01_data_exploration.ipynb

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    "# 1. Data Exploration\n",
    "\n",
    "## Overview \n",
    "In the first section we will look at techniques to load and explore the data to be used for machine learning. The purpose of this exploration to gain an understanding of the data and how it relates to the problem we are trying to solve. This will help us make sensible choices for differents elements of our machine learning pipeline."
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    "### Prerequisities \n",
    "\n",
    "To be able to sucessfully work through this notebook, you will need some understanding of the following \n",
    "* The python programming language\n",
    "* Simple data handling in python\n",
    "* Basic plotting using matplotlib\n",
    "* Environment set up using pip or conda"
   ]
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    "### Learning Outcomes \n",
    "\n",
    "* Loading tabular and gridded datasets using common python libraries\n",
    "* typical techniques for *exploratory data analysis* on different\n",
    "* key characteristics of datasets to use in choosing components for a machine learning pipeline\n"
   ]
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    "## Tutorial - Exploratory Data Analysis\n",
    "\n",
    "The first step in a machine learning problem is to understand the data you have available for training a machine learning algorithm. In any data driven technique, the quality of the results is only going to be as good as the quality of the data. This can only be achieved by matching the appropriate techniques to the data that you have, so understanding the particular dataset to be used is the foundation of a successful outcome.\n",
    "\n",
    "To achieve this, one typically performs an *Exploratory Data Analysis*, where one produce a series of summary statistics and plots that highlight the most salient characteristics of the dataset for the problem at hand. Typical traits to be considered include:\n",
    "* The range of values different input feature can take e.g. min and max temperature, start and end dates.\n",
    "* Important subsets of the data e.g. different seasons or time of the day.\n",
    "* Distributions of different features, both as a whole and important subsets\n",
    "* Correlations between different features, especially input and target features.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
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    "### Best Practices & Values\n",
    "\n",
    "Performing a through data exploration implements the Met Office Machine Learning best practices in the following ways\n",
    "* Ethics - Ensure we can justify our choices from understanding of the data and how it relates to the potential impacts of this work.\n",
    "* Data - Ensure that we understand biases or other issues with the data\n",
    "* ML Pitfall -  Avoid pitfalls through making informed choices based on analysis of data\n",
    "* ML Lifecycle - Ensure we store and proces the data so we can reproduce the results"
   ]
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    "## Excercise 1: Load and explore tabular dataset - Falkland Islands Airfield Rotors\n",
    "\n",
    "The first dataset we will explore comes from a challenge by Operational Meteorologist Steve Ramsdale, around forecasting  localised topographically-driven turbulent wind gusts at the Mount Pleasant Airfield in the Falkland Islands, which occur in northerly winds interacting with mountains to the north of the airfield. The aim is to predict whether or not these will occur, using data from global, coarse-resolution models which are not able to to explcitly resolve this phemomenon. \n",
    "\n",
    "The target is a human-created dataset produced by Operational Meterologists at the airfield, as to whether a rotor occured in any given three hour period."
   ]
  },
  {
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   "source": [
    "import pathlib\n",
    "import datetime\n",
    "import os\n",
    "import functools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f9b4ad3d-958d-4b04-ac0e-5555800898bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4e4c1340-753c-44f8-ac43-7d00fbd045f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "404790fe-fdc9-4890-ae53-497e255b3d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris\n",
    "import iris.quickplot\n",
    "import iris.coord_categorisation\n",
    "import cartopy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "64fdc2e3-7dfa-44b7-96dd-79ba9994aa72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c279ac65-86c2-4199-a270-5fd05147feb2",
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   "source": [
    "falklands_df = pandas.read_csv('./data/rotors/new_training.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 64,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
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       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
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       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>Rotors 1 is true</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>01/01/2015 00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>282.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/01/2015 03:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>280.250</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/01/2015 06:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>278.625</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/01/2015 09:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.250</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>31/12/2020 06:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.500</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>31/12/2020 09:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>31/12/2020 12:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>279.625</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>31/12/2020 15:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>01/01/2021 00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>20106 rows × 95 columns</p>\n",
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      "text/plain": [
       "                    DTG  air_temp_obs  dewpoint_obs  wind_direction_obs  \\\n",
       "0                   NaN           NaN           NaN                 NaN   \n",
       "1      01/01/2015 00:00         283.9         280.7               110.0   \n",
       "2      01/01/2015 03:00         280.7         279.7                90.0   \n",
       "3      01/01/2015 06:00         279.8         278.1               100.0   \n",
       "4      01/01/2015 09:00         279.9         277.0               120.0   \n",
       "...                 ...           ...           ...                 ...   \n",
       "20101  31/12/2020 06:00         276.7         275.5               270.0   \n",
       "20102  31/12/2020 09:00         277.9         276.9               270.0   \n",
       "20103  31/12/2020 12:00         283.5         277.1               220.0   \n",
       "20104  31/12/2020 15:00         286.1         276.9               250.0   \n",
       "20105  01/01/2021 00:00         285.1         279.3               300.0   \n",
       "\n",
       "       wind_speed_obs  wind_gust_obs  air_temp_1  air_temp_2  air_temp_3  \\\n",
       "0                 NaN            NaN         NaN         NaN         NaN   \n",
       "1                 4.1     -9999999.0     284.000     283.625     283.250   \n",
       "2                 7.7     -9999999.0     281.500     281.250     280.750   \n",
       "3                 7.7     -9999999.0     279.875     279.625     279.125   \n",
       "4                 7.2     -9999999.0     279.625     279.250     278.875   \n",
       "...               ...            ...         ...         ...         ...   \n",
       "20101             3.6     -9999999.0     277.875     277.750     277.625   \n",
       "20102             3.1     -9999999.0     277.875     277.625     277.875   \n",
       "20103             3.6     -9999999.0     281.125     280.625     280.125   \n",
       "20104             3.6     -9999999.0     284.625     284.125     283.625   \n",
       "20105             6.2     -9999999.0     284.250     284.000     283.750   \n",
       "\n",
       "       air_temp_4  ...  windspd_18  winddir_19  windspd_19  winddir_20  \\\n",
       "0             NaN  ...         NaN         NaN         NaN         NaN   \n",
       "1         282.625  ...         5.8       341.0         6.0       334.0   \n",
       "2         280.250  ...         6.8       344.0         5.3       348.0   \n",
       "3         278.625  ...         6.0       345.0         5.5       358.0   \n",
       "4         278.250  ...         3.1       338.0         3.5       354.0   \n",
       "...           ...  ...         ...         ...         ...         ...   \n",
       "20101     277.500  ...        12.1       223.0        11.8       221.0   \n",
       "20102     277.875  ...        10.2       230.0        10.8       230.0   \n",
       "20103     279.625  ...        10.3       218.0        11.9       221.0   \n",
       "20104     283.000  ...         9.4       218.0         8.6       212.0   \n",
       "20105     283.250  ...         8.6       241.0        10.2       236.0   \n",
       "\n",
       "       windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN   \n",
       "1             6.1       330.0         6.0       329.0         5.8   \n",
       "2             3.8       360.0         3.2        12.0         3.5   \n",
       "3             5.0        10.0         4.2        38.0         4.0   \n",
       "4             3.9         9.0         4.4        22.0         4.6   \n",
       "...           ...         ...         ...         ...         ...   \n",
       "20101        11.4       219.0        11.3       215.0        11.4   \n",
       "20102        11.6       227.0        12.3       222.0        12.0   \n",
       "20103        12.8       222.0        11.9       225.0        10.6   \n",
       "20104         8.3       218.0         8.7       226.0        10.1   \n",
       "20105        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       Rotors 1 is true  \n",
       "0                   NaN  \n",
       "1                   NaN  \n",
       "2                   NaN  \n",
       "3                   NaN  \n",
       "4                   NaN  \n",
       "...                 ...  \n",
       "20101               NaN  \n",
       "20102               NaN  \n",
       "20103               NaN  \n",
       "20104               NaN  \n",
       "20105               NaN  \n",
       "\n",
       "[20106 rows x 95 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2643e469-3c3a-468d-95b1-40df8aedb431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DTG', 'air_temp_obs', 'dewpoint_obs', 'wind_direction_obs',\n",
       "       'wind_speed_obs', 'wind_gust_obs', 'air_temp_1', 'air_temp_2',\n",
       "       'air_temp_3', 'air_temp_4', 'air_temp_5', 'air_temp_6', 'air_temp_7',\n",
       "       'air_temp_8', 'air_temp_9', 'air_temp_10', 'air_temp_11', 'air_temp_12',\n",
