{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc837c37-995b-48a5-9a45-89c480d00290",
   "metadata": {},
   "source": [
    "# Introduction to Plotting with Seaborn  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9ca5a6-8be5-49ab-855b-ef8cb3c86acc",
   "metadata": {},
   "source": [
    "## Learning Objectives\n",
    "- Be familiar with the use-cases for Seaborn plots\n",
    "- Generate a variety of plots in Seaborn as part of Exploratory Data Analysis (EDA)\n",
    "- Understand the advantages and disadvantages of different types of plots in Seaborn\n",
    "- Recognize and appropriately use strategies for visualizing continuous and categorical data\n",
    "- Visualize and reason about the distributions of data\n",
    "\n",
    "---\n",
    "\n",
    "## Introduction\n",
    "\n",
    "   \n",
    "***Hi Hyosub,***\n",
    "\n",
    "***Just wanted to say thank you for the recommendation to start using seaborn plotting in python. Wow. That's it.***\n",
    "\n",
    "***-Jonathan***   \n",
    "   \n",
    "\n",
    "\n",
    "This message was copied verbatim from an email sent to me by a former PhD mentee, and it kind of says it all. [Seaborn](https://seaborn.pydata.org) is a great Python plotting package that is built on top of Matplotlib. That is, it uses Matplotlib \"under the hood\", but it offers the user a much simpler API (\"Application Programming Interface\"; aka, a set of commands) that enable us to generate a variety of great-looking plots that are particularly useful in data science. You can check out Seaborn's [examples gallery](https://seaborn.pydata.org/examples/index.html) to see some of the cool stuff you can do. Seaborn was written by [Michael Waskom](https://mwaskom.github.io/).\n",
    "\n",
    "Comparing Seaborn to Matplotlib, you could say that Matplotlib gives you every sharp, hard-to-use tool in the shed, and you have to figure out how to learn them all without chopping off a finger. Fortunately in data science you aren't likely to lose any fingers, but Seaborn gives you some DIY-friendly power tools to get data science jobs done easily and looking great. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff8e231-13cd-41db-a73e-da71a1e14955",
   "metadata": {},
   "source": [
    "## Overview of Seaborn Plotting Functions\n",
    "\n",
    "Seaborn is organized into three categories of plot types, as shown below:\n",
    "\n",
    "<img src=https://seaborn.pydata.org/_images/function_overview_8_0.png  alt='Seaborn overview' width=500>\n",
    "\n",
    "*Image source: [seaborn.pydata.org](seaborn.pydata.org)*\n",
    "\n",
    "You can see that Seaborn's functions are organized around the different types of data, and questions we typically ask of data in data science: the `relplot` function plots relationships between different (continuous) variables; the `displot` function plots distributions of data (such as histograms), and the`catplot` function.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba416fff-1919-44b4-be01-616ec4e41933",
   "metadata": {},
   "source": [
    "## Importing the Seaborn package\n",
    "\n",
    "By convention, the alias we use when importing Seaborn is `sns`.\n",
    "\n",
    "We'll also be using some other packages, so we'll import those packages at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "165a9b76-d95a-4438-8f0a-b352258a9353",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0478be1e-48cf-4e62-b95f-ae6b6778c553",
   "metadata": {},
   "source": [
    "## Plotting Gapminder data\n",
    "\n",
    "Mostly we'll work with long-format data from here on, but since we have worked with the Gapminder data up until now, let's see how we can generate a plot similar to what we produced in the [Introduction to Plotting with Matplotlib](./plotting) lesson.\n",
    "\n",
    "First we'll load the Oceania GDP data, and strip the leading text off column names so that they are just the years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5794c60-43c3-4c4f-8253-078788717312",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')\n",
    "df.columns = df.columns.str.strip('gdpPercap_')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1d202f-1c34-4b3a-94c0-e06b8ebdeb47",
   "metadata": {},
   "source": [
    "In the previous lesson, we plotted GDP for each country as a function of year. That is, year is on the *x* axis, GDP is on the *y* axis, and there are separate lines for each country. As discussed previously, in this dataset, year is a continuous variable, as is GDP, but country is a categorical variable. \n",
    "\n",
    "From the figure of Seaborn plot types above, you can see that `relplot()` is the function to use for relational plots — that is, when you want to show the relationships between two continuous variables (year and GDP in this case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1f5b9cd-3962-4369-85f7-f19a306aff0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 461.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=df.T, kind='line')  # For wide-format data we need to transpose rows & columns\n",
    "plt.show() # Always good practice to have this as the last line in a plotting cell"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a635c793-5ccd-4ee4-b68b-6714114d839c",
   "metadata": {},
   "source": [
    "Compared to plotting the data in Matplotlib (below), the results are similar, but Seaborn adds some aesthetic touches by default, such as reducing the number of irrelevant frame lines, putting the legend outside the plot, and making the line for the second condition dashed (which aids in discriminating lines, especially if there are several of them, or if someone has vision problems). As we'll see, Seaborn isn't just another pretty face — it also makes a wide variety of plots easy to make with a consistent and relatively intuitive syntax."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ca5e30c-ddda-4e34-8922-145ce1cb0e21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib version of the same plot\n",
    "df.T.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe9f873-9036-40c1-8701-ff16aadc9691",
   "metadata": {},
   "source": [
    "### Plotting Long-Format Data\n",
    "\n",
    "Let's move to working with some data of the sort we might find in an experiment exploring the psychology of motor control. Imagine an experiment in which each trial involves presenting a string of letters in the center of the screen that is flanked on the left and right by two equal-sized circular targets. Participants are instructed to make a reach to one of the two targets as quickly as possible depending on whether the string is a real word or not. (You've counter-balanced the blocks such that the word and non-word targets show up an equal amount of times on the left and right.) You may be running this experiment because you have some theory regarding sensorimotor confidence as it relates to higher-level properties of a visual stimulus. For instance, people may reach faster and more accurately when they are more certain of their judgments, as in the case of familiar words. The dependent variable in the experiment is reaction time (RT) — the amount of time that passed between the onset of the visual target and the start of the reaching movement.\n",
    "\n",
    "While this is a made-up example, this type of **lexical decision task** is frequently used in psycholinguistics to explore porperties of real words, such as tehir length or how frequently they occur in normal usage. More broadly, these types of experiments in which sensorimotor confidence is assessed by manipulating the visual stimulus and measuring some aspect of kinematics or kinetics is common in the psychological and neuroscientific study of motor control (for [example](https://www.jneurosci.org/content/32/7/2276.short)). Here, we'll assume that people respond with a faster RT when an actual word appears.\n",
    "\n",
    "We've created some data, using a random number generator, to use in our plotting examples. The data were deliberately created with certain properties, so that the expected difference between RTs is reliable. We have data from 5 hypothetical participants, stored in separate data files in the `data` folder. The naming format for the files is `ldt_sXX_data.csv`, where `XX` stands for the subject ID number (two digits).\n",
    "\n",
    "First we'll load the data from the files, practicing our [list comprehension](../3/looping-data-files), and storing the result in a pandas DataFrame called `df`. We need to include the `ignore_index=True` kwarg when concatenating the data, because otherwise there will be multiple copies of the same index in the merged DataFrame (since each data file that is read will start at index 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "879f68a6-612d-4798-a2c6-f2852b4b0e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([pd.read_csv(f) for f in glob.glob('data/rl_s??_data.csv')],\n",
    "              ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce63ff8-55be-4e60-b849-f8a25ca0312f",
   "metadata": {},
   "source": [
    "Let's look at a sample of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c064fbe9-f943-4d0e-ae50-85b7bf526ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>condition</th>\n",
       "      <th>RT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>ldt_s05</td>\n",
       "      <td>word</td>\n",
       "      <td>0.594286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>ldt_s05</td>\n",
       "      <td>word</td>\n",
       "      <td>0.647573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>ldt_s02</td>\n",
       "      <td>nonword</td>\n",
       "      <td>0.596347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ldt_s04</td>\n",
       "      <td>word</td>\n",
       "      <td>0.797847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ldt_s03</td>\n",
       "      <td>word</td>\n",
       "      <td>0.608214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>ldt_s03</td>\n",
       "      <td>word</td>\n",
       "      <td>0.747991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>ldt_s02</td>\n",
       "      <td>word</td>\n",
       "      <td>0.610677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>ldt_s05</td>\n",
       "      <td>nonword</td>\n",
       "      <td>0.815493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ldt_s01</td>\n",
       "      <td>word</td>\n",
       "      <td>0.497075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>ldt_s03</td>\n",
       "      <td>nonword</td>\n",
       "      <td>0.936968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID condition        RT\n",
       "212  ldt_s05      word  0.594286\n",
       "218  ldt_s05      word  0.647573\n",
       "132  ldt_s02   nonword  0.596347\n",
       "150  ldt_s04      word  0.797847\n",
       "66   ldt_s03      word  0.608214\n",
       "67   ldt_s03      word  0.747991\n",
       "107  ldt_s02      word  0.610677\n",
       "225  ldt_s05   nonword  0.815493\n",
       "3    ldt_s01      word  0.497075\n",
