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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###### Modified under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2020 R.C. Cooper"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Seeing stats in a new light\n",
"\n",
"Welcome to the second lesson in \"Analyze Data,\" Module 2 of our series in _Computational Mechanics_. In the previous lesson, [Cheers! Stats with Beers](./01_Cheers_Stats_Beers.ipynb), we did some exploratory data analysis with a data set of canned craft beers in the US [1]. We'll continue using that same data set here, but with a new focus on _visualizing statistics_.\n",
"\n",
"In her lecture [\"Looking at Data\"](https://youtu.be/QYDuAo9r1xE), Prof. Kristin Sainani says that you should always plot your data. Immediately, several things can come to light: are there outliers in your data? (Outliers are data points that look abnormally far from other values in the sample.) Are there data points that don't make sense? (Errors in data entry can be spotted this way.) And especially, you want to get a _visual_ representation of how data are distributed in your sample.\n",
"\n",
"In this lesson, we'll play around with different ways of visualizing data. We have so many ways to play! Have a look at the gallery of [The Data Viz Project](http://datavizproject.com) by _ferdio_ (a data viz agency in Copenhagen). Aren't those gorgeous? Wouldn't you like to be able to make some pretty pics like that? Python can help!\n",
"\n",
"Let's begin. We'll import our favorite Python libraries, and set some font parameters for our plots to look nicer. Then we'll load our data set for craft beers and begin!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"#Import rcParams to set font styles\n",
"from matplotlib import rcParams\n",
"\n",
"#Set font style and size \n",
"rcParams['font.family'] = 'sans'\n",
"rcParams['font.size'] = 16\n",
"rcParams['lines.linewidth'] = 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read the data\n",
"\n",
"Like in the previous lesson, we will load the data from a `.csv` file. You may have the file in your working directory if you downloaded it when working through the previous lesson. In that case, you could load it like this:\n",
"\n",
"```Python\n",
"beers = pd.read_csv(\"beers.csv\")\n",
"```\n",
"\n",
"If you downloaded the full set of lesson files from our public repository, you can find the file in the `/data` folder, and you can load it with the full path:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load the beers data set using pandas, and assign it to a dataframe\n",
"beers = pd.read_csv(\"../data/beers.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Note:\n",
"\n",
"If you don't have the data file locally, download it by adding a code cell, and executing the following code in it:\n",
"\n",
"```Python\n",
"from urllib.request import urlretrieve\n",
"URL = 'http://go.gwu.edu/engcomp2data1?accessType=DOWNLOAD'\n",
"urlretrieve(URL, 'beers.csv')\n",
"```\n",
"The data file will be downloaded to your working directory, and you will load it like described above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK. Let's have a look at the first few rows of the `pandas` dataframe we just created from the file, and confirm that it's a dataframe using the `type()` function. We only display the first 10 rows to save some space."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(beers)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>abv</th>\n",
" <th>ibu</th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>style</th>\n",
" <th>brewery_id</th>\n",
" <th>ounces</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.071</td>\n",
" <td>NaN</td>\n",
" <td>2264</td>\n",
" <td>Rise of the Phoenix</td>\n",
" <td>American IPA</td>\n",
" <td>177</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>0.075</td>\n",
" <td>NaN</td>\n",
" <td>2262</td>\n",
" <td>Sex and Candy</td>\n",
" <td>American IPA</td>\n",
" <td>177</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>28</td>\n",
" <td>0.070</td>\n",
" <td>70.0</td>\n",
" <td>799</td>\n",
" <td>21st Amendment IPA (2006)</td>\n",
" <td>American IPA</td>\n",
" <td>368</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>29</td>\n",
" <td>0.070</td>\n",
" <td>70.0</td>\n",
" <td>797</td>\n",
" <td>Brew Free! or Die IPA (2008)</td>\n",
" <td>American IPA</td>\n",
" <td>368</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>30</td>\n",
" <td>0.070</td>\n",
" <td>70.0</td>\n",
" <td>796</td>\n",
" <td>Brew Free! or Die IPA (2009)</td>\n",
" <td>American IPA</td>\n",
" <td>368</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2387</th>\n",
" <td>2390</td>\n",
" <td>0.059</td>\n",
" <td>135.0</td>\n",
" <td>1676</td>\n",
" <td>Troopers Alley IPA</td>\n",
" <td>American IPA</td>\n",
" <td>344</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2390</th>\n",
" <td>2393</td>\n",
" <td>0.065</td>\n",
" <td>82.0</td>\n",
" <td>2417</td>\n",
" <td>4000 Footer IPA</td>\n",
" <td>American IPA</td>\n",
" <td>109</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2392</th>\n",
" <td>2395</td>\n",
" <td>0.065</td>\n",
" <td>69.0</td>\n",
" <td>1697</td>\n",
" <td>Be Hoppy IPA</td>\n",
" <td>American IPA</td>\n",
" <td>339</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2393</th>\n",
" <td>2396</td>\n",
" <td>0.069</td>\n",
" <td>69.0</td>\n",
" <td>2194</td>\n",
" <td>Worthy IPA</td>\n",
" <td>American IPA</td>\n",
" <td>199</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2396</th>\n",
" <td>2399</td>\n",
" <td>0.069</td>\n",
" <td>69.0</td>\n",
" <td>1512</td>\n",
" <td>Worthy IPA (2013)</td>\n",
" <td>American IPA</td>\n",
" <td>199</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>424 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 abv ibu id name \\\n",
"2 2 0.071 NaN 2264 Rise of the Phoenix \n",
"4 4 0.075 NaN 2262 Sex and Candy \n",
"28 28 0.070 70.0 799 21st Amendment IPA (2006) \n",
"29 29 0.070 70.0 797 Brew Free! or Die IPA (2008) \n",
"30 30 0.070 70.0 796 Brew Free! or Die IPA (2009) \n",
"... ... ... ... ... ... \n",
"2387 2390 0.059 135.0 1676 Troopers Alley IPA \n",
"2390 2393 0.065 82.0 2417 4000 Footer IPA \n",
"2392 2395 0.065 69.0 1697 Be Hoppy IPA \n",
"2393 2396 0.069 69.0 2194 Worthy IPA \n",
"2396 2399 0.069 69.0 1512 Worthy IPA (2013) \n",
"\n",
" style brewery_id ounces \n",
"2 American IPA 177 12.0 \n",
"4 American IPA 177 12.0 \n",
"28 American IPA 368 12.0 \n",
"29 American IPA 368 12.0 \n",
"30 American IPA 368 12.0 \n",
"... ... ... ... \n",
"2387 American IPA 344 12.0 \n",
"2390 American IPA 109 12.0 \n",
"2392 American IPA 339 16.0 \n",
"2393 American IPA 199 12.0 \n",
"2396 American IPA 199 12.0 \n",
"\n",
"[424 rows x 8 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beers[beers['style']=='American IPA']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'abv', 'ibu', 'id', 'name', 'style', 'brewery_id',\n",
" 'ounces'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"beers.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quantitative vs. categorical data\n",
"\n",
"As you can see in the nice table that `pandas` printed for the dataframe, we have several features for each beer: the label `abv` corresponds to the acohol-by-volume fraction, label `ibu` refers to the international bitterness unit (IBU), then we have the `name` of the beer and the `style`, the brewery ID number, and the liquid volume of the beer can, in ounces.\n",
"\n",
"Alcohol-by-volume is a numeric feature: a volume fraction, with possible values from 0 to 1 (sometimes also given as a percentage). In the first 10 rows of our dataframe, the `ibu` value is missing (all those `NaN`s), but we saw in the previous lesson that `ibu` is also a numeric feature, with values that go from a minimum of 4 to a maximum of 138 (in our data set). IBU is pretty mysterious: how do you measure the bitterness of beer? It turns out that bitterness is measured as parts per million of _isohumulone_, the acid found in hops [2]. \n",
"\n",
"For these numeric features, we learned that we can get an idea of the _central tendency_ in the data using the **mean value**, and we get ideas of _spread_ of the data with the **standard deviation** (and also with the range, but standard deviation is the most common).\n",
"\n",
"Notice that the beer data also has a feature named `style`: it can be \"American IPA\" or \"American Porter\" or a bunch of other styles of beer. If we want to study the beers according to style, we'll have to come up with some new ideas, because you can't take the mean or standard deviation of this feature!\n",
"\n",
"**Quantitative data** have meaning through a numeric feature, either on a continuous scale (like a fraction from 0 to 1), or a discrete count. \n",
"**Categorical data**, in contrast, have meaning through a qualitative feature (like the style of beer). Data in this form can be collected in groups (categories), and then we can count the number of data items in that group. For example, we could ask how many beers (in our set) are of the style \"American IPA,\" or ask how many beers we have in each style.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing quantitative data\n",
"\n",
"In the previous lesson, we played around a bit with the `abv` and `ibu` columns of the dataframe `beers`. For each of these columns, we extracted it from the dataframe and saved it into a `pandas` series, then we used the `dropna()` method to get rid of null values. This \"clean\" data was our starting point for some exploratory data analysis, and for plotting the data distributions using **histograms**. Here, we will add a few more ingredients to our recipes for data exploration, and we'll learn about a new type of visualization: the **box plot**.\n",
"\n",
"Let's repeat here the process for extracting and cleaning the two series, and getting the values into NumPy arrays:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Repeat cleaning values abv\n",
"abv_series = beers['abv']\n",
"abv_clean = abv_series.dropna()\n",
"abv = abv_clean.values"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#Repeat cleaning values ibu\n",
"ibu_series = beers['ibu']\n",
"ibu_clean = ibu_series.dropna()\n",
"ibu = ibu_clean.values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also repeat a histogram plot for the `abv` variable, but this time choose to plot just 10 bins (you'll see why in a moment)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6,4))\n",
"plt.hist(abv, bins=10, color='b', histtype='bar', edgecolor='w') \n",
"plt.title('Alcohol by Volume (abv) \\n')\n",
"plt.xlabel('abv')\n",
"plt.ylabel('Frequency');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can tell that the most frequent values of `abv` fall in the bin just above 0.05 (5% alcohol), and the bin below. The mean value of our data is 0.06, which happens to be within the top-frequency bin, but data is not always so neat (sometimes, extreme values weigh heavily on the mean). Note also that we have a _right skewed_ distribution, with higher-frequency bins occuring in the lower end of the range than in the higher end.\n",
"\n",
"If you played around with the bin sizes in the previous lesson, you might have noticed that with a lot of bins, it becomes harder to visually pick out the patterns in the data. But if you use too few bins, the plot is also unhelpful. What number of bins is just right? Well, it depends on your data, so you'll just have to experiment and use your best judgement."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's learn a new trick. It turns out that `pandas` has built-in methods to make histograms directly from columns of a dataframe! (It uses Matplotlib internally for that.) The syntax is short and sweet:\n",
"\n",
"```\n",
"dataframe.hist(column='label')\n",
"```\n",
"\n",
