week1_session3_matplotlib_seaborn.ipynb

{
  "cells": [
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        }
      },
      "cell_type": "markdown",
      "source": "# AI in Medicine <br> Data visualization with *matplotlib*/*Seaborn*"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        }
      },
      "cell_type": "markdown",
      "source": "***Tutor:*** Moritz Seiler <br>\n***Institution:*** Charité - Universitätsmedizin Berlin <br>\n***Email:*** moritz.seiler@charite.de <br>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This notebook introduces data visualization in Python with *matplotlib* and *Seaborn*. The notebook consists of two  parts; while the first part introduces data visualization using the *matplotlib* library, the second part covers the *Seaborn* library.\n"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**First, please make sure, that you're using the Python 3/3.6 kernel!** You can check that in the top right corner of your browser window. If you're using a different Version, go to the Tab 'Kernel' --> 'Change Kernel' and select the correct kernel."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        }
      },
      "cell_type": "markdown",
      "source": "*Data visualization*\n\n*Data set*\n\n*matplotlib*\n- Basics in *matplotlib*\n- Generating a figure\n- Examples of different visualization methods\n- Save plot\n- Exercises\n\n*Seaborn*\n- Basics in *Seaborn*\n- Examples of different visualization methods\n- Exercises"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Data visualization"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Definition:** Data visualization is the graphical presentation of data. \n<br><br>\n<center><i>“Effective analytic designs entail turning thinking principles into seeing principles.”</center>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; — Edward Tufte"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<img src=\"images/ironmandata.jpeg\" witdth=\"50%\">\n\n- [Source](https://o.aolcdn.com/images/dims?quality=85&image_uri=http%3A%2F%2Fo.aolcdn.com%2Fhss%2Fstorage%2Fmidas%2Faf3975a8d226950975d6860ad921352d%2F204458096%2Frobert-downey-jr-iron-man-2016-10-14-01.jpg&client=amp-blogside-v2&signature=658a7b314e3b2b49bb9309b4f204dab95767a3f2*)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Principles of Graphical Excellence**\n- Graphical excellence is the well-designed presentation of interesting data - a matter of substance, of statistics and of design.\n- Graphical excellence consists of complex ideas communicated with clarity, precision and efficiency.\n- Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.\n- Graphical excellence is nearly always multivariate.\n- And graphical excellence requires telling the truth about the data."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Examples"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<img src=\"https://pbs.twimg.com/media/B6AlwGNCAAA6Hwa.png\" width=\"90%\">\n\n*Source: https://pbs.twimg.com/media/B6AlwGNCAAA6Hwa.png*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<img src=\"http://livingqlikview.com/wp-content/uploads/2017/04/Worst-Data-Visualizations-09.jpg\">\n\n*Source: http://livingqlikview.com/wp-content/uploads/2017/04/Worst-Data-Visualizations-09.jpg*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Scales**\n\n*Categorical*<br>\n- *nominal:* represent discrete units and used to label variables, that have no quantitative value.\n- *ordinal:* nominal scale but ordering matters.\n\n*Numerical*<br>\n- *interval:* ordered units that have the same difference\n- *ratio:* interval scale with absolut zero"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**Plotting methods**\n\n*univariate:*\n- Bar Plot\n- Histogram\n- Box Plot\n- Violin Plot\n- Pie Chart\n- ...\n\n*multivariate:*\n- Scatter Plot\n- Scatter Plot Matrix\n- Heatmaps\n- Mosaic Plot"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Data set"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The data we are using in this tutorial is the Alzheimer's disease data set that you have already prepared in the previous tutorial."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Import the Pandas and NumPy library\nimport pandas as pd\nimport numpy as np\n\n# Read the Alzheimer's disease data set \nad = pd.read_csv('data/alzheimers_disease_reduced.csv')\n# Select cross-sectional baseline data\ndata = ad[ad['VISCODE']=='bl']",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        }
      },
      "cell_type": "markdown",
      "source": "## *matplotlib*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Matplotlib is a Python 2D plotting library which produces publication-quality figures. It was designed to serve two purposes:\n\n1) allow for interactive, cross-platform control of figures and plots,<br>\n2) make it easy to produce static raster or vector graphics files without the need for any GUIs.   \n\n<br><br>\n\n<center><i>\"Matplotlib tries to make easy things easy and hard things possible.\"</center>\n    \n<img src=\"https://matplotlib.org/_static/logo2_compressed.svg\" width=\"40%\">\n\n*Source: https://matplotlib.org/_static/logo2_compressed.svg*"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        }
      },
      "cell_type": "markdown",
      "source": "### Installation"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For an installation of the *matplotlib* library, you have to enter the following command in the terminal."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "```python\n!pip install matplotlib\n```"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Importing the library"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A library is imported using the command *import*. In the following, we will use the *pyplot* module, which we will call *plt*."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Import the pyplot module\nimport matplotlib.pyplot as plt",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**First, check your *matplotlib* version!** Version 3.0.3 is required for this tutorial."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib\nmatplotlib.__version__",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "'3.0.3'"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "If you're running an older version of `matplotlib`, you can upgrade the version in the notebook by executing the following command, otherwise skip the next cell. After updating the version of the library, you have to restart your kernel 'Kernel' --> 'Restart'. Since we will need an updated `seaborn` later too, we will include it in the following command."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "!pip install matplotlib  seaborn --upgrade pip",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Requirement already up-to-date: matplotlib in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (3.0.3)\nRequirement already up-to-date: seaborn in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (0.9.0)\nRequirement already up-to-date: pip in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (19.3.1)\nRequirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from matplotlib) (2.1.4)\nRequirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from matplotlib) (1.1.0)\nRequirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from matplotlib) (2.8.1)\nRequirement already satisfied, skipping upgrade: cycler>=0.10 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from matplotlib) (0.10.0)\nRequirement already satisfied, skipping upgrade: numpy>=1.10.0 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from matplotlib) (1.17.3)\nRequirement already satisfied, skipping upgrade: scipy>=0.14.0 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from seaborn) (1.1.0)\nRequirement already satisfied, skipping upgrade: pandas>=0.15.2 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from seaborn) (0.19.2)\nRequirement already satisfied, skipping upgrade: setuptools in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from kiwisolver>=1.0.1->matplotlib) (41.6.0)\nRequirement already satisfied, skipping upgrade: six>=1.5 in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from python-dateutil>=2.1->matplotlib) (1.11.0)\nRequirement already satisfied, skipping upgrade: pytz>=2011k in /home/nbuser/anaconda3_420/lib/python3.5/site-packages (from pandas>=0.15.2->seaborn) (2016.6.1)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now, restart!"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Getting started!"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In *matplotlib*, there are several common approaches to plotting with matplotlib:\n\n- object-oriented API,\n- *pyplot* API.\n- *pylab* API *(not recommended)*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In this tutorial, we'll focus on the object-oriented approach which is recommended to use, especially when you want to create complex graphics, because it gives you more control over the figure. *Pyplot* on the other hand is just a *MATLAB*-like module of the *matplotlib* library which is mainly intended for interactive\nplots and simple cases of programmatic plot generation. The syntax of both approaches is quite similiar, so if you know the object-oriented approach, you won't have a problem to work with the *pyplot* module."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Before we'll create our first plot, we need to look at the parts of a *matplotlib* figure:\n\n-**Figure**<br>\nThe whole figure.<br>\n-**Axes**<br>\nThis is what you think of as ’a plot’, it is the region of the image with the\ndata space. A given figure can contain many Axes, but a given Axes\nobject can only be in one Figure.<br>\n-**Axis**<br>\nThese are the number-line-like objects. They take care of setting the\ngraph limits and generating the ticks (the marks on the axis) and\nticklabels (strings labeling the ticks)<br>\n-**Artist**<br>\nBasically everything you can see on the figure is an artist (even the\nFigure, Axes, and Axis objects)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<img src=\"https://matplotlib.org/3.1.1/_images/sphx_glr_anatomy_001.png\" width=\"40%\">\n\n*Source: https://matplotlib.org/3.1.1/_images/sphx_glr_anatomy_001.png*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "When using the object-oriented approach, we have to initialize the objects *Figure* and *Axes* first. This is commonly done by calling the `pyplot.figure()` function and adding the *Axes* object via `pyplot.add_axes()`."