       "96   ldt_s03   nonword  0.936968"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6243e29c-8270-4d93-909d-ce14cccd2b95",
   "metadata": {},
   "source": [
    "We can see that there are three columns:\n",
    "- `ID` is the unique participant identifier\n",
    "- `condition` is the type of word (real or gibberish)\n",
    "- `RT` is reaction time, measured in seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ce2c97-6e26-4213-a1ba-f06e596f6e16",
   "metadata": {},
   "source": [
    "## Descriptive statistics\n",
    "\n",
    "We can use the pandas `.describe()` method to examine some basic properties of our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1c25c54-1f49-4f35-ba64-b9ba2790b874",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.710869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.159536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.239836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.597494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.708552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.818883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.194955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               RT\n",
       "count  250.000000\n",
       "mean     0.710869\n",
       "std      0.159536\n",
       "min      0.239836\n",
       "25%      0.597494\n",
       "50%      0.708552\n",
       "75%      0.818883\n",
       "max      1.194955"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de5ed586-168a-4728-8693-0ea7614fb0ab",
   "metadata": {},
   "source": [
    "Since `RT` is the only continuous variable in this DataFrame, we only get descriptive statistics for this variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f28f75e-2ad6-461d-830f-0219ed2994cc",
   "metadata": {},
   "source": [
    "## Examining distributions with histograms\n",
    "\n",
    "**Histograms** are a type of plot that allows us to view distributions of values. The *x* axis of a histogram represents the values in the data, and the *y* axis shows the count of how many trials had that value on the *x* axis. Values on the *x* axis are grouped into *bins* — ranges of values. \n",
    "\n",
    "In Seaborn, we use the `displot()` function to plot distributions. Histograms are one type of `displot`, but they are the default, so we can just run the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cb8d3877-4f5f-40c1-abe6-00733fff9612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='RT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705a68e8-382e-481d-a09a-d06c3e29695a",
   "metadata": {},
   "source": [
    "Comparing the range of values on the *x* axis with the `min` and `max` values from `df.describe()`, we see that the entire range of values is represented. The range of RT values have been binned into 11 categories, in order to draw the 11 bars in the histogram. We can see that the most common values of RT are between approximately 0.5 – 0.6 s, which corresponds to the median (`50%`) value shown by `pd.describe()`. We can also see that the data are slightly *skewed* — there is a wider range of values to the right than the left of the peak. Visualizing distributions is thus a useful way of examining your data, and you can likely glean more information from this plot, more quickly and easily, than from the descriptive statistics table above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc63205-1ddc-4fda-a7e8-04fcdd08174c",
   "metadata": {},
   "source": [
    "### Tweaking Seaborn plots\n",
    "We can adjust the parameters of the histogram with a few kwargs, including adjusting *technical* aspects of how the data are treated to create the plot (like the number of bins), and *aesthetic* aspects of how it is represented visually (like the colour)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3474fde1-a5ee-419d-b0c2-80feaec52911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='RT',\n",
    "           bins=25,\n",
    "           color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c2badc-b7c9-4df1-a060-1e7352c81fd0",
   "metadata": {},
   "source": [
    "In the above plot we increased the number of bins from the default to 25. You can see that the histogram looks less  smooth than it did with fewer bins — increasing the number of bins decreases the number of data points in the bins, which in turn results in greater random variability. \n",
    "\n",
    "By default (when you don't specify a number of bins), the software makes a decision based on characteristics of the data, which usually results in sensible bin sizes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8503a3f-cf12-4e0e-8d54-910184d7a27a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### What to plot on the histogram *y* axis\n",
    "\n",
    "By default, on the *y* axis Seaborn plots the count of the number of data points in each bin. This isn't always the best measure. It's fine if you just want to look at how values are distributed over the variable on the *x* axis, but if you want to compare histograms, the scale can be deceiving if the total number of data points in different plots is different. For this reason, we can *scale* the data in different ways. \n",
    "\n",
    "Particularly useful is to **normalize** the data. For example, you can use `stat='probability'` to make the sum of every histogram bar heights equal to 1. This means that the *y* axis now reflects the proportion (percentage) of the data in each bin.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a9936bc0-e9e5-4a78-8ffc-2c10c1c6dc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='RT',\n",
    "           stat='probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b715253-9180-468d-b750-dc761fa61a4c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Kernel density estimate\n",
    "\n",
    "An alternative form of ditribution plot in Seaborn is the *kernel density estimate* (`kde`; often more simply called a \"density plot\"). This is like a histogram, but plots smooth curves over the range of values rather than bins as bars. This is another kind of normalization, but rather than making the heights of the bars sum to 1, the density estimate makes the area under the smoothed curve equal to 1.\n",
    "\n",
    "Note however that while histograms represent, literally, the number of data points in each bin, the KDEs are *estimates* of the true distribution, and are effectively smoothed and thus slightly less accurate representations of the data you have. On the other hand, KDEs are *models* that predict what the true distirbution would be, if more data was collected. \n",
    "\n",
    "In practice, the difference between histograms and KDEs is relatively small in terms of how one might interpret what they show, but this distinction between literal presentations of data and estimates meant to generalize beyond the data is an important concept that cuts through all of data science.\n",
    "\n",
    "To override the default kind of plot generated by `displot()` (a histogram), we add a `kind` kwarg: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2e4ca4a0-4c89-4bef-a5ab-800f53e5dbce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(kind='kde',\n",
    "           data=df, x='RT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ab9475-e624-4fec-8c67-5d60a679d08c",
   "metadata": {},
   "source": [
    "Density plots arguably make more sense for continuous variables like reaction time, since they represent continuous variables in a more continuous fashion. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58023d67-c205-42f9-9d39-1b3f75bb1ff8",
   "metadata": {},
   "source": [
    "## Viewing the data by condition\n",
    "\n",
    "As noted, the experiment had essentially two conditions, words and non-words. So far we've looked at the data overall, combining RTs from those two conditions. But it's easy to look at the data broken down by condition. \n",
    "\n",
    "Firstly, we can use pandas' `.groupby()` method to get descriptive statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "051e6a0b-84b0-43ff-83ae-ba5cfd419757",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">RT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>condition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nonword</th>\n",
       "      <td>125.0</td>\n",
       "      <td>0.797717</td>\n",
       "      <td>0.132815</td>\n",
       "      <td>0.340474</td>\n",
       "      <td>0.716140</td>\n",
       "      <td>0.799589</td>\n",
       "      <td>0.879407</td>\n",
       "      <td>1.133205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>word</th>\n",
       "      <td>125.0</td>\n",
       "      <td>0.611654</td>\n",
       "      <td>0.133936</td>\n",
       "      <td>0.242320</td>\n",
       "      <td>0.530431</td>\n",
       "      <td>0.612513</td>\n",
       "      <td>0.696094</td>\n",
       "      <td>1.042468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              RT                                                              \\\n",
       "           count      mean       std       min       25%       50%       75%   \n",
       "condition                                                                      \n",
       "nonword    125.0  0.797717  0.132815  0.340474  0.716140  0.799589  0.879407   \n",
       "word       125.0  0.611654  0.133936  0.242320  0.530431  0.612513  0.696094   \n",
       "\n",
       "                     \n",
       "                max  \n",
       "condition            \n",
       "nonword    1.133205  \n",
       "word       1.042468  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('condition').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782393e2-e530-442d-9108-cde60859992d",
   "metadata": {},
   "source": [
    "We can add a `hue` kwarg to `sns.displot()` to separate and colour-code the conditions in a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8761a89f-c61b-43f0-b374-a3e0b5648fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='RT', hue='condition')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3558261f-c3d7-4fde-bf90-539e46fe101a",
   "metadata": {},
   "source": [
    "The grey area in the plot represents overlap.\n",
    "\n",
    "You can see that Seaborn does lots of nice things to the plot automatically, that make it easy to interpret: there are meaningful labels on both axes, and a legend telling us what each colour represents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85f2aca9-1d97-474f-a78d-26e68064d05d",
   "metadata": {},
   "source": [
    "## Colour Choice\n",
    "\n",
    "Seaborn has a default *palette*, or set of colours, that it uses in sequence. You can see this with:\n",