"And `pandas` plots a pretty nice histogram without help. You can add optional parameters to set these to your liking; see the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html). Check it out, and compare with our previous plot."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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Ly+0P3AxMiojbJc0EpkXEqwtxuwJ/AD4UEd+v0YYTgBMARo8evdesWbPq7yWwYsUKRo4c2dAy/dVg6ivU7u+Cx5e3vC0TxmzV63V4+w5svdnfKVOm3B0RE8vmdTlmIOn1wDWko/ePkC4XHQr8p6RVEXElIKAsq6jkfT1xG4mImcBMgIkTJ8bkyZO7WmQDHR0dNLpMfzWY+gq1+zujHT9UN33jdjSbt+/A1q7+1jOA/GXSmcDBEbE2l/1c0rbANyRdBSwDxpYsOypPl1VNR0lSbHhKUowzM7MWqmfMYAJwb1UiqPg1sC2wA+msYbykzQsxuwFrWD9GsBDYFHhdSRzA7+pst5mZNVE9yeBJYE9Jwwvl/wCsIh3NzyY9h3B4ZaakocD7gbkRsToX/4yUHKYX1vUB4P6IWNxwD8zMrMfquUz0LeBHwBxJF5HGDKYBRwEXRMQa4B5JVwMXShoGLAZOBMZT9cUfEX+WdAFwqqS/APNJCWM/0jiEmZm1QZfJICJ+nJ8O/hxwCTAC+CPwMeA7VaHHAOcAZwNbA/cCB0TE/MIqTwNWAP8HeDXwIHBERMzpWVfMzKy76vpvLyPiRuDGLmJWAiflV2dxL5MSxtl1ttHMzHqZf7XUzMycDMzMzMnAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwPrRavWvtzuJphZner6by/NumPEsCGMO+X6Xq/n5AnrmFGoZ8l5B/V6vWYDic8MzMzMycDMzJwMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMaCAZSDpQ0m2SVkh6QdJdkvarmj9K0iWSnpH0oqRbJE0oWc8ISedLWipppaQ7JE1qVofMzKxxdSUDSf8KXAfcDbwHOBz4EbB5ni9gNnAA8AngfcAwYJ6k1xRWdylwPHAGcDCwFLhJ0p497YyZmXVPl/8HsqRxwIXAZyLiwqpZN1X9exqwD7BfRMzLy90BLAY+C3wyl+0BHA0cGxGX5bJbgYXAWXk9ZmbWYvWcGRwLvAL8Zycx04AnKokAICKWA3OAQwtxa4Grq+LWAbOAqZI2rb/pZmbWLPUkg32AB4AjJf1R0jpJiyR9rCpmd+D+kmUXAmMljayKWxwRL5XEDQd2baz5ZmbWDIqIzgOkB4CdgNXA54E/ksYMPgJ8KiK+IekhYH5EHFlY9jjgYmBsRDwqaS6wZUTsXYjbH7gZmBQRt9doxwnACQCjR4/ea9asWQ11dMWKFYwcObLrwAGgL/V1wePLe72O0ZvBUys3LJswZquW1F00YcxWvV5HX9q+reD+Ns+UKVPujoiJZfO6HDMgnT28CpgRET/JZb/IYwmnSvomIKAsq6jkfT1xG4mImcBMgIkTJ8bkyZPraPp6HR0dNLpMf9WX+jrjlOt7vY6TJ6zjaws23JWXTJ/ckrqLlkyf3Ot19KXt2wrub2vUc5no2Ty9uVA+FxgN7AgsA7YpWXZUnj6Xp13FLaujPWZm1mT1JIOFNcorR/Ov5JjdS2J2Ax6JiBVV6xovafOSuDXAojraY2ZmTVZPMrg2T6cWyqcCj0XEk6RnDMZI2rcyU9KWwCF5XsVs0vMHh1fFDQXeD8yNiNUN98DMzHqsnjGDG4B5wHckbQf8CTgMeCdwTI6ZDdwBXCHpM6TLQqeSzh6+WllRRNwj6WrgQknDSM8hnAiMB6Y3pUdmZtawLpNBRISkdwPnAl8kXd9/AJgeET/IMa9IOhj4D+AiYAQpOUyJiEcLqzwGOAc4G9gauBc4ICLmN6dLZmbWqHrODIiIF4CP5VetmGWkB9SO7WJdK4GT8svMzPoA/2qpmZk5GZiZmZOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRg1lSr1r48KOu2/m9ouxtgNpCMGDaEcadc36t1nDxhHTNK6lhy3kG9Wq8NbD4zMDMzJwMzM3MyMDMznAzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzOhmMpD0M0kh6exC+ShJl0h6RtKLkm6RNKFk+RGSzpe0VNJKSXdImtTdTpiZWc80nAwkHQXsUVIuYDZwAPAJ4H3AMGCepNcUwi8FjgfOAA4GlgI3Sdqz0faYmVnPNZQMJG0NXACcVDJ7GrAP8MGIuCoifpbLNgE+W7WOPYCjgX+LiIsj4ufAEcAjwFnd6oWZmfVIo2cGXwUWRsRVJfOmAU9ExLxKQUQsB+YAhxbi1gJXV8WtA2YBUyVt2mCbzMysh+pOBpL2AT4EfLRGyO7A/SXlC4GxkkZWxS2OiJdK4oYDu9bbJjMzaw5FRNdB0jDgHuDaiDg9lwVwTtX7h4D5EXFkYdnjgIuBsRHxqKS5wJYRsXchbn/gZmBSRNxe0oYTgBMARo8evdesWbMa6uiKFSsYOXJk14EDQF/q64LHl/d6HaM3g6dWblg2YcxWLam7qBX1lvW3UvdA1Jf251bozf5OmTLl7oiYWDZvaJ3r+BywGXBOJzECyjKLuhm3gYiYCcwEmDhxYkyePLmz8I10dHTQ6DL9VV/qa9l/3N5sJ09Yx9cWbLgrL5k+uSV1F7Wi3rL+VuoeiPrS/twK7epvl8lA0ljgNOA4YNPCNf1N86DyX4BlwDYlqxiVp8/l6TJgbCdxy+pot5mZNVE9Ywa7ACOAK0hf6JUXwKfzvyeQrvnvXrL8bsAjEbEiv18IjJe0eUncGmBRIx0wM7OeqycZ3ANMKXlBShBTSF/gs4ExkvatLChpS+CQPK9iNun5g8Or4oYC7wfmRsTq7nbGzMy6p8vLRBHxPNBRLE/PmPFwRHTk97OBO4ArJH2GdMZwKmks4KtV67tH0tXAhXlgejFwIjAemN6z7piZWXc07beJIuIV0tPENwMXAdcCLwNTIuLRQvgxwGXA2cD1wGuBAyJifrPaY2Zm9av3bqKNRMRGd/9ExDLg2PzqbNmVpKeYy55kNjOzFvOvlpqZmZOBmZk5GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZdSQDSYdJukbSw5JWSnpQ0rmSXlWIGyXpEknPSHpR0i2SJpSsb4Sk8yUtzeu7Q9KkZnbKzMwaU8+ZwaeBl4HPAwcA3wZOBG6WtAmAJAGz8/xPAO8DhgHzJL2msL5LgeOBM4CDgaXATZL27HFvzMysW4bWEXNIRDxd9f5WScuA7wGTgV8A04B9gP0iYh6ApDuAxcBngU/msj2Ao4FjI+KyXHYrsBA4K6/HzMxarMszg0IiqPhNno7J02nAE5VEkJdbDswBDq1abhqwFri6Km4dMAuYKmnThlpvZmZN0d0B5H3z9Pd5ujtwf0ncQmCspJFVcYsj4qWSuOHArt1sj5mZ9YAiorEFpDHAb4F7I+Kfc9lDwPyIOLIQexxwMTA2Ih6VNBfYMiL2LsTtD9wMTIqI22vUewJwAsDo0aP3mjVrVkPtXrFiBSNHjuw6cADoS31d8PjyXq9j9Gbw1MoNyyaM2aoldRe1ot6y/lbqHoj60v7cCr3Z3ylTptwdERPL5tUzZvBX+Qj/OmAdcEz1LKAsq6jkfT1xG4mImcBMgIkTJ8bkyZPraPF6HR0dNLpMf9WX+jrjlOt7vY6TJ6zjaws23JWXTJ/ckrqLWlFvWX8rdQ9EfWl/boV29bfuy0SSRpDuGNoFmBoRj1XNXgZsU7LYqDx9rs64ZfW2x8zMmqeuZCBpGHAN8PfAgRGxoBCykDQeULQb8EhErKiKGy9p85K4NcCiehtuZmbNU89DZ5sAVwLvAA6NiDtLwmYDYyTtW7XclsAheV513DDg8Kq4ocD7gbkRsbo7nTAzs56pZ8zg/5G+vM8BXpRUPfj7WL5cNBu4A7hC0mdIl4VOJY0FfLUSHBH3SLoauDCfbSwmPcA2HpjehP6YmVk31HOZ6F15ehrpC7/6dRxARLxCepr4ZuAi4FrSU8tTIuLRwvqOAS4DzgauB14LHBAR83vUEzMz67YuzwwiYlw9K4qIZcCx+dVZ3ErgpPwyM7M+wL9aamZmTgZmZuZkYDZgrFr78qCq15qroSeQzazvGjFsCOPa8dT1eQe1vE5rPp8ZmJmZk4GZmTkZmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmfWQfy11YPCvlppZj/T2r6WePGEdM0rW719LbS6fGZiZmZOBmZk5GZiZGU4GZmaGk4GZmeFkYGZmOBmYmRlOBmZmhpOBmZnhZGBmZjgZmJkZTgZmZoaTgZmZ4WRgZmY4GQwK/t13M+uK/z+DQaC3f2++Fv/evFn/4TMDMzNzMjAzszYmA0mvlfRjScslvSDpJ5LGtqs9ZmaDWVuSgaTNgV8AbwQ+DHwQeD0wT9IW7WiTmdlg1q4B5OOBXYA3RMQiAEn3AX8A/hX4epvaZWY2KLXrMtE04M5KIgCIiMXA/wCHtqlNZmaDVruSwe7A/SXlC4HdWtwWM7OGtOvZnd6sVxHRayuvWam0Bvh6RJxSKD8bOCUiNrp8JekE4IT89g3Agw1Wux3wTDea2x8Npr6C+zvQub/Ns3NEbF82o50PnZVlIdUMjpgJzOxuZZLuioiJ3V2+PxlMfQX3d6Bzf1ujXZeJngO2KSkfleeZmVkLtSsZLCSNGxTtBvyuxW0xMxv02pUMZgN7S9qlUiBpHPC2PK83dPsSUz80mPoK7u9A5/62QLsGkLcA7gVWAqeTxg++BLwKeFNErGh5o8zMBrG2nBlExIvAfsBDwPeBK4HFwH5OBGZmrdeWMwMzM+tb+uSvlvbkR+wkjZB0vqSlklZKukPSpJK4TSSdKmmJpFWS7pX0vhrrPF7SA5JWS3pQ0kd62sfC+nu1v5L+RtI3JN0naUWOnS1pj5L1dUiKkten+kt/c9ySGv14d0lsr23fFmzbGTX6WXm9uiq2r2/bL0uaK+nZ3K4ZncTWtc0kvVvSb/Pf+MOSTpc0pJvdK1t/r/ZX0o6SzpV0V67jaUk/r7HPX15j+15YV2ciok+9gM1Jv1F0P/Bu0s9TLAD+CGxRx/JXAs+Tfv/oHcBPSGMTexbizgFWA58GpgDfAV4BDizEHZ/Lz8lxZ+f3J/aX/gIfB+4DTs59eA9wB7AK2Kuwvg7SeM7ehder+0t/c9wS4Gcl/RjVqu3bom27fUkf/5H00NKv+9m2/QtwO/A90jjijBpxdW0zYCrwMmlAdgpwUt7nv9Jf+gscDPwJ+Hfgn4GDgOtzfw8uxF4O/Llk++5cV3+a8aE08wX8n7wBd60qGw+sA07qYtk98od6TFXZUNLTyrOrynYgJYIvFpb/OXBfYdk/A98rxH03/7EN6yf93Y58SbCqbCvSMx3/VSjvAH7Zn7dvLl8CXNHF+np1+7aqryXLvj0v+7H+sm1z7CZ5uiu1vxzr3mbAb4FbC3FnAGtoQgJsUX+3BoaWfAYPArcVyi8HHutuf/riZaKe/IjdNGAtcHXVsuuAWcBUSZvm4qnAcOCKwvJXABMkjc/v/5F05FWM+z6wLbBPnX3qqs292t+IeCby3lIVt5w0gD+mCX1oRCu2b716e/u2q68fJn3hzepmu7urRz9AGRGv1FFHXdtM0muBPWvEDQPeVUddXen1/kbE83m7V5etA+6hyX+7fTEZ9ORH7HYHFkfESyXLDidl4ErcamBRSRxV9VQejCu2pxjXE63o70YkbQP8HfD7ktlvztcn1+Zxhn/poh2NaGV/D5H0Ur6ufGfJeEFvb9+Wb1tJm4BkEGoAAARnSURBVAGHA/8dEc+WhPTVbdtIHZTUU9ffbv6yfqlJ7WnLD25KGk5KimV/uztIekbSOkkPSfpcvWMk7fxtolq2ofwnKZaRfq6iu8tW5lemzxePlmvEUbLOYlxPtKK/Zf4v6begioNLt5GuVT9EOkX9EHCJpB0j4uwu2lOPVvV3DvAb0i3Lo0njJtdK+mBEXFGI763t245t+25gS9J16KK+vG0bqYOSeur9262UtftvtyfOBF4DTC+U3wPcTUpGI0hjg+eS/uOw47paaV9MBtDgj9gVYupZtpG4Wu1ppt7u74YzpVOBo4F/qT7FBYiIMwrh10m6FjhN0oXRnOdAer2/EfGJDQJSH+4k/XFcUViuN7dvS7ct6RLR08ANGzWkb2/betW7zTqLa2Z7env9G65YOho4BfhSRNy+QUMiigd2N0haAXxK0lci4g+drbsvXibqyY/YLetk2cr8ynSUpOJGK4ujZJ3bFOb3RCv6+1f5FrwvA6dHxHfrbONVpCONCXXGd6al/a2IiJeBHwGvkbRjIb63tm+rt+2OwP7AlcXrzJ3oK9u2XvVus87OoLam/X+7DZN0CGmQ+NKI+EKdi12Vp13+CmpfTAY9+RG7hcB4pf9jubjsGtaPESwENgVeVxJHVT2V65DF9hTjeqIV/QVA0geBi4CvRcQ5DbSxmUfQLetviWI/env7trqvHwCGUH6JqJa+sm0bqYOSeur621X6DbTNm9Selv3gpqR3kA5mriX918B1L5qnXW/fnt5e1ewX8CnSrVm7VJWNI91ZcXIXy+6ZO/3hwm1YvwfmVJVVbi39QmH5W4AFVe+HkU65LyvEXQI8CwzvD/3N5e/J9czsRhuvIw26dXnvdF/pb8myQ4G7gIdbtX1b3VfSPe739sdtW1hPZ7da1r3NSNfQ5xXiTqd5t5b2en/z/H8EVpCeL2jodmfgG6RnEl7XZWxPP5Bmv4AtSEc9C0i3Z00jPSjzJ2BkVdzOeUOcUVh+FukU7TjSgzo/Jj1o8pZC3Hm5/CRgMvDt/KEdUoj7SC4/O8edld9/rL/0F5iUy+YD/8SGD6S8uSru7XmH+5e8rvfmL4sAPteP+ntUjvsQ6WGjI0kP9wRwZKu2b6v25Rz7lty/0vvb+8m23Rc4jDTYH8C38vvDurPNgANz+Xdy3L/lz+/8/tJf4I2kS1pLch82eKCsUMdtwEeBdwKHkJ69eAX4dl39acaH0uwXMBa4BniB9JTeT4FxhZhx+QM8s1C+GfB14Mm84f8XmFxSxxDSUcLDpLOE+4o7XVXsv5LuwFhNeuLwo/2pv6S7D6LGa0lV3K7AjcDjua8rgF8BR/Wz/u4N/AJ4inSUtpx01je11du3Fftyjv1G7uvoGvP7w7btqLWfdnebkZLevTnuEdJDZ0P6S3+BGbViCnHb5LofzvvKStLB38fJD7d19fIP1ZmZWZ8cQDYzsxZzMjAzMycDMzNzMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzPg/wMPGIkd91586AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"beers.hist(column='abv', edgecolor='white')\n",
"plt.title('Alcohol by Volume (abv) \\n');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which one do you like better? Well, the `pandas` histogram took fewer lines of code to create. And it doesn't look bad at all. But we do have more fine-grained control with Matplotlib. Which method you choose in a real situation will just depend on the situation and your preference."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exploring quantitative data (continued)\n",
"\n",
"In the [previous lesson](./01_Cheers_Stats_Beers.ipynb), you learned how to compute the mean of the data using `np.mean()`. How easy is that? But then we wrote our own custom functions to compute variance or standard deviation. There are some standard numpy libraries that we can use instead. \n",
"\n",
"\n",
"##### Exercise:\n",
"\n",
"* Go to the documentation of [`np.var()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.var.html) and analyze if this function is computing the _sample variance_. \n",
"**Hint**: Check what it says about the \"data degrees of freedom.\"\n",
"\n",
"If you did the reading, you might have noticed that, by default, the argument `ddof` in `np.var()` is set to zero. If we use the default option, then we are not really calculating the sample variance. Recall from the previous lesson that the **sample variance** is:\n",
"\n",
"$$\n",
"\\begin{equation*} \n",
" \\text{var}_{sample} = \\frac{1}{N-1}\\sum_{i} (x_i - \\bar{x})^2\n",
"\\end{equation*}\n",
"$$\n",
"\n",
"Therefore, we need to be explicit about the division by $N-1$ when calling `np.var()`. How do we do that? We explicitly set `ddof` to `1`. \n",
"\n",
"For example, to compute the sample variance for our `abv` variable, we do:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.00018337855205347506\n"
]
}
],
"source": [
"var_abv = np.var(abv, ddof=1)\n",
"print(var_abv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can compute the standard deviation by taking the square root of `var_abv`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.013541733716680264\n"
]
}
],
"source": [
"std_abv = np.sqrt(var_abv)\n",
"print(std_abv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You might be wondering if there is a built-in function for the standard deviation in NumPy. Go on and search online and try to find something.\n",
"\n",
"**Spoiler alert!**\n",
"You will. \n",
"\n",
"##### Exercise:\n",
"\n",
"1. Read the documentation about the NumPy standard deviation function, compute the standard deviation for `abv` using this function, and check that you obtained the same value than if you take the square root of the variance computed with NumPy.\n",
"\n",
"2. Compute the variance and standard deviation for the variable `ibu`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Median value\n",
"\n",
"So far, we've learned to characterize quantitative data using the mean, variance and standard deviation.\n",
"\n",
"If you watched Prof. Sainani's lecture [Describing Quantitative Data: Where is the center?](https://youtu.be/tQ5slNYRcC4) (recommended in our previous lesson), you'll recall that she also introduced the **median**: the middle value in the data, the value that separates your data set in half. (If there's an even number of data values, you take the average between the two middle values.)\n",
"\n",
"As you may anticipate, NumPy has a built-in function that computes the median, helpfully named [`numpy.median()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.median.html). \n",
"\n",
"##### Exercise:\n",
"\n",
"Using NumPy, compute the median for our variables `abv` and `ibu`. Compare the median with the mean, and look at the histogram to locate where the values fall on the x-axis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Box plots\n",
"\n",
"Another handy way to visualize the distribution of quantitative data is using **box plots**. By \"distribution\" of the data, we mean some idea of the dataset's \"shape\": where is the center, what is the range, what is the variation in the data. \n",
"Histograms are the most popular type of plots in exploratory data analysis. But check out box plots: they are easy to make with `pyplot`:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.boxplot(abv, labels=['Alcohol by volume']);"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.boxplot(ibu, labels=['International bitterness unit']);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is going on here? Obviously, there is a _box_: it represents 50% of the data in the middle of the data range, with the line across it (here, in orange) indicating the _median_. \n",
"\n",
"The bottom of the box is at the 25th _percentile_, while the top of the box is at the 75th percentile. In other words, the bottom 25% of the data falls below the box, and the top 25% of the data falls above the box. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Confused by percentiles?_\n",
"The Nth percentile is the value below which N% of the observations fall. \n",
"\n",
"Recall the bell curve from our previous lesson: we said that 95% of the data falls at a distance $\\pm 2 \\sigma$ from the mean. This implies that 5% of the data (the rest) falls in the (symmetrical) tails, which in turn implies that the 2.5 percentile is at $-2\\sigma$ from the mean, and the 97.5 percentile is at $+2\\sigma$ from the mean.\n",
"\n",
"The percentiles 25, 50, and 75 are also named _quartiles_, since they divide the data into quarters. They are named first (Q1), second (Q2 or median) and third quartile (Q3), respectively. \n",
"\n",
"Fortunately, NumPy has a function to compute percentiles and we can do it in just one line. Let's use [`np.percentile()`](https://docs.scipy.org/doc/numpy-dev/reference/generated/np.percentile.html) to compute the `abv` and `ibu` quartiles. \n",
"\n",
"** abv quartiles **"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The first quartile for abv is 0.05\n",
"The second quartile for abv is 0.056\n",
"The third quartile for abv is 0.067\n"
]
}
],
"source": [
"Q1_abv = np.percentile(abv, q=25)\n",
"Q2_abv = np.percentile(abv, q=50)\n",
"Q3_abv = np.percentile(abv, q=75)\n",
