{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Assignment 5: Multivariate exploratory visualization\n",
"\n",
"In Assignment 4, you got a feel for the basic analytic capabilites of `pandas` and `seaborn`, and learned how to calculate descriptive statistics and generate univariate plots. In many cases, however, you are going to want to explore interrelationships between multiple characteristics of your data. In this course, we are going to address this with __multivariate exploratory visualization__. \n",
"\n",
"Multivariate visualization can take a lot of forms, and can get quite complex! We're just going to start with the basics here and work our way up to more complicated visualizations - and fortunately `seaborn` can do quite a lot of things. \n",
"\n",
"Our data for today's notebook are going to come from the 2012-2016 __American Community Survey__. The American Community Survey, or ACS, is an annual survey conducted by the US Census Bureau that covers demographic characteristics of the US population such as income, education, and much more. \n",
"\n",
"To get started, download the required data for this assignment from TCU Online, then upload the data to Colab. Run the cell below to load the required packages and data for this assignment, which are for counties in New Mexico. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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\n",
"\n",
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\n",
" \n",
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\n",
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\n",
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county
\n",
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hhincome
\n",
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pct_college
\n",
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median_age
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\n",
" \n",
" \n",
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\n",
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0
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Bernalillo
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48994.0
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32.8
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36.8
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\n",
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\n",
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1
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Catron
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38142.0
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24.8
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58.1
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\n",
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\n",
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2
\n",
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Chaves
\n",
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41356.0
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19.3
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35.1
