{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derivatives Analytics - Introduction to Object Oriented Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author**: [Gabriele Pompa](https://www.linkedin.com/in/gabrielepompa/): gabriele.pompa@unisi.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "\n",
    "[Executive Summary](#summary)\n",
    "\n",
    "1. [Introduction to dates management: `datetime` module](#datetime)\n",
    "2. [Non-financial Introduction to OOP](#non-financial-introduction-to-oop)\\\n",
    "    2.1. [Without OOP: a person](#nonoopperson)\\\n",
    "    2.2. [With OOP: the `Person` class](#oopperson)\n",
    "3. [Introduction to OOP: Black-Scholes option pricer](#BS-option-pricer)\\\n",
    "    3.1. [Without OOP: a call priced using Black-Scholes model](#nonoopbs)\\\n",
    "    3.2. [With OOP: the `CallOption` class](#oopbs)\\\n",
    "    3.3. [Encapsulation: name hiding, getters and setters](#encapsulation)\n",
    "4. [Including put options: the `PlainVanillaOption` class](#PlainVanillaOption) \\\n",
    "    4.1 [_Focus on:_ effect of interest rates on options limits](#shortRateEffect) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Resources**: \n",
    "\n",
    "- [_Python for Finance (2nd ed.)_](http://shop.oreilly.com/product/0636920117728.do): Chapter 6 Object-Oriented Programming.  From Appendix A _Dates and Times:_ Section \"Python\".\n",
    "\n",
    "\n",
    "- [Object-Oriented Programming in Python](https://python-textbok.readthedocs.io/en/1.0/index.html): Section [Classes](https://python-textbok.readthedocs.io/en/1.0/Classes.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executive Summary <a name=\"executive-summary\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we gently introduce Object-Oriented Programming. We first make a non-financial example, developing the intuition behind the need of a change of programming paradigm to be able to cohordinate together different pieces of code. Once we have established the intuition, we then leverage on some basic financial knowledge to develop a Black-Scholes pricer for call options, first, and then a more general pricer for plain-vanilla put options as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following sections are organized as follows:\n",
    "\n",
    "- In Sec. [1](#datetime) we introduce the `datetime` module, which allow us to easily manage dates.\n",
    "- In Sec. [2](#non-financial-introduction-to-oop) we develop intuition using a non financial example.\n",
    "- In Sec. [3](#BS-option-pricer) we apply the OOP paradygm to price call options under the Black-Scholes model.\n",
    "- In Sec. [4](#PlainVanillaOption) we extend our pricer to price put options too, defining a more general class for plain-vanilla options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the basic imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for NumPy arrays\n",
    "import numpy as np\n",
    "\n",
    "# for Pandas Series and DataFrame\n",
    "import pandas as pd\n",
    "\n",
    "# for statistical functions\n",
    "from scipy import stats\n",
    "\n",
    "# for Matplotlib plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to do inline plots in the Notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# for Operating System operations\n",
    "import os\n",
    "\n",
    "# for some mathematical functions\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction to dates management: `datetime` module <a name=\"datetime\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [`datetime` module](https://docs.python.org/3/library/datetime.html?highlight=strptime#module-datetime), part of the Python standard library, allows you to manage dates in your code with ease. We start importing it and giving it the alias `dt`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for date management\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Today's date? No problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 5, 1, 16, 50, 35, 325577)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today = dt.datetime.today()\n",
    "today"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dates in `datetime` module are objects of [`datetime.datetime` data-type](https://docs.python.org/3/library/datetime.html#datetime-objects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(today)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can specify a date providing year, month and day for that date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1988, 6, 1, 0, 0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_birthday = dt.datetime(year=1988,month=6,day=1)\n",
    "my_birthday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(my_birthday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distance with dates is just the difference of the two dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta(days=11657, seconds=60635, microseconds=325577)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_to_date = today - my_birthday\n",
    "time_to_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which returns an object of [`datetime.timedelta` data-type](https://docs.python.org/3/library/datetime.html?highlight=timedelta#datetime.timedelta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.timedelta"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(time_to_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From `timedelta` objects you can get the number of elapsed days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11657"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_to_date.days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and manipulate them to get number of years elapsed. We'll see this applied extensively to compute the time-to-maturity of an option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.936986301369863"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_to_date.days/365"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often you - either input or read from file - dates which are syntactically Python Strings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18-04-2020'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_string = \"18-04-2020\"\n",
    "date_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(date_string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good to know built-in function is the [`isinstance` function](https://docs.python.org/3/library/functions.html#isinstance)\n",
    "\n",
    "```python\n",
    "isinstance(variable, data_type)\n",
    "```\n",
    "\n",
    "which returns `True` if `variable` is of type `data_type` and `False` otherwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(date_string, str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can convert a date String into a proper `datetime.datetime` object using the [`strptime` function](https://docs.python.org/3/library/datetime.html?highlight=strptime#strftime-and-strptime-behavior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 4, 18, 0, 0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = dt.datetime.strptime(date_string, \"%d-%m-%Y\")\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where the `\"%d-%m-%Y\"` format String is used to specify how to interpret the characters in `date_string` Strings: here as day-month-Year String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(date, dt.datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other way around, you can convert a `datetime.datetime` object into its String representation calling the [`strftime` function](https://docs.python.org/3/library/datetime.html?highlight=strptime#strftime-and-strptime-behavior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18-04-2020'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_string_from_dt = date.strftime(\"%d-%m-%Y\")\n",
    "date_string_from_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where the `\"%d-%m-%Y\"` format String is used to specify how the output `date_string_from_dt` should represent the original `date` object: here as day-month-Year String."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-04-18'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(date_string_from_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(date_string_from_dt, str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An alternative choice could be `\"%Y-%m-%d\"` for Year-month-day output format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'18-04-2020'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime(\"%d-%m-%Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add a certain amount of days to a given date adding a `datetime.timedelta` object to a `datetime.datetime` date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 5, 13, 0, 0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + dt.timedelta(days=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Non-financial Introduction to OOP <a name=\"non-financial-introduction-to-oop\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin with a non-financial example. We define a _person._ \n",
    "\n",
    "We start describing its properties (name, surname etc.) and the things that a person could usually do knowing and/or using his/her properties (e.g. knowing his/her he/she can say it).\n",
    "\n",
    "We start defining these features one by one in a _sequential_ way, noticing that there is no cohordination between the properties defining the person and the actions he/she can take knowing/using the properties. There is no logical consistency. \n",
    "\n",
    "Finally, we define a logic entity - synctactically a `Person` Python class - which logically describes the abstract concept of _person_ and that is able to cohordinate between the properties defining the person and the things that a person can do knowing and/or using his/her properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Without OOP: a person <a name=\"nonoopperson\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knowing name and surname, a person can usually say it. We implement this action as the `say_your_name()` function, whih needs in input the name and surname of our person"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def say_your_name(name, surname):\n",
    "    print(\"My name is {} {}\".format(name, surname))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_name = \"Gabriele\"\n",
    "my_surname = \"Pompa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Gabriele Pompa\n"
     ]
    }
   ],
   "source": [
    "say_your_name(name=my_name, surname=my_surname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also make this person declaring his/her country of birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def say_country_of_birth(country):\n",
    "    print(\"My country of birth is {}\".format(country))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_country = \"Italy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My country of birth is Italy\n"
     ]
    }
   ],
   "source": [
    "say_country_of_birth(my_country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a birthdate String, we can use the functionalities of `datetime` module to compute person's age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_age(birthdate):\n",
    "    \n",
    "    today = dt.datetime.today()  \n",
    "    birthdate = dt.datetime.strptime(birthdate, \"%d-%m-%Y\") if isinstance(birthdate, str) else birthdate\n",
    "    \n",
    "    next_birthday = dt.datetime(year=today.year, month=birthdate.month, day=birthdate.day)\n",
    "    \n",
    "    if today >= next_birthday:\n",
    "        age = today.year - birthdate.year\n",
    "    else:\n",
    "        age = today.year - birthdate.year - 1\n",
    "        \n",
    "    return age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "notice the use of `next_birthday` to check whether in the current year person's birthday is already occurred or not yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'01-06-1988'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_birthdate = \"01-06-1988\"\n",
    "my_birthdate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_age = compute_age(birthdate=my_birthdate)\n",
    "my_age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accordingly we can compute the number of days left to his/her next birthday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_to_birthday(birthdate):\n",
    "    \n",
    "    today = dt.datetime.today()\n",
    "    birthdate = dt.datetime.strptime(birthdate, \"%d-%m-%Y\") if isinstance(birthdate, str) else birthdate\n",
    "    \n",
    "    next_birthday = dt.datetime(year=today.year, month=birthdate.month, day=birthdate.day)\n",
    "    \n",
    "    return (next_birthday - today).days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_to_birthday = time_to_birthday(my_birthdate)\n",
    "days_to_birthday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One think we should notice here is that any time we want to make the person do something (e.g. say his/her name), we need to provide explicitly all the required informations in input. Each time. \n",
    "\n",
    "There is no way to guarantee that `my_name`, `my_surname`, `my_birthdate` (and thus `my_age`) actually belong to the same person. Those are just variables that we declare one-by-one. We have in mind the fact that they describe different properties of the same person, but there is no way to inform Python of this relationship. In other words, there is no way to cohordinate logically between the different data used by the functions we defined..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. With OOP: the `Person` class <a name=\"oopperson\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classes are a way of grouping together related data and functions which act upon that data. They define the logical model according to which data and functions working on those data should cohordinate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the concept of a person as a `Person` class. In Python classes are declared using the `class` keyword,  followed by the class name and a colon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variables `name`, `surname`, `country` etc. are called _attributes_ of the `Person` class:\n",
    "- are the set of informations in common to all the persons;\n",
    "- are the data used by the `Person` class to do things. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions defined within the class, like `say_your_name()`, `say_country_of_birth()`, `compute_age()` and `time_to_birthday()`, are called _methods_ of the `Person` class: \n",
    "- define the range of things that a class can do using its own attributes (and, possibly, other parameters coming from outside the class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Person:\n",
    "    \"\"\"\n",
    "    Person class implementing some typical features of a person.\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        name (str):                   name string;\n",
    "        surname (str):                surname string;\n",
    "        country (str):                country of birth;\n",
    "        birthdate (str; dt.datetime): date of birth. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        age (int):                    age, as computed by compute_age() method.\n",
    "    \n",
    "    Methods:\n",
    "    --------\n",
    "        say_your_name: (returns) None\n",
    "            Prints name and surname.\n",
    "            \n",
    "        say_country_of_birth: None\n",
    "            Prints country of birth.\n",
    "            \n",
    "        compute_age: int\n",
    "            Computes the age, sets self.age attribute and returns it.\n",
    "            \n",
    "        time_to_birthday: int\n",
    "            Computes the number of days left untill next birthdat and return the number.\n",
    "    \n",
    "    \"\"\"\n",
    "    # MIND THE INDENTATION!!!\n",
    "    def __init__(self, p_name, p_surname, p_country, p_birthdate):\n",
    "        self.name      = p_name\n",
    "        self.surname   = p_surname\n",
    "        self.country   = p_country\n",
    "        self.birthdate = dt.datetime.strptime(p_birthdate, \"%d-%m-%Y\") if isinstance(p_birthdate, str) else p_birthdate\n",
    "        self.age       = None\n",
    "    \n",
    "    def say_your_name(self):\n",
    "        print(\"My name is {} {}\".format(self.name, self.surname))\n",
    "        \n",
    "    def say_country_of_birth(self):\n",
    "        print(\"My country of birth is {}\".format(self.country))\n",
    "        \n",
    "    def compute_age(self):\n",
    "    \n",
    "        today = dt.datetime.today()  \n",
    "\n",
    "        next_birthday = dt.datetime(year=today.year, month=self.birthdate.month, day=self.birthdate.day)\n",
    "\n",
    "        if today >= next_birthday:\n",
    "            self.age = today.year - self.birthdate.year\n",
    "        else:\n",
    "            self.age = today.year - self.birthdate.year - 1\n",
    "\n",
    "        return self.age \n",
    "    \n",
    "    def time_to_birthday(self):\n",
    "\n",
    "        today = dt.datetime.today()\n",
    "\n",
    "        next_birthday = dt.datetime(year=today.year, month=self.birthdate.month, day=self.birthdate.day)\n",
    "\n",
    "        return (next_birthday - today).days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have a logical model for a concept, say the concept of a _person_ defined as a `Person` class, we can start making concrete examples from this abstract model. For example, Bill Gates is a person. Mario Draghi is another person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These concrete examples from the person concept\n",
    "- all conform to the logic model defined by the class; \n",
    "- are _instances_ of the `Person` class;\n",
    "- are called _objects_ (of the `Person` class)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The programming style of modeling abstract concepts as _classes_ and concrete instances as _objects_ is called [_Object-Oriented Programming_](https://en.wikipedia.org/wiki/Object-oriented_programming) (OOP in short)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define myself as an instance of the `Person` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gab = Person(p_name = \"Gabriele\", \n",
    "             p_surname = \"Pompa\", \n",
    "             p_country = \"Italy\", \n",
    "             p_birthdate = \"01-06-1988\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We say that `gab` is an object ot class `Person`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you can create an instance of the `Person` class: \n",
    "\n",
    "- the special `__init__()` method is called, taking in input the parameters (if any) required to instantiate the attribute of the `Person`. Here the value of `p_name` parameter is assigned to `name` attribute, `p_surname` to `surname` etc.  \n",
    "\n",
    "\n",
    "- the `__init__()` method is usually called the class' _constructor_ (or _initializer_ )."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the special keyword `self`:\n",
    "\n",
    "- `self` stands for _this object itself_ - like object `gab` itself;  \n",
    "\n",
    "\n",
    "- `self` is used as `self.attribute` and `self.method(...)` to specify that we are refferring to that particular object's attribute and method, respectively.\n",
    "\n",
    "\n",
    "- `self` is the first parameter of each method definition - like `def method(self, ...)` - and informs the interpreter that - whenever that method will be called - the object itself will be passed to that method;\n",
    "\n",
    "\n",
    "- when you call that method - like `self.method(...)` within class definition -  `self` is not an input parameter you provide explicitly; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classes provide an analogy with data-types, such that each class defines its own - user defined - data-type and, then, on each object variable of the class we can call the `type(object)` function - as we would do with Integers, Floats, Strings, etc. - which returns the kind of data-type of that object, that is, the data-type defined by the class we have created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.Person"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is the name of object `gab`? It's his/her `name` attribute. We can access it using the `.` access operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Gabriele'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python knows that `gab.name` refers to the `name` attribute of object `gab`, thanks to the use of the `self` in the attribute definition (`self.name`), which informs that we are referring to the `.name` attribute of the calling `gab` object itself. \n",
