source/notebooks/L7/matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic plotting with pandas and Matplotlib\n",
    "\n",
    "```{attention}\n",
    "Finnish university students are encouraged to use the CSC Notebooks platform.<br/>\n",
    "<a href=\"https://notebooks.csc.fi/\"><img alt=\"CSC badge\" src=\"https://img.shields.io/badge/launch-CSC%20notebook-blue.svg\" style=\"vertical-align:text-bottom\"></a>\n",
    "```\n",
    "\n",
    "```{note}\n",
    "We do not recommended using Binder for this lesson.\n",
    "```\n",
    "\n",
    "As we're now familiar with some of the features of [pandas](https://pandas.pydata.org/), we will wade into visualizing our data in Python using the built-in plotting options available directly in pandas. Much like the case of pandas being built upon [NumPy](https://numpy.org/), plotting in pandas takes advantage of plotting features from the [Matplotlib](https://matplotlib.org/) plotting library. Plotting in pandas provides a basic framework for visualizing our data, but as you'll see we will sometimes need to also use features from Matplotlib to enhance our plots. In particular, we will use features from the the `pyplot` module in Matplotlib, which provides [MATLAB](https://www.mathworks.com/products/matlab.html)-like plotting.\n",
    "\n",
    "Toward the end of the lesson we will also briefly explore creating interactive plots using the [hvPlot](https://hvplot.holoviz.org/) plotting API, which allows us to produce plots similar to those available in the [Bokeh plotting library](https://docs.bokeh.org/en/latest/index.html) using plotting syntax similar to that used normally in pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input data\n",
    "\n",
    "In the lesson this week we are using a subset of the weather observation data from Finland used in Lesson 6, which was downloaded from the [NOAA online databases](https://www.ncei.noaa.gov/cdo-web/). In this case we'll focus on weather observation station data from the Helsinki-Vantaa airport.\n",
    "\n",
    "In case you are working with Jupyter Lab installed on your own computer, Lesson 6 covered [how to download the weather data](https://geo-python-site.readthedocs.io/en/latest/notebooks/L6/advanced-data-processing-with-pandas.html#downloading-the-data). **Those using the CSC Notebooks do not need to download the data**.\n",
    "\n",
    "## About the data\n",
    "\n",
    "As part of the download there are a number of files that describe the weather data. These *metadata* files include:\n",
    "\n",
    "- A list of stations: [6367598020644stn.txt](metadata/6367598020644stn.txt)\n",
    "- Details about weather observations at each station: [6367598020644inv.txt](metadata/6367598020644inv.txt)\n",
    "- A data description (i.e., column names): [3505doc.txt](metadata/3505doc.txt)\n",
    "\n",
    "The input data for this week are separated with varying number of spaces (i.e., fixed width). The first lines and columns of the data look like following:\n",
    "\n",
    "``` \n",
    "  USAF  WBAN YR--MODAHRMN DIR SPD GUS CLG SKC L M H  VSB MW MW MW MW AW AW AW AW W TEMP DEWP    SLP   ALT    STP MAX MIN PCP01 PCP06 PCP24 PCPXX SD\n",
    "029740 99999 195201010000 200  23 ***  15 OVC 7 2 *  5.0 63 ** ** ** ** ** ** ** 6   36   32  989.2 ***** ****** *** *** ***** ***** ***** ***** **\n",
    "029740 99999 195201010600 220  18 ***   8 OVC 7 2 *  2.2 63 ** ** ** ** ** ** ** 6   37   37  985.9 ***** ****** ***  34 ***** ***** ***** ***** **\n",
    "029740 99999 195201011200 220  21 ***   5 OVC 7 * *  3.8 59 ** ** ** ** ** ** ** 5   39   36  988.1 ***** ****** *** *** ***** ***** ***** ***** **\n",
    "029740 99999 195201011800 250  16 *** 722 CLR 0 0 0 12.5 02 ** ** ** ** ** ** ** 5   36   27  991.9 ***** ******  39 *** ***** ***** ***** ***** **\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting started\n",
    "\n",
    "Let's start by importing pandas and reading our data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Datetime in Python\n",
    "For the lesson this week we will be using a datetime index for our weather observations.\n",
    "We did not cover the datetime data type in detail in Lesson 6, but you can find [a brief introduction to datetime in the Lesson 6 materials](https://geo-python-site.readthedocs.io/en/latest/notebooks/L6/advanced-data-processing-with-pandas.html#datetime-optional-for-lesson-6).\n",
    "```\n",
    "\n",
    "Just as we did last week, we'll read our data file by passing a few parameters to the Pandas `read_csv()` function. In this case, however, we'll include a few additional parameters in order to read the data with a *datetime index*. Let's read the data first and then see what happened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define absolute path to the file\n",
    "fp = r\"/home/jovyan/shared/data/L6/029740.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# IGNORE THIS CELL\n",
    "# It is only needed to generate the course website\n",
    "fp = r\"data/029740.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\n",
    "    fp,\n",
    "    delim_whitespace=True,\n",
    "    na_values=[\"*\", \"**\", \"***\", \"****\", \"*****\", \"******\"],\n",
    "    usecols=[\"YR--MODAHRMN\", \"TEMP\", \"MAX\", \"MIN\"],\n",
    "    parse_dates=[\"YR--MODAHRMN\"],\n",
    "    index_col=\"YR--MODAHRMN\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what's different here? Well, we have added two new parameters: `parse_dates` and `index_col`.\n",
    "\n",
    "- `parse_dates` takes a Python list of column name(s) containing date data that Pandas will parse and convert to the *datetime* data type. For many common date formats this parameter will automatically recognize and convert the date data.\n",
    "- `index_col` is used to state a column that should be used to index the data in the DataFrame. In this case, we end up with our date data as the DataFrame index. This is a very useful feature in pandas as we'll see below.\n",
    "\n",
    "Having read in the data, let's have a quick look at what we have using `data.head()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>TEMP</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YR--MODAHRMN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1952-01-01 00:00:00</th>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952-01-01 06:00:00</th>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952-01-01 12:00:00</th>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952-01-01 18:00:00</th>\n",
       "      <td>36.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952-01-02 00:00:00</th>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     TEMP   MAX   MIN\n",
       "YR--MODAHRMN                         \n",
       "1952-01-01 00:00:00  36.0   NaN   NaN\n",
       "1952-01-01 06:00:00  37.0   NaN  34.0\n",
       "1952-01-01 12:00:00  39.0   NaN   NaN\n",
       "1952-01-01 18:00:00  36.0  39.0   NaN\n",
       "1952-01-02 00:00:00  36.0   NaN   NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, you can now see that the index column for our DataFrame (the first column) contains date values related to each row in the DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic x-y plot\n",
    "\n",
    "Now we're ready for our first plot. We can start by using the basic line plot in pandas to look at our temperature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all goes well, you should see the plot above.\n",
    "\n",
    "OK, so what happened here?\n",
    "\n",
    "1. We first created the plot object using the `plot()` method of the `data` DataFrame. Without any parameters given, this makes the plot of all columns in the DataFrame as lines of different color on the y-axis with the index, time in this case, on the x-axis.\n",
    "2. In case we want to be able to modify the plot or add anything, we assign the plot object to the variable `ax`. We can check its type below.\n",
    "\n",
    "In fact, let's check the type of the `ax` variable now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.axes._axes.Axes"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so it looks like we have some kind of plot data type that is part of Matplotlib. Clearly, pandas is using Matplotlib for generating our plots.\n",
    "\n",
    "### Selecting our plotted data\n",
    "\n",
    "Now, let's make a few small changes to our plot and plot the data again. First, let's only plot the observed temperatures in the `data['TEMP']` column, and let's restrict ourselves to observations from the afternoon of October 1, 2019 (the last day in our dataset). We can do this by selecting the desired data column and date range first, then plotting our selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oct1_temps = data[\"TEMP\"].loc[data.index >= \"201910011200\"]\n",
    "ax = oct1_temps.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, what did we change?\n",
    "\n",
    "1. Well, we selected only the `'TEMP'` column now by using `data['TEMP']` instead of `data`.\n",
    "2. We've added a restriction to the date range using `loc[]` to select only rows where the index value `data.index` is greater than `'201910011200'`. In that case, the number in the string is in the format `'YYYYMMDDHHMM'`, where `YYYY` is the year, `MM` is the month, `DD` is the day, `HH` is the hour, and `MM` is the minute. Now we have all observations from noon onward on October 1, 2019.\n",
    "3. By saving this selection to the DataFrame `oct1_temps` we're able to now use `oct1_temps.plot()` to plot only our selection. This is cool, but we can do even better..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic plot formatting\n",
    "\n",
    "We can make our plot look a bit nicer and provide more information by using a few additional plotting options to pandas/Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change line and symbol format, and add axis labels/title\n",
    "ax = oct1_temps.plot(\n",
    "    style=\"ro--\",\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see our temperature data as a red dashed line with circles showing the data points.\n",
    "This comes from the additional `style='ro--'` used with `oct1_temps.plot()`.\n",
    "In this case, `r` tells the `oct1_temps.plot()` function to use red color for the lines and symbols, `o` tells it to show circles at the points, and `--` says to use a dashed line.\n",
    "You can use `help(oct1_temps.plot)` to find out more about formatting plots or have a look at the [documentation on the pandas website](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.line.html#pandas.DataFrame.plot.line).\n",
    "We have also added a title using the `title` parameter, and axis labels using the `xlabel` and `ylabel` parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Commented out here to suppress output for course website\n",
    "# help(oct1_temps.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embiggening\\* the plot\n",
    "\n",
    "While the plot sizes we're working with are OK, it would be nice to have them displayed a bit larger.\n",
    "Fortunately, there is an easy way to make the plots larger in pandas/Matplotlib.\n",
    "We can simply add the `figsize` parameter with the desired figure size listed as a tuple (a set of values in normal parentheses) that lists the width and height of the figure (in inches!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust the figure size\n",
    "ax = oct1_temps.plot(\n",
    "    style=\"ro--\",\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    figsize=(12, 6),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell above sets the default plot size to be 12 inches wide by 6 inches tall.\n",
