7yukqQ8/WKrxFZD1q6+zAG9U3MfwB4/1lyQpo1KpfPt5iEOAGs+nNhEeCILwnzMxNmTB6Hb0mbSBbl29WLlqtc4DhJiYGK1dWfmarg7m4+LiCA4OwsFB9YMWVd29dw9bW1uNDmiT8+ylH+P+mEOunNnJm8sZl1zOuDjnxMJcs8nl9PT0OHD0BCu2bMPW2hrnbFnJlT0rztmzff48G9mzZMZIg99v5kyZWLFmDd379iNrliyULlGCUiVLULpkCVxy507Vz+nylSvI5XJKlyql9dfIP0eOUr5cWcxMdbNmuqpMDA2Y16YO43cf5x9vn0Tbtl/yRqYn0bJUISxMjNKohb+GvLlzM2vSBORyucpXQIWU3bxxnYKurhgaauf12a5tWzZu2sT+AwfYf+BAwv329va4ubri5upKIVdXihTxIJez8w9rxcXF8djHh1u3b/Pq9Sv09fUTnTgrFAoC3r8nKDiYksWLM3TwIDI4OanUzoiICB48fEiVSpV4+OgRSqWSgPfvCXj/nlu3byfsV6yIB6OGDsHYWLWfj39AAJevXefylatYW1nxITAwyT7W1taMGDaMTh06YGBgoFLdFy9e4PfyJVkyZ+b5ixdJtufJnZsFc+dStkwZlepB/M/vwsWL311G2qtzZ6ZNnoyRkRFRakz0LJPJaN2qFSdOnODR48eJtllaWrJ65UoqVqyocr0vPOvWVfsxQrpzFcgjSVJO4DXxkxm2/maffUAfSZK2ASWBT59Dgfc/eOw+oAMw4/PHvV/dv1WSpLlAJuInYbyi6Tch/gMJgpAmTI2NWDSmPb0nradXzx4sW75CJyfhX0yaNAlfX1/at29PjRo1tHYAHhwUyMwpE2neuh0exYpr7XuQyWSMGDaUly9eUrxECYqXKEGx4iXIkiWLxs/x7t07ylashJ2dHYXc3BIOaN3cXHHJkyfVQY6jvR0Aqzb/mej+zBkzkNfZmSLubnRr2ypVJ7yFC+bHQF+foI8fCfr4kWvedxJtNzYyYoBXR7q1aYmhigekAG/evuXwv0cICQ3Fwd4BiO+F4PfqFTt27wbAztaWUiWKU6pECUqVLYebm5tKV4zMzcyoVKUKLi4utGvblhbNm2NjY5Pi41ISFxfHlevXGTxyJGOGD6dF0yZaCd9CPwYTFvKJjNlyqPU4A30ZU5pWx8rUmO2X4n8v9hamfAiNYNnxK2w6d4tWZQrRo0oJnf6N/0xSEzoaG6l2AqdQKHQSxoZHROgkrAoICMDKygojFb8/VX369AnvWzfJkDEjGTNlwtzcIsk+165coX+fXrRs1YYeXp1xVPEK85atW3nx4gVv377l7bt3vPv8MSwsLNn9P3z4wMlTp/D19cXW1oYqlSslu9/FS5fYs3cvt27f5s7de0RERPywHU0aNWT08BE4O/+450hERATLV63i7r173Ll7jydPn6L4wWSnhVwLMnLIYKpXqZzi6/TfY8fZe/AgV65e51kyJ/ZfyGQyOrdvx/DhI7C1tf1hTYDXb96weOlSjh8/ju+TJ8nuY2BgwIB+/RjYv79ar82Fixezet06XiTTXhMTE+bPmUPL5s1VrvfFzVu3mDptGmfPnk2yzdnZmU0bNpA7d26160ZHRzNh4kS1H/er+FUm5lUqlXGSJPUB/gVkwFqlUnlPkqQen7cvBw4BdYif3DAC6PSjx34uPQPYIUlSF+Al0OzzY+5JkrQDuA/EAb2VSqXGExKJ8EAQhDRjZmrM4nEd6TlhHf379WHBwsU6O7nYsmULz58/Z/v27Tg6OtK6dWvatWuHh4eHRs956vgxzp0+xenjx3DJl58WbdtTvXYdDAw06+UQERHB2TNnUCqVPH/+jL92bMfU1JQOnTrTs1dvTExMUl1734H47o+BgYGcPHWKs+fOUa9uXTJlyojk4pLqum/9AziRzJJyepJE+VIlaNWofqpPPv46+A+xyXRXtTAzo12ThnRp1Qwne3u16z5/8YKho8f8cJ/AoCAuXr5CxgwZKFYyTuUTs6HDhgHw+PFjxo4bx9Rp06hfrx7t27enRPHUB00nTp1mwZKlKJVKeg8cxKp165gyfhxlSpVKVb0vlk0Yid+Tx8zddRgDNa/E6ulJDKtbHmtTY1acuMqqLo148DqANaev8yQgCL/AT1r/21YqlUQo5cQplcgkCXM97R7S/PP4NZdffWBs5UIajS3/lv/793TvP4hJo0ZQyLWg1uoCLFy5itt377Fs9kyt9rR6+eoVNRs1o6dXZ3p16aTV3g8jR43i4sWLtGzZknZt25JTS0Mo7t+9S4+uXRK+Njc3J2OmTGTIEB8mZMiYEQmJwA8fWLJoASuXL6VB/fp4eXnhUbjwD2svXLSI58+fJ7vt2x4CAFUqV6Jr5y7UrFH9h8HjbW9vVqxanfC1mZkphdzcKOzujo+vL8eOnwCgWtUqjBs9mkKurj/+IXxmZGTErLnziIyMTLjP3t6OQq5ueN+9w4cP8T0E8ufLy8hhw6lTLeXQ4IuLV66wfWd80CpJEgXy5aVEsWJYWVoyf8nS+O+/YgUmjxtLPpc8SEaq/Q/Q09Njxcr4oeIymYwSxYtToXx5/pg1C4DixYoxf+5c8udTf06VGzdv8uLFCywtLGjSuDEF8udn6IgR5MiRg83r1+Om4s/1W3K5nLNnz6Knp0eN6tV56efH/fv3qVChAqtWrMDa2jpVdQ0NDbl582aqHiukL0ql8hDxAcHX9y3/6nMl0FvVx36+PxCo+p3HTAWmatDkJER4IAhCmjI3NWbJuI70nLCWQYMGMnfuPJ0ECEWKFOH58+dkyZKF8uXL4+LionKXyR9xcHRCHheHTCYjOjqac6dOEh0dTb1GTTQ6wDY1NaVW7Tr8+89hypUvT4OGjalWvTqmWrjy59W5E9t27MDG2pqO7dvTsX17MmbMoHHdzBkz0NSzDjsPxP9vK17Yna5tW1GzUgWNTzbqVavCy9dv8H0ef6XIyd4Or9YtaNuoARbmZqmum8HJCc/atbCwsCA8PIJ9X40rNTQ0pGa1arRo2phqlStjaGiIXF/1E7KyZcty9dr/502Kiori1OnTfPr0iVatWlGndu1UvdYtLMwxMzUlLDwcgFved/Bs0gzPOrWZOHoUOXPkULsmQJVGzXj/5pXawcEXkiTRvUoJbMxMkCSo5e5CDbc8nHr4jJwOmve4+JZfXBT+8mhilArsZYbkNdRseMzX3oVF8seZu8QqlGS0MKFb8dSHat+aMmsOF65cpXXXHhzeuY2sWppj44XfK2YtXEJMbCxRUf1ZvXAeJlqavG/rX7sIDA5myqw5HPz3CPNnTCW/BkHjF9HR0XjfuUNgUBBLli5lydKlVKpYkQ7J9BCLi4sjJCQES0vV5j1RoiRvvvy8ffuGkE+fCAsLw+fxY3y+6Ur+RWxsLDt37WLnrl0ULVoUry5d8KxbN9kQpkH9+oSEhJAhQwYyZswY//Hz5xMnTWLT5s1YWFjQskULenh5kTt3LpXaXK5sWXp270Zhd3c8Chcmd65cCWFDvUaNKV6sGOPHjKF8ubLx36OKV2RlMhm9unfD3NwcN1dXXAsWxMnRkZiYGHLmzUsuZ2dGDB1C44YNkclkyKMjUy76WZ0aNTDQN6BU8WIUK+KBlZUVACvWrCV3LmemjBtLtcqV1H6vy5ghA0MHD6ZggQJUqlgRS0tL7t27x5Jlyxg/ZgydOnZMdQ+bvr17U6N6dRrUq4eZmRn7Dx6kVo0aLF+6FJtUnuADFC1ShIkTJlCnTh2yZc1Kk2bN6Ny5M5MmTNDoeEOSJCZMmEC9+vVTXUMQtEWKDziSVzBPVuW2+QP+u9YIgvDb+hQaQbdxa/Bs0Iw/Zs7UaoCgVCrZv38/7u7uZM+ePcX9L915qHJt388Hotly5FDpSp+9ueo9Bv795x+KFC2i0rwHjpaqn0A/efqU297e1KtbV6UDmrBXqs/EPnnuQt4FBODVphUebj++qqqMjVG5rlKppEzD5hgZGtKjXSsa1arxwzkOTDOl/Hv+1vgpU1m0bDmlShSneZMmNPSsm+RKkTrhwZChQ3n1+jWF3d3jb4ULkyFD8iGNLE71n8WKNWuZOXceNtbWWFtbYW1tjbWVFTbW1jg6ONClQwdsbf9/sr7j3ssfVEu9Ele2fnebpnOB9Jv6Z4r7RCvkXI3+BEB2fROyGqT8tzWjnepLXB969Ippp+8CMK5yIWrk+fFEdC7D5qtUNyQ0lHotWvPgsQ8uuXJxYMdWrD+fcCVHKVe9l+nJs+fo2LsfUdHRlCtVko1LFyVMuJccScUJ25RKJX/u3M24aTMICQ3FwMCAwX160bebV7LvIUqz738/34qLi+PY8eNs2LCBk6dO8eW4NEOGDLRp3Zq2bdqQKVMmFAoF5SpWYt7CxeRSs+t3eHh4/NCCt28Sfbx/7y6PHz367uOcnJwYOGAA7du1U2moUmRkJC1atqRpkyY0adIEMzMzjA00v06nVCo5dvw41apWTfR3pWp48D0PHj7k+s2btGzWLFFQo0548D23vO9QMH++JK8PVXseJOexjw9mZmbJTgop02Cojr+/f/zqFd+poc6cB187d/485cqW/e52dcMPByen6z+YjO+X5FG4sPLE0aNp3QyV2To6/vK/IxEeCIKQbnwMCafr2NU0adGWqVOnpVk71AkP1KVOeKAOdcIDdakTHoSGhak8QaI64UFgcDDXvO9SvXxZlQ641A0PlEolazZsoHqVKmTPlu27+6kTHqhzAq1OeKDuiXlahAeaUiU8AHgYE8YHeQwFDM2xlaX8u1EnPABYevkRW28/w1Cmx0LP4rg6fb8HharhAcDrN2+p3awF7/wDKFuyBNvWrsbIKPn2qxMeAFy4cpW23XsRHhFBcY/CbF21HEuLpOP9QfXw4Iu37/wZMnYcR0+eBsCtQAEW/DEN1/yJu46rEx587fmLF2zevJmtf/5J4OfJ9r50Ae/Qvj29+vQlJiaGGbPnUKVqtVQ9xxcKhYJO7dpw7Wr8/GEGBgbkyZOHAvnzU6BAAfLny0eBAgVwcnJS+e8t7nMvtK/310Z48D2ahgffo43w4Hs0CQ9+RJPwICWpDQ9SIsKDlBUuXFh57MiRtG6Gyn6H35EIDwRBSFeCPoXRdcxq2rTvzPgJaTNBkAgPElMnPFCHOuGBulLT80AV6oQH6lAnPFDXrxwehMhj8Y4JpZiRFcZ6KZ8IqxseKJRKRh+5ydkXAdiYGLKqYWkyWCT/N6xOeABw5/4D6rdqQ3h4BI3re7JszqxkT1LVDQ8Art26TeuuPfgUEoJ7wYJsW7MSWxvrJPupGx5AfHi1c+9+Rk+eysdPn9DX12dAz+4M6Nk9ofdVhJ6hRvOyREdHc+jwYTZu3MiFixeT3adP/wF079k71V3X797x5sg//+CSLx958+alqFtBnazII8KDxER48H8iPEiZCA/SH7G4uiAI6YqtlTkrJ3Vh0/o1TJs6Ja2bIwhCOmahp4+Vnj5Gkm4OZ/QkibFVCpHbzoLgyBiG/3uDiNjvrzOvDrcC+VmzaAEymYzd+w4wfe58rdQFKFbYnV0b1mJnY8Pte/do1L4jAe/fa6W2JEk0a1ifs4f3U7t6NeLi4pi9aAk1Gjfj9t34YR4bN23iiAZdjY2MjGjUsCF7du/mzKlTeHXpkmS+l8UL5jOwb2/Cv7PCQUpc3QoxaOgwPOvVJ49LXp0EB4IgCL8aER4IgpDu2NlYsHJyF1atWMqsmTPTujmCIKRTkiSRy8BMp0tAmhroM6NmEWxMDHkSFMqkE94oftBrUx1VKpRn9uT4Hlbzl61g47btWqkL8eHEns3rcXJw4OFjHxq07cDrt2+1Vt/J0ZH1SxexYv4cbG2suf/wEbWatGDanHn4+PjQo2dPHjzUvBdX3rx5KezuTmwyV3+PHT1C6xZNefHiucbPIwhCOqRUolAofprb70CEB4IgpEsOtpasnNyFJYvmMW/unLRujiAI6ZSpCsMVNJXB3ITpNTwwlOlx7kUAy68kP2N/arRp3pRBfXoBMHz8JI6fPpOwLbkTZnXkzZ2bvVs2kCVTRp4+f0GDNu15/jJ+GMvx02eJidFsuIwkSTTyrMvZfw5Sv3Yt5HI585etYNPmzYSHh9OufXs+fPig0XO8//CBh48f07BBA8qULUceFxdsbGwTtvv6+NCySSPOnT3zgyqCIAiCNojwQBCEdCuDvTUrJ3Vh7uyZLFq0MK2bIwjCb8zVyYYRFeLXf996+xmHHr0C4PDj18TKNbviNLx/X5o1bIBcLqdL3wF4371HeEQEE2Zo3vMqZ/bs/L15IzmzZ8Pv9RsatOmAz5OnLFu3nvVbt2lcH8DBzo7Vi+azZtF8DL+aXd/Pz4/OXboQHR2d+tr29owdPZrFixaxat0G/j5wmHOXr3Lz7gOOnznHtp17mDZzNv7v3vHp0ydtfDuCIAjCd4jwQBCEdC2jow2rpnjxx7QpLF+2LK2bIwjCb6xGnkx08MgFwMyz97jxJpCVVx9z+pm/RnUlSWLetMmUL12KiIgIWnftwaIVq1i/dRtv/TWrDZA1cyb2bt5I3ty5eBcQQIO2Hbh49RqzFy0h+ONHjesDXLt5i8Wr1hDzTW+Jy1euMGz4cH40QXdqGBoakiFDRtwKFaJylao0adYcqx8seSkIgiBoToQHgiCke5mdbFk5uQuTJ45j9apVad0cQRB+Y12K5aZSTifiFEoGHbrG+/Bodt57oXFdQ0ND1i1dRH6XPAS8f8/cJcuIjY1l9cbNWmg1ODk6sHvTevK7uBAYFERcXBwfP31i9qIlWqnvnCM7Xu3b0aBObSy+WRpy2/btLBXhryAIalICcoXyp7n9DnS3fowgCIIWZc1oz4pJXeg6ZiT6Bvp07NhJZ89Vyi1fyjul0rMXulk2LzYsVCd1AQzMLXVSV6HDpRoxMNJJ2aho3bTZODZKJ3UBSt3erZO6x2ef0EldgLHlMumkrrrLKSYn+ONHWrte4PyQ4QlzEtz1/8iVh09wd3dPdd2wsDDOXr1Fztx5ePDYJ+H+9dv+onPfgUlOyNX14MF9Zv0xg0e+vonuX7flT+q1bEdOZ2eN6mPpQIV6jalQrzGvgz9x5+YNLp45zaWzp3nzyo/JU6ZgYONA6QqVUv0Uzva661kQHK6b5QlNvhrGoW0WJrp5n1MY6mZJYQBDHS2pqKvlFEH9JRVVJdPhRK+CoCui54EgCD+N7JkdWDGxCyOGDWXLFu1cjRMEQVCVUqlk685d9B06IslkhuvXr9OotoGBAQ8fPuDYsWOJ7g8LC2XHds3nJsiXLz8dO3WmYEHXRPfHxcUxZ+Z0jet/TV/fAI/iJek1eBgb9hxgzV9/49WnP/t37uD5E9+UCwiCIAjpkggPBEH4qeTM6siyCZ0YNLA/a9esSevmCILwG5Ekid5eXTh54G+KF/FItG3fvn28f/8+1bWNjIwYOHAQR44cpXTp0om2rV+3VuOVFyRJokLFSuz6ey/LV64iX/78CdtOnzzJhfPnNKr/o+fNntOZFh06M23hUrJmz6GT5xEE4dejVIJcofhpbr8DER4IgvDTyZ09A6smezFu7EimT5uq9Ym4BEEQfiS3szP7/tzM5NEjMTE2BiAmJoYtW7ZoXDtXrlxs376D2bPnYG1tDcC7t285dPCAxrUh/mS+arXq7N1/kLkLF5M7Tx4AZs2YRlxcnFae40dk+mLErCAIws9KhAeCIPyUcmZxZP30Hqxfs4IB/fui+E0SX0EQ0geZTEb3Th04dXAfZUoUB2DTpk3ExGg+L4YkSbRo0YJTp07TsFFjAFavWqnVoFRPT4/qNWuxc+8B/pgzj7iYWHbv/Etr9QVBEIRfjwgPBEH4aTnZW7F2WjcunD5K61YttHLQLgiCoI6c2bOxe/MGpkyZSlhYKIcOHdJabTs7O2bNmcv6jZuJiIjg/DntDy2QyWTU8azHnoOHyZQpk+jJJQj/gejo6P+kp8/PT4lC8fPcfgei75ggCD81S3MTlo7vxMg526hTuyZ7/t6n8azkmpDL5Tx79ozcuXOnWRsEQfhv6enp0aFDB6pUqcKOHdu1Xr9suXIcPPwvN65f13rtL/T19SlXoaLO6v/q5HI5MplMqzU/fgxm8+bN2NjYYGtrh61t/Ec7OzusrFK/8sTTp0/xDwjA0dERR0dHzM3MkNLpzP/h4eHExsVhamKCgYGB1toZGRlJeHg4kiShp6eHJEmJP9fTQyL+b9vQ0BB9FYfbhISE8Pr1awICAgh4/x5/f//4zz/f/AMCeP/+Pd27dWPIwIEqt1cul6fyOxUE7RLhgSAIPz1jIwNmDW/NtOV7qVSxPIf/OYKjo+N/9vxKpZKbN2+yZcsWdu/ezY4dO/6z505PIiIjMTI01PoB9Bd7D/+LpYU5JYsWwdREN0uJPXv2jBw5cmjtADUkJAQDAwNMdNReIX3JmjUrgwYNRqlUav1kzNjYmDJly2q15u8oLCyM8PBwnJyctFp30YL5nD93FldXN1wLFcLVzY1cuXKDBks1WlvbEB0dzahRI5Ns09PTw8bGBjs7O4oWKcKEsWNxcHBQqa6trS1Nmjfnxcv4pYNNTU1xdHBICBMcnZxwdHAga9asNGzQAENDQ5Xqbtu+nTVr1+Li4oJLnjzkdXEhj4sLObJnT/X/hZCQEKrVqsWr16+RyWSYmphgYmqKiYlJ/OefP7oVKsSQwYMxNzdXqW5MTAzNW7bk3r17P9yvZcuWTJ44MWH+kZQEBgXRoVMnnj17lux2CwsLli1ZQs2aNdFT4z0ivYY7wu9HhAeCIPwS9GUyxvZqxNKtRylTuiRHj50gZ86cOn3OFy9esHHjRrZu3crDhw8BWLhwIcWLF9dKfYVCgZ+fH48fPeLDhw80bdYMAw3XDA8LD+fSlav4vX7Nq9evP398Q/06teneuZNGByghISFUb9QUJwcH8ud1IZ9LHvK7uJDfxYUMTo4aH/w42tvRqH1nDA0MKF6kMBVKl6J86VK4FyygtcBi9dq1nD13jnZt29KsSROVDxi/Ry6XU6tWTYYNG06DBg20egB47uIl8rnkwd7OTms1v/B994HcGey1Xvd3IA7y0zcTExMa1K9HBicn6tVvQK3atTX+Owfo7NWVnTu2c8fbG7bGT5xpbGxMwYKuFCpU6PPNHWdnZ/T0fjxqWKFQ4Ovry61bN3n//j2SJCUZTqJQKFAoFHRo147OHTtiZGSkUjvDw8N5+OgRFStWZOOmTQBERETw/MULnr94AcQHE82bN6d5s2YqBwcAzZs1Y8vWrUkCdCMjI3I5O+Pi4kL+vHkpWbIklSuq1svF0dGRkcOH07tfP+RyOaFhYYSGhSVsNzU1ZdiQIXTp0kWttlpaWjJ+7Fhat22b7PCBrFmyMGf2bCpWrJji7+tr2bNlY/jQofTs3TvJ7yx37txsXL+ePJ8nKVWHOm0QBF0S4YEgCL8MSZLo3aYGtlZmlC1TmsP//Iu7u7vOns/CwoLTp08nBAdNmzalT58+Wql96+ZNOrRvT2hoCE5OTmzaskXj4ADA1MSE7bt3s2d//MztMpmMmZMn0aF1K41ry/T1yZcnD6fPX8D73v1E26ytrChepDBjhw4hn4v6B07b9uzl/sNHWJibExoWxvnLVzl/+SrT5y/CytKCciVLUL50KSqULkXO7NlUrhsQEMDWbduws7PDztaW/Pnzs2zFCoaPHMm4CRPwrFuXdm3aUK5sWbUP3s6cOYNMJkNfX5++ffuwbt1axo0bT9GiRdX99pN45+/PybPnWLxyNVvXrNTagWVEdAwyPT36rtnNtoHtsDLVXo+JOKUSfR2cWEfJFRjLdHNgHR4RgZmpqfbrhodjZmam9bqxsbFER0erfPVVHW9evyZT5sxar/vk8SNMTM3IlCWLVuu+fPmSv/fswdTMFFMTU0xNTTAxMcXUzIxSJUvx559buXjxIuPHjaV8hQrUrutJlarVVPq9LJg3l5cvX/Dh/QcCP3zgQ+AHPgYHJ9kvKiqK69evcf36NYyNjalXrx5dunQl/1fLZH7t8OHDbNy4AW/v24R9dYL8LSMjIzp27MSIIYNVGr4QGhbGsOHDueXtjY+Pzw8nGK5buzYjRowgX758KdYF2Ld/P/v27ePly5e8ePmSoKCgJPtER0dz/8EDIiIiKFu6NMVVeA989vw5I0eP5sLFi3wKCUl2n/r16jF9yhSyZM5MlBpLmc6cNYtt27bx+s2bJNskSaJz586MHjlS7b/Rs2fPMmjIEJ4/f55kW43q1Vm2dCmWlpZq1YT4ELpz165qP+5XoATkSjEhdnoiwgNBEH45rTzLYmtlRrUqldm5ew8VVbzCoS5bW1vKlSvH7du3sbKyYvXq1Vq76pgte3ZMTU2wsbFm05YtZM2q+gnxj+jp6WFpYRHfld7YmHXLllCxXDmt1A4LC+f0+QtJ7s+eNSte7dvSqmljLFM5H8WRE6c4dOx4sts+hYRy8Ohxrt++g++zZ3Rp05q89hlVqvv02TMmTZmS7Lbo6Gh27d7Nrt27yZ4tG21at6Zh48ZkzJhJpdqzZ8/i5s2bCV/fuHGDhg0bUK9efUaOHEnWrFlVqvOtg/8eoXv/gdjb2fH67VuWrl5Dn26aH1iGRETRY9VOjAz0efsxlDUnrjDIU/O/HaVSybmYYC5Ff6SPeXZM9LQ3rCUgKoaR159TJ4stTbJrr6eEUqnkj/kLOXzsOHs2b8DWxkZrtR8/fkyrVq0YPnw4zZs311pdpVLJuDGjuXvHm1Vr15MhQwat1b5/7y5tWjSjWYuWDBwyTKvDcJbPn83NK5cpVKQYNes1oELV6ph8Fdg89XmMpZUV9o7qDTN4+uQJ8+fNTXG/2NhYThw/zonjxzExMaFylarU9axH+QoVMPzO1fx/Dh/i2dOnKrUjb968tG7dlkaNGqV4oh8cHMSFC+eB+F4LhQoVwt29MI8fP+L06dMANGrUmCFDhpIlSxYsTFTrbWBmasqhf/4hNDQUACdHR9zd3bl2/XrCyX7ZMmUYN2YMxYsVQ67GpJmPHz9m7759P9ynePHi9OrZk9q1amGiYu8AGxsb/j16FIVCgZWlJVZWVrz08wMgl7MzM2fMoFqVKiq382sBAQG8fvMGY2NjcuXKlTB0IXeuXMybO5cSJUqkqq6TkxPPnz/HwsKCGtWrs2v3bgAGDxrEsKFDUx3yymQyoqKiUvVYQdA2ER4IgvBLqlm+MNaWZjRp3JAVK1fTpEkTnTzP+PHjyZo1K0WKFNFoAqtv2draMn/BQnLkzKn1sbl/TJrIx0+fGD5wAC5anNgxc8YM9OnmxeKVqwEoV6okXTu0p0aVShoPK6hVtTLOObLz1979+L9/n3C/k4MD9WpVp17NGhT3KKz2wZmlpSUtmzcnMCiIwKAg3r59y9u3b5Ps9+LlS6bNmMHCxYvp168fXbp4/bCLrFKpxNnZmbdv3/Lu3btE2/bv38eRI//i5eVF79591J7g8+C/R4iOieH153ZOnT2XUsWLUczDQ60637ri+5L7r/wTvt52/iatynqQ0Ub9K2Vfi0LByahAPinj2BjxGi+zrMSi4GVcFC4Gml19Px8QwquIGFY+foehnkS9rNoZwhEYFMzmHTsJeP+eFp282LlxHVapuGKYnN27dxMQ4M+QIYMBtBYg+Pv7c+rkCT58+ECzJo1YvWYdeVW8epySi+fPExcby5+bN3HpwgWmz5pDQVdXjevGxcWipydDkiS8b1zD+8Y1Fs2cRoWq1anh2YBCRYoS8ukTw3p1ZeTkGTh71lK5tpOTEy1btSIiIoKI8AgiIiOIjIggIjKS9wEByV4hj4yM5NbNGxgaGSFJEpWrVk02EG7foSMRkZHY29lj72CPvb09dvb2jB8zhuPHjmJkZEStOnVp0aoVZUqUVDlULl++AtOmTadwYQ/y5s2bMElfixbNKFOmLKNGjcbNzU3ln8EXenp6TJsyBTtbW9zd3cmYIQMRERHkypsXN1dXxo0ZQ5XKlVMVflepXBkzMzOyZctG9uzZuX79OkM+nyjXrVuXXj16UKxYMbXrWltZsWj+fArkz497oUL06tuXgPfvGTJwIP369FF5mEZyOnXsSM2aNSlbpgy7du9m+IgR9O7Vi8GDBmFsbJzqui4uLmzasIHy5cvz4sUL/vn3XxYvWoRn3bqprvnFuNGjOXrsmMZ1BEFT0o+W5CmYJ6ty2/wB/11rBEEQtOy+7yv6TdnIxElT6NGzp06eQ6FQqHzS+uzFS520wRL1ZmKOiorG2Fi1gy9FnOpLYE78YxbBwR/p2rE9BfPl/XHdWPWW1nz6/AVl69TH0d4ez5rVqVezOiWKeCT7sze0S91V19Fjx7JsxQogvvuqi4sLRYsUoViRIhQrWpSsOZ1VnnVbqVQyZMiQH86+b29vz5AhQ+jYpKFKAcvzly+Zu3gpf+7clej+bFmycOLA3mRPcF/vWp5iO496P+bgjfuce/gM+VfLTdUvVpBJLZI/YTs+VfUlCV/FRbI47AWxKClvaIOdzJC7saH0NM+e7P6uxVULm5RKJWt8/fnr+QcAhhTMTPVM3+8l4LFsp8ptfvjYh0Zt2hMYHEwxj8LsWL8G8x90Y442Um2ogEKhYMyYMWzatBFJkpg9e84PA4SoWNWXc/Pz88Orc0eePnmCubkFS5evoHSZMlqpffb0acaNHsGH9+/R19enZ59+dOnWPdnXrX9ohMp1AfzfveXYwf0cObCP137/f4/MkCkTHsVLcnjvHiRJom+//vTt10+jMFKpVNK2TWsuXojvJZUhQwZKlS5NseIlKFmqNFmyZk3VSfSHD+/x6tSRxk2aUr9ho4R5FEw0HG6mVCo5f/48ZcuWTdIuVXseJOfBw4fcf/CARg0aJHkPVafnwbd69OyJjY0N3bt1I0eOHEm2G6bydzd2wgS8Oncme7bke+GpM2zha1v//BPXggUpVKjQd/dJTY8BX19f4uLifjj8Q6bm68zW0fG6UqlUP4n5ibm5FVLu2bc/rZuhsjzOOX7535EIDwRB+OX5vf1Ar4nr6dSlG+PGT0jTCc3SS3igDnXCg8ioKExUvHKjbnhw4co19PSk7wYGX0tNePDq9WtGjhpF4cKFKVqkCEU8PJKMTw2PVr3Nly5dYvXqVVhbW2NlZY219f9vVlZWib52MNZP8XWpVCrZe+gwW3b8xZnzF5KMW/asVZO1SxYlqZNSeADwOugTy/69wMGb9/n6sECSYMfA9uTJmHQWd3XCAwDvmBA2RLwGwAg9olEw1MKZDLKkJ0CqhgcQ/3NZ+ugt+/yC0AOGu2WlUobkewGpEx4A3Ll3n8btOvIpJISyJUuwZfWK7670oWp4AOoFCOqc4AN8/PiRnt27ce3qFQwMDJg24w8aNmqsldrBQUFMGj+WY0f+BaCwRxGmzZqdZFiVuuHBF0qlkvvet/n3wF5OHfmHiPDwJPuULVeOefMXYG+fumEqt2/fZs3q1ZQuXZpSpUsnrK4SE6fZe2h0dDSGhoZJ/v40DQ9+RJPw4Ec0CQ/CwsN/GLKlNjxISWrDA1XoarJCER6kTIQH6Y8IDwRB+C0EBofSe/IGyleqzpKly3S2nGBKfvXwQK26aoYH6khtz4OUqBMeqMM4NlKt/d++e8euffvZvnsPDx/7JNw/c9IEOrVtk2hfVcKDLx69CWDh4XOcf/j/ZcYq5HdmYedGSfZVNzz4qIhlW8RbfOL+f0JY2tCapqZJ56dQJzwAUCiVLLj/hn/eBCOTYGyhbJR2TNoLQ93wAOD6rds069CZsPBwKpcvx8blSzEySjpkRZ3wAFQPENQ9wQeIjo5i2JDBHDp4EICBg4fQs1fvJCe2qamtVCrZu2c3M6ZMIjw8HFNTM0aMHkPDJk0T6qc2PEjUtqhIjh8+yILpU5IEZU5OTixcvERrK9sAGocH3/O7hQcpEeHB/4nwIGUiPEh/xLofgiD8FuxsLFg9xYvbV8/RrGljMfmQ8FPLmCEDfbp15czhgxzf/zfdO3XEwc6OsVOmcffBg1TXzZvJkSVdGrOqR3Ncs8YHMGcePOXG01catfdVXBSLQ18kCg4Arsd8IkKh+UmbniTRr0AmKmewQq6Eqd5+XA8M1bguQNHC7mxdvQITY2NOnj1H1/4DidXCiYqenh5TpkyhXbv2n4e4DE6yxF1qGRkZM2/BIry6dQdg3pzZjB09Ktkl6dQlSRINGzdh594DFClajIiIcMaNHsmAPr0ICgoE4PkTXx4/uJ9CpR+LjYnl3/17k10ZwN/fn9YtW7ByxfIky+EJgvDrUKJErlD8NLffgeh5IAjCbyUmNo7R83YQqTRh3/6DWp3kUBWi58FXdUXPgwTq9jxITmxsLCfPnuPmbW8G9emVsLSnOj0PvqZUKjl+x4dF/5zDytSYDb1bJbpyrW7PgyilnIORAVyI+Zjo/vrGjlQ0TjzRobo9D76QK5RMvePH+YAQjPQkphbJgZvN/7tQp6bnwRenz1+gbdceRMfE0KBObZbPm52oB5O6PQ++SKkHQmp6B3xt88aNTJ40AYVCQcWKlViweAlmZmZERUURGhGp9oSdX5PL5axbs4olCxcQFxuLnb09k6ZO501gMKsWzmPppj+xtrFNVe3AD+/xe/6MkE+fCAsNITQkBP24GD59+sSnjx/5FPKJT58+UaJECYYMGYqRBhPdgeh58DXR8yAx0fMg7bi6uSl3/f3j1TzSk3y5nX/535EIDwRB+O3I5Qpmrt7P3WeB/HvkGBkzqrasnzaI8OCruiI8SKCN8OB7UhsefBErl7P3yl3yZnbELdv//1bUDQ++8I0NZ3vkW4IU8Qf7dnoGjLDIhd5XB9KpDQ8AYhQKJt16ydXAMExkeswomoN8VvFL/2kSHgAcPXmKjr36EhsbS/NGDVj4x/SEE4vUhgfw4wBB0/AA4NjRIwzs34+oqCgKurqyas1aHj96zL9H/mXU2PEa139w/x4jhw7mia8vAJmyZOXNKz88SpRkxsJlyFScZDQlzva6C3tFePB/IjxITIQHaUeEB+mPGLYgCMJvRybTY0S3+lT0yEGZ0iXx/XzAKwhCUgYyGU1LuycKDjSR28CMIRbOlDOMXxUhUBHLg7gwrdQGMNTTY6x7NtxtzIiUKxh94zm+IfHhjEKhICg4ONW1q1eulNDjYMeevQwbP1Er3eZ1OYQBoFr1Gmza+ie2dnbcu3uXZo0bsX3bn2zbspk73rc1rp+/QEG27fqbth06AvDmlR8AN69cZs2ShRrXFwRBENIHER4IgvBbkiSJbi2q0L5+KcqVLcP169fTukmC8NswkvRoZJqBXubZsdMz4Fx06k/ok60v02Ni4WwUsDIlLE7BqBvPeR4WxeVr15m7ZJlGtevVqsmimTOQJImNf25n3NQZ/0mAEB2t2TwthQt7sGPnbnLkyMnr1685fOggSqWSiWPHaGUuBJlMRo4cOTE3T9wDY8em9Zw6+q/G9QVB+D3JFcqf5vY7EOGBIAi/taY1SzKya11q1qjO0aNH07o5gvBbyaVvyhALZzLKjHgvj9ZqbRN9GVM8spPHwphPsXJGXn/OopWr2bB1G2/evtOodtMG9Zg7dTIAK9ZvYPrc+QBcvXoVb2/vVNdNLkDYtfMvACZNmEBoqGaTQNra2tKgUeKVMx49fMDmDes1qgvw8MF9rl+7muykYbMnjeOZr08yjxIEQRB+JmLOA0EQBODa3ScMm/kni5Yso2XLlmndHLUFv9FsNvwfiXz/Vid1je0cdVIX4Mkn3aymkd1ON2OuAzScl+BHQu5e00ldpzqttFovNi4Og89j469auWitbuinj0zu3xO/Z08S7itRuwENegzUuPbFg7s5sDK+W36jTt0Jfh9AUIA/A6bN0aiuQqFgy6I5nNy/G0mSaN6zP7tWLaVmizbUa++VuppyOQc2r+X4ru1ERyWeY8PAyJghSzdg66T50JTY6GhCHt/m4snjXDl3hsiI+BU2MmbJyqzVmzDXYILGPA7WGrfvewz1dTMWX1dj/AFi5LqZp0GXs8Zbmmg2seX3GOjo9wdgoN7UBCqT1JxLwcre4ZcfT/8tVzc35Y7de9O6GSor6JLrl/8diZ4HgiAIQDHXXCyf1IVBA/qycMH8tG6OIPx2DLQ0qd7XIsLCuHHhHBZW1onuv370IMH+modipes2plaHHgDsWbeCc/8ewPvKBXzv39Gorp6eHm36DqZyvcYolUq2L51PXGwMx3Zu49Pn5RDVrimTUb9DV6Zt2U3DTt2xtP3/Chex0VHsWTZPK8MvDIyMKFWhMgPHT2HDgaOM+mMelWvV5dPHYOZNHJ3s0ouCIAjJUSpBoVT+NLffgQgPBEEQPnPJkZG107oxf+5MRo4cIdYPF4SfnKGREeGhIbzwfZzofnlcHCd3bNLKc5TybIRL0ZIAxH2e8X3PupUa11UoFLiXKouZ5f97u0RHRXJoy3qN6ppZWFKrVXumbtxJs37DcMySDYCH1y5x5/xpjWp/y9DIiBLlKtB/7CQ2HDhG7cbNefn0ScoPFARBENIlER4IgiB8JbOTLeun9+DQ3r/o3KmjViYSEwQhbegbGFCneWvmbd1FzcbNEy25dvPEPwS+1Wy4T+DbVyzq15nH1y8nuv/BzWs8uKXZJKx3Ll9g4/yZhId8SnT/2UN78X/tp1FtAANDQ0rU8GTw0o10GjudnAULsXflAiLDtbfyRaLnMzCgWJly5MidRyf1BUEQBN0T4YEgCMI3bKzMWDmpC773r9OwQT0iIiLSukmCIGjAwsqajv2H0G/ROvIWKwXEX9k/sW2DRnXtMmah99xVVGrWFgNDw0Tb9qxbqVHvJY+yFZi+YTtNuvXB9Kt5AhRyOfvWa96z4Qs9PT0KlCxLrz8W02H0VF4+vK+12oIgCMKvRYQHgiAIyTA1MWLBqHboRQdRrWplgjVYG16AuLg4AoPEz1BIWw5ZstN+7Aw6TZyNU3Znbp8+RsDL5xrVNDI1pXpbL6at206pqjUT7ve95829a5d/8MiUGRgaUb1pK6Zs+IuaLf4fUFw/fYIXjx9oVDs52fIWIG/RElqvK6SOUqnkw4cPfPwYTFhYGFFRkcTFxYkhdTqiVCoJCgrixo0b7Nmzh2XLl/Px40et1I6Ojsb3yROOHj/OqjVrOX3mjFbq/g7kCsVPc/sdaH92IkEQhF+EgYE+k/s3Zd76w5QtU4ojR4+TJUuW/7QNb9++xd7eHgMDA53Uf/DoEfnz5tV63djYOO48fMilaze4eP0mb/392bh4ntafRxBSI3fhYvSZt4rrxw9z5Z99eHbrp3FNO6cMdBs5gaoNm7F9+QJ8791h97qVFCxWEknSbLp2U3MLGnXpSaUGTTmwaQ0X/j3I7tXLGPDHAo1rC5rbvWsnixbMx8jYGGNjY4yNjBM+NzX5fJ+xMUWLFqdRo0Yq/84kSeLPP7cye/asJNsMDAzQ19dHX1+fDBkzMn7CREqWKq1S3cAPH7CxtU00jEdTwUFBWFpZIdPi6hJKpVInr+/Q0FCuXb/OixcvePHiBc+eP0/4/MtyqDY2NmzcsAFra2u1at++cwcfHx+ev3jB8xcvePY8vu7rN28SQp/OHTrQqUN7terKdbSyhiCoS4QHgiAIP6Cnp8fgznVZv/s0pUuV5MjRY+TPn/8/ee7Xr1/TqVMnjhw5ovXab9+9Y9zkKRTIl08r4UFsbBy379/n4rUbXLp+g6u3vAn/PNzD1saaPetWkcnJKdX17z94SJsuXkRHRyNJeshkMvT0JPT09JDpyZD09NDTk6hRpQojhgwC1DvgVCqVzJk+FTNzczyKFsOtcGHMzMxT3d4v3n/4gIO9vcZ1BO3Tk8koXsOT6MgIrZ6k5MpfkJHzV3DtzAn+WrWEWxfO4lG2glZq29g70G7gCKo1acnedSt4cP0KBYqV1Ert30FQUCC+Pj4UK15CqyfOjRo34cL58+z9e0+y2y0sLBg8eCj169dX+3XWqlVrjhz5F29v70T3x8bGEhsbS7PmLRg2YqRaJ7knjh9n8qQJ5MqVmzwueciVOw+58+QhTx6XVAfkBw8cYNqUSdja2mLv4IC9vQP2Dg442NvHf3R0xN7egdw5spMjRw6VasbGxtKpcyd8fX2xs7PDztYu/qO9XeKv7ezImSM7Dg4OKtU1MTHh2LFjrFy1KtntOXLkYNuff5LL2VnVbz9BZEQko8aOI+D9+2S3Txo3jr69e6n9OtBmKCMImhDhgSAIggo6Nq6IrZUZlSqUZ9+Bg5QsqdsD9levXlG5cmU8PDy0Wjc2NpaVa9fxx5y5SHp6zJk+TSt1A4ODWbJuI0dOJe6KaW5mxpYlC8idI7tG9Qvkz8fooUPp3q9/stttrK2ZNnE8zRo1jD8oi4lSq74kSVSsWo3eXTqyjvgDNZd8+fEoVozCRYpRuEhRrG1s1G735s2biY2NZeiQIeIKcTplZGKq9ZqSJFG8YlUKly7Hw1s3tF4/Y7Yc9Bg/nbBPH7VeOz2Ijork6P6/qVG/MYZGRlqra21tw+Tx4wgPD6d23brUrVef/AUKpupv8+PHj1y+dImLF85z8cJ5nj59mux+DRo0ZPToMTipGJ7GxsZy8+YNTp06xZkzp7lzJ/llP52dnZk0ZarKvQ2+kMvllClXlsxZsnD37h3u3k1c39jYGGfnXOTOk4fGTZtRqrRq9WvWrs3uXTu5f+8uHz58AJIOq6nj6cmo4SNUbquhoSHTpk6jWvVq+PklP0mosbExXl5eDOineu8hmUxGjerVOXDwIG/evEm0rVjRomzauBH7VIa+OXPkoEb1amze+mei+42MjFi+eBGNGjRIVd3fdYlTJUrkCjFMJz0R4YEgCIKK6lctho2lGXXr1GLjpi3UqVNHJ88TExNDv3798PX1pXv37lqtff3mTRYvX0FYeDiD+vbB0tJSK3UzODrQqlEDTpw7T1xcfPdKI0ND1s2fTaECmvfUaNPZiyPHTyS7rXH9ekybOD5VV/gfP3zAkvlzCQ8LIzw8DEmSUCqVyOVyHty7y4N7d9m6YT0AOXPlpmqlivTv1w9HR8cUa1+6dIk3b96wcdMmXr95w+yZM7Uy/ESpVHLrqR8eubJpXOtbz4JCyWBhgomBdg8P5HIFvn6vyZsjq1brArz1e4FTpizoafnKXFxsLJ8+BGCXMXOqaxgYGuFWIunJ16tnT8icw1njQMncyjrR15+CAomKCMcpi/ZfGzdPH6NgyXIYGhtrte6JQ/v5FBxMjtx5yOmSF2sbW4yMTTh5+CB7tmykWYcuVPVsoPbfjq/PY1avWIGpmSmmpqaYmpphampK1uzZOX3yBOvXrmH92jXkzOlMnXr1adigATly5kyxbkxMDK2aN+POHe9Ecw9IkoS+vj6xn5frdHZ2ZsKkKVQsX17lNq9du4a5c+ckdJ3/okCBAvj4+BAbG4uBgQE9e/aid+8+6Kn4M4mJicGrU0f8Xvnx7u3bH64iFBMTQ9Zs2WjVpi0eRYqkWHv1yhVs2rABf/93392nfIWKDBw8hAIFC2Jpotrr58WLF3h19eLhw4fJdtmXJIlmzZoxdMhQMmfOjIG+an//SqWSRo0bc/7ChSTbPD09Wbp4MSYmJirV+tbf+/bRrVdvYmJiEt1va2vL1g0bKFUydfOJxMbGUqFK1VQ9VhC0TYQHgiAIaihfPD/zR7WjY/u2zJozlw4dOmr9OQwNDVmwYAHFihWjVKlSWq1dqkQJhg7oz59/7aRHVy+t1nbN54KNlRUhoWHExsWxdMYUyhQvqpXa5mZmKBQKLC0tCAmJP7DO4OTE7GlTqF2jeqrrRkVGcvHc2RT3c/coQi3P+nRq1RwbFXogKBQKhgwdio+vLwDbtm3D/907Vq9ahbm5ZsMhtp6+woyd/9CifDGGNKqBsaEBD1+9w87CDAcri5QLfEdgRBQDDlzG0tiQaTWKktXaTKN2fm3Wxm1s2H+Eqb0707iqdrrwA/g9fcLEvt0oWLQ4fcZMSrLiQWopFAp2LZyBz40rtB87g2z5CmqlLoDP3dvMGtqPEpWq0nHQSPS1NJ+JQqFgw6wp+N71pkWvAZSp5am13i6+3jfYOmsSFja2VGnejpI1PTEw1E6PgKP79/DA+3bC1zZ29uTM40JsTAyB7wNYPns6u7dsoEWnruRs2xp9fdUOXV+/es3+fX+nuN+zZ09ZsnA+SxbOp1Ahdzzr16euZ73vBoSGhobExsWiVCpxzpWLMmXKUrpMGUqWKo1nnVp8DA6md5++dPbqiqGar0draxtCQ0OxsbGhfPkKVKxYkfLlK2BsbEShQm4UK1ac6dNn4OLiAkCMiuPgDQ0NefDgfsLkfwYGBlhYWhIUGJiwj5mZGc2at6B9x05kyqx6YKZQKPD3f4ckSWTOkoVXX/UQcHcvzKChwyiZiv9jDo4OCcGBiYkJkZGRCdsqV67M6FGjKVCggNp1JUmiSJEi3Lx5k5atWnHlyhXu3r1Lr549GT9unEbDWYr/j737jq/p/uM4/ro3N3tP2QsJEXvvvffee89Wjdbes0qV6lCtKqoo2v7aqlFF7T0j9iaISCI7997fH5FUKrgrJXyej0ceSc4593O/ci/yfZ/vKFMGjUZD2dKlKV26FJ9/uZSgwEDWrzFsCkQmc3NzioSFcTbC9IukCqEvxYtWbC1S0E+75uN3/7vWCCFEHnH5RhSDpyznneEjGTV6dK49j65zsWNu67dfvT5z8ZPu39G5bnJKCg07dmNAty60b9H0hddaub787n2mS1euYGFuztffreSTJZ/To0snJo354LkjJy7F6jZt4VFMDLv/+hNbOzusrW0YO3I4CY8z9rn3CwigUdPmNGjcFB+/jDvmAa6OOtWNj4+nZ+/e7N6dPZgoGh7OqpUrnxnCfO/Hz3WqC/DJL3+y9I+MugW8PPiwV2sORF5h2/EIlg7thsos+y+/cacP61T33P1YRv12iIdJKdhZqJhYuwSVA54/1Dpfo4461VWrNQybu4g/9h0CoG/Lxozs1h4zs+f/kn7IMUSn2of/3snCyWNJT0ujSKkyjJjxIdY2zw89Lj/SbdvVlMREvpk8khuRZzG3sKTj+1Oytnh8Hi9b3TrTe7b8yjfzZqHRqClUojSDJ83E1v75I4BSdRyunJTwmM8njyHyRMY0iZJVatDl3fexfcHoovhU3TqfEYf2sfGzBcTcy7i77OjmTu123ShbtxEqc3Mexz7iyJ9/UL1lewAKOusePP28ZhXnTp/g6sUL3L5x/YXXBgYFMWjoMBo2avLSTt7169fYvnULiQmJJCYmkJiYSGJiIqdOnuDa1avPXG9rZ0fZsuUoX6ECFStWokh4+HNrnz51Cjd3dzw9PbOORUVFMXH8OCZMmoSv7z8jbCz0GBETGxvL1atXCA8vmm2O+6lTpzh58gQdO3bK9ufWNTwA2L59G/b29vj5+uGRLx+fLPyYJYsX4eXlTfeePWnXrj32T94r+qwaf/PmTe7euU2hwmEcO3qUvr16kD9/AYaPHEntOnWf+f9L15EHAH/t/IuCBQqyYcMGZs2eRXh4OOPHjadatWcDSF1HHgBZuyc5OzvTrEULmjdrRu9evZ57vbkeGdzly1cIDg7i5//9yuIlS1i94tvnToFQ6BFU3Llzl0JFix7RarVldG9N3le4SLh2xdofX3UzdFYuvNAb/xpJeCCEEAa6++ARg6csp0mL1sybN9+kC3DpS9/wQB/6hAcAfx88RJVyZV96nT7hQaYxkybTpEEDKld8cUdO1/DgaX9u/YPZUydTt2EjGjVtTlh40Wd+8dU1PPjtt98YMmwYCQkJz5zz8/Vl9apVWXcPQb/wAGDP2YuM/W4TD+MTsDRX4efmwsU79+hbrwrDmmUf3qpreABwPyGZ8VuOcDrqEQA9SxekV5mCKHMIsHQNDyDj7uTHq39kydqfAKhVriTz3xuEnU3Ow4N1DQ8ATh0+yPzxo0lOSiQotBDvz/kYR2eXHK/VNTwASE1OYvWcSVw4ehClUkmrYR9Qsma9516va3gAcOrgPpZMG09KUiJe/gG8O2M+7l7eObdDj46cRq1m6/rv+Wn5l2jUapzc3Ok5egKhJXIeAaRreAAZ0zgObv2VP3/4jtjojMXgnPN5Uqd9d0JKlWVGjzbU79Kb2u27EeJi2OiapIQErl2+SMTJE3y7ZOFzrysYEsKQYe9Su249vUZXpKSk0KRBXW7fuoWdnR2ly5SlbPnylC1XnmJFi+o8qiEn6enpOT5en/BAX/qEB0/TarWMGP4utevUoV79Bs9MCTF0y7kV3y7Hzs6O5i1aPneBP33Cg0wjR42kUsVKtGjR4rn/z+oTHjwtIiLipYsg6xMeZLp8+QpeXp4vnAKhT3gA4Ojm/sZ3TP9NwoPXj4QHQghhhNj4RN6Z+R2Fwkuz/NsVubal4su8TuGBrgwJDzQajU4hjSHhwfVrV/H29nnhMHJdwgOtVsuRo0e5cvkyl69c4crVq1lfx8XFAeDk5MSK5cuzFt7UNzwAeBD3mPHfbWJPxKVsxz8d2IlqRQpmfa9PeACQptbwyd6zbDhzDYAKfu5Mql0CB6vsQ7D1CQ8y/bxzLx98spTUtDRCA/z4Yvx7+OZ7doV0fcIDgEvnzjJn9LvExz7C09ePsfMW5dgZ1yc8AFCnp/PjJ7M5sXMbAA17DaJK83Y5XqtPeABw/dIFFo4bQcyD+zg4OTNs+ocE5zA9Qp/wINO18xEsmzWFe7duoFAoqNeuM0279Xnmva1PeJApLTWFA5t/YfvalTx+9BAAR1f3rEChZpvODHt3uMFTJrRaLQumjGfX1s0AqMzNcXX3wM0jHwG+PuTz9CRfvnzk8/SidJkyuLrqvtbJkUOHOHnyOGXLladQ4bBsnX0LAzufL/O6hgfAc18jQ8MDXUbKGRIeqNXql+42YGh4oAtDwgNdSHjwchIevH4kPBBCCCMlJafywUdrMLdzZ8PGn7C1Nd1ccV29LeGBrgwJD3Sh68iDnGi1WmJiYrh85QpXr1zh9p07dOncGRcXF4PCg5S0dNbsOsSCn7ZmW43a0caadR/0x8slo636hgeZfo+8ydxdp0hVa/C2t2Zm/TIUdPtnCLwh4QHA8ciLDJixgAePYnFxdOCzMe9SOix7WKBveABw+/o1Zo0cyoOouzi7uTPmw0/wC86f7Rp9wwPICKx+/3oJe39ZD0C1Vh2p163fM50kfcMDgJgH9/h43EhuXLqAhaUlfcdMpnSVGtmuMSQ8AEhOSmTdZwvZs/l/APgXDKX3mMnk8/VHo9Fw6sAegktXMqg2QGpyMnt/28hf61eTEBeb7VyTth3oNWyEQaOxEh7HE3HyOC5uHrh5eGDv6JT1sy7o7mRwe1/mbQoPXsbQ8EAXhoQHupDw4M0k4cHr59WNsRVCiDeEtZUFH33QGTtlIjWqV32yTZUQ2SkUClxcXChTujRt2rRh2NChuLjkPLxeF7vPXGDLsbPPbGMVm5jEyK/XkZZuXMeiYagvX7SshJe9Nbfjk+i/aQ9/nDc+pCoRWoANH02lcFAAD2Pj6DJ+Jhu273r5A1/C2z+AyYuX4hMYRMyD+0wZ1p/zp08aXVepVNKo92Dqde0LwK4N37Nx8Yeo1c9fsV5Xzm4efLDgM8LLViA1JYUlU8ay5cc1vOjGjq6srG3o+t4Y+k2Yjo29PdcvRDJjYE/2/P4LMffv8fXsKUTduGpwfQsrK6o1b0f5+s+ubfK/dWtYMmd6jqvkv4ytnT1lKlUlOCQUBydn2eJUiLecWqPNMx9vAwkPhBDCBFRmZkwa0ooSBVypVLE8165de9VNEm+4OiUKs2pkb36bPIyhTWqS3/Of4f8nr97io01bjH6OEDdHlrWuQnk/d1LSNUz98wQL/j5Dmtq4O5Pe7q78MGcC9SuWJS09ndELv2TON9+jNrKuq0c+Ji/6koJh4STExzHjvcEc3//slmz6UigUVG/TmZZDRqFQKjmy7TdWz55EWkqK0bWtbWx5Z/qH1GjSEq1Wy5rPFrJ68Xw0JrqrXKpqTcZ/9i0hxUuSmpLMdwtm89mk90lJSmLFzAmkJOk/GiPToW2/c3rfrhzvoG77308snDaR9PQ0Y5ovhBDiNSLTFoQQwsRW//I3K37ezx9bthL+ghW7TUmmLWT3Ok5beBFDpi38m1ar5fzte/x2+BS/Hz7NnZhYPurdlgrmhncOM6k1WpYdPs+3RzO2nizm6czns6fi4fLybStf5HkLKUZ4FTeqbnJSEh9P/IATB/dhZmbGgDGTqFK3gUHTFv7t7P7d/DBvKulpaQSEFaXruJlY29kbNG3haVqtls1rV7Fu6acAFK9Qmf7jpqK0tOLY3zspUbmaUXfh/72YYqbiVWvRefQko2qnJicTdf0Kd65e4vaVSzy6cYUrF8+T+PgxFarVZMSUmSbZRlOmLWQn0xb+IdMW3kyFi4Rrv1mz/lU3Q2cVixV+418jCQ+EECIX/L7zGPO+/p0fN26iatWquf58Eh5k9zaGB0/TaLQcv3KDXafP09rHBkcr4ztuALuv3GXajhMkpKbj7uzI4vffyVqv4Pb9aLzdXQ2q+++FFAfPyXnBQ32kp6Xx2awp7N2eMQKj29D3CK3TzKiama6cOcF308eSkphAvoBgekyaS6i/j0lqH971J0tnTyEtNZWAgqEMmjqXT8aNoFbzNlRu+OLtT19Eq9Vy9shB1iz+iPu3b2U717zfMKo0a2Ns07MUdLZFq9VyP+ouVy+cx9HZhdDwosbXlfAgGwkP/iHhwZupUJFw7bLVa191M3RWpUSRN/41kmkLQgiRCxpWL8n0d9vQskUzNm3a9KqbI94ySqWCUvn9ebd5HZMFBwBVgzz5qlVlAp3tuB8TS+dxM1jxvy1otVqmfrmCo+cuGFS3WfVKrJ45DjcnRyKv3WD8gJ5EnjphVFtV5uYMHj+V+q0ydkdYsWg+W1d+ZZL1BIKKFKfvzIXYObsQde0yX34whLs3rwMZnfSH96IMrl2mWi1GzVuMnaMT1y5EMntoX25dvsiPSz8l9mG0wXUf3L3Nwe1/EBfz8Jlzvyz7lCtnTxlcOycKhQIPTy/KVa1ukuBACCHEqyfhgRBC5JKKJUNYPKE7/fv2YumXX77q5ghhEv5OdixtVZmGlcuRrlYz9csVjFzwOcfOXWDE/M94nJhkUN2nF1KMexTD9OGD2PlkpwBDKZVKug8bQdve/QH4a91KfvrMNOsJeAUVoP/sxbh6+RBz7y6z3h3A1fPnuHHpAis+nmtUSFEgrCjjFy0ln68/MQ/uAZD4OJ51ny00uKa7lw8935/Ih2t/pf/EGZSuXgtzy4ypFhq1mpWzJxGfQ7AghBBCZJJpC0IIkcuu3brPoCnL6TdgEOPGT8hzq4ffvxSZK3XVKYZ1MnVh6aT73u/6cPb2zZW6l39flyt1Ae54F8mVutN23uTqjh85//NXaJ8a5uxToT5FO40wuG56ShJ/f/EhyRf/BsC2TDvsq/RCoTRuWHLCiV+I+3MRaLV4lqhCie4fYGZu/KiMlLgYDi4ZR9zNi1hYWRNcvDTnDvxNxzEzKFSuskE1H92PYt8v6zj+52aSEx5nOzdgylzCyxu+xeLTtKnJnNy/h0N/beP0of3kDyvK8DkfY2amMqpukLOdSdr3b262uTPkHSDI3fCdT14k+nFCrtQFSEjJncUorcyNe/1fBRfz3KutNXIh1+dRmuvXaBcfvzd+SPy/ybSF14+MPBBCiFwW4OPO8tn9Wb1iGUMGD0KTi/NJhfivKBQK/Co2xKNY9g7yrf1/cPfE3wbXVVla49R0AnblOwGQcHgtMT9PRpP6z2KHmqRYvevaFm9KqV7jUKrMuXv8bw59Np60JOM7dpYOzlR450OCwkuSmpzEuQMZf/bfly0yeDcGOycXvAIL4Oj27LogPyyeZ9QOCU+zsrahXM26DJ4yh4/W/Uql+o2JPHHMJLWFEMJoWi3qPPTxNpDwQAgh/gPuLg58NaMvh/fuoH27NqSYYIs3Y924ceNVN0HkYWlJCZz4bg5Rx3c/c+7M9wtIjjV8fr5CocS+ck+cGo4BM3NSLu8nes27pMfeRR3/gEe/zzFoWoBXyWqUHTgdM0tros8fZ/8no0iJjzG4nZnSEuLwLhCa7dije3fZvWG1QfVU5uaUqNWAgQuWMWTmAsLKlM86F3P/Hr98u9So9ubExtaOSnUbElaqrMlrCyGEeDNIeCCEEP8Re1trPp3Ug7h7V2nYoB5xcXGvrC2pqakMGjTolT2/yPvMrW0p3W8qVcZ+RWDN1pjbOmSdS0uM59SqedmmMxjCunAtXNt9hNLGmfQHV4hePZSEI+tJuXqIpLNbDKrpFlqSCsM+xMLOkbgbF9k3/z0So+8CGVMmkuP0m/ev1Wi4H3GE03t2PHNuz8bVPLxzK4dH6UahUFCoVFkGTf+IcZ+voGK9xqjMzdn503quRp41uK4QQghhCAkPhBDiP2RpYc6ckR3xsNVQvVoVoqIMX5XdGFOmTOH06dOv5LnFm8XO059CLftTc+pqivcYi2tISQCizx3h+u6fja5v4VUYt86LUbnnR5P0iISjPwIQ99dnqOMfGFTTKSCEisPnY+3sQcL9W+ydP5z421eIPn+cc5u+0quWQqkkoGoT3lmyihZD38fV2y/rXHpaGr8vW2RQG//NKzCYzu+NYeq366nfoRs/f/MF6vR0k9QWQojXkZaMrYfzysfbIO+tiCKEEHmcmZmSsQOa88Wa7VSsUI5t23cQHBz8nz3//v37mT17Nvnz58+159h38BDly5RGqec+1vrSarV5bgHKN5XS3AKvUjXwKlWDxPu3ubl/M1f/2oBrSEnsvAKMqm1m74FD9QE83DgO1KkAaFMSiN32Mc4tphn0HrDL50elER9zYPEYHt+9xr6PR+LgG0z0+RP4V26ES/5w/dqoUlGyVkOKV6/H2f272LV+JVFXL3H+yH4iD+0ltKxpFjl0cHGlSfe+pCYnk5KchI2dvUnqCuP8sWULa9evR61Wo9FqUavVaDUaklJT0Wg0aNQa1Bo1derUoWu37jr92/jo0SMOHzqU8XitBo1Gg1ajffIcGpJT0tBotWg0GqpWr4GbW+4sFCtMIy4ujktXr2KuMic8rPCrbo4QBpHwQAghXgGFQsGAjnVwcbKlSqWK/Pr7ZkqWLJnrz5uYmEj37t3RaDSoVLnzX8A3q75nx67dVCyXO3OnYx7Fsn3332zfuZtenTtQtmSJXHkeYTgbd29CmvaiQKNuJEUbP7om6dyfxG5bmBUcZEq5coCkiG3YhNU1qK6VkxsVh3/Eoc8m8OhqBNHnTwBwZu1iKo/+FKWZ/js8KM3MCK9ckyKVanD+8D52rV/Jb199QnCx0llbI5qChZUVFuTe7gOvSnJyEr9t/BFvP3/8AgLx9PLGzAT/ViUnJzNhzPskJyXh5OyMs4sLzk4Zn52cnbkfHIirqyuurq44OjjoHUjVrlWL73/4gY0//ZTjeQcHByZPmULzFi11ru3g4MCWLX+wft3zd2Nxc3dn8rQZOgUHWq2WP7dvw1ylwsHREScnZxwdHXFwdMTMgPd6pvj4eA4ePIClhSVWVlZYWmZ8trC0zPra8snXuR0o6yMlJYUHDx8SHR3NgwfR/3wd/RCVSsXQgf2xs7XVq2b848dcvnqVy1eucuXaNS5fuZrx/dWrPIiOpkrFiqz44jO923r23Dm9HyNEbpDwQAghXqF2DSvi4mhHvTq1+WHdemrVqpWrz/fBBx9w/vx5AJOHB2q1msmz5/LFN98ya9IEk9a+ev0Gf+zYyZYdOzlw9FjGc40eYXRwsGDRYo4cO4atrS22NrbY2tpga2uLjY0NdjYZX9va2hBWqBDBQUF617916xa3bt2iXLlyRrUzr1KaqbD18DG6jnWhWlgGlSfp7FYSjv+EOuZm1rm4HZ9h6V8KMztXvetqtVoS7t3Cxt2LR1cj/ql56zLX//6VwOrNDG6zQqEgtGwlQspU5MqpY9yIPENwsVIG13sd3bl+FTdPb8wtjN/yMpOVlTWPYmJY8tFcAMzNzbOChJCCBQgMCiIwMJjAoCAcHB31qGtFzz596dGpA6mpqc+9rk2rVkwcP54Af3+d6mo0Go4cPcr/fvuN02fO5HhN9Ro1mDV7Dp6enjq3FyAmJoZSpUrzy88/57jIbpNmzflg3AQcnZx0qqdQKDBTKhkyoF+2BUcVCgX2Dg44PgkU3N3d6T9gIKVKl9aprp2dHbv++ovVq1a+8Lradeowaco0vLy8Xlrz2NEjjHhvOGlpaWjU6iejL7RZX6s1GhQo6NmrFwMGDQYdt129dv0GPfoP4PLVazx+/DjHayqWK8eyzxbrHRx88vkXTJ0957nnWzZtwuJ5H2JpQIiYW2F/XvC27GKQV7y970QhhHhN1KlUFEc7a9q1bc2Sz76gXbt2ufI827dvZ9Gif+Zfm+u5x/SLJCQmMnD4SDZv/xOA2tWrmaz2tz+sY9yMOajV6qxjHVs1p1+3zkbX7tm1C2vW/8iFixdzPG9rY8Oo4e9Sr3Ztg+q7ublRsGBBWrZsyaxZs/DXsUMinqW0tMW2ZAtsSjQj9doREo79RMqVg2hT4ond/gnOzSbrfbdYq04n9sYFYi4+u/5H5P+W41WqGpb2Tka1W6FQvHGhQaY7164y971BVKrXiGqNmpPP1+/lD8pBenoaUXfucOvGde7cvMnj+H8Wk01LS+Pa5Utcu3yJv3dsB8DK2pqWbdrSt/9AXFyfHxolJydzLuIsp06c4PTJE5w+dfK5wUGVSpWYOnkypUu9/LVKSUlh1+7d/O+33/h982ai7t3L8Tp7OzvGTZxI27btdH5vXrlyhW1bt7B161aOHD6c464irm5uTJ8+gyo1df93SaPRcOXyJR5GR+Pj68vNp3bb0Wq1xMXGEhcbS/78BRj9wQfkz19A59p3bt+mSHg4lpaWOYYc3t4+TJg0iTp16+lcs2Sp0vTp249JE8bneD4wMIi58+ZRspRuAUemAH8/5s2cQdO2Of8/2793T6aMG2vQ/4/DBvQn6t49vvj6m2fODenfj4nvjzZ45IX7C97nQvyXJDwQQojXQNliBfh8ci+GDh3E/fv3GDx4iMmfIzExkQ8//JBRo0bh4OBg0jsZd6OisuoVCA4i0N+wTkROOrduybdr1hFx/gIAZUuWYNaEsSZZ62Dn33u4eu1ajudat2jOlPHj8NbhLllOZs6cyYMHD3B3d2f16tVs2LCBESNG8P7772Nvb/g89bjHCXy2diNdGtfHJ5+7wXVysnP7VvwDgwjSo+Ogi/TkRO6fPYhnyepGv24KhRLLwLJYBpYlPeYWcX8tIfX6MZIjd2BdSL+RO0qVOYHVmuFfqSG3Dv3JxS3fk3j/dkabkx5z7JuZVBg216j25mT//36kcIWqOLp5mLTuib27uH3lEqWq1Saf37NBVcz9KJzd8+ld9+H9eyydOQkHZ2ccnV1xcHbBwdkZW3sHUpIS2bJuNVvWraZQidJUa9IC38aNdO58vT9kAMcOHUDzVDj4PM4uLnTq0o22HTri5Oz8wmv/99MmJo8fS/pLFpUMzp+fd0aMonvb1jq9NxMTEylUtCixsbFZx+xsbalbpw6NGzbkt82b2bBpEzWqV2fxwoXYOL24nU+bO2cOn3+2JNuxIkWKUKhwGD+uz5i60KRJUyZPnYqLiwsJKWk61Y2NjaVhnZrEv2CHn0KFCzPy/bGUr1gRK3Pd/2+YOH4c369eleM5lUpFz169GTLsHWxsbHSuCXDq5EmmT53yzHGFQkH3Hj15b+QorK2t9aqZacykyaSkZA+RrK2s+HjuHNq0bG5QTYDVa9c9ExwoFAqmTxhP/149Da6bkpJChRq5OypRCF1JeCCEEK+J0GBvvp7Zj0FTZnLnzh2mTZtu0sUAmzZtSsOGDSldujReXl5s2rTJZLXzBwXx4bQpNKpXl0tXrpisLmT8AtqueRPW/fQ/YuPj+XrhR1iaaJh0SMECpKVl/wW8cKFQ5kybRpVKFY2q/e2332ZNEYGMu6AzZsxg2bJlTJ8+nR49ehg0z/iHP7bz5fqfWbbhfzSoUoHeLZtQPPSfzv72A4epXKIYVpb6/YzuRd1l+oSxpKWm0qlHL7r36Y+llfFz6rVaLWd+WMidIzu4f+YgYe2GorI07Jf+Z2qnJZFy7QiorEm9HYFlwWoozfT/1UapMsevYn18ytXhzrFdXPxjNY/vXCP6/HH2L3qfUj3HYmGn+xD5F7ly+hi/L1vEtu++pFKLDlRp2QELK9P8PHb9soHIY4f59btl+OYvSKlqtShVvTZunt4ArP/8E/wKhFCvXRe91nN49OA+F0+feOl1544f4dzxI6xdsoD6TVvQuGVrvF8yGkGlUqFRq7GwtMTbxxdvP3+8ff3Yu3MHt29m3B338Q+gbedudGjXFisd35M+fn6kp6fj7OJC0WLFKVqsOOHFinPowD6+Xvolrm5uDBw6jBat2qBSqXT+t9bGxoYihQtz6fJlGjVsSJNGjahapUrWUPQ169axYN48enbvjkKhIPpxgk51AUqWLImZmRnlK1Sgbt261KlTFx9fX3777Vd2/LmdqdOm06hxY53rZXJ0dMTW1pa01FSKFC2Kj48vP2/aCICHRz6GDn+Pps1bGPTvUcGQEJRKJUWLFcPd3Z1tW7cCUKZsOaZMnUZIaKjeNQFCCxXC0tIKCwsLEhIyfoZ+/v7Mnvsh5cqVN6hmpnq1a3Pt+g0eREcDEBQQwLdLP6dIYeMWMWxYry7OM2eRPyiIw8eOYWlhwWcfz6dZo0ZG1bW0tKRls6Z8mcOIBiH+a4qchkNlKlLQT7vm43f/u9YIIYQg+lE8w6avoEyFaiz57HOdf1nOLfcvRep1fXp6uk6jGtQpSXrVXfrdaiqVLU2RQi//ZdTSSbdVx7VaLUePH6duk2bY29szZuQIenfv9ty7ps7evjrXXbVqFevWrePnn3PerrB48eLMnz+fWrVqcfn35y+I9m97j59myQ8b2Hfin6H2pQqH0rtlY+pWLMfMr1ZwIvICn08cjZuTI3e8i+hU935UFB9On8Le3TuBjM7XyHGTKFsh5xBl+q5bOtXVarVc3vI9F35bAVoNNh6+lOg5Dgef5+/2ceKqbh25tOhrPPp9Dun3MkalWBWsikPtYZjZOOV4falg3ebOqtPSOL5iDneP7QLA3M6RYh3fwbN4lec+ppyPbmHN3SsX+fXLj7l+LuP1s3dxo06XvhSrXjfHIc2u1rqHIQe3bebwX1s5d/QwGs0/d/IDQgpTqnotrp8/x5Gd28kfXpzuoyfg5eWtU9342EecO3aYuJiHTz5iiI2JJjb6Adcvnn/u45RmZjRv257uAwZj95xdIW7fvIG5uTmu7h5Zf/6kpETa1q1BUMEQ2nfrScVqNTAzM8PNVvd/C9NSU7l3LwpvH99swcCIYUMoGBpKtx69sHlqTnuQu4vOte/fv4+rq2uOr9eDBw+yLV6oT3iQkpJCcnIyjv9ay+Hw4UMEBgY9syiiriMPAG7euEE+T0/Mzc35ZdNGpk2eSM8+/ejeq/czowL0GXkQHx+PVqvBwcGRr5Z+yZdffM77H4yhVes2RoffD+7f59vl3/D5Z0vo2q07I0e//9wRDC56zDJISEwkPv4xRcqUo36d2nz28fxnfuZP06o1etXeuftvho4azcqvvqTiC9a7UeoxNSIxKQnfAiFHtFptGZ0f9AYIKVxEu3jF6lfdDJ3VL1fijX+NJDwQQojXUEJiMlMWb+BmdBI/rF1PWFjYK2uLvuGBrvQND9Rqtc53xnQNDwB++t+vbNm+nUljx+Dh/uJpALqGBwCPHz+mcuXKnDx58plzFhYWuLi44OLiQo8ePWhWyBdzPaeRnL10la83/Y//7dxDWnpGR9HP0wNnB3tOnr+En6cHX03+ANsK9XWuqdVq2bl9KwvmzCT6/n0A6jVqwtCRo3F2yT7nVtfwINPDS6c4sXwWKbEPUKrMKdRqIH6VG+fYwdA1PICMdQseH/yexwdWgUaN0toJxzrvYFXw2Y6+ruFBppirkRxdNo3kmIw57d5la1GkzSAsbB2euVbX8ACejMbY+xdbv/2cR/czdqPwLhBKw15D8C9cNOu6pMfx+LrrPuw90+PYRxzfs5Oju/7kwsljaDXPdn6sbe3o+u5oytaoo3f9TNs3rmXNko+zvvfw9iUwtDClihcnJCyMAqGFs3XQdXXrxnUeRj8gvHjJbO8PfcKD54mLi8PB4dnXT5/wQB/6hAf60ic8eNrunTsJLVQIj3w5T2HRJzx42ratWyhdpizOL5lSoo/pU6dQu05dKlZ68Van+oQHAJEXLvLzr78yYtjQl65DoE94AHDg8GGcHB0JLVjwhdfpEx4AuPj4vfEd03+T8OD1I+GBEEK8prRaLRu3HuST77Yyc+Ys+vXvb9JpDLp6XcIDfegTHiQlJek8d1af8ODIkSMcP34cFxcXnJ2ds8ICFxcXrK2ts72W+ow8+Leo6Id8978/+P63rTyKz756uL2tDdMXLKJ0uQp61XwcH8/STz9hww/fo9VqsXdwYNC7I2jcolXWL9r6hgcAqY9jObVqHvfPHADAs0Q1inQcjrl19g6mPuFBprSoCzzaPJf06KsAWBeujUPNwSit/rnjrW94AKBOS+XCb99xads60GqwdHChaMd3yVc0+89Un/Agq82pKez7ZT27168kNTnj70ORyjWp260fzh5e/LH8MwoXLUrJKjX0rp0p7mE0x/7+i4PbNnPtfMQz5yvWbUinIe9hZaNfJz8lKYnvlywgn48fgSGF8S8Ygq19Rqc8yNnO4Pa+iCnCg+d5m8KDlzE0PMgNuobG+oYHWq1W5/9P9Q0PdCXhwctJePD6kfBACCFec1du3OOD+T8QGlaMZV8vx8Uld37JfZ43PTzQhz7hgT6MCQ8yJSWn0GfybPafzL5dnJlKxejxk2ncoqXeNc+eOsncaZO5eD7jPVC8VGlGjptIUP4CBoUHAFqNhqt/beD8z8vQatRYu3pSosc4HAP+mY5iSHgAoE1PJX7fChIOZ3T0lbauONYbgVVQWcCw8CBTzJUITqz8kISojG0ifcvXJaz1QMxt7FCnpeIZf4GgoiUNqh0fE82fq5dxbPvvaLVaVObmVGjalkf37hCxfzf9Js6iSDnD1+BISkjgswkjuXz2VI7n3b286f3BZPKHhRv8HE+T8OAfEh78N/QND/Qh4cGrU7BwmPaTb/NOeNCofMk3/jWS8EAIIfKA1LR0Fq7YzI6DkaxavYZq1Uy3FeLLSHjwj9c1PEhXq5ny2des/m3rc6/p2rsffQe/fIjuM7XT01m3eiXLliwmOTkJlUpF5569uR3aBDML/fcrz/ToagQnls8k6WEUCjMVoc16E1CjFQqFwuDwIFPq7bM82jwX9aOMgMO6aCMcqvenTCFrNOlpPLoWiUt+/TvK6tQUIv+3nCs7NoBWi5WTG0U7DcfG1ZODC4bT78PPcfHUbR2BnNy5coHNX3/K1dPHsx1XmVswcNqHhJbQb1u6TCf37ebCyWOkJieTlppCWkoKqSkppKelkJqcQlpqCur0dOq360yleo2MHuEk4cE/JDz4b0h48GaS8OD1I+GBEELkIbsPRTDl0w30HzCIiZMmm3S7xeeR8OAfr2t4ABkjD27eu8/NqHvcvHuPG1H3uBV1n8sP47l7+xZxsbHUrt+QsVNnZK0Mr4+7t28zf9b0rAUVbdy9CWs3DLfQUgDEXD6DnVfgM1MQXiQt8TGnv59P1Im/AXAPr0DRziOJuG/8zgbatGTi/l5G4rFNAJg5eFK2xwisnNzYO/9dqoz+FGsXw7ZKfHjpNCe+m0fig4xtHZ2Dw4i5fBYPv0D6zFmCpbV+29Jla7dWy7mDf7Np0RySE/6ZhmJhZc2QmfMJDiv6gkfrx9LMsD3nX0bCg39IePDfkPDgzSThwetHwgMhhMhj7j+MY8In69Gq7Ph+zVoCAgJy9/kkPMjyOocHz5O528Lj+Hju3rmNra0dXj4+BtXKXFDx4zmzeHA/YxFBrzK1KNSyPzf3bebh+eOUHjAdpUr3X4q1Wi03/v6FiA1foFWnYeXkhm39cVj4/DMyQKtRo1Dqv40cQMr148RumYc6LmNhQteQEkSfP46DbwEqvTcfMwvDOqTpKUlE/vw1V3f+lO14aNnKdPhgmt4jPDIlxD3it6ULOf33jmfOWdvaMWz2QvwKGrb93b9JePAPCQ/+IeFBdhIevDoSHrx+cud/DSGEELnG3cWBJRN7UCksH2VKlWTt2rWvukkiD7Czt6dASKjBwQGAQqGgRp16rNr4C/7VmoNCwZ3Df/L3jD5EHd9N9PljnF6zgBfdmMippn/VZlR8byE27j4kP3pA9NoRPD74PVqtBk1yPPG7lhrcZkv/Erh1+xLrohl7rUefPw5A3M2LnFw1X6+2Pk1laU1AtWbYewdlOx55aA87vv/a4PZqNRoKl69K+cat8c4fgvKp0CQp4TGLx73HnauXDa4vhBB5iVqjzTMfb4O8Fy0KIYRAqVTSvVV1yhQNZvSId9j8+28sWvwptgZsiyaEvmzt7AhrMxjvsrU5s+Zj4m9dJi0xHoDbB7dh7ZyPgo2761XTwa8AlUZ9ypkfFnLnyA7i//6alBsnsCnaiISjP2LhWwyrAi/eru15lBY22JVtj+rBWeLvXM06fvvIXzj45id/3fZ610x+FM3ZHz/n8d3rz5zbtX4l+QKCCa9SS++6dk4uhFeplfVYO1K5GhnBlbOnuHz2FFcizrBozHCGf/Qp7rk0EkYIIYTIiYQHQgiRhxUp6Mea+UOYs/R/lCxRjLXrfqREiRKvulniBWJjY9m5cyfVqlXDycnpVTfHKE4BhQjvNIJDi0eTnvTP8OxLf6zCytkDv0oN9aqnsrKhWLcPSHQtReyfi0m9doTU68cAePTHh7i5f4bK0VPvdqrj7xO77WNS71575ty5n7/GziuQfOHl9app5eRKuUEzSEt8zL0zB0m7eIALR/Znbbm4adEcXLx88M5v3BQDS2sbQkuUzlosUaPRcOfaFe7fvombl88r2b5VCCHE20nWPBBCiDfEr38dZd7XvzFu/ATefXe4dCpeI7du3eKnn35i44b17N9/AC8PF0LDivLzL7/m+us0ddPOXKl7LzGde5cj+WPxDOKibj9zXqFU0njEdAJKlNO7dmFXK+5cvcyiscOJexiddTywUBjvfvgpKj3nCmeyS0/i0O6dHNj9F8cPHSA9LWPOuI2tLXOXfotvQNCLC7yotqU5qampHD98kL1/7WDfrr9Qmin59NvvcXEzfA0Os1x8f6Sq1blSN8DFIVfqejrk3siqtMdxuVJXY5076z8AxCWl5EpdCzPD1hd5lZxsrXOtdnrS45df9B9w9Qt84+fT/1uBQmHaj75e+aqbobMWlUu/8a+RhAdCCPEGuXk3mg8++gFvv/x8+91KPDwMW01eGEer1XLu3Dk2btzAxh/XcfHiZaqWLUz1sqFULhWKhbmKLqM/490RY+jXv3+utiU3wwPI+LPG3r3F9VOHuXHqCLfOHiftyd13laUVrSYswD2ooF617R/dYMOXizh/4ugz52q17kCrvkMMarOv/T8djKSEBI4e2Mv+XTs4svdvnFzcmLv0W+zs7Q2qbWeZPdDQaDREnj1NbEwMFapWN6gmSHjwNAkPspPw4B8SHryZJDx4/Uh4IIQQb5i0dDWffb+VX/86yfIV31GvXr1X3SST02q1r/XIigULFjB1yiQaVS9JjXKFKB2eH3NV9l/IL167S59xSzlw6DAFChTItbbkdnjwb+r0NKIuRGSFCQkxD2g1aSEO7rpPNwhxUhF57AiHtv/Bib27SE1Jzna+/5Q5FC1fWe82Px0ePC0tLY3TRw+RkpJChWo19a4Lz4YHpiLhwT8kPMhOwoN/SHjwZpLw4PUjax4IIcQbxlxlxrCuDShfLD/du3aic5fuzJw1CwsLi1x93uTkZKyscm8rtUzr1q2jatWqeHrqP/f9v1KtWjVsrC0Z2bsxquf8Il4gwJM+7WrSqWN79u47gEr1ZvyXbKYyx7twMbwLF6NCu14kx8fy+OED/WqYqQgrU56wMuVJSUrkxJ5dHPxzC5HHD6PVaPhu3gzGLPkGZ/d8Jmmzubk5JcsbthijEEKI3KM2cEcckTtkq0YhhHhDlS9ekB8WDOPovm1UrFCOCxcu5NpzpaamMn/+/Fyrn2nNmjWMGDGCfPlM02nMLaVLl8bfP5C/Dpx94XWdmlTCXJvMjOnT/qOW/fes7B1xC8hv8OMtrW0oV6cBQ2bOZ9p3G2jZdwguHvn4ZtZk1Ok5j34QQgghhOm9Gbc5hBBC5MjZ0ZaPx3Zlza97qVi+HB8tWEC3bt1NPuT/3LlzfPLJJ4wcOTLXRjisXLmS7t2706xZs1ydsqDRaDh48CBeXl4EBAQYXOed4SNYMGcKdSoVfe41SqWSKUNb0+G9T2jYqDHlyum/uODbxMnVjdqtO1C7dQduX71MdNQdPHz8XnWzhAlotVpSUlJIT09HrVajUatJV6tRp6eT/uT7eGsL0tPTsbe3x9fHR+faly9f4fzFC8TGxhEXF0dsXCyxsRmf4+LiiY2N5VFMDPVr1+SdgQP0GgWUlpbGgcNHUCgVKBVKFAoFSqUSpVKR8e+UtR1KRcbXbm5ueHt761VbpVK91lO0hBBvFwkPhBDiDadQKOjYpDKliwQxZsoENv/+G198+RUODqabl3zy5EmioqJYv349nTp1MlndTMuXL6dXr15otdpc6WCnpaWxa9cuNmzYwKZNm6hYsSLr1q0zqmbr1q157913OH/lNiFBz+8w5HNzZEy/pnTq2J4TJ09ja5t787rfJN6BwVlfx0Y/4MLJYzi4uGZ8OLtgbWtndKcrKSGBq5cu4JbPExdXN8zekKklxrp7+xarln2JRz5PPLy8yOflQz4vL9zz5UOlMmztB61Wy6Tx4/hp08YXXte8aVPmz52jV207O1umzZzFmbM5jwSysrJi1qSJdG7XRu/3jLm5OQePHmXWRwteeF3nTp2YNHGiXrX37NlD9549sbKyws7WFjt7e+zs7LC3s8POzg4LK2vs7OwICQ2ldZu2mL1krYIH9++zYsW3WFtZY2VthbW1dbYPK6uMz/a2tnh6emKvw+KhWq2WY8eOYW6uwtraBltbW2xsMj6bciqWVqtFrVaTnp5GWlo66enppKWloVAocHd3N9nzmIpWq+VxQgIPH8bwMCaGmJgYomNisLS0pGnDBnq9z7RaLTGPHuVeY4XQg/wvKIQQb4mQIG9WfjiIj77+leLFwlnzwzrKl9dvb/vnOXHiBACLFi0yeXjw1Vdf0a9fPzIX+DVVm5OSkti6dSsbNmzgl19+4eHDhwCEh4ezfPlyozue5ubmDBw0mO9/+4NJg1u98Np6VYqz6/B5hr/7Dl8u/cqo5zW13FicMjUpkQfXLuEVGm6S2g4urhzbvYMTe3dlHVOZW+Dg7IKDiwsOzq7YO7sQ7O9H/RZtdN5RwcrGhp/WrGL/zj9Rmpnh6uaOWz5P3J98uHk++ZzPk/xBgVhb2+jd9thHj1AqldibMMx7WlpaGkqFwqTBh6e3Dw6Ojqz86otsx5VKJS5u7gT4++Ht7UPBkBC6dOuOjc3Lfy5KpZIPxo3nxInjXL1y5Znzjo6OzJ87h1YtWuj9nlEoFDRt3DjH8KBA/vws/2opoQGGjWBJTkmhWJEieHt5cvvO3WfO+/v789G8eVSrWlXv2lWrVqVz584sW7aMRzl0Hi0tLenXfwBNmzV/aXAA4ObujoWFBfM/+vC511hZWTFkyFD69dNtFxiFQsHVq1cZPvydHNuXGSS4u7vzwQdjqFChok515837kG+/XU5aWlpWUPBvhQoVYsGChXqFB7fv3GH0mDGkJCej0WrRaDRon3zWaDRoNRqcXVyYMnEi+YODX17widTUVEaOHc/R4yd4GJMRGPy7zVUqVmDpp4v0fv9GXrhI+2499HrMm0ILqDWy5sHrRMIDIYR4i1hbWTB+UEu27T1Fk0YNGP7eCN7/YIxOv3i+SGZ4sH//fg4fPkyZMqZZbHjt2rUMHz48KzhQKBQmq3379m2WL1/Oxo3/3Ol0dnZm06ZN2NmZZoX0/gMGEPLRh7zTtT5OL1kp/v2+TWj37if88ssvNG3a1KDnu3LlCq6uriYdVbJ1xRcEFy9NgRJlTVbT3Mqav5YtQGVpRfGGrSlQvhpmBt6xjn8Uw/UL53Dzyj6MPT0tlYf37vLw3l0UCgXlajegSp36OgcH6elp3L5+nRJly3Ng1w40ajX3o+5yP+ouEU9dZ+/gSItOXckfGGhQ+1UqFd1bNqZF+0607NAZWxO99zIpgME9u9CkZRvqNWlm0LQirVbLwwf3uXr5EtcuXeTqpUtcuhD5zHUajYYH96JITHhMmbLlaNuu/UuDg6ioKLZv3cK2rVs5eGA/6TmsY1G9Rg2mzphF8YK6d+aSk5P5Y+tWvl+7lm3b/8yxbttWrZg/70Ps7ez02m3h1u07bN2xg607/mL33n0kJSc/c41CoaB/zx6MHjceWx3CE8gIek6cPMn+/fvZt28fBw4eJD4+PsdrGzdqxMgPxuDrq1vocfvWLfbv38e1q1dRKpVoNJpnrqlTty7jJkwkOCBQp5qQ8XP29vYiNDSUyMjs74mUlBRSUlKoVq06Y8eOxdPTS+e67703gmPHjvH337ufOadUKunffwDDh7+HpaWlzjUBvL28aNm8Ob369s3xfLOmTZk/d67eoxksLCwYO2ok9Zq1IOrevWfODxs0gHGjRho0GqNQSEE+X7iAJm3a6f1YIUxNwgMhhHgL1alUlPCCfoz7eCVbt25h1eo1es3F/beYmBhcXV0pXbq0ScODdu3aUahQIUqUKIGlpSX58+c3Wcc4f/78VKpUKSs8UCqVrF27lvz5DV/c7988PDxo2rQpG7YepFfrF28BaG9rzbR32tC3T29OnjqNh4dHjtcdP36c3r17U7VqVapVq0aVKlWyrn306BGVKlVi1qxZdOvWDaVSv3WRf5g7EY1ajW9IGL6hYXjnD8Xa3oHvpoyiQMmy1Os+kHwBunfgMp3fs50DP36Lg1s+7N09cXDLh52LOzdOH2Hbklns+/5LitZtTpFajbGyd9S5btzDaMZ2av7Ca4pVrEqT7n3xDgwm33O2aszJ4lnT+Gvzr889b+/gSPOOXWjcuj3WtrZY67hVozo9nV7tWuIfEEhwSCj5Q0JxcXNn+eef8uP3K2nftSfN23cwaBTDN59/yv6/dxEQFExAUH4CgoMJCArGx8+fj2ZMYcVXn9Oxey8aNW+FpR47oxzev5fx7wx+6XU2tra0aN+JYYMG4uTk9NLrNRoNrZs35cGDf3bjKFCgINeuXSUtLQ1bWzvGjBtH67bt9L5bu2jJEmbM/md6Q4nixSkUGsqatWuxtLRk7swZdOvSRe+6ScnJlK9Vh5TU1KxjnvnyUa1SRdZu3ARAaMECfDx7JmVKlkSjx+s4Z+5cFi1enO2YnZ0dWq2WhIQEAEIKFmT69OlUr1ZN560ao6OjqV71+Vub+vn7M2HiZGrWqqVzWzONHTuGH39cn+O50NBQpk6dpvNog6cdP348x+DA39+f+fM/pmxZw8PM71ateuaYi4sLH86eTeuWLQ0eDXXw8GFu3b6d7Zi9vT2fzp9H4wb1DaoJkJ6eTs8Bgwx+vBCmJOGBEEK8pTzdnfhyam++WreDksWLsXTZ1zRr1kzvOhqNhu3bt5OcnIyzszPm5qbd775YsWIcPXoUtVrNypWm3e+5T58+3Llzh/nz5/Phhx9Sp04dk9YHeOfd92jZrDHdWlR77raNmcqE56dxtWL07tWDn3/5NcdfYnfu3MnRo0c5evQoCxcuBDJ+Sa9WrRqVKlUiOjqanj17smTJEj755BOd26lOT+fCkQOkpaZw7uAeABRKJXZOLgBcPHaISyeOULJWQ2p17AVWunfyY6NuE/fkIycJMdHsX/s1hzetIrRKXZw7dsTT7+WLVTq4uOLo6oaVtQ1+BUO5deUSd65eBiB/eHGa9xpAcNjzF6x8Ef/g/FhZW+MflJ+42EfcvXUTADt7h4zQoE17bGz1HyVw49pVbl2/xq3r19i3e2e2c/GxsXy1+GPWr15Bh+69adq6LTbWugce5yPOZn3k5H5UFJ/MncXKZUtp37UH9Vu0wlqHu+IBQcEolUq8ff0IyF+AgOD8BAbn56tFH3Pv7h1sbO1o2bETLTt0wd7BAScn3QI+pVJJzdp1OH/uHHXq1aNOvXp4enpRqlg45StUZMbsOfj6+ur8539a21atWL7iO9q2bkX7tm0pXKgQK7//nsNHjvDN0qUUDS9iUF1rKyuqVqpIbFwcdWrWoG7NGoQXLszJM2fZ8Mv/eHfgAN4dNEDvO+KQMSXru5UrKV++PJUqVKBixYoUKVKEsuXLY2ZmxqiRI+nZo4fe/8a6urpSqFAhLCwtqVixEvfv32fDj+uxsLBgwMBB9O0/wOBtdsuVK8+uXbsoXbo0mzf/DmR0mEeMGEnXrt0MXvegRIkSBAQE8ODBg6zgpHPnLowbN97odWHq1KrFuXPnuHM3Y5pJk8aNWfDhh88NbHWvW5PCoaFcuXqV5JQUihQuxPIvPic4KNCouiqVim6dOvLRJ4uMqpMXadHKVo2vGYX2BS9IkYJ+2jUfv/vftUYIIcQrcezsFcYuWEuz5q34aP4CrPXosPyXkpOTDf4l90XeeecdPv7441xb1bxEsXAGtatMxZIhL702NS2drqM+Y/iosfTt1++Z83fv3uXPP/9k9+7d7N69mzNnzrywXvEa9ajTtR8OLm4vvE6jVnPz/FlunD/Lzciz3Dx/lrjo+zlea2FlRfFG7SjZuC3mVi9/ryTGxhBz+zrx96OIfxBF3IO7XDm0h5TEx899TFjZCjTvOQCf4AIvrJ2Wmor5k6H4U3t3xNzSkmY9+xNWpsIzr6evHiMP0lJTMVOpUCqVvNerM1G3b9G8QxeatO2QY2hgp+PIg+TkJM6dPs3lC5FcOn+ei5HnuHT+XI7Xurp70KVXXxq1aKXTdINbN65z6cJ5rl2+xLUrl7l25TLXr14hNSXnO9QOjk606tSFZm07vHC6hFarJS01FYunOsT3ou4ysFNbWrTvRIsOnbOt2RDgovvoILVanW3a1OXLl/l79y66dH125IznS6b+/JtGo8lW48jRo4SEhGCfw59Vn2kL/24zwNETJ7C0sKRI4ULZ22Cte8CUnp7+ZKeGf9p8584d5s6bx9gxY3B3y/53WNeRB5AxJSIzdBg9agQPox8yYdLkHHeUsdBjGlt6ejpmZmacPHmCZs2a0rZtW95/f4xJFjFUq9XUrVub+Ph45s6dR82azx+95WSr3/9Z02fNYumyZcybPZs2rVu/8N/+9KTn/zuVE/9CRWjSsD7zZs7QK/x7Ea1Wi5t/0BGtVmuaYX15RHChwtqZX6541c3QWcfq5d7410jCAyGEEADEPU5i+mebuHE/gbXrfiQsLOxVN+k/8+9OhinFxcUR4O/HuoXvkM9Nt7v1F6/dpc+4pRw4dJgCBV7ceY6OjmbPnj3s3r2bn376iQsXLjxzjYWVFVXbdKVi0zaYW+h+R/Te9SssfX8QqclJOZ63cXKlfNseFKpWD6VS9w7H3Qtn2TDlXbTajHnX1o7OuPgG4uITgItvIKXCCuIVEISNve6d0OTERE4d+JvS1es897XUJzzIlPA4nv+tW0OTth2wtXv+egm6hgf/tvKrL1j++afPHDczU+Hq7o57vnyEFy9Jtz79sTHgjuvVy5fo16ltjgvOmVtY4OntQ4HQQvQYOARPb923P3xwLwora2vscniN9AkP9KFveKAPfcIDfegTHuTkRQuW6hMePO3MmdOEhRV5bl19woNMe/bswcrKitKlSxvUppykpaUxatRIJk2ajLOz8wuv1Tc8+G7VKurWro2np+dLr9UnPEhOTmbN+h/p3rmTycNoV7/AN75j+m8SHrx+JDwQQgiRRavVsmnrIRZ+9wfTp89kwMCBsse4kRYuXMjmTd8xd2RHvR737cZdXIhK56dfnj/v/mn37t2jfPnyXL169bnXOOfzpkHPQYSWq/zS11WjVrN65liunD6Gjb0DNvaOWNs7Znzt4IjWyg4rOwes7B3xDAnD0UP3NTNO/rERhVKZERj4BmL9r3UOCruafnQJGBYe6MqQ8ODe3bt8/eknuLq74+aRD/d8+bI+Ozm7YGZmhpkRf//S09KYNPo9HsfH4eXj+8+Htw9evr7YOTnnSmgm4cE/jA0PXsTQ8OBlDAkPcoM+O73oGx7oQ9+RB7lFwoPXn4QHEh4IIcRb6cqNe3ww/wdCChdl2dfLcXV1fdVNypM0Gg0FCwQzaWATSoYF6fXYG3ce0H/yt9y4mfM6Af+2atUqzpw5g6OjIw4ODlmfN568gqW1DZa2tlha22JpY4OZ2cvnIavV6ajT07GwzLkjfy/x2dXrTeVtCQ90YUx48DKpanWu1JXw4B8SHvw3JDx4MwWHFtZO++LbV90MnXWpWf6Nf41kwUQhhBDPCPLz4Lu5A/lkxWaCAgOoWbMG7dp3pEmTJjg66r5Q3tvujz/+wNpcQYnCgXo/1tvDhQcPoklISNBpgbDOnTvnePxo+s4cj7+MmZlKp5BBCCGEEG+H3JngKYQQIs+zMFcxsncTflv6PmUL2LFsyTx8fbypX68OX331Vbbt1UTOPp4/jw6Nyhs09cPMTIm/jyfnz5/PhZYJIYQQQuhHwgMhhBAv5GhvQ/PaZVk4tgtbvxlLnZJe/LgyY/up6tUqs3jxYm7f1m1o/dvk/PnzHDlyhAbVShhcI8jXncjISNM1SgghhMgjtIBaq80zH28DGY8ohBBCZ7Y2VjSoVoIG1UqQnJLG3qORbP1pJRPGjSW0UAht2nagTZs2BAYGvuqmvnKfLPyYVvXKYWlh+Fx4f09nIiIiTNgqIYQQQgjDSHgghBDCIFaW5tSqGE6tiuGkpaVz4ORFtv+5kdkzp+Pn50ebdu1p06YtoaGhr7qp/7m4uDhWrVrF2oXDjKoT6OvO8bOnTdQqIYQQQgjDSXgghBDCaObmKqqULkSV0oUYN6A5x85cYfv+rVRf+DHOzs60aduONm3bUaxYsbdi68fly5dToURBPN2cjKoT5OvOmi1bTdMoIYQQIg/RAmrN2zEdIK+Q8EAIIYRJqczMKFusAGWLFWB0nyacirzOtn37aNLoK8wtrGjVug1t27ajXLlyb2SQoNFoWPTJx4zr29DoWoG+Hly6dAWNRoNSadgyRYNL+hvdjpwozXNna0KAlEe5sxhnwqUzuVIXwNo/JFfqaly9c6UugIO1Za7UfXz9Yq7UTVO65UpdyL33syZXqmZQa3KnutI897oHFqrc2QYyt34WuUmrznttFkLCAyGEELlGqVRSvHAgxQsH8l7PRpy7fJtte0/RucMPJKWk07p1a1q3aUeVKlUwy4N7iwNotVquX7/OqVOnOHXqFAf278VcoaFUkSCja9vZWGFnZ8PNmzfx98+dEEAIIYQQQhcSHgghhPhPKBQKCuf3oXB+H4Z2rc/lG1Fs23uaQf16cP9hHM2bt6Btu/bUrFkT81y8q22M+/fvZ4UEp04e5+TJE0ScO4+ttRUFg7zJ7+tK6SAPhrTqZrJRFUG+nkRGRkp4IIQQQohXSsIDIYQQr0SwXz76tc9Hv/a1uXk3mm17T/HBe0O4eusejRs3om27DtSrVw8rK6v/vG3x8fGcOXOG06dPc+rkCU4cP8bZiAhSU1IpGOxLfj938vu5MbBNBQr4t8DJwTbX2hLo48K5c+eoW7durj2HEEII8drR8tZsgZhXSHgghBDilfP1dKVHqxr0aFWDuw8esWPfaWZO/oCuXTpTv35d2rTtQKNGjbCzszPp86ampnLu3LknIcFJThw/ypmzZ7l3/wEFAnwoEJCPYF9X2tcpTMHetfBwdfjP12nw93Ih4mzuzdUXQgghhNCFhAdCCCFeK55uTnRsWoWOTasQ/SieHfvPsPij6fTp3Yvq1avRrn1HmjRpgrOzs841NRoNly9fzggJTp3i5PGjnD59mqvXruPr7UEBf0+CfV2pV9qbQa1K4evpipmZYQsUmlqQrwdrt0t4IF4/d+/e5eix41y4eJHePXuYJNxLS0vnQcxD7j94SNSDB9yPfsi96GhiHsXSr29fggICTNByIYQQhpDwQAghxGvL1cmeNg0q0KZBBeIeJ7LzYATffrGAQQMHUKFCedq260DLli1xd3cHMhYvvHPnzlMhwTFOnT5J5PmLuDjaUyDQi/y+bpQOykf7Gs0I9HXH0uL1XF8hU6CPO+cv/PKqm2EyxuwcIZ4vJSWFO3fuYG5unuOHsT/zR7GxHDt+nCNHj3Ls2HGOHjvG7Tt3sLezY9OP6w0ODlJSUxk3ex7HT58l6kE0Dx89euYaNxdnvpw7U+/g4Pet29j2118kJSWTmJREUnIySUlJJCUlk5ScRGJiEmqNmpFDh9CtS2e9RhXFxcczbORoYh49QmWmwsxMiUqlQqlUYmZmhsrMDDMzM+wcnRg2dCi+vr46105PT2fw0KEcOXwYC0tLLCwssLSwwMLCAgtLy6yvzS2t6NWnL4UKF35pzYizZ0lOTsbRyQknR0ccHB1RqYzrBqSnp5OamopSmfFnNzMzy7WRWVqtlpSUFB4/fszjx4+Jj4/HysqKggULmqR+Wloa0Q8f8jA6GhdXVzzz5TO6XtS9e9yJiuJu1D3u3L1LQkICvbt3w8He3iRtfhto0cq0hdeMhAdCCCHyBAc7G5rWKk3TWqVJTEph9+EINn2/lFEj36No0XDQwtmIcygUEBLk82RdAnfqdq5O/oD22Nn892snmIKXuzMPYx4RHx+P/Qt+6bx586ZeHZSXOXbyJLv37KVz+3a4uriYrO7YSZNRKpW0b9OaYuHhRnc2EhISiY6Jwd/Xx0QtzKDVaol9nICTvWmnykRevMT/tm7Hz8cbP29v/H288fRwN3q3EXNzc+bP/4iNGzfmeN7MzAxzc3NKlSrF7NmzCQoK1qnuxYuX6N67N6fPPDv6xd7Ojo3r11GmdGmD221pYcGIAX3oOPCdHIOD4mGFWTpvFj6e+nfmalStwjerVrNj1+4cz4cUyM/nH8+naFiY3u9DB3t73hk0kObtOpCQmJjjNbWqV+ODceP1/nupUqmYPXMmterU4dKlSzleU7ZsWcZPnkJoaCGd6/bq3pWkpKSs721t7XB0csTJ0QlHJ0ccHZ2oWas2Hdu30+nnkZycTIcO7Tlx4kTWMYVCkT1EUamoV68+EydO1Gu02IKPP+aX//2P+Ph4HsfHE//4MWlpaVnna9aowaJFi3Sul+nipUt8sngx0dHRWR8PoqOJjY0FoE+vXkyfOlXvuqmpqYydPJUjx45x524UD6Kj0T7V6Q0ODGTl118ZFBxs3bGDQcNH6P04IXKDhAdCCCHyHBtrS+pXLUH9qiVISU3j4ImLWFioKDC4Aa5Ob9ZdHTMzJQG+npw/f57SpUuTnp6OVqt9ZkeK6dOnc+fOHcaPH0/ZsmUNeq74+Hjs7OxQKBQUKVSIDj16MWfBx7Rs2pRe3bpQqnhxg+omJiWRlpqKnZ0dLZo1pVGLVnz+1TJCChakfetWtGnZAj8Dgw9LSwsatO/CzHHv06JRA4Nq5EShUDBo1sfUq1CGzo3qYG7kXdpMIfmDOb5gEfO/+CrrmEplho+nJ77eXhQPK8zQ3j1x0DO0UCqV9OrVmx07dvAoh064hYUFI0eOolevXnrdcS5QID8fzZ1DkxYts3Xe7O3s2LBuLWXLlNGrnU9Tq9Xs2Luf79Zv5NK168+cb9OkIbPHjsbagEVT70ZFsel/vxH98GGO53t06siUcWOwsbbWu817Dxxg+46dbNvxV47BgYuzMzMmT6RtyxaozXVv+61bt/hzxw527drF7r//JiYm5plrHB0dmTB+PJ07dSI2KUWnumlpaSQkPKZM2XLs3rUz63hCwmMSEh5z+9YtfP38aN+xE/UbNNQ5SLGzs2PatGm0bduWlJSMtmi12qz3iZubG1OmTKVp06Z6hzP9+/Vj008/cf169veFmZkZY8eMYdDAgQaNpimQPz8+3t6sXLUq23ErKyvmf/ghHdq317smZPz9GjawP7UbN+Phv163GlWrsmzJYpycHA2qXadGDXp07sSCT5cY9HghTEnCAyGEEHmapYU5Vcu+fNhuXhbk405kZCSlS5dm586dNGzYkJCQEAoXLkxYWBhhYWEULFiQL774gp9//pn69eszYcIEKleurNfz1GvRims3buDp4YGXZz7MlEpSUlNZ8+OPrPnxR0oWL0bvrl1p0aQJNnpsp/n9D2sZNW48QLbO2vkLF5g2ew7TZs+hcsUKtG3ViuaNG6Frd0ur1TJw1BgSExMZOGoMf+3Zx4yx72Nra6PPHztHP/21h2u3o5j+1Uq+3/wn4/p0oVqpYkbXvXz9On/u2ZvtWHq6mscJidSoWIFu7VpjZ2vY7h3Tp0/LMTioXbs206fPMHhkyqnTp7MFB3a2tvy49gfKGRhSZdJotIyaNot7D6IBsLK0IDklFTMzMyYOH0rvjrrdAc/Jpl9/Y8KMmc8cd3Zy4uPZM2lUz7DdSzQaDV169+Px48c5nm/bqiXTJ47HzdVV79rfrVzJgo8/zvre2to620iBNq1bM3nyZNzd3PSqm5KSQrfOndBoNM+cs7OzY+DgIXTp1h1LS0u927xy5aqs4OBpbdu2Y8KECXqNNnjahQsXOHfuXLZjfr6+fPbZZ0YFVgD79u/P9n1gQADffv01RYsWNaruqTNnnwkOBvTuxZTxY42eIrLpf78a9XghTEXCAyGEEOI1F+DlTETEWQDOnTtHWloaZ86c4UwOQ8kB/vjjD/744w9q1KjBsH59qFqxok6dsLv3okhNTeX6zZtcv3nzmfPHTpxkyIlRTJwxky4dO9CrW1f8/fxeWvdxQkLW14lPdYaetmfffvbs288HEyYwsEc3hvTp+dK7wrfvRrFt525SUlMB+GHTzxw6dpzP5s2mWJhxgdL1u/eIepjREbh08za9Js+lVrmSjO3VmUBvT4Pr+nv7UL5UCQ4cPQ6Ah5srA7t3pUublnrfBf+3+vUboFarOXz4cEZtDw+mTJlK48aNjZoe0r1rV85fuMCGTT+RnJTEj2t/oHy5cka1FcDcXEX/Lh25e/8BXVo1Z8yseURcvMgXc2ZQuazhUyEAWjRuxP82b6Flk8acPHOG1evWU7ViRT79aC5enoa/fubm5rRu3pSk5BTq1KiOSqWi18DB+Pn68NHMGdSuWcPg2rVq1mT3339TvVo1alSvjoWFBfUbNiQ4OJg5s2dTrWpVg+ra2dlRqXIVrG2s2bljR9Y6Be07dGToO+/iYkDQkalevXqcPHkiq6Pv7+/P7NlzqGpgWzMVK1aMDu3b8+tvvxEfH0+jhg1ZMH8+Tk5ORtUFGNCvH6mpqew/cIC6derwxZIlJqlbv05tunXqyPfr1qNQKJg3czqd27czuq5CoWBgn16MnjDJ6Fp5jRZI18iaB68ThfYFi1AUKeinXfPxu/9da4QQQgjxjF93HOXIlUTW/bgRjUbDjRs3OHv2LGfPniUiIoKzZ89y8uRJEp7qpD+tXOnSvDdkMLVrVH9hJzIlJYWoe/e5E3WXy1euMnzMWNLT05+5zsHenqLhRShetCgD+vTB18f7pX+GlJQU4h8/ZuPPv/D++AlZx5VKJeFhYVQsX56K5ctRoVxZHM116+hG3b9Pm179uXj5Srbj5ioVY4cPo1+3ztmGNidc0n3Xio9XrWfxD5sAsLKwwN7WGjsba1wdHRjcviVVS2a/S2ntH6Jz7fX/+41ZnyxhSK9udGjR7IXD8jWuL//ZPm3btm306tWTLl268v777+Po+Pyh0g7W+t1prlm3HjOnTaVihQovvO7x9Yt61c00bMIURg3sh5+3V47nLZz0u+OeaeDwEYQVCmVw3z7PHequ1GMkzdM+/2oZN27eYsyoETmOGElX6X83P9P69eu5cvUqQ4cMwSqH90hMQs5B3PNE3b1L9SqVqFqtOqM/GEPBkJzfs7aWFnrVXbt2LaNGjaRv376MGDES6xeEYBYq3df20Gq1FC5ShNGjRtGzR48X/ttlrueaIRMmTcLOzo5RI0a8dPqDNiXnNS1ykp6eTrHyFVn+xeeUK/PiAEyrfnYkyIu4BQYf0Wq1xg27yGP8Qwpp31+07FU3Q2dDGlR5418jCQ+EEEKI19zZizeZ/uVmTp2JyPF8fHw8JUuWfGZxNTMzM3y9vQnw98Pfz48WjRtRQ8c7gpNmzuLTL5fi4+1F0bAihIcVpmhYGEWLFMHP1wczC/06GJAx97pWw8YZd0LLl6NC+fKUL1MaBweHbNelPHqgU73d+w9wNvICiUlJGavpP/mcmJREYmIyFcqUon/3LlmLEeoaHmi1Wm5E3cfOxho7a2sszF8+UFOf8ODs+QvkDwzAUoefob7hwa+//g9PTy9K67CIob7hwbXr1wnw93/pdYaGB+np6S8c3m1oeBB1/z75nuzI8jyGhgePExJeOM3EmPDgZT8PfcODkydOEPvoEVWrV3/hdYaEB4UKFaJYsZdP69EnPEhJSeH8+fM6TSfQNzy4d+8eHh4eOl2rT3iQkpLCg+hofLxf/vdWwoOXk/Dg9SPhgRBCCPGaS0hMpla3aTxOSMzxLtmiRYvYu3cvwcHBBAUFZX328/Mj9tYNvZ9Po9Gw7+AhCoeG4PKcOcuGdLaSk5PRarUvvDMJuocH+tJn5IG+9AkP9KFveKAPfcMDXRkaHryMoeGBLgwND17GmPDgZfQND3Slb3igD33CA33oGx7oQ5/wQK+6Eh68lH9IIe3IT756+YWviXcaVn3jXyNZ80AIIYR4zdnaWOHoYMf169cJDAx85vzQoUMZOnSoyZ5PqVRSuUJ5k9XLlNPQayGEEELkDfrvcSKEEEKI/1yQnyeRkZGvuhlCCCGEeEtJeCCEEELkAQHeLs9sXSaEEEK8qbRaUGvyzsfbQMIDIYQQIg8I8HIh4mzuzdkXQgghxKuhUChcFArFVoVCceHJ5xwXHFIoFA0UCkWkQqG4qFAoPtDl8QqFYsyT6yMVCkX9p47PUCgUNxQKxWNd2ynhgRBCCJEHBPl6SHgghBBCvJk+ALZrtdqCwPYn32ejUCjMgE+BhkAY0FGhUIS96PFPzncAigANgCVP6gD8ApTTp5ESHgghhBB5QKCvO+cvXHjVzRBCCCGE6TUHvn3y9bdAixyuKQdc1Gq1l7VabSqw5snjXvT45sAarVabotVqrwAXn9RBq9Xu12q1d/RppOy2IIQQQuQBnm5OxMbFExcXh4ODg86Pcw0IypX23Ni/I1fqAphZ2+VKXbuCL9+H3lBxZw/nTuELJ3KnLpCkzJ3t7Szdc2d7yaTbV3OlLoBz8Yq5UjcuKSVX6gJY59L2klYWuVMXMraBzQ0KTXqu1AVIfRyXK3W1GnWu1H3TqLXaV92E/0q+zI68Vqu9o1AoPHK4xgd4ev/lm0Dm1kjPe7wPsP9fj/ExtJESHgghhBB5gFKpJMjPi8jISMqWLfuqmyOEEEKI7NwUCsXTSfKXWq32y8xvFArFNsAzh8eN07G+IodjL0tXDHnMc0l4IIQQQuQRgT5uEh4IIYQQr6cHWq22zPNOarXaOs87p1AoohQKhdeTUQNewL0cLrsJ+D31vS9w+8nXz3v8ix6jN1nzQAghhMgj/L2ciYg4+6qbIYQQQuQ6LaDWaPPMh5F+Bro/+bo78FMO1xwCCioUiiCFQmFBxkKIP7/k8T8DHRQKhaVCoQgCCgIHDW2khAdCCCFEHhHo4y47LgghhBBvntlAXYVCcQGo++R7FAqFt0Kh+A1Aq9WmA0OAP4AIYK1Wqz3zosc/Ob8WOAtsBgZrtVr1k9pzFQrFTcBGoVDcVCgUk1/WSJm2IIQQQuQRQb4erPjl0KtuhhBCCCFMSKvVRgO1czh+G2j01Pe/Ab/p+vgn52YAM3I4PhoYrU87JTwQQggh8ogAHzcuX7mGWq3GzCx3VsoXQugmOTmZb1etJj7+MUnJySQlJ5GUlExiUhJJTz7ik5Kxs7Nn7LjxBAblzs4nQgjxX5HwQAghhMgjbKwscXF24Nq1awQHB7/q5oi3ULpazckLV9h/OoJWNSvj6eryqpuUo/0nz3L9bhTJKSkkJqeQnJJKUkoKSckpJD11rEWtKjSpVsmg57CyssLLy4uJ04eSnp7zVoE1atZi1uw5uLq56VRz3Q8/cPbsGczMzFCpVKhUKsyefFaZqTBTmWGuUlGgYEFq16ylU83z5yOJjY0jPT2d9PQ00tKyf05PTyctLR1Pz3zUqFFT5z//myIpKYlbt29z6/ZtbG1tKVOqlMmfQ61Wo9VqUamk66UPrVb7Nm3VmCfIO1gIIYTIQwJ98xEZGSnhgfjP3Ii6z54TZ9h9/DT7T0UQn5jErME9jQ4OEhKTuHXvHikpqaSkppGalkZKaiopqakUzh9EgI+3wbU9XZ0ZOf9T7j54mON5RztbZg7rR/1K5Qx+jmvXb3D5yhVcXZyJunc/2zlLSwumjh9Py05dUShy2iktZ7Xq1GHpl19w5crlHM+rVCr69h9A2XLlczyfk+joh/To0Y3k5OTnXtO2bTsmTZqsc03IGHnRp29fzl+4gLm5OeYqFaonn81UKsxVKsLDw3l/9Gjs7Oz0qr1l61a2b9+eEXio1aSnpZGuVqNOT0ehVDJk0CCKFy+uV02AS5cv8/nSr7h561ZWYPDwYQwAtWvWYNkXn+tdM9P3GzZx5tw5Yh7F8vDRIx7FxhLzKJZHsbG0a96M8SOGG1T3wuUrTJ77kcHtEsKUJDwQQggh8pBAbxciIiJo2LBhrj3Hd999R6dOnXJlasS96Id45MLd6vOXLhOSXwIVU0tJTWPl79v55pctWceGtGtG61pVja5tZmbG1E++YPfho1nHzFUqRvbpTt3KFYyqnZCcTOGgwBzDg/JFw5g3YhBebq56101KSmb5ypVs+PkXjh4/keM1hUNDWbr4EwoXCuVhmu61T5w4zq+//EJaWmqO54uXKMGMmbMJLVRIrzYXKVKEmjVr8fvvz0yTxtXVldmz51CvXn29akLGyIt+ffvStn17NBpNtnMWFha8N3w4QwYPxtLSUu/alStVYurUqVy4eDHb8cKFC7P4k08IDw/XuyZAcFAQ5ubmbN6yNdvxAX37MG3SRKNGBlQsU5rJcz4kNi4+65itjQ0fTZtMi0YNDK5bMDiIkPzB/Ll7j8E1hDAVCQ+EEEKIPCTAy4VzEabfcUGtVjN16lQqVarE8OHDyZcvH/Xq1TNJ7VOR5zl/+Srm5uYcPXWGycOHmKRuYlISy9esI8DPl+83/sTKTxeapK5Go+Hw8RM4OTqyefufDOvXxyR1AR7GxRNx5Tpebi4E+3iZrC7Amq27sLGyoFGlsqhMFPxYWphTunDBrPCgda0qDG3X3CS1rSwtiImLy/q+QIA/CyeMJjykgNG1z166yo5DR7MdM1MqebdLW/q1boaZmWEbjqlUZsxf9CkPY2JQKBRUrliBlk2bMG7yVJJTUujXqweTxnyAlZWV3rX37dnDN18ve+a4ra0tI0aNplPnLgYFegqFgu3btz1zvH79+sycORs3HadU5OT3zZufCQ4qVqzI/HnzKFDA8Nfx/v372YIDpVLJ4EGDGDVypEFhRCaFQsHFS/+M6lCpVMydOYOe3boaXDPTrbt3swUHIfnzs+yT+RQMNn6ti72HDhtdI69K17z8GvHfkfBACCGEyEOC/DzY/dtJk9edPXs2U6dOxczMDLVazYoVK0wSHpw4e46mvQdh9eQX/qrlShtdM9O0+Z+wYt2PKBQK7G1t0Wg0KJXG7UL95+6/uXr9JmOnz8TB3h5PDw+ThQcP4+IZNHsRRyMvsmjUYJOHB4UDfUlITiEhKRlHO1uT1a1brhQfvduPjTv2MLV/N72G4b/MzBFDaf/OaNo3rs+YAb2z3ifGqlepLEciznP73gP2njiNn6cHC0YNpUSoccGEubk5w4cMRmWuolmjRnjm8+BhTAyzP1rA8i8/p24tw9cMqFW7DteuXaNa9epMnzqFe/fuUbtOHSZNnoqXt+FTOOzt7enXrz+///47ly5dxN7ensmTp9C6dRujX8vWrVpx6fJlduzYgaOjI5MmTqRzp05G/z0MDAxkyqRJTJoyhaCgIBYtXEjZsmWNqplpwpj3UanM2HfgIN9+9SXVqlQxSd2KZUrzwTtDmL1wMa2aNOLDyROxtbUxSe33hw6m8wDThK5CGEPCAyGEECIPCfT14MKFiy+/UEfJycn88ccfPHr0CMgYgQCwYcMG4uPjsbe3N6huYlISfT+YyL0nw8aTU1IAuHH7rtFtjrr/gJ7vjODClatAxqJacY8fc+HyFUIL5Deqtp2tLeNmzAIgLj4edxNOsXBxsKdFjUocjbzIvYePTFY3U/GCuTNtQ6FQUL1UMWqWLo65iRd8Cw0O5PNpE6hRvoxJ6zra2TH7nf70nzaPlrWqMnFAD+xtTNORG5RDmLRry+94uLsbVTckNJRZc+YSFxeLQqFg0aefUb9BA5OENaNGjWbPnr+pWLES8+Z9hK+vr9E1AcqUKUPBAgVwdHBg+vTp5PPwMEldgEqVKtGrZ0/Gjx+PrYleO4Ci4eFUrliRaZMmkt+Ea8colUqqVayAk6Mj3Tu0M2nIVqtqZZPVEsIYEh4IIYQQeUg+V0fiHz/m0aNHODk5GV3P0tKS+/fv8+mnn2Y7npSUxI8//kiPHj0Mqmtjbc3U94bSou/QbMdv3LmDVqs16hfrfO5ufDJjCu37DSYxKSnr+OETp4wOD8qVKsmgXj34dNk3QMbuAqbUoV4NIq/dJOrJIm15hYOJ7qD+m5WlpcmDg6f1b9OMUoVDcq0+gIuzs0nrabXw2x9bcHBwNGndLl260qpVa6NHBfxbnz59CAoMNGlNyFirYdbMmSavCzCofz+Tdu4zlSxWlFLFi5m87ttKC7LbwmvGtP96CCGEECJXKRQKgv29iYyMNFm9Pn36cOjQIcLCwrKdW7FihVG18wf4s3T21Gx3qxMSk3j01Dx3QxUICmTDN1/i5/3P0P/DJ0wznWP0sMEUKpgxvD093bThAcDYnh1MPmVB5Cy3g4Pc4OjoaPLgAKBNm7YmDw6AXAkOgFxZsDVTbgQHuVlXiNeFhAdCCCFEHhPg42ay8CBTkSJFOHToEH36/DMke8eOHVy7ds2ouhVKFWfu2JHZjl03wdQFgABfH378+kuC/P0A04UHlhYWLJo9A5VKRXp6uklqPs1cpaJljUomryuEEELkJgkPhBBCiDwmwMuZiLNnTV7XxsaGpUuXsnr16qy1DlatWmV03dYN6/Fu725Z39+4fcfompl8PPOx4esvCM0fzOVr13kY88gkdYuGFWbEoAGkq00fHoDcoRRCCJH3SHgghBBC5DGBPu5ERJzOtfodO3bk6NGjlC5dmhUrVqA1wZzT4b2707JBHQBu3jHNyINMHm5urP/qc8ILhXL45CmT1R3atxfFioS9/EIhhBAmp9WCWpN3Pt4GEh4IIYQQeUyQrweR50w7beHfChQowJ49e2jYsCGHDh0yup5CoWDumJGUK1GUGyYODwBcnJ1Y++UStBrT/QanUqn4cPJEk9UTQggh8jIJD4QQQog8xt/bjavXrufKfPynWVpasmDBgmcWUjS4noXFMwsompKjgz31a1Y3aU1fb1nYUAghhADZqlEIIYTIc6ytLHBzceLq1asUKFAg15/Pzs7OZLWcHR0Z2a+XyeoJIYR4c8lWja8XCQ+EEEKIPCjQNx+RkZH/SXiQE+vgIoY/9iXnNSacevC0uKSUXKkL4FquVq7UfbB3c67UBYg9uitX6toG5846EXahJXOlbm5ysLbMtdpqTe50qtJyYXvSTGbK3FkoVKmyyJW6AGaWL/sXyzCpcTG5UleI3CTTFoQQQog8KMDbhYiIiFfdDCGEEEK8JSQ8EEIIIfKgQB9XzkWcedXNEEIIIcRbQqYtCCGEEHlQoI87O3459qqbIYQQQuQKLaCWJQ9eKzLyQAghhMiDgnw9uHDx4qtuhhBCCCHeEhIeCCGEEHmQu4sDSUnJPHz48FU3RQghhBBvAZm2IIQQQuRBCoWCIH8vIiMjqVix4qtujhBCCGFSWiA9l3YVEYaR8EAIIYTIowK93SQ8EOI57ty7z4wlX+FgZ4uLkyOuTo5PfXbC1ckRZ0cHLMzN9aqr1WqZ/dECjp04gb2dHXZ2dtjb2WFvb4edbcbnzOOe/oEULlTopTWTkpLY/McfmJmZYaZUYqZS5fi1SqUiMCgIdzc3ndurVqsxMzPT68/4OtJoNDx69IiYmBiCg4NRKHJn20chxPNJeCCEEELkUQFezkSclR0XRN5060EMI75cTWJyKloArRYtGZ1zrVZLmZAgRrZthIONtUH1vTzcKVWkMFMWff7caxQKBZ2bNWLqjJk4OjrqVFehUDCwT2/qNW/JpcuXn3tN984dGf3BWJ1qWltbc/PmTabPmPHca6ysrBg1ciQlS5bUqWamyMhI2rZti0KpwN7ePuvDzs4u6+t6detRs2ZNveoC/PrrryQmJuLu4Y6HuwceHh64uLigVBo3M3rDxo3s2LGD+/fvc//BA+7fv090dDSOjo4s/fJL8ufPb1BdjUbD2YgIFAoFSqXyn8+Ap6cn9vb2Brc5ITERhUKBjbVh79cXOXjsuMlrCmEICQ+EEEKIPCrQ152dp00fHixZsoRBgwaZvG5iYiKnz5yhXNmyJq+t0WhQq9WY63kXWdfaxnaGxLN83JwpGujH6h37sh23tjBneOsGdKxRweCf+4mISFb/8jvHz5577jUVShRl7MA+FC8cip2OwUF6ejoz533EvgMHuX7jRs51y5Zl1tRJFAsPJ11lqXObQ0NDcXR0JDY29plzlStX5qMPPyQoKEjnepmCgoKoUqUKv/zvl2fWSPH29mbihInUqFFD77oAdvZ29O3XN9sxMzMzateuzfTp0/H18TWobuVKlZg4aRL379/POlaieHG+XrYMHx8fg2oCKJVKfli3jk8WL846ZmlpyfujRjFs8GCD62aq1qARSqUZYaEhFA4NpXBoCEWLhBEUEGBU3QuXrxrdtjxJK7stvG4kPBBCCCHyqEBfD75cv8cktR4+fMjo0aMJCQlhzJgxtG3bFnd3d6PrqtVqfv7lF86ePcv9+/dxcHAwWXhw+fJlPvv8c4YOHcq6deto0KABRcLCjK778OFDtm/bhq+vLzv/2kG37j3wNqLDkp6ezuat26hWpTJrf9xIUIA/tWvWQKvVmmzo9cad+/BwdqRyMeP//P8WFZfA7gs3aVM61OS1T1+9me37siFBTO3eGn8PV6Pq3n0QzZr/bc7xXMFAf8YM7E2tCuX0/vmrVCo2/vwL12/cfOacl6cnU8aNoVXzZga9rteuXXsmOHB0dAxjLlsAAGj8SURBVGTypEl07NDB4PeKSqVi+5/bsx2zsrJi0KBBDBo0CBtrG4PqAuzbmz34CQ4K5v3336dx48ZGBW5qtZro6Ois7zt06MCcWbOwsrIyuGampwOJ8uXKsXjhQkIKFjS67u07d7j25H1x5do19h06TNcO7SlTsoTRtTf/+ZfRNYQwBQkPhBBCiDzK38uNa9dvkpaWZvQd90WLFrFs2bKs77dv306HDh2MbSKXL1+m/4ABaLUZt48MvcOZkw/GjGHnrl2cjYjg8OHDhIaEGBUepKQks/ybb0h4/JjPlnyKjY0NiYmJ9OrT9+UPfgGVSsXUWXNwdnbi8NFjeHl60qZlc9LT0pk+aYJRtTMt/OFnEpJT+G7SexQKMOxu7/Mkp6m5cC+GC/diKOjhbNLaYzo0YfJ3m7h+7wHvtWlIh+rlTTLKo1SRwgzs1I7ihUP4/pff2XnwCB6uLozo3Y02DeqiUhm+BsB7Q4egVCrx9/OlRftOWFhYMLhfX94dMgg7W1uD6zaoX58rV69m/T1s1rQpM6ZPx8PDw+CaAObm5nz44YeMHj2ahIQEmjZpyoQJE/D1Nf590qVLF7Zt38bDhw8Z8d4I2rVrZ5LRP97e3qxbu5b2HTowfdo0enTvbrKgbfqUKew/eJCB/frRp1cvk60HEejvT71aNblx8xZ9e3anTfNmWJsg7AAY0KMLf+7Za5JaQhhDwgMhhBAij7KyNMfD3ZkrV64QEhJiUI27d+/SsmVLTpw4ke34li1bjAoPUlNT6dajB3/99VdWcAAQERFhcM1MJ0+eZMPGjVmdlMOHDwNw+coVo+paWlqRlpbGZ0s+BTKmWUBG599Y4UXC+Ol/vwJw5+5dFn32Bb+s+8HoupniE5Po37KhyYMDgABXB8Y0rJDtdTSVYsH+FPD2YOGgLvi5u5isrruLMx8M6IVWq2Xul98wsk93erdtiY218Z25rh0z/l78uOknGtary7QJ4wkKNG5YOoCfnx8Vypfn119/ZfasWTRs0MDompnKli1LgH8AU6dOpVKlSiar6+3tTceOHenYsaNRIxhyEhwUxIb16ylfvrxJ67q4uPDTjz8S4O9v0rrm5uZ88N67hBcubPLFHB3tHUxaTwhDyQQ+IYQQIg8L9stHZGSkwY/39PTku+++e2Ye8ZYtW4zqLFpYWLBs6VJKlSqV7XhUVNQz8671FR4ezt2oKLZtzz4U29jwAKBf/wEU+NcQZmPuUmcK/9eIiLBChahYvpzRdSFjTYYyhQvSp2ldk9R7ntxa3X5273YmDQ6elpqWxrrFHzG0W0eTBAdPq12jBiuXLTVJcJDJ38+P3Tt3mjQ4AHB0cGTz5s0mDQ4gYw2B3r16mzw4gIxgwtTBAWS02dTBQaaiYWG58vckvJBh4XBepwXUGm2e+XgbSHgghBBC5GH+ns5G380vUKAAe/fupUKFClnHbt26xdmzZ42qa2try/erVhEeHp7teMS55y9ipwulUsnCBQuoUqVKtuNXnrPyvT4sLCyYPmNWtmMqlfHDsIsWyR4e9OnRzWSdjHSNhlkDu+XZRR1zs92WFha4OTvlSm0nJ90WWdRHiRIlcHAw/V1me3t7k4ygEUK83fLm/zJCCCGEACDQx41zEcbvuODu7s727dtp0aJF1rEtW7YYXdfR0ZG1a9ZQoECBrGPnjAwPIGOF9G+WLSPsqTv6phh5AFC6TBk6duqc9b1Jpi081U4HB3vatGxhdM1MFioVLg6GbzEnhBBC6ELCAyGEECIPC/RxJ8LIEQKZbGxsWL9+PUOHDgVMEx5ARjCxfu3arAXaTLHuAYCDgwOrV67Ex9sbgHv37vH48WOT1B45enTWQnWmWADOM58Hbq4ZOwh0atsWWxvTD/MWQog3jVqbdz7eBhIeCCGEEHlYkJ8HFy5eNFk9MzMzFi5cyLx589i9ezfJyckmqevj48P6detwd3c3etrC07y8vPh+9WocHTOGkF8x0egDBwdHJkyagpmZmUmmFygUCoqEFQagV/euRtcTQggh/msSHgghhBB5mKuTPampaTx48MBkNRUKBSNGjODrr7/O2snAFPIHB7N+7Vqi7t416cr9oaGhfPvNN1hYWJhs6gJA/QYNqG/CheuKhoVRq3o18gcFmaymEEII8V+RlVOEEEKIPEyhUBDs70VkZCRubm4mrd2uXTvUarVJa4aFhfHF558TExODi4vpVtivWLEiny5ezNWrV01WU6FQMHHSFJPVCy8SRqUKpl89XgghhPgvSHgghBBC5HEBPm5ERkZSuXJlk9c2MzN+m8J/K126tMlrAjRr2pQbN26YtKarCQOZapUrZa17IIQQ4sW0WlBrXnUrxNMkPBBCCCHyuAAvZyLOGr/jgj7cniwmmBsOnYk0/ME2DlyLjjVdY3RkYW/70mts3bxIAtDjl2EzazuD2/RSufRbeXpcTK7UVdmZfmvETJq0tFypa2X98veFoXJrX3klubfyW3piQq7UTcilnwXk3t9BWzvTb8kpRG6TNQ+EEEKIPC5jx4X/NjwQQgghxNtFRh4IIYQQeVyQrweRP+x61c0QQgghTEaLNtdG2AjDyMgDIYQQIo/z93bjxs3bpOXS0GshhBBCCAkPhBBCiDzOwlyFp4crly5detVNEUIIIcQbSqYtCCGEEG+AID8PIiMjKVSo0KtuihBCCGESapm18FqRkQdCCCHEG8Dfy5mIiIhX3QwhhBBCvKEkPBBCCCHeAIHebpyTHRdEHnPt/kO2nDhHYkqqyWtrNBqWLF/J7gOHSE5JMUlNrVbLz7/9zsXLl9Fq5ZaoEOLtItMWhBBCiDdAoK87mw8ceNXNEEIv3i6O9P9iLRPW/E71sPzUK16IKoWDsDI3N7q2Uplxj6zDwGFYWVpQrmQJqpUvS9XyZQkLKZh1Xh8KhYJrN27Qe8gwnJ2cKF2iOGVLl6JsyZKULF4MO1tbg9v71dffsPy7Ffj5+OLn54ufr9+Tzxkf7u7uKBQKvetev36dBQsXYmNjg62tLXa2ttg++XB1daVWzZqYmZnpXVetVjN91ixu3rz1zDkbWxvee+cdAvz99a4L8Mvvv7Pws89JTU0lOSWFlJQUUlIyvm7eqCHTxo/DwcFB77opKSlMmz6NmJiYZ845OTnx7jvv4u7ublCbb9y4walTp/D398ff39+g9j3Prdu3TVZLCGNIeCCEEEK8AYJ8PTh//iJardagDkZu02g0KBQKfvvtNxo3bmyyupl/3s2//kK9ho0N6hAK3ey/cocKQV4mrfnLodO42NlwJyaOP46f44/j57CxNKd6WAHqlyhEpdAgLM31/3U14sJFVqzfSGxcPADJKans2n+QXfsPAuDi5ESVcmWoWr4s1cqXJdjJTefaCYmJAMQ8esS2v3ay7a+dQEZYUTg0hLIlS1KjahUa1aurV5tLlSzJlGnTOHXq9DPnGtSrx/ixYygaHq5XTQBPT08uXLzI3n37stesX58ZU6caFBwAmJmZUbBAQRZ8sijb8apVKrNo/nyDgwOAiuXKMXnmbK7duJF1zMHeno9mTKd182YG17W0tCRfvnx8/fXX2Y43bNiQd4a9Y3BwAODo6Mj4CROyOvqOjo74+fnRonlzBvTvj7kRgdjJU6cMfmxeptWCWvOqWyGeJuGBEEII8QZwcbRDq9Hw4MEDo34BNrWUlBTmzZvHli1b6NatG1988YXJwoOTx48xf/ZM2nXqwpxpUygYWoj8BQoaXff40SPs3bWTgoUKU7dBQxITE7CxMfyOskajQalUEh8fz9dfL+Px48eMGzeeyMhzFCwYYlTgcT/mEYnJKdy4e4+DZ87Rpk517j98RKnCBU0WIh24eodVhyI4fy+GXwa0wNzAzmZOfjsWwZkbd7MdS0xJ4/djEfx+LAJbSwtqhBegTYXi1Kupe93bUfdYsW7Dc88/fPSIn7ds49bduyQkJtLVNxAHB3udav/82+85HtdoNNjZ2lGyeDGqV66k98//5KlTxMXHZzvWoF493h81ilIlS+hV62kqlYqzT62HEhQYyKwZM6hXV79wIyc3b/0z6sDezo6pkybRvWsXo0M8lUrF7bv/vC8qV6jApx99iK+Pj1F1AW7f+ucuvouLCzOmz6BZs2ZG/32Jjo7ONkIgJCSEke+9R/Xq1Y2uvfy774x6vBCmIuGBEEII8QZQKBQEB3hx7ty51yo8ePjwIXPnziUuLo49e/ag0WiIiYnB2dnZ6Nq/bNzA2dOnmDz2fQBOHD1iVHhw9fJlbGxtOXn8GMu/+pLa9Rtw/eoV0tPT6T9kmMF1k5OT6devD3Xq1GP+/I8ASExM4OjRo/z++x8G1wWwt7GhzoCRONjacudBNL/s2oe/pwffTv3AqLqZTt9+wOpDEey/cgeAvy7cpG6hAJPUBqhbLJTo+AQuR0VnO26hMqNEoA8VQgIpVzCAwj759Kob7O/H8L690KLl46XfZDtXulg4TevWplHtmvh4ZtS10DE4AHhv8CBGT5xM/OPHAHh55qN9q1Z0aN2K/EGBerXzaU0bN+KvnTvZ+NNPJgkNMimVSlatWEGb9u157913GTxwIFZWVkbXBRjYvx8nT50iMTGRhfM/ws/X1yR1nRwd+d/aH2jSrj3jRo1kUJ/eJhtVNGbMGM6cPYO3tzczps/A1dXVJHUDAgLo0b0758+f57333qNK5comC/Dq1qrNH1u2mqSWEMaQ8EAIIYR4QwR4uxIZGUnVqlVfdVMAWLVqFT///DM2NjbExcWhVqsB2LFjB61atTK47qUL55k89gPuRWW/Y338yBFatetgcF0zlRnN69fGzS0jfNn+x2a2/7GZyTPnGFwTwMbGBm9vHyZNmpB1bOXKlXTq1NmougBWlhaUL1qYPw8eA+DG3Xv0bNbA6LqZ/Jztsbe0yPp+4/ELJg0P6hUP5ZPfdqJQQJivJ+UKBFAhJIASQT5GrXsQ5O/HyIF9+fbJ6IOcAgND+fv5kZKaQvPGjejYpjU1qlQ2eOj/01xdXfHz82PH1q0mCQ2e5uLiwoE9e/A1Uec+k72dHf369KaqCTvKmRwdHdiyaSNFi4SZtK6DgwPvj36fypUrm7SuUqlkQP/+BAUFmbQuQO2aegy7ecPIVo2vFwkPhBBCiDdEgJcLZ1+jHRdatGjBmjVruHs3eyd/27ZtRoUH+QuGMHDYu7z/7tBsx08cO2JwTQA//wCqVKvBzj+3ZTselD+/UXUB2rRpww8/rMl2rEyZMkbXBahVtlRWeJDxfUmT1AVwtLZkerMqVCvoy9ythzh8PYprD+MIcDHNYnBX7j1kSvuGlC3gj6ONtUlqPs3T3Y2Dv20yOjB4mp2tLaf378XZyclkNTNNmzzJ5DUBQkNCcqUuQLUqVXKlboHg4FypC5g8OMiUG8FBRt3AXKkrhL5kVSEhhBDiDRHk6/Fabddoa2vLxo0b6du3b7bj27Zte84jdFepajUWfvEVtk+tbn/n9m2i7twxqm7nHj2fORYYZHwnpmzZcvj/awE504UHJbK+DgnwxTef6aet1CscyOqejakQ5MWmExdNVrdUsC91ioXmSnAAUL9GNZMGBwBhhUJzJTgQQojXnYQHQgghxBsi0NedyMjIV92MbFQqFV988QVTpkzJOnbhwgWuXbtmdO1SZcry6bLlODg6Zh07buTog+IlSxEWXjTrey8fH6xtbIyqCRlrUrRp0zbrezc3N/z9TTP838PFmaIFMu54mnLUwTPPY2/DwjY1KeLlikYrY4mFEOJtI+GBEEII8Ybw83Tl1u27pKSkvOqmZKNQKJg4cSJfffVV1txwU4w+AAgLL8oXy7/D7ckikSeOHjWqnkKhoFO3HlnfBwcXMKre01q3bpP1denSZUw6R7zmk9AgN8MDyPj51CkUgPI13A5UCPFm0ZKxVWNe+XgbSHgghBBCvCHMzVV4e7px6dKlV92UHPXu3ZuffvoJGxsbk4UHkLEGwpffrsLLx4fjRw4bXa9W3Xrk8/QCTLPeQSZfX18qVqwEQOnSpU1WF6BWuZI4O9hTPMR0YYcQQgjxNAkPhBBCiDdIkK/Hazd14WmNGzdmx44dnDp1Co3GdLdqfP39+fLbVag1GuJiY42qpTI3p0OXroBpwwOAtm0zpi6UKVPWpHWLBAfSsUEtzMzkVzshhBC5Q/6HEUIIId4g/l7OREREvOpmvFC5cuXYtGkT0dHRJq2bz9OTz79ZQXx8vNG1mrdui42NDcEFCpqgZf9o2LARLi4uhIeHm7SuQqFgYNtmJq0phBCvklYLaq02z3y8DWSrRiGEEOINEujj9lrtuPA8BQrkzvB6ZxcXnF1cjK5jZ29Ps9ZtCTTxdnE2Nja8//4YLC0tTVoXwDoXagohhBCZJDwQQggh3iCBPh78b8+eV90Mo6Smq3OlrplSvwGXPfr2x8raFrXm5XeU1Frdp2C0bttG5+vdK9XXua7elGa5UjbtYVSu1LV0Me2Wi0+LI3d+FvZxj3KlLkDqowe5Ujct7mGu1AUIqN4oV+reO382V+oCKJ3ccqVuytuywp54o0h4IIQQQrxBgnw9uHDhElqt1qSr+b+NHJ2cc6WuUs8QQwgh3lYmXBpHmID87yWEEEK8QZwdbVEo4N69e6+6KUIIIYR4g0h4IIQQQrxhgv29OXfu3KtuhhBCCCHeIBIeCCGEEG+YQB/X13q7RiGEEELkPbLmgRBCCPGG8fd05mwe2HFBCCGEeB4tIOtKvl5k5IEQQgjxhgny88gT2zUKIYQQIu+QkQdCCCHEGybQx53I89tedTOEeOXUajUajQZzc/NX3RShh9TUVMzMzDAzM+0WmskpKWz832/Y29ni4OCAk4MDDg72ODk4YG9nZ/LnE+JNI+GBEEII8Ybx9XTlzp0okpOTsbKyetXNydPS09JAoUClkl+ZgFzbAjQtXc32o6doUK6ESesqFAradO3Oog/n4u/na7K6mzf/Tr169U267WZiUhJ/791HnZo1TFr3YcwjNv2xFXs7Wxzt7TM6zvb2ONjZ4evladTr+dP2v7h49QZ+3p4EeHsR4O2Jh6uL0e3XaDRUqFABe3t7ypYtm/URFBRkVHutLC2JfviQd8eOf+acQqHA3s6OMiWKM2vSeAL9/PSqfe/ePYa/9x6hoaEUK1aM4sWLExgQYJK/L59//rnRNfIkLai1r7oR4mnyP6EQQgjxhjFXmeHj5c7FixcJDw9/1c1Bo9Fw9uxZwsPDWblyJY0bN8bZ2dkkteNiH2Hv4MjN69dRKhX4+PmbpK5WqyUpKZGf1q8jrGgxipcsZZK68M/d8HVr19Kpc2eT1c2srVQquXz5Mvnz5zdZ3dS0dLRaDcs2/sqg9i1NVvdw5CXMVWYUDvBlxKffkpicQqtq5Y2um5iUxKdfLqVVs6bcvnOHGo0as3DubJo2bGh07cuXLrHyu+/YtXMn02fMNFlHPy4+njFTpjFu2nR6delCx7atcXJ0NLqug70dP/z8P06fO591rHCB/Ewe+S5+3l5G1baztmHRd2uyHbO1tmZI1/b0btsCCwNHfKSnp+Pq6sq2bdvYtWtX1nFXV1fKli1Lhw4d6Nq1q0E/+8cJCSgUCrTa7L1SJ0cHRg0dTLf27QwaqaJUKom6d48tW7dmHXNwcKBY0aLUrFmTfn37GhzmKkwYJglhDAkPhBBCiDdQkJ8HkZGRrzw80Gq1DBo0iG+//ZaNGzeybNkyjh49yvz5842u/fdffzJ7yiT6DB7K4f37KFSkCF169jG6bvSD+8yZOpkHD+5z7fIlOnTtYbLwYOmXX7D8m2+oWLES589Hmiw8OHzkCNNnzSakYAGcnZxJV6czYexYo+umq9UsWv0jP/zxJ5WKh3PywiWThQcajYZpK9Zz/sYdKoWHotFqGffV9zxOSqZb/epG1f59y1bmLPiYOQs+RqlUotFo6DlwMD06d2LahPFYG9iJS0pKonOnjkRFRbF3zx7MzMyYOm26Se4u/75lK9dv3gRg4sxZzF7wMW1aNKN3166EFQo1uK6ZmRnp6ekAODs5MmpgPzq3bGaS0TQXrl1HZWZGulqNhbmKNg3qMqBja/yNDCXMzMzYti371CuVSkWtWrXo168ftWrVMji0SUxKyhYcqFQqenbqwIjBA3F2cjK4zQkJCZw4cSLbsdDQUDp16kTTJk2MGgW2b98+gx8rhClJeCCEEEK8gfw9nYmIiHjVzSAtLY2bN2+SnJxMs2bNUKvV7Nmzh4EDB1KwYEGjat+4fp1HMQ/55MPZpKakEHXntlHhgUajIerOHS5dPM/e3TtRq9UAHNy3h76DhxrV1mNHj3LlyhU2bdzEnTt32LDhR6ytrdFoNEbduU5JSUGj0fAoNpYdf/3Fnr17SU1NpWP7dka1F+DwmXMkJCWz8c/dPHgUy88796BQKEhOScXK0sLo+kkpqRQJ9OPSrSj2nv5na9FZqzYSn5TEoOb1De6Ue7i7U6taNf76+280mn+Wa1++ajUHDh9m6aJPKBQSonfd1NTUbJ3AVStXolKpmDhpstEBQpWKFbCwMMfb05OwQoUoUqgQRQoXwsbG2qi6CoWCYb17cPTUGYb364WTg4NR9Z7Wr30rfvj1D2pVLEffdi3xdHczSV1ra2sWLVrE0KFDKVCgAH379qV79+7ky5fP6NpD+vYmNi6ONRs2Uad6NSa/P4qC+YONruvn58fgQYP4dsUK2rZtS/euXSlSpIjRdQEmT57M75s3m6SWEMaQ8EAIIYR4AwX5uHHu7OlX3QwsLCzYsGED1apV48CBA0BGJ33UqFFs2rTJoJoajYY9O/8iPS0NCwsLUlNSAIg4c5q7d27j6eVtUF2FQsGfW/9g8fwPsx0/e/oUcXGxODgYPoTc18+P94a/y7Vr17KOJSUlcffuXby9DWsvQEpqKvUbNebCxYtARucW4Oat2wbXzJSWns7AGfNJTUvLOqbVarl44ybhBYzvbNlaWzGzbycqhIXw/hcrs51bvGEzjxOTGd2xuUGd8qqVKhJSoAAtOnbiwqVL2c5FRJ6nbrMWzJw8iS7t2+lcX6vV8tOmjTg6OWF++zZpT34u3y5fjpmZGePGTzAqQLC3t+fcoQPY29sbXON5GtWqSfP6dU1eNzUtjfWL5+HqZPz0in+ztLTkzz//pHr16iZdA8LDzY20tHS+X/o5tapVNVldpVJJpUqVGDlyJHa2tiarC4D27Zz4L1s1vn5kAo0QQgjxBgr09XgtRh6cOXOG4sWLZwUHmX766Sd27NhhUE2lUomVtTXfr/gmq7Ocadd2w3eZUCgUdO7Ri2lzP8o251mj0XDkX+3Xl7u7O99+txJ3d/dsxy896fQbysHenjWrVuL4r7nxt27dMqouQMXi4XzywTDM/tVxi7x6w+jambYePsmEr9fkeG755r+Y+PUPqDX69x7S09OZv3gxDvb2hBYsiK+PN06OjllD9ZOSkxn+wRj6DBlGXFycTjUVCgXduvdg008/c/LUaX7csJEJEyfRrHlztm/bxpzZs5+ZR68PTw+PXAkOAMzNc+d+oZWlZa4EBwB9+/alZs2aJg0OMi2aM9OkwUGmenXrmj44AAIDA01eUwhDyMgDIYQQ4g0U6OPOhYuXcm11fF0VKVKEX3/9lWnTprFixYpsQ8iHDx/OkSNHDNoerWyFiny1ei3j3nuHC5Hnso7/tX0r7bp0M6rNdRo0wtXNnfffGUJ8fEbH8sC+PdSsW8+ouv7+/iz/dgXt27fjcXw8AJcuXaJqtWpG1Q3w92fl8m9o1qp1Vphy+84dk7z2dcqX4cP3BjHio0+zOsbnr5kuPKhbphgHPpvJpdtRXLhxhws373D+ZsbnqJhY1u/cT0JyCrP767c2hEqlYs7UKc8c12q1pKam8jghgccJCSQkJBAbF4+jg34LeFpaWVGyVClKlvpnLYyHDx+SmpqKpaWlXrXEf0+2ZBTCMDLyQAghhHgDOTnYYmGu4u7du6+6KQQHB/PNN98QERFBp06dsjq0J06cYPny5QbX9fbx5bPlK6nToFHWsVPHj/Hg/n1jm0zJMmX58rvVeD6ZUnBg799G3VXOVDgsjKVLv8LCIqODeemScSMPMlWsUIGF8z/K+j45OZno6GiT1G5WozJTB/XK+t6UIw8ArCwsKBLoR4uq5RjVsTlLRw3gr4VT2LdkBt+NG0qZ0PzsORX58kI6UCgUWFpa4uriQoCfH2GFCuHn62OS2i4uLhIcCGFiGk3e+XgbSHgghBBCvKGC/L04d+7cyy/8j4SEhLBq1SpOnTpFmzZtABg3bhzxT+7CG8LK2ppJs+YyZMQolEolWq2WXX8aPnXhaYHB+Vn63feEFg7j7u3b3Lx+7eUP0kH5ChX4ZNGijC0V/zUf3xidOnTg3WH/LOx446bxUxcydWxYh9E9OgJwwYQjD17Eyc6WMqH56VSnCjVLmmbhOSGEEIaT8EAIIYR4QwV6uxIZaZo7tqZUpEgR1q1bx7Fjxyhfvjxz5swxqp5CoaBD1x4s+Hwpjk5O7Ny+9eUP0pGbuwdLvllBxSpVObBvj8nq1qtfnxkzZ3LJhOEBwKTx42ncsCEAt26bLjwA6NemGQPbtSDqYQyP4h+btLYQQojXn4QHQgghxBvK38uZiLNnXnUznqtEiRL89NNPdOjQIdtaCIYqXa4CX61aS2JCAo9iYkzQwgw2NrbM/WQJ+fIZt3f9v7Xv0JGu3boRFxtrsppKpZIvP1tC0fAiJlk08d/e69qOrk3qmXTdAyGEyIlWC+o89PE2kPBACCGEeEMF+Xq81uFBpvDwcJOtqO7l48Oir5aTkpJsknqZVCoVVWvWMmlNgEGDh2BtY2PSmnZ2/2/vvsOaOt83gN+HEPaeIshSUXHVqrjFUbdW66qte9a6V6u17Vft0C7tcmuddbS2tlp33XuLKC6GiCzZIDMhOb8/UH9SFQIknATuz3VxVcI5T27RtuTJ876vFbb9+iuUynyt1gUKpjw+HTccHi7OxV9MREQVCk9bICIiqqC83Z1x995BqWOUOzNzc5iZm0sdQyOCIBQ6FlJbqnl4YMyokVqvCxRMN1R1cdJJbSIi0l9sHhAREVVQ7lUckJCQiJycHJgbyItpAGjV0F8ndfdfDNJJXQCwt9DNLvv5OVmlvte4mPvNq3qXunZR7Bu20Endh5kKndQFgMy8HJ3UNTHW3ZGAPna6aeBUbdRcJ3V1ycVPN//NAIDU2Gid1FVnZuikLpEusXlARERUQRnLZPCo6oLQ0FA0aNBA6jhEREQaEwGoKskRiIaCex4QERFVYD4eLnp54gIREREZFjYPiIiIKjDPKva4ffu21DGIiIjIwHHZAhERUQXm7eGEkFs3pY5BRERUYlo4xZe0iJMHREREFZiPhwvucPKAiIiIyojNAyIiogrM290FoeEREEVR6ihERERkwLhsgYiISGLZOXm4dCMc54LCcSXkPlo39sPEwZ1gLCv7MW82VuawMDNFbGws3N3dtZCWyHCo1WoIggBBELRWMzkpEaIIODk7a60mGb6nDVpt/l17Ss3ZfdITnDwgIiIqZ2q1GrfCovHLjqMY++kv6Dj8C/xx/C7qt+iKXzZuRWRiPibM34DktMdaeT6falVw584drdTSV0ePHkVKSorW64YEXdPZ1IauXhAolUqd1I2OjcOJcxd0UjspORl//LVL63UfPniAXzdu0GpNU1MzDH6rJ1b99AMeZ2RotXZWVqZW65UHQ3xhO3fuXCQlJWm97pgJE/Hl19/gzr17Wq27cv1GrdYzFKJYcFSjoXxUBmweEBERlYPElAzsPnIZc7//HR2Hf4lPf94FtbU3Pv38WzxKSMTxE6cxZ84ctGrVCgf/PYx2Xd7E4JnLEHz3QZmf26uqY4U9rnH9+vWIiIjAsWPHMHDgQK29cM7JzsaDiHAs+3YRbly9opWaT6WmpCAzMxNLf/pRq3Xz8vKQmpqGTz//EqmpaVqtffzseezcfxDfrlij1WbK/kP/YvfefcjMzML706bj73/2aKWuUqHA+jWrcf9+BJZ88xVOHjumlboA8Nfv22FkZITN69ZgYM8u2LphHfJyc8tcNyM9HYvm/w8Xz5/TQsrC4uLjEXJb+w3EjIwMLFiwAI8fa6fRWR7Onz+P3bt3w9/fHzt27NBa3bS0NMQ/eoTFP/2Mlu07IrBzV/y0YiViYuPKXJvLzkhfcNkCERGRDuQplLh26z7OXgvF+esRiE9MRYf27dBr4Ggs/aULvLy8XnmvTCbDokVfoVmz5hg7ehTGv9MBA7o2L/U4rJebPW5XwBMXLl68iDFjxsDc3BxVq1ZFaGgoZsyYgZ9//rlMdTPS0/Dx5PcR+zAKOdnZ+Hvbr2jQuIlWMn++YD5+374drzVqhLS0VEyZNl0rda9dD8boCRNha2OD4Jsh6NKxA9oHttVK7W+Wr8KPazfA1MQEeQoFjp45h46tW5a5bk5OLmZ/Og8xsbGoUd0XarUa4yZPhczYGL26dS1T7V/WrMLKpT/D1NQUoihizqwZ2LB1G/xq1S5TXVEUsWPr5mcTB48zMrD8h8XYsfVXjBo/Ad3e7ANj49L9eL1v9y4c2r8Pxw7/i4WLf0Bgh45lyvpUXHw8WnbuiuZNmmDburVaqfnU3LlzsWzZMoSFheHXX3/Vyci+ti1ZsgQhISEAgIEDB6Jfv35YtmwZXF1dy1Q3JTUNV64FPfv8RkgIboSE4JvFSzBl4gRMeX88zMzMSlX7TmhombIRaQsnD4iIiLRAFEWER8Vj866TmPj5JrQf+jnW7roMr7ptsG7TNiQmJePPv3Zh3LhxRTYOntenTx+cPX8BO4/exLyf/kRuXuneVffxcMHtW7dKda8+c3JyQuvWrZGVlYXQJz9cL126FCtXrix1zfx8JR5GRiI5IQE52dkAgAunTyImqmwTIGdOn8L3i7/DubNnoFQqcOniBUTej4RKpSpTXaVSidXrN+C3P3fiQdRDBN8seFF0Jeh6meo+ladQICEp+dmvAeA7LU0fiBDxzoD+MDczQ1h4BABApVJhzIRJOPDv4TLVrluvPpxdXJCXlwcAyM7OwtT3xyMpMbHMufu+/c4LjyUmPMLXn83DsP59cPzwoRJ/f1QqFaIfRsHc3AJKpRJzpk/BgT3/lDkrAFwLvgEHO3scPn4CZ85rb9nJo0ePcOPGDQDA1q1bsX79eq3V1qX69esX+vzPP/+Ev78/tmzZUqa/174+3mgREAAzM1MEtm6Nj2bNxN6dfyA85AY+nD6t1I0DAHhvxLBS32voVKLhfFQGnDwgIiIqpbSMLFy4Hopz18Nx7loojOUm6NKlC6Z+MAwdO3aEnZ1dmZ+jZs2auHjpCsaMHonhc1Zi8ex34VHFsUQ1vD2ccffevjJn0Tfe3t4YNWoUzp07V2i5wuTJk1GrVi20b9++xDVvXb+OJZ//D2mp/79/giiK2PXbNkz4YE6ps77euAmW/vQTIsLDnz2mVCoQEx0NTw2bSS8jl8vh6+2F+V8uLPT4tetlbx4kp6Zi5PQPcSW48NRK8O07+PfkaXQObFOm+hbm5pgxeSJiYmOxbccfzx7Pz8/HyPETsHntKrxRij9DhUKBsNBQZGVmFXo8Li4W0ydNwJqNm0v9Qi4tNQU3rgeh3Rud4ejkBAdHp2f/dHBygqOjE+wdHEr8DrxMJsOsuZ9gxpy5iIuJQXhYKCLCQhETHQ13D49SZX2qe+dO6BjYFhu3bcfytb+gZbMArUwIuLq64sSJEwgODsbmzZuxcOFCNG/eHP7+/mWurSu5ubmoX78+/vrrr5d+PTExES4uLqWqnZWdjdkzp6NRw4YwNTUtS8wXmJuZa7UeUWkJRXXY6tasJm7/YVr5pSEiInrO4vX7kJ2Th4a1qqGRvzc8qjhKOharzFfhxt0onL12DxeC7yP8QRxat26Bbt17oWvXrqhZs6bO8omiiJ9//gmfL5iPBVP6oU2TOhrfq1Kp0WLgJ0hOSYWFhYVO8klh3759mDdvHi5fvvzC1xwcHHDx4kVUr1792WP7LwZpVDcnOxubVy3Hrt+2Pnsn0tTMDJv+OQhrG5uX3uPraFts3bS0NAx9ZxBCQ/9/M7UVq9egfRHj6c6mmg2JHjl2HEPHjkNeXsF0gKuLM0IuXyzy72NuYvFrsfPz87Hlr934dsVqpKalP3vc368mDm7dACOjF/OZObtplFmtVuOrxd9j87ZtSEh8cfM6U1MTbPll7bPlFw8zFRrVTU5KwqED+3DqxAlcunD+hX0wunbvgUXfLSn0vcks5VRPcUyMy35iyqv4WJS89uPHj2Ekk8GyiP8OOPnUKFWe/Px8PHz4ED4+PqW6X1+lxkbrpK4ys2SbbbrWqntFFEXtrJ8yEFYefmL9ycukjqGx83M6V/g/IzYPiIhIb23YeRwrtx9F185v4Oy581CrlGjk74sGfu54rY43ald3h1yHP5wDQHR8Ms5cvYsLwfdx8fo9+Hh7o2u37ujStRtatmyp9XeYinP69GkMHNAfb3VshHFvd3jpi7eX6T/lJ/z25y689tprug0ogZiYGOzZswe7d+/GkSNHno2q16lTB+fPn4fNkxf8mjYPnrpz8wZ+XPgZHoSHAQBGTpyCAcNGvvRaTZoHAPAoPh7vDOyP2NhYAMAHcz7C6DFjX3m9ps0DADh6/ASGjBn7rIEQfPEc3N1e/UJek+bBU+mPH+OHNeuxbvvvyM8vWGqx5ttF6N6x3QvXato8eEoURUTHxODy1Wu4ci0IV4KCcP3GDeTlKWBmaoptG9ejbauWGjcPnpedlYXz587i1PFjOHXiBJKSCpYtjJ80GeMnTn52XWVpHmiitM2DiorNA+lYeviJ9SYaTvPg4lw2D9g8ICIiyahUaoz4aBXGTZiO9ydMwIMHD3DmzBmcPHEcZ06fwv3IB6hX2wcN/dzxWm0vNKzjBRursr2znpWdi4s3wnEuKAznroUiJ0+JTp06oVv3nujUqVOpR1q1KS4uDn1690LLem4Y1a+dRvd88M02jJzwAd5++23dhpNYZmYmDh06hN27d2PPnj1o1qwZdu/eDZlMVuLmAVCwp8Afmzdg27o1sLWzx/q/98DYWP7CdZo2DwAgIjwc7w4aiLTUVPQfMBBfLPrqldeWpHkAAMdOnMSQ0WORm5eHjatXomcRGw+WpHnwVPiDKHzxw1IcOnEKtWv44t/tm19oYJW0efAyCoUCIbfv4Mq1INy+ewdTJ7wPwb5sG9qp1WrcuX0LJ48fw+mTJzB0xCh06dYdAJsHz2PzoDA2D6TD5oH+YfOAiIj0WsTDRxg9dw0uX70Gb2/vQl9LS0vD+fPncfrUKZw6eRxXrgXBo4oTGtaqhoa1PdHI3xvurkWvP1ar1bgdHoMzV+/hwo37uBUahaZNXke37r3QpUsXNGjQQC93EN+4cSP+3LwCX04foNH1P28+gKq1W2H+/Pm6DaZHVCoVzp07BycnJ9SuXbtUzYOnou5H4KeFn6Nn/4Fo16XbC18vSfMAAIKvX8eIoYNRq3YdbPv91cfFlbR5AADHTp7CkFFjMH7MaHw658NXXlea5sFTJy9cxILFP2La2FHo1anwsgttNA9epjSTB0XJyMh4NpXC5sH/Y/OgMDYPpMPmgf7hholERKTXfKu5Ylif1hg5YhiOHD1e6F1OOzs7dO3aFV27Fry7qlQqcf36dZw+fRqnThzDT7+uhajOf2GpQ0paJs5du4vzwfdxPugenJ2c0KVbdyxYOBmBgYEGsS+Al5cX4pLSi7/wCW93Z1yvgMc1FkUmk6F169ZaqeXp44tvVv2CuyE3tFKvQcOG+GnZCsz5YCZEUdRqg6p92zbYsv4XrFm3QWs1/6ttswAc3LoRl69r5/shBZtX7F9BREQvx+YBERHpvaF92uDInFVYtXIl3p8w4ZXXyeVyNGnSBE2aNMG0adMgiiIiIyNx5swZnDp5AgvXHkT4/UiYmZqiY4f2eOvdcVi5sQuqVatWjr8b7fDy8kJcQkrxFz7h4+GM38p4/F1lZ2RkhDr1G2qtXus2bTDn40+QlpoKewcHrdUFgHZtWsPOtmTTECVlbGyM5o0b6fQ5iKgSEwGVWuoQ9Dw2D4iISO8Zy2T4bEo/jJ77Mbp17/7C8oVXEQQBPj4+8PHxwZAhQwAU7DZuYWEBmUy3Gy3qmru7OxKSUpCvUsFYg9+Lt4cLwsLvQ61Wa7zJIulez15vluls+aK81qB+8RcRERFpiD89EBGRQfCt5oqhfVpj1MjhZXqxZW1tbfCNAwAwMTGBk6MDEpI1W7pgZWEGK0tzxMTE6DgZlZQ+7qlBRET0X5w8ICIigzGsTxuMmLMaq1auxPj335c6juQ8q1VDXEIaqrpoNvLu41EFd+7cMchlGtrQLeA1ndU+dOm6TuqauzrqpC6gux8CHTy8dFNXJ1WJimZf1UPqCJWWCEClm8EsKiVOHhARkcEwlsmwYPJb+OTjuXjw4IHUcSTn6eWFuMRUja/3dnfA3bt3dZiIiIiIKio2D4iIyKBU96yCIb1blXn5QkXg41MdsQmaNw883RxwK6RynbhARERE2sHmARERGZzhb7VFYlwUVq9aJXUUSXn7+OBR8mONr/fxcMGd27d0mIiIiIgqKjYPiIjI4BQsX+iHj+d+VKmXL3h5eSE+KUPj6z2qOCIyMlJ3gYiIiLRIrTacj8qAzQMiIjJINbyqYMibrTB65IhKu3zB09OzRMsWzEzkyM3L02EiIiIiqqjYPCAiIoM1vG9bPIp9gLVr10gdRRJeXl6IfZSkcfPExMQYeXkKHaciIiKiiojNAyIiMlgFyxf64qPZsxEVFSV1nHJnbW0NUxMTpGVka3S9idwYeYqK1zwQRRFKpVLqGFRK6nKa9/3jjz+waNEirSzduXPnDjZt2qT17OX1vSDtE0URe/fuRXa2Zv89LomgoCCt1zQEogio1IbzURmweUBERAatprcbBr/ZEqNHVc7lC9U8qmp8XKOp3Bh5FXDZwt27d7FkyRKd1D5y5IhO6gIwyL+vKpVK6zU3btyIq1evarXmy763gYGBWLBgAXx8fNC6dWssX74ciYmJparv7e2NcePGISAgACdPnixr3GcmT56M/fv3a63eU7du3cKFCxe0XtdQffrpp6X+s38VQRCwb98+uLi4YPDgwdizZw8UWmrWWlpaaqUOUVmxeUBERAZvRN9AxEdH4pdf1kodpdx5enppvO+BsbEMKpW6Qr27uWrVKqxfvx6fffaZVqdPoqKisHv3bvTr1w/JyclaqyuKIuJjYnBk/17Ex8ZorS4AKBQK5ObmIiIiQqt1RVFEWOQD7D1yDGcuX9Fq7b///hu7du1Cr169EBOjve/H0aNHYWpqCkdHR3h7e6NevXro1asXTExMAABnzpzBxIkT4ebmhu7du+PXX39FZmamRrUHDBiAXr16wcLCAleuXEFgYCD69euH8PDwMmW+cuUKzpw5g+7du6N3795a/XPcuXMnWrdujW+++aZC/ftfGocOHcIff/yB1157TauNn9jYWBw8eBBZWVnYunUrevXqhSpVqmDs2LE4evRomRpvq1ev1lpOorJg84CIiAyesUyG+ZP7Ys6HH+Lhw4dSxylXPr6+Gk8eCIIAE7m8wkwfbNmyBePHj8c333yD7OxsTJ8+XSt1c3Nz0a5dO/Tu3Rvp6elYvHixVuoCwOofFuO9d/vjp6++wP2w0DLXy83Nxblz5/DHHztQt24dzJgxHZMnT9JC0v83fOosdB8yCjMXfIm7Ydp7QZuZmYlRo0Zh165diI2NxZtvvomsrCyt1VYoFEhJScGDBw8QEhKCCxcu4PHjwkebqlQq7N+/H0OHDoWLiwuGDRtW5AkuarUa//77Lw4fPozU1P//927nzp2oU6cOZs2ahbS0tFJlvnLlCoKDgwEAu3fvhr+/P+bNm1fmMfjs7GwsW7YM+fn5mD17Nrp27Yr4+Pgy1TRkmzdvxp07dxAbG4v27dtj0aJFWmmoZGVlvTDNkJqairVr12LUqFGYN29eqf9+a7OxZmikPkGBpy0UxuYBERFVCH7ebni3V+VbvuDl7Vui4xpNTeXIzc3VYaLyoVQqX/hBfOfOnThw4ECZ6iYmJmLFihWFxo1//vlnJCUllamuQqHAvFnTsH/XTuTm5CA3JweR4WFlqgkAZmZmWLduLWbNmgmFQoG9e/fAysqqzHUBICzyAfqMeg9XbtxEVnY2Hmdm4U5Y2d5df97t27fh6Oj47POrV69iyJAhWnkh16VLF0RGRuLmzZs4f/48Dh8+jO3bt8PU1LTQdcbGxmjRogU++ugj/P3331i+fDm8vLxeWVcQBJw8eRJDhgx54WtKpRKLFy9GjRo1sGzZshLvw2FhYVHov11GRkZYsWIFevbsiVu3bpWo1vMiIiJgZWUFa2trCIKAf//9Fw0bNsTBgwdLXdNQ5eTkFGr6qNVqzJ07Fz169CjzMoaaNWuiZ8+eAICqVatiyJAh+OWXXxAREYHIyEh88cUXpV5+MGPGjDJlI9IWY6kDEBERacuIvoEY9uEKrFv3C0aPHiN1nHLh5eWFQ7s1bx5UhMkDURSxatUqfPLJJy98bdKkSbh58ybMzMxKVdvBwQGWlpaFmgeZmZlYvHgxFi1aVOrMJiYmmPnJfMx8bxSi7he8e6+N5gEAfPjhHBw+fPjZC08nJ8di7tBMDW8vjH5nICZ89L9nj2mreaBWqxEeHo6AgACkpKQgJSUFQMEyho8++ghff/11meqbmZm90AT49ttvoVKp0LJlS7Rr1w7t2rVDy5YtS/SCThAEWFhYYO/evc8ek8lkcHBwgIODA+zt7eHg4IDz589DJpNh7NixkMlkGtVu3bo1YmJiYGVlBUtLS43vK069evUQGlow5SKKIrKzs5GZmYmsrCyIoghBELTyPIZAJpNhxYoVSE9PR1paWqGPffv2YciQIaX+vmdmZqJdu3aYN28eatasqdXva2WeFCH9IhT17kzdmtXE7T9MK780REREZXQvMg7j/7cO164Hw8PDQ+o4Onfx4kWMHvYOti6eoNH13cZ+g9NnL8Db21u3wcpBRkYGVqxYgSVLliAhIeHZ45999hk+/fTTMtVOT0/H559/jh9//BH5+fmwtLREZGQknJycXnr9oUvXNaqbnJiAmeNGIS4mGl6+1bF6+59FXl/bVbNGwJw5s7Ft21YAwPDhI/DZZ58Xe49xcrRGtbf//Q9mfrYQAGBuZoZ7p4/AyOjVw6tVGzXXqO5TKpUK58+fx969e7F3714EBwdj7dq1GD16dInqFOfSpUvw9/cv8+Zz9+7dQ1ZW1rNmwdN39Il0SRCEK6IoNpE6R3kyr+on+oxZJnUMjd3+vHOp/4wEQXAA8BsAbwCRAAaKovjCmkRBELoC+BGADMBaURS/Ku5+QRA+AjAagArAFFEUDwqCYAFgB4DqTx7/RxTFOcXl5LIFIiKqUPy83TCoR4tKs3zBy8sLsQmab+hnUoFOXLCxscHs2bMRGRmJpUuXPnuneeHChbh//36Zatva2uK7777DzZs30aNHD2RlZeG7774rc2ZHZxd8tXQVHJ2dEf3ggdaOmJw+ffqzaYvnlwJow6A+vTB/5lQAQE5uLqJiYrVaXyaToVWrVli4cCGuX7+OqKgoGBkZIT09XavP07RpU63sWu/n54dGjRrBy8sLNjY2bBwQ6UglO6pxDoAjoijWBHDkyeeFCIIgA7AMQDcA/gDeEQTBv6j7n3x9EIC6ALoCWP6kDgB8J4pibQCNALQSBKFbcSHZPCAiogpnZL9ARD8Ix/r166SOonPOzs7Izs5Fdq5mDQFTE8NftvBf5ubmmDhxIkJDQ7Fx40Z4e3tjypQpWqldq1Yt7NmzBwcOHMDRo0e1crxbFXd3LPp5JSytrBD9ILLsIQG4ulZ5tlTnVdMRZTF28CDMfK+g/t1w7Z7m8F/VqlXDyJEjYWtrq9PnISLSI70BbHzy640A+rzkmgAAYaIoRoiiqACw/cl9Rd3fG8B2URTzRFG8DyAMQIAoitmiKB4DgCe1rgIodlyTzQMiIqpw5MYyfDa5L2Z/8AGiozUbzTZURkZGcK9aBfGJaRpdL5cbV4gNE19GLpdj2LBhCAkJwciRI8t8dN7zunTpgjNnziA/P18r9bx8q2PhT8uR+Eh7a5nHj38f9vb2Wp88eGr6uFEYO3gQbmtx00QiIgIAuIqiGAcAT/7p8pJr3AE8f6RU9JPHirq/qHsAAIIg2AHohYKJhSJxw0QiIqqQ/HyqYmD3ZhgzeiT2HzhUoUeLPT09EZeQBt9qrsVeW5GWLbyKkZER+vbtq/W6crkcbm5uWqtXs46/Vk4WeMrGxgaTJ0+Bo6P2Jw+Ags0C582YgpPnL+qkPhHRf6kMa/WhkyAIl5/7fLUoiquffiIIwmEAVV5y38ca1n/ZDzLFfYeKvEcQBGMA2wD8JIpisWNlbB4QEVGFNbp/ewz5YDk2bFiPkSNHSR1HZ7y8vBGX+MK+Si9lalJxJw8MUVEbD5bGkCFDkZ2drdWazxMEAYEtmumsPhGRAUsqasNEURTfeNXXBEF4JAiCmyiKcYIguAFIeMll0QCqPfe5B4Cnm9C86v6i7gGA1QBCRVH84VXZnsdlC0REVGEVLF/ohw9nzUJMTIzUcXTG27c6YhPSNLpWblzxJw8qM1NTU9jb20sdg4iISmY3gOFPfj0cwK6XXHMJQE1BEHwEQTBBwUaIu4u5fzeAQYIgmAqC4AOgJoCLACAIwhcAbAFM0zQkJw+IiKhCq+VbFQO7NcPYMaOwd9+BCrl8wdvbG1dO7dfoWk4e6Ebnpg2ljlBynhX/KFMiIgPxFYDfBUEYDSAKwAAAEAShKgqOZOwuimK+IAiTABxEwVGN60RRDCnqflEUQwRB+B3ALQD5ACaKoqgSBMEDBcsl7gC4+uRno6WiKK4tKiSbB0REVOGNHlCwfGHjxg0YMWKk1HG0zsvLC/FJmh1rJzeWcfKAiIj0nghAra54Df+XEUUxGUDHlzweC6D7c5/vA7BP0/uffO1LAF/+57FovHw/hCKxeUBERJJRq9XIyMxBanoWUtIzkZaRhdSMJ79+nIN8lRrN6vuixet+sDAzLfXzyI1lWDC5LybMmolOnTrD3d29+JsMiKenJ2IfJWt0rYmczQMiIiIqOTYPiIhIEtm5eZi4YCNCI2Ph7OQIJycnOLs4w8XFFS7u1eHr4gqVSoXde//Bpz/uQJMGNdG2cU0EBvjD2cGmxM9X29cdA7oEVMjlCx4eHkhITkW+SgVjmazIa03kMi5bICIiohJj84CIiMqFKIpIf5yN5LTHSEnLxIa/T8O/YVNcur65yBfys2bNQnp6Ovbv34+/d/6Jnyb/AM+qzqjh6Qonewu4ONjAxdEWLo62cHWyhb2N5St3sB8zoD2GfLACmzZtwvDhw196jSEyMTGBk6MDEpMz4OZS9GZ5XLZAREQGQQRU2jvNlrSAzQMiItKZjMwcXLgeirNBYTh79R6ysnPh6uIMFxcXvB7QFku+/1GjCQBbW1sMGjQIgwYNglKpxNmzZxEaGoro6GhEP3yAi+ceICY2BnFxj5CZmQUXJ/uCDwdrONtbwsXB+lmDYezAdpgxfSo6deqEqlWrlsN3oXxU8/BAbEJqsc0DTh4QERFRabB5QEREOhGflIbuoxehdi0/jB47Dl9+3w1+fn5lXi4gl8sRGBiIwMDAl349NzcXsbGxiI6ORkxMTEGDIfohzoc+QMyx24iNi0PG40ysWbMG8+bNK1MWfeLl7Y24xNRir5Mbs3lAREREJcfmARER6UQVJzv8b1I//LjpEKpUqYJatWqVy/OamZnB19cXvr6+r7xGra54c5A+Pr6IexRS7HUmcmPksXlARER6TgSXLeibly8KJSIi0oI+nQKwfP5IfDhzGqZOmQSlUil1JACAkZHRK/dFMFTePr6IT3pc7HWmcmPk5OaUQyIqSlxcnNQRiIiISqRi/eRERER6p051d2xbMhHXL51EYNvWiI2NlTpSheTp6Yn4pPRir+PkgX5YvHgxtm3bJnUMqiREUcTvv/8udQwiMnBsHhARkc7ZWFngx7lD0bimExo3eg0nTpyQOlKF4+XlhdiE4vc8MDExRm4OJw+kolAoMGTIEBw+fBhjx47FnTt3tFY7JCQER44c0XqDLjMzE3fv3sXmzZu1WpfKzzfffIN3330Xf//9t9RRiMiAsXlARETlwsjICOPe7oD5k/pgQL+38O0330AURaljVRienp6IfZRU7PfURG6M3DxOHkhl9erV2LJlC65fv46srCz0798fWVlZZaqZmpqKdevWoV69eujevTsiIiK0klWtVmPv3r3o3LkzGjdujIULF2qlLpWvpKQkLFy4ECqVCgMHDsT+/fuljkSkMbXacD4qAzYPiIioXLV8vRZ+/XYCft2wCn3f6o2MjAypI1UINjY2MJHLkZaRXeR1JnJj5LF5IImcnBzk5OQUOnEkJCQEEyZMKFMjzc7ODufPnwdQMNnw4MGDMmcFChp+4eHhOHfuHLKyspCUlKSVulS+9u/fDwsLCwCAUqlE3759cfToUYlTEZEhYvOAiIjKnZuLPdYtHAfT/FQ0fv01hIQUf0oAFc+zmnuxxzWayI2Rm8PmgRQuXbqEs2fPQi6XF3p806ZN+OWXX0pdVxAELFu2DB07dgQArTUPAGDy5Ml45513AAApKSlQqVRaq03lY+jQoYiNjUVERAS2bNmC0aNHY/78+bhw4YLU0YjIwLB5QEREkjCRG+Pj9/tgxJvN0LZNa2zdulXqSAbP09MLccXse1AweZBXTonoeW3btsVff/2FR48e4ZdffkHHjh2fTSFMmjQJQUFBpa4tl8uxY8cO1KpVC5GRkdoJjILGxJo1a1C3bl2o1Wqkpha/rwbpH0EQ4OPjg3fffRdLly7FyZMn0aRJE6ljERVJFAWo1IbzURmweUBERJJ6s2MTrFowCnNnz8TECe/zhW0ZeHn7ILa4yQMTY+TytAVJ2dnZYdSoUTh8+DBiYmLwww8/oGHDhhgwYADS04s/MeNV7O3tsXfv3jLvofBflpaW2LlzJ6ytrbl0oQKRyWRSRyAiA8PmARERSa6Wb1Vs+W4iwkMuol7dOjhy5IjUkQySt48v4hOLfvHJyQP94ubmhqlTp+LChQvYv38/wsPDy1SvevXqOtnY0M/PDxs3bmTzgIioEjOWOgAREREA2FiZ49sP38WxCyEYPvRdtA1sh+9/+Amurq5SRzMY3t7eOLwns8hrTOVy5CnYPNBHNWrU0EodLy8vrdT5r7feeouNJyKiSoyTB0REpFfaN6uLP3+eCvP8ZNSrWwcrli+HurKcgVRGXl5eiE1IK/IaE7kx8nL5ApBKx9TUVOoIRFRJiABUasP5qAzYPCAiIr1jYWaKaSO6YeWCUVi78gc0b9a0TJvJVRaenp6IfVT0WLlcLkOeQlFOiYiIiKiiYPOAiIj0lp+3G9Z9ORY9WlZHp47tMW3aFDx+/FjqWHrLxcUF2dm5yC5issDURM7JAyIiIioxNg+IiEivGRkZoW/nZvjj52l4cPsS6tT2w86dOyGKotTR9I6RkRHcq1bBoyI2TTSRG3PygIiI9J8IqNWG81EZsHlAREQGwcHWCp9N6Y/PJ7+F2TOnoGePblo9z76iqFatGmITXn1co4ncGAo2D4iIiKiE2DwgIiKD0qR+dfz2/WTUdDFG40av4atFi6BUKqWOpTe8vX0Ql1h08yA3T8HJDSIiIioRNg+IiMjgyOXGGD2gPX79bgL279qGhg3qYe3atTyDHoC3b3XEJaa98usymRGMjIyQn59ffqGIiIhKQa0WDOajMmDzgIiIDJZHFUf8/MkwTBjYGn9sXglfH2+0C2yDZcuWIS4uTup4kvD29kZ8ctGbSpqayJGbm1tOiYiIiKgiMJY6ABERUVkIgoDAAH8EBvgjJ1eBs1fv4uBfm/Dx3Dnwr1MH/QcOQr9+/eDl5SV11HLh6emJ+CI2TAQAExM58vLyYG1tXU6piIiIyNCxeUBERBWGuZkJOrasj44t60OhzMeFoFAc/fdPfPnFZ/Dy9ET/gW+jf/8B8PPzkzqqznh5eSEuIaXIa0xNTJCXx+MaiYiISHNsHhARUYVkIjdGm6Z10KZpHXz8fm9cuRmBI+cP4scflsDJ0Qn9B7yN/gMGoF69ehCEirNW0cPDAwlJqchXqWAsk730GhMTYy5bICIivSai8hyBaCjYPCAiogrPWCZDs4Y10axhTcwZ2wvX7zzAkXOn0G3NSphZWKJ//wHo2q07zM3Ntfactra2qF27ttbqacrExAQODnZITM6Am4v9y6+Ryzl5QERERCXC5gEREVUqRkZGaOTvg0b+Ppg5qgduhUXj8NmrmDF5F9Ri2d/iyM9XIeTufXh7eyE8PAJGRuW/N3HTJk3w85ZDmDexL0xN5C98nRsmEhERUUmxeUBERJWWIAioW7Ma6tasppV68YlpmLN4Ozp36ogtW7dL0jgAgG2/7cCI4UMx+uM1WDx7MFydbAt93URuzMkDIiLSbyKXLegbHtVIRESkBacu38bgWcvQ/50R2H/gEJycnCTLYmlpid93/Im3B4/EkA+W4/rtyEJfNzVh84CIiIhKhpMHREREZZCvUmHZln+x/9RN/PnXLrRp00bqSAAKpirmfvwJGjR8DSOHD8OUoZ3Rp1NTAIBczg0TiYiIqGTYPCAiIiqlR0np+GjJb7Bzdse1oOtwcXGROtILevbsiVNnzqJXz+64GxmPmaO6w5TLFoiIiKiEuGyBiIioFM5cuYvBs5aid//BOHjosF42Dp6qXbs2Ll2+iqQcOSbM34DsnFxOHhARkd4T1YLBfFQGbB4QERGVQL5KhZ9/PYjPlu/Cbzt24pNP/yfZxoglYWdnh737DqBNxx44d/U2Jw+IiIioRLhsgYiISEOJKRn4aPFvsLR3xbXrwXB1dZU6UonIZDJ88+23aNe+PerXry91HCIiIjIg+v9WCRERkR44d+0e3pm5FN37vI1/Dx81uMbB87p3745q1bRzPCUREZFOiADUBvRRCXDygIiIqAgqlRortx/G7qNB2LZ9Bzp06CB1JCIiIqJyx+YBERHRKySlZuCjJb/DzNoJV4Ouo0qVKlJHIiIiIpIEly0QERG9xIXroRg0fSk69+iHI0ePs3FARERU3qReisBlC4Vw8oCIiOg5KpUaa34/ij//vYJft25Hp06dpI5EREREJDk2D4iIiJ5ITn2Mj3/YAZm5Ha5eC0LVqlWljkRERESkF7hsgYiICMCl4DC8M3MpAju/iaPHTrBxQERERPQcTh4QEVGlplarsXbHMew4cAkbNv2Krl27Sh2JiIiIABhVkr0EDAWbB0REVGklpxUsUxBMbHDlWhDc3d2ljkRERESkl7hsgYiIKqXLN8PxzoylaN2hB46fOMXGAREREVEROHlARESVilqtxvo/T2Db3vNYv3ETunfvLnUkIiIi+i8REFRSh6DnsXlARESVQlZ2LkLCorHx79NQCua4fPUaqlWrJnUsIiIiIoPA5gEREVU4+SoVwh7E4+a9h7gZFoub9x7iYVwi6tfzR69eAzB7zkeQy+VSxyQiIiIyGGweEBGRQRNFEXGJqbh57yFu3IvGrfA4hNyLhId7VTRr3hwder6Luc2bo379+jAxMZE6LhEREZFBYvOAiIgMyuOsHISEPkTw3Ye4HRGP4DuRgCCgadOmaNGyA4a83xxNmjSBnZ2d1FGJiIiolAQAMh7VqFfYPCAiIr2lzFchNDIOwXcf4FZ4HG7ci0ZcQjIaNqiH5i1aYVzPEWjWrBk8PT0hCILUcYmIiIgqLDYPiIhIL4iiiJhHKbhxNwo3Q6NxMywOd8Kj4O1ZDc2aN0fXviMwr1kz1KtXD8bG/N8XERERUXniT19ERCSJ9MfZT/YpiMKtiHjcuBMJuYkpAgKaonmLLhg5pWD5gbW1tdRRiYiIqLyJXLagb9g8ICIinVMo83E3IhY37kUhJDwON+89REJyGhq91gAtWrTC5L7jEBAQAA8PD6mjEhEREdFLsHlARERaJYoiomKTCqYKQmMQEhaDe/ejUd3HG82aN0fvQT3xZUAA/P39IZPJpI5b6fzzzz94f/w4jB8/ARMmToSDg4PUkYiIiMgAsHlARERlkpqehZv3ohB87yFuhcch+E4krKwsERAQgOatuuO9mc3RuHFjWFpaSh210svNzcWUyRMxsk9LXDm9H9UXf4ehQ4dgxswP4O3tLXU8IiKiZwpOWxCljkHPYfOAiIg0lqdQ4k54DILvRSEkLA43Qx8iNe0xGjduhObNW2Hm2xMQEBAANzc3qaPSSyxZvBjV3R0xsHtLDATwKOkNbNtzFo1ea4jOnTrhk//NQ/369aWOSURERHqIzQMiInoptVqNBzFJCL73ACFhsbgZGoPwyBj4+dVAs2YtMGDYW/iuWTPUqlULRkZGUselYsTExGDx4m+x+Zv3nz3m6mSLaSO6YczA9vht71l069IZ0bFxEqYkIiIifcXmARERAQCSUx/jxr0o3LgXXbCp4d37sLOzQ7OAADRv1weTZjfD66+/DnNzc6mjUil8+MFM9OscgGpuTi98zcrCDKP6t8eGnSeRmJgIZ2dnCRISERGRPmPzgIioEsrJVeB2eAxu3H2AkPB43LwXhcdZOWjS5HW0aNEabw5pjoCAALi4uEgdlbTg3LlzOHb0CP5aOv2V1wiCAL/qHggODkbHjh3LMR0REdGLBBGQqbjngT5h84CIqIJTqdS4H52AG3ejEBJesPzgflQs6tSphWbNWuDd0QPRrFkz1KhRg8sPKiC1Wo1JE97HlCGdYWFuWuS1NT1dEBQUxOYBERERvYDNAyKiCiYhOR037kbhZujT5QeRcHF2QkBAAFp26o8ZnzZHw4YNYWZmJnVUKgfr168D8rPRvV2jYq+t6eWKoKtXyiEVERERGRo2D4iIDFh2Th5CQh/ixr2HuBUejxv3HiBPkV+w/KBla/QbWbD8wNHRUeqoJJFtW37FsN6tIAhCsdf6ebth57F/yyEVERFRcUTI1GqpQ9Bz2DwgIjIwh05fx7mgcNwMi0FUzCPUq1sHLVq0wvDx76JZs2bw9fXV6IUiVQ55eXmwttRsyqSGVxWEhUVAqVRCLpfrOBkREREZEjYPiIgMhCiK+HHjAZwJfoBJk6diTvPmaNCgAUxMTKSORnpMoVBALtfsf/fmZiao6uqEO3fuoH79+jpORkRERIaEzQMiIgOgUqnx5cq/EZmQgzNnz3MZAmksT5EHubFM4+trershODiYzQMiIpKUIILLFvQMmwdERHpOqczH3O9/R55giaPHTsDa2lrqSGRAlAqlxpMHAODhaouIiAgdJiIiIiJDxDO5iIj0WHZuHqYu3AxTWzfsP3CIjQMqMYVCUaLJAxdHGzyMitRdICIiIjJIbB4QEempjMxsTJi/Hj61XsOfO//m0YpUKgqlskTNA1cnOzx8GKXDRERERGSIuGyBiEgPJaVmYMKCDejcrRe+/+EnGBmx10ulk5engNxY8//duzraIib6nA4TERERaUKEEfc80Cv8aZSISM/EPErByI9WY9DgEfjhx5/ZOKAyUSqVMCnBngeuTraIiY3XYSIiIiIyRJw8ICLSI+FR8ZiwYANmf/Qxpk6dJnUcqgAUSiXkcs2XLdjbWCIzMws5OTkwNzfXYTIiIiIyJGweEBHpiZv3ojD1y834bsn3GDZsuNRxqIJQ5JVsw0QjIyO4OjsgNjYW1atX12EyIiKiVxNEwEitkjoGPYezsEREeuBicBgmf7EJa35Zz8YBaY0oiiXeMBEAXJ3tER0draNUREREZIg4eUBEJLFj52/is2V/Y8efO9G+fXup41AFkp+fD2NjWYn3zXBxtGHzgIiIiAph84CISEL/HL2CHzcdwv6Dh9C0aVOp41AFk5eXB7lcXuL7XOwt8fDhQx0kIiIiIkPF5gERkUS2/nMGm3afw7ETJ+Hv7y91HKqAFApFiU5aeMrFwQYPox7oIBEREZGmRO55oGfYPCAiKmeiKGLVb0dw4MxtnDl7Dt7e3lJHogqqoHlQ8skDVydb3LkRpYNEREREZKjYPCAiKkdqtRrfrduLoNAEnDl7HlWqVJE6ElVgBcsWSv6/elcnW0RHB2k/EBERERksNg+IiMpJvkqFBUt34lGGGqdOn4WdnZ3UkaiC27Xrb1R1cSjxfa6OdoiJjdNBIiIiIk2JEMR8qUPQc3hUIxFROchTKPHBN9uQLVri8JFjbByQzp06dQqfL5iPBZPfKvG9jvZWSElNhVKp1EEyIiIiMkRsHhAR6VhWdi4mf74Rdq7e2LN3PywtLaWORBVcdHQ0Bvbvh8+m9kc1N6cS328sk8HR3g5xcZw+ICIiogJctkBEpENpGVmY+NlGePrWwuIlPyAhIUGr9eVyOVxdXbVasyLIzc1FRkYGnJycYGRUufrkeXl5eKvPm3i7WwBaN65d6jpVnO0RHR0NT09PLaYjIiLSkAgIPG1Br7B5QESkQycu3kJiSjoSUy6heUATrdZWKpVITk1HXFxchdp4Ua1WIyMjA+np6UhLS3vhn2lpaUhLTUVaWgpSU1OR/uSxjIwMpGdkICMjE2pRDTNTE7Ru3Ro7/thZaaY9RFHE++Pfg4OFgFH925WplqujLWJiYrQTjIiIiAwemwdERDrU+42m6P1GU53UXrn9MBJzLfS6cSCKIs6cOYO4uLhnDYDU1FSkpaYgNTUFaampBU2B9PQnDYMMZGXnwNLCHNZWFrCxsoCVpTmsLc1gZWEKK3NTWJrLYW1pBg9Lc9RxsYGVhSusrcxgbWEOayszWFmYw9TEGPkqNT5buhPtAttg3/6DcHZ2lvrboXOrVq7E6RNHsOnr8RAEoUy1nB0sER0draVkREREZOjYPCAiMkAJyenY9s9ZXLkWJHWUVzp58iRmzZiGtJRE+FarAitLU1iZy2FlbgprSzNUdTOHdQ13WFvWgJWlGayfNAkszc0gk5V9qYHcWIbPpvbH0l8PoUXzABz69wh8fX218DvTT2fOnMEnn8zFhkXvwdLCrMz1XBysERX1QAvJiIiIqCJg84CIyAAt33oYo8eMhY+Pj9RRXhASEoIPZ81EcPA1THznDXQLfEeyfQcEQcDkoV3g7GCN1i1bYM++/Xj99dclyaJLsbGxGNCvLxZM7gsvd+1MWLg62uJieKRWahEREZWUABGCmkc16pPKtYsUEVEFcCciBqev3sMnn34qdZRCoqOjMXLEcAS2aY26Hqb4e9kM9Gj/ul5sWDioR0t8OLobOr/REQcPHpQ6jlYlJyeje7cu6Ne5Cdo29ddaXTsbSyQnJWutHhERERk26X+iIyIijYmiiCUbDmDevAWwtbWVOg4AID09HbNnf4gG9etClhOLXStmYmiftjCR69dwW8eW9bF4zmAMeXcQNm3aKHUcrUhKSkL7dm3xup8rxg5sr9Xayvx8mJiYaLUmERERGS79+smOiIiKdPLSbaRmKjHuvfekjoK8vDwsW7YUXy1ciDZNauG3H6agipOd1LGK9HpdH6z5YgwmzfkAj+Lj8cGHs6WOVGqJiYlo364tWtT3wOQhXcq8QeJ/KZQqmJmVfe8EIiKi0hEhiDyqUZ+weUBEZCCU+Sp8v/EAfl6+BsbG0v3nW61WY+vWrfh47hz4ejhh5YKRqOntJlmekqruWQUbFo3HiDmL4Vu9Bvr16yd1pBJ79OgR2rdri7aNvDHh3U5abxwAQJ5CCVM2D4iIiOgJNg+IiAzEtj1n4OPrh27dukmW4fDhw5g1YzrE/BzMn/AmmtSvLlmWsnB1ssW3H76D98aOQd26dVG7dm2pI2ksPj4e7QLboGPTGhj/zhs6ex6lMh/mZuY6q09ERESGhc0DIiIDcDE4DBv/Oo0z587r5F3m4gQFBeGDmdMRFnoXkwa/gU6tGkiSQ5vq+Xli4uBO6NO7Fy5fuQYrKyupIxUrNjYW7QLboHPzWnhvUEedPleeMh+mZvr/PSEiIqLywQ0TiYj0XGRMIuZ8tx3bf9+BmjVrlu9zR0Zi8LuD0PmNDgio5YA/f56Gzq0bGnzj4Kn+XZuhnq8LRo4YBlEUpY5TpJiYGLRt0wrdW9XReeMAABTKfJibc/KAiIgkIoqAOt9wPioBNg+IiPRYRmY2pn65CV8u/AodOnQot+dNTk7G9OlT8fprDWErpGH3ipkY1KMl5MaycstQXuaM64U7N4OwZPF3Ukd5pYcPH6JN65bo3a4+Rg/Q7qkKr6JQ5MPUlHseEBERUQE2D4iI9JQyX4VZX2/Dm2/1L7fTFXJycvDVokWo5VcDcWHX8OfSaXj/3U6wtKi4LyJNTeT4bvY7+PqrRThx4oTUcV7w4MEDtG3TCv3eaIQRfQPL7XkVynyYmVuU2/MRERGRfuOeB0REekgURXy1ejfsXath8eLvdf58KpUKGzduxKeffIy6NdywbuE4+Hi46Px59UVVFwd8PrU/3h44AFeuXoO7u7vUkQAA9+/fR7vAtninWxMMfrN1uT63QpnPoxqJiEhaYuVYDmAo2DwgItJDW3afRsj9JJw7vw8yme6WCoiiiH379uHDD2bCXC7i6xn90bCOt86eT5+1fL0W3u7WFP369sHJU2dgYmIiaZ6IiAi0C2yLob2aYVCPluX+/Mp8NZsHRERE9AyXLRAR6Zmdhy5i499nsHffAdjY2OjseS5evIjANq0wbdJ7eK9/S6z7cmylbRw8NapfO1gaKzF92lRJc4SFhSGwbRsM791cksYBAOQpVTA1NZXkuYmIiEj/cPKAiEhPKJX5+GbtHly9G4tjJ07C29tbJ88TFhaGObM/wJnTp/De2x3Q+8M+MNbhdIMhMTIywudT+2PwrOXY3KIFhg4dVu4Z7t27h/btAjGmfxv079Ks3J//KWW+ipMHREQkIREQVVKHoOdw8oCISA8kJKdj7KdrkS3Y4PKVa6hTp472nyMhARPeH4+Apo3hbq3AruUz0a9LMzYO/sPa0hyLZ7+LaVOn4vr16+X63Hfu3EG7wLZ4b0BbSRsHAKDg5AERERE9h80DIiKJXbt1H0M+WI4+A4bg713/aH2pQmZmJhbMn4fatWoiI+4O/lo2A2MGdIC5mbRr+vVZTW83fDimB/r07oW4uLhyec5bt26hQ7tATHinHd7qHFAuz1kUpZKTB0RERPT/uGyBiEgioijit33nsPr3Y9iwcTN69Oih1fpKpRJr167Bgvnz0aSuN7Z8NxEeVRy1+hwVWffARohLSEPbNq1w/MQpnZ7AcPPmTbzRsQOmDn0DPds31tnzlESeUsnJAyIiInqGzQMiIglExiRi+dbDiErIxLnzF1GjRg2t1RZFEX/99RdmfzgLTrZm+HHuYPjX8NBa/cpk9ID2MDaWoU3rljh+4hQ8PT21/hzBwcHo/EZHzBjRBd0CG2m9fmlx8oCIiKQlAmoe1ahP2DwgIipH4VHx+OWPEzgXFIqpU6djx4wZsLKy0lr9M2fOYNaMaUhLScCMYZ3R6vVaEARBa/Uro+FvtYXMSHg2gaDNjSyDgoLQpdMb+HBMd3Ru3VBrdbUhT5nPyQMiIiJ6hs0DIqJycC8yDmv/OI4rN+9j+vSZ2PLXJK3ubXD79m3M/nAWrl6+hPff6Yge7QZCJuO2NtoypHebJxMIrXD8xElUr169zDWvXr2Krl06Y87YHujUqoEWUmqXQpnPyQMiIiJ6hs0DIiIdinmUgu83HMDF4DDM+egj7PhnklYnDWJjY/G/Tz/BX3/txIi32uJ/y2fA1ESutfr0/wb1aAljmREC27bB0WPH4efnV+paly5dQo9uXfHx+DfRoUU9LabUHoWCex4QEZGERBEQuWxBn7B5QESkQ/ejE5CUlglRFPH1V1/hrz//gH/duvCvWx916tRBnTp14OXlBVkJj0vMyMjAV18twsoVy9HnjSbYvWImbKwsdPS7oKf6d20OmZER2gW2xdFjx1G7du0S17hw4QJ6du+G/03sg3bN6uogpXZw8oCIiIiex+YBEZEOtW5cG60b14YoikhJy8T96AREPHyEoNN7sOv3TYh4GI/UtAz4+nrDv44/6tStD39/f9SuXRt+fn4wNzcvVE+hUGDFiuX48ovP0aqRH7YvmQw3F3uJfneV01udAyCTGaF9YFscPnoMdetq3gA4e/YsevfqiQWT+6JN0zo6TFl2eZw8ICIioueweUBEVA4EQYCjvTUc7a3RpH7h9fLZOXmIjElExMNHuH/3LM4f34uIh4/wMCYBblVcUau2H/zr1oebW1UsX/YzqrnaYsW8EfDzqSrR74be7NgEMpkROrZvh0OHj6BBg+L3LDh9+jT69H4TX0ztj1aNa+k+ZBkpFEpOHhAREdEzbB4QEUnMwtwU/jU8XjhOUZmvQnR8MiKjExDx8B4u3b2Ej8d1R7OGNSVKSs/r0e51yI1l6NSxA7Zs2w5bW1ukpaUhNTUVaWlpBb9OSUFqajJSUlJw9OhRLJw+EC0alX6vhPKUp1Bw8oCIiKQlqqROUC4EQXAA8BsAbwCRAAaKopj6kuu6AvgRgAzAWlEUvyrufkEQPgIwGoAKwBRRFA8+efwAADcU9AROAZgoikV/w9k8ICLSU3JjGXw8XODj4YL2zaVOQy/TuXVDyIyM8P64kbAwM4W1pTmsLM1gbWEKawtTWFmawtHSDN7e5nj3izGo7llF6sga4+QBERFRuZkD4Igoil8JgjDnyeezn79AEAQZgGUAOgGIBnBJEITdoijeetX9giD4AxgEoC6AqgAOC4Lg96RJMFAUxQyh4EzvPwAMALC9qJBsHhAREZVBx5b10bFlfaljaJUoitzzgIiIqPz0BtDuya83AjiO/zQPAAQACBNFMQIABEHY/uS+W0Xc3xvAdlEU8wDcFwQh7Emdc6IoZjy53hiACQCxuJBsHhAREVEh+fkqGBvLYGRkJHUUIiKqtERAXWmOanQVRTEOAERRjBMEweUl17gDePjc59EAmhVzvzuA8/+5x/3pJ4IgHERBM2E/CqYPisTmARERERWSp8yHiYmJ1DGIiIgMiZMgCJef+3y1KIqrn34iCMJhAC9bv/ixhvWFlzxW3LRAkfeIothFEAQzAFsAdADwb1HF2DwgIiKiQhTKfJiZsnlARERUAkmiKDZ51RdFUXzjVV8TBOGRIAhuT6YG3AAkvOSyaADVnvvcA0Dsk1+/6v6i7nmaK1cQhN0oWOJQZPOA84hERERUiELByQMiIpKaCFHMN5iPMtoNYPiTXw8HsOsl11wCUFMQBB9BEExQsBHi7mLu3w1gkCAIpoIg+ACoCeCiIAhWT5oMEATBGEB3AHeKC8nJAyIiIiqkYPKAmyUSERGVk68A/C4IwmgAUSg4+QCCIFRFwZGM3UVRzBcEYRKAgyg4qnGdKIohRd0vimKIIAi/o2BTxXw8OY5REARLALsFQTB9UusogJXFhWTzgIiIiArhSQtERETlRxTFZAAdX/J4LAqmAp5+vg/APk3vf/K1LwF8+Z/HHgFoWtKcXLZAREREhSjzVTA1Y/OAiIiI/h8nD4iIiKiQPIUSpiZsHhARkYREAKJK6hT0HE4eEBERUSEKZT7MzMykjkFERER6hM0DIiIiKkShyOeeB0RERFQIly0QERFRIQplPszMOXlARETSESFCVJf5CETSIk4eEBERUSF5Sk4eEBERUWFsHhAREVEhSmU+zM3MpY5BREREeoTNAyIiIipEma+CTCaTOgYRERHpEe55QERERIVUc3PEv5euSh2DiIgqNZFHNeoZTh4QERFRIbV93XEj5BbUarXUUYiIiEhPsHlAREREhdhaW8DO2hJhYWFSRyEiIiI9wWULRERE9II6NTxw7do1+Pn5SR2FiIgqJRGiyKMa9QknD4iIiOgFfp4uuHrlstQxiIiISE+weUBEREQvqF29Kq5cviR1DCIiItITXLZAREREL6jt646gpX9BFEUIgiB1HCIiqmxEQFTztAV9wskDIiIieoGLow0EiIiJiZE6ChEREekBNg+IiIjoBYIgoE6Narh27ZrUUYiIiEgPsHlAREREL+Xn5YKrV69IHYOIiIj0APc8ICIiopeq7euGk5cvSh2DiIgqJRHgUY16hZMHRERE9FJ1qnvg2rUgqWMQERGRHmDzgIiIiF7Ko4oD0tMzkJycLHUUIiIikhiXLRAREdFLGRkZoXb1aggKCkLHjh2ljkNERJWKCFHkUY36hJMHRERE9Ep+3q64evWq1DGIiIhIYmweEBER0SvV9qmCy5cuSB2DiIiIJMbmAREREb1SneruCLp2TeoYREREJDHueUBERESv5FPNFVHRMcjKyoKlpaXUcYiIqBLhngf6hZMHRERE9EpyYxlqeLsjODhY6ihEREQkITYPiIiIqEi1fdy4aSIREVElx2ULREREVKRaPq64cvkigIlSRyEiokpCFEWI6nypY9BzOHlARERERart646rV69IHYOIiIgkxOYBERERFcnP2w337oVBoVBIHYWIiIgkwmULREREVCRzMxO4uznj1q1beO2116SOQ0RElYLI0xb0DCcPiIiIqFh1fN1x7do1qWMQERGRRNg8ICIiomL5eTnj6pVLUscgIiIiibB5QERERMWqXd0dVy5fljoGERERSYR7HhAREVGxavu640bIr1Cr1TAy4nsPRERUDrjngV7h//2JiIioWLbWFrC3sUJYWJjUUYiIiEgCbB4QERGRRnw9XXH37l2pYxAREZEEuGyBiIiINOJkb424uDipYxARUWUgihDV+VKnoOdw8oCIiIg04mhrjtjYWKljEBERkQTYPCAiIiKNODvYICY6SuoYREREJAE2D4iIiEgjzvY2iI2JkToGERERSYB7HhAREZFGnB1sEBt3WeoYRERUKYgQeVSjXuHkAREREWnE2cEG8fGPpI5BREREEmDzgIiIiDTiaG+NpOQUqFR8J4iIiKiy4bIFIiIi0ojcWAZba2skJiaiSpUqUschIqIKjssW9AsnD4iIiEhjLk52PK6RiIioEmLzgIiIiDTm4miLuLg4qWMQERFROeOyBSIiItKYo50lJw+IiEjnRIgQ1Vy2oE84eUBEREQac7KzYPOAiIioEmLzgIiIiDTmZG+NmOgoqWMQERFROWPzgIiIiDTm4mCL2JgYqWMQERFROeOeB0RERKQxJwdrxMZdkToGERFVdKLIoxr1DCcPiIiISGMuDraIj38kdQwiIiIqZ2weEBERkcYc7ayQlJwClYrvBhEREVUmXLZAREREGpPLjWFjZYWkpCS4urpKHYeIiCowLlvQL5w8ICIiohJxdrLjcY1ERESVDJsHREREVCIuDjaIi4uTOgYRERGVIzYPiIiIqESc7Kw4eUBERFTJcM8DIiIiKhFHOws2D4iISMdEiGrueaBPOHlAREREJeJsb42Y6IdSxyAiIqJyxOYBERERlYizgw1iY6OljkFERETliMsWiIiIqEScHGwQG3NN6hhERFTB8ahG/cLJAyIiIioRFwcbxD96JHUMIiIiKkdsHhAREVGJONlbIzEpGWq1WuooREREVE64bIGIiIhKRC43hrWVJZKSkuDi4iJ1HCIiqoBEkact6BtOHhAREVGJuTja8bhGIiKiSoTNAyIiIioxZwcbxMXFSR2DiIiIygmbB0RERFRiTvZWbB4QERFVIoIoiq/+oiBcLscsRERERERE9KIkURS7Sh2iPAmCcACAk9Q5SqDC/xkV2TwgIiIiIiIiIuKyBSIiIiIiIiIqEpsHRERERERERFQkNg+IiIiIiIiIqEhsHhARERERERFRkdg8ICIiIiIiIqIi/R+phvCwIeFtMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1584x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(\n",
    "    figsize=(22, 16),\n",
    "    subplot_kw=dict(projection=ccrs.PlateCarree())\n",
    ")\n",
    "\n",
    "# plot arrows over pcolor\n",
    "h = ax.quiver(\n",
    "    dz['lon'], \n",
    "    dz['lat'], \n",
    "    dz['u'], \n",
    "    dz['v'],\n",
    "    transform=ccrs.PlateCarree(),\n",
    "    zorder=20\n",
    ")\n",
    "\n",
    "div.plot(cmap=cmo.cm.balance,zorder=10,transform=ccrs.PlateCarree())\n",
    "\n",
    "ax.set_extent(extent)\n",
    "ax.add_feature(LAND, zorder=0, edgecolor='black')\n",
    "ax.add_feature(cfeature.LAKES)\n",
    "ax.add_feature(cfeature.BORDERS)\n",
    "ax.add_feature(state_lines, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b31ae4b-5399-4f87-a5a8-d2202d61c652",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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