02B-Pandas.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "# Essential Tools: Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pandas is the Python Data Analysis Library. \n",
    "\n",
    "Pandas is an extremely versatile tool for manipulating datasets.   \n",
    "\n",
    "It also produces high quality plots with matplotlib, and integrates nicely with other libraries that expect NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use of Pandas is a data science __best practice.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The most important tool provided by Pandas is the **data frame.**\n",
    "\n",
    "A data frame is a table in which each row and column is given a label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pandas DataFrames are documented at:\n",
    "\n",
    "http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html\n",
    "\n",
    "Get in the habit: whenever you load data, place it into a dataframe as your first step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "#pd.__version__\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fetching, storing and retrieving your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "For demonstration purposes, we'll use a utility library that fetches data from standard online sources, such as Yahoo! Finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close   Volume\n",
       "Date                                                                      \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500\n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000\n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800\n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200\n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "yahoo_stocks = pd.DataFrame(yf.download('YELP',start='2015-01-01',end='2015-12-31', progress = False))\n",
    "\n",
    "yahoo_stocks.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This is a typical example of a dataframe.  \n",
    "\n",
    "Notice how each row has a label and each column has a label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A dataframe is an object that has __many__ methods associated with it, to do all sorts of useful things.\n",
    "\n",
    "Here is a simple method: ``.info()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 251 entries, 2015-01-02 to 2015-12-30\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       251 non-null    float64\n",
      " 1   High       251 non-null    float64\n",
      " 2   Low        251 non-null    float64\n",
      " 3   Close      251 non-null    float64\n",
      " 4   Adj Close  251 non-null    float64\n",
      " 5   Volume     251 non-null    int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 13.7 KB\n"
     ]
    }
   ],
   "source": [
    "yahoo_stocks.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Reading to/from a ``.csv`` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuing to explore methods, let's write the dataframe out to a ``.csv`` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "yahoo_stocks.to_csv('yahoo_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,Open,High,Low,Close,Adj Close,Volume\r\n",
      "2015-01-02,55.459999084472656,55.599998474121094,54.2400016784668,55.150001525878906,55.150001525878906,1664500\r\n",
      "2015-01-05,54.540000915527344,54.95000076293945,52.33000183105469,52.529998779296875,52.529998779296875,2023000\r\n",
      "2015-01-06,52.54999923706055,53.93000030517578,50.75,52.439998626708984,52.439998626708984,3762800\r\n",
      "2015-01-07,53.31999969482422,53.75,51.7599983215332,52.209999084472656,52.209999084472656,1548200\r\n",
      "2015-01-08,52.59000015258789,54.13999938964844,51.7599983215332,53.83000183105469,53.83000183105469,2015300\r\n",
      "2015-01-09,55.959999084472656,56.9900016784668,54.720001220703125,56.06999969482422,56.06999969482422,6224200\r\n",
      "2015-01-12,56.0,56.060001373291016,53.43000030517578,54.02000045776367,54.02000045776367,2407700\r\n",
      "2015-01-13,54.470001220703125,54.79999923706055,52.52000045776367,53.18000030517578,53.18000030517578,1958400\r\n",
      "2015-01-14,52.79999923706055,53.68000030517578,51.459999084472656,52.20000076293945,52.20000076293945,1854600\r\n"
     ]
    }
   ],
   "source": [
    "!head yahoo_data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "And of course we can likewise read a ``.csv`` file into a dataframe.   This is probably the most common way you will get data into Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "      <th>0</th>\n",
       "      <td>2015-01-02</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date       Open       High        Low      Close  Adj Close   Volume\n",
       "0  2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500\n",
       "1  2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000\n",
       "2  2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800\n",
       "3  2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200\n",
       "4  2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('yahoo_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Working with data columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In general, we'll usually organize things so that rows in the dataframe are __items__ and columns are __features__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pandas allows you to use standard python __indexing__ to refer to columns (eg features) in your dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      55.459999\n",
       "1      54.540001\n",
       "2      52.549999\n",
       "3      53.320000\n",
       "4      52.590000\n",
       "         ...    \n",
       "246    27.950001\n",
       "247    28.270000\n",
       "248    28.120001\n",
       "249    27.950001\n",
       "250    28.580000\n",
       "Name: Open, Length: 251, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pandas also allows you to use a syntax like an object attribute to refer to a column.\n",
    "\n",
    "But note that the column name cannot include a space in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      55.459999\n",
       "1      54.540001\n",
       "2      52.549999\n",
       "3      53.320000\n",
       "4      52.590000\n",
       "         ...    \n",
       "246    27.950001\n",
       "247    28.270000\n",
       "248    28.120001\n",
       "249    27.950001\n",
       "250    28.580000\n",
       "Name: Open, Length: 251, dtype: float64"
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     },
     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "df.Open"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "You can select a list of columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
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       "      <th>0</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
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       "      <th>1</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
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      "text/plain": [
       "        Open      Close\n",
       "0  55.459999  55.150002\n",
       "1  54.540001  52.529999\n",
       "2  52.549999  52.439999\n",
       "3  53.320000  52.209999\n",
       "4  52.590000  53.830002"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Open','Close']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Putting things together -- make sure this syntax is clear to you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-01-02\n",
       "1    2015-01-05\n",
       "2    2015-01-06\n",
       "3    2015-01-07\n",
       "4    2015-01-08\n",
       "5    2015-01-09\n",
       "6    2015-01-12\n",
       "7    2015-01-13\n",
       "8    2015-01-14\n",
       "9    2015-01-15\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "241    2015-12-16\n",
       "242    2015-12-17\n",
       "243    2015-12-18\n",
       "244    2015-12-21\n",
       "245    2015-12-22\n",
       "246    2015-12-23\n",
       "247    2015-12-24\n",
       "248    2015-12-28\n",
       "249    2015-12-29\n",
       "250    2015-12-30\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Changing column names is as simple as assigning to the ``.columns`` property.\n",
    "\n",
    "Let's adjust column names so that none of them include spaces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 251 entries, 0 to 250\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   date       251 non-null    object \n",
      " 1   open       251 non-null    float64\n",
      " 2   high       251 non-null    float64\n",
      " 3   low        251 non-null    float64\n",
      " 4   close      251 non-null    float64\n",
      " 5   adj_close  251 non-null    float64\n",
      " 6   volume     251 non-null    int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 13.9+ KB\n"
     ]
    }
   ],
   "source": [
    "new_column_names = [x.lower().replace(' ','_') for x in df.columns]\n",
    "df.columns = new_column_names\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "(Be sure you understand the __list comprehension__ used above -- it's a common and important way to process a list in python.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now **all** columns can be accessed using the **dot** notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    55.150002\n",
       "1    52.529999\n",
       "2    52.439999\n",
       "3    52.209999\n",
       "4    53.830002\n",
       "Name: adj_close, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.adj_close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A sampling of DataFrame methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "A dataframe object has many useful methods.\n",
    "\n",
    "Familiarize yourself with dataframe methods -- they are very useful.\n",
    "\n",
    "These should be self-explanatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         3.809084e+01\n",
       "low          3.659777e+01\n",
       "open         3.732426e+01\n",
       "close        3.733303e+01\n",
       "volume       3.501135e+06\n",
       "adj_close    3.733303e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['high', 'low', 'open', 'close', 'volume', 'adj_close']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         1.138931e+01\n",
       "low          1.114006e+01\n",
       "open         1.128846e+01\n",
       "close        1.126194e+01\n",
       "volume       4.152341e+06\n",
       "adj_close    1.126194e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['high', 'low', 'open', 'close', 'volume', 'adj_close']].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         3.909000e+01\n",
       "low          3.665000e+01\n",
       "open         3.822000e+01\n",
       "close        3.818000e+01\n",
       "volume       2.356000e+06\n",
       "adj_close    3.818000e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['high', 'low', 'open', 'close', 'volume', 'adj_close']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.3242629229785"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.open.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.09083672633684"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.high.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting methods\n",
    "\n",
    "Pandas has an extensive library of plotting functions, and they are very easy to use.\n",
    "\n",
    "These are your \"first look\" functions.\n",
    "\n",
    "(Later you will use specialized graphics packages for more sophisticated visualizations.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.high.plot(label='High')\n",
    "df.low.plot(label='Low')\n",
    "plt.title('YELP Stock Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.adj_close.hist()\n",
    "plt.xlabel('Adjusted Closing Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.title('YELP');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Bulk Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Methods like ``sum()`` and ``std()`` work on entire columns. \n",
    "\n",
    "We can run our own functions across all values in a column (or row) using ``apply()``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As an example, let's go back to this plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.high.plot(label='High')\n",
    "df.low.plot(label='Low')\n",
    "plt.title('YELP Stock Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "It's __almost__ perfect.  The only problem is the $x$-axis: it should show time.\n",
    "\n",
    "To fix this, we need to make the dataframe __index__ -- that is, the __row labels__ -- into dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We have a problem however: the \"dates\" in our data are only __strings__.   We need Pandas to understand that they are actually dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-01-02\n",
       "1    2015-01-05\n",
       "2    2015-01-06\n",
       "3    2015-01-07\n",
       "4    2015-01-08\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The **values** property of the column returns a list of values for the column. Inspecting the first value reveals that these are strings with a particular format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-01-02'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_date = df.date.values[0]\n",
    "first_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To convert these strings to actual dates we'll use the ``datetime`` standard python package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 2, 0, 0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime(first_date, \"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "And to do this for each string in the ``date`` column we will use ``.apply()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-01-02\n",
       "1   2015-01-05\n",
       "2   2015-01-06\n",
       "3   2015-01-07\n",
       "4   2015-01-08\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.copy()\n",
    "new_df.date = df.date.apply(lambda d: datetime.strptime(d, \"%Y-%m-%d\"))\n",
    "new_df.date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Each row in a DataFrame is associated with an index, which is a label that uniquely identifies a row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The row indices so far have been auto-generated by pandas, and are simply integers starting from 0. \n",
    "\n",
    "Fixing this is as easy as assigning to the `index` property of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date       open       high        low      close  adj_close  \\\n",
       "date                                                                           \n",
       "2015-01-02 2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002   \n",
       "2015-01-05 2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999   \n",
       "2015-01-06 2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999   \n",
       "2015-01-07 2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999   \n",
       "2015-01-08 2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002   \n",
       "\n",
       "             volume  \n",
       "date                 \n",
       "2015-01-02  1664500  \n",
       "2015-01-05  2023000  \n",
       "2015-01-06  3762800  \n",
       "2015-01-07  1548200  \n",
       "2015-01-08  2015300  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.index = new_df.date\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now that we have made an index based on a real date, we can drop the original `date` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume\n",
       "date                                                                      \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500\n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000\n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800\n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200\n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df.drop(['date'],axis=1)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now we can see that Pandas handles these dates quite nicely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.high.plot(label='High')\n",
    "new_df.low.plot(label='Low')\n",
    "plt.title('YELP Stock Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Accessing rows of the DataFrame\n",
    "\n",
    "So far we've seen how to access a column of the DataFrame.  To access a row we use a different notation.\n",
    "\n",
    "To access a row by its index value, use the **`.loc()`** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         5.466000e+01\n",
       "high         5.564000e+01\n",
       "low          5.430000e+01\n",
       "close        5.519000e+01\n",
       "adj_close    5.519000e+01\n",
       "volume       1.636400e+06\n",
       "Name: 2015-01-23 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.loc[datetime(2015,1,23,0,0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To access a row by its sequence number (ie, like an array index), use **`.iloc()`** ('Integer Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         5.546000e+01\n",
       "high         5.560000e+01\n",
       "low          5.424000e+01\n",
       "close        5.515000e+01\n",
       "adj_close    5.515000e+01\n",
       "volume       1.664500e+06\n",
       "Name: 2015-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To iterate over the rows, use **`.iterrows()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of positive-gain days is 125.\n"
     ]
    }
   ],
   "source": [
    "num_positive_days = 0\n",
    "for idx, row in df.iterrows():\n",
    "    if row.close > row.open:\n",
    "        num_positive_days += 1\n",
    "        \n",
    "print(\"The total number of positive-gain days is {}.\".format(num_positive_days))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "It is easy to select interesting rows from the data.  \n",
    "\n",
    "All the operations below return a new DataFrame, which itself can be treated the same way as all DataFrames we have seen so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-01-02     True\n",
       "2015-01-05    False\n",
       "2015-01-06    False\n",
       "2015-01-07    False\n",
       "2015-01-08    False\n",
       "Name: high, dtype: bool"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_high = new_df.high > 55\n",
    "tmp_high.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Summing a Boolean array is the same as counting the number of **`True`** values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(tmp_high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now, let's select only the rows of **`df1`** that correspond to **`tmp_high`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6224200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.060001</td>\n",
       "      <td>53.430000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>2407700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>53.869999</td>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>54.660000</td>\n",
       "      <td>55.639999</td>\n",
       "      <td>54.299999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>1636400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>55.119999</td>\n",
       "      <td>55.790001</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1450300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>56.060001</td>\n",
       "      <td>56.160000</td>\n",
       "      <td>54.570000</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>2410400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>56.150002</td>\n",
       "      <td>56.150002</td>\n",
       "      <td>52.919998</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2013100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>53.830002</td>\n",
       "      <td>55.930000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>2885400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>55.529999</td>\n",
       "      <td>57.070000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>2498600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>57.599998</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>56.080002</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>4657300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume\n",
       "date                                                                      \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500\n",
       "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000  6224200\n",
       "2015-01-12  56.000000  56.060001  53.430000  54.020000  54.020000  2407700\n",
       "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999  2295400\n",
       "2015-01-23  54.660000  55.639999  54.299999  55.189999  55.189999  1636400\n",
       "2015-01-26  55.119999  55.790001  54.830002  55.410000  55.410000  1450300\n",
       "2015-01-27  56.060001  56.160000  54.570000  55.630001  55.630001  2410400\n",
       "2015-01-28  56.150002  56.150002  52.919998  53.000000  53.000000  2013100\n",
       "2015-02-03  53.830002  55.930000  53.410000  55.779999  55.779999  2885400\n",
       "2015-02-04  55.529999  57.070000  55.250000  56.740002  56.740002  2498600\n",
       "2015-02-05  57.599998  57.700001  56.080002  57.470001  57.470001  4657300"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[tmp_high]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Putting it all together, we have the following commonly-used patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6224200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>50.180000</td>\n",
       "      <td>51.490002</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>2183300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>3248100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>53.869999</td>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume\n",
       "date                                                                      \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300\n",
       "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000  6224200\n",
       "2015-01-16  50.180000  51.490002  50.029999  51.389999  51.389999  2183300\n",
       "2015-01-21  51.200001  53.500000  51.200001  53.410000  53.410000  3248100\n",
       "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999  2295400"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_days = new_df[new_df.close > new_df.open]\n",
    "positive_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-07</th>\n",
       "      <td>38.220001</td>\n",
       "      <td>48.73</td>\n",
       "      <td>38.220001</td>\n",
       "      <td>47.009998</td>\n",
       "      <td>47.009998</td>\n",
       "      <td>33831600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open   high        low      close  adj_close    volume\n",
       "date                                                                   \n",
       "2015-05-07  38.220001  48.73  38.220001  47.009998  47.009998  33831600"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "very_positive_days = new_df[(new_df.close - new_df.open) > 4]\n",
    "very_positive_days.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Creating new columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To create a new column, simply assign values to it.  Think of the columns as a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "\n",
       "            profit  \n",
       "date                \n",
       "2015-01-02   False  \n",
       "2015-01-05   False  \n",
       "2015-01-06   False  \n",
       "2015-01-07   False  \n",
       "2015-01-08    True  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['profit'] = (new_df.open < new_df.close)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Let's give each row a ``gain`` value as a categorical variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "      <th>gain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "\n",