    "Feel free to change these values if you prefer.\n",
    "\n",
    "```{note}\n",
    "It is also possible to change the default figure size for all figures in a Jupyter Notebook by importing the pyplot module from matplotlib (i.e., `import matplotlib.pyplot as plt`)and then defining the default figure size using `plt.rcParams['figure.figsize'] = [12, 6]`. In this case the figure size should be given as a Python list.\n",
    "```\n",
    "\n",
    "\\* To [embiggen](https://www.dictionary.com/browse/embiggen) means to enlarge.\n",
    "It's a perfectly [cromulent](https://www.dictionary.com/browse/cromulent) word."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other common plot formatting operations\n",
    "\n",
    "#### Adding text to the plot\n",
    "\n",
    "Adding text to plots can be done using `ax.text()`.\n",
    "\n",
    "```python\n",
    "ax.text(x, y, 'Text to display')\n",
    "```\n",
    "\n",
    "This would display \"Text to display\" at the location *x*, *y* on the plot.\n",
    "We'll see how to do this in a live example in just a second.\n",
    "\n",
    "#### Changing the axis ranges\n",
    "\n",
    "Changing the plot axes can be done using the `xlim` and `ylim` parameters of the `plot()` function\n",
    "\n",
    "```python\n",
    "df.plot(xlim=[xmin, xmax], ylim=[ymin, ymax])\n",
    "```\n",
    "\n",
    "where `xmin` should be the minimum bound of the x-axis, `xmax` should be the maximum bound, and the same goes for the y-axis with `ymin` and `ymax`.\n",
    "\n",
    "#### Dealing with datetime axes\n",
    "\n",
    "One issue we will encounter with both placing text on the plot and changing the axis ranges is our datetime index for our DataFrame. In order to do either thing, we need to define x-values using a datetime object. The easiest way to do this is to use the Pandas `pd.to_datetime()` function, which converts a character string date to a datetime object. For example, we can convert 13:00 on October 1, 2019 from the character string `'201910011300'` to a datetime equivalent by typing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-10-01 13:00:00')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_datetime(\"201910011300\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this datetime issue in mind, let's now consider a modified version of the plot above, we can\n",
    "\n",
    "1. Limit our time range to 12:00 to 15:00 on October 1, 2019\n",
    "2. Only look at temperatures between 40-46° Fahrenheit\n",
    "3. Add text to note the coldest part of the early afternoon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(2019-10-01 12:05:00, 42.0, '<- Coldest temperature in early afternoon')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the start, end, and cold times\n",
    "start_time = pd.to_datetime(\"201910011200\")\n",
    "end_time = pd.to_datetime(\"201910011500\")\n",
    "cold_time = pd.to_datetime(\"201910011205\")\n",
    "\n",
    "# Create the plot, including the axis limits\n",
    "ax = oct1_temps.plot(\n",
    "    style=\"ro--\",\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    figsize=(12, 6),\n",
    "    xlim=[start_time, end_time],\n",
    "    ylim=[40.0, 46.0],\n",
    ")\n",
    "\n",
    "# Add text to display the coldest temperature\n",
    "ax.text(cold_time, 42.0, \"<- Coldest temperature in early afternoon\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check your understanding\n",
    "\n",
    "Create a line plot similar to our examples above with the following attributes:\n",
    "    \n",
    "- Temperature data from 18:00-24:00 on October 1, 2019\n",
    "- A dotted black line connecting the observations (do not show the data points)\n",
    "- A title that reads \"Evening temperatures on October 1, Helsinki-Vantaa\"\n",
    "- A text label indicating the warmest temperature in the evening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(2019-10-01 21:20:00, 43.0, 'Warmest time of the evening ->')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "# Define start, end, and cold times\n",
    "start_time = pd.to_datetime(\"201910011800\")\n",
    "end_time = pd.to_datetime(\"201910020000\")\n",
    "warm_time = pd.to_datetime(\"201910012120\")\n",
    "\n",
    "# Create the plot, including the axis limits\n",
    "\n",
    "ax = oct1_temps.plot(\n",
    "    style=\"k:\",\n",
    "    title=\"Evening temperatures on October 1, Helsinki-Vantaa\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    figsize=(12, 6),\n",
    "    xlim=[start_time, end_time],\n",
    "    ylim=[35.0, 44.0],\n",
    ")\n",
    "\n",
    "# Display text on plot\n",
    "ax.text(warm_time, 43.0, \"Warmest time of the evening ->\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar plots in pandas\n",
    "\n",
    "In addition to line plots, there are many other options for plotting in pandas. Bar plots are one option, which can be used quite similarly to line plots with the addition of the `kind=bar` parameter. Note that it is easiest to plot our selected time range for a bar plot by selecting the dates in our data series first, rather than adjusting the plot limits. pandas sees bar plot data as categorical, so the date range is more difficult to define for x-axis limits. For the y-axis, we can still define its range using the `ylim=[ymin, ymax]` parameter. Similarly, text placement on a bar plot is more difficult, and most easily done using the index value of the bar where the text should be placed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 42.1, 'Coldest \\ntemp \\nv')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zZ89q3759+r//+z+9/PLLf3gqQ2F+j40aNdKaNWu0dOlSxcXFKSIiQnXq1FH37t1Vvnx53X777Zo0aZJCQkK0YMECpaSkFPj1FvS1DBs2TKVKlVK7du0UFxen1NRUTZ06VVFRUV5/EAAAmEMpBwAYMWfOHF1++eWaM2eOXnzxReXk5Khy5cpq166dWrVqle/jWrdurc2bN+vBBx/UkSNHVLZsWbVs2VKrVq3ynC9b1G6//XaFh4frlltu0alTp/TKK69c9H4+/PBDlS9fXtdee63XtqFDhyotLU0vv/yyXnvtNdWsWVMPPfSQfvzxR8/3ThfWlVdeqXHjxmnhwoWaN2+ecnJytHr1al155ZVatmyZ7rvvPt18880KDw9Xnz59tHjxYjVv3txrH2+//bZGjRqlsWPHKisrS+3atdPKlStzXWAtP6+++qoeeOAB9e7dW5mZmRo8eLAWLFig+vXrKzk5WU888YTGjx+vtLQ0lS1bVrVq1fKcV16UevfurQYNGmj8+PE6cOCALrnkEr311lvq16+f5z5xcXHavHmznnjiCf3jH//Qjz/+qIiICNWoUUNXX311gY44F+b3OHPmTI0YMUL9+/fX6dOn1bFjR61Zs0aRkZFavny5Ro8erZtvvllly5bVHXfcoWuuucbz1XJ/pKCvpUOHDlqwYIH++c9/6tixY6pQoYLat2+v119/3fP1dQAAsxzXzePLZAEAAP6iHMfRiBEjNHv2bNujAADwh7j6OgAAAAAAllDKAQAAAACwhHPKAQCAX+HMPADAX4nPHCmfOnWqHMfR6NGjvdZ37dql3r17KyoqShEREbr88st14MABO0MCAAAAAFCEfKKUJyUlae7cuWrcuLHX+vfff6/27durbt26WrNmjb788ks9+uijCgsLszQpAAAAAABFx/rV1zMyMtS8eXO9+OKLmjx5spo2baoZM2ZIkvr3768SJUrojTfesDkiAAAAAADFwvo55SNGjFCPHj3UpUsXTZ482bOek5OjZcuWaezYserWrZu2bt2qGjVqaNy4cbm+6/X3MjMzlZmZ6bWfo0ePKjo6Wo7jFOdLAQAAAABAruvq5MmTqly5soKCLvwBdaulfNGiRUpOTlZSUlKubWlpacrIyNC0adM0efJkPfXUU1q+fLmuu+46rV69Wh07dsxzn1OnTtXEiROLe3QAAAAAAC4oJSVFVatWveB9rH18PSUlRS1bttQnn3yiJk2aSJKuvPJKz8fXf/rpJ1WpUkUDBgzQ22+/7Xlc7969FR4ernfeeSfP/f7vkfL09HRVr15dKSkpioyMLN4XBQAAAAAIeCdOnFC1atV0/PhxRUVFXfC+1o6Ub9myRWlpaWrRooVnLTs7W+vWrdPs2bN16tQphYSEqH79+l6Pq1evntavX5/vfkNDQxUaGpprPTIyklIOAAAAADCmIKdQWyvliYmJ2rFjh9fa0KFDVbduXT344IMKDQ3VZZddpt27d3vd59tvv1V8fLzJUQEAAAAAKBbWSnlERIQaNmzotRYeHq7o6GjP+gMPPKB+/frpiiuuUKdOnbR8+XItXbpUa9assTAxAAAAAABFyye+pzw/ffv21csvv6ynn35ajRo10iuvvKJ///vfat++ve3RAAAAAAD406x/T3lxO3HihKKiopSens455QAAAACAYleYHurTR8oBAAAAAPBnlHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALPGZUj516lQ5jqPRo0d71oYMGSLHcbx+Lr/8cntDAgAAAABQhEJsDyBJSUlJmjt3rho3bpxr29VXX6358+d7bpcsWdLkaAAAAAAAFBvrR8ozMjI0aNAgzZs3T+XKlcu1PTQ0VLGxsZ6f8uXLW5gSAAAAAICiZ72UjxgxQj169FCXLl3y3L5mzRpVqlRJtWvX1rBhw5SWlnbB/WVmZurEiRNePwAAAAAA+CKrH19ftGiRkpOTlZSUlOf2a665RjfeeKPi4+O1d+9ePfroo+rcubO2bNmi0NDQPB8zdepUTZw4sTjHBgD4oYSHltke4aLtm9bD9ggXhcwRCPjv3Dwyx1+NtVKekpKiUaNG6ZNPPlFYWFie9+nXr5/n3w0bNlTLli0VHx+vZcuW6brrrsvzMePGjdOYMWM8t0+cOKFq1aoV7fAAAAAAABQBa6V8y5YtSktLU4sWLTxr2dnZWrdunWbPnq3MzEwFBwd7PSYuLk7x8fH67rvv8t1vaGhovkfRAQAAAADwJdZKeWJionbs2OG1NnToUNWtW1cPPvhgrkIuSb/88otSUlIUFxdnakwAAAAAAIqNtVIeERGhhg0beq2Fh4crOjpaDRs2VEZGhh5//HFdf/31iouL0759+/Twww+rQoUK6tu3r6WpAQAAAAAoOj7xPeV5CQ4O1o4dO/T666/r+PHjiouLU6dOnbR48WJFRETYHg8AAAAAgD/Np0r5mjVrPP8uVaqUVqxYYW8YAAAAAACKmfXvKQcAAAAAIFBRygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwxGdK+dSpU+U4jkaPHp3n9jvvvFOO42jGjBlG5wIAAAAAoLj4RClPSkrS3Llz1bhx4zy3v//++/r8889VuXJlw5MBAAAAAFB8rJfyjIwMDRo0SPPmzVO5cuVybT948KDuuecevfXWWypRooSFCQEAAAAAKB7WS/mIESPUo0cPdenSJde2nJwc3XLLLXrggQfUoEGDAu0vMzNTJ06c8PoBAAAAAMAXhdh88kWLFik5OVlJSUl5bn/qqacUEhKikSNHFnifU6dO1cSJE4tqRACwIuGhZbZHuGj7pvWwPQKAfPDeAqA48N7y51g7Up6SkqJRo0bpzTffVFhYWK7tW7Zs0cyZM7VgwQI5jlPg/Y4bN07p6emen5SUlKIcGwAAAACAImOtlG/ZskVpaWlq0aKFQkJCFBISorVr1+r5559XSEiI1qxZo7S0NFWvXt2zff/+/brvvvuUkJCQ735DQ0MVGRnp9QMAAAAAgC+y9vH1xMRE7dixw2tt6NChqlu3rh588EHFxcWpW7duXtu7deumW265RUOHDjU5KgAAAAAAxcJaKY+IiFDDhg291sLDwxUdHe1Zj46O9tpeokQJxcbGqk6dOsbmBAAAAACguFi/+joAAAAAAIHK6tXX/9eaNWsuuH3fvn1G5gAAAAAAwASOlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWhBTkTs8//3yhdzx06FBFREQU+nEAAAAAAASKApXy0aNHq2rVqgoODi7QTlNSUtSzZ09KOQAAAAAAF1CgUi5JmzdvVqVKlQp0X8o4AAAAAAB/rEDnlE+YMEFlypQp8E4ffvhhlS9f/qKHAgAAAAAgEBToSPmECRMKtdNx48Zd1DAAAAAAAASSAl99fdWqVcrKyirOWQAAAAAACCgFLuVdu3bV0aNHPbcvv/xyHTx4sFiGAgAAAAAgEBS4lLuu63X766+/VmZmZpEPBAAAAABAoChwKQcAAAAAAEWrwKXccRw5jpPvbQAAAAAAUDgF/p5y13WVmJiokJBfH3L69Gn16tVLJUuW9LpfcnJy0U4IAAAAAICfKnAp/9+vRevTp0+RDwMAAAAAQCC56FIOAAAAAAD+HC70BgAAAACAJQUu5Wlpafrb3/6m/v376+uvvy7OmQAAAAAACAgFLuVDhw5VbGys+vbtq2uuuSbX95YDAAAAAIDCKXAp37p1q/r166ebbrpJqampOnLkSHHOBQAAAACA3yvwhd6uvfZajRs3TvHx8WrcuLEqVapUnHMBAAAAAOD3CnykfPbs2erXr5/q1q2rVatWFedMAAAAAAAEhAIfKQ8KCtKgQYOKcxYAAAAAAAIKX4kGAAAAAIAlBSrlzZs317Fjxwq80/bt2+vgwYMXPRQAAAAAAIGgQB9f37Ztm7788kuVL1++QDvdtm2bMjMz/9RgAAAAAAD4uwKfU56YmFjg7yZ3HOeiBwIAAAAAIFAUqJTv3bu30DuuWrVqoR8DAAAAAEAgKVApj4+PL+45AAAAAAAIOFx9HQAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZcVCk/fvy4XnnlFY0bN05Hjx6VJCUnJ+vgwYNFOhwAAAAAAP6swN9T/pvt27erS5cuioqK0r59+zRs2DCVL19e7733nvbv36/XX3+9OOYEAAAAAMDvFPpI+ZgxYzRkyBB99913CgsL86xfc801WrduXZEOBwAAAACAPyt0KU9KStKdd96Za71KlSpKTU0tkqEAAAAAAAgEhS7lYWFhOnHiRK713bt3q2LFikUyFAAAAAAAgaDQpbxPnz6aNGmSzp8/L0lyHEcHDhzQQw89pOuvv77IBwQAAAAAwF8VupQ/88wzOnLkiCpVqqQzZ86oY8eOuvTSSxUREaEnn3yyOGYEAAAAAMAvFfrq65GRkVq/fr1WrVql5ORk5eTkqHnz5urSpUtxzAcAAAAAgN8qVCnPyspSWFiYtm3bps6dO6tz587FNRcAAAAAAH6vUB9fDwkJUXx8vLKzs4trHgAAAAAAAkahzykfP368xo0bp6NHjxbHPAAAAAAABIxCn1P+/PPPa8+ePapcubLi4+MVHh7utT05ObnIhgMAAAAAwJ8VupRfe+21xTAGAAAAAACBp9ClfMKECcUxBwAAAAAAAafQ55QDAAAAAICiUegj5UFBQXIcJ9/tXJkdAAAAAICCKXQpf++997xunz9/Xlu3btXChQs1ceLEIhsMAAAAAAB/V+hS3qdPn1xrN9xwgxo0aKDFixfr9ttvL5LBAAAAAADwd0V2Tnnr1q316aefFtXuAAAAAADwe0VSys+cOaNZs2apatWqRbE7AAAAAAACQqE/vl6uXDmvC725rquTJ0+qdOnSevPNN4t0OAAAAAAA/FmhS/lzzz3nVcqDgoJUsWJFtW7dWuXKlSvS4QAAAAAA8GeFLuWdO3dWtWrV8vxatAMHDqh69epFMhgAAAAAAP6u0OeU16hRQ0eOHMm1/ssvv6hGjRoXPcjUqVPlOI5Gjx7tWXv88cdVt25dhYeHq1y5curSpYs+//zzi34OAAAAAAB8SaFLueu6ea5nZGQoLCzsooZISkrS3Llz1bhxY6/12rVra/bs2dqxY4fWr1+vhIQEXXXVVXn+UQAAAAAAgL+aAn98fcyYMZIkx3H02GOPqXTp0p5t2dnZ+vzzz9W0adNCD5CRkaFBgwZp3rx5mjx5ste2gQMHet2ePn26Xn31VW3fvl2JiYmFfi4AAAAAAHxJgUv51q1bJf16pHzHjh0qWbKkZ1vJkiXVpEkT3X///YUeYMSIEerRo4e6dOmSq5T/3rlz5zR37lxFRUWpSZMm+d4vMzNTmZmZntsnTpwo9EwAAAAAAJhQ4FK+evVqSdLQoUM1c+ZMRUZG/uknX7RokZKTk5WUlJTvfT766CP1799fp0+fVlxcnFauXKkKFSrke/+pU6dq4sSJf3o2+K6Eh5bZHuGi7ZvWw/YIF4XMAQAAgOJR6HPK58+fXySFPCUlRaNGjdKbb755wXPRO3XqpG3btmnDhg26+uqrddNNNyktLS3f+48bN07p6emen5SUlD89KwAAAAAAxaHQX4km/XphtnfffVcHDhzQuXPnvLYtWbKkQPvYsmWL0tLS1KJFC89adna21q1bp9mzZyszM1PBwcEKDw/XpZdeqksvvVSXX365atWqpVdffVXjxo3Lc7+hoaEKDQ29mJcFAAAAAIBRhT5SvmjRIrVr1047d+7Ue++9p/Pnz2vnzp1atWqVoqKiCryfxMRE7dixQ9u2bfP8tGzZUoMGDdK2bdsUHByc5+Nc1/U6ZxwAAAAAgL+qQh8pnzJlip577jmNGDFCERERmjlzpmrUqKE777xTcXFxBd5PRESEGjZs6LUWHh6u6OhoNWzYUKdOndKTTz6p3r17Ky4uTr/88otefPFF/fjjj7rxxhsLOzYAAAAAAD6n0EfKv//+e/Xo8euFk0JDQ3Xq1Ck5jqN7771Xc+fOLbLBgoOD9c033+j6669X7dq11bNnTx05ckT/+c9/1KBBgyJ7HgAAAAAAbCn0kfLy5cvr5MmTkqQqVaroq6++UqNGjXT8+HGdPn36Tw2zZs0az7/DwsIKfH46AAAAAAB/RYUu5R06dNDKlSvVqFEj3XTTTRo1apRWrVqllStXKjExsThmBAAAAADALxW6lM+ePVtnz56V9OvXj5UoUULr16/Xddddp0cffbTIBwQAAAAAwF8VqpRnZWVp6dKl6tatmyQpKChIY8eO1dixY4tlOAAAAAAA/FmhLvQWEhKiu+++m68kAwAAAACgCBT66uutW7fW1q1bi2MWAAAAAAACSqHPKR8+fLjuu+8+/fjjj2rRooXCw8O9tjdu3LjIhgMAAAAAwJ8VupT369dPkjRy5EjPmuM4cl1XjuMoOzu76KYDAAAAAMCPFbqU7927tzjmAAAAAAAg4BS6lMfHxxfHHAAAAAAABJxCX+hNkt544w21a9dOlStX1v79+yVJM2bM0AcffFCkwwEAAAAA4M8KXcpfeukljRkzRt27d9fx48c955CXLVtWM2bMKOr5AAAAAADwW4Uu5bNmzdK8efP0yCOPKDg42LPesmVL7dixo0iHAwAAAADAnxW6lO/du1fNmjXLtR4aGqpTp04VyVAAAAAAAASCQpfyGjVqaNu2bbnWP/74Y9WvX78oZgIAAAAAICAU+urrDzzwgEaMGKGzZ8/KdV198cUXeueddzR16lS98sorxTEjAAAAAAB+qdClfOjQocrKytLYsWN1+vRpDRw4UFWqVNHMmTPVv3//4pgRAAAAAAC/VOhSLknDhg3TsGHD9PPPPysnJ0eVKlUq6rkAAAAAAPB7F1XKJSktLU27d++W4zhyHEcVK1YsyrkAAAAAAPB7hb7Q24kTJ3TLLbeocuXK6tixo6644gpVrlxZN998s9LT04tjRgAAAAAA/FKhS/kdd9yhzz//XMuWLdPx48eVnp6ujz76SJs3b9awYcOKY0YUkccff1xNmza94H2GDBmia6+91sg8AAAAABDoCl3Kly1bptdee03dunVTZGSkIiIi1K1bN82bN0/Lli0rjhkhKTU1VX//+99Vs2ZNhYaGqlq1aurVq5c+++wz26Pla9++fXIcJ8+v0AMAAAAAXMQ55dHR0YqKisq1HhUVpXLlyhXJUPC2b98+tWvXTmXLltXTTz+txo0b6/z581qxYoVGjBihb775xvaIAAAAAICLUOgj5ePHj9eYMWN06NAhz1pqaqoeeOABPfroo0U6HH41fPhwOY6jL774QjfccINq166tBg0aaMyYMdq0aZPnfgcOHFCfPn1UpkwZRUZG6qabbtLhw4fz3W92drbGjBmjsmXLKjo6WmPHjpXrul73cV1XTz/9tGrWrKlSpUqpSZMm+te//uXZfuzYMQ0aNEgVK1ZUqVKlVKtWLc2fP1+SVKNGDUlSs2bN5DiOrrzyyiJMBQAAAAD++gp9pPyll17Snj17FB8fr+rVq0v6tQyGhobqyJEjmjNnjue+ycnJRTdpgDp69KiWL1+uJ598UuHh4bm2ly1bVtKv5fnaa69VeHi41q5dq6ysLA0fPlz9+vXTmjVr8tz3s88+q9dee02vvvqq6tevr2effVbvvfeeOnfu7LnP+PHjtWTJEr300kuqVauW1q1bp5tvvlkVK1ZUx44d9eijj2rnzp36+OOPVaFCBe3Zs0dnzpyRJH3xxRdq1aqVPv30UzVo0EAlS5Ys8nwAAAAA4K+s0KWci4CZtWfPHrmuq7p1617wfp9++qm2b9+uvXv3qlq1apKkN954Qw0aNFBSUpIuu+yyXI+ZMWOGxo0bp+uvv16S9PLLL2vFihWe7adOndL06dO1atUqtWnTRpJUs2ZNrV+/XnPmzFHHjh114MABNWvWTC1btpQkJSQkeB7/29fkRUdHKzY29uJDAAAAAAA/VehSPmHChOKYA/n47ePkjuNc8H67du1StWrVPIVckurXr6+yZctq165duUp5enq6Dh065CnbkhQSEqKWLVt6nnPnzp06e/asunbt6vXYc+fOqVmzZpKku+++W9dff72Sk5N11VVX6dprr1Xbtm0v/gUDAAAAQAApdCn/vYyMDOXk5HitRUZG/qmB4K1WrVpyHEe7du264KcUXNfNs7jnt14Qv/1uly1bpipVqnhtCw0NlSRdc8012r9/v5YtW6ZPP/1UiYmJGjFihJ555pmLek4AAAAACCSFvtDb3r171aNHD4WHh3uuuF6uXDmVLVuWq68Xg/Lly6tbt2564YUXdOrUqVzbjx8/LunXo+IHDhxQSkqKZ9vOnTuVnp6uevXq5XpcVFSU4uLivC4Ul5WVpS1btnhu169fX6GhoTpw4IAuvfRSr5/fH5GvWLGihgwZojfffFMzZszQ3LlzJclzDnl2dvafCwEAAAAA/FShj5QPGjRIkvTaa68pJibmoo/CouBefPFFtW3bVq1atdKkSZPUuHFjZWVlaeXKlXrppZe0a9cudenSRY0bN9agQYM0Y8YMz4XeOnbs6Dnf+3+NGjVK06ZNU61atVSvXj1Nnz7dU/IlKSIiQvfff7/uvfde5eTkqH379jpx4oQ2bNigMmXKaPDgwXrsscfUokULNWjQQJmZmfroo488fwSoVKmSSpUqpeXLl6tq1aoKCwvL8+v0AAAAACBQFbqUb9++XVu2bFGdOnWKYx7koUaNGkpOTtaTTz6p++67T4cOHVLFihXVokULvfTSS5J+Pef8/fff19///nddccUVCgoK0tVXX61Zs2blu9/f9jVkyBAFBQXptttuU9++fZWenu65zxNPPKFKlSpp6tSp+uGHH1S2bFk1b95cDz/8sKRfj4aPGzdO+/btU6lSpdShQwctWrRI0q/nqD///POaNGmSHnvsMXXo0CHfK8EDAAAAQCAqdCm/7LLLlJKSQik3LC4uTrNnz9bs2bPzvU/16tX1wQcf5Lv98ccf1+OPP+65HRISohkzZmjGjBn5PsZxHI0cOVIjR47Mc/v48eM1fvz4fB9/xx136I477sh3OwAAAAAEskKX8ldeeUV33XWXDh48qIYNG6pEiRJe2xs3blxkwwEAAAAA4M8KXcqPHDmi77//XkOHDvWsOY7juco3F/UCAAAAAKBgCl3Kb7vtNjVr1kzvvPMOF3oDAAAAAOBPKHQp379/vz788ENdeumlxTEPAAAAAAABo9DfU965c2d9+eWXxTELAAAAAAABpdBHynv16qV7771XO3bsUKNGjXJd6K13795FNhx+deWVV6pp06YXvEo6AAAAAOCvp9Cl/K677pIkTZo0Kdc2LvQGAAAAAEDBFfrj6zk5Ofn+UMiL3pAhQ7R27VrNnDlTjuPIcRzt27dPkrRz5051795dZcqUUUxMjG655Rb9/PPPnsdeeeWV+vvf/67Ro0erXLlyiomJ0dy5c3Xq1CkNHTpUERERuuSSS/Txxx97HrNmzRo5jqNly5apSZMmCgsLU+vWrbVjxw7TLx0AAAAA/F6hS/nvnT17tqjmQD5mzpypNm3aaNiwYTp06JAOHTqkatWq6dChQ+rYsaOaNm2qzZs3a/ny5Tp8+LBuuukmr8cvXLhQFSpU0BdffKG///3vuvvuu3XjjTeqbdu2Sk5OVrdu3XTLLbfo9OnTXo974IEH9MwzzygpKUmVKlVS7969df78eZMvHQAAAAD8XqFLeXZ2tp544glVqVJFZcqU0Q8//CBJevTRR/Xqq68W+YCBLioqSiVLllTp0qUVGxur2NhYBQcH66WXXlLz5s01ZcoU1a1bV82aNdNrr72m1atX69tvv/U8vkmTJho/frxq1aqlcePGqVSpUqpQoYKGDRumWrVq6bHHHtMvv/yi7du3ez3vhAkT1LVrVzVq1EgLFy7U4cOH9d5775l++QAAAADg1wpdyp988kktWLBATz/9tEqWLOlZb9SokV555ZUiHQ7527Jli1avXq0yZcp4furWrStJ+v777z33a9y4seffwcHBio6OVqNGjTxrMTExkqS0tDSv/bdp08bz7/Lly6tOnTratWtXsbwWAAAAAAhUhb7Q2+uvv665c+cqMTHRc9E36dfy98033xTpcMhfTk6OevXqpaeeeirXtri4OM+///fq+I7jeK05juPZ3x/57b4AAAAAgKJR6FJ+8OBBXXrppbnWc3JyOOe4mJQsWTLXRfSaN2+uf//730pISFBISKF/jX9o06ZNql69uiTp2LFj+vbbbz1H4gEAAAAARaPQH19v0KCB/vOf/+Raf/fdd9WsWbMiGQreEhIS9Pnnn2vfvn36+eeflZOToxEjRujo0aMaMGCAvvjiC/3www/65JNPdNtttxXJVfAnTZqkzz77TF999ZWGDBmiChUq6Nprr/3zLwYAAAAA4FHgUn7bbbfp5MmTmjBhgu655x499dRTysnJ0ZIlSzRs2DBNmTJFjz32WHHOGrDuv/9+BQcHq379+qpYsaIOHDigypUr67///a+ys7PVrVs3NWzYUKNGjVJUVJSCgv7URfUlSdOmTdOoUaPUokULHTp0SB9++KHXNQQAAAAAAH9egT/3vHDhQk2bNk29evXS4sWLNWXKFDmOo8cee0zNmzfX0qVL1bVr1+KcNWDVrl1bGzduzLVeq1YtLVmyJN/HrVmzJtfab99x/nuu6+Zaa9++vb766qtCzQkAAAAAKJwCl/LfF7du3bqpW7duxTIQAAAAAACBolCfc+bq2wAAAAAAFJ1CXba7du3af1jMjx49+qcGgl1XXnllnh9nBwAAAAAUvUKV8okTJyoqKqq4ZgEAAAAAIKAUqpT3799flSpVKq5ZAAAAAAAIKAU+p5zzyQEAAAAAKFoFLuWcZwwAAAAAQNEq8MfXc3JyinMOAAAAAAACTqG+Eg3mzZkzR1WqVMn1R5HevXtr8ODBlqYCAAAAABQFSrmPu/HGG/Xzzz9r9erVnrVjx45pxYoVGjRokMXJAAAAAAB/FqXcx5UvX15XX3213n77bc/au+++q/LlyysxMdHiZAAAAACAP4tS/hcwaNAg/fvf/1ZmZqYk6a233lL//v0VHBxseTIAAAAAwJ9BKf8L6NWrl3JycrRs2TKlpKToP//5j26++WbbYwEAAAAA/qQCX30d9pQqVUrXXXed3nrrLe3Zs0e1a9dWixYtbI8FAAAAAPiTKOV/EYMGDVKvXr309ddfc5QcAAAAAPwEH1//i+jcubPKly+v3bt3a+DAgbbHAQAAAAAUAY6U/0UEBwfrp59+sj0GAAAAAKAIcaQcAAAAAABLKOUAAAAAAFjiM6V86tSpchxHo0ePliSdP39eDz74oBo1aqTw8HBVrlxZt956Kx/hBgAAAAD4DZ8o5UlJSZo7d64aN27sWTt9+rSSk5P16KOPKjk5WUuWLNG3336r3r17W5wUAAAAAICiY/1CbxkZGRo0aJDmzZunyZMne9ajoqK0cuVKr/vOmjVLrVq10oEDB1S9enXTowIAAAAAUKSsHykfMWKEevTooS5duvzhfdPT0+U4jsqWLZvvfTIzM3XixAmvHwAAAAAAfJHVI+WLFi1ScnKykpKS/vC+Z8+e1UMPPaSBAwcqMjIy3/tNnTpVEydOLMoxLyjhoWXGnquo7ZvWw/YIAAAAABDQrB0pT0lJ0ahRo/Tmm28qLCzsgvc9f/68+vfvr5ycHL344osXvO+4ceOUnp7u+UlJSSnKsQEAAAAAKDLWjpRv2bJFaWlpatGihWctOztb69at0+zZs5WZmang4GCdP39eN910k/bu3atVq1Zd8Ci5JIWGhio0NLS4xwcAAAAA4E+zVsoTExO1Y8cOr7WhQ4eqbt26evDBB70K+XfffafVq1crOjra0rQAAAAAABQ9a6U8IiJCDRs29FoLDw9XdHS0GjZsqKysLN1www1KTk7WRx99pOzsbKWmpkqSypcvr5IlS9oYGwAAAACAImP9K9Hy8+OPP+rDDz+UJDVt2tRr2+rVq3XllVeaHwoAAAAAgCLkU6V8zZo1nn8nJCTIdV17wwAAAAAAUMysf085AAAAAACBilIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCU+U8qnTp0qx3E0evRoz9qSJUvUrVs3VahQQY7jaNu2bdbmAwAAAACgqPlEKU9KStLcuXPVuHFjr/VTp06pXbt2mjZtmqXJAAAAAAAoPiG2B8jIyNCgQYM0b948TZ482WvbLbfcIknat2+fhckAAAAAAChe1o+UjxgxQj169FCXLl2KZH+ZmZk6ceKE1w8AAAAAAL7I6pHyRYsWKTk5WUlJSUW2z6lTp2rixIlFtj8AAAAAAIqLtSPlKSkpGjVqlN58802FhYUV2X7HjRun9PR0z09KSkqR7RsAAAAAgKJk7Uj5li1blJaWphYtWnjWsrOztW7dOs2ePVuZmZkKDg4u9H5DQ0MVGhpalKMCAAAAAFAsrJXyxMRE7dixw2tt6NChqlu3rh588MGLKuQAAAAAAPyVWCvlERERatiwoddaeHi4oqOjPetHjx7VgQMH9NNPP0mSdu/eLUmKjY1VbGys2YEBAAAAAChi1q++fiEffvihmjVrph49ekiS+vfvr2bNmunll1+2PBkAAAAAAH+e9e8p/701a9Z43R4yZIiGDBliZRYAAAAAAIqbTx8pBwAAAADAn1HKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALDEZ0r51KlT5TiORo8e7VlzXVePP/64KleurFKlSunKK6/U119/bW9IAAAAAACKkE+U8qSkJM2dO1eNGzf2Wn/66ac1ffp0zZ49W0lJSYqNjVXXrl118uRJS5MCAAAAAFB0rJfyjIwMDRo0SPPmzVO5cuU8667rasaMGXrkkUd03XXXqWHDhlq4cKFOnz6tt99+2+LEAAAAAAAUjRDbA4wYMUI9evRQly5dNHnyZM/63r17lZqaqquuusqzFhoaqo4dO2rDhg26884789xfZmamMjMzPbfT09MlSSdOnCiW+XMyTxfLfk0orkyKG5mbR+bmkbl5ZG4emZtH5uaRuXlkbh6Z579f13X/8L5WS/miRYuUnJyspKSkXNtSU1MlSTExMV7rMTEx2r9/f777nDp1qiZOnJhrvVq1an9yWv8TNcP2BIGHzM0jc/PI3DwyN4/MzSNz88jcPDI3r7gzP3nypKKioi54H2ulPCUlRaNGjdInn3yisLCwfO/nOI7Xbdd1c6393rhx4zRmzBjP7ZycHB09elTR0dEXfJwvOnHihKpVq6aUlBRFRkbaHicgkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5v1VM3ddVydPnlTlypX/8L7WSvmWLVuUlpamFi1aeNays7O1bt06zZ49W7t375b06xHzuLg4z33S0tJyHT3/vdDQUIWGhnqtlS1btmiHNywyMvIv9R+gPyBz88jcPDI3j8zNI3PzyNw8MjePzM37K2b+R0fIf2PtQm+JiYnasWOHtm3b5vlp2bKlBg0apG3btqlmzZqKjY3VypUrPY85d+6c1q5dq7Zt29oaGwAAAACAImPtSHlERIQaNmzotRYeHq7o6GjP+ujRozVlyhTVqlVLtWrV0pQpU1S6dGkNHDjQxsgAAAAAABQp61dfv5CxY8fqzJkzGj58uI4dO6bWrVvrk08+UUREhO3RjAgNDdWECRNyfRwfxYfMzSNz88jcPDI3j8zNI3PzyNw8MjcvEDJ33IJcox0AAAAAABQ5a+eUAwAAAAAQ6CjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsMSnvxItkLiuq08//VQbNmxQamqqHMdRTEyM2rVrp8TERDmOY3tEv0Pm5pG5eWRuHpmbR+bmkbl5ZG4emZsXqJnzlWg+4ODBg+rZs6d27Nihhg0bKiYmRq7rKi0tTV999ZWaNGmiDz/8UFWqVLE9qt8gc/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMKeU+oE+fPsrIyNCbb76puLg4r22HDh3SzTffrIiICL3//vt2BvRDZG4emZtH5uaRuXlkbh6Zm0fm5pG5eYGcOaXcB5QpU0b//e9/1aRJkzy3b926VR06dFBGRobhyfwXmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXiBnzoXefECpUqV09OjRfLcfO3ZMpUqVMjiR/yNz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mwp5T6gf//+Gjx4sP71r38pPT3ds56enq5//etfGjp0qAYOHGhxQv9D5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF9CZu7AuMzPTveuuu9ySJUu6QUFBblhYmBsWFuYGBQW5JUuWdO+++243MzPT9ph+hczNI3PzyNw8MjePzM0jc/PI3DwyNy+QM+ecch9y4sQJbd68WYcPH5YkxcbGqkWLFoqMjLQ8mf8ic/PI3DwyN4/MzSNz88jcPDI3j8zNC8TMKeUAAAAAAFgSYnsA/OrUqVN6++23tWHDBqWmpspxHMXExKhdu3YaMGCAwsPDbY/od8jcPDI3j8zNI3PzyNw8MjePzM0jc/MCNXOOlPuAnTt3qmvXrjp9+rQ6duyomJgYua6rtLQ0rV27VuHh4frkk09Uv35926P6DTI3j8zNI3PzyNw8MjePzM0jc/PI3LxAzpxS7gM6deqk2NhYLVy4UCVLlvTadu7cOQ0ZMkSHDh3S6tWrLU3of8jcPDI3j8zNI3PzyNw8MjePzM0jc/MCOXNKuQ8oXbq0Nm/enO9ffb766iu1atVKp0+fNjyZ/yJz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mz5nnIfUK5cOX333Xf5bt+zZ4/KlStncCL/R+bmkbl5ZG4emZtH5uaRuXlkbh6ZmxfImXOhNx8wbNgwDR48WOPHj1fXrl0VExMjx3GUmpqqlStXasqUKRo9erTtMf0KmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXkBnbvZr0ZGfadOmuXFxca7jOG5QUJAbFBTkOo7jxsXFuU899ZTt8fwSmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXqBmzjnlPmbv3r1KTU2VJMXGxqpGjRqWJ/J/ZG4emZtH5uaRuXlkbh6Zm0fm5pG5eYGWOaUcAAAAAABLuNCbj1m3bp02b97stbZ582atW7fO0kT+j8zNI3PzyNw8MjePzM0jc/PI3DwyNy/QMudIuY8JCgpS3bp1tXPnTs9avXr19O233yo7O9viZP6LzM0jc/PI3DwyN4/MzSNz88jcPDI3L9Ay5+rrPmbv3r0qUaKE19pnn32m8+fPW5rI/5G5eWRuHpmbR+bmkbl5ZG4emZtH5uYFWuYcKQcAAAAAwBKOlPuY/fv3KzU1VY7jKCYmRvHx8bZH8ntkbh6Zm0fm5pG5eWRuHpmbR+bmkbl5AZe5vW9jw+9Nnz7drVq1que7+H77br6qVau6zz33nO3x/BKZm0fm5pG5eWRuHpmbR+bmkbl5ZG5eoGZOKfcBkyZNciMjI91p06a5W7dudX/66Sf34MGD7tatW91p06a5UVFR7hNPPGF7TL9C5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZU8p9QNWqVd333nsv3+1LlixxK1eubG6gAEDm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJnzPeU+4JdfflGdOnXy3V67dm0dO3bM4ET+j8zNI3PzyNw8MjePzM0jc/PI3DwyNy+QM6eU+4BWrVrpySefVFZWVq5tWVlZmjJlilq1amVhMv9F5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZ85VoPmDHjh266qqrlJmZqY4dOyomJkaO4yg1NVXr1q1TaGioVq5cqQYNGtge1W+QuXlkbh6Zm0fm5pG5eWRuHpmbR+bmBXLmlHIfcfLkSb355pvatGmTUlNTJUmxsbFq06aNBg4cqMjISMsT+h8yN4/MzSNz88jcPDI3j8zNI3PzyNy8QM2cUg4AAAAAgCUhtgfA/5eRkaEtW7YoNTVVjuMoNjZWzZs3V5kyZWyPBhSbI0eOqGzZsipRooTtUfwW7y0IRLy3FD/eWxCIeG8pfgH53mLz0u/41fnz592RI0e6pUqVch3HcUNDQ92SJUu6juO4pUqVckeNGuWeO3fO9ph+54UXXnATExPdG2+80f3ss8+8th05csStUaOGpcn805w5c9yzZ8+6ruu6OTk57pNPPumWLVvWDQoKckuXLu3ee++9bnZ2tuUp/QvvLXbw3mIW7y3m8d5iB+8tZvHeYl4gv7dQyn3AyJEj3SpVqriLFi1yjx075lk/duyYu2jRIrdatWruqFGjrM3nj2bOnOmWLl3aHTFihHvzzTe7oaGh7pQpUzzbU1NT3aCgIIsT+p+goCD38OHDruu67ssvv+yGh4e7zz77rPvf//7XnTVrlhsVFeXOmjXL8pT+hfcW83hvMY/3FvN4bzGP9xbzeG8xL5DfWyjlPqBChQq5/uL5e59++qlboUIFgxP5v/r167tvvfWW5/aGDRvcSpUquY8++qjruvwft+LgOI7n/7hddtll7vTp0722z5s3z23cuLGN0fwW7y3m8d5iHu8t5vHeYh7vLebx3mJeIL+38D3lPuDMmTOqUKFCvtujo6N15swZgxP5v71796pt27ae223atNGqVas0d+5cjRs3zuJk/s1xHEm/5p+YmOi1rXPnzvrhhx9sjOW3eG8xj/cWO3hvMYv3FvN4b7GD9xazAvm9hQu9+YBOnTppzJgxeuuttxQTE+O17fDhwxo7dqw6d+5saTr/VKFCBaWkpCghIcGz1qBBA61atUqdO3fWwYMH7Q3nx5YvX66oqCiVKlUq15vqmTNnFBTE3wmLEu8t5vHeYgfvLWbx3mIe7y128N5iViC/t1DKfcCLL76o7t27q2rVqmrYsKFiYmLkOI5SU1P11VdfqX79+lq2bJntMf1K+/bt9e9//1sdOnTwWq9fv74+++wzderUydJk/m3w4MGef3/22Wdq3bq15/bGjRt1ySWX2BjLb/HeYh7vLXbw3mIW7y3m8d5iB+8tZgXyewvfU+4jcnJytGLFCm3atEmpqamSpNjYWLVp00ZXXXUVf4krYtu3b9eWLVs0dOjQPLd//fXX+te//qUJEyYYnixwffTRRypRooS6detmexS/wnuLWby3+B7eW4oH7y1m8d7ie3hvKR6B+t5CKQcAAAAAwBL//FMDAAAAAAB/AZRyH1OjRg117drVa61Lly6qWbOmpYn8H5mbR+bmkbl5ZG4emZtH5uaRuXlkbl6gZc6F3nzM4MGDVbFiRa+1vn376ueff7Y0kf8jc/PI3DwyN4/MzSNz88jcPDI3j8zNC7TMOaccAAAAAABL+Pg6AAAA/jSO85hH5uaROYoDpdxHnDlzRuvXr9fOnTtzbTt79qxef/11C1P5NzI3j8zN27Vrl+bPn69vvvlGkvTNN9/o7rvv1m233aZVq1ZZns4/kbl5ZO4bQkNDtWvXLttjBBQyN4/MzTl27JhmzJihESNGaPLkyUpJSbE9UrHh4+s+4Ntvv9VVV12lAwcOyHEcdejQQe+8847i4uIkSYcPH1blypWVnZ1teVL/Qebmkbl5y5cvV58+fVSmTBmdPn1a7733nm699VY1adJErutq7dq1WrFihTp37mx7VL9B5uaRuXljxozJc33mzJm6+eabFR0dLUmaPn26ybH8GpmbR+bmVa5cWTt27FB0dLT27t2rtm3bSpIaNWqkXbt26eTJk9q0aZPq1q1redKiRyn3AX379lVWVpbmz5+v48ePa8yYMfrqq6+0Zs0aVa9enbJSDMjcPDI3r23bturcubMmT56sRYsWafjw4br77rv15JNPSpIeeeQRJSUl6ZNPPrE8qf8gc/PI3LygoCA1adJEZcuW9Vpfu3atWrZsqfDwcDmOw6cUihCZm0fm5gUFBSk1NVWVKlXSgAEDlJqaqmXLlql06dLKzMzUDTfcoLCwML377ru2Ry16LqyrVKmSu337dq+14cOHu9WrV3e///57NzU11Q0KCrI0nX8ic/PI3LzIyEj3u+++c13XdbOzs92QkBB3y5Ytnu07duxwY2JibI3nl8jcPDI3b8qUKW6NGjXczz77zGs9JCTE/frrry1N5d/I3DwyN89xHPfw4cOu67p5Zr9p0ya3atWqNkYrdpxT7gPOnDmjkBDvb6d74YUX1Lt3b3Xs2FHffvutpcn8F5mbR+Z2BQUFKSwszOsv/hEREUpPT7c3lJ8jc/PI3Ixx48Zp8eLFuvvuu3X//ffr/Pnztkfye2RuHpnb4TiOJCkzM1MxMTFe22JiYnTkyBEbYxU7SrkPqFu3rjZv3pxrfdasWerTp4969+5tYSr/Rubmkbl5CQkJ2rNnj+f2xo0bVb16dc/tlJQUzzn9KBpkbh6Z23HZZZdpy5YtOnLkiFq2bKkdO3Z4/p9pFA8yN4/MzUtMTFTz5s114sSJXAdsDhw4oAoVKliarHiF/PFdUNz69u2rd955R7fcckuubbNnz1ZOTo5efvllC5P5LzI3j8zNu/vuu73O0W/YsKHX9o8//piLXxUxMjePzO0pU6aMFi5cqEWLFqlr165cE8QAMjePzM2ZMGGC1+3SpUt73V66dKk6dOhgciRjuNAbAAAA/pQff/xRW7ZsUZcuXRQeHm57nIBA5uaROYoLpRwAAAAAAEs4p/wv4Pvvv+ejd4aRuXlkbh6Zm0fm5pG5eWRuHpmbR+bm+XPmlPK/gIyMDK1du9b2GAGFzM0jc/PI3DwyN4/MzSNz88jcPDI3z58z50JvPuD555+/4PaDBw8amiRwkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5pxT7gOCgoIUFxenkiVL5rn93LlzSk1N5WqPRYjMzSNz88jcPDI3j8zNI3PzyNw8MjcvoDN3YV1CQoK7ePHifLdv3brVDQoKMjiR/yNz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mw5p9wHtGjRQlu2bMl3u+M4cvlAQ5Eic/PI3DwyN4/MzSNz88jcPDI3j8zNC+TM+fi6D9i5c6dOnz6tli1b5rn9/Pnz+umnnxQfH294Mv9F5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZU8oBAAAAALCEj68DAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMr/Ag4fPqxJkybZHiOgkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5vlz5nwl2l/Al19+qebNmys7O9v2KAGDzM0jc/PI3DwyN4/MzSNz88jcPDI3z58zD7E9AKTt27dfcPvu3bsNTRI4yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c46U+4CgoCA5jqO8fhW/rTuO45d/FbKFzM0jc/PI3DwyN4/MzSNz88jcPDI3L5Az50i5D4iOjtZTTz2lxMTEPLd//fXX6tWrl+Gp/BuZm0fm5pG5eWRuHpmbR+bmkbl5ZG5eIGdOKfcBLVq00E8//aT4+Pg8tx8/fjzPvxjh4pG5eWRuHpmbR+bmkbl5ZG4emZtH5uYFcuaUch9w55136tSpU/lur169uubPn29wIv9H5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZc045AAAAAACW8D3lAAAAAABYwsfXfcSPP/6ol156SRs2bFBqaqocx1FMTIzatm2ru+66S9WqVbM9ot8hc/PI3DwyN4/MzSNz88jcPDI3j8zNC9TM+fi6D1i/fr2uueYaVatWTVdddZViYmLkuq7S0tK0cuVKpaSk6OOPP1a7du1sj+o3yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D7jsssvUvn17Pffcc3luv/fee7V+/XolJSUZnsx/kbl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5pRyH1CqVClt27ZNderUyXP7N998o2bNmunMmTOGJ/NfZG4emZtH5uaRuXlkbh6Zm0fm5pG5eYGcORd68wFxcXHasGFDvts3btyouLg4gxP5PzI3j8zNI3PzyNw8MjePzM0jc/PI3LxAzpwLvfmA+++/X3fddZe2bNmirl27KiYmRo7jKDU1VStXrtQrr7yiGTNm2B7Tr5C5eWRuHpmbR+bmkbl5ZG4emZtH5uYFdOYufMKiRYvc1q1buyEhIa7jOK7jOG5ISIjbunVrd/HixbbH80tkbh6Zm0fm5pG5eWRuHpmbR+bmkbl5gZo555T7mPPnz+vnn3+WJFWoUEElSpSwPJH/I3PzyNw8MjePzM0jc/PI3DwyN4/MzQu0zDmn3MeUKFFCcXFxWrNmjc6dO2d7nIBA5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF2iZc6TcR0VGRmrbtm2qWbOm7VECBpmbR+bmkbl5ZG4emZtH5uaRuXlkbl6gZM6Rch/F30rMI3PzyNw8MjePzM0jc/PI3DwyN4/MzQuUzCnlAAAAAABYQin3UR9//LEqV65se4yAQubmkbl5ZG4emZtH5uaRuXlkbh6ZmxcomXNOuY/Jzs7Wzz//LMdxFB0dreDgYNsj+T0yN4/MzSNz88jcPDI3j8zNI3PzyNy8QMucI+U+4r333lO7du1UunRpVa5cWXFxcSpdurTatWun999/3/Z4fonMzSNz88jcPDI3j8zNI3PzyNw8MjcvUDOnlPuAOXPmqH///mrcuLEWL16s9evX6z//+Y8WL16sxo0bq3///po3b57tMf0KmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXkBn7sK6Sy65xH3llVfy3f7qq6+6NWvWNDiR/yNz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mw5Uu4DDh48qPbt2+e7vW3btvrpp58MTuT/yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D2jQoIHmzp2b7/Z58+apQYMGBifyf2RuHpmbR+bmkbl5ZG4emZtH5uaRuXmBnDlXX/cBa9euVY8ePRQfH6+rrrpKMTExchxHqampWrlypfbv36//+7//U4cOHWyP6jfI3DwyN4/MzSNz88jcPDI3j8zNI3PzAjlzSrmP2Ldvn1566SVt2rRJqampkqTY2Fi1adNGd911lxISEuwO6IfI3DwyN4/MzSNz88jcPDI3j8zNI3PzAjVzSjkAAAAAAJZwTjkAAAAAAJZQyv8CvvzySwUHB9seI6CQuXlkbh6Zm0fm5pG5eWRuHpmbR+bm+XPmlPK/CM4yMI/MzSNz88jcPDI3j8zNI3PzyNw8MjfPXzMPsT0ApOuuu+6C29PT0+U4jqFpAgOZm0fm5pG5eWRuHpmbR+bmkbl5ZG5eIGdOKfcBS5cuVdeuXRUTE5Pn9uzsbMMT+T8yN4/MzSNz88jcPDI3j8zNI3PzyNy8gM7chXWNGjVyX3nllXy3b9261Q0KCjI4kf8jc/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMOafcB7Ro0ULJycn5bg8NDVX16tUNTuT/yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c76n3AdkZmYqOztbpUuXtj1KwCBz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mwp5QAAAAAAWMLH131Ujx49dOjQIdtjBBQyN4/MzSNz88jcPDI3j8zNI3PzyNy8QMmcUu6j1q1bpzNnztgeI6CQuXlkbh6Zm0fm5pG5eWRuHpmbR+bmBUrmlHIAAAAAACyhlPuo+Ph4lShRwvYYAYXMzSNz88jcPDI3j8zNI3PzyNw8MjcvUDLnQm8AAAAAAFgSYnsA/H8//PCD1q9fr0OHDik4OFg1atRQ165dFRkZaXs0v0Xm5pG5eWRuHpmbR+bmkbl5ZG4emZsXkJm7sC4jI8O94YYbXMdxXMdx3KCgIDc2NtYNDg52y5Qp486ePdv2iH6HzM0jc/PI3DwyN4/MzSNz88jcPDI3L5Az55xyHzBmzBgdOnRIW7du1a5du9S3b1/deuutOnHihGbOnKmxY8fq7bfftj2mXyFz88jcPDI3j8zNI3PzyNw8MjePzM0L6Mxt/1UArluhQgV38+bNnttHjx51w8LC3FOnTrmu67qzZ892mzZtams8v0Tm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJlzpNwHZGVleZ0jUaZMGWVlZenUqVOSpKuuukrffPONrfH8EpmbR+bmkbl5ZG4emZtH5uaRuXlkbl4gZ04p9wGXXXaZZs6c6bk9c+ZMVaxYURUrVpQkZWRkqEyZMrbG80tkbh6Zm0fm5pG5eWRuHpmbR+bmkbl5gZw5V1/3AdOmTVPXrl3173//WyVLllRqaqoWLlzo2b5hwwZ1797d4oT+h8zNI3PzyNw8MjePzM0jc/PI3DwyNy+QM+d7yn3EoUOH9NFHHykzM1OdO3dW/fr1bY/k98jcPDI3j8zNI3PzyNw8MjePzM0jc/MCNXNKOQAAAAAAlnBO+V/AsWPH9Prrr9seI6CQuXlkbh6Zm0fm5pG5eWRuHpmbR+bm+XPmHCn/C/jyyy/VvHlzZWdn2x4lYJC5eWRuHpmbR+bmkbl5ZG4emZtH5ub5c+Zc6M0HnDhx4oLbT548aWiSwEHm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJlzpNwHBAUFyXGcfLe7rivHcfzyr0K2kLl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5hwp9wERERF65JFH1Lp16zy3f/fdd7rzzjsNT+XfyNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D2jevLkkqWPHjnluL1u2rPhAQ9Eic/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMufq6Dxg4cKDCwsLy3R4bG6sJEyYYnMj/kbl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5pxTDgAAAACAJRwpBwAAAADAEs4p9xGnTp3S22+/rQ0bNig1NVWO4ygmJkbt2rXTgAEDFB4ebntEv0Pm5pG5eWRuHpmbR+bmkbl5ZG4emZsXqJnz8XUfsHPnTnXt2lWnT59Wx44dFRMTI9d1lZaWprVr1yo8PFyffPKJ6tevb3tUv0Hm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJlTyn1Ap06dFBsbq4ULF6pkyZJe286dO6chQ4bo0KFDWr16taUJ/Q+Zm0fm5pG5eWRuHpmbR+bmkbl5ZG5eIGdOKfcBpUuX1ubNm/P9q89XX32lVq1a6fTp04Yn819kbh6Zm0fm5pG5eWRuHpmbR+bmkbl5gZw5F3rzAeXKldN3332X7/Y9e/aoXLlyBifyf2RuHpmbR+bmkbl5ZG4emZtH5uaRuXmBnDkXevMBw4YN0+DBgzV+/Hh17dpVMTExchxHqampWrlypaZMmaLRo0fbHtOvkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5gV05i58wrRp09y4uDjXcRw3KCjIDQoKch3HcePi4tynnnrK9nh+iczNI3PzyNw8MjePzM0jc/PI3DwyNy9QM+ecch+zd+9epaamSpJiY2NVo0YNyxP5PzI3j8zNI3PzyNw8MjePzM0jc/PI3LxAy5xSDgAAAACAJVzozUecOXNG69ev186dO3NtO3v2rF5//XULU/k3MjePzM0jc/PI3DwyN4/MzSNz88jcvIDN3O6n5+G6rrt79243Pj7ec+5Ex44d3Z9++smzPTU11Q0KCrI4of8hc/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMOVLuAx588EE1atRIaWlp2r17tyIjI9WuXTsdOHDA9mh+i8zNI3PzyNw8MjePzM0jc/PI3DwyNy+gM7f9VwG4bqVKldzt27d7rQ0fPtytXr26+/333/v1X4VsIXPzyNw8MjePzM0jc/PI3DwyN4/MzQvkzPmech9w5swZhYR4/ypeeOEFBQUFqWPHjnr77bctTea/yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D6hbt642b96sevXqea3PmjVLruuqd+/elibzX2RuHpmbR+bmkbl5ZG4emZtH5uaRuXmBnDnnlPuAvn376p133slz2+zZszVgwAC5fHNdkSJz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mz5nnIAAAAAACzhSDkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAIAiMWTIEF177bWFflxCQoJmzJhR5PMAAPBXQCkHAKCYDBkyRI7jyHEclShRQjExMeratatee+015eTkFGpfCxYsUNmyZYtnUEPyew1JSUn629/+Zn4gAAB8AKUcAIBidPXVV+vQoUPat2+fPv74Y3Xq1EmjRo1Sz549lZWVZXs8n1CxYkWVLl3a9hgAAFhBKQcAoBiFhoYqNjZWVapUUfPmzfXwww/rgw8+0Mcff6wFCxZ47jd9+nQ1atRI4eHhqlatmoYPH66MjAxJ0po1azR06FClp6d7jrw//vjjkqRz585p7NixqlKlisLDw9W6dWutWbPmgjNd6Lmk/39Ee8WKFapXr57KlCnj+ePCb7KzszVmzBiVLVtW0dHRGjt2rFzXzfc5L/Qa/vfj647jaM6cOerZs6dKly6tevXqaePGjdqzZ4+uvPJKhYeHq02bNvr++++9nmPp0qVq0aKFwsLCVLNmTU2cOJE/fAAAfB6lHAAAwzp37qwmTZpoyZIlnrWgoCA9//zz+uqrr7Rw4UKtWrVKY8eOlSS1bdtWM2bMUGRkpA4dOqRDhw7p/vvvlyQNHTpU//3vf7Vo0SJt375dN954o66++mp99913+T7/hZ7rN6dPn9YzzzyjN954Q+vWrdOBAwc8zylJzz77rF577TW9+uqrWr9+vY4ePar33nsv3+e80GvIyxNPPKFbb71V27ZtU926dTVw4EDdeeedGjdunDZv3ixJuueeezz3X7FihW6++WaNHDlSO3fu1Jw5c7RgwQI9+eST+T4HAAA+wQUAAMVi8ODBbp8+ffLc1q9fP7devXr5Pvaf//ynGx0d7bk9f/58Nyoqyus+e/bscR3HcQ8ePOi1npiY6I4bN67Ac+b1XJLcPXv2eNZeeOEFNyYmxnM7Li7OnTZtmuf2+fPn3apVq+b7evN7Da7ruvHx8e5zzz3nuS3JHT9+vOf2xo0bXUnuq6++6ll755133LCwMM/tDh06uFOmTPHa7xtvvOHGxcXlOw8AAL4gxPLfBAAACEiu68pxHM/t1atXa8qUKdq5c6dOnDihrKwsnT17VqdOnVJ4eHie+0hOTpbruqpdu7bXemZmpqKjo/N97oI8V+nSpXXJJZd4HhMXF6e0tDRJUnp6ug4dOqQ2bdp4toeEhKhly5YX/Ah7YTRu3Njz75iYGElSo0aNvNbOnj2rEydOKDIyUlu2bFFSUpLXkfHs7GydPXtWp0+f5px1AIDPopQDAGDBrl27VKNGDUnS/v371b17d91111164oknVL58ea1fv1633367zp8/n+8+cnJyFBwcrC1btig4ONhrW5kyZfJ8TEGfq0SJEl6PcxynyAp3Qfz++X/740Vea79dxT4nJ0cTJ07Uddddl2tfYWFhxTkqAAB/CqUcAADDVq1apR07dujee++VJG3evFlZWVl69tlnFRT06+Ve/vnPf3o9pmTJksrOzvZaa9asmbKzs5WWlqYOHToU6LkL8lx/JCoqSnFxcdq0aZOuuOIKSVJWVpa2bNmi5s2b5/u4vF5DUWnevLl2796tSy+9tFj2DwBAcaGUAwBQjDIzM5Wamqrs7GwdPnxYy5cv19SpU9WzZ0/deuutkqRLLrlEWVlZmjVrlnr16qX//ve/evnll732k5CQoIyMDH322Wdq0qSJSpcurdq1a2vQoEG69dZb9eyzz6pZs2b6+eeftWrVKjVq1Ejdu3fPNU9BnqsgRo0apWnTpqlWrVqqV6+epk+fruPHj1/wMXm9hqL6WPljjz2mnj17qlq1arrxxhsVFBSk7du3a8eOHZo8eXKRPAcAAMWBq68DAFCMli9frri4OCUkJOjqq6/W6tWr9fzzz+uDDz7wfOS8adOmmj59up566ik1bNhQb731lqZOneq1n7Zt2+quu+5Sv379VLFiRT399NOSpPnz5+vWW2/Vfffdpzp16qh37976/PPPVa1atTznKchzFcR9992nW2+9VUOGDFGbNm0UERGhvn37XvAx+b2GotCtWzd99NFHWrlypS677DJdfvnlmj59uuLj44vsOQAAKA6Oa/IEMQAAAAAA4MGRcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABY8v8ApebXkZ0qufoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define time range\n",
    "oct1_afternoon = oct1_temps.loc[oct1_temps.index <= \"201910011500\"]\n",
    "\n",
    "# Create bar plot\n",
    "ax = oct1_afternoon.plot(\n",
    "    kind=\"bar\",\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    figsize=(12, 6),\n",
    "    ylim=[40, 46],\n",
    ")\n",
    "\n",
    "# Add plot text\n",
    "ax.text(0, 42.1, \"Coldest \\ntemp \\nv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find more about how to format bar charts on the [pandas documentation website](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving your plots as image files\n",
    "\n",
    "Saving plots created using pandas can be done in several ways. The recommendation for use outside of Jupyter notebooks is to use Matplotlib's `plt.savefig()` function. When using `plt.savefig()`, you simply give a list of commands to generate a plot and include `plt.savefig()` with some parameters as the last command in the Python cell. The file name is required, and the image format will be determined based on the listed file extension. Note that because we have not used Matplotlib for any of the earlier plots, we need to import it here first.\n",
    "\n",
    "Matplotlib plots can be saved in a number of useful file formats, including PNG, PDF, and EPS. PNG is a nice format for raster images, and EPS is probably easiest to use for vector graphics. Let's check out an example and save our lovely bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create bar plot\n",
    "ax = oct1_afternoon.plot(\n",
    "    kind=\"bar\",\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    figsize=(12, 6),\n",
    "    ylim=[40, 46],\n",
    ")\n",
    "\n",
    "# Add plot text\n",
    "ax.text(0, 42.1, \"Coldest \\ntemp \\nv\")\n",
    "\n",
    "# Save plot to file\n",
    "plt.savefig(\"bar-plot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you refresh your **Files** tab on the left side of the JupyterLab window you should now see `bar-plot.png` listed.\n",
    "We could try to save another version in higher resolution with a minor change to our plot commands above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZ89q3759+r//+z+9/PLLf3gqQ2F+j40aNdKaNWu0dOlSxcXFKSIiQnXq1FH37t1Vvnx53X777Zo0aZJCQkK0YMECpaSkFPj1FvS1DBs2TKVKlVK7du0UFxen1NRUTZ06VVFRUV5/EAAAmEMpBwAYMWfOHF1++eWaM2eOXnzxReXk5Khy5cpq166dWrVqle/jWrdurc2bN+vBBx/UkSNHVLZsWbVs2VKrVq3ynC9b1G6//XaFh4frlltu0alTp/TKK69c9H4+/PBDlS9fXtdee63XtqFDhyotLU0vv/yyXnvtNdWsWVMPPfSQfvzxR8/3ThfWlVdeqXHjxmnhwoWaN2+ecnJytHr1al155ZVatmyZ7rvvPt18880KDw9Xnz59tHjxYjVv3txrH2+//bZGjRqlsWPHKisrS+3atdPKlStzXWAtP6+++qoeeOAB9e7dW5mZmRo8eLAWLFig+vXrKzk5WU888YTGjx+vtLQ0lS1bVrVq1fKcV16UevfurQYNGmj8+PE6cOCALrnkEr311lvq16+f5z5xcXHavHmznnjiCf3jH//Qjz/+qIiICNWoUUNXX311gY44F+b3OHPmTI0YMUL9+/fX6dOn1bFjR61Zs0aRkZFavny5Ro8erZtvvllly5bVHXfcoWuuucbz1XJ/pKCvpUOHDlqwYIH++c9/6tixY6pQoYLat2+v119/3fP1dQAAsxzXzePLZAEAAP6iHMfRiBEjNHv2bNujAADwh7j6OgAAAAAAllDKAQAAAACwhHPKAQCAX+HMPADAX4nPHCmfOnWqHMfR6NGjvdZ37dql3r17KyoqShEREbr88st14MABO0MCAAAAAFCEfKKUJyUlae7cuWrcuLHX+vfff6/27durbt26WrNmjb788ks9+uijCgsLszQpAAAAAABFx/rV1zMyMtS8eXO9+OKLmjx5spo2baoZM2ZIkvr3768SJUrojTfesDkiAAAAAADFwvo55SNGjFCPHj3UpUsXTZ482bOek5OjZcuWaezYserWrZu2bt2qGjVqaNy4cbm+6/X3MjMzlZmZ6bWfo0ePKjo6Wo7jFOdLAQAAAABAruvq5MmTqly5soKCLvwBdaulfNGiRUpOTlZSUlKubWlpacrIyNC0adM0efJkPfXUU1q+fLmuu+46rV69Wh07dsxzn1OnTtXEiROLe3QAAAAAAC4oJSVFVatWveB9rH18PSUlRS1bttQnn3yiJk2aSJKuvPJKz8fXf/rpJ1WpUkUDBgzQ22+/7Xlc7969FR4ernfeeSfP/f7vkfL09HRVr15dKSkpioyMLN4XBQAAAAAIeCdOnFC1atV0/PhxRUVFXfC+1o6Ub9myRWlpaWrRooVnLTs7W+vWrdPs2bN16tQphYSEqH79+l6Pq1evntavX5/vfkNDQxUaGpprPTIyklIOAAAAADCmIKdQWyvliYmJ2rFjh9fa0KFDVbduXT344IMKDQ3VZZddpt27d3vd59tvv1V8fLzJUQEAAAAAKBbWSnlERIQaNmzotRYeHq7o6GjP+gMPPKB+/frpiiuuUKdOnbR8+XItXbpUa9assTAxAAAAAABFyye+pzw/ffv21csvv6ynn35ajRo10iuvvKJ///vfat++ve3RAAAAAAD406x/T3lxO3HihKKiopSens455QAAAACAYleYHurTR8oBAAAAAPBnlHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALPGZUj516lQ5jqPRo0d71oYMGSLHcbx+Lr/8cntDAgAAAABQhEJsDyBJSUlJmjt3rho3bpxr29VXX6358+d7bpcsWdLkaAAAAAAAFBvrR8ozMjI0aNAgzZs3T+XKlcu1PTQ0VLGxsZ6f8uXLW5gSAAAAAICiZ72UjxgxQj169FCXLl3y3L5mzRpVqlRJtWvX1rBhw5SWlnbB/WVmZurEiRNePwAAAAAA+CKrH19ftGiRkpOTlZSUlOf2a665RjfeeKPi4+O1d+9ePfroo+rcubO2bNmi0NDQPB8zdepUTZw4sTjHBgD4oYSHltke4aLtm9bD9ggXhcwRCPjv3Dwyx1+NtVKekpKiUaNG6ZNPPlFYWFie9+nXr5/n3w0bNlTLli0VHx+vZcuW6brrrsvzMePGjdOYMWM8t0+cOKFq1aoV7fAAAAAAABQBa6V8y5YtSktLU4sWLTxr2dnZWrdunWbPnq3MzEwFBwd7PSYuLk7x8fH67rvv8t1vaGhovkfRAQAAAADwJdZKeWJionbs2OG1NnToUNWtW1cPPvhgrkIuSb/88otSUlIUFxdnakwAAAAAAIqNtVIeERGhhg0beq2Fh4crOjpaDRs2VEZGhh5//HFdf/31iouL0759+/Twww+rQoUK6tu3r6WpAQAAAAAoOj7xPeV5CQ4O1o4dO/T666/r+PHjiouLU6dOnbR48WJFRETYHg8AAAAAgD/Np0r5mjVrPP8uVaqUVqxYYW8YAAAAAACKmfXvKQcAAAAAIFBRygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwxGdK+dSpU+U4jkaPHp3n9jvvvFOO42jGjBlG5wIAAAAAoLj4RClPSkrS3Llz1bhx4zy3v//++/r8889VuXJlw5MBAAAAAFB8rJfyjIwMDRo0SPPmzVO5cuVybT948KDuuecevfXWWypRooSFCQEAAAAAKB7WS/mIESPUo0cPdenSJde2nJwc3XLLLXrggQfUoEGDAu0vMzNTJ06c8PoBAAAAAMAXhdh88kWLFik5OVlJSUl5bn/qqacUEhKikSNHFnifU6dO1cSJE4tqRACwIuGhZbZHuGj7pvWwPQKAfPDeAqA48N7y51g7Up6SkqJRo0bpzTffVFhYWK7tW7Zs0cyZM7VgwQI5jlPg/Y4bN07p6emen5SUlKIcGwAAAACAImOtlG/ZskVpaWlq0aKFQkJCFBISorVr1+r5559XSEiI1qxZo7S0NFWvXt2zff/+/brvvvuUkJCQ735DQ0MVGRnp9QMAAAAAgC+y9vH1xMRE7dixw2tt6NChqlu3rh588EHFxcWpW7duXtu7deumW265RUOHDjU5KgAAAAAAxcJaKY+IiFDDhg291sLDwxUdHe1Zj46O9tpeokQJxcbGqk6dOsbmBAAAAACguFi/+joAAAAAAIHK6tXX/9eaNWsuuH3fvn1G5gAAAAAAwASOlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWhBTkTs8//3yhdzx06FBFREQU+nEAAAAAAASKApXy0aNHq2rVqgoODi7QTlNSUtSzZ09KOQAAAAAAF1CgUi5JmzdvVqVKlQp0X8o4AAAAAAB/rEDnlE+YMEFlypQp8E4ffvhhlS9f/qKHAgAAAAAgEBToSPmECRMKtdNx48Zd1DAAAAAAAASSAl99fdWqVcrKyirOWQAAAAAACCgFLuVdu3bV0aNHPbcvv/xyHTx4sFiGAgAAAAAgEBS4lLuu63X766+/VmZmZpEPBAAAAABAoChwKQcAAAAAAEWrwKXccRw5jpPvbQAAAAAAUDgF/p5y13WVmJiokJBfH3L69Gn16tVLJUuW9LpfcnJy0U4IAAAAAICfKnAp/9+vRevTp0+RDwMAAAAAQCC56FIOAAAAAAD+HC70BgAAAACAJQUu5Wlpafrb3/6m/v376+uvvy7OmQAAAAAACAgFLuVDhw5VbGys+vbtq2uuuSbX95YDAAAAAIDCKXAp37p1q/r166ebbrpJqampOnLkSHHOBQAAAACA3yvwhd6uvfZajRs3TvHx8WrcuLEqVapUnHMBAAAAAOD3CnykfPbs2erXr5/q1q2rVatWFedMAAAAAAAEhAIfKQ8KCtKgQYOKcxYAAAAAAAIKX4kGAAAAAIAlBSrlzZs317Fjxwq80/bt2+vgwYMXPRQAAAAAAIGgQB9f37Ztm7788kuVL1++QDvdtm2bMjMz/9RgAAAAAAD4uwKfU56YmFjg7yZ3HOeiBwIAAAAAIFAUqJTv3bu30DuuWrVqoR8DAAAAAEAgKVApj4+PL+45AAAAAAAIOFx9HQAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZcVCk/fvy4XnnlFY0bN05Hjx6VJCUnJ+vgwYNFOhwAAAAAAP6swN9T/pvt27erS5cuioqK0r59+zRs2DCVL19e7733nvbv36/XX3+9OOYEAAAAAMDvFPpI+ZgxYzRkyBB99913CgsL86xfc801WrduXZEOBwAAAACAPyt0KU9KStKdd96Za71KlSpKTU0tkqEAAAAAAAgEhS7lYWFhOnHiRK713bt3q2LFikUyFAAAAAAAgaDQpbxPnz6aNGmSzp8/L0lyHEcHDhzQQw89pOuvv77IBwQAAAAAwF8VupQ/88wzOnLkiCpVqqQzZ86oY8eOuvTSSxUREaEnn3yyOGYEAAAAAMAvFfrq65GRkVq/fr1WrVql5ORk5eTkqHnz5urSpUtxzAcAAAAAgN8qVCnPyspSWFiYtm3bps6dO6tz587FNRcAAAAAAH6vUB9fDwkJUXx8vLKzs4trHgAAAAAAAkahzykfP368xo0bp6NHjxbHPAAAAAAABIxCn1P+/PPPa8+ePapcubLi4+MVHh7utT05ObnIhgMAAAAAwJ8VupRfe+21xTAGAAAAAACBp9ClfMKECcUxBwAAAAAAAafQ55QDAAAAAICiUegj5UFBQXIcJ9/tXJkdAAAAAICCKXQpf++997xunz9/Xlu3btXChQs1ceLEIhsMAAAAAAB/V+hS3qdPn1xrN9xwgxo0aKDFixfr9ttvL5LBAAAAAADwd0V2Tnnr1q316aefFtXuAAAAAADwe0VSys+cOaNZs2apatWqRbE7AAAAAAACQqE/vl6uXDmvC725rquTJ0+qdOnSevPNN4t0OAAAAAAA/FmhS/lzzz3nVcqDgoJUsWJFtW7dWuXKlSvS4QAAAAAA8GeFLuWdO3dWtWrV8vxatAMHDqh69epFMhgAAAAAAP6u0OeU16hRQ0eOHMm1/ssvv6hGjRoXPcjUqVPlOI5Gjx7tWXv88cdVt25dhYeHq1y5curSpYs+//zzi34OAAAAAAB8SaFLueu6ea5nZGQoLCzsooZISkrS3Llz1bhxY6/12rVra/bs2dqxY4fWr1+vhIQEXXXVVXn+UQAAAAAAgL+aAn98fcyYMZIkx3H02GOPqXTp0p5t2dnZ+vzzz9W0adNCD5CRkaFBgwZp3rx5mjx5ste2gQMHet2ePn26Xn31VW3fvl2JiYmFfi4AAAAAAHxJgUv51q1bJf16pHzHjh0qWbKkZ1vJkiXVpEkT3X///YUeYMSIEerRo4e6dOmSq5T/3rlz5zR37lxFRUWpSZMm+d4vMzNTmZmZntsnTpwo9EwAAAAAAJhQ4FK+evVqSdLQoUM1c+ZMRUZG/uknX7RokZKTk5WUlJTvfT766CP1799fp0+fVlxcnFauXKkKFSrke/+pU6dq4sSJf3o2+K6Eh5bZHuGi7ZvWw/YIF4XMAQAAgOJR6HPK58+fXySFPCUlRaNGjdKbb755wXPRO3XqpG3btmnDhg26+uqrddNNNyktLS3f+48bN07p6emen5SUlD89KwAAAAAAxaHQX4km/XphtnfffVcHDhzQuXPnvLYtWbKkQPvYsmWL0tLS1KJFC89adna21q1bp9mzZyszM1PBwcEKDw/XpZdeqksvvVSXX365atWqpVdffVXjxo3Lc7+hoaEKDQ29mJcFAAAAAIBRhT5SvmjRIrVr1047d+7Ue++9p/Pnz2vnzp1atWqVoqKiCryfxMRE7dixQ9u2bfP8tGzZUoMGDdK2bdsUHByc5+Nc1/U6ZxwAAAAAgL+qQh8pnzJlip577jmNGDFCERERmjlzpmrUqKE777xTcXFxBd5PRESEGjZs6LUWHh6u6OhoNWzYUKdOndKTTz6p3r17Ky4uTr/88otefPFF/fjjj7rxxhsLOzYAAAAAAD6n0EfKv//+e/Xo8euFk0JDQ3Xq1Ck5jqN7771Xc+fOLbLBgoOD9c033+j6669X7dq11bNnTx05ckT/+c9/1KBBgyJ7HgAAAAAAbCn0kfLy5cvr5MmTkqQqVaroq6++UqNGjXT8+HGdPn36Tw2zZs0az7/DwsIKfH46AAAAAAB/RYUu5R06dNDKlSvVqFEj3XTTTRo1apRWrVqllStXKjExsThmBAAAAADALxW6lM+ePVtnz56V9OvXj5UoUULr16/Xddddp0cffbTIBwQAAAAAwF8VqpRnZWVp6dKl6tatmyQpKChIY8eO1dixY4tlOAAAAAAA/FmhLvQWEhKiu+++m68kAwAAAACgCBT66uutW7fW1q1bi2MWAAAAAAACSqHPKR8+fLjuu+8+/fjjj2rRooXCw8O9tjdu3LjIhgMAAAAAwJ8VupT369dPkjRy5EjPmuM4cl1XjuMoOzu76KYDAAAAAMCPFbqU7927tzjmAAAAAAAg4BS6lMfHxxfHHAAAAAAABJxCX+hNkt544w21a9dOlStX1v79+yVJM2bM0AcffFCkwwEAAAAA4M8KXcpfeukljRkzRt27d9fx48c955CXLVtWM2bMKOr5AAAAAADwW4Uu5bNmzdK8efP0yCOPKDg42LPesmVL7dixo0iHAwAAAADAnxW6lO/du1fNmjXLtR4aGqpTp04VyVAAAAAAAASCQpfyGjVqaNu2bbnWP/74Y9WvX78oZgIAAAAAICAU+urrDzzwgEaMGKGzZ8/KdV198cUXeueddzR16lS98sorxTEjAAAAAAB+qdClfOjQocrKytLYsWN1+vRpDRw4UFWqVNHMmTPVv3//4pgRAAAAAAC/VOhSLknDhg3TsGHD9PPPPysnJ0eVKlUq6rkAAAAAAPB7F1XKJSktLU27d++W4zhyHEcVK1YsyrkAAAAAAPB7hb7Q24kTJ3TLLbeocuXK6tixo6644gpVrlxZN998s9LT04tjRgAAAAAA/FKhS/kdd9yhzz//XMuWLdPx48eVnp6ujz76SJs3b9awYcOKY0YUkccff1xNmza94H2GDBmia6+91sg8AAAAABDoCl3Kly1bptdee03dunVTZGSkIiIi1K1bN82bN0/Lli0rjhkhKTU1VX//+99Vs2ZNhYaGqlq1aurVq5c+++wz26Pla9++fXIcJ8+v0AMAAAAAXMQ55dHR0YqKisq1HhUVpXLlyhXJUPC2b98+tWvXTmXLltXTTz+txo0b6/z581qxYoVGjBihb775xvaIAAAAAICLUOgj5ePHj9eYMWN06NAhz1pqaqoeeOABPfroo0U6HH41fPhwOY6jL774QjfccINq166tBg0aaMyYMdq0aZPnfgcOHFCfPn1UpkwZRUZG6qabbtLhw4fz3W92drbGjBmjsmXLKjo6WmPHjpXrul73cV1XTz/9tGrWrKlSpUqpSZMm+te//uXZfuzYMQ0aNEgVK1ZUqVKlVKtWLc2fP1+SVKNGDUlSs2bN5DiOrrzyyiJMBQAAAAD++gp9pPyll17Snj17FB8fr+rVq0v6tQyGhobqyJEjmjNnjue+ycnJRTdpgDp69KiWL1+uJ598UuHh4bm2ly1bVtKv5fnaa69VeHi41q5dq6ysLA0fPlz9+vXTmjVr8tz3s88+q9dee02vvvqq6tevr2effVbvvfeeOnfu7LnP+PHjtWTJEr300kuqVauW1q1bp5tvvlkVK1ZUx44d9eijj2rnzp36+OOPVaFCBe3Zs0dnzpyRJH3xxRdq1aqVPv30UzVo0EAlS5Ys8nwAAAAA4K+s0KWci4CZtWfPHrmuq7p1617wfp9++qm2b9+uvXv3qlq1apKkN954Qw0aNFBSUpIuu+yyXI+ZMWOGxo0bp+uvv16S9PLLL2vFihWe7adOndL06dO1atUqtWnTRpJUs2ZNrV+/XnPmzFHHjh114MABNWvWTC1btpQkJSQkeB7/29fkRUdHKzY29uJDAAAAAAA/VehSPmHChOKYA/n47ePkjuNc8H67du1StWrVPIVckurXr6+yZctq165duUp5enq6Dh065CnbkhQSEqKWLVt6nnPnzp06e/asunbt6vXYc+fOqVmzZpKku+++W9dff72Sk5N11VVX6dprr1Xbtm0v/gUDAAAAQAApdCn/vYyMDOXk5HitRUZG/qmB4K1WrVpyHEe7du264KcUXNfNs7jnt14Qv/1uly1bpipVqnhtCw0NlSRdc8012r9/v5YtW6ZPP/1UiYmJGjFihJ555pmLek4AAAAACCSFvtDb3r171aNHD4WHh3uuuF6uXDmVLVuWq68Xg/Lly6tbt2564YUXdOrUqVzbjx8/LunXo+IHDhxQSkqKZ9vOnTuVnp6uevXq5XpcVFSU4uLivC4Ul5WVpS1btnhu169fX6GhoTpw4IAuvfRSr5/fH5GvWLGihgwZojfffFMzZszQ3LlzJclzDnl2dvafCwEAAAAA/FShj5QPGjRIkvTaa68pJibmoo/CouBefPFFtW3bVq1atdKkSZPUuHFjZWVlaeXKlXrppZe0a9cudenSRY0bN9agQYM0Y8YMz4XeOnbs6Dnf+3+NGjVK06ZNU61atVSvXj1Nnz7dU/IlKSIiQvfff7/uvfde5eTkqH379jpx4oQ2bNigMmXKaPDgwXrsscfUokULNWjQQJmZmfroo488fwSoVKmSSpUqpeXLl6tq1aoKCwvL8+v0AAAAACBQFbqUb9++XVu2bFGdOnWKYx7koUaNGkpOTtaTTz6p++67T4cOHVLFihXVokULvfTSS5J+Pef8/fff19///nddccUVCgoK0tVXX61Zs2blu9/f9jVkyBAFBQXptttuU9++fZWenu65zxNPPKFKlSpp6tSp+uGHH1S2bFk1b95cDz/8sKRfj4aPGzdO+/btU6lSpdShQwctWrRI0q/nqD///POaNGmSHnvsMXXo0CHfK8EDAAAAQCAqdCm/7LLLlJKSQik3LC4uTrNnz9bs2bPzvU/16tX1wQcf5Lv98ccf1+OPP+65HRISohkzZmjGjBn5PsZxHI0cOVIjR47Mc/v48eM1fvz4fB9/xx136I477sh3OwAAAAAEskKX8ldeeUV33XWXDh48qIYNG6pEiRJe2xs3blxkwwEAAAAA4M8KXcqPHDmi77//XkOHDvWsOY7juco3F/UCAAAAAKBgCl3Kb7vtNjVr1kzvvPMOF3oDAAAAAOBPKHQp379/vz788ENdeumlxTEPAAAAAAABo9DfU965c2d9+eWXxTELAAAAAAABpdBHynv16qV7771XO3bsUKNGjXJd6K13795FNhx+deWVV6pp06YXvEo6AAAAAOCvp9Cl/K677pIkTZo0Kdc2LvQGAAAAAEDBFfrj6zk5Ofn+UMiL3pAhQ7R27VrNnDlTjuPIcRzt27dPkrRz5051795dZcqUUUxMjG655Rb9/PPPnsdeeeWV+vvf/67Ro0erXLlyiomJ0dy5c3Xq1CkNHTpUERERuuSSS/Txxx97HrNmzRo5jqNly5apSZMmCgsLU+vWrbVjxw7TLx0AAAAA/F6hS/nvnT17tqjmQD5mzpypNm3aaNiwYTp06JAOHTqkatWq6dChQ+rYsaOaNm2qzZs3a/ny5Tp8+LBuuukmr8cvXLhQFSpU0BdffKG///3vuvvuu3XjjTeqbdu2Sk5OVrdu3XTLLbfo9OnTXo974IEH9MwzzygpKUmVKlVS7969df78eZMvHQAAAAD8XqFLeXZ2tp544glVqVJFZcqU0Q8//CBJevTRR/Xqq68W+YCBLioqSiVLllTp0qUVGxur2NhYBQcH66WXXlLz5s01ZcoU1a1bV82aNdNrr72m1atX69tvv/U8vkmTJho/frxq1aqlcePGqVSpUqpQoYKGDRumWrVq6bHHHtMvv/yi7du3ez3vhAkT1LVrVzVq1EgLFy7U4cOH9d5775l++QAAAADg1wpdyp988kktWLBATz/9tEqWLOlZb9SokV555ZUiHQ7527Jli1avXq0yZcp4furWrStJ+v777z33a9y4seffwcHBio6OVqNGjTxrMTExkqS0tDSv/bdp08bz7/Lly6tOnTratWtXsbwWAAAAAAhUhb7Q2+uvv665c+cqMTHRc9E36dfy98033xTpcMhfTk6OevXqpaeeeirXtri4OM+///fq+I7jeK05juPZ3x/57b4AAAAAgKJR6FJ+8OBBXXrppbnWc3JyOOe4mJQsWTLXRfSaN2+uf//730pISFBISKF/jX9o06ZNql69uiTp2LFj+vbbbz1H4gEAAAAARaPQH19v0KCB/vOf/+Raf/fdd9WsWbMiGQreEhIS9Pnnn2vfvn36+eeflZOToxEjRujo0aMaMGCAvvjiC/3www/65JNPdNtttxXJVfAnTZqkzz77TF999ZWGDBmiChUq6Nprr/3zLwYAAAAA4FHgUn7bbbfp5MmTmjBhgu655x499dRTysnJ0ZIlSzRs2DBNmTJFjz32WHHOGrDuv/9+BQcHq379+qpYsaIOHDigypUr67///a+ys7PVrVs3NWzYUKNGjVJUVJSCgv7URfUlSdOmTdOoUaPUokULHTp0SB9++KHXNQQAAAAAAH9egT/3vHDhQk2bNk29evXS4sWLNWXKFDmOo8cee0zNmzfX0qVL1bVr1+KcNWDVrl1bGzduzLVeq1YtLVmyJN/HrVmzJtfab99x/nuu6+Zaa9++vb766qtCzQkAAAAAKJwCl/LfF7du3bqpW7duxTIQAAAAAACBolCfc+bq2wAAAAAAFJ1CXba7du3af1jMjx49+qcGgl1XXnllnh9nBwAAAAAUvUKV8okTJyoqKqq4ZgEAAAAAIKAUqpT3799flSpVKq5ZAAAAAAAIKAU+p5zzyQEAAAAAKFoFLuWcZwwAAAAAQNEq8MfXc3JyinMOAAAAAAACTqG+Eg3mzZkzR1WqVMn1R5HevXtr8ODBlqYCAAAAABQFSrmPu/HGG/Xzzz9r9erVnrVjx45pxYoVGjRokMXJAAAAAAB/FqXcx5UvX15XX3213n77bc/au+++q/LlyysxMdHiZAAAAACAP4tS/hcwaNAg/fvf/1ZmZqYk6a233lL//v0VHBxseTIAAAAAwJ9BKf8L6NWrl3JycrRs2TKlpKToP//5j26++WbbYwEAAAAA/qQCX30d9pQqVUrXXXed3nrrLe3Zs0e1a9dWixYtbI8FAAAAAPiTKOV/EYMGDVKvXr309ddfc5QcAAAAAPwEH1//i+jcubPKly+v3bt3a+DAgbbHAQAAAAAUAY6U/0UEBwfrp59+sj0GAAAAAKAIcaQcAAAAAABLKOUAAAAAAFjiM6V86tSpchxHo0ePliSdP39eDz74oBo1aqTw8HBVrlxZt956Kx/hBgAAAAD4DZ8o5UlJSZo7d64aN27sWTt9+rSSk5P16KOPKjk5WUuWLNG3336r3r17W5wUAAAAAICiY/1CbxkZGRo0aJDmzZunyZMne9ajoqK0cuVKr/vOmjVLrVq10oEDB1S9enXTowIAAAAAUKSsHykfMWKEevTooS5duvzhfdPT0+U4jsqWLZvvfTIzM3XixAmvHwAAAAAAfJHVI+WLFi1ScnKykpKS/vC+Z8+e1UMPPaSBAwcqMjIy3/tNnTpVEydOLMoxLyjhoWXGnquo7ZvWw/YIAAAAABDQrB0pT0lJ0ahRo/Tmm28qLCzsgvc9f/68+vfvr5ycHL344osXvO+4ceOUnp7u+UlJSSnKsQEAAAAAKDLWjpRv2bJFaWlpatGihWctOztb69at0+zZs5WZmang4GCdP39eN910k/bu3atVq1Zd8Ci5JIWGhio0NLS4xwcAAAAA4E+zVsoTExO1Y8cOr7WhQ4eqbt26evDBB70K+XfffafVq1crOjra0rQAAAAAABQ9a6U8IiJCDRs29FoLDw9XdHS0GjZsqKysLN1www1KTk7WRx99pOzsbKWmpkqSypcvr5IlS9oYGwAAAACAImP9K9Hy8+OPP+rDDz+UJDVt2tRr2+rVq3XllVeaHwoAAAAAgCLkU6V8zZo1nn8nJCTIdV17wwAAAAAAUMysf085AAAAAACBilIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCU+U8qnTp0qx3E0evRoz9qSJUvUrVs3VahQQY7jaNu2bdbmAwAAAACgqPlEKU9KStLcuXPVuHFjr/VTp06pXbt2mjZtmqXJAAAAAAAoPiG2B8jIyNCgQYM0b948TZ482WvbLbfcIknat2+fhckAAAAAAChe1o+UjxgxQj169FCXLl2KZH+ZmZk6ceKE1w8AAAAAAL7I6pHyRYsWKTk5WUlJSUW2z6lTp2rixIlFtj8AAAAAAIqLtSPlKSkpGjVqlN58802FhYUV2X7HjRun9PR0z09KSkqR7RsAAAAAgKJk7Uj5li1blJaWphYtWnjWsrOztW7dOs2ePVuZmZkKDg4u9H5DQ0MVGhpalKMCAAAAAFAsrJXyxMRE7dixw2tt6NChqlu3rh588MGLKuQAAAAAAPyVWCvlERERatiwoddaeHi4oqOjPetHjx7VgQMH9NNPP0mSdu/eLUmKjY1VbGys2YEBAAAAAChi1q++fiEffvihmjVrph49ekiS+vfvr2bNmunll1+2PBkAAAAAAH+e9e8p/701a9Z43R4yZIiGDBliZRYAAAAAAIqbTx8pBwAAAADAn1HKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALCEUg4AAAAAgCWUcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMoBAAAAALDEZ0r51KlT5TiORo8e7VlzXVePP/64KleurFKlSunKK6/U119/bW9IAAAAAACKkE+U8qSkJM2dO1eNGzf2Wn/66ac1ffp0zZ49W0lJSYqNjVXXrl118uRJS5MCAAAAAFB0rJfyjIwMDRo0SPPmzVO5cuU8667rasaMGXrkkUd03XXXqWHDhlq4cKFOnz6tt99+2+LEAAAAAAAUjRDbA4wYMUI9evRQly5dNHnyZM/63r17lZqaqquuusqzFhoaqo4dO2rDhg26884789xfZmamMjMzPbfT09MlSSdOnCiW+XMyTxfLfk0orkyKG5mbR+bmkbl5ZG4emZtH5uaRuXlkbh6Z579f13X/8L5WS/miRYuUnJyspKSkXNtSU1MlSTExMV7rMTEx2r9/f777nDp1qiZOnJhrvVq1an9yWv8TNcP2BIGHzM0jc/PI3DwyN4/MzSNz88jcPDI3r7gzP3nypKKioi54H2ulPCUlRaNGjdInn3yisLCwfO/nOI7Xbdd1c6393rhx4zRmzBjP7ZycHB09elTR0dEXfJwvOnHihKpVq6aUlBRFRkbaHicgkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5v1VM3ddVydPnlTlypX/8L7WSvmWLVuUlpamFi1aeNays7O1bt06zZ49W7t375b06xHzuLg4z33S0tJyHT3/vdDQUIWGhnqtlS1btmiHNywyMvIv9R+gPyBz88jcPDI3j8zNI3PzyNw8MjePzM37K2b+R0fIf2PtQm+JiYnasWOHtm3b5vlp2bKlBg0apG3btqlmzZqKjY3VypUrPY85d+6c1q5dq7Zt29oaGwAAAACAImPtSHlERIQaNmzotRYeHq7o6GjP+ujRozVlyhTVqlVLtWrV0pQpU1S6dGkNHDjQxsgAAAAAABQp61dfv5CxY8fqzJkzGj58uI4dO6bWrVvrk08+UUREhO3RjAgNDdWECRNyfRwfxYfMzSNz88jcPDI3j8zNI3PzyNw8MjcvEDJ33IJcox0AAAAAABQ5a+eUAwAAAAAQ6CjlAAAAAABYQikHAAAAAMASSjkAAAAAAJZQygEAAAAAsMSnvxItkLiuq08//VQbNmxQamqqHMdRTEyM2rVrp8TERDmOY3tEv0Pm5pG5eWRuHpmbR+bmkbl5ZG4emZsXqJnzlWg+4ODBg+rZs6d27Nihhg0bKiYmRq7rKi0tTV999ZWaNGmiDz/8UFWqVLE9qt8gc/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMKeU+oE+fPsrIyNCbb76puLg4r22HDh3SzTffrIiICL3//vt2BvRDZG4emZtH5uaRuXlkbh6Zm0fm5pG5eYGcOaXcB5QpU0b//e9/1aRJkzy3b926VR06dFBGRobhyfwXmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXiBnzoXefECpUqV09OjRfLcfO3ZMpUqVMjiR/yNz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mwp5T6gf//+Gjx4sP71r38pPT3ds56enq5//etfGjp0qAYOHGhxQv9D5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF9CZu7AuMzPTveuuu9ySJUu6QUFBblhYmBsWFuYGBQW5JUuWdO+++243MzPT9ph+hczNI3PzyNw8MjePzM0jc/PI3DwyNy+QM+ecch9y4sQJbd68WYcPH5YkxcbGqkWLFoqMjLQ8mf8ic/PI3DwyN4/MzSNz88jcPDI3j8zNC8TMKeUAAAAAAFgSYnsA/OrUqVN6++23tWHDBqWmpspxHMXExKhdu3YaMGCAwsPDbY/od8jcPDI3j8zNI3PzyNw8MjePzM0jc/MCNXOOlPuAnTt3qmvXrjp9+rQ6duyomJgYua6rtLQ0rV27VuHh4frkk09Uv35926P6DTI3j8zNI3PzyNw8MjePzM0jc/PI3LxAzpxS7gM6deqk2NhYLVy4UCVLlvTadu7cOQ0ZMkSHDh3S6tWrLU3of8jcPDI3j8zNI3PzyNw8MjePzM0jc/MCOXNKuQ8oXbq0Nm/enO9ffb766iu1atVKp0+fNjyZ/yJz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mz5nnIfUK5cOX333Xf5bt+zZ4/KlStncCL/R+bmkbl5ZG4emZtH5uaRuXlkbh6ZmxfImXOhNx8wbNgwDR48WOPHj1fXrl0VExMjx3GUmpqqlStXasqUKRo9erTtMf0KmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXkBnbvZr0ZGfadOmuXFxca7jOG5QUJAbFBTkOo7jxsXFuU899ZTt8fwSmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXqBmzjnlPmbv3r1KTU2VJMXGxqpGjRqWJ/J/ZG4emZtH5uaRuXlkbh6Zm0fm5pG5eYGWOaUcAAAAAABLuNCbj1m3bp02b97stbZ582atW7fO0kT+j8zNI3PzyNw8MjePzM0jc/PI3DwyNy/QMudIuY8JCgpS3bp1tXPnTs9avXr19O233yo7O9viZP6LzM0jc/PI3DwyN4/MzSNz88jcPDI3L9Ay5+rrPmbv3r0qUaKE19pnn32m8+fPW5rI/5G5eWRuHpmbR+bmkbl5ZG4emZtH5uYFWuYcKQcAAAAAwBKOlPuY/fv3KzU1VY7jKCYmRvHx8bZH8ntkbh6Zm0fm5pG5eWRuHpmbR+bmkbl5AZe5vW9jw+9Nnz7drVq1que7+H77br6qVau6zz33nO3x/BKZm0fm5pG5eWRuHpmbR+bmkbl5ZG5eoGZOKfcBkyZNciMjI91p06a5W7dudX/66Sf34MGD7tatW91p06a5UVFR7hNPPGF7TL9C5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZU8p9QNWqVd333nsv3+1LlixxK1eubG6gAEDm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJnzPeU+4JdfflGdOnXy3V67dm0dO3bM4ET+j8zNI3PzyNw8MjePzM0jc/PI3DwyNy+QM6eU+4BWrVrpySefVFZWVq5tWVlZmjJlilq1amVhMv9F5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZ85VoPmDHjh266qqrlJmZqY4dOyomJkaO4yg1NVXr1q1TaGioVq5cqQYNGtge1W+QuXlkbh6Zm0fm5pG5eWRuHpmbR+bmBXLmlHIfcfLkSb355pvatGmTUlNTJUmxsbFq06aNBg4cqMjISMsT+h8yN4/MzSNz88jcPDI3j8zNI3PzyNy8QM2cUg4AAAAAgCUhtgfA/5eRkaEtW7YoNTVVjuMoNjZWzZs3V5kyZWyPBhSbI0eOqGzZsipRooTtUfwW7y0IRLy3FD/eWxCIeG8pfgH53mLz0u/41fnz592RI0e6pUqVch3HcUNDQ92SJUu6juO4pUqVckeNGuWeO3fO9ph+54UXXnATExPdG2+80f3ss8+8th05csStUaOGpcn805w5c9yzZ8+6ruu6OTk57pNPPumWLVvWDQoKckuXLu3ee++9bnZ2tuUp/QvvLXbw3mIW7y3m8d5iB+8tZvHeYl4gv7dQyn3AyJEj3SpVqriLFi1yjx075lk/duyYu2jRIrdatWruqFGjrM3nj2bOnOmWLl3aHTFihHvzzTe7oaGh7pQpUzzbU1NT3aCgIIsT+p+goCD38OHDruu67ssvv+yGh4e7zz77rPvf//7XnTVrlhsVFeXOmjXL8pT+hfcW83hvMY/3FvN4bzGP9xbzeG8xL5DfWyjlPqBChQq5/uL5e59++qlboUIFgxP5v/r167tvvfWW5/aGDRvcSpUquY8++qjruvwft+LgOI7n/7hddtll7vTp0722z5s3z23cuLGN0fwW7y3m8d5iHu8t5vHeYh7vLebx3mJeIL+38D3lPuDMmTOqUKFCvtujo6N15swZgxP5v71796pt27ae223atNGqVas0d+5cjRs3zuJk/s1xHEm/5p+YmOi1rXPnzvrhhx9sjOW3eG8xj/cWO3hvMYv3FvN4b7GD9xazAvm9hQu9+YBOnTppzJgxeuuttxQTE+O17fDhwxo7dqw6d+5saTr/VKFCBaWkpCghIcGz1qBBA61atUqdO3fWwYMH7Q3nx5YvX66oqCiVKlUq15vqmTNnFBTE3wmLEu8t5vHeYgfvLWbx3mIe7y128N5iViC/t1DKfcCLL76o7t27q2rVqmrYsKFiYmLkOI5SU1P11VdfqX79+lq2bJntMf1K+/bt9e9//1sdOnTwWq9fv74+++wzderUydJk/m3w4MGef3/22Wdq3bq15/bGjRt1ySWX2BjLb/HeYh7vLXbw3mIW7y3m8d5iB+8tZgXyewvfU+4jcnJytGLFCm3atEmpqamSpNjYWLVp00ZXXXUVf4krYtu3b9eWLVs0dOjQPLd//fXX+te//qUJEyYYnixwffTRRypRooS6detmexS/wnuLWby3+B7eW4oH7y1m8d7ie3hvKR6B+t5CKQcAAAAAwBL//FMDAAAAAAB/AZRyH1OjRg117drVa61Lly6qWbOmpYn8H5mbR+bmkbl5ZG4emZtH5uaRuXlkbl6gZc6F3nzM4MGDVbFiRa+1vn376ueff7Y0kf8jc/PI3DwyN4/MzSNz88jcPDI3j8zNC7TMOaccAAAAAABL+Pg6AAAA/jSO85hH5uaROYoDpdxHnDlzRuvXr9fOnTtzbTt79qxef/11C1P5NzI3j8zN27Vrl+bPn69vvvlGkvTNN9/o7rvv1m233aZVq1ZZns4/kbl5ZO4bQkNDtWvXLttjBBQyN4/MzTl27JhmzJihESNGaPLkyUpJSbE9UrHh4+s+4Ntvv9VVV12lAwcOyHEcdejQQe+8847i4uIkSYcPH1blypWVnZ1teVL/Qebmkbl5y5cvV58+fVSmTBmdPn1a7733nm699VY1adJErutq7dq1WrFihTp37mx7VL9B5uaRuXljxozJc33mzJm6+eabFR0dLUmaPn26ybH8GpmbR+bmVa5cWTt27FB0dLT27t2rtm3bSpIaNWqkXbt26eTJk9q0aZPq1q1redKiRyn3AX379lVWVpbmz5+v48ePa8yYMfrqq6+0Zs0aVa9enbJSDMjcPDI3r23bturcubMmT56sRYsWafjw4br77rv15JNPSpIeeeQRJSUl6ZNPPrE8qf8gc/PI3LygoCA1adJEZcuW9Vpfu3atWrZsqfDwcDmOw6cUihCZm0fm5gUFBSk1NVWVKlXSgAEDlJqaqmXLlql06dLKzMzUDTfcoLCwML377ru2Ry16LqyrVKmSu337dq+14cOHu9WrV3e///57NzU11Q0KCrI0nX8ic/PI3LzIyEj3u+++c13XdbOzs92QkBB3y5Ytnu07duxwY2JibI3nl8jcPDI3b8qUKW6NGjXczz77zGs9JCTE/frrry1N5d/I3DwyN89xHPfw4cOu67p5Zr9p0ya3atWqNkYrdpxT7gPOnDmjkBDvb6d74YUX1Lt3b3Xs2FHffvutpcn8F5mbR+Z2BQUFKSwszOsv/hEREUpPT7c3lJ8jc/PI3Ixx48Zp8eLFuvvuu3X//ffr/Pnztkfye2RuHpnb4TiOJCkzM1MxMTFe22JiYnTkyBEbYxU7SrkPqFu3rjZv3pxrfdasWerTp4969+5tYSr/Rubmkbl5CQkJ2rNnj+f2xo0bVb16dc/tlJQUzzn9KBpkbh6Z23HZZZdpy5YtOnLkiFq2bKkdO3Z4/p9pFA8yN4/MzUtMTFTz5s114sSJXAdsDhw4oAoVKliarHiF/PFdUNz69u2rd955R7fcckuubbNnz1ZOTo5efvllC5P5LzI3j8zNu/vuu73O0W/YsKHX9o8//piLXxUxMjePzO0pU6aMFi5cqEWLFqlr165cE8QAMjePzM2ZMGGC1+3SpUt73V66dKk6dOhgciRjuNAbAAAA/pQff/xRW7ZsUZcuXRQeHm57nIBA5uaROYoLpRwAAAAAAEs4p/wv4Pvvv+ejd4aRuXlkbh6Zm0fm5pG5eWRuHpmbR+bm+XPmlPK/gIyMDK1du9b2GAGFzM0jc/PI3DwyN4/MzSNz88jcPDI3z58z50JvPuD555+/4PaDBw8amiRwkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5pxT7gOCgoIUFxenkiVL5rn93LlzSk1N5WqPRYjMzSNz88jcPDI3j8zNI3PzyNw8MjcvoDN3YV1CQoK7ePHifLdv3brVDQoKMjiR/yNz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mw5p9wHtGjRQlu2bMl3u+M4cvlAQ5Eic/PI3DwyN4/MzSNz88jcPDI3j8zNC+TM+fi6D9i5c6dOnz6tli1b5rn9/Pnz+umnnxQfH294Mv9F5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZU8oBAAAAALCEj68DAAAAAGAJpRwAAAAAAEso5QAAAAAAWEIpBwAAAADAEko5AAAAAACWUMr/Ag4fPqxJkybZHiOgkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5vlz5nwl2l/Al19+qebNmys7O9v2KAGDzM0jc/PI3DwyN4/MzSNz88jcPDI3z58zD7E9AKTt27dfcPvu3bsNTRI4yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c46U+4CgoCA5jqO8fhW/rTuO45d/FbKFzM0jc/PI3DwyN4/MzSNz88jcPDI3L5Az50i5D4iOjtZTTz2lxMTEPLd//fXX6tWrl+Gp/BuZm0fm5pG5eWRuHpmbR+bmkbl5ZG5eIGdOKfcBLVq00E8//aT4+Pg8tx8/fjzPvxjh4pG5eWRuHpmbR+bmkbl5ZG4emZtH5uYFcuaUch9w55136tSpU/lur169uubPn29wIv9H5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF8iZc045AAAAAACW8D3lAAAAAABYwsfXfcSPP/6ol156SRs2bFBqaqocx1FMTIzatm2ru+66S9WqVbM9ot8hc/PI3DwyN4/MzSNz88jcPDI3j8zNC9TM+fi6D1i/fr2uueYaVatWTVdddZViYmLkuq7S0tK0cuVKpaSk6OOPP1a7du1sj+o3yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D7jsssvUvn17Pffcc3luv/fee7V+/XolJSUZnsx/kbl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5pRyH1CqVClt27ZNderUyXP7N998o2bNmunMmTOGJ/NfZG4emZtH5uaRuXlkbh6Zm0fm5pG5eYGcORd68wFxcXHasGFDvts3btyouLg4gxP5PzI3j8zNI3PzyNw8MjePzM0jc/PI3LxAzpwLvfmA+++/X3fddZe2bNmirl27KiYmRo7jKDU1VStXrtQrr7yiGTNm2B7Tr5C5eWRuHpmbR+bmkbl5ZG4emZtH5uYFdOYufMKiRYvc1q1buyEhIa7jOK7jOG5ISIjbunVrd/HixbbH80tkbh6Zm0fm5pG5eWRuHpmbR+bmkbl5gZo555T7mPPnz+vnn3+WJFWoUEElSpSwPJH/I3PzyNw8MjePzM0jc/PI3DwyN4/MzQu0zDmn3MeUKFFCcXFxWrNmjc6dO2d7nIBA5uaRuXlkbh6Zm0fm5pG5eWRuHpmbF2iZc6TcR0VGRmrbtm2qWbOm7VECBpmbR+bmkbl5ZG4emZtH5uaRuXlkbl6gZM6Rch/F30rMI3PzyNw8MjePzM0jc/PI3DwyN4/MzQuUzCnlAAAAAABYQin3UR9//LEqV65se4yAQubmkbl5ZG4emZtH5uaRuXlkbh6ZmxcomXNOuY/Jzs7Wzz//LMdxFB0dreDgYNsj+T0yN4/MzSNz88jcPDI3j8zNI3PzyNy8QMucI+U+4r333lO7du1UunRpVa5cWXFxcSpdurTatWun999/3/Z4fonMzSNz88jcPDI3j8zNI3PzyNw8MjcvUDOnlPuAOXPmqH///mrcuLEWL16s9evX6z//+Y8WL16sxo0bq3///po3b57tMf0KmZtH5uaRuXlkbh6Zm0fm5pG5eWRuXkBn7sK6Sy65xH3llVfy3f7qq6+6NWvWNDiR/yNz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mw5Uu4DDh48qPbt2+e7vW3btvrpp58MTuT/yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D2jQoIHmzp2b7/Z58+apQYMGBifyf2RuHpmbR+bmkbl5ZG4emZtH5uaRuXmBnDlXX/cBa9euVY8ePRQfH6+rrrpKMTExchxHqampWrlypfbv36//+7//U4cOHWyP6jfI3DwyN4/MzSNz88jcPDI3j8zNI3PzAjlzSrmP2Ldvn1566SVt2rRJqampkqTY2Fi1adNGd911lxISEuwO6IfI3DwyN4/MzSNz88jcPDI3j8zNI3PzAjVzSjkAAAAAAJZwTjkAAAAAAJZQyv8CvvzySwUHB9seI6CQuXlkbh6Zm0fm5pG5eWRuHpmbR+bm+XPmlPK/CM4yMI/MzSNz88jcPDI3j8zNI3PzyNw8MjfPXzMPsT0ApOuuu+6C29PT0+U4jqFpAgOZm0fm5pG5eWRuHpmbR+bmkbl5ZG5eIGdOKfcBS5cuVdeuXRUTE5Pn9uzsbMMT+T8yN4/MzSNz88jcPDI3j8zNI3PzyNy8gM7chXWNGjVyX3nllXy3b9261Q0KCjI4kf8jc/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMOafcB7Ro0ULJycn5bg8NDVX16tUNTuT/yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c76n3AdkZmYqOztbpUuXtj1KwCBz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mwp5QAAAAAAWMLH131Ujx49dOjQIdtjBBQyN4/MzSNz88jcPDI3j8zNI3PzyNy8QMmcUu6j1q1bpzNnztgeI6CQuXlkbh6Zm0fm5pG5eWRuHpmbR+bmBUrmlHIAAAAAACyhlPuo+Ph4lShRwvYYAYXMzSNz88jcPDI3j8zNI3PzyNw8MjcvUDLnQm8AAAAAAFgSYnsA/H8//PCD1q9fr0OHDik4OFg1atRQ165dFRkZaXs0v0Xm5pG5eWRuHpmbR+bmkbl5ZG4emZsXkJm7sC4jI8O94YYbXMdxXMdx3KCgIDc2NtYNDg52y5Qp486ePdv2iH6HzM0jc/PI3DwyN4/MzSNz88jcPDI3L5Az55xyHzBmzBgdOnRIW7du1a5du9S3b1/deuutOnHihGbOnKmxY8fq7bfftj2mXyFz88jcPDI3j8zNI3PzyNw8MjePzM0L6Mxt/1UArluhQgV38+bNnttHjx51w8LC3FOnTrmu67qzZ892mzZtams8v0Tm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJlzpNwHZGVleZ0jUaZMGWVlZenUqVOSpKuuukrffPONrfH8EpmbR+bmkbl5ZG4emZtH5uaRuXlkbl4gZ04p9wGXXXaZZs6c6bk9c+ZMVaxYURUrVpQkZWRkqEyZMrbG80tkbh6Zm0fm5pG5eWRuHpmbR+bmkbl5gZw5V1/3AdOmTVPXrl3173//WyVLllRqaqoWLlzo2b5hwwZ1797d4oT+h8zNI3PzyNw8MjePzM0jc/PI3DwyNy+QM+d7yn3EoUOH9NFHHykzM1OdO3dW/fr1bY/k98jcPDI3j8zNI3PzyNw8MjePzM0jc/MCNXNKOQAAAAAAlnBO+V/AsWPH