{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wind retrievel for RPG radar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6-9RSNp7zde7"
   },
   "source": [
    "**lidarwind** is the package name used to retrieve wind profiles from the radar PPI scans. The package was initially developed to process wind lidar data (https://doi.org/10.21105/joss.04852). Because the physical principle of retrieving wind from lidar and radar observations is the same, lidar wind was extended to support the RPG radar data. Below, you will find an example of lidarwind applied to RPG PPI radar data.\n",
    "\n",
    "You can find more information about lidarwind at: https://lidarwind.readthedocs.io/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps:\n",
    "\n",
    " 1. Dependence installation\n",
    " 2. Importing the required packages\n",
    " 3. Defining useful functions\n",
    " 4. Getting sample data\n",
    " 5. Retrieving wind\n",
    " 6. Visualising the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Dependence installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ya1i4OsxsEC_"
   },
   "source": [
    "The cell below installs an additional package required by lidarwind."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20632,
     "status": "ok",
     "timestamp": 1718263461285,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "MqItOU3p29sg",
    "outputId": "d8998c19-8541-460f-90e5-8b2e9353e4bc",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: tqdm in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from gdown>=4.5.1->lidarwind) (4.66.2)\n",
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      "Requirement already satisfied: cycler>=0.10 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from matplotlib>=3.4.3->lidarwind) (0.12.1)\n",
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      "Requirement already satisfied: pillow>=8 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from matplotlib>=3.4.3->lidarwind) (10.3.0)\n",
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      "Requirement already satisfied: partd>=1.2.0 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from dask->xrft>=0.3->lidarwind) (1.4.1)\n",
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      "Requirement already satisfied: toolz>=0.10.0 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from dask->xrft>=0.3->lidarwind) (0.12.1)\n",
      "Requirement already satisfied: importlib-metadata>=4.13.0 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from dask->xrft>=0.3->lidarwind) (7.1.0)\n",
      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from requests[socks]->gdown>=4.5.1->lidarwind) (1.7.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from importlib-metadata>=4.13.0->dask->xrft>=0.3->lidarwind) (3.17.0)\n",
      "Requirement already satisfied: locket in /Users/jdiasneto/miniforge3/envs/rpg_post/lib/python3.10/site-packages (from partd>=1.2.0->dask->xrft>=0.3->lidarwind) (1.0.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install lidarwind xarray-datatree==0.0.11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ATTENTION:** \n",
    "\n",
    "After executing the previous cell, you may need to restart the kernel in case you get an error message. It is a temporary solution.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Importing required packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we import some basic packages usefull for processing the sample data. Later, lidarwind is also imported and its versions is checked; it should be greater or equal to 0.2.4. After, the RPG related modules are also imported. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 76
    },
    "executionInfo": {
     "elapsed": 5794,
     "status": "ok",
     "timestamp": 1718263486992,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "WBio5B-AvloE",
    "outputId": "204c3c1e-eea3-4d2b-91bd-e2d59d6552b4",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# genneral imports\n",
    "import pooch\n",
    "\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1706,
     "status": "ok",
     "timestamp": 1718263500227,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "i2WDSg8Sr5H1",
    "outputId": "bec445f3-bb0b-4f01-94cf-82985ac51aab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lidarwind version: 0.2.5.dev27+gd45b3ff.d20240702\n"
     ]
    }
   ],
   "source": [
    "# importing the data processing package\n",
    "import lidarwind\n",
    "\n",
    "# checking if the version of lidarwind\n",
    "# it should be equal or greater than 0.2.4\n",
    "print(f\"lidarwind version: {lidarwind.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1706,
     "status": "ok",
     "timestamp": 1718263500227,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "i2WDSg8Sr5H1",
    "outputId": "bec445f3-bb0b-4f01-94cf-82985ac51aab"
   },
   "outputs": [],
   "source": [
    "# importing the rpg radar related modules\n",
    "from lidarwind.preprocessing import rpg_radar\n",
    "from lidarwind.postprocessing import post_rpg_radar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Defining the processing function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is in charge of the main process. Here, the individual PPI files are open and the profiles are retrieved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "executionInfo": {
     "elapsed": 294,
     "status": "ok",
     "timestamp": 1718263584770,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "kN6o_1BqtpCc"
   },
   "outputs": [],
   "source": [
    "def process_one_file(file_name):\n",
    "    \"\"\"\n",
    "    Function to process a single radar file\n",
    "    \"\"\"\n",
    "\n",
    "    ds = xr.open_dataset(file_name)\n",
    "    ds = rpg_radar.rpg_slanted_radial_velocity_4_fft(ds)\n",
    "    tmp_wind = post_rpg_radar.get_horizontal_wind(ds)\n",
    "\n",
    "    return tmp_wind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Getting sample data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, we download a sample dataset needed for this example and create a list of all downloaded files. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_list = pooch.retrieve(\n",
    "    url=\"doi:10.5281/zenodo.7312960/rpg_sample_ppi.zip\",\n",
    "    known_hash=\"md5:952f7b50985cc8623933fbc18f72fd73\",\n",
    "    path=\"tmp_data\",\n",
    "    processor=pooch.Unzip(),\n",
    "        )\n",
    "\n",
    "file_list = sorted(file_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Retrieving wind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this step, the function defined in Step 3 to process the sample files is applied to the file_list defined in Step 4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 114519,
     "status": "ok",
     "timestamp": 1718263884825,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "6bgbXMvjt31a",
    "outputId": "06a23e39-684e-41cb-a4af-c48ea34c01c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47.2 s, sys: 1.17 s, total: 48.4 s\n",
      "Wall time: 56.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# running the function over the selected files\n",
    "wind_ds = xr.merge([process_one_file(f) for f in file_list])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Visualising the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, in this step, we first have a look at the wind dataset structure and later have a loot at some variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "executionInfo": {
     "elapsed": 681,
     "status": "ok",
     "timestamp": 1718263904959,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "GPOmtydDBulj",
    "outputId": "a140d206-7350-4f85-c037-a68487e40676"
   },
   "outputs": [
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       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
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       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\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",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\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",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\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",
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       "\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
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       "\n",
       ".xr-var-preview {\n",
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       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       ".xr-var-dims,\n",
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       ".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",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 631kB\n",
       "Dimensions:                    (range: 339, chirp: 3, mean_time: 42)\n",
       "Coordinates:\n",
       "  * range                      (range) float32 1kB 108.0 129.6 ... 1.157e+04\n",
       "  * chirp                      (chirp) int64 24B 1 2 3\n",
       "  * mean_time                  (mean_time) datetime64[ns] 336B 2022-05-17T10:...\n",
       "    elevation                  float32 4B 74.99\n",
       "    freq_azimuth               float64 8B 0.002778\n",
       "    azimuth_length             int64 8B 72\n",
       "Data variables:\n",
       "    horizontal_wind_direction  (mean_time, range) float64 114kB 82.05 ... nan\n",
       "    horizontal_wind_speed      (mean_time, range) float64 114kB 4.536 ... nan\n",
       "    meridional_wind            (mean_time, range) float64 114kB -0.6275 ... nan\n",
       "    zonal_wind                 (mean_time, range) float64 114kB 4.493 ... nan\n",
       "    start_scan                 (mean_time) datetime64[ns] 336B 2022-05-17T10:...\n",
       "    end_scan                   (mean_time) datetime64[ns] 336B 2022-05-17T10:...\n",
       "    zdr_max                    (mean_time, range) float32 57kB 5.533 ... nan\n",
       "    nan_percentual             (mean_time, range) float64 114kB 0.0 ... 100.0\n",
       "    chirp_start                (mean_time, chirp) float32 504B 111.8 ... 2.03...\n",
       "    chirp_end                  (mean_time, chirp) float32 504B 581.3 ... 1.19...\n",
       "    chirp_azimuth_bias         (mean_time, chirp) float64 1kB 0.0 0.0 ... 0.0\n",
       "    azm_seq                    (mean_time) float64 336B 1.0 -1.0 ... 1.0 -1.0</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-9ea2e11b-d150-4a16-9a0b-7e5d873b8517' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ea2e11b-d150-4a16-9a0b-7e5d873b8517' 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'>range</span>: 339</li><li><span class='xr-has-index'>chirp</span>: 3</li><li><span class='xr-has-index'>mean_time</span>: 42</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-69ca4512-00f6-49a0-939a-522c4c2a3051' class='xr-section-summary-in' type='checkbox'  checked><label for='section-69ca4512-00f6-49a0-939a-522c4c2a3051' class='xr-section-summary' >Coordinates: <span>(6)</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'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>108.0 129.6 ... 1.153e+04 1.157e+04</div><input id='attrs-ab2fbd94-bf1c-4940-b3f3-fcae7dbbc1f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab2fbd94-bf1c-4940-b3f3-fcae7dbbc1f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41513430-e2da-4c02-b3a6-495d31815258' class='xr-var-data-in' type='checkbox'><label for='data-41513430-e2da-4c02-b3a6-495d31815258' 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>units :</span></dt><dd>m</dd><dt><span>name :</span></dt><dd>range</dd><dt><span>comment :</span></dt><dd>height estimated from the original range and elevation</dd></dl></div><div class='xr-var-data'><pre>array([  107.98101,   129.57721,   151.1734 , ..., 11493.517  , 11529.889  ,\n",
       "       11566.26   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chirp</span></div><div class='xr-var-dims'>(chirp)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-f25902cb-8197-442d-8a4a-b37ddc597300' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f25902cb-8197-442d-8a4a-b37ddc597300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee3187d6-a3ea-41c7-8d18-b6a0b1a533c3' class='xr-var-data-in' type='checkbox'><label for='data-ee3187d6-a3ea-41c7-8d18-b6a0b1a533c3' 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([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mean_time</span></div><div class='xr-var-dims'>(mean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-05-17T10:01:35.830779220 .....</div><input id='attrs-779522a0-316c-455e-acb0-b74f100be520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-779522a0-316c-455e-acb0-b74f100be520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d60442aa-6c7f-438f-8576-3ea107df9782' class='xr-var-data-in' type='checkbox'><label for='data-d60442aa-6c7f-438f-8576-3ea107df9782' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>mean time (seconds) from each PPI scan</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-05-17T10:01:35.830779220&#x27;, &#x27;2022-05-17T10:03:00.880759493&#x27;,\n",
