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    "# Short DBS Scans"
   ]
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    "### Introduction:\n",
    "\n",
    "One possible way to retrieve winds using wind lidar is using the Doppler beam swing (DBS) scanning strategy. The DBS consists of four slanted observations of the wind. Each one of the observations is from a different azimuth, equally separated by 90 degrees (0, 90, 180, 270). While executing the DBS, the lidar first observes the wind at azimuths of 0 and 180 degrees and then at 90 and 270 degrees. From those observations, the north-south and east-west wind components can be calculated directly.\n",
    "\n",
    "This example focuses on using lidarwind to retrieve wind speed and direction profiles from the observations collected by the WindCube using the DBS scan strategy. "
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    "### Steps:\n",
    "\n",
    "1) Downloading sample data from zenodo\n",
    "1) Reading the DBS files\n",
    "2) Merging the DBS files\n",
    "3) Retrieving the wind profiles \n",
    "4) Visualising the profiles"
   ]
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   "execution_count": 1,
   "id": "d48102e0-d462-491d-9f93-5a5c9b2b2f09",
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import lidarwind\n",
    "from lidarwind.utilities import sample_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af18abd4-3ac4-41ee-8247-15cd2ab779fd",
   "metadata": {},
   "source": [
    "### Step 0: Downloading the sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766aa9b4-f1cc-4705-b32d-8d398d4907c3",
   "metadata": {},
   "source": [
    "Caching the short DBS sample data"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "386b50e5-c32a-48ac-a65f-88675e43174f",
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   "source": [
    "# 6-beam sample data\n",
    "\n",
    "file_list = sample_data(\"wc_short_dbs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df122154-b517-4d1c-9cc9-69f060ebfd22",
   "metadata": {},
   "source": [
    "### Step 1 and 2: Reading and merging the DBS "
   ]
  },
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   "cell_type": "markdown",
   "id": "52c53bc8-07d2-4cb0-8f7d-8505807f13fd",
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   "source": [
    "Here we are going to read all the DBS files. Be careful to provide a list of files that are compatible with each other. Here we also indicate the variables required for processing the DBS files. Finally, the merged dataset is created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3839b2d4-34d4-42cb-b63a-bc2fdf8fc62c",
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    "var_list = ['azimuth', 'elevation', 'radial_wind_speed', \n",
    "            'radial_wind_speed_status', 'measurement_height', 'cnr']\n",
    "\n",
    "merged_ds = lidarwind.DbsOperations(file_list, var_list).merged_ds"
   ]
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  {
   "cell_type": "markdown",
   "id": "066d4646-1fc7-481e-bef4-464175612124",
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    "Below, you can see the merged dataset and the variables used during the wind retrieval. You can also see one additional variable, the scan_mean_time. This variable is used to identify observations belonging to the same DBS sequence and is used if the parameter method from  GetWindProperties5Beam is set to single_dbs. "
   ]
  },
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   "execution_count": 4,
   "id": "3493e1ed-dbb4-47be-a67f-053f361286a1",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                   (time: 349, gate_index: 159)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2021-07-01T13:03:28.08800...\n",
       "  * gate_index                (gate_index) int32 0 1 2 3 4 ... 155 156 157 158\n",
       "Data variables:\n",
       "    azimuth                   (time) float64 0.0 0.0 180.0 ... 180.0 180.0 270.0\n",
       "    elevation                 (time) float64 75.0 90.0 75.0 ... 90.0 75.0 75.0\n",
       "    radial_wind_speed         (time, gate_index) float64 0.2 -0.07 ... -19.05\n",
       "    radial_wind_speed_status  (time, gate_index) float32 1.0 1.0 1.0 ... 0.0 0.0\n",
       "    measurement_height        (time, gate_index) float64 200.0 250.0 ... 8.1e+03\n",
       "    cnr                       (time, gate_index) float64 -10.05 -9.8 ... -34.31\n",
       "    scan_mean_time            (time) datetime64[ns] 2021-07-01T13:03:35.09280...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ce2a2015-3fcc-4790-8728-1ed475df7a52' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ce2a2015-3fcc-4790-8728-1ed475df7a52' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 349</li><li><span class='xr-has-index'>gate_index</span>: 159</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c459a01a-414a-46be-8ecb-e3b493bb98c9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c459a01a-414a-46be-8ecb-e3b493bb98c9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2021-07-01T13:03:28.088000 ... 2...</div><input id='attrs-134ccd06-b1b9-4d0c-8c5f-0a9ed89bbc05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-134ccd06-b1b9-4d0c-8c5f-0a9ed89bbc05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b29ce34-1888-47a3-b690-184cc5b206e7' class='xr-var-data-in' type='checkbox'><label for='data-0b29ce34-1888-47a3-b690-184cc5b206e7' 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>comments :</span></dt><dd>Number of seconds between time_reference and the end of each ray measurement.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-01T13:03:28.088000000&#x27;, &#x27;2021-07-01T13:03:31.039000064&#x27;,\n",
