From 7b21727285e50b0b050c4ddd70d07542229c5366 Mon Sep 17 00:00:00 2001
From: pants <niklasjo2004@gmail.com>
Date: Mon, 21 Sep 2026 14:17:03 -0700
Subject: init

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 python/atms-310/notebooks/Week 01 F.ipynb   |  445 +++++++
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1bf363de-ca7c-44a6-bd2f-4ca809666863",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<h1>MIDTERM 1 REVIEW</h1>\n",
+    "\n",
+    "<h2>10/16/2023</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a1ad77-a7f8-4438-a7da-f082e340f798",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Introduction to Python and Computer Architecture</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 01 W.pdf</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 09/27/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>Programming is the act of writing an <b>algorithm</b> using a specific syntax to solve a problem.\n",
+    "    <ul>\n",
+    "        <li>A big part of programming is taking a problem apart into smaller pieces that can be understood by a computer\n",
+    "    </ul>\n",
+    "    <li>A computer's structure consists of:\n",
+    "    <ul>\n",
+    "        <li>Input and output devices\n",
+    "        <li>A central processing unit composed of a control unit and an arithmetic/logic unit\n",
+    "        <li>A memory unit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e30ff5c5-2236-400b-9157-73a77cae261c",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Print, Calculator, Variable Types and Operators</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 01 F.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 09/29/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>An <b>integer</b> variable is a number with no decimal point\n",
+    "    <li>A <b>floating point</b> variable is a number with a decimal point\n",
+    "    <li>A <b>string</b> variable is text\n",
+    "    <li>A <b>Boolean</b> variable can take on the values <b>True</b> and <b>False</b>\n",
+    "    <li>A <b>NoneType</b>variable can take on the value <b>None</b>\n",
+    "    <li>Python uses dynamic typing, which means you don't need to define a variable's type when you first use that variable\n",
+    "    <li>The type() command lets you see what type a given variable is. int(), float(), and str() can be used to convert to different variable types\n",
+    "    <li>Combining a float and an integer results in a float\n",
+    "    <li>Combining a string and an integer or float results in a TypeError\n",
+    "    <li>Floats are often prone to rounding errors\n",
+    "    <li><b>and</b> and <b>or</b> can be used to combine Boolean variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8dcb344-0708-4ba9-9ccb-df22e34edea2",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Lists and Dictionaries</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 02 M.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 10/02/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li> A list is a <b>mutable</b>, <b>ordered</b> sequence of elements\n",
+    "    <li> Python counts starting with 0, not 1\n",
+    "    <li> You can also count backwards from the end of a list using negative numbers\n",
+    "    <li> A list can be sliced, where the slice is defined by a range that has an inclusive lower bound and an exclusive upper bound\n",
+    "    <li> Values in a list can be changed by assignment or by particular methods\n",
+    "    <li> A tuple is an <b>immutable</b>, <b>ordered</b> sequence of elements\n",
+    "    <li> A dictionary is a <b>mutable</b>, <b>unordered</b> sequence of elements consisting of key:value pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0af5f1b7-48e9-4131-a331-5e35826a677a",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Functions and Logic</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 02 W.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 10/04/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li> A <b>function</b> is a block of code that can be called\n",
+    "    <li> Functions can be called using either positional or keyword arguments\n",
+    "    <li> Positional arguments can be described via lists and keyword arguments can be desribed via dictionaries\n",
+    "    <li> Python uses if statements for logical constructs\n",
+    "    <li> The code within an <b>if</b> statement is run if the current statement is true\n",
+    "    <li> The code within an <b>elif</b> statement applies if prior statements are not true <b>and</b> the current statement is true\n",
+    "    <li> The code within an <b>else</b> statement applies if all prior statements are not true"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44c499c2-bab1-48be-8edc-c6c59a23659f",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Loops</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 02 F.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 10/06/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li> A <b>for loop</b> lets you repeat an operation a specified number of times\n",
+    "    <li> Indentation is critical for lists in Python\n",
+    "    <li> The range() function will create a list of all values up to (but not including) the specified integer\n",
+    "    <li> Hardcoding is the act of restricting a piece of code so it only works under specific circumstances (generally want to avoid this)\n",
+    "    <li>A <b>while loop</b> lets you repeat an operation until a particular condition is met\n",
+    "    <li>The index in a while loop must be initialized and care should be taken to avoid infinite loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a2a73cf-6155-42d8-b792-7144f1821844",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Modules and Exception Handling</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 03 M.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 10/09/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>module</b> is a library of Python source code that gives access to new variables and functions\n",
+    "    <li>Modules can be imported as whatever name you like\n",
+    "    <li>A <b>raise</b> statement lets you define your own error conditions and stop code when they're met\n",
+    "    <li>A <b>try/except</b> block will let you fix common issues that would otherwise result in exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1972b16e-0c87-482b-8e05-dd4c99b3c11c",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Arrays</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 02 W.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 10/11/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li> An array is NumPy's version of a list, which allows you to work with matrices\n",
+    "    <li> All elements of an array are of the same type\n",
+    "    <li> You can either create a new array or convert an existing list into one\n",
+    "    <li> The zeros() function is useful for initializing an array when you know the shape and size but not the contents\n",
+    "    <li> The arange() function serves a similar purpose to the range() function with lists\n",
+    "    <li> Similar to lists, arrays can be sliced, but arrays can also be sliced across each of their dimensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31e2ef42-a0ec-468a-8c53-189b314186ee",
+   "metadata": {},
+   "source": [
+    "<h3><b>Lecture Name:</b> Testing Inside Arrays</h3>\n",
+    "    \n",
+    "<h3><b>File Name:</b> Week 03 F.ipynb</h3>\n",
+    "    \n",
+    "<h3><b>Class Date:</b> 10/13/2023</h3>\n",
+    "\n",
+    "<b>Key Points:</b>\n",
+    "\n",
+    "<ul>\n",
+    "    <li> You can use functions within NumPy to find the shape, number of dimensions, and number of elements in any array\n",
+    "    <li> <b>numpy.where()</b> will let you find the locations of elements within an array that meet a specified criterion\n",
+    "    <li> Some functions within NumPy allow you to manipulate arrays, such as reshaping, transpoing, unraveling, concatenation, and repeating individual array elements\n",
+    "    <li> When you're in a situation with multiple nested loops, often you can instead perform all operations a lot more easily by using array syntax\n",
+    "    <li> Mathematical operations act on arrays elementwise\n",
+    "    <li> When testing within an array, <b>and</b> and <b>or</b> cannot be used; instead, use NumPy's built-in functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d9362ec-b4ad-421d-8e13-74527bc19ede",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/python/atms-310/notebooks/Week 01 F.ipynb b/python/atms-310/notebooks/Week 01 F.ipynb
new file mode 100755
index 0000000..e39cb51
--- /dev/null
+++ b/python/atms-310/notebooks/Week 01 F.ipynb	
@@ -0,0 +1,445 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>1. Print, Calculator, Variable Types, and Operators</h1>\n",
+    "<h2>09/29/23</h2>\n",
+    "\n",
+    "<h2>1.0 Introduction to Jupyter Notebooks</h2>\n",
+    "First, this is a Jupyter Notebook, which enables a combination of markdown cells (like this one) to provide description alongside actual code that can be run. If you want to change a markdown cell, simply double-click on the text, make any changes you want to make, and click \"Run\" (the little play button) <b>above</b> to execute. You can add HTML-style tags to markdown cells to add details such as formatting and links.\n",
+    "\n",
+    "There are also code cells, which can actually be executed, just like in a normal programming environment. Click on the below code cell and try clicking \"Run\" (or type Shift+Enter). You can also modify the text that's being printed to the screen and try running it again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>1.1 Basic Variables</h2>\n",
+    "Python has <b>dynamic typing</b>, which means that variable types are set automatically based on the input. This is handy if you're coming from an environment like Fortran that requires you to specify each and every time what kind of variable you want to create!\n",
+    "\n",
+    "Here are some of the most important variable types (more on these in a bit):\n",
+    "<ul>\n",
+    "    <li>Integer (short and long)</li>\n",
+    "    <li>Floating point (float)</li>\n",
+    "    <li>Strings</li>\n",
+    "    <li>Booleans</li>\n",
+    "    <li>NoneType</li>\n",
+    "    <li>Lists and tuples</li> (Tuesday's class)\n",
+    "    <li>Dictionaries</li> (Tuesday's class)\n",
+    "</ul>\n",
+    "\n",
+    "Arithmetic operators:\n",
+    "<ul>\n",
+    "    <li>+ is addition</li>\n",
+    "    <li>- is subtraction</li>\n",
+    "    <li>/ is division</li>\n",
+    "    <li>* is multiplication</li>\n",
+    "    <li>** is exponentiation</li>\n",
+    "</ul>\n",
+    "\n",
+    "Comparison operators:\n",
+    "<ul>\n",
+    "    <li>> is greater than</li>\n",
+    "    <li>< is less than</li>\n",
+    "    <li>>= is greater than or equal to</li>\n",
+    "    <li>&lt= is less than or equal to</li>\n",
+    "    <li>!= is not equal to</li>\n",
+    "    <li>== is equal to</li>\n",
+    "</ul>\n",
+    "\n",
+    "<b>Important note:</b> Python is case-sensitive! \"A\" is not the same thing as \"a\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code that is preceded with a hashtag will not be read by the computer.\n",
+    "# These comments are intended for human readers of this code!\n",
+    "# Commenting your code is essential to ensure that you and others\n",
+    "# will be able to interpret it down the road!\n",
+    "\n",
+    "# Now, let's try playing with some numbers...\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that all of the above are examples of Python's <b>dynamic typing</b>: assigning a variable a floating point value (i.e., including a decimal point) results in that variable being a float, whereas assigning a variable an integer value (i.e., no decimal point) results in that variable being an integer.\n",
+    "\n",
+    "You can also convert from one to the other using the int() and float() commands!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert a from a float to an integer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert c from an integer to a float.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the conversion from float to int <b>loses information</b> along the way: Python merely truncates everything after the decimal point.\n",
+    "\n",
+    "On the other hand, the conversion from int to float is <b>essentially lossless</b>: the same information is maintained."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now let's try some mathematical operators.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When applying mathematical operations, Python tries to keep as much information as possible! This means that an integer and a float interacting together will result in a float as an answer. Likewise, division of integers will give you a float by default (this is new in version 3 of Python).\n",
+    "\n",
+    "<b>Why is a*b not exactly -7.56?</b>\n",
+    "\n",
+    "Floats are not exact numbers! There will always be some degree of rounding errors associated with floating point numbers (because we don't have infinite storage to keep an infinite number of digits after the decimal). As a result, it's good practice when comparing floats not to look for equality, but instead to look for \"close enough\" - there are functions to do this that we will see later!\n",
+    "\n",
+    "You can also represent scientific notation in Python!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's represent 6 x 10^23 in Python!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>1.2 Strings</h2>\n",
+    "\n",
+    "Strings are the text variable type in Python: they're surrounded either by single or double quotes (it generally doesn't matter which, as long as you're consistent at the start and finish)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to combine two strings, you can do so easily with the \"+\" command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To add a space, simply throw another string in there.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How about combining a string and a float?\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The <b>TypeError</b> you get in the above example explains the problem: a float cannot be combined in this way with a string. But what if we want to make this change? That's where the str() function comes in handy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try that again!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes you want a new line within the same string. This can be accomplished with the \"escape character\", which in Python is a backslash: \\\\. \"\\n\" will start a new line!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a multi-line string using the newline character.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you don't want to deal with newlines being created automatically, you can create a raw string!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This will give you a nonsense result because \\n is being treated as a new line.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Instead, tell Python you're going to make a raw string by adding the letter 'r'.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>1.3 Boolean Variables</h2>\n",
+    "\n",
+    "A Boolean variable can only have two values: <b>True</b> and <b>False</b> (and remember, capitalization counts!) These are the results of the logical tests mentioned earlier!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also combine conditions using <b>and</b> and <b>or</b>.\n",
+    "\n",
+    "<b>and</b>: Both statements must be <b>True</b> in order for the entire statement to be <b>True</b>.\n",
+    "\n",
+    "<b>or</b>: Either statement (or both) can be <b>True</b> in order for the entire statement to be <b>True</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>1.4 NoneType</h2>\n",
+    "\n",
+    "A NoneType variable can only have one value: <b>None</b> (and capitalization counts!). \n",
+    "\n",
+    "Why would we want this? Consider the scenario where you want to make sure there's no value already in a given variable (so, for example, if you're working with thousands of lines of code and the same variable types keep coming up). Setting that variable to None (via something like <b>a = None</b>) means you're guaranteed not to be messing around with any previous values by accident."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>TAKE-HOME POINTS</h1>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>Python has a variety of data types, many of which we saw today!</li>\n",
+    "    <li>An <b>integer</b> variable is a number with no decimal point.</li>\n",
+    "    <li>A <b>floating point</b> variable is a number with a decimal point</li>\n",
+    "    <li>A <b>string</b> variable is text.</li>\n",
+    "    <li>A <b>Boolean</b> variable can take on the values <b>True</b> and <b>False</b>.</li>\n",
+    "    <li>A <b>NoneType</b> variable can take on the value <b>None</b>.</li>\n",
+    "    <li>Python uses dynamic typing, which means you don't need to define a variable's type when you first use that variable.</li>\n",
+    "    <li>The type() command lets you see what type a given variable is. int(), float(), and str() can be used to convert to different variable types.</li>\n",
+    "    <li>Combining a float and an integer results in a float.</li>\n",
+    "    <li>Combining a string and an integer or float results in a TypeError.</li>\n",
+    "    <li>Floats are often prone to rounding errors.</li>\n",
+    "    <li><b>and</b> and <b>or</b> can be used to combine Boolean variables.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 02 F.ipynb b/python/atms-310/notebooks/Week 02 F.ipynb
new file mode 100755
index 0000000..a5850bb
--- /dev/null
+++ b/python/atms-310/notebooks/Week 02 F.ipynb	
@@ -0,0 +1,301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>4. Loops</h1>\n",
+    "<h2>10/06/23</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>4.0 Last Time...</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>function</b> is a block of code that can be called.</li>\n",
+    "    <li>Functions can be called using either positional or keyword arguments.</li>\n",
+    "    <li>Positional arguments can be described via lists and keyword arguments can be described via dictionaries.</li>\n",
+    "    <li>Python uses if statements for logical constructs.</li>\n",
+    "    <li>The code within an <b>if</b> statement is run if the current statement is true.</li>\n",
+    "    <li>The code within an <b>elif</b> statement applies if prior statements are not true <b>and</b> the current statement is true.</li>\n",
+    "    <li>The code within an <b>else</b> statement applies if all prior statements are not true.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>4.1 For Loops</h2>\n",
+    "\n",
+    "A <b>for loop</b> is used when you're going to be repeating a block of code a predetermined number of times. The format is simple enough: <b>for</b> an index <b>in</b> a list:. The block of code that you'll then be running <b>must be indented</b>: either use Tab or press Spacebar four times at the start of each line. (This is the price we pay for not having to end statements with a semicolon or something similar.)\n",
+    "\n",
+    "The index we refer to here is just a counter that's going to keep track of how many times the loop has been run. Often we just call this index 'i', but you can name it whatever you like. This index will be assigned to each individual item in the list until we run out of list items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try an example. First let's define a list!\n",
+    "\n",
+    "\n",
+    "# Now let's print each value within that list using a for loop.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the index variable changes variable type as it goes down the list, from integer to float to string to integer. Technically, this index should be called an <b>iterator</b>, and iterators are one example of an <b>object</b>, which we'll see in more detail when we get into object-oriented programming later in this course.\n",
+    "\n",
+    "Sometimes you'll want the iterator to just count the elements in a list, rather than actually taking on their values. For this, the range() function comes in handy: you specify the size of the list you want, and it will create a new list counting the number of elements. That is, range(n) returns [0,1,2,...,n-1]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can use the range function in a for loop. In this case, i is an integer for every step of the loop.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's try a meteorological example of a for loop. Let's say you have 10 temperature measurements in Celsius and want them to be converted to Fahrenheit. A for loop is a great way to do this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, let's define our initial 10 temperature measurements.\n",
+    "\n",
+    "\n",
+    "# Next, we'll want to loop over all values in this list.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "    \n",
+    "# Did it work? Let's check.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that, for this example, we had to use the range() function, because we needed to know the index of each temperature measurement we were in the process of converting. If we didn't know how many elements were in temp, we could have used range(len(temp)) instead of range(10). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The range() function is also useful for counting based on a particular pattern, using the format <b>range(first value,final value,increment)</b>. Remember that the first value is always <b>inclusive</b> and the final value is always <b>exclusive</b>!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Let's convert the same dataset from Celsius to Fahrenheit, but this time we'll only convert\n",
+    "# every second temperature, going backwards.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a side note, using an integer in a function in a way that makes that function less versatile (for instance, range(10) above means the loop will only work for lists with 10 elements) is often called <b>hardcoding</b> and is good to avoid when you can. If instead we used range(len(temp)), it wouldn't matter how many elements there were in the list: our loop would still work.\n",
+    "\n",
+    "It's also very possible to combine the logical constructs from yesterday's class with a loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's use our new Fahrenheit temperatures to make some statements!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>4.2 While Loops</h2>\n",
+    "\n",
+    "A <b>while</b> loop lets you loop an indefinite number of times. This comes in handy if we don't yet know how many times we want to repeat a process (for instance, adding a random number to a running total until we hit a particular target value). The syntax is simple: <b>while \\<condition\\>:</b>. The indented code following the while line will be executed repeatedly while \\<condition\\> evaluates as True."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's do a simple example of a while loop that will print the integers one through nine.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That first line (a = 1) is known as an <b>initialization</b> and is a key part of a while loop: if we didn't have a value of a to start with, we couldn't evaluate whether the while statement was True or False. The last line (a = a + 1) is known as an <b>incrementation</b> and ensures that we're not just doing the same check over and over again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's an example of a while loop that will not run because the while condition is always False.\n",
+    "\n",
+    "# There won't be an error message, but the loop will never run.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if the while statement is always True? Then the while statement will go on forever. This... is a problem. If you suspect you may be caught in an infinite loop, you can often hit CTRL-C to stop the process, or hit the stop button in a Jupyter notebook.\n",
+    "\n",
+    "Often this problem emerges because the incrementation step (in the example above, a = a + 1) gets forgotten."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We're not going to run an infinite-loop example here because it may crash your web browser. But it happens!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>An Example!</b>\n",
+    "\n",
+    "Using 1) a <b>for</b> loop, and 2) a <b>while</b> loop, write code that will count integers backwards from 10 to 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# For loop!\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# While Loop!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>TAKE-HOME POINTS</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>for loop</b> lets you repeat an operation a specified  number of times.</li>\n",
+    "    <li>Indentation is critical for lists in Python.</li>\n",
+    "    <li>The range() function will create a list of all values up to the specified integer.</li>\n",
+    "    <li>Hardcoding is the act of (often unnecessarily) restricting a piece of code so it only works under specific circumstances.</li>\n",
+    "    <li>A <b>while loop</b> lets you repeat an operation until a particular condition is met.</li>\n",
+    "    <li>The index in a while loop must be initialized and care should be taken to avoid infinite loops.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 02 M.ipynb b/python/atms-310/notebooks/Week 02 M.ipynb
new file mode 100755
index 0000000..a9fbf99
--- /dev/null
+++ b/python/atms-310/notebooks/Week 02 M.ipynb	
@@ -0,0 +1,380 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>2. Lists and Dictionaries</h1>\n",
+    "<h2>10/02/23</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>2.0 Last Time...</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>Python has a variety of data types, many of which we saw today!</li>\n",
+    "    <li>An <b>integer</b> variable is a number with no decimal point.</li>\n",
+    "    <li>A <b>floating point</b> variable is a number with a decimal point</li>\n",
+    "    <li>A <b>string</b> variable is text.</li>\n",
+    "    <li>A <b>Boolean</b> variable can take on the values <b>True</b> and <b>False</b>.</li>\n",
+    "    <li>A <b>NoneType</b> variable can take on the value <b>None</b>.</li>\n",
+    "    <li>Python uses dynamic typing, which means you don't need to define a variable's type when you first use that variable.</li>\n",
+    "    <li>The type() command lets you see what type a given variable is. int(), float(), and str() can be used to convert to different variable types.</li>\n",
+    "    <li>Combining a float and an integer results in a float.</li>\n",
+    "    <li>Combining a string and an integer or float results in a TypeError.</li>\n",
+    "    <li>Floats are often prone to rounding errors.</li>\n",
+    "    <li><b>and</b> and <b>or</b> can be used to combine Boolean variables.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>2.1 Lists and Tuples</h2>\n",
+    "\n",
+    "A <b>list</b> is simply an ordered sequence of values, similar to arrays in other programming languages. Each element of a list can be assigned to anything (you can have a mix of variable types within a given list), including another list.\n",
+    "\n",
+    "Lists are defined using square brackets (\"[]\"), and individual elements have commas between them.\n",
+    "\n",
+    "Counting in Python starts with <b>0</b>. This can lead to some confusion, but always remember that the first element in a list is actually element 0, the second is actually element 1, and so on.\n",
+    "\n",
+    "You can also count from the end, so that element <b>-1</b> is actually the last element in a list, <b>-2</b> is the second-last element, and so on.\n",
+    "\n",
+    "Finally, to find the length of a list, use the len() command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a complicated list that has a list as one of its elements.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# As a side note, if you want to split a long line of code across\n",
+    "# multiple lines, just use a backslash with nothing after it:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the length of this list.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To get a value from a list within a list:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Likewise, you can make use of the reverse numbering:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A list can be sliced to just obtain a subsection using a colon to separate the lower and upper limits. The lower limit of the range is <b>inclusive</b> and the upper range is <b>exclusive</b>, so that a [1:3] subset will consist of the second and third elements in a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Values within a list can be changed; lists are <b>mutable</b>. Changing by assignment is the most basic way to do this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's change 'hello' to 'goodbye' in the original list.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lists in python actually have some built-in functions called <b>methods</b> that can change the properties of the list. We'll talk about these more when we get into object-oriented programming later in this course, but for now, we'll consider <b>insert</b>, <b>remove</b> and <b>append</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 'Insert' places the requested element into the list \n",
+    "# before the specified element of the list.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 'Remove' gets rid of the first occurrence of the specified element.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Append adds the specified element to the end of the list.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To an extent, you can treat an individual string as a list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A <b>tuple</b> is simply a list that cannot be changed (i.e., <b>immutable</b>), and an error will be returned by Python if you do attempt to change an element within a tuple. Defining a tuple is exactly the same as defining a list, except that you use parentheses instead of square brackets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a simple tuple:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# But note that you cannot change a value within a tuple.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>2.2 Dictionaries</h2>\n",
+    "\n",
+    "A dictionary, as opposed to a list/tuple, is an <b>unordered</b> list: elements are referenced by <b>keys</b>, not position. A key can be anything, as can the value it refers to.\n",
+    "\n",
+    "A dictionary is delimited by curly braces (\"{}\"), and each element is a key:value pair separated by a colon. In order to refer to a particular element in a dictionary, we proceed much as we do with a list, except using a key instead of an element address."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's create a dictionary similar to our list from earlier.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here, our keys are 'value_a','value_b',and 'value_c', and we can refer to \n",
+    "# each of them accordingly.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like lists, dictionaries have <b>methods</b> - and again, we'll go over this in more detail as we get to the object-oriented programming part of this course, but for now, consider <b>keys</b> and <b>values</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# \"Keys\" returns a list of all the keys of a.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# \"Values\" returns a list of all the values of a.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To check whether a particular key is used in a dictionary, use the following\n",
+    "# syntax:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that <b>keys</b> and <b>values</b> will return key:value pairs in no particular order. To make sure one corresponds to the other, you can first sort the dictionary into a desired order using something like <b>sorted</b>, which we'll explore soon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>TAKE-HOME POINTS</h1>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A list is a <b>mutable</b>, <b>ordered</b> sequence of elements.</li>\n",
+    "    <li>Python counts starting with 0, not 1.</li>\n",
+    "    <li>You can also count backwards from the end of a list using negative numbers.</li>\n",
+    "    <li>A list can be sliced, where the slice is defined by a range that has an inclusive lower bound and an exclusive upper bound.</li>\n",
+    "    <li>Values in a list can be changed by assignment or by particular methods.</li>\n",
+    "    <li>A tuple is an <b>immutable</b>, <b>ordered</b> sequence of elements.</li>\n",
+    "    <li>A dictionary is a <b>mutable</b>, <b>unordered</b> sequence of elements consisting of key:value pairs.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 02 W.ipynb b/python/atms-310/notebooks/Week 02 W.ipynb
new file mode 100755
index 0000000..8936046
--- /dev/null
+++ b/python/atms-310/notebooks/Week 02 W.ipynb	
@@ -0,0 +1,427 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>3. Functions and Logic</h1>\n",
+    "<h2>10/04/23</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>3.0 Last Time...</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A list is a <b>mutable</b>, <b>ordered</b> sequence of elements.</li>\n",
+    "    <li>Python counts starting with 0, not 1.</li>\n",
+    "    <li>You can also count backwards from the end of a list using negative numbers.</li>\n",
+    "    <li>A list can be sliced, where the slice is defined by a range that has an inclusive lower bound and an exclusive upper bound.</li>\n",
+    "    <li>Values in a list can be changed by assignment or by particular methods.</li>\n",
+    "    <li>A tuple is an <b>immutable</b>, <b>ordered</b> sequence of elements.</li>\n",
+    "    <li>A dictionary is a <b>mutable</b>, <b>unordered</b> sequence of elements consisting of key:value pairs.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>3.1 Functions</h2>\n",
+    "\n",
+    "A <b>function</b> is a self-contained block of code that can be \"called\" in any other code. It can take user-specified input and return output (which is generally a single output value: you can put multiple outputs into a list).\n",
+    "\n",
+    "To create a function, we start with a <b>def</b> statement that defines the name of the function and then follows with a list of input arguments in parentheses, followed by a colon.\n",
+    "\n",
+    "All the code that you want to be executed within a function should be indented (by default, this is typically indented by four spaces). The amount of whitespace before a command in Python matters! Everything indented together will be run together; there's no need to specify the end of the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can start by defining a function that calculates the surface area\n",
+    "# of a cylinder. \n",
+    "# This can go in its own file so that you can refer to it in the future\n",
+    "# from any code you may want to run.\n",
+    "def area(radius, height):\n",
+    "    a = 2*3.14*radius**2 + 2*3.14*radius*height\n",
+    "    return a\n",
+    "\n",
+    "\n",
+    "# Basically, this function is called 'area' and takes as input two variables\n",
+    "# called 'radius' and 'height' from the user. \n",
+    "# It calculates the surface area of a cylinder, then outputs or 'returns' \n",
+    "# that area to the original code.\n",
+    "\n",
+    "# Running this block of code now will output nothing; you've just defined\n",
+    "# the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now, try calling the function using a radius of 3 and a height of 2.\n",
+    "print(area(3,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An even shorter version of that function would be:\n",
+    "def area(radius, height):\n",
+    "    return 2*3.14*radius**2 + 2*3.14*radius*height\n",
+    "\n",
+    "print(area(3,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The arguments in this example ('radius' and 'height') are what's known as <b>positional arguments</b>, meaning that it knows the first argument corresponds to the radius and the second argument corresponds to the height.\n",
+    "\n",
+    "Alternatively, you can use what are known as <b>keyword arguments</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Can write the arguments out of order if we're using keywords!\n",
+    "print(area(height=2, radius=3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "62.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note that this doesn't work with positional arguments:\n",
+    "print(area(2,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can facilitate long lists of arguments by treating positional arguments like lists and treating keyword arguments like dictionaries. To do this, in the function call, you just need to place an asterisk (\\*) before the list with positional arguments and two asterisks (\\*\\*) before the list with keyword arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Call the function using a list of positional arguments.\n",
+    "positional = [3,2] # plugging values from the list \"positional\" into area function\n",
+    "print(area(*positional))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Call the function using a dictionary of keyword arguments.\n",
+    "\n",
+    "keyword = {'height':2, 'radius':3}\n",
+    "print(area(**keyword))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Call the function using a combo of positional and keyword arguments.\n",
+    "positional = [3]\n",
+    "keyword = {'height':2}\n",
+    "print(area(*positional,**keyword))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This seems like a silly way of going about it, but if you're passing dozens or hundreds of variables to a particular function, it definitely makes life easier!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>3.2 Logical Constructs</h2>\n",
+    "\n",
+    "Logic in Python is handled through <b>if statements</b>. The general idea behind an if statement is that we check whether something is true: if it is, we then execute certain code before continuing.\n",
+    "\n",
+    "If statements are formatted by writing <b>if</b> and then some condition that must be met, followed by a colon. The block of code we wish to execute if the statement is true must all be indented together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ass\n",
+      "booty\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Write a code that checks whether a given variable is equal to 3.\n",
+    "a = 3\n",
+    "if a == 3:\n",
+    "    print('ass')\n",
+    "print('booty')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If the condition is not true, the code ignores what's indented.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also specify what will be done if your conditional statement is not true by using <b>else:</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hell no\n",
+      "buttocks\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now, if the condition is not true, the code continues with what follows\n",
+    "# the else statement.\n",
+    "a = 2\n",
+    "if a == 3:\n",
+    "    print('yes!')\n",
+    "else:\n",
+    "    print('hell no')\n",
+    "\n",
+    "print('buttocks')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Want to deal with more than one scenario? Use <b>elif</b>, which is short for <b>else if:</b>. In other words, if the previous condition is not true, but this new condition is true, do this instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "its 3\n",
+      "end\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 3\n",
+    "if a == 3:\n",
+    "    print('its 3')\n",
+    "elif a == 2:\n",
+    "    print('its 2')\n",
+    "else: print('it aint 2 or 3')\n",
+    "\n",
+    "print('end')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pay close attention to the indentation in the above code!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>An example!</b>\n",
+    "\n",
+    "Write an if/elif/else statement in the box below to check whether a particular variable is positive, negative, or equal to zero. Each possibility should print a string statement: \"I am positive.\", \"I am negative.\", or \"I am zero.\"\n",
+    "\n",
+    "Try testing it with different values to make sure it works!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "its negative\n",
+      "end\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = input('enter a number')\n",
+    "f = int(d)\n",
+    "if f == 0:\n",
+    "    print('its zero')\n",
+    "elif f < 0:\n",
+    "    print('its negative')\n",
+    "elif f > 0:\n",
+    "    print('its positive')\n",
+    "print('end')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>TAKE-HOME POINTS</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>function</b> is a block of code that can be called.</li>\n",
+    "    <li>Functions can be called using either positional or keyword arguments.</li>\n",
+    "    <li>Positional arguments can be described via lists and keyword arguments can be described via dictionaries.</li>\n",
+    "    <li>Python uses if statements for logical constructs.</li>\n",
+    "    <li>The code within an <b>if</b> statement is run if the current statement is true.</li>\n",
+    "    <li>The code within an <b>elif</b> statement applies if prior statements are not true <b>and</b> the current statement is true.</li>\n",
+    "    <li>The code within an <b>else</b> statement applies if all prior statements are not true.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 03 F.ipynb b/python/atms-310/notebooks/Week 03 F.ipynb
new file mode 100755
index 0000000..d296f41
--- /dev/null
+++ b/python/atms-310/notebooks/Week 03 F.ipynb	
@@ -0,0 +1,671 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>7. Testing Inside Arrays</h1>\n",
+    "\n",
+    "<h2>10/13/2023</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>7.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>An array is NumPy's version of a list, which allows you to work with matrices.</li>\n",
+    "    <li>All elements of an array are of the same type.</li>\n",
+    "    <li>You can either create a new array or convert an existing list into one.</li>\n",
+    "    <li>The zeros() function is useful for initializing an array when you know the shape and size but not the contents.</li>\n",
+    "    <li>The arange() function serves a similar purpose to the range() function with lists.</li>\n",
+    "    <li>Similar to lists, arrays can be sliced, but arrays can be sliced across each of their dimensions.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#1. What is the code to create a 4-row, 5-column array of zeros and assign it to the variable a?\n",
+    "import numpy as np\n",
+    "a = np.zeros((4,5))\n",
+    "print(a)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2. Using array a from above, answer the following questions:\n",
+    "\n",
+    "a = np.array([[2,3.2,5.5,-6.4,-2.2,2.4],\n",
+    "              [1, 22,  4, 0.1, 5.3, -9],\n",
+    "              [3,  1,2.1,  21, 1.1, -2]])\n",
+    "\n",
+    "#a. What is a[:,3]?\n",
+    "\n",
+    "#b. What is a[1:4,0:2]?\n",
+    "\n",
+    "#c. What will b = a[1:,2] do?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>7.1 Array Inquiry</h2>\n",
+    "\n",
+    "There are a series of functions within NumPy that we can use to gain information about a given array.\n",
+    "\n",
+    "To return the <b>shape</b> of an array (i.e., its dimensions), we use numpy.shape().\n",
+    "\n",
+    "To return the <b>number of dimensions</b> of an array, we use numpy.ndim().\n",
+    "\n",
+    "To return the <b>number of elements</b> in an array, we use numpy.size()."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Don't forget to import numpy!\n",
+    "import numpy as np\n",
+    "\n",
+    "a = np.array([[2,3.2,5.5,-6.4,-2.2,2.4],\n",
+    "              [1, 22,  4, 0.1, 5.3, -9],\n",
+    "              [3,  1,2.1,  21, 1.1, -2]])\n",
+    "print(np.shape(a))\n",
+    "\n",
+    "# This is the example array from yesterday's lecture.\n",
+    "\n",
+    "\n",
+    "# Array shape.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Array number of dimensions.\n",
+    "print(np.ndim(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "18\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Array number of elements.\n",
+    "print(np.size(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to search within an array for the location of values matching a particular criterion (useful when you have a giant dataset!), you can use numpy.where()."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([0, 0, 1, 2]), array([3, 4, 5, 5]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Search for all places within the array with negative values.\n",
+    "print(np.where(a < 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once you have the locations where the criterion is met, you can then modify only those values!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This code will look for all the negative values in the array and change them to positive.\n",
+    "c = np.where(a<0)\n",
+    "a[c] = np.abs(a[c])\n",
+    "print(a[c])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also create a list of all values within an array matching a particular criterion.\n",
+    "\n",
+    "(We'll see more on array testing soon...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a[a<0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>7.2 Array Manipulation</h2>\n",
+    "\n",
+    "Rather than just finding things out about arrays, NumPy also allows you to manipulate arrays. Some of these come from matrix algebra, but others are just ways to shift the data around into useable formats. Here are a few examples:\n",
+    "<ul>\n",
+    "    <li><b>numpy.reshape()</b>: reshape an array to the desired dimensions.</li>\n",
+    "    <li><b>numpy.transpose()</b>: transpose an array (flip rows and columns, for example).</li>\n",
+    "    <li><b>numpy.ravel()</b>: \"flatten\" an array back into a 1D vector.</li>\n",
+    "    <li><b>numpy.concatenate()</b>: concatenate arrays.</li>\n",
+    "    <li><b>numpy.repeat()</b>: repeat array elements.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5]\n",
+      "[0 1 2 3 4 5 6 7]\n",
+      "[[ 2.   3.2  5.5 -6.4 -2.2  2.4]\n",
+      " [ 1.  22.   4.   0.1  5.3 -9. ]\n",
+      " [ 3.   1.   2.1 21.   1.1 -2. ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create some arrays to play with.\n",
+    "import numpy as np\n",
+    "z = np.arange(6)\n",
+    "x = np.arange(8)\n",
+    "a = np.array([[2,3.2,5.5,-6.4,-2.2,2.4],\n",
+    "              [1, 22,  4, 0.1, 5.3, -9],\n",
+    "              [3,  1,2.1,  21, 1.1, -2]])\n",
+    "\n",
+    "print(z)\n",
+    "print(x)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 1 2]\n",
+      " [3 4 5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, reshape a from a 1x6 to a 2x3 array:\n",
+    "print(np.reshape(z,(2,3)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.   1.   3. ]\n",
+      " [ 3.2 22.   1. ]\n",
+      " [ 5.5  4.   2.1]\n",
+      " [-6.4  0.1 21. ]\n",
+      " [-2.2  5.3  1.1]\n",
+      " [ 2.4 -9.  -2. ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Next, transpose c from a 3x6 to a 6x3 array:\n",
+    "print(np.transpose(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 2.   3.2  5.5 -6.4 -2.2  2.4  1.  22.   4.   0.1  5.3 -9.   3.   1.\n",
+      "  2.1 21.   1.1 -2. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Try \"flattening\" c into a 1-D array:\n",
+    "print(np.ravel(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 0 1 2 3 4 5 6 7]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now try concatenating (combining) the two 1-D arrays:\n",
+    " # This is another function that needs a double-parentheses\n",
+    "                             # because there are other options that can be changed in the function.\n",
+    "                             \n",
+    "                             \n",
+    "print(np.concatenate((z,x)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 0 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print array a and repeat each value 3 times:\n",
+    "\n",
+    "print(np.repeat(z,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>7.3 Array Operations</h2>\n",
+    "\n",
+    "<h3>7.3.1 Looping (slow and steady)</h3>\n",
+    "\n",
+    "As an example of what's going on \"under the hood\", one way you can perform operations on an array is simply to loop through every single value within that array and perform the operation on each element.\n",
+    "\n",
+    "As you might imagine, this can get <i>tedious</i>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create two arrays and multiply them together, element by element.\n",
+    "import numpy as np\n",
+    "\n",
+    "a = np.array([[2,3,2,5,6,7,45,34],\n",
+    "    [123,345,634,3234,234,2,432,4]])\n",
+    "b = np.array ([[123,534,234,423,3,2,23,23],[234,543,645,234,6,23,54,9]])\n",
+    "             \n",
+    "\n",
+    "\n",
+    "# Let's now get the shape of the arrays (they have the same shape, so we'll just get a's shape).\n",
+    "print(np.shape(a))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 8)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Next, let's create a matrix of zeros in the shape of the element-by-element product we eventually want.\n",
+    "ashape = np.shape(a)\n",
+    "print(ashape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finally, we loop over all x values and all y values using arange.\n",
+    "for i in np.arange(ashape[0]):\n",
+    "    for j in np.arange(ashape[1]):\n",
+    "        pr\n",
+    "# ok whatever\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>7.3.2 Array Syntax (quick and efficient)</h3>\n",
+    "\n",
+    "...alternatively, we could do this using built-in syntax that operates on the entire array at once."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[   246   1602    468   2115     18     14   1035    782]\n",
+      " [ 28782 187335 408930 756756   1404     46  23328     36]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "productab = a*b\n",
+    "print(a*b)\n",
+    "\n",
+    " # Note that this method assumes the arrays are the same size.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, okay. That does seem a little easier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6 7 8 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's try another example. Start with a simple 1-D array \n",
+    "# that's just the integers from 0 to 9. \n",
+    "u = np.arange(10)\n",
+    "print(u)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Now try some operations on it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  2  4  6  8 10 12 14 16 18]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(u*2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>7.4 Testing Inside an Array</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's return to logical testing within an array. NumPy has several alternative ways to do logical tests: for instance, we can use <b>\\></b> as 'greater than', or we can use <b>numpy.greater()</b> for the same result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5]\n",
+      "[False False False False  True  True]\n",
+      "[False False False False  True  True]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "t = np.arange(6)\n",
+    "print(t)\n",
+    "print(t > 3)\n",
+    "\n",
+    "          # This uses a more traditional logical format.\n",
+    " # This uses NumPy's built-in function.\n",
+    "\n",
+    "print(np.greater(t,3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unfortunately, the same doesn't apply to Python's built-in <b>and</b> and <b>or</b> functions; in those cases, you have to use NumPy's numpy.logical_and() and numpy.logical_or() functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m/Users/nik/data/python/notebooks/Week 03 F.ipynb Cell 37\u001b[0m line \u001b[0;36m3\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2003%20F.ipynb#X51sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Let's write code that outputs 'True' only when a > 1 and a <= 3.\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2003%20F.ipynb#X51sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m w \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m6\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2003%20F.ipynb#X51sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m((w\u001b[39m>\u001b[39m\u001b[39m1\u001b[39m) \u001b[39mand\u001b[39;00m (a\u001b[39m<\u001b[39m\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m))\n",
+      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's write code that outputs 'True' only when a > 1 and a <= 3.\n",
+    "w = np.arange(6)\n",
+    "print((w>1) and (a<=3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False False  True  True False False]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.logical_and(t>1,t<=3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>7.5 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>You can use functions within NumPy to find the shape, number of dimensions, and number of elements in any array.</li>\n",
+    "    <li><b>numpy.where()</b> will let you find the locations of elements within an array that meet a specified criterion.</li>\n",
+    "    <li>Some functions within NumPy allow you to manipulate arrays, such as reshaping, transposing, \"unraveling\", concatenation, and repeating individual array elements.</li>\n",
+    "    <li>When you're in a situation with multiple nested loops, often you can instead perform all operations a lot more easily by using array syntax.</li>\n",
+    "    <li>Mathematical operations act on arrays elementwise.</li>\n",
+    "    <li>When testing within an array, <b>and</b> and <b>or</b> cannot be used; instead, use NumPy's built-in functions.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 03 M.ipynb b/python/atms-310/notebooks/Week 03 M.ipynb
new file mode 100755
index 0000000..f68849f
--- /dev/null
+++ b/python/atms-310/notebooks/Week 03 M.ipynb	
@@ -0,0 +1,335 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>5. Modules and Exception Handling</h1>\n",
+    "<h2>10/09/2023</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>5.0 Last Time...</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>for loop</b> lets you repeat an operation a specified  number of times.</li>\n",
+    "    <li>Indentation is critical for loops in Python.</li>\n",
+    "    <li>The range() function will create a list of all values up to the specified integer.</li>\n",
+    "    <li>Hardcoding is the act of (often unnecessarily) restricting a piece of code so it only works under specific circumstances.</li>\n",
+    "    <li>A <b>while loop</b> lets you repeat an operation until a particular condition is met.</li>\n",
+    "    <li>The index in a while loop must be initialized and care should be taken to avoid infinite loops.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>5.1 Modules</h2>\n",
+    "\n",
+    "A <b>module</b> is just a library of Python source code files that will give you access to new (frequently more specialized) functions.\n",
+    "\n",
+    "To import a module, the syntax is simply: <b>import \\<module name\\></b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.7568024953079283\n"
+     ]
+    }
+   ],
+   "source": [
+    "# An extremely useful module in the atmospheric science world is NumPy.\n",
+    "# This package lets you use a variety of mathematical (and array-based) functions and variables.\n",
+    "import numpy\n",
+    "\n",
+    "\n",
+    "# As soon as the module is imported, you have access to all its contents.\n",
+    "\n",
+    "# To refer to a function or variable within numpy, just follow it with a \n",
+    "# period, then the function or variable in question.\n",
+    "\n",
+    "# As an example, numpy will let you calculate the sine of a given number.\n",
+    "a = numpy.sin(4)\n",
+    "print(a)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some modules have what are called <b>submodules</b> that can be referred to in a similar way. For instance, the module NumPy has a submodule called ma, which in turn has particular functions defined within it. If you wanted to run the <b>array</b> function inside the <b>ma</b> submodule inside the <b>NumPy</b> module, it would look like <b>numpy.ma.array</b>.\n",
+    "\n",
+    "Modules have detailed documentation (typically easily found via Google) that will let you browse all the available functions at your disposal. This instance of Jupyter has several modules preinstalled; they may be missing if you try running them on a Python build at home. Fortunately, there are generally helpful instructions online for downloading new modules so they can be imported into your code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.7568024953079282"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If you find yourself typing the same module name again and again, \n",
+    "# you can rename it to something a little quicker!\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "a = np.sin(4)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a meteorological example, let's return to our list of Celsius temperatures from yesterday. NumPy will enable us to easily calculate the maximum and minimum temperatures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The maximum temperature is 36.5 and the minimum temperature is 13.5.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, here's our temperature list.\n",
+    "\n",
+    "temp = [23.5,24.5,18.0,26.8,17.7,17.0,27.1,24.6,13.5,36.5]\n",
+    "\n",
+    "# Let's import NumPy.\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "# Now, use the max() and min() functions within the NumPy module\n",
+    "# to calculate the maximum and minimum temperatures.\n",
+    "\n",
+    "maxT = np.max(temp)\n",
+    "minT = np.min(temp)\n",
+    "\n",
+    "# We can even output a nice sentence summing it up!\n",
+    "\n",
+    "print(\"The maximum temperature is \"+str(maxT)+\" and the minimum temperature is \"+str(minT)+\".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>An example!</b>\n",
+    "\n",
+    "In the box below, write a block of code that will apply a NumPy mathematical function of your choice to the list of temperatures <b>(temp)</b> above: https://numpy.org/doc/stable/reference/routines.math.html. Don't forget to include your module import statement!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.99808203 -0.59135753 -0.75098725  0.9953511  -0.91258245 -0.96139749\n",
+      "  0.92243282 -0.50789659  0.80378443 -0.93171689]\n",
+      "[-0.06190529  0.80640949  0.66031671 -0.09631292  0.40889274 -0.27516334\n",
+      " -0.38615761  0.86141805  0.59492066  0.36318541]\n",
+      "[ 16.12272495  -0.73332164  -1.13731371 -10.33455467  -2.23183823\n",
+      "   3.49391565  -2.38874697  -0.58960523   1.35107835  -2.56540287]\n"
+     ]
+    }
+   ],
+   "source": [
+    "temp = [23.5,24.5,18.0,26.8,17.7,17.0,27.1,24.6,13.5,36.5]\n",
+    "import numpy as np\n",
+    "\n",
+    "# Mathematical function #1.\n",
+    "print(np.sin(temp))\n",
+    "\n",
+    "# Mathematical function #2.\n",
+    "print(np.cos(temp))\n",
+    "print(np.tan(temp))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>5.2 Exception Handling</h2>\n",
+    "\n",
+    "An \"exception\" is a very polite way of saying \"error\" in programming-speak. Often we use an if statement to determine whether something is going to cause a problem, and then use a <b>raise</b> statement to tell the code to stop and output a particular error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "12.566370614359172\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's say we have a code that will attempt to calculate the area of a circle\n",
+    "# based on its radius.\n",
+    "# It works well for positive values of radius, but we want to allow for the\n",
+    "# possibility that someone might enter a negative radius.\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "def circle_area(radius):\n",
+    "    if radius < 0:\n",
+    "        raise ValueError('Radius cannot be negative, you fool!')\n",
+    "    area = np.pi * (radius**2)\n",
+    "    return area\n",
+    "\n",
+    "def quadarea(base, height):\n",
+    "    if base < 0:\n",
+    "        raise ValueError('enter a POSITIVE number')\n",
+    "    elif height < 0:\n",
+    "        raise ValueError('POSITIVE integer please')\n",
+    "    x = base*height\n",
+    "    return x\n",
+    "# Now try calling this function with a positive radius to make sure it works.\n",
+    "print(quadarea(2,3))\n",
+    "print(circle_area(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Radius cannot be negative, you fool!",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[15], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Now try calling this function with a negative radius.\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mcircle_area\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+      "Cell \u001b[0;32mIn[14], line 10\u001b[0m, in \u001b[0;36mcircle_area\u001b[0;34m(radius)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcircle_area\u001b[39m(radius):\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m radius \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 10\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRadius cannot be negative, you fool!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     11\u001b[0m     area \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mpi \u001b[38;5;241m*\u001b[39m (radius\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m area\n",
+      "\u001b[0;31mValueError\u001b[0m: Radius cannot be negative, you fool!"
