{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 1.5.2 - Beginner - Introduction to Data Visualization II\n",
        "\n",
        "COMET Team <br> *Anneke Dresselhuis, Jonathan Graves*  \n",
        "2023-01-12\n",
        "\n",
        "## Outline\n",
        "\n",
        "### Prerequisites\n",
        "\n",
        "-   Introduction to Jupyter\n",
        "-   Introduction to R\n",
        "-   Introduction to Data Visualization 1\n",
        "\n",
        "### Outcomes\n",
        "\n",
        "By the end of this notebook, you will be able to:\n",
        "\n",
        "-   Customize aesthetic labels on a graph to communicate the key message\n",
        "    of a visualization\n",
        "-   Use faceted graphs to visually represent complex data\n",
        "-   Identify best practices for creating effective visualizations\n",
        "-   Recognize ways in which visualizations can be used nefariously\n",
        "\n",
        "### References\n",
        "\n",
        "-   Deeb, Sameer. 2005. “The Molecular Basis of Variation in Human Color\n",
        "    Vision.” *Clinical Genetics* 67: 369–77.\n",
        "\n",
        "# Introduction\n",
        "\n",
        "This notebook expands on the concepts introduced in Introduction to Data\n",
        "Visualization 1 and explores ways to create more layering customizations\n",
        "that can help make our visualizations look extra polished. We’ll also\n",
        "see how our new tools perform in a few case study examples.\n",
        "\n",
        "Because we’re already familiar with the theoretical concepts of\n",
        "`ggplot2`, **this notebook serves as more of a** **resource guide with\n",
        "practical case study examples than as a learning module.**\n",
        "\n",
        "## Loading the data\n",
        "\n",
        "In this notebook, we’ll be working with the Penn World Tables data set\n",
        "again. Let’s load it now."
      ],
      "id": "14bf1dcf-80e1-4e9f-a004-54d5f2c52227"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "source(\"beginner_intro_to_data_visualization2_tests.r\")"
      ],
      "id": "a7e24cb7-9be6-46fe-80ad-33dd0890f0d9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import packages\n",
        "library(tidyverse) # contains ggplot2, which is what we'll be using!\n",
        "library(haven)\n",
        "library(RColorBrewer)\n",
        "library(ggthemes)\n",
        "\n",
        "# library(lubridate)\n",
        "\n",
        "# load the data\n",
        "pwt_data <- read_dta(\"../datasets_beginner/pwt100.dta\")\n",
        "\n",
        "# declare factors\n",
        "pwt_data <- as_factor(pwt_data)\n",
        "\n",
        "pwt_data <- pwt_data %>%\n",
        "    mutate(countrycode = as.factor(countrycode)) %>%\n",
        "    mutate(country = as.factor(country)) %>%\n",
        "    mutate(currency_unit = as.factor(currency_unit))\n",
        "\n",
        "NA_data <- filter(pwt_data, (countrycode == \"CAN\")|(countrycode == \"USA\"))\n",
        "\n",
        "# check that it looks OK\n",
        "tail(pwt_data,10)\n",
        "# there will be a lot of missing data"
      ],
      "id": "26d586d3-5e3a-490e-b72a-c3e7e9f134e2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Part 1: Adding Techniques to our Data Visualization Toolkit\n",
        "\n",
        "## Adding and Adjusting Labels\n",
        "\n",
        "In notebook 1 we introduced the `labs()` function which allows us to\n",
        "specify the aesthetic outputs of different labels on our chart, such as\n",
        "the x and y axis titles, the legend title and main graph title.\n",
        "\n",
        "Here are a few best practices to keep in mind when crafting and adding\n",
        "labels to charts (pulled from [FusionCharts\n",
        "2022](https://www.fusioncharts.com/resources/charting-best-practices/5-tips-for-writing-chart-captions)):\n",
        "\n",
        "-   Graph title: should summarize the graph in short, understandable\n",
        "    language that is as objective as possible - avoid using unnecessary\n",
        "    words such as “the”, “a”, or “an”, as well as adjectives like\n",
        "    “amazing” or “poor” which can manipulate your reader’s perception of\n",
        "    the graphic.\n",
        "\n",
        "    -   Example: *“Online Grocery Order Growth 2018 vs 2020”* (a better\n",
        "        title) vs *“The Significant Growth in Online Grocery Orders in\n",
        "        the years 2018 and 2020”* (a worse title)\n",
        "\n",
        "-   Graph subtitle: should be used to add helpful supplemental\n",
        "    information that will help your audience understand the graph.\n",
        "\n",
        "    -   Example: Units of measurement, time frames (if this is secondary\n",
        "        information)\n",
        "\n",
        "-   Axis Labels: units of measurement should always be made known! If\n",
        "    the data is labeled inside the visualization, sometimes axis labels\n",
        "    are not necessary - we should always ask ourselves: what does our\n",
        "    audience need to know?\n",
        "\n",
        "Here, we explore a few tips and tricks for customizing labels to suit\n",
        "the needs of our graph.\n",
        "\n",
        "## The `labs()` function\n",
        "\n",
        "As a refresher, a few `labs()` arguments we’ve already covered are:\n",
        "\n",
        "`x =` specifies x-axis title.  \n",
        "`y =` specifies y-axis title.  \n",
        "`color =` specifies meaning of color outline.  \n",
        "`fill =` specifies meaning of color fill.  \n",
        "`title =` specifies title of plot.\n",
        "\n",
        "> **NEW ARGUMENTS**  \n",
        "> `subtitle =` specifies a subtitle for the graph (positioned below\n",
        "> title).  \n",
        "> `caption =` specifies a caption at the bottom of graph, which can be\n",
        "> great for listing the source of our data.\n",
        "\n",
        "These arguments give us a basic infrastructure for a plot - we can\n",
        "demonstrate these features using a simple bar chart."
      ],
      "id": "0fc6efc9-803a-4fd9-ad36-fa9ccef8f5df"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "basic_plot <- ggplot(data = NA_data,  \n",
        "                aes(x = year,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = country, # specifies the fill to be by country\n",
        "                    color = country)) + # specifies the outline to also be by country\n",
        "        labs(x = \"Year\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"countyr\",\n",
        "             title = \"Canada & US Real GDP per Capita over Time\") +\n",
        "        geom_col(position = \"dodge\") +\n",
        "        theme(text = element_text(size = 20, hjust = 0)) # specifies the x, y and legend label text size\n",
        "        options(repr.plot.width = 15, repr.plot.height = 7) # specifies the dimension \n",
        "  \n",
        "basic_plot"
      ],
      "id": "72ab7c3c-1957-4b2a-8745-97675ff6bede"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Exercise 1\n",
        "\n",
        "Fix the typo from the graph above (in the code below!) so that the two\n",
        "legends on the right hand side are merged into one.\n",
        "\n",
        "> **Hint**: remember that arguments in the `ggplot()` contain aesthetic\n",
        "> mappings, while arguments in the `labs()` function contain labels and\n",
        "> other specifications that are set by us and not the data. What might\n",
        "> be going on here that would create two legends on this graph?"
      ],
      "id": "0a8008c0-8fbe-427b-aa38-a1d7524282c0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# replace the error with your answer: your legend should begin with a Capital letter\n",
        "\n",
        "basic_plot_test <- ggplot(data = NA_data,  \n",
        "                    aes(x = year,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = country, # specifies the fill to be by country\n",
        "                    color = country)) + # specifies the outline to also be by country\n",
        "        labs(x = \"Year\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"countyr\",\n",
        "             title = \"Canada & US Real GDP per Capita over Time\") +\n",
        "        geom_col(position = \"dodge\") +\n",
        "        theme(text = element_text(size = 20)) # specifies the x, y and legend label text size\n",
        "        options(repr.plot.width = 15, repr.plot.height = 7) # specifies the dimension \n",
        "\n",
        "basic_plot_test"
      ],
      "id": "af95c604-a3c3-4c48-9f85-fe167a82667e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "answer_1 <- basic_plot_test\n",
        "\n",
        "test_1()"
      ],
      "id": "058a1b77-e56d-4660-a0a9-0dd1b7b7173b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## The `theme()` function\n",
        "\n",
        "We also learned about the `theme()` function, which allows us to modify\n",
        "components of a theme such as text.  \n",
        "As a refresher, one `theme()` argument we’ve already covered is:\n",
        "\n",
        "`text = element_text()` specifies text attributes that broadly apply to\n",
        "all text components in a graph\n",
        "\n",
        "-   Example: title, labels, caption, etc.\n",
        "\n",
        "> **NEW ARGUMENTS**  \n",
        "> `plot.title = element_text()` allows specifications for the title\n",
        "> text.  \n",
        "> `plot.subtitle.title = element_text()` allows specifications for the\n",
        "> subtitle text.  \n",
        "> `plot.caption = element_text()` allows specifications for the caption\n",
        "> text.  \n",
        "> `axis.text.x = element_text()` allows specifications for x axis text  \n",
        "> `axis.text.y = element_text()` allows specifications for y axis\n",
        "> text.  \n",
        "> `legend.position =` allows specifications for legend position. \\*\n",
        "> example, `\"top\"`, `\"bottom\"` or as a vector:\n",
        "> `c(x-coordinate, y-coordinate)`\n",
        "\n",
        "-   Always ensure that the text is readable to an audience with a range\n",
        "    of seeing abilities.\n",
        "\n",
        "-   Adjusting label size and boldness can help us emphasize important\n",
        "    information about a graph that we’d like our audience to focus on.\n",
        "    Check out more `theme()` arguments in [this documentation resource\n",
        "    created by R\n",
        "    Studio](https://ggplot2.tidyverse.org/reference/theme.html).\n",
        "\n",
        "## The `element_text()` function\n",
        "\n",
        "There are also a quite a few things we can specify using the\n",
        "`element_text()` function:\n",
        "\n",
        "`size =` specifies x-axis title (default in R is set at size 11)\n",
        "\n",
        "> **NEW ARGUMENTS**  \n",
        "> `hjust =` specifies the position of the plot titles (default is left).\n",
        "> \\* `0.5`: centre, `1`: right, `0`: left\n",
        ">\n",
        "> `color =` specifies the text color.  \n",
        "> `face =` specifies typographical emphasis. \\* example: `\"bold\"`,\n",
        "> `\"italic\"`, `\"bold.italic\"`\n",
        ">\n",
        "> `angle =` specifies angular rotation of text.  \n",
        "> `vjust =` specifies the vertical adjustment. \\* example: higher number\n",
        "> is up, lower number is down from the graph (default is 0)\n",
        "\n",
        "Let’s use some of these new functions and arguments to improve our\n",
        "earlier visualization!"
      ],
      "id": "625c5dc2-69ef-4e81-b6d4-2ecc995eb2ff"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "intermediate_plot <- ggplot(data = NA_data,  \n",
        "                aes(x = year,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = country, \n",
        "                    color = country,\n",
        "                    geom_text(mapping = country))) +\n",
        "        labs(x = \"Year\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"Country\", \n",
        "             title = \"Canada & US Real GDP per Capita over Time\", subtitle = \"1950-2019\", \n",
        "             caption = \"Source: Penn World Tables 2019\") +\n",
        "        geom_col(position = \"dodge\") +\n",
        "        theme(text = element_text(size = 20, hjust = 0)) + # specifies the x, y and legend label text size\n",
        "        theme(plot.title = element_text(size = 25, hjust = 0.5, color = \"black\", face = \"bold\")) + # specifies title text details\n",
        "        theme(plot.subtitle = element_text(size = 19, hjust = 0.5)) +  # specifies subtitle text details\n",
        "        theme(plot.caption = element_text(size = 15, face = \"italic\", vjust = 0)) + # specifies caption text details\n",
        "        theme(legend.position = \"top\")  # places the legend at the top of the graph\n",
        "        options(repr.plot.width = 15, repr.plot.height = 9) \n",
        "\n",
        "intermediate_plot"
      ],
      "id": "9cd6f5c7-77a8-417b-90b5-b3525f4fef55"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Formatting Graphs\n",
        "\n",
        "## Scales\n",
        "\n",
        "To visualize only a subsection of our data we can use the following\n",
        "commands which manipulate the scale of our graphs:\n",
        "\n",
        "-   `xlim()` specifies scale adjustments on the x-axis.\n",
        "\n",
        "-   `ylim()` specifies scale adjustments on the y-axis.\n",
        "\n",
        "Both of these functions take two arguments - one lower bound limit and\n",
        "one upper bound limit:\n",
        "\n",
        "-   `xlim(lowerbound,upperbound)` .\n",
        "\n",
        "-   We can use the `xlim()` and `ylim()` functions to examine\n",
        "    subsections of our axis variables.\n",
        "\n",
        "-   In the GDP per capita over time plot that we’ve been working with,\n",
        "    use `xlim()` to view a subsection of data from 2000-2019 below. This\n",
        "    will make the power of changing the scale of our graph much more\n",
        "    obvious.\n",
        "\n",
        "**Note**: scaling can occur on any quantitative attribute, that is, we\n",
        "can scale variables other than year."
      ],
      "id": "608385a1-e6e3-46f2-a255-9e87bdc06706"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# fill in the ... to make the graph\n",
        "\n",
        "scaled_plot <- ggplot(data = NA_data,  \n",
        "                aes(x = year,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = country, \n",
        "                    color = country)) +\n",
        "        labs(x = \"Year\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"Country\", \n",
        "             title = \"Canada & US Real GDP per Capita over Time\", subtitle = \"...-2019\",  # adjust the subtitle to reflect the window of time we're working with\n",
        "             caption = \"Source: Penn World Tables 2019\") +\n",
        "        geom_col(position = \"dodge\") +\n",
        "        theme(text = element_text(size = 20, hjust = 0)) + \n",
        "        theme(plot.title = element_text(size = 25, hjust = 0.5, color = \"black\", face = \"bold\")) +\n",
        "        theme(plot.subtitle = element_text(size = 19, hjust = 0.5)) +  \n",
        "        theme(plot.caption = element_text(size = 15, face = \"italic\", vjust = 0)) + \n",
        "        theme(legend.position = \"top\") +\n",
        "        xlim(2000,2019) + # adjust x-axis scale with lower bound = 2000 and upper bound = 2019\n",
        "        ylim(0,200000) # adjust the max y value to 200,000 rather than the automatic scale of ~64,000\n",
        "        options(repr.plot.width = 15, repr.plot.height = 9) \n",
        "\n",
        "scaled_plot"
      ],
      "id": "cc9c2f1e-59f6-4698-b872-54904464e422"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "*What do you notice about this graph? How do the oscillations (ups and\n",
        "downs) appear different when zoomed in here versus in the large view in\n",
        "the other visualization?*\n",
        "\n",
        "> **🔎 **Let’s think critically****\n",
        ">\n",
        "> > 🟠 What are some good reasons why we might want to adjust the scale\n",
        "> > limits of our axis?  \n",
        "> > 🟠 Is there a subsection of data that would be of interest? Why?  \n",
        "> > 🟠 Are we trying to make our oscillations seem less volatile (as\n",
        "> > seen above) to prove a point? What point?  \n",
        "> > 🟠 Are our scale choices helping our audience gain an understanding\n",
        "> > of the data that is as objective and accurate as possible?\n",
        "\n",
        "Scaling is important because it can communicate a variety of different\n",
        "messages depending on how it is used.\n",
        "\n",
        "## Faceting\n",
        "\n",
        "Let’s say instead of creating one plot at a time, we’re interested in\n",
        "piecing together a visualization that takes on a more dashboard-like\n",
        "effect to display multiple plots in a grid simultaneously.\n",
        "\n",
        "-   A function we can use to achieve this is `facet_grid()`. To *facet*\n",
        "    something simply means to split\n",
        "\n",
        "-   Faceting allows us to arrange graphs into multiple views or layers\n",
        "    which can help us explore multidimensionality and visualize\n",
        "    complexity in a dataset.\n",
        "\n",
        "> **NEW ARGUMENTS**  \n",
        "> `facet_grid(rows = vars(variable))` creates a grid of plots using a\n",
        "> variable or variables split into rows (horizontal split) or columns\n",
        "> (vertical split). The `vars()` function allows our variable(s) of\n",
        "> choice to be correctly evaluated in the context of the data frame.  \n",
        "> `geom_hline()` creates a *horizontal* line across our plot(s) at a y\n",
        "> value of our choosing.  \n",
        "> `geom_vline()` creates a *vertical* line across our plot(s) at an x\n",
        "> value of our choosing.\n",
        "\n",
        "Both `geom_hline()` and `geom_vline()` can be used on single plots and\n",
        "subplots to emphasize particular thresholds, values or time periods. In\n",
        "the graph below, we’ll add a horizontal line to our faceted plot to help\n",
        "us see when the GDP from each G7 country rose above 40,000."
      ],
      "id": "1ce5d8b6-1832-44e2-b1ec-273787bb448a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "g7_data <- pwt_data %>%\n",
        "filter(country == \"Canada\" | country == \"United States\" | country == \"France\" | \n",
        "       country == \"Germany\" |  country == \"Italy\" |  country == \"Japan\" | country == \"United Kingdom\")  # select G7 countries  \n",
        "\n",
        "facet_plot <- ggplot(data = g7_data,      \n",
        "                aes(x = year,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = as_factor(country), \n",
        "                    color = country)) +\n",
        "        labs(x = \"Year\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"Country\", \n",
        "             title = \"G7 GDP per Capita over Time\", subtitle = \"1950-2019\", \n",
        "             caption = \"Source: Penn World Tables 2019\") +\n",
        "        geom_line(size = 2) +\n",
        "        theme(text = element_text(size = 18, hjust = 0)) +\n",
        "        theme(text = element_text(size = 20, hjust = 0)) + \n",
        "        theme(plot.title = element_text(size = 25, hjust = 0.5, color = \"black\", face = \"bold\")) + \n",
        "        theme(plot.subtitle = element_text(size = 19, hjust = 0.5)) +  \n",
        "        theme(plot.caption = element_text(size = 15, face = \"italic\", vjust = 0)) + \n",
        "        theme(legend.position = \"top\") +\n",
        "        geom_hline(yintercept = 40000, linetype = \"solid\", size = 0.25) + # add horizontal line \n",
        "        facet_grid(rows = vars(country)) + # create a set of subplots organized by country\n",
        "        scale_color_brewer(palette=\"Paired\")\n",
        "        options(repr.plot.width = 10, repr.plot.height = 15) \n",
        "\n",
        "facet_plot"
      ],
      "id": "de489d18-2c9b-4341-a43a-9a01b488999b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Faceting allows us to arrange charts to make comparisons clearly.\n",
        "Without faceting, a consolidated line chart of all G7 countries such as\n",
        "the one below might look overcrowded difficult to observe the nuances\n",
        "between countries."
      ],
      "id": "0c8947e7-4281-4e9d-a044-24bc0aab313d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "line_plot <- ggplot(data = g7_data,      \n",
        "                aes(x = year,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = as_factor(country), \n",
        "                    color = country)) +\n",
        "        labs(x = \"Year\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"Country\", \n",
        "             title = \"G7 GDP per Capita over Time\", subtitle = \"1950-2019\", \n",
        "             caption = \"Source: Penn World Tables 2019\") +\n",
        "        geom_line(size = 2) +\n",
        "        theme(text = element_text(size = 18, hjust = 0)) +\n",
        "        theme(text = element_text(size = 20, hjust = 0)) + \n",
        "        theme(plot.title = element_text(size = 25, hjust = 0.5, color = \"black\", face = \"bold\")) + \n",
        "        theme(plot.subtitle = element_text(size = 18, hjust = 0.5)) +  \n",
        "        theme(plot.caption = element_text(size = 15, face = \"italic\", vjust = 0)) + \n",
        "        theme(legend.position = \"top\") +\n",
        "        geom_hline(yintercept = 40000, linetype = \"solid\", size = 0.25) + # add horizontal line +\n",
        "        scale_color_brewer(palette=\"Paired\")\n",
        "        options(repr.plot.width = 10, repr.plot.height = 12) \n",
        "\n",
        "line_plot"
      ],
      "id": "e0148902-7e8b-4ee9-9668-72bc8596e6cd"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can see that this graph above, while it is quite consolidated, does\n",
        "not present the specific patterns of each country as clearly as does our\n",
        "faceted graph immediately before it.\n",
        "\n",
        "## Confidence Bands\n",
        "\n",
        "When creating visualizations that use a predictive element like a\n",
        "regression line, we can summon graph features that visualize how\n",
        "accurate our model is at predicting a variable(s) with 95% confidence.\n",
        "\n",
        "> **NEW ARGUMENTS**  \n",
        "> `geom_smooth()` creates a trendline that aids the eye in spotting\n",
        "> patterns in the data.  \n",
        "> Arguments within this function include:\n",
        ">\n",
        "> -   `method =` specifies the type of smoothing we want to use - in\n",
        ">     this notebook, we’ll use linear regression using the `lm`\n",
        ">     function.\n",
        ">\n",
        "> -   `se =` uses a logical argument to specify the presence of a\n",
        ">     standard error/confidence band (default is `TRUE`).\n",
        ">\n",
        "> -   `colour =` specifies the colour of the trendline.\n",
        ">\n",
        "> -   `fill =` specifies the colour of the confidence band.\n",
        "\n",
        "## Design\n",
        "\n",
        "Before we move into some case studies, we’ll examine a few more design\n",
        "tips and tricks that we can add to our visualization toolkit to make our\n",
        "visualizations look extra polished ✨\n",
        "\n",
        "## Shapes\n",
        "\n",
        "-   `pch =` Allows us to change the shape of the data points using the\n",
        "    argument on scatter plot to distinguish sets of data.\n",
        "    -   E.g., Option 2 (the upwards triangle icon) could indicate growth\n",
        "        or movement upwards, whereas option 6 (the downwards triangle\n",
        "        icon) could indicate decline or movement downwards.\n",
        "\n",
        "The shape options are listed in the chart below - the default, as we’ve\n",
        "seen before, is 19, a simple circle. Note that numbers 21-25 are shapes\n",
        "that have both fill and outline colour options which can be helpful if\n",
        "we are trying to make our data points stand out better. This is\n",
        "particularly useful if we are working with a large data set.\n",
        "\n",
        "![Geom point shape options](attachment:media/pch_options.png)\n",
        "\n",
        "Try exploring a few different shape and size options in the scatterplot\n",
        "below!"
      ],
      "attachments": {
        "media/pch_options.png": {
          "image/png": 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TOTGeM2bMEDolAzLUA1kxKjbowtAwRpXqotAXueHE3XnnnXL11Vd7v7lmGD2v\ndCmCJg2MKDl/NrNr0aKFsFsoFRshE0fp6rZPfUIAZ+fggw/WCrlQ5qXi+OPgkI5iZ2Ym+W+//XYj\njjhIKXsdLGlAGITzV1xxhVZpcNMe7qEBcbAa1bzxlPXEhOYQNaFDUm/lOTG2PukXOfG246knZGfd\nxkEHHVSimMFhQz/39cB5Y8Ibh45JcSbAIY5Zs2apvD7L5qtOsml3sKTBIGSNBiWnhL4MMvLEu+yy\ni1ajkKryrVPSXuRYGWc2nSOf1yDr22+/rVU1G2ywgRL+xhtvLKWdOAVcO2rUKK/Kqcnr77vvvupx\nR8kuE2lwDVVWbLvhawSCDJTCU5jCglvk33rrrXVRKvuKsVWMr7Llc9ysiv8dJGnQ2c4991zd2ZbS\nW4wsB4YI4qAKhZ1EfdtGhMlQ7p9B/f6KnEyWk7ZL84GuSFtQNYQHyqLMCy+8MOPJ9vfolUWO7Gnk\n0863TOqzSJMUDQPSEUcSadCX2fkWY8u6HB8NK/KxewGVjUcccYRGkzxSAUgZ9bRp03RDSpw9okY7\n0oVAcKRByDt16lRd/OXC3CjkDDoqqLbZZhvttEQcbpBGr0vjc1Z8s0kf0ROliuU9XQoAg5P2hXGQ\nBp0TedmIkbQi95PGSMZPUlds5AdZcL8JtuDwiTTok8gwfvx4dXSIIujHcdLgOubkOnToIGxEyXfA\nyaeDscbkPikp9tbCeUEu1p9AGmQIONnAkFQVTh+v7UgPAsGQBoOHzkWEQXiLJ57JS6GTUrq5xx57\n6O1RmePwYXKcDRi5D8EhhxyiRpGVwdmerOVgLyPq4BmgPkQadE5Wfj/++ONy4IEHatvZOgT9uQMC\nIS/es2dPOeaYYzR6xKj6RBrIgkw4M6ytYdcCtgohAnFzGvRlZOd2vkQY9F/fDgiD+9zgBJCSQl5H\neo400CfvQZgQR/fu3XXnZiOO9Gg7CNJwnYz0BTdbwjvJRBgOegYpqSp2SmWQvvnmm14QB6klDGR5\n911i0LEXF4MQAokaXodJmh7RqSMNZGVtBvNRGE1XAYeBwbii88MPP1yJkMiRtIdvpOGwx5BCCjgH\nrF/YZ5999DXeOVujQ570W98OCIN0Y79+/VRX8dX8UdJwsjGGGdM9evSwqioHSgoevScNRxis/ib3\nDWFgTLI5+C7e3KabbqoDkjv6pT1VRYQwZmjxO5IAACAASURBVMwYnRjONlpATqIp0lLg5ENUFSUN\n7iHtdMWeWpA8k8fkxYkq8MbxTDnYBdcX0oC40UX85D7vkANpNraIYWKfNB0T30QY8et5nVYnAL3R\nPnRF+tDdxz4uA6TPvmLoMfoZ5IKzQ0r2nnvu0bHNb9qRPwS8Jw062MUXX6z717CfVEUGD54b+VS8\nWB8293PGkqgjmwOP7fjjj9cbT0GSPgw62ugiDUiDg/cwqBhPChmY08HYOMLgGp9IA7lYl8AOtvGT\nUvFhw4bp1hpsI8JNw9juPn6de02lWRr1ihP24IMPKrlzX3Dkcm2OPkKM3N6W0vjo+zy/4YYbZKed\ndlLS4fuMeTvyh4D3pMFkIJvYsakb+f2KDByI5oEHHtC5EAZm2g9STWeddZZGR9G8cKZ2k8LBI6fE\n0ZcjiTRoO0aIO7rVrl1bJ1Pjd7LziTSYm6Lf4qzET9JT7du31322uL8E83QQR/w695o7VWYbYa/K\nPkBfhdjZ940+iFyuzdFHbvtKJJL0OZtTkppjQ84RI0YUcxJWpSz2v5Yj4D1pYESoNiGUJ+dNyqY8\nxMG1lN6S0iCvGvVa09pJaDOT2sxtsJV2aZ4XpLrzzjtrVVFZ8zxpkjeJNJCTfcSovGHBG5VS5MjZ\nc8sdPpEG+iD9Ej/ZZHLOnDmabsWYsl24u7UtkVb8el5jnMvT7x1euX7EIWNM7r333poNoGiByD4u\nw6GHHqr6jMtHZEzamOo5JtB9mI/LNab5/n3vSQMAMSZs7obnQkUUHS2bNBWEQ2UOqQ48tWy89nwr\nzP1/vMqZM2fqJDCTvknyYkRI2THgFixY4L7qxWOcNJCXtJyrksLosG7FVVW5eQ+fSCNJEeiRiX+8\na8pqScvQpw877DC9MRM31CrNSUj6zXy/hy6Zg2IujjGatO150kQ436Nvc1uDoUOH6rwc79mRXwSC\nIA0gZCBhOAhv8WowKEmG1MGNEWIjOIwQ4TMejk8dkrbiwW2yySY6EYwRjR9gwKTxlClTvKt1Rz46\nJyW3kIWrkmIRmIsmuYaqqt12202NEfpnwt+XifC4vuivyAzJQ4bI5kpuMbq8B5kQhfhGHMhKhEE1\n2ODBg/W+9dHxGScNdMtaIm6mRjGAr9VwcR2H8DoY0kAZdEIiDgwNnTPTvSQgjHfffVe9dBZUkZKi\nk/p2uEiJfDcpNl67AyymT5+uMnKvZd8ORxqkoq677rpiVVJRXfEcPXN/DQiSDs1cgG9GBt0RDZJm\nJcJAJvqlIw36LI4B8yDcTIxt/qP69kW/yMWWISw0Zaw6GaKkQd8lDQVBMucDeUR17ousobYzKNJw\nSqIzMmlIWO+8UvcZnZTbS+KNHnfccV7MYbi2Jz0ywAj7ubcy0RIH72FUGJj333+/l16pIw3u5U4p\nNTlv0o4Yz6QTI8OcFgTKSmKfSAN9QfqsR8CgkibliJMG71FyyyJGthyhCIDv+na4VBWT26yPYkw6\n0mBuhoIWogsiEuYz7EgXAkGSBgaHVAVeCobErZ7lfVakkpLCCEEoIRykZFjgRmkiMjLBSiqD1JUz\nQL7JiTEkJUWVFPtPkbJh0pvJ0KSTzzC6VNhAMmnfJiWqDybyWakP+Uej3iTSQL84B0QcyOtTcYOT\nGRmImkgjo1ecPFLElL1D9hDG+uuvb5PeDrCUPQZJGmDsJsdJVUAcVBG99dZbusANryYUwkBWPLUT\nTzxRJwvxuElzcH9pJlRd+J+yfpdVc/A6SU1RYsu+W9mcrAmguIHv+nIQOeHkRAmDtieRBu9DqJAF\nKVYfIw0nA2PSpaq22GILLZ13ZcVWJZXe3hssaQC5Iw5yo2ypjTd+0kkneTfpnU33YV8qvDO2oOAR\nT9Qnw5lJRkgPPfKYzcm1GFK8WV8O2prU3kyk4eRK+o77zIdH2k+qih2MSUMSUQ4ZMkRTUr7L5gP+\nFW1j0KQBKBgRvLj11ltPt2H2rUoqW8ViUElPEWEwl0G04asXmq3MoV9HBHLmmWfKGWecof04VHkZ\nk6wlYoz6NBcVqj7Kkit40gAAvBZCYQgkZA+GiWL2ZqJqyg7/EaCvQhzxtJX/kpWUgPHJGfL4LCm1\nn+9UCtLwUzUVazUpKaIOOwwBQ8AQyAUCRhq5QNV+0xAwBAyBQBEw0ghUsSaWIWAIGAK5QMBIIxeo\n2m8aAoaAIRAoAkYagSrWxDIEDAFDIBcIGGnkAlX7TUPAEDAEAkXASCNQxZpYhoAhYAjkAgEjjVyg\nar9pCBgChkCgCBhpBKpYE8sQMAQMgVwgYKSRC1TtNw0BQ8AQCBQBI41AFWtiGQKGgCGQCwSMNHKB\nqv2mIWAIGAKBImCkEahiTSxDwBAwBHKBgJFGLlC13zQEDAFDIFAEjDQCVayJZQgYAoZALhAw0sgF\nqvabhoAhYAgEioCRRqCKNbEMAUPAEMgFAkYauUDVftMQMAQMgUARMNIIVLEmliFgCBgCuUDASCMX\nqNpvGgKGgCEQKAJGGoEq1sQyBAwBQyAXCBhp5AJV+01DwBAwBAJFwEgjUMWaWIaAIWAI5AIBI41c\noGq/aQgYAoZAoAgYaQSqWBPLEDAEDIFcIGCkkQtU7TcNAUPAEAgUASONQBVrYhkChoAhkAsEjDRy\ngar9piFgCBgCgSJgpBGoYk0sQ8AQMARygYCRRi5Qtd80BAwBQyBQBIw0AlWsiWUIGAKGQC4QMNLI\nBar2m4aAIWAIBIpAkKTxwQcfyNy5c+WTTz6Rf//9t4Tq/vjjD/niiy9k/vz58u6778q3334r//zz\nT4nr0vzG33//LZ9//rm8/vrrgrx//vlnxub+/PPP8vbbb8u8efNk2bJliZhk/HKeP0AvH374obzx\nxhvy2WefJbbmr7/+UizQJ3J++umn8uuvvyZem9Y30Sf9lX6LnMgUP7jmq6++0j67YMEC7cO+9Vva\nu3jxYpWTfps0PqNyM1YZo/TdL7/8sszro9+157lBICjSoAMuWrRI2rVrJ3Xq1JG99tqrBBlgQE87\n7TTp1KmTNGrUSJo3by59+vSRJ554QuigPhzI+fXXX8taa62lcm6wwQaCXEkHxufaa6+VBg0aSOPG\njeWpp54qgUnS99LyHkayZ8+e2v4jjzyyRLOWLFki++yzj3Ts2FHla9iwobRt21Z22GEHJZISX0jh\nGxjSn376Sfr37y9169aVAw44QH7//fdiLUW/J510knTv3l2aNWumsnbu3FkmTZqk3y3L+Bb7sTy+\nWLp0qYwYMULq1asn2267rUCEmQ5kmjVrlo5R9HrxxRcbaWQCaxW+Hwxp0PmIHnbddVepVq2aVKlS\nRXbZZZdiBhIDevLJJ0vt2rX1mvbt2+vgKywslBYtWsizzz6b+k6JgcFQHnLIIVKjRg2Vc+jQoRpB\nxPsNJPjoo48KxmW11VaTmjVryuOPP14Mk/h30vIafb7//vsCIaJPzrFjxxZr3m+//Sbjx49XHNBh\nhw4dpFWrVlK9enU9hw8frlFksS+l7AV9kn4LyVWtWlX1CQlGIyWM5xlnnKEOArLh6EAcBQUFUqtW\nLTnnnHNKkEzKxNRx9cMPP8h+++2n+qLtm222WamkgcMAkYILff3cc8/1ou+mDfuV3Z4gSOOXX37R\n9AWeN8YDwkgije+++0569Oihg613794alTzzzDNKInTi3XbbrdQ0z8oGv7y/h5EkLbHxxhvrIIII\nkDNOGhALA+7yyy/XaArZuM4H0sBA4lWjF3TlHIAk0iBtgQeKzrfYYguVmRTG5ptvrjrGmyWySqsX\njpwvvviiDBo0qFi/jZPGjz/+KE2bNlWdb7LJJprGIgVHH8ag8hmpyjQeYE/URNpt8ODBRf22NNLg\nO/T1fffdV6+n7xpppEe7QZDGPffco+EuHZEB5Dy2eKTBACX8x3iSsuGgg2J0+Q5pKgZoWg/mL4iI\nMJLIgZeZRBoM0h133FG9bQYbXqkvkQae95VXXqlEjpzIi74gjXHjxhVTzR133KGpKNJujzzySBE5\nkNIAG7533XXXFb1f7MspeDFjxgztr8iJjpw+o6RB/2Qe6vrrr1f5H3vsMZUHo8p19HmIk/mcNB5E\njES36Agd8li/fn1tN+SelJ7ivbvuukvxILKySCNdmg2CNGbOnKmeCCTx/PPPaxiPMY2TxnnnnafX\nYXDfeuutIk2Q4qBDN2nSRCfpij5I2ZPZs2ergSAXzEDs2rVrImlgUEjrkH6bPn26zuFgmHyINEip\nkW5BRwceeKC888470rp160TSQD1EVRjV6HwUpIOszGuRnsPwpvGYNm2a9jlSqkRNpNYgdzzsaHoq\n3naMKoUBRGJcv9566wmpnzQeOAGOxLfaaiuZM2eO9O3bNyNpoCvmJUmpQhjM49AXcH4Yv+jbjvwi\nEARpYFgYdBhLwnY6WRJpMA8AOZC2II3hjlNPPbWITKjSSOtB7huyQ07SEZlIA6OC583kKoOWweYL\nadB25paomCJiQk9t2rTJSBpxXZGWW3PNNdU7xQh/9NFHqSUNIl8iBOREV9mQBqlYPHRIEQ+cnD/V\nVmk1prSLirZXXnlF++33338v/fr1y0gayMf8DhHUOuuso+lYIikjjXhPz9/rIEgD7wRj47yUTKSx\n++67q/GhaorqI3e4CATP9LnnnnNvp+6RAei8ZoxpJtKI4oEQ559/vjekQduR0xlBSCAb0uB7FAgQ\nYeGhkurBk6dfpPWIyomxzIY0SJ9SKYZ8kAbVVHjv0UgrbfJG5aT9mUgDGUhLMQ5XX311+fjjj7VI\nwEgjXRoNgjQcpBgOQttMpEHYT6RB+SnGyB1UpuDJ8L2XX37ZvZ3qx9JII95wn0gj3vZsSANiIPLa\nZpttVL944ZRbZypDjv+PNLzOljSQiXmNhx9+WCf/6c/M+xC10P/TfmQiDdpOlN+yZUt1cM466yxN\nOzKemQPBETj77LM1beccirTLGmr7KhVpHHPMMdr5SE9FF4odf/zx+j6dkwVHPhxGGsu1BGFALCNH\njlTPG8JgLiutOf5Mfas00sCgIiepRmcweU1hhJsopg/zXtqPTKSBbKzDIS1FpHHEEUfIscceq2XW\n6JT3N9xwQ32POSw78odApSINJswJ61mn8eSTTyrqDMjtt99evRsWBZJz9eEw0lhe+fbNN9/I+uuv\nr4TBvM2YMWOE0mrfjkykQf9kvoMU1I033qhzPbzHyRwXETLzd/vvv3+qU1ROH5lIg3kdZIAcoicp\nOFdaziOf0feR3478IFCpSIPJVUpyCenpoHQ8OjHVOXRIVqqWVrWSHxUl/1cjDVEjiYeNYcGYQP4Q\nBgbYnXiwPhyZSIPogd0KIAf66HHHHacRB3LdfPPNGmnwPukcF4WkWd5MpME2OJdccolQYRU9WZfC\neEXHvXr1klGjRnnj2KVZDyvStkpFGgxMiIEOSMTBCmNWpeKhMijPPPNMLwYeCjfSEE3PMEmKp83p\nKsSIJDn57NJLL/XCK81EGjg2fMZCPogRuShFnjBhgpbrQhjMabz22msrYgdW2XczkQZyQoREHNHT\n5jRWmWqy/keVijTomJQ4sgKXXDBGhoFIzpRJcl9SU2i3spMGumR9DTp0pBF/xBFgvyIfPPBMpIGu\nkZUV1eyXhkzI7OYyqCx76KGHvEhNIUsm0shksaiIs+qpTOjk5/2gSMN1Sqqh2GPqzjvvLOFl4s2Q\npmIBGfnvQw89VLfbgDB8MC6um5DnJpxHzquvvlqNhvPW4jvekg8/5ZRTZMqUKUU5cfc7aX+kWojq\nL9bSYBzdgaykZ5A/08nGlC+99FKJPuB+I02P6IzSb2Rhh4N4Wo00FcUbYEGkgZODfFQcxfWdJrni\nbSGKuOyyy7Q/siK+rDFHPyf1hqz0Y/RuR34RCI40soXTGVgGYygdkUV/F154oe63lC0Odp1fCLh+\nC1GE0m/90oC1ttKSho+qx0jgmUF0SSdbqLAVClsw4KElXcN7/IYZHB97gLXZEMg/AkYa+ddB1i3g\nXgRU0rBFSPx88MEHZfTo0brxHSveTzzxROG9+HW8ZtV7mlcQZw2IXWgIGAKrHIGgSQNvmhJa9h9C\nUCbBWQiGt+3jQa6bSIL1JNyYKHoyIcqKdj5jCwYmStlmhE3t3HVsOcGKW65J61baUb2QgmErePL2\n8RN9srgt/j6vmbOKzwlEfzdtz5GTLTOicxOQOjqiz7J3E1VE0c/pw3ynrDmBNMlKWxmLSTqj+gud\nJn323nvvFZM9TTJVxrYESxpMoDLRNnDgQOnSpUuRscWIsqsoA8438mAxF7Iwqc3GjBgVjAmyUErM\ndujcMpTNG6m0ueCCC/QzruPEmFJizC65ad9iA8LHWHJjJUiOlFv0ZCtx1tcgZ/R97toHSbLoz5eD\n25+yOR+FCqQVSTNuuummuq8UsqHzbt266SJG7g/CancKOdjK36eIkZXcLMRER1GdoWP06Ryi6Gfo\nHtl9XLDpS/8rbzuDJA0MBpVR1LZPnjxZHnjgAR1gDDTuo7HzzjurF37TTTepR+pLfh9PjeoatlMg\nVeUOdr5lEzvSUVyDIWFLae4Twv3POSBIvHYiEpSedsJEJ7QTY0KERduj57rrrqtbTSB79H2MKoYG\ngvXlQF/ICDnsscceqsuDDjpIuF8Ipbacd999t1b6ES3utNNOanhvvfXW1OsxqgPIjnJ3bg4W1Rn7\naBEBs/D2oosuKvYZe8HhHFBibkc6EAiKNDA01IGzcR2eGwaFkJ5z7bXX1h00eY6XTbkqu4red999\nXqUyKA1eY4015IorriiaFGfL9y233FK9VLoVxEEkAkEwB0KqBg+WLae5O6EP3qkjDXTEvSPiB544\nizGjKRuuIeoi+vCJNGg3aVRKaVl0yh5MOD5xZwaje/jhhxetK6JaLn5NHKc0vab93F3z/vvvL2oW\nfZObSRH9stodR48x7A4iDCIRIw2HSP4fgyINvGfWLOC1QBhuQEVJw+W6GXDU/tNJMaju2vyrpPQW\nIOPpp5+uxIFhefPNN3VF8Kuvvlosv408pDu4twTRBvl/PDZuowqppP2g/XTOykAa6IN7wlDAQDTM\nnBQ7urKmwR0QPe9xYy2iSPr4008/7YUunQxx0kDHRFFEk9xUjL6MviEV10eNNBx66XkMijQgB+4K\nNnXq1GLedBJpoALIgvw3twSNe6zpUVHJlrATL+0mvUaUwTxFNF3lvsGkI14aREq6I9PtNd31aXqs\nTKRBlEFairQTREEqijw+Tg39EgPKoj4MKikpdM38FR66T/02Tho4cNzCl/k4ImhkhxDJErho2Egj\nTaNyeVuCIQ3nrZEXZVBhdNyRiTTotBMnTtROyzYOvhzIQ3qNCcXmzZvrne6S5ijAhEE5YMAATVUl\npXnSKnNlIg1SL0xqcx9w+iQnNyMizUbkce6552qUyIp4dM/neOY4BNFoJK26dO2KkgZ9E+eHyW+i\nDfovOufmaMh92223qZxGGg699DwGQxoMJKql2NANI0laxp3MAVCOykQjz6PvM2nKJLKbME6PajK3\nhMFFJRRtJ3rAU42SZPSbVFdBLtzRjut8OTAq3PaVe5ywu6nTmXvkRlrIFdUnn2F8IVKqxXw50CUV\nQ6yfceSPp82aGowqaSvm3twcBrrGMaKs2idnJ0oayMJWKOxoG+2XkCD30iD6QDbGpc1ppKsnB0Ma\nDDZuYM8gY2Ebe9W4kxJV8sSU2vI8+j6dFjLxabNCjAb1+xhHJhYZXJlIw815DB48ODGFla7u+L/W\nQBqzZ8+Wxo0b627ETmfukTUobJsd1SefHXXUUeqV+0QapBHpg1R+OdJAp5RMEzlT0ODKcdEzJx45\nG21GDe7/0EvnM0ca9957r+6jhdPz6KOPakThWozeKeJg7oZoig0LjTQcOul4DIY0GEisUYA0CPdd\nuMv7eG3R6ik38Aj1Sd9QbeXT4MNLI61G+SIDilAeWeIHcpMn32ijjfQ6d+Op+HVpfI2O6JxuItzp\nzD1Gq6fcezz6WD0FARAdT58+XQ0o+mV3XiJgNt28/fbb9TnbvDMPh64pxyUC8zE9hTwHH3yw3q42\nqd+SNZg0aZKusWLNkZFGukZoMKQBrHRA0jBHH320hvIOat53pEGH5MCjIV+KF0eu2L3vvpPWRwwj\nOWAMCiTAxOGQIUMSF7NRQYY3x8QqXnj//v2VQPmNtB+0MUoa8fZGSSP6mY+kAUkcdthhestaFsCx\nqyueNms3nPPDWgbSccxxsKvB8OHDNY2TZHSjeKTpOZEGKUTWTlHJRyTBOIwf6B6yIMqi2AO5reQ2\njlL+XgdFGnRAUlSU8BHquw6ZRBqE/wcccIAugOO5LwfkRgoGo4HXyZwF5ZcQn0ttIAsDj7r/rbfe\nWuveFy5cqIMPfKLXpVXuykQayIoeSaFyf3P0ecsttxT1X6dPyJ+0HClVUpOkHl0fT6seo+2CNNih\nYdiwYeroQJalHTh/pF8hDyON0pBatZ8FRRpAh1El5UTJIivBCd+jpMFz8t0YXrw51jf4YERdtyAF\nx2QveWGMDSdRBGWKbjIfQ0KUwWBjTx9eM0DBZeTIkV6kNCoTaaBb9IMTw+Q2E8Hk8uMHkTH3f+Ga\nvffeu2hiPH5dWl9DGqyLol+y0rusAwyopKLowUijLLRW3efBkQbQseKbBXB4bmylMW3aNF3XQCrH\nEQqkgkH1iTBoKxUn7GYb3TvKTaS6m9owl0HOGIJwq2shDnLnhPo+pOMcaWBgWJtAR42epOS4XS/G\nJ/o+sqF3n1aEQxgs3CPCoLyWHD5zHEzysxiTKjJWv5NeJC3J+g30yM3GfEmrMi4hPfZ+69evn87J\nsIivtJOomCoqKuiMNFYdKZT1n4IkDYRmIFLCeMwxx6jxJKogNGbfKSYc2STOp9AemahrZ46C7RYw\nJGxsx8nKYLYRIWVFWo7yTO4bjSHiM3cdcyDMBTAQkxYDltVZVuXnkAalqBhPIiseoyeGlVQNhjT6\nPp5sfG+uVdnuivwvVvaPGjVKyZFUKRs1QhKkFknPcJKSIqIkJcW8B8Z2iy22SCyAqEgbVsV3aDdz\ni/RNSI++XNYJkVJZRpRiRzoQCJY0gBfDgydG7p+SWgwlZOJTdBHtJkyGshKcKAnjGD0ZWNTzcz9l\nwnlKVfHqotdQbYOhZW2DD54bpI6hwWDET7xWdBp/n9cYXp88cPoj1XtRJ4b2Iwd9lpPn0X7Lc/p1\n9DvRvpLG57QZfZJGLc9JtEz0bEc6EAiaNNIB8cprBYRH2oV7RcdP5jrGjx+vhAFxzJw5U0uQ49fx\nGsLwyaiuPATtlwwBQ2BFETDSWFEEU/R90hqsa2DbcPPMUqQYa4ohEBACRhoBKZPKMMoyIQ87DAFD\nwBDIBQJGGrlANU+/yRwOeeNo7jtPTbF/awgYAoEiYKQRqGJNLEPAEDAEcoGAkUYuULXfNAQMAUMg\nUASMNAJVrIllCBgChkAuEDDSyAWq9puGgCFgCASKgJFGoIo1sQwBQ8AQyAUCRhq5QNV+0xAwBAyB\nQBEw0ghUsSaWIWAIGAK5QMBIIxeo2m8aAoaAIRAoAkYagSrWxDIEDAFDIBcIGGnkAlX7TUPAEDAE\nAkXASCNQxZpYhoAhYAjkAgEjjVygar9pCBgChkCgCBhpBKpYE8sQMAQMgVwgYKSRC1TtNw0BQ8AQ\nCBQBI41AFWtiGQKGgCGQCwSMNHKBqv2mIWAIGAKBImCkEahiTSxDwBAwBHKBgJFGLlC13zQEDAFD\nIFAEjDQCVayJZQgYAoZALhAw0sgFqvabhoAhYAgEioCRRqCKNbEMAUPAEMgFAkYauUDVftMQMAQM\ngUARMNIIVLEmliFgCBgCuUDASCMXqNpvGgKGgCEQKAJGGoEq1sQyBAwBQyAXCBhp5AJV+01DwBAw\nBAJFwEgjUMWaWIaAIWAI5AIBI41coGq/aQgYAoZAoAgYaQSqWBPLEDAEDIFcIGCkkQtU7TcNAUPA\nEAgUASONQBVrYhkChoAhkAsEgiONf/75R5588km55ZZbZPbs2fLvv/8Ww+2PP/6Q119/XT/j8+j5\n2muvCZ/7cPz111/y0ksvyc033yyPP/64/PnnnyWazTWffvqpPPvss3LXXXfJY489Jl988YX8/fff\nJa5N6xvIhT5vvfVWefXVV4s18/PPP5cXX3yxmA6j+uSzjz76qEQfKPYjKXmBTp5//nntt+gV3UUP\n+vEvv/wir7zyitx3331y9913q16/++47r/SJXG+88YbK+fTTT5fQDeP3119/VV0j50MPPSRvvfWW\n/Pbbb1E47HkeEQiKNOhwGIn69evLaqutJrvssovwXvT49ttvpXXr1lK7du0SZ9euXWXp0qXRy1P5\nHJkw/t27d5cqVarI0KFDZdmyZUVtxcAwOFFuu3btpFatWlKtWjWpWbOmfuf999/3wtAg55IlS2T1\n1VfX9o8dO7ZIRp5ccMEF0qBBgxJ6dLqlHxx33HEl+kCxH0nBCwiDftehQwepWrWq7LPPPmo4o037\n/vvvZbvttpN69eopFugTva655pry5ZdfeqPPH374QQYMGCAFBQWy2WabFWs3+v7555/loIMOkrp1\n60r16tWlRo0a0rBhQznppJNKYBLFx56vOgSCIA2MJAMPz3O99dbTDokxTSINvHI6JKRCxy0sLCw6\nO3bsKAzONB8MrMWLF8vo0aPVwCSRBlg88MAD0rhxY72mUaNG0rNnT5UTuddZZx3BQ03r4fS5cOFC\n6dOnj8qAkYyTxrnnnquEEdUhOkVGcMHgnHDCCaklDeREn0SD66+/vraZdsdJg+smTJigpA+ptGrV\nSvr161fUzzfYYINU65N+hpw//vijbLPNNtruJNIgmjjiiCOKxma3bt2kadOmqn90efTRRxcjmbT2\n39DbFQRpYOjvuOMO6dy5c5EhzUQa55xzjnowEMcll1yiaY/bbrtNOO+///5Up6dITxCyDxo0qMhg\nJJEGgw8jxMBs2bKlRhx4cJMmTdIB+UTe2QAAIABJREFUyUB89913S6QG0tDZMZAQ2tVXX60RIUYS\nGZNI47333pN77rlHUzWkazhvv/12WXvttbUfEIl9/PHHqZQTrIkuZsyYUaLfxkmDqJHoGDLcaKON\n1LGhL4wfP14dAaIPUlppPNAnfW/WrFka5dIn0WcSaZB+RBY+23vvvfV7OIKQIrITWS1YsCC1TkAa\n8c9Fm4IgDQw+HQqPs3379vqYRBp04F133VUNSvPmzYVUFflT8uYMTDx0rknr8fLLLwtRA14Xj6Rf\n4qRB+0lXYGQZaBAFJML7PC5atEg9PuRN44Euzj//fPWq0SfRHwSPPOPGjSvWZLxXdIYs7sR4ggv4\nkAvnmrQeV1xxhbaVtCFpRBcB77vvvsVSMchIf0WfxxxzjPz+++/aZ6+//nrFhe8x75PGg7bj6NSp\nU0fbSpSE0wIxbL755sUiB0gfPXM++OCDRWMSYqUv8D7RZdL8XRplD7VNQZAG3mWTJk1k4sSJOjFK\nTjuJNJwHzmekbsaMGaN51T333FOeeeYZHYxpVjQGsU2bNuphMuFLZBUnDYwng4/3OXnOoIU8Lrvs\nMvnggw+KDdS0yYtBYK6CeYwzzjhD3nnnHY2Wkkgj2nZIEa+UeSmM8KGHHpp6fUIazGOQekIvRIUQ\nQ5w0kA3CJMdPygavnUlzPHCMKalH5n7SeEAa9957r7Ro0ULTi0yC9+rVK5E0KOpw5MB45LscpFqR\nHaLZf//9U6/XNOphZbYpCNL48MMPhcldPLDPPvtMPTYMZnxOg7QHA4zP6ICQC147zyERBnGavRgi\nCPL8kB/zGhhIZIlOhFP9hTfG+xgg5j6QDU8PeZkjeOKJJ1IrJ4aCTolO0QXGEKIsizT43tSpU1Wf\npHLAKe0HBpQUG/ok3YQXnkQayEGBx8iRIxUHomqiC/ot/fntt99WrzyN8hLpMTbnzp2rcjIR7uZj\n4pEG840uY0DqmPFMf8bZQVbOHXbYQX8njbJWljYFQRpOWXhkpF8YUEmk8dVXXxWlAfDSCe8vvPBC\nvZ7cedu2bYVrfDggkEykweQv8nNCiltuuaWceOKJ0qxZMzVK5Pr5Pnil/UAfZZEGcpBmdJP9kydP\n9s4bLY00kI/J8v79+6s+iaRwACBSopOnnnoq1XNx0T7GZHgm0vj66681HQk5kJYkHUvEQTQGmfL+\nVlttZaQRBTQPzysVaeC1kOd+4YUXilVJUQuOJ46BPfvss/OghvL/y9JIY8qUKUWkMWrUKMG7wxNn\nohFDwwAkpefC//L/91X3jWxIg5TcTTfdpEaFiVTWMvh2lEYa9Fsq3jCapHYo2Hjuuee0BJf3SM3y\n2oejNNJwfRQnAbncyfwUaSteb7/99kYaeVZ0pSINsMZrc6fDnlw4KQ065k477eTeTvVjJtIgpXPV\nVVcVkcZFF11U5IVSxcKEK6Rx1FFHeeGNZ0MaRBmsYSBa7NGjhxY4pFp5CY3LRBoYUhZnEl0g35ln\nnqlkz/ukfXB00CeRpA9OQGmkASzIQMqO0tttt91WDj/8cJk5c2ZROo55SNJWduQPgUpFGpQ4zp8/\nXx555JFii+EI/alOwQs/8MAD86eNcvznTKRBDpn8MYaE9BSLotwggzRIwfHZscceW0Qm5fi3q/zS\nbEiDuSomiJHr+OOPL5J3lTd2Bf5haaTBangmgnFqeM6B44M+IRP0fMghh6R2XiMKS2mkAWEgEyX0\nRMdgQpTFRLqTn0xAmucdo7KG+rxSkQZkQbkfJZnOmGJQWbuBx8ZcCKkqH45MpEHbGXgYUTxTcsOs\nyeA9tm1wVShsK+KDZ1oWaWA8P/nkkyKPO1p144MeXRszkYZzAlykQVXYTz/9pCkaqqhwdEjbUKac\n5vJiJ2dppIHzNmLECOndu7ccfPDB6tjh6BFd0JfBYN68eV7I6eQN8bFSkQbrMvr27aseGwTB4ig8\nNCo2IA2qORiQPhylkQae2Omnn66DDJJYY401VE63QIy8OFVJGNy0H2WRBsRH1Q3eNkYFD9UHueK4\nZyINZKG6auONN1aCYO5tr7320vJyHAIIg3LW+L5c8d9Py+vSSIOxx6JUCIK5KaofSVEx6Y9uKbf1\nwdFJC9a5akdwpMH8BBODdDLWX0S9LwYgi6AwnhAFBhWyYCCuu+66up+TLwYHY4pHhpwMNCIJdyAD\nYT31/+zNhIzIisxMMuKN+xLif/PNNxotYUTIc8cPJsEpyUQ2Um9RHOLXpvk1xECVEHIccMABJSZ7\nKUGmOoy+ii450T1pVSIO9O3DATGwXxZybL311sVIAEJgTzVwQDY3PskMUGrrq0Pgg17K08agSAPB\n8dgYRKwSZ5fTOAnwmrJcFhKRoiI1RdoqzXsxJSmUyd+HH35Y5aTkMokEMCQsAps+fbruwUQ+nBRA\nHJOk30/Le8jJ4q4777xTdyd27XIyYGhYp0A1GNcl4eC+k+ZHyI9FmMhB1MDr+EGun8opokg2Yrzu\nuus09eiT902fZLdltv2h4svp0cmKk0e6kWIO9pqaNm2aXucLKTo5Qn4MjjRQFh2PM94hnSJ5313j\nHjNd676TxkcnR2ltd9f4Kme0/VE5qekn3cgRvSaNesq2TWXpKConRMH1UUyy/T/5vs7Jkant7nOf\nZcw3xrn8/0GSRi4Bs9/OPwJ44SzyovzUVYblv1XWAkOgciBgpFE59ByMlHjXpBOZHGUCmBSkHf4j\nUFaU5b+E4UgQFGkQ1jJxyv5T5O6zPUPIlyIDN2JiIpHKKCaECe8zpQB87MLIwkQqG/W51cIsxmTe\nI8QDeYmqiKZC0iO6Qh7kYj6K+UXmFokc2dqH7UOYm4RI7EgfAkGRBkaSmm72WGKTvmxPVtb6OChp\nMzKT3z/vvPN0iwnWoXAOHz5cJ1YhEq4J4WCSm4lRCIMSW06qqtjx10f9laYT5IEMr7zySl2DQXVV\nKDLSH1l/4ar7WLToFqOiW8rhcQZwAJMKAkrDzT7LPQJBkQYdbOedd9Z1GM6oZPPo641dkJfFiCzk\nixpSJzMLvzbccEPdXtx3rw2DSfWQW/ntZKSmnw0Yfat+K21oIyuRIzu9ujUKp512mpKI78RB+8kE\n0C/RndNj0iMl5baYr7Sekp/PjDSqVPHybmB4a9xfg6jCeWlJA4/PWMdB2srnA08bzzRJVtY2XHrp\npcGkM3AGuKcI62ucTlm3wHukbXw+aD/3CymLMJCba1g/RYm8HelBwEjDU9Jwq9uTjKgzNO6RRVIH\nHXSQt2kqvFMmvN2W904u94hxYUEnq+R9j6hIwbFdPxFGXLcQB3sv+TyHw/oMFvY53ZX1SN9lM0bf\n9Zoek7/iLTHS8JA0GEBs746HXdag43OMD/fewKj6eOCd7r777qV6p+TFx44d620JLsToIgxWQCd5\n4uiRFf4sSvWROJCPld1JqdRM/RiZhw0b5n2E5eO4y9RmIw0PSQNv1N2dL9Ngi7+PseFeIr4dEGQ2\n3inGhVQduxj7NvEPYVBJRISBI4Ascf2513wGqVD4AHHwXV8OKvrWWmutjLI5GeOPbPsT0pyVL/rK\n1E4jDQ9JAwPDPkzxwVXaa9IdvtyoJ9pZqf4aMmRIqYY0Kjc3K2JTPJ8ODD8VYBB7UoQRlY/nXMO1\nFEH4RBpUTHG74bg8Zb1GVioE7UgHAkYaHpIGkQY17WUNtujneKdvvvlmOnpdlq3AILoKoqgspT0n\n73/PPfd4F21QLXXqqafq5HdZkQYOANfiPPh0uM0KS9Nf0mfcO91tGeOTvKG21UjDQ9LAmLJTLZOE\nSYMs6T3uie5TBRUysrNrw4YNs44ynNxsBb948WLvxixpNcigtIliCIMqMt8IA2UwpzF69Ohy65Po\n0dfdi73rhFk02EjDQ9JAr2yNzr1BnKEs7ZH1GnvvvbdX3jcG5sgjj9R7SJQmW9JnkOnJJ5/slbxu\nrEIGJ5xwQmKlGFEUk+C+LvTDEeDWraWRYlyf9F129PVtnsrpM8RHIw1PSQOjSgUVpaal5cGpVMFT\n86nWHQMxZ84czduXlqqJGxj3mu8QWfkYbWBkqBabMmWKlt06mZggJyXJvADG19eDOSoq4ah2c7Jl\nekSPAwcO1MWAvsobYruNNDwlDTojcxu33HKLrpLGu44aWJ6TymCfptdee80rTw3S4N4f3Np03Lhx\nFTqJUtjY0NeDdAyrwJmLQo/c+5w5AZ8JA11QDce2PfRLoohMhIGzw0p/9qHCQbIjPQgERRoYmwMP\nPFDvTteyZUvJ9vR17ym6EUaEuQpuW8vtP5k0pESRW7pOnTpVNzG0hVHpGXDlaQkltaSjJk2apHMY\nvhOGk53+SIUbd9akTJooCqeHkxRco0aNdJsRUrBGGA619DwGRRoMKjY5y3Z3W3cdHnsIB54ot7tl\nEV8IO/eGoJMVlYE+HUr/jGOBk8e6Gna5nThxohx77LG6QSNGiXkbO9KJQFCkkU6IrVWGgCGQCQFI\nkZPog9O9znS9vZ9/BIw08q8Da4EhYAgYAt4gYKThjaqsoYaAIWAI5B8BI43868BaYAgYAoaANwgY\naXijKmuoIWAIGAL5R8BII/86sBYYAoaAIeANAkYa3qjKGmoIGAKGQP4RMNLIvw6sBYaAIWAIeIOA\nkYY3qrKGGgKGgCGQfwSMNPKvA2uBIWAIGALeIGCk4Y2qrKGGgCFgCOQfASON/OvAWmAIGAKGgDcI\nGGl4oyprqCFgCBgC+UfASCP/OrAWGAKGgCHgDQJGGt6oyhpqCBgChkD+ETDSyL8OrAWGgCFgCHiD\ngJGGN6qyhhoChoAhkH8EjDTyrwNrgSFgCBgC3iBgpOGNqqyhhoAhYAjkHwEjjfzrwFpgCBgChoA3\nCBhpeKMqa6ghYAgYAvlHwEgj/zqwFhgChoAh4A0CRhreqMoaaggYAoZA/hEw0si/DqwFhoAhYAh4\ng4CRhjeqsoYaAoaAIZB/BIw08q8Da4EhYAgYAt4gYKThjaqsoYaAIWAI5B8BI43868BaYAgYAoaA\nNwgYaXijKmuoIWAIGAL5R8BII/86sBYYAoaAIeANAkYa3qjKGmoIGAKGQP4RMNLIvw6sBYaAIWAI\neIOAkYY3qrKGGgKGgCGQfwSMNPKvA2uBIWAIGALeIGCk4Y2qrKGGgCFgCOQfgSBI499//5W///5b\nfv/9d/nll1/0/PXXX+WPP/6Qf/75pxjKXPvXX3/Jb7/9VnQt3+P7PhxOTuRzspbVfuTlek4f5HT6\njOqoPPrk2j///FP4nbQfTp/o8ueff1Yd0W+T9IQeo30cfLjOBzkZh7Qd3SArj7yOj8+4vqJjFVzS\nfiAP7XRyImum8Rkdl1zvTvRK/03rEQRpAP6jjz4qW221lbRp00Zq1KghHTt2lD333FPeeuutYthz\n7c033yxDhgyRhg0b6vV8j+t8GHzPPfec7LLLLtKuXTupWbOmtG3bVkaPHi0vvfRSxgF49913S+3a\ntfX6xx9/PON1xYDK4wt0NGvWLNlkk02kZcuWUrduXenatavst99+8uGHHxbT048//ijTpk2Ttdde\nWxo3biz16tWTvn37yuTJk2Xp0qV5lKLsf42Beeyxx2SbbbbRflirVi3tt3vssYfMnTu3mJxcS7/d\neOONpVWrVtKkSRMZOnSo3HrrrWqkyv5v+buCttM/d9ppJ2nfvr3UqVNH++92220nL774YiJButY+\n//zzKi9j+txzz01130XOt99+W/bee2/p0KGDoE/s0ahRo+SRRx4p0Xb67aBBg2TgwIHFzjXXXFNO\nPfVUB0HqHr0nDRT19NNPq2EpKChQwqhfv77wvLCwUJo3by7vv/++DkBIYfr06WpA+RxDWq1aNb1u\n9dVXL0EwadPWm2++qSSBXLQb0qtatarKyvOXX365RJPfeecd6d+/v6y22mpekAaE8eyzz4rTIQMP\nPaEvZO7Xr598++23qk88ujPPPFN1zueQC8aF5zwyWPH00nrMmTNHSRF9Vq9eXQkPffK6d+/e8vHH\nH2vT6bfXXnttESbIxsm14HPbbbel1jMlElqwYEGRnLQbYndyYlSRk3EcPyD9ddddV6/le2knjXff\nfVdJH/1xNmjQQPsi/RHnB4J0B5HEXnvtpbKBRfSkn++///7u0tQ9ek8ahHQ77rijKgcP5qGHHhIM\n5THHHKMDi86G10mn/OGHH2TAgAGqUDz1119/XQccHjtKPuCAAzR1lTot/bdBeDAYFwzqLbfcInRS\nBhKdjI558MEHF6XkwOXee+9VjwfZqlSp4gVpfPfdd+qNMojw1iDC+fPny7hx41RHyHr22Werkfzs\ns8+kRYsW+j6RI9HivHnz1NCAB4N29uzZxTz2tOiWlAVtpp04Nnii6POUU05RPaHnAw88UNtO2gqj\nAyaDBw9WTPDcO3XqpO/h8Hz11VdpEa1YO0i17LbbbkXj88Ybb9TxecUVVxQRH5Ey10UPxuukSZOU\nFOm7aScNyPH444/Xvojupk6dqvq84YYbtB8yBiFAd6DT4cOHKy5rrLGGHH744UXnkUceqRGkuzZt\nj96TBt4I4RypCWf08cwWLVokDCaURfoJpWJQMCR0wAsvvFCJBG+VdAYDEuX99NNPadORtgcPHDkb\nNWokO++8s3rQyLls2TJp3bq1RhIbbbSRvo8XM378ePW88URdRAI5pj09tXDhQunWrZvKedhhh2k+\nGDkhDxdxQJ4Y3QceeEAJplmzZnLPPfeoPrn2+uuvVx0j+8yZM1NJGl9//bWm1NAnRtPl6z/66CPt\nt0SGGBn0zrU4QVxHVMF7UWPMb0CsaTxo5+abb676JIVIOhEdff/999KnTx/ttzgHvHYHhPHkk09q\n/8UAMzbTThouckAXPXv2LDY+R44cqXLi7OHMceAckVpGriuvvFKv5zPwom+j47Qe3pMGyqIjfvHF\nF7J48eIiA/Hll19q3hTPdMyYMUoapKYwnEQkCO4OyIbr8PgYoGk8GGgQ5Oeff64nA4v3SL/Q+TAy\n22+/vXY6Oh65b7zTgw46SCZOnKgejQ+kgfFEzk8++USWLFlSRASvvfaa6g3PHDLhOuTE2GBonTEC\nE3L/yArJPPjgg0V9Ik16xSjQZpwbIiZ3fPrpp6pPDOUWW2xR1HZHFMiNUUHmddZZR50iUlmk7NJ4\nODnpt6SheI2OiPpd2pSUI543B/0aWYjCIH2iLR4xruedd55+nkY5cUpxOLE7pMPdwXuQJuOTVBzy\nceAEkU6ln5I1uP322zW6IPsBFmCU1sN70kgCFiI5/fTTVSF0ODxPlHXooYcW5Y4hGHeQEsCjQYlM\nZPlw0KmQk3CfthNRnXbaaUqOvI/3QpoOI3P++efr5z6QRhL2GMmTTz5ZiQ/jcccdd2T0xBikECbk\n0rRpU/XA0zwAnby0EYN6+eWXq5Gk3xINxw+wwMlhQhlDhIwQKt/15aB/kkZ2UQTjj37KgSPAOEV/\nnTt31oIAIuW0k0YS9hAJ8620H1khQHdAgMiEc+DmVnnOtUcddVRRROKuT9NjcKTB4CMlQdSAEggV\nCQU5qKYiokAx0YiCeQEUSATywgsvpEk/GduCkSAPjpwYjy5dumhlUdIXfCYNBh7GE0KHGLfccssi\nAxOXFQ+NgYkuGaQMPh8IAznQJx4nfRB9jhgxIrH6Cw+ddA7XcJJ6/eCDD7yRE+eN6I8xSPt79epV\nJCe6plqKNA6fv/LKK5o94LlvpEG/I5pwWQB05gob0Pe2226r/Zl+OmzYMM2G9OjRQ20Wzh1jNq19\nNyjSoNPddNNNOvAgDCa7o6WL5MIdaUQnDs866yztlBimaIVD3Cil5TUGhhJjyi4ZeMznPPHEExm9\nTV9JA48Uz5tqGzxP9JlUGs3gYm6HVByDkGuJNlzKIy16y9QOvGuiJ/of+qSklpy+S2VEv0ckdd11\n1+lJWof+zNxdWuc0om1HHuahiI7QEfl/SqtdlIRRRcc4BxMmTNA0HClK30gDO0QBhnNcGZ+XXnpp\nMX1San3GGWeoQ0Q/JdJCfnRJH6BgBwchjUcwpAHoV111lXYwCAOFMfAwPO6gQgGjghFiDsQdVGm4\n90nppPlAHgaaG3h4Zffff78OsEzt9o00IAHkJMLA88aIsE6D6iIGZPTgWgzp7rvvrsTPtRtssEHR\nvE/02jQ+J91EPtt53hQ1YHBcuibeZgwvGPC9Z555RvstMpPiiWMT/24+X9NuUjXMs9Fe+i3VfegZ\nHUIcjENIEJ2zTuGCCy6QKVOmaJoZkmE9C30Cfaf1QB7ShcxfYIdwBKgUwzGIHugKHTv5+QyMNtts\nM/0e5Mk8UBqPIEgD8KkqccxOSAhhxAfeNddcox2QTsnAdAcGh46Mol0qy32WpkfnqZHPZnAhLwv3\noh0vqb2+kQYGMZqSoqqNCNB5pFEZ8cbI8RPSo0Mmj5lET7MBde1Hb0TGeKIYxe7du5dwdBwpvvHG\nG5qOdM4O7/OctA0lqWDA76XxoN8yh0FkTL+l/zrCcO1F5yzeRBY8beRCp1yP8eU99Mv7RB/In7aD\n/klKzaUPqepjPtVVTLn20jcpgmDCPJ5aJG2FvNgwiiTSeHhPGnRIatYppaXDMQCZl/jmm280V0ol\njqtGIIrAoyOqoKaajoryWD2OoihxTHNK47333tMOSVvx1O68806tHkJGJ2eSsfSJNBh4r776arHU\nG6vgIXMnJxVj6B2n4JJLLlHjglFhDQMDjYoqrkW36DiNB0aPEnA8StpO9HvfffcVk5O+iD5dKpJr\nSGngtSIXcwP0ZfoDnnmS7vMtO3piFT9jDGLE8ya9RoWU0ydyItNJJ52kZbiU4rqT8mtHHKzJ4f20\nVorhrFC+jz7Ry0UXXaTrZ5ycpFDRO+2ncoz0HFVi9G10xyPyok9Sj9Ey5HzrMfr/vScNDAj1685D\noVPisTHBxkkn23XXXdXIYEQwLHReyIUFOCyIQ8F0zOOOO65Y3jEKVL6fM/jYXoJ2IivVNXQwJyeP\nO+ywQwmvhnb7RBoUKGBgkJETZyAqJ/qkugavGi8NQ+J0z3MmEx0mDMy0rtPA+6Rklr6IkUGfrt3u\n0ZVQE025xX3IiD7xYCl+cIaY1E8aD8iA8YchRE9E+aQanYw8MhGMk8dYxnBGT1KSOEiMUaoDMbhp\nJEecnWOPPVb1gZyM06gdQk62C3HtJyLmGvR+yCGHCLsDsDSA6IpoilQdYz6Nh/ek4aIMBl7SyaBi\njx7X0fDOMErRa+nQLMCJ1sunSVl4J0ySMeCi7Y4/X2uttXRCON52csOugzJhntbOSLsffvhhHTRx\n2dxr9MmeRRgj1p8gl/ss/sgAvPjii1MpLxP6pC/ibY6+ZjEn5IL+iTaSrsdJItqKp2LjfSBfr/Gy\nHeFFZYs+Zx4nWgIfbSupKDxydAlZprXvIicOTVSu+HPGLwU46JOImCgD2xO9jhQcW4ikNcpAN96T\nBtUY7DG06aabJp5MLDEB7job5MEeTmz6xwpVvBw8Vzwdd02006bhOZ2M8j3KTTPJyftjx44tMeFG\n+++66y5dYAROLB7i99J40C70ic4yyclnrq7/xBNPLPVa8CJ3nkZ5ScFRLptJTqdPRwb0WyKr//zn\nP2pscBDIfzM3V9acVr50De6QQWn6RE52OMhUKUR2ADnRJf04jboEX8iNdpamT/Tt5MTWEHUQUbCd\nCBsXEn3g5BBxpVVOZPWeNAgL8TrxyDKd5H+jSmAA8h0USKdkYKaVMNyAd23OJCPvZ5IDjPgcmfmd\nNB+urdnIibylXYe8aZ0czqbfIl9Sv8Wrpe8iO/qMXpM23dK+0nTEZ4xPSJTJcnCJHnyfz9OsS9rr\n2lmarMgQ1RU2Bx0z10FKDrJIqwMQ1Yn3pBEVxp4bAqEhgJFJu0OzopjjuK2//gbSs1fvYqXwK/q7\n9v3cIGCkkRtc7VcNgRVGAM+UDSaZh4tHHSv84yn5AQjxuuuul9oNekj1BiNljz32Sm3FW0ogy3sz\njDTyroIVbwADj7AWI0OIS4jMc0L9aDi84v9p1f+C87SRMdvTd5lBGZJ44IEHpXGTFlKnXhO54YYb\ngyMO9ESZauMmLaWw4y1S0OUpqd+wlVYShaDDVT9aVs1/NNJYNTjn7L8wuKiDP+GESbLppptJ/wED\nZdAaa8ro0dvJtGnTZenS5VtR56wBOf5hjCf57tmz52R1srgKwvT5ID9+000zpHHTTlK19XSp2u46\nadCwuS4ETOscTUXwxqlhy/fq9YZJYb+vpXDgL1Kt+eGy0UYbK0FW5Dfz/R3G48o68y1Lpv9vpJEJ\nGQ/ex6BeffXV0qBhC6nWYH0pbH2+FHZ5XAo7PyiFzY+RanUHSLfuvXWxI166jwcr3us3aCnV63ST\n6nV6lnF2l7r1Guid/3yUlTZDGFQJNWneTQpaXyKFg/6UwkF/SUGHm6V23Za6PiM+WeyjrMg57803\npU69FlLYc54UDvpbCtf4V8mjep3uKqdvBElxAgsX2c1gRU5uA8vdDtN6BE0aVF2wOIgyRRbXsLAP\nZaJc38NfZDvnnHOlbv3WUtj5fikcsFQKB/2hBgYjUzjwdx2A1VocL63adNaSXR9lnjFjhtRqPEoK\n+nwihf2+KuVcIgW9P5TqNRro5o1pHXCltQtip0S4SdN2Utj2Gikc+JsUriHLz0G/S2Gnu6VRk7bq\nKPhOHKwC33LLraWgyR4xOf+QgtYXSN9+A3SPKZ/6LOXF7dt3kBq1ekiNRjtW7Ky/iTRu0lwXpZbW\nV/L5WbCkQV6f3WtZ+e0WgLGQhhWYrMRlLx+fOmS0k2BcXnnlVWnarK0UdLxjuTeKl+YMTPSRkL/l\n8bLmWkOLbQcf/b00P1fSaLKDFA74OVm+iKwFA5ZJtZrLd/xNs0xJbcOrZh+qJs26SmG7azVVU0Kf\nOAIdrpfmLTurR+vrnBWyPv74k1K3QUcp7PXuf6OM/5Ij/XjAUimoO1QmTz7Vq1Tj8sj/GqlRu4sU\n9HpfCgf8tFyPA3/J7rH/UilhtiTXAAAMzElEQVRo/B8lU8pw03oESRoYVe7Ny2pZlvTHT1ZdspVB\nWjc+K6uzQIi77raXrNZg39iAcwOv+GNB3y+kdsN+eke7sn47bZ9XBtJgDmbWrHulcdPOUtB6anHP\nO0KKSiJEHx1nSp36bfQGXBgq35wfNlrs0rW3VG11TnL/HfS3FHScJU2atZU33pjnVckxNmXgoMFS\ntcXk/zpzxcdiCUegSL//SkH3V6Vug/by8MOPpFrmIEkDo8oqS5bnxwnDveZuWbfeemuqlZPJgDPx\n3aRZJyno/n9let/aSQf9KdXaXqSr3zP9ZlrfD500MPoPPviwNGrcRqq2viI7QzPwdynoNMvLqiqi\njKnTpkv1Wm2WpxozRsh/ymq1R8r+Bxys1YBp7Z/xdhH9PfPMs1KtRmMp7LtICgf9k8UY/Vf1XrXe\n1rLPvgeketNU5A2SNNj0ju2XHUEkPZKyYrNCHyeI2ZyubqO+UtDn8yw65HJPp6DrU9KgQaMSK27j\nnT5tr0MmDSIEtvRv3LSjVG0DYWRjYP7ruQ76S6q2v14aNGymN3BiYtmHgznGTl36SkGHWWX23YJe\nC6Reo566+69P0RTzjTvttLMUNt51eYqqKJrIEHWoLu+QNqt31Hu/p12PQZIGm4Jxb4wksnDvQRrc\nyc9H0mDBV51G/aSg7+IyB54Lhwu6PSt16zbwbuFUyKRB3+MmQzXqdpKCbi9lrcvl0ePfUtDjdale\no53uX+RDpRFt3HffMVLYaNvSU3DOyFI11vRg2Xjk5qn3vqOGHr2yx1v9Bm2koNucsp2BAcukau21\n5aTJJ3sxPoMkDSaR2IbYEUTSI9sPszuoj6TBhosNm3QTPDFHCqU+Uk3VcaZu1eyTx8ZADJk00AV3\noZsw4QSpXnt1qUq6UUtPM3ikEWNa2OdTKazZQ/bZZ4zuXeSDXokymrboJAVdn19eXuvkKe2x7xKp\n3aC/cAM1H2R05EHkN3nyyVKz3ppS2P+7zOOU4obWF0i/fgO90WOQpEFekXsMs51yEmFQRcXtUhcu\nXOhVR3QdElJcd9iGyycSM+WEowNxwFKp2XiU3qjH/YYvjyGTBjrAEFICfuRRLHLrJwXdXy59XgMH\noMfrUqPuABk37lDdcDPtjg8y0meHDFlXqrc6Lrsow/Vf1qm0v0rate+qtwfwiTi41ULHTj2ksN2l\ny0vhnUxFj/9KQe93pWbdTlpi60uKMUjSYDCyayT3FKbENjohTuVUq1at5Nprr/UyykA2SPHuu+/R\nxV4FPd8u3Ttl7UaHG6R9x566ZYMvZOHaGTppICeGkOKN8ePHS836vaSw52vJxKGE8X9Sp8Easvfe\n+3jjmUJqN998s9Sq00YKer65XDZkyfKk+q9m/UEyefLk1O/S7Pqt0+ull14qdRoNEWQokQ3Qcvhj\nZfjw9bW02BdCDJY0UAAe3Hnnnad3d6P8lpMbMnFvCgyvzwee29bbjJZqdYZKYb/F/13Yx0Qq6zWo\nxvh7uUfX7VmpU7+j3qXQF08mqpflpLHt8hCfctOM569S0O8bqVajoZeL++ivTKAeddRRUrNuByns\nAXH89T9Dgz77fCI16vaXPffcS9NaUZzS/JySYu6oWL3BDlKtwwyp1vH2cp63SLVWp+gd/HzLDjBO\nN91sKylofFBxfa7xjxR0eVKatewsb7/9dprVV6JtwZKGk5TByCZ+3PmOFZs+Gk4nS/QRubi71+7c\nApbFRC1OlYIe85YvgmORUNdnpaDJGKlVp5mugqe008cDD7VOvc5S0Gi0FDTesZRzBylouK3UrFlX\nnnzySR9F1TazUvqwww6T6vUHLE9VOSeg51tSWKu3jB17qEYYvghIP/3yyy/1JkojRoyQFTm5wRH3\ni/dtDHPv99r1O0lBz/n/cwIG/ibVGo2WffbZJ7X3fMnUx4InDSc4nZczpAN5IINrr71Ohq+3obRo\nubpUr1FXataqL+3ad5FtttlOXnr5ZS9u7JJJL1TCTZlyukyadFJW56mnnqZGKtPvpf19dIpnftjh\nR0j1ut2loPsr6gxUq9ld9tv/QJ3D8K0f014qp1bGmfb5m6T+Repx3zEHyGr1tpbCgX8ocVRtf4t0\n6dZXPv3006SvpPq9SkMaqdbCCjaOwUgYzPoUvDpObiVJhOWbVxaHAiOBfJBjNifX+mhYonJjZIuq\nqmo1k4Lq7WXvvcfIjz8u8162qJyV5Tn9cf78BdKyVUep2uFuKeizSKrX7SkXXnSxl2lyI43K0nNN\nTq8QgDhIP7Ll/YQJx3tRJeUVwKu4sUSPJ510slStPUSqtjhR+g8YrOnyVdyMlfLvjDRWCoz2I4bA\nykcA4qBggwiL53b4iwD6W7bsJ+nXr780aNBEnnzyKS+jDDTgSIOMBtkNnBtu2ct8HKk4CjqI+Om7\nZDqItDjBoKL9uApfdD/Ej/Lj/BP+Gf+Uf07D7DAEDAFDIBQEsHNsG7Pnnnt6Vf0Wx99II46IvTYE\nDAFDwBDIiICRRkZo7ANDwBAwBAyBOAJR0vjmm2/yn55iwsjSU3E12WtDwBAwBNKBAKSxaNEirdKM\nkwb2e5XPaRhppKNjWCsMAUPAEEhCIE4a7LzhJsKNNJIQs/cMAUPAEKjECCSRBuvEyBCtctKgtJB/\nyqI0GmaHIWAIGAKGQLoQwDZ//vnnerts0lNEGpAGdhv7jR3PScmtK7uNltwaaaSrc1hrDAFDwBCI\nIxAlDXaeyIY03BqNFVqnYaQRV4W9NgQMAUMg/QhAGl988YWwLxyksXTp0jIjjZVKGizyc9GGRRrp\n7zDWQkPAEKjcCERJg/sWQRrslRZNT2VaDb5SIo04abhV4TTMDkPAEDAEDIF0IYBt5jYTbCECaVA5\nBWm4LURw/o000qUza40hYAgYAnlDANJg36n4Go28kEZ0/ymLNPLWJ+wfGwKGgCGQEQFs85IlS3Q+\nw02CZ7tZ4UpPT0Eabl7DSCOjzuwDQ8AQMATyhgC22aWmspkEj+5wu8KkwQ9E5zSMNPLWD+wfGwKG\ngCGQFQKQRjQ1FZ8Ej6/RwM5Hz6z+Sewi3Rrd/YgjjWgFFZPhFmnEULOXhoAhYAikAAFsc3R9RjQ1\nFZ0Ex6bHowzsfkWOEqThiCN6Xw0jjYpAa98xBAwBQyC3CGCbufFSvNS2rI0KK0oYSFMmacBWRhq5\nVbz9uiFgCBgCFUEA2xyfAM9m+5CVRhr8EJFGPNqgYSvyTyoChn3HEDAEDAFDIDMC2GRss1ub4Rb0\nZRNlrIg9LxZpZCKNhQsXagiUufn2iSFgCBgChsCqRIC01Pz583XbkPhcRnwCfGXNZyBfFf5AFu6M\nRhpuQpyJlrlz5ypxrAhDrUpA7X8ZAoaAIRAiAthgCAObzMI+RxhJW6G7CfA4aawILiVIgwZFicNN\niEMcCxYs0HCIkMhOw8D6gPUB6wP56QNEGI4womkp5qBzGWVANkoaPHGRhntMIg4aRL7M3WcDhmPv\ndnJqTMbAfpALdcMsOGGlIoKxNwo7MXKy9zsntyh052effSZ2GgbWB6wPVJY+4Gwfj84mOhuJvcRu\nYj+xo9hT7Cr2FTuLvXU3WlrVhFGMNOLEESUNl6ZyC/4ccbC/CcTBghKEoOzLkQebZ0UJhK17OQHC\nkQnAJJ2AZqdhYH3A+kAofSDJzrn3nE10NjJKFNhRRxZuxTf2Np6SikcYuUhLwREcRZEGL1yU4R6z\nJQ7YLkoeMGGcQByJOCKBPaMnQNlpGFgfsD4Qeh+I2j33HLvIiZ2ME4WLLBxZYG9x2N0cRraEgV1f\nGUcx0qgIcUSjjjh5uNSVIxFHJEQj7oRF7TQMrA9YH6hsfcDZQPeIfeTEXjqiwIZmIgts76omDDgi\na9Ig6iDkiaaq4ukqmA8GhDwcgbgIxKWwAMGdDhx7XN5JDAfDwfpA5eoDzha6R+ykIwlsp7OlLrKI\nRxfRSe9oSgp77TJG7nFlRBmJpFFatBEnDldZFSUPF3k4AkHYKJE4EADETsPA+oD1gcreB5xNdI/O\nZsaJIhpZOLJwN1haVYSRkTSSiAO2cnMcZZEHIRMnQroTEsl0RkGy58tJ1nAwHKwPhNsHMtlC3nc2\n05GEs6cQRXnJYmXNY8AJ7iiRnnIf8OjCmuhjEnG4lBWsF40+XATihI4+RoGx5/8jV8PCsLA+ULn6\nQNQuRp87kogSRTSyKCu6yAVhwAulkkYm4qAxUfKIRh5xAnFEEieTKCD2fLkHYTgYDtYHKncfiNrL\n6HPsqjvjtjfq1EefRwOAlfm8TNIoD3HEycMJ6R6jINjz5VGZ4WA4WB+wPuD6gLOVSY9xsuB1lCSi\nz1cmScR/KyvScF+KNir+PEmg+HtJQNh7//MgDAvDwvpA5e4DcZuZ9Dpue6Ovna3O5eP/AyOHPYmL\nByquAAAAAElFTkSuQmCC\n"
        }
      },
      "id": "3f9e582c-3993-40e7-97e1-0193ebf578ab"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "intermediate_scatterplot <- ggplot(data = NA_data,  \n",
        "                aes(x = hc,   \n",
        "                    y = rgdpe/pop, \n",
        "                    fill = country, \n",
        "                    color = country,\n",
        "                    geom_text(mapping = country))) +\n",
        "        labs(x = \"Human Capital\",  \n",
        "             y = \"Real GDP per capita (expenditure-based)\",\n",
        "             fill = \"Country\",\n",
        "             color = \"Country\", \n",
        "             title = \"Canada & US Real GDP per Capita and Human Capital\", subtitle = \"1950-2019\", \n",
        "             caption = \"Source: Penn World Tables 2019\") +\n",
        "        geom_point(pch = 19, size = 2) + # try exploring a few different shape and size options \n",
        "        theme(text = element_text(size = 20, hjust = 0)) + \n",
        "        theme(plot.title = element_text(size = 25, hjust = 0.5, color = \"black\", face = \"bold\")) +\n",
        "        theme(plot.subtitle = element_text(size = 19, hjust = 0.5)) +  \n",
        "        theme(plot.caption = element_text(size = 15, face = \"italic\", vjust = 0)) + \n",
        "        theme(legend.position = \"top\")  \n",
        "        options(repr.plot.width = 12, repr.plot.height = 9) \n",
        "\n",
        "intermediate_scatterplot"
      ],
      "id": "07da5376-89f0-453d-9765-c1d1b418a068"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Colour\n",
        "\n",
        "In our last notebook, we introduced the `RColorBrewer` theme options and\n",
        "specifications such as `\"blue\"` or `\"Set3\"`. Colours can also be set\n",
        "using hexadecimal colour codes, which are six digit codes that store\n",
        "information about a color by various levels of red (R), green (G), and\n",
        "blue (B) like this: (#RRGGBB). \\> ex. “#FF0000” (red), “#FF6347”\n",
        "(orange), “#FFD700” (yellow)\n",
        "\n",
        "Key points to remember:\n",
        "\n",
        "-   Choose diverse colours when making comparisons\n",
        "-   Use colour hue (i.e. the lightness or darkness of a colour) when\n",
        "    demonstrating concentration or other quantitative measures.\n",
        "-   Too many colours may distract your audience from the main message of\n",
        "    the visualization (\\<10 is recommended).\n",
        "-   Be aware of colour semantics which can vary depending on culture.\n",
        "    For example, in the West, green might be used to indicate growth,\n",
        "    while red might be used to indicate loss or warning.\n",
        "-   While the default background colour for visualization in R is a pale\n",
        "    grey colour though a white background can make a visualization look\n",
        "    a bit cleaner and allow colours to stand out better.\n",
        "\n",
        "To change our background colour, we can add layers to our ggplot\n",
        "visualizations using the following functions which invoke pre-designed\n",
        "themes from the `ggthemes()` package we imported at the beginning of our\n",
        "notebook: \\>`theme_bw()` white background, grey gridlines, black graph\n",
        "border  \n",
        "\\>`theme_minimal()` white background, grey gridlines, no graph border  \n",
        "\\>`theme_classic()` white background, no gridlines, no graph border  \n",
        "\\>`theme_economist()`: a theme based on the plots in the [The Economist\n",
        "magazine](https://www.economist.com/)  \n",
        "\\> `theme_hc()`: a theme based on [Highcharts\n",
        "JS](https://www.highcharts.com/)  \n",
        "\\>`theme_wsj()`: a theme based on the plots in the [The Wall Street\n",
        "Journal](https://www.wsj.com/)\n",
        "\n",
        "You can read more about\n",
        "[ggthemes](https://mran.microsoft.com/snapshot/2016-12-28/web/packages/ggthemes/vignettes/ggthemes.html)\n",
        "here.\n",
        "\n",
        "When working with colours, it’s important use colours that accommodate\n",
        "those who are colourblind.\n",
        "\n",
        "We can view a list of colourblind friendly palettes using the following\n",
        "command:"
      ],
      "id": "416dd94b-1358-4301-9451-52e580665f9b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "display.brewer.all(colorblindFriendly = TRUE)"
      ],
      "id": "84988c35-0169-4af3-9498-be30f9b1dad8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Alternatively, once we have completed and exported a visualization, we\n",
        "can run it through a [Colour Blindness\n",
        "Simulator](https://www.color-blindness.com/coblis-color-blindness-simulator/)\n",
        "like this one to test if our visualization looks okay from the\n",
        "perspective of those who have different colour blindness or impairment\n",
        "conditions.\n",
        "\n",
        "# Part 2: Case Studies\n",
        "\n",
        "Let’s now apply what we’ve learned with a few case studies. In them, we\n",
        "will try to recreate a few different types of graphs with our PWT\n",
        "dataset!\n",
        "\n",
        "## Case Study 1: Labeled Scatterplots\n",
        "\n",
        "Let’s see if we can use our tools to recreate a graph similar to the one\n",
        "below using our PWT data set.\n",
        "\n",
        "What type of plot will we want to specify using the `geom_X` command?"