       "       &#x27;2021-07-01T13:03:33.988000000&#x27;, ..., &#x27;2021-07-01T13:47:05.379000064&#x27;,\n",
       "       &#x27;2021-07-01T13:47:08.345999872&#x27;, &#x27;2021-07-01T13:47:13.800000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>gate_index</span></div><div class='xr-var-dims'>(gate_index)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 154 155 156 157 158</div><input id='attrs-48b23017-65a6-478a-944e-ad42af2cd003' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48b23017-65a6-478a-944e-ad42af2cd003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbdccf88-f3b4-421c-850f-d9bd505fa3e1' class='xr-var-data-in' type='checkbox'><label for='data-dbdccf88-f3b4-421c-850f-d9bd505fa3e1' 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>gate_index</dd><dt><span>comments :</span></dt><dd>Identification number of each range gate. Either a dimension or a variable. When this vector is a dimension, range is a variable and vice versa.</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
       "       154, 155, 156, 157, 158], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d25e2ab-ebe3-4f14-8394-cb8cc62e5ec2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d25e2ab-ebe3-4f14-8394-cb8cc62e5ec2' class='xr-section-summary' >Data variables: <span>(7)</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>azimuth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 180.0 ... 180.0 180.0 270.0</div><input id='attrs-9bb9bba4-cb4f-4483-85cb-48d882492ee2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9bb9bba4-cb4f-4483-85cb-48d882492ee2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b6ea214-1a72-457f-af26-7beccbcb4db8' class='xr-var-data-in' type='checkbox'><label for='data-9b6ea214-1a72-457f-af26-7beccbcb4db8' 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>ray_azimuth_angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>azimuth_angle_from_true_north</dd><dt><span>comments :</span></dt><dd>Scanning head&#x27;s azimuth angle relative to true north when each measurement finished. 0 to 360. 0 is the North, 90 is the East. This angle only incorporates azimuth_correction. The Lidar is not supposed to be moving.</dd><dt><span>axis :</span></dt><dd>radial_azimuth_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "         0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0.,\n",
       "       180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180.,\n",
       "       270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,\n",
       "        90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,\n",
       "         0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "         0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0.,\n",
       "       180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180.,\n",
       "       270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,\n",
       "        90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,\n",
       "         0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "         0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0.,\n",
       "       180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180.,\n",
       "       270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,\n",
       "        90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,\n",
       "         0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "         0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0.,\n",
       "       180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180.,\n",
       "       270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,\n",
       "        90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,\n",
       "         0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "         0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0.,\n",
       "       180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180.,\n",
       "       270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,\n",
       "        90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,\n",
       "         0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "         0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0.,\n",
       "       180., 270.,  90.,   0., 180., 180., 270.,  90.,   0.,   0., 180.,\n",
       "       270.,  90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,\n",
       "        90.,   0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,\n",
       "         0.,   0., 180., 270.,  90.,   0.,   0., 180., 270.,  90.,   0.,\n",
       "       180., 180., 270.,  90.,   0., 180., 180., 270.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>75.0 90.0 75.0 ... 90.0 75.0 75.0</div><input id='attrs-342e22e3-5860-44f3-9aeb-ca103eaf7a3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-342e22e3-5860-44f3-9aeb-ca103eaf7a3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38b8d181-a637-4471-b8de-b7c7a96a3d78' class='xr-var-data-in' type='checkbox'><label for='data-38b8d181-a637-4471-b8de-b7c7a96a3d78' 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>ray_elevation_angle</dd><dt><span>units :</span></dt><dd>degrees</dd><dt><span>long_name :</span></dt><dd>elevation_angle_from_horizontal_plane</dd><dt><span>comments :</span></dt><dd>Scanning head&#x27;s elevation angle relative to horizontal plane when each measurement finished. -90 to 90. 90 is the zenith. This angle does not incorporate any automatic corrections. The Lidar is not supposed to be moving.</dd><dt><span>axis :</span></dt><dd>radial_elevation_coordinate</dd></dl></div><div class='xr-var-data'><pre>array([75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75.,\n",