+     ]
+    }
+   ],
+   "source": [
+    "# Now try calling this function with a negative radius.\n",
+    "\n",
+    "print(circle_area(-2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The syntax for <b>raise</b> is the exception class (for instance, ValueError() if a value is forbidden; this is the most common type of exception you'll have, but we'll see others as we go) followed by user-defined text that will give you more information about the error.\n",
+    "\n",
+    "Luckily, Python also lets you build in ways to deal with common exceptions if you're pretty sure you know where the user went wrong. For example, they may have accidentally put a negative sign on the radius, but you're pretty sure they just meant the positive value. In that case, you can use a <b>try/except</b> block, which will attempt to resolve the error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "28.274333882308138\n"
+     ]
+    }
+   ],
+   "source": [
+    "# As an example, this code will call the area() function, and if it gets\n",
+    "# a ValueError exception, it'll try again with the absolute value\n",
+    "# of the range.\n",
+    "\n",
+    "radius = -3\n",
+    "try:\n",
+    "    a = circle_area(radius)\n",
+    "except ValueError:\n",
+    "    a = circle_area(abs(radius))\n",
+    "    \n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>TAKE-HOME POINTS</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>module</b> is a library of Python source code that gives access to new variables and functions.</li>\n",
+    "    <li>Modules can be imported as whatever name you like.</li>\n",
+    "    <li>A <b>raise</b> statement lets you define your own error conditions and stop code when they're met.</li>\n",
+    "    <li>A <b>try/except</b> block will let you fix common issues that would otherwise result in exceptions.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you can import modules with any name you want"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 03 W.ipynb b/python/atms-310/notebooks/Week 03 W.ipynb
new file mode 100755
index 0000000..38109e4
--- /dev/null
+++ b/python/atms-310/notebooks/Week 03 W.ipynb	
@@ -0,0 +1,487 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>6. Arrays</h1>\n",
+    "\n",
+    "<h2>10/11/2023</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<h2>6.0 Last Time...</h2>\n",
+    "\n",
+    "<ul>\n",
+    "    <li>A <b>module</b> is a library of Python source code that gives access to new variables and functions.</li>\n",
+    "    <li>Modules can be imported as whatever name you like.</li>\n",
+    "    <li>A <b>raise</b> statement lets you define your own error conditions and stop code when they're met.</li>\n",
+    "    <li>A <b>try/except</b> block will let you fix common issues that would otherwise result in exceptions.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>6.1 Creating Arrays</h2>\n",
+    "\n",
+    "An <b>array</b> is similar to a list, but has a lot of additional functionality! \n",
+    "<ul>\n",
+    "    <li>all elements are of the same type (this speeds up operations)</li>\n",
+    "    <li>multi-dimensional arrays clearly supported</li>\n",
+    "    <li>array operations are supported (like matrix multiplication!)</li>\n",
+    "</ul>\n",
+    "\n",
+    "Using an array requires NumPy - remember to import it at the start of your code!\n",
+    "\n",
+    "The easiest way to create an array is to take a preexisting list and convert it into an array using NumPy's <b>array</b> function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[90, 123, 43], [123, 1432, 534]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Start with a predefined list.\n",
+    "\n",
+    "mylist = [[90,123,43],[123,1432,534]]\n",
+    "print(mylist)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  90  123   43]\n",
+      " [ 123 1432  534]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's make this a 2D array:\n",
+    "\n",
+    "a = np.array(mylist)\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>6.2 Initializing Arrays</h2>\n",
+    "\n",
+    "Just like when we were building lists, sometimes you know the size and shape of the array you'd like to build, but not the individual contents.\n",
+    "\n",
+    "In those cases, you can create an array of zeroes where non-zero values will eventually be filled in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0.]\n",
+      " [0. 0.]\n",
+      " [0. 0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's use the zeros() function to make a 3x2 array.\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "x = np.zeros((3,2))\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the more important ways we used lists previously was with the range() function, which counted up from zero to one less than the integer in parentheses. NumPy has an equivalent array version to save time: arange()."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 1 2 3 4 5 6 7 8 9]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use arange() to create an array of 10 elements\n",
+    "# that starts from 0 and counts up through 9.\n",
+    "\n",
+    "import numpy as np # recall you only have to import once!\n",
+    "\n",
+    "a = np.arange(10)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note that you can also have a float version of arange:\n",
+    "a = np.arange(10.0)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>6.3 Array Indexing</h2>\n",
+    "\n",
+    "Just like with lists, arrays start counting at 0, and you can use negative numbers to start counting backwards (e.g., -1 refers to the last element in the array).\n",
+    "\n",
+    "We can also slice arrays, which is a process very similar to slicing lists:\n",
+    "<ul>\n",
+    "    <li>The element numbers are separated by a colon.</li>\n",
+    "    <li><b>Inclusive</b> lower limit, <b>exclusive</b> upper limit.</li>\n",
+    "    <li>If one of the limits is omitted, the range is extended to the end of the range (so if the lower limit is omitted, the range extends to the very beginning of the array)</li>\n",
+    "    <li>To specify all elements, use a colon by itself.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create a 1D array.\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "a = np.array([2, 3.2, 5.5, -6.4, -2.2, 2.4])\n",
+    "\n",
+    "print(a[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.2  5.5 -6.4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a[1:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.  3.2 5.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a[:3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 2.   3.2  5.5 -6.4 -2.2  2.4]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a[:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far so good... but what about multidimensional arrays? In multidimensional arrays, indexing for each dimension is separated by commas. In a 2D array, the indexing is done by [row, col]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 2.   3.2  5.5 -6.4 -2.2  2.4]\n",
+      " [ 1.  22.   4.   0.1  5.3 -9. ]\n",
+      " [ 3.   1.   2.1 21.   1.1 -2. ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create a 2D array.\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "# For the following, I've played around with the spacing\n",
+    "# a bit to make the dimensions more clear, but this is optional!\n",
+    "\n",
+    "a = np.array([[2,3.2,5.5,-6.4,-2.2,2.4],\n",
+    "             [1, 22,  4, 0.1, 5.3, -9],\n",
+    "             [3,  1,2.1,  21, 1.1, -2]])\n",
+    "\n",
+    "# This array has 3 rows and 6 columns.\n",
+    "# Note that Python knew this was all part of the same line\n",
+    "# even without using any special character to warn it.\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Let's try some examples!</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-6.4  0.1 21. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the column with index 3 (the fourth column).\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "print(a[:,3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 1.  22.   4.   0.1  5.3 -9. ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the row with index 1 (the second row).\n",
+    "\n",
+    "print(a[1,:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[22.   4.   0.1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Write a print statement that prints the slice [22, 4, 0.1].\n",
+    "\n",
+    "# [row, column]\n",
+    "# \n",
+    "print(a[1,1:4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>6.4 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>An array is NumPy's version of a list, which allows you to work with matrices.</li>\n",
+    "    <li>All elements of an array are of the same type.</li>\n",
+    "    <li>You can either create a new array or convert an existing list into one.</li>\n",
+    "    <li>The zeros() function is useful for initializing an array when you know the shape and size but not the contents.</li>\n",
+    "    <li>The arange() function serves a similar purpose to the range() function with lists.</li>\n",
+    "    <li>Similar to lists, arrays can be sliced, but arrays can be sliced across each of their dimensions.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Challenge Exercises:</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0.]\n",
+      " [0. 0. 0. 0. 0.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#1. What is the code to create a 4-row, 5-column array of zeros and assign it to the variable a?\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "a = np.zeros((4,5))\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4.  2.1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#2. Using array a from above, answer the following questions:\n",
+    "\n",
+    "a = np.array([[2,3.2,5.5,-6.4,-2.2,2.4],\n",
+    "             [1, 22,  4, 0.1, 5.3, -9],\n",
+    "             [3,  1,2.1,  21, 1.1, -2]])\n",
+    "\n",
+    "#a. What is a[:,3]?\n",
+    "\n",
+    "#[-6.4,0.1,21.]\n",
+    "#print(a[:,3])\n",
+    "\n",
+    "#b. What is a[1:4,0:2]?\n",
+    "#[1,22]\n",
+    "#[3,1,]\n",
+    "\n",
+    "# print(a[1:4,0:2])\n",
+    "\n",
+    "#c. What will b = a[1:,2] do?\n",
+    "\n",
+    "b = a[1:,2] # row 1 to the end, index 2\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 04 F.ipynb b/python/atms-310/notebooks/Week 04 F.ipynb
new file mode 100755
index 0000000..00acbfe
--- /dev/null
+++ b/python/atms-310/notebooks/Week 04 F.ipynb	
@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>8. Text I/O</h1>\n",
+    "\n",
+    "<h2>10/20/2023</h2>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>8.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>You can use functions within NumPy to find the shape, number of dimensions, and number of elements in any array.</li>\n",
+    "    <li><b>numpy.where()</b> will let you find the locations of elements within an array that meet a specified criterion.</li>\n",
+    "    <li>Some functions within NumPy allow you to manipulate arrays, such as reshaping, transposing, \"unraveling\", concatenation, and repeating individual array elements.</li>\n",
+    "    <li>When you're in a situation with multiple nested loops, often you can instead perform all operations a lot more easily by using array syntax.</li>\n",
+    "    <li>Mathematical operations act on arrays elementwise.</li>\n",
+    "    <li>When testing within an array, <b>and</b> and <b>or</b> cannot be used; instead, use NumPy's built-in functions.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>8.1 File Objects</h2>\n",
+    "\n",
+    "A file object is just a variable that represents the file within Python. The process of creating a file object is the same general idea as creating any variable: you create it by assignment.\n",
+    "\n",
+    "For a text file, you can create a file with the built-in <b>open()</b> statement. The first argument in <b>open</b> gives the filename, and the second sets the mod for the file:\n",
+    "<ul>\n",
+    "    <li><b>'r'</b>: sets the file to read-only.</li>\n",
+    "    <li><b>'w'</b>: sets the file to writing mode.</li>\n",
+    "    <li><b>'a'</b>: sets the file to append mode (you can only add new things to the end).</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_io.TextIOWrapper"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Try opening the 'test.txt' file that you added to your server.\n",
+    "data = open(\"../test.txt\", \"r\")\n",
+    "type(data)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you're done with a file, you can use the <b>close()</b> method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Close that file back up.\n",
+    "data.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>8.2 Text Input/Output</h2>\n",
+    "\n",
+    "To read a line from a file into a variable, you can use the <b>readline()</b> method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a test!\n",
+      "\n",
+      "Here's some information:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, open the file.\n",
+    "data = open(\"../test.txt\",\"r\")\n",
+    "\n",
+    "\n",
+    "# Assign the first line of text to the variable aline.\n",
+    "aline = data.readline()\n",
+    "\n",
+    "# Calling readline() multiple times in a row will print the next row.\n",
+    "bline = data.readline()\n",
+    "\n",
+    "# Print those first two lines of text.\n",
+    "print(aline)\n",
+    "print(bline)\n",
+    "\n",
+    "# Close the file. (This is good practice!)\n",
+    "data.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also write a loop to go through the whole file!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a test!\n",
+      "\n",
+      "Here's some information:\n",
+      "\n",
+      "IMPORTANT THINGS TO KNOW\n",
+      "\n",
+      "Okay, that's all I got.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = open(\"../test.txt\", \"r\")\n",
+    "\n",
+    "for i in data:\n",
+    "    print(i)\n",
+    "    \n",
+    "data.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, but that's fairly limiting; more often, you'll want to read the whole file and put each line into a list as an element; this can be done using <b>readlines()</b> (note the plural!)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['This is a test!\\n', \"Here's some information:\\n\", 'IMPORTANT THINGS TO KNOW\\n', \"Okay, that's all I got.\"]\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's open the file again.\n",
+    "data = open(\"../test.txt\", \"r\")\n",
+    "\n",
+    "\n",
+    "# Save the file's contents to a list.\n",
+    "contents = data.readlines()\n",
+    "\n",
+    "print(contents)\n",
+    "print(type(contents))\n",
+    "\n",
+    "# Close that file!\n",
+    "data.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that there's a newline (<b>\\n</b>) character at the end of each line (except the last one).\n",
+    "\n",
+    "To write to a file, you can use the <b>write()</b> method (obviously this doesn't work if a file is in read-only mode)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's open a file in writing mode.\n",
+    "data = open(\"../test.txt\", \"w\")\n",
+    "\n",
+    "\n",
+    "# Write a phrase to the file.\n",
+    "data.write(\"hello world\")\n",
+    "data.close()\n",
+    "\n",
+    "# i didnt run this, and dont ever run this. will overwrite file.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this overwrites everything currently inside the file! To write multiple lines (in list format) to a file, use <b>writelines()</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'_io.TextIOWrapper' object has no attribute 'append'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m/Users/nik/data/python/notebooks/Week 04 F.ipynb Cell 16\u001b[0m line \u001b[0;36m7\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2004%20F.ipynb#X21sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m data\u001b[39m.\u001b[39mwritelines(contents)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2004%20F.ipynb#X21sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m../test.txt\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39ma\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2004%20F.ipynb#X21sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m data\u001b[39m.\u001b[39;49mappend(\u001b[39m\"\u001b[39m\u001b[39mpoopy booty butt balls\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2004%20F.ipynb#X21sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m data\u001b[39m.\u001b[39mclose()\n",
+      "\u001b[0;31mAttributeError\u001b[0m: '_io.TextIOWrapper' object has no attribute 'append'"
+     ]
+    }
+   ],
+   "source": [
+    "data = open(\"../test.txt\", \"w\")\n",
+    "\n",
+    "# Earlier in this notebook we saved the contents of our file to a variable 'contents'.\n",
+    "\n",
+    "data.writelines(contents)\n",
+    "data = open(\"../test.txt\", \"a\")\n",
+    "data.(\"poopy booty butt balls\")\n",
+    "data.close()\n",
+    "\n",
+    "# ok whatever ill figure it out when i need to"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>8.3 Processing File Contents</h2>\n",
+    "\n",
+    "As you might imagine, the contents of files can be pretty unwieldy. Luckily, there are a lot of methods that will make data easier to read!\n",
+    "\n",
+    "Sometimes (as with .csv files) you'll want to take a string and break it into list using a particular separator. <b>split()</b> is a useful tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['3.4', '2.1', '-2.6']\n",
+      "['3.4', '2.1', '-2.6']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create a single string that has three pieces of data in it.\n",
+    "a = '3.4 2.1 -2.6'\n",
+    "\n",
+    "\n",
+    "# The obvious choice for a separator is a space.\n",
+    "print(a.split(\" \"))\n",
+    "a = '3.4,2.1,-2.6'\n",
+    "print(a.split(\",\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If everything we read from a file is a string, we're sometimes going to have to convert to integers or floats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['3.4', '2.1', '-2.6']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We'll need NumPy for this!\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "# Let's look at a typical situation: we've grabbed some numbers from a csv file.\n",
+    "a = '3.4,2.1,-2.6'\n",
+    "a = a.split(\",\")\n",
+    "\n",
+    "# Note that these are still strings.\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.4  2.1 -2.6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can convert these to floats the way we did before!\n",
+    "b = np.zeros(len(a))\n",
+    "for i in range(len(a)):\n",
+    "    b[i] = float(a[i])\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can convert to an array and use the <b>astype()</b> function built-in there. <b>'d'</b> is a float (double-precision), <b>'l'</b> is an integer (long integer)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.4  2.1 -2.6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bnum = np.array(a).astype(\"d\")\n",
+    "print(bnum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>8.4 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>The <b>open()</b> statement lets you open a file in read, write, or append mode.</li>\n",
+    "    <li>Files should always be closed using the <b>close()</b> statement.</li>\n",
+    "    <li>You can read a single line with <b>readline()</b>, and multiple lines with <b>readlines()</b>.</li>\n",
+    "    <li>The <b>write()</b> method allows you to write a single line, and the <b>writelines()</b> method allows you to write multiple lines.</li>\n",
+    "    <li><b>split()</b> lets you break strings based on defined separators.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remember to close files\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 05 F-1.ipynb b/python/atms-310/notebooks/Week 05 F-1.ipynb
new file mode 100755
index 0000000..a39b8c8
--- /dev/null
+++ b/python/atms-310/notebooks/Week 05 F-1.ipynb	
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>11. Introduction to Object-Oriented Programming</h1>\n",
+    "<h2>10/27/2023</h2>\n",
+    "\n",
+    "<h2>11.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>NetCDF is a powerful file type containing global attributes, variables, variable attributes, and dimensions.</li>\n",
+    "    <li>We can read from NetCDF files using similar syntax to that for regular files.</li>\n",
+    "    <li>Using attributes such as 'dimensions' and 'variables', we can learn about individual variables in the dataset.</li>\n",
+    "    <li>We can also write to NetCDF files in a simlar way.</li>\n",
+    "</ul>\n",
+    "\n",
+    "<h2>11.1 What is Object-Oriented Programming?</h2>\n",
+    "\n",
+    "OOP is presented here in opposition to procedural programming. <b>Procedural</b> programs consider two entities: data and functions. Procedurally, the two things are different: a function will take data as input and return data as output (this should sound familiar!). There's nothing customizable about a function with respect to data, which means you can use functions on various types of data with no restrictions... which can you get into trouble.\n",
+    "\n",
+    "In reality, though, we tend to think of things as having both \"state\" and \"behavior\". People can have a state (tall, short, etc.) but also a behavior (playing basketball, running, etc.), and the two can happen simultaneously.\n",
+    "\n",
+    "Object-oriented programming attempts to imitate this approach, so specific objects in the code will have a state and a behavior attached to them.\n",
+    "\n",
+    "<h2>11.2 What is an Object?</h2>\n",
+    "\n",
+    "An object in programming has two entities attached to it: data... and the things that <i>act</i> on that data. The data are called <b>attributes</b>, and the functions attached to the object that can act on that data are called <b>methods</b>. \n",
+    "\n",
+    "These methods are specifically made to act on attributes; they aren't just random functions meant as one-size-fits-all solutions, which is what we would see in procedural programming.\n",
+    "\n",
+    "Objects are generally specific realizations of some <b>class</b> or <b>type</b>. As an example, individual people are specific realizations of the <b>class</b> of human beings. Specific realizations (instances) differ from each other in details but have the same overall pattern. In OOP, specific realizations are <b>instances</b> and common patterns are <b>classes</b>.\n",
+    "\n",
+    "<h2>11.3 How do Objects Work?</h2>\n",
+    "\n",
+    "In Python, strings (like almost everything in Python) are objects. Built into Python, there is a class called 'strings', and each time you make a new string, you're using that definition. Python implicitly defines attributes and methods for all string objects; no matter what string you create, you have that set of data and functions associated with your string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__add__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__delattr__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getnewargs__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__iter__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__mod__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " 'capitalize',\n",
+       " 'casefold',\n",
+       " 'center',\n",
+       " 'count',\n",
+       " 'encode',\n",
+       " 'endswith',\n",
+       " 'expandtabs',\n",
+       " 'find',\n",
+       " 'format',\n",
+       " 'format_map',\n",
+       " 'index',\n",
+       " 'isalnum',\n",
+       " 'isalpha',\n",
+       " 'isascii',\n",
+       " 'isdecimal',\n",
+       " 'isdigit',\n",
+       " 'isidentifier',\n",
+       " 'islower',\n",
+       " 'isnumeric',\n",
+       " 'isprintable',\n",
+       " 'isspace',\n",
+       " 'istitle',\n",
+       " 'isupper',\n",
+       " 'join',\n",
+       " 'ljust',\n",
+       " 'lower',\n",
+       " 'lstrip',\n",
+       " 'maketrans',\n",
+       " 'partition',\n",
+       " 'removeprefix',\n",
+       " 'removesuffix',\n",
+       " 'replace',\n",
+       " 'rfind',\n",
+       " 'rindex',\n",
+       " 'rjust',\n",
+       " 'rpartition',\n",
+       " 'rsplit',\n",
+       " 'rstrip',\n",
+       " 'split',\n",
+       " 'splitlines',\n",
+       " 'startswith',\n",
+       " 'strip',\n",
+       " 'swapcase',\n",
+       " 'title',\n",
+       " 'translate',\n",
+       " 'upper',\n",
+       " 'zfill']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The dir() command gives you a list of all the attributes and methods\n",
+    "# associated with a given object.\n",
+    "a = \"hello world\"\n",
+    "dir(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World\n",
+      "HELLO WORLD\n"
+     ]
+    }
+   ],
+   "source": [
+    "# To refer to an attribute or method of an instance,\n",
+    "# you just add a period after the object name and then put\n",
+    "# the attribute or method name.\n",
+    "print(a.title())\n",
+    "print(a.upper())\n",
+    "# Methods can produce a return value, act on attributes of the object in-place,\n",
+    "# or both!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# isupper() will determine whether the object is in uppercase.\n",
+    "b = \"BALLS\"\n",
+    "print(b.isupper())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "# To count the instances of a particular character, you can use count()\n",
+    "print(a.count(\"l\"))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As another example, let's consider how objects work for arrays!\n",
+    "\n",
+    "Arrays have attributes and methods built in to them just like any other object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.  1.  2.]\n",
+      " [ 3.  4.  5.]\n",
+      " [ 6.  7.  8.]\n",
+      " [ 9. 10. 11.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = np.arange(12.)\n",
+    "a = np.reshape(a,(4,3))\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_function__', '__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', '__array_ufunc__', '__array_wrap__', '__bool__', '__class__', '__class_getitem__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dir__', '__divmod__', '__dlpack__', '__dlpack_device__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getstate__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's look at all the attributes and methods!\n",
+    "print(dir(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Any attributes with two underscores probably shouldn't\n",
+    "# be messed with! This is how Python decides what to do when\n",
+    "# you type '*' or '/'.\n",
+    "\n",
+    "# Some of the other interesting methods include:\n",
+    "\n",
+    "print(np.shape(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  1.,  3.,  6., 10., 15., 21., 28., 36., 45., 55., 66.])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.  3.  6.  9.]\n",
+      " [ 1.  4.  7. 10.]\n",
+      " [ 2.  5.  8. 11.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.,  0.,  0.],\n",
+       "       [ 0.,  0.,  0.],\n",
+       "       [10., 10., 10.],\n",
+       "       [10., 10., 10.]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.round(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.ravel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Practice Exercises!</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The rain in Spain.\n",
+      "THE RAIN IN SPAIN.\n",
+      "True\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = 'The rain in Spain.'\n",
+    "#1. Create a new string b that is a but all in uppercase.\n",
+    "b = a.upper()\n",
+    "#2. Is a changed when you create b?\n",
+    "print(a)\n",
+    "print(b)\n",
+    "# no?\n",
+    "#3. How would you test to see whether b is in uppercase? That is, how \n",
+    "#   could you return a boolean that is True or False depending on whether \n",
+    "#   b is uppercase?\n",
+    "print(b.isupper())\n",
+    "#4. How would you calculate the number of occurrences of the letter 'n' in a?\n",
+    "print(a.count(\"n\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Round 2!</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2.3 8.  3.2]\n",
+      " [4.3 0.4 4.3]\n",
+      " [1.2 0.3 5.4]\n",
+      " [4.3 5.6 6.5]]\n",
+      "[2.3 8.  3.2 4.3 0.4 4.3 1.2 0.3 5.4 4.3 5.6 6.5]\n",
+      "[[2.3 8.  3.2 4.3 0.4 4.3]\n",
+      " [1.2 0.3 5.4 4.3 5.6 6.5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#1. Create a 3 column, 4 row array named a. The array can have any numerical values\n",
+    "#   you want, as long as all the elements are not all identical.\n",
+    "x = np.array([[2.3,4.3,1.2,4.3],[8.0,0.4,0.3,5.6],[3.2,4.3,5.4,6.5]])\n",
+    "x = x.T\n",
+    "print(x)\n",
+    "#2. Create an array b that is a copy of a but is 1-D, not 2-D.\n",
+    "b = np.ravel(x)\n",
+    "print(b)\n",
+    "#3. Turn b into a 6 column, 2 row array.\n",
+    "b = np.reshape(b,(2,6))\n",
+    "#4. Create an array c where you round all elements of b to 1 decimal place.\n",
+    "c = b.round(1)\n",
+    "print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>11.4 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li><b>Objects</b> have attributes and methods associated with them that can be listed using <b>dir()</b>.</li>\n",
+    "    <li>Methods for strings include <b>upper()</b>, <b>isupper()</b>, <b>count()</b>, <b>title()</b>, etc.</li>\n",
+    "    <li>Methods for arrays include <b>reshape()</b>, <b>ravel()</b>, <b>round()</b>, etc.</li>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 05 F-2.ipynb b/python/atms-310/notebooks/Week 05 F-2.ipynb
new file mode 100755
index 0000000..1a68afc
--- /dev/null
+++ b/python/atms-310/notebooks/Week 05 F-2.ipynb	
@@ -0,0 +1,328 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>12. Defining Classes And More...</h1>\n",
+    "<h2>10/27/2023</h2>\n",
+    "\n",
+    "<h2>12.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li><b>Objects</b> have attributes and methods associated with them that can be listed using <b>dir()</b>.</li>\n",
+    "    <li>Methods for strings include <b>upper()</b>, <b>isupper()</b>, <b>count()</b>, <b>title()</b>, etc.</li>\n",
+    "    <li>Methods for arrays include <b>reshape()</b>, <b>ravel()</b>, <b>round()</b>, etc.</li>\n",
+    "</ul>\n",
+    "\n",
+    "<h2>12.1 Defining A Class: The Theory</h2>\n",
+    "\n",
+    "We talked about how all objects are instances of a class. Using <b>dir()</b> in the previous lecture, we saw the list of attributes and methods for the <b>string</b> and <b>array</b> classes. But just as you sometimes want to create your own functions depending on your application, sometimes you want to create your own classes!\n",
+    "\n",
+    "This is going to get a little abstract, so bear with me while we go through this in text form - we'll soon be looking and writing plenty of our own examples! This is complex and a little hard to think about because we're actually messing with the way Python works at a more fundamental level rather than just applying it.\n",
+    "\n",
+    "Similar to the <b>def</b> statement for functions, creating a new class involves a <b>class</b> statement. The indented block after this class statement constitutes the definition of the class.\n",
+    "\n",
+    "Inside your definition, to make life easier, you just refer to the instance of the class as <b>self</b>. For example, if you want an attribute called <b>data</b>, within the class definition it will be called <b>self.data</b>.\n",
+    "\n",
+    "Methods are defined the same way we've been talking about functions: with a <b>def</b> statement. Each method will have a set of arguments, just the way functions normally do. The first argument in a method when defining a new class is generally going to just be the word <b>self</b>, which essentially means \"take everything we have thus far and make use of it in what follows\". This is an argument you won't have to type in when you're actually <i>using</i> the method; it's just for behind-the-scenes work.\n",
+    "\n",
+    "Typically, your first method will be called <b>\\_\\_init\\_\\_</b> (remember, the double-underscores indicate a fundamental method in a given class!). Every time you create an instance of your new class (for example, every time you create a string as an instance of the string class), this \\_\\_init\\_\\_ method will be called. \"Init\" stands for \"initialization\"; this is where you put any important information you'll need when creating a new instance of a class. \n",
+    "\n",
+    "Okay, this has been a lot. Let's see it in action!\n",
+    "\n",
+    "<h2>12.2 Defining A Class: The Code</h2>\n",
+    "\n",
+    "Let's define a class that's called simply <b>Book</b>.\n",
+    "\n",
+    "If we have a bunch of information associated with a book (its author, publisher, title, etc.), we can store it as an instance of the class Book. Rather than having it be a string or a list or a dictionary, it will just be called a Book. We can then come up with some helpful methods that will define what we can do to Books.\n",
+    "\n",
+    "Let's start our class definition. We use the word <b>class</b>, followed by the name of our class (by convention, they're typically capitalized). We then put the word <b>object</b> in parentheses; this argument is a special object that just identifies this as a class that doesn't depend on other classes. For the purposes of this course, just remember: a class definition should have the argument <b>object</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Book(object):\n",
+    "        \n",
+    "    # We'll start by defining the method __init__.\n",
+    "    # Its arguments are self, the Author's last and first names, the\n",
+    "    # title, the place, the publisher, and the year.\n",
+    "    def __init__(self, authlast, authfirst, title, place, publisher, year):\n",
+    "            \n",
+    "        \n",
+    "        # We then set the convention for the rest of our class definition:\n",
+    "        # the name of the object, followed by a dot, followed by\n",
+    "        # the names of our various arguments.\n",
+    "        self.authlast = authlast\n",
+    "        self.authfirst = authfirst\n",
+    "        self.title = title\n",
+    "        self.place = place\n",
+    "        self.publisher = publisher\n",
+    "        self.year = year\n",
+    "        \n",
+    "    # Let's create a second method that will write a bibliography entry.\n",
+    "    def write_bib_entry(self):\n",
+    "            \n",
+    "    # We won't need any additional arguments here, since it's all handled above.\n",
+    "        return self.authlast + ',' + self.authfirst + ',' + self.title + ',' + self.place + ',' + self.publisher + ',' + self.year  \n",
+    "\n",
+    "\n",
+    "        # We could create intermediate steps here, but let's just return the final answer.\n",
+    "    # def make_authoryear(self):\n",
+    "        # return self.authoryear + ' (' + self.year + ')'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have created our new class (<b>Book</b>), we can try creating some instances of the class!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<__main__.Book object at 0x10dd78110>\n",
+      "the evidential power of booty\n"
+     ]
+    }
+   ],
+   "source": [
+    "booty = Book(\"Dubious\", \"Thomas\", \"the evidential power of booty\", \"sanfran\", \"lava press\", \"1999\")\n",
+    "print(booty)\n",
+    "print(booty.title)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2003\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'authfirst',\n",
+       " 'authlast',\n",
+       " 'place',\n",
+       " 'publisher',\n",
+       " 'title',\n",
+       " 'write_bib_entry',\n",
+       " 'year']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "butt = Book(\"martelli\",\"alex\",\"py in nutshell\",\"place in CA\", \"autoparts\",\"2003\")\n",
+    "print(butt.year)\n",
+    "dir(butt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's pretty useful! Let's do a few exercises...\n",
+    "\n",
+    "<h2>12.3 Book Class Exercises</h2>\n",
+    "\n",
+    "After running the code above, answer the following questions:\n",
+    "\n",
+    "<b>1. How would you print out the authorfirst attribute of the pynut instance?</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thomas\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(booty.authfirst)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>2. How would you print the full bibliography entry for the beauty instance?</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dubious,Thomas,the evidential power of booty,sanfran,lava press,1999\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(booty.write_bib_entry())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>3. How would you change the publication year for the beauty instance to \"2010\"?</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2010\n"
+     ]
+    }
+   ],
+   "source": [
+    "booty.year = \"2010\"\n",
+    "print(booty.year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>12.4 Further Class Exercises</h2>\n",
+    "\n",
+    "<b>1. Create another instance of the Book class using a book of your choosing (or get creative and make something up!). Check to make sure it looks okay using the write_bib_entry() method.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "butt,booty,balls,leftcheek,rightcheek,9090\n"
+     ]
+    }
+   ],
+   "source": [
