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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 1 -- Creating Functions\n",
+    "# Create a function that works with the calls to the function at the bottom of this cell.\n",
+    "# Note: To calculate an interest-only payment, multiply the loan balance by the annual interest rate,\n",
+    "#       and divide by the number of payments in a year.\n",
+    "\n",
+    "# TODO: Create Function Here\n",
+    "\n",
+    "\n",
+    "# Function Calls that should work:\n",
+    "# passing arguments by position\n",
+    "print(interest_only_payment(50000, 0.04, 12)) # should return approx. $166.66\n",
+    "# passing arguments by keyword\n",
+    "print(interest_only_payment(rate=0.04, num_payments=12, balance=50000)) # should also return approx. $166.66\n",
+    "# Using default values\n",
+    "print(interest_only_payment()) # should also return approx. $166.66"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 2 -- Calling Functions\n",
+    "\n",
+    "def triangle_number(n=1):\n",
+    "    \"\"\"\n",
+    "    Calculates and returns the n-th triangle number.\n",
+    "    The formula for the n-th triangle number is (n^2+n)/2\n",
+    "    A triangle number is a value where you can organize that number of items into a triangle\n",
+    "    starting with a row with 1 item and each successive row has one more than the previous.\n",
+    "    The first 5 triangle numbers are 1, 3, 6, 10, 15\n",
+    "    \"\"\"\n",
+    "    n = int(n)\n",
+    "    ans = int( n * (n+1) / 2 )\n",
+    "    return ans\n",
+    "\n",
+    "def triangle_sum(n=1,m=2):\n",
+    "    \"\"\"\n",
+    "    Calculates and returns the sum of two triangle numbers.\n",
+    "    \"\"\"\n",
+    "    ans = triangle_number(n) + triangle_number(m)\n",
+    "    return ans\n",
+    "\n",
+    "# TODO: Write and print the output of function calls to the triangle_sum function\n",
+    "# print out sum of call to triangle_sum using only default arguments\n",
+    "    \n",
+    "# print out sum of call to triangle_sum using only positional arguments\n",
+    "    \n",
+    "# print out sum of call to triangle_sum using only keyword arguments\n",
+    "    \n",
+    "# print out sum of call to triangle_sum using a mix of default, positional and/or keyword arguments\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warmup 3 --Trace the following code Using the debugger or Python Tutor.  At any point as the program executes, you should be able to state what line of code will execute next and what the output will be.\n",
+    "\n",
+    "[link](https://pythontutor.com/visualize.html#code=def%20f%28%29%3A%0A%20%20%20%20x%20%3D%201%0A%20%20%20%20print%28x%29%0A%20%20%20%20g%28%29%0A%0Adef%20g%28%29%3A%0A%20%20%20%20x%20%3D%202%0A%20%20%20%20print%28x%29%0A%20%20%20%20h%28%29%0A%0Adef%20h%28%29%3A%0A%20%20%20%20x%20%3D%203%0A%20%20%20%20print%28x%29%0A%0Af%28%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def f():\n",
+    "    x = 1\n",
+    "    print(x)\n",
+    "    g()\n",
+    "\n",
+    "def g():\n",
+    "    x = 2\n",
+    "    print(x)\n",
+    "    h()\n",
+    "\n",
+    "def h():\n",
+    "    x = 3\n",
+    "    print(x)\n",
+    "\n",
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warmup 4 -- Let's make some changes to it! [link](https://pythontutor.com/visualize.html#code=x%20%3D%20100%0A%0Adef%20f%28%29%3A%0A%20%20%20%20x%20%3D%201%0A%20%20%20%20print%28x%29%0A%20%20%20%20g%28%29%0A%0Adef%20g%28%29%3A%0A%20%20%20%20x%20%3D%202%0A%20%20%20%20print%28x%29%0A%20%20%20%20h%28%29%0A%0Adef%20h%28%29%3A%0A%20%20%20%20print%28x%29%0A%0Af%28%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 100\n",
+    "\n",
+    "def f():\n",
+    "    x = 1\n",
+    "    print(x)\n",
+    "    g()\n",
+    "\n",
+    "def g():\n",
+    "    x = 2\n",
+    "    print(x)\n",
+    "    h()\n",
+    "\n",
+    "def h():\n",
+    "    print(x)\n",
+    "\n",
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warmup 5 -- finally [link](https://pythontutor.com/visualize.html#code=x%20%3D%20100%0A%0Adef%20f%28%29%3A%0A%20%20%20%20x%20%3D%201%0A%20%20%20%20print%28x%29%0A%20%20%20%20g%28%29%0A%0Adef%20g%28%29%3A%0A%20%20%20%20global%20x%0A%20%20%20%20x%20%3D%202%0A%20%20%20%20print%28x%29%0A%20%20%20%20h%28%29%0A%0Adef%20h%28%29%3A%0A%20%20%20%20print%28x%29%0A%0Af%28%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 100\n",
+    "\n",
+    "def f():\n",
+    "    x = 1\n",
+    "    print(x)\n",
+    "    g()\n",
+    "\n",
+    "def g():\n",
+    "    global x\n",
+    "    x = 2\n",
+    "    print(x)\n",
+    "    h()\n",
+    "\n",
+    "def h():\n",
+    "    print(x)\n",
+    "\n",
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reminder\n",
+    "\n",
+    "Python has two scopes -- global and local.  The callstack stores variables in frames.  When executing a line of code, the variables in the current local scope and global scope are accessible.\n",
+    "\n",
+    "Some Lessons on executing Functions:\n",
+    "\n",
+    "1. Functions don't execute unless they are called\n",
+    "2. Variables created in a function die after function returns\n",
+    "3. Variables start fresh every time a function is called again\n",
+    "4. You can't see the variables of other function invocations, even those that call you\n",
+    "5. You can generally just use global variables inside a function\n",
+    "6. If you do an assignment to a variable in a function, Python assumes you want it local\n",
+    "7. Assignment to a variable should be before its use in a function, even if there's a a global variable with the same name\n",
+    "8. Use a global declaration to prevent Python from creating a local variable when you want a global variable\n",
+    "9. In Python, arguments are \"passed by value\", meaning reassignments to a parameter don't change the argument outside\n",
+    "10. It's irrelevant whether the argument (outside) and parameter (inside) have the same variable name\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conditionals 1\n",
+    "\n",
+    "## Reading\n",
+    "\n",
+    "* [Downey Ch 5 (\"Floor Division and Modulus\" to \"Nested Conditionals\" and \"Keyboard Input\" to end)](https://greenteapress.com/thinkpython2/html/thinkpython2006.html)\n",
+    "* [Downey Ch 6 (\"Return Values\" to \"Boolean Functions\")](https://greenteapress.com/thinkpython2/html/thinkpython2007.html)\n",
+    "* [Python for Everybody, 4.1 - 4.5](https://runestone.academy/ns/books/published//py4e-int/conditional/toctree.html)\n",
+    "\n",
+    "## Learning Objectives\n",
+    "After this lecture you will be able to...\n",
+    " - write conditional statements\n",
+    "   - using conditional execution ( if )\n",
+    "   - using alternate execution (if/else)\n",
+    "   - using chained conditionals (if/elif/elif/…/else)\n",
+    " - identify code blocks (indentation layers) in a program\n",
+    " - count the number of blocks in a segment of code\n",
+    " - determine the output of code with conditional statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if statement Syntax:\n",
+    "\n",
+    "An if statement has the form:\n",
+    "```python\n",
+    "if condition:\n",
+    "    #block of code to run if condition is true\n",
+    "```\n",
+    "\n",
+    "The `condition` portion is an expression that evaluates to `True` or `False`.  If the condition evaluates to True\n",
+    "then the indented block of code is executed.  If it is false then the indented block of code is skipped.\n",
+    "\n",
+    "### Example usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Word is short\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Determine if a word is short or not.\n",
+    "# If it is short, say \"Word is short\" <= 5 characters\n",
+    "# If it is long, say \"Word is long\"\n",
+    "word = \"hello\"\n",
+    "word_length = len(word)\n",
+    "\n",
+    "if word_length <= 5:\n",
+    "    print(\"Word is short\")\n",
+    "if word_length > 5:\n",
+    "    print(\"Word is long\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You Try\n",
+    "In the cell below print \"even\" if the inputed value is even"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num = int(input(\"Enter an integer number\"))\n",
+    "\n",
+    "##TODO write an if statement that prints \"even\" if num is holding an even value\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if-else statement Syntax\n",
+    "\n",
+    "An if-else statement has the form:\n",
+    "\n",
+    "```python\n",
+    "if condition:\n",
+    "    # block of code to run if condition is true\n",
+    "else:\n",
+    "    # block of code to run if condition is false\n",
+    "```\n",
+    "\n",
+    "The `condition` portion is an expression that evaluates to `True` or `False`. If the condition evaluates to True then the first indented block of code is executed. If it is false then the indented block of code after the `else:` is executed.\n",
+    "\n",
+    "### Example usage\n",
+    "\n",
+    "Bob runs a community bakery. Every day, he recieves a shipment of dough balls, each of which bakes 4 loaves of bread, and he evenly splits the bread among his customers. Some days he may recieve 10 customers, some days 20 customers, and some days none at all!\n",
+    "\n",
+    "Below is the code that tells Bob how much bread he gave to each customer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "How many balls of dough did we recieve?  4\n",
+      "How many customers were there?  4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Each customer gets 4.0 loaves of bread.\n"
+     ]
+    }
+   ],
+   "source": [
+    "balls_of_dough = int(input(\"How many balls of dough did we recieve? \"))\n",
+    "num_customers = int(input(\"How many customers were there? \"))\n",
+    "bread_baked = balls_of_dough * 4\n",
+    "\n",
+    "# If statement to avoid the zero division runtime error\n",
+    "if num_customers == 0: \n",
+    "    print(\"Sorry! No one showed up. All the bread went to waste.\")\n",
+    "else:   \n",
+    "    bread_per_customer = bread_baked / num_customers\n",
+    "    print(\"Each customer gets\", round(bread_per_customer, 2), \"loaves of bread.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You Try\n",
+    "\n",
+    "In the cell below write an if-else statement that prints \"you may enter\" or \"entry denied\" depending\n",
+    "on the value in the `password` variable.  The correct value for entry is **CatNipFOREVER**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter Password: ········\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "source": [
+    "from getpass import getpass\n",
+    "password = getpass(\"Enter Password:\")\n",
+    "\n",
+    "##TODO write an if-else statement that prints \"you may enter\" or \"entry denied\" if they entered\n",
+    "# the proper password (i.e. if password refers to the value \"CatNipFOREVER\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if-elif statement syntax\n",
+    "\n",
+    "An if-elif statment has the form:\n",
+    "\n",
+    "```python\n",
+    "if condition1:\n",
+    "    # code block 1\n",
+    "elif condition2:\n",
+    "    # code block 2\n",
+    "elif condition3:\n",
+    "    # code block 3\n",
+    "...\n",
+    "elif conditionN:\n",
+    "    # code block N\n",
+    "else:  (this final else portion is optional)\n",
+    "    # code block N+1\n",
+    "```\n",
+    "\n",
+    "If `condition 1` evaluates to `True` then code block 1 is executed.  If it is false then `condition2` is evaluated.  If it is `True` then code block 2 is executed.  If it is false then `condition3` is evaluated.  If it is true then code block 3 is executed.  This process continues until the final condition (`conditionN`).  If it is true then code block N is executed.  If it is false then code block N+1 is executed (if present).\n",
+    "\n",
+    "Notice that <u>**only one** code block will be executed and if the final else portion is included then **exactly one** code block will be executed.</u>\n",
+    "\n",
+    "### Example Usage\n",
+    "\n",
+    "**Age Categorizer**\n",
+    "\n",
+    "Assume `age` is an int parameter of the `categorize_age()` function, which categorizes the person's age.\n",
+    "\n",
+    "All the following bounds are inclusive (meaning you should include both ends)\n",
+    "\n",
+    " - Return \"Baby\" if between ages of 0 and 1\n",
+    " - Return \"Toddler\" if between ages of 2 and 4\n",
+    " - Return \"Child\" if between ages of 5 and 17\n",
+    " - Return \"Adult\" if between ages of 18 and 64\n",
+    " - Return \"Senior\" if between ages of 65 and 125\n",
+    " - Return \"Not a valid age\" for all other ages.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N/A\n"
+     ]
+    }
+   ],
+   "source": [
+    "def categorize_age(age):\n",
+    "    if age < 0:\n",
+    "        return \"N/A\"\n",
+    "    elif age <= 1:\n",
+    "        return \"Baby\"\n",
+    "    elif age <= 4:\n",
+    "        return \"Toddler\"\n",
+    "    elif age <= 17:\n",
+    "        return \"Child\"\n",
+    "    elif age <= 64:\n",
+    "        return \"Adult\"\n",
+    "    elif age <= 125:\n",
+    "        return \"Senior\"\n",
+    "    elif age > 125:\n",
+    "        return \"N/A\"\n",
+    "\n",
+    "# This is a lot of tests! Let's try them incrementally.\n",
+    "print(categorize_age(-2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Watch the code run in the [PythonTutor](https://pythontutor.com/visualize.html#code=def%20categorize_age%28age%29%3A%0A%20%20%20%20if%20age%20%3C%200%3A%0A%20%20%20%20%20%20%20%20return%20%22N/A%22%0A%20%20%20%20elif%20age%20%3C%3D%201%3A%0A%20%20%20%20%20%20%20%20return%20%22Baby%22%0A%20%20%20%20elif%20age%20%3C%3D%204%3A%0A%20%20%20%20%20%20%20%20return%20%22Toddler%22%0A%20%20%20%20elif%20age%20%3C%3D%2017%3A%0A%20%20%20%20%20%20%20%20return%20%22Child%22%0A%20%20%20%20elif%20age%20%3C%3D%2064%3A%0A%20%20%20%20%20%20%20%20return%20%22Adult%22%0A%20%20%20%20elif%20age%20%3C%3D%20125%3A%0A%20%20%20%20%20%20%20%20return%20%22Senior%22%0A%20%20%20%20elif%20age%20%3E%20125%3A%0A%20%20%20%20%20%20%20%20return%20%22N/A%22%0A%0Aprint%28categorize_age%28-2%29%29&cumulative=false&heapPrimitives=nevernest&mode=edit&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false).  Notice that the conditions are evaulated one after the other until a condition is found which is true and then that code block is the one that is executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You Try\n",
+    "\n",
+    "In the cell below finish writing the `calculate_letter_grade()` function.  The input to the function is a percentage (float) and the return value should be a letter grade following these rules:\n",
+    "\n",
+    "- \\>= 93 - A\n",
+    "- \\>= 88 - AB\n",
+    "- \\>= 80 - B\n",
+    "- \\>= 75 - BC\n",
+    "- \\>= 70 - C\n",
+    "- \\>= 60 - D\n",
+    "- anything else - F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##TODO finish the calculate_letter_grade() function\n",
+    "def calculate_letter_grade(percentage):\n",
+    "    \"\"\"returns the letter grade for the provided course percentage\"\"\"\n",
+    "    pass\n",
+    "\n",
+    "# Should work with the following function call.  Try different values\n",
+    "print(calculate_letter_grade(86.5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "\n",
+    "Let's work through computing a string representation of a date given the year, month, and day.\n",
+    "\n",
+    "\n",
+    "### Date Printer\n",
+    "\n",
+    "See the requirements below. We will first break down the problem into smaller problems and right a function for each."