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\n",
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\n",
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3
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Cibola
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36160.0
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12.4
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35.8
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\n",
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4
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Colfax
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32693.0
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22.1
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48.3
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"text/plain": [
" county hhincome pct_college median_age\n",
"0 Bernalillo 48994.0 32.8 36.8\n",
"1 Catron 38142.0 24.8 58.1\n",
"2 Chaves 41356.0 19.3 35.1\n",
"3 Cibola 36160.0 12.4 35.8\n",
"4 Colfax 32693.0 22.1 48.3"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"# Assumes the data are in local session storage; \n",
"# modify if the data are in your Drive\n",
"nm = pd.read_csv(\"data/new_mexico.csv\")\n",
"\n",
"nm.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"We note that our dataset has four columns: `\"county\"`, for the county name; `\"hhincome\"`, which refers to the median annual income for households in the county; `\"pct_college\"`, which refers to the percent of the population age 25 and up with at least a 4-year degree; and `\"median_age\"`, which refers to the median age of county residents."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### Bar charts and dot plots\n",
"\n",
"Two common types of charts for comparing quantities, as we discussed in class, are bar charts and dot plots. __Bar charts__ represent values as proportional to the height of bars, and __dot plots__ represent values as dots along a given axis. \n",
"\n",
"Many plot types are available in `pandas` through the built-in `.plot()` data frame method, and also have equivalent functions in `seaborn`. In `pandas` plotting, if no data arguments are passed to the `plot` method, `pandas` will assume that the x-axis values are in the index, and then will plot numeric columns as data series. For example, we can create a bar plot below. Also notice the use of the `sns.set()` function. Within `seaborn`, you can choose from a variety of built-in plot templates: `\"white\"`, `\"dark`\", `\"darkgrid\"`, `\"whitegrid\"`, and `\"ticks\"`, and you can use the `set` function to make all of your plots in this particular style. \n",
"\n",