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Initialize `Figure` and `Axes` object\nfig = plt.figure()\nax = fig.add_axes( [0, 0, 1, 1]) # [bottom, top, width, height]",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sv4dPttPRdQnJnbHtvlbVq5J8NJvB9bex5T3p3nb3o919QXdf0t2XZPPv5Qe6+4yvxfoMsczvgs9n81VuquqCbL7d/ODkkGtomX39bpLXJUlVvTyb0T0xOuXudDjJmxefYr48yaPd/f3tvmlH315ul5DcEUvu6weTPC/JZxefS/tudx84a0OviSX3lqdoyX29PcmfVtV9Sf4nybu727teT2LJfX1Xkn+pqr/J5oeq3uKFzfaq6tPZ/EfgBYu/h783ybOTpLs/ks2/j1+d5FiSx5K8danHtfcAMMMVqQBgiOgCwBDRBYAhogsAQ0QXAIaILgAMEV0AGCK6ADDk/wCYzYqtOC7aMgAAAABJRU5ErkJggg==\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Alternatively, we can use `pyplot.subplots()` to initialize both the *Figure* and the *Axes* object. You can also generate several *Axes* objects in one *Figure*."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Initialize `Figure` and `Axes` object\nfig, ax = plt.subplots(nrows=1, ncols=1) # 1 row, 1 column",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For completeness, the initialization of the objects happens implicitly in the background when you use the *pyplot* library. You just need to call the plotting function. This approach is however disregarded, so use any of the above from now on."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot();",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B4g3IBeDPx3ydWQrDivfM4m9kBfwQeKp7fHTae3qNvf4p8GMW/3Ll1u7cHcD7u+e/w+JfpCwA3wXeMfK5t3afd5Az9C+vJrln4K+B/x75uT4FnD/t/azmz3jka8x8GPyXz5Kkhn+VJElqGAZJUsMwSJIahkGS1DAMkqSGYZAkNQyDJKlhGCRJjf8FFDYZsBaypoYAAAAASUVORK5CYII=\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "So, let's create our first plot using *matplotlib*."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### First plot with *matplotlib*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Plotting in *matplotlib* follows a certain rountine which includes the following steps:\n\n1. Prepare data\n2. Create plot\n3. Plot\n4. Customize plot\n5. Save plot\n6. Show plot"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "So, let's first create a small data set for a simple visualization using *matplotlib*."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Generate data set (STEP 1)\nx = [1,2,3,4]\ny = [1,2,3,4]",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "As seen above, when using the pure *matplotlib* object-oriented approach, we first have to initialize the *Figure* and *Axes* objects. After that, we can simply use a plotting method on the *Axes* object. In this small example we're going to create a simple line graph."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Line Plot using the object-oriented approach\nfig, ax = plt.subplots(1, 1) # Initializing the 'Figure' and 'Axes' objects (STEP 2)\nax.plot(x, y); # Line Plot function (STEP 3)\nax.set_xlabel('x'); #  Set label for x-Axis (STEP 4)\nax.set_ylabel('y'); # Set label for y-Axis\nax.set_title('My first Plot with matplotlib'); # Set title\nplt.show() # (STEP 6)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Before we move on, let's have a look at the *matplotlib* documentation of the line plot method:\n\nhttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot\n\nAlternatively, we can use the following command to open the documentation in the notebook."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax.plot?",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Plotting functions"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "*Matplotlib* offers a huge amount of plotting methods/functions. A good starting point, so you don't have to start from scratch, is the *matplotlib* gallery, where you can find example code for some of these methods/functions (https://matplotlib.org/gallery/index.html). \n\n*Note: Interestingly, just about all methods of an Axes object exist as a function in the pyplot module (and vice-versa).*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A small list of *matplotlib* *Axes* methods:\n\n- **Histogram:** ax.hist(x)\n    * x: numerical (converted into categorical)\n    * distribution of x\n    \n- **Box Plot:** ax.boxplot(x)\n    * x: numerical\n    * distribution of x / identification of outliers\n- **Violin Plot:** ax.violinplot(x)\n    * x: numerical\n    * distribution of x \n- **Pie Chart:** ax.pie(x) *(not recommended)*\n    * x: categorical\n    * proportions of categories in x\n- **Scatter Plot**:  ax.scatter(x,y)\n    * x: numerical\n    * y: numerical\n    * joint distribution of x and y / identification of outliers\n- **Line Plot**:  ax.plot(x, y)\n    * x: numerical/categorical\n    * y: numerical\n    * function *(math.)* / time-series\n- **Bar Plot**:  ax.bar(x, y) \n    * x: categorical\n    * y: numerical (often count of x)\n    * joint distribution of x and y / identification of outliers\n- **Contour Plot**: ax.contour(X, Y, Z)\n    * X: numerical\n    * Y: numerical\n    * Z: numerical\n    * 3D surface on a 2D format\n- ..."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Examples"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Let's look at some examples."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "***Histogram***"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In our first example, we want to visualize the age distribution of the subjects in our data set using a histogram. The variable age is *numerical (ratio scale)* and will be turned into a *categorical* variable due to binning. But before we plot the distribution, let's have a  look at the 5-number summary of our age variable."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# 5-number summary of age variable\ndata['AGE'].describe()",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "count    2246.000000\nmean       73.220392\nstd         7.426809\nmin         0.000000\n25%        68.300000\n50%        73.300000\n75%        78.500000\nmax        91.500000\nName: AGE, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "It seems that we have a typo in the age data, because the smallest value for our variable is 0, which is unrealistic. Let's check how many 0 values we have in our data and what the next smallest value is."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Count cases where age is 0\n(data['AGE'] == 0).sum()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "1"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Next smallest value apart from 0\n(data[data['AGE'] != 0]['AGE']).min()",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "52.0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Let's also check if any 'NA's are in the age data. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "(data['AGE'].isnull()).sum()",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "2"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For now, we ignore these case and leave the subject in the dataset.\n\nBecause in a histogram the numerical values are grouped in bins *(binning)*, we have to first determine the number of bins; and therefore, the width of our groups. In our small example, we simply choose 20 bins for the age variable."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Histogram - object-oriented\n\n# Number of bins\nnum_bins = 20\n\n# Histogram\nfig, ax = plt.subplots(1, 1)\nax.hist(data['AGE'].dropna(), \n        bins = num_bins,\n        color='darkgreen')\n#ax.hist(data['AGE'].dropna(), bins = num_bins)\n\n# Set axis labels and title\nax.set_xlabel('Age')\nax.set_ylabel('Count')\nax.set_title('Distribution of Age');",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We see that the distribution is skewed because we left the subject with age 0 in our data set. The warning appears due to the 'NA's in the data set, which simply get dropped for the plot. ***Be aware of this!!!***\n\n*Note: A histogram in matplotlib returns 3 lists: 1) counts of groups, 2) edges of the bins and 3) patches.*  "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In *matplotlib* you can easily overlay some plots by using multiple methods on the same *Axes* object. For example, we can plot the same distribution conditioned on the gender variable."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data['PTGENDER'].unique()",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "array(['Male', 'Female'], dtype=object)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Split data set into subsets by gender variable \ndata_f = data[data['PTGENDER']=='Female']\ndata_m = data[data['PTGENDER']=='Male']",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Number of bins\nnum_bins = 20\n\n# Histogram\nfig, ax = plt.subplots(1, 1)\nax.hist(data_f['AGE'].dropna(), \n        bins = num_bins, \n        alpha=0.6,\n        color='darkorange', \n        label='Female')\n\nax.hist(data_m['AGE'].dropna(), \n        bins = num_bins, \n        alpha=0.6, label='Male')\n\n# Set axis labels and title, add legend\nax.set_xlabel('Age')\nax.set_ylabel('Count')\nax.set_title('Distribution of Age')\nax.legend();",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "***Boxplot***"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In our next example, we want to visualize the marginal distributions of age and education levels by gender using boxplots. The boxplot method/function does not simply drop the 'NA's in our data, so we have to remove those subjects manually. Additionally, we have to split our data set into two subsets containing either the female subjects or the male subjects. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Select subjects with age not 'NA'\nnonna_age = data[~data['AGE'].isnull()]\n\n# Create a subset for female and male\ndata_f = nonna_age[nonna_age['PTGENDER'] == 'Female']\ndata_m = nonna_age[nonna_age['PTGENDER'] == 'Male']",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now, we plot the marginal distributions for the variables age and education level in one *Figure* object, which means we need two *Axes* objects."