       "       75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75.,\n",
       "       90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75.,\n",
       "       75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90.,\n",
       "       75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75.,\n",
       "       75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75.,\n",
       "       75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75.,\n",
       "       90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75.,\n",
       "       75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90.,\n",
       "       75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75.,\n",
       "       75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75.,\n",
       "       75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75.,\n",
       "       90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75.,\n",
       "       75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90.,\n",
       "       75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75.,\n",
       "       75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75.,\n",
       "       75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75.,\n",
       "       90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75.,\n",
       "       75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90.,\n",
       "       75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75.,\n",
       "       75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75.,\n",
       "       75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75.,\n",
       "       90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75.,\n",
       "       75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90.,\n",
       "       75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75., 75.,\n",
       "       75., 90., 75., 75., 75., 75., 90., 75., 75., 75., 75., 90., 75.,\n",
       "       75., 75., 75., 90., 75., 75., 75., 75., 90., 75., 75.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radial_wind_speed</span></div><div class='xr-var-dims'>(time, gate_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2 -0.07 -0.45 ... 1.42 -19.05</div><input id='attrs-e3b215d0-91e5-42ae-8080-d83c70568fa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3b215d0-91e5-42ae-8080-d83c70568fa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-037f6b7a-bb73-4d4d-a108-891b128f7619' class='xr-var-data-in' type='checkbox'><label for='data-037f6b7a-bb73-4d4d-a108-891b128f7619' 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>radial_velocity_of_scatterers_away_from_instrument</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comments :</span></dt><dd>Wind speed vector projected along the line of sights.</dd><dt><span>ancilliary_variables :</span></dt><dd>radial_wind_speed_ci,radial_wind_speed_status</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.000e-01, -7.000e-02, -4.500e-01, ...,  4.000e+00, -1.322e+01,\n",
       "         2.879e+01],\n",
       "       [-2.700e-01, -4.300e-01, -5.100e-01, ..., -3.052e+01, -3.078e+01,\n",
       "        -3.530e+00],\n",
       "       [ 1.000e-01,  4.000e-02,  9.000e-02, ...,  2.960e+00, -1.959e+01,\n",
       "         1.014e+01],\n",
       "       ...,\n",
       "       [ 4.300e-01,  5.200e-01,  4.000e-01, ..., -1.835e+01,  2.183e+01,\n",
       "        -2.059e+01],\n",
       "       [ 4.700e-01, -3.000e-02, -2.000e-02, ...,  4.440e+00,  1.561e+01,\n",
       "        -2.302e+01],\n",
       "       [-2.160e+00, -1.920e+00, -1.930e+00, ...,  2.310e+01,  1.420e+00,\n",
       "        -1.905e+01]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>radial_wind_speed_status</span></div><div class='xr-var-dims'>(time, gate_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-c65bfa8e-68db-4608-b7cd-7c01102170b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c65bfa8e-68db-4608-b7cd-7c01102170b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8a4fff0-1d94-4112-b871-522432393cc5' class='xr-var-data-in' type='checkbox'><label for='data-d8a4fff0-1d94-4112-b871-522432393cc5' 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>radial_wind_speed_status</dd><dt><span>comments :</span></dt><dd>0 for rejected data and 1 for accepted data. A data is rejected if the confidence index is below a threshold calibrated in factory or when radial wind speed is out of the accepted range.</dd><dt><span>flag_meanings :</span></dt><dd>rejected,accepted</dd><dt><span>flag_values :</span></dt><dd>0b,1b</dd><dt><span>is_quality_field :</span></dt><dd>true</dd><dt><span>qualified_variables :</span></dt><dd>radial_wind_speed</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.],\n",
       "       [1., 1., 1., ..., 0., 0., 0.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_height</span></div><div class='xr-var-dims'>(time, gate_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>200.0 250.0 ... 8.05e+03 8.1e+03</div><input id='attrs-24f2ab40-e3cd-4c65-a842-8016c0969b67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f2ab40-e3cd-4c65-a842-8016c0969b67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c320133-7a32-449e-a71c-dcaea12bf7aa' class='xr-var-data-in' type='checkbox'><label for='data-8c320133-7a32-449e-a71c-dcaea12bf7aa' 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>height</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comments :</span></dt><dd>Vertical distance normal to the ground, between the instrument and the center of each range gate.</dd><dt><span>spacing_is_constant :</span></dt><dd>true</dd><dt><span>meters_to_center_of_first_gate :</span></dt><dd>200</dd><dt><span>meters_between_gates :</span></dt><dd>50</dd></dl></div><div class='xr-var-data'><pre>array([[ 200.,  250.,  300., ..., 8000., 8050., 8100.],\n",
