Data Analysis/Matplotlib/matplotlib-exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib Practice\n",
    "\n",
    "This notebook offers a set of exercises to different tasks with Matplotlib.\n",
    "\n",
    "It should be noted there may be more than one different way to answer a question or complete an exercise.\n",
    "\n",
    "Different tasks will be detailed by comments or text.\n",
    "\n",
    "For further reference and resources, it's advised to check out the [Matplotlib documentation](https://matplotlib.org/3.1.1/contents.html).\n",
    "\n",
    "If you're stuck, don't forget, you can always search for a function, for example if you want to create a plot with `plt.subplots()`, search for [`plt.subplots()`](https://www.google.com/search?q=plt.subplots())."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the pyplot module from matplotlib as plt and make sure \n",
    "# plots appear in the notebook using '%matplotlib inline'\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a simple plot using plt.plot()\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a single Python list\n",
    "plt.plot([1,2,3,4,5]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create two lists, one called X, one called y, each with 5 numbers in them\n",
    "X = [1,3,4,5,6]\n",
    "y = [11,22,44,55,33]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot X & y (the lists you've created)\n",
    "plt.plot(X,y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's another way to create plots with Matplotlib, it's known as the object-orientated (OO) method. Let's try it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot using plt.subplots()\n",
    "fig,ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot using plt.subplots() and then add X & y on the axes\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(X,y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try a small matplotlib workflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import and get matplotlib ready\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Prepare data (create two lists of 5 numbers, X & y)\n",
    "X = [1,2,3,4,5]\n",
    "y = [2,3,4,5,9]\n",
    "\n",
    "# Setup figure and axes using plt.subplots()\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "# Add data (X, y) to axes\n",
    "ax.plot(X,y)\n",
    "\n",
    "# Customize plot by adding a title, xlabel and ylabel\n",
    "ax.set(title=\"This is title\",xlabel=\"X axis\",ylabel=\"Y axis\")\n",
    "\n",
    "# Save the plot to file using fig.savefig()\n",
    "fig.savefig(\"ex-plot1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, this is a simple line plot, how about something a little different?\n",
    "\n",
    "To help us, we'll import NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import NumPy as np\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array of 100 evenly spaced numbers between 0 and 100 using NumPy and save it to variable X\n",
    "X = np.linspace(0,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot using plt.subplots() and plot X versus X^2 (X squared)\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(X,X**2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start with scatter plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of X versus the exponential of X (np.exp(X))\n",
    "fig,ax = plt.subplots()\n",
    "ax.scatter(X,np.exp(X));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of X versus np.sin(X)\n",
    "fig,ax = plt.subplots()\n",
    "ax.scatter(X,np.sin(X));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about we try another type of plot? This time let's look at a bar plot. First we'll make some data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Python dictionary of 3 of your favourite foods with \n",
    "# The keys of the dictionary should be the food name and the values their price\n",
    "food_prices = {\"Samosa pav\":15,\"Vada pav\":12,\"Missal\":60}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar graph where the x-axis is the keys of the dictionary\n",
    "# and the y-axis is the values of the dictionary\n",
    "fig,ax = plt.subplots()\n",
    "ax.bar(food_prices.keys(),food_prices.values())\n",
    "\n",
    "# Add a title, xlabel and ylabel to the plot\n",
    "ax.set(title=\"Bhupend's Fav Food\",xlabel=\"Food Items\",ylabel=\"Prices(Rs.)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the same plot as above, except this time make the bars go horizontal\n",
    "fig,ax = plt.subplots()\n",
    "ax.barh(list(food_prices.keys()),list(food_prices.values()));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All this food plotting is making me hungry. But we've got a couple of plots to go.\n",
    "\n",
    "Let's see a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a random NumPy array of 1000 normally distributed numbers using NumPy and save it to X\n",
    "X = np.random.randn(1000)\n",
    "\n",
    "# Create a histogram plot of X\n",
    "fig,ax = plt.subplots()\n",
    "ax.hist(X);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a NumPy array of 1000 random numbers and save it to X\n",
    "X = np.random.random(1000)\n",
    "\n",
    "# Create a histogram plot of X\n",
    "fig,ax = plt.subplots()\n",
    "ax.hist(X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the distributions (spread of data) are different. Why do they differ? \n",
    "\n",
    "What else can you find out about the normal distribution? \n",
    "\n",
    "Can you think of any other kinds of data which may be normally distributed?\n",
    "\n",
    "These questions aren't directly related to plotting or Matplotlib but they're helpful to think of.\n",
    "\n",
    "Now let's try make some subplots. A subplot is another name for a figure with multiple plots on it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an empty subplot with 2 rows and 2 columns (4 subplots total)\n",
    "fig,((ax0,ax1),(ax2,ax3)) = plt.subplots(nrows=2,ncols=2,figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the subplot has multiple figures. Now let's add data to each axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the same plot as above with 2 rows and 2 columns and figsize of (10, 5)\n",
    "fig,((ax0,ax1),(ax2,ax3)) = plt.subplots(nrows=2,ncols=2,figsize=(10,5))\n",
    "\n",
    "# Plot X versus X/2 on the top left axes\n",
    "ax0.plot(X,X/2)\n",
    "\n",
    "# Plot a scatter plot of 10 random numbers on each axis on the top right subplot\n",
    "ax1.scatter(np.random.random(10),np.random.random(10))\n",
    "\n",
    "# Plot a bar graph of the favourite food keys and values on the bottom left subplot\n",
    "ax2.bar(food_prices.keys(),food_prices.values())\n",
    "\n",
    "# Plot a histogram of 1000 random normally distributed numbers on the bottom right subplot\n",
    "ax3.hist(np.random.randn(1000));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Woah. There's a lot going on there.\n",
    "\n",
    "Now we've seen how to plot with Matplotlib and data directly. Let's practice using Matplotlib to plot with pandas.\n",
    "\n",
