Python_lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python lab: data analysis and plotting with pandas\n",
    "### By Paige Martin\n",
    "\n",
    "This is meant to be a lab that you can walk yourself through on your own time! Any questions and discussions can be posted to the `#python-help` channel in Slack!\n",
    "\n",
    "## This python lab will familiarize you with:\n",
    "\n",
    "- Some of the basic python and jupyter notebook functionality\n",
    "- Using the pandas library to analyze text/spreadsheet data\n",
    "- Loading csv (or other text) files\n",
    "- Manipulating data\n",
    "- Using DateTime functionality\n",
    "- Plotting timeseries with matplotlib.pyplot\n",
    "- Various plotting features in pyplot\n",
    "- Typing equations in markdown cells using latex\n",
    "- Optional advanced section: bar charts with confidence intervals\n",
    "\n",
    "### There are 5 challenges scattered throughout this notebook that ask you to try writing your own code snippets, based on the content you go through here (don't worry, there are tips to help you through!). If you're inspired, post any of your challenges to the `#python-help` channel in Slack! I'd love to see what you've done, and it's a great way to learn from one another!\n",
    "\n",
    "To convert this lab into a pdf, go to the File menu and choose 'Download As...' --> pdf. If this does not work, try first downloading as --> html and then save as a pdf. \n",
    "\n",
    "\n",
    "### In this lab, you will load and plot temperature, air quality, and rainfall data from Canberra, Australia (where Paige lives!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A few notes about python usage:\n",
    "\n",
    "- Python indexing of arrays/dataframes is done using square brackets `[ ]` and starts at 0!\n",
    "- Strings, or words, are denoted by quotes in python, and can be either single ' or double \". They are equivalent!\n",
    "- When doing a loop in python, make sure you indent each line that is within the loop!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First, as always, we write `%maplotlib inline` so that our plots appear within this notebook. Then we load the libraries we want in this lab. See comments for what each individual line does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd # loads the pandas library\n",
    "import matplotlib.pyplot as plt # loads the plotting library\n",
    "import numpy as np # loads the numerical library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load some temperature data\n",
    "\n",
    "#### We are loading in temperature data from Canberra, Australia! In the previous cell, we abbreviated the pandas library as 'pd'. So when we write `pd.read_csv` that means that we want to use the 'read_csv' file from (denoted by the '.') the pandas library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = pd.read_csv('IDCJAC0010_070351_1800_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The above cell loads in what's called a pandas dataframe, so we now have temperature data stored as a pandas dataframe named 'T'. To view the dataset that we just loaded, you can just type the name of the dataframe, or `T`. This will print out a lot of lines though, so we often just us `.head()` to print the first 5 lines of the dataset. (Similarly, `.tail()` prints out the last 5 rows!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Product code</th>\n",
       "      <th>Bureau of Meteorology station number</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Maximum temperature (Degree C)</th>\n",
       "      <th>Days of accumulation of maximum temperature</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4399</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>25.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>31.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4402</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>27.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4403</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>27.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4404 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Product code  Bureau of Meteorology station number  Year  Month  Day  \\\n",
       "0      IDCJAC0010                                 70351  2008      1    1   \n",
       "1      IDCJAC0010                                 70351  2008      1    2   \n",
       "2      IDCJAC0010                                 70351  2008      1    3   \n",
       "3      IDCJAC0010                                 70351  2008      1    4   \n",
       "4      IDCJAC0010                                 70351  2008      1    5   \n",
       "...           ...                                   ...   ...    ...  ...   \n",
       "4399   IDCJAC0010                                 70351  2020      1   17   \n",
       "4400   IDCJAC0010                                 70351  2020      1   18   \n",
       "4401   IDCJAC0010                                 70351  2020      1   19   \n",
       "4402   IDCJAC0010                                 70351  2020      1   20   \n",
       "4403   IDCJAC0010                                 70351  2020      1   21   \n",
       "\n",
       "      Maximum temperature (Degree C)  \\\n",
       "0                                NaN   \n",
       "1                                NaN   \n",
       "2                                NaN   \n",
       "3                                NaN   \n",
       "4                                NaN   \n",
       "...                              ...   \n",
       "4399                            24.8   \n",
       "4400                            25.3   \n",
       "4401                            31.3   \n",
       "4402                            27.9   \n",
       "4403                            27.6   \n",
       "\n",
       "      Days of accumulation of maximum temperature Quality  \n",
       "0                                             NaN     NaN  \n",
       "1                                             NaN     NaN  \n",
       "2                                             NaN     NaN  \n",
       "3                                             NaN     NaN  \n",
       "4                                             NaN     NaN  \n",
       "...                                           ...     ...  \n",
       "4399                                          1.0       N  \n",
       "4400                                          1.0       N  \n",
       "4401                                          1.0       N  \n",
       "4402                                          1.0       N  \n",
       "4403                                          1.0       N  \n",
       "\n",
       "[4404 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Product code</th>\n",
       "      <th>Bureau of Meteorology station number</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Maximum temperature (Degree C)</th>\n",
       "      <th>Days of accumulation of maximum temperature</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product code  Bureau of Meteorology station number  Year  Month  Day  \\\n",
       "0   IDCJAC0010                                 70351  2008      1    1   \n",
       "1   IDCJAC0010                                 70351  2008      1    2   \n",
       "2   IDCJAC0010                                 70351  2008      1    3   \n",
       "3   IDCJAC0010                                 70351  2008      1    4   \n",
       "4   IDCJAC0010                                 70351  2008      1    5   \n",
       "\n",
       "   Maximum temperature (Degree C)  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "\n",
       "   Days of accumulation of maximum temperature Quality  \n",
       "0                                          NaN     NaN  \n",
       "1                                          NaN     NaN  \n",
       "2                                          NaN     NaN  \n",
       "3                                          NaN     NaN  \n",
       "4                                          NaN     NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.head() # print first 5 rows of our dataframe T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we can see the structure of our dataframe! Notice the columns are labeled with different variables, while the rows display different values of these variables. So, the columns are the variables in the dataset!\n",
    "\n",
    "#### Each column in our dataframe appears to be formatted differently. For instance, 'Product code' is a mix of numbers and letters, while 'Year' is just a number. To view the data formats of each row, use  `.dtypes`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product code                                    object\n",
       "Bureau of Meteorology station number             int64\n",
       "Year                                             int64\n",
       "Month                                            int64\n",
       "Day                                              int64\n",
       "Maximum temperature (Degree C)                 float64\n",
       "Days of accumulation of maximum temperature    float64\n",
       "Quality                                         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notice that there are separate columns for Year, Month, and Day. In pandas, there is a specific 'datetime' format which is really handy, but the relevant columns in T are not being interpreted as a \"datetime\" format, but rather as integers ('int64'). Let's change that!\n",
    "\n",
    "#### In the cell below we use the \"to_datetime()\" function from pandas to convert the three columns into a single \"DateTime\" column. Notice how we can create a column simply by calling T 'of' (denoted by the square brackets `[ ]`) and putting the name of our new column in quotes, in this case 'DateTime'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "T['DateTime'] = pd.to_datetime(T[['Year','Month','Day']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's print the first few rows of the dataframe again. Notice that there has been a new column added at the far right: one called \"DateTime\"!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Product code</th>\n",
       "      <th>Bureau of Meteorology station number</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Maximum temperature (Degree C)</th>\n",
       "      <th>Days of accumulation of maximum temperature</th>\n",
       "      <th>Quality</th>\n",
       "      <th>DateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product code  Bureau of Meteorology station number  Year  Month  Day  \\\n",
       "0   IDCJAC0010                                 70351  2008      1    1   \n",
       "1   IDCJAC0010                                 70351  2008      1    2   \n",
       "2   IDCJAC0010                                 70351  2008      1    3   \n",
       "3   IDCJAC0010                                 70351  2008      1    4   \n",
       "4   IDCJAC0010                                 70351  2008      1    5   \n",
       "\n",
       "   Maximum temperature (Degree C)  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "\n",
       "   Days of accumulation of maximum temperature Quality   DateTime  \n",
       "0                                          NaN     NaN 2008-01-01  \n",
       "1                                          NaN     NaN 2008-01-02  \n",
       "2                                          NaN     NaN 2008-01-03  \n",
       "3                                          NaN     NaN 2008-01-04  \n",
       "4                                          NaN     NaN 2008-01-05  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's once more look at the format of the columns to make sure that our 'DateTime' column is being interpreted correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product code                                           object\n",
       "Bureau of Meteorology station number                    int64\n",
       "Year                                                    int64\n",
       "Month                                                   int64\n",
       "Day                                                     int64\n",
       "Maximum temperature (Degree C)                        float64\n",
       "Days of accumulation of maximum temperature           float64\n",
       "Quality                                                object\n",
       "DateTime                                       datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The 'DateTime' column is now listed as a datetime format! ('datetime64[ns]' - the 'ns' is because the time is stored in nanoseconds)\n",
    "\n",