       "       &#x27;2022-05-17T10:04:26.918101265&#x27;, &#x27;2022-05-17T10:05:53.443717948&#x27;,\n",
       "       &#x27;2022-05-17T10:07:19.483846153&#x27;, &#x27;2022-05-17T10:08:51.280384615&#x27;,\n",
       "       &#x27;2022-05-17T10:10:18.800129870&#x27;, &#x27;2022-05-17T10:11:44.335217949&#x27;,\n",
       "       &#x27;2022-05-17T10:13:09.879831166&#x27;, &#x27;2022-05-17T10:14:35.415999993&#x27;,\n",
       "       &#x27;2022-05-17T10:16:00.958662340&#x27;, &#x27;2022-05-17T10:17:26.000480516&#x27;,\n",
       "       &#x27;2022-05-17T10:18:51.040480510&#x27;, &#x27;2022-05-17T10:20:16.576000003&#x27;,\n",
       "       &#x27;2022-05-17T10:21:41.613307693&#x27;, &#x27;2022-05-17T10:23:07.645871786&#x27;,\n",
       "       &#x27;2022-05-17T10:24:33.182886075&#x27;, &#x27;2022-05-17T10:26:05.461358974&#x27;,\n",
       "       &#x27;2022-05-17T10:27:31.496230769&#x27;, &#x27;2022-05-17T10:28:57.029662337&#x27;,\n",
       "       &#x27;2022-05-17T10:30:22.080831168&#x27;, &#x27;2022-05-17T10:31:47.616230769&#x27;,\n",
       "       &#x27;2022-05-17T10:33:13.646615384&#x27;, &#x27;2022-05-17T10:34:39.686358974&#x27;,\n",
       "       &#x27;2022-05-17T10:36:05.727000004&#x27;, &#x27;2022-05-17T10:37:31.261481008&#x27;,\n",
       "       &#x27;2022-05-17T10:38:57.786205131&#x27;, &#x27;2022-05-17T10:40:23.321831176&#x27;,\n",
       "       &#x27;2022-05-17T10:41:47.865307693&#x27;, &#x27;2022-05-17T10:43:19.652743591&#x27;,\n",
       "       &#x27;2022-05-17T10:44:45.691717948&#x27;, &#x27;2022-05-17T10:46:11.727615384&#x27;,\n",
       "       &#x27;2022-05-17T10:47:37.768358974&#x27;, &#x27;2022-05-17T10:49:02.806307692&#x27;,\n",
       "       &#x27;2022-05-17T10:50:28.838871794&#x27;, &#x27;2022-05-17T10:51:53.877717948&#x27;,\n",
       "       &#x27;2022-05-17T10:53:19.907076923&#x27;, &#x27;2022-05-17T10:54:45.441721518&#x27;,\n",
       "       &#x27;2022-05-17T10:56:11.471662337&#x27;, &#x27;2022-05-17T10:57:36.511662337&#x27;,\n",
       "       &#x27;2022-05-17T10:59:02.047807698&#x27;, &#x27;2022-05-17T11:00:33.718896101&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>74.99</div><input id='attrs-50e2ae32-3d90-4b05-91f0-9f24222a803e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50e2ae32-3d90-4b05-91f0-9f24222a803e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ffa5892-a490-4513-9670-b5fdde87c10a' class='xr-var-data-in' type='checkbox'><label for='data-3ffa5892-a490-4513-9670-b5fdde87c10a' 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>Name :</span></dt><dd>Elevation</dd><dt><span>Units :</span></dt><dd>deg</dd></dl></div><div class='xr-var-data'><pre>array(74.99, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>freq_azimuth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.002778</div><input id='attrs-ea7f8403-89c9-46bc-a8ee-07c9e90effc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea7f8403-89c9-46bc-a8ee-07c9e90effc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78f57e6c-c156-48ba-9ac0-5def934af583' class='xr-var-data-in' type='checkbox'><label for='data-78f57e6c-c156-48ba-9ac0-5def934af583' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spacing :</span></dt><dd>0.002777777777777782</dd><dt><span>direct_lag :</span></dt><dd>180</dd></dl></div><div class='xr-var-data'><pre>array(0.00277778)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azimuth_length</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>72</div><input id='attrs-7732e41f-e824-4a5d-9803-dddc2b0fc68b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7732e41f-e824-4a5d-9803-dddc2b0fc68b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-897fb962-8506-4a8f-ad8e-49b389c3b6c6' class='xr-var-data-in' type='checkbox'><label for='data-897fb962-8506-4a8f-ad8e-49b389c3b6c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>size of the azimuth coordinate</dd></dl></div><div class='xr-var-data'><pre>array(72)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-520707e1-fcf8-43b8-8de0-bdc5e166f629' class='xr-section-summary-in' type='checkbox'  checked><label for='section-520707e1-fcf8-43b8-8de0-bdc5e166f629' 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>horizontal_wind_direction</span></div><div class='xr-var-dims'>(mean_time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>82.05 79.69 87.19 ... nan nan nan</div><input id='attrs-fb4dc774-5ed2-4bf4-8576-f8c5de58923c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb4dc774-5ed2-4bf4-8576-f8c5de58923c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9e953c2-9371-4f08-8f86-3d7c0a36d9fd' class='xr-var-data-in' type='checkbox'><label for='data-a9e953c2-9371-4f08-8f86-3d7c0a36d9fd' 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>name :</span></dt><dd>wind direction</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>comments :</span></dt><dd>horizontal wind direction retrived using the FFT method with respect to true north</dd><dt><span>info :</span></dt><dd>0=wind coming from the north, 90=east, 180=south, 270=west</dd></dl></div><div class='xr-var-data'><pre>array([[ 82.04879937,  79.68815761,  87.18918881, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [101.90865544, 102.03428783, 108.98835195, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [105.39191908, 103.52910987,  99.48177222, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       ...,\n",
       "       [ 93.49667527,  97.88265051,  86.54973375, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [122.07450652, 124.91572877, 122.07674197, ...,          nan,\n",
       "                 nan,          nan],\n",
       "       [136.70125445, 144.44080913, 152.55159704, ...,          nan,\n",
       "                 nan,          nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>horizontal_wind_speed</span></div><div class='xr-var-dims'>(mean_time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.536 4.254 4.119 ... nan nan nan</div><input id='attrs-ef4907ed-02ac-40fd-b08a-6956b7b63b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef4907ed-02ac-40fd-b08a-6956b7b63b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5dc1a71-cc33-4692-bb93-c5a22903c7f6' class='xr-var-data-in' type='checkbox'><label for='data-f5dc1a71-cc33-4692-bb93-c5a22903c7f6' 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>name :</span></dt><dd>wind speed</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comments :</span></dt><dd>horizontal wind speed retrived using the FFT method</dd></dl></div><div class='xr-var-data'><pre>array([[4.53636386, 4.25375274, 4.11942253, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [6.68059252, 6.76361453, 6.20638932, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [6.3564044 , 7.33474957, 6.71616601, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [4.72829681, 4.71016782, 5.8194779 , ...,        nan,        nan,\n",
       "               nan],\n",
       "       [4.71299183, 4.6376406 , 5.00992205, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [7.77555032, 8.60158547, 9.39089713, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>meridional_wind</span></div><div class='xr-var-dims'>(mean_time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.6275 -0.7614 -0.202 ... nan nan</div><input id='attrs-cb556397-e96d-4aa5-8630-978772e3e8dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb556397-e96d-4aa5-8630-978772e3e8dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d99c40f-551b-4061-9e1e-a01b63b0a378' class='xr-var-data-in' type='checkbox'><label for='data-5d99c40f-551b-4061-9e1e-a01b63b0a378' 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>name :</span></dt><dd>meridional wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comments :</span></dt><dd>meridional wind retrieved using the FFT method</dd></dl></div><div class='xr-var-data'><pre>array([[-0.62751353, -0.76144543, -0.20200921, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [ 1.37855364,  1.41019342,  2.01940968, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [ 1.68711775,  1.71588661,  1.10637972, ...,         nan,\n",
       "                nan,         nan],\n",
       "       ...,\n",
       "       [ 0.28838176,  0.64597412, -0.35022851, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [ 2.50270048,  2.65445098,  2.66054247, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [ 5.65895045,  6.9975203 ,  8.33372905, ...,         nan,\n",
       "                nan,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zonal_wind</span></div><div class='xr-var-dims'>(mean_time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.493 4.185 4.114 ... nan nan nan</div><input id='attrs-95f3eb7b-db71-4abe-8989-90ee180eb0ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95f3eb7b-db71-4abe-8989-90ee180eb0ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d445ce78-e1cc-4e09-8e2f-5d4a6741816d' class='xr-var-data-in' type='checkbox'><label for='data-d445ce78-e1cc-4e09-8e2f-5d4a6741816d' 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>name :</span></dt><dd>zonal wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comments :</span></dt><dd>zonal wind retrieved using the FFT method</dd></dl></div><div class='xr-var-data'><pre>array([[4.49275236, 4.18504638, 4.11446646, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [6.53681163, 6.6149706 , 5.86866705, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [6.12841828, 7.131219  , 6.62441014, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [4.71949432, 4.66566162, 5.80892959, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [3.99359266, 3.80284108, 4.24509513, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [5.33249121, 5.00219774, 4.32873064, ...,        nan,        nan,\n",
       "               nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>start_scan</span></div><div class='xr-var-dims'>(mean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-05-17T10:00:58.190000 ... 2...</div><input id='attrs-da02ad06-d2ba-48d4-ba68-0341f99f4dd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da02ad06-d2ba-48d4-ba68-0341f99f4dd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa8f2c6-3bfa-4e3a-bbb2-f4b0d3f120ef' class='xr-var-data-in' type='checkbox'><label for='data-caa8f2c6-3bfa-4e3a-bbb2-f4b0d3f120ef' 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>Name :</span></dt><dd>Time</dd><dt><span>Units :</span></dt><dd>Number of seconds since 1/1/2001 00:00:00 [UTC]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-05-17T10:00:58.190000000&#x27;, &#x27;2022-05-17T10:02:22.250000000&#x27;,\n",
       "       &#x27;2022-05-17T10:03:48.280000000&#x27;, &#x27;2022-05-17T10:05:15.300000000&#x27;,\n",
       "       &#x27;2022-05-17T10:06:41.340000000&#x27;, &#x27;2022-05-17T10:08:13.130000000&#x27;,\n",
       "       &#x27;2022-05-17T10:09:41.160000000&#x27;, &#x27;2022-05-17T10:11:06.200000000&#x27;,\n",
       "       &#x27;2022-05-17T10:12:32.241000064&#x27;, &#x27;2022-05-17T10:13:57.281000064&#x27;,\n",
       "       &#x27;2022-05-17T10:15:23.310999936&#x27;, &#x27;2022-05-17T10:16:48.360999936&#x27;,\n",
       "       &#x27;2022-05-17T10:18:13.391000064&#x27;, &#x27;2022-05-17T10:19:38.440999936&#x27;,\n",
       "       &#x27;2022-05-17T10:21:03.470999936&#x27;, &#x27;2022-05-17T10:22:29.511000064&#x27;,\n",
       "       &#x27;2022-05-17T10:23:54.552000000&#x27;, &#x27;2022-05-17T10:25:27.312000000&#x27;,\n",
       "       &#x27;2022-05-17T10:26:53.362000000&#x27;, &#x27;2022-05-17T10:28:19.382000000&#x27;,\n",
       "       &#x27;2022-05-17T10:29:44.442000000&#x27;, &#x27;2022-05-17T10:31:09.482000000&#x27;,\n",
       "       &#x27;2022-05-17T10:32:35.512000000&#x27;, &#x27;2022-05-17T10:34:01.552000000&#x27;,\n",
       "       &#x27;2022-05-17T10:35:27.592000000&#x27;, &#x27;2022-05-17T10:36:52.623000064&#x27;,\n",
       "       &#x27;2022-05-17T10:38:19.643000064&#x27;, &#x27;2022-05-17T10:39:45.682999936&#x27;,\n",
       "       &#x27;2022-05-17T10:41:09.723000064&#x27;, &#x27;2022-05-17T10:42:41.503000064&#x27;,\n",
