virtual_buoy_example_geopolar.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Make a virtual buoy with satellite data  \n",
    "## Background\n",
    "There are buoys in many locations around the world that provide data streams of oceanic and atmospheric parameters. The data are often available through data centers like the those National Data Buoy Center (NDBC https://www.ndbc.noaa.gov) and ARGO floats program (http://www.argo.ucsd.edu). Despite these impressive efforts to monitor environmental conditions, in situ buoy data may not be available for your area of interest. Some locations are hard to access, making deploying and maintaining a buoy impractical. In addition, buoys are expensive to purchase, deploy and maintain. Therefore, limited funding may  prevent installation of a buoy in the first place or the continued operation of a buoy already in place. Using satellite data to create virual buoys can provide a solution to monitoring surface environmental conditions at locations where it is not feasible to install a buoy. For example, the University of South Florida has developed a virtual buoy system for locations off the Florida coast (https://optics.marine.usf.edu/projects/vbs.html).  \n",
    "\n",
    "## Overview \n",
    "The following exercise will demonstrate the use of the ERDDAP data server to create a virtual buoy. For the scenario, we will envision that a virtual buoy is need to continue the datastream for an *in situ* buoy that was discontinued at the end of 2019. For this exercise we will use the National Data Buoy Center (NDBC) buoy # 49259, which is located off the California coast at 34.767N latitude and -121.497E longitude, and pretend that it was discontinued at the end of 2019. The buoy measures several oceanic variables, but we will continue the sea surface temperature (SST) datastream using NOAA GeoPolar Blended SST satellite dataset.    \n",
    " \n",
    "### Steps in the exercise   \n",
    "* __Create the virtual buoy using the ERDDAP server__  \n",
    "\n",
    "* __Bring the virtual buoy data into Python and visualize__  \n",
    "\n",
    "* __Groundtruth the satellite data__   \n",
    " * Download buoy data and virtual buoy (satellite) data for the period from Jan. 2017 through Dec. 2019\n",
    " * Resample the buoy data to match the temporal resolution of the virtual buoy data\n",
    " * Run a linear regression to model the relationship between virtual buoy SST and buoy SST data\n",
    "\n",
    "* __Plot the results__\n",
    "\n",
    "\n",
    "### The exercise demonstrates the following skills:\n",
    "\n",
    "* __The use of ERDDAP to create a virtual buoy__      \n",
    "* __The use of the Pandas module to import and manipulate data__  \n",
    "* __Resampling data to bin into a lower resolution timestep__ \n",
    "* __Generating a linear regression model and statistics__\n",
    "* __Several approaches to plotting timeseries data__  \n",
    "* __Cleaning data to remove outlying data points__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps involed in this exercise    \n",
    "### Create the virtual buoy with ERDDAP  \n",
    "\n",
    "__Visualize the data with a plot__  \n",
    "\n",
    "__Load buoy data obtained from the National Data Buoy Center (NDBC) website__  \n",
    "We will load buoy data store in a CSV file (buoy_46259_2016_2019.csv) into a Pandas dataframe. \n",
    "* The data were obrtained from the NDBC buoy #46259 (https://www.ndbc.noaa.gov/station_history.php?station=46259). \n",
    "* The buoy is at 34.767N latitude and the -121.497E longitude  \n",
    "The file contains sampling date/time, mean wind direction, and surface water temperature for the years 2016-2019.\n",
    "* The sampling resolution for the data is a sample every 30 minutes  \n",
    "\n",
    "__Resample the buoy data to match the temporal resolution of the satellite data__  \n",
    "The sampling resolution for the buoy data is a sample every 30 minutes. However, the temporal resolution for the satellite dataset is daily. We will downsample the buoy data by generating daily means in order to match the temporal resolution of the satellite. \n",
    "\n",
    "\n",
    "__Use the ERDDAP data server to subsample and download the satellite data__  \n",
    "We will generate ERDDAP URLs to subset the satellite data at the latitude and longitude of the buoy and for each day in the downsampled buoy dataset and then download the data.  \n",
    "\n",
    "* Generate the ERDDAP data request URLs \n",
    "* Download the data and load it into a Pandas dataframe.\n",
    "\n",
    "__Plot buoy data and overlay the satellite data__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import some require Python modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from datetime import datetime\n",
    "import statsmodels.api as sm\n",
    "import os\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a virtual buoy with ERDDAP\n",
    "#### Selecting a dataset\n",
    "A good dataset for sea surface temperature (SST) is the NOAA GeoPolar Blended SST dataset (ERDDAP ID: `nesdisGeoPolarSSTN5SQNRT`). It blends together many satellite sensors to obtain good daily coverage of the globe, and then uses an interpellation method to fill in data gaps. \n",
    "#### Query ERDDAP for information about the satellite dataset   \n",
    "ERDDAP has a feature that allows you to select and download metadata about any and all datasets. Click the link below to bring up the user interface for this feature: https://coastwatch.pfeg.noaa.gov/erddap/tabledap/allDatasets.html.  \n",
    "\n",
    "Like all data in ERDDAP, even metadata, the values can be downloaded via a URL-based data request. Details about the feature are presented in the ERDDAP tutorial. For the purposes of this exercise, it is enough to know that we are pulling the metadata from this page into a CSV file and using it in Python\n",
    "\n",
    "__Build a function to harvest metadata from ERDDAP__  \n",
    "\n",
    "* The function builds the URL-based data request, sends the request to ERDDAP for the metedata, and loads the results into a Pandas data frame.\n",
    "* The function also uses the xarray module in OpenDAP mode to obtain a list of data variables.\n",
    "* Inputs to the function are \n",
    " 1. The location of the ERDDAP on the Internet: The NOAA GeoPolar blended SST dataset can be found on the CoastWatch West Coast Node ERDDAP at this URL: `https://coastwatch.pfeg.noaa.gov/erddap`\n",
    " 2. The unique ERDDAP ID for the dataset: For the NOAA GeoPolar blended SST dataset the ID is `nesdisGeoPolarSSTN5SQNRT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable containing the WCN ERDDAP URL\n",
    "my_erddap_location = 'https://coastwatch.pfeg.noaa.gov/erddap'\n",
    "\n",
    "# The dataset ID for NOAA GeoPolar blended SST \n",
    "my_ds_ID = 'nesdisGeoPolarSSTN5SQNRT'\n",
    "\n",
    "# A function to form the metadata request URL and \n",
    "# download the metadata into a Pandas data frame\n",
    "def get_dataset_info(erddapURL, datasetID):\n",
    "    queryURL = \"\".join(['?datasetID,minLongitude,maxLongitude,longitudeSpacing,minLatitude,maxLatitude,latitudeSpacing,', \n",
    "                        'minAltitude,maxAltitude,', \n",
    "                        'minTime,maxTime,timeSpacing,griddap&datasetID=\"'])\n",
    "    \n",
    "    # Form the base URL leading to the allDatasets page\n",
    "    baseURL = '/'.join([erddapURL, \"tabledap\", \"allDatasets.csv\"])\n",
    "    \n",
    "    # Form the data query part of the URL. \n",
    "    # For this exercise we will only request: \n",
    "    # 1) dataset ID, \n",
    "    # 2) the distance between values longitude values, \n",
    "    # 3) the minimum and maximum latitude, and the distance between values\n",
    "    # 4) the minimum and maximum altitude values\n",
    "    # 5) the minimum and maximum and time between time values\n",
    "    fullURL = ''.join([baseURL, queryURL, datasetID,'\"'])\n",
    "    \n",
    "    s=requests.get(fullURL).content\n",
    "    c=pd.read_csv(io.StringIO(s.decode('utf-8')), skiprows=[1])\n",
    "    \n",
    "    #print(c.griddap[0])\n",
    "    # get the data variables using xarray via OpenDAP \n",
    "    c['variables'] = [list(xr.open_dataset(c.griddap[0]).data_vars)]\n",
    "    return c\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Run the function and view the results__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>datasetID</th>\n",
       "      <th>minLongitude</th>\n",
       "      <th>maxLongitude</th>\n",
       "      <th>longitudeSpacing</th>\n",
       "      <th>minLatitude</th>\n",
       "      <th>maxLatitude</th>\n",
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       "      <th>0</th>\n",
       "      <td>nesdisGeoPolarSSTN5SQNRT</td>\n",
       "      <td>-179.975</td>\n",
       "      <td>179.975</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-89.975</td>\n",
       "      <td>89.975</td>\n",
       "      <td>0.05</td>\n",
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       "                  datasetID  minLongitude  maxLongitude  longitudeSpacing  \\\n",
       "0  nesdisGeoPolarSSTN5SQNRT      -179.975       179.975              0.05   \n",
       "\n",
       "   minLatitude  maxLatitude  latitudeSpacing  \n",
       "0      -89.975       89.975             0.05  "
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       "      <th>minAltitude</th>\n",
       "      <th>maxAltitude</th>\n",
       "      <th>minTime</th>\n",
       "      <th>maxTime</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002-09-01T12:00:00Z</td>\n",
       "      <td>2021-04-22T12:00:00Z</td>\n",
       "      <td>86476.212879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   minAltitude  maxAltitude               minTime               maxTime  \\\n",
       "0          NaN          NaN  2002-09-01T12:00:00Z  2021-04-22T12:00:00Z   \n",
       "\n",
       "    timeSpacing  \n",
       "0  86476.212879  "
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       "      <th></th>\n",
       "      <th>griddap</th>\n",
       "      <th>variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://coastwatch.pfeg.noaa.gov/erddap/gridda...</td>\n",
       "      <td>[analysed_sst, analysis_error, sea_ice_fractio...</td>\n",
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       "                                             griddap  \\\n",
       "0  https://coastwatch.pfeg.noaa.gov/erddap/gridda...   \n",
       "\n",
       "                                           variables  \n",
       "0  [analysed_sst, analysis_error, sea_ice_fractio...  "
      ]
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     "text": [
      "Metadata helpful for this project are listed below\n",
      " \n",
      "Time Range: 2002-09-01 to 2021-04-22\n",
      "Data Variables: ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n",
      "Direct dataset URL: https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT\n"
     ]
    }
   ],
   "source": [
    "dataset_info = get_dataset_info(my_erddap_location, my_ds_ID)\n",
    "\n",
    "display(dataset_info.loc[:, 'datasetID':'latitudeSpacing'])\n",
    "display(dataset_info.loc[:, 'minAltitude':'timeSpacing'])\n",
    "display(dataset_info.loc[:, 'griddap':'variables'])\n",
    "\n",
    "print(\"Metadata helpful for this project are listed below\")\n",
    "print(\" \")\n",
    "print('Time Range:', dataset_info.minTime[0][:10], 'to', dataset_info.maxTime[0][:10] )\n",
    "print('Data Variables:', dataset_info.variables[0] )\n",
    "print('Direct dataset URL:', dataset_info.griddap[0]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a virtual buoy with ERDDAP\n",
    "We can make a virtual buoy very quickly using the ERDDAP visualization (make a graph) interface.   \n",
    "\n",
    "### Selecting a dataset and generating a timeseries\n",
    "\n",
    "A good dataset for sea surface temperature (SST) is the NOAA GeoPolar Blended SST dataset. It blends together many satellite sensors to obtain good daily coverage of the globe, and then uses an interpellation method to fill in data gaps. On a separate  can view the dataset on the ERDDAP interface (ERDDAP ID: `nesdisGeoPolarSSTN5SQNRT`). Click on the following link to go to the ERDDAP visualization page for the dataset:  \n",
    "https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph. \n",
    "\n",
    "The NBDC buoy is located at 34.767N latitude and the -121.497E longitude, so we will use those coordinates to view and download data from ERDDAP. Please review the ERDDAP basics tutorial for a refresher on using ERDDAP. \n",
    "\n",
    "__On the ERDDAP visualization interface, do the following:__  \n",
    "* Set the `Graph Type` to linsAndMarkers\n",
    "* Set the `X Axis` to `time` and the `Y Axis` to `analysed_sst`\n",
    "* On the time (UTC) widget, set ` Start` to the Jan. 1, 2020 (`2020-01-01T12:00:00Z`) and the `Stop` to the most recent date (`2020-04-01T12:00:00Z` at the time time of this writing).\n",
    "* Set the `latitude (degrees_north)` to `34.767`, the latitude of our buoy, and the `longitude (degrees_east)` to `-121.497`, the longitude of our buoy. \n",
    "* Click `Redraw the Graph`  \n",
    "* For a review of how to use the ERDDAP data server, please see this online tutorial:\n",
    "https://coastwatch.pfeg.noaa.gov/projects/erddap/\n",
    "\n",
    "The data for the virtual buoy will be displayed in the interface:\n",
    "<img src=\"https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.png?analysed_sst%5B(2020-01-01T12:00:00Z):(2020-04-01T12:00:00Z)%5D%5B(34.775)%5D%5B(-121.475)%5D&.draw=linesAndMarkers&.vars=time%7Canalysed_sst%7C&.marker=5%7C5&.color=0x000000&.colorBar=%7C%7C%7C%7C%7C&.bgColor=0xffccccff\">\n",
    "\n",
    "### You are finished making a virtual buoy!   \n",
    "\n",
    "This is your virtual buoy. If you wanted, you could get to produce an image for you to put on your virtual buoy website. However, you probably want to bring the data into Python to make better plots and to go through some validation steps.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the virtual buoy data into Python  \n",
    "Let's download the virtual buoy data directly from ERDDAP and into Python   \n",
    "\n",
    "### Generate the ERDDAP data request URL for the data   \n",
    "* Go to the ERDDAP visualization page for where you created the virtual buoy. \n",
    "* Near the top of the ERDDAP interface, click the `Data Access Form` link. Clicking brings you to a new page where you can refine your data request. Our goal on this page is to obtain the data request URL to download the data as a CSV file. \n",
    "* Find the `File type:` dropdown and select `.csv`. Then push the \"Just `generate the URL button` and copy the link in the box to the right for th button. \n",
    "\n",
    "__The data request URL should look like the following URL:__   \n",
    ">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(2020-04-01T12:00:00Z)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]   \n",
    "\n",
    "__Getting the most up-to-date data__  \n",
    "At the time this tutorial was written, the most recent date was `2020-04-01T12:00:00Z` (April 1, 2020). However, you may be using the tutorial months later, so hardwiring `2020-04-01T12:00:00Z` into the URL will not give you the most recent data. We can use an ERDDAP trick to fix this problem. By changing the the most recent date (`2020-04-01T12:00:00Z`) to `last`, you will request to most recent data. The new URL looks like the following:  \n",
    ">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(last)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]  \n",
    "\n",
    "__Bring the data into a Pandas data frame__  \n",
    "You could put this URL directly into a browser and the CSV data file would download to your computer, but we will bring it directly into Python as a Pandas data frame.\n",
    "\n",
    "### Create a function to load the CSV data into Pandas  \n",
    "We will be making several request to the ERDDAP server for CSV data that we will put into Pandas. Creating a function will save repeating the same code over again. Instead, just call the function.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the needed modules\n",
    "\n",
    "def get_csvdata(erddapURL):\n",
    "    \n",
    "    '''\n",
    "    The requests module allows you to send a HTTP (URL) request,\n",
    "    that returns a Response Object with all the response \n",
    "    data (content, encoding, status...)\n",
    "    '''\n",
    "    response = requests.get(erddapURL).content\n",
    "    \n",
    "    '''\n",
    "    The StringIO class of the io module creates a file-like object \n",
    "    or memory file from the request response. \n",
    "    \n",
    "    The decode function decodes the request response from \n",
    "    its 'utf-8' URL encoding.\n",
    "    \n",
    "    The Pandas (pd) read_csv function imports the csv data \n",
    "    from the request response into a Pandas dataframe. \n",
    "    \n",
    "    A CSV file from ERDDAP has the column headers in the \n",
    "    first row and the units in the second row. The read_csv \n",