+    "ass = Book(\"butt\", \"booty\", \"balls\", \"leftcheek\", \"rightcheek\", \"9090\")\n",
+    "print(ass.write_bib_entry())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>2. Add a method called make_authoryear to the class definition. This method will create an attribute called authoryear and will set it to a string composed of the author's name followed by the year of publication in parentheses: </b>Dubay (1999)<b>, for instance. This method should not have a return statement, but should instead use a line starting with </b>self.authoryear = <b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>12.5 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>A class can be created using a <b>class</b> statement followed by the name of the class.</li>\n",
+    "    <li>Methods within a class definition are created using a <b>def</b> statement.</li>\n",
+    "    <li><b>__init__</b> is typically the first method defined and is used to initialize the core features of the class.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 05 M.ipynb b/python/atms-310/notebooks/Week 05 M.ipynb
new file mode 100755
index 0000000..2fea63e
--- /dev/null
+++ b/python/atms-310/notebooks/Week 05 M.ipynb	
@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>9. Data Analysis Exercise</h1>\n",
+    "<h2>10/23/2023</h2>\n",
+    "\n",
+    "<h2>9.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>The <b>open()</b> statement lets you open a file in read, write, or append mode.</li>\n",
+    "    <li>Files should always be closed using the <b>close()</b> statement.</li>\n",
+    "    <li>You can read a single line with <b>readline()</b>, and multiple lines with <b>readlines()</b>.</li>\n",
+    "    <li>The <b>write()</b> method allows you to write a single line, and the <b>writelines()</b> method allows you to write multiple lines.</li>\n",
+    "    <li><b>split()</b> lets you break strings based on defined separators.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>9.1 The General Idea...</h2>\n",
+    "\n",
+    "Today we're going to be working our way through various ways of analyzing datasets. The datasets in question are called <b>data0001.txt</b>, <b>data0002.txt</b>, and <b>data0003.txt</b>.\n",
+    "\n",
+    "These datasets are just composed of randomly generated numbers that have particular statistical features. We're going to make use of file I/O techniques to calculate certain statistics for each of them.\n",
+    "\n",
+    "<h3>9.1.1 A Quick Review of Statistics</h3>\n",
+    "\n",
+    "As a review, the <b>mean</b> is what we typically think of as \"average\": the sum of all elements, divided by the total number of elements. A mean is a useful summary, but it is <b>sensitive to outliers</b>. As an example, consider the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = np.array([1,2,3,4,5])\n",
+    "print(np.mean(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "31113.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now consider an outlier: a value that's much higher or lower than the others.\n",
+    "a = np.array([1,2,3,4,155555])\n",
+    "print(np.mean(a))\n",
+    "# the mean is not resistant to outliers\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we suspect there are outliers in the data, we can instead use the <b>median</b>, which is not sensitive to outliers. The median simply organizes all the values in ascending order and picks the middle one (or averages the middle two). The median is said to be <b>resistant to outliers</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.array([1,2,3,4,55555])\n",
+    "print(np.median(a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finding the mean or median of a dataset is only part of the story: we're often also interested in how spread out the data are. Consider the following examples:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22221.000022501237\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.std(a))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The arrays have the same mean (and the same median, actually), but the spread of values is very different. As a result, we use measures of spread such as the <b>standard deviation</b>, which is essentially a measure of the average distance between each value and the mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we can tell that the second array is more spread out than the first.\n",
+    "\n",
+    "Okay, but how does the standard deviation do when it comes to outliers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's a big difference there! So just like how the median can be used as a statistic instead of the mean when we suspect there are outliers, we can also use an outlier-resistant measure of spread called the <b>inter-quartile range (IQR)</b>.\n",
+    "\n",
+    "After sorting the data in ascending order, a <b>quartile</b> corresponds to a quarter of the data. Counting upward through the data, once we've reached 1/4 of the data, we've reached the first quartile. The second quartile is when we've reached 1/2 of the data (so the <b>second quartile is equal to the median</b>). The third quartile is when we've reached 3/4 of the data.\n",
+    "\n",
+    "The interquartile range is simply the difference between the 3rd quartile and the 1st quartile. This function isn't in NumPy (yet!), but it is in scipy.stats."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0\n",
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy.stats as S\n",
+    "import numpy as np\n",
+    "\n",
+    "a = np.array([1,2,3,4,5,6,7])\n",
+    "b = np.array([1,2,3,4,5,6,1552345])\n",
+    "\n",
+    "print(S.iqr(a))\n",
+    "print(S.iqr(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to get fancy with your analysis, there's also <b>skewness</b> and <b>kurtosis</b>, but they're harder to puzzle out by hand.\n",
+    "\n",
+    "<b>Skewness</b> is a measure of how asymmetrical your distribution is: negative skew means a plot of the data has a longer left tail, whereas positive skew means a plot of the data has a longer right tail.\n",
+    "\n",
+    "<b>Kurtosis</b> is a measure of how sharp the peak is in a distribution, as compared to a Gaussian (bell-curve). If the kurtosis is greater than 3, it's got a sharper curve than a Gaussian distribution. If it's less than 3, it's got a more gradual curve than a Gaussian distribution.\n",
+    "\n",
+    "These are more complicated statistics, but you may come across the names, and they can come in handy when you're doing data analysis!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "2.0412414522829976\n",
+      "-1.25\n",
+      "2.1666666665875907\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(S.skew(a))\n",
+    "print(S.skew(b))\n",
+    "\n",
+    "print(S.kurtosis(a))\n",
+    "print(S.kurtosis(b))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>9.2 A Traditional Approach</h2>\n",
+    "\n",
+    "We're going to call this a traditional approach because it's the sort of thing you could do in just about any programming language; it doesn't really take advantage of the power of Python.\n",
+    "\n",
+    "Our goal is to calculate the mean, median, standard deviation, IQR, skewness, and kurtosis of each of the 3 datasets!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start by importing the relevant packages.\n",
+    "import numpy as np\n",
+    "import scipy.stats as s\n",
+    "\n",
+    "# Let's create a function that will read data from any file.\n",
+    "# The function has one argument: the name of the file.\n",
+    "def read(file):\n",
+    "    fileobj = open(file, \"r\")\n",
+    "    # Start by defining a file object. We're opening in read-only mode.\n",
+    "    outputstr = fileobj.readlines()\n",
+    "    # Next, use readlines() to create a variable containing all the data.\n",
+    "    fileobj.close()\n",
+    "    # Close the file!\n",
+    "    outputarray = np.zeros(len(outputstr))\n",
+    "    # Let's initalize an array that will contain all the individual values from the file.\n",
+    "    for n in np.arange(len(outputstr)):\n",
+    "        outputarray[i] = float(outputstr[i]) \n",
+    "    # Finally, let's loop over all the lines and put their values into this new array.\n",
+    "    return outputarray\n",
+    "    # We now have a function that takes in a file name and puts\n",
+    "    # all the data into an array!\n",
+    "    # The final step is to return the data array.\n",
+    "\n",
+    "\n",
+    "# Okay, so let's make use of this function for our three datasets.\n",
+    "data1 = read(\"../datasets/data001.txt\")\n",
+    "data2 = read(\"../datasets/data002.txt\")\n",
+    "data3 = read(\"../datasets/data003.txt\")\n",
+    "\n",
+    "# Calculate the stats!\n",
+    "mean1 = np.mean(data1)\n",
+    "mean1 = np.mean(data1)\n",
+    "mean1 = np.mean(data1)\n",
+    "\n",
+    "\n",
+    "# Printing:\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>9.3 Array Storage</h2>\n",
+    "\n",
+    "We can do better than that! Let's make use of arrays for the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the necessary packages.\n",
+    "import numpy as np\n",
+    "import scipy.stats as s\n",
+    "\n",
+    "# Let's initialize arrays of our final values!\n",
+    "numfiles = 3\n",
+    "mean = np.zeros(numfiles)\n",
+    "median = np.zeros(numfiles)\n",
+    "std = np.zeros(numfiles)\n",
+    "\n",
+    "# Now, let's use a loop to calculate the values!\n",
+    "    # We can use the index from the loop to name each file!\n",
+    "\n",
+    "    # Now, just use readdata() to grab all the data from the file.\n",
+    "\n",
+    "    # Calculate your statistics!\n",
+    "\n",
+    "\n",
+    "# Once the loop is complete, print out the arrays.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That worked fairly well, but the big concern here is that sometimes your files won't be as nicely numbered as they are.\n",
+    "\n",
+    "<h2>9.4 Dictionary Storage</h2>\n",
+    "\n",
+    "How can we use dictionaries to our advantage? This might solve our problem with our filenames! Instead of relying on them to be a perfectly numbered list, we can use them as keys in a dictionary.\n",
+    "\n",
+    "And there's a new import command we can use that will grab all the file names!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The usual suspects.\n",
+    "import numpy as np\n",
+    "import scipy.stats as s\n",
+    "# And a new friend!\n",
+    "import glob\n",
+    "\n",
+    "# Let's start by getting a list of files in the directory.\n",
+    "# We don't want to grab EVERYTHING, so we'll say it has to start with the word 'data' and end with '.txt.'\n",
+    "filelist = glob.glob(\"../datasets/data*.txt\")\n",
+    "filelist.sort()\n",
+    "\n",
+    "# Now initialize our dictionaries as empty to begin with.\n",
+    "mean = {}\n",
+    "median = {}\n",
+    "stddev = {}\n",
+    "iqr = {}\n",
+    "skewness = {}\n",
+    "kurtosis = {}\n",
+    "\n",
+    "# Loop through all files.\n",
+    "for i in filelist:\n",
+    "    # Read the data.\n",
+    "    data = read(i)\n",
+    "    # Assign key-value pairs!\n",
+    " \n",
+    "    \n",
+    "# And, outside the loop, print the results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>9.5 MORE Dictionary Storage</h2>\n",
+    "\n",
+    "Okay, well, what if we didn't want to have to make a separate dictionary for every statistical metric? Remember, the key:value pairs in dictionaries are very flexible and actually allow you to put dictionaries themselves into the values!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Old friends, back again.\n",
+    "\n",
+    "\n",
+    "# First, create a dictionary of metrics with the commands you'll need to calculate them.\n",
+    "\n",
+    "# And we get our files the usual way.\n",
+    "\n",
+    "# Now let's initialize a results dictionary for each metric.\n",
+    "\n",
+    "# Now loop through all files, storing the relevant metrics!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The power of what Python's enabled us to do here is that we can change almost anything very easily: adding or removing files, adding or removing metrics, it's all done with one or two lines of code at most. The first version we saw would have been <b>much</b> more complicated!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>9.6 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>Statistics that are not resistant to outliers include the mean, the standard deviation, skewness, and kurtosis.</li>\n",
+    "    <li>Statistics that are resistant to outliers include the median and the interquartile range.</li>\n",
+    "    <li>By making use of dictionaries, we can create versatile, non-hard-coded programs!</li>\n",
+    "    <li>glob is a package that enables us to grab all files within a directory</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 05 W.ipynb b/python/atms-310/notebooks/Week 05 W.ipynb
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@@ -0,0 +1,685 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>10. NetCDF I/O</h1>\n",
+    "<h2>10/25/2023</h2>\n",
+    "\n",
+    "<h2>10.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>Statistics that are not resistant to outliers include the mean, the standard deviation, skewness, and kurtosis.</li>\n",
+    "    <li>Statistics that are resistant to outliers include the median and the interquartile range.</li>\n",
+    "    <li>By making use of dictionaries, we can create versatile, non-hard-coded programs!</li>\n",
+    "    <li>glob is a package that enables us to grab all files within a directory</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>10.1 Structure of a NetCDF File</h2>\n",
+    "\n",
+    "NetCDF is a commonly used file format in our field because it enables the storage of data as well as the storage of its <b>metadata</b>. Every major language in our field is equipped to deal with NetCDF files, and Python is no exception!\n",
+    "\n",
+    "There are <b>four</b> parameter types in a netCDF file:\n",
+    "<ul>\n",
+    "    <li><b>Global attributes:</b> strings that describe the file as a whole: for example, a title, who created it, what standards it follows.</li>\n",
+    "    <li><b>Variables:</b> entities that hold data, which includes the data, the domain the data is defined on (dimensionality), and metadata about the data (for example, units).</li>\n",
+    "    <li><b>Variable attributes:</b> actual storage of the data's metadata.</li>\n",
+    "    <li><b>Dimensions:</b> not only define the domain, but also might have values of their own (for example, latitude values, longitude values, altitude values, etc.).</li>\n",
+    "</ul>\n",
+    "\n",
+    "As an example, you might have a timeseries of surface temperature for a latitude-longitude grid. The dimensions for that dataset would be <b>lat</b>, <b>lon</b>, and <b>time</b>. The variable <b>lat</b> would just tell you the number of elements in the <b>lat</b> dimension</b>; likewise for <b>lon</b> and <b>time</b>. Finally, you might have a variable containing temperatures called <b>Ts</b> that would be 3-D, with dimensions of <b>lat</b>, <b>lon</b>, and <b>time</b>.\n",
+    "\n",
+    "There are several packages that can read NetCDF files; we're going to learn SciPy today - it's not necessarily the best, but it is one of the easiest to learn, and SciPy is a useful package for many other reasons."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>10.2 Reading a NetCDF File</h2>\n",
+    "\n",
+    "For NetCDF files, we're interested in the I/O functionality of SciPy, so we'll call that part of the package and assign it to an alias."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the I/O functionality of SciPy.\n",
+    "import numpy as np\n",
+    "import scipy.io as S\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a file object, it's actually a pretty similar approach to what we did in our I/O lecture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<scipy.io._netcdf.netcdf_file object at 0x7f1a0448e4d0>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Read in the provided NetCDF file in read-only mode.\n",
+    "fileobj = S.netcdf_file(\"../datasets/air.mon.mean.nc\", mode=\"r\")\n",
+    "print(fileobj)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NetCDF file objects have several <b>attributes</b> that we can call on (more on this when we explore object-oriented programming). One of those attributes is called <b>variables</b>, which is a dictionary. The keys of the dictionary are strings corresponding to the names of the variables, and the values are a special kind of object called <b>variable objects</b> that contain the variable's values as well as any metadata (units, etc.).\n",
+    "\n",
+    "Another NetCDF file object attribute is <b>dimensions</b>, which is another dictionary. The keys of this dictionary are strings that are the names of the dimensions, and the values are the lengths of the dimensions.\n",
+    "\n",
+    "Let's see an example from our .nc file: a grid of monthly-mean temperature values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "b'Monthly mean air temperature NCEP Reanalysis'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's import NumPy and SciPy's I/O functionality.\n",
+    "fileobj = S.netcdf_file(\"../datasets/air.mon.mean.nc\", mode=\"r\")\n",
+    "\n",
+    "\n",
+    "# Now, create a file object!\n",
+    "\n",
+    "\n",
+    "\n",
+    "# First, let's find out what information's in the title.\n",
+    "print(fileobj.title)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'lon': 144, 'lat': 73, 'time': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's explore the dimensions dictionary.\n",
+    "print(fileobj.dimensions)\n",
+    "\n",
+    "# Time is set to 'None' because that's this file's\n",
+    "# 'unlimited' dimension; you can keep adding new times to it\n",
+    "# and it will use the same lat/lon grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'lat': <scipy.io._netcdf.netcdf_variable object at 0x7f19fffc28d0>, 'lon': <scipy.io._netcdf.netcdf_variable object at 0x7f19fffc3450>, 'time': <scipy.io._netcdf.netcdf_variable object at 0x7f19fffc1f50>, 'air': <scipy.io._netcdf.netcdf_variable object at 0x7f19fffc1bd0>}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And let's see what kinds of variables are inside.\n",
+    "print(fileobj.variables)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<scipy.io._netcdf.netcdf_variable object at 0x7f19fffc1bd0>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Okay, that's all very messy!\n",
+    "# Now it's time to grab the values of air temperature.\n",
+    "\n",
+    "temp = fileobj.variables[\"air\"]\n",
+    "print(temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "b'degC'\n",
+      "(755, 73, 144)\n",
+      "[[[-34.926773  -34.926773  -34.926773  ... -34.926773  -34.926773\n",
+      "   -34.926773 ]\n",
+      "  [-35.13935   -35.129673  -35.12742   ... -35.188705  -35.170002\n",
+      "   -35.14935  ]\n",
+      "  [-34.352573  -34.04226   -33.768707  ... -35.333866  -35.002903\n",
+      "   -34.671288 ]\n",
+      "  ...\n",
+      "  [-16.525156  -16.404509  -16.284832  ... -16.795155  -16.737736\n",
+      "   -16.643543 ]\n",
+      "  [-16.190313  -16.202248  -16.21677   ... -16.132574  -16.161928\n",
+      "   -16.178377 ]\n",
+      "  [-17.697733  -17.697733  -17.697733  ... -17.697733  -17.697733\n",
+      "   -17.697733 ]]\n",
+      "\n",
+      " [[-33.311375  -33.311375  -33.311375  ... -33.311375  -33.311375\n",
+      "   -33.311375 ]\n",
+      "  [-34.65034   -34.476204  -34.29689   ... -35.18448   -35.009308\n",
+      "   -34.835514 ]\n",
+      "  [-34.1031    -33.619995  -33.161373  ... -35.606552  -35.103443\n",
+      "   -34.602757 ]\n",
+      "  ...\n",
+      "  [-34.338963  -34.21862   -34.08241   ... -34.359997  -34.42724\n",
+      "   -34.418617 ]\n",
+      "  [-33.795513  -33.896553  -33.977238  ... -33.41517   -33.56758\n",
+      "   -33.690342 ]\n",
+      "  [-32.942413  -32.942413  -32.942413  ... -32.942413  -32.942413\n",
+      "   -32.942413 ]]\n",
+      "\n",
+      " [[-29.716127  -29.716127  -29.716127  ... -29.716127  -29.716127\n",
+      "   -29.716127 ]\n",
+      "  [-29.4471    -29.499353  -29.551613  ... -29.365162  -29.385166\n",
+      "   -29.41258  ]\n",
+      "  [-28.544516  -28.366776  -28.227749  ... -29.282906  -29.01323\n",
+      "   -28.763546 ]\n",
+      "  ...\n",
+      "  [-51.964516  -52.206455  -52.362263  ... -50.628704  -51.18032\n",
+      "   -51.631298 ]\n",
+      "  [-52.846123  -53.07613   -53.290974  ... -52.069355  -52.344517\n",
+      "   -52.60097  ]\n",
+      "  [-54.835476  -54.835476  -54.835476  ... -54.835476  -54.835476\n",
+      "   -54.835476 ]]\n",
+      "\n",
+      " ...\n",
+      "\n",
+      " [[ -6.84033    -6.84033    -6.84033   ...  -6.84033    -6.84033\n",
+      "    -6.84033  ]\n",
+      "  [ -8.222664   -8.076328   -7.9316626 ...  -8.658329   -8.517663\n",
+      "    -8.366997 ]\n",
+      "  [ -8.124661   -7.610333   -7.1173277 ...  -9.681327   -9.180997\n",
+      "    -8.653992 ]\n",
+      "  ...\n",
+      "  [-54.956     -55.16667   -55.29166   ... -53.67867   -54.22433\n",
+      "   -54.649338 ]\n",
+      "  [-55.466324  -55.73534   -55.975994  ... -54.453335  -54.82867\n",
+      "   -55.16467  ]\n",
+      "  [-53.225002  -53.225002  -53.225002  ... -53.225002  -53.225002\n",
+      "   -53.225002 ]]\n",
+      "\n",
+      " [[-16.640314  -16.640314  -16.640314  ... -16.640314  -16.640314\n",
+      "   -16.640314 ]\n",
+      "  [-20.58      -20.478704  -20.383224  ... -20.846767  -20.765804\n",
+      "   -20.679348 ]\n",
+      "  [-21.077736  -20.571283  -20.056448  ... -22.434835  -22.024187\n",
+      "   -21.567738 ]\n",
+      "  ...\n",
+      "  [-44.52612   -44.629353  -44.69258   ... -43.85613   -44.15871\n",
+      "   -44.375164 ]\n",
+      "  [-44.219357  -44.39742   -44.56355   ... -43.50774   -43.770008\n",
+      "   -44.004192 ]\n",
+      "  [-41.401936  -41.401936  -41.401936  ... -41.401936  -41.401936\n",
+      "   -41.401936 ]]\n",
+      "\n",
+      " [[-26.217333  -26.217333  -26.217333  ... -26.217333  -26.217333\n",
+      "   -26.217333 ]\n",
+      "  [-30.25533   -30.281328  -30.300997  ... -30.091661  -30.157658\n",
+      "   -30.210995 ]\n",
+      "  [-31.590332  -31.514662  -31.380667  ... -31.483662  -31.579662\n",
+      "   -31.618664 ]\n",
+      "  ...\n",
+      "  [-32.93566   -32.809     -32.665333  ... -33.02433   -33.059994\n",
+      "   -33.024666 ]\n",
+      "  [-34.10533   -34.120663  -34.118332  ... -33.930668  -34.01266\n",
+      "   -34.066994 ]\n",
+      "  [-33.181664  -33.181664  -33.181664  ... -33.181664  -33.181664\n",
+      "   -33.181664 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can now examine this data more carefully.\n",
+    "print(temp.units)\n",
+    "print(temp.shape)\n",
+    "print(temp[:])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-34.926773\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's be a little more restrained... how about all the data at the first lat/lon pair?\n",
+    "print(temp[0,0,0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "90.0\n",
+      "0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# That seems pretty chilly! What are the lat/lon values?\n",
+    "lat = fileobj.variables[\"lat\"]\n",
+    "lon = fileobj.variables[\"lon\"]\n",
+    "\n",
+    "print(lat[0])\n",
+    "print(lon[0])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Okay, that's reasonable for the North Pole.\n",
+    "# This grid is 2.5 degrees... let's find out what Seattle's weather was like!\n",
+    "\n",
+    "# Seattle is at approximately 47.5 N and 122.5 W.\n",
+    "\n",
+    "# This dataset's lon starts at 0 and counts up to 360.\n",
+    "# So 122.25 W corresponds to 360-122.5 = 237.5.\n",
+    "\n",
+    "# Remember, lat and lon are those weird data types:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 90.   87.5  85.   82.5  80.   77.5  75.   72.5  70.   67.5  65.   62.5\n",
+      "  60.   57.5  55.   52.5  50.   47.5  45.   42.5  40.   37.5  35.   32.5\n",
+      "  30.   27.5  25.   22.5  20.   17.5  15.   12.5  10.    7.5   5.    2.5\n",
+      "   0.   -2.5  -5.   -7.5 -10.  -12.5 -15.  -17.5 -20.  -22.5 -25.  -27.5\n",
+      " -30.  -32.5 -35.  -37.5 -40.  -42.5 -45.  -47.5 -50.  -52.5 -55.  -57.5\n",
+      " -60.  -62.5 -65.  -67.5 -70.  -72.5 -75.  -77.5 -80.  -82.5 -85.  -87.5\n",
+      " -90. ]\n",
+      "[  0.    2.5   5.    7.5  10.   12.5  15.   17.5  20.   22.5  25.   27.5\n",
+      "  30.   32.5  35.   37.5  40.   42.5  45.   47.5  50.   52.5  55.   57.5\n",
+      "  60.   62.5  65.   67.5  70.   72.5  75.   77.5  80.   82.5  85.   87.5\n",
+      "  90.   92.5  95.   97.5 100.  102.5 105.  107.5 110.  112.5 115.  117.5\n",
+      " 120.  122.5 125.  127.5 130.  132.5 135.  137.5 140.  142.5 145.  147.5\n",
+      " 150.  152.5 155.  157.5 160.  162.5 165.  167.5 170.  172.5 175.  177.5\n",
+      " 180.  182.5 185.  187.5 190.  192.5 195.  197.5 200.  202.5 205.  207.5\n",
+      " 210.  212.5 215.  217.5 220.  222.5 225.  227.5 230.  232.5 235.  237.5\n",
+      " 240.  242.5 245.  247.5 250.  252.5 255.  257.5 260.  262.5 265.  267.5\n",
+      " 270.  272.5 275.  277.5 280.  282.5 285.  287.5 290.  292.5 295.  297.5\n",
+      " 300.  302.5 305.  307.5 310.  312.5 315.  317.5 320.  322.5 325.  327.5\n",
+      " 330.  332.5 335.  337.5 340.  342.5 345.  347.5 350.  352.5 355.  357.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# So let's save their values instead.\n",
+    "a = lat[:]\n",
+    "b = lon[:]\n",
+    "print(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array([17]),)\n",
+      "(array([95]),)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now we can use np.where() to find the locations of the values in the dataset.\n",
+    "sealat = np.where(a == 47.5)\n",
+    "sealon = np.where(b == 237.5)\n",
+    "\n",
+    "print(sealat)\n",
+    "print(sealon)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.5006493]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's print it out!\n",
+    "print(temp[0,sealat, sealon])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "b'Monthly Mean Air Temperature'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# If you want the full name of a particular variable, long_name is useful!\n",
+    "print(fileobj.variables[\"air\"].long_name)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>10.3 Writing a NetCDF File</h2>\n",
+    "\n",
+    "Just as with normal files, we can write our own NetCDF files!\n",
+    "\n",
+    "You can create a NetCDF file object in write mode!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.          0.09983342  0.19866933  0.29552021  0.38941834  0.47942554\n",
+      "   0.56464247  0.64421769  0.71735609  0.78332691  0.84147098  0.89120736\n",
+      "   0.93203909  0.96355819  0.98544973  0.99749499  0.9995736   0.99166481\n",
+      "   0.97384763  0.94630009]\n",
+      " [ 0.90929743  0.86320937  0.8084964   0.74570521  0.67546318  0.59847214\n",
+      "   0.51550137  0.42737988  0.33498815  0.23924933  0.14112001  0.04158066\n",
+      "  -0.05837414 -0.15774569 -0.2555411  -0.35078323 -0.44252044 -0.52983614\n",
+      "  -0.61185789 -0.68776616]\n",
+      " [-0.7568025  -0.81827711 -0.87157577 -0.91616594 -0.95160207 -0.97753012\n",
+      "  -0.993691   -0.99992326 -0.99616461 -0.98245261 -0.95892427 -0.92581468\n",
+      "  -0.88345466 -0.83226744 -0.77276449 -0.70554033 -0.63126664 -0.55068554\n",
+      "  -0.46460218 -0.37387666]\n",
+      " [-0.2794155  -0.1821625  -0.0830894   0.0168139   0.1165492   0.21511999\n",
+      "   0.31154136  0.40484992  0.49411335  0.57843976  0.6569866   0.72896904\n",
+      "   0.79366786  0.85043662  0.8987081   0.93799998  0.96791967  0.98816823\n",
+      "   0.99854335  0.99894134]\n",
+      " [ 0.98935825  0.96988981  0.94073056  0.90217183  0.85459891  0.79848711\n",
+      "   0.7343971   0.66296923  0.58491719  0.50102086  0.41211849  0.31909836\n",
+      "   0.22288991  0.12445442  0.02477543 -0.07515112 -0.17432678 -0.27176063\n",
+      "  -0.36647913 -0.45753589]\n",
+      " [-0.54402111 -0.62507065 -0.69987469 -0.76768581 -0.82782647 -0.87969576\n",
+      "  -0.92277542 -0.95663502 -0.98093623 -0.99543625 -0.99999021 -0.99455259\n",
+      "  -0.97917773 -0.95401925 -0.91932853 -0.87545217 -0.82282859 -0.76198358\n",
+      "  -0.69352508 -0.61813711]\n",
+      " [-0.53657292 -0.44964746 -0.35822928 -0.26323179 -0.16560418 -0.0663219\n",
+      "   0.03362305  0.13323204  0.23150983  0.32747444  0.42016704  0.50866146\n",
+      "   0.59207351  0.66956976  0.74037589  0.80378443  0.85916181  0.90595474\n",
+      "   0.94369567  0.9720075 ]\n",
+      " [ 0.99060736  0.99930939  0.99802665  0.98677196  0.96565778  0.93489506\n",
+      "   0.89479117  0.84574683  0.78825207  0.72288135  0.65028784  0.57119687\n",
+      "   0.48639869  0.39674057  0.30311836  0.20646748  0.10775365  0.00796318\n",
+      "  -0.09190685 -0.19085858]\n",
+      " [-0.28790332 -0.38207142 -0.47242199 -0.55805227 -0.63810668 -0.71178534\n",
+      "  -0.77835208 -0.83714178 -0.88756703 -0.92912401 -0.96139749 -0.98406501\n",
+      "  -0.99690007 -0.99977443 -0.99265938 -0.97562601 -0.9488445  -0.91258245\n",
+      "  -0.86720218 -0.81315711]\n",
+      " [-0.75098725 -0.68131377 -0.60483282 -0.52230859 -0.43456562 -0.34248062\n",
+      "  -0.24697366 -0.14899903 -0.04953564  0.05042269  0.14987721  0.24783421\n",
+      "   0.34331493  0.43536536  0.52306577  0.60553987  0.68196362  0.75157342\n",
+      "   0.81367374  0.8676441 ]]\n",
+      "42.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "newfile = S.netcdf_file(\"new.nc\",mode=\"w\")\n",
+    "\n",
+    "# Let's start by putting in 10 latitude and 20 longitude values.\n",
+    "lat = np.arange(10)\n",
+    "lon = np.arange(10)\n",
+    "\n",
+    "# And maybe two different sets of data, one array and one scalar.\n",
+    "data1 = np.reshape(np.sin(np.arange(200)*0.1),(10,20))\n",
+    "data2 = 42.0\n",
+    "\n",
+    "print(data1)\n",
+    "print(data2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# So far so good! Let's create the actual dimension information.\n",
+    "newfile.createDimension(\"lat\",len(lat))\n",
+    "newfile.createDimension(\"lon\",len(lon))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now the names of our variables!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And now we assign the actual values to our variables!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And assign some units!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a title to finish up!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, so having done all that (don't worry if all the details are unclear - this is a fairly advanced topic that will take some practice!), let's try reading our values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>10.4 NetCDF Example</h2>\n",
+    "\n",
+    "Let's pull up some monthly mean surface air temperature data from our air.mon.mean.nc data file. These data come from the NCEP/NCAR Reanalysis 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Let's take a look at the time units.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Well, that seems confusing.\n",
+    "# Let's create a new version of the file where time just starts at 0.0, and change the units string so it just says 'hours'.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# And let's test it!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>10.5 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>NetCDF is a powerful file type containing global attributes, variables, variable attributes, and dimensions.</li>\n",
+    "    <li>We can read from NetCDF files using similar syntax to that for regular files.</li>\n",
+    "    <li>Using attributes such as 'dimensions' and 'variables', we can learn about individual variables in the dataset.</li>\n",
+    "    <li>We can also write to NetCDF files in a simlar way.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 06 M.ipynb b/python/atms-310/notebooks/Week 06 M.ipynb
new file mode 100755
index 0000000..193e7a5
--- /dev/null
+++ b/python/atms-310/notebooks/Week 06 M.ipynb	
@@ -0,0 +1,339 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>13. More Adventures in OOP</h1>\n",
+    "<h2>10/30/2023</h2>\n",
+    "\n",
+    "<h2>13.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>A class can be created using a <b>class</b> statement followed by the name of the class.</li>\n",
+    "    <li>Methods within a class definition are created using a <b>def</b> statement.</li>\n",
+    "    <li><b>__init__</b> is typically the first method defined and is used to initialize the core features of the class.</li>\n",
+    "</ul>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>13.1 OOP Example: Creating a Bibliography</h2>\n",
+    "\n",
+    "Let's create a new class called <b>Article</b> that's similar to our book class from last time, but stores a scientific journal article instead of a book, and writes the bibiliography entry accordingly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start by defining a new class. (Remember, you need the 'object' argument.)\n",
+    "class Article(object):\n",
+    "        def __init__(self, authlast, authfirst, arttitle, journtitle, volume, pages, year):\n",
+    "                self.authlast = authlast\n",
+    "                self.authfirst = authfirst\n",
+    "                self.arttitle = arttitle\n",
+    "                self.journtitle = journtitle\n",
+    "                self.volume = volume\n",
+    "                self.pages = pages\n",
+    "                self.year = year\n",
+    "        # Let's create the make_authoryear and write_bib_entry methods from before.\n",
+    "        def write_bib_entry(self):\n",
+    "            \n",
+    "    # We won't need any additional arguments here, since it's all handled above.\n",
+    "                return self.authlast + ',' + self.authfirst + ',' + self.arttitle + ',' + self.journtitle + ',' + self.volume + ',' + self.pages + ',' + self.year\n",
+    "        def authyear(self):\n",
+    "                self.authyear = self.authlast + '('+self.year +')'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<__main__.Article object at 0x7f0511dc9dd0>\n",
+      "brooks,harold,on the tornado thingies,journal,19,310-319,2004\n"
+     ]
+    }
+   ],
+   "source": [
+    "# And a test!\n",
+    "tornado = Article(\"brooks\", 'harold', \"on the tornado thingies\", \"journal\", \"19\", \"310-319\", \"2004\")\n",
+    "print(tornado)\n",
+    "print(tornado.write_bib_entry())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's also bring <b>Book</b> and our two instances of Book back from last lecture:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Book(object):\n",
+    "    \n",
+    "    def __init__(self, authlast, authfirst, \\\n",
+    "                title, place, publisher, year):\n",
+    "        self.authlast = authlast\n",
+    "        self.authfirst = authfirst\n",
+    "        self.title = title\n",
+    "        self.place = place\n",
+    "        self.publisher = publisher\n",
+    "        self.year = year\n",
+    "    \n",
+    "    def write_bib_entry(self):        \n",
+    "        return self.authlast \\\n",
+    "            + ', ' + self.authfirst \\\n",
+    "            + ', ' + self.title \\\n",
+    "            + ', ' + self.place \\\n",
+    "            + ': ' + self.publisher + ', '\\\n",
+    "            + self.year + '.'\n",
+    "    def writebibalph(self):\n",
+    "        self.sortentriesalph()\n",
+    "        output=''\n",
+    "        for i in self.entries:\n",
+    "            output = output+i.writebibalph()+\"\\n\\n\"\n",
+    "        return output\n",
+    "    def sortentriesalph(self):\n",
+    "        self.entries = sorted(self.entries,key=op.attrgetter('authlast','authfirst'))\n",
+    "beauty = Book(\"Dubay\",\"Thomas\" \\\n",
+    "             , \"The Evidential Power of Beauty\" \\\n",
+    "             , \"San Francisco\" \\\n",
+    "             , \"Ignatius Press\", \"1999\")\n",
+    "\n",
+    "pynut = Book(\"Martelli\", \"Alex\" \\\n",
+    "            , \"Python in a Nutshell\" \\\n",
+    "            , \"Sebastopol, CA\" \\\n",
+    "            , \"O'Reilly Media, Inc.\", \"2003\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say we have a series of instances of the Book and Article classes that we want to pull together into one big bibliography. We'll create a new class called <b>Bibliography</b> for this task, and within Bibliography's definition will be two modules: one that initializes the class with everything we need, and one that sorts all entries alphabetically.\n",
+    "\n",
+    "To do this, we'll need some additional tools. One useful package to import here is called <b>operator</b>, which contains a useful function called <b>attrgetter</b>, which will pull a list of attributes out of an item in question. There are other ways of doing the same thing, but operator.attrgetter() will save us a lot of time! \n",
+    "\n",
+    "We'll also want to make use of <b>sorted()</b>, which is a function that sorts all entries (either alphabetically or numerically) according to a key we specify, which in this case will be the last name and the first name of the author (just in case we have multiple authors with the same last name)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We'll need the operator package.\n",