    "\n",
    "With time this way of reasoning will come automatic. Dont't worry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Pompa'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.surname"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make object `gab` saying his name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Gabriele Pompa\n"
     ]
    }
   ],
   "source": [
    "gab.say_your_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "notice that `say_your_name()` doesn't need any input parameter because it is a method of `gab` object. Therefore, the `self.name` and `self.surname` it uses will be `gab.name` and `gab.surname`, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way of reasoning applies to all the other methods and attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Italy'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My country of birth is Italy\n"
     ]
    }
   ],
   "source": [
    "gab.say_country_of_birth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(1988, 6, 1, 0, 0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.birthdate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'01-06-1988'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.birthdate.strftime(\"%d-%m-%Y\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we instantiate `gab` object, his `age` attribute is left unspecified. We conventionally set it to `None` in the `__init__`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.age is None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we delegate to the `compute_age()` function the initialization of the `age` parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.compute_age()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now `age` is specified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gab.time_to_birthday()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TAKE-HOME MESSAGE**: what's crucial to understand in all of this is that:\n",
    "\n",
    "- classes provide a logical schema that cohordinates between its data (attributes) and functions (methods), allowing them to work together.\n",
    "\n",
    "- once we have isntantiated an object, We don't need to provide data explicitly to its methods to call them, because data on which the method works are its own attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is another instance of the `Person` class. We instantiate a `bill` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "bill = Person(p_name = \"Bill\", \n",
    "              p_surname = \"Gates\", \n",
    "              p_country = \"USA\", \n",
    "              p_birthdate = \"28-10-1955\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.Person"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(bill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Bill Gates\n"
     ]
    }
   ],
   "source": [
    "bill.say_your_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that both `gab` and `bill` share the same data-type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.Person"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in the same way as two different Float variables share the data-type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Introduction to OOP: Black-Scholes option pricer <a name=\"BS-option-pricer\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now see a concrete application of the OOP paradygm: we build a pricer for an european call option according to the Black-Scholes(-Merton) model. For theoretical background (an notation) we refer to [_Financial Modeling 1_ lecture notes](https://finmod1.wordpress.com/lecture-notes/) from Professor Pacati.\n",
    "\n",
    "As before, we start with a non-OOP implementation, notice the intrinsic lack of cohordination between parts of the code and then model the pricer as a `CallOption` class, which is able to cohordinates model and market data with outputs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Without OOP: a call priced using Black-Scholes model <a name=\"nonoopbs\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A call option of maturity $T$ and strike $K$ on an asset $S$ is a contract giving to the owner the right to buy for $K$ the asset at the future date $T$ (when the asset will be worth $S_T$). The payoff of the call option contract at time $T$ is then\n",
    "\n",
    "$$\n",
    "\\max(S_T - K, 0)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We corrispondigly define the `call_option_payoff()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_option_payoff(S,K):\n",
    "    \"\"\"\n",
    "    call_option_payoff(S,K) computes returns the payoff of a call option given the spot price of the underlying asset \n",
    "    and strike price 'K'.\n",
    "    \n",
    "    Parameters:\n",
    "        S (float): spot price of the underlying asset;\n",
    "        K (float): strike price;\n",
    "    \n",
    "    Returns:\n",
    "        payoff (float): call option payoff;\n",
    "    \"\"\"\n",
    "    return max(0.0, S - K)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We consider non-dividend paying underlying asset $S$. At any time $t \\leq T$, the price $c_t$ of the european call option under the Black-Scholes model is given by\n",
    "\n",
    "$$\n",
    "c_t = S_t N(d_1) - K e^{-r \\tau} N(d_2)\n",
    "$$\n",
    "\n",
    "where $\\tau = T-t$ is the option time-to-maturity, $r$ is the continuously compounding short-rate, $N(z)$ is the cumulative distribution function of a standard normal random variable \n",
    "\n",
    "$$\n",
    "N(z) = \\frac{1}{\\sqrt{2 \\pi}} \\int^{z}_{- \\infty} e^{-\\frac{z^2}{2}}\n",
    "$$\n",
    "\n",
    "and the to arguments of $N(z)$ are\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{aligned}\n",
    "d_1 &= \\frac{\\log{\\frac{S_t}{K}} + \\left( r + \\frac{1}{2} \\sigma^2 \\right) \\tau }{\\sigma \\sqrt{\\tau}} \\nonumber \\\\\n",
    "d_2 &= d_1 - \\sigma \\sqrt{\\tau}\n",
    "\\end{aligned}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We corrispondigly define the `call_option_price()` function, which uses the `stats.norm.cdf` function to implement $N(z)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_option_price(S, K, tau, r, sigma):\n",
    "    \"\"\"\n",
    "    call_option_price(S, K, tau, r, sigma) computes and returns the exact price of a call option \n",
    "    of time to maturity 'tau' and strike price 'K' given the spot price of the underlying asset 'S', \n",
    "    the continuosly compounded short-rate 'r' and volatility of the \n",
    "    underlying asset 'sigma'.\n",
    "\n",
    "    Parameters:\n",
    "        S (float):     spot price of the underlying asset;\n",
    "        K (float):     strike price;\n",
    "        tau (float):   time to maturity (in years);\n",
    "        r (float):     continuously compounded short-rate;\n",
    "        sigma (float): volatility of underlying asset;\n",
    "        \n",
    "    Returns:\n",
    "        price (float): call price;\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    if S == 0: # this is to avoid log(0) issues\n",
    "        return 0.0\n",
    "    elif tau == 0: # this is to avoid 0/0 issues\n",
    "        return call_option_payoff(S, K)\n",
    "    else:\n",
    "        d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "        d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "        price = S * stats.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "        return price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independently from the valuation model used to price the option (we assume constant deterministic short-rate $r$ here and everywhere in this notebook), there are non-arbitrage arguments leading to upper and lower limits for the price of the european call options. At any time $t \\leq T$, the price $c_t$ of the option satisfies:\n",
    "\n",
    "$$\n",
    "\\max(S_t - K e^{-r \\tau}, 0) \\leq c_t \\leq S_t\n",
    "$$\n",
    "\n",
    "See, for example, [John C. Hull _Options, Futures and Other Derivatives_ (10th ed)](https://www.pearson.com/us/higher-education/program/Hull-Options-Futures-and-Other-Derivatives-10th-Edition/PGM333301.html) chapter 11.3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We corrispondigly define `call_lower_limit()` and `call_upper_limit()` functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_upper_limit(S):\n",
    "    \"\"\"\n",
    "    call_upper_limit(S) returns the upper limit for a vanilla call option price, \n",
    "    which coincides with the spot price of the underlying asset.\n",
    "\n",
    "    Parameters:\n",
    "        S (float):     spot price of the underlying asset;\n",
    "        \n",
    "    Returns:\n",
    "        S (float):     spot price of the underlying asset;\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    return S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call_lower_limit(S, K, tau, r):\n",
    "    \"\"\"\n",
    "    call_lower_limit(S, K, tau, r) returns the lower limit for a vanilla call option price.\n",
    "\n",
    "    Parameters:\n",
    "        S (float):     spot price of the underlying asset;\n",
    "        K (float):     strike price;\n",
    "        tau (float):   time to maturity (in years);\n",
    "        r (float):     continuously compounded short-rate;\n",
    "        \n",
    "    Returns:\n",
    "        price_lower_limit (float): call lower limit price;\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    price_lower_limit = max(S - K*np.exp(-short_rate * tau), 0)\n",
    "    \n",
    "    return price_lower_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the price, we define a couple of plotting functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "K              = 100.0\n",
    "short_rate     = 0.05 # 5% per year\n",
    "volatility     = 0.2  # 20% per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 80.        ,  80.4040404 ,  80.80808081,  81.21212121,\n",
       "        81.61616162,  82.02020202,  82.42424242,  82.82828283,\n",
       "        83.23232323,  83.63636364,  84.04040404,  84.44444444,\n",
       "        84.84848485,  85.25252525,  85.65656566,  86.06060606,\n",
       "        86.46464646,  86.86868687,  87.27272727,  87.67676768,\n",
       "        88.08080808,  88.48484848,  88.88888889,  89.29292929,\n",
       "        89.6969697 ,  90.1010101 ,  90.50505051,  90.90909091,\n",
       "        91.31313131,  91.71717172,  92.12121212,  92.52525253,\n",
       "        92.92929293,  93.33333333,  93.73737374,  94.14141414,\n",
       "        94.54545455,  94.94949495,  95.35353535,  95.75757576,\n",
       "        96.16161616,  96.56565657,  96.96969697,  97.37373737,\n",
       "        97.77777778,  98.18181818,  98.58585859,  98.98989899,\n",
       "        99.39393939,  99.7979798 , 100.        , 100.2020202 ,\n",
       "       100.60606061, 101.01010101, 101.41414141, 101.81818182,\n",
       "       102.22222222, 102.62626263, 103.03030303, 103.43434343,\n",
       "       103.83838384, 104.24242424, 104.64646465, 105.05050505,\n",
       "       105.45454545, 105.85858586, 106.26262626, 106.66666667,\n",
       "       107.07070707, 107.47474747, 107.87878788, 108.28282828,\n",
       "       108.68686869, 109.09090909, 109.49494949, 109.8989899 ,\n",
       "       110.3030303 , 110.70707071, 111.11111111, 111.51515152,\n",
       "       111.91919192, 112.32323232, 112.72727273, 113.13131313,\n",
       "       113.53535354, 113.93939394, 114.34343434, 114.74747475,\n",
       "       115.15151515, 115.55555556, 115.95959596, 116.36363636,\n",
       "       116.76767677, 117.17171717, 117.57575758, 117.97979798,\n",
       "       118.38383838, 118.78787879, 119.19191919, 119.5959596 ,\n",
       "       120.        ])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_strip = np.linspace(80, 120, 100)\n",
    "S_strip = np.append(S_strip, K)\n",
    "S_strip = np.sort(S_strip)\n",
    "S_strip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.1 , 0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_strip = np.array([0.05, 0.1, 0.25, 0.5, 0.75, 1.0])\n",
    "tau_strip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def plot_multi_tau(S_list, K, tau_list, r, sigma):\n",
    "    \"\"\"\n",
    "    plot_multi_tau(S_list, tau_list) plot call option prices for underlying and maturities in 'S_list' and 'tau_list',\n",
    "    respectively.\n",
    "\n",
    "    Parameters:\n",
    "        S_list (np.ndarray):   list of underlying values;\n",
    "        K (float):             strike price\n",
    "        tau_list (np.ndarray): list of times to maturity (in years);\n",
    "        r (float):             continuously compounded short-rate;\n",
    "        sigma (float):         volatility of underlying asset;\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # color cycle setup: basically a cycle of different shades of blue as many time to maturity there are\n",
    "    plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.Blues(np.linspace(0,1,len(tau_list)+1)))\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(K, 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the price for different underlying values, one line for each different time to maturity \n",
    "    for tau in np.flip(tau_list):\n",
    "        ax.plot(S_list, [call_option_price(S, K, tau, r, sigma) for S in S_list], \n",
    "                '-', lw=1.5, label=r\"$\\tau={}$\".format(tau))\n",
    "\n",
    "    # plot the red payoff line for different underlying values\n",
    "    ax.plot(S_list, [call_option_payoff(S, K) for S in S_list], \n",
    "            'r-', lw=1.5, label=\"$max(S-K, 0)$\")\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a Call Option $(S_t, K={}, \\tau=T-t, r={}\\%, \\sigma={}\\%)$ Vs $S$ (at different $\\tau$)\".\n",
    "                 format(K, r*100, sigma*100), fontsize=12) \n",
    "\n",
    "    # add the legend\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(S_strip, K, tau_strip, short_rate, volatility)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the price $c_t$ of a $(K, \\tau=T-t)$ call option contract for different levels $S_t$ of the underlying asset value (that is, in different market environments) at several times-to-maturity $\\tau$.  The red payoff line is reported for reference. We can notice that:\n",
    "\n",
    "- the longer is the time-to-maturity $\\tau$ of the option, the more the option is expensive;\n",
    "\n",
    "- the more the option approaches expiration ($\\tau \\rightarrow 0$), the more its price approaches the payoff line. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.        ,  5.05050505,  5.15151515,  5.25252525,  5.35353535,\n",
       "        5.45454545,  5.55555556,  5.65656566,  5.75757576,  5.85858586,\n",
       "        5.95959596,  6.06060606,  6.16161616,  6.26262626,  6.36363636,\n",
       "        6.46464646,  6.56565657,  6.66666667,  6.76767677,  6.86868687,\n",
       "        6.96969697,  7.07070707,  7.17171717,  7.27272727,  7.37373737,\n",
       "        7.47474747,  7.57575758,  7.67676768,  7.77777778,  7.87878788,\n",
       "        7.97979798,  8.08080808,  8.18181818,  8.28282828,  8.38383838,\n",
       "        8.48484848,  8.58585859,  8.68686869,  8.78787879,  8.88888889,\n",
       "        8.98989899,  9.09090909,  9.19191919,  9.29292929,  9.39393939,\n",
       "        9.49494949,  9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 ,\n",
       "       10.        ])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau = 2.0\n",
    "\n",
    "K_focus = 5\n",
    "\n",
    "S_strip_focus = np.linspace(0, 10, 100)\n",
    "S_strip_focus = np.append(S_strip_focus, K_focus)\n",
    "S_strip_focus = np.sort(S_strip_focus)\n",
    "S_strip_focus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_tau(S_list, K, tau, r, sigma):\n",
    "    \"\"\"\n",
    "    plot_single_tau(S_list, tau_strip) plot call option prices for underlying in 'S_list' at time to maturity 'tau'.\n",
    "\n",
    "    Parameters:\n",
    "        S_list (List): list of underlying values;\n",
    "        K (float):     strike price\n",
    "        tau (float):   time to maturity (in years);\n",
    "        r (float):     continuously compounded short-rate;\n",
    "        sigma (float): volatility of underlying asset;\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(K, 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the upper limit, price, lower limit for different underlying values\n",
    "    ax.plot(S_list, [call_upper_limit(S) for S in S_list],                    \n",
    "            'k-.', lw=1.5, label=r\"Upper limit: $S_t$\")\n",
    "    ax.plot(S_list, [call_option_price(S, K, tau, r, sigma) for S in S_list], \n",
    "            'b-',  lw=1.5, label=r\"Price\")\n",
    "    ax.plot(S_list, [call_lower_limit(S, K, tau, short_rate) for S in S_list], \n",
    "            'k--', lw=1.5, label=r\"Lower limit: $max(S_t - K e^{-r \\tau}, 0)$\")\n",
    "\n",
    "    # plot the red payoff line for different underlying values\n",
    "    ax.plot(S_list, [call_option_payoff(S, K) for S in S_list], \n",
    "            'r-', lw=1.5, label=\"$max(S-K, 0)$\")\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a Call Option $(S_t, K={}, \\tau=T-t={}y, r={}\\%, \\sigma={}\\%)$ Vs $S$ (with price limits)\".\n",
    "                 format(K, tau, r*100, sigma*100), fontsize=12) \n",
    "\n",
    "    # add the legend\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(S_strip_focus, K_focus, tau, short_rate, volatility)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the price $c_t$ of a $(K, \\tau)$ call option contract for different levels $S_t$ of the underlying asset value (that is, in different market environments). For reference, we report:\n",
    "\n",
    "- the red payoff line;\n",
    "\n",
    "- the upper bound $c_t \\leq S_t$;\n",
    "\n",
    "- the lower bound $c_t \\geq \\max(S_t - K e^{-r \\tau}, 0)$;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. With OOP: the `CallOption` class <a name=\"oopbs\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now group together all the parameters needed to define a call option, together with its payoff, price and bounds in a logical consistent way: we define a `CallOption` class.\n",
    "\n",
    "The class has, as attributes:\n",
    "\n",
    "- the underlying asset value $S_t$ at time $t$;\n",
    "- the strike price $K$;\n",
    "- the option valuation date $t$;\n",
    "- the option expiration date $T$;\n",
    "- the time-to-maturity of the options $\\tau=T-t$;\n",
    "- the short rate $r$;\n",
    "- the volatility of the underlying asset $\\sigma$;\n",
    "\n",
    "and, as methods:\n",
    "\n",
    "- the payoff `payoff`;\n",
    "\n",
    "- the `price_upper_limit` and `price_lower_limit`;\n",
    "\n",
    "- the `price`\n",
    "\n",
    "Notice that each method is defined in exactly the same way as the corresponding function in the previous section. The crucial difference is that - being methods of `CallOption` class, they do not need input parameters, as they can access directly to `CallOption` attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CallOption:\n",
    "    \"\"\"\n",
    "    CallOption class implementing payoff and pricing of a european call option.\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------                        \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "            \n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a vanilla call option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a vanilla call option price.\n",
    "\n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    # constructor\n",
    "    def __init__(self, S_t, K, t, T, r, sigma):\n",
    "        self.S     = S_t\n",
    "        self.K     = K\n",