      ],
      "id": "0fbb5f3c-7f65-4580-96d0-3509a184b1b0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Fill in the ... with your answer below (example: \"geom_histogram\")\n",
        "\n",
        "answer_2 <- \"geom_...\"\n",
        "test_2()"
      ],
      "id": "ef2a9c9b-4ce1-4464-abeb-a01eed9f9781"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![2014 Wellbeing and Financial Inclusion\n",
        "Map](attachment:media/Wellbing_and_financial_inclusion_scatterplot.png)\n",
        "\n",
        "> **Source**: Boston Consulting Group"
      ],
      "attachments": {
        "media/Wellbing_and_financial_inclusion_scatterplot.png": {
          "image/png": 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+m+gTd1gFP/zR77/x5htv/P7LDbehtrpWsYC2ZfeBAMjvX9Pa6hGDjlvVz8qP/rZbS1Y2\nIe8A/iDSOHLlyR4FAeDhn2iu70aSScddNoDNdJLVLHsGqUEOMMjjtVPcHya3Fk2tOaevI75K3FRf\nROD3g2A2KIQ1NRWKu/sAlCSB9wDwi4JYqgQkGEDw7IAS7wAAdo8YdKUB7BWF0BAAppXnzfMg3ZIQ\n3AZgtyiIN40sWmKCkgKBMSSEjmpqSmgOSJAUXouVjw5Mxe0A1JDAmwigd/NhHwMgExaS1ZYWCCnh\naaj9IZM8YATJ8Y23mORDz7OYyYm8Mw2QYZ6vT3kZUYKhXQQADLzAWzkAmdBUw8tUdfJixbj0ikLI\nBADWEO+cBKYjguAHEOCFcHcDTQmhDAFwUFTEo7U20ykJrTYw6JAEIQqgnefFBKnETRW2ibzouzO5\nTvDSYrmCM6TBIOjl1u/OInfo5Vp4+ezZ06dPnz599uUf137f55ZZAHBIUZBKjmSFP7jpO2kauLoT\nx/Hv/bM333jjjTfeePO/++c/rP1wximrK13RhPFko6SFga75jhsv9kfDvhdcMw0nmVvWD+FuOrWi\n/OpAJ4AZcwP3kdHsUY4jgOetbdCt9WDsoY5bPBbCVDTVsdpJnSNZIE2VTNaoULJZaGqNgAR7c0AC\nA2A+nZIEfhqAJShIHQD0/SG+qw/wJkR+agQMJkRBPKzRlOIHVw+69GpqiiAqKqIRYGAWefM81H6R\nlyfBYK8ohJ5prt6+tD0iKBMAjFUX+uiKuKlpSeCfBAOCfQNK3A4CL6+RxBFXMDzeXtiRzkiCEK1U\nN1vk+Qz+hcD3yGGHTuV5sWpwzPQVZF50sECPJCj7AFUUwvWodpsQDDk4EGCQD4rjBASeoBAeJ9Xa\nSTv5UEJTBY/HBT7BARgWeWcfuPGQMGUoFAoTPM/Xwz4DwWBoFweCUV4YqFyIHUk7JL71XwCwSwJv\nAQHLC3yCgCAX4lszAi/6vHfYdN4u13GuTlPhmucO3oo0BAA9oCuwAAHUdOWCLMCl68pnfn7d135Z\nI6nTp0+f/cFCtf2nIhppPxGb0y6QL1R7slrPmq36lDkAMyvtL/2GXGrf+32Npd544403/3mtFmec\ncnu9Tqs5RrPNKkoHkEKTv39E3G8XoqgO9qfnASBdF2YzlXrTESDdLBhrqdhGj7ka2akD0nKm4diz\nlWJ4gh4gXb/9+QYBSKzKUkNB89VabCEgaW/dRZIBKiMWugOyipn5Ku+R+kAG8bY03HD6wTjaVzH6\n1qumDD0HrUU3L8TfAWARFMleMfpaJ4EZK88nOQAToiCWwFQmy3DYV1VTXpPIV9WUppo0NaV28Xwr\nByAlKuJjDdc72u07ZHXy/FRK802FKzQlSINNakrgtwFgKoTI4GlR4E0MwbNCkHcWuw5ZXbzAe6rN\nsBTiu/DsgDghi8Wnj/L8sAoQpPc7+s2HBCHk4IAJRQiXAAsv8IZGmhI1901PXBAPA0BUEEK+Wi+Z\nd/JiIg8GgDcuiCZSiyHw9ocEoXioeKjIC/ww16CmxF06MIgKQrgEQGcZN3UVZT7eOgJSoSngJzU1\nJQmCKPCm/B0++pPnG2iq/P26mkpU26WxREBQcLQBSOVmZhyu/QCWHIfciSgAdDh023pbh4e0ezb4\nnO7nx9Z37XdP13H2p9XOMZTVXjhHIgkvCAqdRXdumgVgc7S+5jsKAH0dvuFhXxQAPDvZgMPtzhTQ\nYt/Z3ZkGmCOlxHC/Y2zdVaD75zWWeuOP33y5LoRraspQSgz37zQARzM5MTG+c2iilBmf1gHs3CiZ\n7W0tdjRIz+nYEVvkGAjQl5gMmMxmO1J7nMXRipck43aaogD6+v3ocWZaLDu7xzWn4mzC7XYYgBGj\nKZd7vu4H0OeEANARmagd2ZvJiYnxzDMqBuOzR3xut0PzW23f4zQ7aq0zL7uq/MWmxhPDie4dAIMj\nwzau01XSjWa65yYBg5zZ2+su9qgAdA7X0ZLZ7dNCG2f7XV1zOwBw/rmEtXc8AIDNjOos/a8V7brN\nrKYI7PHGuCmdUQ5HwmFREMRpEFj4qgtdU1NqUeSd0GgqpKkpXvZDh32xqXgHwNRpKipWfFBdId48\nj3kzz7cCQEoUGmhK3SmEB8SwKPBKhabEatzUyvDOeCUKvTKnLy8KookBtgV5QQyHJUkU+VipSlMW\nSQiqQyFX+qA4lW7jpRIARN1iRBLDvEZTTxdDYsILIx+y5msvWZuinQRKoiB5AIJnBSH0fDNNVcI7\n4wJvImAwrNFUkReCUjgshcWQKOobRvrEXRyAo4og9QCTibgkhUO8EG8dAdAhKUELNEdXggNBf1jg\neUEcvtP324nlxQaaulDjXylRaYjEnCBgsBSaA8ETYkdCtm4HJmRXd6dL9IDgmHhI8XU6Y9MgwG7Z\nun9oSti9LnvzB6dPnz59tvLP2R9Wjs7FtBHydMSaBvrMA76eYmwIMLjMHXanGAWBI1Z07BGFowAy\nik8+UPLFMroWR04YMAC6Q1LO0Rt5bd0BA999oxG/zzWqKa2Tm6WcozfitmGw6BZb+81zu/ut2SIL\neF8zJ1yZkjmyre6G9jlVKMUZEATixdaDnc5Q/2vHSpWX26T7taFps+QhGInPdSvmNtJjTWYtANAh\nyp0Z0RVAYqDoSEjyWK2V7Q0l8oNSqR5GPGp1h8w5c0aPQaHo6i05Yg4vgKiSmzYKU4Za42yttoq+\nuJBwHIr58iDwx3t2KmYLjg0r2QnAkBTkXKkoOVigxSEmXd2OuHIEwLSwZ7RTNE8CM1Nir8M6MJwG\n8s5ir3O8ZI5YNoeaYtYw+oYa46ZIh8gLyZ32HlEz+v4gKEgdIGBzIm/uA9QizztJxTdVMfqmnsMj\n2BdTeE1N7QlpLvSamjKLvDkNtYsXNZoKKaEaTZGdIV5pHZ/eGRaUo2Cq4Z0Eoyt8U9OSwm8DoEN0\nQBHtYLQYcADbgjyf7Ooyd5ndXc6amhpReGFiXNrTYoxHDN1iKAoOfoUX5ISxTeFFBwuCUohvtWGY\nD2e4Rk9SRU11ioo0Ck0FiXU11V43+rx8UDPuhkN86yTyVl4ImrvM5i6z2Z2oq6lgJce9ihDugdca\n5pVDnaP9Ib51RDP6ghYALB+sqKkw7xzmhZCJvbMI4jONYqr8XrUjBETzkNF42OMF6TcRAAGlE8Bk\nq5hTCdD+gksF0m6lD9jLOy8CaVeXCqjmIgsEZPd64uh//IPTjXLqO5ovxGuKaMN1atjdh5bnxSiA\n0d3w5lrzQKFVzgMXJ0GIRdwJBqOieQzwWs15EIyHbAD88yDEKE2v1wp+uYmm3vheo5rS6tSfBohR\nagOBJ5vS8uV7WwDWEX6+HVhS6gbZjOgg2Mn7oYNqdRkAvzKcBwbjoyAgPrkAzBTjaXhNostAAAJP\nLAWC7cH+PLDD6IWhD4R4Qt2ouQ6MUk426hrjRj1ZLfRim+jeDbB7XCqQbu0ngEGwVlLt5ms0xW33\nAqxPNIBgZlh02wigw9CAHzAk3dsBdnjKAHAO8XALMBguAUeUDICo5AAwkQd0c1IUYA+EHe3AduXY\n5lBTt5rTZ2QIAWE47HDzojUN9A0ooWmQCk0BbL/ImycBb3+IlwsAJkKKWKpNluFqphjye6pGX7ii\nptwi3zUPNSfyyTQYpEINRt+EIIQcLGAZEJQUgMdqc/p4Idxk9IU1miLYN6DwdgBPi4LoI4BFUAam\nb46RSYSm2nqlEqbj0pNd4UMFgHOEg1N+ADIvOjgAz03xoWe9siBZ6oRgE7STBI+JgtjJAJgVguKB\nlgY1FarO6dOmHjNVmsqJvLxKMIsQFHfqQGCPKwMdGBQVcQhAtxRstWlGX9CiudrqvqmJ3w0JkdKd\nTS9ZbKKpxVOV40/xiivpmupPgwybCAgMQmeDIRSNjQKAR+wAAsogAJQiTwGpmAcAnheX1nHt75w+\nW1VTp0+fPvsvK4/CNPCs9qHoLGBHyFdh8EDMrl2mag155SII2sQoCPQJcx4EGakWkzE50Llemmqw\n+d54442a1ddg9GmvslgJBE9kK0FuYn9LxZlDEGjwG83G7AzZljWCQD3UD+CI3A3gD4JtIChEugEG\n9oEnQJ6PGKqUEwWDzuYZ+DPxRK38usF4xNdc5ipNeeLbQMD5XCpgyUYBIMEHtFfNbsHdHCRryU6A\nAZsTJyvsF/MDBnkOADyxUUB3IKmCIO10AocHDADgfK1qOE5kPQDnk2dAcETeJDSFW6yQUHMh+5M8\nPwoOgYgSnwbBLC9UfFMib94BcDvDmp/aHxJCpdpCLhz2xYSKmko0+6YYmHnePA/WIQpKCjpMhBpG\n+jw8L/jRAs+AoBwF0Yw+pkJTK13oHhAwFd1G4A1pNPVUkg+PE0JWjOL2DEx1u2IWGMJip3nARADV\nyvM+AIzMa5PsOCsvPTMmi86G8CSbIIQcHAPAwguhXToQdIj1wCmiGX1PV1zowfgeAiBXc6ELIyBg\nyEqa2sWBwU5REY+im+edaXDMuKQFJFSNvopvCsiJvGu+T+YFcfRe0NS7tbHPXMBmCxQImCaaSprB\nAChlDSBAgE8Ak8o7IMBgNgVyODsBjiV2IboumqopqdOnT5/9afWNMaAZFE8nnX2wZKuiyJ61gNUT\nD+8BALLDEBWsIJgORkHg7TXnwWFnhabYEYNnoLReedlMU2+8XHl0qjPZ4LG3GTwDPWDwRCUgAWI/\nC3A+5zwYHJF31lI6pvoAr7jHC7S/MKwDDHIGgOWtUTDYlp0Gy5K9gh3kYITThgA82b2AflicqYVW\n6CYNs6KpWn59JvhMf3g/gJTFW6OpFBgAHn4bGLT4XO0g3TED4VhyWJnQbuBo0N3As/mAoUejqV6+\ncofGmKHiQgf8sR5AdyCZBsAWg142Ic3oOE7ncNlAAK/NYI8NgmnxOVUwMGxumgI6eCHks3gsFo9l\n0OYXguIoOPRFlPg0AIsgiCUAemuIN0+CYFRSxISKgoMXQj2aC32qeaTPVBnp26uN9GlGXzvwjqSI\niTz6DvK1kT6C/bzAPwcOlpgyNdHoQm/jhfDoCpr6A4Cr+6a8osD7GOCRYlyY0hqzLV+3ZaO84ohP\nBaAmxURSHAWHdrNGU3DxIU0wDfHhXe9MSb0N3mqbwod2AeBQUILBqRmAJEJKQyz0vJMPmViABbxx\nIb5HV/FN9QFPhgVeC2dXJ1eoKQB9Tl40F7CT52UVBJ0R3j0JoEMShGbfVIh32mAJCbziv5NH/34T\nTZ0n9YCEykcybCIADEqVpgCwO2MGAEgLRRYjwjsA0JYdBesIF3tzuV5naD0W1/d+0MhTmtEHcJmK\nb2o+VmzHUNZTGTPrlMyJ4eGEWzwMcLMJV5AX+8Fg/1tRMND3ur01mvqjtuFkXIiU1qemCP5pE0u9\n+Z2bAxK801aZFwZ6QOCp0hTfywItPtc8SCNN5WXlYGbcMaX0AeohjaaOgcASHwVgDLcmcrles3QM\n5PmIV2t7nuxeYIdbqHm3LDlXXJB8tXVABtqgmqVtINap9KpqqsXkUuH1ha39uVx/MlSpspGp1pqa\nSveY5bgwsA8ELbk4o134GU1NjQPA0YGdDLg/lNNgwBb5HWpRHO4fHu6fErYDM0NFOS4MjAKsz6mC\n4MjU+OagqUfWVFM8LwhCkBeE8NB8kucP9aE9ExL4EoCjAs+7ZkdSJl6Id+0AQdosCrzZ2sqvTVMh\nTU3tqxp9ZlF0q0BfKy/w5uFWnq+MoAHArCCED+ZhS4hCcBsYPMbzfH+mE2jjg/yh7rryfkdS+N7o\nkNmAfTGFt4MgLwpxEwGBXRT4Lo/NYHEkGwJ4J82iS8ypyPeKST4ZAIE3IQpCCuyoIIg5bcZiMNTf\nK0S6G5q/TeB55+CT1g6QcVEJZQy2Ei80xiW2u+JB2T6bmAO8PK+VYFiLm0q38sHg+Jgt1WH2eRto\niueH/bAleCXcTTAkKmKJwYSZ5+UxMLBLgqAZfTyf4Cq+qUmgR1Li7juJQj++mm8KCIR+t7rE+rBJ\nV1dTLjMYgHXEDCBAn9CVh01TU23ZaXAO6UDnXPdcZ2k942wLzb6pauRUKeYBQNAe6X0ah7MV/yHp\njCQ657q7O0spoEcs7jcYeCuAd96KgoG33+wFh4xkA1oOiL2jtuhACeuci/3d/66Rpv5Yf5PRx5nE\nhGckGusBwbaKjNHUlM7nageDi3WjLxXkRUkKC5FpQD2U01XliiU4CAKjlNNuYhbkYITVupQnGwVG\nnEK1x3WHrNOBCdFUKX8+WMwDARfvh9uka6ApAPC8ZQED1udsh9ckOTq7u+c6eyrGpJpM2ipqI20O\n7rL0DWUnQMD2V9XUM7ExwKC5mXbHSoDOIWtqimfbi+JcZ3d3d6dxEvmc6PP0DWZHgRafSwXZJGrq\nd77+T3RrjDjaBUVRBEVRFEGxoF9S+GQiGeYFvssLtMu8EhQUJcxP8ebJipnGi5IU5LXwznFRSRrA\nYV9EDto1TwQvtKYB7ONlfghgYBaEogqCUZHnQ5LE80LdhZ52hqZ4d0IICYroI8C2sCLwUnEG+0RF\nCIacBa1JM3guogi8wIt27JNkoQMMvMEp/iADgLWGBT6oCEJQMtfbsT4hyuFxAi4jKvwwAYChiCII\nvYckUVC0URqS45MKLzzb+JZ28QofFMIJL0amRCWoKEFFlOtiisGwqASDSqjYDi8/xfsYAP284O4D\nA0+IV3hBEQQ+VreRnlUUPqTICi+EzCOAgecV/lB/XBQU0QgCuygrFgCsoPAmAoJ+fqp1BPD6RFl0\n30EI8akmq+9SnaaqXv2qmqoYfV0VxaONW/EJYETZDwZoy/rBzWUNGwgs/ZdnG8TUy1x1GCtW0jp8\nLKODMTZYieA0ZmuVFJCHWUBVrCB4RzP6Eu48OM03FY1nAATWT1Mt/6zB6nvzR00BCQQAouIxABdj\nPWDgye7TTvP9LQDnc7WD1GmKYJy3EI5DNJbRof2FnA4wTB0DA8tbbQDasrV1+XUVmqqoqT6zULEB\nl4SEDmiPmyoP0yY5QAgmQrJdbKs+YE1NMRjU1NQep4qWXbEV0yCLlVmsBMbwIKqh62yuiaYqRl8q\nO10x+gjyhxTM5KTau3M0bASQynqAFpNzM9HUmnFTPbHIgIYY/xQCXRFRlCK+zEC49SKAUV4SxbCU\n28lHtFBsZtQ8JTtNQ5XJMr7sgOgHh2ezUqwHBPAOZ2N8HxhEs1K2EwCmYjE5DYC0maemnD5j02SZ\nlBwJiVJ4rn8gnGgHZopSiBcFA9jflcR4iN9Xjchlx6UQHwoNHIMlK8WMYPB0LBLT6KfQHwqLoihK\nikOtB+n1xEKxNugwnRUjRhAATzukcEiKmA+LoaCl4h4JCbyzSbNYeIkP8QPuGeC5LikUEsWIs8kv\ns1uR+JAYmRrBB1oJOLRmB4QREMCuSKGQGJJEd/0nc7Gw0iWFxZBkNgBAW1wKhSL84VZJGmcBY1ar\nWG8sEivqwMBcyaxQlPjE5B08+0vL5QpTLS6fqYu6UK+XcDpCwPT36sExtkajD9NZCwiBJf4YMKkM\nQgeUxADgidk3wJS6enjn6dPVeAQ8JZsJAcEzkgc4mp2r7BGyN9ZTDYzeNuABYfJTVhBMxzUXutsL\nRjP65kIT4DAyUFp3SX7vj2tS6o0/WagefdoppwkhhEVGHGMIAgM90GFbNqrRn5jjKjTVqKa8TmVS\nky9KO1TzsA4Vd7OmpgyxTHXPE/J8XU3tA0i/eEQ7YY/MgkCt+aZGwgkwIHhWDnfNV32qnmwKHLTJ\nMgQtPmc7YI95GjdUIZiW5qAjBIDZmQaputAraoqBMTYGGOQ5QoDH4lGAcyTThMNF2QSSGdhdre+E\nEoBOc6Hrq76pzAOiqa9X/r+Nb8owXYOHAfpKidweu77QO2QjAJi9B63W/meebjeNT9u0CitM7A7A\nGxJCPQCODk2PqmDQN9Q2rSmUlH3awwKYt0/bDQDgmdYOAAX/RABqWBEbZv4bMoncQQ9SibYdAFDI\n9A/nOtPA09254dx4/W3y9FDOmjOVDOgztk0HALTY2+wVMsiP+vqLuUTPc43NODCXmQuAQeDAeKZC\nROy0rz/RMwLHsQmtQOmOREg81PSO5p41WXP9GQsHIN1hGi4mOle80iZ81lzvLo8e7PS0PQUAFvu0\nRzMLnxtPFIf7M42rZzw3beqe7LAWE89U9ouJOhL9O6OY9llUAIGh6ekRANz09PSzDIBn7dMeLwD4\nez13EnJHyPHlqv/88kK9OsKmaoJeZzuANrGnQlMEBAYxAQDPhwLAiNJNgLxzOA/0Cc4dAEhhfRf/\nYZWnzp7+Tt1b7YtEAaTlrjwwL7fuAPDcCNSkbACAAgNLZAhAlC8CVTVVdaHbgM7QLIDp2Aa8Jy//\ncUVPvfnPfq8+1uZMVuJK5kQtxxJIxdpiK0ZfS7OaYmBQrFq7cMSiaC/mdJrzh8ASHAUBaxa05XlI\nE01FQWCUSgDYKBmV3gFgkRLVJuZWUgAQaB2w1gTOYKUYnuATYNBicrYDF+NmFYCuVvk7BHkJgN+C\nYdkGeMez0SY1ZYyNAUuyA0De7JwHdD5ZG6fxAM+GEnoA3nnAFFwCuMOxaYA1PUij75vf/OY3//Sb\n3/zmn/7pn37zm9/8029++Nvr/m1e9QK6Wnhii9o+A2BFULQ3pPnWNw5VWjGnLz/DAlw1MImoaqWP\nq+oMaS6XuuY6HKw6r+ZXsUK0f+vZsDMzAGmYeNEt8T0rCFyvqjPVn3vVtPem+U6cqqpk7dpTVwZm\nsl5Ana+XncyoOoC9rQVzp3HoeKlcLpeXl8sn6iyFi6J80OFwHOx+GhkpkxrsT8YdBJiRW7U37Fxk\nfLe/JGUAjMhxkyVlCnkAohuNmEcnBhPmdU5WWfjp6bNnz549+/LJhoN+QZhe8nRpa1/YB4rbjnaK\nnSCzYWfbhMfkVFFwCqMT3VNJlw14R9Royp0Hh52RADAWdFsmfLJcnFm/sPvu//3NN9988803f9RQ\niaqT9zkcDkfCEBDNTxz1ybJ1BkgNmKKjwymQkOabSs6DwZGpTKUJlCL2qpLJVNSUYSoDwBIfBQF2\ni7Ld/6RD7oPON1Az+qIAZtziR7s9VmFyhywPbh9XklUXBqJSsufo3k7F7AsdqN5TdMAUbculakaf\nqx2MrmOg6NnelivWmsqoZH5i+5Dg9Hoiib1RqxzMcAA7LNaMPj9gFwVHNJWI9IAgnQwdavMPic4Z\nEHY8krDs7jAfAGZDw7N7E7JiYms09WBc6K888oj23yOPvPLIIwzzyi1evwxhCGEIIc1zLpmGrl7/\nNKFpi6NVmuKYiguiql4ZUolZI0ztLwGAozsYANgrNqmp2iTI27VALSED1PInzIrVgNe/DFo1ZbqL\nF1JrXu32wkUrQb0kqxSE0a3Is7YQbKWCSbUCtftiyN2vsHny+yfOnHvpZFNcxJQiiiFe6mpH+7Ao\nC9YjjmIemHEWK++dEj8lK50tAEZkRyI+FezQ7s2i8HLQZV8vQZCT3/nuT7/z4+bkE0VeDpq1MEp2\niBemlF1pANFkXBbkwzogqghTRYNFXgJGlb1g0OI75AWH8VAfQPbzipCYNLo3suhTy3df/tGPXv5e\nYznUQ4IohkKiYsBQXFFMk4+Z0wDbzU8pzjEwgokAugNOFQQjrXMVM9YkBrSHdsSVZGesvRxw0dUN\nglllEAyA3c64LEx166FziFU1NeAHgIvDoiy4LTpYBEEZvjiYrHkX/DleVpSd8y2+WhCT/qOgrDgN\n8CgWEOh2OWcA6AaDvMw760PexOLkZX44QPSZkKx0pk17dACbEKpGXyQAXaY46BSmQsf0AI4m7Y64\nzLsNAOA1hgQ5eCgKcCVxSnAUMsN5tDzvVkFgc3Y/CJq66cNGzIY1vxKow+LOwbFCNMcr4h9sKNf5\nYigzaOiLDvNKKHoHxbjXKHjUFvYZSXB5b3VlsvGSkVsS4508xHvcJsBejNqAymTV2uH03r2a9BhR\n2hCYrXECOxad5O6qBQHEkDLUDqq7UxXB2WKYHdFkGhuYJED6ph9X5iob+gDu7qfH1rPOH0kDLVqO\n84b0za9lcvNvSPNrqHqCs1lsK6qyFgWyVxuA8B6ZBEjjatXpIzY9gJaGcM20IX3zm471R3c0mf5e\n/+yOSqlVIO+9ubCsCt3uaMWrmecwMjtWWy4yVV2ZWzWogDffaHNsNZq6VbYTYYEXpXCIV8QX9Btq\nINGwwothSeSVsHUzbK4+nX3N0cuvXK2BAphUpmklUHyVNHWvYXCFRZHneTGc3GDw4XNJqfLLja/i\neB+gCkEhxCsh9zxtLSswosVNUWzifkir59ZIp4yO4f5ek7Gw4V9GDzty/f17hgqboYa8Pc5wXAz1\nT9LGchNNCftpJVBsdRYnLSx7Z2Ybx7Ist1nqKB84mrKxtK3cZJ5POqNUBlBQsUmxmcFS7qagNEVB\nQUFBaYqCgoLSFAUFBQWlKQoKCkpTFBQUFJSmKCgoKChNUVBQ/IbTlLfh3yq4mftU6pbG67D3Mj7c\nu75kOhoxREGxGWlqXlvlWlvLaT7QFCRucA8CsDibpu/NJlOrTflaco633F2hU3x95QRvZ1DmjVAT\nxfRGs+EyzhUL4w46l2ozzm86WQcZz609Cd8jj96mUr1e2u4oKO4LTXmdSgsAW8yqgsCYbdh1isAi\ntoGg9NbuRl6yi6usw0KQiQmGuykywWi2vrHZR+L4YKIH0YHI/o1mNBKKrViJsFtYql5jRBxYc2Ed\nrvjC2jRljh269apsZI91C2oxZpPNMf5Ki6Mjq1/5ni5tQr6aSiLrT7x5HvkG1FR/MAAGT4YPpQEy\nHrM1ZjLLjwI4LGxv/MH+4N7V7tRQzOTvru49dZryCkUABKpjeMO2H9tpXrF4Q/dUTU15O7vWXNmB\nGy6qaz7DJ9z7bzUbUWXROQyiYlPjATXQ6jp/5EGXkdzqIsz6ikIYELKlVpDYvETFrP8WSm8dBdDJ\n8wbAmxC8jedm+TYwOKxMNGa4P776zFRVf5dFrqkpgoIyrK0K6p3Z+EO5af20bmWpTmJrM0mL9dAt\naKad3NLQ7LJk+lOJ4hYTU6x9qGUzladluvTVTU23jW+vN74Jx47al54hHaCb7rgn6rg9fa+fmcF/\n86ubmy6tWlrV4F/Zg3Sebnar0RRgEQYBJBJJD1BwPd+kfWfjowB6Vqqp6P3h5dpWRMCMkLuHGXdP\nLa0nGWct3qkaIpZEUlbM/Yc3s923w5cbWcHo0QFp9t42PIa5iffmHcUlAGAdmdu571SnWXf/KsBj\n1jZFVX25NIBEtqaedXBka0sAt8T6dcDMVG61hxkYHi4Wi8Pm9dEp2dbldO25eA/vgfOYZSV5aHDF\n5Wfc7lUKpJaS8pQrMdHs5ikm9VuNphgYQiYg7xqy9gAXw88AQMBu9LNVNUVuMvre2uvd9ky0QtLc\n7sN2G8Cp7e0qAIwYQHYP2QuVerKUBvWqp/q8dX0pT0o7xU14oXp6olqjJgbjkFoz+sjTBqHYPq+y\n+XZV5QB4U0Bgv1Fbp5YNRD3aK6LlaAsKg4+lKh0j7TFG8/CqapoFAK/BYjF4m40+b7ua5gDd9jxU\nz+HZarl2DD62Nw/OekgFA273JAB4j6oAUHinJ+Ul3Hx1zwVimzamWABgU8BIm9FfYerneGF0szz7\n3370Wz/71qO/tYKTZiXJs9JSd8nb7+3V/+zPf/7xx3/y8+82tMEdr8V8AKB3F9dyC+iNPVp7sfbf\nI5r64JNPr3z6yatNFWCMFfNgAIsoXgRQivuq5eGQCdfcHZx0UAfMOH2rCU2/xLtcyVZhrQVO2Y7O\nhm9DoYSnk3feoaB6/L//H/7Hf/Wv/82/bbyJUthp3L9rSl4hqNTh4ZtLO5MTrdMdPjHRxLfe3hfy\nW42mgHx8GPC7UqYEB0s2CnA9cbdZ7E8DsPCjFZoijWrK5xKSUtEGAGoifsgZ6tbZZFFqzQNcVy7V\nrzjjmtM6JYvOKdkXqQwNpq0RQRbFUQKCo6F39juTroiPBaA/JslJ2SlVZJpaFAWe50VLIiRKfhCU\nlOi47JIj+wkwIYcUWUqOgcAieozJZDJyrAWAzi4JrWHZnxHjsW0ApkVRUQZ60yDoVqo0tUvkYx5g\nLNQx6kw6Iwk9GEDXIQmvhZNjnPWQCgJ/OAoA4/ExENjjspOfSkdFPvY8AYH3WDjplIbbQTDEz3bK\nLjkyxAGAzelok/2b4uE//u2/+IvPPvzww8++9u+aeCoj8AdXrvUdWLqXspjgpx9f/fzq1atXP/7z\nhdqxwqGgKwCALQ7nV3FPEwCqYAYDgvZDw7p7UZRHPrl2/dq1a9evf9q4V0m3KI+BgDhExQKAS9XE\nc3UbeM1pEPZxgOrcw65SNc9JjtWdPJWPeZdcP9an+AAM3vR2WF8X/rf/01/+1V/+1V/91Rf/5oO6\nqBCH8wDSqRX1PlO0sjctI28PdQOA4UhTWb058x81jp2Q27vjNgFNofhaGvud+jkrR0piH4hd9LTP\nDIoJnUZTQI/S9MZtE2VjwGYcyBDA60v684Vxyd4y4ZfNeYDsEuROW59HdABIJ5XoZMAYGa4or/k9\n0/6ARU72gcFF55R7dseISUiBoE06HBjZNxyJVq1vi9DlP7rba/AflAwAPIKSWNqx5HK3A0cTFoOh\nI5QgwERSKaYmR4YVAwgZjJjG+lLmoYC/JzYLwO5IGcZ2hR8D0+CbujjRMxAFbK1Tbsvk5PPxKAjI\naMRnmNz32n6dtaiCYEyLi+iWlwBDOHNxZKKDVSeikQwArjO03zY5GLaygEWZyvl3GNyuHSDAYWWe\nO9S7GXyTr3zr339448aHN27c+OxnjTylmjM5V7q5jdzr5fr//OrVzz//+OrHV69e/XltHC3tzoXG\nCdFoavVuMC93gYBAPTR8L3xTj3x6/ZqG61d+Ur9Kl+IuEQYFOZG0rBzj3CnZqpXCSj4OUF2m1dSU\nIeJrAbNWlRGorc76NkMTWQsIbK6hO7mJ//mv/+qvv/jiiy/++j/8x3/9k2qHHs2uLtlnitabSksS\nkuGmgjLw9pq9t3nZbEI1hU7ZgHEzOv4fXiQOqVAFHwDsGjAAs8G2VdRU3AMgX5RVICVNA2CTzjzg\nMucBdIsWAPPuPQCMogcARJOusQI6Yn4QjDjNMwAG+Vkgr+Q4AE/WR/ryQr/2oSf2FBjMiuM6AI5k\ntZfpTCILGOScF8BQ0A94nWYVgMoC0ayleq20bAXTONKHaHYWKJjdKoBt8SdAkE92zQCYYYlGU37N\n99Y9tQRMC1WBxMUyBBgRHQBQEmeBvXFHC4BjyggAzBsA28hmEFPf/uzGjRsaUX32tUfqbW8ssm00\nPlupiJmJJoeJt7DjXrjS/+zjjz+v4OOrf155VEi7TXtkG6A/lNNoilwcq+iYAqD6ZwC9rA0+qIdy\nwI6jdxvZ+0mVpa5duf5pvS8UnZ2tM4BFHHJamvr49osYj2hqKuCfQdinA9qdJk1N5ccMDYboknSw\nTqTqZM0/PbNb0ywtrc56lT8X6QFBShy8E6v1f/zLv/5Cw9/85X+qHrVn7fWeNK/5mNIEUItFYMnQ\nxD9cIuxv4J2ZyfaqmmKRPlq1cfN96crt5fMgBhsBkJ+sdDRWD6j+i5uBpqb5KLocSEVsOqdDh1S2\nDQAs4iAqLvTDykSzCz0FDqSULQCdsUkAcMgGMEl3HkBG6WM4TLb6AK5X9BIwrGTSNXvK/SCwJXeB\ngLzDR4Gl7DPgOFKPmyIFYZhwBBxTGngKgCW+DRzY52tigDgiLLCkdIIhOCz4gbFYJwMQgIMlW3ML\n52UzSIMLnTCebBTY4fSBEAzGLSCYiPVoj5IbPqSC0dSUZinCE2rTzjFqLEOAwew+EB38ggOIBt+B\nDiQzNbmpIlH+9sMbVXx448Nv10+VIn2TEW2XTLYnqchmD+voKlpHgImE0+Vydty9jvlFlaQ+v/rx\n5x//WeVowe07EnsG0L+g0dQ2tyzLQwCB0e31u4JPzCTcgtBVND+JdnP/xT1JRS7dVY1+cK2OK9de\nraupronYBHS+hGXKju3ForlEAJD9rinl+WIoACDtm5pyzoVMOkCt0NQ7Tll+Idqgpg5WyjZjNDtd\nyZwBAFoecylJ8zRUU5cgdBVbK1ae2jW1A/mcnL+D5/hv/mOFpf76i7/+4q//c1WfSe6apTp4KA2g\nxWfKAzPFF0bdslJMNYqi7oGDtfeQ/ZDTlbRuB+DNFQOOpCJn9IBum9XpcpktADDvbCMZflgl+4tO\nV7KYApAfNqqdLllxeB88TRmyQ2xyGkvBqJrtAIYGjqUsf5DqFIeA2XgbKiN9TE/pcM/hbg/Bfj4K\nApRiUXj3REaj0dloQvADSXMeIMembCDY4TQR9Ll5AGip0xQpDDoSh2ITYDDi+kMCoC0YBdpiRhDS\nONI3LwxrNV0aMFQ2wtZBdyCZBgNyZMjXnxRZYEn+CAAeC/qB6aynun2aJTsLMGB3l/b088Uq46Dh\nZMG5BwA8QQuA/VkLGIBBxejzV40+AzBjfqs7DRAgH8sAuu6sHwyQVopANGgHATJTk3dU7fcLf/fh\njRs3PqyYfR9+rXa8JWf1ItnVDgLSI3V6Bouj3FBnq3QEcCjj9sNO0XK3l/7ex1drPPX5x1d/ylSN\nvj06kysN/aHc0yAYFBMeTyJSAjCqzLkSx2bUUkaRS3PjNrQXFXnP6BNFMXo35aiLqWvXrl37tNZV\nXUV98iOoYttS8DAMc+MxEwEwHU4MenxBIQB4h4Od2zq6JJ9GUy0A6YiX/BZzPNpg9HGEIwBGhNxh\nu0MangHBkNTtGRy2I98zLiud3ZlAtbWFEpO9/MQd3MPf/6v/UhVTX3zxN//P/74q2hIRc0qnZd6j\npEHAmot5YMYakh2WkjDVKEQNTslXEfhp2VravzP0QhrI9wpy7ztP9Ia2AVyxtdPeGZQNIMh3/a7P\n6XgCarLYaT8Wck8C+RecztygJSG1PXiaYsNz2+UU0q2lvaHdICVRdiaTSafzMDCrudCV7QB5zeVy\nORWHrkpTnbEo8v1i0pVMJp3maNXoO6aMgGDHawlgpFUAAC5cpSmuQ1GGHcOxCRCMuP4QAN6Jz1Zo\nisFgnabSwjCYKk0BlvggCHR/mEwD6s6QK+FwhrzAktJdo6lnYhWaYmDJWgAEekW3yScWm0b6iDZK\nUHCaAMATfwIEh7MWcACg01zoY29pNKUsgWDyQCheSgN4OraTgMvE/ACQnnKqiAp2ACSjTG6qmd5f\nq6mp//bGjc9+VnMh9w3MAR+FJkAwU+yvHh2XbEC7HsCEeODuVCHBn39cZ6mrV6/+nNGMvoI7gVTM\nDvaFXB4oJId1gD7BHwGOKKI2wuetGn3WuAVASnsD3Ck+baKpa9WnM+N0wCG3zMZt/reGACByUAek\n5V4CwCEFgGlxEIBX8umAGeeeFiAgjAMoCObKiDT8YuvzPp+pxwtdWvNpBYC8tRYwx9aNPs1r7you\n4Q6i/f/t//JFnab+w1/+V6bSr9PPS+K4ZrA9lkxXByagWpNjAIZEOwhT2/jUXxyY6pgHg8pOrd2R\nCSCfENsALCklEKQJgMFIB3Qg4wNmG6pJjQNRgD0QtgM4MvXRg6cpMuyz8yryxfEO2QYYYx3pkb7J\nyckZwNIQkNA3OTk5OZKuRaH3ZG2YSQz4C5OTOyYn9VU1ldFoyukjmHQKGg2aOK3WohFfQA9LdgIc\nRlwOAGh7axYY1WiqIQp9XshpdV2KPQXAwg+CaGqKlAaGCoAj0gIYpuZANJoixrqaejZrAfLD8dl2\nkGQXCDprRh+DJ7NRoO81E0DgeesJAJ1Zi/bLShR6gwtdB3Aph1TsA/KxDAG3M/scCNCudLGICnYw\nQGZqZBORFJiffai5pir+qb+ttoXBgW1gotkeAqTN/Vo75pCpDnDBq1jvlqZ+1CCmPv/86s8r2e1o\nTUCXEFTdC8N5wJJ9FiCIxktAn/i7WvHa5S4QAO2HrADBbHD6LsrCXGliqes1mnJ9hNnI7lxRFxBK\nIGClgwSIZp8Eo0MmEgDpD80ARJP/mgvdnjUAQEKp+mf8omu4OHxoV77BhQG1aCU1LnQ2FL1wMC6V\n7qj//q9fVE2+L7744ou/+tc/qVayznMoK48CBEa5QlMzwEwxR0BwkU805aP2yFlrXcztzUYBb86s\nA8Fu4XD18GSsBxzQE2vwuPuzHqBlj9MLDoap7k1AU8/n+osAfLLPmge2VZx0AKkEJPQ0u9Df4aMg\nYB1iO3SZWC0oTqMpHFMqRh+Qt/L5Jhd6IhkASFvWD4IR1wEAaItHgVSsRzP69q5q9AFP8IMAdA45\nDTXZrwdhD0ossDRVV1Oe2FD11p/NzgIT8RKAtNwFgrkGNfVkNgpMampqkH8CDAZjdq2+WGtRhQ7+\neN03BQA6z0AJUAcyABnKHgUYGAQHsPctO8hm802B+YcPK36pGzdu3PjwZ9WxbCRcaUANJrwAyYRL\n81p1VGhqJuUZivff3ZWb1VQDTRVaEwTbYh7dC8N5kO6sAQwwwieAyZBPq7t2uUsbJysOtwCYDb5z\nNyVpVlNXqj63dtdHKJh9yjTmp/oBsNJBHcgz2aPVithhFgDgEU1NuUwtYB0xvzo/335MqQ6lGCTf\nTD6f15zXAcvggdhzIGRO6qzsrJ5vbaCpgOuFVEKyA97SELex/vs//02DmvriL/+3hsCKdIkP2QEY\n5TSq46dqscgCSAtF3eTe7bt37z6a15yqBp8k+0EA2CyDu2JRwNtvzoPBduUwCIBCdLQn9gw4oJRN\nV3hj0jI4HnsSYPc4VQBLUx/dnya+ISukw9maAdAt9jugQ1+ov+bx80+NAvBoz6g6kWmaTwFQXb0s\n4BnoqdOUu9HoMwGYk7YB2Bap0lTSrAIYzfrrauqdeBSYlw4SANPZVLXBp4Wc5i+q+Kb4UTCamuqL\nZQiACk3NAcBjgh+wDVSC2CrupyezHgAzchcY7JcNzb6pvqrRZwHBUzEfV/HLvzADYEwYAoC5Ck0x\naIk4CJ6OZQgQzfYAgD3k0XxTANlkagqPfljRUR9+eOPGh9+qvdcFPtna6gwKNjBQfWLy8ExNTc0c\nlqem5FD/3Rqvf1bzTV39/POPr/65ruabImg/5G4vDufR4ogZAGCHcMiLSdEEXV1NEbRrAQl3SVOr\n+qYYLCmdgE9y2pAWimDQEvHpQLqz+0AYZKQAbJr8bwn7NDXFIp8IOZ1Ol1MRKoobhsjztd7ROzUl\nywN+EOQdIblTBRjkG4w+73DQhrRZskCd6t3g/IRX6wN9X3zxxX/8T00nU1PCElaoqaIOwA6hFYfD\nsqIoiqEyxscOSVYVMCSm5Cl5QKMpL4DtQg+AQmZKnpIHejRvcwEEQMA3JSvJAQ8Ydo9TBdkcagr+\nkNABBhY5MQoC7qNYZ17HHinlAYPSTfSwxD2kIby+U9ylcvrOWBsYpIthi5fLz9oJ0NqVB8hH//sI\ndNhh9gEwuITO0rDMmypvkoxoaWE9LmkawIhzFwC0CVGAeZ6f4Lyjcqg2cDs/1a8ZfT2SAYBF8YBA\n53CmkT+UtJF8p8AbAEOyEwCMih9gn5dGvVzatB94NjYLGIREXte3J9SlAoNxC/FWaSoWRaV48AgW\nELA+yePlCnsGsXNqhNNjTEjkOdUuJJeAoykv1zIa6wDykXECqNaggejSrbIX2KvsBwHGk5uLpn7r\nsxt1fPZo9fCTfGtXV1dXa8QOAOSJnPh/GCoigjsQyhz1zgeH7/aV2fKLq/WRvqtX/6zBNwXs5405\n69NgHdmnQAgKwaIek3GfRlMzcpdmpBSHORDMBvffTVlebaCpK9c/qR4eUzqBqGQF5pUcCNjIQQ6k\nM7sPDMHOSAAjVZoyccCM08Qin5A6LB6PZ5ulUM0k4uMqhBV0tgXIdPY5ACCzuXjSDyDf6qqVYyzW\nTYCLLn4pLdo3ehP/uoGm/uYv/21z53aE7FU1pa+qKZ2mpmAYfGLbtieeqA0IsmbFANuUy36ReTJb\npSmC7cphQD0U7PE/YtDUVE+2AAA7knKHgUSzHoA1VWhqE/imUBCyEyDwS5HdIIDqiE2Z3JI8D6hW\nyeQM9Al8wly7a1V2OoN7umIJFSAwHIqZTfKAiQHEKS/AHAgHQNAnWAFgbNjpdEwkqvayQYnkzM4e\nV44DAvFeANgf2wYdDEEx4Y73OM01XRtp1VzomawBBJ6YHQS6hFgAPAN8wmUtiT2AX4sI7hyYAEHA\nHTuUEINPEHiyFkA/Hkv2Tvl2iVEgEBQSRa9GU6PZZ4ERzaZsiw2CAAZnrNgbEiywDLiHfcg7Ys5E\nKNkp2YHOAacvFzOpgJo9CBD4XWLCxCvbAVgGDoMAz0u2TUVTj/zdZ5VghBs3bvzs8Wp7MAW3A8BE\npDprc5vsYjUPsF84CCB9D6ZR/tnHDTT189rDdCcAzLucw9Y8uDnN2xOI+4A+0QcCMGiXu8AAUF/I\n6UAqQvXeWH1XaqFjS0onkPZFgXmhyDFgpYMcMJSNAoTJSAG0H9KcqdXwThacI9a3YlxcOlgRiRl+\nAoBdoykAlqTsRZOaimanwRCkxKQjHtioVP3Pf9PAUv/1cTANHZuMxu2AUZ4H4D1kzdfCOwO8qckM\nB0Dwh8ISusVZANuyjWrqMOARjQCWYj2at1lznoseABpN7XHOgMCgWS0PmKbY6DQLQPU8qXGR3pJJ\nzHn6AKCvx9EzD0Onw17TrHnPRHoo4bNXeCu9/0CiFJ0B8OSzBIDfkwfAWjT7Lb+jAPQXqys1GXoy\nz+yAxQIgb9kNAJPadQJzvaUAorVhX71nn3YPz03nAUwO2gBgwsMCiHZnplnYDYDqWQKAgKcdAAp2\nn2koAKDPngbgHTw2PouZoXYA/rmMvaLoJjvmAa/lKAD0ebTh274On88eAIglk4kC6pAjMZ3mei4C\n89FSwtGmAuDsWnPcYUyY7DYAKHgCAGDw5DcVTeGDn/1FNWrqs9+uv1yS8yAc0kU+DW3kaW4gAGSk\nI2iLWUCQV3J37YDQ/fzq559f1Qb6fqHWLHhnAgA6BSGXBzxZD0AwGLc3+KbUCk21HxrWAXfpQgd+\ncq0WhX7t8Vqf3RvsBMDpgHm5VQXYiOZCbwN0yEg2kP5QGgCRfByQL+5hAXvsneaSLEUOVqbzKIfa\nQeDJGqp00BMzaDRV7X0TWW1+X1SJZbgN389/+j8179Rff/F//qu/X+EFHA95AKNyBASwWmeAmaJV\nBwILb68N89XgTgbQ6kwTgmg2VVNTE0IP4AgZQGCr+qb6AIKi3FcJoEeLyamC2SRG32rMdbvzDQ5B\n3W0mi5JEsf32V7wXk7Y57k4j/bnme9C11DNiyS3ufVPi333rs7/48MMP/+IvvvnbtXadEkwV/9vA\nLLyTADhT5GkgIz2FaKgTIEOi9a5vTNfyo6tXr37++dWrn//8O9U3v6amGKSDYmIGsE0dYgHSz6c1\nNcUAgDrVBSANtZjjAESFtrujzJ98er0yp++Det9+4q3qfOG07M4DLdJBAhSm+jlgwskfAeziIMAN\nSb4WgO3N6YCALNsApP3VLmWIVOf05aYuArb+gWhlQWzOEVMB1ekEMK+Rr0vZDYAbFaTExt9lj/y/\n/uYv/+q/fPFf/stf/g//uXYTg6N6ALsFoR0wxrcBsIi9eWDe7JwHvL18oGGYz5jSDJYEB5PgBwqJ\nmAfw9pv1IPArh4HHRA+QnxuYAwF6Yn0AwU4+BaQPxOwAa3LPgGBJ3hQ0RUjjH62H1mZ73UTOpOlP\nfWoYaZwg1vAT3VRCd1PG1UiSprmb5KYirciO1MvUeJJZcUlSLVhDlkyzEl4121XujtTy1zXk03hd\nsvlWSHvk29/62Y2f/cOjj9cP+WLbtArxDCRgcxXtg6ZIJwgcsacwX5Qy7wy7iorh7gPAmJ/+/OOP\nP/74F3/eUCuFZI4AQHfMOgMGdqk4Pdov2gFMDiQqld8f2mV09qLdeYgDg1mp427L8eqnV65c+fST\nRxqOeaSq75MV5BmAjZl0YGAc6H/HEc8FS8DTrfFjdvPUMB8A4TIDpcPb4IkL3W0OwVptwv6ImJST\nSbnXOyu5hzqTVuEAh0nnoY7BA5E5APCFHUZzJT7BEg/69pfMYubYgO8O3sOf/Nd/9R/+w//yP/2/\n66+PfDEs+0oJMf4EgFRcPmx0Op3yJBANTyW7p4cjmdojJDAIYqujsyi1BoC9ost42FWUEyzyxf8j\nD2B3qBO4OBXs7DCbW90qgLlsHwBMiHKP0VlMDnuh75VnQLAUz2xONXX7wef1ILDHk84X5gbattRi\nhw8FXvnbV5oU4NxwxQdsMyVY75D1taS7hwPBUDENHOl3ujOqfzhwb679Z3+20NT+VIdm+/SZSywA\nYjG7XFYPQJDO9VSahs3U6u4fxczOYwSAof8erILFPL6iF0zkak6Fg6Y0wHUZASDf6UoWLXmfHYCh\nX3Y6+vzWERAUEm5nB3RHrU6X2VFb+yKQ67darcPFDIsht/uQpeVYJ4HXbnUnnZ0cAEz6Ws256kDl\nkZ1utzsxC86RubM5k5/85580fR/pLrbK1RWkPK0ul882YQ4AlsT2TlfS1d04q4Xsft7slJ2ZEQBo\nc7vNHl0pw8E752DBwJbwAIgWW1uNrCcxA4JRp2b1eMyt5mmd0aEHW/LlAeww2bcmTa2vDIZ+KdY6\nEM7kKW1sNqgXm8zcGbauF+9/4+NGVhkcnck/sE6Qrg+CkEDDGhIzWqffMbKGD6Rlpv7GVo/UNI/a\neCf6mXs8H47MGGpF9E4WgJrXSw3ctKojlz5SLcxaC2DP6G569EQlXxVFbAp4/aPdbX6OssIDB2Ea\nP928TNL9XYJ8zbXQyU2NltzH0pAVPoVVnRmr1ghpuhnCEMLc/Ftys2ejwb1xT+9htVoi6zZ6yIom\nUSO1m3/J3NfGQbcTpbgXZvtXePEHWSDmDs8ym+sx3rpayeZrCJuFpggB9UtRUFBQNUXx8Im9Taf2\nVnQvHdkUhSXNptoW6/eUpiiokXmvzdKGXvVA/a3kYTFQKE1RbGXYup97kJe3dN962HEpk8g0LNxn\nmAs8oILm7SbfUG3gj/MYvVvqMVOaothU4sTetaHdVXLZ4Qe4gTTbq6yy/bWh11EpU0oWEmJ9BiQx\nZ01f5c53g13VaPq8Q7QekrZXBxr9UmRwSzULSlMUmwled2zPRuIbu+O+B6ALOM+4RlMmt3qzFTg0\nENyuTYEYDqYxU6ifc4iZr3AbWa4Yy1UqZyJrh66vVlMjrcLslmoWG6IpsqotTsialnr1FCEcoeN4\nFLfHdiEppLGyQa0+WE4AsCl15bSle+XdWqNZEwD6YghAbcZtM1pycsioaRi3qzmL/N78VygTDIIr\nPqJdYTDrbyqIbfs9rqzNpab0tXtjaZeiuPfoMXduaE/NB2ENEOhzPAGpLVrZXCCbs+TKeQEGM24X\ns/aY2n0nCLu7s7J6FRnMppiGpRe41QuzeSlrY8viPT+cGNQBwIhjOHG06k2oreFMule0sAlHOwAY\nxjOZzM4U7YQUt2MAZ/8Rl69qAAKBMQ7QGY5U52ik/Tu0D/PA5JEm69C2pG2FR2Ym79oMVAGM+Gtb\n6/X5q4sRAV6/CiDHa8JpjzMPdXvzOmKDkd3dSgAMQMyamuJUwHaxkRtIoLJT3szk/ZsgVuw6Yq6s\n4GbR1u5SObBLhcY0+aVK4b2F9CYWVhuhKb9gLSXCRgCBpLvUK0RBgJGEpFQTWERPIyOzPUI2AAJM\ni7lhq3Wa9kKK2+BIqMT0x/MAkDd7DNak0luwWGXZ5AWAvCMpJ30FgCQcmFaSI0tFa5eRAECqKMvJ\nUh6FObPT5XS031UpUu6LhkRyqrKV6O5iUpY7WRBYkn19Vt7IOrrkYNFq3g92jztwzKXIOxuIkTUF\nW1LZUYDt7poSikXzLFfshlFwp1NFa5embixmWU4OsfOd5laX01e4P3Vp4x3wSWkAKatTbLWaxy0u\n/0wiPs51dg0XDQDg7XTJ8gspkGi/2+k0ex4GmiJuqw444CqAJNztQMKcBvG4Mhl3JcG8vKvJNB62\nDgk2ADCaaYQ5xXowrcyiFI4CQL6ouEseh+QsdmxLSKMAvL2uNv+QWEwDB9zHZF8PMRzLxHwEBPt4\nucPjk/bDEjIdHhqOzN2FMUgwKxTlY542wTkJgqNCcb/HMeAgQErJvGB1BFqMu1z8+EcOP9g9gmy1\nP9GwPR3BvPMAVNGnB2t3yML4nCNAeosZ2deB3ePPZ+cAAo/o2u/pDc9OhBI9Q4mI4/7EVj2heGCP\neEAwdqxfSsw5Rg3KrmGzw4+Oua7YdgCcTzIN2mXnDhSTx4a6hamxh4Cm1JIBgEXwwyB6QJAamEB7\n7wQ6NGVLuHGl0fmJjBF9YgAMg8ww7YAU62iMJKe0IyU4QADOoRgAtcucBwzKOAjs8SgAT9YOeCRB\nC0fiBnYSYMYs7wAQTcPbDmCmqNzVDvJLsmsJQEnwA/qiax4gmUgKSLslh+aTrRh9rE8cIsBFebz+\n46PZQbAJoQCgpTUJAOiIyNqCKmqsBwx2aBtVzuZ1aQDeXv5+hFQQ+MQ+jE1pixw+kfUDyOciCc2S\n7YiNgWBQ7AQwEwUKHACPZNysI/8bLtagMIahZABAXtR2kjJWhjP8IctKr1wgHgAIujpXrnpJQbEK\ndshWEFWWnwbA+ZwqMN/VTwCDfAxoMbtYADNxkx7RbGUvMu+AgwATDfsWAcDO2F2N8IwJnSBAjzAG\nGGJGgGBJ8QF5p4vVxheHeQAErMnpBaluqq11qFLEwHDGrAWVkT4CDGobPBL0ZUsgeHagaVWmUva+\nRH61u90c8ma+HQAGsxMgaOkX8wDAwRgbA4MEH2g0cwoD45vV6tnQPn0ACskii5JrHgRe9zjA6DDk\n1B5Zb5FTDc1Dt4G4DQCsuzqKuTa6TAvFbdqXRRwEcFCYAAPO55oH2s05Am3HEpvszre3z0yac3nM\nVnd29UYcBLBno7W1WnUGi6cYZu+mGGNKJ1ChqXe03WdVdxLIO1sr/qeqC72yoYpSpym2dZgDDFI3\ngHzFhT6YrSw9XMj2AKRU2WMGADliGeyPtN+PuozGOwBk4nsBwJM9ClIrNodnYmNAuzPZUuOA9tS2\nw5FucFufpgCkXMIE0OlUAXid3QBhMKQ9Cw8fwGyOa8o7IP4LEMw4hcThXrGbdkSKW8MhJc2t5inR\nCAKdzznfpKbG5GCr0+l0xs1pzGaf1HqUd8DBgHRnqxv2ktlDsqAIovfuaKq7SlPMM9knwQB5s5KG\n6mzVqLDa39k92k4F8ke1juQPC61ms5N3eYG8OYmqlgEApGMlgHXEbJUAgJR1SlEUaf5+1GV3JGlu\nNcuhORDAk53QaKqyVJcxNgYcmXJWE6c75SlFjjwUagpkKF60ASi5VBB4ndpuN0NOAFCFTmC62AKw\n22ZnLbMFoGL0wdvZRoBO4SLthxS3QrpL6DKbW7vCw3mA9bnmgXlzTlelKSW5bXBwcNAT1ddp6ukB\nBwPdeHZC62CMR7Ju6+MOSncVk1BRU4eFJZCe7JMggNeszONpZ6Vfs9YGmmrens7It3a1ms2yGG2m\nKQIwSGdLAOvL/gstbVQ85Bkh4zH1PriEnh7mu8xms1k0qyDwZI+CAdtfVVPG2BhgUKo05c2JndvZ\nI5Hxh8E3xRjD3SxQVVN5ZzcAoqkp4ujKA6VhFkgHnclkchaEwBa3NRj8T9DxPopb2imi9uJzJi8C\n3AFXO9D+Qq6qpmxT7lraqEYeFd8U6akafV7N4f185K6MPn/V6FsC7DEPGKDd6QZmqkaffjioOTq0\n8E7D1Fy1I+nMrh0AYI8cXkVNFWIlgMvExgCAcL1TNgDjMfU+1KVf1Da26RL8IHU1haqaeg4YSbo4\njbUs8U4ARx4ONeUPdWo3OSSPgOBpyV5XU4X41M7nHUXZZ+R0Izv6JrWotYD4L2pXuRj00K5IcSuU\nshaAIegZmAZ0Fd9Uf9U3xRUFW3X61QrflCV7uKLHBjI6AjgidzfSp1Rc6EvARLaHgOCokNFoigEA\nfS6o+aYqakqp7aIZkPr1DCEwyK0s8uYkmApNVXxTJQD7s/s1LSb59CDojN0PF3pHdpAhYNARMdZK\nwPaLFZp6JuYHw5mDF7VaHA/7CdAX6X4YaMrnrAQcHI0/AYK9IW1/xCEngPQxh8l34JDL19n4HguI\ndTVl0cJBKSjWQD4RLwAA9g44WLCamqr4puZAYJeebwEQKADRrKUy0hdxEGBH0tUHYNqWn7J6gd1O\nsXBXRp9cM/qgtrrSAOYGDA1qSjP6VHAVF3p9e7pBUdsCx1sUDQ0u9IpNmo6VQHBEMasA947BbVaB\nJXPkPqzu0uILaaN4/kiC1XxTDLiKmmJgjBkAGMOdAEbssIfbAH1nbWfBrUxTnGKq8IzO3c8BiaIK\ngKDDCQA6HQjpybFV0QkAsPG26kevs5VOA6S4FSayJq2JqIpgQ0tCSQPtziIBlvgMAM4hFe32RLgD\nmM1W5jt4swcJgP2S/JjdKo2icyDRlpGt/F1sxESwPT4HAJ0hPwiiIbd9m0MaJ8CMLIMAYJCJHLAP\nm1ldvzwDpmF7OtIqVib1zg2UMOMSAABtVZoqZOcAMD2Sa2johdAT9oHc9Jxs5XvufV0eifVrs3D0\nLtEAjGb3gYAtShXfVE/WD2CmS0oMHhOc6qQc7NxffM3pTG99mrJJYv9wbjg3BqTivaV+IQqCGV9O\nDlur6+t0mxtH+oxW84C5dze8jl0fzSVlA+2IFLdCNPlk5YXY45wAV9qjAqpjnAC2/g4A8BqdSae5\nlAYmkkcrY26tQwDADZqTyeHBFsw4nK3DBrX3rhwMgf79AODR9iNMFZNJ8xAHwGsyVVKkHc7W4jOE\n6zTlAexIdNQEoSlfobriTnh9icqNVfZfVZMeAGixu13J/m3Qzzmdxd35+7G53YSrGhg/5IxqJSBg\nx60VIvY4RwBghyPpcmUCQMrsbO30WvofAppSO8czmUxmfATAblPOlAIA7zM7x7sdxsr8ycHuRsW0\nbed4d2Y8gHz3cH9/5yTthxR3C2JIrxEnzNkqDcyr4h6v30FGAjdfNH9XDiX9SJ/2V8WDXYe4vbpl\nXzuHe7cB4wP1TTU3i9UO6taocZ2OBqFTrMPcavrUsLgIWdlayS1aMLlXBSHrTbnaelirLsNG7lUJ\n11+XDFntwl/xBoxfJU0RhhCi1T5Tuy2GEGaNJe8YjiNEtwUqgWIrUtm9Z6cN5Uzu131Q3EM1RUFB\nQUFpioKCgoLSFAUFBaUpCgoKCkpTFBQUlKYoKCgoKE1RUFBQUJqioKCgNEVBQUFBaYqCgoLSFAUF\nBQWlKQoKCgpKUxQUFJSmKCgoKChNUVBQUFCaoqCgoDRFQUFBQWmKgoKC0hQFBQUFpSkKCgoKSlMU\nFBSUpigoKCgoTVFQUFCaoqCgoKA0RUFBQUFpioKCgtIUBQUFBaUpCgoKCkpTFBQUlKYoKCgoKE1R\nUFBQmqKgoKCgNEVBQUFBaYqCgoLSFAUFBQWlKQoKCkpTFBQUFJSmKCgoKChNUVBQUJqioKCgoDRF\nQUFBcV9pitz0YY3vFBQUFA9cTalc/bM3T+uagoJiU9DU7s6j2gfdtNnltnu1L4aEy+UL0NqmoKB4\n4DSl746LhwEA3E7RmXBJuziAYK8QzFnDriO0uikoKB4sTZEjLyQTwmMAgGgkw0JNRFIA8l1TAcAT\n9umow56CguKB0tSSy1QwCEYAgFUOgMORoAMMPOJhEKBXvkjrm4KC4oHSVMHfgom3ngGASekgAcHM\ncJADeV4aAYC22JN0uI+CguKB0hQA4g8eBoBtAyUwAOeLHEH6BZ4DgGisk9Y3BQXFA6YpggmNpvbz\nFhAQlAbGMOlMAADmY3OamCJERwhHCEd0HOEIIYTodERHdIQQjhDCcUSn4wjRaSlud17H6bTDhHA6\nrvk70ekI4YiOcITTfnG7/G5Of+v877b8d1u++31/97/+Huz1H3z9b+32r/0htfy2hJqCP2gEgO74\nsyDgUIoZYFBMAIB0rFtLYzty8ciRi4anjjTBEKgde6p+aJ3nm483fb/YfOy2+a2a/hb5323577Z8\n9/v+vpL6e4DX3xT1v3XbvyFguFjPzxDIbwGaYlAx+rrjs2DAoGdgDAbBBICBmq3Q1NL27f4GTDR9\n2H7Tidud19CUZf37RHOqdeR3U/rb5H+35b/b8t3v+7vv9feAr//A63+rt//a1wm/3+9Xt4TRV1FT\nxreiAAi6B5ZgkzU11Reb01L12UZGJidHKv+MjEyOTE5Oan+0/ya1E5PVZOs4P6KlqOU3Mlk5PTk5\nOTmiXWxyZB3XWyP9mvnfbfnvtnz3+/6+ovp7YNffJPW/ddt/JbeRytW8W8noG5SGoAPIwYgBO8wu\nAgCpqprSCK32P6P9A5DKEaIdqqa73XnteDXDxu/aX+0TqR25dX43p791/ndb/rst3/2+v/tffw/2\n+g++/rd4+2cajt+3uMh7b/QZAWBsYCcDAjXHs2D7wyoADMY8NCCBgoLiAdMURyaCzxAC5KeKMwwH\nm+AA0BmOgiHwyQG6bgwFBcUDN/oCgh0AYBwYBdATi4IgIBRZwCYm9LS+KSgoHixNPW3qL4rO/p3z\ngGoWO9/ZJfk4ADBK/fbHkoKBVjcFBcUDpqmCLCiuJN+1AwTpHC8IO7UoCjLEC0HzBK1tCgqKTWD0\n1UFGJuoxFDPb6SouFBRbFw928Iv6tCkoKNbFUQ+Oqpj7f3MUFBRbGwun3l0AgJNUTVFQUGxGfP/y\nhcW3Cb7/5eXXL1w+QWmKgoJisympc+XyL0+AnCuXl8vL5fKFU5SmKCgoNhPI8csXFs8QcrlcxeK7\nX7k/h9IUBQXFLWgKWPj+SZwp13FhgaopCgqKTWTzvfvuAoBzdZZaLB+naoqCguLBSadmvHThV4vn\nf3XhBHCpvFgjqsuUpigoKDYHVy2cKS+Xy+VyefnXCzhe56nFFylNUVBQPBi0nHpxoYGuvlyuEtPy\n+41O9PJXPdhHaYqCggIAcOrM+4uLl8+9W6Wplxbrdl75OC7V1dSPKU1RUFA8AFvv3PnlcrlcXl6s\neMgXLi83uM0XTy78qvr58gKlKQqK3zTkb7XTQUH9CkpAcKasOZ8Wy+VzAAjebXCal8vnX8Ll8mJ5\nuby8uPyVR6JTmqKg+Krgn2ABIL93svl42i2PrNkV1UhtcW4SMCYOdIzcl83wGkfyym8DwNvLjTS1\nfBwvLgCnLl0oX/jKp/ZRmqKg+KrQm5wECAzahgF1GMRYAOD8u1cb6E9nn6x2U0vI1W+Vs9uBlgn/\nynSG1N3Q18KFBgtv+QIBcGm5gaXK5xZAFt49SYATL33lFUdpioLiq8Ke19JggEBy/4oT+2Y5QE36\nVjPGCtknK2rKJvQXdHrbUAGYDHavSKY3D7N3UbRmC2/x3RVG369eAk68f7n83vvnHsQqCZSmKCi+\nKiReSwMEF2X7amdn/j8ZrKqmqkafJRutHuxzrhBkYPt9dx50SfBSk4VXPg6CU/Ujyy/hxUo8wi8v\nXKI0RUHxMNNUAUCVplhgxqACINDpQJA276zsjscFqs4r1e9trxl909l94MAQgIy4eqo76XEcAYD8\n8B62QmdsYEeFuo5Mrpe6TjTS1GL5HAjI8fcWl8vl8uLi8hksvFcd8SuXL9GpxxQUD7HRVwBTpam5\nxIwjOcV7CEZ9vp0qDieSrx3w7eoDecItTxX9ANiSosi+2v6WqQGfNuin70xMvfCHvmMzsDmSgtC6\nD7DvcTkP+BwBgHic8pTVAGCbLCvOvesr2tvNRt+JyiVfPHG5XC6/fpKc+WX5/PnF8i/L5XL5V191\n2BSlKQqKr9roOyLbQZARi46j21udOzDm2SOnkbK7ch5PlEU01G3Y3To1Ajwm9RgsiZinskkx64gl\n/ABBy+yQnNg2mOIQbe2Y9bQqAWx/x/3CoGc2D2wLP2M4mnSlMSaOG/bu9K/P6Hu3yeY7fxJYePHt\nl04tAMcXl9/HqS/PXHrxxZfOnFlcXlwsn6M0RUHxG0FT9uAgAIuwBMAuzwN68zEAYM0mHTASMkIV\nMgDUmm8KameE7y4AQHvVN+UF4M9GAfTv0Q64HAAM4f2Yc86s4KJbEdX7jVbfCeCl998rl89fPk7w\nbvkMTgIgCwDefn+5TMM7KSgeZpoqgABHZDuAx+QZECYV94MwRnkeSJt3EkIwFm8DgOJwSzRuAUfq\nI30A8ecG3H4wqPumAKSzUSCfS7CEAKm4BYDOndANBXdvoIN/f7Eef3AOC+cqkZ7ly6fw7ovAyXPv\nX3j9/S9fxMLx5UU69ZiC4qHFntcKAIOLsh0MjHIBwF7eD4IhZR5QzTsBwCL0Z3ZlHLzZu03ZDqYx\nbgoAZ+enJoER12Hte94yZ26NPQvoc3tYABgUEjszGUd8WK+6ZKO2USZmSh99NPfRRx99dOxY97E1\n4hZOaLy0WC4vLuDccs1XdWEBwEvvLS+Xl5fL548DZ5bp1GMKioeapmpqyiinQbCX94PB0NQ8oJoz\nAIO2oDk3PJyzHmOnKzTlqdtrBLDHHgMmXT0AgEKRPzhUij0L6If3cAAwFO8aHh7OWTv1mH9+6i17\nCwDk7Y89ZjQaHzMajcfE+TVK99IFbdmW10/hpYZFW8rHQc5pQ3yL5fLyObx4fvEkpSkKiofY6Kv5\npoxyGkAq7gc0mmo379LUVKqS3KOkKuGdXJWjQDDiShBMVtSUT5gACtkooM9pNOWZWqpf0DYeKa3w\nSRWS6bWKt3D8zOXLZ44vYOFCgzv9lydw6vzyL2vfX8Il6puioHhocUBTUwa5A0wDTZGqmtoFABfF\nDDhCAET5beCgZkebuumIvLOmpog4B65GUy0A4JdKlZ8DBOh36wGAEBAQgCAgrzWTmQBYIACw0DCV\n773jC1h4+9Lx2mpTvyKn3v6qK47SFAXFV4UOKQoApcgEAGMyDSAV9IPBkNwOqO5jAHRIKCMA1Hbo\nFR+A0WzFDYXoEQAoxZ4AJpOPaTTl0wH22H7AazUB4ECsrgIAVYUXAPYcYpupyCavqaZANL2Gd+ve\n9DNV+27hRNUKPEW+8o2CKU1RUHxVaDcLnXa7T+oGCHqCOwBEpd0AeuJpwLsnODSUtMCQlDvtncIQ\nYBQdbb0uuag5wtmiWDR2DEsOPaAWZfthxYBj0q79Obcrp0dLRuyxO+3A2FSyZO+Mj8JX3G/pjg+t\nKINtVTXVTDxv1xxTLwE//unLP/jBT78DnNJMweUHsKEopSkKiq8MaYe5y1zcTwBg1qcCMCQCYGAx\n5UFQMJm7HDbgSMJstpYKAOlxm022o72V8bqLc11mc9HIAkCg19yVKUAdN5vn0qPjHKA63F0mAwB/\nwtxl7Ukjmugym/evpKMReVUXOjl56tRC9cvJqvf8EshPT589ffbs6bP/8sfMjzW/+hlKUxQUDzW4\n/Ixu7bP5ylos3ryu+mFFgnxttZZ8ZQJf3ajz6qunvNo5vffm4AOb3L5Krz/x69fL5ffPVQINFirj\nfF8S8vLZ01X8GCeqsZ+UpigoHl6Qhk5Hbvp3tQ9kDdOMqX1tcBWRWqcmq1pzYGBbRU2d/PWyFm3w\nXmUpqePLi+Vy+Vcv4rt1ljr9MofL2iIvlKYoKCiYW/PcBmnxJjW1kqUuLy9W1hde1FZpWXi9XF5c\n/jV+fLoBZ3+PXCovls+TzVQfFBQUDxt0DTRVnW1DzjVM56vMgzn1+nJ5+Ti+e/r02dOnz54+ffrs\n2dNnf4pT5cXySw+g2JSmKCh+k3Czmnq3cW2E5X/UDi6cWSyfwMunT589e/oHp8+ePXv29Okf4OTi\n8vHNpS4pKCgePoxUaWrh1KVL7y4AuPTLhrURtGXQAeDdEyfx8tkfvPzTUye/8/ZPXz579uzZhYX3\nT5AHUWhKUxQUvzkgGKmEd564/F65fP7CceDUr5qWmjrV4MY/8XItuvO7//I73yELP34wxd4gTaU7\nhmZn6MOm+M3t5ltfTc2DYOFyuVxeXCwvly+8iOONK3cuvlshBQLghwQ/Pv6PFyo7IT+4m2c29owc\notOlmDMdBi9tsRQPPx46Y0NXMfrqGxovv0fwfsNWV7968ZVHGrr88cXapJmW3/uebks8B5I7Nh+Y\nHR92C5KBtmGK3zwxNRG9TYpoarOrqan2FRs0HMe75+u+qV+Rx1+p65I6gS2ewr88e/rthc1PUwzm\nOgAA8xftBdpmKR5izB/OOHY6Ole8jbmk6TY8NnBwUxuGDAKvqTj5euNOob86SX7VuLlxw66kxxuS\n/eMCefn02Zd/uAVUbSERoC2Y4uFXTbDJwp6ESRSa1RNb3HWbX4bHNzNNMQzYPh1e/FUjTS1ewuv1\ngb5Gu+7FRp/V4iX83tnTZ19+ELe3QZpqGXJ60rQVUzz0mHQeIIQY+CKr0ZbWObkXHADAkDW4iCHh\nzGZ2aDEaCTEvNqx7t7h8HGeqawovNi0ffK4pourXuh+fPX367Hc2O00RdAtOOWie22bLE9qSKR5u\nmgIA31SgNnUOAGd23E5NZTazmnrk4o/+5Bc//9FF4NJiI02dOl8uL1748tKpBbQ0eKa+bNoK+cIC\nTp8+ffYHm36yDMnNtY9Z5nJJIU6NP4qH2eibdPoAoFNZAmBrM+5+GhpNEQDt/n1HbQRAfrsOEx3T\n2h7F7IRxMB3auYlp6ns/v3r16tWrVz//0Q9xonHvUJw8fuZdVJcxrmLhcvOO7afw8tnTp39w8qsv\n+AYFqmMIAMikwThJ2zLFQ62mfAAwnJyHbiiYPBRKtANoecEBYJsQV+LhaR2QilgcclFy7QCQ94XN\nyaSUaVBfm4qwCL73i6tXP//8888//vzqL1rqG/Mtnnrk3zEATh0/9/6Z4w3rH5DGQIVy+fxJ8oPT\np0+ffgBW3waNvkJxB41cp/hNUFOuAwD8oT3ArGjMeyckn65KUxM9fe2TCbEPMCjJnoLewttBcDhs\n8c7MRjav0Ud+fvXjzyu4+vOqH32xfHnh3/3Db4McP18ul5eXz5/T1RaXOdOkpt4nC2e3Ak0B6cOv\n/UGetmKKh5+m3M65bp/gHgE33KoD0K08BbAvOKok5M8GgBHFCMBrPgbMOE0AIO0EA0woRbPZ3GU2\nWzbRTf351TpNff7xT3W/Xl4sl8vL53+MR7/1Cvm1tkXfYnn5wslqJbzUOB5YPo7vnD59+vQPFjY5\nTTHoTDoVpb8zWmBpU6Z4qI0+t+B2OjOTwKRrHASYkEYrLnQCEHXEk30KsL3lAUHePQ6MTXWAQCdm\nwAGFd7Zt8wx6PNaOzaOtdD+/+vHVz+tyijm3vFguL59/G//umx/geIN99+vab/6xNo1msXzhJPnp\n2dOnz/7gAZR9g2pKTae373dYk3HZRlsyxcNMU849efWPAIIxxQ4AYzFjLSBhr+kFXhkwALagRlMZ\nYEIZBACpKSDhmH3z0JT+F3WS+vzzq7/ApfLy8uKXPwYe/y1yqtEJVV9SauFX1UCFcvkSTp09e/r0\n2VMPoOx35GfKFyY6R2hLpniYjT7NhQ5gu0ZT/qwRaDnkAJht4VzU1pY1ADbBAyDvPgakFA8AXSUg\nQfOfk4x989zT9z7+vImmfrhw/NyJdyt+/hNNzvILtR+dqm7P9/olkJfPnj599uUHUfY7oCmGhkxR\n/AaoqSpNBeSPAGAiMgqwZgdBXk6wFd+ULTgIaGpqTOmADgiPN0422blp1BSDH9b9559//vnVXzRN\nI36/SU2dr59YOPF+uVy+cPwkIT89ffr02Z/qHkzpN/SS2buXOqUofpPUFMGM2wwAHcIYwBV3AmrW\nAw5HNZrS1NQ4kE7uAoDwwcZ8dm0aNcUwLT9vMvp+DtKw20NTfFR1peGKKlxYWFgAfvzy2dM/+MF3\nCDb9ZBkCY1wpznls+RZ2HYmbt8mgGoxiK6kpt6PadIekbUBa7mcB1nxQ35LnfTNcIBdpA2yKB8CM\neQ4EPiFAZnoGTI1dY+cmMvrwowYX+tWrf9507kKzmlpoOskxAMiPv/OdHz6oom90Tl/Aksm5JPcB\n18TteYdrZCwKiq2kpmx8rvoln5AcO4PFERCQObF4SO0YMOeUTGtCh0BkP4AZwQcg4OITcpdDNDS0\n952byIWOH/6iIW7qFy1N55pm75Xfa66MzANftekOfFNsesyXtPfdJpW+7aBp57YKU6kdjuenabwV\nxdaBN+qvfZ4ZzWTs2oz7mbaM3dti6Sw9h4kUoLdMAuCiBgAI9IyPqt5pdbOqKXy3Fjh19eM/az71\nUtMk43PNHV6ZeNBku2GaIhwArmf/bZKppgFnLinNsQBg6xrg4wP9dGkFit8w7LRvpjkbzE9/cfXq\n1c+vfn71F99d2a1/3RBw/v4Km8/l32JqqhpFn3b6b02wPZKdEDYTmwXAJUQLl5+LdFPrj2LLQEfW\n6Cmk0dVa6RCk4UwTTe3fXG1e//NffPzxx7/40Q9v6vmnzpcXy4vl5fdOfH9hxY+4pH+LqalSYmgi\n7QUwPHTrZyzn9AD65HEwiIoZAKTLSWOtKH7j1NQmA/nhd0m13zd1/u9fWF5eLp9ZABZOnVxo5CU2\nubXUFMFE4tBU3JzZb5emb5mSiydYEBScOwGS0RZO7xiIUjlFQWlqU+CRDx5v/Pr434Kce//yJeDS\nr18/9+7CSjX1gE3XDV9+pi9qTCTl/tu40H1iCsB01gOoRZEFgGdjPbTdUvym0dSmfDP/9t997Zvf\n+tq360z1+D/8FrCwgBfPLJcvAQvvnrh0qlryluT2LaWmqsm5fP52cVMG2fUk2yYmWGCHcxgA0Bcb\np+2W4jcKuzalmmIevfHvP/uLDz/77B/+tnrola999ne/9QEW3v/l6y+CfPmr8+Xy+QuXqmpqq9EU\nOG6db4fJBK8E53TQ4ciUFs+rZru1680X0ul0Oq39ezMKt/x3q6cvbM7yFxqOFFZ837TnGxLcRf4b\nq8/CbWq1IWGh0P6Hh9XCLa9VSLeAXPryvfPv/foSoJsv3O7Z3W172NEO5mt/ceNnN27cuHHjww9/\nGy2FdLrA4tufffizD3B8efGlhu2w3ieYKaRHkn+gFtYuT9NDuE9Lp2yQpnbncplnR2ZuXxbOY5YP\nBQ/tBoFB2AOAIB2bqygt/5h/bGxs+5jf7/dvHxvzN/71V/40pql9Htv66asJNk95mo9vHxtr+r65\nz2+/+/xr9bN9ffXpX1/9+/1LY2NjiWNHtq+d/5jfP5bHyTOVab+XX4R3u9/v3+33b9++ffuYf2zl\ns7sH7WHMi29/duPGjRsf3rhx48Zn//AB+vz+7TYwf/d/fQunzpe/xIuv1wOozoA7MrZbfsfg375a\n/v6x7WNj/u3VG/T7/WPqJqAp0q84emXRmSjN3pqpdN0DJsOMRZY8wEVZU1PpWLd28uLYkgbDksFQ\n+1j/a1iqH19aMizVP9P09yG9YZXjjd8353mDYax6b3eZ/5Kh0g4N66pPQ9Px26T3fRQYM9wi/6U0\nTtY2o1o+fxLzS0uGI4HCpO3ikfvRHgxjI3j8Zx/e0LTUjQ9v/PtHwfoNhqU0PvjW3+HScnmBNC7Z\nufw2VINfbru4av6GJcPS2NKRpbHqczAsGfKbgaZ+dwj69onpXYdiE7dMuFc6wAIYSypp7NCmbsIf\nO1YZ4NSzev0f6fV6vd7L6vV6vVfPsnq9nvWyLKvX6/Us62VZlvX+kV6v97Isyz5c6dnNVJ6Vx1m9\nvvn7gzqvr53X3+L33ubzrL56b5Xk3lr++mpd6BvTe/X6Wk2trz7Z9dX/H+n1+j/S73qsxetl2TXz\n1xPyfsPKc5cBvV7PAQBpKueq5dEqZ/3tQe9lWT2L3/rww4qW+vDGjQ//4RV4WZb1Ao//LY4vX8aP\nVyzpomPVZIr11vJcM3+tFOTB0xTBdGfFpkvfWk11B/eCA4fumAf5YdELANuy0zQggeI3a6Rv/+1S\nNM9S0daje+TVD165f2V69C9qJHXjxo0PP2jo3+//8hwuLS+Wy+XFCnmePwlwybEt5