+    "import operator as op\n",
+    "\n",
+    "\n",
+    "# Define our Bibliography class.\n",
+    "class bib(object):\n",
+    "    # Initialize the class.\n",
+    "    def __init__(self,entries):\n",
+    "        self.entries = entries\n",
+    "        \n",
+    "    # Sort the entries alphabetically.\n",
+    "    def sortentriesalph(self):\n",
+    "        self.entries = sorted(self.entries,key=op.attrgetter('authlast','authfirst'))\n",
+    "    # Now, write a bibliography in alphabetical order.\n",
+    "    def writebibalph(self):\n",
+    "        self.sortentriesalph()\n",
+    "        output=''\n",
+    "        for i in self.entries:\n",
+    "            output = output+i.writebibalph()+\"\\n\\n\"\n",
+    "        return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Book' object has no attribute 'entries'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m/home/nik/data/python/notebooks/Week 06 M.ipynb Cell 9\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m a \u001b[39m=\u001b[39m bib([beauty,pynut,tornado])\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m b \u001b[39m=\u001b[39m a\u001b[39m.\u001b[39;49mwritebibalph()\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39mprint\u001b[39m(b)\n",
+      "\u001b[1;32m/home/nik/data/python/notebooks/Week 06 M.ipynb Cell 9\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m output\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mentries:\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m     output \u001b[39m=\u001b[39m output\u001b[39m+\u001b[39mi\u001b[39m.\u001b[39;49mwritebibalph()\u001b[39m+\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m \u001b[39mreturn\u001b[39;00m output\n",
+      "\u001b[1;32m/home/nik/data/python/notebooks/Week 06 M.ipynb Cell 9\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mwritebibalph\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msortentriesalph()\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m     output\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m     \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mentries:\n",
+      "\u001b[1;32m/home/nik/data/python/notebooks/Week 06 M.ipynb Cell 9\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msortentriesalph\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m---> <a href='vscode-notebook-cell:/home/nik/data/python/notebooks/Week%2006%20M.ipynb#X11sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mentries \u001b[39m=\u001b[39m \u001b[39msorted\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mentries,key\u001b[39m=\u001b[39mop\u001b[39m.\u001b[39mattrgetter(\u001b[39m'\u001b[39m\u001b[39mauthlast\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mauthfirst\u001b[39m\u001b[39m'\u001b[39m))\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Book' object has no attribute 'entries'"
+     ]
+    }
+   ],
+   "source": [
+    "a = bib([beauty,pynut,tornado])\n",
+    "b = a.writebibalph()\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why did we bother doing this? Because it really highlights the power of OOP over traditional, procedural programming. In a lot of languages, we'd have to write a function to format every source entry correctly, depending on the source type (e.g., article or book), which would result in a tree of <b>if</b> tests.\n",
+    "\n",
+    "Another big advantage? Adding another source type would require <b>no changes or additions to existing code</b>, just a new class definition."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>13.2 OOP Example: Creating a Class for Geoscience Work</h2>\n",
+    "\n",
+    "Let's work through another application: as an example, we'll define a class called <b>SurfaceDomain</b> that describes surface domain instances. A domain would be a land/ocean surface where the spatial extent is described by a latitude-longitude grid. We'll instantiate the class by providing a vector of longitudes and latitudes; our surface domain will be a regular grid based on those vectors. We can then assign surface parameters (e.g., elevation, temperature, roughness, etc.) as instance attributes.\n",
+    "\n",
+    "It may be helpful here to think of how best to represent a latitude-longitude grid in code. Let's look at the following example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's say we have 5 longitude values and 4 latitude values.\n",
+    "# We want something that's going to look like the following:\n",
+    "\n",
+    "# Longitude should look like this:\n",
+    "\n",
+    "[[0 1 2 3 4]\n",
+    " [0 1 2 3 4]\n",
+    " [0 1 2 3 4]\n",
+    " [0 1 2 3 4]]\n",
+    "\n",
+    "# Latitude should look like this:\n",
+    "\n",
+    "[[0 0 0 0 0]\n",
+    " [1 1 1 1 1]\n",
+    " [2 2 2 2 2]\n",
+    " [3 3 3 3 3]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's a function in NumPy built in for this called <b>meshgrid</b>: given a longitude array and a latitude array, it will create a nice grid combining the two.\n",
+    "\n",
+    "Let's start our class definition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "class SurfaceDomain(object):\n",
+    "        def __init__(self, lon, lat):\n",
+    "        \n",
+    "        # Let's make sure that latitude and longitude \n",
+    "                self.lon = np.array(lon)\n",
+    "                self.lat = np.array(lat)\n",
+    "                [xall,yall] = np.meshgrid(self.lon,self.lat)\n",
+    "                self.lonall = xall\n",
+    "                self.latall = yall\n",
+    "                del xall,yall\n",
+    "        # are in array format.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then begin to manipulate elements of this domain individually or collectively (e.g., interpolation, etc.)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3 4 5]\n",
+      " [1 2 3 4 5]\n",
+      " [1 2 3 4 5]\n",
+      " [1 2 3 4 5]]\n",
+      "[[0 0 0 0 0]\n",
+      " [1 1 1 1 1]\n",
+      " [2 2 2 2 2]\n",
+      " [3 3 3 3 3]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lon = [1,2,3,4,5]\n",
+    "lat = [0,1,2,3]\n",
+    "\n",
+    "a = SurfaceDomain(lon,lat)\n",
+    "print(a.lonall)\n",
+    "print(a.latall)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>13.3 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>The <b>operator</b> package enables us to use a function called <b>attrgetter()</b> to grab attribute information from various classes.</li>\n",
+    "    <li>The <b>sorted()</b> function lets us sort data alphabetically or numerically as needed.</li>\n",
+    "    <li>NumPy's <b>meshgrid()</b> module lets us create a grid from lat/lon vectors.</li> \n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 06 W.ipynb b/python/atms-310/notebooks/Week 06 W.ipynb
new file mode 100755
index 0000000..1234183
--- /dev/null
+++ b/python/atms-310/notebooks/Week 06 W.ipynb	
@@ -0,0 +1,1666 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>14. Introduction to Pandas</h1>\n",
+    "<h2>11/01/2023</h2>\n",
+    "\n",
+    "<h2>14.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>The <b>operator</b> package enables us to use a function called <b>attrgetter()</b> to grab attribute information from various classes.</li>\n",
+    "    <li>The <b>sorted()</b> function lets us sort data alphabetically or numerically as needed.</li>\n",
+    "    <li>NumPy's <b>meshgrid()</b> module lets us create a grid from lat/lon vectors.</li> \n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>14.1 Why Pandas?</h2>\n",
+    "\n",
+    "Python's standard library has some tools for working with CSV files, but Pandas makes life a whole lot easier! It handles missing data well and also allows for quick calculations and plotting (which we'll be talking about in a couple weeks). A Pandas <b>dataframe</b> is a lot like an Excel spreadsheet, but it's a lot faster and more flexible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's the new library we'll want for today:\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's begin by using a tool we're familiar with that should work pretty well for this kind of data: dictionaries!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[23, 45, 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {\"name\":[\"john\",\"bob\",\"dan\"], \"age\":[23,45,12],\"salary\":[23333,43555,90000],\"title\":[\"CFO\",\"VP\",\"CTO\"]}\n",
+    "print(data[\"age\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far so good, but if we want to get the designation for the employee Sam, that can get a little complex. Pandas will make this endeavor easier and more intuitive.\n",
+    "\n",
+    "Pandas features two data structures:\n",
+    "<ul>\n",
+    "    <li><b>Series:</b> 1-D labeled arrays that resemble dictionaries</li>\n",
+    "    <li><b>DataFrame:</b> (most common) 2-D like a spreadsheet</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   name  age  salary title\n",
+      "0  john   23   23333   CFO\n",
+      "1   bob   45   43555    VP\n",
+      "2   dan   12   90000   CTO\n"
+     ]
+    }
+   ],
+   "source": [
+    "employees = pd.DataFrame(data)\n",
+    "print(employees)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember from our object-oriented programming lectures: a DataFrame is just an object! We can list its attributes and methods using dir()."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['T',\n",
+       " '_AXIS_LEN',\n",
+       " '_AXIS_ORDERS',\n",
+       " '_AXIS_TO_AXIS_NUMBER',\n",
+       " '_HANDLED_TYPES',\n",
+       " '__abs__',\n",
+       " '__add__',\n",
+       " '__and__',\n",
+       " '__annotations__',\n",
+       " '__array__',\n",
+       " '__array_priority__',\n",
+       " '__array_ufunc__',\n",
+       " '__bool__',\n",
+       " '__class__',\n",
+       " '__contains__',\n",
+       " '__copy__',\n",
+       " '__dataframe__',\n",
+       " '__dataframe_consortium_standard__',\n",
+       " '__deepcopy__',\n",
+       " '__delattr__',\n",
+       " '__delitem__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__divmod__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__finalize__',\n",
+       " '__floordiv__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattr__',\n",
+       " '__getattribute__',\n",
+       " '__getitem__',\n",
+       " '__getstate__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__iadd__',\n",
+       " '__iand__',\n",
+       " '__ifloordiv__',\n",
+       " '__imod__',\n",
+       " '__imul__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__invert__',\n",
+       " '__ior__',\n",
+       " '__ipow__',\n",
+       " '__isub__',\n",
+       " '__iter__',\n",
+       " '__itruediv__',\n",
+       " '__ixor__',\n",
+       " '__le__',\n",
+       " '__len__',\n",
+       " '__lt__',\n",
+       " '__matmul__',\n",
+       " '__mod__',\n",
+       " '__module__',\n",
+       " '__mul__',\n",
+       " '__ne__',\n",
+       " '__neg__',\n",
+       " '__new__',\n",
+       " '__nonzero__',\n",
+       " '__or__',\n",
+       " '__pandas_priority__',\n",
+       " '__pos__',\n",
+       " '__pow__',\n",
+       " '__radd__',\n",
+       " '__rand__',\n",
+       " '__rdivmod__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__rfloordiv__',\n",
+       " '__rmatmul__',\n",
+       " '__rmod__',\n",
+       " '__rmul__',\n",
+       " '__ror__',\n",
+       " '__round__',\n",
+       " '__rpow__',\n",
+       " '__rsub__',\n",
+       " '__rtruediv__',\n",
+       " '__rxor__',\n",
+       " '__setattr__',\n",
+       " '__setitem__',\n",
+       " '__setstate__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__sub__',\n",
+       " '__subclasshook__',\n",
+       " '__truediv__',\n",
+       " '__weakref__',\n",
+       " '__xor__',\n",
+       " '_accessors',\n",
+       " '_accum_func',\n",
+       " '_agg_examples_doc',\n",
+       " '_agg_see_also_doc',\n",
+       " '_align_for_op',\n",
+       " '_align_frame',\n",
+       " '_align_series',\n",
+       " '_append',\n",
+       " '_arith_method',\n",
+       " '_arith_method_with_reindex',\n",
+       " '_as_manager',\n",
+       " '_attrs',\n",
+       " '_box_col_values',\n",
+       " '_can_fast_transpose',\n",
+       " '_check_inplace_and_allows_duplicate_labels',\n",
+       " '_check_is_chained_assignment_possible',\n",
+       " '_check_label_or_level_ambiguity',\n",
+       " '_check_setitem_copy',\n",
+       " '_clear_item_cache',\n",
+       " '_clip_with_one_bound',\n",
+       " '_clip_with_scalar',\n",
+       " '_cmp_method',\n",
+       " '_combine_frame',\n",
+       " '_consolidate',\n",
+       " '_consolidate_inplace',\n",
+       " '_construct_axes_dict',\n",
+       " '_construct_result',\n",
+       " '_constructor',\n",
+       " '_constructor_from_mgr',\n",
+       " '_constructor_sliced',\n",
+       " '_constructor_sliced_from_mgr',\n",
+       " '_create_data_for_split_and_tight_to_dict',\n",
+       " '_data',\n",
+       " '_deprecate_downcast',\n",
+       " '_dir_additions',\n",
+       " '_dir_deletions',\n",
+       " '_dispatch_frame_op',\n",
+       " '_drop_axis',\n",
+       " '_drop_labels_or_levels',\n",
+       " '_ensure_valid_index',\n",
+       " '_find_valid_index',\n",
+       " '_flags',\n",
+       " '_flex_arith_method',\n",
+       " '_flex_cmp_method',\n",
+       " '_from_arrays',\n",
+       " '_from_mgr',\n",
+       " '_get_agg_axis',\n",
+       " '_get_axis',\n",
+       " '_get_axis_name',\n",
+       " '_get_axis_number',\n",
+       " '_get_axis_resolvers',\n",
+       " '_get_block_manager_axis',\n",
+       " '_get_bool_data',\n",
+       " '_get_cleaned_column_resolvers',\n",
+       " '_get_column_array',\n",
+       " '_get_index_resolvers',\n",
+       " '_get_item_cache',\n",
+       " '_get_label_or_level_values',\n",
+       " '_get_numeric_data',\n",
+       " '_get_value',\n",
+       " '_getitem_bool_array',\n",
+       " '_getitem_multilevel',\n",
+       " '_getitem_nocopy',\n",
+       " '_getitem_slice',\n",
+       " '_gotitem',\n",
+       " '_hidden_attrs',\n",
+       " '_indexed_same',\n",
+       " '_info_axis',\n",
+       " '_info_axis_name',\n",
+       " '_info_axis_number',\n",
+       " '_info_repr',\n",
+       " '_init_mgr',\n",
+       " '_inplace_method',\n",
+       " '_internal_names',\n",
+       " '_internal_names_set',\n",
+       " '_is_copy',\n",
+       " '_is_homogeneous_type',\n",
+       " '_is_label_or_level_reference',\n",
+       " '_is_label_reference',\n",
+       " '_is_level_reference',\n",
+       " '_is_mixed_type',\n",
+       " '_is_view',\n",
+       " '_iset_item',\n",
+       " '_iset_item_mgr',\n",
+       " '_iset_not_inplace',\n",
+       " '_item_cache',\n",
+       " '_iter_column_arrays',\n",
+       " '_ixs',\n",
+       " '_logical_func',\n",
+       " '_logical_method',\n",
+       " '_maybe_align_series_as_frame',\n",
+       " '_maybe_cache_changed',\n",
+       " '_maybe_update_cacher',\n",
+       " '_metadata',\n",
+       " '_mgr',\n",
+       " '_min_count_stat_function',\n",
+       " '_needs_reindex_multi',\n",
+       " '_pad_or_backfill',\n",
+       " '_protect_consolidate',\n",
+       " '_reduce',\n",
+       " '_reduce_axis1',\n",
+       " '_reindex_axes',\n",
+       " '_reindex_multi',\n",
+       " '_reindex_with_indexers',\n",
+       " '_rename',\n",
+       " '_replace_columnwise',\n",
+       " '_repr_data_resource_',\n",
+       " '_repr_fits_horizontal_',\n",
+       " '_repr_fits_vertical_',\n",
+       " '_repr_html_',\n",
+       " '_repr_latex_',\n",
+       " '_reset_cache',\n",
+       " '_reset_cacher',\n",
+       " '_sanitize_column',\n",
+       " '_series',\n",
+       " '_set_axis',\n",
+       " '_set_axis_name',\n",
+       " '_set_axis_nocheck',\n",
+       " '_set_is_copy',\n",
+       " '_set_item',\n",
+       " '_set_item_frame_value',\n",
+       " '_set_item_mgr',\n",
+       " '_set_value',\n",
+       " '_setitem_array',\n",
+       " '_setitem_frame',\n",
+       " '_setitem_slice',\n",
+       " '_shift_with_freq',\n",
+       " '_should_reindex_frame_op',\n",
+       " '_slice',\n",
+       " '_sliced_from_mgr',\n",
+       " '_stat_function',\n",
+       " '_stat_function_ddof',\n",
+       " '_take_with_is_copy',\n",
+       " '_to_dict_of_blocks',\n",
+       " '_to_latex_via_styler',\n",
+       " '_typ',\n",
+       " '_update_inplace',\n",
+       " '_validate_dtype',\n",
+       " '_values',\n",
+       " '_where',\n",
+       " 'abs',\n",
+       " 'add',\n",
+       " 'add_prefix',\n",
+       " 'add_suffix',\n",
+       " 'age',\n",
+       " 'agg',\n",
+       " 'aggregate',\n",
+       " 'align',\n",
+       " 'all',\n",
+       " 'any',\n",
+       " 'apply',\n",
+       " 'applymap',\n",
+       " 'asfreq',\n",
+       " 'asof',\n",
+       " 'assign',\n",
+       " 'astype',\n",
+       " 'at',\n",
+       " 'at_time',\n",
+       " 'attrs',\n",
+       " 'axes',\n",
+       " 'backfill',\n",
+       " 'between_time',\n",
+       " 'bfill',\n",
+       " 'bool',\n",
+       " 'boxplot',\n",
+       " 'clip',\n",
+       " 'columns',\n",
+       " 'combine',\n",
+       " 'combine_first',\n",
+       " 'compare',\n",
+       " 'convert_dtypes',\n",
+       " 'copy',\n",
+       " 'corr',\n",
+       " 'corrwith',\n",
+       " 'count',\n",
+       " 'cov',\n",
+       " 'cummax',\n",
+       " 'cummin',\n",
+       " 'cumprod',\n",
+       " 'cumsum',\n",
+       " 'describe',\n",
+       " 'diff',\n",
+       " 'div',\n",
+       " 'divide',\n",
+       " 'dot',\n",
+       " 'drop',\n",
+       " 'drop_duplicates',\n",
+       " 'droplevel',\n",
+       " 'dropna',\n",
+       " 'dtypes',\n",
+       " 'duplicated',\n",
+       " 'empty',\n",
+       " 'eq',\n",
+       " 'equals',\n",
+       " 'eval',\n",
+       " 'ewm',\n",
+       " 'expanding',\n",
+       " 'explode',\n",
+       " 'ffill',\n",
+       " 'fillna',\n",
+       " 'filter',\n",
+       " 'first',\n",
+       " 'first_valid_index',\n",
+       " 'flags',\n",
+       " 'floordiv',\n",
+       " 'from_dict',\n",
+       " 'from_records',\n",
+       " 'ge',\n",
+       " 'get',\n",
+       " 'groupby',\n",
+       " 'gt',\n",
+       " 'head',\n",
+       " 'hist',\n",
+       " 'iat',\n",
+       " 'idxmax',\n",
+       " 'idxmin',\n",
+       " 'iloc',\n",
+       " 'index',\n",
+       " 'infer_objects',\n",
+       " 'info',\n",
+       " 'insert',\n",
+       " 'interpolate',\n",
+       " 'isetitem',\n",
+       " 'isin',\n",
+       " 'isna',\n",
+       " 'isnull',\n",
+       " 'items',\n",
+       " 'iterrows',\n",
+       " 'itertuples',\n",
+       " 'join',\n",
+       " 'keys',\n",
+       " 'kurt',\n",
+       " 'kurtosis',\n",
+       " 'last',\n",
+       " 'last_valid_index',\n",
+       " 'le',\n",
+       " 'loc',\n",
+       " 'lt',\n",
+       " 'map',\n",
+       " 'mask',\n",
+       " 'max',\n",
+       " 'mean',\n",
+       " 'median',\n",
+       " 'melt',\n",
+       " 'memory_usage',\n",
+       " 'merge',\n",
+       " 'min',\n",
+       " 'mod',\n",
+       " 'mode',\n",
+       " 'mul',\n",
+       " 'multiply',\n",
+       " 'name',\n",
+       " 'ndim',\n",
+       " 'ne',\n",
+       " 'nlargest',\n",
+       " 'notna',\n",
+       " 'notnull',\n",
+       " 'nsmallest',\n",
+       " 'nunique',\n",
+       " 'pad',\n",
+       " 'pct_change',\n",
+       " 'pipe',\n",
+       " 'pivot',\n",
+       " 'pivot_table',\n",
+       " 'plot',\n",
+       " 'pop',\n",
+       " 'pow',\n",
+       " 'prod',\n",
+       " 'product',\n",
+       " 'quantile',\n",
+       " 'query',\n",
+       " 'radd',\n",
+       " 'rank',\n",
+       " 'rdiv',\n",
+       " 'reindex',\n",
+       " 'reindex_like',\n",
+       " 'rename',\n",
+       " 'rename_axis',\n",
+       " 'reorder_levels',\n",
+       " 'replace',\n",
+       " 'resample',\n",
+       " 'reset_index',\n",
+       " 'rfloordiv',\n",
+       " 'rmod',\n",
+       " 'rmul',\n",
+       " 'rolling',\n",
+       " 'round',\n",
+       " 'rpow',\n",
+       " 'rsub',\n",
+       " 'rtruediv',\n",
+       " 'salary',\n",
+       " 'sample',\n",
+       " 'select_dtypes',\n",
+       " 'sem',\n",
+       " 'set_axis',\n",
+       " 'set_flags',\n",
+       " 'set_index',\n",
+       " 'shape',\n",
+       " 'shift',\n",
+       " 'size',\n",
+       " 'skew',\n",
+       " 'sort_index',\n",
+       " 'sort_values',\n",
+       " 'squeeze',\n",
+       " 'stack',\n",
+       " 'std',\n",
+       " 'style',\n",
+       " 'sub',\n",
+       " 'subtract',\n",
+       " 'sum',\n",
+       " 'swapaxes',\n",
+       " 'swaplevel',\n",
+       " 'tail',\n",
+       " 'take',\n",
+       " 'title',\n",
+       " 'to_clipboard',\n",
+       " 'to_csv',\n",
+       " 'to_dict',\n",
+       " 'to_excel',\n",
+       " 'to_feather',\n",
+       " 'to_gbq',\n",
+       " 'to_hdf',\n",
+       " 'to_html',\n",
+       " 'to_json',\n",
+       " 'to_latex',\n",
+       " 'to_markdown',\n",
+       " 'to_numpy',\n",
+       " 'to_orc',\n",
+       " 'to_parquet',\n",
+       " 'to_period',\n",
+       " 'to_pickle',\n",
+       " 'to_records',\n",
+       " 'to_sql',\n",
+       " 'to_stata',\n",
+       " 'to_string',\n",
+       " 'to_timestamp',\n",
+       " 'to_xarray',\n",
+       " 'to_xml',\n",
+       " 'transform',\n",
+       " 'transpose',\n",
+       " 'truediv',\n",
+       " 'truncate',\n",
+       " 'tz_convert',\n",
+       " 'tz_localize',\n",
+       " 'unstack',\n",
+       " 'update',\n",
+       " 'value_counts',\n",
+       " 'values',\n",
+       " 'var',\n",
+       " 'where',\n",
+       " 'xs']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   name  age  salary title\n",
+      "0  john   23   23333   CFO\n",
+      "1   bob   45   43555    VP\n",
+      "2   dan   12   90000   CTO\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(employees)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like Excel spreadsheets, dataframes are made up of <b>rows</b> and <b>columns</b>. Each column will have the same data type (all ints, all floats, all strings, etc.).\n",
+    "\n",
+    "We can then set a column (or multiple columns) as an <b>index</b> that we can use as a shorthand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      age  salary title\n",
+      "name                   \n",
+      "john   23   23333   CFO\n",
+      "bob    45   43555    VP\n",
+      "dan    12   90000   CTO\n"
+     ]
+    }
+   ],
+   "source": [
+    "employees.set_index(\"name\",inplace=True)\n",
+    "# The inplace argument above means we're replacing\n",
+    "# our 'default' index with our new index.\n",
+    "\n",
+    "print(employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['john', 'bob', 'dan'], dtype='object', name='name')\n",
+      "Index(['age', 'salary', 'title'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(employees.index)\n",
+    "print(employees.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use the <b>loc[]</b> function to access a group of rows and columns by a particular label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age          23\n",
+       "salary    23333\n",
+       "title       CFO\n",
+       "Name: john, dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "employees.loc[\"john\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "age          12\n",
+       "salary    90000\n",
+       "title       CTO\n",
+       "Name: dan, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Similarly, we can use 'iloc[]' to refer to a particular index.\n",
+    "employees.iloc[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's also possible to create subsets of dataframes based on their values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      age  salary title\n",
+      "name                   \n",
+      "bob    45   43555    VP\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's only look at employees older than 30.\n",
+    "print(employees[employees.age>30])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "john    False\n",
+      "bob      True\n",
+      "dan     False\n",
+      "Name: title, dtype: bool\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's look at who's a VP and who isn't.\n",
+    "print(employees['title'] == 'VP')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      age  salary title\n",
+      "name                   \n",
+      "john   22   23333   CFO\n",
+      "bob    45   43555    VP\n",
+      "dan    12   90000   CTO\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can also easily set values or add new columns.\n",
+    "employees.loc['john','age'] = 22\n",
+    "print(employees)\n",
+    "# Change John's age to 22.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      age  salary title      city\n",
+      "name                             \n",
+      "john   22   23333   CFO  montreal\n",
+      "bob    45   43555    VP  montreal\n",
+      "dan    12   90000   CTO  montreal\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's say we now have information about what city\n",
+    "# everyone's in and we want to add that where we have it.\n",
+    "employees['city'] = 'montreal'\n",
+    "print(employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      age  salary title      city\n",
+      "name                             \n",
+      "john   22   23333   CFO   toronto\n",
+      "bob    45   43555    VP  montreal\n",
+      "dan    12   90000   CTO  montreal\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Whoops, Diane's moved to Toronto!\n",
+    "employees.loc['john','city'] = 'toronto'\n",
+    "print(employees)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The <b>describe()</b> function is a helpful summary of statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count        3.000000\n",
+       "mean     52296.000000\n",
+       "std      34182.247337\n",
+       "min      23333.000000\n",
+       "25%      33444.000000\n",
+       "50%      43555.000000\n",
+       "75%      66777.500000\n",
+       "max      90000.000000\n",
+       "Name: salary, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# employees.age.describe()\n",
+    "employees.salary.describe()\n",
+    "# WHAT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      age  salary title      city\n",
+      "name                             \n",
+      "john   22   24333   CFO   toronto\n",
+      "bob    45   44555    VP  montreal\n",
+      "dan    12   91000   CTO  montreal\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can also easily do mathematical operations.\n",
+    "\n",
+    "employees.salary = employees.salary + 1000\n",
+    "print(employees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "title\n",
+       "CFO    1\n",
+       "VP     1\n",
+       "CTO    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Or use things like value counts!\n",
+    "\n",
+    "employees.title.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',\n",
+       "               '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',\n",
+       "               '2021-01-09', '2021-01-10', '2021-01-11', '2021-01-12'],\n",
+       "              dtype='datetime64[ns]', freq='D')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pandas can also be used to create some useful date ranges!\n",
+    "pd.date_range('1/1/2021','1/12/2021')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>14.2 Pandas Examples</h2>\n",
+    "\n",
+    "You'll need to re-run these lines of code to clear out all the changes\n",
+    "we made above..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'my_dict' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/Users/nik/data/python/notebooks/Week 06 W.ipynb Cell 30\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2006%20W.ipynb#X41sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m employees \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(my_dict)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/nik/data/python/notebooks/Week%2006%20W.ipynb#X41sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m employees\u001b[39m.\u001b[39mset_index(\u001b[39m'\u001b[39m\u001b[39mname\u001b[39m\u001b[39m'\u001b[39m, inplace\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'my_dict' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "employees = pd.DataFrame(my_dict)\n",
+    "employees.set_index('name', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>1. Promote Ashley to CEO.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>2. Using methods you learned in this lecture, find the mean and standard deviation of salaries.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>3. A year has passed! Increase everyone's ages by 1.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>14.3 Pandas With Atmospheric Data</h2>\n",
+    "\n",
+    "Let's use a real-life example! This is information from Chris McCray at McGill: Daily weather data for Montreal from 1871-2019. Pandas has a <b>read_csv()</b> function that will come in handy here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/0x/8vc0k5b97mq8hxcghjr_nb300000gn/T/ipykernel_61695/2319455187.py:3: DtypeWarning: Columns (24,25,26) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  mtl_weather = pd.read_csv('http://www.cdmccray.com/python_tutorial/pandas/montreal_daily_weather.csv')\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "mtl_weather = pd.read_csv('http://www.cdmccray.com/python_tutorial/pandas/montreal_daily_weather.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Date/Time', 'Year', 'Month', 'Day', 'Data Quality', 'Max Temp (°C)',\n",
+      "       'Max Temp Flag', 'Min Temp (°C)', 'Min Temp Flag', 'Mean Temp (°C)',\n",
+      "       'Mean Temp Flag', 'Heat Deg Days (°C)', 'Heat Deg Days Flag',\n",
+      "       'Cool Deg Days (°C)', 'Cool Deg Days Flag', 'Total Rain (mm)',\n",
+      "       'Total Rain Flag', 'Total Snow (cm)', 'Total Snow Flag',\n",
+      "       'Total Precip (mm)', 'Total Precip Flag', 'Snow on Grnd (cm)',\n",
+      "       'Snow on Grnd Flag', 'Dir of Max Gust (10s deg)',\n",
+      "       'Dir of Max Gust Flag', 'Spd of Max Gust (km/h)',\n",
+      "       'Spd of Max Gust Flag', 'Date', 'season'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mtl_weather.columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's set the index to the date of observation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mtl_weather.set_index('Date/Time',inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Grouping</b> is a useful feature within Pandas that lets us do the following:\n",
+    "<ul>\n",
+    "    <li>Split the data into groups based on some criteria.</li>\n",
+    "    <li>Apply a function to each group independently.</li>\n",
+    "    <li>Combine the results into a data structure.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Year\n",
+      "1871    184\n",
+      "1872    366\n",
+      "1873    334\n",
+      "1874    365\n",
+      "1875    365\n",
+      "       ... \n",
+      "2015    358\n",
+      "2016    360\n",
+      "2017    351\n",
+      "2018    357\n",
+      "2019     74\n",
+      "Name: Max Temp (°C), Length: 149, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# As an example, let's count how much temperature data is available for each year.\n",
+    "\n",
+    "a = mtl_weather.groupby('Year').count()['Max Temp (°C)']\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "a.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, so there's a lot of missing data in the 90s! We can actually remove all rows with missing data by using <b>dropna()</b>. Since there's probably a lot of data missing, let's specify that we're only removing the rows that have missing temperature data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(54421, 28)\n",
+      "(52456, 28)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mtl_weather.shape)\n",
+    "mtl_weather.dropna(subset=['Max Temp (°C)'], inplace=True)\n",
+    "print(mtl_weather.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use <b>groupby()</b> to calculate the average temperature for each year!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mtl_weather.groupby('Year')['Mean Temp (°C)'].mean().plot(figsize=(25,5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, well, there's obviously something suspicious going on here - it's not that cold, even in Montreal! Note that these are years where a lot of data is missing - it looks like most of that data's from the warm season.\n",
+    "\n",
+    "How would you handle this kind of problem?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year'>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearlycounts = mtl_weather.groupby('Year').count()['Mean Temp (°C)']\n",
+    "yearlycounts.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's set our threshold to about 350 days."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index([1872, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882,\n",
+      "       ...\n",
+      "       2008, 2009, 2010, 2011, 2012, 2013, 2015, 2016, 2017, 2018],\n",
+      "      dtype='int64', name='Year', length=135)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "goodyears = yearlycounts[yearlycounts>350].index\n",
+    "print(goodyears)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now subset the dataframe to only include data from our good years using the <b>isin()</b> function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Year'>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mtl_weather = mtl_weather[mtl_weather.Year.isin(goodyears)]\n",
+    "mtl_weather.groupby('Year')['Mean Temp (°C)'].mean().plot(figsize=(25,5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's also very easy to get extreme values from the dataset. The <b>nsmallest()</b> function will list the smallest values, and the <b>nlargest()</b> function will list the largest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date/Time\n",
+      "1933-12-29   -33.9\n",
+      "1981-01-04   -33.5\n",
+      "1943-02-15   -33.3\n",
+      "1914-01-13   -32.8\n",
+      "1914-02-11   -32.8\n",
+      "Name: Min Temp (°C), dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mtl_weather['Min Temp (°C)'].nsmallest())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Day</th>\n",
+       "      <th>Data Quality</th>\n",
+       "      <th>Max Temp (°C)</th>\n",
+       "      <th>Min Temp (°C)</th>\n",
+       "      <th>Mean Temp (°C)</th>\n",
+       "      <th>Heat Deg Days (°C)</th>\n",
+       "      <th>Cool Deg Days (°C)</th>\n",
+       "      <th>Total Rain (mm)</th>\n",
+       "      <th>Total Snow (cm)</th>\n",
+       "      <th>Total Precip (mm)</th>\n",
+       "      <th>Snow on Grnd (cm)</th>\n",
+       "      <th>Dir of Max Gust (10s deg)</th>\n",
+       "      <th>season</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>49226.000000</td>\n",
+       "      <td>49226.000000</td>\n",
+       "      <td>49226.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>49226.000000</td>\n",
+       "      <td>49215.000000</td>\n",
+       "      <td>49215.000000</td>\n",
+       "      <td>49215.000000</td>\n",
+       "      <td>49215.000000</td>\n",
+       "      <td>41958.000000</td>\n",
+       "      <td>41949.000000</td>\n",
+       "      <td>48913.000000</td>\n",
+       "      <td>12384.000000</td>\n",
+       "      <td>1992.000000</td>\n",
+       "      <td>49226.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1941.422480</td>\n",
+       "      <td>6.522468</td>\n",
+       "      <td>15.725287</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.807128</td>\n",
+       "      <td>2.680293</td>\n",
+       "      <td>6.753986</td>\n",
+       "      <td>12.040049</td>\n",
+       "      <td>0.794034</td>\n",
+       "      <td>2.111869</td>\n",
+       "      <td>0.736919</td>\n",
+       "      <td>2.901108</td>\n",
+       "      <td>10.154797</td>\n",
+       "      <td>20.743474</td>\n",
+       "      <td>1941.841385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>41.283202</td>\n",
+       "      <td>3.448537</td>\n",
+       "      <td>8.799889</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12.444379</td>\n",
+       "      <td>11.722607</td>\n",
+       "      <td>11.986670</td>\n",
+       "      <td>11.006062</td>\n",
+       "      <td>1.850957</td>\n",
+       "      <td>5.781769</td>\n",
+       "      <td>2.854098</td>\n",
+       "      <td>6.503921</td>\n",
+       "      <td>19.577769</td>\n",
+       "      <td>11.753323</td>\n",
+       "      <td>41.286312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1872.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-28.900000</td>\n",
+       "      <td>-33.900000</td>\n",
+       "      <td>-31.400000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1872.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1906.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.100000</td>\n",
+       "      <td>-6.100000</td>\n",
+       "      <td>-2.500000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>1907.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1940.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.700000</td>\n",
+       "      <td>3.800000</td>\n",
+       "      <td>7.700000</td>\n",
+       "      <td>10.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21.500000</td>\n",
+       "      <td>1940.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1974.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21.700000</td>\n",
+       "      <td>12.800000</td>\n",
+       "      <td>17.300000</td>\n",
+       "      <td>20.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>1974.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2018.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36.600000</td>\n",
+       "      <td>26.100000</td>\n",
+       "      <td>30.300000</td>\n",
+       "      <td>49.400000</td>\n",
+       "      <td>12.300000</td>\n",
+       "      <td>90.400000</td>\n",
+       "      <td>46.500000</td>\n",
+       "      <td>94.700000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>2019.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Year         Month           Day  Data Quality  Max Temp (°C)  \\\n",
+       "count  49226.000000  49226.000000  49226.000000           0.0   49226.000000   \n",
+       "mean    1941.422480      6.522468     15.725287           NaN      10.807128   \n",
+       "std       41.283202      3.448537      8.799889           NaN      12.444379   \n",
+       "min     1872.000000      1.000000      1.000000           NaN     -28.900000   \n",
+       "25%     1906.000000      4.000000      8.000000           NaN       1.100000   \n",
+       "50%     1940.000000      7.000000     16.000000           NaN      11.700000   \n",
+       "75%     1974.000000     10.000000     23.000000           NaN      21.700000   \n",
+       "max     2018.000000     12.000000     31.000000           NaN      36.600000   \n",
+       "\n",
+       "       Min Temp (°C)  Mean Temp (°C)  Heat Deg Days (°C)  Cool Deg Days (°C)  \\\n",
+       "count   49215.000000    49215.000000        49215.000000        49215.000000   \n",
+       "mean        2.680293        6.753986           12.040049            0.794034   \n",
+       "std        11.722607       11.986670           11.006062            1.850957   \n",
+       "min       -33.900000      -31.400000            0.000000            0.000000   \n",
+       "25%        -6.100000       -2.500000            0.700000            0.000000   \n",
+       "50%         3.800000        7.700000           10.300000            0.000000   \n",
+       "75%        12.800000       17.300000           20.500000            0.000000   \n",
+       "max        26.100000       30.300000           49.400000           12.300000   \n",
+       "\n",
+       "       Total Rain (mm)  Total Snow (cm)  Total Precip (mm)  Snow on Grnd (cm)  \\\n",
+       "count     41958.000000     41949.000000       48913.000000       12384.000000   \n",
+       "mean          2.111869         0.736919           2.901108          10.154797   \n",
+       "std           5.781769         2.854098           6.503921          19.577769   \n",
+       "min           0.000000         0.000000           0.000000           0.000000   \n",
+       "25%           0.000000         0.000000           0.000000           0.000000   \n",
+       "50%           0.000000         0.000000           0.000000           0.000000   \n",
+       "75%           0.800000         0.000000           2.500000          10.000000   \n",
+       "max          90.400000        46.500000          94.700000         145.000000   \n",
+       "\n",
+       "       Dir of Max Gust (10s deg)        season  \n",
+       "count                1992.000000  49226.000000  \n",
+       "mean                   20.743474   1941.841385  \n",
+       "std                    11.753323     41.286312  \n",
+       "min                     1.000000   1872.000000  \n",
+       "25%                    14.000000   1907.000000  \n",
+       "50%                    21.500000   1940.000000  \n",
+       "75%                    33.000000   1974.000000  \n",
+       "max                    36.000000   2019.000000  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mtl_weather.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>14.4 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li><b>Pandas</b> is a useful way of working with CSV data!</li>\n",
+    "    <li>A <b>dataframe</b> is an object that contains rows and columns, much like an Excel spreadsheet.</li>\n",
+    "    <li><b>loc()</b> will let you identify individual rows, columns, or values.</li>\n",
+    "    <li><b>describe()</b> summarizes statistics for a specified section of a dataframe.</li>\n",
+    "    <li><b>read_csv()</b> will read in a CSV file specified by a file location.</li>\n",
+    "    <li><b>groupby()</b> carries out specific operations on groupings within a dataframe.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 08 F.ipynb b/python/atms-310/notebooks/Week 08 F.ipynb
new file mode 100755
index 0000000..616adc3
--- /dev/null
+++ b/python/atms-310/notebooks/Week 08 F.ipynb	
@@ -0,0 +1,772 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>17. More Matplotlib</h1>\n",
+    "<h2>11/17/2023</h2>\n",
+    "\n",
+    "<h2>17.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li><b>matplotlib</b>'s <b>pyplot</b> module lets us use Matlab's powerful plotting tools in Python.</li>\n",
+    "    <li>The <b>matplotlib.pyplot.plot()</b> function is a simple way to plot 2-D data.</li>\n",
+    "    <li>We can specify axis limits as well as line style and color.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.1 Keyword Strings</h2>\n",
+    "\n",
+    "With a normal scatterplot, you can convey two pieces of information for each point: (1) what the x value is, and (2) what the y value is. You can get additional information crammed into one plot by allowing the size and color of the points being plotted to vary!\n",
+    "\n",
+    "matplotlib.pyplot has a handy function for this particular application called <b>scatter()</b>. By default, you only need two arguments that consist of arrays of your x data and your y data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
+      " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
+      " 48 49]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10e8e6c90>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Let's have our x values just be a count from 0 to 49.\n",
+    "var1 = np.arange(50)\n",
+    "print(var1)\n",
+    "\n",
+    "# And let's randomly generate some y values!\n",
+    "var2 = var1 + 10*np.random.randn(50)\n",
+    "\n",
+    "plt.scatter(var1,var2)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay! So we have some information being shown here: we can see that there's a general positive trend going on here. But what if we have additional information (say this is temperature versus dewpoint temperature and we also know something about relative humidity)? We can convey that information using the 'c' argument in scatter(): the color of the data points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10e9a14d0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now we have a third variable that's a new set of random numbers from 0 to 50.\n",
+    "\n",
+    "var3 = 8*np.random.randint(0,50,50)\n",
+    "plt.scatter(var1,var2,c=var3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More information has been conveyed! Let's try adding even more information - say, a fourth meteorological variable like wind speed - through the size of the circle, which is the 's' argument in scatter(). This marker size is in 'points' squared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/matplotlib/collections.py:996: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10ea0e6d0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    " #This gives random numbers on a N(0,1) Gaussian.\n",
+    "var4 = np.random.randn(50)*100\n",
+    "plt.scatter(var1,var2,c=var3,s=var4)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.2 Categorical Variables</h2>\n",
+    "\n",
+    "Sometimes you have data in the form of categories! You may have, for instance, two different sets of tornado data (like in Homework 3), or three different future climate regimes, or five different locations. Any sort of comparative research will require this sort of categorical data analysis!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 3 artists>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "# Let's look at the example of three pieces of data.\n",
+    "# They might be mean values of three parameters, average grades on an assignment, etc.\n",
+    "names = ['groupa','groupb','groupc']\n",
+    "values = [58.9,90.3,3]\n",
+    "\n",
+    "\n",
+    "# We can start with a bar plot.\n",
+    "plt.bar(names,values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10eaaee90>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can also create a scatterplot as seen above.\n",
+    "plt.scatter(names,values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10eab6c90>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Or a line plot!\n",
+    "plt.plot(names,values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.3 Controlling Line Properties</h2>\n",
+    "\n",
+    "There are a bunch of line attributes you can set! The line type/color/marker examples we saw earlier are shortcuts for common configurations, but there are a <i>lot</i> more. You can find the full list by googling <b>matplotlib.lines.Line2d</b>, or by calling the <b>plt.setp()</b> function with a line or lines as an argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n",
+      "  alpha: scalar or None\n",
+      "  animated: bool\n",
+      "  antialiased or aa: bool\n",
+      "  clip_box: `~matplotlib.transforms.BboxBase` or None\n",
+      "  clip_on: bool\n",
+      "  clip_path: Patch or (Path, Transform) or None\n",
+      "  color or c: color\n",
+      "  dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
+      "  dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
+      "  dashes: sequence of floats (on/off ink in points) or (None, None)\n",
+      "  data: (2, N) array or two 1D arrays\n",
+      "  drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
+      "  figure: `~matplotlib.figure.Figure`\n",
+      "  fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
+      "  gapcolor: color or None\n",
+      "  gid: str\n",
+      "  in_layout: bool\n",
+      "  label: object\n",
+      "  linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
+      "  linewidth or lw: float\n",
+      "  marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
+      "  markeredgecolor or mec: color\n",
+      "  markeredgewidth or mew: float\n",
+      "  markerfacecolor or mfc: color\n",
+      "  markerfacecoloralt or mfcalt: color\n",
+      "  markersize or ms: float\n",
+      "  markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n",
+      "  mouseover: bool\n",
+      "  path_effects: list of `.AbstractPathEffect`\n",
+      "  picker: float or callable[[Artist, Event], tuple[bool, dict]]\n",
+      "  pickradius: float\n",
+      "  rasterized: bool\n",
+      "  sketch_params: (scale: float, length: float, randomness: float)\n",
+      "  snap: bool or None\n",
+      "  solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
+      "  solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
+      "  transform: `~matplotlib.transforms.Transform`\n",
+      "  url: str\n",
+      "  visible: bool\n",
+      "  xdata: 1D array\n",
+      "  ydata: 1D array\n",
+      "  zorder: float\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lines = plt.plot([1,2,3])\n",
+    "plt.setp(lines)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say you want to increase the width of a given line. You'd want to use the <b>linewidth</b> argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10ec75290>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "plt.plot(np.arange(9),linewidth=5.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10eb6ad10>]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Likewise, you can set the color.\n",
+    "plt.plot(np.arange(20),linewidth=20,color='darkblue')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10ed55290>]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# If you have markers, you can change their properties as well!\n",
+    "plt.plot(np.arange(10),'-o',markeredgecolor='red',markerfacecolor='yellow')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.4 Multiple Figures and Axes</h2>\n",
+    "\n",
+    "We often want to deal with multiple subplots within the same figure. As a behind-the-scenes note, pyplot keeps track of the \"current\" figure and axes, which can be referred to using <b>gcf()</b> and <b>gca()</b>, respectively. You probably won't have to worry about this too often.\n",
+    "\n",
+    "The <b>subplot()</b> function refers to a particular subplot within a set. It has three arguments: number of rows, number of columns, and then the specific number of this plot (which ranges from 1 to number_rows*number_columns)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's create a couple of subplots of fairly complex data:\n",
+    "# a damped oscillation and an undamped oscillation.\n",
+    "import numpy as np\n",
+    "# Start by creating a function that will give us a decaying oscillation.\n",
+    "def f(t):\n",
+    "    return np.exp(-t)*np.cos(2*np.pi*t)\n",
+    "\n",
+    "\n",
+    "# Next, let's have two sets of x values:\n",
+    "# the first is more widely spaced than the second,\n",
+    "# but they cover the same range of data.\n",
+    "t1 = np.arange(0,5,0.1)\n",
+    "t2 = np.arange(0,5,0.02)\n",
+    "\n",
+    "\n",
+    "# First, we create a setup where we have two rows and 1 column of\n",
+    "# plots, and we're referring to the first plot.\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.plot(t1,f(t1),'ob',t2,f(t2),'k')\n",
+    "\n",
+    "# Next, we'll refer to the second plot.\n",
+    "\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.plot(t2,np.cos(2*np.pi*t2),'r--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Exercise:</b> Make 4 subplots (2 rows, 2 columns) using the x values below and plot whatever functions you like on them (sin(x), cos(x), 2/x, etc.)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x10ef55d10>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "x = np.arange(0.0,10,0.01)\n",
+    "\n",
+    "plt.subplot(2,2,1)\n",
+    "plt.plot(x,np.sin(x))\n",
+    "plt.subplot(2,2,2)\n",
+    "plt.plot(x,np.cos(x))\n",
+    "plt.subplot(2,2,3)\n",
+    "plt.plot(x,2*np.cos(x))\n",
+    "plt.subplot(2,2,4)\n",
+    "plt.plot(x,4*np.sin(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.5 Working with Text</h2>\n",
+    "\n",
+    "You can use the <b>text()</b> function to place text in any arbitrary location. As seen above, some useful text-related functions include <b>xlabel()</b>, <b>ylabel()</b>, and <b>title()</b>.\n",
+    "\n",
+    "As a side note, if you want to use mathematical expressions in text, it can get a little confusing. You'll want to start your string with the letter r, then surround it with quotation marks followed by dollar signs. The conventions are the same as in LaTeX, and you can find the details by googling 'LaTeX math'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[41.32270515 86.64818776 48.41249733 ... 39.1275195  74.85441181\n",
+      " 59.70951104]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(20, 0.025, '$\\\\mu=60 \\\\sigma=15$')"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's generate a histogram from a set of random values in a distribution\n",
+    "# with a specified mean and standard deviation.\n",
+    "mu,sigma =60,15\n",
+    "x = mu + sigma *np.random.randn(10000)\n",
+    "print(x)\n",
+    "\n",
+    "# Let's create a histogram!\n",
+    "plt.hist(x,100,density=1,facecolor='g')\n",
+    "plt.grid(True)\n",
+    "plt.axis([0,100,0,0.03])\n",
+    "plt.xlabel('grades')\n",
+    "plt.ylabel('frequency')\n",
+    "plt.title('hist of phys midterm2 grades',fontsize=24)\n",
+    "plt.text(20,.025,r\"$\\mu=60 \\sigma=15$\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's also a method of annotating text that is called, as you might expect, <b>annotate()</b>. An example follows!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-2.0, 2.0)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t = np.arange(0,5,.01)\n",
+    "s = np.cos(2*np.pi*t)\n",
+    "plt.plot(t,s)\n",
+    "plt.annotate('local max',xy=(2,1),xytext=(3,1.5),arrowprops=dict(facecolor=\"black\"))\n",
+    "plt.ylim(-2,2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.6 Nonlinear Axes</h2>\n",
+    "\n",
+    "If your data spans many orders of magnitude, it can be helpful to create nonlinear axes using <b>xscale()</b> or <b>yscale()</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z5swxXF+wYAEyMjKa9hImKCpKrXvuzkoJgAsA8MWXC5DpSWlzrQrxjO3xChms8HDLjl0orN2R5F61DLafis2D7xYuRLf0pteZ6rlrhj1VsXcpLCxs9vZV1QWNmn+7aBE00rdkyVIczEpeO2bjW94Avc2G+nq9Ly0xFnY46Qe0vm7atBkZZa0n+KPT+VveAHx9VMalvRXDulGV2Fxo6vjv2x+jQf8r/BIuCagkxq/4aCkKC1MZ8iK2hbPvooTjf0/j+EbqP3DgAAoLjd6dyUZ9fb3jsik/0rNGQ6qqmhoSbd++HXfddRf+/Oc/47LLLkNpaSnuu+8+TJ8+HXPnzuU+M3v2bMycOVP/XV1djb59+2LKlCnIyclJ3osgwhUWFRVh8uTJ8HhSx1Hk7D2B53esAwBcNPESdM9JS1lbrQWJjO07ZWuwp5rW9fbpn4eCHwxKRRebHem7jgM7NwAAxl80Aad1zUy4ruaau2ZYdeAk/r09ov4tKCho9vbvWblAl3iMHjMO2LQSADBm7Fic0zu3yfXbje/+43X4y8ZlAICMzAzAH5FEtcRY2OHwqXpg/VIAwNBzzkHBqD4t3KP45+9F/1iM0qpGHA1m4LvfXkTd+/PGb9HYEJGYNHX8F320FSgvAQBMuewH8LllHDnVAKyPOKbkduqCgoJRTWrDCnevWKD/zb7L79d+jVA0krTde5qNr1b/wIHNQ1c1zYkTpIx56dKlC1wul0HKUl5ebpDGaHj00UcxduxY3HfffQCAc889F5mZmRg/fjweeeQR9OzZ0/CMz+eDz+czXPd4PCkj0qmsGwAkOSZNUOBqkc2mpRDX2HKY4KCCdjNeLpeL+jsZ75XquWsGtztGalqifcr4WYqtL9nlTmp/zMbX5Y59S/Lw1hrnqssV+1ay3Lroj9P5W1rVCAA4UtloKO8m6GtT3410NpJdLng8bsiumFrSH1KabfzYdsi8VE768OlBGQeWHcY9k41MiizLzfIe8bSRMoNdr9eL/Px8gxiqqKgIY8aM4T5TX18PWaa7pBHw71NWXfJV/aH2Y4DaHGhfBruxv9vT7G/ptUwaWYabKSIbFeelWVo04kSt31FagnZvsJtMbyOCe9HmEjmntBxadgiFFWwvqU7q2ojHXvfIqQZ8WyrjqW/2turUFSRS6m00c+ZMvPzyy5g3bx527NiBe++9F8XFxZg+fTqAiMpn2rRpevkrr7wSH330EZ5//nns378fy5Ytw1133YXzzz8fvXr1SmVXWxXIyc+L4CgQAW+dNzDxCIpP1OOR/21HaRU/62trBrlxJGsT2XSkCh9vcO6u3BAIY9Hu401mokk62tK0kfTgs9ssDp+sxw0vrcR3u8qb1GZLMwEbD1ci/5Gv8fPX1tiWpb3c2sZGFg+S6yodm0uaWznFvDhcN7M+2oKCZ5bg2e/2Jq1v8bwluc+E28g3T6nNy/XXX48TJ07goYceQmlpKYYOHYrCwkL0798fAFBaWkrFfLnllltQU1ODf//73/jtb3+LDh064JJLLsHf/va3VHaz1YGcPIJ5iQ9sMKWfvLQCJVWNWHPwJD69Y1wL9SoxUIkZkzQNfvziKgBA344ZGDWgk235u9/dgAXbj+Hm0f0x5+qhSemDoqpwcUjrkj3H0a9TBvp3Tty2xwmCcaRduO+/m7By/0ms2H8CBx8z93q0A/n9mislAYnXlx8EACzcddy2rEoFqWt/SJXkRYsfRNFvh5KX/66LHCie+WYv7rjkjKT0LZ4gdeSQhBUVHhd9X6vrYEUd7nxnA2ZcfBqmnmM042hOpDwa2owZM3Dw4EH4/X6sW7cOF10UM5569dVXsXDhQqr8nXfeiW3btqG+vh4lJSV488030bt371R3s1VBZCVNHKzkpSSq+950pKolutMkpFJ8v/+4szQKC6LJ615bcahJ7ZGElKeq2VB8CjfNXY0Jjy9sUjtOECTWlN24VtQGLO87BdUOsVE8Pn8nymsak9KGFeLZyKh519JishQgmck4SYmkxrSQcYTijUybzIjG8bwlOT1CFt/8vv9uwpajVfj1W+sT71iSIEK5NhGlVQ2o9YfsC8YBMq6RsHkxB2+JOdUxtwWkNEhdMx/+SeLIe5ctR5uPuSQj7LZEkDoSz363D3e8vSHl7ccTtJtWV6agMy0MOYnMC3lY0qYVOX7xMi/JHO94BHykcW/YIirwqfqWj/mjQTAvTUBZVSNGP/otRj6U3NgZQm3kEDybl3ZksEt5yCRZgN8SqgsNPIahOVNiBMLOmcJkjRL5zmydqw+cTFIr5ojne5Mqrpa21UkFkil5IemNxhSHKJuX+Oh3Msc7rtxGlOTFvM+tyQZKMC9NwOqDEaITSCAEtKqqeG9NMTYfqeTe09Ca1EZ1/hCe+no39hyraemumKI1JhBLFKk8AbdkhHqeQWAyNxQ7kAa7zbU5W7XTHJmt45E2tEeGhUQyGXdS0qtLXojFGlbUuFIEtNTQk3uOlcFua5oagnlpAprChS7afRy//3ALrvr3MsO91upt9NiXO/HU13sw+cnFLd0VACa5jZLIvCzefRw/fn55i2WqJqdXsjeU5ha8kM2pnCndnJIXcjOxc5VO1jhRbu9MkzzG7fDJevzpk604dMKZbZId4uGPUjnvWgOSySyS9Ea3eWHmVEsdqOJ5TVLYwlsTWlWtaT4I5qWFsOeY+YZIzp3WJHlZy8lc2pJw4irdFEybtxprD53CjLfWJa3OeJBKl9VkbMpHTtXjr4U7UFIZnxs6jwC6XdYGvclEMA61UbJAfj92c/NyGLdp81bjjZWH8LO5q5LSflxqo3Zu85JMKR/JmGjzljVybikaHo/aiJS28DJha1da03wQzEsrhEJJXlqPGiScLH/dJIFvsJv88TpZlxyPk3hBexslt24pCYqjW19Zg5cW78fPX7WPHUJ2nyeWJiUvqSb28aiNkjFOkXb47QM046bhQEVE4nL4ZHLiEyXKvLSig3bSkCzJi6qqtORFC1LHDFoiZgXJQDxvSamNLIhNsm3vmgLBvDQBqVrYrdVg18qFrrWgMagk3b2zpQh4KjeRZEhe9kTVaTvL7G2g7FQRHmIDTzXzQkpe7KZK8tRG5pKX5lCZxfMe36cIu02RaPpDCjWvtU2f/b4tJ3lxXpbkr6zofGs6vwrmpRWCJBitSW3EEye2RrQmhq9JSPImQoWob2ajF3KTOHqqAX/4eAtl+E32xx9OrbQx2AKu0i3NvLji+N5qCtWVzQWr1yWZl6aoKF9ctJ/6rX3j1qI2ikf2kopo3qmGYF4SgKqqqPOHKBFaMk/7qVYbbTxcibGPfYvPNpXE9Vxz5YFxCjPCmky7l5ZEsgmKSZy0lENVVYpx+vWb6/H2qmJc8yxhrE7cdxqVNFEEQ82/OZPNsGojrzv1ZDg+b6PY321kHwMAvLemGJ9sOArAWk1GJmYMNuFA9sbKg9TvkInkpaUOU/FJ26xtXrSqWhMzK5iXBHDnu5tw9gPzqSilycwHQTIJqeDaH/nfdhytbMBd72yI6zkr///WhPbDvMT+Tsb0Ir9ecwlenv56D0Y98jUOn6zXr5VVRyLK1hExMihpY4ptBOggddZlkyWhIt+PPQQ0h5t4PDYvVFqKFOxVobCCm+auwmNf7kxanXX+EGZ/tAX3/XcTgmHF0tOGZOSaMtfYmFKmBrttwOaF7LLVIbU1nV8F85IA5m+PJGl7nQiZnkypBFlVKrj2TF9iKa1aneTF5Hp7ifWSiORlb3ktPlx3hCsJpDMbN33D9HAMTVk8+fVunKgL4PEFuyzLUXM+xZKXQIvEeYn9HWwBtVFcbrMptnlZuOs4luypwAuL9iWtTn9IgaJGJCn+kGLJrJHTlpWCxQPtwKp5i2lj1RZtXkh6YXVIbU0qpZQmZmzvYIMRJa3eBBJ7xYPTumZh0e5IgraaxiCy0zyOnmttBrtm66i9RNmlbQ+cPXPpE4sAAJk+F34wlE6cRtaRjMO+1yUj6NA+xU7c3KySl7DzdZuKCLtsm06YwKaC3MxVVbWUKKU6MWMqvi8rrbZiXsh3aoraSGvS45IQCMfmFbvBtxjzQsxeRVEtVYdW85Mul5y+JQNC8tIEkJt5ytRGKVjoWWkxnnVPHAHYrHJetCYkqjYKK2qrki6RPYn3xLOrzPhdyc+XDG1IPLYadsPanFGlydN2S8R5MTIvzWvzYjfHaXVl8gcoFawa6+RgxZxTUrAm0Fft8OpxM5KXcOtgXqhM0TbfMeyQeWlNecYF89IEUB88OmGX7qnAnz/d2iTVBa02Sr4UgZQY7Xbg5qqh1UleTK4HEyAWqqri8meW4NInFiWdgQmEFFzz7DI8+Nm2uJ4jv5OTPYQs3znLa7hPV5EMtZFz8mEveYn9nerYRgHKVdpG8pIsV2mLKcmL85JskBuZ3TpOtedJKqgIuWYjNi+01IFqX6XLJgptbDQD4FYX58UmkzsJnss3XVnkv9a0BQjmpQkIcyQvP5u7Cq+vOIS5Sw8kXG+qXaXJxVVS1ZjQc/GgMRhuViv1RIhFfSCMnWU1OFBRF3fE2NdXHMSjX+4wvf/NjmPYeLgSry4/GFe98doeVDbEMr52yjQyL8kmPPFIXuyaTrWROgk6SF1KmyLaMW8oEcnL8r0VuOGlldh33JnklNzM42FeWpGJgyXYlCok0xlkOEeFYl6cvWB5dSOu/NdSvLu6GADtQeeNMp9WEXZbOtio3Tcnx88yzksrmhCCeWkCyA/OTlgtQmZT602Gwe4ryw7g56+u0aVBtK2O8/oTkUhU1Pox+E9f4ZZX7KOwktA8Ev7yxXbzQsxC0uI3JLL5kcQu3gX650+34cVF+7H1aBX3fqInr3jDtB+v8et/83T+dB1NJ0JxMS82zVF2Xhbfb+vRKtz6ympsL6l23DaLYDwRdlOQ24hFIszLjS+vwor9J/Cbt9Y7Kk+pEGw2bDqgYNxds0WzqI1IjyJmPpEkz6nk5eEvdmDL0SrM+mgLAHqMNLWRWZC6NQdP4pwHF+CJot2O2koWyLkbj6pQK8s7cLYi3kUwL8lCMlUqyTbYnfP5dny7sxwfrY/EQHDKZbNIhHn5bGMkloxmIGyFLScl/PjFVThYUYdl+05gyZ4K/GeJcwmWL0pEEjHCc+oqaIXqxqDh2uebSvD0N3sSqo8kFE4kVyTzwtuUyZmUjOnKy8ljBjMm4eLHv8Phk/XUu1oxn9e9uALf7TqOG/6z0nHbLEhvn+aL82IleUl8Oz9W7UxySqoQWEkEi1Tm1AJSrzYKMGojlh7Qkhdn9LWYcPUHaCm0xnxq19i5/taqYgRCCp5JkA4kiviYF+OewPv0QvLSDhHvhmd1oiMlI3aEJh7UB0IA+LY6qUI8k33NcQmbjlRh8Z7jjiRCbM1pHhcAIJBAhFZKipbokHCeu/OdDVQ8oHgQr+SlotaGeYlTDWWHuCQGJs0dPBFJ7uhUVVof9SSrajAyik5B2kTZxnlpopxA2xyt2nE3g8Gu09w1AGOwm6oOJRns/LFiUMip7/Sgc7LOT/1WOMyLRrdbSyRykoSGFAWLdx/Hl1tK+WU584O8pq2DVsS7COYlWWiKpwp7uiGZi2RG7tVOX0qCkpdEEM8mqQU+DYVVR5sGW3WaJnkJxf9OdGAu589TRrVxt2oNsr74JS/G+zTz0oSORUGqjeI52bEIhpVmNdilvATtXKWbwLt8t6scQ/70Fd5bU2z5/vEEkEsU8XjYKCmiP6kE+UqBkEIxEAa1UQKSl5O1dHJWkjHQbF5CnE2fRXN4Hr28ZD++2lpqYFinzVuNX7+1HmUcO8cwZ03wPr2IsNsOEVZV1DaG9N/xfGNFBcqqGvHZphIDIU+mC7ZGIsMJbtSJIB5zD62soqoJbRoxyUv8BIK3eB09l8KxbIrkhauvJv9OQl9JtZGdd50Ni+DY5iUZCFKu0nY9Sxy/eG0tQoqK33+4xXJuNGXeOH3SaRwPIPURds3aagpotVGYCrTG0oNEmJc6JnYUWYfbJkgduU54jEMysfVoFR75Ygemv7meok0kM3eCkSIBTPoKRZMWWktvWxopZ16ee+455OXlIS0tDfn5+ViyZIlleb/fj/vvvx/9+/eHz+fDaaedhnnz5qW6m02Goqio8cdE2fGcHsOKislPLsJd72wwREdNpghSYwgStXlJBAlJXhQ6iJYZgWPTs2uSgERON4navFimj28iYaYNJ+OTvPD6lWy1EeniqzEvqqriq61lVDqAyHXzemSJHqvmZF5SOf3JtWX1/s1xmlWozSketZGxbE1jEAeb4JBg1lbT6qElLazrtFmbiQapo9VGtLcRu/ZI5ulIJb0ukg2aBsSu2zFpvMPb99rm5b333sM999yD+++/Hxs2bMD48eMxdepUFBcXmz5z3XXX4ZtvvsHcuXOxa9cuvPPOOxg8eHAqu5kUhBQVtf6Y5KWGkMLYQVFVvfz64lOM/UUKJC/kRE21zUsc1CmkxIiAk7gUBrVRVPKSSOyGRE5jAD2WbH+aGi8mXpVUDTH/bNVGSeYPGqMMx/xtxzD9zXUY//fvmLbN30CS6L6l3lU6jvWVJJWOZdTSZggDopqcwnmwc5Ue+9i3uPgfC7H7mPMYUU7aSlY9/pBC0QxWjdyUOC9aHirym2k2LyET5oXE0VPxhWFoCqjAj8R7SpCw+UglJv1zIb7efgwA32CX921aEe+SWubliSeewG233Ybbb78dQ4YMwVNPPYW+ffvi+eef55b/6quvsGjRIhQWFuLSSy/FgAEDcP7552PMmDGp7GZSEFZUimEhGRk7lFfHuOXTumYlxfOF97wmzSDXazLVUtz246hfo6mKQtu8OB2DNE/ikpeEPbAs1EZNlWpRJ2AH42jH9CZb8kLWoUleVu4/wS1r1ZrEqI1SHdSruSQvJFKmNnL4KD2/7WxeyL+NDVRH6dxiBx6E8fQrWfX4gwrtvWYheZnx1nqU11irckh6osVPsjLYtWRe4owhFS9ISRlJm6hM6lDx6zfXY9/xOtz++loA7MHGyLxoPDxPEtdSSFluo0AggHXr1mHWrFnU9SlTpmD58uXcZz777DOMGjUKf//73/HGG28gMzMTV111FR5++GGkp6dzn/H7/fD7Y5t/dXUk/kMwGEQwmLhHAg9W9fkDQVTWx4y6ahqs2w8THjEbi2MEX5ZUKl9MWFGb9B6kLYKihBEMBhEi6g+GwgnV7/SZIKE+s3omGAzqaqNgKAxFiTF/Df4AXJypym7omuFcYzDEbcuq/QBxr9FPfztFNf8Gjf7YNw+F6HbrOdK3QCDgOFNxKBx7PmjyTnT52FizfQkGg5SrdCLfnS1PBnurqfcjGPRRXmJkeWvmS6XmSYPf2dpNdF2QmxE7TgDoPDDkBmAzf63KBC3UyGFFcVQ3H87oAzk32PltbC8278Jh876FwsY5FAorqGoMoTMnSKJeJhSrPxAIwgV7ZtVufANBUupNG9c2+APUc6wn43urDmH6hIGmbZN2KtlpbgSDQfgDsTY07WkgOpesvnV5dYPt90p0XgeDQYRC9N6hoYGhU5rnqfYcOX7+KK0JBAgaGJ0HlMotyftrvHWmjHmpqKhAOBxG9+7dqevdu3dHWVkZ95n9+/dj6dKlSEtLw8cff4yKigrMmDEDJ0+eNLV7efTRRzFnzhzD9QULFiAjI6PpL+IQS5ctw0m/BCCiuiivrEFhYaFp+R0lsbKfLtkITQi2ddt2nGiU9N81tXWW9dihIQRon3nbtm0oPLEVh4/Iev2HjxxFYeFhh7XFpouTPr23T8by8phwz+6ZsBIZj52790A5pkIbn6/mL0AGZ6ZWVblAmlSeqjgOQMbO3XtR6NcCQjnrc0VjrOzylatwcqeq/w4EAqbP1gRjz61esxZ1e2Oru464p+GLwi8dJ0XcfTj2nTZs2gRPyUbL8mXlsfKbNm9B5rHN1H2Sf9i8eTMyj21y0Avz8auoiI3/wiVLcSgbOHgg1odI+cjz4bACM/PXsrJSbK0vgfa99+w/iMLC/XH3h8XqcgmNYeCinrFvCQCnqqr1vmzfsQOFVbFAiB8dlLH2uITfDwsj1wtUVsbe0cmcLyoq4vZ18+bN0N6PRdmxY5y67d4zcj8YCDrq195Dse+yZNkyHMk2L7vheIw2HTx0CIWFB7ht72DGDgCe3OLCwVoJ9w8PoRv/vImNJ2L1fzV/Pnz8YeGCHt8Y9lXH+rVhyzaQY71sxSqc2BGb/CzdOLp/Fwrrdpq2ebg2VndtbS0KCwtRHYhck6Di+LEyADI2b9mK3ONbsJsYaxZ7DhSjsPAg5058tNXsuW2nYmPrDwShvefiZSv060uXLkU4SM/rzcQ337xlKzpUbKHo1759+1BYuAeKEt96iBf19c5tglKeVZo9ZVplNFUUBZIk4a233kJubi6AiOrpxz/+MZ599lmu9GX27NmYOXOm/ru6uhp9+/bFlClTkJOTk8Q3iXCFCxbwF88FF47G7vJaYE8kTHxY9qCg4DLTusqWHcQnhyIbrK9TT6Akons8/YxByKhqBI4didxLS0dBwUUJ9/lEXQBYsxAAMHToUBSc3xfzazYBJyLtdevREwUFwxzVdfeKBfrfBQUF9uX/tID6bfVMMBjEQ+u/BQAMPO00XHh6Z/x7e0SkOXHSpdyT3PMHVgD1Mb17/z69sOVUGfr2H4CCgsHcPiuKij98ug1n9czBtAv76fcOnajHwxuWAgBG5I/CxEFd9We9Xi8KCiZy+32suhFYuxgAMHxEPiaf1U2/d7zGD6xdRJW/7Ac/cBwfZdfXe4EjkU38nHPORcHI3pbl3y5bA1SdAgCcdXbkW2sIBoN48cPY3D176DkoOK+PbR+svvnrR1cDNZUAgBGjLsCY0zpj9ec7gGOH9fLa86okmeqOevfshcH9coEDuwAAPXr3QUHB0Lj7Q0JVVdz958j73vWj8cCKmKOALz0TaIgQyTMHDUbB+LxY/dE5u8uVhwcLhuCVI6twqLbKtr1gMIiioiJMnjwZHo+H6mu6R8bZQ88C9vOjRXfp2g0FBSPjek/tvttjTWc0bP5qF1ByCAAw8rwLMHpgZ9Oy/g0lwN6tAIA+ffuhoOAsbttDhgxBwdgB3Hs1HQfhlktO49YvbS3DK7sjjPWlkyc7ymrPG18Sqw6cBLZF6EXfvNOB4hjDNTx/FMYM7ARVBdK9Ljy3fzlQH0urMGrEMBQM72Xa9tpDp4AtkQjh6RmZKCgYh7LqRmDdYrhkGb17dcfGk2UYPOQsFIzujy3zdwMlB+FxSQaD4E5de6CgYLihjXhpq9lzabuOAzs3AAAUyNAW3cjzzgO2R6Ixjxs3Dm8Xb0R1ZUSi1PvcsagtrQL2Rhi4QUPOQsGY/jhZFwDWLgQAnHbaaSiYckbC/XQKTXPiBCljXrp06QKXy2WQspSXlxukMRp69uyJ3r1764wLEFkgqqriyJEjOOOMMwzP+Hw++Hw+w3WPx8Od5E2FqfBbdqEhGLtb6w/B5XKbpiF3uWInA9JEQ4FMGQkqKpr2HlJMjChJMjweD1Ti1KEisfrtnuGpCeye0dRGqiRBJsZHll3cZ1kmOMMXPeWrEre8x+PB8n0V+HB9CT5ECX4+bqBeh0S0p0bHiWzHrO+yi1ANyfRzqmxUG7ncbnjczo6aEjF3ZBd/DEioKlGe6QtA67Ulzn07GOoj/g4qkTEi+0yNhYXWSHbJkGR6PTjpm1UZUqVVz3wGyptJ4o9DVWMo8j4S/32s+sSWS/e6IclWDKv5/HLSrqPvKMXan/bKOrx4Uz4uO7sHgEhk6HSPS2eqyb5+u+s4tpbWYkS/joYqeXNMg8/rNr3ndse2HZcrPjptRtfJ+cN4NSOkSBj11+8QVlTsfPgHRhpuQl80qMTYafTSFV33sizBrcU7YuhrhtdtCKbYGFKT8z1NnnNR64i0gYu9g9vthpegQT9+cRXGnhZjZrV9wuUmjHw53zoV+2s8dabMYNfr9SI/P98g5isqKjI1wB07dixKSkpQWxvjinfv3g1ZltGnj/0psTlgZoulMAa7qgrUW8S+MPNuCSlKwsajPJB1a38nGtMkHiRSr7anKIrKxB0w8zairzvxNiKlHiRhiccbg4SVS2aI0w+ereTe8lqUc8K8x2uwSxpicr2NSKY1GQa7RCONIc1VOrG6KFfpJKTEoKJIM4PRQKxLs3E4FbVfS8TXqLoxSNmapblly/FetPs4fvjcMm56iWSBNbz91RvrAAAn6wI498EFmPTPmISQLHq8xo8fPse3UWRfiXxHl0PdaLK8jchvXM9wLyfr/LoH0sm6gGFt2EXzJqUnrDu0LMXeVXsX7V66x3hIabCJhwQ499DklTN7cjORd01VjSkplu2L2V3yvI1ak6GuhpR6G82cORMvv/wy5s2bhx07duDee+9FcXExpk+fDiCi8pk2bZpe/sYbb0Tnzp1x6623Yvv27Vi8eDHuu+8+/PznPzc12G1umM0r1lUaABW0zqoeyq0vrCbVKyRAMUbGSemUOSIJk9sBYUqE6dJoRFhxxmCx131MnBfehkH2/QjhthhPTASzPrBMDy+GBPs9y6oacekTi3D+X7+hrp+qC2BXWQ3xnIO+EGXs47zY12cHso7GoH0YfDPIEhOnIwneRuR+xMaNaSA2N7P+nqxLjJGo9Ydw7oMLcP5fvtavpXldtuOyobgSLy82s/Mxh9PhNiMjy/dVAKBz9/A2KicefOQ4W9GIeOMXOQHJrDYEaLobIBaGSrTZMSNyyrejVWQ6Ca0Z7X+XJMEl0RF2tbWX4eUwL6xYKAqrLNhmiMeTk8yrFGFezLd+jZkjq29NLtIaUmrzcv311+PEiRN46KGHUFpaiqFDh6KwsBD9+/cHAJSWllIxX7KyslBUVIQ777wTo0aNQufOnXHdddfhkUceSWU344LZNwyrqiG2S01jED1y07jlzXK5hMIKk/U5eZKXUBMkL+QCd3KqSoh5UbT/FTo6pBnzYiJ50TY/3mPk+x451YChvXOjZc0lKFawCjXPc0ll+7zpSCW33gv++g21iTsh8mHFunyyNw3Km0E/UcZfrwT6WyUjPYDV93QyrprnoFPPMA0aw1lN0IJ0j8vReNf6U5cWIa6UF5yilQ0BdMumaRlbjKR/VjSCivabrDgvFpIXs9xGekJFO+aFeF6XvKia5EWKSV6Ye2kcyUt9gH+glSVJf84pTeaVcxRSQVUtmRfeIbc1polIucHujBkzMGPGDO69V1991XBt8ODBphblrQFWaiM2RHqjhfjbLL9QSFGTmtuI9O/XJAF2J3RuPWFnpyoNPJWJlbE2ELN5Casqk5eDP45mQeo0ZpBlHhRFpcb6yKnYaTMZmbbZExNP/aQyr0KexMjxsYpNYQayPW50zDjrswO1CWkMI9GIU9WULEkMs5EExsohM2oueUlMbcTLUxRhXuyfTWX0UrZuqzXM60dlfdDAvLAgJc9Wkhq7IHiJgM9IR8BGA9bat2NeFEVFfTDMZXa1/2VZ0u0a9cNXdP6mxyF5kSVAu+N0/vNjOTk55KiWmcx5uY1aIe8ichvFCyu1EUskrU4VlNqIsUsxUyklAjLDsp6zIoGNmmSCnEhe+CoT8/JhRdUN3SJqo9g9U8mLidooyNlIgcj3IJ8hA0apJt8jcs+841ZqI576gyUuJKG1JFoOiBKdC8l6/JNi82ITldPx1JUYqVASKKXTFBtm46CpQOIOsMspL8sSt53eHWhVOO+b2bbvcKjYIbU6efOG/1RdwHCN7S6pJrc8uNnM00RA1lPHqO+X7KnQ/1bVWFktnYgZ83Lnuxtw/l++xvbSmAeMVlT7nrIUYwS1Q5ZG97lqIxObFzIoJ+/gxwNLF1VVdUTPFY7khWRmdOZFSf53SiYE8xInzD4he6oHjItixb4T+OMnW1DrDzGGnjShpdRGTbV5CRmJeCJqI3IjNvOgIsG3uTBvizypKYpKb4wmm4858xK5zkpewgbJC2HzkuDJ30piw+s3+z3Jk5g2xryNzpHkxWbe0MyLfX124L27anLfChKkpM55tm0rG5p4iLITho+3Mtj5rGHK2bTXJW+8kpVvmmUI3RYnb957nqo32gCxtjFkbjcrw1RyWSbrRE9+YlZtRB5Swqqqt68xHWYb/hebS1EfCOPFRTFbpJhRbuS3S5Z0aZs2b7Wx5quN7FWDTtcN+00V1dmzimJkXngZ4lshv0Ih5Wqj9oZ4JC8swbrhPysBRFzo0ojJQnkEKYqlrnHmexuhAnjy+uGO+kt7MkUXnoUnhhnIDcDJpOaJ6q02CrafTux+2Pp8jNrIaIOiUiqo/cdjXm3xhE8nQWdudeBtZCF5CYQUwMdPTOhkk6VyIXGK02qjZEheYn/zCJ7TNmQmt1FSJC9EFVYqDPYTuWVJXyf1gRB1IlbUWDRVM/DUomFV5dINdgPhS16Sw76YqY2484TT2aoGo+SFBS15ceZpmSxbCrJObU11z/Ehw+vGAUJtFPFkjJR1avPCa0f7XyJsXrS5pM0fnuTFH00ayUqvqcNTgjYvYUV15CnJs3nxumTURRVXfClq6+NkhOQlTpjavKgctZGi4pMNR7GHSWB26ESdqWooFKZVG+SkrqwP4KMNR/HxhqO6Tt4OPIPdRAyCg4xkxA5cyYsFTxBk9Mr0+PAfZNtgDXYNiztML+79FXW6iJlO1haP5MXIHOr1OBgD0j1W22R5evF4JS9cN0qKsbCvzw486RiVW8Wp5IXxNkqG5IWXd4kHVsrgIw4VFTX0GnPEQPIkXiaSF9buJBHJi9ORYqt2x6s24kheWJAGu1ZG16lWG2nSDbcsIyedjhsSVmK0xWOjNuqabYwfxnOVNlMb8VylAb5UKpFkuWy/FUYtboZIN83LhQXz0j5htv/yuN6vtpbhnvc2YvKTi6nrEiRTg8IQI3lR1dhGxDvp2oGW6hglL45tXuL0fOExHJZqIyaWQpizMdrVx9q88Lx/2CzQ20qqDWXji/NC1M8850TyUt1gZF548YGcqCxoHTXnfgJSESvQ6keje6XTuSWBtglJRl5Gsm+NDo1HAXpt1AVCFPfgZM3xykQM0I1lWQaC995JErwY3tNrybwYO6vFvVEZ2kSCNNi1tnnh/90UUAa7UebFJUtGBlGNMZJaLrR47Aq1d9b+d0nxGewCRo8j9qCRqKs0qxa3es5w0CJNFzTbyBR8p2RCMC9xIh61kVmGXVmmCQQdSM4oYtYmKbkMnRpcBih7GqPkxanYlra4ty/vJMYJXZ5wR1RZbyP+c6aSlxB9AiLLs4t2c9RV2Ymkh4eQonD/BpypziqJE61mXM3GqQCcqeris3lJjBo9v3AfXl12IFIfUaHGGJP1Os3uLaVAbURnGbaSAjC/iS6zJ2RH34Bn56Tw38nDbKy8byLZyF6cfke2GM/mRauLV2VlNO6NFQNHMy/OJC+pyCqtMQduORaDRW+bkLy4ZU3ywp+nvG/GukpLRJwX3R4m+n+2j2+V0RiwNi9I1FU64pBgv+YU1XjQpt3BI//TjGrr414E8xInTNVGimrYsOtMfPpZA8UQw2DwdJmRtonJ5LC/QSqGDE/y4myDId/NqTseC2u1Ec1QOXFdNjAvjOTFIAlRjGJVzZMg0SBp5Dux35/LwDFVk1F+NVsXnlFfvGPONfql6rOtzoBj1Y3421c78eDn2xEIKVzbKbJap+MoSfSc3HWsBrM+3IzDJ50naWNBvr7TjRSg10NDIEyxDk7UWby5GlYU7ngbJC+8+m0kL04/I/uePI9BHp3RoElerMagxqnNi808TQQ8LzeXLIHNykAejDS1kRl94V1nbV5ccszmRZs72jiyKisN9UF6X2DH1GmcKYPayKHkJVKOsc/jSVGJ+62QdxHMS7ww+4YhzoSoMws6xZw02Si4Zpx4ImqjAE9tRHQzEfVTKgx2qUB9isp91z3HavDKsgN63Wx1BskLhwlkF7dm8+LEu4kH8puzpx4nqjNK8mLJvNj3hYoPZCN5SURtRLqgGuySwsY5GnQoeQlz5vy7aw7jl9EQ9omAHAueAbQGslmVeaeGQJhS2ySqLuW9H5AcmxenYKv2RHd1lVOGN9e0ecqOF4lap95GKv/vpoC337tdsoFJU5TYd9TURmaSPiuPSe0ZMj1ARU0Ac5ceQEWtHwCQY5JwUlvfzy/chw/XHTGGdEhQ8qKozlWbLI3jHRbjNRVobghvozhhKnlROZIXv3k0RbPNMhhWTC3RE9lg7Qx2ncd5idfmxXzh2/VTUVRuHzXbobCi4vbxAw0nFs3dL8CRMGn1sAxGWFGx51gN3lhxKFYuHsmLhaEvT/LC9okneeEb7DogSpR0jNdX/t9OwZ7E6G+k6cnjl2CFFT5DvIOIrxEvKJsXB5IXRVGxp7yWusfaHjlRZ/HWpaKapKpgVDcJxXlxCIO3EUdtxEoVSFRGvY1YmzESLRnnhScRchNuzGR72itoHjdOJbuR5+n/ZULy8tW2Mny1LZaI2OuWkeaR9bHQ/m4IhLH7WA3+9lUki/MPhvag2kg0SF2YI/03e86K7ocVFZ9vKsGd72wgnml9qiPBvMQJs28eJmxeXLKEsKKanvgkMPYCFIPBWTC6xMS4WdiBlGgEOZt6YjYviRFxK5Eza/NC68Xpd11ffCp6na5PI0ZmBrthRTHGYlFUg0E16yVk9bYhjqGbXo9JlGEN/lCYikFhJXlxGvZbg13StkQIEWWHpDJG1Vp7RLVObV4UVU2KnQtbpwYnG+kjX+zAvKgtj/5cIGxwlbYDX23Ed5U2GuzyJC92Ni/2fYqUc7apmZX1c+zI2FJObV4SiTNlB978IVU6ZHvae7ptXKW5xtcMLSbTA7CQZQlZPjcagxHGr0O6F2XBRjQEwtShhWebBwCFW0oxoHMmzuqVY9I/+rfi2ObFev8IKSrFuGh1s8NhFzE91RBqozhhttRIURwZw4UHNq4FlZhRUU3FiIksejojauKSF7I9Z0Sct3Gbl2czt1p5RJnp5tnEjPw4L/y6qHIJSl4M7dlEGb79tbXUvRjz0nSDXc1t8rmFe7FBY/YoyUsizEvs7zAjHdOkPuR3j0d3n2TehWFerDbSyP8s4wJEVB9Wrt9HKxtw3Qsr8NXWUv0ab+6YqY1Yg12d/yPKJmtvcDI3eeppDdoYsuktSJCHNeeqOvP64gFvHfMMdknPL4+Nt5HZYUsl7GbIxIy89jO8MflAbtQGpj4YBvnpWSY/FFaw+sBJzHhrPQqeWcKtO9JvoxTZkbcRR21EwizMAtteSwtiBPMSJ5xIXnwm/v0aJEkyXRihsMIVB0baJqU1TpkXYxySRILUseXsTnJNURuxm5kZwxGv5CVExHnR6A1vEcdn82L+TXibN9mnHaV0/B9NusWzF3DymVgG882Vh/D3r3bhh88tB9D0OC+U2on5Rpp7Jzl2TiUvrKQtGaC8jSw3UvN26wNhSxuPP32yFasPnsT0N9dz29XAO7UCRskLT8KaKpsX7XuR7xRTiTiTvLAgv7fjIHUJfvfK+gCe+no3ik/Um9bjIvIO6e0ppKu0rF9joarmMVPIw5UkmUcclyWJihukMS8NgRAlsWDHKqSo2F5Sxa2TfhdOvxwsbDu1Ee+eqhoP1S1tByOYlzhh9s1JV2mfjeRFgjnRDIVVoxhRbYrkxXgSNguCZwWecZgVrE52GpbsOY4V+yLu5AGWeSGlQxyvIda4EqBPUmbpGrQ++CwCVDmNcMk+b4iwa8PAaZKwbtFgWNYGu9Z9YomtoqpYureCKkMxH9H66vwhfLD2MDd3jWXfmfa0v8mxc2zzElaTrk8nCW083kYkGoI088KulRNRw0wSvLkTVvjvxybH09c5JXmxURs59Ddi2+ca1FtIXvxBo00TO3ZknaVVjZj90WZuX+wMy53g/k+24qmv9+Da55dF6uRJXlwcyQvBvLA2L0crG3SppxV9VVTao8ks6rJLlqiw+znpESlMfSBM2eKwzHVIUeDk/MTORx5Tku1z4/Jze9LP2TA5ZrY+hkS3QvLStmD2wcjNkpfTQmUIkpnKMagohpMAT/Li2OaFY0/DE/fbgbdQrMDPKh37u7oxiJvmrsYN/1mJQEihcxsZbF6M48H7DiShCHBczkPEN/K5I9+IFxAqHrWRlUs3N0gdx9NLCyPeFJsXdjxUFSivbqTLMPcB4P6Pt+C+/27Gba+tsayf7C9An2CB2LuT7xyf5MVRUccg56t1kDrzOhoCIWazNm9DA19VaOZtxLd5IedIMiQvn248iq93lNv2U3sf3lxrDIWjp2/zdclKHt9ZfZiyg9FASwAT+/DLoox5Ra25C7dbNnobkXNNM1oOKyq+21WOCX//DjPeWm9aH9lnrd+yJMFlEvDP46KZl6xo3Bd2XRgkL2FnNmA85wP2m8iyhK5ZdKRgRbVW6ZoduowHWCF5aVMw++Rh1VryQjIRbFwLEiFOkDptjlqpUsxAZoNuSoRdJ7YiJHj1HjnVoBPGKsJFOKQoRpsXC6bALAw2ma8jaBIvR1vwlpIXZmFbbSBsagcSAZONjOwPAKRH9eL+aLulhBFv7Dl++6qqYuGucpQxjEpYUVFe42fKEvVFK/xkYwkAYH1xJb8BApR0jNmQNaaF9ZxzArPw+U1BvN5GPDQEwzTzws5DzuvxDCbNDXb53kYUbWgi93K8xo+7391ouK6tN54khTcmqhp5xkrlzPvef/tyJ7aX0F5jdpGgrRAKGw93bJ0a3Fy1UYw509RGjcEwbn1lDUKKioW7jgOwk7zEmAuZY1ejweuWqUjG2oGJtb27/Jml9DsqRuk7DzyDXZYG8YyWyThalzHJQSP18g+exizWtl1MKYS3UZxQVf5EJd3UeMwLKRqUJYvASFyJgVHd49TmRYvaqtUdqYfutxMYbV6sy/MkQzf8ZyV+dmE/PHLNOYa6aG8jNsw8a9DG3+xIQhEI8cdRl7x4ZL0uFvHkNuK5C8f6aR3nRSNQpOTlP4v344N1RwzPmakHPttUgrvf3WjIo6KoKo6xDA114o38L0nOiRB5YmQZzJjayJnRJt2v5EteKK8uDvPidcsIhBTLd28I0MHl2DnHm4NmaiMncV60oYvH5sXu21XW89WBMZpCt1/VEMTX28u5z/hDYfoAxTTOUxO+sfIQ3lh5CAcfu5z7HMt0HK1sQCisoH/nTENdYQX4wTPL0T0nzfDePD6Zp9Ih55p22CGzywORMTAxY4m2FasjEueFX87nlinJi8as8g5WJHiOBRqW761ASVUjfpzfh6vKZxkPMg6N3n81tld53UYtgandlpC8tG2YzTmSsPMMdsnYHWyEXRJBDqHTT2QWIlszkJIXLekjbwO1g5n0wyyWjRkD8ObKYgAw2BKwXlFWuY3MxPAykcukzh/mWuPHbF5c+jUW8aUHMJcQ2du8GJmXvxTu4LZj9rnnR+NKsEa+fNdGiboPxHewp1R7Ct9jLiGDXSX5Ni92BrvamFtLXkKWaSp4j5rZC/DKmkXYpZiXJrobmc0bLW4TLQVRMW3eauxiEslqaAzSUg/t733Ha3HT3FXYf7yO+5xVn1hGZuxj32LC4wu5dOVoPXDoZD1WHzzpiJF0u6wNdjXmhV07R07V29q80K7S/G3U63JRB1ndxiasWqqmgyHFVD1348ur8LsPNmHr0SquYwdLc2TJGOuGJ4Fm77PgSV4E89LGYDblSNE0b0KQzIuZ9wFg5m0Ue04v5zisf6zciboALvjrN1RGaqcbB4/rvu7FFTj7gfl6REm6z84ndjisMt5G/E1eg9XJJDstIky86PGY/pp8LmbzohnrGceRZbys3sQqcCA/yjDdHyCWfTYQUtC7Q7ptOyR4Ae2AmC0AVQe3pHNQMYM4jCFAv7NTg10zZtQOVvOWsnnhSF60Mbeapg0BWtJgUOc6tHlx7CqtqXWTKIYyG1eeCjmsqNh0uNK0Ln8ozBygIv/f8fYGLNlTYfKUEWbJHUkm4gRv/hJl2bVlZlPEM9jVimqSEHZ+HD7ZYKlOVxTGVdpkF/UykhdNAhLkxJsiEWLSSfDoyIGKOkuHBLJNtn8qoV7ycvYq5zYvpq/QLBDMS5ww+2Dk6c7HEcWRC5N0tWOhSUeoaxy1kVN3XnIDqaj1J8xoGLluYN2hUwCAb3cYxcx2Rq+s9IcKgqfwI+yS/TXj3boQxmlkrhUgwiQ58TaKx2DXysbDMpYGQUS17LOBcBhDeuZw2zHbp80CsLH2LhGmOPZbl7zEcbLnGX9r0N6d8m6LQ/KSSCZpy9Mx5W1krFwbcyvDSIPNi4PTPjdInQlz5iSStq2XmeVd8+f1aNsmjAQP/pDCHCoidZRVGW20rMB6xWkgDdV505IcWlaaxpsLaR6jwS55WNHUzGxdR07VW84LRWVcpR3avOgGwhwaT4LNG8ZjXhqCYe6Bkid54TFw7CGOvc+ClDZpaOmIu4J5cYgdpdV4a/Vh7KzkT1RK8uIxDiu5MIMW0o4gJ4lbzGDXfEM3gxOjSSeqIwPhJtrnva9d/1hxMbnRRdxwibI89Y9Jn7tm+7jXtT7pgQQ9MeM5XjmnC5MnIVKUSMoBntqEZ5RJqo3M2tUYuv3HaykPDrMcMmzbgTBt35GI2sgfIuZwmL95kOPpWPKiJEYI2W+3fG8Fzv/L1/h6+zHqu5D91uBIbWSQvLDEm9MnExdk3nJwEuelqZGHWSb/kWuGRq6r2iGAPkRYoZFh5rTyLIMw9+ZRmDioq3mfTKRZpBSRK7Uk/jbYvHD67nO7DGojUqrqMZG8HDllLXkh7WZcsmTwGou1T0tetHxSZFgNHvyM2oh3CPIHwwZmgu9tZIxDE1ZjTBzvoM1rT+UwRu1e8vLcc88hLy8PaWlpyM/Px5Il5hEDSSxbtgxutxvDhw9PbQcdYvHu43jw8x1YddyMeYlMBlmiDUdj90nJi2IqOeAZo8ZOZMQ1x8yLfTlHkhemngZKTeZsAZi1abR54bvhkvfN+mzFvPB0vWYGu073UjY6MgD8pXAHJj+5GO+tPWwor9VL9j/dE/U2CimmMWZURKLAXvLPRRj5cJGe98fMkybAbNj+kALySkNAwa/fXOeYCY7Uaa4SaorNS0RMbt4PVVXx6Jc78NmmEuo6+8yNL69CeY0ft7++llEbcSQvHic2L2FLmxeuq7SJ5MVJnBeFMzd4nycehoZV75EnbdZwdO7S/ZZ1+UMKc6iI/M9K7wb1yMbks+h8PVp75dWNdGA8ov06IrI0j26FFHNWmzcmaR6j2oiUvGhZpXnMixVNVNVYe7KN2sjDkbzYGewGGTV6LBFt7JmGYJhrg8IyRbwIwGRYD57khbcmvneu0u+99x7uuece3H///diwYQPGjx+PqVOnori42PK5qqoqTJs2DZMmTUpl9+JCx0wvAMAfNmFeopuF2yVzxYj0qcJCbcTZmLXftPtw/DYvZnCURp3pb3VjzNWZtwB48VOoNhlmxRhh13zTsLKRYGMaUG2S3kZRhot3KufZHZmBVuVF3mHu0gO25SnmxRtLa6DV0SMnjXpOUVXsjhpSBkKKbptgxrywxN/PeNZ8uP4IvtxaBiuwGy6V+ZsNHBj9Tdu8OBxD1foUt774FF5ctB93MflW7OwSNDRyvnFmNOaGVbv1AVrSwE4Jp2ojVeUfEMzivJipVbjXbIaYVd2RzgQRl9zYPc2Y3gz+IL0utDFmN0evS6Zogiblmvn+Jlzw6DdUAkxabRRjXniMb8CEpKgqX+3oc7sMUiFK8iLLhmuA0baHBUmfIokZTQx23TIV84sy2LVRG/k5BwXymYgnHLtPGOeZLBmNloNKjBY4tXlRwQtS146ZlyeeeAK33XYbbr/9dgwZMgRPPfUU+vbti+eff97yuV/96le48cYbMXr06FR2Ly50yvBa3tciUHpdMpcTJzPUhsLm6gHAuHBJdUSsDqeSFwdqIwd1sROajtPCOW3GIXlRFE6EXQvJi5XBrpXkhTw9aKountolGDaq7swQrweY9l7kO2n5TyLMS+R6GqOKU1V6Xmj9dqo2itgr2HaP6Sv9mySoBvsentrIaWJGxdpg1+zWx+uPmq4j8lvwimjMi9U6bGQj7BJ1bj1ahcMnjbYeZvZSvPVqlLxEmRcbOxSnHoKAcWMmpcJmMVPM0Mhs6tq3Zt2KPS4ZnbNi9FLzANxbXgtVBfaUx7yZyOZJ1ToZ4iF2jd8v1hZHQ5rHeJAkv4PHzT+I8qKckyDpiCwBmT5+OhivS8avJgxElywvfjE+jzLYtZa8KNShROszFbuIw2BxvY04cV4oz9g4bF7Y82gL8y6pi/MSCASwbt06zJo1i7o+ZcoULF++3PS5V155Bfv27cObb76JRx55xLYdv98Pvz9mnFhdHRGnB4NBBINBs8fiRo7Pms9rCEZODW5ZgsQ5DtU2xKznI5ujuQiUlQYEou/iD8ZOJv5gyPT9Kmr9+MMn2/CT8/o6Mj71BwIIeq2tH4JB2vj1ZG0shkiDP2Doiz/Id6GO1BVEYyA2Ho2BABWLI6woCBJjEGDeNRRWEOC8ezAYRKcM8yntD4R0dYrm6cFTKQTDCgJE/6DCdKyDIfq0aDfntHdp9Mfqjx5M0RgM6USbVT2GwmGKoNU1RuaEmeSFnUN1DQFbbyOy78eqG/UNXkODP0j8TXuDhMLhyJoj5ltjwNn6C4UVhE3maTAYpMaYxAOfbUOOT8YVTPhzAKbPaEjX1Ybm36whEEZOGsGMRddhSWUDrvgXHVhMq4NV12lgv4ckAQqzQWt98RPjpqiqcW0Ru7gK4336HejvJEHRY/vUNwYQDFuPE4n6xgACPpL5iXxzQ0wUNYzRAzqgYGh3FG49hmD0vbQ1TUmhibVdXR+j4w1+mn7/8ZOteG8vn0moqmuk6IUGjxx5XxJ+ok7ZzO4wHIbfbz6m/kDsXSSoyPby9wZJCaNjmgvL7psAWZbwyvJDkfpDYcu10RAIooGQQtX7AwgGvagnHBDqGoMGGhsgxlh/R8CQTbORqNstGceAm5MtrFA0G4iOQ9A6j1+8iGfPThnzUlFRgXA4jO7d6Qh+3bt3R1kZX1y9Z88ezJo1C0uWLIHb7axrjz76KObMmWO4vmDBAmRkZMTfcROUNwDkcHllFQFCB3v8RCUACeFQAEcOHwYr1Fq3cTOAyIc+Vn4cEdrJn/SRTSJW9/KVq3Bih4odpyS9jk2bNyPz2Cbu86/vkbGuQsZ3uyowIEuFnVnmgq+/Qa61YAk7jsTaBoClq9bpv9es2wC1mF4EO4tlmL1fYWEhDtQA2nh+t3AR9pfFytc3NGLvvv367737D6CwcJ9evrauHt98+x3Y6VtYWIj9lXQ/SWzYtBnF1RIAGWVHjd9IQ21dA778ar5efyAYQGFhIbfsLuI9q2tqo+XM5+6aNWvRsE9FdSDy/hJU7Nm5HYALh4+WojIgAZDQWFcD8rsdPHgIlQHobW3ZvguFtTtQ1+gC7/tGmIvY9SXLlkNVrRlw7R2PNwCPbHSjg5eeO9t27tbbX7l6LchxrqqOvLs/EOvP3v0H4US4W1ldjZJAFbdsYWEh9lSZf9OPl2yCfERTJ8XGfe269abPAMDxksj3P1ZebvhmPpcKf1hCfSCEmtpa/X2WLV+OkmxQ61BDUVERAOBgMX9eHTlaQl2XoGLx4kVUu1XR+VNSF3uXsKIY5p4/HLuvcO6T2HyS7uu6tWvhgowQJCz4+htsrzAfWxar1q5HZ5+qt334yFEUFh5GQwM9B78tWgC3DFzoAwrhhj8URmFhIapqIuVq6v16+TXr1iF4MEI71hyP9WXpigjN0/DeOvM1Vbjgaxw4aqQ3e3btwCm/RF3fvHWb3sbmTRu4715x4hQWLl4Ms3X83XcLsac60tfj5eVYv6LMUNYlqfjqqy+pa7tKI88cPlqCjbVHuW0DwJ59B1BFrPXvFi3GnkygLgi9nT37DyJcoVJ1rFy1GmXH6Petra3Brp07qHI79+zTy+zasQ1sP2rr6sHSlOMVx7F02THqPb/59lt0Mhd0J4T6+nrHZVMeYZc15lJVleueGQ6HceONN2LOnDk488wzHdc/e/ZszJw5U/9dXV2Nvn37YsqUKcjJ4budJoKqhiD+svE7/feZPXKxlQh77cvIBOrrkZWRjrwBXbHsGG2smXfGYODgHgBAbsdOEV1o5QluW6GoVMYtSwgpKkaNOg/jz+iC9F3HgZ0RQj34rLNRcEE/7vNvz1sDVJwCAGTn5gK11dxyGiZcPBG9TOKLaNj77V7gcMygr/+ZZwH7dgEAhp57LgpG9KbKb5m/Gzh6kFtXQUEBVh88CWxdCwAYN348di49AByLMLVurxcDBvQCSiInlX79+qOgYAjuXrEAAOD1peGiCaOADcsM9fYvqcbzO1Zy2x1y9lDUHjwFVJThzNPysPTYIW45l9eLyVPGA6u/BQB4PB4UFFzGLbttQew9fenpKCi4SO8nDyPz83HpkG6RcP7rFsMlyxg5bCje378NHbt0RbDaD9TVoluXjiiuq9Sf69uvH3CqATgVmTN9BuSh4AeDTNtSJBdI/4zh+edh8RfrTPsFRMYPAOYuOwhgd5SRiqF/3mnAkYg9z7nDRwC7Yon3fOkZKCgYj9+tLoJmiLH0mDOtdGZmFrp1yQROGl3uCwoKsGRvBbB9PedJ4PSBeSiYOggAqLEYNnwEsJufGBAAzhp0GhaVHUDnLl1RUJBPPds1Ox0lVY1QVAnHiSDFF1w4GqP6d0TuvhN4YSc9lpMnT0ZRURF69uoNHCs1tNelW3fg5HH9t9vlwsSJ4/GXjTEJzvFGCfNreuOX4/OAzZE5rELC1KlTKZpZ0xgEVkdokSzLpnMTALCljPpOF5x/Ht7avwmhQBjjJ1yMqs1lQPFe8+cJ7Al3xetbT+q/e/TsiYKCYXhy91Kc8Mc2nSsKpkKWJZysC+DP6xZCUSX84AdT8fiOJUBjIwKE1HnEiBGYOjRi3Fu5+jCwNxKkcfiIfEwa0k0vZ7WmLhx7EXYsPwQcO0pdzx9+Lg6dqMc3JTEbtDMGxejwBeeNwtxdtB0VAOR0yMWYsWfp34DF+IsmwHvwJLB/B3r26IFrrzgHD6z/hiqT5nEbvkvVmsP48OAOdO3WA0NO7wzs5wek7NW3H+SqRuBkJHbOhaPH4tw+uZEwF2sXAQA6de+Foad3BvZu058bdd552LT0IFAV+0YdcnMw9Oze+PjgTv1an379gdLI/jRqxHC8s28L1b7H6wMYKUvnzl1w/oUDgW1r9WsTLr4YfTsmT0AAxDQnTpAy5qVLly5wuVwGKUt5eblBGgMANTU1WLt2LTZs2IA77rgDQORUoaoq3G43FixYgEsuucTwnM/ng89nZP88Hg88Hk+S3gbo5HLDJUu6PrBbThr+MTYPryw7gG0l1fBHI9l6XDLcLiNHTRqbhVW7wGfQ6wopYUiyCx6PB5Icq1eFbPp+JKFzotKWXW77sZLojaiWeCGF0xcrtZjH44Ekxd5Fkt0IK0yfifYUSFT9ihp5hldv385Zpu2qhEIv3Wc+9UNhFS5K8ieZjw/Rz5Ci2o6jJMvRbxkRj7pkCem+yDPBcMxjKcPnYR+k7Bf8Yeu2DHZTkG111Fp9ksRnOohgzVCZMooKuN3uuFIr6M9GGjXtk2ohvfF4XNxxkEyMKDVkp2uiRuO3zfR5cMngXHy94xhTZ6QtH6c9rQ6z9cYOi0uSuPUUbj2GGy7oT11zuz2U0aUcpCuzmgcKM64etzvqoh2GKrkM69oKK/afpH4ramTsSJsKtyzB54uMbQZpc+5yQZuS5DzUxhSgbVoUyZy+sQipMlSO9DEzzQuPm8nvRcyldHaNaW2rAGRzaZTL7dJpsdslIzsjzVDG6zb2Py36O0L/zeljWKGN3VVJoxmxAWoIKoZ1KsmyYZ65XbIhBYD2HWQJ8HmNdNDMYJek2QDgcrJvxIl46kuZwa7X60V+fr4uTtVQVFSEMWPGGMrn5ORgy5Yt2Lhxo/5v+vTpGDRoEDZu3IgLLrggVV11BFmW0CE9NrCyJOHH+X1w/Xl9AcS8Ptwuo4EUAEqHaRVkjQSZ9RSwjjpLQiIWhlU5zZBOm6xF24/pLrgs2Ald3RDTTQbDCoJhBR+sPYwjp+qj5W28jRiPCjtvIzaMuZmBZ+csH16eNop7L6zE4rzw3Ltj7+M81w5pAOjEiFqrVxsetyzpRnOBcMwbKo0xpFOZQH4NZq4XJgiEFccRds3enbTbYJmjYNg6aqhle4p1XB0rhshjwqTYeUJket2m5VyyhJ9E1zWvTjahopO+GlxYZcmMX0NlPa33J/sYCCn4L5H7yo4hJdODABG6pRkKh5g0HPFCe5Y0iiXHhk6UqnJpAvludTbeRmao9YdM4rwYDXbJ7+Bh7Mq8hDeQtbcR7SrNA1s3QETYtVkrgbCZt1HsWm1jiBNCwkjvJY63kVa322V0JQf4dExR+YFKWxIpVRvNnDkTN910E0aNGoXRo0fjpZdeQnFxMaZPnw4govI5evQoXn/9dciyjKFDh1LPd+vWDWlpaYbrLYWOGR6ciIbW1+amNnm1CRHxNuIwL4RhpZWrNAltMelxXqiYIk5jaJi343XLCAXCCCsKNh+pxC9ej4gEySRqGlivhCqCeQmEFLyy7AD+WrgTPreMXY9MtQ9Sx3jpkESF9T5hx8vK2wgAxp3RhXs9SBAl1puHRMDGG4xEvIEDY8HconGBZEl3VyS9jdj8WIqqUgTdKlMyDxE3V2dlzTZ+ylWa5+mQgNRFq8uqb1aMMG+tRfpj3WbMVZrPvPTrbBSHa92wsiAzm5csU2MVmZVNpkhW+Z8l+/H4/F36b7sRZ+PxyBLtstuUKKm6q7TMZ1go5iWkcBk7swi7TgMcApGEr4O6Zxuup3mMrtKUtxHDhPo8coTJ58Q0AWJqfPLwxDIGGnjfljyMJuJtRPa9xh/iuEobvY1cktGVXVvHvKzbAH9vUVthnJeUMi/XX389Tpw4gYceegilpaUYOnQoCgsL0b9/RCxaWlpqG/OlNaFjpheIJh/TFoUmvSAlL/w4L2SMDGebo7b49fgPpDTCaQwNG+alPhAJdrT7WK1pOcBa8hJSVCzfF7HF0Jg4Oy8nNqEhFXqeWdxhxrXQLry22YYWChvjvPAQYaacjS+5sTrx7NIWvH6KlyWkRfvSEAzrTJxR8kIzD/WBEK57YYWjPgLGOC+WfTQZ24CNq7RThprXnhUhtHL3Zzcgsk4rWOU2cnHcSyNlo+vQom6rbPFsG2bMy58+3Ub9Jsfmu520XZCdez47dpIk6ZtowCZYmh1iIfJj70F6yWnjqB1O+NGHY3/bxXmxAi+ZpM9tPEhqc1TmMI9pHhdqohINHsPs0w97tKs0D7xPm2icl6AueYk9U+sPGmgUj+lycRgUP8G8sJnNAXNXabbP7T49wIwZM3Dw4EH4/X6sW7cOF110kX7v1VdfxcKFC02fffDBB7Fx48ZUd9ExOmbQaiMgxnlrH9Yt8+O8NAQZtZEJ0SShxSHgB6mzUBsR1VidWknmiO3zB2sPY9zfvsXOsmpD2wAteQmGFMMi0Pp3/oBO3LbJkP8KE2GXTVxpYGYs1EaA8aTBq4cX34AEqSKxTABIDK9ZdFwSWlXa+LhkWQ+AeLIuEFMbGSQv9Gn0aGVDxOjZIfwhxaAPN4PZa5Dts5tLKKwkLHkJm5x0NVgxkmyIfbJOK2iP8b6ty4yoO2JeTOK8GE7EkunGx4Kc6zyGx4pR46mr9DD1DiXAZgjrc5iun4SHYJR46+NUfQBvrDyEU3UB1PuNaslj1Y14bfnBhPqX5nGZxnmRJWNYf00aa6bW16ShqhpbxxqtYd+b92m1ORVUrNdKIKxSkpegidqI/bYz3lqP4pO0t47EibCrrWO3S+ZLXszSAxiSYZq+QrMg5d5G7QkdM4yBl9iJ4XEZJwvAxDYwCVLki0pCYnVFFhN7WgecpwewktBosU7CimpY5Pf9N+Kh8P/+uxmf3THOmnkJc5iXaLuXDe2BxlAYm49U0feJ+v70yVbsLIudnAw5VxSj2sjq9WU5sjGwZSIRdiMLkJePiQR58rEaaZIJc/JNSqsa8WTRbgzrmwsgQvi1ZJKn6gO6RIBlrlRGbVTb6Dw+BxCJP+KU1qgmJf1ETBxe4MB4ElqSCCvmthusStF5ndZvq0kLFNXIwJhJXrRyVkwquyn53HIk7QNzXZYlx0kxyeZ4j4RVFbKJMsvQLqU2ii9InaFdjt0HW5vHJaMxGHl/3vx45IuIx83nG0vQKTNGX7Vvfv2LK3DwhHP3WRKRxIz0Na0PkbD+jNooKgENmdjn6MlcVdUgdUr3uKicY7xv66FsaszndCAUZiQvRrVRrT/EHU+SLgNa1mtG8qJpCWT+XsUD3+alHauN2hty0mLDpXGsrPGex4SbpbJKm+iaWebFy6qNqFO+M4JudbJy6YZ7NPNCEv7K+iBO1PpRXtNIPUumBwiEVcM4aIvfY6JGI9sgGRcgmjSOtXEhJTM2OmMgIgHj5d/RnvNyPMJIkCcfqzVK53pRbTeDv321k/rtlmV0yvTqzJb2/Y2SF5p5YTNm2yGeCLumaiOLjNFhRXUkeeK2ZyFJC9pIdMwYJjuRtqwzL0YVodsk2Z7WlNXGwxJ4tyzBz+lnopIXLvOiqPCYTGdWQkaqjYKKs2zeHpfEPY1zmRemWCxzc9hy/q0+eBLjCVs1rd+JMi5ANDEja7CraEyHkXaT+a60Me/bKV2PpKyVJ+9rzFEaw7zw4NRgt6SyEceJrPA8tVEwrKLWb2/3xlMb6ZIXEyadh0hiRkbykthZJWlIudqoPSHdG6MQGsfKLg4zC+4GJqu0WRZUEjqnHp20lDTCRLT3j/m7dPsTwPoEqvUzojaK9Zm1rch/5GvM30a7jZIeEaGwYiD22objkvlE2k7FQBJ7vuTFTi3At6LXxjLCVJk/T0betWqL3cjMwvVb9dMlS9SpEzBKXhSV/i41NoSSRTzMixlhJSUvGkHVpnrTJC/WzIuV5MVMpWTH3GrfXuHYN9jZvHC9MaLt8cKz865b2byY1R3pt3m/NBRtP4Zxf/sW6w6dNIydLMVUbUGTsPos0k04I96zLNOo0bAGs9j+BBoSNNglQRriRyQvrEQ4Jnkx2rzE6K32jbtk+fDRjDH46p7xOr3UMrxr9QBAbjotB+B9WtJg14p5YVU/2jdkaY0To33JymDXJA8fD4pqXGstLXkRzEscyCSZF8ZgV4PHxIK7gcltxONaWQ8YTV+sLRQ7z5bNR6rw7+/ogFNWi0R7B1ZtRG6SFbUBw3OAMc+Nmc2LR+YvEHtDQ4ZZYcrX2xBDnjtriDBQdLvMU9kDtM2LpdskcysR5gWIqY40kIwyEBHH+xMk6ECUeXFQTlFUbsoEMO1rcShIA02z55y0aTbEkQy7zm06NNiZ38QkL0aibGbzos1B3nwI64yNkRHi9dPMsJ8Hsjkna+kXr6/FkVMNuO21tRzmRYKXcJV2sgGxaSLYdqk8UkwZzW7PCfNSFyfzwqbQYK/xEjOS+ZjYb6wdHkmDXLcsYWS/jhjcI4eaM9ora7T+6Z+MQBcin5Olwa4Sn2ceT20EOGNeIow4fY30NnIqeeEZA7d0biPBvMSBDK9RbcQyKh4HrtJmkgN2A9MNajkJ23g62ZN1RkbDyuZF27zDikotNj8nKZoVAmHV8M4a0XS7+MycnVsxHffFKGZldbsseJsPSTRcMv87aSClDNZqI0by4oBIkzBjXgyu0orK9cDI8FqrvzQEHEhe7v94C8Y89i2OMSpCsg4N2iZNZqUlvUXiQVg1d9kNmXip6PdNXspOfafNd4VjiOiSJF2lyvYT4Nu8mDE22qmX3XjcsuQ4Ppyd2shMfN8YDHNsXmJMOxk6wApmzMv64ko8+91eOsyBieTF7rABRDxoNARCiq0a5tqRvfFTJso4SWusEjPybF5Ig92wrhaKlZGJwx7rbTS0dy7W3H+pZX/dBCNrpXpkwVMbAc4OSi6OhCkW5yUetZGRMReSlzYEntqIFcm5TQx2yQy0oTCfeemeQ0dq1DYGXlZpHtHhnUKdSF5Ybx5/nCfoYFihJB1kTAAztZHd4iXfhRc0qtqGeSHT1HvdpKFc7ETFY3A0kKca7dTx05dX4v/9dxPzHvETFBJunXmxVhv5TVxH7bymyOdVi6jHAPDWqmKUVTfii83G8PYAnXRQ+z5k+/EybhqsDLCDNga7Zi61TtWKPENE2UzyEi3Gm7shRcWqcgmbj9JBHmNqI/oZT1wie5J5MWeqWPDsvkhbj2DYmeSlXyfzEPCPz99FjZ+ZzUu9g3Vx5FSMRgZCCsqq+Ey0hiyf2xAMjhwdvuQlpu5kpbOanRnJnFDMS/RPlWC2SVpPfhuJY0DtJry84rEPM2VeHKy3DJ/bNKu03QGOBM82TDAvbQg8tRF7QjMz2CVh5irdNdtE8sI51fHEjjxGxSoegU7AmQ0iXn0za/PiJ4KtmRHpeNRGPJuIeCQvms6ejN/gkiXLSKkks6CoKjYdqcSyvSfw/tojVDkD8xLnBq6NDSt5YQ122azEZuXM8PrKYhypi6trBtBRP2PfV0NdgsyLVZyXYIjvYhvrUxi3vrIaBU8voa7buQBr466qfOaIa/MSVV/yDCUVVcXb+2LfQpaAP14+RN/cDEbBNjZXJMhX4T0SUhTc8fZ6/LWQzpXjkiWDYXVEbeQskqyGgV0yLe8HQvaSlwYHUjny0WDYAfOSZtyYSbg43jTad5A599J0OqEQzEtsfmtzJkyEcjDzGLOyeWEdEOwQUxsxNi8OYuFkp7lNDXbNPGN5UFUjrUvQPj9pEMxLHCAlL9pEZsOTRyaEdT1BxeiiKEs85iUmpgRoPb5TyYtGxHlB2cwkL/EGiAoyaqOGQFhnEsz0qnZqIzZonVWQPB5cXOZFoSQvLgubl38WxaKYssaybHRgEk7E4yQ0gtaF+fYGyYuJNMyp5AUA/IrD3dIEyVQbDeyaiWduGAGA3gxYPD5/F55fuM+0nu0l1fhu13FsZ9JaxKc2MuryefZQiqriJy+txJ8+2Wq4x87Pd35xIW4fPzC2xlibFxNbMB7IOcbbq7ccqcL/NpfipcX7mTYko82LDKRF6Vh1Y9DRBmSXtJWyvWDq02hYvOsiEFIiyUstkJ3mMRxA2DFlN25rV+nYYVGPw0QUIW1eeKkRSPCukmkZ7Ohf7w7puDGqEjOTvDQ6GNNsn9vUYDfiiWRbBYCo5IWRHrb7IHXtCRmU5CXyP7t43C4ZLpPAWRpU1ejq7JIlg+pA8wrQ47wQk5c9jdb5QyjmuBXqQdk4cU006QRrUxKvGy4bTr8xFI4F7XPx41nYnfhYbyOr9AQ8kJFXNaaT9DYyM8rUQKr52P4GGcaKRLxh+zXiR3obyZLRGLExKnlhu2wVKTjZoFylwzECqG0C8WxQWT43xp7WGUBkPZgxG19s4auwNJAupSTshIfkRmS0H1NNVJ2qaWBA1lhZW7va5sCuV4/LmNuoZ64xwV+kj9Zqo9tei2X6JeepSza6OLskCf2jaqBDJ+odxXkxi2KsoYFRsdLPOrd5IREIKzhmx7z43IY1zA6PIc4LZbDLBqmLqY0qav3R5+mIwQDfVdoJSLWRndo80+fSaUDM24ivov5DwWDce+mZ3HqyOdIpTYrriVNtxEqLhOSlDYFWG0WGjl3YHoeBf1jphixJBsmLl3WVpoLU0c9PeHwh/lm027Q93gmddOMkT59sbhU7BJnomVpQKkA7YRqfsTfYpSVBBslLo3PJC08c7I5j4QL0xu03kcIAfCL9ws/yqejMJDTim0kYg/MiX2pMkc/tolxXWaM7luk5P48f4TgR0OkBYjr/RJgX9uSbSCA6ADhey2de7PTxOvOiqAgwyQtVNRoPhfkGVlWyxvJsEEtW8sKqU7vn+LBi9iQ8fPXZhrrt1EYkyG/kliWOzYuEvKga6EBFnaMIu1YSSoBWlbK1aZK5eNWpgZBqOKCku1TM/kFsk85KcxsYEJa5M0vMKHGMsjWDXUUF/v5VRPJKzgHSvd4uMSOPySRtjexsXjJ97pikJrrW2DWiMS9pHhdG9OvArSc7zcMZA2cHOBIqjAy4sHlpQ0jnSV4MaiN+egAA+PMVZ+l/s8xLRPISY16yfG59I9I9GUhvI4YLrjAh4hp4thExyQt9EmBF8HYIMdEzG4NhvT4zA+Z4crI0BMOG8mzmXRbkd0n3xE48lOTFTr9HgPxeZiokra8sLju7Oy4Z3J1br7aBk1I9r0s2EBXtZO91y9Q8ZOMKse903oCOuO+yQfyXihO8fCsyscnXxxF7hk0Kl2hGajM3aivCKkvERsQJvqU9yTK3Vhv9SYbh176DrJ/WwdynmRfNwPOm0QMMcVW0d1lz8CT2V1gbLpFz0+XiqI0k0MyLg3F3yRE1nxmsDHabInmpYQ4oskSrKbOIDV4Du6LNEjPKktHZIs1CtQ7Q31J7ZTP7Rq7Nixw7jNrZvGR6Y8bIARPJi55PT5aRlcb3CONJXjT4ON5YZhDeRm0cpKu0RmzYiWEV+Cc3PXb65olzSeZl7Omd9ZOBNmdIEW+8xJ4XE4GM80L256mv9ziqU3vNABMxsjEY1omEW5a5p5B4bF7qA2HDQrFj1iibFy9hsEu4cMcjeSHtOYJh58yL1xV5fzOGVusDzZAY+6YRKq9bpjY3D6O3Zr0vXLJMpbVoCniqM9Izx4lHiQbWYDJRyYsZeJtyr6haZtroAVR6ADMvCiMDaf5+p+rojVb7DmZSWA/jhUcWY12TFRXYf7wW//fCChywYV7IMAcuice8xCQvRysbHDEVsiRh3s3nOVovRrVR5Jl4vfACoTCqGfW1i1GnRjZmVvIC5hlG6kBITIyu0jbMCxHU0zYxI+caHaTOer773LI+h3SbF5M4Lx6XhGwTd/bsNI8p7clJ84g4L98XkGqjmMU2x2DXZELkEMwLG8NAliV0JmxeRvbrSETA1TjvWPl4mReezYvW95N1Ace5kkh0jTJbbAj3xqCi2/SYeVWY6XzJWAgaGgIhw/uaBc/T6yFtXgi1kVObFxZ1nKRxQOw0rhEP1qtCI95mDC1P8sJTaWlSD69LpoIZssH2DPNRluIy6nWKmOg5Zt8Rj2qAzbmSaFJHM/CkJC9NG4W5N4/C7ILBlP0Ce6LUk+4x38Bqo2clL7o3oskcM7MFA4A+HWkD2bCiYuV+vq0NC0ryIksIMioxWYpEc9ZSndgxQ1o9A7pk4rmfjrQty456PN5GJIJh1WB752IkL9lpRsnLxEHdAMTU5FYGu+z6Z4OEAvT30+ixmas0CW5uI8Yj0wpet2xQG5m5Sntc1pIXM9qTk25+j4VwlW7jIDcBPbkVa7BrYUuR6TM3rnTJEnxuFwZ1zwYAXDmsV0wywglSF1aUuKy9eYadY6IGk2+tKjZ1xbWCZqMTydVBqI1CYZ1oejlB+yJ5Mvh99xEqHg0NwbDBsJAXkI8Ez+aFDMpl523Eoo5gNgMcyYtGPOoYN1pPdM6YbVRuDvPCY4BjNi8yvG6yLG1T5GXmo8sl2SahTAQBYhPQ+loXh9qIDcGfaDh4M/CWRucsLyYN6R7NeRMrx85FM7WRFXPGzkdtozLbHNhs2GSpJ68fjnP75Mb6o6ooqaQNyM1AMi8eFz/OiyRJOCNKZ5wyL5H67Dc6liZ5E1UbhfhqI5I5z/IZJQdXnNsL824ZhcX/b2Kk7yZB6iRJS44Zu8cGhgToOaCV5SVmZDG4R7bhGrlX2DH6HpdsUBux0hrNVdrtkpBlKnkxVxvFJXlRjO0Lg902BHKiajYIBldpt3nYb1LtxEJ75t1fXogVsy9Brw7phFonUobcwLccqcJ5f/kab6w85KDffLXRDef3Q06aGwcq6rDm4Cnuc1bQ1FwhJoS7P0i4SnPCoIctdL6shT1AJyx0Cg9l8xLzIkhY8hIwkbxozEuUeLCnRV19YKs2Ig12jXZCuuTFLVNMtFuWqI3Q42YlL3JKPJLIE6yWnTwetREbgyPRvEhm4GdtN4Y6UDhxXvRTdRySl1OMDZam8jXbHDwWcy+vSyY+u2McOkc90BQ1ouJxApJZkXlqo2i7Fw50bsgdS4Viv12Y2bzErzZSuJIX8rSfneY2MIEuWcIlg7vrAT8Nkb+V2LyN1Gk85JAgaQQZ2FCbXmz9n98xDjdd2B8PXT3UWBfBvDTaHBa9lNooKnlhaCbpfJBpsrdk+4wGuxpy0p0zL0DryyotmJcEoU0+Q1ZpK8mLRSh3bQ12zPSiZ2569FqMwAK05KW6MYSK2gD+9MlWW3dHt4lxaqbPjYFdswDwbUiG9emASYO7mdarSRsCTAh31tuIXTthC8mL9s6sQbNdqHCzegDSVZr2NorHYLfeoeSF9YLSiJ+t2oggnBLMVY9et0yJzlmvFZYhc6VcbRTz2ohHbZSb7qENdpOtNuLML3LcrOK8xMowkpeg+RxkJS9BIpYGDwbJC2d+SASD5ZR5IRkst4nBLgCMPb0LnELb4J0w+wabl2huo3gPH8GwglqGeZElmi5keF2GPrFdNKqNaFsV1lOP/Qzk86SHWszbiC5/Tp9cPHzNUEOiVYBm/uzygHndsn4Q0WirmWrf66aN37vnxGwn45W8mJXlq1cF89ImoQUNYxePVcK1DBPRHsDXncYCXEUmiWkUUhvjL7MsuUBMlcWLm1IfCOE/00Zh9MDO/Ge9mjqGZ7Abs9Fg21YU84VoFpGUJWR24Nm8kGqjZEleQgbJCyvqtmZeNIJGGuyGmHQLJLwuWvLiYTyTWJsXtytVzIt2go29QzxqI811XE9cGEeuFwD4E+G5xwPPpoAcB6s4L9oyY6eHU8nLuX1y0TcaS8VU8uKAcdYe/XjDUaw+4MzmhZRWRILUGW1eACC/f0eKmbPshyZ5cRDU5LZxedTvRL2N/By1kUuivyvPnd2wGTPrTsvYLHMYMl7EWcpVmjhMarTYLpo62zetek0NbBa92OuSdemcttbM1oi2/jSHkMvO7qHfs4pCnJNuDGDHqp018Jj8JAtL44ZgXhJETPLCc5WOX/LCWwQu4uQFmAfysju1ujiLPNanyKbLY15qG0OQZcmUyGleEcGQyhjsxhLC8Zg5qwiTZrlgapogedHEwaRdTzwZVQHG5iVKQGsagzh0ImIzoHlwmAX4M2NetPclN9ZAWDU1BPS6ZUoFaIjzwnwrtyxzdflNBddVOo4NqmP0ZGqWuNAOZ3bPsrzPixJNx+yInaLZDV6FSpXRYGmwG5W8/G7yGfj0N2NjBrs2TKsVtPbZyLlWICNPc9VGkjbfXOiQzo89BNDRvvU8bjbr5ZVbz8N9lw2mrnkTNNhtDIYN6SZcEgxu5CyzztJRs2HWPgv5Tm4O7SbngHYr4m1kvO8Emjpbk7z88YohXNpMqo0C4UjQPDNVu3bQ+eKucfhoxhgM6BxjiKxyaGWneQzjZaZiVji2YUJt1EahS14MuY3M3WKtbF54hIHMYhr5n/+sLfMiS6bEMtPEVgOIMQxmp0SN8YkwI6TBLu1txK4dRTFnxLSmmix54aiNSFGty8UfE57HAUAzL9qGsGj3cQTDKgZ2ycRZPXMAGMdRe3czuq/1k1QbhBTFsdrILcuWrtLu5lAb6cxLPJKXCPOi9T1e7zm7zZ9nACxxNiJunBdd8sKojSwlLxHmJTfdQ7dj0k2WbvD2l3iYaw3k/OPZ85BVWhlyf/TrMYZ+2EmLLj6zq4F5jlfyojEnrPcWEOn75ef0wA/O7oE5V51N9U3vq2T9O1aXkSHzuIyHPMrmRfc2gq23kRm07645fLhkGVcN7wUAlJG2xyXpaqPFu49j1CNf43UT+0aNQezTMQMj+3U02BeZq42MUhmzg6rKXSeCeWmTIEMsk7DKWWIlpuWqjTRXaU3ykqDaKJKywFptxEOtzrzw+50RfTYQUihmo94f0jcALxNEDbC2eTETw7IiZDuQm1tadNz9TPRR3qI2S3T4zc5y/W/tVP9t9Nrks7rrDBLbT94JjwTvejCkmG7ORrUR6yptVGOmUm1EBvtriuQlXnjd1s8FbAwiyYBjZi6gbNfqLJizk9E4L7np9AHFlAl1oIIh23/79gscqTlJm6vNR6pwrJq2ZSNpk1UfyLbt3L61e1z34Djd6HOi48cjdZqr9As35ePmMQOi9duojUz6HGNeaBWs4Xmifomgx2ZzxA5a/Y36/iHh4auH4u8/Phev3nq+Xs7ndhm+j1kqDFb6z9IwszWWk+4xqo1MmRfjIbndexs999xzyMvLQ1paGvLz87FkyRLTsh999BEmT56Mrl27IicnB6NHj8b8+fNT3cW4cGZuhGjfcH4/ABzJi9s67Hy2iT8+b9PWgxqFNcmLCfNio3y0su8ws1IHgF5Rw2FycZDVaM8GwzRX/sy3e/W/eUH7ImH6reO8sIjbYJeT28gfJNVGxii2AD/KJgvtVK/lXjmrV45+YjRIXmCUrFD95FwPhlXTE7tB8sLE0TEGqZNSpDaKuYpqRFaTjl12Nj+aMAnN5iUemwESJMPGs+nSGEyz6klvI15iRrKMBnYD7he1ayGRw6hizF2l7SUv5LM+j8v0EEGCVBvxDghUnSZzfcbFp1HlYpIX8/bN1q1usOvQ24hNkUJClozvw4Y7sEvMqEErRvabd6BxUeMQ+V9RVT1JbtxqI8aDyCVLyPS5cd2ovuiU6cXNo/ujW7YPN13Y3/JgSYId+xvO74tLBnfDo9eeE+ljHEHqzBhahXPgbNdqo/feew/33HMP7r//fmzYsAHjx4/H1KlTUVxczC2/ePFiTJ48GYWFhVi3bh0mTpyIK6+8Ehs2bEhlN+PCLwcr+HTGhfhxfh8Axoljl9vom99OQN9OxiytvGdkRvJiFp7cTm1kZd/BRvPU0KdjOl6alg+APt2QjIxu88IEqWPbZhdPJGaAieTFZOziZV5otVE0BgtxcjYLJmimNiKhbYwBInBcTPLCVxuZukpzJGKBsIXkhaM2sgpS55bl1EpepNgmqKkZLxzYGTsf/oHl85raKB6jaRLk5p/FyZxLupbzoDWr8gx2mTIaWMnSjRf0M8TzyE2jmRdTbyM2MiwnJiv5aJrHmXecXVJViWiWNzZ/+9E5uO+yQRQzxVOxsDBjbLTN0GkQzE6ZPtM5wXt9dkzs1EgatMMEGdwtInkxql01UN5GJu70duA5eJCYc/VQrJw9CR0zvaa0mQX7HTO8bsy75Tz9gG3WR54nkhmtiEgoW1ecF2ejkyCeeOIJ3Hbbbbj99tsBAE899RTmz5+P559/Ho8++qih/FNPPUX9/utf/4pPP/0Un3/+OUaMGMFtw+/3w++PidOqq6sBAMFgEMFgfKoGOwSDQXhk4Iwu6QiFYkTCJUv64pRUBYrCP2UEg0F0THPh/AEdDVmLJQnG/qrR4EShMILBIEImovAGv03ANsm48F+5OR/BYBBpJuL3939xPrpm+xAMBkEW8bgkaDyAdjAIKaq5mF4JG3Sj/kAAQZPy7DpzyxJCiorqhgD1W0PHDI/u6UGOn0zE+vRImtot8tvrlhEKhbincicbfUMgMrc0SY4sqfBGHzPYWqiRfqkmK11SVe48VcL8TSjTI1Pf0iWp1EbDegtIqgJZTb5bgJ7gjtOmS1LhgnWb2V4JwWAw7pOrBol4p2yfyxC6X5uP5w/oiMV7TkT6TIxzODq+iqqikVEHKYqCYDBoYCdYmx5JVQxzKMNDtyMZYs5GIIP+7ipnHpBVu6A6YvQq642qhTEDO2F5NEJvOBSC1gzPs2RApwhtCxP0TVUi9AcmdA2IejZx5rFs8v5m8LkkdMjw6BG0s3xu/eDiko00UmLmthIO0eNqRoujNLV/p3TsLa+N1hU2HjKI76J9y2AorNNifWwcwvANo3ONRTgM+GSHY2fTByVsHAOfW4YLCsIMHTaza+K5SgeDoZTssU6RMuYlEAhg3bp1mDVrFnV9ypQpWL58uaM6FEVBTU0NOnUyD6j06KOPYs6cOYbrCxYsQEaGUaybDBQVFVG/ZdWFcJTUbFy/Dl6XCt7QFhYWAgDKS2SwQq/ammr9vobdpRIAFw4dOYrCwsM4VGx8DgC++W4Rtz0NjQ0NOHrksP7sDaeFUb17FQp3A/uPRdpgsWzhN9Cco44eibWrhkPQyOq2Tev1Z09V1wEMuZegYv5XX+II0++vv/0OJaX8d6mrraXq8UoKQpBwuKwCgIQ0WUGtErv/89Ma8G2JjEm9FGr8So7G6t+8YR31jrIaRmFhIcrLjH2or60xvAeLjZu3Ivf4FlSccgGQsHHdWmR5+N+8vr4OhYWF2HeY/76Hiw+hsPBA9Ffs+e++/YZb34mSg/CHJb2uQwcOoK5G0vt8oryUamfDhnVo3M/vW1PgD0bmwfHyY1HmKdbmzm1bUXh8i2Wbq5Z8B58LCAZcsBtvHpYtWazXrwYaonx+rJ7yilMAJHQOluO2QUCPdJWaHyf9AOBGMBTC/DU7qP6fPHkKhYWFqK+n+1ZT76d+79q5A7XVMnVt/cql2Em8dkU5/7vv27MLhfU79XdoqK83rH+y/RVLF0EJ2o/VvuISQ3s1pyr0a0ULFuiHjupTxr6tXLEcZVuB6gD0vq1etQLHt8fGjIdwKGDoPwDsMqEvZjhRXgZ3ODaffQiiNvq3DCPt3VNF1790yWLsJgTbR+v4fT5VXYvCwkKo1bExWLZ0CQKN9Bjv2bMbhY2RDNNlUZq1Zds2lFZH1uD27dtQeGKr4/dj61+5YhmOmOS8tBpvEksXL8KONPP7NUFjPT4prH8vCS6o0T7V11SCN18DwSDKj5+g+r5h40a4jyZXK1JfX++4bMqYl4qKCoTDYXTvTuu/u3fvjrKyMkd1/POf/0RdXR2uu+460zKzZ8/GzJkz9d/V1dXo27cvpkyZgpycnMQ6b4JgMIiioiJMnjwZHk9MPPyH9d8gGA0LP/rC85HuceGZbasNzxcUFAAAtszfjSVlB6l7HTvkoqDgQuqatLUMHx/cDGR0REHBBVj40VbgeImh3hHnjwY2rzHtd052JgYO6Izlxw5Hyg8bhoIREQt3ZXMp3t2/xfDM1VdM1UWr6wt3YtmxiKovM82H+uip6OKxF+KFHdF23V4gEMS40ztj6d7ISdfjdqGg4DKs/d8OLI22DQAXTZiAJXW7gFMVhnY75ObgaH1N7HdWOuqrGuHNyAZqatE1NxO1J2IT/JYfXobpHJuO5Z9ux6rjRyL9HDcaz++IfY+MNC8KCibiu/otWH+ilG6/Qy4O11Vzx1HD6YMGo2BcHp7esxSor8e4MRegc6YPj29eZiibnZWJgoJx2PfdPsw/ss9Y18A8FEyNZH2+e8UC/foPpkzG/Wu/M5QfM/IcHKtuxDclEffZQWeejvK9FXqfTx/QD2ui7w0AF55/Hsaf3hkzVxUZ6moKwmpkbvTs2QNel4zNJ2NretTI4Sg4tyf1Piyuic4vqzJWuPSSifjLxoj9XK9unVBZUo0AodbJyMoG6mox9OyzMe3CfobnS6saMWf9YgQVCZtPySCz8nTs2BEFBefjX3uX4VhDLHx+QKUZh3PPGYoDG0tQXFcFIMKsX/mDS+HzxgKUfVG1EVtOlYPF0LPPQsGY/vr7Z2ZG5gkJsv0fTJ6El/evQlVlo+W4pOd0AiorqWv9+/TCllOR7zP1B5fpBp2fndqAnVXHqbIXjRuHob1zUFHrx5/WLQIAjBs7FsP65KK8xo856xdx281MT0NBwQTD9cDGEry33/nm3r9fH8gn61F2KPIOp/XshBPR6N8uGQbau+7QKfx7e4z2XTLxYsoWafexGvx98wpDO7LHh4KCi1G37gi+/WQ7AGDSxIvxxqF1OBWIScUHnn4GCiadDgD4pi5CL4YMOQvVB04CJ4/j3HPOQcGoPo7f7+k9S1Hhj9GvCePHYxAnlQAAVNYHMWe9kQawmHzpJeiRY869nKoP4I9rF1LXuuRmoaBgLACa7pzRtwf2bjPOV7fLjewOWUBNlX7t3HNj+0iyoGlOnCClaiPAaKioqqqp8SKJd955Bw8++CA+/fRTdOtmHuXV5/PB5zMaeXk8HmqSJxNs3RF9b4Rwpnk98BKb6YQzu2LR7uMY2jtHf4bnMu2SZUN/B3aLMF+HTjZE7/HHrT5kLV50u2R4COM8n9ett5WbyTeQ8xIE2Oeh9cIacjNjC0ZzzyP1tB5ZgsfjgctFMxeS7IZi8i6scWl2mgeoakRtlDnMyfACBPOSle7jzifSNicrnY526XO74PF44OYaLNrPTUWNvFcgaueT4fMa2tBrkyJlPS7+6dMT7QsLn49fX/fcDFQ1xjZpn8dN2U9kMTYXaV4P9S2dwueWbZPHAYDH5aIC7AFAhi+yPj6aMQavLDuIzzcZGe5E+tS7Q7oeaTYjLfZ8broxBHogqqbzmoyv1xMbw2BYxaDu2dh1LMI0q4iscdb+gTU583no8PRpLsDn9TK0gf/dfR43VU6OrhUSpBlZVrqPymllBl48JDL1hNfr0WlBGocO+byRb+f1xL69L0rv0rzm88HtMtIvAEj32dNgr1vW7cfSPG50ImhSXpcsrI4yLzKMtNfnpevPzvBZ3tfQGFTg8XhwRo+Ye3K6z2uw3Qmp0OvTv7UUkwx53e649hl2PqT5vKbP52Y6k1ilW9QBADxSkpvB3x9zTTLQkykRNEicPaupiKe+lBnsdunSBS6XyyBlKS8vN0hjWLz33nu47bbb8P777+PSSy9NVReTBnLzcDMRT68b1Rcf/noM3v3laP2aXQIwDQOigc9O1gVQ1RA0NXqzM9JzyTK1KElCbxV7RgOpByVz56R7Yu58WgwV0nuJzbOjIRLkiU8IWYNZzeJec0HOJbw5ctLcpowwOVRsYCvNwI3nfq060NEbDHbdsqmLtdY/M68Hc4NO/vUuWV46txHjKs2+ayKxQiLtmHt9kJAkY7wQbXxH9uuIf90wwvAueSZRRe3wxHXDcPk5PfH+r0ZTdWb63AZvHd3byNRVlv7dlzitazPA7ozFejllcJaSWfsGbyNOGTLQXprHGAqfh+oGIy0g5yblbcQxstXehyyne+ZYeBvZGexaIYfxwOxIbKD9u1ir/tl22flvZlOluSqTc1GSOLmQiIMh6V6vHdbiNdkyJvI1r4ANSGkGNlwHCx4NyEnjMwlmObfIrNJan1rY2Sh1zIvX60V+fr5BR1lUVIQxY8aYPBWRuNxyyy14++23cfnll6eqe0kFOQG9bK4Zl4T8/h2prJ88o1CeVXyWz627Dh46UWfqbVQdZV5O75aFy8/tye0fFUmS+NssGykJkkCQf3vdMjpm0osgg3DvM/OYCSvm+WRY4qNJcrRom5leFwZ2zUSm14Xnf5Zv0etY/Sxjob1DWZVRBO8kSr0/arimGef63OYePdpIm6cHMNtczZgXH53biAlSl+Zl3zUx5qVbjjPmJcPrMrjcepnTJbmBP3n9MHx593j99+XnGOerGfp0ysCzPx2J8/M6UYxxmttlYBJirtJmzAP9vXLS3Jg9dTDSPS48HE2qZ8f4seuKx7yQw0/mu7HbcADau4NNwGkGHkNOSsZIOsMLUqe9Dzls2t9Wc8lsHjtJQUBvpKoeAwgAFS2W2y7TJ0OME5N+aRtx50wvJg3uhvz+HdE9O83ock8Yu2q3nv56D5bvO2FZv3l/jaEMrJDhwF3azguNt7ewLv2RcqrpHPOHFOwojah0tG/a0q7SKVUbzZw5EzfddBNGjRqF0aNH46WXXkJxcTGmT58OIGKvcvToUbz++usAIozLtGnT8PTTT+PCCy/UpTbp6enIzc01bael4XbRzApJMHkLnrfRmdGyAZ0zcLzGj4Mn6k2j0moEyy1LXM5ZZuK8kIQ+wyJlgQaSYWHz6HTM8FKBsEhJjvburLeRoqpcKdL/5YVRzxAfjbkicxJ9ced4hBQlolIygWoleYm+TymHeXHi0qmdxmKu0i4LyUvkf9N4I8SNwT2ysbOsBrnpHtPNoGu2L07JS2LnEysdOokMr9swn9kNi3yV3h0yqLF65oYR2HWsRvf4sAIlASTf2esyqo10V27+OLLxlrLS3PjVhNNw27g8nYDbeUK5GdfadDcnrgrx8h0zPHoaAcOGw2mK9FyLxNMxFhrRrwM2FFfqv9mQ+kAsSGOknth13qk+xrwQkpdo56w2WrNNzwnzQjJXqhqLAQQA/TsbJWJUu8yhzJAuwOYbSpKEubecZ3qflLxo709GsI0/PQAjebFhPJxI2+zi//BIAC/eWLrbaXsxCVRLImWSFwC4/vrr8dRTT+Ghhx7C8OHDsXjxYhQWFqJ///4AgNLSUirmy4svvohQKITf/OY36Nmzp/7v7rvvTmU3mwxaMkGnB+BtHrzgUGaEQTt5HKqos1UbeVwyl4iwJ0SSoLOxBM7qmYPP76ANB81OXF63bMieSjJD2riwvQ4pxoBHz984HON6qKaSF73vsoR0r8uScQFo5oUloNrvck7ESquQ6b+8aCCA2GmMVBuZ5bSyI/wksfjPtFH4v/w++GD6aK66IcvnRprHxQSpky2Z0UTjqHSzCBZGItPHkbww4+3iqCD0e7LEzcDLg0emmTYNaR6XgTm0Uxt5XDI1VhoxNwvIyINblijJCl/yEivQmbDlYDccO7UR2zcNv5syyLKPkkS3JVGSFyMdimVBj13Tht1KWmRGI5yEHiDni6rSa6W/neTFgnEH4peMsMKEIMNAsog3yGJWmpGexQt2TO2kq1zJC4d+ZrjsmSmg9UheUsq8AMCMGTNw8OBB+P1+rFu3DhdddJF+79VXX8XChQv13wsXLoSqqoZ/r776aqq72SSw0ghK8sKZnLwN0lRNEN1ETtUHTTldXfLikrgT2cWcSMjAaCxzcOclp+OcPrSUy0z943PLlIgXoDdPty55oZ8LE0GeNGj9TmfGhlVrOQ0nT9qusAtSW3wzLj4NAHDtiN76vZ9d2B+j+nfk1qnZ2wRCChSCAdPq4xFqrbumEXaJse3bKQOP/98wnNk94n1wzfBe1NzqkhUZa1It42GkaqwEyAkx4oH9rmbI9LkN85kdB15yOxK8TYcHUlVEv7MxirO28VsdSkn7KR4zbOdYELF5iTWQwXkNss+kitWJOo8NCsZjRO2YAy/D3No9y5O86JnRiXomDe5GZZs3VRuZGCyTIGmTCpWKZEyufx75oxKwcqTI8TLvLF3yE9+AH0g0rurRlbEly7VIjhmB9Tc3S8tAgmvzkm7ktNNcztSZGr0TuY3aAcgTEZuZlDdxrIgGCy0TdX0gZMrpkpIXXj1WkpcMBzpiktCSXfC4ZHTKYJkX0ttIk7wY1UaszYu2CbCbL7u4nZ5USEaPZb608Z85+Ux8+OsxmHP12fq9LJ8b//013yZLey4QUiiRvraYzVRHAD86KGBN/J76yQi8+8uY+7xmRGuQvBDfk2UEEpW8dDTxOmCR6URtRPSBR2idqC4B+l0k5p2NKSgiE8BKrE8zL0Zi7kjyQrwqZz/AJYNinpLk/DBE2DVJE0GCp4LhSXFH9uug/+1xyaZGpVZqI/LdeY/3yE3D4J4xF18ztZHBmJtTzsdIXgqidnvnMocoHkjGh8cExysZYWlskJB+8aqKV21Epj/olOk1TdFgBXIeOWGCeXOLJ3lJc6uODjvamLdrtdH3BZTkRZYMBrssuGojk0WQ4Y0ZrNqrjfiZktncRqz9C0/VQ4IkACQj4pIlwwmdzMeh5TVhea5Q2GjzovWJ9TZKlHkh24ycTmK/NQLqdsnI79+RInpWhwlt8wiGVcqNWKsvjSt5MZ5ayfG3IwCDe+bg9G5Z6JGThptG96f6ARjVlKzbsl32ZTOQKSysxpxvsGtu88Ij9k4kLwO7ZJpKGdI8LtMN2mpzIY0WeYbrdhsTm609g2PzMmlIN0w4sysA4II8QlKRgESMx4jyGBpS1SJL5u9hZbDL8zYioYIx3nfobcSqTdgyigqc1jULK2Zfgvd/NZouyFkrlOSF9z5xMhdsEyMJKSxXbdQE5qW7Q7syFhTzkuD65hnsOlYb6cxLOzbY/b6ApCkel0zZc/A2D67ayGSD0BiLBkLycnq3LMrAMWawyz9lsZIXtq0sn1vP28LbqMgFys7XThmMtxEhedHend2gFU56dW3RJEvywkp73LIUc/VjbTJMmDMWGrH2hxTKHkE7/fBsCLSqScKX6XOjKppAz44AZPnc+HomHfyLMthlchuxUoxEdOqzpg7G0N5E/AuPyzS3FDl3eP1j+8BVGxF9liTjHLt17ADcXzDEVDzOUxvx2mZBu90bibndxuRhVDKZHGoqSRLm3jwKpVWNlKGnE5sXFjzpBo+h69ORZjzNhoAreeG8M2/cVVWlTv1mXovsWsv0uXCyji7DU/f0zDXmf+OtFMqI2+bg5QSkU8TDV5+N68+LBTjk08a4qmeYF3u7Mt4UZA32EwHrng5oBrv2zJDPzafrzQ0heUky3EzCP96EdxJbRS+ruQr7Y5KXX140ENeO7K0vXFLywkvw5mLcLNk+kSJz3mIgi7Pz1crmxUykGYnzwqqNImXZU3gyJC8AvShZgipZMGdkuxqxD4RjaiOvW9aft7J5oVR1xBjZZQTnwZBVmhgTVgoSL3G7/JyemD7hNOo7WA15BsfmxehtZLSfIGEWh0Rvw+uydBMe1CMnIcmLndrISZwXKuyAifmC2yWjb6cMahxZKYqTAzxX5cI5CPXuENv4ZUmykLxwJMDR+cJLzEhCUeg1xRs/gMO8cGgfyTime82/M9fbiFHZs4iXeSfJ0k2jB1D9T4raiLB5cerRx4JWGyVP8pLmckaPdEcMYfPSvsAa7PI2j3N752Lc6V1w7ciYoehxjucLQNu8aBt+hteFJ64bjquHR57XmBczyQtLZNkFTUZl5XHe9OZOT1gn3kYs2QlzvI1iaiOGeWEkO4km8iO/g1XgJ7Pl6HFJMbURIXkhA33xbF6yoxFGWemcBqfZdkmQ/Y+oCgn7DxO10fkDIuLva23CeZtJwMyQxfE2Yp+1U0FQBoicAmYE+sNfj8HTPxmO4X07mG5SVnuXncGuras0I9GM5LeyAkkX4ie9PFrCm8u9SObFYgCsJC/kIYj3TRRG8mLm/ccyLzwmJzfdg4euPhtDeubgrklnmPaXBzrGFufgFrfayPwb8sayPmh0TbcCKXnpljDzQq7/BJkXzvc6M1c1JDjlobV4Gwm1URJAfkIr41gNsizhzdsvAAB8tP4oAKC0qsFQDqBtXrRNQqtTOx1XR1UQHrfMFT+7ZdmyTzm2khdSrUKDNOyMSCdIb6PoJGeYeWvJi7XNi1MDVJbJooichYeG2WnCQ0QpDoQVyk1aAyt5GdwjG49eew4AxuaFGGPWKNMJWLUR2Wczg90XfzYC//moCFePz8NHG4zh+sn6AHP3WlmiT6cZXjfq/ES6Ak60YUotxPWeIMpzPq8Zgc7v3xH5UZuERNRGJAHnGuza7AsuWaLDDthQU5qBZSQvDhRHfBU0T+US2xRdkrk3ipXNi9ct48phvVDnD1H2TxoUlf4uPFsWwMggsd6NQMTzZdroAZg2egC3Dg28pUnb8vHURpZVGmAVpJI3x05F4/Y4Bcm88FQ3LHhfjjL8TlRtRFiXf/e7i7H96CmEDq7D1qBzyYtQG7UDsMahVnEtzEAGeiOhGcDW+0N6hF2NGGmbgpbPxMMYppJ9spS8+Eg7FRu9LjNhO2d5qXJ09Fcp+ggjeVGNkhfN8IwX58VODccDu7DIk66TwFksPEQU3UCIz7yQROX6UX3x1T0X6SkeaPd5UvLSdLUROZYG5iVK3LJ8bgzKVXUjajPYeS+wKk/W26hDhvFERzIGvM2EPEnyPq8ThtVUbWTxLKmi4G2+dgyFxyVTUa/N1EaxPlpvtHZgebjeHdK5qkryRB8x2OXXx3McIMf6XzeMwLxbzjO1eSHXlNlGzDIvrHdi5NnE8+O4TA4F+n2TiTGwKz9+jJUqhFeXPxSf5IWktU5o2TM3jDBc60CMYTIkL3ldMjHlrEjKnrYkeRHMS5IhSRJFoJ0kobQCz9tIm/QswXC7+KcsNrS4lc2LncEum6KgW3aMUAbDqmFjBfhxXswkL6zxnluWqAXv1ACPXVZkvB1ryQv/Oqk2irhKhw11kZtwh0xW3UXURTAQLBPnBOSm43HJlNs5qzaKN+Ko3UmOZW4yfS7q9M+LW0F9P0775AbMYxicjJCp5MXifclnsji2GE5sXjSpJwCk22jaLCUvDqY1OTY3XtAP7/ziQi5jl5NGrxezseGtA+eHA2cGuzJhKwbQhx29v7axTsxB0jveRs57n2dvHIkPWE+mKKyWI1vVoO7ZuH6UMWO5Fcj+kt5nZrhwYGdsemAKdY2MQJxoKAQztXCjg2SssTgvCTWdNAi1URLAShbIBWM3tcad3gVL91ZQadxJZHDivGhrtDMT8Mjt4quN7CUvbIZsGiTtM8Znoeui3Xijk5ypL5LbiF4kmqEgKzmQpAhh1LxzElUbkYH5eAnp9OdMtkoyImtZdaOeT4okzCRTwcZJoXNLxZ4xy/FkBdqIUGK82+jxsQoYxwNPIkCOJTs/Mn2M5IWTXZuSvHCaT7MxDnZywjObFlbvS6nDOBU4yW1USTAvdgyIbLPRmkH73uSn+ePlQ/SDzfu/Go1vdh7Di4v2R/tBr3VzyYu1fZsVFJU12DVnQLxuWTdw78TJYu9EfQLYM7E8qSHvfXj532JtOLN56ZDhwfx7LzIta4Xlsy5BSWUDzuqV46h8JnMg6dgEycukwd1wxyWnm94/s3uWbR26q3QL640E85IC0AaK1sTg6Z8Mx3+WHMB1o/pw72vW+cGwCn+QTjbHGst6ZInLLbHZb5sieWFdnIGIHlczOKaNSfkcekhRDDlYNMkIG6vBJUnM5ueMuJJ6/0j9ztRG5xAuwiS8LhlDe+eid4d0HK1swFsriw11kX3vyKhP6BNi0yQvdDh12u2cHB/SEyrWD+u6ecwhaZdDZyc3JqVkDawBemPjrQefh2bGWDghkmbzwko7w84Rp3VqYCUv8SAeW4XYmuKv4fPzOuG8AR1xVs8cDOqRTT0rS5LpR09EfarBaLBrvpV43TIQ1YonW/JCIhEjaBbWkpfY+7LBPeNBrw7plFG1Hdj3ImlLpoPEjSQuOrMrRvTraHr/xvP6QlEljDujCy5/Zim3jLeV2LwI5iUJYDdnu8BJJDpn+TBr6mDT+6QaQIvnotXfmWFeIpIX/gmSkrxI5swLL+gRpTbizNheuWlc5kUDe5q5971N3D4C/JT2dswVD3dNOgPlNX5cPbyX4Tke0V4+6xKU1/hxerdswz0gsml7XDJuHTsAj3yxA1/vOKZf10BKEDqwkhcTewcyJodTsEG9SOkN+Wl5KhwnyQZZkHp9cuwyfW5IkkSpjTpw2rRjPtNsDHadEEnT9AsW73vZ2T1wy5gBVCAyEnZTzS3LukTQCciTuyHOi0U/PRz3ZV6EXs37kGrT0uYl8c2eDVJnZrAL0PO1C495aYLNCwleKhYSsgT81iYXlKXNC0nXHUaFTgVIo2f79AI0eGkBSHjdMn414TTLMpramzUhaG4I5iUF8Lhk3HfZIDQEwtxgS/HA65bhdUXErppLtEaQWbWRWSjwuGxeOCdCj413TM/cdGw6UqX3V4NOCBzMcTNXaVmWqJO7U+YlO82Dp38yQv9N1mvmXmp1GtIWLHuyNfM2YjdxKsaHS8Jbt1+ABdvKcNu4PJs34fSF+B5swD9yE+QzL87rjrXBv69JBSnJC4958VmrjUjJy8WDuuHzTSXokZOGsurGaPuJq42s5otLlvDgVWeb3udHVI2Nh9slxcW8kLUZ4rxYPKcxCeQjTk0dXLJkOq+bxLyodCh5K9UPuUY689RGNhvqrKmD8dLifbi6P98jU4Od5OWZG0bginOtQwVYMcrkdHCajysVIJlGp8zL3350DlYdOIkrbd7fCbT9R8R5aQfgfcPfTDwdv7vMmst3igwf7VWkneAMaiMX3z/CJcvObV44VHHCoK44u1cOfnJeX67kpQchfqeYF+Z/K2iEh5VUeV1y3Bb6PJBj5U0gn4hGMNgx97n5khc2eB9rWDj29C6Yc/VQx/FUrOoys5vhSUHs1Jh2XjAk4dRE1uQYZHAMNym1kY2r9CPXDMX9BUPw4YxYfiknkpeBXWO6ei0cPxB/hFUS5KOaBI9c0y5ZwqgBnQAAfTrYx+wg1048Kg5tTZFj59Q2RZYkXHxmV8ycfCbm3TKKupdo6gjAGKSOpCEsyPfmqo1sJC/TJ5yGlb+/GN1szoF2nnJO9lqrDZmKp9SCzAvJNDplXq4/rx+euG54XPPui7vG4ZcXDTRc19aU8DYSsAUblVITveekuakFa0aM7KL+knpTfiJJF764azwe+9G53AiMt44dAAAoOKcH9bxGs5xw6Dy1UZcsH9K9LuaEl5iIuTPFvCTgKh0dW/bkyLPxAYwuw7TBbuIbqoa7J52Bq4f3wsh+HUztZnhuy7aqEJsNoBsRpyJDl7zEvhlPTUpK9nh7LvlMbroHv7hoIBUl1sn8efDKs3DtyN54/1ej8bcfnUs8a/uoKUgV1+M/Hoav7hmPn57fn6r7H/93Lu685HS8edt5tvWRm7gTb6PrR/UFAPx2ypnR/sTV/egzEQ/EuyadgUsGd6fuJXoQAIAzumdREj8rmxeSZnTJ8hk2ficMvBNmLVG3YRJWQSPJXESsV19zgqQf8aqN4sHZvXLxh4IhhuvauhA2LwK2YBeKtkYlSUKnTK8eI8btkhDguOnbeRtR+YhsCABvcffvnInND04xMFkaZ242yb1uWY+XovWPPMVrlu/ktauGJyb2JE98icV54Uu7yLrIfEes1w055OfndYq7fRb3Tj5T/5tnRA3wDSHtbF7MiPc//m8Yvt15DP83qi++23UcQCw3C7kR806kpOSFJwmxS1BnFpODROcsH564brj++4Erz8LmI1UY3reD7bNmYI2fB/fI0e3OgMj87pGdjt9OGYRgMAijJRcN0kbAidTj0WvPwV2XnqEzcomEXbBiUBLZ7D+eMQYLth/DjItPxxdbSvXrVpnBSeYlzePCaz8/H89+txeLdh+Pu30rNEWSpMGK2R1AJLxsKclLJBFr/GqjZEKbUi0teRHMSxKQ6k/IusqRRLVTpk9nXsxyG8kS4z7JEEFyIdpJBcxO+VyJiI3JS9csH45WNlDtkv08o1uEefn52Dwcq/bjNxNPM40nYQfSRdMqPYAZNELvdcvITnPr9kck80Im3mMZpGF9O6BfpwyMP6MLfj42fjsXK5ipjZzavIzo1wEbiisBmAep+nF+H/w4vw9W7T+hX9NUNeQ34xH1LBtX6T4dM/DEdcMM7rYf/noM1hw8iauHGQ1R7XBrEsaYxytk+dwYe3pn+INK3LlpyLFxInmRZYmSQCUCK7VZx8z4N74R/Trq3irkfLdirFg7ufPzOuHbnTlJZ17MAjC6ZAlhRdWjMVvBipaTzEtLbds+NyOJbhHmRbN5afamKQjmJQlIteHSCSYENXmaIq33zQx2y6v91HXWKJeMNNoUUTILjTM3Gx/ytMZrNz9qT3BG92zMu8VeLG8FUvKSiKGih/KY8MWYF+K6VXTKLlk+LP5/E+Nu1wnMpCW8mCvkJtO7QzpevCkfp3fLwuA/fQWA3pA0ok+C/E55XYwSEb7Ni72r+7UjjaECyPD/LQFeXyVJwpu3XaD/HQ96dUjHHwoGIzvNY3Rhd5AeIJG8XlbL2ed2YdmsS/CXL7ajcEtZ3HVnOzxIBDiBz340sjdeWLQvKVLInrlpKK1qxA/O7sG9v+HPk1HTGHLknmwlTSDDAJSb5KJLNXxu2n6R9WpMJS4d0h0PXHkW3loVCRMh4rwI2IKdJCQRI7OUul0y1+1z3/Fa6jcreenXKRPZPjey0txN8kBgYedsFAjzvWQeu/Yc7CitxhXnmAeTihddmqg2uppQV3XK9OJARZ2hrkSSLCYDZtKwXI4XB7mZ5XXJxFAmrg3JgPncMuoZPSR5kidPov+X3wcbD1fqYcZJ5NjYvLRWmG38TYma/cuLrN1QrZBIs3aJCXt3SEePnMSkO5ef2xOL9xzH6IHWkWIDHDu5M7pnY90fL03K5rvg3otQVtWIM7rzwxzkpHkc28o5VYUcj3rCNTcizEvLqI3Oz+uIvp0ycMW5PXFm9yycYRJWorkgmJc2gIeuHorbX1+r/yZPv6cTERE9csSz4IWfjcSZ3bMx+cnFCCsqBnTJpBgeXlTcNX+8FJJkT5jfvO0C3PPeBjx67bmW5QAivosJPcjv3xGHTtQbrv/k/PhCbjsBpTaKk3n58NejMZII7NTJxPj3NxNPx4Ltx3DThf3RnDCzeWE9ngCa8e3f2RjVuZFIzPaL8QPx9Dd7KIaElKKRtiiP/98wqKrKnT9W0VdbMxLNYJ4InKUHiB9OvK2sospaweOSKTujeMGGekgU2WmepM0xp+ePpnixNQU+jyshb6NkQJMyD+2dazj0tARS7m303HPPIS8vD2lpacjPz8eSJUssyy9atAj5+flIS0vDwIED8cILL6S6i60el57VHev/NFn/TZ6Oz+oZCzHtdkUiqv5gaE8M7JqF+fdchNvH5WH21CEU4eOpaNI8Lm6iNhbjzuiCNfdfismcEzYLjRDwLPOf/+lIXHRGV8P1VIH0NorXUDG/fydqUzaT4vTtlIF1f7yUMqZtDvx2csR994bz+0b/74eze+XgMo4YnfzypOREAzm37pp0Bt7/1WgqOVwtkUGa9DwCzBlf0k7J7yBrbWtBS21QZkikP4m44n+f8cNooD8zdeUrt5yHvC6ZePL64c3YqxhYyXizMi8JhJhIJVIqeXnvvfdwzz334LnnnsPYsWPx4osvYurUqdi+fTv69TOerg8cOICCggL84he/wJtvvolly5ZhxowZ6Nq1K370ox+lsqutHp0yvbjozK7YWVqNMwnxKMm8sBmKT++WhT9ecRYAoKQyFuDJTpRsB6dic+2U/v8uG4RdZTXYcrRKvzf1nJ74eMORJvUjHpA2Lzx373hASl7YPElNTcSZCK4Z0RsXDOykG5A+eu05pmXJ/g0gbFZuGTMAry4/iLsmnaFfc8mSwSZh9MDOmHBmV+T37+j4XTO8LnTL9iEQVqiYQK0dzcm7OGkqnu48fM1QvLR4Hx6yCMKnoaUNL1sTHrjqLFx4WidMHNSNe3/i4G6YOJh/L5XI9rlR4w/h4kHdKFWu07xQyUBT0kmkAil98yeeeAK33XYbbr/9dgDAU089hfnz5+P555/Ho48+aij/wgsvoF+/fnjqqacAAEOGDMHatWvxj3/8o1UzL12yfABqUt7Oa7eeh5CiUpKDrsTpd//xOtNnyX2muU6UGlHslpOGz+8ch7ve2YDPNpXoEoLmJJqUO7iD9++c6TUYSmuYOKgbXll2EPWBsOPkaqmG00jOpNRtMBEt+MGrzsasqYNtT+pet4zXfn5+XH2TJAnLZl2CMDN3WzuaQ200qHs2dh2r4Yb2NyCO7tx0Yf9mV1+2B2R43fjhCH6euZbEF3eNxzc7j+GG8/th4a5y/Xoy8jk5xfeGeQkEAli3bh1mzZpFXZ8yZQqWL1/OfWbFihWYMoVO/33ZZZdh7ty5CAaD8HiMIjK/3w+/P2b5XV1dDQAIBoMIBhNLmmYGrT623oevGoJZH2/Fz8cOSHqb3H4otBHlmIGdsHz/SRQM7Wbafq8cQvLQDH0EIrYYZFsPXzUYl5/THWMHdkIwGMTQnjF7HbOxTSYeumoI9h+vw9k9Mm3befWWfDxSuBMzLz3DUHZY72ysnj0RVQ1BdM3yNtt4NgVaH5VwCE/+3zkRKUi2h+q7C0AwhWqdVNefbPTKjR0M7L5xovP3ndtHYfPRalyY18n22dO7xGyUkjnnLj6zM15dfhAdMzwpmcuXn9MDX2wpww+H90y4/uagD60ZPXM8+Nn5fQAoGJ3XAb07pGF43w5JGw8n49s5w5Xy8Y+nfklNkZ9vSUkJevfujWXLlmHMmFio77/+9a947bXXsGvXLsMzZ555Jm655Rb84Q9/0K8tX74cY8eORUlJCXr2NHqfPPjgg5gzZ47h+ttvv42MDKNBYntEWAFqgkAHG/u34w2AR7Yv11QUFsv4tkTCzHPC6GUTX+xIHZDtAXKbz+NPQMARggrwyUEZQzupGNKh5XUrigosLJUwMFvFgCQ7ehyqAbqkAQmEfrGFPwzsrpIwKFdFCwambVdQ1OZTa649LuFYg4SCvkrKvQXr6+tx4403oqqqCjk51lLtlCvMWL24mUeCVXnedQ2zZ8/GzJkz9d/V1dXo27cvpkyZYvvy8SIYDKKoqAiTJ0/mSoEEIihAxK4kHhWBGNvUQoxvYrjaYbnmGt8rUlZz64aYv6mF1fgWNGM/NM2JE6SMeenSpQtcLhfKyujgR+Xl5ejene+p0qNHD255t9uNzp35sQR8Ph98PqMowePxpGySp7Lu9oJEh0eMbWohxje1EOObWojxTS1aenzjaTtlFjherxf5+fkoKiqirhcVFVFqJBKjR482lF+wYAFGjRolJqyAgICAgIAAgBTHeZk5cyZefvllzJs3Dzt27MC9996L4uJiTJ8+HUBE5TNt2jS9/PTp03Ho0CHMnDkTO3bswLx58zB37lz87ne/S2U3BQQEBAQEBNoQUmrzcv311+PEiRN46KGHUFpaiqFDh6KwsBD9+0dc+EpLS1FcXKyXz8vLQ2FhIe699148++yz6NWrF5555plW7SYtICAgICAg0LxIucHujBkzMGPGDO69V1991XBtwoQJWL9+fYp7JSAgICAgINBW0e5yG2neSfFYLTtFMBhEfX09qqurhQ1OkiHGNrUQ45taiPFNLcT4phatZXy1fdtJBJd2x7zU1EQi3fbt27eFeyIgICAgICAQL2pqapCba538MWVB6loKiqKgpKQE2dnZSc8zo8WQOXz4cNJjyHzfIcY2tRDjm1qI8U0txPimFq1lfFVVRU1NDXr16gVZtvYnaneSF1mW0adPanNT5OTkiAWUIoixTS3E+KYWYnxTCzG+qUVrGF87iYuG1pVpSUBAQEBAQEDABoJ5ERAQEBAQEGhTEMxLHPD5fHjggQe46QgEmgYxtqmFGN/UQoxvaiHGN7Voi+Pb7gx2BQQEBAQEBNo3hORFQEBAQEBAoE1BMC8CAgICAgICbQqCeREQEBAQEBBoUxDMi4CAgICAgECbgmBeHOK5555DXl4e0tLSkJ+fjyVLlrR0l9oEFi9ejCuvvBK9evWCJEn45JNPqPuqquLBBx9Er169kJ6ejosvvhjbtm2jyvj9ftx5553o0qULMjMzcdVVV+HIkSPN+BatE48++ijOO+88ZGdno1u3brjmmmuwa9cuqowY38Tx/PPP49xzz9UDd40ePRpffvmlfl+MbXLx6KOPQpIk3HPPPfo1McaJ48EHH4QkSdS/Hj166Pfb/NiqArZ49913VY/Ho/7nP/9Rt2/frt59991qZmameujQoZbuWqtHYWGhev/996sffvihCkD9+OOPqfuPPfaYmp2drX744Yfqli1b1Ouvv17t2bOnWl1drZeZPn262rt3b7WoqEhdv369OnHiRHXYsGFqKBRq5rdpXbjsssvUV155Rd26dau6ceNG9fLLL1f79eun1tbW6mXE+CaOzz77TP3iiy/UXbt2qbt27VL/8Ic/qB6PR926dauqqmJsk4nVq1erAwYMUM8991z17rvv1q+LMU4cDzzwgHr22WerpaWl+r/y8nL9flsfW8G8OMD555+vTp8+nbo2ePBgddasWS3Uo7YJlnlRFEXt0aOH+thjj+nXGhsb1dzcXPWFF15QVVVVKysrVY/Ho7777rt6maNHj6qyLKtfffVVs/W9LaC8vFwFoC5atEhVVTG+qUDHjh3Vl19+WYxtElFTU6OeccYZalFRkTphwgSdeRFj3DQ88MAD6rBhw7j32sPYCrWRDQKBANatW4cpU6ZQ16dMmYLly5e3UK/aBw4cOICysjJqbH0+HyZMmKCP7bp16xAMBqkyvXr1wtChQ8X4M6iqqgIAdOrUCYD9+L766qvo0KFDqx/fAQMG4JZbbtF/l5SU4MEHH8TGjRsNZW+55RZkZWUlvQ/hcBjvvvsu6urqMHr0aH1sP/jgA72MmLuJ4Te/+Q0uv/xyXHrppdR1QR+ajj179qBXr17Iy8vDT37yE+zfvx9A+xjbdpeYMdmoqKhAOBxG9+7dqevdu3dHWVlZC/WqfUAbP97YHjp0SC/j9XrRsWNHQxkx/jGoqoqZM2di3LhxGDp0KABn4wsAHo+nVY/vxx9/TCWLKykpwZw5czBgwAAMHz48pW1v2bIFo0ePRmNjI7KysvDxxx/jrLPO0on3P/7xD6q8mLvx4d1338X69euxZs0awz1BH5qGCy64AK+//jrOPPNMHDt2DI888gjGjBmDbdu2tYuxFcyLQ0iSRP1WVdVwTSAxJDK2Yvxp3HHHHdi8eTOWLl1quNdWx7ehoQHp6ekYMWJEi/Vh0KBB2LhxIyorK/Hhhx/i5ptvxqJFi6j7JNrK2LYGHD58GHfffTcWLFiAtLQ003Jtdf62NKZOnar/fc4552D06NE47bTT8Nprr+HCCy8E0LbHVqiNbNClSxe4XC4Dp1leXm7gWgXig2b5bjW2PXr0QCAQwKlTp0zLfN9x55134rPPPsN3332HPn366NedjC8ABINBfXznzZuHYcOG4ZtvvsFrr72GH/7wh9ixY4ehzf/85z8488wz4fP5cNZZZ+Htt9/GLbfcggEDBlDl5syZgwsuuACdOnVCTk4ORo4ciblz50JlspIMGDAAV1xxBT766COMGDECaWlpmDNnjn5PUxstXLgQ5513HgDg1ltv1b0oHnzwQaq+vXv3oqCgAFlZWejbty9++9vfwu/36/cPHjwISZLw+OOP429/+xsGDBige1zs3r0bwWAQs2bNwoABA5Cfn4+//OUvuPfeezFs2DA8/fTT+theeeWVVLulpaUoKyvDkCFDcMsttyAQCGD8+PGUmF3M3QjWrVuH8vJy5Ofnw+12w+12Y9GiRXjmmWfgdrv1MRL0ITnIzMzEOeecgz179rQL2iuYFxt4vV7k5+ejqKiIul5UVIQxY8a0UK/aB/Ly8tCjRw9qbAOBABYtWqSPbX5+PjweD1WmtLQUW7du/d6Pv6qquOOOO/DRRx/h22+/RV5eHnXfyfgCgNvtRlFRER599FHcdtttGDhwIICYNGf06NHYs2ePXv6ll17CL3/5S5x77rn46KOP8Mc//hFz5szBwoULDX08ePAgfvWrX+H999/HRx99hGuvvRZ33nknHn74YUPZ9evX47777sNdd92Fr776Cj/60Y8MZUaOHIlXXnkFAPDHP/4RK1aswIoVK3D77bfrZYLBIK666ipMmjQJn376KX7+85/jySefxN/+9jdDfc8++yyWLVuGZ599Fi+//DJ27tyJK6+8ErfddhuOHz+OefPm4e9//zu+/vpr3H777VBVFX6/H3l5efB4PBRhr6+vx1dffYUtW7bgiiuuwNtvvw2Xy4WuXbuiuLgYgJi7JCZNmoQtW7Zg48aN+r9Ro0bhpz/9KTZu3IiBAwcK+pBE+P1+7NixAz179mwftLcFjITbHDRX6blz56rbt29X77nnHjUzM1M9ePBgS3et1aOmpkbdsGGDumHDBhWA+sQTT6gbNmzQ3cwfe+wxNTc3V/3oo4/ULVu2qDfccAPXXa9Pnz7q119/ra5fv1695JJLWo27Xkvi17/+tZqbm6suXLiQcoesr6/Xy1iN7yuvvKICUG+88Ua1V69eqs/nU8eNG0eNb3Fxserz+dQbb7xRVVVVDYfDao8ePdQLLriA6suhQ4dUj8ej9u/f37S/4XBYDQaD6kMPPaR27txZVRRFv9e/f3/V5XKpu3btMjzXv39/9eabb9Z/r1mzRgWgvvLKK4ayN998swpAff/996nrBQUF6qBBg/TfBw4cUAGow4YNU8PhsH79qaeeUgGoV111lTp79mx18eLF6oEDB9Sf/exnKgBVkiR1wYIFqqqqal5enupyufSxvfDCC1UA6r/+9S+9PjF34wPpbaSqgj40Bb/97W/VhQsXqvv371dXrlypXnHFFWp2dra+b7X1sRXMi0M8++yzav/+/VWv16uOHDlSd0cVsMZ3332nAjD80zYjRVHUBx54QO3Ro4fq8/nUiy66SN2yZQtVR0NDg3rHHXeonTp1UtPT09UrrrhCLS4uboG3aV3gjSu7qVuNr8a87Ny5U73yyitVAKrX6zWM79SpU9Xu3burqqqq27dvVwGo//znPw39ufjiiw3MyzfffKNOmjRJzcnJMfSzrKxML9e/f391xIgR3PeMl3mRJEltaGigrs+aNUtNS0vTf2vMy+zZs6ly8+fPVwGoL774ovrzn/9cX/NZWVn6dQ0TJkxQ+/fvr49t165dVZ/PRzFDYu7GB5Z5EfQhcWhxWzwej9qrVy/12muvVbdt26bfb+tjK5gXAYHvKTTm5cCBA+obb7yhAlCXLFliKHfbbbepbrdbVVVVXbJkiQpAffPNNw3lrr/+eop5WbVqlepyudRJkyap7733nrps2TJ1zZo16v3336+3q6F///5qQUEBt5/xMi+ZmZmG6w888IBKCpo15uXxxx+nymnM9gcffEBd18ZqzZo1+rUJEyaoEyZM0H9feuml6sCBA7nvICAgkFwIbyMBAQF07twZQESnzaKkpARdunShyh07dsxQjjX+e/fdd+HxePC///2P8iZhU0RoaA0eDE1B165dsXTpUiiKAlkW5oQCAqmEWGECAgIYPXo00tPT8eabb1LXjxw5gm+//RaTJk0CEHEN7tGjB95//32qXHFxsSFwlSRJcLvdcLlc+rWGhga88cYbTeqrz+fT62pNmDp1KhobG/Hqq6+2dFcEBNo9BPMiICCADh064E9/+hM+++wzTJs2DV9++SXefPNNTJw4EWlpaXjggQcAALIsY86cOVi1ahV+/OMfo7CwEG+//TYmT56Mnj17UhKHyy+/HLW1tbjxxhtRVFSEd999F+PHj9eZj0Rx2mmnIT09HW+99RYWLlyItWvXoqSkpEl1JgM33HADJk6ciOnTp+P3v/89vvrqKxQWFuKBBx7Au+++29LdExBoVxDMi4CAAABg9uzZePnll7Fp0yZcc801uOOOO3D22Wdj+fLlOOOMM/Ryv/zlL/HSSy9h06ZN+OEPf4g5c+Zg1qxZGDFiBDp06KCXu+SSSzBv3jxs2bIFV155Je6//378+Mc/xqxZs5rUz4yMDMybNw8nTpzAlClTcN555+Gll15qUp3JgNvtRmFhIWbPno2PP/4YV199NaZNm4alS5eif//+Ld09AYF2BUlVmWhRAgICAnGisrISZ555Jq655ppWwUgICAi0bwiDXQEBgbhQVlaGv/zlL5g4cSI6d+6MQ4cO4cknn0RNTQ3uvvvulu6egIDA9wCCeREQEIgLPp8PBw8exIwZM3Dy5ElkZGTgwgsvxAsvvICzzz67pbsnICDwPYBQGwkICAgICAi0KQiDXQEBAQEBAYE2BcG8CAgICAgICLQptDubF0VRUFJSguzs7DYfsVNAQEBAQOD7AlVVUVNTg169etlGqW53zEtJSQn69u3b0t0QEBAQEBAQSACHDx9Gnz59LMu0O+YlOzsbQOTlc3Jyklp3MBjEggULMGXKFHg8nqTW/X2HGNvUQoxvaiHGN7UQ45tatJbxra6uRt++ffV93Aqtknn53//+h9/+9rdQFAW///3vcfvttzt+VlMV5eTkpIR5ycjIQE5OjlhASYYY29RCjG9qIcY3tRDjm1q0tvF1YvLR6piXUCiEmTNn4rvvvkNOTg5GjhyJa6+9Fp06dWrprgkICAgICAi0ArQ6b6PVq1fj7LPPRu/evZGdnY2CggLMnz+/pbslICAgICAg0EqQdOZl8eLFuPLKK9GrVy9IkoRPPvnEUOa5555DXl4e0tLSkJ+fjyVLluj3SkpK0Lt3b/13nz59cPTo0WR3U0BAQEBAQKCNIunMS11dHYYNG4Z///vf3Pvvvfce7rnnHtx///3YsGEDxo8fj6lTp6K4uBhAxFWKhXB5FhAQEGg+KIqKhkC4pbshIGCKpNu8TJ06FVOnTjW9/8QTT+C2227TjXCfeuopzJ8/H88//zweffRR9O7dm5K0HDlyBBdccIFpfX6/H36/X/9dXV0NIGKAFAwGm/o6FLT6kl2vgBhbM2wrqUaG14W8LplNqkeMb2rhZHwDIQXBsIJMX6szNTTgp3PXYM2hU1g162J0zPC2dHfE/E0xWsv4xtN+SnMbSZKEjz/+GNdccw0AIBAIICMjAx988AF++MMf6uXuvvtubNy4EYsWLUIoFMKQIUOwcOFC3WB35cqV6Ny5M7eNBx98EHPmzDFcf/vtt5GRkZGS9xIQaA7UBIE/ro1sdE+PDrVwbwSaij+vc6EqIOHv54fgc7V0b6xx94rIvLvxtDAu6CbS3wk0D+rr63HjjTeiqqrK1lu4WY8AFRUVCIfD6N69O3W9e/fuKCsri3TI7cY///lPTJw4EYqi4P/9v/9nyrgAwOzZszFz5kz9t+YnPmXKlJS4ShcVFWHy5Mmtwp2sPaGpY6soKmS5fakXNxyuBNauBhCRaDZFfSrmLo1gOCIFyfAmhwQ6Gd+7VywAAPQ7dzTy+3dMSrupgtbXEcOHoWB4rxbuTeuev6qqGtbmw1/sxMYjlXjj1lFJm2Pxwh9SsOrASYzq38G2D61lfDXNiRO0yKiyH5r9+FdddRWuuuoqR3X5fD74fD48++yzePbZZxEOR/S0Ho8nZR8hlXUDkfGYu/QABvfIwbgzuqSsndaIeMa21h/CPxfsgqKo+Gj9UcycciZuHZuX4h42H9zu2PKUXW64XU03UUv13G2tqGoI4oO1h3HlsF7onpOGa19YitKqRiz9/USkeZInBjEb31BY0f9WJRf+UbQXk8/qjlEDWncICI/b3armSzLmb2lVA+YuOYCbxwxA305Nk87XNAZx1b+XY2S/jvjndcP066+vjNhwfralHDdd2L9JbSSKx+Zvx9ylB3D5OT3x7E9HOnqGHV9VVdEYVJDubR5RYTzftlldpbt06QKXy6VLWTSUl5cbpDHx4je/+Q22b9+ONWvWNKme1oD1xafwyBc78LO5q1q6K60aTyzYjVeWHcRrKw6hxh/CnM+3Y8W+Ey3drZQgGG7bovuwouLG/6zEHz7e0iLt/+HjLXjkix24ed5q1DQGseVoFSpq/Siv9ts/nAQECOblzVWH8OLi/fjxCyuape14oSixudZWpZk1jUF8uvEoav1GdevvPtiEl5cewHUvNn3831ldjAMVdfhw/RH9Gsmo7i6raXIbVliy5zjGPvYtFu8+brg3d+kBAMAXW0od1RVSIuuUxJ3vbMCQP3+FQyfqmt7ZJKNZmRev14v8/HwUFRVR14uKijBmzJgm1f3ss8/irLPOwnnnndekeuJFQyBs+OBNRU1jbMGdrAskte72hB2lRhHj4j30It5ZVo273tmAAxWtb/HZgbRGIze/puCzTaV46PPt1AZlhb3lNfjDx1twtLKhSe1uKD6F5ftO4O1Vxdz7qqrig7WHselwZZPaMcOXUQK+s6wGJZWN+vVA2NqjpqyqEb9+cx1W7m8aUxwIxb7f0VOxsfxuVzn8odR79VTWB3DzvNX4ZIN92AlyrrVR3gV/+mQr7n53I2Z/ZGSWNx+uAgCUVjUa7sWL7SUxGqSZj1YT9Pugw01/xb4TuO6FFdgVJ7Nz09zVOFrZgGnzVhvued3Ot/dgWMEjG1y44t/LKY/f/22OrJs3Vx4CEGEKn1iwC3uOpZYpc4KkMy+1tbXYuHEjNm7cCAA4cOAANm7cqLtCz5w5Ey+//DLmzZuHHTt24N5770VxcTGmT5/epHZbQvJSVR/E8IcW4Ccvxc/BB0IKpr+xDm9EJwUJUoW2t7y2SX1MJkoqG/DTl1fimx3HWrorAIynBACGze+Gl1bis00l+MXra+Ouf1tJFWZ/tBnHqptO5BJD7P2CSWJe/lG0B/OWHcAuh8Tn/15YgbdXFeOXnPGragji/TWHUd0Yn4cCz0dg3aFTuO+/m3H1s8viqsspyKlSQjBi/pD1uN7/8RZ8ubUMP3lpZZPaJ9vJToupA299ZQ2eKNptKH/4ZD0e/Gwbik/UN6ldDS8u3o9Fu4/jnvc2xtVXVxsNU/HJxhIAwOebSgz3umb7ktbOToLZqIu6llfWxw6cW49Wcec7ixv+sxKrD57E7a8nb//yxcG8HK1swKmAhL3H6/D/2zvzMKmKc/9/z+l19mHYBxgWQZFdhkXABVRQRo1GTbyaGDV6ExUTDcnN1eSXoCYGc3NjjAnuRqO5iSZuMclEFlFQQYVhZ1DZdxiWYfbp9fz+6K7TVXXqLN3TPd0z1Od5eOjpPkudOrW89W7VFjIK02q8HTz0j1o8vnwHqh7/wHBMZ5N24WXt2rU455xzcM455wCICSvnnHMOfvrTnwIArr/+ejz22GN46KGHMGHCBKxcuRLV1dUYPDg7dsFUWLu3Hr9dth3vfnYUgXAUa/bUJ32NN9cfwDtbj+Anb20x/EbnV9hel34Jd//JVtzxcg0+3H48qfN+8LeN+GjHCdz2x+QFgUwQihonnk0HGhitQn1rbGJNRQi8/PEP8ZdP92P+XzekXMaOQJuKwg7NRi2BsOWzEkEjaDNpE0j9bT1k1HIt+PsW/PD1TbjnL+tNz998oAG/X76dER5q9tbjssdW4v3P6/TvjjcnzDeZ1kQcoIQXu3o4lIbVOX8fn5v1H3jl0/2G42/+w6d4cdUefPOP6ZnMksnZ0hmaoL98ug8f7Uhu/LFj0Xs7dFOJFb3SJLws+PsWRnhpivethraEMF/fGkJdk3PT5P6THdNw0iQjvKiUkNrYZjS1EfPhqrhZPhfM2GkXXmbOnAlN0wz/XnzxRf2Yu+66C3v27EEgEEBNTQ0uuOCCDt+3M81GNzy3Br9Z9gVeWp3QmhDp+v3P6/DjNzfbDhaiBkJopyTf7Uc7rnk52tiOdfsSAtZb6w/ina1H8PXnP0lK/VcrMNM44cPtxx3ZTDWN7fibDpzC/pPmK0+R5qU5EMbOY6nV2cJ/b8MDb281fL/tsHUdfbTjOP6+QayOj0Y1tIci+OFrG/HiR/YDKw2tbXGiedE0DbMfXYFLHl2BHQKhV9OA1ni7jKYhQ8Lb8VXte58b7e2EK3//If53yRe62hkAbnlhDT470oRbXlijm0WL8xKOelYDeCAcwaqdxzs0wR5KQnjxuNKjeaDL2xZi+/6gsjzD8bviZk4zQfRAfSueWrGT6S9W0NoeO+g6CaXZJA7E+vX9b2zG155Ln09ffUsQv1r8OX72z1o0B8KWJhNa89Ii8IkREY5E8cJHuxmzzms1B5hjiLn/FPdO9p1sxbLao0lrKDuKNwkHf/qdi8pJzIetwdxJ2ZBzexulSjbMRhsoEwVRtd3ywhr83yf78PTKnUld63hzAO9sOYxIVGPUdqlOxDQzHlmOa55YhS0HY7beVur6/9jkzJkLAE61Jt/5avaexNef/wQX/up922Pf3qti0i/ew4fbj+NAfSu+9PuPcP7/vGd6vJmv0T4LgceM9lAET6/YhRdX7cGBevZ8O7Xv1577BPe8sgG7BO/qpj98gpE/eQd/XXsAD/yjNqky0QKLE5+XNXvqdU1BrUDgCkUTfjTpEF6G9BQnzqtrbDcIW9upSZh2opz4s6Wo2XsStBLNStB94O1a3PjsJ3goybqkoX1O7FaQng5EeO053oLb/7gW6/fVM6YYfuFSkULEy3VPrsYj//5MqLkVUUglxrMThOmyhgXHhiNRNKQwFhAO1KdPu0Cgy3ykoZ15Xl7Q9VOar8MNzsryk79vwYP/qMV3/rJO/460HXd8Ztc1L1zdvPDRbtz+0lpc/OsVjkxIHUHTNPx22Xa8uf4AfFQUXTSq4aF/1OK7f1mvl4GuF7Z9Gt+tS431g9YcyrrcbYSXbNMcCDMrOjvHQ9qUfKI5gEk/X4Y7/rQOyz+rY7Q2TldWVoTjk/wnu0/G/qYGpOb2zEnStYca8diy7Y6PX3441hwX/nsbdh5LTGBmHZ43pZBB5KhNBEl7KGIY0OgBnRfSrIYb2kTFD8qapuGjHak7egbDyfm8/HVtwvzgF6w8A9Ql0rGgruiZmHTJaq32UCOm/OJd3MRFylnpL55asQthSnqxcq7+y6cx37n/M3H8pdl1rBmfHTFqCxnNC+ewu+9EK977LGHO6ojm5WvPfYJl247izj+tY1a2TdzKtm+xP+lrH4n7Ya0QRJmIyKcmc7tFSCBECy/GhnLdU6sx/qElBiEfiC223tlyxPB9pqG11Ycb2pj2dvhUu24BANi+dMmjK3Gi2Xq8iEQ1/CVu2vsirgnXNE03W5cVxDIQE0dd2ucFAKo3x+rjWFNA/5wpNh5owG+WfYHvvbqRMRs1tofwh4924+2Nh7C9rhnvf16H0T9djJdX70EkqtlqXojvk52PWGfSbYSXbEUbEVoCESZMd/PBRsdS9h8oc8LeE6zDVEf3F6E7tTc+ELdTg1MqakCnA3rV4x/ggyT9aoCYipuefM06TJjzeSEr2CMWDrYNbSFMeXgZvvLUakZzQw/STZxAZxWZQ78rXsBocfju3lh3wOB/dLSxHWv3nBSWz4xjlG1dpFkJUMVJR4QcvYIl5kciXHy86yRzrNXdvG6VKc/eNDipapqGi369Apc99oFhMjloYTb61strceuLa/BK/DlS0by8uf4A3v+8Tr/PkcZ2dmXLta8lW4/i/jc2CU1hdtE+TscY+rjJDy/TQ2ujUQ0/fG0jXqDGILocP3x9Exb+extzLaJxrhaE4F786xW44081lv4smVA+0PV76FQbY7o5eKoNNz77Ca5/+mNommYYN1bbRJLREUVn9i0EEOs/5DmI8GJmNqJZWisWXtIV1XWcGgNobS2tqQyGo/jp37ciHNXwk79vxfgHl2BTXCsPmPi85KDfdrcRXrKd56UlEMYaarI53hwwDMIH6lvx5voDhomDnnQa28OMwNHODWh1Te2oa3LuREhfm9yXvr7TCZYe/HoX2ju8dcQnodDnYWzWolwNgFF7QBJO1VkIL3uOt6CxPYxNBxrw9saEnwotePArD6uxllaj8kJWvYMw97V7TmL+XzcacvpM/cW7eI5yPnRiNqIFKdHhtPCSDrMRXSbim+HUEZjG61IZ883eFMx+PGGqcezh+iEdPca/M+KA+cg7nwFIXnjZd7IV33t1I255ITEOjexXZKl5OXiqDX/5dD9e+GiP4Xp24a5OZVBe+P3uKzEn65Xbj+Gvaw/gQW5yo3l6xS7hmMO3Mbq9bzxwylnB0gTvJ0iPsS+v3ovVu07g0z0nUdcUMJgKzUwhf1y1Bzf/4VMspzRx7rj5hL5Gz0IivMTeK9Fs9RNo1L4w8WGkM+B2xLRE92vafPUGFyJfnJe4X3MgjGc/3KP/zbdPIDfz/XQb4aWzMGtXLYGwwat8f30rrl70EZ58P+b/cvGvV+B7r27EXz7dx4RD052nsS3E/E1rSUKRKKY8/C6mPPyuY+HgGKUSbYhL1O3U4NTq0GGtnuoIPR0ILy2B5IQX2pRV7HczndDMtMVrOojjI5mc+EFA0zSmbh/+1zYcifuI0I6J/ErdSnqhNWN8p3fiI0RrpojzoOjdhhwIBfQAzq8uASBIm41S1P5+uvsk1sedv+lyktWamZBlNSB7XAoz2bQFw4zQnQqsJo19D/SEbyZsnWoN4VRrMGmzkSgvU/8SPyMkmfnZiHym7IQnp0Io3x6INpHXAgFiTef7Aqds/t60D6BVQIJGdah0+YDQZaYDCxQFeGdrQtuhacZxw0y7/dyHsfDy3yxLhLKTdkq3c7JxJRmniM/ImAHG7Wl2HmsWaj3pDLZmizUeUd3R7+SExeKJF6zKqM03RW1CzcGQeSm8JEnYpK+1BiMGR6fHlm3Hhv2n8Mv4Ko50sFU7jxvOJTS0hZgVND0htVICQX2LM18YehI41RY0XLPFodmIdmxzIoSLpHcrTlITfZ7XxQzwpDP/be1+fOWpVXpYLT8IELMR8XmhBT/yNx3pcbw5iN8tj72jx6hcG/VJ+Ly0Utfj/ZPqeSEI7IDTFowwvhtE+BXl9nASmkgPwmQQo+8XiCiG3+1wqbSQHcZNz3+Cbzz/KaJRjfGNIO0olWR6XrfKTK5r9tRj8sPLhDmQnEKH0R+xCHcm9Xq8OYAXPtrNtO0vjjbbbsnQHAgzdSyq1lBEEwpJ51SUGq7FYxcx4vQ98u2nV3wBIpoARcIL7QdE4LU5dETjvpPOkrOlK8EnPaYR4aV/iR/jB5Yyx0U1TS838ZET5TUBIBSgSbuiF1vEbLTii2O4etFHeC+eAmAcd28gVrf7TrYiGtXwzRfXYMHfjQ7XZGz//EiTZZJSkcaIH/NEaFpCg0TwuOlQ6RA+P9KE71IpENKwM0naycEipUZn+byYWVmaA2GDuWGDidMuv5qiB62GthDaaVOESWN0qp6nOyCZXBnhxaGG5ERzohNFHAyYvN+IiNU7T+imNrqcoUiUGRhJ/fzXa5uwZk89fvnvmDAYNhFeiIq7KcC+j8b2kKHDH21sx9WLPsLfqLBH3txjtTrkBU8akfBCl/nmFz5l8puQOhCFx4py2vDQg3A4omHXsWZMfvhd/KI65rPA+LzEn6klEMYb6w4YtU1x3Co9qIURCEfRFAgjFI0ykxypB7pd0hOTVZPxuFThJOY0kkZEhGo/VsJLMK49+s+X1uLBf9QyWpm9J1oshYfVO09gzILFWBhvjwDQLFgMBMJGB3GAjQACxH3GXvNi+TOAWLvnx6aSfE/8fJHwYizrlkMNhu/4cYD2DanefASLt4r9O+jT+D7slH0nWnH7H9fqfmF0WyQaz5I8j+H9RaKarnkh4fmtwdj7ee+zOsYfKk+w5xUZl4gw6HEpKPbHrrNq5wls2H9KX/yc2bcQJVQKAHK9L442YfPBBiz/rA5/XL03vn9Qos5Ptgax/WgTLn1sJWb+yjzakhd2o1HNkdYmHI0axkZau93YHsJ1T67SUyEARs1LfiftdWRFtxFeOsvnxUywbQmEDapSs1WFx6UyTqC00yyveQlGovp16MHCqdmInhwbWkNoDoSZFbpTzUuQCZ9MeO2bhUyKOtFtL67RhZXG9hBuePZjfOWp1QhFokw520NRZiXO52I4FNcC8WGcA3vEhJfjzUGcbAkatFMNbUbhRbRS4YUOq+G1LWg0nRBEZiO6TezkhJRjTQHU7K3Hnf+3jj/N1GxU3xLEDc98jNdrDjADYFTT8K2Xa3C8OYBnVu4CwJqNiED2079vxfy/bjTNQEwP/nQ7jUbZNkjeUdAm5FKEpqU+iZlBC3tWieaIpmj9vlOG3/adbGWEN56Hq2N+IqR+AbGJMxCOChcbZNLTzxVpXmx8XuzMLu2hCMY9sARPr9jFfE/aLS8T7z/Zit8v32G4Tl1jwHCvSPzkYDiK9z6vM0TbffvlGsx+dAVeXWMeGcaPkSu+OGYaraRpwC8Xf4GnVuzED17biGXbjur7Q7ULtCc98r2M5hBI7CYOxMzTQKydzvnNStz64hrc9Pwn+tgs0kCRMYdcw62qKDTJoVOa78UQKiJv6rDYJpy8ZnXMgsWM4FrfEtS1N7wJ591tR/HS6j0AjMJuWyjiKAAjFNEM7ZS+T2NbGE1cW1QV1rSbC8JLdvbq7oIcawrg453HsL1RPJi1BCP6yrs032OYuOiB3uNSGfU6bQ5qaAsZEkq1hyIo8LmZxuNEPUjKTdhe14wxCxYzv7c61LzQdmKyWrv1hTWo2VuPD/57lq6GJogG8Xc/q8O7n9VhzyOXM9qNSFTDMUqz0x6KCDUv/LV5QaRfiR/eeN1O/NlS9CzwMr83toV0Hx+3qiAc1YSDHm82slLNizQv0aiG7XXNQpUvPUmTNnB2/2JsO9yIY03t+NdmYzpzIDbgRKIathxsQI98rx6m/HD1NqzedQKrd51AATWgnGwJGTQ4bLRRbAJ7K55czyxLtMetAvEm1Mb51NATMpkM6XZOC4GahQgYCEdso6mc7MXU0BbCPzcdwtwx/ZnriUJ6CVYazD0nWpm+qGka46sm6oMiB/hgOCqcCPl+Lta8WNto7arFLOycCJYRxuSl4ZonVwnNJYFwFI3tYUaTQIaE3y/fjscFAg8QG3P++/XNuH5yReI+1O90fwiEI7g5vkfP+p/MRg+u/x5pA57buAeAcfIU1W9pvscwdoQimn5PonnZUdesB1fsOtaClduPYeZZfYTXJL5xpO96XIppAsDSfA/jH1heGvPJaw1GmDrg28zJliDaqJUG3e5IdvOz+xcbtHKtwQiaufFcVYAvjS/Xt0uI1UHUIJw0cZoXHlVhx+HO2mXaim6jeck0L67aje+8shHvHxZXWUNrUB/c+UkTYH1UXCorDNCN4lRryOBARibYiIm2RgRZCdMDkSh5m2PNC1XecFz1+vGuE2gLRYQDJK+W5KFt8JrGmqXaw6zmhR+AmgJhhCPGCcHvcTF+BLzDWkNbSE/QRwZGPpoLEJmNzJ+D15oBMYHi0sdW4rfvGnPc0Noi0gYGlMac5441B0ydfEORKJ5ZuQtXLfoIF/zqPXwaz9mz+UBCnU8LF/y7DoajjPCy/2Qrzv7pO7Y+B7TmgdHsRNkJg7QjZgVJPYuVZiUQiuqreBHNgTCmP7Ict77IalXX7DnJaAN+8LeN+PGbW/Ctl9YywstBQVI0osK3El72cWYj+nl31DWL+5NAe2KmeeHNRkQop9uI1209Sdi9P7M0A6St0vV3zs+WWjpKH2tqZ44n7+zP3PYGA0qNGYMB4NmVu1D12w8MCxcCvWEmb4INhKM41CoOcgDEmpeehV7DBB/TvMTuSYSO41yelzfWHUSY0njTkO9I+/K6VRRxGjRCaZ5X94cBgPx4m2sNhYXlJTS1836Pxne4Yd8pQ1trC0YM3+V73YY6CEaiej89b3gvAE6S1CnMOMz7zGSD7JegizCxogcA4ECLeCVEq6bLBMIL7fDaFowywgsfbcQ7kJHoIHqV1GrR+Gv21mP0gsV44O2ttr4nrcGII49/ejKIRjXsPdGiT0gidbdd8jt6YIhoGqOlaA9FWIfddqPmRfT8PreKmWf1Mb1nQ1tCMCTe9ek0G5EB12p/FXoSJ89IVmTHmgIGNXfi2Ci+oLZy2BQPRaUnUHqsbeOE0hMtQQSiiWv/+VNzVX5LIIyrFn2ER5d+wQx89GowbOLzQk86TO4QC01hezhiKdxsP9qEI43thoRsX3lqNT6k8oksrY1tGLp2bz1jNtov0LwQ/6iAhYMxH2JNBPi6xnZc8ugKoUAi8iELCJIiAjBMekca27Fqx3GmbXs7uD0BP0neODWmAWkJRhCKRJk2Yxcdt+VgI/5AhXOTqsvzstPIK986F//67nn63z3i/jUPV29D7eFGPE4J9fQihdaQ8ePWbS/V4KXtYkGuPRQRCy8FPkN/CjJmo1i5+EVOfWvQVOgLcWYjj0sVhkQDMc3Lf54/DABw6ei+uraiPSguL6GxPcwIIST4gR6n951sNdRRayhsEF7yvC6D03koHNXHVBLmTSNKjKqqChOEIYpm7Gy6jfCSaYfdc+LCC6GYUxUSp8BCnxt+gaMXvf16SyDMDHz05B+MRA2TJ2notOq83cxzGMCvFsccCF9ctcd2ZRaJagiEo9A0Dc99sEtPXsUT4jQvtEmiNRBBWzCC3yz9Qnfa49WSPHTjj2qso1kgFLHUvPB+OwS3qmDmWb1N7xmzCceFlwI2NwMNbzaykl54wdMO3X8pqumfaeGFPBetngdigg59/WNNMR8Es0gJfmV6siXI5nmxaBev1RzAxv2n8Pi7203z7UQ0DQHG8Tv2G+0D9U9q6wmrwTqmeTEvj5WJ1CwJYkQgJNKQsPqQIHxQUYAinxsNbSG8uGoPU07AepNPkSYzGBFrXgp8xnHixuc+YbSQfH6NlkAYj1Ghu3bw/YQJiW0LJZXv595XN+Bn/0zkgyHn+jntULHfg9HlJfjVdeMAGN/fCRPNC72nFW+6+GS32KwJxDQ6ovGgV5HPYHYLRzRds6ULL5zmJRzRzBNjRlizkdulYFS5MSTa61bh97hwVr8ifPqji/H7GyfqwkubibBFaGwLMRowMpbS2u/99a2GcTFmNuI1Ly6DANwWiujjhmihLYpiUqAwwlLEQfRjpuk2wkumHXbLCljnqyG9ClD93fNxzcQBABJOpCV5HmGEACO8BMOWoa9HG9jORDpmmDEbmTd+2jPcSZRKazCCT3efxM//tQ3fiNucNx9owE//vkWPQmF8XqIas2FkSzCMJ9/fgd++u13fKj0ZzcuJ5iDT6dpDUYhCpenyitTziqJgZL8i3DJ9iPCewXA0IbzEVxz0RKHfj8+wazHA08KDk43XQpEomgNhpj6J8HK4od0gXNHn0YPHkcZ2XLXoI0flAmITBt1krJ6JXnnRgz8tVBOhl9AajCAssKUTrAZrO82L1bm86YVgt50Cce7mtwcAgNI8Dx6+Zqzhe6I9KTC5J2CmeTGaOH1u1dQZd9b/vq9/5oW63yz9IqktN/i+43OrKIqXv6Et5MiXyAyywOAXa8SB9eKz+wKItUXRHklATKj+v0/24lRrkNG8EEH9WFOA2dRTxK+XfoFqgZNvrwKvwbxBm41Ioja+CmJtW9zmyDMTB3qPSxW2wVJq8dGn2A+PS9VNla3BiKVA3tQeZpICkn5Pt6H9J1sNY+BP3tpi0JrkeVyG+YjWsIlcHETpDjSwTr5OIk4zjXTYTYIJA0t0VbLf48Ko8mJMrOiBN9YdxOG4vbbI7xY62R2mQvBagxHLfBj8b6QjMT4vJgP6+n312EP5oDjJo9ASCOMoZ+u+9slVCEaiaGgLYdZZffDKmoRdOxzVsINKqNUSCDNbwwP2odK0cEIP1kB8MrMwGwHmwpuiKJg3azizYibEhJfYtcgKVDRp8ivlZMxGdia4N9cdxK+XfoH/vmyk/t2o/rGV2466ZvSJ73jbI98D2vgUikQZ4eizw0343GJHcL5+TjQHHe9tRE/89OBPCy+hsGYQpkXJrQjtFr4ldpoXM+0SYC682DkAk52FRRoRv8eFs/sVGb53kp5AZEINCBx2C3xuR4m/+EWOKGSZ50RzAEtqj+LK8eWGfuhyKSjO86ApEI4JLx2Yg8g783sSbaTA69JNNfS7MUvJ8Mi/P8MH24/jjXUHdSEeSAjQ1z21ytF2EdsEO973KvIZosVEZiOecDRqauYMRbT4FgNxn5e4YOB1q0z7EDm0EuGlndJ8iHhtHZuFnWiH6TLtr28zXGPrIWMd5AvMRkTz5feoTGZfgujZoxo7L6UrP09HkMJLEtAb0ZHVBumgpCEV53mEia1o+3lLIOwoY6rfo6I9FNWldHq1LDIbHahvxZefWMV8ZzWIq0qsUbYEw8xeQpGopjfUv284hL9vYCNgohpnNgpGDKtRMoh/75IzmQyVBKuVcTtnNhKp4q22sjdb0YYiCc0LH8nAQzviEoHkeHMgtnKlBj1aSAhFNNvtFn4dT4ZHEhcCwLBeBehV6MXx5qDuO9Ujn9e8sGYjqwgavlyAM83LO1sOY+2eembTUHqApFdsdHK+2P3ClpuIWg12gXDUtD3wWZGdYmeTJ5OOmfCiCAQLIoCYXTuqwZBlG4gN+vxKPs/jciS88I7Mdg68QGxn+80HG1Czt17fi4fgUVWU5Hlw8FRbXHhJfRL6y6f7MeusPozmhR77vG4VPrcazw0kbhvE7Fezt54pS2N7bCHgdJ8rRTE61vcs8MLNLSRD4agh2ohAykprFcsKvJhY0QMuFVi8NeZTRY+PRKtx/aRBTFJF0Zt1ajbi+woRQOlzguGoo0Sg+V63wWxEHKb5bVgIIq2TpmkG14Fs023MRp0BvadPXny1wYfsFfuNiZEAYNm2o/rn1rizHE/fYjbcuIfuVBo3G0XYlS6PSPK2mjTKCmL3awlEmAHILgIpGI5iJ6V5aQ6EDfZ70uFK8pJX67eHorDaKBGw3nzRZyK8BMNRymFXvOoi0INmVIutBCf9fBkmP7yMOa6Nm8RPCsxQVrhVBaqqGHyqSg3CS5TRbNgJSXw02i8Xf4G9zVSGXUG7uONP6/Dch7vx17WJhH20QEJHifDtryUgNuU5oT0UMW2n+0+2YauFtqE1GMaP3tyMrz/H7g1lp3khgzadR4ngc6vCLNKBcBQ76pqYvW5onv1MxWpuQ0oC34a9btVR1lL+OcwceH/+z1o8G883szm+yd7irUcM93W7FN2fyqnmReQXQfjWyzWMoMe3KxLRY5Y8bTC1INzCbQ64y2J3cQCYRfm3iWSwnoU+w0KSRErSZUuU1aMfQybwPI8Lz908Cf/7lfHsNcIJnxcAuL9qJO6fOxJW0GYjK80Lj6554QRt2oHejOI8t6EOTsY1qEV+t1B4EbWJaFRj2qL0eeli9C5KdGIy2Xu4l1+S57FMbAXEBlyRz8tEkwmMNPQoE23EDgard57Q91CisVqBEntnazDMrLbt/FVim0fSeWrCKKDUj8FwFM0BkvNGPPBZqeADYbHDLi0UmuWvAMyzkgYiCbORneaF9ychE2h7KMqYhowaDus9eXgBlZSVTxXfgxOu2kNGZzwrRMJtYyjxkq1s1rTAwggvlOaFd5BsC6UuvASo1TDPBb96T7hhIaFmbz3+/Mk+JuoIsF8Z6sJLOGqYSPwmWpFgOIpLHl2JRe8Z+5mmaag9lWh3fYt9ePSriQnPIESoilC7w8P7rIkmmykPL8NzH+7Gw9XbmLaZ73UJ70tMZruOtTiKNLRyggdYLTDfrohm2iySifHPi7Cal9U7T1jed/ygUlw7caDp78V+t2EsDlmYjUgQxqFTbXgi/o7JQogeU2ICkMZ8n+9149sXnqEfI3q3uubFxueFJ+Hzwvc5+/7WvyTPMB6SBVaR32260OPhzUZS85JGOmN7AFbzEmuILq6RFvndBoGGpzkQFnqz08KLogBlBbHORRo6k6SOmzxuePZj4XYEVpoX4ljXEmB9TP779U2W5edpDkTgozQ3p9qClOZFrOGwEl54h11yLXogtxJeXKoiDDkOhRMmCDOhygzaDiwKESaIEtNNG9ZT/8zXB5mMBpcVMN/zwhXtb+JkjzSSjE/kkAeId50WH5d4D3QZyHPT1SzaDsGMkf2K9HBaK82LHbyvFcHOYVc3G0U0g9Dl96jC9mOV1fokNzk/dv05+PI5A/S/eRW/x6Uaxo5bpg/BhWeyggJZ4Wqahl8v+RzVm42OqXXM1hq08OI2CLxul4rzR8RyeyytPerIbGTWhwlMFBr3HskYI3KMB8z3QGtsC2HXMWMfp7UlA0rzLJP4KYpicNgNhhNaXd5sVBT/u741pG/mSPonLQSFI4k8VHZ7T9HkOzQbAcDo8mJ8/dxYWLvIYRdI9MHbzxuK288bKrxOuaCOTupmI7fj8tN7QgG54fPSbYSXztgegKxYgITmhR/kvG7VtEH8YM6ZAGKTM21GIkwcXKp/nnlmb33VopuNHEYb0VhJyKQzbTvcyAz2ZuGnZrQGw4y6mE60V+BzCydbuw38+ORvkajGqIZFAxsN/Q6IQ2EwkgiVLvS5HK86AHbiogceXgMhEl6e/kalnleEX+WTVVEpp2nhfV7I4J/ncaG8RJwEjIbcZ+ZZfXD3rOGG31PZzZc1G8XeT3GeR3+/x5MwmeV7XfDF/TcC4aitmceMwyap/0XXmzQ4tjj46RWjKM2LUWPkcanCNmu2+7CiGBPhFfrcUBRFb2O8M7PHbRSQ7rjwDPzxm1MwrFdCkCUZXZfUHsXvTLLY0tBJF2OaF1Y4cKsKLjm7L1QltoEhn8tGhNel4q15M0x/p/1Z+GZFxjA+ERzBbPfpxvawvjs8zV0XDtM/D+yRb9AqkBB4Aj9xByO0zwtrNuLTXwDQF2X0u6I32jQTnkTfkjmjzSbPCxDb9bnQFxsTdOElJBZeSvM9uLZSrIEaUOo31BHJe9SnyGe7/QSB3407F6KNuo3w0hnQtl/S+Hj1sqoopmaj4X0Khd8TBpTmY+yAEvTI9+Chq8bojb1dYDbaeOAUZjyy3HLfEMDa9j97VCyU8fHl25noIaeQgaKF8+GpbwnqjdulGrVTgH3kBm26amgLGVbSVrk2AFa9TgYBOlQ6z+NOKsU1nXabMZnFJ3EiCIqEF7/bldjBlpsAiQ8DLby4VGPKcXLdIr+b+c1MC0NkSZ9HZRzNE7+nILwINC9+t0s3GZqtrkXkeRPCI28mTAei6/3imrFY/v0LceuMIfqATrcJGpHZyMz04VFVZkM/IJHDhTyjQfOiKoZ3R4aN3984Uf+OrHBJAj476Ekx3+sSaF4U9CjwYkY8s+qfP7EeP4BYX5owqBQ/vWKU8PcmixDaIpNcKgSzRUxjW0govPSikqoN7JFncMi9e9ZwXDtxIJ6/eRIA4+KS7n9Gs5FRw0Ten0KN6+FoQjPM+5MQAfk/pgwyXIto69tCEVNBmFCS79H7ecLnhTMbxa/hc7uYSC2amObFuI0AAPQvzXMsvEQ5h93YYjK7AowUXpKAXqmTfBZ853CpRj8YQn+bFbPHpeC1O6dh5Q9nYVBZvp78iagLaVXdF0ebcfBUG/779c2W17TSvNx07mCM6l8MTQO2HTaq3weV5aFycA/BmTEGlsYmxRbODFbfGtJXYIqiGBJtAfaaFzpfSMyHxrgJmRV0pySDQCii6Zln870uQ3ItK+hdl+kJggwEJCU6L7y4VAUel6IPsvygRdoKrWnJ97gMuTNIeGNxnocx0Yl2vqXxulTGH4mQitaX9nkhz+11q7rgZufvQ9O70EcJ51GhD1hHELX70nwPhvUuZDQiwUjUoHmJappQeOEzsRJC0Sj2c5oXMmmT6CDe98TjMmpeSD8ZVV6M5d+/EECsPT3+7na8Ru16bkU75cSZ53Ub/NeIGeXOmWfAKUSLaTau0fc4ozdr/iQ5ZY4l6cje2B4yOOW7FE03YalKbC8zfmIuzffi118dr+eY4YULegdwo8OuQPNCPTPpw+FIYiLntezP3zIZf/zmFHxzhtGMQ8KS24IRy9QBsWNduiaIjH1Gs1Hse59HZUx7A3sk5pnyUqOAR+gf3wvOCVHNGLafbdORFF5ShAxG/Lt3KYqpw2ivIp/we4LHrcLndukDHzF3JHaATb6xWO0ZoygK+sQdSI8LQjxbAhH89dvTmIgAGqItaOGSrp1qDeore1VRhJoXs1BxEsXFD7pmamcz6E5JBqVAOKLnx8n3upj8FHbsp9LwE9X8Z0ca9Vwr4waWAjAKL3nxsFtXfNJwYjbSYIyYIs9fzDnZ2QkvPo+KfEEm144kJwMSPjU+t6qHySfzjgb0yIOPqn87NTrP/NlnWv4u0jjSK2vaYZfXvGgaINq6xcynR9OAffHssEN65mPhNWN1EzN5V/w9PG6jzwv9N+2r8ehS5xl1aSE/32N02CVmjmnDelpGEdGQujKLdCKC4rBeBXjia5XMbwmfl+T6b0NbCHWN7DluJeF3SExGvJabF/r53+kJ1+d2CRc5ND5qgeOJvxM6Yok3G5XkeXDhmb2F6TJIXw1HNTTHtSmXju5rOA6ICTpkHnj/82P46lOrsW5fPXNMW1wDTNrYz68egxumDMINUyr0Y3oWeE0FlP4lyWle+ESD2XbalcJLigTDJmYjVYHHxGxk5jxJ4BuZ38uajVJpLHbnkEyQoonnZEsQLlURJjICEo58sYzBiYb9y3c+01XsqmIcQABzzQsZfHhNSzImCYA3GxH1a1jXCOV5jdoNK/64OpHDgZiNnl25G5oGVI3th0lDYhoqfnVO7kHqgH8dRHihhZBAOGIoG5mEivM8rPBiY/ryUWYdmo7arIkQ6PMkMocm4/MysEc+o/lKJpIKgKlATRA57NJ1Sgsv/L1NNS8Wz7f3RMwH67YZQ5jJw2ciIHsE0Ua0hpJfLdvtLk04RvXjd7YeMYQbEyGa1j6JeO4bk/TPpK7MFmWEl26bYjCN2/m8mHG0MWAYI9wqMLq8CPfNHYmF8QzIvJDAP5OZ1gGI1Snd70QbLNLvL6F5iRqijZxA91Xi4H3ZmH54/c5pKC/x46mvJ8yFPQu8ujDVForg0z0n8fSKXcz1iBaZCFhfP3cwFl4zjtHCKIpirXlx7POiGfqU1Lx0Ma4eHEGBz4UfV8XsvwazkaKYqlf9HheumlBuem2+I5DBnaz0U5lw7BwhSdSNSCVOPNjNGnhJXFvQGogwPiz1rSF9QlBgYjYy0byYCS8d0bzQwov+u1tlzC/JQIRJksJ79qi++ir2COdASjatMxtAyGqWnshCEc1UK1Tk9zCrQVvNC2XWobGLxrGDtreTAZ4k0Tt/RC+D+YCHRIqQx7bbJZ3HZ2Pysw2VjrePUEQz3DuqiX1eTlqYxQ7EM2z3K2G1q2arXpHZiP6bF/gf/eoE03vTWO0KDbDt0KxfP31TJTPROhVeRG1R17yYmNx4aJMHj0uJ9ZM7LjxD99nhtUG80O8x2f2YhKrTWYCFDruM2SjRZkj/EWlYzPC4ElGQxPnd73ahcnAZVt1/MS4b0x8/vOwsVA7uga+fO9g0ezShVe+DbBmuqxyIK8eX47HrJ8Tva6Z58ScVKh3i+pTUvHQxZpVrqPnRRRg7sASAiebFokE/dv0EYd4EVTEKQrRPAJCaqp8IPvxg+NBVowEktCe8ueMnV4zCDy49CwDgM3me0rzYhB3bq0dcNkXwXICF8BLvTEbhRTz4EafhPpxJjlUHk5DzhOrerapMVuFk4JMGulVVF14ONbC+D2TwNHPiNmsrZpNzWT6reeEFE37Opc06NB31MdHt7W5VfwaSE2ZUeTHe/f5My/MH9MhjVv8f7bDO6cFDT66iQZ6ouPkoLgJpH4Fw1Ni2NAiT1PHh0FeOTyxEiNBKp1MAYCogewSJ8Oi/+UlRJICKoPfEEd6XmsxFgtW3LxyGS0f3Y9qr7vNiM1GLNJlkAWa3YzWhZ6FPaL4BYpoXw3c2mhezXdqJEHcWtQ2EUPPCmI1oh13i8+JMIwbEBC8i4OnCC/de75o5HK/fOR0FPrewPDT6AoJb6Pg9LvzuhnNwdTxUX/Seve7YmOV1cftSmQhMUU0zmPo7anruKN1GeOmMPC8EukOIVk92uQfKBDlGRAMDGXiJ130qki5R1tCT+cUj++Ab04Yw9+D5yqSB+mBkqnmJCz4BQaIvgqoo4oRfJpMnGex5nxczm/nzN0/GvFln4LU7pjPf0++AVr8myiUebJ1AhBeiNnWrij65GFLkx4thNoiaTQhmmpfy0jxmgOafgXdC9nlcKEgiqsopLZTDLikPefZ8j/2uI8TBOZlkXTS04Nmn2OhLRvrKjOG98OKtk/HBD2cxv5N6r28NGpLgRTVNqC3kNS9eSntC7kdHwwDm2Z49qlEjSfcTvr04jYzj/UQAVptBX1fUr4nfDZ/mP/a/9UQt6k/kfnbRNYQCrwv9iv3CMopuzy8K+Po2G4vJUHrOoFL9Oz7vC389lyvxrlMxGwGJ90gCEqyCBsyEOAIZz+y0kCKtb79iPxRFMbQBkluMR9OM84/UvKSJzsjzIoIfZNyc5qVXfCVG24JFjVIkHY+MrwpIJJCZpOskZI0uE22iMBNeiigJ3GwAps8lzoy86lhVjU7NgJXZyEzzIhZehvUqwH9dOtIQDizyeSEDqCuuMhYJCE6878mES8JxXaqin8e/ClLTZoOcmWBoNiCVl7KOrvykxj+Tz60mFRLulDZKZU2eTQ9Dj5vKfvsfE0xDuVMRHGmfMfqZehX6DFoMohXzqApmntUHg8rY9kHadCSq4XhzgJngzXxe+K0fPC52oaJAMzjBmgovgiR19FjCT7oivyURxwT9hNbaeGzMRqQM9HGiLLM8tEmEhghodtGFibK60ZcSXugNMkVVyZeJ13SZmXXI+ENvyyEal5l3Qhx2KbORXUJSHn58tOqbZhtHEojwYGf6Eb03ksGbbwO04EijaZrhHWbb50VuzNhBRHle6MbytakVmDi4B8YNKNG/KxR0ElEnGNmvGKoSm7jrmtpNfV6c7JNBN1K6LZtlz6QFHLMJlkTstIeiuq3d51E5DYcxyyUQSxgnLCc3ERKIar9ngVe3n4/qX2w6ONEb2PGaFzIgmSXIsoPXvLgsTIWkfaRX85J4Nt6cEBMKEtqfmM9L8t28d5HP0n+ilXEW5DYdjN/vqgkDMO2Mnpjy8LvM79+YNjjp8gDAr786Hou3HsWNUyoYAa5HfmyDOVqLQ96Reftgvx/ZrwgH4uHOI/sXC81G/H5SHldMcCP3LXALJlMz4cVt1Ei6LDQvTs1GxwSalzzq/TMaFUHdkH5P91nyTFbCi5kwKoo0tCLf62LGpJH9irHxQEP8Wsbjea2CU7MRYfygxLgsGguZHdbj9/p41wk8/+FuADANzjCDF16sIh75/eLMsNO8iLRPRMtE9wOfqpn60P1z82FDRGq6czMlixReOgivtuTNRn6Py5DyW2TLFDWwPK8LQ3sVYOexFmw91Ggq6dKOqMV+tyGbJ8AOVPSgWZJnHy5pJrx4XCp6Fvhw8FSbrq7mBw8F4rDTUNj4LAMLNIEa04uTLUHdbJTvc+Gb552Fww1tmD/7LPMy0w67fjZTMRlQ94t2ZnYwFunCi0YJLyZ1RKrazOeFVsXneVy2quABNmYjvv59gkyuThjYI89SeCGasTyPC/ybpAdAfoL82tQKPHTVGP3vZ26qxLdernFYpnw9yoQOXe+RHwsHpYUXImiYmQ14YaDI78Hbd8/AW+sP4Z5LRjja8dntYoXWIkFXMnuPblU19AtVsMonODYbCXxe8uldnx2ajUQampSEFweKiXyvi3E+pTOZn90/Oc0L/0x2UVpFfg9+d8M5aA6EmfsSaA0xEejo0PVUzUb63xYaSLdLZerGDLOINoKojERQYzKRu83HelFG82xrXrqN2ShbiBJN0Y1F1BhETlFmnWB0eWxlsO2wlfBCrbRNOgNdDnqQFJmN/ue6cey5FhETRE1OVIr8IKaY5HnhVZBFfjfuOjtiuBe5PtG8eFQV82YNx8+vHmuZp4IWCkh9Ezs1eWcHuMRiACz9Q0i1kQRTtMOuWR0pMK5kaej3/tJtU1BRlo/nb54kXLGTTfWs8rwYhZnUTEZ2WxAQn6Q8r8swQTDCCx+6yvWXOaP72UYmEWiBgx6wS/I9jKYNSISRmtV7gdfNaFcKfW6MG1iKn145CiV5HkfCS0zzkjiu2GPsn2aTgZfL82Jw3jVoXsTrzF9eO5b5W5Tun54w3QJzEHPf+M8egYbGyqRqpkFwUo+00KAo7Jh0dv9i/bNbMdYv3/YModIm75/myvHluGFKhXCBQSeGEwlCyZqNeNOUnflU5GzPY2c2EtUB0bzQz5Tnsr8WjfR56eLwg4yL2x5A5I0uEl7MEwnFbJCnWkOmwksDZf4wa3z09/SAUsqpSm87byi+OolNbW02ALtdCnpyDor8c6iKsY4Ao8/LZaP7osBjvBcRUEj2VqcrHbocvO2YCC/fuySW6OzcYWX6b6LMmIRr4jvYisxGpsILcdg1WQHSzzN5SBlW/nAWLj67L1RVwWWj+zHH9i32w6UqjIDq5aJWRJqXVBA5wdLomhevcY8o1seCaw+CtuDU/4UWiujPBV63oZ+1BlkTIY+qKowGlJ9UnCirPC7WJFossMDSggN9DzfnsGunHTNbodtFpPBloMsr6tekTKKQag+1IOjFRVWZOZ460frRY5DGZXKls5ILo424iZnvh2YaTxF2UZGi35PVvPD1ZrfppZPi2+VqETlak/vS7gF+l/21aGS0URfH6HTHSuOixiBapdg5dAYsdt5lNC8WJp5EmRPf851H5JluajaiQoT1+3t44UWseeFTXZNOype/WA9zJnkVnA1GLmpQ432MyCB090XD8fd5M/DAl0brv/Us9OHNu9jIJUKRbn5it2twuxRmYBeRbKg0ADx1UyVev3Oa/jcJC/dymj16AOc1b3YqZTOc7iSc53EZ8xOZmCkAsQ+EUwGLvq6fE2T4hG8kiZ6V2YDemI9vI47MRqrKhuQLquxLVDg1vQWEx6Uy97C7n1kftMvzA7BmI8YcJGh7qm42Mo5hrKDdg9k6xMys5UR4oc/VAFw7cSAKfW58+ZwBbJI4waX45+HbQTJ5WEQaCnqcEl0rmVBpgI1GK8nz2AruTlJ72fu8mJuNaPLcmtS8nE4Y7NaKYphceESmHbPJj1wrGImaOuzSPi+ihuziVnn0QOl2qYwmSJTUyaxzeNyKIWswvwJTFXEUAp8kjZSJry8+cZTTwUijPDF4TRcpj0tVMH5QKRPaaxY1QYdD8xmPeSdtGpEDJI3d4DduYCmuHF+OmWf1xvfnxPPuUAM6Ha4LpM9sNKJPkeXvRIDL9xqFF3oyUrj+IKpbJ5qXa84ZwFyXvqffY9wFmkRDWbUXWiNXxLURkYaIx+tmzUaFArPRucN64u5Zw1Ga72FSwfMaM7tJ3uxnUd3xvht0vdmGSse/EuV5od+jm0vNb6Z5cSIE0iaxqKahX4kfa//fJXj0q+OZe4ouxQjuIlNrEsKFqIteO3GA/lkkCCcjHAHsu+FzU6WKrdnIofDiT9JslG2fF+mw20FE4Y50pxZNaqKObqt5CUdN1XSNlObFLPyR7nf8wOz3qCARlqJB1NRspKooK+CTcrHHOk1SR47hV4O8Ot+xdz9VVfw1+HdGPx+/Iqa/1zMeC/K8mGlWyNepaF7I77+74RzmO3qA8XKTSDrMRn++faowIk6E3+MytA/eGdbjUkB8DlMxG114Zm88Gs8WalYG/p2RaCir9sIILwLzi6pYb2AZe++JZ883qbIfXHoWvj/nTPxtbWJzRbfKCsl2UTmKEstGzK9fRFFI/Uv8jLO1mdAnNBsJ8ryQd0af61bZHCFmGj4nmhfRM9jlmCLYabmTMRvxC4yF14zFZWP6mf4OdMxsZGeadYp9qLS52YgmL0mzkdS8CPjyl7+MHj164Lrrrst2UWwRJqnjJheeHoJEQObRKHHNSziqN5bLx/XHL68dqyf6YjUv4g7MqqjZ35kskoKGbm6KMmpe+JW+qfDCaV4UM80L18mcDhZ0t+JV63x56Gd2qWLNi8el6JOAyOeF1zAQbH1eUhAumPflVhmBoKOal5I8D6YP7+V4EBOZjfj6pp8xFbORWVnIe5o6tKdhVd7qQPNCC7UiPzQ7rYHbpTLPlmch7ymcBpIXkp1EFIvKIzLX9OVydeSZmPH47Kr0Peg+IfqOX6SZCaB8VxJGVVLnDixlHcVtJ2YLwR1w5rBL4Ms6fmApY4YSOuwmbTZKCCx9i8Q5VZLBpSq22h+RNl2oeXE7y3NFsNr0tzPISeHlu9/9Ll566aVsF8MRBoddznnTLH/Lty4Yhh9elgj1Ncs5Qgsv9Er/+skVmFBRCoD1eTHTvFg5B7Kbj4nsuuZ+NHZJucwy7PJmIzIG2GleklEDE/g6MQgv1O+a4HdyDWLuSySpS/i8AOKBjAxQdpqZZEhK8xJ/t1+bEnPCriizjiIi13IqJOZ5XQbTFz+hegSreBo7zYtZ+/v0Rxdj2fwLUNEz3xDhbuewC7CCsShBma0fiovdhNVvIyfSbTe2PYB5nxQhEvxEPi/EyV8vJ52l1s5hVzEeR9oEPzawpjtnZiPhit/rwgu3TMa1Ewfi2xeewfxG31NkNRdlAmbL6LyDKVywBT/WiN5RstvN0Waj3mnQvDjRrIrmIPo9kMR0E3pGDWkPrLDbNy/T5KTZaNasWXj//fezXQxHiLa1Z8IRTQbeH1WdDQD4n3c+B2C+cRnt8xKl8ooAQKGX3XCQXw0R3CrrNMs7tdFmLNH5VtFGZVy0ET+Imfm8CM1GUWNn5NX5jjUv1KjCn8M/Iz85in1eEnsh6Rtlxlce+oTvVkHsIz0LvDi7fzF+dvUY/XwRqeyPSAubHjfv8yI2Gy24YiTODO/GseL++P377O60NES4cLoCy/MazUb8hMrmGDJewypRF2De/noW+tAzvpLl23SbTZ4XgDUbicxkqgo+/x6D26UybSvPZT2Y0xO5hzcbORBeRLKUSPPSjxNe6JW33caMuvnWreKW6UPQEgjr2Ye9nCDBPLtDs1GR32PYSyrP48KskX0wa2Qf4TWsYEO/zbcncIpLVRKLEu5c0cKONtk7gda89CqwF17sxANHwouN2Wjx9y7A/uNN2LnuA2wxyXwuoqM703eUpDUvK1euxJVXXony8nIoioK33nrLcMwTTzyBoUOHwu/3o7KyEh988EE6ypqTpJLnRYTZjrVktR8IJcxGRBDJj2dg1IUXRaxCdHEJsXiBi5kMBeczO6vSqm9VNXRAftJTFPGKkRdezMxGBp8Xhysp2hxrCN+2EF40TROW16Uq1EaZrMMuiWyi627u2H740+1TUR5Xg9ODLF2H0RQGAFrd77NxnCT1qSgKSn0OTCGq+D2YYRdtxF9LbDZKPkMoj9FsZJ3nBWCjjYp8Ip8X6/t6kjAbxcrCm42c3wsQT8R2mhdFYdu7XZ4XWgh84Euj8auvjNe/o9+DpmnMtUw1L1yZRea5VPcYA1jBTKx5SW6KY3f25jRNgvpvcLjhJIHWVDvRIi+4cpTl707MwiKzES24l+R59A0q+ShQK7qcw25LSwvGjx+PW2+9Fddee63h91dffRX33nsvnnjiCcyYMQNPP/005s6di9raWlRUVAAAKisrEQgYJ+slS5agvLzc8L0VgUCAuVZjYyMAIBQKIRRKrmHZQa5HXzfCvWwtGoGiJZZrihZ1VI6oBuFxLsSuHwhHEIpPmgo0hEIh+OMRSqdaY8+vqiYptFUucSxXJh+zP4uxvC4qORS9MoEWQZGP9x9h7x0Jh6FyyaVCoRACYW5Jq0UN9wKAfA+v2RLXEw9tj41EwvC4FD1/hArza4TCEUSjRhOeAg2eeNnaghGEQom8O9FIGKFQiBncvKrC3EOhhBSvW0U4rhkIhSNJt1OXkng2FVEuMytX7mgEoZCWuIdmPTjp9Rt1tpGeR9GgUutDn1tFNBJmTmfmFEF/sIvmdvTOuXGUmI1UQXsm+KkIP79bMxxnJ08oWhQKdWO/y6acGvveotQWGarNM4ZCIYOA8+fbJuvjA00vKsmbW1WgUS9Di0QQMulrpIxm5aC1mZFolNv3R1x+jWtHIg2N2bmGa8F4HD3Wel2K4Xf+/gBw45SB+PqUCvF4SwsoGts3RUqcC0aUpTzPFPtctudeenZv/OxLo/CTt2uFv3vdxmd2gt/FtnfyuT3kfMuUQDBzc6wTkhZe5s6di7lz55r+/uijj+K2227D7bffDgB47LHHsHjxYjz55JNYuHAhAKCmxlk6cCcsXLgQDz74oOH7JUuWID8/X3BGx1m6dKn+OTZ/JapxXU0NjuZr+nefrP4IBywTiCbOra6uNvy6tV4B4MKxE/X4LHoCgAsHD+xHdfVe7D8Y+23/4WMAFGiRCOqOHAavUAsG2nHieJv+/b69e1FdvVv/vaFe1X/btnULqo9tZs7fdCx2H4AMBrFe/O7SJXArgFtxIazFvju0by9z/+XL38XJkyrzXXV1NZpaXKBFqr179mBsBbBz++f6vQBgc82nTB0dPXIY1dUHDfXEc/hQ4p7V1dVQtMT9WpqbBHUdu8eGDRvQtCvx/ghtbW3YuL4GgAvHTp5CdXU1QqHYNVeueB9lPiAYSNxj/97dqK7eqZ+/Zx9VB9GwftzOXexxTtjXnCjvls0b0d6m6tfbu3sHSP2pioYli99hzt25M/G7iPb2NlRXVyOmzLMfHtZ+/BH2NCfahwsRQ922Ue96+xefo7rlM+b3/fvZ9sFzaP8+VFfvsSxHC9eeyApyW+1WVJ/YIjzn88OJcn+4fJkhlX00zF6TZ/OmDTh8VNHLnudmxwaeLfWJ+23auAEntifaWaC93bRNKtBQXV2NCFWeqwZHcKx2NRbXAhN7qlh3IlH49Ws+TpyrRbGttla/7+J3/q0ft516fr2MmzejqG6T6TOQ6x44eBCx/Tdj9z2wR9yOP29g79F86gT4d717+2eobt5me0/AWL9H2xK/N546aajDI63s+QAwAXuwvWYPtgvuRL/z999bjhLKKn74INtO7zo7gj3rP8Se9RZFF3DdUAU7GxUo+9ej+qD9yfvr2Tp0KRoi8fE22NYqnDeMJOpgWp8o3qHaAc2efQdgZ5BRoEGDgk8+XYuWHenVvrS2CrZsMSGtPi/BYBA1NTW47777mO/nzJmDVatWpfNWOvfffz/mz5+v/93Y2IhBgwZhzpw5KC4utjgzeUKhEJYuXYrZs2fD44mtbjRNw/c+TnSoc6dMxqj+RViwbgUA4KKZF1qmP3+3ZTPe3nQYYwcUo6rqXMPvpTtP4JnPapBXUIRhw/sAB3Zh6JDBqKo6G3Wr9+Kf+z6Hv7AEaGyE1+vB4EG9se7EYeYaRQUF6NsrH9tOHQcADBs2FFVzE87C/2rYgG2n6gAAEyeMR9UEVvsV2XQYL++ICTQ+rwfBuJnqiqq5cKkKHqldgSPxvY1Gnjkcyw8nfCpmX3IJFp/ajC8aTujfzZ07Fws2vA9QUvbwYUOB8E6MHzMar+1OTG4XX3g+flf7sa7tGTxoIKqqEnvjmLGkaRNw4ggAoKqqCgs2vIdgW+x+PUpLDHV9z+olAIDx48fj3GFleHDdSrYOCwtwyQVj8eS2T9AGLy69bCa+/8kyQNMw++KL0LfYj9/t+AjH22N7gIweeSaqZg7Tz9/53k4sPhgb3Av8PrTG7f4Vg2PvMhm+ONqEX29eDQCYOqkSK09u1+87dtTZqN4f23slz+NGVdWlABJt96wzR+Bf+819XooLC1FVNQOBcBT/vWaZbVnmXDwLNftO4S87Y+2jON+PqqoLmWP+sP8THGhpAACMGnU2qmYMYX7fv3I33jkgmkpijBg+DFWXnmlZjt/t+AhH24z7r0wYPw5VVK4OmrrVe/HmnpjP2ZVXVBl+X7DhPbS1ma8Ep06qxMYPdgMNsWfzqGDGBp7C7cfx7GfrYudOrsSIPoX4+YYPAQAFBfmoqjqfOZ60SbdLRVXVpXhg43toi5spRo8aharpsQ0uq6qAz4804YpFq5HnUXH+eefh0S0fA4j119Gjh+P1PZ/Fj008Z9PaA3h9D7uit6ovukzl5eUo9nuwum4/AGDsqLNQdcFQw/E9d5/EE7Vr9b+HDOyP2lNHmWMqJ4xFVeVA23sCxvrde7IVv4jXYXnfPqiqmsicu/dEKxZu/JD57qrL55rm8Xlw03toCcfqeM7sS5hoyk/+UYvVdYlw9xuvmGWI7HKCsaVZU7TjOJ6JtxsAyPN69CSRPXsUo6pqmtmpOqQOf3L5SHzj3ArD72R8uGbGaHz61ja4aQ07h9/jQlsoigkTJ2LOqL7CY1KFWE6ckFbh5fjx44hEIujbl32gvn374siRI46vc+mll2LdunVoaWnBwIED8eabb2Ly5MnCY30+H3w+o+OTx+MxHUQ6Cn9tOh+E1+NBnj9RHq/HbVmOX1w7DpVDynDZmH7C4/L9sc4Timq6HtvtcsHj8aAw/puuIlcUeN3GV+p2KXBTfhIet4u5F73rrKi8HuqajH+FL3b/noU+XXjJ4/Zg8Xk9Bj8c1eU22FY9bjcQBvI43wO/z4s8jwtN8c7qs6lPHdo50uNhIy5cquk1KnoWwucV+D+oCsZVlKEkz4P61hA2HWrWO7ff543fI1HH+T62nD4qER59XBRK0u20gGpfeT4PY9cv8NP3dBmu7XaQjdPj8cBl43xKKMr3IY+qL/65AT602/h7vsDfhCbPa//OXSa+LX6Lc5k+ITjGLlGd3+tBO7fBqNW446P7nM/LtDO3at4O3GrsnTAOv1wfHjOoDP/67nnoX5KHIw2JzRljfjni58wTtHPR+xGiqEybzveJn9vLfVcoyKdT4Pc67gN8/eb5EsKF32ts76K+7PN5Dd8R6DrO87Hl4sfWfL8vY3MMjZ97Br/HpQsvXrfxma1wqeZjHwB8+ZyBKC7Iw/hBpZjxyHJxeeLCC5Tk7u2EZK6XtMOuE3jPf03TDN9ZsXjxYhw7dgytra04cOCAqeBCs2jRIowaNcrRsemGtpOqKuvYZvfchT43bp4+xFSC16ONwlE9MoXcjyR3IhN7LOZfHClDyw98kfxMnheRE1/is8hpkDihuVXFkM9EgTHaKBzV9N2T+XvwYbd0fhXytxO+ed4QAMAlZ/eJn0c5jQqe4U+3TcWCK0dh6rCeYofdeBbdi+MREf/ektBuMdFGcXgnRNZZk3LYTcHpzTLDLvUuRQ6ZdsnQyLWcZJgFYg6jtGAociCls0eLXp+dw6aTXB1mj2V17pXjy5HncelthMeuCjwuFYGQM98ggM3gykcbWdW3W/BORA6+o8tLUFbgNTqHJm816wAAL6lJREFUm1SOVbSRHZqmMe3Y6a7Sog0mnWxxAIgjbxg/sySSf5reg7oJH23Ejz3J5nhJFb4NmwVQpANVVVA1tr+eQ0wEedfZjjZKq+alV69ecLlcBi1LXV2dQRuTbubNm4d58+ahsbERJSUlGb0XT2wgSUQC2aWsTgYyUQXCEUNoLmlELXHhxSxNvYtLUsdPYHSoqqgz2EVCENWq123MTquoxvP/86W14KFDNGncLtV0YzkrKgeXYc2PE2pf+j2IBujzRvTCeSN6mf5Ovps+vBfeWH8QWw426L8lwovFQgR/TfoZK3om75dlSNNOR5B4rNuenTCdbB6dWLSRwvzNYyc42oVKJ7HuEdzb/ORehT6s/+lsi0017euKF8Itj6fariHPi8W9yIKAjU4yvw8bMaOYeu1YbcxohwZw0UbiOuT7viibrtm+SE6gtbqiaCC6jPNnn4kbpxpNJjT0dMz3Bas8N5mE7zPseJ1cGUSar2QhfbxbJanzer2orKw0OFUtXboU06eLN7tLF7mieXG7Ymmz77l4BL51wTA9VDZVyMAaoDQvZIAhA0EiIZdY+HC7zPc2Ati9lkQdkr6kSFFAcm14XKphoFQVozbog+3HBfcQCy8eVWEmRLsNEGl6F/kSgoVNuC5TFkEdkomsIF7n9KQlSuzGp0vnw2T/fPtU3DpjiOUu1mbQ70vTNOZ90gJLKnkvksmL4Ytn96Unf9FExOR5EVzfLtzTyQLPTNCwG9z9HpfphG3XTjxJCi9WGXatFgik7dDlcaKpAWITrtm1rZLU2cLleTHXvDgQXmw0L+cNjy0qzu9rbAhMJmCTHFeEYb0LDLs689ARVXah0p0lvPBjOt1fnC42HrpqNC4b3Q9XTUgumlcEGX+6XJK65uZm7NixQ/979+7d2LBhA8rKylBRUYH58+fjpptuwqRJkzBt2jQ888wz2LdvH+644460Fpwnm5oXl2AQ+t5sawdDp9AZdvUkdfF7GNLem+Z54bYH4CV5xh/E2BnoQU6Ul4SYjUT7AqmKsxwWZJzgtQUulTMbJbnSIDCqdJsOL5q0SJWRAYskQSNlBNh64idkZoXoUjF9eC9Mjw/KyULXEZ8RmBZsUpmcklFDk/dibzayFhzpleTPrx6D//fWFnz34hF4/N2YE6/mIO+nWbHNtmVwghOzEd0O7OA1IvT1rboIOU9xKOzw95kytEx4nCiBpp3ARtCgcUnqnGXYFZmN7MyGL9w6GfuON2HLx+8bfmOyNwuKnuzGiTT89Wi/KrNtRDKBVUZ0p2X4xrQh+Ma0IUndt3JwD9TsrTd8T8Ldu1yel7Vr12LWrFn63yTS5+abb8aLL76I66+/HidOnMBDDz2Ew4cPY8yYMaiursbgwYPTV+ocwyr1fkfRhZdIFOG4mo7cg1/lqtyASPCoKrsxI3cMPeGJVqrnj+iNKUPKMKq8GG+sO2D4nTbN8GOfyOdFBBk0eRW+26Uyq7VUtgcA2OeyE6aszEbkfZAtAuhr8zsds/enzEYdXLHR1+JlSTufl1Se3Yz8eLuhn1u0srbTvNATzJXjyvGlCbFIFiK8OBkj6ce66dzBePnjvbGypSjsxq5pLOtFI/tg+WexyDy3qiaV1ItpA1xmZCsTFWlfTjdyZFL7uxQM71OIxfdegF5cNmyRcOvUN1HT2Ocx25gxHWYjj0tFRVk+RAHvbpuxtyM+IXxdMNs7dJK/S+xebN2yGdEzp/15+bYp+NEbm/HWhkPs/YnmpasJLzNnzmRUayLuuusu3HXXXSkXKhUWLVqERYsWIRJxvhJKF4zjXUcM9AJ88YgITUtkpdWFF0EqfrPyqRYDHz3JiTcfU/HXO2LheK/XGIWXhObFuI+R4ljzYtReKErMoTnPxqzlBH5TOcuyCMqbyDIa17yE2ARjADtJ85oXM5+XVKAH1d5FXqY/0kKTaEKxz7DrvGx+r1F4EU1ETN0L7s9MQC7FmIXVgd3ouokDseVgLUaXF2Py0DJdeElV2AXYd/ar68ZBAzBuYIkuvHhcChbdOBHz/rwOD1wxEjDJJyO6nselOvYv0Z2ok9TUxD7H3g3JoErTMYfd1MxGojbp1GHX7voiwStZ4cWqpbHjZOeYjAA7n5fMCVH5XjcqB/cwFV66nOYlV8m+w26MTGleAONmc7wKNra7sfEabpe12YjWvNiVX2Q2Ort/MVxqbIXHn+5UmFMFmpeyfC+Tlh9IfcVj5zRKI9S8EAEl7nOTSD+vCNOn85oXj8taQEyWF26djLrGdgzvU8QMuD5G8yLyebG+bjLtl0w6dBsVTWJ2dc+bOXicDJE3TRuCEX2LMHZgCU61JHKzdCQggi5KvxI/zh/RGwfqE0m0PC4Vl4/rj4tGXga3EkV1tXPhhd/pXcS5w8rw8a6T+FrcydQu2oi+tugZeDri8xIzG1Ht3cRviW9vIkGlI9sDsKY00f3NtZQirI6hx9uOak+TwcrnpSNmUSeIBEJ/VzUbSYzQ7TiTwksbJ7wYNS9m5VOEfjkEfxIrClFzHVSWj9X3XYTSfC9eWbPPUZkMZdSFA0p4iWt06Oc8s69xBekE2jRh67BrMQiS+iFmIyY1v0PNSzpWbbPOSoT30mNIR6ON7Nrv8D6F2FHXDCBhAvDamI3sdpWmJ0Gh8OJgjHSpCmbEfYiKKM1NR/qjyKGWjfSK90OvC6GQvfmI177ZtcM/3DIZmw40YPKQmM+Ky+EiiZ7QrG4hmoDttEFThpbh090nccOUCiafjJnphx9rehX6GLMe0DHNi9W9AOdmMIKVVaHA13ENcCrwQq/Lpr+kE1GdkrFNCi9pIqtmI4eOdCldW1X0bIfEVEHu5/canVtFuFU+2oj9nfF5sZHkzTYS7BPPU8MPFk7rg5znEwgvhxra9O+mDu3p6Ho8dKSA3WpFNOCZbRzJRBExGgjz4yrK0rttBbODtk2YvtM8Lzyv3zkNa/fUIxzV8KvFsay0ZMVMR4CJJiK6zoROlaq1ZsaJwy6Noih4/uZJ2HywAZOH9EjqXPY6xs/JaPB46L7gNtGS0uR73Th3WKK90+dbnUu3NfNAaTPNi3Wh/nTbVBxuaMPgngV4jTIhm4VKizau/dnVY1BRlo+Hq2NbAnQ0nYR+7TSPvTy05iWZqMeOwvrasEJvJn1eALP+Gvsy2z4vnSc+Zph58+ahtrYWa9as6fR7Z9JhF0gMMkR4SeQVYZ3+VEURDlYuzpHXYENlkh5ZNwm79srfXVGsVzN8megBtUd+THhpDiQE0lRzQrht/C7sUAWTF8Ctpi18AEhGTAC4ZqJ5KvRUoKuXfn0is5Fd8zRbyVUOLsO3LzxDmNPF6rkBe5+XQZQwJxIc6QncKRef3Rf3XnJm0itvGpEvGy34JmuSYoRM3mHXwflOxxmnY5DYPGp9rtetYnDP2HYnTsxGvEBB2lcwQm1SmaYxM9PCS4E3O5oX3gGbrq5saF7IWJrtPC/dRvOSTZxGAaSK162iNRjRzUakwSpKLAdKM5VhV0Qswy69auNsqMn4lNgM2EaHXWf1IVLLF/pjzfPHVWfjv1/fhJ9ckdweQDS02SiVDu9SjeXjr0XXHb+aPLt/Yp+tUeXp3XOL1kwwCRJFDrsp+Psw51PvkwiStMZJFArrpdLwi+5fVuDFP79znkEw/ei+i7Cjrhnnj+htWaZMITIb0ZN0kT+54ZMW/L0ue7ORsTzisvHQbcDSsdciJYATUnHYJeUORdI/8aVjHrca3vJ92fd5MQRfZNznxfgdeYfZ1rxI4SUNMLboDDQm0lF4zQsQm0Bo4cWJwy5fRDbPi53mxbrBsgNs7H8nTVyP2KHKQqJOpgwtw3s/mOngKubQZqNUVnqk/oyaF7G/ED+YjxlQgle+dS6G9ylM+t52mGlehD4NggYyok8htsf9WOwEO/p3kc9Lntd4T3Z7APH1xwwwOtkPKM2zTFOeaUTOoF63ir9+exrC0SiKksxWyiZAszcb8bCaIPPjmKgki+v1KDDu8ZNM32BCpU1MP7zwQlbtGRFeTMp+/ohe2LDvFGae1TEhOFuaF3pOURXWf1GUVTidCDUvKtG8SLNRWshmhl2rMOR0QCb0vSdaDfegfQzMtBx2SeoYzUsK0UY0TrOGisoIsANisitbKzwd1LyIIooA1lmbddg1dq1zh/W0zfCZCux+LHah0onPhT431vz4Eiz62kT9O7MNDhO/U6YCQZ6XPI9I85K6n0g2YYQAqi1PGVqG6Wckn2BwYI+EecxFRak5xWmSOqfX9bhUbHnwUpw7rMzRdUXnA4lMyyL467l0zUv6Jz6zor/0zSmo+clsZ8KmVbQRpXnpzDwvrA+TOBQ+UzD7cbkUfPDDWfo9sy28dBvNS85k2M1AW7JyEqWjO8z6k1tVmEmWH1CYaA9bzYt1WelLJzMQkmPpgTedwgv9XKloXkTRUAArLJDf3Ko403GmoAVKui2KfV4Sv3vdKnoX+fSwb8BesKMHSyI4k2yjkahmkuelY3WfLZxqOpyS53Vh40/nwOVKXnABwCWadHi+zXGFPjczqSfTbMl7tQp15oVV8vfAHunXqJltM6Eoip7iwA6r4Y3WvHTElypZWJM/24c6ksfICXQ7qxzcA4PK8imfFym8dHnodpwRzQs3oqiC1S8QX82ZXMNKI2IX7ZEMzLWTuJTotoW+9G23bheOawcfKs1/T9+jI3krUsHcYdc6CRlpV0wEjd3WCdQl87ndviNRzX5jxk4c9DuKU01HMpTki9u0k8tnKp+UmYbJDrfe3s0lHv59k3LfMKUCB+vbcMGZHfdnuveSEfjnpsP45owhHb6WFbQ/VzgDZi8zPIwPk+I4ZD4dKIJ7kf+lz0s3IxONqYVaGcfukfhMTxZmA2x7KMpFKrC/iybgVFEZDU/sf6d5OvjDpgzt0aGy0CSzPYAIPsNu4rp03SXU6J2JqcOuMM9L4jPRFJkJF7FIMfZ8uu5oIc3rUtEeiprkecmsZjJTOHWQ7SxUbgXu6JxkhaIknpNsC9K7yNwUyr9vehFwf1XqDvg0915yJu69JD17yVlFRtJa12AnbkrIp7mg32nmfV7oz7E/bpk+BFeOKzdsN9HZdBvhJZt5XmgyoRbff7KN+ZtW3TNmI1URjmqtwQg3KbHH0OaZjuYNoHU/yQz4ipIQXj667yIcawpgeJ/UEtKJSGZ7ABHkFNpEwl/LiRo9E5hqXgTlEG1TQGv2eC1imBvMaTU1bSIa2rsQnx1uFDrYJrOjdy7hNK9KOnBy/VTySTk5ymnmXp4RfYvw1Ncn4oze5k7oBp+XLmQ2tCITDsdOUBUu2ijjeV6MbaNvsR9943m9skm3EV6y6fOipLhySRX6Hn5OeOkhUEu3hyJsnheujH2L/bhv7kj43Woa9t1JfCaN3ckaxaUoIMNBJqJM3B00G106up/+2etS0RZlsx2T7wHzTeoyBV2/dpoX+t3rZiPKH4D2n1FVxeDkxIRKU8LRn2+fipZAWBjB0pHEbtkkk8kneZxoJ+l5Kr1mo9Q1Y5eN6W/5u5nPS67iVJ+SLeFFAdsuM+/zkvica++u2wgvuUJnvGDackFPUIqi4IYpFVi7px4zz+qN/3ptE4BYiDWzuhIMUHdceEZaysasVuP/O0lSl2lzAmM2SvIdvXzbFJw3PBFd4nEpaItvn8NoXuJCgJnjYKZgBA7q0UTloAVtkdmIllVE1eQ20foV+Nwo4DdUJPfpog67jCCeA+auVDRBTnxYMmkeM4s2ylWcJh4MJbGbeDpRFMV2L7B034+QC6ZTmhzokt2LTLzgWVx+AlpVyIShKjGTxaKvTcRXJg3Sv+fNRh0po2HHX45UVe2Z7hheJotwcvc6f0Rv4aTPX4skY7NyYMwE9ICrKIkMnOJdgxOf6eioxLXE/jOi852axzzdwGyU6faZrMNuOs1GmXQANeR5yQUpMA0Es6V5UZxnWk4HbJvL6K2Spnu0pBwiE5Lw4zecwyRYMsstYtaQ23nNSwcG4j/dPhXjBpbg1W+dK/ydWcUlUReZ7oTuND0/YF7nEypKUV7ix+xRfTt0/WThNVtkghCZjehnJ7/Tghmd8Vv0SkSh0nZ01N8oW6Q7VLqjuDI0adntzNwR+OvluuzidB+tYJY0L3ySusxvD5D4nGt9N8ebUtcjE2rxIr8Hc0YlfC7oCYjJoWEyKbcGw2kLs5wwqBRv330epprsN5OMmpE2O2R6ZdvR7QFo2IR3ic8DSvPw0X0X4a6Zwzt0/WThh1tSJDvhRZSBN0IJQt+bHYvg+OqkxF5MbKi0M6uz10EbzUWYqP8cKDdr3nF2jjONDv05vc/J11t30bxkIsmeExSFEygynE/KKrlptuk2Pi/ZjDZy4tPRUQpNIoKY1Otc4yrN9+BUawgTK3pwSeoyV07RACuqnesqB6KswItnVu7KeJkAPly3Yzdjome4a2VjkuObX6x9RIXRRnRxRWYl2n/mlulDcP6I3hjaq4A6n3LYFWwFIKKrOux2ptnICZkqj2gDykyR67KLY5+XLDrs0uNXpkOlM53DrCPkeFNyTjZ3le4MCn305naJ762cIf8+bwa+c9FwPPzlsZ02ELOTt/l9/vcr45k9lTJvNsqQ5qUT04SbwW/ZQB5PqHkRhErT8P4zw/sUcg6CiXMc+7wwmhdHp+QEqWg6MklqPi/2x4kSkWWKXNe8ZDftmj282Uj6vEhyHjrbLLMxl4VKfnDPAnx/zlkoK/B2mvCSzIBPD5qZFurdafS78Lo6py6dwq8WR/QtQr7XhXJBuLmd2ch276pUzEaUdjDXVM9WdKZGwomQkVJ5kjQbZbo5d6HXL2TRjRNR7HfjD7d0/h56AADOYbcztwfItb7bbcxG3Z0CSvNCN1g2AZj5+Z3leCUUkkzmQ8YBMdPRRmn0u+joJo/p5vrJg/Do0i8weUgPAMD/3T4VbcEISvKMOX+YPC9Cs5H1vdwpOOySKCz+/rlOZwrXTshU6HamHIFF5ILvkCU27f/ycf1RNbZf1p5D5UKlM5+kLvE51/quFF66CEWU5sXUYddi4OE398oUbJI662NTjUxKBSZJXQdXK1Y+L9ngrplnYMKgUkwcHBNe/B6XqUmHfj92Pi8izHIMWWHll5XL5Nr2AKloXpwclWu+PdlkzIBirNt3ylIwz6YApiqc5v00zvMihZcuAq15oaFNGFaScWflBmDzvMQ+m4Ufpit82wkd3duIJtd8Xtwu1fEGd6LtAWjsnM/JSs/vUR0LnF11V+lcm9RT8XVwUmxRVuzTld/fOBG/W74j45s8pooChXWi7cRQ6Vzru1J46SLQ0UbtoYSnu9NIjs7zeXGu4Ul1Q7hU6Oiu0uy16DrvWm5jTAZekc+LTRAFeU9O/V2ArrurNL8hXrbJVD6WXHbK7GzKS/Ow8Jqx2S6GKYrCjvMZ93lhFr0ZvVXS5FhxJGbQqd7bQ4lwcMdmo6w47Mb+uGikOGkbU6ZMbw+QxtU/7YCaA4qXpGAcdlMwG1WU5aPI78a4gc73D2Mj4hyflnVEWsRsQk8eTsvTv8R+jzD6lXcls97piHF7gMx2KLo15NrCQ2peuiD0jp5OU6932ipS4PNyzTkD0LPQi1tfYMPYO1Nd7WE6/OmsebEWXgb3zLc8vyTfg4/vvzipnbNZrVfXqS+6lWR6Tk92ewC7ieSFWybjL5/uw48vPzvJcuTWBCVhURW2HXTm3ka51ja6jfCSzSR1ncXrd07Dgfo2jCov1r9jw3bNz81mtJGqKph1Vh/DsZ0ZbeROY6I0b45FGyWDaBdsAHjlW+fiHxsP4bsXj7C9htkGjGa4XSq+MW0wGttCwl3Pc5Vc8wVJxkds1sg+mDXS2OfskJqX3MYYbdR5Pi+51ja6jfAyb948zJs3D42NjSgpca7S7kpUDi5D5WD2u9SijTpHeLELdUh1E8dUSOf+Ooy2q4vZjdhoo4T25NxhPXGuyZYP6eChq8Zk7NqZgtW8ZP89d0Y+llwzDUhYFC7aqDPzvOSa8NJ1dLgSIU6dITurESYTXtqZUn06nUa7jebFYaizBFByoKo6I7OqlF1ym8E9C5h3lGkzbK5F3NHkQJeUdASnOUc6K6IgmYgIRg3eiXleOnqvrrpLMmDv8yIRkwsDd2fk3Ohq7fl04ZVvnYsvnzMAD1w5qpOTCiY+51rT6DZmo9MV52ajxOfOjjYyP7bzQjTTubeR16GTdC7CbMyYa7GPOUamQpOF93JwDJOkLo2vjs7DlAtCmsQIbdbt1FBpaTaSZAqvQ3NIZ2VKTObajBq8E/O8dFzz0nV9Xuj34zRDriQ3JvXOyPibY/OTRAAbbZRhs1EnLXpTQY5eXRw69brVwNNZO5EmE6HB7tXSedFGHdW8FPkTETNdzeeFrmeP1Lw4JhfG7c5YgORaOKzECJvnRWpeJF2UVKKNMtkG2V1IrY/tzF17PQ63UXDCnFGJpHtHGgIdulZnk8uhj7lMrq06u5q5UpI+OlOgYLV9Gb1V0uSc8LJ//37MnDkTo0aNwrhx4/C3v/0t20WyxSYpaUZxbjZKfM6kliMZzQubdCtTJYrhSaN6dVBZIpGbz5NzXciSzgyz7OooJp9zgVyIfpJkB3r4znQfZrR9OSa95JzDrtvtxmOPPYYJEyagrq4OEydORFVVFQoKCrJdtJwkNc1L5/i82KmgmZQwGU9Sl7h+OmTNZfMvxHMf7MI9DpK65RIKYy/PrcEol8m45iXJ60vNy+lLZ24P0Jn7zyVLzgkv/fv3R//+/QEAffr0QVlZGU6ePCmFFxMYR1SLxtVZzrGM8GJyTM8CL4DO3VOFFvLs9u9xwvA+hXjk2nEdvk5nQ7u5dKVU/VmBEa4ze6tR/YuSOj6dwlQ2NceS5GHzvHSm2Si3hJekR6+VK1fiyiuvRHl5ORRFwVtvvWU45oknnsDQoUPh9/tRWVmJDz74IKXCrV27FtFoFIMGDUrp/NMBOtur1aTM7lGRufJY2Uhfu2Mapgwpw0u3TRGcl2HNC1WY03mwVqXZKCUypRn853fOw3+ePxT3VyW7B1FGiiPpAjCLvk4Mle7yZqOWlhaMHz8et956K6699lrD76+++iruvfdePPHEE5gxYwaefvppzJ07F7W1taioqAAAVFZWIhAwOjouWbIE5eXlAIATJ07gG9/4Bp577rlki3haQfu8hCPms3JnJTayioiYNKQMf71jmvC8TPcL+pkjp7H0Is1GzlE6wdNlzIASjBmQ/HYm0tn69CVKDV/p9OUTQQ/hubbWSVp4mTt3LubOnWv6+6OPPorbbrsNt99+OwDgsccew+LFi/Hkk09i4cKFAICamhrLewQCAXz5y1/G/fffj+nTp9seSwtCjY2NAIBQKIRQKOTomZxCrsdfV6Mmw3Tf0xaqJYfCYdP7a9HEhpWRiPlxHSUSDtN3tbxPmNpEMxqJndcZ9RcKJff8nf5OMwB5Bi2SeD8lfrVbPFumiGpR/bNdPZmNDemE3nQ2Eg4D0fTMJtGo8+fsKKlevzPqt6sQpsbYaCSMUChqcbQzzOo3Sm90rEVzqn2k1eclGAyipqYG9913H/P9nDlzsGrVKkfX0DQNt9xyCy666CLcdNNNtscvXLgQDz74oOH7JUuWID8/X3BGx1m6dCnzd1OTC8RAXl1dnZF7WhN7jV9s34HqwBfCI/Y0JY57f/lyFHszU5K6tsR9TtXXW9bHxmMKgNjmgO8tXw63aqzb9BIr15q1a9G20077kuga2XmnmWH5u8vwvTFAKKpg9fvLsl2cnObgARXEsu60DWSy/e7ekyjPO//+d9pMR3v2JP+cyZG+vpTZ8aFrsPFEYtxcsvidtGqt+fo9FQDI+9u6dSuqT2xJ380EtLa2Oj42rcLL8ePHEYlE0LdvX+b7vn374siRI46u8dFHH+HVV1/FuHHjdH+al19+GWPHjhUef//992P+/Pn6342NjRg0aBDmzJmD4uLi1B7EhFAohKVLl2L27NnweBKJyhbtXIXDbc0AgKqqqrTe0wn3rF4CABg67AxUzRFHv2w80IDfbPkEADD7kovRs9CXkbLsPdmKhzd8CADo2bMMVVWTTY8NbTyMl3dsjpVp9sV47913DXWbTkg93VB1IQb1sBZsybFAdt5pujFruxJz3n9jC3DsEAD7NtAZ9bt58RdYfngPAODyy9PXJmv+9RlWHtkHIDNtPR19SbbfBOrWo8AXGwEAV6SpHZjVb11TAAvWrQAAjB83FlWTBqblfmYQy4kTMhJtxDu3aZrm2OHtvPPOY9SYdvh8Pvh8PixatAiLFi3SVasejydjjZy/tovyO8lqx1IU0/t73IlX7fN6M1ZOH10vqmp5H7re/N6YKiiT723lf83CydYghvWx9zHwe1S0x9Wx3WmwzGT9djdcavL9OpP1m0p5nKBm6LoiOnp92X4Bl8ulf053XfD16/Uk5mKv251T7SOt3j69evWCy+UyaFnq6uoM2ph0M2/ePNTW1mLNmjUZvY+I//3KOPQs8OLnV4/p9HvTRKJW0UaJz5mM7En1Pp2RlryiZz4mDCp1dOz/3X4uhvcpxEvfNEZGSSQSSbbozHgD2iSVaxFuaRVevF4vKisrDXazpUuX2jredpRFixZh1KhRmDzZ3EyRKUaXl2Dt/7sEXz93cKffmyZsIbzQZNJBXe2kkOxMUzm4B5bNvxAXnNk720WRSCQSHS0taTadkUzS0c4mabNRc3MzduzYof+9e/dubNiwAWVlZaioqMD8+fNx0003YdKkSZg2bRqeeeYZ7Nu3D3fccUdaC84zb948zJs3D42NjSgpST70sKPkwou10rzQ5EqGXYlEIpEkh8NhPi04STqaLZIWXtauXYtZs2bpfxNn2Ztvvhkvvvgirr/+epw4cQIPPfQQDh8+jDFjxqC6uhqDB2dXK3E64DR/SeftKp2x20gkGSfnmm/OFUiSDUb2Sy4bc0eg99DKtbVo0sLLzJkzmbwmIu666y7cddddKRcqFXiH3dORqEORPJONMBmfl9M4V5xEIpGkxJl9i/Dn/5yKfsX+jN8rl90Aus3mJtl02M0VnJqNOmtvI6l5kUhyH7vFqCT3mH5GLwzrXZjx++TyEN5thBcJLBszneq8s3xecrvpSyTW5NpKUyLpbNjFaG51iJzbVTpVTmez0et3TsO72+pw64whjo7P5AZb7KXlik4ikUi6KjkmrzB0G+El29FG2aRycBkqB5dZHtNZG7nJCCOJRCLpHuSatoVGmo1OE87uX4SLR/bBDVMGZfQ+ychIg3tmZu8piaQ7kqldrqcM7ZmR60q6PmySutwSZLqN5kVijaIoeP6WzCfwS6aBVw4uw8JrxmJYr4IMlkgiSY1MCQu5RtXYfnjiaxMxpvz00lhL7MnlPC/dRvOSzQy7kgTJWqdumFKBqcPkyk8iyRaKoqBqbH9UZEgTeves4QCAH8w5MyPXl2QORRF/zgW6jebldPZ5ySVy2UYqkUg6n+/PORPXTx6EgT3ysl0USZLkmqmIptsIL5LcIIfbukQiyQKKomBQmfRv6+rkmhm125iNJLmB1LxIuguyKUskCXKtP0jhRZJWpPAikUgk3Y9cG9m7jfAiHXZzA9phV2Ydl0jSxwDpMyKR6HQbnxfpsJsb5LKDl0TSlfmPyYOw61gzzh/RK9tFkZyG5NrQ3m2EF0nukWuNXSJJhlxrvx6XigVXjs52MSSnLbnVIbqN2UgikUgkEklmyDVhXgovEolEIpFILMnzuLJdBAYpvEgyhnTYlXRlpso9fyQS/NelZ+Hycf1x3vDc8rXqNj4vixYtwqJFixCJRLJdFIlE0g24akI53C4F4weWZrsoEknWmBff3iHX6Daal3nz5qG2thZr1qzJdlEkEkk3QFEUXDGuXGaHlUhykG4jvEgkEolEIjk9kMKLRCKRSCSSLoUUXiQSiUQikXQppPAikUgkEomkSyGFF4lEIpFIJF0KKbxIJBKJRCLpUnQb4UXuKp17yBx1EolEIskE3UZ4kXleJBKJRCI5Peg2wosk98ixfbwkEolE0k2QwotEIpFIJJIuhRReJBlD+rxIJBKJJBNI4UUikUgkEkmXQgovEolEIpFIuhRSeJFIJBKJRNKlkMKLRCKRSCSSLkXOCS9NTU2YPHkyJkyYgLFjx+LZZ5/NdpEkEolEIpHkEO5sF4AnPz8fK1asQH5+PlpbWzFmzBhcc8016NmzZ7aLJpFIJBKJJAfIOc2Ly+VCfn4+AKC9vR2RSASaJoNuJRKJRCKRxEhaeFm5ciWuvPJKlJeXQ1EUvPXWW4ZjnnjiCQwdOhR+vx+VlZX44IMPkrrHqVOnMH78eAwcOBA//OEP0atXr2SLKZFIJBKJpJuStNmopaUF48ePx6233oprr73W8Purr76Ke++9F0888QRmzJiBp59+GnPnzkVtbS0qKioAAJWVlQgEAoZzlyxZgvLycpSWlmLjxo04evQorrnmGlx33XXo27evsDyBQIC5VmNjIwAgFAohFAol+3iWkOul+7rdlWg06riuZN1mFlm/mUXWb2aR9ZtZcqV+k7m/onXAJqMoCt58801cffXV+ndTp07FxIkT8eSTT+rfnX322bj66quxcOHCpO9x55134qKLLsJXvvIV4e8PPPAAHnzwQcP3f/7zn3Xzk6RzuWd1TCYeVRrFt8+OZrk0EolEIukKtLa24sYbb0RDQwOKi4stj02rw24wGERNTQ3uu+8+5vs5c+Zg1apVjq5x9OhR5OXlobi4GI2NjVi5ciXuvPNO0+Pvv/9+zJ8/X/+7sbERgwYNwpw5c2wfPllCoRCWLl2K2bNnw+PxpPXa3Yl7Vi8BAPTu0wdVVRMdnSPrNrPI+s0ssn4zi6zfzJIr9UssJ05Iq/By/PhxRCIRg4mnb9++OHLkiKNrHDhwALfddhs0TYOmabj77rsxbtw40+N9Ph98Pp/he4/Hk7GXkMlrdydcqpp0Pcm6zSyyfjOLrN/MIus3s2S7fpO5d0ZCpRVFYf7WNM3wnRmVlZXYsGFD0vdctGgRFi1ahEgkkvS5EolEIpFIug5pDZXu1asXXC6XQctSV1dn6nCbLubNm4fa2lqsWbMmo/eROEeGuEskEokkE6RVePF6vaisrMTSpUuZ75cuXYrp06en81YGFi1ahFGjRmHy5MkZvY9EIpFIJJLskrTZqLm5GTt27ND/3r17NzZs2ICysjJUVFRg/vz5uOmmmzBp0iRMmzYNzzzzDPbt24c77rgjrQXnmTdvHubNm4fGxkaUlJRk9F4SiUQikUiyR9LCy9q1azFr1iz9bxLpc/PNN+PFF1/E9ddfjxMnTuChhx7C4cOHMWbMGFRXV2Pw4MHpK7VEIpFIJJLTlqSFl5kzZ9r6Mtx111246667Ui5UKkiHXYlEIpFITg9ybm+jVJEOuxKJRCKRnB50G+FFknsM612Y7SJIJBKJpBvSbYQXGW2UO7x+5zTcMn0Ivjf7zGwXRSKRSCTdkIwkqcsGMtood6gcXIbKwWXZLoZEIpFIuindRvMikUgkEonk9EAKLxKJRCKRSLoU3UZ4kT4vEolEIpGcHnQb4UWGSkskEolEcnrQbRx2CSSBXmNjY9qvHQqF0NraisbGRrkte5qRdZtZZP1mFlm/mUXWb2bJlfol87aTTX27nfDS1NQEABg0aFCWSyKRSCQSiSRZmpqabKOGFc2JiNOFiEajOHToEIqKiqAoSlqv3djYiEGDBmH//v0oLi5O67VPd2TdZhZZv5lF1m9mkfWbWXKlfjVNQ1NTE8rLy6Gq1l4t3U7zoqoqBg4cmNF7FBcXyw6UIWTdZhZZv5lF1m9mkfWbWXKhfp3maes2DrsSiUQikUhOD6TwIpFIJBKJpEshhZck8Pl8WLBgAXw+X7aL0u2QdZtZZP1mFlm/mUXWb2bpivXb7Rx2JRKJRCKRdG+k5kUikUgkEkmXQgovEolEIpFIuhRSeJFIJBKJRNKlkMKLRCKRSCSSLoUUXiQSiUQikXQppPDikCeeeAJDhw6F3+9HZWUlPvjgg2wXqUuwcuVKXHnllSgvL4eiKHjrrbeY3zVNwwMPPIDy8nLk5eVh5syZ2Lp1K3NMIBDAd77zHfTq1QsFBQX40pe+hAMHDnTiU+QmCxcuxOTJk1FUVIQ+ffrg6quvxueff84cI+s3dZ588kmMGzdOzzo6bdo0/Pvf/9Z/l3WbXhYuXAhFUXDvvffq38k6Tp0HHngAiqIw//r166f/3uXrVpPY8sorr2gej0d79tlntdraWu2ee+7RCgoKtL1792a7aDlPdXW19uMf/1h7/fXXNQDam2++yfz+yCOPaEVFRdrrr7+ubd68Wbv++uu1/v37a42Njfoxd9xxhzZgwABt6dKl2rp167RZs2Zp48eP18LhcCc/TW5x6aWXai+88IK2ZcsWbcOGDdrll1+uVVRUaM3Nzfoxsn5T5+2339b+9a9/aZ9//rn2+eefaz/60Y80j8ejbdmyRdM0Wbfp5NNPP9WGDBmijRs3Trvnnnv072Udp86CBQu00aNHa4cPH9b/1dXV6b939bqVwosDpkyZot1xxx3MdyNHjtTuu+++LJWoa8ILL9FoVOvXr5/2yCOP6N+1t7drJSUl2lNPPaVpmqadOnVK83g82iuvvKIfc/DgQU1VVe2dd97ptLJ3Berq6jQA2ooVKzRNk/WbCXr06KE999xzsm7TSFNTkzZixAht6dKl2oUXXqgLL7KOO8aCBQu08ePHC3/rDnUrzUY2BINB1NTUYM6cOcz3c+bMwapVq7JUqu7B7t27ceTIEaZufT4fLrzwQr1ua2pqEAqFmGPKy8sxZswYWf8cDQ0NAICysjIAsn7TSSQSwSuvvIKWlhZMmzZN1m0amTdvHi6//HJccsklzPeyjjvO9u3bUV5ejqFDh+I//uM/sGvXLgDdo2673a7S6eb48eOIRCLo27cv833fvn1x5MiRLJWqe0DqT1S3e/fu1Y/xer3o0aOH4RhZ/wk0TcP8+fNx3nnnYcyYMQBk/aaDzZs3Y9q0aWhvb0dhYSHefPNNjBo1Sh+8Zd12jFdeeQXr1q3DmjVrDL/J9tsxpk6dipdeeglnnnkmjh49ip///OeYPn06tm7d2i3qVgovDlEUhflb0zTDd5LUSKVuZf2z3H333di0aRM+/PBDw2+yflPnrLPOwoYNG3Dq1Cm8/vrruPnmm7FixQr9d1m3qbN//37cc889WLJkCfx+v+lxso5TY+7cufrnsWPHYtq0aTjjjDPwxz/+Eeeeey6Arl230mxkQ69eveByuQySZl1dnUFqlSQH8Xy3qtt+/fohGAyivr7e9JjTne985zt4++238d5772HgwIH697J+O47X68Xw4cMxadIkLFy4EOPHj8dvf/tbWbdpoKamBnV1daisrITb7Ybb7caKFSvw+OOPw+1263Uk6zg9FBQUYOzYsdi+fXu3aL9SeLHB6/WisrISS5cuZb5funQppk+fnqVSdQ+GDh2Kfv36MXUbDAaxYsUKvW4rKyvh8XiYYw4fPowtW7ac9vWvaRruvvtuvPHGG1i+fDmGDh3K/C7rN/1omoZAICDrNg1cfPHF2Lx5MzZs2KD/mzRpEr72ta9hw4YNGDZsmKzjNBIIBLBt2zb079+/e7TfbHgJdzVIqPTzzz+v1dbWavfee69WUFCg7dmzJ9tFy3mampq09evXa+vXr9cAaI8++qi2fv16Pcz8kUce0UpKSrQ33nhD27x5s3bDDTcIw/UGDhyoLVu2TFu3bp120UUX5Uy4Xja58847tZKSEu39999nwiFbW1v1Y2T9ps7999+vrVy5Utu9e7e2adMm7Uc/+pGmqqq2ZMkSTdNk3WYCOtpI02Qdd4Tvf//72vvvv6/t2rVL+/jjj7UrrrhCKyoq0uetrl63UnhxyKJFi7TBgwdrXq9Xmzhxoh6OKrHmvffe0wAY/t18882apsVC9hYsWKD169dP8/l82gUXXKBt3ryZuUZbW5t29913a2VlZVpeXp52xRVXaPv27cvC0+QWonoFoL3wwgv6MbJ+U+eb3/ym3ud79+6tXXzxxbrgommybjMBL7zIOk4dkrfF4/Fo5eXl2jXXXKNt3bpV/72r162iaZqWHZ2PRCKRSCQSSfJInxeJRCKRSCRdCim8SCQSiUQi6VJI4UUikUgkEkmXQgovEolEIpFIuhRSeJFIJBKJRNKlkMKLRCKRSCSSLoUUXiQSiUQikXQppPAikUgkEomkSyGFF4lEIpFIJF0KKbxIJBKJRCLpUkjhRSKRSCQSSZfi/wMdpNN4FlQ6ZgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's do something called fixing the random state.\n",
