{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "98fcb028",
   "metadata": {},
   "source": [
    "<h1><center>Computing: Intro to Python</center></h1>\n",
    "\n",
    "**<center>IceCube Summer School 2026 </center>**\n",
    "**<center>Sam Groetsch (UW Madison) </center>**\n",
    "\n",
    "<center>With heavy inspiration from Delaney Butterfield and Winnie Wang's notebooks from previous Summer Schools</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89258ff2",
   "metadata": {},
   "source": [
    "# Jupyter Notebooks\n",
    "\n",
    "Compared to running a python file (*.py) from the command line, a Jupyter notebook (*.ipynb) allows a much greater level of interactivity and readibility. While executing a python file requires you to run the entire code by default a Jupyter notebook is broken into multiple cells and while the kernel is still active the variables remain acessible and extendable. \n",
    "\n",
    "Pros:  \n",
    "* View plots and other outputs interactively on your screen   \n",
    "* Can write detailed comments about your code using LateX to include equations, advanced text fonts like *italics* and **bold** lettering, and markdown! \n",
    "* View variables and output from a section alongside the code instead of deciphering many print statements\n",
    "* Can rearrange sections of code (and their output) with ease\n",
    "\n",
    "Cons:  \n",
    "* Can take longer than running a python script  \n",
    "* Many running Jupyter notebooks or 1 very intensive notebook can slow down your computer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea94f282",
   "metadata": {},
   "source": [
    "The basic unit of a jupyter notebook is a `cell`. A `cell` can contain any of the above elements. \n",
    "\n",
    "In a notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end.  Alternately, you can use:\n",
    "    \n",
    "- `Alt-Enter` to force the creation of a new cell unconditionally \n",
    "- `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets\n",
    "\n",
    "- You can also find the following helpful:\n",
    "    * Run All Above/Below\n",
    "    * All/Current Outputs: \n",
    "        * Toggle: Hides or shows cell output\n",
    "        * Toggle Scrolling: Can hide output, show all on the screen, or confine output to a small window you can scroll through\n",
    "        * Clear: Clears all outputs (print statments, plots, etc)\n",
    "        \n",
    "You will also notice that there's a bar that starts with \"File\", \"Edit\", \"View\", ... and continues horizontally with other functions. Using the hotbar, you can:\n",
    "\n",
    "  * Save notebook button\n",
    "  * Add a new cell below\n",
    "  * Cut, Copy, Paste cell\n",
    "  * Move a cell up or down\n",
    "  * Run Cell, stop all execution, or restart kernel\n",
    "    * Clear all variables\n",
    "  * Type of Cell (drop down with options)\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891bf003",
   "metadata": {},
   "source": [
    "# Variables in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38b0893",
   "metadata": {},
   "source": [
    "A variable is a name given to value for reference later. Using this name later tells the python code to substitute the variable' value in its place. Assigning a value to a variables in Python is done with the assignment operator (=). For example, the following line assigns the value 2 to the the variable x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "694d610b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69dbb417",
   "metadata": {},
   "source": [
    "Comments are ways to include plain text in code or non-markdown cells. Comments begin with \\# and will not be interpreted as python code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2e5ed8cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# This is a comment\n",
    "# x = 3\n",
    "# If the comment was run x would have a value of 3 in the following print statement\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fdca9e3",
   "metadata": {},
   "source": [
    "Assigning variables to values is the primary way that data is stored and manipulated in Python code.\n",
    "\n",
    "Variable names must start with a letter and can contain letters, numbers, and the underscore character ( _ ). There are several conventions for variable names like: snake case where variables have lowercase letters with underscores to seperate words (e.g. my_favorite_number = 2) and camel case where variables start with lowercase letters and capital letters seperate words (e.g. myFavoriteNumber = 2) . In addition, there are a number of keywords in Python that are used by the interpreter. So you'll want to avoid using these keywords as variable names. Namely,\n",
    "\n",
    "<CODE>False, None, True, and, as, assert, break, class, continue, def, del, elif, else,\n",
    "except, finally, for, from, global, if, import, in, is, lambda, nonlocal, not, or,\n",
    "pass, raise, return, try, while, with, yield</CODE>\n",
    "\n",
    "are keywords to avoid. Now that we know variables are names that reference a value, let's look at some of the types of values that we have in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "956844ee",
   "metadata": {},
   "source": [
    "# Datatypes\n",
    "\n",
    "In computer programming, a data type is a classification identifying one of various types that data\n",
    "can have. The most common data type we will see in this class are:\n",
    "\n",
    "* **Integers** (`int`): Integers are the classic cardinal numbers: ... -3, -2, -1, 0, 1, 2, 3, 4, ...\n",
    "    \n",
    "* **Floating Point** (`float`): Floating Point are numbers with a decimal point: 1.2, 34.98, -67,23354435, ...\n",
    "\n",
    "  - Floating point values can also be expressed in scientific notation: 1e3 = 1000\n",
    "  \n",
    "  \n",
    "* **Booleans** (`bool`): Booleans types can only have one of two values: `True` or `False`. In many languages 0 is considered `False`, and any other value is considered `True`. In here, `True` translates to 1.\n",
    "\n",
    "* **Strings** (`str`): Strings can be composed of one or more characters: ’a’, ’spam’, ’spam spam eggs and spam’. Usually quotes (’) are used to specify a string. For example '12' would refer to the string, not the integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "60646806",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x)\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "44c77725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3141.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_float = 3.141e3\n",
    "print(my_float)\n",
    "type(my_float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "edeb981e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_bool = False\n",
    "print(my_bool)\n",
    "type(my_bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2de3c574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str = \"Hello World\"\n",
    "print(my_str)\n",
    "type(my_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15abdd92",
   "metadata": {},
   "source": [
    "Different types have different allowed operations and functionality. Some might be unexpected or counterintuitive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "233eb1f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 3\n",
    "print(x+y)\n",
    "type(x+y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "07724f2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3143.0\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "print(x + my_float)\n",
    "print(type(x + my_float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "3e6a88e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.5\n",
      "<class 'float'>\n",
      "1\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "print(y/2)\n",
    "print(type(y/2))\n",
    "\n",
    "print(x//2)\n",
    "print(type(x//2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6ae7e4",
   "metadata": {},
   "source": [
    "The most-commonly used built-in types in Python can be grouped into two categories: Primative and Containers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669fe85f",
   "metadata": {},
   "source": [
    "# Primative Types\n",
    "\n",
    "We've already covered nearly all of the primatives ints,floats,bools,strings, and the only one we haven't covered None. Nones are more like a warning than a variable value. Commonly used to force the user to actually put something meaningful while still defining a name. Also None is the return value of many funcitons not expected to give a value back like print."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8700262b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing\n",
      "<class 'NoneType'>\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "variable = print(\"Testing\")\n",
    "print(type(variable))\n",
    "print(variable)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4afabc0",
   "metadata": {},
   "source": [
    "# Containers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fe105e",
   "metadata": {},
   "source": [
    "Most data in python is stored in Container type data. The data can be as simple as a collection of primative data types or as complex as a database implementing custom math functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c266f9d4",
   "metadata": {},
   "source": [
    "Complex is an extension of floats to represent complex numbers with a real and imaginary part. Each part of the complex data is a float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "5a102ea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'complex'>\n",
      "1.5\n",
      "<class 'float'>\n",
      "2.4\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "z = 1.5 + 2.4j\n",
    "print(type(z))\n",
    "print(z.real)\n",
    "print(type(z.real))\n",
    "print(z.imag)\n",
    "print(type(z.imag))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae25e27",
   "metadata": {},
   "source": [
    "<h4> Lists </h4>\n",
    "A list is an <b>ordered, mutable</b> collection of data (data elements are called \"items\") Lists are constructed using square brackets with list items seperated by commas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "027e84d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[2.3, 5, -43, 74.7, 5]\n"
     ]
    }
   ],
   "source": [
    "list = [2.3, 5, -43, 74.7, 5]\n",
    "print(type(list))\n",
    "print(list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99ce70d",
   "metadata": {},
   "source": [
    "Lists have some built in functions to manipulate and observe their properties. <CODE>len</CODE> gives the number of items in the list. <CODE>append</CODE> adds an additional item to the end of the list. <CODE>sort</CODE> uses a built in sorting algorithm to order the items by their value. Sometimes the sorting metric can be different (sorting a list of ints vs strings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "794cb63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7b563c7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.3, 5, -43, 74.7, 5, 6.67]\n"
     ]
    }
   ],
   "source": [
    "list.append(6.67)\n",
    "print(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "23e343c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-43, 2.3, 5, 5, 6.67, 74.7]\n"
     ]
    }
   ],
   "source": [
    "list.sort()\n",
    "print(list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bc8921",
   "metadata": {},
   "source": [
    "Something counterintuitive about variables is that making a copy doesn't always work the way you expect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "61c3cfc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[74.7, 6.67, 5, 5, 2.3, -43]\n",
      "[74.7, 6.67, 5, 5, 2.3, -43]\n"
     ]
    }
   ],
   "source": [
    "list2 = list\n",
    "list2.reverse()\n",
    "print(list)\n",
    "print(list2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b8b61255",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[74.7, 6.67, 5, 5, 2.3, -43]\n",
      "[-43, 2.3, 5, 5, 6.67, 74.7]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "list3 = list.copy()\n",
    "list3.reverse()\n",
    "print(list)\n",
    "print(list3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745fec16",
   "metadata": {},
   "source": [
    "Note that a <CODE>list</CODE> can contain any type of object. The items in a <CODE>list</CODE> need not be homogeneous. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "13658867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, False, 23.11, [1, 2, 3], None]\n"
     ]
    }
   ],
   "source": [
    "crazy_list = [1, False, 23.11, [1, 2, 3], None]\n",
    "print(crazy_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6cb57a",
   "metadata": {},
   "source": [
    "The items in a list can be accessed using <b>list indexing</b>. Indexing a list consists of adding the item index in square brackets after a list. It's also important to note that in Python <b>list indices begin with zero.</b> So the first item in a list has the index 0, the second item has the index 1, and so on. For example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "df979db8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3\n",
      "5\n",
      "-43\n"
     ]
    }
   ],
   "source": [
    "list = [2.3, 5, -43, 74.7, 5]\n",
    "print(list[0])\n",
    "print(list[1])\n",
    "print(list[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f89016d",
   "metadata": {},
   "source": [
    "Python also supports negative indexing. This has the effect of staring from the end of the list. So the last item in a list has an index of <CODE>-1</CODE>, the second to last item has an index of <CODE>-2</CODE>, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5288ab0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.3, 5, -43, 74.7, 5]\n",
      "5\n",
      "74.7\n",
      "-43\n"
     ]
    }
   ],
   "source": [
    "print(list)\n",
    "print(list[-1])\n",
    "print(list[-2])\n",
    "print(list[-3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2aab19",
   "metadata": {},
   "source": [
    "In addition to indexing a list to get back list items, you can using slicing to get back a sub-list. The syntax for list slicing is given by\n",
    "\n",
    "<CODE>list_object[starting_index : stopping_index : index_step_size]</CODE>\n",
    "\n",
    "As an example we could use 0:3 which would give us all the elements starting with the zero index item and up to but not including the third index item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "eb73603c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.3, 5, -43, 74.7, 5]\n",
      "[2.3, 5, -43]\n"
     ]
    }
   ],
   "source": [
    "print(list)\n",
    "print(list[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385505d6",
   "metadata": {},
   "source": [
    "<h4> Tuples </h4>\n",
    "Tuples are <b>ordered, immutable</b> collection of data. Tuples can be construced in a similar way as lists, but with parenthesis instead of square brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f886db01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "tuple = (83.2, -4 ,5e7)\n",
    "print(type(tuple))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b69963fe",
   "metadata": {},
   "source": [
    "The number of items in a tuple can be found using the built-in len function, and they support indexing and slicing similar to lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "28f5f918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3.2, False, 222, None)\n",
      "5\n",
      "(3.2, False, 222)\n"
     ]
    }
   ],
   "source": [
    "tuple = (1, 3.2, False, 222, None)\n",
    "print(tuple)\n",
    "print(len(tuple))\n",
    "print(tuple[1:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c3de1d",
   "metadata": {},
   "source": [
    "Lists and Tuples are very similar but we mentioned one is mutable and the other immutable. What exactly does that mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4bd43a34",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_list = [0,1,2,3]\n",
    "example_tuple = (0,1,2,3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "3027ab54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1.0, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "example_list[1] = 1.0\n",
    "print(example_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1cda30f0",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[99], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m example_tuple[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(example_tuple)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "\n",
    "example_tuple[1] = 1.0\n",
    "print(example_tuple)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fafabb5",
   "metadata": {},
   "source": [
    "<h4> Sets </h4>\n",
    "A set is an unordered collection with no duplicate elements. They are constructed with comma separated items in curly brackets, <CODE>{}</CODE>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "1e8d2834",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4}\n"
     ]
    }
   ],
   "source": [
    "s = {1, 2, 3, 4, 2, 2, 3, 1}\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fc4e7e",
   "metadata": {},
   "source": [
    "Set objects support mathematical operations like union, intersection, difference, and symmetric difference. Set unions are done with the <CODE>|</CODE> operator or using the set <CODE>union</CODE> method. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ae1a41cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1, 2, 3, 4, 5, 6}\n",
      "{1, 2, 3, 4, 5, 6}\n"
     ]
    }
   ],
   "source": [
    "s1 = {1, 2, 3, 4}\n",
    "s2 = {3, 4, 5, 6}\n",
    "print(s1 | s2)\n",
    "print(s1.union(s2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a634759",
   "metadata": {},
   "source": [
    "Set intersections are done with the <CODE>&</CODE> operator or using the set <CODE>intersection</CODE> method. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8018397b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3, 4}\n",
      "{3, 4}\n"
     ]
    }
   ],
   "source": [
    "print(s1 & s2)\n",
    "print(s1.intersection(s2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a024fedb",
   "metadata": {},
   "source": [
    "<h4> Strings </h4>\n",
    "Even though strings are one of the \"primative\" types in python, behind the scenes nothing in python is really primative Strings really are used to represent a sequence of characters. Strings can be created by enclosing characters in either single or double quotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bc7d63b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str = 'pizza'\n",
    "type(my_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "43b44bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str2 = \"jamesbond007\"\n",
    "type(my_str2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d52176",
   "metadata": {},
   "source": [
    "Strings can also be index just like lists and tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c16e7c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'p'"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "69c1fc24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'i'"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str[-4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5dd541d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'za'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_str[3:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1078ff7e",
   "metadata": {},
   "source": [
    "You can also find out how many characters are in a string using the <CODE>len()</CODE> function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d6a888f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_str)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d127202c",
   "metadata": {},
   "source": [
    "<h4> Dictionaries </h4>\n",
    "Dictionaries are unordered containers for key-value pairs. That is, dictionaries store a mapping for each key to an associated value. Dictionaries are created by placing comma-separated key-value pairs inside curly brackets <CODE>{}</CODE>. For a key-value pair, the key and corresponding value are seperated by a colon, <CODE>:</CODE>.\n",
    "\n",
