ipython3/13_Objects_GUIs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Analyze Big Financial Data**\n",
    "\n",
    "O'Reilly (2014)\n",
    "\n",
    "Yves Hilpisch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img style=\"border:0px solid grey;\" src=\"http://hilpisch.com/python_for_finance.png\" alt=\"Python for Finance\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Buy the book ** |\n",
    "<a href='http://shop.oreilly.com/product/0636920032441.do' target='_blank'>O'Reilly</a> |\n",
    "<a href='http://www.amazon.com/Yves-Hilpisch/e/B00JCYHHJM' target='_blank'>Amazon</a>\n",
    "\n",
    "**All book codes & IPYNBs** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**The Python Quants GmbH** | <a href='http://tpq.io' target='_blank'>http://tpq.io</a>\n",
    "\n",
    "**Contact us** | <a href='mailto:pff@tpq.io'>pff@tpq.io</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Orientation and Graphical User Interfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylab import plt\n",
    "plt.style.use('ggplot')\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object Orientation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics of Python Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "uuid": "1ce92033-098b-4e27-9d87-8e2666ec5cea"
   },
   "outputs": [],
   "source": [
    "class ExampleOne(object):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "uuid": "b456addd-be22-4c83-8540-83e23537a955"
   },
   "outputs": [],
   "source": [
    "c = ExampleOne()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "uuid": "5b6cf9da-239e-43ae-b08b-a68ce7fa1866"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<__main__.ExampleOne object at 0x106d19dd8>'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "uuid": "61841014-fc42-4e7b-af9b-7c00726e30f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.ExampleOne"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "02b764ef-2c2e-4b16-a172-3abe15bfe608"
   },
   "outputs": [],
   "source": [
    "class ExampleTwo(object):\n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "1697f007-4187-458b-9cca-86996f72c187"
   },
   "outputs": [],
   "source": [
    "c = ExampleTwo(1, 'text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "uuid": "66a76e9c-3dce-4fa4-a02f-045e18230ce9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "uuid": "bdef0545-bdbe-4205-94e8-ff4d8d25da8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'text'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "uuid": "5e53f978-44c5-48bb-82c8-488e744ee714"
   },
   "outputs": [],
   "source": [
    "c.a = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "uuid": "2886b794-29e2-4650-afff-cb4528f1837b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "uuid": "97cd1970-5234-45ea-a03f-0c2e82713d42"
   },
   "outputs": [],
   "source": [
    "c = ExampleOne()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "uuid": "8fa2b86a-8182-4689-80b1-a9ddb8aaabeb"
   },
   "outputs": [],
   "source": [
    "c.first_name = 'Jason'\n",
    "c.last_name = 'Bourne'\n",
    "c.movies = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "uuid": "01aaaa84-30eb-4aa1-91f1-45da021090d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jason Bourne 4\n"
     ]
    }
   ],
   "source": [
    "print(c.first_name, c.last_name, c.movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "uuid": "8bab6ecc-0164-4cab-99cc-6dd085c6573a"
   },
   "outputs": [],
   "source": [
    "class ExampleThree(object):\n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "    def addition(self):\n",
    "        return self.a + self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "uuid": "e1972182-b358-43e0-aa65-5edcb5ff22d2"
   },
   "outputs": [],
   "source": [
    "c = ExampleThree(10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "uuid": "d5108fab-7b72-4c84-8d35-e8f10532bcfd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "uuid": "ce192d08-30fd-49fa-9332-f7c12b3041fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.a += 10\n",
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "uuid": "e7be5636-de6c-445f-ad25-7f0bccfcb848"
   },
   "outputs": [],
   "source": [
    "class ExampleFour(ExampleTwo):\n",
    "    def addition(self):\n",
    "        return self.a + self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "uuid": "174ae69d-a1c4-4181-9be6-76246b171787"
   },
   "outputs": [],
   "source": [