       "            profit         gain  \n",
       "date                             \n",
       "2015-01-02   False     negative  \n",
       "2015-01-05   False     negative  \n",
       "2015-01-06   False     negative  \n",
       "2015-01-07   False     negative  \n",
       "2015-01-08    True  medium_gain  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for idx, row in new_df.iterrows():\n",
    "    if row.open > row.close:\n",
    "        new_df.loc[idx,'gain']='negative'\n",
    "    elif (row.close - row.open) < 1:\n",
    "        new_df.loc[idx,'gain']='small_gain'\n",
    "    elif (row.close - row.open) < 6:\n",
    "        new_df.loc[idx,'gain']='medium_gain'\n",
    "    else:\n",
    "        new_df.loc[idx,'gain']='large_gain'\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here is another, more \"functional\", way to accomplish the same thing.\n",
    "\n",
    "Define a function that classifies rows, and **`apply`** it to each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def namerow(row):\n",
    "    if row.open > row.close:\n",
    "        return 'negative'\n",
    "    elif (row.close - row.open) < 1:\n",
    "        return 'small_gain'\n",
    "    elif (row.close - row.open) < 6:\n",
    "        return 'medium_gain'\n",
    "    else:\n",
    "        return 'large_gain'\n",
    "\n",
    "new_df['test_column'] = new_df.apply(namerow, axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "      <th>gain</th>\n",
       "      <th>test_column</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "      <td>medium_gain</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "\n",
       "            profit         gain  test_column  \n",
       "date                                          \n",
       "2015-01-02   False     negative     negative  \n",
       "2015-01-05   False     negative     negative  \n",
       "2015-01-06   False     negative     negative  \n",
       "2015-01-07   False     negative     negative  \n",
       "2015-01-08    True  medium_gain  medium_gain  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "OK, point made, let's get rid of that extraneous `test_column`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "      <th>gain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "      <td>medium_gain</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>2015-12-23</th>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.420000</td>\n",
       "      <td>27.440001</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>1001000</td>\n",
       "      <td>True</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>28.270000</td>\n",
       "      <td>28.590000</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>587400</td>\n",
       "      <td>True</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>28.120001</td>\n",
       "      <td>28.379999</td>\n",
       "      <td>27.770000</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>1004500</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.540001</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>1103900</td>\n",
       "      <td>True</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>28.580000</td>\n",
       "      <td>28.780001</td>\n",
       "      <td>28.170000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>1068000</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>251 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "...               ...        ...        ...        ...        ...      ...   \n",
       "2015-12-23  27.950001  28.420000  27.440001  28.150000  28.150000  1001000   \n",
       "2015-12-24  28.270000  28.590000  27.900000  28.400000  28.400000   587400   \n",
       "2015-12-28  28.120001  28.379999  27.770000  27.879999  27.879999  1004500   \n",
       "2015-12-29  27.950001  28.540001  27.740000  28.480000  28.480000  1103900   \n",
       "2015-12-30  28.580000  28.780001  28.170000  28.250000  28.250000  1068000   \n",
       "\n",
       "            profit         gain  \n",
       "date                             \n",
       "2015-01-02   False     negative  \n",
       "2015-01-05   False     negative  \n",
       "2015-01-06   False     negative  \n",
       "2015-01-07   False     negative  \n",
       "2015-01-08    True  medium_gain  \n",
       "...            ...          ...  \n",
       "2015-12-23    True   small_gain  \n",
       "2015-12-24    True   small_gain  \n",
       "2015-12-28   False     negative  \n",
       "2015-12-29    True   small_gain  \n",
       "2015-12-30   False     negative  \n",
       "\n",
       "[251 rows x 8 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.drop('test_column', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "An **extremely** powerful DataFrame method is **`groupby()`**. \n",
    "\n",
    "This is entirely analagous to **`GROUP BY`** in SQL.\n",
    "\n",
    "It will group the rows of a DataFrame by the values in one (or more) columns, and let you iterate through each group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here we will look at the average gain among the  categories of gains (negative, small, medium and large) we defined above and stored in column `gain`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "gain_groups = new_df.groupby('gain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Essentially, **`gain_groups`** behaves like a dictionary:\n",
    "* the keys are the unique values found in the `gain` column, and \n",
    "* the values are DataFrames that contain only the rows having the corresponding unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "large_gain\n",
      "                 open   high        low      close  adj_close    volume  \\\n",
      "date                                                                      \n",
      "2015-05-07  38.220001  48.73  38.220001  47.009998  47.009998  33831600   \n",
      "\n",
      "            profit        gain test_column  \n",
      "date                                        \n",
      "2015-05-07    True  large_gain  large_gain  \n",
      "=============================\n",
      "medium_gain\n",
      "                 open       high        low      close  adj_close   volume  \\\n",
      "date                                                                         \n",
      "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
      "2015-01-16  50.180000  51.490002  50.029999  51.389999  51.389999  2183300   \n",
      "2015-01-21  51.200001  53.500000  51.200001  53.410000  53.410000  3248100   \n",
      "2015-02-03  53.830002  55.930000  53.410000  55.779999  55.779999  2885400   \n",
      "2015-02-04  55.529999  57.070000  55.250000  56.740002  56.740002  2498600   \n",
      "\n",
      "            profit         gain  test_column  \n",
      "date                                          \n",
      "2015-01-08    True  medium_gain  medium_gain  \n",
      "2015-01-16    True  medium_gain  medium_gain  \n",
      "2015-01-21    True  medium_gain  medium_gain  \n",
      "2015-02-03    True  medium_gain  medium_gain  \n",
      "2015-02-04    True  medium_gain  medium_gain  \n",
      "=============================\n",
      "negative\n",
      "                 open       high        low      close  adj_close   volume  \\\n",
      "date                                                                         \n",
      "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
      "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
      "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
      "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
      "2015-01-12  56.000000  56.060001  53.430000  54.020000  54.020000  2407700   \n",
      "\n",
      "            profit      gain test_column  \n",
      "date                                      \n",
      "2015-01-02   False  negative    negative  \n",
      "2015-01-05   False  negative    negative  \n",
      "2015-01-06   False  negative    negative  \n",
      "2015-01-07   False  negative    negative  \n",
      "2015-01-12   False  negative    negative  \n",
      "=============================\n",
      "small_gain\n",
      "                 open       high        low      close  adj_close   volume  \\\n",
      "date                                                                         \n",
      "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000  6224200   \n",
      "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999  2295400   \n",
      "2015-01-23  54.660000  55.639999  54.299999  55.189999  55.189999  1636400   \n",
      "2015-01-26  55.119999  55.790001  54.830002  55.410000  55.410000  1450300   \n",
      "2015-01-29  52.849998  53.310001  51.410000  52.930000  52.930000  1844100   \n",
      "\n",
      "            profit        gain test_column  \n",
      "date                                        \n",
      "2015-01-09    True  small_gain  small_gain  \n",
      "2015-01-22    True  small_gain  small_gain  \n",
      "2015-01-23    True  small_gain  small_gain  \n",
      "2015-01-26    True  small_gain  small_gain  \n",
      "2015-01-29    True  small_gain  small_gain  \n",
      "=============================\n"
     ]
    }
   ],
   "source": [
    "for gain, gain_data in gain_groups:\n",
    "    print(gain)\n",
    "    print(gain_data.head())\n",
    "    print('=============================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average closing value for the large_gain group is 47.0099983215332\n",
      "The average closing value for the medium_gain group is 39.72307696709266\n",
      "The average closing value for the negative group is 37.38476184057811\n",
      "The average closing value for the small_gain group is 36.53367346160266\n"
     ]
    }
   ],
   "source": [
    "for gain, gain_data in new_df.groupby(\"gain\"):\n",
    "    print('The average closing value for the {} group is {}'.format(gain,\n",
    "                                                           gain_data.close.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other Pandas Classes\n",
    "\n",
    "A DataFrame is essentially an annotated 2-D array.\n",
    "\n",
    "Pandas also has annotated versions of 1-D and 3-D arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "A 1-D array in Pandas is called a `Series`.\n",
    "\n",
    "A 3-D array in Pandas is created using a ``MultiIndex``.\n",
    "\n",
    "To use these, read the documentation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Comparing multiple stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As a last task, we will use the experience we obtained so far -- and learn some new things -- in order to compare the performance of different stocks we obtained from Yahoo finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>IBM</th>\n",
       "      <th>YELP</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>37.840000</td>\n",
       "      <td>10.006667</td>\n",
       "      <td>177.370941</td>\n",
       "      <td>67.919998</td>\n",
       "      <td>37.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>37.619999</td>\n",
       "      <td>9.970667</td>\n",
       "      <td>178.432129</td>\n",
       "      <td>67.660004</td>\n",
       "      <td>36.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>37.470001</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>177.820267</td>\n",
       "      <td>71.720001</td>\n",
       "      <td>36.130001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>37.849998</td>\n",
       "      <td>9.957333</td>\n",
       "      <td>181.367111</td>\n",
       "      <td>72.660004</td>\n",
       "      <td>36.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>37.720001</td>\n",
       "      <td>10.085333</td>\n",
       "      <td>179.703629</td>\n",
       "      <td>78.419998</td>\n",
       "      <td>35.759998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ORCL       TSLA         IBM       YELP       MSFT\n",
       "Date                                                              \n",
       "2014-01-02  37.840000  10.006667  177.370941  67.919998  37.160000\n",
       "2014-01-03  37.619999   9.970667  178.432129  67.660004  36.910000\n",
       "2014-01-06  37.470001   9.800000  177.820267  71.720001  36.130001\n",
       "2014-01-07  37.849998   9.957333  181.367111  72.660004  36.410000\n",
       "2014-01-08  37.720001  10.085333  179.703629  78.419998  35.759998"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks = ['ORCL', 'TSLA', 'IBM','YELP', 'MSFT']\n",
    "stock_df = pd.DataFrame()\n",
    "for s in stocks:\n",
    "    stock_df[s] = pd.DataFrame(yf.download(s,start='2014-01-01',end='2014-12-31', progress = False))['Close']\n",
    "stock_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_df.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Next, we will calculate returns over a period of length $T,$ defined as:\n",
    "\n",
    "$$ r(t) = \\frac{f(t)-f(t-T)}{f(t)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The returns can be computed with a simple DataFrame method **`pct_change()`**.  Note that for the first $T$ timesteps, this value is not defined (of course):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>IBM</th>\n",
       "      <th>YELP</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-02-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-14</th>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.320653</td>\n",
       "      <td>-0.009918</td>\n",
       "      <td>0.321849</td>\n",
       "      <td>0.012379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-18</th>\n",
       "      <td>0.009304</td>\n",
       "      <td>0.361995</td>\n",
       "      <td>-0.018485</td>\n",
       "      <td>0.355897</td>\n",
       "      <td>0.013817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>0.010675</td>\n",
       "      <td>0.317279</td>\n",
       "      <td>-0.016398</td>\n",
       "      <td>0.254880</td>\n",
       "      <td>0.038195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-20</th>\n",
       "      <td>0.011096</td>\n",
       "      <td>0.405798</td>\n",
       "      <td>-0.028728</td>\n",
       "      <td>0.257501</td>\n",
       "      <td>0.036803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-21</th>\n",
       "      <td>0.010074</td>\n",
       "      <td>0.385510</td>\n",
       "      <td>-0.027558</td>\n",
       "      <td>0.170875</td>\n",
       "      <td>0.062081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ORCL      TSLA       IBM      YELP      MSFT\n",
       "Date                                                        \n",
       "2014-02-07       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-10       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-11       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-12       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-13       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-14  0.003700  0.320653 -0.009918  0.321849  0.012379\n",
       "2014-02-18  0.009304  0.361995 -0.018485  0.355897  0.013817\n",
       "2014-02-19  0.010675  0.317279 -0.016398  0.254880  0.038195\n",
       "2014-02-20  0.011096  0.405798 -0.028728  0.257501  0.036803\n",
       "2014-02-21  0.010074  0.385510 -0.027558  0.170875  0.062081"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = stock_df.pct_change(30)\n",
    "rets.iloc[25:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now we'll plot the timeseries of the returns of the different stocks.\n",
    "\n",
    "Notice that the `NaN` values are gracefully dropped by the plotting function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+++9z7bXXsnjxYs455xwiIiI6fOyuRLirehNuQaPRgcqlPx32npuPQCAQCP4UnHHGGezatYtdu3axefNmZs6cyZw5czhy5EiLsddeey2bNm0iKyuLDz74gJtvvrkHZtw+hCWnNyEsOQKBQHDS4qPxYfPVm3vs2CeCn58fqampntejR48mKCiId955h3/9619eY8PCwjjvvPOYN28eZrOZOXPmUFtbe0LH7yqEyOlNCJEjEAgEJy2SJJ2Qy6g3IUkSKpXKK8C4KTfffDPnnHMOjzzyCGq1uptn136EyOlN2FtJIRfZVQKBQCDoYiwWC8XFxQBUVVXx2muvUVdXx/nnn9/q+NmzZ1NWVkZgYGB3TvO4ESKnNyHq5AgEAoGgB/jll1+IiYkBICAggIEDB/Lll18yffr0VsdLkkR4eHg3zrBj9JrA40WLFpGcnIzBYGD06NGsW7fuqOMtFgtPPPEESUlJ6PV6+vbty/vvv99Ns+0iRAq5QCAQCLqBDz74gG+//dbzXJZlz5/RaGTLli1ceumlXtvIsuypktyc4OBgZFluUxT1FL3CkrNkyRLuu+8+Fi1axOTJk3nrrbeYM2cOGRkZJCYmtrrNFVdcQUlJCe+99x6pqamUlpZit5/kmUie7CpRDFAgEAgEghOlV4icl19+mXnz5nHLLbcAsHDhQpYvX84bb7zBggULWoz/5ZdfWLNmDVlZWYSGhgLQp0+fox7DYrFgsVg8r41GY+edQGfRauCxEDkCgUAgEHSEHndXWa1Wtm/fzsyZM72Wz5w5k40bN7a6zffff8+YMWN44YUXiIuLo3///jz00ENtRoEDLFiwgKCgIM9fQkJCp55Hp+AWNGotqIQlRyAQCASCE6HHLTnl5eU4HA6ioqK8lkdFRXkivZuTlZXF+vXrMRgMfPPNN5SXl3PHHXdQWVnZZlzOY489xgMPPOB5bTQae5/QsbssTV69q0RMjkAgEAgEHaHHRY6b5j03ZFlusw+H0+lEkiQ++eQTgoKCAMXlddlll/H666/j49OyKJJer0ev13f+xDsTUSdHIBAIBIJOo8fdVeHh4ajV6hZWm9LS0hbWHTcxMTHExcV5BA7AoEGDkGWZ/Pz8Lp1vl9LUXeW25MgOcDp7bk4CgUAgEJyk9LjI0el0jB49mhUrVngtX7FiBZMmTWp1m8mTJ1NYWEhdXZ1n2YEDB1CpVMTHx3fpfLsUh8td1TS7CkStHIFAIBAIOkCPixyABx54gHfffZf333+fzMxM7r//fnJzc5k/fz6gxNNcf/31nvFXX301YWFh3HTTTWRkZLB27Voefvhhbr755lZdVScNrdXJabpcIBAIBAJBu+kVMTlz586loqKCZ555hqKiIoYOHcqyZctISkoCoKioiNzcXM94f39/VqxYwd13382YMWMICwvjiiuuaNFE7KTD467SNRM5wpIjEAgEAsHx0itEDsAdd9zBHXfc0eq6Dz74oMWygQMHtnBxnfQ0za5SqQEJkIXIEQgEAoGgA/QKd5XARdPAY2iSYWVpfbxAIBAIBMeBLMucddZZzJo1q8W6RYsWERQUxEcffYQkSa3+uZOEnn76aUaMGNHmcaZPn+7ZRq/X079/f5577jkcDkdXnVqrCJHTm2iaQg6g81MeraaemY9AIBAITikkSWLx4sVs3ryZt956y7M8OzubRx55hFdffdXTTmn//v0UFRV5/UVGRrb7WLfeeitFRUXs37+fe+65hyeffJL//Oc/nX5OR0OInN5E0+wqAF+lZQX1FT0zH4FAIBCcciQkJPDqq6/y0EMPkZ2djSzLzJs3jxkzZnDjjTd6xkVGRhIdHe31p1K1Xzb4+voSHR1Nnz59uOuuu5gxY4anKWh30WticgS0dFf5uEVOZc/MRyAQCATtRpZl5KO0F+pKJB+fNgvotsYNN9zAN998w0033cSll17K3r172bt3bxfOEHx8fKiqqurSYzRHiJzeRHN3lW+Y8tggRI5AIBD0duSGBvaPGt0jxx6wYzuSr+9xbfP2228zdOhQ1q1bx1dffdXCFdW87lxcXBz79+8/7rk5nU5+/fVXli9fzn333Xfc258IQuT0JjzZVW6RIyw5AoFAIOgaIiMjue222/j222+5+OKLW6xft24dAQEBntcazfFJhkWLFvHuu+9itSo38Ndddx1PPfXUiU36OBEipzfRtE4OgE+I8ihicgQCgaDXI/n4MGDH9h47dkfQaDRtipfk5GSCg4M7PKdrrrmGJ554Ar1eT2xsLGq1usP76ihC5PQm2nRXda8PUyAQCATHjyRJx+0yOpUJCgoiNTW1R+cgRE5vwi1yNMJdJRAIBIKepbS0FLPZ7LUsLCwMrVZJjmloaGDXrl1e6/39/Xtc2DRFiJzeRHNLjju7SgQeCwQCgaCbGTBgQItlmzZtYsKECYDSGHvkyJFe66dNm8bq1au7Y3rtQpJlWe7pSfQERqORoKAgampqCAwM7OnpKCwcBtVHYN4KSBgHOevhg3MhLBXu7hk/r0AgEAhax2w2k52dTXJyMgaDoaenc0pxtP/t8Vy/RTHA3kTzwGN3TI5wVwkEAoFAcNwIkdObaMtdZa4Gp7NHpiQQCAQCwcmKEDm9iRYix5VCLjsVoSMQCAQCgaDdCJHTm2ieXaXRgd7lbxQuK4FAIBAIjgshcnoTzS050GjNERlWAoFAIBAcF0Lk9BYcdsUtBd4ip61aOU4nWOu7Z24CgUAgEJyECJHTW3BbcaCZyGmjSednc+HlgWASLR8EAoFAIGgNIXJ6C22JHHeGVdP+VXYrHP4NzDVQmtE98xMIBAKB4CRDiJzegpfI0TY+d1tyyvY1Lqs8DE678ry+vOvnJhAIBALBSYgQOb2FpkHHktS4fMAc5XHnJ5C/TXlemtm4XnQoFwgEAoGgVYTI6S20llkFkDINhl0JyPD93YqrqqlVR8TkCAQCgeA4ufHGG5Ekifnz57dYd8cddyBJEjfeeCOgNOq8/fbbSUxMRK/XEx0dzaxZs9i0aZNnmz59+ihd2Jv8xcfH8/TTT7dY3vwvJyeny85TNOjsLdjdIkfbct3sBXBopRJ/s/6VZpYc4a4SdB6Vn36KSqcj+LLLenoqAoGgi0lISODzzz/nlVdewcfHB1B6Rn322WckJiZ6xl166aXYbDY+/PBDUlJSKCkpYdWqVVRWeifEPPPMM9x6662e12q1Gh8fHy8hNXbsWG677TavcREREV11ikLk9BocFuWxuSUHlDTyOc/D1/Ng7YtgCGpcZxIiR9A5OE0mSv75L5AkAmbORN1bGtcKBIIuYdSoUWRlZbF06VKuueYaAJYuXUpCQgIpKSkAVFdXs379elavXs20adMASEpKYty4cS32FxAQQHR0dIvl/v7+nudqtbrNcV2BcFf1FiqzlcfA2NbXD70U+s8Gp83beiNicgSdhNNiAVkGpxNrdnZPT0cgOOmQZRmbxdEjf7Isd2jON910E4sXL/a8fv/997n55ps9r/39/fH39+fbb7/FYrGc8P+ouxGWnN5C8R7lMfq01tdLEpz7MuRsAGtt43IhcgSdhGyzeZ5bsrLxGT68B2cjEJx82K1O3r53TY8c+7ZXp6HVq497u+uuu47HHnuMnJwcJEliw4YNfP7556xevRoAjUbDBx98wK233sqbb77JqFGjmDZtGldeeSXDhg3z2tcjjzzCk08+6Xn93HPPcc8995zQeZ0owpLTW/CInGFtjwmKg7P/oTz3d5n6hLtK0Ek0FTnWrKwenIlAIOguwsPDOffcc/nwww9ZvHgx5557LuHh4V5jLr30UgoLC/n++++ZNWsWq1evZtSoUXzwwQde4x5++GF27drl+bv++uu78UxaR1hyegvHsuS4GXMzhKWCzh/ePVOx5Miyd9q5QNABZGtTS44QOQLB8aLRqbjt1Wk9duyOcvPNN3PXXXcB8Prrr7c6xmAwcPbZZ3P22Wfz97//nVtuuYWnnnrKk4EFimBKTU3t8Dy6AiFyegOmCqgtVJ5HDTn6WElS0sptDcprp02pfOwT3KVTFJz6yLbGgpTCkiMQHD+SJHXIZdTTzJ49G6tV+f7PmjWrXdsMHjyYb7/9tgtn1TkIkdMbKHFZcUJTQB/Qvm20PqD1A5tJseYIkSM4QZpacqx5ecg2G5K2lZIGAoHglEKtVpOZmel53pSKigouv/xybr75ZoYNG0ZAQADbtm3jhRde4MILL+yJ6R4XQuT0BtrrqmqOXxhUu0ROWN/On5fgT0XTmBzsdqy5uej7is+VQPBnILCNkhH+/v6MHz+eV155hcOHD2Oz2UhISODWW2/l8ccf7+ZZHj9C5PQGOipyfMOhOlcEHws6BS+RgxKXI0SOQHBq0jxouDlNXVELFixgwYIFRx3f3qrFXVnduDVEdlVvoD2ZVa3h54qAF1WPBZ1Ac5FjzRK1cgQCwcmNEDk9jc0MZfuV51FDj29bX7fIEbVyBCdO08BjAGvWYa/Xjro6HLW1CAQCwcmCEDk9TVkmyA7wCW272nFb+IYqj8JdJegEWrqrGi05jupqsi+4kMOzZuM0m7t7agKBQNAhhMjpaZrG4xxvrZtgVwO1vM2dOyfBnxK3yFG7CoFZs7I8peJL/v08tsJCHJWV2EtLe2yOAoFAcDwIkdPTdDToGGDwRaDSQv5WKNrdqdMS/Plwixx9376gVuM0mbCXllK3bj01TYIQHUbhshIImtLRvlGCtums/6kQOT1N8V7l8XiDjgEComDwBcrzre923pwEf0rcIkfl64suIQEA8549FD31d69xzjohcgQCAK2rjlR9fX0Pz+TUw/0/1Z5grS6RQt6TOJ0nZskBGHsr7P0a9nwF574CavGWCjqGW+RIWi26lBSsOTkUP/NP7KWlaOPiUAUEYNm3D4fR2MMzFQh6B2q1muDgYEpdLlxfX18k0WLnhJBlmfr6ekpLSwkODm5RnPB46TVXxEWLFvHiiy9SVFTEkCFDWLhwIVOmTDnmdhs2bGDatGkMHTqUXbt2df1EO5PqI0pHcbUewvt1bB8J40BSg60eTGUQGNO5cxT8eWgqchITqPsNT/xNzD+foeLDD7Hs24eztg4A2WrFXlWNNiqyx6YsEPQ00dFKs+RSEavWqQQHB3v+tydCrxA5S5Ys4b777mPRokVMnjyZt956izlz5pCRkUFiYmKb29XU1HD99dczY8YMSkpKunHGJ0jWaijYDqGuQmuRg0DdQZOcSg1+EVBXDHUlQuQIOoyXJSc5xbM86NJL8Js0ieqvlwLgqDVi2rSJor/9HVthIXGvvELgrJk9MmeBoKeRJImYmBgiIyOxNctQFHQMrVZ7whYcN71C5Lz88svMmzePW265BYCFCxeyfPly3njjjaNWWbz99tu5+uqrUavVJ0WjMA8fufp9+Ecpj9HHWR+nOf6RisgxlZ3YfgR/apqKHMNpymdSExFB1F//CoAqUOmrZs3JoWzhq8iuVPLiv/8ddVAgTrMZR1U1jupqfIYPx3fUyB44C4GgZ1Cr1Z12YRZ0Hj0ucqxWK9u3b+fRRx/1Wj5z5kw2btzY5naLFy/m8OHDfPzxx/zrX/865nEsFgsWi8Xz2tgb4grqXNanjgQdN8UtlupOImuWoNfhJXL69yfxow/RJSSgDgoCQB2g9LYx79mLbDajiYxEHR6GJSOT3Btv8tqXyt+f/pv/QBI/+gKBoAfp8eyq8vJyHA4HUVFRXsujoqIoLi5udZuDBw/y6KOP8sknn6DRtE+nLViwgKCgIM9fgit7pNtpLS2uo0HHboTIEXQCTUUOgN+4cWhjGt2fqgB/QLHkAGhjY4l74QV0ffqgjYvDMHQoflOmKOnndXXYy4RlUSAQ9Cw9bslx0zwiXZblVqPUHQ4HV199Nf/4xz/o379/u/f/2GOP8cADD3heG43GnhE6tlZSDaOGnNg+/V2Bn3Ui8E3QcWSrS+ToWo8Pc1tynCaT8josDH1qKn1/+dlr3KEZZ2ErKMBWWIi2EwIHBQKBoKP0uMgJDw9HrVa3sNqUlpa2sO4A1NbWsm3bNnbu3Mldd90FgNPpRJZlNBoNv/76K2eeeWaL7fR6PXq9vmtO4nhoqPZ+HdIHDEEntk+PyBGWHEHHaW7JaY7bkuNGExbW6jhtbKwicgoKYdSozp2kQCAQHAc97q7S6XSMHj2aFStWeC1fsWIFkyZNajE+MDCQPXv2sGvXLs/f/PnzGTBgALt27WL8+PHdNfWOYa7xfh3bCcGZR7Pk7PsJ0r448WMITnmOJXLUgYHer8NCWx2njVV6sFkO7Cfn2mupeFcUqhQIBD1Dj1tyAB544AGuu+46xowZw8SJE3n77bfJzc1l/vz5gOJqKigo4KOPPkKlUjF0qHc2UmRkJAaDocXyXolb5ATGwdh5MPTSE99nWzE5NQWw5FqQnZA4EYJ7KA5JcFJwTEuOfzNLTmjrlhxNrBLHU7XkC5xGI+a96YRcdRUqP79OnK1AIBAcm14hcubOnUtFRQXPPPMMRUVFDB06lGXLlpGUlARAUVERubm5PTzLTsItcvyjYMqDnbNPj8hpZsnZ+bEicAAKdwiRIzgqx2vJ0RzDkuN0ZTDKZjO1q1cTdO65nTVVgUAgaBc97q5yc8cdd5CTk4PFYmH79u1MnTrVs+6DDz5g9erVbW779NNPnzzVjt0i50TjcJridldZjGBrUJ47HbDjo8Yxhbs673iCUxK3yKHNmJwAr9fqNiw5bpHTFOPPP7cyUiAQCLqWXiNy/jSYq5XHzhQ5+kDQGJTndaVgt8Kqf4Axv3GM6FIuOAbHtOQ0EzltWnJiWooc09p1OGpFY0+BQNC9CJHTXVQdgY3/bYyb8QnuvH1LUqM1J+t3eHcGbHhVeT3ofOWxaFfrNXoEAhfHEjmSweBl5VG3mV3VWFtH0unQJiYiW63Ub9vWibMVCASCYyNETnfgdMBnVyEvfxK2vqcs60xLDjTG5fxwLxSngU8IXPY+XPIuqDRQXwGHVolaOoI2OabIkSTU7uBjlcpTCbk5KoMBdahi5dEPHIjvyBEAmDMyOnfCAoFAcAyEyOkO0pZQseYIB7+NwlysdHDuMpED0H8O3LFZydzSGiBikLL8k0sb+2Y1RZbhy5vg50c6d06Ck4pjiRxo7F+lDgk5assGd1yOYfAgDIMHA2DOyOysqQoEAkG7ECKnq7GZafj8X5SmBeCwqKkvcRUk7GyRM2wuRA6Bi96Aqz6DgCaiJ65JQbbSVu6mq3IgfSlsfhPMvaCnl6BHkK1W4OgiR+2viBxNaOvxOG70/foB4DtqVBORIyw5AoGge+kVKeSnMs4Nb1D4mxVk5cLhsLlaVRiCO/dAgy9Q/lpj6kOg1sHWd5TXdgtomlR/NpU3Pi9Jh92fQb+ZMOi8zp2joFfjseTodG2O8Vhy2ojHcRP514cJOPss/KdNw9mgZPzZi4qwV1YeUyAJBAJBZyEsOV1JQxXli17Hamy8M3baXP/yzhY5RyM4EeY83/i6ubWmvonI+fE+2PEhLLmmW6Ym6D20x13VXkuOJiSEgDPPRFKrUfv7o3PVvBIuK4FA0J0IkdOFmD/9GxV7FGOZYfBAAJweS04nu6uOhUoNOlcKsKWZyDE16RZdtq/75iToVRxXTM4xLDnNMQwRLiuBQND9CJHTRcgVORS+uxxkiYBJwwm66BIAnHa3JaebRU7TY7pr9bhpKnKa4rB36XQEvYtGkdO2u8pnxAiQJHxHHV/PNRGXIxAIegIhcrqI8qfvwFKlQe2jIvr5/3qqxTp6ypIDYHCV5W/urmoak9OU+oqunY+gV9EeS07I5ZfTf+tWAufMOa59G4YMAYTIEQgE3YsQOV2AedPPlK/KAiDqvtvRRESg8leaE/aYuwqUyshwdHeV13JRU+fPRHtEDoDa//gbbRoGKWUMbLm5OIwig08gEHQPQuR0MrLdTtETT4BTwn9AMIHX3w3gKaLmtKmUFgxaQ/dPzuOuqvFe3pbIEYUD/1Q0ZlcdXeR0BHVwMNq4OADMmSLuSyAQdA