    "That's a lot of words to say that they act like real dictionaries where a key (the word you are looking up) has a value (the definition) that are paried together. As an example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5134047a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "my_dict = {'MJ': 23, 'longitude': -53.2, 'city': 'Tokyo'}\n",
    "print(type(my_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6063c55e",
   "metadata": {},
   "source": [
    "Here, the dictionary keys are 'key1', 'key2', and 'key3', with corresponding values of 23, -53.2, and 'Tokyo'. In a similar way to sequences, you can access the values in a dicionary by giving the corresponding key in square brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9ee6e9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['MJ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "475e232f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-53.2"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5901a75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tokyo'"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict['city']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff8a9ff8",
   "metadata": {},
   "source": [
    "The keys in a dictionary can be obtained by using the dictionary <CODE>keys</CODE> method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d4394d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['MJ', 'longitude', 'city'])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99afe1d0",
   "metadata": {},
   "source": [
    "The values in a dictionary can be obtained by using the dictionary <CODE>values</CODE> method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "4adb2a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([23, -53.2, 'Tokyo'])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.values()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "921137bf",
   "metadata": {},
   "source": [
    "The size of a dictionary (the number of key-value pairs it contains) can be found with the built-in <CODE>len()</CODE> function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "97634bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c627df07",
   "metadata": {},
   "source": [
    "It is important to note that in the previous example all the keys were strings, but this doesn't have to be the case. The only restriction on keys is that they be hashable. This means that keys must be an <I>immutable type</I>. For example, the following is also an acceptable dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9cdb68f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict2 = {-23: [1, 2, 3, 4], 'desk': 3.2, 7.12: (-3, 'bird')}\n",
    "my_dict2[-23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d6d5c4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict2['desk']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "598f488f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3, 'bird')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict2[7.12]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2203295c",
   "metadata": {},
   "source": [
    "Let see what happens if I try to contruct a dictionary with a list (a mutable object) as a key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "5c3075d4",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[119], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m my_dict3 \u001b[38;5;241m=\u001b[39m {[\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m]: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWOO\u001b[39m\u001b[38;5;124m'\u001b[39m}\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "my_dict3 = {[1, 2, 3]: 'WOO'}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeaa0851",
   "metadata": {},
   "source": [
    "Whoops lists are mutable! So remember to always use immutable objects for dictionary keys!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8a7c78",
   "metadata": {},
   "source": [
    "<h3> Other types </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d88df3",
   "metadata": {},
   "source": [
    "Other useful types can be found in the <CODE>collections</CODE> module in the Python standard library.\n",
    "<ul>\n",
    "    <li>namedtuple</li>\n",
    "    <li>Counter</li>\n",
    "    <li>OrderedDict</li>\n",
    "    <li>defaultdict</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b37b08d",
   "metadata": {},
   "source": [
    "<h2> Type casting </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4db876b",
   "metadata": {},
   "source": [
    "Sometimes it can be useful to change the type of an object. In Python, this so-called \"type-casting\" can be accomplished using several built-in functions:\n",
    "<ul>\n",
    "    <li>int()—casts to integer</li>\n",
    "    <li>float()—casts to float</li>\n",
    "    <li>str()—casts to string</li>\n",
    "    <li>bool()—casts to boolean</li>\n",
    "</ul>\n",
    "Let's see it in action\n",
    "\n",
    "Casting integers to floats is fairly straight-forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f009397a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "<class 'float'>\n"
     ]
    }
   ],
   "source": [
    "a = float(2)\n",
    "print(a)\n",
    "print(type(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9308f85",
   "metadata": {},
   "source": [
    "When casting a float to an integer, Python will round down to the nearest integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "0bcfb531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "b = int(78.81)\n",
    "print(b)\n",
    "print(type(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b615cff0",
   "metadata": {},
   "source": [
    "You can even cast a number to a string! (This effectively just returns the number in quotes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "67488705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1324.1\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "c = str(-1324.1)\n",
    "print(c)\n",
    "print(type(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83128918",
   "metadata": {},
   "source": [
    "Things get a little less straight-forward when casting to <CODE>bool</CODE>. Below are the <CODE>bool</CODE> casting rules:\n",
    "<ul>\n",
    "    <li>Numbers: Zero of any numeric type, for example, <CODE>0</CODE>, <CODE>0.0</CODE>, <CODE>0+0j</CODE>, cast to <CODE>False</CODE>. Everything else casts to <CODE>True</CODE>.</li>\n",
    "    <li>Lists: An empty list, <CODE>[]</CODE>, will cast to <CODE>False</CODE>. Non-empty lists casts to <CODE>True</CODE>.</li>\n",
    "    <li>Tuples: An empty tuple, <CODE>()</CODE>, will cast to <CODE>False</CODE>. Non-empty tuples casts to <CODE>True</CODE>.</li>\n",
    "    <li>Strings: An empty string, <CODE>''</CODE>, will cast to <CODE>False</CODE>. Non-empty strings casts to <CODE>True</CODE>.</li>\n",
    "    <li>Dictionaries: The empty dictionary, <CODE>{}</CODE>, casts to <CODE>False</CODE>. Everything else casts to <CODE>True</CODE>.</li>\n",
    "    <li>NoneType: <CODE>None</CODE> casts to <CODE>False</CODE>.</li>\n",
    "</ul>\n",
    "Here are some examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "bc01aef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "1b984f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool(-178.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "99e356a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "1035ff75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f8637d03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool({})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "dcc6eb26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool({'key': 'value'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6dce03",
   "metadata": {},
   "source": [
    "The following operations are supported for several of the built-in types in Python:\n",
    "<ul>\n",
    "    <li>Addition: <CODE>+</CODE></li>\n",
    "    <li>Subtraction: <CODE>-</CODE></li>\n",
    "    <li>Multiplication: <CODE>*</CODE></li>\n",
    "    <li>Division: <CODE>/</CODE></li>\n",
    "    <li>Floored division: <CODE>//</CODE></li>\n",
    "    <li>Exponentiation: <CODE>**</CODE></li>\n",
    "</ul>\n",
    "Some examples with numerical types..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0012523",
   "metadata": {},
   "source": [
    "<h1> Arithmetic operations </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "8d4f43cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6d405e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "4800164e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "edf18444",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "fb47966c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5//3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "914db106",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9.0**2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce53eca",
   "metadata": {},
   "source": [
    "Some of these arithmetic operations are even defined for compound types. For example, list addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "72eaaeb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "list1 = [1, 2, 3, 4]\n",
    "list2 = [5, 6]\n",
    "summed_list = list1 + list2\n",
    "print(summed_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4342b25",
   "metadata": {},
   "source": [
    "tuple addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "fa4e2578",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2, 3, 4, 5, 6)\n"
     ]
    }
   ],
   "source": [
    "tup1 = (1, 2, 3, 4)\n",
    "tup2 = (5, 6)\n",
    "summed_tuple = tup1 + tup2\n",
    "print(summed_tuple)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fafe509",
   "metadata": {},
   "source": [
    "and string addition are all defined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64122a0a",
   "metadata": {},
   "source": [
    "<h1> Comparison operations </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89521446",
   "metadata": {},
   "source": [
    "In addition to using arithmetic operations to combine objects, it's also using to compare the value of objects as well. The comparison operators defined in Python are:\n",
    "<ul>\n",
    "    <li><CODE>==</CODE> (equal)</li>\n",
    "    <li><CODE>!=</CODE> (not Equal)</li>\n",
    "    <li><CODE><</CODE> (less than)</li>\n",
    "    <li><CODE>></CODE> (greater than)</li>\n",
    "    <li><CODE><=</CODE> (less than or equal to)</li>\n",
    "    <li><CODE>>=</CODE> (greater than or equal to)</li>\n",
    "</ul>\n",
    "A boolean value of either <CODE>True</CODE> or <CODE>False</CODE> will be returned appropreiately from a comparison operator. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "180bccb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 == 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "b2a96971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6f543024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 < 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9234b321",
   "metadata": {},
   "source": [
    "Python also can handle comparing different types to one another. In particular, floats and integers are compared in a natural way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d040795d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 == 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ada4bf",
   "metadata": {},
   "source": [
    "Multiple comparison can also be made at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "003dab82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "a = 25\n",
    "print(15 < a < 30) # Checks whether or not a is greater than 15 and less that 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a23683a8",
   "metadata": {},
   "source": [
    "Boolean values can also be combined using the <CODE>and</CODE>, <CODE>or</CODE>, or <CODE>not</CODE> keywords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "1f89033d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a < 30) and (a > 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "471fe1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a < 30) and (a == 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d272e58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a > 30) or (a == 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0e67fd80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a > 30) or (a < 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d168646e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not (a == 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae80853",
   "metadata": {},
   "source": [
    "<h1>You try!</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce241b6",
   "metadata": {},
   "source": [
    "Work through the next few exercises identifying errors and working with different types in Python. Each block of code has two errors. Comment the errors (and fix it if you'd like) for each block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "f81383d7",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1152230546.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[147], line 3\u001b[1;36m\u001b[0m\n\u001b[1;33m    'second key': 'second value'\u001b[0m\n\u001b[1;37m                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "my_new_dictionary: {               \n",
    "    'first key': \"first value\"     \n",
    "    'second key': 'second value'   \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0567f400",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"float\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)\n",
      "Input \u001b[0;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n",
      "\u001b[1;32m      8\u001b[0m neutrino8 \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mflavor\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtao\u001b[39m\u001b[38;5;124m'\u001b[39m,      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124menergy (GeV)\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m2.3e4\u001b[39m}\n",
      "\u001b[1;32m     10\u001b[0m neutrinos \u001b[38;5;241m=\u001b[39m [ neutrino1, neutrino2, neutrino3, neutrino4,\n",
      "\u001b[1;32m     11\u001b[0m               neutrino5, neutrino6, neutrino7, neutrino8 ]\n",
      "\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mThe energy of neutrino5 is \u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mneutrino5\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43menergy (GeV)\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
      "\n",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"float\") to str"
     ]
    }
   ],
   "source": [
    "neutrino1 = ('tao', '4.2Gev')\n",
    "neutrino2 = {'flavor': 'muon',     'energy (GeV)': 1.3e3}\n",
    "neutrino3 = {'flavor': 'electron', 'energy (GeV)': 8.2e2}\n",
    "neutrino4 = {'flavor': 'muon',     'energy (GeV)': 2.8e1}\n",
    "neutrino5 = {'flavor': 'electron', 'energy (GeV)': 3.6e2}\n",
    "neutrino6 = {'flavor': 'tao',      'energy (GeV)': 9.5e0}\n",
    "neutrino7 = {'flavor': 'electron', 'energy (GeV)': 7.3e1}\n",
    "neutrino8 = {'flavor': 'tao',      'energy (GeV)': 2.3e4}\n",
    "\n",
    "neutrinos = [ neutrino1, neutrino2, neutrino3, neutrino4,\n",
    "              neutrino5, neutrino6, neutrino7, neutrino8 ]\n",
    "\n",
    "print(\"The energy of neutrino5 is \" + neutrino5['energy (GeV)'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66db668",
   "metadata": {},
   "source": [
    "<h1> Control Flow </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36ab9217",
   "metadata": {},
   "source": [
    "Up to this point, we've explored some of the built-in types in Python and how to store values (i.e. variables) for later use. Now we'll look at using these building blocks to make a dynamic program."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7ee744",
   "metadata": {},
   "source": [
    "<h2> Conditional statements </h2>\n",
    "Conditional statements are used to execute a piece of code based on if some condition is met. Let's look at some examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3d35a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Condition is True\n"
     ]
    }
   ],
   "source": [
    "condition = True\n",
    "if condition:\n",
    "    print('Condition is True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c588f373",
   "metadata": {},
   "outputs": [],
   "source": [
    "condition = False\n",
    "if condition:\n",
    "    print('Condition is True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3120931c",
   "metadata": {},
   "source": [
    "Multiple conditions can be combined into a more complex condition using the <CODE>and</CODE> / <CODE>or</CODE> keywords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a110173",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after 'if' statement on line 1 (2080754845.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [83]\u001b[0;36m\u001b[0m\n",
      "\u001b[0;31m    if condition1 or condition2:\u001b[0m\n",
      "\u001b[0m    ^\u001b[0m\n",
      "\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'if' statement on line 1\n"
     ]
    }
   ],
   "source": [
    "if condition1 and condition2:\n",
    "    #code evaluated if both conditions are True\n",
    "if condition1 or condition2:\n",
    "    #code evaluated if at least one of the conditions are True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328e4aa3",
   "metadata": {},
   "source": [
    "For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "8c1f0736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both conditions are True\n"
     ]
    }
   ],
   "source": [
    "b = 5\n",
    "c = 15\n",
    "if b < 10 and c < 20:\n",
    "    print('Both conditions are True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1dde5e7",
   "metadata": {},
   "source": [
    "The <CODE>or</CODE> keywords requires that at least one of the conditions be <CODE>True</CODE>. For example, below the first condition is <CODE>True</CODE>, but the second is <CODE>False</CODE>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "9b83a095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At least one condition is True\n"
     ]
    }
   ],
   "source": [
    "if b < 10 or c < 10:\n",
    "    print('At least one condition is True')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1707ddc",
   "metadata": {},
   "source": [
    "<h3> If-else statements </h3>\n",
    "Sometimes more complicated situations can arise in which you would like to have, depending on if a condition is met, different pieces of code run. This leads us to the if-else statement. If-else statements consist of an <CODE>if</CODE> statement followed by a piece of code that will be executed if the if-statement condition is not met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375ea9c1",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after 'if' statement on line 1 (2168180385.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [86]\u001b[0;36m\u001b[0m\n",
      "\u001b[0;31m    else:\u001b[0m\n",
      "\u001b[0m    ^\u001b[0m\n",
      "\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'if' statement on line 1\n"
     ]
    }
   ],
   "source": [
    "if condition:\n",
    "    #code for True condition\n",
    "else:\n",
    "    #code for False condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "6ef0c24a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b is not 5\n"
     ]
    }
   ],
   "source": [
    "b = 4\n",
    "if b == 5:\n",
    "    print('b is 5')\n",
    "else:\n",
    "    print('b is not 5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77396329",
   "metadata": {},
   "source": [
    "<h3> Elif statements </h3>\n",
    "Sometimes your might like to have many if statements you would like to check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54a7f4d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 is zero\n"
     ]
    }
   ],
   "source": [
    "x = 0.\n",
    "if x == 0:\n",
    "    print(x, \"is zero\")\n",
    "elif x > 0:\n",
    "    print(x, \"is positive\")\n",
    "elif x < 0:\n",
    "    print(x, \"is negative\")\n",
    "else:\n",
    "    print(x, \"is unlike anything I've ever seen...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9d9ece",
   "metadata": {},
   "source": [
    "<h2> Loops </h2>\n",
    "Looping in Python is done via <CODE>for</CODE>-loops and <CODE>while</CODE>-loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65bff9d2",
   "metadata": {},
   "source": [