    "First we'll need to import pandas and create a DataFrame work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import pandas as pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>$5,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>$22,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>$3,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>$4,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,500.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>$7,000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>$6,250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>$9,700.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors       Price\n",
       "0  Toyota  White         150043      4   $4,000.00\n",
       "1   Honda    Red          87899      4   $5,000.00\n",
       "2  Toyota   Blue          32549      3   $7,000.00\n",
       "3     BMW  Black          11179      5  $22,000.00\n",
       "4  Nissan  White         213095      4   $3,500.00\n",
       "5  Toyota  Green          99213      4   $4,500.00\n",
       "6   Honda   Blue          45698      4   $7,500.00\n",
       "7   Honda   Blue          54738      4   $7,000.00\n",
       "8  Toyota  White          60000      4   $6,250.00\n",
       "9  Nissan  White          31600      4   $9,700.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the '../data/car-sales.csv' into a DataFame called car_sales and view\n",
    "car_sales = pd.read_csv(\"car-sales.csv\")\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Try to plot the 'Price' column using the plot() function\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(car_sales[\"Price\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why doesn't it work?\n",
    "\n",
    "Hint: It's not numeric data.\n",
    "\n",
    "In the process of turning it to numeric data, let's create another column which adds the total amount of sales and another one which shows what date the car was sold.\n",
    "\n",
    "Hint: To add a column up cumulatively, look up the cumsum() function. And to create a column of dates, look up the date_range() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4000\n",
       "1     5000\n",
       "2     7000\n",
       "3    22000\n",
       "4     3500\n",
       "5     4500\n",
       "6     7500\n",
       "7     7000\n",
       "8     6250\n",
       "9     9700\n",
       "Name: Price, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the symbols, the final two numbers from the 'Price' column and convert it to numbers\n",
    "car_sales[\"Price\"] = car_sales[\"Price\"].str.replace(\"[\\$\\,\\.]\",\"\")\n",
    "car_sales[\"Price\"] = car_sales[\"Price\"].str[:-2]\n",
    "car_sales[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Colour</th>\n",
       "      <th>Odometer (KM)</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total Sales</th>\n",
       "      <th>Sales Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>150043</td>\n",
       "      <td>4</td>\n",
       "      <td>4000</td>\n",
       "      <td>4000</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Red</td>\n",
       "      <td>87899</td>\n",
       "      <td>4</td>\n",
       "      <td>5000</td>\n",
       "      <td>9000</td>\n",
       "      <td>2021-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Blue</td>\n",
       "      <td>32549</td>\n",
       "      <td>3</td>\n",
       "      <td>7000</td>\n",
       "      <td>16000</td>\n",
       "      <td>2021-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>Black</td>\n",
       "      <td>11179</td>\n",
       "      <td>5</td>\n",
       "      <td>22000</td>\n",
       "      <td>38000</td>\n",
       "      <td>2021-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>213095</td>\n",
       "      <td>4</td>\n",
       "      <td>3500</td>\n",
       "      <td>41500</td>\n",
       "      <td>2021-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>Green</td>\n",
       "      <td>99213</td>\n",
       "      <td>4</td>\n",
       "      <td>4500</td>\n",
       "      <td>46000</td>\n",
       "      <td>2021-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>45698</td>\n",
       "      <td>4</td>\n",
       "      <td>7500</td>\n",
       "      <td>53500</td>\n",
       "      <td>2021-01-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Honda</td>\n",
       "      <td>Blue</td>\n",
       "      <td>54738</td>\n",
       "      <td>4</td>\n",
       "      <td>7000</td>\n",
       "      <td>60500</td>\n",
       "      <td>2021-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>White</td>\n",
       "      <td>60000</td>\n",
       "      <td>4</td>\n",
       "      <td>6250</td>\n",
       "      <td>66750</td>\n",
       "      <td>2021-01-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>White</td>\n",
       "      <td>31600</td>\n",
       "      <td>4</td>\n",
       "      <td>9700</td>\n",
       "      <td>76450</td>\n",
       "      <td>2021-01-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Make Colour  Odometer (KM)  Doors  Price  Total Sales Sales Date\n",
       "0  Toyota  White         150043      4   4000         4000 2021-01-01\n",
       "1   Honda    Red          87899      4   5000         9000 2021-01-02\n",
       "2  Toyota   Blue          32549      3   7000        16000 2021-01-03\n",
       "3     BMW  Black          11179      5  22000        38000 2021-01-04\n",
       "4  Nissan  White         213095      4   3500        41500 2021-01-05\n",
       "5  Toyota  Green          99213      4   4500        46000 2021-01-06\n",
       "6   Honda   Blue          45698      4   7500        53500 2021-01-07\n",
       "7   Honda   Blue          54738      4   7000        60500 2021-01-08\n",
       "8  Toyota  White          60000      4   6250        66750 2021-01-09\n",
       "9  Nissan  White          31600      4   9700        76450 2021-01-10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a column called 'Total Sales' to car_sales which cumulatively adds the 'Price' column\n",
    "car_sales[\"Total Sales\"] = car_sales[\"Price\"].astype(int).cumsum()\n",
    "\n",
    "# Add a column called 'Sale Date' which lists a series of successive dates starting from today (your today)\n",
    "car_sales[\"Sales Date\"] = pd.date_range(\"1/1/2021\",periods=len(car_sales))\n",
    "\n",
    "# View the car_sales DataFrame\n",
    "car_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've got a numeric column (`Total Sales`) and a dates column (`Sale Date`), let's visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the plot() function to plot the 'Sale Date' column versus the 'Total Sales' column\n",
    "car_sales.plot(x=\"Sales Date\",y=\"Total Sales\")\n",
    "\n",
    "# or\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "ax.plot(car_sales[\"Sales Date\"],car_sales[\"Total Sales\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert the 'Price' column to the integers\n",
    "car_sales[\"Price\"] = car_sales[\"Price\"].astype(int)\n",
    "\n",
    "# Create a scatter plot of the 'Odometer (KM)' and 'Price' column using the plot() function\n",
    "car_sales.plot(kind=\"scatter\",x=\"Odometer (KM)\",y=\"Price\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a NumPy array of random numbers of size (10, 4) and save it to X\n",