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Boxplot - object-oriented\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,4))\n\n# Boxplot Age\nax1.boxplot([data_f['AGE'], data_m['AGE']], \n            labels=['Female', 'Male'])\n# Set y-axis label and title\nax1.set_ylabel(\"Age\")\nax1.set_title(\"Patients' age\");\n\n# Boxplot Education Level\nax2.boxplot([data_f['PTEDUCAT'], data_m['PTEDUCAT']], \n            labels=['Female', 'Male'])\n# Set y-axis label and title\nax2.set_ylabel(\"Years of education\")\nax2.set_title(\"Patients' education level\");",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/numpy/core/fromnumeric.py:61: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n  return bound(*args, **kwds)\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 576x288 with 2 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We see a negatively skewed age distribution for females and analmost symmetric age distribution, but with comparable medians for both men and women. Furthermore, the boxplots show a clear outlier (as seen before) in the female age distribution, while we see just some values below the lower whisker in the male distribution. For the education level, we see two negatively skewed distribution of education levels with comparable medians but with a higher kurtosis for the male distribution. "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "***Scatter Plot + Histograms***"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In the last example, we create a more complex visualization; namely, we want to visualise the joint distribution as well as the marginal distributions of age and level of education by gender in one plot. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Define the plotting areas\nleft, width = 0.1, 0.75\nbottom, height = 0.1, 0.75\nspacing = 0.005\n\nrect_scatter = [left, bottom, width, height]\nrect_histx = [left, bottom + height + spacing, width, 0.2]\nrect_histy = [left + width + spacing, bottom, 0.2, height]\n\n# Initialize 'Figure' object\nplt.figure(figsize=(12,6))\n\n# Initialize 'Axes' object \nax_scatter = plt.axes(rect_scatter)\n\n# Customize ticks\nax_scatter.tick_params(direction='in', top=True, right=True)\nax_histx = plt.axes(rect_histx)\nax_histx.tick_params(direction='in', labelbottom=False)\nax_histy = plt.axes(rect_histy)\nax_histy.tick_params(direction='in', labelleft=False)\n\n# Scatter Plots\nax_scatter.scatter(data_f['PTEDUCAT'], \n                   data_f['AGE'], \n                   alpha = 0.2, \n                   color='darkorange', \n                   label='Female')\nax_scatter.scatter(data_m['PTEDUCAT'], \n                   data_m['AGE'], \n                   alpha = 0.2, \n                   #color='blue', \n                   label='Male')\n\n# Histograms - female data\nax_histx.hist(data_f['PTEDUCAT'], \n              bins=25, \n              color='darkorange', \n              alpha=0.4)\nax_histy.hist(data_f['AGE'], \n              bins=20, \n              color='darkorange', \n              alpha=0.4, \n              orientation='horizontal')\n\n# Histrograms - male data\nax_histx.hist(data_m['PTEDUCAT'], \n              bins=25, \n              #color='blue', \n              alpha=0.4)\nax_histy.hist(data_m['AGE'], \n              bins=20, \n              #color='blue', \n              alpha=0.4, \n              orientation='horizontal')\n\n# Set axis limits\nax_histx.set_xlim(ax_scatter.get_xlim())\nax_histy.set_ylim(ax_scatter.get_ylim())\n\n# Set axis labels\nax_scatter.set_ylabel('Age')\nax_scatter.set_xlabel('Years of education')\n\n# Add legend\nax_scatter.legend();\n\n",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oKImIiIhsimorZOrUImGcMlzMEsYpU6cWqbZC16GJbElbMzmr7IewCVETrO3dhs1e+zr7yEc+wl133cVb3vIW7r77bv7jf/yPK6Nir8bHPvYxfvAHf5C3vvWtjI2NXfExv/M7v8Phw4c5cOAAd911F7/3e7+3XuGLiMgWVl24wNST/8xXvvI/mHryn6kuvLzolMgrmZ5rUsxmKGYzGGNWvp6ee/UXokXk1TPWWtcxvMzBgwft4cOHX9L2zDPPcOedd776F2kv9NaYdaq9EbPKfiiMrHOk/eU195GIiGxZ1YULTB05TLFQoJDL0+52aLXbTB44SGVku7u4NEXuuvKVZy8yXMxijFlps9ay2Ap51x3bNjUWY8wT1tqDq9tuufOA/fin/mrl+IP33bypMYm8Flf6Gb7c1lxzBr1E7BqKf4iIyGX67aJXv8XTZ6ZPHKNYKFAsFAFWbqdPHGPSUXJ2aYpcMZthuJilHSVMnVpk8uZhJWh9qlwImK13WWyFNLoxpVzvezdS0vdLZCNs3eRMRETWz6UtSrIDvYJLcat3vPM+NwlRv8WzOq4+SRhr9TqZwiDPzkU0wpRS1mNnKUunXncSD7x0ihywcjs912TyZp3s96ORgSxfffYiQ4WAwXxAvRNxZrHFB96yx3VoPVEDznz1xeMjras/9pUcOLQ+8YhcIyVnIiL9qI9O8oEN26LkmuJJE1iahm4dcoOQG3UXD/RdwmhyA3zzxHmGorOU4xrdTJlvBru46+bNnYq2Wq0dkfEMz56vrYzC7BzK04kSZzHJK1tohty9u8JiK6TeTSjlA24aGWChGbJ3dMB1eCJbztYsCCIicj27dJKfdHsn+Um3d9xecBdTp9rbkmS1TLHX7kLtFFSfhySE3FDvtvp8r92V1QmsMb3b7ECv3QkfO/8MxG3IliFu947xHcXT65ZvnqkSJSnlfECUpHzzTJVVy5mkz9TaEfngpaeL+cCj1o4cRSSytWnkTGSdabH79amvvm/VY1TbCdMnT1NrtSkXC0xsG6LiclQoX6HaaDLdyFLrpJTzHhOlkMrA+m9R8qqEdUjaUK9C3ITMAGRyvXZXNnGPzVfDLk1zy7ZhvrUYsFi3DOdz3L0twi5NA9/hJCYAiwEuZWNm+Vj6lTFw5MwSlUKWcj5DN045cmaJN+4quw5NZEtSciayjvp2sXu/TZHrM9VWyD8fm6XaioiSlMD3OLPY4h37x51836qzp5k6tUAxn2O4VKQdRUxNn2Uy6VDZ6SY5q+b2MfVsr/LfcD5Pu9tmqtpm8sAbcJOemd6UxlwZMiWIG9A8C0V3VQj7LYE17TleiHayawwmfOgm8EIH3tg+5yQe6O1uc2D3EOeWOtQ6MaWcz4HdQ8Rp/1WOlhcZLHDpe2SXj/vTZ44Xr3rfB297nevRRDaRpjW+BsYYfuRHfmTlOI5jxsfH+b7v+75XfN4jjzyy5mNka+jL/WD6cYpcnzkyU2VmsYXvGYYKWXzPMLPY4siMmyl70/MtUutxupXjybkMp1s5UusxPe/uxGK6kaW4/S6KuRwmrFHM5Shuv4vphquLDhaGJnojZpdGzoYmwOFJYzW3j6mZBmG3zXDeEHbbTM00qOb2uQmoMIoJa9CchdppaM72ji8f3dtE5UJAxve4Y0eZt+4d5o4dZTK+R7kQOItJXpm1sM2r8eRTj/KXjzzCk089yjavRh/uxCSyJSg5ew0GBgb41re+RbvdBuDv/u7v2L17t+OopJ/U2hGF4KXrOQqB73Zuft+tg+k/xy42KOcD8kEvqc4HGcr5gGMXG07iOdstMjUX8C9nDY+e8/mXs4apuYCz3atfEd5ovZ/tl/7JKLhcd5IdBL8Ag7th7J7erV/otTvSbwmsHbqFbdE0T8xm+Kvz23liNsO2aBo7dIuTeAAmxgaYrXf4xulFDp+Y5xunF5mtd5gYU2GJftVcmOFfjhxhOBPypu1ZhjMh/3LkCM2FGdehiWxJWzY5uzS97CvPXmTq1CLVVrgur/u+972Pv/7rvwbgs5/9LD/8wz+8ct9jjz3GAw88wOTkJG9/+9t59tlnX/b8ZrPJhz/8Yd72trcxOTnJF77whXWJ60a2Ud/r16NcCGhfVnWsHSVurwr3WyGHPtSbonP5uhfjbOrOye4IT1bLzDe6dBqLzDe6PFktc7Lrbipq2WvTPv8tSJcLcKQh7fPfouy1HQV0M9X8rUwtFPnKiS5TC0Wq+Vuh7G4D2lo7olAahrG7YdcDMHY3hdKwswS2Fcb8a3SAkUyDA/5zjGQa/Gt0gFYYO4nnEgtger9hGJdjnT399DekH50/fRTrZVlIC5xo+CykBayX5fzpo65DE9mStmRydumDNoxThotZwjhdtw/chx56iM997nN0Oh2OHDnCffe9uP7jDW94A//0T//E1NQUv/qrv8ov//Ivv+z5v/Zrv8aDDz7IY489xpe//GV+/ud/nmbT4ZS369xGfq9fj4mxAVphTCuMsdaufO30qnC+0ivpvVrc6rULALdtG2SpE9GJEqy1dKKEpU7EbdvcjMI8X8vQ6bQxfpbcwDDGz9LptHm+5m6Z8ETmLC0KtGweC7RsnhYFJjJnncRTze1jas4QFnYyvPsuwsJOpuaMuymE9N/FmfNz83S6XY61x/l6Zz/H2uN0ul3Oz7kpUAK9qd/bBvO8ac8wb903wpv2DLNtMO9s6ne1FfJPx2b59tklpmebfPvsEv90bFYJ2irztRaen2W+bbjQMsy3Te+4pvVbIhthSxYE2chNLg8cOMCJEyf47Gc/y/ve976X3Le0tMSHPvQhjh07hjGGKHr51dIvfelLfPGLX+Q3f/M3Aeh0Opw6dYo777zzmuK6UfXbhqaVYpbJm4eZnmuy2AopFwLu2OG4GEhlf2+NGfRGzOIWhE3Yebe7mPrMgT0VltoRS+2IWjshkzHsGS5wYI+bBLbe7rC9UiaNO4RJTC6TYXslT73dcRIPQIUak3uGma4mLLZ7xS7u2FOiYhedxDPdyFIcuZniwhQszFEsjMHIJNONLJOOBhgnxgaYOtXrj0Lg044SWmHMHTuGncQzs9BisRXRtAUifDppQhS3mVlwd1Jda0cMX/Z5WAh8Fh0lQ0dmqsxcnKdiFyimLbpekZn6CEcKAe+83d1+cP2k7ZdYrHUZz0ZkshExAQvNgMHhkuvQRLakLZmcbfSH//d///fzcz/3czzyyCPMz794BfKjH/0o3/md38mf//mfc+LECd797ne/7LnWWj7/+c9zxx13rEssN7p++0MPlxK0PiqdXxjpbYJbPdYr6Z2v9BIzl9Ua+6x6ZKWY5X/aP943pfTHshHzYcDQQIGMB3EKS52YsazDtYv5CpWkw+TOVaPAURN8NwlsbWmB4dYpKN8Ew/sh6VBonWLRFAE3yVClmGVyG0yfeIbFep3y4CB37Nvv7OfoQiumZQNG8zEZLyVOUxY6ARda7qY1lgsBs/Uui62QRjehlPMZLmYZKbnpo+NnLjDUPU0+n4dgkHwSMtQ5zfEzRsnZssLgduyFo0Seh5/JEMUhNuxQGHyD69Bes6tVclQVR+knWzI5uzS15NIoCqzv1JIPf/jDVCoV7rnnHh555JGV9qWlpZUCIZ/61Keu+Nz3vve9fOITn+ATn/gExhimpqaYnJxcl7huRBv9vd4qqrbEdHQ7tTCi7AdM2AFH5c95sXpkdqBXNS5u9Y533uc8QeuXpPodt5T42+fahKklTAADec/yjlscXqnusxHYcnSOtilQzBSWYyrQjizl6Bxwq5OYaC9QqT7J5LYB2DXe66Pqk7296Vz8bHsBiQlYCBOMTbAmS2J88Nwl+SMDWb44dYa5RkiYxGT9DGOlLD/2jgkn8djWHC0bcLHh04oiioFPyQ8wrTkn8fSj0SK8YecQRy/WOF2PGcpb3rBziFF39YlEtrQtueZso9f97Nmzh5/+6Z9+Wfsv/MIv8Eu/9EtMTk4Sx1e+MvnRj36UKIo4cOAAb3zjG/noRz+6LjHdqPpyjVef6bd1eaoeuba33/0G3j5eZzsXGAgvsJ0LvH28ztvvdnil+tIIrJ/rjcD6OacJ9UShwcVayDeOv8ATTx/lG8df4GItZKLgpsImANVjnFxM+JPHz/B//f3T/MnjZzi5mDj72R4qVSh4Ia0Y5rsZWjEUvJChkrv1pk+frbFQq+N35yl0Z/G78yzU6jx9tuYknl25Fs8uGFqRpRj0bp9dMOzKOR5JuXQRa/pvnW99ssOf53QdSgPD7B4bpTQwzOl6r11E1t+WHDnbqHU/jcbL/+i/+93vXpm++MADD/Dcc8+t3Pfxj3/8ZY8pFAr8/u///jXFIS/qyzVefabf1uXRqb58n6VMsXfC79DJ+SaPPj/PbL3D+GCe+28dZe+omyS/Ugh4764G03aGWieknM8ysWsPFccjwidbOR49vZPZ+nCvj7I59hZcReNhFp+BTopNgRBMPAt73K3fPTlzik99o8NcGBAmA2RrEUfnF/mxtM1eB5uHjwyP8PjJKtZG+CkkMSwkPvcOuxuhfvz4WbaxwGAlC94ApBH15gKPH8/yvffs3PR4BgYGKdoaz52tUu/CYA72DhUYGChveiwr+mx2gYk6NGOf+SigE0M+4zEapJjI3RpYka1sSyZn0F9TlGRj6Xv9yvpuXV6+QrXRZLqRpdbpFZaYKIVUBtxdzT853+QvnpxhqBCwc6hAvRPxF0/O8IG37HGToF04zFKjynPpBLM2y3gaMtKYpXLhMOx7z+bHw3IfPXacIbvITtOkPjfAX8wv8oG33eakj6bn64ynM+ytlMAvQtKi1WowPb8HVxPF/+pYxHQtYLiYoZS1dOIM07Ve+3+6d/PjaTcWML5Hag3gYf0UzxjaDXejMN3mAkM5H/zlzyQ/S5BLaDXdxHQ+HGBhaZokyRF4AUkSsbA0z/lw8xPFFatnF8CLt9VjvSmym+xbtSKz7ZjzHejEHvlMSpr3+VYtx/duejQiW9+WTc7kBtJnxSX6Tb+ty6vm9jH17GGKhQLD+TztbpupapvJA29wtg7u0efnGSoEDBVzACu3jz4/7yTxOHnyGJ+bHiGM25ikzqKf4UR1hIe8Y+x1lJw9evQUQ90Zhkp58MsMJSE0Znj0aJa937H5o1W16jzDhQrUXoDOEuSHKAzewmLV3QjsUwtFhjI1imkdOilFzyPN5Hhqwc0ozPn5JXYOGNqhpZ0kFHwoZA3n55ecxANw22DI8XoeL2MJPIhSWAoDbht0Mwpz9NwiNTNKNujgxzF+JkPNljh6zk0VUgA6VapmmOm58MULWJU8lchNTIfnCpxqdglMTNGLSFKfU80sh+dyTuLZMEce3vz3PHBo899T+t51lZxZazHm8o1iBXp9c0Pqs+kf/ajfyntPN7IUt99FsXsWwhrF3CCUJ5yWQJ+td9g59NL5eYP5gHNLbjZY/ofTPrV6lWG/S2ASosRnsZ3jH07n+LCTiGB27hw7B7KQWT4hy+QYHEg5N3cO2PzkrJzMMzt3hkWziwa3UQpbDM+dYWTM3VJq4wVEoWU2DghTyHqQwWLyrqajRpxd8qjGGcLUJ+slVDIxt4+kjuKBB2+v8Ny/nOOps23aUUIh8LlptMCDb3UzUnWx1mUp9vG8QTAWEkOaplysdZ3EA1ClzNRMo3cBq+DRjmFqpsHk7rKTC1gzDQiTlNDLkNoMngHSlBmHyzvX29WqOG4EVYaUtVw3yVk+n2d+fp7R0VElaJex1jI/P98rBXyj6bPpH/2oUsxyy3jpZeupXK3Lq7UjhkvDMPhiJlaw1un2B+ODeS7UO4RxSitMKWY9shmP8UE3v1PH6nmGo1NkkxBsStZ4DKVZjtVvdxIPwHjQph6XGFqVZ9TjLOOBmzO0Eb/Ol6qjhInF2CWsyZD1R3loe91JPAC3j8Ijz+epFDyKOY9WnFJtp7x7t5t4khROd/LE+FgMJs1QjzPcmjo8OTQZ8rXjDJsCpUyGwMTka20wNzkJJyKgGzZpdyO6iSXnGwq5gAh3RaWm410U48co1mqQxBT9DJgy0/HbnEzZ7XRTQpvFpDHW9uo4WZul03WX5ItsZddNcrZnzx5mZmaYnZ11HUpfyufz7Nmzx3UYm68/aQboAAAgAElEQVRPi0v0k2or5IXZBvvGBrhzZ5l2lPDCbIOhQuAkQeu3aZYAd+0q88lHjlLxGgz5XapJjmpa4ife7aY6YsG2ONPOczbdSSPJU/I77PIWGS26O6m+f6LC577Z4HQtwJBi8ciaiIfucTMZ9WTDpxmlnIpGVvro5mCBkw2fvU4igrtG4Xi1RLPdot5d3sx8tMRdo2s/dyMcr2fprvyZ713UTMhwvO5uje6j3/w2e0shB4IWpCl4HktRptf+BgcX1KzhfDUkBBJ8fBKy7ZCbHW6wXOvEZBLDs0sZGqFPKWvYOWTodNzsT5cCKQZjA1LAs2CX20Vk/V03yVkQBExMuNkHRfpYvtKbyhisusoZt3rtAvRftcZ+m2YJELeX+M7xeY7XA+a7eYZzCZOD88TtJXAwkWici/x15w7i1GKtx0UzwClvjB/j2U2P5ZKh7bex94XDTDegGWUYCCL2DsQMbb/NSTyPzQ0wkxYZzsfspE6HLDPRdh6ba/FOJxHBQKnMeyY6/Ou5HHPtlLGCxwM7LQMlNyOw59o5LAbLi9PeDYZzbXdrhWYX5hnKFjjZMbRiSzFjGM1ZZhfcXFCbW5ylRUCKh8WQYIhJmVt0dyHYNM7w9cVBYjNK7Fsy1nBqMeRg8Qwu9vDL+r1EzKOX4l9KzLL+pocickO4bpIzkSvqs41x+1G/VWvsx+0PavOnmBgrcMvOF9cd2KjF4vwp2L/54zDPtEYJE0gvbUVpPdLE8kzL0RAMvbWCE7fcxRu7ZyGsQ3aQVm6Xs7WCJ6JRAlun1Y1ZtAE5ExOYXrsrprSLi2eP8pbtOXLZPN0w5GIjZGzXLU7i6abeclp2aSmAwS63u1L0E47OwXC2wwC99ZRH63ludXRt5mzDkmBI8UgxeFgslrMNd+u4G80652ow7FcpEtIiy2xSolFxM2XX9wNyRIT0EjMD5JbbRWT9KTmT69uljXGrx3pTGfOVXmKmYiAr+nEaYb9tf1CmQbvrU2weh7AF2SLtzDbKnpuiAN9ujREQ0SVHiE+WhICQb7fGnMQD/bdW0Pg5LkRxb7zDptRM7+T6Jt9hBbnsIHbwJrALEDbAK2IHt0F20Ek4ZuVU+krtbuwYDHjqQotmUgQvA2lMmnTZMbh5BRlWq8cB8apToRRDSoZ67G7S3vmm4Wb/HDPdEqejIuUg5ubcec433RRNyWUzJEQEvDhyliy3i8j602+WXP8KIyr+8Qr6cRphv5kY8pl67hgMlCgEJdphRGvxGHfcvt9JPLUkoEYGH4MBInw6FLCJu2JI5ULA7MIsi7NnabSalIoDDI/vYmTYTcJYziZ0yJKaoLdWyCR4NqKcTZzEA2AtHNi3k3NLw9S6CaWcz4GhPHHqJhkyJFzpz3yv3Q3rZ9lbjjjThlaSUvThpoEA67u5WNPhynPzrta+GVoRPL+UZckaOgl0YkO3k+U2RzVKPGPI+r1CIEkKGa/3s+6pOJvIhlByJrLF9eM0wmorZHquSa0dUS4ETIwNOI2nUoDJsQ7T3QEWu4ZyxnLHWIdKYe3nbgQvTYnJ4hHik5Lg9Y5TdxUtR/wWX336CEN+k0Evpt7JcGZ2jg+8835g8xP9gpfiZUvk0jZeGpJ6GSKvRMFzV9+7XAhYaFz6HvUSsk6UMlJy9bN9tdEfd6NCDVtkdKzILaYFaRe8Aku2iKtZhCtTh19l+2boJJbnuyMMZdoMehGtNMPz8Qh7EjedlPdSil5IN0mwaW/qZ873yXs3YIVokU2g5EzkBtBP0wirrZCp46cpds8xnNZp1waZqu5k8rab3CVo1lLZ82Ym6zMQzvemoQ2+GVI31dGymZRsFBLikxDgk5IlJJtxd1K9cPpb3B2cYrGbUo89SpmUm3LzLJwusXf35teKT4MB9pcTznbKNGJDKWPZl++QBu5KoI8MZPnSt84RJSnGGKy1BL7HQ/e5qR95tfTCXdoBpeFdNC/M0CkMk8tk6MYxabtNadhNteF+nPq5GGYZKoR0kkGWYksuYxgqxCyGbj4fh70aZ+IusfWJ8bBpim+7DHs1J/GIbHVKzkTWWb+NCvWb6TPnKNaOUiwWIVuhmHSgdpTpMxkmHRTfAHprFZMujK8qJBM1IeumnHbRtrEU8OmdJHqkWFKK1s2m2AC1C0cZ9+bYNlQCL4A0woYNFi8cBd676fEUhvfSWDzOnkKHfDZLJwypdRP27HFX1ffkfJNs4GOMIUos2YxHkPE4Od9k7+jmJ415z9BOU3rJx6XVQpa852462q69d5F0mxyb77DYTRjOwf7RYXbtvctJPD4JyRXSVd/h1M+OX8a3c2BjMD7YGN/22l3IdWdp295aUw9LiqFtM+S62tpIZCMoORNZR305KtRnavOnGC4UILM8ZzBToFCwziojAn1X9TOf1PHJkyfEp1dNLsInn7jbYLmczjMbF1mMBmlEPqUgYdikjBg3JdDHxrZTrEZUl87TaXXJZwNKI3sYG9vuJB6A4xfrbB/MU1hdfCeMOX6xzjtv37bp8VSKHtWGXS4S/2IJ9ErR3djZyMgYX/X2Mzq2yD7TpG4HmDbDvGnE0dpFOlTx6Y2Re0BKZrndlUK+wFKSpcISo16HLnmqyRA35d3Msz7X9sgQE5NZqWiZIeZc2+UYrMjWpd8skXW0MirkR5h8haIfUawdZfrMOdeh9Y0yDdr2pWsV2jZPGXdrhSiMUK28hamLHl95dpapix7VylucVf3MeSEVmsR4NMkR41GhSc5zuOZssMy35gMatQUGO2do1Bb41nzAyKCbq/kDuQy2u0Bcn4XGLHF9FttdYCDn7pqjxUDUgOpxmD3Su40ay6nR5hsOYiomJEfUWydERMWEDAdupusCLDRD9u0YY97fwVTnZub9HezbMcZC083P9q0DDTJ0l6cx9naFy9Dl1gF3n0dD2ZiyF2KzA3RyY9jsAGUvZCjr5vu2GBdWthp48Z/HYuxoUa7IFqfkTGQd1eZPUbg0KmTM8qhQgdr8Kdeh9Y2J7RVanQ6tyGKtpRVZWp0OE9vdbRxebYVMXYSwcifDtzxAWLmTqYu9dhcGfIslJUtClpgsCZaUAd/dOpiF/C3cXbhIyYTU0wwlE3J34SILeTd7eDVmnsScnyITL0IakokXMeenaMw86SQegP0VS+3cUToLJ7GNc3QWTlI7d5T9FTfft3EzT8G2CYgJiAiIKdg2445GOwHOVtvMN7vsqhR5y83D7KoUmW92OVt1M2V3b76Oj0eBiAG6FIjw8dibdzdKPWKWuHdPhh2DWYqBz47BLPfuyTBilpzEExLQJkuERwJEeLTJEqJ9zkQ2gqY1iqyjftsva0V7obcXXKfaW19V2e9sVKiy4w4m248y3TIstrOUMyF3jHSo7Hizk3gApueaFLOZlb3gLt1OzzWdFFLJ+x51SivXqhMMdUrkfXcnjLVkgPGxbWwjhCQBP4+lzGLipgDHwvP/SispUsl2yGdadPCohQELz/8r8AEnMR3IHKeWmaVqSyylAwRexJ7MLAcyx4FbNz2ewegiXfYSklmZkpYhYjC6uOmxXNLoxHidefLzz0G3Sj5XoTtwO42sm+moS0mRcbPEvC0RERAQMWoaLCVu9l0D2D/Y5omwwEgByjnIeB6xybJ/0E0CG3HpM9BcpV1etyMPu46g/xw45DoC5zRyJrKOJoZ8WrPHaIUJNijRChNas8eYGHK3Zw7thd56qqQLhdHe7bmv99pdKIxQmbifyd1F3rW9weTuIpWJ+51uHF5rRxSCl36PCoFPrR05iWeBIQJSUjJEZEjJEJCywJCTeADKuZTZ/O082xjkifkczzYGmc3fTjnnpoJkq91k0A+ZCYd5qrOLmXCYQT+k1W46iQeg0jzKPcMRQbdKc+kiQbfKPcMRleZRJ/FcCLMsMUCHDBEeHTIsMcAFR1X/AErJPOm5x+mEITY3SicMSc89TilxM5o3G+do2Bwvbq1sadgcs7G7zcz3jg9Ra4fMLCWcqsbMLCXU2iF7x938/kdXqY1ytXYRuTYbOnJmjPlZ4CP0PvW+Cfw4sBP4HDAKPAH8qLXW3UIKkXXUb/tlAb0Rs+wAXCoxfum2eszZ5t3VdsT0uYhaPaU8GDGRi5z2UbkQ0I6SlREzgHaUUC64mbZTTUsUTYvA72CtwRhLlHhUUzfVIwFG8h5ffGqOuXaJMC2RXYKx6hw/dv9NTuLxPDjWGcc3KQU6dFOPY8k4DxTdTP0CqDY6vHC2xj6vy52lkLbN8sLZHENeGReTdo92dxITcGnEw+IRYzna3ekgmp5dnW+SlHIcaw6wWPcYzg6wv5Syq/NNYHLT46l1oUGOAjF5OsRkaJCj1nV3WnIy2UHafpz6Yod6CINZGCHPyeQ2XJRMSmwCV9iUu9cuIuttw0bOjDG7gZ8GDlpr76b3m/0Q8F+A37LW3gYsAj+xUTGIbDprqWy7hcnsC7zL/BOT2ReobLsFrLu1QnSqvQqEq2WKvXYHqgsXmDpymDAKGR4aIoxCpo4cprpwwUk8ABNjA7TCmFYY99bBLX89MeZmyl7OT4itRyf2aSU+ndgnth45393J0LcvNFioNfA7CxTCWfzOAgu1Bt++4KZwQscfwaQpvk3Agm8TTJrS8d2NwE63sqSdeU638zzZ2Mnpdp60M890y81I1TxlXr6Hl1lud2MkvsAz9UFaCeQ9aCXwTH2QkdjN738Yg48lwqdNjggfH0vormYKjz1/ken5LljLYABYy/R8l8eedzMd1V5l0/KrtYvItdnoNWcZoGCMiYAicA54EPjg8v2fBj4G/O4GxyGyOYzpjUiVb4Lh/ZB0esfj7tZTka/0SsOv3pw3bvXaHZg+cYzUz3O6GdBYjCllA4YDy/SJY0yOuFl30tuke5jpuSaLrZByIeCOHcPOtj/YxUXOMEKMv6p0dcou3K0VevyFObYxy2CmBmkCnk/dlnn8hQLvcxBPOxhhV1AltpbUGjxjyfiGduAuOTvbDJiOJmjGhiQF3yszkBlkohk4GBOC9AqjHa/UvhlOpjvI2g7GyxNbyBgI0g4n0x1ORoUCE5G3IQ3yJPj4JJToEBg3U5oBjp06TT3NkjGWNEnxfI/YGo6dOg08sOnxeFe5jn+1dhG5NhuWnFlrzxhjfhM4BbSBL9Gbxli11l66JjUD7N6oGEQ2nTW87GJiutzuSp/t4XV2vsaFqEghC+WcoZvAybpPp1NzcgJ7SS9B648F7qX4PBnKJOSw+BgSMsSU4vPOYuo2FynEDcga8Ht/OoKoQbO56CSeoWxMO1fgVGeQRpqj5He5JVd3Vm4c4EI0wHRawdiQ2KZkrMfFNEsxcnWib3n5yNmldjeOmTvYnnmcQiFe+Txqt9scM/fyTgfxDHtdTqXjeCQru53VKDLhOVqTCzRbHVrdDKVcSsb3iG1Kq+vRNG72XvONveKPjG8czgi5jn3m+PoUm/ngba11eR3pPxuWnBljhoH3AxNAFfh/ge95Nc+dnZ3l4MGDK8eHDh3i0CFVb5HrQQrb3wrNGeguQW6wd2wdzpEpjMDO+3ojeO353ojZzrudFeBo2CJeGpHP9D5+8hnodiIa1l11NOiVzZ+ea1JrR5QLARNjA85GzhpJQECMXZ5i5ZMQENNI3JWuvs0/xfGkyEjYJCAiIqCaDHCb72abiG25lL9u7iKyBvBoxTkWm4P8+Li7PQUXzDaq3Srl/ACFjE8nTqh1YhbM5m9A3a9McRTG74GFJ2Bpuvc5NP7WXrsDvetmKV0CLm1CHRA7vZ42kEmZJ2a+E9BNDDnfUDARAxk30wgje+X3vVr7env44Yd5+OGVqoYv2628vlTjf/+F/2Pl+MHvfjcPvufBTYlNZCNs5LTG7wamrbWzAMaY/w58B1AxxmSWR8/2AGcuf+L4+DiHDx/ewNBENki+0quGOLZqVCpqgu+ukAPQOwFyVPzjcqWRnTRnjtPpGHLZLN0wJI1DSjtudhZTtRUydfw0xe45htM67dogU9WdTN52k5MEbSEtkyem7LdJrYdnUsIkYCF1t1bou0pHOV1/IyejEcI0IOtFjJka31VyU4nw6eYQ3dQQ4xNjyGBIbcLTTXcVLZu5HewaWCBqnqMTxuSyGXYNjNLM7XAUUcKV/8y7W7t4WwW+drLObPcNtFKfYithPKnzHW9yE8+5aACLh1nehrp363EucrPeFGDbYIYjCxDRK+ISpQldPA4OuplGGF1lGuzV2tfb6gv0xpi5y+8fHCrz8d/41U2JRWQzbGRydgq43xhTpDet8buAw8CXgX9Hr2Ljh4AvbGAMst76aL+svtRnUwj70a5t28gHHouzZ6k1GpSKA2yb2MvI8MsuiG6a6TPnKNaOUiwWIVuhmHSgdpTpMxkm92/+SphsNiCJoJHkSfDwSfFJyGbdjZwN5Qx35M6RjVOaaY4Br8tE5gJDOTdDDEeqBXqnrr1qU73TVrvc7sZoNma2s8AY8wRBh4g89Y5h1OFUy34zHJ/ldN1i0ga5JKbrZzgdFhiOz+JiL7g6A8R4eNiVmpYxHnXcJWftNAAfvDQlSRI8H/AC2s7qb1wtKdSaM5GNsJFrzr5ujPkz4EkgBqaAh4G/Bj5njPn4ctsnNyoGWWeX9svKDvT2y4pbveOd9ylBu6QwQrXyFqZPHKNWn6U8OMjEvrdQUf+smBgboNoKuemWEQqBTztKnFZGBKjNn2K4UIDM8ol9pkChYFmcPwUOkrPhQpaTzWDlSn6Kh8Vj2OF2A9PmViYKJ3ijNwtpCp5HK80xbW51slaw1vWI8CmYGM8mpManbTPUuu5GhQ7Ej/FEOkcYlOgySoYulXiOA/FjgIuR66slzu7m7D19epa7s+cIO3VaqaXoG7LZQZ4+vZN7HNRNipbT+3RlfV7vNnKYeJxv+QS2S4oHXm/NaWBjzrfc7L3mk5JcYZTMV7VGkQ2xodUarbW/AvzKZc0vAG/byPeVDdKH+2X1m2orZOoiFCt3MjzeSzymLsZM5kNn65f6Tb9VRgQo06Bth1i96q1t85Rxs2dW2auTz4zQiS0RHgEp+Yyh7NWdxANQy9/McO48tOu9qbt+jkJhiMW8m+moOUJaFGnbgJQAz/Ym6+Vwtz/Vgehr1Ab3UI1DogiCACqFhAPR14CfchBR/414zM4vMNSaZt4bBb8AtBlsTTM772rTZ0OvapN32bG7BLYWpsQYcn5KfjltjFNDLXSTDI0ECbPRy5OzkUD7nIlshI0upS9bSafaGzFbLVPsFZkQAKbnmhSzmZXNjC/dTs81+6YSYD+omAaTwTFIqhBUwOwH3I0uTmyvMHWmDaZAIQPtGFqdDnfsdrPdgIm6EIck5DAYEjyIu712R8r5gHanTTEoQLYMNqLdaVPOu5lqOe4vsZgMEC6vETKk+FjGfXebUFdMnXf4jzMdDlBLs5TTkAm/ScW4WnPafyNnxWSOb7R3QpAjTn0yXoaZKMtdhZctJdoUPpaXJ6vecrsbGRKs9YBkpeCmtR4ZR2sFR4oBc0sJdtXomSFhpOhumrWsXfVR1RyvX0rO5NXrs/2y+lGtHTF82QhQIfBZbLm7mt93+nB6bGXHHdwy+/c8eqLNbNswXrDcf1OByo7vdhJPNYIQjzpFQgKyRAREVN1tvcREepypgQkwLQpxnXZmnJYtckd63Ek8o9kOmXZERHZ5LzjIETGadVNuHID8EJWLTzGZG4JiAZI2tJdg/F3uYuozg17E6Wg7mbCNn0YkXobYjHCfN+MkngyWl46cAaTL7W6M5yPmmj5paiG14BkCkzCed5OcdZIslpd++Fh8Og6rx4psZUrO5NVTsYs1lQsB7ShZGTEDaEcJ5YL+iK3ow+mx1XbEC0uGfeWEO8sRbQJeWDIMtSMqDtZ5ne8McJZxPFIMKSEZzjLOeMfdldCK12CsOML/qL6JC90BtueafG/lBBVH+0G1kgwdstjlUg4Wjw5ZWonDP2smRzW3j+nOELU4oJyJmMgvUTGupuz1nwtmB7sys1TTMl1/gBwR27xZLhg3FS09elsxXN7qORxd3F2MWViqU7d5OgTk/ZBB02F30c2i04U2K710aWVeutwuIutPyZm8ev1a7KKPKkhOjA0wdaq3Ke/qYhd37Bh2Ek9f6sPpsdMnjlEsj1Is3ATQW3vWbjF94hiTI9s3PZ5T8SgeCQGXrulbIhJOxW72ggI4GdzJP9YKbMtF3Jo7S50i/1jbR3lkO5tfMgVOhSPY5elnvSTWkuJxKnT3eVS1Jf458x6qXovIRARewJlMkXfYOTS/oGfBG2csmGXCOw02AeNTTwdY8MadxBNfZYTsau2b4eZ8g2dIGE1qZGxMnGRIM3ludjRyFlmwBCQvGT0LiLQHtciGUHImr1pfFrtoL1CdfpTpVoFaXKKcaTGx+CiVifudJGj9WOyi7/Th9Nhavc7w0Ev3xyrk8iwuuVm/1CWLBTpkVtZTecR0cfdz9Kj/boL0EapJmbOMU6RJNq3xqP9uJ8lZnTyslD9npQx6r92NI9zDsVadyNtOnMuQIWa21aQ8upN3Oouqv4zkEy7mxsnZXkXCyOTomhG2uZqyd5XToKu1b4ZC+wwHMwtc9EeppUXK3hLbzAsU2m4uPKQp8LJk1ZKmDnfqlq3ryMNrP2a9HDi0ee/1Gig5k1etH4tdVM8/y9RCnmIhz3AB2nGeqQXLZOFZKhMPOImpl6ApGbuqPpweWx4cpL14mmJzGrpVyFVoD0xQHtrtJB6flHCldqRZ/udTouEkHoBTyTY6Q/dSqB5hILpIFAxRr9xLM9nmJB6LIcEsl/i+VAI9WZWubb4jHKAafp3B5AwDaYfIy1P1xzjCAUfJ2eVrqVa3u3FgqMXXah5nW9toRSnFwGO8mHJgyM2UXdcbLF9JKTyDZZBw+SJN7zZDKTzjJJ541X9Xt8Zour7IRlByJq9aPxa7mL5QpZgfohj0TsiKAWDzTF+oMjnhLCx5JYWRXvGP6rHeVMZ8pZeYOZweOzGSY+rbh6FYoJAdod1t0Fo8zB0TtziJx/d8SA0eyUrakeL32h2xcZskapEd2g12J1nj0Yha2NjNwpOAGF6y+YEBMgS4W5e30IrpJB5n26M0Y4+BTMpIwWOh5WoT6v6r1rh3MOX/aza52B6iTY5CFJG3dfYOujod6b8+MjbieLydRpojSX0aXkLb285d1s36zv7rIXk11qrmeImqOvYfbe8ur9qlYheruS52UaNEwby0OlvBdKjhqnS1vBpVW2Iqup2vhJNMRbdTtW6/X5X6N5nct41srsxiNyGbKzO5bxuV+jfdBGR88rRh1chQnjYYd8nZPv883VaDRuyTenkasU+31WCff95JPN5V/nxdrX0zeEsv8EyjTDs7Sqk0TDs7yjONMt7/z967xdiVnXd+v7X2fZ/7qXuRLLJ46epuSZRKalmtiSWPNY4z4ySIDRiB3zJJAL0NgnlJBkGQhyAPg7wEftVLMg8BgsCYKAgysZOMPbbHdmvUNuVWX9RNitUkiyyybue+73uvPOwim60mpU5E1jrNXj+A2NyLVaw/9tl1zvr2933/b3RTk6L521a/e2wxFV0WQsU5P2IhVExFl3eP9dzbTxukrHPA8k55jnHpYwmBa1VYQjAufXbKc1r0SJ5sE/u0dYPB8MthMmeGT808ml20FzaIH7xNKARYPpQJcRzTXrmkTdM8GZTMI8Mo49rtAaFr0wvdunfx9oDtDY29edM9ur1zbC88trGvKpjc0SLHEzkRFi1SSsCiLiryhL7N0BXrFt5Szo2DlMNE0fMFX1jy2LD0ZM5y4YP6pAV6LvT1nFXxMT07wE4isqrEkxY9O6SKja3dQ354HNJxRuRlQaoEvsxxLJsfHjf5Bxr0SErKJ2yFpKaZYgA3xSUa8ohK2JRILCpcWXBT6PlcCy3FqPxkQB9axhHEYHgemODM8Knphi4Xl5q88dMjDiYJSy2f1y8taDW72DyzxrXREI7/liA7JHYXibpfZuvMmh5BczjDa97YOZwRZvuE969BfEgYLEJ/m51Djb16zTWY3YMiqV8zOwTbr9c1cMY54KhYozxpwi+BCsEZ50CLHoBN6z67xwes4LPgWtiUyOOEzb4mlz1VARbysQxHhThZ10NAzEZxjxiHAoGtFEGRE2DcWh8yyj2OJoJpbpEW4NnQdAQLrp5xA08LL3SGHanVIQ9d4iQmLgSBrQiCgNTSY6Xv2oInxaqubQobDYbngQnODJ+aYZRx82DKhcUGr6y1ifOSmwdTOoGjLUDriinb4V12OMeguETbztgK79