"\n",
"print('The first quartile for abv is {}'.format(Q1_abv))\n",
"print('The second quartile for abv is {}'.format(Q2_abv))\n",
"print('The third quartile for abv is {}'.format(Q3_abv))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** ibu quartiles **\n",
"\n",
"You can also pass a list of percentiles to `np.percentile()` and calculate several of them in one go. For example, to compute the quartiles of `ibu`, we do:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The first quartile for ibu is 21.0\n",
"The second quartile for ibu is 35.0\n",
"The third quartile for ibu is 64.0\n"
]
}
],
"source": [
"quartiles_ibu = np.percentile(ibu, q=[25, 50, 75])\n",
"\n",
"print('The first quartile for ibu is {}'.format(quartiles_ibu[0]))\n",
"print('The second quartile for ibu is {}'.format(quartiles_ibu[1]))\n",
"print('The third quartile for ibu is {}'.format(quartiles_ibu[2]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK, back to box plots. The height of the box—between the 25th and 75th percentile—is called the _interquartile range_ (IQR). Outside the box, you have two vertical lines—the so-called \"whiskers\" of the box plot—which used to be called \"box and whiskers plot\" [3]. \n",
"\n",
"The whiskers extend to the upper and lower extremes (short horizontal lines). The extremes follow the following rules: \n",
"\n",
"* Top whisker: lower value between the **maximum** and `Q3 + 1.5 x IQR`. \n",
"* Bottom whisker: higher value between the **minimum** and `Q1 - 1.5 x IQR`\n",
"\n",
"Any data values beyond the upper and lower extremes are shown with a marker (here, small circles) and are an indication of outliers in the data.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Exercise:\n",
"\n",
"Calculate the end-points of the top and bottom whiskers for both the `abv` and `ibu` variables, and compare the results with the whisker end-points you see in the plot. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### A bit of history:\n",
"\n",
"\"Box-and-whiskers\" plots were invented by John Tukey over 45 years ago. Tukey was a famous mathematician/statistician who is credited with coining the words _software_ and _bit_ [4]. He was active in the efforts to break the _Enigma_ code during WWII, and worked at Bell Labs in the first surface-to-air guided missile (\"Nike\"). A classic 1947 work on early design of the electonic computer acknowledged Tukey: he designed the electronic circuit for computing addition. Tukey was also a long-time advisor for the US Census Bureau, and a consultant for the Educational Testing Service (ETS), among many other contributions [5].\n",
"\n",
"##### Note:\n",
"\n",
"Box plots are also drawn horizontally. Often, several box plots are drawn side-by-side with the purpose of comparing distributions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing categorical data\n",
"\n",
"The typical method of visualizing categorical data is using **bar plots**. These show visually the frequency of appearance of items in each category, or the proportion of data in each category. Suppose we wanted to know how many beers of the same style are in our data set. Remember: the _style_ of the beer is an example of _categorical data_. Let's extract the column with the style information from the `beers` dataframe, assign it to a variable named `style_series`, check the type of this variable, and view the first 10 elements."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"style_series = beers['style']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(style_series)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['American Pale Lager', 'American Pale Ale (APA)', 'American IPA',\n",
" 'American Double / Imperial IPA', 'Oatmeal Stout',\n",
" 'American Porter', 'Saison / Farmhouse Ale', 'Belgian IPA',\n",
" 'Cider', 'Baltic Porter', 'Tripel', 'American Barleywine',\n",
" 'Winter Warmer', 'American Stout', 'Fruit / Vegetable Beer',\n",
" 'English Strong Ale', 'American Black Ale', 'Belgian Dark Ale',\n",
" 'American Blonde Ale', 'American Amber / Red Ale',\n",
" 'Berliner Weissbier', 'American Brown Ale',\n",
" 'American Pale Wheat Ale', 'Belgian Strong Dark Ale', 'Kölsch',\n",
" 'English Pale Ale', 'American Amber / Red Lager',\n",
" 'English Barleywine', 'Milk / Sweet Stout', 'German Pilsener',\n",
" 'Pumpkin Ale', 'Belgian Pale Ale', 'American Pilsner',\n",
" 'American Wild Ale', 'English Brown Ale', 'Altbier',\n",
" 'California Common / Steam Beer', 'Gose', 'Cream Ale',\n",
" 'Vienna Lager', 'Witbier', 'American Double / Imperial Stout',\n",
" 'Munich Helles Lager', 'Schwarzbier', 'Märzen / Oktoberfest',\n",
" 'Extra Special / Strong Bitter (ESB)', 'Rye Beer',\n",
" 'Euro Dark Lager', 'Hefeweizen', 'Foreign / Export Stout', 'Other',\n",
" 'English India Pale Ale (IPA)', 'Czech Pilsener',\n",
" 'American Strong Ale', 'Mead', 'Euro Pale Lager',\n",
" 'American White IPA', 'Dortmunder / Export Lager',\n",
" 'Irish Dry Stout', 'Scotch Ale / Wee Heavy', 'Munich Dunkel Lager',\n",
" 'Radler', 'Bock', 'English Dark Mild Ale', 'Irish Red Ale',\n",
" 'Rauchbier', 'Bière de Garde', 'Doppelbock', 'Dunkelweizen',\n",
" 'Belgian Strong Pale Ale', 'Dubbel', 'Quadrupel (Quad)',\n",
" 'Russian Imperial Stout', 'English Pale Mild Ale',\n",
" 'Maibock / Helles Bock', 'Herbed / Spiced Beer',\n",
" 'American Adjunct Lager', 'Scottish Ale', 'Smoked Beer',\n",
" 'Light Lager', 'Abbey Single Ale', 'Roggenbier', 'Kristalweizen',\n",
" 'American Dark Wheat Ale', 'English Stout', 'Old Ale',\n",
" 'American Double / Imperial Pilsner', 'Flanders Red Ale',\n",
" 'Keller Bier / Zwickel Bier', 'American India Pale Lager',\n",
" 'Shandy', 'Wheat Ale', 'American Malt Liquor', 'English Bitter',\n",
" 'Chile Beer', 'Grisette', 'Flanders Oud Bruin', 'Braggot',\n",
" 'Low Alcohol Beer'], dtype=object)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"style_series.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Already in the first 10 elements we see that we have two beers of the style \"American IPA,\" two beers of the style \"American Pale Ale (APA),\" but only one beer of the style \"Oatmeal Stout.\" The question is: how many beers of each style are contained in the whole series? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Luckily, `pandas` has a built-in function to answer that question: [`series.value_counts()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) (where `series` is the variable name of the `pandas` series you want the counts for). Let's try it on our `style_series`, and save the result in a new variable named `style_counts`."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Herbed / Spiced Beer 9\n",
"Schwarzbier 9\n",
"American Double / Imperial Stout 9\n",
"Belgian Strong Pale Ale 7\n",
"American Dark Wheat Ale 7\n",
"Bière de Garde 7\n",
"Doppelbock 7\n",
"Bock 7\n",
"Belgian Strong Dark Ale 6\n",
"California Common / Steam Beer 6\n",
"American Wild Ale 6\n",
"Baltic Porter 6\n",
"Foreign / Export Stout 6\n",
"English Dark Mild Ale 6\n",
"Dortmunder / Export Lager 6\n",
"Euro Dark Lager 5\n",
"Mead 5\n",
"Irish Dry Stout 5\n",
"Dubbel 5\n",
"Maibock / Helles Bock 5\n",
"English Strong Ale 4\n",
"Quadrupel (Quad) 4\n",
"Munich Dunkel Lager 4\n",
"Dunkelweizen 4\n",
"American India Pale Lager 3\n",
"Keller Bier / Zwickel Bier 3\n",
"English Barleywine 3\n",
"Chile Beer 3\n",
"English Bitter 3\n",
"American Barleywine 3\n",
"Radler 3\n",
"English Pale Mild Ale 3\n",
"Shandy 3\n",
"Rauchbier 2\n",
"Abbey Single Ale 2\n",
"English Stout 2\n",
"Euro Pale Lager 2\n",
"Old Ale 2\n",
"Roggenbier 2\n",
"American Double / Imperial Pilsner 2\n",
"Grisette 1\n",
"American Malt Liquor 1\n",
"Low Alcohol Beer 1\n",
"Braggot 1\n",
"Flanders Red Ale 1\n",
"Wheat Ale 1\n",
"Flanders Oud Bruin 1\n",
"Kristalweizen 1\n",
"Smoked Beer 1\n",
"Other 1\n",
"Name: style, dtype: int64\n"
]
}
],
"source": [
"style_counts = style_series.value_counts()\n",
"print(style_counts[-50:])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(style_counts)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"99"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(style_counts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `len()` function tells us that `style_counts` has 99 elements. That is, there are a total of 99 styles of beer in our data set. Wow, that's a lot!\n",
"\n",
"Notice that `value_counts()` returned the counts sorted in decreasing order: the most popular beer in our data set is \"American IPA\" with 424 entries in our data. The next-most popular beer is \"American Pale Ale (APA)\" with a lot fewer entries (245), and the counts decrease sharply after that. Naturally, we'd like to know how much more popular are the top-2 beers from the rest. Bar plot to the rescue! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we'll draw a horizontal bar plot directly with `pandas` (which uses Matplotlib internally) using the [`plot.barh()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.barh.html) method for series. We'll only show the first 20 beers, because otherwise we'll get a huge plot. This plot gives us a clear visualization of the popularity ranking of beer styles in the US!"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"style_counts[0:20].plot.barh(figsize=(10,8), color='#008367', edgecolor='gray');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualizing multiple data\n",
"\n",
"These visualizations are really addictive! We're now getting ambitious: what if we wanted to show more than one feature, together on the same plot? What if we wanted to get insights about the relationship between two features through a multi-variable plot? \n",
"\n",
"For example, we can explore the relationship between bitterness of beers and the alcohol-by-volume fraction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scatter plots\n",
"\n",
"Maybe we can do this: imagine a plot that has the alcohol-by-volume on the absissa, and the IBU value on the ordinate. For each beer, we can place a dot on this plot with its `abv` and `ibu` values as $(x, y)$ coordinates. This is called a **scatter plot**.\n",
"\n",
"We run into a bit of a problem, though. The way we handled the beer data above, we extracted the column for `abv` into a series, dropped the null entries, and saved the values into a NumPy array. We then repeated this process for the `ibu` column. Because a lot more `ibu` values are missing, we ended up with two arrays of different length: 2348 entries for the `abv` series, and 1405 entries for the `ibu` series. If we want to make a scatter plot with these two features, we'll need series (or arrays) of the same length.\n",
"\n",
"Let's instead clean the whole `beers` dataframe (which will completely remove any row that has a null entry), and _then_ extract the values of the two series into NumPy arrays."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"beers_clean = beers.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1405"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ibu = beers_clean['ibu'].values\n",
"len(ibu)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1405"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abv = beers_clean['abv'].values\n",
"len(abv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that both arrays now have 1403 entries—not 1405 (the length of the clean `ibu` data), because two rows that had a non-null `ibu` value _did_ have a null `abv` value and were dropped."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the two arrays of the same length, we can now call the [`pyplot.scatter()`](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.scatter.html) function."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,8))\n",
"plt.scatter(abv, ibu, color='#3498db') \n",
"plt.title('Scatter plot of alcohol-by-volume vs. IBU \\n')\n",
"plt.xlabel('abv')\n",
"plt.ylabel('IBU');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hmm. That's a bit of a mess. Too many dots! But we do make out that the beers with low alcohol-by-volume tend to have low bitterness. For higher alcohol fraction, the beers can be anywhere on the bitterness scale: there's a lot of vertical spread on those dots to the right of the plot. \n",
"\n",
"An idea! What if the bitterness has something to do with _style_? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bubble chart\n",
"\n",
"What we imagined is that we could group together the beers by style, and then make a new scatter plot where each marker corresponds to a style. The beers within a style, though, have many values of alcohol fraction and bitterness: we have to come up with a \"summary value\" for each style. Well, why not the _mean_… we can calculate the average `abv` and the average `ibu` for all the beers in each style, use that pair as $(x,y)$ coordinate, and put a dot there representing the style. \n",
"\n",