    "c = ExampleFour(10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "uuid": "921600a6-6068-473f-9a6f-91df0d5ae8b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "uuid": "a433da66-70d6-4e55-a16f-cb5ceabbc795"
   },
   "outputs": [],
   "source": [
    "class ExampleFive(ExampleFour):\n",
    "    def multiplication(self):\n",
    "        return self.a * self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "uuid": "55e4676e-552d-4c91-98ac-bcda1f1661bd"
   },
   "outputs": [],
   "source": [
    "c = ExampleFive(10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "uuid": "a00a11b9-64cb-4b84-937f-d4a08e55a866"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "uuid": "602e2b76-9282-41fb-a2ae-f5e8f8700542"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.multiplication()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "uuid": "7e0ec289-2e12-422e-a6e8-928467ced561"
   },
   "outputs": [],
   "source": [
    "def multiplication(self):\n",
    "        return self.a * self.b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "uuid": "eef676a7-1aa2-48bb-8df1-faa0d6e376c2"
   },
   "outputs": [],
   "source": [
    "class ExampleSix(ExampleFour):\n",
    "    multiplication = multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "uuid": "b6914c5b-ef2c-4e89-8658-945230a0d8d1"
   },
   "outputs": [],
   "source": [
    "c = ExampleSix(10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "uuid": "09c11cdf-8a11-44fc-bfa6-ab7d0e5fb1f0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "uuid": "3d48c537-1b7e-47ca-8265-7ec6d22ccdbb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.multiplication()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "uuid": "8ba30bf1-927d-4f0f-bdff-e52902611f47"
   },
   "outputs": [],
   "source": [
    "class ExampleSeven(object):\n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        self.__sum = a + b\n",
    "    multiplication = multiplication\n",
    "    def addition(self):\n",
    "        return self.__sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "uuid": "5789b7e3-eada-4855-a8e7-835d05841f09"
   },
   "outputs": [],
   "source": [
    "c = ExampleSeven(10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "uuid": "cffc73ff-1abd-48d7-8d31-110c5688c8cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "uuid": "4d52d700-8969-4461-84f1-024629dc249a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c._ExampleSeven__sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "uuid": "351770f1-1e91-4d2f-9069-07a828a7b912"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.a += 10\n",
    "c.a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "uuid": "afae2e94-1ff5-4e7e-a17d-a178a4b2db4f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.addition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "uuid": "facaf795-8f14-438c-ac4b-50182c55968f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c._ExampleSeven__sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "uuid": "0cf66833-d07e-413a-938a-c6f0f8add274"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.multiplication()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "uuid": "4ac60abf-3801-4c85-9a70-5225c067802e"
   },
   "outputs": [],
   "source": [
    "name_list = ['Sandra', 'Lilli', 'Guido', 'Zorro', 'Henry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "uuid": "7a94a261-2baf-4ab4-9f76-8de755e2dde3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sandra\n",
      "Lilli\n",
      "Guido\n",
      "Zorro\n",
      "Henry\n"
     ]
    }
   ],
   "source": [
    "for name in name_list:\n",
    "    print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Short Rate Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "uuid": "d6fcc0d8-6c79-4a31-8e48-78a38311e688"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def discount_factor(r, t):\n",
    "    ''' Function to calculate a discount factor.\n",
    "    \n",
    "    Parameters\n",
    "    ==========\n",
    "    r : float\n",
    "        positive, constant short rate\n",
    "    t : float, array of floats\n",
    "        future date(s), in fraction of years;\n",
    "        e.g. 0.5 means half a year from now\n",
    "    \n",
    "    Returns\n",
    "    =======\n",
    "    df : float\n",
    "        discount factor\n",
    "    '''\n",
    "    df = np.exp(-r * t)\n",