"Additionally, we can modify underlying figure characteristics with the `rc` parameter in `sns.set()`. This allows you to specify the output figure width and height in inches; the code below will tell seaborn to make 10 inch by 8 inch plots, which will be more legible than the defaults. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set(style = \"whitegrid\", rc = {\"figure.figsize\": (10, 8)})"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"As we've covered in class, `seaborn` is an excellent choice for creating publication-quality plots with a minimum of code. Bar charts in `seaborn` are available from the `barplot` function. The function has a lot of optional parameters; however, to get a bar chart to display, all you need are columns to map onto the x and y axes, and a dataset you'd like to plot. Let's try a horizontal bar plot with the county name on the y-axis and median age on the x-axis: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x = 'median_age', y = 'county', data = nm)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Looks good! There is much more you can do with the plot - customizing colors, axis labels, and beyond. We'll be addressing plot customization beginning in Week 10. However, there are still some small things that we can do to make our plots more readable. Recall from class our discussion of the `.sort_values()` data frame method, in which we can arrange our data based on one or more column values. When creating bar charts, it is a good idea to show sorted data values, which makes it easier for the reader to make comparisons. \n",
"\n",
"We have a few options here. We could sort our data frame in place, then supply it to the `data` parameter of `barplot`: \n",
"\n",
"```python\n",
"nm.sort_values('median_age', ascending = False, inplace = True)\n",
"sns.barplot(x = 'median_age', y = 'county', data = nm)\n",
"```\n",
"\n",
"We could create a new sorted data frame from the `sort` operation and use it instead: \n",
"\n",
"```python\n",
"nm_sorted = nm.sort_values('median_age', ascending = False)\n",
"sns.barplot(x = 'median_age', y = 'county', data = nm_sorted)\n",
"```\n",
"\n",
"Or, we could tell `seaborn` to work with a sorted data frame within the function call. \n",
"\n",
"```python\n",
"sns.barplot(x = 'median_age', y = 'county', data = nm.sort_values('median_age', ascending = False))\n",
"```\n",
"\n",
"All of these will work. Recall that the `sort` method takes either a single column or a list of columns, and defaults to sorting in ascending order; if you want descending order you will need to tell the method `ascending = False`. Let's try the second option I discussed. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nm_sorted = nm.sort_values('median_age', ascending = False)\n",
"sns.barplot(x = 'median_age', y = 'county', data = nm_sorted)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Sorting the data allows us to more clearly see how the values vary. However, as we discussed in class, it is essential that bar charts have an origin of 0, as bar heights are used to make comparisons. For values that are similar - but for which the differences are still significant - the __dot plot__ may be preferable, which represents values by the position of dots along a a value axis. Indeed, many data visualization experts prefer dot plots to bar charts, as they argue that it is easier for humans to perceive dot position instead of bar heights. Further, dot plots are not constrained by the zero-axis-origin rule. \n",
"\n",