    "    function assumes the header is on the first row. \n",
    "    skiprows=[1] (below) tells read_csv to import row 0 as the header, \n",
    "    then skip row 1 and import the data starting on row 2.\n",
    "    '''\n",
    "    my_df = pd.read_csv(io.StringIO(response.decode('utf-8')), skiprows=[1])\n",
    "    \n",
    "    return my_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the function to create the Pandas data frame with Virtual Buoy data\n",
    "The beginning of the resulting is printed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>analysed_sst</th>\n",
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       "      <td>2020-01-01T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
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       "      <td>14.089994</td>\n",
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       "      <th>1</th>\n",
       "      <td>2020-01-02T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>13.839994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>13.789994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>14.009994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>13.869994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time  latitude  longitude  analysed_sst\n",
       "0  2020-01-01T12:00:00Z    34.775   -121.475     14.089994\n",
       "1  2020-01-02T12:00:00Z    34.775   -121.475     13.839994\n",
       "2  2020-01-03T12:00:00Z    34.775   -121.475     13.789994\n",
       "3  2020-01-04T12:00:00Z    34.775   -121.475     14.009994\n",
       "4  2020-01-05T12:00:00Z    34.775   -121.475     13.869994"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erddap_data_request = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2020-01-01T12:00:00Z):1:(last)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]'\n",
    "# print(erddap_data_request, 'a')\n",
    "vir_buoy_df = get_csvdata(erddap_data_request)\n",
    "\n",
    "# The head method prints out just the top 5 lines of the dataframe\n",
    "vir_buoy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean up the data  \n",
    "__The latitude and longitude columns are repetitious.__   \n",
    "\n",
    "1. Store the latitude and longitude values in a dictionary.  \n",
    " \n",
    "\n",
    "2. Remove the latitude and longitude columns from the data frame with the Panda drop() function. The drop() function removes rows or columns by specifying label names and the axis containing the labels.    \n",
    "\n",
    "\n",
    "* The first argument is the column to drop, by name. \n",
    "* The axis argument indicates if the name is a `row` (0) or a `column` (1). \n",
    "* The argument inplace=True argument applies the row or column deletion to the data frame in use (e.g. vir_buoy_df in this case). By default (inplace=False), a new dataframe would be created.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>analysed_sst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01T12:00:00Z</td>\n",
       "      <td>14.089994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-02T12:00:00Z</td>\n",
       "      <td>13.839994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-03T12:00:00Z</td>\n",
       "      <td>13.789994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-04T12:00:00Z</td>\n",
       "      <td>14.009994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-05T12:00:00Z</td>\n",
       "      <td>13.869994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time  analysed_sst\n",
       "0  2020-01-01T12:00:00Z     14.089994\n",
       "1  2020-01-02T12:00:00Z     13.839994\n",
       "2  2020-01-03T12:00:00Z     13.789994\n",
       "3  2020-01-04T12:00:00Z     14.009994\n",
       "4  2020-01-05T12:00:00Z     13.869994"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# location of the buoy \n",
    "buoy_location = {'lat':vir_buoy_df.iloc[ 1 , : ].loc['latitude' ],\n",
    "                 'lon':vir_buoy_df.iloc[ 1 , : ].loc['longitude' ]\n",
    "                }\n",
    "\n",
    "# Drop the latitude and longitude columns\n",
    "vir_buoy_df.drop('latitude', axis=1, inplace=True)\n",
    "vir_buoy_df.drop('longitude', axis=1, inplace=True)\n",
    "vir_buoy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the virtual buoy data from the satellite\n",
    "Use the matplotlib python library. There are many ways to use matplotlib to plot data. Below is a simple approach. \n",
    ">Documentation for matplotlib can be found here: https://matplotlib.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Import the needed modules\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# the %matplotlib is a magic function allow displaying results in notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# some tools for Pandas to work will with matplotlib\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "# Create the figure and give it a size\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# add a subplot\n",
    "ax = fig.add_subplot()\n",
    "\n",
    "# Assign the x and y data, \n",
    "#    i.e. pd.to_datetime(vir_buoy_df.time), vir_buoy_df.analysed_sst\n",
    "#    pd.to_datetime changes the datestamp into a date objest\n",
    "# Design the look of the plots\n",
    "#    color the plot, color='green'\n",
    "#    define the marker shape: marker='o' is a filled circle\n",
    "#        e.g. '+' are pluses\n",
    "#    define the line type shape: linestyle='-'\n",
    "#        e.g. '--' is dashed, '-.' is dash-dot\n",
    "ax.plot(pd.to_datetime(vir_buoy_df.time), vir_buoy_df.analysed_sst,\n",
    "        color='green', marker='o', linestyle='-')\n",
    "\n",
    "# format the date for the x axis and autoformat\n",
    "ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "\n",
    "# set the axis labels\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.set_ylabel(\"Virtual Buoy SST (degrees C)\")\n",
    "fig.autofmt_xdate()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate the Virtual Buoy measurements\n",
    "How confident are we that the virtual buoy measurement are similar to the buoy measurements? Validating or groundtruthing the virtual buoy data is an important step to be certain that your virtual buoy is accurate. Since both the buoy and the satellite data extend back in time before 2020, we can compare a few years of data (2017-2019) from both datastream to determine how well they correlate.    \n",
    "\n",
    "To do that, we will do the following:\n",
    "* Download the data from NDBC buoy #49259 for the time period between Jan 1, 2017 and December 31, 2019.  \n",
    "* Download the data from the satellite SST dataset for the time period same time period.  \n",
    "* Evaluate, both visually and statistically, how close the two data stream are two one another.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the data from NDBC buoy\n",
    "\n",
    "#### Load the buoy data for 2017-2019 \n",
    "\n",
    "* The data for 2017-2016 has been made available for you already in a CSV file (buoy_49259_2016_2019.csv).  \n",
    "* If you prefer, you can get the data yourself via the NBDC website.  \n",
    "https://www.ndbc.noaa.gov/download_data.php?filename=46259h2017.txt.gz&dir=data/historical/stdmet/  \n",
    "Steps on how to get the data with Python are included in Appendix A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of timesteps = 50900\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>WTMP_degC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:30:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 01:30:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Time  WTMP_degC\n",
       "0 2017-01-01 00:00:00       13.1\n",
       "1 2017-01-01 00:30:00       13.0\n",
       "2 2017-01-01 01:00:00       13.0\n",
       "3 2017-01-01 01:30:00       13.0\n",
       "4 2017-01-01 02:00:00       13.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the buoy data file into a Pandas data frame and view\n",
    "buoy_data = pd.read_csv('data/buoy_46259_2017_2019.csv', parse_dates=['Time'])\n",
    "#buoy_data['Time'] = buoy_data['Time'].dt.date\n",
    "print('# of timesteps =', buoy_data.shape[0] )\n",
    "buoy_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the full resolution buoy data  \n",
    "Use a slightly different method to plot the data than we used above. The plotting uses the plot() function, which is an integration of the `matplotlib` library into Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84c0e511d0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.ylim(12, 20)\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Buoy SST (degrees C)\")\n",
    "plt.plot(buoy_data.Time, buoy_data.WTMP_degC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resample the buoy data\n",
    "The sampling resolution for the buoy data is a sample every 30 minutes. However, the temporal resolution for the satellite dataset is daily. Pandas makes it easy the downsample the 30 minute buoy data to create daily bins of the data that match the temporal resolution of the satellite.   \n",
    "\n",
    "Use the Pandas `resample` method (below) to generate daily means.  \n",
    "* The \"D\" is the resampling rule that sets the resampling interval. Here, \"D\" indicates resampling into daily bins. Similarly, using \"3D\" would resample into three-day bin and \"M\" would resample into monthly bins. \n",
    "* `on='Time'` identifies the column with the Pandas datetime data.  \n",
    "* `.mean()` defines the math operation to be preformed on the daily bins.\n",
    "* An overview of the resample method can be found here: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of timesteps before = 50900\n",
      "# of timesteps after = 1095\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WTMP_degC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>13.120833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>13.110417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>12.970833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>12.912500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>12.968750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            WTMP_degC\n",
       "Time                 \n",
       "2017-01-01  13.120833\n",
       "2017-01-02  13.110417\n",
       "2017-01-03  12.970833\n",
       "2017-01-04  12.912500\n",
       "2017-01-05  12.968750"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('# of timesteps before =', buoy_data.shape[0] )\n",
    "\n",
    "buoy_data_resampled = buoy_data.resample('D', on='Time').mean()\n",
    "print('# of timesteps after =', buoy_data_resampled.shape[0] )\n",
    "\n",
    "buoy_data_resampled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note 1: The resampled data frame (above) contains mean daily values. The \"Time\" column is now the index (first) column and it is in the Timestamp (datetime64) format.*   \n",
    "\n",
    "*Note 2: The length the the data frame (# of time steps) is 1398.*   \n",
    "\n",
    "### Clean up the data\n",
    "The data is likely to contain data points that are outliers and that contain no data. Let's remove those points from the data frame. \n",
    "* Apply a conservative allowable data range. \n",
    " * For the lower end of the range, the freezing point of seawater (ca. -2).  \n",
    " * For the high end of the range, value unlikely to be seen in the area of interest (e.g. 45 degrees C). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of timesteps before cleaning = 1095\n",
      "# of timesteps after cleaning = 1091\n",
      "4 times were removed\n"
     ]
    }
   ],
   "source": [
    "# Get # of timesteps before cleaning\n",
    "b4 = buoy_data_resampled.shape[0] \n",
    "print('# of timesteps before cleaning =', b4 )\n",
    "\n",
    "# Extract the temperature data into a data frame\n",
    "y = buoy_data_resampled['WTMP_degC']\n",
    "\n",
    "# Find the data that is within our acceptable range\n",
    "removed_outliers = y.between(-2, 45)\n",
    "\n",
    "# Invert the results of removed_outliers to get the \n",
    "#   indices of the values outside of the acceptable range \n",
    "index_names = buoy_data_resampled[~removed_outliers].index\n",
    "\n",
    "# Drop values outside of the acceptable range from the data frame \n",
    "buoy_data_resampled.drop(index_names, inplace=True)\n",
    "print('# of timesteps after cleaning =', buoy_data_resampled.shape[0] )\n",
    "print(b4-buoy_data_resampled.shape[0], 'times were removed')\n",
    "# buoy_data_resampled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note that the length the the data frame (# of time steps) went from 1095 to 1091, i.e. four timesteps were outside of the acceptable time range, and they were removed__.    \n",
    "\n",
    "\n",
    "### Plot the results for the downsampled buoy data. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f84e10de410>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.ylim(12, 20)\n",
    "plt.plot(buoy_data_resampled.index, buoy_data_resampled.WTMP_degC, \"b\")\n",
    "\n",
    "# plt.plot(pd.to_datetime(c.time), c.analysed_sst, 'ro', buoy_data_resampled.time_oj, buoy_data_resampled.WTMP_degC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q9wWAWjn4be71YEK5DW5fZMu2a8CPKkfiDnEEMRrwhyLBryFKjzvgS1+m4A+GEQxz2Ex62OSCOjdlfgmCyBGpuD1cF+epJVkeC0GkTZvLh8lxgt8B1UU2WmfoDYRgMeqTvn9tsRUA0NLnld7TF8S4EisaezzoGqDML1EY+AIRza/scBZ5LhhOu0hNZHktRr3iJhEr+P3oaBdm1TiSavAJohBpc3mxs7EXZ8+qzvVQCh7ymyEKgjY5KI1mX4sLX/jLRuV3d9RWqy8YMfFPRI2S+ZWDX38I9aVSQNw9EEhrzASRb/hUTS1C0dEv0i9S88rrzmYywGaSZQ+q4LelV/LNvvqh9fj0nzfGfA+CKHS+8vjHuOGxTUpNCTF8UPBLjFq46uIsgtJofvbKHnx8vEf5PbrIxhcIw2xInvm1mw1wWAxokT/H7Q+ivtQGAOgizS9RAHDONZrfcFhaX+PLrMqNXrpSBfE6q0kHq1FkfiOB9AcHO5Sft53oyUhiQRDDyTv72jDpuy/jcHt/1t+7Q3YjOtCa/fcmtFDwS4xa1NZLRzoGYh6zt7lP83v0xdsXDMFsTG0Z1BZb0NTjgS8YQiDEMU4OCHpI9kAUAIGQFOxGZ35vPWcGvnn+DADS+jnY5hpUOJoMcdNpNRoU2YOQQnz6kQ/xzWe3aY5/e29bmn8FQQwvz246AQDY0Zj9Vt0ioXJAttIkhg8KfolRizo7dDhO8Nvt9sOpKmgbHPymJnsAgJpiK1r6vErRXInVCB3L3P+UIPIBv1wYKjK/ITnzazHqFKnCgC+Ic3+zBqf98p0hvbcIdK2qgjcREL8vZ32NeoavnzMdFXYz/vjuISz40evY0ZD9AIMgMkFcQ2J1Kfzlf/bi2ofXp/3epUVSg5l41zMie1DwS4xa1IHsobbB20SBUBiBEMdNq6Zg4/fOwYQyGzxRmkUp+E0uewCAWqcFzb1e9Psi3sAmg04JGggi1zR0u9HrTk8vKLoVRhe8WQyRgFX4YovAOFUimd/4bg//dcpE3H7eDFw4rwZ7mvvQ6wng92+TiyaRX4jvrVgLav7n3UNKR8R0ENp4agAz/FDwS4xaRDZpbp0Tu5v7sL2hJ+bzFqMeVU4LbCb94MxvIARLqrKHEgs6+n3o9UjBRZHZALNBDx/5lRJ5wmm/fAeXPvh+Wq9Vgl/5ZlAEuGZV5vdgW3rbsR6l4E0/SPYguHJxPQDg0oV1ymP7afuXyDPEjVyPJ/tyNxH0BoJDu7kkhg4Fv8SoRQSyN62aAgB4b1+75nmvP7LVKv4ffcEdUua32ALOI1tSNrOeMr9E3iAuykc702u5HQl+tZpfo16HIrO0Rva0ZBb8Wox6mPQ66FjkQm8z6fHF0ybjpHqp7fGSiaWKFOlYp5sq34m8wiV/H0USJBO+9tQWvLKjWfldXNPomjL8UPBLjFpEo4kqhwUVdjMae7RFOIrOUK4utxpjZ36HovkFgN1NUhFdidUIs0EHX4BOVETuOdqZmU7QH5K1jFFuD3odg80oZX5FIU6pbWg+vB5VoMsYg81kgMcfBuccnkBIuUEFAMYYtvzgPDz06cUAIuuNIPIBcV3pSSAvSkUW5A2E8OK2JkXzDkRuYP1URzLsUPBLjFpE8wqbSY9xpVY0dGuDX7dfG/zGlD0Ew0NyewAkKyYAqHJaYDLo4KO7dCIPOJphkYw3oNX8isyvjjHY5MxvU49k9SfWVLvLl9KF2hO1FqVdmCB8wTA4x6BGMzaTAXPrirPydxFENhHf9+jMr1e1q+hNQQonbDP7VO8zIN8kUhH18EPBLzFq6fdJJw2HxYB6uduaGnWFufR/Q0YFb6LRhdAWV9rNMOkp80vkB+L7H6sKPRXEVqs5Rua3xGqE1ahXPkOvZ2hzebHi3rew6r53NJ7bsfDIa0SsRXEj6lVpgaMpkbPLsQqLCCJXiOA3OvOr7vSZSvArvOn7VN9vD8keRgwKfolRi7goOixGjCuVgl/1RdgbnfmNJXsIpi57cJgNsJsNGPCH4LBITg9mo55OVEReIDJRYc6TBqOxEBd1sR4uWTgOADCuxAqDXodFE0o0x/57axMCIY6WPu8gLX289xaBuZAgRUuT1BSZDGAMpPkl8gpxvm+J6ira7vIpPydbD9LrpRtJdeZX0fwGqYh6uKHglxi1RIJfA0psRviDYc120eDMr7bgLRTmCIR4yplfxhi+fs40zWeb9TpyeyDyAnERDYZ5ShffaP7ywREAEc3vDSsnYe9PVqPSYQYALJ5QqhzrC4axW9VApj9JdjYQCkPHAJ2OAZAyvR5/KCKHiJH51ekY7GYDZX6JvCEsXzMAoLFbm2xR7zwmy/w+/3EDbntGauwi1m04zFXBLyVUhhsKfolRS583AJNeB4tRD4dZKshRXyhjaX7V7Y2VTFeKml8AuPE0yVnCLn+e2UhuD0R+oN4+TVSME4/XdrUCiMh7GGMaLa7oaAhIbcHVF/j+JO2IA+EwjCo5htWkx4luNx54U/Lxjdb8CpwWI2V+ibxBnOsr7CZ4AiF0y+tsV1MvNh6J+Pt6/ImvCbf/PdLRUHy/vapsL11Thh9D8kMIIj9xeYNwyN3b7PL/+31BJVOltlcCpOA3GOb4z84WbG/owQ2nTQaAlGUPgKR/fPP2M5TXmPQ6uksn8gL19mmvJ4C6EmuCowfjsBhwyYI6pcVqNDVOi/KzLxjSSIgGfIkzXcEQ1wa/RgOOdbpxTLZliyV7EGNyeYMIhML4v3VHMW9cMU6dUp7y30QQ2UQEpZPKi9DR78d5v3kPm79/Hi76vdZb25tAtqCWRwBAnycIzrlmDdE1ZfhJGvwyxh4FcDGANs75PPmxZwDMlA8pAdDDOV84TGMkiJhogl+zVByj3n71xih4A4AvP74ZAHDy5DIASFn2IJhWZVd+Nht1VJlL5AXq6vOhyh445+j3BVFqM8U9ploV/Ia5dpfF5UucnQ2GwjDomfJ7dIFbLNkDIGV+Xd4Anv7oBH768h4smlCCF76yMuFnEcRwIYLSeeOKselYNzoHYje68Pjjr7+Gbq0Ptz8kyfVi7UoSw0cqKa/HAKxWP8A5/xTnfKEc8D4H4PnsD40gEtPvDSgZXyFDEBfhjn4ffvCvXQAiF9roC65oiTyUzG80lPkl8oU+b0Dx3/UmuPjGYsAfAudQbiZjIeQQgh63HxV2KVhOlvkNhDkMusg6i16Lljg3oCLz+/YeSZIRoO1gIoeI79/MGgeuP2UCDDqmOf/PrHYASHzzGas5RrfbD3dAupksMukVXTExfCS96nPO1wCI2ayaMcYAXAPgqSyPiyCS4vIG4ZAzvuKi3e8N4oUtDVj60zeV48SFNfqCe1AOfuPpDVPBbNDDFwyh3eXDjY99NGhLK9t8eLgT/9nZnPxAYszR5wmiyiEFqIm2XWMhdkzsCYLf6MYWPe4AKuySxGggieY3GArDqMr8itcJQnHcKRwWA9pcPqw71AlA+hsJIleoXUvGl9oQDHM090YK3ZZPlSQ5iQre+mIUcB5uH1BuIEtsJtpNHAEyLXg7HUAr5/xANgZDEENBK3uIaH4fW3dMOeaZm05VKsyjdYX75G5VonVrOpgMUub3V6/tw1t724Y1MG3t8+JTD2/Alx//OGmwQYw9ej0BVDmloNI7RO9p0bLVYYnfuY0xhuduXo47zp8BQMpWCX29K1nBW4hrZA+TKooAANVOM24/bwbmjyuO+boSmwntLh98wTAmlduo+I3ICmv2t+NQe/+QX6duAV5sldaK0K3PqXXiCysnAUgS/MqZ3y+snIQXvrICALC/1aXIHoqtRrI6GwEyDX6vQ5KsL2PsJsbYJsbYpvb29gw/jiAiuLwB5WKtLnhr6HLj3NnVeO0bq3CKqjjGZtJmtQ62Sic/ETing9kgaX53NvXKv6cfSCdDrRV7Z1/bsH0OMfrwB8PwBEKKLjeR5jAWInh1JFkLSyaWKQVxYS41egGSZ34DoTCMKtnD5ArpPQw6Hb5+znTlBjWaSxbWAZAybefNqUafJ5CWhzFBqPnsoxtxzq/fG/LrfKrgVzRhOdYlnZe/e+EsJSBOJAMSsofvrJ6FheNLUFZkwv5WF9xyA6bSIiO5PYwAaQe/jDEDgCsAPJPoOM75w5zzpZzzpZWVlel+HEEMIlbmt6XXi84BPxZPLMHMGofm+OiiGnHBjw6Kh4LI/Aqrpy537AKIbKC2z/noSEwlEjFGEZnbKjkTO1TZg9ozOxkWlTVgWZEJOpaK7EGb+Z1QJmV+S4viZ5oByVv4rzcsw9rvnIVKhxlhHmlrThAjjQhKNZlfuf12qc2kJGMS7VD0eQMwGSSLTsYYqhxmdA34FfeUEquJ6khGgEyszs4FsJdz3pCtwRBEqoTDHP3+IJzyxdps0MGoZ9gjG+9PKBts16TW/IqMLZCZ7MFskOzTgnKBQvcwBr9iK81uNmDLiR4AwLqDHdDpGNk/jXGEjlAJfocoe0hF8ytQ727YTHrYTIakPr/BKJ/fSocZv7jiJJw+vSLp562aISVNnCKw8ARS2q051N6PnY29uFTuVEcQmaJ4w+t1cFq1md8SmxF6HYPDbEioTe/zBJXvMhBJoIjgt9hmRJgLhxRqxTBcJJ1ZxthTANYDmMkYa2CM3Sg/dS2o0I3IAZxzdLv94DxysZbuoC34+HgPAKklazTq4Fdt21SUgexBdMPqkYPe7jjWN9lAVBAvn1qO3U19aOh24/pHPsS1D28Yts8kRgdiK1V8r5N1mIomFc2vQB3EWkx6mAy6pC4MkuZXe7m5btmEuJ7CsRDBRqq639UPrMGtT29FOEwyCSJCMANJQWzNr5T5FTIIp9UY09FB0OcJwGmNXHNMeqlRkiJ7kN8nno0akR1ScXu4jnNeyzk3cs7rOed/lh//POf8oeEfIkFo+eO7h7BEdnNQX6wnltuUk055kXnQ66ya4DfyfFEGsgdhkya2YrvT6KyVKiKgWTG1HMEwxyNrjwzbZxGjC1FEU+EwgzHgeKcbn/rf9fj4eHdKrxeZ21QyqmrXBptRD4OOKTsf8QiGwzDG0fWmisiW9cZYY+Ewxw//tRNr9kfqSoRd1HBKkYjRhzcDSYE6+C0tkmz+DrcPwCC34gak4DeZ7CFh5lcOqi/+w/uDXvvX9Ufx4DsH0x4/EYFy6sSowuMP4ckPjyu/qzWKE8uLlJ9LYmgJhS0aAFTJGTId02oYh4opyiN4T3PfsGWavKrMLwC8KXufAlLHLWLsIi62xVYjLAY9ntl0Ah8e6dIEg4lfP4Tg16BtU2zU65L6kgaCWs1vOtSWSGv2Uw9vwPaGHs1zG4504v/WH8NnH904aC0Mt/0gMboY6q6IGrXmt8ikh9Uoyd5KbEZIzq+A02JImPn1BcKaa45JluC5/SGY9Dqc6JKs09pdvkHFnT/41y7c/9q+tMdPRKDglxhVXPE/69DYE/FVVGd+J5WLCnIWs2pdk/mV/VCLTAblpJUO0Q0yGro9+PumE9h8LLWM21AQOs76UhscZgMauiPz0NLrzfrnEaMHcbF1Woya77kxRc1gvzeIIpMe+hSysya17MGoh1HPEAwnkT1EaX7TYZLq5vZbz27XSC1e29mi/Nzc41VkSADQRsEvoSKj4Ffl88sYU6z+RLYWkDO/ngDe2duGnY29g98jpF0LolGS2x+E1aTHuXOqlefEte7Gxz7Cn9YcVh6fcder+MdmKrfKBAp+iVFBR78P3/nHdqWgTaDO/ArvUPVdeDSvfWMVvnvhLJTJmWFjBt3dAG3m95vnSf6nf3j7IK753/VZ9yQVml+LQTeoKr9RFQgTYw9RYFNsNWqCU6EjTIbaNjAZ6gu32aCHQZ9c8xsMcRgylD2oA/N9rS489sFR5fe9LS7l52bZ8UVAmV9CjboYNDTEXTq17AGAEvyq24I7LUbsbXHhC499FFO64A+GNUkTIXvw+EOwmfQ4Y0YlnvrSqQAiHsJv7W3Dz17ZE3mPUBg/+veuIY2d0ELBLzEqeHVnC57ZdGLQ4+oMr8gMlahORNHMrHHgy2dMzajITY268l20f23s8SAU5mjry+5F1xsIwahnMOh1SqAiMg6HZbsdYmzS5w3AqGewGHVoc0V2Adwp2oL1+4IpOT0AWs2v2aCDQceSyx6yVLn+2BdOxv1XzcfZs6rw2zf3o9cTQCjMsfFoF5ZNKgMgNYPpU2073/HsNqWgjyDUmd+BFG8OBb5QJPMLRHyuS1TdD9v7tef9aCeUQHTmV5Y9eAIhpRGTsADs9QTiyujICzgzKPglRgXR3dkE6myVsDeLbsMai+wFv5El5LAYNMG4OgjJhjG/JxBSWjWLauH59cVwmA3Yp8p8EWOPPo9URMMYg/pameoWr9ozOxnRF26TQZe0gj4Y5pqMdLqcObMKVy8dj2+cOx1ufwiv7GjGnc9vB+fAGTMlS7SWPq8iAymTi5Ke/7gx488mCgP1muiP0Wo4EeJ7LtaAyPyqC9i+dvY0zWv2t2rPzf5QWLNjaDZIbg/eQAgW+TpXYpW+tz3ugKbVsVqTP9SsNaGFgl9iVBC9fTux3IaXv36akm0FJE1vbbFFueAlQgStUyuLkhyZ2vsAUrMMp0r7JbZbv/rkx7jsj+sy+hxA2q6zyHpOEfRX2s2YWePAtoaelLe4icKj1xPQ6A4B6WYw1cyvyxdMudOhKWrLNpXMr+RZmpnsQc1J44oxrcqO5z9uwHHZZ/VzKybBbjagpdeLV3dIGuBnbjoVJ08qxS9e3aO5GSXGLmrZQzJ/6mhEwKmXv8ui+FidhT15UhnGl0WsNqMbwASCsTW/nkBI0euLTHKPx6/I3QDg7FlVys/BGMHvNf+7Ho+sPTzocWIwFPwSowJxkvrkAqnd6U2rpmBuXfGg4+67aj6+fs70pO8nErHnzK5OfGAS1IFAkVmvyZ61u3xo7fPipe3N2CY3pcgEKTOgkz9L+pzaEgtWzajE9oZerLj37Yw8LInRS583CIcc/D5+4yl4/MZTYDPpUw9+o+yXEhF94U5F8xsIcRh02bvcMMZwxeJx+OhoN/Y0u3DhvBrYzQZMrSzCY+uOKhKpYqsRd5w/E95AGHubaXeEiMr8phv8yjUlnzipFr+6egG+dcFMzXFlKqvN6OA3OvOr1vyKHU6LUQ+LUYced0AT/C4YXxL3bwGAjUe68NOX94BIDgW/xKjA7QtBx4DfX7sQR++9CP91ysSYx50+vTJmUBzNxfNr8btrF+JLp0/JaFzablfazG+by5dy8JEKXpUmTJz0aoutuOWsaTh9egV63AEcbO/P2ucRowdJ9iDdEJ02vQKnTa+A1aRPWfbQ700986vO4JoMOpj0uphZKDWSzjF7mV8AOF+uiu/1BFBul3Z7lkws0xzjtBoxXpZDneh2Z/XzidGJJwPZQ0jOmqiLL69aUq+x2QSAP1y7CKvn1kif4dOuQV8wrJEAmWTZgycQVmQPgCR96HH7NWt44fhifHZ55NrXM4y+8oUOBb/EqGDAH8zYlkyNQa/DpQvHpWTtlAhN5tdk0GTP2l0+TSY206ysR6UJEyft2mIL9DqGey6ZCwDYfkKy1gmHOb773HaNJzJRuPTFkD2kkvl9Z28bHnzn4JA0v9EXboOeJXd7CGfu8xvN1Eq70s65Qi48WjlN2+bbYtSj2mmBQcfQ0O2Bxx/C8U4KgscyGWV+Q4OD31hMKLfhZ5fPAxBD9hCd+dXrEQpzxepMUGIzSplfeQ1Pq7JjQX0JfnzpPDz06cUAgC6VqwlpgIcGBb/EqMDtC8Fmjl30lkuiC97UbSvbXF6NFizT6lyPPxL8iipl0WVocnkRikx67Jat4DoGfHj6oxP43gs7SAs8BujzBjS7DoBUJJos+L316S24/7V98ARCyncpGYNkD7oUmlyEwlmVPQCS9OG0aRUAgHI5+D17VhW+eNpkzXF6HUNdiRUnuty47MEPsOr+d7C7qQ/z73ktpg8rkT/sbOzNetMgdYe3tDO/KSRhhDQtOsD2x8j8AtIOhlXV/KLYakSPJ6AE6z+4eI7imCKs1dTBrz+DznVjEQp+iVFBv5z5zTfUd/B2iwFlKpu1dpdPExRkenLqdvsVJ4vrl00AAEyRvY11OoYJ5UVKn3mPKug5RpmugoZzjj5PcJBm12oyJJU9zKlzKj9X2FMLftVZL7NBB5OBJXd7CPGsyx4AYIUc/FbIgTtjDHdfPEeRgAjm1Drx0vZm7JMr7zcf60KfN4jvvbAj62MissPBtn5c/If38cv/7M3q+3pV50bXEDO/4TAHY9L5NhnCBlCd+Q2FOcJ8sGMKIAW/GtmDzYheleZXnRUWMh91627q8jk0KPglRgVuXzBr9mTZRB38GvU6zBsX0Ru3uXya7eBMg992l0+x1rl22QQcvfcijafxxDIbjsmV7wMqndlR8gAuSDjn4JzDFwzDHwprdh0AwGbUJ836qy+25aoinVSR3B5SaHKRhQ5vsTh/bjWuWzYep07Ryh3WfudsbPzeOcrv3zx/BmpVzjCi4r9bFTwQ+YX4Tv17W1NW31fdfGjIVmdhnlLWF5BuxIrMBvzx3UO4+fHNAAY3yVD/zLnW0rPEapLcHuRgXf2cyPx2D6iDX8r8DgUKfolRwYDc/SbfiG5vvHhCqfJzjzuguevP5OQUCIXR7Q4o2sZYTCy3oaFLarDhCUQ+90gnBb+FyOV/XIdvPLNV2foU3qACawqaX3VmuDzFzK+aiOY3/tY051xyexiG4NdpMeIXV8wfJNkothpR5YwEu9OrHVh/5zm4+6LZAKSOkQDAkP1sNJFdmrPcul3o461GPfp9QysYC3E+pDoRUUT66s4WcM4V6ZumUUxUu3CBovkVnT1VzxVbjWBMK3vwqSzcsi0VKUQo+CVGBW5//md+AWB8mRVfP3uaUpGrPnFnovkVJ7lEwW99mQ3+UBgd/T7K/BY4nHNsPdGDf21twpbjPQCkmx81ZUUm9PuCCdtsq2/I0sr86oXbQ/zvtnCCMGZYXJoNzHIA0SZ7cNNWcf4yXAVcvZ4AnFYD7BbDICeGZITDQwt+i1R1Kp0DfiXzG93eWKCWNhTbjPAFw0rDFvVzBr0OJVajchMHaL/LQ+1cNxah4JcYFbh9+Zn5je5axRjD7efPxCmTpW3YWAUJL29vxsNrDg3pc0TDjETBr9AbH2rvV4z/HWYDjnaQ5rfQUBfR/GeX1NBhSlTDliUTS8E5sPlYd9z3UWeL0sn8GvRS5jeYIPMrnhuOzO9QEUGHWE+uIW57EyPHcAa/xVYjHGbDoGK0cJjjPzub48p4hiJ7ACT7S0Fjt0d531iyB2Cw7AGIJFAsUYmWcaVWNPZ4lN/VN7K9ngDe2duG+7Ksly4kkp6NGGOPMsbaGGM7ox7/GmNsH2NsF2PsvuEbIkFIF/tUfUhHknjWa+KEpj65iuD3lic/xs9fiZyUQmGO9w90JCwa2iI3yah0xA9QlGK4P32Iu/8pLdfZdU6SPRQgIngDgA8OdsBq1KPaYdEcs2hCCfQ6hi0Jgl+vnC2qdJjTvrk06HQJdzVi6RxzhQh+RcbM7Q+RRVSeovaOzua/kQh+7RYDXFG7Ig+tOYQvP/4xXt3ZEvO14TBXurulgto+sKHbo6wFtf7dHC/4lc/nLXLwa41an/UlNjR0R4JftYSpxx3AFx77CH9891DCnZ+xTCpno8cArFY/wBg7C8ClAOZzzucC+FX2h0YQEdz+kOYuOt8RF3q15jdegPCd57bj03/+EG/vbYtboPTQu4cwvcquKaiLRl38Jphb50S7yzdkP0siv1EHv10DfsyudQyqQLeZDKiwm9Da54t+uYIvEJY6pd11btoe2iaDLmHm1xcKKcflGtGURj1/tDbykzCPfKeyuY3f55WcUcqKTOjs1xY8PrFB8kXnPPb3eaiZ32+cO0P5+WjngHINUK8FdaMks8rqrES2LhTZXYshKvgttaKh262MVZ35VcvtdjaQnV8skp6NOOdrAHRFPXwzgHs55z75mLZhGBtBAJBORAP+oEY/lW8Is32BkEOotbeBOAVv6w52AABu+ttmzPnBa4Oe73H70djjwVVL6jUnymhEpkBNfamkA+3z0N3/aOZElxv/72+blJujNpc2oP3ESbUxX1dhN6O9P0HwGwwn/E6lgkHHEmp+RTGcaRiszoaKaA/eqZIjxQp+D7a58MSHx0ZsXMRg1DdU0Y0iBC9tb8J/4mRp4yEyv9UOC1r7IkEi51wJNNVyIDVhzlOyORMsmViKgz+7EOPLrNjT3Bcz82sxxs78Fsvn84YuNxxmw6DPrS+1whsIo0MO4NWa3y/9dZPy8zYKfmOS7q34DACnM8Y+ZIy9xxg7Od6BjLGbGGObGGOb2tvb0/w4YizjDYTBOfI28/vWN8/Aa99YpXlM3NmrM7m+qMzvv7Y2AgCMURmx6BP93hbJm3RmjSPhOEpjZH6F32mizByR/9z76l68tqsVb+xuBTA4+L104biYr6t0mDVZzmh8wZDm4psOBr3U5CJetiy/ZA+R4EIk8GLZXV310Hrc9cLOlNtDE9lHLXWIZ0n21Se34MuPb8bBNlfK76sEv8UWdPRHunCq11S8THMozGEYYuGmQa/D7Bondjf3xcz8ql0ctB3epPN5U693UAMbINLYRdj1RQfsly8ahwq7CQfbqOV9LNI9GxkAlAI4FcC3APydxdkz45w/zDlfyjlfWllZmebHEWMZkZmx52nmd2qlfZDVkjmB5ldw69NbEQiFBwW7IlgRPq5/2yBloObUOpGIWEGMOMkGEmTmiPxHnF1FfNnc44HVqMevrl6AX129QPF/jqbSniT4DWSe+RUZ3WAcXaYS/Opzv37V28qzaqT1FMvuqsctPaYuKCJGlpDqZiqZNEUU+CZ9zzCHPxiGzWRAtdOMMIeSOd3XEgmg41kEBsMcujTkQYsmlOJw+wA+lvX3pjj2Zlov30jAG6v1uHhMFG1GW2l+8/wZmFHtwKF2Cn5jkW7w2wDgeS6xEUAYQEX2hkUQEUT2NF8zv7GIqfmNIXto7vEOqjgX29S3Pr0Vqx9Yi5e3N+PTp07Q+JbGItb9p9heo8zv6EZccIXnZ2OPB3UlFly1pB5XLamP+7pKhxmdA76Yvp/hsOQ7Gu1VPVQMSb5j+ZX5jYxh8YQSAEDXwODgV/iwqguKiJElpLphH4hhSab+Tsd6PhbCbcGgZ0qBqJA+nOiOBNDxai/CYQ5DGvKdT586ASa9Di9skXb7tJnf+IGwOC66eyOgDn6l72+0bd+4EiumVtpxqL0/7q7MWCbds9E/AZwNAIyxGQBMADqyNCaCwBu7W/GmvMUrTmz5rPmNJpbm1x8MDzpBHWrvhy8YRrkqc9zW58PHx7vx721NSjvWc2ZXp/S5XzlzKlZOK8ck2fNVbNEl68BF5Ddip1VY5zX1eFBXYk36unK7GYEQj1nxLbZg1RfcdBDfsXgFnbGM/XOFOst98fw6lNiMeHzDYG2vKC5qpOA3Z6i/TrEyv+qkQbJOhpH3lD2n9SzSIlheU629XjAG2Ez6uMH0UAveBA6LETazXlmHcTO/JrUshynZ39iZX+k5MTci82vQMcyqcYAxhsUTS+DyBvHKjqHposcCSVNpjLGnAJwJoIIx1gDghwAeBfCobH/mB/A5TrcWRBYRgv2j9140ujO/qpPyHf/YhmNRtmO7m/sAADefORWtfV78ae0RtLu8+NPaw5rjZiXR+wq+vXoWACnQDobD+PCwVKsab0uaGB2IbVhh0dXY48WcusQyGCAiFRrwh1Ci7YGh6FnTyfz+5LJ5yq6G+K4HQ2G0ubzocPk1Y8vXzO9J9cW4+Yyp+MWre7H5WBeWTCxTnhPFRY095JGdK7SZ38HBbZeqNXWqmV+xO6HX6ZSgU6yDlj4vKu1m6BhTWgpHM9SCNzVGvQ69spxGbStojSN7AKQ6jtY+X0zNr7D+FDcB4u/YfPd5SqLok/Pr8L/vHcY3n92KFVPLB8nzxjKpuD1cxzmv5ZwbOef1nPM/c879nPNPc87ncc4Xc87fHonBEmMDdZHJgC+I7XK16mjK/Br1g2UPnAO/f/ug5rjtDT0ApOKGOy+cDYOO4aNj3dh6ogffvXAWLl80DksnlqImieQhGpNBB5vJoGzRJfIQJvIfoUHt7Pejx+1HR78PE8qKkrwKsMo3jLEu5iJTZE6j4O0zp07El8+YCkDy+QWkG6xV972DT/x+reZYkfnNVF6RDdR/q91swKdPnQjGgLUHIhuXwVBYydB1uPyD3oNIjbf3tirnt3RIlvlVNxAa8AXxyNrDOJxE3ypcSYx6Fgl+gyL49aGm2CJlfrNY8CYw6XXokwNVdbY33s9AxMEnkeZXFAO6vEHomHSdFFIkg16Hr549Dd5AmPTrUYyeVBoxZlBXp97/2j48tu4oAORle+N4RDS/iTMSovuWwyJZ2dSVWPHy9mYAwLmzq5UAI11EYBIgze+oRmS5Ogd8yndGaFYTYY3KbgGSy8iC+hJFR5xpwZsIar2BELwxLKLyquAt6m8tMhswrsSKw+2RHZmuAb9SWKi2RCOGxg2PRXbv0kFtnxcr+O1W/du8u78dm491Y+2BDvzfDcsSvKfI/DJFaytcElp7vZhQbkOYewYVvB1s60e3249QmgVvgFb2o5Y3qNslR2d+RZe3WMFvkckAxiKa34ZuD2qLrYM6KZbJ2V6XN4hwmCPMeV50W8w1NANE3qGuTt2myhzYjKMn+BUBgT8UTqh1FJXGDjmwH18m6ThNep2i280Eo1KJT5nf0Uq7y6fIZTr7/dh8rBsGHcOC8SVJXysupqJQLhAK49ant+Lc37ynZLwytTqLrjqPJp9kD7H+1vGlNvx7WxNufnwzfMGQsiYB6WaDGDrZ2GnSNLmIEfxuONypBI7ihjCWPEAzLqH51emUGyFxY9g54EOF3QSb0aDRELv9QZz7m/dw9UPrpcxvmtr1eG2M1UTvjojrQXWMnT+djsFuMsAlz01Dtxv1pYPrAESxXJ83gP9+5yAu/N3aQceMRXJ/NiLGDA3d7pRaLaorrFtVnWoqErT2zTfUBQ3RWuVfXnnSoOPtcgAxXm5KManClpW782SV+ET+88qOZgRCHMsmlaFzwI9Nx7oxd1xxSoVqVpP07y9kD6JVajDMlWxavAtxqojCGyHNALQdsgJ5VPBmirGmJpRJa+7VnS14Y3eroquuK7ZottaJ1In2oU6HRE0uOOd47uMGrJ5Xo3k8nlY38p7Sd1Gd+fXKN2d93iCcVqkwTZ35VVughTgyyPzGLnJTE60n/s7qWfj3V1fimqXjYx5vtxgU2UNDt0dpaqRGBL8ubxBrD7TjUHt/TPeXsQYFv8SIcdHv38f8e15PahyvDn6ber341gUzcfjnnxhVBW86HVO0YSaDDrt+dAHm1xfjisXjMC668giRE9R4+UI8sTy5njMVyO1h9HOs0w2bSY+TJ5ei3eXD1hM9WDqxNKXXiousuJir257uly/qsToDDgWR+T3cEdmxUcts8inzyxjDty6YiRe/epry2LdWz8STXzwFFXYz3t7TpgS/M2ocg9rfEqnRlIa+1BcMYebdr+Kfsh2YcGZgDOiPko9tPNKFbncAi1S7H5UOs/JvFw+R+TXomeLoIcl1QvAHw3BajLCZtMGv+rscCoc1MoWhIIJfk0GX8nsY9DrMry+JGyw7LAb0eQPwBUNo6fPGzPyK9dnj9mNnYx/CHOT9Cwp+iRGkV26xu1d1Jx2Lhm5thXV5kSntCttcIi72Jr0ORWYD/nXLSvz66gUxt17LZNudq5fU47ZzZ+CO82dmZQyKzy/d6Y9amns9qCm2oELu6OQPhnHSuOKUXhut+W3ujQQlhzskKUWxNbMdlWJ5q3lPc2Rdq23PfDG6WuWSW86ahpPqI/NXYTdjxbQK1BZb0C0XEwLAzGoH+n3BQfaERHKaVDdZsdjR0ItnN53QPHa80w1fMIxfv7EPQKTJRbHVqGlE4guG8KmHNwAAJldEkgQL6ouTylRENtmg00GnYzDpdfAGwopkx2ExwGzQa/7N1YmDYIinHfyK73+mOy1qSm0mdA8E0NzjBeeR5Ikasau49USPIn8677drxnwBXH6cjYiCR53tdSWRPhzr1Aa/mfqQ5gpxshMaMcYYGGMxgwCh+a1yWnDrudOTtjJOFfHZlPkdvTT1elFXbFXamQKxL3KxEIU14qLX1BMJSkRlfLYyv3tk2z4A8KnWu8j8mvOg4C0RTqsBfd4gOvv9MBl0WChnFf+2frAPMJGYXrlAM55C4Af/3olv/WM73t3XpjwmurSVyW19Rea32GrUFA73eSISiEmq4HdieVFSdw5R+yDOi2ajDt5ASLkmOS1GmPQ6TUMi9Y2cNxhOy+cXiEhushn8VsjZbtGgI1bm16jXwWrUY92hTs3jT314PGvjGI1Q8EuMCGo9oPrkFU2vO4DjXW5Mr7Irj2VakJMrxMnOGKUzZJBOnkVRhubDgVFHmt/RTnOPB7XFFlSoPDpFIUwyRJGo0EKqt6Mjmd/Mgl/hN7q3JRL8qgMGRfNryO/dG6fFiD5PAO39PlQUmbB6Xg1OnVKGxzccow5ZQ0TY6MWbNqETF4VqAHBUTnqURAW/TotR4/agTp6ML7XBKd98VTrM8KgC2VhEMr/Sd9Fi1GPD4U5F2+2wGGAyaIPfgOrnfm8g7YI3oXlXOz1kSqXdjGNdbuxuktZevJtip9UwSL/+0dGurI1jNDI6owpi1NHjiSy8REVvwt1hxdRy5bHRnvmNDn7Fdp6QOgwnBnJ7GNWEwxzt/T5UOy2YXRtpHFGpygInwiIK3lSyh9piqXL8cPsA7GbDoO/nUDHoddAxaGzOfKqfI1Zn+X25EfrJjn4/KhxmMMZw8fw6HO10Y/3hzuRvQCiod/pi3Th4o5q2AMBx2dFEqAq0md9I8CsC4Uc+uxQmgw5rv302PrrrXKVYWGSQYxHR/OqUz9/b4sJtf98KQHKLMCbI/Pb7gmkXvInrQazr2ZKJpZimSvikSnmRCaEwxy9e3QuDjsX1gxc3G2LtA8Dupr4xXfiW32cjomDoHogEvInuzLed6AEAnDKlcIJfU1SmYJzclvaGlZOHfQwR2cPYPcmNZvyhMDgHbGY9SotMmFntQKnNmPJOgUkOTCOZXy9m1jgU3WKmWV9B9DVUHTD4g2HoGPLeW9RpMcLlDaLD5VP01ZctGodJ5Tb8+MXdlP0dAj5V8BjL+1kkQNpdPuxp7sPs7/8HHx/vAQC8s68dV/zxA6UhRHTwq9bnAkCxzYhKhxkTZWvI450Jgt9QpAUwEMlMn+jyKO9pMug050u1ZKzfG8y44M0WI/P73M0r8ObtZwz5PdUJlPpSa9yxiaB31fRK5TGXL4gX5OLCsUh+n42IgqFH1YoyluzB7Q+ix+3HtoYeTK0s0vgajtrgN47sodJhxoGfXYjPr5g07GOIyB4o8zsaERk0UZn+76+txLrvnpPy6xljsBr1msxvXYkVpbLON1O9bzQ//OQcAFGZ31A4b4rdEuG0GuH2h9Da50WFHFTYzQZ88fQp2Nviwq6mviTvQAjUwW+sbml9cvFze78fj284Bk8ghB2NvcrzHx/vUbrDOa1GxcsWiCRPRDZTIILfY1GZ33UHOxSf7JCqyUUsnBajJHsIhZWbnUBQZbnmD6Vf8DYMml+1JCNR6+JFEyR3mFm1Dnzp9Ml45qZTMb3Kjj+tPQwA+MfmBpxIkDEvREaPdxQxqunxqDS/MTK/l/73BzjQ1o8KuxmrZlRoOtpk82QxksSTPagf++En56S13ZUqEdkDZa2yiTcQwvee34FvXjBTyeQPB9EtiNPpxmY1ScGvNxBCtzuAOtk5oqPfr3R/ypS13z4LNpNeCRD9oci2ty8QynvJAwBFO9o54FcyvwBwwdwa3P3PnVh/qBPzUnTZGOuoZQ9uXwiIOsWJrG6HyxezexsAJRhzWg0Y8AXBOQdjTHltdNczh8WIUptxUBB3/SMfApA6zQVEk4s4ut1Sm0nToMhs0Gt2MQCkX/CWQPaQLlcsqccf3z2ENpcvoXzpy2dMATjHNUvHK51SL55fhwfe2o+djb2449ltmFPrxCu3np61seU7+X9GIgqC1j4vGANqnBblrl/NAbmlcUe/DwvHlyi+t8DoL3hLVCDxhZWTcbpqKyrbiBMiyR6yy7v72vD8lkb86N+7hvVzRAY1kxbEVpMebl9QsRossZmUbdC64uwE7uPLbCi3m5ULvBi3NxDCa7taNU4V+Yq6O5h6vJUOM8aXWbHlRHeslxExUGf+3YFIcLu/1YV5P3xNCXjb+32aJhJqRAGc02JEmEduBPvjBL+AFLz2xri+CEJh0eRCe02ZU+vEjy+dC6tJr5y3he432ilHn3bBW3zZQ7o4LUY8/5UVAIDrlsVuhCF9pgG3nz9TCXwBYNnkMnAO/OhF6Rx2UL4GH+90Y0dDb8z3KSQo80uMCMe73Kh1WlBmN2F7Yy9C4fh+iQvHl8BpLZzMby6zXkLbRrKH7BJMsn2aLbLRgthhlqrlFcsxg07JbNaWxC6QSReRNfOFwtjf6sLeFhda+rz49dULsvo5w4E6C14RVYw6v75kTAQE2ULtk6u2KVt7oEMJfE8aV4wdjb0a3/fxZVYUmQxo6fOixx2AjmlbxVuMekXzqw7kBE6rMWHwG4hyexCoM57ivB03+M2ww1u2pUb1pTbs++nqId8gL5tchvpSKz46Kt3U+UNhXP3QOjT1eNHY48HG752DqjgFdIXA6EypEaOOE11ujC+z4ayZVTjcPoDXd7XEPXZWjVMT8JpHefCbaTV9JojgLECyh6ySTDuYLbKR+ZVcDCLNGsxGvTLu8izJHgTqzO/5v12Drz+1BQAwvz7/5QJCFwlIwa6aumIL2rPQshcA/v7RCTy85lBW3itfUWt+1Z7P6gZGS1RdCoVd3lWLx+M/31ilNK8w6HSDdhNc3gCsRn3M86rTalRkEcBgp4mQqsMbAFy2sE5pby0wqYJtYPCuWbprXgxXWLllk3TOD3odw3cvnAUAuP6UCQCAj452K80vXtvdmr0B5iFJr8qMsUcZY22MsZ2qx+5hjDUyxrbK/31ieIdJjHaOy8Hv18+ZDqtRj9d3t2JXUySTotZgmQw6TTX7aJc9GHNY7MMYg1HPKPObZeJlkLKNErBm8B1yWiX/WlF1b9JH1le2OyeKi3C0TrK+NLWmHLlE7Xyh7hwGSF3gPIGQxnUgXb793Hb8/JW9Gb9PPqPW/HpVWeCDbf2YUGbDJxfU4cbTJmOW3MznzJmS9EtkRasdUsZRp9NmfgGp6CxW1heQdNsuVeY3utYhoLg9SO/5wLWLsObbZ2mOEedtUeimtj0D0g9+Rcvk0ixnfjPh4vl1WH/n2fjZZfOUx8Sfd6R9IKnDyb+2NibMtOczqZxRHwOwOsbjv+WcL5T/eyW7wyIKiR63H619PkyuKIJRr8PiiSV4YUsjLvr9+8riKi+StmHPm1M96PWjoVgmFpHMb27N/Q06HY53ucdcNW86pHoid8sV7NHawWwjAtZMimSEhZcIHsxGHW49ZzpWz63BJQvqsjJOgQhU+lXZN7vZkFVj/+Hkw++dgw13DnbTEBrgzv7EHcSGQiFYp724rQmNPR4loyrwBcNKAaHHHwkeG3s8mFvnxB+uW4TxZTacNq0CgFRUCEBx+alySvOtzvyKINQbCMFqir3uiqNkD9GSBSXzmyCAjWR+Q/L/syN7EDdOTkv+BL8AUFtsBWMMf7txGYCIbeGjHxzBb9/YH/M1j75/BHc+vwO3Pr0V3/7HtpEaalZJeubmnK8BMLZbgRAZITRFS+VtrqUTy5TnhO1ZaZEJFqMOv7920aDXD1f3s+FGCX6HOUBKhkHP8NL2Zpx+3ztj2tQ8Ge/sbcOCH72OzceSn+6E7nC478uykfkVzRsUCYVeh5piCx76zJJBdlGZIsbZ3BvpJBer5Wq+Uu20oKZ4sM6xXNYAdwxkR/oAaLtejjScc6W9tRp3DFuyeDT3evC1p7Zg5b1v456owk9fMKxs72uywFFZ22uXTcAVi8bhgrk1eOlrpynJDxEE+0NhmOS22GIteAMhxfovGkn2EFBuLKKztkqHtwQJCSGnENKNQDAMmykiFUq34G1AzvzGy1rnmlhJphe3N8c89scv7cZTG6X2yPEKFvOdTE7dX2WMbZdlEaXxDmKM3cQY28QY29Te3p7BxxHZYuuJHrS5vCP2eR8c7IBJr8OC8SUAgEUTSpTn2uUOP/5gCOfMqh41GaJUEIFArtu6qrVxm46NTMW6NyD5pY4m3tsvnZ/WH0rezUvY9Q23hZy4AGeU+bVKBW9eRfM7fBG7uOFr7JaC33njnHjyS6cO2+eNFBVF2c/8Nvfmbn088eFxnP3r97BVbioEAOsOdWDOD17D+wc6UnoP9fhf3Rmp4eh1B7D5WLciI3lm0wnFUcATCGnqOaZV2fGbTy2EyaDDvHHFSoBZ5ZDm2x8MR2QP8lrwBEJxrxNOixGBEFd2TAYFv0rmN/4aiP68gOxTLdwlMs38Fpnz8xoXy4t7bp1z0GPR2fToOR4tpHsW/B8AUwEsBNAM4NfxDuScP8w5X8o5X1pZOXyWTkTqXPbgBzj9l++MyGcFQmG8uK0J58yuUi7gp0wuV7adRBHJaDHCHwrxmlyMNOotvkStPwEp85Oo/XSqnP2rd3HKz9/SZADzHRFoHkvQIUogMr+icxogtSL+05rD6M1CRq9rwI9edyA7ml+LAZwDXXLglknxXDLEezfIwe9PLp2XNS/hXCIyv5392cv85nJtbJDbNe9vjWTthNXV/a+lpkduUQW/C1QFjTf9bROAiEvPxiNdeGLDcYTCHG5//MBVjdplIJbsIX7mVwpQhfTBNyj4lTW/CbK30Z/nD3EY9TpFrpCu5ldc/7LVVTHbqM8LokD1pe3N+PLfNmuOi5bPuXzBQQHxaCCtMyrnvJVzHuKchwH8CcCy7A6LGG6iTwrDRVOPB50Dfpw1s0p5zGrS41XZWiaS+Q0P2nZ58oun4N4rThqRcQ4H+WB1BmiD70StpQHg1qe3Yv49r6MjwUU+Fa1ik3xh3HaiF90D2cuWDSdiG/jZzQ1Ydyhx9ksEv25V8Pv+wQ787JU9+OnLuzMey7m/eQ8Lfvy6ksHKJFsrLtri33Q4bzItRh0Yi9xkVYwCf99UEAF8Zxa/y0c6BrL2XkNFLOGmnkgALiRRrX2pBfjq16o9d7fJndnUN9H+UBgN3W74guGUrCvLVI4I0cGoJxCGJU4ALQJL8dnRet1gCoWqg90epGuTCKzTDX7vvfIkfO8Ts3BSnjZKUZ8Xzp1djSly0ed/opyZor+3Lm8Qn/jd2lGXAU7rLMgYq1X9ejmAnfGOJUaWfl9Qo7GKRl31ny3rnkR0y1mw8ijfzEp5W0vJ/AYHZ35XTKvAtcsmDPsYhwtjnmR+1Tc6Lm9iTd8bsr3N23vaYj4fCnNc96cNuOXJj+Pqh9XFL19+fDMW/eSNoQ45q1z90LpBmsRYnOhyK9Xn7+1LLNHqly+u6sxvt9zCO1Y716HSJQdZW4/3AEDcTFcqFMvV5SKzl0kWORminXKLLHkR63y0YzHq4TAbEt4UpoLUpUz6eW8OtZLCzupQeySQUXYzElw/1LT0emEx6jCj2q7cpAHamy11nLinWer+l0rmV+2Fq1idicyvPwRrnJtB8dmikVJ0QBaxOou/BhS3B1Xwa9QzOMyZZX6rHBbctGpq3tawqM8LNpM+rsWoCH7/ectK/POWlbhgbjUOtPUr36nRQipWZ08BWA9gJmOsgTF2I4D7GGM7GGPbAZwF4LZhHieRIvN++BpWP7Am7vNe1ckgG9vb8eCco8ftR48cEER7GxZbjdDrGLoG4ge/ox3x9yTaYhsJRFcjIHHml3OunPib4mzJvrarBRsOd+Hl7c14c09sH8hYwV8uK9s/OtqNx9YdTXhMOMzR5vLh7FlVWDKxFBuPJi56E0b96u5Vwsw/m7KCA21SgJRJ5nfltAqUFZnw7OYGAMOb+QUi290OsyGrrVxzTbndlLHm1xcMK1nXXBUKbTjcqWh91bp8l/yddnkDg9wbYnG0cwD1pTZYjHqNnZnIAnf0+zX//rvl1tepZH7Vwa85OvgNhuJ+r0SXvt44wW9AyB5SyfwG1cFvJPOry9PgNVPU5wW72RC3mPdQ+wBKbEYsHF+CheNL8LkVkwAAzYUW/HLOr+Oc13LOjZzzes75nznnn+Gcn8Q5n885v4RzHrskkMgJRxNoFtWZquHU6fxraxMW/vgNpXgouqsNYwwWgy5SmFCImt88aHIBAJNUnqWJMr99nogdVkucYpyPj3XDoGOosJvxzEcnYh7TH+Mz2rOolRwOOgf8CIY5qp0WzK514Kic3bj/tb144sNjg46PJXsQgUSmMpdtqiKkjizodO1mg0aTOZyaXyCS2asokKyvoMJuzjjzq/6+nOjOjfXgZrnoddmkMuV60NHvw2MfHAUgWV11JnG16Brw4+29bZg/rhgWg16z2yji5h9cPEcb/DanHvzaVY4I0T6/Hn8o7nskkj1wzhEaguxBBNv+oKT5FWs+n3x6s4n6vGUzGxArPPjX1kY8tfG45gagVm6RnssCznQorGhjjJOKjZX6JCVMvIcDkTkT2cHSGF1tLEbppBkOcwRCPOfa2Gwj/p5c/11q7+SGbk/cLKx62yreiWxXUx/m1jlx1sxK7GiM3e41ViOA4ykUkQ0H6r/1jme3oS2OA4UIXKudFpTaTOj1BNDW58WD7xzCXS/sHHSjKLLbnhjBb6ayh0sf/ED5ud3lg1HPMu4k57QO3kYeLmwi+LWP/kI3NdnI/AorsSqHGX2eAPzB8IjvivR5AzAZdKh0mpXxfOvZbZpAsS2B7jcc5lj9wBqEuVQYZTbqNLKHzn4fPrt8Im44bbLm3KdkflOQPailAYrVWUBldRYv8ytnnYWFpjrzGwhxpdNlovUkxhwpeAvDaNApNy6fOKk27mtHM+rzQpFJHzOeeGKDZG92yuSIXWmtbA04moqbAQp+85K/fHAEb8XZUk5EMj0noNVzCRPvTLn4D2vxwJtaM2xxdyz0QbEqXKXgN6ycdAst86tYneVY9vDlVVPx1JdOxZTKIrx/sAO//M8+zfOhMEf3gF8pYKl2mmOeyDjn2NPShzl1TkyqKEKbyzfIF7Spx6O0F33080vx5u1nAMhdcY9a7/yPzQ349euxTduF9V+104wSmwlhDjy/pVF5fnuDNtDvj5H5FVnBbHY88iSobB8KamP94dT8ApHMXqHofQXldnPSjGgyRPKhptiCMAdm3P0qvvPc9mwML2X6PEE4LUbYjHrl+9sVVcgXb+cHkBIbbS4fHBYDLl5QpyQxAGk3sc8bVAoE1TeNogg2lcyvmugCNG8gHDf4Fb7VsWQP3mAIoXAYBh1LqLsVEiMhEQwEwzDpGR741EL87tqFGF+W/90K00F9/XVYjAhH3ZRxzrG3pQ+nT6/AL6+arzxuMepRajNS5pfInB+9uBs3/t+mIb8ulROzOlPlz1Lmd2djHx5484DmMZHpDXPpbjzWnbbZqIM3GIp0niqw4FeRPeT479LpGJZPLVe28h9675DynChgW/bzN/G3DdL2/rLJ5Wjo9uBgmws3PvaRUqjS7Q6gxx3A1Eo7JpZLFwC1LdjBNhdW3Ps2HnznIADAbjZiUrkNZoMOf37/SE5cH9TBKQCE4mW9ZWuummILSuQbNbV3aXuUL7bQ/GrWk7ylmknzAl9w8A1pfRYutkKvyNjwt2RWZA8F4vQgqCgyoWvAn5IeNh7i+1itsvL6+6aGjMc2FPq8ATgtBhSZDcp41DsDOgZsj7OrA0QC43/dshIVdjMsRr1ykylucutKpK1wkWyZoPoOD9XLXe27Gwpz+EPhuC3vTQYdrEa9UvCmvvn1BcIIhnjSXRRR2CZ2sHzBEEwGHSZVFOHSheOGNPbRhPq84LQaNOfKrSd68PKOZvR5g1g+tXxQl7raYive3deOvS19IzbeTCmsaGME2dnYizd3Dz07m4xgBjpcUW0OxN+CUGd+s6H5jWdvor4zL43j82kx6OELhJT3KLTMb75ofgXqa7YIRHc39WHjkS4EQhzv7W+HyaDD0omlcPtDOPc3a/DW3jbc9cIOAMCRDsktYGqlHZPKJR3xc5sjF+7dzVIBz9t7JacIu9kAg16yvtrb4sJPXsrcAiwVLv/jB7ju4Q0ABksw4l33djT2oqzIhBqnBaVF0ol924keLJO399pV292+YAiBEIfFqIM/FFbWrCgsHGrm9zdv7Mej7x8BEHv3Zmpl0aDHhoq4WHE+/B0TbSYp0C604LfcbkaYS2vm83/ZqBTzDgURbNaqusiNtO+ryxuEw2qE1aRXdm7U5+uZNU5sOR6/GY74fouAWardkP6uNXKjmBVTywFE/t4LT6pRXp9qEeTp0yswp9apKUATn5Moe+wJhPDI+0ewt6VPI+XwBUMIhnnSmz+LUQe9jim7Oz3uwKCC7UJEfV5wWowa2cNlD36Arz65RXkumtpiCxp7PFj9wFr0ugOY9r1X8Md3Dw7/oDMgP67Ko5DbntmKL/51U0rdoIZCRwaaMrUebfkv3saV/7MOHxzU+pVmO/iNd6FXZ0dK4pzcLbJWTAl+8yRIzBZCq5Zr2YPg83JVLgAc6ZQyNEKbLS5WtcUWTK20a153oLUf4TDHYdkWaXJFEebUOnH+nGo88v4RxTBf6GnFOVQUrZwzW9IcD6e7iJotx3uwXh5TdOa3ayD2GLad6MX8+mIwxjQXujNmSI151M0NxEWxyiEFMG5ly5fLnzG0Nfz7tw7gx/KNQezg1z7osaGS7TbGiShc2YP0vfjWP7bh3X3tii3gUBDnaHUmdFzJyLZ/7vNImV+bUS/pYENh5Tt94bwazK1z4kDr4NbH6tcDkSBILXtYe6ADUyqLUF8q/X0izvzEvIhONlXZw99uPAWv3Hq6pgBNfE4qAfRL25o1yRmXV2rGkGwnjjEGu9mg7O50uf0a3+GxgNNqRLwNDrWns6BadTP36AdHEAxz3Bclr8s3CivaGEF65BPA+iRm+EPhn1saceov3kr79d1RmYjNx7rx7CZtRb5W9pB58Bsv+6Fu+xrvrlmcNCnzOzLcc8lcvPVNWYMrB7K7GntR47Tg4vl1AKTM4OSoTKPLF8TB9n785YOjKC8yob7UCp2O4XfXLkKF3axUiR+WtzzFbplo43nflfMxrsSqXEyGk+jvdPRntvQN3hFp6/Nif5sLSydKXdrVxZkzqx0otRk1Vf7iPUVwJ9aUuOHr9wU162woxLKiE97DmSBkDyNBocoeyuUWx8KfNx3xg9iRm10baRs70rKoPm8ATqsRNvnm1O0Podvtx7mzq/A/n16CSoekbY5XiNfrCcBq1CvnN5HE8AZC+PBIJ1ZNj3RyffeOs/D2N8/QnFNsQ5Q9GHQMOiatbU8Kmd+fXy41RtrV1Ks5H+xo6EVA7taWDLvZgMfWHcWvXtuHXk8g7u5loVJk0seV98TK/KpbPsdzAco38uOqPMrwBUNKJuhge/w75KHy7ObMvjSxug99dLQbA76goiXUahSzEPzGyfyq5RvxrGGEP2ShFrzlW/ALAONLbdDrmKLNa3V5UVdiwZVLxuEzp07EV8+ehrpiC75+znTN687/7Rrsbu7D7efPUAzirSY9PrmgFm/vbYPbH1Q6pAFSNbXINhaZDVg8sXRECiKijdbFtu59V83HqVPKFG2v+vlzfv0eOAfOmyNtzaqzPFMqi1BuN6PDFVlbIvitkoNfkV1W3/Cla4kVK/O7TFVZnS6xLljDRaG6PUT/PfGcQ+LxuzcP4KmNx1Fk0mN8aSTz2z9COyICl1cueJP/ndz+oBTgyd/7CrsZgRCPu6vX5w1opBriPL6vxQVvIIxTp0S+rxPKbZhSaYfTYsTSiaWoK7YMeUeAMQaTQZIYifNWlTP+e1x/ygRct2wCNh3r1rgbbTzaJWV+U9C8i8D/v985CM6B8jEW/Io5j0WsG2m1l32Lal2k4kCVK/LnqjyKONHlVrYERNekbBBdKBNP//u3Dcew9KdvDLozVxcUrZpRiW9dMBONPR7M/eFr+OyfNwKIlj1k/sWMV9yTWuZXyhj4AoUqe8gPqzM1JoMOxVajskvQ2udDlcMCs0GPn1w2D9csHQ/GGG4/bwae/8oKbLjzHM3rV0yt0Py+akYl/KEw/ufdQ9jVFCl2mFnt0Jw860osONbpTrsgornXgxsf+whfe2qLRoIQzfGovvOi8cTcOifOnV2NbncA3QN+fPe57bjswQ8w5wevweUL4pML6jCjWpIXFNuMOGtmJUwGHcaX2VBhN6FZdUKPyB7M8mdIv4tKcgBoS7F7YvQaFpnf/75+EVZOk6Qo5VnIoIpg5ZQsBNLJKFTZQ3Qmu2WIwe9v39yPQ+0DsBj1muBxJHZEAOC9/e14auNxSfZgNaiCXynzK7zYRZCv7gAaCIXxjae3YOuJHsktQhUAWYx6cC41vQAQ1w3hHzevwLo7z0GReei7EGbZS3jtgQ6Y9LqkN4QnTyqFyxtU5ERLJ5Zid1MfginIHoCIlEkw1jK/APCnzy5FfelgSU4sCdWt50zH/ztjCn5zzQLoWETq15GhO8pwMnJ7YQWEyGAtnViKbQ098AVDGRvHh8Mch9r78dnlE9HvDeL5LY14/2AHXtvVgptWTcVkVaOC7/9T6ibd7wtqvoidA37UFVvwzP9bjvFlNrT1eXH/a5Lu5sMjkrbTm2XNb1zZQ0gd/MbOOokTWpf8HmUFdoKZWG5DlcOsaTKRD9hMEYujtj4vVsp632gWT5BkAMVWo5IFmlSuvbAtkaUCf3hbW9ywYHyJ5vfTp1Xif987jIfXHMZvrlk4pPE+9N4h7G9x4S25kG7F1HJcF6ftdXS2SmR+7WYDpshbr3tbXHha3ppbNqkMiyaW4M4LZ2te9+jnT8aAPwSjXodlk8vxh7cPoLXPi2qnZbDsQV5TwRBHTbEFDd2eQe4Q8fBFyTSETdyC+hKsnlujuYnMhJPGFeP+q+bjovnD71FabDMqjVAKCXXAOqvGgS1y6+lUUG8hc0jZs2+cOx1bT/TgoyOJuwlmi0fWHsaHh7vgD4VRaTcrhYm9ngC8gbCyO6C0nu/3YXq1JLk52NaPf25twj+3NmHZ5LKY1nkiKzscGuYSmxE97gC63QOYVetQxh6P5VHntPn1JXjiw2OYUGZLaScuulZgpIsS84FpVXb865aVWPLTNwFIO7jd7kBMzW+JzaScQ1fNqMTaA+247ZltUvIhc9XWsJA/KalRhNjSPGtWFQIhnpU2lW0uH7yBMKZXO3DadCm79vm/fISnNp7Aa7taYr4mOuvaPeBHmd2k3HlXOS346lnTAKg636iCg2xofuNtjQXDatlD4sxva2+kwUAhUVdixca7ztXcuOQDopjD4w+hzxtEVZJ5f/P2M/DuHWfi4++fN8gpwGkx4vsXz1EugJ9fMQkXz6/FNUvrNcedNr0CiyaUJDTPj0X3gB/3vrpX47mbqBjHq7pohcNcsWkrthoxpULK7Ir19IfrFuHvX14+KPAFIkUvAHD+nGpwHrmBjMge5II3lexBVPGnmvmNdqMQWWWnxQiDXpe19sA6HcPVS8cnDRqywX+dMhHP/L9TC6q1MSDN4X1Xzcfrt63C1Co7djX1pVz0JhqfrJhajr98/mQwxvCNc2dgQX0JBvyhEdkebuj2KBKz+lKbkvkVGV67HNRUyjct6uJr9Q7nxiNdmmBQaLwPtPXDZtIPS6BYXmRCR78PvZ5ASkmS2mIrbjxtMgBJMzyrxgFfUJJNpGL1J66N1548HkDhSXhSpdxuxr9uWYlXvn66ksRJVjxbYTfDLtvFxer2mS9Q8JsGomL3zJmSsD/aAD8dRCFErdMyaHHHKoIBBhe4dQ34BwWad1wwE9efMkFZ8Ec63YoGV2R+9zT3DWpWkCoimI4+nwRSzPz6AiGlM1ahbZPmK5K/Z1Bp7FCVZN4r5ex1vIvOjadNxsa7zsWNp03GN8+fgf++fjEWyVljNdUOi/JvnSr7WiM3lsunlGPB+JKE0gn19/iFLY3409ojuGxhHUpsUqGeUc/w721NAIB544rjvY0GUeXvlgNVJfPrFAVvQvbAUeW0oNhqxFZVi+JECFmGQCnoGWJRUD5RbDViycThl1fkgmuWjseMagd+ffUCGPUM7+1vS+l1bvnf+eL5dZpdEZFFy7QrYDLCYa7Ru48vsyrBr9Aui5u9mmILGIsUxQJSYMtYpIOaWgYgCgG3N/SgttgyLFZ6orV0jzsQ1z0oGpGBZizS4v1Qe/+QakvuvngO3rx9FebWpXauGO08+cVT8NzNyzWPLRhfIjU2Ki+CjknFcMkQxc4jJelJBwp+06C93weTXoc5tU5YjfqMu1d9/5878dlHJU1uTbEFp0yObNk4zIa42dXuqMxvl9sfU5hfXmRCt9uPcJjjSEc/ZshbWf5QGG5/EBf+bi1ue2ZrWmN3yV/uMNdmkoXnqY4hbuZTmKO3urwotRkLLlOUr9hMevT7Qkp2MhsZ92KrlAFOlBWodpqHHvyqdlUWTyzB7BoH9jT3xa1EV2v1XtvVAr2O4edXSNXfBr0OE8uL0DXgh8NswMQUm0fYjJGqeCCSzRAZskjmNwyzXodzZlfhrT1tKWXzoi8O3kAIjOWPPR4RG4tRjxVTK/DRkfh+uGpEcCuCAoEIOPuGOUPW3u/TFDiPL7MpPr2i85oIxB0WI2ZWO7DpWEQq99K2JsyocigWZlNU7g2i+OxEl0fx/842FQ4zOvr96HH7U84s21XaYhHop+LzCwCXLxqnvMe0qjzdtx8GVkyriHvjesmCOlx/yoSUbm7E3MdqdZ8vUPCbBp39fpTbTWCModppTnmLMxacc/xtwzGlyrvaaYHVpMd3Vs/Cty6YiXK7SelTLo4XCL3thsOdeHbTCXT2+2MK80vldq29ngCOtA9gpmyd5A+GlcB609HUTuLRqKvT1U4SgRCH02LA1h+ej/n1JTFfK5oENPd4C07ykM/YzQa4fUElEB2pua9yWtDnHZoN2EdHI3rIC+fVYlaNA93ugKYYR436vY91ujG9yq7Z6p8i34jNqXNCl2KnM6uqKh6QTuiMRQqgRPAbkrtHzasrRq8nkFKzC3XGLxzm8AXDsBj0w96IgsicaVV2HOsaiHsjpkYEAUVRspNaOTt5vNMNXzCEFb94S9mZyCYNctZ31YxKzBvnhNNiVHS7oq25uhBt2eQyrD3QgZX3vo1Z3/8PDncM4PbzZyja5emqgFC9czS3LmLhlk0q7GZ0DfjR5w2iOEXPXfXfo+4emorm91dXL8DuH18w9IEWMGfNqsJPLzsppWNF8EuZ3wKjo9+nXPiqnEPfylXTEGW9JDK3N585FbecNQ3FVqOmOYD6y9Q94Ednvw/XPrwB3/rHdrj9IU3nIIHYrt54tAsD/hBm1zph0DEEQpHgN107LrWmxx2I/BwMh2HQ6xJaLIlMb2OPp+CKY/IZm8mAAV8QrX0i8zsycy+C7HjdB6PxBkJ4e28bLjqpFg99egnmjSvGLNkfdU8cnb26UOVo58AgKY14fSxZRjxMBh0MOqa8t8sXhN1kgE3O4omAOxDmMOiZst6iZUmxUK9nf0jySo3XupXIL2qcFngDYfR5g1j9wBrc/c8dcY8V8hZbVOZXeDjvb3Vh87FuNPV68YN/7cz6WMXN4ndWz8RLXzsdQCTTK+QQ6kzpF1ZOhsWo01gHnj69QqknURe+qtfY3BSlREOlUqW5TVX2oM6yqwvSU5E96HVsRPTxhUpBBL+MsUcZY22MsUErkjF2B2OMM8YqYr22UBGZX0C6oA/V71GNKMgRRGejnKpKe0Cbae12B3C0U2vtFCuLJy7G/94qZRROm1YBo14neTnK0gmjIb1Mk8sXGZs68AilsL0kLJFa+rxjspo2V9jNegz4Q2hzeRXrs5Fgdq10oU9VI9/Q7YbbH8J5c6qxep7kwTu7RgpedzbGfg/1d9AXDCtFaYIvnzEFT37pFHzj3OnRL02IVeWQ0e8Nwi53yFJ/ZigsZX6Fxj2V4Fe9LegNhOANZO4cQ4wMoqvVrsZe7G1x4fENx+MeK/6d7VE2X1UOM4qtRuxtcSndOKOzw5my9UQPvvz4ZgDaQNVi1MNs0CmZX3UV/+SKIrx7x1l4944zlcdsJgPuu2o+7rtyPqZVRboOqr+vJ08aHq23ekczXg1JNOq5NqtuKFORPRCZUTQKZA+prLLHAPw3gL+qH2SMjQdwHoD4K74AOdjmwr5WFy6pkTpiVTvMeLNP6oaTzlalqKj9zzdOV7SFapwWo3JyArR3Uo09nkFWY7XFg21mRPD78o5mTKkowvgym2QaHgwrTSrSzfyqg3G15jeVTjriZNvjDoxoB6qxTpFZyvy29flQ5TCP2Bb7rBonHGYDNh7twmWypi4RwhlCbWhfbDNiWpUdHx+TZDrdA34wFvGS9kb5c0ab4dtMhkFexalgM+mVDO+AP4giswEGvQ4mvU7Z8QiGwjDodJHMb5xWymq8gbDmZ18wTJnfUUKNnGi4/pEPkx4r5C3R2UTGGObXF2PzsS6lQKu51yPvAGR+E7TtRA8ue/AD5XdRnCZwWo2K5ldU6Atq5OBedIUEpGvJNbIDgprz51Sj1GYaNrtK9Q5iqsFvJrIHIjNsJj0YG+WZX875GgCxjAh/C+DbSK/L46gkEArj3N+sgT8Y1mR+PYGQUvg1VIRh/5QKOyaUDy7AkTK/kfdWf5l2NfUpRW8OUakbI/Orvms+XbZRM+olva3IKqfbiKHfG1SKCdS+wcFQGPokd9jq4qiR7EA11ikyGxAMcxzrHBhRrbVexzCr1oGDbf0p6STb5bURnb1dMqEUm493g3OORT95AyvvfVt5zu0PaoLHyVkqwCkyGZRiOpc3qGSVrKqgWOx2CMeVrhQyv+obRl+QMr+jiVhyoXgtYcXuQHTmF5B24va39uN9OfMbVjWMyJQ1+9s1v0efk52qbK89hn8rAEyttGNqpT3mc4KHP7sUv7xqfpqjTI5TtTtVbE0twNZkflVraqTbSY9FGGOwmwyjO/iNBWPsEgCNnPNtKRx7E2NsE2NsU3t7e7LD8xbOucaMvFLR/Er/T1f60Dngh8NiSNhKsM8TUIIFsY2wbFIZDrS6FL3xhSfVwKBjMds+qtu1nix3xjHpGQLBsGJVluhu2OMP4fZntmra1wr6vEHlYq++kAdl/WMi1CdeJ8keRgxhVbO7uS9lx4NsUekw42jHABb/5A1c/6cNCY+NlfkFJNeHHncA33xWOv0MqKQObn8Is2qcePeOM/Hy10/D5YuTZ5hTQQpyI1ZnYtdC3TAkGObQ6yOyh3gNYNSItuOAlPn1BijzO1qoK7Hi3NnV+O6Fs/Czy+cBgGIfGI04b0drfgFg5TQpIREIcZw3pxoAcLg9O8FvryegyMtioZY82fLYbUc9zlQ9d9XBr/r6mkp7YyJzisyGwvL5ZYzZANwF4AepHM85f5hzvpRzvrSysnKoH5cXfO2pLVj44zfw8fGII4LI/IqsVGOPN+5dfyLUxXOxqC+1wR8KK1tT4su0aGIJgmGOHQ290OsYvveJ2XjyS7GN5dWeocKKxmTQIRAKK40yEp0PXt7RjOe3NOI3b+wf9JzLG1BORn5N5pfDqEsme1Blfin4HTEmyo4H3kA45m7DcFJeJLmjdLsDWHeoM2EGuM3lhcWoU3Y1BKKr3PMfNw56jccfgs2kx6SKIsytK87aFqc6yPX4Q0pAoc78Chslu9kAo56hKwXZg/qG0RsISd0i8zgIISIY9To88rml+PIZUzFBvoncH6cBi5CHxQowhfsOAFwsd+DL1D5T0OsJoNgqddyri1EMLc67lQ5zyu4nuUCdKEm13beQPUyvckCvY4p9IMkeRoYSm1GRVeYj6XwLpgKYDGAbY+wogHoAHzPGarI5sHzixW1N6PUEsE1VqOOQ9VFi6+tzj27ErU9vGfJ7d/bH9uYVzJALC1be+zb6fUFlG2GGbDWzq7kXJVYjSmympP3OASi9upWCN/nL6QnEt59ae0DK2EeL1wd8QfiCYaWIQt3YIhhOLntQ63ydcbbciOyzanrkJnR86QgHv1FZm4EEtmdtLh8qY2iSp1TYsXhCCa5eInWRUxewuOXgN9tYTQZlrP5QWNk6lYLiIDjncsGbDowxuSX0UGUPUubXTNuyo46TJ5XBZtLH7cbZ0e9DWZEJhhiBlzoYu3BeLWqcFrywpRF/33QCwTRa0AdDYTy+4Rj8wTD6vFLwu/2e8/G2qnhNIHTGp6Rw7cgl6kRJKk0WACnB85cvnIy/3rhM+l2e51j/BkT2EY1JNh7pwtt7W1OSuo0kQ444OOc7AFSJ3+UAeCnnvCOL48pL1K0sxdamujXs+wc7hlz41tHvS9j+VvRWB4DmHo8SgIpGFSe6PJhambquUWwfmQw6eAMhJfiN7jSlprlHyjrvadbaSwld2tQqO97c04ZAlOwhmVE/ZX5zg17H8JNL5+L7/9qF2bXD48sZj+isTa8nEFMHCUAuyBucrdLpGJ7/ykrl/R59/4jynCcQgnUYLIpsRj1aZIs2tZOJzWiA2x9Sdn3ElqpUVJjcz1i9WzLgl28mKfM76rAY9Vg5rQLrDg6+DP7fuqN44sPjiq1ZLF762mlw+0MwGXSYUlmEdYc68e1/bIfdbMAnTqod0lj+uv4YfvzSboQ5VzK/8Wy7vnn+TLT2efGl06cM6TNGGrVsYSjX17NmKqEKzEbJ5cZEDWRGhAq7Ce9v7cA1/7sek8ptOHtWda6HpCEVq7OnAKwHMJMx1sAYu3H4hzVy+IIhNPd6cMUfP8Ajaw/HPEbtnXv2rCr89YZlWCpbuqgv3D3uAI53uQe9Ph7eQAhHOwcwOUHwWlZkUj6j2x1QMr9Tq4ogzgHRFbyJECeOIpMBA/6gsi2RKPMrCnd6ojJZRzukv3WmqmOcIBjiSe+w1dvZVPA2snxm+SRsuvtczBkmU/p4VETtcvS642+Ltbm8SVsvOywGxR8XkGQ4jmHYRdBoe0McBlnSYzXp4QmEEJSDX70+sr5SaRnuU90w9roD8AVClPkdpZw0rhjHutzKOdobCOHnr+zBD/+9CwASWgrOG1es7Nyp3QzUCZdUEfaZjDH0eoIJEwtlRSY88rmTNS2XCxWxrkj2MDKoEx35+P1Kxe3hOs55LefcyDmv55z/Oer5SaM56/ujF3dj+S/exsfHe/DTl/dgb0vfoGPUdl7jS61YNUOrXf5/q6bg5jOnAgDe3ptar3cA2NHYi0CIY2mcdoKCp286FYDkG9rvC8Golwy4q+Ws2PgUipY++O7ZeEe17VVk1mPAF8n8JrpQdw1IQa83ENZsXYjMr+j2o3F7SEH2oNaYJcp+E8NDLhqLiI5WgkRd0NpdvqTBr5DLuLyS9KDHHUjZBH8oWEx6xZYsGA5HMr9yUCyCX4OS+dWnVOmslj30uP1Zs7giRp45tU5wDuyVg8+Xtzfj4TWRhIo7gcRHjdier3SY8cKWRqw/1JnyGI52DODZzQ0ApO9in4dsJAUi+CXZw8iglqfMqxue5ieZMKa/Bb5gCE9+qLUpXv3AWtz+zFaE5YtZIBRGv0+y89LrmFKZq+bOT8zGd1bPwknjivHPrU3Y1+LCj1/crbyHGvVjm2Wv0sUTShKOU109PuCL2CyNL5MCiYkpFC2NK7FqAswis5T5FW4P3kA4ZsFeKMzR7fYrJ2R1pqpVbk4hxqdxewgllz2oGS5/SCK/WFBfjK+eNU2x3IsX/HoDIfR5gxpZUSyEPVO/L4gBOQgtTbH96VAw6XXKzV1I5WQiCt5Cst5dL2eEi8yGlIIdXzCsfPd7PAH4gqT5Ha2IHbynNp7AG7tbcUxODohiuFTaXQPAdy+cjS+dPhlv3LYKJoNuSNnfDYcjgfKAL6jIHgqB525egde+sSrt1wvpBMkeRgbxfV85rRxfWDkpt4OJwZg+y56QJQq//dQC/POWlaiwmzG/vhjPb2nEG3ukE45wQ7jzwlk48NMLcf7c+HV9iyeU4Eh7Py54YA0e/eCIpjUkALy3vx0n3fMaHt9wDIAU/E6uKEpavSou5t3ugKYVsNBDRhcRpUKR3OJWfUKObhAASAE350BdiWXQMf1eyfJJXKzVsodAOLI1nIi3vnkGNt197pDHT4xOGGO444KZ+PnlUo94detuNaIda3R74mhE4anLG0C3vENRnKIJ/lAQTWEAqbBTm/kNIhiWnjOqZA+pZn6tRj0cFgN63AHK/I5ihHTruY8b8KW/bsJj645iSmURXr1Vaif8mVMnpvQ+NcUW3HXRHJTYTFgyoRQfHkk986uuru/o96PfFxyWm8FcsGRiqcYZY6iI6xHJHkaGKxZLBcm/uHx+Xmbb829EI4hoHlFiM2Hh+BJsuvtcPPvl5WAsopsSFdvFNlNSK5iaYiv6VBKJ5l6t5+Pv3twPTyCEu/+5E4++fwTv7mvD4gmlScdpM+lh0uvQNeDHR0e7sHSS9JqrlkpfrgX1Jan9wSqEB1+vJ6KRjBX8ihatdfJ2tVob7JKz0OJkoi54C6m2hhMxtdKek+13IreIALUvTjZMNLhI5unpUMkexI3ccMgeTHJTGEBkfoXbg7bgTa+SPbhTKHjzBSWNb4nNKMkeqMPbqCVaa97nDWL+uGIUmQ04/PNP4Eurhl5UdsqUMuxu7ot7kxhN94AfZoMOVqNeuYalsjM4FhC5mHwMxAqRBeNLcPTei0bcTjNVxuS3oKnHgx/+ayfaZUNydbGV2aBHrdOC451SVli0Hy5NIZtUG+Wj2Nwbyfz6g2Fsb+jF51ZMgkmvw49f2g2LUY8vrZqc9H0Zk4zz39rTCpc3iCWyRvismVXY8+PVmDdu6HqaIrNU+RoKc6XLV6yiN/H3i+BX3Y5VZH6F7ZPG6iyUvMkFMXaxmwzQsfhbwcLPOlkhpF0V/IpdmpJhyHQZ9TqEwpKdWSAUubGzGvXwBcOKHMigcXtILfNrMuhQajOho9+PUJhTh7dRSqyM/b1XSl3P0vXQXTa5DJwDm47GarIq8fzHDXjgzf040jGARz84IhVJWwzY2ShZc1I9hYSeCZ9fui4RYzT4feLDY/i/9ceUpg3FUQUBE8ptOCZLIg60Sablydo7AoOD36aeSOb3aOcAgmGOBfUlmF0rbd18YeVkzKpJrdp+8YRSHJK7/kxS3UlZ0/Q0Vfc9Fy2R1YGtoE3efhadwDwqHeOAP4gis0HRA2tkD6Ew3WETcdHpmNy6O3bwKwLHojg2aAIRHLu8AcWNpGSYZA+A9L2W/Hwjsgcg0nZc0fzKbiqcc7y4rQk/e3l3zPf1h6Tgt9hqRJN8szwcPsXEyDKtyo7/+a/FGUtYFk8ohVHP8OHh2MFvS68Xt/99Gx548wDO+tW7CIQ43P4Q7GYDOmUZ0CQKfgFEnI5I9kAAYzT4FYhuPNHZpUnlRdjR2IvjnW4caHXBYTYMCmxjUaeqZHdYDGhSaX73tUgeuTOqHfjeJ2bjWxfMxFdkh4hU+Po505Wfq5MUAaWCOqgQmeNYsgfRtlnpChbUan5FNytAW/Cm9kIliFgUJwh+RTAZzwNYoJY9HJFvDocj+BXfcV8wLLfujjS5ACLyDXXmN8wl6dPXntqCP609EuNdAZ/c1KLEZlL8tON5shKjh4c+vRgXDtGfNxYWox4L6kvwwaHYhkrv7JPchdRF02rv7PIiE9lIyohmUqlo8YnCZ0wGvy29Ps3v0T6IN5w2GZxzPPL+Yexp7sP0antKxtr1pVbcf9V8bLzrHMysdmBHY6Qj3P5WF/Q6himVRThlSjluOWvakLICkyoi2d6sBL+q7JLo7hMr+G3tk1rMCsspr1+r+XVYDGBMah0Z0GR+Uyt4I8YuTsvg4Lepx4PHPjiScvArbuI+OtqFh947hNOnV8RsjJEpoqjTJ68RRfZgigTfABSpT5FZWl/bTvQo7xGI0a1LZH5LbUZFdkSZ39FPsTV70psV0yqws7EP//PuIc3jnHO8sqMZFXYznrt5BXb/+ALlOfH9o6xvhLsvnoMKuwknT8rvbnbEyDDmopMBXxBv7tFax0RbC82oduDUKeV4a08btp7owbLJ5Sm9N2MMVy8djyqHBSumlmN7Q49SqLCvxYVJ5ba0t8HU2SBTFqyQrPI4xpdZFd1kLNlDa58P1U6LcnyszC8gFwSpMr/SRZ0yv0R8YmV+b358M+55cbciN0omezDqpeKeV3e2IMyB+69aMCxjFVulosWxCHJFoOryaTO/Yl2IoiMgdnGfPxiGSa/TFOmlK2Ui8ods2ot95cypKC8yDbpu/WtrE9Ye6MAnF9SCMcn7/ctnTMVDn16sfP8mlVPwK5hcUYRNd5+nNBMhxjZjLvj9/dsH0OsJKMEcELtd4qlTytHY40EgxHHmzMpBzydj+dQKhDlw76t70eby4kBbf0Y2LdnGIl9gP7d8EixygU2sgrfWPi+qHRYlaPf4IxXvnkBICU6MBp0ms+Xxh2A10vYtEZ9iqxFbjvfg+j9tUDK9QqfY2uuFSa9L6UZPSB8mlttQk4I8KR3EOEQzmEjmVw5+vVrNrxjHJtnLG4hd3OcPhmE26FGsKtKjzO/oJxsJCoHFqMcVi8dhR2OvJsHw8o5mVDnM+P5Fc5THvnvhLKyeV4s5cttyMzmHEERMxtzKEN6+t503PeFxly0ap/yczjbJIlmD9eSHx7H6gbU40jGAGdWZBb/P3bwcz8jd3jLlzBmV+Pv/W44bT5usWCvFkj10DfhRVmSKZH7lY6K3pY16HfwhqZf8b97YrzQGIYh4iABh3aFO7JM7K4pCshPdbmXrNhli56K+1JrkyPQRYxUFnyLItRlja37rSySZ0sYjkUKlWMGvLxhSZA8CWjejl9NiNEHKBgvGl8AfDGN/q1Q7cqDVhff2teMTJ9XGdJL44qopOHNmJa5fNmFYxkMQo50xl5prd/mwbFIZblo1FUa9TrFHimZciRW/u3YhplTYk7bpjYXFqMdF82vx8vZmpT3w/PrMWvwtSdIGeSgwxpTtHyWrG6vJhSeAEptRCZDFMS5ZziGybia9Dn3eAC797/dxVLaJo+1bIhGN3ZGCUOGJrZN3YY53uZM2uBA45IKecSXDF/wK2YPo2mZUZA+xNb81xRYwBqXtMQCNB7hAWJ2VaILfMXdaLhge+8LJmn/zbDFL3jXc2+JCWZEJ1/zvejAGfPH02FaZTosRj31hWdbHQRCFwpg6y+5rceGjo924YrGU1f3CysQeu5cuHJfw+WQ8eP1i/PyyABb8+HUASMuPdyQQwa8vKvjlnCvtMYVMQmR+W2UXCNF+1mTQYUdDL47LmXUAGmkJQUSjlii0KMGv9Ls3EEZRikGgR5Yi1JcOn5l6RPYgMr9a2UOfIntgyvFVDjNa+3yoK7agqdcbW/agWJ2R7KEQMOh1GA6bZqHdvePZbcpjj35+6bB+5wmikBlTsocH3zkIAIoeaiQothnx4PWLceXi+mGpQs8GViWw1Ra8eQNh+INhFNuMSiArtn0bZVsmkW0z6pkm8AXoIk4k5qeXz8MTXzwFFqNOCX7VuyzJnB4EDXIG+ZzZVdkfpIzwsvYEpCDXKPx8zSL4lQJbtYdobbG0NibKgUusgjdfULI6U8seaMeEiMag12FKpfQ9On16BT61dDzOmjl833eCKHQKPvMbCIXR7fajymFBn1dq5fuZ5an1WM8WF82vxUXzM/d8HC4sBq2kQSAyVcVWI4xy8VG/nGUTHsbC/7hF1cpZx4Awp4s4kRinxYiV0ypQW2xFi7yT4FZZ6aUqe3jks0txuGMAU1JoRJMu8TK/Nrmos1uWNllUab9qpzT+SRU2bDzahY+PdePTp2rPPT5Z9iACZYBkD0Rsnr95BfQ6psh8CIJIn6SZX8bYo4yxNsbYTtVjP2GMbWeMbWWMvc4YqxveYabPKzuasfLet/HEh8fQ0e/D0oml1D40CoNeB6OeDSp4Ex2zhG2P02JQtI1NPR44LQblRHzqFMkObmplkRIokOyBSIXaYgsa5ZupXpUGf/GE0pRev2JaxaCgMtuIjK4nyupM3OAJXb9FVV1fbpeC32qnBdctG4/ntzQqbhGAJCvyB8Mw63WaG0VaN0QsSmwmCnwJIkukInt4DMDqqMfu55zP55wvBPASgB9keVxZY9H4UlTYzXhq43G0u3wpZ5PGGhaDflDm987ndwAASmQ9osNiVILf1j6vRrP54H8txrYfno+nb1qu9FCnzC+RChPLbTje6UYwFIZL1X1pkaprVa4RsofXd0teq6KBi8mgg0HHVMFv5Dsv/MNtJj3m1kl6f3WBbSAkFUaZo4LddApsCYIgiNRJur/GOV/DGJsU9Vif6tciANkvb80SE8pt+MRJtfjbhmMIhsKosFPwGwu7xYB+bxDbTvRgwBfEimkV2HK8B0DE0cFhMSguDy5vUNM206jXodgqXeyF9Q5pfolUmFBWhM4BP07I2t0ffnIOZlY7sDSPOjGJhi3CukwdoFpN+pjBrwiYAyGu7J70egJKG3S/7Istjrvnk3Pw1t624fwzCIIgCGSg+WWM/QzAZwH0AjgrayMaBmbWOBRzcAp+Y1NqM6FrwI9LH/wAAHDkF59QnpssF1o4VLKHAV8QpUWxW3iKwCDdbnbE2GJiuVSxvuW41BCiymHBimHyS00Xk177XRZWZ4AkUxDrQi17ELtMRSa9JvgVCHcVIRP6/MrJ+HwSBxqCIAgic9J2e+Cc38U5Hw/gCQBfjXccY+wmxtgmxtim9vb2dD8uIxaOL1F+FoEcoaXcbtJknYQE4rsXzlIyvA6zUalYd/mCcVvPCtkDFe4QqSCav9z+d8nGqdwe+6Yql0R37IrO/ArUN3yfXT4J3794Dq4/ZWLM4FfJ/GaxGxhBEASRnGycdZ8EcGW8JznnD3POl3LOl1ZWDr1NcDaYUe1AhXxBXTU9N2PId8qisrjtLh8Ard2UOvPb7w3CESf4FbIHM13UiRSYGnVDWh5nRyGXqDO90u+R77ZweDDomOZxk0GHG0+bLPv4xgh+5d0oWicEQRAjS1pnXcaYujfwJQD2Zmc4w8d/vrEK6+88m4pJ4uCMqiI+3DEAIDr4NSqa3wFfMK4P6zJZq0lV60QqMMbwq6sXKL9H34jlA4mK0kQDmEQyH6cc/PbFCH4p80sQBDGypGJ19hSA9QBmMsYaGGM3AriXMbaTMbYdwPkAbh3mcWZMhd2s8dIktAzIVfbCj/hwuxT8FkVlfgf8IfiDYQz4Q3FlD7+6egFe/OppcTXBBBHNVUvqlZ9LbPn3vbGbDbhiUaTjo0Ete5B1vmq9bzQOswGMRWl+g9qCN4IgCGJkSMXt4boYD/95GMZC5BDRj35OrRMvb2/G4fZ+AINlDwDQ5vJqfo/GatLjpPr8bOVM5C9/+fzJeG9/e97uzpw/txrPb2kEIHljC0TGN1HmV6djKLYa0Sm7QgCR4Dc6q0wQBEEML1SRRAAAvveJ2Si3m/Bfp0zA/a/tw55myc1OHfyKrdtmuZtbqu1nCSIVzppVhbNm5W/LVrU8QZv5TR78AsD0Kjv2tbiU3/2U+SUIgsgJdNYlAAA1xRb88JNzUWIzodJhxs4mKfgtMkcu6E450ytaG9vjZH4JohBR250Z9LGC38Sn05PGlWBXUy+CsssDuT0QBEHkBjrrEoOYVG5TslLRBW9AJPMbT/NLEIVIvMyvkC0kK/BcNKEE3kAY2xp6AER8fsntgSAIYmShsy4xiMkVkvUUY9D0khca372yJKJW1d6YIAoddfCr10V+TlX2cObMSpgNOry4rRlAJPNLwS9BEMTIQqk7YhBfOXMabCYDFk8s1Rj4i0B4R2MvAGBiGTUMIcYOam2uRvNrkh5Plvl1WIyYVevEIbmYlKzOCIIgcgMFv8QgJlUU4Z5L5g56XGR+D7UPoMZp0QTGBFHoqINUteTHLDe5qElhJ6TIpIfHL8kdKPglCILIDXTWJVJGbW02sdyWw5EQxMijlic4VWtBePemEvzaTHq45eBXsToz0E0kQRDESELBL5EyZoNeyVJNKifJAzG20BS8qSQQLX1SAWgqGniLUQ9PgDK/BEEQuYTOusSQMMpax4kVlPklxhbx/HjPm10NADhZbuudCCnzK3VT9JLbA0EQRE4gzS8xJEpsJgz4PZT5JcYc8TK0ly0ah08uqEupM53NZFA0v95gCEY9g5GaXBAEQYwodNYlhsTZcgeucSXWHI+EIEaWRPKEVFsyW00R2YPHH05qj0YQBEFkH8r8EkPi7otn44wZlVgwviTXQyGIEcWQYoCbCJtRj0CIIxAKwxMIJbVHIwiCILIPZX6JIWE26HHunOpcD4MgRhzGMg9+hT2g2x+CNxCizC9BEEQOoOCXIAhiCKyeW5P2a0Xw6/GH4PFT5pcgCCIXkOyBIAgiRfb9dDUMuvRzBjYR/AZC8ARCsFCjGIIgiBEn6VmcMfYoY6yNMbZT9dj9jLG9jLHtjLEXGGMlwzpKgiCIPMBs0Kdc3BYLq1HKN7j9QVnzS5tvBEEQI00qZ97HAKyOeuwNAPM45/MB7AdwZ5bHRRAEUXDYVLIHLxW8EQRB5ISkwS/nfA2ArqjHXuecB+VfNwCoH4axEQRBFBROqxEAsPVED7Y39CoaYIIgCGLkyMae2w0AXo33JGPsJsbYJsbYpvb29ix8HEEQxOikTm6B/NOX9wCQZBQEQRDEyJJR8MsYuwtAEMAT8Y7hnD/MOV/KOV9aWVmZyccRBEGMairsZo1muM8TyOFoCIIgxiZpuz0wxj4H4GIA53DOefaGRBAEUZjodAyhcOR02eby5XA0BEEQY5O0gl/G2GoA3wFwBufcnd0hEQRBjA1a+7y5HgJBEMSYIxWrs6cArAcwkzHWwBi7EcB/A3AAeIMxtpUx9tAwj5MgCKIg+N21C/GtC2YCAG5aNSXHoyEIghh7sJFULCxdupRv2rRpxD6PIAiCIAiCGJswxjZzzpdGP04O6wRBEARBEMSYgYJfgiAIgiAIYsxAwS9BEARBEAQxZqDglyAIgiAIghgzUPBLEARBEARBjBko+CUIgiAIgiDGDBT8EgRBEARBEGOGEfX5ZYy1Azg2Yh8YoQJARw4+Nx+huRgMzUkEmosINBeDoTnRQvMhQfMwGJoTLbmaj4mc88roB0c0+M0VjLFNsUyOxyI0F4OhOYlAcxGB5mIwNCdaaD4kaB4GQ3OiJd/mg2QPBEEQBEEQxJiBgl+CIAiCIAhizDBWgt+Hcz2APILmYjA0JxFoLiLQXAyG5kQLzYcEzcNgaE605NV8jAnNL0EQBEEQBEEAYyfzSxAEQRAEQRAU/BIEQRAEQRBjBwp+iYKHMcZyPQaCGA3QWiGI1KC1MropmOCXvogRGGNnMsYGmTqPYRziB/qe0ByoobUyCForKmgOItBaGQStFRWjbQ5GffDLGLuUMfZ/ABbkeiy5hjG2mjG2BsB/AfDlejy5hjF2HmPsfQC/Yox9GwD4GK7wpLUSgdaKFlorWmitRKC1ooXWipbRulYMuR5AOjDGGOecM8bOAvATAAEAyxljxzjn3Tke3ogi320xAJ8C8L8AbuScP5vbUeUexlg9gHsA3AvgXQBPM8bKOeffEd+fXI5vpKC1EoHWSmxorUjQWolAayU2tFYkCmGtjLrMb9QX7AiACwB8C8ApAObnbGA5QMwF5zwMoAnAXwEclJ+7mjFWzxgzimNzONQRIepvnAVgB+f8Rc65C8CDAG5jjE2XF+2YmA9aKxK0VrTQWtFCayUCrRUttFa0FMpaGVXBL2PsqwCeZ4zdxhir4Zwf5Zw3c87fBtAK4AzG2LgcD3NEUM3F7YyxCgDvA9gO4H8YY3sBXAPgDwD+KF6Sm5GODFHfDSeA/QBOY4wtlw+pArALwN25GuNIQmslAq0VLbRWtNBaiUBrRQutFS2FtFZGTfDLGLscwOcA/B7S3cXdjLGFqkOeADAD0t2H+nUFtzij5uIkAD8CMA3ASwDeAXAd5/xqADcCuIwxtkS+iy9IYnw3fglJm/ZbAP+PMfYBgPMBXAFgIWNsUiFvT9FaiUBrRQutFS20ViLQWtFCa0VLoa2VUdPhjTF2L4D9nPNHGWMTAVwFYC7n/AbVMbcCKIZ0t3oy5/yXuRnt8BJjLq4BMJNz/kXGmIVz7lUd+ycAj3PO38vVeIebGPPxKQBTOOdflrfnFnHONzLG9AAeAvAdznlXLsc8nNBaiUBrRQutFS20ViLQWtFCa0VLoa2VvM/8qu4aDgO4HgA458cAvAygiDF2ierwpwB8EcAzACqiXj/qSTAXLwJwMMYuiTpBfR/AXAB7R3qsI0GC+fg3gFLG2OWc8wDnfKN83E8AFAFwjfhgRwBaKxForWgZ62sl+rs9ltfKEOZiTKyVIcwHrZUCWit5F/wyxs5ljC0Rv6u2Ef4BwM0Yu1T+vRlSteUcJmEH8DsAOwDM55x/K+r1o4505kJ+3emMsXcgbUFcyTlvHblRDx9pzMdM+XXTGWP/AjAPwG2c88DIjXr4YIwVq35mY3ytDHku5GMLda0MdT4Keq0gytloLK8VpDEXQOGuFQx9Pgp9rRjVvxTqWsmb4Jcxtogx9iqAFyDpjMTj4q6hW37uZvlk3gvADsAiT64XwK2c84s4580jPPysksFcWOXnjwK4hXP+mdE+F0Bm3w35+RZI83FJIZywGWOnyCfdPzHGbmCMmTnnnEnbb8DYWivpzkWhrpW0vxvy84W2Vk5ljD0B4EdysKKXHxcBz1haK+nORaGulbS/G/LzhbZWljPGngVwP2NsTqGvlZwHv4wxPWPsYQB/AvAwgCcBzJafM6juGqwAXoN0t/EwY6wOwCJI/nLgnAc5520jPf5skoW58AMA5/wE53z3SI8/22Txu+HinDeM9PiHA8bYfEj2Ov+Q/zsb8g0B5zwkH1bwawXIeC4Kaq0AWftuFNJamQfJmeAlAG0AbgLwWUBaA/JhY2WtZDIXhbhWsvHdKKS1UgXgvwG8AqATwK0AbgAKd63kPPiVT8r/AXA65/wFAM8DOItJAvsgADDG7oF0x1EN4JuQLDWeBNADyWy6IKC50ELzEZMlAA5yzv8G4A1IWYjjIgvOGPsJxs580FxoofnQciqAvZzzpyDdQLsB/BdjbAow5uaD5kILzYeWBZCK2f4C4NeQrrWXMsZmAQBj7KcotPngnI/4fwDOAHBKjMcZgHMhfRnL5MeqIE3y1KhjbbkYO80FzUcu50P+m30AfgagAcAGAI8CuANSsPMkgGmFOB80FzQfQ5yPBZA0idPk338IKSP+I0gFSgV77qC5oPlIMh+XAfgegIvk3ysBHBB/M4AyeU5+CcBWiPMx0hPugHRH0SWflEvlxxkitmv1kKoK62K8XpfrCaO5oPnI5XzIz82ST0qflX8/A9J21eJCnA+aC5qPIc6HuDm2A7gPwBoA/4S0xX0tgF+p56CQ5oPmguYjyXxUyn/vGgBfhiT5uEp+7l4AD4i/G8BpUCWbCm0+Rlr24AfwNoBPQ2qbeDUgVQNyzjljTMclDc2HkDzkFOTnCslQm+ZCC82HlpjzAQCc872QghyhN9ssHyO2twttPmgutNB8aIl37ujnnH8bwFcB/IVzfjGkNr1zxRwU4HzQXGih+dAyFcAHnPNVnPOHIEkYbpefewrALMbYufLf3QlJ5uADCm8+DMkPyQzG2GcBHAOwjXPewxh7BEAYkgfcaYyxdznn+8XEypWFBwAMqN+nECad5kILzYeWVOdDPvx1AD9kkvXQtZDsdjqAwpgPmgstNB9aUpiPGWI+OOfbIbXoBaQiwA1ytTovhPmgudBC86FFno/jADZCuhk+Ij+uB7AbUntmQLIoexrAA4yxywCcA+mm2QgUzrlDMCwd3uQCixpIOpEwgEOQdDS3cs475GOmQ2qV5+Wc/1R+TAQ5vwXQzzn/ftYHN8LQXGih+dAyxPnwcc5/Ij9mheSAUQVAD+DrfJRXYtNcaKH50JLuuUN+fAmkQp4QgJs454dGePhZheZCC82HlmTzwRjTc85DjLFPA7iEc36N6rXfhuTlPAvAlzjne0b+LxgBePY1JXr5/zMgtT8EpAzzHwA8F3Xs5QD+CMmOxwKgSH6cZXtcufiP5oLmYxjmYzrkQgP52Jpc/x00FzQfeTof0wBY5cfKAZyR67+D5oLmI8fz8XzUMX8FcI38c43qPUy5/juG+7+syR7kLekfA9Azxl4B4IR0JwXOeZAx9nUATYyxM7jcD5xz/gJjbDYkOys7gLMA7OHy7I9WaC600HxoyXA+XgVgZ4ydxaU78pbc/BXZgeZCC82HlmycOxhjZ3Mp8/1ebv6K7EBzoYXmQ0s68wGgH8ARxtiPAVzBGFvNOW/gnPtz8TeMJFkpeGOMnQFJS1IKSTT+E0jGx2cxxpYBSou7HwO4R/W6qwHcBeAdSO3wRn16neZCC82HFpqPCDQXWmg+tGRxPgpB8kFzoYLmQ0s688Ekze8NkCzenADO4gXStCMVsqL5ZYydDmASl8zVwRj7IyTxtAfA1zjnSxhjOkgatN8D+A7n/Ij8OnDO12Y8iDyB5kILzYcWmo8INBdaaD600HxEoLnQQvOhJY35+BYkKcTXAPyVc/5xbkaeO7JldbYZwN9ZpH/8BwAmcM4fg5SC/xqXKgXrAYQ450cA6QtYaF9C0FxEQ/OhheYjAs2FFpoPLTQfEWgutNB8aBnKfIQ558c454c4598Yi4EvkKXgl3Pu5pz7eKR//HkA2uWfvwBgNmPsJUg+cgU90TQXWmg+tNB8RKC50ELzoYXmIwLNhRaaDy1DnI/NgOIIMWbJqs+vfNfBIRkj/1t+2AWpjd48AEc4543Z/Mx8heZCC82HFpqPCDQXWmg+tNB8RKC50ELzoWUo88H56C8ez4Rsd3gLQzJE7gAwX77T+D6kNPv7Y+lLCJqLaGg+tNB8RKC50ELzoYXmIwLNhRaaDy00HymS9SYXjLFTAayT//sL5/zPWf2AUQTNhRaaDy00HxFoLrTQfGih+YhAc6GF5kMLzUdqDEfwWw/gMwB+wzn3ZfXNRxk0F1poPrTQfESgudBC86GF5iMCzYUWmg8tNB+pMSztjQmCIAiCIAgiH8m25pcgCIIgCIIg8hYKfgmCIAiCIIgxAwW/BEEQBEEQxJiBgl+CIAiCIAhizEDBL0EQBEEQBDFmoOCXIAhiBGGMlTPGtsr/tTDGGuWf+xljf8z1+AiCIAodsjojCILIEYyxewD0c85/leuxEARBjBUo80sQBJEHMMbOlNuRgjF2D2Ps/xhjrzPGjjLGrmCM3ccY28EY+w9jzCgft4Qx9h5jbDNj7DXGWG1u/wqCIIj8h4JfgiCI/GQqgIsAXArgcQDvcM5PAuABcJEcAP8BwFWc8yUAHgXws1wNliAIYrRgyPUACIIgiJi8yjkPMMZ2ANAD+I/8+A4AkwDMBDAPwBuMMcjHNOdgnARBEKMKCn4JgiDyEx8AcM7DjLEAjxRohCGduxmAXZzz5bkaIEEQxGiEZA8EQRCjk30AKhljywGAMWZkjM3N8ZgIgiDyHgp+CYIgRiGccz+AqwD8kjG2DcBWACtyOiiCIIhRAFmdEQRBEARBEGMGyvwSBEEQBEEQYwYKfgmCIAiCIIgxAwW/BEEQBEEQxJiBgl+CIAiCIAhizEDBL0EQBEEQBDFmoOCXIAiCIAiCGDNQ8EsQBEEQBEGMGf4/pmry/CXwaOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "buoy_data_resampled.plot(figsize=(12, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the data from the NOAA GeoPolar Blended SST dataset\n",
    "#### Query ERDDAP for information about the satellite dataset   \n",
    "We will continue to use the NOAA GeoPolar blended SST dataset to validate the virtual buoy. ERDDAP has a feature that allows you to select and download metadata about any and all datasets. The user interface for this feature is at the following URL: https://coastwatch.pfeg.noaa.gov/erddap/tabledap/allDatasets.html.   \n",
    "Like all data in ERDDAP, even metadata, the values can be downloaded via a URL-based data request. Details about the feature are presented in the ERDDAP tutorial. For the purposes of this exercise, it is enough to know that we are pulling the metadata from this page into a CSV file and using it in Python\n",
    "\n",
    "__Build a function to harvest metadata from ERDDAP__  \n",
    "\n",
    "* The function builds the URL-based data request, sends the request to ERDDAP for the metadata, and loads the results into a Pandas data frame.\n",
    "* The function also uses the xarray module in OpenDAP mode to obtain a list of data variables.\n",
    "* Inputs to the function are \n",
    " 1. The location of the ERDDAP on the internet: The NOAA GeoPolar blended SST dataset can be found on the CoastWatch West Coast Node ERDDAP at this URL: `https://coastwatch.pfeg.noaa.gov/erddap`\n",
    " 2. The unique ERDDAP ID for the dataset: For the NOAA GeoPolar blended SST dataset the ID is `nesdisGeoPolarSSTN5SQNRT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variable containing the WCN ERDDAP URL\n",
    "my_erddap_location = 'https://coastwatch.pfeg.noaa.gov/erddap'\n",
    "\n",
    "# The dataset ID for NOAA GeoPolar blended SST \n",
    "my_ds_ID = 'nesdisGeoPolarSSTN5SQNRT'\n",
    "\n",
    "# A function to form the metadata request URL and \n",
    "# download the metadata into a Pandas data frame\n",
    "def get_dataset_info(erddapURL, datasetID):\n",
    "    queryURL = \"\".join(['?datasetID,minLongitude,maxLongitude,longitudeSpacing,minLatitude,maxLatitude,latitudeSpacing,', \n",
    "                        'minAltitude,maxAltitude,', \n",
    "                        'minTime,maxTime,timeSpacing,griddap&datasetID=\"'])\n",
    "    \n",
    "    # Form the base URL leading to the allDatasets page\n",
    "    baseURL = os.path.join(erddapURL, \"tabledap\", \"allDatasets.csv\")\n",
    "    \n",
    "    # Form the data query part of the URL. \n",
    "    # For this exercise we will only request: \n",
    "    # 1) dataset ID, \n",
    "    # 2) the distance between values longitude values, \n",
    "    # 3) the minimum and maximum latitude, and the distance between values\n",
    "    # 4) the minimum and maximum altitude values\n",
    "    # 5) the minimum and maximum and time between time values\n",
    "    fullURL = ''.join([baseURL, queryURL, datasetID,'\"'])\n",
    "    \n",
    "    s=requests.get(fullURL).content\n",
    "    c=pd.read_csv(io.StringIO(s.decode('utf-8')), skiprows=[1])\n",
    "    \n",
    "    #print(c.griddap[0])\n",
    "    # get the data variables using xarray via OpenDAP \n",
    "    c['variables'] = [list(xr.open_dataset(c.griddap[0]).data_vars)]\n",
    "    return c\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Now run the function the function to obtain information about the dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datasetID</th>\n",
       "      <th>minLongitude</th>\n",
       "      <th>maxLongitude</th>\n",
       "      <th>longitudeSpacing</th>\n",
       "      <th>minLatitude</th>\n",
       "      <th>maxLatitude</th>\n",
       "      <th>latitudeSpacing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nesdisGeoPolarSSTN5SQNRT</td>\n",
       "      <td>-179.975</td>\n",
       "      <td>179.975</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-89.975</td>\n",
       "      <td>89.975</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  datasetID  minLongitude  maxLongitude  longitudeSpacing  \\\n",
       "0  nesdisGeoPolarSSTN5SQNRT      -179.975       179.975              0.05   \n",
       "\n",
       "   minLatitude  maxLatitude  latitudeSpacing  \n",
       "0      -89.975       89.975             0.05  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minAltitude</th>\n",
       "      <th>maxAltitude</th>\n",
       "      <th>minTime</th>\n",
       "      <th>maxTime</th>\n",
       "      <th>timeSpacing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2002-09-01T12:00:00Z</td>\n",
       "      <td>2021-04-22T12:00:00Z</td>\n",
       "      <td>86476.212879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   minAltitude  maxAltitude               minTime               maxTime  \\\n",
       "0          NaN          NaN  2002-09-01T12:00:00Z  2021-04-22T12:00:00Z   \n",
       "\n",
       "    timeSpacing  \n",
       "0  86476.212879  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>griddap</th>\n",
       "      <th>variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://coastwatch.pfeg.noaa.gov/erddap/gridda...</td>\n",
       "      <td>[analysed_sst, analysis_error, sea_ice_fractio...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             griddap  \\\n",
       "0  https://coastwatch.pfeg.noaa.gov/erddap/gridda...   \n",
       "\n",
       "                                           variables  \n",
       "0  [analysed_sst, analysis_error, sea_ice_fractio...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metadata helpful for this project are listed below\n",
      " \n",
      "Time Range: 2002-09-01 to 2021-04-22\n",
      "Data Variables: ['analysed_sst', 'analysis_error', 'sea_ice_fraction', 'mask']\n",
      "Direct dataset URL: https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT\n"
     ]
    }
   ],
   "source": [
    "dataset_info = get_dataset_info(my_erddap_location, my_ds_ID)\n",
    "\n",
    "display(dataset_info.loc[:, 'datasetID':'latitudeSpacing'])\n",
    "display(dataset_info.loc[:, 'minAltitude':'timeSpacing'])\n",
    "display(dataset_info.loc[:, 'griddap':'variables'])\n",
    "\n",
    "print(\"Metadata helpful for this project are listed below\")\n",
    "print(\" \")\n",
    "print('Time Range:', dataset_info.minTime[0][:10], 'to', dataset_info.maxTime[0][:10] )\n",
    "print('Data Variables:', dataset_info.variables[0] )\n",
    "print('Direct dataset URL:', dataset_info.griddap[0]) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using ERDDAP, subsample the satellite dataset and download the data\n",
    "##### Get the ERDDAP data request URL\n",
    "Two options to get the ERDDAP data request URL for the data:  \n",
    " \n",
    "__Option 1.__ Use the the ERDDAP interface to like we did in CSV format, like we did above in the `Make a virtual buoy` section under `Bring the timeseries into Python`: This is quick but is not usable for automating scripts to download data.  \n",
    "\n",
    ">Go to the ERDDAP interface at https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.graph and do the following:  \n",
    "* Set the `Graph Type` to linsAndMarkers\n",
    "* Set the `X Axis` to `time` and the `Y Axis` to `analysed_sst`\n",
    "* On the time (UTC) widget, set ` Start` to the Jan. 1, 2017 (`2017-01-01T12:00:00Z`) and the `Stop` to Dec. 31, 2019 (`2019-12-31T12:00:00Z`).\n",
    "* Set the `latitude (degrees_north)` to `34.767`, the latitude of our buoy, and the `longitude (degrees_east)` to `-121.497`, the longitude of our buoy. \n",
    "* Click `Redraw the Graph`\n",
    "* Near the top of the ERDDAP interface, click the `Data Access Form` link. Clicking brings you to a new page where you can refine your data request. Our goal on this page is to obtain the data request URL to download the data as a CSV file. \n",
    "* Find the `File type:` dropdown and select `.csv`. Then push the \"Just `generate the URL button` and copy the link in the box to the right for th button. \n",
    ">\n",
    ">The data request URL should look like the following URL:  \n",
    ">https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2017-01-01T12:00:00Z):1:(2019-12-31T12:00:00Z)][(34.775):1:(34.775)][(-121.475):1:(-121.475)]\n",
    "\n",
    "__Option 2.__ Develop a function in Python to generate the data request URL:   \n",
    "\n",
    "To develop the function, use the URL obtained in Option 1 as a model, and break it down into its components. We will generate the components within Python and then put them together to create the URL.\n",
    "\n",
    "The URL components  \n",
    ">1. The direct URL to the dataset: https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT\n",
    "Get this from our dataset_info data frame as `dataset_info.griddap`\n",
    "2. The type of file we want to download: `.csv?`  \n",
    "3. The variable of interest (`analysed_sst`). We can type the name in or get this from our dataset_info data frame as `dataset_info.griddap`  \n",
    "4. The time range: (`[(2017-01-01T12:00:00Z):1:(2019-12-31T12:00:00Z)]`). Get the time values from the maximum and minimun date/time values in the buoy_data data frame. i.e. buoy_data[\"Time_iso\"].values.min() and buoy_data[\"Time_iso\"].values.max()        \n",
    "5. The latitude range: `[(34.775):1:(34.775)]`. Get these values from the `buoy_location` dictionary, i.e. buoy_location['lat']    \n",
    "6. The longitude range:`[(-121.475):1:(-121.475)]`. Get these values from the `buoy_location` dictionary, i.e. buoy_location['lon']   \n",
    "\n",
    "#### The function for Option 2 is below: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_erddap_url(my_var, ds_info_df, buoy_lt_ln, buoy_df):\n",
    "\n",
    "    # my_var is the data variable of interest, \n",
    "    #    which is 'analysed_sst' in this example\n",
    "    # ds_info_df is the dataset_info data frame that \n",
    "    #    we generated with the get_dataset_info() function\n",
    "    \n",
    "    maxTime = datetime.strftime(buoy_df.index.max(), '%Y-%m-%dT%H:%M:%SZ')\n",
    "    minTime = datetime.strftime(buoy_df.index.min(), '%Y-%m-%dT%H:%M:%SZ')\n",
    "\n",
    "    import math\n",
    "    st = '[('\n",
    "    ed = ')]'\n",
    "    md = '):('\n",
    "    url_1 = ''.join([dataset_info.griddap[0], '.csv?', my_var,\n",
    "             st, minTime, md,\n",
    "             #buoy_data_resampled[\"Time_iso\"].values.max(), ed])\n",
    "             maxTime, ed]) \n",
    "\n",
    "    # Check for inverted lat\n",
    "    url_lat = ''.join([st, str(buoy_lt_ln['lat']), ed]) \n",
    "    url_lon = ''.join([st, str(buoy_lt_ln['lon']), ed])\n",
    "\n",
    "    if math.isnan(ds_info_df.minAltitude[0]):\n",
    "        # print('1')\n",
    "        my_url = ''.join([url_1, url_lat, url_lon])\n",
    "        # print(my_url)\n",
    "    else:\n",
    "        # print('2')\n",
    "        url_alt = ''.join([st, str(ds_info_df.minAltitude[0]), ed]) \n",
    "        my_url = ''.join([url_1, url_lat, url_lon])\n",
    "        my_url\n",
    "        # print(my_url)\n",
    "    \n",
    "    return my_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the ERDDAP data request URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://coastwatch.pfeg.noaa.gov/erddap/griddap/nesdisGeoPolarSSTN5SQNRT.csv?analysed_sst[(2017-01-01T00:00:00Z):(2019-12-31T00:00:00Z)][(34.775)][(-121.475)]'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "erddap_url = make_erddap_url('analysed_sst', dataset_info, buoy_location, buoy_data_resampled)\n",
    "erddap_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feed the URL into our get_csvdata() function\n",
    "The function reaches out to ERDDAP to download the data.  \n",
    "__Note: Downloading may take a while, depending on your internet speed.  \n",
    "Note: If you get an error, then run function again. The server probably timed out__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>analysed_sst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>12.859994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.949994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.979994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.819994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.779994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   time  latitude  longitude  analysed_sst\n",