    "      # use of NumPy universal function for vectorization\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "uuid": "8d7ae797-3070-4e98-a516-7c03d219885e"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "uuid": "8febc46f-9d54-4ac9-b4a2-b7b20afd6657"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x108d0f6d8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xZPjH0oifB9iB9JiUSklKFouB+UvtnJvQzdEDXWx9w8eshTYKitQg790yaBpV\neXaq8uw8uiCP/Rc62VLnZWN1G388fZUJLjMPTPOzMM+sBtWVuzZci6Ms/N0W8fMAH/CZUS+RktQ0\nTaOoLI0sj4kD7/rZv8tPc2kvM+apgfPRYjYaWFLkYkmRi47uPt5t8rG1zstL7zXxU6Aiy8rqMjcr\nS9xk2sZ+vEkZf6Id4/iSlPJ7cSjPaFFjHCM0FnW+NnB+MojdbmDuEnvcVpyn4mscsrr43cF6ttZ5\nORseVJ+VZ782qD7aK9UTQSq+zok0OF4GrAS2SikbhBAlwKyhEh/e5jHWAQ8DzUBISrlhiOf4NrAX\nPV37L6SUvw/fVw/Uh089L6X8xDBPpwLHCI1lnVsv6wPnXV0hJk9Po3J67HcZTPXXuKk9yLZ6L9vq\nvVzs6MFs0FhY6GRVqZsFBQ7MxuQYe0r113mkRntw/Bn0N/w3w7d9wAeFEIuklP843MVCCDvwA6BK\nShkUQrwihFgrpdwUcdoTwA4p5XeFEHMBCfw+fN9LUsqnoyyrMs5k55hYvd7NsQN+zhwP0vx+L/OW\n2HG4ku8TcKIoSk/jE7Nz+ItZHs60Btha72VHg5ddjT4cZgNLi12sLnVTlWtX6d+Vm0QbOPKllH8+\ncENKeQV4VAixLcrrlwINUspg+PZO4ANAZOC4hL6wkPD3/RH3rRRCPIG+6PB1KeWuKJ9XGSfMFo25\nSxzkFnRzdF8XW9/0UTVHpWqPNU3TmOKxMcVj46/m5XL4Yifbwqnf365tJ9NmYkWJHkQqslS6E0UX\nbeC4KdezEMIwgutz0VspA7zhY5G+A/xWCPEd9B0Hvx5x399LKd8Lt1wOCCH+REpZE+VzK+PIxGIL\nWR4Th/b4ObKvi0vv9zB7gZ00a3J0nSQyo0FjXoGTeQVO/rq3n33nO9ha7+X1M2384ZQ+M2tgem9h\netpYF1cZQ9G+8W8TQmwEXuZ6ypFPAluivL4ZvbUwwB0+Fukl4MdSyl8KIXKAaiHEJCnlFSnlewBS\nSr8Q4hCwHLghcAghHkVfW4KUEo/HE2XRbmYyme7q+vEo0epcWBTi+KE29r/byvY3O1l+by5FpY5R\ne/xEq288jLTOE/Nz+dP54A30srWmhbdOX+Y3x1qRx1qZnONg3ZQc1k3OIc+VuEFEvc4xeo4oz/t/\nga8CT3I95chL6OnWo7EbKBFCpIW7q5YD3xdCZAG94Q2hioD3w+dfBfoBgxBiLWCWUr4Rvq8CqB38\nBFLKF4GemXxQAAAgAElEQVQXwzdDdzMgpgbUEkN+Eax0uTjwbidvv/Y+JeX6Hucm0913lyRifWPt\nbuq8NN/E0vwJtPo97Gz0sa3ey/d31PP9HfVMz7GxqlTfUz09wXJmqdd5ZMKD48OKOlfVUIQQGVLK\ntijPvQ/4KHqLpUdKuUEI8U3gipTy2XC23b8DDqCvGdkvpfyBEGIm8DT6mEcBcEFK+Y1hnk7Nqhqh\nRK5zX1+I00cD1J7W813NXWInM/vu3qASub6xMtp1ft/XzfZwzqxz3m4MGszJd7Cy1M2SIid289hP\nblCv88iM6nTcAUIID/qK8VD4wX8lpVx2RyWMLRU4Rmg81LmluYdDe/wEukJU3uW03fFQ39EWqzqH\nQiHq24LhQXUvzZ29WIwa8wucrCp1Mb/ASdotEi/GmnqdR2ZUp+MKIZYBv0Tvpop8ULUfhxI3nlwz\nq9e7ORoxbXfOYjsu99h/sk1lmqZRlmmlLNPKp+bkcKqli+31XnY0+tjd5MNmMrC4yMmqEjezJzgw\nqem941607f1vAKvQ11OsEUKY0ffiWBizkinKEMwWfaOo/IndHNnXxbY3fUyfZaO0Uk3bTQSapjEt\nx860HDt/NT+Po5f8bG/wXttLxJVmZFmRi5WlLqbnqDUi41W0gaM7vGLcCCCl7AH+IIT4m9gVTVFu\nraBIn7Z7eK+fYwe7uHihhzmL7CrbbgIxGjTmTHAwZ4KDLyzM48D7nWyv97Klrp2NNW1k2UwsL3Gx\nqkTP3qsC//gRbeAwCCEygCtCiC8DG4HFQGXMSqYow7DaDCxa6aDxbDfHD3Wx5Q0vM+fZmVhiVm9C\nCcZsNLC40MXiQheB3n72nutge8P1NSJ5Tn2NyMoSFyUZaer1S3DRBo5/BR5Cn5b7e+A59Cmzn41R\nuRQlKpqmUVKehifPxME9fg7u8XPxvJmZ821q0WCCspoMrAxvedvR3ceeJh/bG3y8eqKV/zreSqHb\nwsoSNytKXRS6E3eNSCq7ZeAIL/h7VUr5Q2BTxDaxk8IL9FqllP3xKKSiDMfhNLJ8jZPa00FOHwvQ\nermX2Qvt5E+8KemBkkCcFiNryzNYW55Be6CXXY0+djR4+dXRFn55tIWyzDRWhFsieU61j0iiuF2L\nwxUOGgC/A+4duENKeRlACPEtKeXjMSyfokRNM2hUTLOSO8HMwT2d7N3RSVGphaq5NswW1fWR6NKt\nJh6cnMmDkzNp9fewq9HH9gYvLx+6zMuHLlOZbWVliZvlJS48dvWBYCzdLnAYhBBfRV/NnS+E+NQQ\n53wAUIFDSSjuDCMr17k4c0Lf66OlWR849+SpN5vxIttu5oNTs/jg1CwudXSzo0FvifzkQDM/OdDM\n9BwbK0r01epqM6r4u91v/DPA3wPrgQkMvdtffiwKpSh3y2DUmDrTRl6BmYN7/Oze0klphYVps0cn\nZYkSP3lOCx+pyuYjVdmc93azo0FfaPjivkv8eP8lqnLtrChxsazIhTvBUp4kq2g3cnpBSvm30R5P\nAGrl+Aglc517e0OcOtJFXXU3DqeBOYvsTJ6Wn7T1vZVke40b24Jsb9BTwF/w6SlPZuU7WFGsb5vr\nSjMmXZ2jkXApR8YRFThGKBXq3NLcw6H3uujq7GfGnAyKK0IYjanT+kjW1zgUClF3NciOBi87G31c\n7OjBZIDZ+Q4eqCqgKoOk3Bb3VlTguHMqcIxQqtS5tyfEicNdNNR243QbmLvITsZdJkwcL1LhNQ6F\nQtRcCbCjwcfOBi+X/b2YDBpzJzhYUeJiUWFiJF+MJRU47pwKHCOUanUO+m1se/siwUCIiml6wsRk\nb32k2mscCoVo7rPy2uFGdjT6aPX3YjZozCtwsKLEzYKJjqQMIgmT5FBRks3EYgf3PODi+MEA1SeC\nXDyvz7zKyFL/EslC0zSq8l3kmfL4y3m5nG7pYkeDj12NPvac6whn8HWwvNjNgolObGa1YDRa0WbH\n/YyU8qeDjv0n8Ecp5a9iUjJFiTGzxcCcxXYmFJk5vNfPjrc7qJiWxuTpVgxJ3vpINYYbki/mcupy\nFzsafexq8LK7SQ8iCyY6WV7sYsFEJ9YxSgM/XkT78eqTwE8HHXsUeAdQgUMZ1/IKzNzzoIvjB7qo\nPhHk0vke5iy2k56pWh/JyKBpTM+1Mz3Xzl/NGwgiXnY16q2RgSCyotjFfBVEhnTb/wwhxJPhH0sj\nfh5gB9JjUipFiTOLxcDcJQ4mFPVwZJ+f7W916JtFTVOtj2RmNGhU5dmpyrPzufl5nLjsZ2eDvo/I\nrkYfaUaN+SqI3GS4j1Rl4e+2iJ8H+Bh6UaCijFv5E81keVwcO9jFmeNBLp5TrY9UYTRozMxzMDPP\nwecXXA8iu5pubImo7qzoFwB+SUr5vbt5IiHEOuBhoBkISSk3DLq/DPg2sBeYA/xCSvn78H2PAHOB\nPqA2IofWrahZVSOUanWOpr4Xz+utj+5gcsy8SrXXGEanzn39oRtaIm2Bvmtb4w4EkUQaWI/HrKqo\nanuroCGEeCma64UQduAHwJellE8Ds4QQawed9gSwQ0r5LPBP6KnbEUIUAo8Bj0kpnwA+J4RQ+4Ao\nMZc/0cw9D7iYWGKm+kSQ7W/5aLvSO9bFUuJsoCXyhUX5/OTDFfz/64pZOymdk5f9fHvnBT71SjXP\nbDvHtnov/p6+sS5uXEQ7q2oJ8DRQCgzkNtaAPOAvo3iIpUCDlDIYvr0TPUHipohzLgE54Z9zgP3h\nn9cD+6WUA02j3cCDQHU0ZVeUu2FJMzB3sYOC8NjHjrc7KJ+axuSq8d36UO6M0aAxI8/OjDw7n1+Q\nx6nLXexs9LKrqYN3mzqurRNZVuxi4URn0q5Yj7bj9qfAT4AjQCB8TAO+G+X1uehjIgO84WORvgP8\nVgjxHWAR8PURXIsQ4lH0mV5IKfF4PFEW7WYmk+murh+PUq3OI62vxwMVU/rYu6OF6pM+Ll/sZ8W9\neeTmW2NYytGVaq8xxL7Oebmwugr6QyGOXvCyuaaVLTUt7Dn3PmajxqLiDO6p8LBiUnbcEjDG43WO\ntibnpJTfGnxQCPGhKK9vBlwRt93hY5FeAn4spfxleKOoaiHEpPB5FYOurRn8BFLKF4EXwzdDd9Ov\nqfqCk9+d1nfqbCNZuQ4O7/Pz2ivnmDQ5jSkzreMi426qvcYQ3zpPTINHqtz8xXQXZ1oC7ApP8d1Z\ndxWjVs3sfL0lsrjQGdMgMgpjHMOKdkTnZSHER4QQzkHHox0w3w2UCCEG9oFcDrwmhMgSQrjDx4rQ\n9/4AfVva/nD5NgLzhRAD/5lLgdejfF5FGXW5E8zc84CbknILZ88E2brRR0uzGvtQdAZNY2qOjc/O\nz+NH/0c5336ghD+dlsV5Xzf/uucin361hic3NfJG9VXausbn3020s6oGtoiNPFlDnx0VVSeeEOI+\n4KPAZaBHSrlBCPFN4IqU8lkhxArg74AD6FN/90spfxC+9hFgAfqsqjNqVtXoS7U6j1Z9Wy71cHhv\nF/7OfkrKLUyfbcNkTszWR6q9xpBYdR7I4ruz0ceuRi8XfD0YNJieY2NZsZslRU6yR2Fnw4RJciiE\n2A38+eBrgV9KKZfeSQFjTAWOEUq1Oo9mfXt7Q5w+GuDsmSBWu8asBXbyJiTeboOp9hpD4tY5FArR\n0Ba8tkakqb0bgCkeG8uLXSwtcpHrvLO/oURKciiklE03HRTioRGWS1GSjsmkUTXXdi3n1XvbOiks\nMVM114YlLXHm9yuJQ9M0SjOtlGZa+YtZOTS1B9ndqC82HNgetyLLytJifWfDArdl+AeNo6gCx1BB\nI+wHwMdGrziKMn5leUysut9F9YkANSeDNF/sZeY8PaBoWmJ2XymJoSg9jaKZaYiZHt73dV8LIi8f\nuszLhy5TkpHGsiIXS4tdFKdbxvzvKdp1HGdvcZfac1xRIhjDe51PKLRweK+f/bv95DWamDXfjtWm\nWh/K8Ca4LDxclc3DVdlc7uzh3XB31q+OtvDLoy0UuCwsLXKytNhFRZZ1TIJItF1V7egD1wMy0Bfh\n3TQtVlEUSM80smKdk7Nngpw+FmDz616mz7ZRPGnsPy0q40eOw8wHp2bxwalZXO3q5d0mPe3Jb09e\n4ZUTV8ixm1gS7s6a4rFhNMTnbyvawPFhKWX9oGO/E0L8Fj2/lKIogxgMGhVTreRPNHNkXxdH9nVx\nvqGbWQvtOF3JuaJYiZ1Mm4kHJ2fy4ORMvME+9p7Tg8jrZ9r4w6mrZFiNLCly8cAME2X22JYl2jGO\n+sjbQggzMAOoikGZFCWpOF1Glt7joPFsNycOd7H1DR+TZ1gpn5KGIU6fEJXk4k4zsrY8g7XlGfh7\n+th3vpPdTT42n22nydfHN9ZOjOnzRzvG0c+NazgA/MBXR71EipKENE2jpDyNvAIzRw90cepIgAuN\nPcxeaFPb1Sp3xW42sqrUzapSN8HefkJWF/R2xvQ5ox2t2wNMivgqBDKklN+PVcEUJRlZbQYWLnew\nYLmdYKCf7W93cPxQF729w6+nUpThpJkMFGbYYv480X7UeUhKeTWmJVGUFDKh0IIn18SJwwHOng7y\n/rkeZi2wkZufeAsHFWWwaAOHVwjx/6Dv+FcInEPPlvtPUsrUSECvKKPMbDEwe6GdwhILh/f52bO1\nk4klZqrm2Eizqqm7SuKKNnA8C8wEnkTPNZUDPII+LfeJ2BRNUVJDdq6J1evDCwdPBWl+v5eqOTYK\nS9XCQSUxRRs4lgKrpJQDyQ4RQvwK2BaTUilKihlYODixWF84eOg9P+caTMyab8Ohpu4qCSba9rAW\nGTQij49mYRQl1bnSjSxf62TmPBttV3rZstFH9YkA/X1q8FxJHNG2OHYKId4E/oMbu6pUi0NRRpmm\naZRWppFfaObYgS5OHQ1wvrGbWQvsZHnU1F1l7EXb4vgHYDv6GMdvgX8Etoa/K4oSA1abgQXLHSxc\n4aCnJ8TOTR0c2eenp3uoxr+ixE+0K8d70fcA//pw5yqKMrryJ5rx5Lo5dSxAXXWQi+d7mDFXZd1V\nxk60K8fvRe+a+jcp5V4hxCzg08BTUsqOWBZQURQwmTVmzLVRWKLnvdq/209OnT54bneqwXMlvqLt\nMP0H4Pvo27oCHAf2o6/lENE8gBBiHfAw0Iy+5eyGQff/O1AecWgmMF9KWS+EqAfqw8fPSyk/EWW5\nFSWpZGSZWLnOSV1NN6eOdrH5DR9TqqxMUnmvlDiKeqRNSvlqxM99wC+EEJ+L5lohhB1906cqKWVQ\nCPGKEGKtlHJTxGlvSil/HT7fDbwUkVzxJSnl09GWVVGSmWbQmDQ5jQnhwfOTRwKca+hm1nw7WTlq\n8FyJvWgHxx1CiKzIA+Hb1iivXwo0SCmD4ds7gQ9EnjAQNMI+i96aGbBSCPGEEOLrQohlUT6noiQ1\nm93AwhX64HlvT4id73RweK+f7qAaPFdiK9qPJ/8KHBdCvMb16bgPAY9FeX0u4Iu47Q0fu4kQwgCs\nB/454vDfSynfC7dcDggh/kRKWTPoukeBRwGklHg8niiLdjOTyXRX149HqVbnZKqvxwNTpvVzaN8V\njh9q49KFXhYu91AxxXXD4Hky1Tlaqs4xeo5oTpJS/lwIUQf8JTAbaAI+IqXcHeXzNAOuiNvu8LGh\nfAh4TUp5bcWTlPK98He/EOIQsJxBuw9KKV8EXgzfDLW0tERZtJt5PB7u5vrxKNXqnIz1LZsM2bku\njuzzs2NTMyePtDJzgR2XWx88T8Y6D0fVeWQKCgqiOm8kYxy7gF13VBrYDZQIIdLC3VXLge+Hu7t6\npZTeiHM/DXxy4IYQYi1gllK+ET5UAdTeYTkUJam5M/SV541nuzl5JMDWjT7Kp6RROT3aXmVFGV60\n03E/AHwE+BtgCvA79PGRT0optw53fbil8NfA94QQl4EjUspNQohvAlfQkygihJgD1Aya4tsMPC2E\nmAcUAK9KKXdEXUNFSTEDm0blTzRz4nAXNSeDnG/oZtk9duyu4a9XlOFoodDwOXCEEO8A/7eU8qgQ\n4r/RV5HvAp6VUi6NcRnvROjChQt3fLFq3ia/VKpva3MvR/f78Xn7ySswMWOeDbsjNdZ+pNLrPGAU\nuqqGndcd7ayq/nDQyAVmoe/DsRXouqPSKYoSN9m5Jlatd7FgaTYtl3rZ/LqP6pMqcaJy56Id40gT\nQixEH3v4uZSyXwhhA9R2ZYoyDhgMGjPnZZLh6eHYQX3P83N13cycb8OTp/6NlZGJtsXxVfQpuRXA\nM0KIfOAt9ISHiqKMEza7vuf5opUO+vth95ZO9u/uJNCl1n4o0Yt2Ou4uYPGgwyuEEHNHv0iKosRa\nXoEZT66JmlMBak4GuXShhykzrJRVqtQlyvBuGTiEEFYpZSD886pbnPY8MC8WBVMUJbaMJo0pM2wU\nllo4dqCLE4cCNNV1M3O+nWyVukS5jdv9dZwRQswIr7HYCFwc4py82BRLUZR4cTiNLFrp4OL5Ho4f\n7GLXOx1MLDEzfbYNqy3a3mwlldwucFRG5JZ6RUr5yOAThBD/GZtiKYoST5qmMaHQQk6+mZqTAWpP\nhbuvqqyUqu4rZZBbfpyICBoMFTTCfjXqJVIUZcyYTBpTZ9pY/YCLzGwTxw8F2Pamj9bm3rEumpJA\nbjfG8akorv8a8MfRK46iKInA6TKyeFVE99XmDiYWm5k+R3VfKbfvqvpn4FD4Zw19VtUZ9BQh2cA0\n9BxUiqIkoaG6ry6Gu6/KKtMwGFX3Vaq6XeB4WUr5JQAhxHfQU44cHrgznFfqCzEun6IoY2yg+6qo\n1MLxQ12cOByg8Ww3M+bZyMlXiwdT0e3GOL4UcXNBZNAI338IqIpVwRRFSSwOl5FFK5364sEQvLu1\nk707O/F3qsWDqSbaydo2IYQAfiOlDIU3W/oYYItd0RRFSUR5BWY8eSbOng5y5kSA5vd7qJhqpWJq\nGkaT6r5KBdEGjr8GXgV+JoRoAzLQ050/HKuCKYqSuIxGjcrpVgpLLZw41MWZ4wGa6oJUzbWRP9F8\nw86DSvKJanqElHIfUI6+pevfhb9XSCn3x7BsiqIkOJvdwPxlDpaucWAya+zb6efdrZ342vvGumhK\nDI1kB8AeYFsMy6IoyjjlyTWz6n4TDbXdnD6m7zxYWmFhygwrZouavptsVEIaRVFGhcGgUVaZRkGx\nmdNHA9RVd3O+sYepM60Ul1nQ1OrzpBG3wCGEWIc+JtIMhKSUGwbd/+/o3WEDZgLzpZT1QohHgLlA\nH1ArpfxhnIqtKMoIpaUZmLXATkm5hWMHuziyr4v6mm5mzLWRnas+qyaDuLQhhRB24AfAl6WUTwOz\nhBBrB532ppTyHinlPcCHgK3hoFEIPAY8JqV8AvicEKIyHuVWFOXOpWeaWLbGybyldrq7+9m1uYP9\nu9T03WQQr87HpUBDRP6rncAHIk+QUv464uZngZ+Ef14P7JdSDuxzuRt4MIZlVRRllGiaxsRiC2se\ndDO5Ko2LF3rY/LqX08e66O1VW9eOV/FqN+YCvojb3vCxm4TXiKxHT3kS9bVCiEeBRwGklHg8njsu\nrMlkuqvrx6NUq3Oq1RfGvs75+TB7Xg97d7dy5ngH5xr6WLg0m7JKZ8ym7451ncdCPOocr8DRDLgi\nbrvDx4byIeC1iBZGM/qWtZHX1gy+SEr5IvBi+GaopaXljgvr8Xi4m+vHo1Src6rVFxKnzjPnmygo\ncnLsYBdb37rEkQMtzJhrIyN79N+OEqXO8XQ3dS4oKIjqvHh1Ve0GSoQQaeHby4HXhBBZQgj3oHM/\nDbwUcXsjMF8IMfCRZCnweiwLqyhKbGXnmlh1n5PZC210dvSz/e0ODu5Re5+PF3EJHFJKP/rq8+8J\nIf4ncERKuQk9LfvfDJwXTpxYI6XsiLj2HPBt4LtCiOeAH0spq+NRbkVRYkczaBRPSuPeD7gpn5rG\nhcYe3vlvL2eOB+hT4x8JTQuFkvIFCl24cOGOL1bN2+SXavWFxK9zZ0cfJw4FuHi+B5tdY9psGwVF\nd5e+JNHrHAuj0FU17C9cLelUFCUhOJxGFq7Q05eYLQYO7Pazc1MHV1vV7oOJRgUORVESiifXfG38\nw9/Zz463OzjwbiddfjX+kShSZhlnKBQiEAjQ398/bNP30qVLBIPB256TLEKhEAaDgSTtslTGqYHx\nj4IiC9UnA5w9HeT9cz2UT0mjYqoVk1mlLxlLKRM4AoEAZrMZk2n4KptMJoxGYxxKlRh6e3u5cuXK\nWBdDUW5iMmtMm2WjpNzCySMBqk8EaTzbzdSZVopKVf6rsZIyXVX9/f1RBY1UZDKZ6O1V/chK4rI7\njMxf6mD5Wid2h4HDe7vY9paPlks9Y120lJQygUNtLKMo41+Wx8TytXr+q57uELu3dPLe9g58XrX/\nRzypj+AJrqmpieeff57S0lKampp46qmncDgcN533b//2b/h8Ptrb21m9ejX3338/AF6vl3/5l3/h\nN7/5DYcOHYp38RVl1A3kv8qfaKbuTJDqkwG2vuGjpNzC5CoradaU+Tw8ZtRvOMF97Wtf45FHHuGL\nX/wiU6dO5YUXXrjpnAMHDrBr1y6eeOIJNmzYwIYNG/B6vQC89957PPTQQ6orSkk6RqNGxTQr9z7k\npqTcQkNtN++85qX6ZIC+PjXZI5ZSssXR/6sfEWqqu/X9mjbiWUZaURmGP//8bc/59a9/zTPPPMMn\nP/lJ6uvrqa2tJTMzc8hzv/71r1NUVMSuXbuYM2cOAAsXLuSxxx7jiSeeuOHct99+m3nz5gH6eEVl\nZSXvvvsu999/P+vWraOpqWlEdVGU8STNamDmfDtllWmcONzFqSMBGmqCTJ1pIztbBZBYSMnAMVY+\n9rGP8Zvf/IY5c+bwla98hcOHDzN79uxbnn/p0iWsVuu18Rmn0znkitCWlhYqK69vUeJyuVJutayi\nON1GFq100nKphxOHAxzc46fx7DkmV5nw5JnHunhJJSUDx3Atg1jPMhp4k6+srORjH/vYkOd84xvf\noLi4mEAgQCgUQtM0Ojo6hkyX7PF46Oi4lt4Ln8+XcqmkFWWAJ8/MyvtMnG/o4czxbnZvCZJXYGLa\nLBuu9NSZZh9LKRk4xtpAC8Jut/PrX//6tucuW7aMQ4cOMXfuXPbu3cvatfrGid3d3bS2tjJhwgTW\nrl3L888/D0BPTw/V1dUsXrw4tpVQlASmaRqFpRZmzMln3+4LVJ8MsGWjj+IyC1NmWLHa1PDu3TA+\n/fTTY12GWHja5/PdcKCnpwezObrmqsFgoL9/9NMbbNu2jVdeeYXOzk5mzJiB3W4f9ppFixbxwx/+\nkJMnT1JXV8fjjz+OxWJh06ZNPPfcczz88MMUFBRw6dIl3n77bV5//XU+85nPUFVVBcCxY8f45S9/\nyf79++nv76esrAyn03nT8wysIE8Vdrsdv98/1sWIq1Sss9PpwOropniShf6+EI1nu6mvDdLfHyIj\n04TBmHzT9O/mdXa5XAAbhjsvZbLj+v3+qN6oITUXxPX19aXUanmVNTU1DK5zh6+PU0cDvN/UQ5pV\nY3KVleJJFgxJtAI9HtlxVVeVoigpw+kysmCZg6stvZw40sXR/V2cPRNk2iwr+RPvLoV7KkmdvglF\nUZSwTI+JZWucLFzhQNNg3049hfuVy6nV03CnVItDUZSUpGka+RPN5E4w0VTXzeljAXa+00HeRBPT\nZqoZWLcTt8AhhFgHPAw0AyEp5YZB92vAF8M3S4EMKeVnw/fVA/Xh+85LKT8RhyIripICDAaNkvI0\nJpZYqDsTpOZUeAZWqYXJM6zY7KpjZrC4BA4hhB34AVAlpQwKIV4RQqwN7zs+4BGgTUr5H+FrZkXc\n95KU8ul4lFVRlNRkMmlUTrdSXG6h+kSQ+pog5xq7mTQ5jYqpaZgtKoAMiFeLYynQIKUc2B1pJ/AB\nIDJwfAJ4QwjxJSAf+HHEfSuFEE8ALuB1KeWuOJRZUZQUlJZmYMZcG5MqLZw6FqDmZJCG2m4qp6VR\nWpGG0aQG0OMVOHKByIUV3vCxSCWAW0r5/wkhJqMHkWlSyj7g76WU74VbLgeEEH8ipayJvFgI8Sjw\nKICU8qaV05cuXRrRfhyJsndHY2Mj3/nOdygrK6OpqYkNGzYMmR33hRdeuCE77gMPPADApz/96WsJ\nDwF+9KMfDbmqvL+/P6VWm5tMppSqL6g6j5gHikuh9XKQ/e+2cuKwn/raXuYuzKJiqithp/DG43WO\n17tjM3prYYA7fCySF9gDIKU8I4RwA0VAvZTyvfBxvxDiELAcuCFwSClfBF4M3wwNnsccDAajXqeQ\nSOs4Hn/8cR577DHmzp3LT37yE/75n//5piSHBw4cYMeOHbz88sv09vayevVqFi1ahNvtZvr06Xzl\nK1+54fyh6hYKhVJqjr9a05AaRqXOGsxbaqG4XOPk4QA7NzdzeH8LU2cm5hTeUVjHMax4BY7dQIkQ\nIi3cXbUc+L4QIgvolVJ60butJgGEg4YRuCiEWAuYpZRvhB+rAqi9m8L8eN8l6q4Gbnm/dgfZccsy\nrXxuQd5tzxmL7LgXLlzge9/7HqFQiIkTJ/LRj350RPVSFEXnyTWzYp2Ji+d7OHkkwL6dfjKyjEyb\nbcWTm1pJFOMSOMIthb8GvieEuAwckVJuEkJ8E7gCPAv8E/BNIcQ/AOXAp6WUASFEM/C0EGIeUAC8\nKqXcEY9yj7axyI77yCOPMHfuXAC+8IUvAKjgoSh3SNM0JhRayCswc65en8K7e3MnOfkmps60kpGV\nGF3csRa3Wkop3wLeGnTsiYif24H/c4jrjgIfGc2yDNcySKbsuANBA/SEiTt37lSBQ1HuksGgUTxJ\nn8JbXxOk+kSQ7W91MKHIzNQZVpzu5F4DkhrhMcHEKztuR0cHP/rRj/jyl78MQF1dHSUlJTGsmaKk\nFqNRo3yKleJJaZw9HaD2dJCL53ooKtO3sU3WNSAqcMTRtm3bOHfuHD/96U/527/9W7Kzs4e95tln\nn5oTwHIAAArdSURBVOW73/0uW7du5cKFCzz11FMAbNmyhZdffpmXX36Z+fPns2zZMp555hna29t5\n6qmnSE9PJxAIcOLECb71rW8RCoVob2/n8ccfj3U1FSXlmM0aU2bYKK1Io/pEgIbabs7Vd1NakUbF\ntLSk2wddZccdQiLNqooXlR03+ak6x4+/s58zxwM01XdjNMKkyWmUT7FitsR+BpbKjqsoijIO2R0G\n5iyyUzE1jdPHAuGV6N1UTE2jtDIN0zhfRKgCh6IoSow43UbmL3NQcbWXU0cDnDwS4OyZIJXT9NQm\nxnG6kZQKHIqiKDGWnmli8SonrZd7OX20i2MHu6g9HWBylZXC0vG3kVRyjdgoiqIksOwcE0vXOFmy\n2kGa1cDhvV1sed3HuYbuES86HkuqxaEoihJHmqaRk2/Gk2fi0gW9BXLwXT81JwxMSdA0JoOpwKEo\nijIGBjaSyiswcaGphzPH9DQm7gwjU2dayZ1gStgAogJHgmtqauL555+ntLSUpqYmnnrqqSGz4x45\ncoQNGzYwe/ZsnnzyyWvHjx07xs9+9jOKiopoaWnhySefTJjMv4qi6AFkYrGFgkIz5xr1APLe9k4y\nsvQA4slLvACixjgS3Ne+9jUeeeQRvvjFLzJ16lReeOGFIc87efIkS5YsueFYKBTiS1/6Eo8//jhf\n+tKXMBqN/OY3v4lHsRVFGSHNoFFUamHNQy5mLbARCPTz7tZOdm3uoKU5sdaVpeRHz2MH/Hjb+m55\n/51kx3VnGJkx7/YLDGOVHRf0BIrPPffcDccaGhoIBALk5uZeu/7VV1/l4x//+IjqpihK/AxsZVtY\naqHxbDfVJwLs3tyBJ9fE5BlWsnPG/m177EuQQmKVHfdWWlpabujWGun1iqKMHaPxf7d390FW1XUc\nx98Lu+zVjHygBRxzRZ4E1FLSYnQSB/Mhqxkrv1mh45hDM5GahkPDNBOSFeg4IqYV00wqlvlBe9JK\nMRsMCkQlp/4IFDKmmRIciAimvS672x/nANsK7T2w9xy45/OaucOec++59/sb9t7v/h7u79vEqLGt\nnDxqCJs2Vnn1z1V+/5udvHNEM+MnVThuWHEf36VMHP31DI603XEPZNiwYezatWvvcdbrzax4g5ub\nOHV8hZNHt7JpQ5UN66qsfHYnbSObGTepwnEn5P8xXsrEUbSB3h33QNrb26lUKmzZsoW2trb/ud7M\njizNzU2MPq1C++hWXnu1ysb1VVb+Okkg40/PtxaIJ8dz1Ht33K1bt9Z0zfz581myZAkLFy5k3bp1\nzJw5E0h2x+0917F06VJWr17N2rVrefjhh4EkQS1atIgFCxZw991309XVxZVXXjnwDTOz3DS3NDF2\nYoWLPjyU086o8M+tXax4ZidrVuxk+7Z8JtG9O+5+eHfcxuedYsuhDG3u7OzhtVeq/GV9lc7OHk4Z\nfQynTx58UEt4D7vdcSPiIuBjwBagR9Jtfe5vAm5ID08BjpV0XXrfdOAsoAvYKOm7ecVtZnY4a2lp\nYtykCqPGJkNYra0tNDXVt0OQy1BVRBwNfAe4WdJc4MyI6DvYPh3YLmmRpFuAhem1JwGzgFlpqdnr\nI2IsZma2V8uQJIFMfn//BeIOVV49jinAJknV9Ph3wOXAs70e8xngqYi4ERgBfC89fwnwkqQ9KXQV\ncBnwat2jNjOzt8grcbQB/+51vCM911s7MFTSvIgYR5JEJtR4LRExA5gBIOkty043b96caauNsm3L\n0d3dXaqlus3NzaVqL7jNZZFHm/P6dNwCvL3X8dD0XG87gOcBJL0SEUOBd6WPG9Pn2g19X0DSYmBx\netjTd0LszTeTbYtrSQhlmxzfvXs3LS0tDT+J2FsZJk37cpvLYQBKx/Yrr8SxCmiPiNZ0uOo84P6I\nOB7YLWkHybDVqQBp0hgMvA48DdwQEU3pcNUU4N6sAVQqFTo6OqhWq/2uNmhtbaVarf7fxzSKnp4e\nBg0axPDhw2teImxm5ZbbctyI+CDwCeANoFPSbRFxB7BN0vyIeAdwB7AJGA08LumX6bXTgfeSrKp6\npYZVVW9ZjpuF/0ppfGVrL7jNZTEAPY5+l+OW5nscWfiXrfGVrb3gNpdFHonD3xw3M7NMnDjMzCyT\nhh2qKjoAM7MjVGmHqpoO5RYRLx3qcxxpt7K1uWztdZvLcxuANverUROHmZnViROHmZll4sSxf4v7\nf0jDKVuby9ZecJvLou5tbtTJcTMzqxP3OMzMLJNybQHbj/6KTTWiiBgB3A68W9I5RcdTbxExmqS9\na4GTgK2S5hUbVX1FxCDgCZJNRIeQbOlznaT/FBpYnUXEUSRtXiZpVtHx5CEiVgMd6WGXpL51jwaE\nexypGotNNaLzgZ9R4zK8BnA88CNJd0q6CbgqIiYXHVQOVkmaJ+krwNEkfyA1utuBPxQdRM6ekjQ1\nvdXt88s9jn1qKTbVcCQ9FhFTi44jL5Je6HNqELCriFjyIqmb5EOUiGgm6WmtLzSoOouIq0new2cC\nxxQcTp7OiIjZwFHAC5J+UY8XcY9jn5oKRlnjiIgrgKclrSs6ljxExCXAk8CTkl4sOp56iYiJwARJ\nPy46lgIskLQA+BowJyI+UI8XceLYp5ZiU9YgIuJC4ELg5qJjyYukpyVdCoyKiM8XHU8dXQF0RMSX\nSYZiz42ILxYcUy4krUn/7QJWkPyODzgnjn32FptKj88D6tLNs2JFxOUktexvAkZExJSCQ6qriJiY\ntnmP10iLpjUiSV9P53PmAyuBNZIWFh1XvUXEaRHx2V6nxgIb6/Fa/h5HL/srNlVwSHUXERcA1wCX\nAt8G7mrk1TbpRPhzwJ6hmrcB90l6oLCg6ixdSXYnyUqyFmACcKOk1wsNrM4i4uPATJKVZPdJeqTg\nkOoqIk4EvkWyIGAoyf/1Lekc14By4jAzs0w8VGVmZpk4cZiZWSZOHGZmlokTh5mZZeLEYWZmmThx\nmJlZJk4cZmaWiROHmZll4t1xzTJIazysAcYAP5B0fUTcCswGHgSWAXOBN0k2yvycpL+n194PjAcG\nA/9I79sRETOAOcBqko02zwG2A58EHgAqJN8CfiLdwM6sUO5xmGWQbscyjeS9Mzc9fQ/wW5LtHh4D\nrpV0AfAU8FCvy9dJmiZpKsm25remz7mYJEFMJUkgZ5MUIPoSsFzShSR7a32kfi0zq50Th1lGkrYA\nzwDT01OXA78CPg28KGlPrYsfAtMiYmR63BERKyLiOeAqoG8BqVWS3pDULWk2sA24LCImSdoFXFzH\nZpnVzInD7OA8BFyd/hzAUpICSRMjYnlELAd+AmwChqfFsu4Crk57I/NJKvH19q8+x3cCjwOPRsTL\nJAnKrHBOHGYH5+fAiWlxpG5J24G/kfQ49pTunAqcBfwJOBdYL+mv6fUtNbxGm6R7JZ0OzAKWpDvd\nmhXKicPsIEjqIOllfB94ND39CPC+iGgHiIg2YDnJ+2wDMCYiTkgfe0kNL/PNiHhP+vPzJBPuZakN\nb4cxb6tudpAi4nzgp8BISZ3puYuBrwKdQDcwR9LqiBgELCaZAP8jsBP4aHruZeAbJKunlkm6Jn2u\nD5FMoHcDxwIPSlqUWwPNDsCJw+wgRcQE4AuSZhYdi1me/D0Os4wi4lMkw1TXknx3w6xUPMdhlt3Z\nJOU5h0laU3QwZnnzUJWZmWXiHoeZmWXixGFmZpk4cZiZWSZOHGZmlokTh5mZZeLEYWZmmfwXQiSx\nIjZD9dYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108d0f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.linspace(0, 5)\n",
    "for r in [0.01, 0.05, 0.1]:\n",
    "    plt.plot(t, discount_factor(r, t), label='r=%4.2f' % r, lw=1.5)\n",
    "plt.xlabel('years')\n",
    "plt.ylabel('discount factor')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=0)\n",
    "# tag: disc_fact_1\n",
    "# title: Discount factors for different short rates over 5 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "uuid": "849b995a-8873-4a42-94c3-c638799d03e2"
   },
   "outputs": [],
   "source": [
    "class short_rate(object):\n",
    "    ''' Class to model a constant short rate object.\n",
    "    \n",
    "    Parameters\n",
    "    ==========\n",
    "    name : string\n",
    "        name of the object\n",
    "    rate : float\n",
    "        positive, constant short rate\n",
    "    \n",
    "    Methods\n",
    "    =======\n",
    "    get_discount_factors :\n",
    "        returns discount factors for given list/array\n",
    "        of dates/times (as year fractions)\n",
    "    '''\n",
    "    def __init__(self, name, rate):\n",
    "        self.name = name\n",
    "        self.rate = rate\n",
    "    def get_discount_factors(self, time_list):\n",