    "X = np.random.rand(10,4)\n",
    "\n",
    "# Turn the NumPy array X into a DataFrame with columns called ['a', 'b', 'c', 'd']\n",
    "df = pd.DataFrame(X,columns=[\"a\",\"b\",\"c\",\"d\"])\n",
    "\n",
    "# Create a bar graph of the DataFrame\n",
    "df.plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar graph of the 'Make' and 'Odometer (KM)' columns in the car_sales DataFrame\n",
    "car_sales.plot(kind=\"bar\",x=\"Make\",y=\"Odometer (KM)\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the 'Odometer (KM)' column\n",
    "car_sales[\"Odometer (KM)\"].plot(kind=\"hist\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the 'Price' column with 20 bins\n",
    "car_sales[\"Price\"].plot(kind=\"hist\",bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we've seen a few examples of plotting directly from DataFrames using the `car_sales` dataset.\n",
    "\n",
    "Let's try using a different dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import \"../data/heart-disease.csv\" and save it to the variable \"heart_disease\"\n",
    "heart_disease = pd.read_csv(\"heart-disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
       "      <td>294</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>150</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "5   57    1   0       140   192    0        1      148      0      0.4      1   \n",
       "6   56    0   1       140   294    0        0      153      0      1.3      1   \n",
       "7   44    1   1       120   263    0        1      173      0      0.0      2   \n",
       "8   52    1   2       172   199    1        1      162      0      0.5      2   \n",
       "9   57    1   2       150   168    0        1      174      0      1.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  \n",
       "5   0     1       1  \n",
       "6   0     2       1  \n",
       "7   0     3       1  \n",
       "8   0     3       1  \n",
       "9   0     2       1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first 10 rows of the heart_disease DataFrame\n",
    "heart_disease[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a histogram of the \"age\" column with 50 bins\n",
    "heart_disease[\"age\"].plot(kind=\"hist\",bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call plot.hist() on the heart_disease DataFrame and toggle the\n",
    "# \"subplots\" parameter to True\n",
    "heart_disease.plot.hist(subplots=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That plot looks pretty squished. Let's change the figsize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x2160 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call the same line of code from above except change the \"figsize\" parameter\n",
    "# to be (10, 30)\n",
    "heart_disease.plot.hist(subplots=True,figsize=(10,30));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try comparing two variables versus the target variable.\n",
    "\n",
    "More specifially we'll see how age and cholesterol combined effect the target in **patients over 50 years old**.\n",
    "\n",
    "For this next challenge, we're going to be replicating the following plot:\n",
    "\n",
    "<img src=\"images/matplotlib-heart-disease-chol-age-plot.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replicate the above plot in whichever way you see fit\n",
    "\n",
    "# Note: The method below is only one way of doing it, yours might be\n",
    "# slightly different\n",
    "\n",
    "# Create DataFrame with patients over 50 years old\n",
    "over_50 = heart_disease[heart_disease[\"age\"]>50]\n",
    "\n",
    "# Create the plot\n",
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# Plot the data\n",
    "scatter = ax.scatter(over_50[\"age\"],over_50[\"chol\"],c=over_50[\"target\"])\n",
    "\n",
    "# Customize the plot\n",
    "ax.legend(*scatter.legend_elements(),title=\"Target\")\n",
    "ax.set(title=\"Heart Disease and Cholestrol Level\",xlabel=\"Age\",ylabel=\"Cholestrol\")\n",
    "\n",
    "# Add a meanline\n",
    "ax.axhline(over_50[\"chol\"].mean(),ls=\"--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beatiful, now you've created a plot of two different variables, let's change the style."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check what styles are available under plt\n",
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the style to use \"seaborn-whitegrid\"\n",
    "plt.style.use(\"seaborn-whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the style has been changed, we'll replot the same figure from above and see what it looks like.\n",
    "\n",
    "If you've changed the style correctly, it should look like the following:\n",
    "<img src=\"images/matplotlib-heart-disease-chol-age-plot-seaborn-whitegrid.png\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAF8CAYAAAD1mg4lAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABnUklEQVR4nO3deXhM1/8H8Pesmcxks8YaEjut2vd9p9RSFLErpUpRW+0UpVXaWruo2rdSSlttKUUQpLWFIEFsaexJZs0s9/eHn/k2ZtKayWTuJHm/nsfT5owc7+TMnfnMveeeIxEEQQARERERZSup2AGIiIiI8gIWXURERERewKKLiIiIyAtYdBERERF5AYsuIiIiIi9g0UVERETkBSy6iHxAhQoV8OjRowxtO3fuxFtvveXRf2fw4MEO/86zf6tmzZro3LkzOnfujE6dOmH48OG4cOGC/e8MHToU8fHxHs3jK+bMmYOlS5c6fSw1NRVz585Fp06d0LlzZ3Tp0gXbt2+3P96iRQucP3/epX9v8uTJWL16tdt5MxvHfxMdHY2OHTs6tN++fRvVq1d3O8uL8ta/Q+TL5GIHICLviYqKyvSxWrVq4YsvvrB/fezYMbz55pvYsWMHihcvjq+++sobEX2KyWRC37590alTJ3z//feQy+W4c+cOBg4cCADo0aOHKLn+bRyJyHex6CLKAdLT07Fo0SKcOnUKVqsVlStXxrRp0xAQEICDBw/iiy++QHp6Oh49eoQuXbpgzJgxiI6Oxrx586BWq6HT6fDSSy8BAAYMGIAvv/wSRYsW/dd/s0GDBmjdujU2b96M8ePHo0WLFvjss88QERGB999/H4mJiZBKpahSpQrmzJkDqVSK33//HStXroTZbIZKpcKkSZNQvXp1PHjwADNmzMDDhw9x//59FC9eHJ9++ikKFCiATZs2YcuWLVAoFPDz88OcOXNQtmxZJCcnY86cOUhKSoLZbMarr76K4cOHO+Q8c+YMPv74Y6Snp+P+/fto0KAB5s+fj9u3b2PgwIFo2rQpzp49i9TUVEyYMAGtW7eGVqvF1KlTERcXh8KFC0Mmk6FmzZoOff/0009Qq9UYOnSove1ZdrPZbG/bunUrZs6ciUePHqFz584YO3asvX39+vWQSqUoWLAgpk+fjvDw8Az/RkJCAubNm4cnT57AarWiX79+6N69O3Q6ndPf89SpUzOMY2RkJKpWrYrLly9j3LhxKF26NObMmYMnT55AIpFg8ODB6NKly4s90Z6T2fPuzJkzWLhwIfbs2QPg6dnAli1bYv/+/TAajS80bkR5EYsuIh8xYMAASKX/u+KfkpKCChUqAAC+/PJLyGQy7Ny5ExKJBIsXL8aiRYswc+ZMfPPNN1iwYAFKly6N5ORkNG/eHP379wcAXL16Ffv370fx4sUBPL2MuHbtWuTPn/+FMlWsWBF//PFHhrbffvsNOp0Ou3fvhtVqxcyZM3Hr1i0IgoAlS5Zg3bp1yJcvH65evYpBgwbh119/xY8//ohq1aph2LBhEAQBw4YNw+7duzFgwADMnz8fv//+OwoXLoxdu3YhJiYGZcuWxYQJEzBw4EC0aNECJpMJQ4cORVhYGDp06JAhz7p16zB69GjUrVsXOp0OLVu2xIULFxASEoJbt26hUaNGmD59On755RfMnz8frVu3xueffw6VSoV9+/bh8ePH6Nq1q9Oi68KFC6hRo4ZDe5UqVTJ87efnh507d+L+/fto0aIFevXqhRs3buDrr7/G1q1bkT9/fuzcuRMjR47Ejz/+aP8+i8WC0aNH46OPPkKVKlWQlpaGN954A2XLlsWNGzec/p4//PBDh3EsV64cPv30U1gsFrRr1w4TJ05EmzZtkJycjB49eqBUqVIvNN7P+7fnnU6nw/nz5/Hyyy9j7969aNq0KYKDgzFq1Cin41a1alW3MhDlJiy6iHzE88XQzp078csvvwAADh06hLS0NBw7dgwAYDabUaBAAUgkEqxatQqHDh3C3r17kZCQAEEQYDAYAABFixa1F1zuUqlUGb6uWbMmlixZgn79+qFBgwYYMGAASpUqhY0bN+LevXv2S28AIJFIcPPmTQwYMACnT5/GmjVrcOPGDVy9ehWvvPIKZDIZ2rVrh169eqFZs2Zo1KgRmjZtCr1ej1OnTiElJQWfffYZAECv1yMuLs6h6FqwYAEOHz6MVatW4dq1azCZTNDr9QgJCYFCoUDTpk0BAJUrV8aTJ08AAMePH8eUKVMgkUiQP39+tG7d2unPLpFI8CI7pT2bK1WoUCEULFgQDx8+xJEjR9ChQwf7mHbr1g3z5s3D7du37d9348YN3Lx5E1OmTLG3GY1GXLx4EY0bN3b6e3amVq1a9v5MJhPatGkDAAgNDUWbNm1w5MgR1K1b9z9/juf92/Pu9ddfx/fff4+XX34ZO3fuxMSJE/913Fh0EbHoIsoRbDYbpkyZYi8gdDqdvbjo2rUrWrVqhVq1auH111/H/v