       "       &#x27;2022-05-17T10:44:07.552999936&#x27;, &#x27;2022-05-17T10:45:33.593000064&#x27;,\n",
       "       &#x27;2022-05-17T10:46:59.633000064&#x27;, &#x27;2022-05-17T10:48:24.664000000&#x27;,\n",
       "       &#x27;2022-05-17T10:49:50.704000000&#x27;, &#x27;2022-05-17T10:51:15.744000000&#x27;,\n",
       "       &#x27;2022-05-17T10:52:41.764000000&#x27;, &#x27;2022-05-17T10:54:06.804000000&#x27;,\n",
       "       &#x27;2022-05-17T10:55:33.824000000&#x27;, &#x27;2022-05-17T10:56:58.864000000&#x27;,\n",
       "       &#x27;2022-05-17T10:58:23.904000000&#x27;, &#x27;2022-05-17T10:59:55.695000064&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>end_scan</span></div><div class='xr-var-dims'>(mean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-05-17T10:02:13.480000 ... 2...</div><input id='attrs-0af10880-7863-4326-b759-468639573ba4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0af10880-7863-4326-b759-468639573ba4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-459f5aa8-f40d-4ef3-a0e1-e1894c474ea8' class='xr-var-data-in' type='checkbox'><label for='data-459f5aa8-f40d-4ef3-a0e1-e1894c474ea8' 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>Name :</span></dt><dd>Time</dd><dt><span>Units :</span></dt><dd>Number of seconds since 1/1/2001 00:00:00 [UTC]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-05-17T10:02:13.480000000&#x27;, &#x27;2022-05-17T10:03:39.520000000&#x27;,\n",
       "       &#x27;2022-05-17T10:05:05.560000000&#x27;, &#x27;2022-05-17T10:06:31.590000000&#x27;,\n",
       "       &#x27;2022-05-17T10:07:57.630000000&#x27;, &#x27;2022-05-17T10:09:29.430000000&#x27;,\n",
       "       &#x27;2022-05-17T10:10:56.450000000&#x27;, &#x27;2022-05-17T10:12:22.480999936&#x27;,\n",
       "       &#x27;2022-05-17T10:13:47.531000064&#x27;, &#x27;2022-05-17T10:15:13.570999936&#x27;,\n",
       "       &#x27;2022-05-17T10:16:38.610999936&#x27;, &#x27;2022-05-17T10:18:03.651000064&#x27;,\n",
       "       &#x27;2022-05-17T10:19:28.690999936&#x27;, &#x27;2022-05-17T10:20:54.720999936&#x27;,\n",
       "       &#x27;2022-05-17T10:22:19.761000064&#x27;, &#x27;2022-05-17T10:23:45.791000064&#x27;,\n",
       "       &#x27;2022-05-17T10:25:11.822000000&#x27;, &#x27;2022-05-17T10:26:43.602000000&#x27;,\n",
       "       &#x27;2022-05-17T10:28:09.642000000&#x27;, &#x27;2022-05-17T10:29:34.682000000&#x27;,\n",
       "       &#x27;2022-05-17T10:30:59.732000000&#x27;, &#x27;2022-05-17T10:32:25.762000000&#x27;,\n",
       "       &#x27;2022-05-17T10:33:51.792000000&#x27;, &#x27;2022-05-17T10:35:17.832000000&#x27;,\n",
       "       &#x27;2022-05-17T10:36:43.873000064&#x27;, &#x27;2022-05-17T10:38:09.902999936&#x27;,\n",
       "       &#x27;2022-05-17T10:39:35.932999936&#x27;, &#x27;2022-05-17T10:41:00.973000064&#x27;,\n",
       "       &#x27;2022-05-17T10:42:26.013000064&#x27;, &#x27;2022-05-17T10:43:57.792999936&#x27;,\n",
       "       &#x27;2022-05-17T10:45:23.832999936&#x27;, &#x27;2022-05-17T10:46:49.873000064&#x27;,\n",
       "       &#x27;2022-05-17T10:48:15.914000000&#x27;, &#x27;2022-05-17T10:49:40.954000000&#x27;,\n",
       "       &#x27;2022-05-17T10:51:06.984000000&#x27;, &#x27;2022-05-17T10:52:32.024000000&#x27;,\n",
       "       &#x27;2022-05-17T10:53:58.054000000&#x27;, &#x27;2022-05-17T10:55:24.084000000&#x27;,\n",
       "       &#x27;2022-05-17T10:56:49.124000000&#x27;, &#x27;2022-05-17T10:58:14.164000000&#x27;,\n",
       "       &#x27;2022-05-17T10:59:40.195000064&#x27;, &#x27;2022-05-17T11:01:11.400000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zdr_max</span></div><div class='xr-var-dims'>(mean_time, range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.533 6.141 6.962 ... nan nan nan</div><input id='attrs-8a1a0a1c-79df-488d-b5ce-665e279aa81f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a1a0a1c-79df-488d-b5ce-665e279aa81f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7ece13c-0070-4c9c-aaea-7fb20dd22846' class='xr-var-data-in' type='checkbox'><label for='data-c7ece13c-0070-4c9c-aaea-7fb20dd22846' 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>Name :</span></dt><dd>Maximum ZDR</dd><dt><span>units :</span></dt><dd>dB</dd><dt><span>Comment :</span></dt><dd>Maximum ZDR per complete PPI scan</dd></dl></div><div class='xr-var-data'><pre>array([[5.5331583, 6.141076 , 6.962328 , ...,       nan,       nan,\n",
       "              nan],\n",
       "       [8.961918 , 7.182236 , 5.239216 , ...,       nan,       nan,\n",
       "              nan],\n",
       "       [7.1929893, 4.9451785, 6.8622394, ...,       nan,       nan,\n",
       "              nan],\n",
       "       ...,\n",
       "       [6.1269   , 7.221713 , 6.077519 , ...,       nan,       nan,\n",
       "              nan],\n",
       "       [6.898547 , 5.898196 , 5.7665577, ...,       nan,       nan,\n",
       "              nan],\n",
       "       [7.5182233, 7.522902 , 7.0381165, ...,       nan,       nan,\n",
       "              nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nan_percentual</span></div><div class='xr-var-dims'>(mean_time, range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 100.0 100.0 100.0</div><input id='attrs-627f0305-6b99-4e90-bd7e-5225a011e72a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-627f0305-6b99-4e90-bd7e-5225a011e72a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bfef063-30ae-4d94-b275-e43bd208074e' class='xr-var-data-in' type='checkbox'><label for='data-1bfef063-30ae-4d94-b275-e43bd208074e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Percentual of NaN per single PPI scan</dd></dl></div><div class='xr-var-data'><pre>array([[  0.        ,   0.        ,   0.        , ..., 100.        ,\n",
       "        100.        , 100.        ],\n",
       "       [  3.79746835,   1.26582278,   1.26582278, ..., 100.        ,\n",
       "        100.        , 100.        ],\n",
       "       [  0.        ,   0.        ,   1.26582278, ..., 100.        ,\n",
       "        100.        , 100.        ],\n",
       "       ...,\n",
       "       [  0.        ,   0.        ,   0.        , ..., 100.        ,\n",
       "        100.        , 100.        ],\n",
       "       [  0.        ,   0.        ,   0.        , ..., 100.        ,\n",
       "        100.        , 100.        ],\n",
       "       [  2.5974026 ,   0.        ,   1.2987013 , ..., 100.        ,\n",
       "        100.        , 100.        ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chirp_start</span></div><div class='xr-var-dims'>(mean_time, chirp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>111.8 621.0 ... 621.0 2.033e+03</div><input id='attrs-8696225a-9878-4211-a22e-0c19aac9372e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8696225a-9878-4211-a22e-0c19aac9372e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef75b80-37d7-4c52-909a-444f74f76c85' class='xr-var-data-in' type='checkbox'><label for='data-1ef75b80-37d7-4c52-909a-444f74f76c85' 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>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>starting height from each chirp sequence</dd></dl></div><div class='xr-var-data'><pre>array([[ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "...\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ],\n",
       "       [ 111.795395,  620.987   , 2033.4624  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chirp_end</span></div><div class='xr-var-dims'>(mean_time, chirp)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>581.3 1.998e+03 ... 1.197e+04</div><input id='attrs-a4e9b208-abe6-47c8-b496-15ac54044a2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e9b208-abe6-47c8-b496-15ac54044a2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5a2d1a2-395b-4fc5-93db-29c033cdc700' class='xr-var-data-in' type='checkbox'><label for='data-a5a2d1a2-395b-4fc5-93db-29c033cdc700' 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>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>ending height from each chirp sequence</dd></dl></div><div class='xr-var-data'><pre>array([[  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "...\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ],\n",
       "       [  581.33606,  1997.9583 , 11974.834  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>chirp_azimuth_bias</span></div><div class='xr-var-dims'>(mean_time, chirp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-af2d1cfb-fb5b-48cd-8c5c-e8fc261f3d2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af2d1cfb-fb5b-48cd-8c5c-e8fc261f3d2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51cc1bbb-fc05-4a9c-b1a6-830628a39aa1' class='xr-var-data-in' type='checkbox'><label for='data-51cc1bbb-fc05-4a9c-b1a6-830628a39aa1' 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>units :</span></dt><dd>deg</dd><dt><span>comment :</span></dt><dd>wind direction bias correcting factor</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "...\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azm_seq</span></div><div class='xr-var-dims'>(mean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 -1.0 1.0 -1.0 ... -1.0 1.0 -1.0</div><input id='attrs-70c4424a-eb9b-4b4e-b77b-c2fb92cbc04d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70c4424a-eb9b-4b4e-b77b-c2fb92cbc04d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa8169a7-823d-4964-9b76-9a77dbeb2b94' class='xr-var-data-in' type='checkbox'><label for='data-fa8169a7-823d-4964-9b76-9a77dbeb2b94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd> 1: azimuth increasing; -1: azimuth decreasing</dd></dl></div><div class='xr-var-data'><pre>array([ 1., -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,  1.,\n",
       "       -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,\n",
       "        1., -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,  1., -1.,  1.,\n",
       "       -1.,  1., -1.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5ccc49d-0ef5-458e-9393-a60724ab7ee3' class='xr-section-summary-in' type='checkbox'  ><label for='section-d5ccc49d-0ef5-458e-9393-a60724ab7ee3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-13b59726-63ef-4cb4-af2d-c8cfd081a0b6' class='xr-index-data-in' type='checkbox'/><label for='index-13b59726-63ef-4cb4-af2d-c8cfd081a0b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([107.98101043701172, 129.57720947265625, 151.17340087890625,\n",
       "        172.7696075439453, 194.36581420898438, 215.96202087402344,\n",
       "        237.5582275390625,  259.1544189453125,  280.7506103515625,\n",
       "        302.3468017578125,\n",
       "       ...\n",
       "         11238.9130859375,   11275.2861328125,   11311.6572265625,\n",
       "         11348.0283203125,   11384.4013671875,   11420.7724609375,\n",
       "           11457.14453125,   11493.5166015625,    11529.888671875,\n",
       "          11566.259765625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;range&#x27;, length=339))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>chirp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b33aeb2c-0be4-431b-86e0-96a3a01ea01b' class='xr-index-data-in' type='checkbox'/><label for='index-b33aeb2c-0be4-431b-86e0-96a3a01ea01b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chirp&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c937c043-35d4-4267-b572-1e4a665be3d9' class='xr-index-data-in' type='checkbox'/><label for='index-c937c043-35d4-4267-b572-1e4a665be3d9' 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;2022-05-17 10:01:35.830779220&#x27;,\n",
       "               &#x27;2022-05-17 10:03:00.880759493&#x27;,\n",
       "               &#x27;2022-05-17 10:04:26.918101265&#x27;,\n",
       "               &#x27;2022-05-17 10:05:53.443717948&#x27;,\n",
       "               &#x27;2022-05-17 10:07:19.483846153&#x27;,\n",
       "               &#x27;2022-05-17 10:08:51.280384615&#x27;,\n",
       "               &#x27;2022-05-17 10:10:18.800129870&#x27;,\n",
       "               &#x27;2022-05-17 10:11:44.335217949&#x27;,\n",
       "               &#x27;2022-05-17 10:13:09.879831166&#x27;,\n",
       "               &#x27;2022-05-17 10:14:35.415999993&#x27;,\n",
       "               &#x27;2022-05-17 10:16:00.958662340&#x27;,\n",
       "               &#x27;2022-05-17 10:17:26.000480516&#x27;,\n",
       "               &#x27;2022-05-17 10:18:51.040480510&#x27;,\n",
       "               &#x27;2022-05-17 10:20:16.576000003&#x27;,\n",
       "               &#x27;2022-05-17 10:21:41.613307693&#x27;,\n",
       "               &#x27;2022-05-17 10:23:07.645871786&#x27;,\n",
       "               &#x27;2022-05-17 10:24:33.182886075&#x27;,\n",