IV5wANwdAczvCaN8YHt+nt/u+gchAC0hHB6DaDs/8+bGjaxC59Gf76v68DarcH6QCGR/C1f6xFzoZ1xId0mOJRUAdCbVI2LH3jBkgHiGQEZYwqBVgKYWWQ6vkYsSkAh+pjmTN1sq6HDesBR/KQVLaIRUCgYrxqwoJlhtA/RJDxwaS+r0tlYRUldlLydxb0XKPqKSWeT1s/DbrtDu8cOzT9DmEoicqK/Vjxb618uh6iZ01cOPCEEsZ63WAwPGtMcGb41MyjWyPD63TbTbYXHtqy+5Bb+oKhZAjChtHbkE7Aa0HjLOSmrOkh7aM3iNOIMGyBdKDKiaMJ7aM3gC/rEaUKuPjvwfG7EO1DuAhr36zXNVCWGefEXQ5Vn4iAkJhFcUxZajTfOY5ZEkecD0rqUsKKKLfYOQ7Z1qAnJGeGByeDBuoMmiLU2Afzuvsu33e26IkJGxwyocFI9XjdfVeTovlzazyexEyqgNriRpEpC3A4nuh5j6ye0qP4tPXTwGuvEni3uTuFpBT4lmC1Ua/rICuf/Dv1tHWDwfDLYYIzw6dmHt0a52+gsYQHfw1BD7wOlEl9vqxj+zqfbOZv8+fVBUajgEIJbKHoWC7fyt/WJyoZQu8y9K98tKaUtvvIKlPuq4tUSBwKMhzuqyU2y0MtegDGSYlttxURUgsAACAASURBVHk/azAtBE1bsebMSBI9vTldHw6Tj6qt6iLHel0X55spv80HvDFaYi/vsOQk/HrnA843Uy16BAXqCbPxhMbs4p2ZjStgVlpEChwBDUtxZ2bcGh9ymMCD1EdaFQ2rLrF8kEoOk1/4rc+Furz6k9ej1Fr8aXhWPO7qaJwb5wMTnBk+NQ/dGh9mzEC/W+PcDTQW6pMPquXJukaGUcbO4YxxnNMOHDYXG/p6BW0fURWAQikBQtXntsZd9ZzdR+MqIMZhSoOCephxkxnjSk/PCYCwA946tunaKW2rJC0t3kq7fGFZz++/sJ2TwEw9+lOerGtj4QucP/5fOd9tg9WAcgbpGBZ+R4ucp8180zkLblo2GBU5tgWegkrAqBC4ZeMXf/Nz4WkZMn2Zs5sHEUJKQJKXYFt1aHTzwGycDYbPAyY4e4y52sDOIfPo1jh3A42VguWvwWQX0hG4rfq80vekehhlXLtxhzDdo1dNiMctrg3X2L58Tsv9vdP4JkvHf8X5ZgJWCGVENJuy0/imlvI4YO7uo1vFCiPaWJS45FQIRrS5Vaxo0QOA1yNRI+6kLkUFtgRHijpDrIHjaY6DTXliei4Bi5LjqcZSq9YGNFchegDRATh+fd7a0CToafb0+kYyFFgocspSUaAQCBSCQpMmSUk1Z26N0yQnywt8CxoS8gqSsl7XwfzlFg2GFxsTnJ0wjDL+/PoBozinKBS2LdgdRHzrypIJ0E7ohi7bGz12DmcMoox24LC1qtH+HOZvoLHfhTKFpcc29fkMXH1zvHbu7hGOf0IYhuB2CcsExj9h567N9pXzp65n3PkyvXgfjt6FbAfcNsHiqww6mvrNYO7uoz36WJQoLPKTfiqLkj0dJjcnTAipxJT7RYuocgllxhlvwgQ9JgUJNiU2kurRRrrEJtH5sRbvMyxb7CQh49yiXZZsehbdeF+ToPnbVovHBquDeFQwJzQFQzYVTyrMtzX25UlREcicpIDZyYOQwAIp9DhaPu1K6LtCBsOLjQnOTnhrd8juIKYbuISBJC0qdgcxb+0O+fZLy7rlzQ11gDZnwWrQnx8nxDnLwACMj27TCwKwT0ri7IAgUAyOboOG4Kzt28TOIuHyl+uMorSJZYe2r/ntaI7uowqbDBt5sokukVQIXI1P8x/MBMcssh5OcNSUXLgcl4s8mOnb6FsUSKDEwqJEaN4uDg9uce3IJ3QVPT8nVh7XjgTbjVvoKZB9cq+QTqN4T8U41HYgFQKJQlLgaTLfsVFPCc70XaNFJ+VeCvLkpStKGBdwoaund9EEZwbD6aKvqHrOuLE/oeM7+I6FEALfsej4Djf2J7qlGT5LPMzAWF6dgbE87TPO2kyJ1cf7uWLl02aqRc+mfY/I7hN1XkUtXSXqvEpk99m072nRM4/UWTNJifXoj0JiaQzOosqiEC73rbPsiCvct85SCJeo0lOO1pYZFRJFXYKmqB322lKjo+VRRsiUUE0RVU6opoRM2TnSpWn+pnj1rSkCgSMKfFHgiAKBoG/peT96WkCvM9BvqDGerB82KFUhqPBkSUONNSmavwyswfAiYzJnJ6jHCi0eX9XZOG34jDJHGRiAzZUu1+7GIAICG+ICoiRh64yeZ/ldxmyf7bEzLBnEFW1fsnW2SVcNtOh5yDz1nDpk1M/OHn9PkifrmnA7ZGrGcJKRlBLfquh6dj3XTwNn3BGDxCEmoMJCUhIQc8YdadEDMC4kR+UqfzW7xGHVZFFO+ab/UxYKPUG1pKJ6Qi+X1Bh49N2MrhUT45JXEkdWBMT0XT33dvGUz/inrZ8GWVnRtDIKLAplYYsSm5Ks1PW6zV8G1vDpMO6Lv4C3vvfs/8+r3/2l/wsTnJ1wZbnJu/dGCCHwbIu0KBknOa+u62l2NxieFd3VLbbjN9iJBIPYrQd19xO6q1/RI8jv0i0Tttce29TnM7A0OWxyYppye0Do2vRClzgvuXZ7wPaGnp7KCgeb7FHGTJz0nFXocyIUbovjeExfDvBFSoLHcdzjstvSoqdZDUg4S71BLAFFgk+z0hfkH1SLfH96ka6VsGJNmVY+/3z6NX7bv6lFj3jK5vlp66fBQuCxGEVYcoZQoASUlWAh0NNPlT5lG/S09VNBWjjkSJVjVQJLKiwh6rmQGrApKZ5wPWyNmXyD4UXGBGcnXD3bZRznDKOcUZzhWJKzvZCrZ/VtGA2GZ0LQp7v5OtvD6/U8L78L3S/rK7Wcw768eRuwbp0EYy4FDwc+l4gTX0I9qOgBa3KfAotY+tiUrMl9VKTnY+TDbAmbnAq7nlZx0rv0YbakRQ/Ae+oyUpTElcW0bGGJCikU76nLWvSUT8n+PG39NLiw4DEaj7iT+Ewrl6bMOOcnXFhoa1I0f46WDatCqZLQKuvGM6FISouGpUeThXriZDzLZM4MhueCCc5O6IYuv3plaW7KmgyGZ8o8lVoGfei9BLt/CtM9aK7B2V/T2pc3bwPWF+WIUdUgQ1A9cmtULEp9JXvh7CZXvIhJ5RNVklBWtGSCmOmZjHtMEwuFx0cmCQWSY/Q5oz4oOjhixKgKyJSFS0lHZjwodFVgzN8Mr1V3xp9UIa6t6FUpQkqOq5BVd6ZN07yxZE3ZkT7T0n40tiKUFUua+vLm8T4yGF5kTHD2GHPpRGgwvGjExzD4AHqXYelqnTkbfAB+T1uANm8D1vvOjJupwkaddL6qR+u6uMwO77DGSpDgiYpUSYap5AvsaNFT26+rj20PheYuYUHFkCY9d4atEgphMSiahMbX7hH74xhV2cSVS1Z6uBTYZOyPdc2CnD+zC1dmdG2FrHySysWXGW2Z4Eo9c84qfOCTP7teNxgMzxoTnBkMz5r4uJ6X9aiE8IrWrNDcMbxem0g4Jz1nD4/D69qye/M2YD2zOgSkjAkosbEoaBOTWfp6YK92I0b7e4zSkHFlYcuSsyLialdPw/kqR+ywTnHibCmoqJCscqRFD8Cym3KzanBQdE7MOCRFJVg2WaFHvDewyJWNEGCLCiEgVzbvDXSVyD0cYf6kdT1I6ZDis+gXhEREuEwyH6kpUZU/ITD7eesGg+GXwwRnBsOzJD6u+6ncBgQLdVZo7wfa7fTnimTIUPTYOcwYJ7Vb42bXp5vrM3KYtwHrAxaY4CBPxlArBBNCBhoNQbrLF/jW6E/YKZYZVw3acsamvU93+de16PlqcIujuE2CdzIvq6JBzFeDW1r0APQ8xXl/zP0sIKpsQllwxo/pebrKv+YvK7SfesRK4EqFtCqkgLiy2E91XaP5C85Kv8um/4C7WchRGdK0cjb9EaW/ok2T4bOBcWd8MTDBmcHwLBleB1XCcAeyCbitOkjTmBWaN4a0ubY7JQwCeoEkLuDa7pTtM21Ng3pr5qms+Si1kKgTQ5CaDJujVJ9JAX6H7sIK29EBFHfqoebhCvh6snmX3EMO8uscVD0iFRCKmCU54JJ7qEUPQOhBV465GN7DoSDH5rhqEnq67uz5C86wHKICpFPgUpIpi6iwWQj1yJlHJ8IwaPJTlRDKhFDkICTHqsOZQFc/pbHSNxhOExOcGQzPkvEdOL4OxQTKHCwHJi3oX6mzZwZ2inWq/G3uxIppadO0Cnp2wk5xiW2dwuaoHFWV+cnkrrqzChQWBUrbnCMAwVB12Mk7jHObtirYVNDVtNFfto45Z9XlX1IJmiLmnPWAZetYix6AFXtG7IyZqYApIQ4lZ6wxK7aujOf8bao7ns3ETZiVFuPSwbMqum5Bx9PTv2RRPcWJUN/vWmjBpHSZqQZZaeFaJY0yJ9T2bKbiye6VppfSYHgemODMYHiWTPdgcgvCxTprVmX1uavPQW7euBc7PMiXCKbXaedDUqfLreYVktjRF5zNWTlqR07IqxYpLiUSiwqPjI6cnLqWhwzjnD88XGMn7TMrXRpWxmZ2zN/v51oynqKq2K+W6cgZHTUDAfvVMqLa06CmZt0Z4vcUgzJnmiuaTkHPUvSdoRY9koKKT2aD5RPDkdNhoyO5NZQ4ApyTHUipJBsdPWWN4uThx8Ozh38XGrOLxwlkdptAJTRkQSUlmWhzrMcYFYF8YjgvjFujwfBcMMGZwfAsycd1tuxxLKdeNwAwnYyQ0QF+exWsDfwyI50eMJ20gHU9oubMpOSyfZfb2VdOes5qw4sIn8v2T05dy0P+8r7Pm5MWfSdh0Z4RVQ5vTtZp38/5LQ167otlUiU5rBaIlE8oEhbFgPtiWYOams2uzTCxOafuEsgRsewQ2WfY1FTVWE/Ie/K6LnpeScOzmWSKvADHljRcQc/TU0Zonwww/+iqCKDSWtb4IHU46w0gHZBWBZ5lg9fjQarHoMhGkT8hWLVNWaPB8Fwwjz0MhmeJ04TORRAO5NP62LlYrxsAaJYHVLZDgosCElwq26FZHugTlQzrgdiPY4f1ug45KqBJjEBRYCNQNIlJVKBFD8CbgzY9T9G0SyQVTbuk5yneHOgZHnyzOMtOscb9sseganK/7LFTrHGzOKtFD0B36TyL8dv86XiTfzb9Dn863mQxfpvu0nktetRTPuKftn4axIXAUzFNmdF0Cpoyw1MxcaEnU+WT8MnST3GyrgePgnK8S1GWYHkUZUk53sXTlPH05JODsKetGwyGXw6TOTMYniULr8DBj6C1DpYPZQLpABa/olvZ3LDuzvD7LQaJYpwqmq5guR/S11iyh9+tSxkfZsygPvf1pDzuV32Ck81hjo1DQUDC/Uqf42eMS0DCnlgnkQ6+yAkYEz+hbO40+CDuMyGkI6ITUweLkQr5INZ3jW5NJH9cfINl65BL4g4T2eaPi2/Qnkh0hGfiKT1BT1s/DQZJiSVKLCEpAUuAJUoGib5M1ZOCM51ckjf5V6pJlijKqsCSFq7V5KvyphY9gSyZVpKPXxdFIHW+Zi8+xnnx84sJzgyGZ8nKa3W2JTmGYgjSgdaFet0AwOZKl+HdmHOdgMCGuIAojtlc0+jV2L1S95hBnTErIshmsPZFLXIyZTOmiU+GR0yBxZgmnppq0QNwrh/yb3YdemKET0KCz55a4FfO6jG7mJR1P95dtUSJhUVJQMyk1Oe4+cZuRifw6Vh9qLp0pITS543dTEtw5pMzfULw7GucT5VmBVHl0/YFniVIS8k4tUgzPVmhBA8oqQuJHvacVSfreljKdxDVBRpWiSUVpRAUlcVS/qEWPZV6krGMOFk3GAzPGhOcGQzPkqAP539jblz/5pHu6hbb8RvsRIJB7NK2M7b6Cd1VjdnFoF+bfwyvQ3xUv25rX9T2unmiLh0skeTYSCokFZ7Q96T6wtICb927w2HVJi+6OHZFQ+ZcWFrVoqdS9ew3G/UoEzQhpKf0DXw+SBwcVfJGtMk4s2i7JRfdY6aJngDWJ2FKwMc7GCq9JXu2jevY3E8gKQS+rei64Nl6grMSi48CM06O8mRdD5MUvuW9yx21xLj0aVsJ58QBk1TPvIG4rO+fxx0sS+SjdYPB8GwxwZnB8KwJ+mam2c8j6MPq1+DD65COa1fL1Vf1B7Bz9Lq1xZQRNtnJ0GlJhU9KW+jLnKlixuWO4l7iEFcWgSxZ93NUoSkYEhKpFA4FEkWFoMIGoW/DKII+f3lnSt8v6NqKJC/5y0mbXzmnp+f0aeGFxml5+K0ek4MhHhmhLCmVxSR18Vt6zC4eejN+HKHV6kIJB7+asEZBB49QpfgqRgk9MwXrIe8FColCIFAnTqB6yz8NnyOufle3glPFBGcGg+FUGUYZ1/Yh7L5Cb8kizkuu7Rds+xndcD6GQOtmzdonKaAUNgUWNiWWKliz9rVpmsYZi4trXGIIVQTSZcQa01hPwOjZ0MxjUlwKrNqkhBhP56daYwXhWlDsQzYF2US4y9BY1CInxuaTvl/yZF0PygpYtG5jSUWFRJJRVgnKWtOj5ykBxtPWT4N1b8ofFptQ5QhVoUQTpMPf9wZa9ASkKCxqb0uBhQIKAo2OlgbDi4zJSRsMhlNl53BGVcGd44i/uT3kznFEVdXrhprX3BsEMscVOT4prsgJZM5r7g1tmpqtDpWqSPwVVOscib9CpSqaLT1P8/vWjD5jemJGg4SeqM/7lr77SFkNvtk7wm30GAaXcRs9vtk7QlmNX/zNz4GYJw92ftr6aRDk+yy3Q5TTJKU+LrdDglzPg4d6E/SzBimV1s2RKnOUUnX/q9MCO0QphSr19ApuObdJccgQlAgyBCkOW85tLXoMhhcdkzkzGAynyr1hzINxQuDYtH2btKi4dTwjKUq2N/SUNs0bF1sFZ5P7vFtcZKwatJlx0d7lYkvf8OD1zasU7/wxN4aSQRHQs2MuhyPWN7+jRc9V5yb/PHkNRxW0mJErhwlN/m3nTS16AJbcmLi1yOv+3Tpz5jYZOWfourEWPeVTPuKftn4aLDLktlwAR4IQYCsSWbHIkRY9DtkTglWJQ6ZFD8Be0eNqe8SksIlLSWBVtOyCvULP++OKM8PNS7JHmdgKl4IVxzxQ+/+LcWI0/DxMcGYwGE6VaVIgAd+pO198xyLNC6aJvsBj3tj3r7DHCkv2iLXqmEJa7HGGfV/fW3Z/9SJ/9t5dFrJ/w4V8n0m1zIfNX+Erqxe16OnZMzblXW5V60wICUnZlHfp2fo2jK/3DvkfbyccZmtkpY1rFSy6Cf9w41CLHklF9YQOM6nRSr/VbDE4KOkGgpVQMc1hEJe01lpa9DyteFFnN5XwOwRWzoJVgSpBCOLSJXH0ZKmvF2dYYAzwyBn14brBYHj2mODMYDCcKk3fZpbmJHmBZ1ukRUmlFE2Ngce8cS25hGfHREWDMRKPitCu139Xk6bj+ze5kLzJjczhRrpBT5VcTt7k+P4Zzi986dT1HJUtXFHxNfd9fFISPMZlg6NSzyYfgHxKksEgyomznMCFJmU9kF4DHhkFn3SK9DRmhUaNK1xt/YgPZm3uzxy6ds7V1phRQ49ba319Kn7W0fJJ1+20uHxxi79+OydPCvISHAscy+ZrW1ta9ESVR0VFik+GjUuBR0JU6Rs3YDC8yJjdkMHwjBlGGTuHM8ZxTjtw2FxsGKOLx1jvBvhqxuDgQ8bRjGbYYHlpnX430C1tbriXhaTFlK6cYcuCAptJ0eBepsdKG+Dee3/G0XDMeuCz2bRJy4KjYYL93p+x/YXTD84iQtatAwrhkKgAX6Q0rQMi9F2jf7nrkiUDVj2XwnOwycmSjH+52+M/0aCnQcKMT/a7NTRa6cd2j2njEi+LD3CyEbnbYRy+RGjrKdmrc0CfNE3RaXVx/sIr/Mk7dzkqK7JC4KJYsCXnL7yiRY9Hwl0WkQgUghKLGS4X0ZMRNhhedExwZjA8Q4ZRxrXbA0LXphe6tRPh7QHbGz0ToJ2w2cwY3vyAc62AYLFLnCZEgw/YPNfWLW1ucIoxZVVSbx0roKSsSpxirE3T9PhDEtlinDeIEovQKnGlzfT4Qy16+lbEvm2xIGMcxuQ4jCuPvqWvl+OdY5ux6FDlirIqsaSPFAHvHOv5qM2xgYKPf9QXJ+t6aIiUMk2gcwlsB4qccprQEKkWPeop1h9PWz8Nbj04oL20SZBNyfMCx7Fx3Ca3Hhxw/qKGAE1JFBaSj0rPS2xQxlPOYHgemODM8NknPp6boc87hzNC1yZ061+th8edwxnbG/qCs3nK5nXTD9k+22Rn6jJIKtp+wNaCRTf9EFjRomneuKLe4y21QqEsClUbJwQq5YrS544mgZ/OOhSWi6CeD2WXNkudiRY9Vztj/uKwzz21QlT5hDJhSY642tEYwJYOw6yk46T4sqIAhpmHdPWUyBVY1FPNFDx61ayTdT0syyOijsNMOcwKhS0d1jv1ug4+mmj2+DUSWied3Xgwoe1bTEooCnAtaPkWNx5M+LYGPZls0SmnzAgejfboMCWTGkuIDYYXGBOcGT7bxMew9wNwGxAsQBHV52vf0BKgjeOc3s8EPYFjMYj09XjMXTYvGdJtLrDdeqzlXjkQ69mczSPfkNeYOl/iVrnOhAYtIi46d/mG/LE2TZPGZbKjEZNMkikLV5S0ZMqkcVmLnvNnL/BXo5x+NmCBHIWDdBqcP3tBix6Alu9yMI4pKoEloFCCslK0fD0PQsqnBGFPWz8N1r2Isi24fn+fcVzQC2zOrXZY9/RkPH1SCiwUkhKJdTLO3EdPJg8gKgT7hw9oNzwavktelty6/4DlxWUteizPp8wTGiKjVAJLKEplY3n6RjLoxDgtGp43Jidt+GwzvF4HZk6jtmV2GvX58LoWOe3AIc4/3q0Q5yXtQF9z+ePZPCHEo79rmyvmd+sg+nGKqF43AHDe3icUKZftO3zVfp/L9h1CkXLe1jeE+sh7iUgF9KyI884xPSsiUgFH3kta9Bw3v8hrztu8HA4401S8HA54zXmb4+YXtegBuNARbDQSBIpxbiNQbDQSLnT0eP89/ICvO4WqR9kgnR/8/UDwNzfv8+FYcJh6fDiuz/uBnmt0QR5QIgF14kKoKJFckAda9ACErqL8Gb/IEkHo6snmLYaSBI+ZcklwTo4ei6HZQhoMzwOTOTN8tkmGdcbscexQWxZmc7HBtdsDoM6YxXlJlBVsreqb3zV32bzulTq7CfVrVUSQzWBN36Z63ji21njNfY9B1WaqfJoioSfHHFtrnNekKS5K1lfXKOIhSZbjuw7NoEtc6LFOGB/tsrS6yXJ8CPkeOA1UsMngaFeLHoCr/Zh8XJIXKUWpsC2BY9tc7euZc+ZSYpNR4qCQCCpsMlyNdhfvHCiOUgtpVfiWRKmKo9TinQOl5d5ecaZ00jFTmo9s4jtMWHH0OGwCrASCeGmZ2XTCNMtwbJf1pWVWAj3BmV2MsfApEVRIJBUWJXZh5pwZDM8DE5wZPts8zMI4jzmSaczCdEOX7Y0eO4czBlFGO3DYWtVrBvIwm/ew/w00Z/OCfl12OrxeB9F+tw7MNPUJziNj9xxL8Y9YdmY8tPlWVcXA1WM3DtC3U/b9LgvdRRwJeQXjpKBv6yn/aud7xM4iYZWBtMEJiZ1F2vmeFj0AV9tjxn3JUPXIS4ljVXTFjKttPX1wfWvKqFzEfszIocKibw206AF4c69iteXRSnYeDeqehJu8uVfxWxr0ZFjYCHrMUKiTbjNBprP0c6GNL4cM7IJpXNAMJL1mSb+n53NtFCWsiRmVtB9Z6cuqYBTpu0YGw4uMCc4Mn23mMAtTB2jz48w4j9k8gj4E39D38+ecduhzMDvLILOYVg5NmdNzS/qhvh6Pq2db/PXdhKxSZKXCEoKeV3H1jB5TgM12xbWd98GBQOT1fT16n63NVS16ALqtJr+6fJ+dxGJceLTtlE0/otvSo2nBmrFbdshwqRBIFC4ZC5a+jEec5wSTO9yzuqRiBa9ICUd3iFuXtOiZ0aRBSopNgY1FgUfBjKYWPQCbq8sMb/2Qc0GDoNMgTqZE4wdsvvLvatHjqxmF9AjlFKfKyKVLhIevTObMYHgemODM8NnGZGF+IfOYzTP8fPpuwv+VXSZXFkIolBI4WcnvuUNtmq6+tMV48ibDQpJj41DUA4Rf+rIWPd3AYVu+w06xzqBq0JYDtuQ9usE5LXoAaG/QrUq2j9+F8hC8Rei9Cu0NLXIKbFwqLJJHZY3WybouzjpDfph26PkS31IkpcP9tMPX+3rubWH75KmgLWJcUZApm6lyEba+ByFdMWD7pSvs7I8YzGLaYYOtc+t0hZ6M55e8u/yk6pPhM5MNPDJ6csjL3rEWPZ87rn5XtwLDKWOCM8NnH5OF+YXMWzbP8PO5FQW4lkAoSa4ErlA4ouJWFGjrOev2V/jVL1xh5/03GI8GtDs9Nrdep9vXNP4gOYRwBQYzKFKwC+it1Ou68Bdh8EcQLEHnMmQjGHwAq1/XIidWLj4JFfYjC3RJQaz0vRdsBhN+7C9xmBbkRYVjSxq+zWagx4CjI6eMpENMwKQKcGVBW0R0ZK5FD1A72i6ssr249tGaUtp6qX9n8QN+/87XUEpikSNRhCLndxY/0KLneWKcGA3zgAnODAaDYc64kS6zIu8QyBlUFUhJXDW4kZ7TMucIgPiYbvJTtq9cAPvVuoQ4+SnEi1oy1cPxhGvjNmFjkZ4UxJXi2jhj25+gzfczOYSVr9ab6GwMbhs6mycB4+mX7TnkJLSokCgEBRKJhYO+cjTl9zgT5twgpBACz1WcCSOUr6fM+pzYY0duUVTOyTVyKCWcE/pmCs5bL/W5foPtozv8INpkWIV0ZcR2eIdz/cYv/maDwfD/GROcGQwGw5yhhEVUOeyXy0SlTWgVNEWGEBob8B8fWwEfHYfXtWSud+IOlTriTuQxLT2aVkpPJOzEPbZPXc0JyZChtcxOscA4q2hLyaZl0U30lKNZ5FQAKAQSqKhO1nWx775Ekr3Nl9ozHMcnzxMGUcG+q6dPOHAESSGY4tXZxapEVgWBo8faH5i7Xuq3iiuk1oQvNo4pqmNsCals8Vaxru9hkcHwAmOCM4PBYJgz1r2IP85W6LkFoVsSVQF3M5vvaBrUC8zd2Ip7apEP0xazyqKowJYtGrLHBeVpC86GtLm2OyUMAnqBJC7g2u6U7TNtLdk8ISQScTK9SyEAC4EQ+uZTzeweZXOD+6NbFNkY2w2wO+eZ2XoyZzfKDWJcXApcckAQ43Kj1NMnCNSZ6N5LsPunMN2D5hqc/TVtvdQ/nq2yr3wKVVIgsVWFrSx+POua4MxgeA6Y4MxgMBjmjIaV0m+0OIhcosIitEuWGoKGNdEnyu/C8XU4fg/iQwgWof8K9C5qkbPPCvdI6fkZDUpyLO5lLiGeFj0AO8U6YfFDwtEIqoJQ2iA77BRf1xIw5srGJSXFozzJnbmk5ErjR38eocoUWmcfZfNUmUKu58HDTtLBExWuLFAKhICsstlJOlr0ABAfM9x7n510k7HYop1mbO69T9fvaQnQ7qUhu4VDohRpIfBshV8JrFTTOBaD4QXHsGuGDQAAIABJREFUBGcGg8EwZ0zo066GWJ5NbgscS9GoCiZodCEVNuz8H3X2LFiGbFCf9/+xFjkzb51S3ON+IikVWAJsy2HmrWvRA/Xct56l4OGsYAGBpRgkxc/9vueHosKiJ2c4qiAXNtPK4yOBp09YHGMpxZIzfGTLfpw1CAs9zn8lAmk5KOmgFCBACihPCkJ1MLz/Pn9+32NUWBRVgS0tdsce3wrep7v5zVPXMyk99jKXlp0TOgVpZbOXO/RLjaWfBsMLjAnODAaDYc6Yyg5C3cdVPoXwcVWKIGEqNdrEH/wtdC7CbA8m9+pMWudivb76tdPX43ZQzgCqAlWVIC2UE4CrL+PRzvc4oM9ArTItKppS0iOjn++hwxCkZWW0ixglbFLlYQlFW8S0rOzUtTxkxToidmbMSpep8nFUwRnniBUr0aJnNRS8P7ROsngSVIWqKs6H+gLYt+4M2B1LumqfsEpIpc+u6PHWnQHf3jx9PamyCe0ZaSWYVj4OBaGdkaoXxxDEuDQa5onnWnguhOgKIf5ACPETIcR7QohvCiH6Qoj/Wwhx/eSocRKuwWAwzB+ySrnDRSKrQ2hXRFaHO1xEVqk+UcOf1qYEjRVY+lJ9zGb1ugZCK6FIExCAkCCgSBNCTZt8gL6c8PaRYJopWm59fPtI0Jd6ylEvNBVLcoyqClIlUVXBkhxzoakv8Fj3Il7uZlzsSc60FBd7kpe7Geua+ilfPrOAY1k4KsepkvpoWbx8ZuEXf/Nz4sYAOuktfFki3ABflnTSW9zQ4yuDY4OyAirLRym7PloBjnm8bzA8F573r9bvA3+olPpdIYQLhMB/CfxLpdQ/FUL8E+CfAP/Fc9ZhMBjmiGGUsXM4YxzntAOHzcWGGYr9GBVwbsEny12ioktoC7qOpELv091bkc0bD1Y5SB2WvJzXW3c4rylR1Yxuk5YVoyqkqGzssqAjU5qRPgv046rFFxdSBrlgkimaruBcQ3FctbTMp9vqxPzwqE9bRCwwJhc2oypkq6NvePDmQshwlnAuSAgchzjPiRLF5kKoRU+3u8h29y57sfWov3MtiOl2L2jRA7W35ieqKquTdQ10Aof0KCMtFXkpcaioLEEnMD1nBsPz4LkFZ0KIDvBt4B8CKKUyIBNC/AfA3z35sn8G/CtMcGYwfG4YRhnXbg8IXZte6BLnJdduD9je6JkA7YRmd5XZ4SG9VoBnWaRlSRzHNLur2jTdsl7i+7v7dIKMNU8xyXK+v7vIb/eXtQQe0/ExntNlxX7o1mihCsl0rC/wGDtrLDk/YbkhwfKhTFBZwsC5oEVPOTvgrKO4Wy4wrNo0ZMJZ+4hypm9Qd3fpHNuWz87+iME0oh0GbG0u0O0vadETlEds92OuxDFRrggdQTMIEKUeF1KAKz3Fu/kKYnIbrxyTWm3G/gav9vRkPIV0SYsUh5xAlhTKIi0chDTv1wbD8+B5Zs42gQPgfxBCfBn4a+A/A1aUUnsnX3MfWPnZbzw4OOC11157dP7d736X7373u89RqsFgOC12DmeErk3o1m8/D487hzO2N8yHPcD6lW/g5/+CQZIxzm2aVsFyR9C/cvrzxB7yxvQ8neWQTnYXshEdrwmtc7wxXdISnMUEOCgKVQI2qBIHRUygQU1Nu9Mntl8lTO9BOgK3Rdy5QLuhZ3jwbuSQK49QZDhWgSMqcuWxG2nMeHSv0I2P2b5y7uMzvLpXtMi5oq7zbh6z4iV4XkGKzSiHV9V1LXoAri5b7N1/wM1iiWmxTlPlXKwecHX5ghY9wzjnUjAiIiRSPl2REzJiGPun8vO/973v8b3vfe/h6eLP/vtkNOa/+s//60fn3/mNv8t3fvM7p6LNYHgePM/gzAa+CvwjpdQPhBC/T13C+AillBJCfOJR0NLSEm+++eZzlGYwGHQxjnN6P5MhCxyLQaTPpGDe2NzcYhhXnBv+LUF2SOyuEHW/zObmljZNB6pHhyNu5YtExSKhgAVXcqD0tA2r1lo9e61SoAoQCiyrXtfE5mKDa1EG7ZcJHIs4L4mygq1FPcYJD8o2o9LFsyqkUEgUo9LlQdnWogeYuxleV52fMgaGmc+oBMfKOGsdc1VnxZ4SUAk+ctVU9bnSU9boVTOUH7DECLtMKCyfGW28anYqP//xB/RCiE+kfVudNv/tf/ffnIoWg+E0eJ7B2S6wq5Q6GXPPH1AHZw+EEGtKqT0hxBqw/xw1GAyGOaMdOMR5+ShjBhDnJW3Tv/CIbuiy/eor7BxuMDjpy9vS3JcXupL3dgV9R9CwS3Jl8d5AcLmnZ6BxuPwK8vhNVp0xDhk5LsdVm3D5FS164OR12+ixczhjEGX167aqr1y3kCGzykXKFJ+cBIdZ5VJIPf1dAMTHMPgAepdh6WqdORt8AJpmeHWZ8CXnAW+ULzOkzRJjvuTcoPvJop5T462DgoF3jgv+EK+ckFo+A3WOtw4Kvq3h9v5ia8LbhwWlqsiUh1VV+GLMF1ufDUcQ48Ro+Kzx3H6zlFL3hRB3hBBbSqn3gb8HvHvy5z8C/unJ8X97XhoMBsP8sbnY4Nrt2nbsY9mFVWPc+jj1Rn9+yjxXrWPekw2yoIdtQ1ZAXmasWnp6vFb6PeLFPrMpTLMcx3U40+yz0td7H83T62ZLwZo75bhqMKyahDJjzZ1iS0ufqOF1cBvgnGQTHx6H1yE4/bLdoWpxU/lc8Ce8Io+IK5eb6jwd5aCnGBWuTwLa9gxfARX4EpQouT5p8G0Ner6zNmP/cEJuNxDSRlUFTjnjO2stDWpOmaumpcZw+jzvxx7/CPifTpwabwL/MbV9//8ihPhPgVvAf/icNRgMhjli3rILhk9HQ0X8xobg7b0DDgc5vdDhNzba+ErPU+l1uU+xssQNf5VxXOEHkrOdknW5D7ykRRNQZ4aG1+uSS79b91JpKtlbCVJ2ZgvYUtEXCYVwGFQdtgJ9Zhckw3qQ+ePYIcR6NO2wSRjeJ5QZVCWhtKBy2WGVbS2KQDgNOHgXguCjvrz4GLH0dS16zjdTfuvshP/zQcCDVLLiVfyDtQnnm+Y922B4HjzX4Ewp9SPgtSf80997nj/XYDDMN/OUXTB8OtqexB/8mN/sJNCqhz5HEx939Uta9PTlhD869sgrReBAlCveO5Z8ZUHPTDGgDsz2flBnhoKFelO99wNY+4aWAK3fDGmMUnJVG4PYosS3MvpNjWWNfre+Ls5jfXhFVK9rYNx8lV4xheQQyhSETxA0GTRf1aIH4HIr4Z3xeYSY4OUxqQwYect8oaVnht9QtTh0Gvza4j2CYkRsdzh0NhgqqS27aDC8yOhpFjAYDAbDZ4rNxoRodkRUKJQVEhWKaHbEZkNPMHQraeBVEW56CJM93PQQr4q4legx3wA+XrInRH10G/W6BpTXZ8lXSCBXAgks+Qrl6cnkAXUmMZtBPgOl6qNGt8b24jpxnoMqaz2qJM5z2ovrWvQAXO1GnF1qUzbOMG5cpGyc4exSm6tdPVnqnXKdMNklDENE7xJhGBImu+yU+q6RwfAi89no5jQYDAaDVrrZXbY3V9k5zhkkOW0/YGuzTTe7q0XP9bHPSvFjgubD0q8pcXzA9fHXtfTlAHNXspfgMy48mnZCq4hQtsW48Ek4HQv0JxL060zi8Hp9XfwurH1RW+nnptzjmuyB5xMEOXHlEKmALbkH6MkKd7s9vuUk7ExdxklF25dsNjO6DT39lOOySS9oQ3QI5T2wfIJgkUHZ1KLHYHjRMcGZwWAwzCHDKGPncMb4xK1xU7NbI0C3EbDdfSz4yCMoYi1aRDEjCs7zIJ4RpzmB16AVLCOL07H3fiJzVrIXJTM8Evr2DNvOKXA4Lut1Q003eo+Lyy3eOFzjILZY8kteX5rSjd4DflOTqCt04x+wvSgemwUXQVdPsNi2JsRWi7ApoCzBsohp0rY0lhB/CoxLo+GzignODAaDYc4YRhl/fv2AUZxTFArbFuwOIr51ZUlfgNZ/leHuW+zkLuPCo22nbDoTumevapGz6kb88X1J34LQUkQF3B1KvrOhcUPWvVL3mMHHByyvfVGLnCA9Zs2aEssGEQ1sCtYYEaSlFj3A3PXlDTPJzbHDhY7ilX5BXMLNsUPH0thPFfS55V3ljXeuczB+wFK7wetfuMp5XdlFf8K10gZ8AiLi0ifCZsuf7+DMYPisYnrODAaDYc54a3fI7r1drHt/Qfvuv8C69xfs3tvlrd2hNk3DzlWuJRtkZUVPjMnKimvJBsOOnuCs6VqcYQ9LKCJ8LKE4wx5NV6NN/MOSPcurS/YsT1vQAbDh7LPWqDjjDlgW+5xxB6w1KjYcjeNF56wvb0deISwOCKc3EYMPCKc3CYsDdqSeHjiAW0czvv/ulDjcYO3CVeJwg++/O+XWkZ6MZ9eDbfsnuCpiULVwVcS2/RO6nhY5BsMLj8mcGQwGw5xx48MP6Ry/iR8EECzgFxGd4ze5YQm+/dKyFk07U5fw7GuE6T3IJoRuC7x1dqYu2xpiDwX8Sn/A3mzENBc0bcVau6LgzOmLeZygr2Ve15N4fSni+zvQ8yUbQcmksBglCa+f0ZhdnLO+vLG9Rq/415ANoMzBcgjcHgN7TYsegDd+ekQncOiEdfTz8PjGT484v6DD8EbRXTzLdpFCsQd2A+yz1L+FBoPhWWOCM4PBYJgz1OgGOCG4J5bnbgMKUa/zK1o0jeOcXrMHrY8isUApBlGmRU9bzshsh632FKoCpE0kmoRScz/VHM05O79+lq8f/YQ/OH6VB3mLFWfC7/bf5fz6y1r0AHPXl9fO7xOLgJAJUAA2sQho5/eBL2vRdDBJWOsEH1tr+Q57Iz39nbgtSCd1UG35UCZ1D5z7ORhCbTBowARnBoPBMGdccQ54N11BFOBZkJYwVgGvug+0aWoHDvF08Chzhtsi9tZpN/Rsqjf9MddEAxrLBBbEJUTRjC1/rEUPMHf9VLesK/xQdLnaO6CljpiIJj8Ur3PGWuL8qas5Yc768jazH3EtKSBYJ3As4rwkihO2sh8B/44WTUstn0mSP8qYAUySnKWWJpfN9gZYAaRHkI7Aa0F3DRpLevQYDC84JjgzGAyGOePq2Tbj3RlD1WCUShyr4qw34+rZtjZNm82MazffhSAg8NrEaUI0fJetq69p0dNtNdnO7rOT2AyS2qBkqxfTba1q0QN8vJ8KPjoOr2spdXxjsIjTUgwngntpm9DzcVsLvDFY1BeczZmVfre8z3YHdmYug2lF25NsdTK6Gj1TXr+0wPf/ZheoM2aTJGcU5/z6Kyt6BHWv1A8eOpsfD6g1zab7eRiHRsOLgAnODAaDYc7oXvo2vzr9n9nJFhmrFm0xYdM9pHvp9/RpSj9k+2yTnanLIKnqOWcLFt30Q0DDprG9QdcK2E6P6if6Xgu8Tb1P8+esn+p25JEMRwReg0bQJ89TJsMRM++sFj2PmKO+POwm3fhHbDe60A6hjCAeQusr2iSdX2jw2189yxs/PWJvFLPU8vn1V1Y09ZsxdwG1wfCiY4Izg8FgmDd6l+h++ffY3v1TmO5Acw3O/h70LunTlAzpNhfYbomP1pSjLfCgewVGH9Zz1pSqj9YMun9Hjx6Yu34qlUeU/iquO4MixfVcpqKHyk124RGNJQgXAFG/VtKuzzWX7J1faOgLxp7AUDXZyV9inOW0LYdN1dA3asBgeMExwZnBYDDMI71LeoOxn2XOAo8aURvGCU6M48TP//LnzZz1U11oJrwx8xgkPgKFKgSWULzaTLTomUsa69Cb1X2UVQ7SqY0uGuu6lc0Nwyjj2u0BoWvTC13ivOTa7QHbGz19cxcNhhcYE5wZDAaD4RczZ4EHw+vQXP14AJvPtPV3AXNX/rXabbE0ijhIbKJCEdqCJb9ktWtc9h7RPldb6B+/B+kQgkVoX6jXdTJHrp87hzNC1yZ06y3jw+PO4YztjTkKzs58G65u6FZhMPzSmODMYDAYDL+YOQs85q2/6xHz1E/VOkPP/QkXujae65NmGaOohJbmWXDzhL8Iwz+CcAm6lyEb1ff4mp6RFcDcuX6O45zez2TIAsfSNkbDYHjRMcGZwWAwGD4d8xR4zGWZ5XyhnBZf2nqZvb1dxtMpzbDBl7Y2KWyTOXtEcggrX62D+mwMbrt2JUwOAU1lxXPm+tkOHOK8fJQxA4jzknbgnLqWJ9H3ytql0WTNDC8IJjgzGAwGw2ePeSuznEPagUNWdNl6efHRWpQVhLbUqGrOSIbQWK1Ndx6ilN4M7JxlhTcXG1y7PQD4aBZcVrC12tOix2B40THv0AaDwWD47PGwzNLy6k2r5Wkr+5pXNhcbRFlBlBUopR79fXNxflwAtfMwA/s4ujOwc6apG7psb/RwbckgynBtacxADIbniMmcGQwGg+GzyTyVWc4hDzfVO4czBlFGO3DYWjWb6o/RvQK3/h9Ijj9ya/T7cP439Gqas6xwfS+Z+8ZgOA1McGYwGAwGwwuK2VR/GtRH4xjEyblO5s18x2AwnComODMYDAaDwfD5ZHi97jfrXf5oTfdIBjBZYYPhc4zpOTMYDAaDwfD5JBnWpYOPY4f1usFgMGjABGcGg8FgMBg+n8yZ+YbBYDCY4MxgMBgMBsPnk+6V2mwjn9UW+vmsPu9e0a3MYDB8TjHBmcFgMBgMhs8nZiSDwWCYM4whyP/b3r1HW1rW9wH//spIuCSKCFJkUFwRNcR660SJWsVLUi9E1KYWa6ttTWcltfUSW0Xbmpq1mlLNyrWpq6diwWtEYhVdJl6xatYSHUFFQITYqCjKEEUpXnD01z/2O3g4zOHM4JyznzP781lr1tnvs/d+928/+5137+9+nvfdAMDicvINYCBGzgAAAAYgnAEAsDkdenRy/+3zrgL2G+EMAABgAMIZAADAAIQzAACAAQhnAAAAA1gznFXVMVV1VlX9+bR8UlU9Z/1LAwAAWBx7M3J2dpL3JLnbtPz5JC9Yr4IAAAAW0d6Es6O6+9wkP0qS7t6V5IfrWhUAAMCC2ZtwdmNV3SVJJ0lVnZzkW+taFQAAwILZshe3+c0k5yf52ar6yyRHJ/nVda0KAABgwawZzrr7oqp6VJL7JKkkV3T3D9a9MgAAgAWyZjirqqetaLp3VX0rySXdfe36lAUAALBY9mZa43OS/GKSC6blU5J8Msk9q+q3u/v161QbAADAwtibcLYlyc9199eT2e+eJXldkocm+XAS4QwAAOAntDdnazx+dzCbXDu1fSOJY88AAAD2g70ZOftQVb0ryVun5X8wtR2e5Pp1qwwAAGCB7E04e26SpyV5xLS8I8kx3X1jkkevV2EAAACLZM1pjd3dSb6QZFeSp2YWyC5f57oAAAAWyqojZ1V17yTPmP5dl+QtSaq7jZYBAADsZ7c1rfFzST6S5NTuvipJquqFG1IVAADAgrmtaY1PS3JNkguq6n9W1WOT1MaUBQAAsFhWDWfd/fbuPj3JfTP7AeoXJLlrVb26qn55owoEAABYBHtzQpAbu/tN3f0rSbYmuTjJS9a9MgAAgAWyNz9CfbPu/mZ3L3X3Y9erIAAAgEW0T+EMAACA9SGcAQAADEA4AwAAGIBwBgAAMADhDAAAYADrHs6q6qCquriq3jUt37OqLqyqq6rqLVV18HrXAAAAMLqNGDl7fpLLly3/1yS/3933SvLNJM/ZgBoAAACGtq7hrKq2JnlSktdMy5XkMUnOm25yTpKnrGcNAAAAm8GWdV7/HyR5cZKfmZbvkuT67t41LV+d5LiVd9q5c2e2bdt28/L27duzffv2dS4VAICRLC0tZWlpaffiUSuv95mRA826hbOqOjXJtd39yao6ZV/ue/TRR2fHjh3rUxgAAJvC8rBVVdetvN5nRg406zly9vAkT66qJyY5JMkdk/xhkiOqass0erY1yVfWsQYAAIBNYd2OOevul3b31u4+IcnpST7Y3c9MckGSX51u9uwk71ivGgAAADaLefzO2UuS/GZVXZXZMWhnzaEGAACAoaz3CUGSJN39oSQfmi5/IclDNuJxAQAANot5jJwBAACwgnAGAAAwAOEMAABgAMIZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAcAYAADAA4QwAAGAAwhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAcAYAADAA4QwAAGAAwhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZAADAAIQzAACAAQhnAAAAA1i3cFZVx1fVBVV1WVVdWlXPn9qPrKr3VdWV0987r1cNAAAAm8V6jpztSvKi7j4pyclJnltVJyU5I8kHuvvEJB+YlgEAABbauoWz7r6muy+aLt+Q5PIkxyU5Lck5083OSfKU9aoBAABgs9iQY86q6oQkD0pyYZJjuvua6aqvJTlmI2oAAAAY2Zb1foCq+ukkf5bkBd397aq6+bru7qrqlffZuXNntm3bdvPy9u3bs3379vUuFQCAgSwtLWVpaWn34lErr/eZkQNNdd8qG+2/lVfdIcm7krynu39varsiySndfU1VHZvkQ919n+X327ZtW+/YsWPd6gIAYHOpqk9297blbT4zspnsaRteaT3P1lhJzkpy+e5gNjk/ybOny89O8o71qgEAAGCzWM9pjQ9P8k+TXFJVn5raXpbkzCTnVtVzknwxydPXsQYAAIBNYd3CWXd/NEmtcvVj1+txAQAANqMNOVsjAAAAt004AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAcAYAADAA4QwAAGAAwhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAMQzgAAAAYgnAEAAAxAOAMAABiAcAYAADAA4QwAAGAAwhkAAMAAhDMAAIABCGcAAAADEM4AAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZAADAAIQzAACAAQhnAAAAAxDOAAAABiCcAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAOYSzirqsdX1RVVdVVVnTGPGgAAAEayZaMfsKoOSvInSX4pydVJPlFV53f3ZRtdCwAc0L77jeT6K5PvXZ8cckRyxInJoUfOuyoAVjGPkbOHJLmqu7/Q3Tcl+dMkp82hDgA4cH33G8k1FyY//H5y6F1mf6+5cNYOwJDmEc6OS/LlZctXT20AwP5y/ZXJwYcndzg8qZr9PfjwWTsAQxryhCA7d+7Mtm3bbv63tLQ075IAYHP53vXJlsNu2bblsFk7bBJLS0s3fx5MctTK631m5ECz4cecJflKkuOXLW+d2m529NFHZ8eOHRtaFAAcUA45Itn1ndmI2W67vjNrh01i+/bt2b59e5Kkqq5beb3PjBxo5jFy9okkJ1bVPavq4CSnJzl/DnUAwIHriBOTm25MfnBj0j37e9ONs3YAhrTh4ay7dyX510nek+TyJOd296UbXQcAHNAOPTI59qHJQT+VfPdvZn+PfaizNQIMbB7TGtPd707y7nk8NgAsjEOPTA596LyrAGAvDXlCEAAAgEUjnAEAAAxAOAMAABiAcAYAADAA4QwAAGAAwhkAAMAAhLM9WFpamncJw9NHa9NHa9NHa9NHa9NHa9NHa9NHaxuxj3bu3DnvEuZuxNdlHg6UfhDO9uBAeXHXkz5amz5amz5amz5amz5amz5amz5a24h9dN111827hLkb8XWZhwOlH4QzAACAAVR3z7uGW6mqnUm+OMcSjkriq5jbpo/Wpo/Wpo/Wpo/Wpo/Wpo/Wpo/WNu8+ukd3H728oapuSHLFnOoZxbxfl1Fshn641Ta80pDhDAAAYNGY1ggAADAA4QwAAGAAwtkeVNVBVXVxVb1r3rWMqKqOqKrzqupzVXV5Vf3ivGsaTVW9sKourarPVtWbq+qQedc0b1X12qq6tqo+u6ztyKp6X1VdOf298zxrnLdV+uhV0/+1z1TV/66qI+ZZ47ztqY+WXfeiquqqOmoetY1itT6qqn8zbUuXVtUr51XfCFb5v/bAqvpYVX2qqnZU1UPmWeO8VdXxVXVBVV02bTPPn9qH2G9X1eOr6oqquqqqzphHDRtlX94/a+aPpn75TFU9eH6V71/7uk1u1r4Qzvbs+Ukun3cRA/vDJH/R3fdN8oDoq1uoquOSPC/Jtu6+X5KDkpw+36qGcHaSx69oOyPJB7r7xCQfmJYX2dm5dR+9L8n9uvv+ST6f5KUbXdRgzs6t+yhVdXySX07ypY0uaEBnZ0UfVdWjk5yW5AHd/fNJfncOdY3k7Nx6O3plkld09wOTvHxaXmS7kryou09KcnKS51bVSRlgv11VByX5kyRPSHJSkmdMtR2ozs7ev38+IcmJ07/tSV69QTVuhH3dJjdlXwhnK1TV1iRPSvKaedcyoqq6U5JHJjkrSbr7pu6+fr5VDWlLkkOrakuSw5J8dc71zF13fzjJN1Y0n5bknOnyOUmesqFFDWZPfdTd7+3uXdPix5Js3fDCBrLKdpQkv5/kxUkW/ixXq/TRbyQ5s7u/P93m2g0vbCCr9FEnueN0+U5Z8P12d1/T3RdNl2/I7IsxfIoTAAAH7ElEQVTY4zLGfvshSa7q7i90901J/nSq64C0j++fpyV5Xc98LMkRVXXsxlS6vm7HNrkp+0I4u7U/yOwN/kfzLmRQ90yyM8n/mqZ+vqaqDp93USPp7q9k9q30l5Jck+Rb3f3e+VY1rGO6+5rp8teSHDPPYjaBf5Hkz+ddxGiq6rQkX+nuT8+7loHdO8nfq6oLq+r/VNUvzLugAb0gyauq6suZ7cMXfZT6ZlV1QpIHJbkwY+y3j0vy5WXLV09ti2S112Eh+mYvt8lN2RfC2TJVdWqSa7v7k/OuZWBbkjw4yau7+0FJboypaLcwzXU+LbMge7ckh1fVP5lvVePr2e96LPyox2qq6t9nNqXjjfOuZSRVdViSl2U2DY3VbUlyZGZTgf5dknOrquZb0nB+I8kLu/v4JC/MNENk0VXVTyf5syQv6O5vL7/OfnsMi/Y6HOjbpHB2Sw9P8uSq+uvMhsgfU1VvmG9Jw7k6ydXdfeG0fF5mYY0fe1yS/9vdO7v7B0neluRhc65pVF/fPcVg+rvQU61WU1X/LMmpSZ7ZfpxypZ/N7IuQT0/77q1JLqqqvz3XqsZzdZK3TdN7Pp7Z7JCFPnHKHjw7s/11krw1s6lzC62q7pDZh+A3dvfuvhlhv/2VJMcvW946tS2S1V6HA7pv9nGb3JR9IZwt090v7e6t3X1CZidw+GB3G/FYpru/luTLVXWfqemxSS6bY0kj+lKSk6vqsOmb6cfGSVNWc35mH4gy/X3HHGsZUlU9PrOp1k/u7u/Mu57RdPcl3X3X7j5h2ndfneTB076KH3t7kkcnSVXdO8nBSa6ba0Xj+WqSR02XH5PkyjnWMnfT+9dZSS7v7t9bdtUI++1PJDmxqu5ZVQdn9pnt/DnUMU+rvQ7nJ3nWdKbCkzM7tOKaPa1gs7kd2+Sm7IvyJeyeVdUpSf5td58671pGU1UPzOyEKQcn+UKSf97d35xvVWOpqlck+UeZTUO7OMmv7T4Qf1FV1ZuTnJLZt/VfT/JbmX1gPDfJ3ZN8McnTu3tPJ3tYCKv00UuT/FSSv5lu9rHu/vW5FDiAPfVRd5+17Pq/zuxMqQsbPFbZjl6f5LVJHpjkpsze3z44rxrnbZU+uiKzsxFvSfK9JP9qkQ9zqKpHJPlIkkvy4+PwX5bZMT5z329X1RMzO0/AQUle293/eaNr2Cj78v45BZj/ltnZHb+T2We0HfOoe3/b121ys/aFcAYAADAA0xoBAAAGIJwBAAAMQDgDAAAYgHAGAAAwAOEMAABgAMIZsBCm3zn5aFU9YVnbP6yqv5hzTedW1Weq6nm3cx1XV9UR+7GmI6vq15ctH19Vb9lf6wcAVudU+sDCqKr7JXlrkgdl9ltGFyd5fHf/1U+wzi3dvet23ndrkvd3931/gse/Osn9uvv627uOFeu7V5LzuvuB+2N9AMDeM3IGLIzu/mySdyZ5SZKXJ3ldd/9VVT27qj5eVZ+qqv9eVX8rSapqqap2VNWlVfXy3euZRqvOrKqLkzy1ql5YVZdNI2BvWPm4VXVoVZ1TVZdU1UVV9cjpqvcmucf0uA9bcZ9jqupt0+N/vKpOntqPrqr3TTX9jyQ1td+rqj617P5nVNV/mC7fu6o+WFWfnh7/hKq649R20VT3qdNdz0xyn6mmM5evd7XnUVW/VlXnVdV7qurKqvovP+lrBQCLaMu8CwDYYK9IclGSm5Jsm0bTnprkYd29q6qWkpye5E1Jzujub1TVliQXVNV53X3ZtJ5ru/tBSVJV1yS5R3fftMoUw+cl+X53/52q+vkk766qE5M8OauPUv1Rkld298eq6oQk70pyv6n+C7r7d6rqtCTb9+I5vznJf+rud1bVIZl9MfeDJE/p7m9X1V2T/OX0GGckudfumqaRtLWeR5I8IMnfndb7+ar64+7+6l7UBgBMhDNgoXT3jdMxVP+vu79fVY9L8gtJdlRVkhya5MvTzZ9RVc/JbF95tyQnJdkdzpYfh3VpkjdU1TuSvH0PD/uIJK+aHv/SqvpqkntlFhBX87jMRrB2L9+5qg5N8sgkT5zW9Y6quuG2nm9V3TnJUd39zuk+35vaD05yZlU9IsmPkhxfVUfd1rpu43kks+mZ357W/bkkd08inAHAPhDOgEX0o+lfMpsW+Nru/o/LbzCNCD0/yUO6+/ppuuIhy25y47LLfz/JozIbCXtZVd2/u3/4E9ZY02PfIsAtC2sr7cotp6ofMrWt5llJ7pTkwdOI4dW55fPbV99fdvmH8f4CAPvMMWfAont/kqfvHjWqqrtU1d2T3DHJDUm+XVXHZhbAbqWqDkqytbs/mOTFSY5KctiKm30kyTOn2/9ckmOTXLUXdT132ePsnvr44ST/eGr7lSQ/M7V/LcndqurO09TFJyVJd38zyc7ptqmqQ6rqsMyC2bVTMPulJMdN67lh2TpXuj3PAwDYS8IZsNC6+5LMjuN6f1V9JrOTdByT2XFplyX5XJLXZXZM1p5sSfKm6b4XJfnd7l451fCPkxxaVZckeWOSZ60cEduD5yZ5+HSyjsuS/Mup/beSPK6qPpvk1ExTB6fpir+TZMf0HC5btq5nJnnRVONHkxyd5PVJHjbVdHqSK6f1fD3JJ6eTfpy5H54HALCXnEofAABgAEbOAAAABiCcAQAADEA4AwAAGIBwBgAAMADhDAAAYADCGQAAwACEMwAAgAEIZwAAAAP4/91h+wbxV9mlAAAAAElFTkSuQmCC\n",
            "text/plain": "<Figure size 864x432 with 3 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "As you can see, it is possible to use several methods on the same *Axes* object to visualize different categories for example. "
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Save plots"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "You can easily save your created figures by adding the function ```fig.savefig()``` to your code. The only argument you need to pass to the function is a complete path/just the file name (you then save it in your directory).\n\n*Recommendation: Use vector-based graphics; so, you don't get blurry figures in your document later on (e.g. SVG, EPS, PDF)*"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(1, 1) # Initializing the 'Figure' and 'Axes' objects \nax.plot(x, y); # Line Plot function \nax.set_xlabel('x'); #  Set label for x-Axis \nax.set_ylabel('y'); # Set label for y-Axis\nax.set_title('My first Plot with matplotlib'); # Set title\n\n# Save plot\nfig.savefig('line.svg', \n            format='svg', \n            bbox_inches = 'tight'); # bbox_inches = 'tight' created a padding around the figure",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Exercises"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now, it's time for you to get your hands on the keyboard. Please answer the following questions by choosing a **suitable graphical representation** of the data *(think about the scales)* including axis labels, title and legend (if necessary). Before you create a plot, please check whether difficulties can arise and how they can be remedied. For simplicity, 'NA's can be removed from the data. \n\n*Tip:* The following resources might be helpful:\n- www.stackoverflow.com\n- matplotlib documentation: https://matplotlib.org/api/axes_api.html"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "1) **Plot the distribution of diagnoses by gender.** <br><br>\n*Steps:*<br>\n1.1 Determine the unique diagnosis labels in the data set.<br>\n1.2 Create a subset for every variant of the gender variable and count the occurences of the diagnoses in those subsets.<br>\n1.3 Create the plot.\n\n*Tip*: The following functions might be helpful:<br>\n- `pd.dropna()/pd.isnull()`<br>\n- `pd.unique()`<br>\n- `pd.value_counts()`"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "### 1.1 \n\n\n### 1.2\n\n\n### 1.3\n\n\n# Initialize 'Figure' and 'Axis' object\nfig, ax = plt.subplots(1, 1, figsize=(12,6))\n\n\n\n\n\n\n",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x432 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n    \n```python\n\n### 1.1 \ndiagnosis = data[~pd.isnull(data['DX'])]['DX'].unique()\n# diagnosis = data['DX'].dropna().unique()\n\n### 1.2\ndiagnosis_counts_f = data[data['PTGENDER'] == 'Female']['DX'].value_counts()\ndiagnosis_counts_m = data[data['PTGENDER'] == 'Male']['DX'].value_counts()\n\n### 1.3\nind = np.arange(len(diagnosis))\nwidth = 0.25\n\nfig, ax = plt.subplots(1, 1)\nax.bar(ind-width/2, \n       diagnosis_counts_f,\n       width=width, \n       label='Female',\n       color='darkorange')\nax.bar(ind+width/2, \n       diagnosis_counts_m,\n       width=width, \n       label='Male')\n\nax.set_xticks(ind)\nax.set_xticklabels(diagnosis)\nax.set_xlabel('Diagnosis')\nax.set_ylabel('Count')\nax.set_title('Diagnosis distribution by gender')\nax.legend();\n```\n</details>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "**2a) Plot the joint distribution of age and brain volume by diagnosis.**<br><br>\n*Steps:*<br>\n2.1 Determine the unique diagnosis labels in the data set.<br>\n2.2 Create a subset for every variant of the diagnosis variable.<br>\n2.3 Plot the joint distribution of age *('AGE')* and brain volume *('WholeBrain')* for every subset.<br>\n\n*Tip:* The following functions might be helpful:\n- `pd.dropna()/pd.isnull()`<br>\n- `pd.unique()`"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "### 2.1\n\n\n### 2.2\n\n\n\n### 2.3\n# Initialize 'Figure' and 'Axis' object\nfig, ax = plt.subplots(1, 1, figsize=(12,6))\n\n\n\n",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x432 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n    \n```python\n\n### 2.1\ndiagnosis_label = data[~pd.isnull(data['DX'])]['DX'].unique()\n# diagnosis = data['DX'].dropna().unique()\n\n### 2.2\ndata_cn = data[data['DX'] == 'CN']\ndata_dementia = data[data['DX'] == 'Dementia']\ndata_mci = data[data['DX'] == 'MCI']\n\n### 2.3\nfig, ax = plt.subplots(1, 1, figsize=(12,6))\nax.scatter(data_cn['AGE'], \n           data_cn['WholeBrain'], \n           alpha=0.4,\n           label='CN')\n\nax.scatter(data_dementia['AGE'],\n           data_dementia['WholeBrain'],\n           alpha=0.4, \n           color='red', \n           label='Dementia')\n\nax.scatter(data_mci['AGE'],\n           data_mci['WholeBrain'],\n           alpha=0.4, \n           color='orange', \n           label='MCI')\n\nax.set_xlabel('Age')\nax.set_ylabel('Brain volume')\nax.set_title('Joint distribution of Brain volume and Age')\nax.legend();\n```\n\n</details>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "2b) **What kind of relationship can be determined graphically?** "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n\nslightly negative linear relationship\n\n\n</details>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "2c) **How can this relationship be expressed quantitatively?