    "        ''' time_list : list/array-like '''\n",
    "        time_list = np.array(time_list)\n",
    "        return np.exp(-self.rate * time_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "uuid": "00abadb7-63af-4da6-a4f6-d80cc6ac1f75"
   },
   "outputs": [],
   "source": [
    "sr = short_rate('r', 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "uuid": "cb409ce7-30f9-4937-9ac9-85089ca4679f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('r', 0.05)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.name, sr.rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "uuid": "3ac0270c-9950-437c-bb46-65d1cbddf283"
   },
   "outputs": [],
   "source": [
    "time_list = [0.0, 0.5, 1.0, 1.25, 1.75, 2.0]  # in year fractions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "uuid": "cab9697b-e048-4452-bfb8-1090bb48c293"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.97530991,  0.95122942,  0.93941306,  0.91621887,\n",
       "        0.90483742])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.get_discount_factors(time_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "uuid": "0ddbd6e4-8fd9-43e0-a256-5d1b880d3a05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x108e967b8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ndHR0sHTpUtauXYt2FvvUe70KLc0uGuocOOwK8Ul6isvMxM1wsd1CP8+PwuXx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\nmIAsQRhjjAnIEoQxxpiA/g/gfDTwUNKSMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e967f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for r in [0.025, 0.05, 0.1, 0.15]:\n",
    "    sr.rate = r\n",
    "    plt.plot(t, sr.get_discount_factors(t),\n",
    "             label='r=%4.2f' % sr.rate, lw=1.5)\n",
    "plt.xlabel('years')\n",
    "plt.ylabel('discount factor')\n",
    "plt.grid(True)\n",
    "plt.legend(loc=0)\n",
    "# tag: disc_fact_2\n",
    "# title: Discount factors for different short rates over 5 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "uuid": "2acc982a-71a8-4505-bb98-61eb4c85d6b0"
   },
   "outputs": [],
   "source": [
    "sr.rate = 0.05\n",
    "cash_flows = np.array([-100, 50, 75])\n",
    "time_list = [0.0, 1.0, 2.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "uuid": "1d1c180e-b8d2-41f9-9faf-55dd004a3606"
   },
   "outputs": [],
   "source": [
    "disc_facts = sr.get_discount_factors(time_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "uuid": "24992872-9aab-47dd-9c39-f8465e9f21fd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  0.95122942,  0.90483742])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disc_facts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "uuid": "95fed5fe-b198-4790-9ba2-9219ce2d0510"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100.        ,   47.56147123,   67.86280635])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# present values\n",
    "disc_facts * cash_flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "uuid": "22d67111-86c5-438b-ac7f-2d746848ee5a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.424277577732667"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# net present value\n",
    "np.sum(disc_facts * cash_flows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "uuid": "25b83999-44c9-4d70-84ea-5ecbca23ade7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4032346276182679"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sr.rate = 0.15\n",
    "np.sum(sr.get_discount_factors(time_list) * cash_flows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cash Flow Series Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "uuid": "27fec9d5-6849-4ef0-bd69-545e141eb888"
   },
   "outputs": [],
   "source": [
    "class cash_flow_series(object):\n",
    "    ''' Class to model a cash flows series.\n",
    "    \n",
    "    Attributes\n",
    "    ==========\n",
    "    name : string\n",
    "        name of the object\n",
    "    time_list : list/array-like\n",
    "        list of (positive) year fractions\n",
    "    cash_flows : list/array-like\n",
    "        corresponding list of cash flow values\n",
    "    short_rate : instance of short_rate class\n",
    "        short rate object used for discounting\n",
    "    \n",
    "    Methods\n",
    "    =======\n",
    "    present_value_list :\n",
    "        returns an array with present values\n",
    "    net_present_value :\n",
    "        returns NPV for cash flow series\n",
    "    '''\n",
