diff --git a/code/BDATAFML.ipynb b/code/BDATAFML.ipynb
new file mode 100644
index 00000000..9fe1ec4c
--- /dev/null
+++ b/code/BDATAFML.ipynb
@@ -0,0 +1,370 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "from modsim import *\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from pandas import read_csv\n",
+    "\n",
+    "import csv\n",
+    "results = TimeSeries()\n",
+    "filename_csv = \"data/book1.csv\"\n",
+    "tables = read_csv(filename_csv, header=0, index_col=0, decimal='.')\n",
+    "tables = tables.dropna()\n",
+    "\n",
+    "type(tables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tables.columns = ['colonies']\n",
+    "num_col = tables.colonies\n",
+    "num_col[1974]\n",
+    "type(num_col)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-36.046511627906973"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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JkyYBsH79esLCwvjkk09o2LAh8+fPZ9SoUWzfvh0jIxnVVBU6dHCmoKCQjh1d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\nMca0ghMMY4wxreAEwxhjTCv+B8f8Vh2CypZFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x29e8775d630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_estimates(table):\n",
+    "   \n",
+    "    plot(table, ':', color='darkblue', label='colonies')\n",
+    "    \n",
+    "    decorate(xlabel='Year',\n",
+    "             ylabel='Number of Colonies (thousands)')\n",
+    "\n",
+    "\n",
+    "newfig()\n",
+    "plot_estimates(tables)\n",
+    "\n",
+    "\n",
+    "t0 = tables.index[0]\n",
+    "t_end = tables.index[-1]\n",
+    "p0 = num_col[t0]\n",
+    "p_end = num_col[t_end]\n",
+    "type(t0)\n",
+    "t0, t_end\n",
+    "\n",
+    "total_growth = p_end - p0\n",
+    "elapsed_time = t_end - t0\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "annual_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'update_func2' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-34e0badfa8a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.00001\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m.00001\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mupdate_func2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtables\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'update_func2' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "def update_func_q(num_col, year, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    pop = num_col\n",
+    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "    return pop + net_growth\n",
+    "\n",
+    "results = System(alpha=.00001, beta=.00001)\n",
+    "update_func2(num_col, tables.index, results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bees = System(t0 = tables.index[0],#start\n",
+    "t_middle = \n",
+    "t_end = tables.index[-1], #stop\n",
+    "p0 = num_col[first_year], #initial population\n",
+    "p_end = num_col[last_year], #final population\n",
+    "t = tables.index,\n",
+    "alpha=.00001, beta=.00001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#quadratic\n",
+    "def update_func_q(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "   \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "   \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "    return pop + net_growth\n",
+    " \n",
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "   \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    " \n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "    system.results = results\n",
+    "    \n",
+    " \n",
+    "def plot_results(system, title=None):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "   \n",
+    "    system: System object with `results`\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot_estimates(tables)\n",
+    "    plot(system.results, '--', color='gray', label='model')\n",
+    "    decorate(xlabel='Year',\n",
+    "             ylabel='Bee population (thousands)',\n",
+    "             title=title)\n",
+    "    \n",
+    "bees.alpha = -0.02\n",
+    "bees.beta = 0.000001\n",
+    "run_simulation(bees, update_func_q)\n",
+    "plot_results(bees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_middle = tables.index[30]\n",
+    "t_end = tables.index[-1]\n",
+    "p_middle = num_col[t_middle]\n",
+    "p_end = num_col[t_end]\n",
+    "type(t_middle)\n",
+    "t_middle, t_end\n",
+    "\n",
+    "total_growth2 = p_end - p_middle\n",
+    "elapsed_time2 = t_end - t_middle\n",
+    "annual_growth2 = total_growth2 / elapsed_time2\n",
+    "annual_growth2\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "    system.results = results\n",
+    "\n",
+    "def run_simulation2(system, update_func):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t_middle] = system.p_middle\n",
+    "    for t in linrange(system.t_middle, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "    system.results = results\n",
+    "    \n",
+    "run_simulation2(bees, update_func_q)\n",
+    "plot_results(bees)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 't_middle' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-7-5b2473debbf7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mt_middle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtables\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mp0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnum_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m#initial population\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mp_middle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnum_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt_middle\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[0mp_end\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnum_col\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mt_end\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m#final population\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m.0018\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 't_middle' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "   \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    " \n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "    system.results = results\n",
+    " \n",
+    "bees = System(t0 = tables.index[0], #start\n",
+    "t = tables.index,\n",
+    "t_end = tables.index[-1], #stop\n",
+    "t_middle = tables.index[30],\n",
+    "p0 = num_col[t0], #initial population\n",
+    "p_middle = num_col[t_middle],\n",
+    "p_end = num_col[t_end], #final population\n",
+    "alpha = .0018,\n",
+    "beta = 0.0001)\n",
+    " \n",
+    "bees.t_end = 2067\n",
+    "bees.p_end = 0\n",
+    " \n",
+    "array1 = linspace(-.005, 0.006, 25)\n",
+    "array2 = linspace(-0.000003, 0.00000001, 25)\n",
+    "matrix_results = np.zeros((25,25))\n",
+    " \n",
+    "def plot_results(system, title=None):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "   \n",
+    "    system: System object with `results`\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot_estimates(tables)\n",
+    "    plot(system.results, '--', color='gray', label='model')\n",
+    "    decorate(xlabel='Year',\n",
+    "             ylabel='Bee population (thousands)',\n",
+    "             title=title)\n",
+    "   \n",
+    "newfig()\n",
+    "for i in range(25):\n",
+    "    bees.alpha = array1[i]\n",
+    "    for n in range(25):\n",
+    "        bees.beta = array2[n]\n",
+    "        run_simulation(bees, update_func_q)\n",
+    "        matrix_results[i][n] = bees.results[2067]\n",
+    "        #print(bees.results[2067])\n",
+    "        #plot_results(bees)\n",
+    "       \n",
+    "#print(matrix_results)\n",
+    "plt.imshow(matrix_results, cmap='hot', interpolation='nearest', origin='lower')\n",
+    "plt.show\n",
+    "decorate(xlabel='Beta',\n",
+    "             ylabel='Alpha',\n",
+    "             title='Bee Population in 2067')\n",
+    "savefig('bee_heat_map.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/Chap02mine.ipynb b/code/Chap02mine.ipynb
new file mode 100644
index 00000000..cbfbe82c
--- /dev/null
+++ b/code/Chap02mine.ipynb