    "    sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce23288e-5066-4a14-be93-6f1d20c0705e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"550\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f77b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f0e;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#2ca02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#d62728;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#9467bd;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#8c564b;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e377c2;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#7f7f7f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#bcbd22;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#17becf;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48791225-0380-4af6-95a6-cdea50649d1e",
   "metadata": {},
   "source": [
    "So the first category plotted will be blue, the next orange, then green, etc..\n",
    "\n",
    "Seaborn provides a number of different colour palettes, although the default is a good choice as the hues are designed to be easily distinguished from each other, [even for people with different forms of colourblindness](https://gist.github.com/mwaskom/b35f6ebc2d4b340b4f64a4e28e778486).\n",
    "\n",
    "One alternative is the `Paired` palette (note the capital `P`), which is designed for when your data are naturally organized in pairs of categories (e.g., imagine if we had the conditions reward and non-reward, but we wanted to compare these in two different groups).\n",
    "\n",
    "    sns.color_palette('Paired')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "17feeede-0b4c-489a-adc3-887f3886a57a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<svg  width=\"660\" height=\"55\"><rect x=\"0\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#a6cee3;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"55\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#1f78b4;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"110\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b2df8a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"165\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#33a02c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"220\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fb9a99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"275\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#e31a1c;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"330\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#fdbf6f;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"385\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ff7f00;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"440\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#cab2d6;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"495\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#6a3d9a;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"550\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#ffff99;stroke-width:2;stroke:rgb(255,255,255)\"/><rect x=\"605\" y=\"0\" width=\"55\" height=\"55\" style=\"fill:#b15928;stroke-width:2;stroke:rgb(255,255,255)\"/></svg>"
      ],
      "text/plain": [
       "[(0.6509803921568628, 0.807843137254902, 0.8901960784313725),\n",
       " (0.12156862745098039, 0.47058823529411764, 0.7058823529411765),\n",
       " (0.6980392156862745, 0.8745098039215686, 0.5411764705882353),\n",
       " (0.2, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.984313725490196, 0.6039215686274509, 0.6),\n",
       " (0.8901960784313725, 0.10196078431372549, 0.10980392156862745),\n",
       " (0.9921568627450981, 0.7490196078431373, 0.43529411764705883),\n",
       " (1.0, 0.4980392156862745, 0.0),\n",
       " (0.792156862745098, 0.6980392156862745, 0.8392156862745098),\n",
       " (0.41568627450980394, 0.23921568627450981, 0.6039215686274509),\n",
       " (1.0, 1.0, 0.6),\n",
       " (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette('Paired')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcf37ff-ed63-41ee-b913-55f74ed67752",
   "metadata": {},
   "source": [
    "Let's re-plot our 2-condition histogram with the `Paired` palette:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9ed357e9-172e-479b-98bc-753a061c81f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 439.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=df, x='RT', hue='condition', palette='Paired')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b636f4-91d8-4af1-a479-51bce5dd297f",
   "metadata": {},
   "source": [
    "Some palettes are better for some things than others, though — for example, in the histogram above, the overlap is not very distinct from the other shades of blue.\n",
    "\n",
    "We can use the `hue` kwarg with other Seaborn plots as well, like `kde`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e83c14e8-7a86-4b3c-9d1b-4de47868c5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qBMkcqf0cuHqMx+uBS4wxy4GvA3cnMYuymb/uj65Pu2yu/Yra7NLodGhDqAJPxSqL04zMXTSXyFAn4aFOq6MoZStJK2rGmBeAUb/jjDEvG2PiC23WA9OTlUXZz4v1Hcwr81Jtg62xTlZblINgaIjU4C6aZ3WcEbmKoh2Q2tav1Insck/tw8AToz0oIh8TkY0isrGtrW0KY6lkiEQML9Z3ckGdfXbpGM7jclDt6afJNd8WO/OPxF0UXaumbf1KncjyoiYilxEtal8a7RpjzN3GmNXGmNXl5fZq/1aTt/toP52+IBfZtKgZY5juaKIxMs3qKKNyFc4GhGCX3ldTajhLi5qILAd+AtxojOmwMouaOuvqo/+rL5pdanGSkYX7GpjuaKLBb4+tu0bicGXjyq8l2KPTj0oNZ1lRE5Fa4A/Ae40xe63Koabei/WdVOZnMac01+ooI/K3bWGGq5VmnxN/KGx1nFG5iucS0pGaUidIZkv/fcArwAIRaRSRD4vInSJyZ+ySrwKlwA9FZLOIbExWFmUv6w50clFdie3Wp8UF2rYw3dmGAQ51DVodZ1Tuwjna1q/USVzJemJjzB3jPP4R4CPJen1lT4e7fBzuHuTzl8y2OsqoAm1bqCt2Qxcc6PAxvzzP6kgjchfPJeLvIjzUaaujcZSykuWNIiqzvFgfXeVxoU2bRAACbVupq4o2iTR023ikFmvr12YRpY7Toqam1Lr6TvKzXKyYZs8mjPBQN6HeembUzMPpEA7buKgdX6umbf1KxSVt+lGpkbxU38n5s4pxOmx6P619GwC5lSuYVuCgwcb31FwFswAh2FNvdRSlbENHamrK9AwG2dHax3kz7Tz1uAUAT/kKaotybD1Sc7iyceZNI9R70OooStmGFjU1ZV5r6MYYOG9WsdVRRhVo24IjtwKXt4ra4lxbFzUAV2EdIR2pKXWMFjU1ZdYf6kIE1s4osjrKqAIdO/CULgVgRlEODd2DRCLG4lSjcxfU6fSjUsNoUVNT5tVDXSyuyKcwx211lBEZEyHYuQtP6WIgurFxMGw42u+3ONnoXIV1hPuPEAkNWR1FKVvQoqamhDGG9Ye7OGemfaceQ30NmOAA7nhRK84BsPUUpLuwDjCEeg9ZHUUpW9CipqbEvvYBOn1Bzp1ZZHWUUQU7dgLgKVkEREdqAIft3AFZGF3ErvfVlIrSoqamxPpD0aPz7Nz5GOzcBYC7NFrUZhRFz3qz80jNVVgHQKhXi5pSoEVNTZFXDnVRkO1iUYU9t5wCCHTsxJlbdWzLqaIcN3lZTlsXNWduJeLK0WYRpWK0qKkp8eqhLs6pLcJh00XXAMGOXcfupwGICLVFObZegC0iuApmEeo5YHUUpWxBi5pKugF/iK3NvZxTa98mEWMMgc5deGJTj3G1sbZ+O4uuVTtodQylbEGLmkq6DQ3dRAyca+vOx8OYYD/uksUnfH5Gsb13FQFwF84m2FuPMfZdT6fUVNGippLu1cPdALYeqQU7ok0iI43UjvYHGAza+LDQwjpMoI/IkB4er5QWNZV0Gxu6mVOaS6nXY3WUUQU7o+38w++pQXRXEYBGG4/W3PEOSG0WUUqLmkq+1xq6WGPjrbEAAh27Tuh8jDu2Vs3GRS3e1h/UZhGltKip5GrpHaKhe4g1tUVWRxlTsGPnsfVpwx3bVcTGHZCuAh2pKRWnRU0l1YaGbgBbj9SOdz4uPuWxmsJsROw9UnO4c3HmVupaNaXQoqaSbENDNw6BlTY96Rog3NcQ63w8daSW5XJSlZ9FQ7e9NwzWI2iUitKippJqQ0M3S6sK8GbZ95D1QGe88/HUkRrE16r5pjLSpGlRUypKi5pKGmMMGxq6WW3jqUc4vpHxSCM1iBY1O99Tg2gHZKi/ARMOWh1FKUtpUVNJU9/po9MXZM0M+049QnSk5sytxJlTOuLj04uiC7DtvLjZVTAbTIRQ32GroyhlKS1qKmleiy26XmvjRdcQ63wcZZQG0Q7IwWCEDl9gClNNjkvXqikFaFFTSbShoZtsl4OlVflWRxnVWJ2Pcalwrpr72Fo1LWoqs2lRU0mzoaGblTWFuJ32/WcW7m/EBPpO2UlkuPhaNTt3QDrzpoHTo7v1q4xn3582KqWFwhFeb+yxfZNIYJwmEUiNXUVEHLgLZhLUw0JVhtOippJiZ2s/vmDY1ouu4fhp12NNP5Z5PWS7HLaefoRos4jeU1OZTouaSor4TiJrbb49VqBjJ47cilE7HyF2WGhxqpyrpkVNZTYtaiopXmvoojDbxdxSr9VRxhTs2IWnZPRRWtyMolQ4V62OiL+b8FCn1VGUskzSipqI/FREjorI9lEeFxH5rojsF5GtIrIqWVnU1NvY0M2aGUU4HGJ1lFHFOx9H2sj4ZDNSYAH28bb+g9YGUcpCyRyp/Ry4eozHrwHmxd4+BvwoiVnUFBoMhtna3Gf7JpFw/xFMoHdCI7Xaohya+4YIhCJTkOz0HCtq2iyiMljSipox5gVgrHmQG4F7TdR6oEhEqpOVR02dLU29hCPG9k0ixzofJzBSqy3OwRg40mPftn53ga5VU8rKe2o1QMOwjxtjnzuFiHxMRDaKyMa2trYpCadO36bGbgDOnm7v7bEm0vkYd7yt374bGzuyCnBkl2qziMpoVha1kW62jLi5njHmbmPMamPM6vLy8iTHUmdqU2MPZV4PM2KFwK6Odz6WjXvtzGL7r1WD6BSkjtRUJrOyqDUCM4Z9PB1osiiLSqDXG3s4e3ohIvZtEoHono+eMRZdDzc9BbbKgthu/VrUVAazsqg9Arwv1gV5LtBjjGm2MI9KgMFgmB2tfayy+dTj8c7H8aceAXLcTiryPByyeVFzFdYR6juEiYSsjqKUJZJ2cqOI3AdcCpSJSCPwNcANYIz5MfA4cC2wH/ABH0xWFjV1tsaaRM6eXmR1lDEd73yc2EgN4oeF2ruouQtnQyREqK8Rd+Esq+MoNeWSVtSMMXeM87gBPpms11fW2NTYA8DZNfYeqcVPu57oSA1gZnEuO1v7khUpIYa39WtRU5lIdxRRCbXpSDelue5jO9vbVfy068mM1GYU53Coy+6HheoCbJXZtKiphIo2iRTZvkkk0LkLR045ztyJd9PWFuXgC4bp9AWTmOzMuPKng8NFUI+gURlKi5pKmKFgmB0tfay0eZMITK7zMa42Bdr6xeHClT9TOyBVxtKiphJmW3MfoYix/f00YwzBjol3PsYdW6tm+w7IWVrUVMbSoqYS5vhOIkWW5hhPeKCJSKBnQjuJDBffVeRQl313FYHodll6WKjKVFrUVMJsOtJDcY6bWSV2bxKJdT5OcvqxzOshx+2w9fQjRDsgI4PtRPy9VkdRasppUVMJ83pjd0rsJBLfyHiyIzURia5Vs/n0ozvW1q+jNZWJtKiphPCHwmxv6WOVzaceAQKdO3HklE2q8zGuttj+h4W6CmcD6H01lZG0qKmE2NbcRzBsbL8zP0z8tOuRzCjKTYmtsgBCvQetDaKUBbSoqYRIleNmop2POyd0htpIaotyaOnz4w+FE5wscZzZxTiyinS3fpWRtKiphNjUGG0SqSvJtTrKmMIDzafV+RgXb+tv7LbvYaEQ29hYi5rKQFrUVEK83tjDqhRoEolvjzXZzse4+ALsVJiCDOmuIioDaVFTZywQirCtpZdVNl90Dcc3Mj7dkdrxE7DtXdSia9UOYkzE6ihKTSktauqMbW/pjTWJFFkdZVzBjnjnY8Vpff30omwgFXYVqYNwgHC/nrurMosWNXXGjh03Y/MmEYiO1Ca75+NwWS4n1QVZth+pxTsgtVlEZRotauqMbWrsoSjHzexSezeJHO98PL2px7jaohwO232rrHhbvxY1lWG0qKkz9npjN6tq7N8kEva1EPF3n9FIDWJFze4jtfxaEAch3VVEZRgtauqMBEIRtjb3sSoFph6PdT6e4UhtRnEOh21+WKg4PbjyZuj0o8o4WtTUGdnZ2kcgHEmNzsfTOO16JDOLcxkKRWjrDyQiVtLoWjWVibSoqTOyuSm6E/zKlChqO6KnXXsrz+h5UqWtX4uaykQTKmoi8qCIXCciWgTVCTY39eD1OJlb5rU6yriC7dvxlC454+dJhROwIdosEva1EAnau6lFqUSaaJH6EfAuYJ+I3CUiC5OYSaWQzUd6WF5dgNNh7yYRYyIEOnbiKVt6xs+VOidgxzc21tGayhwTKmrGmKeNMe8GVgEHgb+IyMsi8kERcSczoLIvYwybm3pZMa3A6ijjCvUexgT7cSdgpFac48brcdr+BGxXgbb1q8wz4elEESkFPgB8BHgD+G+iRe4vSUmmbO9g5yC9QyHOSoH7acGO7QAJmX48dlhoCkw/gi7AVpnFNZGLROQPwELgl8D1xpjm2EO/FZGNyQqn7G1zU3QnkbNSYKQWaN8BnP6ejyerLc6x/abGjpwyxJ1HqOeg1VGUmjITKmrAT4wxjw//hIhkGWP8xpjVScilUsDmpl4cAkur8q2OMq5Axw5c+TNxZCWmAM8qyWVjQ3dCnitZRAR3YR2hXt2tX2WOiU4//usIn3slkUFU6tnS1MOC8jxyPRP93cg6wY4duMvOfOoxbnZJLh2+ID2DwYQ9ZzK4Cut0+lFllDGLmohUicjZQI6IrBSRVbG3SwF7b/Snkm7zkd6UuJ9mwkECnbsTNvUIHDsMtb7T5s0ihbMJ9dTbevcTpRJpvF+xryLaHDId+M9hn+8DvpKkTCoFdAwEONw9yCdT4H5asHsfRIIJaRKJi2/efKDTZ+vC7i6sw4QGCftacHmrrY6jVNKNWdSMMb8AfiEitxpjHpyiTCoFbIntJGLnH+hxgY5ok4g7AWvU4maXRhebH+iw+0htDgCh7je1qKmMMN7043ti784Skc+f/Dbek4vI1SKyR0T2i8iXR3i8UEQeFZEtIrJDRD54mn8ONcXinY8rqlNgpNa+HcSJu3hBwp6zKMdNcY7b9tOP7uK5AAS791ucRKmpMd70Y3zvo7zJPrGIOIEfAFcCjcAGEXnEGLNz2GWfBHYaY64XkXJgj4j82hhj751iFVuaeplWkE1FfpbVUcYV6NiBu2guDld2Qp93dmku9R0DCX3ORHPl14LDTbBrn9VRlJoS400//k/sv//3NJ57LbDfGHMAQETuB24Ehhc1A+RL9CCuPKATCJ3Ga6kptrmpJyXWp0F0d35P+fKEP+/skly2NPcm/HkTSRwu3IWzdKSmTouI/Bz4kzHm9yLyE+A/jTE7ReQrxpj/N+y6l40x51sWdJiJbmj8LREpEBG3iDwjIu3DpiZHUwM0DPu4Mfa54b4PLAKagG3A3xpjIiO8/sdEZKOIbGxra5tIZJVEQ8Ewu1r7WVFj/6IWCfoIdb+JpzRx99Pi6kq9HOwcJByxd2ehq2geIS1q6gwZYz4ybKbtKyc9ZouCBhNfp/ZWY0wv8DaixWk+8IVxvmakHW5P/u6/CtgMTAPOAr4vIqf8pDTG3G2MWW2MWV1eXj7ByCpZdrb2EYoYzppm/yaRYOcuwOBJ4Bq1uLqSHALhCE29Qwl/7kRyF80l2P2mtvVnABF5n4hsjfUp/FJEZsYGIltj/62NXfdzEflubA/fAyJyW+zzIiLfF5GdIvIYUDHsuZ8TkdUichfRZV6bReTXscf6h339v4vIdhHZJiLvjH3+0tjX/15EdovIr2MzdAk30aIW37T4WuA+Y0znBL6mEZgx7OPpREdkw30Q+IOJ2g/UE92OS9nYG0dinY8pMP14fHusxBe14x2Q9r6v5i6aiwn5CA80j3+xSlkisgT4P8DlxpgVwN8SnQ271xizHPg18N1hX1INXEh0sHJX7HM3AwuAZcBHgVNGYMaYLwODxpizYhvdD3cL0QHKCuAK4N9FJN52uxL4LLAYmA1ccAZ/3FFNtKg9KiK7gdXAM7GmjvF+Pd0AzBOROhHxALcDj5x0zWHgLQAiUkn0L1P39LG5zU095GU5mVNq/zPUAh07EGf2sdb2RJp9bAG2vfeAdBdpB2SGuBz4vTGmHSA2+DgP+E3s8V8SLWJxDxljIrEpxfjJuRcTHbiEjTFNwLOTzHDhsK9vBZ4H1sQee80Y0xi7xbQZmDXJ556QiR4982WifzmrjTFBYIBo08dYXxMCPgU8BewCHjDG7BCRO0XkzthlXwfOF5FtwDPAl+L/Q5R9bWnqZUV1AQ6bn6EG0XZ+d+kixOFM+HPXFufgEPuP1FxF8bVqWtTSnHDqLZ6TDX/cf9LXjnTN6WQYzfDXCzPxvYcnZTInWS8C3iki7wNuA9463hcYYx43xsw3xswxxnwj9rkfG2N+HHu/yRjzVmPMMmPMUmPMr07nD6GmjjGGLU29LE+B+2kQHaklY+oRwO10UFuUY/u1aq78WnB6tK0//T0DvCN2TBgiUgK8THSWDODdwIvjPMcLwO0i4oxNG142ynXBUc7SfIFonXDGZvQuBl6b5J/jjEz06JlfAnOIDhnDsU8b4N