+   ]
+  },
+  {
+   "attachments": {
+    "date%20printer.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![date%20printer.png](attachment:date%20printer.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jan\n",
+      "Mar\n",
+      "N/A\n",
+      "N/A\n"
+     ]
+    }
+   ],
+   "source": [
+    "# first function:  convert a month (int) into a 3 letter abbreviation or \"N/A\"\n",
+    "def month_to_str(month): # fruitful function!\n",
+    "    if month == 1:\n",
+    "        return \"Jan\"\n",
+    "    elif month == 2:\n",
+    "        return \"Feb\"\n",
+    "    elif month == 3:\n",
+    "        return \"Mar\"\n",
+    "    else:\n",
+    "        return \"N/A\"\n",
+    "\n",
+    "print(month_to_str(1))\n",
+    "print(month_to_str(3))\n",
+    "print(month_to_str(15))\n",
+    "print(month_to_str(-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11st\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### second function: convert a day (int) into a string writing '15th', '23rd' or \"N/A\"\n",
+    "def day_to_str(day):\n",
+    "    \"\"\"Covert a day into a date string with proper ending. \n",
+    "    16 --> '16th',    23 --> '23rd', \"\"\"\n",
+    "    last_digit = day % 10\n",
+    "    if last_digit == 1:\n",
+    "        return str(day) + \"st\"\n",
+    "    elif last_digit == 2:\n",
+    "        return str(day) + \"nd\"\n",
+    "    elif last_digit == 3:\n",
+    "        return str(day) + \"rd\"\n",
+    "    else: \n",
+    "        return str(day) + \"th\"\n",
+    "\n",
+    "# st - ends 1\n",
+    "# nd - ends 2\n",
+    "# rd - ends 3\n",
+    "# th - ends 4-0 \n",
+    "\n",
+    "print(day_to_str(11))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# third function: convert a year (int) into a string for year\n",
+    "# 2021 ---> '21     # return a string with '  and the last 2 digits\n",
+    "# 1996 ---> 1996    # if year before 2000 return all 4 digits, as a string\n",
+    "\n",
+    "def year_to_str(year):\n",
+    "    \"\"\"Convert a year (as an integer) into a string. If the year is < 2000, return the full 4 digits\n",
+    "    Otherwise, return the last 2 digits with a single quote in front\"\"\"\n",
+    "    pass\n",
+    "\n",
+    "print(year_to_str(2022))\n",
+    "print(year_to_str(2004))\n",
+    "print(year_to_str(2010))\n",
+    "print(year_to_str(1997))\n",
+    "print(year_to_str(1462))\n",
+    "print(year_to_str(181))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# now put it all together\n",
+    "def format_date(year=2021, month=1, day=1):\n",
+    "    \"\"\"returns a string representing the date, such as Feb 10th of ‘22\"\"\"\n",
+    "    pass\n",
+    "    \n",
+    "print(format_date())\n",
+    "print(format_date(2007))\n",
+    "print(format_date(day=23, year=2006, month=9))\n",
+    "print(format_date(2022, 2, 11))\n",
+    "print(format_date(1997, 10, 22))\n",
+    "print(format_date(1497, 6, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You have learned about the syntax for the three types of conditional statements and you have practiced writing code using each of them.\n",
+    "\n",
+    "- **if** statement\n",
+    "- **if-else** statement\n",
+    "- **if-elif** statement\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s25/Louis_Lecture_Notes/08_Conditionals1/Lec_08_Conditionals1_Solution.ipynb b/s25/Louis_Lecture_Notes/08_Conditionals1/Lec_08_Conditionals1_Solution.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..f035557037d606061129d0b19a8d24d7c42fe8b5
--- /dev/null
+++ b/s25/Louis_Lecture_Notes/08_Conditionals1/Lec_08_Conditionals1_Solution.ipynb
@@ -0,0 +1,827 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "166.66666666666666\n",
+      "166.66666666666666\n",
+      "166.66666666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Warmup 1 -- Creating Functions\n",
+    "# Create a function that works with the calls to the function at the bottom of this cell.\n",
+    "# Note: To calculate an interest-only payment, multiply the loan balance by the annual interest rate,\n",
+    "#       and divide by the number of payments in a year.\n",
+    "\n",
+    "# TODO: Create Function Here\n",
+    "def interest_only_payment(balance=50000,rate=0.04,num_payments=12):\n",
+    "    ans = balance*rate/num_payments\n",
+    "    return ans\n",
+    "\n",
+    "# Function Calls that should work:\n",
+    "# passing arguments by position\n",
+    "print(interest_only_payment(50000, 0.04, 12)) # should return approx. $166.66\n",
+    "# passing arguments by keyword\n",
+    "print(interest_only_payment(rate=0.04, num_payments=12, balance=50000)) # should also return approx. $166.66\n",
+    "# Using default values\n",
+    "print(interest_only_payment()) # should also return approx. $166.66"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "9\n",
+      "38\n",
+      "126\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Warmup 2 -- Calling Functions\n",
+    "\n",
+    "def triangle_number(n=1):\n",
+    "    \"\"\"\n",
+    "    Calculates and returns the n-th triangle number.\n",
+    "    The formula for the n-th triangle number is (n^2+n)/2\n",
+    "    A triangle number is a value where you can organize that number of items into a triangle\n",
+    "    starting with a row with 1 item and each successive row has one more than the previous.\n",
+    "    The first 5 triangle numbers are 1, 3, 6, 10, 15\n",
+    "    \"\"\"\n",
+    "    n = int(n)\n",
+    "    ans = int( n * (n+1) / 2 )\n",
+    "    return ans\n",
+    "\n",
+    "def triangle_sum(n=1,m=2):\n",
+    "    \"\"\"\n",
+    "    Calculates and returns the sum of two triangle numbers.\n",
+    "    \"\"\"\n",
+    "    ans = triangle_number(n) + triangle_number(m)\n",
+    "    return ans\n",
+    "\n",
+    "# TODO: Write and print the output of function calls to the triangle_sum function\n",
+    "# print out sum of call to triangle_sum using only default arguments\n",
+    "print(triangle_sum())\n",
+    "# print out sum of call to triangle_sum using only positional arguments\n",
+    "print(triangle_sum(2,3))\n",
+    "# print out sum of call to triangle_sum using only keyword arguments\n",
+    "print(triangle_sum(m=4,n=7))\n",
+    "# print out sum of call to triangle_sum using a mix of default, positional and/or keyword arguments\n",
+    "print(triangle_sum(6,m=14))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warmup 3 --Trace the following code Using the debugger or Python Tutor.  At any point as the program executes, you should be able to state what line of code will execute next and what the output will be.\n",
+    "\n",
+    "[link](https://pythontutor.com/visualize.html#code=def%20f%28%29%3A%0A%20%20%20%20x%20%3D%201%0A%20%20%20%20print%28x%29%0A%20%20%20%20g%28%29%0A%0Adef%20g%28%29%3A%0A%20%20%20%20x%20%3D%202%0A%20%20%20%20print%28x%29%0A%20%20%20%20h%28%29%0A%0Adef%20h%28%29%3A%0A%20%20%20%20x%20%3D%203%0A%20%20%20%20print%28x%29%0A%0Af%28%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "3\n"
+     ]
+    }
+   ],
+   "source": [
+    "def f():\n",
+    "    x = 1\n",
+    "    print(x)\n",
+    "    g()\n",
+    "\n",
+    "def g():\n",
+    "    x = 2\n",
+    "    print(x)\n",
+    "    h()\n",
+    "\n",
+    "def h():\n",
+    "    x = 3\n",
+    "    print(x)\n",
+    "\n",
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warmup 4 -- Let's make some changes to it! [link](https://pythontutor.com/visualize.html#code=x%20%3D%20100%0A%0Adef%20f%28%29%3A%0A%20%20%20%20x%20%3D%201%0A%20%20%20%20print%28x%29%0A%20%20%20%20g%28%29%0A%0Adef%20g%28%29%3A%0A%20%20%20%20x%20%3D%202%0A%20%20%20%20print%28x%29%0A%20%20%20%20h%28%29%0A%0Adef%20h%28%29%3A%0A%20%20%20%20print%28x%29%0A%0Af%28%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 100\n",
+    "\n",
+    "def f():\n",
+    "    x = 1\n",
+    "    print(x)\n",
+    "    g()\n",
+    "\n",
+    "def g():\n",
+    "    x = 2\n",
+    "    print(x)\n",
+    "    h()\n",
+    "\n",
+    "def h():\n",
+    "    print(x)\n",
+    "\n",
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Warmup 5 -- finally [link](https://pythontutor.com/visualize.html#code=x%20%3D%20100%0A%0Adef%20f%28%29%3A%0A%20%20%20%20x%20%3D%201%0A%20%20%20%20print%28x%29%0A%20%20%20%20g%28%29%0A%0Adef%20g%28%29%3A%0A%20%20%20%20global%20x%0A%20%20%20%20x%20%3D%202%0A%20%20%20%20print%28x%29%0A%20%20%20%20h%28%29%0A%0Adef%20h%28%29%3A%0A%20%20%20%20print%28x%29%0A%0Af%28%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 100\n",
+    "\n",
+    "def f():\n",
+    "    x = 1\n",
+    "    print(x)\n",
+    "    g()\n",
+    "\n",
+    "def g():\n",
+    "    global x\n",
+    "    x = 2\n",
+    "    print(x)\n",
+    "    h()\n",
+    "\n",
+    "def h():\n",
+    "    print(x)\n",
+    "\n",
+    "f()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reminder\n",
+    "\n",
+    "Python has two scopes -- global and local.  The callstack stores variables in frames.  When executing a line of code, the variables in the current local scope and global scope are accessible.\n",
+    "\n",
+    "Some Lessons on executing Functions:\n",
+    "\n",
+    "1. Functions don't execute unless they are called\n",
+    "2. Variables created in a function die after function returns\n",
+    "3. Variables start fresh every time a function is called again\n",
+    "4. You can't see the variables of other function invocations, even those that call you\n",
+    "5. You can generally just use global variables inside a function\n",
+    "6. If you do an assignment to a variable in a function, Python assumes you want it local\n",
+    "7. Assignment to a variable should be before its use in a function, even if there's a a global variable with the same name\n",
+    "8. Use a global declaration to prevent Python from creating a local variable when you want a global variable\n",
+    "9. In Python, arguments are \"passed by value\", meaning reassignments to a parameter don't change the argument outside\n",
+    "10. It's irrelevant whether the argument (outside) and parameter (inside) have the same variable name\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conditionals 1\n",
+    "\n",
+    "## Reading\n",
+    "\n",
+    "* [Downey Ch 5 (\"Floor Division and Modulus\" to \"Nested Conditionals\" and \"Keyboard Input\" to end)](https://greenteapress.com/thinkpython2/html/thinkpython2006.html)\n",
+    "* [Downey Ch 6 (\"Return Values\" to \"Boolean Functions\")](https://greenteapress.com/thinkpython2/html/thinkpython2007.html)\n",
+    "* [Python for Everybody, 4.1 - 4.5](https://runestone.academy/ns/books/published//py4e-int/conditional/toctree.html)\n",
+    "\n",
+    "## Learning Objectives\n",
+    "After this lecture you will be able to...\n",
+    " - write conditional statements\n",
+    "   - using conditional execution ( if )\n",
+    "   - using alternate execution (if/else)\n",
+    "   - using chained conditionals (if/elif/elif/…/else)\n",
+    " - identify code blocks (indentation layers) in a program\n",
+    " - count the number of blocks in a segment of code\n",
+    " - determine the output of code with conditional statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if statement Syntax:\n",
+    "\n",
+    "An if statement has the form:\n",
+    "```python\n",
+    "if condition:\n",
+    "    #block of code to run if condition is true\n",
+    "```\n",
+    "\n",
+    "The `condition` portion is an expression that evaluates to `True` or `False`.  If the condition evaluates to True\n",
+    "then the indented block of code is executed.  If it is false then the indented block of code is skipped.\n",
+    "\n",
+    "### Example usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Word is short\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Determine if a word is short or not.\n",
+    "# If it is short, say \"Word is short\" <= 5 characters\n",
+    "# If it is long, say \"Word is long\"\n",
+    "word = \"hello\"\n",
+    "word_length = len(word)\n",
+    "\n",
+    "if word_length <= 5:\n",
+    "    print(\"Word is short\")\n",
+    "if word_length > 5:\n",
+    "    print(\"Word is long\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You Try\n",
+    "In the cell below print \"even\" if the inputed value is even"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter an integer number 4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "even\n"
+     ]
+    }
+   ],
+   "source": [
+    "num = int(input(\"Enter an integer number\"))\n",
+    "\n",
+    "##TODO write an if statement that prints \"even\" if num is holding an even value\n",
+    "if num%2==0:\n",
+    "    print(\"even\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if-else statement Syntax\n",
+    "\n",
+    "An if-else statement has the form:\n",
+    "\n",
+    "```python\n",
+    "if condition:\n",
+    "    # block of code to run if condition is true\n",
+    "else:\n",
+    "    # block of code to run if condition is false\n",
+    "```\n",
+    "\n",
+    "The `condition` portion is an expression that evaluates to `True` or `False`. If the condition evaluates to True then the first indented block of code is executed. If it is false then the indented block of code after the `else:` is executed.\n",
+    "\n",
+    "### Example usage\n",
+    "\n",
+    "Bob runs a community bakery. Every day, he recieves a shipment of dough balls, each of which bakes 4 loaves of bread, and he evenly splits the bread among his customers. Some days he may recieve 10 customers, some days 20 customers, and some days none at all!\n",
+    "\n",
+    "Below is the code that tells Bob how much bread he gave to each customer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "How many balls of dough did we recieve?  3\n",
+      "How many customers were there?  8\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Each customer gets 1.5 loaves of bread.\n"
+     ]
+    }
+   ],
+   "source": [
+    "balls_of_dough = int(input(\"How many balls of dough did we recieve? \"))\n",
+    "num_customers = int(input(\"How many customers were there? \"))\n",
+    "bread_baked = balls_of_dough * 4\n",
+    "\n",
+    "# If statement to avoid the zero division runtime error\n",
+    "if num_customers == 0: \n",
+    "    print(\"Sorry! No one showed up. All the bread went to waste.\")\n",
+    "else:   \n",
+    "    bread_per_customer = bread_baked / num_customers\n",
+    "    print(\"Each customer gets\", round(bread_per_customer, 2), \"loaves of bread.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You Try\n",
+    "\n",
+    "In the cell below write an if-else statement that prints \"you may enter\" or \"entry denied\" depending\n",
+    "on the value in the `password` variable.  The correct value for entry is **CatNipFOREVER**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter Password: ········\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "entry denied\n"
+     ]
+    }
+   ],
+   "source": [
+    "from getpass import getpass\n",
+    "password = getpass(\"Enter Password:\")\n",
+    "\n",
+    "##TODO write an if-else statement that prints \"you may enter\" or \"entry denied\" if they entered\n",
+    "# the proper password (i.e. if password refers to the value \"CatNipFOREVER\")\n",
+    "if password==\"CatNipFOREVER\":\n",
+    "    print(\"you may enter\")\n",
+    "else:\n",
+    "    print(\"entry denied\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## if-elif statement syntax\n",
+    "\n",
+    "An if-elif statment has the form:\n",
+    "\n",
+    "```python\n",
+    "if condition1:\n",
+    "    # code block 1\n",
+    "elif condition2:\n",
+    "    # code block 2\n",
+    "elif condition3:\n",
+    "    # code block 3\n",
+    "...\n",
+    "elif conditionN:\n",
+    "    # code block N\n",
+    "else:  (this final else portion is optional)\n",
+    "    # code block N+1\n",
+    "```\n",
+    "\n",
+    "If `condition 1` evaluates to `True` then code block 1 is executed.  If it is false then `condition2` is evaluated.  If it is `True` then code block 2 is executed.  If it is false then `condition3` is evaluated.  If it is true then code block 3 is executed.  This process continues until the final condition (`conditionN`).  If it is true then code block N is executed.  If it is false then code block N+1 is executed (if present).\n",
+    "\n",
+    "Notice that <u>**only one** code block will be executed and if the final else portion is included then **exactly one** code block will be executed.</u>\n",
+    "\n",
+    "### Example Usage\n",
+    "\n",
+    "**Age Categorizer**\n",
+    "\n",
+    "Assume `age` is an int parameter of the `categorize_age()` function, which categorizes the person's age.\n",
+    "\n",
+    "All the following bounds are inclusive (meaning you should include both ends)\n",
+    "\n",
+    " - Return \"Baby\" if between ages of 0 and 1\n",
+    " - Return \"Toddler\" if between ages of 2 and 4\n",
+    " - Return \"Child\" if between ages of 5 and 17\n",
+    " - Return \"Adult\" if between ages of 18 and 64\n",
+    " - Return \"Senior\" if between ages of 65 and 125\n",
+    " - Return \"Not a valid age\" for all other ages.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N/A\n",
+      "Baby\n",
+      "Toddler\n",
+      "Child\n",
+      "Adult\n",
+      "Senior\n",
+      "N/A\n"
+     ]
+    }
+   ],
+   "source": [
+    "def categorize_age(age):\n",
+    "    if age < 0:\n",
+    "        return \"N/A\"\n",
+    "    elif age <= 1:\n",
+    "        return \"Baby\"\n",
+    "    elif age <= 4:\n",
+    "        return \"Toddler\"\n",
+    "    elif age <= 17:\n",
+    "        return \"Child\"\n",
+    "    elif age <= 64:\n",
+    "        return \"Adult\"\n",
+    "    elif age <= 125:\n",
+    "        return \"Senior\"\n",
+    "    elif age > 125:\n",
+    "        return \"N/A\"\n",
+    "\n",
+    "# This is a lot of tests! Let's try them incrementally.\n",
+    "print(categorize_age(-2))\n",
+    "print(categorize_age(0))\n",
+    "print(categorize_age(3))\n",
+    "print(categorize_age(15))\n",
+    "print(categorize_age(64))\n",
+    "print(categorize_age(65))\n",
+    "print(categorize_age(126))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Watch the code run in the [PythonTutor](https://pythontutor.com/visualize.html#code=def%20categorize_age%28age%29%3A%0A%20%20%20%20if%20age%20%3C%200%3A%0A%20%20%20%20%20%20%20%20return%20%22N/A%22%0A%20%20%20%20elif%20age%20%3C%3D%201%3A%0A%20%20%20%20%20%20%20%20return%20%22Baby%22%0A%20%20%20%20elif%20age%20%3C%3D%204%3A%0A%20%20%20%20%20%20%20%20return%20%22Toddler%22%0A%20%20%20%20elif%20age%20%3C%3D%2017%3A%0A%20%20%20%20%20%20%20%20return%20%22Child%22%0A%20%20%20%20elif%20age%20%3C%3D%2064%3A%0A%20%20%20%20%20%20%20%20return%20%22Adult%22%0A%20%20%20%20elif%20age%20%3C%3D%20125%3A%0A%20%20%20%20%20%20%20%20return%20%22Senior%22%0A%20%20%20%20elif%20age%20%3E%20125%3A%0A%20%20%20%20%20%20%20%20return%20%22N/A%22%0A%0Aprint%28categorize_age%28-2%29%29&cumulative=false&heapPrimitives=nevernest&mode=edit&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false).  Notice that the conditions are evaulated one after the other until a condition is found which is true and then that code block is the one that is executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### You Try\n",
+    "\n",
+    "In the cell below finish writing the `calculate_letter_grade()` function.  The input to the function is a percentage (float) and the return value should be a letter grade following these rules:\n",
+    "\n",
+    "- \\>= 93 - A\n",
+    "- \\>= 88 - AB\n",
+    "- \\>= 80 - B\n",
+    "- \\>= 75 - BC\n",
+    "- \\>= 70 - C\n",
+    "- \\>= 60 - D\n",
+    "- anything else - F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "B\n"
+     ]
+    }
+   ],
+   "source": [
+    "##TODO finish the calculate_letter_grade() function\n",
+    "def calculate_letter_grade(percentage):\n",
+    "    \"\"\"returns the letter grade for the provided course percentage\"\"\"\n",
+    "    if percentage >= 93:\n",
+    "        return \"A\"\n",
+    "    elif percentage >= 88:\n",
+    "        return \"AB\"\n",
+    "    elif percentage >= 80:\n",
+    "        return \"B\"\n",
+    "    elif percentage >= 75:\n",
+    "        return \"BC\"\n",
+    "    elif percentage >= 70:\n",
+    "        return \"C\"\n",
+    "    elif percentage >= 60:\n",
+    "        return \"D\"\n",
+    "    else:\n",
+    "        return \"F\"\n",
+    "\n",
+    "# Should work with the following function call.  Try different values\n",
+    "print(calculate_letter_grade(86.5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practice\n",
+    "\n",
+    "Let's work through computing a string representation of a date given the year, month, and day.\n",
+    "\n",
+    "\n",
+    "### Date Printer\n",
+    "\n",
+    "See the requirements below. We will first break down the problem into smaller problems and right a function for each."