@@ -0,0 +1,2222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 2: Simulation\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with the same code we saw last time: the magic command that tells Jupyter where to put the figures, and the import statement that gets the functions defined in the `modsim` module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More than one System object\n",
+    "\n",
+    "Here's the code from the previous chapter, with two changes:\n",
+    "\n",
+    "1. I've added DocStrings that explain what each function does, and what parameters it takes.\n",
+    "\n",
+    "2. I've added a parameter named `system` to the functions so they work with whatever `System` object we give them, instead of always using `bikeshare`.  That will be useful soon when we have more than one `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_steps(system, num_steps=1, p1=0.5, p2=0.5):\n",
+    "    \"\"\"Simulate the given number of time steps.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    num_steps: number of time steps\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "    for i in range(num_steps):\n",
+    "        step(system, p1, p2)\n",
+    "        plot_system(system)\n",
+    "        \n",
+    "def step(system, p1=0.5, p2=0.5):\n",
+    "    \"\"\"Simulate one minute of time.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(system)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(system)\n",
+    "        \n",
+    "def bike_to_wellesley(system):\n",
+    "    \"\"\"Move one bike from Olin to Wellesley.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    \"\"\"\n",
+    "    move_bike(system, 1)\n",
+    "    \n",
+    "def bike_to_olin(system):\n",
+    "    \"\"\"Move one bike from Wellesley to Olin.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    \"\"\"\n",
+    "    move_bike(system, -1)\n",
+    "    \n",
+    "def move_bike(system, n):\n",
+    "    \"\"\"Move a bike.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    n: +1 to move from Olin to Wellesley or\n",
+    "       -1 to move from Wellesley to Olin\n",
+    "    \"\"\"\n",
+    "    system.olin -= n\n",
+    "    system.wellesley += n\n",
+    "    \n",
+    "def plot_system(system):\n",
+    "    \"\"\"Plot the current system of the bikeshare system.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    \"\"\"\n",
+    "    plot(system.olin, 'rs-', label='Olin')\n",
+    "    plot(system.wellesley, 'bo-', label='Wellesley')\n",
+    "    \n",
+    "def decorate_bikeshare():\n",
+    "    \"\"\"Add a title and label the axes.\"\"\"\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "               xlabel='Time step (min)', \n",
+    "               ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can create more than one `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare1 = System(olin=10, wellesley=2)\n",
+    "bikeshare1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare2 = System(olin=10, wellesley=2)\n",
+    "bikeshare2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And whenever we call a function, we indicate which `System` object to work with:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bike_to_olin(bikeshare1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley(bikeshare2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And you can confirm that the different systems are getting updated independently:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         11\n",
+       "wellesley     1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         9\n",
+       "wellesley    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Negative bikes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the code we have so far, the number of bikes at one of the locations can go negative, and the number of bikes at the other location can exceed the actual number of bikes in the system.\n",
+    "\n",
+    "If you run this simulation a few times, it happens quite often."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ho0aNamDEtR8VLIoSL8rLJRalOOzGeiIUcnJCX2vYUD7798u5P8VLWZl4i337LZx6qmef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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x14429f739e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_system(bikeshare)\n",
+    "decorate_bikeshare()\n",
+    "run_steps(bikeshare, 60, 0.4, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But this is relatively easy to fix, using the `return` statement to exit the function early if the update would cause negative bikes.\n",
+    "\n",
+    "If the second `if` statement seems confusing, remember that `n` can be negative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def move_bike(system, n):\n",
+    "    # make sure the number of bikes won't go negative\n",
+    "    olin_temp = system.olin - n\n",
+    "    if olin_temp < 0:\n",
+    "        return\n",
+    "    \n",
+    "    wellesley_temp = system.wellesley + n\n",
+    "    if wellesley_temp < 0:\n",
+    "        return\n",
+    "    \n",
+    "    # update the system\n",
+    "    system.olin = olin_temp\n",
+    "    system.wellesley = wellesley_temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if you run the simulation again, it should behave."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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1Md01wOXAVcAYJAQW4PdAb0RpGJIZZ9hofb2ElAaTyLrPwaG4XjuuurpAWRNR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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442a0dfd68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_system(bikeshare)\n",
+    "decorate_bikeshare()\n",
+    "run_steps(bikeshare, 60, 0.4, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variables `olin` and `wellesley` are created inside `move_bike`, so they are local.  When the function ends, they go away.\n",
+    "\n",
+    "If you try to access a local variable from outside its function, you get an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'olin' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-72-45354d82d402>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# NameError: name 'olin' is not defined\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0molin\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'olin' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# If you remove the # from the last line in this cell and run it, you'll get\n",
+    "# NameError: name 'olin' is not defined\n",
+    "\n",
+    "olin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add print statements in `move_bike` so it prints a message each time a customer arrives and doesn't find a bike.  Run the simulation again to confirm that it works as you expect.  Then you might want to remove the print statements before you go on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `if` statements in the previous section used the comparison operator `<`.  The other comparison operators are listed in the book.\n",
+    "\n",
+    "It is easy to confuse the comparison operator `==` with the assignment operator `=`.\n",
+    "\n",
+    "Remember that `=` creates a variable or gives an existing variable a new value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whereas `==` compared two values and returns `True` if they are equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x == 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use `==` in an `if` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if x == 5:\n",
+    "    print('yes, x is 5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But if you use `=` in an `if` statement, you get an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you remove the # from the if statement and run it, you'll get\n",
+    "# SyntaxError: invalid syntax\n",
+    "\n",
+    "if x >= 5:\n",
+    "    print('yes, x is greater than or equal to 5')\n",
+    "else:\n",
+    "    print('no')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add an `else` clause to the `if` statement above, and print an appropriate message.\n",
+    "\n",