7kxFJ2dahrkD5/iOXV9t+ZPzzUSXigCXcC93w82exSr+13FRGHE3dBnU4/prnYTNg3gOdFJEz03MvPAD8VkS8AbUT7GMbyR6LTmNuAvUSnD0dyN7BVRF4/6b7aH4nO6m0hWiO+aIxpEZEp65WY6PBvNbDYaPtUxtsaW5e1PAV2Egm0bQPAU5b4NWpxdaW5/Glna9KeP1HcxXMJ9rxpdQyVZPGtDU/69OUjXPeBkz7Oi/3XEL1tNNJzXzrs/S8BXxrl67/ASd3xxpjngOeGfTziayTCRKcftwNVyQqhUse2WFFbWpUKRW0LAFnlK5L2GnUlubT2+Rnw23vPAFfhXELdbzLCMlCl0spER2plwE4ReY1hN/uMMTckJZWyre0tfcwqziE/2/5nqAXatuDMrcLprRz/4tM0e9gRNEttPHqNtvUPEu5vwpU/3eo4SiXNRH8y/XMyQ6jUsa25l2U2/uE9XKBtC54kjtLgxCNobF3Uhm1srEVNpbOJtvQ/DxwE3LH3NwCvJzGXsiF/KMyetoGUKGomHCDQuQtP+bKkvs68suhavb1tdm/r17VqKjNMdO/HjwK/B/4n9qka4KEkZVI2tau1n3DEsCwFOh8DnbuiB4MmeaRWnOuh3OthT1t/Ul/nTLnyZyDOLF2rptLeRBtFPkl0S5NeAGPMPobtCaYyw7aWaJPIshRqEkl2UQNYUJHH3qP2LmoiDlxFcwl27bE6ilJJNdGi5h9+xpmIuDizVecqBW1r7sPjdDCvPAW2x2rbirhycBfNS/prLajIY7fNixqAu2QBgU4tampqiMgHROT7U/26Ey1qz4vIV4juzHwl8Dvg0eTFUna0vbmXxZV5uJ2T2YjGGoG2rXjKliVle6yTLSjPo20gQJfP3mfbekoWEuo5gAn5x79YqUmKHQxtuYn+dPoy0dXo24CPA48D/5isUMqetjb3pUaTiDHRzsckLroebkFs5LrH5s0i7uIFYCIEe/S+mjqRiHxRRD4Te/+/ROTZ2PtvEZFficgdsaNktovIN4d9Xb+I/IuIvAqcJyIfFJG9IvI8SdqFfzwTauk3xkRE5CGiuzrrKZ0ZqNMXoKl3iKVV9m8SCfc1EPF3Tcn9NICFFXkA7D7az7kzi6fkNU+HuyS6U1Gwc3fS9sNUZ87x949+h+jxLYm0OfLt6z87xuMvAH9H9Gia1UBWbG/HC4F9wDeBs4Eu4M8icpMx5iHAC2w3xnw1tlfkb2LX9QB/JbpV15Qac6QWO/Dtn0WkHdgN7BGRNhH56tTEU3YR30kkFUZq/ilsEoHoriJup9i+A9JdPB9A76upkWwCzhaRfKIbbLxCtLhdBHQDzxlj2mKnr/ya6EbFEN0L+MHY++cMuy4A/HYK8x8z3kjts0SHkGuMMfUAIjIb+JGIfM4Y819JzqdsYltzH5Aiez62bwUET9nSKXk9l9PBnFKv7TsgHW4vrvyZBDt3Wx1FjWGcEVVSGGOCInKQ6IbHLwNbie7QP4fouZdnj/KlQ8aY8LCPLW8gHO+e2vuAO+IFDcAYcwB4T+wxlSG2NvdSkuumuiDL6ijjCrRtwVU0B4cnb8pec0G5N2U6ILWtX43iBeDvY/9dB9xJ9GSW9cAlIlIWawa5g5F3738VuFRESmNTl2+fktQnGa+ouUc6tDN2X22ks3RUmtre0seyqgJE7H8waKBta1I3MR7Jgop89ncMEArbe8Ngd8lCgp17dGNjNZJ1QDXwSuzU6iFgnTGmGfgHovfItgCvG2MePvmLY9f9M9Gpy6exaNep8aYfx+pRtnf/skqYSMSwvaWX96+eYXWUcUX8PYR6DpC/5ANT+roLyr0Ew4aDXYPMLbPvOj538QJMyEe4rxFXQa3VcZSNGGOeYdhgxRgzf9j7vyHaBHLy1+Sd9PHPgJ8lMea4xitqK0Skd4TPC5CdhDzKhg51DdLvD6fI/bTYGWpTPlI73gFp66IW64AMdO7WoqbS0pjTj8YYpzGmYIS3fGOMTj9miK0p1PkYaNsKWFDUyqNFbY/N76t5ShYA6H01lbbsvzWEslx8z8cllfZfo+Zv24Ijpwynt3pKX7fU66EsBTY2duSU48guIaht/SpNaVFT49re3EddSW7KHAzqKVtuSUPLgnIve21e1EQEd/ECbetXaUuLmhrXtuZelqfAcTMmHCDQsZ2sipWWvP7Cynx2tvZjjOVLdcbkLlmo048qbWlRU2MaCobZ2z5g61Od4wIdOyAcwFO5ypLXX1qVT/tAgNY+e28Y7ClZQNjXSnio0+ooSiWcFjU1pl1HYweDpsAZav7W6LKYrAprilr872hbS58lrz9R7tLoTiuB9u0WJ1FqYkTk5yJy20Su1aKmxnR8z0f7Tz8GWjfhyCrCVTjbkteP/x3F/87sKr59WFCLmrKhMz3CRouaGtO25j6yXA7m2XjtVZz/6Ot4KlZZtutJeV4WlflZbG+290jN6a3GkV16bE2fUiIyS0R2icg9IrJDRP4sIjkicpaIrBeRrSLyRxEpjl3/nIh8U0Reix01c1Hs84+LyPLY+2/EN78Xka+LyEdim+T/e+wIm20i8s7Y45eKyF9F5DfAtth13xeRnSLyGFAx0T+L/dvZlKW2t0QPBnXZ/GBQE/ITaN9G4crPWJpjWVU+21vsPVITiW72rEXNnuq/4/kOSTh6pu6zgc+Oc808onv9flREHgBuBb4IfNoY87yI/AvwNaIb3QO4jDFrReTa2OevILpv5EWxzZFDHD9T7ULgV8AtRP9sK4AyYIOIvBC7Zi2w1BhTLyK3AAuAZUAlsBP46UT+oPb+SaUst7W5NyXupwU6dkAkaFmTSNzS6gJ2tPYRjti7A9JTtoxAxw7dA1INV2+M2Rx7fxPRHfqLjDHxzYt/wfEjZwD+MOzaWbH318WuuRB4DMgTkVxgljFmT+zz9xljwrH9JZ8H1sS+9rVhm+dfPOy6JuDZif4hdKSmRtUxEKC5158SnY/+o7EmkcrRTsiYGsurCxgMRtjfPnBs6yw78pQtxQQHCPXU4y6aY3UcNcwERlTJMrxtNwwUTfD6MMdryQai57AdAP5CdDT2UaKFD6JbLI7m5KPjT+s3Qx2pqVHFp9FSo0nkdRxZxbgK6izNsbIm+gvAG0d6LM0xHk/ZMkA7INWYeoCu+P0y4L2MfOTMMbHDQRuAdxA9smYd0eNs1sUueQF4p4g4RaSc6IjstRGe6gXg9th11UTPdpuQpBY1EblaRPaIyH4R+fIo11wqIptjNyfH/AtTUyt+MGgqTD9Gm0RWWn40zuLKfDxOh+2Lmrt0MSB6X02N5/3Av4vIVqL3wv5lAl+zDmg1xvhi70/neFH7I9EDSLcQnVL8ojGmZYTn+COwD9gG/IhxiulwSZt+jLVl/gC4EmgkekPwEWPMzmHXFAE/BK42xhwWkQl3uKjk29bSS3GO/Q8GtUuTCIDb6WBZdb7ti5rD7cVVNIdAh47UFBhjDgJLh3387WEPnzvC9ZcOe7+d4/fUMMb8E/BPsfebGDblaKLb7Xwh9jb8+Z4Dnjvpuk+dzp8lmSO1tcB+Y8yB2JD0fuDGk655F/AHY8xhAGPM0STmUZO0o6WPZdX5lo9+xhNvErH6flrcWTWFvH6kx/bbZXlKlxDUkZpKM8ksajVE51bjGmOfG24+UBxb87BJRN430hOJyMdEZKOIbGxra0tSXDWcMYbtLX0sTZGpR8Dyzse4VTWFdPqCNHQPWh1lTJ6yZQS79hMJ+qyOolTCJLOojfTr/cm/urqAs4HrgKuAfxKR+ad8kTF3G2NWG2NWl5eXJz6pOsXhrkF6h0IsrdImkclaWVMIwKZGe09BRncWMQQ7d1kdRamESWZRawRmDPt4OtA0wjVPGmMGYvOyLxBdlKcsFt+/MBUOBvUf3WSLJpG4FdMKcDmEDQ3dVkcZ0/EOSJ2CVOkjmUVtAzBPROpExAPcDjxy0jUPE1197oot0DsH0F8bbSDezm/3kVq0SWQ7WTaZegTIcTtZXl3Aa4e7rY4yJlfhbMSVe+y0cKXSQdKKmjEmRLR75SmiheoBY8wOEblTRO6MXbMLeJJoi+drwE+MMdqOZQPbm/uYUZRNYY7b6ihjOtYkYtHO/KNZW1vEhoZuIjbeWUQcTjzlK47dk1QqHSR1nZox5nFjzHxjzBxjzDdin/uxMebHw675d2PMYmPMUmPMd5KZR03ctpbU2B7Lbk0icefUFtPnD7H7qL1Pws6qPJvA0c2YSMjqKEolhO4ook4RDEfYfbQ/JbbHih43Y58mkbhzZhYB8OrhLmuDjMNTuQoT8hHs3G11FKUSQouaOsXetgGCYZMS22P5WzaQVbXGNk0icfPL8ijMdrHe5kUtvrYvfsCqUqlOi5o6RfyQS7s3iUSCAwQ6dpBVtdrqKKdwOIQLZpWw7kCn1VHG5C5egLjz8B/dNP7FSqUALWrqFNtaenE6hIU23mUeIHB0M5gwWZVrxr3WChfPKWX30X5a+/zjX2wREQdZFasItGpRU+lBi5o6xY6WPhaUe8lyndGp6knnb90AQFal/UZqAJfMLgXg+Tc7LE4yNk/l2QTatmDCQaujKHXGtKipU2xrTpHtsVo24MqfidNbaXWUEa2aXkhelpPnD9i7qGVVrsKE/dHlEUqlOC1q6gR9QyHqO30sTYkmkY22vJ8W53Y6uGBWCS/YfKQWbxYJ6Ho1lQa0qKkT7GxNjTPUwr42Qr31ZFXZ835a3CVzStnR2kdbv33vq7kK5+DwFOLX+2oqDWhRUyfYliKnXftbNwLgsWmTSFz8vtoLNp6CFBE8lWfrziIqLWhRUyfY1tyH1+NkVnGu1VHG5G/ZAOIgq2Kl1VHGtHpGEbluJ8/ZfgpyFYG2rURCQ1ZHUeqMaFFTJ9je3MuSynwcDnstZj6Zv3Uj7tLFODz2Xnbgdjq4oK7Y1iM1gKyqcyAS1PtqKuVpUVMn2N7SZ/vtsYwx0Z1EbD71GHfx7FK2NffRPmDf+2rZ084HYKjpZYuTKHVmtKipY1r7/LQNBGy/k0iot57IUIftm0TirpwfPdj2qT32PbXdmVuOu3g+/qaXrI6i1BnRoqaOiW+PZfsmkRZ7L7o+2erpRVTkeXh811Gro4wpa9oFDDW9gjERq6Moddq0qKljtrekRju/v2Uj4srBU7rE6igT4nAI1yys4MndRwmF7VswsqedT2SoU3fsVylNi5o6ZltLL+VeDxX5WVZHGZO/dQOeipWI094HmA537aJKugaDtt61//h9tVcsTqLU6dOipo7Z3tzHMrs3iYSDBI6+kTJNInFvnV+OyyE8ZuMpSFfRXBy5FXpfTaU0LWoKgEjEsKOlz/bbYwU6tmNCg7beHmskhTluLqwr4fFdrVZHGZWIkD3tfO2AVClNi5oCYH/HAL5gmBU2H6n5m18DIKtqrcVJJu/aRZVsa+7jcJfP6iijyp52AaGeA4QGmq2OotRp0aKmANjSFO18PKum0OIkY/O3rMeZW4mrYJbVUSbtukUVALbugsyedgEAfh2tqRSlRU0BsLmpB5dDWFxp7x06hppfJav6HETsvePJSBZW5DG7NJdHdrRYHWVUnvIViCuXocZ1VkdR6rRoUVMAbDnSy6LKPFsfDBr2tRHq3k9W9blWRzktIsIty6p5Zn87Xb6A1XFGJE432dMvZvDwM1ZHUeq0aFFTAGxu6uWsafaeehxqXg9AdvV5Fic5fbctryYYNjy8w74NIzm1lxPs2kOor9HqKEpNmhY1RVu/n6beIVZMs3uTyHpwuPBUrrI6ymlbM6OI2qIcHtzaZHWUUeXMuByAwYZnLU6i1ORpUVPHmkTsX9RexVN+Fg5XjtVRTpuIcOvyav68t43uwaDVcUbkLluKI7eCwcNa1FTq0aKm2JwCRc1EQvhbN5KdovfThnv7imkEw8a2DSMiDnJmXMbQ4WcxxlgdR6lJ0aKm2NLUw/TCbMq89t0eK9C+DRPykVV9jtVRztg5tUXMKMrmwa32XQuWU/sWwr4Wgp07rY6i1KRoUVNsPmL/JhF/bD/CVG4SiYt3QT61p40em05BHruvplOQKsVoUctwQ8Ewu9v6WW7jqUeIdj46vdNw5s+wOkpCvH3FNALhCI/utGcXpKugFlfRXG3tVylHi1qG29HaRzhiOMvmRc2fwouuR3JubTE1hdn27oKsfQtDjS9gwvZcU6fUSJJa1ETkahHZIyL7ReTLY1y3RkTCInJbMvOoU20+Yv/tsUIDLYR669OiSSTO4YhOQT65p43eIZtOQc58KybYz9CRF62OotSEJa2oiYgT+AFwDbAYuENEFo9y3TeBp5KVRY3u9SM95Ge5mF2Sa3WUUflji65TdSeR0bx9RTX+UIQ/2XQKMqf2csSZha/+caujKDVhyRyprQX2G2MOGGMCwP3AjSNc92ngQcC+u7ymsY0N3Zw9vRCHw77Tev7m9eD04KlYaXWUhDp/ZgnTCrL5vU27IB1uL9kzLsN34DFt7VcpI5lFrQZoGPZxY+xzx4hIDXAz8OMk5lCjCIQibGnqZfWMIqujjGmo+VWyylficGVbHSWhHI7oQuwndh+17RRkbt11hHreJNi1x+ooSk1IMovaSL/6n/zr3neALxljwmM+kcjHRGSjiGxsa2tLVL6Mt7W5l0A4whobFzUTDhBo3ZR2U49xdp+CzK27BoBBnYJUKSKZRa0RGN5/PR04udVrNXC/iBwEbgN+KCI3nfxExpi7jTGrjTGry8vLkxQ382xo6AawdVHzt76OCQ+RXXO+1VGSIj4F+bst9uyCdBXU4ilbhu+AFjWVGpJZ1DYA80SkTkQ8wO3AI8MvMMbUGWNmGWNmAb8HPmGMeSiJmdQwGxq6Kc11M7PYvnspDjVFO+/ih1emm/gUpK27IOuuZajpJcJDXVZHUWpcSStqxpgQ8CmiXY27gAeMMTtE5E4RuTNZr6smblNjN2tmFNl67dfQkRdxFy/AmVthdZSkeceKafhD9l2InVt3HZgwgwe1QVnZX1LXqRljHjfGzDfGzDHGfCP2uR8bY05pDDHGfMAY8/tk5lHHDfhD7Gjps3WTiImE8R95ieyaC62OklTnzYwuxLbrFGRW1RocuRX43nxk/IuVspjuKJKh3mjqIWLsfT8t0LGdSKAn7YvasSnI3facghSHE++cG/EdfIJIaNDqOEqNSYtahkqJJpEjLwGQleZFDaJTkIFwhEdseiK2d94tmOAAgwf/bHUUpcakRS1DbWyIHjdTVWDftV9DR9bhzK/FXTDT6ihJF98L8vc23Qsyu+ZiHNklDOz/g9VRlBqTFrUMtaGh297304xh6MhLZNekZ9fjyRwO4bbYFKQdj6MRp5vcOTdEdxcJ+a2Oo9SotKhloI6BAPvbB2w99Rjq3k/Y10J2zUVWR5kydj+Oxjv3Fkygl8EGPY5G2ZcWtQz0Yn0nABfWlVicZHRDTdH7aZkyUoPoFOR0G3dB5tRejsNTyMC+P1odRalRaVHLQOvqO/A4HbYeqQ02PIcjpxx38UKro0wZh0O4bcU0256ILU4PuXPehu/NR/SMNWVbWtQy0LoDnZxTW0S222l1lBEZYxhq+Cs5My619cLwZHj78upoF+TOFqujjMg7/+1E/F0MHn7a6ihKjUiLWobp94d4/UgPF86279RjsGs34YFmsmdcbnWUKXdOrAvyoW32LGo5tVfiyC6lf/f9VkdRakRa1DLM+kNdhCOGi+pKrY4yqqHDfwUgp/Yyi5NMPYdDuGFJFU/uOYovELI6zinE6cY77xZ8bz5CJDhgdRylTqFFLcOsq+/EIXD+rGKro4xqsOFZXAWzcBfOtjqKJW5aWsVgMMLT+9qtjjIi74LbMSEfvjcftTqKUqfQopZhXjzQwYppBRRku62OMiITCTPU+ALZMzJvlBZ3yexSCrNdPLTdnlOQ2TUX4MybTv8enYJU9qNFLYMEQhHWH+7iotn2nXoMHH2DiL+bnAwuah6Xg+sWVfLojhZC4YjVcU4h4iBvwTsYPPRnwoMdVsdR6gRa1DLI60d6GAxGuMjG69MGG54FyOiiBtEpyA5fkJcOdlodZUTeBbdDJMTAvgetjqLUCbSoZZAXDkR/q77Qxk0igw1/xV26BKe30uoolrpqQQVZLodtpyA95Stwly6mf9evrI6i1Am0qGWQp/e2sbgyj8r8LKujjCgSGsR/5CVyMrCV/2T52S6umFfGQ9tbMMZYHecUIkL+4vfhb15PoHOP1XGUOkaLWobwBUKsq+/krQvse4L0UMPzmPAQObPeanUUW7hpaTWHugbZ0tRrdZQReRfeAeKkf9cvrY6i1DFa1DLECwc68YciXLWg3Oooo/IdfAJx5ZI9/RKro9jC9YsrEcG2U5AubzU5s66if9evMZGw1XGUArSoZYyn9hwl2+XgYpt2PhpjGKx/gpzay3C47HvG21SqyM/iglklPLzDnkUNIH/xewn3HznW4KOU1bSoZYgndx/l4tml5Nh0v8dg5y5CvQfJmXWt1VFs5cYlVWxp6qW+w2d1lBHl1r0NR1Yx/TvvtTqKUoAWtYyw+2gfe9oGuH6JfTsKffVPAJBbd7XFSezlpqVVADy0o9niJCMTVxbehbfj2/8Q4SF7Lj9QmUWLWgZ4eHv00MkbllRZnGR0g/VP4Clbhit/htVRbGVOmZdl1fk8bNP7agD5Sz+ECfvp36nt/cp6WtQywMM7Wjh7eiEzinKsjjKi8FA3Q00vkVN3jdVRbOnGJVW8WN9JW7/f6igjyipfQVbVOfRtu9uWyw9UZtGiluYauwdZf6iLG5faeJR2+C9gwuTW6f20kdy0tIqIgUd2tFodZVT5yz5KsGsvQ0fWWR1FZTgtamnuvjeOAHD7WTUWJxmdb//DOHLKyKo6x+ootrSyppDaohxbT0F659+GI6uIvq13Wx1FZTgtamnuvjeOsHZGEXPLvFZHGVEk6MNX/xjeubcgDnt2ZlpNRLhxaRV/2ddGv99+Z6wBONy55C16DwP7/0jY12Z1HJXBtKilsR0tfWxu6uVdq+w7ShusfxwTHMA7/zaro9jazUur8IciPLn7qNVRRpW/7CMQCdK342dWR1EZTItaGrvn1UO4nWLrqcf+fb/HmVtJds1FVkextQvrSijNddt2dxEAT+lismdcTu+WH2HCQavjqAylRS1NDfhD/GJDA7ctn0aFXTcwDvQzWP8EufN06nE8LqeD65dU8diuVgIh+52xFle46jOE+4/okTTKMlrU0tR9m4/QMxTib86faXWUUfnqH8OEBvHO06nHibh5aRU9QyGee7Pd6iijypl1Ne7i+fS88d/a3q8skdSiJiJXi8geEdkvIl8e4fF3i8jW2NvLIrIimXkyRThi+PZzb3LWtAIumGXfA0EH9v4Op7ea7JoLrI6SEq6YX47X4+SPNp6CFHFQcNanCbRuwt/0stVxVAZKWlETESfwA+AaYDFwh4gsPumyeuASY8xy4OuA9gMnwO+2NLG3bYD/c8U8RMTqOCMKD3UyePApvPNuRUQnDCYix+3k6gUVPLKjhUjEvqOgvMXvwZFVTM8b/211FJWBkvnTZC2w3xhzwBgTAO4Hbhx+gTHmZWNMV+zD9cD0JObJCMFwhH/5y14WVeRx89Jqq+OMqn/nrzBhP3lL3m91lJRy07Iqmnv9vNbQbXWUUTncXvKXfwzf/ocJdO6yOo7KMMksajVAw7CPG2OfG82HgSeSmCcjfP+lenYf7eeu6xbhcNhzlGaMoW/7T8iqWktWuc44T8Z1iypxOYQ/brPnBsdxhSs/g7hy6H7136yOojJMMovaSD9RR5wzEZHLiBa1L43y+MdEZKOIbGxr04Wdoznc5eP//nkv1yys4G2L7bsjv7/pJYKdu6PrmtSkFOW4uWxuKQ9tb7F1I4Yzt5yCFXcysPcBAp17rI6jMkgyi1ojMHzL9elA08kXichy4CfAjcaYjpGeyBhztzFmtTFmdXm5fU9utlIoHOE9v3kDY+B7Ny+17b00gN5tP0E8BXjnv93qKCnppqXV7GsfYEdLn9VRxlR49ucRZzY9G+6yOorKIMksahuAeSJSJyIe4HbgkeEXiEgt8AfgvcaYvUnMktaMMfztwzt4sb6TH966jNml9twSC6INIr59D5K38F043PbNaWc3L63CIfDAllN+R7QVZ24F+cs/Tv/u+wh27bM6jsoQSStqxpgQ8CngKWAX8IAxZoeI3Ckid8Yu+ypQCvxQRDaLyMZk5UlXkYjhS4/t4kcvH+QLl87h3avs3WvTv/NeTNhP/rIPWx0lZVUVZHPJ7FIe2NJk6ylIgMKzP4c4s+l6+atWR1EZIqm91MaYx40x840xc4wx34h97sfGmB/H3v+IMabYGHNW7G11MvOkm/YBP2+/dyPffu5N7jxvJnddt8jqSGMy4SA9r3+X7JqLtEHkDL3jrGnsbRtgS1Ov1VHG5PJWUbj67xjY9yBDR16yOo7KALpAKAX5AiH+e90BlnzrOf60q5VvX7+YH9yyzNb30QD69/6WcH8jhav/3uooKe/W5dU4HcJvbT4FCdF7a868Gjpe+HuMse8WXyo9aFFLEZGI4cX6Dj75h21M//rTfO7hHSyrLmDD317M5y+ZY/uCZiJhejZ8C3fpYnJmXW11nJRX5s3iinllPLDZ/lOQDreX4gu+TqB1E/27f2N1HJXmXFYHUGPb3z7ALzY28MuNjRzuHiTH7eDGJVV84oJZXFhXanW8CRvY+wDBzt1UXPsb2xfgVPGOFdP48ANb2NjQw5raIqvjjClv4bvo3fwDul78R7yzr8eRVWh1JJWmdKRmU68d7uLae9Yz/65n+bdn9rGoMo9771hJy9eu4jfvOTulCpoJB+la/3XcZUvJnXeL1XHSxk1Lq3A7hd9uOWJ1lHGJOCi77HuEfS10rjtlG1ilEkZHajbT0D3IZ/64jYd3tFKa6+Zfrl7AB9fMoKYwx+pop61v2z2EuvdTecMfdJ/HBCrO9XDNwgrue+MId127CJfT3n+3WVWrKVz1OXo2/Qfe+beRU/sWqyOpNGTv74IMc/8bR1jxH8/z9L52/uXqBRz4yhX84xXzU7qghYe66Fr/dbJnXEZO3XVWx0k77189g+ZeP0/tSY2ddorO+yru4nm