+   ]
+  },
+  {
+   "attachments": {
+    "date%20printer.png": {
+     "image/png": 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IZpdWo3a5Tq5uvJ6PvoWBgiGnEcLovfEzkyum+WbR5k4WKpZslitWb6zv2dTa5tsl2Qc6ODrqO8k7C7iQXLGuy24f3Uc8uj3veNXvPOt9bNchn2Rfqh+F7OvvEeAUaB9kE2xJsQwxC9UMkw0XiGDZTROJiFyN+kH9Gb0Wi44jxgslaOxxSoxOKkxu2TuVQrOfL1UmTfuATbrfwfiMw4cqM9uyhrO/5zIfVjhinxdx9HB+XcGjY++OrxdyFikX250MLTlYeuZU8+nespkzv84xl0uc166wu0CujK06VF0M73PddXOXiJcV6x2vRF3Nu1bX0Hl95MaXJkwzx03JWxq3LVrc7gS2xrSl3E1tP9Bx8F5G56H7mQ+yH+Y+Otx1+PHhJ4e7c5/mPMvqOdSb3pfav/d53F9RA7sHI1/EDCW9PDh87FX5SMPrB6Mv33waB2+JE4Lv5Cd1psyn/WbOvv/0UXk26VPr51/zmgtxXy5/fbfIvmT5PeVHw8/pFe5Vh7XsX53b82+M0EfuQH5GtaMPYRyx4tgF3E18BsGBhptmlPY8XTi9OgOCoZ0xncmCyEDsJR1ltmVhYHnKms1mwg6xN3NEcApxDnHlcOtwf+Ip5TXj/cZXxm/G/1nguKCG4IjQXmF+4VYRb5FV0WIxJbFu8QDxVYljklKSbVKOUlPSqTIiMkOyuTsMdnyTq5L3VKBTaFOMVBJQ6ldOU1FQGVfNU9NW+6ReqmGusah5Xste65d2nY67Llb3hh5Zn6h/1yDSkN+w1yjNWMl42qTE1Ba+d9w2j7KQsnhnWWblYc1q/dymwNbBjmQ3aH/SwdtR2PGD01XnRBdjVwbXYbdK92gPA09az0GvMzuDvRW8V3bd98n39fKT8Fsid/ofC/ANVAxCBQ0G11JSQpxCpcPQYW/Cb0UU746PdI3SoPJGo6JnYwZi2+Pq48sS8vakJsYnhSb77925zy3Fab9Dqn2a/QGHdKeD7hk7DwVkhmZFZ6fkZOYWHC47UpPXePRefl/B6LHPJ1CFEkVexUdP3i9ZOSV72q/sxJnHZ1fLFc4HVJRc6KlCVWvVxNfW1328JHk5pL72ytw1lYb917sbOZrCmjtv8d1OaXnbatXW0i7fcbFT6v7VhwaPhh8ndPM97e053Of0XHQADH4cejf84TV4IzK+a6J2Cj2T+BF8qpgnf9VdUvvpvFq8Mf9b3/s2CkYFgBOHANj4nuNQA0DuRQDE9gDABueednQAOKkDhIApgBY7AGSh/ef8gODEkwBIgAdIAjVgBueXYXBOWQTqQReYAGsQO6QMOULR0AmoGXoN53zSCBdEKqIeMYpkQBog45GXkFNwluaFKkW9gjMxH/QF9CeMCiYV8wzLgw3HtuFIOAquHc+Jj8X3ExQJRYRVGjLNU1p12mo6drpcegR9Ev13hliGJcZEJogpm8hKrCCpkXqZQ1iwLBdYjVmn2DLZpdh7OWI4OThbufy5abiv8XjwInkv8XnCGUGfQJ6grRCT0HPhIhFPUQHRKbGL4tESGpKQZJdUvrQnHJ3zsv07WuQq5QsU9ilSlByVNVT4VCHVMbUW9eMaIZraWvRaI9o1OjG6uno4vT79BoPrhk1Gt4xbTe6Zdpn1mA9ajFpOWy1Yr9ji7FjtRR3UHK2cyM7JLsWurW4zHiRPfa/IneXeAz4EX32/JHKz//dAtaCk4PYQQqhzWEX44m6zyLKouWitmJzY0XilhKN7FpJckx/s005pTbVMm0jPytDOBFl9OVcOn8oryDc7hjx+vzC/OKDE8JR0meBZkXKlCpvKqOrS2ieXQL3qVZsG9xvBTck3T9y+dqe/bamDt9PsQcyjs4+fda/1yPTtfH5k4O4QaZg8cml0dpx7Qm1Sb1r+Pf2HF7NHPu+Ya18w+9L5TWGxZGn5h/3PC8sLqxprKb/ubu4fW/NPhOdfAqgCE+AKQsA+cBzUgU4wCn5AJEgOsoEioKNQA/QSARAScJafhriKeAvn8VbIdGQbcgWljTqA6kazowPRjRg8xhvTiGXEhmGf4KRxebhFvBf+AUGWUESDpImiGad1pn1MZ0jXSq9FfwfOYh8x2jOOwnnqOvE4SZb0lDkCzjybWX3ZaNia2QM5WDkecu7hkuYa5y7iseXF8Xbw7ec3EMAIPBUsFPIVlhVeFekWLROLEjeW4JL4IvlY6rx0ioynrOYOCTl2ebz8qsKc4oTSC+XHKrdVL6qVqB/SoGp6ahlqS+ow6izqDuu16tcbXDVsMGoyvm3SZtpp9ti81+KF5RuraesFmxU7nD2rg5ijupO1s7/LXtcSt5vuwx5rXoI7Lbxjdp316fGDyCr+EQE1gVPBopSQkKuhy+GmEYW7Z6K0qHuj22JRcVbxRQlTiepJR5Kn9xmnVKfSp+05MA3vJ72ZFlkPc8xyu4845I3lpxzjPX63MLCY/mRzqf9pUtmDs3vLVc5/uXC1KrZGqw5zceDyhSvJ17yuqzTSN03cvH77wB2bNva74x01ndQHWo+wXYNPap7u7/Hq03kuMsA0+GjI+eXkq8TXzKPXxpzGVyeqJ92nGWa6PmTOWn5mnHuxcPZryKLKd8SPnuXS1aBfitvzjwQYQLu5A4gDFTgC3EA4OAjOgNtgGF7/gpAFFAtVQIMIGoQRvPI7kDikPfIM8gvKAlWFxqOp6DcYJ3i122AHcGTcT3whQZ0wSXOSVo92hC6Jnp++iyGeUZJxgukM0Y8kQfrO/JCljDWJzZNdj0OKk52LhhvBvcqzzLvKDwSw8A2UR1hWRFvUQSxIfL/ESckbcN49L8u4Q0HOVX6fQoVij9KKioSqu1qBer8ms5a7doXOnJ62/mGDN0aKxjkm42Za5oUWX6zsrC/Z0tqF2T92lHTKcf7gauFW64H3pHg99BbdddBn0s+QXBmADPQPukcRDckInQm3iqiPZIlKoI7FGMVejmdP2LfnY5IbvE5VUqpSOdKOpKMOJmd8yfTIupq9nut0uOrI8lHH/MvHCMcpJx4USRXnnpwrdT11p0z0TD689/uf776gWVlVzVSTWDt10fFSS73olbyrSw3e1x80yjQdbZ6/ZX/78h1Ca2BbazuxI+Be433UA7uHpY8mHks8oXRXPh3v4ey17zvYf/352wHCoNwLhyHqyyPDNa/ujQy8nhpdeLM6Dr3FTmDeYSbB5PLUp+nRmafvmz+Uf8ycjfhk/VlqDjv3Zr55IeuLx1eJr1++tSymLRl9x3zv/JHyU/PnwvKFFY9VwmrjGvkX3a9r6+4b8x8dqKiweXxAtAYAoEfX17+KAoAtAGAtf319pXx9fe08nGyMAHA3bOs/pM2zhhGAs1wbqO/K4r/+y/kfiazUoRVG4s8AAAGdaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjYyNzwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zODU8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KUb6FEQAAQABJREFUeAHsXQm8lNMbfscuhKjsEa1SipI2qWSvpEKhXVFZoiJJ9iVbKmuUCAmt/lEhFYlEm7VVok2ylLKd//Occ7+Zb+6dubPcucvced/fb+bbznnPe57zzXzv9y7nBAxIlBQBRUARUAQUAUVAEVAE0hKB3dJSahVaEVAEFAFFQBFQBBQBRcAioMqc3giKgCKgCCgCioAioAikMQKqzKXx4KnoioAioAgoAoqAIqAIqDKn94AioAgoAoqAIqAIKAJpjIAqc2k8eCq6IqAIKAKKgCKgCCgCqszpPaAIKAKKgCKgCCgCikAaI6DKXBoPnoquCCgCioAioAgoAoqAKnN6DygCioAioAgoAoqAIpDGCKgyl8aDp6IrAoqAIqAIKAKKgCKgypzeA4qAIqAIKAKKgCKgCKQxAqrMpfHgqeiKgCKgCCgCioAioAioMqf3gCKgCCgCioAioAgoAmmMgCpzaTx4KroioAgoAoqAIqAIKAKqzOk9oAgoAoqAIqAIKAKKQBojoMpcGg+eiq4IKAKKgCKgCCgCioAqc3oPKAKKgCKgCCgCioAikMYIqDKXxoOnoisCioAioAgoAoqAIqDKnN4DioAioAgoAoqAIqAIpDECqsyl8eCp6IqAIqAIKAKKgCKgCKgyp/eAIqAIKAKKgCKgCCgCaYyAKnNpPHgquiKgCCgCioAioAgoAqrM6T2gCCgCioAioAgoAopAGiOgylwaD56KrggoAoqAIqAIKAKKgCpzeg8oAoqAIqAIKAKKgCKQxgioMpfGg6eiKwKKgCKgCCgCioAisIdCoAikGoFly5bJ3LlzLdtAICDt27eXkiVLproZ5acIKALFBAFjjAS+XibCT5nDRerUF9lzz8i9W/m1yLIlIiefKlKufOQyG34UWbJQ5L//RE6pJ1K6TMRytt3li0X+/kukZp2IZfSkIpAOCKgylw6jlGYy/u9//5MRI0bIn3/+KVu2bJGaNWvKaaedlma9KFhx//77b7nxxhtl8ODBcuihhxZs49paviHwxRdfyBtvvCF33XVXsI3vvvtOpkyZYo/33Xdf6dKli/z6668yfvx44X1wyCGHSKdOnWTatGnyzTff2HLHH3+8tGrVSj766CP5+OOPhUpI7dq1pW7duvLMM8/Irl27hC9O5513nlSuXFleeOEF+9vbY489pG3btrJo0aKYvBo1ahSUcezYsbLbbrvJ5ZdfHjyXbzvrv5dApxYis6BUeVRjL5GZ63IqYQ8OFhkQwlKeelSkx/VeLTFQ3gJD+orcNSx4zu7MAN5nXRh+7sbuEnhklDvXtBraXxp+XY8UgXRCAH8KSopAviCAB5HBb8Hg4ZMv/IsT07Vr11qs3nvvveLUrYzuy9atW81RRx1loKSH4QClzUBht+MNJc388MMPZv78+cFztWrVMn/99Ze58sorzZ577mnLXXbZZZYHlH2z++67m3322ccMGDDAbNq0yZxwwgm2DKzfZvTo0dBn/jPkwd/e4YcfbmbNmhUXL7+QU6dONVDmrFz+8/my/8RQqKbQT598yJhNG415/D533OfK8OaWLHLnu7Y2ZtW3xrRt6o7XrAyV++F7d67Nmcb8sNaYee+7Y/L/++9QOe7d0suYewcaUx7XmlQNv6ZHikCaIRCgvOmkfKqshYPAt99+K++//76UKVNGzjnnHJk3b56sW7dOqlataq1utApkJ9apVKmStSREs8zRirdgwQLZtm2b5dWhQwfZf//9w1h9+eWX1iJBi8Zhhx1mLQ14SIaV+eOPP+TDDz+UOXPmWAtFhQoVBAqS3HzzzbLXXnjLz6Lff//dWkDIk1aR008/XS644ALvcsLb3ORfvny5xenII48UWlYmTJhg3c1XXHGFtb74G/v++++lXLlyMmPGDDnrrLP8l8L2Y8m/c+dOeeWVVwTKgBxzzDFy7rnnWvxnzpxpcenWrZvFMIxpLgf//vuvQBkQ4sXxrlevnrX+lChRIpdaOS8tWbJEoLDYCwcccIB1vf/222/y+uuvW2vU3nvvbc95Y/XVV18JsV29erUcffTR0qJFC6lSpUoY41j3RTJY0JrMe6l06dJhbSVzQGscLa2rVq2S4447LowFLXannHKK3HLLLXL33Xfba40bN7bb2bNn2y2/WP+ee+6Rzz77TE4++WR7T9Py9tBDD0mvXr1suX/++cde4185cYayZy3j/fv3F+LI+ypeXrYgvsiL7Xj3pHc+0nbDhg1y8MEHC8cwKVq7SmT1CpHGzW11a11DH6QmQjMW/RpieQv6e/8TIps3ihwKtynrHXs8zg0KWusM7tfA+BdE2l0pAqukpWs7igwfK7LiK5HjK4f4eXsX1BX583eRd5d7Z3SrCKQfAlTmlBSBWAg8+eST5sADDzR4iJuKFSvyBSD4adq0qaEVIjvlZpnDg9Y0b97c8jjiiCMMHmx2H4qPgRIUZLVmzRoDRdHAXWStDZSBx++++26wDJQCQwtH2bJlDS0YDRo0CMpGGTyCq8mQP2WH69daTbh//vnn46U921u7VynKNh758cA1++23n7Wi4EFn4D6z1g7ur1+/3nKG8mDl8CwwUGZseVpeWB6KRVCCeOSH4mCgyNpxIqZ9+/a1/SU/9jW7lSjIPMrOY489ZusddNBBdoxoFYJCnTBe9913n4HybHlVr17dED/EVhooavYcxwXKopXikUcesRYpludYckt8nnjiiaCU8dwXiWIBBctA0bRjNHz48GBbyeywf7wf8RITtXrPnj1t39jv1157zVrcFi9eHFZ++/btFqMmTZrY83CZmpNOOslAgQsrx98Dx5e/059//tmUKlXK0Irnp3h5eXWGDBliedJqGI3uvfde+3vk73LFihXRiiV2funnzprW76rweucDy+q7h5/bD3rnFReGn/Md0UppTkAZYGN27fJd8e2Sr1rmfIDobjoiwDcwJUUgLgTuvPNO++cOa5Z5++23zcqVK83AgQPtuY4dO+bgkZsyB4uErTdy5EjrFmJlPsj40D711FODvPjQgoXGUGEj0f1Uvnx507Vr12AZWHcsL1g0gucQ82MflHRhkcgHlgarkMJiaM/xjx7WE1v3gQcesOfi/YpXfrrC+JB9+OGHrQxsmwrR448/HmyKWD799NO23KBBgwxirOwHVrpgmUTlv+GGGyw/KknEBRY2g3grAytKkGc8O8QP1k5bn+X5YGd/3nnnnXiqh5XxFENYCYPnhw0bZsccFjh77tNPP7XKFCyKhsoHCdZIw3uO94annMdzX9jK+IoXC28M2D9Ya73qSW1hxbY43X///VHrI57UwKJlqKCVK1fOXH311RHLvvrqq5bXTTfdZLfRXPEXX3yxgeXculRhlTU7duzIwS9eXqy4cOFC294dd9yRg493olmzZrYMMYNF2Dud/HY7Xl4aHO+Ur+Xhiq2phsfVubXCedcpa0zjyuHn/EcPQqGFbIbbaKTKXDRk9HwaIaAJEPgXUkoMAVgtrIuItegCovsVlgV5/vnnbdB0PNwmTZoksBZZ9yoeeMEqdKnhISK//PKLdd3Q7fXjjz8KrBjWjUoXEiwPQZcdK9L9xmDta665RuiqosuHbtilS5cKlBnLmy7ar7/+WqAoWtee1yADzkl0Q9ItFS/FKz/5wUIj1157rXV/0f1LGehy7NOnj23u7LPPFrpZSQxCj+RmTUZ+usTpeoaiYHnTpZwoEX+61OkyZJYyg/NJDMSHZTUhdj169JChQ4cKXgoESoDAeiUc+86dO8uxxx5reTHoH0q2da1C0QvyZ184VnRBwjJsk2ti3RfBytiJB4t27drZe2/z5s1y2223+asnvI8XHVuHYQbRiFgS1969ewssaWFJEv46l1xyicDiZl2rbdq0kTPPPNN/ObiPFwb7W2DyApMpGEaQneLlxXonnniirQ4FOjub4DEUPfs75j3WsmXL4PmkdhAaIJfhnpoH7MaPEalaPZzN7nuJ/Evd0Uf/4Pe7e5Ss19EjRPrfKdKmiZi+gyXgq6a7ikBxQ0CVueI2ovncHypgcPOEtcJ4OConzFzlQzMe+umnn2wsG6wMYcUZW0R+XvwNs/omTpwojPW6/fbbhbFafMgzxs4jxu1RuXr22Wdt5iBjsaiEkAfccTZGDG5NW5xZg3BJeVXtlkoJ4wAToXjlJ09mpzKr0CO4LGkR9w7DttHOJyM/H7CeIhfWSJwHxJFKF5UrxvldddVVAne6zbqNJmdurHnvwJJrY72olMESaxXzW2+9NViNuJIYb+kpRN7FCy+8MBg3F8994dXjNh4sOC7MDE0FMV6TxBjP3IgvKf369bMvK56iHKk8rIvywQcfiB+r7OXYx4suukhg3RQqptEoHl6sy/HilELZx8HPlzGUSJbwn0pun4pc14tEpnwkwozVdh1z8jm2tsjyD8PPL9oq0rlh+DkevTJapAteluodJzJ2qgQYg6ekCBRjBEJPmGLcSe1a6hCgNYWKk/ewYIA8rXJ8EMaryFEaBlczQeDNN9/MMQcd2+CDhLxpqeH0CHCBBTvx0ksvhSlzn3zyiQ2Wnzx5sg3yp6LhBZjTokHrgWchYRA55fUTlRW47fynYu7HI39MJr4CcCHaI7+S6ruccvn9vKPtT58+3Qbc82HtJYnQKnr99aGpIKLVjXaeSjlc2jbwnwoP3OU2UcMrT1xJtLJySg0/cfoNKujx3hf+uvHs82WE7W7cuFEQ42eTPeKpF6mMp5jBVRzpcvAcLc38MCkkN/JebmKV4+8mVpl4eRFnyk9LdzTiyxiTNJhsw99orLYj8uFvr+OFIq/OELkPin2/OwQZPCI/bxY5/MhQlVqwLE+GMrceVuwjjxH5KmsqkZOzTXv08nMiHbqJNKwgMm2ByL5I1vl+tcgxUOyUFIFiisBuxbRf2q18RIBusU6wmNFdRqsY3V3MGvWIblIqArS+kOim4zEzNT23JhUCZmbygU2XEDNa6SaqX7++fSDwIcKHHF2SrE8FjA8LuiCp4LGuNzEx3adPPfWU0IVEJY4PY7oXqdR5c7bR7YqYIqs8IinAZr1SJsS02QdRdsXBCp7LVzzyM8uP2ZiU9fPPP7fc6C6lfLRA0frkER/+zOKlCxsxdDJq1CjbH56nCzZe+cmb/WK7dFVznx/OTZYo0erCMcB0FxZzKsV16tSxVk8Eu1u3daI8ma2KuEArD618tNT5idnMHDO6vHk/MIuTbkOeR5C9UJGP975IFAtm1jLjmBnRfte/X754972XB8QcRq3i/U74m+A9zN8HXbx+4u+AlmT+PkgMaeAxFS0/8R7hfcOMY1qfOebMbPVTvLy8Ovxdsx1MfeKdyrFlmAV/a8yg9ubOy1Eo1onBeDmgIkdCuIS1zLVvLlLjKHfO+74cChrp6ktEZqN870vd8aWd3ZbfH812ihz3G58ndh66ITe6yYW/+JRnQzQTFsXXXhBZCWy/+9Ltz5oWuq57ikA6IYAHnpIiEBcCTIBg9iGTBfCwMsy8ZOYkg/nxpx/kAXcPfYg5PsxIxQMmWA4KmM2688pyXitm7cFaFyzDLD3E7lhezGLldS/pAlYIO88Wy/Mag8k9Xsy4RIxWWNYflBuDCVptAoJXjnNzQTkNBtYHG45jJ5b8iLsKygOLoOUIV2HwnDd3mNcUHobBucYoH/vNAHoP23jkxzQXQf5eH7nlfGPJEDMaoVBansSb2bAcA/JErF8yLG3mM8cLMYMR6zPw3stu9vrAudSYrOIF9cdzXySKBaY6sRmozJ6Fyz6ibPGeZOII72dYlSNW4ZjCkh02VsSEiTJ+YvIP+Xg4cMtx8BJBvLJMCPKX4X72TNp4eXk8EQNreWICYu9Uji0TWPg/wEQbKJQ5rsd14sK6DDrI+WlYIWf1F58OLzfltfAyo0eEX/fz/X51qOxvv0YvxyQMJUUgzRDQeebwr6cUHwIM1n7uuedsHFp8NWKXwu/FWp7oQqOrhm6iSMS5ujj3VyQ3Dt2ktCLQekWLF+cJozUBD8dIrOxs+eRHXpgWJe6kjUjM4pU/Ut1o52idoSss2hJoxCpV8keTwX+eliO2x7ny/LF//jKJ7GM6CxvsT55QMqNWpWWR48oyTBCIROQR7b6IVD7WOVqiiG+i8+hF4gtl3Vrb2AfPtRmpXFE9hyl7rGWU7vDc5OfvDVPwRP29pbx/v2HuuVXfiVSsIlJiv5SzV4aKQDoioMpcOo5aIcjMhAIGanNpIrpzmD1KFytdXkqKQCwEqCAxK5KTGTN7t2HDhvLyyy+nRGmK1XZhXeekxkwW4gtQJ4QlpBNRgeMLEV/g/CEU6dQHlVURyCQEVJnLpNHOQ1+ZScoYOT9xhn6uMKCkCMRCgJnGzIb1E9ei5UoGxZkYZ8jYUca6pRMxAYSxeXxxS4U1Np36rrIqAumIgCpz6ThqhSAzsz29aSPYPC1ydFEqKQLxIOC5wumW9ojTdnhZvN654rhlQgKTN9KJmLTDhJxooQrp1BeVVRHIBARUmcuEUdY+KgKKgCKgCCgCikCxRUCnJim2Q6sdUwQUAUVAEVAEFIFMQECVuUwYZe2jIqAIKAKKgCKgCBRbBFSZK7ZDqx1TBBQBRUARUAQUgUxAQJW5TBhl7aMioAgoAoqAIqAIFFsE9ii2PdOOFRoCy5YtCy61xWy49u3bR50At9CE1IYVgSwEuD4wl/4688wz7ZrBBQ0Ms8S51FZuy2bFKxPng+Sax1wGLxIxq5hLyq1fv97OE8mM4qJAzHIOfL1MhJ8ymEi6Tn1BqnNREE1lUATSAgG1zKXFMKWXkJx/jvNUcQHuq6++Wr766qv06kAhSMtVFq699lrhYu9KqUWA6/Zy3dNoNHToUOndu7dMnjw5WpGkz3OtYCzrFrE+1zXlpMKc4oft54XYv6ZNmwqWtJPatWvb9YCz8+P6vNWqVbMKK8tiaT7B0nzZi9ljrofLdXALhNZ/L4HmNUWqVhdp3V6kwZkitfcX2bypQJrXRhSB4oCAKnPFYRSLWB+4SDoX/uYC3ErxIUDrzPDhw2Xp0qXxVdBScSMwZswYmThxYtTy8+fPl7Jly0a9nuwFLkeGtXiF1rJI9Ntvv8nFF18sWLdXaDHLC3FeOK7IcumlbvH5SPwGDRokWNvW/i65wsMFF1xgV3f49NNsC9BDEC6f1rFjR6ECmO805TWRWYtFnsQE0ps2ijx+n8jiv0Tu6pfvTWsDikBxQUDdrMVlJPO5H99++62dxR6Lg8s555wj8+bNk3Xr1tkHCBb0TnpyUVrxFixYINu2bbO8OnToYCcr9XeHyyJ99NFH1j1Et1Dbtm3tOqz+MlwfksrjnDlzrCwVKlQQPrC4FBEWAg8W5UNv/PjxQp5YUF1OP/10+1ALFkhwJzf56e4iTliE3K5rOmHCBOtuvuKKKwSL10dsiZMz50ax5N+5c6e88sordtksrnXLFTr4QJ45c6bFpVu3bpKIa41rlc6aNcvixfGuV6+enHfeeQktw0UX2rhx46wrkVao9957z1qHmjdvLqNGjZJjjz1WiIl/XV5ix3bZJq9zCTCOKYlj/dprr1kFqEWLFkJc6TbkWqi0dEUi9iMWUU4qfbzXzjjjDItdXparGzFihPzwww/Sq1eviE17VrHPP/88qsIXsWKEk1QK+SHOL7zwQoQSYn8LVapUsfcjC/Cla9KkSfZ3Q2uen7guK92+tK7nZtVknQ0bNliLYG7rt/p559g/r7XI+7DKNW5uL5le/SVw7S0i8yblKKonFAFFIAoC+ANTUgRiIvDkk08azGJvsAC5qVixIqfxD37gsjFbt27NwQNrcdoyUCZyXIPSYfAwt9fxgDennHKK3YfiY/AgD5Zfs2aNQdydwZJCplatWlYGHmOpoWAZWDgMFoE3sK4YPNBNgwYNgrJRBo8WLVpkyJ+y16xZ0xx11FF2Hw8uAzenVyyubTzyY6kqgwXIDZQUgwedgQJnsKat3YfyYduB1cbKgZUQrCxQPG151mF5KC5BeeKRHwvPGyg9dpyIad++fS1f8mO/sYRWkF88O4899pitd9BBB9kxgnJjoAwmhBf7cOKJJ1o+lKF06dJ2n9jww/Fs3bp1UBwsHWfYDj/Vq1e314nLo48+assgJtNAUbU8DjjgAItXyZIl7THvU4+6du1qcSB/fogBP2wTawx7xez28MMPt+cpH15YLC8orWFlEjng/cH7ES86MatBITVnnXVWzHLxFIBr1MqOl6OYxR955BFbFi9BEcsOGTLEXoflMuJ1nrz33nsttvxvWLFiRdRyCV1Y+jnXCTGm31UJVdPCikAmI4BfjJIiEB8CWJvV/rnDmmWwZqNZuXKlGThwoD0Hl0wOJrkpc7fccoutN3LkSAOXkK27ePFiQ6UGwdtBXrBUGVi/DBU2EhZqN+XLlzd8UHv0+uuvW15YA9M7ZRDzY2B5M7CM2HPkU7lyZasMwspoz7FdLCRu68JKEqwbz0688g8YMMDyf/jhhw1lYNtUUh5//PFgM8Ty6aeftuXgCrOKBpUNWESCZRKV/4YbbrD8qLwSF1imDCxOBlaUIM94dogfrJ22PsvzwU6F55133omnerAMx4317r77bsuLYwhXnoEr0iBW0FCZInGsWa5Vq1bBsVu9erVVdqgIe4rFwoULbTkqQXyRgPvQNGzY0CryXqNwWVssqeg3a9YsiCusb7Zdrxy3bJ8K69dff21PY01VO06bN2/2F4t7H2uxWvnuv//+mHUKQ5mj4kWluk6dOlHl8zAmFtGIuHK8+IFFOFqx+M9vx8tLg+OdMrd8cfz1tKQikOEIqJsV/0JKiSHA2C5YfWwlBnFzEXG6vZ5//nnBAzcuZnTvwEpi3at44AXrMCgbDxFhvBGDuRlv9OOPP0rPnj2tOwiKkPz888/COCeP6Dpiu9dcc400btzY1oPVzcaf0cVJYgYfHtQ2yw/Kn1dVmHhAohuSbqd4KV75yY/xWExuoOx0FTLTkPL36dPHNkcXImMMSY0aNRIoKHbf/5WM/HSJ0/Vcrlw5y4ou5USJ+NNVDKVXmKXsuYfpiqSbNFFi/zlWUKztFgqUxYRudhIzS0l4cQi6A+lmZRIBx4hJCnXr1rVl+EU3Ou8TUsuWLe2xPcAXg/35efDBB+W4444TWP+8SxG3nTp1kkqVKtlrdNnCQmjvRYYVJEp40bFVPH6J1md5ui9h3eULd47qxPDWW28V4pcoMT6T9xjDD/DSELU6rKn2Gl7KopaBomd/x7zHiH+e6C/EyV2Ge2oesBs/xiVE5ImhVlYEMgcBVeYyZ6xT0lMqYNnjkhgzR+WEmZhUIOIhPlD4MGH8lJ/40CU/L/6GD1jGMTHWiw9XuHll2LBhVgn06jHwm8rVs88+ax9ODCynEkIecNPaGDHGVJG46DlctF5Vu6VSkugDO1752cChhx4qcBMH2+QDONIDmgWinU9Gfj5gPUUu2HgCO8QRlhcbm8aYtquuukpgBRO4aqPKmQB7gYvUFuc95REsYQI3aI4pQpgkQGWYir2f/PF/VOqi4RftfDRenpIUKZHAXyfaPuM1SX75opWNdp4KPiy60S5L9+7dE1bmNm3aZJVwvhDNnTs3R+ypvzGOC9zX4imm/mvePmMop06d6h0mv6Ui1/UikSkfiTw4WKRdx+R5aU1FIAMRCD1hMrDz2uXEEWCAPRWndu3a2coMLKdVjg+/eBU5VqRlhkHutMTwgeEntsEHCXlPmzZNLr/8coEbMliEUyZ4lhye/OSTT4SJCLTaIDbKPtA5JQSth4ihEloPPAsJlQLK6yc+sOHG9J+KuR+P/DGZ+ArAvWyP/P3yXU65/H7e0fanT59uk0j4sGbmI4lKwPXXXx+tSp7P80WB48hPmzZtgvyYtML7IfuLRLBALjvENhquuVTL0yXPgsn545IlWnCZeBRJEaViC/dxQqypyDGxg1smlyAe0b708KWK/LIT8ab8nuUz+3Ue82WMSRJMtuFv1FPQI5WNeo6/vY4Xirw6Q+S+W0X63SHI4MHNtlnkcGdZj1pXLygCioBFQJU5vRESRqBz585WeULck82eo7XE7yqlm5QWFmYikuimo0WHDwxOzMqHKxUCTqPAzNQuXbrYjEWWo3JH5YwPX1po+EDjeSpgdL3SRcoHEd2otCwgTsq6TzmXGBUNxPBZawjdi3wI0ipGYnlm+5E/kgIEMVn2QYV4MtsHKn5UIOKleORn5iniveycX8xYRNKFdfdygloqrHxQI5nENsmH//7772+nJ6FyyyxIuhXZV9blwzIe+ckbiRK2XeJFhYzEB7LfPWlPxvii1YVjNnr0aFvys88+E84/RqsnYq4s7lRq4yFvioslS5YEraC8b+hKJFGZ5jXOS0gF/LrrrrPzE9Iy+NZbb1lFAXFt0gmWWrrGeY+RmAlNDKl00A1MxZxjX79+fXudX7ROkgdlR6C+cMxp7aWFka5uL5uUbnjet4glC7rxOW0HXd8cm0TIe3ngOEYj8qY1m2O2a9cuO1Zsm/c8ia5UL4M3Gg/vPC2BlJ8vMSTeO3zB4phTdipmzEJmGVpY+Rvj55lnnrEvZnTZZieOD+vlNpkxwyyINz/MgGU2esI0GC8HVORI6LO1zH0yR2TOB5iqJKeL2RXUb0VAEQhDAA88JUUgLgSYAIGYNsNkATysDDMMmTnJYH786Qd54OHJf+AcH2ak4oEdLAcFzAbBe2Xx8DJNmjQxULiCZZi16mVCMiOR172kC1jvDKwMtjyvQWEJtsmMyx49etikA48Zg+2hONrAdq9NZkFCOTX+rFevfKxtLPkR5xWUBxZByw7zjgXPMfPWT1OmTDFQPoPX2W8G0HvYxiM/Ewy8vvm3XoKBv7149pnRyKxa8iLezIblGPAYsX7xsDDM7IRCYetwnF599VWbwEIezFxl0gPH3sMIColhko0nP+vgJcBg8mnbnpcow+vMEmb2M6bksDx4DkqMTYjwhGPmMKbesFmXvM7+wG1v8LJhi/jvV0wgbKBg2explqVcHJdEiYkjrAurcsSqTAhhBijb8H94DzPZJVFiO34+nuywbFtW/J14Y5C9HO/jSIQYWMsTU51EumzPIeTB/g8w0QZu4ajlcr1wYV2X8AAs0GDo07BCrtX0oiKgCIQQCHAXP24lRSAmAgyCf+6552wcWszCcRbg7cfYIFomaH2iezUSYcoNazGJ5MahNYZWBFrfGMvGechoTYASEImVbYv8yIvznuGhG7FcPCfjlT8eXl4ZWocY75fd/exdJ1apkt/jmduWljC2R7eeP/YvtzqpuMaYPVp3aVlL1DIWqX3y46S5TEiJdm9EqpfsOSZRcI423pteDGiyvAqjHi1ttMbS6peb/Py90YpeEJgWBg7apiKQDgioMpcOo1QEZKRrrV+/fjbBAFNpWAWIiQeRYm2KgLgqgiJQ6AhwYmrG+PEFiO7hdCIqcHwh4gscM4aVFAFFoGgjoMpc0R6fIiMdM0k5XYSfmHTAFQaUFAFFIDICTL5hjB6n70kn4trKzPrmi1tBWmPTCSOVVREoSgioMleURkNlUQQUAUVAEVAEFAFFIEEEkg8WSrAhLa4IKAKKgCKgCCgCioAikHoEVJlLPabKURFQBBQBRUARUAQUgQJDQJW5AoNaG1IEFAFFQBFQBBQBRSD1CKgyl3pMlaMioAgoAoqAIqAIKAIFhoCuAFFgUGdOQ5yJn6szkDj3VPv27aPOmZY5qGhPsyOwdOlSu7oH7xGuBuKth5q9XFE85nyGXHUit9UR4pGbK4FwNQuupOGtVhKpHucz5PJ3mLTbrmIRqUxhnKNcgeWLRf7G8ls16+QUAXMUysYf3VTA3tV9S2DB4vjWcPaq6FYRUARyR0Atc7njo1eTQIBTlnBqA67ZyOWZMHN/Elwyqwon5r322mvt8k6Z0nNOe8HpbniPcJmpokJcEgurd0QUh8tXce44Tjbdu3fviGXiOUklqGPHjlYx45J0XKqsZcuW8uuvv4ZV51JfXJsYK5rYdrmcXTTicmVct7jA6MbuEuCE2yfVxHqqXSM3ew/mqDv6WJFjjgt9Spd1665GrqFnFQFFIAkEVJlLAjStkjsC/fv3t6s6cL1GpfgQoKVn+PDhQmtVphAnofYsuEWlz1zPFkuu2fVnI8nEVSS4Ri6WHrPrwEYqE885Km1cGeKJJ56wv5VHH31UsGyYYKm8sOq03HFVFG/iXq52Eo1KlSplFURvHdxo5VJ2HquUyL0DRcqDo4kiFy12pNdfDH3ewRrIh5R25/VbEVAEUoKAullTAmPxZ8JF4TnxaZkyZexC6fPmzbNLLXEViNNOOy3ppXxoxeNi6du2bRPy4kLd2Zdu4kz6H330kV2knhaKtm3b2qW7/KhzSSEqj3PmzLGycIFyzmLPhyDWkA0W/f3332X8+PFCnvvuu69gDVC54IILgtcT3clNfrrFiBPWrbRLYU2YMMG6m6+44grB+qARm+KC87lRLPn58H/llVcEa3/a5dE4qTMf7lx4ne5MrElqrTy5teG/xoXWZ82aZfHi0lr16tWzC7aXKAFXWRLE5bQ4TuTF8aYCkp1itYn1fWX+/Pm2GpdkoxufShatVrRwcukpnvOPe/Y2oh2PGDFCsK6q9OrVK2IRT9n6/PPPoyp8EStmO0mXMsfEI1r5OMEw71k/cSmzUaNG2fHs27ev/1KOfS6/RbcvLeJcRiw34hJ6XD4vT3TvCFf9w3dF/vw9OquKuHTx5dGv6xVFQBHIOwIw9yspAjERePLJJ+3C4HiIm4oVK9oFuHH32W3Tpk3N1q1bc/Dg4vUsA2UixzUuvt68eXN7HS4rc8opp9h9LtjNhdM9WrNmjV0gHbPQm1q1alkZoJQYzE7vFTF4kBusG2qw5qbh4vUNGjSwvNg2ZfBo0aJFhvx5vmbNmgZrudp9PAQNlACvWFzbeOR/6KGHDNastAvUQ8GwC7xz8XXuc/F3EpZJs3LsueeeVhYoILY8F7XngvBQUoPyxCM/1lA1UGQNx4mYQgGwfMmP/b7xxhuD/OLZeeyxx2w9Ll5Pfli+zUChThgvtoV1Pk358uUtP8rCT40aNewWyllQnFhtwoVvoIjbetWrVzccC8RpGsST2XMcYyiLQX7x7pAP7yG8nMSs0qJFC3PWWWfFLBdPASjwBkt+WdnHjBkTsQrW47XXn3nmmYjXvZNDhgyx5fx4ete4haJsf0fEvnHjxv5Lye+fD7yaVI1cf2BvOJTFmGvaG9OyvjH9exizdlXksnpWEVAEkkYAvzIlRSA+BBDfZB8UsGYZxDuZlStXmoEDB9pziP/JwSQ3Ze6WW26x9UaOHGngOrJ1Fy9ebKjUnHrqqUFefNDB+mUVNp6ExckqBF27dg2WgUXG8sKyScFziB+yD3xYWew58kGQuVUGYWW059gu1p60dWFxCdaNZyde+QcMGGD5P/zww4YysG0qRI8//niwGWL59NNP23KDBg0yb7zxhv3AuhIsk6j8N9xwg+VHxYa48CEO66bZsGFDkGc8O8QP1k5bn+WpJFAReOedd+KpHiwDi5xVuKm4I67LwJ1pt4gVs/z8ykc8bXoKH6xbwTaGDRtm75/Vq1cHzyWyA8uzleX++++PWS1VytzPP/9sPAz4IhKN4lXmkExh+wArX0RWmzdvNnyh4BjyBYkvQnmm3JS52/s6Za7GXsbUKev2qdytWZnnZpWBIqAIhBBQNyv+1ZQSQ4CxXbDS2EoMCKf79bXXXpPnn39e8KCIi9mkSZNsLBDdq3h4BuswW4/ZfYxdOvjgg60ri7FFPXv2tO45KEKCB2DQzcaKVapUse1ec801AmuDrQerm40/gzJjeX/33Xc2yB6KonXHeQ3SLUeiy4uxfvFSvPKTH6w9NrmBstP9SxmgvEifPn1sc2effbaNm+JBo0aNBBYfe97/lYz8dInTpUlXHYku5UQJlkPrKuaC68xS9tzDdHvDsho3O7rSofzbhdvpSidxC8urQIEN4xNPmz169JChQ4faBIpmzZoJrGr2PurcubMce+yxYfziPaB8pEqVKsVbJUc5KLlRXZyUyxtzryLdzHS/w+ImL774osDqLbCueZcT3p544om2Dl6kItZlxiwUYRufxwxiuqnzlbpfJ3J5N5ETqrhm3nwZLleM/9AhIiPG5mvTylwRyCQEVJnLpNFOQV8ZjM1sPj8xZo7KyZYtW2xMnf9atH0G/DOm6b333gsrctxxx9kYPMY9kTp16iQTJ060sV633367MFYLFhgbY+dVZOwVlatnn31WYNWy8VNUCMhjDZQFxtnBrWmLM/CcC4j7iUrJOeec4z8Vcz9e+cmID1D/YuWMl8L7VMQ2op1PRn4qcZ4iF7GxGCcZh0ZFiUH3jPO76qqrBO50gas2qvzRWHIcSLxX/FS7dm3/oR27eNrkfQirsI1tg+VRYNW1Sv6tt94axi+RAy9ejfdLssSXgkceeSRidbiEcyhzLFi/fn374UsKX4jyoswRl5IlS1rFOaIQOEmFMrtSGa1sns8fGR6XZ1pdKgGBMrdoTp5ZKwNFQBEIIaDKXAgL3YsDAVpAqDi1a9fOlmawOq1yVFBoCYqXOK8WEwTefPPNHHPQsQ0+lMh72rRpdmoGuCGDrDn9Ai16Hn3yySfCRITJkyfbIH8qRJxegtZDxPrZwHLP2sIsRMrrJyorcGP6T8Xcj0f+mEx8BeBetkf+fvkuB61FqZLfzzva/vTp021A/tSpU4NJIlQ4rr/++mhVop735mOjsu23PDIZxU+JtMlkDiYkwOVt5YTrPU9B/Z7VkfPHJUuIkxR+kiHeh1SWuY3Xwp29Hf5mKD+t2tGIVlb+VpgkEiupIhqPZM8HfshK8Djy+GRZaD1FQBGIgIAqcxFA0VO5I0BXFpUnBLPLuHHj7BQLflcp3aSIzbEZi+TEjE4+pOhmPPPMM4WKCxUCunmYmdqlSxfrGmM5KndUzqjUIHnAuiR5ngoYXa/MWGR2Jd2onNaCc3RxjrKnnnrKWmZoraFlhe5FKnXeRKwszyklyJ8PsFatWtmHHq067AMVPz7g4qV45GfmKeK3hFtmPyLpwmbkcu4wKqzMEKZbjURFglm8dGHTssKMSlp52FfWZeZhPPKTNxIlbLvEi8oRiQ/3unXr2v14v5i5yjEbPXq0rYIEBuFcZrR6rlixwuJOpTYeoosXCSzW8kS5aAmlkkirK+nTTz+1+CTSJi27dNHSYkgrLMc+L+Qp/MQ+GlFOWqCJM+LYLL6lS5e292m0OpHO04LI+/qSSy6RatWq2bHmvHutW7cOU+SYpc02aSUlMZOX5Yh7JHcyQxKo0HnKc/a2KTezXUm0Znbv3j15V+vMqSK/bBVZuUCE+u9rL4iUQpZ2s6zs8B042amlSO36IqfWE9nwo8hw17ZcFmVeOiuZfikCikDCCOCBp6QIxIUAEyCYMchkATz4DDMvmTnJYH48QII84NqzAda4GcO2DLjGwyhYDgqYQcxQsAwDs5s0aWKgcAXLMGsVcUC2DLNYed1LuoD1zmzatMmW5zUoLEFezLhEXJVNOvCYQYkwUBxtAoInGxQnA+U0LOvVKx9rG0t+TDwblAcWNcsOc5gFz2UPeMc8YwbKZ/A6+81gfA/beOS/++67g/W9PnLLIPtkCC4/m1VLHsSb2bAcAx4j1i8hlkxM8Gcaw5JrvEQSjr2X9ZxIm8yi5tjDbZiQLJEKM/GCcmCS3kiXbfLNgQcemANf3ndMUEmEoMgZxHqG8YKCa5ig4CdYwW3//GPJfd6zkQhuWsvzhRdeiHTZnmP2OXkwiSNp+u1XBgpE/mzPysBmhviFdXOWGXZP0s1qRUVAEYiMQICn8cNWUgRiIkD3DKZQsHFoMQvHWYC3H+e8opWD1ie6VyMRptwQWkAiBWzTLUWLBK1vjGWjNYOWCTzkI7GybZEfeXEm/2RdWmQer/wRBYlyklZNWppooYtExCpV8kfin/0ck0TYHqZ/CYv9y14u3mMo4HaMaNmNRvG2ee+999qkCsrHVRTySlCwbQID7ycvbjOvPHOrT4s1Lcu893n/5pU41xwtqIz/y01+Wvqi3V95lUHrKwKKQMEjoMpcwWOeli3StcYZ+xnzRDcPFSAmHtANp6QIFCQCVGaZrcmJkZkJTFf7yy+/bJNj8ioHJ5Nmgg9fWjoh+SadiAocX2L40uWtGJFO8qusioAikDwCqswlj11G1WQmKdfR9BPj5rjCgJIiUJAIMGuZcXJ+YoZtsokHfj7c50oMjKXklDvpRFwPmZnafNnyZ0+nUx9UVkVAEUgOAVXmksMt42ox25MuTI9okaOLUkkRKGgEPLe6P0KESS9eRnAq5OEUNoiPSwWrAuPBRBsm0UQLLygwQbQhRUARKHAEVJkrcMi1QUVAEVAEFAFFQBFQBFKHwG6pY6WcFAFFQBFQBBQBRUARUAQKGgFV5goacW1PEVAEFAFFQBFQBBSBFCKgylwKwVRWioAioAgoAoqAIqAIFDQCugJEQSNezNvjrPJchJ3zZnHOq8IgBq9zxQgmbdSpU8eu7lAYciTT5oMPPigjR460KyykMqA/GVlSWueLT0VmvYWlLg4V6dw7ZayZBBFYvljk779EatbJyRdz5MlGrDzgn05z3xJYMDf+pecs000bRF56xvHvdp1IyUJIjvj0Qyzb8JnI1i0iR5UTaZ+PqyhwtYYlCwVri4mcgtUbSkfGKyb+OUdEzygCikB+IIAfo5IiEDcCmJDU3HNP9BncuQLBvvvua7A8Vtw8U10Qy1kZLHVkZ/O/4YYbkmKPNV3NO++8k1TdvFTCgvacxNtwtYdCp2lvGHPHTdE/SxbFL+JTj1Kdcp/4a+Vesm+3EM+m1SKXvb1vqIzXPrdbNoWX54oGV2JFhB/Whp/3jj6ZF+Lz5VLvbMFtHxwcat/rRyL4Z5d05lRj2pxpDFdp8NF/XMnltutytjVjiq9U1m48+OespWcUAUUgHxBQy1x+aMjFmCfn3nrkkUeiroPJNSe5ggHXnSws4hqoXBOV22RpzJgxtn7z5s2TZZFUPc5xxjnUDjrooKTqp7TSR7NF7h3uWNIQ5V+QYh2Od8OE0SfFiXGP60U2wdozeKjjl4pvrJIh9w4UGXUv1BtYkCIRLXak1190W34fgI4cUjp0zL2N60XGThHpAGvXkceEX+MR1xedPUOkccHeD1YQrnHaH3M8NgfWL2Gt3YOx/uk2rImaqHXR36sBHUUWgceE8EdA4CfgcNcwkTZnijw2RmTNKpEG2G/eAtZPWDn38JWPB39/m7qvCCgC+YaA75eZb20o42KGAN2X8dDSpUvtihFcRB7rXdrF3rPX48TDCxYskG3bttkVJTp06GDnyspeLp5jLmPEhcg551j9+nj4RiDO8E838HfffWfLtW3bNuoySlywPBalSn7KNXfuXNtcbvObcRF4TmjLvrKfWMtVuOA8LHoRReVSaXR5J0X3PC5yeiORC9uKzF8iUuWkEJsK2ZZK4z0xfgwUBCy6/iuUhErVRK7sKVI2whJbP28WmfueyPq1IhWrQEE6RzBJXIh3vHv3jnAlP3xX5M/fo9eqiEsXXx71uoErMbB9h7u+HYrT9j/c/h6QiQpLdvrpB5E3xzn3bZsrxTRsktzcbuu/F3kDfL5dDkUZyjvcxKZ1Bwn4V1X5/BOR5V84CSpUhZsVrk/SseWTV+ZmTnWKXNfWjpfv2xx2hATGPSfS7kqnuFGx7YP94WNF1q4QOb5yqHS8+Idq6J4ioAjkFwL5YO1TlsUQgWnTphlYi4KL1HPRde/TsWPHsB5fd9111tWKmC+7cDwXL6fr1b+I+M6dOw2sXtaliMmHrVsW97g58sgjzfLly8P4xTrYvn27admypeVFHvwcffTRtm2/m3XNmjV20XLMjm9q1apluGg6F2nHrPnBJrp27WpKlChhz/Oa18f99tvPYCmzYLlUyk+msHYarM1psF6swcSvwXb8O5MnT7ZynXzyyQZKr3Uls6/Vq1f3F7P7UERtH3m9cePGOa7HfWLqBOdy+3KJq3ICXJQrvzHmsrONGT3Snft1qzE19gu55mqWDO0vmBNq6s5+ofOQCwPlPiy/fl2oXKJ7559mTJOqkWsN7O3auKa9MS3rG9O/hzFrV4XKbt0SksOTx7/dvDFUdvaMyGUfvy9UJt69qa+HeB3mw6JWKWN++TnExS+Lf//smqEyie41ON61PfapmDUxQbMxHHO2vWtX5PK54R+5hp5VBBSBFCOAX6iSIhAbAcZwTZw40Vx55ZVWMaNi432yK19U5qhEIJDfUKlYtmyZVUKeeeaZYEO33HJLsIx9YOAKkicMFcBTTz01WC6eneuvv97yuu222wwsVwaWN3PWWWfZc35lDhZFA0uawSLjli3W9jRY7N1QgfMI1kTbLywqb5o1axbsI/vuj2NLpfxe29yOGDEiqjJHBa5s2bIWU5bFerl2PGCF5GEYUXGmEs1xoPLq9TmsUDwHnjJXDX8V9Y5zD/XP5ofXvLajOz/iAWNjrnh10QJ3rrTvL8ZT5s6oZMwcKNA//mDMA7e5cq0bhfNM5Cg3ZcKLmauxlzF1yrq2qJisWRlqYeY0Y54d5q4xXoyKFj/vTg+V4Z6nzLE9KlyMs6MSe9oR4eViHVFBpAz8fPyBK01e993qzl19WYjDr9uc8syyd9/slF4qvn/uCJVJZI/tdGllTNfWDv9Ydb1YPW6jUW74R6uj5xUBRSClCKibFU87pdgIMIarVatWsnLlSoGFSFq3zumi8XOBZUx69uwpUCjkxBNPFFiPZP78+dK9e3dbbNKkSQKrl3Wv3n///cGqrLdw4UKB4hTRLRss6Nt56aWX7OLo3tqxsO4JlCKpVKmSrxQ8cX/+KT/++KOV68MPPxQuSfbzzz9bubyC1apVE36YVXrcccdF7Wcq5ffajrVlf8aNGyctWrSQChUqWBcrM4ahdOaoSvfrY489JlOmTJFLL71UYPHLUSahE21uFClzmMhH/XJWe+oFdw5uw8BtiI3zEzyqQneiPw7twWdF6jR0pRgLNgkuvDfnYIDg6mSmaSqpOzJPL+8mcgLcuaQ3X4bLtYPI0CEiI9Auqdn5Iiu+cvv1Gouc08rtR/vuf7fIQaXc1Ta9RW57IFrJyOcXfuTOD7pW5LRGbp9xfH0Hi9xyj8iTr4g8ATlJzJr1XNAHwhV7xFHufLLfbOe5ifHVHg03NsenTRMxkC2bYz0+HlpKEVAECgQBVeYKBObMa6RMmTJWkfN6TmUQryHeoV3nlbFe7733XvAcd6hAnXbaaQhVihCrFFbSHezYsUO2bNliFU3/5RNOOCGHMtipUyeBhU26desmt99+u8CdKsOGDbMKpb+ut++X1zvnbblObSrk9/jFs7355pttPCFj6yZMmCAbNmwQxvVRmZs5c2YOFn369BF+UkLtOrqYuc0bEa91QjjLrBwDWYL4Lj+dXxsK4BFQSKBA+Mkfe8fztRsjJg8KIXkfc5y/ZN73/UokuJlWl0IpgTK3CMpjJPLdo5Eu23OlodR6dNDB3l782y2bXNmqJ4fXwe9BGiMmbfbX9rdSqGusvjJapAvunXoYj7FTw+P4wqXWI0VAESgCCKgyVwQGIZ1E4NxnXNCbSgQtW8lS5cqVBe5ZefPNN/GsD3/YIx7NWu3i4U2FjIkAU6dORbLd38HF1mfMmGGtex4Pyou4P5uI8fTTT3unhVY9Jl9kJ/Yz0nmvXKrk9/jFs2UWMaZcsZix/K5du4RWzSFDhlgsaQH101133WWtqO3bt5e+ffv6LyW/HykbtcZ+CMxH4sBzk8MSHqgM2+zIA2BR8tMbsDp1usadQR/kcShyIIO50wLubL59B35Y63gfeXx4G3tAkSJt/dltve9dO5EEsY93lJqtp8yOe1Lkss4hnmtWWkVOqu+eXEJFiFPe9l5GAkSHbiINK4hMW+Cspd+vTr2inTcptbYioAj4EFBlzgeG7sZGoFy5cphH9D/p37+/nHfeefLJJ59YJYnKz+zZs60bc+3atcKJezmBcI0aNeSbb76x1jOWoZsW8WiCODfr/mM2aZcuXaySwulMqNyRJxUpJB3EFgglevfuLYMGDZLTTz9dOnfubKclGTNmjK3L9tatWyd03yIWz06ZwgmF6cZ9/fXXZdasWTabldauhg2zXH+oyX6+9dZbMnbsWGF2KftGq96NN95orV2plJ99/fjjj601hgouldLp0zEFBYgYNGrkXHHEh4obM1rp5kbMn83epQWHSq2fNm7cKIMHw20H4jjQvZ2wq5UT1M5717GdOkFk2eeYpqKpyOFHunPed//H3ZQeLTB1RpebAd6xIh/PlcDzj4isQ6FNsLj9CWWPk9CSOvcSeQeKX3m4wYcNd+ceu1sCcMknRMzK/GWryEooHGAvr0EpLHUI3KYXODac0qNTSzetyKn1RDgR7nCHiVzWNbwpupBJD/YUCIKyP0DGSSIz0Ocvl2KS3mNE5sxyZd59C7Kf4BS/RfPduelwXZ57kduP8W1q1ZXAhXVFpn4s0rqRyCVdRKhkPjDE1bxjXIjDjCm49r07/vgD9A+TLpPAQyqf6PZT+f3RbKfIkWfj80SeelSEON6BsWRm7cm1ecVRLPy9crpVBBSB/EcgpRF4yqzYI8CEhl69ehm4QekztQkLZ599djAjtF+/fvY8r2H1BYuHl7XKc/7M16eeesqUKlUqWJ4B+02aNDFQ6BLCkYkMULKCmbZwfxomYRx++OGWNxRPy49Zq7Be2XPMVGVbAwcOtMfMWt20KTSR7Pr1603t2rVt4gblxvQqBu5ZA8UwKFuq5IfiGcSAbfk/zGxl/0iIfTPMqvWwZzkoywaWxqBM/p2mTZtaXoix85+Of5+T6KKNsM+4UZHrP/1YeDnWa17TmImvuvJeID3PM7PUz3fYvaHEicjcc55lIL+fh39/+x+uPCfEvbBuznLDokx6Pfm18LIVIetDQ9zEunPfC7+2fLExzw0PP/fH7znljHaGCRR9rgyvzz6MHxNew98v//6AnuHlUnU0ekROmbx2v18daiUe/EOldU8RUATyGYEA+eOhoKQIJIQA55pj3BiyK23sWEKVfYV5+3EuNLoMOR8akyKSJSY4YPoRa/ljPFs0WrVqlZQuXTouSxWyQIVxeexnpBimVMofTV7vPK2dJFrhaG30XMze9Uhbyp/djR2pXCrOEQvrxvxrJxIeyonss290trAqCmLHDKx8kXCNXjGJK7//5uZy2x2OiApVYO7cP3cmnAdvT5QteXDu5VJxlTh8D/fq/geKYI43JUVAEVAEkkFAlblkUNM6ioAioAgoAoqAIqAIFBEEEgxSKSJSqxiKgCKgCCgCioAioAgoAhYBVeb0RlAEFAFFQBFQBBQBRSCNEVBlLo0HT0VXBBQBRUARUAQUAUVAlTm9BxQBRUARUAQUAUVAEUhjBFSZS+PBU9EVAUVAEVAEFAFFQBFQZU7vAUVAEVAEFIHEERj7FJchSbye1lAEFIGUI6DKXMohVYaKgCKgCBRzBLiaRserRSa9Usw7qt1TBNIDAVXm0mOcVEpFQBFQBIoOAnfe5GR5qD+WrdB554vOwKgkmYqAThqcqSOv/VYEFAFFIBkEliwSqXFKqCbXjD0Na8wqKQKKQKEhoJa5QoNeG1YEihECn8wV4UM+HWj8GJHf3NJo6SBukZOxei2RlV87sSa/JlK+cpETUQVSBDINAVXmMm3Etb+KQIoRsG62gdeIPHxHijnnA7tdWDf20s4iTz+SD8wziOXf/7jOcq3b0mUyqOPaVUWgaCKgylzRHBeVShFIGwQC7/5P5N1lImOniKz6pmjLPW6Uk6//nSLbthZtWYuydH/95aTbe6+iLKXKpghkDAKqzGXMUF7ct9IAADscSURBVGtHFYHUI2D++0+kf/sQ48fuDe0XtT0qIH37hKQaOTS0r3uKgCKgCKQxAqrMpfHgqeiKQKEjEAiIzF0PVxsk6XmpyMD7Cl2kqAJQ1m9/cpcH9hbpc3PUonohBgK7ZT06/vs3RkG9rAgoAgWBgCpzBYGytqEIFFMEAlSQ9ttf5EB0sHxFkcOOKLo93XNPkTKHidQsKbJPCZGSFFopKQR238NV+1uVuaTw00qKQIoRUGUuxYAqO0UgIxFYiV5TqUsHKnm4yI4/0kHSoitjCSjDpJ073Fa/FQFFoFARUGWuUOHXxhWB9EfAZrNy3thD6GtNA9r/IChz29NA0CIsYon9nHB/qFJchEdJRcsgBFSZy6DB1q4qAvmBQOD3bY5tabgw04FKQpnzZE4HeYuijAfAVU36Q+frc0DotyJQuAioMle4+GvrikD6I7Blk+tD6bLp0ZdSmBdty8b0kLWoSrn3Pk6ynzcXVQlVLkUgoxBQZS6jhls7qwjkAwI//OCYHnZkPjDPB5aHwh28cXU+MM4wlkejv5s2ZFintbuKQNFEQJW5ojkuKpUikD4IfM/sBxFDJSkdqNShIivUMpfnoapQVeRHTEujpAgoAoWOgCpzhT4EKoAikOYIrIWVq5qInaYkHbrCRA0s/mATN9JB3qIqY3koc6u/LqrSqVyKQEYhoMpcRg23dlYRyAcEvlkOZa55PjDOJ5aHH2UZBxM38qmZYs/2hMoiX8wp9t3UDioC6YCAKnPpMEoqoyJQlBFYMBXKXK2iLGG4bFnKnKxXF2E4MAkela+AtXhR5++/E6yoxRUBRSDVCKgyl2pElZ8ikEkI7NqFJbLQ4arV06fXR2YlavyUlbiRPpIXLUmrZI35im+KllwqjSKQgQioMpeBg65dVgRShsCXix2rk05OGcv8ZmQOwDxzpHWa0eqASPK7QhVX8csvkmSg1RQBRSBVCKgylyoklY8ikIkIfPax7bUpXyltem8TNWqVElmpFqU8Ddree4sQx4Xz88RGKysCikDeEVBlLu8YKgdFIHMRWDBH5MK6Etgtzf5KqjeAe/jLzB23VPW8WVuRWRNSxU35KAKKQJIIpNk/cJK91GqKgCKQcgTs1B6j3hA545yU8853hidgWo3FM/O9mWLfQP2msMxhFQhdHq3YD7V2sGgjoMpc0R4flU4RKLIIBDzLVuM0mpbEQ7Mi4r04RZpmYnqIJLet39jV+2BWcvW1liKgCKQEAVXmUgKjMlEEMhCB92e4TteonX6dr1rDybxCJ73N0+BxAuaGmKLk7Ul5YqOVFQFFIG8IqDKXN/y0tiKQuQhMfFGka2uRPfZIPwy8TMzlmomZ58FrfaXIyHFq5cwzkMpAEUgeAVXmksdOayoCmYvAlk0iMz4XaXFpemKw114iNfYTWYo+KOUNgYvau/ofZFlq88ZNaysCikASCKgylwRoWkURyHgEprzmIDjz7PSFon5LkQWz01f+oiJ5ufJIgsHUNK+MKioSqRyKQMYhoMpcxg25dlgRSAECox4Q6XGJyAElU8CskFjUwfQk78Ay988/hSRAMWq2+00izyNubvPGYtQp7YoikD4IqDKXPmOlkioCRQOBL5eIzMdSWO27Fw15kpWidn1X86ulyXLQeh4CrbNcrWOf8s7oVhFQBAoQAVXmChBsbUoRKBYIPPWwyAkipsGZad0dU+lEJ/+nH6Z1P4qE8PuWELmzn8hNQ0R2bC8SIqkQikAmIaDKXCaNtvZVEcgrAkx8GD5WZODw9Fv1IVvfA7vvLtK6kchsDdzPBk1yhz3haiWNecJt9VsRUAQKDAFV5goMam1IESgGCDz5kOvEJZ2LQWfQhWYXirw4Vcx//xWP/hRmL0qXERl8g0iv/iLbthamJNq2IpBxCKgyl3FDrh1WBJJEgFa5wUNFHr9PpASm9SgO1Ogs24vAMp1vLiXDecMgx2bo7Slhp0wUAUUgPgRUmYsPJy2lCCgCD9/hMOjap9hgYaqc5Pry/vRi06dC7chBpURGjxS5d4SIKsiFOhTaeGYhEMBi2Sazuqy9VQQUgYQR+Hq5SJVqIi88KXJlz4SrF+kKvTpg8uBPReZ8W6TFTBfh6LIOnAUledsGkY/x2XPPdBFd5VQE0hYBtcyl7dCp4IpAwSBg48n6YsmmU0uL6ZDm05FEguy8i0XmfieyCYqHUp4RCOyGx8ozb4gsQtzcvbfkmZ8yUAQUgdgIqDIXGyMtoQhkNAIBzh02fREe0NPEZoAWNzS8VSzemVzcelZ4/Tm+MhJLnhYZ8rDIzKmFJ4e2rAhkCALqZs2QgdZuKgJJIbAaFqvyFUXuwLQTTH4ornQZliXb/pvIlPnFtYcF3i9G8ASuwWTCT70q8tUykconFrgM2qAikCkIqDKXKSOt/VQEEkVg106Reke6Wh+tF9l7n0Q5pE/5yVhrthWWJ9v4k0iZw9JH7qIuKe+h804R+eJLkS9/FCl7eFGXWOVTBNISAXWzpuWwqdCKQP4iYPOi+vVwcU+vf1y8FTlC2fwCB+hEWJGUUocAXwBemy1yEFg2PkLXbk0dsspJEQhDQJW5MDj0QBFQBIhAYMQDbqWHt7F4+nEVij8oXI6qByxzo4uxK7mwRvGQ0iLzsJYvV/lqDKvnT7DyKikCikBKEVBlLqVwKjNFoBggMGGsyLXIQuRqD2e3LAYdirMLnHJlAVyBS5DsoZRaBA6Hu37+9yL/gG21o0S+hdtVSRFQBFKGgCpzKYNSGSkCxQCBSeNF2nUUub2vmB59i0GH4u+COf0MkRNQXtcWjR+0REoeeTQUus0iJ1USqYRkCM1yTQQ9LasI5IqAKnO5wqMXFYEMQuDl50UuulTk5mvE3P6QBAKBDOo8XMvsb1+4WR99TuR3ZLYqpR6BUoeKvP25CLNcm7cQuftmkb//Tn07ylERyDAEVJnLsAHX7ioC2RGwyQ6P3S3Soat9uBosxZRpilwQk0s7u91xUOiU8geBffYVM+IlZwG9DbGZDWCx+wYrjCgpAopA0gioMpc0dFpRESgGCOzYLoFurUVuuE3k2WEit96XuYoch/PgQ0RuvApz6sHF/A8DvJTyAwH7stDxapHvEDu3+56Ygw5LxdFK9+eO/Giu6PLc/gembFki8tuvkWWMdZ21OJ3Oe29j+peF0a2c69dhubp3YXHeFrmdvF6PzDX22TUrRfgprvQjEn+4FGKylEh9vJUrKQKKQCYi8OUSY6rvzsWZjZk9IxMRiNznVd86TCa/Fvm6nk0tAn//bcyTjzjMS+FefHW0+e+ff1LbRlHlNnWC6/f4MZElzOX6f//+a8x1nVx9/ob54e959YpwXjdeFV5m2hupvR7OLbGjGnsZcyTkLq7UuaXD/s8dyfUwgfpqmUtWY9Z6ikCaImD+/ddlqlatDksUIv7XrhI546w07U0+iM2pWC4/D6te9ORTJh8aUJZhCOyxh0jPG9yUJR2wBjBc3YHqOPfGS2odDQMq/CDw918iw8aIHIvzPRHrSlqC33aPtm6f30zmefgZrOKC/QHI1iZdgLWIf1jr9vN63XHR7yKAgCpzRWAQVARFoMAQgCsmwFUdrukn8uBgkVlYZumY4wqs+bRpqP/ddsLkgK7XWnBDdhgmFX78Bed6rdtKpM0VWDECLtgnkJSydUvByZEuLQXw+O6EJJKvd+Ll7JUQRjOQYEL3LOmBXm77HlyZ9z8pwvkjSaOh5JHyet1x0e8igABef5QUAUWg2COw/nvEJA1w62Q2PQkPgHfd9BDFvuNJdvCkmiJcr/XWrmIw117GJoQkCV+eqp1QReS5iYhbhMV4JJSPXv3dpxssSpd2EWkEK/KeUPKKKvEFYME8EU6WfCGsZNlflhjbNmMKlK/NItt+ydmLWNe9GnvtBaUMbXnEeM96eDH7aLXLxv4V8XFf4yLEkHLlXan6Tdz2LVg9u/bJ23Umr/iJS7eNelzkyHJuaTxcM//9J4FnhonUOlWkTkNXmnFgE1/GFDW1QrU//0TkrTdFDjhApHUHkaOPDV37Hv2ZguX2fkTc30mniLSFkk9rLuP/xsLqeHIdkU2IG1z4kciJ+N3yerT7Y/wYZ+2thnJTJ4iULCly8eWQ+ZjY7XklGHs4Z6Y7OgsW/NMaeVdcfyePx4vgx1g1Z1/EMb4fvBbcyWv9IKNsO8k5crWWIqAIpAUC3682pl9WzMxh8Bq+Mc7YWJu0EL6QhVz2hYt3mTS+kAXJ8OZ/+9WYsU8Z0+D4UOxXnyuNYewXrxUlGnZPSEYvjm3m1JCEzw7LeZ3lECdoKdb1EKecezt3hnjz6sKP3HHbpqGy2/8Ilcnr9RDX0J4Xg7vxJ3eOfWf/GBvn0aBr3bk3X3bnPZy87V4ov3mjK71gbkhe7/r5p0FH/M+YFV/lvMYyPS7xWsq5pRweH297YJztkdu9A3PWHz3StcPYT8iWgz/b8WLm8lo/Z4+CZ9CKkiKgCBQnBOwfHf8Eu13s/lj4Z8WHBP/slRJDoHtbY6gEK3aJ4ZZfpdeuMuaxu8MVu2Y1jLnvVmM+mFX4yt3N1xhzSy9j1qx0Cigf5L2ucGj88H3oQT/xVXdPPTfcnaMyF+t6LEwfGuJ43d7XlZw5zR0P7B1e01Ni8no9nKs7evFp1+YDt7njixq6Y7b58QfGeMokFba//gopc29Pci+ZXrIGEz9ItUq5+jOmGPPrVmOuae+Op08MKXPlweuHtcZs2mDMsdhHW1ETaDxlji8CbL9vN8fvf2/G3x4VViZJrfzGJW+wbzu2GzPqcceL9+Pnn7pzLeu7c1TmPOUz2fpOwqjfu2Uz1OlhOiBQ3NO502EMiqKMP623iQ2BmnBVnAaXxhdzRDgR8KZdIt2uhdl/76IoddGWCZMnywaI+Pzwoi1npkhHl+V1t4rMXYH7eqPIq6NFKlQVGXoPkniawW12oEiVgEsIGH6/W2WC/5cFNc3MQMiBeRqlxP4iO/50o7Jrh9sunO+2N2A+x1ZYB5i/xzKHuXOcsDrWdVcy8jddkTcNcdcYD0s6tKzb/vG72/J721a3D5jyfN1xCv9ue6U7HnCXmztw4tzQ9acfERmP8SI9gOueKxQhvFw2MLAb1JE27e1lWY3x5cTdi7LknTFV5C6Eify0zl1fCDemR5cgLpBu0tLo7/lwsYICDCuJRoBazm/t2j//YldqxTex21sAN65HjOPkcoeeBrXqO5HZ77irtz0M1y/cylzvmZNke5TX+h6fKFs4npXSDoFW+PPa8hcykvAOopTZCKzCn9A7+KObhBgUBj6TenWA8tFbTM3TNNbLIZL8Nx8SdyJmiw9IBuTzgaFUNBAoXUbkkk7u8wTufz7AGXv1+acin85BPBlil/A3GSTqTTURp3XU8SKHH+UUKY7ngQchVgvaTVkUOL5ysHhSO3vshVi/66F4IE6sOeKySP/+47beHG8nneyOs3/Hup69vHf8Fzp56Znu6PUXQwriEegj6dPZdmO/vlnm9k/BMySv10NcQ3t77+PmCxwERbp6NXf+f29CwcXvh/F9XozfFT1Cdfx7JfAiStr5Z/icgxwf0smnuc+pdd1x9m+OIemvnW4b6/tQ3EOkeNr7fq0rW7qKu1941O0md25/yP3bL26/Wg23zf7tKdXJ1s/OL9uxKnPZANFDRaCoIgD7ugTW4I31U7whMgD3jXHOakSBu7RCQO+tImedjzd+/KGC+AKqlAIErgeudz8octt1LoEkBSyVRT4gQMWbnwvahJhTQfr2Kzf9Dq1XtPisXyOyGMreF7Ds+ZW9s6F8vb0oVDeZPSYAUJGb+KqYlu2ctcnjUxEKFGn6JJHOvd2+/zvWdX9Z//5g3JfzoWj0h4LEYH6PaPXzEiIWfyZS4xQkErzurrbp7JS+vFz32sm+ZWIFlTlieyB8kFi2LYDJyWWes5pJnytdckj2etmPKT/qy6/4cJ3osoe7EpxgedOP2Uvn/ThWe54VFf+v5rYHQy/K337pZKsKJX0K/pvf/R+SMNDH7HQqFFFSsvVd7ejfUR2wmXSBwZY3dC2aPY4kW7pOtDiwjzE/rS+aOBchqWyCAgOIF8wx5oUnjWHMy3mnutgLxmfww7iM+wcZM3+2MQy8VcpfBLzJW+e9n7/tKPcCRcDGlzKeib+3VPw39ergfp+jRxjjJUMw7gv3j22rcWV3/ZKzjLn7ZmM6tXDHmDQ41vWIwHBia+8/4fRyxpxRyX1aN3LF/df9/yFbt6TmekShcLJbGyfXo3e5Ert2heRcvjhUK/uzbPFnrtyDg12ZobeH6jF+9erLQny9GDTGKHp0Zz93/bsvvTPhW7YXwP+nR4m0xxi7OmUdf8bRMrGszZnumDF03mTjHA/G4jFusCL2ecx7LK/1PZmjbPPHMsf4hEmvYAJDvA3stjvW3msipul5IU2Wy1swdZem0Ko1kLLcIKRtjh8TOXW47BEizzyKSU4PxZQBeKvwiOV/3iwGmnuA7dLMvPwLmJCPdinOnil5+kQRmkmv6O7SmWkS7QxfO6kJ3gBofT6+IlfbRsr2tTnjizjP0SvPIxYJsq4DH8Y3nFJXzEWXSeA7vPm9+bLIQYeItO/i4jYsY4wiJmgNfPg+5k5CGcZzMJbJS1WPlVq9GW+OkWTL4m1dCtHSub0y3DLV/cP3RM48x/n1N20QOaeFSN0zXCnveruOITcSY6322QcYtndjNR9vsqwzfbLIL1tdyj3jAphi/wGsRI2bi2l2QWiMfe3j3pNAv55u8srdd8NbDMbyjOYi1fAmQ8rtfnAlQt+xMGPJaFMDJDmGEi01PiRV7nu4P2UZ7klO8vnP3zDp7xL5A2+XNLszLuTnTS61fsM6xJksksCqbOxOPwquBdw3o650sRgnAjesb6lUcAgYxNYELsbvpdOZIkt3KP4FB32+tmSnnOFvKVW/p27X4TkBizktb5x4mtMAvbsUlrM5EqDF8I25zsJLt/B4/G96hHgxK0su172iwS3/l2D9CxKtc0FC+AWpRVuRpx+DZet6kf/hOVBjL8iH5zKnMUnFdccl53evAfi/eh3rPV/lrnEalfsH4VkBqxUnK/doNzxj/OZRL46O5Uk3DYG79U+Rx2AZf3aCPWUxbQJsOc8eydv69xl/F4n2xDNY8H/sUSLtsey0xQi5wDPxdTxPhz7juFyL52YZWA33299NjzIAFtJHRnkthLZ5rR/iFHkvipKX/GlmdfANwXtb8LbMYiEN6JnzGjNe/vjdXfeyTbx63Hqpww0ruLqe1s2MIV5nyjrbPbV0Tt4rv3Z8mZ7NshefESrTol5Ic/a3t2WTq+P/9qYp8JfjvqeZ+897adXr10XGYsQDjrP3ZuGvy32mVhMPT6v3X6dskTBidpDXrl9u7vvfbvy8mBHkv/7FQnfMby6rwzdK0l0DQpj563uYeucuP8+Vz/5Ni5xXxtt66dyx7ofsvHLDjGW9t2GvHW69qQGSGcPcUuOzyxbteNZbOfvvycdMrCZVjbnsbGOu7+ze5pjZRgsQMrR0GpFooBbC+XVr3Djedl0hNK5NpgsC9jeb9Tyz1jZmYRYyWTl++TmqFHm9HpHxhh/DT+/802Wkhp+N/4jPt4L0QuTWHuWIpCfE25u81o/QDp4kKSaaR/mgOrumS0X+8H1jmG7M1P4xT7hrVLreeMnNE+SZflmG5CkqkVKHPZMxTZgk/qmyLcydZWjO5T5Nn/zxcG4oHnN6BpKneDQ9yRjKxBTyzz525nWWq4oPTe1Mb45EniJQ7zg3iN4fO+tSMeGNSlctj9lPktc3Kmdz3jXmqUeN2Q/XWWbue6FU5Qip1f9ysClPJNk8jKKlc7vWQ99Db3d8ODcTzd3vTnfHXVu7Mt71WMrcnf2dckHXH+WiuZq40D3BPvCcZ74PtW4M/0S8NQQ5txExjvd+8PPhvqfMRcDMpqPnNjVAMmOYW2p8dtmiHRNzYsA/ePRbFbRoQKXBeW/qBbrAlRQBRUARKCII4OmbYqISx4f6V8tyMj63lrvmVxr48Gd5fkhUVPw+bU/xePw+N3cMLUYsS6sXtyxLX7Q3nw2Vt5u6uw+v86FP8pQ5znWTnciTCmZu5CkC3vw5LEvFkG1QkSN5VhxODEitndeO5qTQ/7nr/KbVhecpo6eY+H3+nJOI1zmfEimSbMTIvzjx/NmuDjCKSJ6yRvlAVh62cfpRrrh33T8ukSxzX2eNKZUx1m/TxNXnN5UonvPHQ4SuhhRv/yLQ8dwPfh7cj4XZb7+4GpvwFkflmTJ5Cn2iY8gJSVmfH++e8u4zWiuVMg4B+9thPBJ/f969lnEoaIcVAUWgqCEQxbEc2SUb19n1n7ti5Y7LWfy7Re5c5Wqha/TdV0dcHYkZL9nJlzoc2B3l7h7qSjRETBzpoTvcfDFeWjCXUGEaMz933CRy9e2uXNa3qY2Yt1TR0dn6WCorDoFxUYxLIzW+MDyOzEup5pQSkSjR1Gry8KdzR+KZ7ZyNzSB8238Kv/Lfv+HH0Y4iTc/gzaeTyHxOydwPkWTyY+ZNDVCmLOIYx7rS3tQAkermNoZ/IjbKI++eYmo87ytvHL3rus0IBOxv54lXkRGJ7l7XCRodVH0lRUARUAQKGYHUK3M1znVdmogEiOxUF0GLpMn4M/RoKZS/JVAioINJif28s9G3HZDAQPKCxDte7Y7rNXXbygg4HQyFDx+mD4s3KaC7Gp4qnnVOGEv+ky8o0jufl235iq72i1NDCyDzzMuj3fnqtd021nd+yJa9TU85Y4o16W8oo8hxSCntgeBREtPKPUrF/eDx8rb+qQHeRqBvXshLVScPpsZn3VdyPQJ5T6iUF85aN50R4BQJ771t58wKvJQVBJ3O/VHZFQFFIO0RSL0y1wNWC1KHbiLNoFjd0AWL3wZEVnwlcuMQe8lO9HjeKcj8PAdWuVru3ENPum2sb2aEDuzjSvW6IjRfTduO7hwzVM8Gz5t6iJ0J//LGsTi6BzPftPsh84YZa6n4g+bs3pxslERrYXdkMp2GrNnBUDBJXXq7baxvKg2pli17m5WqujODO2Nha8jXql72Enk/LnOE49EP9wWzfzj2qbgfskvmTYq5bYsEht/nri5GBtm017OXjO940O2u3GGQ/6p2LpOJk4xOeyO++lqqeCJw5tn4ffcTubInXkazPA7Fs6faK0VAEUgDBFKvzDVsYidMlFLo/bvLkFI8GtMvYJ+pwifDGvXBLKQm43g6/gBfecdNCjjuOfenSMBs6jB3sih76jBP94SVhHRNluLI/ZNqYumWaSKcxoEz4T+MN+Z12+EKuYdXRbxUZ3cU/n3nCJFaEPihZ+Gam+OmGAkv4eTnOU5d4lEw/TnrnHfspUrfgrYfvcuVHoWH/ycbkaoOFzOnqTj62FBKtVeeJb19j1ck2Ww6t2NrvyNh5Lsc3N0zK907eCJrh1OUDIRy+S2Oew9Amn6J7CXc8Z65zGTj4evJkp1DOyjelzZ3YzN8rMhhGKd47ofsfDx8vC2ve/vEjFMD8N7j1ACffuimBuCSMPMxrh6miYwhU+OplJMnU+OffMXxZGq8UmYjcMu9IuedKtIIL6abN2U2Ftp7RUARKFQEAgziyzcJuA7c7lAADiiZswm62xijdRCfkknQxp9CM0Jnr865u/jg5rwviRDj7koenEiN+MsSi333yzl/Xbwc8lM2TwYuacKlYWj9zC/iuHOFguzrhOb1fvDJiwwPCTDeDePP2zvAuelSMa5b8MDm/bpHLkqtTw7dzQAEOHfhibC8nwgvBOfx8l5qMqDr2kVFQBEoOgjkrzJXdPqpkigCioAikD8IfLkEylwNuyauGf5ieMJT/rSoXBUBRUARCEMA5islRUARUAQUgaQR4Iz2M5HoNHKcBIZmxVgmzUwrKgKKgCKQOAKqzCWOmdZQBBQBRSAcASxlJy8giWsAYmRffDr8mh4pAoqAIpDPCGjwTz4DrOwVAUUgQxBgZuuPP7hkrv0RJ4x1m5UUAUVAESgIBFSZKwiUtQ1FQBHICAQMLHOB35Bw07o9MvYxSeQ5rTKi39pJRUARKFwEVJkrXPy1dUVAEShGCHCFCHPPcAlwNZtzL8JUPFNEzrqwGPVQu6IIKAJFEQFV5oriqKhMioAikLYIWIXu4VES+AdTLzVvITLlNZEL26Ztf1RwRUARKPoIqDJX9MdIJVQEFIE0Q4DrSJvHx0hgH7haW2DlkAljRdpg4mwlRUARUATyAQFV5vIBVGWpCCgCikAAE5eboU9JYD9MFt72SpEnsALM1b5VaxQiRUARUARShIAqcykCUtkoAoqAIpAdAetyHfKwBMoejuUH+4n8sFbMXcOwAp3OCpUdKz1WBBSB5BFQZS557LSmIqAIKAIxEaBCZxW5w44UubiDBL5fLfL0eJESsNgpKQKKgCKQAgR0Oa8UgKgsFAFFQBGIC4EPZ4s0OFOkJuahm4JlwI4qF1c1LaQIKAKKQG4IqK0/N3T0miKgCCgCqUSgfmORNStF/sXUJUcfK/LxB6nkrrwUAUUgQxFQZS5DB167rQgoAoWEQLnyIvO2ilx2tsjpjUUev0/Mf/8VkjDarCKgCBQHBNTNWhxGUfugCCgCaYeAMUYCTwwV6T0A05bA9fo05qMrdWja9UMFVgQUgcJHQJW5wh8DlUARUAQyGYFP5ok0bCiyJ0CY/i72m2QyGtp3RUARSAIBdbMmAZpWUQQUAUUgZQjUaSDy4yaRVueKNGoqctt1Irt2poy9MlIEFIHij4Ba5or/GGsPFQFFIA0QsG7XV0aLdOgqUn13kRfmi5xcOw0kVxEVAUWgsBFQy1xhj4C2rwgoAooAELDz0bXvIrJ6hchh1TF9SR2R2/uK/LlD8VEEFAFFIFcE1DKXKzx6URFQBBSBgkeA2a2Bl54R6Xg1pjBB+y+8LXImsl+VFAFFQBGIgIBa5iKAoqcUAUVAEShMBOxyX1f2RCzdD4ijQyxdk3NErjhfZP33hSmWtq0IKAJFFAFV5orowKhYioAioAjI4VgC7KX/ibw7HXPTYcsVIx4aoq5XvTUUAUUgDAF1s4bBoQeKgCKgCBRRBJjhOvJBkRtvF+Gyrs+PEdPmCrFWvCIqsoqlCCgCBYOAWuYKBmdtRRFQBBSBvCGw9z4ifQeLbPxJpHtnkUs6SaB2aVjt/ifMhFVSBBSBzEVAlbnMHXvtuSKgCKQjAmUOE3n0eZGvlomUP1mk2fkSOLOKyNz30rE3KrMioAikAAFV5lIAorJQBBQBRaDAEah8osgErBix8CORkge7CYfPriUyZ1ZmWOo2bxT5conI9j/yBXq7Xi4V5lnTRNauynsblJPy/vZrbF47/xRZMMeObZ6srkyg+Xp57Pa0RNojoMpc2g+hdkARUAQyGoFTTheZggmG588W2QOTDZ9xlgTqHyPy1ptiFZLiCs6jd4mcWEPkswWp7+Hq7yTQAMkmVU8SOetCkWOPxxq6l+cNz/cxvQzlfXtS7vKu+kZk3xIidc8QqV1fAvvgMT3igdzrRLs6qLdIlWoiVA6VijUCqswV6+HVzikCikDGIMCH/1ufQrmBYleuqsgFF0ugCpS7MU9o9muiN8FCKsewap0LS2eHc13tkeMkQCwLghpAebz7ZpGrLxP5Cw32wf7SzwuiZW0jTRFQZS5NB07FVgQUAUUgIgK16oq88g5cekthVbpCpHMvkRJIf72rv5u3LmKlDDl567UiG36M3dm998VEzU+K/O8zNzXMuOdcnbcmxK6b1xLlKyH+EauA3HqfyBMvi/S81HFcDEW9sGgL1g7u262wWtd240BgjzjKaBFFQBFQBBSBdEOA7rURY7Ek2EOwzo0U6X+nyOChIpc2RzbsDWIaN0/dtCbfr4ar9zUoi+tETjpFpC2UyD0iP17s6hZffCLyCaxf//7tUD2tgcip9VKD8G+/wJU5RWQZLFmnw1pZv4mYA0pKoB8mYX4Yq2rsDhtG2SPgjgYO1ZBAEolatA0/e0Yzd7wVSg3p920iY8Hr5Doim35ycYsn1nT93nNPV4bfXywUmQFZtm4W2Qa5kqH1a1yto45121XfikyHq/bcliKrvhP5YKbIhZC3TgPrBg5MHi+y6GMRKqRfvO/qxPMdbQwZm9jkMOAJJsdX5LpzIl2hFO+9t8hP60UmY9xXwzVc5nCRlu1ETqgST2taJtUIILhSSRFQBBQBRaC4I7BzpzFvjDOmWQ1OZGJMaXxmz8h7rxfMdfzI0/ucfxp0tv9y8v5qWaiMV5bbBwfnLBvrzC29HK8PZgVL2jYbHB/eRrc2xgzsE36ObY4eGawXc2fGFFf/uk6u6IqvcvIjzx6XhFg9OyxymVdHh8rktjdulDEHgif5Nq5szJ87XOnXXnDnLmoYzn/zRmOAe3AMWM/7eHWjtRdlDM3vvxlT0cfH47dlkzGfzAvx9863bhStBT2fzwjgFUVJEVAEFAFFoNgjQEtK6/YiM78Q+QYZjt17i11hIq8dvxoWIhItUL9uFbkGbby1QALvTHbn/d+Dr3NH096AdWmi26cbsd8d/lJJ7wdoMZu3UgSGJKGF7o1xcDVfAP5DRK7r5Pgy+3fTBpHLurrjGN82iWRgVtlO14SXLo/DH9Y6fsdi/+nxYv79F8uuwULZPauvE19FAsJOkeeGh9eNdbQI1ksv8bXE/rCIZXtcT5wrMglWuNGwujJBghY54C7Naoh8Dpfsju2wlNWP1Yq7HmUMZe4suHxheSRVxYdzHBK7Q0qLdDvDnpYhN7r7acJYkfPbunP6XeAIRLaDF7gY2qAioAgoAopAgSFQEU/mexJULiIJ9/tvcOlBgSPNmOo+P0GRIS2Eq++cVm7f+96B8iBzTkvB8kPu7OJ5bpuKb07R0qIeXL5Q2JrAJfjsW3CF1nacSx7otoeUESldNu7WAlSWFsJN2qZJiJdX+5JeIkcic5h0PlzLI1+UANfPpTJFugFKYKtL3D7nByTRTRkPDbxHpPn5Ipe3ROzeQiimL4m0Bz+PBvR0bk3v+AooraTbHoacp7r9Uoe6bW7fuY0hM4XPvUikFBiUKA1XalYffgYeS6C0UsGjG5/Ee0qp0BDIpuoXmhzasCKgCCgCikC6IfDnjpDEB0BZ4ufk00TuuAkxcHVD17y9Nh3tXqAq7AhVsmLLLuriXU3N9sX/IaartVPAatYRmfhK8nw5L1w3xIeRhr/ottG+y2YpOn/tdDF1LHfSydFKxz5PRax5C8Q53u/KLodF1U91G/uPnCWSZ6rVCD8f6yjRMSQ/KnOkGme7rX4XOgKqzBX6EKgAioAioAikKQK01EB/s9Sjr0uwYJLF9YMQCF8pZ6fadYIlC6e/xackzD2P3S2Ga81mp3/+cSta/PVX9iu5H7Pe7/BNjoIbd9Zbrixdy6Q9spRH/6S9mze5qVxcifBvWqwuzFKMuLrGYUeEX8/tyLNSMVEhGnGqEU7qm51WfCXy6YcIVDP2I0wWIR1G4Hy0W7bHd9UsxRHLu0WkaJjGM4b7guNPWQocmZc73jXBrGlmuioVOgLZ7oZCl0cFUAQUAUVAEUgnBAZlKWNUdq5q52LmDjxIhHFxJK5KcQxci4zheh8P//U41wOux2lLEMd2ayij9plhzgVJZYTTgjRqKnLzNZZF3F/rVoscVU7k2o5Q0uDmJUFntFQmSxnr181db38O4uaauSzaSEuhXQu3qbfww6CrRRpXdp8ns9yKWWwjbQwm+7XluUIHs4fvuQVuUp9lj7Fn1YHLkUfnnIh4FvqPzNQAlLXAoXhEP/ema4IZq7nRVde7q+3Q9xu7izw4WOTDyaEauWEaawypmHPc+l0lcvEZWHnkBZGb0AaJLuuucMV2bIn4xCyF0l3R7wJEALZuJUVAEVAEFAFFIEkEbhqCSYn/hJXtQcSoTXBMmp6EmLXz3P4KWKDWYXcX3I8/Z1lxkCjAZAGBfiRX3yOmzy0S2LzBld+GGLxy5d3+t1D4opGXEMBVLzw6CJrbeYgXGz7WnalZUuSRLJnaQTn74G2RV2cgtg8yMZ7ttIYi7y7F1BrfijRs4nFxU7mMmRI6/uCb0H6d70LJCJ4MvOrtUwljXNwbcxG/dp2bK278zFB9WtToQq13nMhHqyXw6y8iBx8Sul4NSh7xo1yAQgjFaCQicBUKP+2Z7fF9XAU3RcqAHujzKH9Jt58bprHG8M4RwAsK+EPPOl7/b+9eduPIyjiAn3bstp04pJ17GIgTiZk8AEtYwIqHYAMSi5EQSCzYsGSD2AAbNix4BMQLIMFICIkFCzYz0sxEChrNOImd+JLbxG6bOrE6sjPt7upKdfc51b+SLLe7TlV95/cV4T99qfrxz0P49R9CWGiH8Js/hvDnvx49Hz9XaJmKQPEZ1N6nUKdyfAclQIAAgaYIxLfcYqA6fo25+Fbp/l5xXbl/hvD9HxQBqniF7Ic/K76puRXC7397FFg+KF7BimEq3r/03MrR9dLeLULaL39XXDT3F6PrxNtX7e4Wrxpd/eq28W3WxaWj66T99z/F576+ffTt069/46tjx/lMvIjz34rA+PeP+h8lziF+nu140Os/stSzr67vV8a0Xw97R4jfEI5fMjm2xAjRigE8fl7yeN+PjfFw/AJvRPvxH9ARCBAgQKChApf7hKd28epN/IkXu43L+78K4Uc/PXp8pfjMXQxr8abyMcwVQS4urX8Xr2q9X7xN+JMi9FVZlpZDiD/9lt63WuO6eJ/UePmSCQe5VwHok+LVvj/9pV+FR88NmsPpW526prRpvx729vpGkItPv3oVsqbA2TuM36MLeGVudDNbECBAgMCoAvF6bN+8dbRV/DVX/PQ+k3aveHCzeNvRQoBAJQFhrhKbjQgQIEBgZIH4dty//hHCB8VnwLr7IXy3+ALCd753dBHakXdmAwIEegLCXE/CbwIECBAgQIBAhgLxhW4LAQIECBAgQIBApgLCXKaNUzYBAgQIECBAIAoIc84DAgQIECBAgEDGAsJcxs1TOgECBAgQIEBAmHMOECBAgAABAgQyFhDmMm6e0gkQIECAAAECwpxzgAABAgQIECCQsYAwl3HzlE6AAAECBAgQEOacAwQIECBAgACBjAWEuYybp3QCBAgQIECAgDDnHCBAgAABAgQIZCwgzGXcPKUTIECAAAECBIQ55wABAgQIECBAIGMBYS7j5imdAAECBAgQICDMOQcIECBAgAABAhkLCHMZN0/pBAgQIECAAAFhzjlAgAABAgQIEMhYQJjLuHlKJ0CAAAECBAgIc84BAgQIECBAgEDGAsJcxs1TOgECBAgQIEBAmHMOECBAgAABAgQyFhDmMm6e0gkQIECAAAECwpxzgAABAgQIECCQsYAwl3HzlE6AAAECBAgQEOacAwQIECBAgACBjAWEuYybp3QCBAgQIECAgDDnHCBAgAABAgQIZCwgzGXcPKUTIECAAAECBIQ55wABAgQIECBAIGMBYS7j5imdAAECBAgQICDMOQcIECBAgAABAhkLzGdcu9IJEBhB4MGDB2F3dzeEw8OhW7VarXD16tWwcv780LGpDtje2gqbm5vFdIfPNxTz7XQ64dKlS6lOR10ECBA4VUCYO5XGCgLNEdjb2wubGxsjTSiGv5zD3P3790O32y095wfF+NXV1TA35w2L0mgGEiCQhIB/tZJogyIIjFeg1KtTb5RQZZs3djHVP3Ovf6p4Dk6AQFYCwlxW7VIsAQIECBAgQOCkgDB30sNfBAgQIECAAIGsBIS5rNqlWAIECBAgQIDASQFh7qSHvwgQIECAAAECWQkIc1m1S7EECBAgQIAAgZMCwtxJD38RIECAAAECBLISEOayapdiCUxQoLiQroUAAQIE0hcQ5tLvkQoJTEUg9ygX72JhIUCAwCwICHOz0GVzJECAAAECBBor4HZejW2tiTVJoMrdDMbxylSVOibVh1TmO446JmXoOAQI5CkgzOXZN1XPgEC8n+rjR4/C9vZ22N/fH2nGZ8+eDWu3bo20TZnBH334YZlhEx9z5syZ8N6dO7Uf9+7du+Hll1+OtN/5hYWw2umETnGf1/l5/8SOhGcwAQKVBPxLU4nNRgTGK/DixYvwv3v3RrpR/HgrsveyAvtFCH/48GF4vLUV1tbWQrvdLrupcQQIEKgk4DNzldhsRGB8AvFVOEFufL6T2nMMdfeKQH5wcDCpQzoOAQIzKiDMzWjjTTtdga3Hj70il257RqosBrr4NrmFAAEC4xTwNus4de2bQAWBcfyff/xQ/vLy8kjV5P72YJxvt9sdac7jGBz7uVp8fs5CgACBcQkIc+OStV8CFQVG/bJDmcMsFB/Kv3X7dpmhp45J+Zus/Yr+5s2b/Z4e7bnDw9HG9xndHfHLK3124SkCBAgMFPA260AeKwnkKfDy5ctQd/iK+0x1ia/A1R2C42fd6thn3X1ItQfqIkBgegLC3PTsHZnA2ARiCImfvatriYEkfkMz5WWj5vo2Nzd9eSHlhquNAIHXAt5mfU3hAYFmCayvr4d4rboLxTXP4