"Dot plots in `seaborn` are available via the `stripplot` function; for reference, \"strip plot\" is another name for dot plot. The syntax you'll use is identical to `barplot` in this example aside from a `size` parameter that governs the size of the points on the plot."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.stripplot(x = 'median_age', y = 'county', data = nm_sorted, size = 10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### Scatter plots\n",
"\n",
"Bar charts and dot plots are excellent choices for visualizing quantitative variations amongst different categories, like countries. However: what if you want to visualize how two quantitative attributes might covary? The most common exploratory visualization technique for this type of scenario is the __scatter plot__. \n",
"\n",
"Scatter plots most closely resemble the base __Cartesian coordinate system__ that underpins the charts we've learned how to make to this point. In fact, scatter plots are likely familiar to you from grade-school mathematics in the way they represent the joint distributions of two attributes. The value for one attribute is represented by the x-coordinate (the horizontal axis) on the chart, and the other attribute is represented by the y-coordinate (the vertical axis). \n",
"\n",
"Let's give this a try. We'll be comparing the distributions of counties' educational attainment, as measured by the percentage of residents age 25 and up with at least a 4-year college degree, and the median annual income for households in New Mexico's counties. Briefly, let's examine the characteristics of our data. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
hhincome
\n",
"
pct_college
\n",
"
median_age
\n",
"
\n",
" \n",
" \n",
"
\n",
"
count
\n",
"
33.000000
\n",
"
33.000000
\n",
"
33.000000
\n",
"
\n",
"
\n",
"
mean
\n",
"
40088.818182
\n",
"
21.384848
\n",
"
41.321212
\n",
"
\n",
"
\n",
"
std
\n",
"
15328.603075
\n",
"
10.455744
\n",
"
7.637193
\n",
"
\n",
"
\n",
"
min
\n",
"
21190.000000
\n",
"
9.500000
\n",
"
29.700000
\n",
"
\n",
"
\n",
"
25%
\n",
"
32067.000000
\n",
"
15.400000
\n",
"
35.800000
\n",
"
\n",
"
\n",
"
50%
\n",
"
36160.000000
\n",
"
18.100000
\n",
"
40.400000
\n",
"
\n",
"
\n",
"
75%
\n",
"
41788.000000
\n",
"
25.800000
\n",
"
46.400000
\n",
"
\n",
"
\n",
"
max
\n",
"
105902.000000
\n",
"
64.600000
\n",
"
58.100000
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" hhincome pct_college median_age\n",
"count 33.000000 33.000000 33.000000\n",
"mean 40088.818182 21.384848 41.321212\n",
"std 15328.603075 10.455744 7.637193\n",
"min 21190.000000 9.500000 29.700000\n",
"25% 32067.000000 15.400000 35.800000\n",
"50% 36160.000000 18.100000 40.400000\n",
"75% 41788.000000 25.800000 46.400000\n",
"max 105902.000000 64.600000 58.100000"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nm.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"You can get a sense here of the wide variations among counties. Median household income varies from \\$21,190 to \\$105,902; the percentage of residents age 25+ with a 4-year degree varies from 9.5 percent to 64.6 percent.\n",
"\n",