0P/03RAL9VsdRaUiLmg2EwhE+/vstvOvXr7Og3Msbn7+Ef7xiPvnZqT+Q7nrpH4n4uyi5+Ft6Ly0JrltUSbnXw883NIx/sQ04XDmUXXE3od5DdDz3OavjqDSkRc1ivkCI2+7dyD3rD/PFy+ay7pMXMLcsPXbaGGp6mb5t91Cw8jO6Li1JPC4H7z57Oo/sbKF9wG91nAnJrrmAorVfpn/nL+jbea/VcVSa0aJmoX5/iKvuXs+jO1v5/s3LuOs6+98XmaiIv4e2Jz+Aq2AWxefqbhLJ9ME1MwiGDb9+3f4NI3FF536V7OmX0PHspwl07LA6jkoj6fETNAWFI4Y7frWJ9Ye7+e17zuYTF8yyOlLCGGNof+aThPoaKL/6XhyePKsjpbVl1QWsnl6YMlOQAOJwUn7NvTg8BRz90zsJD3VbHUmlCS1qFvncIzt4bNdRvnfTUm5bMc3qOAnVv/OXDOx9gOLzvkb2tHOtjpMRPrCmli1NvWy08eGhJ3N5qym/9jcEe+o5+vgdmHDQ6kgqDWhRs8B31x3g+y/W87mLZ3Pn+bOsjpNQQ82v0fHXT5M9/RIKV3/B6jgZ492rasjPcvGdFw5YHWVScqZfRNlbfsTQ4WfoeO6ztt8dRdmfFrUp9qedrXz+kR3ctLSKb71tsdVxEirYtY/WR27C6Z1GxbW/RhxOqyNljMIcNx8+p5bfbmmioXvQ6jiTkr/kfRSu/gJ92+6hZ8O3rI6jUpwWtSn0xpEe7vjVJlbWFPLLO1bidKRPi3tooIWWh64HhKqbH8WZW2F1pIzztxfWYYzhey/WWx1l0oov+DreBbfT9fI/0bv1bqvjqBSmRW2KNHYPcv3/vkZJrptHPrQWb1bqr0GLC/Yeovl3lxMeaKHqxodwF821OlJGmlmSy23Lp3HP+kP0DYWsjjMpIg7K3/q/5NRdS8ezn6Z/9/1WR1IpSovaFOj3h7jhp6/R5w/x6IfOobog2+pICRPo3EXzA5cSGWyn6pbHyapaY3WkjPb5S2bTMxTif187bHWUSROnm4rr7iO75iLa/vwhfPWPWx1JpSAtakkWb93f2tzL/e9ZxfJpBVZHShhf/RM0//ZSiISovu1psqedb3WkjLe2tpiL6kr4j+ffZCgYtjrOpDlcOVTe8Ac8Zcs5+qfbGTz8jNWRVIrRopZkx1r3b17GNYsqrY6TECYSpuuV/0vrwzfiKqil+p3P4ylfbnUsFfPPVy3gSM8QP3rloNVRTosjq4Cqm/+Eq3gurQ/fzOChp62OpFKIFrUk+t6L9Xz/xXo+e/Fs/iZNWvcDnbto/t1ldL/6DfIWv5/qd76Au3C21bHUMJfNLeOKeWX82zP76R5MzbVfzpwyqm/9M+7iebQ+cgu+Q3+xOpJKEVrUkuRPO1v53MPbuWFJJf+eBq37kaCPrvX/ypFfryHYtZfyq35G2ZV343Cl7gkC6eybb1tMhy/A157aY3WU0+bMKaPq1qdwF8/nqBY2NUFa1JJgU2M3d/xqE2dNK+TX71qV0q37JhKib/vPaPz5YrrX/wve2Tcw/b1byFv0bt1138ZW1hTy8XNn8sOXD7L5SI/VcU7b8cK2IFrYDv7Z6kjK5rSoJdj+9gGuvedVyryelG7djwT66XnjezT+bBHtT38cV/4Mqt/+Vyqu+w1Ob3rcG0x3/3rNQsq9Ht5//xv4Q6nXNBLnzCk9XtgevZWB/Q9bHUnZmBa1BGrt83P1PeuJGMOTHz2XaYWp1bpvImEGG1+g7c8f5fA9tXQ+/3c4C2ZQcf2DVL/zBbJrLrA6opqEklwP97x9Bdua+/jSY7usjnNG4oXNU76Co4+9k77tP7M6krKp1BxG2FBbv5+r7l5PS6+fZ+48jwUV9t+Z3hhDqOcA/ub1DB5+Gt/Bp4gMtiOefLzzbyN/6UfIrl5rdUx1Bq5bXMmnL6zju+vqWVVTyPtWz7A60mlz5pRSdcuTHH3sdtqf/jjhwXYKV/+9ToOrE2hRS4CW3iGu+J9XqO/08fAH13LOzGKrIx0T8fcS6jtEqPcwob7DhHoPEeprINR7mGD3fiJDHQA4skvJmXUVuXXXkjv7bTjcuRYnV4ny7esXs725l488sIWiHDc3LKmyOtJpc3jyqLzhD7T9+cN0vfR/CPtaKbnom7rPqDpGUm1X7NWrV5uNGzdaHeOYg50+rrlnPQ3dQ/zpw2u5dG7ZlGeI+HsJdO4i2LmbYOcugl17o8Wr9zCRwElNAk4Prvza6FvhLLIqV5NdtRZ36RL9wZDGeoeCXPk/69nc1MOPbl3Oh9bWWh3pjBgTofP5L9C7+XvkzLyK8mt+iTO7yOpYcTp0tJAWtTOw7kAHt/5iI8FwhEc+tJaLZpdOyetG/D0MNj7PUOM6hhqfJ9C2BYj9f3R6cBfNxVUwK1q4CmIFrGAmroJanLmViOit1EzUPRjknb/cyF/2tvPuVTX8x/VLqMjPmvTzGGMYCITp84cYDIbxelzkeZzkepxTPhXYu+1/6fjrZ3AVzKTyuvvtsgmAFjULaVE7DZGI4UevHOTzj+ygriSXRz60lvnlyb2HFgn04TvwKAN7fxddrxMOIM5ssqrPJXv6RWRVrMRdshBXwSzEobPKamShcIR/fXof/++ZfbidwnvPnsHbV1SzdkYx+dmuE6471DXImx0D7G/3sb9jgDfbB3izY4ADHT6GQpFTnrsg28X8Mi8LK/I4d2YJF9aVsLQqH0eSl7QMHXmJo4/dQdjfSckF/0rBys9Y/YubFjULaVGbpPoOHx/93Rae3d/ONQsr+PW7V1GU407Ka0WCA/gOPMbAvt8zWP8kJjyEM2863vm34Z1zA1mVaxDX5H/TVmpvWz//9sw+HtjSxGAwWqBKct04RfCHIwwEwoQjx3825LgdzCn1Mqc0l9mlXqrys8jPcpHtdjIYjI7aGroH2dc2wLaWXpp7/QBU5Hm4dlElNyyp5Mp55Ulb4hL2tdH+9J34DjxKds1FlF76X1aO2rSoWUiL2gR1+QL8x/MH+O91B3CI8O3rF/ORc2oTPt0SCfoYPPQUA3t+h6/+cUzIhzO3Cu/8W/HOfztZ1eda/VuoSiMD/hDPH+hgc1Mvjd2DRAxkux3keVzMLs1lbqmXuWVeqguyJvxv3RjDwc5B1tV38OTuozyx+yg9QyGyXA7eMreM65dU8rbFldQUJnY3GmMM/Tt/Qee6fyDi7yJ/6UcoWvtlXPnTE/o6E6BFzUJa1Max52g/P9vQwI9fOUjvUIh3rJjGt962iNrixHUHhn1t+Oofx/fmIwwefhoTGsSRU4Z37i1457+d7JoLtYlDpaxgOMK6A508urOFR3e2cqDDB8Dq6YVcv6SKG5ZUsry6IGG/IIaHOule/6/0bvkRiOCd/w4KVnyCrKo1U3XPT4uahZJa1ETkauC/ASfwE2PMXSc9LrHHrwV8wAeMMa+P9ZzJLmrBcIQNDd08s6+dx3e18urhbhwCNy2t4qtXLjjjo2NMJEyo9yD+ltcYOvISQ00vEezYAYAzbzq5c67HO+cGsqdfovfGVNoxxrCrtZ9Hdrbw6I5W1h/uwhiozM/i/JnFnDerhPNnFbOsquCEe3ynI9hzkN7N36dv+/9iggO4CmbhnXcL2TMuI7v6XBxZhQn6U51Ci5qFklbURMQJ7AWuBBqBDcAdxpidw665Fvg00aJ2DvDfxphzxnreyRY1YwyB2D0CXyAc/W8wTL8/xNF+P029fpp6h2jsHmR7Sx+7WvsJhCOIwMpphdy+soZ3r6oZ8WBPYwwmPIQJDWKCPkzIRyTow4QGiQT7CftaifiOEvYdJdR7iEDXHkJd+zDh6P0G8eSTXX0eWdPOJ7fuGjzlZ+lCUpVRWvv8PLarlRfe7OClg528GRvFAVQXZLGgPI955V6q87OpzM+iIs9DmdeD1+PC63GS63bizXKS43LidjpwO+WU76HwUDe+Nx9hYN+DDB7+C0RCgOAunoe7ZCHuonk486bhzCnH6a3EkV2Cw5OPw52Hw1N4Ovet9ZvYQsksaucB/2yMuSr28T8AGGP+bdg1/wM8Z4y5L/bxHuBSY0zzaM872aLWPRik5J+eHPMat1OYVpDN4sp8llbls2ZGEZfNLaPU6xnz69qf/hv6tv/vuBnEmY0zfzru4vl4ShZG/1uxEk/Zcp1WVGqY1j4/6w91sbO1j71t/expG2B/+wDtA4EJff3THz+Py+eNvlY0EujH37qBoSMvEmjfFl3b2f0mREY+oqf4gm9QtOYLk/1jaFGzUDLnt2qAhmEfNxIdjY13TQ1wQlETkY8BH4t92B8rfgkTAA7G3s7wAPkyoP3UTweBXbE3223GOkpmW9PMUyPlMr/lP4CE5v5i7G1SnjTGXJ2Y11eTlcyiNtJvKycPCydyDcaYu4G7ExEqmURkozFmtdU5JkMzTw3NPHVSNbdKjGT2hjcCw3dPnQ40ncY1Siml1IQks6htAOaJSJ2IeIDbgUdOuuYR4H0SdS7QM9b9NKWUUmosSZt+NMaERORTwFNEW/p/aozZISJ3xh7/MdFbWNcC+4m29H8wWXmmiO2nSEegmaeGZp46qZpbJUDKLb5WSimlRqP7LSmllEobWtSUUkqlDS1qkyQiV4vIHhHZLyJfHuHxd4vI1tjbyyKywoqcJ2UaM/Ow69aISFhEbpvKfKOZSG4RuVRENovIDhF5fqozjpBnvH8fhSLyqIhsiWW29D6yiPxURI6KyPZRHhcR+W7sz7NVRFZNdcaRTCC37b4P1RQxxujbBN+INry8CcwGPMAWYPFJ15wPFMfevwZ41e6Zh133LNHmndtS5O+6CNgJ1MY+rkiBzF8Bvhl7vxzoBDwWZr4YWAVsH+Xxa4EniK4pPdfqf8+TyG2r70N9m7o3HalNzlpgvzHmgDEmANwP3Dj8AmPMy8aYrtiH64muvbPSuJljPg08CBydynBjmEjudwF/MMYcBjDGWJ19IpkNkB/bzDuPaFELTW3MYWGMeSGWYTQ3AveaqPVAkYhUT0260Y2X24bfh2qKaFGbnNG29RrNh4n+lmulcTOLSA1wM/DjKcw1non8Xc8HikXkORHZJCLvm7J0I5tI5u8Di4huMrAN+FtjzKnHSNvHZP/N25Edvg/VFNGzTSZnQtt6AYjIZUS/mS5MaqLxTSTzd4AvGWPCNjolYCK5XcDZwFuAHOAVEVlvjNmb7HCjmEjmq4DNwOXAHOAvIrLOGNOb5Gyna8L/5u3IRt+HaopoUZucCW3rJSLLgZ8A1xhjOqYo22gmknk1cH+soJUB14pIyBjz0JQkHNlEt1lrN8YMAAMi8gKwguiRR1aYSOYPAncZYwywX0TqgYXAa1MTcdJSdis7m30fqimi04+TM+7WXyJSC/wBeK+FI4bhxs1sjKkzxswyxswCfg98wuKCBhPbZu1h4CIRcYlILtFTIHZNcc7hJpL5MNGRJSJSCSwADkxpyslJya3sbPh9qKaIjtQmwUxs66+vAqXAD2Mjn5CxcMfwCWa2nYnkNsbsEpEnga1AhOjp6iO2eNslM/B14Ociso3o1N6XjDGWHe8iIvcBlwJlItIIfA1wg723sptAblt9H6qpo9tkKaWUShs6/aiUUiptaFFTSimVNrSoKaWUShta1JRSSqUNLWpKKaXShrb0q4wiImGi21O5gHrgvURb8LOAEqI7kxyJXX6TMeagBTGVUqdJW/pVRhGRfmNMXuz9XwB7jTHfiH38AWC1MeZTFkZUSp0BnX5UmewVUm9zXqXUGLSoqYwkIk6i21WdvI2VUiqFaVFTmSZHRDYDHUTvof3F2jhKqUTSoqYyzaAx5ixgJtHTqT9pbRylVCJpUVMZyRjTA3wG+HsRcVudRymVGFrUVMYyxrwBbCF6RIxSKg1oS79SSqm0oSM1pZRSaUOLmlJKqbShRU0ppVTa0KKmlFIqbWhRU0oplTa0qCmllEobWtSUUkqljf8PIQ5iOjmoE+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 439.25x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(kind='kde',\n",
    "           data=df,\n",
    "           x='RT', hue='condition', \n",
    "           palette='colorblind')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f1f4829-51fb-451f-b7c5-2b52dfffbbaa",
   "metadata": {},
   "source": [
    "## Categorical Plots \n",
    "\n",
    "Histograms and KDEs are used to visualize the distribution of continuous variables, like RT in our example data set.  We can also plot categorical variables, by drawing different histograms/KDEs for each category, like for reward vs. nonreward in the plots above. These plots are most useful for visualizing the *shape* of the distributions of the data. This is an important first step in working with data, because some ways of treating data (including statistical tests like *t*-tests and ANOVAs) assume that the data are normally distributed, (the classic \"bell curve\"), and may not work as well or as accurately on data that have very different distributions. On the other hand, in such cases other approaches to analyzing the data could be more appropriate. \n",
    "\n",
    "That said, once we understand the distribution of the data, we might want to focus more on assessing whether there are differences between different categories (such as experimental conditions, or groups). This is where Seaborn's `catplot()` family of plots comes into play. \n",
    "\n",
    "`sns.catplot()` has 6 different plotting options, as shown in the figure at the top of this lesson. All of them allow us to view the distribution of the data, but in different ways, and often with a focus more on showing the mean, or median. This allows us to more easily compare differences between conditions, while considering the extent to which their distributions overlap or not. We'll show a few of them in this lesson. \n",
    "\n",
    "### Box plots\n",
    "\n",
    "Box plots (sometimes called box-and-whisker plots) are a classic and widely-used approach to comparing between categorical variables. The \"box\" shows the **interquartile range** (IQR) — the range between the 25th and 75th percentiles of the data. In other words, half of the data falls within this box. The line inside the box represents the **median**, or the value exactly in the middle of all the values in the data set. The whiskers (lines extending out from the box) end at ±1.5 x the IQR (more on that in a minute), and then any points plotted outside the whiskers are considered outliers. The idea behind this is that the whiskers end at what might reasonably be predicted to reflect the minimum and maximum values in the distribution, and any outliers are likely to be inaccurate measurements or other types of \"noise\". Below is a figure showing how the box plot maps on to the normal distribution, which is a helpful way to think about interpreting box plots.\n",
    "\n",
    "![](./images/Boxplot_vs_PDF.png)\n",
    "\n",
    "*Image from [Jhguch](https://en.wikipedia.org/wiki/User:Jhguch) and shared under the  [Creative Commons Attribution-Share Alike 2.5 Generic](https://creativecommons.org/licenses/by-sa/2.5/deed.en) license*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3d3391-7cf0-4d76-8364-c1c4f8892cab",
   "metadata": {},
   "source": [
    "### Drawing box plots with Seaborn\n",
    "\n",
    "We use `sns.catplot()` with the `kind='box'` kwarg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10f5c654-12ce-4322-8fc3-cdd78e4ee77c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='box',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8f084d7-1f20-4e31-a7f6-22d4ffde47cf",
   "metadata": {},
   "source": [
    "We can see from this that the distributions of the two conditions appear quite different, and indeed their IQRs do not overlap at all. This gives us good confidence that RTs are probably different in the two conditions. You can also see that there are a few outliers (the diamonds outside the whiskers). \n",
    "\n",
    "Although we lose some of the details of the distribution in going from a histogram to a box plot, we gain some simplicity, and it's easier to assess how different our different conditions are. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfba3b57-b343-4448-b0ba-405c4667fe75",
   "metadata": {},
   "source": [
    "## Violin Plots\n",
    "\n",
    "A violin plot is in some ways the \"best of both worlds\", combining a box plot with a KDE. Note that the syntax of this plot is identical to that for the box plot above, except the `kind` kwarg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1dff1d79-8e55-45fe-bab2-3a76241c2948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='violin',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e0b388-a9a9-41ac-98cc-558e0f46c7ba",
   "metadata": {},
   "source": [
    "The box plot has been made more narrow, but the thinker black line inside each \"violin\" is the IQR, and the thinner lines the whiskers. The coloured, shaded area is the KDE. For aesthetic reasons, the KDE is shown on both sides of the box plot (although if you have different sub-categories, you could plot different distributions on each side). \n",