9C5HQyla/8A3NhAyqtk+jnX0WSFMuNyiVyHDapZ6w4cPzuqraWC05J2mvJ6SgiLkcgEHQ3QuR0MoFjkvCLdRB1yXC0A8d5lqv8mlhyujN9vCltuqtcYiZluvLo5+qDJdxVfxpkpxPsSqB5l4kckWElEAi6GVEMsJPRnnELCeMvBdnptdwTk2NXIU+6G6knJteaJcfpbKyTc/6rIKnhjzfgj9fbdmMJTjlke2MmnbDkCASCUwUhcroAyTekxTK1K7sKGeSBl/aQyGklJsdcDU7XBS4gFjQ68AtXXtcJkfNnQbbaPM+7WuRYs7Jw1tej8vXtkuMIBAKBG+Gu6iYkgwFcDQ0dtXU9M4nW3FVuV5U+SBE4AP5ud5UQOX8WZJvV87xNkWMsgsrsDh9DEx6OJjISZBnzvv0d3o9AIBC0FyFyuglJkjzBx05TD4mc1iw5biHjtt4A+EW41omYnD8L7qBjVKrWu4tnr4X/joY3Tz+hJq7CZSUQCLoTIXK6EbWfKy6nroctOa2KnIjGZR6RI7Kr/jQcLbPqwK/wyeVgM4G1DioOdvgwHpGTnt7hfQgEAkF7ESKnG/FYcnpK5LgtOV7uqqNZcspAlrtnboIepc308fRv4POrwW5uXFZ1pMPHERlWAoGgOxEipxvxtHboMZHTSnZV0W7l0R2HA40ix2FtWThQcErSqsjZ+Ql8dTM4bXDa5TD0UmV5VU6Hj+O25FgOHcJpsXR4PwJgz1fw6gjIWgOWWigVRRYFguYIkdONeNLIezrw2FyjWGjyt8HOj5Vlgy9qHKc1NI4Vwcd/ClqInOpc+P4upRTCqOvh4rcgLFVZdwIiRxMdjTokBBwOLAcOnOCs/8TIMqz+N1Rlw9fz4LWxsGg8FO/p6ZkJBL0KIXK6EbVfTwceu4SL7FCEzvf3ADIMuxJSpnmP9Y9SHo0F3TpFQc/QQuRUZisCJzQFzv8/UKkhpI+y7gREjiRJTeJyhMuqwxTubIyNMpVBbZFr+a4em5JA0BsRIqcb8bR26Cl3lc4fJNdbvnoBlKaDTyjMerbl2PD+ymOZuNv+M9BC5LhjcAxB4GpN4hE51R2PyQGRYdUp7PlSeYwfB/7Rjcurc3tmPgJBL0WInG6kMfDY1DMTkKRGN9TmN5XH2Qu8g47dRAxQHsuEn//PQAuRY6tXHjU+jYOCk5TH6jxw2OkoIvj4BHHYlXgcgCkPwv3pMOMp5bUQOQKBF0LkdCPqgB7OroJGlxUovaqGzW19nFvklAtLzp+BliLHZcnRNhE5ATGg1inuTmO+ssxshKW3Q9oX7T6WJ/h4//7G+jyC9pO9Wqlh5RMKqTNArYHgRGWdEDkCgRdC5HQjniaddbU9Nwm9K41cY4DzXml0RTRHWHL+VLR0VzUoj01FjkrVaM1xp5FnfAdpn8PSW2HFU0ovtGOgTUhAFRCAbLNhOXy4s07hz0Oay1U19FJQu94vj5WtmcipOgKmiu6bm0DQyxAipxtxu6vs5RXYKyvbHCfb7dT89BO2kpLOn4T7jm/aI0pQaVu4Y3JMZeJH8k+AR+ToXK093JYcjcF7YPPg45ImRf02LISvbgRbw1GPJYKPTwCrCTJ/UJ4Pu6Jxuft7XVsIdleLjux1SpXq92eJeleCPy1C5HQj7hTy+i1bODRtOraC1jOX6tasofDBhyj59787fxLnvABzP4bJ9x19nM6v8YezXPQZOtVpMyZH20zkuIVx3hblsdQlcgaeByqtYtn54LxjNncVwccdZN8ypfJ0SB+IH9u43D9SEaSyU8mIrM6FL29QahxVHITKrB6bskDQkwiR041owsI8z2WbDUtW680ObcXFyuORLvCvB8XDoPMV18OxiBioPAqX1SmPuwt5i+wqbbNO4UMvUR73fg31lY2WnCkPwvXfgU8IFGyDjy8+qvVAtHfoIGlLlMdhc71dzZLUeFNSth8+vwbqm1hgj2xotPDYrXBwhWIVEghOcYTI6UZ8Rowg6vHHPK8dxtarCTtNyl20vaKH3USeuBxhyTnVcXchl3RuS47L5dTcXZUwHqJPU2J21r+sXEgllSKI+0yGeStBUitF6YyFbR5P31exCFnz8zv9XE5Z6srg8G/K89OuaLneLXJ+uBeK08A3TKmBBbD1XXi+D/z8CGx9Bz65DNa91C3TFgh6EiFyuhFJrSb0+usJOPssAJzG1rs5u7Ov7JWVyCfoS7ceOYLlYAcbKgpLzp+GFpYcWyuBx6BYDMbdpjzf+F/lMTQFdC6LT3hqYzxXyd42j6cKVLL8ejTT8GQjfamS2RY7CsJTsRw8SPGzz2GvqlLWu0VOXbEiNC//sDFup2i34uba86XSBgLEzYvgT4EQOT2AKkD5gXcYW8+ycppcZmSbDWdNx3tHOa1Wcq66muy5Vx410LlNwt2WHJFGfqrTZjHA5iIHlD5WAbGNryMHe6+PHqo8HqXFgMpPiU+TzWZke8dr7pzqyLLceKPTxFXltFrJv+tuqv73P6o+/VRZ7hY5ALOeg+QpkDBOETxu6isg63flubtKskBwCiNETg+gDnSLnDbcVU3ubu3l5R0+jjktDUdlJXJ9PfVbtx3/DiJcd+S1haJR5ymMLMvUrV0LgDrMVRjS465qReRofeCspxtfN8/SixqiPJa0HW+jdokcaCLqBS2oeOtt9g8fgXnTr1CwXREsQy+h8sMPsR5R0vg9GWop00GthzHzYPztyjJ9AMSO8N6pwxWbYxQiR3DqI0ROD6AOcpnq27Lk1Df+6NvLOx6XY9qyxfO8fuvW49+BIajxjl1Yc05ZTBs2Ur95M5JWS+g1VysLPZYcQ+sbnXY5RA9Tnqee5b0u6jTl8SjuKkmnQ9LrAeGyagtZlqn67DNkq5Xar95TFvY9E1udk/I33vSM82SoxY6ExwvgvJe9g5LnvAinP6CIn6bUlYDT0cVnIRD0LELk9ACN7qo2YnKa3NnaKzpuyanf3ETkbOuAJQdEUcA/AeWvvQZAyNVXo42LUxa21tahKSoV3PQz3LJKcYs0xe2uqjh01Jo5bpeVo6fanPRyLAcPYnfVyrJkuATjsCsofeFF5Pp6DEOHgiRhLy5udEe7iwM2JX40nPUU9D3De7nsUOpgCQSnMELk9AAeS05t6yKn6Y++o50ZVg27dpF/732UvvoqdevWYa+ooGHXLs96y/79ODoS3yOCj09pbKWlyudEkgi7pcmdfmttHZqj94f4MS2X+0cpmT2yE0oz29zc08vNJCw5rWFav8Hz3FxmBa0fJmMUxmXLQKUi5p/PoEtSKh2bM9r+P3uIH9dy2VEy4ASCUwFNT0/gz4gqIAAAR007LDntdFeVLnyV+j/+AKDpFurwcNT+/lhzcqjfto2AGTOOb7IijfyUxn0hNQwdiiYionFFa20d2oskQdRQyF4DpRkQN6rVYe7imMJd1Tqmtas9z211ahxj7qbk+f8AEHLlXAyDBmEYPAhrTg7mjAz8T5989B0GRCklAEozcaiCUDfkQW1xF56BQNDzCEtOD6AOVPpHOWrbiMlpGnjcDneVs6GBhu3bAQiYPRttUmOWRcAZ0/GdOAGA4mf+ieXQoeObrBA5pzR165SAY/8pp3uvaKtOTntxByMfpWGk2q8XNKztpTgbGqh3faclFYBE8fJyLAcPoQ4OJuKee4AOVI6+dinGoa9yYLGDygN+SlKBQHAKIyw5PYA6ULHktJUe3tSS42iHJad+23Zkmw1NTAxxr7yMJEnYy8uxHM7CZ+gQHCYT9Vu2Yj18mIKHHibl229a7EO2WkGlQtI0+0i43VU1uUqFVJ1fi20FJyey3Y5p4yYA/KY0i6uxtVHxuL0ExSuPNW0X+3O7qxwiu6oFDTt3ItscaHwc6FOSMaXnKm4qIOKB+1EHBwNNRM7etoO8vdD7Yz6oZGXV5PgQKiw5glOcXmPJWbRoEcnJyRgMBkaPHs26devaHLt06VLOPvtsIiIiCAwMZOLEiSxfvrwbZ3tiuAuhOWprvYr91f72G+Z9+5oFHh9b5Jg2bgTAb9JEJFdWhSY8HL/x41D5+aGNjCThjUUAWA4dQm7WKdppsXB49hxyrrq65c59Q8HP5cYoFxlWpxINaXtw1tSgCgrCZ9gw75Ued1UHLTlBCcpjTV6bQzwxOSLwuAX1axRB4xvlRD+q0cpmGDqU4EsvbXw9bBioVNjy87EVtS8l3GlW3ltzpRZHcRe0jhEIehG9QuQsWbKE++67jyeeeIKdO3cyZcoU5syZQ25u61/AtWvXcvbZZ7Ns2TK2b9/OGWecwfnnn8/OnTu7eeYdw10nB6fTI2jq1m8g/447ybt9vlfPn+MTOZPaHKONjnbt0N4iALlh505shYWY9+zBabG03NgTfCxcVqcSpvXKjYT/5ElIarX3yra6kLeXdllyRExOWzRsUSxsvkP6oh98mmd59N//5vVeqf39MQxR6hK1t0yE3ODOeJMw7RWNOwWnNr1C5Lz88svMmzePW265hUGDBrFw4UISEhJ44403Wh2/cOFC/vrXvzJ27Fj69evHc889R79+/fjhhx/aPIbFYsFoNHr99RQqgwFJpwMaXVYV77wD4EkZdeMoLz9qawdrfj6W/ftBkvCbOLHNcZJOhyrIFQvUrPqxvawx7qfVDCyRRn5KUrdWETl+p09pudLThfxE3VUF0Mxy6EbtL2JyWkO226k/pFhlfE4/E//p0zAMGUL43Xe1tLgBvuOUbuRN62LJTiemPzZT9PTTVH/7rdd4Z31jWr/pgEghF5za9LjIsVqtbN++nZkzZ3otnzlzJhtdFopj4XQ6qa2tJTQ0tM0xCxYsICgoyPOXkJBwQvM+UVRBjS6rhj17qN+82Wu9ZFDuoGWbDWcbAcoA1V9+BShWHM1Rzh/wrG9uHbLmHvE8bzVOSFhyTjnsFRWeOA6/5lk5DptSQwU67q4KjAUkcFigvvXgeZWfOyZHiJymmHdtRbbJqHRO9FOvQBMSQvLXXxFx552tjvcbp6SG12/Ziq2klPK33ubw7Dnk3ngj1Z8vofipp71aZzgbmoicHPMJ98cTCHozPS5yysvLcTgcREVFeS2PioqiuLh9QXEvvfQSJpOJK65opTOvi8cee4yamhrPX15e27EC3YHaXRCwxkjFu++1XB8S4gkutLYyV0ddHeb9+6le+jUAwXPbPnfPPsMUkdO89o41p1HktFqgUFhyTjlMG5TUcf2gQWgjI71XNi3g11YxwGOh1kJAjPK8jbgcEZPTOvWrlgLgG6tBCks+5nif0aOVuJzcXA5Nm0bZK69gy81Vii1qNMgWi9dviGxufH9tdSpsWSLWTnDq0uMix43UtAw5Sknz5sta47PPPuPpp59myZIlRDb/sW6CXq8nMDDQ668nccflmPfupfbXX5VlISGN6/390A9QxIVlX0sLSu68eWRfeBGOsnLUEeEEnHFGizHN0YSGAWCv8HZXWXNyPM9bdVe5G3VW5TTGarSHXx6Ht6YdteqtoGeoW7ceAP/mWVXQ5P2SQKPv+EGOEZfjrngs3FXeWPbsBsAwMLVd49X+/viOaSzK6DNyJDHPPUe/dWsx9Ff6z9WtWcPB6WdQ/uZbje4qSbHgmH5f0YmzFwh6Fz0ucsLDw1Gr1S2sNqWlpS2sO81ZsmQJ8+bN44svvuCss8466tjehsqVRl7x7rsgy/hNm4rP8OGN6339MAwaBIB5n7cFxZpfgHl3mud12I03NXaPPgqacEXkOCobLTmyLHuLnOpWRI5/JBiClQq2FcdRZydtCRTtgpJ21vAQdAuy04lpvVvknN5yQNNCgO240WiTY4kcd+CxSCH3wpxXCoBh0NB2bxP3ysskvPM2/davo89nnxJ8ycWofH3R91OEUum/n8deXEzZwoUed5VvtPLzb9q0qZPPQCDoPfS4yNHpdIwePZoVK7zvJlasWMGko2QLffbZZ9x44418+umnnHvuuV09zU7HUxCwqgqAsHnz0DSxRKn8/TEMUmJhLJneJdvdFyifkSPpv3ULoTff1L5jui05TWrvOCorvWJ+WrXkSFLH2jtYXRcvq7hT702Y09NxVFWh8vfHZ8SIlgNONLPKzTFEjgg8bolst2MtVzIc9cMntHs7TVgY/lOmoAkP91qu69vSGuQWOf6DFMuxaWeGV8yOQHAq0eMiB+CBBx7g3Xff5f333yczM5P777+f3Nxc5s+fDyjxNNdff71n/Geffcb111/PSy+9xIQJEyguLqa4uJiajvRm6iHcBQEBDMOH4Tt2rLfI8fNDP7DRktM0ONC0wXUXPnUK6oCAdrn1ADSumBx7E0uO9cgRrzEOYxv/w3DXj2VlO1NOnY5Gi4BV3Kn3JurWKlWO/SZObN0CeCItHZoS7Kq8fYyYnD9t4LG1Hj44D76/29MN3HowA9kBksaJ9rRjtGloB/rUliLHnULuOyAOlc6Js97c/mKCAsFJRq8QOXPnzmXhwoU888wzjBgxgrVr17Js2TKSXM3nioqKvGrmvPXWW9jtdu68805iYmI8f/fee29PncJxo2oSExQ2bx6SJKGJbOwdpPL3R5+SjKTV4qyrw5av3A3LNhumTUqPKr/TW3E1HAV1mMtd1SQmx5qd4zWmrSrMBMQqj3Ulra9vjjsFGYQlp4dxmkzeItkVj+M3tZV4HGiMyTlRkeO25FTltLpa5fcnCzw2FsHKfzQ2LT3wM+Ssgx0fwbKHQZaxbFfS+vUhEpLf0bMl24PbXeVG8vHxWHJUodH4RSpWo7p2ZrIKBCcbvaatwx133MEdd9zR6roPPvjA6/Xq1au7fkJdjDtzSpeU5Gma2dySI2m16Pv1w5yRQelLL+Osq6MhLQ1nXR3qkBBPEbD2oglzBx4rlhyn1UrVZ58pKyUJZLn1mBxQ4nKg/Q39rELk9AYa9uwl58orCbvpRiIfegiH0UhDmhLP1WrQMTTpW3WCIidSaTlAaaayz2aiqWkxwPYmGpy0FOyAd1zJASXpcM0XsP/nxvXb3oPgBCzp6QDoY4M65bDauDgkgwHZrLgg5YYGZFcxQVVoLH7RFmrzfTBt2EhEG7+/AsHJTK+w5PwZCTrvPALPOYeYfy/wVDDVesXkKBcA/WDFZVX7yy+Y1q/HaTQi6XSE3TIPSXV8b5861DuFvPTf/8a8dy/qoCAi7rlbWdeWJcffFQReV9q+gzUVNsJd1WOYNqwHhwPTFqUarjUrC5xONNHRjVWwm2N39606wZic4ETlc+O0Q9HuFqvdMTk4nU2q8J6CmMrh0yYlHrJ+B7sVDipZlQy7Unlc+TSWNKWgnyE5kc5AUqnQp6R4L3QorjFVWDx+0Yolp2H3bhx/Foua4E9Fr7Hk/NnQhIcT9/JL3suaiBy1K7029OqrseXlo42OxjB8GD7DhmMY0L9d2VQtjumy5DhNJqq//pqqTz8DSSL2Py96Ag9brZMDTUROB9xVFmHJ6SksBw4CjZW03fVSdEcrhnmiHcjdSBLEj4V9P0LeFkj0DqSVfHxApQKnE0ddHSrfDlZX7s3IMvx4H5jKICxVyU50WGHXJ2CuAd9wuGgRNpsvxqVfYMq1ACr0A4/PSns0wubfTs3XS6lbs8ZruSo8EbW/A20g2Ix26rdsIeDMY5eiEAhOJoTI6UWoQ0NBrQaHwxOUaRg8mKQPP+iU/asCApC0WmSbjaKnngYg/C/z8Z8yhfodO4CjWHICmogcWT52anFT641wV/UYloNKoTd7eTmyw+EROdrEdoicjrZ0aIpb5ORvabFKkiRU/v44jcZTN4087QvI/AFUGrjsfSX2Jm8zrP43AEbLKKrm3UL9H5tBVuL01HoHhvFTO20KgTNnEjhzJgfGT2j8fms0SKHKZ8Avyky10YBp40YhcgQeSo1mtuZUEeqnY0JK6EnrThYipxchqVRoIiKwFxd7CqV16v4lCXVYGPbiYrDb8Z04gXBXqXi1u69VWyLHz2VlspvBYgTDMWIGvNxVQuT0BLLVisUdWO5wYK+owJbbDktOZ7mrABKUlgPkbW1VHKv8/BSRcyq6SmryFVEDMP1RiBmuiL68zVBXjKVGQ8HPjVlNPqNGEhRXQWCiBXVq2+UzOorK39/z/Vb5+HgqUvtF1lN90NDuBp+CU5+duVVc/uYm7E4lYeGSkXH886Kh+OlPPslw8s34FEcXH4+9uNjjWupsNKGhyv4jI4n7z3888UBukeM0GpEdjpZdqXW+oA9UBE5daTtETtPA41PwAnYSYMnJgSb1T+wlpY2WnHa5q04w8BggZoRixagrVlLJg71jTdT+ftgB56mWRi7L8N1dYKmBuDEw+X5ledxozxBTfTJgwjB0KHELX0EXH9+lU2p646QyGBQR6xOCIVhxUVtzcpCdzuOO9ROceuwtqMHulAn101Fdb2XpzgJ251fz2tWjGBTj3S2gymTlg405RAbquWZ8Ug/NuG3Ep7mXEfW3vxH1xBP4jh/fJfsPOPss1BHhxC1c6CWk3G0mkOW2G4K643Lak2Hl5a4SIqcnsBw86PXaXlaKzR2Tk3iUwNbOSiEHRRzHjlKeN80mcuEuUGkrKDzxY/Umtr6rBBhrfODiN0Htup+MH+sZUt+gvAcBZ53V5QIHGusSAUi+rvc2IBatnwPUKmSLBXtpOxMLBKc0tRbl5uisQZF8fttEogMNHC4zcdHrG/h0cy6yLGO2OXhzzWGmvvg7r646yBPf7GXT4Ypj7Ln7ESKnl2EY0J/Q665taUnpJML/8hf6rV2L76iRXsslnQ7JFfjZKcHHtibCxtJ2F3VB19Fc5FiP5HouYtqjXVQ7qxigm6GXKo9pS1qscpdBaNiT1mLdSYnTAb8/B788qrw+62kI79e4Pigehs1F7nsW9QeVmwXfcWNb7qcL8LLk+LjirQKikVSgiwgGvJv1Cv681JoVkeOv1zIuOZRl905h+oAILHYnj3+zh6e+T+f8/67n3z/vo9ZsJ8DlxvrHD+nYHc6enHoLhMj5E9JWANkx43LctXLaI3KEJafHsRxU+oy5M/EadmwHlAB0d52mVumstg5uhl4CkhoKtkPFYa9VPsOGAWBO29M5x+ppdn8Ga55X0uZPuwLG3ea9XpLgkrexTvg3jqoqJIMBn6Ht71F1IrjLUoArJgcgUInL0YYroqd5BXTBn5M6t8gxKOIl1E/H+zeM5eFZSrPmjzYd4WBpHREBel66fDirH55OsK+WfcW1fLolt8399gRC5Ag8eEROWwUBA1x1VdolckQxwJ7GbcnxHat0qK7fpogcXULC0TMl3On/nZFdBYo47uvK2kn7wmuVz7DTADAfOIDTfBwd7nsr7npAY26GS95WUuRbweQK8vUZMQJJp+uWqambuKtUPi4B6wo+1gUrlmNrrhA5AqhzuasCmgQaq1QSd56RyjMXDkGSYHBMID/cdTqXjo4nzF/Pg2crHe9f+vUAVSZrj8y7NYTIEXhoFDnVrQ/wWHLa4bcXxQB7FGd9vSf+xm+y0v7D3Qz2qEHH0ETkdJIlBxSrBsCeL5SgXBeamBjUEeFgt2POyGxj45MIoyu2KHJwm2UW7FVVVLz1NgB+E9rfhPNEUfk2WnIkj7vKJXIClIuasOT0TjYeKufWj7axv7h7XP+1zSw5Tbl+Yh+2PH4WP9x9OtFBjb8RV41LZGB0ADUNNl5asb9b5tkehMgReNDGxmLT+PL9Dw1s+aGVRpwdDjwWlpzuxnL4MMgy6rAw9AMHeK3T9+179I0rs5XHoGOIoeNh4LmKZagyS3FbuZAkCZ/TFJdVQ1rLqsgnHbVFyqNLPDRHttkofOhh7CUl6Pr0IeTaa7ttaiovS4478NglcgzK99XmEjmy0+nV70zQs7ywfD8rMkq45t3N5JR3/U1jncUGgH8bKeMRAXrUKm8Rr1GrePoCJcbu0825pBf2jobZQuQIPITPv52y2HEYVWFs/SkHS4Pde8DxtHZoXvFY/GB2K+5Kx/p+/dBGRTWuUKkIuviitjeUZSh3BSyH9++8Cen9FaEDrbisFJFT8+132MvLO++YPYHRJXICW4oc2eGg8LHHMW3YgKTXE/fqQtT+nV8Pqy28A4+9Y3J0GqVprzU3D0dtLdmXXUb2BRfitPYet8OflVKjmV251fg4obzWwjXvbqaopmvboLjdVa1Zco7GhJQwzhsWg1OGf3yf0SuEshA5Ag+6pCQM08/2vM7a2UzMBLo6kdfkHVu0NLXkyA6wWzpploL24I7H0ffrh6aJyPE7ffLRCwHWlSq1XSQVhB3D4nO8DJurPO79Ghw2z+LAc+agCgjAsm8fR669Dtlma2MHvRynozFeLSC2xeralasw/vgjaDTEvboQw4ABLcZ0JU0Dj6VmMTlaVSloNMgWCwX33Y8lIxPLwYOY1q3r1jkKWvJrRjEzG7TcZfThTpMPwwvsPPnyH+TmGtmRW8UfWZ2ftu0OPA7oQPG/x88ZhEGrYktOJT+mFXX21I4bIXIEXpj9IzzPD2xpFmAc0geQlIKApmPccTePwxEuq26lUeSkerkpQq+7/ugblittIAjpAxp9504q5QylV1N9OWSt9izWJSbSZ8kSJIMBa06Op2DhSUddqSLoJXVj/FoT3PEuQeeeQ8D06d08ueaBx66YHL8IkNRIkhNdgiLMTBs2eMYZl7WsbSToXtJ+y2e4VREbvnYYYtMwqkTmh+e28cLLW7jWZdl5deVBvt/dOfWmOmrJAYgN9uGO6akAPLcsk3qr/RhbdC1C5Ai8MJka/az5+6v4csFWlr+zl03fHGbf9mocAX2UlZWHW9+BiwaTE6fc5OMlRE630tSSI0kSSf/7iLiFr+A/5fSjb1juChjsTFeVG7WmzZo5+pRkNOHhgFJ1+6TEHY/jHwWqlnWu3EU2j5q+34W06q5SqT1u6Jh7b8B/+nRUAQEEXnA+ALW//Yazvr7FvgTdw4G0UhJyFSt4v7PjueDeEaRMjaFMq1jSR1k0OB0yz/yQwSsrD/DQl7sxmk/cEtpYJ6djTRFum5pCfIgPRTVm3lh99GtFVyNEjsCLujqlkJMkO0CG0iO1HNpeyo7lR1j1QSbbGlxZMhVtf3DLcmtZvPtuvqx4EaPdZRkSGVbdhqO62lP0T99PKUTnO3YsgbNnH3tjTzxOv6OP6yhul9W+n1p0p1cFBgBHKUbpImtXGUf29r7Kqh6R00o8DoCjVjkvVUBgq+u7Gq/AY98mhR5d8/VNCiThzTcYsHULsc8/jzYhAbmhgbrVq7t5pgIAY0UDq97LQIXEkQA4+5J+JAwKZc7Vg7j0r6OxaiT8ZIlku4qf9yrJIFa7k1/T21Hi4yhY7U4sduU6EKDXdmgfBq2aJ88dDEB6obFHY3OEyBF4kGWZuloHAOMz/4+5T45jzvzTmHxZKv3HKXd7W4tH8JMhDCoOtbmffZuKkFFTbk/hq8rnaXAGtLig9XZqzbZeV7mzvbitONrYWC8XRbtwu6u6wpIDEDcKQlOUwPR9P3mtUrsu/g5j22myxdk1/PzmHpYtSsNc18Ydq9UEB1eA3RU063R0ytSPiTt9vI3MKqfrvNQBx/medBJNLTmSoUl5APd8axvjJyRJImDWbEy+UdT88mt3TVEAns9r2m/5OC1OitROQqfFeNW2Oi0hmHHTlarlQ12urIFWNXF21Qm7rEyWRveSn77jlfdnDYni89sm8N4NY3q0g7kQOQIPlno7NquiuA21xYTH+5MyIoIRZyVy1o2D8YlQIdkNfGe/ALkNkSM7ZbJ2lXleNzhDONhw+knlrtpfXMuE51Zxz+c7e3oqHcLcxFV13HgsOV0UFCtJjdacPd5ZVu7+ac7a1i05siyzaaliQXQ6ZfIyK1s/xrqX4JPL4NMrKHvvfnY8djuOX54CcxentB4jfdxRp4icXmHJ8WlS6LGpyDEbYe/XyHXl7FSNZ/O4v7O6cjjF+8sQdAMVh+H5ZPjyRnL2KHGPW/Q2Zg6LbjF04ETlfUu1qYizqzi/XsdFJh0bDpZTXtfxRA93PI6PVo1G3XGJIEkSE1LCelTggBA5gibUVigVZ7VWI6qGOi8To6SS0I9VhEpK6RSKqlp3V5UcMVJXZUErNTDB/38AHDRPPalEzn9/O4jJ6mBlRilmWzdZAToRTzxO/+MUOZZaJXMOus5dBXDa5crj4d+8yhF43FU1zUROfSXYreRlVlJ4sNqzODe9dZeVM/Nnym19sB7axE/bJ7Kp5mq2ryiC/xsJW97xyuzqVI6SPg6NlhxVL7DkeLmr3JXM170EL/aFr25m97v/4/AB5UJp9E/i61fS+O1/mdQbRUp5l7LvR7DUULN7EzWlDTiQqQvRMiI+uMXQsDh/AkINqJAYblEsLr6yRLAd3lnXSp2zdnK0QoAnI6fGWQg6BbfI8TG77pBtNmhScr4s8jBqhhBgDSGtup5YWW5R1fXgVsUfnKTfxkCf3/mj7hqKbQMxlpfRM/evx0dWWR0/7VEuVlaHk/TCGkYnhfbYfGylpWjCwo6rYaulo5acQpflKigBfLvwnMP6QtwYKNgGe5fChPlAE3dVU0tOURq8OwOGXEKB6q8ABEf5Ul1ST25GJbIse90pWhtsLDt0DQXmwfiryjE5lWDmnfWXMKT2V/yWPQSb34IrPoKowZ17XrVud1XL9HHlvNzuqp75JqhbCzyGxtIQAA4rdY5QNu1XiroNDsqncn8BxdHjydxQxOEdZZx14yCShzdmYQo6TsGhan79XyZYneCQsZv643R+il1W3p8GTTVTB/dDpWrdGhKRFEBtpZkhTi2g3JTG2lW8vz6buWMSSInwR5Zl/v3LPsL99Nw6NeWYc2qtpcPJjLDkCADYua2YPX8oF3eDS+Q0LwSWaz5Cpa/yQ37ImuLlwwfYv7mYtN/yAehnWI+fugp/nZI2e3B/7/ioFR2uoaq47SDot9ZkeZUA2nGkuusn1QbmzEwOTZ1G0SMPtnsbWZY9jTn1/Y8zrsZdiThu9PFt1xGGuds8fOlZpA5yuauaxuTs/hwcVkhfiqlCsQb2GxuFRqei3mglc2MRphrF4mA22fjupS0UmBXxUucSOD4BWuyyjs0hC5UU9oqDsP2DzjmP4r1K1/F9P0GZKzOtDUuOxWTjYN9LqGwwYCxvYNuybMym7qsJJOl0nj5ZkqGJyAlObHw+8lr21J+LU9YQ0zeIcVcMYfC+jxizbxER8X5YG+z8/sl+7NaTz8LZXdSU1ZOb0b7A+O++2k99SQP1VRbqjVasDr1H4ACMNKxmYkpwm9tHJCrWTxyNP1qj/X2xOWSe+VEpxpeWX8Nba7J4dlmmR8AcDU+1Y2HJEZwqVFQ2sO7ddNQodwtukSM3FznGXCIDggmvj6fS1k8JUnXdBeaklbPqQ6X3UIbOxB36LTiRkLRHwJpMSUnbloiqYhMOu5Pw+ICuOD0PxVk1LP3PdnwDdNywYBKqZv7mopoGlu5URNrMwVH8mlHC9iNV3Nqls2qbhpVKmrV565p2b2MvLcNZUwNqNfdvrOTQsjV8NG8cMUE+x944f5vy2B0iZ8A58PNfoWiXUihSo/fEqniyq2QZ9v2gPHdYMRWXABqCwg3EDwwlJ62c3/+3DwC/YD2SBHVVFgySkYnxv7HTejVhcf4Mn5HA0he3k3kwkGHnPUb41gehoY14nuPBboXPr4bqJv2e1Po245n2h06jMHICJd9Woft1B3VVFozlZs68ftCJz6WdqIKCOaIbxM6ldfiu3kForD+hMX0IO20Bof374YifxN5ffgNg5GRffEcOQBMRQWBxOrNPN/PtrwZqK82s+lBxXU28uC/RKUHdNv+TgZ8W7aGqyMRZNw5iwITWBa+b+pIGfIBVPlbsmjI+0j9HmT2eZcZH0TodjDOswLDzCBQPUlzII6716ikXmdjyNzMZDVq1xOr9Zfy2r5T9JY03DfuLaxmdFHLUOZ1o+nhvo3fcXgt6lO2HcjwCB0BnVYIMZUtj8Josy+TW5lLq7+pt09AXcjcDUHCgil/e2YvslJH6+JHmW4YkgVUyYFUrd9/VNa13WrZZHXz9wna+fnFHyzYSnYgsy2z8+hDIUG+0UpLdMrj1nbXZ2Bwy45NDPWbd7blV3Z7+aDTbsNgdOLJ2AeA0tb+EuzFTuejn+Ybx84EqDpbWsXRHQfs2dlty4sccz3Q7RlA8+ISA0w5lypzVrpgcT+BxyV6ozsVpV/SOqVKp1+IXrGfypakMnBRDaKwfkgSmagt1VRb8fKxcHPYEgwfbuOYfE5h16xBi+gaROjoSZNiwI06x1HVGEPKODxWBow+CoEQYcC7ctrpVS05VQQ1FEWMBsFqc1FUp3639m4s9z7saWZbZN+NJDvS7gqpyGwUHqtmzOp81nx1g6YqBvPu6msWPb8Yq+xGszqdPZBGSSkXA2WcBULdyBaNmKVafQ9tLKTxYTcaGzik+d6pgLG+gqkixFK/74iAl2cY2rV5Wsx1Dg7LuL9edxn/OaiBYU0itroq3/M2YglYQrCnCkLsGNr8JPz2ofOaaEJHUKHJUGuU3vK7MzLxxfQB45scMVu9rDBrPLDp2DSpPIcBTROScGmdxCmI129Hq1Eht+GI7k/T8A+hQviw7YlcyZotyR7+3cCcjY5Qf7LKGMhrsDZQG5AIQbEqk/PC3yCl38NOiNBw2J32GhfOapQo/SfnRdmh8saiVC1NNnQGnw9nCelKwvwpLvfKlqi6uJyrZO17hi/1fUGwq5q6Rd6GSOq7Js3eXU3S48cJ2ZG8FManBntcVdRY+26Kc251npHJaXBBatURZrYVdedWMTDz63U9nIMsy//vjCM/+lMnAmEDeKswBwGGWwdYA2qNbY37bV8Lad39hLpATEE18iA/5VQ38vq+UO89IPfrBawoU96OkhpjhnXNCR0OSIGoo5KyD4j0QMxxVgLtOjuvOc99PmKs15KyIxC/KTN0ApWaHX7Ce4ChfZrgsIFaznfL8OqqKTCQdfhr/nHyIPo2y+jLWF6wnuyYbVaoezc4B5Bfo+TBuEhc1VBJ8PPNtHn9mqYM1LyjPz/o7jL3Fa3hdlZnCQ9VUFpqoKqqnJKsaWVITUrUf/eix2G1OJEmiLLeW3/+XSWz/YLR6DVq9mrA4PyKTOj9up2B/FXnFalQaiUkXp6L301BZYKKi0ERlYZ0itmQI86tkuv7/kCpuhdQzCZg5k6pPP6Nu5SoGPPF3dq7Mw1imCO+qIlEosCn5+6s8zy31dr56fhtIEBhmIDjKD12cD8XhGq47PZkjh6qRkDBKTsYNiiSoIAOAitCR1BfDG0wlILE/84cCh1bBgZ/h4K8w/nbPMXz8dfiH6qmrtBCREICl3k51ST0XxIayNKCQIxX1HKlofI/2FbdD5IjAY0FXU5Jj5OvntzF0ejxT53ZRvZIm5FcUkMJASvxz2JL0A7egfMjf2/Emfxs4mii/KHKNigDwC9dg05jR2g1sKzRS+uZubGYHcf2D6XN+Ipmv5zFOo7i5JJ0ftZJMCBbssh5jhZngSF+vYzct6lZT5i1yPt/3Oc9ufhaAcTHjmBAzoUPn53Q42fSNkg0WFO1LTXE9R9IrmHBRY2+mDzbm0GBzMDQukCn9wpEkidNTw/l9fxnXvbeFD28ed0wz74lQVmvhr1/t5ndXqm5OXj6OqmrAB6ddhVx2CCn2tKNuP/9/O7izVMmOGjZlFEtun8jkf//GjtwqquutBPu2bk1TDrheeYwcDLrjaxrZmnhtF9HDGkUOoA5S3B6ewON9P1J1yA/ZAdUlQdj6KyLPL9i73YTOoCE2NZjY1GDYrlgXy0MTOPebc2mwN1rBJkRdyIiiM8mrvJbr417kbVMx0X4tU3NbsPIfsOsTuPhN6HumsmzzG2AqhZBkGHWD13CzycZnz2zB2swyqbGZGJD/I6M+VwKtc9Mr+fG13eRmVJKb0eg+k1QSc58YS1hc52Zhbf0pB4Ahp8cxfEbL/mWWeht2mxO/rQtg/UFPzSTfMWNQBwfjqK7GunsHlz86huLDNfy0KI2aMiFympK/TxE5/cdFUW+0UpZbi6XejrHcjLHcDOlgkmR+0mhw5rluAH1UBPloIW8LAPHDpkMxOFERPHQWjEuEpMmKyMnZ4HHvuolMCqSusoyIhADUOhXVJfVkby3lsXMGcv+S3V7zyyxquwaVGxF4LOhycvaUI8uQvrbAE1TZUZ5blsmjX6fhdLbtcqmpU4SGRaN86awq5WKYV5HFzK9ncvequ/kx60cAkoKScEYp5tjMujnUVlrR6CTO+cswfnBV3ZwYr3wBNT7+lEtBBGkUk3Z1sfcPoizLZO9pNKXm5DWavlfnrWbBlgWe18tzlh//ybvI2FBEdUk9Gl8Nb9hqkIHyPOXOVZZl8irr+XBjDgB3Tk/1ZOu8MncE45NDqbPYeXXVwQ4f/1isyixh9sK1/L6/DJ1GRYivlrGq/djNjV9PR17GUfexdEc+VoeTQWbl/zl40kjign0YGB2AU4Y1B45S5yTzB/jhHhyyhp9K7uaXt/cgH+Xz0pSqYhPv3L+WFe+nt3sbD9Eu0Va8FwC1y5LjrDFCVQ7Ogj0YjyjCxqIPBkArWdAZNOB0KjVFHE2ERNYaxX2k0rDOYaTB3kC4TzjXDLqGawddS/IZATh1VkIbYvApm8DlP1zOO2nv4JSPUfTxjzeUxpv/uxgO/KqktG/4P2XdmU+C2rsq7KHtpVgb7PgEaBk8JZbTL+/HrAuCmbj5HwRqTEiShCRJJA4JZdrVAzhtejwDJ0bTd1QEQZE+yE6Z7b8caWUiHcNudbD+y4MUHqxGpZE8Lqfm6H21+AXpIcyVleeqmSRpNPifNQOA2l9/xeCnJbZfMAANtTYs9SdpQ9VORpZljyVHPyCQC+8bybyXpnDTC6dz8YMjGXpeEiZJxk+WSN9dSnGOqwJ2mE5xn5Yq3/EBY2ZwWlwQPlo1U/u7stgiB4F/NNgbIPcPr+MOPzOeyD6BDJ0Wx5DTlRjJI3vKmZEY5rkxGxKr3DzuL6496rUARAq5oJOoLqlnxeIMZKeMj78WvZ8WH38tfUdHUp6nxLE4HTLpawsYd37baX9Op8yGLw+i1qqYeHFfr3TaIxUm3l6r1Eu4cXIfBka3bgK3mZXjhfglcGHibVjlJUAFQwMHkCUfYHX+as/YhIAEkoaEU1wgo6uZCECctBXtxvX8tHM8AFMjSnnSHko/Pw21mmCCNYVU2JOpKqmnj2s/2TXZHDh8BFNl4wVmT9Y+ZjKKveV7+evav+KUnQyLGEZaWRorj6xkfsK9+Bj0BIa3I4jWhdVsZ8uP2QCs11kpUhdQrIklxq6j4EAV/z1c6OmU2zfCj1lDGu/sg311PH3BEOa8uo7tOZXYHU6v4lhOp8yGw+UMjQ0ixO8oVpI2aLA6eHZZBh//oVjJBkYH8OqVI/n4jyMkbsv0FjkFB9v8ssqyzJKteUiyk7hqRWi6a+RMHxDJvuJanv0pk4LqBk/jvCYbw4/3g93MLp9HycmJgOIycjMrSRoS5hlWV2Vhy49ZqDUqpszt70lpzdlTgd3q5MCWEvxDDEy8+Dg6l3tEzh6QZVSB7hTyWuTMn6grMOC0qdDExKAZNBmcYLCWQ/khWPuC0v9K5w/xYyFxImR+r+xvzM38UbyTs3c4GTVuAteOe9RzyLTaNNb9UM7E3PP4LHQP/7fz/wj3Cefifhe3Pc+mfaiWPajE3liMyvyHXNJi+L5Nyudp1KwkRpylCArTpnxy7SbUTeJ1JEli6NQ4r23Lcmv54rmtHNpWwrjzkgmO8rZ8doQ1nx9g30ZlTmPP6YN/iOHoG7irXZc3CvvAmTOp+epralesJOrJJ9H5aPAN1FFvtFJd0kBUcsfK/59KlB6ppcFoxS7BzT+k8UWCP6MSQ/AN1OEbqGNVhZFDWgfDrRpKC+swmCS0QFi8P+RvBWQI6YMqMJrPbgun3monMsD1XkkSpEyHtM8h63dImeY5bmy/EC5/tDGOLm5AMAX7q8ncWMTCuSN4c81hbpqczDmvrqPOYie/qoHEsLY/V40xOafGeyosOT1ExoZCSnOMlOXWkptRycGtJaT9ns8vb+2hLLfRpLh3XSEOe8s7TUu9japiE7tW5pL2ez47f80lN907Y+S3faWoZAhySOw+1Ho2SUVDBRq78jGIDo3l/rG3YVMrF+yHTnuA7y76jhsG30CIXrkjmBAzgcnjvWM2EnS7kdb9h5fNT9DfUMXW8qV8F+DPf1TVZCSuJUCjBL5Wl9Rjc9p4Y/cb3PTZfLb8T1lulxT3VnVpA1uLt3LnqjtpsDcwOW4y7896n1BDKOY6G0uf38EXC7a2mXZ7JL2C9V8cpKGuMSts18o8GoxW6rSwSZ+Fb8qrVAcr7pHtGaX8mFaEJEFyuB9PnT+kRT2K/lEBBBg0mKwOL1NvvdXO/I+3c917W3jy272tzudYPPHNHo/AueX0ZL69czIDogMYnRTCONU+HE1Ejq2w7V5h245UkVVuoo+tBpXVgqTToUtULq6XjIojyEdLaa2FF37Zz+GyZkUZyw+CqYxa4thWONGzeM9qJcvM6ZRJ+z2PT//xB5kbiti7poDiw9WNm+c3/k92LD/iucCDIr7KcmvbTpMO7w9qHViUu1h3xWMcDuQ9P1KdrfwQB110IdJ4xZKgNRmRF5/f2ODTWqf86K9+TglU1gdi6XcdZz6znFuXOxn1yq9epRCGnpFIhOYQWocvV1XcC8Ca/KNkrzls3oUsq3MVVxXAjKdB5f0TWllooiTbiKSS6D+uUTC7a+Qcq9pxRGIASaeFIct0SlCvw+bk8Hal4OLMeUMYc07ysTcKdwnh2kKlQCTgN2ECqoAA7GVlNOzaBeARYNWlfw6XVU5aOb++l95qQcRt2RV8/qbiGjqksWMH/rfJ2xq3Zn8pVSqXFaXahrZeCTpOGRDqcVWRoLjk/fWaRoHjxu0qPfzbUec5ZIoinDPWFxIbZODZi08jNdKfflGK+/OZHzPIq2z7PTvVYnKEyOkh3Nk9w89KYMYNg5h8WSo6Hw0NtTZM1YqLyidAS4PR6uWvd/PT62l8+vRmT5l7gD++O+zlMvgts4TravXcVmug7OPDpP2R3WI/+yr3obcrP1bhIf6E+ulwqJUPd0VVHSlBKTw09iFWXr6S5Zcu58zEM4mKDcHq21hrJv7cyzCr/BirOsB30gN8rVHmr5bUVGsKOOinuMPyc8p48I1/kv+xmst3PUJoQwwmbTVrkr8FIMAcys3Lb6bSXMnA0IG8NO0l9Go9ZyedTXRtCk4bWEx20n5T4k7sVgeFB6vZsfwI37+6kx//u5vdv+Xxx3eK9cpUY2Hnr8oPzSqtBb+4H5EkJ7U+irVjR2Y+SDbuPiOV3x+a3mgaboJaJTHGZfLdkqO8D8U1Zq54axO/ZiiFD3fnV7f2FntYd7CMSQtWcfU7f/DdrsZMp/WHlLLtr145gifPG4xBq1gMxsRoGSJn47A2WhBq8nLa3P8aVxzPhUFKMUddal9P8cD+UQFsfnwGg2KUi+uB4mY++dyNAGx13I7d6iQ0VonHObK3gvR1BXz9/DbWLTmIzexArVV+Lg7vbHR9ua2OMalKPM3vH++j8GA1RYeq+fyfW/jiua388nYbIlCjU2KAAN6airTnE9Aqd4+W9O2YihW3Z/CFF2KPUESbzlyD6WAFshOsIx5CvmUtnPMfpbt5xEDk2c9z+O/PkFis/FBLdfXU/9Fo3lcZAjkj6A0kHGhzwgk1xbK5aDN2ZxuZffWumDFJBdMeaVyedDqkzvAampdZyXcLlYKKSUNC8Q1stO45PSLn2HE2bgtadcmJi4fCQ9XYLA58A3VKhll78AkB/2iqVCq+3PUWddY6JJ0O/zOmA1C7XOll5RE5nTDPk4FVH2VycGuJ5z122J0UZ9Xw8Qd7+OLlHWiqbViQ+d1HEfU/7SmioLqBHblVfLL5CBsOVVCtUm5YE23Kd6lOkhmaHAJ5SiwZCePankDKdOWxKA1MbdfhSRkRgU+AlvoaK0fSGsfNHKyI7pWZJfzlk+1e2xTVNPDF1jzMNoeIyRGcOA6Hk1KXP3bw5FgGToxhxFmJJA5prDIbGOHjuRM8uKXYa3ubxeGVKRQ/MAStXk15Xh0HXBWH6yx2Mg5VEelU3mIJiV9WbGoxlzV5azG4RE5YqEGJF9ArF5fKysYLok6tI9Y/1hNPEJSqfAEatLVsTwriXM39fKFN4asAHcUaDcGSlvtH3w/Az6HKuRrzbQzYM53Y2lRAxhzdwNdDXuOAQ/lR97MFoXHoiPGL4fUZr+OnVS6484bOI7G+sfbIrlV5fPnvbbxz31q+eWkHm745TF5mFe4s+AObi7HU29i5Ihe71Umh2klB1C6c+mzUkppavfLF15NHQOrLjB989IyDscnK+7Ilu4K9BTVc9PoG9hYYlWBBoKC6oc32D3mV9dz16U4Ka8xsPFzBfUt2UVxjxmxzUFqriMEp/bzFVVztbmSrt0XJXl5MeZ2FsloLpbVmas2N1pG0AuWzMNSiiA9Ds0rHBq3a45M/UNLMkpP7B0Z7BPvLlQq3Z1w7kMQhYSDD6k/2U3qkFp1BzbSr+jPzZmVM1s4yZFnGbnN44qzOvnkIfUdF4HTI/PzmHn54bTeVhYoQLthf1erdr8PhRD7nP4qryWlHWvE31P7Ke151wAdkCZ9Ro9D16YPJqPzw6i01GBuGUVZzFocf/ZQDF84j992tlFdNpGHyWxhz/ZB2pmPVQO4g5X2rXbGy8aAqFREBlfQ1KOJuXPEc6mx17C1vQ4iZXILONwzG3Q5al5n/rKe8sq1qK80se3MP9UYrIdG+TL7M+z1weJpzHjtryu2ONZabjzn2WBzZo3zWk4aGHV+mZuQgngoP5Zl9H/KXlX+h3lZPwBlnAFC/Tcm+dIucwoPVZO0qw3GSNrRtL+6GsJWFJr5csJV37l/L1y9sp+aPMpJsyk3Fbz42kuIDGRwTiNXuZPK/f+OSRRt54pu9NNgcaIIU4at1/VjZAtSkhhsaa1QljG97AgFREDkEkCF7dZvD1BoVgyYpsTnp6xpvqu6ZkcrXf5mESoK9BUaPNWdPfg3n/3c9f/06jX/9lEF+lbL8VEkhFyKnB6jIr8Nuc6L31RDSxOeeNLQxBiIiwd/T+Tt7dzlWc+OdpvviodaqOO+u4Zx7xzBPMOHazw8oxboyS4iQvS1AmuJA9pY1/pjvKt3Fkv2feyw5oS5fvcolcqpq2u43dfbZ45AlJ/sjtvDg2nspjfuIf8bbeSFMsXpcmHI+Vwy4ghB9CAcCyjFrlDkbfaoZPCeSGxaczq6EcEpyHqCuZhL1kmKB+njyF3x9wddE+jbedcb4xzCWqQA4cWIzOyjNMeJ0yvgG6ug7MoLJl6Vy1d/HExrrh93qZNfKPNJcLpdNPg2EJCh3n3eMuAOny/UWYA4DTRX3rb6D0vrGHkrNGddHuViuOVDG5W9uothoJjXSnx/vPp0gHy2yDNnlLasom20O7vhkBzUNNobHBzE4JhBZhuXpxRRUK1k/fjo1Ib7evm/pyEYvVxWAb4OR8f9azthnVzLu2VUM/8evLE8vdlU0rVb+T5WKe6O1dg79XabqA6Xelhz5yCa2mubilFXEDwxBCtcz4rIUTpseT3iCPwPGR3P1PyYwdFo8iUNC0ejV1FVZKD1SS1VRPU6njN5Pg3+Inhk3DiYiMQCzyYbN7CC2XzD+ocpnqWkzzXqjlY1fH+Ld+9ey9DMJy9zvlbgaWz0qjR2rNoCaXOVCH3TxRQAe66beUk1toRNjhnIeTpMJ09p1lL30MjlXXkXhI0r8zQ/jJNTXKHE2tb/9huxoIkINQYzy+waApLKhBJhD2Vi40ev/UmIqYcWRFTjqlJsG/CLALwxu/BGu+6bFHff6Lw9itziITgniisfHtoilOR5LTmC48j00ljeccI2mnL2KtbDpb0t7SA+N43c/5Rx2le3i/tX3oxqguLEshw4h2+0EaBURVniwmp/f3MPXz2+novDk6VF3PDR3uZYeqcVhc2JVw0GNg+JkA5c9NoYXHp7Ep7dO4NapjW7BcH89U/qFc/vUFJ6/cZTXfsaPiEYqzVRcovpAJcD4aPRVhOaxXFaDXQHIuRmV1LjS/SVJYnRSCGNcbWp+31/KuoNlXPn2JspdLv6P/8glp6KeAL2mS7NJu5NTQ6qdZBRnKZaDqD6BXndXSUPCFGuEDOHxAUQkBhAU6UNNaQMHt5Z4fK3uH5KYvkGeH6+Rs5LI2VNBSbaRX99P57/OGlI0ZUAMmSEHSKnpg97hy3u/f8IrVyyg3lbPY+sew4kTvVkpf29wBc9qDcqFqaa67fYHCf3CuO6F8eRs+wVNrh9Wm5pAvQ/RgQGE+4Rz/ag78dH4cN3g6/i/nf/H74NeILBiEhtNU9haUMOLdhsbD1cAEjdO6kP18iJ8HRKG+gACdN5VPB0OJ5Zi5R+zov8H/CX2PvqmxBOdEkRAmMETbH2otJbSKC2aQti2LAeAErWTyHHbSG8oJ84/jhuG3MBZobX8nJ5GgC2UlIC+ZNUeZkfpDmb3md3quZ4WH0SIr5YqVxbJlH7hvHb1KIJ8tPSN8GNHbjWHy+o8LiE3z/yYwZ6CGoJ9tbx+zSh+2VtMxk9Glu0pIskV+JcQ6tuyS++RjdjNzSpEW+EhzRcstF+KBR1OGR5buofIAD3V9TZ0KgljOdjChxHbt6XI6Rep/E8PNbHkOKsLWXdkJvsaFLfLkLMTmL1wLU4Zfr53ClODvcsXaHRqkoaEcXhHKas+zKTvSMUCFR4fgCRJaHVqzr1jGN//3y50Bg1z5p/Gzl+PsGN5LnkZlSQMCmXXilz2rMnHblXu+ouzjPzwWhrnnPcUO9/7kvQBZ2HT+BNTtImBOV8TOFt5T+pcIkdrrcZZXoQTQK0m6X8fYd6zB9OWrTTs2oWtsoL8cMi7aCyPzb6bQy98iaOigobdu/Ed5brA+AQTod1NQpJM3hEVE3IvYHnUcm4bdhtO2clHGR/xyZYvCKiJJHdIBPMA/JTvSGvVoIsO15C1swxJJTH9mgFodC2rex9P36qAMEXk2CwOzHU2fAKU76Usy5TXWcmtrCevsp7cynrsDidXj08iOsg7fsNY3sC6Lw5SU9qASiWRMOj4epEtsioWgFHoydSo2Fi4kafUi7jVYEA2myn+57+o+f43GP+0soGkBE1/+dw2xl+QwvCzEtrst9QWHS5F0A0YyxWhoPfTMHp2H/S+GjZU1/Li2kME+mhYdcdoIgL0RLnGXzwyntPiggjy0RER4F3yYHuwjvpqRVQkpARBnpK5SvwY7yD31uh7Bmx6DQ6vblm7qQlBET4kDg4lN6OSjPUFTLy4MeHgzEGRbMmp5O21WRTXmLE7ZSb1DcPhlNmcrdyM3DOjX4eSKXojQuR0I1aznZWLM8jerdxdRTUrh+4ToCOuvxIZH9svGEmSGDgxhs3fZbHui4OERPsR2y+YinzlQhUU48ui1Yf4dHMuSWG+vHbjML58bivFh2qI9i8nSqX88MmBseSaHPSzQvmhBvJq83h/7/vk1+UTpI1EZwsGwOCnWBR0vsp2NbVH97UHBQTwtwkL+H7lSqwOJ1/eM4XBsd4/4recdgujdn7JgPItFE26kUs3aNmRW825/7cOh1NmYHQA0wdE8OWKAmIdKqqapZnnpJVzcFsJdpsTm9ZMdmgauok19E/yrsq7OauCWz7aRkODnXuiglGXKBfFP/yqKLcoPyIPj3kYvVpPcpwOtVaFw+ZkrN8ksmoPk1mR2abI0WvUfHfn6aQX1hDoo2V8cqgnyyolwp8dudVklXkLwqU78vl0cy6SBAvnjiA+xJfZQ6P510+ZbM2pZGKeIk7jQ5plOdRXQsF2HGbvr6bTquIvmh/4S3gattkvcP4vvuwrruX2/20n1CFxdZ2BHeEXQzgc+B4GlB1g8ORYT60Vd9BhVnkdNofSDPDXt/aQ0zAHcHL6FQNIt1k8Qu6Jb/bw/o1jWwiw8RckU3y4mqoiE9tclV3D