kLXObati2mO\nyQaGgEPPwCCwU/KDEIwLY5SmKO4A5D6mvu8jfbcpzpmmOb9nAHzw6bVr165deZW5L/fK4GtV19SH\nN27cuPHvP2jggOO/fAnnNIJaXtbWRv8+0JLcrq0xx6nb0w8mgmqDFx05Fj7gGbn9XqLvSCUA4ExS\nANjO+wDkBSudfEyxQejUwm1bzZgHAAKe/Lo6sNdzsfpRHey7z8W//eqd55vn/BLyyfXr165du3bt\n+qc/uU9qquKaquCDhip76Zfv4lx5efHy+1+eef/y4rJGU/rkGKAvTAyZgtmhB/MW2JjRR0zK+LAi\nmh1D0VsbfapbNAYmj2SkTgA6h3TYfzQRnqViimJjSJmSSvHW64gwMAsAVDk7Wm9fHZk1J2YZpMFq\nc3Zkc/fb6LudmiJN69G9voBPNJK6du3a9U/vT5l+q2LtfXjjxoc3PvyHRvPyxfPncGL5zLsEABbe\nPrNcPv9jAMk/GDP6XLGs0JvxbwGaAnJ25Of9HQnzwG2m+dgSoSlnMNTjBYAZX0wIi9OUpCg2hqNB\nZ4c9I/lu1UwJrE4A3oRYX26k3bXWa5+BTdlW/TItdd9vo2//7VK817Bm5i/fxtM1lrp27fon96VM\nH/ysQU599u2mqjzxPjl5AvjhS+eOv7QAXCqfJ9zSkOAUYgPFbotNfVANYYMbYBl3avfTYvPeJi07\n4ilZCtUv/ultKu11FBsD1+qcBzBfAACGNK9KUO9bxSQAeOeZ2hGLUsxxN5kC2rlA0FPJgSHaQBAh\n983fcvs5fU07dp4inzbQ1LVr98eR/lufVQMSbnz2Dx80nzu3ACx8eX6xXF48/yXBqRNICWI8Oe6x\neckdyJoHpKZ0pqiOdh+KrwY7+P712BgaTTW26YxzKHmk0rZXZhAQPF/dHdx+Tt+lBpvv8gKuNeHV\nNX508t3j5040zw/eCP7usw9v3Lhx42d/+tnPfvtmcXqqFsj1+osAe2zPoLwbq1blpqWpfQ4xEy1Q\nVzjFV4FA3FqTUPmlaMrQAgCsZR6Ad18BAAkMDU0WncCMbWRSX/3dzKFOg9QGAqDPD3XUQqD696UM\nHMAgELfMDBqP6sGNjIy0A9AHoin/fdL6tw9IwPt1OXWGPN4kpq6tbvWRE+XlsrYR6J3hlW//7P/6\n7LMPP/vsa397E0lh4UKTr2xGBbbYelMEHYmMOS4UHdNLdIcZivsNXU40Vmy3MWdICA7M5UHgz/rB\nYbuUArieUKtT5s3AbK74WiXehcAQieqmTDoA6HQZzJEENyGLQjDS4wXBRTnnlF0Rh659z7DTCKQT\nIUEYsO64L3fgMN62h508X+GpxV+ew6tNNLW6c+rkmUqswHL53J2W62+//Xff+trf/dYqRiVpipA4\nB2y99aYAAt3M5N6SKTlgoMN2FPddTjnDJm2Yr6/bpqZLkWfBYSL7FDjsEyaAaPhwPm+QW4GnC23x\n2jt/SErD4Z4HAVKK7BtRYescUdOZyAQ4BGSnRVVLob2ksJTsBPSllDrvETP35Qa27b59N3mxSgxf\nniSPNBt9f79a+nO/rMmv5Ut33OuZV15ZpftPcgtNcVzlBQAtyYmtQ1N5Y92mJ3rD07QTUdx39Pkk\noeStdnU2Ow0O/uxT4JDit4MzuXQArEkASAnVWGn9cL8elqwFYLAk+2o8oWYt4BAIegDsSDoAr7Oz\nei5pveev3XXnR146897i62cuEQBXmtTU46ukfrtc97kvLi7cAwbQCqrb4ckEj+HdZpp6F0DL1jH6\nCHaHzIDF3pdvob2H4itDqij1qwBAvJPPZafBYSJrAEEqvhvzZh9AmKILYBDlq6ZJ30AHkA53A8DY\n1CB0DADy9OTR7JPgEAhaQDDjdAJqhabYwohyPyKoTp566e0XN0Ykf1+3+q5cWy1wivy6iUZO3GX3\n1+hH3x7tdEqSwntW7NJePrHVjD7iMQDjoaDc2/nsJHWiU3xF8B6LZTgCg6OoCAPTNaMvFZ9AwfkR\nwKDoAoAoX52L5YmZhnq6ZXMeBH5ZC+Y0OIqCEvPU1JRGU65OELSXhpOC2H/PBcPCiffK5eXyhRMb\nyZj59HpNUF1ZLQx94fUmGvnHuyk2AYD8ZPRwgs9GnP3PpHYQnGhWU5cAcPJWc6E7DhimM7lgsJjo\nox2I4iuB3uWchF9xPmnwV4w+A3RICUexw3UMICg6ASAaX6p0poQ8XCz2myN+EIwpowAwEW+1PJWK\nPVmjqXyVppAeDnX4R6aG73VI0KnLy+XF8uLi8vL7Jzfws59UI6euXPlgVfJrppELd8cfttlSIjmQ\nFRI90REtEnKF0Xdqaxl9AAgYWPYDYFV/h/Up6kKn+CpAcEwJoN+Z1nxTLZjIGsAh9dZuzJsdRFNT\nRFNTDACoosnLeluikhGo0lTRrDb6pghUpwmYcXUC9tBRAMnhe9yeyYXqAN5i+f0N6alPrl2/du3a\n9f/v6lP6Tq6gqTsvYTplzLnEmJQrHdVCjAgI0NKcP6nQ1JYx+jT2VStbrlOOoviqeKrobgfvA4Ga\nrRp9QIqfgD5hBgCrzAGIxse0hmkJtQFA2mzWAX55FAARx0HQl7WAg02wADAoHRU15RABQL7XaupM\nebFcLi9rCw0c39BPH3n1k09eXWve8cLlJhr5cqPLR2gxG0/vHsoJvCg6Oy19Kzr08XocV6XgLVts\nZxmCTmsBpLp7IgXFfW2a28YNeW7GHusGDrWOcPO7pIyOwUSso4WdTIhRYDoS1Xk9oqsAYK+wpLVm\nh2wAAXSOcB+wlBwEAGdXQZf2RToJ4BcdM5zXEQ4AamsJ6BT3krxdNN/bAM+Try8uLi+ev3D5wvny\ncvn1k/cu53PlxtnKb2+YojjVbzfxA2LS7LDYyM2aY6EqA8uLy5pJ2SJvNTU12e/sA4CRIbo1KMX9\nhkfJtvYmBxwqEA3JCdmXSKpAPiflrHGH4gPmE1LCmZwTPSCwxLaDAGiPtHoBELRlS8DusB0AnpCS\nvfLzudY80Mmbk72vhYwA2vldgM0pJpyHMvF76X8heKm8XD7z0osATl06U15+995l/uJ7i3fkmiIA\nyPzS9E53LCZYd3ps7JpOtXJ5sby4WF6+/CKw5XxTAADvruQEDBklFqW9iOJ+m3uTnu7u0j4vAEz0\ndEZ1tic4AIWOuc4oLCkA6tCxjhGMjgBIWzQ9pFoq676ozxoAdXYEAMhET2cKTz1LgAn/vH2uMwoA\nLdEjAEY65jx57+C9HLwmuPT+8ZeAhXcvnSLAS+dP3MPMT6wIa1ov2v32g0UxGymOewzzt3LcLBw/\nv1xeXn7vxIL2feupKQAd7kRSGZrkaC+ioLg1W51ZLJ9//cuTOPXiPcx04cRidYuHl9b9I4M90apI\nA65xjy2vuxVHAQB+/O6lS6dOVhO1bL1dj1XPsBwv0gl9FF9NR691KNLQWJs9KqRyova9KZmuKS/t\nD2k6ROpZ3KIYZGFhYf2Wz8LCu2+fXMDC8cVyubx+NlknTr13frG8eP79hXXUHnTsiMcxxYu8kmgL\ncKs4o1b/WQO22kgfgSUu+/oumjNeOtRH8TCCY1btEQsnvnyv/N6vL62v1ZOXLpTL5fLiCYJ3z5cX\nlxffvdfdZeH7l25j72lknbd5jjljEaE10bH76TuVJlxy+xbzTT3baQMwabYWaIumeNjwygeffPrJ\n368yke7UZW0KyfLl9YysLZyrzgy+fAovLZYXyxcW7m1ByXrOqwFLZy6SjRw62LE9fTdX23pxU9CC\nwFTzFB3po3jI8OqV69evX79+7ZOVneLU+eXFxXK5XL7w/nqWJDjzy/qI/gJ5/y5WMlibCU+dujWz\nXNw2nksOZAXf0ESaA+4qhmgLjvRVwProSB/FQwXmk+tXrly7duXKtesrJtORC1qo0uvrWS+T4FK5\nIT7yDN49X14sn7mnRX378uvnf3Xhy7WCsXZYMmZZlKRe4/Z2L1CbYLwOCUZWp6kttJALAIIea2k0\noCcA6EgfxcOlpWprE1xZsafLieramsAHn3zyyau3bvoLlxvjLxffxYXlxfLlWxLcI48/shEl9WW5\nXC4vl8uLlxpopTIop6aMVjEUF8zGvfn1GYgPo5oyTLndwaRvaLSHTj2meKjE1JU1FyG/vFwul5eP\nA69euX7t2vVrVz64Va851bBXTHlx+TguL5fL760dhv6T/3zl+vUrn7663p64cLm21MovK+FYDAcA\n3kLKmAjFQi7r3OwduGROvXvpxNunyBo0tZXUFDDUO5M+Yk9EBPM+2rIpHiJ8cL1x66lPm22+8uLy\nZeCT65VFVq5/wqxtcLy93ERT53C8XF5eezXgV69cv3bt2rXr1z9d5zYyxxuXxat6qEhhd4/JFcvK\nic7ZkTsJFyLHz5eXy8vlX7/4ENAUKdoBQLc9sZvO66N4mPBJE01dabDCTp3XCOGT61euVNJc//u1\n+81LjTtalZfP4ZImxW5naq6Tpxaatkm+DADp3c8kzPGsNNwd3ZG/Mztv4XK5XF4sL5bLvzq+Ck1N\nbDGjb08GAEcw0Uu33aN4mNC4Qd6Va1caohIWLiwvls+QV5qswrU3Tn93cYXR92WD6lmJx680eMTW\ntXvo200LJCyixVNMCiGptdvSl9eiVe9k8P7LqkRbXKWsWy+8c2/cqPFTcZq2bIqHSU01ktCVK680\nNPr3lxfLJ/D316/cdmuqmjCp4yVcKP/y+Fpq5NOmHRoeX0c5z61Yto5LiK07nxhh7kh11BRgI7Fe\nXi0KfYsFJFjkqVJqMu1XOmjLpnhYaarRN0Vwqby4fKmZUa58unZOxxcb9NT7ZOH88pm1OvkjjZle\nuf7qOsp5uXmt8peY3WN3PehOLi83Lw5DbjL6tpKaAgBbTzEi8QNOGt5J8TDh8WtriiXyfvlXLzbv\n+XLlyi38PA37cZZfxLlbLI/wkyu335ZvBZrXKl88uarRs0H8sHF99cVfrixvi7wVwzvztokJP3VN\nUTxkcqphT5crzXFMJ19ffJf82yb77BY0hXfPN2zMQt6+Rbz4Bytoah298ftNq+It4l4ERr202MR9\nvyY3G31bTU1RUDyUYK5cu3blypVrV1aGTQFY+McTTT72a9c+vVVWC2e0fv/+qdvIm1eu1B1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a4DAfT5IJBUtxMgV6VbgHPJczChZ6RUx03oD5Lpakbz/LV9UZtoP09tm9oMguziRAJHcR6ARLsO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\n2HsE1BXLQtzsxab6vX7hCJS1E0t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        }
      },
      "id": "128adb99-5be3-4820-94ad-623dffe49553"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# filter our data for a subset of African and South American Countires\n",
        "casestudy2_data <- filter(pwt_data, year == 2014, country == \"Ethiopia\" | country == \"South Africa\" | country == \"Kenya\" | country == \"Rwanda\" | country == \"Botswana\" | country == \"Nigeria\" | country == \"Mali\" | country == \"Peru\" | country == \"Brazil\" | country == \"Chile\" | country == \"Paraguay\" | country == \"Colombia\" | country == \"Argentina\") %>%\n",
        "\n",
        "# assign each observation to a corresponding \"region\", since we are only given data at the country-level\n",
        "mutate(region = c(\"South America\",\"South America\",\"Africa\",\"South America\",\"South America\",\"Africa\",\"Africa\",\"Africa\",\"Africa\",\"South America\",\"South America\",\"Africa\",\"Africa\")) %>%\n",
        "    select(country, region, hc, rgdpe, pop)"
      ],
      "id": "026b959f-288e-405d-ad9e-3b36efe4afb1"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Trace through the code below to see where we apply what we learned from\n",
        "Part 1!"
      ],
      "id": "c35a2e51-3a33-4d72-980a-b055b0b6703c"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "labeled_scatter <- ggplot(casestudy2_data, aes(x = hc, y = rgdpe/pop, fill = region, label = country)) +\n",
        "    geom_point(color = \"grey\", size = 10, shape = 21, stroke = 1) + # shape 21 allows a stroke to be made visible, the default = 19\n",
        "    theme_hc() + # use a clean, white background\n",
        "    geom_text(size = 7, nudge_y = 10, nudge_x = 0.09) + # adjusting our text\n",
        "    labs(x = \"Human Capital Index\", y = \"GDP per Capita\", fill = \"\", title = \"Human Capital and GDP per Capita in Africa and South America\",\n",
        "        subtitle = \"2014\") +\n",
        "    theme(text = element_text(size = 18), legend.position = \"top\") + # place our legend at the top to maximize space\n",
        "    geom_smooth(method = lm, se = FALSE, colour = \"black\", size = 0.5) + # compute trendlines for the data\n",
        "    scale_fill_brewer(palette = \"Set2\")  # select our colour palette\n",
        "    \n",
        "labeled_scatter "
      ],
      "id": "4381a5df-5776-4ed6-8781-7a30c449b512"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Case Study 2: Confidence Bands\n",
        "\n",
        "Now, let’s see if we can make our own visualization that uses a\n",
        "confidence band!\n",
        "\n",
        "![2010 Line Graph with Probabilistic Population Projects for\n",
        "China](attachment:media/un_population_graphs.png)\n",
        "\n",
        "[Source: Eviews User\n",
        "Forum](https://forums.eviews.com/viewtopic.php?t=17766)"
      ],
      "attachments": {
        "media/un_population_graphs.png": {
          "image/png": 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2/fp1bNmyBe7u7vjwww9x//33m1y+adOmWLp0ab3zr169io0bN+Knn35CXl4e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\nAElFTkSuQmCC\n"
        }
      },
      "id": "448cfb95-7f37-4a67-b0b3-99d7efac5968"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "confidence_data <- pwt_data %>%\n",
        "    filter(country == \"Canada\") %>%\n",
        "    mutate(adjusted_gdp = rgdpe/pop)"
      ],
      "id": "46433171-6184-4a87-9dff-6b815ba61e97"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Trace through the code below to see where we apply what we learned from\n",
        "Part 1!"
      ],
      "id": "94080186-6b76-4a89-8804-4b424d4f83cc"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "confidence_plot <- ggplot(confidence_data, aes(x = hc, y = adjusted_gdp)) +\n",
        "    geom_point(colour = \"red\", size = 3) + # set our data points to be a distinct colour from our confidence interval\n",
        "    labs(x = \"Human Capital\", y = \"GDP per Capita\", fill = \"\", title = \"Human Capital and GDP Per Capita in Canada\",\n",
        "        subtitle = \"1950-2019\", caption = \"95% confidence interval\") +\n",
        "    geom_smooth(method = lm, se = TRUE, colour = \"black\", size = 0.99, fill = \"black\") + # compute trendlines for the data\n",
        "    theme_minimal() + # select a theme with a light grid background\n",
        "    theme(text = element_text(size = 20), plot.caption = element_text(color = \"grey\")) # create a plot caption describing the confidence band d\n",
        "     \n",
        "confidence_plot"
      ],
      "id": "6dd30e2d-998d-4ae1-8520-f07b9372eaa6"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Exercise 2\n",
        "\n",
        "Use the `pwt_data` dataframe to create a graph which looks similar to\n",
        "the one below. Store your object in answer_3.\n",
        "\n",
        "(**Hint**: all relevant styling options are identical to those used in\n",
        "the Real GDP per capita comparison across G7 countries we worked through\n",
        "earlier, except `options()`, which specifies a plot width and height of\n",
        "10 each).\n",
        "\n",
        "![Answer3](attachment:media/answer3.PNG)"
      ],
      "attachments": {
        "media/answer3.PNG": {
          "image/png": 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FyDbVhRG6H/KwId7MCH+yPCwIELBCgcly8gL4PZJAiajwnRR4tMsCC2EEhe8tCyPpv4UA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\nRiUYkSRJkiSpRiUYkSRJkiSpBmX2/wGITBjnaFOZ4QAAAABJRU5ErkJggg==\n"
        }
      },
      "id": "fa477864-ad7a-48a8-86b4-a0dca0ecbf15"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# your code here\n",
        "\n",
        "answer_3 <-\n",
        "\n",
        "test_3()"
      ],
      "id": "d52242f6-c545-4b8e-9749-28d413b7eb51"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We’ve covered a lot of content in this notebook. For further reading or\n",
        "exploration, we recommend visiting the hyperlinks attached throughout\n",
        "this module, as all of them are valuable resources for deepening your\n",
        "understanding of visualization in R!"
      ],
      "id": "e8e3a035-7c6c-4329-97aa-94d33b403ce6"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "name": "ir",
      "display_name": "R",
      "language": "r"
    }
  }
}