    "\n",
    "In adding the distribution plot to the box plot, you gain detail, but perhaps at the cost of the simplicity of a box plot, and its usefulness in making inferences about differences between conditions. \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "I'll comment for the record that \"violin\" plot is a terrible name — if the distribution of your data was actually shaped like a violin that would be very suspicious! I prefer Salem Waligura-Newman's name for them, which is \"stingray plots\". But sadly, once a name is out there, it sticks so you should get used to calling them violin plots. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a76858bb-3088-4646-a8af-620b9b447f12",
   "metadata": {},
   "source": [
    "## Strip and Swarm plots\n",
    "\n",
    "These are quite similar, and both plot the actual individual data points. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d402db5c-8a4f-463a-8ecb-6ee77412d908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='strip',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c598bf-55e3-4b1d-8a6c-6ed48264a96a",
   "metadata": {},
   "source": [
    "Swarm plots shift data points laterally when they are dense, reducing overlap and making it easier to see where the data are more dense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee4d1b64-3ad5-42c2-8e7e-9be0aaa6beac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XQvJMMJthzq06aINOPLXoqVbtor2Qv02EEGfHbNZb1pO/Aq9gGP9PiB9v26a8AD6bovOcABxaCYZJ5/qu68jGluix3ZMRtxDi7Gz6DFa8CkUZOrXrrGt1+ta6UldYg3bdc0Fxtuc6jGjWrjoKGXELIc5O6nLb4+pyOLxWp3bdPhf8I3U2wfoCO8HwB+CnR3SlnLjxMPG5FumyvZPALSy2HynglUV7yCgoY2rfSO4Z3RmlGn02IoRVRF+du7uWcoajm/UoHCAvBTJ3wrD7df1KwwTRg2HwnboU2a2LGrurOAkJ3G3Uu0tT+HR1Ku4uzjwwLp6Lerfnxo/Xk1tSCcAri/bg6+HCDUM7tm5Hxblv8F1wbLte+ufhr5f+bZ9j26Y0B7pdDEPvh7J88AmV+pFnQQK3g/l63WHmbzlCe39PHhwXT8dg7wZtFu/M5OWFuy3Hf5mzBQzDErRr/bE7SwK3OLHibHD30euzp30AU97SlXLMVZC9W68SqeXsruezMzbDD/fp+fDIRLjys8bLoomTksDtQGZvSOPpedssx+tT81j6l9GsT83j3aUpVJrM3DK8IxsP5dtcZxiQVVSOi5OyqVHZJcy3xfou7Eh5Acy+QQdmN184/1+QeAskfQR/PK/Lm8VNgI4jdd5tjwCY9CK4+8K8u6wV5I8kwa9/1dXixWmRwO1AFmzPsDk+cryMRduP8fDsZKpMOiBvOJjHw+O7NLh2RHwIfp5uvLhgFyWVJgbHBnLP6M4t0m9hZ1a+bh1NVxbpOpIhCbDoGaDmF//+xXpZ4BWfgZu3LkdWeMQatGsd296CHXccErgdSEygl82xs5MiJbvYErRBj67LKqu5fWQsX6w9hJuzEw+Mi6d3VAC9owK4rH8kReXVUpdSnFjWLttjczWkLsMStGtl7oRts2HpS1BdoUflwV2sG24A4sY1e3cdkQRuB3LvmDjWHshlb2YxLk6Khyd0oVekf4N2nUN9uXxAFE9e0B0FODlZV454ubng5SY/FuIk4sbD3oXWY8920PdaWPVf2yrwwfGw8Enr8dp34Pzn9Wg9e7fO2T3uHy3WbUci/0IdSJifB4seOo/dx4oI8XUn2McdwzC4elAMszYcxmzABT3Dmdo3AtAjciFO28Db9Gaard+Cb7gOvgFRcO23egdlSQ70u05PkdRXeBQSpsCadEhfD/sWQY9LW/wt2DtJMtVGZBWVU20yiAg4SYY2IU7Hzvm61mRVGQy6Ewbfoc8fWqPXcftFwrc3YjOFMvav8EedTTbKCe5eDaHdW7TrdkSKBbdlob4yZy2aUG4KfHuT3kwD8MtfIDAWjibDktrArKD/9XBgmd5NOegOqCq1vY9hhpQlErhPkwRuIdq6jZ/Brh914B35qJ7+OJXUZdagXWv/b7D5yzonDDi4Ch7aCiW5eu129p6G9wrvefKvVV4Iu+brJ+sJU8HDT69GWfCYflAafz5c+Jo+30ZI4G4DKqvNLNmTRZXJzLhuYXi6SYVtUWPDh/Dzo9bjg6vg7lVwaBWsfVefG3ovdBhme11474b3CuupV5jUZa6CNe/Ab/8EUyUEdYHe0/UuS+UMQ++B2POs7bd8A7t/gqB4vUVeOcH7oyE/Vb++4lW4fYlOD1t7bttscPOCi/97Nt8JuyKB28GVV5m4/L3VbD9SCECHIC9+uHc4AV5urdwzcU7Y9p3tcdYO2POL3mBjrtLn9v0Kd68BpeD3f0H+QT3yHf00rHpDB+S+10Lfa/RqkTVvW+/X/wZY/A/rvXL3Qoeh8MRBPYIuyYbqSnBxg/Uf6FF0rYMroc9V1gAN+mtv+Mj2XG3bNkQCt4NbtOOYJWgDHMotZc7GdG4b2akVeyXOGf6RtsdOrrpSe22gBR2Yd/0IGz+B44f0uYxkOP85eGQXLH8F0tbBL0/A6Kd0Aqmjm/VI2sPf9mEk6KCbdwC+uU7n4/YOgcs/1qPtutLXQ+fRDfvs6gm+EVB01Houov+ZfgfskgRuB1dWaWpwrrSRc6KNGvWkXgVSmK6nLsb+FfwiGrZTyhq0a+1eoANw0sf6OH2DbnPtt9BhOKT8rndMtuuoR8q1uk/Ruy1riyiUZMP8ByA0wfb+zm7Q60rY+DkUH9PnfML0VEtkf53zJC8FYkfBxOeb4rthNyRwO7hJPcN5bfFesosqAPB1d+HSfpGnuEq0GcFx8GCyrjwTEKODtqkKts3Ra6wBul6op0GWvqhXh9QK6qxH4nXtW6zrSH45DSpq/tLrcRlED4Hjh/Wa7YG36fXedeWnwuUfQdpaKM0FlB69B8fDXStgy0w9tdLnavAJ0R8PbNI7Ml3cm+u7c86SddxtwLGCcmauP0ylycz0xOhGMwYK0UD2HkBBSE1um42fwsKn9JK+sJ56ZD37Rj2lUcsvCqISYef3tvd6cIseeVdX6iRVi/8OW762vt55HFw/FypLdBGGoM66vWh0HbcEbiHEn1dRBMVZOrCCHqnPvBqKM8HdX6d3Xf+BTjJV112rIHc//PQwlOXplK6h3eHIJojsB+P/D7yDW/79nPtkA44QbV5VGaDA9Qw3ZLn76o9akQPgoW2w+m09312aCwNu0mu6a3dMRg/Wo+ePJ+lsgqBTuoYlwD2rz/y9tGESuIVoCwxD575e/4F+0Dj4Lpjwf01z76Uvwcr/6M+Tv4Qh98KNP+pVIpXF+mHi8UPWoF0rc0fTfP02SAK3EG3B3oW266tXvQGxI/WKjH2/6pF41wsaTwx1KrWrSmpt/AR6X6nnuSuL9X97Xg7+MdaVJKCzDJ6NyhK9kacsXz8ADYg+u/vZEQncQrQFGVsbnjuyGZa8oOepQa8quX0JuPvBps8gczt0Hqs325yMu6/OFljLzQdWv6mDdq3tc+Dq2bDhfT2l0u0iGPlYg1tZVJXp9dq11r2vH2b6hOnVJmE94ZMLIGOLfn3ZK3D77xDS9eR9dRASuIVoCzqNhqUv2J5zcrEGbdDL9TZ9rgP29podlRs/hUkvQf8b4ccHYMc88I+Gya9A/ATdZuxf4fu7dcIolD6um6+7ll84XFdnp+bOH2DJizqH98DbYPiDuvjC3Nt1H9r3hWkf6fXhv/zFel3aOpjytjVog56GSfoYLnj5zL9HdkQCtxBtQcxgHexWv6nzfwx/iAYVawBK83RwrivpE71JZtu3+jg/Fb69GR7dpXdaegbqEmWVxRA1UK+99ovUwdsw62uih4Crt15iGNJVj7q/vdmaqGrx3yGwEyx/VQdt0Lsz59/fcLVJWX7DKjzACRZgOCQJ3EK0Ff2v1x+1ygtst467+egNLkkf2aZfdffVm2rqqizS6VoXPgkFafpc7+l6ow5A/Hi4dbH+JeAXqYsGvz2g5rXzoevkhtkFU5bajqJBb50fem+9N6L0Rp49P+vXQU/vDLz1dL4bds2pOW+ulJqklNqjlNqvlHqykdf9lVI/KqW2KKV2KKVubs7+CCHq8PCHO5boZFEjHoY7lkF4DzivztyzsxuMeaphdkB3fx24a4M2wNZZOh/3kY3w44OwfS4Muh38o2DPAmu7fb/qKjn1RfaDjiNsz8WO1FkCY2q+vrO7LkIcHAc3LYCpM3Q5tHvW6pF+G9FsG3CUUs7AXmACkA5sAK42DGNnnTZPA/6GYTyhlAoB9gDhhmFUnui+sgFHiBZwbLterhc7Um+DryrTmfu2z9MPMS94CZJnwtZ6iaEmvwaLntKJqUAnkBp8F/zxrG27Mc/oh4/Lairo9LtO59QuytCbdNI3QMxQuOh1a37w/EOAoUfZATF6Dbnja/ENOIOA/YZhHABQSn0DTAV21mljAL5KKQX4AHlAdf0bieZRUlHN1+sOcyivhEk92jMiXnauiRrhPW0LHLh6wtR39EddW2dhmSsP6KB3R5rqjLtKssHFQ4+UTTpfDk4uelVJWIIO6maTdUOQf03tysaUF8BnF+n/gi6XNvnfZ/1W7VFzBu5IoM7fUaQDg+u1eRuYDxwFfIHphlH7NMNKKXUHcAdATExMs3S2rTAMA/17Em77LIk1B3IB+HLtYd6+ph8X9W4kM5xom5I+hq2z9Yh39FMNl9p1Gg03ztcbbbyDYfDdkPxVw/uEdIMbftDryA2zDtZhNZkAnV31R2WpHm0HdtIbhACOp+kHnP7Rel585X+sQRtg/fsw/AEd7NuY5gzcjQ3x68/LTASSgbFAZ2CxUmqFYRiFNhcZxvvA+6CnSpq+q46joLSKRTuP4ePuwvjuYbi56McYP209yvM/7yK3pJJp/SO5eXisJWjX+nrdYQncQkv+Wk9Z1Dq0RieKKi+wTo/0uVrn3I49DwrSdRGGXlfoTH65+3WbzmP1h5OTLqAAUFWu7+Phr493fK/TulYUQHAXuPobvbrls4uhuky36XEZlB2v10lDlzXzb6bvwTmsOQN3OlB3K1MUemRd183AS4aeaN+vlEoFugH1HmGLP+Po8TKmvrPKksK1b3QAc+4aSl5JJQ/PSqbKpH/nzVyfRoivO04KzHV+Dfq4yyIjUWPXT7bHxcd04qifH9UJpQDWzNBlznbM00UUDJN+aHn1TJ2Mavt3es31ZxfDhH9B1ABY9abeIl9dpleGXPS6fpBZUTOSztmrlwYqJ2vQBtgxFya9DAeWWM9FDbKO3NuY5vyXugGIV0rFAkeAq4Br6rU5DIwDViilwoCuwIFm7JNDm7n+sCVoAySnHWf5vmwMA0vQrpWSVcINQzvy6eqDAHi7OXPPmLiW7K44lwXG2h4rJ/3AsjZogw7mW2bqQFy7tK+iQOftjp+ga0GCzlPy9RVw3VxY/Dfr9du/0yPsursuAXL26SmT+jqN0vfY+QO066A37bRRzRa4DcOoVkrdBywCnIGPDcPYoZS6q+b194BngU+VUtvQUytPGIbRyDoh8WdUVDd4PEB5lZmBHQNxc3ai0mR9vU+0PyG+7txxXiei23kyuVd7gnzaXkJ6cQLDH4LU5XBsq95kM/pJ69RGXWaz7dZ20ME95Q/bc6W5upZlfQXpEN4Ljm2znus2WU+/7PvV+guh02idBja0O8SNO5t35hAkH7cD2Z9VxJS3V1lKk8UEenHTsA7sOFqEt5szy/dlk1tcySX9Itl4KI+dGTpbW5C3G/PvH0FkgOfJbi/aoqzd+sGjd7CeY35/lLUMWbuOeu33d7fWpHGtMe7veo66blIrZ3fd9v1R1tUlAJd/ondb/v5/+muFJehMhl6BOlinJ+mlf72nn3kqWvsmhRTagtScEuZtSsfb3YU9x4qYu/mI5bV7Rnfm8Und+HXHMe74YqPNdfePjePR89tGgh5xFsoL9VQF6ORTHn763Jq39brv+Ak6r0n5cZhzix55e7bT89N9pusAv+RF/fqAm/TmmlqH1sCnk63b5H3CdXmyM8lY6DikkEJbEBvszSPnd8VkNuj+N9tEP7M2pPH4pG6YzA1/WdefAxeiUR5+ttvma8+Nedr2XFUZdBypdzz2u14nmNr/G/z8mE5mlTAFBtTbKL31G2vQBj2HnvIHdL+4ed6LHZPA7aCcFPh6uJBbYt0M4e/lSmW1mR4RfnQK8eZAdgmgCwhfNbDt5DIWZ6myRCecKi9oPA92QTq8N1KXKAOdcfDWRTqpVG0B4dosg+fX2VHpHdLwazV2TkjgdlRKKZ6Y1I0n527FbICLk2JC9zCGvPg7eSWV9Izw45EJ8Tg7OTG1bwRR7bxau8vCHpiq4eOJ1oeJy16B2/+wFhQGvdKkNmiDLp6Q9Ik1aNdKT4K9v+qVJsWZ0H2qXk2SV7OwLOESiBnSrG/HXkngdmBXDoxmSKcgth45Ts8If6a8vZLCcp1RYPvRQuLDfHl9et/W7aSwLweW2K4AqSzSFW8mvWg959RIWPGPBo8A26V/EX1h9g3W9dqbPtUJo8ISdNvI/k3efUfRrNkBReuLCfLiot4RGGAJ2rX2HCtq/CIhTqixZ2VKZwpc8R84vA76XqvTxdYK7gq9psH0L3XlGjdf6HsddBhhu8kG4PAavdNSgvZJyYjbwW06nM+alFy6t/clNtib1JwSy2uju8r8oThNnUbryjQZyfrY3R/M1fD5FGubya/q6u1r37OWP1POOtPgtd/qHCPlBTplrJMrmKus10YltuCbsV+yHNCBzd6QxuPfWWsNXjUompyiClKySxjfPZTHJnbF3cW5FXso7FJVmX64WHYcuk+BGYNtN+H4R8Ml78IXl+igDjrgXzUT3h4Ihen6nJMLjHpSF24oydF5Ti5+A1xkI1gdshywrXlvWYrN8febj7DlH+dLsBYnVlkCv/4V9v2mdylOehGCOuvXTFV667urp7XSjdnU8B6GAeveswZtgANL9Vx4bdAG/XpRBjy6W9/Hqd7PpakK1n8A6ev1ssKBtzZs00bJHLcQwmrx33U614LDsG8RzLpeB+KFT8ELkfByR1j1X2t7J+eGpcWGP9D4A0qPgIbnPNvp/5pNOj3sitd0rhLQhRsWPVWTxOovsOiZpniHDkECtwO7c5Rtop7rh3Qgr866biEaqJ9jJGsHbPgQ1s7QW9UrCnVwP7IRKoph81fg2x6u+FSXQLvpZxh8Jwy9TxdQqNXtIj1K7zLJes4/Rpc2A5g5HebdCb//C94dpndRJs+07Uvy183ylu2RTJU4sOkDY4gL9WX1/hyKK6uZue4wH6xIpVekPx/ckEi4f5vM/SBOJqyHdR016G3ndY9rHV4L8+7SaVhBryK5czn41Dzwjh4Id63SUyah3fQuSaV0ru0DS3XaWDcvKDyqq+TU/YVhqtTXeQVZCxlDw2rvbZiMuB3cgA7tuGVELF+uOWRZDrjtSAGv/rqnlXsmzkkTX9CrRkAH40vfhdhR9RopqK6wBm3QAXZLnRFxXip8PhU2fKC3uf/xXM2lSk+1JH0Iq9+ED8fpJYT1KSe9q7J2ysXZTef0FoCMuB3SzqOFPP7dFnYcLWR452DuHdOZkkrbh0j7MmUNt2hEQAxcPw82fqpXd4T31iPdCf+Cdf/T0x+jnrBdwlfLVOfcilfrPIg0YNUbkHiLfvhZtxiCYdbpW+Mm6EINoL/GkLshehB0GK6LA0clgk9oM71p+yOB284czCnBx8OF4JPkzn7gm83sz9LLs1buz8HT1YmYQC8O55Va2ozpJv8IRCPK8uF/o/TDSdAVa+5aCcMf1B+1ygtg6cvWdl5Buvbj6reg8zgoOmZ7X8MMxVng1a7h13Rx0+ldd/4AhUf0fLi7L8y7W68Xjz1PF1EQFhK47URReRW3fZbEutQ8nJ0Ud5zXiScmdWvQrrC8yhK0ayWnF/DNHUN46ZfdpOaUMLFHGPdKtRvRmB3zrMEYdIa+bbNh0B1wcKXOBBg5QBdVuGOpzuhnqoTsvfrhIoD6h35AWTdHd3AXiOina0/2ukInqQJw8YRhD+qpkU6jrfPYn14EB1foz7N2QlUpTHmrud+93ZDAbSc+X3OIdak6cY/JbPDu0hSm9Imge3s/m3Z+Hq50C/dld53t7P1jAvh63WH2ZxXTOcSHKwZE4+osjzdEYxrZ71FZCjOGQm7NMr3uF8OVX4B3kF4KWJoHr9QZCBgmnUDq8k90eTL/aD1ad3KCnP3gFQJdL4Twnjrla85eeL2HXtMd0Q8uec8atGvtsU1R3NbJv147cbDOVvVa+zKLeH3xXqa9u5q/fr/NstTvrav70T8mAHcXJ8Z1CyXE152PVqaSmlPCb7syue3zJBrbMbv2QC6frkq1zH9XVJt4Zt42ev9zEZPeWM7q/VJVzuH1uBQCOliP/SJ14d/aoA2w60c4tKrehfV+ngyTHmWH9dBJozzb6RUkH4yFdTNgz8+w9l09Lz7vLh20Qc9nL3leB/u6QqTIR10nHXErpWIMwzh8sjaiZZzfI5xvN1p3nfm6u7DhYB5frNX/ezYeyudAdglf3z6E8iozfaIDGNM1lGuHdOCq99fY3Gt/VjHp+WVEB1pTuf574W5mLNU7LZ0UvHNNf/ZnFfPVOn3/wmNF3PnFRtY+PQ5vqQbvOAqOWEe6Ts7gGaCX9W3/Ts9L95wGS15oeF1Jtl4NUpCmc5H0uQaSv6x5Uel57vdHWXdPbpuj13DXVnMHvSY8+SsoybK9d+YOmPImfHc7lOboXySTXmqOd2+3TvUv8HtA0nSdAyYkhPHaFX2YtSENP09XHhgXx91fbrJpszoll2V7s7ntsw2Wijbzko/QPdyPvZnWee92Xq6E+lkfbpZWVvPhylTLsdmAd5buJ8DTzeb+RRXV7MwoZGDHwOZ4i6KlLX0Jlr2sA3RgJ7hhvi6KkPK7HvmG9wY3H13vMelja+Fe71DYt1gHXdCJpm74QRfxzd4N8efDytdtt7ynLtMrROoLiIGQ7pC9y3qu81hdDq3vtRDWHXpN19Msa97Rub59wmDMM206g+CpAnejCU5E65g2IIppA6IsxzGBXhw5bk2LGezjxk9bjtqUITuQXcJ9Y+I4lFfC9iOFBHm78dK03ri7OFNeZSKnuAJ/T1fM9cqZVVab6RsdwMo60yOers50CfNtxncoWkxBujVog95ks+I1XQD4t39Y26VvgGkfwE0/waYv9GqPhCnw6YXWNhUFsOYtuPxj67nGtrx3maSnSDK26OOwXtDrcug4AhY9rR9Cxk3Qffr2xjr3L9b5URbVlkfbpufQH94B7j5N8d2wO6cK3JFKqTdP9KJhGA80cX/EaXjmwu7c+tkGMgsr8HZz5l9Te5J0ML9Bu3A/D24ZHktucQWX9o8i2MedH5KP8Nfvt1NUXk2PCD8u7N2eH5Ktu9RuGR7LlL4RHMorZcG2DML9PPjHxQn4e7q25FsUzaXwqG19R9DBPHWZ7bntc3Sh35Q/9Kg4tIftSLpWeaHONbLpc52TpPuFsHeRNd9294shog/c/Avs+QXc/fTI2tlFT4WMeVqPvl084aV689trZ+i5cpuvdxzS1kLc+LP5Ltitk6Z1VUodAv5+otcNw/isOTp1MpLW1VaVycyeY0V0CPLC18OVtLxSLp2xmpziCgBGdQnh6PEy9tUsEYzw92DmHUOY/N8VNptypvWP5LwuIezKKOK8LsEM62zdXmwyGzg7yR9fDsVUDW8PgPyD1nOXvAfr/6enSWq5+eriwGtnWM9FDQSUztoH+vORj+pNN7Xc/eDmBXBotV7f3WUSbJ0NC5/Qa8A7joQrP9dru7+6XM+Vu3jCBf/Wbaqsew4I6aYD//JXrOeUEzyQDO3qPEh1TGeU1jW3NYKz+PPKq0xEBnji66FHwtGBXix5bBR/7M4iyNudrKIyHpltzcl9tKCcL9YcarCTcn92Ca9d2Zfx3avxcrNNndlY0DYMA6UkmNstZxe48UdY/qre9NJzGvS9Wq+j/uYavTYbYPSTen67rvQNcN8m2DpLrzZJvBm2zLJtU1EIuSl6PTfoJYM/PqgTVYFe7rf8Fb01viBNn6su0yllh9ytp20Ayy+FLhN1fpSDK3SAH/vXthC0T+hUgbvRVHJKKWfgKsMwvmr6Lok/65VFu/lgeSpVZjMX947gtSv74OrshK+HK1P7RgK6mEJ9/p4udAjy4lCudVST2KEdl7yziuS048QGe/PalX3oH9Nwl9sPyUd4YcEu8kuqmDYgkn9N7Slrwu1VQIxevVFX/AR4cIseKYf11AmiUpdBXp3c7p6BsHMerPyP3vqesw/iG5my8A2H3Qv0iLu63Bq0a2Xu0CPuuioKIPFWvSrl2FY9Mg/vqV+76Se9CsbDT8+1t2GnCtwTlVJPAZHAfGAxcB/wGJAMSOBuQXVHuRsP5fPOEus/pvlbjjKscxDd2vvxt++3cyC7mAkJYTxxQTciAzwtDzEDvFyZNiCaC3tH8PLC3RzMKWVijzC2Hy0gOe04AKk5JTw8K5mlj422GVUfKyjn0dlbqK55kDlzfRpxob7cOiK2hb4DokX4ReiHhrUmPAvZe+D4IT0FMvavOld27Rx55jY9fdJhuF7frZx1CtdvrtXL+UDnKfEO0csIa8WN17lLltVZURKZCP6R+qNjI6tQ/COb/v3aoVMF7s+BfGANcBvwF8ANmGoYRnLzdk3UWp+ax9PztnEgu5ix3cJ49Yre7G0kSdTuY4W8/tteMgv1yOb75KMEeLkx/77hzN10hEqTmcv6R9Le3xOAt67uT3ZxBRH+Hgx58Xebex3KLSUtr5Qv1x1mz7EixnQNob2/pyVo19p8OB+QwO3QQrvBA5shd78ePWdsafhgszBdz2nnpujR8OK/W4M2QNIncNVXut7k8TToeZm1AIOrh36QGdINRj/Vcu/Ljp0qcHcyDKMXgFLqQyAHiDEMQ1LLtZAqk5l7v95EdpEOxr/tyuTlhbu5Z3Qcrs7KZulf9/Z+fLr6kM3161PzUEqhlM6oqWqedSzdk8Vj324hp7iSziHe9Ir0J7PQ+mdrjwg/Hv9uK2sP6G32y/Zmc/eoTrg5O1Fpsv6jHRQra7rbBCdn6+7FyESd8rVuruyYYbpCjakS+t+o57RtGLrgwg0/6MPyAph/P6QsgfBeukZloAwA/qxTBW5LnkbDMExKqVQJ2i0rPb/MErRrbT58nOhAL164tBdfrTtMZbWZG4d14NJ+UbyyaA85xdZHEz0i/Lj4rZWWqZIPlh9g/v3DeXzOVku7lOwS2vt7MLVvBCv35ZAQ4cdD4+KZ9p7tjsvfdmXx1jX9ePmX3eQUV3BFYjTXDm67D4jaLBc3uHE+LH1RLyvsOlmnbS3N1a9v+gLG/1OXPqsV1kvvzqy18CnrBp6iozqZ1Z3LW+od2L1TBe4+SqnCms8V4FlzrADDMAy/E18qmkJ0O0/a+3uQUVBuOdc7yp9p765m46F83FyceHRCF6YPjAHg7Wv68/TcbRzMLWFstzB6RPrZbJXPL63im/VpZNX7ZZCaU8qXtw2xHJdVmvBxd6G4wrpmt32AJxN7hDOxR3hzvV1hL4LjrRtuNn1uDdqgV4eU5sC1c2D7XD290mkU/PZPPc/d/3o90q4rY4sepXvJX3B/xkkDt2EYUlK5lbk4O/HudQP42/fb2Z9VzPiEMHzcXdl4SG+0qaw28/LC3Uzu1R5PN2f2ZxXz8IQujO8eiqebC3M3pTe4p6+HK32i/NmSbs0bMbYmP/fW9ON8sCKVymoT0wdG8/mag1SZDIK83Xh8oiT6EY3w8G/kXIBeoRI/AdLWwyeTrcUXtnyjp0fqTrUExDReTFg0SrIF2YG+0QH8eP8Iy/Edn9tuQDIbsD41l+d+3kV+qf7H0T8mgG/vGsaknuHMWJpiydHd3t+DywdEMaVPBC8s2MWujELO6xLC4xO7kVFQxlXvr6W0Zo23i5Pik5sH4u7iTO8ofzxc5fe4aETXyXqO+/BqfRzcRVer2bcYOo3RDybrVszJ3AYjHoLiTF0oISAGLv2fzkci/hQJ3HZoQkIYv+7MtBwHerux/UiBJWgDbDp8nNUpOYyMD2H+fcP537IDlFeZuHt0ZwK8dPKoKxOj+XlrBu283KisNvPbrixL0AaoNhusPZDLXyY2LNgghIWzq67ufnC5nu5Y+hLMraneHjlArwevr10s3LlMV9xx95egfZokcNuhKxKjKamoZt7mI4T4evDIhC7MTmq40abKZKbaZObuLzexbK9eP7s2NY+vbxvMin3Z3FUnu+DinZk8OK5hVZzapYNCnJRTTQWbdf+DnDqFqI9shIRL9TRI+XF9Lv58iBqgP/dspJSZOCUJ3HbqpuGx3DTcunzqmsExzNqQRlmVHjF3CfNhZHwIv+3KsgRtgC1px5m7KZ3fd9vuWNt2pIBQPw8m9wpnwTZdL3BwbCCX18lGKMQplRc2POfmqdd47/oJIvq22cRQTUkCt4PoEubLLw+O5IfkoxzMLaa00sQ7S/bj00jRg+yiCgLqZflTCiqqzQR4uTE4NpCR8cHcNza+pbovHEWvy/XSwMqa/O+egZC1Bxb8RW/aieind1nK6pGzIoHbgXQM9sbP04V5m/XT+kU7MhnUMRBfdxeKapb1uTk7cVGfCAwDlu7N5njNvPg1g2J47Nstlvwl61LziGznyaX9ZMQtTkNgLNz+ByR9Ci6uEDsKvrzM+vrRzbogwri/Wc9VFMPv/9I5Udr3gQn/0nlOxAlJ4HYwczbaLv9bfzCPL28dzI9bjlJlMnPd0A50CfOlstrM/WPjWLEvh9FdQ+kV6W8pU1ZrfvJRCdzi9KSugJ8e0oUZulygA3F9x21397LwSdj8hf48e7dOJHXzz83eVXsmgdvBBPm42xx7uDqhlEFRRRWV1QalFXoO/OHZyfy8VRdoXbonmycndcVJ6aWFtdoHyINJcRqqK+Dbm6w5Svb8DF5B+qPuBp2EqbbX7V1ke3xopR6Ft9HqNn+GrMGxE/kllXyblMYfuzMx1Uv0VNcjE7rg66F/HysFt4yI5dbPkliw7Ri/7crkxk/Ws2p/Dgu2Zdhc9+PWDO4bE0dt6u0OQV7cM7pzs70f4QDMZji2HUpqgnL+QdvEUqDXad/0s873HTsKLn1fF0UAnZCqOLthBXf/GHDzbu7e2zUZcduB1JwSLpuxyrJOe1SXED67ZRALtx/j+QU7yS2u5LL+kfzj4h70jQ5g1ZNjSTqYR+cQH5btzaa8ypoUymQ2WLonC1cn22RRXm7OPHJ+V6YPiuFYQTl9ovxxkTzb4kSOp+m565y94Oym56UH3gY+4TrvSK3IAbDxUx3UY4ZC94v0ypOZV9mmgC1Ih/xUvSV+6lt61CFOSAK3HfhkVarN5pple7P5Y3cWD8zcbAm+X649TMcgb24b2Qk/D1fGdgsDIDKguMH9OoX4cPt5sZZ83m7OTjwwLp60vFJmLN1PZmEFl/SLZEqfiBZ4d8IuLXtZB23QGQF//Rv0ugKmfwkLHtWj6a6ToegY7P1FtzuyEUpyIDhOB23QleM3fwF3rgQPX/CL1Bt6xElJ4LYDZfXKjAHsOlpoM2IGXVxhdNdiZiel4ebsxDWDYxjTNZQLe7e3zGcPjwvi0n6ReLg6MyIumD3HipjQI5wQH3fGvLrUkkXwj91ZOCvFhb3bN/8bFPanbq1K0FvaM3fClpl6frr7FDj/OXi13pLS3T81nOMGPdpOmNJs3XU0ErjtwDWDY/g++Ygl93bXMF+mDYjiv7/vswnescHeTHl7pWXb+jcb0vjtkfN455r+PDqhmCqTQddwXfLprd/38c7S/VRUm0k6lM+ViVGWoF1r/pYjErhF4xKm6vqPtQI7wdp3Ye8CfZyXonNut+toW/YsMBa6XWRN6Qp6y3vseS3SbUchgdsO9Itpxw/3juCH5CME+bgxfWAM/p6u/Peqvjy/YBc5xRVM6x+FUsom10hOcQW/7siktLKat/7YT6XJzC3DY5mQEMZri/da2v20NYO4kIYPg2S7uzihgbfpDTU7vtdFe0c/CW8l2rbZ9ytcNwe+vRnK8vT89eRXIWaILpyw6XO9Qee8x8AzoDXehd2SwG0nEiL8SIiwpj/PLCzH292FBQ+OxK+mwvv7y1MaXJdTXMG/F1lzR/z3930Ullc1aJddXMldozrzv+UpGAZ0CvbmrlGyqkScgFK6gnttFXeA0O66wK/luJvOX/LILj3qDooDl5rlqn2v0R/ijEjgtkPfJqXx1NxtVJsNfNxd+PDGRIZ0CuLKxGi+XneYgzW7H/tGB+Dq3PDpfJXJaLQEmbebCyNuGYyXuzN9ogJwdpIn++I0XPxf+PZGOH5Yp2qNGQb/G6ULKYz9qzVoi7OmDOPEa4LPRYmJiUZSUtKpGzqoKpOZgc//ZtmqDjpAf3+vrohdXmViye4s3F2dOC8+hJ0ZhUx5e5XNPT68IRE3Fyf++/s+isurGdMthK/XHaawXG+Lf3BcPA9P6NJyb0o4DrMZijL0Q8hfHree920PD27VZc/E6Wh09CQLde1MZbWZgjLbqY7sogqW7Mli4uvLGfbSH2w8lM+oLqG4ODvROyqAZ6f2INTXnQAvVx6t2aCzOiWXawfH8PMDI0jJLrEEbYAZS/eTV1JZ/0sLcWpOTuAfCXt+sT1flKE344gmIVMl55j8kko+WpnKobxSJvcM54Jetqs6vN1dOD8hjEU7rIUULugZzt1fbrRstPlwZSoRAZ7cOKwjC7ZlkF1UwXvXD6B/TDvmbU5n+vtrLdeu2JfTIEhXmQwKy6oI9JbRkThDwfFwoE5dSSdXvcJENAkJ3OeYGz9Zz9aaWpA/bjnKq1f04fIBUSQdzGP9wTz6Rbfj9el9+WB5KjuOFjAiPpiodp58uDLV5j7rUnPZfayQ2Uk66dRbS/bzxvS+fLLqoE27H5KP8PTk7pYalgCDOgbSMVi2HIvTZDbBjnmQtVOXLEtPgqObwMUTzn9WlzMTTUIC9zlkb2aRJWjXmrMxjeLyKv75407LuccndeXB8daNDVlF5bg6K8s6b9D5ud9Zst9ybBjw0cpUXOttY3dSiisGRBMR4MmiHceIDfbm5joFGoT40+Y/AMlfWo8v/R9ED9ZJpjz8TnydOG0yx30OCfB0bbCSI8jHnXeX2S7ze3epPt6VUci3SWlUVJl59Yo+hPq64+KkuKx/JDcM6YhTvXwPzk6Ke8d0xqXO17hhaAd2HSuknZcbr1/Zl4fGd8HbzZnZG9L45/wdLKmplFNeZeLxOVvo+tdfGPPqUst5IQBda3LL17bn1s7QG27qBu3ibPjxIfhwPCx5AarlWcqZkFUl55hXF+3h7ZqRcqC3GzNvH8L1H60jq6jC0sbX3YUHxsXx/ILdgK7G/vY1/ZnYI4xvk9L5aVsG4X7umMwG3206AuigPePa/kzsEU5qTgnL92bTIciLt//YT1LNNEmf6AC+uX0Iz3y/jbk11wE8O7UHeSVVvP6bddOOl5sza58eZ1lDLtq48gL4d2fbau6RiXDLIqgotFa8+WSyNU8JwND7YOLzLdtX+9LoqhKZKjnHPDaxK5f0iyQtr5TBnQLxcnPhjvM68dzPuyxtbhkRy5u/W6dBqs0Gb/y2V4+Kv7NugIjw9+CjGxNZuT+HA9klzNt0BF8PF4Z1DiY22Jt5m9MtQRt0PcpZSYf5frM1aAN8vuZQg9zcpZUmdh0tZHCnoKb+Fgh75OEPg+6Ate/oY+Wst7H/pzuUZEH0EL3O+5Dt0lR2zZfAfQaaNXArpSYB/wWcgQ8Nw3ipkTajgTcAVyDHMIxRzdmnc0lWUTkbD+bTI8KfmCAvy/m4UB/iQq1J5G8b2YmECD82pObTNyaAwbGBNvPXAMUV1fy09ajNuaMF5VSazHyz3lpE+Lddmcy/bwQJEX7kFjf8M7WgrAo3FyebVLBebs4MiGnH8jpFh73cnOkeIfOWoo5JL0CX8yFrl07h+vkUPRIHSFsLq/4LXsG2ObsDO7VOX+1cs81xK6WcgXeAC4AE4GqlVEK9NgHADGCKYRg9gCuaqz/nmiV7shjx8hLu/moTo15dwlfrdDmnrKJyPlt9kLmb0imvsuYd8fNwpWu4L/1jAvBwdW5Qff26IR2IrDcqVgr2ZRZZgjbo0XltEYXJvdrbFBP2dHXm0r5RNlvdXZwU94+N585RnbgyMQp3Fydig715eVovXl20h8tmrOK1X/dQUd0wg6FoQypLYfHf4Y/n9M5Jw2QN2rWydsCFr4FbzaDEL1JnEBSnrTlH3IOA/YZhHABQSn0DTAV21mlzDTDXMIzDAIZhtJknXq8s3ENltR7VGgb8e+EehnQKZNq7ayy7Ij9ZdZB59wzjuZ938enqgwC083Jl1p1Dee6SnvSLCWDbkQJGxAUzqWd7jhWUs3J/DinZJTgpuG9sPN3b+zf42n6eLjwyK5nktOOMiAvC18MVF2cnbhjagZggLx4a34XRXUPZnVHIsM7Blr8Gnr+0F/++XNcQvP6jdazYp0dOmw4fp6i8mn9O6dHc3zZxrlrwmDXjX/qGmm3vHWzrS3YeCz0ugbhxOi1sSHdwltnaM9Gcq0oigbQ6x+k15+rqArRTSi1VSm1USt3Q2I2UUncopZKUUknZ2dmNNbE7x0ttpymKK6r5eu1hm63s244UMHfTET5bc9ByLr+0ineXppCWX8YPyUf5YfNRvtt0hOyiCsL9PXj2kp6M7x7KdYM7cN2QGMZ2C2VSD2vF7MGxgSzdk83czUc4kFPCwh2ZlFebefGyXnRvb5366BsdwFWDYogJ8iLpYB5jX1tK/DO/cN2H6ziUW2IJ2rV+2W5bCk20Mbt+tD3eswCumqmDdUAMDL4bRj+lX3P3hfBeErTPQnN+5xp7Glp/CYsLMAAYB3gCa5RSaw3D2GtzkWG8D7wPelVJM/S1xV2RGM1/f99nOZ7SJwLVSLmmoooq6i/8yS+t5MFvNlvWfC/emYmzUtwwrAPXfbjOUvB32b5sfntkFO9dP4BNh/Mxmw36RgcQ94ztduSlJ1naZzIb3D9zMxkF5QCs3J/Da7/uIdTX3WalS4dA2bDTprXraJsZMKCDLmHmF6HXcg+6Q5JMNaHmDNzpQHSd4yjgaCNtcgzDKAFKlFLLgT7AXhzcQ+PjiWrnyeqUXHpE+HH90A6k55fxzYY0iit03pCuYb5cN7gD328+yrYj1vnCS/tG8uCsZJv7rUvNxcvN2aZK+6HcUlbvz+GX7cf4dmM6zk6KW0fE0jnEm5TsEku7+DDrg9Dsogr+9dNONh3KJ7FjO+48r5MlaNfadqSQ5y7pySOzt1BcUU2orzt/vah7E353hN254N8w61pdzd3DH3pfqWtS1tr9M9y5XGpJNpFmW8etlHJBB+BxwBFgA3CNYRg76rTpDrwNTATcgPXAVYZhbD/RfR19HXd6finztxzF18OVS/pG4OvhyvHSSj5dfZD0/DIu6t2e0V1Dmfj6cvZkFlmuG901hE7BPny8ynbr+2Pnd+HVX21/D/7flB68tyyFjIJyYgK9ePe6/vSI0HPhN32ynqV7rNNR47qFkJJdYkkVC3DVwGhemtabkopqDuaWEB/qi5uL7OVq86rKdR3KoDj49ibYt8j29TuWQkS/1uiZPWvZddyGYVQrpe4DFqGXA35sGMYOpdRdNa+/ZxjGLqXUQmArYEYvGTxh0G4Lotp5cc/oOJtzAV5u3Dsmjg9XpPLxqoMkHcznhct68sy87ew+VsTAju147pKeACzYlsGxQj1CvrhPBMUVDVd7GIbByifGkllYTkFZFYt3ZrLnWBEX9Y6wWfIHsGJ/Lj/cO5y/fb+dPZlFjOkaylOT9eja293FEvCFwNUD2vfWn3u2a/i6R0CLdseRyc5JO/F/P+6wSRA1uVc4M64dwFt/7GN2Uhp+Hq48en4XhnYKZsW+bI4WlNEhyBsXJ8X1H623XOfspFj00EjiQn1ZnZLDDR+tp7pmfmVM1xCyiirYcbTQ0r5PlD8/3Deixd6ncBBZu+GTC3TJMoDEW+Gi/7Run+yT7Jy0Zz9usX08sHD7Mb7blM5rlmmQMu78YiO/PzqaD1eksv6g/gfTJcyHf05JYOa6NFycFYNjA9mSVkC4vyefrDpoCdoAS/Zk8+61/Xh54R4O5pbSKdibFy/r3VJvUTiS0G7w0FY4sFRXwJEpkiYlgdtOhPt7kFNnp2OwjzvrUnJt2lSZDD5ZZQ3aAHszizGZYe49w7jknVV8XDNqf+P3vcSH+jb4Ol3C/Vjy2GhySyoJ8nZrsNLFMAzMBlLWTJyauy90v7i1e+GQJHCfwwrLq/h89UEO5eqiCodySykqr8bdxYl/XNyD3JIKZm9Mt7nGv5GkTwVlVSzYlsG+rGLLubS8MsZ1C8PNxcmyEWhSj3A6h+gVJsE+7pRXmXB2UpZUsHM3pfPCgt0UlFVyab9InruklzyUFKIVSOA+h9366QY2HLQmgXr5sl60D/Dkp61H+fei3cSH+jCxRzi/7crEy9WZKxKjSOwYSDsvV/JrNvJ4uDoxtW8Eaw/kNrh/mJ8Hvz50Hh+sOMDx0kou66+30VebzDwzbzvfbUrH082Zh8Z34YKe4fxlzlZMNVMrs5PS6RLmy20jJdeEEC1NAvc5an9WsU3QBpiXfISuYb6WqjaHckvp3t6P9U+P444vkvh41UE+XnWQgR0D6RXpR1F5NaWV1fzt++0Mjwuy2TTTzsuVS/tFsmBbBl+tOwzAz9uO8dD4eEJ9PZiVpDe9FpVX8+xPO3FSWIJ2rS31ij4IIVqGBO5zlJ+HC04Kmw01AZ5uLK23XG9XRiHfbUxn46HjlnMbDuZx8/COzFi6n+1H9AqR1Sm53D8mDg83Z0xmg8sHRBHu78H7yw/Y3O/DFalc3Me2ziXoR9tuzk5UmqxZAwfHBp79GxVCnDaZoDxHhfp52ExD+Hq4cN/YOLqG2T5QDPF1p6SRtdq7MgotQbvWsn3Z3DisI+28XJmdlEZKdnGDjWwKGNY52Oaci5NifEIYM67tT1yoD4HebtxxXieuGRRzdm9SOA7DgC3fwE8Pw9bZNMjTIJqUjLjPYU9P7s7UvhEczi1lWFww/p6u/O2iBNLzy9iZUUiIrzuvXtGHyAAP3l2eYnnI6OXmzJQ+EXyw4oBNXu3IAE+m/2+NZZ32u0tTuHl4R95bZh113zmqExf3iSAtv5Sv1h7G18OFhyd0IaqdF1HtvBifENay3wRhHxb/DVa/pT9P+ljn5B7/j9btkwOTDTitYPPhfJbvzaFruC/nJ4ThdAZL67KKygn0csOlZsXHpsP5fL76IM5OTtw8vCM9I/35Ys1Bnv1pF5UmMxH+Hjw8oQt/mbPV5j5XD4rh8gGRrEvNo29UAMPighv7cieVXVTBmgO5dA3zpWt4wyWGog14PgKqrPlv8PCHJw+3Xn8ch2zAORf8kHyEh2YlW/6SvG5IDM9d0otZGw7z4YpUnJ0U94yJY0qfiJPeJ9TXw+a4f0w7+sfYbjO+fmhHLujVnj92Z9Eh0BOjkZ8BV2fFgA6BDOjw5+ar92cVsT+rmMGxQbTzdmN1Sg63fLrBMrL/y8Su3Dsm7hR3EQ7H3cc2cLv5Qs4+ne7VPwoSLgEXt1brnqORwN3C3l9+wGb675v1aYzrFsoT322znHvwm83Eh/rg6qx4/bd9ZBaUM7VfJNcP6XBaX6vKZObhWcmW3Nn9ov0Z2LGdZbWKn4cLNwzt2Oi1B3NK+GrdIUxmuGZwDHGhPryzZD+vLNoDgLebM5/dMog3fttnMx3z1h/7uGlYR7zd5UerTRn7V5j/ADpzs9LZAd8dBqaaTWNbZ8N1c1qzhw5F/nW1MKd6TwOVgrUH8mzOGQYs25vFxysPWpbvJR3Kx8PFiSsSo9mXWcTuY0UM7hTYYORd1687Mm0KHmxOK+BfU3tw07BY8koqmNgz3HL97mOF+Hu60t7fk6zCcqa+s4qCMr0WfHZSGt/dPdQmf3hJpYk3fttHYVmVzdesqDZTXmWSwN3W9L9BFwRO36Dzb//+f9agDbB/MWTuhLCEE99D/Gnyr6uF3TmqE/fP3GwZdV87uAN9ogMatHN2crIpVACwaMcx8koqefGX3QC4uzjx8U0DGR4XzNHjZSzfm01ssLel8np2UXmD++aXVNmMsgtKq7jhk/VsSTuOk4Kbh8cS3c7TErRBV+f5Ifmo5eGn5V6llVw9KIZ/zLdk6mVC9zCCfCRhfpsU0kV/nIjk4m4yErhb2EW9I4gN9mbFPv1wcnSXEAwDbhjagZnrD+OkFLeMiGVyr/a8uGCXzTruyABP3vjNOuqtqDbzn8V7cXFS3PDxeipqAutNwzryzyk9mNgznFcW7aGkUi8XdHNxIiLAg2fmbSOynSfXD+nAJ6tT2ZJ2HNBrxj9amcqjExr+44ts58noriE2ubqnD4zmhqEdCfV1Z8meLLqE+XLdaU7nCAc19F7YuwhMNYOP+IkQKsU2moqsKjmHlFRU46QUnm7OgF6u95/Fe6gyGfSI8OOtq/sx7j/LbObI40J9iAjwtMmj7aRg3dPj8XZ35uetGazan4uTE3QJ8+WlmtE6QL+YADqH+DCnXr6Tt67uxwcrDlhKo3UL9+W7u4cB8Nmag+zPLGZs91Au6n3yB6iijctNqfNwcio4N8yjI05JVpWc6+rPC989ujPTB0aTV1JBXE0mvwt6hrNg2zFLm6sGRvPrjkyb68wGrD2Qy9PztlFUXo2HqxNvTO/LNxvSbNptPnycqX0ibQJ3gJcro7uGcEHPcFbuz8FsGIyMD7Ekmqpf5EGIEwrqDCMeau1eOCQJ3Oe4QG83Ar2ty6j+c2VfBnQ4zO6MQkZ1DeGi3hGE+XnYpHId1y2U/y1Poahc164srzLzj/k7GNTRdsmfUjCueyhBPm7MTkqrqbTTGd+aDIOju4a2wDsUQpwuCdx2xsPVmVtHxAKQV1JJtcnMxX0iCPF1Z/HOTGKDvbl8QBTDX/rD5rrsogpuGxnL0r3ZloB+9aAYogO9iA704uI668arTWbLxh4hxLlHArcdOlZQzl1fbiQ57Tghvu68dFkvxnUPY0jNapLyKhMX9m7P52sOWa6Z2COcPtHtWPH4GJbvyyGqnSdllSY+XHGAkfEhdA33pbiimsfnbGHh9mO09/fk/6b0kC3uQpyD5OGkHXrom818n2wtZdbOy5W1T4+jpMLEg99sZsW+HCIDPBgWF8yxgnJCfNwpLKvCAG4c1pHzuoTwwoJdlsyATgreuaY/W9ILeG9ZiuW+3m7OrH16nGXqRAjR4uThpKPYfazI5ji/tIpjBeW8uzTFsuHmyPFyft+VxZy7hjL5zRWW3Y1L92Yz8/bBfFqn8LDZgBlLU/Cp93C0pNLEvqziBlvphRCtSyYy7dDIeNtEULHB3sQEelmW79XKK6nk+81HbLakm8wGi3dmYqr3l1a12WBQvfza/p6udA/3a+LeCyHOloy47dCj53elymTw265Motp5Mj0xhmqzwdDOQezMsObgjvD3oHeUf4PrO4f4cGViFDPXW5cH3joilov7tCenuIKftmYQ1c6Tv1+UYFlTLoQ4d8gctx1747e9/Pf3fRiG3lX50Y2JfLLqIIt3ZdIp2Jt/TulBQns/HpyVzI9b9Jz4yPhgPrghETdnJxZsz2B3RhHndQmxjLazCsvxcndpMG0ihGgVjc5xS+C2U8cKyhn+8h82dSCvGBDFK1f0abT9wZwSqkxm4sMaz5ddUlHN3V9tYvnebDxcnXhofBfuGtW5WfouhPjT5OGkI8kuqmhQvPdYYTkbDuYxc91hvN1duGVELLHB3gB0rPnviXyyKtWybb68yszLC3czsUe45XohxLlDAred6hHhR1yoD/uzii3n+kUHcPX7a6muCeg/b8tgyWOj8fd0JS2vlGqzYROIc4orSMkqpndUAHszi23ubxi60rwEbiHOPRK47ZSTk+LLWwfz3rIU0vNLubB3e7alF1qCNuhVJb/vymTl/hzmbjoCwJiuIbx3/QDmJx/lmXnbqTSZCfBy5aZhHW3u7+Pu0mCViRDi3CCB2465OCu6hfsyMj6Y0V1DOXq8Yf7to8fLLEEbYMmebOZsTOPfC/dSadLLBI+XVrEmJZe/X5TAtxvTCfJ24+EJXfD3lI03QpyLJHDbqb2ZRUx7d7Ul78i4bqG8dmUffkg+Ypn2mJAQ1ujqkAPZJTaFEkDPj98yIpZbavKgCCHOXRK47dTHK1MtQRvg991ZpOWV8cuD57EuNRcfdxd6RwWQllfKC7/stlSvcVIwtW8k+7JKbHJ4Tz1FcWIhxLlDAredqqhXRgwgp6SCt/7Yx8GcEs7vEU7vqACiA7348tbBvL88hUqTweSe4aTmlPD05G4ktPdjV0YhIb7ubE0/ziOzkrlnTGdL7m8hxLlJ1nHbqaSDeVz9wVqqTPr/X69If9ycFRsPH7e0eXlaL6YPjLEcz9pwmCfnbsMwwNlJ8d+r+uLt7sLNn2ywtAnydmP542Ok2K8Q54ZG13FLrhI7ldgxkB/uHcFdozrz94sSeGlaL5ugDTA7ybYk2b8X7rGUPTOZDV5dtIeftmTYtMktqWR1Sm5zdl0IcZZkWGXHEiL8SIjQSaByiytwcVI2ywEDvd3IKa4gq7CCLqE+FFVU21xfWF5NZIBHg/tGNHJOCHHukBG3gwjyceeeMdZ6kH4eLkQGeDL0xd+Z/OYKJr25got7t7e55upB0dw8PJaE9jr4K6UrxPeIaJiYSghx7pA5bgezP6uI1JxSOoV4c/7ry222xV81KJrekQEkp+UzKDaIaf0jUUphGAbbjxQS4OVKdKBXK/ZeCFGP5CppC+JCfYkL9WXT4fwGuUyO5Jfx0mW9uWaw9YHlvswiPlyRSkllNdcMjpHALYQdkMDtoHpH+hPVzpP0/DLLuZHxIXy+5iC+Hi5c0LM9ZZUmLn9vjWUzzi/bj/Hd3cPoGx3QSr0WQvwZErgdlIuzE1/dNpj//r6Po8fLGN45mBlL93O8VAfpT1Yd5PohHWx2UJrMBvOTj0rgFuIcJ4HbAZVWVpNRUE6nYG/+c2VfAF5csMsStAG2pheQWdgwt0mYn3tLdVMIcYYkcDuYH5KP8My87RRXVNMpxJuPbxxIx2DvBvPdAF3CfJncK5wF244BOlXsVYNiGrQTQpxbJHA7kLJKE3/9Xgdt0Mmk/r1oNzOuHcBVg2L4ZkOa5bUuYT6M7hrK+T3C2ZVRSGllNf2i2+Hk1OhDbCHEOUQCtwPJKa6wSTwFOnjvyijkk1WpDOscRLCPO93a+zKwQyC3fZ7EjiMFDO0cxLNTe0rQFsJOSOB2INGBXnQL92X3sSLLuaGdg7jivTWWkbabsxM/3j+Cv8zZwtb0AgB+2pqBUoq3ru7XKv0WQpweCdwO5qObBvLKwt3szy5mXLcw2nm7WYI2QKXJzLxN6ZagXWtNSk5Ld1UIcYYkcDuYyABP3rjKOnJeuP1YwzaBXnQK9uZATonlXM9I2eYuhL2QXCUOxjAMtqQdZ2+mni4Z3z2U0V1DLK/3jQ5gWv9I/jO9L51qCgH3ifLn2ak9W6W/QojTJ7lKHEhxRTXXfbiO5LTjAFzYuz1vX90PpRTb0guoNJnoH9MOpawPIUsqqiX3thDnLslV4uhmbUizBG2An7dmMD0xmkBvNz5aeYAqk0HVUIMhnYIsbSRoC2F/5F+tAzlWUNbg3O5jhbzx2z5KK00ALNpxjB/vH0H3mlSuQgj7I3PcDuSi3hE411mL7evugslsWII2QLXZYMG2jMYuF0LYCRlxO5A+0QF8fssgvlp3CA9XZ24f2ckmO2CtcH+pcCOEPZPA7WCGxwUzPC7YctwlzJcJCWEs3pkJwMCO7bisX1RrdU8I0QQkcDs4ZyfFBzcksiujkCqTmd5RAa3dJSHEWWrWOW6l1CSl1B6l1H6l1JMnaTdQKWVSSl3enP1py7q395OgLYSDaLbArZRyBt4BLgASgKuVUgknaPcysKi5+iKEEI6kOUfcg4D9hmEcMAyjEvgGmNpIu/uB74CsZuyLEEI4jOac444E0uocpwOD6zZQSkUClwJjgYEnupFS6g7gDoCYGEn0fyrvLNnPV2sP4e3uwiMTunBBr/at3SUhRBNqzhF3Y1s16++vfwN4wjAMUyNtrRcZxvuGYSQahpEYEhJysqZt3k9bj/LKoj0cLShnX1Yx983cTFpeaWt3SwjRhJpzxJ0ORNc5jgKO1muTCHxTkzsjGJislKo2DOP7ZuyXQ1uTkmtzbDIbrEvNIzrQq5V6JIRoas054t4AxCulYpVSbsBVwPy6DQzDiDUMo6NhGB2BOcA9ErTPTu+ohulZPVydeGRWMvd9vYn1qXmt0CshRFNqthG3YRjVSqn70KtFnIGPDcPYoZS6q+b195rra7dllw+IZkt6Ad8mpeHp6sytIzrxxJytlNTLVdItXHKVCGGvJK2rg6qoNuGsFDPXH+ZvP+ywee3+sXE8en7XVuqZEOI0SFrXtqC4oprnftrJin05dG/vx9huDR/mhvlJrhIh7JkEbgfz7I87mZWkV2EeOV7GsYIyxncP5bddepl8Yod2TOsvuUqEsGcSuB3M8n3ZNsfbjxby5W2DScsrpaLaTGLHwFbqmRCiqUjgdjDd2/uRUVBuOY4M8OTLtYd4b9kBqkxmbhjagacnd7cpXyaEsC9SSMHB/OPiBBJqqttE+Htwx3mxvPrrXoorqqmoNvPBilR+aaTyuxDCfsiI28F0CPJmwYMjyS2uIMDLjc/XHGzQZmt6AZNlG7wQdksCt4MK8nEHYHBsEEpB3VWfQzsHneAqIYQ9kMDtYNLySvluUzpuLk5cMSCahAg/Xrm8D+8s2U9ltZlbRsQyqovkexHCnskGHAeSllfK5DdXUFReDeg57oUPn4efh2sr90wIcYZkA46j+25TuiVoAxwtKGfR9mMYBry1ZB9V1Qa3jOjIHed1bsVeCiHOlgRuB+Lu4tzgXFZRBa8s2mM5fmHBbuLDfBnTNbQluyaEaEKyHNCBXJEYRWSAp+W4e3s/3Jwb/qW17oBkCBTCnsmI24EE+7iz8KGRLNqRibuLExMSwthxtLBBu77RDVO/CiHshwRuB+Pr4crlA6y5SAZ0aMdTF3Tj7SX7qTYZ3DisIxN7hLdiD4UQZ0tWlbQRZrOBATg7yVZ3IeyIrCppy5wkYAvhMCRwO5ic4gq+TUqnvMrE5QOipNakEA5IArcDKSqvYurbqzhyvAyAj1elsuCBkRK8hXAwErgdyK87Mi1BG6CovJpvN6YzrX8kX6w5RJXJzNWDY6TepBB2TgK3A3F3bbgs32w2uOSdVeSXVgEwOymdnx8YQacQn5bunhCiicgGHAcyvnuYJRc36Fwlvh4ulqANUFZl4ofko63RPSFEE5ERtwPxcHVm7j3D+G1XJuVVZib2CGPZ3uwG7QK8JOmUEPZMAreD8XB15qLeEZbj8xPCSezQjqRD+QB0CfNh2gApFiyEPZPA7eDcXJyYfedQVqfkUmUyMyI+GFdnmSETwp5J4G4DnJwUI+KDW7sbQogmIkMvIYSwMxK4hRDCzkjgFkIIOyOBWwgh7IwEbiGEsDMSuIUQws5I4BZCCDsjgVsIIeyMBG4hhLAzEriFEMLOSOAWQgg7I4FbCCHsjATuNmLT4XzWHcjFbDZauytCiLMk2QEdXLXJzM2fbmDFvhwAekf5M/P2IXi7y/96IeyVjLgd3G+7Mi1BG2BregHfbUpvxR4JIc6WBG4Hl1Nc2fBcUUUr9EQI0VQkcDu48xPC8PWwTou4uThxcZ+Ik1whhDjXyUSngwv18+C7u4fxyaqDVJnMXDekA/Fhvq3dLSHEWVCGYV+rDBITE42kpKTW7oYQQrQE1dhJmSoRQgg7I4FbCCHsjARuIYSwMxK4hRDCzkjgFkIIOyOBWwgh7IwEbiGEsDMSuIUQws5I4BZCCDsjgVsIIeyMBG4hhLAzdperRCmVDRxq7X7YqWAg55SthGha8nN35nIMw5hU/6TdBW5x5pRSSYZhJLZ2P0TbIj93TU+mSoQQws5I4BZCCDsjgbtteb+1OyDaJPm5a2Iyxy2EEHZGRtxCCGFnJHALIYSdkcAtbCilblJKvd3a/RCiMUqpT5VSl7d2P1qbBO42Tinl3Np9EKIx8rN5YhK47ZhS6nGl1AM1n7+ulPqj5vNxSqkvlVJXK6W2KaW2K6VernNdsVLqX0qpdcBQpdTNSqm9SqllwPDWeTfiXKCU6qiU2qWU+kAptUMp9atSylMp1VcptVYptVUpNU8p1a6m/VKl1MtKqfU1P0Mja84vUEr1rvl8s1Lq7zWfP6uUuk1pr9T8bG5TSk2veX20UmqJUuprYFtNu7eVUjuVUj8Doa3znTm3SOC2b8uBkTWfJwI+SilXYASwD3gZGAv0BQYqpS6paesNbDcMYzCQAvwfOmBPABJaqvPinBUPvGMYRg/gODAN+Bx4wjCM3sA24B912rsYhjEIeKjO+eXASKWUH1CNdUAwAlgBXIb+uewDjAdeUUq1r2kzCHjGMIwE4FKgK9ALuB0Y1sTv1S5J4LZvG4EBSilfoAJYgw7gI9H/4JYahpFtGEY18BVwXs11JuC7ms8H12lXCcxqwf6Lc1OqYRjJNZ9vBDoDAYZhLKs59xnWnyWAuXXadqz5fEVNmxHAz+hBhRfQ0TCMPTXnZxqGYTIMIxNYBgysuXa9YRipNZ+fV6fdUeCPpnub9sultTsgzpxhGFVKqYPAzcBqYCswBv0P7TAw4ASXlhuGYap7q+bsp7A7FXU+NwEBf7K9CWtM2YAeRBwAFqMTTd2ODu4A6iT3K6l3LD+f9ciI2/4tBx6r+e8K4C4gGVgLjFJKBdc85LkaPaqpbx0wWikVVDPNckWL9FrYkwIgv3b+Griexn+WLGr+eksDrkT/LK5A/5yuqGmyHJiulHJWSoWgR9brG7nVcuCqmnbt0QOTNk9G3PZvBfAMsMYwjBKlVDmwwjCMDKXUU8AS9OhmgWEYP9S/uKbdP9HTLBnAJkCe5ov6bgTeq5nuOID+K+9UVgDjDMMoVUqtAKKwBu55wFBgC3pE/bhhGMeUUt3q3WMe+jnNNmAvp/iF0VbIlnchhLAzMlUihBB2RgK3EELYGQncQghhZyRwCyGEnZHALYQQdkYCtxAnUTcbnVLqQ6VUQs3nT9drt7o1+ifaJlkOKMRJKKU+BX4yDGNOvfPFhmH4tE6vRFsnI27hUJRSN9RksNuilPpCKdVBKfV7zbnflVIxNe0+VUq9qZRarZQ6UGdUfcJsdDWZ8BKVUi8BnkqpZKXUVzWvFde5/kRZ75YqpeYopXYrpb5SSp1s27cQJyQ7J4XDUEr1QO8iHW4YRo5SKhCdEOlzwzA+U0rdArwJXFJzSXt0sqNuwHxgDrbZ6MKAncDHdb+OYRhPKqXuMwyjbyPdqJv1LhjYoJRaXvNaP6AHcBRYhc6Yt/Ks37hoc2TELRzJWGCOYRg5AIZh5KG3VX9d8/oX6EBd63vDMMyGYexEB2k4+2x0p8p6l24YhhmdT6bjad5bCEACt3AsilNnkqv7et0seOoEbc6kDydSP+ue/MUrzogEbuFIfgeuVEoFAdRMlawGrqp5/VpOPTXxZ7PRVdVkU2zs+j+T9U6IMya/8YXDMAxjh1LqeWCZUsoEbAYeAD5WSv0FyObUWe3+bDa694GtSqlNhmFcW+/6P5P1TogzJssBhRDCzshUiRBC2BkJ3EIIYWckcAshhJ2RwC2EEHZGArcQQtgZCdxCCGFnJHALIYSd+X8hBreMXFAN4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='swarm',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "719e896f-7b33-4f9f-b709-d4babb37eb49",
   "metadata": {},
   "source": [
    "## Bar plots\n",
    "\n",
    "These are classic ways of representing data, that focus more on showing and comparing means for different conditions, than on the distribution. The \"error bars\" (black bars at the top of each bar below) represent 95% confidence intervals, which are a way of representing variance that is better-suited for comparing between means, than for assessing the shape or breadth of the distribution. You can see below that the bar plots lose a lot of the information about the distributions of data, relative to box plots or any of the other plots above. \n",
    "\n",
    "Another limitation of bar plots is that they are \"anchored\" at zero. For some data this makes sense, especially if the data values relative to zero are meaningful. But in many cases — including our current RT data — this doesn't make a lot of sense. An RT of zero would be considered invalid data, since the human nervous system can't execute a response to a stimulus at the speed of light. So our experiment is not really concerned with how much slower than zero a person responded. As well, if your data values are far from zero, and the differences between conditions are relatively small, then bar plots make make the differences look meaninglessly small, because the *y* axis has to span the entire range from zero to the maximum value in the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd65d82b-e050-494d-9f3d-299e0c500fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='bar',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a18a0f-b16a-40d3-bc11-d61c04a8f5ed",
   "metadata": {},
   "source": [
    "## Point and Line Plots\n",
    "\n",
    "Point plots generally are a better choice when you want to focus on comparing means between categories, because they simply show the means as points, with 95% confidence intervals. The scale of the plots will be more appropriate, without the visual bias that bar plots create by filling the range from zero to the mean of each condition with a big block of colour.\n",
    "\n",
    "By default, Seaborn draws lines connecting the points in a plot. This is only really appropriate if your categories are \"connected\" in some meaningful way, such as different time points (e.g., pre- vs. post-treatment for some intervention), or when you have multiple categorical variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "56e4d278-ff0d-4435-8ff2-82d344a0f1b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='point',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b144e1-cdb4-4c35-9733-8e3b81a3ac90",
   "metadata": {},
   "source": [
    "We can remove the lines with `join=False`, and colour-code the different levels of condition with `hue='condition'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "35aeb163-4bf2-48b9-9a17-ffcf3764210d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(kind='point', join=False, hue='condition',\n",
    "           data=df, \n",
    "           x='condition', y='RT')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b0980f-071f-493c-ab53-9f0d6eb0bf50",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Summary\n",
    "- Seaborn makes it easy to generate plots useful for EDA\n",
    "- `relplot()` is used for visualizing relationships between two continuous variables\n",
    "- `displot()` is used for visualizing distributions of data, as histograms or kernel density estimates (KDEs)\n",
    "- `catplot()` is used for making comparisons between different levels of categorical data, such as different experimental conditions or groups\n",
    "- Different kinds of `catplot()` plots vary in how much they communicate information about the distributions of data in each category, versus on differences between the means of the categories\n",
    "- In EDA, it's often useful to visualize the data many ways to really understand it. Even still, it's important to have an understanding of the strengths and limitations of different kinds of plots\n",
    "- Seaborn makes it relatively easy to generate plots and adjust properties such as colour and font size, and as well as making plots accessible to the widest range of viewers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c4d5f9-17a9-4820-b0f5-00c9c591bd07",
   "metadata": {},
   "source": [
    "---\n",
    "This section was adapted from Aaron J. Newman's [Data Science for Psychology and Neuroscience - in Python](https://neuraldatascience.io/intro.html)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}