    "#### Take note of the 'NaN' values under \"Maximum temperature (Degree C)\" in T. This means \"Not a Number\", i.e. the value is undefined. This is very common especially in observational data, as every data point in time may not have been recorded properly due to instrument failure, for instance. Pandas and python treat NaNs pretty intuitively (e.g. they are not included in the plots below). But, they can sometimes be a bit of a pain when manipulating data, as we will see a bit later"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the temperature data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's see if we can plot the data! We call upon the pyplot library (that we imported at the start of this notebook) and use the `plot()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1194daeb8>]"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.plot(T['Maximum temperature (Degree C)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Great, look how easy that was to plot! But, notice that the x-axis is clearly not formatted as a date, so let's fix that. We can pass the \"x\" variable into the plot() function before the temperature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1194afe48>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO2dd3wVVdrHfw8hQKiBEGqA0KSIAhIUxEZREXzt9XUtuypr27UroO7a1rWs6O6+q7so9rqCiiIWFFBRWmihI9IhQOg9kOS8f9yZ5GYyM/fMzDln7tyc7+eTT+6dO3POmZkzz5zznKcQYwwajUajiR41wm6ARqPRaPyhBbhGo9FEFC3ANRqNJqJoAa7RaDQRRQtwjUajiSg1VVbWtGlTlpubq7JKjUajiTzz5s3bwRjLtm5XKsBzc3ORn5+vskqNRqOJPES03m47twqFiNKIaAERTTK+v0FEa4loofHXS1RjNRqNRpMYLyPwOwEsB9Awbtv9jLHxYpuk0Wg0Gh64RuBElANgOIBX5TZHo9FoNLzwqlBeBPAAgDLL9r8QUQERvUBEte0OJKIRRJRPRPlFRUVB2qrRaDSaOBIKcCI6H8B2xtg8y0+jAHQF0BdAEwAP2h3PGBvLGMtjjOVlZ1dZRNVoNBqNT3hG4AMAXEBE6wB8AGAQEb3DGCtkMYoBvA7gZInt1Gg0Go2FhAKcMTaKMZbDGMsFcBWAqYyx3xBRSwAgIgJwEYAlUluq0Wg0mkoE8cR8l4gWA1gMoCmAJ8U0SaPRaFKDHQeK8dWSrdLK9+TIwxibDmC68XmQhPZoNBpNynDduDlYVrgPSx87F/Vqi/eb1LFQNBqNRhIbdh0CAJRKSpyjBbhGo9FIhiSVqwW4RqPRRBQtwDUajUYSsnMOawGu0Wg0kolZW4tHC3CNRqOJKFqAazQajSTkKlC0ANdoNBppmCpwbYWi0Wg0mkpoAa7RaDQRRQtwjXIOFpfgn9/9gpJSa3h5TbJSVsbwr2mrsffwsbCbIoRt+47gP9//itIy2VpquWgBrlHOc1+vxPNTVuHzgi1hN0XDyferivDc1yvx6GdLw26KEO77aBH++uUKdBw9OeymBEILcI1yDhaXAACOlugReFQoLikFUHHvok7xMfl9r6yM4ajkWaYW4BrlmJNWkrY2r5GFJH8U5ag4jz9/trRcRSNLUaMFuEY5rEKCazShUEOBBH971nrpdWgBrtFokoIP5mzA/A27ldRVI0UkH/dpEFEaES0goknG9/ZENJuIfiGiD4molrxm8rP38DHpAWQ0muqGikdq5MeLcclLP8uvCGpG4PHIkkle3kN3Alge9/0ZAC8wxjoD2A3gRpEN88PaHQfR87Fv8M7sDWE3RePCnkNHAajRoExdsQ2fLNikoCZNlJAVXEo1XAKciHIADAfwqvGdAAwCMN7Y5U3EEhuHyq/bDwAApq3YLrTcXQePCi2vOjNtxXZ8Z9wfFQ/R797Ix90fLpJejyZapIb45h+BvwjgAQCmTUwWgD2MMdOmaBOA1nYHEtEIIsonovyioqJAjU1EmTFNqSHw7izYsBsnPTEFny2K2SzvPXQMj362tNysSuONOet2lX9W+RAdOabvlwhSxXJI9QA8NCsUIjofwHbG2Lz4zTa72raRMTaWMZbHGMvLzs722Uw+yo0bBN6dpVv2AQBm/roTAPDM1yvwxs/rMHGBdkLxQ1iPf4rMmJWz9/Ax/GniEhSnmM1+qnQHnhH4AAAXENE6AB8gpjp5EUAmEZlplnMAhC7RzIUCGTfHFADHjI7MpAeKTE3CEqQbjeSyqcK2fUeULNaP+WYl3pq5Hh/N2yi9LpWo1oGv2rpfSrkJBThjbBRjLIcxlgvgKgBTGWPXAJgG4DJjt+sBTJTSQg+YYQ1ErjDHPyKFew+X15EqiyCqiZ+Cq7yEQ8b8gCWb96qrUCLzN+zGKU99hwnzN0uv65jR4Yv2F0uvy2RunJpNFqqf3iv+M1NKuUGsIR8EcA8RrUZMJz5OTJP8Y+rA00QqwQ3mrN2F/n+diokLYw/NA+MLhNdR3VD9Dty274jQ8ibM21TeH1Sy0hjN5SsQdOYof9W2A9LrMrn833KEnSomLy7Eh3MrW8LJiplVM/EuFTDGpgOYbnxeA+Bk8U3yz55DRqQ0kYLB6MCrDQuXkohHLwubMCcu6WlivTfu/Shm3XJhL9v1e2mYAxUVs8BUdamQeelue3e+vMItpIg/UoyHP10CQL2RvoafeKGj2qJBtAAPi7Iy8dZWjnWlqgRPEVKjR1tQ0bE1/gjz1tSqmRodQ8ZaT6K6TFJnbJQaJ5IyAnzMNyvLP8taxNQEJ/7WyBQGxSWlVSxPvl0u1sErLGSu9VSpKwVVhowxrNmhTqcvk5QR4P+Yurr8s8xuXSc9ZS5ZKKiakT84vgCnPzut0raXp/+qpnLJVFhCxYTRA+MXYYGkIFCpqEL5cO5GrCk6GHYzhJCS0kjm4k7rzAxpZVcHzDgospm+Sq7Xb5hU+DsQ9h0pwX/zN+G61+ZIqas09eQ3FqeIOSmQsgJcZtnqdWert+9Pmew18fJA29L7g1lG4IC8Wad1BJ4KtywVzsEkJQW4SNWgdQap+t4X7S/GkDE/4E8TlyiuWQ4pOCNXjukFXIPiFjQl6cNTUQeeKvFcgJQV4PJukOq3txmE6cdfdqitWBKqQhCU+wRIQpbOmQcW5w1cEcBNTseMetZ2O1LJSs2TI09UEDk1t8abCOvtnWrBhIBoG3Ld/eHCUOq984MF2GBY12zefVhKBM54UnER0yof1hQdQIfs+iG1JhgpOQKXqwOv+KwimND942PefqoW/2QTf8mirIsMS6xNXLgFCzbsAQB8sbiw0mhcBtYReCqoH974eV2l7zsORPfZSgkBfqC4pNJ3oTpwy/f4B0XF4GT22li8i1R04U8FYRA25QJcUvkp2O2qEOUUjCkhwG98Y26l72kydeDSSq4eRPdRqUyyPPOydeBWFcqm3akVlheIdp9MCQFujlJNxOrAK3+vpEIRVgt//VEnVc5HdTz4uet2oaS06jqI2QpZOnCrCmXRptSxoTaJcp9MCQFuRaZudcPOihGI6PCk1Y0o68BVMm/9blz+75n4x3e/VPmNSY5MqGoR06rGuPBfPykzYbz6lVmYVBB6PhpfpKQAlxkLZX+cvn3iwmje9DBJT4uPRqjhYbsxULCLyS1bvqpK5GA9j0Ub9+Cwwjymd7y3QFldIklRAS6urLUuQW9S0cRKNie0blT+ef+REpc9NSYVuV6d99m857CUun+1iRkiY9FPP0n+4ElqXIeI5hDRIiJaSkSPGdvfIKK1RLTQ+Oslv7l8iJxOvjNrg+NvqeilJpv4e/PAhOhmNVL57narK/43VSnjJhUUCi8zypYgYcIzAi8GMIgx1hNALwBDiaif8dv9jLFexl84ng02qNKtavnNz5LNe7HdJhHvqm1ykr3Kxipvbnt3Hp7+coXSOoHKs8C/2+jIZbBFwmjf7lFKFe9jmfAkNWaMMVOPkG78JbXo2rT7MJYX7pNej1ah8HP+P2fgrL9Nr9Jxznnhh1DaI5rJi7fi39/LCVdrWrx8tXRrld/i+6A1/rksZPR6u0fplnfmKXmOowyXDpyI0ohoIYDtAKYwxmYbP/2FiAqI6AUiqu1w7Agiyiei/KIiNSE+vygoxHl//1F6PakivktKy/Dtsm3Sp7GHjqpblEol3G5LGLNAGd3kvL/bv8j3HpYb0ybqcAlwxlgpY6wXgBwAJxNRDwCjAHQF0BdAE8Sy1NsdO5YxlscYy8vOzhbU7OQgVfR2/5r2K256Kx/TVirIWJMalyyJUH9BZdjA2y2WAjq/bSI8WaEwxvYglpV+KGOs0FCvFAN4HUmWoV4FMlUoy7bsQ+7IL6SVH48ZHElFTAjVDjCqKdx7GJ1GT8a0FWrSt6XCCNzOQclEy293eKxQsoko0/icAWAIgBVE1NLYRgAuApAaAas9IPPheW/OenmFh8jXS7eF3QQhOM2+Ji0qREkZw4sCFxTduln8IELVhFD0zNMt1V1Q+c0Yw4mPfo23ZyV+nrZH0DGPZwTeEsA0IioAMBcxHfgkAO8S0WIAiwE0BfCkvGYmJy9P/xVf2ywsieBYSWqOVKcqGpmGhTnDEDlwdBOY8T+JnN1s2XMYs9bsTFinCDbtdrZqCToCZwzYd6QEj3yaeHw5/J8zglUWAgnjgTPGCgD0ttk+SEqLIsY/p/6Cc49vIbzcY2Xq43/r2So/iWSYOlPWipb075AlrNwhY77HoaOlqJNeA0eOVe6LoocWbi+eZVv2ISO9Jrq3auizbH5UeZ2KJJKemO/N3oDckV/gmIvuTMOP+QDdP74A4+dtCrk1qYGql2H8aDi3aT1h5ZoWQ3ZqQtEjcDdV5CMTl2LYP/xblMXPXkpKy/DJgtTq35EU4E9/uRwAcKhYrlnaS9NXJ9wn1WJavzBllfAyF6dgBLvCvfb6UlMYqUrYXEmFIsVAu+om0acm0xggvuRXZ6zF3R8uklZXGERSgJffFMnPyLNfrUy4T0qskktWt09cuFluBUmIqm5RaRFTcvmykFlFfNm7DkY3844TkRTgKB/lhNuMVERfU+98vqhqVMr89buFhRt2d+SJt0IRLwntBLjoLiLT2zK+/TVTKZuxQSQFeGraZ6hl9fYD+PGXqp6xMhwnUv2l8MqPa8o/x8u7K/4zU0j5bot88b+IGi2v3FoRn8ZOPz15yVZs3y/O5G7FVjXxcLQATxLKg9iH3I4oM2TM97h23Jwq292cKqLCh3OdI0jKoCBOx686NsklL/1c/rlmDTGPcyJTz+WF+3CdTd9JRiqbWcqui+G92Wr7XiQFOC+dRk/GqI8XS60j1V4iWxwW54KgakHP5MEJcu85L6q9JLPq11JW12YX2+1kQqXn77SV2zH6E7V9L5ICnFn+O1FSxvD+HMlvxBTQD4StkhIRMuDTBZtRuDcaQsUrvJqR135aJ6Y+nh4RkW6vMlzRQclWcXYkdORJRlIkhpRGEEeOleKuD2Ph6Js3tA2KKZT/uISNfW3G2krfc0d+gVevy8OQ7s1918fb3xdt3OO7jlRFZcjnMMZyER2Bx26KFuRikB1VkadfBxmFxz+k2/bJ96Zzi/u908ZU7VMfZpT/mrYauSO/wNGS6K9JiCB35Bd45ivvCTPie7ZseRFG5MRICvBykkCAL9q4B6UplJqnZ5vMsJuQcvh5sM2XhMrEvibJ6pzmFvTKCZWDvDCMXCInwE96Ykp5bIZkCU368fzouufe9ObcSlexds0aKNpfjLdmrhNXieSOnewzMT8DMzPfag1SP07hea6SU8TboPTiuV8VGTPdyAnweG+qZHlwj4QwShLFt8urmozd/u58/GniUqwpOmBzhMYrfkbg5qQurQZ5evA/mLMhkPni5j2HuTyQRSFbfRf/Mvpg7kapdd3yzjzX32ev3SW8zsgJ8HiSRH6nHLsOxV6SolRDyTolV4Wfsy8r93XwdvTIjxcHciB6iNMMToRp6IHiErQfNTlwOW7Evx92HAg32mCxhPWMSAtwANgZ8k0BkBKmhCZhRnj0W7eX10wY8TD8CLv4c/rMxlXfjd2H/J/jboXXZ5eCDFDJlHg8FBUKEdUhojlEtIiIlhLRY8b29kQ0m4h+IaIPiUidF4EBYwz3jy9QXW0Voia+l25xjg64YMMeHA4p+XDnh77Eiq1ys5CPnBC8v3h9DP0sbsU/7D/+ssN7AT45Vsp3diJmZyrWsFSJbx5rIRnvEp4ReDGAQYyxngB6ARhKRP0APAPgBcZYZwC7AdwovnnuMAAHjpSorrYKURuAD/9H5cwj1o5VZMxqRJ2Xl3L8hJ71MrIJxarDzyKmcUp+hFwQlVUJZyKRA8UlOFDs7dkrKS2rNMtSYbylYgC+59BRHPfwl4nbIuF1klCAG4mLzdWsdOOPARgEYLyx/U3E8mIqhTEkxfBXio5XUsezGzlZt1XEQ1F/cYOqGxIhYkpdfMybqsffImasnXaLzDJpWp/fEWqPR1XNkDHfo/NDFYJO9gImoGaUv50zk09YI3AQURoRLQSwHcAUAL8C2MMYM1/BmwC0Ft88dxhYMshvKcjqdvd9VDWgvVX3LHpk5OUe+VM38O/702r7PI9e8DqK9zMCN8/pj+8v8HxskJlTXrvG3Pt6nS2t21nZOkaJesNDJX6jFfIeJWPGwSXAGWOljLFeAHIAnAygm91udscS0Qgiyiei/KKiquFLA8GSQ32RDG3g5ZMF/F6BIs7raEmZp9V3X95sybNOZYvq9KZBbpsXIXPru/MD1KRGveGlihLJOp3Q7cAZY3sATAfQD0AmEZmxVHIA2C6VM8bGMsbyGGN52dnZvhtaUlqGgxad29uz1mPRxvDTdcmQ3yqmlyo454XvMc4SH8QNf/I7ua/Vh/ly7Y9FovJaqujjXlVmxSXy1khCGYETUTYRZRqfMwAMAbAcwDQAlxm7XQ9govjmVXDLO/Nw/J+/rrTtn1NXc01nverqvLJok/ggQsktkvixTpsT4UsHnioXSxBBbLRVXksVVXk9Hz+22ryXO6wReEsA04ioAMBcAFMYY5MAPAjgHiJaDSALwDjhrYsjyGLOE5OWez5m025+wfP+nI14bcZaLBQYDW7iwsoTmtaZGWCMYcw3K7F+50Fh9bgRhmbIT51aflcmyH1LtWvp9Xz8yVi+Ky7j2iYMJ8sYKwDQ22b7GsT04UkPr2lUPHe8523x6PFJywAA654e7rkuHhpmpKNg0178Y+pqTFpciKn3niWsbCfHD9WJGAB/OvBUUTclA0pH4Arq+mGVt3W3w0dL0SgjXUpbZDgVRd4TUxZheiTasWLrPlz4r58AiG/b3HW7hZZnsmWP9wQLvqxQvB+S2gR476p0N1fhJek1I1e/v34nqSUhmhFWR8K0LLF7iKI4yPSTIcfPqP8Dj1mXZC5URZk1RQcwfp66yJpR7NN2cOvAJdRdLQS4H1kcVgCmA8UlyHvy21DqthKGasLPi/Nv36zytH+Xh7/yXkmE8Ntz1ytIwhzPviPHlNYXNjKep0imVPNKlF70hzjck1W9XIJeNz/9NYysJrzsPnjUV1YY1fhdu1B95a8aO0txjXLgjR2kVSg+8XPhkliOKGtbKCNw5TXy8/yUldJjSodJGIvWqcDbM9dz7SfDxr5ajMD9EEZXfmLSMtStlRZCzfaEoaNMZhkSlcx5fq9hEl/6pKaU80GR4ZFbLQS4r9XuECQJr8fieo/OMX4JQ14lswolKotufq+gqks/9odf0S6rnprKfMAYkzIbCcUOPBWIyHOXdKzdcRAdmtZDzTRvmjbGGB6ZuAS5Ph5SVUIkf90u5OU2UVNZRFC1tvLU5OReRygpY0hPE38teklIGF4tdOB+JHjyjgO9U1Jahjs/WIBftu33dNzv356HJwwHJS8cKC7BO7M24MkvvHvAqhqBX/Zv/2nHNOFx6Kj8+P9eeyDvzCyzrngHoWohwH0FxU8hCb50yz5MXLgF9/y3aijZRExZts3zMUFmPA9/ugQ//6ouA403ojGX822FEoE+f8Nrc6XX4dn9nvMIGZe3WghwP4sHqvtymcQVMnORpYYPN0c/zWIBFms27DqE/31ltv8CkpQVW/cps+rxrQMX2go5zFknPrO7Fc+3KcT3erUQ4MkebhTgX8n2gyk4/LipHywu8Rw8KwrXWzVDX/wRr/+0TkldO/0mJo6CBFeArP4rY2G0eghwX0YoanuziCSxTphF+9Ev7y8uwZnPTfd0TFSsNbwS9LyWuCSTFs3yQu/JocPyPk423vqZz67bhLdbaBWKT/w8d9bkEbLxKhyKOPPwARUvhzRFLyUVQYpMjihMUhylF9MrP67xfEwUdOBe8Ouq/5fJyz0t3ocZDbN6CHAf13fFVm8WG0HxqkLp+xf+eCll5TpwT1X4RmV3FhmDPRFRUg19PJ8/dV6qMmWp9wV4Ey9ZpLhH4BJekNVEgHt78NbuUJMwIR6pKhRjUVGViZ6IAcmkAtsMfVUIckYqTNLiSXYVRXK3zjuqZoK81ci4/zwp1doQ0TQiWk5ES4noTmP7o0S0mYgWGn/DhLdOEF5v48C/Tfdd18qt+7H3kPepm0wrFHPkqE6ABz8Xrwk1/HDnBwul1xEmcz1abKRaLJSgAjwKiUJ4RuAlAO5ljHVDLJnx7UTU3fjtBcZYL+NvsqxG8l7I/+nZKtDxADBnbTAzpXNf/AGX/ftnz8fJHC2YRc9Yrca+WkW3L9pfjNXbDwQqY/56b4ksgt4i1fJx274jaitMMoKOiYSPqcJQoTDGChlj843P+xFLaNxafFPc2sC338Au9lnvee/D96uKcMV/gnvo/eJDsEg1I5RWskN9Cio849lpGDLm+0CjxlQbcVrxOuPyY2aazATth/sO882keauR4Z7vSQdORLmI5cc0PS3uIKICInqNiBo7HDOCiPKJKL+oyFt+OhPeC+TUYXlvpJ8UYKLw42zEG4dYxOjeyyxGhe7xsGF9cu04/04/KS6/PQvkVLseQfshr9MQbz21PMYU4oG7RCKqD2ACgLsYY/sAvAygI4BeAAoBPG93HGNsLGMsjzGWl51tP0JOBK/wcOqAYdpp8uKnsz04oUBCS+zxMp1UOeIvLkn85nMSZF4FXPJrRCsjYs3jnRtPEdAS9YybsRYPf7okUBlHOfrW+3M24IuCQq7yvAaF44GrRCJKR0x4v8sY+xgAGGPbGGOljLEyAK9AYoZ6fjMdpxF48j96flrI7awh4PS9vGCS7XrXq2UfdFN16FrVA4Q0j2+o6SurzpAzEsSn7/PEFGzfr1bXvp/DvttPEDYrT3+ZOGpioqTJFzisy4mCxwqFAIwDsJwxNiZue8u43S4GEOx1JwCn7lqwSZ0HnF/8WKHwCiARKg1vAjxwdcJITyM8ftHxtr/JijqnmhVPDLXd7jX2zT+nrq6yLVEX23nwKKat2O6pnqAs3eLdy9QPm0NUqfLCEw98AIBrASwmItPuajSAq4moF2Lju3UAfi+lhfBgZ+nQ2fZyLkaIIqteLc/HyMw5IULwhCG8vlu+DYO7NQ9UxogzOqBRhn0YT6+LmBPmq8vY7gWnF7kXz1ve9RQ7kvXFVh3gsUKZwRgjxtiJ8SaDjLFrGWMnGNsvYIzxKYJ8sPsQX3CeoIbyombUfoIJzVzj3cSPVwCJeL7CGIH/xUc8cS+o8kyVjZOqxEv44DU7gplkJmKX3wBbkqhdswZ+N6B92M0ITCQy8lzyEp9ddRRX0c0kCw9OcNel2cF7uiJ00t4WMcVIcFEDO6fTV+0ZKat/1iCgecPa2LavcnycHQf44+WsKPQfOoLnPv3mVXEhgkVcxmtOaYemDbzPlL0iWyZFYgzCq4tyu1Y8QkzUyLFriwbc+579wg84+4UffNXDO4IUcVpXerCPj0ry350eBJwIlmyWo7slIpzXo2XiHV249yP70ToBeP23fQOVXbS/GMt8REd0QoT9vqrBnmz1UiQE+I2n8U113G7KV0u2CmpNYmrXVHNZeRcxRXQiLwtHz34lJuehbGuWgwH0vn5YVrgP/5pWdaFQBDKv1cAuzZDdoLbv44N6N8vAq4VOshIJAV6T+2I777eLQ48u6q2sysOPvxa1Q+IvBb0sZatQwuC5r1dKKVf2KSbTNRTxeBGpOSetQvGA28VSqe9UpotXOAIPg6i2OwyCmIq+NXNdwn1aZdZx/C1R1ckYhldVbHytQgG4h5puu6lc4FTlIMI7MVH1+PxadAC5I79QVBsfBEpC8eGPsdf2cfwtyLrDo58tdfzNnE2Ou95ZD646BIWItYt4FcotZ3YMXF5YREKAT1/BF0MlWYITzVu/GxPmybcZ5j1bUbFJEj04vBnss+rVwrOXnZhwv2QcuTlh1amOHtbVd1mTF9tb5J7WuanjMbLXC9x04P8nSa/vxC3vzA9cRryvBhFwXPP6gcu0Q6tQAKzcxmfi5HateJx5RKpZVHdqFUxfWYR1LskuVKZSU02ihBuvXNen0uJ1/w7OwtaNdTsO4rZ37QUUgfDA0C62v/kJhlZebpIMfESRaKDx7KUn4tr+uZW2fXP3mXjpmpM81bM7CWzbIyHARcAT10DkiE/FIjdva0XJ1Xs/WoSzXJJdePKY5dhXRLvdZFO7rLrc5bwzyz7R7SntmwAAeuZkYv4jZ3tqmx37jzhnCSICbjurk+1vQV6esrsqT9Mm3NoffXNtA5p65ndv5rv+fkXfNrZWKHntvNV/yzvzPO0vg5QS4A0dXKZ5ETmAFKUH75nTyPE33vYm37iYlKlHsurVclQvdG7Gb6/v5Ek4qGszrHt6OLLq1xYyXT5a6m8oLcv2XuHSf/mn//6+f6CSFinKk7p+5yHufe8a0llKG1JKgJ9sjIYAoH7tcJ1MVSxkLty4hysru6rogLwBuXhNuII2+8Ure1WZKlvbAcRyY+aO/ALvzd4QqL54FVxuU/7RfTzHXAS4XZe6rn87AMHuMW9X7dTMn56Yp3xVZn22dfs8rsTDWzM3q57PWtxJGQFu1V9N/uPpOPf4YIGQguA1Epwb0+87y/G3ro98lfD4ZFRNm02S+Z67qHdrpNUg9G7rPjXesT82un5puvd1C6f2N6jjbzboKsAtoiazbjoev7AHAGcVyg+r/CVRseODEf3Qo3VDz8fx9L/4MyOKzWyUI6EvtsrMAAA0qhtMO+BEygjwYSdUdiVulVkHf7u8p6cyRMq5Eo6p8Ow1O7nKym3q/e39+OfLyk36VKkrvCTOMB/qoOEPeMhuUNt19FjTSHWVaKEyESJeRiWlzm2wlh8/y3Nq+nWvzUlYJ+/ifdP6tfHxrQOQKUEYEVU292zvo8/LIHfkF8h3yMzDc7/vHnIcXrrmJJx1nL9kNolIGQFupWZaDc+jIJEjVZ68mFeOnZVwH9NCYMKtpzruYyd4XvtpbflnVSNw3oU0ooqXipsFhMhmd7ARCFOWbUPuyC9woDi2cOiWgUWFocasNTsxzyXRsrUJ8d95khz4wXretWrWCBx3xbYey2cVfdY6QHB6kU1ebO9ZzDPJrlWzBoad0FKapU/KCvCwGfLx9dkAACAASURBVNApS0g55mp5H5cV8kkFW8qjGlr5dtm2hHFMWhvTvKBwW6GAlOjA4xlzZS/H3/46ORa21k8YYDdM/TQvV42d5Wp+ahUCf/qf7uWfp9lk05GFDAurmA48HF1fItla6mCjqTqapR1agEuirkMaL6/wuPze+cFCx4iGN72Vj3Ez1tr+ZvIch1MND14eP9MxxO3lIXLQUr92TbRqZO8OziP8nB7W+O3W9pr6aVFYW3Bhr9ZyCk60uwwBbrmOKtR+LRplVPrvdF7HHPRTyWA+n1DKEFEbAG8BaAGgDMBYxtjfiagJgA8B5CKWkecKxpjz/C8C8HaaSX84DZl103HaM9Mc9xEVayEZOgkvXkZQ53RvjnHX56Fw7xHH5LMROnUA8kdkUeoLXokJ7QpUDMYvPak1MjPSEy6YOrVFdU5V2zZw7FMC4F7GWDcA/QDcTkTdAYwE8B1jrDOA74zvSUf/DvyqDN5O06N1I+Q0rmwmFm/CCIgLV5kqYS+v7VehTiCKqQMGd2vu6yEIY6rNawontw1yKnCNIWTzq9cXFe/duv+cLsiqVwtdWiS2dHGL38ILEWFI9+blFmNRfNJ4UqoVMsbmG5/3A1gOoDWACwG8aez2JoCLZDUyEWd3dzYXPCfOlNBP4mBeGltW5kUJ3uOa8zubhA2/DpxzPweB5fc2yrj7iWSqWwAqkXhJImJSsGmPY9wV01zQblbaStCaSTxEwKmdmmLeI2dz+XC88fM64W1wYpXD+lIypOTz1AQiygXQG8BsAM3NPJjGf9t5CBGNIKJ8IsovKpKz0NKrTSbXfh1GT3b93e8D/u09Z+KU9pVH+iIEeIuGdTB6WLfA5ajCTQUV/1u8YC4u8Z5UIazFrkTY3XEZwk4UF/zfT45xV0zsRtu5NiEINu5y9krkeRKS9JYCgKNlUDK0mVuAE1F9ABMA3MUY407PwhgbyxjLY4zlZWf7s4V0s+ho2agOLu7tvJij4iJ3alYfN5yaW2mbCP1Y/45ZqKUou088zXxmX3G71k6/zXKxhXe6hL5H4JL7QphBoQLFQrFptltxQ3u0QN1aaZW2rS5yNpvlaZnVFNbrS7qsjHH5Xrjh9f5FRoATUTpiwvtdxtjHxuZtRNTS+L0lgO1ymgic1slZ8M8cNdh1lONpBOTjjpgOB1bPyzQBctfaiWeNGoyJtw/AfeccF7xwt3p9HHPkWClemv6r5+PcLCmcnie/FgqyAz7Z7aPqIX/sgh7o4kHdduhoRdCsI8eqCr5yRyubkyIinNrRMuMM+PKyHu71sl372mx0eujLQG3wSjLMBBOKGYq9lsYBWM4YGxP302cArjc+Xw9govjmxQhiUuTFnd5rLV/eeTo+uc3ewUaECsXanhaN6qBnm0z8jjNHqBteFnd5eOWHNa6/nxgXlCv+YTVnKi1tTPycFsv8PjdBlkC+cNAVJwv9O2bh67vP4NqXMYbuf/o6UH3Wa3m0pMxWoM1bvwufLdzsuXyzqFMsxgFO/LSaz6vZDa9PbDIk7+YZJw4AcC2AQUS00PgbBuBpAGcT0S8Azja+SyHIi846LXJ6ax4tKXN1Y7ajW8uGyKxby/Y3tyTKR46V4hmOxL9O5y3CXK1xvYpFV2s1fq73kQS67Iw4u/j4W2J+Pr5V1aiLpWXMVkd+sNg55Ko7/jpS4d7DWLE1cUz6JLAqwyUnJbYN5zmXRFhVHje9