+    "Replace the `==` operator with one or two of the other comparison operators, and confirm they do what you expect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have a working simulation, we'll use it to evaluate alternative designs and see how good or bad they are.  The metric we'll use is the number of customers who arrive and find no bikes available, which might indicate a design problem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we'll make a new `System` object that creates and initializes the system variables that will keep track of the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we need a version of `move_bike` that updates the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def move_bike(system, n):\n",
+    "    olin_temp = system.olin - n\n",
+    "    if olin_temp < 0:\n",
+    "        system.olin_empty += 1\n",
+    "        return\n",
+    "    \n",
+    "    wellesley_temp = system.wellesley + n\n",
+    "    if wellesley_temp < 0:\n",
+    "        system.wellesley_empty += 1\n",
+    "        return\n",
+    "    \n",
+    "    system.olin = olin_temp\n",
+    "    system.wellesley = wellesley_temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now when we run a simulation, it keeps track of unhappy customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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YS21IKBs3wt69sty7N2QGFUQVFbbNsgP4HUn8P+BxrfVvlVJDgeOAm7XWpwG/\nAy7ocE0MHccZKdZ0b2OOUxiccIK9vHZt9PGNDIaY4XzXJ04Ulzuvbe3Er5BQwJPB5ROANODl4O9S\nYESHa2LoOOPH2xnsysqij6hmCMvevbB+vSynpYl34X77ye/mZjOH0ZBAnL0XpWKee8avkKgC+gSX\nZwHrtdaW0nsssL3DNTF0nNxcGDVKlgMBbz2loV2sWmXPrh4xQkxARrtnSDjNzfDtt/bvoqKY557x\nKyTeBn6vlLoGOAV4AUAp9UPgD8B/O1wTQ2wwyYnigrNDZgkHo90zJJx162xd54AB8rFyzwBs2yZh\nezqAXyFxBTJauAl4E/hTcP3dwBrgmg7VwhA7TMsVF9wjehDtXlqaLK9fb9sODYZOw6v3kpkZOuG2\ng+2Ar3kSWuvtwPEemw7TWm/qUA0MsWXsWHlImpps74Y+fdrezxCWmhox8UDohPe8PBg5UgREICAq\nqQMOSFw9Dd0Qr94LiMD45htZ1hqCAQ3bQ1QzrpVSw5VS/6uUuibo5TRYKRU56Lqhc8nODg3sZUYT\nHcZ5CUeNEtOPhdHuGRJGY2Oox4RbSFho3aEUAtFMpvszolp6HPgjMAy4HViulBocYVdDZ2Narpji\nFBLOSwsxdyQxGPyzZo0d1HPIEMjPt7eNHGn3ZnbtEttEO/E7me63wOXA1cA4xAUW4PdAP0RoGJIF\nY5eIKV5qX4tx40K9jq3gvAZD3HF2AN0PZno6TJhg/+5AO+B3JHEx8Hut9b3Aevu8+gPgemB2u2tg\niD0x9m7ozlRV2fkjMjPF5OMkJyd07pLxOjZ0GpGGuO51HdAo+BUSw5BJc16sA2IW4kwp1VMpdZ9S\nqlwpVamUKlFKTYzV8bsFmZniemNhRhPtxnnpxozxjrxstHuGTqe+3s4lA6GjBosY2SX8RoFdjXg3\nvemx7UjEVhEr/gocDpwO7ETcbecrpSZoretieJ6uzX//az9EWoe2ZO5osnFMop6MlJZKGtKKCigo\ngNnBcbDXuttvl4Y/L8+7swbyLj7+uITQWbZM5jbNnp284cO9/n80dfV7/Tp6zOLi+JwrZQj3Xi5e\nLInFvvhCfvfqBVdfLcvO9/WWWySVaWOj/N68WWaBusu1gV8hcQ/woFIqC3gFCABjlFJHAL8Bfuv7\njG1zChIX6j0ApdTvgK+AicDyGJ6na+M0YlVWSi/Ccur3oqVFFOq9etlK9i5IaSk8/LCokVpaRBtn\n5aS3gvY5Ob4yAAAgAElEQVQ51+3aJZeupkbyWpeWtm6Qdu4UNVNLi/xeswbmzpXlZGu8SkvhkUck\nR01mpuSpiqaupaVSfu9eOYb7+mVmyiTg9hyzuVk8trdtg88/F3fizz+3y7nPlZ0d/blSksbG0EB9\n9fWwZYv925lLxklamrQDltG6qsoWElHgd57EI0qpgYj94VeI4fpFoAG4S2t9f9RnDs824MdKqReA\nSuB8YBexHa10fXr1sudL1NdDXR306BG+/JdfijDJz5deSiSBksKUlMDXX4emCd8eDCrjTPTlXpeR\nIQE2589v3RgtWCBTUay8L1VVkm/Cq2yiKSmR//bNN3YMqp49/de1pET+nzN1gftaDR0q2o9ojhkI\nSMfY2RZ+8EHrKT7uc40dKzG0kvFax4SGBli61PZiAum55OTYv50dQjd9+9pCorIShg2Lugq+u4xa\n69uAAiTA3znAicB+WuvfRX3WyFyEBAzcAtQCFwInaK1NurVoSEvzn62urs7eXlnZpUOarlsXKiBA\n3j/nO+i1rl8/GWCVl7c+ZkVF64EbeJdNNBUVonUAaZi3bpVlv3V17m/hvlZbt8qoKppj1ta2jmpd\nW9u6rPtcVl2S8VrHhO3bWz+cTjIywo8kQIZclmagnR0/XyMJpdR4rfUqrfVuXHGalFI9gD9ora9u\nVw1aMw7YDFwC7EDcbv+plJqutS6L0Tm6B/n5tmdTZaUocL1wC5DKSukOdkEyMuzlnBwZcNXUyPvj\nzDDnXJedLW7n4N0RKygIvYTWcjs6bXFnyJDQrJbR1nXoUOnhW/TrZ1+rrCzRjLS0SIM/aZK/YxYU\n2DPaQdz7e/aU47iz/lnnSkuz1YCNjaEeZl0K54PVu7c8jLt2yYVJT5cbkhmhGc/NFaNZVZWdlCxK\n/NokFiqljtE61E1GKXU88ABQiDTmHUIpNRp4BDhCa/1hcN1PgG+AK4GrOnqOboWze1tVFd4u4c6F\n24WFhLMxHDZMIroOGiSXZdAge5vXOoBZs1ofc/ZsMVpnZIiOvK5OPl5lE83kyfDCC/bvPXuko+q3\nrtOnw/PPy3JmpgiCIUPkWlVV2T36qir/x5w9W1R2FsOHy72ZMQPefz+0rHVfysvtx7ayMjmvdYdx\n56meMEGk544dsP/+/o8zcGCoLjVKovFuWhQUFF8H7RP3AGcBnwNHtLsGoUwBMoCl1gqtdaNS6hNk\nhGGIhrw8u3vX0CDjd7fhKhCQnokTP4buFCQQkMtQVCQ91379pEG6IJgya/58aXyGDfNeN2uWt97b\nWldeLg5lPXvCkUcmp448J8f+/zU1UtdjjvFf17w8e//cXBGyF14o2x57TFRHPXuKB7bfY06ZIveh\nrk7q9J3vwOmny/4TJ3rfl3vugU8+kXMddFByXusOYw2TQEYQeXkJqYZfIfF94N/IiOIu4NdANtKz\nv1dr3RKj+liDzgMIejIppdIQz6aSGJ2je2C5uD30ECwPOoWdeSYcfXRouS1bpHvo5uabu9xoYvt2\nsUcMHiyN2913h6qfIgmAtiguhssvh5dekt/J6iCmtfz/wY5AOl5zP/zsf/rp8L3v2duKimxPTMtf\nwmlfDUdZmRioDzlEvu+4w+6fFBd734ObboK77pJlqx3tcvzsZ6IPBZGkljT2S4zc1309ysH5CScB\ni5B5C18CRVrre2IoIAA+Bj4EHldKHaGUKgL+DowE7ovheboPbc30CjfRrgvOCnP+1fHjQwVELIhh\nTLW40NAg7rlu/M61DATCBx0FUZlbau+WltBcOJFwH9PPANYZVGDr1taD4S5BpHgwnUjYkYRSyiu2\n7L3AKOBAYJpSap+zrtb6fY/yUaG1blZKnQjcBjwP9EJUT0dqrddH3NngjfNNtpz5nd1c5xs6ZIjt\nf601zJzZKVXsLCKFuokFI0aIl/HevaKx27pVLmmysHq17SgzcKCMqlpapCdfXS2NfCQqKqQciJrH\nyw6qlG2E1tqf6jyS4AlHVpa4v1r7au09IE5ZWlpCY7z4vTBxIJK6aQkyac6NJef/FdyeFvyOSb8s\nmLsiynGVISxDh8oYfvdusUmUldmuOoFAaG/lxBPtmUlWV7iL2CXcfzUe75wVU82aQ7BiRXIJCef/\nnzRJjO1WpOmVK+HQQ/3vP2GC96OhFLz1liz7GYw2N0seDotohLdSXVhIrF8vRhoQ45nbg6ITiaRu\nOho4xuNztONzjOPbkIykpYVPxrxpkxjHQLqRU6bY3cmaGtneRdi82fbDz8trtzdgmyRzAF639iLa\nmFN+tB/ObH0bNnjPdXCyfr09Lad//+iccNyPdbKp9zqEu0eTwM5a2JGE1npRZ1bEEEeUkpgSIA/f\n979vLzvLpKXJ99Kgc9mKFfFrTTsZ91+Nl2HZyy6RDIOxujqZSAhSnwkTRFi+/rqsa0ug+R2JeWXr\nO/DA8MftSFtYWCiG8fp6UZ3t2NEhT8/kIt660SiIZJO4DviH1roiuByJQHBGtiEZcT5kq1bJGD8j\nw1sZ7BQSWoe6r6Qw7dF7t4dhw2QwVl0tcxA2bUoOOfvtt3ZsqeHDxaYwZowduWXLFjsqixdlZfao\noE+fyI5vRUUiJEAeIb9CItq2MCNDRi5ffim/V6yAI2LljJ9ImppCrf4JtEdAZJvErUjU14rgciQC\niLHZkIwMGCCfHTuk27V+veTh9DKMOd9UL0N3ChIIdJ4N0OqlL1smv7VODiHhJSQt46/VUGsN06b5\n2z9Sj18pCULs3s9NLNpCpbqgkFi71vbrHTTIjjyZICKpm9K9lg0piKVGsqavrlghDb9lGMvPtx3n\nBw0SQ9muXbJ9wwYRKCmMNXEMpJcfLjpJrCgqChUSxx4b3/P5IdxIqqgoeiHRVo/fytbX0iIjqXCe\nU2vW2G3h4MHy2EVLsqr3OkSSuL5aRNX4K6XSlFITlVKHKaXGxKtShjjgtqi6H0TrzbIEikUXmC8R\n7q/GC/elbonlTKJ2UFNju6W6s1r6udXReiD5zdYXC2+z4cPtici7d7cOPpiSdJZu1Ce+hYRS6mok\nMusXiHvsKqXUBqXUOfGqnCGGOB+21avthCXube7fyeai0w46+50bPNjW7VuDsUSycqXt+VNYKOE0\nLEaNsmdF79hhh+J24vZAcgfd8yKcQ52TWHSYY5jKOTloaAjNOJcqQkIpdSVwJ/AWcDZwHHAuEjrj\nCaXU2XGroSE29Otnq5QaG0O7hpGExKpVkUMVJzktLZH/ajxwD8YS3XBF6rFnZIh6yKus1zq/I7G2\n/n99fejsb6/sm37xI5BSBueMx4KC1gk1EoDfkcRlwF+01mdprZ/XWr+ttX5Wa30K8BBwY/yqaIgZ\nXt21gQNbdw0HDLAn7zQ2hvZsUoxEzUly68oTSVv2hLbq2p6RmOU5BbbnlJPVq0WNBeIR1pG20B1U\nIKXnS8R7xmc78CskCnDlkXDwTyRJkCHZ8Xrowo3zk6mV6wDuBrKzjJrJMhjbvVvCaYCMGsaObV3G\nbZdwNrJNTfasbHfZSGRlRR6hxLItLCgInQPqzE2RciTR/AgLv1FgFwGnAgs8tn0X+ChmNTLEjwce\nCM0YAxJgaMmS1hEjX3pJclyCJBp+5RVJwA5w1FGhZRcvbr3OwmckSr8J773WFReH3//ee2U0kZcH\nU6f6qkpMGDBA1MtffCHzC666Cs45J3xUWa/6RxP+Otz+//63eFrV1kpsqc8+a33cESNEmKxaZc+F\nsEJ1/+c/8OGHsn7QIHFZ9VsvpeTR2LhRjj1zpn1f7r9fwn/n5UlY9Y5gqfdef13OdemlUsdYPUMd\n2d/3Pbz4Yhlavf++7aLV2CjSNkbRXNtLpMl0P3H8XATcopQaiowctgD9gFnAj5GEQIZkJytLZlFZ\n/qAQfvaUMyWi1R21WhCra2pRWyvr0tLsDPVRUFoKDz8ss2YbG8Vt8s03Q6sXbt3SpRJE7quv7OM5\ny+7YYWcwe/ddOOCAzsk9UFoqM5ytS71ypR0Wy33+0lLZVl0t7URzc/iy4c41d66ca/fu0OuycKFd\nh7Q07+MuWyaNq1WutFQ6tPvvL/0Ha31GRnT1qq+3O8ZNTVKnN9+U+2Hdl9paePttaeQ7cl+cEWq3\nbLHPBR1/hqLdPz9f1kdzrQA7MRjIexpNDPc4Emkk8bTHulOCHzcPAXNjUiNDfMnPt9/6vLzwDbqV\n5KS21rb+WsGPnJZgsLuhIK4zU6ZENQGvpERUGk7Z4054H24dSCPn1mm7y/boIV488+d3jpAoKZFL\nbblkVlZKuAqv85eUyCX89FP5rZQEBvRb15ISiTz7ySeh7rbu62I1bO7jWnW1Mt1u3iwfv/uH47PP\n7Gx99fXyiLjvi9UWdvS+OFVi1dXyifUz5Hf/zEwJlhj18+Y03ITrvCWASG/y6Cg+Zs5EquA0Urfl\ny9ieQDh1da0z2rdBeTls2xa6zp3wPtw68A4i5y5r/VUrvWa8qagIfc9375YG3Ov8FRWh/9+K1u63\nrtb+7vkYzuuSnW3r7d3HraiQAaDbXuPcPyPD/j9+67VlS+vJwvG6L1VVrZMuxvoZ8rt/U5OMiiHK\n/5WkQiLSjGuTv6Erkp8vBrH6+tCEz16MGCGtg+UeZDX+7inLu3dLBq09e+R3VVVUD3mPHvaLl5Ul\nsqmuThot56m81oEMWtwhuZ1lc3Ptv9rWX44VBQWicnAOxnbvlhDdXmVLS+3flkDxG86joMAehYA0\nzDk59nVxfkPra2DVddIkWw0EofsNHGhrP/xew4ICeczy8sQ+A6H3pUeP2N2XYcMk1enmzfazFMtn\nyM/+ffrY7XxlpZTx/b8aG0P1gk51b4Lxa7g2dCWcuSsjkZEhgsLC0geNHx9arqJCylmG7l27ZNaW\nT5xRP/r3l8P37SvvitNl1WsdwE9/akccaavsrFm+q9UhZs8WnXR+vt3LrKz0Pv+RR8Jzz9m/W1pE\nXeK3rscdBy+8YP8eP16EhNd1gdbHterar19oaAy/+4fDOq7zUYjXfbHO5ZzpHY9nKNL+CxbY4Vgs\n84Lv/+W0R/TuHfu0iR3ACAlDbHCOHCwLrM8HPRCQwU1ZmQiJ4cPthPfz58uQfdgw73WzZonOd+JE\n/2U7A+s8jz0mMrRnT2m8vc7fq5f9/2tqpOzUqf7rOmCATEYrK5NrOXZs+OvidQ2s336uazTX0Ou4\n8bovfs/V0Wco0v7f+Q5ceaX0kbKz4eSTozRaWySRqgkgLZDSM08EpdQoYO1bb73F8GQIudldueUW\nO1HR5Zf7yl3Z1ARz5tiB3m6/vX2B3pKVPXvE/RVEJXH33aFhMQCeflo8r5yMHw9XX+3vHK+8Aq++\nKstHHQVnm/gHCeOhh2D5cln+8Y/hGL/