4xutE37Beq78m2LlkCSJhMFh7Fiey+FdZRzeWeoRN5FJAQw+PZZN3x6mJNvIR2+qcNgu8vwiFcVMxBo3gGT0qM12ql39xsoCqohSvj6U9Qli0KhR+I4aRegNN3DEeIRLl56LRtLw/Rn/Qq3T4ztmDHW//YY5M7NR5BiU792k8TV8kRtM34qRZB7ZxD//+CeZh7OIPjCYi8sfRi2ryTD/4joxRdA57E5kWUajbbwY5aQpE0odHdlmbRunq1WFu6ozgN3h5K21WejUKi4aGee5GGq0avyC9ZiqLdSUN3DY2MDT36eTUWSkvpXquV9sy2fxTWMZFBOIw+5k54pcti/LwW5zolJJTHLF+7WXPWV7WFt7GLUs80ylkcIrP+TOVXfya95KLo4OICzHTPWSJfgCfY/8RPSD99J3VCRrPt3Pkb0VbFx6iOy0MmbOG3LsTC4XG5ceYs/v+Vzy19FEJHRte5eO4LaGhEb7MfLsRAqqG3jl5TSQ4LFzBinvXV0pHFiuuJwi+pMa2fp5BEf4ekROWJw/bHEHHR/FVeUmcZISrG/MV5IGItquozZkShy5GZVkbiwitl8I/qF6wmL9OWtQJP/+eR/5Vco5nT88lv9cPox9RbVc+sZGksJ8uWFSn/b/c3o5QuR0I9m7yz0CBxqDNZsyc95QqkvqPXUoRs5MpDirhiN7Kvjp9d1c9MAojyXn60Ml/LBTuUPMr2qg0GFjyPkJ7Poqh0mmEJySIjiSk5PZWFpAPyCuph9Pb3yaLcXKF2uM73x8XHE7ej/l42BwiZy6NkROemENB0pqmZwazu/7SrE6nPSP8mdQTMsvtSRJjPZPAnkz/XxNLL1jMjd/sJVclz/4nNNi6BPmR6HGyWAbZO8uY8ycPp4CfrtW5nn2ZY+oBUkm2+gdQL08vZi7P9uJ1e4ECX4KdnD1+HjeX3WYhv7fYXNaGR8znjMTz/TMKTDMQFVxPX0kxeqxr3Jfq+fqJjHMt9W0y74RykWtadbSvmIjj3+jZHDdc2Y/pg9QXG/xIb4Miw8iLb/Gk1UVH9IkHd5cAx9fAk4bdnUs0Ggid8ROhcCdUH0E7edz+aLPHCaWXUlpLUy2avCxg9ZWh0q2YyGYtN/ySfstn5jUIM6ZP4zYIB98dWrqrQ4O5taQ8cVhSrK1qLEybMB2ckIHeVLpAX7fX8aqzFLOGhxFU0Ki/bj8sbEsf3cvRYeUWKCIZv1zmgqjmL5BaPRq7Bbl4hyZFMDY85KVGBFJIjoliJ8WpVFbYUatVTGy9COseUbSB99EhSaUr57fRvygUKxmBya/Kg5GFzDU9favjapix/aF/GXEX9Cr9azOWw2SxOiYMSQEKMXudElJANhycxsn6BI54f4VDJ0+lD2/5zNz/80UF2UxtXoKUhMvfnjJWHLD3yPRL4L6Wgtv/3M5mDQUjt1O9Gl+9AvuR+luX0CChLZdNY46ZZ3KX/lfybLM375L91TZfv6XfZw5MJK5YxMYHBuIzaDM4bGPdrDBasbqineRJIgN8iEh1IfEUF+2H6nicJmJuW9t4oOrx7Dvs0OetgJx/YOZeuUATzB5e3l1+2sAnFtXT5KxgqSgfrw49UUeXPMguwOrObPJ2AG+R0h2pcmfe+cwMjcUsf7LgxQdquHXd9O5+MFR7YoFOryzDLvNSeb6QiKuar33V0/ituQEhvsgyzJPfbeXequDMUkhzB2TADv+B9/fpQyOGw23tu1OCor0ofBgNZJKIjTaD/JcgfFHCzp2o/NVYtiy1yhZhUcROUnDwvAL0mGqsfLja7sx+Gu56fnJ9I3wJzXSn0Olddw8OZknzx2ESiUxPCGYVQ9OI9hHh07TOy1qHeHUOZOTgOImwcKTL0slfkBLn6dvoM4jcADUahWzbh1KTGoQVrODH/67i7Jc5QdzS1UdAQYNmqBt6MJX8cGub7m/4BYOhe1ALavROnVKCfdRMeRolB/JWGN/9uUoGVmXpl7Fqu0B6NyNOV2WHB+XL76+zlvk5FXWc89nOzn3/9Zz/5LdTHhuFf/4QSlgddHIuJZuFzf+rvgaUympkf58c8ckxvUJxV+v4eKRccSF+HBI78SJTGlOLaVHjPz42m6PwAlx9SfyHaxc9HNqcjy7/nxLLn/5eDtWu5NBA3bgl/ocWdafeWdvHvlBB2jQ7kYtqXl07KNe83MHd0Y54gHIrMjsUPxD3wjlAtLUkvP09+mYbU6m9AvnnhnerqOzBymiobxOsTQlhLqEk6UWPr4UOX8nJXsjKduqzEXlq6x3SEFw5xaYdDdIagJzfmbRoHQAIh3K17hPzjJmNHzNeXcNJ2VkBCqVRNGhGrLTylCpJPpF+qOS4ef/7qYk24hWMnFh6FN8XGbm3s93scKVLebOJtua03rZAb9gPRc/OIo5809jzDl9PG6r1lBrVEy+NJXk4eGce+cwLnt0DH1OC/e8F2Fx/lz+2BjGnpfMxQ+OIrF/DOGV6YzPeZ+AcAPGcjMZ65RYo60xP5PRpI7dnj4S7+19jyt+uILdZbv5LVe5sJyRcIZnjC5J2cCa0ySd1xCsPJqrmXBBCjGpQeicBhKrByOhIkG/mYtCn8SuqifQEsYXmrGkq1R888ZWtEZ/tA4DSX9MJvPHSp75/TkaipX36oPqRW3+H5pbct5Yc5jPtiiWviGxgdidMr9mlDDvw21MXPAbOyqUm5eGKitWh5OzB0ex8oFp7PvnbDY8eiaf3zaRFy4bztK/TGZ0UghGs52Fb++gqsiET4CWs24azIX3jzwugeN0yny0Yw2bSzYiyyrOq3TdA5dmMCNpBn+f+HdyI7y/4+rwxlgfSZIYfHosVzw+Fo1eTdHhGjKblBRoC6vZ7mkTkbWr7PiLSnYDbktOYIQPy9NLWJlZikYl8dwlpyluuT1fNA4u2g3Otru0B0Uovz3BUb6oG0qUsgSSCuLa2TPOE5fz+1GHqdUqTjsj3vPaXGejsqgeSZJYfONYPr11PH87b5CXWzEpzI+gZjGCJztC5HQj7oyo2bcNZcRZiW2LgmZodWrOvXM44Qn+NNTasDbYcSJToZZ584YB+MR+jT5iBb+U/YcGyliT8Av4KaImPD6AMSmhWHxUZGtsqGU1o/NmkxKUgrlkFk7XHTYSHpO2X4DyJTSbzNTU2/huVwF/+Xg7M15aw/e7C5Ek6Bfpj1OGeqsDnUbFhSPiWp070Chy6pTg3jB/PUtun8COv51NQqgvWrWKkFAfcl1C7OsXtpOXWYVGp2J3ooaXJCNXvjiZlNFKXESOMQdZlnntt4M8unQPThkmD88mX/UFKq0RQ/SPlBsW4xujdLCeO2AuqSHeRfDcIsenIRC1pKbKUkVJvXKRN9lMrM5bTVHdsX+gU1yWnKyyOqUBYrGRP7IqUasknr90GOpmd7HN09QTQnzAaoJPrkDO20ZexTDSKs+g0k8JXNSlKIUgHTU1oPeHmf+CKQ8CMMUvn/HJoZ4MOv+6Agz9UkkaGsac24ZyWoLSFb4s/RAAqZEBxDhUGOqdWCQnl4Q+SoxuH1VhjcGQfcJ8uWCEErTYoqZOEyRJImVEBOMvSGk1BqUpQ6fGcc5fhnmJm6b4+OsYd14ye1Vb2RWkXNwLJgfy2aDnKAxQ5l5jKGd/+FZKUkJRh4ehiY7mtqteIswQRlZNFtctu46dpUpqr7fIUSw51lYsOZhr0PlouOiBUUy+IIyBoZu4MuxeLoheRNysi/ELULIRq6ou5qPf46jOsmJTWajso9wkjCg6k6vSH0NCRaVPMXsbdlJnbf1/5qhRvvuqgAC+2ZnPC7/sB+Cp8wbz0z1TWH7fVOadnkyonw5JklEFKYkGF6ZG8s0dk3j7utGkRvqj13j/r4N8tXx48ziGxwcR6krGGjkziQHjo9v9+1JqNDP3rU0M/NsvPLfhLQDsNSOptbkukK4g14tTL8aR4v09r6n0zvwE5eI97jzl87vpm8NtNqh0406kADDVWCnJOXZ/pe7GLcL+vfYgD32p1MO5fVoK/aNcVsyaJk1wnXZFuLRB/IBQVCqJ5GHhkO9yVUUOAUM7+5W56+XkrFPqOB2FUbOSuOyRMUT2UfZdekT53yaE+jKpb+vfx1MN4a7qJiwNdo+bKbpvSzdVc/Iq63E4ZaKDDBi0avQ+Gs6/ewTfvLSD6pJ6qlUyUSE+6HzKAOXOR5bV2ConcnrUdVxyxxBWfZDBkCmx6DQqJqWGsy6tlOQ6Lf3LxzIj+Awe+LWEYFn5kOt9NR5F7+vvixXQOGyM/tcK7E3urE5PDeexcwYyJDaI4hozh0rriAjQExd8lCrEft4iB5SLpE7T9A7Cl8yiKvrY1TgdMoHhBuTJ4fy6Rqm6uvpAGYP79AHgiPEIT3+fzoebjqDSF9J/0HrSLDsASPQZyZH63WiDlB+iYH0wd4y4o8WU3Bks1YVmUhJSOFh9kI8yPqKoroh1BeuwOCykBqey9IKlR/0hSArzRaOSMFkd5FU28JGrANisIVHEtvI/GRrXJIhZhlCzhbSXX6Og6HQKbfMxOwPAZYGOLv6DsclG2LsXh7HRCihHD8Mu69CW7OW9q0fx4c4NAATU5aPvN1cZlPEtkdXLgIGUZhwC62guHhlHxXYlJdovpIFwbT6O4D58dt/FLN6QzbM/ZXL9xD4eF1zzOKOu5JPMT/j3ln+jDpVJvElNdtRWsEs0jPyM2JIB5PhmIktO+sYOIeWbf4JKRb+wMMYkTuSFrS/w/WGl2vHA0IHE+sd69usuimjNz0e225E0GvAJVlY2VAOgUkmMKHkcdOshIAau+RmC4jlj5ZksYzqxRsUaZ1NZ+LX/Yh6//B5iSvuz6sNMaFD+V9XhBcjIpJWnMSl2kte5OaqrsRxSxNrCgzbePaB8Nm+dksyNkxUxMCA6gL+dN5jH5gzkywNf8eVPPzO4/Dp8bPIxm8P66zU8fcEQVqQr3wFnaPsDRp1OmQe/3M3m7EokTTV+AYp1cHjgBfxcsoWZ6u2wfiHEjUEadB5Tp14H7zzn2b6movWChcPOjCft9zzqKi1kp5XTb0wUsixjNtmor7FSb1T+7FYHNou3CMraWUZ0yrF/I7uDqmITab/lU3CgGoBip4M6i5PEUF/uPtNlpZVlqFEyOdEFgLVWiZcJbb3wYlRyILe8MhWtXg0rPlIWxrfTigMQdRr4hkN9uVIpOWlSm0MlSSIqOZDYfsGe4rNMVoK8HXZZmcMpjhA53URJVg3IysXVL0h/1LEbDpVzzbtKcSi1SmJgdAD3zujHzCHRXHDvCD56fScbqmoYkRhJjlG5q1Q1DKIm5zr0Gg1P3DSCmBBfrv1nYwXbaf0jWJFRQmmwishqJzlfmgj3gQlJoZBej6GJiVLtSiHXOezYnTIpEX7MGRrN7CExDI0L9Fz0o4MMLbI6WsW/pchpTnK4H3/oyjlbpyE1KZgJV/Zj1qINnvW/7y9jtEvkVFuq+d++dRhiN6AN2k2BBTSShqsGXcV1/e9i1pvvIUV9iFOycNeIuwjSt/zBdKf4Htlbwekl13I45Rn+l/E/rzGHqg+RVp7G8Ii2u3Jr1SpGJ4WwObuSxRuz+WaHckd3w8Q+rY5XqySm9Ivg+92FzG5Qsfmdw9AkykHtsOBfm0dNcF+KoyewK0gile9x1NRQU1bP/j+K2b8piNrKTznX/gLqAuXOzMdWjcZhRt+/vxK8vOxhIlyF9SoaYnD+9jynz34GU2o0h7aXMiauDMpA7fqBvGlyMlePT0SvUVNUo9y15lbWY3M40XYw46WioYKbl9/MhJgJPDru0TbF4saCjfx7y78BSArtS7Y6m7HRY7i8/+XMSJzBqzteZU+G8lkYHDYYTUSjNSxIH8Szpz/L7D6z+Xz/51w76FqvfWtiYpB0OmSrFVtxMbr4+CbuKpdwdDqhwFWn5JovIXooAH2DG7jU8Xd2ms5njy6ZX/v8DynCwsSYiajj1ITF+vPzW3uoLDThP8AJJthduruFyKlbvwGcThri+/DugXpA4qwxxaw3v8bKIw9yVtJZjfNVq/jm0FcYDcp74HaTFB6sxi9Y73F1NKevr4GNsoQDmafWHuDVRH+PWD0aH2zMYd3BcgxaiTnT9rOywMm46HFM8R/J0/t1XBBQwBm138PSW+Gmn5k16grWxvyH+CIlcFY2mbA77WhU3pcStVrFwAkxbFuWw941BeTsKScvo5KG2rYtDwGhBmorzRQcqGpzTHez/suDXpXkq1VOHprZn/OHx3qKd2IqB4cFkJR2C/t+VJqb9p/Z5n494sLosgCFtt26pwUqlXKcvV8rVrajiBw3ka64udIjtTgcTr5fuIvyvFoueXh0pzeC7W0IkdPJmKotlOQYsTbYsdTbsdTbsNTbPSbYmL7BLbYpr7Owv7iWw2V1jEoM4VNXMKJaJeFwyqQXGrntf9u5aXIfHp0zkJ2JWvaZHFyWEEx2jRKFmRTYhzRU3D41pWXGDnDGwEgkCT51mngoKgRKzFxh0jM0KoRD6fXo/RpFjqRT7gQnJgZw+QNT28wSaDdNYnLaYnh8MB9JR1iXpOHevwzj1ZUHKa+z4K/XUGex8/u+Un7LLIHEIFTaGvySG+Mf5iTP4a4Rd5EYqNy1b3/oTvJqzyfbeJgzE85s9XiJQ8KYcFEK237KgbJgoiOSkeIamN1nNnOS5/BB+gf8mPUj3x/6/qgiB+CKMQmKyNmQA8DQuEDGJbeesmuqtjDarGaZDMNsMjIQq88kYcJpBBpLsS18Cp/UvmifWsxPr+8hL99J9eiHUclOav7WtDGfmg3V1zIoUzmmf7XyqO+XCr88BqYygqMHo61xYrPpqcrYTdjsxkrbUTaXmTyxsbO72xUSHWjwBCkfqagnNbJjP4Irj6wkqyaLrJos4gPimRQ7iTpbHSariTpbHfX2esZFj+P9ve8DcGm/S3lq4lMtxNBFqRfxUYZyxzsorPU6IlPipzAlfkqL5ZJKhTYhAevhw1hzjrhEjiJ67RUV1P/yCwHjBiPZzSCpIWJg48YRA4it/Z1YXQYDb/qOPXtDmDtwLmpXmm9wlC9XPDEWU7WFn8pKYDPsLtvdYg51a5S2EbsSY/Dr9xxDAqdx0LGRSnMlD699mAWnL+DspLNRq9Tk1eaRWZmJweCHEyd1lRbWfrafPWsKkCRIHRPFmDl9WsTaFGcpgq1CC3tLajn3/9bx5LmDuWZ8227xfcVG/v3LPiR1HX2Hfc/KgjQArh10LT72QEDiadv1nNG3TrmYfnYl+ltWMWLJ95gy02m4/UEMFpn0inSifKOot9djc9iwOhQBlDI2kW3Lcig8WO11XIO/Ft8ALTprMcUVjS6avqMj2bUil5ryBnoDNWX1XtXmbcgEBhm468x+zQa6kiP8oyBqiCJy8rfCyqdhxDUQ3rKkg4dal7svILrtMa2RerYicvZ+DWc8gdlhYdGuRSQHJbfahy0iSfkNr8ivY9M3hz3vyaoPM7nskdG9NnW/M+g1ImfRokW8+OKLFBUVMWTIEBYuXMiUKS1/tNysWbOGBx54gPT0dGJjY/nrX//K/Pnzu3HGrVNwsIoV77XdMfrTnBLue0r5UvQJ96W4xuwpxAQQYNAoKb7AN3dMIiJAz3vrsnl3fTaLN+SwNaeSAlfq38jEEN47mAPAZcNGcv/osUzr13oQaFywD2cOiGTVvlJeslRxmVpHnEPNod+UOwmDX+NHQdIrIqd/iJ7YExU4oHz5QbnjsdYrGQLNGOUKdk0rqKGwuoG31ioWqmcvHspT36dT7Upt9rdFgVb5QZ8aP5V7Rt7DgFDvbAytWkVKcB9Sgvu0OSWVSmL07D4UH64hZ08FC4a+xMgzkj0XhAv6XsCPWT/yc87P/HXcX9Grva1vFoeFj9I/4vP9n3NFv6sI0CdQ6yqH/vg5g9q8sKz+dD91aeVcGeaPLDtQYeOCm6JQj5hKztwrcchOgi6+mLBBYZx102BWvJ9ObYASVyJJkDAolNQxUWz4ZAdVjgR2rqsBVPjX5aMOD0dTvk1p4iepkC76LxGf2CjMbiCzoC+x2wuprTSDBJFGV2p0K3eBkiSRHO5HeqGRrLK6DoucbSXbPM9f2PpCq2N8ND402BtQS2puH3Z7q/+3fiH9mJE4gz1lexgX3Y4MlGbokpIUkZN7BJjsseSUriikJvN+4h6fTyBAcIJ3R/HIQUoGC9A3ejSfJE1vsW+1WkVgmA8jGAFAWlkaTtmJSlIuGrLDgWndOgCW99mLStNAZr1S0kAtqbE77Ty89mHCfcKZ3Wc2NqfyOTdrTeyP3Myg0onsWaN8R2UZDm4t4eC2EvqOjGDMOX0Ij1e+n8Uu8TppXCyHLUbWHyrnyW/3snp/Kf++dBjh/t6fX7PNwX2f78JGNeH9PiC3vhCD2sAdI+5gesJ0ahqUeRyptmKc/y6Bn5wDZfvgs7lE3PQLIYPHchDwtcCVP13Tar2Wy/tfzpCUcyjOMuIToOXseUOUbLuKTPhmPnLxHj7TvEqVXbk5SRkezq4VuVhMys2hvocDYNPXFoIM0alBVOpgWU4Zg2JbuXlxW2OC4hubm2Z8qzzmbIBbVrR9EHexSf+otse0xqDzYdnDUJmFLWs1D+cs9TRS9tP6MbOPtxUpKMIHrUGNzexgtyuhQ6NVUZZby84VuYye3ef4jn8S0Svk25IlS7jvvvt44okn2LlzJ1OmTGHOnDnk5rYevJWdnc0555zDlClT2LlzJ48//jj33HMPX3/9dTfPvCX+wQZCEvzRx/tiitJxOBD+0NtYY7Dxg6+V38311Fns1Fns7C0wUl5nRZKU2I6oQD21Zjtmm5M+Yb6cFhdETJAPT543mPduGEOwr5a9BUaq6m1o1RJDYgPJMeYAMCg8lTMGRB61AJe79oEV+CXUSUiT+iaGJpYcd8VjuUljwxPCLwKCEgAZcta3OqRPmC+hfjqsdid3frqDequD4fFBXDA8lqku4Rbiq2XhrIe5oO8FfDj7Q16f8XoLgXO8hEQrd8R1pXavC+y46HFE+kRSa631BLQ6HE7y91eyKvs3Lvr2Iv5v5/9RWl/Ke+lvM3u4ckd61qBIJvUNb/VYZpON3HSlzH5cjSJkwzS5qGOHYsnKpmH3blCrCTr/PECpu3LtPycw8MCn9D/wOdfc25fz7xnBoEkxjOqfA0BDQ2PQsb5vstJRHGDCHRA/hogUJftlt+k8fn5HSZMPDpXQOauUVgVhzbqSu/DE5bRSybk9yLLM1uKtAET5RqFVaQnWBxPnH8eAkAGMihxFclAyDXZFsM9InEGMf0yb+3tl+iusvHxlq67HY+GOy7EdcWVYuSw51irl823PU+JlCGkWQ+G26mgMSrfzo9AvpB8+Gh9qbbVe1pyGtDQc1dWYfdTsS6hHdjS6m16a/hLXD76eIH0Q5Q3lfJz5MUv2K41HNSoNWxOWgdaVQJDgz2WPjiFlZATIcHhHGUv+tZXvXtvB4Z2lHNqmXCxTBoby0c3j+Nt5g9GpVazMLGX2wrWsyizxyh58cfl+9pfn4d/nbcxSIZG+kSw5bwk3Db0JSZII9tURHai4og9US3D1EiUOpHgPrHwalb/y/1DJYLCCSlIRqAsk3CecWD8lJuqrA18RO1PDgAnRXPzgKBL6B6HZ/F945wwo2YOk0THIZ5VnTpFJgfi4+n253XQ9RU1Zg6dB6qKiUl4sLSVd52hR8FMZ7IrHCYpvabXJ3wIFO9o+UK1L5ByvJUfvD6ddBsDHm1/0CByAv234G59mforZrkSi19vquW3FbdSFNPacGzUrkWnXKL+dW37MprKo++LvupteYcl5+eWXmTdvHrfccgsACxcuZPny5bzxxhssWLCgxfg333yTxMREFi5cCMCgQYPYtm0b//nPf7j00ku7c+otWFlezd9rGz9MqEDlC0Nig5iSEspDyWH0jfTH5nCSXW4iOtBAvyh/fHUaduRWcckipbz8BcNjvS66MwZF8fO9U7jns51szaliREIwKpWDgjrlLiI56Njdhaf0C2+sj3BGCpeM78N3C3dSnlfnVbTL7a6SrZbO+Jcod3mpZ8H2xXBoRau+akmSGJUYwsrMEnbmVgPw6BzFInLb1BRKjGbuPasfk1LCOStlQovtO0pIjCJymn/J1So1g8IG8f/tnXd4FOXah+/Zkmx674XQe0eqVAVp0iwoimIBRbFXPOdY8Cgez2fvHfsRFUUQERULvfdeQ3rPpidb3u+P2ZJNNg0SkuB7X1eubHZnZn87mZ155qmZyZkkGhMZFDmIle/sJHlvAZk+iRg7lRAeFI5eqyelKIWEtvt4PHwsM/rVXGV2cncWVot6obGa1d9h+pPgMx3j558B4HPxMJecE79gL+ItxzFnZJB5242Ie+8lYOoUeg3ypjDxJ3J1PdHgT3DeQTyIUpuEBSXA6H8AENMpiD2/JbvoiAzMgWLUfhs1eJza2Urjj6QXcjSjkOOZRZzILCIh1IfLe0e7XacypwpOkVOWg6fWkx9n/FjNEwbqyXf+r/PZm72Xm3vcXOv2zqUKxCPBVmGVaLtpshk5lhILoMFqzARvqieKRvVSfwfE1rif7Og0OsYnjOe749/x7t53eetSdVL5mZ/V8RE7EqxY8MQ37w7un+xNhaWCMXFjuCT+Eu7tdy8bUjfw48kf+SPpD7x0XkxqN4nPDn1GVt/9dEkdyiU3dsUcVIz2sjQq2h6naIuBoJR4kvfnk7w/H1ANoba9Q9FoFG65uC3DOoRwz5e7OZJRyC0fb6djuC//mNQVD62GD7dswzvhfdDnE+Mbw/vj3ifWL9blM3WJ8iO9oIxlu1LoO7UH2onPwzc3w5nNKAYDaLVgseBVAf+d8Aqj4kY51l24biErT67kzdQX+OTGT9T/34p7YMcSdYHOE+GSx+n5xgjSK7oQPHI6Wr2GgFAvSgsqMGaVEt6mntVGZ4mwCrauPIXOQ+PiySgvNfPjm3spLzFT4KVwUmdF2Kbq1GnkuLtp2PouTH+7+vMVJVBuywtrqCcHoP+NsOMj9hqPgbeBBX0WsDV9K1vTt7J462Le3fsuN3S/gRP5J9ictpmjYWf4V+f/0mtIAuFt/BFCcGxbJmcO5LD2k0PMeKh/g7tUg/o9fnrz03hqPVk4aKHb73pz0uxGTkVFBTt27ODRRx91eX7cuHFs3LjR7TqbNm1i3DjXC+Vll13GBx98gMlkQq+v7uYsLy+nvNx50S4oaJoyxR4xAWgU9ffgdiEMbhfMgIRg/A3VNTnKD230iw/i7jEdWH5wB9/m3MK+1R25r/999ApTT7ZRAV58OXcwqw+k0zs2kDMFZ7AKK756X0IMdc+mURSFt6/vx8YTOVw7MB69VsPUe/tyclcW7fo4L6yKh3qQWssbycgBp5FzrGbXbf82qpEDMKZLOEPaq5+pR0wAX902pMb1zoWgKDV0lpde/U7GnuOTWJjIsW0ZJO9Vj5nw4jbMOvBPLp3TjaMBO/jH+n+w7NhXrL7yJvSa6v9ns8nC0S0Z7P8rpdprYfoTCL0fxuXLAQicXj2eHrXoKdKfWoQpNZW0hQvJ/eQTIm6YyEj/dyGoLenGGeRZzWgLbWHSKa85QoIJvUKZMngb/sc/JL/TPA6WjKO35hunkVMD9tL473al8N0up25FgQEJQUQF1FJNB2xPV0NVvcN613jS89Z789H4jyisKDwrD019cZSR2z05thwxmxMJqzHHZuRUSf6M6qPuy8p5OrUwt+dcfjjxA+tT1rMzYydpxWlof/qWNsDBjv6UJN7EiI49ubqz62gIvVbPqLhRjIobRalZnVd1IOcAnx36jFWeX2Aclsh7G/eSWVoppy0eAsLC6Zcylk7ZAwiN9mPK3X1cyvm7RPqzfMEw/u/nI3yyOZFjmUUs+GIXQQFGvNu8g0ZfSIJ/Au+Ne49In+qehIk9ovjjSBZfbDnD8cwi3hjbgTCAvNMogNbPD0t+Pq8NfI7elQwcgPv638ea02vYnbWbY/nH6OQbD3tULxWTX4L+N4GioAuOZoLyPPQeCnQjINyL9JPG8+LJ2ftHsmP8S9veYQRH+WC1WFnz/n7y0orxDvDgfaWQyl176jRyPCrlSvW8Wu2fs/9bGLvImZtop8iWj6PzcrY1aAjRfcE/hlStaoF1CurEnB5z+O7Yd3y4/0PSitN4acdLjsVzfVLRDM0mPF69niiKwujrO/PlU1vIOFXA3rVJ9Lk03u1b2TFbzWSWZDoqGMvMZdy99m62pKuFMqlFqbw65lUMuvp1uj4fNHu4Kjs7G4vFQkSEqyUbERFBenr1HgwA6enpbpc3m81kZ2e7XWfx4sUEBAQ4fuLi4hrnA1ShV0wAu58Yxw8LLuaxiV0Z0yXCrYFTE/eP68yUi9MoqDCyPWM7c1bP4UT+CcfrOq2Gyb2iiQv2doSq2ga0rfedbodwP24YkuComDH46Ol2cTQG30qJx552T07tPRgaRLuRoNFD3inIOeF2EftAOI0Cj4yv34XlXAm2hatKjBWUl7h+3ja2XJik/GTWLVVL2fdF/olXDCgVWn579whBu7oQqA8iszSTQzmH3L7H+qXH+P2zw2r5JmrXXzthvlmUbN+BOT0djb8/vqNHV1vfd+RI2v20ivCHHkTj50f5oUOcWfgCxRkeUJKLOUVNPtcZLHDxfdB2hGNdRVGI6xlJgC6dNvzFhHndCc1bpb5Yi5HTv02Qo+upr6eOPnGBBHmrw0j3p9R9g2A3cgZEDMBksVJQ5v5Y0iiaJjVwoEoZucUC3sGI+OFYTba8mUJbNU/VcJWiQL8bXDrRPvPjQW5eso1dZ6pXAMX5xzGp3SQAbvn5Fv6z6hHaZFgRCpja/QNrWSy9YgNr1eql88Jb702vsF7E+8VTai7l1zO/klmaiVbR0jW4K1d1uopFQxfxwtRn+aPDF3w04DGG3BnlmHFVGYNeyz8nd2P7Py+lb3wgReUmsg1fotEX0i6gAx+N/8itgQNw9UVxvDSzNz4eWraeymXSp7YE24pCKMl1hKw66qt7L8O9wxkSrR5fa8+shTMbVavSL9ph4GSWZLIuor3a19tW3WavHitoIiNHCEFWUiHbfzrNpmXO89DRreq1ZuO3JzhzIBedXkPclHjycJ2s3jbUTXPFykYOwJxVMP45mP6OOnjWUuH0YBVnwxnVIHCGqiLq9BTWSGRP0nSqryLaNxpPrSfXdLmGH2f8yKKhi0jwT3BZfF/2Ppe/fYMMDL1C9T5tXn4SY5baAFYIQeKBHJdzYkZxBrN+nMVl317GY+seI6kwifv+uI8t6Vvw1nnjpfNiU9omFqxd4AhDtwSa3cixU/UiLYSo9cLtbnl3z9tZuHAhRqPR8ZOUlOR2uXMlrzyHG3++mg/2fUBWSVbdK7hhc5qzisZkNblY45Wx54rUJ1TVEBzhqsb05Hj6OSt5jv/qdpEBbYK4bWQ7np3ek86R52d+jYeXDp9A29T1KnOz4vxVQ7jwjDoosURfyM52q7n24aH0GqOe0Pb+msyko7ehsWo5mFM94TzlSB4HbB172/cNY8Q1negyRM09UbAQElBC/vffA+A/cYIjH6oqGk9PQm65hfZrfsZnqJosXJRmgHKj08iJ6wRjHq++sq0kmowD6sWkNA/0Ps5wjBtiAr3YvPASNi+8hH1PjuP7O4cxuot6J3owtW4jZ3+OOu2+a3BPZr6ziV5PrmH6mxt4+88TnKylyWBTYC8jx2TClKY2eDR3vNrxutVu5NTQ18TO4fQC3lt3irWHM5n+5kYe/HoPmYVlLss8OOBBhkUPwyzM9DuhnpNMHbuy6ozqYRlQz8nOnlpPvp/6Pa+NeY17+t3De+PeY9OsTSy9fCmPD3mc6R2nMyhqEEOjh1KhK+WHxOW1bs/foOe5Gb3w9D+Ezuc4WkXPG5e8RqiX+/wxO9P7xrLy7uH0jAkgs1QhTdgSb/NOgY9q5FiL3OdzXBKvDn5de2YtHFdzb0rbjeTHU6u4/ZfbGfvNWO4oP8YnAX6w9t/w+dX4+6kX1cb05FSUmTmxM5O1nx7i40c3sPSZbWxZfhKL2er47h/dqnpa96xVrwuXzOnGvhL1fzupZxTzR7Xnhat6V2vuqYqtYuQkDIPB89VS70G2YphtH4C5Ar6eAx+Og6RtTk+ObwPzcSpREt6VPK16bEV5hanZ6YBeo2d6x+l8P/V7lk9bzhNDngDUxPiqdLs4muiOgVhMVo5tUw2vk7uzWPnaHn75SD2nHc49zKxVsziUe4iwojj033fi8ZdfY33yegxaA29c8gZvXfoW3jpvtqRtYcFvCygx1T778HzR7OGq0NBQtFptNa9NZmZmNW+NncjISLfL63Q6QkLch208PT3xrOEC0pisPLmS4/nHeXnny7y26zWGxwxnWsdpjIgd4TaUUZXcslzHHKUl45dwy8+38Gfyn6w4sYLJ7SY7jLiiiiKWHVsGwLg2NfdjOBsciceNaeQAdByrduk89gsMuq36+2oUFk5wXyLclARHeVOcX052cpFLE7I2/qonxytFvRCcCTxIv+i+eHkaGH51J6LaB/LbkoP4pUcxtvwmDnZ0NXJMFRbWfqb+L7sPj2bUdap3qiCnFP23gmjNLhQPfwp/UY2+wGnT6tSqCwrC77LLKN64kfJ89Xiy5KlxfV2bruqJtSphXQEFirPUiwmo1Rna2o/H4CpTiLtHB7BsZwoH04w1rKFiLDeSVKheLD753cJO2xiSXWfy2XUmn+d+OkyHcF8u6x7BzAHxbmeCNSYuZeSJahm5JdjZ5VmYbM3oghLcrn8iq4hdZ/LZk5QPQJifJ1mF5XyzI5nV+9O5bUQ74kO8+W5XCgWlJp6/8r/c2H0/fuvfBLazzCMBi1Uwo2+Mw1tZHyqHsWpiWodpbEjdwEf7PyLMK4yZnWfWeKOXWLaJwPjvKLHATd1vrJaDUxNtQ334dv5Q/v3jQc7sCCdKyWXzjh2cya6gJ2AtKnS73si4kWgUDYdyD7Gv9DS/BAWytGgbxes2uCz3h7c3txgL4djPBOgGA30cs6LOlYoyM58/vpmSAmcRhc5DQ2znINr0CKHDgAg++cdGCnPK+PMLtQv1oClt8e/ozx+fqt7bYR1CmTWohjBOSa6zQirATXSg6xTViClKh63vqOc/sHmubP8nv7PIx7GRFhQDKeArwP/NoRDZC65zjpjQarS0C2iH2aqGtHZl7uLro1/TN6yvowu8oqhT6lOP5TuMS3t3/sT9Ofy6/08e2/MQZaZyhpSNo+/RiVjLFcKL4zHgxaxZlzEgUm1m+PbYt5n/63y2pm/lzt/u5J2x7+Chbd5p5s1u5Hh4eNC/f39++eUXplfKR/jll1+YOnWq23WGDBnCihUrXJ5bs2YNAwYMcJuPcz65uvPVBHgG8N2x79idtZs/kv/gj+Q/CDYE0yO0B9vStzGh7QSeGvqU2/W3pKmuzE5Bnegf0Z+rO1/Nl4e/5LH1j/HTqZ9YOHAhcf5xfHvsW4pMRbQLaOe2P8i54PDkmBqpuspOh7Hwy+PqF91UCvra8zrOF0FRPiQdyuPPL46w+fsTBIR54R/mRZehkeg1emJzVeMkMegAV0Y5DcoO/cMx+Oj44fVdtM3ryZ4jK+Bi53a3/HCSgqxSfAI9GTLDmZDoH+LFDdckoVvzPIWn+iNKS/Fo2xZD79r78dgxdFbLVMuN6v/JbPcmRERxMv8kyUXJJBUmkVyYjF6j566+d6HvcKma9H1K7dlC3+vdbbpWutnyEQ6m1e7JsXu0Qj2jWbu7CA+dhtev7UtGYTlrDqSz6UQOxzOLOJ5ZxE/70ln74KgGa2kojjLyxEQYNgyL0fkZrGbbxaZSPsXe5Hw+WH+KB8Z2Zu7H212qzP57ZS8CvPQ8+cMB9iQbeeGXoy7vdcVbm1hyXW+029XuwRtCOzOobTCLr+jZ6G30L21zKZfGX8qvZ37lmS3PkFacxr397q32PpvTNvPAn+o4kO4h3Znba26D3sdDp+GxiV35aUcEcJjtu3fio1fzLiyF7o2cYEMwfcP7siNjB7O8SsHLH6wVxPjGMKX9FPpH9OfWNbey32DgZPsRHEvbxvDTnwF9KMorx1xhqXNkSF1kJRZSUlCB3lNLt2HRxPcIJrpjoMsU+c4DI9V8OQWMMQbu33eKM385b1gGt3Pf84rETfDtrahdXmPVasWq6Dzgolvg92fg10rn/JzjqncbzsmTk+odCEB0RYXac6cwDczloHO9oW8f0B4FBZPVxKJNi+gV2ovPJ33ueN0/TP1fFmSr55KcZJu3VcCyb/6gq/5i+mSPxqPUBytqj6j8jBJ6pIxk3wvl5HbaRc9RsfTt05e3L32b23+9ne0Z21l6ZCnXd2v4uaYxaXYjB+D+++9n9uzZDBgwgCFDhvDuu+9y5swZR9+bhQsXkpKSwiefqA3Bbr/9dl5//XXuv/9+5s6dy6ZNm/jggw/48ssvm/NjAGqPghkdZzCj4wxOGk+y/PhyfjjxA9ml2fyV/BcAy44t44ZuN9A+sD2gem9WnFjBjyd/JLlIdX0OiVLj2Q8OeBBfvS8fHfiIdSnr2LJ8C9M7TmfFCdXIu7H7jY6eHI2FM/G4kY2c8K5qTL4wVe0f0fHSutc5D3QaGMmpPdkU5pRRXmImM7GQzMRCUo/l07lXTwLLIrAoZpIDDjMk+gmXdWO7BNNucDAn1ufhfyKecks5nlpP0k8Z2fub6s0YdV1nPL1cv2oGcxYoJoz71IttwLRp9b4AlsSrSeLmUgVTiQar7d90R/E6diz/ptryXYK7MHH8c/DWn2p+QGAbaDOsQfsInEZOUm4pxlITAV7ubygO5KgXd71Fvfu95qI4xnVXT+SzB7fBWGrijyOZPLB0Dyezi0nOK3HbwLIxcZaRqxVW5hxnkzerVQu9Zros/+yqQ2w+mcu2U7mkGp0hqZhAL4Z3DEOrUfjujmEs25XCj3tTyS0x0TcukN1J+exOyuf1V77mwdJScj39EB068s7s/tXmTjUGOo2OF0e9yIf7P+TlnS/z4f4P+e7YdwQaAgn0DCTAI4AJbSfw0+mfAJiQMIFnLn4GfR1ePHcY9Fq0IQmQ9ydhpnSydGq4ylJYc/jxrr538Z91/+BQcQpxVoVHLn2N4bHDHeesWN9YkouSuUaTQWlEGP/MysDTU1BernB0WwbdhtVdyVcb2baLdWyXIC6+2n1TvmFXdcCjvR83LttFRZHqyVAU6Brpz+TeUY4kfErz1RL6uEGw8RX4fTEICwS3h6s+qjmvpv8c+Ou/6nfPIewY+Ntymc7Fk6OoXshos9n5ZO4pCHfNadT+9hQDvaLZUqoWEezN3uuSEmKf5Wf3oOWkOP+n3dOdN9EGXz3dL46m/8QEjm/PZPevZ8hNLSb5cB7JR/KY8UA/+nTowwMDHmDRpkW8v+99ruh0BV665ruhbRFGzsyZM8nJyWHRokWkpaXRo0cPVq1aRRtbVURaWppLz5y2bduyatUq7rvvPt544w2io6N59dVXm718vCrtAtpxX//7uKvvXaxPWc/RvKNsSdvC1vStfLDvAya2m8iyY8v4Pel3hzvRjt1F7aH14O5+d3N5+8t5dsuzbE7b7OilMShyEJPbTW503Y7E48YOVymKatjs/ET1KrQQIyciwZ8bnhmKqcJCQVYpxqxSfvv4ECXGCvocVz03aX4n6RLZiQ6B1UtEB4zqwIn124jP68H+M4fpE9uTtZ8cQgjw7FLGqznPcuL7E9zb715Gx9sSi0tyqCjSUnIqHxSFgKlT6q33HzueZmYgROZDcbrNINUIdljT8dB60i6wHbG+saQWp3Iw5yD7c/Yzsd1EGPkIrH3amS/QQAK89cQEepGSX8rhtAIGtQuhsKKQZ7Y8Q5BnEGPbjKVPeB92Z6j5OJnZag7P2G6uJ/EALz1T+8SwZONpdp3JZ8vJXGL7N7GR4ygjVyusLHlOI0dED1aTRG1kFZaz9ZT6ut3AGdstgjKThVkD4x15GRqNwpX9Y7myvzPsU1RuZvKr6+iwV8192BvbnQ9vGkSgd9O57BVF4Zaet+Dn4cfirYvJK88jr9yZGP1H8h8Oo+L23reflYFjJ6JNF8iDeCWTRL0axs3OyKWmzJ7+Ef1ZmjCTkh/vw6vDOJS4kS6vD44ezDdHv3Ekqn4Q5M/T5h1sPz2AP788QlCEN1EdAs9ab3aS6mUKjas5x0+n15LnrVChqP26nprag77xgdULRn64Cw79oBZQ2Jo20msmTHrB6ZVxh284dJ+hNum0k3MCbJ2z8au5P1RdpBaraRtR5krzv3KOuRo5+Umw4RXuM3ixbOQdLD36tbpYWY4jJyvAZuQU5ZdTmFtGaaEJgZUiDyN+FUFEtPOn58hY2vcLc3jBug6NouvQKPIzS9j03QlO7srit48PMfOfA5nWfhof7PuAlKIUlh5Zyo3dbzzrz3iutAgjB+COO+7gjjuqD1IEWLJkSbXnRo4cyc6dtTRZakHoNDpHbH1g5EBm/zSbFSdXsOKkM+TWI6QHMzrNoI1fGzx1ntVGCbQNaMu7Y99lTeIa3tv7HhdFXsT9A+6vV55PQ9E4+uQ0sicH1JDVzk/UvJwJ/2n87Z8Deg8tITG+hMT4cmJXJke3ZOCbpp6AjoZv5bEBD7j1toTG+lEcnINPbgh7NyRyhAzy0nSU6gpZ4ruYskQ11PH54c9djBzjafXE4jNkCPrI+rmsj+cdZ0PqBoaEK0TmC4ozbEnTPgooCv+++N9MaDsBgO+Pf8+/NvzLmRA94kHofY3zDvIs6BbtT0p+KX8czWJQuxCWHVvGjyd/BOCzQ5+hE/6YrOUoWig0RuDnqWNQW/d5coPahrDrTD6bT+ZwRf9Yfj6QTl5xBdcMrL2M9WxwVFjZbpbMuZU8OWUVLnfhqw+kU2kmLYoC/5rUrV65Q76eOl65pi/Z/1MTwIfMupw2IW4qcpqAqztfzWUJl5FenI6x3Iixwsgvp3/hp9M/YRVW+oX3o11gA2YkuaFLt16wG4ZoD1JgUPdHeloOvrV544zJeAuhdpSuwqCoQXxzVPU+atGQptOxIeRr2vqNJnVfIT+9s48rHx2Ab6Ane39PJryNOmyyvmTZPDmhlRqfuiOjQDVmu0cHMLKTrZ3Gjo+hvBCG3KkeBJm275HVBHpv1bjpM6t+QgbPh/3fqMnJeafVflZ2Y/NseuTYSC1WixpcPDk5x10Xso3T6V5WSvf+D7AhdSMpRSkkFSY5jByDrx69pxZTuYXTe9UKZaMhmz1Df+D1i98kPDqwRg2B4d6MuaErmacLMGaVsun7E4yY2Ynbet3G4xsf5+fTP3NDtxuabeJ5izFy/i70Ce/DsJhhbEjZQLh3OJfGX8qMjjPq1blXURQuS7iMyxIua1KNSlMlHgO0GwUaHeSegNyTDRtMdx5p3yeco1vUhMJivZHwXgb6R/SvcXnfnibEn5C5owKdScGAji1tV9A7rgcdgjrw+aHP2Ze1D7PVjLHcSEhJDkWpahzcf8rl9db1+WE1jn4mDAYehSKbkZPvA31DejA+Ybxj2e4h3QE4lHPIOWogoPZk0y8OfcHBnINE+UZxbZdrCTa45iOM7RbBLwczeOuPE8QFefNrzh8AWEpj0HjkYNYWoGhBCAVLWQwjeoQ5StGrMrhdMG//eYLNp3I4kGpk/mc7sAroGRtA92hnAnhRuZkVe1IpKjNzZf9Ygnwa7hWx98oxJSUhLBaKMpytJkoKXKuDVu1VK7DuGNWefSlG+sYFNig5upu2hOPF2aDV0nNq4xYF1EWAZ4BLSf6I2BGcMJ7gaN5Rru167blvP7qT43F/j8Nk4c+eI8nM+c/vzBmawD8ndUVXdQ5S1eqjSgyNHkqkTyRxfnFc7NuWl44v5SOPMgz6BcwN/zelmbDqzX0k9Aphx0+J6A1arn18EH7BdfdhsZit5NmafIbG1W7kpNuMnHB/Wy5L1hFYcbf6uM1QiOmnJhkDDL0bBtzUsHNXdB+4fQN4B8MbA9UKxzy1KrLB3Y4rkVakHqtRwx6AklL48z+QXdXIqdRWpaKYWL9YUopSOFNwhr7hfQH12uIf6kVOShEnd6tVwTneqQxs269WA8eOp5eO0bO7sOLVPez7PZl2fcKY3HEyWo2WCQkTms3AAWnkNAuvj3mdoooiAu3TkFsY9sRjq6kR++TYMfhD3GBIXK+WlQ5smUZOXPdgdB4azBVWuo2I4r5LXql1+WsmT+TT9ZvxLVWNApN3CZ/c9TpeHgYsVgvLjy+nyFTE/X/cz+9Jv/NqhQ9xFerFwO5lqIv8snxHLlZGlBdQgrVMdR0bfRQeGbTQ5WTSNqAtBq2BEnMJpwtO0y6g9n19MOcgi7c6O4yvOLGCfwz6B11DuhJiCEFRFK7qH8uJzCLe+esk/1qxDe8OO0GB0pRZ9IpsQ4/2mZR57GfvCR+OWQ1cUUsH6AEJwWg1Ckm5pdz7v90O78naQ5kOI2fl3lQeW7aPgjL1TvXV347xnyt7MbFn/Vz8QggOpRWSlAOxOh0ak4l7Xl1N3y1HsPfNLq1UAp1dVM6WU+rojWsHxvNwcMPDaKV71V4knp07ofVt3gnPnlpPPhr/EYdzDnNR5EXnvkGfMLVxXZkRrU7tIeNjUg2EJRtPcyq7mNdm9XUN9TiMnOqeHH8Pf9ZcsQaBwJp9HLa+wy++fuz3KOHD2EVcX/hPclKKHDkipjILf35xhEl39qrzwpmXXozVIvD01tVpFGUWqDd09lEWbKnUoXj/txDRA0ptRs6we8Cn9tJ7t9hDSCEd1AGeAJ7+1fszNYDUIpsnp92lkK5Wh5FzzHWhykZOeSHxfvFsSdvCmULXsUn+oQZyUopIPqyGOnO9UxnsX/9ZcfHdQug+PJoD61JZ+8khrvnXQKa0r38YvqloMX1y/k7oNLoWa+CA05ODyaQ2T2tsOqj9M2rrftzc6D20DJrSjjY9Qrh0Yv868xiC/QJp298ZlrlodAe8PNQTplajdXSt/j1JHfi4giJHVY/Gq35Jed8c+4ZySzldg7vSfeQMl9cCDILuYa59b3QaHV2C1RPrgewDdW7/y8Nq4n6fsD7E+qp3e3f8dgejl45m+FfDufGnG/nowEc8OqELM/rGoHgdAcWKKA9nxe1TWX7nSJ4ZfxUvjHmK1bc8xLqHRzOmS82ueF9PHX3jAgE4lulMdPztcCZlJgv/+n4/C77YRUGZmXahPnSJ9KOw3MxdX+5izQH3jUKrct9Xu5n46jpu+2I3yV6qAZq07yh+Zc73E6XOcuXV+9VQVa/YAOIaaOBkv/0OSXcuoGSrWiHp1avmPkTnE38PfwZGDWycu2lFgSs/grFPo+hVqzRSFPHPSV0x6DX8eTSLK97cyJmcSj1SjLaLaQ1eREVR0CgadD6h3Gws5IuUVOb3nEexZz7ftX8Nq0Y9Bxn9MxAaK4n7c6pNNreTcbqA9+//i60rTpKdpP6Pg6J9XMKPbtezeXIi/A2qx2Z3pSKW/cucZeKKFrxqqLaqLyGVEqD7z3E7sLg+mK1mskpVr0uUb5RzblZ2VSOnUr+2imLi/dSbqqQC115x/mGu56Ecn9R6txmwM/SKDviFGCjMKWPDt8frXuE8II0cSTXsOTnQRHk5Hceqv0/9Baay2pdtRvpcGs/kBb1dhpfWxuBL1JCjRqvQZ4Tr3Vmf8D4uf2/WCaeR4133Sc5kNTmMkOu7Xc+Y/ldyupL90MPL/Z1q91A1ZOWuUaGdYlMxW9K2sOqk2gn5gQEP8MWkL5jSfgrxfvEoKBjLjezM3MlLO17ieP5xnpnek5Bw9SQ2KGo4PWJcuxZrNUq9jISXr+nD/FHtGZgQzD8mqj2S9iTnM/3NjXy6WU0SvnN0e9bcN4If7x7OjL4xWKyCBV/uYm9yfq3b/u1QBt/vTkWrUegdG4A5UvUqzWuvp7uPs5OtxlRBma1Xzqp9qvu/vp4iO0IIsl5+maLffiPvC/X/5NWzZRg5jU6HS2DY3Whj1NBVN68ybh3ejq9vG0qEvyfHMouY9uYG9iUbwWqBAtXb4LaPTGUMAYCCAtzRaSb/HfFfjIHp/NzxQ04H7WNl+7c5EroVgOM7MjFZTFisrjdhm747QXmJmZ0/n+HQRvV/+XtGPoOe/ZXV+9NqfOuMyuGqHUvU7swRPVRNhalw0NZs0SfsrJL2XaicpDx4/llvpqCiAGEbOhHoGahWeYHqcSpx5pxRUjlcVeRoclrVk2NPPgYo9MwlKeCwwyCqLx4GHWNuUL/HB9elcuZgToPWbwqkkSOphtLURk5EDzXZzlwKqbsaf/vNRHgbfy69qRsT5/fCJ8C1T0WfsD4ufxcqisPI+SZpJeuS15FcmIxVuLaRt/Nb4m9klmQSYghhfMJ4Ogd3puMwZ2mnf5D7YYb2vJzdmburvVZhqeDjAx8z7ptx3LrmViqsFXQN7krvsN4EGYJ45uJn+HHGj2y9bitfX/41/cLVBnp/Jv8JmgrwVr1Ddw2aVue+qYnYIG8eGd+FpbcPYe6IdnSP9kcIOJRWQJC3niU3XcRDl3VBp9Wg1Sg8f2UvLukSToXZym2f7iAp131X1YIyE0+tUA27W4e3ZfmCixkwTE3m76cpQlPgbGjoaa5gX3I+2UXlbD6pnpQnNdDIsbqZhefVq2eDttHa0LZRjwdRkA+ouVTL77yYnjEB5BZX8MQP+1UPiNWsekDqyj3RaMErUH1cksv4tuNZMn4JJTEZrO7yPqVeBZwIVs8Xx3dmcs+bT/KPZ95g4/fH2P9XCnt/TyLliBpqsZitqrdHgQ2Uk11Uwe2f7WTBFzvJKaqea5hhD1f5aGHre+qTQxZAZ3VMB7u/UH9XnT91NvSZpe6PIQvA/+xL5PPL8wHw8/BDp9Gpk8n9bNurnHxcXMnQqOzJKXT15PiFOG+UNrb5DkUvahz5URuxnYPoOVr1AP3+6eFq43LONzInR1IdnU69W7FasZaX0+jdPRRF7dVSlOF6l9FSsJjV5l1RvaH7tAat2nmQ+5NCr7Be+Op9sQgLvQI7sSttt+2eFV489BblJ9THBq2BhIAEpneYzqyuzsqNTw99CsDMzjMdHUSjx4wlcZnaQVUb6L6T7sBINaZ+IOcAeWV5BBmCsAorP536idd2veaYYh/uFU6sXyx39b2rWljDoDOovXbaTmRn5k7+Sv6LKJ8oSswlxPrGVqsEPBcm94rmQGoBA9oE8dqsvtUGgeq0Gl66pg/T3tjAyaxiJryyjicu7+Yo435hzVFO5xSTkl/KmdwSogIM3D1GdeN7tFFP7uUnT2I1Oo0cLYKdxzM5mlmkJj7HNDxUZR8XYUfj44NHu5aZb9ZYaDoOBX6mIqeM1LvmEr7oeSKDgvhwzkUM+89adp7J5/CRg3QBtaJPU48ziVeQmpRbqhorPUJ78PXlX7M7S/2+3Pfb/ZTrSqEQuh9Qw967Ulwv1kGR3o4RLekhOvLMgt5xgexPMbJybxqbTuTw9LQeDm9dUbmZonI15ys69WfVc+MbAT1mQFk+7PnCWVnVGEZOdB/4Rxq46QRsMRpJefhhAiZOJKCGZrh28svyAZsXx05Ed1X/0Z+dM9cqh6vKCx0hqIKKAozlRkeielSHQAIjvNGEV3DKZy9tDBHozOXg0XAzYci09pzZn4Mxq5T1Xx/jkhu7NXgbjYX05EiqoSiKs+txU3hywHnHVprfNNs/Fw58B+tfhK9vVBt+NQI+eh8+HfYcXw7/PyaFD8BQabeOaD+WDoEd0Gv0lFnKOJx7mP9u/69j9sverL3szdqLXqPnqs5XOdbz6udMCtQEuy/TjvCJoENgBwSCzWmbMVlNzPtlHo+ue5SUohTCvcJZNHQRa65cw8cTPna0Z3fHiFh18OeerD18fkit8prcfnKjVk7MHd6WlXddzP/mDa5x0rm/Qc8nNw+kf5sgisrNPPTNXuZ9uoMP1p/i9d+Ps3JvGrvO5ONn0PHeDQPw8VRP0vYKq9Ldu9UNaZ0X3T3H0886VAVgSk11+dvQoweKttFvD1oUuq7OQbDGX9aT84+boTCdMD9PZvRVQ4Prtts8tXVU9Tnwshnrpc4+PyFeIVwSfwmj4kbRIaQ9p4Kc85cORGzA2jWHhF6hhMX7Ed7Wn+LBwfhHeePpq+d7k9on57Vr+vL9HcPoEulHTnEFd3y+k+W7VQM/0xaq8vXUYthuSzi+6Fa1a3CIvS+WLannHMq9XdB5um0eaFy5kuI//yLn/ffr3ITdk1PZyKmIHE/+SS/E9iVq52OoFq7y0nk5PDT/3fZfTBbV0+LppeO6pwajH5sJCsRln4b/1bNEvgp6Ty2X3NgVjUbB4KNH1JUU1YRII0fiFkcZeVMZOfbEa9vdSIti/7fOx38+B8nbz32bZUY6fDmb9l9cz5j8HLoVqScWxcuLF8e8xHdTv2PrdVtZOX0lUT5RmK1mtmeo7/vxgY8BmNB2gstARcUvgriROYT2KMD3oprzPy6OUWdNbEjZwK6MXWxJ24JBa+DuvnezcsZKpnecjrYed9lRvlF0DOqIVVgd04wbuxmlTquhR0xA9TLkKsQGebP0tiE8Mr4Leq3CLwcz+PeP6hT40Z3DuLhDKB/fPNAlV0gfrxo5VtsYAq9evVSvJbD9SBqbT6p5DA0NVQGYUm2eHL0eNBr8J01s8DZaG7rISKL//SQBvdVQaf5fB7E83xO+u507uqgVa9kptknfNiPHahWczCpi+e4UFq04yJ1f7OR0pZEZjqTeSkaOHY2i4c1L3kTXqwCjZxb7O/7GunZL2dThOybd0YurH7uI9AEBPPPHMZaHWvCYEkOhAr1jA4gP8aZnbAA/LLiY2YPV4+Dhb/byzI8H+XqHWv01xvuUGj7XesKAm9U3DWnvKqIxPDm1ULJVrboypaQ6hk7XhLFc9UZWbhmQ/tUW0rYGUXisSL1Zg2ol5KB2otYoGpafWM78X+c7tgXOMFacyQwn/zjrzxLVIZDr/z2EYVd2RHE32PQ8IcNVErdoPDyw0kS9cqDlenJKcp1T0kM7QfZROLMZYmv2cNSLoz9Dme2ktOktXssVnCIcjY+zUZxOo6ONfxuGRg/l22PfsjltM8fzj7MmcQ0As7vNdt2m3oBvGz2+UUW15jsMjR7KkgNL2Ji6kTBvtdHZJW0uafD8IoDL213OizteBGBK+ymOIabNgVajMH9Ue0Z2CuP+pbs5nF5In7hA3rthgFsjSR8ViaLXI2ytEXwuHkb58eNYCwvRmyuwWAU9YvzPamCoKU315ATPupbwhx5C0f09Tq0BV87Ef+p0SidcSkVyFvnH9YRovyR+z5cs9+mB3uZJISCWd/48weu/H6ewzLW7u6dOw/NX9CKvxESYw5OTizsifCJ48cpnSbosCQ/tSMZ+8wMHcg5gLDfiofjy0cbTAGxPyie3TP0/X97bmffiodPw5JTupOaX8tvhTN5bd8rx2nVCbWpJ75nOEvGAeLWvl70jvU/TGTlCCEq2qzc21pISLPn56IJqHuhq9+QEeTqXseSqxmFJpgf+W95Rm3+6lJCrFWdT2k8h0DOQh/58iC3pW5j902zeuOQN4vziHEZOvL3BoMUM2rM7nuvTz6ipkZ4ciVvsnhxTRkbTvEFL9eQcXql2NA3vDj2vVp9L233u27VXZwBYTbVWVg2OUju4fHrwU17a8RIA9/W/z1EO7oJ9KGAtd5j9IvrhpfMiqzSLpUfUCcX22WgN5cbuN7Jy+kq2zNrCMxc/c1bbaGy6RfuzfMEw3rthAJ/cMrBGL5Ci1aKPc1b4+F58MRqDehL2NKsey7MJVYEzXKWLivrbGDh2FL0HwXMXAJCX1gHRbQYoWnpb9tNNo1bI7Va6sPinwxSWmfHUaegbH8jkXuq+/vVgBvd+tZuLnvmVlcdt1ZZuPDmO91MU4v3jifSJpF1AO6zCytb0rXy+JZHcYqfn+WRWMVqNwpQ+rsm9Wo3CG5eH81f8+0yPUFsRRJLDRWW26eiDKlU8aXWufWya0JNTcfIklhxnkrApJbWWpZ1GTmVPjrVc3X+lOZ6QulOtYDVXmuhe4WydMCJ2BJ9M+IRw73BOGU9x/arrOZJ7hD1ZewBoYzJhNSlYzuw/14/WrEgjR+IW78GDAMj8z/NYy5qgzLulenL22QZc9rxCTRAESN19btssL3J6h2xYfVQPiDsjZ1DUIJe/53Sfw809bna/7REPQrdpalfWGvDUejK1vZrEWFChVgHZDamGolE0tPFvg7e+aWdNNRRPnZax3SKqzxuqgj0vRxsQoObN2HoUXRznS4CXnml9zm7khdkWrtJHndtAydZKwNQpaAMDMaVnURhwLdy7j2Pd7uIV83RmG17l5o1qGOrGIW3Y/9RlfHfHMF65pi+hvh4UlJlZaesyfbxQNRALcjPr9b724/jdrat5ZpUaruwS6SzRHtExlHC/6t4Ewy+PEZ+5lpeM9wMwSHMIDVaI6Q8RVZJkK4esmtDIKdm61eVvU0pKrcu7y8kRZarnvSzPQ3U+/f6s60oVRZTs3InZZkx1Du7Ml5O+pGtwV3LLcpn3yzwySzIJVnRcVFrGqV9COX7FTZQdOaquf+wX+OHulnfergVp5EjcEvHgg+jCwqg4dYqsl15u/DdoiZ6cwgw4rVYr0eMKiOqjPs45rs6wOVuO/wLmMrUNfFs1WdMaprZTd2fkBBmCHF6bHiE9uLffvTVvu98NcPXHaiJjLdzU4yZ0inoBaRfQjgifRkqgbGV4JCQA4D10CIpW6/DkPDiyDTv+eSnRgWc3LdleXaWP/nsaORqDgcBrrwEgd8kSCIghZNLjvGS+inX5oeQWV9A1yp/HJnVFb/O0aTWKYzo9wLhuEfgHqcfl1kMnWLhsL++vO8nvhzP5aV8at368jc9svZPsdA1UR60czN+BEDBzQBxvXtfP8fqMfjUkPFcK4fQI19NLYwtbxbrpCu1IPqbxEo/dULJtm8vfdRo5bqqr7J4crIKyXA84s0nddqYHZXk6Sk+lkzjrOo6PHecwXMK9w3l1zKsYtAZyy9Qw4VUEYM3WU1Ggx1pSRvJdd2HZsQy+vAZ2fgyb3miET3x+kEaOxC3awECi/v00ALmffEJxlbuMc6YlenIOfg/CCjEDICgBfMNswywFpO2tY+VaOPWX+rvTBJj0Egy9C2v8GKDmbsf/Gvwv5vacy7vj3q1XUnBdRPtGM6md2vNjeMzwOpa+cAm+YTYBM2YQfu+9gHP/K+VldSY714SoqMCcpZbp6qPPfqJ0ayfo2mtBr6d01y5K9+4l2MfD4VXx0mt57dq+eOpcj+UJPVQjR1Hg4fFduGJ4DwAMJiNfbk3i3z8e4qYl25j/+U5+PZTJEz8c4FCa6o1cfyybRV+XI4QGjUcOT0yP5D9X9qJdmC+zB7dhdOcwxnarwSixn3+Ab8YLZkTaPEfRfasvex48OUIIim1Jx159VQ319uRU6p5v9+QAlJjUMJulXCHxjxDO/B5CWaJq3ImSEs7ccAOmdFu4zieSOT3mAGpu4MwKDUUpTg+Y6cwZUh9+CGGx5ensWALmJipKaWSkkSOpEd+RIwm48goQgrTH/oG1uLjulepLU3hyTvwOH01Uh+udDY5Q1ZXO5+zenNrycoQAq/smfgCcUdv8Ez8YQjvAuH878hhr6nbcK6wXd/e7Gz8PP7evnw2PDXqMp4Y+xfw+Z99ltbWjj4oi+tlnHGEre7jKWuoaki3dt5/ke+9zXARqw5SRAUKgeHigDT7Hlv+tGH14OAET1aqy3CVqReC0vjFoNQr/ntaDDuHV53gNax/KHaPa88y0nnQI9yXA5snpEWzl7jEdmNQzii6RfkQHGOgS6YfFKvjHd/vILirn1k+2kVukwdOiXsz9gpxJxE9P68FHNw3EoK/hBqHImWtoOP0bQUY11EV0v+rL2jsJaz2c560qCLO5XsdKTVScOo0lOxvFwwP/CeqQ3bqMHHtFlIsnp1JqQWmpGnqtKNaBVcFSoaX0TL7jdYvRSPabbzn+vrnHzUzvMJ1HL3qUsLJCCm1GTnAvULSCohQPsjP6g1+UOtn80A9OMds+gO0fqedCVKNNNMXsw7NAGjmSWol49FF00VGYkpPJ+O9/G2/DTeHJ2f05JG6AXZ82fN28REjeCijQfbrzeXteTvq+mtf933Xwen9HeaYLZUZnI7E4Z18bUaL2wKnPSIfGwlvvzYyOM/DR+9S98N8Ee7hKlJW6PJ/+9NMUrl5N3ldf1bmN4o1qSEAfFdWs05ZbAsFzbgSg4OefMaWmcvvI9ux7chxX9HcfNtJoFB4e34VZg2zjA2wl5IEUc/+4zrxxXT9W3zuCjQsvYclNA/Hx0LLzTD5zP9lOmclKtyh/buxzKQCb0zbXX2hhpYKKre+AqQQ8fF1DU3ai+6qdhNuPcdvbBiDr5Zc5PnoMxuXL3b5eF/Z8HK/evfFoqzaRbGhOjrBYoJJhUXoyA+EdjrnUeZkvOa2eo3xGqN7c/GXLqDijjnfw0nmxaNgiZnaZSUV6PhUFelAEoR3SiOyvvlf2n+kUeaj7m12fqb/T98GP98PKe+Gv/wMg9YEHOTp0GMWbG/A/aSKkkSOpFa2vL9HPqslr+f/7qlrc+Kyp7Mmpox9EvbFXZJxNovCBZervhItdy7Ht83YKa7hLM5fDkVWQe9L9+yZvB4Ta4bnSdq0l6kVV49OyEnj/bii2mV+VPTllR45QtlcNT1acPOV2PTtZb75J+hNPAOA95OySuS8kDF274j1oEFgs5H2lVvJ5N6RjruPmp3oJeWSAgQW27tW7bB6JG4e2YXjcMAC2pG2pcSyKC1ar6omoSlQf93OpDP5w71649n81bjLn/Q9ACFIfefSsCjXs51XvgQPRx6geGFNKSo29coQQ1frkiMrvq9Viyc3D1P46zKVOb5bJqM76Crj8cnyGDwezmew3quTXCEFxknp+8g6rQOshCBzVl8CrrwQhSPl4MyXZekjZoe7LXZ871/3935R/+wwFq1ZhLSwk6fb5jZ/q0ECkkSOpE5/Bg/G//HIAiv5a1zgbtZ/MrGb3HpCzwe4VSt1de/jIHftsDQArh6oAvO3Nydz37SA/CUc31KzD1V9Psn3B41wrpqzN4MmRVEdjsIWrKnly8r/+xvG44lTNRk7x5s1kv/Y6AME330zEY481kcrWRcAU9Vzh6CzdEOx9ciqK3OZ83DQsgRhbcrifQcflvaPpEdoDH70P+eX5HM518x2sSkmOs+/N5a86n4/sUfM6Wn2NXhwAfazTU5X7acM8yUIIp5Fz0UWOvC57rxxQQ1N/Jf/l6E5cZCrCLNTPYPfkWCv1NDP0UGfWlWr7Yu52S3W90dGE3XOPuu0fVlB+vNKsK1Mp9vZGnh07q0UYs74i8p//wqtPH6xFxST+ForxqAmyj8Bem7ezgzp4OfeDdwB1BqIoKyPj6X+rXqZmQho5knph6K6WVVYkJdWxZD3Re6tNtqDx8nJszfaoKHQdUFcXWUcgYx9o9NB1iutr9g6sJTX07cg7XWk77owcWz5OpVAVOI0cRRo5zYrG5skRNk+OtawM4w/OXIOKxES3J2hLfj6pjzwKQhB49dVEPPwQGo/qs4j+jnh26gxA+bFjDV/ZNokccHteMOi1/GtyNzQK3HJxW7w9dOg1ei6KUKuiNqVuqvs97Pk43iHQ/0a4bLHqae1zdiMMAKxFzv4zBStWNmhdU2Ii5sxMFL0erz690RgM6MLCHK+lFaUx68dZ3Pnbncz7ZR7GcqMjVOWl88Kgs4dc1WNY0evx7qdWnZXs3oXJzQxbfXQ0Xj264zf2UhCCrFdfc75YmoepWPX+6AbNgCs/BIM/iocHce+9i99ll4FQyD3so5aol+aq4bxr/4e5x60YE9VzWtzC2QRedSVxb7/VrCNOpJEjqRce8WrM3GSL354ziuIMWTVWXk7lk2JDppvvt4Wq2o9xem7s1OnJOe18XNXIsVqcIyGkJ6dF4kw8Vj05hb/8grWgAF10lHonWl5O6Z69LrkFQgjSHn8Cc0YGHgkJRDz6SLNob6l4dmgPioIlNxdzdgMH8Gq0Tm9OYZrbRcb3iGT/U5dxzyUdHc8NjlZDhfXKyymyhZ59beHjIXeo4aiosxs0K0wmh8cFwJxZvx4/doptXhxD717O5pRd1RYSqdvXcePqGzlTqJ53t2ds5/pV17MvS80RdGkEaKusUgwGvPr0AaB09x7MGVX06HTowtUqsbC77wZFoXDNGkoPHFBfL8vHXKIaJfqYKo0U/fwIvf02AEwlWmfycZ9rQasjP6cTwqJgCKrA++hzRD0w1xF+ay6kkSOpFx62TrEVSUl1zlSpN/aQVWN4coRwNZZSd9Z/XXs34h4zqr/m4j53M+Kisicns4qRk3lI9Sp5+EK4a4MxaeS0DKqGq+yhqsArrnBUYCXOmsWZOTdR+PvvABiXfUfhmjWg0xH9f/8n/4dV0Hh5OW6Kyo8ebfgG7Mn+Z2o2WLw9dC5J3vYO3jszdlJmriMnxp507Nc4PW/Mua5eXkt+foMqi+zzqrwvcvbo8erRE4C/fv2ItOI0EvwTePOSN4n0ieR0wWkWrl8IVGkEaOuRoxg8HUZO+dGj1UKu+ohwh2fFs2NH/Cer8+eyXnlFXaA03+HJcdfcUheh7jdLuRZhd3L2uQ5hMpH3+RcABA8MRhEmZ7i+GZFGjqRe2GPO1sJCl7uWc6IxPTmmEnUcg536enKyjkDWITVU1Wl89dcNgaDYviYlbrw5lY2c4kzXZeyhqtgB1Wa/OI0cWenUnFQOV1WcPq1WuWg0BM6YgUfbti7L5n35JRWnT5P+jDrOIuyeu/Gy5T5IXPHs1AmAsrMxcmwNMx39peqzSkBbwr3CqbBWsDtrd+0LV/XknCOWXLV7sDY42DHZvqrhUxOV83F8BjpD2oaean5QbFIpHYM68tH4jxgeO5wvJn5B95DujgRrl/JxW8hVY/BCHxGuNqa0WjElJ7u8pz7S1bgLW3AnaLUU/7WOkl27EMW5mGzJyu76PmmDglD06vnMXKaF+KEQ0p6Cn9dgzsxEGxaK3wibwdaQtIEmQho5knqh8fJyxokbKy+nMT05VQ2ltL3qYDl3bHoDXu4J2cedXpz2o10ahDnQaJzeHHchqzzXDqwuIasako5BenJaCorDk1NG/rdq8rnP8IvRR0VVM3KK160n6c4FiJISvAcOJOTmGkZtSBxGTvnRs8jLsRs5p9epId96oCgKfcL7AHDy4CZERS2N6hrbk5OtGjm6sDC0weq5wpJTvzCdKTkZc3o66PV49elDWlEa96y9h7fKfwEgOgf+238RoV7qwNAw7zA+vOxDLom/BIAE/wTHtuyeHI1B7X5ubyroQFE98PqIEJenPdq0wf+ycQCUbN6MOS0ZhAIaHOd8l80oiiPcZSrVQN/rAbVpLKhNITUR6v9fGjmSVoXe5oKuONNIRk5jenLsScdeweDprw6lc5cIDLD1Pcg/AzuXwIHv1Oe6Tat52/bk48SN8MNdcHq9+rcQTk9OgK3PR+Yh53o1JB0DWEvUijJp5DQvdk+OtbCQ/O++ByDwSrXCziPeOcxTGxgIQlBx4gTaoCCi//NcsyZTtnQ8O6r5MmeVfBzZGzwD1O90ev07jXcO7kzXM4K+d75L+tP/rnlBe+JxI41osHtydCEh6EJUY6S+uUiO/jg9e5JHCfN+mcfapLV8mrGCLH/1Ah2Z5Fp96q335sVRL/LphE+5f8D9juftpeuKp3pM20NWoFY6eQbajBx/HZhcQ3r6WPVYN+flYUpNsS3nWeMxrotQvWDm4MHQYwalu3dTtncvil5P0MyZzn5D0siRtCbseTmmpLqTj0u2b6/7i96Ynhz7NryCnAmE7vJyijIhzxaj3vGJ2qhP6wFdJta8bXvy8dqnYecnsGQSfHW9GhIrV1vM09HWIMv+pXa8j6KOiaiC05NzdrOSJI2DYkv0LN6wAUt2NtrQUPxGjQLA0N0Ziopc9BSgeigSln6FPurvO76hPjg8OcePN7x8WKuDBLX3DSf/rPdqXYK70DFVvZAXb9lS84KNbOTYPTnakBB0oaEuz9VF2QG1Uai2Vzfm/zqf0wWnifSJJNw7nMRYtVqvdF91Q0+jaOgT3gcvnfP8IcrticfVPTm68HA8Q215OGe+gM+vdOlPpg2yeaDy8jGlqeE8fXDNoXRdhOrJMbe/CvReDi+O/+WXowsJqWTknGi8PmhniTRyJPVGH29PPk6udbmidetIvH42af/4Z+0bbExPjn0bXoHO+TPu8nIqJ8LZmmnRZbIzJOUOuyfH7i0COLQC3lPnT+ET7vxS25sG2t8nvCumggpS7n+ArDffxGSrvBDFMlzVEtB4ue7/wOnTUPTqJHNDly7EvvUm7Vb8gP+4cbRb9SNtv/naYexLasajTTyKlxeitJSKxLOoyDyLvJxOQZ0INaoXVFNSkvsxNBaz0/vq1zg5OWYXT44aCvpl99d8evBTfk38lT1Ze0grSsNYbuSVna/w4J8PUlShlpzbB7t+U7yeQ7mHCDYE897Y91hzxRrGTr4LgIKVP9ar2MPuydHYPDmGzp0cRrwuPJyIkf7EDMvFL7ZMDQXaG6AC2qBAACx5eZgy1c+jD/Wv8b304aqBaMrIpCI5hYKf1wDqbDj1QVtAUW8Ci7Pq1N6UNKAVpeTvjkdc/crIjT+sAKBk926EEDW3urd7chrjS2D35BgCnUZOihtPTrKbbP++19W+bW/XGDZT34R9X8NJtdqGoDbOu8IiW7lmpVBV3v++pGDVKgDyvviS9j+udJQsSyOnefHs2AH0ejCZULy9Cbz6apfX/UaPdi7brt35ltdqUbRaDJ07q2GMgwfxbNe27pUqYzdyzmxSmwLq6u5BFOEdQVSRDjCBEJQfP45X7ypl4Xu+UEvTDYEQ2bNhmmrA4vDkBHOy8DSBQOKp3Xy8reZRMP0j+nNtl2upSE0FYKdyBl+9P++MfYeEgAQAwmZcifH1tyg/coSi3//Ab8zoGrcHzuGcdsNG0evx6tmTkm3b0IWHo9Psxj+uUpjqlyfVocEe3ugcnpw8zKZ8AHThVc57ldBF2sJV6enkfPA+WCz4DB2CoUsX2wKeEBgP+Ymqd7uJBpvWB+nJkdQbe45CRaKabGvOyalWKmmtqKDIVmprNRod05ndYh9+eXS16yyZs6GyJyfGNmQv40D1sm+7hyVGbZaFfwy0q/3kgXcVL0+XiTD7O5i1VO2tM/Qu55fY7gqvlHRcOfnSkp1NzgcfOhIjpZHTvHjExdHxrz9p99MqOv71p/TSNCKGbmrbhLKDB7EYjSTffQ/HxoyhZFc9Kh/DuoJ3qFo1mbKjXu+nKApRRXrH39UquyqKYa1aGcfIh8Hz3IffCiHIS1PD33vMiazMVzvCd7CGMcN7KL1DehHtE41O4+pP+Cv5L04bT2M8o4a3C4M8eeOSN+gS3MWxjDYwkKBZ1wKQ/c7bdXpzHInHnp6O57wHqUUPHm3aQEElD7x/DBjPwJp/Ot4LwJyXiylH9TJVrcKqjN4Wrio7dAjjt6pHKOS2210XaiF5OdLIkdQbD9udrDkri8I//uDYxcPJfPEll2WKN2506f5ZceJEzRtMuBhiLwJzGWx8tebl6oM9lGQIVLuXegWrJeXp+53LmMqcIazLX4HBd8C0N9UGZLVhD1eBGtbyClKbGXa6TDV2uk119eSYy53vEzfI0Ssk6DrVY5Tz7ruOzUkjp/nRBQXh2bYtWt/qU7IlZ4+hW1cAijdv4tTVV1O4Zg3m1DSSbp1L6Z49ta+s0UBbdYhkQ0JWQfnOisryI1WMnE1vquXjgfFw0a313mZNHM49zNw1czmdqObMLEn7jlxv1RDpvCOTa/71F//5I5rVV6xmx/U7WH/Ner6dolbwbUvfxrzls/EqVfOVHpr0H/pFVJ+AHjxnDoqnJ2V79lJSW54RzhJy+zw2gJCb5hD93+cJueVmGPMv0HrCjStgiq3D8fYP4NAK15ycfHU7+ujqPXLs2HvlVJw6haiowKtvX7wHXuS6UKitWaM0ciStBa2fn8NNmfP++yCEw2tjp+oU3vJjtRzgigIjbd1it38IFW76j9cXR7gqQN2uvaLptO0EabXC8jtVg8ovCiJ6wPjF0G5U3duu3AU5qAa3u92TU25UQ1WWcvAOwWqIdJTch94xH8/OnZ3r6PUochSA5ALF7skpP3gIU+IZdJGRePXrh7W4mDO33ErpvprDOUCD83IshYXoS5yl4y6NCIuyYMPL6uNLnlDDKXWQ97+vyH73PYcHJbs0mzWn13Aw5yD/XP9Prl5xNVvStxBoS/0xeisM6zHJZRsFK1eSv/RrNIqGAM8AOgZ2JMoninJLOZostZeO4uvDsE5j3WrQhYY6qv2y336nVr3WctecHACNjw8Bl1+O1t8fRjwIC5PU/drhEhh6t7rQ8gVotWr4XJSWUpGnGor6Nm4mstt1Rbh6eUJvv616WkLl5ONmRBo5kgbh2UE9cEu3qy7kisRER6VQ0foNFP60GhRFnXALroPf3NHhUtUwMZWo8duzpXK4CtQwEsDx39Tfvz0J+79R52VNe7PWYXvVqOzJCa4hL8MQqN4lARyyza6JG0S5zZOlDQ1FFxJC+EMPOddpQFdUiaS1YT9X2Al/4AHi338PrwH9sRYVqYbOfnWUQMm2baQ/8yyWSl5g2o5UfydvrdcNkCnVdQxE8eGDzhDPn/9Ru5ZH94XubjqbVyK3LJc/968k7aknyXrxRZK2/YEQgnt/v5cH/nyAmStnsvzEcgSCCW3GE2xrnPfeNUu5cnB1D1HGs886QmeKojA8Rj03hhao2jyiax97EHLLzaDTUbJ5c62hvqo5OW6pbNyN+Ze6P8ry0fx8L+jUkJq9i7G+Y81jLux9cgA8u3XFZ8SI6gu1GQqj/wkDbqpZz3lAJh5LGoRn+/YUr1/vfEIIyo8exbNjR9IffxxQwzJevXtTvG6d4yJfI4oC/rFquMmYAuFdz05Y5cRjgPZqsyzObIb1L8EGW8vyKa87DaD6UtmTE1yDJ0dR1JCV8Qwcthk5sRc5+oQYOqmuW9+Lh+HRoT0Vx5v37kYiaWqqein9x1+GotcT/847nJk7j9KdOzlzyy0kfPE56YsXU37wEIqHngj7jUBwOzV3pCBF9Y62rz13zpSmJvHmR/nhn16IpqCI1Tu+ZEfRLnQnv6W7rzfdh91BggKbUzby4f4PKTGXUGoupdRcSqRPJJklmSQVJjH4kJX7bfbRZ28vQBHXsydrDxpFg0bR0Cu0Fw8MeIAuJQGcsKjf97DoDlgLCx16NN7eePXvT/G6daTcfz9tv/4ajZcXUztM5dtj3zLBqz+wpc52BProaAKmTcX4zbdkvvACbT791G0xR9VmgHWi84ArPoB3RqCc2YjWuz2WAtWLo/W0ogmNr3FVjYcHuogIzBkZhM5z48UBiOiu/jQz0pMjaRCeHau7MMsOHybzxZcwpaaij4kh/L571SF92Ppk1FX+GKCOjHBJjGsojmaAgervkPZq7N1qgl+fVJ8b8091kFxDqVxdVVO4CpwhqwK1mRYx/Rwuc3tzNID4Dz7E0K0bIfPmNVyLRNKKCJk7F4DIJx53lOZrfHyIe/ddvHr3xmo0kv3Gm5QfPgKo1YfmHFuPGUVpUMjKZKtU8tXnUWK7zr/y57N8lbiaz/19eSwslKnbn2bIF0O47dfb2JK+hX3Z+zief5yUohR2ZOwgqVANLQ9NcyYlX3TEyheH1ZlMU9tPZcf1O/h4wsf0DO1J+qKnAfAePBiNh4cjgRfAs0sXop9bjC4sjIrjJ8h49lkAeoX1YvOszYw1qFWgOjejE6oStmABiqcnpdt3ULR2rdtlHAM6PWvx5FQlpD1MelHVoRQ4ntb5adW8qFqI/s9zRD7xOH62bsktFenJkTSIqi5ogPxvvqVsv5rgG7noKTQ+PmqSskbjqLDSh9dSQhhgc9caz8HIsYer7J4cRVG9OTs+Uv/uPweGP3h2265PuAqqNxeL6EH5MbVJlr05GqiVCW2XfXt2WiSSVkToXQvwv3wyhkrHP4DW14fQO+8gad5tFKxerebMoeaE5H70EeEP2r6rbUfAni/rZeSYbUZOmKGMRJ2aRN6tQsMlRiNWFA4kDOBw4RlKzGroa2bnmQyPGY6H1gODzkBKUQrBnsH0COtB1pczqUC9cYrNgagcQVqIwvXdrkdjm2VnXPYdxRs2oHh6EvXkEwAuHYK9+vRBFxJC9H+f58xNN5P/9TcUb9qMtaSEtt9+g9nWI8fdEMyq6CMjCZ4zh5x33iHzv/+H74gRDqPRjihroCfHTu+ZcHgF2rUbne9XSyNAOz6DB+MzeHDD3qsZkEaOpEF4VDJytAEBWIxGh4ETcOUV+A5TO5VqPD3RR0ZiSk3FlJJSu5HjbzdyUs5eWOXEYzs9r1KNnC6TYeILDcvDqYx3sDrA02pS73xqonIvCN9I8AmlzFY+XtmTI5H8XdB4eFQzcOx4DxiAotc72lBog4Kw5OVRtG6908hJsFVYpe5UvbWVv99VsOfk6L0thGDGhI5/J6fhHWuAy56Bfjdgtpo5aTyJQWsg3t81HNM3XPWsmNLTqTh9GjQavHr1onT3bqZnxFHRpT+Rm08gLuuAOSubjOeeAyDs7rvxSEhwbCfk9tso3rCR0HmqF8tn8GBCbr+NnLfedgzLLN6yxdEIsL7ds0Pm3kr+119Tcfo0eUuXEnyda38va/lZeHLsdByH1tOZhqAPq6U5aitDhqskDULr64vO9qX0v/xyx/O68HAiHn7YdVlb909LXh0TeQNsvUlyT8KX18KvTzVcWNXEY1Bbwz98CmZ+Vm0KeIPQ6mHqG2rZeW1NrSp7ciK6Y87NxWIbbeHOAyaR/J2x56zYCZiink8qzpxB2Dw7BMap3lNhVWfH1YCoqHBUa+l9LGh0aojcalHg2i+h3w0A6DQ6OgV1qmbgVKZ482ZAHevhY7tpm+Y5kOnfZZJy3/1kvfYa6U8+ibWwEEOvXgTPudFl/fB776Xt10tdQldhd95J2APOOVOm1FSnkVOPcBWo597QBXcCkP36G1gq5f+A6gUD5zy2BhHVG62n1fGnPqpxukG3BKSRI2kwQVdfhUe7doTccjMaPzV2HfnkE2qZYiUcE3lz3Uzvrow9XJW0GY6sgvUvNqzssChLHcgJ1cczeAefvQenMr1nqiGv2qhsAEV0dzQB1MfFyX44EokbfIYNdTz2v3wK6HSI0lLMtvEnAMQPUX+n7q5xO7mff4EpKQmtpwWfiHIUrWrkCF2gc/16UrJZ7UfjM3iQwwAxpaVRvE5t9Jfz1ttq6wy9nuhn/l2vQa2KTkfo3LmE3q2OajAlnsGUbpsR1YA5aEFXXYVH27ZY8vLIee99l9fOyZMT1tVRHAqgj0to+DZaKNLIkTSY0Pnzab/qR/RRUcS98zaxb7yO35jqFUu6IDWXxZxTl5ETW/25nR/XX9CWt9Tf0f1qdWc3OZU9OZE9nUnHNbjrJZK/O7620mNtYCCGrl3wiFXPBRWnTzsXCrN1Ac467HYb5uxsst94A4DwXoVoPRU0NiPHGtGv7maflRBCOIZ7eg8e7DBA7L2uKhN2x/wGh6HtDfZKtm1TR4kYDI7eY/VB0esJf0gN5eV+/LHDGwTOnByloTk5ADoPdKHOmzR9woUTXpdGjuSc8O7XD79LLnH7mjZENXLq9OT4RQNVvC27Pldn1tRFmRG2vqc+Hv5A3cs3JVXCVfbycXcVaRKJBAydOxP7+mvEvf0WilbryG1xMXLsbSUqGTnlJ08hflsMn19N5kM3Yi0qwhDrT0C7Eug0HsUWrhKhDZtPZUpMVBOC9Xq8+/VzhOZd9ACeXbsScmvDuybbjRx7JZhHQgJKHVVMVfEdPRrvAQMQ5eVkv+NsEOhoBlhbn5xa0EYlOHV2qLlHTmtDGjmSJkMXbPPk5NVh5Og8XEM9Hr5Qkg2HV9T9JlvfUyfdhnWFzhPPQW0jYA+76QwQ0tHhyakp8VIikYDfpZfi1acPgNPIOXXauYDdk5NzHH7+BwX3D+TkxIlkvPwWpVvWYtykhrYjupxQI9NtR6CJVvuzWP3aNEhLsS1U5d27Nxovr2qhJI+EBOI/+Zg2Sz6qVt1UH/RVGv95tE1o8DYURSHkttsAKPptraNFh6MZoOdZeHIAbZy6nxWNQBvfpY6lWw/SyJE0GdoguyenjsRjcIasvENg8Hz18Y4lta9TUQKb31QfD7+/zr4OTY5/tJqcfOWHCK2+kifnwnH9SiRNiVtPTkCseuNjNcOm18nfqQ79zTvhTdr2UEDBv6sX3pE60PtAx3FoYlQjR5TXwxtcieItatKx9xC1NFpjMKANdraQ0MfE4DNwINqAswuL6yPCXc5Tnm3PbrK996CBKF5emLOyKD+serjO1ZPj0W8kIPAM06LoL5xxM81u5OTl5TF79mwCAgIICAhg9uzZ5Ofn17i8yWTikUceoWfPnvj4+BAdHc0NN9xAqs39J2k51DvxGJxl5PFDoN+NoGjU3hi1JSDv/BhKciAooc5W7eeN/nOgyyRMKanquAu93qW8VCKR1IxbI0dRIEyd+WYuVyjOsHkqrArluQqKtzfhb6+Gx1Lg0UQIae9IvrWWldb7vYXVWinp2Nn/pbI3Rx9T+wiGulD0epe5Tx5ta2kuWgsaDw98bBPGi/5SE6LrNdahFjz6jqXtC3cT9867dS/cimh2I2fWrFns3r2b1atXs3r1anbv3s3s2bNrXL6kpISdO3fyr3/9i507d7Js2TKOHj3KlClTzqNqSX3Q2UrIzfUxcuz9MLpPV8tGO9gG1tmb+VXFXAEbbJPLh917biXiTUD5MVvScbt2Z+XWlkj+jtjDNxXJyY7+OYAajgaKUgwgFBSts9w59PbbbR4SrdruAWcZtf3CXx/Kjx3DkpeH4uWFV09nLk/lEu9zNXLU7Tmb/52tkQPgO1JN2i5apzZKtJadmycHwDDpDnRdhp31+i2RZr0yHDp0iNWrV7N582YG2azS9957jyFDhnDkyBE6V57YbCMgIIBffvnF5bnXXnuNgQMHcubMGeLj3fc/KC8vp7zcecAXFBS4XU7SeDjDVbkIIdzPN7Fz0a3QbQr42SoNBtwEx36G3V+og+SqTg3e8yUUpqoTxfvMaqJPcPaUyyaAEkmD0YWHo3h7I0pKKD95EoP9GmDz5BQkeQEQ0rWIsjw9IqxvtT41QCVPTlm937vE1h/He8AAl7lbukb05IBq5JTuUAcce55FTo4dn+GqkVO6azeWggLHwN+z9eRcqDSrJ2fTpk0EBAQ4DByAwYMHExAQwMaNNTd+qorRaERRFAIrNV+qyuLFix0hsYCAAOLi4s5FuqQe6GzhKlFRgbW4jinCGo3TwAHVk+MXrYajDlVJQLaY1aGbAEPvqm4AtQBk+bhE0nAURcFn4EAACn9e43whqhdCQGmuegH3jS4nbnge8R99iMajev6I05NTfyOnuFJ/nMpUHrugj6l7BENd2D05uogIND51j0+oCY/YGHRhYWCxUHbggON5zVkmHl+oNKuRk56eTribdv/h4eGk2xol1UVZWRmPPvoos2bNwr9KM7rKLFy4EKPR6PhJctP3QNK4aLy9HXcVlroqrKqi1Tm6lLK9Ssjq4PeQd0qdKVVXg75mQpaPSyRnh/8ktUqyYNUq53DftiMxD3sSazmggGeACQLbVG/+acPhySmvn5EjzGa1dw3gPch1HlNj5uQAeNiiDZ7taxkRU0/s55fKRo705LjSJEbOk08+iaIotf5s374dwP3I+LpCGzZMJhPXXHMNVquVN998s9ZlPT098ff3d/mRND0NSj6uSr8b1ATkxPWQnwR//Ac2vALrXlBfH3wHeJz9nVBTIUwmyk+dAmT5uETSUHxHj0Hx9KTi9GnKDx1Sn1QUKnxVD4tHeIDa3y92QI3bsDfEE6X1M3LKDh3GWlSExt8fQ1fX8ml7To7i4YEuNLSBn6Y6/uMvI2TePJcxD2eLR3vVyCndrxo5il7f4L47FzpNkpOzYMECrrnmmlqXSUhIYO/evWRkZFR7LSsri4hKGejuMJlMXH311Zw6dYq1a9dKo6WFogsOwZyaVnfXY3cExEBkT0jbA3u/gj+edb7m4QcDG96M63xQcfo0mExofHzQRZ+7e1si+Tuh9fXBd9QoCn/+mYJVqzB06wZA+XG10tKjez+4bDZ0m1rjNjQGNXenvp6cipPqtg1dulQb02Do2hWfkSMwdO3aKAaExseH8PvvO+ftgHMmnn1IsvTiVKdJjJzQ0FBC62HxDhkyBKPRyNatWxloi8Nu2bIFo9HI0KFDa1zPbuAcO3aM33//nRBbFY+k5eHw5DQ0XGXHbuTs/MT1+YG31uiqbm7K7Pk4HTvWyyMpkUhc8Z84kcKff8a4ahVh99+PotFQfvw4YMtzG3JnretrGujJKbeVrLtr96Do9cRX6izckvDsoIa87NPN9fKmqhrN6tfq2rUr48ePZ+7cuWzevJnNmzczd+5cJk+e7FJZ1aVLF7777jsAzGYzV155Jdu3b+fzzz/HYrGQnp5Oeno6FRUNa/wkaXoc86vOJlwFENlL/Z2fqP5uczGMXQQjH2kEdU2DbAIokZwbviNHoPH2xpyaRunuPQBOI6d93XluisOTU78S8opajJyWTNW8Hr9xY5tJScul2YN3n3/+OT179mTcuHGMGzeOXr168emnn7osc+TIEYxGIwDJycn88MMPJCcn06dPH6Kiohw/DanIkpwf7N1C69X12B12I8fOgJtg2D2g9zpHZU2H42QsjRyJ5KzQGAz4XqrOxLMnIJefUENK9Unmd3hyaqiuMufkkPavfzm+qxWn1Zuo1mbkaAMD0YY5oyb+48c3o5qWSbN3UAsODuazzz6rdRlHhj1qLk/lvyUtG12orSFgTvbZbSCiu+vf0X3PUVHTY0qxDd9r475nk0QiqZuASZMo+GEFBatXE3LrLViNRtBo6tVAz+HJqcHIyV2yhPyvv8FSUEjMyy85PTnn0LemufDs0IGSrGw8O3ZslIqtC41m9+RILmx0YWEAmLOyzm4DBn8Isp3UDAEQfHazXs4n9gnDuirD/SQSSf3xGTIEbUAAluxsx7Rtj/j4evWBqcuTU7prt/p7317MmZmI0lLQavGIjW0c8ecR7379AQiYNq15hbRQpJEjaVIcRk7mWRo5AFG2kFV0X3WOTQvGUlSs3nEikwAlknNB8fDAb9w4APK//B8AfmMvrd+6hpo7HguzmVJbNZI5NY0SWzsTj9jYVjmCJWTeXNp8+gnBN81pbiktEmnkSJqUc/bkAHS8TP3deVIjKGpazGmqF0fj74/W17eZ1UgkrRt7Y0AAFIXAmTPrtZ59fpM7T0750aMuzxf8oHZUb235OHY0np54X3SR7I9TA82ekyO5sNHZOlpbCwqwlpWd3fC4PrMgYRgEtPwcF3uoSnpxJJJzx/uii9CGhmLJzsZnxPB6h5PsnhxRUYGwWl0MgNI9e1yWLfrzT6D1GjmS2pGmn6RJ0fj5odhi6DV5c6zFxeqAuZpQFAhKUOdbtXBMaWmAayt4iURydihaLSE334zGx4fQ226v93qVb6aqenPsJemVq5IADJUmj0suHFr+VUPSqlEUpdaQlbBaOTZ6DMcvuRTrBdDnyF5ZJT05EknjEHLzTXTesR3vfvWvrKzc+bdyrxxzTg5FGzYAEDTT2ZXfZ9gw/CdOaAS1kpaGNHIkTY49ZGXOzKz2mjk9XQ1lFRZitnlBWgvW4mIyFi+mZOcux3POcJX05EgkzYWi0aDYppOL0lJANXaSF9yFJTsbjzZtCL7xBvQxMehjYoj+z3Myp+UCRf5XJU1ObRVW9kGWAFbbyehsKTt0iNRHHj23JOcGkPvZ5+R+/AnpTz7peM4RrpKeHImkWXFWWJUjhCD98ccp3bULjb8/sW+9hdbPj3Y/raLdjysbZfCmpGUiE48lTU5t4aqKk5WMnMLCc3qfpPl3YE5Pp/zECdp+8/U5basuhBAYV/wAqNUappQU9DExMvFYImkhaAwGrAUFiPIyct57H+PyH0CrJfbll/Bsp/be0ti8PZILF+nJkTQ5tYWrKip5ciyFRef0Pub0dMA5kbc+5C1dSvqiRQizuUHvVX74MBW2qcgAhX/8gTCZHJ9RNgKUSJoXuyfHuGIlWS++CEDkP/+BTy3DnyUXHtLIkTQ5tXlyyk+ddDy2Fp2bJ6dysqHdo1IX6Y8/Qd4XX5K/bFmD3sto661hj/sX/f4HpoxMsFpR9Hrp/pZImhl7Z+TcDz8EIOi66wi69trmlCRpBqSRI2lynEaOO0/OacdjyzmEq0RFBaJSFUXR+vV1r2O1Oh4Xb9zkeFy6bz+nr7uenA8+dL+exULBjz8CELpgAQAlW7ZQcUId9qeLipJJjBJJM6N4OYf46qOjiVj4aDOqkTQX8kwsaXJ04e4Tj63FxY4QE5xbTo4pIwMqDW4tXr+hznWsxcWOx2X79iGEIPfTzzg9axalO3aQ99VXbtcr2boVc2YmmoAAgufciC4sDGEyUfTXOkDm40gkLYHKM64MvXuh6GQK6t8RaeRImhx9VBRotViMRvK/+cbxfLlt8q+dc/HkmFJdy89Ldu2scx2LbcYUgCklhaS588h45hkwmaq9Xhl7qMp//Hg0Hh54tGkDQPGWzYBsBCiRtAQUL2f42tC1WzMqkTQn0siRNDlaPz9C5s0FIO2JJylap3o8Kk6ccFnOeg6JxybbzChD9+4AWLKysZaU1LqOJd/ViClevx70ekLvmK/qKShAWCyuGsvKKFyzBoCAyycDoI9Xx03YE5GlJ0ciaX40npWMnG7SyPm7Io0cyXkh7O67CZg6BSwWku+5l9IDB5zJu7bJv+eSeGxPNPbs0hlNQAAAFUnJta5jLXA1cvQxMSR88Tmh81UjByGqhdCKfv8da3Ex+uhovPr1A8AjPs51O7IRoETS7FS+QTF069qMSiTNiTRyJOcFRVGIevppvIcMRpSUkHTrXNVzoiiEzFW9POdSQm52zIyKxiNONTpMSWdqXccejjL07kXsm2/S9vvv8OrZE0WvR+Pjoy6Tn++yjiNUNXmyI7nYI951cKj05EgkzU9FYqLjsS44uBmVSJoTaeRIzhuKhwexr76KZ+fOWPLyAPAdM8Zxl3VOicepzk7Dds9KxZmkWtexGNWhoLrQMPzGjEbr5+d4TWvzBlXOyzHn5TlCbQFTLnc8r4+TRo5E0tKo3INL8vdFGjmS84rWz4+4d99xNMsLmXMjGl/VuLCcS7jKMU4hymF0VNTTk2M3aCqjCXQ1coQQ5H7yCZjNeHbrimeHDo5lq4ardJGRZ/kpJBJJYxH55BMAhN17b/MKkTQrsqZOct7RR0TQdtm3mJJT8OrZg9IDBwCwFpydkSNMJpeZUXajw1SXJ8eWk6P196/2WlVPTvqTT5FvKykPuuqqastqAgKwGo1ow0JdSlclEknzEHjVVfhefLHsPv43Rxo5kmZBFxSELigIwBEmshQ1PCdHCEH6oqcRpaVoAgLQR0ait+XkVCTVFa6yGTmB1T052oBAdZl8I6a0NNXAURTCH3yAwJkzqy3vERdHmdGIPkqGqiSSloCiKDJ0LJHhKknzo7EZOaKkpMEzpHI//JD8r78GjYboxYtRPDwcicCm1NRat2e15eRo3HlyKoWrijdvAcDQqycht9zitpux/T3lSVUikUhaDtLIkTQ7Wl9fx2NrA7w5BWvWkPl/LwAQ8egj+I0ZDagDQRUPDzCbHWEsd9SWk+Pw5BiNlGxWRz74DB5S47bsydOenTrWW79EIpFImhYZrpI0O4pej2IwIMrKsBQVoQ0MdLtc7qefofHxIXDGdEr37iX14UdACHXw3uzZzu1pNOjj46g4foLyY8cdJeVVsRSonhytvzsjx+bJyc+nZIvqyfEZPKjGzxA0ezaeHTviPXBgvT6zRCKRSJoeaeRIWgRaPz/MZWU1lpFXJCaqIxcAjZeB9GeeRZSV4TNyBBELH0VRFJflfQYOpOL4CQrXrHF4eKpSe06O+lzp7t2YMzNRPDzw6tu3Rv0aT098R46s+4NKJBKJ5Lwhw1WSFoE9L6em+VVlBw86Hqfcdz+W7Gw8u3Qh5oUX3Q7e8580CYDCX3/FWmk6eWWsxlqqq2yGj8mWvOzVrx8ag6HachKJRCJpuUgjR9Ii0PipeTk1eXLKDh12+VsXFkbc22+h9fVxu7xX377ooqKwFhVR9OefLq8ZV6zk9HXXO2Zbuc/JcX3Ou3//+n0QiUQikbQYpJEjaRFofevw5Bw+BIBX794Yevci7p230dfSdE/RaPCfOAGAnPfex1pW5ngt+403KN2xw7ag4vAiueipYuR49e5V/w8jkUgkkhaBzMmRtAjshkbaowsxLl+OLiQUXUgw2pBQfAZeRLnNkxP+6CN415IbU5mga2eR//U3lO3bR+rChcS88ALmrCwqTp92LKN4eaFotdX1VDFyDD17nuUnk0gkEklzIY0cSYugsuekZNNml9ey7A8UBUPnzvXepkdsDLGvvcaZW2+l8KfVZMXF49mhvcsywhayqk2PNiTE0bhQIpFIJK0HaeRIWgRBM6/GnJWFoXs3POLiMGfnYMnNoWT3bkq3q6ElfVQUGm/vBm3XZ9BAohYtIm3hQnLefdfRDbkuKo9m8GjTpkHvKZFIJJKWgTRyJC0CQ7duxL35RrXnLYWFHL1I7T2jCws7q20HTp+GKekM2W++5aiWivr30+R9/TWB06fXub5HQsJZva9EIpFImheZeCxp0Wj9/Ih7/3082rYl9K67zno7oXfdhf/kyeofOh3+EybQ9quvCLrmmhrX8b3kEgBCbr3lrN9XIpFIJM2HIoQQzS2iOSgoKCAgIACj0Yi/mz4pkgsPa0UFmf/9PzzbtSXo2mvrXF6YTFgKC9EFB58HdRKJRCKpDw25fstwleRvg8bDg8h/PFbv5RW9Xho4EolE0oqR4SqJRCKRSCQXJNLIkUgkEolEckEijRyJRCKRSCQXJNLIkUgkEolEckHS7EZOXl4es2fPJiAggICAAGbPnk1+fn6917/ttttQFIWXX365yTRKJBKJRCJpfTS7kTNr1ix2797N6tWrWb16Nbt372b27Nn1Wvf7779ny5YtREdHN7FKiUQikUgkrY1mLSE/dOgQq1evZvPmzQwaNAiA9957jyFDhnDkyBE61zKnKCUlhQULFvDzzz8zadKk8yVZIpFIJBJJK6FZPTmbNm0iICDAYeAADB48mICAADZu3FjjelarldmzZ/PQQw/RvXv3er1XeXk5BQUFLj8SiUQikUguXJrVyElPTyc8PLza8+Hh4aSnp9e43n/+8x90Oh133313vd9r8eLFjryfgIAA4uo5qFEikUgkEknrpEmMnCeffBJFUWr92b59OwCKolRbXwjh9nmAHTt28Morr7BkyZIal3HHwoULMRqNjp8k26BGiUQikUgkFyZNkpOzYMECrqll8CFAQkICe/fuJSMjo9prWVlZREREuF1v3bp1ZGZmEh8f73jOYrHwwAMP8PLLL3P69Gm363l6euLp6Vn/DyGRSCQSiaRV0yRGTmhoKKGhoXUuN2TIEIxGI1u3bmXgwIEAbNmyBaPRyNChQ92uM3v2bC699FKX5y677DJmz57NTTfddO7iJRKJRCKRXBA0a3VV165dGT9+PHPnzuWdd94BYN68eUyePNmlsqpLly4sXryY6dOnExISQkhIiMt29Ho9kZGRtVZjVcU+fF0mIEskEolE0nqwX7ft1/HaaPYp5J9//jl3330348aNA2DKlCm8/vrrLsscOXIEo9HYqO9bWFgIIBOQJRKJRCJphRQWFhIQEFDrMoqojyl0AWK1WklNTcXPzw9FUSgoKCAuLo6kpCT8/f2bW141Wrq+qrQWva1Fp53Wore16LQj9TYNrUWnndak9++sVQhBYWEh0dHRaDS11081uyenudBoNMTGxlZ73t/fv0UfMC1dX1Vai97WotNOa9HbWnTakXqbhtai005r0vt31VqXB8dOs491kEgkEolEImkKpJEjkUgkEonkgkQaOTY8PT154oknWmwvnZauryqtRW9r0WmntehtLTrtSL1NQ2vRaac16ZVa68ffNvFYIpFIJBLJhY305EgkEolEIrkgkUaORCKRSCSSCxJp5EgkEolEIrkgkUaORCKRSCSSCxJp5EgkEolEIrkgkUaO5Jxo6cV5ZWVlzS2hQWRmZrb4fWpn+/btrW7/SiSSvxd/CyMnLS2NTZs2cfr06eaWUo3k5GS++OILNm3aRH5+fnPLqZO0tDSuuuoqvvrqK0CdAdYSOXXqFL179+bZZ59tbin14tSpU0yZMoVHHnmEgwcPNrecWjl58iRTp05l4MCBLF26tLnl1EpiYiKLFi1iyZIlbNmyBWi5xyxAUlISK1asYN++fVgsFqDl3kgkJSXxzTffsHPnTkwmE9Bytebm5pKdnQ207P8/tOzrlTta/L4VFzj33HOPCAkJERdddJHw9vYWb7zxhsjPz29uWcJqtYq7775b+Pn5iZEjRwofHx9x0003idTU1OaWVitPP/20UBRFDB48WBQXFwshhLBYLM2syonVahW33Xab0Ol04sorrxRZWVnNLalGrFarEEKIjz/+WAQFBYmrr75abNu2TaSnp7u83lKwWq1i/vz5QqPRiKlTp4rAwEDx7bffNresGnnkkUeEt7e3mDBhgujSpYuIjY0VBw4caG5ZNfLAAw8Ib29vcckllwh/f38xf/58ceLECSFEyzsWHn30UWEwGMTgwYOFp6enuOWWW1qs1scee0yEhYWJZ555prml1ElLvV7VRGvYtxeskZOYmCguv/xyMWTIELFhwwZx6tQp8dBDD4mePXuKn3/+uVm1nTp1SowaNUoMGzZMbNq0SZSVlYl3331X9OjRQ/z666/Nqq0uJkyYIK655hoxcuRI8cQTTwghWo6Rc+zYMREUFCS6d+8udu7c2dxy6oXFYhETJkwQL774ouM5u/HYkvjuu++El5eXGDhwoNi8ebMQQoghQ4aIW2+9VQjR8i5s3377rejfv79Yu3atEEKIXbt2iSFDhojnnnuumZW554MPPhBDhgwR69atEyaTSSxdulQMHz5cjBw5srmlVWPz5s2ia9euYuXKlaK0tFR8+umnYsSIEWLo0KHNLc2FvLw8cfPNN4uBAweK/v37i8svv1xs3bpVCNHyjteWfL1yR2vatxdUuEpUcpXu27cPb29vXnvtNYYOHUpCQgLPP/88WVlZzRIWqqzNbDYzbdo0PvjgAwYPHoynpyfTpk1Dq9XSsWPH867NHaKK29lsNgMQFRXFzJkzGTp0KEuXLuXQoUNoNJpmc1NXfl+9Xk90dDQXX3wxffv2ZePGjTzwwAM8++yzrF69msLCwmbRWJmq++mPP/7g+PHj3HXXXWzcuJGpU6cyY8YMFixYwObNm92uc76o/L5ZWVl89tlnbNmyhUGDBlFaWkr79u3Jzc2lpKQERVGaRaOdqvto+fLleHl5MXr0aAD69OmDh4cHEyZMqHGd84n9ve2/v/32W9q3b8/FF1+MTqfjqquuok+fPvz111+8//77za63Mt9//z0Wi4VJkyZhMBi4/vrree6559i7dy8vvfQS0DK0enl50aZNGxYuXMgLL7xASkoK3333HSaTCUVRWoRGO/v3729R1yt3VN5frWnfXjBGTmlpKRUVFY6/e/fuzd13303//v0BNVZoMpmIiYlxxLqbS1tsbCw33ngjnTt3BiAjI4NZs2ZhMplYtGgRy5cvP6/6qlJVrxACnU4HwLZt2+jUqRPTp08nMjKSt99+m4qKimbJI6mqMy4ujkWLFvHuu+8yfvx4Zs2aRWJiIkuXLuXWW29lwYIF511jZarqBfDx8SE3N5eVK1dyzz330K1bN4YMGcK+ffuYOHEi6enpzWJAVNV6yy23MGPGDAAsFgteXl6EhoZy/PhxvL29mzUWX1Wr2Wymc+fOnDlzho0bNzrynfbu3cvDDz/MXXfd1ayGWWW9iqI4LmLx8fEu+9FgMNCpUycWLlyI2WxuFr32i1VlXeHh4Xh5eVFSUuJ4bvDgwTz44IM8/fTTlJeXN6tW+/nd09OTe+65h2nTpjFy5EhGjx7NX3/9xS+//HLetVXFrtV+89inT58Wc71yR9XvmIeHR4vdt9U4r36jJuLRRx8V/fr1E5deeql45ZVXqsUw7eGU06dPC19fX7Fnz55m02Y0Gl1eP3r0qDAYDGL8+PFiyZIlYsaMGaJTp07NFuOsSa/FYhHJyckuuTgvvviiCA0NFYqiiFdffVWUl5c3u87c3Fxxww03iGHDhok9e/aIsrIyIYQQ7777rujcubN48803z5vG+ujdtGmTuOSSS8RFF10kbr31Voert6CgQPTo0UPcfPPNQojzGxKsqrWgoMBFg/33ypUrhb+/v0hMTDxv2urSav/uHzhwQMycOVNMnjxZaDQaMWHCBPHrr7+KF154QbRr105cf/31Lp+lufTm5eUJIdR8nAEDBoinnnpKZGdni4ceekiEhYWJL774QkRFRYm33nrrvOoUQogXXnhB/Pvf/672/Pvvvy/69+8vfvjhB5fnT506JWJiYsTixYuFEOc3bFGTViGc/+PExEQxdOhQMXfuXEeuXnOEVqpqraqhOa9X7qjp3GW1WlvcvnVHqzZyysvLxZVXXim6desm/ve//4kbbrhBdOvWTUyaNMllOfvO/vLLL0XPnj3Py4mtvtqEEGL37t2Ox2azWTzwwANiyJAhoqSkpMl1NkRvQUGBGD58uCgpKRHLli0TwcHBIiAgQPTq1cuxTFMf2DXpnDhxomOZQ4cOiW3btrl8CXNycsTkyZPFvHnzhNlsblKN9dE7YcIEIYSafzNjxgyhKIp49913hRDCoe/DDz8UMTExorCwsFm1ujtmhRBi+fLlom3btmL9+vXnRV9l6tqvQqjH4ueffy4mTpzoMNSEEOKHH34Q3t7e5zUpvSa9l112mRBCiMLCQnHPPfeIDh06iODgYNGjRw+xZcsWIYQQF198sUvOVlOzdetWMWrUKKEoiujXr5/YuHGjEEKIiooKIYQQRqNR9OzZU9xxxx0iIyPDsV5ZWZmYM2eOuOmmm87bd6wmrVXP8fbz0ssvvyz69+8vPvroo2qvtRStds7n9cod9T0f2PU1576tjVZt5Bw8eFB07NhRrFmzxvHc+vXrhZeXl3j++ecdO9j++8EHHxS33XabY9m1a9dWuxs539rcMXXqVDFp0iRRUVFx3g6SuvQKIcRvv/0moqKiRI8ePURgYKD4v//7P/HOO++IPn36iDfeeEMI0fR3xvXRWRX7PuzQoYOYP39+k+qrSm167Xe8P/30kwgJCRFjx451WXfhwoVi1KhRori4+LwcB/U9Zisbjh4eHmLlypUuz58P6tJqv8guXLhQTJ8+3WXdl19+WXTq1Om8eqDqcxwIIURSUpLYu3ev4++ysjIRFhbm+H6dD55++mlx5ZVXio8++kiMGzfOkVwuhNPQeeONN0SnTp0chrmdYcOGiVtuuaVFaK38nbE/LisrExMnThRXX3212Lt3r/jss89q9AA1l1Y75/N65Y6Gng+ac9/WRqs2cnbs2CEURRE5OTlCCOeBsnjxYhEUFCSOHj3qWNZsNou+ffuKr776Spw8eVKMGTNGeHh4iK+++qrZtVVm06ZNYsSIEeKLL75oEl01UZvewMBAcfLkSWEymUS3bt3EvHnzxKlTp4QQQqSmpoqrr75ajBgxwhEaai6dte3Xn376SVx00UViw4YNTa6xMrXpDQgIcJTdPvnkkyIkJET861//EkePHhWHDx8WI0eOFIsWLWoRWt3t2/z8fDFixAjxwAMPnDeNDdV63333iUsuuUT8+OOPwmw2i4MHD4oRI0aIO++8s0XpPXLkiMvy9tc/+eQTMWjQoPPSWsL+nomJiQ4vw+LFi8WgQYPE0qVLhRBCmEwmx/KzZs0Sffr0Ee+8847Iy8sTO3bsEP369RP/+9//WoTWqka3/e/vv/9etGvXToSEhAgPDw/xf//3fy1O6/m+XrmjIeeD5tq39aFVGzm7du0S3bt3F6+99poQwvlPqKioEG3btnU5+e7Zs0f4+fmJCRMmCJ1OJ2bOnOniwm4ubRaLRRw4cED88ccf4vbbbxd+fn7i3nvvddwxnS9q05uQkCDuvfdeIYQQGRkZ1e46Dhw4cF4MnLp0Vt2v+/btE2vXrhW33XabCAgIEI8++uh5DVXVR699v6anp4t3331XBAYGih49egg/Pz9x0003nbf9Wh+t9n1rv9CZzWbRsWNHcfvtt7eo47Xyft2yZYuYOnWq8PDwEOPGjRO+vr7i5ptvFqWlpS1Kr33fms1mkZ2dLZYtWybmzZsnfHx8xKJFi4TVam0W1/+JEyfEtGnTxLRp00Rubq4QQjhy706cOCEef/xxodVqRf/+/YWXl5e45ZZbzvuxUJvWqsbD8ePHxQ033CAURRHz588XRUVFzSG1Tq3n+3rljoYcs0K0nH1blVZt5OTm5opp06aJmTNnOu507CfgF154QURHRzsOnG+//VYoiiLGjBkjZmfOPwAADi5JREFUdu3a1aK0ffnll2LSpEli3LhxzZZkVpfeqKioGuPcLUln5f368ccfi9GjR4vRo0e75D21JL1V92tKSorYvHmzOHbsWIvTWnnf2k9sn3zySTUvREvQGhUV5dCYmpoqvvvuO/H666+LgwcPnnet9dFbed9mZmaKBx98UFx66aXNdtwK4fx+f/DBB2LQoEE15gXt379frFy5Uhw6dOh8ynOhvlofeughERsb6xISPN/UR+s333xzXq9X7mjIMStEy9i37mixRs6xY8dcYoGVqewy/eCDD0Tv3r3Fyy+/7LLM+++/L7p37y5OnjwphBAiLS3N0RispWkrKSlxhH+agsbSe/r06SbT2Jg67fu1qKhIHD9+vMXrPX36dJMbjK3lGBCi8bQ25XeqMk2ht6nu2uurtfLfRqNR3HTTTWLMmDGOEMWOHTuEEE2bh9XYWoVoOr2NpXXbtm1CCDU3q7GuV+7Yv3+/eOihh9zeoDT0fFC16rKl0SKNnD179ghFUURoaKjLSbXyTiwtLRVffvmlEEKIG2+8UQwZMsTloHjyySfFqFGj/lbaWrPe1qKzNeqVWqXe+mqtqKgQS5YsqfbaqlWrxKhRo8R1110nxowZIxRFcYRZpNbG1Zqdnd1kWsvLy8WcOXOEoijirrvucmn7UflGq6Ucs41BizRytm3bJsaPHy8iIyPFHXfcUe31V155RQQHB4upU6cKIdSD7LrrrhMeHh5i/vz5Yt68ecLPz8/RW6Ix75JbsrbWrLe16GyNeqXWpqM16a2v1iuuuKKaUZCYmCjat28vFEUR11xzjWO+mtTaerR+8MEHws/PTwwdOrRaSKnycdeSjtnGoEUaOe+884649tprxW+//SZ0Op2jX4QQQrz++usiISFBfP755y6WstVqFc8++6yYO3eumDhxYpNV0bRkba1Zb2vR2Rr1Sq1Sb0O0Vr1o/fbbb8LX11f06dNHbN++XWptpVqHDh0qunbt6mhAuWPHDrFq1Spx5MgRRyL+a6+91qKO2cagRRo5S5YsEY888ogQQh0CaG/0Zs/ar9ok73xaki1Zmztai97WotNOa9IrtTYdrUlvQ7Xayc7OPu8tLaTWxsOeY7Nx40bRrl078dRTT4kpU6aIdu3aie7du4uIiAhx1VVXOZatWhXV3N+xc6XZZletWrXKPlai2ms7d+6kuLgYgC+++ILVq1czYcIEhg8fzqFDh/Dy8nJZvrHnpLRkba1Zb2vR2Rr1Sq1Sb2NrtW8nJCSEa6+9VmptJVqr6tXpdAghGDJkCKNGjWLx4sUEBwezbNkyvvzyS95//31WrFjBU089hU6nw8fHx2VbzT1895w531bVypUrRUxMjFAUxeHyqtqZ+JprrhG//vqrEELN4vby8hJ6vV588803f1ttrVlva9HZGvVKrVKv1Cq11qXX3k4hKytL/POf/xQpKSku673wwgsiJCSk2XocNSXn1ZOzfv16Xn/9daZPn8748eO55557AKelKGxWsqenJx9//DEDBw5k4cKFPPbYY/j4+JCYmPi31Naa9bYWna1Rr9Qq9UqtUmt99Gq1WoQQhIaGsnDhQqKjo13WjY6Oxmq1cuTIkfOq+bxwPiwpu8V79OhR8eKLL4qTJ0+K7du3C29vb/H+++8LIZwldSUlJWL69OkiJCRE3HnnnSI5OVkIIcRzzz0nFEVp9N4XLVlba9bbWnS2Rr1Sq9QrtUqtZ6O3JubPny9mzJjR5FqbgyY1cnbs2CHy8/NdnrO7zUwmk3jggQdEWFiYo3W9/bWtW7eKAwcOuKxXVlYmnn/++UZrONSStbVmva1FZ2vUK7XK40BqlVrPVm9VTp06JY4fPy5uueUWER8fL77//nshROtPNK5Kkxg533zzjYiNjRXt27cX8fHx4vHHHxdpaWlCCOEyg+XkyZMiLi6u2gyMpqQla2vNeluLztaoV2qVeqVWqfVc9VY2Xg4fPizuvPNOER4eLkaNGtUso1nOF41u5Gzbtk106dJFvPzyy2LPnj3izTffFGFhYWL+/PmOaab2g8NqtYo333xT6HQ6Ryv+8vJyUVxc7Hj976KtNettLTpbo16pVeqVWqXWxtJbVlYmysvLhdlsFj///LP466+/mlxzc9NoRo79H/zWW2+J2NhYYTQaHa+9/vrrYvDgweLpp5+utl5OTo4YOnSomDp1qtixY4cYN26c+PTTTxv1gGnJ2lqz3taiszXqlVqbjtakV2qVWhtT79ixY8+L3pZEo3tyHn74YTFmzBiHdSuEOijxzjvvFEOHDhX79+8XQri6+j766COhKIrQaDRi8uTJNTZPupC1tWa9rUVna9QrtUq9UqvU2ph6K6/7d+CsjZw1a9aIu+66S7z88ssubayXL18uDAaDOHHihBDCubPXrFkjhg0b5jJWvry8XLzxxhtCo9GIkSNHOv5B50pL1taa9bYWna1Rr9QqjwOpVWptrXpbMg02clJTU8XkyZNFeHi4uO6660TPnj1FQECA4x9RWloqunTpIubNmyeEcC1dGz58uMsAs/T0dHHPPfeIjz/++Fw/R4vX1pr1thadrVGv1CqPA6lVam2telsDDTJyiouLxY033ihmzpzpSGQSQoiLLrpIzJkzRwihWpaffPKJ0Gg01YZ4XXfddWL06NGNILt1aWvNeluLztaoV2qVeqVWqbW16m0tNKjjsbe3N56ensyZM4e2bdtiNpsBmDx5MocOHQJAq9Vy9dVXM3XqVG699Vb+/PNPhBCkp6dz7NgxrrvuusbvaNjCtbVmva1FZ2vUK7VKvVKr1Npa9bYaGmoVVZ5tYc/Qvv7668XcuXNdnistLRWjRo0S4eHhYty4cSI6OloMHjxYnDlz5mwNslatrTXrbS06W6NeqVXqlVql1taqtzWgCOFmrGoDGTFiBDfffDNz5sxBCIHVakWr1ZKRkcHevXvZtm0bCQkJzJo1qzHssgtGmztai97WotNOa9IrtTYdrUmv1Cq1QuvT2+I4VyvpxIkTIiIiQmzfvt3xXHl5+blutlFoydrc0Vr0thaddlqTXqm16WhNeqXWpqE1aRWi9eltiZz1FHJhcwCtX78eX19f+vfvD8BTTz3FPffcQ2ZmZuNYYReYNne0Fr2tRaed1qRXam06WpNeqbVpaE1aofXpbcnoznZF+7j5rVu3csUVV/DLL78wb948SkpK+PTTTwkPD280kReSNne0Fr2tRaed1qRXam06WpNeqbVpaE1aofXpbdGcixuotLRUdOjQQSiKIjw9PcVzzz13LptrVFqyNne0Fr2tRaed1qRXam06WpNeqbVpaE1ahWh9elsq55x4PHbsWDp27MiLL76IwWBoLNurUWjJ2tzRWvS2Fp12WpNeqbXpaE16pdamoTVphdantyVyzkaOxWJBq9U2lp5GpSVrc0dr0dtadNppTXql1qajNemVWpuG1qQVWp/elkijlJBLJBKJRCKRtDTOurpKIpFIJBKJpCUjjRyJRCKRSCQXJNLIkUgkEolEckEijRyJRCKRSCQXJNLIkUgkEolEckEijRyJRCKRSCQXJNLIkUgkEolEckEijRyJRNJimTNnDoqioCgKer2eiIgIxo4dy4cffojVaq33dpYsWUJgYGDTCZVIJC0SaeRIJJIWzfjx40lLS+P06dP89NNPjB49mnvuuYfJkydjNpubW55EImnBSCNHIpG0aDw9PYmMjCQmJoZ+/frx2GOPsXz5cn766SeWLFkCwIsvvkjPnj3x8fEhLi6OO+64g6KiIgD++OMPbrrpJoxGo8Mr9OSTTwJQUVHBww8/TExMDD4+PgwaNIg//vijeT6oRCJpdKSRI5FIWh1jxoyhd+/eLFu2DACNRsOrr77K/v37+fjjj1m7di0PP/wwAEOHDuXll1/G39+ftLQ00tLSePDBBwG46aab2LBhA//73//Yu3cvV111FePHj+fYsWPN9tkkEknjIWdXSSSSFsucOXPIz8/n+++/r/baNddcw969ezl48GC1177++mvmz59PdnY2oObk3HvvveTn5zuWOXHiBB07diQ5OZno6GjH85deeikDBw7k2WefbfTPI5FIzi+65hYgkUgkZ4MQAkVRAPj999959tlnOXjwIAUFBZjNZsrKyiguLsbHx8ft+jt37kQIQadOnVyeLy8vJyQkpMn1SySSpkcaORKJpFVy6NAh2rZtS2JiIhMnTuT222/n6aefJjg4mPXr13PLLbdgMplqXN9qtaLVatmxYwdardblNV9f36aWL5FIzgPSyJFIJK2OtWvXsm/fPu677z62b9+O2WzmhRdeQKNR0wyXLl3qsryHhwcWi8Xlub59+2KxWMjMzGT48OHnTbtEIjl/SCNHIpG0aMrLy0lPT8disZCRkcHq1atZvHgxkydP5oYbbmDfvn2YzWZee+01Lr/8cjZs2MDbb7/tso2EhASKior47bff6N27N97e3nTq1InrrruOG264gRdeeIG+ffuSnZ3N2rVr6dmzJxMnTmymTyyRSBoLWV0lkUhaNKtXryYqKoqEhATGjx/P77//zquvvsry5cvRarX06dOHF198kf/85z/06NGDzz//nMWLF7tsY+jQodx+++3MnDmTsLAwnn/+eQA++ugjbrjhBh544AE6d+7MlClT2LJlC3Fxcc3xUSUSSSMjq6skEolEIpFckEhPjkQikUgkkgsSaeRIJBKJRCK5IJFGjkQikUgkkgsSaeRIJBKJRCK5IJFGjkQikUgkkgsSaeRIJBKJRCK5IJFGjkQikUgkkgsSaeRIJBKJRCK5IJFGjkQikUgkkgsSaeRIJBKJRCK5IJFGjkQikUgkkguS/wclEN8lDir+MgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7h/j4eADA2rVrccsttyAsLAy9evXCoUOHVB8gUTDSYzl6f1BSU0it13sbQKnB13IAem7EK7UzjshIFC+TtWvXDp988glGjBiBNWvWYOLEiQCA48ePIyEhQfUBEgUrvZWj9we5N/ev/jdT43g9vKlJ5M+ijo58LQeg1+KKgVhyJNKS4pmhZ599Fk888QTatGmDnj17Ijs7G0DdLFHXrl1VHyBRMLNWNR7e5RKXy0LBRu7N/d28Qy4btXpTk8ifRR0dZaUlwRyjfNOurzWmtJy1CdSSI5GWFP+/9K9//Sv69u2L4uJiXHHFFbbHr7vuOowYMULVwRFRcJFKHnfkmBQtJ/k8KS4KJZbzyCssQ1ZaUkBbXISHmTAwoxk+3HVU9mt8zRvTctbGn33kiPzJqwrUKSkp6Nq1K8LC/nx5VlYWOnbsqNrAiCj4eEoed8Uxp0fq9QJAWUUVJv6/H2wzS6cqqpBqjnH7flpX+u7Tvomi433JG9N61sbXnC8ivVIcDFVUVGDatGno3bs32rVrh0svvdTuS2sLFixAWloaYmJi0L17d2zatMntsR999BEGDhyIJk2aICEhAdnZ2VizZo3mYyQi99wlj7vjeINV8voSywU8/MEuDLuiLrAIxO69lAR5n3P8tW2x/P5e2Dy5v1eBkD8SxQO55KgEk7tJKcXLZPfddx82bNiAnJwcpKamwmTy31ToypUr8dhjj2HBggXo06cPFi1ahOuvvx4FBQVo1aqV0/EbN27EwIED8cILLyAxMRFLly7FTTfdhO3btzO/iUhDUsUN6yePf7W3GO/mSe9EtS59Wc+54clrsfPQKZSUX8CsL36ya3NhZV26+eyHYvzz9q6Y9eU+u5kNc4NI3N2njaYFLq1Le55mVFLNMZg48DKfAjJ/JIoHcslRLiZ3kzdMQghFIXNiYiK+/PJL9OnTR6sxudWzZ09069YNCxcutD2Wnp6Om2++GXPmzJF1jssvvxy33XYbnn32WZfPV1ZWorKy0vZ9eXk5WrZsCYvFwt1yRDIovRnlFZZh1OJtkudNiouyq9mTFBeJEV0uQfPEBpj15T7J1y+/vxey0pLwxvpfsXRLEU6ft+8RpuXN0rp8BbhuN6JGOYVP849iwop8yeNeHdkFw7tc4tV71NQK9J27XrJg6ObJ/W2BnT+rfrsryKnmdSbjKC8vh9lslnX/VrxM1qhRIyQl+b8yblVVFXbu3OnU8mPQoEHYunWrrHPU1tbizJkzHsc/Z84cmM1m21fLli19GjdRKPEmZ8U6cyJ1e3QsXniyohpLthyUFQgBdUs36wpKMP/rX+wCIanxqcEfdaXkzsaUnqn0evlIacHQ3L3F6Dt3PUYt3oYJK/Jd7hBUSyDrSZHxKQ6GZs2ahWeffRbnzp3TYjxulZaWoqamBs2aNbN7vFmzZigpKZF1jpdffhkVFRW49dZb3R4zdepUWCwW29eRI0d8GjdRqPD2ZqQ0qdpbyQ2jA3qzHJKZis2T+2P5/b3w6sguPuUHuSInqAwzAbO+3OdTYCI3sPP3Fnwmd5MvFOcMvfzyyygsLESzZs3Qpk0bREZG2j2/a9cu1QbnimOOkhBCVt7S8uXLMWPGDHz66ado2rSp2+Oio6MRHR3t8ziJQo0vOSvWG6zj8lpSXKTLXCAlrEs3EAhY8UUra10prc49/aYMjHt/F0yAy6DPMc7ztimsVMHQQGzBN0pyN+mT4mDo5ptv1mAY0pKTkxEeHu40C3T8+HGn2SJHK1euxL333ov//Oc/GDBggJbDJApZvt6MXN1gS8ovYOLKfK/HVH/pprSi0uOxUuMzAndBZZjJORACfAtMPAV2gaj6bYTkbtIvRcHQxYsXAQD33HOP33NpoqKi0L17d6xbt86uuOO6deswfPhwt69bvnw57rnnHixfvhxDhw71x1CJQpIaNyPHG2xeYZlPY0qplxgt91xGv1k6BpWlZyo95lVpEZgEYpZGTkHOFA3rSZGxKQqGIiIi8I9//AN33XWXVuPxaNKkScjJyUGPHj2QnZ2Nt956C4cPH8bYsWMB1OX7HD16FO+++y6AukBo9OjRePXVV9GrVy/brFKDBg1gNpsD8hmIgpUWNyOlFasBYNrQdCTHRzst3YTSzbJ+UPlpvrzq12oGJoGYpfG0TOiPelJkbIoTqK+77jp8++23GgxF2m233Yb58+fjueeeQ5cuXbBx40asXr0arVu3BgAUFxfj8OHDtuMXLVqEixcv4uGHH0Zqaqrta8KECQEZP1EwU7rTSOk5pVgrSY/pk+ay15uc6tXThgbfzTIQgYlUMrdWVb/9sWuPgpPiOkOLFi3CjBkzcMcdd6B79+6Ii4uze37YsGGqDjDQlNQpIGNSUgfFnzVTjEqLoneuzlmfkjoyns6lx+J8vv7MeVMbSI1x+KO2klpjpeCk5P6tOBiq34/M6WQmE2pqapScTvcYDAU3JTduVraVT4ubkfWc6wpK8En+Mbu6Q0r/HVbvOYaHPtjt9LjeivOp9TPna2Di7Tj4/xkKJE2DoVDDYCh4KalWy8q2geUYXHVv3Qg7D53yKtiyzpR4mmWSM1OiNbV/5nwJaJSOo/6/V3JcNGACSs9WcpaG/ErJ/Vvx1nqiYKCkDgr+99/+rJlCf/J0E/emrUQgtn0rpUWdHqnaQGqNw9O/V6CuJ5EUxcHQc8895/F5dz2/iPREabVavd88taCHvAt3sxLeFgsEjFGcT6uATWnRR6Xj0OLfi8gfFAdDH3/8sd331dXVKCoqQkREBNq2bctgiAxBixuikYv1OdJDrodWVYyNUJxPLwGbknEEouo0kVoUB0O7dzsnHZaXl2PMmDF2xRCJ9EyLG6KWN089dP529de9luPSanbECPWG9BKwKRmHEZYfidxRJWcoISEBzz33HG688Ubk5OSocUoiTSm9IQby5unPWRolf92vKyjRdFxazY4YoTifXgI2JeP4Ys8xWecMphnUQNPDUnawUFx00Z3Tp0/DYrGodToiTSkpEKhFMUG59Nr5+431BzQflzezIzW1AnmFZfg0/yjyCsvcdqDXe3G+QP7MeTsOvcxmhYrcvcXoO3c9Ri3ehgkr8jFq8Tb0nbte9d8JoULx1vrXXnvN7nshBIqLi/Hee+/hqquuwvLly1UdYKBxa31w83edIaUFHv29BfzT/KOYsCJf8rjEBpE4fd51N3m1xqW0WKA3/z56/8taD7lbcsehVnFHksZSH/JoWmcoLS3N7vuwsDA0adIE/fv3x9SpUxEfH698xDrGYCj4+asCtdIbW15hGUYt3iZ53uX391ItB0Pue8qhxrjkFgsM5puDXgI2OeMIZNXpUGGUOll6oGmdoaKiIq8HRqRHSrYbK92abOXNlmO9dv42x0bi9DnXs0Jqj8u6nOUYRNbvRh/su5i8/ZkLxDjk/HuRb5iorg3FwdA999yDV1991WkGqKKiAo888gjefvtt1QZHFAy8vVnrtfP33b3TMO/rX/w2Lqligbw56Is3xR1JPr2UXQg2ihOo33nnHZw/f97p8fPnz+Pdd99VZVBEwURpgUcrvXb+Ht+/nd/HZZ2VcNWNnjcH/fH070W+YaK6NmTPDJWXl0MIASEEzpw5g5iYejs4amqwevVqNG3aVJNBEumR3FwOb2/WgdwCLvXXvZ62pvPmQKFEL2UXgo3sYCgxMREmkwkmkwkdOnRwet5kMmHmzJmqDo5Ir5QkQ/tysw5kDkZ4mAlZaUm2gGhH0UlbQKSn3BDeHCiUGKFOlhHJ3k22YcMGCCHQv39/rFq1CklJf/5iiYqKQuvWrdG8eXPNBhoo3E1GjpTuXFJjy3EgdhTJ3U6th9wQ7mKiUKOXsgt6punW+kOHDqFVq1YwmUIj6mQwFDzUuHF7u63VaDdrI25V98fNQS/BHxHAn0cpmgZDALBp0yYsWrQIv/32G/7zn//gkksuwXvvvYe0tDT07dvX64HrEYOh4KDWjdKX2j96/kuu/i/V5LhoPP6fH1BSbrw6JlreHPT870dEzjStM7Rq1Srk5OTgjjvuwK5du1BZWQkAOHPmDF544QWsXr3au1ETacSbGj/u+LJzaWBGCuJjIrHl11IcO30elyQ2QO92yeh1aWC3e7u6yXui563qWtXkUfNniIj0R3Ew9Pzzz+PNN9/E6NGjsWLFCtvjvXv3xnPPPafq4Ih8pXZBPm+Tod0FHP/8tjCgswvubvJyhMpWdV9+hriMQWQMioOh/fv346qrrnJ6PCEhAadPn1ZjTESqUbsgn5KdS9Yb4dcFJViy5aDbcxYHaHbB001ejgN/nEFeYVnQ3+C9/RnishqRcSguupiamopff/3V6fHNmzfj0ksvVWVQRGqRO3ux5ddStx3O65PbxXtdQYmto7SnQKi+mZ8XSI5Bbld2OaRu8lLe+KYwJDple7M0ap1xc7y+1mW1YL5eREakOBh68MEHMWHCBGzfvh0mkwnHjh3DsmXL8MQTT+Chhx7SYoxEXpO7rPXGN7/KvqlLVWgG4PJG6Im7KtT15e4ttgVYE1bkY9Tibbhy9jqs3nNM9vvUp9YyV7Df4JUujUotqwHyAl8i8h/Fy2R/+9vfYLFYcO211+LChQu46qqrEB0djSeeeALjx4/XYoxEXpNa1qpPSTKsuwrNANB37nqvl57cBSjucntOVlTjoQ9248HfT2PqDRmK3kutiszB0AzVE6VFHdkrjch4FM0M1dTUYMOGDXj88cdRWlqKHTt2YNu2bThx4gRmzZql1RiJvOZpWcuR0r/aXfVf8nXpyVWAIie3Z9HGIqzeo2xmRk7vs5SEaCy7ryfGX9vO47nkzGwZldylUWsQyF5pRMajKBgKDw/H4MGDYbFYEBsbix49eiArKwsNGzbUanxEPnO3rOWK0pu6Yw5PicW5ibEcnpqbyg2wpn26V9HSi5yb/Ixhl6NPu2S0bybv/+PBeoOXWhqtP5PIXmlExqN4maxTp0747bffkJaWpsV4iDRhXdaat24/3vimUPJ4OTd1V7uFkuKivB6ju35CcgOMsooqxUsvcnuM8QYv3bzWir3SiIxHcTA0e/ZsPPHEE5g1axa6d++OuLg4u+dZpZn0KjzMhD7tmsgKhqRu6u5yeE5VVCkel9R2ayUBhjczM3Ju8rzB15FT1JGNNImMR3EwNGTIEADAsGHD7PqTCSFgMplQU1Oj3uiIVKbGTV3ObiFXrDfGMb1bo2WjWCQ1jEZKgnQhvqy0JCTFReJkRbWHs9fxdmZG6ibPG7wycmfciEgfFAdD33zzjRbjIPILNW7qcnN4HAMYb2+E4WEmPD88Ew99sNvjce5yjtTCG7wycpfViCjwvGrUGkrYqDU4+VId+NP8o5iwIl/yPebd1gUpCTGq3QjnrC7Aoo1FLp8zwX+d5NligoiMQNNGrUTBwJe/2uUuRaUkxKhaR2bqDRm4okUjPPPpXpysl5vk7xYPWjVDJSIKFAZDFLK8van7M5nYcRZmcGYKBmdy6YWISE0MhogU8lcyMRt9EhH5h+LeZESkrAifN0K10aeajWiJiORSPDM0Y8YM3H333WjdurUW4yEyDK12C0lt3Q/WPmCcCSOiQFE8M/T555+jbdu2uO666/DBBx/gwoXgLL9PJIer/mS+UtLoM1iE6kwYEemD4mBo586d2LVrFzp37oyJEyciNTUV48aNw3fffafF+IhCjtwq0t72QdMbOUUs5TbPJSLyhlc5Q507d8a8efNw9OhRvP322zh69Cj69OmDTp064dVXX4XFYlF7nEQhQ+7W/Vlf7guKGZNQnAkjIn3xKYG6trYWVVVVqKyshBACSUlJWLhwIVq2bImVK1eqNUaikGLdui+14HaqoioolpDkzoR503eNiEgOr4KhnTt3Yvz48UhNTcXEiRPRtWtX7Nu3Dxs2bMDPP/+M6dOn49FHH1V7rERBzbqT6os9xzDyypYe+5wBwbOEJHcmzNu+a0REUhTvJuvcuTP27duHQYMGYcmSJbjpppsQHh5ud8zo0aPx5JNPqjZIomDnaidVYmwkLtbU4myl++bH9ZeQjFoV2p9FLImIXFEcDP3f//0f7rnnHlxyySVuj2nSpAlqa2t9GhhRqLDupHIMBCznqiVnh6yMvITkryKWRETuKF4mmzZtmsdAiIjkk7OTSg6jLyFpXcSSiMgTr9px/P777/jss89w+PBhVFVV2T33yiuvqDIwolAgtZNKjsTYyKBYQtKqiCURkRTFwdB///tfDBs2DGlpadi/fz8yMzNx8OBBCCHQrVs3LcZIFLTUWN46fa4aa/aW4IbOxp898bZ5LhGRLxQvk02dOhWPP/449u7di5iYGKxatQpHjhzB1Vdfjf/7v//TYoxEAaFVn6z65y09UynrNQ2jPf/dMn75Lqzec0yN4RERhRyTEELRb/j4+Hjk5+ejbdu2aNSoETZv3ozLL78cP/zwA4YPH46DBw9qNNTAKC8vh9lshsViQUJCQqCHQ36iVZ8sV+d1TBquzwSgUVwkTlZUyzr/m8yvISICoOz+rXhmKC4uDpWVdX/NNm/eHIWFhbbnSktLlZ6OSHe06pPl7ryeAiEAGNFF/oYFo9ccIiIKBMXBUK9evbBlyxYAwNChQ/H4449j9uzZuOeee9CrVy/VB0jkT1r1yfJ0XnesO6kGZKTIfg3bVhARKac4gfqVV17B2bNnAQAzZszA2bNnsXLlSrRr1w7z5s1TfYBE3qqpFYp3Jinpk6Uk0VfJrjGTCZjQvz0eua49wsNMqKkVSDXHyH69kWsOEREFguJg6NJLL7X9d2xsLBYsWKDqgIjU4G3OT0m5NgGHkg7zQgDz/3sAHVPjMSQz1VaUcOz7u2S93ug1h4iI/M2nRq1EeuRtzk/u3mLM+uInWe+hNOA4WVElfZCD+stxQzJTseD2bpLNW4Ol5hARkT/Jmhlq1KgRTCZ5hc9OnmS+AgWOVM6PCXVBxsCMFLslM3ctMRx52ycrqWG0ouMB5+W4wZkpMMdG4vQ59zvLWJ6QiEg5WcHQ/Pnzbf9dVlaG559/HoMHD0Z2djYAIC8vD2vWrMG0adM0GSSRXN7k/MhNbvalT1ZKgndLV/WX43YUnfQYCAHAqXPVhm7aSkQUCLKCobvuusv233/5y1/w3HPPYfz48bbHHn30Ubzxxhv4+uuvMXHiRPVHSSST3FwexyBDTnJyUlwUZo/I9JhzVHWxFu/lHcShk+fQOikWOdltEBURZuvMrrT1Rv3lOG8+GxERSVOcQL1mzRrMnTvX6fHBgwdjypQpqgyKyFtyc3m8CTKeGZruMRCas7oAizcVof6u+9mr9+H+fmmYekMGhl2RikUbi2S9l6vlOG8+m5q82Z1HRGQEioOhxo0b4+OPP8aTTz5p9/gnn3yCxo05NU+BZZ2BKbFccLns5UuQkWJu4Pa5OasLXAY6tQJYtLEIv5VW4OuC47Lex91ynDefTS1aVeQmItIDxcHQzJkzce+99+Lbb7+15Qxt27YNubm5+Ne//qX6AImUCA8zYdrQDDz0gfM2dK2CjKqLtVi8yfOMzzqZgRD+916uggzrFvtx7+9yauHhSz6TFHfJ5dbdeQvZAoSIDE7x1voxY8Zg69atSExMxEcffYRVq1bBbDZjy5YtGDNmjAZDJJIvd28xZn1Z4PI5a0Vnd0EG4LwbS06Q8V7eQajRAeP6zBQsv78XNk/u7za4GJKZioV3dkOK2X42y91n85VWFbmJiPRE8cwQAPTs2RPLli1TeyxEPpHaHj/NQ86PNchwXApyN0tT36GT53wZts1Xe0swvEtzyZmdIZmpGJiR4pf8Ha0qchMR6YlXwZDV0KFD8a9//QupqZwip8CS2h5vAjDry30Y/L+Kzq54G2S0Tor1bfD1uKqB5Ep4mMkvwQd3sBFRKPCpAvXGjRtx/rz8NgNEWlEyg2FVUyuQV1iGT/OPIq+wDDW1whZkDO9yCbLbNpY125KT3QZqTcrordFqoHewERH5g08zQ0R6oXQGQ83dUVERYbi/X5rHbfMDM5rKTqLW0yxLIHewERH5i08zQ61bt0ZkZKRaYyHympIZDHe9y4otFzD2/V149esDihOCp96QgQevSnOaIQozAQ9elYbFo6/ExAEdZI9RL3xNLiciMgKTEILbQDwoLy+H2WyGxWJBQkJCoIdDbtTUCvSdu15yBmPDk9fi6pe+kawEnZIQjRnDLlc8S2StQH2wrAIA0KVlIzRPbGCbOenz9/+ipLzS5WutY9w8ub/uggvWGSIio1Fy/1YcDB04cABbt25FSUkJTCYTmjVrht69e6N9+/Y+DVqvGAwZh3XGB3Bdg2fhnd1gbhCFUYu3yTqf6X+vUXqz9xQ4AJAco16DC1agJiIj0SQYslgsGD16ND7//HOYzWY0bdoUQgicOHEC5eXluOmmm/Duu+8GXcDAYMhYpGYwPs0/igkr8mWdy5uZGnfb++sHOwA4y0JEpDEl92/ZCdSPPPIIioqKkJeXh549e9o9t337djzwwAN45JFH8M4773g3aiIVSG2PV5KPo7SGjlSBQhPqgqDNk/v7rU4QERFJk51A/dlnn2Hx4sVOgRBQV4Rx0aJF+PTTT1UdnCsLFixAWloaYmJi0L17d2zatMnj8Rs2bED37t0RExODSy+9FG+++abmY6TA8rQ93ro7SknYIWd3V02twL+3FMne3u/NFn4iItKGot1kJpP7X9ienlPLypUr8dhjj+Hpp5/G7t270a9fP1x//fU4fPiwy+OLiopwww03oF+/fti9ezeeeuopPProo1i1apXmYyV9qr87Si6p2aTcvcXoO3c9Zn25T9b59LR1noiIFARDN910E+6//358//33Ts99//33GDt2LIYNG6bq4By98soruPfee3HfffchPT0d8+fPR8uWLbFw4UKXx7/55pto1aoV5s+fj/T0dNx3332455578I9//MPte1RWVqK8vNzui4KLrb9Xgucgx4S6XB5PNXTcbdP3RE9b54mISEEw9Prrr6N58+bIyspCUlISOnbsiPT0dCQlJaFnz55ITU3Fa6+9ptlAq6qqsHPnTgwaNMju8UGDBmHr1q0uX5OXl+d0/ODBg/H999+jurra5WvmzJkDs9ls+2rZsqU6H4B0ZUhmKrZM6Y+JA1zvgpRTQ0eqBYirc0oFV0RE5H+yE6gTExPx1VdfYd++fdi2bRtKSkoAACkpKcjOzkbHjh01GyQAlJaWoqamBs2aNbN7vFmzZraxOCopKXF5/MWLF1FaWuqyp9rUqVMxadIk2/fl5eUMiIJUeJgJEwZ0wGUp8V41aJVqAVIfCxQSEemX4nYc6enpSE9P12IssjjmJgkhFOUyWSsJuHtNdHQ0oqOjfRwlGYm3DVqV5P7ICa6IiCgwFAVDQggcPHgQLVu2REREBKqqqvDxxx+jsrISN9xwA5KTk7UaJ5KTkxEeHu40C3T8+HGn2R+rlJQUl8dHRESgcWPtO36TcXjTBV5u7s+0oekY0yeNM0JERDolO2do//79SEtLQ7t27ZCeno6ioiL07t0b9957L8aNG4f09HQcOHBAs4FGRUWhe/fuWLdund3j69atQ+/evV2+Jjs72+n4tWvXokePHuypRj6T2qZvzRFiIEREpG+yg6HJkyfjiiuuQH5+Pm688UbceOONaNGiBU6dOoVTp06hT58+eO6557QcKyZNmoR//etfePvtt7Fv3z5MnDgRhw8fxtixYwHU5fuMHj3advzYsWNx6NAhTJo0Cfv27cPbb7+NJUuW4IknntB0nBQa2MSUiCg4yG7H0bRpU6xduxZdunRBRUUF4uPjsXHjRvTt2xdA3c6tkSNH4tChQ5oOeMGCBXjxxRdRXFyMzMxMzJs3D1dddRUAYMyYMTh48CC+/fZb2/EbNmzAxIkT8dNPP6F58+aYPHmyLXiSg+04SAqbmBIR6Y8mvcliY2Px888/o1WrVgCA+Ph45Ofno23btgCAI0eOoH379rhwIbgKyjEYIjmM3MTUyGMnInJHk95kzZs3x+HDh23B0IsvvoimTZvanj9x4gQaNWrk5ZCJ9E0qYPAmAVsPOKtFRKQgGBowYAB+/vln27LYuHHj7J5fu3YtunXrpu7oiHQgWAMGa/Vsx6nhEssFjHt/Fxbe2c3Qn4+ISC7Zy2RSioqKEBMT47KQoZFxmSz01J8FOlhagXlfO++StM4JGTVgqKkV6Dt3vduikSbU1UbaPLk/l8yIyJA0WSaTkpaWptapiALG1SyQKwJ1AcPMzwswMCPFcAGDVPVsAaDYcgE7ik4acvmPiEgJ1YIhIqNzt2zkjj8CBq2Sm+VWz1ZSZZuIyKgYDBFBedPV+rQKGLTMVZJbPVvucURERia76CJRMFPSdNWRFgGDdZbKcUzW5ObcvcU+nV9u9eystCSf3oeIyAgYDBHBu9kdrQIGT7NU1sdmfl6Amlrv9z6wejYR0Z9kB0O1tbV46aWX0KdPH2RlZeGpp54KugKLFLqUzu5oGTAoSW72xZDMVCy8sxtSzPafPcUcY9hdckRE3pCdMzR37lw888wzuO6669CgQQO88sorKC0txVtvvaXl+CiEuUoeBuqChZLyCzh5thJJcVFIMTfwObHYumxUYrkgK28oRcM6Q3Jnqbb8esLnxOohmakYmJHCCtREFNJk1xm67LLLMGHCBDz00EMAgNzcXNx88804f/48TKbg/cXJOkOB4Sp5ODE2EgBw+ly10/FqJBbn7i3G2Pd3uX1+4oAOaJMcq3nAkFdYhlGLtyl6jTefn204iCiYaVJn6NChQ7jxxhtt3w8ePBhCCBw7dgyXXHKJ96MlcuBui7urIMiq2HIBY9/fhTc1XN65LKWhX5aOlM5SAcqrRgdrVW0iIm/IzhmqqqpCgwYNbN+bTCZERUWhsrJSk4FRaPJlizsATPnoR68Si63v6461wKIvSctyeUpudkdJYrXWO9WIiIxGUZ2hadOmITY21vZ9VVUVZs+eDbPZbHvslVdeUW90FHJ82eIO1M0evbH+V0wY0F7V9/V3RWZrcrOcathWcsYotVPNyFW1iYi8JTsYuuqqq7B//367x3r37o3ffvtN9UFR6FKjgOHSrUUY37+dopu5HisyOyY3H/jjLN745lfJ13kao96CPiIiPZAdDH377bcaDoOojhoFDE+fq1Z8M9drRebwMJPtc+QVlskKhjyNUY9Bn78xcZyIHKnWjqOwsBD3338/1q9fr9YpKUh5uhl5kzzsitKbudT7Wru4B7Iisxpj1GvQ5y9MHCciV1SrQH327Fls2LBBrdORAdTUCuQVluHT/KPIKyyTlVycu7cYfeeux6jF2zBhRT5GLd6GvnPX25J2vUkedkXpzVzvFZmtAeT1mSm23J765I4xlNtwMHGciNxhOw7yilRQ4+41cm5G7iojJ8ZGwtzA82SmLzdzvVZkrn+t395yEADgWNpL7hj1HvRpxR8tTojIuGQXXZTyww8/oFu3bqipqVHjdLrBoovO3NUBst4+Xd2Ua2oF+s5d7zZ517rEs3lyf9uN2F0F6jfW/4p5X//i8hzu3t/d+Vzd9PWUU+LuWlvd26cNBmSkKB5jqC0XyS1kufz+XkwcJwoSmhRdJKqpFdj2WxmmrPpR8dZsb3Yx1U8erm/CgPa4LKWh083cU4sMJTd/d+/rb1I1l0wAVu8twVNDlc/khFobDiaOE5EnsoOhrl27emy7ce7cOVUGRPrkKphwxd3WbLVvRkpu5u5mV5RWbfY3rbfB6yXo84dQTxwnIs9kB0M333yzhsMgPZNaqnHFMajR4mYk52buzyKD1pmzvMIyAALZlyajV9vGXp+XsxnqMcJuQSIKHNnB0PTp07UcB+mUt+0xrEGNNf+mpPwCkuIicbLCdX8xuTcjpfk8/ioymLu3GFM++tGuf9ob3xQiMTYSf7+lk1czT8lx0bKO42yGNGvi+Lj3d8EE2P08B3PiOBHJIzsYOn78OJo2ber2+YsXL2LXrl3IyspSZWCkD0rbY9QPauQurXm6GdUPfg6WnsPyHYdRUi4/6dcfsyueut2fPlftVQPZ3L3FmPGZ+15pgH5mM/SUcO6JuxYnnnLNiCg0yA6GUlNTUVxcbAuI0tPTsWbNGrRq1QoAUFZWhuzs7KDbTRbqSiznZR9bP6hZV1Aie2mt/s3IPvip+F/w474ZsFTej9a5IjW1QjJoAYAZn/0keylOzrKkXmYzjLYrLdQSx4lIHtnBkOMO/N9//x0XL170eAwZW+7eYsz6cp/s461BzcCMFPSdu97jLqikuCg8dX1HnD5fjaSG0TA3iMLqPccw68t9imaipPJ+tM4VsS4BSikpr5S1FCd3WTKhQQTm/qVzQAMOoyamh1LiOBHJo+rWek+7zchYlCRNJ8ZG4p+jutmShfMKyyTzdMoqqjD7q31uc4iU8JT3o3WuiJLlNTnHyl2WrBUC/Ts2k/3eavNnYjoRkdZYgZqcKEmaNgH4+y2d0Kd9su2mJzdAUCMQqs/d+2pZWVrJ8pqcY+VeuzMXatBrzn8D1kJCSWI6EZHeKZoZOnPmDGJiYiCEgMlkwtmzZ1FeXg4Atv8l45M7O5EUF4kXRjjvlArU7iZP76tVrkhWWhJSEmIkl8oSG0TIWopTcu1OVlQFbDmK2/6JKJgoCoY6dOhg+28hBLp27Wr3PZfJgoPcG9i0Gy93eRNWq/O8XHLzfrTIFQkPM+HZG9Px0Ae7PR4n9f8NWwkCy3nEx0TgzIWLHo+vLxDLUSxiSETBRHYw9M4779h2jlFwk3sDS0lwfZynPB216WFXVSMZ9YBOnat2m0AttwSBK2rVSVKKRQyJKJjIDoYeeeQRvP7668jJydFyPKQDatzo3NV0aRwXhbKKKtXGqocaMb4sGXlT3duXMaiFRQyJKJjIDoZeeOEFPPzww/jkk0/w1ltvoXFjbk0NFq6K5qlxo3OVp9O9dSNc/dI3Xi+hpSREY1RWK7RJjtNNjRhvl4y8re7tyxjUxCKGRBQsTEJBcaCioiLce++9KCgowFtvvYVhw4ZpOTZdKC8vh9lshsViQUJCQqCHozpPRfMAaFJQzzobAkgvoaWaYzDyylZokxyrm+DHUU2tQN+56yVn0jY8eS12HjplCwxrawXuWLJd8vyecois5948uX/ArotRKlATUWhRcv9WFAxZvfHGG5g4cSLS09MREWE/ubRrl+u2BEYVzMGQpyUaE4CFd3bTrFqvuyBs2tAMNIqLMtyN1V2AZx35A1el4bMfiu0+b2KDSJw+L11e4J4+bbB0y0G359ZrcUMiokBScv9WXHTx0KFDWLVqFZKSkjB8+HCnYIiMQWqJRgCY+tGPGJiRguy2jW1//X+x55gqQYre2yJIzXY4Pj8wI8XtktGwK1Lx1sYip2stJxACgIEZKchKS+JyFBGRRhRFMosXL8bjjz+OAQMGYO/evWjSpIlW4yKNyakldOpcNd5YfwCXpcRrslwmtdU9UMsvUv22PD2/eXJ/lzlS3uYFJcVFoqT8AlISnJfZ9BQ8EhEZmexlsiFDhmDHjh2YP38+Ro8erfW4dCNYl8k+zT+KCSvyJY+LjQrH+aoap5u5Gks0noKdQDUAdbd0WH+5y9Usj7vrkVdYhlGLt6kyNj03QCUi0htNlslqamqwZ88etGjRwucBUuDJ3X10rqrG5eO+9p9yFeykJMRgVFYrWM5X4e3/5cjUp1YDUHdBmFS/LQBYvMk5ELI+7+p6qLnlXe8NUImIjEp2MLRu3Totx0F+lpWWJDuB1x1vC/657XZefgHzvv7F4/sBwJSPfkR8dKStMawSnmaczA2iJJcOaz3Mo7q6HmpueWcDVCIibbBRa4gKDzPh7j5pqpxLyeyHGrV1Tp+rxh1LtqPv3PWKGpVagzDHgMc64/J1QYkPo/pT/ethLWCpVtiitAFqTa1AXmEZPs0/irzCMtR4iuaIiEIUg6EQNr5/OyTGRvp8HiWzH3KbwMphDWLkBERylsA+zj+qyriSG/7ZnsNaqRmAagERIC8Azd1bjL5z12PU4m2YsCIfoxZvUxxAEhGFAgZDISw8zIS/39LJ5XPWG3dsVLjb15tQt8SkpP+UWrMvwJ9BzMzPCyRnPKSCMAHgZEU1GkarUCrCYSjWSs0pZvWWzKQCUKlZMAZERER/YjAU4oZkpuLNO7sh1eFGnWKOwYNXpblNoAbq7vlK+k/V1ArVZl/qj0HOspHcpbyzlfK7xbtTWlHp9NiQzFRsntwfy+/vheszU7w+t5wAVM4smJwAkogoVLBiInnsIeZJo9hIDMyQf2PfUXQSJyu8T9j2xFWwU3/XWOkZ5wBFK65mbaxj+bqgBF/tlTc75m1fODmzYIHodE9EpFcMhgiAcwHEvMIyWUUZldxQteys7hiAuNo1ZjIBypvPyGftE+Y4a+NqLFLncdW+Q27FabnX2d+d7omI9IrBELnkzQ1VqmK0Fp3VXQUg7rbuax0IAc6zNp76v7nz2IAOGN+/Ha7q0BR5hWUABLIvTZZdSkDudQ5Ep3siIj1iMBTkvG1pofSGKqditHWbubvu7t5wzFtSY+u+N1zN2ng7Fsv5KvSdu97uWq7adVR29Wmp6+xuBouIKFQxgTqI+bK1Wqo+Tv1EXrk7l3zdZh4d4fzj6lgaQM2t+3JNG5qOzZP7OwUq3o7l7S0HfdoF5uk6y807IiIKJQyGgpSvW6vl3lABKNq55Ms288qLtU6PWc5V230ef+fBpJpjMKZPmsvAwpuxuItPlO4Cc3edU8wxbOdBROSAy2RBSGprtdyWDtYbqlMPsXpLQlKJ1q52LjnuXktuGI3H/18+/iivVLyk5Ph5/J0HM+yKVLfXUMlYrDvHlLb78MTVLkF2uicicsZgKAipubVa6obq7c4lx91rM4ZdjnHv73LaTi5H/c+jdl6S1A60z34oxt+GpLsMMJSMJcUcgxsyU7DERYNaR0pmnByvsz94m6dGRBQoDIaCkNpbqz3dUNXaueRuFkqJLb+eQFZaEqbflOF1YOVIageap6DSutToaizW7+/p0wYDM1KQlZaEHUUnZQVDet4FJieRnohIb5gzFIT8ubVaSaK1FGuV5vHXtvNqLG98U4i+c9cDABbe2Q1mFfquyeEpqPSUu/Pmnd3w7E2XI/t/W+bVvJaBwBYgRGRUDIaC0KmKSreJuIC6N1W1dy6Fh5nQp12y1+Ox3ngvXqxVtTGqJ3JmvaytOF4d2QXL7++FzZP7Y2BGil1HeeDPpHRXBDznKAUSW4AQkZFxmSzI5O4txsMf7JZcHlJza7WcRGslfMn7sR7/yMp8TYssAsrq9TguNXpaTnrgqjQs2ljk8jxvbSxC11aNdLfkxBYgRGRkDIaCiJwif2Em4I1R6m+tVnPnkqdcG7n8EQgB3gWV7qpSW2e1pJb35OwE9De2ACEiI+MyWRCRU+SvVgCN4qI0eX/r7MfwLpfY8mC85S7XJtUc41PXd285fhRv6/VILScJAKfPuW9mW3+GRU/YAoSIjIwzQ0Ek2P46dzfbtKPopOzO776yxkBvjOqGRnFRKLGcx8mKKiQ1jIa5QRRqaoWioE+tCtl6+zdkCxAiMjIGQ0EkGP86d7WtX4seZ+7Uz3vK3VuMF9fs92nbuFpBjN7+DaXKCABsAUJE+sVlMgOpqRV2u48cd+ZYgwR39L41Wy5fe5zJMf7adrZdX9ZASI1t474GMXr+N2QLECIyKs4MGYScYnbhYSYMuyLV7U4kIHj+One3gy3M5LmlhVztmzW0zUip1d4EkLeclBgbiVPnqg05w8IWIERkRAyGDEBq95H1r+7cvcV4y0Mg9MBVaUH117mrG++piio8/IHztVKq/gyOmtvG5SwnzbmlEwCoVqrA3wLRAoSIyBcMhnRO7qxE/47NPG6rN8FzHy2jcnXjXRjWDU99vBcnK6oUn89Voq/aiely6zJxhoWIyD8YDOmc3FmJ9/IOsujd/wzJTEX/js3Qa87XOFnhfpu6I3fLUFokpstZTuIMCxGRfzCBWufkzjYcOnlO1nFfF/hnS3qgRUWE4YURnWCC+zYhiQ7FDd0l+mrVM0zNukxEROQ9zgzpnNzZhtZJsbKOW7LlIK5MS/Jb3klNrQjYUo/UcpSnmRnHcU8bmo6HP9htyKRmIiLyjMGQzsktZpeT3Qb/2lwkq/aOv9o5yNkBpzWp5ShXy1Duxv3AVWn47IdiQyY1ExGRe4ZZJjt16hRycnJgNpthNpuRk5OD06dPuz2+uroakydPRqdOnRAXF4fmzZtj9OjROHbsmP8GrQK5XeGjIsIw/aYMWbuo/NHOQa26PGpQshzladxvbSzCtKEZTt3nGQgRERmbYYKh22+/Hfn5+cjNzUVubi7y8/ORk5Pj9vhz585h165dmDZtGnbt2oWPPvoIv/zyC4YNG+bHUatDbjG7gRkpiI0Kl3VOLds5SO2AA+pmpxyLRvryfp6KUSo5j9S4Z31ZgKy0JOb5EBEFEUMsk+3btw+5ubnYtm0bevbsCQBYvHgxsrOzsX//flx22WVOrzGbzVi3bp3dY6+//jqysrJw+PBhtGrVyi9jV4uc3Uc7ik7iXFWNrPNp2c5Brbo8cvKN1FyKU7OeEBERGYchgqG8vDyYzWZbIAQAvXr1gtlsxtatW10GQ65YLBaYTCYkJia6PaayshKVlZW278vLy70et9qktlrLne1JjI3UtJ2Dkro87gIeOUGO3GKUWoybiIiChyGCoZKSEjRt2tTp8aZNm6KkRN5W8QsXLmDKlCm4/fbbkZCQ4Pa4OXPmYObMmV6PNZDkzvbc3TtN0+UdueM4WHoOfeeudwp4hl2Rirc2FnkMcgZmpKjWIkPpuPXWJJWIiHwT0JyhGTNmwGQyefz6/vvvAQAmk/MNTQjh8nFH1dXVGDlyJGpra7FgwQKPx06dOhUWi8X2deTIEe8+XABI1cMB6maFxvdvF9BxWPtvzf/6F5eJyotcBEKAfb7Rtt/KZC9pueOYa9S9dSNN6gkREZG+BXRmaPz48Rg5cqTHY9q0aYM9e/bgjz/+cHruxIkTaNasmcfXV1dX49Zbb0VRURHWr1/vcVYIAKKjoxEdHS09eB3y1PfK6u+3dNI86Veq/5b1e08BjzvWICevsEzWWNwtablbhrPOSrGeEBFR6AhoMJScnIzk5GTJ47Kzs2GxWLBjxw5kZWUBALZv3w6LxYLevXu7fZ01EDpw4AC++eYbNG4c/Emv7goNBqK+j7uChyOvbIl5Xx/w8R3k7RhztaTlKdforY1FrCdERBRiTEIIdfY3a+z666/HsWPHsGjRIgDAAw88gNatW+Pzzz+3HdOxY0fMmTMHI0aMwMWLF/GXv/wFu3btwhdffGE3g5SUlISoqChZ71teXg6z2QyLxSI5q6Qngaz8LDWOL/Ycw4QV+T6dd9m9PfHEhz9IFqPcPLm/3eeuqRVOeUquXrfhyWux89CpgF8/IiLyjpL7tyESqAFg2bJlePTRRzFo0CAAwLBhw/DGG2/YHbN//35YLBYAwO+//47PPvsMANClSxe747755htcc801mo85kLxt8ql2EOVqHL4kIFuDlV5tG3tcigNcL2nJ3T6/89Apbp8nIgoRhgmGkpKS8P7773s8pv4kV5s2bWCQSS/d8Ff7DKkWI+44BjlSvcdcjVnP2+f1MptHRBRqDBMMkbbUrtnjiZwE69iocKcCkomxkZhzSye7ccgpRlk/yCg9Uwk5/L19Xg993IiIQhWDIYNScxZBqg2FNzV7pLib1UmMjcSpc9UuK2mfOlft8lyelgRdBRlhJsBdxw7rMpw/t8/7MxAlIiJnDIYMSO1ZhEC1oXCc1UluGI3H/1++2+OVBmXuggxPgRDg3+3zgQhEiYjInmEatVIdLbrBBzKPpn5H+TCTCSXl7pex5BRStPIUZFg5xhaOjW/9QUkgSkRE2uDMkIFoNYuglzYUagZlUkEGUDdDNG1oOpLjowOWsKznhG4iolDBmSED0WoWQU77DH+0oVAzKJMbPCTHR2N4l0uQ3bZxQJah9BKIEhGFMgZDBqLGLIJjP66aWmHb3QXAKSDyZx6NmkGZUYIMvQSiREShjMtkBuLrDV4q8VppzR6lpHbASW25B+QHZVK1jAKxa8wVNT8zERF5xzDtOAJFT+04rK0klLagANzvrLIeZU0c1qrwn5IdcGrtlrN+ZsB1kKGnLeusM0REpC4l928GQxL0FAwB3t3g5fbjctXHy11gpCRokhuIOY5ZjaDMSEEGK1ATEamHwZCK9BYMAcpv8HmFZRi1eJvkeZff38tWR8jTewCQ/f5SgRgANI6LQt7U6xAVoU0KG4MMIqLQE5SNWulPclpQ1Kc08dpTReSx/5uVcuSuWrKcLe5lFVXoNedrvDCikyazNd42rSUiotDA3WQGVb9YodS2cCWJ11K1jNyxPjfz8wLU1CvxLDcQO1lR7XXRSCIiIl8wGAoBSrZvy5nJccdVnSMlW9cFnIMpIiIirTEYCgFK6gipUem4/jmkAjFHxZYLeGP9AZ/HQEREJBeDoRBhrSOUYrafqXHsx6VGEcL656gfiMk17+sDXC4jIiK/YQJ1kHK1g0pO4rVUsUIpjeOi0L11I7vHrIHYUx/vxcmKKlnnYad2IiLyF26tl6DHrfVSlGy9dxU0rSsocVnLSC5371V1sRa95nyNkxXVss5Tf6s/ERGREkru31wmCzLWbfGOSdDWre/1l59y9xaj79z1GLV4GyasyMeoxdvQd+56AHC5pCaXq/cCgKiIMLwwopPs87BTOxER+QODoSAiZ1u8dbeWVNAEAJsn98fy+3th3q1XICkuSnYStLtt9kDdktnEAR1kncdd/pKrZrNERETeYs5QEJHaFm/d+r6tsMxj0GTCnzk71mWqBlHhLpuJSr3XjqKTTktd7Zs29HgeT01UjdReg4iIjIEzQ0FE7rJS3m+lsoKm+vWC3O1Gk1JSbv8+uXuL8fAHztWtHbnq1K5kCVBNnIkiIgpunBkKIvK3xctb8HIMrurvRtvyayne+OZXyXPM+uInNIgMw5DMVI/LeFZhJuCNUV1dJnrLnc1ScwcaZ6KIiIIfZ4aCiNxK03J3aLkKrqxtQCYO7CCrmGL9NhtyqlvXCqBRXLTT43KXAOvPZvkqUDNRRETkXwyGgojcStO9Lm0suz2HI+uS0Rd7jmHkla1cvpcjAWDKqh+x+dcTsj6Hq+U+pc1mfaUkGZ2IiIyNy2RBxprb47i0k+KwtDP9pgyXCdGO7Tnqc7VklBgbiYs1AmcrL3oc1+nz1fjnN4WyPoOrGSklzWbVoGQmirWQiIiMjcFQEJJTaVpu0GRlXTJynAexnKv2qjCjK552kUlVxvb0Wm/4eyaKiIgCh8FQkLLm9ngiJ2gC5C0Z+crTjBTw5xKg0tksb/l7JoqIiAKHOUMhzho0De9yCbLbNnYZTMhJfPaVY8NYV+Q2m1WD3GR0tWaiiIgocDgzFMJc9SVzFQwpWQqSW5QRAMZf2xbtm8V7fG9HjrNZyQ2jAQGUVlQir7BM9nmk+HsmioiIAofBUIhSUj9H7lLQX7u1wOZfT6CkvFLW8X3aNfEq+dg6m5W7txhP/OcHzWoAKc2rIiIiY2LXeglG7FovxV0ytHWOw3HJqaZWoO/c9W6Tl+tLSYjByCtb4t9bD+L0edfd6a3Jzpsn9/d6ZkXpZ/CF3Bk0IiLSD3atJ5dqagW2HCjFlFU/Kqqf46l+kaM/yi/g1f8ewG1XtoDJxfFqLDH5uwaQnLwqIiIyLgZDISJ3bzH6zl2PO5ZsdztjA7iv5Cy3N5k1/Pjsh2L88/aumiQ7B6IaNRERBS/mDIUAd0tKnrhKmrYmL/97SxFmfbnP7WutwUijuGhsntxf9SUm1gAiIiI1MRgKcnKao7qS3NC5PxhQt2SUHO/6OUfHz1yQVe9IKdYAIiIiNXGZLMh5XSPIQ/QU6GCENYCIiEhNDIaCnLdLRaUV7rfHZ6UlITE20uPrE2MjNQtG5DakZaIzERHJwWAoyHk7O2N9nbVL/af5R5FXWCZ7h5bWYYg/q1ETEVFwY86QAfhS50aqwamj+g1P3RVmHHllS5w+535HGgCcOleteUd3ub3ViIiIPGEwpHNKKkW74qmthKP6S0zrCkpc7kArsVzAvK8PyBq7P3ZzaZGgTUREoYXLZDpm3RLvmABdYrmAce/vQu7eYlnncbek5DiBYl1iGpiRokqXeqklOm+X4IiIiNTEmSGdkqqybEJdleWBGSleNThtGh+D7q0bYeehU05LTHmFZT51qa+/1OaOrzNeREREamEwpFNKqizLXSZytaTk6rW+dqkXAKYNdb+by10RSOuMFxOgiYjIn7hMplPrCkpkHedrXo6rpSq5O9AmDujgtj3HrC8LXC7j+buvGBERkRTODOlQ7t5ivL3loKxjfSls6G6patrQDI870KzLYOP7t0P7pnF46IPdTse4m+XRYsaLiIjIF5wZ0hnrzIkc3lRZts4EPff5TxjrJjn74Q92YdgVdQGMp6KGANz2KHM3y8O+YkREpDecGdIZJe0z6ldZllOLyNVMkCNrcra16/ysL/fZHZ9SL8lZKtHa1SxPoFt5EBEROWIwpDNyZ0Rio8Jt/y1nZ5aSzvVyu857M8sjVQRSzk40IiIiNTEY0hm5MyLnqmow7v1deOCqNLy1scjjzixPdYM8cew67zj7lBwnr3t9/c/kqQgk+4oREVEgMBjSGaXtMxZvcg6EAPtaRPExkV7VDaofxLiafUpJiEFibCQs56oVzfJYi0A6nY91hoiIKAAYDAWIuxyf+jMnUgQA4SFisi535RWWKRqbYxDjbontj/I/Azalszze9BXzpUcbERGROwyGAkAqx8c6czJl1Y84fd5zQ1R55C+QOQYxVRdr8dTHez3OPiXGRiI6Igwl5ZW25+TM8ijpK8aK1UREpBWTEJ7mFqi8vBxmsxkWiwUJCQk+n8/dLIs1CKlfl2fLgVLcsWS7z++57N6eeOLDH2QtvdUPMHL3FuOpj3/EyQrpgGzZfT0RZjJpMmuj5JoREREByu7fnBnyI6X9xnq1bSy588pkAtwVa7Yud/Vq21iyc/29fdpgQEaKLYhRsvsMAErPVmJ4l0tkHi2f2j3aiIiIHLHooh8pqb4M/LnzCnBf/PD+fml1QZGb563LXe4616eaY/Dmnd0w7abLkd22McLDTB4DEHe0qguk9JoREREpxZkhP/KmLo+cnVddWzWStTNLbtKyksKPWtcFYsVqIiLSGoMhP/K2+rJUEKNkZ5acpGWlgYWWdYFYsZqIiLTGYMiPfKm+LBXEKNmZJUVuYNE4LgqzR2RqmrzMitVERKQ15gz5kZwcID1UX7YGIJ5GkRQXibyp12m+i8so14yIiIyLwZCfuUtkTjHH6GaLuFQAYgLwwohOiIrwz4+PEa4ZEREZF+sMSVC7zpCVEaop663QoRGuGRER6YOS+zeDIQlaBUNGwQCEiIiMiEUXSTVqJmYTERHpEXOGiIiIKKQxGCIiIqKQxmCIiIiIQhqDISIiIgppDIaIiIgopBkmGDp16hRycnJgNpthNpuRk5OD06dPy379gw8+CJPJhPnz52s2RiIiIjIewwRDt99+O/Lz85Gbm4vc3Fzk5+cjJydH1ms/+eQTbN++Hc2bN9d4lERERGQ0hqgztG/fPuTm5mLbtm3o2bMnAGDx4sXIzs7G/v37cdlll7l97dGjRzF+/HisWbMGQ4cO9deQiYiIyCAMMTOUl5cHs9lsC4QAoFevXjCbzdi6davb19XW1iInJwdPPvkkLr/8clnvVVlZifLycrsvIiIiCl6GmBkqKSlB06ZNnR5v2rQpSkpK3L5u7ty5iIiIwKOPPir7vebMmYOZM2c6Pc6giIiIyDis9205XccCGgzNmDHDZeBR33fffQcAMJmc+2EJIVw+DgA7d+7Eq6++il27drk9xpWpU6di0qRJtu+PHj2KjIwMtGzZUvY5iIiISB/OnDkDs9ns8ZiABkPjx4/HyJEjPR7Tpk0b7NmzB3/88YfTcydOnECzZs1cvm7Tpk04fvw4WrVqZXuspqYGjz/+OObPn4+DBw+6fF10dDSio6Nt3zds2BBHjhxBfHy8oqDKH8rLy9GyZUscOXIkJJvIaoXXVX28ptrgdVUfr6k2AnFdhRA4c+aMrM1TAQ2GkpOTkZycLHlcdnY2LBYLduzYgaysLADA9u3bYbFY0Lt3b5evycnJwYABA+weGzx4MHJycnD33XfLHmNYWBhatGgh+/hASEhI4P9pNcDrqj5eU23wuqqP11Qb/r6uUjNCVobIGUpPT8eQIUNw//33Y9GiRQCABx54ADfeeKPdTrKOHTtizpw5GDFiBBo3bozGje27rUdGRiIlJcXj7jMiIiIKLYbYTQYAy5YtQ6dOnTBo0CAMGjQInTt3xnvvvWd3zP79+2GxWAI0QiIiIjIiQ8wMAUBSUhLef/99j8dIZYy7yxMyqujoaEyfPt0ux4l8x+uqPl5TbfC6qo/XVBt6v64mIWfPGREREVGQMswyGREREZEWGAwRERFRSGMwRERERCGNwRARERGFNAZDBnPq1Cnk5OTAbDbDbDYjJycHp0+fdnt8dXU1Jk+ejE6dOiEuLg7NmzfH6NGjcezYMf8NWocWLFiAtLQ0xMTEoHv37ti0aZPH4zds2IDu3bsjJiYGl156Kd58800/jdQ4lFzTjz76CAMHDkSTJk2QkJCA7OxsrFmzxo+jNQ6lP6tWW7ZsQUREBLp06aLtAA1I6TWtrKzE008/jdatWyM6Ohpt27bF22+/7afRGofS67ps2TJcccUViI2NRWpqKu6++26UlZX5abQOBBnKkCFDRGZmpti6davYunWryMzMFDfeeKPb40+fPi0GDBggVq5cKX7++WeRl5cnevbsKbp37+7HUevLihUrRGRkpFi8eLEoKCgQEyZMEHFxceLQoUMuj//tt99EbGysmDBhgigoKBCLFy8WkZGR4sMPP/TzyPVL6TWdMGGCmDt3rtixY4f45ZdfxNSpU0VkZKTYtWuXn0eub0qvq9Xp06fFpZdeKgYNGiSuuOIK/wzWILy5psOGDRM9e/YU69atE0VFRWL79u1iy5Ytfhy1/im9rps2bRJhYWHi1VdfFb/99pvYtGmTuPzyy8XNN9/s55HXYTBkIAUFBQKA2LZtm+2xvLw8AUD8/PPPss+zY8cOAUDyF2qwysrKEmPHjrV7rGPHjmLKlCkuj//b3/4mOnbsaPfYgw8+KHr16qXZGI1G6TV1JSMjQ8ycOVPtoRmat9f1tttuE88884yYPn06gyEHSq/pV199JcxmsygrK/PH8AxL6XV96aWXxKWXXmr32GuvvSZatGih2Rg94TKZgeTl5cFsNqNnz562x3r16gWz2YytW7fKPo/FYoHJZEJiYqIGo9S3qqoq7Ny5E4MGDbJ7fNCgQW6vYV5entPxgwcPxvfff4/q6mrNxmoU3lxTR7W1tThz5gySkpK0GKIheXtdly5disLCQkyfPl3rIRqON9f0s88+Q48ePfDiiy/ikksuQYcOHfDEE0/g/Pnz/hiyIXhzXXv37o3ff/8dq1evhhACf/zxBz788EMMHTrUH0N2YpgK1ASUlJSgadOmTo83bdoUJSUlss5x4cIFTJkyBbfffntINiEsLS1FTU0NmjVrZvd4s2bN3F7DkpISl8dfvHgRpaWlSE1N1Wy8RuDNNXX08ssvo6KiArfeeqsWQzQkb67rgQMHMGXKFGzatAkREfz17siba/rbb79h8+bNiImJwccff4zS0lI89NBDOHnyJPOG/seb69q7d28sW7YMt912Gy5cuICLFy9i2LBheP311/0xZCecGdKBGTNmwGQyefz6/vvvAQAmk8np9UIIl487qq6uxsiRI1FbW4sFCxao/jmMxPF6SV1DV8e7ejyUKb2mVsuXL8eMGTOwcuVKl8F+qJN7XWtqanD77bdj5syZ6NChg7+GZ0hKflZra2thMpmwbNkyZGVl4YYbbsArr7yCf//735wdcqDkuhYUFODRRx/Fs88+i507dyI3NxdFRUUYO3asP4bqhH866MD48eMxcuRIj8e0adMGe/bswR9//OH03IkTJ5wickfV1dW49dZbUVRUhPXr14fkrBAAJCcnIzw83OmvlePHj7u9hikpKS6Pj4iIQOPGjTUbq1F4c02tVq5ciXvvvRf/+c9/MGDAAC2HaThKr+uZM2fw/fffY/fu3Rg/fjyAuhu5EAIRERFYu3Yt+vfv75ex65U3P6upqam45JJLYDabbY+lp6dDCIHff/8d7du313TMRuDNdZ0zZw769OmDJ598EgDQuXNnxMXFoV+/fnj++ef9PuPOmSEdSE5ORseOHT1+xcTEIDs7GxaLBTt27LC9dvv27bBYLOjdu7fb81sDoQMHDuDrr78O6Rt4VFQUunfvjnXr1tk9vm7dOrfXMDs72+n4tWvXokePHoiMjNRsrEbhzTUF6maExowZgw8++CBgeQJ6pvS6JiQk4Mcff0R+fr7ta+zYsbjsssuQn59vl2sYqrz5We3Tpw+OHTuGs2fP2h775ZdfEBYWhhYtWmg6XqPw5rqeO3cOYWH2IUh4eDgA6abrmghI2jZ5bciQIaJz584iLy9P5OXliU6dOjltrb/sssvERx99JIQQorq6WgwbNky0aNFC5Ofni+LiYttXZWVlID5CwFm3gC5ZskQUFBSIxx57TMTFxYmDBw8KIYSYMmWKyMnJsR1v3Vo/ceJEUVBQIJYsWcKt9Q6UXtMPPvhAREREiH/+8592P5OnT58O1EfQJaXX1RF3kzlTek3PnDkjWrRoIf7617+Kn376SWzYsEG0b99e3HfffYH6CLqk9LouXbpUREREiAULFojCwkKxefNm0aNHD5GVlRWQ8TMYMpiysjJxxx13iPj4eBEfHy/uuOMOcerUKbtjAIilS5cKIYQoKioSAFx+ffPNN34fv17885//FK1btxZRUVGiW7duYsOGDbbn7rrrLnH11VfbHf/tt9+Krl27iqioKNGmTRuxcOFCP49Y/5Rc06uvvtrlz+Rdd93l/4HrnNKf1foYDLmm9Jru27dPDBgwQDRo0EC0aNFCTJo0SZw7d87Po9Y/pdf1tddeExkZGaJBgwYiNTVV3HHHHeL333/386jrmIQIxHwUERERkT4wZ4iIiIhCGoMhIiIiCmkMhoiIiCikMRgiIiKikMZgiIiIiEIagyEiIiIKaQyGiIiIKKQxGCIiIqKQxmCIiAh1Hbc/+eSTQA+DiAKAwRBREDKZTB6/xowZ4/G4FStW2M61aNEiXHHFFYiLi0NiYiK6du2KuXPn2p6fMWMGunTpIjmm8+fPo1GjRkhKSsL58+clj//oo4/Qo0cPJCYmIi4uDl26dMF7773ndNyCBQuQlpaGmJgYdO/eHZs2bZK+QAbTpk0bzJ8/P9DDIApaEYEeABGpr7i42PbfK1euxLPPPov9+/fbHmvQoIHtv5cuXYohQ4bYvT4xMREAsGTJEkyaNAmvvfYarr76alRWVmLPnj0oKChQPKZVq1YhMzMTQgh89NFHuOOOOzwen5SUhKeffhodO3ZEVFQUvvjiC9x9991o2rQpBg8ebPtsjz32GBYsWIA+ffpg0aJFuP7661FQUIBWrVopHqO/VVVVISoqKmjfj8gwAtIRjYj8ZunSpcJsNrt8DoD4+OOP3b52+PDhYsyYMR7PL7cZ6DXXXCPefPNNsXDhQnHttddKHu9K165dxTPPPGP7PisrS4wdO9bumI4dO4opU6Z4PM8vv/wi+vXrJ6Kjo0V6erpYu3at07X429/+Jtq3by8aNGgg0tLSxDPPPCOqqqqEEHUNkE0mk/juu+/szvvaa6+JVq1aidraWpfv27p1azFr1ixx1113iYSEBDF69GghhBBbtmwR/fr1EzExMaJFixbikUceEWfPnhVCuG5qK4Tr6z5v3jzRunVr2/d33XWXGD58uHjhhRdEamqqaN26ta1586pVq8Q111wjGjRoIDp37iy2bt1qe93BgwfFjTfeKBITE0VsbKzIyMgQX375pcdrSmRkXCYjIrdSUlKwbds2HDp0yKfzFBYWIi8vD7feeituvfVWbN26Fb/99pvs1wsh8N///hf79+/HVVddBaBulmPnzp0YNGiQ3bGDBg3C1q1b3Z6rtrYWt9xyC8LDw7Ft2za8+eabmDx5stNx8fHx+Pe//42CggK8+uqrWLx4MebNmwegbtlqwIABWLp0qd1rli5dijFjxsBkMrl9/5deegmZmZnYuXMnpk2bhh9//BGDBw/GLbfcgj179mDlypXYvHkzxo8fD6BuubBFixZ47rnnUFxcbDfrJ8d///tf7Nu3D+vWrcMXX3xhe/zpp5/GE088gfz8fHTo0AGjRo3CxYsXAQAPP/wwKisrsXHjRvz444+YO3cuGjZsqOh9iQwl0NEYEWlLamYoJiZGxMXF2X0VFhYKIYQ4duyY6NWrlwAgOnToIO666y6xcuVKUVNTYzuHnJmhp556Stx8882274cPHy6efvppybGfPn1axMXFiYiICBEdHS2WLFlie+7o0aMCgNiyZYvda2bPni06dOjg9pxr1qwR4eHh4siRI7bHvvrqK8lZshdffFF0797d9v3KlStFo0aNxIULF4QQQuTn5wuTySSKiorcnqN169Z210EIIXJycsQDDzxg99imTZtEWFiYOH/+vO118+bNsztG7sxQs2bNRGVlpe0x68zQv/71L9tjP/30kwAg9u3bJ4QQolOnTmLGjBluPwdRsOHMEFGImzdvHvLz8+2+WrZsCQBITU1FXl4efvzxRzz66KOorq7GXXfdhSFDhqC2tlbW+WtqavDOO+/gzjvvtD1255134p133kFNTY3H18bHxyM/Px/fffcdZs+ejUmTJuHbb7+1O8ZxFkYIYXvshRdeQMOGDW1fhw8fxr59+9CqVSu0aNHC9prs7Gyn9/7www/Rt29fpKSkoGHDhpg2bRoOHz5se/7mm29GREQEPv74YwDA22+/jWuvvRZt2rTx+Jl69Ohh9/3OnTvx73//226cgwcPRm1tLYqKijyeS45OnTq5zBPq3Lmz7b9TU1MBAMePHwcAPProo3j++efRp08fTJ8+HXv27PF5HER6xgRqohCXkpKCdu3aeTwmMzMTmZmZePjhh7F582b069cPGzZswLXXXit5/jVr1uDo0aO47bbb7B6vqanB2rVrcf3117t9bVhYmG1sXbp0wb59+zBnzhxcc801SE5ORnh4OEpKSuxec/z4cTRr1gwAMHbsWNx6662255o3bw4hhNP7OAZU27Ztw8iRIzFz5kwMHjwYZrMZK1aswMsvv2w7JioqCjk5OVi6dCluueUWfPDBB7J2fMXFxdl9X1tbiwcffBCPPvqo07GeksDDwsKcPkt1dbXk+1lFRkba/tv6+a0B7n333YfBgwfjyy+/xNq1azFnzhy8/PLLeOSRR9yOh8jIGAwRkSIZGRkAgIqKClnHL1myBCNHjsTTTz9t9/jf//53LFmyxGMw5EgIgcrKSgB1wUj37t2xbt06jBgxwnbMunXrMHz4cAB1O9KSkpKcxn/48GEcO3YMzZs3BwDk5eXZHbNlyxa0bt3absyu8qbuu+8+ZGZmYsGCBaiursYtt9wi+7NYdevWDT/99JPHgDQqKsppFq1JkyYoKSmxmwnLz89X/P7utGzZEmPHjsXYsWMxdepULF68mMEQBS0GQ0Qh7vTp006zK/Hx8YiLi8O4cePQvHlz9O/fHy1atEBxcTGef/55NGnSxG5p6fz580434oYNG8JsNuPzzz/HZ599hszMTLvn77rrLgwdOhQnTpxAkyZNnMY1Z84c9OjRA23btkVVVRVWr16Nd999FwsXLrQdM2nSJOTk5KBHjx7Izs7GW2+9hcOHD2Ps2LFuP++AAQNw2WWXYfTo0Xj55ZdRXl7uFKi1a9cOhw8fxooVK3DllVfiyy+/tC2H1Zeeno5evXph8uTJuOeee+xKFsg1efJk9OrVCw8//DDuv/9+xMXF2RKeX3/9dQB1CdsbN27EyJEjER0djeTkZFxzzTU4ceIEXnzxRfz1r39Fbm4uvvrqKyQkJCgeg6PHHnsM119/PTp06IBTp05h/fr1SE9P9/m8RLoV0IwlItKcVAK1q685c+YIIYT48MMPxQ033CBSU1NFVFSUaN68ufjLX/4i9uzZYzvH9OnTXZ7j6quvFv/4xz9EYmKibUt6fdXV1SIpKUm8/PLLLsf29NNPi3bt2omYmBjRqFEjkZ2dLVasWOF03D//+U/RunVrERUVJbp16yY2bNggeU32798v+vbtK6KiokSHDh1Ebm6uUwL1k08+KRo3biwaNmwobrvtNjFv3jyX13HJkiUCgNixY4fk+7pKhBZCiB07doiBAweKhg0biri4ONG5c2cxe/Zs2/N5eXmic+fOIjo6WtT/tb1w4ULRsmVLERcXJ0aPHi1mz57tcmt9fdYE6t27d9seO3XqlAAgvvnmGyGEEOPHjxdt27YV0dHRokmTJiInJ0eUlpZKfj4iozIJ4WIBnYiIZJk9ezZWrFiBH3/8MdBDISIvcTcZEZEXzp49i++++w6vv/66y+RnIjIOBkNERF4YP348+vbti6uvvhr33HNPoIdDRD7gMhkRERGFNM4MERERUUhjMEREREQhjcEQERERhTQGQ0RERBTSGAwRERFRSGMwRERERCGNwRARERGFNAZDREREFNL+P7GWWXMv08vEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rets.TSLA, rets.YELP)\n",
    "plt.xlabel('TESLA 30-day returns')\n",
    "plt.ylabel('YELP 30-day returns');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "There appears to be some (fairly strong) correlation between the movement of TSLA and YELP stocks.  Let's measure this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The correlation coefficient between variables $X$ and $Y$ is defined as follows:\n",
    "\n",
    "$$ \\text{Corr}(X,Y) = \\frac{E\\left[(X-\\mu_X)(Y-\\mu_Y)\\right]}{\\sigma_X\\sigma_Y} $$\n",
    "\n",
    "Pandas provides a dataframe method to compute the correlation coefficient of all pairs of columns: **`corr()`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>IBM</th>\n",
       "      <th>YELP</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ORCL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007218</td>\n",
       "      <td>0.026666</td>\n",
       "      <td>-0.083688</td>\n",
       "      <td>0.131830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>0.007218</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.196371</td>\n",
       "      <td>0.769623</td>\n",
       "      <td>0.411348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.026666</td>\n",
       "      <td>0.196371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.104705</td>\n",
       "      <td>0.343697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YELP</th>\n",
       "      <td>-0.083688</td>\n",
       "      <td>0.769623</td>\n",
       "      <td>0.104705</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.131830</td>\n",
       "      <td>0.411348</td>\n",
       "      <td>0.343697</td>\n",
       "      <td>0.264703</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ORCL      TSLA       IBM      YELP      MSFT\n",
       "ORCL  1.000000  0.007218  0.026666 -0.083688  0.131830\n",
       "TSLA  0.007218  1.000000  0.196371  0.769623  0.411348\n",
       "IBM   0.026666  0.196371  1.000000  0.104705  0.343697\n",
       "YELP -0.083688  0.769623  0.104705  1.000000  0.264703\n",
       "MSFT  0.131830  0.411348  0.343697  0.264703  1.000000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "It takes a bit of time to examine that table and draw conclusions.  \n",
    "\n",
    "To speed that process up it helps to visualize the table.\n",
    "\n",
    "We will learn more about visualization later, but for now this is a simple example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7d+QvInXP/6FM/ZZtnfAoIBBZWVkAgPt35Ne5e1u2zT6DuiMuJh7nz16R2fYpEahYqTzceo3F+4REhc6vsGVmZuHJg2do1rqJRHmz1o1x/85DufsE+D9Cs9aNJcqat2mCJw8CkZX1+dYk9wlu+NFjJEYPmIzHD57mGUOvgV0RF5uAS37XvuNMCk9KSipevnwjfgUGBiEiIgod2rcS19HS0kKrlk1x44Z/nu3cvHUXHdq3lChz7tAKN25+3qdUKT3k5Eh+K87OzoaGRu6fwz17D6OBQwc4NHIRv8LCIvDbqg3o/MNgZZxuocnKzMKLRy9Qv6XkbcL1WzbAs7t5v3++hUAggJa2llLbpMKjUM9ARkYGFi9ejLNnz0JLSwszZ85Ejx494Ovri7lz50IgEGDKlK9/u9fR0ZEZEsjMKHpdmR8+pCEk9PNs6bDwKDwLegkjQwPYWFt+Yc/iZ6vPbqzasAyP7gfinv8DDBzaG7ZlbPCHb+6M2hnzJ8PaxhLTxs8DAOz1PYShIwdg7s/TsX/3YTR0rId+g3tiyphZ4jZ9N+3Fgb+3Y+zkEfA7dQHOndqieesm6NdlhMSxBQIB+g7qjsMHTiA7W/KeXE1NTfjs+BW16tbAqIGToKGpIf4GnZiQiMzMrIK8LN9sx8Y/sHz9YjwOCESA/yP0G9ITNmWtsX/nYQCAx9wJsLSxwOyJiwAA+3f+hcHu/TB7yVQc3H0U9R3roPeg7pg+bq64zZETh2DKrHGY/uM8hL2LEF+HD6kf8CE1TVxPIBCg54CuOHrgH5nrWZysXbcVs2dNQvCL13jx4jVmz5qEDx/SsG//EXEd3+1rEB4egbnz/gcAWLduGy6cP4wZ08fj+Ikz6Na1I9q3b4nWbT7f9fL3P37wnD0Z796F4Ungc9SvXxtTp4zBjp37AQDx8QmIj0+QiCUzMwuRkTEICnpZCGeuXEe3HoHH79Pw4mEwnt57BtdBrrCwtcDJPbl3oAybNQxm1mZY9dMq8T52NXO/1OmW1oORqRHsalZCVmYm3gXn3inWd0JfBD8MRsTbSGhpCeHY1hHtereDz1zZO2WKLTXrGVAoGVi0aBHWr18PZ2dnXLt2DX379oW7uzsuXrwILy8vDBo0CFpaJSczfPwsGO6TPn+4rVi3GQDQvVMHLJs3TVVhFYh/jp6BiakRJs8YAwsrCwQ9fQH3ARMQFprbDWhpZS6x5kBoSBjcB0zAvKUzMGRkf0RHxmCx53KcPvF5Rva9Ow8wedQsTJs7ER6eExDy5h0mjZqFgLuS3YgtWjdFmXK2OLT3qExc1rZWcO6Ue6/9ycuSt/oM6DYSt67l/S1RlU4d84OxqREmTBsFCytzBD97ibEDpyI8NPdWSAup6xkWEo6xg6Zi9s8/YdCIvoiOjMGyub/i7N8XxHUGDe8DbR1trN2+QuJY3is3w3vl51u/mrVujDLlbIrlXQT/tfJXH+jp6cJ77S8wMTHC7dv30anLIKSkfB4iKl/OVuIBajdu+mOQ23gsWTwTixfNwMtXbzFw8I+4fefzEMSUqfOweNFMrFv7CywtzRAeHoUtW/fg56W/F+r5FZYrJ67AwNgQA6YMhKmlKd4GvcWiYQsRE5Y7N8fE0lRmzYF1p9eJ/12lbhW06dkWUe+iMLJ57loMOnq6GL90PMxszJHxMQOhL0Lx29RfceWEbM9ecaVuTy1UaJ0Be3t7rFy5Ej179sSDBw/QoEED9O/fH7t37xbfTvativo6A0WZMtYZUFcFtc6AuijIdQZKusJaZ6CkKuh1BtIu71BaW3qthiutrYKi0Cf4u3fv0KhRIwBAvXr1oK2tjVmzZn13IkBERFSkcJggb5mZmdDW/vxNSktLC0ZGRkoPioiISKV4a+GXLViwQHzrYEZGBpYuXSqTEKxatUrerkRERMUDewby1qpVKzx//lz8c7NmzfDqleRYf15P/iIiIqKiSaFk4OLFixI/x8bGQiAQwMxM/hKrRERExZKaDRMovOjQ+/fvMWHCBJibm8PKygqWlpYwNzfHxIkT8f79+wIIkYiIqJDxQUV5i4+Ph5OTE8LCwjB48GDUqFEDIpEIT58+xY4dO3Du3Dlcv34dJibf9wAMIiIiKjwKJQNLliyBtrY2Xr58CSsrK5ltLi4uWLJkCX7/vWQu3kFERGqCwwR5O3r0KH799VeZRAAArK2tsWLFChw5ckTOnkRERMWImg0TKJQMREREoFatWnlur127NiIjI787KCIiIio8CiUD5ubmePPmTZ7bX79+zTsLiIio+GPPQN5cXV0xd+5cZGRkyGxLT0/H/Pnz4erqqrTgiIiIVEKUo7xXMaDQBMLFixfD0dERVapUwYQJE1C9enUAQGBgIHx8fJCeno7du3cXSKBERERUMBRKBsqWLYsbN25g/Pjx8PT0xKcHHgoEAjg7O8Pb2xvlypUrkECJiIgKTTHp3lcWhZ9NYGdnh1OnTiEhIQHBwcEAch9tbGpqqvTgiIiIVKKYdO8ryzc/e9jExASNGzdWZixERERFg5r1DCi8HDERERGVLN/cM0BERFRicZiAiIhIzXGYgIiIiNQJewaIiIikqVnPAJMBIiIiaf+/jo664DABERFREeLj4wM7Ozvo6urCwcEBV65c+WL9vXv3ol69eihVqhRsbGwwYsQIxMXFKXRMJgNERETSVPSgogMHDmDq1KmYO3cu7t+/j5YtW6JTp04ICQmRW//q1asYOnQoRo4ciSdPnuDQoUO4c+cORo0apdBxmQwQERFJU1EysGrVKowcORKjRo1CjRo1sHr1apQrVw4bNmyQW//mzZuoWLEiJk+eDDs7O7Ro0QJjx46Fv7+/QsdlMkBERFSA0tPTkZSUJPFKT0+XqZeRkYG7d+/CxcVFotzFxQXXr1+X23azZs0QGhqKkydPQiQSISoqCn/++Se6dOmiUIxMBoiIiKQp8RHGXl5eMDIyknh5eXnJHDI2NhbZ2dmwsrKSKLeyskJkZKTcMJs1a4a9e/eif//+0NbWhrW1NYyNjbFu3TqFTpfJABERkTQlDhN4enoiMTFR4uXp6ZnnoQUCgcTPIpFIpuyTwMBATJ48GQsWLMDdu3dx+vRpvH79GuPGjVPodHlrIRERkTQl3lqoo6MDHR2dr9YzNzeHpqamTC9AdHS0TG/BJ15eXmjevDlmzJgBAKhbty5Kly6Nli1bYunSpbCxsclXjOwZICIiKgK0tbXh4OAAPz8/iXI/Pz80a9ZM7j4fPnyAhobkR7mmpiaA3B6F/GLPABERkTQVrUDo4eGBIUOGwNHREU5OTti8eTNCQkLE3f6enp4ICwvDrl27AABdu3bF6NGjsWHDBnTs2BERERGYOnUqGjduDFtb23wfl8kAERGRNBUlA/3790dcXByWLFmCiIgI1K5dGydPnkSFChUAABERERJrDgwfPhzJycnw9vbGtGnTYGxsjHbt2mH58uUKHVcgUqQfoQDZmdVTdQjFVtDzI6oOodga7zhL1SEUa2tm5/+bB0m6ujBK1SEUay5R+wu0/bRt05XWlt7IX5XWVkFhzwAREZE0ER9UREREpNZEOUWi07zQ8G4CIiIiNceeASIiImkqmkCoKkwGiIiIpKnZnAEOExAREak59gwQERFJU7MJhEwGiIiIpHHOABERkZpTs2SAcwaIiIjUHHsGiIiIpBWNlfoLDZMBIiIiaRwmICIiInXCngEiIiJpvLWQiIhIzXEFQiIiIlIn7BkgIiKSxmECIiIi9Sbi3QRERESkTtgzQEREJE3NhgmU0jOQk5ODEydOoEePHspojoiISLVEOcp7FQPflQwEBwfD09MTZcuWRb9+/ZQVExERkWrliJT3KgYUHiZIS0vDwYMHsW3bNty8eRPZ2dn4/fff4e7uDn19/YKIkYiIiApQvnsGbt++jTFjxsDa2hre3t7o3bs33r17Bw0NDXTo0IGJABERlRw5Ocp7FQP57hlo1qwZJk2ahNu3b6NatWoFGRMREZFqFZPufWXJdzLQrl07bNu2DdHR0RgyZAg6duwIgUBQkLERERFRIch3MnD27Fm8e/cOvr6++PHHH5GWlob+/fsDAJMCIiIqWYrJXQDKotDdBOXKlcOCBQvw+vVr7N69G9HR0RAKhejevTvmzJmDe/fuFVScREREhUfN7ib45lsLnZ2dsW/fPoSHh2PSpEk4deoUGjVqpMzYiIiIqBB896JDJiYmmDRpEu7fv487d+4oIyYiIiKVEuXkKO1VHChtOeIHDx6gUaNGyM7OVlaT38TNvR/GTBwOSytzBD17iZ/nrsCdm/fzrN+kmQPm/jwdVatXRlRkDDat24E/dhySqOPatT08PCegfMVyCHnzDr8u88bZf86Lt1+5fxJly5eRaXv3tv1YMNMLQqEQ0+ZORJsOLVC+QlkkJyfj2qVbWL5kDaIjY5R38kWIf8Aj+P7xJwKfvUBMXDzWeM1H+1bNVB1WkdPGzQUdx3aHkaUxwoNCcWCJL4LvPJNbt0HHxmjj1hHlalaEUFuI8OBQnFh9EE8uPyjkqFXj4IMQ7PR/jdjUdFQ208f01tXRsKyp3LoLzjzEicBwmfJKpvo4PKwFAGDUoVu4G5ogU6eFnQXW9XBQbvAqVm64MypO6AptS2OkPg/Fs/m78P6W/PfZfxk3qgrHowuR8uwdbrafLS4vXa0s7Gf2hWHdStArb4Fn83ciZPOpgjyFwldMuveVpUQ9qKhLj46Yv2wm1q/agi5t++POzXvwPeAD2zLWcuuXLV8G2/evx52b99ClbX/4/L4VC71mwbVre3GdBo51sW7rChw5+Dc6t+6LIwf/hve2FajvUEdcp3uHwWhUo5345dZrDADgn2N+AAA9PV3Urlsd3r9uRtd2/TFumAfsKlfAlr1rCvBqqFZa2kdUs6+EOR7jVR1KkeX4QzP0XzAC/3gfxpLOMxF85ykm75gLU1tzufWrNqmJwKsPsHbEL1jadRae33iMiVtno1ytioUbuAqceR6BlRefYmTjStg3uBkalDHBxKN3EZGUJrf+jDY14Demrfh1elQbGOlqwbmqlbjOb10bSNT5c0hzaAoEcK5iJbfN4sqquxOq/TwMr1Yfwc0Os5Fw6xka7psN3TJmX9xPaKCH2t4TEH/lscw2TT1tpL2NRvCyP5AeJZtQUfFTopKBUeOH4ODeIziw5wheBr3Gz3NXIiI8EoPd5S+VPHhEX4SHReDnuSvxMug1Duw5gkN7j2L0hGHiOu7j3HD14k1sWL0dr4LfYMPq7bh++TZGjB0srhMfl4DY6Djxq51LK7x5FYJb1/wBAMnJKRjSexz+OXYWr168RYD/Iyya/T/UrV8rz0SluGvp1AiTxwyDc5vmqg6lyHIe9QOuHjyPqwfOI/JlGA4s2YGEiFi0dnORW//Akh04s+k43jx8ieg3kTiych+i30SgXnvHQo688O259wY9apdFrzrlUMlMHzPa1IC1gS4OPQyRW99ARwvmpXXEr8CoRCR9zES3WmXFdYx0tSXq3AyJg66WBpyrlqzfyYrjuiDsjwsI23sBqcHheD5/Fz6GxaHscOcv7lfj19GI+OsaEv2DZbYlBbxC0JK9iDx6AznpWQUVumpxAmHxpKUlRO16NXDlwg2J8isXbsChUT25+zR0rCtT//KF66hTvyaEwtwRlAaN5NQ5fx0OjeW3qaUlRI++XXDoj6NfjNfAUB85OTlISkr+Yj0qmTS1hKhQuxICr0h28T+58hCVHfK3qJdAIIBOaT2kvk8piBCLjMzsHDyNSoJTBckek6blzfEg/H2+2jj6OBRNypvB1lDvi3U6VrWBnlbJeZirQEsTBnXtEHfxoUR53KWHMHasmud+tgNao1QFK7z69c+CDrHoUrMHFeX7XZ+UlPTF7cnJqv1QMzEzgVAoRGx0nER5bEwcLKzkd7taWJojNkaqfnQctLS0YGJmjJioWPl1YuJgbim/TZfO7WBoZIA/9x3PM1ZtHW3MXDAFxw+fQkpyan5Oj0oYfRMDaAo1kRTzXqI8OeY9jMyN89WG8+iu0CmlA/9/ris/wCIkIS0D2SIRTEtpS5SbldZG3Nv0r+4fk/IR197E4pdOdfOs8zjyPV7EpWChS+3vjrco0TY1hIZQE+kxiRLlGTGJ0LE0lrtPKTtrVJk3EHe6LYYou3h8kBWIYvKNXlnynQwYGxt/cXEhkUiU78WH0tPTkZ4u+UssEuVAIPj+jgqRSPI/oEAgkCn7Wn3pcrl18mizn1tPXPr3Wp4TA4VCIdZtXQ4NgQbmz1iW94mQWpB5FwkEEMmWymjcrTm6Te2L9aNXIDnuy4l6SSH910UkAgQypbKOB4bBQEeItvZ5zwU4+jgU9mb6qG1t/H1BFllS7ykB5P8N0xCgzoZJeLniT3x4FVEokVHRkO9k4MKFC0o7qJeXFxYvXixRZqRrCZNS3z5WlxCXgKysLJleADNzU5negk9ionO/+UvUtzBFZmYm3scn5l3H3FSmtwAAypS1QfPWTfDjMA+5xxMKhfDevhLlypfBoB6j2SugxlISkpGdlQ0jC2OJcgNzIyTFJsrf6f85/tAMQ5f/iE3jV+HptUcFGGXRYKKnDU2BAHEfMiTK4z9kyPQWSBOJRDj2JAxdathCS1P+l420zGyceR6JH53slRZzUZERn4ScrGzoSL3PtM2NZHoLAECorwejBpVhUKciqnuNAAAINAQQaGigQ9he3Ov/C+KvPimM0FVOxJ4B+Vq3bq20g3p6esLDQ/IDs27F75tolpmZhccPnqJFm6YSt/21aNMUfqcuyt3nnv9DtO/YSqKsZVsnPAoIRFZW7qSY+3ceokWbpti+cY9Enbu3ZW/n6jOoO+Ji4nH+7BWZbZ8SgYqVymNQ91F4n/DlP/hUsmVnZuHt41eo0aIu7p+5LS6v2aIuAvzyXq+jcbfmGLZiPLZMXo1HF9RjxU8tTQ3UsDLEzbexaPefb/c3Q2LRprLlF/e9GxqPd+8/oEftsnnW8QuKQEZ2DjrXsFVazEWFKDMbyQ9fw6x1HUSf+vy+MmtVB9Fn/GXqZyWn4Xrr6RJl5Ya7wLRFLTwY9TvSQkrmrdByMRmQLycnBzk5OeKJdQAQFRWFjRs3IjU1Fd26dUOLFi3y1ZaOjg50dHQkypQxRLDVZzdWbViGR/cDcc//AQYO7Q3bMjb4wzd33YAZ8yfD2sYS08bPAwDs9T2EoSMHYO7P07F/92E0dKyHfoN7YsqYWeI2fTftxYG/t2Ps5BHwO3UBzp3aonnrJujXZYRU/AL0HdQdhw+ckFlrQVNTEz47fkWtujUwauAkaGhqwNwy97aexIREZGaWvNm4Hz6kIST0833eYeFReBb0EkaGBrCx/vIfcHXht/VvjFw1CW8fvsTLe0FoNagDTG3NcWnvWQBAz5mDYGJliu3TvAHkJgIjfpuIA4t98ep+MAz//9te5scMpCV/UNVpFAq3hhUx7/RD1LQyQl0bY/z16B0ikz+iT93yAIC1V58jOiUdS10l5wUcfRyGOtZGsDc3yLPto4/D0KayJYz1vtzLUFy92fgP6nhPQOKDV0j0D0LZIR2gW9YcoTv/BQDYzx0AXWtTPJ7kA4hESHkWKrF/RmwSstMzJcoFWprQr5qbYAm0NaFrbQqDWhWQlfoRaW+iCu/kSGnynQyMHDkSWlpa2Lx5M4DcCYONGjXCx48fYWNjg99//x3Hjh1D586dCyzYr/nn6BmYmBph8owxsLCyQNDTF3AfMAFhobljX5ZW5hK38oWGhMF9wATMWzoDQ0b2R3RkDBZ7LsfpE+fEde7deYDJo2Zh2tyJ8PCcgJA37zBp1CwE3JXsnm3RuinKlLPFob1HZeKytrWCc6e2AICTlyUXNBrQbaT4FsSS5PGzYLhP+pxUrViX+77p3qkDls2bpqqwihT/v69D31gfP0zpAyMLE4QHvcPaEb8gPiwWAGBsaQLTMp+HqFoNcoZQS4jBS0dj8NLR4vLrf16E7/T1hR5/YepYzQaJHzOx+dYLxKamw97MAOt6OIjvDohNTUdksuSaA8npmTj3IhIz2tTIs923Cam4H56ADb1K7u2ZUcduQNtEH5U9ekPHyhgpz97h/qD/4WNo7vtMx9IEumXkT4jOi461KZzOLxf/XHFCV1Sc0BXx1wLh32uJUuNXmWKycqCyCERfml33H1WrVoW3tzdcXHLvgV6/fj2WLVuGp0+fwsjICLNmzcLt27e/eW6BnZn8W/Xo64KeH1F1CMXWeMdZX69EeVozu+R1rReWqwv5Dfp7uETtL9D2k8d3UlpbBj5Ff3XGfPfNh4WFoUqVKuKfz507h969e8PIyAgAMGzYMDx5oh4TS4iIiEqSfCcDurq6SEv73A138+ZNNG3aVGJ7SkrJXvyEiIjUBFcglK9evXrYvXs3AODKlSuIiopCu3btxNtfvnwJW1t2GRIRUfEnEomU9ioO8j2BcP78+ejcuTMOHjyIiIgIDB8+HDY2NuLtR44cQfPmXIeeiIiouMl3MtC2bVvcvXsXfn5+sLa2Rt++fSW2169fH40bN1Z6gERERIWumHTvK0u+kwF3d3esWbMGU6ZMkbt9zJgxSguKiIhIpdQsGcj3nIGdO3dKTCAkIiIqqUQ5IqW9ioN8JwPFZRIEERERKUahB3fn96mERERExVox+UavLAolA1WrVv1qQhAfH/9dAREREamceq1GrFgysHjxYvGKg0RERFQyKJQMDBgwAJaWfOIcERGVbMVl4p+y5DsZ4HwBIiJSG2qWDPBuAiIiIjWX756BHDV7tjMREakxNfvIU2jOABERkTpQtzkD+R4mICIiooLn4+MDOzs76OrqwsHBAVeuXPli/fT0dMydOxcVKlSAjo4OKleujO3btyt0TPYMEBERSVPRMMGBAwcwdepU+Pj4oHnz5ti0aRM6deqEwMBAlC9fXu4+/fr1Q1RUFLZt2wZ7e3tER0cjKytLoeMyGSAiIpKiqmGCVatWYeTIkRg1ahQAYPXq1Thz5gw2bNgALy8vmfqnT5/GpUuX8OrVK5iamgIAKlasqPBxOUxAREQkLUd5r/T0dCQlJUm80tPTZQ6ZkZGBu3fvwsXFRaLcxcUF169flxvm8ePH4ejoiBUrVqBMmTKoWrUqpk+frvCDBZkMEBERFSAvLy8YGRlJvOR9y4+NjUV2djasrKwkyq2srBAZGSm37VevXuHq1at4/Pgxjhw5gtWrV+PPP//EhAkTFIqRwwRERERSREqcM+Dp6QkPDw+JMh0dnTzrSy/yJxKJ8lz4LycnBwKBAHv37hU/LmDVqlXo06cP1q9fDz09vXzFyGSAiIhImhKTAR0dnS9++H9ibm4OTU1NmV6A6Ohomd6CT2xsbFCmTBmJ5wbVqFEDIpEIoaGhqFKlSr5i5DABERFREaCtrQ0HBwf4+flJlPv5+aFZs2Zy92nevDnCw8ORkpIiLgsKCoKGhgbKli2b72MzGSAiIpIiylHeSxEeHh7YunUrtm/fjqdPn+Knn35CSEgIxo0bByB3yGHo0KHi+oMGDYKZmRlGjBiBwMBAXL58GTNmzIC7u3u+hwgADhMQERHJUtE6A/3790dcXByWLFmCiIgI1K5dGydPnkSFChUAABEREQgJCRHX19fXh5+fHyZNmgRHR0eYmZmhX79+WLp0qULHZTJARERUhIwfPx7jx4+Xu23Hjh0yZdWrV5cZWlAUkwEiIiIpyryboDhgMkBERCSFyQAREZGaU7dkgHcTEBERqTn2DBAREUkTyV/xr6QqMsmAjqa2qkMotsY7zlJ1CMWWj/9yVYdQrLnWH6fqEIotN6GlqkOgL+AwAREREamVItMzQEREVFSIcjhMQEREpNY4TEBERERqhT0DREREUkS8m4CIiEi9cZiAiIiI1Ap7BoiIiKTwbgIiIiI1JxKpOoLCxWSAiIhIirr1DHDOABERkZpjzwAREZEUdesZYDJAREQkRd3mDHCYgIiISM2xZ4CIiEgKhwmIiIjUnLotR8xhAiIiIjXHngEiIiIp6vZsAiYDREREUnI4TEBERETqhD0DREREUtRtAiGTASIiIim8tZCIiEjNcQVCIiIiUivsGSAiIpLCYQIiIiI1x1sLiYiISK0o1DNw+fLlfNVr1arVNwVDRERUFPDWwi9o06YNBILcCyTKY6qlQCBAdnb290dGRESkIup2N4FCyYCJiQkMDAwwfPhwDBkyBObm5gUVFxERERUShZKBiIgIHDlyBNu3b8eKFSvQuXNnjBw5Eq6uruIeg6Jm4Ig+GDneDRZW5njx/BV+mbcKd28F5Fm/kVNDzF4yFfbVKiE6KhZbvXfhwM6/xNv7uvVA936dUaV6ZQDAk4fP8Puy9Xh0P1Bc55z/MZQpbyvT9t7th/Dz7BXKO7kioo2bCzqO7Q4jS2OEB4XiwBJfBN95Jrdug46N0catI8rVrAihthDhwaE4sfognlx+UMhRF13+AY/g+8efCHz2AjFx8VjjNR/tWzVTdVhFTrehXdFvXF+YWZriTdBb+CzagEe3H8uta2ppinELxqBqnSooY1cGR7Yfhc+ijYUcsepUH9YBtcd1hp6lMd4HheH2wj2Iuv1cbl3LRlXhOHcAjOxtINTVQUpYLJ7vOY/ALafl1rfr1hRtNkzE29P+OD9ydQGeReHiBMIv0NbWRv/+/XHmzBk8f/4cdevWxcSJE1GuXDnMnTsXWVlZBRXnN+nU3RmeP3tg42pf9GzvBv+bAdi8fw1syljJrV+mvC02/bEa/jcD0LO9Gzat9sXcZdPh8kNbcZ3GzR3wz5GzGNbrRwzo7I6I0EhsO+gNS2sLcZ0+HYehRW1X8WtEnwkAgDPH/y3YE1YBxx+aof+CEfjH+zCWdJ6J4DtPMXnHXJjayu81qtqkJgKvPsDaEb9gaddZeH7jMSZunY1ytSoWbuBFWFraR1Szr4Q5HuNVHUqR1aZra4xfNA5/rPsDY11/xKPbj+C1exksbS3k1tfS1kJiXCL2rt2Hl4GvCjla1bLr1gSNF7nhwdrjON5xHqJuP4fznhkobWsmt37Wh3Q89fXDqV5LcaTNTDxYcwwNZ/ZB1cFtZeqWLmOGRgsGIfKm/OS/OBOJBEp7FQfffDdBuXLlsGDBAvz777+oWrUq/ve//yEpKUmZsX234eMG4fAfx/Dn3mN4FfwGXvNXITIsCgOH95Fbf8CwXogIi4TX/FV4FfwGf+49hr/2HYf7eDdxnRk/zsc+3z/x7HEQXr94i/key6ChIYBTq0biOglx7xEbHSd+tXFugbev3+H29XsFfs6FzXnUD7h68DyuHjiPyJdhOLBkBxIiYtHazUVu/QNLduDMpuN48/Alot9E4sjKfYh+E4F67R0LOfKiq6VTI0weMwzObZqrOpQiq8+Y3ji1/zRO7juNkBfv4LNoI6LDY9B1aFe59aNCo7B+4Qb4Hf4XqcmphRytatUa3QnB+y8ieN9FJL4Ix+2Fe5AaHofqQ9vLrR//5C1eH7uB90FhSAmNxau/riH84iNYNakmUU+gIUBr7/G4/+thJIdEF8apUAH6pmQgPT0df/zxBzp06IDatWvD3Nwc//zzD0xNTZUd3zfT0hKiVr3quHbxlkT5tYu30KBRXbn71HesI1P/6oWbqFWvJoRCTbn76OnpQigUIjFBfiKkpSVEtz6d8Ncfx7/hLIo2TS0hKtSuhMArkl38T648RGWHannsJUkgEECntB5S36cURIhUAgm1hKhapwr8L0sm13cv30Utx5oqiqpo0tDShFldO4Rdkhw+Cb/0GJaOVfLVhmmtCrBwrILIG5Lf/uv91BMf45IRvP+S0uItSkQi5b2KA4XmDNy+fRu+vr7Yv38/7OzsMHz4cBw8eFDhJCA9PR3p6ekSZTmiHGgIlLfsgYmpMYRCIeJi4iXK42LiYG4pv3vMwtIMV2PipOrHQ0tLCBNTY8REx8ns4zF/IqIiY3D98m25bbbv1AYGRvo4sv/vbzyTokvfxACaQk0kxbyXKE+OeQ8jc+N8teE8uit0SunA/5/ryg+QSiQjU0NoCjWREJMgUZ4QkwBTCxMVRVU06ZgaQEOoiY+xiRLlabGJ0LM0/uK+/fzXQtfUAAKhJgJW/YXgfRfF2ywdq6DqwDY45jynAKIuGtRtzoBCyUDTpk1Rvnx5TJ48GQ4ODgCAq1evytTr1q3bF9vx8vLC4sWLJcrMStnAvHQZRcLJF5lbIAWCPG+LzK0vVfD/7wd5e4ycOARderpgaM9xyEjPkNten8HdcOXcDURHxeY/6GJG5toIBBDJvWKSGndrjm5T+2L96BVIjitaQ0xUDMj93VZNKEWd7N/BvG8P/+Rkz5+hVVoHFg3t4TCnP5JeR+H1sRsQltZFq3U/4tqMrUhPKLk9esVlrF9ZFF6OOCQkBD///HOe2/OzzoCnpyc8PDwkyhwry05O+R4J8e+RlZUl0wtgZm4q01vwSUy0bK+BmbkpMjOz8D7+vUS5+3g3jJ0yAu59JiAo8IXc9mzLWsOpVWNMGjHz20+kCEtJSEZ2VjaMLIwlyg3MjZAk9U1EmuMPzTB0+Y/YNH4Vnl57VIBRUkmTGJ+E7KxsmFhK9kiamBsjITYhj73UU3p8MnKysqEn9TuqZ2aEjzFf/h1NeRcDAEh4Fgo9CyM0mNYLr4/dgGFFSxiUt0SHHdPEdQUauR+cw97uxF+tZiD5LecQFDcKJQM5OTlKOaiOjg50dHQkypQ5RAAAmZlZePLgGZq1boJ/T14Ulzdr3RjnT8tfSTHA/xHaurSUKGvepgmePAhEVtbnBMd9ght+/GkkRvWfhMcPnuYZQ6+BXREXm4BLfte+72SKqOzMLLx9/Ao1WtTF/TOfh0lqtqiLAL87ee7XuFtzDFsxHlsmr8ajCyVvUiUVrKzMLAQ9CoZDy4a4dvrz75ZDy4a4dvaGCiMrenIysxH38DVsW9VGyGl/cbltq9oIOXM3/w0JBNDQzv24SHwRgSPtZktsbjizD7T09XBrwW6khssOpxZHHCYoQXZs/APL1y/G44BABPg/Qr8hPWFT1hr7dx4GAHjMnQBLGwvMnrgIALB/518Y7N4Ps5dMxcHdR1HfsQ56D+qO6ePmitscOXEIpswah+k/zkPYuwhxT8KH1A/4kJomricQCNBzQFccPfBPiV6R0W/r3xi5ahLePnyJl/eC0GpQB5jamuPS3rMAgJ4zB8HEyhTbp3kDyE0ERvw2EQcW++LV/WAY/v83lsyPGUhL/qCq0yhSPnxIQ0houPjnsPAoPAt6CSNDA9hYW6owsqLjz82HMXvNTAQ9DELg3UB0GdwFlmUscWJ37tyckbPdYW5thuVTV4r3qVyzEgBAr5QejMyMUblmJWRlZuFtcIhKzqGwPNlyCi3X/Ii4B68QffcFqrm1RekyZni2+xwAwGF2P5SyMcGVKZsA5K5JkBoeh8QXue9By0bVUHtsZzz1zf2dzk7PxPvnoRLHyEjK/d2VLi/O1G3E6ZuSgbi4OJiZ5X4Ivnv3Dlu2bEFaWhq6du1apJ5LcOqYH4xNjTBh2ihYWJkj+NlLjB04FeGhkQAACytz2JaxFtcPCwnH2EFTMfvnnzBoRF9ER8Zg2dxfcfbvC+I6g4b3gbaONtZul1w8yHvlZniv3CL+uVnrxihTzqZE3kXwX/5/X4e+sT5+mNIHRhYmCA96h7UjfkF8WO4cCWNLE5iW+bzmQKtBzhBqCTF46WgMXjpaXH79z4vwnb6+0OMvih4/C4b7pFnin1es2wwA6N6pA5bNm5bXbmrl4olLMDQxxJCpg2FqaYo3z9/Cc+g8RIfldk+bWZrCsoxk4rT57OdFhqrVq4oOPdsh8l0kBjsNLdTYC9vr47egY2KAej/1RClLYyQ8D4XfkJVIDcv9Bq9nZYzS/1kXRKAhgMPsftAvbwFRVg6S30bD3+sAnu8+r6pToEIgEH1tFsl/PHr0CF27dsW7d+9QpUoV7N+/H66urkhNTYWGhgZSU1Px559/okePHgoHUt2y0dcrkVwtS1VUdQjFlo//clWHUKy51h+n6hCKLTcRe3m+x4iwPQXa/nWb3kprq1nEYaW1VVAUGqifOXMm6tSpg0uXLqFNmzb44Ycf0LlzZyQmJiIhIQFjx47F//73v4KKlYiIqFCo2wqECg0T3LlzB+fPn0fdunVRv359bN68GePHj4eGRm5OMWnSJDRt2rRAAiUiIqKCoVAyEB8fD2vr3DF2fX19lC5dWmLBIRMTEyQnJys3QiIiokKmnHvnig+FJxBKP52wqD6tkIiI6FuJoF6fbQonA8OHDxevEfDx40eMGzcOpUuXBgCZJYaJiIio6FMoGRg6dKhET4Cbm5vcOkRERMVZjpotNKBQMrBjx44CCoOIiKjoyOEwQd569er11ToCgQCHDxf9eyqJiIjywjkDX2BkZFRQcRAREZGKKJQM+Pr6FlQcRERERQZvLSQiIlJz6jZMoNznBhMREdF38fHxgZ2dHXR1deHg4IArV67ka79r165BKBSifv36Ch+TyQAREZGUHCW+FHHgwAFMnToVc+fOxf3799GyZUt06tQJISFfftR2YmIihg4divbt2yt4xFxMBoiIiKQoMxlIT09HUlKSxCuvRfpWrVqFkSNHYtSoUahRowZWr16NcuXKYcOGDV+Md+zYsRg0aBCcnJy+6XyZDBARERUgLy8vGBkZSby8vLxk6mVkZODu3btwcXGRKHdxccH169fzbN/X1xcvX77EwoULvzlGTiAkIiKSoswJhJ6envDw8JAo+7Ss/3/FxsYiOzsbVlZWEuVWVlaIjIyU23ZwcDBmz56NK1euQCj89o90JgNERERScpR4M4GOjo7cD/+8SD8AUCQSyX0oYHZ2NgYNGoTFixejatWq3xUjkwEiIqIiwNzcHJqamjK9ANHR0TK9BQCQnJwMf39/3L9/HxMnTgQA5OTkQCQSQSgU4uzZs2jXrl2+js1kgIiISIoqnk2gra0NBwcH+Pn5oWfPnuJyPz8/dO/eXaa+oaEhHj16JFHm4+OD8+fP488//4SdnV2+j81kgIiISIqqHlro4eGBIUOGwNHREU5OTti8eTNCQkIwbtw4ALnzD8LCwrBr1y5oaGigdu3aEvtbWlpCV1dXpvxrmAwQERFJUdVyxP3790dcXByWLFmCiIgI1K5dGydPnkSFChUAABEREV9dc+BbCEQiUZF4anN1y0aqDqHYalmqoqpDKLZ8/JerOoRizbX+OFWHUGy5iSxVHUKxNiJsT4G2/5f1IKW11SvyD6W1VVDYM0BERCQlR87s/ZKMyQAREZGUItFlXoi4AiEREZGaY88AERGRFFVNIFQVJgNERERSlLkCYXHAYQIiIiI1x54BIiIiKapYgVCVmAwQERFJ4d0EREREpFaKTM/Ai/fhqg6h2Lq3pJmqQyi2uILe9zkdsFHVIRRbyxzmqzoE+gJ1m0BYZJIBIiKiooK3FhIREak5zhkgIiIitcKeASIiIimcM0BERKTm1G3OAIcJiIiI1Bx7BoiIiKSoW88AkwEiIiIpIjWbM8BhAiIiIjXHngEiIiIpHCYgIiJSc+qWDHCYgIiISM2xZ4CIiEiKui1HzGSAiIhIClcgJCIiUnOcM0BERERqhT0DREREUtStZ4DJABERkRR1m0DIYQIiIiI1x54BIiIiKbybgIiISM2p25wBDhMQERGpuXwnAzk5OVi5ciWaN2+Oxo0bY86cOfj48WNBxkZERKQSIiW+ioN8JwPLly/H7NmzUbp0adjY2GDVqlWYPHlyQcZGRESkEjkQKe1VHOQ7GdixYwfWrVuHs2fP4tixYzh69Ch27doFkah4nCgRERHJl+9k4O3bt/jhhx/EP3fs2BEikQjh4eEFEhgREZGq5CjxVRzk+26CjIwM6OnpiX8WCATQ1tZGenp6gQRGRESkKurW563QrYXz589HqVKlxD9nZGRg2bJlMDIyEpetWrVKedERERGpQHH5Rq8s+U4GWrVqhefPn0uUNWvWDK9evVJ6UERERFR48p0MXLx4sQDDICIiKjrUbQVCpS069PLlS7Rr105ZzREREamMut1aqLTliFNSUnDp0iVlNad0C+Z7YNTIwTAxMcLt2/cxacpcBAYGfXGfnj07Y/GiGahcqQJevnqL+QuW49ix0+LtmpqaWLhgGgYO6AlrawtERERj1+6DWPbLGrm3XPqsX44xo93gMW0h1q7bqvRzLAwHH4Rgp/9rxKamo7KZPqa3ro6GZU3l1l1w5iFOBMrebVLJVB+Hh7UAAIw6dAt3QxNk6rSws8C6Hg7KDb4I6ja0K/qN6wszS1O8CXoLn0Ub8Oj2Y7l1TS1NMW7BGFStUwVl7MrgyPaj8Fm0sZAjLvr8Ax7B948/EfjsBWLi4rHGaz7at2qm6rBUqtGQDmg2tgsMLIwRHRyG04t3I+TOc7l1a7g6wtGtA6xrVoBQWwvRwaG4+PthvLz8SKKermEptJvRDzVcHaFnWBoJoTE4u3Qvgi88KIxTIiVTi2cTzJg+HlOnjIH7qJ8QHPwKczyn4PTJfahZuxVSUlLl7tO0iQP27d2AhYtW4uixU+jRvRP2/7ERrdv0xO079wEAM2dMwJjRQ+A+ciqeBD6Hg0M9bNuyComJyVjnvU2ivW7dOqJx4wYIC4so8PMtKGeeR2DlxafwbFcT9W1NcPjRO0w8eheHh7aAjaGeTP0ZbWpgcotq4p+zc0Tov+canKtaict+69oAmdmfE6fEtAz033MdzlWsUNK16doa4xeNw9q56/D4zhP84NYFXruXwb3tKESHx8jU19LWQmJcIvau3Yfeo3upIOLiIS3tI6rZV0KPzi74ae5SVYejcrV+aArXBUPwz3xfhPgHwXFQO7jtnIn1HWYiMTxOpn6FxtXx6spjnFtxEB+TUtGgb2sM2jYdW3osQOSTtwAATS1NDNkzG6lxSTj441okRcTDyMYU6aklZ1Xa4vF9XnnU4tkEkyeNgtf/1uLo0VN48uQ5RrhPRalSehg4oGfe+0wehX//vYzlK7zx/PlLLF/hjfPnr2Ly5FHiOk2bOOD4iTM4eeoc3r4NxV9//QO/fy/BwaGeRFu2ttZYu3oZhg6biMzMrAI7z4K2594b9KhdFr3qlEMlM33MaFMD1ga6OPQwRG59Ax0tmJfWEb8CoxKR9DET3WqVFdcx0tWWqHMzJA66WhpwrmpdWKelMn3G9Map/adxct9phLx4B59FGxEdHoOuQ7vKrR8VGoX1CzfA7/C/SE2Wn8QS0NKpESaPGQbnNs1VHUqR4DSqE+4duIh7+y8i9kU4Ti/Zg8SIODi6dZBb//SSPbi26W+EP3yF+DdROLfyIOLeRKJa+4biOg36tYGesT72j/4d7/yDkBgWixD/IEQ9lf+3oDhSt3UGSnwyYGdXHjY2VvD79/MQRkZGBi5fuQknJ8c892vaxAF+/16WKDvrdwlOTT/vc+36bbRr2wJVqlQCANStWxPNmzXGqdPnxHUEAgF2+q7Fb6s2fHVYoijLzM7B06gkOFUwlyhvWt4cD8Lf56uNo49D0aS8GWzl9CL8t07HqjbQ0yrZnVZCLSGq1qkC/8v3JMrvXr6LWo41VRQVlTSaWpqwrWOHl1cku/hfXn6Ecg5V8tWGQCCATmldpCWmiMuqOTdE6L1gdPl5OKb7+2D82f+h5YRuEGio2ay7EiTff3EbNGgAgSDv/9AfPnzI90HT09NlFisSiURfbP9bWVtZAgCiomIlyqOiYlChfFl5u+TuZ22BqGjJrtqo6BhYW1uIf16xcj2MjAzw5NElZGdnQ1NTE/MXLMeBA8fEdWbOmICsrCyZYYPiJiEtA9kiEUxLaUuUm5XWRtzbry88FZPyEdfexOKXTnXzrPM48j1exKVgoUvt7463qDMyNYSmUBMJMZLzJRJiEmBqYaKiqKikKWViAA2hJlJjEyXKU2MToW9hlMdekpzGdIZWKR08+fuWuMyknCXsnGri4bHr2Dt8BczsrNH55+HQ0NTEpbVHlHoOqlJcJv4pS76TgR49eijtoF5eXli8eLFEmUBDHwJNw+9ue+DAntiwfrn4527dhwKAzIQ+gUDw1ecqfG2ffv26YdDA3nAbOgGBgUGoV68WVv26GOERUdi9+xAaNqiDSRNHolET1+89rSJDOl0TiQCBTKms44FhMNARoq193nMBjj4Ohb2ZPmpbG39fkMWJ9HtQIJApIvpeMn/rBIJ8DYrX7uaENlN7Yf+oVUiNS/q8u4YAqXFJODF7K0Q5IkQ8fgMDKxM0G9ulxCQD6vZrmO9kYOHChUo7qKenJzw8PCTKTMyqK6XtEyfO4vbt++KfdXRyv8laW1sgMjJaXG5paY6o6FiZ/T+JjIwR9yqI97Ewl+hhWO41HytWeuPgweMAgMePn6FC+bKYNXMidu8+hBYtmsDS0hyvX94W7yMUCrFyxQJMnjQK9lWbft/JFiITPW1oCgSI+5AhUR7/IUOmt0CaSCTCsSdh6FLDFlqa8kem0jKzceZ5JH50sldazEVZYnwSsrOyYWIpeSeGibkxEmJl764g+hYfEpKRk5UNfQtjifLSZoZIkeotkFbrh6bovmI0Do5fi1fXnkhsS45+j5ysbIhyPn9kxrwIh4GlCTS1NJGdma20c6DCke85A9HR0V/cnpWVhdu3b3+xzic6OjowNDSUeClriCAlJRUvX74RvwIDgxAREYUO7VuJ62hpaaFVy6a4ccM/z3Zu3rqLDu1bSpQ5d2iFGzc/71OqlB5yciTzx+zsbGho5F7WPXsPo4FDBzg0chG/wsIi8NuqDej8w2BlnG6h0dLUQA0rQ9x8K5lA3QyJRT1b4y/uezc0Hu/ef0CP2nkPy/gFRSAjOweda9gqI9wiLyszC0GPguHQsqFEuUPLhnjiH6iiqKikyc7MRvij16jcUnLorXLLOnh3NzjP/Wp3c0KP38bi8OT1CD4fILP9nX8QTCtYSfzdNrOzRnJUQolJBNRtAmG+ewZsbGwQEREBS8vcb8s1atTAmTNnUL58eQBAXFwcnJyckJ1d9N4Ia9dtxexZkxD84jVevHiN2bMm4cOHNOzb/7k7y3f7GoSHR2DuvP8BANat24YL5w9jxvTxOH7iDLp17Yj27VuidZvPdyD8/Y8fPGdPxrt3YXgS+Bz169fG1CljsGPnfgBAfHwC4uMlv+VlZmYhMjIGQUEvC+HMlcutYUXMO/0QNa2MUNfGGH89eofI5I/oUzf3PbD26nNEp6RjqavkvICjj8NQx9oI9uYGebZ99HEY2lS2hLHel3sZSpI/Nx/G7DUzEfQwCIF3A9FlcBdYlrHEid1/AwBGznaHubUZlk9dKd6ncs3cyap6pfRgZGaMyjUrISszC2+DS84s7u/14UMaQkI/r28RFh6FZ0EvYWRoABtryy/sWTLd2HoKvX7/EeEPX+PdvWA4DGwHI1sz+O/NnejcfmZ/GFqb4IhH7poVtbs5oeeqcTi9eDdC778Qzy3I/JiB9OQ0AMCdPf+i8XAXuC4agts7zsLUzhotJ3THrR1nVHOSBYBzBvIgPeYUGhqKrKysL9YpKlb+6gM9PV14r/1FvOhQpy6DJNYYKF/OFjk5n3O4Gzf9MchtPJYsnonFi2bg5au3GDj4R/EaAwAwZeo8LF40E+vW/gJLSzOEh0dhy9Y9+Hnp74V6foWlYzUbJH7MxOZbLxCbmg57MwOs6+EgvjsgNjUdkf//x+KT5PRMnHsRiRltauTZ7tuEVNwPT8CGXnnf3VESXTxxCYYmhhgydTBMLU3x5vlbeA6dh+iw3F44M0tTWJaR/PDafPbzIkPV6lVFh57tEPkuEoOdhhZq7EXZ42fBcJ80S/zzinWbAQDdO3XAsnnTVBWWyjz5+yZKmeij9eSe0Lc0RnRQKPYOX4nEsNxePgNLYxjZmonrOw5qB00tIbosHYEuS0eIywMOXcbR6ZsAAEkR8dg95H9wnT8EP572QlJUAm75nsbVDScK9+QKUNH8NCs4AlE+P8E1NDQQGRkp7hkwMDDAgwcPUKlS7jeVqKgo2NrafnPPgFC7zDftR0DS2j6qDqHY6rr0qapDKNZOB3AFxG+1zGG+qkMo1ha93Vug7f9UcYDS2vr9zX6ltVVQSvbN3ERERN+guIz1K4tCyUBycjJ0dXXFawKkpKQgKSn3dpNP/09ERFTcidRsoEChZKBq1arif4tEIjRo0EDi54JYNIiIiIgKVr6TgZ07d4rvHCAiIirJOEyQh0mTJmHdunUYMmRIQcZDRESkcup2a2G+Fx365ZdfMGHCBPTu3RtxcbKPvSQiIqLv5+PjAzs7O+jq6sLBwQFXrlzJs+5ff/0FZ2dnWFhYwNDQEE5OTjhzRvH1HvKdDIwfPx4PHjxAQkICatWqhePHjyt8MCIiouJApMSXIg4cOICpU6di7ty5uH//Plq2bIlOnTohJET+wmKXL1+Gs7MzTp48ibt376Jt27bo2rUr7t+/L7d+XhSaQGhnZ4fz58/D29sbvXv3Ro0aNSAUSjZx7969PPYmIiIqHlQ1TLBq1SqMHDkSo0aNAgCsXr0aZ86cwYYNG+Dl5SVTf/Xq1RI///LLLzh27BhOnDghMcn/axReZ+Dt27c4fPgwTE1N0b17d5lkgIiIiD5LT09Herrko951dHSgo6MjUZaRkYG7d+9i9uzZEuUuLi64fv16vo6Vk5OD5ORkmJqafr3yfyj0Sb5lyxZMmzYNHTp0wOPHj2FhYaHQwYiIiIoDZd5N4OXlhcWLF0uULVy4EIsWLZIoi42NRXZ2NqysJB/1bmVlhcjIyHwd67fffkNqair69eunUIz5TgZcXV1x+/ZteHt7Y+hQroNOREQllzIXHfL09ISHh4dEmXSvwH9Jr9mT33V89u3bh0WLFuHYsWPiRwfkV76TgezsbDx8+BBly+b9GFoiIqKSQJk9A/KGBOQxNzeHpqamTC9AdHS0TG+BtAMHDmDkyJE4dOgQOnTooHCM+b6bwM/Pj4kAERFRAdHW1oaDgwP8/Pwkyv38/NCsWbM899u3bx+GDx+OP/74A126dPmmY3P2HxERkRRVPZvAw8MDQ4YMgaOjI5ycnLB582aEhIRg3LhxAHKHHMLCwrBr1y4AuYnA0KFDsWbNGjRt2lTcq6CnpwcjI6N8H5fJABERkRRVLUfcv39/xMXFYcmSJYiIiEDt2rVx8uRJVKhQAQAQEREhsebApk2bkJWVhQkTJmDChAni8mHDhmHHjh35Pi6TASIioiJk/PjxGD9+vNxt0h/wFy9eVMoxmQwQERFJyRGp17MJmAwQERFJUa9UQIG7CYiIiKhkYs8AERGRFHV7hDGTASIiIimqurVQVThMQEREpObYM0BERCRFVesMqAqTASIiIimcM0BERKTmOGeAiIiI1Ap7BoiIiKRwzgAREZGaE6nZcsQcJiAiIlJz7BkgIiKSwrsJiIiI1BznDKiIq3V9VYdQbF1dGKXqEIotN6GlqkMo1pY5zFd1CMXW3Ls/qzoEIrEikwwQEREVFeq2zgCTASIiIinqNmeAdxMQERGpOfYMEBERSVG3dQaYDBAREUnh3QRERERqTt0mEHLOABERkZpjzwAREZEUdbubgMkAERGRFHWbQMhhAiIiIjXHngEiIiIpHCYgIiJSc7ybgIiIiNQKewaIiIik5KjZBEImA0RERFLUKxXgMAEREZHaY88AERGRFN5NQEREpOaYDBAREak5rkBIREREaoU9A0RERFLUbZhAoZ6BkJAQtes6ISIi9SNS4v+KA4WSATs7O8TExBRULERERKQCCg0TsFeAiIjUgbp93nHOABERkRR1mzOgcDKwdetW6Ovrf7HO5MmTvzkgIiIiKlwKJwMbN26EpqZmntsFAgGTASIiKtY4TPAV/v7+sLS0LIhYiIiIigR1GyZQ6G4CgUBQUHEQERGRivBuAiIiIinFZX0AZVEoGVi4cOFXJw8SEREVdzlq9uVXoWRg27ZtmDhxIkqVKgUA8Pb2xtChQ2FoaFggwRWUzkO6oNfYXjC1NEVIcAi2LN6MJ7efyK1rYmmCkfNGwb6OPWztbHHC9zi2LN4iUcfJtRn6TewHmwo2EGoJEf46HEe2/IULf10ojNMpVOWGO6PihK7QtjRG6vNQPJu/C+9vPfvqfsaNqsLx6EKkPHuHm+1ni8tLVysL+5l9YVi3EvTKW+DZ/J0I2XyqIE9BZaoP64Da4zpDz9IY74PCcHvhHkTdfi63rmWjqnCcOwBG9jYQ6uogJSwWz/ecR+CW03Lr23VrijYbJuLtaX+cH7m6AM9CdRoN6YBmY7vAwMIY0cFhOL14N0LuyL9+NVwd4ejWAdY1K0CorYXo4FBc/P0wXl5+JFFP17AU2s3ohxqujtAzLI2E0BicXboXwRceFMYpFTn+AY/g+8efCHz2AjFx8VjjNR/tWzVTdVgqoW49AwrNGQgNDUV2drb45zlz5iA2NlbpQRWkll1bYvTC0TjofQCTO0/Gk9uPsWjnYljYWsitr6WthaT4RBz0PoDXga/l1kl5n4yD6w5ges/pmNhxAv495Iepv/6Ehq0aFuSpFDqr7k6o9vMwvFp9BDc7zEbCrWdouG82dMuYfXE/oYEeantPQPyVxzLbNPW0kfY2GsHL/kB6VEJBha5ydt2aoPEiNzxYexzHO85D1O3ncN4zA6Vt5V+7rA/peOrrh1O9luJIm5l4sOYYGs7sg6qD28rULV3GDI0WDELkza8nZcVVrR+awnXBEFzxPoaNXeYi5PYzuO2cCaM8rl+FxtXx6spj7B2+Ept+mIs31wMxaNt0WNeqIK6jqaWJIXtmw7isOQ7+uBbr2s3AiVlbkRRZct+HX5OW9hHV7Cthjsd4VYdChey7Fh0qjnMIeozqCb8DZ3F2/1kAwJbFW9CwlQM6D+mMnct3ytSPDo3G5kWbAQDO/ZzltvnopuS3jePbj6Nd7/ao2agm7l2+p+QzUJ2K47og7I8LCNub2+PxfP4umLWph7LDnfFi2f4896vx62hE/HUNyM6BRSdHiW1JAa+QFPAKAFBl7qCCC17Fao3uhOD9FxG87yIA4PbCPSjTug6qD22Pu/87KFM//slbxD95K/45JTQWFTs5wqpJNQTt/dzjJNAQoLX3eNz/9TCsmlSDtmGpAj8XVXAa1Qn3DlzEvf0XAQCnl+xB5dZ14ejWAedWHJCpf3rJHomfz608iGouDqjWviEi//+6NujXBnrG+tjWazFysnK/5CSGFa8vN8rW0qkRWjo1UnUYRYK6DROo1SOMhVpC2Nexx/3L9yXK71+5h+oONZR2nHrN66Fs5bJ4fFv2m3BxJdDShEFdO8RdfChRHnfpIYwdq+a5n+2A1ihVwQqvfv2zoEMssjS0NGFW1w5hlyTfD+GXHsPSsUq+2jCtVQEWjlUQeUPy23+9n3riY1wygvdfUlq8RY2mliZs69jh5RXJpPvl5Uco55C/6ycQCKBTWhdpiSnismrODRF6Lxhdfh6O6f4+GH/2f2g5oRsEGrxritTvQUXftQJhVlYWduzYAXNzc4k6X1t0KD09Henp6RJl2aJsaAryXsxIGQxNDaEp1ERC7HuJ8oSY92hoYfJdbZcyKIWdt3dBS1sLOdk52DDPBwFXAr6rzaJE29QQGkJNpMckSpRnxCRCx9JY7j6l7KxRZd5A3Om2GKLsnEKIsmjSMTWAhlATH2Mlr11abCL08rh2n/TzXwtdUwMIhJoIWPWXuGcBACwdq6DqwDY45jynAKIuOkqZ5F6/VKnrlxqbCH0Lo3y14TSmM7RK6eDJ37fEZSblLGHnVBMPj13H3uErYGZnjc4/D4eGpiYurT2i1HMgKuoUSgbKly+PLVs+T56ztrbG7t27JerkZwVCLy8vLF68WKKsiqE9qhrl/Q1TqaS6fwQCwXcPeaSlpGGy6yToltZD/eb1MHL+KESGRMoMIRR/UtdJAJnrCQDQEKDOhkl4ueJPfHgVUSiRFXUy7zHB14faTvb8GVqldWDR0B4Oc/oj6XUUXh+7AWFpXbRa9yOuzdiK9ISUL7ZRUsheP4HM21Ge2t2c0GZqL+wftQqpcUmfd9cQIDUuCSdmb4UoR4SIx29gYGWCZmO7MBkgtRsmUCgZePPmjVIO6unpCQ8PD4my/rX6KaXtL0mKT0J2VjZMpHoBjM2N8F6qt0BRIpEIEW9zP/ReB75CWfty6Duhb4lJBjLik5CTlQ0dC2OJcm1zI5neAgAQ6uvBqEFlGNSpiOpeIwDk/vEVaGigQ9he3Ov/C+Kvyr+Do6RJj09GTlY29KSunZ6ZET7KuXb/lfIu95HhCc9CoWdhhAbTeuH1sRswrGgJg/KW6LBjmrjup+7tYW934q9WM5D8Nlq5J6IiHxJyr5++1PUrbWaIlNgvX79aPzRF9xWjcXD8Wry6Jvl+S45+j5ysbIhyPv/Rj3kRDgNLE2hqaSI7M1u6OVIjxaV7X1lU8tRCHR0d6OjoSJQV9BABAGRlZuHFoxeo37IBbpy5IS6v37IBbp29qdRjCQQCaGlrKbVNVRJlZiP54WuYta6D6FN3xOVmreog+oy/TP2s5DRcbz1doqzccBeYtqiFB6N+R1pITIHHXFTkZGYj7uFr2LaqjZDTn6+VbavaCDlzN/8NCQTQ0M79lU18EYEj7WZLbG44sw+09PVwa8FupIbHKSX2oiA7Mxvhj16jcsvaePaf91rllnXw7Gze1692Nyd0XzkGhyd5I/h8gMz2d/5BqNO9mUTPoJmdNZKjEpgIkNpRKBm4desW4uPj0alTJ3HZrl27sHDhQqSmpqJHjx5Yt26dzAd9UXJ06xF4/D4NLx4G4+m9Z3Ad5AoLWwuc3HMSADBs1jCYWZth1U+rxPvY1awEANAtrQcjUyPY1ayErMxMvAt+BwDoO6Evgh8GI+JtJLS0hHBs64h2vdvBZ+76wj/BAvRm4z+o4z0BiQ9eIdE/CGWHdIBuWXOE7vwXAGA/dwB0rU3xeJIPIBIh5VmoxP4ZsUnITs+UKBdoaUK/atncf2trQtfaFAa1KiAr9SPS3kQV3skVsCdbTqHlmh8R9+AVou++QDW3tihdxgzPdp8DADjM7odSNia4MmUTgNw1CVLD45D4IhwAYNmoGmqP7Yynvrl3wWSnZ+L9c6nrm/QBAGTKS4IbW0+h1+8/Ivzha7y7FwyHge1gZGsG/72516/9zP4wtDbBEY+NAHITgZ6rxuH04t0Ivf9CPLcg82MG0pPTAAB39vyLxsNd4LpoCG7vOAtTO2u0nNAdt3acUc1JFgEfPqQhJDRc/HNYeBSeBb2EkaEBbKzV65k0HCb4gkWLFqFNmzbiZODRo0cYOXIkhg8fjho1amDlypWwtbXFokWLCiJWpbhy4goMjA0xYMpAmFqa4m3QWywathAxYbnfVE0sTWXWHFh3ep3431XqVkGbnm0R9S4KI5u7AwB09HQxful4mNmYI+NjBkJfhOK3qb/iyokrhXdihSDq2A1om+ijskdv6FgZI+XZO9wf9D98DM29HUvH0gS6Zcy/0ookHWtTOJ1fLv654oSuqDihK+KvBcK/1xKlxq9Kr4/fgo6JAer91BOlLI2R8DwUfkNWIjUs9xu8npUxStt+vnYCDQEcZveDfnkLiLJykPw2Gv5eB/B893lVnYJKPfn7JkqZ6KP15J7QtzRGdFAo9g5fKb4V0MDSWGLNAcdB7aCpJUSXpSPQZekIcXnAocs4Oj034UqKiMfuIf+D6/wh+PG0F5KiEnDL9zSubjhRuCdXhDx+Fgz3SbPEP69Yl3tbdfdOHbBs3rS8diuR1G2YQCBSYOacjY0NTpw4AUfH3HvF586di0uXLuHq1asAgEOHDmHhwoUIDAxUOJAfyndReB/KNTndQNUhFFthQpWMlJUYb4Xq9QdTmebe/VnVIRRrWuaVCrT9SuYNlNbWq9j7X6/0Hz4+Pli5ciUiIiJQq1YtrF69Gi1btsyz/qVLl+Dh4YEnT57A1tYWM2fOxLhx4xQ6pkLrDCQkJMDKykoiAFdXV/HPjRo1wrt37xQKgIiIqKgRiXKU9lLEgQMHMHXqVMydOxf3799Hy5Yt0alTJ4SEhMit//r1a3Tu3BktW7bE/fv3MWfOHEyePBmHDx9W6LgKJQNWVlZ4/Tp3Sd6MjAzcu3cPTk5O4u3JycnQ0io5k+aIiEg95UCktJciVq1ahZEjR2LUqFGoUaMGVq9ejXLlymHDhg1y62/cuBHly5fH6tWrUaNGDYwaNQru7u749ddfFTquQsmAq6srZs+ejStXrsDT0xOlSpWS6Lp4+PAhKleurFAARERERY1IJFLaKz09HUlJSRIv6YX3gNwv2Xfv3oWLi4tEuYuLC65fvy43zhs3bsjU79ixI/z9/ZGZmZnv81UoGVi6dCk0NTXRunVrbNmyBZs3b4a2trZ4+/bt22WCIiIiUmdeXl4wMjKSeHl5ecnUi42NRXZ2tsRwPJDbKx8ZGSm37cjISLn1s7KyFHqQoEKzpywsLHDlyhUkJiZCX18fmpqSawMcOnQIBgaczEZERMWbot37XyJvob0v3YIvEEg+H0MkEsmUfa2+vPIvUSgZcHd3z1e97du3K9IsERFRkaLMp/LKW2hPHnNzc2hqasr0AkRHR8t8+//E2tpabn2hUAgzsy8/Xv6/FEoGduzYgQoVKqBBgwbF8vHFRERERZW2tjYcHBzg5+eHnj17isv9/PzQvXt3ufs4OTnhxAnJtTHOnj0LR0dHhSb0K5QMjBs3Dvv378erV6/g7u4ONzc3mJqaKtIEERFRkaeqFQg9PDwwZMgQODo6wsnJCZs3b0ZISIh43QBPT0+EhYVh165dAHI/l729veHh4YHRo0fjxo0b2LZtG/bt26fQcRWaQOjj44OIiAjMmjULJ06cQLly5dCvXz+cOXOGPQVERFRiiJT4P0X0798fq1evxpIlS1C/fn1cvnwZJ0+eRIUKFQAAEREREmsO2NnZ4eTJk7h48SLq16+Pn3/+GWvXrkXv3r0VOq5CKxBKe/v2LXbs2IFdu3YhMzMTgYGB0NfX/6a2uALht+MKhN+OKxB+H65A+O24AuH3KegVCK2Nayitrcj3T5XWVkH5rr+EAoFA/MSvnBzFVlkiIiIqqtStt1uhYQIASE9Px759++Ds7Ixq1arh0aNH8Pb2RkhIyDf3ChARERUlqlqBUFUU6hkYP3489u/fj/Lly2PEiBHYv3+/QrcuEBERUdGjUDLwaQ1kOzs7XLp0CZcuXZJb76+//lJKcERERKqgbsMECiUDQ4cOVWhFIyIiouJIVbcWqorCiw4RERGVdOrWM6DwBEIiIiIqWXiTNRERkZTicheAsjAZICIiksJhAiIiIlIr7BkgIiKSwrsJiIiI1JyiDxgq7jhMQEREpObYM0BERCSFwwRERERqjncTEBERkVphzwAREZEUdZtAyGSAiIhIiroNEzAZICIikqJuyQDnDBAREak59gwQERFJUa9+AUAgUre+EAWlp6fDy8sLnp6e0NHRUXU4xQ6v37fjtft2vHbfh9dP/TAZ+IqkpCQYGRkhMTERhoaGqg6n2OH1+3a8dt+O1+778PqpH84ZICIiUnNMBoiIiNQckwEiIiI1x2TgK3R0dLBw4UJOovlGvH7fjtfu2/HafR9eP/XDCYRERERqjj0DREREao7JABERkZpjMkBERKTmmAwQERGpOSYDREREak4tkoF3795h5MiRsLW1hba2NipUqIApU6YgLi5OXKdNmzYQCAQQCATQ1tZG5cqV4enpifT0dIm2Xrx4gREjRqBs2bLQ0dGBnZ0dBg4cCH9/f3EdgUCAo0ePFtbpFZhP1yOv1/DhwwEAFy5cQNu2bWFqaopSpUqhSpUqGDZsGLKysgAAFy9ehEAgwPv37796TBcXF2hqauLmzZsFeGaqNXz4cPTo0UP87/9eUzMzM7i6uuLhw4cS+3zaLn1d0tPTYWZmBoFAgIsXLxbSGRQOkUiEDh06oGPHjjLbfHx8YGRkhF27duX5/oyMjAQALFq0CPXr18/zOP/93dfR0UHVqlXxyy+/IDs7u6BOrcB9el+NGzdOZtv48eMlfn+jo6MxduxYlC9fHjo6OrC2tkbHjh1x48YN8T4VK1aUub5ly5bFokWLvvp34s2bN4V01vQ9Snwy8OrVKzg6OiIoKAj79u3DixcvsHHjRpw7dw5OTk6Ij48X1x09ejQiIiLw4sULrFixAuvXr8eiRYvE2/39/eHg4ICgoCBs2rQJgYGBOHLkCKpXr45p06ap4OwKVkREhPi1evVqGBoaSpStWbMGT548QadOndCoUSNcvnwZjx49wrp166ClpYWcnByFjhcSEoIbN25g4sSJ2LZtWwGdVdHj6uoqvqbnzp2DUCjEDz/8IFOvXLly8PX1lSg7cuQI9PX1CyvUQiUQCODr64tbt25h06ZN4vLXr19j1qxZWLNmDcqXLw8AeP78ucR7MyIiApaWlvk+1qff/efPn2Py5MmYN28efv31V6WfU2EqV64c9u/fj7S0NHHZx48fsW/fPvF1A4DevXvjwYMH2LlzJ4KCgnD8+HG0adNG4m8jACxZskTi+t6/fx/Tp0+XKCtbtqxMvXLlyhXaOdN3EJVwrq6uorJly4o+fPggUR4RESEqVaqUaNy4cSKRSCRq3bq1aMqUKRJ1evXqJWrYsKFIJBKJcnJyRLVq1RI5ODiIsrOzZY6TkJAg/jcA0ZEjR5R6Hqrm6+srMjIykin//fffRRUrVvzivhcuXBABkLhG8ixatEg0YMAA0dOnT0UGBgailJSU74i46Bo2bJioe/fuMv/+5PLlyyIAoujoaHEZANG8efNEhoaGEu9lZ2dn0fz580UARBcuXCiE6Avfjh07RPr6+qJXr16JcnJyRG3bthVfs/y8txYuXCiqV69entvl/e536NBB1LRp0+8PXkU+va/q1Kkj2rNnj7h87969ojp16oi6d+8uGjZsmCghIUEEQHTx4sUvtlehQgXR77///tXj5rceFT0lumcgPj4eZ86cwfjx46GnpyexzdraGoMHD8aBAwcgkrPu0oMHD3Dt2jVoaWkBAAICAvDkyRNMmzYNGhqyl83Y2LhAzqGos7a2RkREBC5fvvxd7YhEIvj6+sLNzQ3Vq1dH1apVcfDgQSVFWXykpKRg7969sLe3h5mZmcQ2BwcH2NnZ4fDhwwByh78uX76MIUOGqCLUQjNs2DC0b98eI0aMgLe3Nx4/fozNmzcX6DH19PSQmZlZoMcoDCNGjJDoTdq+fTvc3d3FP+vr60NfXx9Hjx6VGRIl9VKik4Hg4GCIRCLUqFFD7vYaNWogISEBMTExAHLHIfX19aGjo4P69esjJiYGM2bMELcFANWrVy+c4IuJvn37YuDAgWjdujVsbGzQs2dPeHt7IykpSaF2/v33X3z48EE8Puzm5qY2QwV///23+I+ygYEBjh8/jgMHDshNOkeMGIHt27cDAHx9fdG5c2dYWFgUdsiFbvPmzQgMDMTUqVOxadMmmSGAsmXLiq+hvr4+qlWr9k3HycnJwenTp3HmzBm0b99eGaGr1JAhQ3D16lW8efMGb9++xbVr1+Dm5ibeLhQKsWPHDuzcuRPGxsZo3rw55syZIzNnBQBmzZolcY3Xrl1bmKdCBaxEJwNf86lHQCAQAAAGDx6MgIAA3LhxA/369YO7uzt69+4tty7l0tTUhK+vL0JDQ7FixQrY2tpi2bJlqFWrFiIiIvLdzrZt29C/f38IhUIAwMCBA3Hr1i08f/68oEIvMtq2bYuAgAAEBATg1q1bcHFxQadOnfD27VuZum5ubrhx4wZevXqFHTt2SHzLK8ksLS0xZswY1KhRAz179pTZfuXKFfE1DAgIwJkzZxRq/9MXAV1dXXTr1g1ubm5YuHChssJXGXNzc3Tp0gU7d+6Er68vunTpAnNzc4k6vXv3Rnh4OI4fP46OHTvi4sWLaNiwIXbs2CFRb8aMGRLXeOjQoYV4JlTQSnQyYG9vD4FAgMDAQLnbnz17BhMTE/Evh5GREezt7dGwYUPs2bMHly5dEn87rVq1KgDg6dOnhRN8MVOmTBkMGTIE69evR2BgID5+/IiNGzfma9/4+HgcPXoUPj4+EAqFEAqFKFOmDLKyssTfgkuy0qVLw97eHvb29mjcuDG2bduG1NRUbNmyRaaumZkZfvjhB4wcORIfP35Ep06dVBCxanx6b8hjZ2cnvob29vaoWLGiQm1/+iLw8uVLpKWlYdu2bShVqpQSolY9d3d38bf/vJJHXV1dODs7Y8GCBbh+/TqGDx8ukwyZm5tLXGN1HRotqUp0MmBmZgZnZ2f4+PhIzKgFgMjISOzduxf9+/eX+21fS0sLc+bMwbx58/DhwwfUr18fNWvWxG+//SZ3lnx+bptTFyYmJrCxsUFqamq+6u/duxdly5bFgwcPJL55rF69Gjt37hTfoqguBAIBNDQ0ZN6zn7i7u+PixYsYOnQoNDU1Czm6kunTF4Fy5cqVuGvq6uqKjIwMZGRkyL1NU56aNWvm+/eXSgb5aXYJ4u3tjWbNmqFjx45YunQp7Ozs8OTJE8yYMQNlypTBsmXL8tx30KBBmDNnDnx8fDB9+nT4+vqiQ4cOaNWqFebMmYPq1asjJSUFJ06cwNmzZ3Hp0iXxvq9fv0ZAQIBEe/b29iXuNrBNmzYhICAAPXv2ROXKlfHx40fs2rULT548wbp16yTqPnr0CAYGBhJl9evXx7Zt29CnTx/Url1bYluFChUwa9Ys/PPPP+jevXuBn4uqpKeni++JT0hIgLe3N1JSUtC1a1e59V1dXRETEwNDQ8PCDLNIi46OxsePHyXKzMzMxBOA09LSZH4f9fX1YW9vX1ghqoympqa4R1M60YmLi0Pfvn3h7u6OunXrwsDAAP7+/lixYkWJ/p0jWSU+GahSpQr8/f2xaNEi9O/fH3FxcbC2tkaPHj2wcOFCmJqa5rmvtrY2Jk6ciBUrVmDcuHFo3Lgx/P39sWzZMowePRqxsbGwsbFBs2bNsHr1aol9PTw8ZNq7cOEC2rRpo+QzVK3GjRvj6tWrGDduHMLDw6Gvr49atWrh6NGjaN26tUTdVq1ayezv7++PBw8eyO0SNzAwgIuLC7Zt21ai/zCdPn0aNjY2AHLPuXr16jh06FCe7xWBQCAz7qvu5E0YvHHjBpo2bQoACAoKQoMGDSS2t27dusQt1JSXvBJHfX19NGnSBL///jtevnyJzMxMlCtXDqNHj8acOXMKOUpSJYFI3n11REREpDZK9JwBIiIi+jomA0RERGqOyQAREZGaYzJARESk5pgMEBERqTkmA0RERGqOyQAREZGaYzJARESk5pgMEBERqTkmA0RERGqOyQAREZGa+z8I1U0BqB898gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(rets.corr(), annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Finally, it is important to know that the plotting performed by Pandas is just a layer on top of `matplotlib` (i.e., the `plt` package).  \n",
    "\n",
    "So Panda's plots can (and often should) be replaced or improved by using additional functions from `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "For example, suppose we want to know both the returns as well as the standard deviation of the returns of a stock (i.e., its risk).  \n",
    "\n",
    "Here is visualization of the result of such an analysis, and we construct the plot using only functions from `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(rets.mean(), rets.std());\n",
    "plt.xlabel('Expected returns')\n",
    "plt.ylabel('Standard Deviation (Risk)')\n",
    "plt.xlim([-.05,.1])\n",
    "plt.ylim([0,.3])\n",
    "for label, x, y in zip(rets.columns, rets.mean(), rets.std()):\n",
    "    plt.annotate(\n",
    "        label, \n",
    "        xy = (x, y), xytext = (30, -30),\n",
    "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
    "        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
    "        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To understand what these functions are doing, (especially the `annotate` function), you will need to consult the online documentation for matplotlib.  Just use Google to find it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "rise": {
   "scroll": true,
   "theme": "beige",
   "transition": "fade"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}