+    "# Typically when you generate random numbers, every time you\n",
+    "# run the code you'll get a different result.\n",
+    "# Setting a particular random seed will ensure that we can reproduce\n",
+    "# the same results every time for demonstration purposes.\n",
+    "\n",
+    "np.random.seed(19680801)\n",
+    "\n",
+    "# Let's just make up some data in the interval (0,1)\n",
+    "y = np.random.normal(loc=.0,scale=.4,size=1000)\n",
+    "y =y[(y>0) & (y<1)]\n",
+    "x = np.arange(len(y))\n",
+    "\n",
+    "plt.subplot(2,1,1)\n",
+    "plt.title('linear')\n",
+    "plt.plot(x,y)\n",
+    "plt.yscale('linear')\n",
+    "plt.grid(True)\n",
+    "plt.subplot(2,1,2)\n",
+    "plt.title(\"logarithmic\")\n",
+    "plt.plot(x,y)\n",
+    "plt.yscale('log')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>17.7 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>The scatter() function can make use of keyword strings to set the shape and color of points on the plot.</li>\n",
+    "    <li>We can also use categorical variables to plot groups of information.</li>\n",
+    "    <li>There are many line properties that can be edited!</li>\n",
+    "    <li>Subplots can be added using the subplot() function.</li>\n",
+    "    <li>Text can be added or annotated on plots.</li>\n",
+    "    <li>Nonlinear axes can be added using xscale() or yscale().</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 08 M.ipynb b/python/atms-310/notebooks/Week 08 M.ipynb
new file mode 100755
index 0000000..8f43515
--- /dev/null
+++ b/python/atms-310/notebooks/Week 08 M.ipynb	
@@ -0,0 +1,442 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>15. Missing Values</h1>\n",
+    "<h2>11/13/2023</h2>\n",
+    "\n",
+    "<h2>15.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li><b>Pandas</b> is a useful way of working with CSV data!</li>\n",
+    "    <li>A <b>dataframe</b> is an object that contains rows and columns, much like an Excel spreadsheet.</li>\n",
+    "    <li><b>loc()</b> will let you identify individual rows, columns, or values.</li>\n",
+    "    <li><b>describe()</b> summarizes statistics for a specified section of a dataframe.</li>\n",
+    "    <li><b>read_csv()</b> will read in a CSV file specified by a file location.</li>\n",
+    "    <li><b>groupby()</b> carries out specific operations on groupings within a dataframe.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>15.1 Masked Arrays</h2>\n",
+    "\n",
+    "<b>Masked</b> arrays are just like normal arrays, except that they have a \"mask\" attribute to tell you which elements are bad.\n",
+    "\n",
+    "Recall how arrays normally work:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 5 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's create a 2D array that contains the numbers 1-6.\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "a = np.array([[1,2,3],[4,5,6]])\n",
+    "print(a)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we have some information that maybe the last two values are suspicious and may consist of bad data, we can create a <b>mask</b> of bad values that will travel with the array. Elements in the array whose mask value corresponds to \"bad\" are treated as if they did not exist, and operations using the array automatically consider that mask of bad values.\n",
+    "\n",
+    "This is extremely useful! Sometimes we have a dataset that's read-only, or we want to be aware of precisely which data are suspect, so instead of deleting them, we just keep all information and have a flag on which values are bad.\n",
+    "\n",
+    "For this purpose, NumPy has a function called <b>numpy.ma</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1 2 3]\n",
+      " [4 -- --]]\n",
+      "[[False False False]\n",
+      " [False  True  True]]\n",
+      "[[1 2 3]\n",
+      " [4 5 6]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy.ma as ma\n",
+    " # This saves us having to type 'np.' at the start of every instance of numpy.ma.\n",
+    "\n",
+    "a = np.array([[1,2,3],[4,5,6]])\n",
+    "b = ma.masked_greater(a,4)\n",
+    "\n",
+    "print(b)\n",
+    "# Let's set our mask to everything greater than 4.\n",
+    "print(b.mask)\n",
+    "print(b.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3 6 9]\n",
+      " [12 -- --]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now, if we try to do an operation on our masked array:\n",
+    "print(b*3)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we have a masked array, any operations applied to elements whose mask value is set to True will create a resulting array that also has the corresponding elements' mask values set to True. Masked arrays thus transparently deal with missing data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>15.2 Constructing and Deconstructing Masked Arrays</h2>\n",
+    "\n",
+    "There are several different ways to construct a masked array; we saw one example above, but (as always!) Python provides us with options.\n",
+    "\n",
+    "We can explicitly specify a mask!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-- -- 3]\n",
+      "[1 2 3]\n",
+      "[ True  True False]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = ma.masked_array(data=[1,2,3],mask=[True,True,False])\n",
+    "print(a)\n",
+    "print(a.data)\n",
+    "print(a.mask)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A lot of the time, we'll determine whether or not data values should be masked on the basis of some logical test (e.g., whether data values are beyond an acceptable value - like negative rainfall amounts!).\n",
+    "\n",
+    "We can make a masked array by masking values based on conditions! This can be done with some specific functions like <b>numpy.ma.masked_greater()</b> and <b>numpy.ma.masked_where()</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 3 -- --]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Mask all values greater than 3.\n",
+    "data = np.array([1,2,3,4,5])\n",
+    "a = ma.masked_greater(data,3)\n",
+    "print(a)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1 2 -- -- 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Mask all values greater than 2 and less than 5.\n",
+    "b = ma.masked_where(np.logical_and(data>2,data<5),data)\n",
+    "print(b)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes we might want to export our results to a file that doesn't support object attributes (for example, a text or comma-separated value file). In those cases, it makes sense to replace masked values with some value that we know is nonsense, which we can do using <b>numpy.ma.filled()</b>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-- -- 3.0]\n",
+      "[-1.e+23 -1.e+23  3.e+00]\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = ma.masked_array(data=[1.,2.,3.],mask=[True,True,False],fill_value=-1e+23)\n",
+    "print(c)\n",
+    "\n",
+    "d = ma.filled(c)\n",
+    "print(d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>15.3 An Example</h2>\n",
+    "\n",
+    "As an example, let's revisit the <b>air.mon.mean.nc</b> NetCDF file from before. This dataset consists of air temperature in Celsius for the global domain. Let's look at the first time slice of this dataset and mask out temperatures in all locations greater than 45N and less than 45S, then convert the remaining temperatures to Kelvins (K = 273.15 + C)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, import the important packages.\n",
+    "import scipy.io as sc\n",
+    "\n",
+    "\n",
+    "# Open the file in read-only mode.\n",
+    "fileobj = sc.netcdf_file(\"../datasets/air.mon.mean.nc\",mode=\"r\")\n",
+    "\n",
+    "# Create three variables: temp, lat, and lon.\n",
+    " # Remember, we only want the first time step!\n",
+    "temp = fileobj.variables[\"air\"][0,:,:]\n",
+    "lat = fileobj.variables[\"lat\"][:]\n",
+    "lon = fileobj.variables[\"lon\"][:]\n",
+    "\n",
+    "# Use meshgrid() to create a lat-lon grid.\n",
+    "\n",
+    "[lonall,latall] = np.meshgrid(lon,lat)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>1. With the above code to get you started, create a masked array called ma_temp that masks all latitudes greater than 45 and less than -45.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]\n",
+      " ...\n",
+      " [-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ma_temp = ma.masked_where(np.logical_or(latall>45,latall<-45),temp)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>2. Next, convert all temperatures in the unmasked region (between 45N and 45S) to Kelvins.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]\n",
+      " ...\n",
+      " [-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]\n",
+      " [-- -- -- ... -- -- --]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ma_temp = ma_temp+273.15\n",
+    "print(kelvin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check the results with the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "North pole:  [-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --]\n",
+      "South pole:  [-- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --\n",
+      " -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --]\n",
+      "Equator:  [298.989990234375 298.8916015625 298.92547607421875 299.78387451171875\n",
+      " 297.4780578613281 294.6690368652344 295.40869140625 296.71514892578125\n",
+      " 296.8448181152344 296.437744140625 295.9793395996094 293.3296813964844\n",
+      " 292.00128173828125 294.366455078125 293.1861267089844 294.57452392578125\n",
+      " 301.221923828125 301.32000732421875 298.9042053222656 298.9080505371094\n",
+      " 298.2835388183594 298.419677734375 298.5970764160156 298.6822509765625\n",
+      " 298.8238525390625 298.6919250488281 298.71484375 298.830322265625\n",
+      " 299.1283874511719 298.9522399902344 298.6025695800781 298.3016052246094\n",
+      " 298.3628845214844 298.5619201660156 298.70159912109375 298.83966064453125\n",
+      " 298.58514404296875 298.8529052734375 298.94903564453125 298.3051452636719\n",
+      " 296.6951599121094 296.3338623046875 298.24224853515625 298.41387939453125\n",
+      " 296.9270935058594 294.44805908203125 294.5732116699219 297.2270812988281\n",
+      " 296.748046875 297.1932067871094 298.6135559082031 298.6080627441406\n",
+      " 298.5787048339844 297.7358093261719 298.6219177246094 300.23065185546875\n",
+      " 300.4396667480469 300.650634765625 300.4158020019531 300.04449462890625\n",
+      " 300.2264404296875 300.44580078125 300.19580078125 299.89288330078125\n",
+      " 299.8716125488281 299.91741943359375 299.8864440917969 299.64739990234375\n",
+      " 299.6064453125 299.6180725097656 299.51739501953125 299.43450927734375\n",
+      " 299.3219299316406 299.2790222167969 299.062255859375 299.01611328125\n",
+      " 299.1012878417969 299.0425720214844 299.07806396484375 298.74322509765625\n",
+      " 298.59063720703125 298.52288818359375 298.23419189453125\n",
+      " 298.3409729003906 298.168701171875 298.0899963378906 298.0496826171875\n",
+      " 297.89288330078125 298.1625671386719 297.9141845703125 297.8422546386719\n",
+      " 297.8786926269531 297.53643798828125 297.52288818359375 297.2799987792969\n",
+      " 297.0796813964844 297.2138671875 297.080322265625 296.75933837890625\n",
+      " 296.722900390625 296.6477355957031 296.57708740234375 296.4858093261719\n",
+      " 296.3219299316406 296.5787048339844 296.8248291015625 297.05352783203125\n",
+      " 296.7248229980469 297.19903564453125 297.5367736816406 297.294189453125\n",
+      " 297.6383972167969 293.062255859375 290.7722473144531 295.3493347167969\n",
+      " 296.72320556640625 296.6377258300781 296.75933837890625\n",
+      " 296.89935302734375 297.1480712890625 296.15484619140625 296.649658203125\n",
+      " 296.330322265625 296.31549072265625 297.60162353515625 297.9100036621094\n",
+      " 298.46514892578125 298.4815979003906 298.3829040527344 298.1735534667969\n",
+      " 297.87225341796875 298.0712890625 298.0290222167969 297.97064208984375\n",
+      " 298.06097412109375 298.2054748535156 297.97967529296875 298.169677734375\n",
+      " 298.4061279296875 298.37774658203125 298.5574035644531 298.3951416015625\n",
+      " 298.58740234375 298.7112731933594]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('North pole: ',ma_temp[0,:])\n",
+    "print('South pole: ',ma_temp[-1,:])\n",
+    "print('Equator: ',ma_temp[36,:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>15.4 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li>A masked array has a <b>mask</b> attribute that allows us to identify suspicious or unwanted data.</li>\n",
+    "    <li>We can use direct assignment, assignment by condition, and filling to create a masked array.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 08 W.ipynb b/python/atms-310/notebooks/Week 08 W.ipynb
new file mode 100755
index 0000000..7f54ae8
--- /dev/null
+++ b/python/atms-310/notebooks/Week 08 W.ipynb	
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>16. Introduction to Matplotlib</h1>\n",
+    "<h2>11/15/2023</h2>\n",
+    "\n",
+    "<h2>16.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>A masked array has a <b>mask</b> attribute that allows us to identify suspicious or unwanted data.</li>\n",
+    "    <li>We can use direct assignment, assignment by condition, and filling to create a masked array.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>16.1 What is Matplotlib?</h2>\n",
+    "\n",
+    "There's another object-oriented programming language out there called Matlab that handles plotting especially well. Unfortunately, Matlab costs a lot of money, so Python's overtaken it in popularity for good reason!\n",
+    "\n",
+    "And there's no reason we can't make use of Matlab's helpful plotting interface in this lovely open-source language...\n",
+    "\n",
+    "The submodule we want is called <b>matplotlib.pyplot</b>. It's fast and efficient, and the plots look great! You'll see a lot of Matlab-style plots applied pretty much right out of the box in published scientific articles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'y = 1/2x')"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's start by creating a straight line.\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot([1,2,3,4])\n",
+    "plt.ylabel('ints')\n",
+    "plt.xlabel(\"indeces\")\n",
+    "plt.title('y = 1/2x')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, so far so good! But why does the y-axis go from 1-4 when the x-axis goes from 0-3? If you just provide a single list or array to the plot() command (rather than giving it both the x and the y values), it assumes you're dealing with a sequence of y values and automatically generates x values for you. And, of course, Python defaults to counting starting at 0, so it's created a default x vector that has the same length as y but starts at 0: [0,1,2,3].\n",
+    "\n",
+    "If you're not working with evenly spaced data, you can provide both x and y values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3,4],[1,4,9,16])\n",
+    "plt.xlabel('yea')\n",
+    "plt.ylabel('no')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you wanted to plot x squared with more data, you can specify that as well..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1111ea8d0>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "x = np.arange(1,4,0.001)\n",
+    "plt.plot(x,x**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>16.2 Formatting your Plot</h2>\n",
+    "\n",
+    "Every time there's an x,y pair of arguments, there's an optional third argument that is called the <b>format string</b> that will indicate the color and line type.\n",
+    "\n",
+    "Here's the list! You can also specify things outside this list, as we'll see, but these are the quick ones:\n",
+    "\n",
+    "<b>Line Style Specifiers</b>\n",
+    "<table style=\"width:100%\">\n",
+    "    <tr>\n",
+    "        <th>Specifier</th>\n",
+    "        <th>Line Style</th>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'-'</td>\n",
+    "        <td>Solid line (default)</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'--'</td>\n",
+    "        <td>Dashed line</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>':'</td>\n",
+    "        <td>Dotted line</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'-.'</td>\n",
+    "        <td>Dash-dot line</td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "<b>Marker Specifiers</b>\n",
+    "<table style=\"width:100%\">\n",
+    "    <tr>\n",
+    "        <th>Specifier</th>\n",
+    "        <th>Marker Type</th>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'+'</td>\n",
+    "        <td>Plus sign</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'o'</td>\n",
+    "        <td>Circle</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'*'</td>\n",
+    "        <td>Asterisk</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'.'</td>\n",
+    "        <td>Point</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'x'</td>\n",
+    "        <td>Cross</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'square' or 's'</td>\n",
+    "        <td>Square</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'diamond' or 'd'</td>\n",
+    "        <td>Diamond</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'^'</td>\n",
+    "        <td>Upward-pointing triangle</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'v'</td>\n",
+    "        <td>Downward-pointing triangle</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'&gt'</td>\n",
+    "        <td>Right-pointing triangle</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'&lt'</td>\n",
+    "        <td>Left-pointing triangle</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'pentagram' or 'p'</td>\n",
+    "        <td>Five-pointed star (pentagram)</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>'hexagram' or 'h'</td>\n",
+    "        <td>Six-pointed star (hexagram)</td>\n",
+    "    </tr>\n",
+    "</table>\n",
+    "\n",
+    "<b>Color Specifiers</b>\n",
+    "<table style=\"width:100%\">\n",
+    "    <tr>\n",
+    "        <th>Specifier</th>\n",
+    "        <th>Color</th>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>r</td>\n",
+    "        <td>Red</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>g</td>\n",
+    "        <td>Green</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>b</td>\n",
+    "        <td>Blue</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>c</td>\n",
+    "        <td>Cyan</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>m</td>\n",
+    "        <td>Magenta</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>y</td>\n",
+    "        <td>Yellow</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>k</td>\n",
+    "        <td>Black</td>\n",
+    "    </tr>\n",
+    "    <tr>\n",
+    "        <td>w</td>\n",
+    "        <td>White</td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1111f4090>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# How about plotting some red circles separated by dotted lines?\n",
+    "plt.plot([1,2,3,4],[1,4,9,16],\"ro:\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also specify the axes that we want to see using the <b>matplotlib.pyplot.axis()</b> module, which takes as input a list of [xmin, xmax, ymin, ymax]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot([1,2,3,4],[1,4,9,16],\"g-.^\")\n",
+    "plt.axis([0,10,0,30])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also plot using arrays! We're not just limited to lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t = np.arange(0,5,0.2)\n",
+    "\n",
+    "# Create an array that counts from 0 to 5 at 0.2 intervals.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>1. Create a plot where the x values are given by t and the y values are given by t. Use red circles separated by dashes.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1105bcf90>]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t,t,\"r--o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>2. Create a plot where the x values are given by t and the y values are given by t squared. Use blue squares separated by dots.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14aa2b990>]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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05JPSp59KV11ldRrEAgoIAKDLHA7piSeMtT9Y6wPBoIAAANrl8Uheb8vjNTXSn/4kzZplXHaRKB8IHgUEANAmj0fKzZVqa9s+Z/Fi6cAB9nBBaLgLBgDQJq+3/fIhSfX1rY+QAO2hgAAAANNRQAAAgOkoIAAAwHQUEAAAYDoKCAAAMB0FBADQpvJyqxPAriggAIBWHTki/ehHHZ/ncklud+TzwF5YiAwA0KoTJ6SkJCkjQ/rd76T09NbPc7tZhAyho4AAAFqVlia9846xyNg551idBnbDJRgAQKP6eulvf2t6fvrplA9EBgUEACDJ2Mn2gQek731PWr/e6jSwOwoIAECSVFdnXG6pqzPmfwCRxBwQAIAk6bTTpJdekt57T7rsMqvTwO4YAQGAOLdvX9PP3btTPmAOCggAxLGNG6Xzz5d+8QtjDghgFgoIAMSxXbuMO1/+8Q+rkyDeMAcEAOLYz34mDR0qXXON5HBYnQbxhBEQAIgzFRXN73KZMsWYgAqYiREQALAhj8e4pfZUR45Id94pjRwpvfyylJhofjZAooAAgO14PFJurlRb2/Y5//3f0t690oUXmpcLOBmXYADAZrze9suHZNzxwl0vsBIFBAAAmI4CAgAATEcBAQAApqOAAAAA01FAAMBmmFyKWEABAQAbCQSk556zOgXQMQoIANhIdbX00Ucdn+dySW535PMAbWEhMgCwkaQk6b33pJdekkaNavs8t1vKzjYvF3AqCggA2MCBA9JZZxk/9+0rPfCAtXmAjnAJBgBiXGGhdMEF0htvWJ0ECB4FBABi2IkT0gcfSH6/tGeP1WmA4HEJBgBiWLduxq62r70m3XCD1WmA4DECAgAxaO/epp9PO43ygdhDAQGAGPOHP0hDhkgLF1qdBOg8CggAxJiDB40FxzweVj1F7GIOCADEmJ//XBozRsrPlxwOq9MAnUMBAYAo4vFIXm/L4598Ip13npSaaiwg9v3vm58NCCcKCABECY9Hys2VamvbPsflkkpLWcUUsY85IAAQJbze9suHZLze2ggJEGsoIAAAwHQhF5AtW7Zo0qRJyszMlMPh0IYNG5q9HggENHfuXGVmZqpHjx4aP3689p58wzoAAIh7IReQmpoaDRs2TEVFRa2+/uyzz2rhwoUqKirS9u3blZ6erokTJ6qqqqrLYQEAgD2EPAk1Pz9f+fn5rb4WCAS0aNEiPf7447ruuuskSStXrlRaWppefPFF3XfffV1LCwAAbCGsc0AOHjyoiooK5eXlNR5zOp267LLLtHXr1nB+FAAAiGFhvQ23oqJCkpSWltbseFpamv75z3+2+tf4/X75/f7G5z6fL5yRACAmrF4t9e5tdQrAPBG5C8ZxytJ8gUCgxbEGhYWFSklJaXxkZWVFIhIARK01a6TbbpMefFByOts/1+WS3G5zcgGRFNYRkPT0dEnGSEhGRkbj8cOHD7cYFWkwZ84cFRQUND73+XyUEABx5fLLjVVOr75amjVLOnq07XPdbhYhgz2EtYDk5OQoPT1dxcXFuvDCCyVJx48fV0lJiZ555plW/xqn0ylnR5UfAGwsLU3atk1KTjb2dhkwwOpEQOSFXECqq6u1f//+xucHDx7Url271KdPH2VnZ2vWrFlasGCBBg4cqIEDB2rBggXq2bOnbrnllrAGB4BYFQhITz4pXXaZdMUVxrGUFGszAWZzBAKhbeb8zjvvaMKECS2OT5s2TS+88IICgYCeeuopLVu2TMeOHdOoUaO0ePFiDR48OKj39/l8SklJUWVlpZKTk0OJBgAxYcUK6c47pZ49pf37pZOuWAMxK9Tf3yEXkEijgACwO79fuvZa6Qc/MCaeAnYQ6u9vdsMFABMEAsb8Dsm40+V//kfqxm5ciGP84w8AEfbtt9JNN0mLFzcdo3wg3vGvAABE2EsvGY+CAqmszOo0QHTgEgwAdJHHI3m9bb9+ySXS7NnGXS8scwQYKCAA0AUej5SbK9XWtn2OyyWVlrKAGHAyLsEAQBd4ve2XD8l4vb0REiAeUUAAAIDpKCAAAMB0FBAAAGA6CggAADAdBQQAAJiOAgIAAExHAQGAEAUC0q9/bexk63Yb63y0x+UyzgPQhIXIACBEv/619PDDUlGR9Le/GYuMtbfOh9vNImTAqSggABCiW2+Vli6VZs6Uevc2HhQMIDQUEAAIQiAgORzGz2lp0u7dUo8e1mYCYhlzQACgA+Xl0ujR0l/+0nSM8gF0DQUEADpQWCh9+KH0wANSXZ3VaQB74BIMAHTg2Wel6mrppz+VuvNfTSAsGAEBgFb83/81/exySX/4g5STY10ewG7o8gDiksfT9q2zGzdK8+dLS5ZI995rbi4gXlBAAMQdj0fKzZVqa9s/r7hYuueeprtfAIQPl2AAxB2vt+PyIUmzZ1M+gEihgABAG7rxX0ggYvjXCwAAmI4CAgAATEcBAQAApqOAAIg7f/qT1QkAUEAAxJVjx6Rly6xOAYACAiCufOc70u9/3/GS6i6X5HabkwmIRyxEBsD2DhyQKiulCy80nk+ZYiy13tZKqJJRPrKzTYkHxCUKCABb+/hjaeJEKTHR2NG2f3/jeHY2BQOwEgUEgK2dfbaUkWFcUmFhMSB6UEAA2E4g0LSEenKy9OabxtyPnj2tzQWgCf9/AICtVFdL110nvfJK07EzzqB8ANGGAgLAVhYvljZskO69V6qqsjoNgLZwCQZAzPB4Or5z5eGHpU8+kR54QEpKMi8bgNBQQADEBI9Hys2VamvbPsflkkpLpZUrzcsFoHO4BAMgJni97ZcPyXi9vRESANGDAgIAAExHAQEAAKajgAAAANNRQAAAgOkoIAAAwHTchgsgqn34odSjh9UpAIQbIyAAotamTdK4ccbS6qedZqzz0R6Xy1iMDED0YwQEQNQaNcrYyXbIEGnAAGORsY5WQs3ONi8fgM6jgACIKj6fsYOtJPXtK23dapQQh8M4TsEA7IFLMACixsaNUk6OVFzcdCwz0ygfAOyFAgIgamzcKH35pVRUZHUSAJHGJRgAERXMDrYNl1V+8xtjw7lZs0yJBsBCFBAAERPMDrYJCdKBA0YJ6dFD+vGPzcsHwDpcggEQMcHsYFtfL61ZY04eANGDAgLAcmPHWp0AgNkoIAAs16uX1QkAmI0CAgAATEcBAQAApqOAAIiYr76yOgGAaEUBARB2x49Ljz4qTZlidRIA0YoCAiDsunWT/vpXqapK6t7BakPsYAvEJxYiAxAWgYDxp8NhlI7Vq6W//U0aPpwdbAG0RAEB0GVHj0p33y1deaU0fbpxLCfHeEgUDAAthf0SzNy5c+VwOJo90tPTw/0xAKLIn/4kbdggPfaYVFlpdRoAsSAiIyCDBg3S22+/3fg8ISEhEh8DIErce6/0v/8r3XmnlJJidRoAsSAiBaR79+6MegAxrr1dbA8elF59VVq50pjv4XAYO9kCQLAiUkD27dunzMxMOZ1OjRo1SgsWLNBZZ50ViY8CEAHB7GIrSf37S888Y04mAPYS9gIyatQorVq1Sueee64OHTqk+fPna+zYsdq7d6/69u3b4ny/3y+/39/43OfzhTsSgBAFs4utJF1+eeSzALCnsE9Czc/P1/XXX68hQ4boyiuv1Ouvvy5JWrlyZavnFxYWKiUlpfGRlZUV7kgAIqRfP6sTAIhVEV+IrFevXhoyZIj27dvX6utz5sxRZWVl46OsrCzSkQAAgMUivg6I3+/Xp59+qnHjxrX6utPplNPpjHQMAAAQRcI+AvLII4+opKREBw8e1AcffKAbbrhBPp9P06ZNC/dHAYiAL7+U3nrL6hQA7C7sIyCff/65br75Znm9XvXr10+jR4/Wtm3bNGDAgHB/FIAwO3JEGjJEOnzY6iQA7C7sBWTt2rXhfksAJunXz1hO/b33pM8+szoNADtjN1wgjgUC0rp1Uk1N07HFi6U//9nYpbY97GILoCvYjA6IY/fdJ/32t9JDD0mLFhnHUlKMR2kpu9gCiBwKCBDHrr9eWrVKam3nhOxsCgaAyKGAADbS3v4tklRVJSUmSmPGGM+vusrY1yUjw5x8ANCAAgLYRLD7t2RmSn//u5SUZDynfACwApNQAZsIdv+WM86QqqsjnwcA2kMBAeLM0qWMegCwHgUEiDMOh9UJAIACAgAALEABAWygtlZascLqFAAQPO6CAWzgzjulNWusTgEAwWMEBLCBhx+WUlOtTgEAwaOAADGmulqaM8dYQr3BiBHGBnLs3wIgVnAJBogxa9ZITz8tfec70g9/KJ1+unH8nHPYvwVA7KCAABbraPl0t9tYvbT7v/5tveMOadMm48+UlObnsn8LgFhBAQEsFMzy6QkJxiWWbduMNTy6d5fWrzcvIwBEAnNAAAsFs3x6fb304YdSSYk5mQDADBQQIAYsXSqNH291CgAIHwoIEAO+9z2rEwBAeFFAAACA6SgggEWOHZOWLbM6BQBYg7tgAItcdZW0fbvVKQDAGoyAACb59lspEGh6PmuWdNZZlsUBAEtRQAATvPyydN55xgJiDW66SXr7bZZPBxCfuAQDdEIwq5eevCLpjh3SgQPS889L11xjHOvWTcrJYfl0APGJAgKEKJjVSxMTpb/8RRo71ng+e7axd8vMmS3PZfl0APGISzBAiIJZvfT4cekXv2h63qeP9JOfSL16RTYbAMQKCggQIb17N590CgBoQgEBImT2bGPzOABASxQQAABgOiahAkE6cUJ6/XWposLqJAAQ+yggQJAWLZIeflgaNMjqJAAQ+7gEA7ShpkY6fLjp+W23Samp0ogR1mUCALtgBARxJdgFxF5+Wbr/fmnSJOmFF4zXUlOlsjLjEsxLL7V/Ky6rlwJA+yggiBvBLCDmchkrkw4YIH35pfThh8aaHomJxuuJiUZBYfVSAOgaCgjiRjALiNXWGud973vGPi3jx0sJCS3PY/VSAOgaCgjQhiuusDoBANgXk1ABAIDpKCAAAMB0FBDYVnW19NvfSrt3W50EAHAq5oAg6gV76+ypHnpI+sMfpDvvlH7/+8jlAwCEjgKCqBbsrbO7dknvvy99//vGeh2SdMcd0rvvSsOHmxIVABACLsEgqgV76+y0aUbhWLGi6fjFFxvrdUyfbjx3u42y0h4WEAMAczACAlu4+mpj2fS+fZuOORzNz2EBMQCIHhQQ2ML3vy/97GdStw7G9FhADACiA5dgELUCAWnfvuDO7d694/IBAIge/CcbUauqytiBFgBgP1yCQUSEeuvsoUPGBNJjx6RnnjGOJSdLEyZIxcWRzQoAMB8FBGEX7K2zf/+7seusJJWXS3PmSE6n9PjjRvmQpMJCCggA2BGXYBB2wd46O29e0/Nhw6Tbb5eKiprvPtuvH7fOAoAdMQICy7z1ljHR1OEwHiev4dGAW2cBwJ4oILDM8uUt1+poDbfOAoD9UEDQqLN7rkiS3y/98Y/Sli3SjBnBfV5aWugZAQD2QAGBpOAnjpaWGiWktlaqqJDOPNN4LSFBevhhqbJSuvxyUyIDAGIYBQSSgp846vVKe/ZIP/yhNGKEsdmbZCwENmOG8ScTQgEAHaGAIGSDBknffGOMmhw/LiUmGsfnzzf+/Ogj67IBAGIDBSTGdWXexskCgeA/88wzpU8+kc47r/VJpA27znZ0OYeREgCIXxSQGBbqvI2TVVVJX37ZtBBYXV1on33++W2/xq2zAICOUEBiWLDzNg4dkjIzjfkZkrR6tTR1qrGF/aZNxrHTTgtvNm6dBQC0hwJikXBdOgnG+PHSK68YhUMyRk0CAeMuFgAArEABsUBXLp2c7MSJ4D7v66+lDz5oKiDf/a70xRdSRkbQkQEACCvbF5BwjjSE671CueU1M1P6/POm9TYkae5cafFi6dZbg0ktrV0r3XBD0/Pu3VuWDyaOAgDMFLECsmTJEv3yl79UeXm5Bg0apEWLFmncuHGR+rhWhWukIdzvFazKSqlXL+nbb6WaGqlHj6bXvF7ps8+Ce5+BA5tv8NYaJo4CAMwUkQKybt06zZo1S0uWLNHFF1+sZcuWKT8/X5988omyTfwNFspIQ0exOvNegYDk8xl3mPTt23SsqCi4/MnJUs+exvuWlUnnnmscv+MO6d/+zbi08tprwb1XMJg4CgAwS7dIvOnChQt111136e6779b555+vRYsWKSsrS0uXLo3Ex0WV6uqmn59+Wjr9dGn27KZjDof08svBvZfDYay3UVPTVD4k49bZ4cONcgIAQCwKewE5fvy4du7cqby8vGbH8/LytHXr1hbn+/1++Xy+Zg+zvf66dPHF0oIFzY/37y/17m1cfgnWkSNNP6emGn9+9VXzc267Lfj3y8iQurXxv1LDvI32MG8DABCNwn4Jxuv1qr6+XmmnbHWalpamilbu+ywsLNRTTz0V7hgh8XqlrVuNuRInq642Rh86uvRyst69m36+5Rbp5ptbjlTcc4/0X//V+bwNmLcBAIhVEZuE6jhlje5AINDimCTNmTNHBQUFjc99Pp+ysrIiFatVl11mrJVx8p0mkvThh8YdI1lZxgZswejXr+nnkyeNRgrzNgAAsSjsBcTtdishIaHFaMfhw4dbjIpIktPplNPpDHeMkJx5pjGn4lQnz7sIJ255BQDEu7AXkMTERI0YMULFxcW69tprG48XFxdr8uTJ4f64mMSlEwBAvIvIJZiCggJNnTpVI0eO1JgxY7R8+XJ5PB7df//9kfi4NoVzpCHcoxZcOgEAxLOIFJAbb7xRR48e1bx581ReXq7Bgwdr06ZNGtCw9apJwjnSwKgFAADh4wgEAgGrQ5zM5/MpJSVFlZWVSk5OtjoOAAAIQqi/vyOyEBkAAEB7KCAAAMB0FBAAAGA6CggAADAdBQQAAJiOAgIAAExHAQEAAKajgAAAANNRQAAAgOkishR7VzQszOrz+SxOAgAAgtXwezvYBdajroBUVVVJkrKysixOAgAAQlVVVaWUlJQOz4u6vWBOnDihL774QklJSXI4HGF9b5/Pp6ysLJWVlbHPjIn43q3B924Nvndr8L1b4+TvPSkpSVVVVcrMzFS3bh3P8Ii6EZBu3bqpf//+Ef2M5ORk/gG1AN+7NfjercH3bg2+d2s0fO/BjHw0YBIqAAAwHQUEAACYLq4KiNPp1JNPPimn02l1lLjC924Nvndr8L1bg+/dGl353qNuEioAALC/uBoBAQAA0YECAgAATEcBAQAApqOAAAAA08VNAVmyZIlycnLkcrk0YsQIvfvuu1ZHsr0tW7Zo0qRJyszMlMPh0IYNG6yOZHuFhYW66KKLlJSUpNTUVE2ZMkWlpaVWx7K9pUuXaujQoY2LMY0ZM0ZvvPGG1bHiTmFhoRwOh2bNmmV1FFubO3euHA5Hs0d6enrI7xMXBWTdunWaNWuWHn/8cX388ccaN26c8vPz5fF4rI5mazU1NRo2bJiKioqsjhI3SkpKNH36dG3btk3FxcWqq6tTXl6eampqrI5ma/3799fTTz+tHTt2aMeOHbr88ss1efJk7d271+pocWP79u1avny5hg4danWUuDBo0CCVl5c3Pvbs2RPye8TFbbijRo3S8OHDtXTp0sZj559/vqZMmaLCwkILk8UPh8Oh9evXa8qUKVZHiStHjhxRamqqSkpKdOmll1odJ6706dNHv/zlL3XXXXdZHcX2qqurNXz4cC1ZskTz58/Xd7/7XS1atMjqWLY1d+5cbdiwQbt27erS+9h+BOT48ePauXOn8vLymh3Py8vT1q1bLUoFmKOyslKS8csQ5qivr9fatWtVU1OjMWPGWB0nLkyfPl3XXHONrrzySqujxI19+/YpMzNTOTk5uummm3TgwIGQ3yPqNqMLN6/Xq/r6eqWlpTU7npaWpoqKCotSAZEXCARUUFCgSy65RIMHD7Y6ju3t2bNHY8aMUW1trXr37q3169frggsusDqW7a1du1YfffSRtm/fbnWUuDFq1CitWrVK5557rg4dOqT58+dr7Nix2rt3r/r27Rv0+9i+gDRwOBzNngcCgRbHADuZMWOGdu/erb/+9a9WR4kLubm52rVrl7766iu98sormjZtmkpKSighEVRWVqaHHnpIb731llwul9Vx4kZ+fn7jz0OGDNGYMWN09tlna+XKlSooKAj6fWxfQNxutxISElqMdhw+fLjFqAhgFzNnztTGjRu1ZcsW9e/f3+o4cSExMVHnnHOOJGnkyJHavn27nn/+eS1btsziZPa1c+dOHT58WCNGjGg8Vl9fry1btqioqEh+v18JCQkWJowPvXr10pAhQ7Rv376Q/jrbzwFJTEzUiBEjVFxc3Ox4cXGxxo4da1EqIDICgYBmzJihV199VZs3b1ZOTo7VkeJWIBCQ3++3OoatXXHFFdqzZ4927drV+Bg5cqRuvfVW7dq1i/JhEr/fr08//VQZGRkh/XW2HwGRpIKCAk2dOlUjR47UmDFjtHz5cnk8Ht1///1WR7O16upq7d+/v/H5wYMHtWvXLvXp00fZ2dkWJrOv6dOn68UXX9Rrr72mpKSkxpG/lJQU9ejRw+J09vXYY48pPz9fWVlZqqqq0tq1a/XOO+/ozTfftDqarSUlJbWY39SrVy/17duXeU8R9Mgjj2jSpEnKzs7W4cOHNX/+fPl8Pk2bNi2k94mLAnLjjTfq6NGjmjdvnsrLyzV48GBt2rRJAwYMsDqare3YsUMTJkxofN5wbXDatGl64YUXLEplbw23mo8fP77Z8RUrVuj22283P1CcOHTokKZOnary8nKlpKRo6NChevPNNzVx4kSrowFh9/nnn+vmm2+W1+tVv379NHr0aG3bti3k36lxsQ4IAACILrafAwIAAKIPBQQAAJiOAgIAAExHAQEAAKajgAAAANNRQAAAgOkoIAAAwHQUEAAAYDoKCAAAMB0FBAAAmI4CAgAATEcBAQAApvv/kkRntsSOT68AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t,t**2,\"bs:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>3. Create a plot where the x values are given by t and the y values are given by t cubed. Use green upward-pointing triangles.</b>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x14ab5b990>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(t,t**3,\"g^\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>16.3 Take-Home Points</h2>\n",
+    "<ul>\n",
+    "    <li><b>matplotlib</b>'s <b>pyplot</b> module lets us use Matlab's powerful plotting tools in Python.</li>\n",
+    "    <li>The <b>matplotlib.pyplot.plot()</b> function is a simple way to plot 2-D data.</li>\n",
+    "    <li>We can specify axis limits as well as line style and color.</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/python/atms-310/notebooks/Week 09 M.ipynb b/python/atms-310/notebooks/Week 09 M.ipynb
new file mode 100755
index 0000000..fc0ce74
--- /dev/null
+++ b/python/atms-310/notebooks/Week 09 M.ipynb	
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1>19. Contour Plots and Animation</h1>\n",
+    "<h2>11/20/23</h2>\n",
+    "\n",
+    "<h2>19.0 Last Time...</h2>\n",
+    "<ul>\n",
+    "    <li>You can save a figure to a file using savefig().</li>\n",
+    "</ul>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>19.1 Basic Contour Plots</h2>\n",
+    "\n",
+    "Contour plots work much the same as line plots for things like saving figures to files, etc.\n",
+    "\n",
+    "The <b>matplotlib.pyplot.contour()</b> function generates a basic contour plot. It typically has four arguments: the first two are the x and y locations of your data values, the third is the 2D array of values to be contoured, and the final argument is an optional parameter that tells you how many contour levels to make."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Locator attempting to generate 3761 ticks ([-41.02, ..., 34.18]), which exceeds Locator.MAXTICKS (1000).\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# This is going to be an ongoing example using our\n",
+    "# NetCDF file from earlier...\n",
+    "\n",
+    "import numpy as np\n",
+    "import scipy.io as S\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fileobj = S.netcdf_file('../datasets/air.mon.mean.nc',mode='r')\n",
+    "T_time0 = fileobj.variables['air'][0,:,:]\n",
+    "T_units = fileobj.variables['air'].units.decode('utf-8')\n",
+    "lon = fileobj.variables['lon'][:]\n",
+    "lon_units = fileobj.variables['lon'].units.decode('utf-8')\n",
+    "lat = fileobj.variables['lat'][:]\n",
+    "lat_units = fileobj.variables['lat'].units.decode('utf-8')\n",
+    "\n",
+    "[lonall,latall] = np.meshgrid(lon,lat)\n",
+    "\n",
+    "map = plt.contour(lonall,latall,T_time0,5000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use the <b>levels</b> keyword to get specific levels where we want contours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "map = plt.contour(lonall,latall,T_time0,levels=[-20,-10,0,10,20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make negative contours dashed, set colors to 'k'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "map = plt.contour(lonall,latall,T_time0,colors='k')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By saving this contour plot to a variable, we can then make modifications using functions that ask for a contour map object as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<a list of 25 text.Text objects>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can add contour labels this way!\n",
+    "map = plt.contour(lonall,latall,T_time0,colors='k')\n",
+    "plt.clabel(map,fontsize=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes you'll want to fill in the contours! This is done using <b>contourf()</b> instead of <b>contour()</b>. You can also specify a particular colormap using the cmap keyword - there's a list of them here: https://scipy-cookbook.readthedocs.io/items/Matplotlib_Show_colormaps.html."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x1284f1290>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x1500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5,15))\n",
+    "plt.subplot(3,1,1)\n",
+    "map1=plt.contourf(lonall,latall,T_time0,1000,cmap=plt.cm.flag)\n",
+    "plt.colorbar(map1,orientation='horizontal')\n",
+    "plt.subplot(3,1,2)\n",
+    "map2=plt.contourf(lonall,latall,T_time0,1000,cmap=plt.cm.Paired)\n",
+    "plt.colorbar(map2,orientation='horizontal')\n",
+    "plt.subplot(3,1,3)\n",
+    "map3=plt.contourf(lonall,latall,T_time0,1000,cmap=plt.cm.prism)\n",
+    "plt.colorbar(map3,orientation='horizontal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>19.2 Animations</h2>\n",
+    "\n",
+    "You can create your own animated images in Python!\n",
+    "\n",
+    "To begin, you'll create an initial state of a plot (the first frame of the animation), then use a function to \"progress\" to the next frame of the animation, repeat that process, and save the final animation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start by defining a figure and axes, \n",
+    "# then create a line with our old friend plt.plot.\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Define a function called init to initialize\n",
+    "# the plot!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For each frame, create a function that will take an index\n",
+    "# and return the desired frame in the animation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# We'll now create a new runtime configuration that\n",
+    "# lets us animate the whole thing!\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>19.3 Take-Home Points...</h2>\n",
+    "<ul>\n",
+    "    <li>contour() allows you to create customizable contour plots.</li>\n",
+    "    <li>contourf() allows you to create customizable filled contour plots.</li>\n",
+    "    <li>FuncAnimation allows you to create animations in Python.</li>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
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