**"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n    \n```python\n\n# Pearson correlation between 'Age' and 'DX'\ncorr = data[['AGE','WholeBrain']].corr(method='pearson')\ncorr\n\n```\n\n</details>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "3) **What are the 10 most occuring jobs in the data set?**<br><br>\n*Steps:*<br>\n3.1 Determine the Top 10 jobs in the data set. *(When using the function `pd.value_counts()` you can access the indices with the pandas `Series.index` attribute.)*<br>\n3.2 Create the plot.\n\n*Tip:* The following functions might be helpful:\n- `pd.str.lower()`\n- `pd.value_counts()`"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "### 3.1\n\n\n\n\n\n\n### 3.2\n\n\n# Initialize 'Figure' and 'Axis' object\nfig, ax = plt.subplots(1,1, figsize=(16,6))\n\n\n\n\n\n\n",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1152x432 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n    \n```python\n\n### 3.1\njobs = data['WORK'].str.lower()\ncount_jobs = jobs.value_counts()\ncount_top10_jobs = count_jobs[:10]\nlabel_top10_jobs = count_top10_jobs.index\n\n### 3.2\nind = np.arange(len(count_top10_jobs))\n\nfig, ax = plt.subplots(1,1, figsize=(14,4))\nax.bar(ind, count_top10_jobs)\nax.set_xticks(ind)\nax.set_xticklabels(label_top10_jobs)\n\nax.set_xlabel('Job')\nax.set_ylabel('Count')\nax.set_title('Top10 jobs');\n\n``` \n</details>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Seaborn"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Matplotlib has proven to be an incredibly useful and popular visualization tool, but it comes with drawbacks:\n\n- defaults are not exactly the best choices,\n- API is relatively low level,\n- lacking compatability with *pandas DataFrame*s\n\nAdressing these drawbacks, *Seaborn* is statistical plotting library build on top of *matplotlib* and provides an API that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and provides a close integration with *pandas* data structures.\n<br><br>\n\n<center><i>“If matplotlib ’tries to make easy things easy and hard things possible’, seaborn tries to make a well-defined set of hard things easy too.”</center>\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; - Michael Waskom, Introduction to Seaborn\n\nBehind the scenes, *Seaborn* uses matplotlib to draw plots; so, if you know *matplotlib*, you’re already halfway through *Seaborn*. Many tasks can be accomplished with only seaborn functions, but further customization might require using *matplotlib* directly. \n\n<img src=\"https://www.datacouncil.ai/hs-fs/hubfs/DataEngConf/NYC16/OSS%20Projects/Seaborn-Logo_256px.png?width=256&name=Seaborn-Logo_256px.png\" width=\"20%\">\n\n*Source: https://www.datacouncil.ai/hs-fs/hubfs/DataEngConf/NYC16/OSS%20Projects/Seaborn-Logo_256px.png?width=256&name=Seaborn-Logo_256px.png*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Installation"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For an installation of the *Seaborn* library, you have to enter the following command in the terminal."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "```python\n!pip install seaborn\n```"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Import the library"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A library is imported using the command import. In the following, we will call the library ```sns```.\n"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Laden der seaborn library\nimport seaborn as sns\n\n# Default seaborn theme\nsns.set()",
      "execution_count": 27,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Getting started!"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Let's have a look at some of the drawbacks of *matplotlib* and how *Seaborn* adresses these."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Close integration with *pandas*"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "As mentioned before, *Seaborn* is closely integrated with *pandas DataFrame*s; so, for most of the functions you simply specify your data set in your plotting function and call a variable by using its header/column name. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Convert our toy data set into a dataframe\ndf = pd.DataFrame({'x': x, 'y': y})",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Line plot\nsns.lineplot(data=df,\n             x='x',\n             y='y');",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Here, another example of the close integration with *pandas* can be seen in the plot; *Seaborn* loads the full data set and uses the selected header/column names as axis labels."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Some plots in *Seaborn* don't support the option to load in the full data set and then to select a variable by its header/column name, e.g. histogram (```sns.distplot(x)```)."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Histogram\ng = sns.distplot(data['AGE'].dropna(), bins=20)\ng.set(xlabel='Age');",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The kernel density estimator is returned per default, if you don't want to visualize it just add the argument ```kde=False``` to the function."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### High-level functions"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In *matplotlib*, tasks like plotting counts for categorical data, like you did in ex.1 and ex.2, is tedious. First, you have to count your categories and then pass these to the bar plot function. In contrast, *Seaborn* provides high-level functions for these tasks which will do all the work for you. Let's create the plot from ex.2."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Bar plot with counts\ng = sns.countplot(data=data,\n                  x='DX',\n                  hue='PTGENDER')\n\n# Rename x-Axis\ng.set(xlabel='Diagnosis');\n\n# Rename legend title\ng.legend(title='Gender');",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We see that we don't have to build a subset for every category like in *matplotlib*, we can simply split the data by passing a conditioning variable to the argument *hue*."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Another example is the visualization of joint distribution with the marginal distributions of two variables like the last example in the *matplotlib* section."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Joint distribution and respective marginal distributions of age and brain volume\ng = sns.jointplot(data=data, \n                   x='AGE',\n                   y='WholeBrain',\n                   kind='scatter', # kind=['scatter','hex','kde']\n                   alpha=0.3,\n                   height=8); # figsize not possible\n\n# Rename axis labels\ng.set_axis_labels('Age', 'Brain volume');",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": "/home/nbuser/anaconda3_420/lib/python3.5/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n",
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 576x576 with 3 Axes>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Plot functions"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "A small list of *Seaborn* functions:\n\n- **Boxplot:** sns.boxplot(x)\n- **Violin Plot:** sns.violinplot(x)\n- **Bar Plot:** sns.barplot(x,y)\n- **Line Plot:** sns.lineplot(x,y)\n- **Scatter Plot:** sns.scatterplot(x,y)\n- **Faceted Scatter Plot:** sns.relplot(x,y)\n- **Scatter Plot with regression line:** sns.lmplot(x,y)\n- **KDE Plot:** sns.kde(x,y)\n\nVisualizations with multiple plots\n\n- **Scatter Plot with histograms:** sns.jointplot(x,y)\n- **Scatter Plot Matrix:** sns.pairplot(df[['x','y',...]])\n- ...\n\nOptimized function for plotting categorical data:\n- **Plotting categorical data:** \n    - sns.catplot(x,y,kind) *(kind=['swarm', 'violin', 'boxplot'])*\n    - sns.sns.stripplot(x,y)\n    - sns.swarmplot(x,y)\n\nCheck out the tutorial or the gallery for more examples.\n\nhttps://seaborn.pydata.org/tutorial.html\n\nhttps://seaborn.pydata.org/examples/index.html"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Exercise"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now, it's again time to practice. Please answer the following questions with a suitable representation."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "1) **Plot again the joint distribution of age and brain volume by diagnosis.**"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Create the plot.\nplt.figure(figsize=(12,6))\ng = ...\n\ng.set(xlabel='age', ylabel='Brain volume');\nlegend = g.legend()\nlegend.texts[0].set_text(\"Diagnosis\")\n",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n    \n```python\n\n# Specify colors analogous to ex.3\ncolors=('blue', 'red', 'darkorange')\n\n# Scatter Plot\nplt.figure(figsize=(12,6))\ng=sns.scatterplot(data=data, \n                  x='AGE', \n                  y='WholeBrain', \n                  hue='DX', \n                  palette=colors, \n                  alpha= 0.4)\n\ng.set(xlabel='age', ylabel='Brain volume');\nlegend = g.legend()\nlegend.texts[0].set_text(\"Diagnosis\")\n\n```\n\n</details>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "2) **Plot a Scatter Plot Matrix for the variables Brain volume and the cognitive test scores by diagnoses.**<br><br>\n*Steps:*<br>\n2.1 Create a Dataframe with the variables brain volume *('WholeBrain')*, cognitive test scores *('ADAS11')*, *('ADAS13')* and diagnoses *('DX')*.<br>\n2.2 Create the plot.<br>\n\n*Tips:* The following functions might be helpful:\n- `pd.dropna()`"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "### 2.1)\n\n\n### 2.2)\ng = ...\n\n\n\ng.fig.get_children()[-1].set_title('Diagnosis')",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<details>\n<summary>Solution</summary>\n    \n```python\n\n### 2.1)\ndf = data[['WholeBrain', 'ADAS11', 'ADAS13', 'DX']].dropna()\n\n### 2.2)\ng = sns.pairplot(df, \n                 hue='DX',\n                 plot_kws={'alpha':0.2})\ng.fig.get_children()[-1].set_title('Diagnosis')\n\n```\n\n</details>"
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "file_extension": ".py",
      "version": "3.5.4",
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