lY9Xf6zqb3DpyzPx7XLvk/NkT+ZxrLTM1/qNaBIu9zLGZsD55TRYbHPkU8aANJuz6fPkFNdYzF7ZfcjZtXncjLV4mUPdILMLu78ExNfs9OKsaEXV3zfvOYwuD3+FdU8PL9/2+aIt+MP7C3y1we+Iqf9fp3LtlwyJEcZc0Qubdh3GHJv4HUX7i/HWzHUY0i3xrDTRpbILujVh/ibcfEYHzpZWJhm8Gr3cvsHPf+/6jKsiCQxhEmN9+HMa+1/Z/+vk5baxQ5yEd6OAMcbtcIu5wYNsOeFnBJ7omHiPy8ped+Spzmkr/S+1yMgYlAxC28qTF9t7gN4/fhH+OXU15qy1D85kh6N3oo0A99OvZQTGUsEGF6sblURCgFvlbZBIZa/OWOsYXcwO8+XhFotEFjIXSawP5sjzuuLqk9vG6hVc198u74m83Cb4x9W9q9RtfuSPZOhfYMr0A/CCl/7nB6crdKg4NuVP9CLLa9c4Yd87zBGHngcnay23RVQ3dgRIeJwMswCvREKAW/tS0FFPepxp3p5DRxM+2N/deybe/N3Jger0g1QViuUa3nJmx3LrFtEvjsv65AAATmgd03PH19wys06l3xIR5NaPvS7P1elLFP06uC+8XfbvmVzlnOiSjckNZ/NLvvt628CKLO1OQu3pS6rGzyn2MQJ3up28PfCrJYX4Mm6BOQkMQ5QSbtoaTqwLGkHfk6bJU9H+YvT9y7e4c7BzuiMiQsfsWCzpV67Lw5RlzouTwpHYGeOvoTm7kK0OaNM4A0O6NccdgyryOh7fqhEm//F0dGnRAI0y0vH4pGWuZQRpYb8OWejXIas8TroVP9N5a3um3nsmWsRZ1ARZjPN/P5w8WM0QvomOTlxvD5sX7uY9hyt9jzdVdKwrYJfjyVDfOjOjvG13DLTPKRpVIjECtw6Qg950c9q2bd8RAMA3y7ZxHXd29+Z49jJvSSLs4G2/14f/ew/ZV8w2XJGXgzrpMacMr+qMeHiOqZlWA69en1fFc7Z7q4ZIq0FCzCOD0NAmfryTc8jFvVvj9M5NcelJOZW2d8iuLywSpV9L1EQOUBPmJTDrs7ES8sOJj37DvW8VO3BLhxp+YstK8XScsHtmxt/av3zdrH9HF/PZ6GlQoiHARc+LzM5iFru8kNuxVCm8IQJMbk6QjTueGjaLh9brkozInCTYLUw99rn9jKBZw9p4+8ZTfKfK2nmgOOGin99TddYrx27sSofYHkHrteKUM/Lru+Lt1d1ro0p2Sok7pp0pY4M66WjT2F+O0mQnEgI86NqTNbO72cFLBUsqkYEDv73nDNx8ur1JlpMQ85PR3O4KJIOHmR1jvlmJZQJets96iH/+2aItttuDeh72efJb3P3hQtd9/KpQnI7ifY76d8yS+hLvEhd4y/kUYw0Y0r05hh7fAqOHdeNq01gbhzJCXAYol2PNtti1yYypf+hoCfYmgfmgSSQEeFAdeAuLl9/nBbGHUrRZ2YJHzqn0PYjJW6dmDaTqpO1G240y0vG/p7TF2zee4nrsERsLhI/nbxLZvEowxlBWxvCPqauxZHNiAf74hcfjz3HZaqxckdeGu26nUbKIGDWJvDtFqVDMFzJvzs/aNdPiUt55q/tfLhmF4mlaP+YA57iIaTQ1Iz0N/762D1pnZlQZbPCeT6UZCcf52M1gnjDWZs54dhp6Ps6vGpJNJAS4dSrmVbBZB6bz1u/GpIIteH/2hqBNq4R1Ov3b1+faCrug5koizJ3MMqxRAp+6+AT0jFPd2MXXuOH1qolyt+3zb76ViDLm7WV7WZ8c/HZAcH36xl2HHGc1fi1EvOD3Plvlmnnp/AxYvLbhua9Xcu2XyEywd9tYH4xPRm1tvp0tuh2xdG0c+xn/3V6cOw6ITbsXlEhYoVhd3BN1qWxLND1rx2WM4Y73/HnzeeWzhVtwRV/+EZ8qyh+cBB37qUt64NKXK5u9zVoj147ZCmNMWdyJDTsPoW1WXfy0egeueXW2436Duso3R3RiwSNnu0a7tOZwNC+dCBd60Ti9IK7Ia4N+HbLQLsvZ58NLEu1E9VVtU9WyN+1ODuedeCIxAuedKgHAvIeHYNp9Z7ker1LFq9pRb9yMtY5B+uMJYnFicvhoKe7+cCGK9ssbfQPAxws2exo9BpmhnPHcNEwq2IIpnJZJiQjS17q1tI8u2LheLdfFU+uM1c/IW9Uz4rQwSUQ2wrvyvhMX2q9PWKlBxNXPzX2c1GNuKRTDIhIC/K4hnXFFXg5Gntc14b5Z9WtXyehh9RpTuUQnI2+eW5FPTFqWMEh/fBlOC5ajh3XFuzc568LXFB3Apws345MFm/H8N3zTZr88ML5Aihu8E4s37RWW8aVJPftgZzz079gUMx4c6Pk464DFlwA3/idTpIA+7So7SI36eDHXcTXiEm66nc8xY72jTnqa6xqKG/ecfRzyHx7i61g/REKAZ9athWcv61nuUOO1UxVbBbhCYeDV5fjh4d08WUnEk+VBWJjhbp3WE0ac0REDOjV1PH7Q89/jm6XqnJpUesGLrKpNE//ma0SoknuVhyAzzpevOclzfXbsOSReV3wpR6RFO8jhs5Um9Wrh92d0wHs3n4KWjfzFW6qTXgNN6/tLiOKHSAhwE1GCd+dB/s4VdARil3HdrczzT2zlyUoiHi82yef1aInf9GuLh4f7T9m20tCpqhileVKhJNGoMRELNtin6wL822Nb14Cccjra0VBA8LbDR0vx2k/rXPfx8yT7NqvkPIyIMGpYNxzXvAFq+jQB6mETFlkm0RLg5Z+CPaHrd4azGLFi676ELyGu7OcO29cUHeRuS3paDTx50QnICjBaqHig5EtMFiyAo7e6FM7QfvvGXMff/Aqslo0yUCsuJdQF//cTdwTC8rUR0+3eR/3/zd/Iva+KAFJEFGcWyVdfml3MaQ5OdZm1yiBSAtwkSiMsk9lrdmLoiz/irZnrbU0LTVSdG389zjsW7j2csKyfRw7ib5QLV78yS0g5PKhc5HZbIwnSFVpbQi6v28n5cjfXRsyvPhrx58+Wcu97jpExy28eVl68Rjf0MwJf/Og5iXcSDE9KtdeIaDsRLYnb9igRbbZk6JGO3wcrbL/CjbsOYePumLD782dLXR8mnkXPZIlBbapat+494riPNfmtX9w8MG87qyNGD0u8wG2lp0OoAtH9Zdnj5zr+Zt7J1durJgUOcpurzCI4T0p1SNU/DOqMBY+cjWYNq6bUCxM/nrYNbGLpyIZnBP4GgKE2219gjPUy/iaLbZY9plH/wC7NVFQHALjIJekuL6c/O63SG33yYufFP55uo3KKz9OPp65w9jhVIRBOzGmEEWfEhUDlqHLpY+fio9/3t/3tgMDMTABcg1sREcrKGIaM+d7mt9j/PB+x6K095O1Z67mOEzU2SFSM2YdrUMwsUja102uU18dDsgySEsGTUu0HIsqV35TEdGpWH4v+fA4a1qmJ0Z/wmRABwabEj5zvz5zICm+SY54RuAjTRGXdU0lF3iupV9u56yfK7ykSIuC1n6oGYAIqXn7vj+jH7XVoYu3zizfv5WtPeQFVtgilQkWjpieOuaIX3pq5Dr3b8L0MRcQ16psrPwlMEB34HURUYKhYHFtKRCOIKJ+I8ouK+MOdOtEoI13p21FEdnkg5r7PA5cAFxk1SzJeb5WIzEdBR/1qdeAV1jxOpKfV8Byi1m8ccvPZyjBUX25d7elLTvBVRxg0b1gH95/blfvZCSpjlj8+FO/d3C9QGTz4FeAvA+gIoBeAQgDPO+3IGBvLGMtjjOVlZ2f7rC4YyZDhmtsxJMlkc9DmeD3+wxH9sOIJO42dM6JesiYqnYYIhJoOFg/BdOA+22PUOfa6PNx3znGu6QuvMlLw2bF2h/06z5MXxfJ11jNeSMk6FgnaroxaaUhPk28j4isWCmOs3M+YiF4BMElYi6o5ydahrdYMXvGq7qmZVgM1Pa57Wp+ToBM0la/7rfuOOI72gowCfQtw43/rzAzcMcg5U1UiVmy1X3RON15W79/cD18v3YrMuvL1337wkx4uDHy9IoioZdzXiwFU9VbR+EKG670dvMKhWYM6+OF+7+7cFfX4PtRDHZUrCVrld8vFxEHhxSnJcRjvclH3K8NB5WPeq7ZZdXHzGfbx7pMBr0lewlrz5DEjfB/ATABdiGgTEd0I4FkiWkxEBQAGArhbcjsjw+d3nBboeC8dwY91gh/qpPufCqqwQrGafAV9CR45pnb0tWpbVRNCQLAZITdi7teijXtst6saoFQXeKxQrrbZPE5CW6Thty9/dIu9mZkbPVo39FeZAW8H//WpmOl9x9GJLTifvfREPDChAABwWqemmLF6B9plqUkxpeJ5NXN6qqxTBUFefn7Fdy3JettkUxGKIqzTiqQnpl9euS7P0/4qRgud4wLWeyGtBnEv3p3VJRtnd495vP2mX1v8+tQwNE8yx4kgWM21ZFkpmS9NP7x6XR6u9Bnjxg/XnOK8wOjE6GFdAw9AEpGqI/Am9dQFsIqnWghwcwSe7jG+gYrRwpR7zqz0vbaAVF12mNegBvELfpMgkQBlPq8fjOiHv1/VS5lZaRBrlyHdmyPPo11wEGuY2wd2wkPD+AOVEcUiUHq9lsc1r49MD0HUVJnAen3WrXi99O/cdHKg+vxSLQS4X/wIhiDC5IUre8oRRhTv+ea9/CCCxK8a4CqOLEY9WjfChQI8ZVXh9SoGuu5ESoTlN3efiQWPnM29vyoVSuXM997xeu1bZQaz1vJLJFKqBcW8FV6fB9X6uot750gpl0AoNQW4j1d2ELM6v9ewT7vG+GCue1S7VNWnmgQ1R/dyeYJcSi+DDr9hWr3SIbs+vvjjadhkxCDySu+23mZLYXXFyArwV67LQ2uPbz0vqdkA//q6F67siaL9xXhq8gpfx8ugLE6F4vnYADoUvzMKnhojp0/1eBmdUnvxkoyXR+U9O75VIxzvMz53h2xnB6ZkIrIC3FyU80LjemqihV3cO4crL6UqiIDGhp7Sq0s2EGwk6Ptx5ckinoQCyg1ej+CCR8/Bx/M24dSOWYHq8yIsVa0jRO6lm+REVoD7wWpuloggnY0njKqsBUsrBOCJi3qgb24TXwF2goQi8HsJeRb8UlUYNKyTjhsGtA+7GZ5oUq8WdnFkunILIhZlwopeWC0WMf06NfjRF5uceVziuC9T7j4z4T6iaFgnHb/p185XR/OqQYlPKu23Y3fITmxemaoCXBTW7PQyeeZSvjyu/To0SbyTCx2y6+Hk9sHKkIG2A1eAV4uIII4UPIKrrTJnmqAmVd4EQbOGamxiRa6HXdSrlbjCHFAZ5RDwtnZxz9nHSWxJBUH74tR7z8J/HeK4i8RrK5PWlb46kypWDkETQHgdyKm6bFZh8Psz/cfWePGq3q6/n945eK5D1RlbBnblT3xy+8BOgepKpkclSmFug1KtBDhRYqHcIs5DMSpZOWTTslHsmjx3Gd80OSxGndcN654e7umYvHaNuab1b994it9mlTPshBZKhUsnTi9fVWEVVDFAcWJhQH0qOpNqIcDjB6DPX9HTdd94mR1Ufjf24KGWzNSrXRPrnh6OyzldwaP04ht/66n4YIT8KTkQuy5uMbRl0CRBurK2Teri+wDRJk2S6ZaLcGDi7cOqDBGcqBYC3IQotpjnRnZcduygi2S8sbTr1UrDHQGnsMlEKp1L1Ln3HDW67WTCT0JiK7wljDovlkw7qM2+X6qFAL9jUEyg5DSum3CkcNPpFXrUIGFUAf5Fq6WPD8V953YJVFcycVFvMe7t79/cL1Ascg1wzSnt8N5NzuofUdmqGmYkz2xTxNqVU5YkKzcMaI91Tw8XnhWKl2ohwP+nZyuse3o46teuaaur6tqiAW47qyNaZ2aUv73T0wgtGwWLb6Da6sAJlc3o0ryBsLL6d8xSZqkDiFmoTEpcZIuoPto3twmeulidfv9/XaItlgo4qQZ10jHuem/RS8OgWgjweOxG4O/edAoeGNoVP40cVL7Nj6enFRWCc9gJLbhszlWRTLpQr7x+Q1/b7W/81n67Xz6/4zRccpK6IFyqFtiGn9Ay8U6CuNTl+tUM4sARx+BuFTLgszsGCClTNDwZeV4jou1EtCRuWxMimkJEvxj/1aSGEYDd4kRW/XBi+YrgpWv64M3fhRPK0uT2gR3LP0fZucap7aJjp5+Q0whjruhl+1vDOuI9Fd1uyQNDuwqrJz6CX4ZHr2evON2r4Se0rLSOJYoTczK5ImSqhudV9QYAa5rwkQC+Y4x1BvCd8T0SWG+71asrGTLYi0a2KueOgRXJb1XI7/qS3LGd2q7ynTRr9GDhZW7fX1xl23X922HBI2fjgp7iHJjMbta4bjqWP2EVGWJxEuCDPNi+e6VxAoueMOBJqfYDEeVaNl8I4Czj85sApgN4UGC7pGG97/072AcMEjHtDOpAEwVmPDgQGXFxX1QIu2/vOROb9xwSXq6T6VjddHXxO2SoO1Zt3V9l252DOwsXSGUBYs57xazi+FYNsX1/MYqMl1SEJ4C+8Kssas4YKwQA47/ja4+IRhBRPhHlFxUV+axOHNYHpLrdcNFUzQgv/oL+cXDnSt9bNKqDPu3UxcOQtZBq50Ckqj/KUBs2ykhHVr1aePSC44WXbSX+JTH3oSG40AiFUN2eZ+mLmIyxsYyxPMZYXnZ2+IttVhWJdbQgctBsLevqk5NPh+aVr+86A3fGCVTr8yLDmorXo1AGQUO6utEqoJUTL6rUgulpNTDvkbPxPw5qmZ5tMoXVZT62Zoz/m07rgIz0tFC8MMPE79xwGxG1ZIwVElFLANtFNkomXy7ZWum7o8CRIIhUa1T65jbG5t2HPeUsTESXFg2wff+R8u/mg/TSNSfhtnfnp9wQSPXpyKgvbE1ewaPnYEXhfqEmpubAyzy3E3IaSde7J2PP9jsC/wzA9cbn6wFMFNMc+Zx/YmVTJ6sKwFzB7tA0eEYO68gnSI5DP5x7fAv8PGow0tPETrRO75yN6/q3Q+vMDDQ1puJdWsQezq4CH1KTMNcSpJrg2RQto76wV2Ia1knHye2boJHAgUS5AA/x7G44NTe0uk0SjsCJ6H3EFiybEtEmAH8G8DSA/xLRjQA2ALhcZiNF0t4imHMs7u79OmThnRtPCRy3GKg68lEhh4Z0a4Zvl8cmRDJjkjx+YQ88fmHF947Z9fH+zf3Qu624aXIyIHMEbiesZdQXJCVesmLOnGWf2hV5OY7hN7q3aii3cg54rFCudvhJvL2TAsyHpkGdmnj5mj4Y0KmqjvM0SR55N5/RAR/N2wQAuKyPnATGr17fF499vhSv/7ROSvlu9JeoLw4L1YG5ZNQ2tEcL/OeHNRJKDg8qF+ByJfizlzkHv0sGn4fUzG/kgnnNCfIEtYm1ax0Xp1742+XuURED1ZsEA67sBrXLTbtEcutZHRPvJBDrI3r7wI7YfeiYmLLtVCgShILVsUWV3Jk1ajAOHi2RUjZZdOAqyM2Kzd7/MKgTpizbhsESbc55qX4CXGFdYduBhzk++OrO07HjQOIciV64oGcrPCjQc5CHbi0rT5PvP1du/TLumbUb9s1VY4LZopFYD9Z42jSuix6tG2L0ed2k1WHl8rwctM2qi1PaN8G95yRH8LlqJ8BVkgQD4dDIql9bmK2xyvfg4K7N8N2K2BrChFtPRc+cRtLqshPWKkbHUQjSlIhaNWtg0h9OV1onEaGfg+NfWFQ/AR6+2koKcx4ajMNHSwFUjPyTQEUnhFaZsYXmri3FW7i40aed3BA/qlQodWtVjkuiOrWbRh7VToArTX1kM3L8/ZkdkJkhPqZCswYV01Wz2hSR3zi5fRN8ctup6Jkj38IlFWdNUQ7WpnGn2oaTVfGgPnd5zypWLqPO66ZsIS5Kqc0S0bttYyGpspIJczChIk/mzFGDEu+kiRzVT4ArrKtPu8Z496Z+CmvUBCWMhWeGWMxxmfG0gyYn0SQn1U6FYpJaY7nKJIMZYVR5+PzumLbyeyV1/XFIZ2zcfQjDT2yJhnXScVaX8M3SNNGi2gnwMNQKfzq/O46Vlimr73jDQ6xDdvBwANWNjtmxwFkqktS2zszAezerm6HdPeQ4KckONOFR/QS48V/lIPV3p7VXWBtwZd826N22cXl8Eo03Zo0aHDihdTJy55DOiXfSRIrqJ8BTWXdiQERaeAdApgOKRiOS1BtmcFIN5LhGo0lxqp0AV2oHrtFoNBKpdgJco9FoUgUtwDUajSaiBFrEJKJ1APYDKAVQwhiLfpQcjUajiQgirFAGMsZ2CChHo9FoNB7QKhSNRqOJKEEFOAPwDRHNI6IRdjsQ0Qgiyiei/KKiooDVaTQajcYkqAAfwBg7CcB5AG4nojOsOzDGxjLG8hhjednZ2QGr02g0Go1JIAHOGNti/N8O4BMAJ4tolEaj0WgS41uAE1E9ImpgfgZwDoAlohqm0Wg0GneCWKE0B/CJEd2vJoD3GGNfCWmVTAxHzAxLmimNRqOJGr4FOGNsDYCeAtuihEYZ6XhwaFece3zzsJui0Wg0gah20QgBKEtpptFoNDLRduAajUYTUbQA12g0moiiBbhGo9FEFC3ANRqNJqJoAa7RaDQRRQtwjUajiShagGs0Gk1E0QJco9FoIgoxxtRVRlQEYL2yCuXRFECqJbFIxXMC9HlFiVQ8J0DMebVjjFUJ56pUgKcKRJSfaunjUvGcAH1eUSIVzwmQe15ahaLRaDQRRQtwjUajiShagPtjbNgNkEAqnhOgzytKpOI5ARLPS+vANRqNJqLoEbhGo9FEFC3ANRqNJqJoAQ6AiNoQ0TQiWk5ES4noTmN7EyKaQkS/GP8bG9u7EtFMIiomovssZQ0lopVEtJqIRoZxPkY7hJyTUzlhIfJeGb+nEdECIpqk+lws7RDZBzOJaDwRrTDK6x/GORltEXledxtlLCGi94moTkTO6RoiKjD+fiainnFlBZMXjLFq/wegJYCTjM8NAKwC0B3AswBGGttHAnjG+NwMQF8AfwFwX1w5aQB+BdABQC0AiwB0j/g52ZYT9XsVV949AN4DMCkV+qDx25sAbjI+1wKQGfXzAtAawFoAGcb3/wK4ISLndCqAxsbn8wDMNj4Hlhd6BA6AMVbIGJtvfN4PYDliHeZCxB4GGP8vMvbZzhibC+CYpaiTAaxmjK1hjB0F8IFRhnJEnZNLOaEg8F6BiHIADAfwqoKmuyLqvIioIYAzAIwzOyVAkgAAAohJREFU9jvKGNuj5CRsEHm/EEsBmUFENQHUBbBFcvNt8XFOPzPGdhvbZwHIMT4HlhdagFsgolwAvQHMBtCcMVYIxG4aYqMDN1oD2Bj3fRNCFHYmAc/JqZzQEXBeLwJ4AECZpCb6IuB5dQBQBOB1QzX0KhHVk9hcboKcF2NsM4C/AdgAoBDAXsbYNzLby4OPc7oRwJfG58DyQgvwOIioPoAJAO5ijO3zU4TNtlDtNAWck9ByRBG0PUR0PoDtjLF5whsXAAHXuSaAkwC8zBjrDeAgYtP5UBFwvxojNjptD6AVgHpE9BuxrfTcJk/nREQDERPgD5qbbHbzJC+0ADcgonTEbsa7jLGPjc3biKil8XtLANsTFLMJQJu47zkIaZoHCDsnp3JCQ9B5DQBwARGtQ2zqOoiI3pHUZC4E9sFNjDFzljQeMYEeGoLOawiAtYyxIsbYMQAfI6ZbDgWv50REJyKmqruQMbbT2BxYXmgBDoCICDGd4XLG2Ji4nz4DcL3x+XoAExMUNRdAZyJqT0S1AFxllKEcUefkUk4oiDovxtgoxlgOYywXsfs0lTEW2ohO4HltBbCRiLoYmwYDWCa4udwIfLY2AOhHRHWNMgcjpntWjtdzIqK2iL1wrmWMrYrbP7i8ULFqm+x/AE5DbOpSAGCh8TcMQBaA7wD8YvxvYuzfArG35z4Ae4zPDY3fhiG2Kv0rgIeifk5O5UT9vCxlnoXwrVBE9sFeAPKNsj6FYQGRAuf1GIAVAJYAeBtA7Yic06sAdsftmx9XViB5oV3pNRqNJqJoFYpGo9FEFC3ANRqNJqJoAa7RaDQRRQtwjUajiShagGs0Gk1E0QJco9FoIooW4BqNRhNR/h9osrpxdeQpGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T['DateTime'],T['Maximum temperature (Degree C)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we better add some labels, and let's make the figure a bit bigger as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '${}^{\\\\circ}$C')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the figure size to be 12 inches wide and 4 inches tall\n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.plot(T['DateTime'],T['Maximum temperature (Degree C)'])\n",
    "\n",
    "# Add plot labels\n",
    "plt.title('Temperature in Canberra, Australia (${}^{\\circ}$C)',fontsize=18) # add title\n",
    "plt.xlabel('Year',fontsize=16) # add xaxis label\n",
    "plt.ylabel('${}^{\\circ}$C',fontsize=16) # add yaxis label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Notice that the degree symbol (for degree Celsius) was written in latex form. In a Jupyter notebook markdown cell, you can use latex formatting for equations, which is very handy! Latex formatting must be written between dollar signs (`$` or `$$` to center your equations - see below). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing out equations using latex\n",
    "\n",
    "#### As a quick aside, let's look at an example of latex. For instance, let's write out the formula for finding the mean of the temperature dataset. The formula is written in the cell below - run the cell to see how latex formatting looks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Take the time mean of the temperature data T\n",
    "\n",
    "$$\\text{mean} = \\frac{\\Sigma_i T[i]}{n},$$\n",
    "\n",
    "where $n =$ length of dataset.\n",
    "\n",
    "What this states is that the mean is defined as the sum ($\\Sigma$) over each point in time ($i$) of the temperature value at time i ($T_i$) divided by the length of the timeseries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's calculate the mean (or average) temperature just for kicks! For this, we use the numpy library's function `mean()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.36161469664002"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(T['Maximum temperature (Degree C)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Back to plotting!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Great, let's get back to our plot. Let's make a few changes to the plot just for fun. Let's:\n",
    "\n",
    "- Trim off the extra x-values that have no data at left and right ends of the plot\n",
    "- Change the color of the plotted timeseries (a list of named colors for pyplot can be found here: https://matplotlib.org/3.1.0/gallery/color/named_colors.html\n",
    "\n",
    "#### I've added comments describing the newly added lines of code in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "# Add the argument 'color' to the plotting function\n",
    "plt.plot(T['DateTime'],T['Maximum temperature (Degree C)'],color='darkorange')\n",
    "\n",
    "plt.title('Temperature in Canberra, Australia (${}^{\\circ}$C)',fontsize=18)\n",
    "plt.xlabel('Year',fontsize=16)\n",
    "plt.ylabel('${}^{\\circ}$C',fontsize=16)\n",
    "\n",