    "        self.t     = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        self.T     = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        self.tau   = self.__time_to_maturity()\n",
    "        self.r     = r\n",
    "        self.sigma = sigma\n",
    "        \n",
    "    # string representation\n",
    "    def __repr__(self):\n",
    "        return r\"CallOption(S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.S, self.K, self.t.strftime(\"%d-%m-%Y\"), self.T.strftime(\"%d-%m-%Y\"), self.tau, \n",
    "                       self.r*100, self.sigma*100)\n",
    "      \n",
    "    # time to maturity method (private)\n",
    "    def __time_to_maturity(self):\n",
    "        return (self.T - self.t).days / 365.0\n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        return max(0.0, self.S - self.K)\n",
    "    \n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        return self.S\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        return max(self.S - self.K*np.exp(-self.r * self.tau), 0)\n",
    "\n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        \n",
    "        if self.S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif self.tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()        \n",
    "        else:\n",
    "\n",
    "            d1 = (np.log(self.S / self.K) + (self.r + 0.5 * self.sigma ** 2) * self.tau) / (self.sigma * np.sqrt(self.tau))\n",
    "            d2 = d1 - self.sigma * np.sqrt(self.tau)\n",
    "\n",
    "            price = self.S * stats.norm.cdf(d1, 0.0, 1.0) - self.K * np.exp(-self.r * self.tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can instantiate a `call` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "call = CallOption(S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we defined the special `__repr__` method. This special method is actually provided by default by Python anytime you define a class and it provides the string representation of the class: simply how it should be printed on screen. We simply re-define it (aka _override_ ) to give a more informative representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CallOption(S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.CallOption"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(call)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `.` access operator, we can inspect the `call` attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 12, 31, 0, 0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7013698630136986"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can call its methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.payoff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.487402470943657"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.price_upper_limit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.price_lower_limit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can test the consistency of `price_lower_limit()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(call.S - call.K*np.exp(-call.r * call.tau), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize again our option price modifying our plotting functions: we pass in input an `option` object, so that we can use its attributes and methods in the `plot_multi_tau()` (and `plot_single_tau()`) function body."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multi_tau(option, S_list, tau_list):\n",
    "    \"\"\"\n",
    "    plot_multi_tau(option, S_list, tau_list) plot option prices for underlying and maturities in 'S_list' and 'tau_list',\n",
    "    respectively.\n",
    "\n",
    "    Parameters:\n",
    "        option (CallOption): instance of CallOption class;\n",
    "        S_list (List):       list of underlying values;\n",
    "        tau_list (List):     list of times to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # color cycle setup: basically a cycle of different shades of blue as many time to maturity there are\n",
    "    plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.Blues(np.linspace(0,1,len(tau_list)+1)))\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # auxiliary variables\n",
    "    numS = len(S_list)\n",
    "    numTau = len(tau_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(option.K, 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the price for different underlying values, one line for each different time to maturity tau\n",
    "    for i in np.arange(numTau)[::-1]: # loop over reversed range\n",
    "        \n",
    "        option.tau = tau_list[i] # reset of time-to-maturity\n",
    "        price = np.zeros(numS)\n",
    "        \n",
    "        for j in np.arange(numS):\n",
    "            option.S = S_list[j] # reset of underlying value\n",
    "            price[j] = option.price()\n",
    "        \n",
    "        ax.plot(S_list, price, '-', lw=1.5, label=r\"$\\tau={}$\".format(option.tau))\n",
    "    \n",
    "    # plot the red payoff line for different underlying values\n",
    "    payoff = np.zeros(numS)\n",
    "    for i in np.arange(numS):\n",
    "        option.S = S_list[i] # reset of underlying value\n",
    "        payoff[i] = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, payoff, 'r-', lw=1.5, label=\"$max(S-K, 0)$\")\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a Call Option $(S_t, K={}, \\tau=T-t, r={}\\%, \\sigma={}\\%)$ Vs $S$ (at different $\\tau$)\".\n",
    "                 format(option.K, option.r, option.sigma), fontsize=12) \n",
    "\n",
    "    # add the legend\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(call, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_tau(option, S_list, tau):\n",
    "    \"\"\"\n",
    "    plot_single_tau(option, S_list, tau) plot option prices for underlying in 'S_list' and time to maturity 'tau'.\n",
    "\n",
    "    Parameters:\n",
    "        option (CallOption): instance of CallOption class;\n",
    "        S_list (List):       list of underlying values;\n",
    "        tau (float):         time to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # setting legend labels\n",
    "    upper_limit_label = r\"Upper limit: $S_t$\"\n",
    "    payoff_label      =r\"$max(S-K, 0)$\"\n",
    "    lower_limit_label = r\"Lower limit: $max(S_t - K e^{-r \\tau}, 0)$\"\n",
    "        \n",
    "    # auxiliary variable\n",
    "    numS = len(S_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(option.K, 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the upper limit, the price and the lower limit for different underlying values\n",
    "    upper_limit = np.zeros(numS)\n",
    "    price       = np.zeros(numS)\n",
    "    lower_limit = np.zeros(numS)\n",
    "    payoff      = np.zeros(numS)\n",
    "\n",
    "    for i in np.arange(numS):\n",
    "        option.S = S_list[i] # reset of underlying value\n",
    "        \n",
    "        upper_limit[i] = option.price_upper_limit()\n",
    "        price[i]       = option.price()\n",
    "        lower_limit[i] = option.price_lower_limit()\n",
    "        payoff[i]      = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, upper_limit, 'k-.', lw=1.5, label=upper_limit_label)\n",
    "    ax.plot(S_list, price,       'b-',  lw=1.5, label=r\"Price\")\n",
    "    ax.plot(S_list, lower_limit, 'k--', lw=1.5, label=lower_limit_label)\n",
    "    ax.plot(S_list, payoff,      'r-',  lw=1.5, label=payoff_label)\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a Call Option $(S_t, K={}, \\tau=T-t={:.1f}y, r={}\\%, \\sigma={}\\%)$ Vs $S$ (with price limits)\".\n",
    "                 format(option.K, option.tau, option.r*100, option.sigma*100), fontsize=12) \n",
    "\n",
    "    # add the legend\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "call_focus = CallOption(S_t=3.0, K=5.0, t=\"19-04-2020\", T=\"19-04-2022\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(call_focus, S_strip_focus, call_focus.tau)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3. Encapsulation: name hiding, getters and setters <a name=\"encapsulation\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In plotters `S` and `tau` attributes have been intentionally changed. But this might be something which is error prone. You may unintentionally change an attribute value and then not being able to understand the behavior of your class anymore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution to this issue is based on the concept of _encapsulation._ The idea is simple: \n",
    "\n",
    "- Hide the attributes to the outside of the class (that is, do not make them accessible). This is done prepending an attribute name with `__`. `__AttributeName` attribute is called _private_ attribute. For example:\n",
    "\n",
    "  - change underlying attribute from `self.S` to `self.__S`;\n",
    "  \n",
    "  - From outside of the class you cannot access `S` anymore, neither as `call.S`, nor as `call.__S`;\n",
    "  \n",
    "  - this way there is no risk to unintentionally change its vlaue.\n",
    "\n",
    "\n",
    "- Provide, if you want, _getters_ methods to retrieve the value of an attribute from outside the class. For example: \n",
    "  \n",
    "  - you make attribute `S` accessible from outise through getter method `get_S()` which returns the current value of the attribute.\n",
    "\n",
    "\n",
    "- Provide, if you want, _setters_ methods to set an attribute from outside the class. For example:\n",
    " \n",
    " - you allow to set attribute `S` to a new value, through the setter method `set_S(new_value)` which sets `S` to `new_value`.\n",
    "  \n",
    "\n",
    "The naming convention \n",
    "\n",
    "- `get_AttributeName()`\n",
    "\n",
    "- `set_AttributeName(new_value)`\n",
    "\n",
    "is standard and should be followed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CallOption:\n",
    "    \"\"\"\n",
    "    CallOption class implementing payoff and pricing of a european call option.\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------            \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a vanilla call option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a vanilla call option price.\n",
    "            \n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, S_t, K, t, T, r, sigma):\n",
    "        self.__S     = S_t\n",
    "        self.__K     = K\n",
    "        self.__t     = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        self.__T     = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        self.__tau   = self.__time_to_maturity()\n",
    "        self.__r     = r\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return r\"CallOption(S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.get_S(), self.get_K(), self.get_t().strftime(\"%d-%m-%Y\"), \n",
    "                       self.get_T().strftime(\"%d-%m-%Y\"), self.get_tau(), self.get_r()*100, self.get_sigma()*100)\n",
    "    \n",
    "    # getters\n",
    "    def get_S(self):\n",
    "        return self.__S\n",
    "    \n",
    "    def get_K(self):\n",
    "        return self.__K\n",
    "    \n",
    "    def get_t(self):\n",
    "        return self.__t\n",
    "\n",
    "    def get_T(self):\n",
    "        return self.__T\n",
    "\n",
    "    def get_tau(self):\n",
    "        return self.__tau\n",
    "    \n",
    "    def get_r(self):\n",
    "        return self.__r\n",
    "\n",
    "    def get_sigma(self):\n",
    "        return self.__sigma\n",
    "    \n",
    "    # setters\n",
    "    def set_S(self, S):\n",
    "        self.__S = S\n",
    "        \n",
    "    def set_K(self, K):\n",
    "        self.__K = K\n",
    "    \n",
    "    def set_t(self, t):\n",
    "        self.__t = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        # update time to maturity, given changed t, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "\n",
    "    def set_T(self, T):\n",
    "        self.__T = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        # update time to maturity, given changed T, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "    \n",
    "    def set_tau(self, tau):\n",
    "        self.__tau = tau\n",
    "        # update expiration date, given changed tau, to keep internal consistency\n",
    "        # we could have updated valuation date as well, but this is a stylistic choice\n",
    "        self.__update_T()\n",
    "\n",
    "    def set_r(self, r):\n",
    "        self.__r = r\n",
    "\n",
    "    def set_sigma(self, sigma):\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "    # update methods (private)\n",
    "    def __update_tau(self):\n",
    "        self.__tau = self.__time_to_maturity()\n",
    "\n",
    "    def __update_T(self):\n",
    "        self.__T = self.__t + dt.timedelta(days=math.ceil(self.__tau*365))\n",
    "\n",
    "    # time to maturity method (private)\n",
    "    def __time_to_maturity(self):\n",
    "        return (self.__T - self.__t).days / 365.0\n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        return max(0.0, self.get_S() - self.get_K())\n",
    "\n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        return self.get_S()\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        return max(self.get_S() - self.get_K()*np.exp(-self.get_r() * self.get_tau()), 0)\n",
    "    \n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        \n",
    "        # some local variables retrieved to be used repeatedly\n",
    "        S = self.get_S()\n",
    "        tau = self.get_tau()\n",
    "        \n",
    "        if S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()\n",
    "        else:\n",
    "            K     = self.get_K()\n",
    "            r     = self.get_r()\n",
    "            sigma = self.get_sigma()\n",
    "\n",
    "            d1 = (np.log(S/K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "            d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "            price = S * stats.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that we can use getters also from inside the class: this is a good idea, since we are sure that if there is an error with the value of `S` attribute somewhere in the class, then we know we should be looking directly at `get_S()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "call = CallOption(S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CallOption(S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.CallOption"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(call, CallOption)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that having defined `self.__S` for underlying asset value attribute, hides it from exterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raises: AttributeError: 'CallOption' object has no attribute '__S'\n",
    "#\n",
    "#call.__S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can instead use the corresponding getter to retrieve the value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_S()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and the corresponding setter to set it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "call.set_S(105)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how now the value is changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_S()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly for other attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 4, 19, 0, 0)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_t()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 12, 31, 0, 0)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_T()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7013698630136986"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_tau()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that, for internal consistency:\n",
    "\n",
    "- when we change the valuation date $t$ (`set_t()`) or the expiration date $T$ (`set_T()`) of the option, then we automatically update the time-to-maturity $\\tau=T-t$ with the `__update_tau()` method;\n",
    "\n",
    "- when we update the time-to-maturity $\\tau$ of the options, then we automatically chose to extend or anticipate the expiration date $T$ with the `__update_T()`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same way we define private attributes as `__AttributeName`, we can define _private_ methods as `__MethodName()`. Private methods cannot be called from outside the class. Examples: \n",
    "- `__update_tau()`,\n",
    "\n",
    "- `__update_T()`,\n",
    "\n",
    "- `__time_to_maturity()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "call.set_t(\"01-06-2020\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 6, 1, 0, 0)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_t()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raises: AttributeError: 'CallOption' object has no attribute '__update_tau'\n",
    "#\n",
    "# call.__update_tau()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5835616438356165"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_tau() # tau gets updated automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5835616438356165"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(call.get_T() - call.get_t()).days / 365 # consistency check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "call.set_tau(1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_tau()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raises: AttributeError: 'CallOption' object has no attribute '__update_T'\n",
    "#\n",
    "# call.__update_T()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 6, 1, 0, 0)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.get_T() # expiration date gets updated automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(call.get_T() - call.get_t()).days / 365 # consistency check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "call = CallOption(S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CallOption(S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use getters and setters in our plotting example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multi_tau(option, S_list, tau_list):\n",
    "    \"\"\"\n",
    "    plot_multi_tau(option, S_list, tau_list) plot option prices for underlying and maturities in 'S_list' and 'tau_list',\n",
    "    respectively.\n",
    "\n",
    "    Parameters:\n",
    "        option (CallOption): instance of CallOption class;\n",
    "        S_list (List):       list of underlying values;\n",
    "        tau_list (List):     list of times to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # color cycle setup: basically a cycle of different shades of blue as many time to maturity there are\n",
    "    plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.Blues(np.linspace(0,1,len(tau_list)+1)))\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # auxiliary variables\n",
    "    numS = len(S_list)\n",
    "    numTau = len(tau_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(K, 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the price for different underlying values, one line for each different time to maturity tau\n",
    "    for i in np.arange(numTau)[::-1]: # loop over reversed range\n",
    "        \n",
    "        option.set_tau(tau_list[i]) # reset the time to maturity\n",
    "        price = np.zeros(numS)\n",
    "        \n",
    "        for j in np.arange(numS):\n",
    "            option.set_S(S_list[j]) # reset of underlying value\n",
    "            price[j] = option.price()\n",
    "        \n",
    "        ax.plot(S_list, price, '-', lw=1.5, label=r\"$\\tau={}$\".format(option.get_tau()))\n",
    "    \n",
    "    # plot the red payoff line for different underlying values\n",
    "    payoff = np.zeros(numS)\n",
    "    for i in np.arange(numS):\n",
    "        option.set_S(S_list[i]) # reset of underlying value\n",
    "        payoff[i] = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, payoff, 'r-', lw=1.5, label=\"$max(S-K, 0)$\")\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a Call Option $(S_t, K={}, \\tau=T-t, r={}\\%, \\sigma={}\\%)$ Vs $S$ (at different $\\tau$)\".\n",
    "                 format(option.get_K(), option.get_r()*100, option.get_sigma()*100), fontsize=12) \n",
    "\n",
    "    # add the legend\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(call, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_tau(option, S_list, tau):\n",