       "       [ 200.,  250.,  300., ..., 8000., 8050., 8100.],\n",
       "       [ 200.,  250.,  300., ..., 8000., 8050., 8100.],\n",
       "       ...,\n",
       "       [ 200.,  250.,  300., ..., 8000., 8050., 8100.],\n",
       "       [ 200.,  250.,  300., ..., 8000., 8050., 8100.],\n",
       "       [ 200.,  250.,  300., ..., 8000., 8050., 8100.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cnr</span></div><div class='xr-var-dims'>(time, gate_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-10.05 -9.8 -9.45 ... -34.23 -34.31</div><input id='attrs-5838a964-dd3f-4f39-aca7-c101f946d526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5838a964-dd3f-4f39-aca7-c101f946d526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-521e439f-d75d-4edc-9791-4637d051b3e9' class='xr-var-data-in' type='checkbox'><label for='data-521e439f-d75d-4edc-9791-4637d051b3e9' 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>carrier_to_noise_ratio</dd><dt><span>units :</span></dt><dd>dB</dd></dl></div><div class='xr-var-data'><pre>array([[-10.05,  -9.8 ,  -9.45, ..., -33.15, -35.21, -33.98],\n",
       "       [ -9.91,  -9.64,  -9.39, ..., -29.94, -32.74, -36.97],\n",
       "       [-10.02,  -9.75,  -9.43, ..., -35.26, -33.84, -35.07],\n",
       "       ...,\n",
       "       [-11.56, -11.29, -10.95, ..., -34.75, -33.7 , -35.23],\n",
       "       [-11.52, -11.33, -10.95, ..., -33.06, -33.59, -35.97],\n",
       "       [-14.6 , -14.37, -14.23, ..., -32.85, -34.23, -34.31]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scan_mean_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'>2021-07-01T13:03:35.092800 ... 2...</div><input id='attrs-d7a95301-c599-427e-9af3-cbbb674e4f2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7a95301-c599-427e-9af3-cbbb674e4f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7133bdcb-0180-4006-b1bf-3e1ea8e78e49' class='xr-var-data-in' type='checkbox'><label for='data-7133bdcb-0180-4006-b1bf-3e1ea8e78e49' 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;2021-07-01T13:03:35.092800000&#x27;, &#x27;2021-07-01T13:03:35.092800000&#x27;,\n",
       "       &#x27;2021-07-01T13:03:35.092800000&#x27;, &#x27;2021-07-01T13:03:35.092800000&#x27;,\n",
       "       &#x27;2021-07-01T13:03:35.092800000&#x27;, &#x27;2021-07-01T13:03:55.400000000&#x27;,\n",
       "       &#x27;2021-07-01T13:03:55.400000000&#x27;, &#x27;2021-07-01T13:03:55.400000000&#x27;,\n",
       "       &#x27;2021-07-01T13:03:55.400000000&#x27;, &#x27;2021-07-01T13:03:55.400000000&#x27;,\n",
       "       &#x27;2021-07-01T13:04:15.740600064&#x27;, &#x27;2021-07-01T13:04:15.740600064&#x27;,\n",
       "       &#x27;2021-07-01T13:04:15.740600064&#x27;, &#x27;2021-07-01T13:04:15.740600064&#x27;,\n",
       "       &#x27;2021-07-01T13:04:15.740600064&#x27;, &#x27;2021-07-01T13:04:36.017200128&#x27;,\n",
       "       &#x27;2021-07-01T13:04:36.017200128&#x27;, &#x27;2021-07-01T13:04:36.017200128&#x27;,\n",
       "       &#x27;2021-07-01T13:04:36.017200128&#x27;, &#x27;2021-07-01T13:04:36.017200128&#x27;,\n",
       "       &#x27;2021-07-01T13:04:56.294000128&#x27;, &#x27;2021-07-01T13:04:56.294000128&#x27;,\n",
       "       &#x27;2021-07-01T13:04:56.294000128&#x27;, &#x27;2021-07-01T13:04:56.294000128&#x27;,\n",
       "       &#x27;2021-07-01T13:04:56.294000128&#x27;, &#x27;2021-07-01T13:05:16.678199808&#x27;,\n",
       "       &#x27;2021-07-01T13:05:16.678199808&#x27;, &#x27;2021-07-01T13:05:16.678199808&#x27;,\n",
       "       &#x27;2021-07-01T13:05:16.678199808&#x27;, &#x27;2021-07-01T13:05:16.678199808&#x27;,\n",
       "       &#x27;2021-07-01T13:05:37.022400256&#x27;, &#x27;2021-07-01T13:05:37.022400256&#x27;,\n",
       "       &#x27;2021-07-01T13:05:37.022400256&#x27;, &#x27;2021-07-01T13:05:37.022400256&#x27;,\n",
       "       &#x27;2021-07-01T13:05:37.022400256&#x27;, &#x27;2021-07-01T13:05:57.286200064&#x27;,\n",
       "       &#x27;2021-07-01T13:05:57.286200064&#x27;, &#x27;2021-07-01T13:05:57.286200064&#x27;,\n",
       "       &#x27;2021-07-01T13:05:57.286200064&#x27;, &#x27;2021-07-01T13:05:57.286200064&#x27;,\n",
       "...\n",
       "       &#x27;2021-07-01T13:44:47.426800128&#x27;, &#x27;2021-07-01T13:44:47.426800128&#x27;,\n",
       "       &#x27;2021-07-01T13:44:47.426800128&#x27;, &#x27;2021-07-01T13:44:47.426800128&#x27;,\n",
       "       &#x27;2021-07-01T13:44:47.426800128&#x27;, &#x27;2021-07-01T13:45:07.706799872&#x27;,\n",
       "       &#x27;2021-07-01T13:45:07.706799872&#x27;, &#x27;2021-07-01T13:45:07.706799872&#x27;,\n",
       "       &#x27;2021-07-01T13:45:07.706799872&#x27;, &#x27;2021-07-01T13:45:07.706799872&#x27;,\n",
       "       &#x27;2021-07-01T13:45:28.053200128&#x27;, &#x27;2021-07-01T13:45:28.053200128&#x27;,\n",
       "       &#x27;2021-07-01T13:45:28.053200128&#x27;, &#x27;2021-07-01T13:45:28.053200128&#x27;,\n",
       "       &#x27;2021-07-01T13:45:28.053200128&#x27;, &#x27;2021-07-01T13:45:48.323800064&#x27;,\n",
       "       &#x27;2021-07-01T13:45:48.323800064&#x27;, &#x27;2021-07-01T13:45:48.323800064&#x27;,\n",
