mshackman/bofa/Calculations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.71828183e+00,   4.00584723e+00,   5.90329225e+00,\n",
       "         8.69949785e+00,   1.28201789e+01,   1.88926982e+01,\n",
       "         2.78415807e+01,   4.10292700e+01,   6.04635568e+01,\n",
       "         8.91032597e+01,   1.31308697e+02,   1.93505536e+02,\n",
       "         2.85163079e+02,   4.20235945e+02,   6.19288618e+02,\n",
       "         9.12626340e+02,   1.34490900e+03,   1.98195049e+03,\n",
       "         2.92073868e+03,   4.30420159e+03,   6.34296777e+03,\n",
       "         9.34743397e+03,   1.37750222e+04,   2.02998210e+04,\n",
       "         2.99152137e+04,   4.40851183e+04,   6.49668652e+04,\n",
       "         9.57396450e+04,   1.41088532e+05,   2.07917773e+05,\n",
       "         3.06401944e+05,   4.51534998e+05,   6.65413057e+05,\n",
       "         9.80598489e+05,   1.44507744e+06,   2.12956560e+06,\n",
       "         3.13827447e+06,   4.62477730e+06,   6.81539020e+06,\n",
       "         1.00436282e+07,   1.48009819e+07,   2.18117459e+07,\n",
       "         3.21432903e+07,   4.73685653e+07,   6.98055788e+07,\n",
       "         1.02870306e+08,   1.51596765e+08,   2.23403430e+08,\n",
       "         3.29222673e+08,   4.85165195e+08])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff60c7a3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(1, 20)\n",
    "y = []\n",
    "\n",
    "w = 500\n",
    "for _x in x:\n",
    "    d = 500/1.2**_x\n",
    "    d = 10 if d < 10 else d\n",
    "    \n",
    "    if w > d:\n",
    "        w -= d\n",
    "        y.append(w)\n",
    "    else: \n",
    "        y.append(0)\n",
    "        \n",
    "y = np.exp\n",
    "\n",
    "plt.plot(x,y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class map in module builtins:\n",
      "\n",
      "class map(object)\n",
      " |  map(func, *iterables) --> map object\n",
      " |  \n",
      " |  Make an iterator that computes the function using arguments from\n",
      " |  each of the iterables.  Stops when the shortest iterable is exhausted.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  __next__(self, /)\n",
      " |      Implement next(self).\n",
      " |  \n",
      " |  __reduce__(...)\n",
      " |      Return state information for pickling.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(map)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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