    "    \"\"\"\n",
    "    plot_single_tau(option, S_list, tau_list) plot option prices for underlying in 'S_list' and time to maturity 'tau'.\n",
    "\n",
    "    Parameters:\n",
    "        option (CallOption): instance of CallOption class;\n",
    "        S_list (List):       list of underlying values;\n",
    "        tau (float):         time to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # setting legend labels\n",
    "    upper_limit_label = r\"Upper limit: $S_t$\"\n",
    "    payoff_label      =r\"$max(S - K, 0)$\"\n",
    "    lower_limit_label = r\"Lower limit: $max(S_t - K e^{-r \\tau}, 0)$\"\n",
    "        \n",
    "    # auxiliary variable\n",
    "    numS = len(S_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(option.get_K(), 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the upper limit, the price and the lower limit for different underlying values\n",
    "    upper_limit = np.zeros(numS)\n",
    "    price       = np.zeros(numS)\n",
    "    lower_limit = np.zeros(numS)\n",
    "    payoff      = np.zeros(numS)\n",
    "\n",
    "    for i in np.arange(numS):\n",
    "        option.set_S(S_list[i]) # reset of underlying value\n",
    "        \n",
    "        upper_limit[i] = option.price_upper_limit()\n",
    "        price[i]       = option.price()\n",
    "        lower_limit[i] = option.price_lower_limit()\n",
    "        payoff[i]      = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, upper_limit, 'k-.', lw=1.5, label=upper_limit_label)\n",
    "    ax.plot(S_list, price,       'b-',  lw=1.5, label=r\"Price\")\n",
    "    ax.plot(S_list, lower_limit, 'k--', lw=1.5, label=lower_limit_label)\n",
    "    ax.plot(S_list, payoff,      'r-',  lw=1.5, label=payoff_label)\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a Call Option $(S_t, K={}, \\tau=T-t={:.1f}y, r={}\\%, \\sigma={}\\%)$ Vs $S$ (with price limits)\".\n",
    "                 format(option.get_K(), option.get_tau(), option.get_r()*100, option.get_sigma()*100), fontsize=12) \n",
    "\n",
    "    # add the legend\n",
    "    ax.legend(loc='upper left', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "call_focus = CallOption(S_t=3.0, K=5.0, t=\"19-04-2020\", T=\"19-04-2022\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(call_focus, S_strip_focus, call_focus.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Including put options: the `PlainVanillaOption` class <a name=\"PlainVanillaOption\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A put option of maturity $T$ and strike $K$ on an asset $S$ is a contract giving to the owner the right to sell for $K$ the asset at the future date $T$ (when the asset will be worth $S_T$). Their payoff at maturity is then\n",
    "\n",
    "$$\n",
    "\\max(K - S_T, 0)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could develop for put options an independent `PutOption` class. In fact, if we were not to use OOP, we would be obliged to develop put option payoff, pricing and price boundary functions independently. But this would be a waste of time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the end, put options are just a different kind of plain vanilla options. The only difference w.r.t. to call options is the payoff they provide to the owner. Underlying, model and market data could (and should) be shared by both call and put options. \n",
    "\n",
    "Moreover, from the financial point of view, call $c_t$ and put $p_t$ prices are related by the so-called _put-call parity_\n",
    "\n",
    "$$\n",
    "c_t + Ke^{-r\\tau} = p_t + S_t\n",
    "$$\n",
    "\n",
    "which we can use to compute the put price $p_t$, once we know the call's price $c_t$ and the other contract variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then can model a unique `PlainVanillaOption` class and distinguish between calls and puts through a `type` discriminant String."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PlainVanillaOption:\n",
    "    \"\"\"\n",
    "    PlainVanillaOption class implementing payoff and pricing of plain-vanilla call and put options.\n",
    "    Put price is calculated using put-call parity\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        type (str):               type of the options. Can be either 'call' or 'put';\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------   \n",
    "    \n",
    "        getters and setters for all attributes\n",
    "        \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a vanilla option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a vanilla option price.\n",
    "            \n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it, using call_price() or put_price()\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, option_type, S_t, K, t, T, r, sigma):\n",
    "\n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "        \n",
    "        self.__type  = option_type\n",
    "        self.__S     = S_t\n",
    "        self.__K     = K\n",
    "        self.__t     = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        self.__T     = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        self.__tau   = self.__time_to_maturity()\n",
    "        self.__r     = r\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "        # informations dictionary\n",
    "        self.__docstring_dict = {\n",
    "            'call':{\n",
    "                'price_upper_limit': r\"Upper limit: $S_t$\",\n",
    "                'payoff':            r\"Payoff: $max(S-K, 0)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $max(S_t - K e^{-r \\tau}, 0)$\"\n",
    "            },\n",
    "            'put': {\n",
    "                'price_upper_limit': r\"Upper limit: $K e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $max(K-S, 0)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $max(K e^{-r \\tau} - S_t, 0)$\"}\n",
    "        }\n",
    "                \n",
    "    def __repr__(self):\n",
    "        return r\"PlainVanillaOption('{}', S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.get_type(), self.get_S(), self.get_K(), self.get_t().strftime(\"%d-%m-%Y\"), \n",
    "                       self.get_T().strftime(\"%d-%m-%Y\"), self.get_tau(), self.get_r()*100, self.get_sigma()*100)\n",
    "    \n",
    "    # getters\n",
    "    def get_type(self):\n",
    "        return self.__type\n",
    "    \n",
    "    def get_S(self):\n",
    "        return self.__S\n",
    "    \n",
    "    def get_K(self):\n",
    "        return self.__K\n",
    "    \n",
    "    def get_t(self):\n",
    "        return self.__t\n",
    "\n",
    "    def get_T(self):\n",
    "        return self.__T\n",
    "\n",
    "    def get_tau(self):\n",
    "        return self.__tau\n",
    "    \n",
    "    def get_r(self):\n",
    "        return self.__r\n",
    "\n",
    "    def get_sigma(self):\n",
    "        return self.__sigma\n",
    "    \n",
    "    def get_docstring(self, label):\n",
    "        return self.__docstring_dict[self.get_type()][label]    \n",
    "    \n",
    "    # setters\n",
    "    def set_type(self, option_type):\n",
    "        self.__type = option_type\n",
    "        \n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "        \n",
    "    def set_S(self, S):\n",
    "        self.__S = S\n",
    "        \n",
    "    def set_K(self, K):\n",
    "        self.__K = K\n",
    "    \n",
    "    def set_t(self, t):\n",
    "        self.__t = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        # update time to maturity, given changed t, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "\n",
    "    def set_T(self, T):\n",
    "        self.__T = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        # update time to maturity, given changed T, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "    \n",
    "    def set_tau(self, tau):\n",
    "        self.__tau = tau\n",
    "        # update expiration date, given changed tau, to keep internal consistency\n",
    "        # we could have updated valuation date as well, but this is a stylistic choice\n",
    "        self.__update_T()\n",
    "\n",
    "    def set_r(self, r):\n",
    "        self.__r = r\n",
    "\n",
    "    def set_sigma(self, sigma):\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "    # update methods (private)\n",
    "    def __update_tau(self):\n",
    "        self.__tau = self.__time_to_maturity()\n",
    "\n",
    "    def __update_T(self):\n",
    "        self.__T = self.__t + dt.timedelta(days=math.ceil(self.__tau*365))\n",
    "\n",
    "    # time to maturity calculation\n",
    "    def __time_to_maturity(self):\n",
    "        return (self.__T - self.__t).days / 365.0\n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return max(0.0, self.get_S() - self.get_K())\n",
    "        # put case\n",
    "        else:\n",
    "            return max(0.0, self.get_K() - self.get_S())\n",
    "\n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_S()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_K()*np.exp(-self.get_r() * self.get_tau())\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return max(self.get_S() - self.get_K()*np.exp(-self.get_r() * self.get_tau()), 0)\n",
    "        # put case\n",
    "        else:\n",
    "            return max(self.get_K()*np.exp(-self.get_r() * self.get_tau()) - self.get_S(), 0)\n",
    "            \n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.__call_price()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.__put_price()\n",
    "          \n",
    "    def __call_price(self):\n",
    "        \n",
    "        # some local variables retrieved to be used repeatedly\n",
    "        S = self.get_S()\n",
    "        tau = self.get_tau()\n",
    "        \n",
    "        if S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()\n",
    "        else:\n",
    "            K     = self.get_K()\n",
    "            r     = self.get_r()\n",
    "            sigma = self.get_sigma()\n",
    "\n",
    "            d1 = (np.log(S/K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "            d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "            price = S * stats.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price\n",
    "    \n",
    "    def __put_price(self):\n",
    "        \"\"\" Put price from Put-Call parity relation: Call + Ke^{-r*tau} = Put + S\"\"\"\n",
    "        return self.__call_price() + self.get_K() * np.exp(- self.get_r() * self.get_tau()) - self.get_S()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we define the so-called public interface of the class making use of  public methods \n",
    "\n",
    "- `payoff()`,\n",
    "\n",
    "- `price_upper_limit()`,\n",
    "\n",
    "- `price_lower_limit()`,\n",
    "\n",
    "- `price()`\n",
    "\n",
    "\n",
    "which then call private methods like `__call_price()` and `__put_price()` (which uses the put-call parity) depending on the value of `type` String (through `get_type()` getter)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a call object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('call', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call = PlainVanillaOption(option_type='call', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.PlainVanillaOption"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(call, PlainVanillaOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.487402470943657"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call.price()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and here is a put option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('put', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put = PlainVanillaOption(option_type='put', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.PlainVanillaOption"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(call, PlainVanillaOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.041330587465126"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put.payoff()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we accept only `'call'` or `'put'` values for the `type` discriminant String"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Raises: NotImplementedError: Option Type: 'other' does not exist!\n",
    "#\n",
    "# PlainVanillaOption(option_type='other', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multi_tau(option, S_list, tau_list):\n",
    "    \"\"\"\n",
    "    plot_multi_tau(option, S_list, tau_list) plot option prices for underlying and maturities in 'S_list' and 'tau_list',\n",
    "    respectively.\n",
    "\n",
    "    Parameters:\n",
    "        option (PlainVanillaOption): instance of PlainVanillaOption class;\n",
    "        S_list (List):               list of underlying values;\n",
    "        tau_list (List):             list of times to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # color cycle setup: basically a cycle of different shades of blue as many time to maturity there are\n",
    "    plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.Blues(np.linspace(0,1,len(tau_list)+1)))\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "        \n",
    "    # auxiliary variables\n",
    "    numS = len(S_list)\n",
    "    numTau = len(tau_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(option.get_K(), 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the price for different underlying values, one line for each different time to maturity tau\n",
    "    for i in np.arange(numTau)[::-1]: # loop over reversed range\n",
    "        \n",
    "        option.set_tau(tau_list[i]) # reset of time to maturity\n",
    "        price = np.zeros(numS)\n",
    "        \n",
    "        for j in np.arange(numS):\n",
    "            option.set_S(S_list[j]) # reset of underlying value\n",
    "            price[j] = option.price()\n",
    "        \n",
    "        ax.plot(S_list, price, '-', lw=1.5, label=r\"$\\tau={}$\".format(option.get_tau()))\n",
    "    \n",
    "    # plot the red payoff line for different underlying values\n",
    "    payoff = np.zeros(numS)\n",
    "    for i in np.arange(numS):\n",
    "        option.set_S(S_list[i]) # reset of underlying value\n",
    "        payoff[i] = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, payoff, 'r-', lw=1.5, label=option.get_docstring('payoff'))\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a {} Option $(S_t, K={}, \\tau=T-t, r={}\\%, \\sigma={}\\%)$ Vs $S$ (at different $\\tau$)\".\n",
    "                 format(option.get_type(), option.get_K(), option.get_r()*100, option.get_sigma()*100), fontsize=12) \n",
    "\n",
    "    # add the legend ('best' loc parameters places the legend in the best position automatically)\n",
    "    ax.legend(loc='best', ncol=1)\n",
    "        \n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('call', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "call = PlainVanillaOption(option_type='call', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(call, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('put', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "put = PlainVanillaOption(option_type='put', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(put, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 _Focus on:_ effect of interest rates on options limits <a name=\"shortRateEffect\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are limits for call option prices, independently from the valuation model used to price the option, there are non-arbitrage arguments leading to upper and lower limits for the european put options as well. At any time $t \\leq T$, the price $p_t$ of the put option satisfies:\n",
    "\n",
    "$$\n",
    "\\max(K e^{-r \\tau} - S_t, 0) \\leq p_t \\leq K e^{-r \\tau}\n",
    "$$\n",
    "\n",
    "See, for example, [John C. Hull _Options, Futures and Other Derivatives_ (10th ed)](https://www.pearson.com/us/higher-education/program/Hull-Options-Futures-and-Other-Derivatives-10th-Edition/PGM333301.html) chapter 11.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_tau(option, S_list, tau):\n",
    "    \"\"\"\n",
    "    plot_single_tau(option, S_list, tau) plot option prices for underlying in 'S_list' and time to maturity 'tau'.\n",
    "\n",
    "    Parameters:\n",
    "        option (PlainVanillaOption): instance of PlainVanillaOption class;\n",
    "        S_list (List):               list of underlying values;\n",
    "        tau (float):                 time to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "    \n",
    "    # auxiliary variable\n",
    "    numS = len(S_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(option.get_K(), 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the upper limit, the price and the lower limit for different underlying values\n",
    "    upper_limit = np.zeros(numS)\n",
    "    price       = np.zeros(numS)\n",
    "    lower_limit = np.zeros(numS)\n",
    "    payoff      = np.zeros(numS)\n",
    "\n",
    "    for i in np.arange(numS):\n",
    "        option.set_S(S_list[i]) # reset of underlying value\n",
    "        \n",
    "        upper_limit[i] = option.price_upper_limit()\n",
    "        price[i]       = option.price()\n",
    "        lower_limit[i] = option.price_lower_limit()\n",
    "        payoff[i]      = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, upper_limit, 'k-.', lw=1.5, label=option.get_docstring('price_upper_limit'))\n",
    "    ax.plot(S_list, price,       'b-',  lw=1.5, label=r\"Price\")\n",
    "    ax.plot(S_list, lower_limit, 'k--', lw=1.5, label=option.get_docstring('price_lower_limit'))\n",
    "    ax.plot(S_list, payoff,      'r-',  lw=1.5, label=option.get_docstring('payoff'))\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a {} Option $(S_t, K={}, \\tau=T-t={:.1f}y, r={}\\%, \\sigma={}\\%)$ Vs $S$ (with price limits)\".\n",
    "                 format(option.get_type(), option.get_K(), option.get_tau(), option.get_r()*100, option.get_sigma()*100), \n",
    "                 fontsize=12) \n",
    "\n",
    "    # add the legend ('best' loc parameters places the legend in the best position automatically)\n",