    "<h3> For loops </h3>\n",
    "The basic syntax of a <CODE>for</CODE> loop is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "934b4323",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after 'for' statement on line 1 (2686819945.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [89]\u001b[0;36m\u001b[0m\n",
      "\u001b[0;31m    #code using interating_var here\u001b[0m\n",
      "\u001b[0m                                   ^\u001b[0m\n",
      "\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'for' statement on line 1\n"
     ]
    }
   ],
   "source": [
    "for iterating_var in iterable:\n",
    "    #code using interating_var here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d179dc0",
   "metadata": {},
   "source": [
    "We've run into several iterables already: lists, tuples, strings, and dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "011dcc07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "for item in [0, 1, 2, 3, 4, 5]:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95b6ea48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P\n",
      "y\n",
      "t\n",
      "h\n",
      "o\n",
      "n\n"
     ]
    }
   ],
   "source": [
    "for letter in 'Python':\n",
    "    print(letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e490d6",
   "metadata": {},
   "source": [
    "Built in <CODE>range</CODE> function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15f0bf1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n"
     ]
    }
   ],
   "source": [
    "for item in range(3, 20):\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "461ba136",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MJ 23\n",
      "longitude -53.2\n",
      "city Tokyo\n"
     ]
    }
   ],
   "source": [
    "k = {'MJ': 23, 'longitude': -53.2, 'city': 'Tokyo'}\n",
    "for key in k:\n",
    "    print(key, k[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c599e6b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MJ 23\n",
      "longitude -53.2\n",
      "city Tokyo\n"
     ]
    }
   ],
   "source": [
    "# If using Python 2, use k.iteritems() instead of k.items()\n",
    "for key, value in k.items():\n",
    "    print(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34893d70",
   "metadata": {},
   "source": [
    "<h3> While loops </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8306d0c",
   "metadata": {},
   "source": [
    "Loop over code until condition is evaluated to <CODE>False</CODE>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20901e8d",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block after 'while' statement on line 1 (2838938086.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [140]\u001b[0;36m\u001b[0m\n",
      "\u001b[0;31m    #code using iterating_var here\u001b[0m\n",
      "\u001b[0m                                  ^\u001b[0m\n",
      "\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'while' statement on line 1\n"
     ]
    }
   ],
   "source": [
    "while condition:\n",
    "    #code using iterating_var here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "136d0c19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "n = 0\n",
    "while n < 10:\n",
    "    print(n)\n",
    "    n = n + 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c317dbbf",
   "metadata": {},
   "source": [
    "<h3> List comprehension </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8e3a777",
   "metadata": {},
   "source": [
    "One way to create a <CODE>list</CODE> is to use the <CODE>append</CODE> method inside a <CODE>for</CODE> loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fbef448",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16]\n"
     ]
    }
   ],
   "source": [
    "squared_list = []\n",
    "for i in range(5):\n",
    "    value = i**2\n",
    "    squared_list.append(value)\n",
    "print(squared_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f225f047",
   "metadata": {},
   "source": [
    "While this approach gets the job done, it's a little too verbose. Python has another, less verbose, syntax for creating lists—list comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af3bf55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16]\n"
     ]
    }
   ],
   "source": [
    "squared_list = [i**2 for i in range(5)]\n",
    "print(squared_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc57cac4",
   "metadata": {},
   "source": [
    "<h2> Functions </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c54784",
   "metadata": {},
   "source": [
    "Functions allow for the consolidation of several pieces of code into a single reusable object. The basic syntax for defining a function is shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a341a210",
   "metadata": {},
   "outputs": [],
   "source": [
    "def function_name(some_input):\n",
    "    #code utilizing input goes here\n",
    "    return some_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ce8c622",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add(value1, value2):\n",
    "    total = value1 + value2\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0757ce2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "800.0\n"
     ]
    }
   ],
   "source": [
    "print(add(4,5))\n",
    "print(add(1e3,-200))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "278f1c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pythonrules\n"
     ]
    }
   ],
   "source": [
    "print(add('Python', 'rules'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2940a32d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_zeroth_item_to_3(parameter):\n",
    "    parameter[0] = 3\n",
    "    return parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17830b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 2, 3, 4, 5]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "change_zeroth_item_to_3([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8600f4a",
   "metadata": {},
   "source": [
    "<h1>You try! (again!)</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6586ce18",
   "metadata": {},
   "source": [
    "Comment the errors (and fix it if you'd like) for each block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ed163b1",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "cannot assign to subscript here. Maybe you meant '==' instead of '='? (2413209417.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Input \u001b[0;32mIn [10]\u001b[0;36m\u001b[0m\n",
      "\u001b[0;31m    if neutrino[flavor] = 'muon':\u001b[0m\n",
      "\u001b[0m       ^\u001b[0m\n",
      "\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to subscript here. Maybe you meant '==' instead of '='?\n"
     ]
    }
   ],
   "source": [
    "number_of_muon_neutrinos = 0\n",
    "\n",
    "for neutrino in neutrinos:\n",
    "    if neutrino[flavor] = 'muon':    \n",
    "        number_of_muon_neutrinos += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7a0bb14",
   "metadata": {},
   "source": [
    "<h1>Write your own code!</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d93e6f",
   "metadata": {},
   "source": [
    "Using the array of neutrinos below, please\n",
    "<ul>\n",
    "    <li>Count how many have an energy above 1000 GeV</li>\n",
    "    <li>Using a for loop, add a new key to each neutrino called <CODE>\"triggered\"</CODE> with a value of <CODE>True</CODE> if the neutrino has an energy greater than 100 GeV and <CODE>False</CODE> otherwise.</li>\n",
    "    <li>Using a for loop, calculate the average of all neutrino energies</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74188d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "neutrino1 = {'flavor': 'tao',      'energy (GeV)': 4.2  }\n",
    "neutrino2 = {'flavor': 'muon',     'energy (GeV)': 1.3e3}\n",
    "neutrino3 = {'flavor': 'electron', 'energy (GeV)': 8.2e2}\n",
    "neutrino4 = {'flavor': 'muon',     'energy (GeV)': 2.8e1}\n",
    "neutrino5 = {'flavor': 'electron', 'energy (GeV)': 3.6e2}\n",
    "neutrino6 = {'flavor': 'tao',      'energy (GeV)': 9.5e0}\n",
    "neutrino7 = {'flavor': 'electron', 'energy (GeV)': 7.3e1}\n",
    "neutrino8 = {'flavor': 'tao',      'energy (GeV)': 2.3e4}\n",
    "\n",
    "neutrinos = [ neutrino1, neutrino2, neutrino3, neutrino4,\n",
    "              neutrino5, neutrino6, neutrino7, neutrino8 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fdbe70",
   "metadata": {},
   "source": [
    "<h1> NumPy </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bcf0f49",
   "metadata": {},
   "source": [
    "The NumPy (Numeric Python) package provides efficient routines for manipulating large arrays and matrices of numeric data. It contains among other things:\n",
    "<ul>\n",
    "    <li>A powerful N-dimensional array object (<CODE>numpy.ndarray</CODE>)</li>\n",
    "    <li>Broadcasting functions</li>\n",
    "    <li>Useful linear algebra, Fourier transform, and random number capabilities</li>\n",
    "</ul>\n",
    "By convention, NumPy is usually imported via"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "562ceddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca95386",
   "metadata": {},
   "source": [
    "The fundamental datastructure that NumPy gives us the the ndarray (usually just called \"array\"). According to the NumPy documentation:\n",
    "\n",
    "<div><I>An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)</I></div>\n",
    "\n",
    "\n",
    "Generally, think of an array as an (efficient) Python list with additional functionality. BUT keep in mind that there are a few important differences to be aware of. For instance, array object are homogenous—the values in an array must all be of the same type. Because arrays are stored in an unbroken block of memory, they need to be fixed size. While NumPy does support appending to arrays, this can become problematic for very large arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed7dbdf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "array = np.array([1, 2, 3, 4, 5, 6])\n",
    "print(array)\n",
    "print(type(array))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394854ea",
   "metadata": {},
   "source": [
    "The datatype of an array can be found using the <CODE>dtype</CODE> array attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d1ba1a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "print(array.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f39fcd",
   "metadata": {},
   "source": [
    "If not specified, NumPy will try to determine what <CODE>dtype</CODE> you wanted based on the context. However, you can also manually specify the <CODE>dtype</CODE> yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6495ba32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3. 4. 5. 6.]\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "array = np.array([1, 2, 3, 4, 5, 6], dtype=float)\n",
    "print(array)\n",
    "print(array.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58fe29e6",
   "metadata": {},
   "source": [
    "Array attributes reflect information that is intrinsic to the array itself. For example, it's shape, the number of items in the array, or (as we've already seen) the item data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9333122b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 2. 3.]\n",
      " [4. 5. 6.]]\n"
     ]
    }
   ],
   "source": [
    "array = np.array([[1, 2, 3],[4, 5, 6]], dtype=float)\n",
    "print(array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86750962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "6\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "print(array.shape)\n",
    "print(array.size)\n",
    "print(array.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c2dd96",
   "metadata": {},
   "source": [
    "In addition to array attributes, <CODE>ndarrays</CODE> also have many methods that can be used to operate on an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f1356aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.0\n",
      "1.0\n",
      "6.0\n",
      "3.5\n",
      "[ 1.  3.  6. 10. 15. 21.]\n",
      "1.707825127659933\n"
     ]
    }
   ],
   "source": [
    "print(array.sum()) # Sum of the values in the array\n",
    "print(array.min()) # Minimum value in the array\n",
    "print(array.max()) # Maximum value in the array\n",
    "print(array.mean()) # Mean of the values in the array\n",
    "print(array.cumsum()) # Cumulative sum at each index in the array\n",
    "print(array.std()) # Standard deviation of the values in array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d193c658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "M = np.array([[1, 2, 3],\n",
    "              [4, 5, 6],\n",
    "              [7, 8, 9]])\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d0cde3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4, 7],\n",
       "       [2, 5, 8],\n",
       "       [3, 6, 9]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22c8feb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 5, 9])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M.diagonal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78c005c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 32, 50])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M.dot([1, 2, 3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3c73e2",
   "metadata": {},
   "source": [
    "M.trace()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ce21d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  8 10 12]\n"
     ]
    }
   ],
   "source": [
    "array1 = np.array([1, 2, 3, 4])\n",
    "array2 = np.array([5, 6, 7, 8])\n",
    "print(array1 + array2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0065f706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 6, 8])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "2*array1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7f0efb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "array1**2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93df2dff",
   "metadata": {},
   "source": [
    "<h1> Matplotlib </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9ad6e1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be68a2ec",
   "metadata": {},
   "source": [
    "Matplolib has several plotting capabilities. For example,\n",
    "<ul>\n",
    "    <li><CODE>plot</CODE> — plotting x and y data points</li>\n",
    "    <li><CODE>errorbar</CODE> — plotting x and y data points with errorbars</li>\n",
    "    <li><CODE>hist</CODE> — plotting histograms</li>\n",
    "    <li><CODE>hist2d</CODE> — plotting 2D histograms</li>\n",
    "    <li><CODE>matshow</CODE> — display a matrix</li>\n",
    "    <li>etc...</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ab4c998",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 4*np.pi, 100)\n",
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00842f2a",
   "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(x, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38391aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 4*np.pi, 100)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "966c0a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y1)\n",
    "ax.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4be6432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y1, label='Sine')\n",
    "ax.plot(x, y2, label='Cosine')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "887454f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jY9o9bnsg3ltNa9Xx2fYurPw/QEJXloNOCzjoQd8J3DpZNcaWsKvZcK2VmJh4UTPg6tWrSUhIQK/X4+joSHx8/EXbJCcnM3To0PYMtWX0LjB8Llz3IfSYAgNuUzqiJmXkVzD9/W1cu2wT6fnlAOh12nqJUmNW7s+hosbE1tSCuhZAQbA44/mxhzh7wb074PKXoLFECSAsAe7bBfG3y7+vfgIO/WTVMNuqrNrIXR/v4Ovtp+puC/B0rpcoNcTNyYHRwWaifF25ql+ItcMUOjKzGZKfgpUPARLE3wHTPgcHR6Ujq8euWpbKyso4fvx43e+pqans2bMHHx8fIiIimD9/PllZWXzyyScAzJ49mzfeeIN58+Yxc+ZMNm/ezPvvv8+XX35Zt4958+Yxffp0EhISSExM5J133iEjI4PZs2e3+/Nrsbip0Oua+k2SJiPobOvfqdGAh7MDznodndxa98Jfdks8m0/mM7Szr+gCEKzCwVSJw0cT5ffT8AflF6yTe8vu7OoDV7wmd8ttewe+mwVeoRAab92gW+md9SdYeziPbWkFTIgLwsul5a0gI4MlFsxIxMPV8oNqBQEASYIf74G9X8i/j30cRjxkk+NxbevbtRk7duxgzJgxdb/Xjhu67bbb+Oijj8jJyalXHyk6OpqVK1fy4IMP8uabbxISEsLrr79eVzYAYNq0aeTn57No0SJycnKIi4tj5cqVREaeH6tgk2pfTDXlclZefApu+sqmXmThPq58MzsRk1lqUWvShbRaDcO6+NW7zWAyo9epujFUaC9mE/FpS9GU7Iey0zBgBrj5NX+/v5uwGApS4XgyfHkT3LUWvJvuNm5PD1zWDSe9jiExvq1KlGo568+Pidx6Mp/Vh07zxJSelgxR6Mh2fignSloHuPK/0O9mpSNqlF0lS6NHj26yS+ajjz666LZRo0axa9euJvc7Z84c5syZc6nhKaPoFOz6GEw1sPtT+UNfQTVGM2n55XQLlAeOtrQMQFMMJjPP/HKI9IIK3r9tIDqt7SSEgn3SrnmSoJK9SA7OaG76qm2JEsitudd9AB9MBEdXq0xtvhQ6rYZ7xnS55P2UVhm46+MdlFYbGd3dnxFd/S0QndDh6RxB7wZjH7PpRAlUPmapQwjoAWOfkK+vmg+FaYqG8+7Gk1z95l9sPHbGYvtMO1vO8h2nWHfkDFtP5ltsv0IHteMDdNvfBsB05VIIu8SuM2dPmP4d3PYzuCufRFQZTCzfnoHZbLmxfh7Oeq6ND+Oa/qHEhXg1fwdBaIn+t8I9W2HwP5WOpFl21bIkNCLxHjjyK2Rsgh/ukT+0te2fB0uSxLojeVTUmDhTWt38HVqoa6AH/7muL856HUO7tLEFQBAASnPht8cBOBR8HV1jr7TMfj2C6v9eUyG3NCnguZUpfLI5na0nC3hlWj+L7feJKT1Fq65gGRdW5LahbuumiJYlNdDq4OqlcnNm+p+wdZkiYWg0Gr6alcibNw/gmv6hFt33FX1DGN+z8XIOgtAiaxeBoRxzaALHAqdYfv9VJbDiX7B0sJwwKaB7kAcueh1XWngW298TpSqDyaL7FzqI04fgzUGQal8L1otkSS18omHCs/L1NQsh77AiYei0Gi7vE2zVGWzl1UZ2phdYbf+CigX0BGcvzOOfq6sQbFE6PRxdDUUZ8Ndrlt9/C9wyOJI//z2G0d0DrLL/ihojC38+yOWvbxQJk9A6xhr4/m7IPw6b31Q6mlYRyZKaxN8OXcbL9ZiKM9vtYauNJn7am23RMRKNOXmmjFH/WccdH26nqEKsyyS00tB74cFDSKEDrLN/vQskLZKv//Vau74PL2SJiRWNMZgkVuzL4cSZctam5FntcQQV2vQ65O4Dl3OlN+yISJbURKOBq96EOVug62Xt9rDv/5nK/V/uZvZnO63+WJG+bvi5O9LJzZGsokqrP56gQi2tpdRWPa+GiKFyle/kp6z7WOfkl1Uz+9Od7M8stvpjebnoeen6vnzyj0Fc3ifY6o8nqERVsZwsAUx8Hjzsa1iFSJbUxiMQPNv3A8zN0QE3Rx0T44Ka3/gS6bQa3p2RQPKDo+rWkROEJtVUwCdXw7F2WvRUo4FJzwMaOPANZGyx+kO+tf4Eqw7m8tgP+9ul4v3Ibv6M7Kb8zD/Bjmx9W06Y/HtA7+uVjqbVxGw4tZIkOLpKXsIh0rpLt9w2NIrJvYPxc2+f8vThPvIsI4MYLyG0xKbX4eQfkH8C7tsBDtbroqoT3BcGTIddn8Cv/4aZf1h1hurNgyM5W1bDlf1C2r3ifZXBREWNCZ9WVukXOpCq4vNjlEY9rMhs7UtlfxELLbP5DfjyRvjtMTlxsjJ/D6d2/5CWJIn9BRoOZLVs1WihAyrOgj+XyNeTFrVPolRr7BPg6CFX+M4/ZtWHivZz49Vp/RhjpUHdjVlz6DQjX/yDZ1ektOvjCnbm4A9QVQR+3eVuajskkiW16jMNHFwgexccX2OVh/huVyZHckutsu+WeGdjGu8d0fHMysNisV2hYVuWymOHIhLb/0PaPQCmfQL37wL/7u372O3Ez8OJvNJqtqXli5lxQuMGzIBbv4NJL5yvr2RnRLKkVu4BMPBO+fq65y3eupRfVs387/YzYckGDmUr07JzVb9gPPUScaGeVBvNisQg2LDqUrkbDGD4PGXWTew8tu1LqbTAop8P8cKqwxRV1FjtMZrSL9yb929LYO280fXWkROEejQa6DIOOo9pflsbJZIlNRt6v9y6lLUDjq+16K4rDSYuiw2kX7g3scEeFt13SwV5OrMg3sTjk3uID2rhYnu+gOoS8O0KXdpvdmiDJEkuxmdBmYUVfLw5jWXrTnDiTJlF990a42IDcXQQXyVCA2rKobJI6SgsQrzC1cwjEBL+IV9fb9nWpbBOrrx5ywC+vjux3ccqXUgnVl8QGmI2wZZzleyHzFZ2QKnJAO8nwbJEiyZMod4uvH1rPP8YFk18pI/F9nspMguVqVou2Cbt9ndhSR/Y/r7SoVwykSyp3bAH5JXQM7fLM4IszFbOKNPzy/n98GmlwxBsyWUL5BalvjcpG4dOf76chwWXItJoNFzWM5Anr+hpsX22VVFFDTe8tZnxr2ygoFyZLkHBtuhM1Wi3LoXqYnBSpvfBkmzjm06wHo9AGHCbPJVZuvRxPWazxCeb0yi2oerZuzOKGP3SOuZ9vVcMMhVkWh30uhpu/RYc3ZSOBobMkS/3Lofys8rGYgVeLnoqDEZMZomd6YVKhyPYgNCiLWgqC6BTNMRdq3Q4l0wkSx1B0jNw9waLjNv4/XAeT/54kImvbcBoso1B1X3CvAjr5EK/cG9xVivYpvDBENIfTNWw48NL2pUkSdz/5W6Wb8+wmZMDjUbDC9f2YcPDY8SC1wIAkWfXyVfib7fbGXAXEslSR+BguWJxegctPYI8uLJfCA4623j56LQaVj0wko/uGESIt4vS4QhKS34SNrwEFTa02LJGA0Puka9vfxeM1W3e1bbUAn7am83Cnw9hsJETFoBeIV4EeTkrHYZgC/IO4VNxAknrAP1uVjoaixAVvDuSqmJI+Rn63tzmAa+juvkzsqufzU3Vd3MSL2UBKD0tD+w21UDMaHC1jYHPAPS8CpKfgNIcOPg99L2xTbvpGujB/Ek9qDKY8XDWWzhIy6isMeHiaP+tCULbaHd/CoDUbRIa9/YtlGotttE0IFif2QRvDoYf74HUdZe0K41GY7NT9StqjKw/ekbpMASl7HhfTpTCBkFYgtLR1OfgCINmytcP/dTm3fi4OXL3qM48cFlXCwVmOUaTmblf7WbA08lioeuOyliD9uA3AJj7TVc4GMsRyVJHodVBjyny9Z0ft/ruVQYT64+ewWS23UrZ+WXVDHluLf/4aDvZ4oO64zEZz48HGvJPZWNpTPwdMO0zmPap0pFYhYNOS25JFZUGE8kHc5UOR1CCgyPG238jJfhapJjRSkdjMSJZ6kjib5MvD6+Asta1vqxJOc1tH2zjurc2WSEwy/B1d6JXiBfhnVxEstQRnVwH5Xng6guxVygdTcNcfeTY2jDg1WyWWPDTQbalFtj08j4PT+zBD/cM47ahUUqHIijFJ4ajQVeBRj0phhjo0ZEE9YaQAfJ6cXu/kGswtVBxpQFvVz3DOltv6QZL+O/N/fFxdUSrFdUqO5x9y+XLuGvl2ka2znxuJlsLE6e/Tpzlo01pfLszk62PjcPV0TY/vgdEdFI6BEEpkqTMskLtwDbfbYL1xN8uJ0u7PpGXQ2nhC/uWwZFcHx9OtdE2pio3xs+9HVeVF2xHdRkc/kW+3measrG0xJ+vwtZ34PKXocfkFt0l2MuFGweG4+3qaLOJ0t9JkqRohX+hna1ZAPnH0STer3QkFqeeNjKhZeKuBUd3yD8O6X+16q6ODlqbnX3zd5Ikia64jqS6FGKvhKA+EBqvdDTNKz8LpdnnW8NaoEuAO89f24dHJvWwYmCWYTCZWbLmKJe9st6mCtgKVmSslk/CD/8C5eqbZCOSpY7GyV1OmDRayN7dorvYW9KRWVjB2JfXM/n1jdTYWIkDwUo8g2Hq2zBrvX10A/S5Qb488qtc0kNlHLQaVh3I5cSZcn7am6V0OEJ7OPwLVBaARwhS53FKR2NxIlnqiEb+H8w9AEPva3bTlJwShj7/O7e8twWzDc+Eu1Cwlwtl1UaqDCaO5JYqHY7QnpRcMLc1gvqAfw+5oncLygh8tyvTrhap1Wg0PDi+G6/d2I/rE8KVDkdoD7s/ly/73wpa++gmbg31PSOhed4t//DamV6IRgMeTnq7GTSt02p4/7YEYvzdcRfFKtUv7S9w9oTAOPtoVQI5zj43wNpFclfcgMbr0WQVVTLv671oNbDj8fH4uFmuIr81TegVpHQIQnupKJBno0Kbi63aOvFN0tFVlza5IvStQyK5LDaQihpjOwZ16fqEeSsdgtBeVj0Cufvgmnegrx0M7q7V+1yylLYRik41ehJTXGFgSIwPWo3GbhIloYM5/AtIJgjsDb6dwaC+cWp20mYtWFxVMXw0BV7qBlUlTW4a5OVMjL97OwVmefbSfSi0QV6KnChpHaDreKWjaR3vcIgcLl8/8E2jm/UM8eSrWYl8/I9B7RSY5VQZTHyzM5N/f7PPpmtDCZfo4A/yZa+rFA3DmkSy1FE5ecprVBkq4OhvDW5itKFFOtsiJaeEf3y0nVmf7lQ6FMFa9n0tX3ZNsq114Foq4Q4YNAtaMCBWbyMLV7eGWZJ4/If9LN9xioPZTZ+UCXasaxKEJkDPa5SOxGpEN1xHpdFAz6th40vyop59rq/354oaIyNfXMfYHv4svDLOLhfFdHLQ8vvhPHRaDUUVNXi7ii4MVTGbYf//5Ou1s8vsTe/r5J9GZORXEOjlhJOD/b3/AFwdHbgtMQpnvQ5fd/H+U60hs+UfFbO7U5WlS5cSHR2Ns7Mz8fHxbNy4sdFtR48ejUajuejn8ssvr9vm9ttvv+jvQ4YMaY+norxeV8uXx9dc1BW38dhZzpZVs/lkPs56u3uZABDj787TV/Xit7kjRaKkRhmboPiU3ErabZLS0VjF7M92kvDMGjafyFc6lDabPzmWB8d3I9jLRelQBKHN7OpbcPny5cydO5fHHnuM3bt3M2LECCZNmkRGRkaD23/33Xfk5OTU/Rw4cACdTsf119dvRZk4cWK97VauXNkeT0d5gXHg01mevvy3rrjLYgNZPmsIT1ze064r8E5PjKJLgP2OtxKaUDtOIvZK0DsrGsolMRnlmURrn5aXizinuMJAfnk1lTUmugc1PglDEBRTUQB7voDKIqUjsTq7SpZeeeUV7rzzTu666y5iY2NZsmQJ4eHhLFu2rMHtfXx8CAoKqvtJTk7G1dX1omTJycmp3nY+PnY49qEtNJrzrUuHfqj3J51Ww+AYX5LE9F/BVmVsli9jpygbx6Uy1cAXN8pd4qcP1N3s5apn8yPj+OX+4XY/C06SJHZnFLLxmPoqO3doh1fAD/+ET65UOhKrs5sxSzU1NezcuZNHHnmk3u1JSUls2rSpRft4//33ufHGG3Fzc6t3+7p16wgICMDb25tRo0bx7LPPEhAQ0Oh+qqurqa6urvu9pETuwjIYDBgsOGWydl+W3OdFuk1Bv/FlpGPJGMsKmiwjYGtaenwO5ZTw094cYoM9uapvcHuEZhPa5fWjpDuS0WRsRgob2OqpyjZ1bDR6dDGj0R79FdPBnzD71l/OpLOvS7vHaenj8/O+HOb9bz/dAtxZcd9Qi+xTSTb1+lGQ7sD3aAFT10mYLzgW9nR8Whqj3SRLZ8+exWQyERgYWO/2wMBAcnNzm73/tm3bOHDgAO+//3692ydNmsT1119PZGQkqampPPHEE4wdO5adO3fi5NTwoqyLFy9m4cKFF92+evVqXF1dW/GsWiY5Odni+6wjSfTzGUGBezeyktdg0jrxR7YGowTxfhI+drAubXPHZ12Ohu/TdHT1NKPPatkSL2pi1dePLUj5o813tZVjE1EVSn+gdMdy1pfFYZbAFmrAWur4VBvBVafDzVTCj7+sxE6HQV7EVl4/StAby5h4rhDlujPelDUwfMUejk9FRcsq42skOyl+kZ2dTWhoKJs2bSIxMbHu9meffZZPP/2Uw4cPN3n/u+++m02bNrF///4mt8vJySEyMpKvvvqKqVOnNrhNQy1L4eHhnD17Fk9Pz1Y8q6YZDAaSk5MZP348en37LGBrNkuMfHkDp0uqeefW/ozp7t8uj9sWLT0+GQUVvPHHCcbHBjK+Z+MthmqjxOun3UjSJVXrtrljU34Wh9d6opHMGO7dw+s7q0hOOcOcUdFMimv/rnBrHB+jyYyDHZY/aIjNvX4UoNn7BQ6/3I8U0BPjzA31/mZPx6ekpAQ/Pz+Ki4ub/P62m5YlPz8/dDrdRa1IeXl5F7U2/V1FRQVfffUVixYtavZxgoODiYyM5NixY41u4+Tk1GCrk16vt8oLw1r7bYjBZOaBcd3440geI7sHotfb/pTl5o5P50AvXr1xQDtGZFva8/XTLgrT4eMrIPYKSHrmkpImmzk23sEQPgQyNqE/sZrklFgO55ZilDSKxmfJ42MLh9nSbOb1o4TDPwOg6XVNo8fAHo5PS+OzmzTf0dGR+Pj4i5r1kpOTGTq06T7wr7/+murqam699dZmHyc/P59Tp04RHNxxxrYAUJwFm99En76BmwdH8O6MBJztIFESOqAjK6EoHbL32M9acC3RY7J8eXgFn901mJeu78u4Hk2fCNqjgvIaiitsfyyL0ITKwvNrwfW8WslI2o3dJEsA8+bN47333uODDz4gJSWFBx98kIyMDGbPlothzZgxg/nz5190v/fff5+rr74aX1/fereXlZXx0EMPsXnzZtLS0li3bh1XXHEFfn5+XHONeiuRNmj7e/Dbo7DzY6UjsZozpdV8veMUNUb7rkze4R1eIV/WJhdq0f3c8ylKx89Fy3XxYXi52vZZeWst/jWFgc+uYfmOhsu9CHYiYyuYDeAfC/7dlI6mXdhNNxzAtGnTyM/PZ9GiReTk5BAXF8fKlSuJjIwEICMjA622fv539OhR/vzzT1avXn3R/nQ6Hfv37+eTTz6hqKiI4OBgxowZw/Lly/HwsJ9ZYRbRfRL8+QqGo2swVlXj4mwHI7tbQZIkLn99I3ml1YR6uzCsi5/SIQltUVEA6edmv3ZXWbLk2xn+uRkCYtXVYnaBsE6umMwSh3NLlQ5FuBTdJ8K/jshLZnUQdpUsAcyZM4c5c+Y0+Ld169ZddFu3bt0aXcDRxcWF335reF20Dic0nkoHL1wMxbz80ec8MvsfSkdkURqNhjHdA0jJLcEoFta1X8dWy6ubB/QCn2ilo7G4ik7dePx/exndPYDLewejs4UpcRZ0Zd8QRnb1I9LXrfmNBdvmEST/dBB2lywJVqLVke03jM65K7nCtekZg/bquam9Vffl0+GotQvunL+O5/Pdrix2njzDFXH+gLq64bxc9Hi5qOs5CR2DXY1ZEqyr81C5VELPsi0KR2IdIlGyc4YqOL5Wvt7j8qa3tVPRfm58Fb2C34x3oqkdQCsItmTdC/DxlXD04qEtaiaSJeG8LuNAo0WTdwiKTikdjdWYzBJ5JVVKhyG0Vk059LsJwgZCcD+lo7GKLgHuDAl1wtlYfL4VTWWKKw08+v1+Jr22EaNJTLawO4d/htT1UFmgdCTtSiRLAgBny6rB1Uf+ItI51lujSk22nsxn8HNrmPnpTqVDEVrLzRcufxnuWqPaAdAAdD/XanbkVzCrL5lwd3Jg5f4cUnJK2HOqSOlwhNYoyYbc/YAGulymdDTtSoxZEqg2mhj54h8EeDjx7Q2v4RsUDk7uSodlFdH+bpwtq6HGaKas2oi7k3gLCLZhR1oBbk4O9IgajsbRA8pyIXs3hMUrHZpF6bQaHpsci5+7E3GhXkqHI7TGsXN1DkPjwa1jzSgW3xQCR3PLqDGaKa8x4RPRQ9Vn7QEeznw/ZyhxoV7oVbL0QodQnAXFpyA0AXTq/Nh6ZkUKe04V8dL1fbmu8xhI+QmOr1FdsgRwfUK40iEIbXHs3DilbhOUjUMB4ttCoHeYF7ufHM+Htw9Ec2GipMIuAID+EZ1EomRv9i2HDybAt+oqaVHLaDLj5+6Is17L8C5+8vhBgBO/KxuYINQyVp+v2t11vKKhKEGdp2hCq3k46883iR9eAeueh8hhMOl5ZQMTBDifNEQOVzYOK3HQaXnvtoFUG004Oeig81j5D5nboaoYnNXXXXWqoIK1KafpEuDB8K4dq0vHLmVshpoycA+EoL5KR9PuxOm1cDHJDLn74Jh6C3Z+uiWdm97ZwrbUjjWjwy7VlEPGuXIWtUmESjk5nFuP0TtCXnNr+INgMioak7X8b2cmC34+xJfbxNIndkHnKA/q7jEFtB0vdRAtSx3cL/uy+XlvNtcOCCOp17lqrDGjQauHgpNw9jj4dVE0RmvYmVbA5pP5DDiax6BoH6XDEZqS9qe8DpV3hLwkiMoYTWZqTGZcHf/2cXyDetdpBBjXI4BtqfkMiRHvP7sQOVT+6aA6Xnoo1PPbwdP8dvA0+7OKz9/o5AFRw+TrKm1dumFgOE9f1YsbB0YoHYrQnNouuM5jVTn5YFtqAf0WJTNv+R6lQ2lXfcO9+WpWItMTo5QORRCaJZKlDu7ukTHMG9+NSXHB9f/QNUm+PKbOKq1DO/sxPTGKcB9XpUMRmlNbtbvzOGXjsJId6YXUGBuZTFFdKtdbKuk4C5YKNigvRZ6R2oGJZKmDiwv14v5xXekZ4ln/D13PTQ1N+0v+wBYEJRRlQP4x0OggeqTS0VjFfWO7sGbeSOaMaaC7e/mt8OWNkPJz+wfWTmqMZnZnFCodhtCU3x6DV3vCrk+VjkQxIlkSGubbGbwj5bEi6ZuVjsYqyquNrE05zU97s5UORWiMZyjctRamvAou3kpHYxUajYYuAR50CWigEGzMaPlSpSUEKmqMxD+TzDVLN5FbLJYgsknGakjfJF8PVV/Nr5YSyVIH9uW2DHamFzS8PpNGA3FToff18jIoKrQ9rYA7P97BC78eRpIkpcMRGqLVQVgCxN+mdCTKqJ39l7YRjDXKxmIFro4OxPi74+vmSFp+udLhCA05tQ2MlXLJgIBYpaNRjJgN10EVVxp4/IcDmMwSf/57DGGdGhi7c9mCdo+rPQ2O9qVLgDuDo32oNppx1uuUDknoYJatO0F+WTU3DAynW6DHxRsE9gY3fyg/A5nbIEp9dabem5GAr5sjWq36Bu+rQup6+TJ6pConWLSUSJY6qNIqAxPjgsgtrmo4UeoAXBx1rJk3SukwhMbk7IXt78l1XVS6vMLXO06ReracITG+DSdLWi3EjIH9X8sD3VWYLPl7OCkdgtCUk7XJUsf+rBTdcB1UWCdX3rx5AN/+s5m6GZIEpw/C2WPtE5gg1DryK+z6BPZ8rnQkViFJEvPGd+PGgeEMbqrWkFj6RFBKVQlk7ZSv146f66BEsiQ07fenYdlQ2PRfpSOxqszCCgwNjd0SlFNXX0mdJQM0Gg1X9A3h+Wv74OGsb3zDmDHyZc5eKM9vn+Da2f92nOKqN/7k401pSociXCj9L5BM4BMD3h178WPRDdcBlVUbMUsSnk19QNcKHyxf1vZbq9D1b21ie1ohX9+dKKp524rKIsjcIV9X+RInzfIIhKnvyjORVDrZIr+8hr2Zxfh7OHPb0CilwxFqhQ+Ga98Hs0npSBQnkqUO6IfdWTz54wFuGhTBs9f0bnrjyKGgdYDCNPmnU1Q7RNi+gr1c0GmLOJZXKpIlW5G6Xj6j9eumyjNas1nix71ZDInxJdjLpfk79LnB+kEpaGKvIAI8nBjaWSyoa1NcfaD3dUpHYRNEstQBHc8rwyxBgIdz8xs7eUBoApzaIg/0i4+yenzt7d+TevD01XF4ubSgpU1oHyfXyZe1XVAqc+R0KQ8u34uro469TyWh13XsERFRfm5E+bkpHYYgNKpjv0M7qAVX9mLz/LHcPLiF66LFnJsFodKuuFBvF5Eo2Zq0P+XLGHXOwCmrNtI33JvEGN+WJ0o7P4avb5MXuBYEa0vdABv+A7kHlI7EJoiWpQ6qRU3/taJHwfoX5DePJHXoWhtCO6gpB8kMaFS7yvnAKB9+vGcYJnMriqHuWy4PuO08Rh5wqzKlVQZ+P5zHmdJq7hqhvudnd/Z9Dbs/lccPBj2rdDSKEy1LQvPCBoLeVS6Ml3dI6WisYuvJfOZ8vpNXko8qHYrg6Ab37YSHjoFLJ6WjsSpdawox1tZYqm11U5mc4ioe+GoP//ntCNVGMaBYUZJ0vr5SBy8ZUEskSx3Mwp8P8si3+ziS24rFcR0cYeLzcOt3qjyjBXk2zsr9uaw+mKt0KEItd3+lI7CKyhoT5ta0KNWqTZZSN8pfZirTNcCd4V38mJEYSVWNKOOhqMJUKM4ArR4iEpWOxiaIbrgOxGyW+H53FkUVBm4Y2MoZRipfm2tIjC8PJXVjSIyv0qEIJiPo1PvRtGz9CT7dnMb947pyx7Dolt8xbBDonKAsF/JPgF8X6wWpAI1Gw2d3DVY6DAHOtyqFDQSnBhZ47oBEy1IHIgGv3NCXu0fF0DvUS+lwbIqPmyP3ju1KQpQoHaCokhx4IRI+v0G1tV22peZTWGHAzbGVCaHeWf7yAkjbYPnABKFW7WQelU6waAv1nr4JF9FpNYztEcjYHoFt20HqBjj6G/SZBsF9LBucIIA8gLmmTG490apzYeNP/jGY/VlFxPi14Yw9ajik/ymPW0r4h+WDswGSJHHiTBmh3q64OKrzNWDTzGb5sx46/HpwFxItS0LLbX8PNr8BR1cpHYlVGExmdqYX8PnWdKVD6bhqP6SjRigbhxU5OmiJj/Shk5tj6+8cPQI0OjBUWj4wG3Htsk1c9soGtqSqc2kXm1ecAYYq0LvJVeMFQCRLHYbJLPHZlnSOnS5Fauvg0NqzjFR1dgEUVxq4dtlmHvv+AIXlNUqH0zHVzvRScbJ0ScIHw7/T4KYvlY7EaroGeODkoCWrUL0JoU3rFCW/xmaulSf3CIAdJktLly4lOjoaZ2dn4uPj2bhxY6PbfvTRR2g0mot+qqqq2rxPe3Uou4THfzjA1GWbWlfb5UK1s3Eyt4NRfcmEn7sTg6J9mBQXRFm1UelwOp6SbCg4ARotRKpzBs6zKw7x37XHyCupan7jhuj04Oxp2aBszMMTu7P3qSRuHRKpdCgdl4MjBMQqHYVNsasxS8uXL2fu3LksXbqUYcOG8fbbbzNp0iQOHTpERETD1ag9PT05cuRIvducnc8v89GWfdojg9nM0M6+dHJzxKGtSyv4dQNXX6jIh+zdEKG+mStf363OL2m7UNuqFNwXnNU3AaHKYOLjTenUmMxM6Rty6Ts01qjyzN/X3UnpEAThInbVsvTKK69w5513ctdddxEbG8uSJUsIDw9n2bJljd5Ho9EQFBRU7+dS92mPBkR04ouZQ3jjpv5t34nmgorK6X9ZJjBBqKXy8Uoms8TjU2KZlhBOlK9r23dUlAHvjoXX+qiy3pKgoLwUeGMgrJqvdCQ2x25almpqati5cyePPPJIvduTkpLYtGlTo/crKysjMjISk8lEv379ePrpp+nfv/8l7bO6uprq6uq630tKSgAwGAwYDIZWP7fG1O7Lkvu8VNqwIehSfsac9hemIfcpGos1j09ZtRG9VoOT3n5n49ji66cpmqB+aKMzMEeORLJyzEocG0ct3JQQCgmhGI2X0M3r5IPD6YNojFUYcg/JLb4WpvRrZ/3RM3y8OYP+4d7cN7azIjE0RenjYy3akxvQnT2K2T0I0yU8N3s6Pi2N0W6SpbNnz2IymQgMrD/tPTAwkNzchqsu9+jRg48++ojevXtTUlLCa6+9xrBhw9i7dy9du3Zt0z4BFi9ezMKFCy+6ffXq1bi6XsIZYyOSk5Mv6f7VJnDQgCUWNveqMDEaqMjcz9oVK2xinbhLPT5/9/FRLXvyNdzR3UwfH/s/c7f08bEef/C+Ew5XwOGV7fKI9nNs6hvqHIN/2SEOrXiLNP/LrPY4Sh2f7Wc0bDyuIyP3LJ2rjjR/B4XY6+unMfGp3xEGHKny4+jKS38P2sPxqaioaNF2dpMs1dL87ctZkqSLbqs1ZMgQhgwZUvf7sGHDGDBgAP/97395/fXX27RPgPnz5zNv3ry630tKSggPDycpKQlPT8sNvjQYDCQnJzN+/Hj0en2b9/P2hlSWrj/JzOFR3DvmEs/SzCYMheNx8unCZIUTJUsdn7/bYjzErvxMXEO6Mnms/VZJttbxUYP2PjY1RjObT+YzIMIbD+dLfzztxkOw4RC9PYrpOXmyBSKsT+nXTkJpNVEHchkU5UNssEe7P35zlD4+ViFJOLz+MABdL5tBl8jhbd6VPR2f2p6h5thNsuTn54dOp7uoxScvL++ilqHGaLVaBg4cyLFjxy5pn05OTjg5XTwIUa/XW+WFcan7PZBdSkWNCS9XJwvEp4egnpe4D8uy9HGfM6Yr943rRoi3i8X2qSRrvS4tKmsXeIW3+3pw7XVs9mUXcNenu/Fzd2L7Y+OaPBlrkc6jYcPzaNP/QuvgYLUWXqVeO6E+eu4aafsnKnbx3mqpgpNyMVidIw6RQ8ACz8sejk9L47ObAd6Ojo7Ex8df1KyXnJzM0KFDW7QPSZLYs2cPwcHBFtunPVh6ywBWzR3BFZaYgdMBhPu4qiZRshv/ux1e6nJ+RpzKFFcaiPR1JSGy06UnSgChA8DBBSrOwpnDl74/QUg/N043NB704vPv7+ymZQlg3rx5TJ8+nYSEBBITE3nnnXfIyMhg9uzZAMyYMYPQ0FAWL14MwMKFCxkyZAhdu3alpKSE119/nT179vDmm2+2eJ9qoNVq6BFkwdosZWdg5UNw9ij8c5NNjFsS7FhxJhSly5Wpg/sqHY1V1C4zVG200Hp3Dk5y6Y6T6+QEU4U1cSprTGxNzSenuIqbBqmnjIvNqk2WItXTUGBJdpUsTZs2jfz8fBYtWkROTg5xcXGsXLmSyEi5eFlGRgZa7fnGsqKiImbNmkVubi5eXl7079+fDRs2MGjQoBbvU2iAsxccWw2GCjhzBAJ6KB2Rxe09VcTyHaeI9HHl7lG2NxtHVTK2yJfBfcHJ9sanWJKTgwVnV3a/HJy9wSfacvu0IVlFFdz+4XacHLRMHRBq2WMnXMwzBHw6i2SpEXaVLAHMmTOHOXPmNPi3devW1fv91Vdf5dVXX72kfdq7V5KPUmM0c0NCGDH+bVi4syEOjvLq56nr5XpLKkyW0gsq+GJrBj2DPUWyZG0Zm+XLCHUWBDWbJbRaK7S+Dp4l/6hUZ393+oZ50SXAg/Jqk0iWrG3s4/KPqN3VILtLloSWkySJr7ZlkFdazZju/pZLlgAih51LljbBwDstt18bMSTGh5kjohkU7at0KOqXXpssDWl6Ozv1/p+pfLEtgzuGRTEjMUrpcOyGRqPhx3vbPiNLaCMxrKJBIllSMbMED0/swY60AvqGe1t25xdW8pYk1b3BAjyceexy25r1p0qVhZB3SL6u0mRpa2oBqWfLqTJYaLzShSQJ8k+AVgs+MZbfv9AxFGeCexDoRErQGLuZDSe0nk6r4br4MJ6/tg/Olq5EHZYAWj2U5kBhqmX3LXQcp7YDkjxWwj1A6Wis4uUb+vL+bQlMigu2/M5/fxreiIdN/7X8vm3I2bLqti8ALjTvs2vhhUjI2Kp0JDZLJEtC2+hd5CmmcH4WhcpIksTxvFLWppxWOhT1CukP17wNIx9SOhKr8XLRMy42kHAfy1f3J+TcWo+1g+RVRpIkrnrjTxKeWcPh3JYVDxRaqfxc+YmaMvDrqnQ0NkskSyq2NuU0eSVV1nuA6JHnanJY4UvABhzPK+OyVzZwzxe7MJjMSoejTu7+0PdG6Hez0pHYp/BzXZd5h+QuTZXRaDR4uToCcDinVOFoVKr2ZDegJ7j6KBuLDRMdlCp1prSaOz/egUYDe55MwsvFClVUxzwKYx+z/H5tRGd/d4I8nYnyc6WgvIZAT2elQxLszCeb09BoNCT1DLTO68fdH3y7Qv4xOLUNuk2w/GMobNGVvfB21eN9LmkSLEzUV2oRkSyp1JnSanoGeyKBdRIlUN2g7r/TajVsemSsdaZ9C5B3GI4nyy2UKi1GuWzdCXKKq+js72a9ZDtiiJwsZWxWZbIU5eemdAjqlv6XfCmSpSaJbjiV6hniycoHRvDTvcOs/2A15XK/twqJRMmKjq6C1Y/D+heVjsQqjCYz0waGM6KrH/0sPRv1QrX1qVQ6bkmwoupSOH1Avh4hkqWmiJYlldPrrJwP//kqrH0aBt4Fk9X5pQdgMktoNVhmXS9BVluMUqVntA46LXMv62b9B6otuZC1EwxVoFdfd/GaQ6f5eV82ST2DuLyPFWYVdlSZO0Ayg3cEeIrj2hSRLKmQ2Syhaa8vdq9wkExwSp1TTiVJYuYnO9hysoCf7xtOtOgSsAyz+XxLiErrK7UbnxgY9W95ZpxGnZ0Fe04V8eOebHRajUiWLKlTJIx5DHRiPFhzRLKkQptO5POv/+3h8t4hPHmFlQsr1n7R5e6H6jJwsmCVcBug0WgoqjBQVm1kR1qBSJYs5ewRqCqSZ1IG9VE6Gqs4kltKjL+b9Vt3NRp5soWKje8ZiF6nZXhXUVHfonxiYNTDSkdhF0SypELb0wo4XVJNQXm19R/MKww8Q6EkC7J3yYN1VWb+5Fhc9Dq6B6l7kdd2VdsFF5YAOitNQFBQWbWRSa9twNFBy5b548RMrkvUN9zb8qsQCEIriGRJhWaP6kxiZ1/cHNvp3xs+GA5+J1d/VWGyFB/ZSekQ1Cdd3Yvnpp0tx8NZj4ezQ/skSiYjpK6Tx6CMfFhe/kQQmlKcKY9zCx8MHkFKR2PzRLKkQi6OOobEtGNzdcQQOVlS6bglwQqydsqXKh2vFBfqxe4nxnO2rB1ad2stnwGGcoi9EgLVt66hwWTmUHYJNSYzA6NE8cRLdnQVrPgXxIyGGT8qHY3NE6cfwqULHyRfZm6TB+6q0Pa0Av7z22G2pxUoHYo6/HMT3PGrfFarUlqthoD2KmSqc4DwgfL1DHUuP/TD7iyuevMvXvj1sNKhqEPtOnDh6jxhsTSRLKnM2pTTvL3+BEdy23FpgMDe0OdGGPM4mA3t97jt6Ltdmbz5xwmSD4l14ixC7yyXDHAUA+YtRuX1luIjO+HlosffwwlJEovqXrLanoAI9Z6wWJLohlOZ73ZnsWJfDkaz1H4DknUOMPXt9nkshYztEYjRJDE4WjT/C01LySnhse/3M6pbAA9c1o4Lk9Z2adaOB1OZaD83dj8xXhSKtYTSXChKl0tNhCYoHY1dEMmSyozo4ofRZG7fMUsdwPiegYzvGah0GOrw3Sxw8oRhD4B3uNLRWNz2tAJ2ZRTh5tTOH6+hCfKXX0kmFGeBV2j7Pr6VaTQata+w1H5qW5UCeoGzp7Kx2AmRLKnMjYMiuHFQRPs/sCTB2aNyvaXe17X/4wv2oaYc9n8jFzId/qDS0VhFUs8gXB0d6OTaziURnNwhsJf8HszcBl7XtO/jtyOTWUInWpjarm680iBl47AjYsySYBlVxfDmYPj2TijLUzoaqzlbVk3a2XKlw7BfWbvkRMkzTHUtH7WCvJy5Lj6McbEKtESGnfvyy9rV/o/dDjILK5i69C9GvviHGLd0KerGK4nB3S0lkiUVycivoLLGpMyDu3hDQKx8/dQ2ZWKwsi+3ZZDwzBqeWXFI6VDsV+2HdO3MLcGyEu+Bf26GyxYqHYlV+Lk7sT+rmKyiSjILK5UOx35d9wFMfReiRykdid0Q3XAqct9XuzmQVcx7MxIY0yOg/QMIHwx5h+DUFoid0v6Pb2XdgzzQaOTqzEIbZW6XL8PU2fx/IKuY1LPlDIr2IbC9ygZcyLdz+z9mO3LW63jr1ni6BLgT1slF6XDsV6dI+UdoMdGypBIms0RBeTUms0SXAIXWZ6utmZOhzuKUfUK92PtUEl/NUmfVaauTpPOtjiqtr/TNzkzu+3I3y9adUDoU1RoXG0ikr1v7LBQuCOeIliWV0Gk1bHx4LNlFlQR7KXBGC+frdeTsAUOVXEtHRRx0WjytvSiqmuWfgMoCcHCGoN5KR2MVod4u9ArxZJCSJSaOJcO+r+XKzP1vUS4OwTZteElejzHuOtWOG7QGkSypTIi3gk3TnaLBzR/Kz8gJkxg8KFyo7DR4RYBnCDioc2HZmSNjmDkyRtkgTh+A/V+DsUqVyZIkSazYn8PO9ELmje+Gh7P6FmK2GrMZNr8BlYUQOVwkS60gTpMFy9FoLuiKU2cV4fT8cu79Yhf/+Gi70qHYn6hh8OB+mP690pGoW+17MHO73PWpMhqNhud/PcyHf6Wx91Sx0uHYl/zjcqLk4ALBfZSOxq6IliUVkCSJm9/dSoy/G/9K6o6Pm4Jn7Yn3woAZqq3f4eSg45d9OWg1UF5tbP/Cg2rg6Kp0BFZRUWPERa9TfixNSH/QOkBpDhSfAm8F6q5Z2dT+oZRUGfHzUGcLpdWcOncSGzpA7ooTWkx80qvAqYJKNp/MZ2d6IU9MUXi18Uh1D34O8nLm8ctjiQ32RC/GL7Wc2QRoQKveY3b/l3vYm1nEs1fHkdQrSLlA9C7ymLDs3fKAehUmS/OSuisdgn2qnWARJkp3tJZ6P7k6EB93R966dQCPTOqBs16ndDiqd9eIGIZ18cPRQbx9Wix1PbwYDT/dp3QkVnMwu5gzpdX4ujspHUr9rjhBqJW5Q75Uacu/NYmWJRVwd3JgYlyw0mGcl7FFnpETPRJiRNEzATi1HaqKoKZC6Uis5o+HRrM/q5i4UBtYaytsIGx963wRUBWSJInMwko8nB3wdhXdcc2qKoYzh+XromWp1cSpsWB5B7+HjS/B4RVKR2IVJrPEroxCPvorVSy50FKZ6q6vBHLBxIFRPjg52EDrbvggeVFdSTrXBao+sz/byYgX/2DVgVylQ7EPZ4/J45S8I8FdgaLFds7ukqWlS5cSHR2Ns7Mz8fHxbNy4sdFt3333XUaMGEGnTp3o1KkTl112Gdu21V+K4/bbbz+3mvX5nyFD7GfKe3m1ka+3n+J4XpnSoZxXe9ai0i4Ak1nixne2sODnQ6SKdeKaZzbLLUsgljlpL17h8EgG3L0etDaQvFlBlwB39DoNeaXVSodiH8ISYH4mzPhB6Ujskl0lS8uXL2fu3Lk89thj7N69mxEjRjBp0iQyMjIa3H7dunXcdNNN/PHHH2zevJmIiAiSkpLIysqqt93EiRPJycmp+1m5cmV7PB2L2HuqiIe/3cdtH9jQemy1yVLuPjCob/0mRwcto7r5c1lsADUms9Lh2L6zR6G6WJ6uHBindDRW8eyKQyxdd5wztvLFrdGAk4fSUVjVrJGd2b9gAveP66p0KPbDwQl8FK4DZqfsaszSK6+8wp133sldd90FwJIlS/jtt99YtmwZixcvvmj7zz//vN7v7777Lt988w1r165lxowZdbc7OTkRFKTg7JVLNCjKh0hfG5qO7R0BbgFQngc5e1VZnPLdGQlKh2A/arvgVDpducpg4sO/0jCaJa7sG6J0OBczm1TZuuTlor7XkmC77CZZqqmpYefOnTzyyCP1bk9KSmLTpk0t2kdFRQUGgwEfn/pLEaxbt46AgAC8vb0ZNWoUzz77LAEBjffpVldXU119/gyypKQEAIPBgMFgaOlTalbtvpra58BILz6/M6HZ7dqbLjQe7dFfMaVvwRwcb5XHaMnx6chs5fjo0regBUyhCZht5H9lyWNTWW3k/5K6ciyvnAA3B8WPd53iTHTfz0RTmo3x3j1ya1ML2cprx1bZ3fEpOIHDt3dijhqOefwzVn84ezo+LY1RI9nJCNXs7GxCQ0P566+/GDp0aN3tzz33HB9//DFHjhxpdh/33HMPv/32GwcOHMDZWV63bPny5bi7uxMZGUlqaipPPPEERqORnTt34uTU8BTgBQsWsHDhwotu/+KLL3B1taEWHgV1Of0LvbK/Jst7IDui1TtdvMYEOg2IkkuNiz6TTHDRDo4HXk6ep6ga3F605hom75uNTjKS3PM/VDgFKh2Sxe0v0LAlT0M3L4lRwXbxVaaI8Pw/GZDxDvluXfmz2xNKh2NTKioquPnmmykuLsbTs/GZrHbTslTr79VxJUlqUcXcF198kS+//JJ169bVJUoA06ZNq7seFxdHQkICkZGRrFixgqlTpza4r/nz5zNv3ry630tKSggPDycpKanJg91aBoOB5ORkxo8fj15/cZOzwWRGp9Gg1dre6tuadC/47GtC9KVMnjzZKo/R3PGxtjlf7GHd0TO8N30AQzv7tvvjN0fp43Oe/P+3pY5L2zk21qU5+xZk7WBMFzek3i1/H9rL8Tm7JYMDRw7j6+/H5MkD2u1x7eX41NL++jtkgHfceCZfZp3P4wvZ0/Gp7Rlqjt0kS35+fuh0OnJz608TzcvLIzCw6TOml156ieeee441a9bQp0/TZ7bBwcFERkZy7NixRrdxcnJqsNVJr9db5YXR2H5/3p/JUz8e5PqEcJ68QuHK3X8XOQTu2YbGtyt6K1dtttZxb46TXofBJHH4dDmjetjumDeljo89uNRjI0kSW1ML6B3qZZtL34QPhqwdOOTsggGtX1TX1l87Y2ODQKNlUJSPInHa+vGpk70TAF3EYHTtGK89HJ+Wxmc3nQeOjo7Ex8eTnJxc7/bk5OR63XJ/95///Ienn36aVatWkZDQ/Lltfn4+p06dIjjYhoo8NmJfZjGl1cbWDEVoP3pn8O+u6uUtHhzfjY0Pj2GW0qvM27KCk1BRoHQUVpNVVMmN72yh/9PJVBttsJ5R2LnPvNrKzSoT7efGncOj6R3mpXQotqumHE4flK+Lyt1tZoOnQo2bN28e06dPJyEhgcTERN555x0yMjKYPXs2ADNmzCA0NLRuZtyLL77IE088wRdffEFUVFRdq5S7uzvu7u6UlZWxYMECrr32WoKDg0lLS+PRRx/Fz8+Pa665RrHn2VKPXx7L9QlhuDna1b9RNTr7uysdgu1b+X9wfA1ctRT6t75lw9adLqki1NsFHzdH2yhG+Xe1ydLpA3IZD72LsvEI7S9rF0hm8AwFTxucrWkn7Opbdtq0aeTn57No0SJycnKIi4tj5cqVREZGApCRkYH2gpaMpUuXUlNTw3XXXVdvP0899RQLFixAp9Oxf/9+PvnkE4qKiggODmbMmDEsX74cDw/br1HioNPSK8SGz6jOHIX1L8hv1Os/VDoaob1J0vkWjYAeysZiJfGRPvz1yFgqaoxKh9Iwr3BwD4Sy06ot41FWbWRPRhEGs5kx3UVl6ovUFgcOs6VRg/bHrpIlgDlz5jBnzpwG/7Zu3bp6v6elpTW5LxcXF3777TcLRSZcRKOFA9+AzgmMNeCgvvWbNhw9w9qU04zpEcBo8UFdX/4JeT04nRME9lY6GqtytdXWXY0Guk2EinzQ2vbYkbbacPQMcz7fRa8QT5EsNcTBCTpFQZjogrsUNvoOF5rz6/4cDmaXML5nIH3DvZUOp2G+ncGlE1QWwun9EGqdektKWn/0DB9vTkcCkSz9Xda5VqWQfqpMlO3Gla8rHYFV9Y/wJtzHhe6BHi2eHd2hJN4j/5jFagOXQiRLdurnfdms3J+Lm5OD7SZLGo289Mmx1XJ3jAqTpbE9ApAkGNPDX+lQbE9t83+oOpv/d2UUMv/b/YzpEcAjk9TZzWgPgr1c2PjwWKXDsH0qnmzTHkSyZKcm9ArC1dGBRBus71NPbbJ0ahsMvlvpaCxuWBc/hnXxUzoM21Q7XkmlYyV2pRdy5HQp4T52UIhWkqAoHVz9wElMTOgwqstA7yoSJQsQyZKduqpfKFf1C1U6jObVLqpb28ogdAyGSnkGFqg2WbqmfyjhPq54OtvBWKCPr4C0jTDtM4i9QulorKbaaLLNWYlK+f1p2PMFjHsSBs1UOhq7JtJNwbpCBwAa+ay2LE/paKxCkiRSz5ZzPK9U6VBshyTBlFdh8D/lGVkq5OvuxIReQbbfugvnV5pX6UlLen45419Zz6gX12EnK3i1j8ztUF0ijx0VLolIluzQ8bwy8suqm9/QFjh7QVAcBPVRbbL07saTjHlpHa+uabzqe4fj6AoDZsCk51u1gKtgJXUtvDuVjcNKAjycST1bTm5JFbklVUqHYxsMVZCzT76u0tbd9iS64ezQo9/tZ1taAf+9qT9X9LWDImOz1oNWvU3jcaFeODpoMZvFGW1HcSCrmANZxQyK9iHGHoqT1n5ZZu8CkxF06vrod3HU8emdg+ka6I6fe8MLoHc4ufvAbAA3f/COVDoauydaluyMJElUnVtWITbY9gtnAqpOlAAGRflwYMEElt2qvtl+bbbrE7lysNkGlwCxgJX7c3jku/28vf6k0qG0jF93cPIEQwWcSVE6GqtI7OwrEqUL1U6wCE0QrbsWoK7Tiw5Ao9Hw073DKa404GGLC3c2xVgtF8ZT2cwMB526ns8lK8mBn+6Ti5I+ckqVs68ifV0ZEuPDkM4+SofSMlothPSH1PXyOJYgdRcJFThf5yxMnMRZgviUt1NeLnq0Wjs6W/j4CnguFM4cVjoSwdpqP6QDeqoyUQKYNjCCr2Ylck3/MKVDaTmVj1symMx8sTWDR77dR41RFGCs17IkXDI7a5oQ7JbZLPefZ+2AwJ5KR2NxB7KKeXn1EVwcdSy9pYOfydUVo+zgx8HWdE0CYxV0VmcBRwethhd/O0xRhYGbB0fQJ8xb6ZCUYzZD7+vlz9vQAUpHowoiWbIz09/fSidXRx6e2J2wTnZQDK9WWDyk/ymf7QyYoXQ0Fueg0/DHkTO4OeowmSV09tTqZ2m1LRe1LRkqU1plwNXRwf7+xxGD5R+V0mg03DI4AgBvlw6+vI5WC+OeUDoKVRHJkh0pqqhh47GzACy8spfC0bRSbVNw1i5l47CSrgEeLLyyF/3CvbGzr1DLMhnlGVeg2unKz61M4ac92Tx6eSy3DBazjGzJ/00Qy84I1iGSJTvi4qjjszsHcyyvlE5udnbmVPvFmXcQasrB0U3ZeCxMp9Vw29AopcNQ3pkUecaVk6c8A0uFDueWUl5jwtfNDmdeVZdC1k7QOUFkotLRCNZyajv4RIObWIrJUkSyZEecHHQM7+rH8K52+AbwDAGPECjNhuw9EDVM6YgEa6gdrxTSX3WzHmt9M3sox/JKCfV2UTqU1tv9Oaz6N3SdoNpkyWgyc+R0KdF+brg6dsCvOLMZPpsqV+7+5yYItLNeCBvVAV9JgmLC4iElWx50qMJkqcpgYmtqAen55cxIjFI6HGX0vgF8u8hlA1RKp9XQI8hT6TDapraFN3O7vCSNCuvvTPnvnxzOLeWjOwYyunuA0uG0v/xjcqLk4KLa1l0liGTJTkiSxGdb0ukV6kXfMG/7G1wK0OUyQAO+XZWOxCpKqgzc9sE2NBp5kVUPe1hg1dKc3CF6pNJRCI0J6g06R6gsgIKT4NtZ6YgsrkeQB1mFlZwtq1E6FGXUlgwI6ae6Su1KEkfSTmQUVPDEjwdx1GnZvzAJnT1WxY6/Xf5RqQAPZxJjfAn0dKK82tQxkyWVW7LmKNVGMzckhBPtZ4fj7hyc5IQpa6c82UKFydKiq+N45QYH+6pDZ0m1dc5E6Q6LEsmSnag2mhnbQ25SdnKww0Spg/hy1hClQ1BO1i7Y/w3EjIZuSUpHYxVfbsvgdEk1o7v522eyBPLM1Kyd8pdqn+uVjsbiPDv6SUpty5JKZ6MqRSRLdqJboAcf3K6CujWSJDf/OziBlx1VPxaad+J32PImlOaoMlkymSXmXtaNPRlF9A7zUjqctgtLgG1vywmToC41FXD6oHxdVO62KPWOwhRs06pH4L8DYNs7SkdiVcWVBiRJUjqM9lX75avSM1qdVsNNgyJ44bo+9j3LqrZ7JmcfGNU5rufTLenc8PZmVu7PUTqU9pWzFyQTuAeKk1ELs+N3fMdhMkuYjSZ1dL/VTmNV6fpUJrPEpNc2cPR0GX89MtY+p5e3hSSJtajshU8MXPEaBPcDexz72AIn8srYllpAz2BPJvcOVjqc9uPbBa5aCsZKVc50VJJIluzAwewSbnxvG8O6+PHRHYOUDufS1H6RZu8Gs0l1H9Y6rQa9Tm6wPZxT0nGSpeJMKM8DrQME91E6GqvYllpAlJ8rAR7OSodyaTQaVU+0ALi6fyg9gz0ZFO2jdCjty90f+t+idBSqJJIlO3AguwSDSSVdOv7dwdEDakohLwWC4pSOyOKWTOuHn7uT/VVZvxS1M3ACe4FefQmi0WTmtg+2UWkwsfZfo+js7650SEIT+oV70y/cW+kwBBURyZIduGlgGKN7BFJlMCsdyqXT6iC0P6RukL9gVZgsdQ30UDqE9pep7unKZ8tqiPBxJbekimhfO50Fd6GqEjjwLRSlw2ULlI5GsITyfNj/NYQNkgsACxYlkiU7oNFoiFTDB3St0AQ5WcrcofrugA6j4KR8qdLxSkFezvz24EiqjSZ11O8xG+GXufL1YQ+ASydFw7GGM6XV7EwvwM/diYSoDtAdd2qLPIEmoCfM2ax0NKrT6mTpyJEjfPnll2zcuJG0tDQqKirw9/enf//+TJgwgWuvvRYnJztcYFJoP7WzpVQ8dfl/O06x+WQ+c0Z3pktAB2hpuvELedySk7qfqyomWQC4+kCnaChMld+HXS5TOiKL+2JrBq+uOcrV/UI6RrKk8tZdpbW4dMDu3bsZP348ffv2ZcOGDQwcOJC5c+fy9NNPc+uttyJJEo899hghISG88MILVFdXWzPuDiOtFBatOMzvh08rHYrlhA2CYXNhzGNKR2I1P+zJ4rtdWWxLLVQ6lPah0YB3OLh4Kx2J0FJ1Jy27lI3DSuIjO9EjyENdrfJNyRLFKK2pxS1LV199Nf/3f//H8uXL8fFpPEvfvHkzr776Ki+//DKPPvqoRYLsyA4Xafj1QAalVUbG9ghUOhzLcPeH8QuVjsKqpvYPIz7SRwwyVYGyaiOTXttAn1BvXpnWVz2tS6EJsP9/51skVGZ4Vz9Wze0g6xSazZC1W76u0q5wpbU4WTp27BiOjs3P7klMTCQxMZGaGnUWO2tvnT1hxpAIhnT2UzoUoRWuje9ABeFWPwH5xyHxHogarnQ0FrfvVBGnCioxm1XUDQcXtCztkOtkibo89uvsUXmGsd4V/HsoHY0qtThZakmiBFBRUYGrq2uLtxea1tVLYvLkHuj1KlvvqLoMTm2FqmKIm6p0NMKlOLYazhyG/tOVjsQq+kV488XMwZRUGpUOxbKCeoNWDxX58qy4TlFKR2Q1VQYTznoVJbp/V9sFF9IfdGLeljW0abmT0aNHk5mZedHtW7dupV+/fpcaU5OWLl1KdHQ0zs7OxMfHs3Hjxia3//bbb+nZsydOTk707NmT77//vt7fJUliwYIFhISE4OLiwujRozl48KA1n4IAcln+z6bCb+rtqjWYzBzIKianuFLpUKynqgTOHJGvq3SshKujA0M7+zExLkjpUCzLwUlOmABOH1I2Fiv540geQ55by+zP1DuZBDg/WUYM7raaNiVLnp6e9OnTh6+++goAs9nMggULGDlyJFdeeaVFA7zQ8uXLmTt3Lo899hi7d+9mxIgRTJo0iYyMjAa337x5M9OmTWP69Ons3buX6dOnc8MNN7B169a6bV588UVeeeUV3njjDbZv305QUBDjx4+ntLTUas+jpbKKKjlbhTrXGAvpBxqdvOhqSbbS0VjFvK/3MuW/f/L97iylQ7Ge7N2ABF4R4B6gdDRCa019B/6dBj0mKx2JVfi4OpJbUsWBrGJ1fo7WSnoGbl8JA2YoHYlqtSlZ+umnn3juuee46667uPnmmxk+fDjvvfceK1as4KWXXrJ0jHVeeeUV7rzzTu666y5iY2NZsmQJ4eHhLFu2rMHtlyxZwvjx45k/fz49evRg/vz5jBs3jiVLlgByErJkyRIee+wxpk6dSlxcHB9//DEVFRV88cUXVnseLfXhpnSe3u3Ai6uPKR2K5Tm6yfVAQLUDTHuHeuLp7ECNUQXFRBtTNwNHnWe0Z8uqeW/jSXamq3RWo19XVdZYqhUb7MnyWUNY/39j0Kh5TJajG0QNk/+fglW0uXNz9uzZpKen88ILL+Dg4MC6desYOnSoJWOrp6amhp07d/LII4/Uuz0pKYlNmzY1eJ/Nmzfz4IMP1rttwoQJdclSamoqubm5JCUl1f3dycmJUaNGsWnTJu6+++4G91tdXV2vNEJJSQkABoMBg8HQ6ufWmPIqAzqNRFd/F4vu11ZoQ/qjO70f06ltmLtOavX9a4+JrR6bmweGcdvgcLRajSIxtsfx0Z3agRYwBffHbKP/h4a09NhsPn6GZ1akEBvkwU/3JLZHaDbB1t9bLaUBBoR7ApJFn4tajo+1WPr4HM4tpbO/W926m5bU0hjblCwVFhZy1113sXbtWt5++23Wr19PUlISL774InPmzGnLLpt19uxZTCYTgYH1p88HBgaSm5vb4H1yc3Ob3L72sqFt0tPTG41l8eLFLFx48dT31atX4+rq2vyTaaERzpA4CKTs/azM3W+x/dqKiHw9/YGC/WvYVDWwzftJTk62XFAqZLXjI0lMOLkJZ2BTejUF+Sut8zhW1NyxOVasoXcnDYG6YlautL/n1xJdc38ioPQA+0NvocQ1st7fxHurabZwfEILt9Cp/Dg53gnku9vWTDhLHJ8yAzy2wwFHrcSzCSYcLTxOv6KiokXbtSlZiouLIzo6mt27dxMdHc3MmTNZvnw5c+bMYcWKFaxYsaItu22RvzelSpLUZPNqS7Zv7T7nz5/PvHnz6n4vKSkhPDycpKQkPD09m30OLWUwGEhOTmb8+PHqmw0HkBcN776PX00GkydOkNeNawXVH59LZPXjU1OG7mx3pLyDDLnmbnnasp1ozbF5oJ1iUoruy4/Q5hxmZJQj5gR57JKa3ltny6r5fk82xRVGHkqyTDeVLR0f3Xffok1bTVSvQZiH2cbYM0sen9IqI9rw02QWVnL1uC4WivC82p6h5rQpWZo9ezaPPfYYWu35JrFp06YxbNgw7rjjjrbssll+fn7odLqLWpHy8vIuahmqFRQU1OT2QUHy7Jbc3FyCg4NbtE+Qu+oaWtJFr9db5Y1jrf0qLrgXOLqjqSlDX3RCXrG+DWz5+Kzcn8OHf6WS2NmPeeO7KRKD1Y6PvhPctRrMJvStTHRthS2/dtpN+EA4+Tu6nN3o/nYs1HB8asw1vPjbMRx1WuZN6G7RWlk2cXyy5WKUuohBF/3/lGaJ4+Oj13PT4CjLBNSAlsbXpg7AJ554ol6iVCssLMxqzZKOjo7Ex8dftP/k5ORGx0olJiZetP3q1avrto+OjiYoKKjeNjU1Naxfv96q46+Ec7Q6uPY9mP0n+HVXOhqrKKsysj2tkK0n85UOxXrsNFFqTmWNiSqDSekwrC9U3Ws1Rvi4MrV/KPOSumE0qWxGXFkeFGcAGrnGkmA1LW5ZysjIICIiosU7zsrKIjQ0tE1BNWbevHlMnz6dhIQEEhMTeeedd8jIyGD27NkAzJgxg9DQUBYvXgzAAw88wMiRI3nhhRe46qqr+PHHH1mzZg1//vknIHe/zZ07l+eee46uXbvStWtXnnvuOVxdXbn55pstGrvQiO6tH9htT0Z08+Ol6/syIMJb6VAsz1AFemelo7CaH/dk8fgPB7hhYDjPXdNb6XCsp7Y2T/4xqCxU3ew4jUbDK9P6KR2GddQmuP7dwdlyQ0BshdkskZxymr5h3gR5KftZ0+KWpYEDBzJz5ky2bdvW6DbFxcW8++67xMXF8d1331kkwAtNmzaNJUuWsGjRIvr168eGDRtYuXIlkZHyoMSMjAxycnLqth86dChfffUVH374IX369OGjjz5i+fLlDB48uG6bhx9+mLlz5zJnzhwSEhLIyspi9erVeHioe/V0oX0Ee7lwXXwYMf7uSodiWSYj/KcLvDlEPrtVoSOnSzGaJbxcbKtrw+LcfM9X71bporqqVVt2RaXrwaXml3P3pzsZ9Z8/MJiULcHS4pallJQUnnvuOSZOnIherychIYGQkBCcnZ0pLCzk0KFDHDx4kISEBP7zn/8waZJ1WgzmzJnT6Iy7devWXXTbddddx3XXXdfo/jQaDQsWLGDBggUWilBoFbMZdn4AmTth8n/ASWVJhVqdSZHXoirJAld1rlv45JSe3Dk8GocGhhyoTmgCFKbJyVKXcUpHYxWVNSaOnC5V1+LWKq9zVlxpIDbYEw9nB6uUDWiNFidLmZmZvPDCCzzzzDP8+uuvbNiwgbS0NCorK/Hz8+OWW25hwoQJxMXFWTNeQW20WtjwMpRmQ/9bVLkQa0F5DZtOnAVgSp8QhaOxkMwL1qJSaTKh0WgI62Q/M/wuSVgCnPgdJHWO0SqtMtB/UTJGs8SeJ8fj7aqStUtLT8uXKm1ZGhDRiV8fGIHJrPxYsxYnS/379yc3Nxd/f3/+9a9/sX37dnx9fa0Zm9BRhMVDSrbc/67CZGlHWgH3frGb7oEe6kmW6s5o1fkh3eEMvAsGzwaVVrn2cNYT1smFSoOJzMJK9SRL92yRl4tyU/dSQzqt8q/LFidL3t7enDx5En9/f9LS0jCbVbyEg9C+QhMg5WfVLnvSL8KbuFBP4iM6NVvDy25k1i7cqc5k6YfdWexML+TyPsEMiekAJ4U6lY/LAn66bziezip8np4qOQH7G1v7rGxxsnTttdcyatQogoOD0Wg0JCQkoNM1PGX45MmTFgtQ6ADC1D11OcDDmV/uG6F0GJZTVQJnDsvXVbrK+aoDuaw6mEtoJ5eOkSxdyKTOJTxUmSip2K6MIu79YhejuweweKrys1FbnCy98847TJ06lePHj3P//fczc+ZMMWNMsIzgfqDRyoOFS3LAM7jZuwgKyt4NSOAVDh6NF2+1ZzcMDCO0kwvDu6hz8HqDdn0C656HnlfBuEVKRyM057Nr5c/N8U9DgG0tc2IJe04VkVNcxZnSKqVDAVpZwXvixIkA7Ny5kwceeEAkS4JlOLmDfyzkHZRblzynKB2RVUiSRHGlwf7HSzh7Qd+bwFW9LS5jewQytoc6E8FG6ZzkExaVdocDLPjpINvTClh2SzwRvnY8eN9QCSfXgdkIl7+sdDRWcdOgcOJCPHFQeBZcrTYtd/Lhhx9aOg6howuLh7xDUKDOLtzjeaXc9O5WNMDWR8fZVF98q4X0g2veUjoKwdJqu8Nz9oKpRtlYrGT3qSIOZpew+1ShfSdLOfvkRMktQG7hVSFXRwcG21AXeJuSJUGwuDGPQdIzcquFCoV6u1JQLn8BnS2rwd/j4rUFBdtwIKsYF0cd0b5uaG1gFk678YkBZ2+oKkJz+qDS0VjFnNGdMZklBkf7KB3KpblwNqo9n3jZEZEsCbbBI0jpCKzKxVHHj/cMo7O/Oy6OdryWWlUxFGeCfw/Vrgn39C+H2JpawH+u68P1Ceo8a2+QRiMP2D+xFk32LkB9Ywcn9FLJ50zmdvlSpRMs9mcWs+nEWRI7+9InzFvpcIA2LqQrCELrxYV62XeiBHDiD1g2FD5U75p+jg5anBy09FVTpeeWOtcVJydLgs2qLd2h0jpnyYdyWfzrYT7elK50KHVEy5JgO3Z8CPuWQ/zt0PdGpaMRGlLb/B/YS9k4rOjTOwdjMJnRdcTujdDaZGknRFyucDDWcfJMGTvTCxkc7Wuf45ZKT0NxBqCBkAFKR2MVPYI9mdgriBFdbWc2qkiWBNtRlA4Zm8G3iyqTpSqDiQ/+SuVgVgmv3djPZmZ5tIrKi1HWUnodKsWExkPIAMwh8WBUZ+HhRb8cYt2RMyy4oie3D4tWOpzWqyyEyGFgrAZnT6WjsYrJvYOZ3Nu2uoFFsiTYjlB1F6d01GlZ9scJSquNzBnTmV4hdjaY3WSEnD3ydZU2/3d4br4w6w/MBgOsXKl0NFYxJMaXimoTndzstIRHQA+4YyVIyq+X1pGIZEmwHbVfwHkpUF0KTuqq46XVapg5MgYXvQ4/dzucDZd3CAwV4OQJvl2VjsYqbnh7M856HQuu6EmMv7vS4QhWMHtUZ2aP6qx0GJdOpd3E+WXVuDo62Nz4TpEsCbbDI0iuGVJ8Sq4SHT1S6Ygs7v5xdpxk1Lb4hfQHrfq6qUqrDGxPK0CS5IVXOzRDBR6VWUpHIfyd2QzVJeDirXQkVrNkzTG+2JbBwxO6c7cNJbXq+8QT7FvtVNjaqbGC7biwtosKuTo68NM9w3nxuj4duw5W7gEc/hPNsOPPqbqrx2yWqDKYlA6jdc4chhci4Z3Rqv3fnCqswGSWCO3konQo9YiWJcG2hA2EQz+cH0isQmXVRvZnFtM33AtXRzt6C/afLrf8dR6ndCRWodNq6B3mRe8wOxtLZmm+XUCjxclYiqEoDQK6KR2Rxb21/gRv/nGcO4ZGMS+pu9LhtFztCYveTbXdcB/dMYic4kqba90VLUuCbQlLADd/cO2kdCRWM/m1jdz07hb2ZBQpHUrrRAyB0Y9A+EClIxGsSe+MFCSv8q5R6WQLN0cdpVVGDmSXKB1K69Su2xemzmKUtYK9XHB3sq0TSduKRhDCB8NDx1R71gTQO8wLk1mipMqodCjCOZIksWz9CWKDPRnW2Q9Hh459HimFJkD2LjlZ6n+T0uFY3MS4YPpHdKJHkJ1NIqlNXsPECUt7E8mSYFtUnCTVeuWGvjg52NZMj2adXC/PUIxIlKeXq0xWUSUvrjqCg1bDgYUTlA5HcVJoPGw/V5xShfw9nOxvXFp1mTwjFVRb5+z5Xw9TUmVgRmIkPYJsq4ZUxz59EmyXJMkfDipkd4kSwOY3YfktsP9/SkdiFSazxNT+oST1CsRZb4f/HwuTait55+4HQ5XC0QiAPENYMoNnKHjaVsFGS/lxTxZfbM2gqMKgdCgXES1Lgu1J3QDfzoROUXDnb0pHI0jS+dmJKm3+j/R145Vp/ZQOw3Z4RVDl4ImzsQRy90H4IKUjsrijp0v5eW82vm6O9lHJW+WzUSVJ4skpPdl9qojeobY3yUIkS4Lt8QiGslyoKgKTAXS2NSvCEpatO8GPe7K4d2wXpvQJUTqcphWchMoC0DnBuYG/gsppNBwPmExsr97ovMKUjsYqjp0u47+/H6dXiKd9JEtBfaDfrRA1XOlIrEKj0TCpdzCTbGyZk1oiWRJsj09ncPaWk6XTB+QiiCpzuqSKw7ml7EwvtP1kqXZQaXBfcLDTJSKaYDSZKa0y2u/yF1ZyInAy3QdNRqdX38kKQHxkJ64dEEZClJ3MvO0yTv4RFCGSJcH2aLVyccoTa+WpsipMlq6LDyOxsy8DIuzgg1rlXXApOaVc8caf9A335sd7hikdjtBOgrycefmGvkqHIZzz++HTRPi4EuPnjlZrexN9xABvwTaFqXtR3bhQLyb0CrKPGTl1yZI6a7scPV0KgJeLOltQLkn+cdjzBZTnKx1Jx1aQCjl75cWsVchgMjPn811c9soGTp61zYk9omVJsE21U2PFsifKMlRC7n75ukpblq6ND2NsjwCKK21vBo7SHL77hzxdfdrnEDtF6XCsoqC8hqzCStuu3L7jfdj0Xxh4F1z+stLRWFxhRQ19wrxJzy8nxs82F7AWyZJgm2pblvKPQ0UBuPooG48VnCqoYNOJs4R4uzCiq7/S4TTMwRnu2SZPW/YKVzoaq+nk5ijGLDVAColHk3dIPmlRYbK0M72Aa5dtJtjLmc3zbXg8UG3l7pABysZhJQEeznx9dyJms2STXXAguuEEW+XqAz2vhiFz5BlxKvTT3mz+/e1+vtp+SulQGqfRgG9n6H1dhygYKtRnrmvh3aFsIFbSI8gTB60GV0cdZdU22sVlrJFPVkCVJRwuZKuJEoiWJcGW3fCx0hFY1aBoH4bE+NDXlpv/VW7LyQK+25PD2B4BXNHXxmclKqC2OCXZu+TxMjp1fWW4OTmw56kkm1uHrJ7T+8FYJc8Q9u2idDRWIUkSGhs/GRMtS4KgkIFRPnw1K5FZIzsrHUrjfrpfHitRZWcLjrbQXyfy+X53FhuPnVE6FNvk1xWcPMFQcX6pDZWx6UQJ4NQFs1FtPKFoi6KKGvouXM3097dSYzQrHU6j7CZZKiwsZPr06Xh5eeHl5cX06dMpKipqdPuCggLuu+8+unfvjqurKxEREdx///0UFxfX206j0Vz089Zbb1n52QgtVl0GqRvBbFI6ko6nOAt2fQzJT4JWnUuAXBYbwAPjujLZRgvhKU5zrowHiMkWSqk97irtgttzqoiSKiOZhZU2vYC1jafU5918881kZmayatUqAGbNmsX06dP5+eefG9w+Ozub7OxsXnrpJXr27El6ejqzZ88mOzubb775pt62H374IRMnTqz73ctLdIvYBLMZXukJ1cXwz80Q2FPpiKzCZJYorKjBz93GygjULq8Q0Asc3ZSNxUr6hnmREO2ndBi2LWwgnPxDHrc08E6lo7G4smojj32/n4PZJay8f4TtfWFnbpMvVTobdXgXP1bcP9wm14O7kF0kSykpKaxatYotW7YwePBgAN59910SExM5cuQI3bt3v+g+cXFxfPvtt3W/d+7cmWeffZZbb70Vo9GIg8P5p+7t7U1QUJD1n4jQOlotBPeBtI3yB4YKk6U/juRx7+e76BXqxdd3JyodTn2Z6l6LSmih3tdD6ADVrnTv5qhj/dEzFFUYOJRTQr9wb6VDOk+S4Mr/yl1xoeqsc+ag09IrxPYbKOwiWdq8eTNeXl51iRLAkCFD8PLyYtOmTQ0mSw0pLi7G09OzXqIEcO+993LXXXcRHR3NnXfeyaxZs9BqGz+7qK6uprq6uu73khJ5PIfBYMBgsFx2XLsvS+7T3mhDEtClbcScvhVTn1vq/U0NxyfYw5HyGhMn8sqorq6x6GyQSz0+ulPb0ALG4AFIdnyMG2IwGMiugN3p+cSFdUKvs7HWBIXVe+14R8s/8g0KRmU9T0zugY+bI9E+Ti16v7TrZ0/4MPlHfkDrP54F2NNnc0tjtItkKTc3l4CAgItuDwgIIDc3t0X7yM/P5+mnn+buu++ud/vTTz/NuHHjcHFxYe3atfzrX//i7NmzPP74443ua/HixSxcuPCi21evXo2rq2uL4mmN5ORki+/TXgQWwxCg/Mgf/K5b2eA29nx8zBI83AeCXY2sWvWrVR6jLcdHIxmZnLkLLbD+RDllWQ0fe3v2e7aWF97byYQwM5PDbXdgqZLs+b3VGg5ACbDuaOvu11GOT1s1d3zyq2BLnpbOnhI9vKV2iqq+ioqKFm2naLK0YMGCBpOOC23fLg9ua2haYUunG5aUlHD55ZfTs2dPnnrqqXp/uzAp6tevHwCLFi1qMlmaP38+8+bNq7f/8PBwkpKS8PT0bDaeljIYDCQnJzN+/Hj0Kl3Mslnlg2DJq3hU5zB5zFBw8a77kzg+Tbuk45OzF4c9NUjOXoy85h/yQF8VMRgMLD+5Fk9nB64f05tR3Wy0KKhCLnrt5O5He+QXJN8uSHHXKx2e4trrs0e7/T0k9wCkmDHg5GG1x7G0lh6fb3ZlsXr3QRIcvJl3szID2Gt7hpqjaLJ07733cuONNza5TVRUFPv27eP06dMX/e3MmTMEBgY2ef/S0lImTpyIu7s733//fbMv7CFDhlBSUsLp06cb3beTkxNOThcPxtXr9VZ541hrv3bBOxh8YqDgJPrTe6HrZRdt0qGPTwu06fiUngKtHk1oPHpHGxt4biHTYsx8NGkMOgc9OhsuhqekutdOzk7482XoPA7636x0WFaxL7OIHWmFXNkvpMWTLaz62WMywO8LwVgJ92wHd/tbxaC54xPj78F18WHEBnsq9hne0sdVNFny8/PDz6/5mSiJiYkUFxezbds2Bg2Ss8+tW7dSXFzM0KFDG71fSUkJEyZMwMnJiZ9++glnZ+dmH2v37t04Ozvj7e3d4uchWFnYICg4KQ/ybiBZsndny6r54M9UcoqreHVaP6XDkfW6BrpNggp1L6Cq0WhEotQStTOxMnfIs1SbGNNpr/797X5SckoI9nJmki2UksjdLydKzl6qLUY5OMaXwTG+SofRInYxZik2NpaJEycyc+ZM3n77bUAuHTBlypS6wd1ZWVmMGzeOTz75hEGDBlFaWkpSUhIVFRV89tlnlJSU1DW3+fv7o9Pp+Pnnn8nNzSUxMREXFxf++OMPHnvsMWbNmtVgy5GgkL43yrNxYsYoHYlVOGg1LF13AoAnp/S0nTXK9M7gFap0FFYhScqMj7BbgXGgd5XLeJw9AgGxSkdkcaO7+xPs5Yyni420UtfWVwobqMrk1N7YRbIE8Pnnn3P//feTlJQEwJVXXskbb7xR93eDwcCRI0fqBmvt3LmTrVu3AtClS/2sPDU1laioKPR6PUuXLmXevHmYzWZiYmJYtGgR99xzTzs9K6FFOo+Rf1TK29WR2aM6E+XrioNOtHK0h7s/301qlo7guCIGdRbjlZqlc5CnrqdthFNbVZks/XtiD6VDqK8uWVJnMcqzZdVoAF9bqy/XCLtJlnx8fPjss88a/XtUVFS9s8XRo0c3e/Y4ceLEesUoBUEpj0yyoQ/qwyth3WKIuxaGz1U6GoszmSW2pRVSXq3BWa/OyuRWET74XLK0HeJvVzoa9Tt1rhhluDqLUX6yKY3Xfz/OXcOjeXyK7dfQE217gn0oSIVdn0D6JqUjUb+MTZC7DwrTlI7EKrQa+OGfQ7i1i4muAeqsTG4V4efq3J3aqmwcVlZtNFFebVQ2iLI8KEoHNKotRnm6RK5VGOVnH+9BkSwJ9mHXJ/DTfbDnc6UjsZozpdX8djAXg0nhmj91Z7SDm97OTmk0GqJ83RjoL+EgilG2XG0l9/I8ec1GFXpuZQq9n1rN8u2nlA0ka5d86d9DHuCtQi9c14e9TyVxVb8QpUNpEbvphhM6uNrZOLUrcKuMJElc9sp6iisN/HTvMPqEeSsTiLEasnfL11W6cKfQRq4+cO9OuZSHSgcce7noqTGZOZzbsto7VtNtAjywF8rVPRvVy1YG07eASJYE+1CbLJ09ApWF4NJJ2XgsTKPRkBDZiczCSkqrFOwCyNkLphpw9ZO/FFXov2uPEdHJmWqT0pHYIT91TmGvdX1CGFP6BBPhY/mVGFpFo4FOUfKPYBNEsiTYB3d/6BQNhamQtRO6qK/e0jszEpSv+VM7HiV8sPyBrTKF5TW8nCyvafGsOteFFS5BgEfztfiES7f41xRyiqq4fVgUAyLs48RXnW2pgjrVdcVtUzYOK1E8UYILkiV1dsHVmMzclhhJUs8A3O2nB8B2VJXAtzPhzcFyhWnB8k4fhK9ugW3vKh2J1fx2IJef9mZTXGk/ryGRLAn2o/YLXOWzcUDBoomeoeAVodrB3YGeziy8Ko43b+qndCj2ydEdjq2GM4flCtMqtPdUEQt+OshHf6UqE0Dan3D4Fzi6SpnHbwfPXtObeeO7MSDcPlqVQCRLgj2JGCJfZu4EszoHnLy8+gjDnv+dX/blKBPApBfgwf3nj7UgXEirveCkRZ0tvCfOlPHRpjR+2putTAAZW+TLcPW+B4d18eP+cV3xcrWf5l2RLAn2I6An3LQc5u4DrTqLCZZWGckqqmRneqGygahwvJLBZCYjv0IsdXKpVN7COyjah9sSI5k1UoEJDpJ0PlkSJyw2RQzwFuyHVgfd1V1x/aZBEST1DKRvuHf7P3j5WXDxUe208P1ZxUxduokeQR78fE+i0uHYr7rilOpsWQrr5MrCq+KUefDiU1CaDVoH1Raj/HlvNmGdXIgL9UJvR3XO7CdSQegAugd5MLSLH25OCpzHfHo1vBgFaX+1/2O3g7Sz5eh1GsI6KTwt3N6FDACNDkoyoThT6WjUJeNca11wX3BU3+vUYDLz0P/2cs3STaTnVygdTquIliXBvlQUwOY3If8YXP2e0tGoR3WZPAtHMqu2tsvUAWFM7h1MUYX9zMCxSU7uEBQn1+Q6tQ28wpSOyOIkSeJUQSWnCisY1sWv/R44Y7N8qdLxSkUVBoZ38SP1bDkxdrLMSS2RLAn2xcEJ/nwVJBOMXaB0NFZx4kwZfxzOI6yTCxPjgtvnQbN2yomSVzh4hbbPYyrAWa8jyEuHwSASpksSMRTMZlWObQM4kFXCFW/8ibernl2Pj0fbXmU9jNWgc4QIdc5G9fdw4v3b7XNhYJEsCfbF0U1uos7ehebUVkB9TdXrj5zhmRUpjOnu337JUt16cOqsryRY2MTFqk2UAHoEe+Dp7EC0nxtFlQZ83Bzb54GvfhMuf1nVx9ZeiWRJsD8RQ+RkKXMbMFrpaCxuSIwvl8UGMrJbOzb/X1i5W4VW7s/h252ZXNkvhKv6qbflrN2o/Mtcr9Oy84nxygxA1quzirgkSVQbzTjr7XMmsxjgLdifc1/oWpVOXe4Z4sl7tyUwIzGqfR7QbIZMdbcsbTx2hrWH8ziUrfACqWpjqJKreqtQuydKKq0dV+tUQSW9F/zGDW9vxmy2v/IdIlkS7E9t/ZG8QziY7GtGhU06kwJVxaB3g0CFpkxb2YzEKB6/PJZJvdupW7Mj+P1ZeD4ctr2jdCRW1W51uT6bCsuGn6+zpDK7TxViMEkYTeb2GwNmQaIbTrA/HkHQKQpNYRqdyo8rHY3VVBlM5BRXEW3tWSNOnjDiITBWgc5+Kuq2RmywJ7HBnkqHoS6uvmCqOT+DS2XMZonZn+1ke1oBv80dSYCnFbvHTAa5bICxEpy9rfc4CrqybwgDIjrZ1XpwFxLJkmCfwocgVRbhZCxVOhKr2JZawM3vbiHcx5U/Hhpt3QfzDodxT1j3MQT1iTxX2PPUNrkLSWVV9bVaDZmFlRRWGNieVsjlfazYKpm773yi5NfNeo+jII1GQ7iPK+FKB9JGIlkS7NPk/2Cc8jqZv66ij9KxWEH3QA9MkkRFjZGyaiPuShSpVIlNx88iAf3CvZUp9qlWgXFyq2R1CZw+IM9SVZnHL4/F1cmBXiFWbpW8cIkTlVbQt3fivyLYJ2dP0Kj35evlquevf49ly/xx1k2Uys7AkV+hUuG16Kxoydpj3PLeVn5WamFUtdLqzk8ISN+kbCxWMrSLH/3Cva0/2Ltu8Vx1zkbddOIsT/14gA1HzygdSpup99tG6DhUOoskxNsFjbWnaB9fA1/eCJ/fYN3HUVC0rxuh3i4kRHVSOhT1iTjXFafSZKldSNL50h0qXTx3bUoeH29O57eDuUqH0maiTVqwW9otbzL+wOto/R6EYfcpHY59yjj3JafSD2mAF66TO2rbbVZTRxI5VL7M2Cx/6auw/tLO9EL+PHaWcbEBxIV6Wf4BClOh7LRcuTtkgOX3bwPG9gjAZJYY0yNA6VDaTCRLgv0yGXA15GNW6ernRpOZZ1aksC21gC9nDcHLxQoz1dLPzWSq/dJTMau30nVEIQMg9gq5+8hkAId2qnTdjj7ZnMaPe7KRkKyTLJnN0GeaPLNQpQUph3Xxa9819qxAJEuC3ZLOjZfQnNqiyrNaB52W9UfPkHq2nF3phZY/Kys7Iy9IDKodK1FZY8LFUV2ztGyK3hmmfaZ0FFY1tkcAkgRxIVZIlAD8usBUddeqUgORLAl2Swruj1mjQ1ueJzdl+8QoHZLFzb2sKzqthv4R3pbfeW19nICe4Opj+f0rzGSWSHx+LUGeznxw+0BCvF2UDkmwQ1f1CxVL5FyCg9nFuDk6EOnratetu2KAt2C/9C4Uup5LkNL+UjYWK7mqXyhT+oTg7WqF7o3aZKl2kK7KnDxTRlGFgVMFFQR4OCkdjnpJEhSmw8EflI7E/lSVQO5+uStOpZ5bmcLol9bx9Y5TSodySUSyJNi1fPce8pV0dSZLVlU7g0ml45W6Bnqw4/HL+PCOQTgosSBqR1FdCq/3g//dBiU5SkdjNcXnEm+LOvkHvDUcPpxk2f3aCEmS0Go06HUa+oXb92xU8Qki2LWztcmSSluWAE4VVPC/Hac4dtrC1cqvfR+u/C9Ej7Lsfm2In7sTg6LV18VoU5w9z68pmKHOEgJfbsug39OreXZFimV3nPanfBnU27L7tREajYZP7xzM/gUT6BrgrnQ4l0QkS4JdK3DrihTUB3pMBmON0uFYxYu/HeH/vtnHyv0WrlHi1wUGzAB3f8vuV+h4alsn09W5Tly3QA8kCc6WVVt2x7UneVHDLLtfG+Os19nl4rkXEgO8Bbtm0jljvPN39Hp1LgALMKyzL1mFFQR5iXE3LXXyTBlv/HGckV39ubq/GJxrdRGJsPUt1S6q2zfMi22PjSPAw4JT+ysKIO+gfD1yuOX2a0PUVNvMblqWCgsLmT59Ol5eXnh5eTF9+nSKioqavM/o0aPRaDT1fm688cZL3q8gtKcbB0Xw3ZxhTBsYYbmd/rEYtr4N5Wctt08b8teJfL7blWX3g0rtRm3L0umDUFmkaCjW4KDTWjZRgvNjBv26q7J1V5Jgwmt/8Y+PtpNXUqV0OJfMbpKlm2++mT179rBq1SpWrVrFnj17mD59erP3mzlzJjk5OXU/b7/9tkX2K9gYQxVkbFU6CvtgrIY/X4VfH1btmnADIry5Z0xnpg4IUzqUjsE9AHw6Axcs3SE0LV3dXXB5VZCaX8Ffx8/i5Wr/Lf920Q2XkpLCqlWr2LJlC4MHy8Xz3n33XRITEzly5Ajdu3dv9L6urq4EBQVZfL+CDTFUwqvdwVABDx4CL3V2u5jNEqVVxkv/4MneDaZqcPMH3y6WCc7G9Arxope1iggKDYtMhIITchLQbYLS0VhcYXkNz61M4cjpUv43c9Cl7zBto3wZqc5kyd8Zvp89hFPF1Tg52H9hWLtIljZv3oyXl1ddQgMwZMgQvLy82LRpU5NJzeeff85nn31GYGAgkyZN4qmnnsLDw+OS9ltdXU119fmBfiUlJQAYDAYMBsMlPdcL1e7LkvtUk7rjgwM6v25oc/ZgPLkeKe56hSOzvNWHTvPoDweJj+jE27f2b9F9Gnv9aFP/RAeYwwZjMhotHapdEO+tprXl+Gj6zoCYcUjhQ0CFx9VRK/HLvmwqDWYO5xQBl/b60YxdiCb9T8xh6jteBoMBrQa6B7gQF+pp0++zlsZmF8lSbm4uAQEXL/UQEBBAbm7jM4RuueUWoqOjCQoK4sCBA8yfP5+9e/eSnJx8SftdvHgxCxcuvOj21atX4+rq2pKn1Cq18QoNS05OppcpmC7sIfPP5ezNcFM6JItLL4XiSgd2peaxYsXKVq3s8vfXT+Lx7wkADpZ5cXLlSssGagNyKsAkQYgrNDcBR7y3mtb646ODk9utEostuDJcg7cjHN21GUedJV4//WDDTkuEZpPs4f1VUdGy2lmKJksLFixoMOm40Pbt8huvoTLpkiQ1WT595syZddfj4uLo2rUrCQkJ7Nq1iwEDBrR5v/Pnz2fevHl1v5eUlBAeHk5SUhKenp5NPp/WMBgMJCcnM378eFXP9mqrC4+PY5oOvv6VSOkUoZMnKx2axRlMZoYNKyU22AN9CwssNvj6MdXgcGA2AD0m302PgJ7WClkx//fNfn7Yl8O9o2N4YFzD3YzivdU0cXwaVvvJIo5P047lFrPkh83celk8iV1se/B6bc9QcxRNlu69996LZqf9XVRUFPv27eP06dMX/e3MmTMEBga2+PEGDBiAXq/n2LFjDBgwgKCgoDbt18nJCSeni6dx6/V6q7xxrLVftdDr9ThEDwc0aApOoK/KB4+Gx6nZK70e4qPbVjqg3usne4c8tsvVD31wb9DazRyPFnPU63B3cmBQjF+z7xvx3mpaq4/PmaNw8HvwCIT4260Wl61o8+tn85vyWpbRo8DR8r0RStuUWsTqLC0Vf2UwMjZE6XCa1NL/n6LJkp+fH35+fs1ul5iYSHFxMdu2bWPQIHlg3datWykuLmbo0JYv1XDw4EEMBgPBwcEW3a9gA1y85Sq4ufvkqri9r1M6ItuUfwy0DhA9QpWJEsCL1/XluWvUWRHZ5mXvhnXPQUh/1SZLR0+XsvFoHpq2zoavKobVj4NkhnkpqkyWuga4E+9nJqnnxcNc7JVdfFrGxsYyceJEZs6cyZYtW9iyZQszZ85kypQpdYOws7Ky6NGjB9u2bQPgxIkTLFq0iB07dpCWlsbKlSu5/vrr6d+/P8OGDWvxfgU7EnWusJtK14mrqDHy+tpj3PHhNkzmNhZ7GzAD/p0OSc9aNjgb46DTivXglBA9Qr7M2avKeksAC38+yNMrDnOoqI0VqTO2yolSp2jwtO1Wl7Ya2tmXGV3N3DQwXOlQLMZuPk0+//xzevfuTVJSEklJSfTp04dPP/207u8Gg4EjR47UDdZydHRk7dq1TJgwge7du3P//feTlJTEmjVr0Ol0Ld6vYEd6TZWTgIEzm9/WDjnqtLy78SR/HDnDgazitu/IyV3V5RUEBXmGyOUoJPP5oosqM6Z7AMO7+OLZ1t7b9HPrwam0vpJa2cVsOAAfHx8+++yzRv8eFRVVr7R6eHg469evv+T9CnYkfKD8o1IOOi33jumCu7MDYZ1cWr8DSaJV0+js0OTXN+Llouf5a/sQ7ae+WZF2IXok5B+H1A3ymo0qc9eIGG4bEs7Kts4krV08V6VLnKSdLUcFNSgvYjfJkiAIcPeozm2/8x/PwfFkGHofxF1ruaBsRHZRJYdzS9FpNfi6OyodTscVPRJ2fCAnS0J91aWQvUe+rtKWpad/OcTaw3nc1FmDmlJlkSwJ6lKWB8fXyIOY+9ygdDS25eQ6eQCuoVLpSKwi2MuZPx4aTUpOCZ7OKjy1tRdR58Yt5R2U1x50a34Sjz0ymuFsWTXBnVrxWju1FSQTeEWAtwXXerQhRZVykcdQV3V1idvNmCVBaJGMLfDDP2HDf5SOxGqKKw0kHzrNyTNlLb9TdSlknSt+Fz3SOoEpTKPREO3nxuTewUqH0rG5+UFAL3DyhLNHlY7GKn7Yk80j23U8veJw6+6YWfseHGH5oGzEt/8cyuZ/jyJUZb3gomVJUJfoEaDRyh/SxVmqHMj8+A8H+HlvNg+M68qD47u17E7pm+Uz2k5Rqj2jFWzILV+DexDo1PkVE9bJBYNZw4kz5a2746iHVdkF/nd+7k7NVs+3N6JlSVAXl05yjReQu51UaGhnX2L83PB0aUXzf+q5yQ5R6jyjPZ5XxoKfDrLh6BmlQxEAvMJUmygB9An1Yn5fIz/fk9i6O2o04NdF/hHsinpfzULHFTNG7nI6+Qf0v0XpaCzuxoHh3DSola1DtSucR4+yfEA2YN2RPD7alEZafjkju9n28godjgpnYTo6aAlybXi5rI7KYDIz+bWN9A7z4vFJLWzxtiOiZUlQn85j5MuT68BsVjQUa2j1B3RlIeTsk6+rdKxE33Bvbh4cwZQ+6izyZ5c2vgKv9YV9XysdiW347TFYPh1ObVM6EqvYl1nMsbwy/jich5uj+tph1PeMBCFsIOhdofyMPCMnSL1LX5RVG3F3auZtXFMGcVOh9LTq1syrNTDKh4FRPkqHIVyoqggK0+QSAn2nKR2NxdWY4IXfjrIjvYivZg3BWa9rfGNJgoM/QEkmxN/WbjG2p57Bnnzyj0Hkl1ejVduAJUTLkqBGDk4Qea6GSc5eZWOxkt0ZhYx88Q9ufGdz8xt7hcN1H8AdK6wfmCDUijo36zJNnfWW9Fr4aW8Oe04VsTO9sOmN80/IiZLOESLUue6oi6OOkd38uaZ/mNKhWIVoWRLUaeJicF4G7uocvxLi7UJGQQWOOm3LWpdU7EBWMZ3cHAn1bkNVc8F6IobI9c6KMuQWpk5RSkdkURoN3D+2Mx4ujsSFeDW98ck/5MvwwapcOLcjEC1Lgjr5dVVtogQQ6OnMF3cNZteT45tMlByM5ZCXIncDqNSTPx5g2PO/89PebKVDES7k5A6hCfL1k80vPWWPpiWEcVW/ULyaW9/jxLlkqXY8pcrszijkvY0nW1f7zc6IZEkQ7NTQLn7NtigFF+9C/+4I+EKd1cxNZgkHrRadVkN8ZCelwxH+rjY5OL5G2TiUZDKen40ao85k6YfdWTyzIoX3/0xVOhSrEcmSoF5HVsFHU+CPxUpHopiAkv3ylaA+ygZiJTqthq9nJ7LnyfGiG84WdblMvjy5DkwGRUOxljOl1Xy/O7PxcUvZu6C6BJy9Ibhvu8bWXvqGezOymz/jYgOUDsVqOu5AB0H9qorkM7rqUhgzX+lorOKrbRmsScnjqSt6Eu7zt7EQZhMBpeeSpdovLZXyEGvB2aaQ/hAaDyEDoKYcXLyVjsji3tt4krc3nOS6+LCGWzeN1fIMXe8I0DYxY86OTR0QxtQB6hzYXUskS4J61RZgzNkLFQXgqr6p5d/uymR7WiGjuvszfUhkvb9pcnajN5UjOXmiCRuoUITWZTZLqpymrBpaHcz8XekorGpkN3/+OnGWrgHuDW8QPQLuWqPKmm8dieiGE9TLMxj8YwHp/HIfKnPL4EgeSurGsM6+F/1Nc26ciBQ9WpVLT5wuqWLAM8nc8/kuzGb1DmAXbNuwLn78ct8I7h7VuekNter8uj2YXUyVwaR0GFanvk9QQbhQ5zFwJkWejdLrGqWjsbir+ze+ULDmpHxGb+48TpVnRZtOnKWowsCpwgrRumTrTAY4tRUCeqqyhbdRZXly3TfnZkoL2Kkao5kb3tqMSZJY9cBIovzclA7JatT4GSoI58WMli9Vuqhuo8rz0WTvBkCKGatwMNZxZd9QvpszlP+b0F3pUITmfHIVfHQ5HP1N6UisRpIkThVU1L9x48vwQjT8+aoyQVlZZmEF7s4OuDk6EPH3MZMqI5IlQd0ih4FWD0XpchVdFTKazGxPK+D3w6fP3+jkgemmrzkUfL3cHalCOq2GARGdGNFVvfW0VCMiUb5UaQmBnOJKBj67lqRXN1BtvKBL6sQfIJnAJ0a54Kwoxt+dLfPHseL+Eapv3RXdcO3EbDZTU1PTqvsYDAYcHByoqqrCZFJ/n3BrNXd89Ho9Oid3uXVJowVjVfsH2Q7WpJxm9me7iPFzY2yPQPlGB0ekmDEcO1xJV2XDEwR5NubGl+DEWjCbVDcrLMjTue76yTPlxAZ7QkEqnD0CGh1Ej1QwOuvSaDQEeTk3v6GdE8lSO6ipqSE1NRVzK2dDSJJEUFAQp06dav1K8x1AS46Pt7c3QTd/jUalgytBLk7p5+5EXKgXVQZT0wt6qsSnm9MoKDdwVb8QVY+TUI2wgeDkBZWFkL0bwhKUjsiiNBoNX80aQriPC04O595/tV2OkUPBRX0FUyVJ6lDfSyJZsjJJksjJyUGn0xEeHo62FV/aZrOZsrIy3N3dW3W/jqKp4yNJEhUVFeTl5QEQHKzOrigAT2c92x8bd/6D6/Qh2PM5mi5JygZmRR9vTud4XhmdA9xEsmQPdA7QeTQc+lHuilNZsgTQ5e+lA47+Kl92m9D+wbSD73dn8c6Gk9w0KILbhkYpHY7ViWTJyoxGIxUVFYSEhODq2roBcLVdd87OziJZakBzx8fFRa7onJeXR0BAALrSLHlWjm8zU3ztUL0zvCMrYPMbaPNPgtuNygVlJWazxMwR0fx+OE+MV7InXS6Tk6VjyTD6EaWjsa6qEkj7S77ebZKysVjJ+qNnOJxbyukSdQ5v+DuRLFlZ7VgaR0dHhSPpmGoTVMPW99Ctfhj63gTXvKVwVNZTUF6D97E1aAGp81jIVToiy9NqNUwbGMG0gRFKhyK0Rm0V+aydqi0Su+pADp9sTuduv32MMhvAtwv4dVE6LKt46opejO0RQM9gT6VDaRciWWonHalv15bUHffAOPny2GpVDjAFmP3pTjYdOsFe5+2AXF+J3P0KRyUI53iGwOSX5OVPnL2VjsYqMgsr2XQiHy9zOKOmvAo69Z4k+7g5clW/xuu8qY1IloSOIaivXBiuIh8yt0PEEKUjsjhvVz3DNAfQSCbw6w5e4YC6kqXyaiMbjp5heFc/sR6cPRo0U+kIrGpiXBAajYbLYgPAV4ylUxMxEEZok6ioKJYsWaJ0GC2nc4Au4+XrR35VNhYruWdMF17uf67WkkoXzt147Cz//HwXV7/5l9KhCMJFwjq5cufwaCJVnigt+vkQP+zOorKm45S0EcmSHbjjjjvQaDQX/Rw/ftzqj/3RRx/h7e190e3bt29n1qxZVn98i+p+bqClSqsIh3s54pqaLP+i0hk4RrOZGD83MbDbnh1bA9/dDae2Kx2Jdez+HLa+AyU5SkdiFalny/ngr1Qe+t9eaowdZ3Fg0Q1nJyZOnMiHH35Y7zZ/f+W+MJR87DbrMk4uEHcmBQrToFOU0hFZVnEG6JzAxUeuXK7CxWWn9AlhSp+QDvUhrTr7/wf7vgJXXwgfqHQ0Fidt+i+aMyl8e7iCK2+9H71OXW0Sro467h3ThcKKGrxcO05XuLr+iyrm5OREUFBQvZ8777yTq6++ut52c+fOZfTo0XW/jx49mvvvv5+HH34YHx8fgoKCWLBgQb37FBUVMWvWLAIDA3F2diYuLo5ffvmFdevWcccdd1BcXFzXmlV73793w2VkZHDVVVfh7u6Op6cnN9xwA6dPn19+Y8GCBfTr149PP/2UqKgovLy8uPHGGyktLa3b5ptvvqF37964uLjg6+vLZZddRnl5uaUOoVwYrnbZhSOrLLdfW+ETw6nbt/NCxFvM/mKv0tFYlaOD+OiyW7FXyJcpP4OksoS+MA3NmRSMaFmYEsz21AKlI7K4QE9nHprQnWev6a10KO1KfOJ0AB9//DFubm5s3bqVF198kUWLFpGcLHfXmM1mJk2axKZNm/jss884dOgQzz//PDqdjqFDh7JkyRI8PT3JyckhJyeHhx566KL9S5LE1VdfTUFBAevXryc5OZkTJ04wbdq0etudOHGCH374gV9++YVffvmF9evX8/zzzwOQk5PDTTfdxD/+8Q9SUlJYt24dU6dORbL0h+nwB+H6j6DfzZbdr43Q6nQs22vkt0O5nCmtVjociyquNFj+9SC0v85jQe8qt4TmqCypP3cSlu3Rj8sHxeLjrt7ZcB2N3XTDFRYWcv/99/PTTz8BcOWVV/Lf//63wfE0AGlpaURHRzf4t6+//prrr78eaHhK/7Jly5g9e7ZlAreQX375BXf38xViJ02ahJtbywYR9unTh6eeegqArl278sYbb7B27VrGjx/PmjVr2LZtGykpKXTr1g2AmJjziz56eXnJa/8EBTW6/zVr1rBv3z5SU1MJDw8H4NNPP6VXr15s376dgQPlpnaz2cxHH32Eh4cHANOnT2ft2rU8++yz5OTkYDQamTp1KpGRkQD07m2FM5eu6hz4THUpOLgQ6u3CwxO70yfUm04qayKf8f5WCipqeO3G/gyIUN/yER2Go6s8ASHlJ7l1KaSf0hFZzrmq3RGJU1k8tI/CwVje3lNFaDUa4kI9O1w5HLtpWbr55pvZs2cPq1atYtWqVezZs4fp06c3un14eHhda0jtz8KFC3Fzc2PSpPoVVT/88MN62912223WfjqtNmbMGPbs2VP38/rrr7f4vn361H/TBgcH1y0DsmfPHsLCwuoSpbZISUkhPDy8LlEC6NmzJ97e3qSkpNTdFhUVVZco/T2Ovn37Mm7cOHr37s3111/Pu+++S2FhYZtj6nA2/Ade7gY7P2LO6C4M7+qHg4rGShRXGjiWV0ZmYSVhnVyUDke4VLFXypcpPysbhyV1gKrdLycf5Yo3/uTDv9KUDqXd2UXLUkpKCqtWrWLLli0MHjwYgHfffff/27vzuKjK/YHjnxmWYYcEFRdUVBBRcddQc8lEy7LSyqWrLWp200zNrpam1L1iWXqra5m2qGn96tZVUzOvuITXFEEM9zQVATPElR1mhjm/P0ZIBEaQGc6Mft+vly/gcM5zvvMIh+885znfh8jISI4fP06rVq3KHePk5FRuNGTt2rUMHz68zAgNXFts1cLIiT3w9PSkZcuylWC1Wm252xIGg6HcsS4uZUcYNBpN6aK+JUuC1ERlCyreuN1SHE5OTsTGxrJ79262bNnCv/71L2bNmsXevXsrHSG8ZVfTIPn/QKOBPn+zbttqUBTzH538S+ZaUrchX3cX9s2+j6TUK9Tzvv1XOL/thUaB1gUuHocLx6Fu+Wu4wzm1HUwGqNOitGr3uasFXMrV066x4/9eKoqCn7sL7i5O9A51wAd8asghkqU9e/bg6+tbmigB3H333fj6+rJ79+4Kk6UbJSUlkZyczIcffljue5MmTWLcuHEEBwczduxYnnvuOYtrsRUVFVFU9Od8kOzsbMCcqNyYrBgM5nkWJpOpNDGoqusToZI2rhcQEMDhw4fLbE9OTsbFxaXMthuPVRSldFvbtm05e/Ysv/76a4WjS87OzhQXF1cYe0kbYWFhpKWlkZqaWjq6dPToUbKysmjVqhUmk6n0tdwYx43bIiMjiYyMZPbs2QQHB7NmzRqmTp1qsX8q6psSJec2GAw4OZmrdmsupeD8UwyKzgdjtxfAWVfhsQ4j8ygul0+jOOkwNusLBgPnrhaw8eA5rl7RMKCCBNoRuWjg7mZ+Fb4hqK6SNqzR1u3I5v3j5IFTcB80Oecozs5A8Wt+82PsSEX9o710Gq3WBVPIQEwGA5sOZfDSvw/SvrEv303oXllTDmXhY20pMrRG5+Jk8WfDkX6/qhqjQyRLGRkZ1KtXr9z2evXqkZFRtcWvPvvsM1q3bk2PHj3KbP/73/9O//79cXd3Z9u2bbz88stcvHiR2bNnV9rW/PnzeeONN8pt37JlS7nFcp2dnQkMDCQ3Nxe9Xl+lWG9kMBgwGo2lSVmJ7t278+6777Js2TK6du3Kv//9bw4dOkRERETpvkajEb1eX+ZYo9GIwWAgOzubjh070qNHD4YOHcq8efNo3rw5J06cMFehve8+6tatS25uLhs2bKBt27a4u7vj4eGByWSisLCQ7OxsunXrRps2bRg5ciTz58/HaDQyffp0evbsSWhoKNnZ2RQVFVFcXFwmjsLCQkwmE9nZ2ezbt4+4uDjuvfdeAgICSEpK4sKFCzRp0qTc677R9U/U3Uiv11NQUMDOnTsxGo3mjYqJKJe7cC+6wv5v3yHDt1N1/0vsSqs/1hIGZHiFk7B1JwBbzmr4Id2J1n4a2l6bzC/Ki5W+sciW/ePkNZxiHx0cvgqHN9nsPLZUtn9a4NLmAzR5xeg3bSJLD1qcyM26yvqNm7gTH+B0hN+v/Pz8Ku2narIUHR1dYdJxvcREc+GyqtzmqUxBQQFfffUVr7/+ernvXZ8UdejQAYA333zTYrL06quvMm3atNKvs7OzCQoKIioqCh+fsosKFhYWkp6ejpeXF25u1bt9oCgKOTk5uLi44OzsXK7tRx99lNmzZxMdHU1hYSHPPPMMY8aM4fDhw6X7Ojs74+rqWuZYZ2dnXFxcSretXbuWV155hfHjx5OXl0fLli2JiYnBx8eHAQMGMGHCBMaOHculS5eYM2cOc+fORavV4ubmVtrG999/z+TJkxk8eDBarZaBAwfywQcflH5fp9Ph5ORUJg43Nze0Wi0+Pj40aNCAhIQEli5dSnZ2Nk2bNuXdd99