    "# Align the axes with the first and last values in the timeseries, eliminiating extra white space\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !Challenge #1! It's your turn to do some coding and test your knowledge! --------------------------------\n",
    "Copy and paste the above cell (tip: use the buttons near the top of the window - hover over them to see what they do!) below this one. Switch the color of the data (go to the above website or just try typing a color you know, like 'red'!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEhCAYAAACjjxwNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9eZQtW13n+f1FnJPzPN17m6c8tOlSnB7WlUJRCx6giJS8KrVbG1gPxXqlpbVAtEStVSV2lxaOYK8uqX4yvWqR0gaqQEQbSqABS5HLIDzqKSCDIvfenDNvTudkRPz6j30iMzIy4sQcsXec32etXJl5hogde/zt3/4NxMwQBEEQBEEQBKE6rKYLIAiCIAiCIAhtR4RuQRAEQRAEQagYEboFQRAEQRAEoWJE6BYEQRAEQRCEihGhWxAEQRAEQRAqRoRuQRAEQRAEQagYEboFQRAEQRAEoWJE6BYEQRAEQRCEihGhWxAEIQQRPZmImIie33RZqoKInj94xic3XRahPKL6btX9mYi+mogcInp6FdcP3Oc+IuoT0WOrvI8gVIUI3YKQgsGClfbn7qbLqyNEdA8RvVTqByCiKSJ6ERG9j4i2ieiUiG4T0dsHwnCn6TK2GSJaJKKTwXh9bo33besY+HUAf8LM7wy+SERPIaI/J6L3EtHXxn057Xhg5v8C4OMAfqnSpxGEipCJXRDS8bzQ/98C4AEADwJ4X+i9jVpKZB73APg5AO8B8LlGS5LMewFMAjgt+8JE9D8C+AMA/xOA/wrg3wHYBLAG4GkAXgvgcQB+qux7C2c8B8AYgM8CeAGA367pvk2NgSr78zcCeDqA+yLe/mUAzwAwD+C3ADw14vtZx8NvAHiIiL6KmT9R6sMIQsWI0C0IKWDmC4vyQPPyAIA/Db83ChCRDWCcmY+aLotPmWViZg/ASfFSXYSIJgG8DcCXAfhuZn5z6CO/RETfAOAbyr53kyS1TQP96QUA3g3gLQBeQURfzsx/XdO9U1NWvVTVnwf8cwBbAN4e8V4HwDEAG0A3/GbO8fBmAK8E8MMA/kXh0gtCjYh5iSBUABGNE9HPEtEnBsfYu0T0+0T0+IjP+ra1TyWif0NEnyeiYyL6ABE9cfCZf0hE7yeiQyK6SUT/OuYaTxscX3+eiHpE9DEi+r4iZQxd+18T0V9DLeD/MxHNEtG/HZR1c3DPTxPRy4hoKnCNl0JprADg3QFTnNf578eZ5hDR54joPWnLlLX+Y+omyi7Wv+e9RPSTRPTXg+f9JBHdn+a6AH4IwN8D8GsRAgYAgJk/yMy/ObhnqvotWL5Omj5TRn9Jej/L8+aFiL4eSuP8EIDXQ2l/fyDic6n7JBFNDD7/V0R0NKibjxPRrwSvh+FjoLJ6ienPheualPLhPgDvZOYoLfrLAPw5gD8C8LMR72caD4P/D6BOF783TRkFQSdE0y0IJUNEXahF5psA/N8A/k+o49V/CuBPiOhbmflGxFdfBqUR+g2oo++fAPD/DgSmV0OZsrweSnj534josxFa9l8CMA2lCWIoYeINRDTBzK8rWMZfhdJW/RaAfQB/BeBRUAvnmwD8DgAHwD+EOgp+PIBvH3z3zQCuQZ0O/CKARwavF9UuXipTgfpPyy9CHdX/XwB6AH4EwOuI6NPM/CcJ3/2ewe8HU94rbf0WKV9inymxvyS9n+d5s/ICAIcA3sTMh0T0BwDuJ6J/M9AI5+HfA/hBAP8RwMuhxvFjAdwb+EzaMVBXvZRxzb8PYAZKsL4EM/8ugN8d8v2s48HnTwF8OxF9BTP/ZcbvCkJzMLP8yI/8ZPwB8HwoAeX5Ee/9+OC9bw+9PgfgbwC8J+ZaHwYwFnj9uwavOwC+IfD6GICbUKYt4Wt8HsB84PX5wWvbACbzlDFw7b8CMBX6/BiAbkQd/O+D7zwh4jpPjvj8Swfv3R3x3ueG1FlUmTLVf0z7PjncvoF7fiTUTo+CEm7fkOK6WwD2M/SzPPWbqnxZ+kxZ/aXM/pRz3E4Mnut1gdeePbj2d+Ttk4Nrvj3F/YeNgVLqJabvRr1WuK6hNmgM4Ltytkem8RD43nMH9/3uIv1BfuSn7h8xLxGE8nkugL8E8CEiWvF/oBa5dwL4ZlK2jGFeycz9wP++g+afMfMH/RcHn/lzKE1a1DX2Ap/dA/AfACxCLbxFyvhKDtmWMnOfB8fKRNQhFRViBcohCgD+QUQZy+RSmZC//tPym8F2Yua/A/BJRLdHmDko7WUqctZv1vKl6TOl9Jek92voT/8E6rkeCrz2BwDWoTTVedkD8FVE9NUFruFTS72UdM3Vwe/trPcfkGk8BNga/F7LeV9BaAQxLxGE8vlKqOP9YVFMVgD8bei1zwT/YeYdIgJUhIUwOwCWI15/JOK1/z74/WUFy/jJqA8R0T+Hcmr6Klz2E1kccv0yiCpT3vpPy2ciXtsC8OgU390HMJvlZjnqN2v50vSZ0vpL0vsV96cXQD3DF0hFzfB5J4DvJaIVZt7Mcd0XQZndfJyIPgPlpPn7AH6fs5us1FYvJVyT/UvluT9yjIfQ/XjopwRBM0ToFoTyIahYsi8e8pko4cWN+Wzc61FELUJRC2KeMl7SWhLRiwH8GoB3APg/AHwRQB/KpOF1SO+sPWzxHDZPRWlS89Z/WuLaI43g8TCAbyWiL2PmKOH44gXz1W/W8qXpM6X0l6T3S+xPlyCixwB4CtSzxG0IngvgFYO/U/dJZn7LwOHymVB20U+DEvDfR0RPC51gJVFLvZR0Tb/Nl7Lef0Cm8RDAv5+EZxWMQoRuQSifT0Edu74rh5arKI8D8NbQa185+B1c1Moq4/Og7Fu/I3gdInpGxGeHCTH+8fQSAvGLiWgCyvns0xnK1GT9J/EmAN8K5cAWFc0hTJb6zUuaPlNXnVb5vD8AJXD/UwC7Ee//WyhB2Re6M/VJZt6Givf926SOqF4G5ZT4bAD/j/+xnGWvol7KuObDg995M0RmHQ8+/inFw0M/JQiaITbdglA+/xHAVcRoBYnoSoX3/hEimg/cax7q+HgXwP9XQRldKEHiTDM6CCP20xGfPRj8jtKK+ZrHp4Ve/3Fkn6earP8kXgXlKPeTRPTsqA8Q0d8fHPsD2eo3L2n6TF11WsnzEpEF5aj4cWZ+FTO/MfwD4A0AvppUXGggZZ8kIpuIFoIfYGbfoRW42N+HjYFhVFEvZVzzI1AmIk/MWYas48HniQBuM3M4Io4gaI1ougWhfH4DKkPbrxDRvQDeBbUwfSlURrYTqGPuKtgE8AEieg3UYvoDg/v+UMg5q6wyvhEqg9wfEtGboRyj/ldEZ777IAAPwL8iokWosG2fZeYPQDlv/SVUKMRlKDv2b4ZaXLPa2DZZ/0Nh5iMiehaU895/IaJ3QNkTb0Fpkp8CFartlwdfyVK/eUnTZ+qq00zPS0SfA/BoZk4y7fk2AF8CFXozjjdBRSx5AVRfTdsnZwHcJKK3Qgmh6wAeAxWqcQfKtttn2BgYRhX9oPA1mdkdfPfZRDTOzL0sBcgxHkBEM1AZgV+T5V6CoAMidAtCyTDzKRF9J1SmtucB+PnBW1+EijryUNx3S+AlUAvSjwG4AmUW8Bxm/p2KyvgrUILaC6AEs1tQcXlfi3NnPP+ef0NEPzgo4yuhYhE/BOADg8X72VC2pf8Cyrb0HVD2sUmxry/QcP0nwsyfJpVQ5p8B+G4A/woq1vE2gBsA7oeKmwxkqN8CJPaZGus06/PODMqQxAsGvyMTsAAAMz9MRJ8E8H1E9OPMfJyyTx5BmaQ8FUorPgMV0vOtAP4dM38xcI/YMZBQ/ir6QVnXfCXUKcKzoDYumcg4HjD4zBRUHHpBMApSp2CCIJgMqUxzrwXwFGZ+T7OlEYTqIaKvBfAXAH6QmV+b9HmhOojojwBMM/O31HCvDwH4PDP/k6rvJQhlIzbdgiAIgol8O5TQ3ejJhQBAZc/9RiL6tipvQkT3AfgaqJMCQTAO0XQLQgsQTbcgCIIg6I1ougVBEARBEAShYkTTLQiCIAiCIAgVY3T0kpWVFb777rubLoYgCIIgCILQcj70oQ9tMvNq3u8bLXTffffduHHjRtPFEARBEARBEFoOEX2+yPcbs+keZPH6CBG9bfD/64jos0T00cHPPU2VTRAEQRAEQRDKpElN9wsBPAKVBcvnXw7S8QqCIAiCIAhCa2hE001EdwH4TgCvauL+giAIgiAIglAnTZmXvALATwHwQq//AhF9jIheTkTjUV8kogeI6AYR3djY2Ki8oIIgCIIgCIJQlNqFbiJ6FoB1Zv5Q6K2fAfAVAL4BwBJiMk4x84PMfJ2Zr6+u5nYgFQRBEARBEITaaELT/SQA30VEnwPwnwDcS0S/zcw3WdGDyqz3hAbKJgiCIAiCIAilU7vQzcw/w8x3MfPdAL4PwLuY+blEdA0AiIgA3Afg4brLJgiCIAiCIAhVoFOc7tcT0SoAAvBRAD/ccHkEQRAEQRCEEeXkxEOnQ+h0qJTrNSp0M/N7ALxn8Pe9TZZFEARBEARBEHx2dlwQAVevdku5XmPJcQRBEARBEARBZ5jLu5YI3YIgCIIgCIJQMSJ0C4IgCIIgCELFiNAtCIIgCIIgCBUjQrcgCIIgCIIgVIwI3YIgCIIgCIJQMSJ0C4IgCIIgCELFiNAtCIIgCIIgCAG4zFiBA0ToFgRBEARBEISKEaFbEARBEARBECpGhG5BEIzj6MiD45R/9CcIQvUcH3vo972miyEU5PDQheNwJWYYbaXTdAEEQRCysrfnggi4erXbdFEEQcjI7q4LALh2TfR+psLM2N/3AKjN05UrHVgWNVsoA5AeLwiCUfhaFVGuCIIgNEN4/nXdZspRFVVp70XoFgRBEAShFsQUoR2Em5FapOTu9z3cuuXg5ESilwiCIAiCIAhCJZyeKmG71yvf70CEbkEQBEEQhAKcnjIODlpmYzGENh9YVPlsjQndRGQT0UeI6G2D/x9DRB8gok8R0e8S0VhTZXNd8cYVBEEQhLJp69K6uengzp3RjcjSJvMSH6rgoZrUdL8QwCOB/38JwMuZ+bEAdgC8oIlCuS5jfd05864WBEEfPI9bu2hH4TiMO3dcUQIIgiGMylht82O2TtNNRHcB+E4Arxr8TwDuBfDGwUceAnBfE2XzBhvVKgzoq0YJJOaVWxDS4LqM27cdHByMjjZpf9/FwYGHo6PReWZBEIS20pSm+xUAfgp+gEdgGcAuMzuD/78A4FFRXySiB4joBhHd2NjYqL6kBnH7toNbt5zY95kZe3suPE8Ec8E8zjfEoyeAqni4giAIetJGfV8Va03tQjcRPQvAOjN/KPhyxEcjm5CZH2Tm68x8fXV1tfTytVlTfHzMODrysL8vpjOCubR4iF7CEld3wXD6fW+kHAxHhTbLSv6jeRXoOprISPkkAN9FRM8EMAFgDkrzvUBEnYG2+y4AX2ygbGe00SlAEASzsG0CwJietsDMcF2g05HJyXT808ZRyOC3taUE7pkZu+GSCEJ+Tk8Z3W7x8Vq7HoWZf4aZ72LmuwF8H4B3MfNzALwbwPcMPnY/gLfUXba2oRZpvvA/UI1HriDUzah14709FxsbjpiHtYDbtx3cvh1vCthWRsER2nHUWG2zJrjtHB5eVnFvbpYzXnU6vHwJgBcT0aehbLxf3UQh2jRODg48rK87cJyLD3V05GF7e/QmfMFsfCG7TWM0C8fH6sFNff79ffcs6YQwmty+7WB9vd1rz/6+i6MjD/2+9HUTYGbs77uX5KSqaMK85Axmfg+A9wz+/gyAJzRZHgBwW2R61uupTqQ0YxfVgjIhCIL+RAnYJgrdzIzDQw+Hhx6uXes2XRxBEAQASuY7PPRwcuJhbU3NTZZVjT03oJemu3EcR0X3ANp5dB11ZCIIgv6YKGgLQpAqUmrriJh/mU+VfjMidAeo63ihLsI23MGdmyzigmn4fXYU+66YZQims73domNkoTXzcJTZYuuS4+jG0ZGHw8P2TwgSfkwQ9IaZIzf/wdfESUsQ9GUUhqbrttchVj1bdQ8nYhjUIra/713oRG00L5mfVyGbRPgWBD05PPSwseFc0mwH56N+n+G02xdtpDk8dCUDaQtoq1AKAOvrDnZ2Lioq2/K8KvhEddcX8WuEUDF/g8cpLRklgtASfGHb12zLEB099ve9M98iQRDqow6ZSITuALLAtZfT02qPjAShDOK6aPjkrY0ncYIgCG1HhO4Ad+60S7vgL+CjvkC7LmNz08H+vhzZtok29+u2Cdmy3xWEdiDKq2KI0B1gbMzwlS0lo5ZkxH/OkxMRuk1mFCZ7P7a+TxsiKvV63iX7T0EQ2oWESkyHCN0BLOtc6DZdsxRFG58pDa4r9rGCmfhJrEzuuzs77sgn4zo6irbTbsOmSriIP1aPj0dLySMhIdMhQrfQevzJwGTBRRAEc9nbi45IsrEhYWjayslJ+xYcZm6tcM1cj4ww8kL3KCedECFUEMxExq4gCHXjOBj5U6uijLTQzawc7NqKLMxCmxjl/jzKz942RsE3IS2+6Z9gBm202657OI600B1GJkNBEEyg3x8te1GT8Dwe6RPULIh5jVmIiFSckRa6h3UgapHXYa/nRW4oJOuZIJiJhL/Ul50dF5ubjihxUtD2KvI8xsbG6Uhtwu7ccbG7K5upOEZa6B4V9ve9SDusgwNZuAVzadG+OJG2CCej0Ga+gBXXZm1pSyGZkxOG4wAHB+10PgzT73s4OPBwfGxmJxdHSiE1ScfNMtELbcNt8TrGrE6ohPYT1IgH/25D+6fJjdA2u+6otbYtm80kOUJO4JKpXegmogki+nMi+gsi+gQR/fzg9dcR0WeJ6KODn3uqLktbBFHPY2xtDZdAiNoz8IXRJGq8ttW2eX/fbW1orraTtK6kXXe2t13jE3p5KYrf7mAGqrHbZK7aVuqSBzv13OYCPQD3MvMBEXUBvJ+I/nDw3r9k5jc2UKZLmDRGoia27W0n1YQnCDpyesqwLMC2hw9Ezzt3XBsfb8/BXXjstkVBEMXRkYfDQxerq92mi1IL4bZkjl9vdnZcXLvWnn4dxaisU34ipE7HIOFCKJ3aRzMrDgb/dgc/2i4prmumJ3o4nbQgmMTmppM6ssH2ttIKtzGcVRxWi+SwvT0XjtO+6FH7+y52di72YccZvp60rApGkrg23NhIP6fpSvIpjnTgJBqZuonIJqKPAlgH8E5m/sDgrV8goo8R0cuJaDzmuw8Q0Q0iurGxsVGoHFEahzDr6472x1/Sz0cX1+XWmlik7dcmboqLMjXVIql7QNvmseNjvpSVcGPDwe7u6JgNmXRinJedHUeidbSE1jpSMrPLzPcAuAvAE4joqwH8DICvAPANAJYAvCTmuw8y83Vmvr66ulpqudpyzDUqntKC2hQm2fOPCm0T2kYFXzBry/xbBOnD5nFywonROkQDrC8jlRyHmXcBvAfAM5j55sD0pAfgtQCeUHd5TE1vGh7Qd+6M9uq1vn4qG48WIutWuwkfvR8cuFhfP22oNIKQTFLklbY5UMrmoThNRC9ZJaKFwd+TAJ4G4C+J6NrgNQJwH4CHqy6L9J/2wcxwXdl4mEzUxH5y4kXabLdsTRMC3LnjtTosZBSyJpnF+no6sxLHAOuTft/DzZunhUI4Bvuv57Hx0XeqoInoJdcAPERENpTQ/3vM/DYiehcRrQIgAB8F8MMNlM1I0k7UUQJKr+dhe9vF2lonMVKECcii1T5u3z4V0wNBMADXZTjOeSQh2RQrTPA78TNU93qMqaniDbe766LXY6ytUStki7KoXehm5o8BeHzE6/fWXZZRJSiYHh6qgXZ6yq0YGCJ0m0+4DYcJ3CYsZlVgUj9PKiuRWc9TNsOe3TShdWPDATNaH+YwLXfuRB/VMCsn24kJ0sYERZWDh5qQ+G91Osna+6TMrDrSWkfKutjedrC9Hd8z2mKfVOQx/O9qMu4LczkiTTvaOA0bG6cj50UfNCMaoaYWWkq4D4+PmzUxyxi8SJzCoNdj7O66ODjQ5wjPlwGi1tDjYy/3WtoW2aIsWi1093rc+njVu7sOdnaSDR9dF7h167JA1jahe5RxHCR60ZuAZGJsD1kX6lHaJKchHHJQaAe+MF7Efrou7tzxsLvrDqK0qILrop03kSZsuoUSSStkxR3DmzDoy4SZZcLQnFE1GWkjvvmaIAhm4ssIWePLm7h/Tlp7er3i81mrNd1JmNgpyiD43G2TP5M096Pa5oLQBFHp7G/dyh4G8OgoOnpN22ibpr9lj3OBUeiPQPtkhGHs7Q3fWPia/iKMtNAttHtSjMLE5+31PNH+psDEts2Lyc86rOxR7/X7Hvb2XOzvm2l25DgGN1ZKmBk3b17cTK2vn6bWjjIzjo7y2w03QRqzzrYySoJ42YjQPSLETWajForNxOfd3naxuTncQdKkxUq4zOGhe0loaTN+TOA0m0k/lJmpMbvDSX/i2Ntzjc0uG55++v1sMdZ7PcbenmtUfgVTk+llJe/SovOSxMw4PHTBzJlOLMowTR1pm+6kThHVGFtbDjyPsbrarahUzaDzAMlCUPh0HL40Mar327dNb7vDcJs5PWXs75sjbJSBL0j3+94FrVnUPNQG5+A0+HUSxLYbKEgJZN08mORYKJjPwYGHgwMPlkWxYR2rQjTdQ4jSwvT7bER2qWG0RcBOYmvLSbTREtrFrVunQ8OENoXnnWtWwpRhJ2gaozIHpWFYXbiu2pjoTlntKWYL7UPHse5v8jyPM51+l9E/ReiOgdlMUwThnFFqv7YuVlm9xZn11Porm2QP/T7j4ECZkhQxCTLBnGhz04mNXnLu8Hy54x4eukOPfD1P2Q9HaYbros76N9XkRBCEy4y00G3AulUao/Kso/KcYdoqdGeJ2R0U0Dc2TrG+ro+NdHAD6CfE8PtqW/vsMHvtuGfu95WpzbATKt9k7OSkOaF7Y2N44rVRo619uC56vfQ+DroxNzfSYmRmpLZCWFZ8Zqa2EPVcjsMjecwttIegE5bjmOF4d3rKjWpsdcOfm6JOqfp9laJah4RerqvniUpTtHWtrALPU6ddQfy1t0mhO++46nTM1vjUPY+0Tuje2nJSRwGImii6XUK3S2DmkbIH3t/3Mge/F+rl5s3TWPvOYYve0ZEnDkoaEWwrE+x1qyBugU9SeNy+7bRyntraEq35qHDnzuXILr6ZlQlmY8Mwsfi2nV7qljjdEWQJ4zOsg4ffMn0wSDrhdpA1TJXn8SAUmSzqOtLGcFxpSJpP4943/bnjSDOumZWGVKekLI6j7Ot3d0djflEnLcXqP/j942NVfzq16dGRMnXJo6gxwY9qb8+9cLo4OZle6C7j+VondPuk6cRtncBHmVFt07jnbrvdsGA+eY932+rHEEevx7hzx9MqSZAfbs30iF5pOTjwcOuWU0jwjlKA6WQK57dlWqHbtHHYtDlfa4XuOK95n6Oj7Fn+TNd2C+YJn3nMD6SfXqQM7ZRunJxw5sguOuEv7Lu77gWBzeRm8jXR1V1f/dZJm1iWsGjK+PTliiLPHfWovixiSDVk0syb8kx1UbvQTUQTRPTnRPQXRPQJIvr5weuPIaIPENGniOh3iWisynLs7bmZTS6COyRTJgnBXA4P82WoC2pidHA6axJmxq1bTuPJZ6qYLrJEdhGqx3FQSUbFOLv3tmLC2po2y6jJDMu+aNrJRpzihYhq3xQ0oenuAbiXmb8OwD0AnkFETwTwSwBezsyPBbAD4AVFblJFRTZ9LFEHJkx4owBztiyF4WbztWHSnApdxm7bBSYhOyaP0bLXi+NjbmQzmec0zNRsoWlpkyywv6/MgnSgdqGbFQeDf7uDHwZwL4A3Dl5/CMB9xe4ztAxDvysLo/mY3IbD+mfcc4UjOoSPoE2uDyEeXRfGvOXK8r1hmri6uHPHHenIQJaV3AZZmymrs3gZbG25mYWyNM+eFZ2G89aWa7QJWxBf6RKeX5qYPxux6SYim4g+CmAdwDsB/DWAXWb2e/0XADyqibL5RLXFxMR5dZkYxF7QH89T5hBFj6k3Nx30+562QplQzgKra/i8vMfvpnXXg4PhiXzaThpt79hYsnAabve6Ex+lXc+D5Wzb3Br1PE1sgNpOI0I3M7vMfA+AuwA8AcBXRn0s6rtE9AAR3SCiGxsbG8PuUUpZz6+nEuf4SGpePTF9HnQc9QBlxAM1acL0vPY5O9aBrqFA64jG0FR/uawta6QYxpAmiVD4ZG5nR//1Vdo9HaN8EhRFo9FLmHkXwHsAPBHAAhF1Bm/dBeCLMd95kJmvM/P11dVVMHMqT1rXZezvuxcymkVfP997glAGo9rHbt92KlloJWugWWTp/8fHerStSZtbXUmKNqYjVczVJs7/SWZeOzvumTJJaCZ6ySoRLQz+ngTwNACPAHg3gO8ZfOx+AG9Jc73dXRe3b6ujzGHC98GBh8NDr1SHqjbu4HRZyEaVsqONmDSJVyEg62wG5jiMgwPzhA3hMnVo3U0ay6NAlvaYmGje/yAPjsOlnKbplPwnzChEL7kG4N1E9DEAHwTwTmZ+G4CXAHgxEX0awDKAV6e5WLBD+MI3cLkifSFmf99LVclpPtOkPWW/7+HwsPz7B+0Tg6cDJsDMZ8kawnQ6kS/DcdR3dHnGJKE7r/OYDk5no0xU/9redkWQCmFyfeSdQ1yXMwllOg3lNI+sU3mFbOhqvmYyMaJIdTDzxwA8PuL1z0DZd5d0n/O/e72LGu48x4HMUanh85auOEVtyicmaOiAcl3G+rraxIyPE8bH9Z85XffcljRoI2hZwPy8faHOPI9BBGxtOfA8YHraMm5xaMtJS1ALUvYz6damsvkRwuzsuK0PPyfozfGxV6mA7XlqU6rb/NeEDNfajJRBwhqlPBrqk5NyTVOaJincUTDVsE5ph4eRZTzfvu1gc9MxVrMW3BQNQ6fsdXEET6hGORKEYIamu4oyDnM81bVO2rLpz0ubbLp3d6s9ddvZcbVIKKTDWKpd020qTWe0q5vgrlf37FNJ9mJxwrjj6KcJTUsaW+UqsuNVTdmTYtPt6yc5qnLz47oM2za0I7eEqhdz3QTcXs+TjKhCJuqIaGQCrRG6y7LJZVYLtbeAsRMAACAASURBVA47IiEdvqZ0Zsb8gxvpd+XStE1ir8eVn5CtrzsYHycsLZk/neviW6EbugndWRyU27Ke6tYGTdGW9kxDFc9qvpQSw6h0CuGcNJEgTHIMHUYLHqEWdAvnVlXf0yE04tGRV0p8ed2RsTd6pDXnK0ob1iad0UHbbr5qRBASCJoYOI7ZiY38Z5G5WdAN3x5/cjK/Luc8eg8hJj/aSCL10SxFzMMmJ0nr+XqUBP3NzYsbp6gAGVUjmu4RJk16XgCYmtKnmzAzdncdHB97cN18WQx1jt1c1QTYtG2zMDoUicmrgyYqiabWllESjtpEUtCCpjHR9ycLuo2b1mi6y6rXsTEamexJk5MW+v3kVc7SR+YGs0rgc3ycfnVOE6ao388WK7cq0jrs6jaR6I5sOuojGI0mKzpviHVBt7BrcRS1h20yxNzJiYfTU8bsbPFYjlkeoYnn7fXqE7pNcPoeHyfMztqXtOJloZE4VS55hJKFBRuzs62tkpFl2Dzmd5MqUpDriOPkOx0ok/19t9aJflTY2XFGRmGgG00cU+tOUfmxyXCnOzsuDg68UgM06EoRQX9xMdumJC55nU4QodK4+a2VMPN0cts2R4uQh7W1dAcbOntp52lXXZv08NCtJKtoHK7L2NhwGj9OPDysP9yYrn2gTE5O2JiY+iYTNQfpPGcK+albWGbm2hUjRebGbjfbl02Rr6qs/tYI3Tpli9QV26ZUO7g6vLQFZUoSZ04S13+LzFm+5mgUIkxEwczY2XEaNWGQ5D/pMGRtPqNq52zlv1LpLYQhFBGCs/RlZmBjw5F5oiaSmrWKeag1QndY01BkggpWtCmxn1dWOqlMY6anzz+TtkPpNNnn1XSbssP2May4qQkvXnVp+olU5JqTE+WI2xRVJ5oyIQNpmZyeKq2g63Jt2uYmzLN2dtzGT6hGgaz9KDhPd0rwkPPHrw4hQNvCsOHKrEwuiVCbT5cZEmUKwpqGsuZFnZwIh6EEy8uvlzER6ESbzEui8I/rkpxN8tRDWFioU1CJI6zpL1OeSRudp000ba9fN5ubDg4PPayvOyNzQud5zY/bNExMqMXTpC4Z7keOw0Oj8QSVcvPz5RkC67xmBcuWtZy69dujIw+9Hhd6pqwYIlI2h2ka0jBFYua2hbm5Cr0iSsYXmuIWqmLRAC7+r6OgkjQpp90Eh4etZRFOTtqvKTRJwCmLus2Fmq7jk5N6ErUUJeqUOKupRdNsbblDnezLFg8MFzcSEQ2+CN2CYWTV5I2PU+o4qTpofqsiOJl7HhoTQIPNF5UevUzziOC9iNJlLC2bur31dRBUyiDLc7RdUInDhEgQbWBYVts0Srk8G406+3QTWXs9j7G1pU+0pTrr22ihu6oFJm0DmD7Zm17+stFB85vUp/t9xva2k9O85PxvHUIkVukspGy4+cL/TVCGDffysjknNaNA1rE3PW1VkmCsiU1kFtq0vqRp86AATqQUPlkoGte8KbKasDIz+n31s7tb/TqkW50aLXTXsUvSaeJwnGY0sTp12qxlMU1z7T9f3HP6Nmg6tUmdBM2lhjm+xPk4AKpu9/ZcHBw0v/FIQ5aMdszK3KJIVkgdyDK36z4WpqetzKHV0iLJhOohbX/05xzmYrKDSWatRJTbd0yX/ltnJtzahW4i+hIiejcRPUJEnyCiFw5efykR/R0RfXTw88wGylb3LTOxsdG8JrbpBT3rAlt1tAghG0WPT4MRehYW8mmAmdXmxZRoEH5dpU3YsLnpaHGSUQSdBem4sg07pq9qztzcdLTfYOnclmkJOnzHabCJLvqcnJyoB89ix9wWR+i5OSs28pvuj1i1HDhU6CYii4j+ERF99ZDPfA0R/aMM93QA/AQzfyWAJwL4USJ63OC9lzPzPYOft2e4ZmFmZiwtI304DqfaZdfRkZm5FQu6bvT75xN62nYsal4