    "    def __init__(self, name, time_list, cash_flows, short_rate):\n",
    "        self.name = name\n",
    "        self.time_list = time_list\n",
    "        self.cash_flows = cash_flows\n",
    "        self.short_rate = short_rate\n",
    "    def present_value_list(self):\n",
    "        df = self.short_rate.get_discount_factors(self.time_list)\n",
    "        return np.array(self.cash_flows) * df\n",
    "    def net_present_value(self):\n",
    "        return np.sum(self.present_value_list())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "uuid": "50136851-d7f8-467a-a04b-4b88bc6dc630"
   },
   "outputs": [],
   "source": [
    "sr.rate = 0.05\n",
    "cfs = cash_flow_series('cfs', time_list, cash_flows, sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "uuid": "f6245d43-7dde-44b4-9b35-40eea1c6af66"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100,   50,   75])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfs.cash_flows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "uuid": "04da2bcd-a388-4830-a152-8ab6cd958e68"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0, 2.0]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfs.time_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "uuid": "7de29949-494c-4156-bbc9-4165cdb87e47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-100.        ,   47.56147123,   67.86280635])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfs.present_value_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "uuid": "a7822201-d6b3-4715-9911-de550887f5e3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.424277577732667"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfs.net_present_value()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "uuid": "94f0003a-b9f3-4c92-b814-e0a6e6f9977c"
   },
   "outputs": [],
   "source": [
    "class cfs_sensitivity(cash_flow_series):\n",
    "    def npv_sensitivity(self, short_rates):\n",
    "        npvs = []\n",
    "        for rate in short_rates:\n",
    "            sr.rate = rate\n",
    "            npvs.append(self.net_present_value())\n",
    "        return np.array(npvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "uuid": "523386c6-0394-40f2-88af-6dcc23b1f742"
   },
   "outputs": [],
   "source": [
    "cfs_sens = cfs_sensitivity('cfs', time_list, cash_flows, sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "uuid": "743dbb63-8728-49d5-83b0-f2213c37c4f8"
   },
   "outputs": [],
   "source": [
    "short_rates = [0.01, 0.025, 0.05, 0.075, 0.1, 0.125, 0.15, 0.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "uuid": "bbb146fa-1f7a-459b-b89e-8baad84eda8a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 23.01739219,  20.10770244,  15.42427758,  10.94027255,\n",
       "         6.64667738,   2.53490386,  -1.40323463,  -8.78945889])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "npvs = cfs_sens.npv_sensitivity(short_rates)\n",
    "npvs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "uuid": "e96a2807-b45d-4c02-b5b6-6ca80026f340"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x108fe0358>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108fcf5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(short_rates, npvs, 'b')\n",
    "plt.plot(short_rates, npvs, 'ro')\n",
    "plt.plot((0, max(short_rates)), (0, 0), 'r', lw=2)\n",
    "plt.grid(True)\n",
    "plt.xlabel('short rate')\n",
    "plt.ylabel('net present value')\n",
    "# tag: net_present_values\n",
    "# title: Net present values of cash flow list for different short rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://www.pythonquants.com\" target=\"_blank\">www.pythonquants.com</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a>\n",
    "\n",
    "<a href=\"mailto:analytics@pythonquants.com\">analytics@pythonquants.com</a>\n",
    "\n",
    "**Python Quant Platform** |\n",
    "<a href=\"http://oreilly.quant-platform.com\">http://oreilly.quant-platform.com</a>\n",
    "\n",
    "**Derivatives Analytics with Python** |\n",
    "<a href=\"http://www.derivatives-analytics-with-python.com\" target=\"_blank\">Derivatives Analytics @ Wiley Finance</a>\n",
    "\n",
    "**Python for Finance** |\n",
    "<a href=\"http://shop.oreilly.com/product/0636920032441.do\" target=\"_blank\">Python for Finance @ O'Reilly</a>"
   ]
  }
 ],
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  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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