       "       &#x27;2021-07-01T13:45:48.323800064&#x27;, &#x27;2021-07-01T13:45:48.323800064&#x27;,\n",
       "       &#x27;2021-07-01T13:46:08.556999936&#x27;, &#x27;2021-07-01T13:46:08.556999936&#x27;,\n",
       "       &#x27;2021-07-01T13:46:08.556999936&#x27;, &#x27;2021-07-01T13:46:08.556999936&#x27;,\n",
       "       &#x27;2021-07-01T13:46:08.556999936&#x27;, &#x27;2021-07-01T13:46:28.794200064&#x27;,\n",
       "       &#x27;2021-07-01T13:46:28.794200064&#x27;, &#x27;2021-07-01T13:46:28.794200064&#x27;,\n",
       "       &#x27;2021-07-01T13:46:28.794200064&#x27;, &#x27;2021-07-01T13:46:28.794200064&#x27;,\n",
       "       &#x27;2021-07-01T13:46:49.117799936&#x27;, &#x27;2021-07-01T13:46:49.117799936&#x27;,\n",
       "       &#x27;2021-07-01T13:46:49.117799936&#x27;, &#x27;2021-07-01T13:46:49.117799936&#x27;,\n",
       "       &#x27;2021-07-01T13:46:49.117799936&#x27;, &#x27;2021-07-01T13:47:07.486249984&#x27;,\n",
       "       &#x27;2021-07-01T13:47:07.486249984&#x27;, &#x27;2021-07-01T13:47:07.486249984&#x27;,\n",
       "       &#x27;2021-07-01T13:47:07.486249984&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d29225a-30d9-4749-ad51-4a42b0c50a30' class='xr-section-summary-in' type='checkbox'  ><label for='section-0d29225a-30d9-4749-ad51-4a42b0c50a30' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-22959e98-929b-46ca-b966-99632c2686d1' class='xr-index-data-in' type='checkbox'/><label for='index-22959e98-929b-46ca-b966-99632c2686d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([   &#x27;2021-07-01 13:03:28.088000&#x27;,\n",
       "               &#x27;2021-07-01 13:03:31.039000064&#x27;,\n",
       "                  &#x27;2021-07-01 13:03:33.988000&#x27;,\n",
       "               &#x27;2021-07-01 13:03:39.444999936&#x27;,\n",
       "                  &#x27;2021-07-01 13:03:42.904000&#x27;,\n",
       "               &#x27;2021-07-01 13:03:48.364999936&#x27;,\n",
       "               &#x27;2021-07-01 13:03:51.336999936&#x27;,\n",
       "                  &#x27;2021-07-01 13:03:54.300000&#x27;,\n",
       "               &#x27;2021-07-01 13:03:59.762000128&#x27;,\n",
       "                  &#x27;2021-07-01 13:04:03.236000&#x27;,\n",
       "               ...\n",
       "                  &#x27;2021-07-01 13:46:36.592000&#x27;,\n",
       "               &#x27;2021-07-01 13:46:42.076999936&#x27;,\n",
       "               &#x27;2021-07-01 13:46:45.052999936&#x27;,\n",
       "               &#x27;2021-07-01 13:46:48.032999936&#x27;,\n",
       "               &#x27;2021-07-01 13:46:53.474999808&#x27;,\n",
       "               &#x27;2021-07-01 13:46:56.951000064&#x27;,\n",
       "                  &#x27;2021-07-01 13:47:02.420000&#x27;,\n",
       "               &#x27;2021-07-01 13:47:05.379000064&#x27;,\n",
       "               &#x27;2021-07-01 13:47:08.345999872&#x27;,\n",
       "                  &#x27;2021-07-01 13:47:13.800000&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=349, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>gate_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff663233-f861-4a1d-bd90-1277b5d4e804' class='xr-index-data-in' type='checkbox'/><label for='index-ff663233-f861-4a1d-bd90-1277b5d4e804' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
       "            ...\n",
       "            149, 150, 151, 152, 153, 154, 155, 156, 157, 158],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;gate_index&#x27;, length=159))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c91e54f-926c-410f-b95a-45b57a7f590f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6c91e54f-926c-410f-b95a-45b57a7f590f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                   (time: 349, gate_index: 159)\n",
       "Coordinates:\n",
       "  * time                      (time) datetime64[ns] 2021-07-01T13:03:28.08800...\n",
       "  * gate_index                (gate_index) int32 0 1 2 3 4 ... 155 156 157 158\n",
       "Data variables:\n",
       "    azimuth                   (time) float64 0.0 0.0 180.0 ... 180.0 180.0 270.0\n",
       "    elevation                 (time) float64 75.0 90.0 75.0 ... 90.0 75.0 75.0\n",
       "    radial_wind_speed         (time, gate_index) float64 0.2 -0.07 ... -19.05\n",
       "    radial_wind_speed_status  (time, gate_index) float32 1.0 1.0 1.0 ... 0.0 0.0\n",
       "    measurement_height        (time, gate_index) float64 200.0 250.0 ... 8.1e+03\n",
       "    cnr                       (time, gate_index) float64 -10.05 -9.8 ... -34.31\n",
       "    scan_mean_time            (time) datetime64[ns] 2021-07-01T13:03:35.09280..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1579299b-809e-41de-9d1b-f0153a7b0c5a",
   "metadata": {},
   "source": [
    "### Step 3: Retrieving wind profiles "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a13c5c5-7b18-4cb4-9c65-b77cfbf3634e",
   "metadata": {},
   "source": [
    "Once the merged dataset is created, you can use the dedicated class to retrieve the wind profiles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f7e87cad-afc2-4c89-accb-f2d0f6074021",
   "metadata": {},
   "outputs": [],
   "source": [
    "wind_obj = lidarwind.GetWindProperties5Beam(merged_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca5741f-d5bc-48a8-b1f3-eb428b4f0349",
   "metadata": {},
   "source": [
    "As indicated below, you can read the wind profiles directly from the wind_obj (wind object). Since they have different timestamps, it is helpful to resample the data into a regular time grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21db6318-294a-4a7f-8c72-232302de0d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "hor_wind_speed = lidarwind.GetResampledData(wind_obj.hor_wind_speed, time_freq='20s', tolerance=10)\n",