       "               &#x27;2022-05-17 10:26:05.461358974&#x27;,\n",
       "               &#x27;2022-05-17 10:27:31.496230769&#x27;,\n",
       "               &#x27;2022-05-17 10:28:57.029662337&#x27;,\n",
       "               &#x27;2022-05-17 10:30:22.080831168&#x27;,\n",
       "               &#x27;2022-05-17 10:31:47.616230769&#x27;,\n",
       "               &#x27;2022-05-17 10:33:13.646615384&#x27;,\n",
       "               &#x27;2022-05-17 10:34:39.686358974&#x27;,\n",
       "               &#x27;2022-05-17 10:36:05.727000004&#x27;,\n",
       "               &#x27;2022-05-17 10:37:31.261481008&#x27;,\n",
       "               &#x27;2022-05-17 10:38:57.786205131&#x27;,\n",
       "               &#x27;2022-05-17 10:40:23.321831176&#x27;,\n",
       "               &#x27;2022-05-17 10:41:47.865307693&#x27;,\n",
       "               &#x27;2022-05-17 10:43:19.652743591&#x27;,\n",
       "               &#x27;2022-05-17 10:44:45.691717948&#x27;,\n",
       "               &#x27;2022-05-17 10:46:11.727615384&#x27;,\n",
       "               &#x27;2022-05-17 10:47:37.768358974&#x27;,\n",
       "               &#x27;2022-05-17 10:49:02.806307692&#x27;,\n",
       "               &#x27;2022-05-17 10:50:28.838871794&#x27;,\n",
       "               &#x27;2022-05-17 10:51:53.877717948&#x27;,\n",
       "               &#x27;2022-05-17 10:53:19.907076923&#x27;,\n",
       "               &#x27;2022-05-17 10:54:45.441721518&#x27;,\n",
       "               &#x27;2022-05-17 10:56:11.471662337&#x27;,\n",
       "               &#x27;2022-05-17 10:57:36.511662337&#x27;,\n",
       "               &#x27;2022-05-17 10:59:02.047807698&#x27;,\n",
       "               &#x27;2022-05-17 11:00:33.718896101&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;mean_time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-501930ea-eac7-4ccc-8fc0-4c40d75c5569' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-501930ea-eac7-4ccc-8fc0-4c40d75c5569' 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> Size: 631kB\n",
       "Dimensions:                    (range: 339, chirp: 3, mean_time: 42)\n",
       "Coordinates:\n",
       "  * range                      (range) float32 1kB 108.0 129.6 ... 1.157e+04\n",
       "  * chirp                      (chirp) int64 24B 1 2 3\n",
       "  * mean_time                  (mean_time) datetime64[ns] 336B 2022-05-17T10:...\n",
       "    elevation                  float32 4B 74.99\n",
       "    freq_azimuth               float64 8B 0.002778\n",
       "    azimuth_length             int64 8B 72\n",
       "Data variables:\n",
       "    horizontal_wind_direction  (mean_time, range) float64 114kB 82.05 ... nan\n",
       "    horizontal_wind_speed      (mean_time, range) float64 114kB 4.536 ... nan\n",
       "    meridional_wind            (mean_time, range) float64 114kB -0.6275 ... nan\n",
       "    zonal_wind                 (mean_time, range) float64 114kB 4.493 ... nan\n",
       "    start_scan                 (mean_time) datetime64[ns] 336B 2022-05-17T10:...\n",
       "    end_scan                   (mean_time) datetime64[ns] 336B 2022-05-17T10:...\n",
       "    zdr_max                    (mean_time, range) float32 57kB 5.533 ... nan\n",
       "    nan_percentual             (mean_time, range) float64 114kB 0.0 ... 100.0\n",
       "    chirp_start                (mean_time, chirp) float32 504B 111.8 ... 2.03...\n",
       "    chirp_end                  (mean_time, chirp) float32 504B 581.3 ... 1.19...\n",
       "    chirp_azimuth_bias         (mean_time, chirp) float64 1kB 0.0 0.0 ... 0.0\n",
       "    azm_seq                    (mean_time) float64 336B 1.0 -1.0 ... 1.0 -1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the wind dataset\n",
    "wind_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 2141,
     "status": "ok",
     "timestamp": 1718263972570,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "VwmcnBd1t6wc",
    "outputId": "f434f6fc-4377-4d58-c4b9-ca5c0cc4ce61"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9e9pbLItTkCGyqO+4r68vGjdujFq1aqF3795IS0vDG2+8gXvuuQdbtmwpMhHD9Vx5TGdkZCArKwvt27eHruvYs2cPateuXajMiy++iJUrV+Krr75Cs2bNnN4mEVVcDIao0jp//jxSUlKwYMECh8xhV0pKSipy+V9//QVd1zFlyhRMmTKl2LI1a9bEDTfcgCZNmmDZsmX2i7tly5ahevXquP322+3rZ2VlYfbs2Vi4cCHOnDnjMDYiNTVV9BpPnDiBhg0bFsrgVdCt7sSJEw7Lr74IKLg4vHTp0jW34+3tjW7duon28XqWLFmCkJAQ3HnnnQ7L8/PzMWbMGDz88MMO2adKytvbG6tWrcL999+Pfv36Abh8cThnzhy8/PLLDt3WHn/8cZw6dQqvvvqqfbxV27ZtMWHChELrukrdunUd/j569Ch0XUfDhg2LXL+g+1XBZ3r1ep6enqhXr57T+3Hw4EG88MILWL9+PSwWi8NzRR2X77//PhYuXIj333+/xF0Hrz7uzGYzABTq0mc2m697LEq5w7Hv4+OD3NzcIp/Lzs6+bjdQHx8fe/fPq8sWPH/lv8WtW9x2EhIS0KtXL5jNZvv4pOIsX74c2dnZ1+0ip+s6li5dihYtWqBVq1aFnr/vvvvg4eGB77//3r7sX//6Fxo2bIjnn3/eYWxfSZ08eRJTp07Fd999V+jzLeqYXr16NWbMmIHJkyfbzxVEVHUwGKJKy2azAbicXrm4O5xF/ThfWXb8+PH2MT9Xu/KO5QMPPICXX34ZFy5cQEBAAL777jsMGDAAHh7//xUbPXo0Fi5ciLFjxyI2NtY+EWf//v3t2yttxV3cXBmYFcVqteL8+fMl2kZISEixd7+vdvLkSWzZsgUjRowoNNbik08+wZEjR/D+++8XmnMpLS0Nx48fR1hYmH0MUFGaN2+OAwcO4NChQ7h06RKaNWsGHx8fPP300+jcubPDui+//DLGjx+PgwcPwmw2o2XLlnjuuecAAI0aNSrR63HG1RekNpsNmqbhxx9/LPJzKo2ALCUlBZ07d0ZgYCBmzpyJ+vXrw9vbG7t378bEiRMLHZc7duzAU089hUcffdRhEP/1FHfcFbX8ymOxqCQIABySO6juQ1ke+zVq1IDVakVSUpLDuJTc3FxcvHgRkZGR16y/Ro0aOHPmTKHlBS2YBeVr1KjhsPzqdYvaTmpqKu68806kpKRgy5Yt192XJUuWwGw2O7Q8F2Xbtm04ceIEZs+eXei5f/75B6tXry50syokJAQdOnQoNB9RSVitVtxxxx1ITk7GxIkT0aRJE/j5+eHMmTMYMmRIoWP62LFjGDhwIO644w689NJLTm+PiCo+BkNUaYWGhiIgIABWq9XpO7sFd9g9PT1LVPaBBx7AjBkzsHz5coSHh8NisRTKYvbVV19h8ODBeO211+zLsrOzkZKS4rBecReARYmOjsbvv/8Om83m0Dp0+PBh+/OucOrUqUItGcXZsGFDibMvFZdhCrgcKOXl5RXZjfGTTz7BJ598gm+++QZ9+/a95jY0TXMYCP7DDz/AZrMV+blendlt7dq1qFWrFpo0aVKi16Oifv360HUddevWvWbwVfCZHj161KHlMS8vD8eOHXOqm+LGjRtx8eJFfP311w4D248dO1Zo3fPnz+Pee+9F69at7ZnbSltB683V35GrWzwB5743znDlsd+6dWsAl7NQ9uzZ0758586dsNls9uevVX7Dhg2wWCwOSRQKupEWlG/RogU8PDywc+dO3H///fb1cnNzsXfvXodlwOXzUJ8+ffDnn39i7dq11+0mdu7cOWzYsAFDhgwpsivelZYsWQJN0/Dggw8Wei4xMRFA0cFtXl5ekZMjX8/+/fvx559/YvHixQ6JOIrq6piVlWVPdvLf//633ObIIqLyxWCIKi2j0Yh+/fph6dKlOHDggMPkgcDli7vQ0NAiy4aFhaFLly54//33MXr0aPud1uLKNm3aFC1btsSyZcsQHh6OGjVqFMqaZDQaC92FfueddwpdCPj5+QEofAFYlJ49e+Lnn3/GsmXL7OOG8vPz8c4778Df379Q64dUaY0ZWrp0KWrXru0QgBTo379/kReHd999N3r27Inhw4cjJibGvvzw4cPw9fUtcjxAgaysLEyZMgU1atRwGGdVlGXLluG3337Df/7znzK5SLrnnnswefJkzJgxA5999pnDxb2u60hOTka1atXQtm1bhIaGYv78+XjkkUfsLRGLFi0q0TFzpYLWkiuPy9zcXLz33nsO61mtVvTv3x+5ublYvnx5iVv+VEVHR8NoNGLz5s0OQe/V+wc4fm+uN4bFGa489m+//XaEhIRg3rx5DsHQvHnz4Ovri169etmXXbhwARcuXEDt2rXtrZ/33nuvPT1+QZr5nJwcLFy4EDExMfZuh2azGd26dcNnn32GKVOmICAgAMDlSVPT09Pt6fuBy5/tAw88gPj4eHz77beIjY297uv8/PPPYbPZrttFLi8vD19++SU6dOhQ5PeyQYMGMBgMWLZsmT3LHwCcPn0aW7ZsKfK8cD1FHdO6ruOtt94qtO7jjz+OP//8E/Hx8Q7jya6nqHPNyZMnkZmZ6XDjpKjPkIjcD4MhqtT+/e9/Y8OGDYiJicHw4cPRrFkzJCcnY/fu3Vi7du01U+a+++676NChA1q2bInhw4ejXr16SExMRHx8PE6fPo19+/Y5rP/AAw9g6tSp9nk/rr6A7t27Nz799FOYzWY0a9YM8fHxWLt2rcPcNsDlu7tGoxGvvPIKUlNTYTKZcPvttxeZ7nXEiBF4//33MWTIEOzatQt16tTBV199hW3btuHNN9+0XwSpKo0xQwcOHMDvv/+OSZMmFXlXv0mTJsW2yNStW7dQi1DTpk3RuXNnhzlp7r//fkRGRqJZs2awWCz4+OOP8c8//2DVqlUO783mzZsxc+ZMdO/eHdWqVcOvv/6KhQsXokePHoXS+06fPh0zZsxwqgWsJOrXr4+XXnoJkydPxvHjx9G3b18EBATg2LFj+OabbzBixAiMHz8enp6eeOmll/DYY4/h9ttvxwMPPIBjx45h4cKFTo8Zat++PYKDgzF48GCMGTMGmqbh008/LRS0z58/H+vXr8fjjz+ODRs2ODwXHh6OO+64Q/n1F8VsNuO+++7DO++8A03TUL9+faxcubLIsX5t2rQBAIwZMwZxcXEwGo2FWmclXD1m6MUXX8TIkSNx3333IS4uDlu2bMFnn32Gl19+GSEhIfZ1586dW+g4i4mJwX333YfJkycjKSkJDRo0wOLFi3H8+HF89NFHDtt6+eWX0b59e3Tu3BkjRozA6dOn8dprr6F79+7o0aOHfb1nnnkG3333Hfr06YPk5GR89tlnDvUUlep7yZIliIyMvO7x/9NPP+HixYvFBk2hoaEYOnQoPvzwQ3Tt2hX33HMP0tLS8N577yErKwuTJ092WL9g/p+ru81eqUmTJqhfvz7Gjx+PM2fOIDAwEMuXLy80dmjVqlX45JNP0K9fP/z+++/4/fff7c/5+/tfs8W5qHPNoEGDsGnTJofvTlGfIRG5oTLOXkdU5hITE/WRI0fqUVFRuqenpx4REaF37dpVX7BggX2dolL16rqu//333/qgQYP0iIgI3dPTU69Zs6beu3fvIlMtHz161J4aduvWrYWev3Tpkv7II4/o1atX1/39/fW4uDj98OHDhdIR6/rlmeLr1aunG41Gh5S9RaV5TUxMtNfr5eWlt2zZstDrKC7Ns65fTlU9bdq0Yt+/0jJp0iQdgP777787VQ7FpNZGEWmlX3nlFb1Jkya6t7e3HhwcrN911136nj17CpX966+/9O7du+vVq1fXTSaT3qRJE3327NkOqasLPPPMM7qmafoff/xxzf28Xmrt8+fPF1lu+fLleocOHXQ/Pz/dz89Pb9KkiT5y5Ej9yJEjDuu99957et26dXWTyaS3bdtW37x5sygN8LZt2/R27drpPj4+emRkpD5hwgT9p59+cjjuCva5qMeV2ysutfZvv/1Wovdg8ODBup+fn8Oy8+fP6/369dN9fX314OBg/bHHHrOnar7yOM/Pz9dHjx6th4aG6pqm2dNsu+Oxv2DBAr1x48a6l5eXXr9+ff2NN95wSD2u6///Hl2drjsrK0sfP368HhERoZtMJv3mm2/WV69eXeR2tmzZordv31739vbWQ0ND9ZEjR9rT7Rfo3LnzNdOFX+3w4cM6AH3cuHHXfZ39+/fXPT099YsXLxa7Tl5env7OO+/orVu31v39/XV/f3/9tttu09evX19o3erVq+vt2rW77nYPHTqkd+vWTff399erV6+uDx8+XN+3b5/DMVNwbBb1iI6Odnh/rv5OFXWuKXgfr1TcZ0hE7kXT9euMHiUiIrtbbrkF0dHR9skb3UnB3ecr71gTVQaHDh1C8+bNsXLlSofuhEREqthNjoiohCwWC/bt22dPv01EZWPDhg2IjY1lIERELseWISKiSuLKlqHz589fMwW1l5eXwxgVIiKiqogtQ0REldDNN99cZArqAlcPACciIqqK2DJERFQJbdu2DVlZWcU+HxwcbM/ARkREdKXZs2fj66+/xuHDh+Hj44P27dvjlVdeQePGje3rZGdn45lnnsHnn3+OnJwcxMXF4b333kN4eHix9eq6jmnTpuGDDz5ASkoKbr31VsybNw8NGzYsi5dVJAZDRERERERk16NHD/Tv3x8333wz8vPz8dxzz+HAgQM4dOiQfV63J554AqtWrcKiRYtgNpsxatQoGAwGbNu2rdh6X3nlFcyePRuLFy9G3bp1MWXKFOzfvx+HDh2Ct7d3Wb08BwyGiIiIiIioWOfPn0dYWBg2bdqETp06ITU1FaGhoVi6dCnuvfdeAJcnJG7atCni4+PRrl27QnXouo7IyEg888wz9omjU1NTER4ejkWLFrlkbjgJjhlyEZvNhrNnzyIgIKDICSSJiIiIqHzpuo60tDRERkYWmhzdHWRnZyM3N7dU6tZ1vdA1qslkgslkum7Z1NRUALAn3tm1axfy8vIcJqVu0qQJateuXWwwdOzYMSQkJDiUMZvNiImJQXx8PIOhiu7s2bOIiooq790gIiIious4deoUatWqVd674SA7OxvVfXyQUUr1+/v7Iz093WHZtGnTMH369GuWs9lsGDt2LG699Va0aNECAJCQkAAvLy8EBQU5rBseHo6EhIQi6ylYfvWYomuVKQsMhlwkICAAwOUvV2BgYDnvDRERERFdzWKxICoqyn7d5k5yc3ORAWCsh4brt9U4JwfAm+npha5TS9IqNHLkSBw4cABbt2518V65BwZDLlLQ7BgYGMhgiIiIiMiNufOQBhMAk6v3738pApy9Th01ahRWrlyJzZs3O7SkRUREIDc3FykpKQ6tQ4mJiYiIiCiyroLliYmJqFGjhkOZ1q1bO/FiXMv9OksSEREREVVRBg0wuvhhcDK20nUdo0aNwjfffIP169ejbt26Ds+3adMGnp6eWLdunX3ZkSNHcPLkScTGxhZZZ926dREREeFQxmKxYPv27cWWKQsMhoiIiIiIyG7kyJH47LPPsHTpUgQEBCAhIQEJCQn2+evMZjOGDRuGcePGYcOGDdi1axceeeQRxMbGOiRPaNKkCb755hsAl1vjxo4di5deegnfffcd9u/fj0GDBiEyMhJ9+/Ytj5cJgN3kiIiIiIjchod2+eHSOp1cf968eQCALl26OCxfuHAhhgwZAgB44403YDAY0K9fP4dJV6905MgReyY6AJgwYQIyMjIwYsQIpKSkoEOHDli9enW5zTEEcJ4hl7FYLDCbzUhNTeWYISIiIiI35M7XawX7NsVLg7eLxwxl6zpezNXd8nWXN7YMERERERG5iYJxPi6t07XVVSocM0RERERERFUSW4aIiIiIiNwEW4bKFoMhIiIiIiI3YdT0UgiGmCKgOOwmR0REREREVRJbhoiIiIiI3ISxFFJrs5tc8dgyREREREREVRJbhoiIiIiI3ITRUAoJFDhkqFhsGSIiIiIioiqJLUNERERERG6CqbXLFluGiIiIiIioSmLLEBERERGRm2DLUNliMERERERE5CYYDJUtdpMjIiIiIqIqiS1DRERERERugqm1yxZbhoiIiIiIqEpiyxARERERkZswwPUtQwa2DBWLLUNERERERFQlsWWIiIiIiMhNlEo2ORfXV5mwZYiIiIiIiKoktgwREREREbkJg+Hyw6V1ura6SoXBEBERERGRm2A3ubLFQJGIiIiIiKoktgwREREREbkJtgyVLbYMERERERFRlcSWISIiIiIiN2E0XH64tE7XVlepsGWIiIiIiIiqJLYMERERERG5CYN2+eHqOqlobBkiIiIiIqIqiS1DRERERERuwmDQYHRxU87l1g/dpXVWFgyGiIiIiIjcBLvJlS12kyMiIiIioiqJLUNERERERG6CqbXLFluGiIiIiIioSmLLEBERERGRm+CYobLFliEiIiIiIqqS2DJEREREROQmOGaobLFliIiIiIiIqiS2DBERERERuQmDpsHg6klXdYCTrhaNLUNERERERFQlsWWIiIiIiMhNMJtc2WIwRERERETkJkolgQJ7yBWL3eSIiIiIiKhKYssQEREREZGb0AyuT6CgsWWoWGwZIiIiIiKiKoktQ0REREREboIJFMoWW4aIiIiIiKhKYjBEREREROQmDIbSeThj8+bN6NOnDyIjI6FpGlasWOHwvKZpRT5effXVYuucPn16ofWbNGkieIdci8EQERERERHZZWRk4IYbbsC7775b5PPnzp1zeHz88cfQNA39+vW7Zr3Nmzd3KLd169bS2H2ncMwQEREREZGbMJRCNjnD/7LJWSwWh+Umkwkmk6nQ+nfeeSfuvPPOYuuLiIhw+Pvbb7/Fbbfdhnr16l1zPzw8PAqVLW9sGSIiIiIichOl2U0uKioKZrPZ/pg9e7by/iYmJmLVqlUYNmzYddc9evQoIiMjUa9ePQwcOBAnT55U3r4qtgwREREREVUBp06dQmBgoP3volqFnLV48WIEBATgnnvuueZ6MTExWLRoERo3boxz585hxowZ6NixIw4cOICAgADl/ZBiMERERERE5CYkCQ+uW+f/uskFBgY6BEOu8PHHH2PgwIHw9va+5npXdrtr1aoVYmJiEB0djS+++KJErUqlpVy7yV0vU4Wu65g6dSpq1KgBHx8fdOvWDUePHnVYJzk5GQMHDkRgYCCCgoIwbNgwpKenO6zz+++/o2PHjvD29kZUVBTmzJlTaF++/PJLNGnSBN7e3mjZsiV++OEHl79eIiIiIqLKYsuWLThy5AgeffRRp8sGBQWhUaNG+Ouvv0phz0quXIOh62WqmDNnDt5++23Mnz8f27dvh5+fH+Li4pCdnW1fZ+DAgTh48CDWrFmDlStXYvPmzRgxYoT9eYvFgu7duyM6Ohq7du3Cq6++iunTp2PBggX2dX755RcMGDAAw4YNw549e9C3b1/07dsXBw4cKL0XT0RERER0lYIECq5+lIaPPvoIbdq0wQ033OB02fT0dPz999+oUaNGKexZyWm6ruvlugf/o2kavvnmG/Tt2xfA5VahyMhIPPPMMxg/fjwAIDU1FeHh4Vi0aBH69++PP/74A82aNcNvv/2Gtm3bAgBWr16Nnj174vTp04iMjMS8efPw/PPPIyEhAV5eXgCASZMmYcWKFTh8+DAA4IEHHkBGRgZWrlxp35927dqhdevWmD9/fpH7m5OTg5ycHPvfFosFUVFRSE1NdXnzIxERERGps1gsMJvNbnm9VrBvO27yhL/RtcFLulXHLbvzSvy609PT7S02N954I15//XXcdtttCAkJQe3ate37W6NGDbz22mt4/PHHC9XRtWtX3H333Rg1ahQAYPz48ejTpw+io6Nx9uxZTJs2DXv37sWhQ4cQGhrqwlfrHLfNJnfs2DEkJCSgW7du9mVmsxkxMTGIj48HAMTHxyMoKMgeCAFAt27dYDAYsH37dvs6nTp1sgdCABAXF4cjR47g0qVL9nWu3E7BOgXbKcrs2bMdsnFERUWpv2giIiIiqtIMWilkk3Myttq5cyduvPFG3HjjjQCAcePG4cYbb8TUqVPt63z++efQdR0DBgwoso6///4bFy5csP99+vRpDBgwAI0bN8b999+PatWq4ddffy3XQAhw4wQKCQkJAIDw8HCH5eHh4fbnEhISEBYW5vC8h4cHQkJCHNapW7duoToKngsODkZCQsI1t1OUyZMnY9y4cfa/C1qGiIiIiIgqsi5duuB6ncdGjBjhMDTlasePH3f4+/PPP3fFrrmc2wZD7q64SaqIiIiIiKRKZdJVt+0LVv7c9q0pmJ02MTHRYXliYqL9uYiICCQlJTk8n5+fj+TkZId1iqrjym0Ut467zZBLRERERJWbVgoTrmpue8Vf/tz2ralbty4iIiKwbt06+zKLxYLt27cjNjYWABAbG4uUlBTs2rXLvs769eths9kQExNjX2fz5s3Iy8uzr7NmzRo0btwYwcHB9nWu3E7BOgXbISIiIiKiyqdcg6H09HTs3bsXe/fuBXA5acLevXtx8uRJaJqGsWPH4qWXXsJ3332H/fv3Y9CgQYiMjLRnnGvatCl69OiB4cOHY8eOHdi2bRtGjRqF/v37IzIyEgDw4IMPwsvLC8OGDcPBgwexbNkyvPXWWw7jfZ566imsXr0ar732Gg4fPozp06dj586d9uwXRERERERlQTNopfKgopXrmKGdO3fitttus/9dEKAMHjwYixYtwoQJE5CRkYERI0YgJSUFHTp0wOrVqx1muF2yZAlGjRqFrl27wmAwoF+/fnj77bftz5vNZvz8888YOXIk2rRpg+rVq2Pq1KkOA77at2+PpUuX4oUXXsBzzz2Hhg0bYsWKFWjRokUZvAtERERERFQe3GaeoYrOnfPWExEREZF7X68V7Nvvt5oQ4OHalpy0fB2ttuW45esub247ZoiIiIiIiKg0MbU2EREREZGbYGrtssW3hoiIiIiIqiS2DBERERERuYnSyP7GeYaKx2CIiIiIiMhNFEyU6uo6qWh8a4iIiIiIqEpiyxARERERkZtgN7myxbeGiIiIiIiqJLYMERERERG5CYNWCqm1XVtdpcKWISIiIiIiqpLYMkRERERE5CY0g+vH+HDMUPH41hARERERUZXEliEiIiIiIjdhMGowGF08Zsjm0uoqFQZDRERERERugqm1yxbfGiIiIiIiqpLYMkRERERE5CY0owbNxd3kNHaTKxZbhoiIiIiIqEpiyxARERERkbsohTFDbP4oHt8aIiIiIiKqktgyRERERETkJphau2yxZYiIiIiIiKoktgwREREREbkJZpMrWwyGiIiIiIjchKbB9ZOuarpL66tM2E2OiIiIiIiqJLYMERERERG5iVLpJufi+ioTtgwREREREVGVxJYhIiIiIiI3wZahssWWISIiIiIiqpLYMkRERERE5CY0owGa0bXtFZrRpdVVKmwZIiIiIiKiKoktQ0REREREboJjhsoWgyEiIiIiIjfBYKhssZscERERERFVSWwZIiIiIiJyE5qhFFqGDGwZKg5bhoiIiIiIqEpiyxARERERkZtgau2yxZYhIiIiIiKqktgyRERERETkJphNrmyxZYiIiIiIiKoktgwREREREbkLo3b54eo6qUhsGSIiIiIichMF3eRc/XDG5s2b0adPH0RGRkLTNKxYscLh+SFDhkDTNIdHjx49rlvvu+++izp16sDb2xsxMTHYsWOHU/tVGhgMERERERGRXUZGBm644Qa8++67xa7To0cPnDt3zv7473//e806ly1bhnHjxmHatGnYvXs3brjhBsTFxSEpKcnVu+8UdpMjIiIiInIT7pBa+84778Sdd955zXVMJhMiIiJKXOfrr7+O4cOH45FHHgEAzJ8/H6tWrcLHH3+MSZMmObeDLsSWISIiIiKiKsBisTg8cnJyxHVt3LgRYWFhaNy4MZ544glcvHix2HVzc3Oxa9cudOvWzb7MYDCgW7duiI+PF++DKzAYIiIiIiJyFwbt/5MouOphuDxmKCoqCmaz2f6YPXu2aBd79OiBTz75BOvWrcMrr7yCTZs24c4774TVai1y/QsXLsBqtSI8PNxheXh4OBISEkT74CrsJkdEREREVAWcOnUKgYGB9r9NJpOonv79+9v/37JlS7Rq1Qr169fHxo0b0bVrV+X9LEtsGSIiIiIichOlmU0uMDDQ4SENhq5Wr149VK9eHX/99VeRz1evXh1GoxGJiYkOyxMTE50ad1QaGAwREREREZHY6dOncfHiRdSoUaPI5728vNCmTRusW7fOvsxms2HdunWIjY0tq90sErvJERERERG5C6MGuDibHIy6U6unp6c7tPIcO3YMe/fuRUhICEJCQjBjxgz069cPERER+PvvvzFhwgQ0aNAAcXFx9jJdu3bF3XffjVGjRgEAxo0bh8GDB6Nt27a45ZZb8OabbyIjI8OeXa68MBgiIiIiInITkklSS1KnM3bu3InbbrvN/ve4ceMAAIMHD8a8efPw+++/Y/HixUhJSUFkZCS6d++OF1980aHb3d9//40LFy7Y/37ggQdw/vx5TJ06FQkJCWjdujVWr15dKKlCWdN0XXcuVKQiWSwWmM1mpKamOgxMIyIiIiL34M7XawX7dmlmfQR6Ozkx0PXqzrYieOrfbvm6yxtbhoiIiIiI3EVBOmxX10lFYgIFIiIiIiKqktgyRERERETkJjSjAZqLEyhoTiZQqErYMkRERERERFUSW4aIiIiIiNyFoRTGDBk4Zqg4DIaIiIiIiMjt/P77706XadasGTw8Sh7iMBgiIiIiInIXzCZn17p1a2iahpLOBGQwGPDnn3+iXr16Jd4GgyEiIiIiInJL27dvR2ho6HXX03UdLVq0cLp+BkNERERERO6CLUN2nTt3RoMGDRAUFFSi9Tt16gQfHx+ntsFgiIiIiIjIXRg01yc8qKAJFDZs2ODU+j/88IPT22BqbSIiIiIiqpIYDBERERERuQujoXQeldSpU6cwdOhQcfnK+84QEREREVGllpycjMWLF4vLc8wQEREREZG7YAIFB9999901n//nn3+U6mcwREREREREbqlv377XnWtI0+TBHrvJERERERG5C4P2/61DrnpU0GxyAFCjRg18/fXXsNlsRT52796tVD+DISIiIiIicktt2rTBrl27in3+eq1G18NuckRERERE7oJjhhw8++yzyMjIKPb5Bg0aOD0f0ZXcumXIarViypQpqFu3Lnx8fFC/fn28+OKLDtGfruuYOnUqatSoAR8fH3Tr1g1Hjx51qCc5ORkDBw5EYGAggoKCMGzYMKSnpzus8/vvv6Njx47w9vZGVFQU5syZUyavkYiIiIjIrmDSVVc/KqiOHTuiR48exT7v5+eHzp07i+t362DolVdewbx58zB37lz88ccfeOWVVzBnzhy888479nXmzJmDt99+G/Pnz8f27dvh5+eHuLg4ZGdn29cZOHAgDh48iDVr1mDlypXYvHkzRowYYX/eYrGge/fuiI6Oxq5du/Dqq69i+vTpWLBgQZm+XiIiIiIiurb//ve/12wtcoamq3SyK2W9e/dGeHg4PvroI/uyfv36wcfHB5999hl0XUdkZCSeeeYZjB8/HgCQmpqK8PBwLFq0CP3798cff/yBZs2a4bfffkPbtm0BAKtXr0bPnj1x+vRpREZGYt68eXj++eeRkJAALy8vAMCkSZOwYsUKHD58uET7arFYYDabkZqaisDAQBe/E0RERESkyp2v1wr27dInNyLQ1+jaujOtCB60xy1ft0RgYCD27t2LevXqKdfl1i1D7du3x7p16/Dnn38CAPbt24etW7fizjvvBAAcO3YMCQkJ6Natm72M2WxGTEwM4uPjAQDx8fEICgqyB0IA0K1bNxgMBmzfvt2+TqdOneyBEADExcXhyJEjuHTpUpH7lpOTA4vF4vAgIiIiIqLS5cq2HLdOoDBp0iRYLBY0adIERqMRVqsVL7/8MgYOHAgASEhIAACEh4c7lAsPD7c/l5CQgLCwMIfnPTw8EBIS4rBO3bp1C9VR8FxwcHChfZs9ezZmzJjhgldJRERERPQ/pTHGpwKPGSptbt0y9MUXX2DJkiVYunQpdu/ejcWLF+M///kPFi9eXN67hsmTJyM1NdX+OHXqVHnvEhERERFRpffjjz+iZs2aLqnLrVuGnn32WUyaNAn9+/cHALRs2RInTpzA7NmzMXjwYERERAAAEhMTUaNGDXu5xMREtG7dGgAQERGBpKQkh3rz8/ORnJxsLx8REYHExESHdQr+LljnaiaTCSaTSf1FEhERERH9j27UoLs4Fbar6ysPWVlZ0HUdvr6+6NChA06cOIFvvvkGzZo1Q/fu3cX1unXLUGZmJgwGx100Go2w2WwAgLp16yIiIgLr1q2zP2+xWLB9+3bExsYCAGJjY5GSkuIwWdP69eths9kQExNjX2fz5s3Iy8uzr7NmzRo0bty4yC5yRERERERUdv71r3/hk08+AQCkpKQgJiYGr732Gv71r39h3rx54nrdOhjq06cPXn75ZaxatQrHjx/HN998g9dffx133303gMszzo4dOxYvvfQSvvvuO+zfvx+DBg1CZGQk+vbtCwBo2rQpevTogeHDh2PHjh3Ytm0bRo0ahf79+yMyMhIA8OCDD8LLywvDhg3DwYMHsWzZMrz11lsYN25ceb10IiIiIqqKDKX0qOB2796Njh07AgC++uorhIeH48SJE/jkk0/w9ttvi+t1625y77zzDqZMmYInn3wSSUlJiIyMxGOPPYapU6fa15kwYQIyMjIwYsQIpKSkoEOHDli9ejW8vb3t6yxZsgSjRo1C165dYTAY0K9fP4c3zWw24+eff8bIkSPRpk0bVK9eHVOnTnWYi4iIiIiIqLTpRtd3a9Ndm6m7XGRmZiIgIAAA8PPPP+Oee+6BwWBAu3btcOLECXG9bj3PUEXiznnriYiIiMi9r9cK9u3iV20Q6Ova9gpLZj6q3bvLLV93SbVq1QqPPvoo7r77brRo0QKrV69GbGwsdu3ahV69etmzRDurEjSaERERERFVEpr2/+m1XfXQKn4ChalTp2L8+PGoU6cOYmJi7PkBfv75Z9x4443iet26mxwREREREdG9996LDh064Ny5c7jhhhvsy7t27WrPJyDBYIiIiIiIyE3oBg26iydJdXV95SUiIqLQtDe33HKLUp3sJkdERERERFUSW4aIiIiIiNyEbrj8cHWdVDS+NUREREREVCWxZYiIiIiIyE2wZahsMRgiIiIiInITulErhUlXK0cChbNnz2Lr1q1ISkqCzWZzeG7MmDGiOhkMERERERGRW1u0aBEee+wxeHl5oVq1atCumDtJ0zQGQ0REREREFR27yRVtypQpmDp1KiZPngyDwXUvqBK8NUREREREVJllZmaif//+Lg2EAAZDRERERERuo6BlyNWPim7YsGH48ssvXV4vu8kREREREZFbmz17Nnr37o3Vq1ejZcuW8PT0dHj+9ddfF9XLYIiIiIiIyE1wzFDRZs+ejZ9++gmNGzcGgEIJFKQYDBERERERkVt77bXX8PHHH2PIkCEurZfBEBERERGRm7BpgM3FLTm2SjDNkMlkwq233uryeitBoxkRERERUeXABApFe+qpp/DOO++4vF62DBERERERkVvbsWMH1q9fj5UrV6J58+aFEih8/fXXonorQZxIRERERFQ52Ayl83DG5s2b0adPH0RGRkLTNKxYscL+XF5eHiZOnIiWLVvCz88PkZGRGDRoEM6ePXvNOqdPnw5N0xweTZo0KfE+BQUF4Z577kHnzp1RvXp1mM1mh4cUW4aIiIiIiMguIyMDN9xwA4YOHYp77rnH4bnMzEzs3r0bU6ZMwQ033IBLly7hqaeewl133YWdO3des97mzZtj7dq19r89PEoeiixcuNC5F1FCDIaIiIiIiNyEO6TWvvPOO3HnnXcW+ZzZbMaaNWscls2dOxe33HILTp48idq1axdbr4eHByIiIpzbmVLGbnJERERERFWAxWJxeOTk5Lik3tTUVGiahqCgoGuud/ToUURGRqJevXoYOHAgTp48ec31b7rpJly6dKnE+9GhQwecOXOmxOsDbBkiIiIiInIbumCMT0nqBICoqCiH5dOmTcP06dOV6s7OzsbEiRMxYMAABAYGFrteTEwMFi1ahMaNG+PcuXOYMWMGOnbsiAMHDiAgIKDIMnv37sW+ffsQEhJSon3Zu3ev0wEegyEiIiIioirg1KlTDgGLyWRSqi8vLw/3338/dF3HvHnzrrnuld3uWrVqhZiYGERHR+OLL77AsGHDii3XtWtX6Lpeov3RNOcnVGIwRERERETkJnSDDt1Qsot/Z+oEgMDAwGu23jijIBA6ceIE1q9f73S9QUFBaNSoEf76669i1zl27JjT+1WrVi2n1mcwRERERETkJiSpsEtSpysVBEJHjx7Fhg0bUK1aNafrSE9Px99//42HH3642HWio6NVdrNEmECBiIiIiIjs0tPTsXfvXuzduxfA5RaavXv34uTJk8jLy8O9996LnTt3YsmSJbBarUhISEBCQgJyc3PtdXTt2hVz5861/z1+/Hhs2rQJx48fxy+//IK7774bRqMRAwYMKOuX54AtQ0REREREbsIdWoZ27tyJ2267zf73uHHjAACDBw/G9OnT8d133wEAWrdu7VBuw4YN6NKlCwDg77//xoULF+zPnT59GgMGDMDFixcRGhqKDh064Ndff0VoaKjzL8iFGAwREREREZFdly5drpm0oCQJDY4fP+7w9+eff666W6WCwRARERERkZuwGXTYXJxAwdX1VSYcM0RERERERFUSW4aIiIiIiNyETSuFMUPOT7/jFoKDg0s8d1BycrJoGwyGiIiIiIjI7bz55pv2/1+8eBEvvfQS4uLiEBsbCwCIj4/HTz/9hClTpoi3oeklndKVrsliscBsNiM1NdVlk1kRERERkeu48/Vawb4d2NsGAQGuba9IS8tHi9a73PJ1l1S/fv1w2223YdSoUQ7L586di7Vr12LFihWiejlmiIiIiIjITRSk1nb1o6L76aef0KNHj0LLe/TogbVr14rrrQRvDRERERERVWbVqlXDt99+W2j5t99+i2rVqonr5ZghIiIiIiI3YdNcn/CgoiZQuNKMGTPw6KOPYuPGjYiJiQEAbN++HatXr8YHH3wgrpfBEBERERERubUhQ4agadOmePvtt/H1118DAJo2bYqtW7fagyMJBkNERERERG7i8hgfV0+66tLqyk1MTAyWLFni0joryVtDRERERESV2d9//40XXngBDz74IJKSkgAAP/74Iw4ePCiuk8EQEREREZGb0Eshk5xeCa74N23ahJYtW2L79u1Yvnw50tPTAQD79u3DtGnTxPVWgreGiIiIiIgqs0mTJuGll17CmjVr4OXlZV9+++2349dffxXXyzFDRERERERuwqbpsGkuHjPk4vrKw/79+7F06dJCy8PCwnDhwgVxvWwZIiIiIiJyE1ZD6TwquqCgIJw7d67Q8j179qBmzZrieivBW0NERERERJVZ//79MXHiRCQkJEDTNNhsNmzbtg3jx4/HoEGDxPUyGCIiIiIichOuTp5Q8KjoZs2ahSZNmiAqKgrp6elo1qwZOnXqhPbt2+OFF14Q18sxQ0RERERE5Na8vLzwwQcfYMqUKThw4ADS09Nx4403omHDhkr1ligY+v33352uuFmzZvDwYKxFRERERFRSTKBwbREREcjKykL9+vVdEmuUqIbWrVtD0zToesneSIPBgD///BP16tVT2jkiIiIiIqLMzEyMHj0aixcvBgB7rDF69GjUrFkTkyZNEtVb4nBq+/btCA0Nve56uq6jRYsWop0hIiIiIqrKSiP7W2XIJjd58mTs27cPGzduRI8ePezLu3XrhunTp5duMNS5c2c0aNAAQUFBJaq0U6dO8PHxEe0QERERERHRlVasWIFly5ahXbt20DTNvrx58+b4+++/xfWWKBjasGGDU5X+8MMPop0hIiIiIqrKbABs2nVXc7rOiu78+fMICwsrtDwjI8MhOHJWJWg0IyIiIiKiyqxt27ZYtWqV/e+CAOjDDz9EbGysuF6nUzDouo6vvvoKGzZsQFJSEmw2x1jz66+/Fu8MEREREVFVZiuFMUOVZZ6hO++8E4cOHUJ+fj7eeustHDp0CL/88gs2bdokrtfpt2bs2LF4+OGHcezYMfj7+8NsNjs8iIiIiIhIxqaVzqOi69ChA/bu3Yv8/Hy0bNkSP//8M8LCwhAfH482bdqI63W6ZejTTz/F119/jZ49e4o3SkRERERE5Iz69evjgw8+cGmdTgdDZrOZ8wcREREREZUCq3b54eo6KwOr1YpvvvkGf/zxBwCgWbNm+Ne//qU0+arT3eSmT5+OGTNmICsrS7xRIiIiIiKikjp48CAaNWqEwYMH45tvvsE333yDwYMHo2HDhjhw4IC4XqfDqPvvvx///e9/ERYWhjp16sDT09Ph+d27d4t3hoiIiIioKrMZXJ/woDIkUHj00UfRvHlz7Ny5E8HBwQCAS5cuYciQIRgxYgR++eUXUb1OB0ODBw/Grl278NBDDyE8PFwprzcREREREdH17N271yEQAoDg4GC8/PLLuPnmm8X1Oh0MrVq1Cj/99BM6dOgg3igRERERERXGMUNFa9SoERITE9G8eXOH5UlJSWjQoIG4XqcbzaKiohAYGCjeIBERERERkTNmz56NMWPG4KuvvsLp06dx+vRpfPXVVxg7dixeeeUVWCwW+8MZTrcMvfbaa5gwYQLmz5+POnXqOFuciIiIiIiKURrzAlWGeYZ69+4N4HL+goJhOrquAwD69Olj/1vTNFit1hLX63Qw9NBDDyEzMxP169eHr69voQQKycnJzlZJREREREQArJoGq4vH5Lu6vvKwYcOGUqnX6WDozTffLIXdICIiIiIiKlrnzp1LpV5RNjkiIiIiInI9dpMr2urVq+Hv729P4vbuu+/igw8+QLNmzfDuu+86ZJlzRokSKDg7ECktLU20M0RERERERFd79tln7THJ/v37MW7cOPTs2RPHjh3DuHHjxPWWqGUoODgY586dQ1hYWIkqrVmzJvbu3Yt69eqJd4yIiIiIqKqxlcKYIVslGDN07NgxNGvWDACwfPly9OnTB7NmzcLu3bvRs2dPcb0lCoZ0XceHH34If3//ElWal5cn3iEiIiIiIqIreXl5ITMzEwCwdu1aDBo0CAAQEhLidC+2K5UoGKpduzY++OCDElcaERFRKMuc1JkzZzBx4kT8+OOPyMzMRIMGDbBw4UK0bdsWwOVAbdq0afjggw+QkpKCW2+9FfPmzUPDhg3tdSQnJ2P06NH4/vvvYTAY0K9fP7z11lsOwd3vv/+OkSNH4rfffkNoaChGjx6NCRMmuOQ1EBERERGVBCddLVqHDh0wbtw43HrrrdixYweWLVsGAPjzzz9Rq1Ytcb0lCoaOHz8u3oCKS5cu4dZbb8Vtt92GH3/8EaGhoTh69KjDAKk5c+bg7bffxuLFi1G3bl1MmTIFcXFxOHToELy9vQEAAwcOxLlz57BmzRrk5eXhkUcewYgRI7B06VIAl8dEde/eHd26dcP8+fOxf/9+DB06FEFBQRgxYkS5vHYiIiIiIrps7ty5ePLJJ/HVV19h3rx5qFmzJgDgxx9/RI8ePcT1anrBbEVuaNKkSdi2bRu2bNlS5PO6riMyMhLPPPMMxo8fDwBITU1FeHg4Fi1ahP79++OPP/5As2bN8Ntvv9lbk1avXo2ePXvi9OnTiIyMxLx58/D8888jISEBXl5e9m2vWLEChw8fLtG+WiwWmM1mpKamIjAw0AWvnoiIiIhcyZ2v1wr2bcmFNvANdDrh8zVlWvIxsPout3zdrvbvf/8bjz/+OIKCgkq0fomyyZWX7777Dm3btsV9992HsLAw3HjjjQ7d9Y4dO4aEhAR069bNvsxsNiMmJgbx8fEAgPj4eAQFBdkDIQDo1q0bDAYDtm/fbl+nU6dO9kAIAOLi4nDkyBFcunSpyH3LycmBxWJxeBARERERqSiYdNXVj6pi1qxZSE5OLvH6bh0M/fPPP/bxPz/99BOeeOIJjBkzBosXLwYAJCQkAADCw8MdyoWHh9ufS0hIKJQFz8PDAyEhIQ7rFFXHldu42uzZs2E2m+2PqKgoxVdLREREREQqnO305to2OBez2Wxo27YtZs2aBQC48cYbceDAAcyfP7/cJ3+dPHmyQ05zi8XCgIiIiIiIlFihwQrXtuS4ur7KxK1bhmrUqGHPJ16gadOmOHnyJIDLWesAIDEx0WGdxMRE+3MRERFISkpyeD4/Px/JyckO6xRVx5XbuJrJZEJgYKDDg4iIiIiIKg5RMLRlyxY89NBDiI2NxZkzZwAAn376KbZu3erSnbv11ltx5MgRh2V//vknoqOjAQB169ZFREQE1q1bZ3/eYrFg+/btiI2NBQDExsYiJSUFu3btsq+zfv162Gw2xMTE2NfZvHmzw/xIa9asQePGjR0y1xERERERlSabppXKwxmbN29Gnz59EBkZCU3TsGLFCofndV3H1KlTUaNGDfj4+KBbt244evTodet99913UadOHXh7eyMmJgY7duxwar9Kg9PB0PLlyxEXFwcfHx/s2bMHOTk5AC5ncSvozuYqTz/9NH799VfMmjULf/31F5YuXYoFCxZg5MiRAABN0zB27Fi89NJL+O6777B//34MGjQIkZGR6Nu3L4DLLUk9evTA8OHDsWPHDmzbtg2jRo1C//79ERkZCQB48MEH4eXlhWHDhuHgwYNYtmwZ3nrrLYducEREREREVUFGRgZuuOEGvPvuu0U+XzC1zfz587F9+3b4+fkhLi4O2dnZxda5bNkyjBs3DtOmTcPu3btxww03IC4urlAPrrLmdGrtG2+8EU8//TQGDRqEgIAA7Nu3D/Xq1cOePXtw5513FptwQGrlypWYPHkyjh49irp162LcuHEYPny4/fmCSVcXLFiAlJQUdOjQAe+99x4aNWpkXyc5ORmjRo1ymHT17bffLnbS1erVq2P06NGYOHFiiffTnVM1EhEREZF7X68V7NuC5Halklp7RMivotetaRq++eYbe0NDSaa2KUpMTAxuvvlmzJ07F8Dl3ABRUVEYPXo0Jk2aJH9xV+nZsyc++ugj1KhRo0TrO/1OHzlyBJ06dSq03Gw2IyUlxdnqrqt3797o3bt3sc9rmoaZM2di5syZxa4TEhJin2C1OK1atSp2PiMiIiIiooru6qlgTCYTTCaTU3Vcb2qbooKh3Nxc7Nq1C5MnT7YvMxgM6Natm306nJLs77UUBHk//PBDicsAgmAoIiICf/31F+rUqeOwfOvWrahXr56z1RERERER0f/YSmFeoIIxQ1dnPp42bRqmT5/uVF0lmdrmahcuXIDVai2yzOHDh4vdVlBQELQSvhdWq7VE613N6WBo+PDheOqpp/Dxxx9D0zScPXsW8fHxGD9+PKZMmSLaCSIiIiIiAmwwwObihM8F9Z06dcqhm5yzrUJlbcOGDfb/Hz9+HJMmTcKQIUPsidLi4+OxePFizJ49W7wNp4OhSZMmwWazoWvXrsjMzESnTp1gMpkwfvx4jB49WrwjRERERERUelwxHcyVU9tcOS4nMTERrVu3LrJM9erVYTQarzkdTlE6d+5s///MmTPx+uuvY8CAAfZld911F1q2bIkFCxaI5yB1OuzUNA3PP/88kpOTceDAAfz66684f/48XnzxRdEOEBERERHRZdb/dZNz9cNVSjK1zdW8vLzQpk0bhzI2mw3r1q0rtszV4uPj0bZt20LL27Ztq5SiW9wG5+XlhWbNmuGWW25xyMpGREREREQVV3p6Ovbu3Yu9e/cCuJw0Ye/evTh58mSJprYBgK5du9ozxwHAuHHj8MEHH2Dx4sX4448/8MQTTyAjIwOPPPJIifYpKioKH3zwQaHlH374YaGxUM5wupvc3XffXeRAJk3T4O3tjQYNGuDBBx9E48aNxTtFRERERFQVWWGA1cVjhpytb+fOnbjtttvsfxfMvTl48GAsWrQIEyZMQEZGBkaMGGGf2mb16tXw9va2l/n7779x4cIF+98PPPAAzp8/j6lTpyIhIQGtW7fG6tWrCyVVKM4bb7yBfv364ccff0RMTAwAYMeOHTh69CiWL1/u1Ou7ktPzDA0ZMgQrVqxAUFAQ2rRpAwDYvXs3UlJS0L17d+zbtw/Hjx/HunXrcOutt4p3rKJx57z1REREROTe12sF+/ZmSif4uHieoSxLPsYGbXbL1+2M06dP47333rNnoGvatCkef/zxsm0ZioiIwIMPPoi5c+fCYLgcZdpsNjz11FMICAjA559/jscffxwTJ07E1q1bxTtGRERERFTVuEPLkLuqVasWZs2a5dI6nQ6GPvroI2zbts0eCAGXJ00aPXo02rdvj1mzZmHUqFHo2LGjS3eUiIiIiIiqrpSUFOzYsQNJSUmw2WwOzw0aNEhUp9PBUH5+Pg4fPoxGjRo5LD98+LB9siNvb+8ST5BERERERESX5WtG5GtGF9fp1KgYt/T9999j4MCBSE9PR2BgoEOsoWla2QVDDz/8MIYNG4bnnnsON998MwDgt99+w6xZs+w7sWnTJjRv3ly0Q0REREREVZUNmsu7tdlQ8RspnnnmGQwdOhSzZs2Cr6+vy+p1Ohh64403EB4ejjlz5tgnTgoPD8fTTz+NiRMnAgC6d++OHj16uGwniYiIiIio6jpz5gzGjBnj0kAIEARDRqMRzz//PJ5//nlYLBYAKJSVonbt2q7ZOyIiIiKiKiQfBuS7uGXI1fWVh7i4OOzcuRP16tVzab1Kefsqcmo+IiIiIiKqGHr16oVnn30Whw4dQsuWLeHp6enw/F133SWq1+lgKDExEePHj8e6deuQlJSEq6cpKkiiQEREREREzrHCCCtcm0DBCtv1V3Jzw4cPBwDMnDmz0HOapoljEKeDoSFDhuDkyZOYMmUKatSowaxxRERERERUqq5Ope0qTgdDW7duxZYtW9C6detS2B0iIiIioqorH0bku7hlKL8StAyVFqeDoaioqEJd44iIiIiIiFzp7bffxogRI+Dt7Y233377muuOGTNGtA2ng6E333wTkyZNwvvvv486deqINkpERERERIUxm9z/e+ONNzBw4EB4e3vjjTfeKHY9TdPKLhh64IEHkJmZifr168PX17dQJofk5GTRjhARERERVXW2UkigYKug3eSOHTtW5P9dSdQyREREREREVFb++ecfl88xBAiCocGDB7t8J4iIiIiICMjXjTDqLk6goFfMlqErNWjQALVq1ULnzp3RpUsXdO7cGQ0aNFCuV6kDYXZ2NiwWi8ODiIiIiIjIlU6dOoXZs2fDx8cHc+bMQaNGjVCrVi0MHDgQH374obhep4OhjIwMjBo1CmFhYfDz80NwcLDDg4iIiIiIZC4nUDC6+FExEyhcqWbNmhg4cCAWLFiAI0eO4MiRI+jWrRu++OILPPbYY+J6nX5nJkyYgPXr12PevHkwmUz48MMPMWPGDERGRuKTTz4R7wgREREREVFRMjMz8fPPP+O5555D+/bt0apVK+zbtw+jRo3C119/La7X6TFD33//PT755BN06dIFjzzyCDp27IgGDRogOjoaS5YswcCBA8U7Q0RERERUleXDCKPLJ111bX3lISgoCMHBwRg4cCAmTZqEjh07uqRXmtMtQ8nJyfZMDoGBgfZU2h06dMDmzZuVd4iIiIiIiOhKPXv2hNVqxeeff47PP/8cX375Jf7880/lep0OhurVq2fP892kSRN88cUXAC63GAUFBSnvEBERERFRVWV1+Xgh189bVB5WrFiBCxcuYPXq1YiNjcXPP/+Mjh072scSSTndTe6RRx7Bvn370LlzZ0yaNAl9+vTB3LlzkZeXh9dff128I0REREREVV2+7gGD7vQl+nXqrPiptQu0bNkS+fn5yM3NRXZ2Nn766ScsW7YMS5YsEdXn9Dv99NNP2//frVs3HD58GLt27UKDBg3QqlUr0U4QEREREREV5/XXX8fGjRuxdetWpKWl4YYbbkCnTp0wYsQIdOzYUVyvU8FQXl4eevTogfnz56Nhw4YAgOjoaERHR4t3gIiIiIiILsuHEQYmUCjkv//9Lzp37mwPfsxms0vqdSoY8vT0xO+//+6SDRMREREREZXEb7/9VqL1nnzyScycORPVq1cv0fpOJ1B46KGH8NFHHzlbjIiIiIiIriNfN5bKo6r47LPPYLFYSry+02OG8vPz8fHHH2Pt2rVo06YN/Pz8HJ5nEgUiIiIiIioPuq47tb7TwdCBAwdw0003AUCh3N6apjlbHRERERER/U8+DKUwZsjpzmBVhtPB0IYNG0pjP4iIiIiIiMqUa5OYExERERGRmFX3QL6L5xmy6laX1leZMBgiIiIiInIT+boRmosTHlSlBArOYgdCIiIiIiKqFB566CEEBgaWeH22DBERERERuQkrPGBw8SW6FRWzm5wz85u2atUKADBv3jyntsFgiIiIiIiI3E7r1q2haVqx6bILntM0DVarLOBjMERERERE5CY4Zuj/HTt2rNS3wWCIiIiIiIjcTnR0dKlvg8EQEREREZGbyNcNpdAyVHlyph06dAgnT55Ebm6uw/K77rpLVB+DISIiIiIicmv//PMP7r77buzfv99hHJGmaQAgHjNUecJEIiIiIqIKzgoP5Lv4Ya0E7R9PPfUU6tati6SkJPj6+uLgwYPYvHkz2rZti40bN4rrrfjvDBERERERVWrx8fFYv349qlevDoPBAIPBgA4dOmD27NkYM2YM9uzZI6qXwRARERERkZuwlkI2OWsFzSZ3JavVioCAAABA9erVcfbsWTRu3BjR0dE4cuSIuF4GQ0REREREbiJfNwJMrV1IixYtsG/fPtStWxcxMTGYM2cOvLy8sGDBAtSrV09cL4MhIiIiIiJyay+88AIyMjIAADNnzkTv3r3RsWNHVKtWDZ9//rm4XgZDRERERERuwqp7QNNde4ludXF95SEuLs7+/wYNGuDw4cNITk5GcHCwPaOcBLPJERERERGRWxs6dCjS0tIcloWEhCAzMxNDhw4V18tgiIiIiIjITeTrxlJ5VHSLFy9GVlZWoeVZWVn45JNPxPVW/DYzIiIiIiKqlCwWC3Rdh67rSEtLg7e3t/05q9WKH374AWFhYeL62TJEREREROQmbLoBVt3o0odNd+6Sv06dOtA0rdBj5MiRRa6/aNGiQuteGbSoCAoKQkhICDRNQ6NGjRAcHGx/VK9eHUOHDi12v0qCLUNERERERGT322+/wWq12v8+cOAA7rjjDtx3333FlgkMDHSY70clqcGVNmzYAF3Xcfvtt2P58uUICQmxP+fl5YXo6GhERkaK62cwRERERETkJqy6B1BK2eQsFovDcpPJBJPJVGj90NBQh7///e9/o379+ujcuXOx29A0DRERES7YW0cF2zx27BiioqJgMLi2YxuDISIiIiIiN2EthUlXrf+rLyoqymH5tGnTMH369GuWzc3NxWeffYZx48Zds7UnPT0d0dHRsNlsuOmmmzBr1iw0b95ced8LREdHIyUlBTt27EBSUhJsNpvD84MGDRLVy2CIiIiIiKgKOHXqFAIDA+1/F9UqdLUVK1YgJSUFQ4YMKXadxo0b4+OPP0arVq2QmpqK//znP2jfvj0OHjyIWrVquWLX8f3332PgwIFIT09HYGCgQ2CmaZo4GNJ0XdddsodVnMVigdlsRmpqqsNBRkRERETuwZ2v1wr2renJd2EM9HFp3VZLFv6oPVL0uuPi4uDl5YXvv/++xGXy8vLQtGlTDBgwAC+++KKzu1ukRo0aoWfPnpg1axZ8fX1dUifAliEiIiIiIirCiRMnsHbtWnz99ddOlfP09MSNN96Iv/76y2X7cubMGYwZM8algRDA1NpERERERG7DphtL5SGxcOFChIWFoVevXk6Vs1qt2L9/P2rUqCHablHi4uKwc+dOl9VXgC1DRERERETkwGazYeHChRg8eDA8PBxDhkGDBqFmzZqYPXs2AGDmzJlo164dGjRogJSUFLz66qs4ceIEHn30UZftT69evfDss8/i0KFDaNmyJTw9PR2ev+uuu0T1MhgiIiIiInITVt1QCtnknO8MtnbtWpw8eRJDhw4t9NzJkycdUlxfunQJw4cPR0JCAoKDg9GmTRv88ssvaNasmdJ+X2n48OEALgdeV9M0zWFeJGcwgYKLuPOAPCIiIiJy7+u1gn1rcHwBjIGuHRdjtWTirzoj3PJ1lze2DBERERERuQmbboRmc23LkHTMUFXABApERERERG7CphthdfGjsgRDmzZtQp8+fdCgQQM0aNAAd911F7Zs2aJUJ4MhIiIiIiJya5999hm6desGX19fjBkzBmPGjIGPjw+6du2KpUuXiutlNzkiIiIiIjdh0w2AIOHBdeus4F5++WXMmTMHTz/9tH3ZmDFj8Prrr+PFF1/Egw8+KKq34r8zRERERERUqf3zzz/o06dPoeV33XUXjh07Jq63QgVD//73v6FpGsaOHWtflp2djZEjR6JatWrw9/dHv379kJiY6FDu5MmT6NWrF3x9fREWFoZnn30W+fn5Duts3LgRN910E0wmExo0aIBFixaVwSsiIiIiIvp/um4slUdFFxUVhXXr1hVavnbtWkRFRYnrrTDd5H777Te8//77aNWqlcPyp59+GqtWrcKXX34Js9mMUaNG4Z577sG2bdsAXJ4Bt1evXoiIiMAvv/yCc+fOYdCgQfD09MSsWbMAAMeOHUOvXr3w+OOPY8mSJVi3bh0effRR1KhRA3FxcWX+WomIiIiI6P8988wzGDNmDPbu3Yv27dsDALZt24ZFixbhrbfeEtdbIeYZSk9Px0033YT33nsPL730Elq3bo0333wTqampCA0NxdKlS3HvvfcCAA4fPoymTZsiPj4e7dq1w48//ojevXvj7NmzCA8PBwDMnz8fEydOxPnz5+Hl5YWJEydi1apVOHDggH2b/fv3R0pKClavXl2ifXTnvPVERERE5N7XawX7VvPPz2EIcO08Q7a0TJxp1N8tX7czvvnmG7z22mv4448/AABNmzbFs88+i3/961/iOitEN7mRI0eiV69e6Natm8PyXbt2IS8vz2F5kyZNULt2bcTHxwMA4uPj0bJlS3sgBABxcXGwWCw4ePCgfZ2r646Li7PXUZScnBxYLBaHBxERERERlY67774bW7duxcWLF3Hx4kVs3bpVKRACKkAw9Pnnn2P37t2YPXt2oecSEhLg5eWFoKAgh+Xh4eFISEiwr3NlIFTwfMFz11rHYrEgKyuryP2aPXs2zGaz/aHSV5GIiIiICAB03VAqj4pu6NChWLx4caHlFosFQ4cOFdfr1u/MqVOn8NRTT2HJkiXw9vYu791xMHnyZKSmptofp06dKu9dIiIiIqIKzmYzlMqjolu0aBGefPJJjBkzBjabzb48KyuryCCppNz6ndm1axeSkpJw0003wcPDAx4eHti0aRPefvtteHh4IDw8HLm5uUhJSXEol5iYiIiICABAREREoexyBX9fb53AwED4+PgUuW8mkwmBgYEODyIiIiIiKh2rVq3CDz/8gLi4OFy6dMkldbp1MNS1a1fs378fe/futT/atm2LgQMH2v/v6enpkGbvyJEjOHnyJGJjYwEAsbGx2L9/P5KSkuzrrFmzBoGBgWjWrJl9natT9a1Zs8ZeBxERERFRWbjcrc3VqbXd+pK/xJo1a4bt27cjLy8Pt9xyiz2Rggq3Tq0dEBCAFi1aOCzz8/NDtWrV7MuHDRuGcePGISQkBIGBgRg9ejRiY2PRrl07AED37t3RrFkzPPzww5gzZw4SEhLwwgsvYOTIkTCZTACAxx9/HHPnzsWECRMwdOhQrF+/Hl988QVWrVpVti+YiIiIiIgK0TQNAFCtWjWsXbsWjz/+OGJjY/Hqq68q1evWwVBJvPHGGzAYDOjXrx9ycnIQFxeH9957z/680WjEypUr8cQTTyA2NhZ+fn4YPHgwZs6caV+nbt26WLVqFZ5++mm89dZbqFWrFj788EPOMUREREREZUs3XH64us4K7srZgDw8PPDhhx+iWbNmePLJJ5XqrRDzDFUE7py3noiIiIjc+3qtYN/CD66AIcDPpXXb0jKQ2LyvW77uktq0aRNuvfVWeHg4tuWsXbsW27Ztw7Rp00T1VviWISIiIiKiykK3GaC7OPubq+srD507dy5yebdu3QrNF+oMBkNEREREROR2xo0bhxdffBF+fn4YN27cNdd9/fXXRdtgMERERERE5C5sxssPV9dZAe3Zswd5eXkAgN27d9uTKFytuOUlwWCIiIiIiMhdMIGC3YYNG+z/37hxY6lso2K+M0REREREVCXk5eXBw8MDBw4ccHndbBkiIiIiInIX7CZXiKenJ2rXrg2r1eryutkyREREREREbu3555/Hc889h+TkZJfWy5YhIiIiIiJ3oZdCy5BesVuGAGDu3Ln466+/EBkZiejoaPj5Oc7FtHv3blG9DIaIiIiIiMit9e3bt1TqZTBEREREROQubBrg6klSbfLU0+5i2rRppVIvgyEiIiIiIqoQdu3ahT/++AMA0Lx5c9x4441K9TEYIiIiIiJyE5rNCM3FY4ZcXV95SEpKQv/+/bFx40YEBQUBAFJSUnDbbbfh888/R2hoqKheZpMjIiIiInITmm4slUdFN3r0aKSlpeHgwYNITk5GcnIyDhw4AIvFgjFjxojrZcsQERERERG5tdWrV2Pt2rVo2rSpfVmzZs3w7rvvonv37uJ6GQwREREREbkLm6EUEihU/M5gNpsNnp6ehZZ7enrCZrOJ66347wwREREREVVqt99+O5566imcPXvWvuzMmTN4+umn0bVrV3G9bBkiIiIiInITTKBQtLlz5+Kuu+5CnTp1EBUVBQA4deoUWrRogc8++0xcL4MhIiIiIiJya1FRUdi9ezfWrl2Lw4cPAwCaNm2Kbt26KdXLYIiIiIiIyE1oViM0q4tbhlxcX3nRNA133HEH7rjjDpfVyWCIiIiIiIjc3rp167Bu3TokJSUVSprw8ccfi+pkMERERERE5CYMNgMMzCZXyIwZMzBz5ky0bdsWNWrUgKZpLqmXwRARERERkZvQbIZSSKBQ8YOh+fPnY9GiRXj44YddWm/Ff2eIiIiIiKhSy83NRfv27V1eL4MhIiIiIiI3oemG/7UOufChV/xL/kcffRRLly51eb3sJkdERERERG5n3Lhx9v/bbDYsWLAAa9euRatWreDp6emw7uuvvy7aBoMhIiIiIiI3YbAZYXD1JKkVdNLVPXv2OPzdunVrAMCBAwcclqskU2AwREREREREbmfDhg2lvg0GQ0REREREbqJgnI+r66Si8Z0hIiIiIqIqiS1DRERERERuQrMaYbC6doyP7uL6KhO2DBERERERuQmDzVAqD2dMnz4dmqY5PJo0aXLNMl9++SWaNGkCb29vtGzZEj/88IPK21BmGAwREREREZGD5s2b49y5c/bH1q1bi133l19+wYABAzBs2DDs2bMHffv2Rd++fQtlfXNH7CZHREREROQm3CWBgoeHByIiIkq07ltvvYUePXrg2WefBQC8+OKLWLNmDebOnYv58+c7ve2yxJYhIiIiIqIqwGKxODxycnKKXffo0aOIjIxEvXr1MHDgQJw8ebLYdePj49GtWzeHZXFxcYiPj3fZvpcWBkNERERERG7CYDXA8L8kCq57XL7kj4qKgtlstj9mz55d5D7ExMRg0aJFWL16NebNm4djx46hY8eOSEtLK3L9hIQEhIeHOywLDw9HQkKCa9+cUsBuckREREREVcCpU6cQGBho/9tkMhW53p133mn/f6tWrRATE4Po6Gh88cUXGDZsWKnvZ1liMERERERE5CYk2d+uR/9ffYGBgQ7BUEkFBQWhUaNG+Ouvv4p8PiIiAomJiQ7LEhMTSzzmqDyxmxwRERERERUrPT0df//9N2rUqFHk87GxsVi3bp3DsjVr1iA2NrYsdk8JW4aIiIiIiNyEwWawj/FxFd3Jlqbx48ejT58+iI6OxtmzZzFt2jQYjUYMGDAAADBo0CDUrFnTPuboqaeeQufOnfHaa6+hV69e+Pzzz7Fz504sWLDApa+jNDAYIiIiIiJyE+6QWvv06dMYMGAALl68iNDQUHTo0AG//vorQkNDAQAnT56EwfD/dbZv3x5Lly7FCy+8gOeeew4NGzbEihUr0KJFC5e+jtKg6bqul/dOVAYWiwVmsxmpqamivphEREREVLrc+XqtYN9afH4WRl/X7ps104ID/SPd8nWXN7YMERERERG5CYNVg8GqubRO3cX1VSZMoEBERERERFUSW4aIiIiIiNxEaabWpsL4zhARERERUZXEliEiIiIiIjdhsGkw2Fw8ZsjF9VUmbBkiIiIiIqIqiS1DREREVcyTtsfEZd8zvO/CPSGiq2lW10+6anNxfZUJ3xkiIiIiIqqS2DJERFRBLM+PFZX7VosRb/MT45visu+gh6jcaKwWb5NKxkPLF5d9Tn9QXHaWtlRclqiq0KwaNBfPC+Tq+ioTBkMksjP5ZnHZOmc9xWWrt/hFXJbc0yJbF3nZnF7isn19NojL1slLFpc97hkiLmv0MIvK1cE58TbH6UPEZf012f5+pN8m3mbDjHRx2YhUWWeJRjW3i7e5OV1+LjXJ4xnUDIoUlz1hi5BvmNdjRNdl0F2fQMGg88tXHHaTIyIiIiKiKoktQyRyOsAoLluX6R0rpXWZsjvcF3zDxdts5X1YXPasXk1c1uhpE5f90xYlLhtuuCgqF4pU8TZP28LEZfMNsvNEiJYm3maKV6a4rEJ7h9hhv0BxWW89T1w23CY/Jg7qdcVlZ+BeUbm6tiTxNgcZNovLEpUHdpMrWwyG3MTZP2VjASIbxbt4T0qmr+ev4rIn/duJy8ovX8vHPyfkr7VetPw9Lg9bfaNF5c4oXGx7wCovq2WJy1oVGtVTbAHistUNKaJyvnqOeJvpNl9x2SCDPKiR+sdLfpa4q+YPLtyTkhmhrROXfVm7R1w2EPKgsZYuD0y2ZLcVlYv24jiy0hSfKu+uGZ4ivzkqleIvP/enp8nLUuXEYIiIiIiIyE2UxqSrrq6vMmEw5CZ+ry27U3EqRX4354KP/O7227hLXPb5MPldGcNfshY0ANhdW961KdMo+6rsqN1EvM3XxSXLx+95jUXlzAaLeJv+BvndbT8tW1w2X6FlKF+X30WVllVpybIq/Ex4I1dULkCXt9odhKyFEkCFG9zvB/kxnAmTuGwdJIrLzvKeJCr3ia3sW+2qkm1meXfluBxZS2HLsB3ibaqweFoAyJK7UOXEYMhNWDxkH0WurzywkF7gA4BHvjyNkadCC/XRGvLt+ilkXrJpsp1uaDgj32gF08e4TVROJSNce+894rKhunzMRKYmv5D0EB5LKlT2tzx46vL36HXDItftiJsbC3mAoJIlsHG+PBiKs70sKnc661HxNgcFiYtWGTnwEpcNyGQuLlfTrJcfrq6TisYjmIiIiIiIqiS2DLmJPE3W/cVmlPfryNbk8/1YrPIMSGle8n0+4iOfo6VtqiwTFwB09ZUlM1hi7SjeJsp+TKoS6d18lfl+kiFPRuBtk2fi8rPJun8BgLcmT2YgpdKtL9smv2N8QQ8SlRtk4MScpe11bZG47M40effsHB/Z8TTOvFC8zQ0Z74jL3ub3m7hsRaLS5TI5QHbuz0i4RbzN5hHl08WurHDMUNliMORi9+bMhmeOt9Plepj8RNuLzr0kKgcAR7xqiMum5csvQiGPwRCZL+/aVB5ZJQcat5T9RstJhXut5dQuPlChbK+cGaJyq0zTxNscX7F62FEZaBsiDxAa2Z4UlRumyW+afGFqLy7755kYUTmViXjLg8qNpRyP86JymZ66eJtbLfKAPMPi/v3F2E2ubLGbHBERERERVUlsGXKxr0yTEWiSdyFzmrwHi0I+OGCa0kuUD4RdnS1vVvdWSKBA5A5UWniIXOU/6C0u64GaLtyTkpF2QweAo9Vlt9MbibdYPhJs8vm5En1PiMqFZ8qbKhJ95Z9pZp68RaqsaLbLD1fXSUVjMERlbn+SPKDJDpY3ZrYNqViTmBJRxfC59VZx2f7CLIzlaTxWyguXQ38UpS68FWzsplSQJp8gOVeTfaiBWfKr8/M+8oDG4P6xEJUxBkNERERERG5Cs+nQrK6N2jQbo8DiMBiiMqcy0dpBhTuwRESlIUBh3rWq0vJA7i1f4UA84BElKnd/xDLxNtejh7hslhf7zJMjBkMVXHyqPKNKrLnipQz11OXN6irZZzoEVrz3iojKRppw0mwid6GSar08JMNfXDYb8mkVygrHDJUtnsErOF/5dCcVUrYmP2TP+jLHPhG5XkUc90NUkU3DV+KyFljwb5hduDdU0TEYIiIiIiJyE5q1FMYMubi+yoTBUAV3Q2jV6r5V4Sb2JCIiInKGTb/8cHWdVCS3nnR19uzZuPnmmxEQEICwsDD07dsXR44ccVgnOzsbI0eORLVq1eDv749+/fohMTHRYZ2TJ0+iV69e8PX1RVhYGJ599lnkXzXgdePGjbjppptgMpnQoEEDLFq0qLRfHhERERERlSO3DoY2bdqEkSNH4tdff8WaNWuQl5eH7t27IyMjw77O008/je+//x5ffvklNm3ahLNnz+Kee+6xP2+1WtGrVy/k5ubil19+weLFi7Fo0SJMnTrVvs6xY8fQq1cv3Hbbbdi7dy/Gjh2LRx99FD/99FOZvl4iIiIiqtoKusm5+kFF03RdrzDvzvnz5xEWFoZNmzahU6dOSE1NRWhoKJYuXYp7770XAHD48GE0bdoU8fHxaNeuHX788Uf07t0bZ8+eRXh4OABg/vz5mDhxIs6fPw8vLy9MnDgRq1atwoEDB+zb6t+/P1JSUrB69eoS7ZvFYoHZbEZqaioCAwNd/+KJiIiISIk7X68V7Nvt0y7Cw9u1+5afbcH6GdXc8nWXtwo1Zig1NRUAEBISAgDYtWsX8vLy0K1bN/s6TZo0Qe3ate3BUHx8PFq2bGkPhAAgLi4OTzzxBA4ePIgbb7wR8fHxDnUUrDN27Nhi9yUnJwc5OTn2vy0Wi9JrexM9ReXq5CWLt9nX81dx2fKyKidGXLaXabsL94SIiIioFHDMUJly625yV7LZbBg7dixuvfVWtGjRAgCQkJAALy8vBAUFOawbHh6OhIQE+zpXBkIFzxc8d611LBYLsrKyityf2bNnw2w22x9RUbJJx4iIiIiIqHxUmJahkSNH4sCBA9i6dWt57woAYPLkyRg3bpz9b4vFohQQBehFB13XLZdnFW8TnvKiKj7SbxOXbXdJ4c5GhLwoERERUZmw2i4/XF0nFalCBEOjRo3CypUrsXnzZtSqVcu+PCIiArm5uUhJSXFoHUpMTERERIR9nR07djjUV5Bt7sp1rs5Al5iYiMDAQPj4+BS5TyaTCSaTSfm1FRimbZAV9HXZLpQZ8WsFEO9zswv3hIiIiIiqMrcOhnRdx+jRo/HNN99g48aNqFu3rsPzbdq0gaenJ9atW4d+/foBAI4cOYKTJ08iNjYWABAbG4uXX34ZSUlJCAsLAwCsWbMGgYGBaNasmX2dH374waHuNWvW2OtwRuKf3ZDp7/zbuq2BrMWjn0e8qFxFFWuuWvMqEVHl9rn1VnHZ/sZtLtwTInIbNtvlh6vrpCK5dTA0cuRILF26FN9++y0CAgLsY3zMZjN8fHxgNpsxbNgwjBs3DiEhIQgMDMTo0aMRGxuLdu3aAQC6d++OZs2a4eGHH8acOXOQkJCAF154ASNHjrS37Dz++OOYO3cuJkyYgKFDh2L9+vX44osvsGrVKqf3+Vz1fKQFOB/Y9POQXeTvvihvKbmpmjywSNsh/wF//5ZgcdnxWCkuuz1F/l7FBDEIIyLXY0BDRIXYSqGbHIOhYrl1MDRv3jwAQJcuXRyWL1y4EEOGDAEAvPHGGzAYDOjXrx9ycnIQFxeH9957z76u0WjEypUr8cQTTyA2NhZ+fn4YPHgwZs6caV+nbt26WLVqFZ5++mm89dZbqFWrFj788EPExcU5vc+tQzaKUhb+B72dLgMAd+ZponIAcPov51u+CtS6Rd4i5SHMnKfKP1v+XhERuZvvcuXZNe/yYnZNIiLAzYOhkkyB5O3tjXfffRfvvvtusetER0cX6gZ3tS5dumDPnj1O7yMRERERkatoNhs0F7fkuLq+ysStg6GqJFjPKPNtZpvK54uRr5DRPeFwe3HZrLCqk2N/c7qsS2Anf3YHpKpL2lpeq4G8pXzfeXn33fpWhdZuheya/fKeF5dd7vmyfMPl4OesW0TluvvsuP5KROQWGAy5iWaWdFE5nxyjeJuBaQrTTClMq6Qy7gdN5EXPJ8l+1AD5BUutc17ibZ6omSsuezTEX1Suk3iLRBVf6ClZcHHS0E68zRvqyW9AHEyQn9NU3GPYXC7blfrnhPzzsYVff52ibLXIg9xGp2XzXlgC5FNtBKXKryXebyGLrG/OOi3eZqUPNplau0wxGHIT0ixp6zzlJ9ymORVvDM3OZPnrDUuXn+xzPWWtSl7Z4k3CU+Gub1S2rKXx7Cn5OLLIRlUrsyG5J5WxkIaasnJ+CucWFVle8tbu/Qo3hyL9FFrZ/WTFVAK/alnySx3ffFmAEaZws9EzV3butync38z0lV8oR9kuiMrdvlnhe+P8kG6iYjEYquC6+ip0a2rkuv0oK3XOymeK9RD+wABASojsBzHgFnmmqByFwM9beIPQ4i+/s/iP8E5oh8Dy6Zq3IUP+/t7mx+6EpUnlTn71FPkFVkKNfFE5n0z5Vejhs/KL/LBs+Wu9pNCKkOol3640CPNRSBZ0PkT2uarIEd5AA4C0QNlnY9Pk28xWmDaxWl6OqJyWJ9+mSnbYDIvCZPVlham1yxSDoQpO5S7oPxHyHwiVsSUqfeRrGOTBkFXhaK9T51d5YaFUb3nZNE/ZxUqnyLJ/nSrp4YNU7shXlxcl9yW9cQEAvlmyoMbqIb8IDUkpn5/hmgoB3Amz/D2+5Ct7r8zpCvtbTX4RaBa27qvsr7QngkGXB4x5RvkxXD1LVvZiPfnvuadCly8PxgR0FQZDLnbixO0ICHD+bfUXnjizzfJvdb0E+ceffVY+z1CtIPk4Gk/5MBp4KnRZO35cdqdaJYgKzhIXRXCW7GJlN8p+3iqbQm/NLIUkIMkmnv7cVb5CcHGiuvyYaHtQdm4yKFxcpYbIC6u0SPldlL/HfvXk+1wtXfaFv6AQgEVflL9PGd6y15opLAcAkcIeEEnh8huc4Zfk50NbmGy7Kt/zSs9qA6wubsHimKFi8WrAxQJTjQi0On+32kN4kV8jXf4Regjv5gCA16lMcVlDTR9xWatJfuWs8noDLLIWiJP/yLv7mHzlr9UvW/bj76XQlfBwjqz7S9tIeSvjukx58JZiVGh6oxKRZjX0lOX/AACEpcmPYekNE88U+UVonpf8HK4ShOX5yd+nminysv6ZsnOptLUEAMwKyQEyaijcgRPKE94v9M+Qv06V8UblsU1fhTHPtoowXprd5MoUgyEXCzxrRaCv8+WsPrKTmFHYAgAA+X4KXYys8h8mTeH76JmmcKfEID8B+gjvZvoodOPyDpL/Ukgvks4J7/CpbDPpkDxdes0g+Wfa/KSsnzsApayGKqQtlCoXko1qyifnNAkPp3CFcT/ZXvITjNd52YWvli0/L3nmls/PcK7CvQDvXPm5Sdoa4K1wAZunkGjCR/haVcbvSMcMeQtvggFqgbW0rMrNNwius4iKw2DIxbQcGzSj819w6U+/IUHel0qrK7/9aqsmH22p5cvPuppCEJanEPwZc2TbNeYq/Ah7KFxwCO8shibLTwnHashGw0ZlKlzkKIz7iWjyi7xwOZG2UJ6vLh+pfOGAPFj1ry77bKXdhgEgRCGpgGaVBci6b/m00OcqtO6oZLpUuehO95Nd6Hso3MxSudCXJkLwKYfWBw+FPBGaQn9laU8EFaEX5OONvIU3N8uU1VoK3eScq2/27Nn4+uuvcfjwYfj4+KB9+/Z45ZVX0Lhx42LLLFq0CI888ojDMpPJhOxshRNOGWAw5GK6pwG6p+DEIDxZ28LkXc4MueXTZKrlyX/8bcIWNEAe0ACALjzXa3kKgzxV7r4Kg6Fchbvq1aTj3oQDqgEgQGHMxJ9nYsRlPfLlP6ahifJjWHJqAdQuHFTuqpuEGcBMCgGyd2rZj0PQcuQXLcZc+fHg4Vk+F3Uq5wlpVy6VjHDhF+SXOtKgxkNhagRp8KYS0OgG+fdGej5U6SZXPsnsq5ZNmzZh5MiRuPnmm5Gfn4/nnnsO3bt3x6FDh+DnV3yO/MDAQBw5csT+t6a5f/DJYMhdCE8KNlP5nBJs8hwI4sACADwy5BcduqDFroBB2iKlEG9KAxpAfgGbr3A4BaaV/bEYcVKhj7zC2S9LYUyLV0bZtwR4y4f4KX1fDcKLM2PZZ0IGANi8ZceT4aK8y6UtVGHsg8IxnBYkPw5VzhMQBlKZCkG5yhw60qBGpbumNIFCrnf5JCSQBm8qCRSkczFVFLrNCt3FLUO6zbn6Vq9e7fD3okWLEBYWhl27dqFTp+KnaNc0DRERsol4ywuDIRczZObDAMEXXNhKo4eXzyBwq0mhb7JCy5AhVd7dJy9C/l4Z8mX7bPWSn7BVUoFLZyL3UpjLw1t4N1/ldapcqKu0FAYoZPpTGTMnDRJUtqlyt1nKoNTdR+H8kiY8vyjcaFFJCqMy7kd6jgDU0jCnCy/YjQrHcJpCMKTS+iyV6S+8maUQXKiMK5RS2V+lro8KXfUrA4vF4vC3yWSCyXT9oQ+pqakAgJCQkGuul56ejujoaNhsNtx0002YNWsWmjdvLt/hMsBgyMV0kxG6pLVG2P9Fy5F/qXWFgEalhUYlkYEuvHMLqGVP8kqTlVO50FEh7eeuEgxJgxqVVgCVYEilpRCSGx7/oxKYSMeXlEfrDgDkekpfrPx7riucX6SJYfQAeTfEDIUWmnR/+cGkEtCodG/yFP50eJZDtzNAnlpbZdJVX2GrksrnohKYqHQblsr0kX+omcKbm2XKZr38cHWdAKKiohwWT5s2DdOnT792UZsNY8eOxa233ooWLVoUu17jxo3x8ccfo1WrVkhNTcV//vMftG/fHgcPHkStWrWUX0JpYTDkYvkBRuT7Of+2eqTI7kiqjKFRYVNo8VC5CDUqTLqq8kORGyDbZ2mKVEAtSMgXXuj4KtwFlU48qfIe+V6Sl80RfqYA4KHQRcOoMFZPGkiptFBmKdxVzzQJjwmFFg+bh8LnqpCgRcoSIH9/zwbLL5ZqJct/O1KFSRAAIFB4jlE5f2cpdFkLEKYCNyn0gCiPMTgqAaO026RKEGVTGONU1Z06dQqBgYH2v0vSKjRy5EgcOHAAW7duveZ6sbGxiI2Ntf/dvn17NG3aFO+//z5efPFF+U6XMgZDLmbMtsFocHE0fw0qrR1KCQUUAhqVfu5eFxUiBLM8kJK2eqhkbFIZM5QpLJut0OdcmiZV5UdNpRVApbWkvOSYZTt9PkJ+TlIZ+5Aj/N5cqi7f3yCFi3xplkyVVnZvhQzvYWny7arc+FDpUiVtfc4WBtaAWjIDKe+csk8/rhJcZPootBQK04h75amkAi+flsIyU4rZ5AIDAx2CoesZNWoUVq5cic2bNzvduuPp6Ykbb7wRf/31l1PlyhqDIRczpObBIMggZhNeqHsqDMZW6VufY1aZ80FcFF7e5TOPgrSoygW3tN84APgK5wlM9ZeffKXpVVUuyjKDy6fFI0DhFqxnmkJ6eOF3J1lhfIi/Utpc2WtVudhWOb/kB8gCKZtCVjeTwg0Tla5CKgPQfbMUulgLL9izTfJj2Kww35v0Zo2/RaWVXVxULN2v7CMED4VrfZXguDzeX6fZbKXQTc65z1jXdYwePRrffPMNNm7ciLp16zq9SavViv3796Nnz55Oly1LFeGQqFBsAZ6w+Tp/4pVmhVNpZQEUupMozI1hU7ibb/NSCIZUBmULN6vSuqPSYiK9E6oySFlKJdOTrzQNI9TuLKaGKEzsmSHfZ2nXSZXxFiqTR2YKG2MtCsdEtYviokpBTXlQGZeicjNApQXClC3MiKjQ1VPlRph0TiWVG2E+6bJy6Qrjz1SygUrnjvJTCFLzjQrp7MspW2VFM3LkSCxduhTffvstAgICkJCQAAAwm83w8bk8rcugQYNQs2ZNzJ49GwAwc+ZMtGvXDg0aNEBKSgpeffVVnDhxAo8++mi5vY6SYDDkYvn+BuQLJveUdjtTSWSQGSo/EfmeV0hxLbz7CgDZCi1SKqRjNVT6cKuUlZJmerpM9ib5C/vkA4opc8upv3q+T9nfMVYJaHLkvUshzZ9QHkE5IL+5pHKnWeVCXeV9Ko9B74D8vKayvz4Krc/Sc395dMXyTymfzITpxU85Q1LW/MsPV9fphHnz5gEAunTp4rB84cKFGDJkCADg5MmTMBj+//t16dIlDB8+HAkJCQgODkabNm3wyy+/oFmzZkq7XtoYDLmYbtREgY1HVtmfOZUu1BVaaFTmQlAZuK4yfkd6sZOvMDeGSnYfaZe1LC+F4FrYbcE3W2W+k/JJzaoyz4rKhbO0bPhFeURzOkyezl7aXVM6aB1QHEReDqnLVY6H6snywiop01XmrZK2qiplOxMeh4B8XjCVTKLS40llHLDK8SBtZVc5BwdfUpiAPZ3JF0pC16//Pm3cuNHh7zfeeANvvPFGKe1R6WEw5GI2D9ndRelAcF0hc5JKUOKpkLhB5YdJhUrXPk34w6bSFctHoQtBlnC8kUqWKIMue60qgYVKKvBMYcpcAPBVGEejlJZY+N1R6bLjL+zWBACX/GTHoUrLm1JLi/Bi0qjQQm+NkP8MG5VaN8VFlQK4i0Gy98pT4SZ5sEKqdmO27IAyKtx8U7nZKKUSSJlTZe+vyrWASlc3k8I8cWVFt5bCpKuuTshQiVTAfEpERERERETq2DLkYjk+QI6v8+U8hHcqVJriVbo6qNxV9BYODi1P4m4LKne+FO7USZNjBCiM55JSad1RGQsgHRityveSSipxWblswTmpQJBF/jOR6ivrYqdyTKjcMfawyAprOQotqgpZcPLK6Rc8JUT+es+UPKOvA7PC+TBSZXJmD+GE6ArZWo1Zsvc3J0R+QKhkppW2WKtQSYikUrbM2PIvP1xdJxWJwZCLeeUAXoJ31TNB1qZuCJGnK/NKURirIUgSUUClf71Kd5/yyBSlKUziqOXL36hc77IPaqTpdr0zy2ccmMocRSo/psYseeHcINkpWyVAyAyUX/hKpxFR6TqpROECVkqly5nK8Z8ZKH+taQqJSzI8hJOYKgwmPxspH/cW4iv7gAKSFdKPZ8nOTSpJEFSyy2b7yo4llXM/kSsxGHIxn4R8+EhODMJZ6Y2p8pO8LkznDagFNAbhawWAtBryffY/r5IBTHbSzgiSn+wDzqtkHZPO7yL/bKqfl3025ZFQQLWsr8r8Xgoz00tvBuQpJPJQmaNIOulqhsL8OWYv+TlCOnmq7in/rkovJAEgQGE8V2J1eXBxKkhcFNma7KBIVTiGfX3lx3C6t+w3NkJyV/R/pBNYS1NcA4B3jsL4Y+Fnk6uQLj1QYZSHwiVI2bGVQjY5tgwVi8GQi2k5VmiC32I9UCF/rZA0nTcgDw4AwKQ0Uay4qNKcTNIuayoJFHICFO7UCZMDqMzULg0uUoUDqgHAP13hB1Hh4spLoYudIVdhZnphQ7D0eADkcwUBQJqnLDCpqdAqmqEwcTBqyw5ilaQNidXlN7SSg+Tb/TtE/l1P8TCJy2ZrsgMqT/LD+j/eCn3Cpbk8MiPkF57BwhaTPIXsmkaFrrRS0qQ7gNqktuUxbYWzdKsVuov78zGBQvEYDLmY1ewBq6BZXdrtTGWeIRUqd9VzFfo1K21XIQOed6rsR0YlW46nQheYXOFkjCqpqtOE3akSFYKh8sh+BwCXFLqO1Twrjy6kx7/0eACAVGFrCQAYSpCatchtKrSWeMljC/ExrPK9uSDMuAcAZ73l/aJUggurpnAu1YXjyHT5dy7FU/6dC8+WncQV7ishVZjpNSBHvs08ha6pmcKbNAaFbqnBgQo3TFTuqlKlxGDIxS5GG5Dr7/zFg59Ftj2DtFM+1CYwVbmzojLuR4XKdqVlfS7JT7o2hbTp0jk5VC5CU4VdUTwV4nnpjzAAZCoECAq9CeFTTaElLEN2IKpMuuqtMO7topfsIvScwo0LX4U+MD7CrkI5CsdSnsLYNRWhefL8wv+YgsVlPSA7/gOtaeJtKgVvwpvzCtnhxd1Ls8u+gwkAIE3YTdQovFkCAKkKLcDpegUIhphAoUwxGHKxU2H58BMMTI2G7CymMgmpyqBqlTkfVLKkGRUuklSy5Uiz+0DlnCscaAzIu+2oBCZS/gpdzoxeFeBH7SoqmdKkQa5KK5jKHW5p60NQnvwE4ydM5AEAgWmystkKc7YlBMpfa1C+vOk5RDh/DgAkeMmbIPxssn0OyJOfnIRJDQEodJNTCEykAXKmwhVdtlGhy7EmvUkjP7nUNMg/1IrQTY7KFoMhFzvjb4Svv+BtjZB9sZtZ5LfGVbphKQU06fIf/zw/+S+MOKCBwvgqhQx2KokmpGOVVLpKGIW7q9JqodIypCI8Rf5G+SmMc5J248pSCBrzDPL9zTDIPqBQyC+2pVkNAcAvRfa98fJWuKjzlZdNVcjqdklhn3MVLh2q2WQtUiotLeVxk0elC6M0yD3pJcxbDsCiyQcNBeqyMVlGhbuF6Sb5jYAMpWkryog13/U5wF2dkKESYTDkYt5WHd5W57/gocIZnFV4ZMlPRPk+8gsOzabQV13hiFXJ+JTnJysrmXOqgP95hax7vrKT3gWFz1V6N7O6R9lnKwMAUzn9Lqi0yErvaKp0n8kwyr+vp7XqonK1NXmXKE+F1nKvFNmHkxchPxBDLyh8OArJFw6Y/cRlMzR5AoVz4rdKfkwEKMwD5SM8njxVxjh5yG4i5CuMA0vU5V0fj9hqi8rdaDwq3mayt/y3KjO3AgRDVKYYDLlY7RQd/oI+9tXPyz4Kn4sKrR3CyeQAwFK9fDJxXQiVX0kG+sp/KFLMsu2q3KX2VMg6dk6YiU76IwwA540BwpLJ4m1ajPL9DcqXtz54K6Sblo77Acqn+2O2Qf4zccEWJCqXqzCOJkuhtSRYmPZcJcDNU2jdtCp097mkyYMhC+Rlz0IWIAca/hZvM9NL/gXwFI6Zq5YrD1QzhDeIVJJihBsuicvmG2TbNdvkWf7OePmLy2Z5uX8LiW7Ng67QFbC4OqloDIZcLOyiEQGC29XiLmsqFw3B8rKnw+VN1GGX5Ifd2WCF1iyFjE/pwjEBKnM3qFwkmYQ/4Nkm+WdjEmaJUhncnGqQN72lesnLBnldFJdNMcsvzDzyZe/VJR/xJsWpkAHAW9jdLVt4cQWozZVllXRxhlqL9aVg+YWZSuIGlYDmtDVMXDYfss9WfrMFCPCS/17558iOJ5UkK9KkJaG6PCmGp0ILvdEoK2tTyGqUo3BeylH4zaHKicGQm5DOH6Ir9Oe/ECo/+QWlyy9WpBd0AJCpkGFNZQ4df2Gw6qXQuqOS/U46vVGaJr9qDhLe5ZNOwggAFpTD5BgArJo8GFIZv2MWDuryV0jxbvGVHxP5umx/M4VjjQDAwyofDJknTNCiEgydV0gRnGqSn9PSdfnnmm6Tf+9ydGEXO4We5EEK4y+liRBUEo9kCnttSLroF1AZvyN1wiBrJQSAMD1VXNZDoQtjWdGt+aXQMuT+LWLlhcGQi2X62GAUdNOwCYMac6KoGAB5ZioACFIY46SSycVTYV4C3yz5Pp8X3r1VeY/z/eVlVVpbyn6b8s8lR5iFUZX0YgUAQhQuOnwzhecJhQDMz6zQnRCyKCxXobuPyvnF1eOVS0IlaYlKd8IEq/xC1NsgPyakF6JJepB4m8B5cUnp8ZTqJf9gMwyy85pVkx/AFoM84YO0lTFFl3d1M2vyLnYKwwrLDlNrlykGQy72Z4QOP8GdvjrJsgtfP4v8I0xXmJVepXuGSupblYxCFxUm95ResCQpdIExKaRglrYMqQQXoTbZAGeVfu4qVO6Mp3koTBxskDfTSJMDqLSKqgzKjoSwBa2cxjd7WmTf16xA+fHgrzBRplVh8shUD3m3s3CPC+Ky+cLLDg9Nfv5WmgBVGNQkeci7IUrlCVtiAeC8ZhaXVQlqpJIhP35zwLEz5IjBkItd8PJCppfzb2teddkFkke+wsRj8oRASFNIGV1L4UdNepEPACcVJpk1C1NxSifsA4AsL/kVYa5C90kpafYkafplAAhEhrhsCuQ/4FYoZDJSaMzyEMZRKjcvzkN+kRQKWVcWi0J3zXxjurisJhwfoiIkTX4Be0EhWUSGQle3VJs8hfPfWXVF5eoEnBVvU+V3Q+o4wsVlm9tOicqpdHVTySYnnUhXhcrNrByFoLGs6LY8lyc80G0MAovDYMjFjLCJTkjStMQqLSUqVO60pSkEYSqztauUlaZwjrykcKdOYaC9dDI7X4X5XYzlcDs/UGHAsLQLF6CWNlelW5SUSvIQaUY4ADAJ+7yHKowHUJnUVpp+Xzr/kyqVi/yLOfJucirybLKbHyotQyrJDKSTvfopTOTnJ7xolXavU5UtHAfmr9DVLUWXtwzl6rz0JUc8ItxEdeGEoN4qE4go3M1RGZBqzpb/gqvMg+ObrzBWQ3hDpbwmCpSOrSqPgaXewix0AOCt0AfaW9rMAiA0R77P0mMJkA/UV0nB/HeebA4RAIg2JYjKeSkch9JxVQCQU112bsr1lB+HWSaVFmD5uTQ5PUJc1qiQWt4gDGpUMtjlGf4Rl5V+X0N0+bxI0vGX0iAKAIIUWlQP5jcQlfPwkJ0fACBbl/coyNUrwDxD1jxAc3FLDlNrF4vBkIt56jZ46s5fdEu7U2UrjPuRzlkCqF3QhSbLD7tjCuN+QrIUEhJIB9H6lU+XQGnLXbImv9vmJZxsRamVRWE8izTTGaDWymjTFNIhC49/lWMpMUfe3eeMl6z1wWiQHxMXQpLEZSOFN5dUAppMhe6weQopyJESKd+unzwhgTQYUumal6GQ8ATCrmcqY+2skE70qjD1hML5MFvY2peQL2+dVDke8vPlrXZUOTEYcjGjrsMouOsgvdhOU+gzrpJ1SWVwv0mhZUilRUqllSZT+E3xV3iteQpdm/KEY4YydHlGISmVVoDdhnrisgm2auKyaZ7HxGUDcuWtCEnCYOh0gPxCJylR3jKU7rdPVO4fraZ4m6m+8hSbviGy9/eSr/y7mm2UnyNUukV5ZshvfGTny8drWG2yfb7oFSTeZranQtAoPPef0kPFm4y2ySZA9VLItqpyPpSmS/87XdaiBAAR3vIxZPm2sh8b6Czdmg/dxS1DTK1dPAZDLhaYa4NfrvM/qNIxKXkKV/gqLUMqA7JzhBOYAmpjlVTGakgvWHxy5BHnJV+VPvKyD9ek0HUsE8LBYApB+ZE82WBsQN7PHQAuKkzYGuJhEZfNFp4nLnrIL171XHnZf3JlgZTZKO9ilOgrP6C8heNDTvnIbyKopME/rNUSl/XOkCcQSc2QXzgjT/ZenVKYcyU7RH6p422THRMqY+3SjLKWFqvCPHx/ZDUUl5VKtdQQlw01yVuArRwzRFfhEUFERERE5CZ0Wy50m2uz7eg2hdm3KzkGQy7maZUNfJdmHTtdTd56YJYn4lIakJ2rMAFkmjDbEwD45sm3m+Ip+6qoDKxO9pa/Vmk3OTPk2X2Shamq/9CjxdtUyTBl1uQtNCppcwO95f3VG6TKfsysCq0lhix53/zzwvFGRzNaiLfZseZucdkAYRe7XYb64m1WUzgOD+bKuxl55shbRjWFbgWGjCBRuUyFC8PU6vKW3EBN9p27aJWnqj7iJRvPZVRIipSQKe+a6u8lO4YNFnlXwkR/eauSNVv+O0eVE4MhFwvK0uAvaKoOTJNdrFiry09+PrkKF9sK41nyFW52SLsPAECoJr8IlaaqvuQnf5/OeMrnd/HTZSmyz+ry7i/H82U/4Ofy5FmtfI3yHzUPyAPVDZntxGVr+CWLy0Z6ySYxzdTkF77eqSHisp5RsgtJW6784jUD8i5rySbZT2KKTT7+xlthEt7fU28Ql/X0kd8N0xS6rHlmyT5bzSr/4RB34YV8XNa5LPkNk5oesqBcKcOawncuJV/2nfNROLekKQRDtjT5/HRlxpoHaC6+RGc2uWIxGHIxrzxNlFwgU5gIQWneHYWARiX5gk1hnNNpo/zk6e0n72OcK8wMlOYl/3z26/LkANEGWcrS3dnNxds0CltpUvKCxNs8lS4fM9Qm+Ddx2dMWeUvAeT95kPuPryygP6nL0xIb8+SD9KVzyhi85EFuosJA8BAPYXphhYz0x63yrG7Z6fJsXLnV5YkmbPny4MLmIbsg043yN/mMrpC1zOAnKpdllQcX0glF/TWF7h4K2QVtwgsC/0tB4m1mhshvQGgKiY3Kim7Ng66QkbC4OqloDIZczGADDII8tpeE8zZkeihkiVKYK+K8SX6BFJkp/0KqzJKdr/BeSbOsZXvLLzgSFdKOSl3IlW/TKkzNmpYj/0zz8uQXZek2+cVKfrZ88LnKwGppsonfMuUtCDaFi9A8qywYsqXJj8MT1eUXdeEmWcubSprfi/lB4rIqbD7y7nlQ6LKW7yMLdFWy3/0tTOQBAOfzZMeiyjxOHv6yrp7SIAoAdIUux3X3ybq1pgfLb3ro3vLWHT2P3eTIEYMhFztVzQY/wWzk0haeJA/ZXSsAOKcw98I5Td5Ck+cnT4l5JFfeElDNQ5auFADyhdlnshW69Zk0WVc3QK3bjlR6ruyCUKV7RrC/fNK+SwotUioSrfILfWk3mMQseZcSFZ5GWRcw7xT5WIKT2fIL35qewu5JwhYwADiVUUdcFrpCE72mkE5UYR4o6UV3vknezVllbGFKjqylUbsgHwsZFCnLprgpPVa8TZXgwu+crJylusIxqHL8qpQtI7otF7rK4Oxi6qSiMRhysT99zfDxdf5tDbTJTvR/QaGLBeQ/4ClW+cW2n1H+o/a3RT5g+LxnirhsA2/ZvDK/ZcnvyLf0PiwuK73gTsmWX6jnSAfaW+WtjCrznXgqjNVQ2efjChfrdbxPisrlCed2AQCbt/wuqrTdztci70p4MU0+EHy/h+wiNMrrjHibmSppqvPl53CYFMZN5MhvwhmEqbWtCvt7Okd+TEhbvH1T5DcMzZqsu2aCRX5uUaELExtdrP23fKPC4wgAkK/Qr5UqJQZDLva7Xh9egru3QQbZj7BKNiE/g/wiJ0mhC5e3SX4RKh2XAgC5NnmXKimVMS2BRnk3ljPCH/+cZPlFg0emLBjK95W/zjzhwF0AMJrkiQxMyfIuMBnV5BO2SgMpT4UB7/L2SfmFZPjJIPE2L9WSn5vOeMi+r01Dj4q3qQmzqwGAKVR+LGWnyceRqQwaNQm7u2X6pYi3eS5Vfh728U4VlctTaMnKEHZ3U0k8AqP8HJHvW/ZjcAw5Cq81pwK0DFnzoINjhsoKgyEX+9VyM4xw/kQWIswSpdL9paH/EXHZf1KbiMv6KfQTViEdzA0A+cKLupx0+V1fi79CSmPh3XFfhYt8q3BgtMqc2HkKd6itvvIfGp80+WeTKxxHAwDJwpsQvp7yu+oqwZD05oV3kvyo8EoPEpe1hchaeFQm8PU/L2/dT/ORT05rTJOfm1Qyu0l5+Mm7ORvONBKXza7xl6xggCyIUqEJz8EAYPSQf9OzzMKkGF7ygNGQIb8Rprl4/h6q+BgMudilpEYwZDo/uDpd+AOeG3pKVA4Ajgv78wNAXmaQuGyGWX5HJzlVPuO6OVDYsRkK6Z8V7rZlKmQjkmYGqnZWfrc4qbbsQlLabQaA0qR0eWaFNLQKaYkzU+Stb5pJdiPBoNBH3iNX/vlIEyhY6sjvNOsK41mMwnNi/CX5WI2ax+XfubQ6h8RlrQqBlMp3Ftmy85pR4VxqTpTf5EmtfVBUzhpyWrzNupCNhawWKL8eyFHocnyu/nFZQYXzt0qWS02hbFnRrbnQpRlzrlEnFY3BkIuZLCEwCAavByXK5iRI8pX/oF1SmDhPy5bfkb+okLFMpUuJyn06o1l2geWp0M/dyyC/UxeQJGsxNCgM2NSEXWdsKhdlWfKxa8mX5AOcwxRSwmZFyVtkpQN/fT2FKaMBwCJvBUuzyi6ajXmuvQgoKV3aAmyRBzSp4fLjX+Vi0jNQPtWA9XwdcVnvdFkmxjSlC2eFssIupn7CiUgBYI/eUFROpfeDNNkJAKQLr0M8FFpxDfkKAY2Vl77kiEeEi+WbcmAQpJ32S5VdNHhmyi8GcxWyx0DhAlYllbLKXV+DQpe1TF9ZFw2Dwv5GesnTcqdIAxOjPECWzh+iEtB45Ch0lciKEpf1SVcYq6TQHcUqnN8l3CQ/ltJT5S2UF3NkF74+l+QtQ1ZPeQuCVZgERFNoPVOhKXx3jP4XxGW9k+XZ/qSp2vPz5C0XpsyyDxKkXd8B4Eye7OaoSlKYln6yFjAA2Boi+03XTsu72xtUurpVgG5yui0XunCy92vVSUVjMOQmPLLL/k6oSp9xa5A8pXFOhjwYgkIfYyj0MbYKu/sEeMv7ufsZ5MGqtBuXRaG1MF/Yt97zTGPxNnMC5RccNQ+1FJdVabnw8pSPmcvMlV0QZih0ufTKVPiZSJNl1DImyu+qq9ww0YwqI9hkAi/I56zK8pddNAOAfy3ZXDYAkJcjHyOVGShro/cyyVs3rV7yY8KSIQv86vsKxxoBSM6X/UZ6KQRDdYzyKS+SgmSB1LF/Wou3aVP4nquULSuXu8m5OBhiN7liMRhyMWOeJwyC/qjS6yObwsWrSp9b43n5XfXcoPPispq3/AdRUwj+8oV35FXu1GXYFJIDCFtpVI6JEOGcP5dM8u5qKsGxKVP+WrP8VdIKlL0Tyc3EZZsozNYuHVuipyl0HVMgHZeSrzBw3SdZHlgbouR3uFVaC4+Z5Md/vpesbKBCt7O0YIVEE0my85NH2FbxNs+kybLfNRIGJQCQr5C5rJlJlk3xqMr5W2ESXmsOs6qRIwZDLuaV6Q+j5vyXVDq+2eYpv9j2UDiZ+KfIW3cuKARD/j7ydMhIaSUumiZM65rpI7/rK0hKaCe9O+5rkR8T0oknVeYPUZk8T6V1JzlS3jLq5yFPvpAt7GJny5LP26MyP6G4G6NNYVLPYHmiFKtw/ijNVz4iMU8hT0p5UUkbbROWNShMq5CtkPDEJ03+3ZHKE3bXDKsu7/q4O6e5uKw0GFK5MeqbqtDDJN/9L30vp9Z2dcsQg8DiuP8RUcEEXQiCUTDfipYnu+JQSRFZ7aw8w06Wv/zHBZ7yH9L0LPlEdsEKXTsMwhTOaanyzGF/Klw05/qniMp55cgn7TN6yFrtUlUSeXjK71ArXFuJ318AkLf3Ad7CrkJZwsQAAJAZrJDmOkt4pV9DfqHj7xMvLptmkSUe8fSRt1rk+soPRJWgJEdhsL0Km/C9UukClqPQ4m0Qjr9USbcOXbbNBh7ybHL70uU3CyM95TeHpKwe8vOSSlmqnBgMuVi+hxW6h+THTfZR2KrJ03cajtYXl00Nl999DQ/+R1w2c09vcVlTpvzHSZdeOSvcVvf3UMgUKAze/M/L3yPpvXGVrFZ5Ckkx0qrLg82wv+XjnBJVkmrUlI3zyMqSBxc5vvKLUOmFZG64/DhMuyQP6KVz2Ujv5AOAV6Y8UM2p9ae4bDWPFHHZowpz6EhvYLTx2yfe5qHjw8VlL9z1X1E5D4UZ1EwXZDfR8uvKj6W0bPk5IsNP9nvj6S8f82n1lHfVtykkWSkrui0HOnQX18kxQ8VhMORiHvlGGPOdPyFJW4Y8FDJTeWXI75apdG0K8pQnFbio8CNc7Yw8pbd0cjiVSfBybfILQml/6nxv+ck3JUfWaicdjwWoZfGyVJcfhxF/yyc7Tk2RZ+LKrSG7m68rZB2TBjQAkCX8vibXlPcRrfn7TeKy+d0+E5VLTJMn41BotIOXlzwZR7hR3qXKQ9riByDXU3YsSjOsAUB6iMK8YDmyQDfDW/4eeeTKzokq4348PeStjKlW2WcqndcLAPIVxhvZcivWmE8qfQyGXMw71Qseuc5fsKTWl91FMvvIf9BU0teqDFxvZDomLntUoVuIZ47CVYdwYLXKJHjZVoVBQ9JtBsh/nGzCrh26MEMaABiEYzwAIM9bfiFpSpcHCHk+8u2mZArns1EIaHxT5cFqbjVZhqochXEaKmnPw02yiYMTIQ+GEurJExkEecvP/zUN8vEansKJUwHAEP27qNz5PPnNrPTgFHFZaevzUWsL8TbL/syvJsQou7H0R65ClsvKPs+QNQe67tqWIbBlqFgV4IioWDxyNHgYnA8yUmrILvJtCrcVDdkKM7UrZGaLNsj7FxsU5pXxUMiKJW3hCfJMEW/zVLosoxAA+GQJUzAHyi/UPYWTE2bny8cuGPLkZVUGRhuzFbIKKMgXpqU3CrtNAsD/tXffUVGd6R/AvzPMwNCbVAXU2GIJlihCTMAGq8Jq1lUP7CK2tCWuynF3bRFZN/a1azQm0SRGUWNNbOuSKEaJLjgQQUPUiC0UUenSZt7fHx7uz5Gmzx2K8nzO4Rzn3ve+947PzNz3vW/TqWW8V+pEHvn0iUfKzeitsa3VtIpJagF9LGNR+1Tyse1kzAhXLOi/pUod/b7jZnmTdFxehR35nNTZNQEAxEK3Ipc+S6ZlPq016lolvYvow1L672G+Ne169TIqQ+WW9G7keiFjEWr2QuLKkJEVO1ZAZfHsP7wK4pNbOf18dRb0G5pFHv3mL+cmTO0+AABl1jJmqKqgndfChF65KCcuWAkAzg9o3RYKHOndEHXEirmpjIKknOlVqV24AKDUgd7VTc5CsZpCWgFLyJikQq+U8XSS2D2vxJZeWFHLmCilkvgZVsnormluRa/QUL9zAFAko/1BTtdJ6sQNJRUyZuaUgfogwfE2fUyL07XGf6+KB/Suvw/s7GgHymjZF8TeGnKPbSxCV270liGhb/7vu6lwZcjIyizKUUkYcGyTS3tCImOiaVnkrPJ+tZJ+k5BTqGuKtWHkFFbU2TKeLN6nFQjvu9LjqtPTbmxyCpJ2d+kPA36zo3975FQQ5Dyltr1Lq4SVWdDHTAgT+nu1u0Nr3czzuEY+p0NGJ/Kx1DEXJa3oC1YqiuhjYbJlzLDW3pTWQgMAmiJ6S2NWEa31orKCXnkzIa5tBABW92nd88o19HOaELt6Upc3AADzfPpDKer4VjljPmEtp/QjYzmHRiJ0ZRDCuD0QuDJUO64MGZlNjgVU5s9esSmzpo0ZknODUJY1zYKtuib62OW0y6AfTOzK5ammjUEAgOxc4vgQAMQea7KeqhcTu3DJWWbFPI9+89fIGZciY7ydMKG3UFrn0gqhehf6d71SLeN6iS2Upa/ImCXz1y7kYzUKWuWCurgsAMgp7uit6Q9brBT0VmudWsa0xnfbko5TylhGotKCPvW5xU3aw7sie/o5K+xp91edjOm8qYvhAvQFfOXMrGlhTp8AR19J/+yzFxNXhoxMc08HlebZv+CFrWilV1FML9DldqT/+JV2/h/5WFsl/SZRKWMCBYs2tIG7AFCUTXva/L8i+sxWFTJWea+woD3NV8mo5LbW+pCOU5c2zc+QTS69VUn5C/2pui44j3ys5TVaC8/tl+lT2Npn0seu5bSlDdIvK6N3f3SUMYV+LwVt8cjjMmZXE470z5JaRsuQo0JGYV3Gb5PeivZE3/kS/bdUzgQKemLLqJxW63uetPEw9M67QFkr+oO7S/d70g40p4/7sTaV0aVbLWOdxEbCLUONiytDRiZMFBAmz/7U+IErba0V0yI70nFyadT0Zmbq01cA0Mn48bQzoxcICzW095t7rwP5nHLWPacWQkst6WM13P9HWxsjqzf9xmRBbHkAgFbXZDzNz6LPiKgop69Jo6igPZGvMKd/X+UsTkttQTbJoXcRbQrlNvQuO8p79EHv+TKmJs4X9K5uZTJmRFQQx5GZVNBbhuR0TS21on137DPpLfsg9lgzU8joCi6jy1qlnO5uRF5u9IcIFeVloE9bwl5EXBkysmJXQEXouVZOHMytljGA3KyE3sUoq4Q+zSnoD8xk6WJOe+oLALfVxEU2C+n9sO1y6P9RN7yTScfJ6e5TSlwDp9iWPj2wSwn9ibxCRx8Lo9fRC1f2t+ktLcKa9n7VD+kF30oN/emkZQHtelUV7uRzFjjm0Y9V0K63lcsl8jnNT4SRj82S0apUCvrvP/V+BdAf4Mm5X5XZ0acRp07aU2pF70qoKaS9VznTjytkdEOkrsNnfac9+Zx+XeiL8JaaVuAo+ejG8WgCBWO3DNE+kxs2bMDy5cuRlZUFb29vrFu3Dv369as1/Z49e/DBBx8gIyMDHTt2xNKlSzF8+HDqZTcKrgwZmUmFEiaqZ59pR09cxLTCjj5wV50mY+rmW8TKAYAbzvSCjpzCupwWKUUebZCznFlrLO7Tu6yp7WjTl1cU0J9m5rvQnhaXONBn0wJcyUcKNX1GLF0BfXp4uxx6y1D+S7SbI3XiBUDe1NrU2Qndf6Z/Dm9455GPTdPRfhPlLHHgcJreqnTXn/6bpoGMNUdkzCYniONETCro51TI6FFAnUa8oBX9nC7XaA/CiivpD0dNSukPTPTEWeFMKulFUBPIWBpExrEtza5duxAVFYVNmzbBx8cHq1evRlBQENLT0+HsXP13+uzZswgNDcXixYsRHByMHTt2YNSoUbhw4QK6d6evvdXQuDJkZDq1HgpC4UFlSRsMaGNO7/qlLqEPAre+R2/xuFRA/0LIaQnLrqQ/NdMU2pGOe+iRTj6n+gG9IqUkFjhU1vRWGmp3EjkVXDkVGkWFjOmmS+mDd4vsZUy3TpyhyvE2/ftqQf9I4GYP2kQIHpt+JZ/TyaEP+dikrrRxKfok+lNPUUHvwviSNb21W86aNHLW9yq3yiMdV0kYi1tFJWOMiM09O9Jx2W1vkM9JrQyVVNArNHLWXbMooN2XlTp6GeQXPf3zW/4cLD6q15dCIYxbRNeLZ28ZWrlyJd566y1MnDgRALBp0yYcPnwYn332GWbNmlUt/Zo1a/C73/0Of/vb3wAACxcuxIkTJ7B+/Xps2rRJ3htoQFwZMpKq+eAf4j5M8OyF2Mp8WjNz7l16Ad/lPr1AZ/uQ/iQ0sx39Rup0gz44WutIn2VK8TCPdJzuLr2wXplPL4WqHxIHrj+UMb1qOe0Jt0kuvZBTUU4fBK66Sx8fZeJJH8xdqKIPVFaU0b53Jnfp3zn9dXoXIzwgzpJZRu+GpbxHr2zev9yRdFznJHorbqUz/QFP3n36Lfy2jMpQuYLePQ8ltM9wjh19unV1Kf0zXKqjPXDUPZSxjpmgHVtym97qbFVMb8nSldC6HMtpn7mYQ1+io7LwUXnL2Ov4GJNOGL/1qirPggLD+6aZmRnMzKqXrcrLy5GUlITZs2dL25RKJYYMGYKEhIQaz5GQkICoqCiDbUFBQThw4IDMq29YXBkyksLCRz8kF9+mD5hvbLQpG4zgb/RDm+yam8B3cg5eZqyraN5kFMmazu7GPyW9nUWmjbTDMuWc86Ccg2nonSZlimmqE7OGJOvzT9SS7q1VCgsLYWsrZ6oi4zM1NYWrqysuZSU2SP5WVlbw8DCsSEZHR2PBggXV0ubm5kKn08HFxXCYgIuLC37++eca88/KyqoxfVZWk/1KPhWuDBmJu7s7bt26BWtraygU9KZf9mwKCgrg4eGBW7duwcaG/lSMGRfHpfni2DRPHJfmi2PTfFFiI4RAYWEh3N3p45cbikajwfXr11Fe3jBd+YQQ1cqoNbUKtTRcGTISpVKJNm3aNPVltFg2NjZ8k2qGOC7NF8emeeK4NF8cm+brWWPT3FqEHqfRaKDRNP505U9q1aoVTExMkJ1tOMlRdnY2XF1rnrzI1dX1mdI3F/QBDYwxxhhjjLEXjqmpKfr06YO4uDhpm16vR1xcHHx9fWs8xtfX1yA9AJw4caLW9M0FtwwxxhhjjDHGDERFRSEiIgKvvvoq+vXrh9WrV6O4uFiaXW78+PFo3bo1Fi9eDACYNm0a/P398e9//xsjRoxAbGwsEhMT8fHHHzfl26gXV4bYc83MzAzR0dHc57WZ4bg0Xxyb5onj0nxxbJovjk3DGjduHO7evYv58+cjKysLPXv2xLFjx6RJEm7evAml8v87mfn5+WHHjh2YN28e5syZg44dO+LAgQPNeo0hAFCI5jy3IGOMMcYYY4w1EB4zxBhjjDHGGGuRuDLEGGOMMcYYa5G4MsQYY4wxxhhrkbgyxBhjjDHGGGuRuDLEmkR8fDxCQkLg7u4OhUKBAwcOGOwXQmD+/Plwc3ODubk5hgwZgitXrtSZZ2ZmJsLCwtCpUycolUpMnz69xnR79uxBly5doNFo0KNHDxw5csRI7+rF0BCx2bdvH4YOHQonJyfY2NjA19cXx48fr5Zuw4YNaNu2LTQaDXx8fHD+/HljvrXnWkPE5YcffsBrr70GR0dHmJubo0uXLli1alW1dByXujVEbB535swZqFQq9OzZs9o+jk3dGiI2J0+ehEKhqPaXlZVlkI5jU7v64rJv3z4EBgbC0dERCoUCycnJT5XvTz/9hNdffx0ajQYeHh5YtmxZtTRcBmBP4soQaxLFxcXw9vbGhg0baty/bNkyrF27Fps2bcK5c+dgaWmJoKAglJaW1ppnWVkZnJycMG/ePHh7e9eY5uzZswgNDcXkyZOh1WoxatQojBo1CqmpqUZ5Xy+ChohNfHw8hg4diiNHjiApKQkDBw5ESEgItFqtlGbXrl2IiopCdHQ0Lly4AG9vbwQFBSEnJ8fo7/F51BBxsbS0xPvvv4/4+HhcvnwZ8+bNw7x58wzWhOC41K8hYlMlLy8P48ePx+DBg6vt49jUryFjk56ejszMTOnP2dlZ2sexqVt9cSkuLsaAAQOwdOnSp86zoKAAgYGB8PLyQlJSEpYvX44FCxYY/J5xGYDVSDDWxACI/fv3S6/1er1wdXUVy5cvl7bl5eUJMzMzsXPnzqfK09/fX0ybNq3a9rFjx4oRI0YYbPPx8RHvvPMO6dpfdA0Rmypdu3YVMTEx0ut+/fqJyMhI6bVOpxPu7u5i8eLF9DfwgmrIuLz55pviz3/+s/Sa4/JsjB2bcePGiXnz5ono6Gjh7e1tsI9j82yMFZvvv/9eABAPHjyoNQ3H5uk9GZfHXb9+XQAQWq223nw2btwo7O3tRVlZmbTtH//4h+jcubP0mssArCbcMsSanevXryMrKwtDhgyRttna2sLHxwcJCQnStoCAAEyYMOGZ8k5ISDDIFwCCgoIM8mW1M1Zs9Ho9CgsL4eDgAAAoLy9HUlKSQb5KpRJDhgzh2DwFY8VFq9Xi7Nmz8Pf3B8BxMQY5sdm6dSt+/fVXREdHV8uXYyOf3O9Nz5494ebmhqFDh+LMmTPSdo5N45gwYQICAgKk1wkJCXjjjTdgamoqbQsKCkJ6ejoePHggpeEyAHsSV4ZYs1PV77pqheMqLi4uBn2yPT094ebm9sx515cvq52xYrNixQoUFRVh7NixAIDc3FzodDqODZHcuLRp0wZmZmZ49dVXERkZiSlTpgDguBgDNTZXrlzBrFmzsH37dqhUqmr5cmzko8bGzc0NmzZtwt69e7F37154eHggICAAFy5cAMCxaSxubm7w9PSUXtd2f6/aV1cajkvLVv0XlrHnxBdffNHUl8BqUVdsduzYgZiYGBw8eNCgjz1reLXF5fTp0ygqKsKPP/6IWbNmoUOHDggNDW3kq2vZHo+NTqdDWFgYYmJi0KlTpya8KgZU/9507twZnTt3ll77+fnh2rVrWLVqFb788svGvrwWa/HixU19CewFwS1DrNlxdXUFAGRnZxtsz87OlvbJybsh8m0p5MYmNjYWU6ZMwe7duw26KrRq1QomJiYcGyK5cWnXrh169OiBt956CzNmzMCCBQsAcFyMgRKbwsJCJCYm4v3334dKpYJKpcI///lPpKSkQKVS4bvvvuPYGIEx7zX9+vXD1atXAfD3pqnUdn+v2ldXGo5Ly8aVIdbstGvXDq6uroiLi5O2FRQU4Ny5c/D19ZWVt6+vr0G+AHDixAnZ+bYUcmKzc+dOTJw4ETt37sSIESMM9pmamqJPnz4G+er1esTFxXFsnoIxvzN6vR5lZWUAOC7GQImNjY0NLl68iOTkZOnv3XffRefOnZGcnAwfHx+OjREY83uTnJwsdaXj2DQNX19fxMfHo6KiQtp24sQJdO7cGfb29lIaLgOwapp6BgfWMhUWFgqtViu0Wq0AIFauXCm0Wq24ceOGEEKIJUuWCDs7O3Hw4EHx008/iZEjR4p27dqJhw8fSnmEh4eLWbNmGeRblWefPn1EWFiY0Gq1Ii0tTdp/5swZoVKpxIoVK8Tly5dFdHS0UKvV4uLFi43zxp8DDRGbr776SqhUKrFhwwaRmZkp/eXl5UlpYmNjhZmZmdi2bZu4dOmSePvtt4WdnZ3IyspqvDffjDVEXNavXy8OHTokfvnlF/HLL7+ITz75RFhbW4u5c+dKaTgu9Wuo37PH1TSbHMemfg0Rm1WrVokDBw6IK1euiIsXL4pp06YJpVIp/vvf/0ppODZ1qy8u9+7dE1qtVhw+fFgAELGxsUKr1YrMzEwpj1mzZonw8HDpdV5ennBxcRHh4eEiNTVVxMbGCgsLC7F582YpDZcBWE24MsSaRNXUpE/+RURECCEeTXn6wQcfCBcXF2FmZiYGDx4s0tPTDfLw9/eX0lepKU8vLy+DNLt37xadOnUSpqamolu3buLw4cMN+E6fPw0RG39//zrzrLJu3Trh6ekpTE1NRb9+/cSPP/7YwO/2+dEQcVm7dq3o1q2bsLCwEDY2NqJXr15i48aNQqfTGRzHcalbQ/2ePa6mypAQHJv6NERsli5dKl566SWh0WiEg4ODCAgIEN999121c3NsaldfXLZu3Vrj/ujoaCmPiIgI4e/vb5BvSkqKGDBggDAzMxOtW7cWS5YsqXZuLgOwJymEEMK4bU2MMcYYY4wx1vzxmCHGGGOMMcZYi8SVIcYYY4wxxliLxJUhxhhjjDHGWIvElSHGGGOMMcZYi8SVIcYYY4wxxliLxJUhxhhjjDHGWIvElSHGGGOMMcZYi8SVIcYYY4yxZmDx4sXo27cvrK2t4ezsjFGjRiE9Pd0gTWlpKSIjI+Ho6AgrKyuMHj0a2dnZ0v6UlBSEhobCw8MD5ubmePnll7FmzRqDPPbt24ehQ4fCyckJNjY28PX1xfHjx+u9voCAACgUCixZsqTavhEjRkChUGDBggW0N/+USktLMWHCBPTo0QMqlQqjRo2qlmbChAlQKBTV/rp169ag18aeT1wZYowxZnRt27bF6tWrm/oyGHuunDp1CpGRkfjxxx9x4sQJVFRUIDAwEMXFxVKaGTNm4JtvvsGePXtw6tQp/Pbbb/jDH/4g7U9KSoKzszO2b9+OtLQ0zJ07F7Nnz8b69eulNPHx8Rg6dCiOHDmCpKQkDBw4ECEhIdBqtfVeo4eHB7Zt22aw7c6dO4iLi4Obm5v8/4R66HQ6mJub469//SuGDBlSY5o1a9YgMzNT+rt16xYcHBwwZsyYBr8+9hwSjDHGGNHWrVuFra1tte05OTmiuLi48S+IsRdITk6OACBOnTolhBAiLy9PqNVqsWfPHinN5cuXBQCRkJBQaz5/+ctfxMCBA+s8V9euXUVMTEydafz9/cV7770nHB0dxQ8//CBt//DDD0VISIjw9vYW0dHR0vYvvvhC9OnTR1hZWQkXFxcRGhoqsrOzhRBC6PV68dJLL4nly5cbnEOr1QoA4sqVK3VeixBCREREiJEjR9abbv/+/UKhUIiMjIx607KWh1uGGGOMGZ2TkxMsLCya+jIYe67l5+cDABwcHAA8avWpqKgwaBHp0qULPD09kZCQUGc+VXnURK/Xo7CwsM40VUxNTfGnP/0JW7dulbZt27YNkyZNqpa2oqICCxcuREpKCg4cOICMjAxMmDABAKBQKDBp0iSDfABg69ateOONN9ChQ4d6r+VpffrppxgyZAi8vLyMlid7cXBliDHGmomAgABMnToV06dPh729PVxcXLBlyxYUFxdj4sSJsLa2RocOHXD06FHpmNTUVAwbNgxWVlZwcXFBeHg4cnNzpf3Hjh3DgAEDYGdnB0dHRwQHB+PatWvS/oyMDCgUCuzbtw8DBw6EhYUFvL296yxYVTl58iQmTpyI/Px8qU9+1XiBJ7vJKRQKbN68GcHBwbCwsMDLL7+MhIQEXL16FQEBAbC0tISfn5/BtQHAwYMH0bt3b2g0GrRv3x4xMTGorKwk/g8z9vzQ6/WYPn06XnvtNXTv3h0AkJWVBVNTU9jZ2RmkdXFxQVZWVo35nD17Frt27cLbb79d67lWrFiBoqIijB079qmubdKkSdi9ezeKi4sRHx+P/Px8BAcH15hu2LBhaN++Pfr374+1a9fi6NGjKCoqAvBobE96ejrOnz8P4FHlaceOHTVWrKh+++03HD16FFOmTDFanuzFwpUhxhhrRj7//HO0atUK58+fx9SpU/Hee+9hzJgx8PPzw4ULFxAYGIjw8HCUlJQgLy8PgwYNQq9evZCYmIhjx44hOzvboEBTXFyMqKgoJCYmIi4uDkqlEm+++Sb0er3BeefOnYuZM2ciOTkZnTp1QmhoaL2VDj8/P6xevRo2NjZS3/yZM2fWmn7hwoUYP348kpOT0aVLF4SFheGdd97B7NmzkZiYCCEE3n//fSn96dOnMX78eEybNg2XLl3C5s2bsW3bNnz44YfE/13Gnh+RkZFITU1FbGwsOY/U1FSMHDkS0dHRCAwMrDHNjh07EBMTg927d8PZ2RkA8NVXX8HKykr6O336tMEx3t7e6NixI77++mt89tlnCA8Ph0qlqpZ3UlISQkJC4OnpCWtra/j7+wMAbt68CQBwd3fHiBEj8NlnnwEAvvnmG5SVlUlje7p16yZdw7Bhw0j/B59//jns7OxqnGiBMQA8ZogxxpoLf39/MWDAAOl1ZWWlsLS0FOHh4dK2zMxMaXzAwoULRWBgoEEet27dEgBEenp6jee4e/euACAuXrwohBDi+vXrAoD45JNPpDRpaWkCgLh8+XK911zbmCEvLy+xatUq6TUAMW/ePOl1QkKCACA+/fRTadvOnTuFRqORXg8ePFgsWrTIIN8vv/xSuLm51XtdjD3PIiMjRZs2bcSvv/5qsD0uLk4AEA8ePDDY7unpKVauXGmwLS0tTTg7O4s5c+bUep6dO3cKc3Nz8e233xpsLygoEFeuXJH+SkpKhBCPfqOmTZsmhBBi/fr1om/fvsLS0lKkpaUJIYTBmKGioiLh6OgowsLCRHx8vLh8+bI4fvy4ACC0Wq10rkOHDglbW1tRUlIigoODxZQpU6R9GRkZ0jXcvn272vXXN2ZIr9eLDh06iOnTp9eahrHq1XjGGGNN5pVXXpH+bWJiAkdHR/To0UPa5uLiAgDIyclBSkoKvv/+e1hZWVXL59q1a+jUqROuXLmC+fPn49y5c8jNzZVahG7evCl1vXnyvFUzQuXk5KBLly4N8t6q3seT7620tBQFBQWwsbFBSkoKzpw5Y9ASpNPpUFpaipKSEh6TxF44QghMnToV+/fvx8mTJ9GuXTuD/X369IFarUZcXBxGjx4NAEhPT8fNmzfh6+srpUtLS8OgQYMQERFRa0vqzp07MWnSJMTGxmLEiBEG+6ytrWFtbV3ntYaFhWHmzJnw9vZG165dq+3/+eefce/ePSxZsgQeHh4AgMTExGrphg8fDktLS3z00Uc4duwY4uPjpX1yx/icOnUKV69exeTJk2Xlw15sXBlijLFmRK1WG7xWKBQG2xQKBYBH4wmKiooQEhKCpUuXVsunqkITEhICLy8vbNmyBe7u7tDr9ejevTvKy8trPe/j5zCmms5R13mLiooQExNjMG1wFY1GY9RrY6w5iIyMxI4dO3Dw4EFYW1tL44BsbW1hbm4OW1tbTJ48GVFRUXBwcICNjQ2mTp0KX19f9O/fH8CjrnGDBg1CUFAQoqKipDxMTEzg5OQE4FHXuIiICKxZswY+Pj5SmqpzPA17e3tkZmZW+82q4unpCVNTU6xbtw7vvvsuUlNTsXDhwmrpTExMMGHCBMyePRsdO3Y0qNTV5tKlSygvL8f9+/dRWFiI5ORkAEDPnj0N0n366afw8fExePDD2JO4MsQYY8+p3r17Y+/evWjbtm2N/fXv3buH9PR0bNmyBa+//joA4IcffjDqNZiamkKn0xk1zyq9e/dGenq6UWeVYqw5++ijjwA8mkzlcVu3bpVmYVu1ahWUSiVGjx6NsrIyBAUFYePGjVLar7/+Gnfv3sX27duxfft2abuXlxcyMjIAAB9//DEqKysRGRmJyMhIKU1ERES1NYTq8uREDo9zcnLCtm3bMGfOHKxduxa9e/fGihUr8Pvf/75a2smTJ2PRokWYOHHiU513+PDhuHHjhvS6V69eAB61rFXJz8/H3r17qy04y9iTuDLEGGPPqcjISGzZsgWhoaH4+9//DgcHB1y9ehWxsbH45JNPYG9vD0dHR3z88cdwc3PDzZs3MWvWLKNeQ9u2bVFUVIS4uDh4e3vDwsLCaN3X5s+fj+DgYHh6euKPf/wjlEolUlJSkJqain/9619GOQdjzcnjhfnaaDQabNiwARs2bKhx/4IFC6RZHWtz8uRJwtXVf1xVC02V0NBQhIaGGmyr6T3euXMHarUa48ePf6rrqKrU1cXW1hYlJSVPlR9r2Xg2OcYYe065u7vjzJkz0Ol0CAwMRI8ePTB9+nTY2dlBqVRCqVQiNjYWSUlJ6N69O2bMmIHly5cb9Rr8/Pzw7rvvYty4cXBycsKyZcuMlndQUBC+/fZb/Oc//0Hfvn3Rv39/rFq1itcKYewFUVZWhtu3b2PBggUYM2aMNJaQscakEE/zGIIxxhhjjDEj2rZtGyZPnoyePXvi0KFDaN26dVNfEmuBuDLEGGOMMcYYa5G4mxxjjLFaDRs2zGDxxcf/Fi1a1NSXxxhjjMnCLUOMMcZqdefOHTx8+LDGfQ4ODnBwcGjkK2KMMcaMhytDjDHGGGOMsRaJu8kxxhhjjDHGWiSuDDHGGGOMMcZaJK4MMcYYY4wxxlokrgwxxhhjjDHGWiSuDDHGGGOMMcZaJK4MMcYYY4wxxlokrgwxxhhjjDHGWqT/A66zQJP2GWUlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting wind speed and direction\n",
    "plt.figure(figsize=(10,6))\n",
    "wind_ds.horizontal_wind_speed.where(wind_ds.nan_percentual<50).plot(y=\"range\", cmap=\"turbo\", vmin=0, vmax=20)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "wind_ds.horizontal_wind_direction.where(wind_ds.nan_percentual<50).plot(y=\"range\", cmap=\"hsv\", vmin=0, vmax=360)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 593
    },
    "executionInfo": {
     "elapsed": 1026,
     "status": "ok",
     "timestamp": 1718264008474,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "BNycQrdQKxzf",
    "outputId": "83a98c19-6287-403b-f430-a74bbde351d4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the maximum ZDR from each scan\n",
    "plt.figure(figsize=(10,6))\n",
    "wind_ds.zdr_max.where(wind_ds.nan_percentual<50).plot(y=\"range\", cmap=\"turbo\", vmin=0, vmax=7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this last cell, you can save the retrieved wind data as a NetCDF file.\n",
    "To do it, you just need to uncomment the line below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 826,
     "status": "ok",
     "timestamp": 1718264025618,
     "user": {
      "displayName": "Christine Unal",
      "userId": "02384359149360931920"
     },
     "user_tz": -120
    },
    "id": "MGlVA3i2K-JI"
   },
   "outputs": [],
   "source": [
    "# wind_ds=to_netcdf('retrieved_wind.nc')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}