       "       'air_temp_13', 'air_temp_14', 'air_temp_15', 'air_temp_16',\n",
       "       'air_temp_17', 'air_temp_18', 'air_temp_19', 'air_temp_20',\n",
       "       'air_temp_21', 'air_temp_22', 'sh_1', 'sh_2', 'sh_3', 'sh_4', 'sh_5',\n",
       "       'sh_6', 'sh_7', 'sh_8', 'sh_9', 'sh_10', 'sh_11', 'sh_12', 'sh_13',\n",
       "       'sh_14', 'sh_15', 'sh_16', 'sh_17', 'sh_18', 'sh_19', 'sh_20', 'sh_21',\n",
       "       'sh_22', 'winddir_1', 'windspd_1', 'winddir_2', 'windspd_2',\n",
       "       'winddir_3', 'windspd_3', 'winddir_4', 'windspd_4', 'winddir_5',\n",
       "       'windspd_5', 'winddir_6', 'windspd_6', 'winddir_7', 'windspd_7',\n",
       "       'winddir_8', 'windspd_8', 'winddir_9', 'windspd_9', 'winddir_10',\n",
       "       'windspd_10', 'winddir_11', 'windspd_11', 'winddir_12', 'windspd_12',\n",
       "       'winddir_13', 'windspd_13', 'winddir_14', 'windspd_14', 'winddir_15',\n",
       "       'windspd_15', 'winddir_16', 'windspd_16', 'winddir_17', 'windspd_17',\n",
       "       'winddir_18', 'windspd_18', 'winddir_19', 'windspd_19', 'winddir_20',\n",
       "       'windspd_20', 'winddir_21', 'windspd_21', 'winddir_22', 'windspd_22',\n",
       "       'Rotors 1 is true'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6699d1-6b34-4f2d-a7f6-1fb99699b0b2",
   "metadata": {},
   "source": [
    "We perform some initial cleaning of the dataset to make it easier to work with. Ideally this could be done once and saved out for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2fb3d1b3-6cd7-4ddb-b92b-2256e13cdea5",
   "metadata": {
    "tags": []
   },
   "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>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>282.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>280.250</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>278.625</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.250</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-01 12:00:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.375</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>273.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>329.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>338.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>2020-12-31 06:00:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.500</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>2020-12-31 09:00:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>2020-12-31 12:00:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>279.625</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>2020-12-31 15:00:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17486 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DTG  air_temp_obs  dewpoint_obs  wind_direction_obs  \\\n",
       "1     2015-01-01 00:00:00         283.9         280.7               110.0   \n",
       "2     2015-01-01 03:00:00         280.7         279.7                90.0   \n",
       "3     2015-01-01 06:00:00         279.8         278.1               100.0   \n",
       "4     2015-01-01 09:00:00         279.9         277.0               120.0   \n",
       "5     2015-01-01 12:00:00         279.9         277.4               120.0   \n",
       "...                   ...           ...           ...                 ...   \n",
       "20101 2020-12-31 06:00:00         276.7         275.5               270.0   \n",
       "20102 2020-12-31 09:00:00         277.9         276.9               270.0   \n",
       "20103 2020-12-31 12:00:00         283.5         277.1               220.0   \n",
       "20104 2020-12-31 15:00:00         286.1         276.9               250.0   \n",
       "20105 2021-01-01 00:00:00         285.1         279.3               300.0   \n",
       "\n",
       "       wind_speed_obs  wind_gust_obs  air_temp_1  air_temp_2  air_temp_3  \\\n",
       "1                 4.1     -9999999.0     284.000     283.625     283.250   \n",
       "2                 7.7     -9999999.0     281.500     281.250     280.750   \n",
       "3                 7.7     -9999999.0     279.875     279.625     279.125   \n",
       "4                 7.2     -9999999.0     279.625     279.250     278.875   \n",
       "5                 8.7     -9999999.0     279.250     278.875     278.375   \n",
       "...               ...            ...         ...         ...         ...   \n",
       "20101             3.6     -9999999.0     277.875     277.750     277.625   \n",
       "20102             3.1     -9999999.0     277.875     277.625     277.875   \n",
       "20103             3.6     -9999999.0     281.125     280.625     280.125   \n",
       "20104             3.6     -9999999.0     284.625     284.125     283.625   \n",
       "20105             6.2     -9999999.0     284.250     284.000     283.750   \n",
       "\n",
       "       air_temp_4  ...  windspd_18  winddir_19  windspd_19  winddir_20  \\\n",
       "1         282.625  ...         5.8       341.0         6.0       334.0   \n",
       "2         280.250  ...         6.8       344.0         5.3       348.0   \n",
       "3         278.625  ...         6.0       345.0         5.5       358.0   \n",
       "4         278.250  ...         3.1       338.0         3.5       354.0   \n",
       "5         277.875  ...         1.6       273.0         2.0       303.0   \n",
       "...           ...  ...         ...         ...         ...         ...   \n",
       "20101     277.500  ...        12.1       223.0        11.8       221.0   \n",
       "20102     277.875  ...        10.2       230.0        10.8       230.0   \n",
       "20103     279.625  ...        10.3       218.0        11.9       221.0   \n",
       "20104     283.000  ...         9.4       218.0         8.6       212.0   \n",
       "20105     283.250  ...         8.6       241.0        10.2       236.0   \n",
       "\n",
       "       windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  rotors  \n",
       "1             6.1       330.0         6.0       329.0         5.8     0.0  \n",
       "2             3.8       360.0         3.2        12.0         3.5     0.0  \n",
       "3             5.0        10.0         4.2        38.0         4.0     0.0  \n",
       "4             3.9         9.0         4.4        22.0         4.6     0.0  \n",
       "5             2.3       329.0         2.5       338.0         2.4     0.0  \n",
       "...           ...         ...         ...         ...         ...     ...  \n",
       "20101        11.4       219.0        11.3       215.0        11.4     0.0  \n",
       "20102        11.6       227.0        12.3       222.0        12.0     0.0  \n",
       "20103        12.8       222.0        11.9       225.0        10.6     0.0  \n",
       "20104         8.3       218.0         8.7       226.0        10.1     0.0  \n",
       "20105        10.5       232.0        10.5       227.0        11.3     0.0  \n",
       "\n",
       "[17486 rows x 95 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df = falklands_df.rename({'Rotors 1 is true': 'rotors'},axis=1)\n",
    "falklands_df.loc[falklands_df[falklands_df['rotors'].isna()].index, 'rotors'] = 0\n",
    "falklands_df['DTG'] = pandas.to_datetime(falklands_df['DTG'])\n",
    "falklands_df = falklands_df.drop_duplicates(subset=['DTG'])\n",
    "falklands_df = falklands_df[~falklands_df['DTG'].isnull()]\n",
    "falklands_df = falklands_df[(falklands_df['wind_speed_obs'] >= 0.0) &\n",
    "                            (falklands_df['air_temp_obs'] >= 0.0) &\n",
    "                            (falklands_df['wind_direction_obs'] >= 0.0) &\n",
    "                            (falklands_df['dewpoint_obs'] >= 0.0) \n",
    "                           ]\n",
    "falklands_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4012374d-291e-47fd-a56d-ed3d6125d939",
   "metadata": {},
   "source": [
    "Our target variables is the *rotors* column, labelling whether or not one or more rotors occured in a given 3 hour period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8872aba2-5442-40dc-8513-1bbf8baa7e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    17037\n",
       "1.0      449\n",
       "Name: rotors, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df['rotors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d121cb54-cad1-4213-b68a-ccce047b82a4",
   "metadata": {},
   "source": [
    "We see that this is an *imbalanced* classification problem. As a result we will have to consider cerefully how to take this into account in our ML pipeline, including\n",
    "* train/test split\n",
    "* possible resampling strategies\n",
    "* classification metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7a9355b9-3143-43a7-8de5-bdcd8b4a3953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DTG', 'air_temp_obs', 'dewpoint_obs', 'wind_direction_obs',\n",
       "       'wind_speed_obs', 'wind_gust_obs', 'air_temp_1', 'air_temp_2',\n",
       "       'air_temp_3', 'air_temp_4', 'air_temp_5', 'air_temp_6', 'air_temp_7',\n",
       "       'air_temp_8', 'air_temp_9', 'air_temp_10', 'air_temp_11', 'air_temp_12',\n",
       "       'air_temp_13', 'air_temp_14', 'air_temp_15', 'air_temp_16',\n",
       "       'air_temp_17', 'air_temp_18', 'air_temp_19', 'air_temp_20',\n",
       "       'air_temp_21', 'air_temp_22', 'sh_1', 'sh_2', 'sh_3', 'sh_4', 'sh_5',\n",
       "       'sh_6', 'sh_7', 'sh_8', 'sh_9', 'sh_10', 'sh_11', 'sh_12', 'sh_13',\n",
       "       'sh_14', 'sh_15', 'sh_16', 'sh_17', 'sh_18', 'sh_19', 'sh_20', 'sh_21',\n",
       "       'sh_22', 'winddir_1', 'windspd_1', 'winddir_2', 'windspd_2',\n",
       "       'winddir_3', 'windspd_3', 'winddir_4', 'windspd_4', 'winddir_5',\n",
       "       'windspd_5', 'winddir_6', 'windspd_6', 'winddir_7', 'windspd_7',\n",
       "       'winddir_8', 'windspd_8', 'winddir_9', 'windspd_9', 'winddir_10',\n",
       "       'windspd_10', 'winddir_11', 'windspd_11', 'winddir_12', 'windspd_12',\n",
       "       'winddir_13', 'windspd_13', 'winddir_14', 'windspd_14', 'winddir_15',\n",
       "       'windspd_15', 'winddir_16', 'windspd_16', 'winddir_17', 'windspd_17',\n",
       "       'winddir_18', 'windspd_18', 'winddir_19', 'windspd_19', 'winddir_20',\n",
       "       'windspd_20', 'winddir_21', 'windspd_21', 'winddir_22', 'windspd_22',\n",
       "       'rotors'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ecb5ed-b32e-4500-a4d9-597788f232b0",
   "metadata": {},
   "source": [
    "Above we see the forecast variables from the Met Office Global Forecast model, which we want to use an input to our algorithm. We see there are 4 variables, each on 22 height levels, including\n",
    "* temperature\n",
    "* specific humidity\n",
    "* wind speed\n",
    "* wind direction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b01037-db7e-4965-be42-026fe3e1af55",
   "metadata": {},
   "source": [
    "Next we split into two groups, to see how the distribution of input variables differs for times with and without rotors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5245c7a1-f6b8-4cb0-b7ec-9dbf64f9d7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_rotors_df = falklands_df[falklands_df['rotors'] ==False]\n",
