3a_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workshop 3:  pandas, netCDF, xarray\n",
    "\n",
    "# Part I:  pandas\n",
    "\n",
    "Pandas is a an open source library providing high-performance, easy-to-use data structures and data analysis tools. Pandas is particularly suited to the analysis of tabular data, i.e. data that can can go into a table. In other words, if you can imagine the data in an Excel spreadsheet, then Pandas is the tool for the job. In the climate context it is useful for time series analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Data Structures: Series\n",
    "\n",
    "A Series represents a one-dimensional array of data. The main difference between a Series and numpy array is that a Series has an _index_. The index contains the labels that we use to access the data.\n",
    "\n",
    "There are many ways to [create a Series](https://pandas.pydata.org/pandas-docs/stable/dsintro.html#series). We will just show a few."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice     30\n",
       "Bob       15\n",
       "Claire    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['Alice', 'Bob', 'Claire']\n",
    "values = [30, 15, 22]\n",
    "ages = pd.Series(values, index=names)\n",
    "ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Series have built in plotting methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q01Z72bHKJRRJLflSkj9aujPJZazDj3L2CFxSM7qvwLsB+Bn/H+x5Bp+Hcn5V/U9fs/XBgEtqTpIXAAtr4XdX1af7nKcvBlySGuUauCQ1yoBLUqMMuCQ1yoBLUqMMuCQ16n8BRqN+K5TK9eQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Arithmetic operations and most numpy function can be applied to Series.\n",
    "An important point is that the Series keep their index during such operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice     0.003779\n",
       "Bob       0.012036\n",
       "Claire    0.006386\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(ages) / ages**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the underlying index object if we need to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alice', 'Bob', 'Claire'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing\n",
    "\n",
    "We can get values back out using the index via the `.loc` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.loc['Claire']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or by raw position using `.iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pass a list or array to loc to get multiple rows back:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob      15\n",
       "Alice    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.loc[['Bob', 'Alice']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can even use slice notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice     30\n",
       "Bob       15\n",
       "Claire    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.loc['Alice':'Claire']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bob       15\n",
       "Claire    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.iloc[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we need to, we can always get the raw data back out as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 15, 22])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.values # a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Alice', 'Bob', 'Claire'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ages.index # a pandas Index object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Data Structures: DataFrame\n",
    "\n",
    "There is a lot more to Series, but they are limit to a single \"column\". A more useful Pandas data structure is the DataFrame. A DataFrame is basically a bunch of series that share the same index. It's a lot like a table in a spreadsheet.\n",
    "\n",
    "Below we create a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>30</td>\n",
       "      <td>180</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>15</td>\n",
       "      <td>155</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Claire</th>\n",
       "      <td>22</td>\n",
       "      <td>160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  height  weight\n",
       "Alice    30     180    70.0\n",
       "Bob      15     155    52.0\n",
       "Claire   22     160     NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first we create a dictionary\n",
    "data = {'age': [30, 15, 22],\n",
    "        'height': [180, 155, 160],\n",
    "        'weight': [70, 52, np.nan]}\n",
    "df = pd.DataFrame(data, index=['Alice', 'Bob', 'Claire'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas handles missing data very elegantly, keeping track of it through all calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, Alice to Claire\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   age     3 non-null      int64  \n",
      " 1   height  3 non-null      int64  \n",
      " 2   weight  2 non-null      float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 96.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A wide range of statistical functions are available on both Series and DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        15.0\n",
       "height    155.0\n",
       "weight     52.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        22.333333\n",
       "height    165.000000\n",
       "weight     61.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        7.505553\n",
       "height    13.228757\n",
       "weight    12.727922\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.333333</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.505553</td>\n",
       "      <td>13.228757</td>\n",
       "      <td>12.727922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>157.500000</td>\n",
       "      <td>56.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>65.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age      height     weight\n",
       "count   3.000000    3.000000   2.000000\n",
       "mean   22.333333  165.000000  61.000000\n",
       "std     7.505553   13.228757  12.727922\n",
       "min    15.000000  155.000000  52.000000\n",
       "25%    18.500000  157.500000  56.500000\n",
       "50%    22.000000  160.000000  61.000000\n",
       "75%    26.000000  170.000000  65.500000\n",
       "max    30.000000  180.000000  70.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get a single column as a Series using python's getitem syntax on the DataFrame object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice     180\n",
       "Bob       155\n",
       "Claire    160\n",
       "Name: height, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['height']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or using attribute syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice     180\n",
       "Bob       155\n",
       "Claire    160\n",
       "Name: height, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing works very similar to series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        15.0\n",
       "height    155.0\n",
       "weight     52.0\n",
       "Name: Bob, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Bob']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        22.0\n",
       "height    160.0\n",
       "weight      NaN\n",
       "Name: Claire, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we can also specify the column we want to access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Claire', 'age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alice    30\n",
       "Bob      15\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:2, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make a calculation using columns from the DataFrame, it will keep the same index. We can easily add as another column to the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>30</td>\n",
       "      <td>180</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.604938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>15</td>\n",
       "      <td>155</td>\n",
       "      <td>52.0</td>\n",
       "      <td>21.644121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Claire</th>\n",
       "      <td>22</td>\n",
       "      <td>160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  height  weight        BMI\n",
       "Alice    30     180    70.0  21.604938\n",
       "Bob      15     155    52.0  21.644121\n",
       "Claire   22     160     NaN        NaN"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BMI'] = df.weight / (df.height/100)**2\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also index using a boolean series. This is very useful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>age</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>BMI</th>\n",
       "      <th>is_adult</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>30</td>\n",
       "      <td>180</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.604938</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob</th>\n",
       "      <td>15</td>\n",
       "      <td>155</td>\n",
       "      <td>52.0</td>\n",
       "      <td>21.644121</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Claire</th>\n",
       "      <td>22</td>\n",
       "      <td>160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  height  weight        BMI  is_adult\n",
       "Alice    30     180    70.0  21.604938      True\n",
       "Bob      15     155    52.0  21.644121     False\n",
       "Claire   22     160     NaN        NaN      True"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['is_adult'] = df.age > 18\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting\n",
    "\n",
    "DataFrames have all kinds of [useful plotting](https://pandas.pydata.org/pandas-docs/stable/visualization.html) built in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='height'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='scatter', x='age', y='height', grid=True, s=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Indexes\n",
    "\n",
    "Indexes are very powerful. They are a big part of why Pandas is so useful. There are different indices for different types of data. Time Indexes are especially great!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "two_years = pd.date_range(start='2014-01-01', end='2016-01-01', freq='D')\n",
    "timeseries = pd.Series(np.sin(2 *np.pi *two_years.dayofyear / 365),\n",
    "                       index=two_years)\n",
    "timeseries.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use python's slicing notation inside `.loc` to select a date range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeseries.loc['2015-01-01':'2015-07-01'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The TimeIndex object has lots of useful attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "            ...\n",
       "            12, 12, 12, 12, 12, 12, 12, 12, 12,  1],\n",
       "           dtype='int64', length=731)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseries.index.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
       "            ...\n",
       "            23, 24, 25, 26, 27, 28, 29, 30, 31,  1],\n",
       "           dtype='int64', length=731)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseries.index.day"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data Files: Weather Station Data\n",
    "\n",
    "In this example, we will use NOAA weather station data from https://www.ncdc.noaa.gov/data-access/land-based-station-data.\n",
    "\n",
    "The details of files we are going to read are described in this [README file](ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/README.txt)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zsh:1: command not found: wget\n",
      "zsh:1: command not found: wget\n"
     ]
    }
   ],
   "source": [
    "# some shell code to download some data\n",
    "# if this does not work, you can manually download the files by copying the addresses\n",
    "# (Windows) in your Windows Explorer address bar\n",
    "# (Mac) in the Finder 'Go/Connect to Server' Menu\n",
    "! wget ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/HEADERS.txt\n",
    "! wget ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/2017/CRND0103-2017-NY_Millbrook_3_W.txt\n",
    "! head -2 data/HEADERS.txt | tail -1 > data/data.txt\n",
    "! cat data/CRND0103-2017-NY_Millbrook_3_W.txt >> data/data.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a text file on our hard drive called `data.txt`. Examine it.\n",
    "\n",
    "To read it into pandas, we will use the [read_csv](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) function. This function is incredibly complex and powerful. You can use it to extract data from almost any text file. However, you need to understand how to use its various options.\n",
    "\n",
    "With no options, this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WBANNO LST_DATE CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY SUR_TEMP_DAILY_TYPE SUR_TEMP_DAILY_MAX SUR_TEMP_DAILY_MIN SUR_TEMP_DAILY_AVG RH_DAILY_MAX RH_DAILY_MIN RH_DAILY_AVG SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64756 20170101  2.422  -73.74   41.79     6.6 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64756 20170102  2.422  -73.74   41.79     4.0 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64756 20170103  2.422  -73.74   41.79     4.9 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756 20170104  2.422  -73.74   41.79     8.7 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756 20170105  2.422  -73.74   41.79    -0.5 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WBANNO LST_DATE CRX_VN LONGITUDE LATITUDE T_DAILY_MAX T_DAILY_MIN T_DAILY_MEAN T_DAILY_AVG P_DAILY_CALC SOLARAD_DAILY SUR_TEMP_DAILY_TYPE SUR_TEMP_DAILY_MAX SUR_TEMP_DAILY_MIN SUR_TEMP_DAILY_AVG RH_DAILY_MAX RH_DAILY_MIN RH_DAILY_AVG SOIL_MOISTURE_5_DAILY SOIL_MOISTURE_10_DAILY SOIL_MOISTURE_20_DAILY SOIL_MOISTURE_50_DAILY SOIL_MOISTURE_100_DAILY SOIL_TEMP_5_DAILY SOIL_TEMP_10_DAILY SOIL_TEMP_20_DAILY SOIL_TEMP_50_DAILY SOIL_TEMP_100_DAILY \n",
       "0  64756 20170101  2.422  -73.74   41.79     6.6 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "1  64756 20170102  2.422  -73.74   41.79     4.0 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "2  64756 20170103  2.422  -73.74   41.79     4.9 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "3  64756 20170104  2.422  -73.74   41.79     8.7 ...                                                                                                                                                                                                                                                                                                                                                                                                          \n",
       "4  64756 20170105  2.422  -73.74   41.79    -0.5 ...                                                                                                                                                                                                                                                                                                                                                                                                          "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/data.txt')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas failed to identify the different columns. This is because it was expecting standard CSV (comma-separated values) file. In our file, instead, the values are separated by whitespace. And not a single whilespace--the amount of whitespace between values varies. We can tell pandas this using the `sep` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>WBANNO</th>\n",