p2H7/e3ukPmeOSJxOoKysjGMlAuVorfU6rzJhDddKqd8o\npTpJg2voErRjAp4zKnJ7I4EmM7162baFlpbQ0NgWXpdqzRo7XlJbJFk8uG5Nu0KE+JnxmEAieTfd\nhHguoZRqVkp14lQkQ0rSjuix3aGBixTHaOdOmc8IYhi2jKfNzaFuneGor0+6eHDdGq90LG3ifCjG\njIl6nlG8iWSTqAKuUkqNA9KA/1FKhZ0nrrV+MtaVM6QYVoLjQMBOcGzFcQ5DEoaqiTlFRfDWW7Ic\nKTzFuHGS8c1ygdVa9OSRcMdA8srbYOg8rAjAlZUyd2XjRh8+HEmWP8JNJCFxG/AXZPJcALghQtkA\nYIREdyfKBMcNDaGRQLuqkIgkO90jqYICeOed1tvCkYShfro1VoiQj4KBirT2ISSSfDgdVt2ktb4P\n6IsE70sDTgwue31Gxr2mhtQgCrtELCOBJjM9etgNhTOVaiDQuhM5YYI9qW39eumNRiLJO6Hdkqi0\nrjt32jMps7JCfXiThIgusFrrWqBWKfUz4EOt9Y7OqZYhZfGb4Jik70DFFKUklhNIw37QQdI27Nol\n63JzRZCkp8uUkw0b7MHYAQd4H3PvXhEkIILFPX3FkBicwvrbb9vwBnfrGzOTb1aCrwA7WusngHSl\n1B1KqY+UUiuUUkuUUrcppYa0eQBD98FKcAx2guMwdAd7hIWX14vz/48fb182v4OxVavs+VcjR7Zp\n/jF0EgMG2OFG6uvtzoEnKaAv9JuZrhD4BLgcMWiXAnXAHOBTpZTJJ2EQfCY43rvXfnnS0jqWmSwV\nGDvW7k2Wl4vsDKcq8quu6E4jsVTDl6B36xuT9Cb6DdV5J1ALTNBaf19rfa7W+nvABERo3B6vChpS\nEB+tnLMXPHx46+BsXQ237NQ6fCPvHIyVldkhsdykQPvSbfEl6L30jUmIXyFxHHCj1nqjc2Xw983A\n92NdMUMK46Mb1R0Nrs7/+c47tiauZ8/QYH65uaHxrLwGY3v22BnY0tNDs8AZEo9TSKxebU8YDcH5\nEkyYEFV4/c4kmlqFUy7vBow21GDTVoJjuqeQcDYczpnXTo8mi7bkrFNwjB7dOtSHIbHk59sTI5ua\nQl2995Ei+kK/QmIp8Isw2y4BlsemOoJS6gKl1Eql1F6l1DKllN8IKIZkICsrNLSAq5WrqZFJRtC9\nesFjxngnG/MSkm2pK1KkfenWRAzRkSL2CPAfBfZG4F2l1KfAC8BmYCiSunR/4PhYVUgp9VPgfkT4\nLAYuBeYppSaFC0BlSEKKiuyXQGuYNm3fJmcveNSo7tMLzswU2eluMLzah7FjpXxTk+RIqKoKTTGQ\nIu1Lt6aoCBYtkuVWo8GKivD6xiTDl5DQWn+olDoB+BNwKzK5LgAsA07QWr8di8oopdIQG8cdWuvH\nguuuBo4BZgDrYnEeQyfw1FOUftBESc1RVPyrBwX3zmP29qcAuDP9Gr7aVUBeZgPjxi6FNQsSnuy9\nsyh6414Wf3MEG2v6U9uUTb+cWjZufZGCwetDrkHWZReT+f7pfLR5HLVN2fx68bdcWLSE4hVP8fbY\nC3jtox9S25RNr6x6dm15AYas7TbXMFWY8LfL2fr2eWys6c+Spmz2vLCcE3c8AUBJzilUbD2agsxt\nzN7vc4p/8YukvX++Z25orRcAC5RSeUA+UKW1rolxfRRQiIxWrPO2AAfF+DyGOFNaN4lHqsazvSmf\n2pYerF2XS8nuayAN9ubkEwg0UxPIpnTbKEq3FtJJaR4STn1zJisqh8oMq/QAmWktPLriCNIg5BqU\nbi1EVw6lpiELmpv4rHwwN2z7PkV1g/lky1RqGtIhvYWM7BYe1zPITGvpNtcwVfhm11DKavpTU58J\nLU28u3YEC/bIO9A3t4GM5nr2tuQwd9d42Lo8ae9f1NP7rFnYcagLiEstQL5S6m1gErACuEZr7ZEj\ny5CslGyczNa0fHTDfrKiGrY39QUCDGQ30EB6Zjp9svYyv2z/pH1BYs2nO4aT0VhHc5P4/+b3Ef9W\n9zUo2TiJ/Oxa1tfnQSDA7sYsdtf144vGE+jTuBcQd5n8Xt77GxJPycZJ9M2oZk+9+PVsbuzlegdg\nS/NADsnemtT3L9l8rqzoPU8Ac4FZwJfA20qpzsnCYYgJFbV92UZojsemQCZNAbtf0j+wkwxaKK9J\nrkxc8WTL3j4MRKLbZNBCvzTxk3dfg4ravvSmmlxCk0rUBnrsW04jwAC2e+5vSDwVtX0ZFNgWss79\nDtTQk73kJfX9S7ZAIZY38R+11s8CKKV+CRyJGLIvT1TFDNExpEcVVYyD7BYIBBiRu43GynTSgMLs\nzWQFGhiUuRMa+zCsf7wGpslHQcY2GrK20ye9ml7pteQ0tgD5DOsZ6iZckFfFpupMJudotjf3oyUj\nG7KyyKqqZ1jPamhoID+9mp7NDUD/VvsbEk9BXhUt25qYnFPP7uZekJNDY7O8A71ym9jT3AMyM6hs\n6MGE/C2Jrm5Ykm0kEcx9yRfWCq11APiGYAIkQ2pw4IAymgPpkJlJbi6MHlTD5B7fMrnHtxT22cWw\nrK1kpTVBQwOzhn+V6Op2GrN7LyEzrZmCzG30Tq+ReOnQ6hrMHvElNDTQI72eEVmbKey/m8JBtVw1\n8EkKB9VSmFVO34zq4P6BbnUNU4XZBZ9CUyP9MnZTmF1B4eC9+96Bofl14g+dlk5VQ15S3z9fIwml\n1MvA3VrrhfGtDsuBGsSGtzR47jRgIvBmnM9tiCE5Gc0U9a2grKY/vbPrGN5rFxcMeBqA+XmnUV6b\nxrDMrczqU0Lx4JwE17bzKA58DH3LmV9zJOVNgxmWXs6sUZ9TPDi0J1k8cC30/Iz5LYdLuX41zCpc\nRvGKl5lY1If51f0o39uPYZlbmDX8U4oHmwDNyUZx9mfQ9wu51+n7Max35b534MW+F7B69yB6ZjYw\nvOcupgxan+Dahsevuul7wD3xrAiIUVwpdTfwR6XUFmREcSkwFvhhvM9viB0rjvsVg0fCYODnP4dp\n04qAkwEorqmBq66SCUXpPeDuuxNa106jqQkOPZTixkaK+QoI9h7POgtmzgwte9VVFKffLeWG7oCb\nbw5uuI5ioPjZZ2FRMDvRCSfAySd3zn8w+OeHP6R44CK5hycUBu+R3KcpAQj82poqMYqy66eSrFFS\n/aqb/gucpZTqDBvGjcCfEaH0BXAY8H2t28hgY0gaGhtD8zO3muzlnDzU0hIao6Irs2aNdxAfr0e7\nrbglUSR3MiSICPfQymDnVTTZ8Nvo7wbOA85QSn0NuONSBrTWMZl1HbRB3Bb8GFKQNWuk0wwSvybf\ny3FDKTs2x4oVMGlSp9UvYbgTzFjCUWsZVTkDOLUVd8MZW33tWqir6z5T11OBykqZKg8ydX7MmFZF\niopg6VJZ1hq+971OrF8U+B1JFALvITkl6oEs1yc7LrUzpCS+4gpFleOxi+D8n0cfDb17y3JNjR3S\nFaTBd2aq8Uq20atX9xyNpQrODsHYsZ5Bu5yvwMqVchuTEb9hOY6Od0UMXQdfEV6t0MgtLdJA1tR0\n7aQS9fXS47dQSj7OruSIoFb622/tFmP4cBEIXihlCxetu8doLFXw8RIMGgT9+klKibo6SUWbhCmu\no3OBVUrlKqWOUkqdqZTqp5RK3qhUhoTgbgvDZpxzJlkJBMJmsOsyrF4toTgAhg2TUUQ4u4LfOOrG\nLpG8+IjAmCp2Cd9CIjiprRxYCDyDzFt4SCn1plKqC3cBDdHg7ATvt5+tUfEkFd6QWOGlgwunb/Ab\n4nX8eNuOsWED1HafSYlJzfbt8gFJSejMIOUiYjjxJMFvjuufA/cCjwPHIlFgAR5F5jTc7L2nobsR\nVTKh7tQT9rowlr4BbH1Dba00+CACYPz48Mfs0SN0NLZqVezrbYget4OCldzcA3ciKsvhI5nwO5L4\nNXCX1vr/ITkeANBavwRcD/woDnUzpCBRJcMZO9Z+gcrLYffuuNUroezdKwIApOG3dHBufcOKFTKi\nsJJ/FxaKIIhEd3QASHai6Cn17y99BRDvaKeqNlnwKyRGA2+E2fYFkoDI0M2JphMMQHZ2qGtgVx1N\nrFplN/wjRkCeI9uvezQVbTYhIySSi3ZknEv2AbVfIVEGTA2z7eDgdkM3x9kWjhwZ2haGpTvYJSL1\nLN36hq++Cl/Wi3HjxEsMZDRWHS4VvaFT2LrVzuneo4ftsRaBZJfzfoXEY8ANSqk52IH2eiilTgJ+\nBzwZj8oZUouo7BFeBbuqkIikg3PrG7YEYzhlZITmCQ9HTk73GI2lCs57bbl5t4HzkVizZl/Mx6TB\nr5C4DREEdwHWU7gY+DcSsuOPsa+aIdWIyh5hMXq0PdFo61ZxGu9K7Nljz2VIT5eevxsviTp6tAgA\nP3SH0Viq0I7k4336QEGBLDc3h4a0SQZ8CQmtdUBrfTHwHSTg3vVIboeDtNZna62b41hHQwpQXQ2b\ngoHew7WFnmRmhhbuao2cc/7HqFHeoTO8GhPfQzG6x2gsFXDbI6K4h8l8C6MN2LcquE9fYKvWOslk\nnqEtSkuhpAQqKqT3Mns2FIfJm+hVFrz3//e/YdkyMV4PHw6ffx7+uK144w3brUNraTQXB53ojjrK\nLue1zlrvtc6rLHROwvmLL5bvb78VWwFIqI01a1qf/4EH4MMPQ9dVVcGrr/qr65//DO+/b8+z2LBB\nRiFe18XCeVyrrm787h8Or+PG6774PVdHn6FI+x96qLwEIE4ZN98sHhw+/pdS8MILEs7ss8/k/Yn0\nbnYmvoWEUupq4DfAAMe6TcB1Wuun41A3Q4wpLYW5c2HbNnHLb2yU39D6YSwtlWe7vFzKag3z5sm2\nPsEks1rDwoXixbR8uUTWAHkvwh3Xk7597eWdO8UCbrnDOn3/vdZZ673WWWVzc2WWcwR/9bhR6cgY\n5xnpEGlQ8vLsyXDp6W3MQnSRni43xTrXqlUiJKzrkp4ukRbDhfew2LULduywvQ+c+w8cGHqf/NLY\nKA+RpWh33pcePeS++NDb+2LvXgmqZ002iOUz5Gd/p56ob9/QgI1tUF0dGudxw4Yo36E44jfp0JXA\nncALwMvAVsTt9QzgCaVUQGv9TNxqaYgJJSXSjnzzjfxubhYNyPz5rR/EkhLp1VgurWBPIh04MLSs\n1rbgALst9DquJ717i9qpqUkalYoKu8GsqLDLea2z1nutc5Ztaur8wDgNDaENv/MiuenXzy7bp0/0\nDWd+vi0kdu6Ub+d12bYNpk4Nf9y9e+HLL20B4d5/82bZ3yNQXUS+/VbO7Twm2MdtaRFXuI4SCMDX\nX9s9Fa9zhVtnrW/rGWprfz8dgjAsXCihy/bskb9SWQkDBkTxDsURv0/iZcBftNZnaa2f11q/rbV+\nVmt9CvAQkgPCkORUVEhH0cJq9C1tiLustd2iqcl7RqgzGkRurt0Weh3Xk7Q028MnXrj/TGfgbDTa\navid/3/IkOjPNWhQ5OM3NER2j925M1RAuGluDv0/fggEbIEVjljdl7q6UAGRSDIzxWstCioqQuVK\nVZV8+36H4ohfdVMB4sXkxT+RXBOGJKegQNRIFrW10nZ4hLqnf//QTvCYMXYb4p4k16OH2CHS0mQ/\nqx5d5NsAACAASURBVK0aNiyKyo0dK71pKymPdXLnybzWWeu91lmVb2mRnnJ9vX+PoVjgbFTbUtX0\n6QMHHyxSOMpeKCA34eCDQ2et19aGjjAqK8PXw1nXIUOkPrW1Mmy0GvLKyuiEeXW1HdQwO1tmkFv3\nJS1NHqiaGrnn0Y5QItW/d28YOjR2z1C0++fnR/2cFRTIZbYc4ay/E9U7FCf8ColFwKnAAo9t3wU+\nilmNDHHjqKPguedC11VVwaxZrcs6o7f26SMPa2amd6f/hz8Uu6kbr+OGxdJ7W1g6X8s3MNw6a73X\nOpBG0XKrraxsXy+9vUSrfojGDuFFz56h4dat6+IUElasJyeBgN11BXtW+KpVEqXREhLOMn5w/v/+\n/aUu1n3p1UuESCAggm3AAO9j+MVZt8GDQ8/V0WeovftHwezZotp1y86o3qE4EVZIKKV+4vi5CLhF\nKTUUGTlsAfoBs4AfA1fGs5KG2NCrl7jaOdM3HHigt84zI8Mu26+fjBQuuEC2zZ8vw+Bhw+QhLi6G\niRO91yec/PzECIm6OvmAXMyOCoD24hw5WD17twF/zx5bj5idHRovqndvezRWWxvdaCySkMzPt9Vf\nlgK+vVhKfIv2GNgTjPWubNgg71zPnnDMMcnxDkUaSXh5LJ0S/Lh5CJgbkxoZ4obW0skaPNheF252\n54oVdtmrrw4dXXs9uMXFHXig4+mSumYN3HGHLPfrB7d1Ulbciy+250Tsvz9cfnn4sh39/23t//vf\n24bWOXPgO98J3X7eebYQmzoVzj8/dPvdd9szJX/2M5g+ve06NTbCIYfYwueOO0IFxddfw1//KsvD\nhsFNN7V9zHBUVNh16tkT7rorKs+iZKG4GC69VJxGIDHOeF5EMlyPjuLjodU2JBtek3S2bw81ZoPY\nGi2HlKys5MyW5RvnBLZdu0I9beJJu6afx4m2Zmo56+o1Aaw9M73Wro2c6DyWEYCddZowISUFhEUy\nTp4PO5LQWq/vzIoY4suuXXZYoKwsaTst9arWMGOGXdb5cI4fL7aIlCU9Xf7EF1/Ib2s4FU/aEQk0\nrigF77wjy+4Ics3NofmxverqjkBnOfNHoi0hacWcsh7ClSthypTIxwxHO2c5JyNjx9re4BUVIjsj\neU53Bn7nSWQDvwQOA7wscAGt9fGxrJghtrjzsk+cGF5IJFMnOCYUFYUKiSOPjO/53JFAYzEPoCNY\nvetAQPJa1NXZo6t168TOAGIXcE+CATF25+RIuZ07ZejpVc5JW6MTkIfLeghXrGifkGhpSS6B3EGy\ns2Xk7nw3E22X8DtP4n4kuF8RkOXxyY5L7Qwxw/0eeXUOoUPhZ5KXcH82XriHYrGaUdxeevYUzwOQ\nRtU5W9hPA5uREWqUaiuetd9E57HQrZSVhU5CHJr6qW2SLY6TX0XCqcCNWutb41kZQ3wIBFp37EaO\nlM5kXZ10erduFdXxtm22M1BubuI7wTFh+HA77EV1tYzj4+mAnoxStqhIptCDPAyTJ9vLzjKR9v/y\nS3ufI44IX9ZvovMxY0T32dhoRwC20rn6xT3sTWF7hIVS8MorspwM+SX8dnECwIdtljIkJTt22BNf\nc3JEe5CeHtrBs9o1tw0w0Z3gmOBOExrP7lmy2SMsvP5/Y2NovKFIdXXvH2k05ldIZmaG5sxoz31J\nRoHcQZzR87dta3vSerzx2wQ8DpyvlOoKTUa3w639sJxKvNqNLmePsOgsIeHMDterl/SkkwGn2sua\nKLNmTWQPJCfWaAzEmrp5c/iy0QjJjuhWmptDVWdd5IFNtuj5ftVNNwLLgZVKqWWAO0hKQGt9fuvd\nDMlAuIbf/X52SXuEhdefjYdqwp2ZLFnUH7m54tK2Zo3895UrQ6M3ttXApqdLmU8+kd9ae88wrq0V\n4zj4S3TeHs8pi/XrbaN7//5tG9NTiKIiOxDnihVw2GGJq4vfkcEdgELChB8CHOnxMSQhbnuE853c\nbz87ikN1tYTCtzrBPXsmTyc4JgwdavsS1tbaQXJiTTJLWXeDHG1d/YzGok10bnlOgehVogn4565/\nsgjkGBCNdi/e+B1J/C8iKK7TWiewuoZo2bLFnqeUlxeal91S1S9fLr+tfBHQZWyANtaftSIcrljh\nK0l9VLS0hGaiS0YhYU3n/fLLUGV3OA8k9/4W4UZj0Qoey3PKMopr7T+IYJfVjYrstBxLrDmg8Z7e\nEw6/I4lm4A0jIFKPtvKyO98ta7Kde32Xwd2TjjUbN0q0WZD4QYl6q8NhzdQC6bH78UByUlBgl6up\n8R6Ntcdo7xQmfu9LNEb3FCScY0lC6uKz3DNAp9sclFLTlVJNSqmZnX3urkJb72ykeU5dDuefXbXK\nDmMdK5Jd/WHN1HLjd8TTlpdYdbUtOKJJdN4e3cratXZY+cGDo3edTQHi3afxi1910xbgp0qpb4FS\nwJ29JKC1DpMot30opXoCTwFJEuYq9fBjiB4yRDq9zkjLXWROUmsGDhQD586dYvBcv947mUZ7SQX1\nR1FR69Sb0dS1qAiWLpVlreF737O3OY87erT/aLHOeSyW51RbYbeT2fYTI/xo9zoDv0LiAmAn0mB7\nhYCMhxrqL0AZ4LM7YnDz2muSn722VjpaZWWt55BZncNXXxVtSW2tDHOXLk18OICYk5YGH3xg69U2\nbhS7RDQJ76F11NWLLxbVzQcf2KOTujp48sn4RrhtD08/DZ99Zv9OSxM32MxMf3V95BHbrvP++3Ks\nd9+V38OG2anU1q0TdZCfY15yiUSFtYzWF10kx1q8uPX1t+6Ls2ezbZusT7Zr3UGGD5fB2cqV8l6m\npcEZZ8h7WVoq5qWKCpGns2d7v69+y0XCl5DQWndqHFCl1AnA/wCzgc8789xdhdJSePBBO6NjRgY8\n+qg8aO6HxO0BFQgkTxL2mJOfbwsJa06DFWfp66/tcs51WVniqROpZ7xnjy0gcnPt2EjJhjM/BMhc\njmgiOObm2nGcmprE4GxdK2d2uWiz6+Xn20KirEyOWVkZek/APpezS52C+SP8sHSp9GOsd3jRIvjw\nQxmkrV0rl2DoULmVXu9raams375d5Oneve17r5NucpxSaiDwKDJ62ZXg6qQsJSXeOV/mz29d1hkE\ntK2yKY+z8aqvlzfIShC0fbv9ca6rqAg1knoRbRa6RJGeHtqoRlvXtLTQfXbtsq+VZSNIT48+dKmz\nTtZ1d98T532x7BY9e4qtpQtSUhJ6qaur5RK89ZZ8b9smMtSaD+l+X0tK5FKtWAGbNskUGa9ybeE3\nCuwq2lApaa19+ND54iFgntZ6vlJqeIyO2e0oLw+1M1gPm1di9aoqOxWyMz1vMiRhjzk5OaJ72xVl\n/2PXLumyhYtTkipCAqT7uWuXNPjt8cAaOjTUFc7N4MHRx3PJyxPBEm1eiS5pPBMqKsSMtm6dLQhA\nVE+WDG5ulvd3wIDW72tFhZjfrEGjJVejfa/9jjPfo7WQ6AVMBXKBe6I7rTdKqZ8CBwMHxOJ43Zm8\nPPvBysmxtR9ece0KCqTsnj2ifbBG8smQhD0u7L+/vFlO+wFI/HQLa52lWmlulq6cl2qjpSW0cUt2\n9cfAgXDooaKDbI9arG9f0VdYehDn9cvKal8ChLQ0CTq4e3fofXHeE/e5cnLkge2iFBTICGDKFDsd\nOIjGMDfXzp9lZX91v68FBfDVV/Zv67GM9r32a5M4z2u9UioLeBloY1qlb84DhgOblZj2LcVjiVLq\nCa31L2J0ni7PqFH2cn6+3fB7JVafPVt0le53OxmSsMeF9PRQl0mroXSGdbDW9etnxymqqvIWALt3\n2921vDz/Xj2JIi3NnmrfXnr0sHNhe12/9pCR0fq+uI8Zq3OlANZ7mZ0dmgL8pJPEKcUSEpbGwP2+\nzpoF//yn/dsa4Eb7Xnco55jWulEp9VfgH8ANHTlWkHMARxZ2hgLvIvaJBTE4frchEBDPwLIyee+G\nD5eHI1x+ahBdZXm59DTCle125OfbQqKy0jt2eiqpmgwpQ6T3ctw4uPJKGf0DnHVW6/d12DCZP1lW\nJuaiCRPi6N3UBv2BmCTY01pvcv5WSgXHlmzSWm+NxTm6A01N4rI+eLB8/vSn0J6IF8XF3UQoROsm\nWVUFv/mNLGdmwj0emtVjj7UN2xdfDIcc0rE6xpOOuonGw820i7muxpJw7+Xhh8OPfmQ/dl4T5les\nsNuAKVPgwgvbVwe/huufeKzOAEYAc4DF7Tu9IR44J6MOHNi2gDBEoG9fMY5u3izSd/Xq0MlbfrOw\nGQwxRilbSGgtZiYnsUpr4nck8XSEbe8Dv2p/FcKjtS7DtksYfNINJqN2LkVFtspJ69CL6szCNnx4\nlzakGpKLoiJ4/XVZdoftiGWsSb9CwmsyXQDYrbWu9NhmSCBGSMQYpWDhQllesQJOPtnelgqhOAxd\nkjFjRAPa1CQeyZYLO0iqEMsRrF8//4F1vfDr3bS+/acwdCaNjfakGTDaj5jgvIjr1snbZ3nZJGOq\nUkO3ICtLDNPO1MPTpslyLFN/hxUSSqkbozhOQGv9h/ZXwxArVq+250cUFCS/y35K0KuXqJLKymQc\n/+23MGmSzGqysrv5ycJmMMSYoiJvIRHLvkukkYQfl9Y0bJuBERJJgNF+xAnLnxjkDZw0KTQLW2Fh\n21nYDIYY4xW53fJu9CrTHsIKCa11VrhtcmL1CyRbXRpwbceqYYgVxh4RJ5SCN9+UZUsSG1WTIcFY\n2V+tMGTbt0vEFcu7cdCgjns3Rj1PQik1GpgLzEQmuF2ktd4QcSdDp1BXJypzEO2HsUfEkPHj5aIG\nAnZoTjNsMySYzEyZWGeF39A6NCxZLB7LqKJwKaXmIKG7DwYu0FrPMgIieXB7Y3Y08oLBQY8e0m0D\nERTLl0tgHYguC5vBEGPcKqdYaxP8TqZTwGPAYcA84BKtdUXHT2+IJaZjG2eKiuyhmuWgDuKLmOzx\nmgxdFqcg+OYb8aewiEU7EFFIKKXSgd8CNyIpS8/SWr/Q8dMa4oEREnFGKTsY/86doesNhgQxYoQM\ndPfuDQ1GXFDQvoC8bsKqm5RSBwAfA7cCLwHfMQIieampCc1Bb+wRcWDsWIlU6sZ4CBgSSLj3PVZ9\nl0gjiaVIfKYqYBDwnAp/1oDW+vjYVMnQHlauDPXGTNbsmSlNTo6olpz+hVlZkk/SYEggSoWmLofY\n9V0iCYn3sRMNRXSHNXQeXonNAe68Uzwc8vKMDTWuvPkmrHcEIMjPh8suk2UTzdSQIIqKYOtWcbyr\nrRWnlerq2Bw70jyJmbE5hSFWlJbCww9L2A0rjvwLQQVgerqMJGpqpFxpaTcJ/d3Z5Oe3FhIGQ4LZ\ntEmiLVjzIwCeeUZsFR1tB6JMRGtIJCUlEoy0vFwMVLt3S+9h61Y7FIeVgz7aZOcGn/TuHZq/2QgJ\nQxIwf37oo2gtx6IdMEIihbASmztpagpNkj50qNhWo012bvBJerro+UAEhle2F4Ohk6mokEx0aWny\niA4dKutj0Q7EIjOdoZMYMsTOZwuw//4y4xIklFBGhh0+KNpk54YoGDNGLnBubsfCaxoMMaKgQCbS\nTpsmQsJqF2LRDpiRRApx0EHQ3CzLubnQv78YqcePFxVTz552mxVtsnNDFKSlibLXCAhDkmA5sGRn\n2wICYtMOmJFECpGTYwcj7d1bJtFYeWu9kqUbDIbugfW+x6MdMEIihXAmNv/Zz2D6dHubEQqdhHFz\nNSQpxcXxaQeMuilFaGqyk56DiQRhMBg6ByMkUoQ1a2wf6MGDJW+twWAwxBsjJFIEk0zIYDAkAiMk\nUgQT4dVgMCQCIyRSgPp6WLvW/m0ivBoMhs7CCIkUYPVqe37EsGGxiRFvMBgMfjBCIgVwqpqMPcJg\nMHQmRkikAE6jtbFHGAyGzsQIiSRn7147MnVamrFHGP7/9u49SKo6O+D4dxgUhHIRYYER5KGRo5OF\nlbgmQcIrugTUOLWvlJWNSixqk5XVBC3R1Q3o4uJjU67Jrq9oImt8bVbFdaMQVxBWtHRHEApfR57y\nGqR8IIMDI6/8cX6XuXPpnunpmenu25xPVVfP3O6+93eG5v7u73F/x7nC8kqixK1Z05RxbvDgpgX8\nnHOuELySKHE+9dU5V0wlt3aTiPQH7gAmAccBrwPXqOpbRS1Ykfh4hHOumEqqJSEiXYD5wHCgBjgH\n+AxYJCJ9ilm2Yti921Z8BVsj3nNXO+cKrdRaEl8FRgPVqvougIhcAnwCXAA8nOuOamst3WddnSXk\nmDLFVkjMtB0yv7e9cj1WtuPPnw/Ll1ti80GDYPVqX+3VOVdYpVZJbAIuBGKdLBwMzzkvaVdbCw8+\naOuqb9tmGZvmz7cTbXRlHnn0UXvu0cMS+TQ02GehfSfk2lq4/XabmRSlF40fK9PxI1FZN25s+mxF\nRceUyznn2qKkKglV/Rh4LrH5Kmxs4oVc97NggVUM69fbc2TFiiPvVo5yRnfpAnv3wqZNNs104cL2\nnYyff95mJn3xReZjtbQtU1njic29knDOFUpJjUkkichFwK3AnVH3Uy7q6uyk2ycxitHQcOR79+9v\nuloH+PRTm3La3gTia9c2ryAyHSvbtmRZe/WyB3RMYnPnnMtVSbUk4kRkKvAA8AQwsy2fraqCrVvh\njDMsZ310n8H69fZ73KpV9nzggJ2sGxutRXHaae0rf7xl0LevHXfVKus2Gjmy+fGT2+JlraiwvLVR\nOuWOSGzunHO5KsmWhIjcCDwE3AdcqqoHW/lIM9FgMFhe6O7d7TFtWtPP0WPYMDsZR1fqADt3tj+B\neL9+TT/37t10rGHDjjx+clu8rN26NVUQ0DGJzZ1zLlcl15IQkZnALcAsVZ2Tzz5aSgpeXd18+7Rp\n9t577rHxgZ49YcSI9vX7HzhgLZLTT7eB8hNPtIHo6FiZjp9LWTsqsblzzuWqpCoJERkJzAX+C3hA\nRAbEXq5X1c9z3Ve2pODZts+ZYw+wE/yhQ82v4Nvigw9sH/36WUUxd27zfWUrV65ldc65Qim17qaL\ngUrgcqAu8ZjRmQceONBaEQD19Tb4na/kXdL5VjbOOVdsJdWSUNUbgBuKceyKCjuhr1gRlSX/QWLP\nR+2cKxel1pIoqvjaSPGF9dpi/36b/pppn845lzZeScTEr/rff7/5jXi5Wr8e9u2zn/v1s5lNzjmX\nVl5JxPTv3zQVtqEBNm9u+z581VbnXDnxSiImGpeIxE/4ufLxCOdcOfFKIiF+Ym9rJdHYaN1NEU81\n6pxLO68kEuItiTVr7Ma4XK1b1/T+k046cjFB55xLG68kEvr2bVoYsLHRluvOlacadc6VG68kMsh3\nXMLHI5xz5cYriQzyGZfYs8eW4wAbAPfxCOdcOfBKIoN4S2Lt2qb7HlqyZk3TkuQnn9w805xzzqWV\nVxIZnHCC3TMBdgf1hg2tf8a7mpxz5aik1m4qJV27wvLldlNdfT3MmGErstbWWnrUujpLbhTlrrj3\nXti+3VoQEycWt+zOOddRvJLIoLYWVq6Ez8PC5CtXwtSpMGCAVQRx8+bZc7du9tzQAC++CKee6st8\nO+fSz7ubMliwwLqcIocO2TpO771nz/FHfb09IscfD5WVlizIOefSziuJDOrq4JhjYPDg5rmqGxqO\nfO/+/fYA+8yQIfbztm2dX07nnOts3t2UQVUVbN0KQ4daRRHNWlq3zrqR4t58055HjbIKJUowlG8u\nCuecKyXeksggGowGO/FXVtpj6tSmn6PH4MHWeqisbJ6BbvLkghfbOec6XLm0JCoBtidHlfNUVQU1\nNbB0KXz4oU2HHT8eRo6EL3+5+faaGvtM8r1VVbBlS4cUxznnOkXsnFmZ7T0Vh6K+lBQTkb8AXi52\nOZxzLqXGquqyTC+US0uiFhgL1AFtWLfVOeeOapVAFXYOzagsWhLOOec6hw9cO+ecy8orCeecc1l5\nJeGccy4rryScc85l5ZWEc865rMplCuwRRKQSuAWYChwPLASmq+qHxSxXvkTkPqCrqk6LbZsE3AEI\nsAa4TlUXFKmIOROR/li5JwHHAa8D16jqW+H1tMY1CPgZcC52AbYQuFpVt4XXUxlXRET+HFgGnKeq\nS8K2VMYkItXA2xleGquqy9IaF4CITANmAicD7wDXquri8Fqb4yrnlsRNwGXApcA4YBDwVDELlA8R\nqRCRHwP/kNheDTwL/BoYBfwGeEZE/rjwpcydiHQB5gPDgRrgHOAzYJGI9ElxXBXAc0BvYCIwHpt/\n/tvweirjiohIT+C/id2Zm/KYRgAfYf9G8cfraY5LRC4D7gZuw2JcCjwrIkPzjass75MQkWOxL8BV\nqjovbBsKbADGqOqrxStd7kTkFOA/ga8ADcDvopaEiNwPiKpOiL3/JWCNqn6vCMXNiYiMAlYA1ar6\nbtjWDfgE+D4whnTGNQC4C7heVTeGbTXAM8CJ2H/a1MUVCd+34cAEYKKqLknrdxBAROYA41R1fIbX\nUhlXuFDZADysqrPCti7Y/7c7sAuXNsdVri2JM7EupiXRhvAfdyN2Z3ZanANsxq4IkklUxxKLL1hC\n6ce3CbgQiCV85WB47k1K41LV7ap6cayCGIS1/mpV9VNSGheAiJwPXABclXgptTFhF17vZnktrXEJ\nMAT4VbRBVQ+q6pmq+hh5xlWuYxKDwvPWxPZtWD9dKqjqI8AjACKSfHkQKYxPVT/GumXirsLGJl4A\n5pDCuOJE5BmsK+1TrOsJUvrvJSJ9sdbs32PxxKUypuArQHcReQ0YCrwF3KCqfyC9cQ0PzyeIyGIs\nxvew1u2r5BlXubYkegAHVXVfYnsj0L0I5ekMPYC9iW2pi09ELgJuBe4M3U/lENe/AH+GDfL+TkQG\nkt647geeVdVMuRZTGZOIHAecAvQCrgUuwk6WS0XkDFIaF/Cl8PxL4EFgMlb5LW5PXOXaktgDdBGR\nrqq6P7a9G/B5kcrU0fZg8cSlKj4RmQo8ADyBzcaAMohLVVcDiMjFWHfhZaQwrjAIOgoYmeUtqYsJ\nQFX3iEhvoFFVG+Hwd/Es4ApSGhcQXRT/JHQvISLTse6k75NnXOXaktgcnqsS20/iyOZWWm0mxfGJ\nyI3AQ8B9wKWqGo1LpDIuEekfKoXDVLUBWAcMJJ1xTcW6KLaLyG6axpEWhCnZaYwJAFXdFVUQ4feD\n2JTYk0lvXFH5VkcbVPUQNvYyjDzjKtdKYhVQj43mA4dnNw0Ffl+cInW4ZcTiCyaSgvhEZCZ2D8ss\nVb0yfJEjaY1rCPC4iHwt2iAivbDBxHdIZ1x/B1RjE0HOBP4qbJ8GzCKdMSEiZ4nILhE5K7atEovx\nbVIaFzaL6XPg7GhDmPFUjV2s5BVXWU6BBRCR27AroanADuAeYG98+leaiMgSYG1sCuwIYDnWn/84\n8LdY/+qfRFNLS5GIjMS+zL8Ebky8XI/1Facxri7YTJEvAd/Dmv63AadiJ59hpDCuuDBjazNNU2DT\n+h3sin0HvwCmA7uB67BZd6cD/UlhXHB4au90rCJfjXWf/SP2HTyWPOIq15YEwI+AR7HZQS8BHwDf\nLmqJOlDo9/4GFtNKbPDtr0v9SwxcjN2QdTmWJCr+mJHWuEJ3xTexMv8vdhPTLmC8qu5Oa1wtSWtM\nYZxyCtZ99lvgD8AA7L6JHWmNK5gF/BS7Z2c1MBqYpCavuMq2JeGcc679yrkl4Zxzrp28knDOOZeV\nVxLOOeey8krCOedcVl5JOOecy8orCec6ULh5ybmyUa5rN7mUEJF52NpGLVmqqhPCDYX7VfW8Ti9Y\nHkTkh8B+bJ56oY75R8Ai4MywJHk++5iA3Us0VlWXteG4L2A3Yu3M57guHbwl4YptDnbDT/R4E6hN\nbLsivPcK4MoilDFXc4CehTpYuMv7IeD2fCuIYAX2d16V6wdUdS3wNPDv7TiuSwFvSbiiUtV12Loy\nAIjILqy18FqG975TyLKlwLewHAIPtGcnqroLOOLvnYM7gC0icpeqrmhPGVzp8krCpUayu0lEDmHZ\n38ZhSX72Aj/HliS4CzuJ7sHWibo+WkhQRPpg6yrVYBkMl2MJ4V9p4dhdgB8D38VWztyGrX8zW1X3\nhbIAzBaR2apaET43ArgdW675IPB/wNWquiW8PgHr6pkM3Ax8FctCOEdVH2/lTzITeDKeNyWfv0my\nu0lEbsKWT5kJ/ASriDaGMj0SHUtVd4jIIuCHwHdaKatLKe9ucmn3r1g+8xpszaSbsbV4GrC1lJ7G\nTnbfBBCR7lgf/gXYye3bWMa1RSJydnLnMddh3V03A5OAe8N+bwivjwYOYFncRodjDQdewXJcX4It\n/DcC+H1YITbuCWw1zm9g6+o8FnJkZySWqvBrwFPt/ZtkMQjrSvoZ9rfaCDwsIqcl3vckUCMiBetm\nc4XlLQmXditU9Z8BRGQVYdVfVf1B2LYYu/ofjZ1QL8GS6Pypqr4R3rMAO4nOBb6e5TjjgTdUdV74\nfamINAA7AVT1tZBidkusq2w2tsLoeaq6OxxrKbAe+AF2lR55QlWjxEsLw8n4R8BvspTnL4FD2PhN\ne/8mmfTEFn97KXzmfWyRzPOBf4u97w3gGGAMNpDtyoy3JFzavR79EPJnH0hsO4S1FE4Im87Fkqys\nFJGuYdnoLtgV9zgROTbLcV4Cvi4iL4vItSJSraq/iHe/ZHAusBjYGzvWR6F8ycroscTvTwNniUiP\nLPs+BfhYVeszvNbWv0k28e63LeE52WLYGJ6HtrIvl1JeSbi0y3SSbCkdYx+sK2Vf4jEbW2+/b5bP\n/RS7+u+BjTG8LSJvicjEVo713QzHmoCNa8RtS/y+A6jA8jBn0ovscbb1b5LJAVX9Ivolljkwec6I\n9putnC7lvLvJHW0+w9I5Xprl9Y8ybQwnybuBu0WkH9btciPwlIj0jw8eJ461gObdM5HGxO99iM3y\nwhLfHAA+aaGcrbUECqF3eM74d3Pp55WEO9osxRLObFPVw1fvIaPXELLc2CciL2N9/f+kqjuAr7Qi\nXQAAAYdJREFUeWHw+S6sC2YndlJPHqs6fO5g2E8l8D9Y98/q2HsvxMZFIt8CXonnYU74AOglIsdn\n6XIqlEHheVMRy+A6kVcS7mjzEHZD3osiMhcbn7gQuBq4OZFvO24JcL2IfAi8CgwErgEWxe443gmM\nEZFxwMvYlNnXgGdF5D+wrqYrsfGIexP7v1ZE9mA3tl2OTYU9t4U4okHiMcDCHOLuLGOwLqes04dd\nuvmYhDuqhFlGY7Er+TuB57F7FK5U1Zta+OhNwC3YCXxh+OxC4G9i77kVm5a6ABioqqvCsbpiqXR/\nhd2Xcb6qvpjY/wxsOu4zWD7syaq6tIU4NmAVypTWYu5kU4DnVHVvkcvhOomnL3WuiPJZNyn22e8A\n9wMnFeMkLSKDsem8Z6vqm4U+visMb0k4l15PAmuxm/SK4Rrg115BlDevJJxLqTB+cgk2ntG7tfd3\npHCzXw0wvZDHdYXn3U3OOeey8paEc865rLyScM45l5VXEs4557LySsI551xWXkk455zL6v8BgMDn\n7nSDZskAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442a0e64e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_system(bikeshare)\n",
+    "decorate_bikeshare()\n",
+    "run_steps(bikeshare, 60, 0.4, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the simulation, we can print the number of unhappy customers at each location."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.wellesley_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Let's add a \"clock\" to keep track of how many time steps have elapsed:\n",
+    "\n",
+    "1. Add a new system variable named `clock` to `bikeshare`, initialized to 0, and \n",
+    "\n",
+    "2. Modify `step` so it increments (adds one to) `clock` each time it is invoked.\n",
+    "\n",
+    "Test your code by adding a print statement that prints the value of `clock` at the beginning of each time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "unexpected indent (<ipython-input-78-c6319c4360ac>, line 10)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-78-c6319c4360ac>\"\u001b[1;36m, line \u001b[1;32m10\u001b[0m\n\u001b[1;33m    bikeshare.clock =+ 1\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m unexpected indent\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here's a copy of step to get you started\n",
+    "bikeshare = System(Wellesley=10, Olin=2, clock=0)\n",
+    "def step(system, p1=0.5, p2=0.5):\n",
+    "    \"\"\"Simulate one minute of time.\n",
+    "        \n",
+    "    system: bikeshare System object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "        bikeshare.clock =+ 1\n",
+    "        print (clock)\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(system)\n",
+    "       \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(system)\n",
+    "       "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(wellesley=10, olin=2, clock=0)\n",
+    "def step(system, p1=0.5, p2=0.5):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(system)       \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(system)\n",
+    "    bikeshare.clock += 1  \n",
+    "    print(bikeshare.clock)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "step(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the simulation, check the final value of `clock`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(bikeshare.clock)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Now suppose we'd like to know how long it takes to run out of bikes at either location.  Modify `move_bike` so the first time a student arrives at Olin and doesn't find a bike, it records the value of `clock` in a system variable.\n",
+    "\n",
+    "Hint: create a system variable named `t_first_empty` and initialize it to `-1` to indicate that it has not been set yet.\n",
+    "\n",
+    "Test your code by running a simulation for 60 minutes and checking the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = System(wellesley=10, olin=2, clock=0, wellesley_empty=0, olin_empty=0, t_first_empty=-1)\n",
+    "def move_bike(system, n):\n",
+    "    \n",
+    "    olin_temp = system.olin - n\n",
+    "    \n",
+    "    if olin_temp < 0:\n",
+    "        system.olin_empty += 1\n",
+    "        \n",
+    "        if system.t_first_empty == -1:\n",
+    "            system.t_first_empty = system.clock\n",
+    "            print(system.t_first_empty, system.clock)\n",
+    "            print('ran out at olin')\n",
+    "        return\n",
+    "    \n",
+    "    wellesley_temp = system.wellesley + n\n",
+    "    \n",
+    "    if wellesley_temp < 0:\n",
+    "        system.wellesley_empty += 1\n",
+    "        \n",
+    "        if system.t_first_empty == -1:\n",
+    "            system.t_first_empty = system.clock\n",
+    "            print(system.t_first_empty, system.clock)\n",
+    "            print('ran out at olin')\n",
+    "        return\n",
+    "    \n",
+    "    system.olin = olin_temp\n",
+    "    system.wellesley = wellesley_temp\n",
+    "    \n",
+    "for i in range(0,60):\n",
+    "    step(bikeshare)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the simulation, check the final value of `t_first_empty`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'System' object has no attribute 't_first_empty'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-81-c6143a77b215>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbikeshare\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt_first_empty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mC:\\ProgramData\\Miniconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   3079\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3080\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3081\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3082\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3083\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'System' object has no attribute 't_first_empty'"
+     ]
+    }
+   ],
+   "source": [
+    "print(bikeshare.t_first_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we go on, let's put `step` and `move_bike` back the way we found them, so they don't break the examples below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def step(system, p1=0.5, p2=0.5):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(system)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(system)\n",
+    "\n",
+    "def move_bike(system, n):\n",
+    "    olin_temp = system.olin - n\n",
+    "    if olin_temp < 0:\n",
+    "        system.olin_empty += 1\n",
+    "        return\n",
+    "    \n",
+    "    wellesley_temp = system.wellesley + n\n",
+    "    if wellesley_temp < 0:\n",
+    "        system.wellesley_empty += 1\n",
+    "        return\n",
+    "    \n",
+    "    system.olin = olin_temp\n",
+    "    system.wellesley = wellesley_temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Returning values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a simple function that returns a value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def add_five(x):\n",
+    "    return x + 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = add_five(3)\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you run a function on the last line of a cell, Jupyter displays the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_five(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "But that can be a bad habit, because usually if you call a function and don't assign the result in a variable, the result gets discarded.\n",
+    "\n",
+    "In the following example, Jupyter shows the second result, but the first result just disappears."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_five(3)\n",