       "0  2017-01-01T12:00:00Z    34.775   -121.475     12.859994\n",
       "1  2017-01-02T12:00:00Z    34.775   -121.475     12.949994\n",
       "2  2017-01-03T12:00:00Z    34.775   -121.475     12.979994\n",
       "3  2017-01-04T12:00:00Z    34.775   -121.475     12.819994\n",
       "4  2017-01-05T12:00:00Z    34.775   -121.475     12.779994"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_data = get_csvdata(erddap_url)\n",
    "sat_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean up the data\n",
    "The data may contain data points that are outliers and that contain no data. Let's look for outliers and remove those points from the data frame if they exist. \n",
    "* Apply a conservative allowable data range. \n",
    " * For the lower end of the range, the freezing point of seawater (ca. -2).  \n",
    " * For the high end of the range, value unlikely to be seen in the area of interest (e.g. 45 degrees C). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of timesteps before cleaning = 1089\n",
      "# of timesteps after cleaning = 1091\n",
      "0 times were removed\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>latitude</th>\n",
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       "      <th>analysed_sst</th>\n",
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       "      <td>12.859994</td>\n",
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       "      <th>1</th>\n",
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       "      <td>34.775</td>\n",
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       "      <td>12.949994</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.979994</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.819994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05T12:00:00Z</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.779994</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                   time  latitude  longitude  analysed_sst\n",
       "0  2017-01-01T12:00:00Z    34.775   -121.475     12.859994\n",
       "1  2017-01-02T12:00:00Z    34.775   -121.475     12.949994\n",
       "2  2017-01-03T12:00:00Z    34.775   -121.475     12.979994\n",
       "3  2017-01-04T12:00:00Z    34.775   -121.475     12.819994\n",
       "4  2017-01-05T12:00:00Z    34.775   -121.475     12.779994"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get # of timesteps before cleaning\n",
    "b4 = sat_data.shape[0] \n",
    "print('# of timesteps before cleaning =', b4 )\n",
    "\n",
    "# Extract the temperature data into a data frame\n",
    "y = sat_data['analysed_sst']\n",
    "\n",
    "# Find the data that is within our acceptable range\n",
    "removed_outliers = y.between(-2, 45)\n",
    "\n",
    "# Invert the results of removed_outliers to get the \n",
    "#   indices of the values outside of the acceptable range \n",
    "index_names = sat_data[~removed_outliers].index # INVERT removed_outliers!!\n",
    "\n",
    "# Drop values outside of the acceptable range from the data frame \n",
    "sat_data.drop(index_names, inplace=True)\n",
    "sat_data.shape\n",
    "\n",
    "print('# of timesteps after cleaning =', buoy_data_resampled.shape[0] )\n",
    "print(b4-sat_data.shape[0], 'times were removed')\n",
    "sat_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change time to match buoy time format and move time to the index location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>analysed_sst</th>\n",
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       "      <th>Time</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>2017-01-01 12:00:00+00:00</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.859994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>2017-01-02 12:00:00+00:00</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.949994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2017-01-03 12:00:00+00:00</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.979994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>2017-01-04 12:00:00+00:00</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.819994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>2017-01-05 12:00:00+00:00</td>\n",
       "      <td>34.775</td>\n",
       "      <td>-121.475</td>\n",
       "      <td>12.779994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                time  latitude  longitude  analysed_sst\n",
       "Time                                                                   \n",
       "2017-01-01 2017-01-01 12:00:00+00:00    34.775   -121.475     12.859994\n",
       "2017-01-02 2017-01-02 12:00:00+00:00    34.775   -121.475     12.949994\n",
       "2017-01-03 2017-01-03 12:00:00+00:00    34.775   -121.475     12.979994\n",
       "2017-01-04 2017-01-04 12:00:00+00:00    34.775   -121.475     12.819994\n",
       "2017-01-05 2017-01-05 12:00:00+00:00    34.775   -121.475     12.779994"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Put the time column into datetime64 format\n",
    "sat_data['time'] = pd.to_datetime(sat_data['time'], errors='coerce')\n",
    "\n",
    "# Cut time part of datetime and put into new column \"Time\"\n",
    "sat_data['Time'] = sat_data['time'].dt.date\n",
    "\n",
    "# Put the Time column into datetime64 format\n",
    "sat_data['Time'] = pd.to_datetime(sat_data['Time'])\n",
    "\n",
    "# Make the index column the time column\n",
    "sat_data.set_index('Time', inplace=True)\n",
    "sat_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trim buoy and satellite data to time in both datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timesteps before in buoy data = 1091\n",
      "Timesteps after in buoy data = 1085\n",
      "Timesteps before in sat data = 1089\n",
      "Timesteps after in sat data = 1085\n"
     ]
    }
   ],
   "source": [
    "# keep only the buoy_data_resampled times that are in sat_data\n",
    "print ('Timesteps before in buoy data =', buoy_data_resampled.shape[0])\n",
    "buoy_data_final = buoy_data_resampled[buoy_data_resampled.index.isin(sat_data.index)]\n",
    "print ('Timesteps after in buoy data =', buoy_data_final.shape[0])\n",
    "\n",
    "# keep only the sat_data times that are in buoy_data_final\n",
    "print ('Timesteps before in sat data =', sat_data.shape[0])\n",
    "sat_data_final = sat_data[sat_data.index.isin(buoy_data_final.index)]\n",
    "print ('Timesteps after in sat data =', sat_data_final.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot both datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f84a0f05810>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "# set the minimun and maxium for the y axis\n",
    "plt.ylim(12, 20)\n",
    "\n",
    "# set the the plot.\n",
    "# The \"ro\" indicates a red filled circular marker. The \"b\" incicates a blue line.\n",
    "plt.plot(sat_data_final.index, sat_data_final.analysed_sst, \"ro\", label=\"Satellite\")\n",
    "plt.plot(buoy_data_final.index, buoy_data_final.WTMP_degC, \"b\", label=\"NDBC Buoy\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "#plt.plot(sat_data_final.index, sat_data_final.analysed_sst, \"ro\", buoy_data_final.index, buoy_data_final.WTMP_degC, \"b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a linear regression of satellite vs. buoy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y-Intecept:  0.5298046099717714\n",
      "Slope:  0.9558409867765579\n",
      "R2:  0.9216794169492041\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.922</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.922</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>1.274e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 23 Apr 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:23:01</td>     <th>  Log-Likelihood:    </th> <td> -519.16</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1085</td>      <th>  AIC:               </th> <td>   1042.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1083</td>      <th>  BIC:               </th> <td>   1052.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.5298</td> <td>    0.128</td> <td>    4.127</td> <td> 0.000</td> <td>    0.278</td> <td>    0.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>    <td>    0.9558</td> <td>    0.008</td> <td>  112.893</td> <td> 0.000</td> <td>    0.939</td> <td>    0.972</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>31.741</td> <th>  Durbin-Watson:     </th> <td>   0.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  61.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.177</td> <th>  Prob(JB):          </th> <td>4.67e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.111</td> <th>  Cond. No.          </th> <td>    165.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.922\n",
       "Model:                            OLS   Adj. R-squared:                  0.922\n",
       "Method:                 Least Squares   F-statistic:                 1.274e+04\n",
       "Date:                Fri, 23 Apr 2021   Prob (F-statistic):               0.00\n",
       "Time:                        14:23:01   Log-Likelihood:                -519.16\n",
       "No. Observations:                1085   AIC:                             1042.\n",
       "Df Residuals:                    1083   BIC:                             1052.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.5298      0.128      4.127      0.000       0.278       0.782\n",
       "x1             0.9558      0.008    112.893      0.000       0.939       0.972\n",
       "==============================================================================\n",
       "Omnibus:                       31.741   Durbin-Watson:                   0.750\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               61.392\n",
       "Skew:                          -0.177   Prob(JB):                     4.67e-14\n",
       "Kurtosis:                       4.111   Cond. No.                         165.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "#Y1 = np.array(buoy_data_final.WTMP_degC)\n",
    "Y1 = np.array(sat_data_final.analysed_sst)\n",
    "#X1 = np.array(sat_data_final.analysed_sst)\n",
    "X1 = np.array(buoy_data_final.WTMP_degC)\n",
    "X1_b=sm.add_constant(X1)\n",
    "model = sm.OLS(Y1, X1_b, missing='drop')\n",
    "results = model.fit()\n",
    "\n",
    "\n",
    "print('Y-Intecept: ', results.params[0])\n",
    "print('Slope: ', results.params[1])\n",
    "print('R2: ', results.rsquared)\n",
    "\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot satellite vs. buoy data and overlay the regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EyieIVlppnW5sQx8GFCKD3J+4VEL3StALocRK0fzz8aDuhVd64kwMGUgzlRXAVuKFWR0pJXN/MK4eg8EQJxQKsWXLFv5w/x94+q2nqR5ZLS4YBQuecdQvy8rK8OX64ArEXTMLmQRS+fMXAkWubasRI1+LE+wdJPtKimG0E7ulx2j48geuAdqThgYiwNeI5k4YvBlebhl9S4dJydwfjOE3GAxEo1EmTp7I3LlziWXEoBpZxRcTD+JW9ati/vz53Df1vsQMnxpkFb6Jeqmd3ADMQFS6snBW+QBRZOW+AX68Gd50uW0m9YQHC4AXSJRnWA30A9/jPjgCotdG4/sy12Ti8XgIh8MdMkWzKRhXj8HQAUn23U+aMomSNSXExsfgLuAUZAV/h/UaB3wHMRVjy5YtlJWVMWz4MFGjVEiOflJVLeD47Y/DmSBsebAs8EXh/aBj9Hf5IfsOeHA7cI41htlIFtAMoAyyPshCV2jH6Fv3qb6smjlz59D18K7tvvVhW2NW/AZDByIajTJpyiRK55fiDXqJhWJSMbtkCdGxUafZSarV+xCIzIhwxjlnkJGbQTQUpc8xfdiycAvRzKjINKQqoKoE9uBk91gr92FRWPIFsEsOvXAYvNDbOi9gnXcGovAVBH/Yz9CzhjJ54mTOvexcqRR284ZoAcWuj+21OUtHx6z4DYYWYl+bg7ckqdoYLnlmCVGfa/WcrjArE8iCmiE1hG4NER4b5pPKTxgxYgQb3tjA8Scc77QwtK+z3Pp9qHO9Lh7Qn1pGH3i6lwRv40bfyvTxLfPBDMj4NgP/535GXTGKRfMX0adPn/q6PnYVcRotoPb0b9AeMIbfYGhmGtuEpLVJ2Rc2E8JnhomGoo4hdRdmgRRa2WJrIMZ8HZAthnXJkiX06NGDd998l7FXjcU/10+wOEigJEBedZ74FTLl1AfXwY5pzph6/wiuAgghxnsr8Gc4/oTj2fDqBrZ9uY1Nb23isy2fMeG2CYTD4XghmW+1zxnjdiResJf+vQbBGH6DoZlJtaq25YfbilAoxKuvvupo7biN+VqctMoKJAjbD3gcMcTPAdusbTEkB389kq2T7RhWn89H8axidmzbwRsvvMHw4cOpDdeCB058APRvYeIbMp7/Ph1UBnx6KtKM9SFgGpKhUw4ffvEhs+fOpkuXLsycM5NefXolTKL3/vpedJkW//9DSDvGMAdM68O2pkUbsTQXphGL4UAhbROSCgiUBCgvK2/VTBPbpz9//nyUX1G9uxomIoLpdkOTGmAZ8CPEoNv+9TqkOjaMFGn5gWtx/PR/Bg4B/xd+dmzbkfC5JkyawIJnFhC+tIo3/gwDt8n2WgWdT4LQJte1FdI/96ckxAC827yMKhzF0heWOk8pVq7+kNOG8OSzTxIaGXLiCi9Rr0lL9tpsCgcXdlgff3tpxGIwHNQ01FCkLVwOEydPpPipYsJjw1SPr5YCq8dJbGiSixjgM5D+sz2BI5AJ4i7rpw+RR8gEypEG5d8CX0AkEuHue+4mGo0SCoV45513mD9/PpcfXUVsumP0r7gGsnwQ+jbp2t2tV1IgORaJsXDhwkTXlOW3X7lypbinapHGKllILcChiFaP5WoqHNxxpJabgsnqMRiakXoKljZt4HIIhUKSlz8+5ozlUsSlsxvH5fMSsrp/AhFM+4D6GT3XIW6VTYjxryaushmrirFgzQJeeuUlPt78MZ0yoToUhjVy+os94cLhoCPIthuSrn2Dde2LEANub/eDz++jNtfuluLs8wV9XDP4GlatXZUgGJcdyubmMTfXk182JGJW/AZDM2IHHrPXZidkt7SFlMCWLVukGMs9AXmBK3H84S8g7pFxyCp/LrIcTH5ieQNZlY8DfoGkZn5nnW9p8G98byP3HF3N199Vx087dhwMugW0x7qPP8W1c3F0e2wqwBvxUlddl9ZvP3P6TAoHFxIoCSSs8GfPmN3uWx+2NS224ldKLUAKustdzdYfB46xDskHdmutT26pMRgMbYFbwbIlujvZsgoAffr0SWngQqEQW7dulZW5++nDDuoCLEV0dMZb+y9G+uPNTTonTcMVt5LmcdXwfgR4V3b/Pw/c8wPE0GNd7+8kNjvHtS+EU9FbAWqFYuzYsXg8nkQZaFdXrvz8fGZOn8l9U+8zlbpNRWvdIi/gXER6aVOa/Q8Av2nMtQYMGKANhgONiooKvXnzZl1RUdEs14tEIvr2O27X3iyvJojGh/b6vfr2O27XkUgkfsz4O8frQDCgg92DGh+aQ9FMQvNbNAPQ5Mm55Fg/z0Tza+un39p+JJrJ1jkj0eRavye9VCf0SwVojfM65FQ0R6EZaF2vE5oMNEegOQPND13XnoymF5psayzBxM8U/zy58nkCuQE9/s7x8c9raBjgLZ3CprZoVo9SqiewRlsrftd2BXwBXKC13rK365isHoMBxk0cx5wn56Cv0072ywrwhrz87IqfUTyrmLvvuZsFzySukHkCcbNkI9k6GtlXjaRofosInmUiq/hsJE9/A+KCqUFiAG5dfeDKd+CpJ533Q32wqhBYgvN0YK/wFTAPCeYuA74hMasnA64efDX3/s+9KZ9i7C5cZlXfNNJl9bSV4T8X+FOqAaXCGH7DwUZTDVkoFCK/S35ioBbEaD8MZIHf4ycSjRC7LQaHuI6pQHRuvIh+vh1ctUXP8oH3EKOc6toepDiqC3A95GbArj+C1zIdbyg4sz/UvQsMB55EJolk7gd6IcHjrogWzyEydtZK+ubu8t3GsDcj7S2d80bgsYYOUEqNUUq9pZR6a8eOHa00LIOhZdnXqt4NGzYQ87jUMG1f/TxkVR6D8LFhYl1j8Bqw0zoOHA37Oupn1AwB3keMb3LQNR9ZlXuQVfk2+J/psOcPjtE/8QY4/VdQdyXOE0UlKQOyRIBPkSeOamSC2GTdZwjEorF47MLQsrR6OqdSyoeUgQxo6DitdQlQArLib4WhGQwtjruqtzFCYrZccnFJscgYP4oY9G6IsXZr369Gjnkb+AgxricihjhE2sboBBARteSg6zokL/4G6F0LH7uG96eTYfI1rmMrrPs9h0wwC4ERyIreHltX5MnBXQS2GskMuhgnlTMNxt3TfLTFiv9C4EOt9VdtcG+Doc1IqZWzFyGxSVMmUfJoCXUFdU7R062Ij9wuwLKuwxBkpX8bkqnTD8nG2YoY3FpSd8MKAVngXel1ull9i/j4r4e/Pplo9LtkwOTdJIqxPYFMMjdb48tBmrM8hGT+dLXGZht995jXy/18UR99+vSp9x20V+2jA5mWTOd8DPgJ0EUp9RVwr9a6FPgZe3HzGAwHI2mrejNBZSm2bNnCKaecEt8cCoWYP28+0VhUVs/2eXZwNtXqPWjtfwHJs7f99lYgmMXASNe25YCCfsf044P3P4h3s8IDF/vg2T85l795CDxyEmLMOyEG3Ycj6fAf4HNE4K0fIqd8GXCkda8tacYcAO9qL2PGjEm5km/qU5Jh77TYil9rfaPW+nCtdYbW+gjL6KO1vkVrXdxS9zUY2isJVb2QIJRWFa7izHPPTFjJlpWV4Ql46rto7IycVH70asRl4pZksM+5zjpmFvHGJpedfBnDbx7OB7s/iDdhyb4d9kTgWasOa2M3yLjHMvp268PBiLyDB3HtZCFPGa6mLSpL4X/NL08ayYqf7jFXQNFVRcx4YEa972xfnpIMe8dU7hoMrUS9ql531exdEB4bTlDxLCgokMrVZINpq2em0r7vh9Ppyj1Z1FivLKRCtwYyMzPp3r07S5YsIXatZAtNfhUqH4JcK6o2oBucOAyiXhyfvN0YvdZ6BYDzqefC0bWaGwfdKJW1C4N4lTdRSrkC/E/7GTt2LHNnz8Xnq++AaG/aRwcLaQ2/Uur/a82BGAwdgenTplM4uBD/XD+8Sb1VuXslGwwGGTV6lBjWxxG/O4jh3G69ZiClkLMRF8+31k9rJZ0gv/wI4pb5QrbnHpLLsueXQS78MCqyyfc/L7coHgAqH9bn4jwhPIQEe63G6Hb/W2oQOQc3uZCRm8Gv7voV5WXlvP3S2+ws28mtV92aILEw6rJRzHpwVtrvq95Tko2RW94vGvLxDwb+u7UGYjB0BHw+n2jMDC/k7MFnU5VblXiAayXbq1cvampqnFTIOUiQtg7JibsakVe4DjG8PuQpogRZkS9H5I6/IzH75wngEPh297eQDyu+gKEuL8thd0G5RoK7ESRT6BBkdb8B+ASn+Gs7kupZS2JWToUEa+0MnL59+wI0WWLBfkpasHYBVRdVyaSmIPt5kW0w2T37RkOuHq9SqpNS6tBUr1YbocFwENKnTx90lW5wJTtpyiSWvrC0voTxyUj6o0KM+Q9xjG4MMY4+pHnKu9T39V8PbIHzBoJ+F4bWya5RF4H6rWX0n7DuMwW4EwiCx+Ph2iHXklWbRU5eDr5NPrzVXlHfspu4WJ+hIVE6eyJorNGedt80euf0lolvMTAHeuf0Ztp90/Z2qiENDRn+Y5GM4FQvU0ZrMOwHe1PxBCidX0r4krDjn89F0iE3uN67/f8vIFr5ExFjPQxxEyX5x7P8sK0G/rFa3n+aB5mnQ+kriOtoBlIncKl1guWzr4vW8ew/nkWhuOKiK/B6vMRuicnE0h1xN1nnDxs0rNlE6abcPYVPKj+RzzVZPt8nlZ8w5e4pzXL9jkhayQal1Dta61NS7mxljGSDoS1o6YKhaDTK+DvHs3jxYrzZXqKVUUaMGMHsGbP56KOPOPn0k4lEIhKorUKCqoMQA/tzRELhKWAHcA1SxetW0KxBfPuubbe/CbP/6ozhTC+8Pgo4HIkP/Nm6VyqbOgO4CZGHeNpPbFuMyOSIs9/S5clZnsP6V9bH3Tv7Q3vraHagkU6ywTRiMRiSsNsVls4vxRv0EgvFKBpVxPRp01NmnuzPPRYvWkzUEyX8XRhf0MeSJUvIzMykpqaGSH6kvq7OOsQw29t2Iav8OdTXus9CJosnoGAwfF3i7FrigxEaEWabbx1bAxyPVP2mkk6utrZlQfjKsEwE3yKduez71UJddV2zBV0bk9XTHBNMR6Oh/8X1kmqVUp0QDX0joWA4aGmNgiH7HuHjwhJ8HQLR3CjRiijFq4rR23Rq/fuHrPczkQwdgL6I0/ZDGSuZOIb7NFj0OoxwGf0jxsDXBTiTyaGIAf8a6YIVtLa7etcmpHFa48nIzcC71kv48rDEFSLgf97PsGHDmm0V3p46mh1MNOTj76GUOhZAKZWllPo7Es//Ril1YauMzmBoZVqjYCh+j4uqRKQsKfgavTZKLBoTA+4mFzKCGfz7tX/zjzX/cBqXbAc+Rgz4PMS98yic/gDo6dbKHhjvAzXZMvrW9RiCjOEKa9ufgdNwfPYPIEtAO43Txsra6ZXXSxQ8S+VVu7OWJUuWNJukQnvqaHYw0dCK/6fAVOv3EdbPrsj6YjHwtxYcl8HQJjTGtVBQULBfvv/4PTTphdP8iEH/oWu71Y7Q6/UyZ94c8ctfT6IkQw1k3AofLoJeVqvab3zQU0E4ncxDNjKWLOib35cv53+JN8dLKBKCo6395cBXyIRQC4E1AY7ue7QEXU8h/tRSl1tHuCLcrE9ILd3RrCPS0Iq/1uXSuQT4s9Y6prX+ABMbMByk7K1g6IEZD+y3WFj8HorErJwaRMjsWyAMnhc99SpzwzVhBp49kMcff9wx+iBPBxfAyEqofRB67ZbN518P3RXU5nrSSyZUAQp8ER9v/PMNysvKWbFoBTmdc+SJwIu4gZ4ApgEPwRX9r2DLh1vSPrU05xOSXftgF4KVl5Uzc/rMZou3dEQa+uZqlFL9EB3A85FMYpvsFh2VwdBGJBQMJfV57d23N8teXCa+/0xgO8xbM49IJELxrMbLTwWDQUYWjmTuo3OJ1cWcQqtNSPplCHm23g7qIYXO0nHhNDTUqTonkBsDXoBub8M3Nc49Vh4L1/0UUJDxtwwKOhWwtWKr3Cs5YNwPAs8FKLq1iPz8fADOOuss6qrqJJj8HU73LWsC8ng8+HJ9DT61NHfw1V0IZtg/GjL8dyIPj12B6VrrzwCUUpcB77T80AyGtuGXk3/J1i+28vzc5/EFfcQqYwwbPozFixYTHhOG1xERtGyoqaphbslcYrEYc2bOIRwO77URuo3KU5Jr/wSyvErS1tceje6ipVjrfSRtcwgy6Uy3jnsdZm+C211Gv+eRsPVQ5ImiAiJ7ImwNb5VJohzx2QeQLB0f+Mv98awlm2AwyLDhwyiZV5LYcjEXuAGeKnkKXacTn1rcQeVaE3xtz7Ro68XmwuTxG1qDcDjMqWefysYNG8UwVkHfY/vy6j9e5bvvvmPAeQMI9QqJ792d8fIEeCo8HNPjGDZ/uJm6mKzIvREvY8eOZcYDMxLcEgm56bYRd2fwgNMu8TYkyyb5mOeg/2fw9jbnlP+6EKadbZ07GyhC+tva6Z9VyOq8B+JS6g7Zn2fzz+f+mSAHbY/x5Zdf5pqbryEyMUIyweIgV19yNavfWE1VThV85rp+FfiCPsb8fAyzZ8xu4r+CoTlpch6/UupK4D2t9Vbr/W+AoUirhon2E4DBcLBw6tmnsnHnxgQN+49WfMQFgy/gtX+8RrQiKiv