    "rotors_present_df = falklands_df[falklands_df['rotors'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8a380eb0-4467-4aec-bd94-4fff8093f157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17037, 95)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_rotors_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9c7a73b9-0067-45c4-b6ea-1bb6c35c0a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(449, 95)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_present_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649cfa28-a4eb-4c63-81c0-e1197ffc6200",
   "metadata": {},
   "source": [
    "We start by looking at the surface observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "87ce6744-b5f5-471a-843c-ff1f786eef65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'wind direction')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('distribution_wind_spevalue_countsirection', figsize=(16,8))\n",
    "ax1 = fig1.add_subplot(1,2,1,title='distribution of observed wind speeds')\n",
    "falklands_df['wind_speed_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('windspeed in m/s-1',)\n",
    "ax1 = fig1.add_subplot(1,2,2,title='distribution of observed wind directions')\n",
    "falklands_df['wind_direction_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('wind direction',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0a4f1180-69d2-4c0e-856d-ae751f00464f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'dew point temperature (K)')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('distribution_temp_dewpoint', figsize=(16,8))\n",
    "ax1 = fig1.add_subplot(1,2,1,title='distribution of observed air temperatures')\n",
    "falklands_df['air_temp_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('air temperature (K)',)\n",
    "ax1 = fig1.add_subplot(1,2,2,title='distribution of observed dew point temperatures')\n",
    "falklands_df['dewpoint_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('dew point temperature (K)',)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d6a3bc-7fb1-47bb-9f49-c52eca161c94",
   "metadata": {},
   "source": [
    "Now compare for rotors present/absent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9cfc7721-7260-4462-bf4f-27e699a59f02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['air_temp_obs', 'dewpoint_obs', 'wind_direction_obs', 'wind_speed_obs']\n"
     ]
    },
    {
     "data": {
      "image/png": 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cDTAxMcHU1NReaeTu2r59e2fa0nXr1+6Yc/rEyvnnmeY2n5/vzcFxWw7WKG1Pk1VJkpaHrcDWqrqqHf8bmmT1riSrqmpbklXA3f0WrqqNwEaAdevW1eTk5BI0eX5TU1N0pS1dd+aGy+acvn7tDi64fmH/Gm45fXIALRpvvjcHx205WKO0Pb0MWJKkZaCqvgLckeTxbdGJwI3ApcAZbdkZwCVDaJ4kSbvwzKokScvHrwJvT/I9wBeBF9IcuN6c5CzgduC5Q2yfJEnfZbIqSdIyUVXXAev6TDpxiZsiSdK8vAxYkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnrBh2A6RRtGbDZQOpZ8v5Jw+kHkmSJGnceGZVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOmfFfDMkORK4CPg+4N+BjVX1p0kOBt4FrAG2AL9QVV9vlzkXOAt4EPi1qvpgW/5k4EJgJfB+4OVVVYMNSV2wZsNlw26CJEmSpBG2kDOrO4D1VfXDwAnAOUmOATYAV1TV0cAV7TjttNOAY4GTgDcm2aet603A2cDR7eukAcYiSZIkSRoT8yarVbWtqj7dDt8P3ASsBk4BNrWzbQJObYdPAS6uqgeq6jbgVuD4JKuAA6vqyvZs6kU9y0iSJEmS9F27dc9qkjXAk4CrgImq2gZNQgsc1s62GrijZ7GtbdnqdnhmuSRJkiRJO5n3ntVpSQ4A3g28oqq+mWTWWfuU1Rzl/dZ1Ns3lwkxMTDA1NbXQZnba9u3bxyaWfnrjW792x3AbsxdMrBx8XF16Pyyn9+c4Mj5pfPkcCEnL1YKS1ST70iSqb6+q97TFdyVZVVXb2kt8727LtwJH9ix+BHBnW35En/JdVNVGYCPAunXranJycmHRdNzU1BTjEks/vfGdOYYd6/q1O7jg+gUf31mQLadPDrS+xVhO789xZHySJGnczHsZcJpTqG8Bbqqq1/VMuhQ4ox0+A7ikp/y0JPslOYrmQUpXt5cK35/khLbOF/QsI0mSJEnSdy3kNNFTgOcD1ye5ri17JXA+sDnJWcDtwHMBquqGJJuBG2meJHxOVT3YLvdSHvrpmg+0L0mSJEmSdjJvslpVH6f//aYAJ86yzHnAeX3KrwGesDsNlCRJg5FkC3A/ze+g76iqdXP9brokScO0W08DliRJI+9pVXVcVa1rx/v+brokScNmsipJ0vI22++mS5I0VCarkiQtHwV8KMm17c/Ewey/my5J0lAN9nc4JElSlz2lqu5MchhweZLPL3TBrv4G+nL4Dd6l+u3y3fk98XHf5oOwHN6bS8VtOVijtD1NViVJWiaq6s72791J3gscz+y/mz5z2U7+Bvpy+A3epfrt8t35PfEu/U54Vy2H9+ZScVsO1ihtTy8DliRpGUiyf5JHTg8DPwV8jtl/N12SpKHyzKokScvDBPDeJND0/++oqr9P8in6/G66JEnDZrIqSdIyUFVfBJ7Yp/xeZvnddEmShsnLgCVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS56wYdgPULWs2XLbHy65fu4MzF7G8JEmSJE3zzKokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLn+IAlSZIk7ZbFPJCx15bzTx5IPZLGk2dWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNWDLsB0nI2qB9VB39YXZIkSePFM6uSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqStEwk2SfJZ5K8rx0/OMnlSW5p/x407DZKkjTNZFWSpOXj5cBNPeMbgCuq6mjginZckqROMFmVJGkZSHIEcDLwlz3FpwCb2uFNwKlL3CxJkmZlsipJ0vLwJ8BvAv/eUzZRVdsA2r+HDaFdkiT1tWK+GZK8FXgOcHdVPaEtezXwYuCr7WyvrKr3t9POBc4CHgR+rao+2JY/GbgQWAm8H3h5VdUgg5EkSbtKMt2PX5tkcg/rOBs4G2BiYoKpqamBtW8xtm/f3pm29Lr+y98YWF3r1w6sqjlNrIT1a3cszcpaXdx3g9LV9+YoclsO1ihtz3mTVZoE88+Bi2aUv76qXttbkOQY4DTgWOBw4MNJHldVDwJvounkPkmTrJ4EfGBRrZckSQvxFOBnkjwbeDhwYJK/Bu5KsqqqtiVZBdw9WwVVtRHYCLBu3bqanJxcgmbPb2pqiq60pdeZGy4bdhN22/q1O7jg+oX8azg4W06fXNL1LaWuvjdHkdtysEZpe857GXBVfQz42gLrOwW4uKoeqKrbgFuB49sO8MCqurI9m3oR3hcjSdKSqKpzq+qIqlpDc1D5H6rql4BLgTPa2c4ALhlSEyVJ2sVi7ll9WZLPJnlrz6PuVwN39MyztS1b3Q7PLJckScNzPvDMJLcAz2zHJUnqhD291uNNwGuAav9eALwISJ95a47yvrp6X8xijcL14Yu5V2UY97ospa7Ht9j31ii8PxfD+EbbuMe3lKpqCphqh+8FThxmeyRJms0eJatVddf0cJI3A+9rR7cCR/bMegRwZ1t+RJ/y2erv5H0xizUK14cv5h6bYdzrspS6Ht9i7/sZhffnYhjfaBv3+CRJ0q726DLg9h7UaT8LfK4dvhQ4Lcl+SY4Cjgaubh+Hf3+SE5IEeAHeFyNJkiRJmsVCfrrmncAkcEiSrcCrgMkkx9FcyrsF+BWAqrohyWbgRmAHcE77JGCAl/LQT9d8AJ8ELEmSJEmaxbzJalU9r0/xW+aY/zzgvD7l1wBP2K3WSZIkSZKWpcU8DViSJEmSpL3CZFWSJEmS1Dkmq5IkSZKkzunu73BIkiRJC7RmET+/12vL+ScPpB7oZpukUeKZVUmSJElS55isSpIkSZI6x2RVkiRJktQ53rMqSZKkoRjUPZ2SxpNnViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6Z8WwGyBJkiR1xZoNlw2knvVrd+C/2tLieGZVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmd4yPKpDGx2KcXrl+7gzM3XMaW808eUIskSZKkPeeZVUmSJElS55isSpK0DCR5eJKrk/xTkhuS/G5bfnCSy5Pc0v49aNhtlSQJTFYlSVouHgCeXlVPBI4DTkpyArABuKKqjgauaMclSRo6k1VJkpaBamxvR/dtXwWcAmxqyzcBpy596yRJ2pXJqiRJy0SSfZJcB9wNXF5VVwETVbUNoP172BCbKEnSd/k0YEmSlomqehA4LsmjgfcmecJCl01yNnA2wMTEBFNTU3uljbtr+/btnWlLr/Vrdwy7CbttYuVotrurBrk9u/geX0pd/ZyPqlHaniarkiQtM1V1X5Ip4CTgriSrqmpbklU0Z137LbMR2Aiwbt26mpycXKrmzmlqaoqutKXXmYv8ObFhWL92Bxdc77+GgzLI7bnl9MmB1DOquvo5H1WjtD29DFiSpGUgyaHtGVWSrASeAXweuBQ4o53tDOCSoTRQkqQZPHwmSdLysArYlGQfmoPVm6vqfUmuBDYnOQu4HXjuMBspSdI0k1VJkpaBqvos8KQ+5fcCJy59iyRJmpuXAUuSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqnHmT1SRvTXJ3ks/1lB2c5PIkt7R/D+qZdm6SW5PcnORZPeVPTnJ9O+0NSTL4cCRJkiRJ42AhZ1YvBE6aUbYBuKKqjgauaMdJcgxwGnBsu8wbk+zTLvMm4Gzg6PY1s05JkiRJkoAFJKtV9THgazOKTwE2tcObgFN7yi+uqgeq6jbgVuD4JKuAA6vqyqoq4KKeZSRJkiRJ2sme3rM6UVXbANq/h7Xlq4E7eubb2patbodnlkuSJEmStIsVA66v332oNUd5/0qSs2kuGWZiYoKpqamBNG7Ytm/f3vlY1q/dscfLTqxc3PJdt1zi6/p7dE+NwudvMYxPkiSNmz1NVu9KsqqqtrWX+N7dlm8FjuyZ7wjgzrb8iD7lfVXVRmAjwLp162pycnIPm9ktU1NTdD2WMzdctsfLrl+7gwuuH/Txj+5YLvFtOX1y2E3ZK0bh87cYxidJksbNnl4GfClwRjt8BnBJT/lpSfZLchTNg5Subi8Vvj/JCe1TgF/Qs4wkSZIkSTuZ9zRRkncCk8AhSbYCrwLOBzYnOQu4HXguQFXdkGQzcCOwAzinqh5sq3opzZOFVwIfaF+SJEmSJO1i3mS1qp43y6QTZ5n/POC8PuXXAE/YrdZJkiRJkpalPb0MWJIkSZKkvcZkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnrBh2A7R4azZcNuwmSJIkSdJAmaxK2skgD35sOf/kgdUlSZKk5cXLgCVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRpGUhyZJKPJLkpyQ1JXt6WH5zk8iS3tH8PGnZbJUkCk1VJkpaLHcD6qvph4ATgnCTHABuAK6rqaOCKdlySpKEzWZUkaRmoqm1V9el2+H7gJmA1cAqwqZ1tE3DqUBooSdIMJquSJC0zSdYATwKuAiaqahs0CS1w2BCbJknSd60YdgMkSdLSSXIA8G7gFVX1zSQLXe5s4GyAiYkJpqam9lobd8f27dsH2pbrv/yNgdSzfu1AqllSEyth/dodw27G2Bjk9vyzt18ykHoGae3qRy3Zugb9OV/uRml7mqxKkrRMJNmXJlF9e1W9py2+K8mqqtqWZBVwd79lq2ojsBFg3bp1NTk5uRRNntfU1BSDbMuZGy4bWF2jZv3aHVxwvf8aDsq4b88tp08u2boG/Tlf7kZpe3oZsCRJy0CaU6hvAW6qqtf1TLoUOKMdPgPo3ikcSdKyNL6HeyRJUq+nAM8Hrk9yXVv2SuB8YHOSs4DbgecOp3mSJO3MZFWSpGWgqj4OzHaD6olL2RZJkhbCy4AlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjpnxWIWTrIFuB94ENhRVeuSHAy8C1gDbAF+oaq+3s5/LnBWO/+vVdUHF7N+Sd22ZsNlA6lny/knD6QeSZIkjY5BnFl9WlUdV1Xr2vENwBVVdTRwRTtOkmOA04BjgZOANybZZwDrlyRJkiSNmb1xGfApwKZ2eBNwak/5xVX1QFXdBtwKHL8X1i9JkiRJGnGLugwYKOBDSQr4P1W1EZioqm0AVbUtyWHtvKuBT/Ysu7Ut20WSs4GzASYmJpiamlpkM7th+/bteyWW9Wt3DLzOPTGxsjtt2RuMb3gG8bnZW5+/rjA+SZI0bhabrD6lqu5sE9LLk3x+jnnTp6z6zdgmvRsB1q1bV5OTk4tsZjdMTU2xN2I5c0D3BS7W+rU7uOD6xb6lusv4hmfL6ZOLrmNvff66wvgkSdK4WdRlwFV1Z/v3buC9NJf13pVkFUD79+529q3AkT2LHwHcuZj1S5IkSZLG0x4nq0n2T/LI6WHgp4DPAZcCZ7SznQFc0g5fCpyWZL8kRwFHA1fv6folSZIkSeNrMdf8TQDvTTJdzzuq6u+TfArYnOQs4HbguQBVdUOSzcCNwA7gnKp6cFGtlyRJkiSNpT1OVqvqi8AT+5TfC5w4yzLnAeft6TolSZL6GdTvOktaGH9LXUthb/x0jSRJkiRJi2KyKkmSJEnqHJNVSZIkSVLnmKxKkrQMJHlrkruTfK6n7OAklye5pf170DDbKElSL5NVSZKWhwuBk2aUbQCuqKqjgSvacUmSOsFkVZKkZaCqPgZ8bUbxKcCmdngTcOpStkmSpLmYrEqStHxNVNU2gPbvYUNujyRJ37XHv7MqSZKWjyRnA2cDTExMMDU1NdwGtbZv387U1BTr1+4YdlNG3sRK3I4D5PZcmIV8l0x/zjUYo7Q9TVYlSVq+7kqyqqq2JVkF3D3bjFW1EdgIsG7dupqcnFyiJs5tamqKyclJztxw2bCbMvLWr93BBdf7r+GguD0XZsvpk/POM/0512CM0vb0MmBJkpavS4Ez2uEzgEuG2BZJknZisipJ0jKQ5J3AlcDjk2xNchZwPvDMJLcAz2zHJUnqBK9NkCRpGaiq580y6cQlbYgk9VizgEv416/dsaBL/becf/IgmqQO8cyqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hzvWR2ihVyjL0mSJEnLkcmqpM4bxIGd9Wt3MLn4pkiSJGmJeBmwJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmds2LYDZAkScvTmg2XLbqO9Wt3cOYA6pEkdY9nViVJkiRJneOZVUnLxiDO4kzbcv7JA6tLkiRJu/LMqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNWDLsBkjSK1my4bCD1bDn/5IHUI0mSNG5MViVJkiSNvEEdSB4kD0ovjsnqHtjTD8L6tTs4s4MfIkmSJEnqGu9ZlSRJkiR1jmdWJWmIBnXJ0vq1O5gcSE2SJEndsOTJapKTgD8F9gH+sqrOX+o2SJKkh9g3S9Lycf2XvzGQWxOX4n7cJb0MOMk+wP8Gfho4BnhekmOWsg2SJOkh9s2SpK5a6jOrxwO3VtUXAZJcDJwC3Li3V9zFp4NJ0iB18XvOpyCOhKH1zZIkzWWpk9XVwB0941uBH1viNkiSlsigEugLT9p/IPWoL/tmSVInpaqWbmXJc4FnVdUvt+PPB46vql+dMd/ZwNnt6OOBm5eskXvXIcA9w27EXmR8o834RpvxLdwPVNWhA6pr5I1B3zzu7/2l5LYcLLfn4LgtB6uL27Nv37zUZ1a3Akf2jB8B3DlzpqraCGxcqkYtlSTXVNW6YbdjbzG+0WZ8o834tAgj3Tf73hgct+VguT0Hx205WKO0PZf6d1Y/BRyd5Kgk3wOcBly6xG2QJEkPsW+WJHXSkp5ZraodSV4GfJDm8fhvraoblrINkiTpIfbNkqSuWvLfWa2q9wPvX+r1dkTnLp8aMOMbbcY32oxPe2zE+2bfG4Pjthwst+fguC0Ha2S255I+YEmSJEmSpIVY6ntWJUmSJEmal8nqgCQ5MslHktyU5IYkL2/L35Xkuva1Jcl1Pcucm+TWJDcnedbQGr8AuxtfkjVJ/qVn2l8MNYB5zBHfcUk+2cZwTZLje5YZh/3XN74x2n9PTHJlkuuT/F2SA3uWGYf91ze+Edx/D09ydZJ/auP73bb84CSXJ7ml/XtQzzIjs/+058a971lK497PLbVx71eX0rj34Utt7P5nqCpfA3gBq4AfbYcfCfwzcMyMeS4A/mc7fAzwT8B+wFHAF4B9hh3HAONbA3xu2O1ebHzAh4CfbsufDUyN0/6bI75x2X+fAn6yLX8R8Jox23+zxTdq+y/AAe3wvsBVwAnAHwEb2vINwB+O4v7ztaj3xlj3PV3YluPSz43A9vS9ufvbciz68BHYnp1+b3pmdUCqaltVfbodvh+4CVg9PT1JgF8A3tkWnQJcXFUPVNVtwK3A8XTUHsQ3UuaIr4DpI3mP4qHfHhyX/TdbfCNljvgeD3ysne1y4Ofb4XHZf7PFN1Kqsb0d3bd9Fc1+2tSWbwJObYdHav9pz41737OUxr2fW2rj3q8upXHvw5fauP3PYLK6FyRZAzyJ5uzAtKcCd1XVLe34auCOnulb6emAu2yB8QEcleQzST6a5KlL2cbFmBHfK4A/TnIH8Frg3Ha2cdl/r6B/fDAe++9zwM+0k54LHNkOj8v+my0+GLH9l2Sf9lLOu4HLq+oqYKKqtkHT+QKHtbOP7P7Tnhv3vmcpjXs/t9TGvV9dSuPehy+1cfifwWR1wJIcALwbeEVVfbNn0vPY+chv+ize+Ucz70Z824Dvr6onAf8NeEfvvQZd1Se+lwK/XlVHAr8OvGV61j6Lj+L+my2+cdl/LwLOSXItzaUw/zY9a5/FR3H/zRbfyO2/qnqwqo4DjgCOT/KEOWYfyf2nPTfufc9SGvd+bqmNe7+6lMa9D19q4/I/g8nqACXZl+ZN8faqek9P+Qrg54B39cy+lZ2PaBxBxy8V2Z342ksz7m2Hr6W5n+BxS9vi3TNLfGcA08P/l4cuMxmX/dc3vnHZf1X1+ar6qap6Ms0/tF9oZx+L/TdbfKO4/6ZV1X3AFHAScFeSVQDt37vb2UZu/2nPjXvfs5TGvZ9bauPery6lce/Dl9o4/c9gsjog7X0zbwFuqqrXzZj8DODzVbW1p+xS4LQk+yU5CjgauHppWrv7dje+JIcm2acdfgxNfF9cqvburjniuxP4yXb46cD0pWbjsv/6xjcu+y/JYe3fhwG/DUw/4W4s9t9s8Y3g/js0yaPb4ZW03yk0++mMdrYzgEva4ZHaf9pz4973LKVx7+eW2rj3q0tp3PvwpTZu/zOsGHYDxshTgOcD1+ehn6d5ZVW9HziNGQ9/qKobkmwGbgR2AOdU1YNL2N7dtVvxAf8J+L0kO4AHgZdU1deWqrF7oG98wIuBP22P4P8rcDaMz/5jlvgYn/13dJJz2vH3AH8FY7X/+sbH6O2/VcCmtrN8GLC5qt6X5Epgc5KzgNtp7rEZxf2nPTfufc9SGvd+bqmNe7+6lMa9D19qY/U/Q6q8xFuSJEmS1C1eBixJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVfWV5MIkvz/sdgAk+f4k25Pss4TrXJOkkqxYZD2TSbbOMf0vkvzOYtYxCpL8bJI72v34pGG3R5LGmX24ffgg2YdrmExW1XlVdXtVHVBVD84376A6qKVSVS+pqtfMN1+SLUmesRRt2kteC7ys3Y+fGXZjFqN9fz122O2QpFFgH24fvlhJppL88lKvV91gsiotc0v0T8EPADfsyYJLfDR+r26LUfkHTJI0Gka9D09jr+Uje7t+7X3uPAGQ5ElJPp3k/iTvAh4+Y/pzklyX5L4k/y/Jj7TlL0zydz3z3Zpkc8/4HUmOa4crya8l+WKSe5L88fQXSJKHJfntJF9KcneSi5I8qp2205HW9gjba5J8om3vh5Ic0q7yY+3f+9rLVX58jphnXWePFyW5M8m2JOt7lj0+yTVJvpnkriSvm2f7rm/XsS3JC3vKv3upVpJDkryv3cZfS/KPbRvfBnw/8HdtTL/Zzv8zSW5o559K8sM99f5oks+02+f/JnlXz3omk2xN8ltJvgL8VZKD2nV/NcnX2+EjeuqbSvL77b7fnuTvknxvkre32+BTSdb0iXu/JNuBfYB/SvKFtvyH2zrva2P4mRnb5E1J3p/kW8DT+tQ713tgzm3Tp65Kck6SW4Bb2rIXt+/lryW5NMnhbfn0++uf2u3wi3PN36/+NF7fvh++keSzSZ4wW/skaT6xD7cPH70+/LwknwC+DTwmyU+07fhG+/cn2nnPA54K/Hnb9j9vy/vOP0f9Z7bv3fuT3Jbk9Ln2uTqkqnwt8xfwPcCXgF8H9gX+C/Ad4Pfb6T8K3A38GM0X1hnAFmA/4DHAfTQHPla19Xy5Xe4xwNeBh7XjBXwEOJjmi/ufgV9up70IuLVd5gDgPcDb2mlr2mVXtONTwBeAxwEr2/Hz+807T9wLWec7gf2BtcBXgWe0068Ent8OHwCcMMs6JoEdwO+12/bZNF+cB7XTL+zZzn8A/EU73740X85pp22ZXnc7/jjgW8Az23l/s43le3r258vbaT8H/FvPeqbb9IftPlwJfC/w88AjgEcC/xf42571TbX1/yDwKODGdv89A1gBXAT81RzbuoDHtsP7tnW9sm3r04H7gcf3bJNvAE+heV89vE99c70HZt02c7Ttcpr35cq2PffQvO/3A/4M+Fi/WNrxhczfW/+zgGuBRwMBfhhYNezvAV++fI3mC/tw+/DR7MNvB45t1z9B8157fjv+vHb8e3vm/+We5Q9ewPy99T8K+GZPG1cBxw77s+trYS/PrArgBJovnz+pqu9U1d8An+qZ/mLg/1TVVVX1YFVtAh6g+XL/Is2X1HHATwIfBL6c5Ifa8X+sqn/vqesPq+prVXU78Cc0XzAApwOvq6ovVtV24FzgtMx+ectfVdU/V9W/AJvb9e+uhazzd6vqW1V1PfBXPe39DvDYJIdU1faq+uQc6/kO8Hvttn0/sB14/CzzrQJ+oJ33H6uab9U+fhG4rKour6rv0NxPshL4CZr9uQJ4Q1vPe4CrZyz/78CrquqBqvqXqrq3qt5dVd+uqvuB82j2X6+/qqovVNU3gA8AX6iqD1fVDpqOcaEPXTiB5p+D86vq36rqH4D38dC2Bbikqj5RVf9eVf86Sz2zvQfm2jaz+YP2ffkvNO+Lt1bVp6vqAZr3xY/3O+rcWsj8vfV/h+afiR+i+UfmpqraNkfbJGku9uH24aPYh19YVTe06/8p4JaqeltV7aiqdwKfB/7zLMuevID5e+vfQbPNnpBkZVVtq6o9uqxZS89kVQCH0xxJ7f1S/VLP8A8A69vLPe5Lch9wZLscwEdpjvT9p3Z4iuZL8ifb8V53zFjHdB2Hz1jnl3joaFs/X+kZ/jbNF+fuWsg6Z2vvWTRHRj/fXn7ynDnWc2/7ZTlfe/+Y5mjlh9pLVTYstO3tPxN3AKvpvz/v2HlxvtrbgSR5RJL/015O9U2aS7EenZ3vNbmrZ/hf+owvdB8cDtwx4x+gL7Vtn629/cz2Hphr28ymd30zl98O3DvH8guZ/46e6f8A/Dnwv4G7kmxMcuAcbZOkudiHz75O+/CHdK0Pn7XfnaXOmW2Yb/7efvdbNAcIXgJsS3JZe0BGI8BkVQDbgNVJ0lP2/T3DdwDnVdWje16PaI9kwUMd3VPb4Y8ye0d35Ix13NkO30nTofZO28HOX6YLMdtRzH4Wss6+7a2qW6rqecBhNJfi/E2S/XezrTupqvuran1VPYbm6OB/S3Li9OS52t7uuyOBL9N/fx658+K71Lee5kjxj1XVgTT/tEBzmeqg3QkcmZ0fePD9NG2frX27W/9s22Y2veubufz+NJdYzbb8QubfKZ6qekNVPZnmEqXHAf99jrZJ0lzsw2dfp3344A2qD5+13+1T55zbbyFtqKoPVtUzac5+fx548wLaqA4wWRU0927sAH4tyYokPwcc3zP9zcBLkvxYGvsnOTnJI9vpH6W5gX5lVW0F/hE4ieYf9s/MWNd/T/MggCNp7sd4V1v+TuDXkxyV5ADgfwHvmnE0cyG+SnOpx2MWMO9C1vk77RHLY4EXTrc3yS8lObQ9snhfO++8j+WfS5oHYDy27aC+2dY3XeddM2LaDJyc5MQk+9J0VA8A/49mfz4IvKzdn6ew8/7s55E0R1bvS3Iw8KrFxDKPq2ju1fnNJPsmmaTp2C8eUP1zbZuFeAfwwiTHJdmP5n1xVVVtaafP3Bfzzb+TJP+h/SztS7Md/pVFvnckLWv24fbho96Hvx94XJL/r435F4FjaC4vhl2333zz7yTJRJoHWu1Ps523Y787MkxWRVX9G80N/GfS3KD+izQPKpiefg3NPS9/3k6/tZ13evo/03zw/7Ed/ybwReATtevvql1C83CZ64DLgLe05W8F3kZz6cptNP/A/+oexPJtmns1PtFe7nTCHLMvZJ0fpYn3CuC1VfWhtvwk4IY0T8n7U+C0Oe7LWKijgQ/TbMsrgTdW1VQ77Q+A325j+o2quhn4JZqH+dxD01H85/b+ken9eRZNJ/xLNF/gD8yx7j+huV/mHuCTwN8vMpZZte37GeCn2/W9EXhBVX1+QPXPum0WuPwVwO8A76Y5wv2DwGk9s7wa2NTui19YwPwzHUjzz+PXaS5bupfmfiVJ2m324fbhjHgfXlX3As+hSdrvpXng1HOq6p52lj8F/kuaJx2/YQHzz/Swdt47ga/RXDXwX/e0vVpa008pk/a6JAUcXVW3Drsty02Sq4C/qKq/GnZbJEmjxz58eOzDtZx5ZlUaQ0l+Msn3tZfHnAH8CHvxSKskSRoM+3DpISarGmtJ/iLNj0jPfP3FsNu2lz0e+Cea3zpbD/yX8udRJEkjxD7cPlya9zLgJA+nuR9gP5pHgv9NVb2qvYH7XTQ/vLwF+IWq+nq7zLk019o/CPxaVX2wLX8yzY8Fr6S5Ofrl5XXIkiRJkqQZFnJm9QHg6VX1RJofbT6pveF9A3BFVR1Nc+P6BoAkx9A8XORYmhvY35iHfufpTcDZNDehH91OlyRJkiRpJ/Mmq9XY3o7u274KOAXY1JZvAk5th08BLq6qB6rqNpqnsB2fZBVwYFVd2Z5NvahnGUmSJEmSvmvFQmZqz4xeCzwW+N9VdVWSienr56tqW5LD2tlX0zw2e9rWtuw77fDM8jkdcsghdeihh7L//ov6reaR9K1vfcu4lxHjXl6Me3iuvfbae6rq0KE2YsQdcsghtWbNmlmnd2E/D8o4xQLjFc84xQLjFc84xQLGs7cdcsghfPCDH/xgVe1y1e2CktX2d7aOS/Jo4L1JnjDH7OlXxRzlu1aQnE1zuTATExP88R//MQcccMBCmjpWtm/fbtzLiHEvL8Y9PE972tO+NNQGjIE1a9ZwzTXXzDp9amqKycnJpWvQXjROscB4xTNOscB4xTNOsYDxLIUkh/QrX1CyOq2q7ksyRXOv6V1JVrVnVVcBd7ezbQWO7FnsCJof4d3aDs8s77eejcBGgHXr1tUBBxzQuQ26FLr4RloKxr28GPfyslzjliRJu2/ee1aTHNqeUSXJSuAZwOeBS4Ez2tnOAC5phy8FTkuyX5KjaB6kdHV7yfD9SU5IEuAFPctIkiRJkvRdCzmzugrY1N63+jBgc1W9L8mVwOYkZwG3A88FqKobkmwGbgR2AOe0lxEDvJSHfrrmA+1LkiRJkqSdzJusVtVngSf1Kb8XOHGWZc4DzutTfg0w1/2ukiRJkiQt6HdWJUmSJElaUiarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnzPs7q5L2njUbLvvu8Pq1OzizZ3x3bTn/5EE0SZKkJbNmEf3ezH7TflAaP55ZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x5+ukcbEYh7/38tH/0uSJKkLPLMqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjpnxbAbIEmSJHXFmg2XDayuLeefPLC6pOXIM6uSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeocnwYsSdIykWQLcD/wILCjqtYlORh4F7AG2AL8QlV9fVhtlCRpmmdWJUlaXp5WVcdV1bp2fANwRVUdDVzRjkuSNHQmq5IkLW+nAJva4U3AqcNriiRJDzFZlSRp+SjgQ0muTXJ2WzZRVdsA2r+HDa11kiT18J5VSZKWj6dU1Z1JDgMuT/L5hS7YJrdnA0xMTDA1NTXrvNu3b59z+igZp1ige/GsX7tjj5edWLnz8oOKazFtmml32tS1fbMY4xQLGM8wmaxKkrRMVNWd7d+7k7wXOB64K8mqqtqWZBVw9yzLbgQ2Aqxbt64mJydnXc/U1BRzTR8l4xQLdC+eMzdctsfLrl+7gwuuf+hf2S2nTw6gRYtr00y706au7ZvFGKdYwHiGad7LgJMcmeQjSW5KckOSl7flr07y5STXta9n9yxzbpJbk9yc5Fk95U9Ocn077Q1JsnfCkiRJvZLsn+SR08PATwGfAy4FzmhnOwO4ZDgtlCRpZws5s7oDWF9Vn247uWuTXN5Oe31VvbZ35iTHAKcBxwKHAx9O8riqehB4E80lRJ8E3g+cBHxgMKFIkqQ5TADvbY8TrwDeUVV/n+RTwOYkZwG3A88dYhslSfqueZPV9mEL0w9euD/JTcDqORY5Bbi4qh4AbktyK3B8+9tuB1bVlQBJLqJ54qDJqiRJe1lVfRF4Yp/ye4ETl75FkiTNbbeeBpxkDfAk4Kq26GVJPpvkrUkOastWA3f0LLa1LVvdDs8slyRJkiRpJwt+wFKSA4B3A6+oqm8meRPwGprH4L8GuAB4EdDvPtSao7zfunZ64uAoPbFqkIx7/PU+cXDmUw2HZam3/XLa372MW5IkaW4LSlaT7EuTqL69qt4DUFV39Ux/M/C+dnQrcGTP4kcAd7blR/Qp38XMJw4ecMABI/PEqkEapSd1DdIoxL1mYE8KfOgjOPOphsMyqKcpLtQo7O+9wbglSZLmtpCnAQd4C3BTVb2up3xVz2w/S/NEQWieKnhakv2SHAUcDVzd3vt6f5IT2jpfgE8clCRJkiT1sZDTOE8Bng9cn+S6tuyVwPOSHEdzKe8W4FcAquqGJJuBG2meJHxO+yRggJcCFwIraR6s5MOVJEmSJEm7WMjTgD9O//tN3z/HMucB5/UpvwZ4wu40UJIkSZK0/OzW04AlSZIkSVoKJquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS56wYdgMkSZKkxVqz4bJhN0HSgHlmVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkLRNJ9knymSTva8cPTnJ5klvavwcNu42SJE0zWZUkafl4OXBTz/gG4IqqOhq4oh2XJKkTTFYlSVoGkhwBnAz8ZU/xKcCmdngTcOoSN0uSpFmZrEqStDz8CfCbwL/3lE1U1TaA9u9hQ2iXJEl9rRh2AyRJ0t6V5DnA3VV1bZLJPazjbOBsgImJCaampmadd/v27XNOHyXjFAt0L571a3fs8bITKxe3/FLYnW3dtX2zGOMUCxjPMM2brCY5ErgI+D6ao7Ebq+pPkxwMvAtYA2wBfqGqvt4ucy5wFvAg8GtV9cG2/MnAhcBK4P3Ay6uqBhuSJEma4SnAzyR5NvBw4MAkfw3clWRVVW1Lsgq4e7YKqmojsBFg3bp1NTk5OevKpqammGv6KBmnWKB78Zy54bI9Xnb92h1ccH23z7tsOX1ywfN2bd8sxjjFAsYzTAv5hO8A1lfVp5M8Erg2yeXAmTQPZTg/yQaahzL8VpJjgNOAY4HDgQ8neVxVPQi8ieao7CdpktWTgA8MOihJe27NIv5xmGnL+ScPrC5Je66qzgXOBWjPrP5GVf1Skj8GzgDOb/9eMqw2SpI007z3rFbVtqr6dDt8P81TBFcz+0MZTgEurqoHquo24Fbg+PaI7YFVdWV7NvUifJCDJEnDdD7wzCS3AM9sxyVJ6oTdunYiyRrgScBVzHgoQ5LphzKspjlzOm1rW/addnhmuSRJWiJVNQVMtcP3AicOsz2SJM1mwclqkgOAdwOvqKpvJpl11j5lNUd5v3Xt9BCHUboJeJCMu7v2xgMdRuFBEbtrIftxFPb33mDckkbZIG8ZkaTZLChZTbIvTaL69qp6T1s820MZtgJH9ix+BHBnW35En/JdzHyIwwEHHDAyNwEP0ijd/DxIoxD3Yh4IMZtReFDE7lrIgyVGYX/vDcYtSZI0t3nvWU1zCvUtwE1V9bqeSZfSPIwBdn4ow6XAaUn2S3IUcDRwdXvJ8P1JTmjrfAE+yEGSJEmS1MdCTuM8BXg+cH2S69qyV9I8hGFzkrOA24HnAlTVDUk2AzfSPEn4nPZJwAAv5aGfrvkAPglYkiRJktTHvMlqVX2c/vebwiwPZaiq84Dz+pRfAzxhdxooSZIkSVp+xusGOUmSJPW1ZsNlrF+7Y688d0GS9oZ571mVJEmSJGmpmaxKkiRJkjrHZFWSJEmS1Dnes6plwx8wlyRJkkaHZ1YlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5K4bdAEmStPcleTjwMWA/mv7/b6rqVUkOBt4FrAG2AL9QVV8fVjsl7WrNhssGUs+W808eSD3SUvHMqiRJy8MDwNOr6onAccBJSU4ANgBXVNXRwBXtuCRJQ2eyKknSMlCN7e3ovu2rgFOATW35JuDUpW+dJEm7MlmVJGmZSLJPkuuAu4HLq+oqYKKqtgG0fw8bYhMlSfquee9ZTfJW4DnA3VX1hLbs1cCLga+2s72yqt7fTjsXOAt4EPi1qvpgW/5k4EJgJfB+4OVVVYMMRpIkza6qHgSOS/Jo4L1JnrDQZZOcDZwNMDExwdTU1Kzzbt++fc7po2ScYlm/dgcTK5u/42AUYtmd985c77VBxblU7+Vx+tyA8QzTQh6wdCHw58BFM8pfX1Wv7S1IcgxwGnAscDjw4SSPazvHN9F0cp+kSVZPAj6wqNZLkqTdVlX3JZmi6YvvSrKqqrYlWUVz1rXfMhuBjQDr1q2rycnJWeufmppirumjZJxiOXPDZaxfu4MLrh+P52uOQixbTp9c8LxzvdfOHNQDlnajPYsxTp8bMJ5hmvcy4Kr6GPC1BdZ3CnBxVT1QVbcBtwLHt53fgVV1ZXs29SK8J0aSpCWT5ND2jCpJVgLPAD4PXAqc0c52BnDJUBooSdIMi7ln9WVJPpvkrUkOastWA3f0zLO1LVvdDs8slyRJS2MV8JEknwU+RXPP6vuA84FnJrkFeGY7LknS0O3ptRNvAl5D8xTB1wAXAC8C0mfemqO8r5n3xYzSddWDZNyD1fX7Wkbh3pvdtZD96Pt8eVmucXdBVX0WeFKf8nuBE5e+RZIkzW2PktWqumt6OMmbgfe1o1uBI3tmPQK4sy0/ok/5bPXvdF/MAQccMDLXVQ/SKF1PPkh7K+5B3e+xt4zCvTe7ayH3xvg+X16Wa9ySlqc1u/G/x/q1Ozr/v4q01PboMuD2HtRpPwt8rh2+FDgtyX5JjgKOBq5uH4V/f5ITkgR4Ad4TI0mSJEmaxUJ+uuadwCRwSJKtwKuAySTH0VzKuwX4FYCquiHJZuBGYAdwTvskYICX8tBP13wAnwQsSZIkSZrFvMlqVT2vT/Fb5pj/POC8PuXXAAv+PTdJkiRJ0vK1mKcBS5IkSZK0V4zX01wkSZIk9bU7D3yaz5bzTx5YXdJsTFYl7TUL6RQX8vRDO0RJkqTlx8uAJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNWDLsBkiRJkpanNRsuG1hdW84/eWB1qRs8sypJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5PmBJkiSpwwb5ABppUOZ6X65fu4Mzfd9qADyzKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZKkZSDJkUk+kuSmJDckeXlbfnCSy5Pc0v49aNhtlSQJTFYlSVoudgDrq+qHgROAc5IcA2wArqiqo4Er2nFJkoZu3mQ1yVuT3J3kcz1lsx6FTXJukluT3JzkWT3lT05yfTvtDUky+HAkSVI/VbWtqj7dDt8P3ASsBk4BNrWzbQJOHUoDJUmaYSFnVi8ETppR1vcobHuE9jTg2HaZNybZp13mTcDZwNHta2adkiRpCSRZAzwJuAqYqKpt0CS0wGFDbJokSd+1Yr4ZqupjbafW6xRgsh3eBEwBv9WWX1xVDwC3JbkVOD7JFuDAqroSIMlFNEduP7DoCCRJ0oIlOQB4N/CKqvrmQi90SnI2zUFnJiYmmJqamnXe7du3zzl9lHQhlvVrdwysromVg61vmMYpFhiveIYVy976rHbhe2CQRimeeZPVWex0FDbJ9FHY1cAne+bb2pZ9px2eWS5JkpZIkn1pEtW3V9V72uK7kqxq+/NVwN39lq2qjcBGgHXr1tXk5OSs65mammKu6aOkC7GcueGygdW1fu0OLrh+T//965ZxigXGK55hxbLl9Mm9Um8XvgcGaZTiGfS7qN/h2ZqjvH8lM47ejlL2P0jGPVhdP1o5TkdUd8dC4h7Hz4Gfby219lkRbwFuqqrX9Uy6FDgDOL/9e8kQmidJ0i72NFmd7SjsVuDInvmOAO5sy4/oU97XzKO3BxxwwMhk/4M0Skc9Bmlm3GsGdkS520crx+mI6u5YSNx760jpMPn51hA8BXg+cH2S69qyV9IkqZuTnAXcDjx3OM2TJGlne/qf8WxHYS8F3pHkdcDhNA9SurqqHkxyf5ITaB7m8ALgzxbVckmStGBV9XH6X+kEcOJStkWSpIWYN1lN8k6ahykdkmQr8CpmOQpbVTck2QzcSPN7budU1YNtVS+lebLwSpoHK/lwJUmSJElSXwt5GvDzZpnU9yhsVZ0HnNen/BrgCbvVOkmSJEnSsrSQ31mVJEmSJGlJmaxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5K4bdAEmaz5oNlw2kni3nnzyQeiRJkrT3eWZVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ2zYtgN0Hhas+GyPV52/dodnLmI5SVJkiSNPs+sSpIkSZI6x2RVkiRJktQ5XgYsSZIkSa2Zt7Mt5ha1LeefPIgmLVueWZUkaRlI8tYkdyf5XE/ZwUkuT3JL+/egYbZRkqRei0pWk2xJcn2S65Jc05bN2vElOTfJrUluTvKsxTZekiQt2IXASTPKNgBXVNXRwBXtuCRJnTCIM6tPq6rjqmpdO96340tyDHAacCxNZ/nGJPsMYP2SJGkeVfUx4Gszik8BNrXDm4BTl7JNkiTNZW9cBjxbx3cKcHFVPVBVtwG3AsfvhfVLkqSFmaiqbQDt38OG3B5Jkr5rsQ9YKuBDSQr4P1W1kRkdX5Lpjm818MmeZbe2ZZK0JBbz+78z+cAELTdJzgbOBpiYmGBqamrWebdv3z7n9C67/svf2Gl8YiX82dsv2e161q5+1KCaxPq1OwZW18TKwdY3TOMUC4xXPMOKZVDfOzPbvph4uvhdOErf0YtNVp9SVXe2CenlST4/x7zpU1Z9Z5zRIY7SBh2kUY57MV9Q4/RlvTuMe7Qs9rM5yp/vxViucXfYXUlWtQeXVwF3zzZje0B6I8C6detqcnJy1kqnpqaYa3qXzXzi5/q1O7jg+t3/d2nL6ZMDatGubVqMPY2ni8YpFhiveIYVy6A+d4P6HoDBfhcMyih9Ry/qXVRVd7Z/707yXprLemfr+LYCR/YsfgRw5yz17tQhHnDAASOzQQdplN5IMy2mYx2nL+vdYdyjZbGdzyh/vhdjucbdYZcCZwDnt393/xSiJEl7yR7fs5pk/ySPnB4Gfgr4HA91fLBzx3cpcFqS/ZIcBRwNXL2n65ckSQuX5J3AlcDjk2xNchZNkvrMJLcAz2zHJUnqhMWczpgA3ptkup53VNXfJ/kUsLntBG8HngtQVTck2QzcCOwAzqmqBxfVekmStCBV9bxZJp24pA2RJGmB9jhZraovAk/sU34vs3R8VXUecN6erlOSJEmStDzsjZ+ukSRJkiRpUUxWJUmSJEmdY7IqSZIkSeock1VJkiRJUueM3o8bSpIkddyaRfzeuKQ94+du/HhmVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWO96xKkqSR571qkjR+PLMqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkrht0AdcuaDZcNuwmSJEmS5JlVSZIkSVL3eGZVkiRJkvaCLl61eOFJ+w+7CQvmmVVJkiRJUueYrEqSJEmSOsfLgCVpDyz2sp71a3dw5obL2HL+yQNqkSRJ0njxzKokSZIkqXM8sypJkoaiiw8ekSR1h2dWJUmSJEmdY7IqSZIkSeock1VJkiRJUud4z6okSZIkLRPXf/kbnDmAZwYsxS8aeGZVkiRJktQ5nlkdAz5NUZIkSdK48cyqJEmSJKlzTFYlSZIkSZ3jZcCSNESDvIx/KR50IEmStFSW/MxqkpOS3Jzk1iQblnr9kiRpZ/bNkqQuWtJkNck+wP8Gfho4BnhekmOWsg2SJOkh9s2SpK5a6suAjwduraovAiS5GDgFuHGJ29EJ813+t37tjoH8BpIkSXOwb5YkddJSJ6urgTt6xrcCP7bEbZCksTSo+1+993XZsW+WJHVSqmrpVpY8F