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       "      <th>LATITUDE</th>\n",
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       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
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       "      <td>64756</td>\n",
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       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>64756</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
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       "      <td>64756</td>\n",
       "      <td>20170103</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170104</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170105</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.177</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.1</td>\n",
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       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WBANNO  LST_DATE  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "0   64756  20170101   2.422     -73.74     41.79          6.6         -5.4   \n",
       "1   64756  20170102   2.422     -73.74     41.79          4.0         -6.8   \n",
       "2   64756  20170103   2.422     -73.74     41.79          4.9          0.7   \n",
       "3   64756  20170104   2.422     -73.74     41.79          8.7         -1.6   \n",
       "4   64756  20170105   2.422     -73.74     41.79         -0.5         -4.6   \n",
       "\n",
       "   T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  ...  SOIL_MOISTURE_5_DAILY  \\\n",
       "0           0.6          2.2           0.0  ...                  -99.0   \n",
       "1          -1.4         -1.2           0.0  ...                  -99.0   \n",
       "2           2.8          2.7          13.1  ...                  -99.0   \n",
       "3           3.6          3.5           1.3  ...                  -99.0   \n",
       "4          -2.5         -2.8           0.0  ...                  -99.0   \n",
       "\n",
       "  SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "0                  -99.0                   0.207                   0.152   \n",
       "1                  -99.0                   0.205                   0.151   \n",
       "2                  -99.0                   0.205                   0.150   \n",
       "3                  -99.0                   0.215                   0.153   \n",
       "4                  -99.0                   0.215                   0.154   \n",
       "\n",
       "   SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "0                    0.175               -0.1                 0.0   \n",
       "1                    0.173               -0.2                 0.0   \n",
       "2                    0.173               -0.1                 0.0   \n",
       "3                    0.174               -0.1                 0.0   \n",
       "4                    0.177               -0.1                 0.0   \n",
       "\n",
       "   SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "0                 0.6                 1.5                  3.4  \n",
       "1                 0.6                 1.5                  3.3  \n",
       "2                 0.5                 1.5                  3.3  \n",
       "3                 0.5                 1.5                  3.2  \n",
       "4                 0.5                 1.4                  3.1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/data.txt', sep='\\s+')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! It worked. \n",
    "\n",
    "If we look closely, we will see there are lots of -99 and -9999 values in the file. The [README file](ftp://ftp.ncdc.noaa.gov/pub/data/uscrn/products/daily01/README.txt) tells us that these are values used to represent missing data. Let's tell this to pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.0</td>\n",
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       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.151</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
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       "      <th>2</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170103</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170104</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64756</td>\n",
       "      <td>20170105</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.177</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   WBANNO  LST_DATE  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "0   64756  20170101   2.422     -73.74     41.79          6.6         -5.4   \n",
       "1   64756  20170102   2.422     -73.74     41.79          4.0         -6.8   \n",
       "2   64756  20170103   2.422     -73.74     41.79          4.9          0.7   \n",
       "3   64756  20170104   2.422     -73.74     41.79          8.7         -1.6   \n",
       "4   64756  20170105   2.422     -73.74     41.79         -0.5         -4.6   \n",
       "\n",
       "   T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  ...  SOIL_MOISTURE_5_DAILY  \\\n",
       "0           0.6          2.2           0.0  ...                    NaN   \n",
       "1          -1.4         -1.2           0.0  ...                    NaN   \n",
       "2           2.8          2.7          13.1  ...                    NaN   \n",
       "3           3.6          3.5           1.3  ...                    NaN   \n",
       "4          -2.5         -2.8           0.0  ...                    NaN   \n",
       "\n",
       "  SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "0                    NaN                   0.207                   0.152   \n",
       "1                    NaN                   0.205                   0.151   \n",
       "2                    NaN                   0.205                   0.150   \n",
       "3                    NaN                   0.215                   0.153   \n",
       "4                    NaN                   0.215                   0.154   \n",
       "\n",
       "   SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "0                    0.175               -0.1                 0.0   \n",
       "1                    0.173               -0.2                 0.0   \n",
       "2                    0.173               -0.1                 0.0   \n",
       "3                    0.174               -0.1                 0.0   \n",
       "4                    0.177               -0.1                 0.0   \n",
       "\n",
       "   SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "0                 0.6                 1.5                  3.4  \n",
       "1                 0.6                 1.5                  3.3  \n",
       "2                 0.5                 1.5                  3.3  \n",
       "3                 0.5                 1.5                  3.2  \n",
       "4                 0.5                 1.4                  3.1  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/data.txt', sep='\\s+', na_values=[-9999.0, -99.0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great. The missing data is now represented by `NaN`.\n",
    "\n",
    "What data types did pandas infer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 365 entries, 0 to 364\n",
      "Data columns (total 28 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   WBANNO                   365 non-null    int64  \n",
      " 1   LST_DATE                 365 non-null    int64  \n",
      " 2   CRX_VN                   365 non-null    float64\n",
      " 3   LONGITUDE                365 non-null    float64\n",
      " 4   LATITUDE                 365 non-null    float64\n",
      " 5   T_DAILY_MAX              364 non-null    float64\n",
      " 6   T_DAILY_MIN              364 non-null    float64\n",
      " 7   T_DAILY_MEAN             364 non-null    float64\n",
      " 8   T_DAILY_AVG              364 non-null    float64\n",
      " 9   P_DAILY_CALC             364 non-null    float64\n",
      " 10  SOLARAD_DAILY            364 non-null    float64\n",
      " 11  SUR_TEMP_DAILY_TYPE      365 non-null    object \n",
      " 12  SUR_TEMP_DAILY_MAX       364 non-null    float64\n",
      " 13  SUR_TEMP_DAILY_MIN       364 non-null    float64\n",
      " 14  SUR_TEMP_DAILY_AVG       364 non-null    float64\n",
      " 15  RH_DAILY_MAX             364 non-null    float64\n",
      " 16  RH_DAILY_MIN             364 non-null    float64\n",
      " 17  RH_DAILY_AVG             364 non-null    float64\n",
      " 18  SOIL_MOISTURE_5_DAILY    317 non-null    float64\n",
      " 19  SOIL_MOISTURE_10_DAILY   317 non-null    float64\n",
      " 20  SOIL_MOISTURE_20_DAILY   336 non-null    float64\n",
      " 21  SOIL_MOISTURE_50_DAILY   364 non-null    float64\n",
      " 22  SOIL_MOISTURE_100_DAILY  359 non-null    float64\n",
      " 23  SOIL_TEMP_5_DAILY        364 non-null    float64\n",
      " 24  SOIL_TEMP_10_DAILY       364 non-null    float64\n",
      " 25  SOIL_TEMP_20_DAILY       364 non-null    float64\n",
      " 26  SOIL_TEMP_50_DAILY       364 non-null    float64\n",
      " 27  SOIL_TEMP_100_DAILY      364 non-null    float64\n",
      "dtypes: float64(25), int64(2), object(1)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One problem here is that pandas did not recognize the `LDT_DATE` column as a date. Let's help it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 365 entries, 0 to 364\n",
      "Data columns (total 28 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   WBANNO                   365 non-null    int64         \n",
      " 1   LST_DATE                 365 non-null    datetime64[ns]\n",
      " 2   CRX_VN                   365 non-null    float64       \n",
      " 3   LONGITUDE                365 non-null    float64       \n",
      " 4   LATITUDE                 365 non-null    float64       \n",
      " 5   T_DAILY_MAX              364 non-null    float64       \n",
      " 6   T_DAILY_MIN              364 non-null    float64       \n",
      " 7   T_DAILY_MEAN             364 non-null    float64       \n",
      " 8   T_DAILY_AVG              364 non-null    float64       \n",
      " 9   P_DAILY_CALC             364 non-null    float64       \n",
      " 10  SOLARAD_DAILY            364 non-null    float64       \n",
      " 11  SUR_TEMP_DAILY_TYPE      365 non-null    object        \n",
      " 12  SUR_TEMP_DAILY_MAX       364 non-null    float64       \n",
      " 13  SUR_TEMP_DAILY_MIN       364 non-null    float64       \n",
      " 14  SUR_TEMP_DAILY_AVG       364 non-null    float64       \n",
      " 15  RH_DAILY_MAX             364 non-null    float64       \n",
      " 16  RH_DAILY_MIN             364 non-null    float64       \n",
      " 17  RH_DAILY_AVG             364 non-null    float64       \n",
      " 18  SOIL_MOISTURE_5_DAILY    317 non-null    float64       \n",
      " 19  SOIL_MOISTURE_10_DAILY   317 non-null    float64       \n",
      " 20  SOIL_MOISTURE_20_DAILY   336 non-null    float64       \n",
      " 21  SOIL_MOISTURE_50_DAILY   364 non-null    float64       \n",
      " 22  SOIL_MOISTURE_100_DAILY  359 non-null    float64       \n",
      " 23  SOIL_TEMP_5_DAILY        364 non-null    float64       \n",
      " 24  SOIL_TEMP_10_DAILY       364 non-null    float64       \n",
      " 25  SOIL_TEMP_20_DAILY       364 non-null    float64       \n",
      " 26  SOIL_TEMP_50_DAILY       364 non-null    float64       \n",
      " 27  SOIL_TEMP_100_DAILY      364 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(25), int64(1), object(1)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/data.txt', sep='\\s+',\n",
    "                 na_values=[-9999.0, -99.0],\n",
    "                 parse_dates=[1])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It worked! Finally, let's tell pandas to use the date column as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.68</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.152</td>\n",
       "      <td>0.175</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>4.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>13.1</td>\n",
       "      <td>0.68</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.205</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.173</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.85</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-4.6</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.215</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.177</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            WBANNO  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "2017-01-01   64756   2.422     -73.74     41.79          6.6         -5.4   \n",
       "2017-01-02   64756   2.422     -73.74     41.79          4.0         -6.8   \n",
       "2017-01-03   64756   2.422     -73.74     41.79          4.9          0.7   \n",
       "2017-01-04   64756   2.422     -73.74     41.79          8.7         -1.6   \n",
       "2017-01-05   64756   2.422     -73.74     41.79         -0.5         -4.6   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY  ...  \\\n",
       "LST_DATE                                                            ...   \n",
       "2017-01-01           0.6          2.2           0.0           8.68  ...   \n",
       "2017-01-02          -1.4         -1.2           0.0           2.08  ...   \n",
       "2017-01-03           2.8          2.7          13.1           0.68  ...   \n",
       "2017-01-04           3.6          3.5           1.3           2.85  ...   \n",
       "2017-01-05          -2.5         -2.8           0.0           4.90  ...   \n",
       "\n",
       "           SOIL_MOISTURE_5_DAILY  SOIL_MOISTURE_10_DAILY  \\\n",
       "LST_DATE                                                   \n",
       "2017-01-01                   NaN                     NaN   \n",
       "2017-01-02                   NaN                     NaN   \n",
       "2017-01-03                   NaN                     NaN   \n",
       "2017-01-04                   NaN                     NaN   \n",
       "2017-01-05                   NaN                     NaN   \n",
       "\n",
       "            SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "LST_DATE                                                     \n",
       "2017-01-01                   0.207                   0.152   \n",
       "2017-01-02                   0.205                   0.151   \n",
       "2017-01-03                   0.205                   0.150   \n",
       "2017-01-04                   0.215                   0.153   \n",
       "2017-01-05                   0.215                   0.154   \n",
       "\n",
       "            SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "LST_DATE                                                                     \n",
       "2017-01-01                    0.175               -0.1                 0.0   \n",
       "2017-01-02                    0.173               -0.2                 0.0   \n",
       "2017-01-03                    0.173               -0.1                 0.0   \n",