SRBe7ujRcPMIdrumOo4qxwsyVZtm8erWTKjqC/2x122k2KSzosogPM/fIm6wtDbo8f5iisouJz+WfSgVPNrKctrZFSTQxYZ1FrwnThA25TiRpup8L4A0ADod85g6ANxDR96e5ITPfZOYPD/6+A+ARAI9K890qmZ21zxbxsG1WkLqV4RsbDjY2kkdinkx0nU62h/Gd3JocNLpOxEXIY9ucR8jRIVuhDu1XRRmOjlSWzaocVLNqX3Q8tCNSm4cqrlsnZW54o+h0VD0lhQ01BVPC7qZluNCtfge1vDrMeWUS9fxZnnFnx63FltsnbdnqmkfSCN2vZebPxn2AmT8H4NUA7s96cyK6G8DjAXxg8NKPEdHHiOg1RLQY850HiOgGEd1oy1FMGcQdMQ7rSJ0OYXU1/SI4KtXd73uNH/Ml2bodH2cv3yglTiGiWvurbxPonxocHXnY2mqJ2qokhiWp0vGUsSzyLOa6mzqmgYgwN2dhebldjTtMKRdHOkfMnAVqkLxlDtZH02stEO8fVMWGMemSXw/gHSmu818BXM9yYyKaAfAmAC9i5n0ArwTw5QDuAXATwK9FfY+ZH2Tm68x8vY6JycSBkIU0z+cPCj/EW5N1Usf43NpyK0/DG5xo6ujHusRHbSIkVpgqIkmE2dtzc50IZY14EIdp85ZJsfTzUGVm4yZklqT+5Qsr09N25hNV3ck6tuLMLJpke9vBzZunWjgyVqUM2ttLv46H28i21YZxaenihrEMh+ik3jALYCfFdXYGn00FEXWhBO7XM/ObAYCZbzOzy8wegN8C8IS01xtFmjiyOzwcHVVpU7Z1Gmz6K8XfbFStdQ8vjFVvOoJRC8q4DpDepjfpOiZgUnnzaObSRKDx68CEhGbhTLJh4jaPWaqOWUXrun1bD+HQJ62/iL9G59nkn55ypYoffz6sKoJTlvFclY/Y0ZEXmQsiCb/s09P2JROvMsK3JvWGTQCPTnGdLx18NhFSar1XA3iEmX898Pq1wMf+MYCH01yvCoZ1mOB7aZ0cq6CJRcqfRJrUXJjk9DeMuoTruTm9HITr3FQ0sYEp099hebmDuTkrl22vSUJs28mS9+Hq1Q6mp/UXuuui12N4HnB42Lw/ik9ce5Y95vIIjFnRwamx6g1VmetAGafSSaP7/Uhnq/38wWfT8CQAzwNwbyg84C8T0ceJ6GMAngLgx1Ner1KG1bHnIZWTo84Eny+pP/lHMEU0cEXJqm1vmxNPHnSyD61LEE77yFtbTqmZ/fp9LmSjeFHTTZiezjfY6m7zYdqqyUnfQT3++zr10SSq7MNElLku6txcnp4ybt48re1+/lgyqX/kIW/406LkPXFMs662vMlyk6QDewWA9xPRywG8hJn7wTcHZiK/CuBeAN+c5obM/H4AUc1Ra4jAIuiYOaqMSWl8nM5ii0ZRx857GMfH2e8/O5tXaMn1tdRE9Z8qtDljYxaOj4dfV7e+nJdOJ7tZkJ8ZLW8/8Rn1BaZK853paWskTNvC4zCtwmBz08Hyso2xseo1DKYklEqL53Elmtas88HkpKVFdKmZGStVptPl5Q76fS5tM+S63JpoPUkMFbqZ+U+J6CegnBqfQ0TvAPD5wduPBvB0AMtQcbf/rNKSNoQuO+xRi4Or4h4zxseV5sd160kZ2wR+F0uyk8xDVZnw6iSL0OV/drjdZUt2GYag5tD8dT4xQTgcBK2dm7MqGSdZqKr7hIXs+XkbnpfOIffgwMPSUrVCt4rqVOwatl1v9r8k9vbcoYqmKLKKBJqIEIXodunC5qTToaFmplmfeX3dwbVr3bzFM4pEa09mfgURfRjAT0PZWk8O3joG8B4AL2Pm91VWwgYYZj4RpeVmLm/HF0cTWubFRbux3ffpKWNnx8XUlIWxMWqdwK2T3Lez42BsjLCwUL3xd5aYqXnqSIcFTqe2NYWy2q3f92rR+GYh7doQDq1nWYTpaQv9vh5z39ZW/nKsrHTguoxul1JnVa56HG1uOoW13DrMN2WSxl69bc/sU1Q5kJZUqywzvxfAe4nIArAyeHmLmfWYDUomydM8LBDcueNVHvJKB+9tX0tRp1DR73vo9eq7n09bJ5YoXFfF/V5YqPY+p6f1nVYMa7+qx1LdzkmWddk2c5T6b5CtLReW5WJmxsptD59EVfNfm4/Xu11KdepWp+mmDmuqUD1pEpbVuanIpBJgZo+Z1wc/rRS485AlzatJECmB7OSE4Xlcq7Bd1smBrsJHvXVZ372GkccmH8gS/UGPBy1if5wmtFyYsmJ7V0XRZklq17A9uedVY6qlM5p0/UxElTkcucWkU6O0oQTjMCV5mS7zbFrS2KjXSaLQPcgaKQzBsD6YC53s8PIgdrxCXRTpannmkjbY7Q8j6fnqHtpZ7re0ZH7Sn+NjL1XEkrTxqH3b9Sx9fRTWWJ1J27bnkYrOMwInmevqQl1lGWpeQkS/BcAhoklmfn49RRKiyHtkXWRBDnbCft+rdSceFJJ1zLhWlIsZKfNf59q1Lra2nFJMGqrwTdjcdNDpoFR78clJ0ibDZtlMTKSLHtAWokxjgviLuGnMzytJY2zMzPIHSRuxJO1as7LSSZX8JTjvHx5Wb8KZljo1vXVH9Yh7tMlJC7adbNc/M2NjYeHiRXQSrHUgafvytcz8IwAeV0dhdCTcYZrqQFk1zWNjhGvXurmOq6MIHtcyAzdvnuY2FxDKpaxkRVm9+NNweso4Pi73ugsLHVy5Eu3pbvoEX5bWWsd6iCrTykoHi4vJwtTyso2VleayPDEzjo48MKeLw06ktINlCmjDklzp2N5x2DalSo2+s+Pizp3zNabf12+9saxq616nPCC+LJE2lKWv4Z6ZsWPraBQPoJOq73NE9IsA/raOwphKEw4ZSZ21jIkgbsHwbdhNiNkaVw9Xr2qUprFEioRd8jzVrr2eV0nmzyy2zlUmNcoay1t3mrax7PWGt2u4eMGx5wtgnQ5hYcGOPYoeG7MaNaO5c8fD3p5b6IQlSms/MZHstH/+Wb2isoTJasKXtdvqOG67XTrTwIefx6+OIsOzbKG01/NSORZG4Y/RtIl8LEsp/iYnLayu1rfenp5yoWhvVU+nSTXxHABfgwZTsutAt0vwPD7TNocbRcfJoErqd6jMd8O5OSu3k1kTyXGaxhfetrdVR28yburMjJ14/CwomvZX8PtLWogIlnXZTnRy0sLkpFVrxsO0+IoGZuQ+uVlY6GBy0rtQX9PTFk5O0tXfqGfX1VGbT0SYmqJIm+fZWRVTXqdyZx2rYSYn83VC21YCex3RqzY39RbIkpLjOAA+UlNZtCE8SFZWOuj3PWxtuQMHAQ0lpgpImizq0rAlVffEhHLaCGuhJifLPd41jZkZNUGOShUMc9ipk6rmhza1Y5x5kK6UobUs+v2pqWYSA+WLl9+izpqT6Wm7srCVefBPMoXhxPX35eVy2rL1e+egHVyUTdz0tJWpMtseKSANdew5styDKDlsWtZFwPOArS2nlolqWNGGOWIl9ds8qc1N3VAuLNhnzmtNUGa1LS/bkcexZQky/b6KRlGXWVxRQVMHqhwWY2OUymF0WPubKOT6vihpx62Bj3iGDgqBLFpmHcpbN0n9q6ykW3rMaDUR5QU8PW2lqsxuVx2PzM1ZuYSZpigyUSU5P9QxCZ6elhMf3G+/tPR6jH6fcfu2g/X1Zo67O51zbXUUZWfec5x6Y7GXyeSkBcuiyD5pWgSMsTEr0jl2mCNdFnyH2by2nXUS18ebigqSPmZ82s8Rlpc7pUT36fe9Rp0Nx8ezzUcLCzYWF+0LG6smMi9nJc+616RCwCfLJluUi9XRSqE7blCEA+/PzVmpw/EQ0Zm5Ql67Jh0ZNoFEvVe1oO26xZwghuGHPQKyaYbyxig/OVEaxSJOiVkXsijS2oIWSepSJUX7nA4LXhkQ+RGJ/P/TfS98euF/z9QN1iiS1NZHR8r8sUiq9qJ0OoS5ufTzlWWdRzHxT3fa2ieDUcTm520tHfmLbOrztFuUQml9/RRbW3rZZK+udkozLQFGTOjOI8DEhWMrS+tUFeVELxn+fhWZOHd2XOztuaUk44kq//Jyp7aEFX5IxbwxtMs6Ms4SCrCqRc9Us5W01Pl4/pw0zKSq12O4LuPgwMWtW86F+jc5/XWdp2xHR17iHBdW5FRFy4dP7OlOkLbUwcQEaWcONDlJWF0t7meR5bGi2tt186+XVdHpUKmnyq0UuoODs0jnHqYhXFxsVuoOT0B1jeGqQ1hVbUNt25Rbe8zMODx0U5fR1274As/pKZ8d6YfbL+vuPsti35bFKi91LHB1HosvLtpYWrITY/BvbztncY6DfaCpxEJltIM/51Rt6+15KnlLeFyGx1LWY/hgHRAlK29MSQ0u6E9WP6my0SXnSdO0Uugua0Ie1inKSkhSN/5CncZRImmRrGLQ+BPDsHBxSbFtfcoWtvp9xv6+lzqUXfgYf3PTwc6O+m5Qg0aUbXc/OUkXsrNl0SKbYt+ctul0CKMW9juoepNjWek2jsFQpuvr6p8mwvHlHYazs5efsdNRJjZ12XT7Qq/fpv749Yl7tuTIT8DVq13tNJ6CkAfTbcDr9BGpfckioi8honcT0SNE9AkieuHg9SUieicRfWrwezHvPdJWoMkawKiyp3meiQnC4qIdqSlN44Q2ymuEX79Z+03488ycKvRXlNAxjCz2lHUQ3Axk0QRPTFDq+OorKxft7UwUYupYsEw0GxqmPNGxmasYfyavUYJeDPff8k9lL/6floUFdfqWJWV98BZNjudOB6Vl7k5DE6u0A+AnmPkrATwRwI8S0eMA/DSAP2bmxwL448H/lRLXQdJ2ABMdtIiU80rUoPJfm5o6zyA17Bl1XPh0omjq2zKcKJskeDSeNr7w2BhhcbEDy6JUJjS2Xa69XRlkabexsWxRdcrE5PGr4+ZKxzLVRV227bozM2OBaPhGOms32dx0SjmhqnJzn/b0TWhA6Gbmm8z84cHfdwA8AuBRAJ4N4KHBxx4CcF/ee6TZQS0u2qnNFOIIaoF9jU8dDmNR9yhjvvftC8fG6Mx8JryQ6LCu6FCGIvh2tj55M9yFiVv0m6qvfEk1mrt3WWQ1eTG9PzdFmREF8lJ95lrVkfNm1s1zv4MD95L52zDm5+0L5m5FGGb2yKyXQ3ZUvYyPW7h6tVuq5rQsx+csfbWOOWnYPZpI5FNX12p0a0JEdwN4PIAPALjCzDcBJZgDWIv5zgNEdIOIbqS5R1xFxml7L94rzR0U29suej0Pt245xkYHmJqysLRUfDPSNGtrHayspHd0TWvGkXVQxoVmqzsWbRb/A43WtFyRKs43vxUUSMhFVf4vVZ5wVLVhzCvMZDm2L4LjKKVAlkQqZQpow7Sxe3tuLXNn0vP4IWDbOMfo8kz7+/nCl+m0KYujMaGbiGYAvAnAi5h5P+33mPlBZr7OzNfz3Hd8PH0s0SzHhf0+n4VmKytBQdYOVLS/EakjovBzBwVYE45QbZsyHaUFPxvnDxBsiyqroIojwKbMoJqY//wNb1yEjjqOwQ2Y9wGU04+TnnV1tdMKTX7cXJzXkTL9fcu9XvL9mhUo5+ftoScYdQjdSacKuoW0C8PMRgifw2SJvMUvIzJT1WOtEaGbiLpQAvfrmfnNg5dvE9G1wfvXAKxnve6w8Eu+4LG01MH09HAhpO6JLitRHTJc1qyddpjzqemeyXGsrnawutrB+LjS8Ps/TVLXMXIcZSwoUUfUdeE733U60eNXt6hDReYYf2HVaSMcPCVbXrbR6ZAxm5A8VF33fkKvqkMkAmrDGjZ9Y+YLUXCiKLMKiC77aAR9HnTo62Vt3NMoQ/I87q1bDra3L2uK04xD3WWfKqi7TzURvYQAvBrAI8z864G33grg/sHf9wN4S/57XH4tT2ix5LBPdGGRMWF3GYaIsLLSweJiOmEzKEg1+bgrK52zyc93Ds1Kp0OBRCOWto4gRW1Xs8wpacMhDsN11RF1OLxaGmZniz7ruRf+6mo2M6Omqcs+uMzrhYW0YWR9viYXfl2mctsmrK11K90seh7jzh0Xm5vO2abbcRinp4zNzeT8AZVrBgPTchHfKdVflTJg2PeTnqdoWwQDFkQRVHLlrVvdtfHA5WfL4keQ9pppqFtua0LKeBKA5wG4l4g+Ovh5JoCXAXg6EX0KwNMH/2eiiYkyOEDKcojzPB4cEUW/H/V6nDY6TSfsdim140cwIsXBgVeKw0Ov551lb0zThkQ0KPP5a1XbPGYRLqJIO7Cj2qvMZ6tjV593HE5NWYVPVYKPl9XMKOuxrP/58HcOD9ud0YRZZbvc3HRq908wnaLDb2rKyhxKNIn9fRcHBxfb0bJUwq4kLTdw8ZmqXoMdB2dZVn3b6rScnjIODrLZqzdB8LS1iaR3dd43SJF2YWZ4HucUunPfNhe1q4GY+f0A4qrmqVXdt6pOVHaDOQ5jY8PBxASd2Yhb1vDMZMvLNrpdKiwY5uH42Es010nCPwqbnEzr0FjdKIkTSo+PPXQ6eoR200ULVzZJ2fmq5tYtB7YNrK0lp0NmZty65WBqyiqUcKiIZj94FLy21jlLgFM1BwfeJSHNZ5SOpYH6n9c3SbBtwvGxl9mG1fOU4Dk7e+67E7W2TExYhTaP5ZqcXPzfX+cch3NpnZPmT1UvjMlJuqBIm5mxMgv6eWjrCU/ScwUVeFnL4c9JaU/sg9S9nup5nl4Ca2uds1jTTXBycq69zYI/qH2BG7isxQ53krGxy86P/mJe9QAuO01xWk33xf/LLUMcvoNslfcbNaFlGL4QnmVhjYsYkxY3o7KliKbXtss5SlbXokQTurIWl7Tzmg72t3kJ27Zmj1xUva13HgFjb8/F4eFFYT3u2dI+gsntPIzwvDM1ZWFx0RxztSDMnGqDdu4nUnWJLp+QFDHbyXJS7uObBrde010HRFToSD6LM0FUg/V6jF5PdYK02tsy8Dvt4qJ9tphXPSG2VesaRVnmQ0UJ1/nMjA3HcVOFegx2h7k5K3XSmioZHyd43uWx4pubKMeq+o6EmTnTuPHbY2rKqtXcIuvYu327HE24KWP+5MQDczVz8MQEGblBTpvJOG0bq5wOxcpUN0knpUkbrarbvYo1O+u8VMdGqswTXF/5l8Xctanx21pNd5NkOfJL00nSds7JSeUUmcepUCifYDulnVCiM4XGXxdQm62VlU7mhAx1RERIg21Hlz8qkkESfl3FReOJzxJaXJJMG9++rKQuc3M2xsao8Yg3ZVPW8+zsuNjddStJtDE5aWFh4WI4RKLmTaTSclHLeLl+jo68VHPW8nKnEgEtTcQS3U4fmqDf93B4WJ5Coo7oJd1u9HwfPDXP2rb+57MokZrqD4ZMEe3k6MjD3p6LlZVOKWH5lIPh5denpqzKFmZTtF5lwqw2S2mFXOb8wf7DjI9TZjvgsGBgItPTVqrjR8tSE3pY+Jmbsyrvq3nrNu9C1+kQlpfTT+GdjnJES/IRSWJYPfr2sEVYWLDheeUtigcHXuaMiWm1oVlpevwV0WrXyeSklehYt7vrpjrFGBaUIIqm2mh+3r5kM55k7ra1pT5Q1K/Kp9tVvmTBuTaPGdMwiKIDP9SZVHBsjGo/mfTRQ9VlMEUmLN8E5TzDFacKs5ZVKzc/bxuh/c5qA99UTNGTE850VH905KU2TUl6lqWlYhs03eJUp2Vuzk6tme926ZLANj1tY2YmfvEo4oTc1DF02nL4+FF//DCKeRzlVGSlbN/xtfFpkzRZFpXaT3UUKnUiqx+DaezsuJfm62F9wu97dc+VU1PW0M1hlanR/fC709PWJSWgCbJDEuGIP8vLnVxhpMvA/NqMoM7Fzi5xExi38DetHamLKM1G1sgQdQ6kJE92HRf7oGaojcetScS1SRVh/tJWb1X9JM7Exh8jebTdeezCiQhLS53GN3x+qMN83734v9+2pg0hE5OfFC2rb+6ZRlk1M2NhclIJnpOTF0+Im66zPGOPmbG35yZurHwfOH9j3jaannuCGC90lyn0+mSZmKanrdyCXngOODlJtwo2EWc07hmrzjqYRTCsMmJNlICS9Ox56ibqcUdROG6SuLX5+NgbehxZtJ2KNvPSkn1hkxrOnHd6yrWEPPPRrdvu7rpYX3eMTGIWRdPzwsyMmeLDsOb3hWxf8IwyrSiz2mdm8kWhSUuvxzg68ozLHeCbcO7uOpEafs/jwk6TRSNd5cXMURMgbWD+quanpJ1hmkUuTebLIE0cB66udrC2dlmg7fX4LJRe06iwaZqt9A0wN2eVkqo468QGnGtW2sjurou9PXdQL+ev5wkNGqTTUSc6CwvFNoxjY1biNepcYJo6vg3j9+NgGNYkwvWUpt7Gx/VPeV/mhqOsdOhFuXPHzbQGEUW359SUdclxu+qNzexstaafOvXHNMpM3xl9fJxweKjMMqM2DLdvO5k0/zopAIx3pCzDcadKXPeiU8LBgYtOJ1/a8iYZJsyenDCOjhzMztolZ08cvkjoflSaZ8Ir41nCTjXhay4s2Km08P6kdu1acrIYn6Mjr1aHmKwE49DmXZDCk32cMJclznFRgbsqfBvutOO6jFTOZXNywjg5OW8z5mxli7Jj94Wxi07K6UN2po10UxVlCGNNZUsMwsyDxCjAtWvF1tSqI8/4jsxCPL4jJ1HxLNBB0kQFqwuzJL8Ihh0b5MUffFnDlaXhzh3vzFlSd6Exiijthr8jvXOnfA2nTjv1tjA5aV1yKjQl1FlZDBN68va58Dg26dQl7kRua8tJlenS7z9+3QUzHpoMc1479rjXCWtrndROpWXjt4+fBXiUaHotWV6OPi0WLhPenHhefn8MQC8ZqzVCd5lCw9iYhdXVTmYnvupMWKq5bh50OTLWgeAk7rrK/iyomW96kg8zrB/lOaHo9bxKNlp1MEwgLCudunLK0mjwDgg/+8mJh40NJ9JMJq1mzhco/D6vk+NSmDTj0v9M2sg0WTTXtn05uo6QjyxzrO+P4Xn580n3JgAAHl1JREFUnIiLNpllFUvaB6iMosNMVqv2sSqCX+dZ65E5/eY/jjQmLXVhtAhlWSoM1dpaJ1O82jR0OuknRv9jIpCWT9SkquN6tbfn4vg4XardYVQhqBP5NsPFtGuOwxc2FdvbLg4O9LDnT0va06UyTGQsC42ajaTLqHvuXFnkmYmUMFlnGum8lDnGfM110bFVJlnstoskVqlr41DWbXwzMM8DNjerEeDKIs5P7OjIiwyk4Ld5UDC9efNUK1M/v4zBE8ArVzoXHEnj6raoWU5cgILg2K1LSWa0mGjbaqIf5kBXp6e6bWcLt2OieckwqnbwDNeTr0nUJbNiVuoy6fBthov4EbguY2PDKZQ2XketbxxlaIya1mamub3pMZr9mONZKEMQCdatSZrr8HKYdzzrYiaRtLwXdXJuiuVlO3NktihZR6eoJZOTar0Oxsy2LIpUVqaV2/xcJ3mJGrtVD2UzpZUcNDUppolewjz8c4bM5+j3udQFLejsNj9vXwpRZVlKmKzSdnZYWCy/bHH2ZmXu92ZnLSwtNadN848Gi0SqSZM9rmp0MPmpay7qdikxwsTGRnoV0rCF0H+muhUJeU4X+/3LpmBhdOgneWFWm8boUGvl3CPOTKLueguG2Y1qzybC65YBUbwi0RR5IEzceh2cD5OUAFGnrdvbyXOYTnXW/CpYIk1NlMMadH/fS9SaHRwom0rTtU7AcC3S6SnnPnmoIh57uvsmj9b1dSfy+CtqIxU8ho6qimAyhiAzMzbGx5sJLdXve6WcGMWNk7gkLlVCpGLwxp02VDFJ171xIiLMzdm5k10EhTbP41TOd+dCt76mB36G2DbMt1euXO7AzGpOyuMAqiNJ66tPv59/fTEJvz6CcoXnqec3kaj2DY7NoEyxseFcCkmb1qTTX1ujxkydtEro9tFpVwPEa7H9CcLUwZKF01PG5qaTygZYp/ZLI7CE53nP48GO/PJnk+Je63hMvbXlni1uZWnKfNbWOo1pwCcmrKH3dl3G1paD09N08crjNoZ+k05PW5VunOJISmLizz/hZ9zaOhfaDg89Lecpv26Dm9U6zLZ0GaYmRcgpk6gTnO1tt1ZziqbN5YL22xsbjrERaS6G3VS/g3NNeN7J4zdFRFhctHHlSrUn42mofQUgotcQ0ToRPRx47aVE9HdE9NHBzzPrLlcZxE3Epg6GvDBfNjPxF/SDA5XVz3WTQwA1vbCtrHSGCt3M0UeaGxtOpmN7E/DbM0ro9uvAcXiojWjUhqLMuO5piEvnHcZ1lUa031ebxSTHq/HxeNvA5eUOZmbqC6GX1eTCX8SOjy9WTvD0RleHWSLClSsXQ/AVrWcVicjcObuoM3cZVNnVLSt+g1ul4+B5bPbLfa5pRkDBf0Zexc8wk506aULF9DoAz4h4/eXMfM/g5+15LuxrreoW1oKT/LDjreCE4DhcitZQt4gpzIydHRebmxdtJoNtsren0jGXFZqtKtLUbdTiXLY2WHe2t10wM/b3zRFUkuaIvT33QvsXMUXodgmzs/Ut0GtrnQtHqE1vXqsm70IadULV78eHwbwo2Edf03dWbnJeDh6/+2Hy2oQOa55lNec8q3NYwDxkrca85nK6ULtxCzO/l4juruLac3OWtkkZjo68C5NhGZrQK1c6DWwwhr/veeeaFtc9P+rN0iZ543k2QZbU0mF0siktoinp9zmVT4IJ7RmkSNs2CREZV9dlkvfZd3dd9Ho8MF243PZprjs7a2FmxtJCowYoATwuupNO4eSiiMoaOzWlImDElb2N/d621WbON50pI6vllSudRJv/2Vmr9pPINIyPx7d/0eseHVUv1GuwZzzjx4joYwPzk8W4DxHRA0R0g4hubGxshN+7NNnpEpYvbPyfhWEJGnTbYAQ1K2k2FlFaJV0eKakcaSLThGnDMeDW1uXID2k2EDq0q++0mcZ5M0v76pLeOy1ty0Aa7Ft5x5hvO5qmn8bNu00eYc/NZVvOi8SqjqPMMb683LmkRJuft4dmii5yyqirBnVuzi5du5+mj87M2LX72zS5RkxMWLhypVNJJvIgugjdrwTw5QDuAXATwK/FfZCZH2Tm68x8fXV1ta7yDSUY4q7e++o3SYQnPWbG9rYTG08zyla07ggISZSpKQoKe+GJNE/M4Sbo982N/FD1xJrGBKFuosphamz7MglvHP16ihPcdGnPOOK0kr5j98bGaeVlmJ62MD1tJTrvpqHbJczMZDPL6vXyRzCpWtjKS1S/a1OUljwZKquijg2zFr2MmW8zs8vMHoDfAvCEpsukE7pP9sPwzU3u3DHXtjAYzD9IHtu64MQe1iI0oWlRpyXZJ/E85lG69OO0E2vRyV2X542i6g2tbmZvUYTnJP8aptpBx9XB7q6LoyOvFLOE5DKoMJVNKkzKNgtbWelkPkUom3B1tkjmHjm0ELqJ6Frg338M4OG4z+pM0Gs8KSlFFpqIYxxHnZNp2Kav7okm6VFN1fYGuXKlU1vcUp2F0CjaInQHx+zystIcRp3S+Ccvp6eM9fVkragJ81K3S7Gh3XxzEmbGyYlnvCAT1990DPVYJWU7GqokU/pEKgFE6DaZ2s+ziegNAJ4MYIWIvgDg5wA8mYjugfJe+RyAf1Z3uYrgD3Lm86PJ8XHC4WE519dl8QZUbFLXtWoPIVZnHYyPE3o9zrXBCE74lhV9VD0/bxey8S8TXUx4miIcJq8oWaqziar3T1qi4o775gl37rhGbyiDpzazs9ZA6Ixv56MjL1U6dN0FnbixXFa5l5ZsI8LfOo4KWbuzk161b9o0qHvkrywE+22adtAptX0emohe8v0RL7+67nKUSbCjTE0pgbRq26CrV5ux/yVS4c98odu2h2t880z43a7yTras6GtXPUEuLl7UamS5X/B5477nb1ymp63ICcSy9HXqKYKOAn6apDd50fF5y4RIOWTWYbaQhenpi5vaoO9E+PRsff1Um0gjVVK0Ky4u+hlx9Re6j48Zx8eadUoNWViwjY5HbypmeG4VoI7oJWpxVTeamVFOJGVqiqIcZHRZ0FdXOzg9ZWxtRT9wHvvI2VkLp6eMqSmrEc1KWXUb1wf8jQug7AU3N50L/XNtrf5hWbUWr0xzqzKZny9He6fJcIykqrIR6eX8u7Bgo9MhdLuEoyPvkgnNuf/C+Wuum94cQVdNt795L6udo0L1+UoAFT1E486eE93Hb5nrvb8JnZy0ROhuAH1mzJZwnrWqvBlax1iZPkQ0mISjB2/Wo6CrVzsgIoyPx39G5/rIy8VUuO17Pl2jZZTdl5QjWTPp3rMQ1cXSdrughkynvhp0TC7yfKYxO6vyUwwLl5fFrjtK6PbrrslxPD7e0gaskU5Hr41yHSws6GWPP1q1L2jPsEV8etoCEWIdo3QgqzasqXCTdaPr83U6SkhOY9ObliSnq7rDfaWteyJlbpOURlyX3Ad1o2uYtjSKHtOdKas2p9S9L8elvc/C+Dhhfr7ZyDJJVDHEdMudoLc6pgR0SBkrlINlqbitTU4aY2OEa9e6pV1Pl3V8VAUp4KKQnMaWvix7e90Wv16PE7PUmUhWR604dI3jnIRm3SwXKstqCx4kJ2X4HUxNXc4wubhot0rzTXQ5SZRu/aY9tR3D0lIHvR7XYpKg6xF6Ffj2k0I5aDYvjCxTU1ZiZJk2tFXQDyUrvsZXt8UsCc/LrzGcm7O0d26Oa44yNvaGNXUrCI6zqup/YqJdMsvkpJ6p64O0q8YjsG2qTRgOplbOo2G/dq1rzOQ2Oan/IlQWWYSLvAucLhOFKf1PF0yor7LLmOZURNd6yTM+r17tnJ2GLC/bWFrSy0a0CuqK3a8LOvbXNJGwBPNovdDdFET5zBDKsN0ylVF89k5HadGadvYIC1JTU1applmmLBp57XZHIexcGyiqKBgb09dJtsyTh/CldDvVKNvURxelR5DgnGzbyh5bN/tk3YhyAtYNPWePEcQXupaWzNQwLC7ahRwc5+dtLC+b+exBskZrIVLZznQT2qpyVl1ctM+yIupE3tMwf4LPskGp234+6j5FnAJNtv/XfUEW0hHcPBUNRzo5SY0rPaKYmVHP5SujomyyBfMwX8ppCX64K1MH1cSEBccZnvltGJOTo+0oowtVN8HEhFVpQpqmMK3rTk5aOD52Y7OmDsMEwdVvD2Z9o44I5VB07KlTPf0G8PS0fSkSUpGubII5aBntoPtwF0235uioFYwj2Nmzag5F4G4Wv+38Sb5sJ9k2NW9U3WRZLPzvNxvz2MK1a91c7ewfcUc5Yemy4PknD5Z1sUx5TiZMotul0p+tzGhNQv1YlorNbYJCr4wTVt3XmpZOPfUS1KqUjalhqjojcoaic8zwLMzOqn42Oal8EbIIkSZoUKomi