+    "add_five(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "When you call a function that returns a variable, it is generally a good idea to assign the result to a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "y1 = add_five(3)\n",
+    "y2 = add_five(5)\n",
+    "\n",
+    "print(y1, y2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a function called `make_system` that creates a `System` object with the system variables `olin=10` and `wellesley=2`, and then returns the new `System` object.\n",
+    "\n",
+    "Write a line of code that calls `make_system` and assigns the result to a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system():\n",
+    "    system = System(olin=10, wellesley=2)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare = make_system()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running simulations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we go on, I want to update `run_steps` so it doesn't always plot the results.  The new version takes an additional parameter, `plot_flag`, to indicate whether we want to plot.\n",
+    "\n",
+    "\"flag\" is a conventional name for a boolean variable that indicates whether or not a condition is true.\n",
+    "\n",
+    "This version of `run_steps` works even if `num_steps` is not an integer.  It uses the `int` function to round down.  See https://docs.python.org/3/library/functions.html#int"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_steps(system, num_steps=1, p1=0.5, p2=0.5, plot_flag=True):\n",
+    "    \"\"\"Simulate the given number of time steps.\n",
+    "    \n",
+    "    `num_steps` should be an integer; if not, it gets rounded down.\n",
+    "    \n",
+    "    system: bikeshare System object\n",
+    "    num_steps: number of time steps\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    plot_flag: boolean, whether to plot\n",
+    "    \"\"\"\n",
+    "    for i in range(int(num_steps)):\n",
+    "        step(system, p1, p2)\n",
+    "        if plot_flag:\n",
+    "            plot_system(system)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now when we run a simulation, we can choose not to plot the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KAVLyDn/TJiljC1IDpa4uhQ8tX+6WrZWCKYqiKBGn5B1+RkVNpk519Q2HhqQk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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x14429e5bbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2, \n",
+    "                   olin_empty=0, wellesley_empty=0)\n",
+    "run_steps(bikeshare, 60, 0.4, 0.2, plot_flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But after the simulation, we can still read the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's wrap all that in a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation():\n",
+    "    system = System(olin=10, wellesley=2, \n",
+    "                    olin_empty=0, wellesley_empty=0)\n",
+    "    run_steps(system, 60, 0.4, 0.2, plot_flag=False)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And test it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>olin_empty</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin                0\n",
+       "wellesley          12\n",
+       "olin_empty          6\n",
+       "wellesley_empty     1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(system.olin_empty, system.wellesley_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we generalize `run_simulation` to take `p1` and `p2`, we can use it to run simulations with a range of values for the parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(p1=0.4, p2=0.2):\n",
+    "    bikeshare = System(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "    run_steps(bikeshare, 60, p1, p2, plot_flag=False)\n",
+    "    return bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When `p1` is small, we probably don't run out of bikes at Olin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = run_simulation(p1=0.2)\n",
+    "system.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When `p1` is large, we probably do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = run_simulation(p1=0.6)\n",
+    "system.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Exercise:**  Write a version of `run_simulation` that takes all five model parameters as function parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(p1=0.4, p2=.02, olin=10, wellesley=2, num_steps=60):\n",
+    "    bikeshare = System(olin=olin, wellesley=wellesley, \n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "    run_steps(bikeshare, num_steps, p1, p2, True)\n",
+    "    return bikeshare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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t9VVX5fd/+OGT2yIqoiKSCgr4IWtqigP+Sy9ZOtGeHns9MLlaT4+1ZTKWgrRB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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442a2af550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_simulation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## More for loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`linspace` creates a NumPy array of equally spaced numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1_array = linspace(start=0, stop=1, num=5)\n",
+    "p1_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use an array in a `for` loop, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "0.25\n",
+      "0.5\n",
+      "0.75\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    print(p1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will come in handy in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The function `linspace` is part of NumPy.  [You can read the documentation here](https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html).\n",
+    "\n",
+    "Use `linspace` to make an array of 10 equally spaced numbers from 1 to 10 (including both)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "2.0\n",
+      "3.0\n",
+      "4.0\n",
+      "5.0\n",
+      "6.0\n",
+      "7.0\n",
+      "8.0\n",
+      "9.0\n",
+      "10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "floopy_array = linspace(start=1, stop=10, num=10)\n",
+    "for floopy in floopy_array:\n",
+    "    print(floopy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The `modsim` library provides a related function called `linrange`.  You can view the documentation by running the following cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function linrange in module modsim:\n",
+      "\n",
+      "linrange(start=0, stop=None, step=1, **kwargs)\n",
+      "    Returns an array of evenly-spaced values in the interval [start, stop].\n",
+      "    \n",
+      "    This function works best if the space between start and stop\n",
+      "    is divisible by step; otherwise the results might be surprising.\n",
+      "    \n",
+      "    By default, the last value in the array is `stop` (at least approximately).\n",
+      "    If you provide the keyword argument `endpoint=False`, the last value\n",
+      "    in the array is `stop-step`. \n",
+      "    \n",
+      "    start: first value\n",
+      "    stop: last value\n",
+      "    step: space between values\n",
+      "    \n",
+      "    Also accepts the same keyword arguments as np.linspace.  See\n",
+      "    https://docs.scipy.org/doc/numpy/reference/generated/numpy.linspace.html\n",
+      "    \n",
+      "    returns: array or Quantity\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(linrange)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `linrange` to make an array of numbers from 1 to 11 with a step size of 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "3.0\n",
+      "5.0\n",
+      "7.0\n",
+      "9.0\n",
+      "11.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "floppy_array = linrange(start=1, stop=11, step=2)\n",
+    "for floppy in floppy_array:\n",
+    "        print(floppy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Sweeping parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following example runs simulations with a range of values for `p1`; after each simulation, it prints the number of unhappy customers at the Olin station:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ])"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1_array = linspace(0, 1, 11)\n",