       "2017-01-04                    0.174               -0.1                 0.0   \n",
       "2017-01-05                    0.177               -0.1                 0.0   \n",
       "\n",
       "            SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "LST_DATE                                                                 \n",
       "2017-01-01                 0.6                 1.5                  3.4  \n",
       "2017-01-02                 0.6                 1.5                  3.3  \n",
       "2017-01-03                 0.5                 1.5                  3.3  \n",
       "2017-01-04                 0.5                 1.5                  3.2  \n",
       "2017-01-05                 0.5                 1.4                  3.1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('LST_DATE')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now access values by time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WBANNO                     64756\n",
       "CRX_VN                     2.422\n",
       "LONGITUDE                 -73.74\n",
       "LATITUDE                   41.79\n",
       "T_DAILY_MAX                 19.3\n",
       "T_DAILY_MIN                 12.3\n",
       "T_DAILY_MEAN                15.8\n",
       "T_DAILY_AVG                 16.3\n",
       "P_DAILY_CALC                 4.9\n",
       "SOLARAD_DAILY               3.93\n",
       "SUR_TEMP_DAILY_TYPE            C\n",
       "SUR_TEMP_DAILY_MAX          22.3\n",
       "SUR_TEMP_DAILY_MIN          11.9\n",
       "SUR_TEMP_DAILY_AVG          17.7\n",
       "RH_DAILY_MAX                94.7\n",
       "RH_DAILY_MIN                76.4\n",
       "RH_DAILY_AVG                89.5\n",
       "SOIL_MOISTURE_5_DAILY      0.148\n",
       "SOIL_MOISTURE_10_DAILY     0.113\n",
       "SOIL_MOISTURE_20_DAILY     0.094\n",
       "SOIL_MOISTURE_50_DAILY     0.114\n",
       "SOIL_MOISTURE_100_DAILY    0.151\n",
       "SOIL_TEMP_5_DAILY           21.4\n",
       "SOIL_TEMP_10_DAILY          21.7\n",
       "SOIL_TEMP_20_DAILY          22.1\n",
       "SOIL_TEMP_50_DAILY          22.2\n",
       "SOIL_TEMP_100_DAILY         21.5\n",
       "Name: 2017-08-07 00:00:00, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2017-08-07']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or use slicing to get a range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>WBANNO</th>\n",
       "      <th>CRX_VN</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LST_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-07-01</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>19.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.163</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.9</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-02</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.8</td>\n",
       "      <td>18.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>27.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.129</td>\n",
       "      <td>0.162</td>\n",
       "      <td>26.8</td>\n",
       "      <td>26.4</td>\n",
       "      <td>24.5</td>\n",
       "      <td>22.3</td>\n",
       "      <td>20.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-03</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.162</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-04</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.8</td>\n",
       "      <td>12.6</td>\n",
       "      <td>19.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.161</td>\n",
       "      <td>25.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-05</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.90</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.161</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>20.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-06</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.7</td>\n",
       "      <td>14.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.160</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.7</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-07</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.8</td>\n",
       "      <td>16.8</td>\n",
       "      <td>21.3</td>\n",
       "      <td>20.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>13.88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.160</td>\n",
       "      <td>24.2</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-08</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>22.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.92</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.159</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.4</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>18.6</td>\n",
       "      <td>19.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.158</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.5</td>\n",
       "      <td>20.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-10</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.6</td>\n",
       "      <td>11.8</td>\n",
       "      <td>19.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.158</td>\n",
       "      <td>24.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>23.6</td>\n",
       "      <td>22.5</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-11</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.4</td>\n",
       "      <td>19.2</td>\n",
       "      <td>23.3</td>\n",
       "      <td>22.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17.79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.157</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-12</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>16.27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.157</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>22.8</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.5</td>\n",
       "      <td>18.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.4</td>\n",
       "      <td>23.3</td>\n",
       "      <td>13.61</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.156</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>24.4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-14</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>18.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>5.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.155</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-15</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.6</td>\n",
       "      <td>16.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>21.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.163</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.155</td>\n",
       "      <td>24.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>22.2</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-16</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.9</td>\n",
       "      <td>13.3</td>\n",
       "      <td>20.6</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0.155</td>\n",
       "      <td>25.4</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.2</td>\n",
       "      <td>16.1</td>\n",
       "      <td>22.6</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.47</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.143</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.6</td>\n",
       "      <td>24.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-18</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>30.3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.99</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>27.0</td>\n",
       "      <td>26.7</td>\n",
       "      <td>24.9</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-19</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>31.2</td>\n",
       "      <td>19.1</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.69</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.156</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.4</td>\n",
       "      <td>25.6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-20</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>31.8</td>\n",
       "      <td>16.6</td>\n",
       "      <td>24.2</td>\n",
       "      <td>23.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>21.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.156</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-21</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>30.6</td>\n",
       "      <td>16.6</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.55</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.155</td>\n",
       "      <td>27.1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-22</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>27.7</td>\n",
       "      <td>15.6</td>\n",
       "      <td>21.7</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>16.04</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.156</td>\n",
       "      <td>25.9</td>\n",
       "      <td>26.1</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-23</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.4</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.03</td>\n",
       "      <td>...</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.155</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-24</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>19.4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>17.1</td>\n",
       "      <td>16.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>9.10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.154</td>\n",
       "      <td>23.1</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.9</td>\n",
       "      <td>23.5</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-25</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>18.6</td>\n",
       "      <td>13.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>16.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.153</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.5</td>\n",
       "      <td>21.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-26</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.7</td>\n",
       "      <td>11.2</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.152</td>\n",
       "      <td>22.9</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-27</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.154</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-28</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>26.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.7</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.154</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>22.8</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-29</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>24.2</td>\n",
       "      <td>10.4</td>\n",
       "      <td>17.3</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.28</td>\n",
       "      <td>...</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.154</td>\n",
       "      <td>23.3</td>\n",
       "      <td>23.6</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-30</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>16.8</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.117</td>\n",
       "      <td>0.154</td>\n",
       "      <td>22.8</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>64756</td>\n",
       "      <td>2.422</td>\n",
       "      <td>-73.74</td>\n",
       "      <td>41.79</td>\n",
       "      <td>29.4</td>\n",
       "      <td>10.1</td>\n",
       "      <td>19.7</td>\n",
       "      <td>20.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.49</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.116</td>\n",
       "      <td>0.153</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>22.7</td>\n",
       "      <td>21.9</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            WBANNO  CRX_VN  LONGITUDE  LATITUDE  T_DAILY_MAX  T_DAILY_MIN  \\\n",
       "LST_DATE                                                                    \n",
       "2017-07-01   64756   2.422     -73.74     41.79         28.0         19.7   \n",
       "2017-07-02   64756   2.422     -73.74     41.79         29.8         18.4   \n",
       "2017-07-03   64756   2.422     -73.74     41.79         28.3         15.0   \n",
       "2017-07-04   64756   2.422     -73.74     41.79         26.8         12.6   \n",
       "2017-07-05   64756   2.422     -73.74     41.79         28.0         11.9   \n",
       "2017-07-06   64756   2.422     -73.74     41.79         25.7         14.3   \n",
       "2017-07-07   64756   2.422     -73.74     41.79         25.8         16.8   \n",
       "2017-07-08   64756   2.422     -73.74     41.79         29.0         15.3   \n",
       "2017-07-09   64756   2.422     -73.74     41.79         26.3         10.9   \n",
       "2017-07-10   64756   2.422     -73.74     41.79         27.6         11.8   \n",
       "2017-07-11   64756   2.422     -73.74     41.79         27.4         19.2   \n",
       "2017-07-12   64756   2.422     -73.74     41.79         29.4         18.5   \n",
       "2017-07-13   64756   2.422     -73.74     41.79         29.5         18.3   \n",
       "2017-07-14   64756   2.422     -73.74     41.79         18.5         15.9   \n",
       "2017-07-15   64756   2.422     -73.74     41.79         26.6         16.5   \n",
       "2017-07-16   64756   2.422     -73.74     41.79         27.9         13.3   \n",
       "2017-07-17   64756   2.422     -73.74     41.79         29.2         16.1   \n",
       "2017-07-18   64756   2.422     -73.74     41.79         30.3         19.3   \n",
       "2017-07-19   64756   2.422     -73.74     41.79         31.2         19.1   \n",
       "2017-07-20   64756   2.422     -73.74     41.79         31.8         16.6   \n",
       "2017-07-21   64756   2.422     -73.74     41.79         30.6         16.6   \n",
       "2017-07-22   64756   2.422     -73.74     41.79         27.7         15.6   \n",
       "2017-07-23   64756   2.422     -73.74     41.79         26.4         18.5   \n",
       "2017-07-24   64756   2.422     -73.74     41.79         19.4         14.8   \n",
       "2017-07-25   64756   2.422     -73.74     41.79         18.6         13.7   \n",
       "2017-07-26   64756   2.422     -73.74     41.79         24.7         11.2   \n",
       "2017-07-27   64756   2.422     -73.74     41.79         24.2         15.2   \n",
       "2017-07-28   64756   2.422     -73.74     41.79         26.5         16.9   \n",
       "2017-07-29   64756   2.422     -73.74     41.79         24.2         10.4   \n",
       "2017-07-30   64756   2.422     -73.74     41.79         25.5          8.2   \n",
       "2017-07-31   64756   2.422     -73.74     41.79         29.4         10.1   \n",
       "\n",
       "            T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  SOLARAD_DAILY  ...  \\\n",
       "LST_DATE                                                            ...   \n",
       "2017-07-01          23.9         23.8           0.2          19.28  ...   \n",
       "2017-07-02          24.1         23.7           4.0          27.67  ...   \n",
       "2017-07-03          21.7         21.4           0.0          27.08  ...   \n",
       "2017-07-04          19.7         20.0           0.0          29.45  ...   \n",
       "2017-07-05          20.0         20.7           0.0          26.90  ...   \n",