3hWVf1yO/584Piq+tUZ850NnN2OPh64F7hnyRraHYdg3MuJcS8vxj08P1BVhw65DZ2xiL755jmq7cJ+HpRxigXGK55xigXGK55xigWMZ2+7B6CqTpo5YanPrG4FjuwZPwK4c+ZMVbUR2Dg9nuSaqlq395vXLca9vBj38mLc6pA96pvnMk77eZxigfGKZ5xigfGKZ5xiAeMZpqV+GvCngKOTHJXke4DTgEuXuA2SJOkh9s2SpE5a0jOrVbUjycuADwL7AG+tqhuWsg2SJOkh9s2SpK5a6suAqar3A+/fzcUWdNnRGDLu5cW4lxfjVmfsYd88l3Haz+MUC4xXPOMUC4xXPOMUCxjP0CzpA5YkSZIkSVqIpb5nVZIkSZKkeQ09WU1yZJKPJLkpyQ1JXt4z7VeT3NyW/1FP+blJbm2nPWs4LV+c3Y07yZok/5Lkuvb1F8Nr/Z6bLe4k7+qJbUuS63qWGdv9PVvcy2B/H5fkk21s1yQ5vmeZcd7ffeMel/0Nc8b+xCRXJrk+yd8lObBnmZHf5+Nqb31nJzk4yeVJbmn/HtTFeJI8M8m17fv22iRPn6XeVyf5ck8dz+5gLAv6nhmhfXN6T/l1Sf49yXF96u3SvllU39fBfTNbnzaKn5tF9c8jtG86+7mZVVUN9QWsAn60HX4k8M/AMcDTgA8D+7XTDmv/HgP8E7AfcBTwBWCfYcexBHGvAT437HbvrbhnzHMB8D+Xw/6eI+6x3t/Ah4CfbsufDUwth/09R9xjsb/nif1TwE+25S8CXjNO+3xcX3vrOxv4I2BDO7wB+MOOxvMk4PB2+AnAl2ep99XAb3R83yzoe2ZU9s2M8rXAF7u+b+boA0byczNHPCP3uZkjlpH83MwWz4xlO/W5me019DOrVbWtqj7dDt8P3ASsBl4KnF9VD7TT7m4XOQW4uKoeqKrbgFuB43etudv2IO6xMEfcACQJ8AvAO9uicd/fQN+4x8IccRcwfWbtUTz0m47jvr9ni3tszBH744GPtbNdDvx8OzwW+3xc7cXv7FOATe3wJuDUvRLADLsbT1V9pqqmP6c3AA9Pst9StHU+e7FfGYl9M8PzZikfir3Y93Vt3/SNZ0Q/N4vtn0di38zQqc/NbIaerPZKsobmaMxVwOOApya5KslHk/yHdrbVwB09i22l58tsFC0wboCjknymLX/qMNo6SDPinvZU4K6quqUdH/f9PW1m3DDe+/sVwB8nuQN4LXBuO9u47+9X0D9uGLP9DbvE/jngZ9pJzwWObIfHbp+PqwF/Z09U1TZo/tkCDht4g+exG9/F034e+Mz0weQ+Xpbks0neulSXAE4bcL8yivvmF5n7n+6u7JtXsLi+r2v75hXM3qdNG5XPzStYXP88ivums5+bXp1JVpMcALwbeEVVfZPmZ3UOAk4A/juwuT2qlj6Lj+wjjXcj7m3A91fVk4D/BrwjPfd8jZo+cU+beZRn3Pf3tJlxj/v+finw61V1JPDrwFumZ+2z+Djt79niHqv9DX1jfxFwTpJraS5V+rfpWfssPrL7fFyN23f2bsQzPf+xwB8CvzJLlW8CfhA4jubzfMEg2zuXcetX9mDf/Bjw7ar63CxVdmnfjHTftxvxTM8/Sp+bke6f92DfdPZzs4vFXEM8qBewL82Pkf+3nrK/ByZ7xr8AHEpzZODcnvIPAj8+7Bj2dtx9lp0C1g07hkHF3ZavAO4CjugpG+v9PVvc476/gW/w0E9nBfjmctjfs8U9Tvt7tthnTH8ccPW47fNxfe2N72zgZmBVO7wKuLmL8bTlR9DcB/aUBda/hiW6B31v9CujtG/aaa8HXjkK+2axfV/X9s1cfdqofW7mimXGsiPxuZkvnq5+bvq9hn5mtT1r+Bbgpqp6Xc+kvwWe3s7zOOB7gHuAS4HTkuyX5CjgaODqJW30AOxu3EkOTbJPW/4Ymri/uKSNHoA54gZ4BvD5qtraUzbu+xv6xL0M9vedwE+2w08Hpi/rGvf93TfucdnfMHvsSQ5r/z4M+G1g+omKY7HPx9Ve/M6+FDijHT4DuGSwLe9vD76LHw1cRpNIfGKOelf1jP4szWXve9Ve7FdGYt+0yzyM5raCi+eot0v7ZrF9X9f2zWx92qMZvc/NYvvnkdg37TKd/NzMalhZck+2/h9pLnX4LHBd+3o2TZL21zQb59PA03uW+R80Zxxvpn3S1ai9djdummv+b6B5Wtyngf887BgGGXc77ULgJX2WGdv9PVvc476/2/Jr2/iuAp68HPb3bHGPy/6eJ/aX0xxl/2fgfNojvuOyz8f1NcjvbOAvac9IAN8LXEHzD9QVwMFdjIfmwMq3eua9joee0t8bz9uA69t6L6U9w9KxWGb9nhnFfdOWTwKf7FPeyX3DHvR9Xd43s8Uzip+bOWIZyc/NPO+1Tn5uZntNnx6WJEmSJKkzhn4ZsCRJkiRJM5msSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isLkNJtid5zB4uO5Xkl3dzmTVJKsmKdvwDSc7Yk/XvrsXEOoB1vzrJXw+gnguT/P4c04cW41JJ46+SfD3J1cNujyQNg/330rD/Hhz7by3WimE3QEuvqg4Y8vp/em/Um2QK+Ouq+suedQ011qWwkBiTTNJsmyP2eoP2jv8IPBM4oqq+NezGLEaSM4Ffrqr/OOy2SBotw+7T7L8Hy/5770uyBrgN2Leqdiz1+rV4nllVp0wfvdVoSbLPXl7FDwBb9qSjW+r31N5en58RSV3kd9NoGvX+2z53/JmsjokkL0zydz3jtybZ3DN+R5Lj2uFK8th2+MIk/zvJZUnuT3JVkh/sWe6ZST6f5BtJ/hzIAtqyT5LXJrknyReBk2dM/+6lSEnOTPKJJK9P8jXg1Un2a5e/PcldSf4iycqe5U9Jcl2Sbyb5QpKTkpwHPBX48/aymj/vE+ujklyU5KtJvpTkt5M8rKcdH2/X+/UktyWZ9whyksOTXJrka+02f/GMWR6e5F3ttv10kif2LPtbSb7cTrs5yYlzrOqgOfZRb4zPTnJjO9+Xk/xGkv2BDwCHt9tme9vu/ZL8SZI729efJNmvp97fTLKtnfbLfd43b0ry/iTfAp6W5OQkn2n3yx1JXt1T1/SlZC9sp309yUuS/Ickn01y3/Q+67ONzwL+Evjxtu2/25a/uN3mX2v3weEztsk5SW4BbulT53R7zmjfZ/ck+R890+fcNjPq6vce7vteS/LDwF/0xHJfW8d8782Z9T82yUfTfC7vSfKuOd47kjos9t/239h/z9gmC+m/z0pyO/APafrX327fG3e375VHtYt8rP17X9uGH59r/lnqf3iSv05ybxvvp5JMzLHPNUhV5WsMXsBjgPtoDkCsAr4EfLln2teBh7XjBTy2Hb4Q+BpwPM1l4W8HLm6nHQJ8E/gvwL7ArwM7aC5hnKstLwE+DxwJHAx8pF3ninb61HQdwJltnb/arn8l8CfApe2yjwT+DviDdv7jgW/QXFLyMGA18EMz6+1pS2+sFwGXtHWuAf4ZOKunHd8BXgzsA7wUuBPIPLF+FHgj8HDgOOCrwInttFe3dU5vv9+gvRQFeDxwB3B4O+8a4AdnWces+6hPjNuAp7bDBwE/2g5PAltn1Pt7wCeBw4BDgf8HvKaddhLwFeBY4BHA29j1ffMN4Cntfnh4u4617fiPAHcBp/bEVzSJ2sOBnwL+Ffjbdv2rgbuBn5xlG5wJfLxn/OnAPcCPAvsBfwZ8bMY2uZzmPbSyT33T7XkzzXvuicADwA/Pt21madvM9/B877WPz6hjvvln1v9O4H/0bPv/OOzvIF++fO3ZC/vvnertaYv9d9l/96lvuj0XAfvTvO9eBNxK83k5AHgP8LYZ86/oqWMh8/fW/ys07+VH0LzHngwcOOzvjuXyGnoDfA1wZzZfnj8KnAZsBK4Gfgh4IXBpz3wzv7T+smfas4HPt8MvAD7ZMy3AVubv7P4BeEnP+E8xd2d3+4x1fIueL37gx4Hb2uH/A7x+lvVOzWzbdKztl8sDwDE9034FmOppx6090x7RLvt9c8R5JPAg8Miesj8ALmyHXz1j+z2MtjNq23Q38Aya+yjm2p6z7qM++/P2Nq4DZ9Qxya6d3ReAZ/eMP4vmUh2At9L+g9GOP7bP++aiedr9J9P7ioe+/Ff3TL8X+MWe8XcDr5ilrjPZubN7C/BHPeMH0PxjsaZnmzx9jrZNt+eInrKrgdPm2zaztK33PbyQ99rHd3P+22es8yKaz/gRs8Xoy5ev0Xlh/z01s23Yf0/PM4n9d2990+15TE/ZFcB/7Rl/fFvnCvonqwuZv7f+F9EcEPiRxXzOfe3Zy8uAx8tHab7U/lM7PAX8ZPv66BzLfaVn+Ns0XxwAh9N0oABU84m9g/nttBzNUeK59M57KE1Hc217qcV9wN+35dB0MF9YQBtmOgT4nhlt+RLNEcFp390OVfXtdnCuhx8cDnytqu6fo87e7ffvNP8sHF5VtwKvoOkQ705yce9lMH3Mto9m+nmazvBLaS4T/fF52j9zexzeM613v/Tb7zuVJfmxJB9pL9P6Bs0R+kNmLHNXz/C/9Blf6AM1dmp7VW2n6Tz7bvs5zPXen23b9NO7roW813otZP6ZsfwmzT+GVye5IcmL5mibpO6z/56d/Xf/9i/3/rt3nn7bYwUw26W6C5m/t/63AR8ELm4vrf6jJPsuoI0aAJPV8TLd2T21Hf4oC+vsZrONpnMBmseP944vdDng++eZv3qG76H50ju2qh7dvh5VDz0x7w7gB3epYdd6ZrqH5qjZD8xo15fnadtc7gQOTvLIOers3X4PA45ol6Oq3lHNE2F/oG37Hy6iLbR1fqqqTqG5NOdvgen7nvptmzvZdXvc2Q5va9u6Sxy9q5sx/g6ay7+OrKpH0VwyNO89Untop7a39/V8Lztv+7neD7tVPztvm35mvofneq/NbNdC3ps7LVNVX6mqF1fV4TRH4t84fT+SpJFk/z07++/+7V/u/XfvPP22xw6ahHqh2296/l3qr6rvVNXvVtUxwE8Az6G5ekFLwGR1vHwUeBrNNf5bgX+kuXfhe4HP7EF9lwHHJvm5NE9D+zXg+xaw3Gbg15IckeQgYMNCV9gevXwz8PokhwEkWZ3kWe0sbwFemOTE9gb51Ul+qJ12F839B/3qfbBt13lJHpnkB4D/Buzx76hV1R00l4X8QXvz/Y8AZ9HckzLtyT3b7xU0lzJ9Msnjkzw9zQMR/pWmg39wT9sCkOR7kpye5FFV9R2a+5Wm67wL+N6eBw5Ac9/jbyc5NMkhwP/koe2xmWY7/3CSR7TT5vNImiPV/5rkeOD/W0w883hH277j2m34v4CrqmrLgOqfa9vMaQHvtbuAI5J8zwLn30WS5yaZ/mfk6zSd6qLeP5KGyv7b/tv+e8+9E/j1JEclOaCt813V/FTNV4F/Z+f311zz7yLJ05KsTfPk5G/SHDyxz10iJqtjpKr+GdhO08lRVd8Evgh8ov2y39367gGeC5xPc4nG0cAnFrDom2kul/gn4NM0N67vjt+iufH9k0m+CXyY5n4Cqupqmnt4Xk/zgICP8tDRsT8F/kuaJ9W9oU+9v0pzP80XgY/TfGG+dTfbNtPzaO5vuBN4L/Cqqrq8Z/olwC/SJBTPB36u7Yj2o9mu99BcInQY8MpFtoV2HVva7fYS4JcAqurzNF/OX2wvzzoc+H3gGuCzwPU0++r32/k/ALyB5uEatwJXtvU/MMe6/yvwe0nup+kcN88x76JU1RXA79DcJ7ON5mj9aQNcxazbZoHmeq/9A3AD8JUk9yxg/n7+A3BVku00R8NfXlW37Ub7JHWI/bf9N/bfi/FWmkt1P0bzIKx/pXnPTF8Wfh7wiXb7nTDX/LP4PuBvaBLVm2jeu3t8sES7J81tDJI0uzQ/ufI5YL/ZjjxKkqRusf/WqPPMqqS+kvxse2nSQTT34/ydHZ0kSd1m/61xYrKqPZLmh76393n9xbDbNmizxLk9yVOH3ba97Fdo7vX4As29GS8dbnMkSYtl/23/LY0SLwOWJEmSJHWOZ1YlSZIkSZ1jsipJkiRJ6pwVw27AfA455JBas2bNouv51re+xf7777/4BnXAOMUC4xWPsXTTOMUC4xXPMGK59tpr76mqQ5d0pWNmvr55nN6jMH7xgDGNgnGLB4xpVHSpb+58srpmzRquueaaRdczNTXF5OTk4hvUAeMUC4xXPMbSTeMUC4xXPMOIJcmXlnSFY2i+vnmc3qMwfvGAMY2CcYsHjGlUdKlv9jJgSZIkSVLnmKxKkiRJkjpn3mQ1ycOTXJ3kn5LckOR32/JXJ/lykuva17N7ljk3ya1Jbk7yrJ7yJye5vp32hiTZO2FJkiRJkkbZQu5ZfQB4elVtT7Iv8PEkH2invb6qXts7c5JjgNOAY4HDgQ8neVxVPQi8CTgb+CTwfuAk4ANIkiRJktRj3jOr1djeju7bvmqORU4BLq6qB6rqNuBW4Pgkq4ADq+rKqirgIuDURbVekiRJkjSWFnTPapJ9klwH3A1cXlVXtZNeluSzSd6a5KC2bDVwR8/iW9uy1e3wzHJJkiRJknayoJ+uaS/hPS7Jo4H3JnkCzSW9r6E5y/oa4ALgRUC/+1BrjvJdJDmb5nJhJiYmmJqaWkgz57R9+/aB1NMF4xQLjFc8xtJN4xQLjFc84xSLJEkarN36ndWqui/JFHBS772qSd4MvK8d3Qoc2bPYEcCdbfkRfcr7rWcjsBFg3bp1NYjf+Rmn30Aap1hgvOIxlm4ap1hgvOIZp1gkSdJgLeRpwIe2Z1RJshJ4BvD59h7UaT8LfK4dvhQ4Lcl+SY4CjgaurqptwP1JTmifAvwC4JLBhSJJkiRJGhcLObO6CtiUZB+a5HZzVb0vyduSHEdzKe8W4FcAquqGJJuBG4EdwDntZcQALwUuBFbSPAXYJwFLkiRJknYxb7JaVZ8FntSn/PlzLHMecF6f8muAJ+xmGyVJkiRJy8xu3bMqSWqs2XDZQOrZcv7JA6lHkiRpIeb7H2b92h2cuYD/c5bif5gF/XSNJEmSJElLyWRVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkjRGkrw1yd1JPtdT9sdJPp/ks0nem+TRPdPOTXJrkpuTPGsojZYkqQ+TVUmSxsuFwEkzyi4HnlBVPwL8M3AuQJJjgNOAY9tl3phkn6VrqiRJszNZlSRpjFTVx4CvzSj7UFXtaEc/CRzRDp8CXFxVD1TVbcCtwPFL1lhJkuZgsipJ0vLyIuAD7fBq4I6eaVvbMkmShm7FsBsgSZKWRpL/AewA3j5d1Ge2mmXZs4GzASYmJpiampp1Pdu3b59z+qgZt3jAmEbBuMUDxtQV69fumHP6xMr55wGWJG6TVUmSloEkZwDPAU6squmEdCtwZM9sRwB39lu+qjYCGwHWrVtXk5OTs65ramqKuaaPmnGLB4xpFIxbPGBMXXHmhsvmnL5+7Q4uuH7+NHHL6ZMDatHsvAxYkqQxl+Qk4LeAn6mqb/dMuhQ4Lcl+SY4CjgauHkYbJUmayTOrkiSNkSTvBCaBQ5JsBV5F8/Tf/YDLkwB8sqpeUlU3JNkM3EhzefA5VfXgcFouSdLOTFYlSRojVfW8PsVvmWP+84Dz9l6LJEnaM14GLEmSJEnqHM+sSpIkSVKHrZnnoUjjyjOrkiRJkqTOMVmVJEmSJHXOvMlqkocnuTrJPyW5IcnvtuUHJ7k8yS3t34N6ljk3ya1Jbk7yrJ7yJye5vp32hrSPJJQkSZIkqddCzqw+ADy9qp4IHAeclOQEYANwRVUdDVzRjpPkGOA04FjgJOCNSfZp63oTcDbN77gd3U6XJEmSJGkn8yar1djeju7bvgo4BdjUlm8CTm2HTwEurqoHquo24Fbg+CSrgAOr6sqqKuCinmUkSZIkSfquBd2zmmSfJNcBdwOXV9VVwERVbQNo/x7Wzr4auKNn8a1t2ep2eGa5JEmSJEk7WdBP11TVg8BxSR4NvDfJE+aYvd99qDVH+a4VJGfTXC7MxMQEU1NTC2nmnLZv3z6QerpgnGKB8YrHWLppb8Syfu2OgdSzJ+1y30iSpOVgt35ntaruSzJFc6/pXUlWVdW29hLfu9vZtgJH9ix2BHBnW35En/J+69kIbARYt25dTU5O7k4z+5qammIQ9XTBOMUC4xWPsXTT3ojlzAH93tmW0yd3exn3jSRJWg4W8jTgQ9szqiRZCTwD+DxwKXBGO9sZwCXt8KXAaUn2S3IUzYOUrm4vFb4/yQntU4Bf0LOMJEmSJEnftZAzq6uATe0TfR8GbK6q9yW5Etic5CzgduC5AFV1Q5LNwI3ADuCc9jJigJcCFwIrgQ+0L0mSJEmSdjJvslpVnwWe1Kf8XuDEWZY5DzivT/k1wFz3u0qSJEmStLCnAUuSJEmStJRMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEmSJKlzTFYlSZIkSZ1jsipJkiRJ6hyTVUmSJElS55isSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSNEaSvDXJ3Uk+11N2cJLLk9zS/j2oZ9q5SW5NcnOSZw2n1ZIk7cpkVZKk8XIhcNKMsg3AFVV1NHBFO06SY4DTgGPbZd6YZJ+la6okSbMzWZUkaYxU1ceAr80oPgXY1A5vAk7tKb+4qh6oqtuAW4Hjl6KdkiTNx2RVkqTxN1FV2wDav4e15auBO3rm29qWSZI0dCuG3QBJkjQ06VNWfWdMzgbOBpiYmGBqamrWSrdv3z7n9FEzbvGAMY2CcYsHjGkx1q/dsdfXMW1i5cLWtxRxm6xKkjT+7kqyqqq2JVkF3N2WbwWO7JnvCODOfhVU1UZgI8C6detqcnJy1pVNTU0x1/RRM27xgDGNgnGLB4xpMc7ccNleX8e09Wt3cMH186eJW06f3Ott8TJgSZLG36XAGe3wGcAlPeWnJdkvyVHA0cDVQ2ifJEm78MyqJEljJMk7gUngkCRbgVcB5wObk5wF3A48F6CqbkiyGbgR2AGcU1UPDqXhkiTNYLIqSdIYqarnzTLpxFnmPw84b++1SJKkPeNlwJIkSZKkzjFZlSRJkiR1zrzJapIjk3wkyU1Jbkjy8rb81Um+nOS69vXsnmXOTXJrkpuTPKun/MlJrm+nvSFJv0fmS5IkSZKWuYXcs7oDWF9Vn07ySODaJJe3015fVa/tnTnJMcBpwLHA4cCHkzyufWDDm2h+o+2TwPuBk4APDCYUSZIkSdK4mPfMalVtq6pPt8P3AzcBq+dY5BTg4qp6oKpuA24Fjm9/1+3Aqrqyqgq4CDh1sQFIkiRJksbPbt2zmmQN8CTgqrboZUk+m+StSQ5qy1YDd/QstrUtW90OzyyXJEmSJGknC/7pmiQHAO8GXlFV30zyJuA1QLV/LwBeBPS7D7XmKO+3rrNpLhdmYmKCqamphTZzVtu3bx9IPV0wTrHAeMVjLN20N2JZv3bHQOrZk3a5byRJ0nKwoGQ1yb40ierbq+o9AFV1V8/0NwPva0e3Akf2LH4EcGdbfkSf8l1U1UZgI8C6detqcnJyIc2c09TUFIOopwvGKRYYr3iMpZv2RixnbrhsIPVsOX1yt5dx30iSpOVgIU8DDvAW4Kaqel1P+aqe2X4W+Fw7fClwWpL9khwFHA1cXVXbgPuTnNDW+QLgkgHFIUmSJEkaIws5s/oU4PnA9Umua8teCTwvyXE0l/JuAX4FoKpuSLIZuJHmScLntE8CBngpcCGwkuYpwD4JWJIkSZK0i3mT1ar6OP3vN33/HMucB5zXp/wa4Am700BJkiRJ0vKzW08DliRJkiRpKZisSpIkSZI6x2RVkiRJktQ5JquSJEmSpM4xWZUkSZIkdY7JqiRJkiSpc0xWJUmSJEmdY7IqSZIkSeock1VJkiRJUueYrEqSJEmSOsdkVZIkSZLUOSarkiRJkqTOMVmVJEmSJHWOyaokSZIkqXNMViVJkiRJnWOyKkmSJEnqHJNVSZIkSVLnmKxKkiRJkjrHZFWSJEmS1Dkmq5IkSZKkzjFZlSRJkiR1jsmqJEnLRJJfT3JDks8leWeShyc5OMnlSW5p/x407HZKkgQmq5IkLQtJVgO/BqyrqicA+wCnARuAK6rqaOCKdlySpKEzWZUkaflYAaxMsgJ4BHAncAqwqZ2+CTh1OE2TJGlnJquSJC0DVfVl4LXA7cA24BtV9SFgoqq2tfNsAw4bXislSXrIimE3QJKWszUbLtvtZdav3cGZfZbbcv7Jg2iSxlR7L+opwFHAfcD/TfJLu7H82cDZABMTE0xNTc067/bt2+ecPmrGLR4wplEwbvGAMS3G+rU79vo6pk2sXNj6liJuk1VJkpaHZwC3VdVXAZK8B/gJ4K4kq6pqW5JVwN39Fq6qjcBGgHXr1tXk5OSsK5qammKu6aNm3OIBYxoF4xYPGNNi9DtIvbesX7uDC66fP03ccvrkXm+LlwFLkrQ83A6ckOQRSQKcCNwEXAqc0c5zBnDJkNonSdJOPLMqSdIyUFVXJfkb4NPADuAzNGdKDwA2JzmLJqF97vBaKUnSQ0xWJUlaJqrqVcCrZhQ/QHOWVZKkTpn3MuAkRyb5SJKb2h8Sf3lbPuuPiCc5N8mtSW5O8qye8icnub6d9ob2MiRJkiRJknaykHtWdwDrq+qHgROAc5Icwyw/It5OOw04FjgJeGOSfdq63kTzJMGj29dJA4xFkiRJkjQm5k1Wq2pb1f/f3v3HW1bXhf5/vWMQUTQh4DTMUINJJDCFOpdLUd1TaCL0beh+L4ZfUiapMb9o+r3T1wbrUfaD7526YVk38U7JBUpFbmpMghmRR6MUREIGGIlRRhkYmdQUjn0veuh9/1ifA3vO7H32nnP27P3Ze7+ej8d+nLU/a+213p/PWmd/9nuvtT8r7yjTj9EMxrCKzjcRXw9cm5mPZ+YDwE7g9DLC4LMz8+OZmcA1eONxSZIkSVIbBzQacESsAV4A3Ernm4ivAh5sednuUraqTC8slyRJkiRpHz0PsBQRRwDvA96YmY8u8nPTdjNykfJ22+r5xuO9GqebEI9TXWC86mNd6nQw6jLIm3Mv1Olm3aO4v8bpOJMkSf3VU7IaEYfSJKrvysz3l+JONxHfDRzf8vLVwMOlfHWb8v0cyI3HezVONyEep7rAeNXHutTpYNRlkDfnXqjTzboHcXPufhun40ySJPVXL6MBB/BOYEdmvrVlVqebiG8DLoiIwyLiBJqBlG4rlwo/FhFnlHW+Cm88LkmSJElqo5czq2cCrwS2R8SdpezNwBba3EQ8M++JiOuAe2lGEr4kM58or3stcBVwOPCh8pAkSZIkaR9dk9XMvIX2vzeFDjcRz8zLgMvalN8OnHogAUqSJEmSJs8BjQYsSZIkSdIgmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRNiIh4TkT8eUR8JiJ2RMT3R8RREXFTRNxf/h457DglSQJYMewAJKmbNZtvWNbrN62dY8PmG9i15dw+RSSNrLcBf5WZ/ykingY8A3gzcHNmbomIzcBm4JeGGaQkSWCyKknSRIiIZwM/DGwAyMxvAN+IiPXAdFnsamCGASWry/0iqpVfRknS+PEyYEmSJsNzgX8G/kdE/GNE/ElEPBOYysw9AOXvscMMUpKkeZ5ZlSRpMqwAXgi8PjNvjYi30Vzy25OI2AhsBJiammJmZqbjsrOzs4vOn7dp7Vyvm++ql+0tVa/1GSXWqX7jVh+wTsvRz/fLbqYO7217g6i3yaokSZNhN7A7M28tz/+cJll9JCJWZuaeiFgJ7G334szcCmwFWLduXU5PT3fc0MzMDIvNn7ehn5cBX9h9e0vVa31GiXWq37jVB6zTcvTz/bKbTWvnuHx79zTxYL7vzut6GXBEXBkReyPi7payt0TEQxFxZ3mc0zLv0ojYGRH3RcRLW8pfFBHby7w/iIjof3UkSVI7mflF4MGIOKkUnQXcC2wDLiplFwHXDyE8SZL208uZ1auA/wZcs6D89zLzd1sLIuJk4ALgFOA44G8i4rsz8wngCprLhz4B3AicDXxoWdFLkqQD8XrgXWUk4M8BP0PzxfV1EXEx8AXg/CHGJ0nSk7omq5n5sYhY0+P61gPXZubjwAMRsRM4PSJ2Ac/OzI8DRMQ1wHmYrEqSNDCZeSewrs2sswYciiRJXS1nNODXRcRd5TLh+RuIrwIebFlmdylbVaYXlkuSJEmStJ+lDrB0BfCbQJa/lwOvBtr9DjUXKW/rQEYc7NU4jT42TnWB8aqPdTk4ljsC3vyodv2szyBH5Vuo0yh9teyvA1HTcSZJkuqypGQ1Mx+Zn46IPwY+WJ7uBo5vWXQ18HApX92mvNP6ex5xsFfjNPrYONUFxqs+1uXgWO4IePOj2vVz1LpBjsq3UKdR+gYxKl+/1XScSZKkuizpMuAytP28nwTmRwreBlwQEYdFxAnAicBt5Sbjj0XEGWUU4FfhaIOSJEmSpA66nlmNiPcA08DREbEb+DVgOiJOo7mUdxfwGoDMvCcirqMZCn8OuKSMBAzwWpqRhQ+nGVjJwZUkSZIkSW31MhrwK9oUv3OR5S8DLmtTfjtw6gFFJ0mSJEmaSMsZDViSJEmSpIPCZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVJ0Vww5A0vhas/mGYYcgSZKkEeWZVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXH36xKkiRJ0kHg+B3L45lVSZIkSVJ1TFYlSZIkSdXxMmBJ0kHT7fKnTWvn2NDDJVK7tpzbr5AkSdKI8MyqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqzophByBJg7Jm8w3DDkEaqog4BLgdeCgzfzwijgLeC6wBdgEvz8x/GV6EkiQ9xTOrkiRNjjcAO1qebwZuzswTgZvLc0mSquCZVUnSPjwDPZ4iYjVwLnAZ8J9L8XpgukxfDcwAvzTo2CRJasczq5IkTYbfB94E/FtL2VRm7gEof48dQlySJLXlmVVJksZcRPw4sDczPxUR00tcx0ZgI8DU1BQzMzMdl52dnV10/rxNa+eWEkpbvWxvqXqtzyixTvUbt/rAZNapn+9zgzJ1eG9xD2Jfdk1WI+JKYL6TO7WUdRyQISIuBS4GngB+ITM/XMpfBFwFHA7cCLwhM7O/1ZGkyeXlu1rEmcBPRMQ5wNOBZ0fEnwGPRMTKzNwTESuBvZ1WkJlbga0A69aty+np6Y4bm5mZYbH58zb08ZjddWH37S1Vr/UZJdapfuNWH5jMOvXzfW5QNq2d4/Lt3c9pHsz33Xm9XAZ8FXD2grK2AzJExMnABcAp5TVvLyMPAlxB843sieWxcJ2SJOkgyMxLM3N1Zq6h6af/NjN/GtgGXFQWuwi4fkghSpK0n67JamZ+DPjKguL1NAMxUP6e11J+bWY+npkPADuB08u3tc/OzI+Xs6nXtLxGkiQNxxbgJRFxP/CS8lySpCos9Ter+wzIEBHzAzKsAj7RstzuUvbNMr2wXJIkDVBmztCM+ktmfhk4a5jxSJLUSb8HWIo2ZblIefuVHMAgDr0apx90j1NdYLzqY132VcugAr0OFDAqxqk+NQ3iIEmS6rLUZLXTgAy7geNbllsNPFzKV7cpb+tABnHo1Tj9oHuc6gLjVR/rsq9aBhXodaCAUTFO9alpEAdJklSXpd5ntdOADNuACyLisIg4gWYgpdvKJcOPRcQZERHAq3AQB0mSJElSB73cuuY9wDRwdETsBn6NZgCG6yLiYuALwPkAmXlPRFwH3AvMAZdk5hNlVa/lqVvXfKg8JEmSJEnaT9dkNTNf0WFW2wEZMvMy4LI25bcDpx5QdJIGbv5enZvWzlVzGa8kSZImz1IvA5YkSZIk6aAxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdVZMewAJEmSJKkWazbf0POym9bOseEAlteB8cyqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6K4YdgKT+WLP5hmGHIEmSJPWNZ1YlSZoAEXF8RHwkInZExD0R8YZSflRE3BQR95e/Rw47VkmSwGRVkqRJMQdsysznA2cAl0TEycBm4ObMPBG4uTyXJGnolpWsRsSuiNgeEXdGxO2lrOM3tBFxaUTsjIj7IuKlyw1ekiT1JjP3ZOYdZfoxYAewClgPXF0Wuxo4bygBSpK0QD/OrP5IZp6WmevK87bf0JZvby8ATgHOBt4eEYf0YfuSJOkARMQa4AXArcBUZu6BJqEFjh1iaJIkPelgDLC0Hpgu01cDM8AvlfJrM/Nx4IGI2AmcDnz8IMQgSZLaiIgjgPcBb8zMRyOi19dtBDYCTE1NMTMz03HZ2dnZRefP27R2rqdt96KX7S1Vr/UZJdapfuNWHxidOh3Ie9PU4f19L6tBr3UaxL5cbrKawF9HRAL/PTO3suAb2oiY/4Z2FfCJltfuLmWSJGkAIuJQmkT1XZn5/lL8SESsLH32SmBvu9eWPn4rwLp163J6errjdmZmZlhs/rwNfRzFfNeF3be3VL3WZ5RYp/qNW31gdOp0IO9Nm9bOcfn28brBSq91Opjvu/OW27JnZubDJSG9KSI+s8iy7b66zbYLHsC3t70alW9yejFOdYHxqs8w69Lvb/XG6ZvCcaoLjFd9avr2dtxFcwr1ncCOzHxry6xtwEXAlvL3+iGEJ0nSfpaVrGbmw+Xv3oj4AM1lvZ2+od0NHN/y8tXAwx3W2/O3t70alW9yejFOdYHxqs8w69LPMxQwXt8UjlNdYLzqU9O3txPgTOCVwPaIuLOUvZkmSb0uIi4GvgCcP5zwJEna15I/7UTEM4FvyczHyvSPAb9B529otwHvjoi3AscBJwK3LSN2SZLUo8y8hfZXOQGcNchYJEnqxXK+mp8CPlAGZlgBvDsz/yoiPkmbb2gz856IuA64l+Zeb5dk5hPLin5I1vTpDNauLef2ZT2SJEmSNG6WnKxm5ueA72tT/mU6fEObmZcBly11m5IkSZKkydCP+6xKkiRJktRX4zFCRw+2P/S1vg9AI0mS6uBPdCRp/HhmVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdWZmPus1mip94TbtHZun3vGek84SZIkSePGM6uSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6DrAkSZIkaeQtdfBS1cszq5IkSZKk6nhmdQz081skb4PTm05tvvC2Qt3Y3pIkSVJ7JquqXr+ScRNDSZIkaXSYrOqgOJAE80DPRkqSJGm4lnIyod1nPk8maDEmq5oY/uhekiRJGh0mq5IkSUW7LzaXcgWQZ4skafkcDViSJEmSVB2TVUmSJElSdbwMWBoif0crSZIktWeyKkmSVDFv4aZx5hf3WoyXAUuSJEmSqmOyKkmSJEmqjpcBax9eiiFJkiSpBiarkiRJOiA1frl91dnPHHYIkvrMy4AlSZIkSdUZ+JnViDgbeBtwCPAnmbll0DFIkqSn2Df3X41nHiWPS42agZ5ZjYhDgD8CXgacDLwiIk4eZAySJOkp9s2SpFoN+szq6cDOzPwcQERcC6wH7h1wHJIkqWHfPCFaz6ptWjvHhjE7y7b9oa+NVZ3GcR9JB2rQv1ldBTzY8nx3KZMkScNh3yxJqlJk5uA2FnE+8NLM/Nny/JXA6Zn5+gXLbQQ2lqcnAff1YfNHA1/qw3pqME51gfGqj3Wp0zjVBcarPsOoy3dm5jED3ma1DlLfPE7HKIxffcA6jYJxqw9Yp1FRTd886MuAdwPHtzxfDTy8cKHM3Aps7eeGI+L2zFzXz3UOyzjVBcarPtalTuNUFxiv+oxTXUZY3/vmcduv41YfsE6jYNzqA9ZpVNRUp0FfBvxJ4MSIOCEingZcAGwbcAySJOkp9s2SpCoN9MxqZs5FxOuAD9MMj39lZt4zyBgkSdJT7JslSbUa+H1WM/NG4MZBb5c+X1Y8ZONUFxiv+liXOo1TXWC86jNOdRlZB6FvHrf9Om71Aes0CsatPmCdRkU1dRroAEuSJEmSJPVi0L9ZlSRJkiSpq4lIViPi7Ii4LyJ2RsTmYcdzoCJiV0Rsj4g7I+L2UnZURNwUEfeXv0cOO852IuLKiNgbEXe3lHWMPSIuLfvpvoh46XCibq9DXd4SEQ+VfXNnRJzTMq/muhwfER+JiB0RcU9EvKGUj+q+6VSfkds/EfH0iLgtIj5d6vLrpXzk9s0idRm5/aLejHp/O2+U+91549T/wnj1wfPsi+uuD4xXnzxv5PrmzBzrB81gEZ8Fngs8Dfg0cPKw4zrAOuwCjl5Q9jvA5jK9GfjtYcfZIfYfBl4I3N0tduDksn8OA04o++2QYdehS13eAvxim2Vrr8tK4IVl+lnAP5WYR3XfdKrPyO0fIIAjyvShwK3AGaO4bxapy8jtFx897e+R729b6jKy/W5LvGPT/y5Sn5F+L7Evrrs+Jcax6ZN7qFOV+2kSzqyeDuzMzM9l5jeAa4H1Q46pH9YDV5fpq4HzhhdKZ5n5MeArC4o7xb4euDYzH8/MB4CdNPuvCh3q0kntddmTmXeU6ceAHcAqRnffdKpPJ9XWJxuz5emh5ZGM4L5ZpC6dVFsX9WRc+9t5I9Hvzhun/hfGqw+eZ19cd31gvPrkeaPWN09CsroKeLDl+W4W/8epUQJ/HRGfioiNpWwqM/dA8+YAHDu06A5cp9hHdV+9LiLuKpcozV8GMjJ1iYg1wAtovlkb+X2zoD4wgvsnIg6JiDuBvcBNmTmy+6ZDXWAE94u6Gqf9N2797ryRfB/pYizeS+yLgUrrM0598rxR6psnIVmNNmWjNgTymZn5QuBlwCUR8cPDDuggGcV9dQXwXcBpwB7g8lI+EnWJiCOA9wFvzMxHF1u0Tdko1Gck909mPpGZpwGrgdMj4tRFFh/FuozkflFX47T/JqXfnTeq+24s3kvsi/dRXX3GqU+eN0p98yQkq7uB41uerwYeHlIsS5KZD5e/e4EP0Jx6fyQiVgKUv3uHF+EB6xT7yO2rzHyk/MP/G/DHPHVZRPV1iYhDaTqTd2Xm+0vxyO6bdvUZ5f0DkJlfBWaAsxnhfQP71mXU94s6Gpv9N4b97ryRfh9ZaBzeS+yL665Pq3Hqk+eNQt88CcnqJ4ETI+KEiHgacAGwbcgx9SwinhkRz5qfBn4MuJumDheVxS4Crh9OhEvSKfZtwAURcVhEnACcCNw2hPh6Nv9GVfwkzb6ByusSEQG8E9iRmW9tmTWS+6ZTfUZx/0TEMRHxnDJ9OPBi4DOM4L7pVJdR3C/qyUj3t/PGtN+dN3LvI4sZ9fcS++K66wPj1SfPG7m+OSsYlepgP4BzaEYk+yzwy8OO5wBjfy7NCFyfBu6Zjx/4NuBm4P7y96hhx9oh/vfQXErwTZpvZi5eLHbgl8t+ug942bDj76EufwpsB+6i+WdeOSJ1+UGaSzjuAu4sj3NGeN90qs/I7R/ge4F/LDHfDfxqKR+5fbNIXUZuv/joeZ+PbH/bUoeR7ndb6jE2/e8i9Rnp9xL74rrrU+Ibmz65hzpVuZ+iBCBJkiRJUjUm4TJgSZIkSdKIMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVnVfiJiNiKeu8TXzkTEz/Y7pn6LiA0Rccsy19GXukbEroh4cYd5PxQR9y13G7WLiKmI+FhEPBYRlw87HkkaRfbfPa/D/rtP7L91sK0YdgCqT2YeMewY1MjMvwNO6rZcRLwFeF5m/vRBD+rg2Ah8CXh2Zuawg1mOiLgK2J2ZvzLsWCRNFvvveth/D0ZEbAB+NjN/cNDb1mB4ZlXSoiJiEF9qfSdw71I6ugHF17q9Q0Z5/ZKkyTAO/ffBrsOgP0PowJmsToiI+JmI+MuW5zsj4rqW5w9GxGllOiPieWX6qoj4o4i4oVzicWtEfFfL614SEZ+JiK9FxH8DoodYnhcRHy2v+VJEvLdlXkbEL0TE58q8/xoR39Iy/9URsSMi/iUiPhwR39ky73si4qaI+EpE3BcRL2+Z920RsS0iHo2I24An69Al1h+IiE+WWD8ZET+wYJHviojbyvzrI+Ko8rqnR8SfRcSXI+Kr5bVTi2zqtIi4q6znvRHx9LKe6YjY3RLPL0XEQ2Vf3BcRZ0XE2cCbgZ8ql4B9uix7XKnzV8r+/rmW9RweEVeXdtwREW9asJ1dZVt3AV+PiBURsTkiPlu2fW9E/GTL8hsi4u8j4vdKfT9X2m5DObb2RsRFHdr4KuAi4E0l/hdHxGER8fsR8XB5/H5EHNbaJiW+LwL/o806N0TELRHxu6WOD0TEy1rmd2ybdvFFxBURcWNEfB34kYh4fjSXkX01Iu6JiJ8oy24ELmypy1+W8rbLL7L+c0obP1b29y92ik/SeAv7b/vvyeu/5+P5CvCWiPjWiLgmIv45Ij4fEb8SEd8SEc8H3gF8f9n+V8s62i6/yPo7HteqQGb6mIAH8FzgqzRfUKwEPg881DLvX4BvKc+T5pIUgKuArwCn01w2/i7g2jLvaOBR4D8BhwL/DzBHcznGYrG8B/jlEsvTgR9smZfAR4CjgO8A/ml+fcB5wE7g+SWWXwH+ocx7JvAg8DNl3gtpLks5pcy/FriuLHcq8BBwS5c4jyrt8sqyzleU599W5s+U9Zxa1vs+4M/KvNcAfwk8AzgEeBHNJTLttrMLuA04rmxzB/DzZd40zSWl0FxO9CBwXHm+BviuMv2W+W23rPejwNtLG58G/DNwVpm3pcw/ElgN3DW/nZaY7gSOBw4vZeeXGL8F+Cng68DKMm9D2fc/U+r7W8AXgD8CDgN+DHgMOKJDG1wF/FbL898APgEcCxwD/APwmy1tMgf8dln34W3WtwH4JvBzJZ7XAg8D0a1tOsT2NeDMUvdn0RyHbwaeBvxoqdtJHepyaA/Lt67/6cAe4IfK/COBFw77PcSHDx/DeWD/bf89ef33HPD6su8OB64Brqfpf9fQHFsXtyx/y4J1dFt+4fo7Htc+hv8YegA+BrizmzfKFwIXAFvLG+z3lDeobS3LLezs/qRl3jnAZ8r0q4BPtMwLYDfdO7tryvZXt5mXwNktz/9v4OYy/aH5N5vy/FuAf6W5BOWngL9bsK7/DvxaefP9JvA9LfP+v4Vvbm1ieSVw24KyjwMbyvQMsKVl3snAN8r2Xl3eoL+3h/2yC/jplue/A7yjTE/zVGf3PGAv8GLg0AXreAstnR1NJ/UE8KyWsv8CXFWmPwe8tGXez7J/Z/fqLnHfCawv0xuA+1vmrS37cqql7MvAaR3WdRX7dnafBc5pef5SYFdLm3wDePoisW0AdrY8f0aJ59u7tU2H2K5pef5DwBcpHw5L2XuAt3SoSy/LX7Ngm1+g+cDU9gOSDx8+JuuB/bf992T1319oeX4I8DhwckvZa4CZluVvOcDlv7Bgmx2Pax/Df3gZ8GT5KM0bxQ+X6RngP5THRxd53Rdbpv8VmB/A4TiaDhSAbP7jH6S7N9F0jLdFc0nkqxfMb13H58t2oOnU3lYuU/kqzTfGAawq8/79/Lwy/0Ka5OQYmm/PFq63m+PaLPf5sr1OsR5K8431nwIfBq4tl8H8TkQcusi2OrXxkzJzJ/BGmo5tb0RcGxHHLVyuJfavZOZjHWLfZ9/Rfr/tUxYRr4qIO1va91Saus57pGX6/y8xLyzrdfCPhW3fehwA/HNm/q8u63iyTTPzX8vkEXRvm3Za2+I44MHM/LceX9/L8gvb//+k+WD5+XJp0vcvEpuk8Wf/ve96u7H/bjGC/Xdr/EfTXJW0cJ2d+txell/YZt2Oaw2Ryepkme/sfqhMf5TeOrtO9tB8AwhARETr804y84uZ+XOZeRzNt11vj/Ibm6J1Hd9Bc/kmNG8ur8nM57Q8Ds/MfyjzPrpg3hGZ+Vqay2fm2qy3m4dpOtFW30Fz6VCnWL8JfCkzv5mZv56ZJwM/APw4zTfZy5KZ785mxLvvpPnm87fnZ7WJ/aiIeFaH2PfQXD7Urh5Pbm5+IprfFv0x8Dqay6ieA9xND79xWqKFbd96HOwT2xLXvVjbtNO6vYeB46Plt1gLXt9uXyy2/H6vycxPZuZ6msuo/oLmEjhJk8v+e9/1dmP/XYxo/926zJdo9s3CdXbqc7stv99rejiuNUQmq5Plo8CP0PxGYDfwd8DZwLcB/7iE9d0AnBIR/zGa0dR+geab0EVFxPkRMf9G+y80bxpPtCzy/0bEkRFxPPAGYP6H7u8ALo2IU8p6vjUizi/zPgh8d0S8MiIOLY9/FxHPz8wngPfT/Ij+GRFxMs2AAN3cWNb5f0UzQMFP0Vwq9MGWZX46Ik6OiGfQ/E7jzzPziYj4kYhYG83Iro/SvHE+sd8WDkBEnBQRP1oGKvhfNN90zq/zEWDNfEKUmQ/SXMb0X6IZLOJ7gYtpfrMETfJzaWnnVTSd2GKeSbOf/rnE8jM038weLO8BfiUijomIo4FfBf6sHyvuoW26uZXm9z5vKsfZNPB/0PyuCpp98dwDWH4fEfG0iLgwIr41M79Jc/ws69iRNPLsv+2/J7L/LsfAdcBlEfGsknz/55Z1PgKsjoin9bj8fno4rjVEJqsTJDP/CZil6eTIzEdpfvvw9+Wf+0DX9yWaH+1vofk9w4nA3/fw0n8H3BoRs8A24A2Z+UDL/OuBT9H8puIG4J1lex+g+Sby2oh4lOabwZeVeY/RDAJwAc03eF/kqR/wQ/NmfkQpv4o2I9C1qd+Xab5R3VTq9ybgx0u95/1pWd8XaX6U/wul/NuBP6fp6HbQfNBYbrJ1GE1bf6ls71iaQXsA/mf5++WIuKNMv4JmYIGHgQ8Av5aZN5V5v0Hz+6QHgL8psT7eacOZeS9wOc1vfh6h+U1LL/t6qX4LuJ1m4IjtwB2lrF8Wa5tFZeY3gJ+gOfa+RDMIxqsy8zNlkXcCJ5fLrf6ih+XbeSWwqxznPw+M6v33JPWB/bf994T336+n+dL3c8AtwLuBK8u8vwXuAb4YEV/qYfl2uh3XGqL5kTGlKkREAieW33doQCLitcAFmfkfhh2LJGn02H8Ph/23xp1nVqUJFBErI+LMaO5TdhLNt88fGHZckiSpM/tvTRqTVR0UEfGOaG7QvPDxjmHH1ioifqhDnLPDju0gexrNrQEeo7mE5nqay1MlSRPM/rt69t+aKF4GLEmSJEmqjmdWJUmSJEnVMVmVJEmSJFVnxbAD6Oboo4/ONWvWLHs9X//613nmM5+5/IDGmG3UG9upO9uoO9uou4PVRp/61Ke+lJnH9H3FE+Q5z3lOPu95zxt2GAdsVP/vRjHuUYwZjHvQjHuwao67U99cfbK6Zs0abr/99mWvZ2Zmhunp6eUHNMZso97YTt3ZRt3ZRt0drDaKiM/3faUTZmpqqi9986CN6v/dKMY9ijGDcQ+acQ9WzXF36pu9DFiSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVMVmVJEmSJFVnxbADUF3WbL6hL+vZteXcvqwH6oxJkiQtX7/6eLCfl8aRZ1YlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1uiarEXF8RHwkInZExD0R8YZS/paIeCgi7iyPc1pec2lE7IyI+yLipS3lL4qI7WXeH0REHJxqSZIkSZJG2YoelpkDNmXmHRHxLOBTEXFTmfd7mfm7rQtHxMnABcApwHHA30TEd2fmE8AVwEbgE8CNwNnAh/pTFUmSJEnSuOh6ZjUz92TmHWX6MWAHsGqRl6wHrs3MxzPzAWAncHpErASenZkfz8wErgHOW24FJEmSJEnj54B+sxoRa4AXALeWotdFxF0RcWVEHFnKVgEPtrxsdylbVaYXlkuSJEmStI9eLgMGICKOAN4HvDEzH42IK4DfBLL8vRx4NdDud6i5SHm7bW2kuVyYqakpZmZmeg2zo9nZ2b6sZ5zNzs6yae0TfVlXP9t609q5vqynXzF5LHVnG3VnG3VnG0mSNNl6SlYj4lCaRPVdmfl+gMx8pGX+HwMfLE93A8e3vHw18HApX92mfD+ZuRXYCrBu3bqcnp7uJcxFzczM0I/1jLOZmRkuv+XrfVnXrgun+7IegA2bb+jLevoVk8dSd7ZRd7ZRd7aRJEmTrZfRgAN4J7AjM9/aUr6yZbGfBO4u09uACyLisIg4ATgRuC0z9wCPRcQZZZ2vAq7vUz0kSZIkSWOklzOrZwKvBLZHxJ2l7M3AKyLiNJpLeXcBrwHIzHsi4jrgXpqRhC8pIwEDvBa4CjicZhRgRwKWJEmSJO2na7KambfQ/vemNy7ymsuAy9qU3w6ceiABSpIkSZImzwGNBixJkiRJ0iCYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6JquSJEmSpOqYrEqSNCEi4pCI+MeI+GB5flRE3BQR95e/Rw47RkmS5pmsSpI0Od4A7Gh5vhm4OTNPBG4uzyVJqoLJqiRJEyAiVgPnAn/SUrweuLpMXw2cN+CwJEnqyGRVkqTJ8PvAm4B/aymbysw9AOXvsUOIS5KktlYMOwBJknRwRcSPA3sz81MRMb3EdWwENgIcc8wxzMzM9C2+QZmdnTXuAek15k1r5/q2zX600Si2NRj3oBn34JisSpI0/s4EfiIizgGeDjw7Iv4MeCQiVmbmnohYCezttILM3ApsBTjppJNyenp6AGH318zMDMY9GL3GvGHzDX3b5q4Lu2+vm1FsazDuQTPuwfEyYEmSxlxmXpqZqzNzDXAB8LeZ+dPANuCisthFwPVDClGSpP2YrEqSNLm2AC+JiPuBl5TnkiRVwcuAJUmaIJk5A8yU6S8DZw0zHkmSOvHMqiRJkiSpOp5ZlSRJKtb0acCfXVvO7ct6JGmSeWZVkiRJklQdk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRVx2RVkiRJklQdRwOWJEmq2PaHvsaGPoxS7AjFkkaNZ1YlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1VnRbICKOB64Bvh34N2BrZr4tIo4C3gusAXYBL8/MfymvuRS4GHgC+IXM/HApfxFwFXA4cCPwhszM/lZJkiRJB9OazTcsOn/T2jk2dFlGkrrp5czqHLApM58PnAFcEhEnA5uBmzPzRODm8pwy7wLgFOBs4O0RcUhZ1xXARuDE8ji7j3WRJEmSJI2JrslqZu7JzDvK9GPADmAVsB64uix2NXBemV4PXJuZj2fmA8BO4PSIWAk8OzM/Xs6mXtPyGkmSJEmSnnRAv1mNiDXAC4BbganM3ANNQgscWxZbBTzY8rLdpWxVmV5YLkmSJEnSPrr+ZnVeRBwBvA94Y2Y+GhEdF21TlouUt9vWRprLhZmammJmZqbXMDuanZ3ty3rG2ezsLJvWPtGXdfWzrTetnevLevoVk8dSd7ZRd7ZRd7aRJEmTradkNSIOpUlU35WZ7y/Fj0TEyszcUy7x3VvKdwPHt7x8NfBwKV/dpnw/mbkV2Aqwbt26nJ6e7q02i5iZmaEf6xlnMzMzXH7L1/uyrl0XTvdlPUDfBmjoV0weS93ZRt3ZRt3ZRpIkTbZeRgMO4J3Ajsx8a8usbcBFwJby9/qW8ndHxFuB42gGUrotM5+IiMci4gyay4hfBfxh32oiSZIkLVO3kY4PxK4t5/ZtXdIk6uXM6pnAK4HtEXFnKXszTZJ6XURcDHwBOB8gM++JiOuAe2lGEr4kM+evLX0tT9265kPlIUmSJEnSPromq5l5C+1/bwpwVofXXAZc1qb8duDUAwlQo6mf30pKkiRJmjwHNBqwJEmSJEmDYLIqSZIkSaqOyaokSZIkqTomq5IkSZKk6pisSpIkSZKqY7IqSZIkSaqOyaokSZIkqTomq5IkSZKk6pisSpIkSZKqY7IqSZIkSaqOyaokSZIkqTomq5IkSZKk6pisSpIkSZKqY7IqSZIkSarOimEHII2iNZtv6Mt6dm05ty/rkSRJksaNZ1YlSZIkSdUxWZUkSZIkVcdkVZKkCRART4+I2yLi0xFxT0T8eik/KiJuioj7y98jhx2rJElgsipJ0qR4HPjRzPw+4DTg7Ig4A9gM3JyZJwI3l+eSJA2dyaokSRMgG7Pl6aHlkcB64OpSfjVw3uCjkyRpfyarkiRNiIg4JCLuBPYCN2XmrcBUZu4BKH+PHWKIkiQ9yVvXSJI0ITLzCeC0iHgO8IGIOLXX10bERmAjwDHHHMPMzMxBifFgmp2d7Rr3prVzfdlWP9tn6vD+xNXPmLrF06+YD8Qfvuv6Za+jibsPwRSD+j/p5diukXEP1ijGbbIqSdKEycyvRsQMcDbwSESszMw9EbGS5qxru9dsBbYCnHTSSTk9PT2ocPtmZmaGbnFv6Nd9tC9cfDsH4g/fdT2Xb1/+R7Z+xtStnTatnetLzIPW77j72eaL6eXYrpFxD9Yoxu1lwJIkTYCIOKacUSUiDgdeDHwG2AZcVBa7CFj+6SlJkvpg9L7ykiRJS7ESuDoiDqH5svq6zPxgRHwcuC4iLga+AJw/zCAlSZpnsipJ0gTIzLuAF7Qp/zJw1uAjkiRpcV4GLEmSJEmqjmdWJUmSJB2QNX0YjGzT2jmmlx+KxphnViVJkiRJ1TFZlSRJkiRVx2RVkiRJklQdk1VJkiRJUnUcYEmSJKnP+jH4zLxNa/uznn7GpN4Mqs03rZ1jQw/b2rXl3AFEI/WPZ1YlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1uiarEXFlROyNiLtbyt4SEQ9FxJ3lcU7LvEsjYmdE3BcRL20pf1FEbC/z/iAiov/VkSRJkiSNg17OrF4FnN2m/Pcy87TyuBEgIk4GLgBOKa95e0QcUpa/AtgInFge7dYpSZIkSRIrui2QmR+LiDU9rm89cG1mPg48EBE7gdMjYhfw7Mz8OEBEXAOcB3xoKUFLS7Fm8w19Wc+mtXP08K8jSZIkaRmW85vV10XEXeUy4SNL2SrgwZZldpeyVWV6YbkkSZIkSftZ6umhK4DfBLL8vRx4NdDud6i5SHlbEbGR5pJhpqammJmZWWKYT5mdne3LesbZ7Owsm9Y+Mewwqjd1+PzZ1eUb12PS/7fubKPubCNJkibbkpLVzHxkfjoi/hj4YHm6Gzi+ZdHVwMOlfHWb8k7r3wpsBVi3bl1OT08vJcx9zMzM0I/11Kh/l7c+weXbvby1m01r5/rWTrsunO7Lemozzv9v/WIbdWcbSZI02ZZ0GXBErGx5+pPA/EjB24ALIuKwiDiBZiCl2zJzD/BYRJxRRgF+FXD9MuKWJEmSJI2xrqeHIuI9wDRwdETsBn4NmI6I02gu5d0FvAYgM++JiOuAe4E54JLMnL+u9LU0IwsfTjOw0sQPrtSvM6KSJEmSNG56GQ34FW2K37nI8pcBl7Upvx049YCikyRJkiRNpOWMBixJkiRJ0kFhsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqmKxKkiRJkqpjsipJkiRJqo7JqiRJkiSpOiarkiRJkqTqrBh2AJIkSZIOvjWbbxh2CNIB8cyqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6JquSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjsmqJEmSJKk6JquSJEmSpOqsGHYAkiRJy7Vm8w1dl9m0do4NPSwnSaqDZ1YlSZIkSdUxWZUkaQJExPER8ZGI2BER90TEG0r5URFxU0TcX/4eOexYJUkCk1VJkibFHLApM58PnAFcEhEnA5uBmzPzRODm8lySpKEzWZUkaQJk5p7MvKNMPwbsAFYB64Gry2JXA+cNJUBJkhYwWZUkacJExBrgBcCtwFRm7oEmoQWOHWJokiQ9ydGAJUmaIBFxBPA+4I2Z+WhE9Pq6jcBGgGOOOYaZmZmDFuNSbFo713WZqcN7W642oxj3KMYMxj1oU4dT3XtJL2ZnZ417QExWJUmaEBFxKE2i+q7MfH8pfiQiVmbmnohYCext99rM3ApsBTjppJNyenp6ECH3rJdb0mxaO8fl20fvo88oxj2KMYNxD9qmtXO8vLL3kl7MzMxQ23tgL0Yxbi8DliRpAkRzCvWdwI7MfGvLrG3ARWX6IuD6QccmSVI7o/cVjCRJWoozgVcC2yPizlL2ZmALcF1EXAx8ATh/OOFJkrQvk1VJkiZAZt4CdPqB6lmDjEWSpF54GbAkSZIkqTomq5IkSZKk6pisSpIkSZKqY7IqSZIkSaqOyaokSZIkqTomq5IkSZKk6pisSpIkSZKqY7IqSZIkSapO12Q1Iq6MiL0RcXdL2VERcVNE3F/+Htky79KI2BkR90XES1vKXxQR28u8P4iITjcmlyRJkiRNuF7OrF4FnL2gbDNwc2aeCNxcnhMRJwMXAKeU17w9Ig4pr7kC2AicWB4L1ylJkiRJEtBDspqZHwO+sqB4PXB1mb4aOK+l/NrMfDwzHwB2AqdHxErg2Zn58cxM4JqW10iSJEmStI+l/mZ1KjP3AJS/x5byVcCDLcvtLmWryvTCckmSJEmS9rOiz+tr9zvUXKS8/UoiNtJcMszU1BQzMzPLDmx2drYv6+mnTWvnhh3CPqYOry+mGvWznWo7Jvulxv+32thG3dlGkiRNtqUmq49ExMrM3FMu8d1byncDx7cstxp4uJSvblPeVmZuBbYCrFu3Lqenp5cY5lNmZmbox3r6acPmG4Ydwj42rZ3j8u39/v5i/PSznXZdON2X9dSmxv+32thG3dlGkiRNtqVeBrwNuKhMXwRc31J+QUQcFhEn0AykdFu5VPixiDijjAL8qpbXSJIkSZK0j66nhyLiPcA0cHRE7AZ+DdgCXBcRFwNfAM4HyMx7IuI64F5gDrgkM58oq3otzcjChwMfKg9JkiRJkvbTNVnNzFd0mHVWh+UvAy5rU347cOoBRSdJkiRJmkhLvQxYkiRJkqSDxmRVkiRJklQdk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRVx2RVkiRJklQdk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRVx2RVkiRJklSdFcMOQJpkazbf0Ld17dpybt/WJUmSJA2bZ1YlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVWfFsAOQJEmSpOVas/mGvqxn15Zz+7IeLZ9nViVJkiRJ1TFZlSRJkiRVx8uAJe2jH5fQbFo7x4bNN3gZjSRJkpbMM6uSJEmSpOqYrEqSJEmSqmOyKkmSJEmqjr9ZlcZEv4ZrlyRJkmrgmVVJkiZARFwZEXsj4u6WsqMi4qaIuL/8PXKYMUqS1MpkVZKkyXAVcPaCss3AzZl5InBzeS5JUhVMViVJmgCZ+THgKwuK1wNXl+mrgfMGGZMkSYsxWZUkaXJNZeYegPL32CHHI0nSkxxgaQkcyEaSNGkiYiOwEeCYY45hZmZmuAEtsGntXNdlpg7vbbnajGLcoxgzGPegTR1OX99L+tUG3WKanZ2t7j2wF6MYt8mqJEmT65GIWJmZeyJiJbC304KZuRXYCnDSSSfl9PT0gELszYYevkjetHaOy7eP3kefUYx7FGMG4x60TWvneHkf30t6eR/oxa4LpxedPzMzQ23vgb0Yxbi9DFiSpMm1DbioTF8EXD/EWCRJ2ofJqiRJEyAi3gN8HDgpInZHxMXAFuAlEXE/8JLyXJKkKizreoGI2AU8BjwBzGXmuog4CngvsAbYBbw8M/+lLH8pcHFZ/hcy88PL2b4kSepNZr6iw6yzBhqIJEk96seZ1R/JzNMyc1153vaebRFxMnABcArNfd7eHhGH9GH7kiRJkqQxczAuA+50z7b1wLWZ+XhmPgDsBE4/CNuXJEmSJI245SarCfx1RHyqDGkPne/Ztgp4sOW1u0uZJEmSJEn7WO4Y12dm5sMRcSxwU0R8ZpFlo01Ztl2w5V5uU1NTfbkfUD/vKzSK97Hqxajeo2vQbKfu5tto1O7lNUijeK+zQbONJEmabMtKVjPz4fJ3b0R8gOay3k73bNsNHN/y8tXAwx3W++S93NatW9eXe7n1875C/bqHU21G9R5dg2Y7dTffRt3uUzbJRvFeZ4NmG0mSNNmWfBlwRDwzIp41Pw38GHA3ne/Ztg24ICIOi4gTgBOB25a6fUmSJEnS+FrO6aEp4AMRMb+ed2fmX0XEJ4Hryv3bvgCcD5CZ90TEdcC9wBxwSWY+sazoJUmSJEljacnJamZ+Dvi+NuVfpsM92zLzMuCypW5TkiRJkg6mNV1+8rdp7VzPPwvcteXcfoQ0sQ7GrWskSZIkSVoWk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRVx2RVkiRJklQdk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRVx2RVkiRJklQdk1VJkiRJUnVMViVJkiRJ1Vkx7AAkSdJkWrP5hmGHIEkHVb/e53ZtObcv6xk1nlmVJEmSJFXHZFWSJEmSVB2TVUmSJElSdUxWJUmSJEnVcYAlSZIkSUPhQGuDN0qDPnlmVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVWfFsAMYlO0PfY0N3nRYGqhRuum0JEmS6uKZVUmSJElSdUxWJUmSJEnVmZjLgCWNLi8nliRJmjyeWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUHZNVSZIkSVJ1TFYlSZIkSdUxWZUkSZIkVcdkVZIkSZJUnRXDDkCSBmXN5hv6tq5dW87t27okSZK0P8+sSpIkSZKqY7IqSZIkSarOwC8DjoizgbcBhwB/kplbBh2DJC3Xci8p3rR2jg2bb/ByYlXBvlmSVKOBnlmNiEOAPwJeBpwMvCIiTh5kDJIk6Sn2zZKkWg36MuDTgZ2Z+bnM/AZwLbB+wDFIkqSn2DdLkqo06MuAVwEPtjzfDfz7AccgSdXo5wjF/eKlyRPHvlmSVKXIzMFtLOJ84KWZ+bPl+SuB0zPz9QuW2whsLE9PAu7rw+aPBr7Uh/WMM9uoN7ZTd7ZRd7ZRdwerjb4zM485COsdSUvsm08F7h5ooP0xqv93oxj3KMYMxj1oxj1YNcfdtm8e9JnV3cDxLc9XAw8vXCgztwJb+7nhiLg9M9f1c53jxjbqje3UnW3UnW3UnW00MAfcN4/qvjHuwRnFmMG4B824B2sU4x70b1Y/CZwYESdExNOAC4BtA45BkiQ9xb5ZklSlgZ5Zzcy5iHgd8GGa4fGvzMx7BhmDJEl6in2zJKlWA7/PambeCNw46O3S58uKx5Rt1BvbqTvbqDvbqDvbaECW0DeP6r4x7sEZxZjBuAfNuAdr5OIe6ABLkiRJkiT1YtC/WZUkSZIkqauJSFYj4uyIuC8idkbE5mHHU6OI2BUR2yPizoi4fdjx1CAiroyIvRFxd0vZURFxU0TcX/4eOcwYh61DG70lIh4qx9KdEXHOMGMctog4PiI+EhE7IuKeiHhDKfdYarFIO3k8DUm3vjMaf1Dm3xURLxxGnAtianscLVhmOiK+1nJM/eowYl2oWz9caXuf1NKOd0bEoxHxxgXLVNHey+nTh/k5skPc/zUiPlOOgw9ExHM6vHZon+2W8/mgwvZ+b0vMuyLizg6vHUp7L/dzxjDbuyeZOdYPmsEiPgs8F3ga8Gng5GHHVdsD2AUcPew4anoAPwy8ELi7pex3gM1lejPw28OOs8I2egvwi8OOrZYHsBJ4YZl+FvBPwMkeSz23k8fTcPZH174TOAf4EBDAGcCtFcTd9jhasMw08MFhx9om9kX74Rrbu80x80WaeyVW195L7dOH/TmyQ9w/Bqwo07/dqf8Y5me7pX4+qLG9F8y/HPjVmtp7OZ8zht3evTwm4czq6cDOzPxcZn4DuBZYP+SYNAIy82PAVxYUrweuLtNXA+cNMqbadGgjtcjMPZl5R5l+DNgBrMJjaR+LtJOGo5e+cz1wTTY+ATwnIlYOOtBWY34cVdfeC5wFfDYzPz/sQNpZRp8+1M+R7eLOzL/OzLny9BM090auyjI+H1TX3vMiIoCXA+8ZVDy9WObnjOrzpElIVlcBD7Y83834dFz9lMBfR8SnImLjsIOp2FRm7oHmzQE4dsjx1Op15fKkKyf98tZWEbEGeAFwKx5LHS1oJ/B4GoZe+s6q+9c2x1Gr74+IT0fEhyLilMFG1lG3frjq9qa5P2+nD/E1tjf09j5ce7u/muaMezs1frbr9n5ec3v/EPBIZt7fYf7Q23sJnzNqbm9gMpLVaFPmEMj7OzMzXwi8DLgkIn542AFpZF0BfBdwGrCH5pKZiRcRRwDvA96YmY8OO55atWknj6fh6KXvrLZ/7fL/dgfNparfB/wh8BcDDq+Tbv1wze39NOAngP/ZZnat7d2rmtv9l4E54F0dFqnts10v7+fVtjfwChY/qzrU9l7i54ya2xuYjGR1N3B8y/PVwMNDiqVamflw+bsX+ADNZQHa3yPzl12Vv3uHHE91MvORzHwiM/8N+GM8loiIQ2k6kHdl5vtLscfSAu3ayeNpaHrpO6vsXzv8vz0pMx/NzNkyfSNwaEQcPeAw99NDP1xlexcvA+7IzEcWzqi1vYte3oerbPeIuAj4ceDCLD8+XKi2z3Y9vp/X2t4rgP8IvLfTMsNs72V8zqiyvVtNQrL6SeDEiDihfPN3AbBtyDFVJSKeGRHPmp+m+eH+3Yu/amJtAy4q0xcB1w8xliot+A3VTzLhx1L5jcs7gR2Z+daWWR5LLTq1k8fT0PTSd24DXhWNM4CvzV9yNiyL/L+1LvPtZTki4nSaz0JfHlyUbWPqpR+urr1bdDzjVGN7t+jlfbi6z5ERcTbwS8BPZOa/dlimus92Pb6fV9fexYuBz2Tm7nYzh9ney/ycUWt7P2XQIzoN40Ezgt4/0Yx29cvDjqe2B80IYJ8uj3tsoyfb5T00l6l8k+abp4uBbwNuBu4vf48adpwVttGfAtuBu2je8FYOO84ht9EP0lxScxdwZ3mc47HUczt5PA1vn+zXdwI/D/x8mQ7gj8r87cC6CmLudBy1xv260td9mmZwmh+oIO62/XDt7V3iegZN8vmtLWXVtfeB9OnAccCNLa8d2ufIDnHvpPmd4fwx/o6FcXc6poYcd9v389rbu5RfNX9MtyxbRXsv8r5X/fHdyyNKkJIkSZIkVWMSLgOWJEmSJI0Yk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRVx2RVkiRJklQdk1VJkiRJUnVMViVJkiRJ1TFZlSRJkiRV538DsnMkBpJWJNAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x1728 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vars_to_plot = ['air_temp_obs', 'dewpoint_obs', 'wind_direction_obs', 'wind_speed_obs',]\n",
    "print(vars_to_plot)\n",
    "fig1 = matplotlib.pyplot.figure('comparing rota events',figsize=(16,6*len(vars_to_plot)))\n",
    "for ix1, var_name in enumerate(vars_to_plot):\n",
    "    ax1 = fig1.add_subplot(len(vars_to_plot),2,ix1*2+1, title=f'{var_name} histogram for no rotors')\n",
    "    no_rotors_df[var_name].hist(bins=20,ax=ax1)\n",
    "    ax1 = fig1.add_subplot(len(vars_to_plot),2,ix1*2+2, title=f'{var_name} histogram for rotors')\n",
    "    rotors_present_df[var_name].hist(bins=20,ax=ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79428b80-ba0e-4a42-a76f-acfcb9592ff5",
   "metadata": {},
   "source": [
    "The above diagrams already show some differences for conditions at the surface when a rotor is observed compared to when not. Most obvious is the wind direction, where rotors are found mostly in northerly winds, as is already known. Other surface variables show smaller differences."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b558c455-e830-41e5-a5c9-e5d2c703464e",
   "metadata": {},
   "source": [
    "We might also be interested in the link between occurences frequency at different times of day or in different seasons. The plots below show a small peak in the middle of the day (which may due to observation bias), but no other obviously exploitable patterns in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9d02ad7d-33f6-40b6-be26-3607a2f25b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'hour of the day')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('time of year',figsize=(16,16))\n",
    "ax1 = fig1.add_subplot(2,2,1, title='number of periods without rotors by month') \n",
    "no_rotors_df.groupby([ no_rotors_df['DTG'].apply(lambda x: x.month)])['DTG'].count().plot.bar( ax=ax1)\n",
    "ax1.set_xlabel('month of the year')\n",
    "ax1 = fig1.add_subplot(2,2,2, title='number of periods with rotors by month') \n",
    "rotors_present_df.groupby([ rotors_present_df['DTG'].apply(lambda x: x.month)])['DTG'].count().plot.bar(ax=ax1)\n",