"So how do these two distributions of values covary? We can draw a scatterplot with __seaborn__: "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rr5bD8de3rFixImaFAQAAoH8iCng/+clPNGHCBP3N3/xNrOsBAADAAEUU8Ox2u370ox/FuBQAAABEQ0T34I0ePVrV1dUxLgUAAADREFEHr6amRuXl5XI6nXI6nQqFQrLZbNqzZ0+s6wMAAMB5iijgPffcc7GuAwAAAFESUcDLyspSZWWlXnvtNfn9fk2YMEG33HJLjEsDAABAf0R0D96zzz6rf/3Xf9WXv/xljR49WuvXr1dZWVmsawMAAEA/RNTBq6io0MaNG+V2uyVJM2bM0Le//W19//vfj2lxAAAAOH8RdfAkhcOdJKWmpvba8BgAAACJI6KAl5WVpeeee05+v19+v18///nPdfHFF8e6NgAAAPRDRAFv2bJl+u1vf6tx48Zp3Lhx+vWvf60lS5bEujYAAAD0Q0TXWYcNG6bHH39c6enpCgQC+uCDD5SVlRXr2gAAANAPEXXwNmzYoO9///tKTk7WiRMnNG/ePL344ouxrg0AAAD9EFHAe+GFF7Rx40ZJ0ogRI1RRUaFf/OIXMS0MAAAA/RNRwAsEAn1W0dpstpgVBQAAgP6LKOBddtllKi0t1eHDh3X48GE99dRTuuSSS2JcGgAAAPoj4lW09fX1uuWWWzRjxgzV19frRz/6UYxLAwAAQH9EtIo2PT1da9eujXUtAAAAiIKIAt7777+vf/u3f9PHH3+sUCgUHn/mmWdiVhgAAAD6J6KAV1JSoq985Su69tprWVwBAACQ4CIKeJ2dnVq8eHGsawEAAEAURLTIYuTIkWpsbIx1LQAAAIiCiDp4wWBQ06ZN0+jRo+VyucLj3IMHAACQeCIKeDfeeKNuvPHGWNcCAACAKDhnwGtvb5fb7db1118f1YO+8sorWrt2rTo7O3Xddddp8eLFqqmp0YoVK+Tz+TR16lQVFxdLkvbv369Fixapo6NDOTk5WrZsmRwOh44ePaoFCxbo+PHjuvTSS1VaWqqUlBS1trbqoYce0uHDh+XxeLRmzRplZGREtX4AZxcMhtTQ3KGW1k55hiYrMz0l3iUBwKBzznvw7rzzTknS17/+deXm5urrX/96+L/c3Nx+HfDw4cNaunSpysrKtHXrVr3zzjvauXOnFi5cqLKyMlVWVqqurk47d+6UJC1YsEBLlizRjh07FAqFVF5eLunU5suzZ89WVVWVsrOzVVZWJklas2aNcnJytH37dt12221avnx5v+oEcP6CwZDe2Neg+auqtXBdjeavqtYb+xrkcER0sQAAECXnDHgvvfSSJOnAgQPav3+/Dhw4EP5v//79/Trgb37zG910000aPny4nE6nVq9ereTkZI0cOVIjRoyQw+FQYWGhqqqqdOTIEZ08eVLjxo2TJBUVFamqqkp+v1+7du3S5MmTe41LUnV1tQoLCyVJ06ZN06uvviq/39+vWhF7wWBIRxrbte9PTTrS2K5gMPTZb0LCamju0OqNe+TzByRJPn9AqzfukYZ8Ps6VAcDgEtH/Vp88eVK//vWv1dLS0muj47//+78/7wMeOnRITqdTc+fOVUNDgyZOnKgrrrii12VUr9erY8eOqbGxsdd4RkaGjh07phMnTsjtdoe7Aj3jknq9x+FwyO12q6WlRcOGDYuovrq6uvM+p2iora2Ny3HjyeFwqNmXqnWb35HPH5DLmaT7iv6v0l1t6u7ujumxB+N8W+Gk/QvhcNfD5w+o/WSQObcY820t5ttazPdniyjgPfjgg/roo4905ZVXDnij40AgoN27d2vDhg363Oc+p/vuu08XXXRRr88NhUKy2WwKBoNnHO/589POVlcoFJLdHtFuMJKk7OzsXiuFrVBbW6trrrnG0mMmgiON7frxqupe3Z51m9/RUw9OVJbXHbPjDtb5tsKRxna5nEm9Qp7LmST3RXaNvYo5twrf49Zivq3FfJ/i8/nO2ZSKKOC999572rFjx3kFpbNJT09Xbm6uPB6PJOmGG25QVVWVkpKSwq9pamqS1+vV8OHD1dTUFB5vbm6W1+uVx+NRW1ubAoGAkpKSwq+XTnX/mpubNXz4cHV3d6ujo0NpaWkDrhvR19LaecZuT0tbZ0wDHmInMz1FxbPGhy/TupxJKp41Xupq+uw3AwCiJqLE9sUvfjFql8yuv/56vf7662ptbVUgENBrr72mKVOm6ODBgzp06JACgYC2bdumvLw8ZWVlyeVyhVuxW7ZsUV5enpxOp3JyclRZWSlJqqioUF5eniQpPz9fFRUVkqTKykrl5OTI6XRGpXZEl2doslzOpF5jLmeSPKnJcaoIA2W325Q7JlNPPThRj3//G3rqwYnKHZMZ80vuAIDeztnBW79+vaRT97jdeeed+ru/+7teYak/9+CNHTtWd999t2bPni2/36/rrrtOs2bN0mWXXaZ58+bJ5/MpPz9fU6ZMkSSVlpZq8eLFam9v1+jRozVnzhxJ0tKlS1VSUqJ169YpMzNTq1atkiTNnz9fJSUlKigoUGpqqkpLS8+7RljjbN0ettW4sNntNmV53XRhASCOzhnw3n33XUmS2+2W2+3WwYMHo3LQGTNmaMaMGb3GcnNztXXr1j6vHTVqlDZt2tRnPCsrSxs2bOgznpaWxhM2LhA93Z5LMieqpa1TntRTe6bZ7QO7zxMAgMHunAFvxYoVkqTdu3dr7dq1On78uCVFIXbOtAltPAMV3R4AAKIvokUWS5Ys0be//W1dddVVA15Fi/jp2YT29EuiuWMy6ZoBAGCQiALekCFD9L3vfS/GpSDWzrYJ7SWZsd2WBAAAWCuiVbSXXXaZ9u3bF+taEGPn2pYEAACY45wdvJ5HfnV0dGjWrFnhR4n1ePnll2NbHaKqZ1uS0zehZVsSAADMcs6A9+ijj1pVByzAtiQAAAwO5wx4X/3qV62qAxZgWxIAAAaHiBZZwBxsSwIAgPkG/nBZAAAAJBQCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYVtECwCASDIbU0NyhltZOeYayVRJgKgIeAAwSwWBIb+xr6LPZee6YTEIeYBgu0