l2LBhN+0fb2/vSn8OCgsLcXd3p3fv3mX6X+saDwkf09UtleIHKv+/dgTOn7wNQN3eY3kg4gEAwi7kcXH9EZppLzFgwIByt0AdyfLdqfx88hJP9WjCPS0DrNKmwWAgNjbW4fvGVqR/LKtq/wweaMDH3fH7T280UWgorvJrcaSfn5u9GS+lqOjChQvKsWPHLP4rKChQPvvsM8XX17fc8b6+vsrnn39+0/N88cUXiouLi5KZmXnTfXft2qUASkZGRpVfR1ZWlgIoWVlZ5b5XUFCgHD16VCkoKKhyeyWKi4uVK1euKMXFxdU+9k5Qlf6ptP/P7lOUuT6K8o9ARSnKtXGkNnTxpPl1RN+lKHmXynxLr9cr69atU/R6vUrBWcfgD3YqTWdsVFbHn7Fam7dL39hKrfZPUa6iXDpl+/NYUbn+ybn53xZHtu1YhtLi1R+Uad8kV2l/R/r9svT3+3qqjiwFBAQQEHDzd4qRkZFkZWWRkJBAt27dANi7dy9ZWVnlbqtV5LPPPmPIkCFVqjr9yy+/4ObmVmlJAnGbaNgJ7gqGKynmteLaPaZ2RLfm143mj8H3gEcddWOxkfeGd+SHg38wqI19P4QhbsHxzfDt0+byAc86aKHY/Mvwz3AIjIDRa8HNp9wuhYZiDMUmh138eX/qVYwmBU+dk9qhqMYh7qK2bt2aQYMGMX78eOLj44mPj2f8+PE8+OCDpZO7f//9d8LCwkhISChz7MmTJ9m5cyfjxo0r1+6GDRv45JNPOHz4MKdOneLTTz9l1qxZPPfcc+h0Dj7pV1im0UDba7f4Dq9RN5aaCImCXtOgU8XlLowm2H78ArlFxloOzHpa1vPipftC8PeS38nbTmBbMBZAWjzknL/5/vbo141QrAdDQYWJ0vKfU+g6byuf7UpRITjrmD6wFdtf7sO4Xo41Ed+aHCJZAvjyyy9p164dUVFRREVFERERwapVq0q/bzAYOH78eLnJWp9//jmNGjUiKiqqXJsuLi589NFHREZGEhERwfvvv8+bb77JwoULbf56hB1oOwxcvcHT33GXXajXGu6bC22HVvjt9w47MWH1L2z/NbOWAxOiCnwbm0d5Uf4cJXU0h74zf2xX8fxKX3cXcgqN7D3t2EufNK/rRRN/j5vveJtyiKfhAOrUqcPq1asr/X6zZs0qXAohJiaGmJiYCo8ZNGgQgwYNslqMwsHUD4dXToLL7Vu3J8xPQa91o1BfrHYo1Xb6Qi6r4lMZ0r4hHaVi9+2rzaNwbj8c/Aa6jlU7murJPQ9n/mf+vE3Fb1ii2gTy9XPudGvmmLfJlSo+SHW7c5iRJSFswlETJUWBH2fCb7FgqjwRimpkYuf03jzRNajSfezV+gPnWP7zGT7Y9pvaoQhbingCNE6QvhcunlQ7mmrRHlsPigkadYE6FRfP9dI5c3dzf7Rax0s4Lufp6fnWdv6x8SiG4urVCbzdSLIkhKKYF/TMd6Bh8rP7YO8S+PcYMFReJ8TVCYe8SAN0D/bnkQ4NGda5sdqhCFvyDjSvFQeQ/KW6sVST5uha8ydtKy9xcqOK7oDYqx8O/cG5rELiUy7hchstn3Qr7uxXLwTAd8/A0t5w+D9qR1J1ydduSYc/DDpvy/tec+pCrg0Dsr7IFv68N6IjD0Y0VDsUYWsdRpk/Hvy3w8wfdNdfRHs2AdCYbyXexKr4VAa9t5Pdpy7ZPjgrGdE1iE/HdOHlAbfBcjQ1JMmSEI27mj+WTNS0d4aCP5/gK/kjY0GxSWHoRz/Tf2EcxzMqr3YuhGpa3Q/955rLBzjI/JgiZx+Mj34KvaeDT4Ob7n/0XDa/ZuTwffLvtRCddbg4abkvvD79wsqvoHGnkWRJ3DKNRsO6devUDqPm2jwKaCA9Hq6mqx3NzR3bCEXZ4NcEmva66e5OWg11vXW4OGk4ePaq7eOrIUOxieU/p3Al79aWBxIOyFkH90wDP8eZW2fSuqKEPwL3Vm0FgCe7N2H+0HbMeiDctoEJm5BkSVQqMzOTCRMm0KRJE3Q6HYGBgQwcOJA9e/YA8Mcff3D//ferHKUV+DSEpj3Nnyd/pW4sVVEyr6P9KLCw4PP1XnugNfGv9ufxLvb/xyj26Hne2HCUhxbvcqj5HUJY0raRLyO7NcHXw/4LUxabFJ77Yh+r41MpNDjek7S2IMmSqNSwYcM4cOAAK1eu5MSJE6xfv56+ffty+bJ5InRgYODtU7yz89Pmj0nLodiOV8q+mg6nfzJ/3mFklQ9r6u/pMEUd3V2daNPQh0c6NJJHlu80p3bA6scgaYXakVik/SmG0Ix1kHdB7VBsYs+pS2w5ep4Fm391lLuiNifJkqjQ1atX2bVrF2+//Tb9+vWjadOmdOvWjVdffZXBgwcDZW/DnTlzBo1Gw5o1a+jXrx8eHh60b9++dBSqxO7du+nduzfu7u4EBQUxefJk8vLyavvllRc+BDzrQs4fcNyOV0DPPW8uRNnsHrir2S01ka+372re/VrVY+OLvXjpvhC1QxG17fwROBkL+79QO5LKFVxBu3cJrf9Yg+byqWofHnfiAs+uSOTslaqtdq+GsAbezHqgNX/t2xKd8527xMn1JFlSSb7eSL7eWOY2g95oIl9vpMhYXOG+JtOf+xqKzfveOERa2b7V5eXlhZeXF+vWraOoqKjKx82aNYvp06eTnJxMaGgoI0eOxGg0/3E+dOgQAwcOZOjQoRw8eJBvvvmGXbt2MWnSpGrHZ3XOuj+XDDm2Qd1YLGncBf66G0ZU/xFrQ7GJl77+hc5/38q5qwU2CM56NBrNHf+o8h0pYjhoneH3JMj8Ve1oKpb8FRpjAVluQSiNu1f78GU7T7H910xWx6fZIDjrCPDSMb53c/7at4XaodgNuRqpJHzOfwmf818uXzeJddnOU4TP+S9zvz9SZt+u87YTPue//H7dH7gv9qQSPue/zPjPwTL79np7B+Fz/svJ6x4T/y7pbLXjc3Z2ZsWKFaxcuRI/Pz969uzJa6+9xsGDBy0eN336dAYPHkxoaChvvPEGqampnDxpLjT3zjvvMGrUKKZMmUJISAg9evTggw8+4IsvvqCwsLDaMVpd17Ew8ht4dKnakVim0YCbb7UPc3HS8kdWIQWGYrYds791uAoNxWw9eh7jHV787o7mVde83iHYZ80lkwkSPwUgpW7/W3pyb/w9zXm2ZzAjHLBQ7J1MkiVRqWHDhnHu3DnWr1/PwIED+emnn+jUqRMrVqyo9JiIiIjSzxs0MD9Om5lpXpcsKSmJFStWlI5aeXl5MXDgQEwmEykpdrDIpE9DaDUItHY67JwWD/qa3bKceX8Y6yf15C93N7VSUNaz+XAG477Yx/Bl8WqHItTU4Unzx4Pf2N/8wdM74PJpFJ03Z+/qcUtN9G1VjzkPhdMswNPKwdWcyaQw5/vDJKRclocrbuAwa8Pdbo6+ORAAd5c//zA/17sFz/YKxumGisuJs+5Fq9Xidt294zGRTRnZLQjtDe9sds3oB1Bm38dqUAHZzc2NAQMGMGDAAObMmcO4ceOYO3cuTz/9dIX7u7j8+aRHyeRck8lU+nHChAlMnjy53HFNmjS55RhtwqgHFPPtOXuQkwErHwInV/jrz7c8X6mTHa+xlq8v5i4PF/qE1lU7FKGmkCjwqm+en3fou2o9yGBz10aVTO1GUFzsoEslWRB34gJf7Ell7f7fiX+tP546SRFKSE+oxMO1fNe7OmtxrWCwz8PVGe0Nj4i7OGkrnNNRUbvWnPsRHh5+y7WVOnXqxJEjR2jZsqXV4rGJhE8g7m3o+6r9LOwZvwSK9dCw4y0nSjcyFJvsal7QqO5NGNqpEUaTvKO9ozm7QvfnYdsb8PP71+Yx2cHP6dU0OLEZAFPnZyChZuvYnbmYxxd7UhkcEUjnpvaxyG7zup6M6t4Ef09XSZRuYAc/gcIeXbp0iXvvvZfVq1dz8OBBUlJS+Pbbb1mwYAEPP/zwLbU5Y8YM9uzZw8SJE0lOTua3335j/fr1vPjii1aOvoZMRvMjwYmf2cfSC4VZsO9z8+c9p9S4OUVRWBp3isj52/k1I7vG7VmTm4sTXnKRFl3HQlB36DUVsIPfwRLtR0HIQAgIrXFTS3ee5vOfU/hslx1MQbimqb8nMY+24+UoWd7kRnJVEhXy8vKie/fu/POf/+TUqVMYDAaCgoIYP348r7322i21GRERQVxcHLNmzeKee+5BURRatGjB8OHDrRx9DbUfCVvfgMwjkLYHmt7a3ASr2fe5uWJ33TAIHVTj5jQaDQfPZnExt4j/25vGGw+3tUKQty6vyMgfWQW0rFe1Ne7EHcDNF8ZuUTuKsvyawCMfmt9AGWtefuPpHs34I6uA4V3tbAqCqJAkS6JCOp2O+fPnM3/+/Er3uX4CYLNmzcpNCPTz8yu3rWvXrmzZYmcXwRu5+0HE4+ZaLwnL1E2WDIXmW3AAPV+y2u2Iyf1DuDesHkM6qL9I7fKfU3h3ywme6NKYBY+1VzscISyzUpXGVoHerHimm1XaqqmzV/JZFZ/KMz2CCfS9/eZiWYPchhOiIl3Hmz8e/V7dei8HvzZPdPVpBG0fs1qzrQK9Gda5sV3MWbqYq0ergZ4tA9QORdgbfR7Efwwbp6oXQ7ERfpgO54+qF4ONLf/5DEvjTvPKdwfUDsVuqX+lFMIeNYiAsAdBMcH2v6sXx8XfAA1ETjRPfLUBRVFUXf8pekgbtr3clwcj1B/lEnYm+w/YPNN8K1qtZOXg15D4iflpVGPVC/RWld5o4vNdKXydoF6Ryt6hdekWXIexvYJVi8HeSbIkRGX6zwGNFo7/CFfOqBPDwHnwYtKf1cWtLCn1Mo9+tJuYTcds0n5VBQd4liuZIQQBLaH1Q+bPd39Q++c3FMKOa1MRek2xSSmRHw6d482NR5n/469k5atTV6pPaF3+PSFSynZYIMmSEJWp2woeeMe8vIiVHte/Jf4tQOdlk6aLDCaS06/yffI5CvS1O7q049fMMhXshahQrynmj4e+NS8kXZsSP4Hss+bb4CW35q1sSPtG9GoZwIxBYXjq1C2IKwtXV04meAthSddx6pw3dY/5iaD64TY9TWQLf6IfCmdwREPcXWvvQp2ZU8hfv0zCSaPhh8n32GU1Y2EnGnWG4N6QshP+9y489H7tnLcwC/630Px531fBxTYTn520GlaPq/4ac9bwr22/4e+lY3jXIBnZvQkZWRKiqi6dMq8NZWv6PFjzHCy9B07Y9slBjUbD0z2Dqetdu5XKL+fpaVHXi9BAb5r6e9TquYUD6jPT/DFpJaQn1M45f/4ACq5AQCtzOZFaUlvLjJzMzOX9bb/x2tpDJKRcrpVzOjJJloSois2vweIucGSN7c8V9zZkpYF3g1ovW/Db+ZxauViHBfqw8cVefDqmiwz9i5tr1vPamnEKbHrF9sVic85D/Efmz/u/Dk61cxNm35nLDFuym+T0qzY/VzN/D2YNbs3QTo2IbOFv8/M5OkmWhKgK97v+fDLOaMN5NhmHYfdi8+cPvGuzuUoVmb/pGFHv7WTDwT9q5XwajQZ/LztZe0/YvwF/Nz+hOvQTq9U6qpSbL9w721ytO+xB257rOv+XkM7+tKv8M/aEzc/l7KTlmZ7BLHqig83PdTuQZEmIqrj7r+BZz/xU3G4bzZkwmWDDS6AUQ+sh0Krm1bqrw8PVGUWBgzZ8V7vhwDlW/JwiK5qL6vP0hxFfQt2aLzVyUy5u5nIdT/7b9onZdaYPDOXJ7k145/EIm51DbzTJ798tkGRJqGbFihX4+fmpHUbV6LxgwBvmz3fEwJld1j9H0ufw+z5w9Yb737Z++zfx174t+L/xdzP7QdtMKj+fXciraw4RveEo65J/t8k5xB3kwgnr344ruAr56s3faeDrzrxH21HP23ZVtBduOc7wpfEcz8ix2TluR5IsCYsyMjJ48cUXad68OTqdjqCgIB566CG2bdtW47aHDx/OiRO2H262mvYjzf8UE3z3LORmWq/tnPPm9ejAXN/Jp/YLNLo6a206d6Get47pUaH0bOnPQ1KAUtRE7Bz4qDscWWu9NhUFvp8IS/vA7/ut124NxJ++hKHYeg+V5BQa+CohjYQzl/n9ar7V2r0TSLIkKnXmzBk6d+7M9u3bWbBgAYcOHWLz5s3069ePiRMn1rh9d3d36tWrZ4VIa4lGA4MXQt3W5iVI/jPWeu9sPfyh70xodo95xXWV5RYZeevHX8kqsF6RvJIn71aP7Y6zHSyzIhyYi4f5TcvmmZB9zjpt7vkQft0IuRnWaa+Glvx0ihHL4q1aMNbbzYXNU3oz8/4w7g2rb7V27wRyxRKVeuGFF9BoNCQkJPDYY48RGhpKmzZtmDZtGvHx8QCkpaXx8MMP4+XlhY+PD0888QTnz58vbePAgQP069cPb29vfHx86Ny5M/v27QPK34aLjo6mQ4cOrFq1imbNmuHr68uIESPIyflzuFhRFBYsWEDz5s3x9PSkV69efPfdd7XTIQCunvDESvAKhC7PWm8+g5OzeY7EUxtAq25hOoC/rk7i47hTRK8/UuO20i7lY7zu3bE8/SZqrNdU8yP9uefNy5DknL/5MZak7YWtc82fD4yBRp1qHmMNNa9rrj2mKNYtJ9DIz53n+7SwWnt3CkmW1KLPq/yfobAa+xZUbd9qunz5Mps3b2bixIl4epYvGOjn54eiKDzyyCNcvnyZuLg4YmNjOXXqFMOHDy/d78knn6Rx48YkJiaSlJTEzJkzcXFxqfS8p06dYt26dWzcuJGNGzcSFxfHW2+9Vfr92bNns3z5cpYsWcKhQ4d44YUXGDNmDHFxcdV+jbesbit46QC0ebRm7SiKuehd3sU/t9lJIjF1QCj1vHU1Xisq7VI+I5btYfiyeM5nF978ACGqwlkHT34LvkFw6SR88TDkXbq1tvIuwXfPgMkIbYepV4j2BgPbBLJp8j1ED2lTozcYhYZinl+VVCvlCG5nUsFbLTEW5myERJkvBNdoFoaCoZL7y017wTM//Pn1e+0gv4KLRnRWtcI7efIkiqIQFhZW6T5bt27l4MGDpKSkEBQUBMCqVato06YNiYmJdO3albS0NF555ZXSdkJCQiye12QysWLFCry9vQEYPXo027ZtY968eeTl5bFo0SK2b99OZGQkJpOJUaNGkZSUxNKlS+nTp0+1XmONXF/NN3UP5JwzX2irSlHgxxmQsBQOr4HnfgKnypPI2tapyV3s/Fs/3FxqNsplNJnIKTKiy9PjqZPLjbCiu5rCU+th+QNw4RisehjGrAePOlVv48Jx+PZpyP4d/Fuaq4PbyRsWgPCGPqWfZ+UbuJKvr3a1+w+2/cbmIxnsT7tild/pO5XDjCzNmzePHj164OHhUeUnqBRFITo6moYNG+Lu7k7fvn05cqTsbYUrV64wevRofH198fX1ZfTo0Vy9etX6L8DBlAz7WnpHc+zYMYKCgkoTJYDw8HD8/Pw4dsx8n33atGmMGzeO++67j7feeotTp05ZPG+zZs1KEyWABg0akJlpnkh99OhRCgsLGTBgQOltv8aNG7Nq1aqbtmsz+ZfNc5e+e9b82P+NI30VURTzBNWEpYDGfPvNjhKlEtdfVFMv5TFu5T6uVHMtt+Z1vfh0TBf+b/zdeEmyJKytTnPzrWvPepBxCE5tr97xP78PmUfBsy48sQp03jc/RiVvbDzCoPd3svaXs9U6bmK/ltzXuh7vj+goiVINOMzVS6/X8/jjjxMZGclnn31WpWMWLFjAokWLWLFiBaGhofzjH/9gwIABHD9+vPQP8qhRozh79iybN28G4LnnnmP06NFs2LDBZq8FgNcsTErUlP2BVl4+gUZbSV6ruWH7lEM1DMwsJCQEjUbDsWPHeOSRRyrcR1GUCpOp67dHR0czatQofvjhB3788Ufmzp3L119/zaOPVnwL68ZbdBqNBtO1JUZKPv7www80atQIk8lEbm4uXl5euLu73+pLrRmdj/kJuf8thKQV5qUYHlsO9SoYkTPq4fB/zBNJz1/7f3rwn9BhVK2GXF2KovDi//3CwbNZfJuUznO9K5/vkK83ErPpGGMimxFa3/w71r25VAcWNhQQAmO+h3P7od1jf27Pv3zzUaZBb5lHku6dA972O+G50FBMRlYheqOJJnUsjyzlFhn5dl86o+9uirOTFk+dM58+1bWWIr19OUyy9MYb5seqV6xYUaX9FUXhvffeY9asWQwdOhSAlStXUr9+fb766ismTJjAsWPH2Lx5M/Hx8XTvbl7I8JNPPiEyMpLjx4/TqlUrm7wWwDxRuDr7VpYs1aRdC+rUqcPAgQP58MMPmTx5crl5S1evXiU8PJy0tDTS09NLR5eOHj1KVlYWrVu3Lt03NDSU0NBQpk6dysiRI1m+fHmlyZIl4eHh6HQ60tLS6NOnDyaTiezsbHx8fNBWtX+szcnZvBxCs17m9dwyj8KyvtA00jys328WuPuZ15j68O4/n7Rx8YCof0CXZ9SJuxo0Gg0LH2/PmM8TeLhDo9LtJ87ncOZiHj1bBpTeYtt48A9Wx6ex++Ql/ju1Ny7y1JuoDfXDyy46fXaf+fZcxOMQ/ijk/GGe23TppPn3cchic5Lk5gMPf6ha2FXl5uLEl+O680v6VTo1uat0+1s//oqzVsMzPZvh76UzzyP98GdOZubi6erME12DLLQqqsNhkqXqSklJISMjg6ioqNJtOp2OPn36sHv3biZMmMCePXvw9fUtTZQA7r77bnx9fdm9e3elyVJRURFFRUWlX2dnZwNgMBgwGMo+am0wGFAUBZPJVDoyUlUlt8JKjq9tixcvplevXnTr1o3o6GgiIiIwGo1s3bqVjz/+mMOHDxMREcGTTz7JokWLMBqNTJo0iT59+tCpUyfy8vL429/+xrBhwwgODubs2bMkJiYydOjQMv1R8rHk9V7/Wq/f5unpycsvv8zUqVMxGo307NmTjIwMDhw4gJeXF0899VS512AymavVGgwGnJxsOATdpBeM+wmn9S+gTfkJTm1HOf0TxnujwWAAZy+c6rZCA5i6jsfUcYx5CRWD9R7Nv1HJz+KNP5O3olkdNza92AMvnVNpe0vjTvKf/ef4YHgE97cNBKB9Q2/q++h4fXAYmIoxmIprfG5bsGbf3I4cvX+0x37AqbgIfllt/ncDY4POKB1H33L7avVPuwZepedUFIXPdp3GUKzwWKcG+OjMb0we69SQrxPP4uvupNr/nyP9/FQ1xts2WcrIML+Dr1+/7NBq/fr1SU1NLd2nojo/9erVKz2+IvPnzy8d6breli1b8PAou4K6s7MzgYGB5Obmotff2ppi1z86X5v8/f3ZsWMHCxcu5OWXX+b8+fMEBATQvn173nnnHXJycli5ciUzZsygb9++aLVa+vfvz9tvv012djZ6vZ6MjAzGjBnDhQsX8Pf358EHH2TatGlkZ2dTWFiIoiilyWZRURHFxcWlXwMUFhaWjiABTJ8+HR8fH+bPn8+ZM2fw9fWlffv2TJ06tcxxJfR6PQUFBezcuROj0Wj7TvN9mrtCe+Nd+Ds6Qza//Xdr6bfcPB6lqIUPylVn2LHH9rFcExsba5N2r2Zo8XXVsHXPLyhpJYk9vBoO2Sf2sskB6o3aqm9uF47bPx24K/R1WmRuxrcgjXzXuuTqAsnTBZLrFsils54U/7GpxmdRs39MCtzbQEu+ARL/t4MD194L1jXB5BAoPJXIJpWmcpZwhJ+f/PyqFefUKCouEhMdHV1h0nG9xMREunTpUvr1ihUrmDJlyk0nYe/evZuePXty7tw5GjRoULp9/PjxpKens3nzZmJiYli5ciXHjx8vc2xISAhjx45l5syZFbZd0chSUFAQFy9exMfHp8y+hYWFpKen06xZM9zcqlfCXlEUcnJy8Pb2lto0FahK/xQWFnLmzBmCgoKq3f+OzmAwEBsby4ABAyyWa7gTSd9YJv1jmfSPZY7UP9nZ2QQEBJCVlVXu7/f1VB1ZmjRpEiNGjLC4T7NmzW6p7cBA822BjIyMMslSZmZm6WhTYGBgmQKKJS5cuFBuROp6Op0Ona78aukuLi7lfjCKi4vRaDRotdpqz6spuR1Vcrwoqyr9o9Vq0Wg0Ff7f3Cnu5Nd+M9I3lkn/WCb9Y5kj9E9V41M1WQoICCAgIMAmbQcHBxMYGEhsbCwdO3YEzLdk4uLiePtt8yKlkZGRZGVlkZCQQLdu3QDYu3cvWVlZ9OjRwyZxCSGEEMKxOMxwRVpaGsnJyaSlpVFcXExycjLJycnk5uaW7hMWFsbateaFFTUaDVOmTCEmJoa1a9dy+PBhnn76aTw8PBg1yvyoduvWrRk0aBDjx48nPj6e+Ph4xo8fz4MPPmjbJ+GEEEII4TAcZoL3nDlzWLlyZenXJaNFO3bsoG/fvgAcP36crKw/K1X/7W9/o6CggBdeeIErV67QvXt3tmzZUqbo4ZdffsnkyZNLn5obMmQIixcvroVXJIQQQghH4DDJ0ooVK25aY+nGueoajYbo6Giio6MrPaZOnTqsXl3+0VIhhBBCCHCg23COTsWHDu9o0u9CCCFqSpIlGysphHirNZZEzZTU0LD3JzKEEELYL4e5DeeonJ2d8fDw4MKFC7i4uFSrBIDJZEKv11NYWCilAypgqX8URSE/P5/MzEz8/PxsW71bCCHEbU2SJRvTaDQ0aNCAlJSU0srhVaUoCgUFBbi7u0tRygpUpX/8/PxKa24JIYQQt0KSpVrg6upKSEhItW/FGQwGdu7cSe/eveU2UgVu1j8uLi4yoiSEEKLGJFmqJVqtttrLbTg5OWE0GnFzc5NkqQLSP0IIIWqDTIQRQgghhLBAkiUhhBBCCAskWRJCCCGEsEDmLFlBSeHD7Oxsq7ZrMBjIz88nOztb5uRUQPrHMumfyknfWCb9Y5n0j2WO1D8lf7dvVsBYkiUryMnJASAoKEjlSIQQQghRXTk5Ofj6+lb6fY0i60HUmMlk4ty5c3h7e1u1HlJ2djZBQUGkp6fj4+NjtXZvF9I/lkn/VE76xjLpH8ukfyxzpP5RFIWcnBwaNmxosfizjCxZgVarpXHjxjZr38fHx+5/4NQk/WOZ9E/lpG8sk/6xTPrHMkfpH0sjSiVkgrcQQgghhAWSLAkhhBBCWCDJkh3T6XTMnTsXnU6ndih2SfrHMumfyknfWCb9Y5n0j2W3Y//IBG8hhBBCCAtkZEkIIYQQwgJJloQQQgghLJBkSQghhBDCAkmWhBBCCCEskGTJjn300UcEBwfj5uZG586d+d///qd2SHZh/vz5dO3aFW9vb+rVq8cjjzzC8ePH1Q7LLs2fPx+NRsOUKVPUDsVu/P777/zlL3/B398fDw8POnToQFJSktph2QWj0cjs2bMJDg7G3d2d5s2b8+abb2IymdQOTRU7d+7koYceomHDhmg0GtatW1fm+4qiEB0dTcOGDXF3d6dv374cOXJEnWBVYKl/DAYDM2bMoF27dnh6etKwYUPGjBnDuXPn1Au4BiRZslPffPMNU6ZMYdasWfzyyy/cc8893H///aSlpakdmuri4uKYOHEi8fHxxMbGYjQaiYqKIi8vT+3Q7EpiYiLLli0jIiJC7VDsxpUrV+jZsycuLi78+OOPHD16lIULF+Ln56d2aHbh7bff5uOPP2bx4sUcO3aMBQsW8M477/Cvf/1L7dBUkZeXR/v27Vm8eHGF31+wYAGLFi1i8eLFJCYmEhgYyIABA0rXC73dWeqf/Px89u/fz+uvv87+/ftZs2YNJ06cYMiQISpEagWKsEvdunVTnn/++TLbwsLClJkzZ6oUkf3KzMxUACUuLk7tUOxGTk6OEhISosTGxip9+vRRXnrpJbVDsgszZsxQevXqpXYYdmvw4MHKs88+W2bb0KFDlb/85S8qRWQ/AGXt2rWlX5tMJiUwMFB56623SrcVFhYqvr6+yscff6xChOq6sX8qkpCQoABKampq7QRlRTKyZIf0ej1JSUlERUWV2R4VFcXu3btVisp+ZWVlAVCnTh2VI7EfEydOZPDgwdx3331qh2JX1q9fT5cuXXj88cepV68eHTt25JNPPlE7LLvRq1cvtm3bxokTJwA4cOAAu3bt4oEHHlA5MvuTkpJCRkZGmeu0TqejT58+cp2uRFZWFhqNxiFHcmUhXTt08eJFiouLqV+/fpnt9evXJyMjQ6Wo7JOiKEybNo1evXrRtm1btcOxC19//TX79+8nMTFR7VDszunTp1myZAnTpk3jtddeIyEhgcmTJ6PT6RgzZoza4aluxowZZGVlERYWhpOTE8XFxcybN4+RI0eqHZrdKbkWV3SdTk1NVSMku1ZYWMjMmTMZNWqUQyyueyNJluyYRqMp87WiKOW23ekmTZrEwYMH2bVrl9qh2IX09HReeukltmzZgpubm9rh2B2TyUSXLl2IiYkBoGPHjhw5coQlS5ZIsoR5ruTq1av56quvaNOmDcnJyUyZMoWGDRvy1FNPqR2eXZLr9M0ZDAZGjBiByWTio48+UjucWyLJkh0KCAjAycmp3ChSZmZmuXcxd7IXX3yR9evXs3PnTho3bqx2OHYhKSmJzMxMOnfuXLqtuLiYnTt3snjxYoqKinByclIxQnU1aNCA8PDwMttat27Nf/7zH5Uisi+vvPIKM2fOZMSIEQC0a9eO1NRU5s+fL8nSDQIDAwHzCFODBg1Kt8t1uiyDwcATTzxBSkoK27dvd8hRJZCn4eySq6srnTt3JjY2tsz22NhYevTooVJU9kNRFCZNmsSaNWvYvn07wcHBaodkN/r378+hQ4dITk4u/delSxeefPJJkpOT7+hECaBnz57lykycOHGCpk2bqhSRfcnPz0erLftnwcnJ6Y4tHWBJcHAwgYGBZa7Ter2euLg4uU5fU5Io/fbbb2zduhV/f3+1Q7plMrJkp6ZNm8bo0aPp0qULkZGRLFu2jLS0NJ5//nm1Q1PdxIkT+eqrr/j+++/x9vYuHYHz9fXF3d1d5ejU5e3tXW7ulqenJ/7+/jKnC5g6dSo9evQgJiaGJ554goSEBJYtW8ayZcvUDs0uPPTQQ8ybN48mTZrQpk0bfvnlFxYtWsSzzz6rdmiqyM3N5eTJk6Vfp6SkkJycTJ06dWjSpAlTpkwhJiaGkJAQQkJCiImJwcPDg1GjRqkYde2x1D8NGzbkscceY//+/WzcuJHi4uLSa3WdOnVwdXVVK+xbo+7DeMKSDz/8UGnatKni6uqqdOrUSR6Nvwao8N/y5cvVDs0uSemAsjZs2KC0bdtW0el0SlhYmLJs2TK1Q7Ib2dnZyksvvaQ0adJEcXNzU5o3b67MmjVLKSoqUjs0VezYsaPCa81TTz2lKIq5fMDcuXOVwMBARafTKb1791YOHTqkbtC1yFL/pKSkVHqt3rFjh9qhV5tGURSlNpMzIYQQQghHInOWhBBCCCEskGRJCCGEEMICSZaEEEIIISyQZEkIIYQQwgJJloQQQgghLJBkSQghhBDCAkmWhBBCCCEskGRJCCGEEMICSZaEEEIIISyQZEkIIYQQwgJJloQQ4gYXLlwgMDCQmJiY0m179+7F1dWVLVu2qBiZEEINsjacEEJUYNOmTTzyyCPs3r2bsLAwOnbsyODBg3nvvffUDk0IUcskWRJCiEpMnDiRrVu30rVrVw4cOEBiYiJubm5qhyWEqGWSLAkhRCUKCgpo27Yt6enp7Nu3j4iICLVDEkKoQOYsCSFEJU6fPs25c+cwmUykpqaqHY4QQiUysiSEEBXQ6/V069aNDh06EBYWxqJFizh06BD169dXOzQhRC2TZEkIISrwyiuv8N1333HgwAG8vLzo168f3t7ebNy4Ue3QhBC1TG7DCSHEDX766Sfee+89Vq1ahY+PD1qtllWrVrFr1y6WLFmidnhCiFomI0tCCCGEEBbIyJIQQgghhAWSLAkhhBBCWCDJkhBCCCGEBZIsCSGEEEJYIMmSEEIIIYQFkiwJIYQQQlggyZIQQgghhAWSLAkhhBBCWCDJkhBCCCGEBZIsCSGEEEJYIMmSEEIIIYQFkiwJIYQQQljw/8l8EoSqsDSNAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "#ax.scatter(x, y1, label='Sine', color='r')\n",
    "ax.plot(x,y1,label='Sine', linestyle=':')\n",
    "ax.plot(x, y2, label='Cosine', linestyle='--')\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "ax.grid()\n",
    "ax.legend(title='Functions')\n",
    "plt.title('Example Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090a5cd2",
   "metadata": {},
   "source": [
    "What happens if you want to change your axes to log-scale?\n",
    "Use <CODE>plt.loglog()</CODE> to automatically change the scale of the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d004e2fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2HGlXQGxbtzb/9oJdVNkd3NSvKWFW9xdF9cjzsKIIXe52lKRu7mvzuPrE6dOJ0MiRIxk5cuQZv//aa68xceJEJk2aBMDrr7/OnDlzeOedd3jhhRdo3LhxrR6g/fv306tXrzO298ILL/D000+fcv+vv/5KcHAwAD12v0Vs0aYztvFzp3drbnfd+z4JBWvOeOzcDq9hM6jttjk0k/jC9dj1Zhx6E3adGbvejF1vwqE3syVxbM2xUcVbCSs/ePz7Zuy64485frvImoSiV/+0OsWGgh50Pt35J8Q5zZ07V9Pz7yyEVUf0JAQrDExUP2QpCjy/0kCFXUdK+S7igtRj/8jWMWO3gc5RDpQsR00b/95gwKiHcS3sJAZr8VMILRhtJXTf+x4JhesAWHVIITuyu1vPsW6PnvmH9KzcvIOrWznO/QAnuet5qHdUMmDbMwRVHmVh26cptcS7pd1qpaWldT7WpxOhs6msrGT16tU8/PDDte4fPnw4S5YsAaBnz55s2rSJAwcOEB4ezuzZs3niiSfO2OYjjzzCfffdV/N1YWEhKSkpDB8+nPDwcAAMX36CPv/Mv+BRo0bV3DZ8MwN93pmPHT7yYjBa1GO//wF99s4zHpty7RQIVid563/+HcP2qWc8tipjDUQ2UY+d9ySGZVNQDGYwWtWLKQiMVhSjFftlH0KjZgDo/pqFfvscFKMVTFYwBh0/Xn2co90lEBKjnqRgH7rCA2CwoBxv7+S2MZjhLL1vQtRVVVUVc+fOZdiwYZhM7v+kezp5JZX8vvUIF3SIJ9SivkzOWHOAJZs307dFFKNG9ag5dr1+K3odjBnYgoggNb7o3XmYYrJJTQxnVHoyAPmlVexbOh+A0RcMITpUfe7/vvUIfx0qYkLvFI98khcaK8vHOHU4usJdKAYLjs7j6dZ9LMSlqt8vLwRruMunaby/gAPfbeaV67qRGGF1ub2/c/fzULdhOsb1WShBUQzu1QUlqasbojyhekSnLvw2ETp69Ch2u534+NpZZHx8PNnZ2QAYjUZeffVVBg8ejMPh4MEHHyQ6OvqMbVosFiwWyyn3m0ymE3/4S96AqjMnN7X+QUa9BEPPnHiZrCEnkoXzJkPaZVBVBrbyk67LwVaGKSQSjMfbTkiDNiPBVlbz/ZOvTUHhUB2HvRIAnb1SvV1x4p9DB+iNhhPH5qyHDV+cMV5D0z4Qmah+kfkN/P7sGY/lxl+gaR/19tpP4Y/XTiRJppMTrCA4716IP/6icGwPHN4CYQkQlgghsaA3nPk8ImDUeh562DX/t4Qdh4sJCzJzUackAHq2iOHOwa3olBxRK44nL0k75fHntYnnvDa1X5tiwo0suH8Qmw4WkNAotOb+OZmH+XbNAbKLKnjhsk4e+omEZmY/BXm7IDwZ3VWfYUjqQs0r2qr/g9+egsv+B22G16vZnMJypszfwcMj2xFsNtKjeQxz7h1w1ukf7uC25+HqDwHQ9b0TY9Oerrf3N/WJ0W8ToWp//6MrilLrvksuuYRLLrnEfSeMaHzuY6qFJ9X92ISO6qUuet6sXupi+L9g0MOnSbCOX4eemL9Aq6FgjThtcoWtDIJPSiItERDV8tQ2OT4vy3TSJ5Liw5B35t4uul134vb2uTD7/hNf6/QQGn8iMRr4IFR/cig+rF7CkyCokfRAiXorKq/iixVZrN9XwFtXd6157RjaPh6zQY/xpPk8LWNDuX+E8/M6dDodzWJCaBYTUuv+Ye3jyTxYyC0DWtaKS6fT1fRGCT+1a4H6QRDg8g8hqUvt7+ftgvIC+OUhaDGwZoSgLj5ZuodPlu4lv7SKN8arr4knv/ftPlpCo2ATkcFmF38IDyjNg8oSdeSg2/VaR+O/iVBMTAwGg6Gm96fa4cOHT+klCmimIPVSF83OUy910esW9XIyRVF7narKwHziEy+dr4Imvc+cjEWfeAPAEq4mOkXZUJwDigOKDqkX1kLfu08cm/n9iaTJYD6RLIUlQFgSpE+EmNbq9yuK1bYsYZIwiRp6nY5//7qNSpuDO7Nb0T5RHaJ4YERbHh7ZzisxjOyYyAVpCbXexN5esJMvV2TxxMWpjOma7JU4hJspCsx7Rr2dPkl9Dfy7AQ/Chq/VhGjpW9D/H3VuPjUxgg5J4YzqmHDK9x7+ZgNfrtzHExelctN5zZ39CTwnOAruXAFl+RAUqXU0/psImc1munfvzty5cxkzZkzN/XPnzuXSSy/VMLIAptOpn2j+/qkmPKnuvWOdx6kXAIcdSo5A4UE1MSo6BDFtThzrsKu9VKW5agKWn6VeqnUYDRxPhNZ/oSZNppATCVN44onbqZdCxPE3HEWRZKkBcjgUFmw7zLacYm4bqCbfIRYjtw1sSWyouWbFF4DByyu7Tk6CHA6FRduOcKy0ijCLzBnyWzodjP8SFrwIQ548/THWcBj2NHx3Kyx7R/2gZ6jb3/zCTomnTYIA2iWEYdDrOJhf5mz03uEDSRD4eCJUXFzMjh07ar7evXs369atIyoqiiZNmnDfffcxYcIEevToQZ8+fXj//ffJysritttu0zBq4TZ6w/FE5fRPdnrfpl5sFWrvUXWyVH0d1eLEsSVH1euqEnWY7u9DdY27n0iEVnwA8587qXfp+HVMG/W46Fagl1V4/mZLdiE3TV2FUa/jks5JJEWqPaX3DWtzjkd6l16v4/uMfsz76zBD2sfV3F9pc2A2yv+dXwmNg4teO/sxaWPh18eh5LA6NaDdqLMff5IzzQca2z2ZizonERNa96E2r1EUsFeB0XeG7Hw6EVq1ahWDBw+u+bp6Rdf111/P1KlTGTduHLm5uTzzzDMcOnSItLQ0Zs+eTdOmTbUKWWjBaFFXyR1fKXdagx+BfncfT5KyTwy3Vd+OPOl/pugQlOerlyNbTm3r2m+h1RD1duEhUOwQ3lh6kXxMYXkVOw4X061JIwA6JEUwtH08LWJDfD6hMBr0jOhw4gNAUXkVl7+zlCt6JDPxvOYenxArXGS3gaGOb68GE3S6Uh0aW/fZOROhI0UVbDlUSK8WUViMp19IEmY1EVbfmL0lbxe800+dEzX+S5943fTpRGjQoEGn3RTxZHfccQd33HGHlyISfs0cos5HOnlO0un0vw86jaudKBUegOxNkL3hxGRtgOXvwp+vqxO6k7qpPUaNu6q3g6WmnVY2HSjgqveXYTUZWPzQYKwm9Q3jg+u6+2USMXPtAbbmFPHBH7u4vHuyb06AFSd8c5M6GXjoU3VbBNPlGjUR2jYHyo6piz/O4JfN2Tw+cxPntYrh00ln3hev2t8XEGkuZ7O6+KbokE8kQeDjiZAQmrCEQVw79fJ3Dnvt5fxlx0BnUIfmtv2sXqo1ag6T5kHI8dV2MvfIa9omhBFuNRJqNXKooJzmx1dq+dQbQj1c27spdodC1yaNJAnydQX7Ycsstad42L/q9pj4VLjgRWg55KxJEKhzyOLCLPRtdeatYADKq+zc9cVa1mYdY+EDgwnxlRWIh4/3ssd10DaOk/jIb0YIP/H3PY0ueUN9AcveCAdWw8E1cGCNOgepvKB2r9CMm+DoNrVHqXF39RLXvs6TI8XpKYrC/K2H+W3LYZ4bnYZOp8Nk0PPVbX1oHBnkt8nPyXQ6HTf0q736Z19eKUaDjsSIOq4KFd6x6Vs1CWra78T+aHXR+/Y6HXZ932ZM6N2USvvZd4+2mgxsOVTI0eJKVu89xoA2sXWPxZMOb1av6/O78TBJhE5jypQpTJkyBbvdrnUowh+Yg6FJL/VSrewYHNtbuwdo/0oo2Ac5m2DtNPU+oxUSO6tLa4c94924G4jDRRXcNm0NlXYHQ9rFMaS9un1GcqOGW8NibdYxJn68iqRIK1/d2odgs7yU+4y/ZqnXHcac/TgX6PU6rHXYaPZfl6bRKMRMhyTXd652m5xM9TquvbZxnESePaeRkZFBRkYGhYWFREREaB2O8EdBjU7t4p74q9pbVNNztBYqCmDfcnUVxclm3avuhdRxbO3VbwJQi52aDOqE5/hwK7cNaklFlZ305oExL+vk1UDFFTZJhHxFUQ7sW6Heblv31V81tvwImT+om8w273/Kt0/+v6+Lwe3izn2QN1WVn1ixK0NjQgSg6v2U2l+kfu1wqCsoDqyuvZS0qgxWf6x2r89/FlJ6qZO3O4wJ+AnYDofCtGV7eWfBTmbc3qem18fXlsB7WkpUMJ/f3IumUSEEmaUEjc/Y9jOgqIsl6lOFoNr2X2HjV+qy+9MkQmPe/pNKm4NXr+hCx2Q//JCeu0Pd2NYaceZtUTTg22tIhWjI9HqIaaVuIHlyN7rigFGvQMvz1RIj+5bDT/fBq23hy2tgz5/axawxnQ5+3nSI7MJyPl6yR+twNNUuIbxWElRYXnWWo4VX/PWTet3uQuce32KQer1rwSnfKq20seVQEdtyiokNq/v+QEt2HmXK/B3sOFzsXExupailnFoM8qmFI9IjJISvMYeo5UHSJ6r7FG2aAeunQ85Gdf5By8HQrJ96rN2mTuD2oRcVd8strsBxfBcNnU7HExd1YHXWMcanp2gbmA/5bu1+nvx+M9Mm9qJzSqTW4QSudhequ9y3u8i5xzcfqF7nbFLrKIaeGNoKNhtZ+sj5bDpQQEI9qsu/v2gXC7YeISLIRKu40HM/wJMSOsK132gbw2lIj5AQviw8EfreBbcvhtuXqFvwd7jsxPfXTIU3u6nb+Oft0ixMT/lpwyFGvPEni7JPJHqpSeFM6N0UYz3mSjRkiqIwa/0hCsttTF+1T+twAlv3G+C670+/9UZdhMSc2Hdo18JTvh0XZuX8dvWrpTmwTSyXdkmiSVTDXTzgKukREsJfxHeA4X/blyTzezUBWvCCemlg84nyyyopKLOxPlePw3H2zVUDlU6n4/WrujB95T5u+tsSe+GHmvVXt+PYvwI6XeFyczf60v9EVTmY6t6b5S3ykUoIf3bVFzDm/dPPJ/r6BnVCtp8prrDV3L66ZxNeviyNO1Pt6L1cCNWfhFlNTOrfQn5HWtryI+S7oUeueuf6Q+tr7lIUhSe+38QXK7Ioq/TjbV3eHwgvNYN9K7WOpBZJhITwZ5ZQdbL1hO/g3kwY/izEd1TnKVSW1C4Om7NZ3d3aR+WVVJLx+Rqu+WAZtuObxel0OsZ0TUJGwerOZnfwydI9HC4s1zqUwFF8GKZfC693hLJ819pK7ALo1Ofw8efr3txSPlm6lye/3+x0vefiChvlVRomUYqi7q1WdszneqtlaEyIhqJ6PlHfu9Skx3GiZ4Vje+GdvmrZjx43Qs9bfa6LutLmYNG2I5RW2lm99xi9Wpy9hIA4vYe/3ciM1ftZm5XPf8Z10TqcwLB3iXod3wGCIl1rK7oVPHpAXTRxnNGg47aBLamyO85YaPVsbpq6kt//Osz/ruvB0NT6zTFym+LDao0xnR4ifGuhgyRCQjRE8X/brCxnM5hC4NhumPsErPo/GP6cuspFwxVnJ28QlxBh5bUruxAfbqFTcqRmMfm7Cb2b8vtfh+ne9Ow1q4QbVSdCTfu63pZeXysJAnWX9IdHOjkBG4gIUsv47MktcSk0l+RnqddhSbX3TfMB0uF8GlOmTCE1NZX09HStQxHCPdqNgge2wyVvQlgiHNsD06+BaaNPFEH0snlbchj0ygI2HyyouW9YarwkQS7qnBLJkofP59reTbUOJXC4MxHygAdGtGXN48OY1F/DXeqLDqnX4UnaxXAGkgidRkZGBpmZmaxc6VsTuoRwiTlE3br/zlXQ/x9gsKgbt/3fCHU+kZd9vWo/B/LLmDJ/h9fP3dBZTbLbtNeU5av7/gA0cVMidGA1fDgcpqlbZezNLamZN+eMpMggokI07oUpzlGvfWhH6WoyNCZEoLGEwpAnoOsE+PWfkNyjdle8w4HTMzLr4dkxabSKC+XO81t5/FyBasP+fP796zb+c2VnokPrvhuxqId9ywFFndsT5qb5N6ZgtV1TCKUVlQz69wJMBj0rHx1KRLDJPefwtuoeIR9MhKRHSIhAFdUcrvoM+k0+cd+uBfDeANiz2K2nUhSFDxbt4s1522vuiwm1cP+IttJ74SGKovDYd5tYtO0I//ltm9bhNFx7j5e8ceewWEwbMAZBVQk5uzdjNRoItxqdToIUReG9hTt55NuN2pViadRcLa+R0Emb85+F9AgJEehOniy94CW1lMfUCyF1tLqBY2QTl0+xdGcuz83egk4H57ePo0OSHxaM9DM6nY6nLkll2tK93H1+a63Dabh63gKNu7vleVJDb1AXPBxYRXPbbjY/PYajxRVON6fT6fjgj90cLa7g6p5NtCnY2m2CevFBkggJIU4Y9ynMfw5WfwSZM2HbL9DvHrXXyOz8Fv19W8VwfZ+mtIoPIzUx3G3hirPr3jSK7k19a8+WBiciWb24W0wbOLAKcnei1+uIC3dtu4trejVBURQi/XVozYNkaEwIcUJINFz0Gty6CJqeB7ZyWPgSvJUOW3+pczOKovDd2v21NnB7+tI0JvRuiq4BF4gVwm2iW6rXue5ZTHDvsDbcN7wtKVrVHKsq0+a8dSCJkBDiVAkd4YZZcMXH6uZnhfvVzdDq6InvN3Pv9PU8/eNmDwYp6upAfhlP/7iZf83K1DqUhmXfSlj0b8ha7v62o9VFBHu2rePZWZnklVS6/xzeUlUOzyXAi02gvODcx3uZJEJCiNPT6aDDaLhzJVzyljpnqNreJVCSe8aHDu8Qj9mop1VcGIoPl/UIFIfyy/jozz1MW7bXv99Qfc22n+H3f8G6T93fdkwbHJHN2FgSyf8W78ZkcL0ntbzKzoF8DXpmqpfOV5WDxfeGxmWOkBDi7ExBtSc5luWrdZUMFrh2BsR3QFEUcksqiTm+RLt/61gWPzjY5XkNwj26N23Ejf2aMbhtHI1kjoj7HNqgXntiJVR8KmW3r6Zw3QHuLqwgzOra321t1jHGvL2ExpFB/Pnw+W4Kso6KstXrsHhNd7I/E0mEhBD1U3IUgqIgdzv830jKxn7Mo2sbsWJ3Hj/dfR6RwerGbZIE+Q6dTseTF3c494GifrKPJ0KJnT3SfIjFyDW93LNDeOPIIACKyquw2R0YvVnJuOSIeh2qUZ2zc5ChMSFE/cS0gom/qrvoVhRgnX4ljXb9QHZhOct2nXm4TIgGpSjn+JCP7tTafu7mcL1qfGyYhTWPD2P9k8O9mwQBlB5/XQj2zULKkggJIeovOAomfAepl6KzV/JExavM77OBCzr43q6x4oSySjvTlu5h8pdrtQ7F/2VvVK+jW51SJNVdjv76b+wvtcDx29Mut6XT6YgKMWuzalMSIf8jRVeFOLPyKjsPzdjAb9sL4PKPoNdtADRZ/QKs+UTj6MTZFFfY+NesLcxcd5A1Wce0Dse/Za9XrxM9t1PyT5uPYijL5dAuP199WZMI+eaeVjJH6DQyMjLIyMigsLCQiAjZAVeIk/3fn7uZvmofv2Zms/ih8wm54EV1Q7nN30HaWK3DE2cRG2bhtoEtiAox0yY+TOtw/Fv28UKrHiwZkW1KASCqPMst7c3ZnM0f248wsE0cw1K9OF8nuhW0Ggbxad47Zz1IIiSEqJdJ57VgbVY+1/VpSojl+EtI37vUniHD8ZUtiqJWtLeEaheoOK37hrfVOoSGYcx70P8+jw73PHTNhfDfR7EW7VXnCeldq8u3ak8eny7LIshk8G4i1ONG9eKjJBESQpzTxv0FNfWJzEY9H1zX49SDDCct7130b9j4NVz7DUSmeClKIbzIaFY3HvWkiBTQG9HZK9Ql6BGNXWquf+tYgkwGejb3zbk6WpE5QkKIs3r9t21c/NZipi3dU7cHlBeotcqOboX/DT0xqVT4DEVR+HPHUe7+Yi1VdofW4Ygz0RsgLEm9XXjA5eYGtInlvuFtOa91jMtt1Ytdo4r3dSSJkBDirCxGtTt+f113pLVGqMvrY9tDcTb830jYtcBzAYp6cyhwz5dr+WH9QZbvytM6HP+zZzF8fyds+Npjp/hx/UEmfbyKI4bjSUvBfo+dy+Neag7PJ8OxPVpHclqSCAkhzuq2gS348pbePDKyfd0fFJEMN/2iFm6tLIJPL4cNX3kuSFEvBr2O8T2bcEPfZiREyMaX9Za1DNZOg53zPHaKdfvy+W1LDjtM7aDFILeVpiirtLPnaIn3St/YKtTXgMoi9UOSD5I5QkKIWlbvzeOzZVm8fHknjAY9Op2O3i2cmFMQFAkTvoXvboPN38K3N0PRIeh3j9tjFvX3D5k07bzqivDVFeI9YEzXxjSLCcGc2AeaumfZeXmVnfZP/ALA+ieHExHkhXIrpcd7HHUGsEgiJITwcUXlVdw0dRUFZVW0TQjj1oEuvtAbLTD2QwhPgqVvqaU5hPB3R7er19GtPXaKtMYRpDV2b+JgNRmICDJRaXOQV1LppUTopD2E9L45CCWJkBCiRpjVxAuXdeTH9Qe5trd7ahyh18OI56DDGEg+zWozoaldR4rJLiynb0svT6D1V4qi1tkDiPFcInQKW6W6Us1FSx4+n2CzwXs7TPv4rtIgc4SECHiF5VUcPGki9KiOibx9TbcTewS5y8lJUHkBbJnl3vZFvc3fepjzX13Iw99s9N6cEX9Xmqv+/6KDqBYeOUWlzcGyXbnszS1BObIVXm4B/3FPPbMQi9G7ZTbKjg+N+XBvsCRCQgSwrNxSxkz5kxs/Wklxha3mfo++UJbmwXsD4KsJsHeJ584jzqlX8yjCLEaaxYRQdNLfX5xF9bBYRAqYgjxyioP5ZVz1/jIueP0PtSelNBdKDqsTj/1NeYF67aMTpUESISECmtmop6jcRkFZFdkFdVwe76qgRpDSGxQHzLgJio9457ziFMFmIyv/OZRPbupJuNUL80Uagupl7DGtPHaK0ko7zWNCaBYTgi44GozHV/a5YS+hxduP8th3G/lq5T6X26qT0ARoPRwad/PO+Zwgc4SECGAJEVY+ujGd2FALceFeWkat08GFr8LBNXB0G3x3C1zzjc9OpGzorCbXyjYEnE5XQNuRUFHosVOkJoUz//5BJ+4Ibwx5O6HggMvDcX9lF/LZ8iyKym1cme6FXd/bXqBefJi88ggRQKrsDp76YTOr956oPN4hKcJ7SVA1Syhc8TEYg2Dn77D4Ve+eX5yivMpea3hUnIUlVF0J6S3VpTXc0CPUo1kU9wxpzUWdEl1uq6GQROg0pkyZQmpqKunp6VqHIoRbvTlvO1OX7OH2T1dTVmnXNpj4VLjw3+rt+c+ru/UKTbz1+3a6PjOXz5bt1ToUcTrhyeq1G3aX7pISyb3D2jC8Q4LLbdWJw/dLuEgidBoZGRlkZmaycuVKrUMRwq1uHdiS3i2ieOGyjgSZfWBIpMs10Hm8Ol/o18fVpcnC6yKCzZRV2Wv1FIrTsNvg40vgx8lQWeKx07zw8xYmfbyKpTuPLz13Y4+Q131xFTyXBBtnaB3JGckcISEauGMllTQKUfcfCbEY+eLm3t5dPns21fOFzCEw6BH1a+F1F3VMpEtyJGmN3VPGocHK3wu7F8K+FXDhax47zfJdeazbl8/l3Y/3BMWnqWU2Ytq43LaiKJRU2jlWUklKVLDL7Z1TeQFUlYDBdyfjSyIkRAP2x/Yj3PHpGp67rCOXdFbnNPhMElTNHKImQ0IzjULMNcmyOIu83ep1VAuPTu5/YERbdh8toVPy8SXnHUarFzcoqbST9uQcALY8c4Hne4arJ5XL8nkhhBYWbTtCUYWNb1bv958N89Z9Drv/0DoKIU5VcHzJeWQTj56mX6sYru3dlKRI9+9TFGI2YDbosZr05JdVur39U/jBPkLSIyREA/bIyPYkNwrmqp4pvtcTdDprP4Pv74DQeLhtMYTGaR1RwLA7FP4zdxu/bclhxu19CXX3zuINQXUiFJGszfltleoQkwvPZZ1Ox4anhntv24TqRMjiu8Ou0iMkRANisztq9f7o9Tqu79sMi9EHJkbXRYcxENseinPgm0ng0HhlWwAx6HV8t/YAf2UXsS4rX+twfFP1qi0PJkJ5JZUs25XLvrzSE3faKuHFJvBsLJTnu3wOryVBdhtUFh8/aaR3zukESYSEaCAUReH2z9bwj6/X8/pv27UOxznmYLhiKpiC1Umpi/6tdUQB5Z6hrfnPuM60TwzTOhTf5IVEaOWePK56fxl3fbH2xJ1G84kVlSVHPXZutzt500mr9AgJITxMp9MxuG0cVpOe1CTffdE5p7h2J1bkLHgB3Z5F2sYTQK7skcKYrslEh1q0DsU3VfdueHCOkKJA85gQmseE1P5GSIx67YZE6Mf1B3n0u438/leOy22dleKAVsOg+QBZNSaE8I6rezVhcLtYEiM8UwzSa7qMh72LYe2nGGbehqX5P7WOSAi4dZG6f5DBcyvsLkhL4IK002x2GBwDebugxPXafCt25/H58iyiQ8yc3y7e5fbOKCQGrvXd/YOqSY+QEH5s9d5j3PLJKsqrTsyl8fskqNrIVyAuFUqOEFe4UetoAsbB/DK+X3eA/cdKz31wIDKHaNO7ERKrXrshERrcLpbJQ1vTt2WMy201BNIjJISfKq+yc9unqzlSVMGU+Tv4x/C2WofkXuZguOJj7Lm72be1nI5axxMgHv52I4u2HeGZSztwXZ9mWocjqrlxaOz8dvGe7Qmqpih+sUmq9AgJ4aesJgP/vaoLF3RI4PZBLbUOxzNi26C0PF/rKAJKr+ZRdEqOkOXzf7d9LkwbA3++4dHTPPD1em6aupLNBwtqf6O6R6jUjyZLr/tMLa/xzc1aR3JW8p8uhB9xOBSOllQQF6ZWi+/bMiZwurfzdkLWn5A+UetIGrSMwa3IGNxK6zB8T84m2Pk7hHh2b6ulu3LZf6zs1L9BXHtoeT5Et3b5HIqiUFxho7jC5tmh9MoStbyGo8pz53ADSYSE8BMVNjv3f72BdfuO8e3t/YgNC5yVPdbKPIzv9VP3FWraT11ZJoQ3eWHpPMC/Lk0jp7CcFn9fNdbxcvXiBpsPFnLRm4uJDbOw8rGhbmnztKpX2ZlDzn6cxmRoTAg/UVJhZ8P+fA7ll7N+X77W4XhVuTkKpdVwQIFFr2gdTkBQFKXWJPyA56VEaHC7OK7q2cSjtd+q2y6r9PDft7JEvTaHevY8LpJESAg/ERViZuqNPfn4pp4MTfXCREcfY+9/v3pj0zdwZJu2wTRwX67IIv2533hh9hatQ/Ed+dXlNVK0jcPu+jBTYriVrc9ewKanR7ghoLOoSYSkR0gI4aTNBwtYsvPE5MjmMSH0axUgc4L+LqETtL0QtVfoZa2jadBCLEaOFleybn/BuQ8OFNU9QpGeS4TyS9XyGruPlpz6zZLc42U24lwuPaPX67xTdkeGxvzXlClTSE1NJT09XetQRADbdKCAce8t45ZPVrM1u0jrcHzDoIfU603fwFE/LSPiBwa0jmXGbX2YfktvrUPxDeUFUHE8KQxv7LHTrNuXz1XvL+OOz9ac+k1rhBqH4oDSPI/F4FYyNOa/MjIyyMzMZOXKlVqHIgJY6/hQOiSFk9Y4nIQIq9bh+IbEzmqvkOKQuUIeFBFsokezKO8V5/R1JUchOBqCGoHFs2/qLWJCaBYdfOo3DEYIijoej+ubKr69YAePfreRvbmn6X1yl+hWkNLb4/OqXCWrxoTwURajgQ+u74HFqPef6vHeMPBB2PsnxLT2mw3bhJ+LbgkP7oKqco+eZlDbOAa1Pcvy/JBYKMtzSyI0c+0BtuUUc2HHRJpGe2jo6nz/KI0jiZAQPkJRFF79dRtJkUFc3Ust6hhu9d1ChZpJ6gL3bVF3nhYec6igjG9W76e8ysH9IxrYruXOMmncMxsSC0e3uiURGt+zCfmlVTSObCAleVwgiZAQPmJuZg5vzd+BXgc9m0fRKs63x9U1JUmQxx0rqeLfv24jxGxg8tDWGA0yk0Jz1WU2SnNdburGfs1dbqOhkERICB8xLDWea3s3oX1iuCRBdaEosOM3yN4A/f+hdTQNTtuEMMZ0bUzHxhHYHAoBPTr721NwcB30uRNae24Dwhdmb2H74WJu7t+CPi2jTz0g+Ph9bqg35hVvpUNFEUyY6dOboEoiJISGyqvsmA169HodOp2OZ0dLadE6O7wFPrscdHpIHa3O4xBuY9Dr+M+4LlqH4RsOroNd86HTOI+eZuWePNZk5XNljzNMLk5Ig1ZDoVEzl8/lcCgUVdjQ6yDMU0PwRdlQUQgG3x7il75OITRSUFrFNf9bztM/bkZRFK3D8T/xqdB6xPEVZP/WOhrRkFXPyQmN9ehp7h/elpfGdqRjcuTpD+hxE1z7DXS9xuVzvfTLX3R++lf++5uHtqFQFNlHSAhxdiv25LEm6xjfrT3AwQLPrkZpsKr3FdowHXJ3ahtLA5VfWsmG/flah6Gt4sPqtYcLrvZtFcO49CZemcAcalEHhEo8VWbDVq5+SAGfT4RkaEwIjQxLjeelyzrRKSVCVm44q3F3aD0ctv8Kf7wKo9/WOqIG5XBROT2fm4deB1v+dUFgbuPgcEDp8Tk5oZ5NhOrM4QC9a/0YNw9owS0DW3jub1p50v5EJt9e3CA9QkJ40Y7DRZRU2Gq+vjI9hXYJ4RpG1AAMfFi9Xv8l5O3SNpYGJjbUQrjVSEK4lcOFFVqHo42yvBM9G8GnmcDsrtNU2lm6M5cdh8+yi/yeP+H5ZHhvgMvns5oMnk1sq4fFTMGg9+0EWhIhIbxkTdYxLnt7Cbd9upoKm1T1dpvk7tBqGCh2WPSq1tE0KDqdjhWPDWXJI0NIifLtT/UeUz0sFhTl0Um/u44WM/6DZVz1/rIzH2S0QmURlOd7LA63qe4R8vHeIJChMSG8Rq/TYXMolFTYKK9yBOYwg6cMelgtitl6mNaRNDgBX2ajslgtrREa79HT2OwKLWJCiAoxn/kga4R6XV7o8vkO5JcxbelerCY9k4e2cbm9U+gMkNwTrL7f4y2JkBBe0iUlki9u7k3r+FCCzfLUc6vkHnDHUim3IdwvpSc8tMfliu/n0jklkt/vH3T2g6oToYpCl+cJHSup5N2FO4kPt3gmEYprB5Pmur9dD5BXYyE86JvV++nVIorkRmr3cOeUSG0DasgkCfKIPUdL+O+87dgdCm+M76p1ONrxhXkuNb0ripoMBUU63VR8uJWb+jUnOvQsPVABQhIhITzkyxVZPPztRprHhPD9nf2kbpg3VJXBqo+g5DAMfUrraBoEnQ6+W3sAs1GP3aFg0EvCqRmjRZ0nZCt3ORGKDbPwxMWp7ovNj8lkaSE8ZGDbWBpHBnFhx0TCLPKZwyuyN8GcR2DJm5C3W+toGoTkRsE8MKItb47viiMQN/6c/wJ8cin89ZNHT/PJ0j3c+NEKvl934OwH1swTKvBoPC5b+ym82h5mP6B1JOckiZAQHpIYEcTse/pz/4i26GTYxjtS0qHl+eCwwdK3tI6mQTDodWQMbsWIDgmYArHw6sE1sGuBx+t7bT5QyPytR9h/rOzsBzbtBy2HgN71HmaHQ6GwvAqb3eFyW6cozYWig26Z2O1pAfhfLYRnlFfZmfzlWlbtyau5LyJIhsO8rs+d6nXm9x6f4CoCQE15Dc9upjiuZwovj+3EoLbnKONxxUcw4Vu3FDHt/cI8Oj31K9tyil1u6xRVxxM6k+9vFiuJkBBu8vb8Hcxcd5DbPl1Dmae2rRfn1nyAOnxQcgT2rdA6mgahrNLO+n35rN6bd+6DG5ri44mQh8trdGvSiCvTU+iQFOHR85ysusxGUXmV+xu3HS8b5AeJkExcEMJNbh/Uig0HCrilfwuCzD6wwiRQGUzQ5gK1/thfs6BpH60j8nvz/srhzs/X0rVJJN/d0U/rcLxHUdSJ9wAhMdrG4gHf3tEXq8ngmb2iqo4nQkaL+9t2M+kREsIFJ/f8BJkNTL2xJ31bNbwXTL/T7iL1esuP6puZcEmruFBiQi3EhPr+m5pbVRSCvVK97eGhsQ3789lyqJDyqnP0Js99Ui2zsfBll88ZGWz23IaZ1T1CRt/vEZJE6DSmTJlCamoq6enpWocifNiuI8UM+89CZqzer3Uo4u9aDVHrQiV2PlHzSDitXUI4q/45lA+u66F1KN5VPSxmDvP4EM9NU1cy8r9/sPtoyTmOVNQyG2X5Ho3HZTVDY1Zt46gDSYROIyMjg8zMTFauXKl1KMKHfb/uIPuPlfHewp1U2jyw6kI4zxwC/9gG46aBJUzraIS/qixSa4yFeba8BkB0iIXoEDPh51pg4cbl879syualX/5i2a5cl9s6RVgCxLb3+Nwqd5A5QkI4afLQ1pgMOq7q2QSzUT5T+ByDvLwJFyV1hYd2e2X14Zx761hR3nJ8d2k3FF79/a8cvlq1n1CLkd4tol1ur5ahT/nNpqbySiFEPWw6UECHpHB0Oh06nY47z2+tdUjiXI5uV4tmNsDJrt40e+MhPvhjF71bRPPQBa4v3fYrvlBeo5o1Ur2ucH1/nn6tYgixGOmU7L2Var5IPsYKUUffrd3PpVP+5PnZW1BkAq5/mHkHvNUD1n+hdSR+r6TCxtqsfNbvy9c6lMDmxqGxS7s05smLO9C/9Tn2LmrgJBESoo5sdgW7Q+FocSUOyYP8Q2Jn9XrLLG3jaAD6tYrhrau78sylaVqH4j0LX4GPL1E35/SgHYeLuPGjFTz1w+ZzH+wvJTa+GA9vpcPepVpHck4yNCZEHV3RI4XGjYLo3TwavRSe9A/tLoSfH4R9y6H4sMeXQDdkSZFBJEX6/lJotzq0DnYvhPYXe/Y0BeXM33qEdgnl5z44JAaS0yEi2S3ndjgUbA7F/fMc83bB0W0nth/wYdIjJMQZKIrClyuyau3r0bdljCRB/iQiWZ3wiuLxopmiAfJSeY1WcaG8PLYTGYNbnfvg6JYw6Te4YqrL5/1s+V5aPDqbe75c63Jbp/CjnaUlERLiDF6Zs5WHv93IrdNW45CxMP9VvbniXzI85qo9R0v4Yf1BNh3w8WEZdymu3lXas4lQYkQQV6ancHHnJI+e5+/Mx4volp1rE0dn1OwsLfsICeG3BreLI9hsYGj7OOkF8mfVwxq7Fvr+vAof99Gfu7n7i7X8uP6g1qF4h5d6hLRyYadEVv1zKO9c0939jdskERLC76U3i2LhA4OZ0KeZ1qEIV8S2hejW4KiC7XO1jsavdUqOpEfTRoExV6iq7MSu5B7eeuFAfhl/ZReSX1rH+TTv9ocXUuDwFpfOG2w2EhNq8UxtRD/aWVomSwtxXKXNwQs/b+HWAS1JiFCfvLFhAVZbqaEa8jjoTdBysNaR+LWx3ZMZ2909k3R9XnXvoc5wYhNDD/lg0S6mLtlDxuCWPDCiDns0leer+whVlno0Lqcpil/1CEkiJMRxz8zazKfLsli2K49Zd52HQYbDGo7US7WOQPibyhJ1I069EXSefS2wGPXEhJppFGyu2wNMwep11bnqkp3d4aJyvl61H5NBxy0DWrrUVi32Kohpo84T8oPJ0pIICXHcrQNasmRnLg+PbCdJkBDnoCgKOg8nCJqKbgkP7VF7NzzskVHteWRU+7o/oDq5qCpz6bx5JZW8MmcrMaFm9yZCRjPc6T+1OiUREgHt5BfzlKhgfp08AKNBps41SHm7Ye00MFhg0ENaR+O3nvh+Ez9tOMRTl3Tw+ionTfhismcKUa8rXesRig6xMK5HCpHB5yj02sDJK74IWPmllVz74fJaJQMkCWrA8vfCH6/Cive9UkSzoSooqyK3pJLsgjps/ic8w009QrFhFl66vFP9eqMaIHnVFwHr1V+38eeOXCZPX4fN7tA6HOFpTfupBStLj0LWMq2j8Vt3nd+KWXedx5XpKVqH4lmZ36vlNRa/7vFTPThjPfd8uZZ9eXWc/GyuniPko5Olc3fC233gE/+YmyeJkAhYD49sxwUdEnhvQnfpCQoEBhO0Hanels0VndYqLoy0xhFEBDXw4ZTcnWp5jaPbPX6qOZtz+H7dQSpsdeypjG4NjXuok7ndQFEU9xaSriiCw5lwZJv72vQgefUXAeXkchkhFiPvTuhOm/gwDSMSXlW9y/SWWV6ZBCv8WPXyeatnl84D/PPC9vzzwvbEhddxqfmQx+HmedDxcpfP3fWZX2n56GwOuXOo04/2EAJJhEQA2XWkmCGvLuT7dQe0DkVopeX5YAyCgizI3qB1NH6psLyK79cd4IsVWVqH4lkVhep1dbV3D7qiRwqT+rcg3Or9Xja7Q8GhuLnMhh/tIQSSCIkA8s2a/RzIL+Pt+TupkjlBgckcDK2GqLe3yPCYM/JLqrjny3U8/eNm9w6n+JqaHiHPJ0Jamn1Pf1Y8NoSmUcHua9SP6oyBLJ8XAeQfw9piNhi4pncTTDInKHC1uwj2LlE3yhP1FhduoXeLKBLCrVTaHViMHijP4AuqEyEP7ypdWmljX14ZkcEm4us6NLZ6Kix8BdpdCKNedun8yY3cmABVsx1fzSaJkBDa25dXSnKjIHQ6HXq9jnuGttY6JKG1tLHQ8QowyMufM6wmA1/e0kfrMDzPSz1CmQcLufzdpTSNDmbhA3UsAWOrgML9UJzj0dicZqtQr2WOkBDaWrozlwteX8TLc7Y27C58UT9GsyRBom50eo8nQpV2B1EhZmJD61HXsKbEhmv7CAHM3niIdxbsZMfhIpfbqmEwQVgShMS6r00PklcD0WDtPlpCSaWdjfsLqLIrmI0+uEOs0I6iwJGtEFeHIpci8Ez6Tf0f8fCHqL4tY1jz+LD6PahmQ0XX9xH6bPle/tyRS1KklVZxblpBmzZWvfgJSYREg3V1ryZEhZgZ1DYWs1E6P8VJKktgSi8o2A//2Aph8VpH5Fdem7uNL1ZkMfG85tw20I01qnyNTuejJTbct6Fi/9axJEYE0TjS94ujeookQqJBWbz9KD2aNcJqUidwXpCWoHFEwieZQyA0Dgr2wdafoMdNWkfkV2x2B0eKKqTMhlbM7hsaa9CJbB3Jx2TRYMxYvZ8J/7ecOz9fI8vjxbmdvLmiqJfxPZsw667zmNxQFx+U5qnlNb66zuNDY3Mzc5j85Vq+Wrmv7g9yY4+QRyx7F/43FFZ8oHUkdSKJ0GlMmTKF1NRU0tPTtQ5F1ENSpBWzQU9cuBWDL3ZnC99SnQjtXnRilYuok5SoYNIaRxAZbNY6FM8ozVPLa+yc7/GhsS2HCpm57iBrTyr+fE7WSIhtB9GtPBWWa47thv0rofCg1pHUiQyNnUZGRgYZGRkUFhYSEdGwN9NqSPq2jGHWXefRKi4UnSRC4lxiWqtvKOX56qTpxE5aRyR8RYX3NlPs3zqGYLOhfqV+YttAxnK3nP/Nedt5f9Euru7dhEdGuqkKvb1SvTb4R6IsPULCbymKwtQ/d3O46MQ8hdbxYZIEibrR6SA+Tb2ds1nbWPxMhc3O9+sO8P6inTgcDXBrCi/uKt21SSMm9W/BgDbaLDW3KwpFFTaKy21ubLRKvTb4R2Fe6RESfuu9Rbt48ee/+HxFFj/ceV7NBGkh6iy+A+xdDDmbtI7Er+jQcc+X6wAY2y2Z6PrsgeMPvLSrtC+4tndTLumcRFSIG3tvJBESwjtGpiUw9c89XJXeRJIg4Zy2F6if+lsM0joSv2I26hmWGk+I2YC9QfYIea/gak5hOTaHQlSwmSBzHV/HHHZ4py9UlsJtiyCokdPnjwm1EOPuRNZRnQj5x9CYJELCbzWNDmHufQMI06Bis2ggWp6vXkS9fXBdD61D8BwvDo09PnMTv2bm8NyYNK7p1bRuD9IbIHenmnBUlrqUCHlEdY+Qn9TzkzlCwm9U2R088u1GNh0oqLlPkiAhhNvZKrxSXgNAr9NhNuoJqm+vttk9S+j35pbwydI9zN54yKV2ajEFqb87kwcKunqAf6RrQgBv/b6DL1ZkMW9LDgsfGFz3bmQhzqYoG7I3qqvIGjXTOhq/oyhKw1ugMPABGHD/iZ4ND3p3QneA+tdDNAWrPVcuJkKZBwt54vvNpDdrxKiOiS61VeOy993TjpdIj5DwG5P6N6dnsyheHNtRkiDhPrPvh88uh79+0joSvzJt2V7Sn/uNJ75voCvudDq1QK/XTlfPZLK6t6XStUQoKTKIUR0T6NMyxqV2/Jn0CAmf5nAo6PXqC0SY1cT0W3s3vE+fQlvxabDlR1lCX096HRwpquCQlNnQhpt2l+6cEsnb13R3Q0D+SxIh4bMKyqqY9PFKbujbnAs7qV22kgQJt4vvoF7LEvp6GZ6aQOfkSJIaYrHOXx5RC/L2vw+Sunr0VM/8mEm5zU7G4Fb1K3zqpjlCHjHrPsjdAef/E1J6ah3NOcnQmPBZny7by8o9x3jyh82UVrpxsy8hTladCB3ZCnb5P6ur2DALaY0j3Lv/jK/YtRC2/ABl+R4/1cx1B/h8eRYlFfX834tsCjFtwOCDezgdWKWWKCkvOPexPkB6hITPum1gS44UVTAuPYVgs/yrCg+JbAamEKgqgbxdavkCEdi8uHz+zsGtKCq3EVvfvXzGuqeg6ZZDhdzw0QqiQyzMvqe/W9qs+UDhJ8vn/SNKETAKSqsIDzKi0+kw6HU8dUkHrUMSDZ1eD3Ht1U+xOZskEaqHH9Yf5FB+GVelNyEiuAFtZeHFROim85p7/BznklNYgd3hxgal1pgQzsnKLWXUG3/w+m/btQ5FBJqaeUIyYbo+nvspkxd+/ousPB+cp+Isuw0qi9TbXkiEtNY8JoRZd53Hl7f0dl+jDimxIYRT/tx5lAP5Zfyw/iA3D2hBqEX+PYWXdB4PTfr4xcROXzK0fTwlFTaspgb0mbqi8MRtD9caszsUjhRVEGQy1L9HbclbsO4z6HI19L3L6RisJgNpjd2c8FUPjUkiJET9jO/ZBB1wfvs4SYKEdzXto15EvTw3pqPWIbhf9bCYKdjj+wjlFJbT98XfMRv0bHtuZP0eXJoLhzOh4IBngnNF9dCY3j8SoQaUxgt/tPtoCZW2E4PTV/VsQlyYVcOIhBABrbJYLa/hhcrzFTYHBr3OuR41N+0jVGGzM2P1fr5YkVX/3a3PxGBSkyDpERLi7DYdKODaD5fTu3k0b17dFZNB8nKhof2r4MBqaDkEYlppHY1faVBlNhI6whN5Xtmfp3lMCDufH0WVMzOVTcf3HHI5EXJw/9frARjbLRmz0Q1/x/syXW/Di+SdR2gmr6SS0go7OUXllFfZtQ5HBLqFL8HPD8LuBVpH4jfmbM6mx7O/MfHjVVqH4l46HZhDvHY6pz4E1myoWObSuS1GPYPaxjIsNR6Hu3qE/Iz0CAnNDGgTy7SJPUlNCpcq8kJ78R1g+6+ycqweLEY9R4ulzIYm3DQ0ZjEamHpjYC8SkERIeNXarGM0iQom+vjmYb1aRGsckRDHxaep15II1Vn3po346e7zSAhvQPP6Mn+AjV9Dy/Ohx40ePdWmAwV8tWofreJCua5Ps/o92E1FV93OYYfPx6nzgy57HyxhWkd0TjI0Jrxm+a5crvnfcq79cAX5pZVahyNEbTV7CWVCgA4R1FeY1USHpIiaDzYNQs5mtbxG9gaPn2rnkWI+WbqXOZuz6/9gaziEJUKwj32YtFfBjrmwdbbWkdSZ9AgJr4kJsxBsNhITasZqMmgdjhC1RbdSV7pUFkF+FjRqqnVEQguVxeq1OdTjp2oTH8bdQ1qT7Ezh2haD4B9/uSWOi99cTF5JJV/c3Jsm0cGuNWY/6UOunyyfl0RIeE3L2FC+ub0P8eFWSYSE7zGYILYd5GxUewUkEaqTXzdns+toCRd2TCQlysU3UV9gOz7fyeREclJP7RPDaZ/o+WX653Iwv4zckkrKbW5YtOI4qXisLJ8XAuZvPUxihJV2CeqTvWm091ZiCFFv8R1OJELtRmkdjV94d+FO1mTl0yw6uGEkQlXHEyFjAxruO4cPb0gHIKWRG/5+1T1COj3o/eMDryRCwmOW7DjKLZ+sItxqYmZGv4bxIikatr53Qo+bID5V60j8xoA2sTSNDiGmocwTsh1fjm70fI9QSYUNh6IQZDJgrO8S+rxd8M3N6mTk62a6FEeXlEiXHl+L/XidMT8ZFgNJhIQHdUiKoE18GM2iQ0iIaECrSkTDldAAS0Z42OShbbQOwb2qe4RMnn/Nev23bXzwx25uHdCCR0a1r9+D7VVwYBUENfJMcM7ys8rzIImQ8KCIYBOf39ybELMTn3aEEEIL1XOEvNAjVHZ8I1mn5kxWz7+p7oFxwR/bj5BXUknvFtHEu7oVQvUcIYP/pBf+E6nwC9+vO0Cw2ciw1HgAIoL8p3tUCAA2fQP7VqhDZLFttY5GeNs1X6u7NXthou+/Lk3jnxc6OQyrd18i9PIvW9l4oICPbkh3PRGKbQtP5rslLm+RREi4zbJdudw7fR0GvY6ZGf3okBShdUhC1N/qj2H3QnWDRUmEzumP7Ue464u1tIkL46vb+mgdjuv0BrB4fuk8gE6nc34FbfXQk931Pdk6p0QQHmQkPMhNKYFOB0YZGhMBqEfTRoxMSyQi2ET7BO2XhArhlPgOaiIkO0zXiUGvI7+0imOySap31fRYKepuzi6s0Hp2dGDPjZNESLiN0aDnv1d1waDXNZxK1CLwVO8wfVgSobronBzJr/cOILyh1Av8+WGoKIQBD0BUc4+e6uMlezhUUM6Yro1pm1DPUhQnD93ZK0Hv+TlNdXJ4Cyx4ASKbwPBntY6mTmQGq3DJx0v28PaCHTVfGw16SYKEf6tOhLI3SamNOgixGGkTH9ZwVoZmzoR1n6nJkIfNXHeAdxfuJCvPiXphBjNYIyAktvYmhloryobM72HH71pHUmfSIySctmF/Pk/+oH5q7takEb2lgKpoCGLbqZvBleVBcQ6EJWgdkfCmKu/tIzS6S2O6NWlE8xgn9lgzWuDhLLfE8dxPmSzcdoSMwa24tEtj1xqrniTtJ7tKgyRCwgWdkiO5b1gbKmx2ejWP0jocIdzDFARRLSF3uzpPSBKhs7I7FL5atY+SChsT+jTFYvSP3YTPyOa9naWv79vM4+eoi+zCCrblFJNb7IZ5Xg5JhEQAcDgU9Hp1+OvuIa1RFEWGw0TDEt9BTYSObodWQ7SOxqfpgEe+3QjApV0aExvmx4mQoni11pivyBjckvHpKTSPdUMJJNlQUTRkiqLw33nb2XmkhP9c2blmk0RJgkSDM+wZGPVvCI3VOhKfp9fruLBTIia9Dr2/vxTYKk7cNnp+zlNZpR2LUV/zwbLevrgayo7BZe9DZIrTcbRz5ypf+/H5Snr/SS/8J1KhuZ1HSnjr9x3YHAqjuyQxpH281iEJ4RlSeb5eplzdTesQ3KO6zhh4pUeo89O/Uml3sPSR80mMcOJ8+5ZBaS5UFrs/OGfVDI1Jj5BfmzJlClOmTMFut2sdik9pFRfKW1d3Y/+xUkmChBANT3WdMZ3B43NcbHYHlXYHAEEab6q443ARu46UkBIVTPtEF3uHaobGZI6QX8vIyCAjI4PCwkIiIgJ7d2RFUSirshNsVv9VLkiTiaMiQCx6RS21Mfw5iG1ghUXF6YXGw0N7aw+ReYhBryPzmRGUVzmc34OppsyGa8vnZ6xWl/FPPK85j1/kZMmPal2uhbTL1d2l/YTsIyTOSFEUnv4xk6veX0ZBmf/UjRHCLbb/Btt/hUPrtY7E593z5Vq6/Wsuszce0joU1+j1EBQJYZ7v8dbpdASbjUSFmJ2fI1RTeNW1HqGUqCC6Nokk0R17QRmMaokSsxsmXntJvXuEtm7dyhdffMEff/zBnj17KC0tJTY2lq5duzJixAjGjh2LxeL5ZYfC87ILy/lh/UGOlVaydGeu9AaJwBLfQZ2DkbMJuELraHxaSYWdvJJK+cDkbW5KhK7p1ZRregXuvLg6J0Jr167lwQcf5I8//qBv37707NmT0aNHExQURF5eHps2beKxxx7jrrvu4sEHH2Ty5MmSEPm5xIggPp3Yi605hZIEicBTvcO01Bw7p39e2J4HRrQlMdLPd5c+sg2WvgkRTWDgAx491eHCcj5euoeoEAsTz3OylEd1IuTwoQT0r59gyyxoPgC6jNc6mjqpcyI0evRoHnjgAaZPn05U1Jk3z1u6dCn/+c9/ePXVV3n00UfdEqTwHrtD4XBRec0KhtSkcFKTpICqCEDxaer14Uxt4/ADzWL8ZxjkrAqyYM0nEN/R44nQoYJypszfSVKE1flEyBSiXnypFMyhDbD+c3XVXUNLhLZv347ZfO7lcH369KFPnz5UVkolYn9jszu4/+v1LNmZy/Rb+9C8oby4CeGMuPbqdeEBKM2DYNk9vcGrXjVm8nzPVlSImev7NCU8yIXVVRPnuCWWOZuzeXvBTno2a8RjF7o4WbohL5+vSxIEUFpaSnBwcJ2PF76jpNLOX9lF5JVUsj2nSBIhEdis4WoF7fwstVeo2XlaR+SztucUsXrvMVKigunXKkbrcJxXU17D84lQSlQwT1+a5vHz1EV+aSXr9+UTE+KG9+2a5fP+syjdqVVjgwYNYv/+/afcv3z5crp06eJqTEIjEUEmPp3Uiw9vSGd4B5kTJATxaWAOg+LDWkfi0xZtP8rD325k+sp9WofimgAsrwFwXutY/nddD+4Z2tr1xqqX8vtRj5BTiVB4eDidOnXiyy+/BMDhcPDUU08xYMAALrnkErcGKDyrwmZn3b78mq9jQi0MbCNlBYQAYMx78Mg+SLtM60h8WouYEIa0iyOtsZ/PJ6ypPO/5HiHFHfN6Fr4Cn46F7XNdaqZxZBBDU+PplBzpekzVPUL6Br6h4g8//MC7777LpEmT+OGHH9izZw9ZWVn89NNPDB061N0xCg+psNm5/dM1LN5xlA+v70H/1pIACVGL1c/f2L1kcLs4BreL0zoM13mxR2jelsPcPG0V3Zo04pvb+zrXSPZ62PEbtB3p3uBcUTNHyH+GxpyO9LbbbmPv3r289NJLGI1GFixYQN++Tv4xhSZ06DAcL5Sow392ARVCCI+onixt9PzWLzaH4vpir5oSG67tLJ1fWsnmg4VYjHp6NHNxUYC9AU+WPtmxY8eYNGkS8+bN47333mPhwoUMHz6cl19+mTvuuMPdMQoPMRv1TLm6G9tyikhrHNilRIQ4o29vhQOr4OqvILql1tEIT+pzB3S52iuJ0OB2sax4bAh6V0pRuKnW2KYDhVz74XLaJYTxy+QBLrXFqH/DsGfAFOxaO17k1ByhtLQ0cnJyWLt2LTfffDOffvopH374IY8//jgXXnihu2MUblRSYWPWhoM1X5uNekmChDibI39B7g7ZT+gsdh0pZsDL8xnxn0Vah+IacwhENIYQz698sxgNxIVZiQl1IenSH+/LcDERCg8y0i4hjGbRblgpbAmF0Dj12k84lQjddtttLFq0iObNT2wCNW7cONavXy/7B/mwCpudGz5awZ2fr2Xasr1ahyOEf4hIVq9l5dgZGfV6svJKycor1TqUwFLTI+TaztKdkiP5ZfIA3p3Q3Q1B+R+nhsYef/zx096fnJzM3LmuzV4XnmM26OnVPJq/sotIk92ihagba6R6XXZM0zB8WXyEhW9u70OIxX8myJ7Wus8heyO0u9Dj+0ZtOlDAou1HaBkbyghntyupToR8qcTGyg/h8BboeAU06aV1NHVS5x6hrKysejV84MCBegcjPEun0/GP4W2YM3kAXZs00jocIfxDUKR6XZ6vZRQ+zWI00L1pFO0S/PwD1vZfYdnbkL3J46dauy+fl3/ZyndrXHivrF6Z5XBtsrRbbf8VVn4AR7dpHUmd1TkRSk9P5+abb2bFihVnPKagoIAPPviAtLQ0vv32W7cEKFyTX1rJm/O243CoyxN0Oh1JkYG1WZgQLqlOhMrytYxCeIOtQr32QomNljEhXNE9mT4to51vZOjT8GS+OjnZBdkF5Yx/fxkTPlzuUjsAOOzqtc6pmTeaqHM/5pYtW3j++ee54IILMJlM9OjRg6SkJKxWK8eOHSMzM5PNmzfTo0cPXnnlFUaO9KF9DQKU3aFw3f+tYMP+AgrLq1yvISNEIAo63nsqQ2NnNTczh2MllQxLjaeRO0o1aKFmQ0XPf1js2yqGvq6WI9Eb3BKLzeFg6a5cLEY3JC+KQ71uiInQ/v37eemll3j22Wf5+eefWbRoEXv27KGsrIyYmBiuueYaRowYQVqab9ROEWDQ67i5fwue+2kLV/ZI0TocIfxTcDQERfnVcmAtPPXDZg7klzEzoZ//JkI27xVd9SXRIRbeGN8Vs0ESobPq2rUr2dnZxMbG8o9//IOVK1cSHe1Cl57wios7JzGkfRzBZj+fxCiEVjqMUS/irPq0jCavpJIgk3t6KTThxRIbbrFtDqz7DFJ6q3sgOSnIbOCSzknuiUk5PjTmpt4qb6hzyhYZGcmuXbsA2LNnDw6Hw2NBCeflFJZz3/R1FFecmDwnSZAQwtP+fUVn/u+GdNomhGkdivO8WH3+rd+3k/bkHJ77yYX9qfJ2Q+b36oafvqJ6u2xXNor0sjq/Q44dO5aBAweSmJiITqejR48eGAynz/iqEybhXYqicOu01azbl4/NofDG+K5ahySEEP6jukfIC7XGyqrsFFfYqLK7UGfD4J4NFRVFYe2+fKpsDro1bYTJlSGyhjw09v7773PZZZexY8cO7r77bm6++WbCwvw482+AdDod/7o0jYe/3cADI9pqHY4QDUNlCXw+Tl0+P+l3MPrp/BdxbjfOVv/eEZ6fU3lL/5Zc0T2FUKsLPfZu2lDRocBlby8BYO3jw1yb43X5R1BVCiH+U8S7Xn+BCy64AIDVq1dzzz33SCLkIxRFQXe8G7JjcgSz7jqv5mshhIuMVtjzh3q7vABC/ecF3pv+M3cbM9cd4KZ+zbm+bzOtw3FO9S7i3jhVsImIYJNrjeiPP97FREivg6bRweh1OtdHtMLiXWzA+5xKRT/66CN3xyGctOdoCf/4ej2vj+tCSpS6qkWSICHcSG8ASwRUFKi9QpIInVZBWRV7c0s5XFSudSiBw1CdCLk2NKbT6Vj4wGA3BOSf/GcQT5zWP2duYvXeY/xzpud3QhUiYAUdL0wsewmd0XV9mvL1bX24tndTrUNxjsMBv/4Tfn8WKj1fM+3PHUeZ+uduNuzPd76RmhIbPrSz9LJ34NfH4UgD3Fla+KbXruzMiA7x/PuKzlqHIkTDVbOpYr6mYfiyFrGhpDeLIjHCT3eut5XDkjdh0SsnJvx60A/rDvLUj5n8sf2o8424qUfIrdZ/CUvegHz/Kewt66r9UKXNgfn4DqBx4Vbem9BD44iEaOCqC69KvbGGy3bSkJ4Xls93TI6guNJGq7hQ5xtpNRQePXiiZ8gFt05bRYXNwb+v6ExMqMX5hmpWjfnPFA1JhPxM5sFCbv5kFa9e2ZneLWRDSyG8QuqNnVNOYTmr9x4j1GJkQBs/nEdVvXRebzyxLN2Dru3d1PVhRIPpRK+QixZtO0pZlZ2ySrtrDTXk5fPCN7y/aCcH8sv472/b6XVzlEyMFsIbQmLVUhu4sOdLA7c2K587PltDtyaR/pkI1Wym6KdDey56bkwaDgXXy6PUJEL+s7O0JEJ+5sWxnYgNs3DXkNaSBAnhLRe+ql7EGcWHW+jZLMp/d5au2UzRT8prABzbCwteBGs4jHzJpaYu6+amrQOkR0h4Qm5xBdHHx2ytJoNUkRdC+JyuTRrx1W19tA7DebYK9dpLPUL3fLmWJTtzeeKiVC52ts5XeQGs/xzCEl1OhNzGcXxozY8SIf+JNECt2J3HwFcW8PnyLK1DEUKIhsvm3R6hvJJKjhRVUGlzYYWaG1eNbc0uYtOBAsqr3DRHyI+KrkqPkI9btO0IxRU2Zm88xFXpKej1MhwmhNftWwm/PwMRTWD0FK2jEZ6Q1BVuX+q11U7Pj+lIUbmNpEgXEi83ldgAuPqDZeSWVPLrvQNoE+/C8OY1X6u9a438Zz8pSYR83D+GtyEx0srYbsmSBAmhlapS2L0IYttpHYnPKq20ceV7SympsPPL5P5YjP7TIwCAOQTivTftoLoSgEsM7imxARAbZsFk0OPy20x0S5dj8TZJhHzQ1uwiWseFotfr0Ol0XNPLfzJrIRokWT5/TmaDnk0HCgEoqbD7XyLkj2p6hFwfGvtl8gCX2/BXkgj5mPl/HebWaasZl57CM5d2kJVhQvgC2VDxnIwGPVNvTCfYbCTU4odvLQdWw/a5ENceUi/1+Ol+2ZRNYVkV/dvEOL8bd3XRVcWulgjR+8C03yVvQmUJdL8BwhK0jqZOfOC3Jk52rLSSKoeDw0Xl2ByyZ4kQPqG6R8hWfmKZtTjFoLZx9GweVbPzvV/ZvxoWvACbvvHK6f47bzsPfrOBbTnFzjdy8maKDteHx9xiyVvq77H4sNaR1Jkfpu0N22XdkokPt9KzeRQmgx++mAjREFnC1Q3iFLs6PGYKzE33GrTqVWNeWj7fq3kUiRFWYkJd2MDQHAr3bz++w7RrGyE+8f0mjhRV8OAF7WgeE+J8Q7KPkHDGH9uP0L1pI4LN6p+jX6sYjSMSQtSi04E1Asry1OGx8EStI/JJa7KOkVNQTtcmjUiI8KONCQGqju8s7aXl809d0sH1RvR6CI1zvR1gwdYjZOWVMql/i4BLhPwn0gbqh/UHuf7/VjDp41Wu798ghPCc6jIbMjR2Ri/+/Be3f7aG1XuPaR1K/QV4iY3JQ1vzr9FppES5+PPLPkKivhpHBhFkMpDcKEiGwoTwZRnL/aqithZSE8NxOBRCrX741mLzbo+Q2/zyiDo5eciTEOJ8IW73ldjwv52l/fC/tWHp3rQRP951Hs2iQ2SfICF8mSRB5+SW4R6tVHl3jtClby0mv6yKD69Pp1VcqPMNrfkEKouh3z0uJUJuoxxf5ONHiZD/RNqAfL/uAAfzT3Svt4gNlSRICCG0VDM0ZvHK6bLyStmbW4qiuLg62E2bKh7IL2PnkWJKK22uxeOHc4SkR8jLvlu7n3unr6dJVDA/3NmPyGDXZvoLIbxk9cew8WvoMAbSJ2odjXC3QY9Aj4kQ7mQB1HqaNrEXFTY7yY1c3GG6erWYi8vnJ05dyV/ZRXw2qZdrC3ZumKUWXg3znwUFkgh5Wa/m0TSJCmZYajwRQaZzP0AI4Rvys2DPH+qGe+K0Zqzez8dL9jCkfRyTh7bROpz6adTUq/Wx0hpHuKchN+0uHWoxEm41uj4CnNTVxQa8TxIhL0uKDOKHO/sREWSSXaOF8CdBjdTrMj9cEeUlx0oq2XiggJaxLiy/FvWjP/42bndtSGvG7X3dEIx/kkTICz5dtpf2ieF0b6q+kMpwmBB+SOqNndPwDvG0jAuhcaQbCop627rPoTQX2l0EUc09frqZaw9g0OsYlhqP1eTCUnM31htziz//C+igx01gcWESuBdJIuRhP288xD9nbiLUYuSXyf1dHw8WQmhD6o2dU9PoEJpG+2lv0LJ3IHsDxLbzeCJkdyhMnr4OgLWPD3NPIuQrJTbmPqFed75KEiGhGtg2lt4toujWpBGNIwNzoy4hGoSaHiEZGmuQbBXqtdHz+wg5FIX+rWOosjuwmFxcXXXNV+qS9RDXKhK8OW87u3NLuKlfc+fnL528Ak5WjYlqwWYjH9/UE7NBL3OChPBnNXOE8jUNw5cVlVexfl8BCgr9W8dqHU79VNca80IdOZNBz7SJvdzTmJtWuS3YdoTVe48xokOCC4mQ48RtSYQC2weLdhEVYmZsd3WnTovRf7YaF0KcgTVSLbyqN6iffOWDzSn25pZy7YfLiQ+3sPzRoVqHUz/Vtca80CPkiyb0bsrw1HjaxIc534gkQgLUAqrPzd6CTgftE8NJTQrXOiQhhDuEJ8ETuZIAnUVEkIl2CWHEhnlnU0K3svlpIrTmEzi8BdIuh+TuTjczumtj12NxnFQvUxKhwHVeqxiu7d2EmFCLJEFCNCSSAJ1TSlQwv0weoHUYzqkuseGFWmNHiioY/8EyrCY9s+7q71pjW2bB9jkQl+pSIuQW0iMU2BRFQafTodPp+NelaTIfSAgh/IXDfmLVlRdqjVXY7Ow4XIzF6IZkobrEhourxo6VVFJusxMRZCLY7GRqcHIiJNXnA8sb87ZTWFbFYxe2r0mGhBAN0C+PQPZGGPY0NNb407dwIx3cNEftFbK6acfns4gJtfDFzb1drzMGJ+0j5FoidPeXa/lj+1FeH9fF+WEyoxVu+ElNiAz+s1+eJEIu2nSggNfmbgPg/HZx9HWlRosQwrcdWA37lkPBAUmEzuDmT1aRU1jOlKu7kRLlJ/um6fXQpLfXTmc1GejT0k2V4muKrrq2oaJBr8NkcPFDvMEIzc5zrQ0NSCLkorTGETw7Oo2icpskQUI0dLKp4jltOlDAoYJyjpVW+k8i5NfcMwIx9caebmnHH0ki5ARFUbA7FIwGdXz32t7eK9QnhNCQ1Bs7p2dHp6Eo0DTKj3aYriqDtZ+qt9MneXxifEFpFUt35RJmNbpW6d3XVJbCus/U31+PiX6zwEASoXpSFIXX5m5jw/4C3pvQ3bWt0YUQ/kXqjZ3TkPbxWodQf5WlMPt+9bYX3sB355Zw26erSW4UxOKHzvfoubyqoujE7zF9krax1IMkQvW0L6+M//2xm7IqOwu2HuaCtEStQxJCeIsMjQk3CDIZ6NYk0j37LQ19Cvr/w+USG1+syGLTgQIu7dKYns2jnGuketWYzr86CAIiERozZgwLFixgyJAhzJgxw6W2mkQH8383pLMtp0iSICECjdQbO6fdR0s4mF9Gk6hgmSN0Bm0Twvj2jn7uaSw8EXD9vWjh1iP8sjmb9onhbkiE/GcPIQD/itZJd999N5988onTj1cUhcLyE0sT+7SM5vq+zdwQmRDCrwQ1Uj/tnryDrqjlzXnbueZ/y5m98ZDWoYh6uLBTIpOHtqajs3XGwG8ToYDoERo8eDALFixw+vGvzt3G4r0lfHlLb+LC/Gz7dSGE+3S8AjqN85tJoFpIjgqmbXwY4UEmrUMJDJnfq3tbtRrq0hYAF3d2Q/HW6kTIjzZTBB/oEVq0aBEXX3wxSUlJ6HQ6Zs6cecoxb7/9Ns2bN8dqtdK9e3f++OMPr8b4y8ZD7DpSwp87jnr1vEIIH6M3SBJ0DvcNa8OcewcwvmcTrUPxWct25XLJW4t5+JsNrjf212xY9ArsX+l6W65SjveUSo9Q/ZSUlNC5c2duvPFGxo4de8r3p0+fzuTJk3n77bfp168f7733HiNHjiQzM5MmTdQnWvfu3amoqDjlsb/++itJSa5nuf93YzpbjtoY0zXZ5baEEEIEtvzSSjbsL8Bs8J2EoazSTqXNgcWkd341dPVO2ZII1c/IkSMZOXLkGb//2muvMXHiRCZNUpfivf7668yZM4d33nmHF154AYDVq1e7JZaKiopaCVVhYSEAiWFm2jaOoarKtS3MhRD1V/2884nnX0URhh/vgvJ87Nd863cv+OIMDFZ0V34OgGKzebzXr1NSGO9f25Uwq9Hl/2uD4kAP2O12HC609cg3G5m5/hAPX9CGif2anfL9Oj0PrdHoxn0BOj2Kxs/X+vxeNU+EzqayspLVq1fz8MMP17p/+PDhLFmyxO3ne+GFF3j66adPuf/XX38lOFhWPwihpblz52odAnpHFRdvnQXALz9+Q5XRjzYN9JJtBTrmHtCRFAxjmjnO/QBfs/1nr52qDJi92bU2uu0/QAqwZctf7Myb7XQ7Bw/qAT2ZmVuYXZB5xuPq/Dzc6nws7lBaWlrnY306ETp69Ch2u534+NobdMXHx5OdnV3ndkaMGMGaNWsoKSkhOTmZ7777jvT09FOOe+SRR7jvvvtqvi4sLCQlJYXhw4cTHh7u/A8ihHBaVVUVc+fOZdiwYZhM2k/AVTLvQldVyrD+6dComdbh+BzdpmymZG4gPDKSUaMCt2yDtxh+mAXHoH37drTtPcrpdobb1aRVr9Oh15/aI+Zrz8NzqR7RqQufToSq/b2au6Io9arwPmfOnDodZ7FYsFhO3eDKZDL5xR9eiIbMZ56H1kioKsVkKwZfiMfH9Ggew+vjupAQYfWNv1ddVJXDpm/U212u9vjQ2MH8MnYeKSY2zEK7BBc/ZB8fnjUYDBhc+H3X9aFnfR6W5qk9QaZgSLvM6VjcoT7/ez49wB0TE4PBYDil9+fw4cOn9BIJIYRXSL2xs0qKDGJ018b0buGm6ureUFkC39+hXqon/HrQvC05TPhwBf/9bbvHz+VVBfvg+wyY86jWkdSLTydCZrOZ7t27nzImOXfuXPr27atRVEKIgCb1xoSLwoNMtEsII7lRkOuNDX4Eblmg7m/lgl82HeK5nzJZtO2I841IiQ3nFBcXs2PHjpqvd+/ezbp164iKiqJJkybcd999TJgwgR49etCnTx/ef/99srKyuO222zSMWggRsKTe2FmVVdrJPFSIUa+jc0qk1uH4pEu7NObSLo3d01ijZm6Zq/bnjlymLdtLsNnIgDaxzjUiO0s7Z9WqVQwePLjm6+rJytdffz1Tp05l3Lhx5Obm8swzz3Do0CHS0tKYPXs2TZs21SpkIUQgC4pUP/FWlWkdiU/ad6yUse8sITrEzOrHh2kdjqijfq1iCDYb6N60kfON1Owj5F+bjmqeCA0aNAjlHGOyd9xxB3fccYeXIhJCiLO46HW4dIrfvdh7i8mgp2l0MJFSYsM7tv4ChzOh+UBI7u50MxekJXBBWoJrsUiPkBBCBACjWesIfFrzmBAWPjD43AcGsK9W7uPbtfu5oEMCN/Rr7lpjm7+DDV/C8GddSoTcwuGfJTb8K1ohhBDCz+3JLWHZrjz25tV90z9PszsUKm0OquwubILpp0VXpUfoNKZMmcKUKVOw2+1ahyKE8DX7V8Pi1yCyKVzwvNbRCHewhMIVU9XbXhjyvKRLEu0Tw2ke4zs7k7/8y1+8t2gXN/dvzmMXpjrXSGxb9fdoDnVrbJ4midBpZGRkkJGRQWFhIREREVqHI4TwJWXH4K9ZEN9R60h80v5jpTzx/WbCrEb+e1VXrcOpG6MFOozx2unaJYS7vpGim1VvUuzSNkohMV79PbqLJEJCCFEf1RsqyvL50yqttPP7X4eJDpG5VP5k8tDW3DG4JWZD4M2YkURICCHqQzZUPKv4MCsvX94Ji9GP3lBtFSeKhKaO9vjw2O6jJeSVVNA4MpiECKtHz1VXVpMBq8nFuT1F2bB3CQRHQYtBbonLG/zoP1UIIXxA9YaKlUVgr9I0FF8UEWziyh4p7tsw0BsqiuHrG9SLF0psvLNgB2PfWco3a/Z7/FxedWgDzLgR5j6pdST1Ij1CQghRH9aT5g2WF6jzIoSoh0YhZppGB9Mo2A3DhwPuh+7Xq5P3XbBsVy5LdubSqXEEQ1OdrOUp+wgJIUQAMBjBEg4VherwmCRCtZRX2dl1pASjQUeb+DCtw/FJj4xszyMj27unsZjW6sVFK3bn8ca87Vzdq4nriZCfLZ/3r7RNCCF8gTVSLbNRUah1JD4nK6+UUW/8wfj3l2kdiqiHjskRTOjdlF7No5xvRPHPDRWlR0gIIeorYzmYgqTMxmkY9Dpiwyw0CpYSG16x83c4uh2a9IbEzk43M7htHIPbxrkWiwyNCSFEgDAHax2Bz2oZG8rKx4ZqHYZPe+3XrWw6WMjE85rTr5WLQ6vrp58oseFCIuQWfpoI+Ve0QgghhJ9buy+f3/86TE5hudahuJef1hqTHqHTkBIbQoizWv+lurt0u4uh8zitoxGuMofApW+rt70w3HnLgBZc3CmJbk0aefxcdfXh4t28MHsLl3RO4rVxXZxrJLmH+nsMdXGIzcskEToNKbEhhDirw5mw5UeIaCKJ0N8czC/judlbCLMYeXFsJ63DqRuTFbpe47XT9W8d67Vz1YfNoWB3ZR+lRs3Ui5+RREgIIeqrelPFsmOahuGLiits/LThENEhZl4cq3U0oq7GpadwUadE13eX9kOSCAkhRH1JvbEzig218PQlHbCa/GieiK0Sds1Xb7ce7vHhsR2Hi6i0KTSJDibU4htvw6EWo+ux5O+DnM3q0Fjjbu4JzAv86D9VCCF8hNQbO6NGIWau79uMcelNtA6l7iqK4PMr1YsXSmzc+flaRr3xB2v2NrAexV3z4YtxsPAlrSOpF99IRYUQwp9UD41Jj5BwQlSImdgwC0FmNwxD9b0LOl4BMa1caibzYCGLth+hWXQwF6QlOteIny6fl0RICCHqq3poTOYInaLCZudQfjkGvY6UKNlv6XQ+v7m3+xpLSFMvLlq/P58Xf/6LYanxAZcI+Ve0QgjhC6qHxiqKNQ3DF+3NLWXQvxcwesqfWoci6qF5TAhjuyXTt2W0843IPkJCCBEgIprAI/vBHKp1JD5Hr4Mwi5EQH5kE3ODtWQx5u6Bxd4jv4HQzvVtE07uFC0kQnJhfJYmQEEI0cHo9WKSy+um0igtj49MjtA7Dpz04Yz0llXYeHdWexpFBrjW2ZtqJEhsuJEJuIUNjQgghhDiXXzNz+GnDIcoqbVqH4l5SfV4IIQLIvH/B0a0w6BHtP4kL15iDYdS/1dteKLHx6Mj2lFbaiA21evxcdTV74yEemrGBXi2i+N/16c410nyA+nuMauHe4DxMEiEhhHDGzt/h4Broco0kQifJLijn379uJdRi5KlL/OT3YgqCnjd77XRXpqd47Vx1ZXMoFFXYKK10ocZmQkf14mckEToNKboqhDgn2VTxtArLq5ixej/RIWb/SYQEg9vGMv/+QQRJiQ0BUnRVCFEHUm/stGJCLTx0QTuC/KnEhr0Kspapt5ud59HhMUVR2HmkBOPxfZYMes8PxdVFmNVEmNXkWiPH9kJ+FoQlurzBozdJIiSEEM6QemOnFRVi5vZBLbUOo37KC+Hji9TbT+Z79FR2h8LQ1xYCsO6JYUQGmz16Pq/a+BX8/ix0ux4ueUPraOpMEiEhhHCGDI0JJ9gcCpHBJmx2BaPBDb1mvW6BdqMgzrVhyH15pczfepjoEAsXdnJyZ2mHfy6fl0RICCGcIfXGTqvK7uBYSSU6nY7YMIvW4fgcq8nAuieGu6/Bxt3Vi4u2ZhfxxPeb6ZIS6XwiVL2PkN6/5hlJIiSEEM6oGRor0DYOH7P7aAnD/7OI6BAzqx8fpnU4oo7iw61c2DGRZjEu1Ifz0w0VJRESQghndLwcOoyWMhunYdDr0PvIJOAGb99KyN8LCZ0gto3TzXRMjmDKNd1ci0U2VBRCiABicrE0QgPVJj6Mnc+P0joMn3WspJJnZmViNel54bJOrje48n8nSmy4kAi5RU2PkH8NjflX2iaEEEL4seIKG9+tPcB3aw9oHYr71SRC/tUbKD1CQgjhjPJCmPMoVBTCFR/73Yu/OIkpCIY945VThQeZeGxUe5/7d1m1J487PltD85gQpt/ax7lGWg+H4GhI6ure4DxMEiEhhHCG3gBrp6m3K0vAInOFAA4XlvPW/B0Em408PLKd1uHUjTkY+t3jlVNFBJm4eYDv1eKqsiscLqogIsiFTRWbnade/IwkQkII4QxTMOhN4KhSl9BLIgRAflkVnyzdS3SI2X8SIUHH5Ah+uvs8rFJiQwghRJ3odOqmiiVH1E0VI5K1jsgnRIWYuXtIa4LNfvSGaq+C7A3q7aRuHh3mrLI7yC2uRKdTl6z7ilCLkQ5JLpaUOrYXSo+qJTbCk9wTmBfIZGkhhHCW1Bs7RUyohfuGteG2gX5UZqO8ED44X7142L68Unq/MK+mzEaDsuQN9Xe4eqrWkdSL9AgJIYSzpN6YcIJRr8Porn2WetwILQdDYheXmskrqWTelhyCzAYu6uRkb45sqNhwTJkyhSlTpmC327UORQjhy6Te2CnsDoWSShs6cL2aeQPUIjaUHe7cZ6lJb/Xion15pTwwYwONI4OcT4Qc1Rsq+tGwKDI0dloZGRlkZmaycuVKrUMRQvgyqTd2ip1Hiun01K8MemWB1qGIeggPMjG4bSx9WkY734jsIySEEAFm1Ctw0WtSZkNo59B6KDwIsW0hyvll+c1jQvjoxp6uxaIo6rUMjQkhRICoHhoTNVrFhrL12Qu0DsNnHSmq4I1527Ga9Dx2YarrDS59+0SJjb53ud6eK6T6fOCy2+1UVVVpHUZAMZlMGAz+9WQTIhDo9TosfvZG6E1F5VVMW7aXMKvRPYmQL5HJ0oFHURSys7PJz8/XOpSAFBkZSUJCAjo/G48WDcjBtWrRy4gmMOghraMRzjIFwcCHvXKqRsFm7hnSGovJt5KFPUdLuOGjFYRZTfx4l5O7Q7e/CBo1g2QXh9i8TBIhF1QnQXFxcQQHB8sbspcoikJpaSmHDx8GIDExUeOIRMAqyoa1n6qb8EkiBMDhonI+XLybYJORe4a21jqcujEHw+BHvHKqRiFm7h2mcZX407A5FPbklrpWYqP9xerFz0gi5CS73V6TBEVHuzDLXjglKCgIgMOHDxMXFyfDZEIb1fsIyYaKNfJLq3hv4S6iQ8z+kwgJkhsF8c3tfTDqfaunyhskEXJS9Zyg4OBgjSMJXNW/+6qqKkmEhDZk+fwpIoNN3Ny/OcFmP3p7sdsgd7t6O7adR5d/2x0KxRU2dDoI96F9lqwmA92bRrnWSP4+qCyGsIQTHxL8gB/9p/omGQ7TjvzuheaqV42VF4DDAQH4afrv4sKs/jcJuLwA3j6+KeGT+R491d7cEs5/dSFhViMbnxrh0XN53S8Pw1+z4MLXIH2i1tHUmSRCQgjhrOoeIcUBFYWynF54X5erIaWnenFBWaWdeX/loEPHhZ2cnHcp+wgJIUSAMVnBGAS2MnV4TBIhFEXB7lDfEI0G/3pD9IbmMSFsf26k+xpsMVC9uOhYaSV3fr4Ws0HvQiJ0vMSGn22fIP+l4hTNmjXj9ddf1zoMIfyD1BurZfvhYlo99jO9np+ndSg+SafTYTLoMflYkmgx6unVPIqezV2YJ+Sn+wj5V7QN0A033IBOpzvlsmPHDo+fe+rUqURGRp5y/8qVK7nllls8fn4hGoRbFsAjByCxs9aRiEB0eAvsWgD5WS41Ex1qYfqtffh0Ui/nG/HTREiGxnzABRdcwEcffVTrvtjYWI2i0fbcQvidsAStI/ApLWNDWf/EcJC1DKeVW1zBe4t2YTHq+cfwtq43uPh13yuxIdXnRX1ZLBYSEhJqXSZOnMjo0aNrHTd58mQGDRpU8/WgQYO4++67efDBB4mKiiIhIYGnnnqq1mPy8/O55ZZbiI+Px2q1kpaWxqxZs1iwYAE33ngjBQUFNb1Q1Y/9+9BYVlYWl156KaGhoYSHh3PllVeSk5NT8/2nnnqKLl26MG3aNJo1a0ZERARXXXUVRUVFNcfMmDGDjh07EhQURHR0NEOHDqWkpMRdv0IhhI8w6HVEBJtc25ivASsoq+L9RbuYumSP1qG4n/QICS18/PHH3HfffSxfvpylS5dyww030K9fP4YNG4bD4WDkyJEUFRXx6aef0rJlSzIzMzEYDPTt25fXX3+dJ554gq1btwIQGnpqBW1FURg9ejQhISEsXLgQm83GHXfcwbhx41iwYEHNcTt37mTmzJnMmjWLY8eOceWVV/Liiy/y3HPPcejQIcaPH8/LL7/MmDFjKCoq4o8//kCpXmEghD/b9A3s/B1aj4DUS7SORjjDZPVab0pEkIlbBrTAYvStZKGwvIor312KosDse/pj0DvRpddhDCR0hFjf2zn7bCQR8gGzZs2qlYSMHDmSkJCQOj22U6dOPPnkkwC0bt2at956i3nz5jFs2DB+++03VqxYwZYtW2jTRv3HbNGiRc1jIyIi0Ol0JCScuWv/t99+Y8OGDezevZuUlBQApk2bRocOHVi5ciXp6ekAOBwOpk6dSlhYGAATJkxg3rx5NYmQzWbjsssuo2nTpgB07Nixrr8eIXzb/tVqmY3gaEmEgKPFFXy6bC9BJgO3DmypdTh1Yw5Rh5a8IDrUwqOj2nvlXPWhKPBXttqLb3coziVC3W9wb1Be4lspqY+YMmUKqampNW/ynjZ48GDWrVtXc3njjTfq/NhOnTrV+joxMbGmBte6detITk6uSYKcsWXLFlJSUmqSIIDU1FQiIyPZsmVLzX3NmjWrSYL+Hkfnzp0ZMmQIHTt25IorruCDDz7g2DEpSSAaiJpVY/I/DZBXUsnrv23n/UW7tA5F1EOI2cC0iT35bFIvjM4kQX5MEqHTyMjIIDMzk5UrV3rlfCEhIbRq1armkpiYiF6vP2XoqLqsx8lMptrj8DqdDodDHaetrsflCkVRTruD89/vP1scBoOBuXPn8vPPP5Oamsqbb75J27Zt2b17t8vxCaG5mnpj+ZqG4Ssigkxc06sJY7snax1K3TnscGyvevHwkL2iKFTZHVTZHR49T30ZDXr6t46lX6sY9M4mQoWH1N9hZal7g/MwSYR8VGxsLIcOHap137p16+rVRqdOndi/fz/btm077ffNZjN2u/2sbaSmppKVlcW+fftq7svMzKSgoID27evevavT6ejXrx9PP/00a9euxWw2891339X58UL4LKk3Vkt8uJXnxnT0yeGfMyrLh/92Ui8etvtoCa0f+5lu/5rr8XN53VcT1N/hrgVaR1Ivkgj5qPPPP59Vq1bxySefsH37dp588kk2bdpUrzYGDhzIgAEDGDt2LHPnzmX37t38/PPP/PLLL4A6nFVcXMy8efM4evQopaWnZvFDhw6lU6dOXHPNNaxZs4YVK1Zw3XXXMXDgQHr06FGnOJYvX87zzz/PqlWryMrK4ttvv+XIkSP1SqSE8FkyNCa01PFydX5T034uNeNwKPz+Vw6/ZeY431vlp6vG/CvaADJixAgef/xxHnzwQdLT0ykqKuK6666rdzvffPMN6enpjB8/ntTUVB588MGaXqC+ffty2223MW7cOGJjY3n55ZdPebxOp2PmzJk0atSIAQMGMHToUFq0aMH06dPrHEN4eDiLFi1i1KhRtGnThn/+85+8+uqrjBzpxm3mhdBKdY9QWYGmYQj/0DQ6hPVPDufPh893T4Oth6kr3hp3c6kZu6Jw09RVTPpkFaUVZx8pOCPH8cf5WUFsnSJrmM+osLCQiIgICgoKCA8Pr/W98vJydu/eTfPmzbFarRpFGNjkbxAYqqqqmD17NqNGjTplLppPOLQB3usPoQlw/1ato9Hc9pwiLnxjMVEhZpY9OkTrcOqmJBdeOb6i9sl8v3sjdweHQ2H023+i0+mYNrEn4dbaz7U6PQ/fGwiH1sHVX0Ob4Z4P+izO9v79d7J8XgghXBHXHu7f7nfDAZ6iAJU+OBm4wcrdCaV5EJEM4U4WSwX0eh0/3HmeGwPzH5IICSGEKwwmCI3TOgqf0TwmhKWPnI8+AHtV6uJYSSVTl+zBYtJzx6BWrje48GXfKbHhpyQREkII4TYmg57ECNe37miojpVW8t952wmzGt2TCAmXSSIkhBCuKDgAi/8D1nAY8oTW0QhnGC2QPskrpwqzmpjQuylWk+8NpV753lIqbQ7+d30PYkIt9W+gw2hI7qEO0/kRSYSEEMIVpbmw8gN1srQkQuSVVDJj9T6sJgPX9WmmdTh1YwmFC1/1yqliwyz8a3SaV85VX+v25VNpc1Bhc3J+13n3ujcgL5FESAghhNscLa7g+dl/ER1i9p9ESADw9tXdUICoYLPWoXiVJEJCCCHcJsxq5LJujQm1+NHbi8MBZXnq7eDogFw+DzA0Nd61BsqOqXsJWcLU4UY/4Uf/qUIIIXxdYkQQr13ZResw6qfsGLzSUr39ZL5HT7XrSDHD/7OIMKuRtU9ou9eO230y2mf2EaoP35utJXxC9Y7SQggh3MvmULA53LSXcfuLYfBjkNLL5aZW7sljyY6jlFba3BCY/5BEKEAdPnyYW2+9lSZNmmCxWEhISGDEiBEsXboUgEOHDkkJDCGEcLOUqGCWPTKE3+4b6J4G218EAx+ElJ4uN3XzJ6u4+n/LOZhf5obA/IcMjQWosWPHUlVVxccff0yLFi3Iyclh3rx55OWp4+QJCQkaRyiE8Ec7Dhdx2dtLiAoxs+CBwVqH43NMBj0JEb5ZEqhVbChF5TaM+sDqIwmsn1YAkJ+fz+LFi3nppZcYPHgwTZs2pWfPnjzyyCNceOGFQO2hsT179qDT6fj2228ZPHgwwcHBdO7cuab3qNqSJUsYMGAAQUFBpKSkcPfdd1NSUuLtH08I74ptC3evg0lztY7EJzgUKCy3UVQeWMMrminYDzmboeSoy03NuL0vc+4dQLOYEDcE5j8kEfKA0kobpZU2Tq5nW2lzUFppo8JmP+2xjpPGi6vs6rHlVXU7tr5CQ0MJDQ1l5syZVFRU1Plxjz32GPfffz/r1q2jTZs2jB8/HptNfbHbuHEjI0aM4LLLLmPDhg1Mnz6dxYsXc+edd9Y7PiH8itECUc0hsonWkfiEptHBzPvHQL65va/Wofik/NJK3lu4k4/+3O2eBuf9C97pC+u/cE97AUgSIQ9IfWIOqU/MIa+ksua+9xftJPWJOTz5/eZax3b/12+kPjGHAyeNyX6ydC+pT8zhoW821Dr2vJfmk/rEHHYcKa65b8bq/fWOz2g0MnXqVD7++GMiIyPp168fjz76KBs2bDjr4+6//34uvPBC2rRpw9NPP83evXvZsWMHAK+88gpXX301kydPpnXr1vTt25c33niDTz75hPLy8nrHKITwTxajgZaxoQHXq1BXeSWVvPDzX7w2d5vWoYjjJBEKUGPHjuXgwYP88MMPjBgxggULFtCtWzemTp16xsd06tSp5nZiolrl+PDhwwCsXr2aqVOn1vQ2hYaGMmLECBwOB7t3u+mTjxC+qPAQzH0CFr6idSTCWUYzdL5avXhYqNXI2G7JXNolyePnqq97vlzL1R8sY+dJH7brpc0F6u8wPNG9gXmYTJb2gMxnRgAQZDLU3HfLgJbcdF5zDPraG3WtfnwoAFbjiWOv69OU8T1TTqnevPihwacce3l352u6WK1Whg0bxrBhw3jiiSeYNGkSTz75JDfccMNpjzeZTDW3dcdjczgcNde33nord9999ymPa9JEhgxEA1ZyBP78r1piY+ADWkejufzSSn7ccAirUc8VPVK0DqduLGEw5h2vnCouzMqrV3b2yrnqa9WeYxzIL3N+ftfgR9wbkJdIInQaU6ZMYcqUKdjt9nMffBrB5lN/rWajHvNpOuBOd6zJoMdkqPux7pKamur03kHdunVj8+bNtGol1ZSFCGSHiyp4fOYmokPM/pMICQCevDiVcpuDplHBWofiVZIInUZGRgYZGRkUFhYSERGhdThul5ubyxVXXMFNN91Ep06dCAsLY9WqVbz88stceumlTrX50EMP0bt3bzIyMrj55psJCQlhy5YtzJ07lzfffNPNP4EQwleFWoyMTEvwrxIbigJVpeptU3DAltgY3sHFbVOqykBxgMECBv/5+/tPpMJtQkND6dWrF//5z3/YuXMnVVVVpKSkcPPNN/Poo4861WanTp1YuHAhjz32GP3790dRFFq2bMm4cePcHL0QwpclRQbxzrXdtQ6jfkrz4JUW6m0Pl9jYc7SEi99aTLjVxJ8Pn+/Rc3nd/13glyU2JBEKQBaLhRdeeIEXXnjhjMecvPS/WbNmtb4GiIyMPOW+9PR0fv31V/cGK4QQDYhDUdy7x1Kb4RCWAEndXG7qr+xCSivttIoLJdxqOvcDGghJhIQQQggvSW4UzPz7B6F31+hb2lj14gZ3fb6W7YeL+fzmXvRtGeOWNv2BJEJCCCHcZueRYq75YDmRwSZ+mTxA63B8jtmop7mP7rGUEGGl3GbHYgysnXUkERJCCFfEtIbbl4JeXk4B7A6F7MJyp3a9F04oPgyVxRAUBUGRLjU1baLrFez9kTxzhRDCFaYgiE/VOgqf0SQqmFl3nYfREJgrr86loKyK79cdwGTQM76nG/ZY+/Vx2PAlDH8W+t7lensBSBIhIYQQbmM1GUhr3PC2HXGX3OIKnvh+M2FWo3sSIeEySYSEEMIVRTmweiqYQ6CvFBn2SwYTpDq3h1p9hViMjOqYgPWkygO+4vnZW9h1pJg7z29Nl5TI+jfQYiA0agqhcW6PzZMkERJCCFcU58CC59USG5IIUVBWxdzMHCxGPRd39r16WqdlDYcrP/HKqeLDrbx9jW/us7Ridx7r9uUzLt3Jnqphz7g3IC+RREgIIYTb5BSWc//X64kOMftPIiQAyBjcirySCtonhmkdildJIiSEEMJtgs0GBraJJcwqby/+ZlhqvNYhaCKwNgsQXjN16lQiIyO1DkMI4WXJjYL5+KaevHW16zsde01JLjwVoV7+tmO+u2XlltLr+d8Y8uoCj55HE+8NVH+H2/yrwoAkQgEsOzubu+66ixYtWmCxWEhJSeHiiy9m3rx5Lrc9btw4tm3b5oYohRCi4bA5HOQUVnC4qMI9DbYYBD1vgYSOLje1L6+Uv7ILKSircj0uPyJ9lwFqz5499OvXj8jISF5++WU6depEVVUVc+bMISMjg7/++sul9oOCgggKCnJTtEII0TAkRQbx093nYXBXjY0u49WLGzz0zQaW7Mzlv1d14dIujd3Spj+QHqEAdccdd6DT6VixYgWXX345bdq0oUOHDtx3330sW7YMgKysLC699FJCQ0MJDw/nyiuvJCcnp6aN9evXM3jwYMLCwggPD6d79+6sWrUKOHVo7KmnnqJLly5MmzaNZs2aERERwVVXXUVRUVHNMYqi8PLLL9OiRQuCgoLo3LkzM2bM8M4vRAjhFruPlnD+vxcw5u0/tQ7FJ1lNBjokRdAuIVzrUE4REWQiJtSM2RBYqYH0CHlCZcmZv6czgMlax2P16q615zrWXL+6NXl5efzyyy8899xzhISc+tjqyvKjR48mJCSEhQsXYrPZuOOOOxg3bhwLFiwA4JprrqFr16688847GAwG1q1bh8l05orFO3fuZObMmcyaNYtjx45x5ZVX8uKLL/Lcc88B8M9//pNvv/2Wd955h9atW7No0SKuvfZaYmNjGThwYL1+RiG8JrolTJonJTaOq7I72HW0hOgQs9ahBIayfLBVgCW03u8Ff/fOtb65rN/T5JnrCc+fZclo6+Fwzdcnvn6lFVSVnv7YpufBjT+d+Pr1jlCae+pxTxXUK7wdO3agKArt2rU74zG//fYbGzZsYPfu3aSkpAAwbdo0OnTowMqVK0lPTycrK4sHHnigpp3WrVuf9bwOh4OpU6cSFqYuzZwwYQLz5s3jueeeo6SkhNdee43ff/+dPn36ANCiRQsWL17Me++9J4mQ8F3mEEjuoXUUPiO5URBf39YHo9vKqzcsheVVzNmUjdmod8/w088PSYkNF0kiFICU46sidLozv1Bt2bKFlJSUmiQIIDU1lcjISLZs2UJ6ejr33XcfkyZNYtq0aQwdOpQrrriCli1bnrHNZs2a1SRBAImJiRw+fBiAzMxMysvLGTZsWK3HVFZW0rVrV6d+TiGE9wWbjaQ3i9I6DJ91tKiCB2ZsIMxqDKh5OL5MEiFPePTgmb+n+9u26g/sOMuxfxunnbzR+ZhO0rp1a3Q6HVu2bGH06NGnPUZRlNMmSiff/9RTT3H11Vfz008/8fPPP/Pkk0/y5ZdfMmbMmNO2+fdhM51Oh8OhVqiuvv7pp59o3Lj2i4PFYqnXzyeEVxUfgfVfgDkY0idpHY1whsGo9tZ7QbDZyOC2sQSbfe/t94NFu1i/P5/xPZvQr1VM/Rto0lstrxES7f7gPMj3/hINQX3GaT117FlERUUxYsQIpkyZwt13333KPKH8/HxSU1PJyspi3759Nb1CmZmZFBQU0L59+5pj27RpQ5s2bbj33nsZP348H3300RkTobNJTU3FYrGQlZUlw2DCvxRnw9zH1RIbkghRWF7F4u1HMRv0DPWXDfqsEbWnLHhQQoSVj27s6ZVz1deKPXnMzcyhb8sY+rVyooGRL7k9Jm+QRChAvf322/Tt25eePXvyzDPP0KlTJ2w2G3PnzuWdd94hMzOTTp06cc011/D666/XTJYeOHAgPXr0oKysjAceeIDLL7+c5s2bs3//flauXMnYsWOdiicsLIz777+fe++9F4fDwXnnnUdhYSFLliwhNDSU66+/3s2/ASGEJ2QXlHPHZ2uIDjGzOnXYuR8gfMZV6Sn0aRFNt6aRWofiVZIIBajmzZuzZs0annvuOf7xj39w6NAhYmNj6d69O++88w46nY6ZM2dy1113MWDAAPR6PRdccAFvvvkmAAaDgdzcXK677jpycnKIiYnhsssu4+mnn3Y6pn/961/ExcXxwgsvsGvXLiIjI+nWrRuPPvqou35sIYSHBZkM9GweRbj1zCtIhW8a0t5PevDcTKcoHt5P3I8VFhYSERFBQUEB4eG193woLy9n9+7dNG/eHKvVeoYWhCfJ3yAwVFVVMXv2bEaNGnXW7Rk0k70R3j1PHRq7f6vW0QhnlOTC62nq7UcPwlkWkrhqX14pEz5cTojFyE9393e9wW9v9cqqsTo9Dz8cAdkbYNw0aDXUY7HUxdnev/9OeoSEEEKIM21j4u7T2B3syS11X1Hapn3Vyd5x7c997DkcKaqgvMpOZLCJMGd69Gzl6u/x+OIXfyGJkBBCCOElSZFBzLitD3p37bPU/Xr14gaPz9zEL5uzeXZ0Gtf2buqWNv2BJEJCCCHcZm9uCbd/uoaIIBNf3NJb63B8jtVkoIeP7rNkMekJNhsCbjNMSYROY8qUKUyZMgW73a51KEIIX9eoGVz3AxikpARAhc1B5qFCKbHhLVVl4LCBwQJG137n/70qMDevlUToNDIyMsjIyKiZbHU2MtdcO/K7Fz7BEgYtZO+rao0jg/j4pp6YDIHVq1BXxRU2Fm49gtGgY0SHBNcb/HGylNhwkSRCTqqeNV9aWkpQUNA5jhaeUFqqTm70yZVEQgSoEIuRgW1itQ7DZx0uLCfj8zWEWY3uSYSEyyQRcpLBYCAyMrKmVlZwcPBZa3cJ91EUhdLSUg4fPkxkZCQGg+HcDxLCU0pyIfM7MAVDl6u1jkY4w2BUi1x7gdVkoFfzKEIsvvf2O23pHjYdKGR018b0aelEmYykLmAOhaBGbo/Nk3zvL+FHEhLUbL46GRLeFRkZWfM3EEIzRQfhp3+o+whJIkRxhY2Ve/IwG/TO1avSgjUCbvzJK6dKigxi+q19vHKu+vpzRy6/bM6mY3KEc4nQxf91f1BeIImQC3Q6HYmJicTFxVFVVaV1OAHFZDJJT5AQPuhgfhk3frRSLbHxuJTY8CejuybRMTmCLimRWofiVZIIuYHBYJA3ZSGEACxGPR0bRxARJHP3/M0FaYlah6AJSYSEEEK4TdPoEH68yzvzbdymNA/e6qHefmCnR0ts7D9Wys2frCbUYuDr2/p67Dya+PgSyNkEYz+EloO1jqbOJBESQggR2BQFSnO9cqpKm4MthwrdV2IjuQfYKyG6tctNFZRVUWlzEGoxEmR2YpSjvED9Pdr9a6qIJEJCCCGElyREWJk2sScGd+3e3PNm9eIGD83YICU2RG3VG/YVFhZqHIkQgauqqorS0lIKCwt9c8+oomKoUMBoB3mtYH9eKY98u5GwICNvX9Nd63DqpqRI/RuC+jf08FYoneMtx0/lW/8vlWXFOCpKKSspOiW2Oj0Py2zq77G4RPPnQnX8ddl4V6fI9rxntH//flJSUrQOQwghhBBO2LdvH8nJyWc9RhKhs3A4HBw8eJCwsDDZLPG49PR0Vq5cqXUYp6VFbJ46pzvadbUNZx5fn8fU9djCwkJSUlLYt28f4eHh9YqnoZLnoefP6a425XmoDUVRKCoqIikpCb1ef9ZjZWjsLPR6/TkzyUBjMBh89kmgRWyeOqc72nW1DWceX5/H1Lf98PBwn/3f8zZ5Hnr+nO5qU56H2jlXrdBqZ0+ThPibjIwMrUM4Iy1i89Q53dGuq2048/j6PMaX/5d8nS//7hrK89Bdbcrz0PfJ0JgQwqcVFhYSERFBQUGB33wSFaKhacjPQ+kREkL4NIvFwpNPPonFYtE6FCECVkN+HkqPkBBCCCEClvQICSGEECJgSSIkhBBCiIAliZAQQgghApYkQkIIIYQIWJIICSGEECJgSSIkhPBbs2bNom3btrRu3Zr//e9/WocjREAaM2YMjRo14vLLL9c6FKfI8nkhhF+y2WykpqYyf/58wsPD6datG8uXLycqKkrr0IQIKPPnz6e4uJiPP/6YGTNmaB1OvUmPkBDCL61YsYIOHTrQuHFjwsLCGDVqFHPmzNE6LCECzuDBgwkLC9M6DKdJIiSE0MSiRYu4+OKLSUpKQqfTMXPmzFOOefvtt2nevDlWq5Xu3bvzxx9/1Hzv4MGDNG7cuObr5ORkDhw44I3QhWgwXH0eNgSSCAkhNFFSUkLnzp156623Tvv96dOnM3nyZB577DHWrl1L//79GTlyJFlZWQCcblRfp9N5NGYhGhpXn4cNgSRCQghNjBw5kmeffZbLLrvstN9/7bXXmDhxIpMmTaJ9+/a8/vrrpKSk8M477wDQuHHjWj1A+/fvJzEx0SuxC9FQuPo8bAgkERJC+JzKykpWr17N8OHDa90/fPhwlixZAkDPnj3ZtGkTBw4coKioiNmzZzNixAgtwhWiQarL87AhMGodgBBC/N3Ro0ex2+38f3v370r/Hgdw/BWSAQPqGGQ+w0nkRzGoMxnUyT8gq7Ip/gSDwtmUSZ1VWWTAYJASyckqQiFWUobjDre+5X6He7t1z8f1fjy2855ey7uen/fn8zmfXC73ZT2Xy8XT01NERDQ1NcXKykoUi8Wo1WqxuLgYnZ2dWYwLP9I/2YcRERMTE3F+fh5vb2/R09MT29vbMTw8XO9x/zUhBHxbf33m5/Pz88taqVSKUqlU77EgKX+3D//vb2u6NQZ8O11dXdHY2PjlqjMi4vn5+berU+C/kco+FELAt9Pc3ByDg4Oxv7//ZX1/fz/GxsYymgrSkso+dGsMyMTr62tcXV39+n1zcxMXFxfR0dERvb29MT8/H9PT0zE0NBSjo6OxsbERd3d3MTs7m+HU8LPYhz6xAWTk8PAwisXib+szMzOxubkZEX/+kdvy8nI8Pj5GoVCItbW1GB8fr/Ok8HPZh0IIAEiYZ4QAgGQJIQAgWUIIAEiWEAIAkiWEAIBkCSEAIFlCCABIlhACAJIlhACAZAkhACBZQggASJYQAgCSJYSApLy8vER3d3csLS39Wjs5OYnm5ubY29vLcDIgC74+DyRnd3c3pqam4vj4OPL5fAwMDMTk5GSUy+WsRwPqTAgBSZqbm4uDg4MYHh6OarUap6en0dLSkvVYQJ0JISBJ7+/vUSgU4v7+Ps7OzqKvry/rkYAMeEYISNL19XU8PDxErVaL29vbrMcBMuJECEjOx8dHjIyMRH9/f+Tz+VhdXY3Ly8vI5XJZjwbUmRACkrOwsBBbW1tRrVajtbU1isVitLW1xc7OTtajAXXm1hiQlMPDwyiXy1GpVKK9vT0aGhqiUqnE0dFRrK+vZz0eUGdOhACAZDkRAgCSJYQAgGQJIQAgWUIIAEiWEAIAkiWEAIBkCSEAIFlCCABIlhACAJIlhACAZAkhACBZfwDKDD6nWFsNiQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "#ax.scatter(x, y1, label='Sine', color='r')\n",
    "ax.loglog(x,y1,label='Sine', linestyle=':')\n",
    "ax.loglog(x, y2, label='Cosine', linestyle='--')\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('f(x)')\n",
    "ax.grid()\n",
    "ax.legend(title='Functions')\n",
    "plt.title('Log Scale Example')\n",
    "plt.show()\n",
    "\n",
    "# If you want to save your figure as an image, you can use the function:\n",
    "# plt.savefig('figname.png') to save the plot as an image to whatever directory you are working in"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88626b29",
   "metadata": {},
   "source": [
    "<h1>Practice plotting!</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba6f3a9",
   "metadata": {},
   "source": [
    "Using matplotlib and numpy, create two plots with three functions each:\n",
    "<ul>\n",
    "    <li>f(x)=ln(x)</li>\n",
    "    <li>g(x)=e<sup>x</sup></li>\n",
    "    <li>h(x)=log(x)</li>\n",
    "</ul>\n",
    "Make the x- and y- axes range from 0 to 100. Be sure all three functions have different line colors and styles, and include legend, axes labels, a plot title, and grid background. Make one plot with linear scale and the other with log scale. No need to set a y-axis limit for the log-log plot."
   ]
  }
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