6Bj26qOg8lcqsTf0PptXJSxMSWw69zuc3M25uftS2U8jwjRRKmqZ2Wlc8FUIu88XHaf0Zm2K3xmZy+PA13J2hZRCZMmJpT9u660f0TVwDChW9f010I+dNHklc3MjD2InpN9ch4bo1bFevVtKdvEtWtdzMzouxCVjWWpqFVEFKPpTt/PTRbKioSE073PtHGcCsUhqi9iXR70LZmBRAlkaY5v43ahpmqLZ2as0hYqf9Niakzw1VVDGxHZNhimaFLiODdHIMzO2pmP1A2Wy0aGiQlqvXkJcD4WFxf1FZiF4iwv2xgbuzzx6KzlrRM/g7VutHjqqQ//mDjvcXFcxzA1kkmZTE5amc0dqiSrdqXTIaytjW472jZya9DrZGbGSh2SSxzzktGpuf3m6nbpzGm0zUK3ZSnzpbxabp3abhhBu/1RxLYp0ryiLczNWReUVlevnv/tbyyHWRLMzdm4elU/f4TRlQZKpNOJj8mdZgILa3HHxgi2HR3JZHHRjEyQpkzcWQkvZFGZ/cbHCb0eBz5TR8maZ2WlA2bG1tZ5Rse1Nf0mvSjUkWR8Q83OWuj1GP3+xbbudgmnp+YK4dPTFvr9+Ayc4b5sIsFTDJ+sEVtMpc1zj28+NDlJ2N/P36Am1dHUlIWjo+HPaqJSYHW1A9cNr6NWrKxj28DKir+2nM9fJrTliO4R9WFsjDA3d7EZlpc7WFiI3g+NjZERQneQoLNh0AzBhAHiY9vRWu6oZzDd1CKKNKZO3S6lcvw1qd19iPKfZOnMxIQVqzAImmP4mGguNT2tkk9NTqq51rJg/EZCOLfdDZ6iRWm9o0wwTCXP2mLCfNvp0KWkU36519Y6WFy0tT8tTUvtqwgRvYaI1ono4cBrS0T0TiL61OD3Yt3lqgOiy/ZW4UmjjczOnj9z0MHBpMdeW+teeA6fqA2Qild+jknPGcewyb6NwmgSU1NWKzdXQaKyu5m24Qcw0IZaZ4morlzppjJJmJ0tNyurjljW+RG9PzdHzXOmsLLSwcyMMksYG4t3qGuryV9wUzw1pTabpmax9NdN26azE+Y2RNNp4gleB+AZodd+GsAfM/NjAfzx4P/WcfVq98IkYPKASKINguYw/OfrdmkkHZZmZ62zTWTbhc8o5udtrKx0zk4A2r5xHkVmZpSAbjJhBUAYImX7urhoY27OqjWUZRXYtnKE7nQIy8udVE6FJg/dsLljeFPsbzZNJKrYJsQaT6L20cXM7wWwHXr52QAeGvz9EID7ai1UhQzr73kGhInjx8QyZ+GynTdC/7ezAsKbyFFkfl4JLKO48TAZEzX2eUgyo5mbU0LpxIS5wtmoQnTuT9bptEMLPAro0kpXmPkmAAx+r8V9kIgeIKIbRHRjY2OjtgIK2QjP3xMT1JpEMqNCUDAJC9ejsD6vrXUu2LJHOSgRUaFYyLrjP/P8vG2kPXccwQQyPqPQp4NcudJpTd8dH08Xm9lv42Bbt6HdV1fPY6r705Tpz9XWTaBxI46ZH2Tm68x8fXV1teniCClZXIx3DjWZqHmhLZPF0pKNpSUVs9rUzKhFsO1ox9C2tG8Wul3zHLiHEWWrLacV5rK0lM6UZH7ehmWpedsefFyXcLRloU4tRtPXxgR0aZXbRHQNAAa/1xsuT2mEQ4yNCiMolwyl2yXjjv8s67JHeRralJ1SGA0sa/SSyYzaHG1ZShC9ckXlDVhe7mB5uX1t3ukQrl7tjuQm0gSbb12kgLcCuH/w9/0A3tJgWUqlLJvXURxAJpEkaCqverMn+JWVDpaWlKZo2LF0m/pq0KSkLce2aTFhActL+LRibIxap/EULhI2KYo7yRLMZHHRNsKEtXaVFBG9AcCTAawQ0RcA/ByAlwH4PSJ6AYC/AfC9dZerKuKOZLMu3EtLNhyHjTnaDiafMKTIheh0zkOsTU8nJzAwESVME65cSV6o2mT/CyghzcSkE0WYmbFwfKz68SiMYUEQmmdiIp8pmyn+CbWvjMz8/TFvPbXWgjTIykonc/xXyyKjgvzHlTWctnZ21koMa6Uj4RjGRHSWcnbUhLMo2mT/O4qMjZExG/wyGRujVpkEzs3Z2N2Nzjg6Ku3b6ag2bdtJRlubb3GxXQqbMGZsDVpGt0utProF4p1TbJsuaEFnZmwjHSyHTXijspgVxTQzlMlJC7ZdnslYG1lYsM9OfUxkedm8uWgYJilqqmJuzsLycvvCeo6ibsd3fg1H1jLJX6pdM4xgFKYuzMB52Udx4isKEWFlpXM2gZqCbRPW1sxOlhLH/LyNvb3LGtFul+C6nHqsTkxYxhzzjgIyP6n5pk2bj8VFGwcHntHrZ17GxiwsL19U2KSJWqMTInQLQg7Gxiw4zmhOfGXQNq2T6UxNWTg95Uu+CAsLNk5PufUnc6PE7KyVKyqRoAejvrE13flVhG6hctpobjE3Z2Fysl2xi4XLqPZl47TyZdE2LeGoEeU7ZFkkm15BaAgRuoXasW1la2iSHVYYEUZGg+lpC2NjoxdarIX75FhUDP127qp8AVudVgButE+lIAg1IUK3UBpTUxZ6PU50NPMTEwiC7sjmqv20PZnT8rINxwEOD10cH4uRtyA0SbtnG6FWbJtav4AJgtBu5udtI8OYxkFE6LbT/1cQjEMkJEEQBAEzM+1M6hTHzIwVafMsISEFQagKmV2EyjAlLWtViLOSYBLBk6pRMKmZnbUxPd1OW+4oZmdtjI8TJiba37aCoCui6RYqY5RDG62uZs86KghN0+0qwbsjK0PrsG3C0pI0rCA0iYxAQagACSUomIqc0AiCIFSD6OIEQRAEQRAEoWJE6BYEQRAEQRCEihGhWxAEQRAEQRAqRoRuQRAEQRAEQagYrRwpiehzAO4AcAE4zHy92RIJgiAIgiAIQnG0EroHPIWZN5suhCAIgiAIgiCUhZiXCIIgCIIgCELF6CZ0M4B3ENGHiOiBqA8Q0QNEdIOIbmxsbNRcPEEQBEEQBEHIjm5C95OY+esBfAeAHyWibw1/gJkfZObrzHx9dXW1/hIKgiAIgiAIQka0ErqZ+YuD3+sA/jOAJzRbIkEQBEEQBEEojjZCNxFNE9Gs/zeAbwPwcLOlEgRBEARBEITi6BS95AqA/0xEgCrX7zDzHzVbJEEQBEEQBEEojjZCNzN/BsDXNV2OKlhYsKH2EoIgCIIgCMIooo3Q3WYmJ7Wx4hEEQRAEQRAaQKRBQRAEQRAEQagYEboFQRAEQRAEoWJE6BYEQRAEQRCEihGhWxAEQRAEQRAqRoRuQRAEQRAEQagYEboFQRAEQRAEoWJE6BYEQRAEQRCEihGhWxAEQRAEQRAqhpi56TLkhog2AHy+6XIYxAqAzaYLIZSOtGs7kXZtJ9Ku7UTatZ2E2/XRzLya92JGC91CNojoBjNfb7ocQrlIu7YTadd2Iu3aTqRd20nZ7SrmJYIgCIIgCIJQMSJ0C4IgCIIgCELFiNA9WjzYdAGESpB2bSfSru1E2rWdSLu2k1LbVWy6BUEQBEEQBKFiRNMtCIIgCIIgCBUjQrcgCIIgCIIgVIwI3QZDRF9CRO8mokeI6BNE9MLB60tE9E4i+tTg9+Lg9a8goj8loh4R/WToWs8gor8iok8T0U838TyCoqx2jbuO0AxljtfB+zYRfYSI3lb3swjnlDwPLxDRG4noLwfX+8YmnkkovV1/fHCNh4noDUQ00cQzCbna9TlE9LHBz38joq8LXCuz3CQ23QZDRNcAXGPmDxPRLIAPAbgPwPMBbDPzywYdYZGZX0JEawAePfjMDjP/6uA6NoBPAng6gC8A+CCA72fm/177QwlltmvkdaRdm6Gsdg1c78UArgOYY+Zn1fkswjlltisRPQTgfcz8KiIaAzDFzLt1P5NQ6jz8KADvB/A4Zj4mot8D8HZmfl39TyXkaNdvAvAIM+8Q0XcAeCkz/4O8cpNoug2GmW8y84cHf98B8AiARwF4NoCHBh97CKpDgZnXmfmDAE5Dl3oCgE8z82eYuQ/gPw2uITRAWe065DpCA5Q4XkFEdwH4TgCvqqHowhDKalcimgPwrQBePfhcXwTu5ihzvALoAJgkog6AKQBfrLj4Qgw52vW/MfPO4PU/A3DX4O9ccpMI3S2BiO4G8HgAHwBwhZlvAqqDAVhL+PqjAPxt4P8vQIQzLSjYrnHXERqmhHZ9BYCfAuBVVEQhBwXb9csAbAB47cBs6FVENF1hcYWUFGlXZv47AL8K4G8A3ASwx8zvqLK8QjpytOsLAPzh4O9ccpMI3S2AiGYAvAnAi5h5P88lIl4Tu6OGKaFdS72OUA5F24OIngVgnZk/VHrhhNyUMM46AL4ewCuZ+fEADgGIf03DlDBeF6E0oI8B8D8AmCai55ZbSiErWduViJ4CJXS/xH8p4mOJcpMI3YZDRF2ojvN6Zn7z4OXbA7sl335pPeEyXwDwJYH/74IcfzVKSe0adx2hIUpq1ycB+C4i+hzUkea9RPTbFRVZSEGJ8/AXmNk/jXojlBAuNERJ7fo0AJ9l5g1mPgXwZgDfVFWZhWSytisRfS2UKd+zmXlr8HIuuUmEboMhIoKy/3uEmX898NZbAdw/+Pt+AG9JuNQHATyWiB4zcN75vsE1hAYoq12HXEdogLLalZl/hpnvYua7ocbqu5hZNGcNUWK73gLwt0T09wYvPRWAOD03RInr698AeCIRTQ2u+VQoO2KhAbK2KxF9KdRG6XnM/MnA53PJTRK9xGCI6JsBvA/Ax3Fu2/mzUPZJvwfgS6EG/Pcy8zYRXQVwA8Dc4PMHUB7V+0T0TCg7URvAa5j5F2p9GOGMstoVwNdGXYeZ317TowgByhyvgWs+GcBPSvSS5ih5Hr4HSqM2BuAzAH4g4MQl1EjJ7frzAP4XAA6AjwD4IWbu1fk8giJHu74KwHcD+Pzgsw4zXx9cK7PcJEK3IAiCIAiCIFSMmJcIgiAIgiAIQsWI0C0IgiAIgiAIFSNCtyAIgiAIgiBUjAjdgiAIgiAIglAxInQLgiAIgiAIQsWI0C0IgmAIRPRGItomoisR7z2ZiDwiemETZRMEQRCGIyEDBUEQDGEgbH8CwLuZ+XsDr08C+BhUFrVvYWYv5hKCIAhCQ4imWxAEwRCY+TaAFwH4HiK6L/DWS6HSEP9gXQI3EdlE1KnjXoIgCG1AhG5BEASDYObfBvA2AL9JRAtE9PUAXgzgpcz8V8HP0v/f3v2D6hTHcRx/f/wpGXQnA4NS98p2y8BCWU3KoCQLkwxSYkEZFQMLURhEIhZJJn8mMYkSEXElocif7k1fwzmPbrd7DZcz3Mf7Vb9O/c73nOec7dN5vud3ko1J7iX5luRTkgtJFk+o2ZLkVpL3Sb4keZBk04SaeUkqyf4k+5K8BEaBwW7vVpL6h+0lkjTDtMH5EXAFGKb5vPSqqvo5rmYncAQ4CVwFBoCDQAHDVfWtrTtA05byrD10LbAH2FpVZ9qaecB3YAR4AhwDfgD3qupDl/cqSf3C0C1JM1CSbTSBegxYUVUPx+0bAN4AZ6tq+7j5IeAxsKOqjk9yzlk0/4CeBoaqamU73wvdr4DBqhrt7MYkqU/ZXiJJM1BVnQLeAlfHB+7WamA+cC7JnN4AnrdjTa8wyfIkF5OM0DwxHwM2A8sm+dlrBm5Jmh5fgpGkmWu0HRMtbLd3pzjuBfx+In4T+AjsbudHaV/WnOS4t39zsZL0PzN0S1L/6fVZbwKeTrL/c7tdDSwG1lfV/d7OJHOnOK/9iJI0TYZuSeo/t2l6sJdW1fk/1M1vt2O9iSQLgXUdXpsk/ZcM3ZLUZ6rqY5K9wOEki4AbwBeap9prgetVdQm4A3wFTiQ5CCwA9gPvaNb9liT9I4ZuSepDVXW0XU97F7AFmE2zoskt4GFbM5JkA3AIuAy8pllmcAlNX7ck6R9xyUBJkiSpYy4ZKEmSJHXM0C1JkiR1zNAtSZIkdczQLUmSJHXM0C1JkiR1zNAtSZIkdczQLUmSJHXM0C1JkiR17BcTD9OS9ppJbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "# Add the argument 'color' to the plotting function\n",
    "plt.plot(T['DateTime'],T['Maximum temperature (Degree C)'],color='lavender')\n",
    "\n",
    "plt.title('Temperature in Canberra, Australia (${}^{\\circ}$C)',fontsize=18)\n",
    "plt.xlabel('Year',fontsize=16)\n",
    "plt.ylabel('${}^{\\circ}$C',fontsize=16)\n",
    "\n",
    "# Align the axes with the first and last values in the timeseries, eliminiating extra white space\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So, the plot above looks very nice if we are simply interested in looking at how the temperature in Canberra has fluctuated over the last 12 years or so. \n",
    "\n",
    "#### Now let's take a look at the trend in the data. Sometimes, we may want to detrend our data, meaning that we remove a linear fit to the data. This is important if doing, e.g., Fourier analysis. To detrend our data we can use the *scipy.signal* function 'detrend'. First, we have to import the signal library within the scipy package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import signal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's try to detrend the temperature timeseries... (you should get an error message!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array must not contain infs or NaNs",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-0e60bf3d2876>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mT_detrended\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Maximum temperature (Degree C)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/scipy/signal/signaltools.py\u001b[0m in \u001b[0;36mdetrend\u001b[0;34m(data, axis, type, bp, overwrite_data)\u001b[0m\n\u001b[1;32m   2580\u001b[0m             \u001b[0mA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNpts\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mNpts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2581\u001b[0m             \u001b[0msl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2582\u001b[0;31m             \u001b[0mcoef\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2583\u001b[0m             \u001b[0mnewdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2584\u001b[0m         \u001b[0;31m# Put data back in original shape.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, cond, overwrite_a, overwrite_b, check_finite, lapack_driver)\u001b[0m\n\u001b[1;32m   1151\u001b[0m     \"\"\"\n\u001b[1;32m   1152\u001b[0m     \u001b[0ma1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_validated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1153\u001b[0;31m     \u001b[0mb1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_validated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1154\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Input array a should be 2-D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/scipy/_lib/_util.py\u001b[0m in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m    237\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'masked arrays are not supported'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m     \u001b[0mtoarray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray_chkfinite\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck_finite\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 239\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobjects_ok\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'O'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda3/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    496\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtypecodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AllFloat'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m         raise ValueError(\n\u001b[0;32m--> 498\u001b[0;31m             \"array must not contain infs or NaNs\")\n\u001b[0m\u001b[1;32m    499\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs"
     ]
    }
   ],
   "source": [
    "T_detrended = signal.detrend(T['Maximum temperature (Degree C)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oh no!! We got an error message *(tip: the important bit is at the end of the printed message)*. But that's ok, it tells us what went wrong! You should get an error that says something like: *'array must not contain infs or NaNs'*. So it's complaining about our NaN values, as discussed earlier. \n",
    "\n",
    "#### We can use the \"isna()\" (named for \"is NaN?\") function to test what values in a column are NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "        ...  \n",
       "4399    False\n",
       "4400    False\n",
       "4401    False\n",
       "4402    False\n",
       "4403    False\n",
       "Name: Maximum temperature (Degree C), Length: 4404, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(T['Maximum temperature (Degree C)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### That printed out the row number and either True (is a NaN) or False (is a regular number). We can use the isna() function combined with the detrend function to apply a detrending only to the actual numbers, avoiding the NaNs. To do this we will use a logical \"not\", depicted as \"~\" in python. That is, we want to apply detrend to all of the values that are not NaN.\n",
    "\n",
    "#### In order to not overwrite the data, let's copy (using numpy's `copy()` function) the temperature column into a new column of the dataframe, and then detrend this new column. Let's call the column 'T_detrend'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "T['T_detrend'] = np.copy(T['Maximum temperature (Degree C)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Product code</th>\n",
       "      <th>Bureau of Meteorology station number</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Maximum temperature (Degree C)</th>\n",
       "      <th>Days of accumulation of maximum temperature</th>\n",
       "      <th>Quality</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>T_detrend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IDCJAC0010</td>\n",
       "      <td>70351</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008-01-05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product code  Bureau of Meteorology station number  Year  Month  Day  \\\n",
       "0   IDCJAC0010                                 70351  2008      1    1   \n",
       "1   IDCJAC0010                                 70351  2008      1    2   \n",
       "2   IDCJAC0010                                 70351  2008      1    3   \n",
       "3   IDCJAC0010                                 70351  2008      1    4   \n",
       "4   IDCJAC0010                                 70351  2008      1    5   \n",
       "\n",
       "   Maximum temperature (Degree C)  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "\n",
       "   Days of accumulation of maximum temperature Quality   DateTime  T_detrend  \n",
       "0                                          NaN     NaN 2008-01-01        NaN  \n",
       "1                                          NaN     NaN 2008-01-02        NaN  \n",
       "2                                          NaN     NaN 2008-01-03        NaN  \n",
       "3                                          NaN     NaN 2008-01-04        NaN  \n",