95DTL66HuoTrRs3cFXGMVMUpWl+DxeBKCnGVlZXiyPU4j8nQB3izgn8AZicd4Y/DOFjjB6kgayoDDpkBVputcH6LZ0x0YRaJr531gLDAP6nx1Cc1Pdu/ezYRJE1ixYgXebC+RikjaVMpZD86i0+86Mad4jrR8HJl0H0O7paHg7v8iD5copa5AeusUIrWCRk/fcFCxfft2WelfRz0N+40bNhIKhRh63VARSUtnpD0kBlxzITokGg9yhkIh3n//ff74wB+p/LYSlgCLcJQ03VQgE8PniECK5U658T2ITnWM/mAP5N7hMvr2uRHE/56qx24dEkfIgCOOOAK/3x/vctXl8C4se3EZ4bFhKm+vFL2eFSQEme1Uyvz8fO6beh8Zvox694kOibJw4UKjl99OacjHr7XWtnTgtUCp1vpt4G2l1O0tPzSDofXYsGGDBExrEPeLXclaA/hl/4P3P8ijjz6KrtCJxn8bEnxNky5Z66ll2MhhrPvrOqIqSswXk788Zb0yEONqTzr2ivlkJLD6LhzaD759wLnsuh5w2ddIKmVysdVyRDjty9TjwY+4gICtVVvj+v+lfy0lRixx8roUCfDOgGCXYL1UyrKyMnxBn3xnO3EmQVNZ265pyPArpVQQWWsMQso0bPwtOiqDoRWJRqMsX7VcjPcjyP94u0dsAAjD8lXLeeqvT+E9xEt0dVQMrR9YgDwXZ+Gs3JPdIlUx/rL6L07BVRbyu9f6mYmkS87A6a5l6/JUwbQ/wV3rnUv2yYaPv7bGdikizjYbR9PHiyRgp5NeiCJ9cf8CkS4R5s+fD0D4+rBjvG28wBWQ80UOKxat4KyzzkoIWHfr1o3q3dUSg3BrCp1mgrvtmYYM/4OIqOse4AOt9VsASqlTkDVOgyilFiD1gOVa636u7ROQJm1RYK3W+r/2dfAGw/4SCoW4dfytrHp9lePbX4Nkv7iybB5b8xi1W2uJ3RaDNxBDC6JiORExevNInS55EvKXZLcpzEfEzixtedYSd71wE/FVc79vYOMcZ6z3Hg+/Pw1prBJEqnWrkKyf86z7KWscXXG6dCWPpz+ir3ODfA7t13h8HokH2Bk6SZNFXXVdPaMPcM/v7kEVqHryDt4lXoYPH24qa9spaQ2/1nqBUupZ5L/2Bteu7UgYZ28sQnr3LLE3KKXOR9pHnKi1rlFKdduXQRsM+4stkjZ//nzChGUZ8jpwDpJPnxTArb6iWnRy/IihHYAIo9lukeeQ1fG3JK6+7ZX7J0AOkrZ5KBJ49SNPGScgKRPVQCV4cuCf8+GMr5zx5nmgYiuw2Tr/Y8TIr0IcsVYKZdyw1yJ/qbuQv8IM5DPa47E+Fz6oqa2Ried5UrqOAmsCKeURbOmJ6NhovThCbEaMRQsXkZGR0azidobmocF/Da3118hDqHvbXlf71nEvK6V6Jm2+DfiD1rrGOqa88UM1GJoH9yo/PDacuBp+joazbLYjPWufxSmiqkGyfUYjq+3RyGrePsdWtrwZmSw+IdHV8wnyNKBgyGOwKhy/K1d3gaeiSGqm24evkKeHbYiLyJ5EFDK5rEfy9sdb156JZPJ0dq4dd/tMQiaKFYjUs63TkyHVvEW3FqWUR2hI3oI8qLm6hgXPNK+4naF5aO1m632Bc5RSbyilXlJK/TjdgUqpMUqpt5RSb+3YsaMVh2g4WLEzV7oe3pVHVj5C9RfVssqP4WS8vE9aaQNv1Evg1YAEO79BDK3t189GjGp/xFWUhWP03e4VP7LyH4cURo0D8iAvADrqGP1XMsHjg6e+x0mTxPp5GGKc7wR+hSyn8pEnB0tLh+uQGIAdsD4FcSm5JSDciptWBhMbIOfjHLK8WVw56Eo+2/JZWnmEhuQtqJKxVF1Uxbx589i+fXvKfxOQifijjz4yGUCtSGsbfh/QCTgdafWwXCmlUh2otS7RWg/UWg/s2rVra47RcJASl0IeGxYBknHICv5JxNViG/A+SDWtbdC+BR6Dvsf0ZdiFw2Q1XY0Y8ieQ1MlK6/hBiHPU7mQ1GzHGg6z9Yerp2vz+cPh+jzPO42+HcyeA9iKB30NcH6IGcUW50yc7I5KKHwCXIav+v1n3spQ6eReZEGYjjdNnuMZlkyuZO4POGoRSihdfe5GjjzmasePGptQjSqecyWpkonkJmAc1nhp6Ht2znq6RPRHvr/aRoek0yvGmlDob6KO1XqiU6goE97GA6ytglSX+9qZSqg55uDRLekOLYvujEzo5ZSPLkPeQlb7tLvkA+V85E/kLqZVjPyj/gFCFtSrNBL5Hlk7zEB+6HUi9FMfHfjNimCsQf7yPuLul707YPMsZ4x/7w6+ucg06G0mtcAdbK0jZTjFetLXQ+vktYtiTA7sBJEbwOVIY5nVdowLCu8M8t+k5wseG49pBJfNKeP2N11n/r/X1Vv5u5cxqXe3EEbT1+a1YSU1FTT3Fztboe2BIzV5X/Eqpe4FfAndbmzKIZwE3mb8AF1jX7YuT/WswtCgp/dEvIFIDR+I0NL8NMcxWA3K6WPt+IT+/jHwphvVE69guyPtsxP0zA1lRv4c8BczBWWF3A6Kg9sDfFica/UOz4FeXuMZmxwU8JD59WPo7Kd0rUcSP34n0xVsVSFzAQ73CLJZDHXWEs6y0znHWZ78DNpZvZPyd4+t9r27lzJuG3kSgR0AC3+/QoGJna/Q9MKSnMa6eIcBVyH9jtNZl1F9v1EMp9RjiQT1GKfWVUqoIyXrupZTahLR8GGG6eRlag3r+6BrgbcQXPQQx3M8Bc5FV9g7EeCZV4nID8ldThqR1Xm+9vxWYgAR+w8hKugsSlL3MOuZsuOxoqPsTDLKel396OagesCsHeUoA5+kggBRibceZUOYgMYLVpPbXd0ayjpLbMNrjz0Xq78db581EspVmg9qhqIvUydPAdhLjHzfA4sWL0xrkYDDIovmLKLqsiKwFWWkrnO2irsb0PTC0HI1x9dRqrbVSSgMopXIac2Gt9Y1pdt3c2MEZDM1FNBrlyJ5H8tHyj8R4W8qU8cDmM8BGZKWfiRhuD/VLFXORSeJ84DEkfz4bMaJdEDWrGYjf/1CkZVE25GipvLU7F65XcGoOxJ617hexzrPTQBUy8dj2rxti8Lda+77EyeZJTtPsjhN4Ti7ecvXNZSSS6nkd8G/QO3V919ALyESSK8JrDVXi2qv/X//y1/Q8uic1FTUpNX7soi7TUrHtaMyKf7lSai6Qr5QajYSN5rfssAyG5iGuQdO9Cx99/pG4Yx5CWgVWI/nr25CAbWccl89EJHtmQdIFXRkrZCMrY1v9EhxJhCxEbmEc/PIUCEUco39KNgzwQOyHyFNCIY6bqQox+Lb7yc78CSNuqUyc+oCR1mcYjRhn219fi5Pjny6Lxx5rjnW95ICx7RpaT1xMrrEGuXv37owePbrBdommpWLbstcVv9b6fqXURcgD8DHAb7TWz7f4yAyGZmDSlEmUrislNj7mrGQfQYKfdtZMKWIoh1JPdZMZyMRwOInGsxYxmM8Bx7puaIuraTjqfPjUpa8z82S4YyNwJeJfvwrHCGP97keM+eU4ukGHIBW9D1tj6u467xQkcpa8Uod4dlGgU4DqXdWi/ePO4rFiBZmrMtFBTSQ3SX7ZfrrZDtmvZVNYVNhog9yYdommpWLbsVc9fqXUH7XWv9zbtpbE6PEbmkooFGLDhg0MumQQNbcmuRxWArtJNJbLkVX2xUkXuh9ZbQcQd4ylQ8NKZLVfhxjHWkQiYTuQB09+BFe5shK73QU7gsCfgOFIXfv11Oupix3wVSRW/54MLAUmJ40vZo2xDmf1HrCuew74F/p57eXXKJ5fzLIXlyV0FQusCTD0jKFMu28avfr0Ssx4ssfzEPiz/IwaNWqfKnBDodBe+xM35hjDvpFOj78xrp6LUmy7dP+HZOiotGTBTjQaZdzEceR3zefs886mRiUZ/RpgC/XdGtcg8sfubBk7774Ox/B/gKy8a4Af4GT8jAO+gUE7QL/vGP0R14D6rWX0K3DcMLXA30l0xaxC4gLdSCzwsgOt1UnjA6gCFVN4tEfGGkPcV1uBYuhzTB9OOOEEZs+YTeHgQgIlAYLFQQIlAYouLWLhvIV07949pdslsCbAzTfdzI5tO/a5x63dLrEhg96YYwzNS1rDr5S6TSm1EcnKec/1+gxJVjMYmkRrFOxMmjKJkkdLiBXE4HYk8JlszN158DHEXTMPcavMst5/j6RRBpBSw+us/ZXWtm9xNHKAQBZ8uwP+ZrVA/BDIOBKW9Hbd1+pvy1ok5bEAJ1tnBiIMV47EFuyu1ravfRPy15oim8fj9bD1k6307dVX3EB3EI8PfBL6hElTJu21Yfn0adPTTgzGIB98pHX1KKUOQTKC70MKw20qtNbftcLY4hhXz8HBhEkTWPCMq4n5t+Bf6+fGQTfyq7t+1eRH/WQXQSgUomv3roRjYUdk7TmcVonZiJzCRhwlTvd+t9tnO5KSOBr5K3gKSfG8AVntP2JdA5j4Ojz4rDOu0zLgzd6IBo/GKQzLRCaaAcQll5mFPEmMJjGO0J1Et9P94M/0S2GV1e/XdgPlfJLDK+te4axzz0rprgmUBCgvK2/Ud2vcLgcX++Lq0Vrrz5E/oQrXC6XUoS0xSMPBS0LBjp0zXwLh7WEWLlxIv9P70fXwro16Akj35PDhhx9K0NMtsjYIR3RsGlKYdDJiXL9FjGhSoVE8V/8EJK/fLsiy3UO5QDUc+TXo3zpGv7QfKB+8WYfIJo9B3DrXIwHgPGvbxYjRXo345X2Ii8e+vzubBuIup7pwnVTbTgJ+bv08A+qqpNqsOfLijdulY9CQ0+5RRE//bRzlcBsN9GrBcRkOMhIKduxV9glIyuMQiORGiFRE6pXsp1qBjps4jiXPLCE8MiwpmN9D8dJiHn74YWl0noGTH+5FDO0JSPbONciq+03EqGeSutApB/gxUme+2RqzfVwmPBqEG+c5pxSMhW3PA6cixnkVUr1qn7MTCSjb9wwjk8sAxC9UQWKaZQAnJrAaUHDzTTfz57V/lsmzC/H0x8KiQvr06WPy4g2NpiE9/iusn0e13nAMByvxyll7lW1LGCfp3lddXkVpSSlT753KPb+7h9L5pXiDXmKhGCMLRxKJRJg3b56cM4+4Lkw0LwojkCDoh9TTlPes8aAzNXqednz1PtI2HmEPjsLmUcQLos76Dv650Dn0Vg/MzUYKtewiKi/i/5+NuHGWwvXXX8/TTz8tk5W9ku+MGHb3BGHf3047tbpx+b7zMfnOyWTnZKdMf/T5fBSNKmLB2gUJmTv2xGBW8AY3Dfn4+zd0otZ6fUP7mxPj4z84mDBpAnNWzCFWGZO89EeRQGQSweIgV19yNavfWJ1gxHyLfehcTexaV07+akT6bwKyLYaUGL6NZOP4wRf10feYvrz/3fsSpH0DmXwyEMOah0wah+D4+G3Jhq7yeybwcRSOjMkYvwzC0Z2gdheych9HYk4+wAOI3/5TGDNmDJmZmZSuK5WmLvb4bb0cW3rZ/kxdkSeOXKA20U+fzg9vN5cpLXUmhqKiItMIpQOTzsffkOH/ewPX01rrC5prcHvDGP6Dg927d9O5e2dxx9xG/RU/xIORuk4TvjWcqEo5k9TNRGYgmTduw1sj13l0/qOcfvrpTp7669QP5q5AirTs4qmTkITlKuAJGK2gZKtz6XMD8IpGiqdqkf50drDYPa6ZiB++BLJqs9j+1Xbu+d09zCmeQywjJk8D9jXeQ9w7VeDN9xIbFotPRNlrsykcXNhoxUoToDXYpDP8Dbl6zm/ZIRk6GuXl5WR3yib0w5Bk1/Sjnksme2021w69lr888xdnBf8CsoLPxHHv2C4VWyJhO04xVI281zWaCy+80IkvZCIr/ST3Etchk0cVMrFY7R+6A9u+cMb/WD/4+UnWmO/EmWh8JLZArEBSQS01TqrBl+ejvFxSKIf/fDhnnn8mUR2V+IFdpDUAsp/I5trLr2Xl/JX7XM1qB2gNhnSkNfxKqWsbOlFrvar5h2M4mIn7+c9E3C1vI/8DLbExv/JTWFTI1HunsnLFSjGg9gp9PKnFw6yMF8/fPNRdUCf+/Q1ABkQiEW6bcBtT750qkgXbSd9W0Z5A3pHrznsSRr3jHNLjePjyOmRSqbVetuG/GOnKNQNxG1Uhk9pGpMK3H9RtrosHWI877jgyVAbRMdF6LRp1WDNn5hzmzJxjVu2GFqMhx9+VDezTyBrHYGg0tjDXgnVWAPI8YDv4X/Ez9KyhFM8qjhu5olFFlD5dKu0Rk1foQ5DA6QDgL6AyFHXb6kTouxMSOO4MsYoYy55YxrJHlonBf4H0wVyrJ+6P58Kbrzm7Jv0EHvwX0tzcNvp2IZX9pFKFZCcNwPHLZyGTUBACFYnNyuPfg13TYBn95ECsWbUbWoqGXD0jW3MghoOfUCjEbaNvo7a2lqUlSxsMQE6fNp1du3bxSNkj6btNPQx0BX2Yljz49xDjbLuDzkfcLWU4GvoKeBxpVeh6gvCdDBsfhWMtTfzv/HDEGKheY13rQ+SJ4SXEwEcQlU/LL8/JJCpkWk8i/s/9FI2q36w8WaAsGooyZOgQpt47dd++XIOhCTRGpO0w4P8DCrTWlyqlfgScobUubY0BggnuHujEs01cqZnDRwznjtvvoEePHg2Kd3Xp3qW+yFoFsuLPQlbqJ5IooWC7g2oRv36qQK6V8UMtDOsBSz52Ln9hBrxgr+RtUbaZOJUsOUh650nIJPBPRGrheuJtFr2rvNx43o3MmTmnQVfN7t27mTBpAitWrMAX9BELxeIThcnEMewvTc7qcZ24Dunk+Wut9UlKKR/wjtb6hJYZan2M4T+wSM4qqSfV0IRMlaKx4hqqJzt8KNJAPANxPCZnB32LdKtKlW0zW47vUgY7HnN2PZUNV9ciWjkXI9HdWmSyOBQYjKzyrwF6IE8dLyABYx8yGWQAUfD6vOws24nP52vQV78/343BsDf2R52zi9Z6OVYXUq11FMm12NsNFyilyq02i/a23yqlvlZKvWu9LmvCZzC0c3bv3s3Pbv4ZnQ/rTP9z+tPt8G6MHTeW+fPnp+2t+uqrr/LOO++kVeqcPHEyaqcSgzsDMdqHIob9ZCTlMVWbvxrStx/MhhnPJBr93plw9cnIU8Q3iATyTOvVCZkIaq3rHmod9wJOQ3G7eUs34BTw5/n3Kkhn+s4a2orGGP5KpVRnZF2FUup0RLtwbyxC1kjJTNdan2y9/trokRraLbYUcqfDOvH4449Tm1VL5Z5Kqo+tZvG6xZKznsIAV+tqzj7vbPqf25/8LvmMmziO3bt3J0g29+jRgyxPFvwIqab1I0qVhyO59tfjyBXXIC6XdUglrd1+0MVJn4D+Du74j7z/bx+obPh0OCK1EEFcOR5QPiXunS+QSWc+EiReS3qdn+uA96D6+2pWvr6S6rHVhG4NUT22mgXPLGDSlEnxsTSl72xLSlkbOh6NMfy/QLQJeyulXgWWkLLeMhGt9ctIroPhIGfSlEnMfWSuuEgmWq9xwHdQk19DpCJSX0feNtS3A3dBbHyMOU/OocvhXRhw3gC6dO/CreNvxe/3M2r0KPwVfjGQNyNFURcjOfIaCaguAKYDyxCDfAKSYWPJGHtj8NbD8O5SuX2NB3J/AfdNRPzy6xGXzgmInEMd8jQxHkcXPw/og7iA5iLunVRPFBlADKdC19qevJKv1wDe9d3Y+jqtIWVt6Hg0xvD/B0m8OxMpbzkeyXHYV8Zbuv4LlFKd0h2klBqjlHpLKfXWjh079uN2hpYkFAoxf958MWCperZ+AL6AD//T/kQd+eVIcLSzc7y+ThMjRmhkiJpba5j71Fz6n96f+6beR6+8XtJgJAsxuM8hhv4RxPHoR9I4f4EYanvJ0R2ufwiiU2FAuWy63Af+fAg9jNQJWKt0DsVJy/TJeOqpdr6HTA43IU8HKYy2t9ZLoFNgryv5xvSdnTRlEgueWdDgk4PB0FQaY/hf11pHtdb/0Vpv0lpHkD+XfWEO0Bvxzm5D1ExSorUu0VoP1FoP7Nq16z7ezrCvNNa1UFZWhifgEfdIqtVvADwxDyMuGkGgJCASx7MQA5vcx83yv1OBZONcCBs3bOS0c07js6rPnPz5dYhv/TbATnUPI2mcz1nXGAL560G/BsutVrIvBMBzBPzV3cR8O1JM5keWOIcjhj9NoZcn4BGXzxPIU8Fy6nWtGjVqlEglN7CSt0nVAKVwsFTqJsQAMhGFz0wTAzDsPw1V7nZHmssFlFKn4CSz5eH0B2oSWutvXNefhxTuG9oRqVIvG0ovLCgooK66TlbdqQqjQnDL6FuY9eAsdu3axfK/LBf3yWwkXTL5+Crg30j1rfW/7P1N78tqewciR7wecSe9jvj07cwdV1XvfQp+VeNc+lgvbK7Fyd+HxGKwCCLDkIu4oNIUetVV18EoRNZ5N/LEMhvJ5w/B0ScezawHZ5GRkdEopUy7M9Z9U++jrKyMvLw89uzZQzgclkk1xyOfM6n5iifbQ1lZmSnyMuwTDSUKXwLcAhyBtIi2qQD+e19uppQ6XGu9zXprN5QztCPG3zmexc8vJjw2HDdYyRr5boLBIKNGj6J4WTHR1dF6naxOOFH6vU6aMomn33rayg1D8uOTdHriPWd34KRn2m6hFdZ5lcjq3K27Y6+Gc+G4c+H9Rc74pv4YfvMNsrL3UX/JYvvk3S7zrDTjexyZeBYhE5094Vxk7Vfw8aKPCYfD9Qq09qa54/f7mTlnZsKEO2z4MMLfh53MIdfkFt4TNhr7hn2mMXn8Q7XWK5t8YaUeA36C/Kl8A9xrvT8ZCcl9Dox1TQRpMXn8LY+dmVNSUpIy972h9n3RaJSJkycyd95cYtEYZIE34qWwsJCHH3qYcDhMt8O7JapjXo0jj2znwPtwFCsvJbEK9iEkeLzd2jYM+AtwnFxDBeAfu+Fc13/nfL+VfmYXYa2wrnFF4mdjBvI/sgAnTvE9khlkxxWqRRpCH64l4vUkaSWl337p7fhKvLFKmany+QNPBwh/Fkbfoev9e/hm+9i1Y5fR8TE0SJPVOW201iuVUpcjQV2/a/vv93LejSk2t1q1r6FpTJoyiSXPLHEEy9y4gpKpXAs+n4/ZM2bzx//9I1u2bAGgT58+caOUkLY4CMl/n4O4R6KIsc9FjP8pSKqkLcJm3Z8snAYmmcDfkdX/drjyEnjqSWc8Q72wSiFunQIcMbUbkAnkDOIVtjyBxA4y5FrMIN5akf7AOYg7aRFotDwBZJLWFZTsw2+MUqbty0/ol5sL1WdVi8xEin8Pf77fuHoM+8xeDb9Sqhh5QD4fCWtdh3g4DQcJtuEJjwxLgHQf2/cFg0FOOeWUetvz8vKI7ImIQe+MGHR71exHDLHd5201Tnrleda27TiTgw8ppOoEuV/Cnk+BT+Wwf/0AzroB6mYjrhi30QdnApmL9MStsu5/AiJJaLtzuiIVKPa5P0QmKY/re0nhCtrXbldp8/m749Qi7MO/h8GQjsZk9ZyptR4O7NJa/w75Mz2yZYdlaE3ihqczjkFzZar4n/YnqEs2FjsHvVefXtRl1UExEs6PIYHRj5DV9LPISvtxxABvQozuM0h06XHrnJ2I4Vdwz/uwx1U/fuKtcMZoqDsEMepZOG4hmwrkCWMCTrPyIiSbpwYxrj8FPkhxXsw61/5eBuE0cb+fhGycppI2n78WvBleAmsCadM9DYZ9oTEqUNXWzyqlVAGybjN9eA8iEgzPICQlcibynBeCEWNH7NWgpfJlu3PQEwK19+OIfuQh/8NORiJBK5HV9R5EChkc3/9JcPSpsOVh574PDIC7rnQNxM4Milif42ckNkfpT6I7KwsnhTQLJ9hrN3axM4UGWMe7V/lngOdrDzf+5MYESemmfk9xmeYUWUC3jL4Fj8fT6CCxwdAotNYNvoB7gHxgKPLnsA34/d7Oa87XgAEDtKFlGX/neB04JqAZiMaPphOaDPTxJx+vI5FI2vMikYicGwzoYPegDgQDeuy4sfrNN9/U/qBfMxnNb12vyWgy0RyJs28yml7IvX2uV641ljPR3IleF0BrnNehvjTXyUdzvIwfP5qg9TMTzaQU4/GjuTtpfFnWd+BDcxqae6zXma5r+tBev1fv2rVrr99vqu9p/J3j499tfH+utT83cX9FRYXevHmzrqioaJZ/b0PHAHhLp7Cpe83qcaOUygL8WuvGaPU0Gyarp+WJRqP0P70/G8s3JihhZq/JpvDS9EqRqbJRWA6+73xEVVTEy9zUANNIr5rptbbf5Izh4kfgWZfb5qYr4dEBSPlfL8Q1k4n4wxXiwIwhTwoTgb8iKaKHI9k67hRNu0XiFTir++5IfKEC0aW9AaetIzjKnydD8KvELJ50NFaF0/TLNTQn+9Js/cfAl1rr7db74ciqfyvwW611q+nwGMPf8oRCISflspGpnA2dwyzrd7tlos1WxN0zJcUgZiAunlFABmRnwfaZkGs1R9nYFU6JQuwmxC3zEOK6AXgf8e0PQiJQtSTGBrJITBkNpHgfJbGfr5U26TvSR/iqcKK7SgPXQ2B++jTX/fluDYbmYF/SOecCF1onnwv8AQmLnQyUINk9hoOExihFJq9qGzqHbMTYLidRS//vyKo/TZUvHmAx3KVgWrWze0AerL8CeAxZ1S9HKkRcfXGJIKt7u4HKYUjw+A7X/VchQewTgEeB4da+Z4FdSOqCZfSz12ZzyxjxsRfPLiaaEZUJ5WTgTMhe17gsnn35bg2GlqQhw+91rep/CpRoKeRaqZR6t8VHZmhVEgK8jUwdLCgoEOXNrYh7xM61347k2OfjSBr4kBX1cYgxtoOkmdbxzwEKfngofO5y68zJhduPQTJ9nkMmhnlIM3M7y0cjKZjXkyjd8BWyVHFLNFxrjeciZHLKtPZ9B/mRfMJzw9IJyxVE9fl8TL13KuPvHM/KlSvxbfURe7/xQdZ9+W4NhpakQcOvlPJpabwyCBjTyPMMByANZZYMGz6snt85Go1y9z13U1tbKxWxtYjx3YFkxdThtELsj7hdJlg3ex9pbvIQTgvEGlgRg6Euo3/YXVCureM0soK/GpFLthqUx6tubaMPjgbPDBzDjmtfNjLZVED249nUVNSAhmgn0W24ZvA1zJw+k/z8/Php+fn5LFu0jOJZxU32wTf03e5L3r/BsL80ZMAfA15SSu1EEu5eAVBKHU3jGrEYDjBS6cv07tubxYsW88jKRxIE28bfOZ7Fzy125ATsQOkJwOmIXLJtiDsjTwJhRKahDidXPg/OC8E/XFo5RVfBguNxVshZyNPCVUkDzkUmjTpSu5v8OGmZNla6p/+ffkaMHUFlZaU0TLmimlBuCCpg1dpV5P8uP6020b64ZZqq3WMwtCQNZvVY3bYOB57TWlda2/oCQa31+tYZognutjZ2ZskDMx5g2YvLElepa7LpGegpipkN9LNlFomB3eeQLg75yIp9J2RdC1vnw2GVcsgnWXCchsgpOOqcVTjCbm63jX2/mQ3sm4FIDA4lYXLy7vBy29jbmHrvVAqOLGjVoKvJ2jG0JvvUc1dr/S+t9Wrb6FvbPmpNo29ofYLBIAUFBSxdsrR+P9grqnh/4/uSQZMuqFuDBEDdWvWnIBk7lwPvw7g+EJ7hGP0zfwZHK4h4kBjAOBzN/MORlMukimKWW9cdmGbfjxDZhtnIJDADfnrOT9lZtpOZ02dSXl7e6NaHzYX9xGCMvqEtMb76DkJTV5opM1GqEfE0jRhXW3vHxq6azUX0eOYj/vkAEuwNQEEFfF0DvCinLDkRRgxBMnXsil07NgBOH9vZSMcuO1Bcg/jvr0MmoXWue9UiTwk/t95bOfk5y3P4/W9+H/fdm6CroaPSGK0ewwHMvvZsLSgoIBqKOjo16xCphfeR/zUBErV3bB9/P8QnX4UjqxwGgrC4Er5e5NzjB7+AEdciRt9O5/ST/knix0h7xTDiLspHXD3TgXet+1UjE0I/a4xY48mSJirJypl7a31oMByMmBX/QU4qvZz5T88nEolQPKs45Tl2xk4kGhGXSRSnCKoziXn5y5FK3CgyIXyN+PJrkEDvDjj9Snh9hXP98T1gtsLp6WZfx9blT5XjX2UdvxZxI72HuHGsezCEuNSyWqHwfOEhVhHbawaNCboaOiJNkmxoK0xwd99osLJ2BowZM4bZM2bXa6kYlxe4tErSMD1IquZGUgd0ZyGplt0RV80W4HbIOAQ+nAa9LB397TnQ8xio2WBd04sTwD0JOBNx5XQnMSd/OTLxaJzK2unANUgqqd2dyjUm32wfPp8vnpNfVJS+faT9XZmgq+FgY58bsRgOXBqsrM2FJc8sIXNKJjOnz4wbPq/Xy7ySedQU1Yjb5VtkVb2NhgO6WUgVbRcgDwo/g1JXR+WfZMBLWYir6HjgC6RZujtlE6QGIIwEY/3W712AEUjuv52/H0aeKgKpx+TP9/PyX18mJyenUcZ8X9M0DYYDkRYz/EqpBYj0VbnWul/SvrsQB0FXrfXOlhpDR6eh4CXVEL45TGlpKbW1tSxevJiYN0a0Mir/K4qBvojxfc86r47U16ogHlDtdiZ8sxLx/QMrfgjX7wFuTTp3unVOl6Rr1SDSCznW+86Isc/BMforELfPs8jTwhrqtWqMVcYSuoAZDAaHlgzuLkL6GCWglDoSKZj/ogXvbcAJXvqf9iemOq5GXCadodZTy/zH5lMTqyHqjYrxjCEr6c2IYT0MOBFZ8adKm+wK9IWH6yyjb9FzFFz/cyTgahv5LGRi6URiuqcdHM5BjLq2ftYg7QdnI1k7M61jxwO/QFxP5Ujw2bpOYE3ABGcNhgZosRW/1vplpVTPFLumA/+FNN4ztDDTp00nEokwd8bcxF6yg5CVcVVM1CwLcRqhu2WLlwOHIqv+0Yg7ZzYJxVX9Y/D2NueeUwbC/R8ixVNQv03hOuR/3mHWtQJIRk8XxIUzHjH4C5DJxos8MXyLNEAfSWK65/WIa2izfL6hNw81wVmDoQFa1cevlLoK+FprvUEptbdjx2DpA/Xo0aMVRndw4Q5WFs8qRmvNkmeWEL457DQaX4GsrG2xtPUkBkqzEeP8LuLyKUWM+ESgErw58O790C9m3TMDDpsCVRoRVbNdO4OQ/P+ZOLr5dpD4Ius4hWi+2gVgCnlKGIqIstVa5+aQOs7gB7pDVlkWc2bOSRvENRgMrWj4lVLZwK+R0p69orUuQUwBAwcObP+pR+2EaDTKpCmTKJ1fijfojevrzHhgBpmZmZSWllKtqyX9si+yis5FsmaySTSqLwDf4Rhp2030CtzYHR5d5Rx6yc3w3NGuc/sjrhs7O+cM4BPreu5cfSvHHnAKvZR1rp/EPsCXI08ZqeIMUSAMfY81VbEGw95ozWVRb6RXr73aPwJYr5Q61W72Yth/UuXtL1i7AICZ02dSOLyQsy8+m6phVWLY3XnzbqNaQ/0ngFw4dDB86+p5u84Hl4E8Gbg5DXgLp9I2jBM/iJDaeFciUac5QB9EH78C54lhnrU/WePfjlmcAR+XfEwoFDLG32BogFar3NVab9Rad9Na99Ra90TU0vsbo998hEIhSueX1tfXubyK0tJSQqEQRx11FDWhGjHIu5H8eVtW2V5Z25k6SamS9z+baPT7+OCygUhF7SqcrJyt1nUGApMQd9LNyMrerqhNFSRWwAXIBHENkka6HJmQLgZGQ1a3LDJ2ZYhP/yGcvP9BtKjGjsFwMNGS6ZyPAT8BuiilvgLu1VqXttT9OipuX35jOj09MOMB9KFaXDvXI+6dFxAD6s6ht5uqVEC/Ktg4x7ncb7ww9Trgn4iL5RIkEGvr5lv6+uQjBV0xGQO1SNrly8DbOLn6tYiRLwP+Awywtm2HzrHOVJVUxatqi4qKmHrvVLof0Z2aq2ucBjBgNHYMhkbSklk9N+5lf8+WundHIJUvf/iI4dIRK4UbJRqKct+0+1i0aJFkytQhWTyDkNX0AMTF4gGGAUtBnQBFi2GeVWlRB+T/ACqOQDppHYEY+xhi6AsQ0TS3Pn8Jkq2zBnmiCCATxU+AB5GJ4hbr3M3g+cZDoCZA7L0YI0aMYNaDswiHw/WqakePHu00L7fy+01jE4OhcZjUhwOUVL78JWuW4M/xU7OiJtEAr4Cc3ByWvrC0fqD2b4hFfxenUnYZHOeFuf+Bc2pFCmdUDjxVi7hj7PB8LmLINyAZN6nSLGcjk0kYmWRsahFfvyKeZeTHz2eff8aePXsSjHyqqlqjsWMw7DtGq+cAZG8aPJyAdLiyc+2PI73OzkwkMGsFS7N2wa8Xwy+/hwoNd/lgUdA69kSkFttVIcssnKKsO1IM9iGkXeIyJBffTiVdDXyJPAWcDTwBN19wM0sXLm3yd2E0dgyG1BitnoOIBjV4/Eiw9ceubZ8iipnJ/WczkdW+ZfTP/xSK10Df3bBEweRs2DkRMdT/Rpb+tt6+HZC1nxJqSa+q2R15Mphr3TOK45ffBLwLXuXljtvvaHJGjtHYMRiajtHjPwBJ0OBx8z3ia1+MZOosRFbczyKGeRrwF+s4O5Dqh84eWLQaXlwCHg0XDoMR+bCzFkdq4WLEgM9GdPndjdLrEEOenKljp1na1+mMuHfqkHTNMfLek+MBDRdcdUGj+wUYDIZ9x6z4D0BsDZ7i1cVEh0SdFfhixDi7c9xXIEY+CzG+HyNuH6uL1XAPPDATDqmF/3cO/O+5EA4j0g52OqV9vTMQhaVvkLTM95GWiLYP/0vE1RRADHx/JJ9/ORIArgR+hgRya5HgrwJPvofoyGi82Xnp06Xs2rWL4lnFxn1jMLQAxsd/gJDsy965cyddf9BVjG42YlTrED97srvlIepl3PR5FIq3wwUaXs2EMTfC+0fhrNTt3Pg/IU8R7rTLTUgf3O1IWd6H1n1fQ4q2PNZYAkAYeh/dm4UlC3nk8UdYsmRJPBh71VVX8Ze//IWaW2tkXDEktXQ94JNg76hRoxrU0TcYDOkxPv4DlFRpmyMLR/L8354Xf/kEHFnkFdT3+2ciKZOW0c+Mwn+th1+XQ1jD2ENh3h7Qj1BfxK0KMcYepKDqnzgSyecDS4FTkZX+IUiOPoha5hXEFTa3Pb+N5auWUzyrmPv/cH9C3cHaF9ZSk2t1ankBmUysauFwRZgFaxcQiUT4xR2/iE96JqBrMOwfxsffznGnbYZuDVE9tpq5j8xly/dbxJ3yJWL0bddOst9/O3FtnLO3wjvFMPXv8Jdj4dh8KLla7DMexKiPRvz5VcjKPxupwD0aEUxbjzxddLf2ZeAEdmuQ1M5rEX9+F/nprhy2g7HBYJC8vDwieyKiF2RLRNgKnhCvOp47dy79z+lP1+5dOXHgiXQ9vGuT+gcbDIZEzIq/HWNLMCSkbfog9n0MeiIr6z/jZNYoRDrhWpyJ4O/QKQx/XAmjN8Lnh8ClN8EzdqC2u+v8GFLEZb/3IIVddv59LmLoj0aMfRWS7XMKMkmcR32hN+u8qC/KF198wY9+9COi0SjjJo5j8aLFxDJj0vTlmPTnkguVN1TCv2Djjo0w1vl8bh0ig8HQOMyKvx0QCoX46KOPCIVCCdtTpm0+hxjfzxAd/YnAXdbPw5DV8wzrNQtuzIAPFIzcCP83EI4fZxl9d8ZNGGlgPgr5H3EuMomMRaps3Xn7NUhv3OVIC8Wd4Nvow7vNK7n6e6j/1FEBkVCEGbNnEI1G6X96f0rmlVCTVUM0HIVj5TrpzqUaeRrZhBOngHo6RAaDoXEYw9+GRKNRJkyaQLfDu6V0XRQUFBCtiIroWY31+g/OatvW0S9DDOQQa/sx0MsLzxwGj26BzzvDgCD88l2ocgub2Rk3PsRXX4sEZP+DiLetpb6QmgfJx99OPKvHq71s3rgZf6ZfpJ6TO2utlustXbqUwjGFbCzfKMHgiYg//3tErtkDgacDqVNCa0j7RGCE2QyGpmFcPW1IQxLK06dN5+577iYSjUjQNoJU4GYhGTOZOFk0Wciq2AM+D0z+Fu7dKaeMD8Cc76DOixjZlxGD/QHiU/cgBv8RZNLohmQArUdW/e6m5znAT3HSMVcDn0Gdp46PP/4Yb45XJownSezSZQWLIx9HWLp0aWIFsVvaoTMcmXkkX5Z8iTfHS2hnSJ5EBiFFX2m0+I0wm8HQNIzhbyNCoRDz588nPDZc33VRUkokEmHpC0uJjY8lVspaBp4IstIfD7wBrIfTPFBSBSduh5V94I4glP0H+VeutX5ejXRCtrteFSOibXY2z8WIa2cg8DD4sn3ijtGIK8jdQGUIMBsitREeefwRKr+rhGdw0j7PQLJ9LBG1aCga99knkItMEhfDlyu+5NOPPmXPnj08MOMBlr24jKoqS2a6HzIJutJSjTCbwdB0jKunjSgrKyOWEUtpBCPeCAsXLqTqjCpHZiEXKaSy8/brEAP4BuSVwexj4LUq6BSEq7rAdQEouxrx09utDJ9ADL7d9eov1rWKEMN+KmL0K5BVuweuvORKuVceaQ22ylI88eoTspK3X98CbxI3+ixHJoNqUvvxLWkHb46XPXv20LdvX2bPmE3h4EICJQGCxUH8H/o5ocsJ8feBkgCFg40wm8HQVMyKvwVoTJ55Xl5eagnl7yFaaa2wnyTBVRLPqgkhBjUDhv4bHsqEw7bCQ6fBPRdAKISs5LcBf0X+lSOIG8ftgvkRkhnkR3z7DyMr9D1Ig/UceOpfTzn5/am0eEKg6zThK8P13TczwPexD2/US52qI3JLxGnC4m7o7go0u902Pp+PmdNnct/U+xK+T5PHbzDsHx12xZ8uk2Z/2Fuw1s2ePXvICGTU17exZRcmIgHQcUgg9QWc3rI/gx618PQyWBGB7blw2iiY9BMIPYO0KMxCmqNHEbnkQ5CiqknAz62fVyF+++2IYT8FcQV5EU3+MMSGxMS/34/UXbP8JPbQtcmFnM45rPnzGj7f8jk+7ZPJZpD1+WYCD5AQaM5ek01RUVE9Y+7O/U/13mAwNI0Ot+JP14y8OWQB9tbv1k1BQQHeOi+RQyPOKrwSyaVP1rW3fOlsBW8d3PEoTK0D/TVM8sHMn0Ls30igtztOn1x7Nb0eeUqwV+yujlVUISmihwHnIE8ZA3CyaDojq/Ft1u+zkVV7CHEfnWxdP8XTQF1VHWeddVZcW2jBWqtxysXIJLPM+rwfAP+G3if2Nm4bg6EVaLEVv1JqgVKqXCm1ybVtqlLqPaXUu0qp55RSrZ6KkaoSdsEzC5g0ZdJ+Xbcx/W7dBINBRo0eRXYoW6plfw70ol6fW/s6+GBsBUQj8Kc6eLEPHH88PKggtggJ9HoQF0vypPEuEsBNtWKPgWenRxqbzyGhf208i2YQos2zCVnd70HSNicB54A3w0tgTWIaZvbaxNX79GnT4/567keeRo4HfgHcBNwKH3/0MeFwuGlfvMFgaDIt6epZhOSPuJmmtT5Ra30y0ozvNy14/3o01Tg3hb31u3311VfrXX/qvVMZctoQ/Av9ZP85GzbjyB+4OHwb6BAU75H3/3cmXPVz+OI6pG1hJaKdk67yNRNx69jVunYu/3bY8v4Wvt/5PTdddxOBHgHJxPHKOHxBH77VvoRm52QjDVmuB6Ji4MeOHkvRpUUNBl1tf/2rL71Ktj9bJo2LkYnOknYw+fgGQ+vQkj13X1ZK9Uzatsf1NgdLJqa1aEwz8n1t6pGgkZ/k8gjtDDF0+FDqquooGlXEtPumMeXuKXF3UyQSQUe0GFVbbM1KWZy/Aoo2OZc7chJ8dYjr+lnIKrw7afPcCSO590chsgrWMcGFQerq6ggGgyyav4hOUzoltDIcOXIkAAtLFsa3Hd33aLZs3oKv2JfQ7tDn89ULwqaiT58+6LCWz5nl2mHy8Q2GVqNFZZktw79Ga93Pte1/geFIveb5Wusdac4dg7TqoEePHgO2bt263+NpqGVhoCRAeVn5fgUMJ0ya4DQAd+vhd0UCq5YLpHdObz6p/CTxuNWIMdwBeODUWngj5lx74iB46FUc/73Nt4iLZiLSPH07iRkzy61tKeSaU33mVBkzydv2N6sm1feUvTabwsGFRnPHYGhG0skyt7rhd+27G/Brre/d23WaU4+/JY1OPHBcWppYeXopjt6N21Cn0M33jYZNj8Mx31qH++HIX0B1JhKETTbsK5EVfTaSkWMVc5GBZOqciOTh70k8ry0NbfL3FKuMUVTUPAF2g8Hg0B4N/w+Btan2JdMcht9epXbr1o17fndPixgd+x55eXls2LCBocOHUnl7ZeJBO5GUzcn1zx/+f7C4ynl/YRBeuAvJsKlAnGOv4BRG1SDOsiwkT1/jBGWPBs82D54qD1mHZBHeE0ZphT/f3+Bnbs0ceZOPbzC0LO2iEYtSqo/Weov19iqkd1OLki59s+yLMsrLy5vF6KS6x7Dhw4hVpfD5K+oVQ3WphB3TnEOePAauGQJMR0Lgm3CKruxpMgdHh78a0cjZDp4dHgKHBKj7oo6ioiKm3js1/jmBtIa2JdNc02EapRsMbUOLGX6l1GPAT4AuSqmvgHuBy5RSxyDOh63ArS11f5um5NY3FXvFamvKuO+xbO0y+hzTh0/WJvrys5/PpvdJvePbH3oFJrzpXLP3HfDpodabQ5HKWndevtWnliwcv70dI+gOWd9n8cozr9CnT5+4cc/Pz49fP52hbcnvyWAwtC8O6p67+xPMbcgNkbw6TunLrwD/XD8jRoxI6DNbVCRZPTOKbuGXjz4ev+bdXvjD4Yj6ZS4NxwJmALchBVXu7bMhJy+H9a+sb9JKuqWD3gaDoW1I5+o5qCUbGpO+mUxjZBeSi8CYiOjJv5B4D1/Qxy/u+AXlZeW8/dLblJeVM3PadPxnnBE3+nVZWXTJzuIPP0WM/Swkz34uaaUQ8FM/ETYXCIjOT1NTIvflezIYDAcuB7XhT8itd9NAzvjeKnvTFYExBMmmqal/j7i2zNq1kJEB774LwNdz51K1cyc/HVWE91mvBGhtOYQTcYK6SWOPt1lM3h6CESNGNHp1busV5eXlNfl7MhgMBy4HteEHuHbotfW6OiXLCdg0prK3odUxAeINz7PXZjN8+HDKysoIffEFKAU/+xkAH/Y4kpwcP8feO5luh3fj5X++LLLHE4E7gQnIE0QXHClla+zeVV6O7nM0geeSOlUthxNOPIHZM2bv9TtJfqrp1acXRx9zNNlrshv1PRkMhgObgzJpOtkHX7urFu/DXvx5fuqq6uLVpsk0xuXRUIUuFZCzPIe66jp69+3NokWL6LOwlMlVjpto6vCb+MObq6m6PhwPom5avklE0pKfIB6E4084nk9KPsET8FCzRx4nyvLKqN1Ti2+2D3++n2goyogRI5j14KxGZeCkCuRueXoLPTJ7xLtfuatyDQbDwcVBueJPdtfExsfI7JHJkEuHiJ99+syUBrIxriFbaTJ7bf3V8dixY1n/ynpuvulm1DfvE66siRv9/5erGFl0C/etWlXfTXQDkrJZ47pnLgS7Bln1+Cp2lO1gyKVDyOyRSWxcjKrbq4iNi5HRI4OrL7maHdt2UDyruFFGP91TTfjKMB9t/oi6WB1XX3I1ZV+Upf2eDAbDgc1BZ/jTGbbqK6pZuXJlg+c2ZNTTKU3mPJxDVnEWN19wM7MenEVB9+4MnzuPDV85egv5WXCPT7No8SKiKio5+W7cbiIb12QTCoVYsWIF1VdWJ36mK6tZtWpVk76fBl1VuVDz0xpWv7Gae353T5OuazAYDhwOOsPfkGGL+qJ88cUXDZ7vNuoNKU1Onzad4cOHE62MkpGbwdIlS1k45GqChxzC2VbGzbVHgeoJ348n3pIwkh+BdcjqfidOANfWt4f4ZDNy5EjuvuduevbuSY2nplmybhp6qrHbHzaHWqnBYGi/HHSGvyHDFglFmDF7RoPn2/LBCSmYKVwek6ZMYukLS6m5tQbPLSGqQtWMXvNXAF5X4M2E1TsQbZ3XkYYjtlvnXeBPwKPAdPAt9tHvhH4EFiVONgALnllATVGNZPw0Q9ZNuqeaePvDLEwap8FwkHPQOXCDwSDDRwxn7vK5YmTdla0nwdKlS3ngjw+kVZ50XyddEZTtTqoeW81v3obf/cPZd6IPNh4G/Czp3i8g+vO2W+c64IfO/nPPPpfXX3o9PhYgsaiqP/V61WavzaawqLDJWTf200tpSSnVulraM9p9fcGkcRoMBzkH3Yof4I7b7yBjd0Zi05HuwKXOSrYp/XFt7Lz3LVu2cIxfoR9wjP79Z4D6LWz04xh4qJ/jX4Gs3rs7+6NDoixcuBCQJ5aysjK2bNmS6LKye9XOBh6QquBkF1RjcT/V3DQ0qQGLSeM0GA56DroVP0CPHj3waR+R0RFHsTKLhJVsU7RpEtJDczys2lnNO7G6+P7O/wXfZeMUV3UnkVwkoLsd+BuOS8W135vj5dbxt7Jq5Sq8QS/RUJRINCJdCw5BjPLFwADIWpDFZx9/RvfuyTdqGukasJg0ToPh4OagXPHH/djPZ4uBtYy+vZIFmtSC0Z4kzr24mopvKrnIMvrDunlQk11G/wnkG61NGlAFooe/BPgGOK3+/urd1ax6fVU8BTU8NgzdwbvIK1IO1nHZz2czevTo/Tb6No2NaRgMhoOHg/av2+3HTl7Jfvrpp41uwRgKhXhs3nzKa8LkWCLS73WD/jcCJYpASSB+/dpILbETYvV88SyHH/b8ITu+30FVnyp4Mmn/E6DRTrpmDHgdYttj8i80BzICGXi1l8JRLbMaNxLJBkPH4aA1/PZKNlUf2Iaqb5ODmtW//z07K8Px9wPGwHprd/CQAC//9WVycnIoKCjg7nvupnRdKdWHVosv3tbd6QLfBL4hUhGBHyNds2YgYmthwAP+fD9VuVYXlhcQt5BLjtn7tJcRF40wEskGg2G/OShdPW7iAmlJGTt7LdT67DNQiq7TpEPKnJMkeGsbfXuS6NOnT/z606dNZ/iFwyVd0w9UIv78UVIZq1B4l3khE5FVvkV++n7gEzmGCiQAvB7niQDilbVLliwxufUGg2G/OegNfzrSFmr935/g2muhV6/4sf89upC7avYuYObziQxzzqE5cDPSXtHW6M+VVb3eo8Wod0ZE2DpD9NooCiVictuRQLCRSDYYDC3EQevq2RspXUH//jdkZjoHlZZCYSG/j0apmJLdqMyXgoIC6qrqnKCyTQVEQ1ECnQJU5ib14bUmhavPuJqVT6wkHA43yg1lMBgM+0KLrfiVUguUUuVKqU2ubdOUUh8qpd5TSq1WSuW31P0bSzAYpO+RRxI86ii44ALZ2Ls31NRAoVTPNiXzpSE30ogRI2RSSFOBWzyrmB3bdnDzzTcTWNM4KWmDwWBoMlrrFnkB5yIe7k2ubRcDPuv3PwJ/bMy1BgwYoFuMmTO1Buf12muNPrWiokJv3rxZV1RUJGyPRCJ6/J3jdSA3oIPdgzqQG9Dj7xwf3559bLZmMprfopmMzj42W4+/c3yjzjcYDIbGArylU9nnVBub6wX0dBv+pH1DgEcac50WMfxffplo8IcPb/SpccMctAxzMLVhTjUxNMWop5tYDAaDoTGkM/wt2mxdKdUTWKO17pdi39PA41rrZWnOHQOMAejRo8eArVu3Ns+gtIbhw2GZ67ZffQU/+EGjLzFh0gQWPLPAKQCzdXMGFzY63bKhZu4Gg8HQHKRrtt4mhl8p9WtgIHCtbsQABg4cqN966639H9Brr8FZZznvZ82CceOadIlQKJQonmZTAYGSAOVl5caQGwyGdkE6w9/qWT1KqRHAFcCgxhj9ZqG2Fo45Bj7/XN537y55+n5/ky/VmPaMpgLWYDC0Z1o1j18pNRj4JXCV1rqqVW46fz5kZTlG/+9/h23b9snoQ+PaMxoMBkN7piXTOR9DWpAco5T6SilVBMxC1srPK6XeVUoVt9T9AVi4EEaPlt+vuw7q6uAnP9mvSza2PaPBYDC0V1rM1aO1vjHF5tKWul9KfvQjOOMMePRR6Nmz2S7bkACcwWAwtHdaNLjbXDRbcLeZMZk5BoOhPdNugrsHE0bK2GAwHIh0WJE2g8Fg6KgYw28wGAwdDGP4DQaDoYNhDL/BYDB0MIzhNxgMhg6GMfwGg8HQwTCG32AwGDoYB0QBl1JqB9BMusz16ALsbKFrNwfteXzteWxgxre/tOfxteexQfsZ3w+11l2TNx4Qhr8lUUq9laqyrb3QnsfXnscGZnz7S3seX3seG7T/8RlXj8FgMHQwjOE3GAyGDoYx/FDS1gPYC+15fO15bGDGt7+05/G157FBOx9fh/fxGwwGQ0fDrPgNBoOhg2EMv8FgMHQwOpThV0otUEqVK6U2ubZNU0p9qJR6Tym1WimV347GNtUa17tKqeeUUm3W0DfV+Fz77lJKaaVUl7YYmzWGVN/fb5VSX1vf37tKqcva0/is7ROUUpuVUv9RSv1fexmbUupx1/f2uVLq3bYYWwPjO1kp9S9rfG8ppU5tZ+M7SSn1ulJqo1LqaaVUXluNLyVa6w7zAs4F+gObXNsuBnzW738E/tiOxpbn+v0OoLg9fXfW9iOBZ5ECuy7taXzAb4G72mpMjRjf+cDfgCzrfbf2Mrak/Q8Av2ln391zwKXW75cB/2hn4/s3cJ71eyEwta3Gl+rVoVb8WuuXge+Stj2ntY5ab/8FHNHqAyPt2Pa43uYAbRaJTzU+i+nAf9GGY4MGx9cuSDO+24A/aK1rrGPKW31gNPzdKaUUcAPwWKsOykWa8WnAXkUfApS16qDcA0k9vmOAl63fnweGtuqg9kKHMvyNoBBY19aDcKOU+l+l1JfATcBv2no8bpRSVwFfa603tPVYGmC85S5boJTq1NaDSaIvcI5S6g2l1EtKqR+39YBScA7wjdZ6S1sPJIk7gWnW38b9wN1tO5x6bAKusn6/HnkybjcYw2+hlPo1EAUeaeuxuNFa/1prfSQyrvFtPR4bpVQ28Gva2WSUxBygN3AysA1xWbQnfEAn4HRgCrDcWmG3J26kDVf7DXAbMMn625gElLbxeJIpBMYppd4GcoHaNh5PAsbwA0qpEcAVwE3acsq1Qx6lfT0u9gaOAjYopT5HXGTrlVLd23RULrTW32itY1rrOmAe0GYBwDR8BazSwptAHSLu1S5QSvmAa4HH23osKRgBrLJ+f4J29m+rtf5Qa32x1noAMnF+0tZjctPhDb9SajDwS+AqrXVVW4/HjVKqj+vtVcCHbTWWZLTWG7XW3bTWPbXWPREj1l9rvb2NhxZHKXW46+0Q5PG7PfEX4AIApVRfIJP2oehocyHwodb6q7YeSArKgPOs3y8A2pUrSinVzfrpAf4HKG7bESXR1tHl1nwhM+82IIIYqiLgY+BL4F3r1SaZM2nGthIxVu8BTwM/aE/fXdL+z2nbrJ5U399SYKP1/T0FHN7OxpcJLLP+jdcDF7SXsVnbFwG3ttV3tpfv7mzgbWAD8AYwoJ2NbyLwkfX6A5ZKQnt5GckGg8Fg6GB0eFePwWAwdDSM4TcYDIYOhjH8BoPB0MEwht9gMBg6GMbwGwwGQwfDGH5Dh0ApFbOUHDcopdYrpc5s4ftdoZR6x7rf+0qpsdb2Y5RS/7DG8oFSqkQpdYlLCTNkqXW+q5Ra0pJjNHRcTDqnoUOglApprYPW75cA/621Pm8vp+3rvTIQtdJTtdZfKaWygJ5a681KqWeBh7XWT1rHnqC13ug69x+IouhbLTE2gwHMit/QMckDdgEopX6ilFpj71BKzVJK3WL9PshatW+0RN6yrG2rXcdfpJRalXT9XESH51sArXWN1nqzte9wpMgHa99GDIZWxhh+Q0chYLlPPgTmA1MbOlgp5UcqV3+qtT4BMeS3AS8CxymlulqHjgQWus/VWn+HVApvVUo9ppS6ySrdB5GxflEptU4pNUm1UeMfQ8fGGH5DR6Faa32y1vpYYDCwZC9KmMcAn2mtP7LeLwbO1eIbXQrcbBntM0gh5a21HgUMAt4E7gIWWNsXAschwmI/Af5luYIMhlbDGH5Dh0Nr/TqigtkVkeJ2/x34rZ8NTQoLgZsRyeIntNPIJ/k+G7XW04GLcCmraq3LtNYLtNZXW/fvt6+fxWDYF4zhN3Q4lFLHAl7EB78V+JHlvz8EWaWDKKH2VEodbb0fBrwEYrgRdcj/QdxBydcPKqV+4tp0snUflFKDreAvloR1Z+Dr5vt0BsPe8bX1AAyGViLgahiugBFa6xjwpVJqOaLguQV4B0BrHVZKjQSesHTp/02itO4jQFet9fsp7qWA/1JKzQWqgUrgFmvfxcAMpVTYej9FtyMpa0PHwKRzGgz7gFJqFvCO1rq9dX4yGPaKMfwGQxOx2ulVAhdpq1G6wXAgYQy/wWAwdDBMcNdgMBg6GMbwGwwGQwfDGH6DwWDoYBjDbzAYDB0MY/gNBoOhg/H/A2oTt2ngke4/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_predicted=results.predict(X1_b)\n",
    "plt.scatter(X1,Y1, s=40, facecolors='green', edgecolors='black')\n",
    "plt.xlabel(\"Buoy SST\")\n",
    "plt.ylabel(\"Satellite SST\")\n",
    "plt.plot(X1,Y_predicted, \"r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DONE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix A - Get data from NDBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 2017\n",
      "Got 2018\n",
      "Got 2019\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#YY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>hh</th>\n",
       "      <th>mm</th>\n",
       "      <th>WDIR</th>\n",
       "      <th>WSPD</th>\n",
       "      <th>GST</th>\n",
       "      <th>WVHT</th>\n",
       "      <th>DPD</th>\n",
       "      <th>APD</th>\n",
       "      <th>MWD</th>\n",
       "      <th>PRES</th>\n",
       "      <th>ATMP</th>\n",
       "      <th>WTMP</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>VIS</th>\n",
       "      <th>TIDE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2.99</td>\n",
       "      <td>11.76</td>\n",
       "      <td>8.76</td>\n",
       "      <td>317</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>13.1</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>12.50</td>\n",
       "      <td>8.91</td>\n",
       "      <td>313</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.14</td>\n",
       "      <td>11.11</td>\n",
       "      <td>9.05</td>\n",
       "      <td>307</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.36</td>\n",
       "      <td>12.50</td>\n",
       "      <td>9.46</td>\n",
       "      <td>302</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>999</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>3.12</td>\n",
       "      <td>11.76</td>\n",
       "      <td>9.28</td>\n",
       "      <td>313</td>\n",
       "      <td>9999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #YY  MM  DD  hh  mm  WDIR  WSPD   GST  WVHT    DPD   APD  MWD    PRES  \\\n",
       "0  2017   1   1   0   0   999  99.0  99.0  2.99  11.76  8.76  317  9999.0   \n",
       "1  2017   1   1   0  30   999  99.0  99.0  3.00  12.50  8.91  313  9999.0   \n",
       "2  2017   1   1   1   0   999  99.0  99.0  3.14  11.11  9.05  307  9999.0   \n",
       "3  2017   1   1   1  30   999  99.0  99.0  3.36  12.50  9.46  302  9999.0   \n",
       "4  2017   1   1   2   0   999  99.0  99.0  3.12  11.76  9.28  313  9999.0   \n",
       "\n",
       "    ATMP  WTMP   DEWP   VIS  TIDE  \n",
       "0  999.0  13.1  999.0  99.0  99.0  \n",
       "1  999.0  13.0  999.0  99.0  99.0  \n",
       "2  999.0  13.0  999.0  99.0  99.0  \n",
       "3  999.0  13.0  999.0  99.0  99.0  \n",
       "4  999.0  13.0  999.0  99.0  99.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select buoy years\n",
    "buoy_years = ['2017', '2018', '2019']\n",
    "# select buoy ID\n",
    "buoy_no = '46259'\n",
    "# set up url parts\n",
    "url_base = 'https://www.ndbc.noaa.gov/view_text_file.php?filename='\n",
    "url_end = '.txt.gz&dir=data/historical/stdmet/'\n",
    "\n",
    "for i in range(0, len(buoy_years)):\n",
    "    buoy_url = ''.join([url_base, buoy_no, 'h', buoy_years[i], url_end])\n",
    "    response = requests.get(buoy_url).content\n",
    "    print('Got', buoy_years[i])\n",
    "    if i == 0:\n",
    "        #print('N')\n",
    "        buoy_df = pd.read_csv(io.StringIO(response.decode('utf-8')), skiprows=[1], sep=' ', skipinitialspace=True)\n",
    "        continue\n",
    "    else:\n",
    "        #print('Y')\n",
    "        temp_df = pd.read_csv(io.StringIO(response.decode('utf-8')), skiprows=[1], sep=' ', skipinitialspace=True)\n",
    "        buoy_df = buoy_df.append(temp_df, ignore_index=True)\n",
    "        \n",
    "buoy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Edit the dataframe to include only the needed columns__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#YY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>hh</th>\n",
       "      <th>mm</th>\n",
       "      <th>WTMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    #YY  MM  DD  hh  mm  WTMP\n",
       "0  2017   1   1   0   0  13.1\n",
       "1  2017   1   1   0  30  13.0\n",
       "2  2017   1   1   1   0  13.0\n",
       "3  2017   1   1   1  30  13.0\n",
       "4  2017   1   1   2   0  13.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buoy_df = buoy_df[['#YY', 'MM', 'DD', 'hh', 'mm', 'WTMP']]\n",
    "buoy_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Set up a dictionary to rename the date/time columns__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'#YY': 'year', 'MM': 'month', 'DD': 'day', 'hh': 'hour', 'mm': 'minute'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename_dict = dict(zip(['#YY', 'MM', 'DD', 'hh', 'mm'], ('year', 'month', 'day', 'hour', 'minute')))\n",
    "rename_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Use the date columns to create a date object column__  \n",
    "Put the date object column in the index position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#YY</th>\n",
       "      <th>MM</th>\n",
       "      <th>DD</th>\n",
       "      <th>hh</th>\n",
       "      <th>mm</th>\n",
       "      <th>WTMP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:30:00</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 01:00:00</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 01:30:00</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 02:00:00</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:30:00</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:30:00</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50900 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      #YY  MM  DD  hh  mm  WTMP\n",
       "2017-01-01 00:00:00  2017   1   1   0   0  13.1\n",
       "2017-01-01 00:30:00  2017   1   1   0  30  13.0\n",
       "2017-01-01 01:00:00  2017   1   1   1   0  13.0\n",
       "2017-01-01 01:30:00  2017   1   1   1  30  13.0\n",
       "2017-01-01 02:00:00  2017   1   1   2   0  13.0\n",
       "...                   ...  ..  ..  ..  ..   ...\n",
       "2019-12-31 21:30:00  2019  12  31  21  30  14.0\n",
       "2019-12-31 22:00:00  2019  12  31  22   0  14.1\n",
       "2019-12-31 22:30:00  2019  12  31  22  30  14.1\n",
       "2019-12-31 23:00:00  2019  12  31  23   0  14.1\n",
       "2019-12-31 23:30:00  2019  12  31  23  30  14.1\n",
       "\n",
       "[50900 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#buoy_df.index = pd.to_datetime(buoy_df[['#YY', 'MM', 'DD', 'hh', 'mm']].rename(columns=mapper))\n",
    "#df1\n",
    "buoy_df.index = pd.to_datetime(buoy_df[['#YY', 'MM', 'DD', 'hh', 'mm']].rename(columns=rename_dict))\n",
    "buoy_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Remove the unneeded columns from the dataframe__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>WTMP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>13.1</td>\n",
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       "      <th>2017-01-01 00:30:00</th>\n",
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       "      <th>2017-01-01 02:00:00</th>\n",
       "      <td>13.0</td>\n",
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       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 21:30:00</th>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:00:00</th>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 22:30:00</th>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:00:00</th>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 23:30:00</th>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50900 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     WTMP\n",
       "2017-01-01 00:00:00  13.1\n",
       "2017-01-01 00:30:00  13.0\n",
       "2017-01-01 01:00:00  13.0\n",
       "2017-01-01 01:30:00  13.0\n",
       "2017-01-01 02:00:00  13.0\n",
       "...                   ...\n",
       "2019-12-31 21:30:00  14.0\n",
       "2019-12-31 22:00:00  14.1\n",
       "2019-12-31 22:30:00  14.1\n",
       "2019-12-31 23:00:00  14.1\n",
       "2019-12-31 23:30:00  14.1\n",
       "\n",
       "[50900 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#buoy_df = buoy_df[['#YY', 'MM', 'DD', 'hh', 'mm', 'WTMP']]\n",
    "buoy_df.drop(['#YY','MM', 'DD', 'hh', 'mm'], axis=1, inplace=True)\n",
    "buoy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>WTMP_degC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01 00:30:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01 01:00:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01 01:30:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01 02:00:00</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>50895</th>\n",
       "      <td>2019-12-31 21:30:00</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50896</th>\n",
       "      <td>2019-12-31 22:00:00</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50897</th>\n",
       "      <td>2019-12-31 22:30:00</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50898</th>\n",
       "      <td>2019-12-31 23:00:00</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50899</th>\n",
       "      <td>2019-12-31 23:30:00</td>\n",
       "      <td>14.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50900 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Time  WTMP_degC\n",
       "0     2017-01-01 00:00:00       13.1\n",
       "1     2017-01-01 00:30:00       13.0\n",
       "2     2017-01-01 01:00:00       13.0\n",
       "3     2017-01-01 01:30:00       13.0\n",
       "4     2017-01-01 02:00:00       13.0\n",
       "...                   ...        ...\n",
       "50895 2019-12-31 21:30:00       14.0\n",
       "50896 2019-12-31 22:00:00       14.1\n",
       "50897 2019-12-31 22:30:00       14.1\n",
       "50898 2019-12-31 23:00:00       14.1\n",
       "50899 2019-12-31 23:30:00       14.1\n",
       "\n",
       "[50900 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buoy_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size:30px\"><img src=\"images/cw_logo_80.png\" align=\"left\">CoastWatch Satellite Training Course</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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