       "2017-07-06          20.0         20.3           0.0          19.03  ...   \n",
       "2017-07-07          21.3         20.0          11.5          13.88  ...   \n",
       "2017-07-08          22.1         21.5           0.0          21.92  ...   \n",
       "2017-07-09          18.6         19.4           0.0          29.72  ...   \n",
       "2017-07-10          19.7         21.3           0.0          23.67  ...   \n",
       "2017-07-11          23.3         22.6           8.5          17.79  ...   \n",
       "2017-07-12          23.9         23.1           1.9          16.27  ...   \n",
       "2017-07-13          23.9         23.4          23.3          13.61  ...   \n",
       "2017-07-14          17.2         17.5           4.1           5.36  ...   \n",
       "2017-07-15          21.5         21.0           0.8          21.13  ...   \n",
       "2017-07-16          20.6         21.0           0.0          27.03  ...   \n",
       "2017-07-17          22.6         22.9           0.0          20.47  ...   \n",
       "2017-07-18          24.8         24.7           0.0          24.99  ...   \n",
       "2017-07-19          25.1         25.0           0.0          27.69  ...   \n",
       "2017-07-20          24.2         23.4           0.7          21.53  ...   \n",
       "2017-07-21          23.6         23.6           0.0          25.55  ...   \n",
       "2017-07-22          21.7         21.2           0.5          16.04  ...   \n",
       "2017-07-23          22.5         22.2           0.0          19.03  ...   \n",
       "2017-07-24          17.1         16.7          29.2           9.10  ...   \n",
       "2017-07-25          16.2         16.2           0.0           7.35  ...   \n",
       "2017-07-26          18.0         18.3           0.0          22.22  ...   \n",
       "2017-07-27          19.7         19.5           0.0           8.28  ...   \n",
       "2017-07-28          21.7         20.9           0.0          21.06  ...   \n",
       "2017-07-29          17.3         18.1           0.0          21.28  ...   \n",
       "2017-07-30          16.8         17.3           0.0          27.68  ...   \n",
       "2017-07-31          19.7         20.1           0.0          25.49  ...   \n",
       "\n",
       "           SOIL_MOISTURE_5_DAILY  SOIL_MOISTURE_10_DAILY  \\\n",
       "LST_DATE                                                   \n",
       "2017-07-01                 0.157                   0.136   \n",
       "2017-07-02                 0.146                   0.135   \n",
       "2017-07-03                 0.141                   0.132   \n",
       "2017-07-04                 0.131                   0.126   \n",
       "2017-07-05                 0.116                   0.114   \n",
       "2017-07-06                 0.105                   0.104   \n",
       "2017-07-07                 0.114                   0.100   \n",
       "2017-07-08                 0.130                   0.106   \n",
       "2017-07-09                 0.119                   0.103   \n",
       "2017-07-10                 0.105                   0.096   \n",
       "2017-07-11                 0.106                   0.093   \n",
       "2017-07-12                 0.108                   0.094   \n",
       "2017-07-13                 0.134                   0.110   \n",
       "2017-07-14                 0.194                   0.151   \n",
       "2017-07-15                 0.190                   0.163   \n",
       "2017-07-16                 0.171                   0.154   \n",
       "2017-07-17                 0.155                   0.143   \n",
       "2017-07-18                 0.142                   0.132   \n",
       "2017-07-19                 0.126                   0.118   \n",
       "2017-07-20                 0.111                   0.103   \n",
       "2017-07-21                 0.100                   0.093   \n",
       "2017-07-22                 0.092                   0.086   \n",
       "2017-07-23                 0.087                   0.082   \n",
       "2017-07-24                 0.145                   0.118   \n",
       "2017-07-25                 0.167                   0.133   \n",
       "2017-07-26                 0.155                   0.128   \n",
       "2017-07-27                 0.144                   0.122   \n",
       "2017-07-28                 0.137                   0.117   \n",
       "2017-07-29                 0.126                   0.108   \n",
       "2017-07-30                 0.113                   0.099   \n",
       "2017-07-31                 0.101                   0.090   \n",
       "\n",
       "            SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "LST_DATE                                                     \n",
       "2017-07-01                   0.144                   0.129   \n",
       "2017-07-02                   0.143                   0.129   \n",
       "2017-07-03                   0.139                   0.128   \n",
       "2017-07-04                   0.136                   0.126   \n",
       "2017-07-05                   0.131                   0.125   \n",
       "2017-07-06                   0.126                   0.124   \n",
       "2017-07-07                   0.123                   0.123   \n",
       "2017-07-08                   0.122                   0.123   \n",
       "2017-07-09                   0.119                   0.121   \n",
       "2017-07-10                   0.113                   0.120   \n",
       "2017-07-11                   0.110                   0.120   \n",
       "2017-07-12                   0.108                   0.118   \n",
       "2017-07-13                   0.108                   0.118   \n",
       "2017-07-14                   0.114                   0.120   \n",
       "2017-07-15                   0.119                   0.122   \n",
       "2017-07-16                   0.123                   0.123   \n",
       "2017-07-17                   0.124                   0.122   \n",
       "2017-07-18                   0.122                   0.122   \n",
       "2017-07-19                   0.118                   0.122   \n",
       "2017-07-20                   0.114                   0.121   \n",
       "2017-07-21                   0.108                   0.120   \n",
       "2017-07-22                   0.104                   0.119   \n",
       "2017-07-23                   0.100                   0.118   \n",
       "2017-07-24                   0.102                   0.117   \n",
       "2017-07-25                   0.107                   0.116   \n",
       "2017-07-26                   0.108                   0.118   \n",
       "2017-07-27                   0.109                   0.118   \n",
       "2017-07-28                   0.110                   0.119   \n",
       "2017-07-29                   0.108                   0.118   \n",
       "2017-07-30                   0.104                   0.117   \n",
       "2017-07-31                   0.099                   0.116   \n",
       "\n",
       "            SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "LST_DATE                                                                     \n",
       "2017-07-01                    0.163               25.7                25.4   \n",
       "2017-07-02                    0.162               26.8                26.4   \n",
       "2017-07-03                    0.162               26.4                26.3   \n",
       "2017-07-04                    0.161               25.9                25.8   \n",
       "2017-07-05                    0.161               25.3                25.3   \n",
       "2017-07-06                    0.160               24.7                24.7   \n",
       "2017-07-07                    0.160               24.2                24.2   \n",
       "2017-07-08                    0.159               25.5                25.3   \n",
       "2017-07-09                    0.158               24.8                24.8   \n",
       "2017-07-10                    0.158               24.7                24.7   \n",
       "2017-07-11                    0.157               25.6                25.4   \n",
       "2017-07-12                    0.157               25.8                25.6   \n",
       "2017-07-13                    0.156               25.7                25.7   \n",
       "2017-07-14                    0.155               23.0                23.3   \n",
       "2017-07-15                    0.155               24.6                24.4   \n",
       "2017-07-16                    0.155               25.4                25.3   \n",
       "2017-07-17                    0.156               25.7                25.6   \n",
       "2017-07-18                    0.156               27.0                26.7   \n",
       "2017-07-19                    0.156               27.6                27.4   \n",
       "2017-07-20                    0.156               27.0                27.0   \n",
       "2017-07-21                    0.155               27.1                27.0   \n",
       "2017-07-22                    0.156               25.9                26.1   \n",
       "2017-07-23                    0.155               26.0                26.0   \n",
       "2017-07-24                    0.154               23.1                23.6   \n",
       "2017-07-25                    0.153               21.9                22.2   \n",
       "2017-07-26                    0.152               22.9                23.0   \n",
       "2017-07-27                    0.154               22.5                22.7   \n",
       "2017-07-28                    0.154               24.1                24.1   \n",
       "2017-07-29                    0.154               23.3                23.6   \n",
       "2017-07-30                    0.154               22.8                23.0   \n",
       "2017-07-31                    0.153               23.8                23.8   \n",
       "\n",
       "            SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "LST_DATE                                                                 \n",
       "2017-07-01                23.7                21.9                 19.9  \n",
       "2017-07-02                24.5                22.3                 20.1  \n",
       "2017-07-03                24.8                22.8                 20.3  \n",
       "2017-07-04                24.6                22.9                 20.6  \n",
       "2017-07-05                24.2                22.8                 20.7  \n",
       "2017-07-06                23.9                22.7                 20.9  \n",
       "2017-07-07                23.4                22.4                 20.8  \n",
       "2017-07-08                23.9                22.4                 20.8  \n",
       "2017-07-09                23.8                22.5                 20.8  \n",
       "2017-07-10                23.6                22.5                 20.9  \n",
       "2017-07-11                24.1                22.6                 20.9  \n",
       "2017-07-12                24.2                22.8                 21.0  \n",
       "2017-07-13                24.4                23.0                 21.0  \n",
       "2017-07-14                23.4                22.9                 21.2  \n",
       "2017-07-15                23.2                22.2                 21.2  \n",
       "2017-07-16                23.9                22.6                 21.1  \n",
       "2017-07-17                24.4                22.9                 21.2  \n",
       "2017-07-18                24.9                23.2                 21.3  \n",
       "2017-07-19                25.6                23.7                 21.5  \n",
       "2017-07-20                25.6                24.0                 21.7  \n",
       "2017-07-21                25.5                24.0                 21.9  \n",
       "2017-07-22                25.3                24.1                 22.0  \n",
       "2017-07-23                24.9                23.8                 22.1  \n",
       "2017-07-24                23.9                23.5                 22.1  \n",
       "2017-07-25                22.4                22.5                 21.9  \n",
       "2017-07-26                22.3                22.0                 21.7  \n",
       "2017-07-27                22.4                22.0                 21.4  \n",
       "2017-07-28                22.8                22.0                 21.3  \n",
       "2017-07-29                23.0                22.2                 21.3  \n",
       "2017-07-30                22.4                22.0                 21.3  \n",
       "2017-07-31                22.7                21.9                 21.2  \n",
       "\n",
       "[31 rows x 27 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['2017-07-01':'2017-07-31']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>T_DAILY_MAX</th>\n",
       "      <th>T_DAILY_MIN</th>\n",
       "      <th>T_DAILY_MEAN</th>\n",
       "      <th>T_DAILY_AVG</th>\n",
       "      <th>P_DAILY_CALC</th>\n",
       "      <th>SOLARAD_DAILY</th>\n",
       "      <th>SUR_TEMP_DAILY_MAX</th>\n",
       "      <th>SUR_TEMP_DAILY_MIN</th>\n",
       "      <th>SUR_TEMP_DAILY_AVG</th>\n",
       "      <th>RH_DAILY_MAX</th>\n",
       "      <th>...</th>\n",
       "      <th>SOIL_MOISTURE_5_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_10_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_20_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_50_DAILY</th>\n",
       "      <th>SOIL_MOISTURE_100_DAILY</th>\n",
       "      <th>SOIL_TEMP_5_DAILY</th>\n",
       "      <th>SOIL_TEMP_10_DAILY</th>\n",
       "      <th>SOIL_TEMP_20_DAILY</th>\n",
       "      <th>SOIL_TEMP_50_DAILY</th>\n",
       "      <th>SOIL_TEMP_100_DAILY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>336.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.720055</td>\n",
       "      <td>4.037912</td>\n",
       "      <td>9.876374</td>\n",
       "      <td>9.990110</td>\n",
       "      <td>2.797802</td>\n",
       "      <td>13.068187</td>\n",
       "      <td>22.396703</td>\n",
       "      <td>1.624451</td>\n",
       "      <td>10.248626</td>\n",
       "      <td>88.974176</td>\n",
       "      <td>...</td>\n",
       "      <td>0.183804</td>\n",
       "      <td>0.181000</td>\n",
       "      <td>0.156533</td>\n",
       "      <td>0.138286</td>\n",
       "      <td>0.162844</td>\n",
       "      <td>12.344231</td>\n",
       "      <td>12.308516</td>\n",
       "      <td>12.060989</td>\n",
       "      <td>11.960989</td>\n",
       "      <td>11.971978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.502087</td>\n",
       "      <td>9.460676</td>\n",
       "      <td>9.727451</td>\n",
       "      <td>9.619168</td>\n",
       "      <td>7.238628</td>\n",
       "      <td>7.953074</td>\n",
       "      <td>15.043183</td>\n",
       "      <td>11.044597</td>\n",
       "      <td>11.448891</td>\n",
       "      <td>9.012024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047493</td>\n",
       "      <td>0.052697</td>\n",
       "      <td>0.042775</td>\n",
       "      <td>0.019207</td>\n",
       "      <td>0.013814</td>\n",
       "      <td>9.367742</td>\n",
       "      <td>9.350273</td>\n",
       "      <td>8.760899</td>\n",
       "      <td>8.082595</td>\n",