       "4                                          NaN     NaN 2008-01-05        NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we apply the detrend function only to the values that are not (~) NaNs (pd.isna())."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "T['T_detrend'][~(pd.isna(T['T_detrend']))] = signal.detrend(T['T_detrend'][~(pd.isna(T['T_detrend']))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (You may have gotten a warning after running the cell above, but you can just ignore that for now!)\n",
    "\n",
    "#### Ok, let's plot both the original temperature timeseries and the detrended timeseries as subplots in the same figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "# Make subplots with 2 rows and 1 column, with the third number indicating the 1st plot\n",
    "plt.subplot(211) \n",
    "plt.plot(T['DateTime'],T['Maximum temperature (Degree C)'])\n",
    "plt.title('Original timeseries',fontsize=18)\n",
    "\n",
    "# Align the axes with the first and last values in the timeseries, eliminiating extra white space\n",
    "plt.margins(x=0)\n",
    "\n",
    "# Make subplots with 2 rows and 1 column, with the third number indicating the 2nd plot\n",
    "plt.subplot(212)\n",
    "plt.plot(T['DateTime'],T['T_detrend'])\n",
    "plt.title('Detrended timeseries',fontsize=18)\n",
    "\n",
    "# Align the axes with the first and last values in the timeseries, eliminiating extra white space\n",
    "plt.margins(x=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating subplots is really handy, especially if you have lots of figures you'd like to show together. When publishing papers in research journals, they usually want you to have one file per figure, so that's a great time to use subplots!\n",
    "\n",
    "#### The two plots look almost identical, but notice the different y-axis scales! \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !Challenge #2! It's your turn to do some coding and test your knowledge! --------------------------------\n",
    "Plot the two temperature datasets (original and detrended) on the same axes. That is, plot them together on the same figure, but as one plot, not using subplot this time! Remember to add a new cell (click the \"+\" button!) first!\n",
    "\n",
    "Tip: just use the `plt.plot()` function twice within the same figure!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### While sometimes you may want to remove a trend from a timeseries, other times you may be interested in the trend! Let's look at just the trend itself to see how temperature has evolved in Canberra over the past 12 years. We do this by subtracting the detrended data from the original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "\n",
    "plt.plot(T['DateTime'],T['Maximum temperature (Degree C)']-T['T_detrend'])\n",
    "plt.title('Temperature trend',fontsize=18)\n",
    "plt.margins(x=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The line increases in time - this implies that the average temperature in Canberra has been increasing over the last 12 years! Not too surprising given the predicted climate change impacts on Australia..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !Challenge #3! It's your turn to do some coding and test your knowledge! --------------------------------\n",
    "Plot the original temperature dataset and the trend line on the same axes. This should be very similar to the last challenge! But this time, I want you add a legend by typing `plt.legend()` on the last line of your cell. You will also need to type the following at the end of the plot() function (but still within the parentheses!): `,label='data'` for the temperature data and `,label='trend'` for the trend line.\n",
    "\n",
    "Remember to add a new cell (click the \"+\" button!) first!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !Challenge #4! It's your turn to do some coding and test your knowledge! --------------------------------\n",
    "Now recreate the plot you just made but convert from degrees Celsius to Kelvin. The conversion is T(K) = T(C) + 273.15. Below this cell, add a new markdown cell and write out this equation using latex. Then add a new cell below to create the plot that is in Kelvin. Make sure you change the necessary labels on the plot!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fancy plotting example: plotting two different datasets (with different units) on the same plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In order to plot two datasets, we need to load a second dataset. Let's load a dataset of air quality measurements in Canberra. We call the data 'aqi', short for air quality index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Name</th>\n",
       "      <th>GPS</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3_1hr</th>\n",
       "      <th>O3_4hr</th>\n",
       "      <th>CO</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>AQI_CO</th>\n",
       "      <th>AQI_NO2</th>\n",
       "      <th>AQI_O3_1hr</th>\n",
       "      <th>AQI_O3_4hr</th>\n",
       "      <th>AQI_PM10</th>\n",
       "      <th>AQI_PM2.5</th>\n",
       "      <th>AQI_Site</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>21/01/2020 04:00:00 PM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.61</td>\n",
       "      <td>4.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>21/01/2020 03:00:00 PM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>4.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>21/01/2020 02:00:00 PM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>21/01/2020 01:00:00 PM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.91</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>21/01/2020 12:00:00 PM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.65</td>\n",
       "      <td>4.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name                       GPS                DateTime  NO2  O3_1hr  \\\n",
       "0  Civic  (-35.285307, 149.131579)  21/01/2020 04:00:00 PM  NaN     NaN   \n",
       "1  Civic  (-35.285307, 149.131579)  21/01/2020 03:00:00 PM  NaN   0.033   \n",
       "2  Civic  (-35.285307, 149.131579)  21/01/2020 02:00:00 PM  NaN   0.034   \n",
       "3  Civic  (-35.285307, 149.131579)  21/01/2020 01:00:00 PM  NaN   0.032   \n",
       "4  Civic  (-35.285307, 149.131579)  21/01/2020 12:00:00 PM  NaN   0.032   \n",
       "\n",
       "   O3_4hr   CO   PM10  PM2.5  AQI_CO  AQI_NO2  AQI_O3_1hr  AQI_O3_4hr  \\\n",
       "0     NaN  NaN  14.61   4.36     NaN      NaN         NaN         NaN   \n",
       "1   0.033  0.0  14.33   4.23     0.0      0.0        33.0        41.0   \n",
       "2   0.032  0.0  14.00   4.18     0.0      0.0        34.0        40.0   \n",
       "3   0.031  0.0  13.91   4.36     0.0      0.0        32.0        39.0   \n",
       "4   0.029  0.0  16.65   4.60     0.0      0.0        32.0        37.0   \n",
       "\n",
       "   AQI_PM10  AQI_PM2.5  AQI_Site             Date      Time  \n",
       "0      29.0       17.0      29.0  21 January 2020  16:00:00  \n",
       "1      28.0       16.0      41.0  21 January 2020  15:00:00  \n",
       "2      28.0       16.0      40.0  21 January 2020  14:00:00  \n",
       "3      27.0       17.0      39.0  21 January 2020  13:00:00  \n",
       "4      33.0       18.0      37.0  21 January 2020  12:00:00  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use pandas library to load csv file\n",
    "aqi = pd.read_csv('Civic_Air_Quality_Station.csv')\n",
    "aqi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are many columns of data here, and you may need to scroll to the right to see all of the columns (depending on your screen size). The number we will plot here is called *PM2.5*. This refers to the concentration of particulate matter (PM) in the air that has a diameter of less than 2.5 microns. \n",
    "\n",
    "The reason why I decided to use this data in the lab was to show you just how bad the air quality was in Australia during the horrendous bushfire season in the last year. For those of us living for days on end in thick smoke, it was the PM2.5 particles that we were most concerned about breathing in due to their negative health effects. But PM2.5 particles can also come from human-caused pollution sources, not just from forrest fires.\n",
    "\n",
    "#### Anyway, back to this exercise. It looks like there is already a single column with datetime information, but let's double check that it is being correctly identified as datetime by pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name           object\n",
       "GPS            object\n",
       "DateTime       object\n",
       "NO2           float64\n",
       "O3_1hr        float64\n",
       "O3_4hr        float64\n",
       "CO            float64\n",
       "PM10          float64\n",
       "PM2.5         float64\n",
       "AQI_CO        float64\n",
       "AQI_NO2       float64\n",
       "AQI_O3_1hr    float64\n",
       "AQI_O3_4hr    float64\n",
       "AQI_PM10      float64\n",
       "AQI_PM2.5     float64\n",
       "AQI_Site      float64\n",
       "Date           object\n",
       "Time           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqi.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nope, the 'DateTime' column appears to be an object. Let's fix this by telling pandas to interpret as a datetime format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqi['DateTime'] = pd.to_datetime(aqi['DateTime'],yearfirst=True,dayfirst='True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's take another look at the dataset. This time let's look at the last 5 rows using the \"tail\" function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Name</th>\n",
       "      <th>GPS</th>\n",
       "      <th>DateTime</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3_1hr</th>\n",
       "      <th>O3_4hr</th>\n",
       "      <th>CO</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>AQI_CO</th>\n",
       "      <th>AQI_NO2</th>\n",
       "      <th>AQI_O3_1hr</th>\n",
       "      <th>AQI_O3_4hr</th>\n",
       "      <th>AQI_PM10</th>\n",
       "      <th>AQI_PM2.5</th>\n",
       "      <th>AQI_Site</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>2020-01-21 16:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.61</td>\n",
       "      <td>4.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>16:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>2020-01-21 15:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.33</td>\n",
       "      <td>4.23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>15:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>2020-01-21 14:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>14:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>2020-01-21 13:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.91</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>13:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Civic</td>\n",
       "      <td>(-35.285307, 149.131579)</td>\n",
       "      <td>2020-01-21 12:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.65</td>\n",
       "      <td>4.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>21 January 2020</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name                       GPS            DateTime  NO2  O3_1hr  O3_4hr  \\\n",
       "0  Civic  (-35.285307, 149.131579) 2020-01-21 16:00:00  NaN     NaN     NaN   \n",
       "1  Civic  (-35.285307, 149.131579) 2020-01-21 15:00:00  NaN   0.033   0.033   \n",
       "2  Civic  (-35.285307, 149.131579) 2020-01-21 14:00:00  NaN   0.034   0.032   \n",
       "3  Civic  (-35.285307, 149.131579) 2020-01-21 13:00:00  NaN   0.032   0.031   \n",
       "4  Civic  (-35.285307, 149.131579) 2020-01-21 12:00:00  NaN   0.032   0.029   \n",
       "\n",
       "    CO   PM10  PM2.5  AQI_CO  AQI_NO2  AQI_O3_1hr  AQI_O3_4hr  AQI_PM10  \\\n",
       "0  NaN  14.61   4.36     NaN      NaN         NaN         NaN      29.0   \n",
       "1  0.0  14.33   4.23     0.0      0.0        33.0        41.0      28.0   \n",
       "2  0.0  14.00   4.18     0.0      0.0        34.0        40.0      28.0   \n",
       "3  0.0  13.91   4.36     0.0      0.0        32.0        39.0      27.0   \n",
       "4  0.0  16.65   4.60     0.0      0.0        32.0        37.0      33.0   \n",
       "\n",
       "   AQI_PM2.5  AQI_Site             Date      Time  \n",
       "0       17.0      29.0  21 January 2020  16:00:00  \n",
       "1       16.0      41.0  21 January 2020  15:00:00  \n",
       "2       16.0      40.0  21 January 2020  14:00:00  \n",
       "3       17.0      39.0  21 January 2020  13:00:00  \n",
       "4       18.0      37.0  21 January 2020  12:00:00  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dates here should look strange - they aren't in order! We can force them into ascending time order by running a sort function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "aqi = aqi.sort_values(by=['DateTime'],ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you rerun the 'aqi.tail()' cell now, you should see that the dates appear to be in order, with the most recent data at the end.\n",
    "\n",