377YWCWq3O0r974cIFlC9fPkNbyZIl8dtvvyE6OhonTpzAoEGDMGfOHNhsNtSvXx+ffvqp/e8mJSWhcOHC+Pjjj3Hu3Dm8/vrrqFu3LiwWiz3jokWLcOXKFRw7dgxffvkldu/ejXnz5kEQBGzZsgX+/v4AgEePHsHPz88hY9++fVGhQgU0btwY7du3x9mzZ+19KxQK+9lDiUSS4fv+WUzJZDKnP3+1atWwceNGh/YDBw7g9OnTmDRpEgBALv/fS+mzQs1mszl8nyAIsFgs9q+tVisCAwOxe/due9uDBw8QGBgIPz8/p7/nFi1aOPT7bJytVqvTn/Of/6YrMnveAUD37t3RtWtX9OjRA2lpaahTpw60Wm2m4/b48WO3MhDlJrx7kSgHaNSoETZu3Ij09HTYbDZMnz4dixcvRmJiIrRaLcaMGYMWLVogOjra/neckclkL/wG/Mcff+DQoUN44403MrRv2rQJ77//Pho1aoQJEyagUaNGuHjxIurXr4+oqCgkJCTYv/+1116D0WjE0aNHMWDAAHTp0gUFChTAsWPHYLVa8ejRIzRt2hQhISEYOHAgxowZg/PnzyMgIADVqlXDmjVrADydM9S7d28cOHAgQ5bU1FScP38e48ePR5s2bfD333/j5s2bmf78zzRu3BjfffcdbDYbUlJSHPp9pk2bNtBqtfjqq69gtVoBALdu3cKCBQtQpkyZ//w3fvrpJ/tdhjt27EBISEiGs1Xh4eFQqVT2oispKQkdO3bEhQsXMv09A5mPY0REBORyOX799VcAQHJyMn755Rc0aNDgX7NmJrPnHfD0LFrVqlUxY8YMdO/eHQBeeNyI8iqe6SLKAd5++20sXLgQXbt2hdVqRaVKlTB58mSo1Wo0a9YM7du3h1KpRPny5VG2bFkkJiZCqVQ69NOuXTv069cPS5cudTiDdfr0aXTu3BnA07M1hQsXxurVq1GoUKEMf69Lly44efIkOnToAH9/fxQtWhT9+vVDcHAw5syZg3HjxkEQBMjlcqxcuRIajQYjR47ERx99hM8++wwKhQI1atTAzZs3kT9/fowYMQIDBw6ESqWCTCbD3LlzATw9A/bBBx+gU6dOSE9PR8eOHfHaa69lyBIUFIRhw4aha9euUKvVCA0NRY0aNZCYmIiSJUtm+vscNWoUZs6cifbt2yN//vwOv4tnlEol1qxZg48//hidOnWCTCaDTCbDiBEj0K1bt38ds4YNG2LgwIEYMGAAbDYb8ufPjy+++CLDvD2lUokVK1Zg3rx5+Prrr2GxWPDuu++iZs2aqFSpktPf8/Pj+E8KhQIrVqzA3LlzsXTpUlitVowcORL16tVDdHR0pln1er3Dcg5btmzJ9Hn3TI8ePfDuu+9i5cqV9rbMxu2fl1WJ8iqJ8CITFoiIiIgoS3h5kYiIiMgLWHQREREReQGLLiIiIiIvYNFFRERE5AUsuoiIiIi8IEcsGRETEyN2BCIiIqIX5mxrsRxRdAGO4S9duoRKlSqJlIae4Tj4Bo6D+DgGvoHjID6OQeYni3h5kYiIiMgLWHQREREReQGLLiIiIiIvYNFFRERE5AUsuoiIiIi8gEUXERERkRew6CIiIiLyghyzThcRERHRggULEBsbi/v378NoNKJkyZLIly8fPv/8c4/9Gxs2bEDfvn091t8zLLqIiIgox5g8eTIAYOfOnbh27RrGjx/v8X9j5cqVLLqIiOjFXb9+HVu2bIFer8drr72G2rVrix2JyOO0Wi2mTp2KtLQ0PH78GD169ECfPn3Qr18/5MuXD6mpqVixYgUmT56Me/fuoWjRojh16hSOHj2Ky5cvY+7cuQCAkJAQzJ8/Hxs2bEBKSgpmzZqFWbNmeTQriy4iolxo7dq1GDFiBCwWCywWCxYvXoz+/ftjxYoVkEgkYscj8pjExES8+uqraNOmDZKTk9GvXz/06dMHANCpUye0bt0aa9euRYkSJfD5558jISEBHTt2BABMnz4d8+fPR9myZbF9+3Z8/fXXGDt2LDZs2ODxggtg0UVElOs8fPgQw4cPh9FotLfp9XqsX78evXr1QtOmTUVMR+RZBQsWxNq1a/Hrr78iICAAFovF/lh4eDgAICEhAU2aNAEAlClTBvnz57e3z549GwBgNpvtfz+7sOgiIsplfvnlF8jlji/ver0emzdvZtFFuco333yDatWqoU+fPjhx4gT++OMP+2PPzuqWL18ef/31F1q1aoWbN2/i8ePHAJ4WZQsXLkSxYsUQExOD+/fvAwAEQciWrCy6iIhyGZlM5vQSokQicVqMEeVkzZs3x6xZs7Bnzx6EhIRAJpMhPT09w9/p3r07Jk+ejMjISBQrVgx+fn4AgFmzZmHSpEmwWq0AgHnz5gF4ejZs/PjxWLRokUez8ugjIspl2rVrZ38T+SeVSpUtd2QRiaFbt272/9+3b5/D4+vXr7f//8WLF9G9e3c0atQIN27cwF9//QUAeOmllzL8PWff60ksuoiIcpng4GBs2LABkZGRkEgksFqtkEgkGDt2LOrVqyd2PCKvK1myJMaNG4dly5bBYrFgxowZouRg0UVElAt17doVN2/exM6dO2EwGNChQweUK1dO7FhEoihUqFC2nb1yBYsuIqJcqmDBghg2bJjYMYjo/3HvRSIiIiIvYNFFRERE5AUsuoiIiIi8gEUXERERkRew6CIiIiLyAt69SERERPQcm82GWbNm4fLly1AqlZg7dy5KlSqVpT55pouIiIjoOfv370d6ejq2bt2K9957DwsWLMhynyy6iIiIiJ4TExODxo0bAwCqVauGCxcuZLlPFl1EREREz9FqtQgICLB/LZPJYLFYstQniy4iIiKi5wQEBECn09m/ttlskMuzNhWeRRcRERHRc2rUqIHDhw8DAM6cOYPy5ctnuU/evUhERET0nNatWyMqKgq9evWCIAiYP39+lvtk0UVERET0HKlUijlz5ni2T4/2RkREREROsegiIiIi8gIWXURERERewKKLiIiIyAtYdBERERF5AYsuIiIiIi9g0UVERETkBSy6iIiIiLyARRcRERFRJs6ePYt+/fp5pC8WXURERJSjbdy4EaVLl4ZUKkXp0qWxceNGj/T71VdfYdq0aTCZTB7pj0UXERER5VgbN27EsGHDkJiYCEEQkJiYiGHDhnmk8AoLC8PSpUs9kPIpFl1ERESUY02dOhV6vT5Dm16vx9SpU7Pcd9u2bSGXe26bahZdRERElGPdvHnTpXYxsegiIiKiHCssLMyldjGx6CIiIqIca968eVCr1Rna1Go15s2bJ1KizLHoIiIiohwrMjISX375JUqVKgWJRIJSpUrhyy+/RGRkpEf6L1GiBLZt2+aRvjw3O4yIiIhIBJGRkR4rsrITz3QREREReQGLLiIiIiIvYNFFRERE5AUsuoiIiIi8gEUXERERkRdk292LXbp0QWBgIICnt1sOHz4ckydPhkQiQbly5TBz5kxIpVJs27YNW7ZsgVwux4gRI9C8efPsikREREQkmmwpup7txr1+/Xp72/DhwzFmzBjUrVsXM2bMwIEDB1CtWjWsX78eO3bsgMlkQp8+fdCwYUMolcrsiEVEREQkmmwpuuLi4mAwGDB48GBYLBaMGzcOsbGxqFOnDgCgSZMmiIqKglQqRfXq1aFUKqFUKhEWFoa4uDhUrVo1O2IRERERiSZbii6VSoUhQ4agR48euHHjBoYOHQpBECCRSAAAGo0GaWlp0Gq19kuQz9q1Wq3TPi9dupTha6PR6NBG3sdx8A0cB/FxDHwDx0F8HIPMZUvRFR4ebl+OPzw8HCEhIYiNjbU/rtPpEBQUhICAAOh0ugzt/yzC/qlSpUoZvr506ZJDG3kfx8E3cBzExzHwDRwH8XEMgJiYGKft2XL34nfffYcFCxYAAJKTk6HVatGwYUNER0cDAA4fPoxatWqhatWqiImJgclkQlpaGhISElC+fPnsiEREREQkqmw509W9e3e8//776N27NyQSCebPn498+fJh+vTpWLx4MSIiItC2bVvIZDL069cPffr0gSAIGDt2LPz8/LIjEhEREZGosqXoUiqV+OSTTxzaN2zY4NDWs2dP9OzZMztiEBEREfkMLo5KRERE5AUsuoiIiIi8gEUXERERkRew6CIiIiLyAhZdRERERF7AoouIiIjIC1h0EREREXkBiy4iIiIiL2DRRUREROQFLLqIiIiIvIBFFxEREZEXsOgiIiIi8gIWXURERERewKKLiIiIyAtYdBERERF5AYsuIiIiIi9g0UVERETkBSy6iIiIiLyARRcRERGRF7DoIiIiIvICFl1EREREXsCii4iIiMgLWHQREREReQGLLiIiIiIvYNFFRERE5AUsuoiIiIi8gEUXERERkRew6CIiIiLyAhZdRERERF7AoouIiIjIC1h0EREREXkBiy4iIiIiL2DRRUREROQFLLqIiIiIvIBFFxEREZEXsOgiIiIi8gIWXURERERewKKLiIiIyAtYdBERERF5AYsuIiIiIi9g0UVERETkBSy6iIiIiLyARRcRERGRF7DoIiIiIvICFl1EREREXsCii4iIiMgLWHQREREReQGLLiKiLBIEATabTewYROTjWHQREbnp8ePHiIyMhEqlQtWqVdGqVSskJCSIHYuIfJRc7ABERDmRIAho1qwZ4uLikJ6eDgA4ePAg6tWrh/j4eAQHB4uckIh8Dc90ERG54Y8//sC1a9fsBRcA2Gw26PV6rF+/XsRkROSrWHQREbnh8uXLsFqtDu16vR7nz58XIRER+ToWXUREbqhSpQpkMplDu0ajQfXq1UVIRES+jkUXEZEbGjZsiEqVKsHPz8/eJpVKERAQgMjISBGTEZGvYtFFROQGiUSCAwcOYMCAAQgICIBSqUTnzp1x6tQpBAYGih2PiHwQiy4iIjcFBgbiiy++QFpaGs6cOYOdO3eiZMmSYsciIh/FoouIiIjIC1h0EREREXkBiy4iIiIiL2DRRUREROQF2VZ0PXz4EE2bNkVCQgISExPRu3dv9OnTBzNnzrRvDLtt2zZ069YNPXv2xMGDB7MrChEREZHosqXoMpvNmDFjBlQqFQDgww8/xJgxY7Bp0yYIgoADBw7g/v37WL9+PbZs2YLVq1dj8eLFGbbTICIiIspNsqXoWrhwIXr16oXChQsDAGJjY1GnTh0AQJMmTXDs2DGcO3cO1atXh1KpRGBgIMLCwhAXF5cdcYiIiIhEJ/d0hzt37kT+/PnRuHFjfPnllwAAQRAgkUgAPN0iIy0tDVqtNsMCghqNBlqtNtN+L126lOFro9Ho0Ebex3HwDb42DjabDdHR0bh69SrCwsLQqFEjyOUef7nxKb42BnkVx0F8HIPMefxVcMeOHZBIJDh+/DguXbqESZMm4dGjR/bHdTodgoKCEBAQAJ1Ol6H931ZxrlSpUoavL1265NBG3sdx8A2+NA5paWlo1qwZrly5ArPZDKVSiYIFC+LYsWMoUqSI2PGyjS+NQV7GcRAfxwCIiYlx2u7xy4sbN27Ehg0bsH79elSqVAkLFy5EkyZNEB0dDQA4fPgwatWqhapVqyImJgYmkwlpaWlISEhA+fLlPR2HiLxsypQpiI2NhVartR/ft27dwtChQ8WORkQkKq+c7580aRKmT5+OxYsXIyIiAm3btoVMJkO/fv3Qp08fCIKAsWPHZtg4lohypo0bN8JkMmVos1gs2LdvH8xmMxQKhUjJiIjEla1F1/r16+3/v2HDBofHe/bsiZ49e2ZnBCLyMqvV6rRdEAT7cjFERHkRF0clIo/q2rWrw9ksqVSKxo0b82w2EeVpLLqIyKM++ugjFC9eHAEBAQCe3plcoEABfP311yInIyISV+6+h5uIvK5w4cK4dOkSduzYgb/++gsVKlRA79697UUYEVFexaKLiDxOpVIhMjISkZGRYkchIvIZvLxIRERE5AUsuoiIiIi8gEUXERERkRew6CIiIiLyAhZdRERERF7AoouIiIjIC1h0EREREXkBiy4iIiIiL/jXxVE/+eQTSCQSp4+NGzcuWwIRERER5Ub/WnRFRER4KwcRERFRrvavRVfXrl0BABaLBVu3bkV8fDxKly6N3r17eyUcERERUW7xQnO6ZsyYgVu3bqFhw4a4c+cOpk2blt25iIiIiHKVF9rwOjExERs3bgQAtGrVCr169crWUERERES5zQud6TKZTDAYDAAAo9EIq9WaraGIiIiIcpsXOtM1YMAAdO7cGeXKlUN8fDxGjx6d3bmIiHKEx48fY/fu3bhx4waGDBmCkiVLih2JiHzUCxVdhQoVwrZt23Dr1i2UKFEC+fLly+5cREQ+74cffkCvXr0gk8lgsViwcOFCzJ49GxMnThQ7GhH5oBe6vLh06VKEhITg5ZdfZsFFRATgyZMn6N27NwwGA7RaLYxGI4xGI2bNmoWzZ8+KHY+IfNALnemSSCQYOXIkwsPDIZU+rdO4OCoR5WV79+61vx7+U3p6OjZs2IBXXnlFhFRE5MteqOh6/fXXM3yd2Sr1RL4gNTUVv/76KwCgTZs2CAoKEjkR5UZmsxmCIDi022w2mEwmERIRka97oaLr/PnzmDFjhv3riRMnokuXLtmViXKQEiVKiB0hgx07dqB///6QyWQAAKvVinXr1jl8cCDKqvbt2zu9k1utVqNHjx4iJCIiX/evc7o2btyIRo0aYfv27WjUqBEaNWqEhg0bIjk52Vv5yMcFBgaKHcEuKSkJ/fr1g16vR1paGtLS0qDX69GvXz8kJSWJHY9ymSJFimDRokXw9/eHXC6HRCKBWq1Gnz590KhRI7HjEZEP+tczXZGRkYiMjMSqVaswfPhwb2Uicsv27dudXu4RBAHbt2/nUifkcSNHjkSLFi2wceNG3LlzB0OHDkX9+vU5BYOInHqhy4vdunVDfHw8ZDIZvvrqK/Tv3x8VK1bM7mxELtHr9bBYLA7tFosFOp1OhESUF1SqVAlz587FpUuXUKlSJbHjEJEPe6ElIyZNmoQHDx5gyZIlaNiwIebNm5fduYhc1qFDBygUCod2hUKBDh06iJCIiIjof16o6LJYLKhduzZSU1Px6quvwmazZXcuIpdVrVoVQ4YMgUajgUQigUQigUajwZAhQ3j7PhERie6FLi+azWZ8+OGHqFWrFk6cOMG9F8nOarXa7xT0BZ9//jlef/11bNiwAYIgoG/fvmjWrJnYsYiIiF6s6FqwYAGioqLQo0cP7N+/Hx9