QLAINHQ3BEOd9KpZ1Gv3rhHDc0dca4MQLQR8ABgkGhp7ez1LGrpVMhraeuMU0UAYoWABwCDhGdoslzOpF5jLmeSPKnJcaoIQKwQ8ABgkMhMT1HxrPHhkNdzD15mekqcKwMQbSyyAIBBwm63KXdMpi7JnKiWtk55UllFC5iKgAcAg4jdblOW160srzvepQCIIS7RAgAAGIaABwAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYAh4AAIBhCHgAAACGIeABAAAYhoAHAABgGAIeAACAYQh4AAAAhiHgAQAAGIaABwAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYAh4AAIBhCHgAAACGIeABAAAYhoAHAABgGEe8CwCAgQgGQ2po7lBLa6c8Q5OVmZ4iu90W77IAIK4IeIgKfskiHoLBkN7Y16DVG/fI5w/I5UxS8azxyh2TyfcfgEGNgIcB45cs4qWhuSP8fSdJPn9Aqzfu0SWZE5Xldce5OgCIH+7Bw4Cd7ZdsQ3NHnCuD6VpaO8Pfdz18/oBa2jrjVBEAJAYCHgaMX7KIF8/QZLmcSb3GXM4keVKT41QRACQGAh4GjF+yiJfM9BQVzxof/v7ruT0gMz0lzpUBQHxxDx4GrOeX7On34PFLFrFmt9uUOyZTl2ROVEtbpzypLPABAImAhyjglyziyW63KcvrZlEFAHwKAQ9RwS9ZAAASB/fgAQAAGIaABwAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYAh4AAIBhCHgAAACGIeABAAAYhoAHAABgGAIeAACAYQh4AAAAhiHgAQAAGIaABwAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYAh4AAIBhCHgAAACGIeABAAAYhoAHAABgGAIeAACAYRzxLgCItWAwpIbmDrW0dsozNFkOB9/2AACz8ZsOcXd6AMtMT5HdbovaZ7+xr0GrN+6Rzx+Qy5mk+4r+r4LBUNSOAQBAoiHgIa7OFMCKZ41X7pjMqASwhuaO8GdLks8f0LrN72jUJV5led0D/nwAABIRAQ9xdaYAtnrjHmV84Tr5urr71dH7dEfQ1xUIf3YPnz+glrZOAh4AwFgEPMRVS2vnGQPYrnc+0v/7zbvn3dE7vSM488Yvy+VM6nUMlzNJntTkqJ8LAACJglW0iCvP0GS5nEm9xlzOJAWDp/7e09FraO6I6PNO7wj+dtchzbzxyvAxeu7By0xPid5JAACQYOjgIa4y01NUPGt8r3vwbr/xSlXWHAy/5nwuqZ7eEWz++KT+s+agfvQPX1dIIXlSk9Xc8GcWWAAAjEbAQ1zZ7TbljsnUJZkT1dLWKZfTodLnd6v545Ph15zPJdWejuCnQ15bh19fSL0oHBA/Otwd3ZMAACDBcIkWcWe325TldWvM5Rn6P19K03cLRve6pFo8a3zEl1R7OoL9fT8AACagg4eEcnpHz5N6fqtoB/p+nF0s9ysEAERXXAPeE088oRMnTmjlypWqqanRihUr5PP5NHXqVBUXF0uS9u/fr0WLFqmjo0M5OTlatmyZHA6Hjh49qgULFuj48eO69NJLVVpaqpSUFLW2tuqhhx7S4cOH5fF4tGbNGmVkZMTzNHGeejp6/d3GZKDvR1+x3q8QABBdcbtE+8Ybb+ill16SJJ08eVILFy5UWVmZKisrVVdXp507d0qSFixYoCVLlmjHjh0KhUIqLy+XJC1btkyzZ89WVVWVsrOzVVZWJklas2aNcnJytH37dt12221avnx5fE4QMMjZ9iuMdHUzAMBacQl4H3/8sVavXq25c+dKkvbu3auRI0dqxIgRcjgcKiwsVFVVlY4cOaKTJ09q3LhxkqSioiJVVVXJ7/dr165dmjx5cq9xSaqurlZhYaEkadq0aXr11Vfl9/utP0nAIGfbr7ClrTNOFQEAziUul2iXLFmi4uJiNTQ0SJIaGxt7XUb1er06duxYn/GMjAwdO3ZMJ06ckNvtDj80vmf89M9yOBxyu91qaWnRsGHDIqqtrq4uKud4vmpra+Ny3MGK+T4/zs+ln3HD6GBXh2prP4joM5hzazHf1mK+rcV8fzbLA96LL76ozMxM5ebmavPmzZKkYDAom+2v9/GEQiHZbLazjvf8+Wmnf/3p99jtkTcqs7Oz5XK5zueUBqy2tlbXXHONpccczJjv8xcMhlQ8y9nnHrwxX86U3X7JZ76fObcW820t5ttazPcpPp/vnE0pywNeZWWlmpqaNH36dP3lL3/RJ598oiNHjigp6a9PM2hqapLX69Xw4cPV1NQUHm9ubpbX65XH41FbW5sCgYCSkpLCr5dOdf+am5s1fPhwdXd3q6OjQ2lpaVafJnBBiHRlLKuTAeDCYnnAW79+ffjvmzdv1ltvvaVly5Zp0qRJOnTokL70pS9p27Zt+ta3vqWsrCy5XK5wWt+yZYvy8vLkdDqVk5OjyspKFRYWqqKiQnl5eZKk/Px8VVRUaO7cuaqsrFROTo6cTqfVpwkkvDOtjJ337XG67isXy+Ho2/VmdTIAXDgSYh88l8ullStXat68efL5fMrPz9eUKVMkSaWlpVq8eLHa29s1evRozZkzR5K0dOlSlZSUaN26dcrMzNSqVaskSfPnz1dJSYkKCgqUmpqq0tLSuJ0XkMjOtDL2J+V/0NCUIRp7RQbdOQC4gMU14BUVFamoqEiSlJubq61bt/Z5zahRo7Rp06Y+41lZWdqwYUOf8bS0ND3zzDPRLxaIg1huLny2lbHvHDwu7xc+R6cOAC5gCdHBA9BXrDcXPtNze13OJAWDUktbJwEPAC5gPIsWSFCx3lw4Mz1F8749rtdze2+/8Uq99ocP5UlNjsoxAADxQQcPSFDn2lw4Gt01u92m675ysYamDNE7B48rGJR+8+YhfbdgtDLTUwb8+QCA+CHgAQnqbJdQo9ldczjsGntFhrxf+Jxa2jr1tzkj2P4EAAzAJVogQWWmp6h41vhel1CLZ42PenetZ/uTMZdnKMvrJtwBgAHo4AEJis2FAQD9RcADEhibCwMA+oOAh5iI5f5tAADg3Ah4iLpY798GAADOjUUWhgoGQzrS2K59f2rSkcZ2BYMhy44d6/3bAADAudHBM1C8O2ix3r8NAACcGx08A8W7g9azf9unRXv/NgAAcHYEPAOdq4NmBav2bwMAAGfGJVoDWfEEhHNh/7YLGyugAeDCR8AzUE8H7fR78KzsoA3W/dsu9HAU7/s3AQDRQcAzEB20+DAhHJ3t/s1LMicOurAOABcy7sEzlJXPF43nliyfxcra4r24JRriff8mACA66OBhQBK5a3W22tyO2Hzbm7A9TLzv3wQARAcdPAxIInetzlabhnw+JsczYXsYVkADgBno4GFArOpa9Wfxwtlqaz8ZjFpdn5YIi1sGivs3AcAMBDwMiBWX9Pp7Gfhstbkvik3j2pRwNFhXQAOASbhEiwGx4pJefy8Dn602df0larWdzsrFLQAAnA0dPAyIFV2r/l4GPlttv/99Q9RqAwAgERHwMGCxvqQ3kMvAXG4EAAxGXKJFwmNlJwAA54cOHhJeoi5euNAfSwYAMBcBDxeERLvUmkgbPBM0AQCnI+AB/ZAoz2xNpKAJAEgc3IOHhJTIz7eVEueZrYn8JBEAQPzQwUPCuRC6UonyzFYTnn8LAIg+OnhIOBdCVypRVvaa8PxbAED00cFDwrkQulKJsrLXhOffAgCij4BnIVY7RiZRLn9+lkRY2ZsoQRMAkFgIeBY5131l6I2u1PlJhKAJAEgsBDyLnGtbDfRGVwoAgIEh4FkkUbbVuFDQlQIAoP9YRWsRVjsCAACrEPAskijbagAAAPNxidYi3FcGAACsQsCzEPeVRY4tZQAA6D8CHhLOhfCoMgAAEhn34F0AgsGQjjS2a9+fmnSksV3BYCjeJcXUhfCoMgAAEhkdvAQ3GLtZF8KjygAASGR08BLcYOxmsaUMAAADQ8BLcINxg2S2lAEAYGC4RJvgerpZnw55pnez2FIGAICBoYOX4BK5mxXLxR89W8qMuTxDWV434Q4AgPNABy/BJWo3azAu/gAA4EJBB+8CkIjdrMG4+AMAgAsFAQ/9MhgXfwAAcKHgEq2hYv2or8G4+AMAgAsFAc9AVtwf17P44/RjJMriD55jCwAYzAh4Bjrb/XGXZE6M2pMgWPwBAEDi4h48A/X3/rjz3faExR8AACQmOngG6s/9caZ0vniOLQAAdPCM1J/NkU3pfPEcWwAA6OAZqT/3x5nS+UrkxR8AAFiFgGeonvvjIg1npmx7kqiLPwAAsBKXaCEpsZ95e74ScfEHAABWooMHSXS+AAAwCQEPYed7WRcAACQmLtECAAAYhoAHAABgGAIeAACAYQh4AAAAhiHgAQAAGIaABwAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYAh4AAIBhCHgAAACGIeABAAAYhoAHAABgGAIeAACAYQh4AAAAhiHgAQAAGMYR7wJgvWAwpIbmDrW0dsozNFmZ6Smy223xLgsAAEQJAW+QCQZDemNfg1Zv3COfPyCXM0nFs8Yrd0wmIQ8AAENwiXaQaWjuCIc7SfL5A1q9cY8amjviXBkAAIgWAt4g09LaGQ53PXz+gFraOuNUEQAAiDYC3iDjGZoslzOp15jLmSRPanKcKgIAANFGwBtkMtNTVDxrfDjk9dyDl5meEufKAABAtMRlkcXatWu1fft2SVJ+fr4efvhh1dTUaMWKFfL5fJo6daqKi4slSfv379eiRYvU0dGhnJwcLVu2TA6HQ0ePHtWCBQt0/PhxXXrppSotLVVKSopaW1v10EMP6fDhw/J4PFqzZo0yMjLicZoJyW63KXdMpi7JnKiWtk55UllFCwCAaSzv4NXU1Oj111/XSy+9pIqKCv3xj3/Utm3btHDhQpWVlamyslJ1dXXauXOnJGnBggVasmSJduzYoVAopPLycknSsmXLNHv2bFVVVSk7O1tlZWWSpDVr1ignJ0fbt2/XbbfdpuXLl1t9ignPbrcpy+vWmMszlOV1E+4AADCM5QEvIyNDJSUlGjJkiJxOpy6//HLV19dr5MiRGjFihBwOhwoLC1VVVaUjR47o5MmTGjdunCSpqKhIVVVV8vv92rVrlyZPntxrXJKqq6tVWFgoSZo2bZpeffVV+f1+q08TAAAgbiy/RHvFFVeE/15fX6/t27frO9/5Tq/LqF6vV8eOHVNjY2Ov8YyMDB07dkwnTpyQ2+2Ww+HoNS6p13scDofcbrdaWlo0bNiwiOqrq6sb8Dn2R21tbVyOO1gx39Zjzq3FfFuL+bYW8/3Z4rbR8Xvvvad7771XDz/8sJKSklRfXx/+t1AoJJvNpmAwKJvN1me8589PO/3rT7/Hbo+8UZmdnS2Xy3V+JzNAtbW1uuaaayw95mDGfFuPObcW820t5ttazPcpPp/vnE2puKyira2t1fe+9z390z/9k2699VYNHz5cTU1N4X9vamqS1+vtM97c3Cyv1yuPx6O2tjYFAoFer5dOdf+am5slSd3d3ero6FBaWpp1JwcAABBnlge8hoYG/eAHP1BpaakKCgokSWPHjtXBgwd16NAhBQIBbdu2TXl5ecrKypLL5Qq3Yrds2aK8vDw5nU7l5OSosrJSklRRUaG8vDxJp1blVlRUSJIqKyuVk5Mjp9Np9WkCAADEjeWXaJ999ln5fD6tXLkyPDZz5kytXLlS8+bNk8/nU35+vqZMmSJJKi0t1eLFi9Xe3q7Ro0drzpw5kqSlS5eqpKRE69atU2ZmplatWiVJmj9/vkpKSlRQUKDU1FSVlpZafYoAAABxZXnAW7x4sRYvXnzGf9u6dWufsVGjRmnTpk19xrOysrRhw4Y+42lpaXrmmWcGXigAAMAFiidZAAAAGIaABwAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGAYAh4AAIBh4vYs2kQTCoUkSV1dXXE5vs/ni8txByvm23rMubWYb2sx39Zivv+aV3ryy+lsobP9yyDT1tamd999N95lAAAAROzKK69Uampqn3EC3v8KBoPq6OiQ0+mUzWaLdzkAAABnFQqF5Pf7lZKSIru97x13BDwAAADDsMgCAADAMAQ8AAAAwxDwAAAADEPAAwAAMAwBDwAAwDAEPAAAAMMQ8AAAAAxDwAMAADAMAS8O2tvbNW3aNH344YeSpJqaGhUWFmrSpElavXp1nKszz9q1a1VQUKCCggI9+eSTkpjzWHrqqad00003qaCgQOvXr5fEfFvhiSeeUElJiSTmO5buvPNOFRQUaPr06Zo+fbrefvtt5juGXnnlFRUVFWnq1Kl67LHHJPH9HbEQLPWHP/whNG3atNDo0aNDhw8fDnV2doby8/NDH3zwQcjv94fuuuuuUHV1dbzLNMZ///d/h26//faQz+cLdXV1hebMmRN6+eWXmfMYefPNN0MzZ84M+f3+UGdnZ+j6668P7d+/n/mOsZqamtDXvva10A9/+EN+psRQMBgMTZgwIeT3+8NjzHfsfPDBB6EJEyaEGhoaQl1dXaFZs2aFqqurme8I0cGzWHl5uZYuXSqv1ytJ2rt3r0aOHKkRI0bI4XCosLBQVVVVca7SHBkZGSopKdGQIUPkdDp1+eWXq76+njmPka9+9av6xS9+IYfDoePHjysQCKi1tZX5jqGPP/5Yq1ev1ty5cyXxMyWW3n//fUnSXXfdpZtvvlnPP/888x1Dv/nNb3TTTTdp+PDhcjqdWr16tZKTk5nvCDniXcBgs3z58l5fNzY2KiMjI/y11+vVsWPHrC7LWFdccUX47/X19dq+fbu+853vMOcx5HQ69fTTT+tnP/uZpkyZwvd4jC1ZskTFxcVqaGiQxM+UWGptbVVubq4effRR+f1+zZkzR3fffTfzHSOHDh2S0+nU3Llz1dDQoIkTJ+qKK65gviNEBy/OgsGgbDZb+OtQKNTra0THe++9p7vuuksPP/ywRowYwZzH2AMPPKA33nhDDQ0Nqq+vZ75j5MUXX1RmZqZyc3PDY/xMiZ2rr75aTz75pFJTU+XxeDRjxgw9/fTTzHeMBAIBvfHGG3r88cf1wgsvaO/evTp8+DDzHSE6eHE2fPhwNTU1hb9uamoKX75FdNTW1uqBBx7QwoULVVBQoLfeeos5j5E///nP6urq0lVXXaXk5GRNmjRJVVVVSkpKCr+G+Y6eyspKNTU1afr06frLX/6iTz75REeOHGG+Y2T37t3y+/3hQB0KhZSVlcXPkxhJT09Xbm6uPB6PJOmGG27g58l5oIMXZ2PHjtXBgwd16NAhBQIBbdu2TXl5efEuyxgNDQ36wQ9+oNLSUhUUFEhizmPpww8/1OLFi9XV1aWuri797ne/08yZM5nvGFm/fr22bdumLVu26IEHHtDf/u3f6t///d+Z7xhpa2vTk08+KZ/Pp/b2dr300kt68MEHme8Yuf766/X666+rtbVVgUBAr732mqZMmcJ8R4gOXpy5XC6tXLlS8+bNk8/nU35+vqZMmRLvsozx7LPPyufzaeXKleGxmTNnMucxkp+fr7179+qWW25RUlKSJk2apIKCAnk8HubbIvxMiZ3rr79eb7/9tm655RYFg0HNnj1bV199NfMdI2PHjtXdd9+t2bNny+/367rrrtOsWbN02WWXMd8RsIVCoVC8iwAAAED0cIkWAADAMAQ8AAAAwxDwAAAADEPAAwAAMAwBDwAAwDAEPAA4h71792rJkiUx+ewvf/nLamlp0ebNm3XvvffG5BgABicCHgCcw5/+9CeedQnggsNGxwAGlTfffFOlpaW6+OKL9f777+uiiy7SypUrNXz4cD322GPas2ePkpKSdMMNN2jWrFl6+umn1dbWpkceeUQrVqw46+d2dHT0eX9xcbHa29u1bNkyHThwQDabTd/85jf14IMPyuE484/ftrY2LV++XO+++274sVgPP/ywHA6Hdu7cqdLSUtntdl111VWqqanRL3/5S33pS1/Siy++qI0bNyoYDCotLU2PPvqoLr/88lhNI4AERwcPwKBTV1enO++8Uy+//LKKioq0YMECPf300/L5fKqsrFRFRYX27NmjDz74QA888IBycnLOGe4knfH9b731lh577DGlpaXp5Zdf1q9+9Sv9z//8j372s5+d9XMef/xxjR49Wps3b1ZFRYVOnDih9evX68SJE3r44Yf1L//yL9qyZYu+9rWvhTuLb731lioqKvQf//Efqqio0N133637778/qnMG4MJCBw/AoDNq1Cjl5ORIkr71rW/pn//5n+X3+/XII48oKSlJSUlJev755yVJmzdvjugza2pqzvj+f/zHf9TGjRtls9k0ZMgQzZw5U88995zuueeeM35OdXW19u3bp02bNkmSTp48KenUg+4vv/xyjRo1SpJ066236rHHHgu/59ChQ5o5c2b4c1pbW/Xxxx8rLS3tPGcHgAkIeAAGnaSkpD5jn3zyiWw2W/jrhoYGXXTRRRF/psPhOOP7g8Fgr/FgMKju7u6zfk4wGNRTTz0Vvrza2toqm82mXbt26fQnS9rt9vB7pk+frgULFoS/bmxs1Oc///mI6wdgFi7RAhh0Dhw4oAMHDkiSXnjhBV199dWaPHmyXnrpJQWDQXV1demBBx7Qrl27lJSUdM5A1iM3N/eM758wYYKef/55hUIhdXV1qby8XN/4xjfO+jkTJkzQz3/+8/Dr77vvPj3//PMaP3686uvrw3Xv2LEjHP4mTJig//zP/1RjY6MkaePGjfrud78bhZkCcKGigwdg0ElPT9eaNWt05MgReTwePfnkk/J4PFq+fLmmT5+uQCCgm266SZMmTdKhQ4f005/+VPfff7/Wrl171s+8//77z/j+a6+9Vo899pgKCwvl9/v1zW9+U3Pnzj3r5yxatEjLly8Pv/4b3/iG7r77bjmdTq1atUo//OEPZbfblZ2dLYfDoeTkZE2YMEH/8A//oLvuuks2m01ut1tr167t1TkEMLjYQqf3/AHAYG+++aZ+/OMfa9u2bfEu5by0t7errKxM8+bNU3Jysv74xz/q3nvv1WuvvUaQA9AHHTwAiMD777+v4uLiM/7bpZdeqjVr1sT0+G63W06nUzNmzJDD4ZDD4dCaNWsIdwDOiA4eAACAYVhkAQAAYBgCHgAAgGEIeAAAAIYh4AEAABiGgAcAAGCY/w/TejD+uvKXeAAAAABJRU5ErkJggg==",
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