       "      <td>7.170197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-12.300000</td>\n",
       "      <td>-21.800000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>-16.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>-12.100000</td>\n",
       "      <td>-29.800000</td>\n",
       "      <td>-22.800000</td>\n",
       "      <td>49.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.069000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>-0.700000</td>\n",
       "      <td>-0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.900000</td>\n",
       "      <td>-2.775000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.275000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.225000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>-6.325000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148000</td>\n",
       "      <td>0.137000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>0.118000</td>\n",
       "      <td>0.155000</td>\n",
       "      <td>2.275000</td>\n",
       "      <td>2.075000</td>\n",
       "      <td>2.575000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>4.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.450000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>10.850000</td>\n",
       "      <td>11.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.865000</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>0.198000</td>\n",
       "      <td>0.169000</td>\n",
       "      <td>0.147000</td>\n",
       "      <td>0.166000</td>\n",
       "      <td>13.300000</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>12.850000</td>\n",
       "      <td>11.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.850000</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>18.150000</td>\n",
       "      <td>18.450000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>19.740000</td>\n",
       "      <td>35.900000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.221000</td>\n",
       "      <td>0.219000</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.152250</td>\n",
       "      <td>0.173000</td>\n",
       "      <td>21.025000</td>\n",
       "      <td>21.125000</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>19.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>33.400000</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>26.700000</td>\n",
       "      <td>65.700000</td>\n",
       "      <td>29.910000</td>\n",
       "      <td>45.700000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.800000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>0.321000</td>\n",
       "      <td>0.231000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.192000</td>\n",
       "      <td>27.600000</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>22.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_DAILY_MAX  T_DAILY_MIN  T_DAILY_MEAN  T_DAILY_AVG  P_DAILY_CALC  \\\n",
       "count   364.000000   364.000000    364.000000   364.000000    364.000000   \n",
       "mean     15.720055     4.037912      9.876374     9.990110      2.797802   \n",
       "std      10.502087     9.460676      9.727451     9.619168      7.238628   \n",
       "min     -12.300000   -21.800000    -17.000000   -16.700000      0.000000   \n",
       "25%       6.900000    -2.775000      2.100000     2.275000      0.000000   \n",
       "50%      17.450000     4.350000     10.850000    11.050000      0.000000   \n",
       "75%      24.850000    11.900000     18.150000    18.450000      1.400000   \n",
       "max      33.400000    20.700000     25.700000    26.700000     65.700000   \n",
       "\n",
       "       SOLARAD_DAILY  SUR_TEMP_DAILY_MAX  SUR_TEMP_DAILY_MIN  \\\n",
       "count     364.000000          364.000000          364.000000   \n",
       "mean       13.068187           22.396703            1.624451   \n",
       "std         7.953074           15.043183           11.044597   \n",
       "min         0.100000          -12.100000          -29.800000   \n",
       "25%         6.225000            9.800000           -6.325000   \n",
       "50%        12.865000           25.150000            1.500000   \n",
       "75%        19.740000           35.900000           10.600000   \n",
       "max        29.910000           45.700000           21.000000   \n",
       "\n",
       "       SUR_TEMP_DAILY_AVG  RH_DAILY_MAX  ...  SOIL_MOISTURE_5_DAILY  \\\n",
       "count          364.000000    364.000000  ...             317.000000   \n",
       "mean            10.248626     88.974176  ...               0.183804   \n",
       "std             11.448891      9.012024  ...               0.047493   \n",
       "min            -22.800000     49.800000  ...               0.075000   \n",
       "25%              0.200000     87.000000  ...               0.148000   \n",
       "50%             12.400000     92.900000  ...               0.192000   \n",
       "75%             20.400000     94.700000  ...               0.221000   \n",
       "max             28.000000     97.800000  ...               0.294000   \n",
       "\n",
       "       SOIL_MOISTURE_10_DAILY  SOIL_MOISTURE_20_DAILY  SOIL_MOISTURE_50_DAILY  \\\n",
       "count              317.000000              336.000000              364.000000   \n",
       "mean                 0.181000                0.156533                0.138286   \n",
       "std                  0.052697                0.042775                0.019207   \n",
       "min                  0.074000                0.069000                0.100000   \n",
       "25%                  0.137000                0.118000                0.118000   \n",
       "50%                  0.198000                0.169000                0.147000   \n",
       "75%                  0.219000                0.188000                0.152250   \n",
       "max                  0.321000                0.231000                0.170000   \n",
       "\n",
       "       SOIL_MOISTURE_100_DAILY  SOIL_TEMP_5_DAILY  SOIL_TEMP_10_DAILY  \\\n",
       "count               359.000000         364.000000          364.000000   \n",
       "mean                  0.162844          12.344231           12.308516   \n",
       "std                   0.013814           9.367742            9.350273   \n",
       "min                   0.128000          -0.700000           -0.400000   \n",
       "25%                   0.155000           2.275000            2.075000   \n",
       "50%                   0.166000          13.300000           13.350000   \n",
       "75%                   0.173000          21.025000           21.125000   \n",
       "max                   0.192000          27.600000           27.400000   \n",
       "\n",
       "       SOIL_TEMP_20_DAILY  SOIL_TEMP_50_DAILY  SOIL_TEMP_100_DAILY  \n",
       "count          364.000000          364.000000           364.000000  \n",
       "mean            12.060989           11.960989            11.971978  \n",
       "std              8.760899            8.082595             7.170197  \n",
       "min              0.200000            0.900000             1.900000  \n",
       "25%              2.575000            3.300000             4.100000  \n",
       "50%             13.100000           12.850000            11.650000  \n",
       "75%             20.400000           19.800000            19.325000  \n",
       "max             25.600000           24.100000            22.100000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,4:].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Values\n",
    "\n",
    "We can now quickly make plots of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=2, figsize=(8,8), constrained_layout=True)\n",
    "\n",
    "ax[0,0].set_title('temperatures')\n",
    "df.iloc[:, 4:8].boxplot(ax=ax[0,0])\n",
    "df.iloc[:, 10:14].boxplot(ax=ax[0,1])\n",
    "df.iloc[:, 14:17].boxplot(ax=ax[1,0])\n",
    "df.iloc[:, 18:22].boxplot(ax=ax[1,1])\n",
    "\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        ax[i,j].set_xticklabels(ax[i,j].get_xticklabels(), rotation=30);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas is very \"time aware\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LST_DATE'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.T_DAILY_MEAN.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: we could also manually create an axis and plot into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pandas Made This!')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df.T_DAILY_MEAN.plot(ax=ax)\n",
    "ax.set_title('Pandas Made This!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LST_DATE'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temps_list = ['T_DAILY_MIN', 'T_DAILY_MEAN', 'T_DAILY_MAX']\n",
    "df[temps_list].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We easily calculate the running mean by applying a [rolling](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rolling.html) window and specifying a function (in this case the `mean` function) to apply over a moving window along the index. We specify the size of the window and, optionally, the weights. We also use the keyword `centered` to tell pandas whether to center the operation around the midpoint of the window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='LST_DATE'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[temps_list].rolling(30, center=True).mean().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Exercises</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Convert the following dictionary into a Pandas Series and display on overview of basic statistics (count, mean, std, min,... see DataFrame subsection above). `d1 = {'a': 100, 'b': 200, 'c':300, 'd':400, 'e':800}`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Convert the following data into a DataFrame:\n",
    "```\n",
    "exam_data={'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "              'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "              'attempts' : [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "              'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "```\n",
    "Calculate the mean score, select those whose attemps>2, and calculate how many rows are missing a score (you can use the built-in `.isnull()` function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Create a a daily time series from 2015-2020 with an noisy annual cycle. Resample the data by taking the mean over each month using `resample('M')`. Create the climatology (monhtly means) using the groupby(timeseries.index.month) operator.\n",
    "<img src=\"figures/3a3.png\" width=\"600\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <span style=\"color:green\">Solutions</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    100\n",
      "b    200\n",
      "c    300\n",
      "d    400\n",
      "e    800\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count      5.000000\n",
       "mean     360.000000\n",
       "std      270.185122\n",
       "min      100.000000\n",
       "25%      200.000000\n",
       "50%      300.000000\n",
       "75%      400.000000\n",
       "max      800.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Convert the following dictionary into a Pandas Series\n",
    "# and display on overview of basic statistics\n",
    "# (count, mean, std, min,... see DataFrame subsection above).\n",
    "d1 = {'a': 100, 'b': 200, 'c':300, 'd':400, 'e':800}\n",
    "new_series = pd.Series(d1)\n",
    "print(new_series)\n",
    "new_series.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean score:   13.5625\n",
      "Number of attempts in the examination is greater than 2:\n",
      "      name  score  attempts qualify\n",
      "1     Dima    9.0         3      no\n",
      "3    James    NaN         3      no\n",
      "5  Michael   20.0         3     yes\n",
      "This many rows are missing a score:   2\n"
     ]
    }
   ],
   "source": [
    "# 2. Convert the following data into a DataFrame:\n",
    "# Calculate the mean score, select those whose attemps>2, \n",
    "# and calculate how many rows are missing a score (you can use the built-in `.isnull()` function).\n",
    "\n",
    "exam_data={'name': ['Anastasia', 'Dima', 'Katherine', 'James', 'Emily', 'Michael', 'Matthew', 'Laura', 'Kevin', 'Jonas'],\n",
    "           'score': [12.5, 9, 16.5, np.nan, 9, 20, 14.5, np.nan, 8, 19],\n",
    "           'attempts' : [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "           'qualify': ['yes', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes']}\n",
    "exams = pd.DataFrame(exam_data)\n",
    "print(f'The mean score:   {exams[\"score\"].mean()}')\n",
    "\n",
    "print(\"Number of attempts in the examination is greater than 2:\")\n",
    "print(exams[exams['attempts'] > 2])\n",
    "\n",
    "print(f'This many rows are missing a score:   {len(exams[exams[\"score\"].isnull()])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n58lVhyap4XR7s2qKJHtGmpjtR3ulHgIhhOQ8CuAE+PKIrw2KjbIjkjnaHb4GnMvjxYMbDmHTwa40j4hEsukATVcRQkiqUQAXxb0fHMDxHhvt3MwAr3zVMuIyDo6Xt/t2dDa09aV7SAnLhaoboX8TLCceESGEZBcK4GJ4aXvzkCmvxz89ItlYhOiyOvDEp0dyYi2VkE0lbWZ7GkaSGtT4nRBCSKIogPPrF1iqom/AleKRJGfLkR6YB1w42pP9bae+aOwWfGy25YA6LHb8Zf1BHO22Dbl8/wkLlecghBASEwVwfq9+PXKKLiCbplAD/SnlOVAeXyaL/hh6QuqpZdFTBA7geI9vh/CJMBnE5l7biMsIIYSQUBTA+ZmjZNZ4FoUHXn+0yVjur0w63Jk9WcbhOzPdWVSwlxBCSOahAE6AbMrABcYaI3mVFdN0sjiyiJkcrA44PSN2Zro8WfRLRQghJONQAOcXKKSa7QIZuFjBT6ZvxgByp0l6uMfhHfapIJvq2AHCpqw559h/wpJ1j40QQrIBBXDwFeqNJpvefgKL4lvN0bswZEPAGiuLmM2iB6fZ9cAjPZZdLWas29WGXS3m9A6IEELyAAVwAA52xGhgnyUZhNB1VV8ezu6+rs9vOYZ+R+ZP85LIrP6d3fQ8EkKI+CiAEyA7wrfcks313WLJrvzaUJxz9Ibs/g29fLjc30ZDCCHSoQAuh7BcWTSW47L5A8GXR3rw5GdN6O53CL5NNu3iJoSQbEEBnADZ0tWAFotnplx6Wlp6fWsrrfbBwtcDTg8aO2MsQyCEECIqCuAEoCb2JFGccxwatsaSIbeCOgB485s2qYdACCF5Je8DOIfbg920S46kyLEeGz5saJd6GKLgnAc3JgiZrg8ekmPBKiGEZIK8D+DCtTIi2SdTl//ZXSPLtbi9PGPHG803zeZg+zKarieEEGnlfQCXS+gtNXtkY/xzvGewR+v6hg4JR0IIISTvAzgqdUBSKRszbeF8erBryFq+wFRqNDny0AkhJCPlfQBHSCpFCmKyLbDb2pTdhaEJISTX5HUAN+D04LUdLVIPg5CcloWzxYQQkvHyOoD7ptkEjzd33l6Gr6vqtAgvtkpSI1KmLRvXwBFCCMkckgZwjLEzGWP7GWOHGGO3hbn+FsbYDv+/3YwxD2OsxH9dE2Nsl/+6bekfPckk/Q5PcIdkZhE2Vxoaz2Xb9Gok1BmEEEJSR7IAjjEmB/AggLMATAdwOWNseugxnPM/cc7ncM7nAPglgI8556GLcVb6r1+Q0BgSGzrJUE991iT1EATL9djG6fai1xYoOSLxYAghJAdJmYFbBOAQ5/ww59wJ4HkAa6McfzmA59IysixFPSczT6RArduaidlC8bz5TSv2tvZJPQxCCMlZUgZwYwAcD/m+2X/ZCIwxHYAzAbwccjEH8D5jbDtj7LqUjTKMhrY+dPTlTgHg5l4bXJ6RBWdJcrxeHrGP7pGu/jSPJr2OhdSMI4QQIj4pA7hwuYlIKaRzAWweNn26jHM+D74p2BsZY8vD3glj1zHGtjHGtnV2diY3Yr93d5/As18eE+VcUjPZnPj3tmZ8tI8Ks4rt6+MmfLA3/jZab37Tir9/cjgFI0rM9qO9Sd1+Z4tJnIHkoVS8fhFCcoOUAVwzgLEh31cDaI1w7GUYNn3KOW/1/98B4FX4pmRH4Jw/yjlfwDlfUF5envSgc43D7cu8dVkzZ8dqrvSmPdaTWJaNc8Bij10oN12SfT4cYdqJEWHo9YsQEomUAdxWAJMYY3WMMRV8Qdobww9ijBUCOAXA6yGX6RljxsDXAFYD2J2WUWeweBeLbz/ak5ELzBPJWhFCCCH5RCHVHXPO3YyxmwC8B0AO4AnO+R7G2PX+6x/xH3oBgPc556HpjEoAr/rLFCgA/Itz/m68Y8j3MgebDnRhUV0JAKCjL3MycLkiE4PjROT5nwkhhGQkyQI4AOCcrwOwbthljwz7/kkATw677DCA2SkeXl7Itc0LH+xtx+nTK6UeBiGEEJJSed2JIRdwzrG7xZxTHSWSkSvr5wghhJBoKIDLcvtOWPDB3nZqNp6B2mlamhBCSIpQAJfl7C5fnbHPG7thi1BzLBrK26VO4LkhhBBCxJbXAVy2L872ejk27h+sDfXO7rakzsdzZdV9FqIfPSGEkHjkdQCX7QaGZXgSqrcVEjjsb7ckOaLM0dvvpICUEEJIzqIAjgRlUvHYZLSZB/DkZ03Ycdwk9VCy3itfNed831ZCCMlGkpYRIeIyD7ikHkJSOOdwilDWpNPi2zxwwpw7/WqlcrSbepoSQkgmogCOZASPl+NvmxpFabu0viE3+rpyzvO+2DQhhJDwaAo1z/GQRXBSLhlraOujnpmEEEKIQBTAJaE7gxrAi0HKRf/uFBQiFmM6Vkq0B4MQQkgkNIWahD67Gz39TujVClQVaaUeTkJyOUhwe3L4wRFCCMlreZ2BE2N10Vs72/DC1uMinImILZuWj4Ubay6Fn0e7+3G0u1/qYRBCSM7I6wCODJVLAUO2MdlG7iDOpTp2r3zVgle+apF6GIQQkjMogMtzORQjEEIIIXkjbwO4HcdN2NPaJ/UwktIkwpTUrhZz8GsK5jILPR2EEEIiydtNDBv2ZX+tsPf3tEs9BEIIIYRIIG8zcGQknmM5n2zaxBAOZUQJIYREQgGcSNxZXnOMZJ5cC6gJIYSIhwI4kfQ7PVIPgQzTbXXieA/18iSEEJJ7KIBLwmtf51ZZBK1SLvUQRGWxu/HS9maph5Ewq90t9RAIIYRkKArgSNDG/Z1SD4GEeI82qRBCCImAArjhOMcY89c488BvcfX2i6B3dkk9IpKnPN7cW1d57wcHpB4CIYTkhLwtIzKcwjOAWe2vYtaJ11A6cARupoSCu1Bp3YvDJculHl7aeP1N5WWyLN/CGaLL6kCZQS31MOLWZXXC6nDDoM6tP1POOVi2bxEmhBCJUQbOb9nRh7HiyL1wyvV4b+J/48l5LwMACu3Zu4YqEQ9/3Ii/f3pY6mGIaiCLN5gcaLdIPQRCCCEZKLc+2iehvH8/Wo2z8EL9E74LOIdDrkehvVXagaWZ0+2Fk9bOZwzKUxFCCAmHMnB+hfYWmDRjBy9gDGZNFQrtubXTlGSXXJxqpALFhBCSPArgAMi9ThidHTBrqodcbtZUC55C3dbUk4qhEUIIIYSMQAEcgAJ7Kxg4TJoxQy43q8f4plB57N2AO5vNMY8hhID6SxBCiAgogAOC06Tm4QGcZgwU3EmlRIhkvjluknoIhBBCMpCkARxj7EzG2H7G2CHG2G1hrl/BGDMzxnb4//1W6G3jUeSfJh0+hWryf19E6+CIRHr6neh35N6ukj67C3ZX9u4OJoQQqUkWwDHG5AAeBHAWgOkALmeMTQ9z6Cec8zn+f7+P87aCFNqb4ZRpYVOWDLk8kJHLxFIinFaC5w1vjj3XnHM8/skRPLH5iNRDIYSQrCVlBm4RgEOc88OccyeA5wGsTcNtRyi0t/iCtWE7/izqUfBClpE7Ue/78GBKz9/U1Q+L3ZXS+yDC5Fb4Bvz1o0MAAIcr9zpNEEJIukgZwI0BcDzk+2b/ZcMtZYx9wxh7hzE2I87bgjF2HWNsG2NsW2dn+F6fRfbmEdOnAOCVKWFRV6LQkVkBXDqyb69+3YJnvjiW8vvJNR0WOzotDqmHQXKEkNcvQkh+kjKAC1fganhk8hWAGs75bAB/BfBaHLf1Xcj5o5zzBZzzBeXl5eCcY9OBkBdC7kWho3XEDtQAs2aM4AzcliPpKSWSrhk1WqMUv2e/OIZnvjgq6jlzbAaVxGH46xchhARIGcA1AwipnItqAEPaHnDO+zjnVv/X6wAoGWNlQm4bic3pwfajvcHv9c4uKLyOETtQA+IJ4DYfSs9uVXo/zzP0hBMiGK0PJvlCylZaWwFMYozVAWgBcBmA74QewBgbBaCdc84ZY4vgCzi7AZhi3TaSgWFZpaJgCZGRU6i+y8dA7+qB0mODS64T+thSKtcWtecil0e89V1tfQMo1ClFO59QZhutgSTZ4XiPDesb2rF+Xwe2HOnB6dMrcdeFs2DUpP/vhpB0kSyA45y7GWM3AXgPgBzAE5zzPYyx6/3XPwLgYgA/Zoy5AQwAuIz7Pl6Fva2Q+/V6hwY/gR2mpogBnO/yAnsruvUT43yUqZGL8ZvQT81aZw/AGAaUxSkeUXJCs7zJemfXCUwdVSDa+YR4d/cJNLT1CTpW4RnAOPNWHC5ZnuJREeLj8XLsON6LDxs6sL6hHQfarQCA8eV6nDVzFN7c2YY9rX146Ip5mDY6vX87hKSLpM3s/dOi64Zd9kjI1w8AeEDobYUYFr+h0N4ML2SwqEeFPd6s9k2tFtmbMyaAy+cM3PkNN8Mp1+HlmQ9LPZSoBpzZvX4wNHgrt+5HleUbfDP6krDHrjz8J8zseBPPzn4aHYZp6RoiyTNWhxufHOjEhw0d2Li/A939TshlDItqS/Cbs8di1bRK1JXpAQCXLxqHnzz3Nc5/cDPuWDsTlywcG+PshGQfSQM4KXAMz8C1+MqFyMKn2k3BWnCZtRM1LpxD7bHCoTBKPZKIhDRtL7Q3Y5R1L9xMBbnXCY9MlYaRJWbHcRO0zh4MqEpiHyzAgXYLGtr6sHZO+LWaqTT7xEuY1f4aBhRFOFC+esh1Nb2fY2bHmwCAsaZtFMARUTX32rC+oQMfNrTjy8M9cHq8KNQqsWJKOVZNq8Qpk8tRqB352r14fCnW/exb+NnzX+MXL+/ElqYe3LF2JrQquQSPgpDUyKsAzmJ3j5h+LArUgIvAoSiAXW7IqAAu3gTckuN/x4KWp/Cv2U+jRzc+NYNKkpAp1AndGwEACu5EhbUBbQWzUzyqxFVa9uI7O6/CpzU3Ymv11Umf7+2dbckPKkGBVnKnHr4HzYXzYFOVAQBUbitOO3QnurV1kHEPxvZtw3ZcKdk4SfbzTY2a8NG+dqxv6MC+ExYAwPgyPa46qQarplViQU0xFPLY++/KDGo8/f3F+Mv6g/jrRwexq9mMB6+Yh4kVhlQ/DELSIq8COJvTPWL6sdDejMbSFZFvxBjMmuqM6sYQzxSq1tWL+S3/hNLrwGmNd+HFmX8DWHa2wJ3YvRFmdRUKHa0Y0/dNRgdwgd+Xk48+CLdMha+rBO2xyUh6Zye6tXUodLTitMa78MbUPwOM4eSjD8Do7MDz9Y9jWsc6TO9YB5nXDa8sr15WSJL6HW58ctA3Nbph3+DU6MLaYvzm7Gk4dWoFxpcnFnTJZQw/P30yFtQU4z9e2IG1D3yKuy6qx3mzq0R+FISkX9690obGPiq3FTq3KeIGhgCzZgzK+lPb+SAe8STgFjQ/DYXXga1jvoeFLU9jZvsb2D3q/FQNLWGxplD1zi5UWXbi83HXYWrHO6iyfJOmkSVG6zYBAI4WLsKKI/fCLdNg16gLg9cXDhzHWPM2NJauyPgNGQZnFw6XnIzd2rU4pek+TO98G32qUZh94mVsr/oOThhnwejowJwTL6HSuhdtBfVSD5lkELvLg06LA+19drT3+f+32NHR50CLaQA7jpng9HhRoFFgxZQKrJpWgRWTK0Tdeb18cjne/unJ+Mm/vsZPn/saW4/04DfnTINaQVOqJHvlXQAXmr0qjFFCJMCsGYPxPZvAuAecSf8HLzQDp3N2YfaJf2Nf+Zn4tOYmjLbswrea7sfhkm/BpipN8SjjE2sKdUL3x2DgOFS6EgX2Vkzo2eSLxgWsnXtpezNuPn2yWEMVROfqBQfD69P+D+fs/yVOa7wLGncf1G4Lxvd8gtIBXx/QcaYtWDf1rrSOLR6Mu6Fz9aBfVY6vqy7DxJ6NOOXw/8GhKIBJU43Pxv0YAHC8cD4AoNq8PWMCOO5xw2W3Sj2MnOXyeNFldQSDso4+O06EBGkdfQ60W+wwhSlHo5LLUFGgRmWBBt9b6p8arS2GUsDUaKJGF2rx3HVL8Kf39uPRTYex47gJD10xD2NLMqM8FCHxysMAbvDrwQAu+sJws2YMFNwFg7Mz4m7VdBoe68xreQYy7sG2Md8bEtAsbH4Kcq8bX4y9FmAMH074Fa7ccTmWH7kX7075Q5pHnZyJPRvQoxmHbu14tBbMwcyON1E8cBS9ulqphxaW1tULu6IAHrkGb029G2sb/hMnH30QHiZHS8E87Bx1IYrszZjb9gK2W76LduOM2CdNkxe2DrZQ0zl7wMBhVZWDMznem/g7XLnjchQ6WvHizEfglmsAAHZlETp1kzDWvA1bx14j1dCHMLXsR/ETJ0k9jJzxj81H8MnBrmAmrbvfMeK1SC5jKDeoUVmgxrhSHRbWFWNUgQYVBRpUFmhQWaBGpVGDIp1S0MYlsSnlMvxqzTQsqCnGf/77G5x9/yf4v0vm4PTplWkfCyHJysMAbvAVpyhGDbgAc8hO1EwI4IbPodafeAXF9uNQuy3YXHMjwBj0jg7Un3gFeyvOhlnr20Lfq6vF1uqrseT439FQcTaOFi+VYPDxU7vMqDZvw1dVVwCModXoy/CM6duRsQGcztULm39q1CNT4/Wpf8bYvu1oM9YHdwMr3f2Y0vU+vtX0V7w082FB2cR0aDXZg18bnL62c/3+jQtmbTXenPq/MDi70OLPugUcL5yPWe2vZswOYWYXrxZfvttx3IT/eXMv6sr0qC3VYdaYQn9Q5gvIRhVqUFGgRqleDbksM36Po1k9YxTeHlWAG//1FX749DZct3w8bjljSkozgISILe8CuFCF9hYMKArhVERfIBsawDUPe9NKJ6fbC8/wQnacw+DsxICiEItangIDx6c1N2FR8z/A4MGXY38w5PAt1Vdjcuf7WNV4N56a+wI8/gxKunVY7GjqsmFRna/MRvDTeJhp0fG9n0LOPThYeioAoFdbA5uiCFWWbzJyPR/gy8CFrm3zyDVoKl425BiXQo8vxl6LUw//CbW9n6GpZNnw00gusAPVqhrsw3m0OHxW63jhAsxrex6jLLvRUjgvLeOLhtnNUg8hZzzw0SEUapV48ycnw6DOjbeNcaU6/Pv6pbjz7QY8uukwvj7Wi79ePg+jCqV5TSQkXnn3ceNwZ3/w60J7c8zsGwD0qUfBC7mgUiInzHbc+8EB7PdvfxfTM18cxSMfNw6pZaf2WKH02rG1+ip8M+oiLGx5Gqc13olZ7a9hT8Va9GmG7rbyyNT4tPYmFDpaMdoqqHlFSvx7WzM2H+oKBqSBNXDf3XEFVjb+75B54ondG2BRVaA9UGOMMbQWzEZVX+ZuZNC6TII2J+yqvAAmTTW+dfSvYDzziv8GM3DKspjHthTOgxcyjDVvT/WwBJHZTVIPISfsO9GHDxvacc2y2pwJ3gI0SjnuOH8m/nLZHOxp7cOa+z/BJwc7pR4WIYLkXQC3u2XwU3mRvTnm+jcA4EyBPs0oQaVEDrT7Ard1u8Sv22UeGLkY2ODsAABYVJX4aPwv8M2oizCr/XVwMHwZYS1Sl34SAKBAwtp2gSSbwz0YtMi9TpTbDmLOiZew7OiDAAClx4Za0xc4VLpySPmT1oLZKLYfh87ZndZxC6UNmUKNxitTYnPNDSizNWJaR9yNRVJO7+yEFzLYVLEfi0NhRKd+CqrN29IwMgEGTFKPICc8uKERepUcV59UK/VQUmbtnDF446aTUWZQ4XtPbMG9HxwYOdtBSIbJuwAuQOZ1w+hoj7kDNcCsHpNRxXwD9I7AGqVygMnw0fhbsXnc9fi47j9hjbBez6ISnlFMFaXM96vn8vheJBlj0Lp8a5bM6iosankK81v+idrez6HwOnwBXIiWgjkAkJHlRBj3QOs2Y0BRJOj4A6Wn4YRhOk469gjkHnvsG6SR3tkNm7IEnAnLvBwvnI/Rll0Z8ThkDpPUQ8h6R7r68fbOVnx3aQ2KdNKva0yliRUGvHbjMlwwdwz+sv4grnpiC7qsDqmHRUhEeRvAGR0nIIMn2CorFrNGWACX7s9sgSkuq9q/RokxbBn7A+wcfVHE23hlCljUlSh0tKZjiIJwzqF1mQAAm2p/hv2lp2F50/04+egDsCmK0DKsaG+HfircMnVGTaNaHW4AgMZlBgMXXt+NMXxS+1MYnb6NJ5nE4Or0fTgQ6HjhAii4C1WWnSkclTDMboJDrpd6GFnt4Y2HoJTLcO3JmdnBRWw6lQL/9+3ZuOeiemxt6sHZ93+CLUd6pB4WIWHlbQAXmA4VMoXqO64aOrcJKnf0ulJCWkKJKRjACVijFMqsqcqojOLG/Z3BDJxNVYp3J/8eTUVLUGRvRmPJKSMyQF6ZEicM0wUHcHtaU7+g/bWvfT9Pndv/OOIo0NtcOB+9mrEYbd2dkrElSu/shFUl/HerpWAOvJBLvg7uQLsFLW2tsCsKJB1HNmsxDeCVr1pw+aJxKDeqpR5O2jDGcMnCsXj1hmXQKuW4/LEv8MjHjfDSlCrJMHkbwBUJLOIbYM7QpvYGZwcGFIVx7ybNmCnhkNdEncv3SdemLIJXpsSbU+/BtjFXYlv198LetLVgNir690EhYLpuT2ufKMONxO3xBtcoBjKJA8qiuM7RryrLuDV9BmdXXBk4l0KPE8bpGCtwHdzrO1pS8qHn7Z1tkNtNwZItJH5/+7gRjAHXLc+P7Ntw06sK8OZPTsYZMypx9zv78MCGQ1IPiZAh8jaAK7Q3w81UQ8ojRCM0gEvHZ7TQ9zu9s0vwYwhl1oyB3tUjKPgRW6tpIDjdGGow8PFlrtxyLT6p/SlM2nFhz9NinAM592CUkN20KX5i/v7pETjdXgAYzCQqS+I6R7+yNFi2IxPIvC7oXL1xZeAAXzax0roHSo8t5rGHO/vh9HgTHWJUareFMnAJ6rDY8fzW47hwbjWqirRSD0cyRo0SD35nHs6uH40HPjqEw53U2YNkjrwO4MyaKsGN3Xs1vmK4xQNHox+Y5iy7wdkRLLIaj0B5kUJH+rNwW5vCrynRuXvhYXI45MKyJm0FswAAVX07xBpa3Hr6nWjutWHAObibNhDAxZuBs6lKoXdlTgYuMJZ4f7+OFy6AnHsw2rJL0PEMqSn8qnH3wUEBXEIe/+QI3B4vfrxigtRDkRxjDL87dzo0Shl+/erutC+TISSSvA3giuwtgqdPAd/UUJ+qMtjDMlMYHJ2wqirivl0go1hgT/9GhkgtdLSuXgwoigV3JHAoCtClmyBoHVyLaSCuMQr11GdN+Pe2oeVldP4Azi5wF2pAv6oMak9/1Kyo3eXBJwc701LiQO8M2eEch06dr0xNiU3avxWNu48ycAkw2Zx45oujOKe+CrVltAkEACqMGtx21jR8frgbL3+VAUtPCEG+BnCc+zNwwjYwBPTo6mK+KfE0puACjcYTmkJV+zNwEqyDG/4J1uWfQtMN614gRJtxFkZZ94o2NjEE+qB6ZfEVPe1XlgIAdFGycJsPdWFbU29KCkUPF64LgxADymI45HoU2Y8LOj4lHcQ4pynUBP1jcxP6nR7cuHKi1EPJKJctHIv5NcW48+296Ol3Sj0cQvIzgNO5eqDyDgjqwhCqR1uHkoEjAI+8Zied2XWdswcyeGFVx5+BG1AWwynTojBGBu7BDYeGFD8WQ2g3DAD44rAvYPG1nyqK61y9mnHQus0xdwcDSNsuMp3LBFuc2TcA6Ff5Ajh9lI0Mbv9j8KbhF214H1TBGINJMw7FA8dSMCphFF4HFNxJmxjiZHW48eRnTTh9eiWmjKKfXSiZjOGPF8yCxe7GH9c1SD0cQvIzgBssIRJfANetq4PS60CB40TEY9IRwAXuIuE3WABgDH2aqphr4JxuLzbs64j//HHY1uSbctS6THGV3gDi2x2cqsXyw2ldvRhQxbeBAQBsAjJw6aR3dsELedzPCQD0ascKzsClgtrt23VMGbj4PPPFUZgHXLiJsm9hTRllxHXLx+Ol7c34rDFzNhyR/JSXAVyghEjcGThdHQDp1/YEBNpoJbIGDvAFP1KsgQsVOuU8vAG8EMEATsBmjHRlR7Xu3pRl4NLJ4Oz0jUngRp9QJs1YFNjbIPOObP+WDhoK4OJmd3nw90+O4FuTyjB7bJHUw8lYP101CeNKdPjNq7thd2Ve/2KSP/IygCu0N4ODjWj0Hs38mmL0aGsBAKUDhyMel879ScEivgmsgQN86+AK7S3pnfeNQOZ1QeOxpjQDly5CG9kPN6AshheyjNmJmmiJGgAwacdBBq8kz8t7e05QAJeAF7YeR5fVQWvfYtAo5fjD+TNxuKsfD29slHo4JI/lZQBXZG+GRVUBj0x4b7/KAg3syiL0K0tQYmuKeFw6tpjva/O9ORmcnfCwxKa4AF/wo/IOQOs2iTi6xAyW3ojvsTgVBgwoCjMngOPehAM4zuQYUBYJqgX3wd52UT/9O9we7Gk1D/n99WXgEgvgerW+sjtCplHF/pPZ29oHtdu3yYPKiAjjdHvxt48bsaCmGIvr4p/+zzfLJ5dj7ZwqPLyxEYc6qDYckUZeBnC+HajxTZ8GBDcyRJCOXNaX/t58Bkcn+pVlCU1xASG14DIg+NElWDsNEN4WLB07hDXuPsjgTehxAL5p1GjdGALdHgDgeE/sQrlCbdjXiff3tKPNPFjCRO/sCk7rxsvkr5tYNCDNOjiN27fxhjJwwrz2dQtazXbceOrEiGV+yFD/fc50aFVy/OrVXVQbjkgiTwO4lrjXvwVe07p1dSi1Hc6IaUeDsyOhHagBUtaCGy6QBYy3ewEQaAsm/WMABgPRRLOiNmX0Yr4tvampZ9fv74wRKOki9zqhdZsTnkK1K4tgVxSg2C7NTlSNPwNHAVxsHi/Hwx83YuaYAqyYnNjznY/KDGr88qyp2HKkB//e3hz7BoSILO8COKXHBr2rB2ZtfDXgAnp0dVB7+iNOc4XGdWJmSMLRx9mncjgpa8ENF5xCTWDxf5+mCgWOVjAu/YLiRKeCA+Lph5qKjxCBSiuB3+9kfr96NWMlzMD1wQsZnHKdJPefTd7e1YYjXf24cQVl3+J1yYKxWFhbjD+ua0C31SH1cEieybsArjDOJvbD9Wh9O1Ejb2QYfFt9aXszHO7UBRUGZ0fCGRLA12u0X1kiSTut4QYb2ccf+Jg1YyDn7uCmjnTgnOPzxpGBVqCfa6IZuP5ABi7NGV6bfz1d4DHpgxtkEihR42cSWEokFVPbanefrwZcgssL8oXXy/HgR4cwscKAM2aMkno4WSdQG67f4cadb1NtOJJeeffqFqgBF/cUqv//7hilRIa/76bqfVjpsUHt6U8qgAN8wY+UGbj2Pt+nVq3LBC/kCRVeHZwKjv44xHwuHG5vsABxqEAgOqBIcApVVQo5dwfrmKWLzT+F2t7nWwNnECEDZ9KMQ4HjBOTe9GcmqI2WMB82tGN/uwU3rJgAmYyyb4mYVGnE9adMwCtft2DzIaoNR9JH0gCOMXYmY2w/Y+wQY+y2MNdfwRjb6f/3GWNsdsh1TYyxXYyxHYyxbULvsyjBIr4BNmUp7IoClAw0hb0+XXkTgyNQAy7JAE5dldb1Y01dQ7swWB2+Rfm+NlqFCWVMpCglEikYTLSRfUCgnVa6S4kMnznTJ1miBvDtRGXgwb+5dKIALjbOOR7ccAhjS7Q4b7bwkkpkpBtXTkRtqQ6/fnUX1YYjaSNZAMcYkwN4EMBZAKYDuJwxNn3YYUcAnMI5rwdwB4BHh12/knM+h3O+QOj9FtpbMKAojDvTE3yDYww92lrfRoYw0jXzJcYaJcAX/Bgd7ZB53WIMK6ZXvw4fZPm6MCRWvsCiGgUv5Jmxls9tgl1ugFemTOj2UhTzPdLVj37H0DcdX4kaBeyKwoTPK+VOVLXbQm20Yvj0UBe+aTbj+lMmQCHPu8kYUWmUctx5wSw0ddvw4IZDUg+H5Akp/2oXATjEOT/MOXcCeB7A2tADOOefcc57/d9+ASCxtFmIInszzHEU8A2nW1cXMQN3oD31TcaB0C4MyQVwfZoxkMEDozNye7B00Ll6Es5aeWUKWNSVMQO4dMTWugS6SYSSop1WuF63wSK+SSxqN2nHAYgdwKXiQw9l4GJ74KNDqCxQ4+L5Sb+sEgDLJpbhwrlj8MjHjTiYpvcBkt+kDODGAAh9ZW/2XxbJDwC8E/I9B/A+Y2w7Y+w6oXeaeA24wTeyHm0ddK7e4HSZFIJdGJIoIwJkTimRRIvfBpg1Y1DokL6USCLtwEIF+toKKeabymyvb4dz4hsYAMChMMKmKIpZSkSsHrVujxf7TvjWDlIAF922ph58eaQH1y2fALVCLvVwcsavz54GvVqBX726C16v9KWmSG6TMoAL99E+7G88Y2wlfAHcrSEXL+Ocz4NvCvZGxtjyCLe9jjG2jTG2bcBmQ4H9RNwbGIaLtZEhlFhvssNLkhicnXDI9XAlWSZB6lIigZ+P1tWb8M5NIP2bMSIlpnxTwYk/DqdcD7dMLWgK1en2Yv+J1HzS93VhCB/ATao0CD6PSRu7lMhnIi38/vRQF97ZdQLgXv8UavYHcKGvX52d4u2yfmDDIZToVbh80VjRzkmAUoMavzprGrY29eLFbdKU0CH5Q8oArhlA6KtHNYARKRTGWD2AvwNYyzkPvqtxzlv9/3cAeBW+KdkROOePcs4XcM4XGDRKyOBJKAMX+obdoxsPABGnUVPhpWGFIg3OzqSnTwFfBs/D5JJmr3x9UC1JZ+D0rh4oPamtvRdLMlPBAADGBkuJxPBhQzvW7WpDVwrqT0Xrg7pm5mjB5zFpxsUsJeIWKVNh9e+kVXlskMGbExm40Nev8nJxiuzubjFj4/5O/ODkOuhUClHOSQZ9e0E1FtWV4I/rGtBpodpwJHWkDOC2ApjEGKtjjKkAXAbgjdADGGPjALwC4ErO+YGQy/WMMWPgawCrAeyOdYdy7gQAmDSJFfENsKgq4ZRpI25kSAd9En0qQ3Emh0U9WtINAIHaaYmW3gCETQWnvN0N90LrMicViAKx22kN5/aI+7gUHjs0HkvEDFw85SZ6tWNhdHZA4bFHPa6x04o9rSPX4sWD+ZP6g220aBNDOA9uOASjRoErl9ZIPZScxJivNtyAy4M/vL1X6uGQHCZZAMc5dwO4CcB7ABoAvMg538MYu54xdr3/sN8CKAXw0LByIZUAPmWMfQNgC4C3OefvxrpPhddXsiLREiJBjKFHVytoClUMNufIHaIGRwesquTWvwWYNWOiBj5iZUgi0bqTaz8FCJ8K3t1iDraNEpvabYEMHtiSCEQBCM7ApYpYO5yBkJ2oUbJwB9uteGNHK97f0570/QGhbbQS30Gbqw51WPDunhO4amktCjSJ7ZQmsU2sMODHKybi9R2t2HQgfQXGSX6RdO8453wd53wy53wC5/xO/2WPcM4f8X99Lee82F8qJFguxL9zdbb/34zAbWORe51wM5Wgqcc544qGfD8859CjHZ+2KdQ3vxkWXHEv9K6upKrkh/LVgose+FhTFPQ4Pd6kGtkHBGvBRekqYXd58cHedrz8VWrqkonxOABfMd94MnBiM4hQAy5A6E5UMQWKIFMZkZEe2tAIjUKO759cJ/VQct4NKyZgfJkev3ltN9WGIymRV8V/5Nzpe6MXUCxWHqN8Qo+uFkZnB1Ruq1jDi8g6rEaXztULOfckvQM1wKwZA53bBKW7P+IxB9stsNhdotxfqC8P9wxOoSaRgbMrCuGQ66MWJQ60bOq2OhO+n2i0bhMAJFzPLqBfWQqd25S22nzDBYr4JrsLFfD1QwUguKl9m3kg6fvU+AO4XFgDJ6Zj3Ta8/k0rvrN4HEr0KqmHk/M0Sjn+cMFMHOux4f71B6UeDslB+RXAeV3BTE04FQXq4Nfjy/VDrhve5Lnb3xO1ZCD6NOo7u9tEX3slZoYE8DWDBxB1I8PG/Z34+yepmTJOpg9qEGOStwULttFKcg2czV/MV+s/Xyxi9x/Xu3xTqFZl8gGcS6FHv7JEcAbujR3Jb6ahAC68RzY1Qs4Yrls+Xuqh5I2TJpThonnVeHTT4ZTtGCf5K78COO4UXEKEMQatKnJ9pO7ATtQY6+COdttEr9c1WMRXpAycWtpacIE+qMm+4cYM4FK8h2Ewk1iU1HnS3U5r+I/F4OyCm6lEK8PRq60R1NQeAJRJdAQIBLKa4BQqBXABJ8x2vLStGRcvqEZlgUbq4eSVX589DUYN1YYj4surAI5xb1wbGEIDr+FJjj5NFdxMhdIUb2Rwur3oGxg6dTm4yFykNXAaaWvBaZPogxrKrPYX8+XiFIaN12Af1GR3oQov5psK+kANOJFSeybNWBQPCJtCFYPabYGbqeCWqWMfnCce3XQYHs7x41MmSD2UvFOiV+HXZ0/H9qO9eG5r+v4OSO7LqwAOQNJFfAM4k8OkHSt4bU+itjWNnEYzODrghSzYNzNZdkUhnDIdChxtopwvXr4ArkjQsYXayDvnzJoxUHgdae0jGkrn6oVDrodHltz6IpsE/VBDRasBlwiTdiz0ru6oayzFEJii0rjNvg0MYs8tZ6luqwP/2nIUa2dXYWxJcoW/SWIumjcGS8aX4O539qHDEr2kDiFC5V0AF20NXCSrplWEfS/oU4+CwSFO6YNIwpXwMDg7YVOWgDORinAyBou6Ekb/1Gy66Vy9ghf+F8QI4IDIO1Ed7tRm5pJtBxYQ+Fmksx9qqGhdGBLRq/HvRLWnZvfvcBq3hda/hXhi8xE43F7csJKyb1JhjOHOC2bB4fLijrcapB4OyRF5FsCx4IL9eEyqCF+OwKqqSGnQ4/XysMu2DM4OUWp0hbKqK1IejEaidfViQFEk6NhoOZVgABdhKnh4NwuxJdsOLMAjU2FAUShpBk7MAM6kjW8narLU1Ac1yDzgwtOfHcWZM0ZhYoTXMZIeE8oNuHHlRLz5TSs27pfmwzLJLXkVwHlkiqSnt0JZ1RW+kh7e1LRL+cv6g/jqaO+Iy/XOLljV4gZwlhQHo9Ho3MIzV722yCVALOpR4GCC1vI5U5CN08URiMZiU5akZRODzemGK+RnofDYofb0izY9D4QU8xWwE9U84Eq6N6rG3Uc14Pz++XkTLA43blw5UeqhEADXrxiP8eW+2nADTqoNR5KTXwEcE7f2kUVVCQAwOKJX2hZ735FYfVBDWdWV0Du70l57TOZ1Q+PuExzAWeyRx+eRqWBRVQgK4J7YLP7mE99avuQzcEB87bSSWen1t48P41jPYP/YQAmRfhFKiAS45RpYlWWCN8l8fdyU1P2p3RbqwgBfcP74p0ewYko5Zo6hn0cmUCvk+OMFs9DcO4C/UG04kqS8CuDsysRexCKthQ4U0jU40zf1KPfYoXWbRSshEmBRVYCBB9/A00UTLH4rTuBj1oyJWs8uQPRPv5xD6zYlXUIkQKp2WoGgUawM3LcXVPvPVxask5dqGncf9UEF8K8vj6HX5sJNlH3LKEvGl+KSBdV47JPD2HeiT+rhkCyWVwHcgMifygMZOKMjfVOPBn9pCbEzcBZ1IJuY3nVwOpFKbwRIVcxX7bFCzt2iBaJStdPSBwM4cTJwo/w1x2zKYsGbMpLZPCrzuqH29Of9GjiH24PHPjmMxXUlWFCbXGcQIr5fnuWrDff7N/eKXuid5I+8CuDEFsjAGdOYgTM6TgDwrfcSUyCjl+51cIHaaTaRMldmzRgYnJ2Qe9K7VX+wBpw4b5b9yjKovAMpL70xXCADa1OKk4ELdDCxqUqCwXoqqT2+UiL5XsT3pe3NaO9z4CenTpJ6KCSMYr0K/7FqEj5r7MZH+2hDA0kMBXBJcMl1sMuNMKQxA1fg9AVwfWIHcBJl4MTogxpqsJRIertK6EQORIO14FI4jRquwbbe2Q0v5KJNBQcMKEugc/ZA9LYkfjanb22kOthGK3+nUN0eLx75uBGzxxZh2UTxNqMQcV2xpAbjy/S4c10DXB5pio+T7EYBXBThi3gMZVVXBFtbRTyPiG9agQBLrEb2AQ65AU6ZLq3TwYD4U6gW9WgAg5nKdBHShUGvjtyabbgJ432t2lJZSuSLwyPPrXd2waYsBmfCxypEv7IECu6EypOajOKBdisAXw04AHm9iYED+PEpE/GLM6aM6OFMModSLsMv10zD4c5+PLeFOjSQ+FEAlySLqgLGNGatChwn0K8sgUfsNkGMwSIgGBWb1tUDL2SirVkKrOVL5XOyu8WMo922IZcJCeDGlxmi9tcdwuB7HKks5hvuY4XO1S1qDbiAgWBx4tRsZAiUhdG4zQCQ12VElHIZvrN4HJZNFP95JOI6bVoFlo4vxb0fHIB5WMtEQmKhAC4ECynIUG5Qo9zgC5KifYi1qivTGvQYHSeCQUq8yozRgz5rmoNRANC5TLArC0XL+FhVZfBCltIM3Ad727Fu19C2Y4GpYFuMOnBC8yFunS/Dmu5ivr4ivuJNuwUeb38wgIu9Do4lURhFHczA5fcaOJIdGGP49dnTYBpw4cENh6QeDskyFMCFCA3UVAoZzp1dhYvnV0OtiBxcWFQV0Lt6IPdGLjB7os+OY8MyNokyOtoT2sCgU8lx5ZKaqMdYUhCMujzeqOs7tO7IxW/n1cQ/rcqZAv2qsrRuLAF8a9WcMh08ck3U44TOaHnURfAwubAMnIizZHpnd1w14E6ZImw3dCAzmaqMYqtpAICvhAhAARzJHjPHFOLiedV4cnMTjnand9MSyW4UwIUY/j6oUcpjNn8OLv6PEvj8e1szXv5KhDZOnPsCOFX8Gbjh02VzxhWNOMaqqoDe2QXGxSvm+8BHh/DwxsaI12tdpoilNxaFKX8gZArSoq5MeyaxZKAJvdpx4p2QyWBTlqY1A8e4BzpXT3ADhRDzxgkLsgPn1DlTsxP1SJfvjS8QwOX7LlSSXf7rjCmQyxj+9919Ug+FZJG4AjjGmIwxlrOvjIms9x2snyZu5mr/CcuIy9QeC1RemyglRORhHqxVXQEZvNA7xS3m6/HyiNMDOldPxNIb4Z4PIftBLKrKtG/GKLUdRrdufNRjGBM+PciYr5RHOhvaa10myOAVdQo1IJBlFboGzuvlCW3+0bj64JTp4JUp4r4tIVKpLNDgR6eMx7pdJ7C1KT0Fr0n2ixnAMcb+xRgrYIzpAewFsJ8xdkvqh5Z+iay9CdRPEzL1uEFgvZ8Bp2fEGitAeA24mtKRWcPh74XDg6N5NcUhGwAij5Nzjh3HTXC44+tkEKn3qC8DVxTXuWKxqCt9U6gxAgCzzQXOOcy25BYPq919MDg70a2bEPW4ZRPL4vqQ0K9KbwYuELiL2UYr8Hi9MgUGFIWCAlKH2+PrA3ws/myd2kNdGEh2um75eFQWqPGHtxvg9VJxXxKbkAzcdM55H4DzAawDMA7AlakclGSivLlWGMOvbbIECuAKmLLbIbDHoydC4FHgEFYDLty0VqySKKdMLh/sLBFl/dixHhs27OvAxv3R+78KwbgbWrc54s7NsBm4YY/DoB6ZabGqK6HwOoI7EiN5YvMRfHWsF09sPoJOiyPqsQ1tfXhs0+GwWaFS22EAiJmB0yjj26jRryyFwRn752y1u7Gz2RTXucMJBFepyMABgE0prJhv4Ee8u0VYm6HQ50TjtsCuzNlJApLDdCoFbjljKr45bsKbO9Nbx5JkJyEBnJIxpoQvgHudc+6C+P3ZM4IsSnpEq5Jjbph1Yy6FHna5IS07UQdrwCW2CzUUA8PowqFBabC3a5QMnMvje+odETJq8dC6fAFWpKKx4TKigqZQ/T+fAgE7UZt7fYvfY23h/3BvO6wON9xhPhkLDeAAxFWXy6wdA72rO2Y3htd3tGJ9Q0ewmG2iAhm4eNbAxcMXwAmfHhI6hXrfh4NNwTXuPjjkFMCR7HTh3DGYOaYA//vOvrCFtgkJJSSA+xuAJgB6AJsYYzUAcrIDLwPw/ZPr8MPlsd+IQ1nVFWlZc1XgOAEPUwRLMiSrzDC0rIhDboRTpo0RjCYeu7f3DW1vFat2WqI1SC0qX4Yynq4Sse4rcH24mKLUdhhOmU5Qd4xJFQZh4wFDr9a3a7jYLqzI55GufnRZHcEdmTENeyyBrg/WJKZQKwsi78K1KYuhdcYRwCVw/2p3H2XgSNaSyRh+vWY6Ws12PP7pEamHQzJczACOc34/53wM53wN9zkKYGUaxpZ2jAGFWmXYabloxKyfdqDdgrYIb8C+EiKVAIt/83C4NXAjghbG/MFo5McSyFQlElt9sHfoeQMBgy3SJoYE7gMIKeabplIipbZGdOvqBD0vJ00Qnt0KBnADRwUd//6edvzz86N4YetxwfcRSu/shl1uiFkKJZrvLB6HBbWDAXloxtGmKk1ZId8AjasPdsrAkSy2dEIpVk+vxEMbDqHDkt6eziS7CNnEUMkYe5wx9o7/++kArkr5yCSQcMZHXQmDSMHC2zvb8NbOkRsYAH8RX5WwHainTavEmlmjR1y+NEYAYVVF78aw6YBvmu1odz+2H41vkfnwdWYlNt8nzECgMly4KW0h02o2ZTHcTClqKZFAIDLgHDmtIWQH6vDzCGHSjIUXMpQIDOCSpXd2pWz6FPA9LxqPNWrNxFAmmwsvbI2vxZDGY8nrLgwkN/xyzTQ43F7c+8HB2AeTvCUklfMkgPcAVPm/PwDgP1I0HknF2oUaCB0W1Q3NGFlVldC7eiDzprYVSjxdGGZVF2LKqMhvZJEeqdAaai4Px6YDyW1kKLMdwoCiEP3K8EFDomVEwGQxM4mJemLz0GkNjcsEvatnRAB35dLoRZOF8MhU6NOMFpyBS5beFV8R34Afr4i++3b2WF9f0kCmVRtHFq7VJDwDIffYofA6aAqVZL26Mj2uXFqDF7Yew74TObliiYhASABXxjl/EYAXADjnbgA5ubpSaHJkeDFZi1p4KZFEF6Yy7obB2SWoBlwy/astKSjmG0lZfyO6dBMjDjhctur8uWMwNUpgGmBRVQqaQrU6fI8z0R9Zqc1XpHh4CZESnSrBM/oEHnqvtiZ9AVyCbbQ0Sjk0SjmWjA9/2zFFvrI2tjjaaQlx//qD+Gjf4HMcaGRPmxhILvjZqkkwapS48+2GhGoiktwnJIDrZ4yVwp+AYowtARC9PkOeCZbfELCR4Y0dkbeHR/sj1Tu7IIMn4T6ogXMH7yJCxGJVV/qL+aa4/hj3otTWiC79xLhuNrZEh7NmjQ5Or0Yqj2JVVwraxNDRF718SCxlwQAuvo0v4YSb8u7V1KB44BjAk9/1G4vOmXgj+x+vmBBzet6m8gdwcWxkiMbj5fjm+OBLkcbjb6NFGTiSA4p0Kvx01SR8crALG5Oc7SC5SUgA93MAbwCYwBjbDOBpAD9J6agkEs/6pFDWODJwkRalujzeIeUQhivwByNCdjqGI/QD3GAwmtoNAIWOVqi8A+iKUfw2ku8trcFZsyL/LPrUo2B0dIDx1CaLS22H4ZDrgwWdI/n+yXUxzzVllHFE67YeXS2UXjuMKS5To3T3Q+UdgC3CdLYYBvuhxhfA9fQLWzOncVEfVJJbrlxSg9pSHe58uwHuKD2lSX4Ssgv1KwCnADgJwI8AzOCc7xTjzhljZzLG9jPGDjHGbgtzPWOM3e+/fidjbJ7Q2yZiSmWMqTl/EDQ8zLPGUcw3kliFcQNdGBKtAbdqmu92gYxVpPV+8QSjySjt92Wu4s3ABRTrVZg6KvIbtVVVCRk80AnMJCYavPs2MEwYMQ08/HSFWmXU86ycOjQADJ1CBYTvRE2UPljEV7wuDMMF1jrGG8A99VmToKUH6kAfVDltYiC5QaWQ4ZdrpuFQhxXPJbi7nOQuIbtQvwfgOwDmA5gH4HL/ZUlhjMkBPAjgLADT/eedPuywswBM8v+7DsDDcdw2LufOroq66D8ap8IAh1yfVNDTa4ueZQgEh4k0sgeA6VXCshKJ9HZ9bssxNLQNXWj74IZDUVshldl8vVG7tclPPYYjdimRsPEd53HtQI2k1KDCnLFFYa8TM4CzuzwRp+mDbbQEroEbU6RFfXVhXPfvlmvhkmkSKiXiFJB9CDSypylUkktWT6/E4roS3PfBAfTZU7tRjmQXIVOoC0P+fQvA7QDOE+G+FwE4xDk/zDl3AngewNphx6wF8LS//twXAIoYY6MF3jYuySz8B/zlN1I47Wh0nIBdboRTIawQbMBZs0ZhxZTywQsCWcQIj9chN8Il08QV+Jww2/Hubl+GsKPPjm6rA063Fx9HySqW9R+CWV0Fl0Iv+H6GZ6miGezrmrrnROfqhtZtTjqAm11dFPG6fmUpHHJ90gGcyebEwxsbsasl/PLVYAZO4BTqJQvHBrO6sSjlIbXg4uzGEA+1fxMDTaGSXMIYw2/Ono7ufice2tAo9XBIBolZsZZzPmS9G2OsEMA/RbjvMQBCc8LNABYLOGaMwNsCABhj18GXvUPJmNjrkBLla6CeeAYuVvzoKyES//q3aNOMYYM4xmBRJd5Z4tkvhdXtKktgA0NVofACs+kI4AIttMKt44s0JStjvl3MA04PVkwpx9wwfWuBkCluxtCrHZd0Lbhu/zqyI139qA8TMA5m4MSfQq0r0+PUqRX4aF+H4H6okZhsTnzTbMbySSPHqXd1w8MUcMjj+5CTyUJfv8aNGyfxaIhUZlUX4sJ5Y/DEp0dwxeJxI9bKkvwUf0l/wAbflGaywk5KCTxGyG19F3L+KOd8Aed8gU6X3C99cP1YmDdnMbsxhGN0Rq8Bd8ViYS/uMTahAvDv4ExhFwO514nigWO+EiIxCFn8H06gLVg8z8m+E31wuMOvtQq3ZjCeHqjB8zCG60+ZgJtPnxwxeBtOjFIiganTFtNA2PVkOqcv+ElF9ooxhtn+KWKbqiSpXahv72rDV0d70WUdueTA4OzwrUdNoFNJpgp9/SovL499A5KzbjljCmQy4H/f3Sf1UEiGELIG7k3G2Bv+f28B2A/gdRHuuxnA2JDvqwEMr7ER6Rght41LsmV2LOpK6JIo5htrEb3R0RE1gCvWJ153TKUY+mtgiaMI7scJbG8vsR2BDB5BGbjQxf/hnqJwz9uF88b4MolqYbXgAF+XiHd2ncBDGxrDdlsIp9R2GAOKwpTu3AR8AVyB4wQUnsTb6gSWkDlc3rClbPSuLl+dthQHPzZlcVJTqIFA2hvmiTc4OmFVUZBDctPoQi2uWz4Bb+1si7sLDslNQl6t/wzg//z/7gKwnHMuxq7PrQAmMcbqGGMqAJfBV64k1BsAvuffjboEgJlz3ibwtnERYw0cAw9ORYlJ6bFB6zZHnEJdVFcypO2UWiEPexwwGPCEBozLJg6djrKqKqF3dgsq5vtVAi8kgdppQjJwADDaP3WqifK4QgXe5C3qUcHdu7GEbtFfv09Y0Fdma/Rl35L95Qkj9JSBjQxFApvaA8Cb37Si1z9tah5wYcP+wSnxrv6Rte/0SdSAGy5aRxObshRalynhunZy/yuWxxsmgHN2BHdRE5KLfrR8PCqMavzh7b1U3JcIKiPycci/zZzzZjHu2N/R4Sb42nQ1AHiRc76HMXY9Y+x6/2HrABwGcAjAYwBuiHbbZMajkCX3JhzoxhDvrke3x4t7PziA4z22iMcYY9SAqy0buhGgSBe9ZEUAj1AWxaIOlOBIzWLzsv6DcDMVerVjIx6zesZgtvHi+dX4zuJxKAzzuCaUR17vZFEJawsGYMgPwe0Z+cI4IuPDOUpE2IEqRI8/gItnHdyhDmswaHt3d9uQrGK4AEvv7Ba8gSEWtzdycGZTFkMGT3DHaLwCH1RGBHCcw+jsgCVGPT5CsplercB/nTEFXx8zReyZTfJHxE0MjDELws9aMQCcc570YhnO+Tr4grTQyx4J+ZoDuFHobZMhjyOAC3ekVWD5jeFxgN0dOxMRrAEXoYQIGzYmjTJypirwMGUMCLwHDm8aH6xr52xHfwoyGmW2Q+jR1YKzyHtoZlQNlqhQyGWoLAi/geHUqRURd1ZaQ6a1vbLoQW2sPrjOYc+T3tkJjcealgDOpBkHDhb3OrjA8xsmWTWCztWNNuPMBEY30pRRRnx9zBT2ukA7Lb2rG3ZlUdznDvydDg/g1O4+KLyOmAWVCcl2F82rxpObm3D3O/tw+vTKqK/3JLdFzMBxzo2c84Iw/4xiBG+ZRiGLPZscLWMd7GAQIwPn9nK0mAbiGlusDFw85tcWY864IsweWxTMKg2fAQxmE+PciRppA8BwZbZGwdOnsciiBN4WdSUYuKD6fKE/g0jtuUKVReiBmgpuuQZ96lEotjXFdbtIUyzDn2/G3dC5emFLoA9qOKMLtRGvCza0dya2hifwYWN4RjSwA5ymUEmuk8sYfnP2NLSYBvCPzU1SD4dISPCKZcZYBWNsXOBfKgclBY0yucXbTrkeTplO0JTd2zvj229hdJyAFzL0i7BAW62QY+WUCijlsuAb/PAMXCAYjbeunZAaRWqXGQZnp2gBXDTxlBL5vDG+3q+J7ECNx/CwtFdbg5IB4WvggNgbcwKBqs7ZAwaOfmXqujAEBPuhilwLLhCk0yYGkg9OmliG06ZV4MENh9BlTa6fM8leQnahnscYOwjgCICPATQBeCfF40qrC+aOQZEu8V2cAIK7HoVkexyu+BZwG50n0K8qg1cWfsox0TX0wSnUYb8FDkUBXDK1aF0MQgU6MHTpU5+5SqavK+c86iLhUtth9CtLgv09Uy1YSiSOhcvhdmqGM9hGK3YGbu64IsH3H04gAyd6AOcIZOAS61RCSLb55ZppsLs8uPeDA1IPhUhESNrpDgBLABzgnNcBWAVgc0pHlWbDNwEkyhpH+Y14GB3tUUuIAIkFcd4IGTgwBquqMuFivtHEuwM1GYPttITtRA3gHHhoYyOe+qwp4jHxtNCqS+D3a/hT0qsdB5XXFtcuZyFr34D4ivjOrxEWsOpU4dfl2BUF8EIuegBndHaAg6Uli0hIJphQbsB3l9TguS3HcKDdIvVwiASEBHAuznk3ABljTMY53wBgTmqHlZkGS3CEv75fWZaSNkG+AC759W/DDW5iGHmdVVWekob2Zf2HYFcUiDIdHItbrsWAojDuoJpz36aFXlvkmn5F9uPB8h6RBHbSGtQxG57ElEhP1Ehr+YY/3XpnfG20hLh6WS2uWx4mwGUyDCiLRP870Ts6YVOWRMxSE5KLfrZqEgxqBf64rkHqoRAJCAngTIwxA4BPADzLGPsLgNgFwvKQTVkEbRJtgsLi3pQFcGX+4r8G9cgdmlZ1OfTO+Iv0xrxP2yFf6ykRa6edP3cMrjqpNvh9uVEd/DqeosRCxarLFzCjqhBrZo3GKVOSD1YTKSUiOAPn8mXgxNrEAPjWWuojBK79SXZjCMfobA9uviEkXxTrVfjpqknYuL8zoaLqJL0OdVjx7Uc+w+FOqyjnExLAbQJQBOBnAN4F0AjgXFHuPctMq/Jtvh0XoQ+dXVkEpdeRVMX84XSuXii4M7ieK5xYJTAiWTK+FJcuHItRYXqMWlUVMDg7Ey64GhbnKLUdRqdejE5sg+rK9CgJ6UQRGhtaVeK3BYtnV/CUUUYo5cl3N7CqKuCSaeLLwHGO3S3msEVvQ+mc3bArCuCRJbkOVKCBBPuh9vQ7caSrH8DIziUGZyeVECF56cqlNRhXosOdb+8dUpCcZJ671jVgX5tlSIehZAh5Z2HwFczdCMAA4AX/lGpWmzmmMPZBw4wp0uLm0ydH3PAwoCgCAGjdpiRGNlSgBlysYCFWK65wZDKGqqLwJR+sqnLIudtXNV8kBY42qD39aSm9EWBRx7+W71iUosrA4HNiUY9OeFyxDV+XKEOvdhyK7cIDuG6rEx/sbUenZeguteG/Knpnl6jTp7H4GtrHn4F7b0/ktYwGRwcFcCQvqRVy/PKsqTjQbsWL20Sps09S4NODXVi/rwM3nToRpQZ17BsIIKQTw/9wzmfAV1C3CsDHjLEPRbl3CS0ZXyL6OQf8hUljTaPGE2sFdoKmYgo1msCboUHEadTgDtQ0bGAIsKgroXWbE86K/uvLkaU7Chy+Cuh9ad7x2KOtTbqpfTh6V7egHahiSbQfaqRdtQrPADQeC6xqKiFC8tOZM0dhYW0x/t8H+2GxJ9aPm6SOx8vxh7f3YmyJdshyn2TFM7fTAeAEgG4AWf9RN5GMVSyBkhJiZq2M9kC2J73BQuDNUMyNDKX9gR2oaczAqXyBr9CeqMO1940M/IyOdnghF613qFC92hoU2lsh9yZX96nf4Rky1SJmH1QhbMpSKL12KD3RM52hBpweeEOmgjtCnpfBGnBUQoTkJ8YYfnP2dHRZnXhgwyGph0OGeWn7cew7YcFtZ04TtXOGkDpwP2aMbQSwHkAZgB9yzutFG0EOGVD4pmXFnnZ0yrSwK+Kf8k1GKjJwJQNN6FNVwqUQp2yLEIOlRMRbB2d0nIBVXR61FVgqmDXVYOAJB6OhAmvJwLkvA5fWKVTfB514NjL868tj6LI6g99/FlJ4OTBFTn1QST6bPbYIF8+vxhOfHhn8+yaSszrc+PP7BzC/phhrZok7kyYkA1cD4D845zM457/jnO8VdQQSWjGlHJcvEq+pRHAKVcQ1cAWONvRpRou6a1OIflUpvJDF7O0ajyL7cZiiNLBPhcDUc4FdvMbPBY429KVo/dss/9rMMsPIdZaBdZAFIgRwAWqPFQqvI70ZOJG7MQQ+ZNAUKsl3vzhzCtQKOe54K2feprPe3z5uRKfFgd+cPU30mT8ha+Bu45zvEPVeM8TcccVhd2AmylekVCZ6Bi5VwUI0nClgU5aImoErtLfArBkj2vmEsKgr4ZapUTzQJNo5hRRWTtSUUUbcfPrksCU4Ar8HYgajOn8NOFuaNzEAYgZwvuwqbWIg+a7CqMHPVk3CR/s68NE+8YvKk/i0mgbw6KbDWDunCnPHid+1J/n6BmQQk8GuLBQcwL2w9VjUav+A7806VgAXGtQvrBVvc4ZVLV4xX6W7H3pXD8yaalHOJxRncvRoa1A6cESU8zHugcGZmrp8sVjV5fBCDqNDxADO30YrkBWLRaVI/iVD9ADO0QG73Ai3PPyOakLyyVUn1WJ8uR53vNUAh9sj9XDy2p/e2w8A+MWZU1Ny/rwN4KL1uUzGgKJI8BRqq8kOpzty3R6V2wqNxyK4XMXNp0/GyZPEmwoL1oITQZHdt73dpIk9hXrZInGnWXu0dSixNYlyLp2zG3LuCW6OSCfOFLCqy0WdQg3UYxOyBu76UyZArUh+AW5gs4+YU6hWKuJLCADfh6zfnjMdR7r68Y/NTVIPJ2/tbDbh1a9bcO236jAmQrmuZOVtAJcqAyJ2YwiWq9Ckdwp1fLlvk4GvnZY4AVyhvQUAYIqRgTNqFBhdmNwv+/BlBt26OhQ6WqHwDCR1XiD0OUl/AAf4plELRM3A+YKoQFAVjTZCf1OhAi9iHpkKdrlRtL8To6ODNjAQEmLFlAqcNq0Sf11/MOxOepJanHP84a0GlBlU+PGK1JXNogBOZL4AzhT1GC8HTDZn1GOA0Hpj6Q3gqot9b7RWVQU07j7IRegsEcjApXsNHODLwAHxtaGKJNCFIdVTqJGWulrUo0QO4HrBwTCgTP0u53NmD/4e25TF0IvUTsvgpCK+hAz33+dMg8vD8b/v7JN6KHnnvT0nsKWpBz8/fYoovbAjoQBOZAOKImjc5qjHeLxcUGo7sFhdik0MANAfrAWXfBauyH4cNmUxnApD1OOS3aUT7o+lR+cP4GyHkzo3MLgDNFprs1TqU4+GwdEJxpNvR8y5r+j0gKIw7SVRbKrS4Pq7ZMi8LuhcPTSFSsgwNaV6/HB5HV75ugXbj4rbe5hE5nB7cNc7+zCl0ohLFqR2zTcFcCLzZeDMovQQNTra4Japg4u+082qEq+Yb6G9Oeb0abJuXDkR1yyrHXG5STMWHiYXZSOD0XECdrkxZiCaKn3qUZDBA4NDnKltnatH0PSpGEJ79vZqxqHUdtgXRSZB7+wCAw/+rhJCBt2wYiJGFWhw+xt7Y/ZEJuL45+dHcbTbhl+fPQ0KEfpgR0MBnMgGlEWQwQO125L0uQodbehTVaa9BlyAmMV8i+zNKZ8+VSlkYf9gvDIFTJpxomxk8NXlS/36t0iZyGApkSSnURkDdjaboXP1pu0DQuhD6jBMgdZtTroocbALA2XgCBlBr1bgl2umYleLGf/edlzq4eS8nn4n/rL+IFZMKcfyyan/UEkBnMjEbGhvdLShT1OV9HkSFczAJZntkXudMDraBe1ATZUeXa0oU6hGR3tad6DKhgVyFpGL+WqdPcHOCKkW+lg6DNMAABX9ya3PMToDXRiojRYh4Zw3uwoLa4txz3v7YR6gPqmpdP/6g7A5Pfj1mmlpuT8K4JI0o6pgyPeDDe1NSZ+7wHFC0GL5VOXnnAoDnDJd0lOoBfZWMPC014AL1a2tQ5G9BXJv7M0j0RgFPiepEujGIFYtOJ2rV3ANuGTJQn5RO3UT4YUcldbkAji9/8MFTaESEh5jDLefNwMmmxP3fXhA6uHkrEMdVvzzi6O4fNFYTKo0puU+KYBLklw2NHwSq6G9wjMAnasXZrV0GTggUMw3uQzcYA249O9ADejR1UEGD4oGjiV8jsG6fGmYQo1wuUeuQb+yRJSdqDKvCxqPJW1r4EIzcB65Bt26OlRYG5I6p9HZDpdMDYeiIPbBhOSpGVWFuHzRODz9+VEcaE9+eQ8Z6e53GqBTyvEfp01O231SAJeg2WMLUVOqG3G5WFOowd2OEmR7QteV+2rBJZeBK4yjiK8Y2cRw6+IHS4k0JXzewHqtPgkzcECglEjyU6iBOmxSrIEDgA7DVFT270tqI4PB4S8hItE6UUKyxX+u9pW0+J8396SskH2+2nyoCx82dODGUyeizKBO2/1SAJegU6dW4sJ5I6cExZpCLXC0AoCka+AAcboxFNmb4ZTp0pbpCadHWwMOhlJb4jtR01UDLhTHyBfaPvVoGEXoh6oLBnBp2oU6LMhqN0yDztWb1AcEg7OTasARIkCJXoX/XD0Zmw9147094nVzyXceL8cf3m5AdbEWV59Um9b7pgBOZG65Fi6ZOukq8wX2OLI9KUw+WFXl0Ds7kyqLUmhvhkk7RlCW5LRpqVmM7pFrYNZUoSSBUiJWh6/mWkGGZOD61KNQ4GxPugTHYBcGacrUdOh9/QErklgHZ3B2wKqm9W+ECPGdReMwdZQRd7zVALuL+qSK4eXtzWho68NtZ02FRpl8q8F4UACXpHDvofH0Q42kwNEKD1OgXxW7tylLYQRnVVdAzj3BbE0ifCVEYk+fnlM/GuPCTEuLxdcTNf4A7rFNvt2rRkeb4OckWYFYt7ZUP+I6i3o0FF5H0r1E052BG65TPxleyFCZ6Do47qUMHCFxUMhl+N25M9BiGsDfPk5+V36+63e48ef392PeuCKcPSv9BffzNoATawVAbVmYdXCBYr5J8O12rARnsSP6VC7/GSzmm9g0KuMeFNhbBRXx1YvUciRSYqpHV4figWMJdzEwOk7411ul/s+GMYZrltXi7PqRLwqDO1GTmwbR+gPAWGvg5tcU48aV4vfzc8s16NHVJlxKROfqhZy7YVFTCRFChFo6oRRn14/GQxsPobnXJvVwstrfNh1Gh8WB35wzPekuQomQJIBjjJUwxj5gjB30/z8iBcAYG8sY28AYa2CM7WGM/SzkutsZYy2MsR3+f2vS+wgGTawYuV3YLqAfaiwFjjbBLbRS+WszWMw3sXVKBkcHFNwlSQ/U4bq1dVBwJwrtrQndvsDZntbp0yKdCsowhYn7NOIU89W5euFmSjjlI7N8oWSMQaVIzUtFh35awqVEAh8qqIQIIfH51ZppYAy4ax31SU1Um3kAj25qxLmzqzBvnDSzGFJl4G4DsJ5zPgnAev/3w7kB/CfnfBqAJQBuZIxND7n+Xs75HP+/dakfsnA2RRG07mTXwMURwKUw8k+2mG9RHDtQxRJu4T8Q2hM1sY0MUteAC7CI1I0h2EYrxu9PpJ+nGNoNU6F3dQfrucXD4N9UQlOohMRnTJEWN6yYiLd3teGzxi6ph5OV/vTefng58Iszpkg2BqkCuLUAnvJ//RSA84cfwDlv45x/5f/aAqABgPRpHAGSzcDJvQ4YXF2SNbEPfbu2qUrghQz6BDNwRXZf+5ZU90ENNbw2X0CPthYAEuqJyrgbBken5BsYAMChMMIuN4gwhZq+NlqRdBh8Gxkq++NfBxdso0UBHCFxu275eFQXa/E/b+yF25N87+58srPZhFe+asEPTq7D2JLUrduORaoArpJz3gb4AjUAUV+BGWO1AOYC+DLk4psYYzsZY0+Em4INue11jLFtjLFtNlt65vsHlEVQe/oh8ybWtiRYrkIj/RQqZwrYVKUJr4ErtLfAzZSCelWKlUhUK8KvG3QqDLCoKhLKwBmcXZDBI6wzRhqWQljUo1GQZCkRnSt9bbQi6dRPBgdLaCeqwdkJL+RRO0koIgTz2SL09auzM/mexIQEaJRy/Obs6djfbsGzXyZe4DzfcO4rG1KqV+GGFRMkHUvKAjjG2IeMsd1h/q2N8zwGAC8D+A/OeZ//4ocBTAAwB0AbgP+LdHvO+aOc8wWc8wU6XXoi5QFFct0YAlNjZsFTqAndjWC+Yr6JT6H2aaoEbcZIhx5dXUKlRIxxFFZO5a7ggD4RivnqXL2SlRAJcMl16NHWJhTAGR3t6FeVRv3dKjemr6hmKoS+fpWX01o/Iq4zZlTi5Ill+L/396OnP7k2g/nivT3t2HKkBz9fPRlGjVLSsaQsgOOcn8Y5nxnm3+sA2hljowHA/3/Y+TnGmBK+4O1ZzvkrIedu55x7OOdeAI8BWJSqx5GIYDHfBEuJBDIrFsGbGFIbMPiK+SY2hVpoP57W6dNYerS1KLE1xV3XbvA5kX4KFfBtZEiqHyrnvj6oAjJwqS7aHuzIEKcCR5vkha4JyWaMMfzu3Onod3rw5/f3Sz2cjOd0e3HXOw2YXGnApQvSt647EqmmUN8AcJX/66sAvD78AOZbmf84gAbO+f8bdl1oZHMBgN0pGmdCku3GYHS0wQtZxqztsarKE9vEwDmKBlokbWI/XLduPFTeARjjDEiNTuFFfNMzhToKGo8VKrc1odszVz8UXofka+AAoF0/FQZnJ3TO+BZTF9pbYFZHXxZLHbYIiW5SpRFXLa3Fc1uOYXdLcuWvct3TnzfhaLcNvz57OhRhKgSkm1QjuBvA6YyxgwBO938PxlgVYyywo3QZgCsBnBqmXMg9jLFdjLGdAFYCuDnN449qMIBLbCdqoaMNVnUFvDKBddFSPYWqLofGY4HCY4/rdlpXL1ReW8Zl4ACgxBZfEUujox0DikK45dqYx6YjZuhLcieqfMAXLA2opF0DBwxuZKiwCs8AyL1OGJydMFMGjpCk/ey0SSjRqXD7G9QnNZLefifuX38Qp0wuxymTM2M5gyQBHOe8m3O+inM+yf9/j//yVs75Gv/Xn3LOGee8fni5EM75lZzzWf7rzgtsiJDaSRNKASTf0N7oOBHXDtTUr4FLrBZcYAeq0AxcOtaOdevGA4BvGjUOvudE2PRpOrI+yQZwioFuANJ1YQjVoZ8CDhbXTlSjow0MPCPqCxKS7Qq1SvzizCnYdrQXb3yTWJ3MXPeX9Qdhdbjx67OnST2UIOlzgDng3NmjcU5IxXy7ohAAEu7GUGBvjS+AS+heIhv+ASzRbgxF9hYAwkuIiBn4RColYlcWwaYoQulAfBm4Akeb4PVv6ajIbUmyG0MgA5cJU6guhR692nFxbWQIFGPuU0fPwKXjQwEhueDb88eivroQf1zXgH5HYt1qctXhTiue+eIoLls0DpMrRxbvlwoFcCKYWGHEpEpjsH6aV6aAXW6EJoEMnMzrhsEZX72xVAcMwQycI/4MHAcTvNBczEdx+aJxEa/r0dXFl4HjHAV26Yr4/nD5eMweWzjkMpuyBG6ZOuFSIu6+Tv95pM/AAb7G9vH0RC30fzigKVRCxCGTMfzu3Blo73PgwQ2HpB5ORrnrnX3QKOW4+bTJUg9liLwN4FI9zT+gLE6oAbzB2Q4ZvBm1uy7RDFyhvRkWdSU8MpWg48UMRKOVj+jR1qJkoEnwL4HaY4XKaxPcc1PseNqgVkCrHLYekjH0qSoTLiVy7PhRAL7f01jSsSKm3TAVRmcHtM4eQccXOlp99QUjtNG6eH7mrLskJFvMrynGhfPG4O+fHEFTV7/Uw8kInzV24YO97bhh5YSMK0uUtwFcqg0oCxPahVoQR72xgFRPErkUejjk+rjXwJXYmtCriZwJGy5dNVd7dLXQus2CN5kEWjZZVAIDuDRN21mSKCWic/XAIdfDI8uMF6ROva8dTbntoKDjg8sMWIyXMJpBJSQut505FUo5wx/e3iv1UCTn8XL84a0GjCnS4vvL6qQezgh5G8ClepnSgKIooSnUAv/aHnOMtT2h0rFoPt5ivnKvA2W2Q2g3CF/wmY61YwDQo/X3RBVY0DfYsklgBm755LLEBhZFuB+Nr5hvYgFcPG20atLQKqZTPwkAUN5/QNDxhfaWjMpSE5IrKgo0+OmqSfiwoQMb9idW/zNXvPJVM/a29eHWs6ZCo8yMYvSh8jaAS88Uqinu2wUycEKDBQCQpSHw6VeVQx9HAFfWfwhy7ka7Ybrg26QvA+cL4EoFroMz+tf+WQTW5ZtRVRj7oDiF+9H0qUdD7+qBPM7yLkBII/so1EoZfrpqEmrL9HGfP152ZREsqgrhAZyjlXagEpIi1yyrw/gyPe54cy+c7vzsk9rvcONP7+3H3HFFOLdemr7kseRtAJdqA8oiaFymuCPF8v79MGnGCF43tmJKuWifDEYXaiJeZ1VVxLWJodLqS79nYgbOoqqEU6aNKwPHwdCvEj+zlgxLsJRI/OvgdAIzcJF28ybqR6eMj3hdp36yoABO7bZA4+6LWcSXEJIYlUKG/z53Og539ePJz+JvPZjtnG4vbvzXV+i0OvCbs6en7b0pXnkXwJ02rRJFOiUMaoFFchM0oCiCgrug9NgE30bmdWOseRuOFS4WfJs6EbMjF8+vxg0rJ4Qt5NinroTB2QW51yHoXJXWBtiUxfGt5UvX3whj6NXWonjgqKDDDY4O2JQl8Mqk7Xs3XF8SpUS0AtpoleiEfYiIh04V+e+uQz8ZJbajMX/HAssMaAqVkNRZOaUCq6ZW4C8fHkSLaUDq4aSNx8tx84s7sHF/J+66YBbm12TGTv1w8i6Am1FVgGuW1YmeWQCAWWMGp84S6Yc62rITak8/jhYLD+DEpJDLoFaEz+a1G6ZDBo/gUg+V1gZf9i2OqCwdU8EBPbpalNiEfbI0OtthUUvb1ixcHjdQQqPYfiyuczHugdZlihrAMQasnZPeDFeXfjJk8KA0RpeMQkeghEjs8WXm52ZCssN/n+PLPl3yyOc41JFY275swjnHb17bhbd3tuFXa6bisijlqDJBXgVwpQY1ZClcaKUPyeol0g+1xvQlvJDjWOEikUcWn6qike2i2grqfdf1fRPz9grPAEpth9Guj69itSJdi+AAdGvrUOBsF5QhNTg7YBW4A/XkSembZrWqKmFVlmG0ZVdct9O4+yCDFwNRplBrS/XQqtK7aLdD76uxFGsaNbjRhzJwhKRUbZkez1+3BA63B99+5DPsOG6SekgpwznH3e/sw3NbjuOmlRNx3fIJUg8pprwK4NIZICQWwH2ONuNMOBUGwbdJRcmKsWF2HQ4oi9GjGScogKvoPwAZvILWvynlvvGfIuJaPiF6dTUAhLXUMjg6BGfg0vk7BsbQWjBH0HMSSufy1VrLlCK+AWbNGDhlupgBXKG9BXa5AQ5FQZpGRkj+mjmmEC9dfxKMGiUuf/QLfHwgvnqg2eKhjY3426bD+N7SGvzn6swq2BtJXgVw6RRvP1SNy4RK6z4cLV6SukElqbVgNqosO2NuzBjcwBB7B+rYEh1uPn0y5o1LbzAxWEqkKepxSnc/NB5rsBtFpmkpmI1CRxsMcayD0zp99e8yoY3WEEyGLv1ElPdHrwUnZAcq9eMmRDy1ZXq89OOlqCvT4wdPbsVrX7dIPSRR/fOLo/jTe/txwdwxuP3cGRm7aWE4CuBSJFCiQWix2BrTl2DgaCpaGtf9KBXp+0VrM9ZD6zbHXPxfad0Lq6oc/erwVfIzgUkzFh4mj7kTNVD7TuoALlJA0lowGwBQ1bdT8LmCGThVhgVw8E2jlvcfAHjk0gUF9taoGxhWTBn8vcuWF2JCMl2FUYPnf7QEC2qL8R8v7MDjn+bG7tTXd7Tgt6/vxmnTKnDPxfUpXWYlNgrgUsQp18PD5IKnUGtMX2BAUYgOw1TB9/GdxeOi7uoTWzBYsEQPFoIbGDKYV6aAVTcu5hSq0envwiDxJoZIOvWT4JRpUWURPo0aaPEWLQPH09JAa6RO/WSoPf0odLSGP4B7UWhvjVpCZOYY8evwEUKAAo0ST16zCGfOGIU73tqL/313X9iqBdniw73t+PmL32BxXQke+M48KOXZFRJl12izCWOwK4qETaFyjhrTlzhWuBCcCV8HVlkQuW5bKvRoazCgKIy65krltqJk4ChOxFHAN1V+cupElEXpXTdQODF2Bs5f+07oJoZ040yBNuMsjOnbIfg2WlcPvJDBnoFryDpjbGTQO7uh4E7awECIRDRKOR68Yh6+s3gcHt7YiFtf3gm3J/uK/X7e2I0b/vUVZlYV4O9XLczITguxUACXQjZlMbQuc8zjSm2NMDg7cbQ4vunTtGMytBlnYXSUDFxF/34A8RXwTRWFXAZ5lCm0/oLxKBpohszrinjMYBstYdPB06vSHxS1FsxBWf8hqNzCtvnrXL2+TTZR+oimsp9riT5yfblu3QR4IYu4Di6QmRNaAy57JkMIyR5yGcOd58/Ez1ZNwovbmnH9M9sx4PRIPSzBdjabcO1TW1FTosOT1yxKeV3YVKEALoUGlEWC1sDVmj4HABwtkqb+WzxaC+pROtDk6zIRRqVF+AYGqVmNEyCDB0X24xGPMTo6YFMUCWr6ftKE0oh19JIVbUqztaAeMngFlxMR0oUhlVOoZ86MXNzZLdegV1sTMQNXaPfXgIsyhcog3RQwIfmCMYabT5+MO9bOwPp9Hbjy8S9htkX+MJwpDrZbcNUTW1CsV+GfP1iM4igfKDMdBXApJHQKtcb0Jbq1dXH1P5VKqzH6OrhK616Y1VWw+8uoxCLlIvP+Al9bp2g7UQ3ODlgzdP1bQJthJryQCy4nIqQPaipVFmiifuKN1lKrwB/A9WkyszchIfnmyqW1eODyedjZbMYlf/scJ8zx92ZOl+M9Nnz38S+hkMvw7LWLMSpK+8hsQAFcCtmURTE3MSg8dowxf42mTJ8+9TthmA4PU0QMFir7M38DQ0C/0VeoMdpGBoOzXfIdqADCt2Lwcyn06NRPFryRQSsgA5fKKdRYOvWTUOA4AbW7b8R1hY5WWFXlgjKiQBrbsxGSx86uH40nr1mI5l4bLnr4MzR2Zl7Xho4+O777+Jewu7x45geLUVMqXhtKqVAAl0J2ZRE07j4wHnltwJi+r6DgThwtiq/+209XTUp2eAnxyDXo0E8NO12ncZlQZG/JmgDOo9ChTz0q6kYGo6MDlgzIjBbF6EvaUjAboy27IPO6Y55L5+qJWcRXyinIwY0MI9fBFdpbBLXQyuKNcYRkpZMmluGFHy31d234HN9kUNcGk82JKx/fgk6LA09esxBTRhmlHpIoKIBLoQFFERg4NGEyCQG1vV/ALVOjuWBuXOdORS9XoVoL6jHKunfE4v8K6z4AmbGBQagebW3EDJzcY4fWbYYlA3agThttxJgwLc4CWgvmQOl1oNy/iSQSudcBtac/84r4hhgM4EY+lgJ7K8zq6BsYGGPQqX1rEcsMwjJ1hJDkBbo26NVyXP7YF9iUAV0b+h1uXP2PrTjS1Y/HvrcAc9NcND6VKIBLoUCpg7HmbWGvZ9yD8b2foKVgDjxyYXPx6epPecqUyLsuW42zofA6UNG/b8jlgUb3HXEEcFLPcPVoa31r4MIUjg0W8c2ANXCMsajrNQYL+kafRg1sqom1Bk7KKVSbqhT9ypIR6+BkXheMzg5BO1ArjBpcunAslk1MX29aQoiva8PL15+EmlI9vv/kVry+Q7quDXaXB9f9cxt2tZjx1+/MzbnXAwrgUqip+CR06SbgpKOPhJ3amt7xNorszdhVeYHgc16zrBY/OmW8mMMMK1prq9YIje0rrXvRqxkHhyJz0tOxpgJ7dHVQeu0wOtpHXGcMlBDJhDVwiLoMDv2qMpg0YzDGsiPqOQaL+GbuFCoQ2MgwdAq1wNEGBi5oChUAqoq0kmaqCclXFQUavPCjJZhfU4yfPb8DT0jQtcHt8eKnz32NzYe6cc9F9ThjRuTd79mKArgU4kyOzTU3oNh+DDM63hhyncJjx0nHHkGrcRYOlp4q+JxqhTyt3RfCsfmDhdCdqHKvE6OsezN++lQpH/qG3qOtBYCw6+AMjszuwjBcq9Hf2D7KArBAGY5MbKMVqlM/GaW2w0Om6QvsvhpwMadQUzoyQogQBRolnvq+r2vD79/ai3vS2LXB6+W49eVdeH9vO24/dzouml+dlvtNNwrgUuxw8bfQYpyNJcceg8IzuL16butzMDg78UntTzN2q9w1y2oxbXT4wrStxtmo6vsGarcFC5ufxPe3rYXR2YGjxfFtxki30ILhjPkycED4nagZl4GL8eLXUjAbeldP+Lp2nGN224s48+Dt6FeWokdbl6JRiqNTPxly7h4SWAeK+FIXBkKyQ6Brw+WLxuGhjY247eVdKe/awDnH79/ai5e/asbNp03G1csy+7UuGRTApRpj+LT2JhhcXZjb9hwA3zqkhS1P4VDJKWgtmCP4VJMqDSkaZHhFOhVWTw+/gL/NWA+9qwc/3LoGJx99EN36CXh5xgPYW352WscYL29IEMTgWws2oChEadgMXAfsigK45ZE3DwDAwtrMyGZFWgenc3ZjbcPNOPXwn3C8cD6emfMsnIr0/i7Fq0M/BQCwqPlJKD02AL7soZsp0a8S1hWDECI9uYzhjxfMxE9PnYgXth3H9c98BbsrdV0b7vvwIJ78rAk/OLkOP101MWX3kwmys39ElmktmIPDxSdjQfPT2Fl5IRYffxxKjx2f1twk9dBikkVYQ9RUvBT9yhIcK1yI7WOuRKdhSkLnd7ql66E3Z1wR9rT2oUdXh+KBoyOuNzg7YBGQfcuUBGqPthYDikIsO/Ywpna+C4eiAA6FARN6PobK3Y+Pxt+Cb0Z9O3MGHEWvrhafjb0OS48/hnLrfrwz5U4U2lthUY+O2S84Cx4eIXmFMYafr56CUoMat7+5B1f8/UucPWs0inRKFOtUKNQpUaRVokinQoFGAUWCTeUf//QI/rL+IL49vxq/OXuapIXi04ECuDT5tOZGXLnjOzit8W5M6NmA3ZVr0aurlXpYCevTVOHRRe8lfZ7RRemthH3enCq8scM3Fafyv0j0aGsxsXuDb+1YyB+80RG7C0NtmS51gx1myfhSuD0cu1oi9NdlMmyuuQHjezZB4+6D0dYOtdsCs3oMPpj5G3TrJqRtrGL4ctwP0VI4D2ce+C0u23kNXHIdTmRBizZCSHhXnVSLUoMKv3hpJ7YfjdxmskCjQJFOhSKdEoX+wK7YH+QV6lT+YC/wz/f9+oYO3PHWXpw5YxTuunBWzgdvgEQBHGOsBMALAGoBNAG4hHM+4tlkjDUBsADwAHBzzhfEc/tM0q2fiIbyNZje+TacMi0+H/dDqYeUEZbUlab0/Kunj8JzW47B4+UYX65HuXGwLligVMYJwwzMan8NpbZGdOsHU+4GZzs6EswspoJGKcdp0yux70QfXJ7w6+F2jboQu0ZdmOaRxU+tlMHqiH1cs3/K9/RDf8DEno9h0oxN/eAIISlzTn0Vzpo5Gha7CyabC6YBF3ptTphtLphsTpgGfJeb/ZebbC409w7AZHPCPOCCN8pS4G9NKsNfLp+TcAYv20iVgbsNwHrO+d2Msdv8398a4diVnPOuJG6fMT4b9yOM7/kY26qvgk2VW/VoEhVpilYs5UY1zq4fHcy6hbu3IyUnA43AhJ5NwQBO5nVB7+oRNIVK4rd2zhgc6rAKKvRpVxbhzal/Ql3v5uDauGjy4ZM3IdlMLmP+DFt8jeS9Xg6L3Q3TgDMY/AUCO86Bby+ohlqRnlqpmUCqAG4tgBX+r58CsBHxBWDJ3l4SFs1oPLrwHcFFe0l69KvK0GaYifE9m7Bl7PcBhBbxlb4LgxQ0KX4RLNQqMb+mWHildsZ8gTYhJG/JZAyFOiUKdUrUpHbyJitIlWes5Jy3AYD//0hpDg7gfcbYdsbYdQncPuNQ8JZBQhI1jSXLMdq6B3qnL9kbKOwbKwMnZceCVJhXU4wVU8qxcmrW/EkRQkheSlkAxxj7kDG2O8y/tXGcZhnnfB6AswDcyBhbnsA4rmOMbWOMbevslL4vG8lMh0u+BQCo6/kEgG8HKpAZbbTSbe64YmiU+TMNkcno9YsQEknKAjjO+Wmc85lh/r0OoJ0xNhoA/P93RDhHq///DgCvAljkv0rQ7f23fZRzvoBzvqC8PLvrR6WpiHXeCF0q1a2bALO6ChN6NgHw1YADMqeIL8lPufT6RQgRl1RTqG8AuMr/9VUAXh9+AGNMzxgzBr4GsBrAbqG3J+KZUJHZRV/jEXGBO2NoLFmOceatUHgGYHS2wyHXZ3zBW0IIIflJqgDubgCnM8YOAjjd/z0YY1WMsXX+YyoBfMoY+wbAFgBvc87fjXb7TDB1VOY0chfLebOzu3XRmCItCrVKLBkffdXr4ZLlUHgdGGfaAoOzQ3D2rb66EBUFaswcUyjGcAkhhJCYJNmFyjnvBrAqzOWtANb4vz4MYHY8t88EM6oKse+ERephkBAapRzfP9nXD8/qcAcvH56LaymYC7vcgAk9m2AQUMQ3wKhR4orFNWINNyZfFpHm0wkhJJ/lR7W7NJLLc2tXYiplWrkur0yBpuJlGN/7CQocJzK2jdYlC8ZicV1m9F8lhBAiDQrgRFZVqMHyybTYOBatSo5rTqqTdAzh1sM1liyHztULvas7YzcwlBvVOGkiFYImhJB8RgGcyBhjmF9TLPUwMl5NiQ6FOmXa7zdW44ejxUvh8TdLz8cSIoQQQrIDBXAkr8hCsm7hYjmHwoiWgnkAAIsqf7owFGrTH0wTQghJHAVwKbJsYhmqiqjrQiRSrX8Tcr+NJacAAPo0o1M8msxRaoivJyEhhBBpSdULNectqivBoroS3PvBAamHkpGkKkosExDB7Rx1Ifo0o9GjG5+GERFCCCHxowxcFqHCEckLDd8ixXJemRKHS4R1bSs3qJMfFCGEEBInCuCyQC5Ob0k1hSokAxePebRhJSlaFfVcJYSQRFAAR/JKaPzGwm5jSPx8JH5Xn1Qr9RAIISQrUQCXBSbmUC9SqUXshZro+UQIAvOZRkkZOEIISQQFcBnu5tMno766CAAwx/8/IYQQQvIb7ULNAga1AjefPlnqYZAwYhUGJoQQQlKBMnCEJEHsTRFSCeymrSqk2oWEEJINKANH8pYYsVeOxG8YW6LDrOpCGDXUkYEQQrIBZeAISYLYmyKkwjkkC96KJeiJSwgh2Y4COCKIWkm/KrmMS1gm+sqltbjp1ImS3T8hhGQjelcmgojdcSBX1o7lCq+EbT7kMgalnF6KCCEkHvSqSQQR8/19QoUByyeXi3hGkiyvVM1pQ1yxeJzUQyCEkKxBARxJu+WTyqiAa4bJgPgNFQW0A5YQQoSiAI4IQhOeI+XS4nueCREcIYQQwSiAI4LQ2/tQC2tLcPWyOknHsHJqhWjnknINHCGEkPhRAJdip4r4JktIqDlji0Q7VyasgSOEECIcBXApNlvEN1mSOXJtEy3Fb4QQkl0ogCNpU6jNnTVjuaZEr5J6CIQQQuJAAVwGO3PmKKmHEFW8tdxG+fts5kLNr1xJwClkDNd+qy743BBCCMkO1AuVCDKjqgAtvQNDLivRK9FldQo+x+nTKzG/phh6tbS/dlcsHofjwx5LPqP+p4QQkn2yPxVC0mJGVSFuPn3ykMsUchnKDMKn3pRyGSozoNZXRYEG82uKkztJrqTgCCGEZCUK4AhJAMuRCG5siU7qIRBCCEkABXCEZDGVIrk/4SXjS0UaibQyIbNLCCHpJEkAxxgrYYx9wBg76P9/xHwWY2wKY2xHyL8+xth/+K+7nTHWEnLdmrQ/CJLXMqWMyJVLa3Dx/OqEb58pj4MQQkh8pMrA3QZgPed8EoD1/u+H4Jzv55zP4ZzPATAfgA3AqyGH3Bu4nnO+Lh2DJmFQBCCpAo0yqWnQXKn/Rr+GhJB8I1UAtxbAU/6vnwJwfozjVwFo5JwfTeWgCBFKneTUJRHHhAqD1EMghBBJSPUuVMk5bwMA//+x+k1dBuC5YZfdxBjbyRh7ItwUbABj7DrG2DbG2LbOzs7kRp2kRXUlOHlSmaRjIOKYXV0k9RBEke2Zq3H+7CMDUFWUe+vgMun1ixCSWVIWwDHGPmSM7Q7zb22c51EBOA/Av0MufhjABABzALQB+L9It+ecP8o5X8A5X1BeXh7/AxHRsollmFxhlHQMUrh80Thcs6xW6mEk5dKFY1FmVAe/l8myPPLxy/YpVIX/edCpFbh4/ljcdOpEiUckrkx6/SKEZJaUVVTlnJ8W6TrGWDtjbDTnvI0xNhpAR5RTnQXgK855e8i5g18zxh4D8JYYYyapoVPLUZDlxWLLDGpolXKph5Hz5tcUY/vRXsHHlxvVOH16JSZWGCCXMchzpLwLIYTEItUU6hsArvJ/fRWA16McezmGTZ/6g76ACwDsFnV0RLB8ebvM9qnGbFFTGv+GjJljCqGh4JoQkmekCuDuBnA6Y+wggNP934MxVsUYC+4oZYzp/Ne/Muz29zDGdjHGdgJYCeDm9AxbBBQIZCV62jITPS+EkHwlSVNKznk3fDtLh1/eCmBNyPc2ACMqjXLOr0zpAAkZhlEKLi1ypcMFIYSkGjWzT4PaMh2KdcJ7hmYT2bDAZlShBifMdolGkzoUVmSmXNlMQggh8aJiVmlwwdxqrJgSq1JK9uEcOHvW6CGXzRpTOOK4TH2LjWcHJiXgMpNSRi9hhJD8RK9+aRZPIJCJQcOECkNwXBwchTolrj9lgrSDSgPGGBbXlUg9jJwX7++8Qp6BfySEEJIGFMCRuJw3uwrfWTRuyGVaVX7sAEymZVWm4sjuQnDUEYMQkq9oDRyJnz/p4RX43p9LGwDOm1MFm8Mj9TAIgCuWjINCTgEcISQ/UQCXZsNDmTKjGl0WhyRjSVRwp2CYRWQ5FKuFNaE8t3pvZtquz3imRKn2GyEkn9HHV4ldPK9a6iHEbXANXHbL9WBTiEybQs3V3dqEECK2vM/AuVwuNDc3w25PT+kLL+c4qdQd/L6p8QBOKnWFPdbZdRwNvZkXY3u8vscgkznQ0NAAAMHH4OlpxkmlQ6cYjzYeGFFuREwajQbV1dVQKuNr16VRynHWrFF4Z9eJFI0s8+mUmfUSQFk1QggRJrNevSXQ3NwMo9GI2tratKzV8ni96LI6g99XFmjQ3hc+eCzQKjOy/6bb40V3vxNyGUOZwdfgPfAYCjRK9NmHBqRlBjXkKarXxTlHd3c3mpubUVdXF/ftp44qyNsA7qqTalGoy64etYvHl+DLwz1SD4MQQiSXeemdNLPb7SgtLU3jQnuatxMTYwylpaVpy6BmqsCv79o5VVAJ3JlZos+O6crVMyqlHgIhhGScvA/ggNzaJZlJ0vVjpedvsCPGuBIdDOr8SKynclqeEEIyHQVwGeZPd/0BD91/LxgDVBFKJDzyyCN4+umnAQBXX301XnrppZSNZ8WKFdi2bVv4K8Osf9co6VdKCoEp6szakpACIQ8wXwJVQggJh14BM1SFURPxuuuvvz6NIwkjyi7UbM6GGTUKWOzu2AdmoEsXjsWBdguUUeqiVRVpMH10IT5saE/jyAghhKQCpUsywH1/+l8sm1+Pb5+3Bo0HDwQvf+yxx7Bw4ULMnj0bF110EWw2GwDg9ttvx5///Och51i/fj0uuOCC4PcffPABLrzwwiHHvPPOO7jkkkuC32/cuBHnnnsuAODHP/4xFixYgBkzZuB3v/td2HEaDIM10N587RX85PprAQCdnZ34wXcvwxkrlmHhwoXY8sVnAIDPPv0Eq05ejPnz5mLu3LmwWCxx/2zSaXy5XuohJKzMoMZJE8oA+DZ2RJJpZUPC+eHy8bhu+fiw12X+6AkhJD0oAxdi4/4OdIpcVLfcqI7ayH779u147ZV/44NPvoDH7cbpy5eifs5cAMCFF16IH/7whwCA3/zmN3j88cfxk5/8JOx5Tj31VNx4443o7OxEeXk5/vGPf+Caa64Zcszpp5+OH/3oR+jv74der8cLL7yASy+9FABw5513oqSkBB6PB6tWrcLOnTtRX18f9r6G59h+9rOf4bobf4LFS5fBYerAaaevxidbd+Ch++/DXX++F2tOW4kBWz80mshZRZJ61cXZ0QosdGp0YW1JuHrRhBCS9ygDJ7FPPvkEZ51zHnQ6HYwFBThjzdnB63bv3o1vfetbmDVrFp599lns2bMn4nkYY7jyyivxzDPPwGQy4fPPP8dZZ5015BiFQoEzzzwTb775JtxuN95++22sXbsWAPDiiy9i3rx5mDt3Lvbs2YO9e/cKfgwffvghfnXLz7Hq5MU477zzYLH0wWqxYNlJJ+F3v7oVD/z1fphMJigU2fV54YcRskCZLlKgtnR8KYya7CkbcvPpk3HypLIhl1EwRwghPtn1jppi0TJlqRRp3djVV1+N1157DbNnz8aTTz6JjRs3Rj3PNddcg3PPPRcajQbf/va3wwZMl156KR588EGUlJRg4cKFMBqNOHLkCP785z9j69atKC4uxtVXXx22LMfgOBkcjsFMpdfrxVsfbIRWqx1S1+63v/kVzl97Lt595x0sWbIEH374IaZOnSrsh5IBsnWR/MqpFdjVYh5xuUzGUFfmmyamHZyEEJLdKAMnseXLl+Pdt97AwMAArBYL3n9nHRT+HYUWiwWjR4+Gy+XCs88+G/NcVVVVqKqqwh/+8AdcffXVYY9ZsWIFvvrqKzz22GPB6dO+vj7o9XoUFhaivb0d77zzTtjbVlZWoqGhAZx78c6brwd3Pq5evRpPPPpw8LjdO78BABw+fBiz6+tx6623YsGCBdi3b5/gnwtJXKyiyd9eUI1rTq5Nz2BElg1r+AghJB2yM8WQQ+bNm4cLLv42Tjt5MarHjsPik5ZB7e++cMcdd2Dx4sWoqanBrFmzBG0CuOKKK9DZ2Ynp06eHvV4ul+Occ87Bk08+iaeeegoAMHv2bMydOxczZszA+PHjsWzZsrC3vfvuu3HOOedg7NixmDFjBvr7+wEA999/P358ww047eRF8LjdWLhkGe6576+47777sGHDBsjlckyfPn3ElG6mKDOq0SXy2sdMli1r4QghhETGou1YyzULFizgw2uaNTQ0YNq0aWkbg8fL0WUdDBYqCzQw2ZxwuL3By0r1KiiilIOI5qabbsLcuXPxgx/8IOmxJiowhVpZkL5NC8k8jx4vB+ccnxzswo7jpuDlN58+Gfd+cCD4dTZ5eGMj7K6hPWmz7TEE7G4x44O9vtIn82uKsf1oLwDhj4cxtp1zviBlA0yTcK9fhJDcFu31i6ZQc8j8+fOxc+dOfPe735V6KFlFLmNQyGU5NT03dZRR6iEQQghJIZpCTTMZ85XhSEWosH379hSclWSjUyaXD8kmEkIIyS2UgUszxhgq0ji1SIRjIyrcZS+ZjAU3w+SS3MmREkJIcigDl0GMGgU4j72LkJB8lU9rdgkhJBoK4DKInDGoVXKph5E0GWPwZvkbrUqR/cnpXCz1Vl2shVYph8uT3b9fhBCSLArgMkCuvRWV6FXweHPtURGpjS/XY2KFEROlqbdNCCEZJfvTDCQsk8mEhx56KPj9xo0bcc4554Q9dsWKFRCzPIFcxnIigwUAq6ZVYNnEstgHZrArl9ZkbVuwUDoVfd4khJCA3HiXJSMMD+BIfALTj/XVRVhUVyLtYJJUoFFmbVswwFcSpb66ECdneSBNCCFikiSAY4x9mzG2hzHmZYxFLLDJGDuTMbafMXaIMXZbyOUljLEPGGMH/f8Xp2fk4mtqasLiubPw85t+jHlzZuOKK67Ahx9+iGXLlmHSpEnYsmULAKCnpwfnn38+6uvrsWTJEuzcuRMAcPvtt+P73/8+VqxYgfHjx+P+++8HANx2221obGzEnDlzcMsttwAArFYrLr74YkydOhVXXHHFiAXhjz/+OG6++ebg94899hh+/vOfjxizwWDArbfeivnz5+O0007Dli1bgvf/xhtvAAA8Hg9uueUWLFy4EPX19fjb3/4WHMOqVaswb948zJo1C6+//nrw5zBt2jT88Ic/xIwZM7B69WoMDAwA8HV6mD59Ourr63HZZZeJ9rMfbny5Pvh1qV6VsvtJF70/aMv2tXAKuQyrplVCmwPrQwkhRDSc87T/AzANwBQAGwEsiHCMHEAjgPEAVAC+ATDdf909AG7zf30bgP8Vcr/z58/nw+3du3fwm3W3cv7EGnH/rbt1xH1yzrnb4+Eej5cfOXKEy+VyvuGzrdxmd/J58+bxa665hnu9Xv7aa6/xtWvXcs45v+mmm/jtt9/OOed8/fr1fPbs2Zxzzn/3u9/xpUuXcrvdzjs7O3lJSQl3Op38yJEjfMaMGcH727BhAy8oKODHjx/nHo+HL1myhH/yySecc85POeUUvnXrVm61Wvn48eO50+nknHO+dOlSvnPnzhFjB8DXrVvHOef8/PPP56effjp3Op18x44dwXH97W9/43fccQfnnHO73c7nz5/PDx8+zF0uFzebzZxzzjs7O/mECRO41zv4c/j6668555x/+9vf5v/85z8555yPHj2a2+12zjnnvb29YX+eQ57HJDV1WfmA0y3a+aTSN+Dke1rMUg9DcgC2cQle58T+F+71ixCS26K9fkkyr8I5bwB8NdGiWATgEOf8sP/Y5wGsBbDX//8K/3FPwRcI3pqa0aaGXDaY/Kyrq8O8ObOhUSkwY8YMrFq1CowxzJo1C01NTQCATz/9FC+//DIA4NRTT0V3dzfMZjMA4Oyzz4ZarYZarUZFRQXa29vD3ueiRYtQXV0NAJgzZw6amppw8sknB6/X6/U49dRT8dZbb2HatGlwuVyYNWvWiPOoVCqceeaZAIBZs2ZBrVZDqVQOGe/777+PnTt34qWXXgIAmM1mHDx4ENXV1fjVr36FTZs2QSaToaWlJTjeuro6zJkzB4Cvq0TgXPX19bjiiitw/vnn4/zzz4/zJx2/mlJ97IOygFGjxPQqpdTDIIQQkgKZvDBmDIDjId83A1js/7qSc94GAJzzNsaYOPvSzrpblNPES61Wo0Dre6OVyWRQq9XBr91uN4Dw9a8CAXDgeMDXrD5wm3D3E+u4a6+9Fn/84x8xdepUXHPNNWHPo1Qqg/cdbbx//etfccYZZwy57ZNPPonOzk5s374dSqUStbW1sNvtYccXmEJ9++23sWnTJrzxxhu44447sGfPHigUmfyrSwghhKRWytbAMcY+ZIztDvNvrdBThLks7toUjLHrGGPbGGPbOjs74715xli+fDmeffZZAL4dpWVlZSgoKIh4vNFohMViift+Fi9ejOPHj+Nf//oXLr/88oTHe8YZZ+Dhhx+Gy+UCABw4cAD9/f0wm82oqKiAUqnEhg0bcPTo0ajn8Xq9OH78OFauXIl77rkHJpMJVqs14XERkk1y5fWLECK+lKUxOOenJXmKZgBjQ76vBtDq/7qdMTban30bDaAjyjgeBfAoACxYsCBri5PdfvvtuOaaa1BfXw+dToennnoq6vGlpaVYtmwZZs6cibPOOgtnn3224Pu65JJLsGPHDhQXJ7435Nprr0VTUxPmzZsHzjnKy8vx2muv4YorrsC5556LBQsWYM6cOZg6dWrU83g8Hnz3u9+F2WwG5xw333wzioqKEh4XIdkkV16/CCHiY+Gm5tJ254xtBPBfnPMRRcgYYwoABwCsAtACYCuA73DO9zDG/gSgm3N+t393agnn/Bex7m/BggV8eL2zhoYGTJs2LfkHk0POOecc3HzzzVi1apXUQxGMnkcSCWNsO+c84m73bBHu9YsQktuivX5JVUbkAsZYM4ClAN5mjL3nv7yKMbYOADjnbgA3AXgPQAOAFznne/ynuBvA6YyxgwBO939PkmQymTB58mRotdqsCt4IIYSQfCPVLtRXAbwa5vJWAGtCvl8HYF2Y47rhy8wRERUVFeHAgQNSD4MQQgghMVAnBkIIIYSQLEMBHMKX6CDZg54/Qggh+SbvAziNRoPu7m4KArIU5xzd3d3QaDRSD4UQQghJm7yvhlpdXY3m5mZQjaXspdFogh0mCCGEkHyQ9wGcUqlEXV2d1MMghBBCCBEs76dQCSGEEEKyDQVwhBBCCCFZhgI4QgghhJAsI2krrXRjjFkA7I/jJoUAzHHeTbpuUwagK8X3kY7bxPs4ErmPRG6Tjuckkfuh36/4bzOFc26M8zYZhzHWCeCoxMNI5HdcCjROcWXDOLNhjED846zhnJeHvYZznjf/AGyL8/hHE7iPdN0mJx5LvI8jjeNK+XOSxnHl7e9Xos8L/cvunyWNM//GmQ1jFHucNIUa3ZsZfJt03Ec+P5Z0PI5E7iefn5NEb0MIITmHArgoOOdxv1mk6zbpuI98fizpeByJ3E8+PyeJ3oYQQnJRvgVwj0o9ABHlymPJlccB0GPJVLn0WKSWLT9LGqe4smGc2TBGQMRx5tUmBkIIIYSQXJBvGThCCCGEkKyX1QEcY2wsY2wDY6yBMbaHMfYz/+UljLEPGGMH/f8X+y8v9R9vZYw9MOxcGxlj+xljO/z/KrL4sagYY48yxg4wxvYxxi7KtsfBGDOGPBc7GGNdjLH70vU4xHws/usuZ4ztYoztZIy9yxgry+LHcqn/cexhjN2TzseR4GM5nTG23f/z384YOzXkXPP9lx9ijN3PGGPpfjyZLtLPO1MxxuSMsa8ZY29JPZZIGGNFjLGX/K/PDYyxpVKPKRzG2M3+53w3Y+w5xphG6jEBAGPsCcZYB2Nsd8hlYf/+pRRhnH/yP+87GWOvMsaKEr4DqbfUJrkddzSAef6vjQAOAJgO4B4At/kvvw3A//q/1gM4GcD1AB4Ydq6NABbkyGP5HwB/8H8tA1CWjY9j2Hm3A1iejc8JfD2HOwLPg//2t2fpYykFcAxAuf/7pwCsyvDHMhdAlf/rmQBaQs61BcBSAAzAOwDOSudjyYZ/kX7eUo8rynh/DuBfAN6SeixRxvgUgGv9X6sAFEk9pjBjHAPgCACt//sXAVwt9bj8Y1kOYB6A3SGXhf37z8Bxrgag8H/9v8mMM6szcJzzNs75V/6vLQAa4PulWwvfHwj8/5/vP6afc/4pAHv6RxudyI/l+wDu8h/n5ZynrbhhKp4TxtgkABUAPkndyEcS8bEw/z+9P8NTAKA15Q8ghIiPZTyAA5zzTv/3HwJIW4bXP7Z4H8vXnPPAz3sPAA1jTM0YGw2ggHP+Ofe9mj4duA0ZFOXnnXEYY9UAzgbwd6nHEgljrAC+N/bHAYBz7uScmyQdVGQKAFrGmAKADml+3YqEc74JQM+wi8P+/Usp3Dg55+9zzt3+b78AUJ3o+bM6gAvFGKuF75P2lwAqOedtgO/FB743fyH+4Z+u+28pp1KSeSwh6dg7GGNfMcb+zRirTOFwo42lFsk/JwBwOYAX/G+ykkjmsXDOXQB+DGAXfC+A0+F/8ZZCks/LIQBTGWO1/hf18wGMTd1oo0vgsVwE4GvOuQO+IKQ55LpmZGhgkimG/bwz0X0AfgHAK/E4ohkPoBO+95uvGWN/Z4zppR7UcJzzFgB/hi/j3gbAzDl/X9pRRZXMe4xUvg9f5j8hORHAMcYMAF4G8B+c874ET3MF53wWgG/5/10p1vjiIcJjUcAX0W/mnM8D8Dl8f4RpJdJzEnAZgOeSH1Vikn0sjDElfAHcXABVAHYC+KWogxQ+lqQeC+e8F77H8gJ8GdEmAO5ot0mVeB8LY2wGfFMWPwpcFOYw2pYfgch/06JjjJ0DoINzvl3qscSggG9a7WHO+VwA/fBN+WUU/xqytQDq4Hvd0jPGvivtqHIHY+zX8L12PpvoObI+gPO/Ob4M4FnO+Sv+i9v90yPw/98R6zz+TxuBKYJ/AViUmhFHJtJj6QZgA/Cq//t/w/dikTZiPSf+Y2fDt15AkhdlkR7LHADgnDf6s4gvAjgpNSOOTMS/lTc554s550vh6y18MFVjjiTex+KfWnsVwPc4543+i5sxdPqiGhkyRZRpIvy8M80yAOcxxpoAPA/gVMbYM9IOKaxmAM2c80AW8yWk+TVaoNMAHOGcd/pnEV6BBK9bcUjoPUYKjLGrAJwDX+Io4Q+NWR3A+ac5HwfQwDn/fyFXvQHgKv/XVwF4PcZ5FMy/K9D/QnUOgN3RbiM2sR6L/5fhTQAr/BetArBX1MFGIdbjCHE5JMq+ifhYWgBMZ4wFGhKfDt86orQR83lh/h3a/k/oNyDN643ifSz+ZQVvA/gl53xz4GD/NIuFMbbEf87vQfjvZd6I8vPOKJzzX3LOqznntfBl7T/inGdcxohzfgLAccbYFP9FaX2NjsMxAEsYYzr/78AqpPl1K06JvsekFWPsTAC3AjiPc25L6mRCdjpk6j/4dslx+Kakdvj/rYFvp9x6+DID6wGUhNymCb5FhVb4PglNh2/H3Xb/efYA+AsAeTY+Fv/lNQA2+c+1HsC4bHwc/usOA5iazb9f/suvh+/Fbyd8AXZpFj+W5+B7w9kL4LJMf14A/Aa+aaodIf8q/NctgO/DWiOAB+Avbk7/Yv+8pR5XjDGvQGbvQp0DYJv/Z/oagGKpxxRhnP8DYJ//b+SfANRSj8k/rufgW5fn8r82/SDaa1mGjfMQgOMhf0uPJHp+6sRACCGEEJJlsnoKlRBCCCEkH1EARwghhBCSZSiAI4QQQgjJMhTAEUIIIYRkGQrgCCGEEEKyDAVwhBBCCCFZhgI4knKMsSLG2A0h31cxxl5Kwf3czhhrYYz9XuxzD7ufFYyxk0K+f5IxdnGY4yb4e+taUzkeQggh+YcCOJIORfBV7AcAcM5bOecjAh6R3Ms5/22Kzh2wAgJaynBf66w5KR4LIYSQPEQBHEmHuwEEslF/YozVMsZ2AwBj7GrG2GuMsTcZY0cYYzcxxn7OGPuaMfYFY6zEf9wExti7jLHtjLFPGGNTY92pPyP3FGPsfcZYE2PsQsbYPYyxXf5zKf3HrfLf3y7G2BOMMbX/8ibG2P8wxr7yXzeVMVYLX1eFm/2P51v+u1vOGPuMMXY4XDaOEEIIERMFcCQdbgPQyDmfwzm/Jcz1MwF8B8AiAHcCsHHO5wL4HL7+lADwKICfcM7nA/gvAA8JvO8JAP5/e/evGkUYhWH8OZrCItZiZyFYieIFeAMRxEIsxEIEwd4LsNfe2CnYCSJYaBdELAUlKmITWzGCmgUNwX0tZkIGQXfZ/FlneX7N7AxzPs7ALBzONzPfAnAWuA8sJTkO/AAWquoAcBe40B6fA6514leTnAJuA9eTfAQWaTp9J5M8b887TLPk0BmaglWSpF1jAaf/wVKStSSfgW80a4UCLANHqmqeZsryQVW9Au7QFEzjeJJkox1rP/C0OzZwDFhJ8qE9fg843Yl/2G5ftuf/zaMkwyTvgENj5iZJ0kTmpp2ABKx3fg87+0Oae3Qf8HXC58nWAZIMq2ojW4v/bo5dY+b2i3//X7rXMGpMSZK2xQ6c9sIacHDS4CTfgZWqOg9QjRM7lNt7mi7f0Xb/EvBsRMy2rkeSpO2ygNOuS/IFeFFVb6rq5oTDXASuVNVr4C3NM207kdtP4DLN9OwyTWducUTYY+DcHy8xSJK0Z2prRknqt6q6AQyS3Jp2Ll1VNUgyP+08JEmzww6cZskAuLrbH/Id1+aHfIFP085FkjRb7MBJkiT1jB04SZKknrGAkyRJ6hkLOEmSpJ6xgJMkSeoZCzhJkqSe+Q0pamhDhBRGcgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Create a a daily time series from 2015-2020 with an noisy annual cycle.\n",
    "# Resample the data by taking the mean over each month using `resample('M')`.\n",
    "# Create the climatology (monhtly means) using the groupby(timeseries.index.month) operator.\n",
    "\n",
    "dates = pd.date_range(start='2015-01-01', end='2020-01-01', freq='D')\n",
    "timeseries = pd.Series(.5*np.sin(2*np.pi*dates.dayofyear/365)+np.random.rand((len(dates)))-.5,\n",
    "                       index=dates)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10,6), sharey=True)\n",
    "\n",
    "timeseries.plot(ax=ax[0], label='daily values', alpha=.5)\n",
    "timeseries.resample('M').mean().plot(ax=ax[0], label='monthly means')\n",
    "ax[0].legend()\n",
    "ax[0].set_xlabel('time [years C.E.]')\n",
    "ax[0].set_ylabel('values')\n",
    "\n",
    "timeseries.groupby(timeseries.index.month).mean().plot(ax=ax[1], label='climatology over five years')\n",
    "ax[1].legend()\n",
    "ax[0].set_xlabel('time [month]')\n",
    "plt.savefig('figures/3a3')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}