    "    ax.legend(loc='best', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "call_focus = PlainVanillaOption(option_type='call', S_t=3.0, K=5.0, t=\"19-04-2020\", T=\"19-04-2022\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(call_focus, S_strip_focus, call_focus.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a focus on the price limits of put options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_focus = PlainVanillaOption(option_type='put', S_t=3.0, K=5.0, t=\"19-04-2020\", T=\"19-04-2022\", r=0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+peWQY6Bzbj1BF5MFocfJBNvpk9D3WmrS/V7IhPS+m1KVU9/DnqVcP+ltz4j2lcLA0vmVUSTHmdkGghEx5imPf3IZBGxyzo3KYL6LgGoH57NgXOeVQO3sfrnnhvyef34QWsdfEwx9uDiV5wrs+i/o7y8zzGwJcL0LTu7LcyI9BnrOYQN55DshN+X1fSWaCvqA1yJ5nnMutZaY1KwHHjWzai44CWYPwc+5+VJ+zz+f6E/Q8rY45RMFff0X9PeXGc65hrmdQ3oycQwUz/L6vhJNKpBF8olQd5aM+neLYGb1gA8JToDqmMvpiIjkO+piISIiIiISRifpiYiIiIiEybNdLMqVK+eqVasW9bg7duygRInsnugveZm2ceGg7VzwaRsXDtrOBV9ubuNly5b94Zw7MuX0PFsgV6tWjaSkpKjHTUxMpHHjxlGPK9GjbVw4aDsXfNrGhYO2c8GXm9vYzL5Pbbq6WIiIiIiIhFGBLCIiIiISRgWyiIiIiEgYFcgiIiIiImFUIIuIiIiIhFGBLCIiIiISRgWyiIiIiEgYFcgiIiIiImFUIIuIiIiIhFGBLCIiIiISRgWyiIiIiEiYuGgFMrMNwDZgP7DPOVc/WrFFRERERCIVtQI5pIlz7o8oxxQRERERiZi6WIRbtYriv/yS21mIiIiISC6KZguyA+aYmQOedc6NjWLsjO3bBxdfTJ2//4bzz4dKlXI7IxERERHJBeaci04gs0rOuZ/NrDwwF7jVOfdRinl6AD0AKlSocPqECROikttBJb/4gjq9e7O7fHk+e/JJ9pYqFdX4Eh3bt28nPj4+t9MQz7SdCz5t48JB27ngy81t3KRJk2WpnRcXtQL5kKBmA4Dtzrnhac1Tv359l5SUFL2kQj4bNYq6994LtWrBBx9AQkLUcxC/EhMTady4cW6nIZ5pOxd82saFg7ZzwZeb29jMUi2Qo9IH2cxKmFnJg/eBlsCaaMTOrC1168Lbb8PKldChA+zcmdspiYiIiEgUReskvQrAx2a2ElgKTHfOzYpS7Mxr3x5efx0WLIBLLoE9e3I7IxERERGJkqicpOec+xaoE41YOaZzZ0hOhh494MorYfx4iI3N7axERERExLNoj4Ocv3TvDtu2Qe/eULIkPPccxGhkPBEREZGCTAVyRu66C7ZuhYEDgxP2Ro4Es9zOSkREREQ8UYEciQEDgiJ51CgoVSp4LCIiIiIFkgrkSJgFLcfbtsHDDwctyXfdldtZiYiIiIgHKpAjFRMDY8f+v09yQgLccENuZyUiIiIiOUwFcmbExgbDv23fHoxuUbJkMNqFiIiIiBQYGpIhs4oWDS4kcu65wfBv06fndkYiIiIikoNUIGfF4YfD1KlQty506gSJibmdkYiIiIjkEBXIWZWQALNmwXHHBZekXro0tzMSERERkRygAjk7ypaFuXOhfHlo3RrWrMntjEREREQkm1QgZ1elSjBvHhx2GLRoAV9/ndsZiYiIiEg2qEDOCdWrBy3Je/dC8+awcWNuZyQiIiIiWaQCOaecfDLMng1//RW0JG/alNsZiYiIiEgWqEDOSaefHgz79sMP0KoVbNmS2xmJiIiISCapQM5p55wDkybB2rXQrh3s2JHbGYmIiIhIJqhA9qFVKxg/HhYvho4dYffu3M5IRERERCKkAtmXTp3ghReCk/cuvxz27cvtjEREREQkAiqQferWDZ58Muhycd11cOBAbmckIiIiIhmIy+0ECrzbboPkZHjwweDqe08/DWa5nZWIiIiIpEEFcjTcfz9s3QrDhwdF8qBBuZ2RiIiIiKRBBXI0mMHQoUFL8uDBUKoU9O2b21mJiIiISCpUIEeLGfz3v7BtG9x7b9CSfNNNuZ2ViIiIiKSgAjmaYmPhlVdg+3bo1QtKloQrr8ztrEREREQkjEaxiLYiRWDiRGjSJBjlYvLk3M5IRERERMKoBTnMiBEwcuRETjhhQLrztW/fnj59+gDQuHFjunXrRrdu3fjjjz+45JJLMozTrVs3uk2Zwt7GjXEXX8yyhx+m0YMPsm7dOnr27Jnh63v37k2HDh3+mX/QoEGcddZZLFy4kH79+mX4+pTzP/vss9SoUYOpU6cyYsSIDF+fcv63336bcuXK8fLLL/Pyyy9n+PqU8ycmJgIwfPhwpk2bluHrw+dftGgR77zzDgD33XcfixYtSve1ZcuW5dZbb/1n/s2bNzN27FgAevTowfr169N9/YknnnjI/GXLlmXw4MEAdOrUic2bN6f7+kaNGh0yf6NGjQ7ZlzKSI/te2Pwp96WM5Kd9b8uWLZQuXfqQabm974XPr30v+/sekCf3vdRo38v6vnfSSSf9M19e2ffy6nEvNflh3xswYECGeUSbWpBD9u2Dl156l59/fp7vvjvSf8D4eJLHj2fj4YfT4LHHIIOdTERERESiw5xzuZ1DqurXr++SkpKiGvPnn7dx3HGN2bVrLcOGzaBPn6b+g/76K5x3HmzaBImJULeu/5iFXGJiYkStFpK/aTsXfNrGhYO2c8GXm9vYzJY55+qnnK4W5DCVKpVk7NiBFCt2AnfffQHPP7/Yf9CjjoJ584JRLVq2hHXr/McUERERkTSpQE6hSpUSLF48h7i4o+jRow0ffrjJf9BjjgmKZDNo3hy+/95/TBERERFJlQrkVNStW5EPP5xHqVJDuPzy8mRw/kLOOPFEmDMnGAKuefOg64WIiIiIRJ0K5DScc041Fi3qyYEDcN55y1i06Af/QevUgZkz4ZdfoEUL+PNP/zFFRERE5BAqkNNRsyZMm7aHTZs6cv75zVmz5jf/Qc88E6ZMgfXroU2b4Mp7IiIiIhI1KpAzcMYZRRkzZgJ79/5EgwYt+e67v/wHbdYsuJjIsmVwwQXw99/+Y4qIiIgIoAI5Ij17nsXgwZPZtetLatduy6+/bvcf9MILg8tSz58Pl14Ke/f6jykiIiIiKpAjde+9Lbjnngls3/4pZ501gl27ohC0a1cYMwamT4err4b9+6MQVERERKRw06WmM+HxxzsSF/cBgwY1onNnePttKFLEc9CePWHrVujbF+LjYezYYDg4EREREfFCBXImPfbYeRx9NPTq9Rv16o1mxYoBxMV5boi/556gSB40CEqVgmHDVCSLiIiIeKIuFllw881w0UWTWbPmEerU6cWBA1G4XPejj8Itt8CIEcF9EREREfFCLchZNGlSTxo12sDixUNo1CiBRYuGEBPjsVXXDJ58Mhj27aGHgktT3367v3giIiIihZQK5Gz45JNB1KmTzNKlQ2nduhRz5vTzGzAmBp5/PiiS77gDSpaE667zG1NERESkkFEXi2yIiTFWrHia6tWvZO7c1xgxYof/oHFx8MYb0LIldO8Ob73lP6aIiIhIIaICOZvi4mL4/POXaNPmE/r0KcErr0QhaLFi8O670KhRMBTcrFlRCCoiIiJSOKhAzgHFi8fx7rtH0KTJHrp168o997zrP2iJEsH4yLVqwcUXw4IF/mOKiIiIFAIqkHNI8eIwfvxe4uO/Y9iwLgwePMd/0FKlYPZsqFoV2rULLk0tIiIiItmiAjkHVahQgtWrp1O8+Mn063cR//3vx/6DHnkkzJ0LRxwBrVrB55/7jykiIiJSgKlAzmHVqpUhKWkORYpUoVevdrzxxgr/QStXhvffh6JFoUUL+O47/zFFRERECigVyB6cckp5FiyYR5Ei1bj55p188UUUgh53XNCSvGsXNGsGP/0UhaAiIiIiBY8KZE8aNqzC6tUrKF78bFq0gLVrozAE3CmnBCNa/PFH0JL8xx/+Y4qIiIgUMCqQPapRI4Y5c+DPP5+ibt06LF/+s/+gDRrA1KlBN4vWrWHrVv8xRURERAoQFcie1a4NTz/dkH37fuWss1qyfv1m/0HPPx/eeQdWroT27WHnTv8xRURERAoIFchRcP31DRkxYiq7d3/Naae15scfk/0HbdsWxo2DhQuhUyfYs8d/TBEREZECQAVylNx1VxMeeOAtdu78jFq1LmDbtv3+g152GYwdG/RLvuIK2LfPf0wRERGRfE4FchQ98kgHbrnlNbZu7ULnzrHRadS9/noYOTLoctG9Oxw4EIWgIiIiIvlXXG4nUNg8/XQXTj0VevaEDh3WMnVqTYoWjfUb9M47ITkZBgyAhAQYNQrM/MYUERERyadUIOeCHj3g+++/Z9CgMzj11Mv5/PPniI31XLA+9FAwosUTTwSXqB440G88ERERkXxKXSxyyWOPVeXcc3uzfv0LNGjQmwMHnN+AZjBiRNDl4pFHYPhwv/FERERE8qmotiCbWSyQBPzknGsfzdh5UWLiw9Srt5UVK56gadNSJCb29xvQDJ59FrZtg7vvDrpb9OjhN6aIiIhIPhPtFuTbgWhceDlfiIkxkpKe4Pjjr2X+/AHccMM0/0FjY+G114Jh4G68EcaP9x9TREREJB+JWoFsZpWBdsDz0YqZH8TFxbBmzXPUq/csL7zQhuejsXaKFoW334bzzoOrrw6uvCciIiIiAJhznvu+Hgxk9jYwGCgJ9Emti4WZ9QB6AFSoUOH0CRMmRCW3cNu3byc+Pj7qcffuNR54oBZLl/5N585TuPHGU7zHjN25kzq9exP/zTesGjKELfXqeY+ZF+TWNpbo0nYu+LSNCwdt54IvN7dxkyZNljnn6qecHpUC2czaA22dczebWWPSKJDD1a9f3yUlJXnPLaXExEQaN24c9bgQXBG6evWr2LRpAv37T2bAgHb+g27eDI0bw3ffwfvvQ8OG/mPmstzcxhI92s4Fn7Zx4aDtXPDl5jY2s1QL5Gh1sTgbuMDMNgATgKZm9nqUYucbhx8OSUmjOfzwOjz88CWMGjXff9CyZWHOHDjqKGjTBlat8h9TREREJA+LSoHsnLvPOVfZOVcN6AJ84Jy7Mhqx85sqVRJYvnwWxYody513tuell5b6D1qxIsybF1ToLVvCV1/5jykiIiKSR2kc5DyoRo1yLFw4l7i48vTocS+rV0ehn3i1akGRvH8/NG8OP/zgP6aIiIhIHhT1Atk5l6gxkDNWr14l3n//fcqVe5uWLY2vv45C0Jo1g+4WW7dCixbw229RCCoiIiKSt6gFOQ8777xqvP/+EezZs4vTTruZTz/90X/Q006D6dPhxx+hVSv46y//MUVERETyEBXIedzJJ8Mzz3zL9u3jOOecFqxdu8l/0LPPhkmT4IsvgguKbN/uP6aIiIhIHqECOR+49NKTefrpaezZ8z0NGrTi+++3+A/asmVwlb2lS+Gii2DXLv8xRURERPIAFcj5xC23nMsjj7zL33+v5dRT27Fp0w7/QS++GF58MRgfuUsX2LvXf0wRERGRXKYCOR954IHW9Okznm3bvuaii75j9+4oBL3mGnj6aZgyBa69Fg4ciEJQERERkdyjAjmfGTasE//979csWlQr1KgbhSHgbrkFHnsMxo2DXr0gSpcnFxEREckNcbmdgGTeTTeVZO9euP32gdSs+Q3r1r1EXJzn/3Xuuy8Y/m3oUEhIgCFDwMxvTBEREZFcoBbkfOq226BZsxi+/fZV6tW7jQMHPLfqmgVF8Y03BkXy4MF+44mIiIjkErUg52Nz5txPw4ZbSUoazrnnluKTTx7zG9AMRo+Gbdvg/vuDluRbbvEbU0RERCTKVCDnYzExxpIlQznllGQWLhxEmzYJzJzZ13dQeOmlYGzkW28NiuSrr/YbU0RERCSK1MUin4uJMVau/C/Vql3FrFlHMWZMFIIWKQITJkCzZsHIFpMmRSGoiIiISHSoQC4AihaNZd26V+jQ4Rp69YIxY/7wH7R4cZg8GRo2DMZInjvXf0wRERGRKFCBXEAULWpMnAh16y7g5pur0a/fFP9B4+Nh+nQ46aTganuffOI/poiIiIhnKpALkOLFYdq0upQoUYvBgy9j6NB5/oOWKQOzZ0PlytC2LaxY4T+miIiIiEcqkAuYSpVKsnLlDIoXr0HfvhcyduxC/0ErVIB586B0aWjZEr780n9MEREREU9UIBdAxx13BEuWzKFIkUr07NmW6dO/9R+0SpWgSI6NhebNYcMG/zFFREREPFCBXEDVrn0UH344j4SE27n22mqsWxeFoCecAHPmwM6dQZH8yy9RCJgh4uEAACAASURBVCoiIiKSs1QgF2Bnn12VpUsfBmJo3HgDn3zyvf+gtWvDzJnw66/QogVs3uw/poiIiEgOUoFcwNWoAbNmHWDTpg40adKcVat+9R+0YUOYOhW+/hratIHkZP8xRURERHKICuRCoF69GMaMGcvevb/QsGFLvvnmT/9BmzSBt94KRrXo0AH+/tt/TBEREZEcoAK5kOjRoxGPPz6ZXbvWUadOG37+eZv/oB06wKuvwoIFcMklsGeP/5giIiIi2aQCuRC5557m3HffRHbsWMYZZ9zPrl1RCHr55fDsszBjBlx1FezfH4WgIiIiIlkXl9sJSHQNGnQh+/ZNY9iws7jsMnjnHShSxHPQ7t1h61a4+24oWRKeew7MPAcVERERyRoVyIXQ0KGtOfZYuOmmnZxxxmiWLLmLokVj/Qbt0ycokh99FBISYMQIFckiIiKSJ6lALqRuvBESE9/lzTfvoU6dr1m79hliYjwXrAMHBiNaPPEElCoF/fv7jSciIiKSBSqQC7EJE65k48YvWLhwEA0bJrBkyVC/RbJZUBwnJ8OAAUFL8p13+osnIiIikgUqkAu5BQsepW7dZJKShtOyZSnmzXvAb8CYmKAP8rZtcNddQZF8/fV+Y4qIiIhkgkaxKORiYozly5/k2GOv5v33n2bw4Chc+S4uDsaNg1atghP4Jk70H1NEREQkQiqQhbi4GNaufYHWrZfSr19ZXn45CkGLFYN334VzzoGuXYNh4ERERETyABXIAkDx4nFMnlyVFi0c1157H717v+0/6OGHB5ekrl0bOnWC+fP9xxQRERHJgApk+UexYjB+/G5KllzAyJFX8Oijs/wHLVUKZs+G6tWhfXv49FP/MUVERETSoQJZDlG2bHFWr57GYYedwoMPXsx//rPAf9By5WDuXDjySGjdGtas8R9TREREJA0qkOVfqlYtzbJlcyhatCq33tqO119f5j/o0UfDvHlBM3aLFvDNN/5jioiIiKRCBbKk6qSTjuTjj+cSF3cUN920gc8/j0LQY48NiuS9e6FZM/jxxygEFRERETmUCmRJU4MGlVm5cg3x8Z1o0QLWrdvrP+jJJwd9kv/8M2hJ/v13/zFFREREwkRcIJvZSWb2oJmNDj2uaWa1/aUmecHJJxdl7lxITn6XU089laSkn/wHPf10mDYNNmwIxkressV/TBEREZGQiApkM7sUmA8cDVwVmhwPjPSUl+QhtWrBf/5Thb17f+Lss1vw5ZdRaNU97zyYNCk4Ya99e9ixw39MERERESJvQR4ItHTO3QjsD01bCdTxkpXkOddc04AnnpjKnj3fUa9ea374Yav/oK1bwxtvwKJFcPHFsHu3/5giIiJS6EVaIJcnKIgBXNhfl/rsUhDdcUdj+vd/m7//XsWpp7bnzz93+Q96ySXw/PMwZw5cfjns2+c/poiIiBRqkRbIy/h/14qDugBLczYdyesGDGjH7bePIzn5LK64ohh79kQh6LXXwqhRQZeL66+HAweiEFREREQKq7gI57sNmGNm1wMlzGw2cCLQ0ltmkmeNGnUZtWpdRvfu0LHjj7zzzlEULx7prpRFt98Oycnw0EOQkABPPQVmfmOKiIhIoRRRVeOc+9LMagLtgWnARmCac267z+Qk77rhBvj11808+GB9atduy+efP09cnOdRAx94ALZuhREjgiL5scf8xhMREZFCKaIC2cyOBnY65yaGTStjZpWccz97y07ytAceKMu8eTcyf/7DNGiQwLJlTxAT47FV1wyGDQtakgcNglKl4J57/MUTERGRQinSJr/JQOUU0yoDk3I2HclvPvigP6eddgefffYkTZoM8B/QDMaMgS5doG9feOYZ/zFFRESkUIm0QD7RObc6fELocc2cT0nyk5gYIylpJCeeeD0ffTSQrl3H+Q8aGwuvvhqMj3zzzcFQcCIiIiI5JNIC+XczOz58Qujx5pxPSfKbmBhj9epnOfXUIbzxxkU891wUghYpAhMnQuPGcPXVMGVKFIKKiIhIYRBpgfwi8I6ZtTezk82sA/A28Ly/1CQ/KVo0lqSkvrRpU4IePbbRr99c/0EPOywojOvXh8sug/ff9x9TRERECrxIC+QhwOvAcOBTYFjo8RBPeUk+VLQovPMOHH30/Qwe3JaHHprmP2jJkjBjBtSoARdeGFx1T0RERCQbIiqQnXMHnHPDnHM1nXMlQn+HO+d0xQY5xGGHweLFj1KixGk88sgljBz5of+gRxwRXGmvYkVo2xZWrsz4NSIiIiJpSLNANrPzwu43TesWnTQlP6lcOYEVK2ZSrNjx9O59AS+8sMR/0KOOgnnzghblli1h/Xr/MUVERKRASq8F+b9h919I46Y+yJKqE04oy+LFc4mLK0+PHjfw2WdR+LGhalWYOxecg+bN4fvv/ccUERGRAifNAtk5Vyvs4fHOueqp3I6NQo6ST9WtW5H335/HkUe+R+vWMXz1VRSC1qgRdLdITg6K5F9/jUJQERERKUgy7INsZrHAdjMrFoV8pIA577zqJCZWZ9++AzRoMJAlSzb6D1q3LsycCb/8EnS3+PNP/zFFRESkwMiwQHbO7QfWA2X9pyMFUc2a8NJL37N16wjOPbc5a9Zs8h+0USOYPBnWrQtO3Nu2zX9MERERKRAiHeZtHDDNzK4xs2Y6SU8yq0OH6vz3vzPYu/dHGjRoyXff/eU/aPPmwcVEkpKCIeB27fIfU0RERPK9SAvkm4AywACCE/MydZKemRU3s6VmttLM1prZw1lJVvK3m246m0GDJrFr1xfUrt2OX3/d7j/ohRfCyy/Dhx8GFxPZu9d/TBEREcnXIh0HObUT9DJzkt5uoKlzrg5QF2htZmdmNWnJv+67ryX33DOe7dtX0q7d8ug06l55JYweDVOnwjXXwP79UQgqIiIi+VVcek+amQHdgVrAcufcy1kJ4pxzwMHmwiKhm8vKsiT/e/zxi6lS5RxuvbU8XbrAW29BkSKeg958czCyxX33ceK2bdC0KZh5DioiIiL5kQW1axpPmo0ArgAWAOcCY51z/bMUKBgNYxlwPDDaOdc3lXl6AD0AKlSocPqECROyEipbtm/fTnx8fNTjFkaTJlXiqaeWUKnSdF55pQdxcZH2+Mm66s89R9U33uCHzp35tmdPFckFmD7LBZ+2ceGg7Vzw5eY2btKkyTLnXP2U0zMqkDcCzZxz683sZGCKc+6E7CRiZqWBScCtzrk1ac1Xv359l5SUlJ1QWZKYmEjjxo2jHrewatVqMHPm9OOUU25i1arRxMR4Llid46eOHTl6yhR45BF44AG/8STX6LNc8GkbFw7azgVfbm5jM0u1QM6oya6Uc249gHPuc+CI7CbinNsCJAKts7ssyf9mzbqXhg3vYe3aMZx9dj//Ac346rbb4Kqr4MEH4amn/McUERGRfCXdPsgE3ZCrAweb9WJTPMY5921GQczsSGCvc26LmR0GNAcez2LOUoCYGQsXDuHUU5NZvHgIrVolMHv2fX6DxsTAiy8GYyPffjskJEC3bn5jioiISL6RUYFcAviasIIY+CbsvgNiI4hTEXgl1A85BpjonJuWmUSl4IqJMVauHM0JJ2xjzpxdjB4NvXp5DhoXBxMmQIcOcP31ULIkdOrkOaiIiIjkB+kWyM65HDlryjm3CjgtJ5YlBVNcXAxffvkqnTvHcMstUKTIdnr08Nxhv1gxmDQpuBz15ZcHw8C1auU3poiIiOR5/ocNEIlQsWIxTJgAZ565lp49j6dv30n+g5YoAdOnwymnQMeOsGCB/5giIiKSp6lAljyleHGYNKkq8fHVGTq0C4MHz/UftHRpmD0bjjkG2reH5cv9xxQREZE8SwWy5DlHHRXPqlUzKF68Jv36XcSYMZ/4D1q+PMybB2XKBN0svvjCf0wRERHJk1QgS55UvXoZPv10DkWKVObmm9vxzjtRKFgrVw6K5Lg4aN4cvvvOf0wRERHJc7JUIJtZEzM7L6eTEQlXq1YFPvpoLvHxXejZs3p0GnWPPx7mzoVdu4Ii+eefoxBURERE8pKICmQzm29mZ4fu9wUmAOPNLApXdpDC7Mwzj2H58meIiytOs2Zb+PjjH/wHrVULZs2CTZugRQv44w//MUVERCTPiLQFuRawOHS/O9AYOBO40UNOIoc44QSYPdvx++8X06RJE1as+MV/0AYNgmHfvv0WWreG5GT/MUVERCRPiLRAjgGcmR0HmHPuC+fcRqCMv9RE/q9OHeOZZwaxb99vNGrUgvXrN/sP2rgxvP02rFwZjG6xc6f/mCIiIpLrIi2QPwb+AwwHJgGEimX99ixRc/31ZzJ8+Hvs3v01p53Whp9+2uY/aLt28Prr8PHHwZX29uzxH1NERERyVaQFcjdgC7AKGBCaVhN4MudTEklb795NeeCBt9i5czn16t0cnUbdzp1h7NigX3LXrrBvXxSCioiISG5J91LTBznnNgP9Ukyb7iUjkQw88kgH/v77HUaMqMcll8DkyVC0qOegN9wQ9EPu3RtKloTnn4cYjZIoIiJSEEU6ikUxM3vMzL41s62haS3N7Ba/6YmkbvjwCxk7tgozZ+7nrLOeZ8+e/f6D3nUX9O8PL70U3HfOf0wRERGJukibwJ4gGMmiK3CwKlgL3OQjKZFIdO8O1103g2XLunPqqTeyf38UCtb+/eGOO+DJJ2HAAP/xREREJOoi6mIBdASOd87tMLMDAM65n8zsaH+piWTshRc68NVXD7BgwaM0aJBAUtJwYmLMX0AzGDkStm2DgQMhISHodiEiIiIFRqQF8p6U85rZkUAUxtoSSV9i4kBOP30rK1aMpGnTUiQmPuQ3oBk8+2xQJPfpExTJ3bv7jSkiIiJRE2kXi7eAV8ysOoCZVSQY9m2Cr8REIhUTYyQljeKEE7oxf/4Q7r//e/9BY2PhtdegbVvo2RMm6KMgIiJSUERaIPcDNgCrgdLAV8DPwMN+0hLJnNjYGFavfo6WLZcwaFBVxo6NQtCiReGtt+Dcc+Gqq2DatCgEFREREd8iKpCdc3ucc3c45+KBCkBJ59ydzjldNUHyjGLF4pg69VTatIGePZ/j9tsn+g96+OHBJanr1oVLLoEPP/QfU0RERLxKsw+ymR2bzutKmgUnQjnnvs3ppESyqmhRmDhxP5Urv8ZTTy2idOl4Hn64rd+gCQnBRUTOPx8uuADmzYOGDf3GFBEREW/Sa0H+mqArxdfp3L7ynaBIZsXHx7J69VQOP7w2Awd24okn5vsPWrYszJ0L5ctDmzawerX/mCIiIuJFmgWycy7GORcb+pvWLTaayYpEqkqVUqxYMZtixapz110deOmlJP9BK1YMWo8PPxxatICvv/YfU0RERHJcpq6Va2bHmFkjM6viKyGRnHLiieVYuHAucXFH0qvXJ6xcGYWg1asHLcn790OzZrBxYxSCioiISE6K9FLTFc1sPkG3ineBb8zsIzOr5DU7kWyqV+9oPvtsJWXL3k7LlvDFFwf8Bz3pJJg9G7ZsgebNYdMm/zFFREQkx0TagjwGWAmUcc5VBMoAK4BnfCUmklNOOSWeefNgz55F1KlzOmvXbvUftF49mD49aEFu2RL++st/TBEREckRkRbI5wC9nXM7AEJ/7wHO8pWYSE6qUQNGjy7C3r3fcMcdfVmz5nf/Qc85ByZNgs8/h3btYPt2/zFFREQk2yItkP8CTk4xrQawJWfTEfHniivqM3r0NPbt+54GDVrx7bdR2H1btYLx42HJEujYEXbt8h9TREREsiXSAnkoMM/MhpjZTWY2BJgbmi6Sb9x883nccMMwdu1aQ5067fnllx3+g3bqBC++GIxw0aUL7N3rP6aIiIhkWaRX0nsO6AyUAzqE/l7unIvGBX1FclTXrrXo2/cNduyowmWXFWHnzigEveYaeOopmDIFrr0WDkThZEERERHJkjSvpJeSc+4D4AOPuYhEzZAhl1CnziV07QoXXvgXU6aU5PDDI/44ZM2tt0JyMjzwQHD1vdGjIXRFShEREck7IqoIzKwo0A2oC8SHP+ecuzrn0xLx7/LL4c8/d3LLLWdz8slnsH79ixQtmqmhwTOvXz/YuhWGDYNSpWDwYL/xREREJNMibTJ7BagDTAV+85eOSHT16nU4U6d2Yfbs/tSpU5I1a54iNtZjq64ZPP44bNsGQ4YELcn33ecvnoiIiGRapAVya6C6c06jVkiBM3Pmg5x9djKLFo3gzDNLsXTpo357PpgF3SuSk4MW5YQE6NXLY0ARERHJjEgL5B+AYj4TEcktZsbHHw+jbt1kkpIeo23bKsyc2dNv0JgYePnlYGzkW24JiuSrrvIbU0RERCKSZoFsZk3DHr4KTDGzJ0nRxSJ08p5IvhYTYyxfPoYGDY5k1qwLGTIE7r3Xc9AiReDNN4OLiFx7LcTHB2Mli4iISK5KrwX5hVSmDUrx2AHH5lw6IrknLi6WpKTHuPpquO++ffz222KeeOIcv0GLFw+GfmvRIhgjedq04L6IiIjkmjRP2XfOVY/gpuJYCpTY2KDnQ82agxg1qjG33TbFf9D4eJgxA2rWhIsugoUL/ccUERGRNEU0ppWZ1TWzKimmVTGzOn7SEsk9RYrAggV3kpBwOk8/fRkPPfS+/6BlysCcOXD00dC2LaxY4T+miIiIpCrSQV9fB4qkmFYUeC1n0xHJG8qVK8maNTM5/PATeeSRCxkxYpH/oBUqBJejTkiAVq3gyy/9xxQREZF/ibRAPsY59234BOfcN0C1HM9IJI+oUuUIli+fQ7FiR9GnT2dmz97tP+gxxwRFslnQF3nDBv8xRURE5BCRFsg/mlm98Amhxz/nfEoieUeNGhVZuHAe1au/TadOxVi8OApBTzwR5s4NhoBr3hx++SUKQUVEROSgSAvkJwiGebvVzNqa2a3AJGCkv9RE8oZ69arxySdnULEiNG36PDNn/uA/aO3aMHMm/PortGwJmzf7jykiIiJAhAWyc+454C6gHTAs9Le3c26sx9xE8oyKFWHChE3s2tWHDh2a8dFHv/oPeuaZ8N578NVX0KZNcHlqERER8S7SFmScc28551o7504J/X3bZ2Iiec3pp5dn/PgZ7N//M82atWT58j/9B23aFCZOhOXLoUMH+Ptv/zFFREQKuXQLZDM73cxqhT0+0szGmdlKM3vGzOL9pyiSd3TufBZjx05h3751NGrUli+/jEKr7gUXwKuvwkcfwaWXwp49/mOKiIgUYhm1II8Cjgp7/DxwIjAWqAUM9ZSXSJ7VvXtzhg9/kz17ltG06Rx++y3j12TbFVfAmDEwfTpcfTXs3x+FoCIiIoVTepeaBjgJWABgZqWBNkAt59x6M3sPWAjc7DdFkbynd++LqFJlHddeeywtWsCHH0LZsp6D9uwJyclwzz1QsiSMHRsMByciIiI5KqMCOQ44+HvumcCvzrn1AM65jaGiWaRQuuyyYzniCGjbNpGaNd9g3boxHHFErN+gd98NW7fCY48FFxQZPlxFsoiISA7LqIvFWuDS0P0uwLyDT5jZ0cBWT3mJ5AvNm8OVVybxxx/PcfLJN7N9u/Mf9JFH4NZbYeTI4L6IiIjkqIxakPsCU83sGWA/cE7Yc52BT3wlJpJfvPhiH/76azOTJw+hVq1SfPHF4xx2mMdWXTMYNSrobtG/f9CSfMcd/uKJiIgUMukWyM65j83sGIIT89Y758JP2Z8OTPCZnEh+8e67g2jRIpn33x/GaaeVYtWq+yla1GPAmBh4/vngant33hkUyddd5zGgiIhI4ZHhOMjOuW3OuWUHi2Mzuzw0fZ1zTpeaFgHMjDlznqZhw6tYt+5LunZ17NvnOWhcHIwbF1xpr3t3eOstzwFFREQKh4gvFBLm2RzPQqQAiImJ4eOPX2TYsFd4+22jW7e9HDjgOWixYvDuu9CoEXTtGlyeWkRERLIlKwWyTpkXSUNcXBx9+sTQp8+PjBtXm1at3sH5Pm+vRIlgfORTT4WLL4b58z0HFBERKdiyUiAvyPEsRAqY/v3LcPTRZZg373I6dpztv0guVQpmzYJq1YJLUicleQ4oIiJScEVUIJtZpYP3nXNtw6af5iMpkfwuPr4Eq1fP4MgjT2HKlI5cddXH/ovkI4+EefOCK5a0agVr13oOKCIiUjBF2oI8x8yOCJ9gZmcAM3I+JZGCoUyZ0qxePZvSpY9h3Lh23HTTZ/6DHn10UCQXKwYtWsC33/qPKSIiUsBEWiCPJSiS4wHM7CzgPeD6SF5sZlXM7EMz+8LM1prZ7VlLVyR/qVChPJ99NpeqVZvz7LOVGTo0CkGPOw7mzoXdu6FZM/jppygEFRERKTgiKpCdc08BU4AZZtYGmARc6ZyLtAV5H9DbOXcSwSWre5nZyVlJWCS/qVq1Ct988w5dupSjb989DBwYhYL1lFNg9mzYvDm43N/vv/uPKSIiUkBEfJKec+4R4FPgTeBS59y8DF4S/tpfnHPLQ/e3AV8AR2cyV5F8KzYWXn0VKle+lv79z2Xo0CgMIV6/PkybBhs2BH2St+rK8CIiIpEwl8aZQ2a2EUj5ZAxQGvjz4ATn3DGZCmhWDfgIqOWcS07xXA+gB0CFChVOnzAh+hfq2759O/Hx8VGPK9GTm9t41aovufPOPhw4UJVbbnmeTp12e495xOLF1HrgAZJPPplVQ4dyoHhx7zHzAn2WCz5t48JB27ngy81t3KRJk2XOufopp6dXIJ8fyYKdcxEPuhrqwzwfeMw5925689avX98l5cJQVYmJiTRu3DjqcSV6cnsbz5mTSJs2rTlwoBZjx35A9+4J/oO+9RZ06RJ0t3jvveAkvgIut7ez+KdtXDhoOxd8ubmNzSzVAjkurRdkpvCNMIEiwDvAuIyKY5GCrGXLxkyc+DaXXtqRHj2uonTpKVx6qeegl14K27bB9dfDFVfAm28Gl6oWERGRf4l0HOR3zezcFNPONbO3I3y9AS8AXzjnRmY+TZGCpVOn9rz22gTq1h3AFVfA5MlRCHrddfDEE8GlqW+4Af/XwRYREcmfIj1J73xgYYppi4AmEb7+bOAqoKmZfRa6tc3oRSIFWdeunZg//zROPx0uuWQy772333/QO+6Ahx+GV14J7nu/eomIiEj+E2mBvAsokWJaPLA3khc75z52zplzrrZzrm7opouMSKGXkAD9+3/M/v0d6dixBzNnRqFV98EH4a674Omn4aGH/McTERHJZyItkGcDz5pZAkDo73+AWb4SEyks2rQ5h7vvfogDB16kQ4fezJ3ruVXXDIYPD7pZPPooDBvmN56IiEg+E2mB3BtIAP40s00Ew7yVAu7wlZhIYfL44wPo2fMO9u8fRdu2D/Phh54DmsEzz0DnznDPPfDss54DioiI5B8RncbunPsLaGdmFYHKwEbn3K9eMxMpRMyMMWNGsm1bMm+8MZC2bTsxe/apnHeex6CxsfDaa7BjB9x0U9Df4/LLPQYUERHJHyK+kh4EV8QDkoBNZhZjZpl6vYikzcx49dWxTJqUSPXqp9K2LXzyieegRYrAxIlw/vlw1VXBGMkiIiKFXKTDvFUys0lmthnYR3By3sGbiOSQ2NhYLrroPD74AMqUmUWzZhNZtMhz0MMOCwrj00+Hyy6D99/3HFBERCRvi7QF+FlgD9AM2A7UA94DbvSUl0ihVqGCo3r1Eeze3ZVmzaazZInngCVLwsyZcMIJcOGFsHix54AiIiJ5V6QF8lnAdc65zwDnnFsJXE9w8p6I5DAzY9q0d6hduy67dnWiadNEli71HPSII2DOHDjqKGjTBlat8hxQREQkb4q0QN5P0LUCYIuZHQnsAI72kpWIkJCQwAcfzOL444/j77870LTpUv9FcsWKMG8exMdDy5awfr3ngCIiInlPpAXyEuDgle9mA28C7xKcsCcinpQtW5bExLlUqVKeuLiJtGiB/yK5WrWgSD5wAJo3hx9+8BxQREQkb4m0QL4KmB+6fwfwIbAGuMJHUiLyf5UqVWLZsiWsWjWMcuWITpFco0bQ3SI5OSiSf/vNc0AREZG8I6IC2Tm3xTn3Z+j+3865R5xzfUPDvomIZ+XKleOYY4yXX/6aPXsa06zZRv9Fct26MGMG/PRT0N3ir788BxQREckb0rxQiJkNjGQBzrmHci4dEUlPiRLJFCmygl27WtCs2UfMm1eehg09BjzrLJg8Gdq3h7ZtYe7coH+yiIhIAZZeC3KVCG8iEiX16tVjxozpxMb+wJ49rWjRYov/cZJbtIAJE+DTT4Mh4Hbt8hxQREQkd6XZguycuzaaiYhIZM455xymTJlM+/bt2bu3HS1azGHWrBKcc47HoB07wosvwjXXBBcTeeed4Cp8IiIiBVCmLxVtZuXN7GIzq+kjIRHJWMuWLRk/fjx16sRSseJeWreG+fMzfl22XH01/Oc/MHUqdOsG+/d7DigiIpI70i2Qzaxy6BLTX5rZS2Z2CvAF8Aywysy6RCVLEfmXTp06sXBhIgsWlKZy5V20br2PDz7wHLRXLxg0CN54A265BZzzHFBERCT6MmpBHgP8CdwJGMEYyDc458oDlwL9/KYnIumJiYmhXLl9lCvXluLFr6Nt2wPMmeM56H33Qd++8MwzcO+9KpJFRKTAyahAPgu4yTk3E7gZqABMBnDOTQGq+k1PRDISFxdHmzbN2LLlNeLjb6VDB8fMmZ6DDh4MN90EQ4cG90VERAqQNE/SCyninNsD4JzbaWbbnDukucj8pSYikerXrx9bt25l2LBhVKhQigsvHMRbbwWDTnhhFvRH3rYN7r8fSpaEW2/1FExERCS6MiqQ48ysCf8vhFM+jvWWmYhEzMx4/PHHSU5O5tlnB3PMMeW55JI7eP116NzZU9CYGHjppaBIvu02SEgIRrkQERHJ5zIqkDcBL4Y93pzi8aYcz0hEssTMGD16NMWLF+eKK1rQpw9ccUUwbLG3ujUuLhgjlzryCQAAIABJREFUuUMHuO66oCX54os9BRMREYmOdAtk51y1KOUhIjkgNjaWUaNGATBjhqN581V061aH3buhRw9PQYsXD66216IFdOkC06YFl6YWERHJpzI9DrKI5A/jxv2PvTsPq6paHzj+XYd5dMJ5NicUEAfMCUUR55xy6jqVmmb107zZvXYbtKuWlVlaebNs0LTS9GpOaWJiOd3CCXIeQEVxHgCZYf3+2IAiqIDncADfz/Psh3P22nutddgHeFln7Xd9Tmhoc5o3X824cTB3rgUbc3GBDRugcWPo2xe2b7dgY0IIIYRlSYAsRAk1dOhQ/Pz8CA8fTLt2m3npJZg1y4INli4NmzZBjRrQsyfs3WvBxoQQQgjLkQBZiBLK1dWVDRs20LBhQ/bu7UuXLjt59VUjjbHFUhdXqACbNxvBcteucPiwhRoSQgghLEcCZCFKsDJlyvDLL79QtWpV9uzpzTPPxDBrlrEIXnq6hRqtXh2Cg8HGxpiXHBFhoYaEEEIIy3hQFgshRDFXsWJFgoODCQsLo2dPd8qXN9b3iI2Fr74yElGYXb16xkhyhw7QuTP8/jtUqWKBhoQQQgjzkwBZiEdAjRo1qFGjBgD+/utJT/di9uyaxMXB99+Dg4MFGvX2hp9/NgLkoCDYtg08PCzQkBBCCGFeMsVCiEdIXFwco0Y9w+rVnZkx4wKrVhkpjG/dslCDjz8Oa9fCyZPQrRvExFioISGEEMJ8JEAW4hHi6urKmjVriI6O5ocfgvjkk2ts2WIM8F67ZqFGAwJgxQo4cMCIxuPjLdSQEEIIYR4SIAvxiGnVqhVr1qzh+PHjLF7cncWLY9mzx5gufO6chRrt1Qu+/daYizxgACQnW6ghIYQQ4uFJgCzEI6hTp04sX76cPXv2cPPmt/z8M0RGQtu2cPy4hRodMgQWLDDmJQ8bBmlpFmpICCGEeDhyk54Qj6jevXuzZ88efHx8UAq2boXu3Y0geeNGaNbMAo0++6yRPuPll8HNDb74Akzyf7oQQoiiRf4yCfEIa9KkCUopDh06xOLFE/jttzScnIxpw1u3WqjRv/8d3nzTyDH38ssWXLVECCGEKBgZQRZCEBwczMcff0xCQgI7dnxO166Kbt2MFHD9+1ugwWnT4OZN+OgjKFXKeC6EEEIUERIgCyGYMGECly5dYubMmZQqVYrffnufXr0UAwfCp5/Cc8+ZuUGlYM4cY7rFW2+Bu7sxsiyEEEIUARIgCyEAmD59OjExMXzwwQeUKlWK4OA3GDIExo+HqCiYPt2Ia83GZILPP789J9ndHcaMMWMDQgghRMHIHGQhBABKKT766CNGjhzJr7/+ir19CqtWGffVzZwJzzwDKSlmbtTGBpYsMe4OHDsWli0zcwNCCCFE/skIshAii8lkYuHChaSkpGBnZ0d6ejoLFpioVg2mToULF4w1P1xdzdiovb1RabduRvo3V1fo2dOMDQghhBD5IyPIQohsbG1tcXJy4ubNm3Ts2JEVK37kzTdh4UIIDjYyXFy8aOZGnZ1h3Tpo0sRYSCQkxMwNCCGEEHlXrEaQU1JSiIqKIjEx0WJtlCpVisOHD1usfmF9xeEaOzo6Uq1aNezs7KzWB1tbW9LS0hg6dChubm6MHt2NSpVg0CBo3dpY76NBAzM26O5uJGDu0MFYknrLFmjZ0owNCCGEEHlTrALkqKgo3NzcqFWrFsqsdwvdFhsbi5ubm0XqFkVDUb/GWmuuXr1KVFQUtWvXtlo/XFxcWL9+PZ06daJ///5s3LiRnj3bs3WrEb+2bg2rVhnxrNl4eMDmzdCunTHl4rffwMvLjA0IIYQQD1asplgkJiZSrlw5iwXHQhQFSinKlStn0U9K8qpUqVJs3LiRmjVr0qtXL0JDQ2nZEnbvhkqVICgIvv3WzI1WqWLM5XByMho4ccLMDQghhBD3V6wCZECCY/FIKErv8/LlyxMcHEzz5s0pVaoUALVrw44dxkDviBHGOh9mXRCvTh1jJDklBTp3NvLMCSGEEIWk2AXIQojCV7VqVbZu3Uq9evXQWnP58mXKlDGmDD/9tLHWx4gRkJRkxkYbNYJNm+D6dSNIvnTJjJULIYQQ9yYBshAiX6ZMmULLli05d+4c9vbw1VcwY4aRzrhLF7h61YyNNW9uZLc4cwa6doUbN8xYuRBCCJE7CZCFEPkyYMAArl69SlBQEJcvX0YpeO01+O47Y27y44+DWZOE+PvDf/8LBw8a+ZFv3TJj5UIIIUROJTpA3r17N0899RTNmzfnqaeeYvfu3Wapd+bMmTRu3BgfHx98fX353//+x40bN5g/f/59z2vTpg0AkZGReD3EnfkLFizgueeeA4zUd8OHD2fkyJGkmH2ZMyFy8vPzY+3atURERNCtWzdu3rwJwFNPGemLY2OhVStj+oXZdOt2OwLv18/MczmEEEKI7EpsgDx16lQCAwNZtmwZe/fuZfny5QQGBjJ16tSHqnfXrl2sW7eOvXv3EhYWRnBwMNWrV79vgKy1Jj09nZ07dz5U25nCwsLw8fEhJiaG7t27U6NGDRYtWmTVnLni0dKhQwdWrlxJWFgY/fv3R2fcode6Nfz5p3ETX8+e8NFHZrx5b8AA+PJL4+a9p56C1FQzVSyEEEJkVyID5N27dzN79mzi4+Oz/nCnp6cTHx/P7NmzH2okOTo6Gg8PDxwcHADw8PCgSpUqTJkyhZMnT+Lr68srr7xCZGQknp6ePP/88zRr1oyzZ8/imsv6vKdOnaJp06b8+eefLFmyhJYtW+Lr68u4ceNIS0vLtQ/h4eGULVuWgIAABg4cyMyZMwv8eoQoqB49evDdd9/x8ssvZ8u6UaMGbN8OffrApEkwdiwkJ5up0aefhrlzjQTMo0ZBerqZKhZCCCFuK5EB8ty5c0lISMi1LDExkblz5xa47i5dunD27Fnq16/P888/z7Zt2wCYNWsWjz32GPv37+f9998H4OjRo4wYMYJ9+/ZRs2bNHHUdPXqUJ598kq+//hpXV1eWLVvGjh072L9/PzY2NixdujTXPoSHh/N///d/vPXWW4wbN67Ar0WIhzVw4EB69OgBwO+//05qxqiuqyusWAGvv24sUR0UBJcvm6nRCRNg+nQjAfOECWbOLyeEEEKU0AD52LFjWSPHd0tPT+f48eMFrtvV1ZU9e/bw+eefU758eQYPHsw333yT67E1a9akVatWuZZdvnyZPn36sGTJEnx9fdmyZQt79uzBz88v6/mpU6dynJc5Eu3t7U10dHSBX4cQ5nTw4EE6dOjAs88+S3rGqK7JZMSx330Hf/wBLVrA3r1mavC112DyZPj0UyMKF0IIIcyoWC01nVf169dn//79WX+o72Qymahfv/5D1W9jY0NAQAABAQF4e3uzaNEiAgICchzn4uJyzzpKlSpF9erV2bFjB40bN0ZrzciRI3nnnXfu23ZYWBhNmjThiy++oFWrVvj5+dG0adOHej1CPKzGjRszdepUpk2bhpubG3Pnzs2advHUU1C/vnFvXdu28PnnMHz4QzaoFLz3HsTEwNtvg7s7/POfD/9ChBBCCEroCPLEiRNxdHTMtczR0ZEJEyYUuO6jR49mG4Hev38/NWvWxM3NjdjY2DzXY29vz+rVq1m8eDHfffcdgYGBrFixgksZiyFcu3aN06dP5zgvPDwcb29vKleuzMKFCxk8eHBWFgEhrOnNN99k0qRJfPzxx7z55pvZypo3hz17jOwWI0bAxInGInkPRSmYPx/+9jeYMgX+85+HrFAIIYQwlMgAuVWrVkyePBlnZ2dMJuMlmkwmnJ2dmTx58j2nPeRFXFwcI0eOpFGjRvj4+HDo0CGmTZtGuXLlaNu2LV5eXrzyyit5qsvFxYV169bx4Ycfcvz4cWbMmEGXLl3w8fEhKCgo1ykUmQEyQFBQEIMGDWLUqFEFfj1CmItSig8++IDRo0czY8YMfvvtt2zl5csbCSgmTYJ584x5yQ+9OJ6NDXzzDTzxBLzwgrFaiRBCCPGQ1L3m6pq1EaW+AnoBl7TWeUoA3KJFCx0aGppt3+HDh/H09Mxzu7t372bu3LkcP36cevXqMXHixAcGx7Gxsbi5ueW5DVH8FJdrnN/3e1GRlpbGmjVr6Nev3z2PWboUxowBDw9YuRJatnzIRhMTjbxy27YZdwf27UtISEiuU59EySHX+NEg17nks+Y1Vkrt0Vq3uHt/Yc1B/gb4BFhcSO0Bxkjyw4wWCyHyz8bGJis4PnDgAMeOHWPgwIHZjhk6FBo1MuYlt2sHH34Izz9vzJooEEdH+Okn6NwZBg+G9evBtkTeYiGEEKIQFMoUC631b8C1wmhLCFF0TJs2jaeeeoq1a9fmKGva1Mhq0aULvPiiMZU4H9P4c3J1hQ0boEED6NMH97/+eojKhBBCPMoKZYoFgFKqFrDuflMslFJjgbEAFStWbP7DDz9kKy9VqhR169a1YC+Nj4dtbGws2oawruJyjU+cOFHsb8C8desWkydP5uTJk8yaNYtmzZrlOCY9HX74oQZfflmbqlUTeOutv6hdO77Abdpfu4bvhAnY3bjBgY8+Is7CvzOE9cTFxeW6AJMoWeQ6l3zWvMYdO3bMdYpFkQqQ72SOOcgFUVzmp4qCKy7XuLjOQb7b1atX6dChA5GRkQQHB99z2lNICAwZYowif/bZQ6aCO32axJYtcdQafv/dGFUWJY7MTX00yHUu+YriHOQSmcVCCFF0lCtXjs2bN1OpUiU++uijex4XEAD79oGfn5EK7tlnIb6gA8k1a3Jg9mxjUnPnzpBLykQhhBDiXiRAFkJYXOXKldm2bRuLFi16wHEQHAyvvgpffmmsvhcWVrA2E6pXh19+gbg4I0i+cKFgFQkhhHjkFEqArJT6HtgFNFBKRSmlRhdGu6LoK6wpPsL6qlatioODA1evXqV///6cOXMm1+NsbY3F8TZvhuvXjRRwn34KBXqrNGli3LgXHW0kXr4m9woLIYR4sMLKYvGU1rqy1tpOa11Na/1lYbQriqYLFy7Qpk0bZs2aRefOnalTpw7Dhg27Z8AkSpZz587x66+/0rlzZy5evHjP4wIDjdHjwEAjy0W/fnD1agEabN0aVq+GY8ege/eHTJUhhBDiUSBTLPIpMjISL6/s9xlOmzaN2bNnF2o/7rzbs02bNvk6N/P4GzduMH/+/Dyft2DBAp577jkAUlJSGD58OCNHjiTlHmsGh4eH06tXr2zbpUuX2LdvH0OGDGHKlCl88cUXDB06lCVLllCjRo18vQ5RPPn4+LB+/XrOnTtHly5duH79+j2PLV8e1q6FOXOMgWBfX2MtkHzr3BmWLzfWu+7dGxISCv4ChBBClHgSIBcDWmvS09PvWb5z58581Zd5fH4D5LCwMHx8fIiJiaF79+7UqFGDRYsWYWdnl+vx3t7erFu3LttWoUIF9u/fT1BQEGAE0T4+Pvnqvyj+2rZty+rVqzly5Ag9evQgLi7unseaTMby1Lt3g5MTdOwI//gHJCXls9E+fYxlqbdtg0GD4B7/2AkhhBASIJtRZGQkDRs2ZOTIkfj4+DBgwADi4+PvuT/TkiVLaNmyJb6+vowbN460tDQiIyPx9PTk+eefp1mzZpw9e/ae7WaOJme2M2bMGLy8vBg6dCjBwcG0bduWevXq8ccff2Q7fsqUKZw8eRJfX19eeeWVB76+8PBwypYtS0BAAAMHDmTmzJkARERE0KdPH1q0aEHLli05evTofes5fvw4DTLSbpUpU4bly5ff9x8AUTIFBQXxww8/cP369fuOImdq1sxYWGTsWHj/fSPbRb5v4Bs2DObPh3XrjFQZaWkF67wQQoiSTWtdJLfmzZvrux06dCjHPnOLiYm5b3lERIRu3Lhxtn1Tp07V77//vo6IiNCA3r59u9Za62eeeea++7U2XlOvXr10cnKy1lrr8ePH60WLFumIiAitlNK7du3KtR8uLi45HkdERGgbGxsdFham09LSdLNmzfQzzzyj09PT9erVq3WfPn1yHH/3a+nevbs+d+5crm2WLl1ae3h46DVr1mTtS05O1p06ddInTpzQWmu9fv16/fTTT9/3e2htD7rGRUVhvN+LgqSkJK211mlpaTolJSVP56xfr3WlSlrb2Wk9a5bWqak5j9m6deu9K5g1S2vQ+tlntU5PL0CvRVFw32ssSgy5ziWfNa8xEKpziUNtrRyfP5S8JJXu1asXkydPzjr+6aef5umnn+bKlSsMGDAg27EhISEPrE8pdd/91atXp23btgAMGzaMefPmMWDAgFz3T548mS1btrBnzx78/PwASEhIoEKFCrRv356aNWvec1GFe6lduzbe3t4ANG7cmMDAQJRSeHt7ExkZ+cDzN2zYkOv+s2fP4urqSr169YiOjs7av3r1ag4ePMiTTz4JQGpqKv7+/vnqs3i02dvbo7Vm1KhRpKamsnjxYkym+3+41aMHhIfDc8/BlCnGgPDixVC7dh4b/ec/4eZNeOcdcHc3hqTv8bMthBDi0VOsA2RrKFeuXI6Pg69du0btjL/MdwfQmc/vtV9rzciRI3nnnXeylUdGRuLi4pLv/jk4OGQ9NplMWc9NJhOpqan5ri9TWFgYTZo04YsvvqBVq1b4+fnRtGlTDhw4wMyZMxk9WjL3iYJTStGgQQP+9a9/4ebmxvz58+/5z2gmDw/48UdYssTIcuHtDe++C+PHG/OWH2jmTIiJgQ8+gFKl4I03zPNihBBCFHvFOkDOy4jvvY738PDI9/lgzN+tXLkyW7ZsITAwkGvXrrFx40YmTpwIwJkzZ9i1axetW7fm+++/p127dvfdHxgYSJ8+fZg0aRIVKlTg2rVrxBZSGio3N7c8txUeHo63tzeVK1dm4cKFDB48mD///JPKlSuzadMmnnnmGUwmE+Hh4Xh5eT0wuBHibq+++io3b97k3Xffxd3dnVmzZj3wfaSUsSR1hw7Gynsvvmgkq/gyL4kklYJ584y0b2++aYwkZ/wcCyGEeLTJTXoFsHjxYmbMmIGvry+dOnVi6tSpPPbYYwB4enqyaNEifHx8uHbtGuPHj7/v/kaNGjFjxgy6dOmCj48PQUFB2aYwWFK5cuVo27YtXl5eWTfp9ejRg/Pnz+c4NjNABuPmqkGDBjFq1ChGjRpFeno6np6e+Pr68u6770pwLArsnXfeYfz48bz33nu89957eT6vRg3YuBG++goOHAAfH1i+vNqD78EzmYxoul8/eOkl+Prrh3sBQgghSgSli+hKZi1atNChoaHZ9h0+fBhPT0+LthsbG4ubm1uBzo2MjKRXr1789ddfedovrONhrnFhKoz3e1GUnp7O888/z6BBg+jUqVO+zz9/3pibvHYttGplBM0P/DYmJcETT8CWLfDDDzBwYME6LwpVSEhInu5FEcWbXOeSz5rXWCm1R2vd4u79MoIshChSTCYTn332WVZwHBERka/zq1SBn36C118/xPHjxuIiU6dCYuJ9TnJwgFWrjFX3hg41hqOFEEI8siRANqNatWrlOkp8r/1CiPtbtWoV9evXZ9WqVfk6TykIDLzEoUPGYPC//21MuwgOvs9JLi5GOgwvL+jfH37//eE6L4QQotiSAFkIUWQFBQXh5+fHkCFD+OWXX/J9foUKRpaLX34BrSEoyFgr5OLFe5xQujRs2gQ1a0LPnnDXNC8hhBCPBgmQhRBFlqurK+vXr8fT05O+ffuyY8eOAtUTFGTkTX7zTSM1XMOGsGAB5LqAY/nysHkzlC0L3brBoUMP9yKEEEIUOxIgCyGKtDJlyrBp0yaqV69Oz549uXDhQoHqcXSEt94yslz4+ho38rVsCTt35nJwtWrGfAw7O+jcGU6dergXIYQQoliRAFkIUeRVrFiR4OBg3n//fSpVqvRQdTVsCL/+aky9iI6Gtm2NXMo5shvWrWuMJCclGUHyuXMP1a4QQojiQwJkIUSxUL16dZ599lkAQkND853d4k5KGckqjh6FV181FhepX99YiS8p6Y4DvbyMjBZXrhjzNK5cechXIYQQojiQAFkIUawkJyczYMAAsyyq4+oKb79tTDMODIQpU4yYeNUq46Y+APz8jKTKERHQtSvcvPnwL0IIIUSRJgGyEKJYsbe3Z9myZVy4cIGgoCCuXr360HU+9piRO3njRmPacf/+4O8Pu3ZlHNChA6xcCWFh0KsXxMc/dJtCCCGKLgmQ88nGxgZfX1+8vLwYOHAg8ff4Q9mjRw9u3LhRyL0T4tHw+OOPs3btWk6cOEH37t2JjY01S71duxox8IIFcOIEtGlj5FE+fhzo0QOWLjXu6nvySUhONkubQgghih4JkPPJycmJ/fv389dff2Fvb89nn32WrVxrTXp6Ohs2bKB06dJW6qUQJV/Hjh358ccf2bdvHx988IHZ6rW1hbFjjQB52jT4+Wdo1AgmTIBLAYPg88+Noea//Q1SU83WrhBCiKJDAuSH4O/vz4kTJ4iMjMTT05Pnn3+eZs2acfbsWWrVqsWVjBt6Fi9ejI+PD02aNGH48OEAXL58mSeffBI/Pz/8/PwKnN9ViEfZE088QUhICK+99prZ63Z1NZaoPnECRo+G+fOhTh147dRobs380Jhy8eyz90imLIQQojiztXYHCuqll2D/fvPW6esL06fn7djU1FR+/vlnunXrBsDRo0f5+uuvmT9/frbjDh48yMyZM9mxYwceHh5cu3YNgIkTJzJp0iTatWvHmTNn6Nq1K4cPHzbr6xHiUdC2bVsALl68yNy5c5k+fTo2NjZmq79SJfjsM5g0ycij/M478InbS/wUcJOAb6aBuzt89JGRGkMIIUSJUGwDZGtJSEjA19cXMEaQR48ezfnz56lZsyatWrXKcfyvv/7KgAED8PDwAKBs2bIABAcHc+iOFbpiYmKIjY3Fzc2tEF6FECXP2rVreeedd7h06RJffPGF2etv0AC++85ICzd1KnRc9SafOtzk+XkfkuxUCvtZ/zZ7m0IIIayj2AbIH31kmXofdK9P5hzku7m4uOR6vNYalcvIUnp6Ort27cLJyalA/RRCZDdmzBjOnDnD9OnTcXd354knnrBIO97e8N//Qmio4s03PsBuYyzPvjud4APutPh+MnLrgRBCFH8yB9nCAgMDWb58eVYqqswpFl26dOGTTz7JOi63oFsIkT9vvfUWEyZM4MMPP2Tx4sUWbatFC9jws6Lxb5+xrfJgOm98hamVP+e11+DyZYs2LYQQwsIkQLawxo0b89prr9GhQweaNGnC3//+dwDmzZtHaGgoPj4+NGrUKEc2DCFE/iml+PDDD3nmmWf49ddfuXXrlsXbbONvQ4fIxdxs14MPE58j4u3vqVXLmLMcFWXx5oUQQlhAsZ1iYS1xcXE59tWqVYu//vor277IyMisxyNHjmTkyJHZyj08PFi2bJlF+ijEo8xkMvHFF1+wfv36e059Mjt7e0r9sgK6d2fpjhF82MqNf3zci08+gSFD4OWXjZuAhRBCFA8ygiyEKHFsbGxwd3cnOTmZv/3tbyxfvtzyjTo5wZo1qKZN+fuOAZxdvJUXXjCWrW7aFDp1gvXrJSucEEIUBxIgCyFKrNTUVKKiohg6dCgbNmywfIPu7sbKInXrUnnsE3z01P+IioL33oNjx4xVqhs3Nlbqy+XDKCGEEEWEBMhCiBLL2dmZtWvX0qRJE5588km2bdtm+UbLlYPNm6FiRejendJnwnjlFYiIgCVLjIHm556DqlVh4kQ4csTyXRJCCJE/EiALIUq0UqVKsXHjRurUqUOvXr34888/Ld9o5coQHAzOztClCxw/jp0dDB0Ke/bAjh3GaPJ//gOentC5s5E6TlauFkKIokECZCFEiefh4cHmzZupV68eycnJhdNo7dpGkJyWZkTAZ84AxoJ7bdrA0qVGlouZM43pF08+CbVqweuvw8mThdNFIYQQuZMAWQjxSKhSpQqhoaFZS1PnlpHG7Bo2hE2b4MYNCAqCixezFVeoAP/6F5w6BatXQ5MmxlLWdetCx47GlIz4eMt3UwghRHYSIAshHhkmk/Er7+OPP8bb25uowkhU3KyZkb7i7Fno2hWuX89xiK0t9OljHHb6tDGqfPYsDB9uzNZ47jljWobWlu+uEEIICZDFI0BLVCHu0qZNG65du0ZQUBCXC2PZu3btjCHiw4ehR4/7prCoVs0YVT52DLZuNQLnxYuNKh57zJiCcfiw5bsshBCPMgmQRYl04cIF2rRpw6xZs9i9ezfDhg2jTp06DBs2jLNnz1q7e8LKmjdvzrp16zh9+jRdu3blxo0blm+0Sxf4/nv44w/o2xcSE+97uMkEAQFGcHzxIixaBPXqGVMwGjWC5s1hzhxZrU8IISxBAuQCcHV1tXYXsvWhTZs2+To38/gbN24wf/58s/YrvxISEujQoQNpaWkALFiwgOeeew6AlJQUhg8fzsiRI0lJScn1/PDwcHr16pVtu3TpEvv27WPIkCFMmTKF1q1b8+9//5uhQ4eyZMkSqlevXqC+RkdHM2TIEFq0aEH9+vXp2LFjns7buHEjDRo0oG7dusyaNStrf3JyMu3btydVUhdYhb+/P//973/566+/6NWrV+Fch/794euvYcsWY4m9e7yv7+bmBiNGGNOZz52DDz80AuiXX4bq1Y3R5XnzJFgWQghzkQC5GNBak36f5bd27tyZr/oyjy8KAfJXX31F//79sbGxASAsLAwfHx9iYmLo3r07NWrUYNGiRdjZ2eV6vre3N+vWrcu2VahQgf379xMUFJR1XHh4OD4+Pg/V1+HDh9OvXz9CQ0M5duwY8+bNe+A5aWlpvPDCC/z8888cOnSI77//nkOHDgFgb29PYGCgLDluRd26deO7775j+PDh2NraFk6jI0bAxx/DTz/BM8/ke2m9SpXgpZfgzz/h6FGYMQNiY42cytWrQ6tW8P77cPy4hfovhBCPAAlbiJc/AAAgAElEQVSQzWTOnDl4eXnh5eXFRx99BMB7772XFURNmjSJTp06AbBlyxaGDRsGwJIlS2jZsiW+vr6MGzcuayQ1MjIST09Pnn/+eZo1a3bfaQGurq5ERkbSsGFDxowZg5eXF0OHDiU4OJi2bdtSr149/vjjj2zHA0yZMoWTJ0/i6+vLK6+8ct/XN3DgQF588UXatWtHzZo12b59OyNGjKB+/fqMHj0667gVK1bQqlUrmjRpQrt27bLmd3bs2JHNmzcD8PrrrzNhwgQAli5dSp8+fbLODw8Pp2zZsgQEBDBw4EBmzpyZVRYREUGfPn1o0aIFLVu25OjRo/fs7/Hjx2nQoEHW8zJlyrB8+fL7/qNxP2lpaYSEhNChQ4esfd7e3g88748//qBu3brUqVMHe3t7hgwZwk8//ZRV3rdvX5YuXVqgPgnzGDBgAOPGjQPgr7/+KpyR5BdfNO7EW7oUXnihwHff1a8Pr70GBw4YC47MnGnkUv7HP4yyhg2Nx9u3G9nmhBBC5JHWukhuzZs313c7dOhQtucdOnTIsX366adaa61v3bqVa/nXX3+ttdb68uXLOcq01jomJiZHu3dzcXHJ9jw0NFR7eXnpuLg4HRsbqxs1aqT37t2rd+3apQcMGKC11rpdu3baz89PJycn62nTpunPPvtMHzp0SPfq1UsnJydrrbUeP368XrRokdZa64iICK2U0rt27XpgH1xcXHRERIS2sbHRYWFhOi0tTTdr1kw/88wzOj09Xa9evVr36dMnx7kRERG6cePG2ert3r27PnfuXI72GjRooD/44AOttdZvvPGGrl+/vj5//rxOSkrSpUuX1omJiVprra9cuZJ1zrRp0/Qnn3yitdZ627ZtukOHDnrJkiW6R48eOjU1VSclJemKFStma6d06dLaw8NDr1mzJtv+5ORk3alTJ33ixAmttdbr16/XTz/9dK7fmwfJyzXOTdeuXXWFChX02LFj9fbt27P2X7t27Z7n/Pjjj3r06NFZzxcvXqxfeOGFrOepqanaw8Mj13Pvfr+L/Nm6dWu+jj9z5ox2dnbWI0aM0GlpaZbp1J3S07X+xz+0Bq3/+U+zVh0ZqfXHH2sdFKS1nZ3RRLlyWo8YofUPP2h9x49psZbfayyKJ7nOJZ81rzEQqnOJQwvpM8WSbfv27fTr1w8XFxcA+vfvz++//8748ePZs2cPsbGxODg40KxZM0JDQ/n999+ZN28eW7ZsYc+ePfj5+QHGfNwKFSpk1VuzZk1atWqV537Url07a1SzcePGBAYGopTC29ubyMjIPNWxYcOGHPsSExO5ceMGL730EgBOTk6MHj2aypUrA8Zyvvb29gB88803LFu2jKSkJC5cuMDbb78NQPv27dFaM2fOHEJCQrCxseHixYuULl06q52zZ8/i6upKvXr1iI6OztaH1atXc/DgQZ588kkAUlNT8ff3z/P35l46d+7MhQsXcuyfOXNmtpFtgJ9//pkdO3awZs0aunXrxrfffkvfvn2ZNGkS33zzTa7161xGBpVSWY9tbGywt7cnNjYWNze3h3sx4qFUr16dKVOm8Oabb+Lm5sbHH3+c7VqZnVIwaxbExMC770KpUvDqq2apumZNY5D6xReN6jdtgjVrYO1a46Y/paBlSyPrXLdu4OdnpJoTQghhKNa/EkNCQu5Z5uzsfN9yDw+P+5bnR25BEICdnR21atXi66+/pk2bNvj4+LB161ZOnjyJp6cnW7ZsYeTIkbzzzju5np8ZcOeVg4ND1mOTyZT13GQyPdTHxgcPHqRZs2ZZOWQPHDjA+PHjAYiKiqJKlSoopVi8eDF//PEHv/76K66urrRv357GjRsDxtSJ6OhoPDw8sgJBJycnEu+4kz8sLIwmTZrwxRdf0KpVK/z8/GjatGlWmzNnzsw2ncMcgoOD83ysUop27drRrl07rl+/TlhYGI6Ojhw5coTZs2czefLkHOdUq1Yt2/SYzO/XnZKSknB0dCz4ixBm8/rrrxMTE8Ps2bMpVapUtik+FqEUfPqpMYn4X/8Cd3djyoUZubvDwIHGlppqzF3etMnYZsyAf/8bSpeGTp2MrWNHY/lrS/5vIIQQRZ3MQTaD9u3bs3r1auLj47l16xarVq3KGt1s3749s2fPpn379vj7+/PZZ5/h6+uLUorAwEBWrFjBpUuXALh27RqnT58utH67ubkRGxv7wOPCw8Np0qRJ1vPMG+nACFwzH4eHh9OmTRtcXV1ZuXIlO3fuxNvbm+joaIYOHcpPP/2Ei4sLmzZtAox5wWlpaVlBcnh4ON7e3lSuXJmFCxcyePBgbt68CUDlypXZtGlT1hzi8PDwQs1vvGnTpqwlii9dusT27dsJCgrCw8ODYcOGZQXHgYGBnDt3Lus8Pz8/jh8/TkREBMnJyfzwww/07t07q/zq1auUL1/+njchisKllOK9995j7NixvP3226xevdryjZpMRmaLPn2MId/Fiy3WlK0ttG4N06bBrl1w+TIsW2Yk1wgNNZpv3NhYnOSpp+Dzz418zJJKXAjxqJEAuQDi4+OpVq1a1hYSEsLTTz9Ny5YtefzxxxkzZkzWyKe/vz/R0dG0bt2aihUr4ujomBU8N2rUiBkzZtClSxd8fHwICgrKMbXAksqVK0fbtm3x8vLKukmvR48enD9/Pttx4eHh+Pr6AsZ0i4SEBMqUKQNkD5ZHjhzJvHnz8Pf359ixY9SpUwelFP379+eDDz7A09OTN954g2nTpmXV3aVLF7Zv357VTuYUkaCgIAYNGsSoUaMAGDVqFOnp6Xh6euLr68u7775r2Y+/77JixQo8PT1p0qQJvXr1Yvr06bRu3Tpr1BsgPT2dEydOULZs2azzbG1t+eSTT+jatSuenp4MGjQoa1QdYOvWrfTo0aPQXod4MKUU8+fPZ+HChTzxxBOF06idHfzwAwQGGpktVq0qlGbLloVBg+DLLyEyEk6ehIULoXNn2LYNxo2DBg2gYkUjiJ4zx0jjnMfsdEIIUWypwhyFy48WLVro0NDQbPsOHz6Mp6enRduVuaCFa9++fcyZM4dvv/220No05zVes2YNK1euZMqUKaSlpfHVV18xZ86cPJ/fv39/3nnnnWwZNzIVxvu9JAsJCSEgIOCh64mKiiI0NJS+ffs+fKceJC7OWFBkzx5Ytw7uSFVY2LQ2Ro9/+83IgrF9O5w6ZZQ5OxtzmB9//PZ218yhQmGuayyKNrnOJZ81r7FSao/WusXd+4v1HGRR/DVt2pSOHTuSlpaWlQu5OOndu3e2KRP5CY6Tk5Pp27dvrsGxKDpef/11lixZwsqVK3PcuGl2rq6wfr0xEbhvX/jlF2jb1rJt3oNSxuhxgwbw7LPGvvPnYccOI1jeudMYUc4cTa5WzQiUW7Y0Vvlr2tQYoRZCiOJIAmRhdZnTKB419vb2jBgxwtrdEA/w8ccfc+TIEQYNGsT69evp3LmzZRssU8a4g659e+jRA0JCjGizCKhS5fYNf2Cslr1vnzHt4n//M7aVK28fX6sWNGtmbE2bQpMmRh1yA6AQoqiTAFkIIe7Dzc2NDRs2EBAQQJ8+fQgODqZ169aWbbRiRQgONtaQ7tLFmOdQBKfbODoaN/3d+e24etUImvfuvb3997+3y8uUAR+f25u3NzRqZCynLYQQRYUEyEII8QBly5bll19+wd/fn1dffZWtW7da/ibR6tWNINnf35iLvH27MSRbxJUrZ9zkd+dAe0wM7N8P4eEQFmZsX39tTLnOVK2aESh7et7+2qABlC8vI85CiMInAbIQQuRBpUqVsnJ8F1oGlXr1jHnIAQFGxPn770YOtmLG3d2YMdK+/e196elG5ozwcDh0CA4fNr4uXAi3bt0+rlQpY9nsevWMr/XrQ926UKeOpJ8TQliOBMhCCJFH1atXB4x0hy+99BKvvvoqNWvWtGyjPj7w889GCrigICP/Wrlylm2zEJhMRpBbp46RAjpTejqcPWsEzMeO3d527oTvv88eFLu4tKNevdv11K5trCKYucm0DSFEQUmALIQQ+RQZGcmyZcvYsmULv//+O5UqVbJsg48/bqwT3b27sQUHG8OyJZDJdDvA7dYte1liopGr+eRJI+Xcb79dICmpGocOGck/kpKyH1+mjFFPjRrGjJVq1W5v1atD1arGPGohhLibBMhCCJFPDRs2ZMOGDQQFBdGlSxdCQkKyLRBjER07wo8/Git2PPEEbNwITk6WbbOIcXQ0VvrLXGvH1/cEAQHVAGPk+cIFOH0652YE03DjRs46S5c2MmtUrmxsmY8rVsy+lS1rBO9CiEeDBMhCCFEArVu3ZvXq1fTs2ZP27duzdu1aateubdlGn3jCWIp66FAYMMBYcc/e3rJtFhMmkxHcVqmSPavGneLi4Nw5iIq6vUVHG/mdo6ONKd7nz+e+UqCNDVSoYNw0WL48eHhkf+zhYQTR5crd/uriIjcYClFcSYAshBAF1LlzZ37++WdGjx5NQkJC4TT61FMQG2usAz18OHz3nRG9iQdydb29+Mm9aA3XrsHFi7lvV67A5ctG+rrLl3Mflc5kb29M88htK13auAExt83NzZhB4+oqo9ZCWEuhBchKqW7AXMAGWKi1nlVYbZubjY0N3t7epKam4unpyaJFi3B2djZb/fPmzeM///kPzZo14/HHH896vHTpUrO1kVcJCQl069aNX3/9FRsbGxYsWMC+ffv47LPPSElJYdSoUZhMJhYuXIidnV2+6o6OjmbSpEmcOHGCmJgYqlatytatW3M9duPGjUycOJG0tDTGjBnDlClTAGM1us6dO/Prr79iayv/74nC16lTJ44dO4adnR1aa3bu3ElbS69+N3askTvtlVfA3Z3do0Yxd948jh07Rv369Zk4cSKtWrWybB9KKKWM0d9y5Yx0cw+SkmIEzdeuGTmgM79mbtev394uXoQjR4zHN28a00IexNXVCJbd3IzN1fX25uZmjFLfa3N2zn1zcgIHBxndFuJ+CiWiUErZAJ8CQUAU8KdSao3W+lBhtG9uTk5O7N+/H4ChQ4fy2Wef8fe//91s9c+fP5+ff/6Z2rVr07Bhw6zH1vDVV1/Rv3//rGWgw8LC8PHxISYmhv79+/P4448zc+bMAtU9fPhwnn32WQYPHgxAeHh4rselpaXxwgsvsHnzZqpVq4afnx+9e/emUaNG2NvbExgYyLJlyxg6dGjBXqQQDynzn8Mff/yRwYMH889//pO3334bkyWH/yZPNqKsGTP485tvWJaaigb279/PmjVrmDx5Mm+99Zbl2hcA2Nndnr+cH1obUz5u3jRGoW/evL3FxhpbTEz2r3Fxxtdz524/josz0uIVJOWdo6MRLGdujo65bw4O99/s7XN+tbc3vjd3f7378d2bra3xVYJ3YW2FNeTWEjihtT4FoJT6AegDFDxAfuklI/O8Ofn6wvTp+TrF39+fsLAwAPr27cvZs2dJTExk4sSJjB07ljfeeAMPDw8mTpwIwGuvvUbFihWZMGECc+bM4auvvgJgzJgxvPTSSzz33HOcOnWK3r17c+zYMbTW9O7dm1GjRjFp0qR79mPgwIFUrFiR/fv3c/bsWZYuXcrnn3/O7t278ff358svvwRgxYoVzJ49m4SEBNzc3Fi1ahXly5enY8eO/Otf/yIoKIjXX3+dmJgY5s2bx9KlS/nuu++y2gkPD8ff35+AgADGjRvHuHHj8vX9ypSWlkZISAhLlizJ2uft7Z3rsX/88Qd169alTp06AAwZMoSffvqJRhnDO3379uXVV1+VAFlYXb9+/Xjuued49913OXr0KAsWLMDd3R1HR0fS09OJjY3NcY6joyMODg6kpaURd+fKGRmcnJywt7fPtfzP9u05ZmPD/6WmchX4EIxhyfh4Frz3HgHNmtGubVvs7OxISUkhPj4+R/0uLi7Y2tqSnJyc6zQRV1dXbGxsHlielJREYmJijnI3NzdMJhOJiYkk3Z1mAnB3d0cpRUJCAsnJyTnKS5UqBUB8fDwpd00OTrx40Ygo71GulMI9I9vHrVu3SE1NzVZuMplwy8gFFxcXR1pa2j3LY2NjSb9ryNfGxgZXV9d7ltva2uLi4gJATEwM+q4o1s7OjmpuzlRzg5vuN6E6OcozP528mfE672Rvb4+TkxPp6ZrLl2OIT4D4W5CQAPHxkJbmQFqaI3Fx6Vy/EUtiglGWmGhsKSmOpKY6EB9vvLeSkoz9ycmQeB1upjhxKcmepKQ0EhON8pQUI1tISiqAE2APpAK3cvQPnAE7IAXI+d4DF4wwJBnI/t5SgJ2dK3Z2NmjdAHuHM9jZGgG0ra0x9cTOzhVbWxtMpiRsbBIxmYz9trbGrCN7ezdsbEwolYhSSdjYGOWZXx0c3LG1VWidACRn7TeZwGQDTo6lsLGBtLR4IAWlbp+rTODsVAqTCVJTb5dnnm9jo3B0dMdkgpSUW2idmlVmvDdMODq6YTJBcnIckAYKTIqMeky4uLiBgqTEWLRON9rNKLexscHJyRWlICExFkV6VplRbouTswsKSEiIAXTWPx1KGe8tR0dnlIJbt25mOxcFtjZ2ODllL88813hv2uPo6ARobt2KyVHu4OCAvb0jWqcTHx97u+6MY+ztb//eS0yM49yhJAjI5S1iTVpri2/AAIxpFZnPhwOf3O+c5s2b67sdOnTo9pOJE7Xu0MG828SJOiYmJke7d3NxcdFaa52SkqJ79+6t58+fr7XW+urVq1prrePj43Xjxo31lStXdEREhG7atKnWWuu0tDRdp04dfeXKFR0aGqq9vLx0XFycjo2N1Y0aNdJ79+7VWmtds2ZNffny5RyPtda6e/fu+ty5czn61KBBA/3BBx9orbV+4403dP369fX58+d1UlKSLl26tE5MTNRaa33lypWsc6ZNm6Y/+eQTrbXW27Zt0x06dNBLlizRPXr00KmpqTopKUlXrFgxWzulS5fWHh4ees2aNQ/8Pj1I165ddYUKFfTYsWP19u3b73ncjz/+qEePHp31fPHixfqFF17Iep6amqo9PDzy3G5ernFRkO39LvJt69atVmk3PT1dz507V5tMJg3oqVOnaq21PnfunAZybLNnz9Zaa3306NFcyxcsWKC11jo0NDTXcgX6K2MAUTbZZJOtWG4p2Fjl97XWWgOhWueMQwtrBDm3D0t0joOUGguMBahYsSIhISHZykuVKnV7BCafI715lZaWlusoz50SEhLw8fEBjDvZBw0aRGxsLO+//z7r1q0D4OzZs+zfv5+WLVtSunRptm/fzqVLl/D29sbe3p7g4GB69OiRNeLQs2dPNm/eTN26ddFaExcXh4ODQ7bHAMuWLQPI1sfExESuX7/O6NGjiY2NxWQyMWzYMFxdXUlKSsLJyYmkpCSSk5NZsGABK1euJDk5mYsXLzJ16lRiY2Np2rQpqampzJ49m/Xr1xMfH090dDTu7u5ZbUVFReHi4sJjjz1GRERErt+n3r17c/HixRz733zzTXr27Jlt3/Lly9m9ezcbNmygW7dufP755/Tq1SvHuZkjQ5ntJSQkZHsOxn/D58+fzxrtuZ+8XOOiIDExMcfPgMi7uLg4q33/fHx8mDdvHocOHaJ8+fKEhISQkJDA888/n+NYV1dXQkJCiI2NzbXcxsaGkJAQrl27lqP8xx9/5PLlyzwLbAXuXj7E1cWFvv36UcrdnZsxMUREROSov+5jj+Hq6sr169c5feZMjvL69erh7OzMlStXiDp3Lke5Z8OGODg4cOnSJc5HR+cob9yoEXZ2dly4cIELufxu8PbywsbGhnPnznH5ypUc5b5NmgBwNiqKq1evZivT6ek0bdoUgNOnT3P9rjvm7GxtaZyRE+5URAQxMTHZyh3s7fH09ATgxIkTxN3KPgrq5OREg/r1ATh67FiOEXRXFxfq1q0LwOHDh0m6awTc3d2dOhnT4w4ePEjKXSPYZUqXzlpoJjw8nLS7RqDLlStH9WpGGrv9Bw5wt/IeHlStWpW0tDTC//orR3mlihWpVKkSKSkpHDyU8wPbKpUrU6FCBZKSkjh85EiO8mpVq+Lh4UF8fDzHjh/PUV6zRg3KlClDXFwcJ06ezFFeu3Zts7z3oqOjuXjpUo5ya773TMqEt7c3WitOn47k+vXM955Ca7C1taNBg8ZoFKcjTxIba7z3tDaOsbNz4LG6xqegpyOPc+tWnBEUZZQ7ODhRq5YnGjgdeYSEhHjuDKWcnFypVs14b0ZEHCQlJcmINDPKXZxLUbly3YzyMFLT7njvaXB1LUOFisanshGn9uX49MPNrTweHjXRGiIi9tw+NaMBd/eKlC1XnfS0NE6f2ZdVb6bSpStTunQ1UlOTORt1x3s345gyZarj7l6J5JREos+HU7ZcPSoUsb93hRUgR5H9w6NqwPm7D9Jafw58DtCiRQsdEBCQrfzw4cN5CoAeRmxs7APbcHJyyppWkSkkJITff/+d//3vfzg7OxMQEICNjQ1ubm6MGzeOH3/8kQsXLjB27Fjc3NxwcHDAwcEhqy0HBwccHR1xc3NDKYWrq2uOx/dy7NgxmjdvnvVR5JEjRxg/fjxubm5ERUVRtWpV3N3dWbx4MQcOHGDbtm24urrSvn17mjdvjpubG+Hh4Vy6dAkPDw+qVKkCQGpqKsnJyVltnzp1Cl9fX7744gtatWqFv79/1h+nTPe6ye5eunTpQpcuXbh16xbHjx/P9XXWq1ePpUuXZpVdvXqVWrVqZTs2OTmZ8uXL5+lGwbxc46LA0dExx/dX5F1ISAh3/w4pTLm13b179/ue88QTT9y3vH///tmeX7t2jeXLl5OWns63dx1rMpkY3Ls3zb+9XdL8vrWD333K6j7gXGuUh4SEUDfj+1wU+1eY5fdJzAGA5wPKGz+g3OcB5b4PKH+Y915USAgD7vOzbO3vff0HlOc+efC2Zg8of/wB5W0eUN7+AeUdH1BeGKz9+zo3hZVA5k+gnlKqtlLKHhgCrCmktgvFzZs3KVOmDM7Ozhw5coTdu3dnlfXr14+NGzfy559/0rVrVwDat2/P6tWriY+P59atW6xatQp/f/8CtR0eHk6TjP904faNdAAHDhzIehweHk6bNm1wdXVl5cqV7Ny5E29vb6Kjoxk6dCg//fQTLi4ubNq0CYAyZcpkzA9KzDrf29ubypUrs3DhQgYPHpzrvLi82LRpU9Z8w0uXLrF9+3aCgoIACAwM5NwdI1V+fn4cP36ciIgIkpOT+eGHH+jdu3dW+dWrV/McHAtRkkycOBHHeywF5+joyIQJEwq5R0IIUTIUSoCstU4FXgQ2AYeB5Vrrg4XRdmHp1q0bqamp+Pj48MYbb2RLsWRvb0/Hjh0ZNGhQVjaIZs2a8fTTT9OyZUsef/xxxowZk6fRwh49enD+fPbB9/DwcHx9jf/fExMTSUhIoEyZMkD2YHnkyJHMmzcPf39/jh07Rp06dVBK0b9/fz744AM8PT154403mDZtWlbdXbp0Yfv27VntZN5IFxQUxKBBgxg1alSBvl8rVqzA09OTJk2a0KtXL6ZPn07r1q1JT0/nxIkT2VYls7W15ZNPPqFr1654enoyaNCgrI9NwRi17tGjR4H6IURx1qpVKyZPnoyzs3NWxgyTyYSzszOTJ0+WVG9CCFFQuU1MLgrbA2/SsxBL3MCVlpammzRpoo8dO2b2ui1t7969etiwYYXWXnh4uJ40aVK+zunXr58+cuRIno+Xm/QeDda6Sc8adu3apYcMGaKbN2+uhwwZonft2mXtLhWKR+kaP8rkOpd81rzGWPkmvUfWoUOH6NWrF/369aNevXrW7k6+NW3alI4dO5KWlpY1+m1JXl5ezJkzJ8/HJycn07dvXxrcb2ksIUq4Vq1ayWixEEKYkQTIFtaoUSNOnTpl7W48lIJOoygM9vb2jBgxwtrdEEIIIUQJIqu8CyGEEEIIcQcJkIUQQgghhLhDsQuQtc6xvogQJY68z4UQQgjrKVYBsqOjI1evXpXgQZRoWmuuXr16z/y2QgghhLCsYnWTXrVq1YiKiuLy5csWayMxMVECkxKuOFxjR0dHqmUsMSuEEEKIwlWsAmQ7OztqZ6xrbykhISGyvG8JJ9dYCCGEEPdTrKZYCCGEEEIIYWkSIAshhBBCCHEHCZCFEEIIIYS4gyqqGSGUUpeB01Zo2gO4YoV2ReGRa/xokOtc8sk1fjTIdS75rHmNa2qty9+9s8gGyNailArVWrewdj+E5cg1fjTIdS755Bo/GuQ6l3xF8RrLFAshhBBCCCHuIAGyEEIIIYQQd5AAOafPrd0BYXFyjR8Ncp1LPrnGjwa5ziVfkbvGMgdZCCGEEEKIO8gIshBCCCGEEHeQAFkIIYQQQog7SICcQSnVTSl1VCl1Qik1xdr9EeanlKqulNqqlDqslDqolJpo7T4Jy1BK2Sil9iml1lm7L8IylFKllVIrlFJHMn6mW1u7T8K8lFKTMn5X/6WU+l4p5WjtPomHp5T6Sil1SSn11x37yiqlNiuljmd8LWPNPoIEyIDxxxT4FOgONAKeUko1sm6vhAWkAi9rrT2BVsALcp1LrInAYWt3QljUXGCj1roh0AS53iWKUqoqMAFoobX2AmyAIdbtlTCTb4Bud+2bAmzRWtcDtmQ8tyoJkA0tgRNa61Na62TgB6CPlfskzExrHa213pvxOBbjD2pV6/ZKmJtSqhrQE1ho7b4Iy1BKuQPtgS8BtNbJWusb1u2VsABbwEkpZQs4A+et3B9hBlrr34Brd+3uAyzKeLwI6FuoncqFBMiGqsDZO55HIYFTiaaUqgU0Bf5n3Z4IC/gI+AeQbu2OCIupA1wGvs6YSrNQKeVi7U4J89FanwNmA2eAaOCm1voX6/ZKWFBFrXU0GINZQAUr90cC5Awql32S/66EUkq5AiuBl7TWMdbujzAfpVQv4JLWeo+1+yIsyhZoBvxHa8H8Ei8AAAXoSURBVN0UuEUR+EhWmE/GHNQ+QG2gCuCilBpm3V6JR4kEyIYooPodz6shH+WUSEopO4zgeKnW+r/W7o8wu7ZAb6VUJMZUqU5KqSXW7ZKwgCggSmud+QnQCoyAWZQcnYEIrfVlrXUK8F+gjZX7JCznolKqMkDG10tW7o8EyBn+BOoppWorpewxbgRYY+U+CTNTSimMOYuHtdZzrN0fYX5a61e11tW01rUwfo5/1VrLqFMJo7W+AJxVSjXI2BUIHLJil4T5nQFaKaWcM353ByI3YpZka4CRGY9HAj9ZsS+A8THVI09rnaqUehHYhHGn7Fda64NW7pYwv7bAcCBcKbU/Y9+/tNYbrNgnIUTB/B+wNGNQ4xTwjJX7I8xIa/0/pdQKYC9GBqJ9FMHliEX+KaW+BwIAD6VUFDAVmAUsV0qNxvjnaKD1emiQpaaFEEIIIYS4g0yxEEIIIYQQ4g4SIAshhBBCCHEHCZCFEEIIIYS4gwTIQgghhBBC3EECZCGEEEIIIe4gAbIQQgghhBB3kABZCCGEEEKIO0iALIQQVqKU+kYpNeMhzo9USnXOw3EHlVIBBW1HCCEeNRIgCyFEHimltFKq7l37pimlllirT3mhtW6stQ6xVP1KqaFKqT1KqRtKqWtKqRClVFVLtSeEEJYmS00LIUQxo5Sy1VqnWrsfAEqpp4HXgcEYywKXA/oC163YLSGEeCgygiyEEGaSMeVhslIqTCl1Uym1TCnleEd5U6XUXqVUrFJqGeB41/lVlFIrlVKXlVIRSqkJd9X9T6VUGHBLKWV7R9krSqmVd9X1sVLqozvO7XxXXbn2UynVTCm1L6OPP2aU3W8ayGhggdZ6jzZc0Vov1FrHF+y7KIQQ1icBshDi/9u7nxArqziM49/HtAj7M0SlaE0jBG0mcWMILTJcNFT2x8qKxMRaRkibIlq5jBbiSiiYzEorMUEXtY0onIiIIiooyaFUsBpcZGnD0+I9A2cuzdz3zgyNE88HLpz7vu8593l3Pw7nnBtzazMwBKwCVgPbACRdChwG9gHXAO8BD010krQIOAJ8CawENgA7JN1Vjf04cA/Q1zGD/CYwJKmvjLWYZkZ3Xy85S8b3gddLxv3Ag13e9xywXdJmSdd2eTYiYkFIgRwRMbd22/7F9m80Be+acn0dsATYZfuC7YPAZ1W/tcB1tnfaPm/7R+BV4LGOsUdtn6t/0PZJ4CPgkXJpCDhj+/Mec66jWXq3u2Q8BIx0ed+twIfAK8BpSUckXQ8g6U5JA136R0RcdFIgR0S0N05T5NaWABeq76eq9h/AFaW9AvjZtqv7P1Xtm4AVZaPbmKQx4EVgWfXM6DTZ9gJbSnsL088eT5Xz3zJO95vYPmV7h+1+4Daa2ejny+3tgLrkiIi46KRAjoho7wQw0HFtFZML3amcBFZKqgvG/qo9Chy33Vd9rrR9d/VMXbh2OgysljQI3Au81SJTm4w3tu1cZqy/ApZKug/YCAxL2jqDLBER8yYFckREe+8AL0m6QdKisvFtI3CwRd9Pgb+BZyUtlrSJZsZ1wghwtmzEu1zSJZIGJa1tE8z2nyXH28CI7RO9vFiVcRx4pmS8vyPjJJJekHS7pMvKZxuwHhgGjgJf2F5v+40ZZImImDcpkCMi2tsJfAJ8THOM2cvAE7a/7tbR9nlgE82mvd9pNtEdqu6P0xTba4DjwBngNeDqHvLtBW6l+/KKbhmfAsZolmocBf6aostVNMXwrzSz648CG2wfA24GvptJjoiI+abJS80iImKhktQPfAsst312jsY8BuyxPdxjvweAAdu75iJHRMR/KTPIERH/A+WYuOeAA7MpjiXdIWl5WWLxJM2muw9mMNT3wNMTZzFHRCwk+Se9iIgFTtJS4DTNZsGhWQ53C/AuzakWPwAPl2PkemL7G2BwllkiIuZFllhERERERFSyxCIiIiIiopICOSIiIiKikgI5IiIiIqKSAjkiIiIiopICOSIiIiKikgI5IiIiIqKSAjkiIiIiovIPBqIRTrBmliQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(put_focus, S_strip_focus, put_focus.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the price of the put can become smaller than its own payoff. This cross is due to interest rates. In nowadays scenarios, rates are typically extremely low (when not negative). Let's see that the crossing disappears for $r=0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_focus_zero_rates = PlainVanillaOption(option_type='put', S_t=3.0, K=5.0, t=\"19-04-2020\", T=\"19-04-2022\", r=0.0, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(put_focus_zero_rates, S_strip_focus, put_focus_zero_rates.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And nothing prevents us to consider even negative rates, as is common nowdays. Let's use a unrealistic $r = -5\\%$ just to magnify the result graphically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "put_focus_negative_rates = PlainVanillaOption(option_type='put', S_t=3.0, K=5.0, t=\"19-04-2020\", T=\"19-04-2022\", r=-0.05, sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/cM5tA1YQNJB8FO+lPKFl7SToenAKwTE6oeSOfYlJeOzEObeJoJvIstDzPwm208ehc2Jikj2nhCm5c1qS0uscHmF/r59Qbslty7D2lazOkvlVUSQsZraNYGSKBcrj71yeBn51zg1NYb52QMUT81kwbvJaoHpaT8YZIavnnxWE1vFmguEGP0nkNa3/RGjd/I+ZfQr8nwsuvMtUwj12RjiHbWSSc0lGy8z7SnrK7oNFi2QI51xirSSJ2QQ8ZWYVXXCRyRGCn1WzpKyefxbxGEGr2CcJX9D6T5rWzf845y7J6BySkopjp3iQmfeV9KRiWCQDhbqkpNQfWwQzuxBYTHCRUfsMTkdEJNtQNwkRERERybF0AZ2IiIiI5FiZtptEyZIlXcWKFb3H3b9/PwUKpPXifMmstH2zP23j7E3bN/vTNs7eMmr7rl69eqdzrlRir2XaYrhixYqsWrXKe9y4uDhiY2O9xxU/tH2zP23j7E3bN/vTNs7eMmr7mtn3Sb2mbhIiIiIikmOpGBYRERGRHEvFsIiIiIjkWCqGRURERCTHUjEsIiIiIjmWimERERERybFUDIuIiIhIjqViWERERERyLBXDIiIiIpJjqRgWERERkRxLxbCIiIiI5FgqhkVEREQkx1IxLCIiIiI5VnRGJ5CZzJ0LYBmdhoiIiIh4omI4ZMsWaNlyCmZDqF07PzExR5Oct3Xr1vTp0weA2NhYunbtSteuXdm5cydXXXVVirESzt+7d2/atGnDxo0b6dGjR4rvTzj/008/zWWXXcaKFSt46KGHUnx/wvnfeOMNzj77bKZPn87zzz+f4vsTzj9p0iRKlizJmDFjGDNmTIrvTzh/XFwcAEOGDGHGjBkpvj/+/CtXrmTy5MkA9OvXj5UrVyb73uPHj7N06dK/59+1axcjRowAoHv37mzatCnZ91epUuUf85coUYJBgwYB0LFjR3bt2pXs+y+99NJ/zH/ppZf+Y19Kifa9lPe9PXv2ULRo0UTfn5H7XokSJf4xv/a9k9v3fvjhB2JjYzPlvpcc7Xvh73sJv8OZZd/LzMe95GS2fe+6665LMaZv6iYRUqkSdOu2l2PHPuWzz/Zw+HDujE5JRERERCLMnHMZnUOiatWq5VatWuU9bvv2zzFtWj8KFqzNV1/Npnz5It5zkMiJi4sLqxVMsi5t4+xN2zf70zbO3jJq+5rZaudcrcReU8twAj17Xsz997/Pvn2rqFatMT/+uD+jUxIRERGRCFExnIjBgzvw6KNTOXSoCa1a5ee33zI6IxERERGJBBXDSRgwoBWzZz/D5s1GnTrfsGbNfzM6JRERERFJZyqGk9GsGUyffpTvvmtL7dr1+fTTHzM6JRERERFJRyqGU9C4cTTDh4/hr79+pV69+ixd+l1GpyQiIiIi6UTFcBh69LiMsWMXcuzYHzRqVJ/587/N6JREREREJB2oGA7TjTfWYsKExRw/fpi2bR/l668zOiMRERERSSsVw6lw9dU1mDFjOYULj6RBA1i7NqMzEhEREZG0UDGcSi1bVmH58oLExOzl4ovb8Pbb/m8MIiIiIiLpQ8XwSTjrLJg48XecW89NNzVmxIjk780tIiIiIpmTiuGTVK9eeZYvX0ru3KXp0aMpL720JKNTEhEREZFUUjGcBpdcUo7PPltCTEx5evVqweDBizI6JRERERFJBRXDaVSzZhm++CKOQoVieeSR8syYkdEZiYiIiEi4VAyng3PPPYVt22Zx/vmVad/eMWTIFxmdkoiIiIiEQcVwOileHBYuhNNPf4X776/N3Xe/l9EpiYiIiEgKVAynoyJF4OOPu1GkSD1efbULt9wyJqNTEhEREZFkqBhOZ6edVojNm2dRvHgTRo3qxvXXj8jolEREREQkCSqGI6Bkyfxs2fIRpUq1ZNy4u3n88R8yOiURERERSYSK4QgpWjQvW7dOpX79OAYMKM+zz2Z0RiIiIiKSkIrhCCpYMIYFCy6lc2d48MHxNGz4BMePu4xOS0RERERCVAxHWO7c8O67cNZZi4mLe4y6dR9SQSwiIiKSSagY9iBXLvj66zeoVq0Hn3zyDLVq3aeCWERERCQTUDHsSXR0FOvXD6dmzZ58+eVQzj//Do4dU0EsIiIikpFUDHsUFWWsXv0il1zSl6+/LsGttxrHjmV0ViIiIiI5l4phz6KijBUrBvHoo08yejS0b7+FQ4eOZnRaIiIiIjlStK9AZrYN2AscA44652r5ip3ZREUZAwbA8eO7eOqpOlSu3IBvvhlPwYIxGZ2aiIiISI7iu2W4oXOuZk4uhON78skStGvXn59/nkylSh3Zs+dQRqckIiIikqOom8QJf/0Ft95Kge++8xp26tRedO48nF9/nUHlyleyc+cBr/FFREREcjJzzs+IBmb2HfA74IA3nHMjEpmnO9AdoHTp0hdNmDDBS24AeXbs4MK77sIOH2bdkCHsq1LFW2yAwYNXMHv2w5QufR+jR19Jvny6si4S9u3bR8GCBTM6DYkgbePsTds3+9M2zt4yavs2bNhwdVI9E3wWw6c557ab2SnAfOBu59zSpOavVauWW7VqlZfc/rZlC4fq1iXv4cMwZw5cconX8A8+OI/nnrucOnXyMWsWFCniNXyOEBcXR2xsbEanIRGkbZy9aftmf9rG2VtGbV8zS7IY9tZNwjm3PfTvr8BUoLav2GGrVIkvhw6FEiWgaVNYvtxr+Geeacb77+fj00//oHLlW9iyZbfX+CIiIiI5jZdi2MwKmFmhE38DzYD1PmKn1uFTT4UlS6BsWWjeHBYt8hq/Y0d48sk17Nz5Dued14gNG37zGl9EREQkJ/HVMlwaWG5ma4HPgJnOuTmeYqde2bIQFwdnngmtWgVdJjzq168BTz89nUOHNnLBBbGsWfNfr/FFREREcgovxbBzbqtzrkboca5zbqCPuGlSujQsXgzVqsGVV8KHH3oN369fM158cTaHD39P7doN+Pzzn7zGFxEREckJNLRackqWDLpJXHABXHUVfPCB1/C9esXy+uvzOHYsLx06HGPbNq/hRURERLI9FcMpKVoU5s2DOnWgc2d4912v4Xv0uIwVK9awf38F6tU7zvLl273GFxEREcnOVAyHo3DhoN9wbCzceCO8+abX8JdcEsWiRbB794M0aFCLjz762mt8ERERkexKxXC4ChSAGTOCESZuvRWGDfMavmZNmDChK+Bo1y6WDz5Y5zW+iIiISHakYjg18uWDadOCC+ruuguef95r+LZtz2HWrCWYxXDNNQ15553VXuOLiIiIZDcqhlMrT57gQrpOnaBPHxjod2CM5s2rsHDhUnLlKsxNN7Vm8eIDXuOLiIiIZCcqhk9G7twwfjzccAM8/HDw8HRba4DY2DNZtmwpZcq8S5s2+VmyxFtoERERkWxFxfDJio6GMWPglluC1uH77/daENepU47PP29MuXLQtOkYBg9e4C22iIiISHYRndEJZGlRUfDGG5A3b9B/+NAhePnlYLoHp50GCxb8ReXKL9G37wYOHpzCY4+19BJbREREJDtQy3BaRUUFBXCfPsEIEz16wLFj3sKXLZubdesWkD//uTz+eDv69p3qLbaIiIhIVqdiOD2YweDBQd/hN9+Ebt3g6FFv4c86qwRff72QggUvYvDgTvTsOdFbbBEREZGsTMVwejGDJ5+Ep56Cd96B666Dv/7yFr5ChaJs3DiPIkXq8sorWxk71ltoERERkSxLfYbTW//+QR/iPn3gyBGYODEYjs2D004rxLZt8+nUKYauXWHXrl3cd18JL7FFREREsiK1DEdC797w6qvw4YfQrh0cPOgtdNGiMUyfDvXrb6R377Po2PFlb7FFREREshoVw5Fy550wciTMnQutW8P+/d5C580LM2eeQZkysUyZ0pOWLQd7iy0iIiKSlagYjqRbboG334a4OLjiCvjzT2+hCxaMYfPmiZQv35nZs/vSsOETHD/ubxxkERERkaxAxXCkXX89TJgAn3wCzZrBnj3eQufPn5tvv32XSpVuIi7uMdq1G+/zviAiIiIimZ6KYR86dYJJk+CLL6BRI9i1y1vomJhcfPPNW9St+yrTp19F795eb5QnIiIikqmpGPblyiuDC+o2bIDYWNixw1vo6Ogoli27k3vuycOLL+7i0ksHcfTocW/xRURERDIrFcM+tWgBM2bA1q3QoAH8/LO30GYwdCg0bjyeTz99iHPOuZUjR/zdKU9EREQkM1Ix7FvjxjBnDmzfHhTE33/vLbQZzJt3F/XrP8q3377F2WffxKFD/u6UJyIiIpLZqBjOCJdfDvPnw86dUL8+bNniLXRUlLFkyQCaNRvItm3jqFz5WvbtO+ItvoiIiEhmomI4o1xyCSxaFIw/XL8+bNzoNfzcuQ9x5ZUv8PPPa+jY8XcOH/YaXkRERCRTUDGckS68EBYvhqNHgy4T69d7DT9t2r288MIa5s0rTZs2R9m1y9+d8kREREQyAxXDGe3882HJEsiVKxhl4ssvvYa/994CjBoF8+d3p3Lllvzyyz6v8UVEREQykorhzKBqVVi6FAoUCMYh/vRTr+FvvhnuuKMJe/Yso0qV5vzwwx9e44uIiIhkFBXDmUWlSkFBXLw4NG0Ky5d7DT9s2HX07j2RvXs/45xzmrJ1626v8UVEREQygorhzKRChaAgLlMGmjcPLrDzaMiQjjz88BT2719L9ept+fVX3apOREREsjcVw5lN2bJBH+IzzoBWrWD2bK/hn3yyDU899RFHjjxBo0bGL794DS8iIiLilYrhzOjUUyEuLuhL3K5dcBtnj/r3b87cuY3Ytg0uuOBdPv/c353yRERERHxSMZxZlSwZdJOoWROuugo++MBr+IYNYeLEnfzyy11cdll9li/3d6c8EREREV9UDGdmxYoFd6q75BLo3Bnefddr+FatSjJ69HyOHdtNbGx9Fi7c7DW+iIiISKSpGM7sCheGOXOCm3LceCOMGuU1fNeuF/Pee4s5fnw/zZrVZ+bMb7zGFxEREYkkFcNZQcGCMHNmMMLELbfAsGFew19zTU2mTIkDjtO58yLWrfMaXkRERCRiVAxnFfnywbRp0LYt3HUXvPCC1/Dt2p3HypVfU6TIHTRsCJ98csRrfBEREZFIUDGcleTJA5MmQadO0Ls3PP201/C1axdn6VKIifmcyy6rzKhRfu+UJyIiIpLeVAxnNblzw/jxcP310L8/PPIIOH83xzjzTJgypTS5csVwyy1NePXVZd5ii4iIiKQ3FcNZUXQ0jBkD//d/8NRT8MADXgviSy8tz6efLiUm5nTuvvsKnntuobfYIiIiIulJxXBWlSsXjBgBd9wBQ4bAPffA8ePewl944WmsXh1H3ryVeOCBVrz00mpvsUVERETSS3RGJyBpEBUFr74KefMGF9QdPgyvvx5M9+C880qzbt1iLr98CH36VKdCheCGeSIiIiJZhVqGszqzoGW4f38YORK6doWjR72FP+usEnzzzSAuuig3V121gwce+MhbbBEREZG0UjGcHZgFfYeffBLeeQe6dIG//vIWvmjR4EZ5pUo9wnPPtadHj7e9xRYRERFJC3WTyE4efjjoMnH//UGXiYkTg+HYPChUCNaufZFq1b5jxIiuHDhwiHfe6e4ltoiIiMjJUstwdtOnD7zyCnz4IbRvDwcPegt9yikF2Lx5OqVKteDdd3vQqdMr3mKLiIiInAwVw9nRXXcFI03MmQNt2sD+/d5CFyuWl82bp3Dqqe2YNGk4gwb5K8ZFREREUkvFcHZ1660wdiwsXgwtWsCff3oLXbhwHrZseZ+2bZfw0EP5eOKJY95ii4iIiKSGiuHs7IYb4L33YMUKaNYM9uzxFjp//txMnlyKLl2O8thj11Kv3sMcP+7vxiAiIiIi4VAxnN1dfTVMmgRffAGNG8OuXd5CBzfKi+Lss4vw8ccDqV37fhXEIiIikqmoGM4J2rULLqj76iuIjYUdO7yFjo6OYv36Nzj//LtYvfp5atS4m6NH/d0pT0RERCQ5XothM8tlZl+a2QyfcYWg3/DMmbB1a1AQb9/uLXR0dBRr1rzMxRf3Yf36YdSseR/H1I1YREREMgHfLcM9gQ2eY8oJjRsHI0z89BPUrw8//BabMhgAACAASURBVOAtdFSU8ckng4mNfZKvvrqam27yeqM8ERERkUR5K4bN7HSgFfCmr5iSiMsvD24Xt3NnUBBv3eotdFSUsXjxwwwceBnjxkGDBh9x4IC/O+WJiIiIJGTO+bmgycwmAYOAQkAf51zrRObpDnQHKF269EUTJkzwklt8+/bto2DBgt7j+lZw40ZqPPAAx2NiWPP88xwsX95r/OHD9/L++20pVuwK3n77PgoWzO0lbk7ZvjmZtnH2pu2b/WkbZ28ZtX0bNmy42jlXK7HXvBTDZtYaaOmcu8PMYkmiGI6vVq1abtWqVRHPLaG4uDhiY2O9x80Q69ZBkyYQFQULF8K553oN37nza0yceCclSjRn06apFC+eL+Ixc9T2zaG0jbM3bd/sT9s4e8uo7WtmSRbDvrpJ1AXamtk2YALQyMze9RRbklK9OixZEhTDsbGwZo3X8BMm3EHXrqPYtWselSq1YseOfV7ji4iIiHgphp1z/ZxzpzvnKgKdgUXOuet9xJYUVKsGS5dCvnzQsCF8/rnX8KNH38xtt73Dnj1LadBgrs8b5YmIiIhonGEBKlcOCuJixYIRJz7+2Gv44cO78PLL37BlS0eaNoXdu3VjDhEREfHDezHsnItLqb+wZICKFYOCuEwZaN4c4uK8hr/77spMngxffLGM8uXrs3HjTq/xRUREJGdSy7D8z+mnB32IK1QIbtIxd67X8G3bwuOPH2D//lXUqNGQ9ev93SlPREREciYVw/JPp54atApXrRpUp9Onew3fv39zhgyZyeHDW7noogasWvWz1/giIiKSs6gYln8rVQoWLYIaNaBDB5g0yWv43r0bMWzYXI4c2c6ll9Zn1Sq1EIuIiEhkqBiWxBUrBgsWwCWXwDXXwLhxXsPfcUc93nprAVFRTenQoSRbtngNLyIiIjmEimFJWuHCMGcONGgAN9wAb73lNXy3brX55JPXOXAgF3Xr/sTs2Ru9xhcREZHsT8WwJK9gQZg5E5o1g//7P3jtNa/hL7gAFi927N7dhVat6jN16nqv8UVERCR7UzEsKcuXDz78ENq0gTvvhBde8Br+/PONDz98A7NoOnaMZdy4L7zGFxERkexLxbCEJ08emDwZrroKeveGp5/2Gr5Fi6rMn7+UqKgCXH99Y0aN+tRrfBEREcmeVAxL+HLnhvfegy5doH9/ePRRcP7uFteoUSWWLFlKdHQJunfvw9KlulOdiIiIpI2KYUmd6GgYOxZuvhmefBIefNBrQVy3bgVWrlzCmWdOoUULY+FCb6FFREQkG1IxLKmXKxeMHAm33w6DB0OvXl4L4lq1yrJ8eSkqVjxCs2adeeKJ2d5ii4iISPYSHe6MZlYNuAo41Tl3p5lVBWKcc+silp1kXlFRMGwY5M0LL74Ihw7B8OHBdA9Kl4YPP9xP9erf8thjV3Lo0Ac8/fSVXmKLiIhI9hFW5WJmnYAlQFnghtDkgoDfYQUkczGD55+Hfv1gxIig68SxY97CV65cjA0bFlKgwIUMGnQV9977vrfYIiIikj2E2zL8BNDMObfGzK4JTVsL1IhMWpJlmMHAgUEL8WOPweHD8PbbwcV2HlSoUJSNG+dxzjmtGDr0Wg4fPsZrr13rJbaIiIhkfeH+pn0KQfEL4OL9q8v5JSiIH30Unn0WJkwIbt985Ii38GXLFubbb+dQqtSVDB9+FiNGaLcUERGR8IRbDK/mf90jTugMfJa+6UiW9sAD8NJLMHUqdOgQ9CP25JRTCvD991No0aIWPXrA/fd/7i22iIiIZF3hFsP3AE+Z2RKggJnNBZ4E7o1YZpI13XMPvPEGzJoV3LHuwAFvofPlC+rwiy+eypAhtWnT5nlvsUVERCRrCqsYds59A1QFhgEPA6OB851z30YwN8mquneH0aNh0SJo0QL27vUWOk8eiItrQ7lyVzNjRh+aNHnKW2wRERHJesK6gM7MygIHnHPvx5tWzMxOc85tj1h2knXddFNQmV5/PTRrBrNnQ9GiXkLnz5+bTZvGce65eVi48BHq1TvE0qVPEhVlXuKLiIhI1hFuN4lpwOkJpp0OTE3fdCRb6dwZPvgAVq+GJk1g1y5vofPmjeabb0Zz9tm38PHHA7nxxlU+7wsiIiIiWUS4xXAV59x/4k8IPa+a/ilJttK+fdCRd/16aNgQfv3VW+jcuXOxfv0bdOiwjHHjLubuu+H4cW/hRUREJAsItxj+zcwqx58Qeu6vqU+yrlatYMYM2LwZGjSA7f561kRHRzFpUj369IFhwxZw881jOHLE341BREREJHMLtxh+C5hsZq3N7BwzawNMAt6MXGqSrTRpAnPmwE8/BQXxDz94C20GgwdDgwYr+f77sVSt2o1Dh456iy8iIiKZV7jF8DPAu8AQ4HPgudDzZyKUl2RH9evD/Pnw22/B31u3egttBnFxj3DRRffx3XfvcNZZ13HgwF/e4ouIiEjmFO7Qasedc88556o65wqE/h3inFMPTEmdOnVg4cJguLX69WHTJq/hhwxpQ5s2Q/jppw+oVOkq9u71d6c8ERERyXySLIbNrH68vxsl9fCTpmQrF10EixcHt2yuXx+++spr+I8+6k2nTq/yyy+l6NQpmoMHvYYXERGRTCS5cYZfA84L/T0qiXkccGa6ZiQ5Q/XqsGQJNG4MsbFB94maNb2Ff//9O3nzTUf37kbjxj8ybVpxTjmlgLf4IiIikjkk2TLsnDsv3tPKzrkzEnmoEJaTV60aLF0a3Ee5USP4/HOv4W+5xRg16jArVzbmrLOu4Kef/vQaX0RERDJein2GzSwXsM/M8njIR3KaypWDgrho0WDEiRUrvIbv1i0PvXo9xZ9/fkLVqk357rvfvcYXERGRjJViMeycOwZsAkpEPh3JkSpWDAri0qWDWzfHxXkN/+KLV/PQQ5PYv38N557bmI0bd3qNLyIiIhkn3KHVxgEzzOwmM2usC+gk3Z1+etCHuEIFaNEC5s3zGn7gwCt56qkPOXhwA5dc0ptffvEaXkRERDJIchfQxXd76N/HE0zXBXSSfsqUCVqFmzaFNm1g8mRo3dpb+P79r6BQofk8+OA5NGgQjAB3+unewouIiEgGCHec4cQuntMFdJL+SpWCRYuC0Sbatw8KYo/uuace8+cXZ/v2Q5xzTneWL//ea3wRERHxK9li2ALdzexlM+vqKSfJ6YoXhwUL4OKL4ZprYPx4r+Hr1oWRI7ewd+/7xMbWZ+HCLV7ji4iIiD8ptQwPAQYApwKDzGxA5FMSAYoUgblzoV49uP56GD3aa/jOnc9l/PhFHD++n2bN6jNr1kav8UVERMSPlIrhq4EGzrmrgcbAdZFPSSSkUCGYNSsYcu3mm+H1172Gv/baC5kyJQ44SuvWDZg8eb3X+CIiIhJ5KRXDRZxzmwCcc18DxSOfkkg8+fPDRx8FF9LdfjsMHeo1fLt25zFjxhJy5TqFm28+zurVXsOLiIhIhKVUDJuZnWFmZ5rZmUCu+M9D00QiK2/e4EK6jh3h3nvhmWe8hm/RoiobNqyhePHqNGrkmDbtR6/xRUREJHJSKoYLAJvjPQoDW+I9/zai2YmcEBMDEybAdddBv37w+OPgnLfwlStHsXQpxMS8Svv25/Lqq8u9xRYREZHISbYYds5FOedyhf5N7JHLV6IiREfD229Dt24wYEBQFHssiMuVg7lzOxATU4a7727O4MGLvMUWERGRyAj3DnQimUOuXPDmm0H/4WefhV69vBbEF15YltWrl5Anzxn07duKJ56Y4y22iIiIpD8Vw5L1REXBsGFBIfzyy3DbbXD8uLfw5513Kv/5Txz58lXjscc6MGqU7t0sIiKSVYV7O2aRzMUMXngB8uWDQYPg8GEYNSpoOfbgrLNKsmHDQpo3X0aPHqeSL1/QnVlERESyFrUMS9ZlBgMHBv2Hx44Nbs7x11/ewleoUIzPP29LvXrQpcsMuncf5y22iIiIpI+Tahk2s4bAMefc0nTORyR1zODRR4Ph1/r2DVqIJ0wIRp/woFAhmDnTUbHicEaOnM2BA4d4993/8xJbRERE0i6slmEzW2JmdUN/9wUmAO+Z2UORTE4kbA88AC+9BFOnQocOcOiQt9AFChibNk2iVKnmjBt3Cx06DPMWW0RERNIm3G4S5wGfhP6+FYgF6gC3RSAnkZNzzz3BLZtnzoQ2beDAAW+hixXLx5Yt0yhTpi1Tp95Fy5YveIstIiIiJy/cYjgKcGZWCTDn3Abn3I9AscilJnISevSAMWNg0SJo0QL27vUWulChPGzZMony5Tsxe/YWHn7Y+Rz1TURERE5CuH2GlwOvAmWAqQChwnhnhPISOXk33QR58gQX1DVvDrNnQ5EiXkLny5ebTZvGc+edUQwcaOzevZNXXy1BVJR5iS8iIiKpE27LcFdgD7AOeDw0rSrwUvqnJJIOOneGDz6AVaugcWPYvdtb6Dx5ohkxIoqbb97B8OEXcNFFfTl2TE3EIiIimVFYxbBzbpdz7iHn3GPOuX2haTOdc0PDeb+Z5TWzz8xsrZl9ZWYD0pK0SFjatw8uqFu/Hho2hN9+8xY6KgpGjCjFBRdcyZo1z1G9ek/++svfjUFEREQkPOGOJpHHzAaa2VYz+yM0rZmZ3RVmnMNAI+dcDaAmcIWZ1Tm5lEVSoVUrmD4dvv0WGjQgZtcub6Fz5Ypi1apXqFOnN19//QrVqt3GkSMqiEVERDKTcLtJvEgwokQX4MTvvV8Bt4fzZhfYF3qaO/TQ78biR9OmQb/hH36gZs+e8OOP3kJHRRkrVjxHw4b92bJlJBde+CxHjngLLyIiIikwF8bl7mb2X6Cyc26/me12zhUPTd/jnCsaViCzXMBqoDIwzDnXN5F5ugPdAUqXLn3RhAkTwv8k6WTfvn0ULFjQe1yJvMLr13N+374cLVyYtS+8wKEyZbzGf+KJFSxefCd16hxlwICviIlRK3Ek6DucvWn7Zn/axtlbRm3fhg0brnbO1UrstXCL4e+B6s65P04Uw2ZWCvjEOVcpNcmYWVGCESnuds6tT2q+WrVquVWrVqVm0ekiLi6O2NhY73HFj9VvvMFF/fpBgQLB8GtnneU1/ogR0KPHAc44YzCrVvWjePE8XuPnBPoOZ2/avtmftnH2llHb18ySLIbD7SbxATDWzM4ILbAMwVBrqW66dc7tAeKAK1L7XpG02nv22bB4cXDb5vr14euvvcbv3h3uuWce3303gMqV27Njx0Gv8UVEROSfwi2GHwK2Af8BigLfAtuBsEaFMLNSoRZhzCwf0AT4JrXJiqSLGjUgLi74u0EDWLvWa/iXXmpH9+4j+P33OVSp0oafftrvNb6IiIj8T7hDqx1xzvVyzhUESgOFnHP3OufCvRSoDLDYzNYBnwPznXMzTi5lkXRwzjmwdCnkzRsMu+a5S84bb9xKz55j+PPPxVSt2oKtW//0Gl9EREQCSd6BzszOTOZ9hcyCO2o557amFMQ5tw64INXZiUTSWWcFBXHjxsFj9my47DJv4YcOvZH8+fMwaFBvmjXbwbJlhfF8TZ+IiEiOl1zL8GaC7hCbk3l8G+kERSLqjDNgyRIoXRqaNftf9wlPnn76GubM2cQvv5zF5Zc7NmzYl/KbREREJN0kWQw756Kcc7lC/yb1yOUzWZGIKFcuKIgrVICWLWHePK/hmzfPz4IF8NNPj1CjxmV8+ukOr/FFRERysnAvoAPAzMqb2aVmVi5SCYlkiDJlglbhKlWgTRuY4bdLe506MHRoQ/76azN168ayZMl2r/FFRERyqnBvx1zGzJYQdI2YAmwxs6VmdlpEsxPxqVSpYOzh6tWhQweYMsVr+Ntua8yYMXM5fvwnGjWqz5w5P3iNLyIikhOF2zI8HFgLFHPOlQGKAV8Cr0cqMZEMUbw4LFgAtWrB1VfDe+95DX/TTZczYcJ8nNtJq1aN+fhj3btZREQkksIthusBvZ1z+wFC/z4A+Lv0XsSXIkVg7lyoVw+6dIExY7yGv/rqOnz00SJKlnyGFi1iWLbMa3gREZEcJdxi+HfgnATTzgb2pG86IplEoUIwaxY0aQLdusHrfn8Ead36Qr74oiNly0KTJh8ycuRXXuOLiIjkFEmOM5zAYGCBmY0CvgcqAN2ARyKVmEiGy58fPvoIOnWC228PbuHcs6e38GXLwrx5h6lUqSfdu+/nwIH59OxZ01t8ERGRnCDcO9CNBK4BSgJtQv9e65wbEcHcRDJe3rwweXJwQV2vXvDss17DlyuXhxUrFhATk49evRry9NOfe40vIiKS3YXbMoxzbhGwKIK5iGROMTEwcSLceCM8+CAcOgSPPgqhuzBGWq1alVm1aim1azemf//GHDgwm6eequsltoiISHYXVjFsZjFAV6AmUDD+a865G9M/LZFMJjoa3nkH8uSBxx8PukwMHOitID7//IqsXbuECy5ozMCBsyhbti633+4ltIiISLYWbsvwWKAGMB3Q7bEkZ8qVC0aNCgriQYPg4EF44QVvBXGVKqezZcundO9ehDvugD/+OMyDD+bxEltERCS7CrcYvgI4wzmn0SMkZ4uKguHDg77EQ4cGXSaGDQume3DqqUWZPBnatfuWfv2asnbtK4wf38ZXPS4iIpLthFsM/wCoCUoEgpbgF18MCuJnnw26TIwcGbQce5A7N4wdW5Jq1U5hwoQO7N//Hh9+eJUKYhERkZOQZDFsZo3iPX0b+NDMXiJBN4nQhXUiOYtZ0FUib14YMCAoiMeODfoWe1CyZDG+/XYB55zTkunTr6FJk7eZP7+LrwZqERGRbCO5M/eoRKY9neC5A85Mv3REshCz4GK6vHmhX7+gIB4/Phh9woOiRQuzceMczj+/LYsW3UCTJkWZN6+Vr3pcREQkW0jytOmcO8NnIiJZ1oMPBgXxvfdCx47wwQfBcw8KFSrIhg0zueKKJ1i8OJZrrgnq8Tzq1CQiIhKWsH5UNbOaZlYuwbRyZlYjMmmJZDG9egUX1s2YAW3bwoED3kLny5ePJUsGMXRoAaZM2Uvt2uN9hhcREcnSwu1h+C6QO8G0GOCd9E1HJAu77TZ46y1YsABatoR9+7yG79kT2rUbyrp1XTj33EH8+afX8CIiIllSuMVweefc1vgTnHNbgIrpnpFIVtatG4wbB8uXQ7Nm8McfXsN/8EE/6ta9jm3bHqJq1cfYudN5jS8iIpLVhFsM/2RmF8afEHq+Pf1TEsnirr0W3n8fVq2CJk1g925voaOjo1my5G2aNr2Z//73CapW7cv27SqIRUREkhJuMfwiwdBqd5tZSzO7G5gKvBC51ESysA4dYMoUWLcOGjWC337zFjpXrlzMmTOStm1vZ/fucdSt+xvff+8tvIiISJYSVjHsnBsJ3Ae0Ap4L/dvbOTcigrmJZG2tW8P06bBpE8TGwn//6y10VFQU06YNY/r01ezZcwr16jk2bDjuLb6IiEhWEfYQ/c65D5xzVzjnzg39OymSiYlkC82awezZ8P330KAB/Pijt9BmRqtWp7JokWPXrp5ccMHNfPHFMW/xRUREsoJki2Ezu8jMzov3vJSZjTOztWb2upkVjHyKIllcgwYwbx7s2AH168N333kNf8EFxq23luLw4bHUqXM9y5b95TW+iIhIZpZSy/BQ4NR4z98EqgAjgPOAwRHKSyR7uewyWLgwGF2iQQP49luv4V966RH69RvMX39NoGHDa5g167DX+CIiIplVSsVwNWAZgJkVBVoAXZxzw4BrgTaRTU8kG6lVCxYvhoMHg4L466+9hn/66ft56qmXOXZsKq1bX8u0aRplQkREJKViOBo4Evq7DvCLc24TgHPuR6BoBHMTyX5q1IAlS8C54KK6deu8hu/f/25efHEElSvfxFVXGe++6zW8iIhIppNSMfwV0Cn0d2dgwYkXzKws4PeOAiLZwTnnBAVxTAw0bAirV3sN36vXraxefSUNGsANN8zh+ef3eo0vIiKSmaRUDPcF3jCz3QTDqT0b77VrgI8jlZhItlalCixdCoULQ+PGsHKl1/CFCsHIkduJimpHnz7NePjhPTj1mhARkRwo2WLYObccKA80Bc50zm2M9/JM4N4I5iaSvZ15ZtBCXKpUMATb0qWew5/GhAnvERW1moEDG3PXXbtUEIuISI6T4jjDzrm9zrnVzrm9AGZ2bWj6RuecbscskhblywcF8emnwxVXwIIFKb8nHXXq1J5p06aSK9dXvPZaI7p0+ZVjGopYRERykLBvuhHPG+mehUhOdtppQUFcuXJw17qZM72Gb9OmFbNmTSc6+lvee28i11wDhzXymoiI5BAnUwxbumchktOdckow7Np550H79jB1qtfwzZo15Ztv/sOQIXcxeTK0aePYv99rCiIiIhniZIrhZemehYhAiRJBN4lataBTJ5gwwWv4SpUq0bu3MWDAeubPr029et+xe7fXFERERLwLqxg2s9NO/O2caxlv+gWRSEokxypaFObOhbp1oUsXGDvWewqtWh2mYMGtrFlzOXXqbGK7rgwQEZFsLNyW4XlmVjz+BDOrDcxK/5REcrhChWD2bGjUCLp2hREjvIa/6KKL+PjjxRQteoTNm+tTu/ZXbN7sNQURERFvwi2GRxAUxAUBzOwy4CPg/yKVmEiOlj8/TJ8OLVtCjx7w8stew1evXp0VK5ZQokQU27fHUqfORtau9ZqCiIiIF2EVw865l4EPgVlm1gKYClzvnFPLsEik5M0bXEjXvj307AmDB3sNX61aNVauXErbti3Jm7c8DRrA8uVeUxAREYm4sC+gc849CXwOTAQ6Oef8DogqkhPFxMDEidC5M/TtC088gc87Y1SuXJlp08ayYkU+SpbcQ5Mmn/se+U1ERCSiopN6wcx+BBKedaNCj3fNghHWnHPlI5adiEDu3PDuu5AnDzz2GBw6BAMHgvkb5bB8eahZ8y6++24abdvOYPToWG680Vt4ERGRiEmyGAau95aFiCQvVy54662gIB40KCiIn3/ea0H8yivP8dVXX7JpUwtuuulDdu5sxn33eQsvIiISEUkWw865JT4TEZEUREXB668HfYlffDEoiF99NZjuQZkyZVi6NI4mTZqyfn0beveezK+/tmbQIK81uYiISLoKd5zhKWZ2eYJpl5vZpMikJSKJMoOhQ+GBB2D4cLj1Vjh2zFv4UqVKsXjxIi68sAZFitzHs8/+xS23wNGj3lIQERFJV8l1k4ivAdApwbSVwLT0TUdEUmQGzzwD+fLBgAFBC/HYsRAd7tc5bYoXL86CBfPZvft3xo7NzYABsHNncMO8fPm8pCAiIpJuwv199RBQIMG0gsBf6ZuOiITFDB5/HJ5+GsaPD0abOHLEW/giRYpwxhkVeewxR716t/PRR6Np1gx+/91bCiIiIuki3GJ4LvCGmRUGCP37KjAnUomJSBj69Qv6D0+eDFddFbQSe3TkyBHy598K3MzKla9z+eXw009eUxAREUmTcIvh3kBhYLeZ/QrsBooAvSKVmIiEqVcveO214I51V14JBw54C50nTx4+/PBDWrduzbFjt7N581Auuwy+/tpbCiIiImkS7h3ofnfOtQLKAa2A051zbZxzeyKanYiE5/bbg6HX5s+HVq1g3z5vofPmzcvkyZPp2LEjhw/fy549Q6hXD1as8JaCiIjISUvVmEzOuf8Cq4BfzSzKzMIdjaKcmS02sw1m9pWZ9TyZZEUkGd26BTfnWLYMmjeHP/7wFjomJoYJEybQrVs3XnnlfEqWhMaN4aOPvKUgIiJyUsItZk8zs6lmtgs4SnDh3IlHOI4CvZ1z1YA6wJ1mds7JJCwiybjuuuD2zZ99Bk2bwu7d3kJHR0fz1ltvcdNNzfn4YzjjjM9o184xcqS3FERERFIt3JbhN4AjQGNgH3Ah8BFwWzhvds791zn3RejvvcAGoGyqsxWRlHXsCFOmwNq1QfPsb795T2Hr1k/55ps6lC/fi+7dHY89Bi7hzd1FREQyAXNhnKFCLcLlnXP7zWyPc66omRUHVjjnqqYqoFlFYClwnnPuzwSvdQe6A5QuXfqiCRMmpGbR6WLfvn0ULFjQe1zxIydt32KffcZ5jzzCoTJlWPvCCxwpXtxbbOccr732GpMmTaJcuRv48ccxtGixg/vu20R0dGSr4py0jXMibd/sT9s4e8uo7duwYcPVzrlaib0WbjH8K1DOOXfYzLYBFwN/Ajudc4XCTcTMCgJLgIHOuSnJzVurVi23atWqcBedbuLi4oiNjfUeV/zIcds3Lg5at4ayZWHhQjj9dG+hnXP079+fQYMGUaNGV9aufZMWLXLx/vsQyeNgjtvGOYy2b/anbZy9ZdT2NbMki+Fwu0l8CrQM/T0XmAhMIbiYLtwkcgOTgXEpFcIikk5iY2HuXPjlF6hfH7Zt8xbazBg4cCADBgxg7dox3H77DObODVLascNbGiIiIskKtxi+gaBFF4KxhRcD64HrwnmzmRkwCtjgnHshtUmKSBrUrQsLFgS3h6tfHzZv9hbazHj00UdZtmwZr712JdOmBWMQX3opbNrkLQ0REZEkhTvO8B7n3O7Q3wedc0865/qGhloLR12CgrqRma0JPVqm9CYRSScXXwyLF8PBg0FBvGGD1/D16tUDoGzZL6hf/3r+/PMQl10GH3/sNQ0REZF/iU7qBTN7IpwFOOceDWOe5YClIi8RSW81awZ9iBs3hgYNgtbi6tW9prB27Vrmzh1H3bq/8ssv02jcOD/vvAOdOnlNQ0RE5G/JtQyXC/MhIlnFuefC0qUQEwMNG8Lq1V7Dd+vWjdGjR7Ny5UJKl25JzZp7ufpqeP55Db0mIiIZI8mWYedcN5+JiIgnVaoEBXGjRkEr8Zw5UKeOt/Bdu3YlT5483HDDDdSq1Zwrr5xDnz6F+e47eOklyJXLWyoiIiKpux0zgJmdYmYdzCxV4wuLSCZy5plBQVyqVHCnuqVLvYa/Y2MfWwAAIABJREFU9tpref/99znttFOZMCEvffrAsGHQvj3s3+81FRERyeGSLYbN7PTQbZi/MbPRZnYuwd3jXgfWmVlnL1mKSPorXx6WLAnGHr7iiqAPsUcdOnRg8uTJ5M0bQ9++Oxk06Fdmzgy6M/833EtzRURE0iilluHhwG7gXoIL4OYCtzjnTuH/2bvvuKzK/oHjnwPIRlFRH9yaC5kODAeKIq58XLnKmZo+ammW9SvN0ictK7My9dFypGnlytEwc6GZK1eQe6Di3gqyBK7fH5eQCCgqnJvxfb9e58V9znXOub43B/TLdV8DugCjcjY8IUSOKl1aJ8RVqujFOX75xdTqDcNAKUWHDh2YPz+IOXPOc+gQPP00hIWZGooQQogC6mHJcANgsFJqNTAEKAWsAFBKrQQq5Gx4QogcV7KknnbN0xM6dIAVK0ytPmVxjsjISMaPb8ySJZEkJenpkVevNjUUIYQQBdDDkuFCSqkEAKVUDBCl0q7fLNOlCZEfFC+ul2uuUwc6d4ZFi0ytvkmTJvz2229cunSJIUMas3RpRGpj9fTppoYihBCigHlYMmxjGEZTwzCaGYbRLIN9GfctRH7h6gq//QYNGsDzz8P8+aZWX79+fdavX8/NmzcZPbo/v/8OzzwDQ4fCiBGQlGRqOEIIIQqITKdWu+sSMOee/av37V/K9oiEEJbj4qL7JnToAH37QlwcDBxoWvV169YlNDQUV1dXnJ1h+XJ47TX47DM4fhwWLtQhCiGEENnlgcmwUqqiSXEIIXILJyf48Ud49lkYNAji4+Hll02r3ufuqnhJSUm8+uqr9OvXj2rVfBk2TPcj/vFHqCCjFYQQQmSTR55nWAhRANjbww8/6BbiYcPg449ND+HixYv88MMPNG3alHr1drF6NZw+DfXqwdatpocjhBAin5JkWAiRMTs7WLwYunWDN96A994ztfrSpUuzefNmihQpQnBwME5OW9m+HQoX1itJf/ONqeEIIYTIpyQZFkJkrlAh3VG3d2945x0YPRrSTCiTsypVqsTmzZspVaoULVq04OLFTezYobtL9O4No0ZBcrJp4QghhMiHJBkWQjyYtTXMnQsvvgjvvw8jR5qaEJcrV45NmzZRo0YNlFIUKwZr1uhwPvhAd22OjjYtHCGEEPnMw2aTEEIIsLKCmTPBwQEmT9azTHzxhT5uAnd3d3bu3InV3frOnz/NzJnl8fSEV1+F+vVh5UqoXNmUcIQQQuQj0jIshMgaw9BznL3+ul4JY+BAUyf/TUmEly1bRtWqVVm+/AeGD4dff4WzZ8HfXy+kJ4QQQjwKSYaFEFlnGPDhh7r/8OzZ0KcPJCaaGkLz5s2pU6cOXbt25bvvviMkBHbuhFKlICQEli8vY2YvDiGEEHmcdJMQQjwaw4Bx4/RsE6NH63mIv/1WD7YzQZEiRVizZg1t27alR48exMfH07dvX7Zvh549YcqUqsTEwLRpYGtrSkhCCCHyMGkZFkI8nlGj4JNPYOlSPYotPt60ql1cXFi9ejXNmzenX79+/P333xQuDCtWQI8ep5g1C5o1gwsXTAtJCCFEHiXJsBDi8b36qm6C/fFHaN8eYmNNq9rR0ZFVq1axePFivLy8AD2eb8CACBYtgr17oXZt2LbNtJCEEELkQZIMCyGezJAhMGsW/PYbPPOMqfOc2dvb07lzZwC2b9/O5MmTAejaVSfBDg7QpImeCEP6EQshhMiIJMNCiCfXvz/Mnw+bNkGrVnDrlukhfP3117z22mvMmzcPpRQ+PvDnnxAcDP/5j56XOC7O9LCEEELkcpIMCyGyR8+e8P33sGOHntbh+nVTq582bRp9+/bl66+/ZtSoUakLdPz0k+7ePHu2biU+c8bUsIQQQuRykgwLIbJPly6wbBns26dHsF25YlrV1tbWzJ49m3//+99MnDiRV199FaUU1tYwYYIO68AB3Y94wwbTwhJCCJHLSTIshMhe7drp5eAOHYKgIFOndLCysmLEiBEMHz6c06dPk3TPoiCdOulG6+LFdcP1xImQnGxaaEIIIXIpSYaFENmvVSv4+WeIiNB9E86eNa1qwzD49NNPWbRoETY2Nly7di01Ka5ZUy/Q0bkzvPUWdOwIN26YFpoQQohcSJJhIUTOaNYM1qyB8+ehcWM4dcq0qg3DwMbGhpiYGBo3bkzv3r1JvLtSnouL7tr8+efwyy9Qt67u1SGEEKJgkmRYCJFzGjWCdevg2jWdEB87Zmr1jo6O9OzZk2+//Zbu3buTkJAA6EX0hg3Tk1/ExkL9+jB3rqmhCSGEyCXy1HLMd+7c4cyZM8Tl4PxIRYoU4eDBgzl2f2FZeeX52tvbU7ZsWQqZtMRxjqpXT49YCwnRCfGGDVCjhmnVv/nmm9jb2zNixAieffZZlixZgr29PQANGujFOZ57Dvr1g82bYepUcHIyLTwhhBAWlqeS4TNnzuDi4kLFihUxDCNH6oiKisLFxSVH7i0sLy88X6UUV69e5cyZM1SqVMnS4WSPWrUgNBSaN9d9iNetA29v06p/5ZVXsLOzY8iQIbz22mtMmzYttaxkSb1eyH//C++9p/sUL14Mnp6mhSeEEMKC8lQ3ibi4OIoXL55jibAQuYFhGBQvXjxHPwGxCC8v3S/BxkbPMrFnj6nVDx48mEWLFvH222+nK7O2hnHjdFJ85Qr4+8PXX5sanhBCCAvJU8kwIImwKBDy7c959eq6L4KLix5gt2OHqdV37doVd3d3EhMTGTt2LDdv3kxT3ry5HkwXEAAvvAB9+sDt26aGKIQQwmR5LhkWQuRxTz2lW4jd3HT2+fvvpoewe/du3n//fZo3b861a9fSlLm7w9q18O678M03eraJsDDTQxRCCGESSYaFEOarUEEnxGXL6jmJ1683tfqnn36aH374gbCwMJo1a8bly5fTlFtbw9ixOim+cUOPAfziC1DK1DCFEEKYQJJhIYRllCmjB9VVrgzPPAOrV5tafdu2bfnxxx85cuQITZo04fz58+nOCQ7WrcLBwXoqtvbtTV1hWgghhAnydTK8fft2nnvuOerUqcNzzz3H9u3bs+W+EyZMwNPTEx8fH/z8/NixYwc3btxg+vTpD7yuQYMGAJw8eRIvL6/Hrn/mzJn85z//AfR0c7169aJPnz7cuXPnse8phEWUKgUbN+ql4dq318s4m6hFixasXr2a69evExkZmeE5JUrATz/BZ5/pNUR8fExvyBZCCJGD8m0y/O677xIcHMyiRYvYs2cPixcvJjg4mHffffeJ7rtt2zZ++ukn9uzZQ1hYGOvWraNcuXIPTIaVUiQnJ7N169YnqjtFWFgYPj4+3Lp1i9atW1O+fHnmzZuXP+akFQWPm5uee7h2bb1O8uLFplbfpEkTTpw4Qb169QA9/d79DAOGD9fj/YoU0VMmv/UW3F3DQwghRB6WL5Ph7du3M2nSJGJiYlB3O/klJycTExPDpEmTnqiF+Pz587i5uWFnZweAm5sbpUuX5s033+T48eP4+fnx+uuvc/LkSTw8PBgyZAi1a9cmMjISZ2fndPc7ceIEtWrV4s8//2TBggXUq1cPPz8/Bg0aRFJSUoYxhIeHU6xYMYKCgujSpQsTJkx47PcjRK7g6qrnNatfX6+A8c03plbv4OAAwIwZM/D09OTo0aMZnufnB7t2Qf/+MHGiDjcPrOEihBDiAfJlMvz5558TGxubYVlcXByff/75Y9+7RYsWREZGUq1aNYYMGcKmTZsAmDhxIk899RT79u3j448/BuDw4cP07t2bvXv3UqFChXT3Onz4MM8++yxz587F2dmZRYsW8ccff7Bv3z6sra1ZuHBhhjGEh4fz8ssvM27cOAYNGvTY70WIXKVwYd1vOChIz2k2a5bpIQQEBBAbG0uTJk04cOBAhuc4OcFXX8GyZXDqlG7QnjpVBtcJIURelS+T4SNHjqS2CN8vOTk501afrHB2dmb37t18+eWXlChRgm7duvF1JrPzV6hQgYCAgAzLLl++TPv27VmwYAF+fn6sX7+e3bt34+/vn7p/4sSJdNeltDB7e3tnOOBHiDzNyUl30G3VCl58Ee5ZKc4Mfn5+hIaGopQiKCiIsAfMqdapE4