    "ax1.set_xlabel('month of the year')\n",
    "\n",
    "ax1 = fig1.add_subplot(2,2,3, title='number of periods without rotors by hour of the day') \n",
    "no_rotors_df.groupby([ no_rotors_df['DTG'].apply(lambda x: x.hour)])['DTG'].count().plot.bar( ax=ax1)\n",
    "ax1.set_xlabel('hour of the day')\n",
    "ax1 = fig1.add_subplot(2,2,4, title='number of periods without rotors by hour of the day') \n",
    "rotors_present_df.groupby([ rotors_present_df['DTG'].apply(lambda x: x.hour)])['DTG'].count().plot.bar(ax=ax1)\n",
    "ax1.set_xlabel('hour of the day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "ae7a2943-7f53-4b9c-90b4-3ca630227312",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_dict = {\n",
    "    'air_temp': [c1 for c1 in falklands_df.columns if 'air_temp' in c1 and 'obs' not in c1],\n",
    "    'dewpoint': [c1 for c1 in falklands_df.columns if 'sh' in c1 and 'obs' not in c1],\n",
    "    'wind_speed': [c1 for c1 in falklands_df.columns if 'windspd' in c1 and 'obs' not in c1],\n",
    "    'wind_dir': [c1 for c1 in falklands_df.columns if 'winddir' in c1 and 'obs' not in c1],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "94a527e9-d168-4329-a5ee-85019dc6ddde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x2304 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_vars = len(features_dict.keys())\n",
    "num_plots = 3\n",
    "fig1 = matplotlib.pyplot.figure(figsize=(16,8*num_vars))\n",
    "for ix1, (feature_name, fl1) in enumerate(features_dict.items()):\n",
    "    ax1 = fig1.add_subplot(num_vars,num_plots,ix1*num_plots+1, title=f'average {feature_name} with height for no rotors periods')\n",
    "    no_rotors_df[fl1].mean().plot.bar(ax=ax1)\n",
    "    ax1 = fig1.add_subplot(num_vars,num_plots,ix1*num_plots+2, title=f'average {feature_name} with height for rotors reported')\n",
    "    rotors_present_df[fl1].mean().plot.bar(ax=ax1)\n",
    "    ax1 = fig1.add_subplot(num_vars,num_plots,ix1*num_plots+3, title=f'difference in {feature_name} with height for rotors reported compared to no rotors')\n",
    "    (rotors_present_df[fl1].mean() - no_rotors_df[fl1].mean() ).plot.bar(ax=ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0de52c-532e-4bc9-8f01-69c89d019c13",
   "metadata": {},
   "source": [
    "Next up we want to explore the relationship between the forecast data and the observations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94271903-b87e-4d7d-b67a-206630f23433",
   "metadata": {},
   "source": [
    "### Comparison with human performance\n",
    "\n",
    "This dataset also has some data contyaining the prediction made by human OpMets ahead of time. This is the baseline that we would need to match or improve upon for the algorithm to be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "33fba922-9f72-4a3d-9cd6-14100965946f",
   "metadata": {},
   "outputs": [],
   "source": [
    "opmet_predictions_df = pandas.read_csv('./data/rotors/rotors_opmet_performance_2016_2021.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "56510f49-b9a7-42a7-8724-9c6d382bd6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df.observation.isna()].index,'observation'] = 0.0\n",
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df['opmet_forecast'].isna()].index,'opmet_forecast'] = 0.0\n",
    "opmet_predictions_df['DTG'] = pandas.to_datetime(opmet_predictions_df['DTG'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "cf845855-a8f0-4477-aa81-140b8c7bbffa",
   "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>DTG</th>\n",
       "      <th>observation</th>\n",
       "      <th>opmet_forecast</th>\n",
       "      <th>Hit</th>\n",
       "      <th>False Positive</th>\n",
       "      <th>True Negative</th>\n",
       "      <th>Miss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09-19 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-19 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-19 02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-19 03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-19 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38971</th>\n",
       "      <td>2021-02-28 19:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38972</th>\n",
       "      <td>2021-02-28 20:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38973</th>\n",
       "      <td>2021-02-28 21:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38974</th>\n",
       "      <td>2021-02-28 22:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38975</th>\n",
       "      <td>2021-02-28 23:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38976 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DTG  observation  opmet_forecast    Hit  False Positive  \\\n",
       "0     2016-09-19 00:00:00          0.0             0.0  False           False   \n",
       "1     2016-09-19 01:00:00          0.0             0.0  False           False   \n",
       "2     2016-09-19 02:00:00          0.0             0.0  False           False   \n",
       "3     2016-09-19 03:00:00          0.0             0.0  False           False   \n",
       "4     2016-09-19 04:00:00          0.0             0.0  False           False   \n",
       "...                   ...          ...             ...    ...             ...   \n",
       "38971 2021-02-28 19:00:00          0.0             1.0  False            True   \n",
       "38972 2021-02-28 20:00:00          0.0             1.0  False            True   \n",
       "38973 2021-02-28 21:00:00          0.0             0.0  False           False   \n",
       "38974 2021-02-28 22:00:00          0.0             0.0  False           False   \n",
       "38975 2021-02-28 23:00:00          0.0             0.0  False           False   \n",
       "\n",
       "       True Negative   Miss  \n",
       "0               True  False  \n",
       "1               True  False  \n",
       "2               True  False  \n",
       "3               True  False  \n",
       "4               True  False  \n",
       "...              ...    ...  \n",
       "38971          False  False  \n",
       "38972          False  False  \n",
       "38973           True  False  \n",
       "38974           True  False  \n",
       "38975           True  False  \n",
       "\n",
       "[38976 rows x 7 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5f44cd2b-8e78-4202-adc3-6ca04de34f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    37726\n",
       "1.0     1250\n",
       "Name: observation, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_predictions_df['observation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7b410f2c-9fe8-4e83-a148-93d43fd6fcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0     33551\n",
       "1.0      5424\n",
       "11.0        1\n",
       "Name: opmet_forecast, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_predictions_df['opmet_forecast'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a84acc0b-8d11-4657-9c35-dd86f7082e3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "opmet_predictions_df['truePositive'] = ((opmet_predictions_df['observation'] == 1 ) & (opmet_predictions_df['opmet_forecast'] ==1))\n",
    "opmet_predictions_df['falsePositive']  = ((opmet_predictions_df['observation'] == 0 ) & (opmet_predictions_df['opmet_forecast'] ==1))\n",
    "opmet_predictions_df['trueNegative'] = ((opmet_predictions_df['observation'] == 0 ) & (opmet_predictions_df['opmet_forecast'] ==0))\n",
    "opmet_predictions_df['falseNegative']  = ((opmet_predictions_df['observation'] == 1 ) & (opmet_predictions_df['opmet_forecast'] ==0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "add0e52f-b894-464f-b101-d8e015a65e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "opmet_predictions_df['result_category'] = 'trueNegative'\n",
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df['falseNegative']==True].index,'result_category'] = 'falseNegative'\n",
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df['falsePositive']==True].index,'result_category'] = 'falsePositive'\n",
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df['truePositive']==True].index,'result_category'] = 'truePositive'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "abd91668-6ee8-4647-99dd-65ba3a31b8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'true/false positive/negative proportions'}, ylabel='result_category'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('comparisonn of hits/isses/etc.')\n",
    "ax1 = fig1.add_subplot(1,3,1,title='rotor events')\n",
    "opmet_predictions_df['observation'].value_counts().plot.pie(figsize=(18,6))\n",
    "ax1 = fig1.add_subplot(1,3,2,title='opmet forecasts')\n",
    "opmet_predictions_df['opmet_forecast'].value_counts().plot.pie(figsize=(18,6))\n",
    "ax1 = fig1.add_subplot(1,3,3,title='true/false positive/negative proportions')\n",
    "opmet_predictions_df['result_category'].value_counts().plot.pie(figsize=(18,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6a0c4e-c69b-47fc-a345-86de0ad21330",
   "metadata": {},
   "source": [
    "Exploration of this data shows us that OpMets routinely overpredict rotor events. This is what you would expect, as the cost of missing a rotor event (false negative) is much higher than the cost of predicting an event and it not happening (false positive). So the challenge for our ML algorithm is to eliminate false neagtives while maintaining an acceptably low false positive rate so as to maintain trust in forecast output."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bcd1b7-1f3f-4448-b263-c2feb1184f22",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "There are further example notebooks looking at loading and exploring data in python in the following locations:\n",
    "* [Introduction to Data Analsyis in Python](https://github.com/informatics-lab/intro_python_data_analysis/)\n",
    "* [Pangeo Lectures](https://github.com/informatics-lab/PangeoLectures)\n",
    "* [Using Climate Data](https://github.com/Informatics-lab/UsingClimateData)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e70e46b-e202-47b9-8d6d-a479a11e2b25",
   "metadata": {},
   "source": [
    "## Dataset Info\n",
    "\n",
    "### Falklands Rotors Challenge Dataset\n",
    "Crown Copyright 2021 - This dataset was created by Met Office Chief Operational Meterologist Steve Ramsdale from Met Office forecast and observation data.\n",
    "* Model Data - Met Office Global 10km resolution model\n",
    "* Observations - made by meteorologists at Mount Pleasant airfield in the Falkland Islands.\n",
    "\n",
    "### ERA5\n",
    "ERA5 is Renanlysis data created by ECMWF. Reanalysis combines observations from many sources. by assimilating these into a forecast model (ECMWF's IFS in this case), to provide a consistent physically valid gridded dataset that is a close to observations as possible\n",
    "https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5\n",
    "\n"
   ]
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    "## References\n",
    "\n",
    "The format of this notebook is based on the [template for tutorial notebooks](https://github.com/geo-yrao/notebook-dev/blob/main/templates/NCAI_Training_Notebook_template%20-%20Distribution%20Copy.ipynb) developed by NOAA, available on GitHub."
   ]
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