    "#### So, we have our data loaded with the two columns we are interested in. Let's plot the PM2.5 numbers on the same plot as the temperature data! This is a bit trickier than just adding two lines to the same plot, since we need to account for different units (Celsius vs a concentration of particles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(735599.0, 737445.6666666666)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(16,6))\n",
    "ax.plot(T['DateTime'],T['Maximum temperature (Degree C)'],label='Max. daily temperature',\n",
    "       alpha=0.5)\n",
    "ax.set_ylabel('Temperature (${}^\\circ C$)',fontsize=14)\n",
    "ax.tick_params(axis='y',labelsize=12)\n",
    "\n",
    "#plt.xlim(aqi['DateTime'][17809],aqi['DateTime'][0])\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(aqi['DateTime'],aqi['PM2.5'],label='Concentration of PM2.5 particles')\n",
    "ax2.set_ylabel('PM2.5 concentration ($\\mu g/m^3$)',fontsize=14)\n",
    "ax2.tick_params(axis='y', size=12,labelsize=12)\n",
    "ax2.set_ylim(0,700)\n",
    "plt.fill_between(aqi.index,0,aqi['PM2.5'],alpha=0.3)\n",
    "ax2.yaxis.set_label_coords(1.075,0.45)\n",
    "plt.xlim(aqi['DateTime'][17809],aqi['DateTime'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We have both timeseries on the same plot - hooray! Now you can see that the PM2.5 concentrations were very small for years before 2020. Clearly, the air quality does not show a yearly cycle like the temperature does! Also very clear is that the air quality was nearly two orders of magnitude worse than usual at the start of 2020. For reference, the Australian health authorities consider a PM2.5 concentration greater than 200 to be hazardous to health.\n",
    "\n",
    "#### Let's try to make the plot look a bit nicer by specifying different colors for the different data. Since we will use the color argument a couple of times, let's define the colors as their own variables. Let's also add a horizontal line at the 200 concentration level to visually highlight the hazardous concentration number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define color names\n",
    "Tcolor = 'red'\n",
    "AQIcolor = 'k'\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(16,6))\n",
    "ax.plot(T['DateTime'],T['Maximum temperature (Degree C)'],color=Tcolor,label='Max. daily temperature')\n",
    "ax.set_ylabel('Temperature (${}^\\circ C$)',color=Tcolor,fontsize=14)\n",
    "ax.tick_params(axis='y', colors=Tcolor,labelsize=12)\n",
    "\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(aqi['DateTime'],aqi['PM2.5'],color=AQIcolor,label='Concentration of PM2.5 particles')\n",
    "ax2.set_ylabel('PM2.5 concentration ($\\mu g/m^3$)',color=AQIcolor,fontsize=14)\n",
    "ax2.tick_params(axis='y', colors=AQIcolor,size=12,labelsize=12)\n",
    "ax2.set_ylim(0,700)\n",
    "plt.axhline(200,color='grey',linestyle='dashed')\n",
    "ax2.yaxis.set_label_coords(1.075,0.45)\n",
    "plt.xlim(aqi['DateTime'][17809],aqi['DateTime'][0])\n",
    "\n",
    "plt.title('Temperature and PM2.5 in Canberra, Australia',fontsize=18)\n",
    "\n",
    "plt.savefig('Canberra_temperature_fig.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can see that the left y-axis labels are now in red, the same color as the temperature data! And what's nice about having defined our plotting colors at the top of the cell is that you can easily change the colors of either of the datasets! Play around with different colors until you find a combination you like! (Color names listed here: https://matplotlib.org/3.1.0/gallery/color/named_colors.html, or you can define your own color using RGB html color codes - click on \"Specifying Colors\" at the above website!)\n",
    "\n",
    "#### Just for fun, here are another couple of other nice functions for visualizing data. You can use the \"alpha\" argument to change the transparency of plotted data. You can also fill in the area beneath a curve with a solid color. I've also zoomed in below to emphasize the grey shading beneath the PM2.5 data curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define color names\n",
    "Tcolor = 'red'\n",
    "AQIcolor = 'k'\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(16,6))\n",
    "ax.plot(T['DateTime'],T['Maximum temperature (Degree C)'],color=Tcolor,label='Max. daily temperature',\n",
    "       alpha=0.5) # make the temperature data semi-transparent\n",
    "ax.set_ylabel('Temperature (${}^\\circ C$)',color=Tcolor,fontsize=14)\n",
    "ax.tick_params(axis='y', colors=Tcolor,labelsize=12)\n",
    "\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(aqi['DateTime'],aqi['PM2.5'],color=AQIcolor,label='Concentration of PM2.5 particles')\n",
    "ax2.set_ylabel('PM2.5 concentration ($\\mu g/m^3$)',color=AQIcolor,fontsize=14)\n",
    "ax2.tick_params(axis='y', colors=AQIcolor,size=12,labelsize=12)\n",
    "ax2.set_ylim(0,700)\n",
    "plt.axhline(200,color='grey',linestyle='dashed')\n",
    "plt.fill_between(aqi['DateTime'],0,aqi['PM2.5'],color=AQIcolor,alpha=0.3) # fill the area under the curve with a lighter shade of AQIcolor (grey in this case)\n",
    "ax2.yaxis.set_label_coords(1.075,0.45)\n",
    "plt.xlim(aqi['DateTime'][17809],aqi['DateTime'][0]) \n",
    "\n",
    "plt.title('Temperature and PM2.5 in Canberra, Australia',fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### !Challenge #5! It's your turn to do some coding and test your knowledge! --------------------------------\n",
    "Copy and paste the previous cell and make some changes to the plot! Change the colors, the line thicknesses (use the `linewidth=` argument within the plot function), the fontsizes, the x-axis limits, etc. Play around! If you like your final plot, share it on Slack in `#python-help`! I'd love to see your visualizations! :)\n",
    "\n",
    "Remember to add a new cell or copy and past the above cell to get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional section: making a bar chart!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this exercise, we will load in a new dataset of rainfall in Canberra. Then we will organize the rainfall by monthly average and make a bar chart to display the data!\n",
    "\n",
    "#### Load the rainfall data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain = pd.read_csv('IDCJAC0009_070307_1800_Data.csv')\n",
    "rain.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Again, we need to convert some columns to 'DateTime' format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain['DateTime'] = pd.to_datetime(rain[['Year','Month','Day']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's just plot the dataset as is to see what it looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot daily rainfall\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(rain['DateTime'],rain['Rainfall amount (millimetres)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It's a bit hard to interpret what's going on here, with daily rainfall amounts over 40 years! \n",
    "\n",
    "#### We can bin the rainfall into different time intervals. For instance, if we want to bin the rainfall into 10-day intervals, we can `resample()` using the string '10D' to mean 10 days. Then we use the function `sum()` to sum over each day in the 10-day interval. In the plot, you can see that the data is starting to look less messy now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate and plot 10-daily rainfall\n",
    "rain = rain.set_index(rain['DateTime']) # first we need to set the datetime as the index of our dataframe\n",
    "rain_10day = rain.resample('10D').sum()\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(rain_10day.index,rain_10day['Rainfall amount (millimetres)'])\n",
    "plt.xlim(pd.Timestamp('2015-01-01'),pd.Timestamp('2020-01-01'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In many ways, it is more intuitive to plot daily rainfall accumulation as a bar chart instead of as a timeseries. So let's do that! And to make it slightly more interesting, we adjust the width of each bar in the plot according to the number of days that has been summed over. (Although it is summed over 10-day intervals, there are some days with no rain.) This time, let's sum over monthly rainfall accumulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rain['DateTime'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate monthly accumulation rainfall\n",
    "rain_m = rain.resample('M').sum()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "# Use the 'bar()' function and add the 'width' argument to specify the width of the bars\n",
    "plt.bar(rain_m.index,rain_m['Rainfall amount (millimetres)'],width=rain_m['Period over which rainfall was measured (days)'])\n",
    "plt.xlim(pd.Timestamp('2015-01-01'),pd.Timestamp('2020-01-01')) # set the x-axis limits\n",
    "plt.ylim(0,200) # set the y-axis limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's say we want to plot the average rainfall accumulation for each month across the 40-year timeseries. We will need to sum over all days in each month and then average each month across all of the years. To do that, we can use a 'for loop'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make loop to define pd series of monthly average rainfall\n",
    "\n",
    "# Initialize an array with length 12\n",
    "month_rain = np.arange(12)\n",
    "\n",
    "# Loop through 12 times to define the monthly average rainfall accumulation\n",
    "for i in np.arange(12):\n",
    "    month_rain[i] = rain_m[rain_m.index.month == i+1].mean()['Rainfall amount (millimetres)']\n",
    "    \n",
    "month_rain # print out the month_rain just to verify that it has worked!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we will go ahead and create a new pandas dataframe that we will call 'rain_bar' with this new data and with the list of each of the months for our 'DateTime' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new pandas dataframe with average monthly rainfall totals\n",
    "\n",
    "rain_bar = pd.DataFrame({'DateTime':rain['DateTime'].dt.month_name().unique(),\n",
    "                         'Monthly Rainfall': month_rain})\n",
    "rain_bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It looks good so far, so let's go ahead and plot it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.bar(rain_bar['DateTime'],rain_bar['Monthly Rainfall'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We just made a bar plot! Let's fancy it up a bit. This is the kind of thing a lot of data science programmers might do! I won't go through each step here, but see if you can figure out what the code is doing in the following cell! (Ask on Slack or search online if you want to learn more about this!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "bars = plt.bar(rain_bar['DateTime'],rain_bar['Monthly Rainfall'])\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "#plt.tick_params(top='off', bottom='off', right='off') #, labelbottom='on')\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False) #,         # ticks along the top edge are off\n",
    "plt.tick_params(\n",
    "    axis='y',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    left=False,      # ticks along the bottom edge are off\n",
    "    labelleft=False)\n",
    "\n",
    "# Label rainfall amounts on each bar\n",
    "for bar in bars:\n",
    "    plt.gca().text(bar.get_x() + bar.get_width()/2, bar.get_height() - 5, str(int(bar.get_height())) + ' mm', \n",
    "                 ha='center', color='w', fontsize=11) #ha is horizontal alignment\n",
    "    \n",
    "bars[4].set_color('salmon') # color the minimum rainfall month to highlight it\n",
    "    \n",
    "plt.title('Average monthly rainfall totals in Canberra, Australia',fontsize=24)\n",
    "plt.xticks(fontsize=14,rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Doing some statistics on your data is always a must-do! So let's add some confidence intervals to these bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Redo loop to define pd series of monthly average rainfall, this time also defining the confidence intervals for each month\n",
    "month_rain = np.arange(12)\n",
    "err = np.arange(12) # defined as: mean +/- 1.96*std/sqrt(N)\n",
    "for i in np.arange(12):\n",
    "    month_rain[i] = rain_m[rain_m.index.month == i+1].mean()['Rainfall amount (millimetres)']\n",
    "    err[i] = 1.96*rain_m[rain_m.index.month == i+1]['Rainfall amount (millimetres)'].std()/np.sqrt(len(rain_m))\n",
    "month_rain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "bars = plt.bar(rain_bar['DateTime'],rain_bar['Monthly Rainfall'],yerr=err,capsize=5)\n",
    "for spine in plt.gca().spines.values():\n",
    "    spine.set_visible(False)\n",
    "plt.tick_params(\n",
    "     axis='x',          # changes apply to the x-axis\n",
    "     which='both',      # both major and minor ticks are affected\n",
    "     bottom=False,      # ticks along the bottom edge are off\n",
    "     top=False) #,         # ticks along the top edge are off\n",
    "    \n",
    "plt.title('Average monthly rainfall totals in Canberra, Australia',fontsize=24)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### That's it! You made it to the end! So now you've had several examples of manipulating data and plotting. Test your skills by playing around with any of the cells here and by doing the other python labs available!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}