SHQ9Om8PLL0Lo1nDtnYrBCCCGyRb5MhqtVq4aVVcZvzcrKimrVqj3R/a2trQkKCmLcuHFMnTqVZcuWZXiek5NTpvcoUqQI5cqV448//gB0v+I+ffqwb98+9u3bx+HDhxk7dmy668LCwvD19WXhwoVMmDCBvXv3PtF7ESLXcXCA5cv1gLqXXoJPPjG1ek9PTzZt2oStrS3NmjXj6tWrmZ7r7g4//wzTp+u1RLy9dYuxEEKIvCNfJsPDhw/H3t4+wzJ7e3uGDRv22Pc+fPhwmpblffv2UaFCBVxcXDIceJMZW1tbVqxYwfz58/n2228JDg5m6dKlXLp0CYBr165x6tSpdNeFh4fj7e2Nu7s7s2bNolu3bulW0RIiz7OzgyVLoGtXGDkSxo83tfpq1aqxefNmPvroI4oXL/7Acw0DBg+GvXv1eiKdO0PPnnDfWh5CCCFyqXyZDAcEBDBy5EgcHR1TW4itrKxwdHRk5MiRmXZdyIro6Gj69OlDzZo18fHx4cCBA4wdO5bixYvTsGFDvLy8eP3117N0LycnJ3766Sc+/fRTjh49yvjx42nRogU+Pj6EhIRk2A0iJRkGCAkJoWvXrvTr1++x348QuVahQrBwIfTqBWPGwNtvm9oxt3Llyqm/W5s3b04dH5CZ6tXhjz/0Yh2LFoGnp+7xIYQQInczMutba2l169ZVu3btSnPs4MGDeHh4ZPke27dv5/PPP+fo0aNUrVqV4cOHPzQRjoqKwsXF5bFiFrlfXnq+j/rznm8lJ8OgQXpA3Wuvwccf6+bYTISGhhIUFJRt1SulCAgIIDw8nJUrVxISEvLQa/bu1WMAw8P1188+0xNmiCeX3c9X5D7yjPM3Sz1fwzB2K6XqZlRmY3YwZgoICHiiVmAhRC5gZQUzZ4K9ve4/HBcHU6bo4yYwDIMff/yR5s2b8+9//5tly5bxzDPPPPCaWrX0FGzvvQcffADr1ukZKFq3NiVkIYQQjyBfdpMQQuQzVlY6AR45Us8wMWgQZDIPd04oWbIkGzduxMvLi44dO7J8+fKHXmNrq5Phbdv0Qh1t2sALL0hfYiGEyG0kGRZC5A2GAR99pPsOz5oFfftCYqJp1RcvXpx169ZRp04dVj7CstH+/rB7t16x7ptv9MrTMuOEEELkHpIMCyHyDsPQza3jx8OCBfD883DnjmnVu7q6snbtWmbdXRAkPj4+S9fZ28P77+uuE2XK6BknOnUCmSpcCCEsT5JhIUTeM3o0TJqkp1/r3BmymJRmB2dnZ2xsbLh48SK+vr58+eWXWb7Wzw927IAPP9SL7Xl4wOzZsnqdEEJYkinJsGEYcwzDuGQYxt9m1CeEKABee02vg7xqFXToAJkswZ5TihQpQpUqVRg0aBBffPFFlq+zsYE33oCwMJ0cDxigV7E7eDAHgxVCCJEps1qGvwZamVSXyENy69R+Io8YOlRP07BmDTzzDNy+bVrV9vb2/PDDD3Ts2JFhw4bx0UcfPdL1VavChg06/LAw8PXV3aFNzumFEKLAMyUZVkptBmQMtQDgwoULNGjQgIkTJ9K8eXMqV65Mz549OX36tKVDE3nRgAEwfz5s2gStWmFtYkJsa2vLokWL6N69O//3f//3SF0mQE+SMWAAHDoE3bvDhAng5aVzeyGEEOYwbdENwzAqAj8ppbwecM5AYCBAqVKl6nz//fdpylM+lsxJSUlJWFtbZ1p+6tQpunbtyo4dO1KPvf/++zg7Oz/RMs+Pyt3dPXWFuubNm7Nu3bosX5ty/o0bN1iyZAkvvvhilq6bM2cOYWFhfPbZZ9y5c4chQ4ZgZWXF1KlTKVSoULrz9+/fz9ixY9Mcmz59Onv37uX48eMMHjyYiIgIFixYwJgxY7Ic/5N42PPNTY4dOyZLbT+CEqGheIwfz82nnmL/pEkkmri4SlJSEvPmzaNTp064PsHqGnv3uvLpp9WIjHQkKOgSQ4cew80tIRsjzfuio6Nxdna2dBgiB8kzzt8s9XybNm2a6aIbKKVM2YCKwN9ZPb9OnTrqfgcOHEh3LLvdunXrgeURERHK09MzzbF3331XffzxxzkWU3JyskpKSkpzzMnJ6Ynvm9F7eZAhQ4aoadOmqZs3b6rg4GA1atSox6r3/fffT32WK1asUIsXL36s+zyOhz3f3MSMn/d8Z8UKlVSokFK1ail1+bJFQoiPj1ezZ89WycnJj3V9XJxS//2vUnZ2Sjk7K/XRR0rFx2dzkHnYxo0bLR2CyGHyjPM3Sz1fYJfKJOeU2SSy0cmTJ6lRowZ9+vTBx8eHzp07ExMTk+nxFAsWLKBevXr4+fkxaNAgkpKSOHnyJB4eHgwZMoTatWsTGRmZab0pf2Gl1DNgwAC8vLzo0aMH69ato2HDhlStWpWdO3emOf/NN9/k+PHj+Pn58frrrz/0/YWHh1OsWDGCgoLo0qULEyZMACAiIoL27dtTt25d6tWrx+HDhx94n6NHj1K9enUAihYtyuLFi0lOTn5o/UI8VPv2/D1+vB6N1rQpXLxoegjffvst/fv3Z+jQoY/1c21nB2PGwP79+i288Qb4+MBvv+VAsEIIIaRl+H5P0jIcERGhALVlyxallFIvvPDCA48rpd9T27ZtVUJCglJKqcGDB6t58+apiIgIZRiG2rZtW4Zx3NsynPI6IiJCWVtbq7CwMJWUlKRq166tXnjhBZWcnKxWrFih2rdvn+78+99L69at1dmzZzOs09XVVbm5ualVq1alHktISFDNmjVTx44dU0op9fPPP6u+ffs+8HtoSdIynP9t3LhRqfXrlXJ0VKp6daXOnDG1/uTkZPXGG28oQPXr108lJiY+0f1++kmpp55SCpTq2FGpiIjsiTOvklbD/E+ecf6WG1uGbcxIuA3D+A4IAtwMwzgDvKuUmv2k9w0KCnroOW3btmXkyJGp5/ft25e+ffty5coVOnfunObc0NDQh97PMIwHHi9XrhwNGzYEoGfPnkyZMoXOnTtneHzkyJGsX7+e3bt34+/vD0BsbCwlS5akcePGVKhQgYCAgIfGdK9KlSrh7e0NgKenJ8HBwRiGgbe3NydPnnzo9b/88kuGxyMjI3F2dqZq1aqpfZUBVqxYwf79+3n22WcBSExMJDAw8JFiFiLbNWumR6G1aQONG+tpGypUMKVqwzCYOHEi9vb2/Pe//yUuLo558+ZhY/N4/9w+8wwEB8PkyXqAnYeHbi1+4w1wcsrm4IUQogAyJRlWSj1nRj1mKF68ONevX09z7Nq1a1SqVAlInyyn7Gd2XClFnz59+OCDD9KUnzx5EqfH+J/Ozs4u9bWVlVXqvpWVFYlPsHRtWFgYvr6+fPXVVwQEBODv70+tWrX466+/mDBhAv3793/sewuRIxo1grVroVWrfxLip54ypWrDMBg3bhz29vZ8+OGHnDhxgmrVqj32/eztYdQo6NULXn8d/vtfmDMHJk7Ui/Bl8je6EEKILDAlGc4pWWnJzex8Nze3R74edH9bd3d31q9fT3BwMNeuXePXX39l+PDhAJw+fZpt27ZRv359vvvuOxo1avTA48HBwbRv354RI0ZQsmRJrl27RlRU1CPH9ThcXFyyXFd4eDje3t64u7sza9YsunXrxp9//om7uztr1qzhhRdewMrKivDwcLy8vDJtQRfCVE8/DevXQ4sW/yTEd/urm+Gtt97ihRde4F//+hegPzl53BZigHLl4Pvv4aWX4JVXoGdP+OIL+OQTuPvBkxBCiEckA+gew/z58xk/fjx+fn40a9aMd999l6futjh5eHgwb948fHx8uHbtGoMHD37g8Zo1azJ+/HhatGiBj48PISEhaboh5KTixYvTsGFDvLy8UgfQtWnThnPnzqU7NyUZBggJCaFr167069ePfv36kZycjIeHB35+fnz44YeSCIvcpXZtCA2FxERo0gT+NnchzJRE+MMPP+SZZ55JM3j2cTVqBDt3wty5EBmp9zt3hmPHnvjWQghR8GTWmdjSW24dQPcgmU1V9qhTmImcIwPo8r9MB2ccPKhU6dJKFS+u1J49psaklFJz5sxRhmGooKAgFRUVlW33jY5Watw4PV7QxkapoUOVunAh226f68jgqvxPnnH+lhsH0EnLsBCiYKhRAzZv1qPOmjWDexbOMcMLL7zAggUL+P3332nZsmW2Laji5ATvvKNbhQcMgBkzdNfosWPBpB5XQgiRp0kynI0qVqzI3xl8BJvZcSGEyZ56SifExYpBSAhs2WJq9c8//zyLFi1i586dtGzZ8okGtd7P3R3+9z84cABat4Zx4/Tb/eILSJBF7IQQIlOSDAshCpYKFXRCXLo0tGypB9WZ6Nlnn2X58uUMGDDgiQbTZaZaNViyRDd8e3rCsGF6zODcubrbtBBCiLQkGRZCFDxlysCmTVC5sp7I99dfTa2+bdu2DBgwAIBNmzZx4cKFbK+jXj2d569eDcWLQ79+ULMmLFwISUnZXp0QQuRZkgwLIQqmUqVg40a9ikW7drBypekh3L59my5dutCkSRPOnDmT7fc3DD3N8p9/wooV4OCgp2Pz8YGlS0FWQRdCCEmGhRAFmZubnoe4dm09N9nixaZW7+TkxPLlyzl//jyNGzfO0iqRj8MwoH172LtXv0WloEsXqFVL70tLsRCiIJNkWAhRsBUtCr/9BgEB8NxzsGCBqdU3bNiQ9evXc/36dRo3bsyxHJws2MpKJ8Hh4fDNNxAfD926gZeX3pc+xUKIgkiSYSGEKFxY9xsOCoLevWHWLFOr9/f3Z+PGjcTGxvLll1/meH3W1rq7xP79umXY1la/7erV9VuX2SeEEAWJJMNCCAF6wt6fftIzTLz4IkybZmr1fn5+7Nq1iw8++ADQCyLlNGtr3VK8d6/uMl2smH7rlSvrJZ5v3crxEIQQwuIkGX5E1tbW+Pn54eXlRZcuXTJdWrVNmzbcuHHD5OiEEE/EwUGPNGvXDl56CSZPNrX6ChUqYG1tzenTp/H392f37t2m1Gtlpd/yzp26gbxaNRg5EsqXh7feApNWiBdCCIuQZPgROTg4sG/fPv7++29sbW2ZMWNGmnKlFMnJyfzyyy+4urpaKEohxGOzs9NTLXTpAq+9BhMmmB5CYmIiV69eJTg4mO3bt5tWr2H8M/Xyzp3QogV89BFUrKhXtzt0yLRQhBDCNJIMP4HAwECOHTvGyZMn8fDwYMiQIdSuXZvIyEgqVqzIlStXAJg/fz4+Pj74+vrSq1cvAC5fvsyzzz6Lv78//v7+/PHHH5Z8K0KIexUqBN9+qzvWvv02jBmjp2AwSeXKldm0aRMlSpQgJCSEzZs3m1Z3Cn9/3Z/48GHo31/PT+zhAW3awJo1pn47hBAiR2X/8kcmeeUV2Lcve+/p5wfvvZe1cxMTE1m9ejWtWrUC4PDhw8ydO5fp06enOW///v1MmDCBP/74Azc3N65duwbA8OHDGTFiBI0aNeL06dO0bNmSgwcPZuv7EUI8ARsb+Ppr3VI8fjzExelmUsMwpfry5cuzadMmgoODadWqFevWraNBgwam1H2vKlVg+nS9vPOMGfp1q1ZQo4Ze3a53b93dWggh8ippGX5EsbGx+Pn5UbduXcqXL0///v0B3dcvICAg3fkbNmygc+fOuLm5AVCsWDEA1q1bx0svvYSfnx/t2rXj1q1bREVFmfdGhBAPZ20NX34JQ4bApEk6+zNxpYrSpUuzadMmnn32WTw8PEyrNyMlSugG8lOn9DRsTk7621K2LLz+Opw4YdHwhBDiseXZluHPPsuZ+z4sH03pM3w/p0yaRpRSGBm0JCUnJ7Nt2zYcHBweK04hhEmsrGDqVLC31wPq4uN1E6mVOW0JJUuW5JtvvgEgLi6OnTt30rhxY1Pqzoitre490qMHbN0Kn38On36qZ59o2RIGD9YrXFtbWyxEIYR4JNIynMOCg4NZvHgxV69eBUjtJtGiRQumTp2ael5GCbYQIpcwDN0yPHo0fPUV9O1rkRUqxo0bR7Nmzfj+++9Nr/t+hgENG+p+xadOwTvvQFiYXumuUiXds+TCBUtHKYQQDyfJcA7z9PRk9OjRNGnSBF9fX1599VUApkyZwq5du/Dx8aFmzZrpZqUQQuQyhqEzvPfe0/0EevSAO3dMDWHUqFE0aNCAHj16MH/+fFPrfpAyZWDsWDh5EpYt04t3jBkD5cpBx456+mZZ3U4IkVvl2W4SlhIdHZ3uWMWKFfn777/THDt58mTq6z59+tCnT5805W5ubixatChHYhRC5KC339ZdJl5/XXeZWLRID7IzgYuLC6tXr6Z9+/b07duXuLg4Bg4caErdWVGoEHTqpLejR3Uj+rx5eurm0qWhTx/o108PyhNCiNxCWoaFEOJRjRwJX3yhl23r2BFiY02r2snJiR9//JHWrVszevRorl+/blrdj6JqVT35xpkzsHw51KoFH36ojwcFwZw5ssKdECJ3kGRYCCEex0sv6Zkmfv0V2raF27dNq9rBwYEffviBLVu2ULRoUdPqfRyFCkGHDrqrxOnTeg2Ts2f13MWlSsFzz8Evv0g3CiGE5UgyLIQQj+vFF3U/gNBQPfmuiU2ddnZ2VK9eHYCxY8fyXlYnSbegMmVg1Cg4cgS2bdNdJn77Tc8+UaaMnj9+xw5Z0EMIYS5JhoUQ4kn06gXffQfbt+v1i2/cMLX65ORkIiIieOeddxg9ejQqD2SShgEBATBtGpw/r/sUBwbC//6nj1euDG++CXv3SmIshMh5kgwLIcST6toVli6FPXugWTO4uxS7GaysrJg7dy4vvvgi77//PiNHjswTCXEKW1s9HdvSpXDxIsydq1e3mzQJatfWM1OkTNvjlqsyAAAgAElEQVSWh96WECIPkWRYCCGyQ/v2ekDdwYPQtKnO7ExiZWXFzJkzefnll5k8eTLDhw83re7s5Oqqp3BevVrPUTxzpp6ebcIE8PXVg+9mzKjMjh2mLgQohMjnJBkWQojs0rq1Hil24gQ0aaJHipnEMAw+//xz3njjDfz9/U2rN6e4ucHAgbB+PZw7pxPjKlVg2bKyBARA+fLw8suwbh0kJFg6WiFEXibJsMj38tJHxiIfCA7WM0ycPasT4tOnTavaMAw+/PBDevXqBcCuXbtIzAfTNJQqpRPjX3+F5cu3smABPP00zJ4NISE6ce7SRY9lvHzZ0tEKIfIaSYZFvnThwgUaNGjAxIkT2b59Oz179qRy5coMGDCA0yYmJ6KACgyEtWt13+HGjeH4cdNDOHnyJI0aNeL555/njskr5eUkZ+dEevTQK91duQI//qinZ9u6VXexKFUKGjTQXSt275buFEKIh5Nk+DE4OztbOoQ0MTRo0OCRrk05/8aNG0yfPj1b43pUsbGxNGnShKSkJABmzpzJf/7zHwDu3LlDr1696NOnT6b/mYeHh9O2bds026VLl9i7dy/du3fnzTffpH79+vz3v/+lR48ezJo1i/Llyz9WrOfPn6d79+7UrVuXatWq0bRp0yxd9+uvv1K9enWqVKnCxIkTU48nJCTQuHHjfNFyJzIQEAAbNkBUlG4hPnzY1OorVqzIBx98wJIlS+jcuTPx8fGm1m8GR0c9xfPMmXpxj9274d13dbeJt9+GunXhX//SE34sWACXLlk6YiFEbiTJcB6glCL5Ac0bW7dufaT7pZyfG5LhOXPm0KlTJ6ytrQEICwvDx8eHW7du0bp1a8qXL8+8efMoVKhQhtd7e3vz008/pdlKlizJvn37CAkJST0vPDwcHx+fJ4q1V69edOzYkV27dnHkyBGmTJny0GuSkpIYOnQoq1ev5sCBA3z33XccOHAAAFtbW4KDg2VZ7vysdm09B3FCgk6I9+83tfoRI0Ywffp0Vq1aRYcOHYg1caU8sxmG/na/+y7s2qUH4H3zjZ7tbs0anRCXKqXPef113eUiOtrSUQshcgNJhrPJ5MmT8fLywsvLi88++wyAjz76KDVhGjFiBM2aNQNg/fr19OzZE4AFCxZQr149/Pz8GDRoUGoL6cmTJ/Hw8GDIkCHUrl2byMjITOt2dnbm5MmT1KhRgwEDBuDl5UWPHj1Yt24dDRs2pGrVquzcuTPN+QBvvvkmx48fx8/Pj9dff/2B769Lly689NJLNGrUiAoVKrBlyxZ69+5NtWrV6N+/f+p5S5cuJSAgAF9fXxo1asTlux34mjZtytq1awF4++23GTZsGAALFy6kffv2qdeHh4dTrFgxgoKC6NKlCxMmTEgti4iIoH379tStW5d69epx+AEtbUePHk1dkACgaNGiLF68+IF/VDxIUlISoaGhNGnSJPWYt7f3Q6/buXMnVapUoXLlytja2tK9e3dWrlyZWt6hQwcWLlz4WDGJPMLbGzZvBisrvQ7xvn2mVj948GBmz57NmjVrmDVrlql1W1KpUtCzp24RvnBBJ8jjx0ORIjBlih7rWKyY7tEydqx+RHFxlo5aCGERSqlcudWpU0fd78CBA2n2mzRpkm6bNm2aUkqp27dvZ1g+d+5cpZRSly9fTlemlFK3bt1KV+/9nJyc0uzv2rVLeXl5qejoaBUVFaVq1qyp9uzZo7Zt26Y6d+6slFKqUaNGyt/fXyUkJKixY8eqGTNmqAMHDqi2bduqhIQEpZRSgwcPVvPmzVNKKRUREaEMw1Dbtm17aAxOTk4qIiJCWVtbq7CwMJWUlKRq166tXnjhBZWcnKxWrFih2rdvn+7aiIgI5enpmea+rVu3VmfPnk1XX/Xq1dUnn3yilFJqzJgxqlq1aurcuXMqPj5eubq6qri4OKWUUleuXEm9ZuzYsWrq1KlKKaU2bdqkmjRpohYsWKDatGmjEhMTVXx8vCpVqlSaelxdXZWbm5tatWpVmuMJCQmqWbNm6tixY0oppX7++WfVt2/fDL83D5KV55uZli1bqpIlS6qBAweqLVu2pB6/du1aptcsWbJE9e/fP3V//vz5aujQoan7iYmJys3NLcNr7/95F1mzceNGS4eQsaNHlSpXTilXV6V27jS9+q1bt6qkpCTT681u2fF8b99W6rfflPq//1Oqbl2lDEMpUMrOTqnGjZV6+22l1q5VKjr6yeMVjy7X/g6LbGGp5wvsUpnknNIynA22bNlCx44dcXJywtnZmU6dOvH7779Tp04ddu/eTVRUFHZ2dtSvX59du3bx+++/ExgYyPr169m9ezf+/v74+fmxfv16Tpw4kXrfChUqEBAQkOU4KlWqhLe3N1ZWVnh6ehIcHIxhGHh7e3Py5Mks3eOXX36hdOnSaY7FxcVx48YNXnnlFQAcHBzo378/7u7u2Nra4ujoiK2tLQBff/019erVw9fXl+nTp2Nvbw9A48aNUUoxefJkvv/+e6ytrbly5Qqurq6p9URGRuLs7Iy3tzfnz59PE8OKFSvYv38/zz77LH5+frzxxhup934SzZs3T23Rv3e7t/U2xerVq1m2bBlFihShVatWrFixAtCt/plRGcxkYRhG6mtra2tsbW2Jiop64vcicrkqVXTzY7FiesaJP/4wtfr69etjZWXFiRMn6NSpE9evXze1/tzE0VHPQjFxIvz5J1y9CqtWwUsvQUwMvP++Lnd11bNWvPqqXhTk3DlLRy6EyAk2lg7gSYSGhmZa5ujo+MByNze3B5Y/iowSHoBChQpRsWJF5s6dS4MGDfDx8WHjxo0cP34cDw8P1q9fT58+ffjggw8yvN7JyemR4rCzs0t9bWVllbpvZWX1RIO09u/fT+3atbGy0n87/fXXXwwePBiAM2fOULp0aQzDYP78+ezcuZMNGzbg7OxM48aN8fT0BHT3h/Pnz+Pm5oaLiwugk+q4ez6XDAsLw9fXl6+++oqAgAD8/f2pVatWap0TJkxI0yUjO6xbty7L5xqGQaNGjWjUqBHXr18nLCwMe3t7Dh06xKRJkxg5cmS6a8qWLZumi0vK9+te8fHx2ZLYizygYkXYtEknwy1b6qkQsjgQM7scOXKEn3/+mWbNmvHbb79RokQJU+vPjYoWhX//W2+gxzxu3aof1ZYtepnoTz/VZRUq6Nkq6tfXibKvL9zzT68QIg+SluFs0LhxY1asWEFMTAy3b99m+fLlBAYGppZNmjSJxo0bExgYyIwZM/Dz88MwDIKDg1m6dCmX7g5xvnbtGqdOnTItbhcXlyy1SIaHh+Pr65u6nzLIDXSSmvI6PDycBg0a4OzszLJly9i6dWtqK2+PHj1YuXIlTk5OrFmzBtD9eJOSklIT4vDwcLy9vXF3d2fWrFl069aNmzdvAuDu7s6aNWtS+/yGh4ebOn/wmjVrSLg7s/+lS5fYsmULISEhuLm50bNnz9REODg4mLP3LLTg7+/P0aNHiYiIICEhge+//5527dqlll+9epUSJUpkOkBQ5ENly+osq2JFaNNGj+4yUatWrVi1ahWHDh2iadOmXLhwwdT68wIXF/23yvvv68b8mzdhxw6dENerpx/fsGE6GXZx0cdeflkP2Dt0SKZzEyKvkWT4McTExFC2bNnULTQ0lL59+1KvXj2efvppBgwYkNqiGRgYyPnz56lfvz6lSpXC3t4+NVGuWbMm48ePp0WLFvj4+BASEpKue0BOKl68OA0bNsTLyyt1AF2bNm04d99ngeHh4fj5+QG6y0RsbCxFixYF0ibGffr0YcqUKQQGBnLkyBEqV66MYRh06tSJTz75BA8PD8aMGcPYsWNT792iRQu2bNmSWk/KoLSQkBC6du1Kv379AOjXrx/Jycl4eHjg5+fHhx9+mKa7QU5bunQpHh4e+Pr60rZtW9577z3q16+f2poNkJyczLFjxyhWrFjqdTY2NkydOpWWLVvi4eFB165dU1vLATZu3EibNm1Mex8il/jXv/QsEzVqQLt2uoXYRC1btuSXX34hIiKCJk2apPudF2nZ2uqE95VXYPFiPY3b6dN6ruMRI8DJCb7+Gnr3Bg8PPUgvMBCGD9cLgfz9N8gMikLkYpl1Jrb0lpUBdDnhSQZYiUe3Z88e1bNnT9Pqy+7nu3LlStW7d2914MABFR4erkaMGPFI13fs2FEdOnQowzIZQPd48tTgm2vXlPL3V8rGRqklS0yv/vfff1chISHq5s2bptf9uHLr801MVOrvv5WaO1epl19WqmFDpRwd9cA8UMreXqk6dZTq10+pzz9XKjRUP36RXm59xiJ75MYBdHm6z7DI+2rVqkXTpk1JSkpKnWs4L2nXrl2abg+TJ0/O8rUJCQl06NAhzRRwooApWhTWrdPdJbp1g/nzoUcP06pv1KgRa9aswTAMbt++zeXLl6lYsaJp9ecn1tbg6am3vn31saQkOHIE9uyBvXvhr7/0hwBz5vxzXbly+hovr3+u9/CAXLC2kxAFhiTDwuJSukIUNLa2tvTu3dvSYQhLK1xYrwDRrp1eGSI+Hkz8nUjpbtSvXz+2bNnC+vXrqVGjhmn152fW1jqx9fD4528cpfS8x2FhOjn+6y+9FsvGjfrRp6hYUV9Xo4beUl67uekFRoQQ2UeSYSGEsDRnZ/j5Z+jYEfr316s/DBliaghjxoyhefPmNGnShHXr1mVpURnx6AwD3N311rLlP8cTE+HECZ0Yp2yHDumu5fcuHFisGFStmvFWpIjpb0eIfEGSYSGEyA0cHGDlSujSBYYO1Qnxq6+aVr2XlxebNm2iWbNmBAUFsXbtWmrXrm1a/QWdjQ1Uq6a3jh3/OZ6cDJGRcPCgTo4PHYKjR/WMFgsWpL2HmxtUrqy3p57653WlSlCmjK5DCJGe/GoIIURuYWenV3fo0QNee00nxKNGmVZ99erV2bx5M82aNeP5559n//79ebIvf35iZaXnNq5QAVq1SlsWGwvHj+vk+OhR3bJ8/Djs3AlLlug+yymsrfWsfhUr6i3lnuXK/bM94tT2QuQbkgwLIURuYmsL330H9vYwerROiMeNM62j6FNPPcXvv/9OVFSUJMK5nIODHnjn5ZW+LDFRtygfPw4nT+rt1Cn9df16OHtW91++V9GiOikuW1a3JN+/lS4NxYtLn2WR/0gyLIQQuY2NjZ641s4O3ntPJ8QffmhaFlK+fHlAT735zjvvEBQURHBwsCl1i+xhY6O7R1SqlHF5QoJOiCMj026nT+t5lP/8Ey5fTn9doUJ6muyUfs/u7nq/VCm9lSz5z2tnZ0mcRd4gybAQQuRG1tbw5Zc6If74Y50Qf/aZ/tzcJLdv32bFihV8/PHHLF++nNatW5tWt8hZtrYPTpZBJ8znz+uk+exZOHdO76dsx4/r5aqvXs34egcHKFEi4614cd3HuXjxf14XK6aTbSHMJsmwEELkVlZWMHWqTog//VQnxDNmmJYQOzs7s3HjRlq0aEH79u1ZvHgxHTp0MKVuYXm2tv/0LX6QhATdinzpEly8+M926ZI+nrIdPKi/xsRkfi8XF3B0DKB0ad1to1gxvbm66n1X17RbkSL/bI6O0hItHo8kw0IIkZsZBnzyiW5me/99PRnt7NmmTQ3g5ubGhg0baNWqFZ06deK9995j9OjRptQt8gZb23/6FWdFTIxuTb56Fa5cSfv1+nU4cOAGtrb/4vp1PcXctWv6eELCg+9rY6On7U5Jjl1c9H7K5uKSdnN2TvvaySntV5l9o+Aw7VEbhtEK+BywBmYppSaaVXd2s7a2xtvbm8TERDw8PJg3bx6Ojo7Zdv8pU6bwv//9j9q1a/P000+nvl64cGG21ZFVsbGxtGrVig0bNmBtbc3MmTPZu3cvM2bM4M6dO/Tr1w8rKytmzZpFoUf8fOv8+fOMGDGCY8eOcevWLcqUKcPGjRszPPfXX39l+PDhJCUlMWDAAN58801Ar+LWvHlzNmzYgI38yyXyK8OACRP0oLp33oH4eHa89BKfTZvGkSNHqFatGsOHDycgICBHqnd1dWXdunW89tprVK1aNUfqEAWHo6PeypXLuDw09BBBQf9KdzwuTifFN27o7fp1uHkz4+3WLb2dPw+HD0NUlD4eF5f1OG1tdWLs5KTjvf+ro6P+GzXldcq+g4P+VU15fe+xzDZbW1N7QIn7mJI9GIZhDUwDQoAzwJ+GYaxSSh0wo/7s5uDgwL59+wDo0aMHM2bM4NVsnA90+vTprF69mkqVKlGjRo3U15YwZ84cOnXqlDqqPCwsDB8fH27dukWnTp14+umnmTBhwmPdu1evXrz44ot069YNgPDw8AzPS0pKYujQoaxdu5ayZcvi7+9Pu3btqFmzJra2tgQHB7No0SJ6mLiMrRAWMWaM/p/zjTe4uHQpy5OSiAf27dvHqlWrGDlyJOPGjcuRqp2dnZk5c2bq/pQpU7h58yZvvfWW/CEqTGFv/8+gvceVmAjR0XqLivpnu31bH8voa0yM/pry+uZNnWTHxOjp7WJi9PawluuHsbXVPaLs7fXXlC3l+L37D9sKFUr7+mGbjU36/ZTt/v37N2trncjn5S4qZv0LVg84ppQ6AWAYxvdAe+Dxk+FXXoG7CWm28fPTI7cfQWBgIGFhYQB06NCByMhI4uLiGD58OAMHDmTMmDG4ubkxfPhwAEaPHk2pUqUYNmwYkydPZs7dReoHDBjAK6+8wn/+8x9OnDhBu3btOHLkCEop2rVrR79+/RgxYkSmcXTp0oVSpUqxb98+IiMjWbhwIV9++SXbt28nMDCQ2bNnA7B06VImTZpEbGwsLi4uLF++nBIlStC0aVNGjRpFSEgIb7/9Nrdu3WLKlCksXLiQb7/9NrWe8PBwAgMDCQoKYtCgQQwaNOiRvl8pkpKSCA0NZcE9s8ZntuLVzp07qVKlCpUrVwage/furFy5kpo1a6Z+39966y1JhkWBsD0wkCWFCvHJnTv8APQG7iQnQ0wMX378MW0DA/H398/xOA7v3MmChQvZsHw5X3zxBeXKlcPBwQFbW1sSExO5fft2umscHR0pVKgQd+7cISaDjqNOTk7Y2NiQkJBA7L3Lrt1Tbh0dTcLlyxmWOzs7Y21tTXx8PHEZNAG6uLhgZWVFXFwc8feufXxX4cKFMQyD2NhYEjLIbB5WXuTuEnAxMTHcuXMnTZlhGBQuXBjQAxMTExPTlFtZWeHi4gJAdHQ0SfdOEpyFcmtra5ydnQGIiooiOTk5TbmNjQ1OdycSvnXrFuq+edUKFSqU+gnnzZs30723h5Xb2tri4OCAUopbt26lK7ezs8Pe3p7k5GSioqLSldvb22NnZ0dSUhJxFy9y8/TpTMujo6PTXZ/Vnz115w5GTAwugIsT4AT8K2s/ew8q1997a27ejOfmzThiY3WPprg4iIsHAxfu3LEiKiqO6Oh44uNJ3RISQKnCJCQYxMTEEhOTQEICxCfAnQR9TlJSYe7cMYi9GkvsHV2ekAB37qR8LUJiIiQkxJCQcIfkNI/XAArffX0bSLwveivA5e7raCDpEcutAWdsrMHKKgobm2Ssrf9Jkq2tbShUyAkbGzCMWxRzq0Do7nTfQstSSuX4BnRGd41I2e8FTH3QNXXq1FH3O3DgwD87w4cr1aRJ9m7Dh6tbt26lq/d+Tk5OSiml7ty5o9q1a6emT5+ulFLq6tWrSimlYmJilKenp7py5YqKiIhQtWrVUkoplZSUpCpXrqyuXLmidu3apby8vFR0dLSKiopSNWvWVHv27FFKKVWhQgV1+fLldK+VUqp169bq7Nmz6WKqXr26+uSTT5RSSo0ZM0ZVq1ZNnTt3TsXHxytXV1cVFxenlFLqypUrqdeMHTtWTZ06VSml1KZNm1STJk3UggULVJs2bVRiYqKKj49XpUqVSlOPq6urcnNzU6tWrXro9+lhWrZsqUqWLKkGDhyotmzZkul5S5YsUf3790/dnz9/vho6dGjqfmJionJzc8tSnVl5vrlFmp93kWUbN260dAg5qnv37sowDPUiqCQ9VaxssskmW57ZTjtXssi/ncAupTLOOc1qGc6o8VylO8kwBgIDAUqVKkVoaGia8iJFivzzF+UjtuBmVVJSUoZ/td4rNjYWHx8fAOrXr0/Xrl2Jiori448/5qeffgIgMjKSffv2Ua9ePVxdXdmyZQuXLl3C29sbW1tb1q1bR5s2bVL/en/mmWdYu3YtVapUQSlFdHQ0dnZ2aV4DLFq0CCBNjHFxcVy/fp3+/fsTFRWFlZUVPXv2xNnZmfj4eBwcHIiPjychIYGZM2eybNkyEhISuHjxIu+++y5RUVHUqlWLxMREJk2axM8//0xMTAznz5+ncOHCqXWdOXMGJycnnnrqKSIiIjL8PrVr146LFy+mO/7OO+/wzDPPpDm2ePFitm/fzi+//EKrVq348ssvadu2bbprU1pZUuqLjY1Nsw+61eLcuXOpLSeZycrzzS3i4uLS/Q6Ih4uOjs7X37c9e/aglOIr4Cjgd195iRIl6Nqli2nxxMTEcPz4cZKSk6lQvjxFixYlOjqaY8ePpzu3UsWKFClShFu3bnEiIiJd+VOVK+Pi4sL169c5dV/LIEC1qlWxtrYmOjqayDNn0pVXr14dB3t7Ll++zNlz59KV1/TwwNbWlgsXL3LhwoV05V5eXthYW3Pu3DkuZTDJrq+PD4ZhEHnmDFfvm0/MyjBS/184deoU12/cSFNuY2ODl6cnACciItK1ntrZ2uLh4QHA8ePHibqv9dPBwYHq1aoBcOTIEWLua510dnKiSpUqABw6dIi4+1q+C7u4pH66tv/AgXQt165FilCxYkVAfwKYdF/LcvFixSh3t5Pvvr/+Sve9KeHmRpkyZUhOTiYsgy5vpUqWxN3dnTt37rD/QPoPhUu7u1OyZEni4+P5+++/sb6v603ZMmVwc3MjJjaWI0eOpLvejJ89R0dHrl69Kj97T/izV9rTk+O57N9os5LhM8C9XeXLAul+WpRSXwJfAtStW1cFBQWlKT948OBDk50nFRUV9dA6HBwcUrtGpAgNDeX3339nx44dODo6EhQUhLW1NS4uLgwaNIglS5Zw4cIFBg4ciIuLC3Z2dtjZ2aXWlfIRkouLC4Zh4OzsnO51Zo4cOUKdOnVSP6I7dOgQgwcPxsXFhTNnzlCmTBkKFy7M/Pnz+euvv9i0aRPOzs40btyYOnXq4OLiQnh4OJcuXcLNzY3SpUsDkJiYSEJCQmrdJ06cwM/Pj6+++oqAgAACAwOpVatWmlgyGwCXmRYtWtCiRQtu377N0aNHM3yfVatWZeHChallV69epWLFimnOTUhIoESJEg8dxJeV55tb2Nvbp/v+iocLDQ3l/n878pPatWtz7NgxkpOTCQVC7ymzsrKiW/PmjJo2zdSYfDI4dn+Sfr/aDynPrKNHyvN9+gHXVnnIvaU8Z8urPaTc4yHllx/yO5zRz9u9cupnD/R7l5+9JyvPjf9GmzV28U+gqmEYlQzDsAW6A6tMqtsUN2/epGjRojg6OnLo0CG2b9+eWtaxY0d+/fVX/vzzT1q2bAlA48aNWbFiBTExMdy+fZvly5cTGBj4WHWHh4fj6+ubup8yyA3gr7/+Sn0dHh5OgwYNcHZ2ZtmyZWzduhVvb2/Onz9Pjx49WLlyJU5OTqxZswaAokWL6v5bd/vehYeH4+3tjbu7O7NmzaJbt24Z9h3LijVr1qT2ubt06RJbtmwhJCQEgODgYM6ePZt6rr+/P0ePHiUiIoKEhAS+//572rVrl1p+9erVLCXCQuQHw4cPx97ePsMye3t7hg0bZnJEQgiRt5mSDCulEoGXgDXAQWCxUmq/GXWbpVWrViQmJuLj48OYMWPSTHFka2tL06ZN6dq1a+qsDLVr16Zv377Uq1ePp59+mgEDBmSpFbBNmzacu+8jmPDwcPz89N/CcXFxxMbGUrRoUSBtYtynTx+mTJlCYGAgR44coXLlyhiGQadOnfjkk0/w8PBgzJgxjB07NvXeLVq0YMuWLan1pAxyCwkJoWvXrvTr1++xvl9Lly7Fw8MDX19f2rZty3vvvUf9+vVJTk7m2LFjFCtWLPVcGxsbpk6dSsuWLfHw8KBr16543v3IB3RrdJs2bR4rDiHymoCAAEaOHImjoyNWd+disrKywtHRkZEjR+bY9GpCCJFvZdaZ2NLbQwfQ5ZCcGGCVlJSkfH191ZEjR7L93jltz549qmfPnqbVFx4erkaMGPFI13Ts2FEdOnQoS+fKALr8L78PoEuxbds21b17d1WnTh3VvXt3tW3bNkuHZIqC8nwLMnnG+Zulni+5YABdgXXgwAHatm1Lx44d8+Rk9bVq1aJp06YkJSWltmrnJC8vLyZPnpzl8xMSEujQoQPVq1fPwaiEyH0CAgKkFVgIIbKBJMM5rGbNmpw4ccLSYTyRx+0KYQZbW1t69+5t6TCEEEIIkUfJ4n9CCCGEEKLAkmRYCCGEEEIUWHkuGdZ9oIXI3+TnXAghhDBHnkqG7e3tuXr1qiQKIl9TSnH16tVM55IVQgghRPbJUwPoypYty5kzZ7icwVKF2SUuLk6SkHwsrzxfe3t7ypYta+kwhBBCiHwvTyXDhQoVolKlSjlaR2hoqCyBm4/J8xVCCCHEvfJUNwkhhBBCCCGykyTDQgghhBCiwJJkWAghhBBCFFhGbp2ZwTCMy8ApC1TtBlyxQL3CHPJ88z95xvmbPN/8T55x/map51tBKVUio4JcmwxbimEYu5RSdS0dh8gZ8nzzP3nG+Zs83/xPnnH+lhufr3STEEIIIYQQBZYkw0IIIYQQosCSZDi9Ly0dgMhR8nzzP3nG+Zs83/xPnnH+luuer/QZFkIIIYQQBZa0DAshhBBCiAJLkmEhhBBCCFFgSTJ8l2EYrQzDOGwYxjHDMN60dDwiexmGUc4wjI2GYRw0DGO/YRjDLQLGPCwAAAd7SURBVB2TyH6GYVgbhrHXMIyfLB2LyH6GYbgahrHUMIxDd3+X61s6JpF9DMMYcfff578Nw/jOMAx7S8cknoxhGHMMw7hkGMbf9xwrZhjGWsMwjt79WtSSMYIkw4D+DxSYBrQGagLPGYZR07JRiWyWCLymlPIAAoCh8ozzpeHAQUsHIXLM58CvSqkagC/yrPMNwzDKAMOAukopL8Aa6G7ZqEQ2+Bpodd+xN4H1SqmqwPq7+xYlybBWDzimlDqhlEoAvgfaWzgmkY2UUueVUnvuvo5C/ydaxrJRiexkGEZZ4BlglqVjEdnPMIzCQGNgNoBSKkEpdcOyUYlsZgM4GIZhAzgC5ywcj3hCSqnNwLX7DrcH5t19PQ/oYGpQGZBkWCsDRN6zfwZJlPItwzAqArWAHZaNRGSzz4A3gGRLByJyRGXgMjD3bleYWYZhOFk6KJE9lFJngUnAaeA8cFMp9ZtloxI5pJRS6jzohiqgpIXjkWT4LiODYzLnXD5kGIYzsAx4RSl1y9LxiOxhGEZb4JJSarelYxE5xgaoDfxPKVULuE0u+HhVZI+7/UbbA5WA0oCTYRg9LRuVKCgkGdbOAOXu2S+LfDyT7xiGUQidCC9USv1g6XhEtmoItDMM4yS6m1MzwzAWWDYkkc3OAGeUUimf6CxFJ8cif2gORCilLiul7gA/AA0sHJPIGRcNw3AHuPv1koXjkWT4rj+BqoZhVDIMwxbdaX+VhWMS2cgwDAPd1/CgUmqypeMR2Usp9ZZSqqxSqiL693eDUkpalfIRpdQFINIwjOp3DwUDBywYkshep4EAwzAc7/57HYwMkMyvVgF97r7uA6y0YCyA/tipwFNKJRqG8RKwBj2CdY5Sar+FwxLZqyHQCwg3DGPf3WOjlFK/WDAmIcSjeRlYeLfR4gTwgoXjEdlEKbXDMIylwB707D97yYXL9opHYxjGd0AQ4GYYxhngXWAisNgwjP7oP4K6WC5CTZZjFkIIIYQQBZZ0kxBCCCGEEAWWJMNCCCGEEKLAkmRYCCGEEEIUWJIMCyGEEEKIAkuSYSGEEEIIUWBJMiyEEEIIIQosSYaFEEIIIUSBJcmwEEKYzDCMrw3DGP8E1580DKN5Fs7bbxhG0OPWI4QQBYEkw0II8RCGYSjDMKrcd2ysYRgLLBVTViilPJVSoTl1f8MwehiGsdswjBuGYVwzDCPUMIwyOVWfEELkBFmOWQgh8gjDMGyUUomWjgPAMIy+wNvw/+3dT4hWVRzG8e9jWob9GforWtMIQZtJLDCEFhkGDZVlVlokJtoyRKIwopXLaDHYpkgws3+TfxJ0UduIwgmTNKKCkhxKpSnNSEsbnhb3CndedO68M5PO8D4feOHOPfec97mz+nE457wspfgJ3auBRcDRCxgrIqJpmRmOiBilctnCc5L2SfpDUo+kqZX22yR9KelPST3A1Ib+MyRtk/SrpAOSVjeMvVbSPuAvSZMrbc9L2tYw1quSuit972kY66w5Jd0uaW+ZcUvZNtRSjlXA67b3uNBve4PtEyP7L0ZEXBgphiMixsYSoAuYBcwGVgBIuhjYAWwGrgK2AI+c6SRpErAT+AqYCSwA1ki6tzL2E8D9QFvDzPDbQJektnKsyRQztZubyVlm/BB4s8z4HvBwzfueBFZKWiLpmppnIyLGrRTDERFjY73tX2z/TlHczinvzwOmAN22T9veCnxR6TcXuNb2OtunbP8IvAE83jB2n+2T1S+0fQj4BHisvNUF9Nve02TOeRTL5taXGbcDvTXvuxz4GHgFOCJpp6TrACTdLamjpn9ExLiQYjgiot4ARUFbNQU4Xfn7cOX6BHBZeT0D+Nm2K+0/Va5vAmaUm9COSToGvAhcX3mmb4hsm4Bl5fUyhp4VPlfOs2Uc6juxfdj2GtvtwB0Us8xry+aVgGpyRESMCymGIyLqHQQ6Gu7NYnBRey6HgJmSqsVhe+W6Dzhgu63yudz2fZVnqkVqox3AbEmdwAPAO8PINJyMNw63czkTvR+YJulBYCGwUdLyEWSJiDivUgxHRNTrAV6SdIOkSeWmtIXA1mH0/Rz4F1gtabKkxRQzqWf0AsfLTXKXSrpIUqekucMJZvvvMse7QK/tg828WCXjAPBMmfGhhoyDSHpB0p2SLik/K4D5wEZgF7DX9nzbb40gS0TEeZViOCKi3jrgM+BTiqPDXgaetP11XUfbp4DFFBvqjlJscNteaR+gKKznAAeAfmADcGUT+TYBt1K/RKIu4yrgGMVyi13AP+focgVF4fsbxaz5UmCB7d3AzcB3I8kREXEhaPASsYiImGgktQPfAtNtHx+jMXcDr9ne2GS/RUCH7e6xyBER8X/LzHBExARWHs32LPD+aAphSXdJml4uk3iKYkPcRyMY6nvg6TNnHUdEjHf5BbqIiAlK0jTgCMVGvq5RDncL8AHF6RI/AI+WR7c1xfY3QOcos0REnDdZJhERERERLSvLJCIiIiKiZaUYjoiIiIiWlWI4IiIiIlpWiuGIiIiIaFkphiMiIiKiZaUYjoiIiIiWlWI4IiIiIlrWf3h6aqEwlU9/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(put_focus_negative_rates, S_strip_focus, put_focus_negative_rates.get_tau())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}