    "ver_wind_speed = lidarwind.GetResampledData(wind_obj.ver_wind_speed, time_freq='20s', tolerance=10)\n",
    "hor_wind_dir = lidarwind.GetResampledData(wind_obj.hor_wind_dir, time_freq='20s', tolerance=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a14bf1a2-a1b9-4d2d-aa95-4e6993f24d7b",
   "metadata": {},
   "source": [
    "### Step 4: Visualising the profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eee2170-e2d0-4dee-b1d9-029411db645c",
   "metadata": {},
   "source": [
    "After resampling, you can use the xarray methods to plot the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03ab9f9a-680b-4988-81f2-c31d58cbef8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pRQkFQRAUotx5GDsDBwF3SLrV474MTJR0lB9fRFrXAjObI+k84C7SCKujuhkh1RBdKgwzu7ani7tL4+vDDjOzF3x/L+B4kjacBpzo4SV+yaXAJyXNJHVwL3SlcgXwrZqO7r2AY3ssWRAEQX9RosLw72pX61Oc0sU1JwAnlJODrumx01vSO4BvAK/x9CJZk9bp9sLUN3Fxal0xAviNmf1J0o3AeZIOBR4C3u/pLyeZwOYBL5PGCmNmCyR9A7jR0x2fO8CDIAgGBc0107vPFBkldTLwHuAOH8FUCDO7H9i6k/hngZU6yv3eR9XH+7kzgDOKyg6CIOhXmsBbbcY7zD9PeyMAADPbvadriyiMR4A7e6MsgiAIWorerIcx8JxP6jD/OdCrvo4iCuMLwOWS/krNMn61vfdBEAQtTXOZpJaZWSFXIPUUURgnAC+SfKg3zyMJgiDoL5pLYfzeR7heTMdGQI99w0UUxoZmtmUDmQuCIBjaNFEfBml0KnScyGfAa3u6sIjCuFzSXmb2577kLAiCYMjTRC0MM9u0r9cWURifAP5H0hLgPxQfVhsEQdAaNMcSrUCb25FPALt41DUk103/6enaHhWGma3dUO6CIAiGOs01Suo0knr7iR8f5HGH9XRhd84HX21mT3Z3cZE0QRAEQ57m6sPYzsxq58hdJem2Ihd2tx7G5QWuL5ImCIJgaFPRehgVsVzSZvlA0mspOB+jO5PU1pIWdXNeQHfngyAIWoMm6vQmjY66WtL9pJy/BnfF1BPdOR8cXk7egiAIhjhNpDDM7EpJE4HNPepeM1vS3TWZgVhxLwiCYGjRBApD0u5mdpWk99Sdep0k8kJN3REKIwiCoFGG0wyjpP4buAp4ZyfnjLTGRreEwgiCIGiUJpiHYWbH+e7xZvZA7TlJhSbzdTdKKt/o7CJxQRAELUtzjZK6sJO4C4pcWKSFsUXtgaThwJuL3DwIgqAlaI4+jNeTvufr1vVjrENyLtsj3U3cO5a0juzImuG1Apbia2cHQRAENMvEvc2BdwCj6NiP8QJweJEbdDes9tvAtyV928xiDe0gCIIuWQHDXxnoTHSLmV0CXCJpJzP7V1/uUcSX1LGSNmLl5fz+1heBQRAEQw8jGV+ago9LutvMngeQtB7wfTP7WE8X9qgwJJ0ITAXuon36uAGFFIb3edwEPGZm7/De+JnA+sBs4CAzWyppNeAsUv/Is8AHzOxBv8exwKEu/9NmdkUR2UEQBP1DUymMrbKyADCz5yRtU+TCIp3e7wY2LzoTsBM+A9xN6lgB+A5wkpnNlPRTkiI4zcPnzOx1kqZ6ug9IeiNJYW0BbAj8RdIkM+vVWrRBEATV0VQKY5ik9czsOQBJoyk4xaLHYbXA/fRxhLGkjYG3A7/wYwG70z6E60zgAN/f34/x83t4+v2BmWa2xMcOzwO270t+giAIqmEFSWEU2Qac7wP/kvQNSd8A/gl8t8iF3Y2S+iFJbb4M3CrpSjqu//rpAvc/GfgCkNfUWB943syW+fGjwEa+vxHwiN97maSFnn4j4Lqae9ZeU5vf6cB0gPHjxxfIWhAEQVk0TwvDzM6SdBOp8g7wHjO7q8i13TVDbvJwNnBpbzMl6R3A02Y2W9Kuvb2+t5jZDHy475QpU6xqeUEQBO2sAF4q5U6SNiH1525A0kQzzOwUSZOBn5LmTCwDjjSzG9wScwqwH6mCf7CZ3dyDmNHAS2b2S0ljJW1aP/u7M7obVntmV+cKsjPwLkn7kQq4DqlQoySN8FbGxsBjnv4xYBPgUUkjgHVJnd85PlN7TRAEwSCg1BbGMuAYM7tZ0trAbEmzSGajr5vZH/27+l1gV2BfYKJvO5D6hHfo6uaSjgOmkOZl/JLU5fBr0je7W4q4BrlD0u11298lnSRp/a6uM7NjzWxjM5tA6rS+ysw+DFwNvM+TTQMu8f1L/Rg/f5WZmcdPlbSaj7CaCNzQU76DIAj6j6wwGu/DMLMncgvBzF4gDRrayIXkwUPrAo/7/v7AWZa4jlQpH9eNiHcD78KbRGb2OO3dBt1SpGf8j6ThrL/x46nAGsCTwK/o3PNhd3wRmCnpm8AtwOkefzpwtqR5wAKXg5nNkXQeaVjvMuCoGCEVBMHgInd6F2KM9yFkZrhJfSUkTQC2Aa4HjgaukPQ9UmX/LZ6srf/Xyf28T3Qhf6mZmSRzGWsWzXgRhbGnmW1bc3yHpJvNbFtJHykixMyuAa7x/fvpZJSTmS0GDuzi+hOAE4rICoIg6H96ZZKab2ZTekokaS2So8CjzWyRV7I/a2YXSno/qZK9Zx8ye56kn5FaIocDHwN+XuTCIgpjuKTtzewGL8R2JO/vkGr8QRAELU65o6QkrUJSFufULGw0jTSvDeB8fLoCvejn9Q7y3wKvJy2xvTnwf2Y2q0i+iiiMw4AzXNvldbwP82bMt4sICYIgGNqsAF4s5U7+UT8duNvMflBz6nHSIkjXkIbEzvX4S4FPSppJ6uxeaGadmqPcFHW5mb0JKKQkainiS+pG4E2S1vXjhTWnz+utwCAIgqFHr/owemJn4CCS+f9Wj/syyaPsKT6KdDE+7wy4nDSkdh5pWO0hPdz/Zknb+be9V3Q3ce8jZvZrSZ+riwegTvMFQRC0MOWZpMzsWpI1pzNWWovIR5Me1QsROwAflvQQaaSU/DZb9XRhdy2M3HNeaLhVEARB62LAfwY6E0XZu68Xdjdx72cefr2vNw+CIGgNBr9rEEnrmNki0oJJfaKIe/NJpJmDG5jZlpK2At5lZt/sq9AgCIKhxXLK6vSukN+QVtybTdJwtWYvA17b0w2KjJL6OfB5ILc4bpf0GyAURhAEAdAMLQwze4eHm/b1HkUUxhru4Ko2LuZfBEEQtDH4FYakbbs7X8BhYSGFMV/SZqQngqT30fWU8yAIghZk8CsM0joYkJzBTgFuI5mltiJ5J9+ppxsUURhHkdyGv17SY8ADQCGXIEEQBK3B4FcYZrYbgKSLgG3N7A4/3hL4WpF7FJm4dz+wp8/sHubeE4MgCII2Sp24VzWbZ2UBYGZ3SnpDkQuLjJJaDXgvMAEYUTNx7/g+ZTUIgmDIUd4CSv3A7ZJ+QVoDA+DDwO1FLixikroEWEgairWkh7RBEAQtyOA3SdVwCPAJ2h0Z/o00daJHiiiMjc1snz5mLAiCoAVoHoXhS0mc5NtKSLrQzN7b2bkiCuOfkt5Ua/MKgiAIammqPoye6HICX3fOB+8gqc0RwCGS7ieZpAo7qgqCIGgNmqeFUQDr6kR3LYx3VJCRIAiCIciQUhhd0p3zwYcaubGk1UmdKau5nAvM7DhJmwIzgfVJHekHmdlSH411Fsl977PAB8zsQb/XscChJIctnzazKxrJWxAEQbmUt4DSIKAr1+oMq1DoEmB3M9samAzsI2lH4DvASWb2OuA5kiLAw+c8/iRPh6Q3AlOBLYB9gJ9IGk4QBMGgIfdhFNkGPV/s6kSRTu8+4Yt6ZJW7im9GWlrwQx5/JmmG4WnA/rTPNrwA+JEvVbg/MNPMlgAPSJoHbA/8q6q8B0EQ9I7Bb5Kq6ZfulNwvbWZ/7ipNZQoDwFsCs4HXAT8G7gOeN7PsvPBRYCPf3wh4BMDMlklaSDJbbQRcV3Pb2muCIAgGAYNfYdDeL51X5zvbww8XvUGlCsPMlgOTJY0CLgZeX5UsSdPxNW7Hjx9flZggCIJOGPwr7uV+aUlvM7Ntak59SdLNwJd6ukeVfRhtmNnzwNUkb4ijfBFzgI2Bx3z/MWATAD+/Lqnzuy2+k2tqZcwwsylmNmXs2LFVFCMIgqALVjCMlwttgwBJ2rnm4C0U1AWVKQxJY71lgaSRwNuAu0mK432ebBrJ9QjApX6Mn7/K+0EuBaZKWs1HWE0Ebqgq30EQBL3HGMayQtsg4FDS4KEHJT0E/AT4WJELqzRJjQPO9H6MYcB5ZnaZpLuAmZK+CdwCnO7pTwfO9k7tBaSRUZjZHEnnAXeRFm46yk1dQRAEgwK5wmgGzGw2sLWkdf14YdFrqxwldTuwTSfx95NGOdXHLwYO7OJeJwAnlJ3HIAiCsmgWhdGIB/JKO72DIAhaAQGrFky7uMqMFKPPHshDYQRBEDTIMIorjJ6QtAnJ68UGpOFXM8zsFEm/BTb3ZKNIUxQm+zW98YbRZw/koTCCIAgaZBiwVsG083tOsgw4xsxulrQ2MFvSLDP7QE4g6fukVkK9N4wNgb9ImtRNX2+fPZCHwgiCIGgQkVxZlIGZPQE84fsvSLqbNFn5LkhjYoH3k7xmQO+9YfwXcLCkB+ilB/JQGEEQBA3Smz6MXt1XmkAaPHR9TfRbgafMbK4f99Ybxr59zU8ojCAIggbppcIYI+mmmuMZZjZjpXtKawEXAkeb2aKaUx8Ezu11HqV1/D4v9PbaTCiMIAiCBullp/d8M5vSXQJJq5CUxTlmdlFN/AjgPaRlIDKFvGEAvyH5k5pN6kyvdWNudLPSXiYURhAEQYP0ptO7J7yP4nTgbjP7Qd3pPYF7zOzRmrhLgd9I+gGp07tTbxhm9g4PN+1r3kJhBEEQNEjJfRg7AwcBd0i61eO+bGaXk0ZDdTBH9dYbhqSzSYvb/d3M7ulNxkJhBEEQNEiZCsPMrqWLVe/M7OAu4nvjDeMMUsf5DyVtRnLR9DczO6WnC0NhBEEQNEiZE/eqxsyulvQ3YDtgN+DjpDkcoTCCIAiqpqphtVUg6UpgTdI8jb8D25nZ00WuDYURBEHQIM2kMIDbSaOstiTNFn9e0r/M7JWeLgyFEQRB0CDDSFX2ZsDMPgvgbkcOBn4JvBpYradrQ2EEQRA0SDP1YUj6JKnT+83Ag6RO8L8XuTYURhAEQYM0mUlqdeAHwGwz69UiHqEwgiAIGqSZFIaZfa+v14bCCIIgaJBmUhiNMKyqG0vaRNLVku6SNEfSZzx+tKRZkuZ6uJ7HS9KpkuZJul3StjX3mubp50qaVlWegyAI+kJWGEW2ZqYyhUH7IiBvBHYEjvKFPr4EXGlmE4Er/RiSy92Jvk0HToOkYIDjgB1IPt6Py0omCIJgMDCc5EuqyNbMVKYwzOwJM7vZ918A8iIg+wNnerIzgQN8f3/gLEtcB4ySNA7YG5hlZgvM7DlgFtCn5QWDIAiqoFVaGP3Sh1G3CMgGvqIUwJOkdWshKZNHai7Li4B0FR8EQTAoaJU+jMoVRv0iIMlzb8LMTJKVJGc6yZTF+PHjy7hlEARBIcpconUwU2UfRleLgDzlpiY8zD5MuloEpNDiIGY2w8ymmNmUsWPHlluQIAiCbsgT94a6SarKUVJdLQJyKZBHOk0DLqmJ/6iPltoRWOimqyuAvSSt553de3lcEATBoCAvoDTUO72rNEl1uggIcCJwnqRDgYeA9/u5y4H9gHnAy8AhAGa2QNI3gBs93fFmtqDCfAdBEPSK6MNokO4WAQH26CS9AUd1ca8zSP5OgiAIBh2hMIIgCIJCNJPzwUYIhREEQdAg0cIIgiAIChEKIwiCIChEMy2g1AihMIIgCBokWhhBEARBIaLTOwiCIChEtDCCIAiCQggYsWKgc1E9lfqSCoIgaAkMhi0rtvVEV4vP+blPSbrH479bE3+sLz53r6S9qylktDCCIAgaRitgxOLSbpcXn7tZ0trAbEmzSEtB7A9sbWZLJL0KwBemmwpsAWwI/EXSJDNbXlqOnFAYQRAEjWKg/5R0q+R09Qnff0FSXnzucOBEM1vi57Kn7/2BmR7/gKR5pNVJ/1VOjtoJk1QQBEGjGLC04NYL6hafmwS8VdL1kv4qaTtP1m+LzEULIwiCoFGywijGGEk31RzPMLMZ9Yk6WXxuBDAa2BHYjuT1+7UN5buXhMIIgiBoFAOKm6Tmm9mU7hJ0sfjco8BF7tn7BkkrgDEUXGSuDMIkFQRB0CjLgRcLbj3QzeJzvwN28zSTSFM/5pMWn5sqaTVJmwITgRtKKNVKRAsjCIKgUXpnkuqJrhafOwM4Q9KdLm2atzbmSDoPuIs0wuqoKkZIQSiMIAiCxilRYfSw+NxHurjmBOCEcnLQNaEwgiAIGqXcFsagJRRGEARBo7SIwqis01vSGZKedntbjhstaZakuR6u5/GSdKpPbb9d0rY110zz9HMlTasqv0EQBH1mBZXMwxhsVDlK6lfAPnVxXwKuNLOJwJV+DLAvqWd/IjAdOA2SggGOA3YgzVw8LiuZIAj6D3Fz2h4nbXv5tl7aLlPa0DjfTkpbq7CC0kZJDWYqUxhm9jdgQV30/sCZvn8mcEBN/FmWuA4YJWkcsDcwy8wWmNlzwCxWVkJBEAQDS0UzvQcb/d2HsYH7SQF4kuRMC7qe2l54yruk6aTWCePHjy+cIb3Bd36ZAtux8KVDHn07hXbswOYj6D/EXwAw9vTjX/uZ3VKw4TMp/PPdHn8jAO/ksHT4TP574/epHv3Od/7Pw/z3XysFw/6QwhUf8/irU2C3l5iJ3k3ca1oGrNPbzExSae+TT62fATBlypQO9z3FB6h9piZ2Q48b5i/Vin942p1WTtuyHJmCrFTt7q6TNiv5g2idj1ZsQaYDIJ7z4+yu6AoPX+3hkx7e56ErlLW8+7FC04uu851cW88f6nd6eKOHG9CRXKQqVjrKfRhDnP6e6f2Um5rwMHtb7Gpqe79NeQ+CIOgzYZKqhEuBacCJHl5SE/9JSTNJHdwLzewJSVcA36rp6N4L6LWBZC8PxQPtkZ/YtGOinVNw9GwPyRMlLwTAeH9vxXaLvPZjT6Uwt3ger2vZaDNPd1973ARP+9DrPWIVT1NmExtg3WSeYL1knhjjcucPwtaX+GHauflTKTzXT4z28NhkSjHGevr8+3Zca0aPp9A2rCafZVBtqyjZc1b3/8ritpaE/0HINtzbPHzJQ6+2j3wlhS+OrCBvzo65ieF1yYVupc4tiskeelZXZBPV6I7xpdIiw2orUxiSzgV2JXlmfJQ02ulEkofFQ4GHoO0rfDmwHzAPeBk4BMDMFkj6Bu2NzOPNrL4jfSVumQ3rChb6h23/fGJhjZLIWuTPHu7ottm29moOK3oLzkmBsguyjVOwp3+Ur/xECg++P4WmRW2Xbsc6ADzkWdvjnmqy2GbsfV1SGNPcu/6BnscLsp/M8z38hYe/9/BnNbfaL/3JjWo6ibbh0wDcsq3b0ZYOT2G2oHAEAO/nYj8+28Nkp1Aen7FhViDl51McBMDV/sHfLS9XcH6SZQfmdHkk+rUAnEV6GT6K20vbzEBVKIz0NV3G9X786g7x7Yojf32z7enWjrcZm/8321I+edyM/1ZrucJ4nUdnBZEVyAc9zF+OfHmZ5FFSQ5zKFIaZfbCLU3t0ktaAo7q4zxkkHyqFWTECFo0GLfSId3v4j5pE2Z55bY7wt2n55h3TjvY/5Za9yUEB8n8+/6+8svTko368WcdsnuZKAuACVya5ZXHrqSXnrQ3/KPgfIVuzf5B/DdcnbWW4KwXDnk/hiqdq7zXPw2oUxm25hzPXfHfMX4ecuVRTbn8F/q/ufP4wfqGS/CVSJ9CFbccnp2CvmQD8Z6lr4lXzujdnAfC5tvS5EvPtqjJIfjGXMdGPs4LIsuu/ivklyAokf61zHa8KhZF/M//Nh3ur5smRHaLbWjsjfSXTh49LYYf3siRapA8jZnoHQRA0Spikmpfhy2DU0/BsruTkauXWNYlyDf8lOpIH8WbzSm6Jl93CyKbhXBvyitycXJHzER+XnJzCK3LLA+CjHnpl79nallOprJmCo1Owarbm5KZ+rnzmyrw/3xW5st6he6B2dHT5rMBbhm2mkWxrfI+H6d/8OG8GYAyzO1y/yKudS5/xWurY8vO4qrd+JrfFeBPRR5/dmBtfy9POiOG3AJA78ObnH/7fPj50Uvl5bG8J5rZt/gPkFzMvzZDKsgV3ADCHH3l8boHUD1Eqk/xlrjMbr+W/3aRsWsi2Uf+z7XhvChfkd6VEQmE0L3lZqmf3TJ226zydbPCLfl+T6IsetnWAuXaZcFkK1zsmhe+tKJP55cot+A/WHU/yl/vI9HIvrs37uXVpi0876SX+h9wlmUDuy+MNspLNiiIrvWkdL2PDv9Tcq1ZbV8Fadcf5g5UfdP6hkxZ7nsMBWObDSNu0YjZXVKAwVmdZh5y1PSi36rQpDK/MvGnSYqDd2DOXN6adbKO6rPw8TvDO7sf907A0m+p+t0sK994vhSN/CsCcNgWTn2/uMew4mKBc6iZa5Ac2KQ+oPMvD/NtnY+o3U7DfZ/24RHNZKIzmZclWMPePkJXAojcnhdH2zwM40EdafD7/S3ONysebv9cVRo9d7H0k18JzxTtXzLK8V7wWlD++NXbX9b3P4tncN/MkFZH/kElD/DhHr1knN49/z//XtnHutR/xu0rPXUfyJJH8oLKGzZnZ3sP04F9F+hg/nv/ly31ARP7+ld2iBBaxqecwj9bzd86fY1ud4OEUrOotiPanmD586/yh/Lxl2hWFT8TbyxXF0Z4gv6+Tcq3heA/zC5yb7vUKvEy+6mH+bfOPljOXX8z6P5kfv+KKosyBXCtY2VoxBBmSCiMIgqBfiU7vJuYl0gKFB3hNI5tKHq5N5DWiXDtua7bemoJc08+Vk7IH94yuO86DdrLlJleYc8VtanvSl77lO7lGv1vJeWsj19ZTJh46yA/387mTV29Ue5p1RqVwkWUbcp4dDNXWOKH935prm7n2WT+PIPdljPJjz2MeaTO6wvkDblK60FsYq/sQ38X+mK/Mw2b998yvQlsX1cI0VHjRxtXlcGmbKcltpPn9264+ZX7O71jpDh3DCvib/0a5AfGit8a3vdwj6lsa+V3wcbf5ge5ZYp7CJNXEPEUasXiAv+Wf+r6feE9NIrdnbu1vzSuuEUbW2aCqMknd6mH+2GeFkf+vT9ad3+UrbZcufsoX1spWtMq+xcn29Faf4/D33fKcydTxuerJaZLc0p3rr/Nn2+HhVabVnPThGuMjsPMjebBtcL5/XR7033lC6kAekbzJsOwc/wjl575LFXlMH64/8gEApvNbAGa0VVq8H2B4el+zhSMv6rz3uucBMO7RcieRdsTHc+cnONkP66ddtH0dX+qQfgLPA/BgdXbS9jxkPZDnEG6b85R7iXKtKys3fwerGFYbCqOJWZ1Umfh3mkOx/6Q0vv2SmmrShvwVgMfXzc7V8ifG0xzgM4EPyG9ByTXP/LIf4pO0ru7CaL5LmlC4Bd9qi5rzVVcYufO7ipmrQLZHz8hi6pTn0vzI/IO36AP5TK7h1SroynrmnfRAs0U995j8qE1p+e+YJ0EckxTJMl4DwLCPp+if5Y/RMVXkMc9ZSG2HtpbDh/PEltTntrorvXz+yLbrU8wTX6tSYeQai7e8NnN7f/72ZsWxbv7x8wNN/5v8qX6wspoW7S2LrAdWGk+RE6TnPdFbdHPzO1jFBLtQGEEQBEEhog+jiVmDVOHxCt3WPtrkkhqbep7KcGLbELzcFM81rFwbzDWo08rNY9u8dpdzpLcwcvN6v9wPkGYB55ozwHsn+AivY72W90oFQ3qA7EOhbRmctgaamxtyprL9/525FZY7V95Yc6+q/Sak55izmGu6j7j7jUs6+LCszc9XO8Qexg2+tz1lc4iba8711u0c99T/Vg4F4F+s3iFH+VXYt+0Obl47zv1m8anS89j+BH1ts+3cDJkfV/1jbPt/pCZIuyPYCvus8q1zy3rkzb7zYl2CNHT6nW5S/UHO/EplKIFwDdLEjH0FjrgTbk4f0txinVhj1ml/nXO7Nv89t647ru24LZG2OQreE7+jv/Qv5rHhuR8gVVs6/v2yscI/3Pf9vxSWrDdWdWNv23D/tep2sm79uiuKbIn6cD5RO5S2vpe/XHZws0NW83n6zP4vp3DEGm6mOCYrYp8O90oyW654Wzq87OKkKOq7csvgDNdFv9w+Da9dx/Ocn9bdPtQ3V13yEzufN/leVma586oKhZFnu7pJaZfUb8J1bgZrq0WnnTGk+ULzvX+g3QN+hRM1J2W/b7kzIo/zzurKTaHnJ4U8/sBPe7w/t/3y0N8Se73DJBUEQRAUIhRGM/MKcCtsm6ZC5Ypxbbdre9N5tB+nGtEK0izQZawPwAierSiP9eN235WCnbMPkNR6GMbvAPhJp9e+WJu09BbGhj4z+UFmpYjc2NrazRS5Mnqyh20DobKtYGbN3VYaSlUq9V3qeQT1mmv4ziveChqZp8ed7MfeY793qo2+M6+8WH4Wk+N+AEtflmzRy3nN72Q25OU25hh3v7HUw0VtXmurIEldx5edafORvKNPNtw1tY5GXPMroN1R8W7etsjWnvkdPH2Wzcke5ncqt9F8xNYrPtz7wDTM9udt1+X/zYc9LHG4VKy418wsB17kCr4MrOwYFla2sGa3DYd5eGpliqKe5E9oC/+Dzhm5vMPZE10pfLXDT5U/ej7BZE/vQyh5JFf+7D/4Fm+6ZzuPf/jWd3dMz2aLyTn5yvzpq1ESz/ifuAKXG9Cuu/KIoqzb8kAyRubZ1emX39BNKY/n8cxrnQ7Ad9d1j7FVqAyf2jDRf+t670x5bsha3teRz2cTVVaKL2a36JWQ/iX5E3z+cjdFDU+LrwybnXzpf9nNZ7tmFzvfSabdNm/yFTLCBxovy9WoV9zUNNL/4blb8ohkVp7T5mcspR/Ttm5biUSnd/MiHmY1PtF2nJ231dZx57XtpQ9I7iTNo/ZH+B9iMW+rJpNtfSSJu9tUWK5vptpP7lFZ2qEPIHdM5vpcViDlDrdsU6r5K+AjBYZ9LYULV/d4f2jbjEwf21vymhOvHNB+syqWxazhQZJDvrt9SGruim331pBrk+k5Ps6rANjC089ZNSmMqlyHAfznVx2Pj/Q87OEfsEf9ncvKLjsxz7NfbvXwxIrylxjdIQ9ZUZzsCvZo/2J8PS/dXTekexb5paiuRbmxP6fjPfxo7tLJc6jynKYj0n/prZ73Rzw8rIpMRad3EARBUIjow2he1iEt9fdOL97VbmaqNTG2OdLzmn6uiea6+oq2R1PFGDxoN6I87PKyXfq7Hc63t0M2o508JveTHh5JFbRZoS/K5pxU71zx9jTLbcVMdxHys2RumuypbslGwJHZVQMwMreVNqogp5Cfya3eYpjbZcsw/cITvVafG09zDkxmvc1ILY0q+jCu9lbWXPIMx1Q9n8HXAJjuNebcstvEF/fa4L9SWL8WZBWs6uPMFnmevunhZ9xCe3RuOHit/pqD85Up18vahiNX52zyYZf1Yq7S50b5Bu5J180Fe5A86uZ/Tn4D2ybWl0mLKAylxe6GFpKeIS0B2xNjgPkVZ2cwyW1V2a1Y5oGU3Wxlfo2ZNdS7JulPLrsI881sn0bkDRRDUmEURdJNZjal55RDQ26rym7FMg+k7FYsc6swbKAzEARBEDQHoTCCIAiCQrS6wpjRc5IhJbdVZbdimQdSdiuWuSVo6T6MIAiCoDit3sIIgiAICjKkFYakV3montJWIHuV/pbpcjf0sGXK7LIHstz9LtPlDuT7PSBldtkDVu5WZ0gqDElrSvoB8GtJo6wf7W6S1pb0Q+BESWWvBN6d3FzmKySt3wpldtkDXe6Tgc9Lqmzdw07kDvT7fTL9XGaXPWDlDhJDTmFI+iDJ3+BzwPvN7Pl+lL0KyTnmMGAu8FVJ0/tB7juBOcDLwM5m1l+eEweszC57IMu9Lsnd4ghgOHCapKocj9XK/RAD934PSJld9oCVO2hnKLoGWQKsbWbfAJC0MfC0mfXHxP0xwKZmNtVlPw7sLeldZnZphXIXA8PN7Ksu93XAU2b2QoUyM69iYMoMyRnDQJZ7IzN7l8t+CXibpEVmdn2FchfTz++3pOFmtpwBKLOkEWa2jAEod7AyTT9KStLmJD/ffwb+aWZLJV0DPAAsI7kLGkZac/Iuf/HLkv0aYJyZXVcTdxlwrpmdI2k0MBXYHPhqWR+yLuReRKp9rUb6Y68ATgWuMrPFZch1OZuRFhS4GrjBzJZI+iPw6yrL3I3sS0iuIPqj3IcDfweuNLPFki4GTjOzP0vaBDgCeNbjSpFdI/da4C8u92+k2nbV7/fawK+BXwB/MLMVki4EflZlmTuRfbmZLe+v/3XQNU1tkvLm8IWkcnyc9jHYR5A+LPeY2TuBO0lejUtz1y/pC6SX9yhJW3rcaqTlDf5L0lpmtoDkP9DKkt2ZXOd/SD4XZ5vZXsAfSMsabFOSXEk6HvgdyQf254Af+emqy1wv+7O0L7LeH+X+Juk9WwIcQvt7djOwjaSRZvYIcAfwGmBYox2yncidBvzKTx8BfIhq328B6wM7AdsCr5W0JnALFZW5C9nbQNuCFp+g4nIHPWBmTbuRal4n+/7awHXAB/341TXpRgL3ApuXJHc10uoQhwD/S3KVOsLPvRk4BTjKjwXMLkN2F3KH15wfV7Mv4Bpgp5LKvDlwAjDWjzcifSxeBWxRVZl7kD2hH8q9KfBpYLQf7wCc4vv7klozB/jxmsD9te9eyXJPBlb14/FVvd81950CXAWcDhzkcXt7PkovcxeyfwEcRDJHAWxcdblj63pr6hYGya65QNJoS6aPU4H3SHq1mT1Zk+5NwD1AKeYRM1sCXGxmvwQeBCbSvmLMnaRa7jRJuwJbkUxFDT/rLuS+peb8EzXJJ5Ns/M83Kte5DzjTzJ6RNIy0VOAc0rqX91FRmbuRfSfwtCRVWW4ze8DMTjWzBZK2x1s5ko4wsz+SnsHhkrYmLSV1Z4Vy18N92ZvZwzXJS32/a1gbOAOYBewkaRxJUd8FHCppMiWWuQvZfyEpyzGS1jSzR2vSVFXuoCsGWmMV2YDVu4h/F3A28LqauD8BH/P915KWHb6ONLKiNNk158eQFkI+FnhVTfxHSC/8v4EPVyw317xXAzYkmYiuA6ZWUWZPsympFbFuWWXug+x1/HiVqstN+oBNJ7XwxgPX017r/pTLngd8qGK5/wIO7Y/3G3g78EPfPwe4G/iyP+8jGylzH2TfBXwNWAOYAJzXSLlj69s24BnoMYPpJbmYZMMd73HDgDf7/q9Jppn80ZwK/NH3t8TNJGXJ9vgd6tK9nWSS2cmPh3u4Wj/LHUGy8R/RD2U+DDjL91UT3+MHv0zZNXHrVVXu2vLVxB8I3FFzPLof5d5W8fu9o4f7kfoGvwg8RmpJ7Npomfso+w5gFz+3RSPljq3v26A1SUl6s6QbSUve/QT4IPDffnpfktkD4GekP5evOM2GJBs2ZnanmbUvrleCbElvByZJWiV38pnZH0g1ra9Lehl4n8cv6We57zGzBWb2s6rLTFrY8DxJBwD3SNrb89TrkTKNyJb0b0l7mdlzVZWbzoefbwD8Nh9Y6uzvL7kXuMzK3u+clDSwYStS5/Pvgd0ljXX5vSpzg7IvIw3hHW1mc/pS7qAEBlpjdbWROlN3qzn+MfAB3x9el3YXkuL4O2kUx+R+lD2MNErjCdLQx12bTW5vZXvctaR+gt8Db20F2cAo0sftTyS7/lbNJrcPsifW7L8emNSssmNrfBvwDLRlJJlS3t3JS7M6aZTGE8AlpCbsRjXnh3k4HNi6n2Wv4uFrgcObRW6DskeQlpX+FT4irUVki9RH9BXgkGaR26DsPCJrWP21zSA7tvK3Ac+AmUEy4bwA/BPvxKKjXfwjHm5PGl55jB/vQBqT34gttRHZnwPWaya5Jcj+H9JHeyX7+hCXfQwwspnkliD7s9QMamgm2bFVsw2WPoxngaNJw2L/W9IGZmaSRgCY2a89vIE0QiP3DTxH6vzstS21JNlnmtlzTSa3Udm/NLOl5v/sFpL9KzN7pcnkNir7LDNb2KSygwoYLArj72Z2OmnY3hLSSBAs+ZBpw8eBTwae8fP/tsYdzg2U7FYsc6vKbsUyD7TsoAIGhcKoeYHuBG4Etpa0DSTnY5LGSDoduBy4zMx+28WtmkZ2K5a5VWW3YpkHWnZQDf2mMCR9QdJHfL9TnzOWHIjdROoI29Wjx5Katn8ljYg5tVlkt2KZW1V2K5Z5oGUHA0BnHRtlbqSZqt8jNTcfokBHFqkT7CpSh9kJzSa7FcvcqrJbscwDLTu2gduqu3G7M77hwNt9/2zge91dQxpCeCPJR897m0l2K5a5VWW3YpkHWnZsA7+Vf8P0cnyP5LJiL4/LrjJeQ3LNvYUfdzo8EvhEM8luxTK3quxWLPNAy45t8GylLqDkNswfk1w3/BE4mORl8xfmbjIknUhyR/zuTq4fZmYrmkl2K5a5VWW3YpkHWnYwyChT+5BeqH/S7rt+b1KN5CM1aVYFbictjrIFNW4CmlF2K5a5VWW3YpkHWnZsg2srdZSUmS0irdNwsEf9g+Tb6S2SXu1plgJn+bm8Wl7Tym7FMreq7FYs80DLDgYXVfyoFwOTJY0zsxdJtY7FwDgApQV2Dge+bWavN7Mrh4DsVixzq8puxTIPtOxgkFCFwrgWmI/XRszsZtJwujX8/COkcddfGUKyW7HMrSq7Fcs80LKDQUJn/vYbwsyekHQJcKKkeaShdIuB5X7+vrJlDrTsVixzq8puxTIPtOxg8FDqKKkON5b2JfmOeQvwIzP7USWCBpHsVixzq8puxTIPtOxg4KlMYQBIWgUwq3M21h8MlOxWLHOrym7FMg+07GBgqVRhBEEQBEOHGPoWBEEQFCIURhAEQVCIUBhBEARBIUJhBEEQBIUIhREEQRAUIhRGEARBUIhQGMGgRdIoSUf6/oaSLhgEeRor6XpJt0h660DnJwj6k5iHEQxaJE0ALjOzLftZ7oiuJqVJmgrsaWaH9WeegmAwEAojGLRImgnsD9wLzAXeYGZbSjoYOABYE5hIWgluVeAgYAmwn5ktkLQZaeGfscDLwOFmdk8Xsn5F8o20DclF94/rrwVWBy4FRgKPATuZ2StllzsIBiulOx8MghL5ErClmU3OrY2ac1uSPu6rA/OAL5rZNpJOAj4KnAzMAD5uZnMl7QD8BNi9G3kbA28xs+WSrqy/1sx2l/R/wBQz+2S5RQ2CwU8ojKBZudrMXgBekLQQ+L3H3wFsJWktkoO889MKowCs1sM9z3dl0Zdrg2DIEwojaFaW1OyvqDleQXqvhwHPm9nkXtzzJQ/7cm0QDHlilFQwmHkBWLsvF/qyog9IOhBAia2rvjYIhjKhMIJBi5k9C/xD0p3A/+vDLT4MHCrpNmAOqQO9P64NgiFJjJIKgiAIChEtjCAIgqAQ0ekdtBSSvkJaYrSW883shIHITxA0E2GSCoIgCAoRJqkgCIKgEKEwgiAIgkKEwgiCIAgKEQojCIIgKEQojCAIgqAQ/x9tSvOxTrFloAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t1 = merged_ds.time.values[0]\n",
    "t2 = merged_ds.time.values[-1]\n",
    "\n",
    "hor_wind_speed.resampled.sortby('time_ref').sel(time_ref=slice(t1,t2)).plot(y='range', cmap='turbo')\n",
    "plt.show()\n",
    "\n",
    "ver_wind_speed.resampled.sortby('time_ref').sel(time_ref=slice(t1,t2)).plot(y='range', cmap='turbo')\n",
    "plt.show()\n",
    "\n",
    "hor_wind_dir.resampled.sortby('time_ref').sel(time_ref=slice(t1,t2)).plot(y='range', cmap='hsv')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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