"Better yet! We'll make the size of the \"dot\" proportional to the popularity of the style in our data set! This is called a **bubble chart**.\n",
"\n",
"How to achieve this idea? We searched online for \"mean of a column with pandas\" and we landed in [`dataframe.mean()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html). This could be helpful… But we don't want the mean of a _whole_ column—we want the mean of the column values grouped by _style_. Searching online again, we landed in [`dataframe.groupby()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html). This is amazing: `pandas` can group a series for you! \n",
"\n",
"Here's what we want to do: group beers by style, then compute the mean of `abv` and `ibu` in the groups. We experimented with `beers_clean.groupby('style').mean()` and were amazed… However, one thing was bothersome: `pandas` computed the mean (by style) of every column, including the `id` and `brewery_id`, which have no business being averaged. So we decided to first drop the columns we don't need, leaving only `abv`, `ibu` and `style`. We can use the [`dataframe.drop()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html) method for that. Check it out!"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"beers_styles = beers_clean.drop(['Unnamed: 0','name','brewery_id','ounces','id'], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have a dataframe with only the numeric features `abv` and `ibu`, and the categorical feature `style`. Let's find out how many beers we have of each style—we'd like to use this information to set the size of the style bubbles."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"style_counts = beers_styles['style'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(style_counts)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"90"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(style_counts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of beers in each style appears on each row of `style_counts`, sorted in decreasing order of count. We have 90 different styles, and the most popular style is the \"American IPA,\" with 301 beers…\n",
"\n",
"##### Discuss with your neighbor:\n",
"\n",
"* What happened? We used to have 99 styles and 424 counts in the \"American IPA\" style. Why is it different now?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK. We want to characterize each style of beer with the _mean values_ of the numeric features, `abv` and `ibu`, within that style. Let's get those means."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"style_means = beers_styles.groupby('style').mean()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>abv</th>\n",
" <th>ibu</th>\n",
" </tr>\n",
" <tr>\n",
" <th>style</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Abbey Single Ale</th>\n",
" <td>0.049000</td>\n",
" <td>22.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Altbier</th>\n",
" <td>0.054625</td>\n",
" <td>34.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American Adjunct Lager</th>\n",
" <td>0.046545</td>\n",
" <td>11.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American Amber / Red Ale</th>\n",
" <td>0.057195</td>\n",
" <td>36.298701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American Amber / Red Lager</th>\n",
" <td>0.048063</td>\n",
" <td>23.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tripel</th>\n",
" <td>0.089750</td>\n",
" <td>23.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vienna Lager</th>\n",
" <td>0.050429</td>\n",
" <td>24.357143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wheat Ale</th>\n",
" <td>0.060000</td>\n",
" <td>24.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Winter Warmer</th>\n",
" <td>0.069500</td>\n",
" <td>24.625000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Witbier</th>\n",
" <td>0.050417</td>\n",
" <td>16.208333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" abv ibu\n",
"style \n",
"Abbey Single Ale 0.049000 22.000000\n",
"Altbier 0.054625 34.125000\n",
"American Adjunct Lager 0.046545 11.000000\n",
"American Amber / Red Ale 0.057195 36.298701\n",
"American Amber / Red Lager 0.048063 23.250000\n",
"... ... ...\n",
"Tripel 0.089750 23.500000\n",
"Vienna Lager 0.050429 24.357143\n",
"Wheat Ale 0.060000 24.000000\n",
"Winter Warmer 0.069500 24.625000\n",
"Witbier 0.050417 16.208333\n",
"\n",
"[90 rows x 2 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"style_means"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking good! We have the information we need: the average `abv` and `ibu` by style, and the counts by style. The only problem is that `style_counts` is sorted by decreasing count value, while `style_means` is sorted alphabetically by style. Ugh."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that `style_means` is a dataframe that is now using the style string as a _label_ for each row. Meanwhile, `style_counts` is a `pandas` series, and it also uses the style as label or index to each element.\n",
"\n",
"More online searching and we find the [`series.sort_index()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) method. It will sort our style counts in alphabetical order of style, which is what we want."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"style_counts = style_counts.sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Abbey Single Ale 2\n",
"Altbier 8\n",
"American Adjunct Lager 11\n",
"American Amber / Red Ale 77\n",
"American Amber / Red Lager 16\n",
"American Barleywine 2\n",
"American Black Ale 20\n",
"American Blonde Ale 61\n",
"American Brown Ale 38\n",
"American Dark Wheat Ale 5\n",
"Name: style, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"style_counts[0:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above, we used Matplotlib to create a scatter plot using two NumPy arrays as the `x` and `y` parameters. Like we saw previously with histograms, `pandas` also has available some plotting methods (calling Matplotlib internally). Scatter plots made easy!\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"style_means.plot.scatter(figsize=(8,8), \n",
" x='abv', y='ibu', s=style_counts, \n",
" title='Beer ABV vs. IBU mean values by style');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's rad! Perhaps the bubbles are too small. We could multiply the `style_counts` by a factor of 5, or maybe 10? You should experiment. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But we are feeling gung-ho about this now, and decided to find a way to make the _color_ of the bubbles also vary with the style counts. Below, we import the [`colormap`](https://matplotlib.org/api/cm_api.html) module of Matplotlib, and we set our colors using the [_viridis_ colormap](https://matplotlib.org/examples/color/colormaps_reference.html) on the values of `style_counts`, then we repeat the plot with these colors on the bubbles and some transparency. _What do you think?_"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib import cm\n",
"colors = cm.viridis(style_counts.values)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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y/20zTIVzyXE7D79iZhe6kZjZFqLRt5d8XmJ/QZSwvY/onPWJ+e7IzF4a978sXZbkmZruqc/5VB+/9XNs8nJj+ThRl4T/HZ/npsfZYfOYh1Bqg/oISiVdW9Lx24hO0q8kao56lGg+NADc/VEz++9EU8XsN7N/JRpZ2EnURPgCov6Dj8cP+VOipq0fAV5sZv9B1I9vLdFI2JuIao8W1Cdwmjkv6ebuD5nZI8Dzzewad99fsvo5Jc9/arDIa4m+8L9C1A+rnE8Cv0X0/GAec8XNENvTZvYAUbNTCviruFao1I3A58zsfqIrnpwlmj7jDUSjoj9SUvZ7iV6LTzLzfIcL8VNmNjUAo5Goc/rUiO+fKxksg7s/YWbvJhrN+Vg8UOcY0fHxaqJj6i6iWtpl4+7ZuF/jF4jmDPwS0QCcZBzTi4jmFnxV/JCPEB1X/xAP6BkCXkj0mt9NNMJzKf0HURP0fyM6Jj5Upsw3iQZVvcnM1hDV1m0lqh2+i2ieuvn4ENFx8jEzewVRcv8yogSrXDP4O4mOgQ+b2Y8T1VqNEiU6zyX63Ez96Hsr8E4z+yrRKO8xonkeX0FUS1Z28uwZHAUeNbPPxdt/M1HT9Y+5e7muBp8kGvC1jqjW9ESZMjP5I2CDmd1NdH5LEM2deT3w1+5+BsDdR+P+ji8ys78ADhB9Lv50WkyXFYu7nzWztxL1F94Xn2+fIqpV30p03H6SZ360Sy3zKhi6rFt93Zh5+phxogTwNyiZs2zaY59LNMHrKaLpLk4Tzcf3HmBjmfJvBb5K9As6S3RS/yLRl0rp3GCfYI7pR6ZtNyAatJAH1sxR9mfjbf9u/P+LZ3j+o0RfsO8CUnNs81/ix+y9wvfiZ0r2f8lUG0RTovwe0Zf/WaIaiYNEXwLT58W7k1mmtCiz7anXYT7TxxSJOuj/E/DiWbb5CqJk5Ez83gwSjfB8B9PmvZsjtvfG+71kX8TTiszwuEumSYmXbyT6EXMgPg4H4mP9T4DbppV9GdEkyCNE80p+lujHziXHaMlrfmeZfc64bo7n/kclr/v2GcqsjuM5SfS5fZDoszb1nr53nq/Ld8XHfDZ+zz5INFDmMGWmliFKRH6FaJT0GNFn5imiOQa/r6Tc7UTXqt5HlEyPEiXgv83C5xFsIqpRO0l0/D8MvHGOx94VP/Z7F/ja/xDR5PgH4te1nyjh/UkgOa3stUTnsqGS92vzQmOZ6T2L1+2K3+djROfbc0STof8ecO1Cnptu1Xuz+M0WERGRKxRPBXWAKIHc4BdPAVW3sUj1Uh9BERGRxfO9RK0ef1kFiVc1xSJVSjWCIiIiVygexLKRqBtCErjG4z599RyLVD8lgiIiIlfIzA4TjeJ9nOh6019WLFILlAiKiIiI1Cn1ERQRERGpU0oERUREROqUEkERERGROqVEUERERKROKREUERERqVNKBEVERETqlBJBERERkTqlRFBERESkTikRFBEREalTSgRFRERE6pQSQREREZE6pURQREREpE4pERQRERGpU0oERUREROqUEkERERGROqVEUERERKROKREUERERqVNKBEVERETqlBJBERERkTqlRFBERESkTi17ImhmG8zsT8zsPjMbNzM3s81lyjWY2R+Y2Skzm4jL31GmXGBmv2xmh81s0sy+Y2bfvxzPRURERKSWVaJGcBvwg8AA8PVZyv0l8BPAe4DXAKeAL5rZTdPK/SbwXuCDwHcD9wN/b2avXtywRURERFYWc/fl3aFZ4O5h/PePA38ObHH3wyVlbgQeBt7u7h+PlyWBfcCT7v66eNkq4Bjwe+7+ayWP/wrQ6+43LM+zEhEREak9yeXe4VQSOIfXAXngMyWPK5jZp4F3m1nG3bPAK4E08NfTHv/XwMfMbIu7H5ptRz09Pb558+aFPAURERGRinjwwQfPuXvvYm1v2RPBeboOOOTu49OW7yNK/LbFf18HZIGny5QD2AXMmghu3ryZPXv2XHHAIiIiIkvNzI4s5vaqddRwF1EfwunOl6yfuh/0S9u3p5cTERERkWmqNRE0oFznRbvMchevNHuHme0xsz19fX2XGaKIiIhIbavWRPA85WvzOkvWT913mtn0xG96uYu4+0fdfbe77+7tXbRmdhEREZGaUq2J4D5gi5k1TVu+C8jxTJ/AfUAGuLpMOYDHlixCERERkRpXrYngXUAKeOPUgnj6mB8CvhSPGAb4N6LE8K3THv/DwN65RgyLiIiI1LOKjBo2sx+I/7w1vv9uM+sD+tz9a+7+sJl9Bni/maWIRv6+E9hCSdLn7mfN7H3AL5vZCPAQUbL4UuD1y/R0RERERGpSpaaP+ftp/384vv8a8OL477cBvw38FtABfAd4lbs/NO2x/wsYBf4HsAZ4EvhBd//84octIiIisnIs+5VFqs3u3btd8wiKiIhILTCzB91992Jtr1r7CIqIiIjIElMiKCIiIlKnlAiKiIiI1CklgiIiIiJ1SomgiIiISJ1SIigiIiJSp5QIioiIiNQpJYIiIiIidUqJoIiIiEidUiIoIiIiUqcqda1hERGRi+TyBYbHsoyMTXJuaIzBkXEKxRB3SCYCGjMpVnW20N7aSEtThuaGNGZW6bBFapoSQRERqRh35/zwOAdP9HP09MCF5Q3pJJlUdJsqNzaR4/GhMxSLDkB7SwM7Nq9iTXcbqWSiIvGPj0xw8sBphvtH6Vzdztqtq2loylQkFpHLoURQREQq4tS5IR59+hTDY5OkU0l62psJgplr+NIpaG5MX/h/fDLHN/ceJZkwtm/sZcfGVSSXMSEcODvEfZ/fg4dOujHNyQOneeqhQzzvdbtp6WhetjhEroT6CIqIyLKazOXZ8/hRvv7wQdyd1V2tdLY2zpoEltPUkGZ1VwvtLY08cfgsX35gP+cGx5Yo6ou5O9+5ex+Zxgxdaztp6Wime10XYeg8fv/+ZYlBZDEoERQRkWVz5vwI/37/kxw9PcjqrlaaGtJzP2gOyUTAqs4WzOCre/bz6NMnKYbhIkQ7s/GRCUYHxmhqa7xoeVt3C6ePnKOQLyzp/kUWi5qGRURkWRw/M8j9jx6mvaWBjkxq0bff1JCmIZ3iySNnGZ/Mc+vOq0gmlqa+w8zwMsvdIdAAFqkhqhEUEZEld/zsIPc9cojOtkYaliAJnBIExqrOFo6dGWDPY0eXrGawqbWRrjUdjJwfvWj5UN8Q67evIZlSPYvUBiWCIiKypPqHxrj/0cN0tTeRXoYEyWwqGRzk0adOLtl+brhjJ0FgnDvRz8DZIc6d6KeptZEdz962ZPsUWWz6ySIiIksmXyjyrX1HaW3KLEsSOMXMWNXVwv6jfazrbWdVV+ui76O1s4U73vhc+o71Mzo0RltXK70bulQbKDVFR6uIiCyZxw+dYWwyx6rOlmXfd2BGR2sj33rsKK+4fceSJKLpTIr129Ys+nZFlouahkVEZEmcHx7nySNn6Gmv3Jx6jZkU2XyBxw6erlgMItVMiaCIiCyJp4720ZhJLXh+wMXW3dbMgRP9TGbzFY1DpBopERQRkUU3Ppnj+NlBWpsbKh0KQWAYcOzMYKVDEak6SgRFRGTRHTs9gFn1zKnX1tLA/qNnl3yiaZFao0RQREQW3YET/bRXQW3glEwqyWSuwODIRKVDEakqSgRFRGRRTebyTGTzyzpdzLw4jI5nKx2FSFVRIigiIotqdDxH2euvVVhDJsnZgdG5C4rUESWCIiKyqIbHJqmSroEXaUgnOTeoRFCklBJBERFZVKPjWZLJRKXDuEQ6lWR8Io97FVZXilSIEkEREVlUxTCkwlMHzswgVCIocoESQRERqRvVmp+KVIoSQRERWVTJREAYVl+tm7vjXj1zG4pUAyWCIiKyqNqaG8gXqm/i5lyhSEtTBlMiKHKBEkEREVlULU2ZqmyDnczm6e1srnQYIlVFiaCIiCyqlsYMVOGAjGy+QHe7EkGRUkoERURkUWXSSZqbMmRzhUqHcjE32qrosnci1UCJoIiILLptG3oZGp2sdBgXTObyNDemaW9prHQoIlVFiaCIiCy6Davaozn7qmT08PDoJDs2rSKo2gkORSpDiaCIiCy6hkyKTWu6GBqdqHQoFMMQCwLW9bZXOhSRqqNEUERElsT2jT3kCkWKxcpOJdM/NM6Ojb1k0smKxiFSjZQIiojIkmhvaeRZV6/l3NBYxWIYm8jR3Jhmx6ZVFYtBpJopERQRkSVz9VW9dLQ0MjK2/ANHimHI6HiW23ZtJJlMLPv+RWqBEkEREVkyyUTA7us2ks0Xmczml22/oTtnB0bZuXW15g4UmYUSQRERWVIdLY284KatDI9llyUZDN05e36U7Vf1snPzmiXfn0gtUyIoIiJLrrezhRfevJWR8SyjE9kl20+hGHKmf4TtG3u4cft6TRcjMgclgiIisixWdbXykt3bMTPOnh+hGC7uaOLhsUnOD49z8471SgJF5kmJoIiILJvOtiZefts17Ni8inMDYwyPTeJXeF3ibL7A6f4RGjNpXv7sa9i+cRVmSgJF5kOTKomIyLJKJhNcf/U61va089ih05zpHyWRMDpaGkkm5lc/4e6MjGeZyOZpzKS5decGNq3tIhGofkNkIZQIiohIRXS3N/PCm65mZGySo6cHePr4OQqFEAySQUAmnSQwA4sSv3yhSC5fZKr+cE1XK7t3baS3o0XNwCKXSYmgiIhUVGtzA9ddvZadW9YwNpFlZDzLwPA4gyMT5ItF3KNpaBobUvR2tNDa3EBLY5p0Sl9hIldKnyIREakKQWC0NjfQ2tyg6wKLLBN1phARERGpU0oERUREROqUEkERERGROqVEUERERKROKREUERERqVNKBEVERETqlBJBERERkTqlRFBERESkTikRFBEREalTSgRFRERE6pQuMSciInIFioUifScGGDg7RFgIaWhJs2pDD62dzZUOTWROSgRFREQug7tzeN9x9n/7MPlsnmQmSRAEFLJ5Hrv/aXrXd3Pdc7crIZSqpkRQRERkgdydvffu59DeY3Su6SCVbr1k/fDAKPfctYfnveYW2rtbZ9iSSGWpj6CIiMgCHdt/ikP7jtOzoZtU+tI6FTOjrauFTGOaB774CPlcoQJRisxNiaCIiMgChGHIU98+THtvK0Fgs5Ztam0kO57jzNG+ZYpOZGGUCIqIiCzAwJkhxkcnyTSk51W+uaOJA48cXeKoRC6PEkEREZEFGBuewGz2msBSjc0ZRgbGKBaKSxiVyOVRIigiIrIAYTHE5mgSns7McPclikjk8ikRFBERWYBMY5qwGM67fD5XIJlKkEgmljAqkcujRFBERGQButd1kkgE827qHe4fZcv1GxfUnCyyXJQIioiILEA6k2Lzrg0M9g3PWbZQKOLurL969TJEJrJwSgRFREQWaNuNm2hpb5o1GSzkC/SfHOD6526nua1xGaMTmT8lgiIiK5y7MzmeJZfNVzqUFSPdkOL2776J1q5m+k6cZ/j8KMVCkTAMyU7kOHfyPEPnRrjpRbvYvGtDpcMVmZEuMScisoLlcwUe/upeTh86Cwbbb9nKjt1Xq7/aImhoyvC877mFgbPDHHnsBH0nzlMsFGloznDdc7azdssqGpoylQ5TZFZKBEVEVrCnHjrI6YNn6V7fhYfOEw88TeeqdlZv6q10aCuCmdG1up2u1e2VDkXksqhpWERkBes/OUBLVwtmRpAISGdS8xrkICL1QYmgiMgK1t7dyvjwOBD1FcxP5mjpaKpwVCJSLdQ0LCKygl1z29UM9g1x7kQ/7rDxuqtYu1VTmYhIRImgiMgK1tCU4flveDajg2MEiYCWjmYNFBGRC5QIioiscIlkgvaetkqHISJVSH0ERUREROqUEkERERGROqVEUERERKROKREUERERqVNKBEVERETqlBJBERERkTqlRFBERESkTikRFBEREalTSgRFRERE6pQSQREREZE6pURQREREpE4pERQRERGpU0oERUREROqUEkERERGROqVEUERERKROKREUERERqVNKBEVERETqlBJBERERkTpVtYmgmT3fzL5kZmfNbNjMHjKzt08r02Bmf2Bmp8xswszuM7M7KhWziIiISC2pykTQzG4AvgykgJ8Avh/4FvCXZmU25XoAACAASURBVPbOkqJ/Ga9/D/Aa4BTwRTO7aXkjFhEREak9yUoHMIM3AQngte4+Gi/7dzO7EfgvwJ/Gf78FeLu7fxzAzL4G7AN+A3jd8octIiIiUjuqskYQSAN5YGLa8kGeifl1cZnPTK109wLwaeCVZpZZhjhFREREala1JoKfiO8/YGbrzKzDzH4CeBnwvnjddcAhdx+f9th9RInktmWJVERERKRGVWXTsLvvNbMXA58D/mu8OA/8lLt/Ov6/Cxgo8/DzJetFREREZAZVmQia2Xbgs0S1ez9F1ET8euAjZjbp7n8DGODlHj6P7b8DeAfAxo0bFytsERERkZpSlYkg8DtENYCvcfd8vOwrZtYN/LGZ/R1RzV+5LK4zvj9fZh0A7v5R4KMAu3fvLpdMioiIiKx41dpH8FnAd0qSwCkPAN3AKqLawi1m1jStzC4gBzy95FGKiIiI1LBqTQRPAzeZWXra8tuBSaLavruI5hl849RKM0sCPwR8yd2zyxSriIiISE2q1qbhDwJ/D3zezD5M1EfwdcCbgfe5ew542Mw+A7zfzFLAIeCdwBbgrZUJW0RERKR2VGUi6O7/z8xeDfxP4C+ABuAA8NPAn5UUfRvw28BvAR3Ad4BXuftDyxuxiIiISO2pykQQwN2/AHxhjjITwM/HNxERERFZgGrtIygiIiIiS0yJoIiIiEidUiIoIiIiUqeUCIqIiIjUKSWCIiIiInVKiaCIiIhInVIiKCIiIlKnlAiKiIiI1CklgiIiIiJ1SomgiIiISJ1SIigiIiJSp5QIioiIiNQpJYIiIiIidUqJoIiIiEidUiIoIiIiUqeUCIqIiIjUKSWCIiIiInVKiaCIiIhInVIiKCIiIlKnlAiKiIiI1CklgiIiIiJ1SomgiIiISJ1KVjoAEZFa5e5ADjwHno3/LpSUCMBSYBkgDZbBLFGZYEVEylAiKCIyT+45CIfxcBDCU1A8EyWBZnEBwEr+8KkFBuZREeuGxBos0QPWBtaMTT1eRGSZKREUEZmFexYvHIPCkxAORwmdG1gjWBsWzP806h6CT0L+CTxfiBPIFJ7cjCW3YkHX0j0REZEylAiKiJTh4Xk8fwCKB4AQrB2CVVdUe2cWgDUBTc/sxwuQP4Tn9+NBN6R2Yom1mKWu/EmIiMxBiaCISMw9jxdPQf4xCPuBFATdS9qvzywJie5o/+EoZO/BLY0nd2DJLVjQsmT7FhFRIigidc/d8cIRyO+JBn1YG5ZYu+xxRElfC+55yO/D84/iyaux9I2YNSx7PCKy8ikRFJG65uEonvsWFE9EtX9V0E/PLAWJVVGfwsIhvHgMT9+OJTZoYImILColgiJSl9xDvHAAcnvA0lhiXaVDuoRZECeEk5D9Gp7YBOlbsKC50qGJyAqhCaVFpO54OIZnvwK5b0LQhQWdlQ5pVmYNEKyF4ml88p8JC8cqHZKIrBBKBEWkrnixH5/8NwiHscS6mhmda2ZYojuaezD7NcLco1HTsYjIFVAiKCJ1I8wfwSe/CKSrvhZwJmYZCFZD/jt49t5okmsRkcukRFBE6kKYfwpy/xk3Bdd2HzuzRNRUHB7Ds/coGRSRy6ZEUERWvDC/H3L3Q7Aas3Slw1kUZoYFqyE8i2f/U8lgGYV8gYEzgwz2DROGakYXKUejhkVkRQvzh+JBIaujyZtXGAtW4eEZPHsPZO5Ykc/xckyMTfLNf/k2I+dHcHfWbl3NLS97Fonk0k0OLlKLVCMoIiuWF/sgd298abiVmyBZsDoaUZz/Nu5e6XCqwlMPHmR8aJye9d30bujh5IEznHj6dKXDEqk6SgRFZEXycBTPfi26SkiNjAy+IsEqyD8ZzY0ojAyM0tDyzNVY0pkU48PjFYxIpDopERSRFce9gGe/AQQ1PzBkvswCCHohd39UE1rnetZ3MzIwirtTLBTJTuToWNVe6bBEqo4SQRFZcbywH8J+LOiodCjLyiwF1o7n7o+uV1zHtt20mY0719N/6jwDZ4fY9bxrWL2pt9JhiVSdldtpRkTqkodDkHs4qh2rQxY04+FpPP84lr6h0uFUTCKZ4OaXXM/1z7+WIDANEhGZgWoERWTFcA/x3ANgDdFce/XKeiH/KB6er3QkFZdKJ5UEisxCiaCIrBheOAjFM3XXJDydWQKsGc8+gHux0uGISBVTIigiK4L7JOQfrNsm4eksaIOwHy8crnQoIlLF1EdQRFYELxwDwvqYKqZEIV9kbHiC0ZEJJkazgJNuSNHS1kRTSzOZYB+e3BKNKhYRmUaJoIjUPPcQCo+B1c/0ILlcgTPHznHm+AAeOkEQkEwlwGB4cJyzxwdwD+ldk6N74xHaurdUOmQRqUJKBEWk9oVnIRzDEmsqHcmyGOof4eDjJwmLIc0tjQQJK1vO3RkdmuT0fV+kd/Nr2LprHUGgmkEReYYSQRGpeZ7fD9ZY6TCWRf/ZIQ7sPU5TSwOp9OyncDMj1dBNT1M/T377cSbGslx32xYlgyJygc4GIlLTPByF4nGw1kqHsuTGRiY4uO84zW2NcyaBzzCCRMCGjeMcevwkR548taQxikhtUSIoIjUtGhUbrPjBEGEx5NDjJ0k3pEkucF68QqGV5qbDdK9u5rE9hxgZ1DV3RSSyss+cIrKiRYNEnoCgs9KhLLnB8yNMjGVpaEwv+LHuKSwo0NAwTDKV5OBjJ5YgQhGpRUoERaR2+RiQq4spY04fPU9D4+U/T/eAVHKQ1s4mThw8S3Yyt4jRiUitUiIoIrUrHAIvP2J2JZmaKzDdsPDawClh2EA63U8QBDgwPKDmYRFRIigiNczDPqiD2sDsRA64soQ3DDOkkoNAEQNGBsYWIzQRqXFKBEWkdhVP1cW0McUwxK644jPaQDIxTiKVIDuZv+K4RKT2KREUkZrkno+ahmmodChLbtEavx0SiZHoSiRXnlmKyAqgRFBEapOPAYbVQUKTSifxRdiOe4JEYoRioUhrZ9MibFFEap0SQRGpTZ6DRUmPql+mMUUiMMLilT1f9yTJYAKA5raV36QuInNTIigitcmzi9hmWt3MAnrXdzA+OnlF23FPEhZHaGppoLVDNYIiokRQRGqU+2S9VAgC0LOmgzAMCcPLf9LuCXITw2x71gZdb1hEACWCIlKzcsDCLrVWyxqbG1i7qYexocuf/298rEBLe5J1W1YtYmQiUsuUCIpIbfKw0hEsu7Wbemhub2L0MpLB7GSOsBhy1dU9JBI69YtIRGcDEalRddJBsEQiEbD9WRtoamtkeGBsXoNH3J2x4QmKhZAdN28k05RZhkhlORXyBfK5QqXDkBqVrHQAIiKXxQLqqpNgLJlKsuOGjZw5cZ4TB86CGY3NGZKpi5vJw6IzMZ6lUCjStaqdjdtWkUonwCcqFLkstsG+YfY/eJAzR84B0L22g2tu3UrP+q4KRya1RImgiNSoBqD+mocBgkTA2o09dK1q4/zZYc6eGGBiNBu18TjgTpBK0LWqjd51HTS1NmAY7jmwlT8Bdz3oPzXAfZ9/kHRDiu51HQCMD0/wjbv2cNt33cC6q9dUOEKpFUoERaQmWZCpw/rAi2Ua0qzd2MPajT0XmgfdIZlKkMoksenN554Ha65MsLJo3J1H/vNxGlsbaGp9Zj7I5vYmUg0pHvnPx1m9qZdEsn4GU8nlUyIoIjUqU4/dBGeUTCVJpmY/pY+PTnLiSMDJo98kO5EjCAKa25vYvHM9q6/qmvPxUh1GBsYYHRqnZ13nJevSmRTDuREGzgypiVjmRZ96EalNlqEe+whejtxkkb0P9HHy4CkstZa27tW0draAO9mJHN+++zGSqSTX3LyJLddtqIvL9tWyYr7IrG+RGYV8cdnikdqmRFBEapM1A0ncQ8w0AcJMJscLPPDlE4yNFOhencQya7EgdWF9U0uCppYGCvkie+9/irGRCa5/zvayyeDo4BiHHztOIZdn/ba19G7oXs6nIrGmtkZwCIshwbSpgNwdDz0qIzIPOnuKSE0yCyDoAb/8CZZXumIhZM/dpxgfK9C5qgELArDyl5ZLphL0rOvi0N7jHHj02CXrx4bHuedzD3D0sROcOXKOe+/aw5kjfUv9FKSMTGOajbvWc/7M0CXrhs4Os3pTD21dLRWITGqREkERqV2JNUoEZ3H2xBgDfRN09DTEE3An4ib18oLA6F7bwZMPHSI3mb9o3Zkj58hnC3Subqetq5WW9maefvjw0j4BmdGu27ezemM3fcf7GTgzxODZYfqOn6e1q4UbX7Sr0uFJDVHTsIjULAu68DqdQmY+DuwbpLktHf3jkxB0XDqSeJpEMgEOp470sWnHugvL3dUfs5okU0lue+VNDJ4d5syxc+BOz7ouutZ26DrSsiBKBEWkdgXtlY6gao0MZBnsm6Rn3VRTcB6CS0eZltPS0cSBvccuSgTXbO5l/54DDJwZIpEKmBzLcv0LdixB5DJfZkbn6nY6V+tzIJdPiaCI1CyzRjzowMNxLCjf961eTU4UsaC09i+EoHVej800pjl3apAwDC/ULjW3NfHC77udQ3uPUiwUNVhEZIVQIigitS15HeTuBZQIlrqoKTecBGsFW9gAgrAYXtTM2NLRzLNesHOxQhSRKqCOBCJS0yy5FiyBe6HSoVSVZDIomWZxHBJXzdk/cEoYOmamK1OI1AElgiJS08zSkLwGwsFKh1JVWjrSBAmjmM8BKUjM/yoTo4NjrNvaq4mlReqAEkERqXmW3AKW18jWEulMgk072hg+PwSJ9Rjzr93LTuYuGigiIiuXEkERqXkWtEOwBnyk0qFUlQ1bWykWChS9Z96PGR0ap62zhc5VbUsYmYhUCyWCIrIiWGon+Filw6gqrR05rnvONvpPZykU5r727PjoJPlcgVtevEvNwiJ1QqOGRWRlCFZD0IaHo1igy2u5h+CjbL7+FVg6z977nibdkKS1s/mSCYfzuQLD50dJpZM897tvorWzuUJRi8hyUyIoIiuCWQLSz8Un/w33puhaxEtgfCTHyEAWM6Ojt4F0Q5WeRr0fktsJkqvZsgs6V7Vz5PGTHD9wiqgrpQEODumGFDtu3cKGratpaJ75EnQisvJU6RlMRGThLNGDp3ZBYT/YqkXddiEf8tg3T3P0ySFwB4uuzbtj9yq2Xt9VVU2p7pNAEkvfdGFZR08rHS/cwY5bNzPYN0IhX8QM0o1pula1aaoYkTqlRFBEVhRLXY8XjuA+gVnjom13772nObZ/kO51TQTxFTuKhZC9954mmQzYtHN+l29bau4O4XnIvAizS2v3GpoyrNmkWj8RiWiwiIisKGZpLPMcCM9H/eQWwdhQjmNPDdKz/pkkECCRDOhc3ciTD/ZRLC7Ovq5Y2A+JzVhiQ6UjEZEaoERQRFYcS6yF1C4Izy7K9ob6JzEo2/ybziTIZYuMDeUWZV9XwsNRsBSWvqWqmqpFpHopERSRFclSN0JiDV48d+XbspKrtZXjXvHEyz0HPoo1vAgLdN1lEZkfJYIisiKZJbHMcyHI4OHwFW2rozfqaxiGl6aD2YkCja0pmtvTV7SPK+FehLAP0s/HgvlfSk5ERImgiKxYZo1Y5kVAIWo2vUyNLSmuvqGbvhNjFArP9AXMTRYZ7Jtk17NXX9R3cDlFSeAZSN9KkNpUkRhEpHZp1LCIrGgWdEDDS/HJr+Ahlz3Z9LW7V5FKBzz9nX7CouMOmcYEu1+xgbVbKnM5tgtJYOoGLLmzIjGsZMPnRzm87xinDp4lSBhX7VjHpp0baGxpqHRoIovG6v0i7bt37/Y9e/ZUOgwRWWIeDuKTXwaCKDm8TPlcPDDEoK2roYI1gfloMEzqFiylS8Ittv5TA9z/+QcJkglaOptxd0bPj5FIJXje626ltVNXr5HKMLMH3X33Ym1PTcMiUhcs6MAaXgXWgIdnudwfwal0go7eRjp6GiuXBIZj0TQx6RcQpK9TErjIisWQh77yKE3tTXSsaiOZSpBKJ+lc044ZPHrPE5UOUWTRKBEUkbphQQvW8HIINkB4CvdCpUNaMA8HgBzW8F0EqS2VDmdFGjwzxORotuzl9lo6m+k/NcDY0HgFIhNZfOojKCJ1xSwNmefhhW7IfRu3hitqKl4u7jkIz0FiHZa+HQuaKx3SipXL5qM5g8owM8yMXDaP3gFZCZQIikjdMQuw1E48sQ7PPYAXT0LQi1mq0qFdIrpkXD8Y0fQwyc1qCl5iDc2ZGbsOhKHjoZetLRSpRUoERaRuWdAOmZfhhYOQfxAPAwg6MauOXjMejoEPQnIrlrpZE0Uvk47eNtp7WhkZGKO18+J6v6G+YdZtW0Njs0YOy8pQHWc7EZEKMQsIUtuwhu+B5EYI+/DwNO7ZisTjHuLhebx4CiwBmZcSZJ6vJHAZmRm3vvxZBGb0nxxgbHic0cFxzp04T0tHM9c/b0elQxRZNKoRFBEhHkiSuR1P34AXjkLhsXhgRiNY25I3x7pno9o/J6oBTG6DoEvNwBXS0tHMHT9wO6cOn+XM4XMEgbHu6mtYtbGbZEpfnbJy6GgWESlh1oilduDJ7RCexfNPQPEkjoE1gzVilrji/UR90LLg40Au2nbqVix5FWaNV7x9uXLphjSbrt3Apms3VDoUkSVT9Ymgmb0aeDdwCxAC+4Ffcvf/iNd3An8AvAFoBO4Dfs7dH61MxCKyEpgFkFiDJdbg4QhePA7FUxCei67o4Q6WBjJgKSBZtm9hlPAVgTx4DnySqNrPIGiLav8S6yBYVTV9E0WkflR1ImhmPwl8ML79JlGfxpuApni9AXcBW4D/DgwAvwx81cxucvfjlYhbRFYWC1qxYCekduIego9COISHfVAcAMbBh/CwCFg0wndq0KkBNIA1QmJ1lPAFHRC0RVPZiIhUUNUmgma2GXg/8Ivu/v6SVV8s+ft1wAuAl7r7V+PH3QccAn4J+JllCVZE6oZZANYWJXJcdWF5VPOXB0Kiixob0W/XlGr6RKRqVfPZ6e1ETcEfmaXM64CTU0kggLsPAZ8HXr+04YmIPCOaaDiNWQMWNEV9DS2jJFBEqlo1n6FeADwBvMnMDphZwcyeNrOfLilzHbC3zGP3ARvNTFcFFxEREZlBNSeC64DtRANBfg/4LuDfgQ+a2f+Iy3QR9Quc7nx837nUQYqIiIjUqqrtI0iUpLYCd7r7P8TL/iPuO/jLZvYBLu6SXWrWibfM7B3AOwA2bty4WPGKiIiI1JRqrhHsj+//fdryLwGrgbVENX9dZR47VRNYrrYQd/+ou+929929vb2LEauIiIhIzanmRHDfDMunavvCuMx1ZcrsAo66++hSBCYiIiKyElRzIvi5+P6V05a/Ejju7qeJ5hBcb2YvmlppZm3Aa+N1IiIiIjKDau4j+K/AV4E/M7Me4CDwA0SDRt4Wl7mL6Eoif21mv8gzE0ob8PvLHrGIiIhIDanaRNDd3czeAPwu8OtE/f6eAN7q7n8blwnN7DXAHwIfBhqIEsOXuPuxykQuIiIiUhssmg2/fu3evdv37NlT6TBERERE5mRmD7r77sXaXjX3ERQRERGRJaREUERERKROKREUERERqVNKBEVERETqlBJBERERkTpVtdPHiIhIJJ8rcOLp0xx69Ci5yRxdazrYesMmutd2zv1gEZFZqEZQRKSK5bJ5vvkvD/HIfz6OBUZzexMDZ4a45x8f4PA+TZcqIldGiaCISBU7vO8YA2eH6N3QRaYxTSKZoLWrha41ney95wnGhscrHaKI1DAlgiIiVSoMQw4+cpT23rZL1iVTCQgCzhzuq0BkIrJSKBEUEalSYTEkny2QSpfvzp1KJxgfnVzmqERkJVEiKCJSpRLJBI3NGXKT+bLr89kCrZ3NyxyViKwkSgRFRKqUmbHtli0M9Q0z/brwuck8QWCs2byqQtGJyEqg6WNERKrYVTvWcf7UAMf3nyLTnCGZSpAdy+Hu7H7ljWQa05UOUURqmBJBEZEqlkgE3PSS69h47XqOPXmS7ESOq3asY/22NTS3NVU6PBGpcUoERUSqXBAE9Kzvomd9V6VDEZEVRomgiMg8FAtFshM5gAvz+YmI1DolgiIisxgbnuD4U6c4tPcYxUIRgEQqydZnXcWGbWtoam2scIQiIpdPiaCIyAz6TpznW196BIC2rmaSqeiUmc8VePrhwzz98BFu/+6b6F7TUckwRUQum6aPEREpY+jcCPd/4WGa2xrpWt1+IQkESKWTdK3uoKm1gW/+67cZPj9awUhFRC6fEkERkTL2P3SITENq1ulZGpoyJJIJDj56dBkjExFZPEoERUSmGR+Z4PSRPlo65r5qR2tXM8efOs3kWHYZIhMRWVxKBEVEphkdHAeDILA5ywZBABijQ2NLH1gNKBZDJsezFPKFSocyq+xEjtxkrtJhiFScBouIiEwThiHG3EngM5ww9LmLrWDFYsjhvUd5+uHD5CcLWGBcde06tt+yhcbmhkqHd0Hf8X6e+NYBBs8OAdC5uoOdz95G97rOCkcmUhmqERQRmSbdkLrk2r6zcYd0JrWEEVU3d+c7d+9j7737aWxpoHt9J+2r2jj+5Cnuu+tBJsero9n85MEz3Pv5B8lN5i5M0D05Psk37voWZ470VTo8kYpQIigiMk1HbxsNzRlyk/k5y06OZ2npaKS9p3UZIqtO508Pcmz/KXo3dJGKE+JEIqBzTTvjo5Mcfux4hSOMJgR/9OuP09HbdtHcj81tTbR1t/LI1x8nDMMKRihSGUoERUSmCYKA7TdvYaBvaNaaQXdn6NwI227chNlCmpJXlhMHTpNpTJV9Ddq6Wzi891gForrYYN8wuck86YZLa24zjWmy4zmGzo1UIDKRylIiKCJSxsYda7lq+1r6Tg5cuKJIqUKhSN+J82zeuYH129ZUIMLqkZ/Mz3jJvWQqQSF36eu33IqFuft9hgXVCEr90WAREZEygiDgxhftorm9iQOPHCEshiTTSXDI5wskEwl2PnsbV9+wMR45XL+613Vx6uDZstPtjA2N07O+8gMxWjqacI8G9UwfDR6Gjjs0tetygVJ/lAiKiMwgkQjYcetWtlx/FX3HzzM6EF1BpLWrhd4N3aTSOoUCrN2yiie++RQTo5M0tjwzQriQLzI+MsHNL7m+gtFFmlob2XDNWk48ffqSEcLnTw+ycee6qhrdLLJcFnQWM7P3zFHE3f03ryAeEZGqk86kWH/1amB1pUOpSpnGNLd/zy088IWHGRs6TyKZoFgMMYcb79hFz/quSocIwHXP30F2MsfZo/0kkkFUQ1gMWbtlNbuec02lwxOpCFvIFAlmNlsHCgdw9/IdRarU7t27fc+ePZUOQ0RWoFw2z5mj5+g7PkA+lyedSbF6Uw+rNnRddO3ilaKQL9B3rJ/RwXHSTSlWXdVTdbVs7s7g2WH6Tw0QBEbX2k7ae1rrerCP1BYze9Dddy/W9hZ0JnL3SzrCmFkn8FrgXcAbFikuEZGaFYYhTz18hIOPHKVYCGlozhAkjOFiyPGnT5NKJ7nmls1sue6qFZWAJFNJ1m6t7lpTM6NzdTudq9srHYpIVbjin6TuPgD8lZl1Ax8CXn3FUYmI1KgwDHn4a49z4ukzdK1pv2Q0bWtHM4V8gb3feIrxkSzXPWfbikoGRaS2LOZQt+8Adyzi9kREas7T3znKiafP0LO+c5YpVZL0bOji4KNHOLb/1DJHKCLyjMVMBF8D6Bo9IlK38rkCBx45Quea9jlr+YLA6Oht58kHD+mKFiJSMQsdNfyxMovTwPXAs4BfW4ygRERq0dnj/RTzRZIz1AROl25IMdQ/Qv+pQXqrZGStiNSXhfYRfCnx6OASk8AR4P3AJxcjKBGRWtR37DyZpsyCHpNMJTl/WomgiFTGQkcNb16iOEREal4hXyBILGzgRyIZVMUl2ESkPtX3dZFERBZRKpOiuMDr1RbyRVINqSWKSERkdgtOBM2sw8x+3cy+ZGb74vv3mlnHUgQoIlIr1mzqITuRW9BjioUiPesqfy1eEalPC0oEzexG4Cngl4EG4LH4/leA/Wb2rEWPUESkRvSs6yTTkCKfK8yr/OR4ltaOZjpXtS1xZCIi5S20RvADQD+w3d3vcPc3uvsdwDXAeeBPFjtAEZFakUgm2HHrVgbODM05JUyxUGTo3Ag7dm/RhNIiUjELTQRvA37V3Y+ULnT3w0RTxzx7keISEalJG69dx/abN3PuxMCMzcQTY1n6Tw1y/fOuYe3mVcscoYjIMxY6fUw/kJ1h3WS8XkSkbpkZ1+7eSltXM08+eJhzJ84TJBMEiYCwUCQshrR2NfPsV97Amk29lQ5XROrcQhPBPwV+0cy+5O6TUwvNrBH4BaJrDYuI1DUzY/3Va1i7ZRUDZ4YY6Bsmny2QyiTpXtNBR2+bmoNFpCrMmQia2W+U/gtsAo7a/8/efcVYmt75ff8+bzo5n8rVXdV5unsym8NZkssVNyjsrlYQpAsbsAU4QHf2hQHbEHxn2DeCAcM3BizYgA0IFgwLsKWVVpTJXQZxOENOZk/n7qrqyunk/KbHF1Vd7JrK1ZW6+f8AMz085z3veU4RffCrJ/z/Sv0FsAQMAH8MdID4cQxSCCFeRoZhUBjKURiSU8FCiLNJaf31RiFfu0CpgxTF0lrr/fVWOiNu3bqlP/nkk9MehhBCCCHEnpRSn2qtbx3V/facEdRaS9FpIYQQQohX0EH3CAohxJkQao0XBgQ6JNAaBZjKwFCKiClfbUIIsR/72SMYAL+jtf7V+jLxbmvJWmst38BCiCMVak3Ld2m4XUrdNkvdBqvdFqF+fufK2uELrTVxy6YvlmQgliLjREnbUaLW4dq4BX7A8myJucdLeF2PSNxh9OoQhaEcpikLJkKIl9t+Qtt/C8w+99+7byoUQogj0vZdpptV7lYW6QYeWoNpKOKmQz4Sw1TbBzEvDFjuNJlpVggBNPTHktzIDTIQS2EZ+wtw5cUqn/zwNr1OEwxP9QAAIABJREFUj1gyimmbNGst5p4skUjHuPWHb5AupI7uAwshxAnb87DIq04OiwhxtmitWe22eFhfYapRRilF1o6+8HJvw+vR9FwipsWN3ABjyRxJO7Lj9dWVOh/8i0+IZ2LEEtEtz7fqHbyey3f/zjdJZhMvNDYhhNivEz8sIoQQJ6Xa6/DL5aesdJtETYv+aBLjiOrtpewIKTuCFwb8ujTPF6U5bmQHuJkbwjG3Fju48+FDIonItiEQIJGOUVv1uf/xE2790ZtHMkYhhDhpEgSFEKcuCEMe1Jb5fHWOqGUxFE8f23vZhkl/LEmoNfeqy0y3qny7f5y+WHLjmnqpQXmxSt9oYdd7pfJJFiZXaDc6xFOxYxuzEEIcF9npLIQ4VdVehx/OPeSz1VmK0QRZ52QClaEUA7EkSsMPZu/z+eosbhAA0Ki0UMbeM5GGoUBBs9o67uEKIcSxkBlBIcSpedqo8PPFiWOfBdxNwnaIWTb3qsvMtmp8f/gyYbj/vdNKcaDrhRDiLJEZQSHEqXhcW+Fni0/IR+InNgu4k2ezg27o88O5B/g27PccnQ4hGt/50IkQQpxlEgSFECfucW2FD5ef0hdNbHtQ47RknRho+DhYJLQVbs/b9fpuq0cqFydTlBIyQoiXkwRBIcSJmmqU+cXSWgi0jbMTAp9JO1Es06Q0qlicLxEG27dbD/yAWqnBtVsXUUd0slkIIU6a7BEUQpyYUrfFz5cmz2wIfCbjRAku5mj0VlmaLpFIxUhmExiGIgw1jUqTXtvlje9cZejCwGkPVwghDk2CoBDiRPhhyIfLUyQt50wtB+8kH4nTu5ql/2KKyGyPxamVtSeUYuTyIOPXR8gNZE53kEII8YIkCAohTsS9yhI1t8tg7OXZT9cXTTLZbfDH37vBW793g8APsGwTy5avTiHEq0H2CAohjl2p2+LL8hzFyMvVis0yDBKWw0dLUyjLIBqPSAgUQrxSJAgKIY5V8GxJ2I5gGafzleP3fHqNHr1GD6+z+0ngr0s7Ucq9Ng9qy8c0OiGEOD3yq60Q4lgtdRpUeh2GT7hgdBiENFdarDxcobHUWKv8DKA1iWKC/mv9pPqTGNbe4bQvmuSr8gJX0n0vxf5GIYTYLwmCQohjdaeySMo+2YLLrXKbqV9M4bZdnLhNsi+xqcRLr9lj8oNJzIjF+PtjpPqTu9xtbYnYDwPm2zXGU/njHr4QQpwYWRoWQhybmtthqdMgaTkn9p7NlSaP/vIRhqFID6SIpqJb6vxFkhFS/UnsiMnjnzyhOlvd874pJ8qdyiJ6vy1HhBDiJSBBUAhxbCbqZWzDPLGCy916lyc/myCajuAk9g6fdtQmnosy+eFTWqXWrtcmLIdKr02p1z6q4QohxKmTICiEOBZuEPCwtnyifYSX7i1hmAZ21N73ayzHwolZzN9e3PNax7B4XFt9kSEKIcSZIkFQCHEsam6HIAxP7KSw2/GoTFeJZaIHfm0kGaG53KBb7+56XcaJMtOqyPKwEOKVIUFQCHEsam4XTrAFb2W6AkqhjIO/qVIKwzIpT1V2vc4yDNwgoO0frASNEEKcVRIEhRDHYrnTIGruf4n2RbVWWjixwxdCiCQcGsuNPa9TChre7jOHQgjxspAgKIQ4FsvdJrETDIKB62OYh/9KMwxF4AZ7XqdQVHqdQ7+PEEKcJRIEhRBHrhf4tHz3RIsvG5aBDg+/d09rva/i0jHLZrFTP/T7CCHEWSJBUAhx5Nq+y4luEASimRhezz/0672uTyy990GTqGmt7X8UQohXgARBIcSRC7RGcbIna3Pnc/ta2t2J3/UoXCrseZ1C4Yfhod9HCCHOEgmCQogjF+qTD0rxXIxEIYHbcg/8Wq/r4yQcEoXEntcaSuGfwucTQojjIEFQCHHk1AkvCz8zcKOfbqNLGOw/qOlQ0652GLgxcKjSM0II8TKTICiEOHJrLeVOPlRlhjMMvTFMY7m5rzCoQ01jucnAa/3kx/P7eo9QaywlX51CiFfD4YtuCSHEDswT6i28nYHr/ShTMf/lPKZtEstEt5SV0aGmU+vguwEDNwcYujG4737IodbYJ9QtRQghjpsEQSHEkYtbzqm1YVNKMXCtn/RAitJkmdJECR2G6PUZSrX+r8KFPPkLBRL5+IHu3wt9cs7BXiOEEGeVBEEhxJGLmBZJ26EX+ETM0/maiWVjjL4zwuCNAdrlNoG3dqLYsEzi+Th29HDj6gQe1+OpoxyqEEKcGgmCQohj0RdLsdCunVoQfMaKWKSH0kd2vzDUZJy96w0KIcTLQDa6CCGOxWAsSS84fIHnM0tBWoKgEOIVIUFQCHEsXsWw5IchEcM60R7KQghxnCQICiGORcaJYRomXnj4bh9nTc3tMJbK7fuEsRBCnHUSBIUQx8I2TF7L9FN1O6c9lCPjhiGX0sXTHoYQQhwZCYJCiGNzMZXHD8NTKyXzonSo8Xs+Xten3ulQjMbJR6R0jBDi1SGnhoUQxyblRBmOp6m53Zdmz6DWmk61Q2myTHmygkYDinqvw9sXxlgIV+gbzmPZ5mkPVQghXpgEQSHEsXotN8hfzj18KYJgu9Jm5tM52pU2lmOSKMRRhsLXIdq3SGLz6U/uYTsW194ZZ+zakOwXFEK81CQICiGO1WAsRV80Qc3tnun6e/XFOhMfPMWJWaQHNheMrrs9rmSKpBIJUukEvutz+8NHNKttbrx3EUNazgkhXlLy7SWEOFaGUrzfP04n8PDD8LSHs61Wuc3Ez6eIpSNEkpFNzzV9l4wTZSj2m6LUlmNRHMoxcW+Wh188PenhCiHEkZEgKIQ4dtlIjLcLI6x0m6c9lC10qHn6y2kiCQcrsnmRJNAaLwi4lu3bMuunDEVxKMejL6eprjZOcshCCHFkJAgKIU7Ea5l+iutLxGdJc7VFr+niJJwtz9XcLpcyReJWZJtXgmEYOFGb6YcLxz1MIYQ4FhIEhRAnwjQM3u8fpxt4Z6r13OrjVZzY1u3Sda9HxokyHNu9T3Eql2D28RLdtntcQxRCiGMjQVAIcWKykRi/N3iJUreNGxys44jf8ylPrrLw5RyV6TKB9+IdS7yuT22hTiS1dV+gY5jcyA3seRDEMAzCULO6UHnh8QghxEmTU8NCiBM1kszy3aGL/HxxgmIkgWPuXY+vW+vw+EcP8DouhmUQeCHRTJTLf/gaTnzrku5+Ba6PQm0qAdP0XAyleDM/TGSfPYVNy6TXkRlBIcTLR2YEhRAn7kIqz+8OXmS116K7j2Xi6Y+m0FqTGkyTKCZJD6Vx2y7zn8280Dh0qNcLRq9peD0sw+CtwghRa38hENYOjvjuq9NTWQjx20OCoBDiVIyn8vz+8BXqbpfKLv2I3VaP1kqDWDa26fFEPkHl6YstERvW2ldgoDUVt0PUsnmrMEzsACEQIAxCnNjBXiOEEGfBSxMElVI/UEpppdR/97XHc0qp/1UptaqUaimlfqSUeuO0ximE2L+RRIY/HbtJzokx167jhVtDnQ7XWrxtoUBr1v91OHbUxlUhlXaL8WSed/Ij+14Ofl4QhCQz0oNYCPHyeSmCoFLq3wfe2uZxBfxL4G8C/xnw9wAb+LFSavREBymEOJSUHeH7w1f4dv8YlV5ny+ygk4wQzcboNTaXnelUOmRHs5jO4bY6+2HIstdi4GofV608Y6n8oTqEuD2PWNyhMJg51DiEEOI0nfkgqJTKAv8j8F9s8/SfAd8F/kOt9T/TWv9g/TED+K9ObpRCiBdhKMXlTB9/OnaTvBNnoV2n1GsT6BClFOffH8fvBTSXGnRrHeqLdZShGH7n4L/vdQOfxU6DUq/Nm7lh/v777xHVJvqQM4uNSotLr4+eSpu5IAgJz2i3FiHEy+FlODX8j4E7Wut/ppT6P7/23J8B81rrHz97QGtdU0r9OfB3gP/8BMcphHhBKTvC749codJr86Re4lFthVBrMtko1//265SnyvSqHeKFONnxAnZ0f8u4WmtqXpeO75O0Hb5ZPM/5ZHbjQMjo5UHmJ5fJD2YPNN5Oq4dtmwyN9x/4s76IMAy599EjJm5PYyi49t5lLr01vun0sxBC7MeZDoJKqe8C/4BtloXX3QS+2ubxO8A/UEoltdZnr6eVEGJXuUicW31x3sgPMduqcqeySM1w4WKSKCkipgXW9jNwWmt6YUDHd+mFAWv7CzWjiQzXMv30x1IYXwtMN791iWa9TWW5Rq5/f0u83VaPdqPDt//WW0RfoITNYUx+Nc3jzycpjhbRYchXP39AIhNn6MLAiY5DCPHyO7NBUCllA/8L8D9orR/scFkemNrm8fL6nzlAgqAQL6mIaXEpXeRSukjH96h7Xaq9DkudBsudJr3Q33SMRK+fK8nYUcaSefpiSVJ2hLQTxTZ2rldoOxbv/cHrfP6z+yzPlUllE0QT27eV872ARqWJMhS/8zffJNe3e+eR47A6WyaZTWIYCgyTaCJKab4iQVAIcWBnNggC/zUQA/77Xa5Z+1V/+8d3fpFS/xD4hwDnz58/7PiEECcoZtnELJuBWIpr2bWlWC8MCLVGa41Saq04NNBzfZrtHtVSh9VOEz9Y20dnmQbpRIxsKkoyFiEWsTeWU52oza3fv8HC01WefDXL6nwFyzaxHAulFIEf4HY9bMfi4s1Rzl0ZJJ6MnsrPIpGJU5qrEE+vldTxeu7GfwshxEGcySColDoP/DfAfwpElFLP/2oeWT9A0mBt5i+/zS1y639u2/NJa/1PgH8CcOvWrcPXnhBCnKrnZ/mqzQ5P58tMLpQIAo1WYBoK2zI3wp7WmumlKjoMAYXjWFwczjM2lCcZi2BaJqOXBhi52E+t1GRxepVu2yXwQyIxi/xAlv6RPJa9dzeU43Tp7XFWZkuszpXQWlMcyXPu2sipjkkI8XJShz0pd5yUUn8N+PEel73D2mGQv6613nR0UCn1vwPf11qP7fVet27d0p988skhRyqEOE1aaxZLde5NLVOqtbAtk0wyimka6DCk2+wBEE1GUNuc6vX8gHqzix+EDBXTvDY+QDGTOLLxBX5Aq97BNA3i6diRHubwXJ/aah2lFNn+DKZ55otACCGOgFLqU631raO635mcEQS+AL6/zeM/Bv4p8L8Bj1mrIfgfKaV+T2v9UwClVBr428DXTxgLIV4hnZ7HF4/mmF6skE5EGSikNp4rT68yd2cWt70WBO1YhNHXR8mfL266h22ZFLIJtNZUmx3+6pOHXD3Xx40Lgzj24b8ewzBk8vY0jz6bxF/vfJLKJbj57WsUR7ZbxDg427EoDh/NvYQQv73OZBDUWleBn3z98fXfpp9qrX+y/r//JfAh8E+VUv8la0vB/4i1PYL/+ISGK4Q4YTNLFT69PwsKBgupTTNtq1MrTPzyCYlCgtT6CWCv5/Hko8eEoaY43rflfkop0okoyXiEJ3Ml5lfqvHfzPMVs8lDju/+rxzz+fIrsQAZ7veB1u9HhF3/+Kd/5O7coDOX2uIMQQpyMl3otQWsdAn8K/BD4n4H/BwhYWxZ+sW70QogzR2vNvalFPrw9RSLukE/HN4XAwA+Y/fU0qb4UduQ3NQbtiE2ymGL219ME/s69iQ2l6MslMUzFjz99zPTittuMd9Wqt3nyxVMKI/mNEAgQT8WIp6Pc/ejRge8phBDH5UzOCO5Ea71lg43Wugz8x+v/CCFeUVpr7kwucndikb58EnObPX/tahvfC4hvc5jDtE18z6ddbZMqprY8/7x41MG2TD66M0WoNeND+1+CrSzWQLFW2uVrEuk4q3NlOs0usVM6cSyEEM97qWcEhRC/PZ7MrnJnYpH+fGrbEAigQ81u5zGUUuhwfwfkbMukkEnw8d2nLKzW9j3OINi95ZsylLSFE0KcGRIEhRBnXrXR4YtHc/TnktvOtD0TW6+lt13QevZYLL3/mTjbMsmm4/zyzjSdnrev12SKqR37Frtdj2gsIrOBQogzQ4KgEOJM84OQj+9NE4s6e5ZIsaM2/ZcGaSzXN8386VDTWK7Tf2kQO3qwdnCR9dPDXzyc2zHgPS9TTNE3WlhbIn5OEIRUlmpc/eYljB1mNIUQ4qS9VHsEhRC/fR7PrFBrdujP776v75mRmyOEQcjKxNJ6nxHQaPovDTJy83BFl3PpGDNLFUb7M5wb2P3Er1KKb/zhG3z2V1+xPF1aP8yy1vnk5revce7q0KHGIIQQx0GCoBDizOq6HncmFykcoMizYZmMvTvO4NVBWpUWAIlcgsgLLMcqpcil43z5eJ7hvsyOexSfcaIO7//xu9RLDRrlJoZpkBvMEo1v379YCCFOiwRBIcSZNbdSQ8OhumZEktEXCn9b7udY1JodVqstBvY5O5kupEgX9netEEKcBtmoIoQ4k8JQ8+DpMpnE2TlYEYvYPJxePu1hCCHEkZEgKIQ4k8r1Fu2uS8Q5OwsXyXiEpXKDZqd32kMRQogjIUFQCHEmleudM3e6VimFUopGS4KgEOLVcLa+ZYUQYt1KtUn0DM0GPmOaBuV667SHIYQQR0KCoBDizNFas1ptEn2uX/BZEXUslivN0x6GEEIcCQmCQogzp+f5+EGIdYjTwsct6thUm53THoYQQhyJs/ctK4T4rRcE++sHfBoMQxGGmnCfPYuFEOIskyAohDhzNGc8ZOmXYIxCCLEPEgSFEGeOodRpD2F36iUYoxBC7MPZO5InhPitZ5kGOtS06x38nk8YhChDYUcsYunYev/e0xGs7108zTEIIcRRkSAohDhTfC9gda7C3Bcz+C0X0zIADSi01sSSUQYu9ZEZSGPZJ/8V1nE9Ctn99z4WQoizTIKgEOJM0FpTWqjy2b97gNv1SNg2rSTEHAtlGChjrZiz1/WY+nIW01SMv3Oe7EDmRMfZ7XlcHuk70fcUQojjIkFQCHHqVudK/OSff8TDz5+SG8rSP9ZPZabEwlKFaMQBwLRMMgNpUoUk6WISv+dx/+ePGLkxTP/5Ak7MQRnHv1yrQ8gkz07/YyGEeBESBIUQp6rd6PDj/+sXPH20hGFZTHwxzdyjJYZujBJJRInF1oJg6IeU5yqUpksYEQtlGrg9j/mJZYoXCmT6Mwxd6CM/kMVyzGMZaxhqUBIEhRCvDjk1LIQ4VY1Kk6cPF2lU2jSrLTL9aXSoScSjRCM2nh8AYFgGkZhDq9llaWqFylyFaCJCpi9Nt9rFMBVP781x95eP6B1TL+Bas8P4UJ6oc/Y6ngghxGFIEBRCnKp2o8vc5Ap+EBBLRQn8AMsxUYaikI7jemtBUIea8lKNwA9IZhOgFNW5CspQBF6A3wtIF1KEoebBZ5N4Pf/Ix+r6AReHC0d+XyGEOC0SBIUQp+rR7VliqRjaD2jX27hdj8HLQwAkYxFM0yAIQzqtHm7PxV7vP2xHLHwvpFlqYjkWtcUaALFklF7HY2WufKTjbHZ65FJxsqnYkd5XCCFOk+wRFEKcGs/zuf3RIy6+NU7gBQRBgBN1NsKeMg0G8ylml6t0Ki3sry3JOlGbdq1DIp+gU+/guz6WY5FIx1h6usrgWBFjH/2KwzCk1/Hotl0810eHYJgKJ2ITjTuYjkGr7fKtW2NSP1AI8UqRICiEODXLM2W8XoAdsTfC39elE1ESjk2p55JMfm02Tq2Vnem1eiilCIMQANM2CeoB7UaXZDa+7X211rSbPVYXapQW62i99phSCrV+32dd5Nqux7vvjhE15StTCPFqkW81IcSpmXm8iOXs8TWkFMVMgmkUYagxvlYixnZM2tU2iXwSHW7u//ssGH5dt+3y9OEyjWob0zKIp6Jb7vtMp+eRjlh4VZe/+sGvGb88wLUbwzh7jVsIIV4C8k0mhDg13baLae1d6sWxLXJRh2YQYCuL5zObYZp4XQ/Qm5eBFRjG5mVhrTWrCzVmHq9gmAbp3Pazhc/0XJ8gCHnj0hDxmEMYaqYnV1icq/Duty5SKKYO8nGFEOLMkcMiQohTFY3ZeO7uJ3wtxyQRc8jGIri+T6ifm/lTa3v8lFKY9lqoDIIAZShiycjGZVpr5iZWmbq/RCwRIf7cc9vpuj49z+fmxbUQCGAYinwxiWkbfPCT+8zPHu2BFCGEOGkSBIUQp8aJ2PQNZ+nuUfdPGQa5gQymH9KfjOP5Id76sq/WmsAPyQxmNmYEO/UOA+eLG8EQYH6yxMLTMqlcfL1/8fa01jTbPYIg5M1LwyTjzpZrYjGHTC7Oxx8+ZmmxepiPLoQQZ4IEQSHEqckPpIk86xwSbr+f75lUNoltWxhhyGAmgWUYdD0fr+dhORapYhKAXsdFKYO+kfzGa+uVNvNPS6Ry8R33AgL4QUC10SGbiPLWlWES24TAZxzHIpOL89kvJ+h03IN8bCGEODMkCAohTs3opQFA0TeSo1Xr7HqtaZuMXB4EwG+7FBNRcvEo7UaP+EAaTGhWWvg9n2u3LhBZD3G+FzB1f5FYIrJjCPSDgFqzS7fnc21sgGvjA0T2cRgkErHRGu58Ob12ylgIIV4yclhECHFqkpk4fSM56pUWnVaPZrW9Y7kXADtqc/7aMNWVBtWVOl6zSz4e5cLlIuVKi8xAhtGxPqLP9QJema/huT6pr903CEJ6no/nBdi2yYWhPMVsAts+WJ/ibD7O/EyFC5eaFPrk8IgQ4uUiQVAIcaquvHmOD/7Nl1x4bYip+wvUy00S6diOp4lN2yI7kMGyTdqNLr//925x6Y3zxNJRyo0OU0sVStUWvh8ShiGPHyxgRy1qjS6gYb0gtG0ZZJJR+rMp0smdy8fsRSlFJGYz+WRZgqAQ4qUjQVAIcaoKg1ne+NYlbn/0mIs3RlhdrLE4XcL3OutdRiwMQ6FDjef59NouhmEQS0b53p+9y1vfvbbR7SMejzI6kENrTafnMTm5QmemTrawFtAMBbZlEovYB575200yFWVhrkKn7RLbZV+hEEKcNRIEhRCnbvz6CEopbn/0mEQqypvvX6ZRa7M4XaLT7hF4IaZtEIna5M4XsCyT194d59q749u2fFNKEY86aDegv5Am83y9QA0ccZe4tdlETb3WliAohHipSBAUQpw6pRTj10dI5RN89eEj7n/8iDDUJHNJhs4PYVoWnusReCF9ozku3hylOJTds+/vwkyZZqNLebVBo9ah5/qg9dqMYjxCOhsjlV77xzBfLB2apkGl3GJgKPtC9xFCiJMkQVAIcSY0Ki1u//Q+jUqT/qEs9Uqb2lKV0myJ8ZvnuPLuOCMX+klmdu8GAlApNXl0f4GPP3xCIhXBsS2ciEkkutarWGuN5wUsL9RYmClj2SaDIzn6BjJY9uGKKURiNtVy81CvFUKI0yJBUAhx6lr1Dr/4808xTUXf6Fr9v77RArB2und1rgx+sGcI9LyAh3fnmHi4iGVbJJIRMtu8RimF41gb/YKDIGR2usTSfJULV/rJ5BIH/gyGYeB5wYFfJ4QQp0mCoBDihWmtKS9WeXpvlupyA8sxOf/aCMMX+3Gie++Zm/j1UwLfJ1PYuqxqmmvFoR99NsW5a8PEU7Ft79FqdvnVB49oN3sU+lLo3xwQ3pNpGqTTMTwv4P6deYZHcoyOFVFSaVUI8YqTrzkhxAsJw5A7Hz7kg//3Y1Zny9iOSeiHfPXBfX76zz+ivsdyqdvzmL4/T6aY3vEawzRQpmJ+Ymnb51vNLr/4yX18L6DQl8IwDJRSKMM4UKFn2zbJZOLMz1WYnlxZO1iyT4EfEInY+3+BEEKcARIEhRB76rZ7LD1dYenpCt325r7AMw8WmPjyKcXRAulCCjtiE01EKA7nUYbBxz/4gsDfecn0WZ9h09z96ygSc6itNLY87nkBv/rgEQCp9G9mCw1DkUhE8NyDLdcqBZlMnKWFKovz++8j3O16FPt2DrNCCHEWydKwEGJHYRjy8JMnPP786cbkmAIuvT3GtW9eAuDx55Nk+tLbFmROZuKszJRYnSszMNa37XsYhtrXrJ0O9bZh8dG9eVrNHsVtijmnMjGW5qs4kYN91Sm1VhtwZmqVTDZOLLH38rYO195PCCFeJjIjKITY0cPPJnnwyQS5wQzFkRzFkRy5wQwPP53k4acTdBpdOq0ukdjOQSkSd1ieLu34fDwdIxKL4Ha9XcfSbffoHytueqxabvH4/gL5wvaHO7LZOEFwuB7Apmlg2yaTj5f3XCIOghBDKTK7tMcTQoizSIKgEGJbbtflyedTFIZzGM/NxBmmQWEkx5MvntLruHuGJGUognDn5VnDMLj89hjVlfqO13TbPZyITf+5wqbHJx4tEo3ZGMb2X2WJZJRozMbz/N0HuYNY3KHV7NBsdHe9rlHrcP5i34FnHoUQ4rRJEBRCbKtRbqG13rbnr2kahDrE63nYjoXn7hy0um2X4nB+1/c6/9owQxf6WZ4tbbqX1ppGpUWj0uLWH72JZf8maHXaLnMzZZI7nCKGtWXnoZEc7Za76/vvxrZNlhZrOz4fhiG+FzA2XtzxGiGEOKskCAohtrdH6RWFwrItLr49TmV5+6DUbfewHWvH/YHPmJbJu3/wOjfev0K73mF1vkJpocLqfJV0McXv/t33KAznNr2mtFJHwbZ7E59X7EuSTEXptA8XBqOxCOXVBmEQbvt8pdTi0tUh0rIsLIR4Cck6hhBiW5liCtMy8T1/00wcgO8FmJZJupgi25+mulRlfmKZdCFJLBEl8APq5SY6CPnWn7yL7ez9VWNaJpffGufCzXM0yk3CUBOJR0ikt5/xK602cfZRrsUwDMYv9fHlx1N4XRcdrq1lm6aBFbFwIvampe+vU2tthOl2fOLJzXshW80u8XiEK9eH9hzHy6Dd6DD7aIHl6VUs2+LctWEGxopb/v8XQrw65G+3EGJblm1x/VuX+fKn98gNZjbCnOf6VBZrvPG71zYee/cP32RoYoknXzxlZa6MaRmcf22E8RujJLMH69JhWibZ/sye15VXG0SjuwfBwA+pLNdZeLqCW+swt1TU6eiMAAAgAElEQVQjErU39RVWhiJTTJEuJHcJlppu190UBNutHp4b8J2/dhXb3rp8/rIpL1b56F99htaaWDpKt9Xj0x/+mvxglvf++J19hW4hxMtHgqAQYkdjN0ZRSnHvV4/xXR+lwLQt3vq964zdGN24zjQNRq8MMXpliDAM14o577etxyF12i7ZXVrBNattJu7O0eu4xBJRhs7nSWRjLM5VsBxrI7yFoaa22qC6XKcwlN22FI6h1Ka9i7VKG4Bv/95rpF+BkjGBH/Dxv/2SWCpKNBHZeDyRiVNeqPDw0wle//a1UxyhEOK4SBAUQuxIKcXYjVFGrw7RqKwdHknnk9seIHlmpxO8R01rvWMLuZWFCpN3FogmHNL55Mbj6XQM2zJZXKjSabvrJ44V0XiEMAxZna/QafUYHCtuXi5WijDUuK5PtdxiYCjLm++MEX8uNL3MVufKuF2XdCG55blMX5rpu7Ncu3VpX0v8QoiXi/ytFkLsybRMsmesa4ZlmoShxjQ3p8HyYo3JO/MkM3FMa2sojcUdzo8XqZRbVMstQh3i2BaWbRBPxWjXuyxOrzI03odaD4Ddjkut2iaRjPLuNy8ycr6w5yGVl0mn1d1xBte0TEKtcbuuBEEhXkHyt1oI8VJK5+J02j3i8d/MyvU6LpP350lkYptCYK/dw+16a4WfDYUTtSn0JcnlEzQbXWrVNp22h0aDAaWFGloZpHIJDFMRjTm8/92rXL0xvGcrvJdRJBbZsbtLGISgFbbsERTilSRBUAjxUir0pXh8v7kpCM48XsYwFJZlotG0Km0qS1U6LRelNAqF1ms1sCMxm/xghnQuSSYbR+u1pd8g0AReQLfT47WbwyRTMcqlJmMX+17JEAhQHMlj2xa9jrulS0xttcG5a0NyWESIV5QEQSHEmeR2PRamVnh6b45uq4dlm/SN5jl3bYhMIUW+kNxU26/bdqks10jlEuhQszyzSm2ljhN1ti1B47seCxMrpPJtBsb6MEyDyPNhR4e0qx2iUYdI1CYW37vf8MvKdize+cM3+PgHX9BudEhm4gRBSLPSIplNbPSVFkK8eiQICiHOnOmHC9z+4D6EkMjGiWdi6FAz92SJqbtzDJwv8sZ3rxGJOrg9HydiUVmprx1UUbAyU6K22iCeju+4981ybEzbolFpo9QqA+t7Ap+JJaMsTpcwoxavvzN27KegT9vA+SLf+/vvM3V3hpXpEpZj8fp3X2Pk0gBO9NUNwUL8tpMgKIQ4U57en+eLn96lMJTdUsj42YGV1fkyn/7lbcYvD/Hg7jzF/jT1cgs7atNt9qiu1ImnYnuGN6UU8VSUerlJKp8kkflNdxDLMmn1OvhewNDo7i3yXhXpfJI3v3v9tIchhDhBr+aGFyHES6lVa3P7gwfkBzO4XY/aaoNGuUngB5uuyw9mqa7UcettonGHdqtHs9bGdixqK3Usy9z3DJ5SCtuxqSxVt46n3ePc+cKehauFEOJlJTOCQohTFYYh5aU63VaPh19MMT+xxMRX02gvxLQNdKgxLZOB8T6GLvRhmGs1DLN9aWYezPPOH73Fhz+7TxCEBH5Ao9wilooeaAx2xKLd6OB23Y1l0E7bJRZ1GDlXOPLPLIQQZ4UEQSHEqei2XRamlnny1SzdtgvAp395m+pKjUjUIRJ1yBZTpHIxDMNg7vEi7XqHS2+NYZgGlm2tdfvwfV57fZS7v5rAMhVKceD9fM86oXg9Hyfq0Ot5BGHIufEi1ivQPk4IIXYiQVAIceJWF6p88ld3CIKQVC5BMpug2+rSLDXI92cwTZMwCCkv1ygv1Rg4lydTSFFZqrI6n6Z/fZZOGQa9jsu1myOMXRpg6vECYRjSaXRolBsEro8Ti5AqJHFie3cB0aGm03YJwpDXXh+l2+wSfYVPCwshhARBIcSJWpop8fGP7pDKJzbVrKss1dAazPWlX8M0iCWiBEHIwtMSodbE0zEWJpfpG8mhDAMFKENRW6kzNJRidWaV2eUaYc8nlohgR228nsfK9Cp954o48Z3DoA41zVaPYirKtasjRKM2vVaP+AGXmY9avdykVe8QBiGGaZDMxEnt0mNZCCEOQoKgEOLE1MtNPvnxXdLF5JYCxY1Kk2jCwfeCTcuxpmkQS0ZYmikzerEf3/Vxux6ReIROq8uvf3qXwA9pt3rUV2p0Sg2smIPnBxgabMcEpaiv1ime79syJj8I6HU8ej2Pi9cGGTlfxDAVtXKT4bHiqRSRDoKQ5ZkST27PUFmpYSiDtb4fGh1CppBg7LURRq8MbARnIYQ4DAmCQogT8+TOLLZjbdulQoeQ7c9QWa5j2ZsLQBuGgeNYlJZqZHMJNFArNZh5MM/r718hNZREh5qVpTrJbByv62EbYMesjdZx3XqXWLOLYSieb6ZmWSbpVJThN89x7sJaUNRa43Z9xq4OHuNPY3tuz+Pzn95jaaZEMhOnb/g3pWvcrsvU3VkefPyYf/cvPmF4vMif/ie/T9+oHGgRQhyOBEEhxInotLrMTyyTH8hu+3wyF6e2Wsc0DXzXx3I2fz3ZEZtmvU0iFcW2Te7/apriSI5UPgmsLRGPXR7gye0ZwkDT63hkCmmyuSS9rofbdRkYyqABQyks28RxbJQBrWqb4fHixnvVVpsMj/eRLSaP7eexHd/z+ezHdykv1egf2Vy7MAxDHnwygddzKYzkQGtKizX+7//pL/gP/tHfJZ0/2bEKIV4NUkdQCHEi5idXwVAoY/sTvcWhPMowGBrvw11f/v063/UxbIuVuQrKNLhw89ym5/tHcoyO92HaJqEf0Ky1AAg8n8HxPjK5BNlcgnQ2TjwRQRnQrLQ4/9rQRjHpdrOLYRrc/MbJdxOZvDvH6nyF/EBmy3ONUpNOs7PWLYW1k87FoSz1cpNPf/TrEx2nEOLVIUFQCHEilmZKxJNbe/4+E0lEGLrYj9vzGb08gGkbtGptOs0uvY5LvdxAh+D1PK594wIjY0Usa+v+uFt/cIPh8wUM26JealBZrpHuS210JYG1PXjNaptWrcP4jREGzq3NBjbrHdyOz3u/f4PoLgdLjoPv+Ux8NUOmmNr2+V7HRamtX9mpXJJHX04ThuE2rxJCiN3J0rAQ4kS4PQ/T2v13z5FLAximwfzjRbJ9GfysT7Paxuv5FIZyXHl7HCvmcPXdC0zenibwA8yvhcFozOF7f/Yuv/7lE5qNLgNjfbSqLZrV9sY1hmkwOF6kOJQlEo/gez7V1RbpfJxvff8GqWz860M7ditzFTzXx3a2/1qOxB203hr2dKgxLIPyUo3iUO64hymEeMVIEBRCnAilFJtOaWx3jWEwfHGA/tE81ZU6vbaLZVuk8gni6ThhGFIvNzEtk/Gbo0zdnd12z6FhKEbO5bnxnWsszFaorNTx3RA7YhGJ2kRjNqGGbselXu3iRCyuvzvG+NXBLcHypCzNlIhEd65ZmMqniKdjNCstEtkESkG31QMFfeeKrMyWJQgKIQ5MgqAQ4kRE4w6tegdnH317LcemOLL1JKzvBRu1B8dvnmP24SL1cnPTQYkwCCktVhm+NMCl189x6fVz1Motyss1VhfrNGttfD/EidoMjAySH0hR6M+cegeRXsfdNYQahuLqNy4y82CB8kIFrSGZjXPl3QuEod52T6UQQuxFgqAQ4kSMXhrg8393n3hq532Ce2lUW7z2zjgA8VSM3/nb7/Lx//drZh8tYNkmpm1hKMWF189x/b3LG4c9soUk2UKSi9dH9nyPXsfF93yUMnCiFpZ9Ml+TpmWi9e5Tpk7E4dKbY4zfGCUM9cYy8tosqWz5FkIcnARBIcSJ6B/NY1nmtvv69kOHGkLN8IU+Aj9gfmKJJ18+pd3ogFK06j0Gx5Pc/PY1Bse2Fo7eTRCElBYqPLk9Q2mhilKg9dos3PnXhjl/dejYy7OkcgmWZ0ok0nsHZdMyef4n6Ln+xqlnIYQ4CAmCQogTYTsW468NM3F3jvxABt8LaFRaeK5PGARYjkUsEd0xCNUrLYbG+7Adi09+dJulp6uk80mK6wWXw1DTrLT41b/9kre+d4Ox14b3Na5Wvc3HP/qKZrVNLBmlMJTdmEkM/IDZR4tM3Zll7PoIN751+dg6jYxc7OfhZ5NorVFKEYYapdizhE0YarTWDJ4v7nqdEEJsR4KgEOLEjF8f4cmdWR58Pkmz2iYMQpQyUEqhdYjWmkQqzuB4kWwxhbEeujqtHjoMufbOOHc/eszKTIn+c5v3EBqGIl1IEveifPmzuyQzcQpD2xevfqZV7/DBv/p8rSbf8NaDFqZlku1LE4aaqbtz+F7AW797DcMwqJcazE8u47ZdMv0phsb7cXY57LGXZCZOupBk4qtZGpUGXtcHBalsnMHxflL55LYhtFFpMXKxn1hy957IQRBSXqrRaXbxPR8nYpNIx8n2pU68XqIQ4uyQICiEODGlxQrdVo/SQpVo3CGd31ozr9d1efzraTKFJJdeP7feFcTj/b/xBoaheHp/jsIup2Mt2yIad3j85RSFobd3vE5rzWc/vrMWtnKJXcdtGIriSI6Zhwtk+1J0Gx0efT61vi/RZPr+HPc+fMQ3/+bbu45tN9MPF1iaWln7fAMZ0vnkWqu7jsvDz6eIxR0uvzNOLPGbwNfrru1nvPzm2I737XVc5ieWeXJ7hm6ntxa8DYUOQ3S41tHl0hvnGBrrO/IDM51WlydfTDH7cAHTMrnwxjnGb547sX2XQoi9yd9GIcSJmJtY4tMf36N/NE9xOMuTr2aplxprS8LJyEax5EjUwYnYrC5UqazUeeN3rvDdP3mbVC7B5N1ZlFIYO3Qn6XV6PPnyKY1yE98P1opFn99+v2B5qUZ1tUHf11q57UQpRbYvzSc//ArLhP5zhY0ZS1gr5fLxD77g+//edzZONu/X9MMFvvjJHYYu9JPKxnn05QyGaeBEbCLxCJF4hE6ry/2PJ7j+3iWi8QjtRpd2s8N7f/3NHYNso9LiVz/6im6rRzqfILnNdd12jy9+ep+ZwUXe/f4NovHDz2o+z+26/OJffEK33SVdSBOGIXc/fEh5ocqtv/EWhiGHW4Q4C+RvohDi2DUqLT7/2QPy/WlsxyISdbhx6wLX37tItpiiWWvTqDY3/mnWWvSP5hke7yM/kN4IOu1aGzuy/e+vYRjy6NNJOo0umWIaHWh+8S8/we1tX1bl6b25Awc2J2oz82gBZRqbQiBANBHB9wIWp5YPdM9uu8ftDx6QG8hiOxa5gSzXb11Ar9dMbDe7BEG4Xvg64P4nE6zMV1AGfOdP3t3Sk/iZVr3Dh//mC9CawlAWO7J92Z5oPELfaJ56pcnHP7q98fMKw5B2o4Pn+gf6PM/MPlqg3WiTH8yt9XWO2PSdK7I0tUJlqXaoewohjp7MCAohjt3TBwtYloH1XNeMTrPH6myJ6mINFWqy/Wlyg9n1GUELO2KjQ838kxVee/cCsUQUr+ez9HSFhcllwkBjRyz6RvKkC0k816fd6JAupAiCEG0oVhZr3P7oMalckngyQjITJ5WNoxTMT61u29N3N2EY4vc8eh132+edqE11uc7Y9f3fc2FyGZ4rBQOQyid549tXaVbbLM2UaVRb66etDXQQ8s73rjNyqX/HvX1aaz7/yV2UYez7NHG2L01pocr9TybJ9aW499HDtdqGhsHY6+e4duvigZZ0V+fKxLYpFaQsg1qpcegldCHE0ZIgKIQ4UmEYUl1p8OCzKeanlmnWOkzdWzspPH59mMJAlma1xYNPJjBMg1gyQhhqKotV6qUmN751eWP2ShkKDMX0w0V6rS73P5lg7vESuYEMhmnidl2qy3Us22LsxjB+oFmcq1CvdfFcDy9UzE2u4CzUCIIAUNiOxdiVAXzP33GJeSdKKWzHpNPqbfu87wVEEgfrUTx5Z5ZkfuuSrTIMUvkkqa+VrVmdr+C53q4HPKorDaqrDYr7XPZ+JjeQ5quPHuEY0HcuTyqfJAhCnnwxie/5vPW9G/u+VzwVozRfgfTmx3UYEj3gTKwQ4vhIEBRCHAmtNTOPF/n8J/eZfrRAt+1iOzadVod6uUWn2ePp/fm1Zd4gYGi8b6PLiGFCMpegXe/w9N4cV79xceO+yXSMH//zX9I3mEEpqK02qK00sGyTaDJCKp9AmRaf/eQ+PtCqtghcn2x/huvvXaJ/dPPpYs/1efDlNI9/PUMsESFT2HpgZSdKKQojedq19pbnwiDE93yGL/Qf6OfWbXbJbtMmbye2Y621ltvF0wfzhzrBbBgGy09XGBwrbLzeNA0KIwVm7s1x9d2Le55OfubctWEmfj2N1/M2gn273sGJ2BRHt3aNEUKcDgmCQogXFoYhtz98zC/+4nM6zR6JdJxULrk2a7WsSaRiRBMRtNbUVxssTa/Qbblcees8xnMdMeKpGLWVOm7XJfDXWsXNPl5k8qtZ5nJx0rkEo1eHmL43R3mpgT/rEwYhdjJONJ0gkYoyeL6IE3W4dusC6UJ6y1htx6J/JM/sg3m++tUEV944R/9onjAIaVSa6FATT8d2DFKZYppozKY0XyHTl8KyLTqtLo1SkyvvXiB9gGAJYJgma02Y9zc7GYZ6y/7E560V214m27/1s+/r/kFAo7o56BqGAqXoNLv7DoKZYppv/NEb3P7ZPTw/gBDi6Sjf+pNv4OywX/EwGpUmzWqbaNwhd4BALYRYI0FQCPFCtNbc+dUEP//zzwj8kPxgdtOyZRiEGxlHKYUTtUmkopSWqugvNNfeHV9bAgZQ4Hk+9z5+gtv1MQxYnFyhWWsTTzrUVhvUyy163R4osByLrhtQLbeJ9zy0n6R/NM/b37+B7eweNs5dHWbi9lOmHiyiDEVpZpXqcg3DNLBsk9fev7qlHV4Yhli2yV/7B7/HwuQyU7en8d2AVD7BN/7oTUYuDx7451cYylItNUhldy9h80zg+WSLO4dNzw3Wu6Ic7ixgOpekWetseuxZ0epYan8h8JmRy0MMjPVRLzVRhiJTTB3JaWGtNeXFKp//5W2+/OldTNskW0zzjT98gzcOsHwthJAgKIR4QUszJX71w9v4fkCmsLU4sWGZa+3hgCAM8bWm5wUo22JmepUe0Deaw7EtCELmHy1y4c0xUvkEnXqHXqeHZRkkUzGUUjRqLdr1Hvn1YtHdlQbR6NpMX3E4TyTubDp4sZNsfxrTsnCiNvc+foIRBBt76tqNDjP357j2zcubXlMrNRm5NEimkCJTSHHtGxcJQ/1C3UYuvH6OD//is30FQc/1sSP2tsWvn3nR2tBDV4b46mf36HVcIjGHwA8oL1QZf310Uw3D/bJsi/zg0c3UhWHIVx88YOL2NI8+myCVS2IYimqpyQ/+j59SPN/H0PjBWgwK8dtMyscIIV7Ig8+f0qi2SWUTax1C0ARhSBAGuJ5HoDX1Zpe5pRqzSzUqnR69QNPreZgRi9WFCu2OS6nU5O4nT+igWKq2KFdadNo9WvUusUR0I2Aq1k7n1sst6o0uqWyCbF8aZRiYtrHesi7cc9y2YzF2Y4ROq4vv+tQrrY3nnIiN2958MrhV72AaBlfe/k3xZqXUC7ecyw9myBbT1EuNXa/TWlNZqnHl7bFdezVbtolSa4HpMJLpONfeu0Tg+pTmyjRKDa5+4wI3f+faoe531J7enWXqzgyFgbUT5tF4BCfqbMySfvrDLw/92YX4bSQzgkKIQ6uXm8w8msf1A6rtLu2uR6vTo9v2aDfX/uy4Ln6tjWWa2I6FpRRWLILf6qAU+D2fdqVF6PpEYxEGL/aDUpSqLdqVNq1mh8LQb8q8rC35apRl0mz2iMUjoBSB5xNLxtB6/+PvG8kTeAEPP52gttKg0+wSiUdo1VoMX1nrVRz4AbVSA9uxef9vvbVjL+TDMgyDb/zB63z4rz+nslgl05/esnzquT6VpRrj10cYvzm66/1My2T4Yj9LM6UDHYR5pllt8fb3rnP+2hBu18OyzTPTCSQIQh5/PkW2P4PlWCRzCZrVFvFMHLftksjE8Hoe5YXqgU9MC/Hb6mz87RZCvHS6PY9ffviQL+/NgwbL9fBdn3atS+CH9PyA0IREIoqvFL16B0+HeBqs0MCIRNBBgGFpGtUWmUKa3Eh+Y4YtHrHRMZsQRbnaRpkGiXgEJ+YQTUQpl5qEgcZzfQI/xI7axNMx9B6HKb5ucLyPeDpGGAYsTq8QjTn0ne8jloqxMlfGskzGr49w4cbovg9KHFQ8FeN3/uQdPvo3n/PFetu7wmCOVC5BGGicmMMb37nK2PWRfe2xG7s2zOyjpQOPIwxDlKEYHOvDMAyi8YOVwjlurVqbXsfdKKlz6e1xpr6aob7aIJaMcuHNC3g9n/+/vTuPjuy+Djv/ve/VvqGqsC+9obvZ7GaTlMimRFKytVqSLVG2xnZsJfZYnokTOx5PcjLOZLwko3iseCY5mZPESTTjJB7Jss9RJnI8ViaxLUWWRI21kBTFvdnsvRtbN4ACUPvy3vvNH68AooHC1g0UCsD9nFOniFe/V/Wry6rXt37r7bFZTQSV2iRNBJVSW+J5hms3Z3j5wgRXLkxiGUM0HqEwV6ZaruMKlB0HS4SQ7XdhBmNhnGoD43lLYwbr+GPrrHCYhiMEw4E7Wp48z8Mz0DOSoVGuMbdQplp3yKZiZPrTlKoOXrFKpVwjkYoxcmqQWqXO4VODbHYG7qJUNsEj73+YrkyM/pGMv5CybRFLRukZyqya5ZqbmufS81doNByOnDnEyMnBe4ypx4VnL5OfyXPk9BCFXJH8bJ5kOsq5DzxE70j3lrqg071J0r1JCnOlDfdRXm7uVp7D9w1s2zZz225Fc284EuLUueMYY5aGDizMFjCbGBqglPJpIqiU2rRiqcYLr97k1nSe7kycRDSIZYT5mSJO3cWzoFRpEAxYd0waEUuIZOJUcyW8hosVsAiKhecZ6q5DowELc2ViqRiWJbiOS7Vco/9IN+5QFxe/fYkgUKs2mHWLZLriBMJB0tEQYlt0ZeIkM3Gchke6J0Wj3vCTTHvtsXQrhaNB6nWHo/cPr1suP1vgm3/8LOFYGMu2+O6XXgS4p2QwNznPjQvj9B3qQUTI9qcxxjAzNkswaG95HKKI8NZ3n+ab/+/3KC6USWxid5H56TypbJxTjx6727ex46LJKHbAwmm4BIJv/r9d/lmrVxpLE4mUUhvTySJKqU3JzZf42jcvkM9XGOhNEQzY2LZNqVijXmngWUKxRRK4yLItotk4diiA23BxGw4iELBsPDEU8lVmby0s7W87MNpHur+LzFCWoVNDNGoNTN2hUq4zPVvAcTxqFX9f3L7DPZTmK0RjYV755gVe+NprPP+VV7ny8g1K+dWLP7ciIribaEmauj6NWBaJdJxYMkoik+DG+fGtBXOFWzdmCEVCdybPIgTDQW7dmLmr54wnozz+gw8jIsxOzFGvtt5zuVKqMj2Wo6s7wdt+4MFtXeNvuwVDAY6dPcz87fmWj1dLNUKRIL26YLVSm6YtgkqpDeXmS3zjOxeJRULElm8P5hkajosxhnKltmYSuEhsi0gmjue4OJU6jXIdp+oQiAVo1BrM5UoMjPbRO5xeGgtnWcLIAyOE42Gmr01TyVcoFyt4LiRTMbr70zRq/kzhSrFKLBnFsi2M57Ewk2d2PMfow4fpHlx/zJjnegTDG18S7YCN57pLf7uOSyBwb7+p7aDVcqaz8bx7mqiRTMf5vqceYeLqbS6/fJOF2QKWJYhYeMYDz+8Wf/Q9Z+g/3L3ubOROceKtR5m7vcDtm7N09SSXlrjJ5/zFwB//8CMdM7lFqb1Avy1KHRCLu3VE4uF1k7WVCsUqf/HMJeLRENFlu20YYyguVIimIsxMLWCFAhs+7+J6glbAJpSMEoiFqRWrDJ8doTZXplSoMDWVp2/kznXyLEvoH+2jezhLMVektFDm5pVpoqkoZ588xcTlSRJd0aWtzMDfqzeWjOJGXa68dJNYIko0ufaM31qlQf/I2uvzLRo63s/Vl24wMz6LZVkYYzi5bEu8uzF4tI83nr2C67hLyZjruLgNj4F7XBMvFAly9PQwh08Nkru1QLVcp1FzCIUDxFNRunpWr/3YyQLBAI998GEmLt/i0gvXmBnPYdkWh08Pc/TMCIlNLsytlPJpIqjUPler1HntW28wdnESgEQ6ztl3nNpU95nnGZ5/5QYB27ojCQQoLlQwriGYjOJOLWB5BtZoUDKuR7Vcw2n4rV6BgEUoFsZruES6YsS7YoSCNo1yjcJCmZmZIr29by594rkeTq2BFbBJD6ZJD6apNTw8AYOH8cwdSeBydsDGDljcvjnLkTNrL71SrzfI9nWt+fiiaDzCOz72GJNXbuO5Ln2Hera8rdxKXT0pzr7jFK9+88KbK0IbwwPvPHXPz73Isix6BjdOdPeCQDDA4fuHOXz/MK7rNVs5904yq1Qn0URQqX3MGMNzX3qBhek82cEMluXvF/ud//Q83/ejb6erZ/39aK/enGE2V6S/Rbnc1AJWwMa1hUhXlMZCBcsWZMXyJsYzlAsVDBAI+o+5rkd5vkQwHKBn0F+qJBSPEM3GqU/luXFthkwmTiBgUS1WmTh/E6fuIkDfyUFC0RDdg13MlxrcuDhFYoNlXaLJCDMTOY6cGabVjGLXcQkGA3T3b5wIgp8Mjj54eFNlN2v0oSP0H+lldnIOgO7BDPFNTPI46O51QW+lDjr9Bim1j83fXiA3uUBmwE8CAaKJCIFwgKuv3Fz33HKlziuvj9O9RldbYa6MYzzsUJBMfxfBZAS35uA5d451cxounnfnP9hiwHFcoj0JUs0dIUSE1ECaZG+C8myJiZuzeJ7H1IVxMBBJRJCAzbXvXsV1DGfffZq+4QyTE3NL720tluWPwfPc1qtNz88WGD0zfMdM1N0Q74ottXRpEqiUagdtEVRqH6sUq37WtUIkHiE/k1/33LGJHACBFhMIPNejVq6xUK4SDGEqzz8AACAASURBVFjEMnEwMG/laeSreA0HsS2soO1v92X5S8AZx/WTMUuI9yRJpONYy7r0LMsiPZLFDgUpux6Tl24xdmFyaRmYxW5ez/OoluqcePAQF5+5TH6hQqY7seZ7cRsugVAAy16dMJaLVWKJCKOnh9aNh1JK7UeaCCq1j0UTEWixIkqlWGX4eP+a5zmux8Wrt+laY3KF53q4nqFSdYhHg4CQyiYIhoPk50vUChWccp1GpYHnOHjVBsa2sEIBQqkQsWQUEQhHVy9cLCK4DQ/X84jEQmSGsthBi3AsgmULtVINK2Bz+fnrBII23Yd6uDU+SyodW7ObsFyoMHxygJXdwtVynWq5xpMffGjXWwOVUmo3aCKo1D6W7uuiezjL7ESOTH8ay7YoFyq4dYejDxxa87zZXJF6wyW91nIiItQdt5lWvZlcReNhovEw9XqDcr5KrdLAdRwK8yUsW0ikYgSDAZyG45dfMbbPGEP+dp7iTIG+4z14QZvjbz/J5Gs3cap1EGHw1DCJ5hZjruNi58tIwGJ6PEemN7UquayVathBm56hOydKLOQKGA8e/4GzpNdpTdzLSvkyM2OzeMYfc5jK7s/3qZS6e5oIKrWPiQjnPvAQ579zibE3JjCeIZmJ88RHz607G3V6tkBonTXl7ICF63hLy8GsFAoFCfW8OYvXcRzy0wWq5Tp1t0E4FqSrJ3XHvrkGQ2G6QClXIhwPE46GqFQb2OEgRx49jlN3sAMW9rI14uyAn+A5jks6EiQ/V8LMl4jHI9gBoV5pEIqEuP/ccYLhEMYzFBbK1Kp1+keynH1slNgO7R+8266fH+Plp88v5emea7jvsVFOPXpcZ9gqpZZoIqjUPheKhHj4XWc488R9uI5LOBraMBGYmSsSWWeHCRHBCttQbJ0IrhQIBMgOZvzxgnBHArioWqhSypUIhgLYy9YkbDRcgrEQoRbdyAAB2yLZnaQ7k+Dc/QNMj89x4+IUtapDZjhLKpOkXK5TLtcRgYFD3Rw9NUimd2+tn7cVxfkSL339NdL96aUub8/1uPDMZXqHu+neJ8vIKKXunSaCSh0QwVCAYGjjr7zreizkK2Q3WJg32hVjZmr9CScrtUoAwW8NLM36SaDjeMTjzaRPoFpvEIu1TgIXiQipniT5uQof+PiTWJZFo+4wOzXP9FgO13HJ9HcxNNpH8ADsOnF7bBax5I5xj5ZtEY6FmLhySxNBpdSSjl0+RkR+TET+UESui0hFRC6IyG+JSHJFuYyI/BsRmRGRkoj8FxF5cLfqrdReV6s7GMMds3lbiXVFQFize3grnKqDU20gAQswhBNhwF9ypl531z+5KRwJUspXmLtdwBjDtVdu8uyffI9rL19n7PVxXvzqK3zrP37Xn0m9z3mO27K1UywLt7G5eCqlDoaOTQSBXwZc4FeBDwGfBn4B+LKIWADiX+m+2Hz8l4AfBYLAV0Vk7S0ElFJrMmZziV0wEiLZk6Baqt3za5YXyoht0ag2iHVF75j9u9idvB4DzVnIQa6/Mcn4pUle+/ZFMgNpeoazdA9l6B3pplKo8Oyfvbip59zLuoeyuI6HtyxJN8ZQKVYYPNa3izVTSnWaTu4jecoYM73s76+LSA74LPBu4M+BjwLvBN5rjPkqgIh8C7gK/I/Af9/WGiu1T2xm5FwsEiDek8Ap1XHqDoFNdDuvZIxBRKiX6iB+13Eis2Ih5Q0qY4yfBAYCNnYywszEPHPjs3T1JlctJ5PqTjI9liM3OU/PcHbL9d0r0r0pRh86zOWXrhOJRxCBSqHKyMlBeg9tvLWgUurg6NhEcEUSuOjZ5v1w8/6jwMRiEtg8b0FE/iPww2giqBTgJ1y5qXluXBinvFClqzfJ4VNDLWcO27aFYeNWwXgsjNgWfcd7mHh9ChHB3uRafG7DZeHGbaoLZexggLoDgViQ7pEs1vLkzaw9rnBRo+EQj4WxLMGIRblUxXYaayZ6dsBifjq/rxNBEeGBJ0/Rf6SXsYuTeK7H0DsH6Dvcs2E8lVIHS8cmgmt4V/P+fPP+AeCVFuVeBf5rEUkYY4ptqZlSHcoYw6vffIMrL10nHAsRjAS58Xqeqy/d4KHvP82RM3eOogiHAgRsG9f11t3HdXFWcSQRYeBkH1MXpwm4HsHI2rONFy1cv00tXyEUj9Ao1yiMzXHo7ccJrTjXdT2i68xeBqg7Lv2Z5sQW09zKznmztXF1PNh0wrqXiQi9I930jmgLoFJqbXvmp6GIDAO/AfwXY8xzzcNZYK5F8VzzXqfGqQPv1vUZLr1wje7hLKnuJNF4hExfF+n+Ll58+jXyuTt/K4kI3ZkE1Vpj3ecNhYIEAxaO4xLrijF8ZhAraFHOV3CdtSckGGOoLpSwo0Hq1QZ2JEQ8E8NqNTbRQGiDLmfX9Ugl/B1QnIZLLBmh73APhbnSqrKeZzCeR+8+bg1USqmt2BOJoIgkgD8GHOBnlz8ELfuw1h1VJCJ/TUSeE5Hnpqdb9UArtX9ceek6iUwMy7rzaxEI2gSCAcbemFx1Tk93gkp1/UTQEhjsT1Ou+OXCsRBD9w/QcySL67hUChUqhSqNagO34eI2XBrVBtViDdc1OLUGqd4E2ZE0sXQU17lzAocxBgRC67Teua5HMGCTSvqLQhcXKhw60c/pt53AqTkU5kpLk18atQaz4zlGHzxCYoOlcZRS6qDo+K5hEYngzwweBd5ljBlb9nAOv1VwpcWWwFathRhjfgf4HYBz587d+9oXSnWwfK5IIh1r+Vg4FiI/W1h1vCebwNvE7OHubJzrY7NL3bCWZdHVlyLVm6RarFEr16jka7jNLeWC4RCpVJh0X5zcpUkwHtW5EtnRPuanS8xNLuDUXYwxOJ5HJBIknywSTUQIR4OrunpL5TqHhjP++EBjcB2X4dE+4sko7/zYY5z/9kVmJuZA/HUUz77z1Lpb6yml1EHT0YmgiASBPwTeBrzfGPPyiiKvAh9oceoZ4IaOD1QKYsko9WqDaGJ1y1q92qD3UHTV8XQqSjoVpVypE1tjRw+AcDBAX0+S2bkSiXh46biIEE1GiCYjpPtbn5vsTVGcKVCYKVJ1DK5rqJaqhGMhLLFo1AyRYIDcrQJmKk8oEiTbnySeiiIiuK4HAt0Zf//cUr5C/0iWeNJ/P109KR7/yKNUyzVcxyUSj6w75lEppQ6ijr0qNtcK/APgfcAPG2O+3aLYF4FhEXnXsvNSwFPNx5Q68EYfOkQhV1y1PqDnejRqDQ6dHFp1johw32g/xU2sETgylEEAZ51xgZ7rUZzOM/nqGDefv8bN717l+nevMnVpGs8IsWSE7EgW2/Zb/IwYAgGLRDJKNB729wM2MHltlls3cjgNl0KpxpGRbsLhAI2aQ7Vc5+TDh1e9diQWJp6KaRKolFItdHKL4L8Efhz4FFASkceXPTbW7CL+IvAt4PdF5O/gdwX/Cv4YwX/U5voq1ZEGRwcYPjnN+MUpEpk4oUiQaqlGOV/h1GPHyfR3tTyvvzdFPB7eVKvg6JEeLly+RToV9Rf1azKex/z4HPNjOTzHIxAJIJZF/naeSr6KZQu1hTLJvhTRbIL0SIa5m3N4Atls4o7BvoGgjR2IUCpUyZ+f5Mipfvp7UtRrDRamizzy7vtJ96xeDkcppdTaOvkn8g82738NP9lbfvurAMYYD/gI8GXgXwF/hL8byXuMMTfbXWGlOpFtW7z1vQ9y7gMPE46GqRRrpLJJnnjqHKfOHV/zvEDA5tGHDpMvVe/YoaKVbCZBb0+KfOHNFkTP9bh1YZLctWmCsRDRTIxgJERpvkyj6hLtihFJRrFDNgsTc+Qn5wnHwsQHUtiA5RncFTuAiAiBUADX9XDyNWZvzVOcK/PY+x9gWHfMUEqpLevYFkFjzNFNlssB/03zppRqwbYthk8MMHxiYEvndWcSnBrt59LVaXq7E2uWE2D0cA+u47KQr5CIh5i5cpvSTJFo5s0ZutVijcpClVAstNRwaNkWoUSY8mwBLAh1xTnz2DGqC1UWpgvUPA87aGNZguN61GsOfdkk5XwVY1l830feSnKNyTBKKaXW17GJoFKqM9x/YoDcfInZ+RLd6yy7YlvCydE+Ll+bZmIsR35ynuiy7eI8x2NhOk8wEmDlOs8iQjAWYn58njNHeoknosQTUTIDXZQWypTzFcqlGkaE+04NMTCSoSuTYD5XolF3duqtK6XUvqeJoFJqXYGAzdsfGeVbz11mdq5Ed2a9ZNDixNE+CjdnmXE9AnWXUNi/zNSKNYxrsMKrR6QYz1B3XNJdUaT2ZmJn2RaxdAw3YNN3tJdjh3uILtt9JBILcvn1CbJ9qW18x0opdXB08hhBpVSHCIcCPPnYcXq6E9yaKdBYb4aw42JKde67b4hA0KZUrlGrOZTmygSbu4QYY5qzlh1KhRqFfIV4OEQsGSE3nsPzPBzXI1+sUio3GD3cw+mTg3ckgQCJVJRbY3OUixvPblZKKbWatggqpTYlFAzw+COjXLs5w0uvjRMK+jt6rFzkuV5tIBgi0SCHh7upVGrcni5QrtQJhAJ45TpOtYHneDSaO4NEI0GqC1VK82Xq5ToSj5AdSnPkSC89mfia28yJCCJCYb5ELBFuWUYppdTaNBFUSm2aZQmjR3rp60nx8vkxpqbzWJZFOhUl0Fynz3gei7s8WgLxWJj+rKEUX6BYrFKv1Gl4HiIWsXiY8GKSJxALhYkEbRLBAFapAZUGgZ71Oy7sgMV8rkj/iO4frJRSW6WJoFJqyxLxME+cO06hWOXGRI6r12dwXBeM4NbqVGsNwnUHY8Azhonrs8zeyhOJh8lkEyTjEUIh219z0BgQwbIEAcqFKr0DXYgI49emyc+VOHF2mECw9eUqGLQp5qvtDYBSSu0Tmggqpe5aMhHhgfuGOHV8gFKpRqlc49Z0nqlXx7EEAqEAxfkypu7S15skkY4ttRy+6c2uZafuEooGsWwbEUhl4hQXKlx6ZZyTDx7CDrRoHVxMJpVSSm2ZJoJKqXsWsC26UlG6UlGGBtIkjeGN710nEo/w6mSewcE0t8fnWySBd6rX6vQf6b1jeZlEV5T8XInJGzOMjK5eNNp1PUIrJpEopZTaHJ01rJTadkPH+nE9j8uvTxAKB4lEQyDrn+M6HpZtE+9avTh0oivGxPVZSoXVXcD1mkNGt5ZTSqm7oomgUmrbxZIRjtw/xO3xOYLhAMGQjeCvF9iK63hUyzUGjvRgt2g1tCwhGLK5NZZbfbLxWw2VUkptnSaCSqkd4XrC0dNDFOeLVMt1kukYtdqdu4AYD6rlOrVyjcFjfS1bAxdF42Fyt/I0lj1HrVInnoyQ0i3mlFLqrugYQaXUtjPGcHs8x9H7h+kfzjJ5fZpSoUIpX8G4HiLgeR6IkMwkyPSmCMdC6z6niIBApVwj2NytpLBQ4aG3j2JZ/m9a1/WYnchRXCgTS0ToGc6uOdtYKaWUJoJKqR1QLdep1xxyk3PUyjV6BzMcOjlI4sUbzEzOEUtGcWoNDBCJhRBLKBdrOA0HzzWILQQDAb9bOWjfMb6wXKySysQpFaqk0jGGj/QAfuvgM3/yPeZvLyC2hXE9Yqkoj3/40XVbGpVS6iDTRFApte1q1To3LozjVOoEgjZjFyc5cmaEBx4b5bXvXmNhtkBuagHH9SgWalhBi0xfF4Iglt9lDGCAYNAi05skkYxiByzqtQaNukOlVOPRDz2IHbABuPDcJfKzBXpGupfqkZ8t8OLXX+XJjz62C1FQSqnOp2MElVLbrjhXJj+Tp6snRbwrTqo7ydgbE1gCJx8YZuLKbebzFcoVh3A0BJ7BtoVoIkwkFiaa8G+xRBixLaYnFrh2cYpivorT8JifLfLIO0+SziYAcB2XG+cnSPd13VGPVHeS2cl5yoXKboRBKaU6niaCSqlt5y/8/GZ/rmUJGHA9j6mJOQwQCPgziS2rWW6NNaEDAZtoIkwgGODmlduMX5vh0Xfex9DhnqUyxhiMMYi1eo0aATzX27b3ppRS+4kmgkqpbdc7nCHeFSM/m6derbMwUyA7mOHG5WlmphYYGe0lkQgRiQYpFSt4nsGyLUyLHUI8119apl5tkO5OMHi0h5tXZ3Aa7lKZQDBA3+EeCrniHedWSlViqSixlC4vo5RSregYQaXUtgsEAzz5Q2/lhafPY1tC932DeJbN1FiOZDpGsiuGHQxQXiiT7UsSjkcoFatUS/VVz2XbFl2ZBImuKE7d4ejJfqanFjj/4g0ePHdsqdyZx0/yF3/8LLMTOSKJCPVyDWPg8Y88ujSrWCml1J00EVRK7YgTZ0eYvJmjdzBNqVDhteevk0jHlpaByQ6kyQ6kl8r34Lf+NeoOxvhbCAeC9tJkkFK+Qu9gmkAwQLY3ydULUwyMZOkd8McFJjMJ3vXjTzB+cZLc1DypEwOM3DdEIh3fjbevlFJ7giaCSqkd0ZWN0zecZn6mwPj1HKFo8M3xgGuwbMufPLKC63q4rkffSMYvZwmpTIyXn73Cuz/88FKLXzQe4cRbjq06XymlVGvaX6KU2hEiwtlzoxTzVRbmi/5+w3fBGENpvszh4/1EYuGl45FoiFKxTm66sF1VVkqpA0cTQaXUjoknI/QOZ6hXnDsmd2yWMYbCXJlsf4re4fSqxyOxINcu3t6Oqiql1IGkiaBSake5jsf9bzlCpVSjXKxu+jyn4ZKfK5HtT3Hs/qGWEz5i8TAztxZazjZWSim1MR0jqJTaMY26Q7lUo38kQ1c2xrXXp8jnSgTDAX9rOVk9ZrBRd6iWali2xfEzw2T7Ui3LgT+j2HFcKuU6sXi4ZRmllFJr00RQKbVj6nWHxYWlI7Ewp95ymMJCmVtjORZyJX8RaQGM/x8GQzgS5PB9/WR6UgRDG1+iBKFeczQRVEqpu6CJoFJq56zosRVLSGXipDJxf6HoSh2n7mIw2M0Zw5tJ/la9iHYNK6XUXdFEUCm1Y2zbwqyxd5xlW8QSkXt/ERFsW4c7K6XU3dCrp1Jqx4SjQYLBAO4O7fVrjN8aGEtot7BSSt0NTQSVUjtGROjuTVIpr946bjvUqg2SmdjS7iNKKaW2RhNBpdSOGj7aTXWHEsFSvsrh0b4deW6llDoINBFUSu2o3oE0waBN4y4WlF6P63pgweBIdlufVymlDhJNBJVSOyoQtDn10Ahzs8Vtfd65mSInTg8TjgS39XmVUuog0URQKbXjDh/vo6cvxcJcaVuer1iokuyKcvz+wW15PqWUOqg0EVRK7TjLsnj47aOIWBQLm99mrpVyqUaj7vDIEycIBHWSiFJK3QtNBJVSbRFPRHjivafBwFyueFf7A+fny9RrDk+89wzJdGwHaqmUUgeLLiit1B6Sm5rn8ovXmB7LYQdsjjwwwpHTw0Tj27Aw810wxrAwU2Ds0i1K+QrGGKKJCMOjfWQHurCsO39rJruivPMHHuDV711n/PoMiVR0U1vDVSt18vNl+ocynD13lPh2LEStlFIKuZtf5fvJuXPnzHPPPbfb1VBqQ2MXJ3n+Ky8TiYWJd8VwXY/CbJFILMQTHz1HLBlta32mx3O89sxl8rNFgpEgoeakDafuUK82iCbC3PfWYxw6ObDqXGMMtybmuPjKOPNzJSyxCEeDhMMBxBKMgXq1Qa3awHUNya4IJx8YZvBQdlVyqZRSB4mIfNcYc267nk9bBJXaA+rVOi99/TXSfV1Le/FatkV2MM38dJ7Xn73EI+99sG31ufHGJC98/TzJTJzeNZZvqVXrfO9rr1HKlzn1yDFEZOkxEWFgOEv/UIaFuTJzMwVmbxfIz5VwHBfbtkhl4nT3Jsn0JEl3x+84Xyml1PbQRFCpPWBmfA7X8ZaSwOVS3UnGL97i7JOnCEVCO16X22M5Xvj6ebID6XUna4QjIXqGM7zx/DUisTBHTw+vKiMipLNx0tk4x+5b3XKolFJqZ2kfi1J7QK1aR+zWLWKWJYgFjbqz4/UwxvDady6RzCY2NWPXsiyyA2nOP3ulLfVTSim1NZoIKrUHxJNRPLf1eF6n4WLbNuHozrcGzt3OU5gvE93EBI9FgaCN67jcvjm7gzVTSil1NzQRVGoP6B7OEktEKBcqqx6bv7XAsQcPEQju/EiPsUtThCJbf514KsqVV8d2oEZKKaXuhSaCSu0Btm3x2Icexqm7zE7MUZwvk58tMD2Wo/9oDyfecrQt9SjOl+9qHGIoGqK0UN6BGimllLoXOllEqT2iqyfFe37iCSau3GZ2PEcgFGDoeD/dQ5m2LaliPMPdTN4V4a4WkFZKKbWzNBFUag8JRUIcPTPC0TMju/L6kUSY8q0qkdjmxwiCP5GlHWMYlVJKbY12DSulNm1otI/ZiTlmJ/1bcb60qZa+4ly55fIxSimldpe2CCqlNmSM4ebFSS48e5kbr48TigSxbXtpS7mhEwN0D6Rbnut5BgMMHetrb6WVUkptSFsElVLrMsZw/juXeOGrrxGKhjj16ChiWSQycbp6kojApeevMn75Vsvzc1NzHL1/kMgWlpxRSinVHpoIKqXWNX5piosvXKNnOEs4EqJvJEv/oW7yuSKuZwiGg6R6Eoy9McH8dH7pPM8zzEzk6Bvp5vRjx3fxHSillFqLdg0rpdbkeR5vfO8aXT1JLMufLiwiHL5/iFAk6LcCGogmIkRiISav3CKWilKcL+O5LkdODXPm7cexAxvvQqKUUqr9NBFUSq1pYbpAaaFM73D2juOWCEPH+ugdzpK7nef2jRkadZfcrRl6D/Vw/7ljDB3rJ5aM7FLNlVJKbYYmgkqpNdUqdWSdhQODoQD9I1n6R7IYY5idnOfxDz1Ez1B2zXOUUkp1Dh0jqJTaFosJ43qJo1JKqc6iiaBSak2xZBTjeZsq63kG8JeTUUoptTdoIqiUWlOqO0Gmr4tSvrJh2eJ8iYGjvcSS0TbUTCml1HbQRFCpfch1XKbHckxcucXtm7M06s5dP9d950YpzpdwHHfNMo26Q7VS58RDR+/6dZRSSrWfThZRah8xxnD11Ztc/O5V6rXG0ng9O2Az+tBhTr71KJa1td9/fSPdvOVdp3nxG68TjUeId8WWlpLxPENxvkS1Uufc+86S7ktt+3tSSim1czQRVGofOf/MJS4+f5XsQJquUHLpuNNwufDcZSqFCg99/+ktJ4NHTo+QzCa4/OINbt2YRkRY3GF4aLSf0bOHSPdqEqiUUnuNJoJK7RPztxe49L3r9A5nsew7E71A0KZ3OMv18xMMjfbTd7hny8+f7U+T/UCaSrFKpVRFEH8had06Timl9ixNBJXaJ66/PkEoEliVBC4SEeJdUS6/dOOuEsFF0UREZwYrpdQ+oZNFlNonbt+YIZGOrVsmnooyMzmHt8klYZRSSu1vmggqtU+YpVF7a9PFnpVSSi2niaBS+0TPUDflfHXdMqV8hWxfasuTRZRSSu1P+q+BUvvE0TPDVMu15g4frZUWyhx/y9H2VUoppVRH00RQqX0i09/F0QdGmB6fxV2x+LPnekyPzzE02k/vSPcu1VAppVSn0VnDSu0TIsLZd5wiHA1x+cXreJ5BBDwPLAuOnR3h/seOY68xq1gppdTBo4mgUvuIZVmcOnecY2cPMTs5T61SJxQJkh1IE4npen9KKaXupImgUvtQKBJi8FjfbldDKaVUh9M+IqWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0oTQaWUUkqpA0qMMbtdh10lItPA9d2uxyb0ADO7XYkOpzHamMZoYxqjjWmMNqYx2pjGaGOtYnTEGNO7XS9w4BPBvUJEnjPGnNvtenQyjdHGNEYb0xhtTGO0MY3RxjRGG2tHjLRrWCmllFLqgNJEUCmllFLqgNJEcO/4nd2uwB6gMdqYxmhjGqONaYw2pjHamMZoYzseIx0jqJRSSil1QGmLoFJKKaXUAaWJYBuJyCER+YKILIhIXkT+g4gc3uS5ERH5xyIyKSIVEfmWiHx/i3KWiPyKiFwTkaqIvCgiP7r972b77XR8ROQ+EflnIvKSiBSbZb8oIg/vzDvafu34DK045+MiYkRkbHvewc5rV4xEZFhEfldEpkSkJiJXReS3tvfd7Iw2XYu6m9+3K81yV0XkX4jIti17sZPuMUb/UES+JCKzze/PJ9Yp+3Mi8nrzM3RBRH5+297EDtvpGInIoIj8log813yNaRH5ykbXrU7Srs/RsnOeFBGvWT6wqUoaY/TWhhsQAy4CrwA/Avww8DJwGYhv4vw/AOaBnwPeB/wHoAK8ZUW5TwE14JeB9wD/J+ABP7TbMdjt+AD/HfAS8D80Y/Mx4FtAFXh0t2PQCTFaUT4NTAGTwNhuv/9OihFwFBgH/j/gLwHvAn4G+F92OwadECNAgL8ApoFfAN4N/A1gtvmdk92Oww7HqAB8A/gsYIBPrFHu55rX5081r0m/2fz7F3Y7Bp0QI+AjwBXg7wE/AHwY+E/NGH1kt2PQCTFaUT7YfP7JZvnApuq524E6KDfgbwIucGLZsWOAA/ztDc59uPk/9WeXHQsAF4AvLjvWh58E/oMV538FeGm3Y9AB8elZ+Q8Q0AXMAb+32zHohBitOOd3gD8DPsPeSQTbEiPgT4FngOBuv+dOjBFwX7PcX1tx/s83j5/a7TjsVIyaZa3m/QnWTnICwG3gsyuO/y7+AsId/dlqU4zSrEhmln3ent7tGHRCjFaU/1X8pPNTbCER1K7h9vko8G1jzKXFA8aYq/i/mn94E+c2gH+37FwH+DzwQREJNw9/EAgBv7/i/N8HHhSRY/f0DnbWjsfHGDNjmt+WZeUWgDeApsD0/wAACGhJREFU4e14EzusHZ8hAETkHcBPAb+4PVVvmx2PkYgcx/+u/bYxprG91W+LdnyOQs37/Irz55v3nf5vz73ECGOMt4nXeALoZfX1+nNAN/DOTdd2d+x4jIwx883P1/JjDvAC+/+avdnPEbB0Xfo1/Jb3LV2XOv3LuJ88gJ+pr/QqcGYT5141xpRbnBvC/7WwWK4GXGpRjk28zm5qR3xWEZEscBY4v/mq7pq2xEhEgvitgf94+QVsj2hHjN7RvK+IyJebY7vmROT3RKT7biveRu2I0avA08DfE5FzIpIQkbcBfx/4E2NMp3/f7iVGW3kNWrzOXrheQ3titIqIhPCT6E7/DEF7Y/Rp4AvGmKe3eqImgu2Txe+CXCkHZO7h3MXHF+/nV7Z6tSjXidoRn1Z+G3880z/dqIIdoF0x+rtAGNgTEx9WaEeMhpr3v4vfmvyD+DH7MPBnItLp19Udj1HzGvRD+F14z+KPdfoO/nivvTB57V5itJXXoMXr7IXrNbQnRq18EhgB/rcdfI3t0pYYichPAeeAv3M3529uRonaLq0WbZRNnCebPHez5TrVTsfnzgdFfgX4y8B/u4davnY0RiJyAr974WPGmOrWq9cRdvpztJjofc0Ys9h1/uciskCzixT4k81UdBe147v2r4HH8ccFngdOA/8A+IKIPLWVbq9dstPX0sXn2suL+bb13xsR+cvA/4Q/KesbO/U622xHY9Ts1fonwK8aY27fzXNoItg+c7T+hZeh9S+G5XJAq+nmmWWPL95nRERWtAquLNeJ2hGfJc0lGv4h8OvGmN/dQj13Uzti9M+BPwe+LSLp5rEQIM2/a8aYypZq3V7tiNFs8/7LK8p9qXn/Vjo7EdzxGInIh4GPA+83xnyl+djTInIFP05PAX+8xXq3073EaLOWt/xNLjueXfF4p2pHjJaIyFP4E9f+rTHmf97u598h7YjRbwK3gP972TU70rzvEpGqMaa03hN0ehfGfvIqb44JWe4M8Nomzj0mIrEW59Z5c0zgq/hdesdblGMTr7Ob2hEfAETkp4F/BfwTY8yn7q66u6IdMTqD36U3t+z2cfzu0Dk6v7u4Xd8zWLslp9NbutoRoweb98+uKPdM8/705qq6a+4lRlt5DVq8zl64XkN7YgSAiLwP+PfAHwF/fTufe4e1I0Zn8L9vs7x5zf67zcdm8Jd7Wpcmgu3zReBxERldPCAiR/EHnn9xE+cGgR9fdm4A+AngS8aYWvPwn+JfjP/KivN/CnilOVupU7UjPojIx4D/C/g3xphf3q7Kt0k7YvST+OuZLb/9Gf4F5T3Av9iG97GT2hGjb+Ovr/ihFecv/r0y+ek07YjRVPP+bSvOf3vzfvxuKt5G9xKjzfoW/veq1fU6hz+ztJO1I0aIyBP4rcdfAX5qDwwpWK4dMfpbrL5mf7b52PuBX9/wGXZrfZ2DdgPi+L+WX8afNv5R4EX8wdOJZeWO4K8x9PdXnP95/Ez/r+Iv4voF/IWQH1lR7n9tHv/b+Iu4fhq/heKp3Y7BbscH+P7mseeBJ/HHLy3e3rrbMeiEGK3xup9h76wj2K7v2c/gtwj+H8AH8JdsmAO+SucvltyO71oKP9mbwF9Q+j3N+yngxvLX6cTbNsToXcCP4S9ib/B/QP0Y8GMryv08/vX5N/Gv17/R/PsXdzsGnRAj4H78pPhaMz7Lr9mP73YMOiFGa7zuJ9EFpTvzhj+25g/x19YqAP8PcHRFmaPN/4GfXHE8CvzvzQtpFX8G3rtbvIaN/wvgOv5SMi9t9KHplNtOx2fZl6PV7dpuv/9OiNEar/kZ9kgi2M4YAT+NvzREDX+M12/T4QlOO2MEHAL+LXC1We4q/gSS4d1+/22I0dfWuta0eJ2/jj/7vIa/C8Xf2O333ikxAj6xVplWsezEW7s+RyvO+yRbSASleZJSSimllDpgdIygUkoppdQBpYmgUkoppdQBpYmgUkoppdQBpYmgUkoppdQBpYmgUkoppdQBpYmgUkoppdQBpYmgUkptAxH5jIiM7XY9lFJqKzQRVEoppZQ6oDQRVEoppZQ6oDQRVEqpdYjICRH5nIhcFZGKiFwRkU+LSGaN8k+KyLMiUhWRayLyS8see5uIGBF5qsV5nxaRaREJ7uT7UUqp5TQRVEqp9Q0BY8DfAj4I/AbwPuA/tyibAv4d8FngR/D3Cv3nIvIJAGPMM8AF/H2Kl4hICPhLwOeNMY2deBNKKdWK7jWslFJbICIB4HHgG8AjxpjvNY9/BvgZ4OPGmM8vK/9l4D78jeaNiPwa8OvAgDFmoVnmR4A/At7eTBaVUqottEVQKaXWISIhEflVEXldRCpAAz8JBDi1orgL/OGKY58HDgPDzb9/HwgDP76szE8DFzQJVEq1myaCSim1vt8CPomfwH0YeBvwXzUfi6woO9eia/dW834YwBhzHXiaZvewiKSbz/u57a64UkptJLDbFVBKqQ73k8DvGWN+c/GAiCTWKJsRkeCKZLC/eT++7NjngH8tIkfwxx2GgD/YxjorpdSmaIugUkqtL4bfHbzcz65R1gZ+dMWxnwRucGci+O+BKvBX8FsGnzbGXLvnmiql1BZpi6BSSq3vT4GfEZGXgUv43cJPrlG2APwjEekBLgIfB94PfMIsm5lnjMmLyBeBXwQGgZ/bwforpdSaNBFUSqn1/RIgwKeaf/9n/ASv1cSOPH4L4D8DHsQfH/g3jTGfbVH2c8BP4LcMfmGb66yUUpuiy8copZRSSh1QOkZQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA0kRQKaWUUuqA+v8BLrd3pKDiByYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"style_means.plot.scatter(figsize=(10,10), \n",
" x='abv', y='ibu', s=style_counts*20, color=colors,\n",
" title='Beer ABV vs. IBU mean values by style\\n',\n",
" alpha=0.3); #alpha sets the transparency"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like the most popular beers do follow a linear relationship between alcohol fraction and IBU. We learned a lot about beer without having a sip!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Wait... one more thing!_ What if we add a text label next to the bigger bubbles, to identify the style? \n",
"\n",
"OK, here we go a bit overboard, but we couldn't help it. We played around a lot to get this version of the plot. It uses `enumerate` to get pairs of indices and values from a list of style names; an `if` statement to select only the large-count styles; and the [`iloc[]`](http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.iloc.html) slicing method of `pandas` to get a slice based on index position, and extract `abv` and `ibu` values to an $(x,y)$ coordinate for placing the annotation text. _Are we overkeen or what!_ "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ax = style_means.plot.scatter(figsize=(10,10), \n",
" x='abv', y='ibu', s=style_counts*20, color=colors,\n",
" title='Beer ABV vs. IBU mean values by style\\n',\n",
" alpha=0.3);\n",
"\n",
"for i, txt in enumerate(list(style_counts.index.values)):\n",
" if style_counts.values[i] > 65:\n",
" ax.annotate(txt, (style_means.abv.iloc[i],style_means.ibu.iloc[i]), fontsize=12)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What we've learned\n",
"\n",
"* You should always plot your data.\n",
"* The concepts of quantitative and categorical data.\n",
"* Plotting histograms directly on columns of dataframes, using `pandas`.\n",
"* Computing variance and standard deviation using NumPy built-in functions.\n",
"* The concept of median, and how to compute it with NumPy.\n",
"* Making box plots using `pyplot`.\n",
"* Five statistics of a box plot: the quartiles Q1, Q2 (median) and Q3 (and interquartile range Q3$-$Q1), upper and lower extremes.\n",
"* Visualizing categorical data with bar plots.\n",
"* Visualizing multiple data with scatter plots and bubble charts.\n",
"* `pandas` is awesome!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"1. [Craft beer datatset](https://github.com/nickhould/craft-beers-dataset) by Jean-Nicholas Hould.\n",
"2. [What's The Meaning Of IBU?](https://beerconnoisseur.com/articles/whats-meaning-ibu) by Jim Dykstra for The Beer Connoisseur (2015).\n",
"3. 40 years of boxplots (2011). Hadley Wickham and Lisa Stryjewski, _Am. Statistician_. [PDF available](http://vita.had.co.nz/papers/boxplots.pdf)\n",
"4. [John Wilder Tukey](https://www.britannica.com/biography/John-Wilder-Tukey), Encyclopædia Britannica.\n",
"5. John W. Tukey: His life and professional contributions (2002). David R. Brillinger, _Ann. Statistics_. [PDF available](https://www.stat.berkeley.edu/~brill/Papers/life.pdf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problems\n",
"\n",
"1. Many beers do not report the IBU of the beer because it is very small. We may be accidentally removing whole categories of beer from our dataset by removing rows that do not include the IBU measure. \n",
"\n",
" a. Use the command `beers_filled = beers.fillna(0)` to clean the `beers` dataframe\n",
" \n",
" b. Repeat the steps above to recreate the plot \"Beer ABV vs. IBU mean values by style\" \n",
" scatter plot with `beers_filled`. What differences do you notice between the plots? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Gordon Moore created an empirical prediction that the rate of semiconductors on a computer chip would double every two years. This prediction was known as Moore's law. Gordon Moore had originally only expected this empirical relation to hold from 1965 - 1975 [[1](https://en.wikipedia.org/wiki/Moore%27s_law),[2](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)], but semiconductor manufacuturers were able to keep up with Moore's law until 2015. \n",
"\n",
" In the folder \"../data\" is a comma separated value (CSV) file, \"transistor_data.csv\" [taken from wikipedia 01/2020](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors). \n",
" Load the csv into a pandas dataframe, it has the following headings:\n",
"\n",
" |Processor| MOS transistor count| Date of Introduction|Designer|MOSprocess|Area|\n",
" |---|---|---|---|---|---|\n",
"\n",
" a. In the years 2017, what was the average MOS transistor count? \n",
" Make a boxplot of the transistor count in 2017 and find the first, second and third quartiles.\n",
"\n",
" b. Create a semilog y-axis scatter plot (i.e. `plt.semilogy`) for the \n",
" \"Date of Introduction\" vs \"MOS transistor count\". \n",
" Color the data according to the \"Designer\"."
]
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