nXYqi7xFbkYbJ7s7lbdxUS2e1yE1/hq2sWLF0O81lvVJb4tGvcVfywECBDIQUCYy6FLaiRQUSCGsPgzK0v8ssE4vkAyK37mSYBAngLeZs2zb6omQIAAAQIECLwSEOacCAQIECBAgACBjAWEuYybp3QCBAgQIECAgDDnHCBAgAABAgQIZCwgzGXcPKUTIECAAAECBIQ55wABAgQIECBAIGMBYS7j5imdAAECBAgQICDMOQcIECBAgAABAhkLCHMZN0/pBAgQIECAAAFhzjlAgAABAgQIEMhYQJjLuHlKJ0CAAAECBAgIc84BAgQIECBAgEDGAsJcxs1TOgECBAgQIEBAmHMOECBAgAABAgQyFhDmMm6e0gkQIECAAAEC8wgIEMhL4PqNG6HdbudVdIOrff78eXj44EGDZ2hqBAikLiDMpd4h9RF4Q2BpaSksLy+/8aw/pyVwcHAwrUM7LgECBF4JeJvViUCAAAECBAgQyFhAmMu4eUonQIAAAQIECAhzzgECBAgQIECAQMYCwlzGzVM6AQIECBAgQECYcw4QIECAAAECBDIWEOYybp7SCRAgQIAAAQLCnHOAAAECBAgQIJCxgDCXcfOUToAAAQIECBAQ5pwDBAgQIECAAIGMBYS5jJundAIECBAgQICA23k5Bwg0XODRo0fh2bNnpWcZ7/t69erVgePvr6+Hvf39gWOOrzx37lxYXV09/lTfx5ubmyHe67Tssri4GK5cuTJw+PoXX4T9bnfgmOMrV1ZWQqfTOf6UxwQIEEhaQJhLuj2KI/D2Ak+fPg1PdndL7ygGpGFhbqfY3/7eXul9zrVapcLc0ydPQqy37LJX3Kd2WJjb2dkJ3RHC3Jm5OWGubAOMI0AgCQFvsybRBkUQIECAAAECBKoJCHPV3GxFgAABAgQIEEhCQJhLog2KIECAAAECBAhUExDmqrnZigABAgQIECCQhIAwl0QbFEGAAAECBAgQqCYgzFVzsxUBAgQIECBAIAkBYS6JNiiCAAECBAgQIFBNQJir5mYrAgQIECBAgEASAsJcEm1QBAECBAgQIECgmoAwV83NVgQIECBAgACBJATcziuJNiiCwPgE2gsLYaH4KbvEe7MOWxaLMa1hg46tny95/IVivwsvXx7bcvDDUrUWtyfbG+HWY2VrHVyZtQQIEJicgDA3OWtHIjAVgWvXr4f4U+dyc22tzt293teNGzdeP67rwdqtW3Xtyn4IECCQpIC3WZNsi6IIECBAgAABAuUEhLlyTkYRIECAAAECBJIUEOaSbIuiCBAgQIAAAQLlBIS5ck5GESBAgAABAgSSFBDmkmyLoggQIECAAAEC5QSEuXJORhEgQIAAAQIEkhQQ5pJsi6IIECBAgAABAuUEhLlyTkYRIECAAAECBJIUcNHgJNuiKAL1Cdz99NPwcoS7KnQ6nXB9wMV7u91u+OTjj8Ph4WHpIq8XFy3urK4OHb+xsRE2Hj4cOq43YG5uLrx3507vz76/P/vss/Bkd7fvun5Pnj17Nozrosj9juc5AgQIvK2AMPe2grYnkLhA9+BgpOB1UIwftpQZc3wfZcfHcaOExBgshy0HxZi69znsmNYTIEBgkgLeZp2ktmMRIECAAAECBGoWEOZqBrU7AgQIECBAgMAkBYS5SWo7FgECBAgQIECgZgFhrmZQuyNAgAABAgQITFJAmJuktmMRIECAAAECBGoWEOZqBrU7AgQIECBAgMAkBYS5SWo7FgECBAgQIECgZgFhrmZQuyNAgAABAgQITFJAmJuktmMRIECAAAECBGoWEOZqBrU7AgQIECBAgMAkBdzOa5LajkVgCgIXi3ui7u/vlz7ycnFv0kFLvB/qxUuXQnGPrEHDTqwbts/e4HPnzoXDErcT641vFbUMW7524UJYXFwcNuz1+na7/fqxBwQIEMhBQJjLoUtqJPAWApcuX36Lrb+6aavVCteuXfvqihqeiWEu/tS5dDqdOndnXwQIEEhOYPh/1iZXsoIIECBAgAABAgR6AsJcT8JvAgQIECBAgECGAsJchk1TMgECBAgQIECgJyDM9ST8JkCAAAECBAhkKCDMZdg0JRMgQIAAAQIEegLCXE/CbwIECBAgQIBAhgLCXIZNUzIBAgQIECBAoCcgzPUk/CZAgAABAgQIZCjgosEZNk3JBEYR2N3dDd1ut/Qm8W4Jy8vLp44/LO78sL29fer6fivOFneVKHNnhS9fvAjPi5+yS7yA8YXiDg+DlqdPn4a9vb1BQ06sm5+fDysrKyee8wcBAgRSFhDmUu6O2gjUILC+vh72RwgzMRwtv/POqUc+KG639cXnn5+6vt+KeMeIV7cA67fy2HPbOzthc2Pj2DPDHw4Lc3F/MdCVXZaWloS5sljGESCQhIC3WZNogyIIECBAgAABAtUEhLlqbrYiQIAAAQIECCQhIMwl0QZFECBAgAABAgSqCQhz1dxsRYAAAQIECBBIQkCYS6INiiBAgAABAgQIVBMQ5qq52YoAAQIECBAgkISAMJdEGxRBgAABAgQIEKgmIMxVc7MVAQIECBAgQCAJAWEuiTYoggABAgQIECBQTUCYq+ZmKwIECBAgQIBAEgLCXBJtUASB8Qm0xrfr8nsu7qE6tWXEY8f7vVoIECCQk4B7s+bULbUSqCBw6/btEO+nWnY5c+bMwKFx/bfefTccHh4OHHd8Zbx5fZnl8uXLodPplBn6akyZ4PVOcZ/Zbrdbep9zc/4btzSWgQQIJCFQ7l/YJEpVBAECVQTKBqlR9r2wsDDK8NJjY5Bqt9ulx5cZGMPnsIBaZj/GECBAIFUB/wmaamfURYAAAQIECBAoISDMlUAyhAABAgQIECCQqoAwl2pn1EWAAAECBAgQKCEgzJVAMoQAAQIECBAgkKqAMJdqZ9RFgAABAgQIECghIMyVQDKEAAECBAgQIJCqgDCXamfURYAAAQIECBAoIeA6cyWQDCGQksDzZ89Cd38/pZJmupYXz5/P9PxNngCB6QsIc9PvgQoIjCRw//79kcYbTIAAAQLNFvA2a7P7a3YECBAgQIBAwwWEuYY32PQIECBAgACBZgsIc83ur9kRIECAAAECDRcQ5hreYNMjQIAAAQIEmi0gzDW7v2ZHgAABAgQINFxAmGt4g02PAIHpCrRarekW4OgECDReQJhrfItNMDeBdrudW8nqHSCwsLAwYK1VBAgQeHsBYe7tDe2BQK0CFzqdWvdnZ9MV6KyuTrcARydAoPECwlzjW2yCuQl0ijDn1bncuta/3qWlpXD+/Pn+Kz1LgACBmgSEuZog7YZAXQJzc3Ph5tqaQFcX6JT2E4Nc7KPPzE2pAQ5LYIYEWofFMkPzNVUC2QgcHByE3Z2dsLW1FfbivVj9TzX53sXgFj8jF99aja/ICXLJt0yBBBohIMw1oo0mQYAAAQIECMyqgLdZZ7Xz5k2AAAECBAg0QkCYa0QbTYIAAQIECBCYVQFhblY7b94ECBAgQIBAIwSEuUa00SQIECBAgACBWRUQ5ma18+ZNgAABAgQINEJAmGtEG02CAAECBAgQmFUBYW5WO2/eBAgQIECAQCMEhLlGtNEkCBAgQIAAgVkVEOZmtfPmTYAAAQIECDRCQJhrRBtNggABAgQIEJhVAWFuVjtv3gQIECBAgEAjBIS5RrTRJAgQIECAAIFZFRDmZrXz5k2AAAECBAg0QuD/xSh8Dw79Lg4AAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![date%20printer.png](attachment:date%20printer.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jan\n",
+      "Mar\n",
+      "Jul\n",
+      "N/A\n",
+      "N/A\n"
+     ]
+    }
+   ],
+   "source": [
+    "# first function:  convert a month (int) into a 3 letter abbreviation or \"N/A\"\n",
+    "def month_to_str(month): # fruitful function!\n",
+    "    if month == 1:\n",
+    "        return \"Jan\"\n",
+    "    elif month == 2:\n",
+    "        return \"Feb\"\n",
+    "    elif month == 3:\n",
+    "        return \"Mar\"\n",
+    "    elif month == 4:\n",
+    "        return \"Apr\"\n",
+    "    elif month == 5:\n",
+    "        return \"May\"\n",
+    "    elif month == 6:\n",
+    "        return \"Jun\"\n",
+    "    elif month == 7:\n",
+    "        return \"Jul\"\n",
+    "    elif month == 8:\n",
+    "        return \"Aug\"\n",
+    "    elif month == 9:\n",
+    "        return \"Sep\"\n",
+    "    elif month == 10:\n",
+    "        return \"Oct\"\n",
+    "    elif month == 11:\n",
+    "        return \"Nov\"\n",
+    "    elif month == 12:\n",
+    "        return \"Dec\"\n",
+    "    else:\n",
+    "        return \"N/A\"\n",
+    "\n",
+    "print(month_to_str(1))\n",
+    "print(month_to_str(3))\n",
+    "print(month_to_str(7))\n",
+    "print(month_to_str(15))\n",
+    "print(month_to_str(-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11st\n",
+      "4th\n",
+      "22nd\n",
+      "43rd\n"
+     ]
+    }
+   ],
+   "source": [
+    "#### second function: convert a day (int) into a string writing '15th', '23rd' or \"N/A\"\n",
+    "def day_to_str(day):\n",
+    "    \"\"\"Covert a day into a date string with proper ending. \n",
+    "    16 --> '16th',    23 --> '23rd', \"\"\"\n",
+    "    last_digit = day % 10\n",
+    "    if last_digit == 1:\n",
+    "        return str(day) + \"st\"\n",
+    "    elif last_digit == 2:\n",
+    "        return str(day) + \"nd\"\n",
+    "    elif last_digit == 3:\n",
+    "        return str(day) + \"rd\"\n",
+    "    else: \n",
+    "        return str(day) + \"th\"\n",
+    "\n",
+    "# st - ends 1\n",
+    "# nd - ends 2\n",
+    "# rd - ends 3\n",
+    "# th - ends 4-0 \n",
+    "\n",
+    "print(day_to_str(11))\n",
+    "print(day_to_str(4))\n",
+    "print(day_to_str(22))\n",
+    "print(day_to_str(43))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'22\n",
+      "'04\n",
+      "'10\n",
+      "1997\n",
+      "1462\n",
+      "181\n"
+     ]
+    }
+   ],
+   "source": [
+    "# third function: convert a year (int) into a string for year\n",
+    "# 2021 ---> '21     # return a string with '  and the last 2 digits\n",
+    "# 1996 ---> 1996    # if year before 2000 return all 4 digits, as a string\n",
+    "\n",
+    "def year_to_str(year):\n",
+    "    \"\"\"Convert a year (as an integer) into a string. If the year is < 2000, return the full 4 digits\n",
+    "    Otherwise, return the last 2 digits with a single quote in front\"\"\"\n",
+    "    if year < 2000:\n",
+    "        return str(year)\n",
+    "    else:\n",
+    "        last_two = str(year % 100)\n",
+    "        if len(last_two) < 2:\n",
+    "            last_two = '0'+last_two\n",
+    "        return \"'\"+last_two\n",
+    "\n",
+    "print(year_to_str(2022))\n",
+    "print(year_to_str(2004))\n",
+    "print(year_to_str(2010))\n",
+    "print(year_to_str(1997))\n",
+    "print(year_to_str(1462))\n",
+    "print(year_to_str(181))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jan 1st of '21\n",
+      "Jan 1st of '07\n",
+      "Sep 23rd of '06\n",
+      "Feb 11st of '22\n",
+      "Oct 22nd of 1997\n",
+      "Jun 8th of 1497\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now put it all together\n",
+    "def format_date(year=2021, month=1, day=1):\n",
+    "    \"\"\"returns a string representing the date, such as Feb 10th of ‘22\"\"\"\n",
+    "    return month_to_str(month)+\" \"+day_to_str(day)+\" of \"+year_to_str(year)\n",
+    "    \n",
+    "print(format_date())\n",
+    "print(format_date(2007))\n",
+    "print(format_date(day=23, year=2006, month=9))\n",
+    "print(format_date(2022, 2, 11))\n",
+    "print(format_date(1997, 10, 22))\n",
+    "print(format_date(1497, 6, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You have learned about the syntax for the three types of conditional statements and you have practiced writing code using each of them.\n",
+    "\n",
+    "- **if** statement\n",
+    "- **if-else** statement\n",
+    "- **if-elif** statement\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
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