//HF25yIfZ7VacfXqVSQnJ6Nx48ZOF4kUg0QiQbNmzVhoERGRz3mhd8qSJUtCqVRi1apVKFCgADQaTXbnIh92+PBhFC1aFHXq1EGHDh1QokQJnDhxQuxYREREPu2Fiq4ZM2bg7t27iIqKgk6nw6RJk7I7F/moBw8e4NVXX8X9+/ft62AlJSWhbdu2SE1NFTuexx06dAgNGzZEgQIFUL9+fRw4cEDsSERElEO9UNF18+ZNvPvuu/Dz80OLFi2QlpaW3bnIR23ZssXpnD6r1YodO3aIkCj7/Prrr+jQoQOOHTuGR48e4cSJE3jttdewd+9esaMREVEO9EJFl9VqxaNHjwAAWq3WZ+bvkPfdu3cPBoPBod1kMuH+/fsiJMo+48aNc/hZ9Xo9N3snIiK3vFD1NGbMGPTu3RsXLlzAG2+8gZEjR2Z3LvJRLVq0cDqnT6lU5rrJ63FxcU7b4+Pjna58T0RE9G9e6O7FOnXq4JdffsGjR4+QL18+bnGRhzVt2hRNmjTBH3/8Ab1eDwDQaDRo27Yt6tSpI3I6zwoNDcXdu3cd2gsWLMhjgIiIXPavRdcbb7yR6ZvLli1bsiUQ+TaJRIIffvgBa9euxbfffguDwYDRo0cjMjJS7GgeN3XqVEyYMMFeXAKAWq3GlClTRExFREQ51b8WXYsXL/ZWDspB5HI5hgwZgiFDhuTq/eZGjBgBrVaLefPmIT09HQqFApMmTcK7774rdjQiIsqB/rXoKl68OADg77//xvz585GQkIDSpUvj/fff90o4IjFJJBJMnDgR48aNw8OHD5E/f36nezsSERG9iBeaSD9t2jR07twZmzdvRteuXTF16tTszkU5RF64k1UulyM0NJQFFxERZckLvWOaTCa0bNkSQUFBaNWqFSwWS3bnohyifPnyYkcgIiLKEV7o7kWr1YrLly+jQoUKuHz5Mu/cysSlS5fw8ccf48KFC6hbty7ee+89lC5dWuxY2YrPBcppbt68iZ9//tn+4bFZs2aoUqWKyKmIKC94oaJr2rRpmDJlCu7du4fQ0FB88MEH2Z0rxzly5AjatWsHk8kEq9WKM2fOYO3atTh+/Dhf0Il8xOXLl1G3bl3o9XqYzWbIZDL4+flhz549aNGihdjxiCiXe6Giq3LlyrluixdPGzFiRIalBcxmMywWC9577z3s27dPxGRE9MyECROQmppqX9zWarVCr9dj+PDhuHLlisjpyBOKFi0qdgSiTL1Q0bVs2TJs3LgRMpnM3nb06NFsC5XTGAwGp6uXC4KAI0eOiJCIiJz5448/nO4mcP36daSmpiIoKEiEVORJHEPyZS9UdB08eBAHDx6ESqXK7jw5klKphFKpdLonYXBwsAiJiMiZkJAQpKamOrTL5XK+vuUSeeGOasq5XujZWaBAAcjlL1Sf5UkymQwDBgxweNFWq9UYPXq0SKm8w2w2ix0hz9q2bRvCw8MhlUrRvHlzrFmzRuxIPm/MmDFQq9UZ2lQqFXr37g2lUilSKiLKK/61kho3bhwkEgkePHiArl27oly5cva71T755BOvBMwplixZguTkZPz888/w8/OD0WhEnz59MGHCBLGjZav4+PhcuyK9L9u5cycGDRpkn0eYnJyMd955BxaLBUOHDhU5ne969913cfXqVaxZswZ+fn4wmUxo1aoVli1bJnY0IsoDJIKzCQ7/Lzo6GtevX0fJkiWhUChw6tQp5M+fHxEREahbt67XQsbExKBmzZoZ2nx1+5lbt27h2rVrqFixIkJDQ8WOk+18dRxyuwoVKjid+F2kSBEkJSWJkChnuXfvHuLi4lCqVCmUKlXKI33yWCB6iseC87oF+I8zXSdPnsTVq1excOFC+Pv7o1ixYliwYAEePnzo1aIrJylZsiRKliwpdgzK5W7cuOG0PTk5GWazmavn/4u0tDRs27YNx48fR+XKlfHmm2/miQ9IeYXNZuO8LvJZ//rMPHz4MD777DP4+/sDAEqUKIElS5bg4MGDXglHRM6Fh4c7bed2Rf/u7t27qFChAiZNmoRNmzZh7ty5KFeuHM6ePSt2NPKQ69evix2BKFP/WnT5+/s7rDiuUCig0WiyNRQR/bsPP/zQ/mHoGbVajblz54qUKGeYNGkS7t+/b58LZzQakZaWhsGDB4ucjDwlPT1d7AhEmfrPouvWrVsZ2m7dupWrtn6xWq34888/cf78eafr9+QWP//8M2rWrImQkBDUr18fhw4dEjsSZUHXrl2xdu1aREREQCqVokiRIli+fDmGDBkidjSftnfvXtSuXRt//PEHHj16hL/++guvvfYazp07B61WK3Y8Isrl/nVO1/jx4/H222+jfv36KFmyJO7evYujR49i4cKF3sqXrX7//Xf06tULRqMRNpsNhQoVwq5du/DKK6+IHc2jduzYgf79+9s/3Z84cQIdOnTA7t270bp1a5HTkbt69OiBHj16AODE1RfVuHFjbN682X62Pl++fNi0aRNGjRrFZXGIKNv965mucuXKYdOmTahcuTIMBgOqVKmCzZs3o3Llyt7Kl22SkpLw2muv4cGDByhRogSKFi2KGzduoEWLFjAajWLH86j33nsvwxZFwNNV9HP7chZEz1u6dKnD9AiNRoNPPvmEi6MSUbb7z492gYGB6NKlixeieNe6detQt25drFu3DsHBwZBIJLh9+zb69u2LPXv22M8g5HQWiwU3b950+tilS5e8nIZIXGFhYU7bQ0JCvBuEiPKkPHtfbVpaGnbv3o3ixYsjICAAGo0G5cqVw88//4z79++LHc9jZDJZplsRFS5c2Mtpsp/BYLDfkVa2bFnMnTvX6fZMlDdlNh/Vl+apPtvLNSUlRewoedrdu3dx9epV2Gw2saNQLpJni67evXtn2MAbeLpnl1KpxKuvvipSKs+TSCSZrkFUvHhxL6fJXjabDS1btsT8+fMRHx+PhIQEzJ8/Hy1atOALJwEAEhISoNPpMrTpdDpcvHhRpET/IwgCFixYgEKFCqFOnToIDQ3F4MGDeTeel925cwcNGjRAREQEqlWrhhIlSuC3334TOxblEtlSdFmtVrz//vvo1asXIiMjcfPmTSQmJqJ3797o06cPZs6caX8T3LZtG7p164aePXt6df2vSpUqOdxyDwB+fn4eW6HaF9hsNly9etXpY+fPn/dymux14MABnD9/PsOZLYPBgAsXLmD//v0iJiNfMXToUIwZMwYPHjywLxfxySefoG3btqLfvbxhwwZ88MEH0Ol0SEtLg8lkwpYtWzB27FhRc+UlgiCgRYsWOHnyJEwmE/R6PZKSktClSxckJCSIHY9ygWwpup4VT1u2bMHo0aPx4Ycf4sMPP8SYMWOwadMmCIKAAwcO4P79+1i/fj22bNmC1atXY/HixV77VCeVSp2+yGZl01uLxYLdu3fjk08+wa+//uozZ1cyW505t63afPLkSYcbBoCnZzJOnjwpQiLyNadPn8bXX3+NwoULo3jx4sifPz9mzpyJ5ORkpKamippt/vz5Tm94WbNmDUwmk0ip8pZjx44hKSkJVqs1Q7vZbMbKlStFSkW5SbbcI92qVSs0a9YMwNPr4gULFsShQ4dQp04dAECTJk0QFRUFqVSK6tWrQ6lUQqlUIiwsDHFxcahatWp2xHLgbB6Hu3M7kpKS0KBBAzx8+BAmkwlKpRJlypTB4cOHERQUlNWobpNKpejYsSP27t0Li8Vib1cqlejZs6doubJD8eLFoVarHdZb0mg0KFGihEipyJeEhoYiLS0NgiDg0aNH9nalUin6os/JyclO2202G9LS0uDn5+flRHnPnTt3nLabzWZcu3bNy2koN8q2Ux1yuRyTJk3CBx98YD91/6yg0Wg0SEtLg1arRWBgoP17NBpNjl2gcOjQobh9+zbS0tKQnp4OrVaLS5cuYfLkyWJHw8qVK1GyZEkEBgZCLpcjMDAQZcqUwaJFi8SO5lHdu3eHUqnMUDhLJBIoFAp0795dxGTkK6ZOnQq1Wp2hTa1WY8SIEaKv01WvXj2n7fnz50f+/Pm9nCZvqlOnDsxms0O7Wq1Gq1atREhEuY1EyOaJDPfv30fPnj2h1Wpx6tQpAMD+/ftx7NgxNGzYEEeOHMGsWbMAACNHjsTw4cPx8ssvZ+gjJibG4YXSaDRmaV2dUqVKOfQJPL2mn5qairt3775wX2azGTVq1HA4JQ08XXIjOjra7ZyeYrFYcOjQISQmJqJs2bJo1KiRw40E7sjqOADAtWvXsHr1asTFxaFy5coYMmQISpcu7VZfV69exfjx4+3LZISFhWHRokUoV66cy32VK1fO6Rux1WrFlStX3MqXHaRSKfR6vU+tM6VQKJA/f374+/vDZDLh0aNHPnGJTBAErF69GqtWrQLwdCy7deuG999/P8tFV1aPhcuXLyMyMtK+WDMAqFQqzJ8/H+3atXOpr+DgYISGhjo9xp88eYKkpCS3c/q6rI7DjBkz8OOPP9rnhioUCoSGhuL7778X/WxoTuGJ9wVPk0qlCAwMhEwmg06ny/bXI71ej5o1azo+IGSD77//Xli1apUgCIKQlpYmNG/eXBg4cKBw4sQJQRAEYfr06cKPP/4o3Lt3T+jYsaNgNBqF1NRUoW3btoLRaHTo7/Tp0w5tFy9ezFLGlJQUwWazObTbbDZBp9O51JfJZBJkMpkAwOFPYGBglnL6uqyOw4kTJwSNRmP//clkMiEgIEA4depUlvq9ffu2cPv27Sz1kZNYLBaxI+QoBoNBiI+PF9LS0jzWZ1aPBUEQhNjYWKFnz55C6dKlhVatWgkHDx7MerA8JqvjYLVaha+//lqoVq2aUKZMGWHixInCw4cPPZQub/DEsZDTOatbBEEQsuV8eps2bfD+++8jMjISFosFU6ZMQZkyZTB9+nQsXrwYERERaNu2LWQyGfr164c+ffpAEASMHTvWa/MWNm/ejKFDhzrM4bLZbPjhhx/Qq1evF+5LqVSiadOmOHToUIbJ8wqFAq+//rrHMudG77zzToZb+K1WK7RaLUaPHo1jx4653W9uWw7jv3jirGVeolKpUKZMGbFjOKhcuTK2bt0qdow8TSqVYsiQIdzHlLJFthRdarUan332mUP7hg0bHNp69uwpyoTuhIQELF26FG+//bb9soLFYsHkyZNRvnx5l/v7+uuvUa9ePej1emi1WgQEBKBQoUL4+OOPPR3dp/xzTp6rBEFATEyM08d4tyEREeU2eXaH127duqFly5b49ttv0aVLF6Snp2Pbtm24fft2puta/Zvw8HBcu3YN27ZtQ1xcHKpVq4Zu3br5zB1HiYmJ+Oyzz3Du3DnUqlULo0ePRrFixbLcb9GiRd3+XolEgsDAQAQHB2PUqFGoXr06YmJisHTpUqdLPxAREeVkebboqlevHgYPHoxvvvkGs2fPtq9GP2/ePLeXF9BoNBg0aJCHk2bdn3/+iaZNm6JcuXKIiIjATz/9hFWrVuHEiROoWLFilvrO6mWtOXPmYNCgQfDz84Ofnx8aN26MESNGYN26dVnql4iIyNfk2aILAJYuXYrIyEh89913UCqV6NOnD1566SWxY3nc2LFjsWfPHtSuXRsWiwUKhQIHDx7E+PHjsXfvXlGzjRo1CsD/Fmr18/ODQqHA22+/LWYsIiIij8vTRRfw9IxXZuvj5AZWqxX9+vVDvXr1MtzC27x5c8TFxYmY7Clnq+L72kr5ZrMZMpkMVqsVCoVC7DhO+eIt2kRElJFvvbuRx0mlUvTt29fhDVmtVvPunP9gNpthNBqhUCgglUqhUChgNBp9cgNisbewoazNbySivIFFVy4nkUgyncyflTsPnxFE3iQ4O6WkpDgUqyqVyid3TShYsKDYEfK8kJAQsSMQkY9j0ZWHeeIy3pMnT7IexEfly5fPpXYx+dolWSIicsRX6jwgs0283d3c+5/+/vvvLPfhq55tJfS827dv5+ozfCSeqKgoNGzYEEFBQahcuTK2bduWpf4EQbA/Vz3xnH38+DGOHj2a6bFBRP+ORRdRJjZs2JBhtXwA0Ol0WLt2rUcKVqJ/OnbsGNq0aYNjx44hLS0Nly5dwqBBg/DFF1+41V9sbCx69eqFTZs2Yffu3Rg0aBD27NnjVl+CIGDixIkoVqwYOnbsiAoVKqB9+/Y+eamdyJex6CLKRLdu3TBmzBjcunULNpsNd+7cwfjx49GpUyexo1EuNGXKFIdFgfV6PaZMmQKr1epSX2azGS1atMD27dvRt29fdOnSBWvXrkWvXr1w7do1l7N98803WLFiBeRyOcqWLYvg4GAcPHgQQ4cOdbkvoryMRVceYTAYcOHCBVy9ehUXLlyA0WgUO5LPq1KlCmbNmoXly5ejTZs2+PTTTzFlyhS88sorYkdz8M89P0kcrhZGzzt37pzTdp1Oh8ePH7vU16+//gqDweBwSdFsNmP16tUuZ1u0aBHGjx+P5ORkHDhwANevX8emTZuwb98+nu0ickGeX6crL0hMTETdunWh1Wqh0+kQEBCAAgUKIDo6GqGhoWLH82nFixfHggULPNbf82+CnrpMmZiYiPDwcI/0Re5JSkpyezcLAAgLC3NaXCkUCgQHB7vU1/37950W4mazGUlJSS5na9myJcaPHw+1Wm1va9++PZYtW4a0tDQEBAS43CdRXsQzXXnAW2+9hQcPHtjnJ2m1Wty5cwdjx44VOVneYrPZIJFIMvzx1BkqnrkUX1paWpa+f86cORmKGuDpenrvvvuuy4vyNm7c2OmZt4CAALRt29blbJMnT3YorPz9/dG9e3cUKVLE5f6I8ioWXbmczWbD/v37HV6ALRYLdu/eLVKqvMdkMsFkMjm0W61WGAwGERKRJ1gsFixbtgwvvfQS2rVrh+nTp7tdfL322mtYuXIlQkNDoVQqERgYiPfeew9z5sxxua8yZcpg8ODB0Gg09ja1Wo1KlSqhW7duLveX2cKvcrnc524qCQsLEztCjnH9+nUMGjQI4eHhaNSoEX788UexI+V6vLyYB2T2opjVzarpxUVFRTndbkoQBERFRaFVq1YipKKseuONN7Bv3z77BPiPP/4Y33//Pf78808olUqX++vfvz/69euH1NRUaDQayOXuv0QvW7YMzZs3x8qVK6HT6dCnTx8MGzbMra2sZDIZBEFweC3xxdeQfxaalLnr16+jevXq0Gq1sFqtuHHjBnr27ImPP/6Ye99mI57pyuWkUilat26N6tWr47vvvkNcXBw2bdqEypUro3PnzmLHyzPWr1/vtPi1WCxYs2aNCIkoq86dO5eh4AKentFMTEzEjh073O5XIpEgODg4SwXXs366d++OAwcO4MSJExg9enSW9uf0tTNa2e3JkydISkrKtWvyzZ07115wPaPX6zF58mSnZ+XJM1h0+bDbt29j1apV+Oqrr3Dv3j23+3n55Zdx5MgRdOnSBRUqVEDPnj0RHR3NiddeFBQUhJEjR8JgMECn00Gv18NgMGDixIlunREh8Z04ccJpu1arxaFDh7wbhjzm3r17aNu2LUJDQxEeHo5y5crh6NGjYsfyuEOHDmV6x21CQoKX0+QdvLzooz7//HNMmjTJPuF69OjR+PrrrxEZGelSPzabDQMGDMhwyl0mkyEgIADt27fPUsbU1FSHxUPJuXHjxiE8PBz79u1D586dIZVK8cMPP+DOnTuIjY0VO16ecu3aNSxcuBAnTpxAxYoVMXnyZFSvXt3lfooXL+708ppKpeIHmhxKEAS0atUKcXFxMJvNAJ4WIO3atUNsbCxKlSolckLPKVmypNM129LT01G4cGEREuURQg5w+vRph7aLFy96pO+kpCThm2++EdavXy88fvzYI31mVVxcnKBSqQQAGf74+/sLf//9t0t9Wa1WwWq1On3MaDS6le/KlStCvXr1BIVCIcjlcqFx48bC9evX3eorL/n8888FmUwmSCQSAYAgl8uF+fPne6RvTx0PuV1sbKwQGBgoyOVyAYAglUoFtVot/PLLLy73ZTabhWLFiglSqTTDcRoQECAkJSVlQ3rKbsePHxcCAgIcXnuVSqUwadIkseN51L59+wS1Wp3h51SpVMLrr7+e5b75euS8bhEEQcjTlxeXL1+O8PBwjBo1CiNGjEDx4sV94o6+bdu2wWKxOLRLJBLs2rXLpb6kUmmmywm4c9ecTqdDw4YNER0dDbPZDIvFgqioKDRo0IDzAP7DqFGjkJCQgE8++QSLFi1CXFwc3n//fbFj5SkTJkyAVqu1H182mw16vR4jRoxwee6OXC7HkSNHUK1aNahUKqhUKpQqVQr79u3jMgoiysoyLImJiU7nrqWnp+Py5ctZieVz2rZtiyVLliA4OBgBAQHw8/NDx44dsXbtWrGj5Wp59vLipUuXMGHCBIeCpHfv3rh9+zby588vUrKnB7izFw6bzWY/5e0Ki8UCvV6fYQ0gvV7v1p1H27dvd1jp2mazQavVYvfu3ejZs6fLfeYlpUqVypb10UJCQjzeZ2509OhRp8XVrVu3kJqa6vIipBEREYiJicHt27cRGxuLNm3a5LkJ577m6tWrqFChglvfW7NmTaevsWq1Gs2aNctiMt8zbNgwDBw4ENevX0ehQoVEfd/LK/Lsma5NmzY5PbhkMhl++OEHERL9T7du3TK9y8idff9sNhv27dsHg8GAlJQUGAwG7Nmzx62iKyEhwem2HwaDAdevX3e5P/IMnll5MZm9qchkMoeFSV1RokQJhIWFseDyAVk501W2bFl069Ytw3NBoVAgX758GDRokCfi+RylUokKFSqw4PKSPFt0GY1Gp3duWK1W0Vf3rl69Ot5++22o1WpIpVLI5XL4+/vjgw8+cGsi56hRo9C7d28UK1YMTZs2RZEiRTBw4EBMnjzZ5b5q1qzpdMsPlUrl1mRk8gy+2b+Y9957z6G4UqlU6N+/v1vrV1Hus27dOsydOxdly5ZF0aJFMWTIEMTExCAoKEjsaJQLSARXJzKIICYmBjVr1szQdunSJVSqVMntPvfv34/WrVs7tEskEly/ft0n7lKJiYnBd999B4VCgV69eqFy5cou92Gz2eDv74/09HSHx4KDg/HkyROX+rNYLKhWrRquXr1q79PPzw9VqlTBqVOnIJXm2TqecgBBEPDee+9h5cqV8PPzg8lkQseOHbF+/fosrWEFPN0GKDAw0ENJyV1ZfW+grOMYOK9bgDw8pys+Ph5yudxhwrpCocDly5d9ouiqWbOm00FzhSAITiflA3Br4rtcLkdUVBRmzJiBLVu2wGq1YuDAgZg1axYLLso2zz4bSiQSpyujvyiJRILFixdj+vTpuHLlCsLCwjLd4sZVLLiI6L/k2XfJixcvOi1GpFJprrpLRSaToVGjRg5vUlKpFO3atXOrz+DgYHz22WdITk7GkSNHsGjRIqeXHIk8wWq12terA54WTu7cUPJP+fLlQ926dT1WcBERvYg8W3RVr17daaEgl8vx0ksviZAo+3zxxRcIDg6Gv78/gKd34hQoUACffvqpW/3dv38fffv2RUBAAGrWrInBgwfj0aNHHkxM9JTRaHR6RlYmkyElJUWERERE7ssxlxff+OJ4hq9rFZagUiXAkG7FwDUnHf5+95ol0KNWSTzSpWPEhhiHx3vWaIbg4GCYFQEIafcugKefoDUaDVZdUcEamoxWlUORcF+LKTvPO3z/qBbl0KhcQcTeTcGcPRcdHp/YrgJqlsqPmMRH+Gif45mzGZ0qo0qxYBy9+gBLf7/q8Pj8bi+jTKEA7L+YjK+OOK4avOSNaigW4o89Z+9iw4lEh8dX9q2J/Boltp++he9iHqPF3N1ISvobWp0WQYFB2D6qOYoWKoD1x29g77kkh+/f+lZ9AMCXhxNw4NL/tiASBAEnj0fhztatsFgsCG7QCz+bKuDQxA2oXbsWAAnyqZVY1e/pZdGF++LwZ+LjDH0XDVbh015PJ93P3hOLi3dTMzweUUiDD7tVBQC8v/Mcrt3PuOp95WJBmNmpCgBgzJa/kJSS8caHGqXyYVK7igCA4etj8FifcT5bw7IFMbplOQDAgG9OwmjOeENFy0qFMaxJGQDA68uP4O+//0aaNg0BmgAULVoEXWqEoV/90m4/9/rWK4VOrxTD3ScGjN16xuHxoY0jctFz77bD498OqgN/peyFnnvfR1+FRhNgv9NWpZBh7eA6MJvNmLU9GnetGS/p5abn3vOveQDQsWpRPvf+47kHwCPPvX++7gH/e+4BwOcHriIq/kGGx/nc+99zb+K+u1AffpLh8bzw3Pvn697EWs63d8sxRZenKZV+OHnyJN4ePxUnpVJIJBIUDi2MMhFlxI5mZ7FYcPnyZaSlGbM8X0QuV6BkyZL2r0OCQ9zq58GDB0hPT89waVYQBJhMJjx69DhX3XZ87do1nDwZDavVBpvNhgfSB7h58yYalPAHUFrseHmCTqeHRuN4RtpqteLmzUTIi+eus9JElMt5Yzn8rMqubYBsNptw+PBhYfTo0cL48eOFv/76K8t9espvv/0m5M+fXwgMDBQCAgKEokWLCtHR0WLHEqZNm+awRQb+f0ubDz/8UOx4HtWhQweHLV4kEonQsmVLsaPlGVeuXBFatmwpPHz4UHjy5Inw5MkTIS0tTYiMjBT2798vdjzyEUajUVi3bp0wbNgwYdy4cUJycrLYkfI0bgOU+TZAefZMlyAIGDFiBDZs2AC9Xg+JRILly5dj5syZmDRpkqjZ/v77b3Tu3Bl6vd7eptVq0bp1a9y5c0fUSesVK1ZEQECAwwKp/v7+bq8C7at+++03h4UWBUHAoUOHsnQHXXYwm81ZXmdKEARcuHABFosFVatWdWvxXE8rV64cqlatijJlyqBmzZpQKBSIiYlBgwYN0KJFC7HjkQ948uQJ6tati7t370Kr1cLPzw9fffUVDhw4gNq1a4sdjyiDPDuR/sSJE9iwYQN0Oh0EQYDNZoPBYMCsWbNw69YtUbNt3Lgx022Avv/+exES/c/rr7+OoKCgDG/IcrkcBQoUQMeOHUVM5nlKpfNr8r64iGZ8fHyWvv+vv/5CeHg46tevjyZNmqB48eI4fPiwh9JlzSeffIKtW7eiQIEC8PPzw/Lly7Fjxw6fKnpJPPPmzcONGzfsHwRNJhPS0tLQt29fl/fTJMpuebbo2rlzZ4YzSc9IpVL8+OOPIiT6nwcPHjhdFT89PR0PHz4UIdH/qFQqnDhxAm3btoVMJoNMJkOHDh1w/PjxLBUjz14cfelFsn///vDz88vQ5ufnh969e/vMG35KSgrOnDmTpTv59Ho9WrRogcTEROh0Omi1WiQnJ6NDhw64d+/ef3eQzSQSCdq0aYOtW7di165d6NGjh0+chSPfsHXrVqeLP9+8eRN3794VIRFR5vJs0aVSqZy+cEul0iyvTJ1VLVq0gEajcWiXy+Vub7pqMBgQFRWFQ4cO4dixY05fpF5UyZIl8eOPP8JkMuHs2bPYvXu323v/Wa1WTJ8+HUuXLsXRo0fx+eefY+bMmU63aPK2BQsWIF++fBnagoOD8cknn4iU6H9sNhvee+89FClSBE2bNkWzZs0wbNiwTBfC/Te7du1y+n1WqxUbN270RNw8wReesznJrVu3sGjRIsyePRunT592u5/nPxg9IwhCpmeriUTjnSllWZMdE+kvXrwo+Pv7O0wI9/f3Fx4+fJilvrPKarUKbdq0ETQajT2XRqMRIiMj3ervzp07QlJSkpCamioIgiCkpqYKN2/eFO7du5flrFkdhxkzZghqtTrDGKjVamHu3LlZzpZV8+bNc3iO+Pv7CzNmzBA7mvDxxx87/N78/f2FSZMmudzXkiVLBKVS6fQGiQkTJmRD+tzp9u3bYkfIMTZv3iz4+/sLSqVSkEqlglqtFoYNGybYbDaX+/roo48cjlOZTCY0aNAgG5LTi+BE+swn0ufZoksQBGHZsmWCSqUSNBqNEBAQIKjVamHXrl1Z7tcTzGazsHr1aqFJkyZCy5Ythc2bNwtWq9Wtvk6ePCmYzeYMbSaTSThy5EiWc2Z1HEJCQpy+2RcsWDDL2bKqYMGCTrOFhISIHU0oUqSI02wBAQEuv3HFxMQ4FHDP+vr555+z6SfIffhG82KePHni9AOvRqNx647U9PR04dVXXxXUarWgVqsFjUYjhIWFCYmJidmQnl4EjwXevejUyJEj8frrr+Pnn3+GQqFAx44dERISInYsAE8vJQ4ePBjt27eHTCZD4cKF3erHZrOhevXqkMszDrVSqUT16tU9EdVtgiBkOhfp8ePHTtu96VkGPz8/FClSBH///TdMJhNSUlJEv3sxs9+PTqeD1Wp1GO9/U6NGDbz66qv46aefoFarIZPJkJqaipo1a6JNmzaeikwEAPj111+dPj91Oh02btyIli1butSfQqHA3r178ddff+HUqVMQBAFvvvkm5/2RT8rTRRcAFClSBIMGDfJIXxaLBT/99BOuXr2KqlWromXLlm5vAn327FlERkba70p7+eWXsXnzZpQtW9blvoRMJqdn1u4tEokEQUFBTguv4OBgERJlVKNGDXTo0AHjx48H8DTvp59+iu3bt7tVcNlsNofng7O2F1GzZk0cO3bMob1ixYouFVzPbN68GY8fP0ZQUBAEQYBOp0NgYKDPbGKenJyMXbt2IT09HZ06dULp0qXFjkRukkqlTu/OfvaYu6pXr47q1avj0qVLLLjId3ntXFsWZNflRU+6e/euEB4eLgQGBgpKpVIICAgQqlWrJqSkpLjc1+PHj4Xg4OAMp96lUqkQGhoqGI1Gl/s7fvy4YDKZMrQZjUbh8OHDLvf1vKyMg81mE2QymdPLZEqlMsvZsuratWuCVqvN0KbVaoWEhASX+9Lr9YLBYHDanpaW5nJ/J0+eFNRqtX3xVolEIqjVauHAgQMu95VdzGazkJSU5PDcc9WmTZsEf39/Qa1WCyqVSlCpVMKCBQs8lNJzfO01yVc9efJEkEgkTo/77du3Z7l/joP4OAaZX170jY+xucCwYcNw+/Zt1KtXD2+++SaqVauGS5cu4f3333e5r82bN8NsNmdos9ls0Ov12LNnj8v9PXz4EHfu3EFqairS09ORmpqKa9euiX6mC8j8k60vLMkQHh7ucBepRqNBRESEy33t2rULEyZMgMFggF6vh8FggMFgwMyZM7FlyxaX+6tduzZOnjyJmTNnYtasWZgwYQKOHDniMwuGfvbZZyhUqBDCw8ORP39+TJs2LdOzG//mwYMHGDx4sP33ZjQaYTQaMXv2bJw/77g/G/m+M2fOOL3jUCaT4ezZsyIkIvKePH950RPMZjNiYmJw7tw5FCtWDHK5HFarFbGxsejevTuWL1/uUn/Xr193uoaYyWRya+HWyZMnIy4uDq+++ioqVKiACxcuYN++fWjQoAGOHDnicn+eIpFI0LFjR+zZsyfDkgVyuRxdunQRLVd2uHnzJlatWoXvv/8eXbt2hVQqxe7du5GYmIjp06e71WeVKlVQpcrTDXCtVqvPXFJZt24dpkyZkuE5vGTJEvj5+bn8s/7www9Of6709HRs3rwZL7/8cpbz+hpBEJCamgqNRuPWpWJfd+vWLac/l9VqzfIiv0S+Lvcd0S66f/8+9u3bB4VCgQ4dOiAoKMjlPgRBwIoVKxAREZFhXZhq1aph6tSpLvdXv359p1vtKBQKl7e1sFgsiI2NhSAI2L17d4bHTp503Cne25YvX46YmBg8fvwYaWlpCAgIQMGCBfHZZ5+JHc2j6tSpA5VKhTt37mDZsmX29oCAANSpUyfL/ftKwQUAc+bMcfjQoNfrsWjRIkydOtWleTtWq9XpGVlBENxak+yZmJgYnDlzBmXKlEHTpk194swqABw9ehTly5dHcHAwDAYDTp8+jaZNm/rM3DpPqFWrltOx02g0aNq0qQiJiLzIaxc4syC75nStWrVKUKlUQkBAgBAYGCio1Wph7969bvX1/JIMz7gzX8dsNgsvv/yyoFAoMsxxatKkicvLAdhstgzrff3zT2hoqMvZnueJcTCZTMLWrVuFWbNmCdu2bcvyHCBfZLPZhIYNGwoqlcr++1epVEKNGjUEi8UidjyPcrYcAP5/7SSdTudSX3fu3MnwO3v2R61WCydPnnQ5m8FgEFq2bGlfWiAgIECoVKmST6xZd+LECadzCA8ePJilfn1Rz549MyxTolQqhdKlSzv8/O7gfCLxcQw4p8vB5cuXMXbsWBiNRmi1WqSlpUGv16Nnz55uLVeQ2ZkGtVrtVl9FixbN0CYIAsLCwlz+RC6RSDBy5Ej4+/s75BozZozL2bKDUqlEz549MXPmTPTo0SNXriItkUiwf/9+TJkyBeHh4ShVqhTGjx+Pw4cP+9RZKk/I7JJf0aJFHZ6H/6VYsWJYtGgR/P39oVAoIJVKoVarMXz4cLc2M54zZw6ioqKg1+vtWx7Fx8djyJAhLvf1vIoVK2bp+4OCgpzOIaxVq5bDHM+cbtOmTZg/fz7Kly+PEiVKYPjw4Th9+rTTnTiIchXv1n7uyY4zXdOnTxfkcrnTBSG//fZbt/p8/iyUO6srC4Ig7N+/3+nZKbVaLZw9e9bl/tLT04WBAwcKKpVKCAoKElQqlfDOO++4vdjqP/ETDT3vjz/+cDjbpVarha1bt7rd55UrV4QPPvhAmDZtmvDnn3+63U9oaKjTs3AKhcLp3aXe9OjRI6ftBoNBuH//vkf+DXdfk3ISviaJj2PAM10ODAaD073SrFYrDAaDy/2dO3cOjx49ss/D0mq1uHv3Lq5du+ZyX/v374dOp3Oa7ffff3e5P4VCgTVr1uDWrVs4cOAA7t69i6VLl/rUPJHY2Fjs2LEDly5dEjtKtjIYDPj555/x448/Or1Zwl3u3BmYXZo0aYL9+/ejadOmKFCgAGrVqoUdO3agZ8+ebvdZrlw5TJs2DR988EGWFvU1mUxO24UszhHzhHPnzjlt12q1bo2vs+8zmUzcI5JIRL7zrutlbdq0cTpB12g0om3bti73N2DAAERERGDixIlYtmwZRo0ahfLly7t1Ca9AgQJOb6lWKBTInz+/y/09U7BgQdSqVcthE2cx6fV6tGrVCrVr18bgwYNRs2ZNtG3b1q3C19f98ssvCA0NRa9evdCnTx8ULlzY4eYGdyUnJ3ukH09p0KABDh06hAcPHuDUqVNo166d2JEAAJ07d3Z659wrr7yCgIAAERL9z+LFix0+bOl0OkybNg2BgYEu9XXz5k00adIEN27cgFarRUpKCh4/foz+/ftj7969noxNRC7Is0XX9evXnb74yuVyJCQkuNSX0WjE+fPnkZqaipUrV2LUqFH49ttvodfrceDAAZezRUZGOp3nI5VK0bVrV5f782Xjx49HVFQUDAYDUlNTYTAYcPjwYUyePFnsaB5dyf/hw4fo1q0bjEYjatSoYZ+n07t3b9y9ezerUfHkyZMs95EXLFiwAKGhofa5Q/7+/ggODsaaNWtETga0bNkSHTt2xNGjR5GSkoLY2FgMGTIEKSkpLs+FO3jwIK5cuYIyZcqgQYMGaNOmDQoXLozt27fjhx9+yKafgIj+S54tui5cuOD0coJMJnP5EpdcLs90PR13Pj0XLVoUO3fuREhICIKCghAYGIhChQph3759Ln/izW5FihTJ0vevXbsWRqMxQ5vRaPSJN8H4+HiHAksQBNy4ccPlvnbs2IEmTZrYt7PZuXMn7t27h1atWmHr1q0eSkz/pUiRIoiLi8Mnn3yCgQMHYvbs2YiPj/eJ9b7eeecdVKxYEa1atUKpUqVQu3ZtPHz4EF9++aXLfYWEhNinD5w/fx4nT56ExWKBXC5HgQIFPB2diF5Qni26qlWr5vROGblcbl9w8kXJ5XL06dPH4ZKgv78/3n77bbfyVa9eHa+++irMZjNsNhu6deuGypUru9VXdsrKBuGCIDgUXM/4wuXFw4cPO20/evSoy2e7TCYTtm/fjnz58iE4ONj+Z/PmzZxj42UBAQF46623sGbNGkyYMAEFCxb0SL9ZnVcnlUqxcuVK3LhxAzt27MD58+fx22+/ufVBq127dk4/CCoUCgwePDhLOYnIfXm26HrjjTcQFBSU4TKeUqlEREQEmjVr5nJ/n3/+ORo1amS/XKFSqdCpUydMmTLF5b5MJhPq1auHbdu2wWAwQKfTYc2aNWjatKnbL+yPHz/GkiVLMGjQICxfvhypqalu9fO8rCwqKZFI0LhxY4c+JBKJW2Pgab1793aarUePHi7/3JldFpZKpejRo4fbGcl3XL582SP9FClSBC1btkSZMmXc7sPPzw+//vorChcujKCgIAQFBUGtVuPLL7/M8tIWROS+PLsivUajwb59+9C5c2f75aLy5cvj559/duuuvoCAAOzfvx/x8fFITk5GyZIlERYW5la2HTt24P79+xnW5klPT0dCQgIOHDiA1q1bu9TflStXUL9+fRiNRuj1emzbtg1z5szBqVOn3M7oKcuXL0eDBg1gMplgMpmgUqng5+eHpUuXipoLQKbzaJzd5PBfSpQo4fRytp+fn+hjQLlTrVq1cPfuXZw5cwZmsxkvv/wy18EiElmeLbqMRiO6dOmCO3fu2Nvi4+PRvXt3REVFuX0Gp2zZsihbtmyWsp05c8ZhCyDgaeF1/vx5l4uut956C48fP7ZfEnu2cfDYsWOxY8eOLGXNqipVqiAuLg4rV67EX3/9hZo1a2LEiBEIDQ0VNVd2cPacEgTBp5buyAvMZjOkUilMJhOUSiVsNluuXJAXeDpHtWbNmh7py2QyYceOHTh27BjKli2Lfv36cX4YkYvybNH13XffOZxNMhqNOHfuHKKiotCoUSPRslWsWBEajcbh9nE/Pz+UK1fOpb6sVisOHz7sMAfJZrPh559/znJWTyhatCjmzJkjdgwHVqs10415Xd2IODU1Fbt370aXLl3sc3S0Wi1+/fVX+3pWuc2TJ0+QkJCAsLAwFCpUSOw4AIC0tDTIZDKo1Wr7bhEGgwGPHz/2qaVUfM3jx49Rr1493L17F1qtFv7+/pg5cyYOHz6MV155Rex4RDlGnv2IferUKadnk6xWK86cOeP9QP/wxhtvQKPRZDgDIpfLUbBgQbRv396lviQSSaYFgkKhyFJOALl6EviKFSucbty8YsUKl/vasWMH3n77bfTt2xe7du3Cnj17MHDgQPTr1w+bN2/2VGSfYLPZ8O6776Jw4cJo0KABihUrht69eyM9PV3saDCZTA5bc/n7+7u1XVdeMnv2bPuaXwDsS7z069dP5GREOUueLbrKly/v9IVWoVAgIiJChET/o9FocPz4cTRr1gwymQxyuRzt27dHVFSUy2dYpFIpunfvDqVSiaCgIFSuXBkajQZ+fn4eecFMSkrKch+epNfrMWvWLERERCA8PByzZs1ye+X3CRMm4N1330VycjIsFgvu3buH8ePHY9y4cS7fvXj//n2YTCb88MMP6Nq1K1577TXs2LEDer0e9+7dcyvfP/nSJcp58+Zh6dKlMJvNSE9Ph8ViwZYtWzB8+HCxo2V6GVEqlTrdBYKe2rZtm9Oi+fLly7h//74IiYhyKC9sQZRl2bH34pMnT4R8+fIJEonEvv+aXC4XwsPDBYvFkqW+n/HEPmcmk0kwm81Z6uPRo0fCli1bBIPBIKSmpgp6vV5Yv369oNVqs5zv77//znIfnmK1WoW6desKKpXKPqYqlUqoU6eOW/tMqtXqDP08+3+lUuny2EZHRzvdTzMgIED4/fffXc72PE/so+kp//y9/fOPVCr12LHlrl9++cXp7+rq1auCXq/PUt+5eb+5sLAwp2OqVCoz3TNSLLl5HHIKjgH3XnQQHByMqKgo1K1bF3K5HAqFAi1btsTRo0edrgb/X5KTk2E0Gu1LOjzbwzElJSVLOZVKpctnt56XL18+9OzZEyqVCoGBgfD390dkZKRH7mTylbk6APDbb78hNjY2w9pfRqMRFy9exG+//eZyf7169bKfGXnWp1KpRPfu3V2+0aJ27dp49dVXMXnyZMTGxuLixYuYMWMGmjdv7pHlMXzpTFdmZxZtNhvS0tK8nCajZzepPDtrY7FYoNPpsHr1apdXfc9LhgwZ4vD7kclkqFu3LufCEbkgz06kB4BKlSrh+PHj0Gq1kMlkWXrR/fzzzzFt2jT7m/Gz/y5btgxTp071SN6scLbelCf40pv9qVOnnF4i0ul0OHXqlMt7ai5evBgxMTFISEiwr+ZdunRpt5azkEgk2LJlS4ZJ+NOmTYNMJvPYWPgKuVye6ebRYi9Z8M4776Bfv35o06YNateujatXr2Lt2rVYvXq1qLl83aRJk/DHH38gOjoaVqsVCoUC+fLlw8aNG8WORpSj5Omi65msbnSbnp6OwYMHZyjapFIp1Go1mjdvntV49IJKliwJtVrtUHip1WqULFnS5f6Cg4Px119/4dChQ7h48SIqVqyI5s2bu11oPn9TgyduZPBFXbt2xfbt2x3a69SpI/rPrFar8d133yEqKgrHjx9HmTJlsGfPHrfObuclfn5+2L9/P06ePInTp0+jVKlSma56T0SZkwiCG7v3/guz2YwpU6bgzp07SE9Px4gRI1C2bFlMnjwZEokE5cqVw8yZMyGVSrFt2zZs2bIFcrkcI0aMyLRAiYmJcVhr5tKlS6hUqZIno7stPT0dUqnU6QuQXq8X/c4onU7n9AxDWlqaz+3lmBVarRb58uVzOMsil8vx+PHjLBfX9GJSU1NRt25dxMfH288QFipUCDExMShatKjY8bKNL70m5WUcB/FxDJzXLUA23L34ww8/ICQkBJs2bcJXX32FDz74AB9++CHGjBmDTZs2QRAEHDhwAPfv38f69euxZcsWrF69GosXL/aJW8rdoVQqM91D8NGjR15O42j06NHQ6XQZ5pvpdDq89957IifzrNOnTzu9O02hUODUqVNu92u1WvHgwYNML5n5Ag9/dsqSoKAgxMbG4qeffsKiRYvw3Xff4ebNm7m64CIizxMEAQ8fPoTJZBI7isd4vOhq164d3n33XfvXMpkMsbGxqFOnDgCgSZMmOHbsGM6dO4fq1atDqVQiMDAQYWFhiIuL83QcrzAYDFi2bJnDZS2tVov58+e71aezPRbd2XfRYrFgzZo1aNasGfbu3Yv4+Hh8//33aNCgAdauXetWtqxmyi5RUVFOi1+TyYSoqCiX+xMEAYsXL0bBggVRokQJFChQAAsXLvSpAucZZ2vOiUkqlaJ169Z477330LlzZ16GIiKX7NmzB2FhYShWrBhCQkIwfPjwXFF8efyV8NllLK1Wi9GjR2PMmDFYuHChfbKwRqNBWloatFpthktbGo3mX984Ll26lOFro9Ho0CYWo9GIadOmITAwEIMHD4bZbIZMJsOiRYuwcuVKjBo1yqX+tFotlEolypcvb58nptPpEBMTg5CQEJfmxQiCAH9/f5w+fRqdO3fO8FjBggXd/h0qFAoEBgbCbDbDaDRmWNlfLIIgwM/PDwaDIUO7n58fBEFw+WfdunUrPvroI3t/JpMJs2fPRkpKiltrnCmVSpQoUcI+fmazGXfu3PHIC4nRaIRKpXL7+5/Nh3s2X81msyE9PR3Xr1/PcrasKlOmDORyeYZsNpsNCQkJPlX0+9JrUl7GcRBfVsfgzJkzGDx4cIYP0d9++y1u376Njz/+2BMRxZMd61PcvXtX6Nq1q7B9+3ZBEAShcePG9sd+++03Yfbs2cL+/fuFmTNn2tvffvtt4dy5c077y451ujzJZDLZ160JDAwUKlasKPj7+9vXJnLVF198IRQuXFiYNWuWEB8fL8TFxQkTJkwQChQoIOzcudPl/iZPnuywdpJarRY++ugjl/t6nifGwWazCdHR0cLatWuFU6dOub2+WUpKihAcHOywllBwcLCQkpLicn/Fixd3ujZR4cKF3cqXnXzpePA0Z+tn6fV6n1qbTBBy9xjkJBwH8WV1DDp27Oj0tVelUgn379/3UMrs5bV1uh48eIDBgwdjwoQJ6N69OwCgcuXKiI6OBgAcPnwYtWrVQtWqVRETEwOTyYS0tDQkJCSgfPnyno7jFRKJxH4mLy0tDXFxcfazI+5cVomPj8e9e/cwa9YslC1bFhUrVsTHH3+MtLQ03Lhxw+X+PvjgA/Tp0wcqlQpBQUFQqVR46623fGJOV2pqKho0aIAWLVrg7bffRrNmzdC4cWO3LpcFBQXht99+Q6FChSCTySCTyVCoUCH89ttvCAoKcrm/v//+22n7/fv3ffISY26U2Y4H/v7+SExM9HIaIvKGq1evOm1XKpW4c+eOl9N4lseLrlWrViE1NRUrVqxAv3790K9fP4wZMwZLly7FG2+8AbPZjLZt26JQoULo168f+vTpgwEDBmDs2LHw8/PzdByvsNlsma615M4t8rVq1XJ6p51SqUS1atVc7k8ul+Orr77CnTt38Mcff+Dvv//G4sWLfWKNrbFjx+Kvv/6CTqez/zl9+jQmTpzocl+CIGDJkiXQ6XSwWq32GwYWL17sVpFUoUIFp+1lypTJdWtr+arjx4873d8zLS0NR48eFSEREWW3unXrOl3GxWw2i75NX5Z583Sbu3z98qIgCEKjRo0ybCmE/99WqE+fPi73ZTKZhPLlywtKpTLDadW6det6ZGshT8rKONhstgzb6/zzj0ajcbm/6Ohop1vQqNVqITo62uX+fv75Z/tl4md//P39hd27d7vcV3bztePBU7Zs2SJERUVluMRoNBqF+Ph4YcGCBSImc5RbxyCn4TiIL6tjcOXKFSEwMDDDe6pGoxGmT5/uoYTZj9sAZbOvvvoKISEh9jW5NBoNChcujEWLFrncl1KpxIkTJzBs2DAUKlQIRYoUwejRo3HgwIFcd4Ylswn47iwfsn//fqeT0k0mk1vbALVr1w579uxB3bp1ERwcjFq1amHXrl147bXXXO6L3NOpUye8/vrr+Oyzz/Dw4UOkpKRg7dq1aNasGSIjI8WOR0TZoFy5cjh+/Dg6dOiAkJAQlCtXDp999hlmz54tdrQs433cHlKxYkVERUVh7NixuHz5MmrUqIElS5a4vTZRvnz5sHTpUre2nMkpJBIJmjVrht9//z3D5T+pVIpWrVq53F++fPng5+fnsPefn5+f2/vDtWzZEi1btnTreynr1Go1Dh48iCJFikAqlUIQBERGRqJ27dooUaKE2PGIKJtUqVIFe/fuFTuGx7Ho8pCzZ8+icePGSE9Ph8lkQnJyMg4fPoxTp06hdOnSYsfzWStXrkTdunVRuHBhlCpVComJiXjw4AGWLVvmcl89e/bEhAkTHNolEgl69uzpibg+K8fPc/gXFStWdGirXr26CEmIiLKGRZeHDB06FGlpafavDQYDTCYTxo8fj++++07EZL6tXLlySEpKglQqRXp6Ovz8/GCz2ZyuLP9fChQogN27d6N79+729ZukUim+++47FCxY0NPRfUpOvQmFiCgvYdHlAUajEX/++adDu81mw759+0RIlLM8Kxg8sRlyy5Ytce/ePRw7dgyCIKBBgwZuFXBERESexqLLA+RyOWQymdNb25+tKE/eo1Ao0LRpU7FjEBERZcC7Fz1ALpeje/fuDmdUVCoVhg4dKlIqIiIi8iU80+UhK1asQEJCAi5cuACpVAqLxYJmzZph5syZYkejXMhms8FsNmeYy2U0GiGXy7m5tJdduXIFmzZtwp07d/Dmm2+ibt26YkciIh+V51+dBUFAXFwcFApFllYaDw4OxvHjx/Hnn3/i6tWrePnll1GlShUPpyV6auHChUhISMDs2bNRtGhRPHjwAPPmzYNCoXBrbThyz6pVqzBu3DiYzWZYrVZs2rQJAwYMwPLly3PdmnpElHUSQfD9TeRiYmJQs2bNDG2XLl1CpUqVstTv0aNH0bt3bzx+/BiCICAsLAw7duxA5cqVs9RvXuKJcSDXFSlSBMnJyQCeXt62WCwAni7Km5aWxjd8L0hOTkbp0qVhNBoztKvVavz6669o2LChSMnytqSkJLfXRyTP4PuC87oFyMNzupKTk9GuXTvcvn0bOp0Oer0ely9fRrNmzZyuak7kS548eWL//2cFFwDo9XqnN3SQ5/38889OL+UaDAZs27ZNhEQEAKGhoWJHIMpUni261q9f7/DmJAgCjEZjrlwFl3KXzOYNValShXO6vCSz37NEIuEyJSKSSvPs2xrlAHn22Xn79m2HywLA070Ak5KSREhE9OKWLFmCgIAAyGQyAE/faNRqNZYvXy5ysrzj1VdfdXpW0c/PD3369BEhERH5ujxbdDVr1gwBAQEO7VKpFI0aNRIhEXnKnTt3sHLlSqxYsQJ37twRO062qFGjBmJiYtC/f3+8/PLLaN++PU6cOIEmTZqIHS3PyJcvHzZu3Ah/f39oNBr4+flBpVJh+vTp3KaIiJzKs9chOnbsiIoVKyI2NhYGgwHA0wmw7dq1Q7Vq1cQNR2774osvMGbMGPtE8vfeew+ffvop3nrrLZGTeV758uXxzTffAODEVbF07doVt27dwq5du3Djxg0MGTKEe60SUabybNEll8tx+PBhLF26FOvXr4dCocBbb72FN998U+xo5KbExESMGTPG4bLxmDFj0LZtW74ZUrYoUKAAhgwZgkuXLvE55gOsVqv9sjuRr8mzlxeBp1v0TJw4EefPn8eff/6Jt956iwdrDrZjxw44WwFFEATs3LlThERE5G1///232BGIMpWniy7KXaxWK2w2m0O7zWbLsKwCEeVeqampYkcgyhSLLso1Onfu7PQ2frlcjs6dO4uQiIiI6H9YdFGuUb58eUydOhX+/v6QyWSQyWTw9/fHlClTUKFCBbHjERFRHpdnJ9JT7jR16lR06dLFviJ4jx498NJLL4mcioiIiEUX5UJVqlTB7NmzxY5BRESUAS8vEhEREXkBiy4iIiIiL2DRRUREROQFLLqIiIiIvIAT6YmIiP7h4sWL+Pbbb5GSkoIuXbqgbdu2kEp5joKyjkUXERHR/1u9ejVGjRoFs9kMi8WCTZs2oXnz5ti1axcLL8oyPoOIiIgAPHnyBKNGjYLBYLBvHabVavH777/jhx9+EDkd5QYsuoiIiAD8/vvvUCgUDu06nQ5btmwRIRHlNiy6iIiIAKhUKqftEokEGo3Gy2koN2LRRUREBKBly5aQSCQO7f7+/hg8eLAIiSi3YdFFREQEwM/PD3v37kVQUBACAwOh0WigUqkwadIkNGzYUOx4lAvw7kUiIqL/16hRIyQlJeGnn35CWloaWrdujRIlSogdi3IJFl1ERET/oFar0b17d7FjUC7Ey4tEREREXsCii4iIiMgLWHQREREReQGLLiIiIiIvYNFFRERE5AUsuoiIiIi8gEUXERERkRew6KJcRRCEF2ojIiLyNhZdlGuYzWYYjUaHdqPRCLPZLEIiIiKi/2HRRbnG999/j1WrVkGn08FqtcJqtUKn02HVqlX4/vvvxY5HRER5HLcBolzjypUrmDFjBjZv3ow+ffoAADZv3oxTp05h9uzZIqcjIqK8jkUX5RpVqlRBQEAATp06hVOnTtnbAwMDUaVKFRGTERER8fIi5SIdO3ZEaGgoFAqFvU2hUCA0NBSdOnUSMRkRERGLLspFFAoFjh8/jl69ekGj0UCtVqNXr144fvx4hkKMiIhIDLy8SLlKwYIFsW7dOqxbt07sKERERBnwTBcRERGRF7DoIiIiIvICFl1EREREXsCii4iIiMgLWHQREREReQGLLiIiIiIvYNFFRERE5AUsuoiIiIi8gEUXERERkRew6CIiIiLyAokgCILYIf5LTEyM2BGIiIiIXljNmjUd2nJE0UVERESU0/HyIhEREZEXsOgiIiIi8gK52AFeVJcuXRAYGAgAKFGiBIYPH47JkydDIpGgXLlymDlzJqRS1pDZ7flx6Nu3L4YPH47SpUsDAHr37o0OHTqImDD3++KLL/D777/DbDajd+/eqFOnDo8FETw/DpUrV+ax4GU7d+7E999/DwAwmUy4dOkSNm3ahPnz5/N48BJnY7BlyxYeC5nIEXO6TCYT3njjDezatcveNnz4cAwaNAh169bFjBkz0LhxY7Ru3Vq8kHmAs3HYvn070tLSMHjwYPGC5SHR0dFYs2YNVqxYAYPBgG+++QaxsbE8FrzM2TgUKVKEx4KIZs+ejYoVK+LgwYM8HkTybAykUimPhUzkiPI/Li4OBoMBgwcPRv/+/XHmzBnExsaiTp06AIAmTZrg2LFjIqfM/ZyNw4ULF3Do0CFERkZiypQp0Gq1YsfM1Y4ePYry5ctj5MiRGD58OJo1a8ZjQQTOxoHHgnjOnz+P+Ph4vPHGGzweRPLPMeCxkLkccXlRpVJhyJAh6NGjB27cuIGhQ4dCEARIJBIAgEajQVpamsgpcz9n4zBs2DD06NEDL730ElauXInly5dj0qRJYkfNtR4/foy7d+9i1apVuH37NkaMGMFjQQTOxoHHgni++OILjBw5EgB4PIjkn2NQtWpVHguZyBFnusLDw/Haa69BIpEgPDwcISEhePjwof1xnU6HoKAgERPmDc7GoXHjxnjppZcAAK1bt8bFixdFTpm7hYSEoFGjRlAqlYiIiICfn1+GNxUeC97hbByaNWvGY0EEqampuHbtGurVqwcAGeZv8XjwjufHoHXr1jwWMpEjiq7vvvsOCxYsAAAkJydDq9WiYcOGiI6OBgAcPnwYtWrVEjNinuBsHEaOHIlz584BAI4fP44qVaqIGTHXq1mzJo4cOQJBEJCcnAyDwYD69evzWPAyZ+MwbNgwHgsiOHXqFBo0aGD/unLlyjwevOz5MRgyZAiPhUzkiIn06enpeP/993H37l1IJBKMHz8e+fLlw/Tp02E2mxEREYG5c+dCJpOJHTVXczYOfn5++OCDD6BQKFCwYEF88MEHCAgIEDtqrvbRRx8hOjoagiBg7NixKFGiBI8FETw/Dvnz5+exIIKvv/4acrkcAwcOBABcv36dx4OXPT8GsbGxPBYykSOKLiIiIqKcLkdcXiQiIiLK6Vh0EREREXkBiy4iIiIiL2DRRUREROQFLLqIiIiIvIBFFxHlel9++SUaNWoEk8kkdhQiysNYdBFRrrdnzx506NABP/74o9hRiCgPyxF7LxIRuSs6OhphYWHo1asXJkyYgG7duuHcuXOYPXs2NBoNChQoAD8/PyxYsADr16/H3r17IZFI0KFDB/Tv31/s+ESUi/BMFxHlatu3b0ePHj0QEREBpVKJs2fPYubMmViwYAHWrVuHsLAwAEB8fDx++uknbNq0CZs2bcL+/ftx7do1kdMTUW7CM11ElGulpKTg8OHDePToEdavXw+tVosNGzbg3r17KFeuHICn+yj+9NNPuHLlCu7evWvfyiQlJQU3b95ERESEiD8BEeUmLLqIKNf64Ycf8Prrr2PSpEkAAIPBgJYtW0KlUiE+Ph5ly5bF2bNnAQAREREoW7Ysvv76a0gkEnz77bcoX768mPGJKJdh0UVEudb27dvx0Ucf2b/29/dHmzZtULBgQUyZMgVqtRoKhQKhoaGoWLEi6tevj969eyM9PR1Vq1ZFaGioiOmJKLfhhtdElOds3LgR7du3R/78+bFkyRIoFAq88847YsciolyOZ7qIKM8pUKAABg8eDLVajcDAQCxYsEDsSESUB/BMFxEREZEXcMkIIiIiIi9g0UVERETkBSy6iIiIiLyARRcRERGRF7DoIiIiIvICFl1EREREXvB/JJT5qxbaOssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reproduce the same figure as above with the \"seaborn-whitegrid\" style\n",
    "\n",
    "# Create the plot\n",
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# Plot the data\n",
    "scatter = ax.scatter(over_50[\"age\"],over_50[\"chol\"],c=over_50[\"target\"])\n",
    "\n",
    "# Customize the plot\n",
    "ax.legend(*scatter.legend_elements(),title=\"Target\")\n",
    "ax.set(title=\"Heart Disease and Cholestrol Level\",xlabel=\"Age\",ylabel=\"Cholestrol\")\n",
    "\n",
    "# Add a meanline\n",
    "ax.axhline(over_50[\"chol\"].mean(),ls=\"--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful, you've changed the style of the plots and the figure is looking different but the dots aren't a very good colour.\n",
    "\n",
    "Let's change the `cmap` parameter of `scatter()` as well as the `color` parameter of `axhline()` to fix it.\n",
    "\n",
    "Completing this step correctly should result in a figure which looks like this:\n",
    "<img src=\"images/matplotlib-heart-disease-chol-age-plot-cmap-change.png\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replot the same figure as above except change the \"cmap\" parameter\n",
    "# of scatter() to \"winter\"\n",
    "# Also change the \"color\" parameter of axhline() to \"red\"\n",
    "\n",
    "# Create the plot\n",
    "fig,ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "# Plot the data\n",
    "scatter = ax.scatter(over_50[\"age\"],over_50[\"chol\"],c=over_50[\"target\"],cmap=\"winter\")\n",
    "\n",
    "# Customize the plot\n",
    "ax.legend(*scatter.legend_elements(),title=\"Target\")\n",
    "ax.set(title=\"Heart Disease and Cholestrol Level\",xlabel=\"Age\",ylabel=\"Cholestrol\")\n",
    "\n",
    "# Add a meanline\n",
    "ax.axhline(over_50[\"chol\"].mean(),ls=\"--\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beautiful! Now our figure has an upgraded color scheme let's save it to file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the current figure using savefig(), the file name can be anything you want\n",
    "fig.savefig(\"heart-disease-ex2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reset the figure by calling plt.subplots()\n",
    "fig,ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extensions\n",
    "\n",
    "For more exercises, check out the [Matplotlib tutorials page](https://matplotlib.org/3.1.1/tutorials/index.html). A good practice would be to read through it and for the parts you find interesting, add them into the end of this notebook.\n",
    "\n",
    "The next place you could go is the [Stack Overflow page for the top questions and answers for Matplotlib](https://stackoverflow.com/questions/tagged/matplotlib?sort=MostVotes&edited=true). Often, you'll find some of the most common and useful Matplotlib functions here. Don't forget to play around with the Stack Overflow filters! You'll likely find something helpful here.\n",
    "\n",
    "Finally, as always, remember, the best way to learn something new is to try it. And try it relentlessly. Always be asking yourself, \"is there a better way this data could be visualized so it's easier to understand?\""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}