9Prrr9seI6CQuXlkbh6Zm0fm5pG5eWRuHpmbR+bm+XPmHCn/C/jyyy/VvHlzZWdn2x4lYJC5eWRuHpmbR+bmkbl5ZG4emZtH5ub5c+Zc6M0HnDhx4oLbT548aWiSwEHm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJlzpNwHBAUFyXGcfLe7rivHcfzyr0K2kLl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5hwp9wERERF65JFH1Lp16zy3f/fdd7rzzjsNT+XfyNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D2jevLkkqWPHjnluL1u2rPhAQ9Eic/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMufq6Dxg4cKDCwsLy3R4bG6sJEyYYnMj/kbl5ZG4emZtH5uaRuXlkbh6Zm0fm5gVy5pxTDgAAAACAJRwpBwAAAADAEs4p9xGnTp3S22+/rQ0bNig1NVWO4ygmJkbt2rXTgAEDFB4ebntEv0Pm5pG5eWRuHpmbR+bmkbl5ZG4emZsXqJnz8XUfsHPnTnXt2lWnT59Wx44dFRMTI9d1lZaWprVr1yo8PFyffPKJ6tevb3tUv0Hm5pG5eWRuHpmbR+bmkbl5ZG4emZsXyJlTyn1Ap06dFBsbq4ULF6pkyZJe286dO6chQ4bo0KFDWr16taUJ/Q+Zm0fm5pG5eWRuHpmbR+bmkbl5ZG5eIGdOKfcBpUuX1ubNm/P9q89XX32lVq1a6fTp04Yn819kbh6Zm0fm5pG5eWRuHpmbR+bmkbl5gZw5F3rzAeXKldN3332X7/Y9e/aoXLlyBifyf2RuHpmbR+bmkbl5ZG4emZtH5uaRuXmBnDkXevMBw4YN0+DBgzV+/Hh17dpVMTExchxHqampWrlypaZMmaLRo0fbHtOvkLl5ZG4emZtH5uaRuXlkbh6Zm0fm5gV05i58wrRp09y4uDjXcRw3KCjIDQoKch3HcePi4tynnnrK9nh+iczNI3PzyNw8MjePzM0jc/PI3DwyNy9QM+ecch+zd+9epaamSpJiY2NVo0YNyxP5PzI3j8zNI3PzyNw8MjePzM0jc/PI3LxAy5xSDgAAAACAJVzozUecOXNG69ev186dO3NtO3v2rF5//XULU/k3MjePzM0jc/PI3DwyN4/MzSNz88jcvIDN3O6n5+G6rrt79243Pj7ec+5Ex44d3Z9++smzPTU11Q0KCrI4of8hc/PI3DwyN4/MzSNz88jcPDI3j8zNC+TMOVLuAx588EE1atRIaWlp2r17tyIjI9WuXTsdOHDA9mh+i8zNI3PzyNw8MjePzM0jc/PI3DwyNy+gM7f9VwG4bqVKldzt27d7rQ0fPtytXr26+/333/v1X4VsIXPzyNw8MjePzM0jc/PI3DwyN4/MzQvkzPmech9w5swZhYR4/ypeeOEFBQUFqWPHjnr77bctTea/yNw8MjePzM0jc/PI3DwyN4/MzSNz8wI5c0q5D6hbt642b96sevXqea3PmjVLruuqd+/elibzX2RuHpmbR+bmkbl5ZG4emZtH5uaRuXmBnDnnlPuAvn376p133slz2+zZszVgwAC5fHNdkSJz88jcPDI3j8zNI3PzyNw8MjePzM0L5Mz5nnIAAAAAACzhSDkAAAAAAJZQygEAAAAAsIRSDgAAAACAJZRyAAAAAAAsoZQDAIAiMWTIEF177bWFflxCQoJmzJhR5PMAAPBXQCkHAKCYDBkyRI7jyHEclShRQjExMeratatee+015eTkFGpfCxYsUNmyZYtnUEPyew1JSUn629/+Zn4gAAB8AKUcAIBidPXVV+vQoUPat2+fPv74Y3Xq1EmjRo1Sz549lZWVZXs8n1CxYkWVLl3a9hgAAFhBKQcAoBiFhoYqNjZWVapUUfPmzfXwww/rgw8+0Mcff6wFCxZ47jd9+nQ1atRI4eHhqlatmoYPH66MjAxJ0po1azR06FClp6d7jrw//vjjkqRz585p7NixqlKlisLDw9W6dWutWbPmgjNd6Lmk/39Ee8WKFapXr57KlCnj+ePCb7KzszVmzBiVLVtW0dHRGjt2rFzXzfc5L/Qa/vfj647jaM6cOerZs6dKly6tevXqaePGjdqzZ4+uvPJKhYeHq02bNvr++++9nmPp0qVq0aKFwsLCVLNmTU2cOJE/fAAAfB6lHAAAwzp37qwmTZpoyZIlnrWgoCA9//zz+uqrr7Rw4UKtWrVKY8eOlSS1bdtWM2bMUGRkpA4dOqRDhw7p/vvvlyQNHTpU//3vf7Vo0SJt375dN954o66++mp99913+T7/hZ7rN6dPn9YzzzyjN954Q+vWrdOBAwc8zylJzz77rF577TW9+uqrWr9+vY4ePar33nsv3+e80GvIyxNPPKFbb71V27ZtU926dTVw4EDdeeedGjdunDZv3ixJuueeezz3X7FihW6++WaNHDlSO3fu1Jw5c7RgwQI9+eST+T4HAAA+wQUAAMVi8ODBbp8+ffLc1q9fP7devXr5Pvaf//ynGx0d7bk9f/58Nyoqyus+e/bscR3HcQ8ePOi1npiY6I4bN67Ac+b1XJLcPXv2eNZeeOEFNyYmxnM7Li7OnTZtmuf2+fPn3apVq+b7evN7Da7ruvHx8e5zzz3nuS3JHT9+vOf2xo0bXUnuq6++6ll755133LCwMM/tDh06uFOmTPHa7xtvvOHGxcXlOw8AAL4gxPLfBAAACEiu68pxHM/t1atXa8qUKdq5c6dOnDihrKwsnT17VqdOnVJ4eHie+0hOTpbruqpdu7bXemZmpqKjo/N97oI8V+nSpXXJJZd4HhMXF6e0tDRJUnp6ug4dOqQ2bdp4toeEhKhly5YX/Ah7YTRu3Njz75iYGElSo0aNvNbOnj2rEydOKDIyUlu2bFFSUpLXkfHs7GydPXtWp0+f5px1AIDPopQDAGDBrl27VKNGDUnS/v371b17d91111164oknVL58ea1fv1633367zp8/n+8+cnJyFBwcrC1btig4ONhrW5kyZfJ8TEGfq0SJEl6PcxynyAp3Qfz++X/740Vea79dxT4nJ0cTJ07Uddddl2tfYWFhxTkqAAB/CqUcAADDVq1apR07dujee++VJG3evFlZWVl69tlnFRT06+Ve/vnPf3o9pmTJksrOzvZaa9asmbKzs5WWlqYOHToU6LkL8lx/JCoqSnFxcdq0aZOuuOIKSVJWVpa2bNmi5s2b5/u4vF5DUWnevLl2796tSy+9tFj2DwBAcaGUAwBQjDIzM5Wamqrs7GwdPnxYy5cv19SpU9WzZ0/deuutkqRLLrlEWVlZmjVrlnr16qX//ve/evnll732k5CQoIyMDH322Wdq0qSJSpcurdq1a2vQoEG69dZb9eyzz6pZs2b6+eeftWrVKjVq1Ejdu3fPNU9BnqsgRo0apWnTpqlWrVqqV6+epk+fruPHj1/wMXm9hqL6WPljjz2mnj17qlq1arrxxhsVFBSk7du3a8eOHZo8eXKRPAcAAMWBq68DAFCMli9frri4OCUkJOjqq6/W6tWr9fzzz+uDDz7wfOS8adOmmj59up566ik1bNhQb731lqZOneq1n7Zt2+quu+5Sv379VLFiRT399NOSpPnz5+vWW2/Vfffdpzp16qh37976/PPPVa1atTznKchzFcR9992nW2+9VUOGDFGbNm0UERGhvn37XvAx+b2GotCtWzd99NFHWrlypS677DJdfvnlmj59uuLj44vsOQAAKA6Oa/IEMQAAAAAA4MGRcgAAAAAALKGUAwAAAABgCaUcAAAAAABLKOUAAAAAAFhCKQcAAAAAwBJKOQAAAAAAllDKAQAAAACwhFIOAAAAAIAllHIAAAAAACyhlAMAAAAAYAmlHAAAAAAASyjlAAAAAABY8v8ApebXkZ0qufoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar plot\n",
    "ax = oct1_afternoon.plot(\n",
    "    kind=\"bar\",\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date and time\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    figsize=(12, 6),\n",
    "    ylim=[40, 46],\n",
    ")\n",
    "\n",
    "# Add plot text\n",
    "ax.text(0, 42.1, \"Coldest \\ntemp \\nv\")\n",
    "\n",
    "# Save plot to file (high resolution, PDF)\n",
    "plt.savefig(\"bar-plot-hi-res.pdf\", dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive plotting, a teaser (optional)\n",
    "\n",
    "One of the cool things in Jupyter notebooks is that our plots need not be static. We can easily create plots that are interactive, allowing us to view data values by mousing over them, or to click to enable/disable plotting of some data. There are various libraries we could use to do this, but we'll use the [hvPlot plotting API](https://hvplot.holoviz.org/), which allows us to create interactive plots with little additional effort.\n",
    "\n",
    "[hvPlot](https://hvplot.holoviz.org/) is a high-level plotting package that can accept several different common Python data types and plot them using using the [bokeh visualization library](http://bokeh.org/). The great thing with hvPlot is that it takes almost no effort to move from plotting in pandas to creating interactive plots.\n",
    "\n",
    "To get started, we need to import the pandas submodule of hvPlot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
       "  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n",
       "  var reloading = false;\n",
       "  var Bokeh = root.Bokeh;\n",
       "  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks;\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "    if (js_modules == null) js_modules = [];\n",
       "    if (js_exports == null) js_exports = {};\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    if (!reloading) {\n",
       "      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    }\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "    window._bokeh_on_load = on_load\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    var skip = [];\n",
       "    if (window.requirejs) {\n",
       "      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n",
       "      require([\"jspanel\"], function(jsPanel) {\n",
       "\twindow.jsPanel = jsPanel\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-modal\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-tooltip\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-hint\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-layout\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-contextmenu\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"jspanel-dock\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"gridstack\"], function(GridStack) {\n",
       "\twindow.GridStack = GridStack\n",
       "\ton_load()\n",
       "      })\n",
       "      require([\"notyf\"], function() {\n",
       "\ton_load()\n",
       "      })\n",
       "      root._bokeh_is_loading = css_urls.length + 9;\n",
       "    } else {\n",
       "      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
       "    }\n",
       "\n",
       "    var existing_stylesheets = []\n",
       "    var links = document.getElementsByTagName('link')\n",
       "    for (var i = 0; i < links.length; i++) {\n",
       "      var link = links[i]\n",
       "      if (link.href != null) {\n",
       "\texisting_stylesheets.push(link.href)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      if (existing_stylesheets.indexOf(url) !== -1) {\n",
       "\ton_load()\n",
       "\tcontinue;\n",
       "      }\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
       "      var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
       "      for (var i = 0; i < urls.length; i++) {\n",
       "        skip.push(urls[i])\n",
       "      }\n",
       "    }    var existing_scripts = []\n",
       "    var scripts = document.getElementsByTagName('script')\n",
       "    for (var i = 0; i < scripts.length; i++) {\n",
       "      var script = scripts[i]\n",
       "      if (script.src != null) {\n",
       "\texisting_scripts.push(script.src)\n",
       "      }\n",
       "    }\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (var i = 0; i < js_modules.length; i++) {\n",
       "      var url = js_modules[i];\n",
       "      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    for (const name in js_exports) {\n",
       "      var url = js_exports[name];\n",
       "      if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
       "\tif (!window.requirejs) {\n",
       "\t  on_load();\n",
       "\t}\n",
       "\tcontinue;\n",
       "      }\n",
       "      var element = document.createElement('script');\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.type = \"module\";\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      element.textContent = `\n",
       "      import ${name} from \"${url}\"\n",
       "      window.${name} = ${name}\n",
       "      window._bokeh_on_load()\n",
       "      `\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "    if (!js_urls.length && !js_modules.length) {\n",
       "      on_load()\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.1/dist/panel.min.js\"];\n",
       "  var js_modules = [];\n",
       "  var js_exports = {};\n",
       "  var css_urls = [];\n",
       "  var inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "\tvar NewBokeh = root.Bokeh;\n",
       "\tif (Bokeh.versions === undefined) {\n",
       "\t  Bokeh.versions = new Map();\n",
       "\t}\n",
       "\tif (NewBokeh.version !== Bokeh.version) {\n",
       "\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "\t}\n",
       "\troot.Bokeh = Bokeh;\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
       "  function load_or_wait() {\n",
       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
       "    // versions of Bokeh and its dependencies at the same time.\n",
       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
       "      load_or_wait();\n",
       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
       "      setTimeout(load_or_wait, 100);\n",
       "    } else {\n",
       "      Bokeh = root.Bokeh;\n",
       "      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      if (!reloading && (!bokeh_loaded || is_dev)) {\n",
       "\troot.Bokeh = undefined;\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "\trun_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.2.1/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.1/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
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     "output_type": "display_data"
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     "data": {
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       "  var output = handle.output;\n",
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       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
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       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
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      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(\n",
       "    > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
       "  ),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import hvplot.pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the submodule imported, we can simply take a slice of data from the `data` DataFrame, the month of July in 2014 in this case, and create a plot just as we would in pandas. The only difference here is that we will use the `hvplot()` method rather than the `plot()` method from pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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gZJydEIAAKimk3J0QgAAHF6VcnRCAACY8JVydEIAAJAVl3J0QgAABM2YcnRCAACAX5lydEIAAHiEmnJ0QgAA7DuccnRCAABozpxydEIAAGDznXJ0QgAA1KqfcnRCAABQPaBydEIAAEhioXJ0QgAAvBmjcnRCAAA4rKNydEIAADDRpHJ0QgAApIimcnRCAAAgG6dydEIAABhAqHJ0QgAAYJLCcnRCAABIAcZydEIAADBwyXJ0QgAAnEzMcnRCAAAY38xydEIAABAEznJ0QgAAAE7QcnRCAAD4ctFydEIAAGwq03J0QgAA6LzTcnRCAADg4dRydEIAAFSZ1nJ0QgAA0CvXcnRCAADIUNhydEIAADwI2nJ0QgAAuJracnRCAACwv9tydEIAACR33XJ0QgAAoAnecnRCAACYLt9ydEIAAAzm4HJ0QgAAiHjhcnRCAACAneJydEIAAPRU5HJ0QgAAcOfkcnRCAABoDOZydEIAANzD53J0QgAAWFbocnRCAABQe+lydEIAAMQy63J0QgAAQMXrcnRCAAA46uxydEIAAKyh7nJ0QgAAIFnwcnRCAACUEPJydEIAABCj8nJ0QgAACMjzcnRCAAB8f/VydEIAAPgR9nJ0QgAA8Db3cnRCAABk7vhydEIAAOCA+XJ0QgAA2KX6cnRCAABMXfxydEIAAMjv/HJ0QgAAwBT+cnRCAAA0zP9ydEIAALBeAHN0QgAAqIMBc3RCAAAcOwNzdEIAAJjNA3N0QgAAkPIEc3RCAAAEqgZzdEIAAIA8B3N0QgAAeGEIc3RCAADsGApzdEIAAGirCnN0QgAAYNALc3RCAADUhw1zdEIAAFAaDnN0QgAASD8Pc3RCAAC89hBzdEIAADiJEXN0QgAAMK4Sc3RCAACkZRRzdEIAACD4FHN0QgAAjNQXc3RCAAAIZxhzdEIAAACMGXN0QgAAdEMbc3RCAADo+hxzdEIAAFyyHnN0QgAA2EQfc3RCAADQaSBzdEIAAEQhInN0QgAAwLMic3RCAAC42CNzdEIAACyQJXN0QgAAqCImc3RCAACgRydzdEIAABT/KHN0QgAAkJEpc3RCAACItipzdEIAAPxtLHN0QgAAeAAtc3RCAABwJS5zdEIAAOTcL3N0QgAAYG8wc3RCAABYlDFzdEIAAMxLM3N0QgAASN4zc3RCAABAAzVzdEIAALS6NnN0QgAAME03c3RCAAAocjhzdEIAAJwpOnN0QgAAGLw6c3RCAAAQ4TtzdEIAAISYPXN0QgAAACs+c3RCAAD4Tz9zdEIAAGwHQXN0QgAA6JlBc3RCAADgvkJzdEIAAFR2RHN0QgAA0AhFc3RCAADILUZzdEIAADzlR3N0QgAAuHdIc3RCAACwnElzdEIAACRUS3N0QgAAoOZLc3RCAACYC01zdEIAAAzDTnN0QgAAiFVPc3RCAACAelBzdEIAAPQxUnN0QgAAcMRSc3RCAABo6VNzdEIAANygVXN0QgAAWDNWc3RCAABQWFdzdEIAAMQPWXN0QgAAQKJZc3RCAAA4x1pzdEIAAKx+XHN0QgAAKBFdc3RCAAAgNl5zdEIAAJTtX3N0QgAAEIBgc3RCAAAIpWFzdEIAAHxcY3N0QgAA+O5jc3RCAADwE2VzdEIAAGTLZnN0QgAA4F1nc3RCAADYgmhzdEIAAEw6anN0QgAAyMxqc3RCAADA8WtzdEIAADSpbXN0QgAAsDtuc3RCAACoYG9zdEIAABwYcXN0QgAAmKpxc3RCAACQz3JzdEIAAASHdHN0QgAAgBl1c3RCAAB4PnZzdEIAAOz1d3N0QgAAaIh4c3RCAABgrXlzdEIAANRke3N0QgAAUPd7c3RCAABIHH1zdEIAALzTfnN0QgAAOGZ/c3RCAAAwi4BzdEIAAKRCgnN0QgAAINWCc3RCAAAY+oNzdEIAAIyxhXN0QgAACESGc3RCAAAAaYdzdEIAAHQgiXN0QgAA8LKJc3RCAADo14pzdEIAAFyPjHN0QgAA2CGNc3RCAADQRo5zdEIAAET+j3N0QgAAwJCQc3RCAAC4tZFzdEIAACxtk3N0QgAAqP+Tc3RCAACgJJVzdEIAABTclnN0QgAAkG6Xc3RCAACIk5hzdEIAAPxKmnN0QgAAeN2ac3RCAABwApxzdEIAAOS5nXN0QgAAYEyec3RCAABYcZ9zdEIAAMwooXN0QgAASLuhc3RCAABA4KJzdEIAALSXpHN0QgAAMCqlc3RCAAAoT6ZzdEIAAJwGqHN0QgAAGJmoc3RCAAAQvqlzdEIAAIR1q3N0QgAAAAisc3RCAAD4LK1zdEIAAGzkrnN0QgAA6Havc3RCAADgm7BzdEIAAFRTsnN0QgAA0OWyc3RCAADICrRzdEIAADzCtXN0QgAAuFS2c3RCAACwebdzdEIAACQxuXN0QgAAoMO5c3RCAACY6LpzdEIAAAygvHN0QgAAiDK9c3RCAACAV75zdEIAAPQOwHN0QgAAcKHAc3RCAABoxsFzdEIAANx9w3N0QgAAWBDEc3RCAABQNcVzdEIAAMTsxnN0QgAAQH/Hc3RCAAA4pMhzdEIAAKxbynN0QgAAKO7Kc3RCAAAgE8xzdEIAAJTKzXN0QgAAEF3Oc3RCAAAIgs9zdEIAAHw50XN0QgAA+MvRc3RCAADw8NJzdEIAAGSo1HN0QgAA4DrVc3RCAADYX9ZzdEIAAEwX2HN0QgAAyKnYc3RCAADAztlzdEIAADSG23N0QgAAsBjcc3RCAACoPd1zdEIAABz13nN0QgAAmIffc3RCAACQrOBzdEIAAARk4nN0QgAAgPbic3RCAAB4G+RzdEIAAOzS5XN0QgAAaGXmc3RCAABgiudzdEIAANRB6XN0QgAAUNTpc3RCAABI+epzdEIAALyw7HN0QgAAOEPtc3RCAAAwaO5zdEIAAKQf8HN0QgAAILLwc3RCAAAY1/FzdEIAAIyO83N0QgAACCH0c3RCAAAARvVzdEIAAHT99nN0QgAA8I/3c3RCAADotPhzdEIAAFxs+nN0QgAA2P76c3RCAADQI/xzdEIAAETb/XN0QgAAwG3+c3RCAAC4kv9zdEIAACxKAXR0QgAAqNwBdHRCAACgAQN0dEIAABS5BHR0QgAAkEsFdHRCAACIcAZ0dEIAAPwnCHR0QgAAeLoIdHRCAABw3wl0dEIAAOSWC3R0QgAAYCkMdHRCAABYTg10dEIAAMwFD3R0QgAASJgPdHRCAABAvRB0dEIAALR0EnR0QgAAMAcTdHRCAAAoLBR0dEIAAJzjFXR0QgAAGHYWdHRCAAAQmxd0dEIAAIRSGXR0QgAAAOUZdHRCAAD4CRt0dEIAAGzBHHR0QgAA6FMddHRCAADgeB50dEIAAFQwIHR0QgAA0MIgdHRCAADI5yF0dEIAADyfI3R0QgAAuDEkdHRCAACwViV0dEIAACQOJ3R0QgAAoKAndHRCAACYxSh0dEIAAAx9KnR0QgAAiA8rdHRCAACANCx0dEIAAPTrLXR0QgAAcH4udHRCAABooy90dEIAANxaMXR0QgAAWO0xdHRCAABQEjN0dEIAAMTJNHR0QgAAQFw1dHRCAAA4gTZ0dEIAAKw4OHR0QgAAKMs4dHRCAAAg8Dl0dEIAAJSnO3R0QgAAEDo8dHRCAAAIXz10dEIAAHwWP3R0QgAA+Kg/dHRCAADwzUB0dEIAAGSFQnR0QgAA4BdDdHRCAADYPER0dEIAAEz0RXR0QgAAyIZGdHRCAADAq0d0dEIAADRjSXR0QgAAsPVJdHRCAACoGkt0dEIAABzSTHR0QgAAmGRNdHRCAACQiU50dEIAAARBUHR0QgAAgNNQdHRCAAB4+FF0dEIAAOyvU3R0QgAAaEJUdHRCAABgZ1V0dEIAANQeV3R0QgAAULFXdHRCAABI1lh0dEIAALyNWnR0QgAAOCBbdHRCAAAwRVx0dEIAAKT8XXR0QgAAII9edHRCAAAYtF90dEIAAIxrYXR0QgAACP5hdHRCAAAAI2N0dEIAAHTaZHR0QgAA8GxldHRCAADokWZ0dEIAAFxJaHR0QgAA2NtodHRCAADQAGp0dEIAAES4a3R0QgAAwEpsdHRCAAC4b210dEIAACwnb3R0QgAAqLlvdHRCAACg3nB0dEIAABSWcnR0QgAAkChzdHRCAACITXR0dEIAAPwEdnR0QgAAeJd2dHRCAABwvHd0dEIAAORzeXR0QgAAYAZ6dHRCAABYK3t0dEIAAMzifHR0QgAASHV9dHRCAABAmn50dEIAALRRgHR0QgAAMOSAdHRCAAAoCYJ0dEIAAJzAg3R0QgAAGFOEdHRCAAAQeIV0dEIAAIQvh3R0QgAAAMKHdHRCAAD45oh0dEIAAGyeinR0QgAA6DCLdHRCAADgVYx0dEIAAFQNjnR0QgAA0J+OdHRCAADIxI90dEIAADx8kXR0QgAAuA6SdHRCAACwM5N0dEIAACTrlHR0QgAAoH2VdHRCAACYopZ0dEIAAAxamHR0QgAAiOyYdHRCAACAEZp0dEIAAPTIm3R0QgAAcFucdHRCAABogJ10dEIAANw3n3R0QgAAWMqfdHRCAABQ76B0dEIAAMSmonR0QgAAQDmjdHRCAAA4XqR0dEIAAKwVpnR0QgAAKKimdHRCAAAgzad0dEIAAJSEqXR0QgAAEBeqdHRCAAAIPKt0dEIAAHzzrHR0QgAA+IWtdHRCAADwqq50dEIAAGRisHR0QgAA4PSwdHRCAADYGbJ0dEIAAEzRs3R0QgAAyGO0dHRCAADAiLV0dEIAADRAt3R0QgAAsNK3dHRCAACo97h0dEIAAByvunR0QgAAmEG7dHRCAACQZrx0dEIAAAQevnR0QgAAgLC+dHRCAAB41b90dEIAAOyMwXR0QgAAaB/CdHRCAABgRMN0dEIAANT7xHR0QgAAUI7FdHRCAABIs8Z0dEIAALxqyHR0QgAAOP3IdHRCAAAwIsp0dEIAAKTZy3R0QgAAIGzMdHRCAAAYkc10dEIAAIxIz3R0QgAACNvPdHRCAAAAANF0dEIAAHS30nR0QgAA8EnTdHRCAADobtR0dEIAAFwm1nR0QgAA2LjWdHRCAADQ3dd0dEIAAESV2XR0QgAAwCfadHRCAAC4TNt0dEIAACwE3XR0QgAAqJbddHRCAACgu950dEIAABRz4HR0QgAAkAXhdHRCAACIKuJ0dEIAAPzh43R0QgAAeHTkdHRCAABwmeV0dEIAAORQ53R0QgAAYOPndHRCAABYCOl0dEIAAMy/6nR0QgAASFLrdHRCAABAd+x0dEIAALQu7nR0QgAAMMHudHRCAAAo5u90dEIAAJyd8XR0QgAAGDDydHRCAAAQVfN0dEIAAIQM9XR0QgAAAJ/1dHRCAAD4w/Z0dEIAAGx7+HR0QgAA6A35dHRCAADgMvp0dEIAAFTq+3R0QgAA0Hz8dHRCAADIof10dEIAADxZ/3R0QgAAuOv/dHRCAACwEAF1dEIAACTIAnV0QgAAoFoDdXRCAACYfwR1dEIAAAw3BnV0QgAAiMkGdXRCAACA7gd1dEIAAPSlCXV0QgAAcDgKdXRCAABoXQt1dEIAANwUDXV0QgAAWKcNdXRCAABQzA51dEIAAMSDEHV0QgAAQBYRdXRCAAA4OxJ1dEIAAKzyE3V0QgAAKIUUdXRCAAAgqhV1dEIAAJRhF3V0QgAAEPQXdXRCAAAIGRl1dEIAAHzQGnV0QgAA+GIbdXRCAADwhxx1dEIAAGQ/HnV0QgAA4NEedXRCAADY9h91dEIAAEyuIXV0QgAAyEAidXRCAADAZSN1dEIAADQdJXV0QgAAsK8ldXRCAACo1CZ1dEIAAByMKHV0QgAAmB4pdXRCAACQQyp1dEIAAAT7K3V0QgAAgI0sdXRCAAB4si11dEIAAOxpL3V0QgAAaPwvdXRCAABgITF1dEIAANTYMnV0QgAAUGszdXRCAABIkDR1dEIAALxHNnV0QgAAONo2dXRCAAAw/zd1dEIAAKS2OXV0QgAAIEk6dXRCAAAYbjt1dEIAAIwlPXV0QgAACLg9dXRCAAAA3T51dEIAAHSUQHV0QgAA8CZBdXRCAADoS0J1dEIAAFwDRHV0QgAA2JVEdXRCAADQukV1dEIAAERyR3V0QgAAwARIdXRCAAC4KUl1dEIAACzhSnV0QgAAqHNLdXRCAACgmEx1dEIAABRQTnV0QgAAkOJOdXRCAACIB1B1dEIAAPy+UXV0QgAAeFFSdXRCAABwdlN1dEIAAOQtVXV0QgAAYMBVdXRCAABY5VZ1dEIAAMycWHV0QgAASC9ZdXRCAABAVFp1dEIAALQLXHV0QgAAMJ5cdXRCAAAow111dEIAAJx6X3V0QgAAGA1gdXRCAAAQMmF1dEIAAITpYnV0QgAAAHxjdXRCAAD4oGR1dEIAAGxYZnV0QgAA6OpmdXRCAADgD2h1dEIAAFTHaXV0QgAA0FlqdXRCAADIfmt1dEIAADw2bXV0QgAAuMhtdXRCAACw7W51dEIAACSlcHV0QgAAoDdxdXRCAACYXHJ1dEIAAAwUdHV0QgAAiKZ0dXRCAACAy3V1dEIAAPSCd3V0QgAAcBV4dXRCAABoOnl1dEIAANzxenV0QgAAWIR7dXRCAABQqXx1dEIAAMRgfnV0QgAAQPN+dXRCAAA4GIB1dEIAAKzPgXV0QgAAKGKCdXRCAAAgh4N1dEIAAJQ+hXV0QgAAENGFdXRCAAAI9oZ1dEIAAHytiHV0QgAA+D+JdXRCAADwZIp1dEIAAGQcjHV0QgAA4K6MdXRCAADY0411dEIAAEyLj3V0QgAAyB2QdXRCAADAQpF1dEIAADT6knV0QgAAsIyTdXRCAACosZR1dEIAABxplnV0QgAAmPuWdXRCAACQIJh1dEIAAATYmXV0QgAAgGqadXRCAAB4j5t1dEIAAOxGnXV0QgAAaNmddXRCAABg/p51dEIAANS1oHV0QgAAUEihdXRCAABIbaJ1dEIAALwkpHV0QgAAOLekdXRCAAAw3KV1dEIAAKSTp3V0QgAAICaodXRCAAAYS6l1dEIAAIwCq3V0QgAACJWrdXRCAAAAuqx1dEIAAHRxrnV0QgAA8AOvdXRCAADoKLB1dEIAAFzgsXV0QgAA2HKydXRCAADQl7N1dEIAAERPtXV0QgAAwOG1dXRCAAC4Brd1dEIAACy+uHV0QgAAqFC5dXRCAACgdbp1dEIAABQtvHV0QgAAkL+8dXRCAACI5L11dEIAAPybv3V0QgAAeC7AdXRCAABwU8F1dEIAAOQKw3V0QgAAYJ3DdXRCAABYwsR1dEIAAMx5xnV0QgAASAzHdXRCAABAMch1dEIAALToyXV0QgAAMHvKdXRCAAAooMt1dEIAAJxXzXV0QgAAGOrNdXRCAAAQD891dEIAAITG0HV0QgAAAFnRdXRCAAD4fdJ1dEIAAGw11HV0QgAA6MfUdXRCAADg7NV1dEIAAFSk13V0QgAA0DbYdXRCAADIW9l1dEIAADwT23V0QgAAuKXbdXRCAACwytx1dEIAACSC3nV0QgAAoBTfdXRCAACYOeB1dEIAAAzx4XV0QgAAiIPidXRCAACAqON1dEIAAPRf5XV0QgAAcPLldXRCAABoF+d1dEIAANzO6HV0QgAAWGHpdXRCAABQhup1dEIAAMQ97HV0QgAAQNDsdXRCAAA49e11dEIAAKys73V0QgAAKD/wdXRCAAAgZPF1dEIAAJQb83V0QgAAEK7zdXRCAAAI0/R1dEIAAHyK9nV0QgAA+Bz3dXRCAADwQfh1dEIAAGT5+XV0QgAA4Iv6dXRCAADYsPt1dEIAAExo/XV0QgAAyPr9dXRCAADAH/91dEIAADTXAHZ0QgAAsGkBdnRCAACojgJ2dEIAABxGBHZ0QgAAmNgEdnRCAACQ/QV2dEIAAAS1B3Z0QgAAgEcIdnRCAAB4bAl2dEIAAOwjC3Z0QgAAaLYLdnRCAABg2wx2dEIAANSSDnZ0QgAAUCUPdnRCAABIShB2dEIAALwBEnZ0QgAAOJQSdnRCAAAwuRN2dEIAAKRwFXZ0QgAAIAMWdnRCAAAYKBd2dEIAAIzfGHZ0QgAACHIZdnRCAAAAlxp2dEIAAHROHHZ0QgAA8OAcdnRCAADoBR52dEIAAFy9H3Z0QgAA2E8gdnRCAADQdCF2dEIAAEQsI3Z0QgAAwL4jdnRCAAC44yR2dEIAACybJnZ0QgAAqC0ndnRCAACgUih2dEIAABQKKnZ0QgAAkJwqdnRCAACIwSt2dEIAAPx4LXZ0QgAAeAsudnRCAABwMC92dEIAAOTnMHZ0QgAAYHoxdnRCAABYnzJ2dEIAAMxWNHZ0QgAASOk0dnRCAABADjZ2dEIAALTFN3Z0QgAAMFg4dnRCAAAofTl2dEIAAJw0O3Z0QgAAGMc7dnRCAAAQ7Dx2dEIAAISjPnZ0QgAAADY/dnRCAAD4WkB2dEIAAGwSQnZ0QgAA6KRCdnRCAADgyUN2dEIAAFSBRXZ0QgAA0BNGdnRCAADIOEd2dEIAADzwSHZ0QgAAuIJJdnRCAACwp0p2dEIAACRfTHZ0QgAAoPFMdnRCAACYFk52dEIAAAzOT3Z0QgAAiGBQdnRCAACAhVF2dEIAAPQ8U3Z0QgAAcM9TdnRCAABo9FR2dEIAANyrVnZ0QgAAWD5XdnRCAABQY1h2dEIAAMQaWnZ0QgAAQK1adnRCAAA40lt2dEIAAKyJXXZ0QgAAKBxednRCAAAgQV92dEIAAJT4YHZ0QgAAEIthdnRCAAAIsGJ2dEIAAHxnZHZ0QgAA+PlkdnRCAADwHmZ2dEIAAGTWZ3Z0QgAA4GhodnRCAADYjWl2dEIAAExFa3Z0QgAAyNdrdnRCAADA/Gx2dEIAADS0bnZ0QgAAsEZvdnRCAACoa3B2dEIAABwjcnZ0QgAAmLVydnRCAACQ2nN2dEIAAASSdXZ0QgAAgCR2dnRCAAB4SXd2dEIAAOwAeXZ0QgAAaJN5dnRCAABguHp2dEIAANRvfHZ0QgAAUAJ9dnRCAABIJ352dEIAALzef3Z0QgAAOHGAdnRCAAAwloF2dEIAAKRNg3Z0QgAAIOCDdnRCAAAYBYV2dEIAAIy8hnZ0QgAACE+HdnRCAAAAdIh2dEIAAHQrinZ0QgAA8L2KdnRCAADo4ot2dEIAAFyajXZ0QgAA2CyOdnRCAADQUY92dEIAAEQJkXZ0QgAAwJuRdnRCAAC4wJJ2dEIAACx4lHZ0QgAAqAqVdnRCAACgL5Z2dEIAABTnl3Z0QgAAkHmYdnRCAACInpl2dEIAAPxVm3Z0QgAAeOibdnRCAABwDZ12dEIAAOTEnnZ0QgAAYFefdnRCAABYfKB2dEIAAMwzonZ0QgAASMaidnRCAABA66N2dEIAALSipXZ0QgAAMDWmdnRCAAAoWqd2dEIAAJwRqXZ0QgAAGKSpdnRCAAAQyap2dEIAAISArHZ0QgAAABOtdnRCAAD4N652dEIAAGzvr3Z0QgAA6IGwdnRCAADgprF2dEIAAFRes3Z0QgAA0PCzdnRCAADIFbV2dEIAADzNtnZ0QgAAuF+3dnRCAACwhLh2dEIAACQ8unZ0QgAAoM66dnRCAACY87t2dEIAAAyrvXZ0QgAAiD2+dnRCAACAYr92dEIAAPQZwXZ0QgAAcKzBdnRCAABo0cJ2dEIAANyIxHZ0QgAAWBvFdnRCAABQQMZ2dEIAAMT3x3Z0QgAAQIrIdnRCAAA4r8l2dEIAAKxmy3Z0QgAAKPnLdnRCAAAgHs12dEIAAJTVznZ0QgAAEGjPdnRCAAAIjdB2dEIAAHxE0nZ0QgAA+NbSdnRCAADw+9N2dEIAAGSz1XZ0QgAA4EXWdnRCAADYatd2dEIAAEwi2XZ0QgAAyLTZdnRCAADA2dp2dEIAADSR3HZ0QgAAsCPddnRCAACoSN52dEIAABwA4HZ0QgAAmJLgdnRCAACQt+F2dEIAAARv43Z0QgAAgAHkdnRCAAB4JuV2dEIAAOzd5nZ0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fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}]}};\n",
       "  var render_items = [{\"docid\":\"d8db8e4b-eda0-4723-945f-46f2a9b00f6c\",\"roots\":{\"p1002\":\"afb592ed-7077-4b56-b62b-841409e1d69b\"},\"root_ids\":[\"p1002\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":NdOverlay   [Variable]\n",
       "   :Curve   [YR--MODAHRMN]   (value)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "july2014_df = data.loc[(data.index >= \"201407010000\") & (data.index < \"201407310000\")]\n",
    "\n",
    "july2014_df.hvplot(\n",
    "    title=\"Helsinki-Vantaa temperatures\",\n",
    "    xlabel=\"Date\",\n",
    "    ylabel=\"Temperature [°F]\",\n",
    "    ylim=[45.0, 90.0],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have an interactive line plot where you can place your mouse cursor above the line to see the plotted values at a given location, zoom in and/or pan the plot to areas of interest, and click on the legend items to hide or display them. And as you can see, hvPlot makes it very easy to start creating your own interactive plots from pandas DataFrames.\n",
    "\n",
    "That is all we will explore for the moment, but you are welcome to have a look at the [hvPlot User Guide](https://hvplot.holoviz.org/user_guide/index.html) to learn more about the types of visualizations available in hvPlot and how to use them."
   ]
  }
 ],
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