+    "p1_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0\n",
+      "0.1 0\n",
+      "0.2 0\n",
+      "0.3 6\n",
+      "0.4 6\n",
+      "0.5 20\n",
+      "0.6 26\n",
+      "0.7 36\n",
+      "0.8 34\n",
+      "0.9 46\n",
+      "1.0 50\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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fDrfcAoiEiYYG8S8YzF8JJiVc/oMfiDRJPSX05JPhox8FYNm3xEQC8LnPCdWi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\nvl98yKNHWcObMwQunnuOpGhAD02sYxWdnOwJX2c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OwNU4T/WQi968nwZAUQpicIJBUhed\n7NutvUahDGsx5DJ14JuHXlKSUq2ukEgxql7+WGxb/wsSQ/fJQ7dUW9Rtq58GwK+Qi22yDAR1NQ8f\njaxf42q/j4oe+tSBnx56rABZLumydmR76HZkneXiRwzdC4PuFFaWbQAmQcgl2UXZRjZdyMVHIY/J\nHkP3xA8NhUKNwK3AeUAFsAG4MhKJeLdbxAf4tihaUpKaJVBA+J226IuHbkaB0hZ999CTpD7yYepi\noQx6oRZFj0QPPRQKqcDvgFbgIuB04BDw11AoVJ9v+37C1ywX7fWT5WLe+p9TyCUXY1vwGLoimg/Y\nFIS8XiwsRJaLTfYu2UU/vddCecpHukEHTgBOA/4jEok8EYlEXgQ+BFQDb/egfd9QuDz0wmLSpy1O\nEQ/dmraYaF62kc0UEni9eOjmHYEyOadA2qIXZus14EIgYjqmrw5Nz6ahzk5Ys0bs+B4YyFyiIRCA\nadPgnHPyr3uiqxTpwhaKImqqzJgh6rt4js9+ll2vXszW8WaG4xVU7uuic9/PxfZtieonzz0HDz4I\ne/eK31swCLW1RkXJcJgUZSWLchDkpB7UPVzDE71z6Xqlno6jYHTUWF/atQtuuMF2/drb4S9/gQOJ\nQlelpaIOA4jt7G54J/qBt7fDz38uagkD3HEHfPzj4u/KypyUi57fOZ0Huxazd3Qa8VdKmNEJx/zo\nGtp6bzM+dNddxt/z5qWWPMgHv/gFPPHE/2/v7IOsqK4E/ps3b4aPARxWDQxCgq6Zi4iWrJnsRgNq\nshLdD60Yt2I2roOJBRoim9WAX6uSqIvGrWi5LJHSNUKMMWvp+llDxYpRJJQytX4UErgbV2VBBnBA\nQXEEZl7vH6d7Xk9Pz3vd73X36x7ur+rVzOt+r889/W6fPn3uvefAK6/I+8ZG2L1b/o+4X/VXSeod\nwaQ10Dn172jb+UzkVaCG5JZb5Nw58mKorDWIBx8UA/G+ZHrk/ffFUMQpMyRVG3St9W7gWc/mhUgs\n/TdBj9PZCYsWwcsvywVfjr4+2LtXDNQHH1SePGvlSqlG1N1d7BuWJb/VddfJ+6iNeufuY9lwQNFn\nPyDt723k/s1fBiDifFxFmfbNsqtL9CwUxKgfPChVhBz7F7X8zg0jeXnXcWz9uJmevqJXZVki+/nn\n5d4x6PcbqqJ1XLiT0VdAZyfcuXoGXT3N/dv27IElvd+Hwg7ac7+QjX19sWR57CfiTJl+dK632Ly3\npf/9Rx/B/XsvhEIXbdi5cXp7B6YujYNcrqhjFb9daJkOKcy4GPkZUEqdBywFfqq1Dux+dHRIVaAw\n165lifGvppqQU6XI+9sUCnLDWLWqsuOWomPrDEbUFRUdYSfQX73txOiFOTI7pPQiDHReLUvqi4Lr\nHEZUOQigY00T+w6O5FDB33c4dGiI38/bhrCP8EGmLUZY5KKjA7bsHjNo+8fWKFbxDwPbFaUh8D6J\nJGBwOjy/1ciR8nd14ezixt7e6OXXQNeayKyCSA26Umou8Bjwa2BxmO92dUFPT3iZ1VYT2rlz6N+l\nt1cq7ERN1yfNTBv3ntQqqs8xfbw0fvv+5jLfrEJmV/HJx2vQnQps/ecwQk+n6/08vYV6u2TZYMNa\nKAzx+3mD69XEZIcy6H56VhiL7eqCg731AzTM56HXyrODiRUdsyJiLE7i0NVVx4nN26nDoj5X6E/G\nuZ1jBn4wzgFgiLayVlCZKTfokQ39KaVuAG4FlgELtdahfs2WFhg1SgoWh7l26+qqqyY0YYJ4iH7H\nzedhYgzXYsvovRSObOaLY9+DfJ5G20Of1PRh9MIcmfb5LRSKBZpB9HQ8rEmTgG0UV/1F0GFbju6l\nob6OXJ1FwbJwG/Wy1aBGjAgWf/MjiDFxfuQIBvBaWmBUYy/0QZ+Vo66xgVwe8nW9TKzbGdn5HIT3\nScR5Ofm6LSvSJy6AlhaLwsj9tB29hXx9gXyjGPJJ+Z1g1SWTJ9zpPA5JyfSukksZkbhiSqnFiDG/\nSWt9ZVhjDlIdpLU1/IKehobqqgm1t4vd8FJfLwOGl1xS2XFLce4UmZ7f2FjXb8wBzpm8MXphjkz7\n/MLgEnuOhxV1hSSQijrNDftpyPkbzZLVoJK6YCMweOeeC60T5IZcX1foryU6pq6HS3K/HPjhuA1B\nzJ7ruV+T9o+sP0Q+V9TlnPxvk4ljO5iQyyCimId+MvAvwAPAfUqpia5XU9DjtLXBnXfKhd3cXL5f\nOKXhTj+9umpC7e0wa5YYb8cxbWyUQhdLl8Yzy6XtM1tcVWYsJo/5gMumrZVZLjHhnN/Zs0W/fF48\n86OPFv0jqO/gL3dGD189RjN1zB6aGg/1l/VzZtiUrAYVlTcUsYfqR1sb3Hnhes6apGnKH6ShweL4\n42HJUctor38ovjb4nZeYjU7bF6yB/XcyUj0o/1r5tlVDLUIuXlJu0KMIuVwE1APfsV9ubkQ890C0\ntcHjj5f/3LJlsGGD/L9gAZx8clAJ/igFn/2s/H/XXRICiJUVK2jDO6Pk/JiFyvl95BG46qqB2xcv\nLtYyHVBZyeGGG2QaEMh0MafUUUCOHbebWYW3YPxuOOkkLrhADPmQOPLdVYu++U34yleCCy1nTNx6\n/uxn8Prr8v/ll8PMmcHluGib2s3jc+wRw/475O3y2rgR7rlH9p14IixcWJGMklx8sUzfW7QI9u2T\nbXfcIR5SxPRXSWpogBtnwY2Pyo4HHihOm7z0UimHFQc33wzPPgtvvy3vFy0qlhqLi+9+Vy4Ux0jN\nmQPf+Ea8MkMSxbTF64HrI2hLYKJe9ZvYgqIU4KdfWZ2rPOH53MDMe4HPcTULVYLMcolCTlDiWpDi\np2fS+WPcJLWIKqlEWRnL5ZKe5YohiPKcWlaCWRZTgJ9+ZcctqjSs+Tr70dTH3sQm100Yg15Nhyp1\nE0lyBWeSKzZLyY7a4NViVazJhx4/Uf6O7rTNsdYRTQlODNtNKA+9gou0IQoPPazcMPHbJC7SWuU5\nSSJlby1k12IZ/uGQnKsWRPk7JpYyNyV4+yQEMLBVduJ8buDgUWCDnpQ3FIeH7iUuXfyeCmppdJL0\nYJP2lk0+9HiI8pweTvFzB+9TSNmnkipDLkUPvW7Q4QLLrcbQlgu5xHGRJuW5+t1E4vZcaxVaqnVm\nSTAeehxEeU4PR4PuvQbLzqirOoY+MOQS+EmoGkMbJuQSVYcqZeiS8OySGhQtxXBOoZuBbIuZNOhR\nFis5nAZEHUJPD66yE/eHXHwiAoHlmlku/pQLucRtdJK8caVtlovx0KPBeOgJk4Zpi3EapiTkJGkI\n0jJtcTgM/pppi/ETl0E/HAZFK6LK2Sb90xZtFz3V0xbjCrkcLtMWk/LQIXlvOQODopn0Sd96S/Kg\nb98O990n+fOrJZeDI4+UFaOxFLVIEZs2ycLFnh5J2HXaaSV0PuEEya/rdN6HHy5eSAELF7y97yjW\ndB1P97Zm0JKA7Qc/KJNqYP58eO45yW0M8OSTxZWW7iIXJ5wgqwW9F1ehIDkOnEQ1peSsXl1cWfnM\nM/CjHw2WM9R316wpvv/ww2Lqyu5ueOyx4r4FC+Cll4rv582DRx+VVJN+S5ODFoXw3kTmzwetJY0o\nyN8JE+T/OIowuG9c8+fDrl2webO837wZ1q0ryp4/f+B33edu9uyB+8q19eabJb/1FjtdxrvvB1rV\nJQAADPVJREFUSh9xy3If303QIile7rsPnn5aCggANDUV5aekwEXmPPTOTimis22bOItRpYsoFKS4\ny3XXSY704crKldIfnVTFPT1SrSmwziHzV3S+OYr/fPtUdvQcQW9fjt5e+Q2XLpW/JYkqV0c5D71a\nOU61jlLfLZUdsFCQBRGVdOYa5HIpSbn8O5YlFTHc+/r6gmXV9N68giRv6+sr/9sElQnJZ3gMSeYM\nekeHGPM4+mmcRS3SwlCGO7DOIQsXdKwdS/enYwdt37o1ZFGSUndvpwST8wp7obmNkONhh6G7W8oT\nuauz+N1E/IyBk5D+wAG5u0Zx44rboJcKLZWTrTW89loxGdPBg1IebP36YjnAoJSTdeCAHHvPHnly\niOJc1CIhWAgyZ9C7uqQPxHVzjKuoRVrYuVPCLA5OLvSSOnsnqvf1+X/Oh67uPL2F4vcdx2r//gBF\nSbyjp0PF770XVtjO4S0JFyY22tsb/Hz4GQNvW6MwErX00MvJ3rVL/n74oex36sZaln9hgmpkOeE6\nByesFoZSTwUpNOiZi6G3tEjoav/+UHYlEHEWtUgLEyYMtFdNdoLjkjrn8xXX9Ww5qpfmEQc4VKjn\nYP1IRo+X89zUFKAoifdGUuldPMzCorBywnw2iDEIq6Oft5ykFxnGQy+nW7mwS7W5yY2Hnj6cQg1x\n1J+Ns6hFWmhvl+tg3Dh5Oc5pWZ3dRi+E0Tn39H1MadrD6IaDNI8+0P+7TZkSsKCG8wgRUm5oKp3i\nFKZNSRmDuOO8pY5ZSsdq21IqH3pcfeNwK3CRNGELYQTBKZYRZ1GLtNDeDtdeK95xLid/r702gM4V\nFmhom76f62eu5oyW/2HsiF7GjoUzzpDB50AFNaq9aIO2Ownj4GcMojRytYiheyl1Myn3PixJG1e/\ngdiUDYxmLuQCwQthGPxpbw9x09q0Sf6++KJMWQSZ9vXtbweW118MYeZMKSARBGca2KpV8Pvfy/8X\nXyzlldy8+qrMgRyKcgbdkXPPPTKXE+DKK2HGjGBtfOMNWL588L4FCwZ/9tZbZTQYpGjIQw/BvffK\nIKHD2WfDhReWl+3gNSgrVkhRkEcekfdnngnf+lbw44XFfX5XrJBBriVL5P3EicUpoAB33w1XX118\nf/PNA/c3N0tBjqFw63rbbTKIumyZvJ8xQ343d1u8fP7z8MMfllVpSJnz50tBiyuuKN5Ali8fPAZT\nQzLnoRtqRK2WPJdbyBFVWyrVL8xn/XJWeL8fhT5pybboHcQup2vYp8Ba6Zni5f/GoBuCUelq0TA5\nVcrJ9bt4yrUlqMxKL9Kh5PvJ9ZPhlRVFVskkV4qGWREbpa5eWUkl5/LKNQbdkEkqXfJcbYyx3MWT\nVg+9UoMeRRKyWhqcUrLLeezl+koaknNBqvO5GINuCEYUnbhaD91PbrkLuRIPPYx+YQyJn7ErZ+Qq\noZbZFsN46NWmRE46OVca0hOXwRh0QzBq1YnLyY0q5FKpflHH0KsptZe2ItFeXaK8edWiHFwanoDK\nYAy6IRiVduK4Y+hxhFzCGNVqY+hZ9NBLGfT6+qLuTioGh3I3r7D9I8rCCFmQGwBj0A3BqFUnLmec\nompLEh66ny5ReugOST5N+ZW/GuomHPV4QS3yoUOqPfRI5qErpeqBW4G5wFhgNbBAa70ziuMbUkBW\npy3WapaLH366RDltMS0hgXx+4A3LWR4c5ywXE3IBovPQlwDtwCXAbGAy8FipLxgyxuEUcjGzXMLL\ndFMLDz3IzSGq9MTD2aArpRqBfwSu11o/p7V+FbgIOF0pdVq1xzekhCgMehxya+2h19qg+1FrgzOU\noY1y2iKUfnrzO1Yl2fxqXbM1LJZlVfVqbW39Ymtrq9Xa2jrVs/2d1tbWa0p8b2pra6u1detWy5By\n5s2zrCOOcDJXVPdqbLSsadOCyx07Nn6Z8+ZZ1pgx0ejnvBoaLKu5WY7tyGhqilaG3yuXk1fccoY6\nv9OmJSO/FnrW6uXpv1u3brX8bK5lWZGEXCbbf9/zbN8OTIng+AaDIUtUmMjNUD1RGPTRQEFr7X32\nOACM9Pm8IYvU6iIdTsZhOOliSCVRGPQeIKeU8s6YGQHsj+D4hrRQC4OUyyUj1z1/ulrq6vyPlWRW\nvlrePJKUbW6SA4jCoNv5QGnxbJ/E4DCMIcsMZy99KCNc6bGG2h5FAv9q2pAESekIxqB7iOLMvwF8\nBJzhbFBKTQWmAmsiOL7BYDAYAlD1wiKt9QGl1HLgX5VS3cAuYDnwotb65RJfrQfYMZwrMg8XenrE\n68rnq68Mk8/L2P22bcnJLSczSv2geKxcTo69bVv0MkrJdkiqRJr7/FpW/DpC6kvBRYqn/7ps5qAY\nXp1lWVXLs+PndyCLixoorhTtLvGdLwMvVS3cYDAYDk9maa3XujdEYtArQSk1AmgDuoAKZvwbDAbD\nYUk9MmbZqbU+4N5RM4NuMBgMhmgx2RYNBoNhmGAMusFgMAwTjEE3GAyGYYIx6AaDwTBMiKTARVRk\nqVCGUupeIK+1vsy1bQ7wE0ABfwSu0Vp3uPZ/BlgGzAEOAj8HbtBaJ5bjVCk1wW7jHGAU8Apwtdb6\nzQzpMBm4C/gq4pSsBq7SWm/Pig6utvwFsBb4S631C/a2TLRfKTUd2Oiza5bWem2G9LgMWIwkE/wD\nsEhr/by9LxM6OKTNQ19CygtlKKXqlFI/BuZ7tk8HngIeBWYCTwJPKKVOdH3sMWAisqp2LnAp8KME\nmu20MQf8F9AKnA+cBuwFfquUOjIjOtQBzwLjgbPsdrQAT9v7U6+Dg1KqCfgFrgUiWWo/cBLQjZx/\n9+uVrOihlGoH/h24HdHnReAppdTUrOjgJjXTFu1CGd3AQq31g/a2qcA7wOla63W1a52glDoO+A9g\nBvAJ8JzjoSulVgBKa32m6/O/A/6otZ6nlPoSsA44Tmv9jr2/Hfg34GjvfNKY2j8TeBWYrrXeZG8b\nAewBrgBOz4AOE4G7gWu11u/a284HngD+BLkwU62Dq10rkJvrmcBZWusXstCPXO26BZittT7DZ1/q\n9bCdg3eAVVrrm+xtOeQa+QlipFOtg5c0eeinIGGWF5wN9gX7LjCrJi0azGlIMrKTkI7gZhauttu8\nQLHts4Atzg/v2j8W0T0J/g/4G0C7tjnrpseTAR201ju01he5jPlk5GmpU2v9ARnQAUAp9VfAXwML\nPbsy0X6bGcCmIfZlQQ8FfA74tbNBa13QWp+itX6YbOgwgDTF0FNfKENr/RDwEIBSyrt7MqXbPtR+\n7M+8EllDh0BrvRsJV7hZiMTSfwPcQsp1cKOUegIJHX2AhF8gA7+DUuoo5EnvUqTtblLffhczgJFK\nqZeRZHxvIqUo15MNPVrtv81KqecRfTYjT3/rSrQxTToMIE0eetYLZYwGPvVsc7d90H5bV4sa6aeU\nOg9YCvzUDsFkTYcbgT9HBhWfU0odQzZ0WAE8pbVe7bMvC+1HKTUKOA44AlgEnIcYsxeVUif4tZP0\n6THO/rsSuB84B7kpPZ8hHQaQJg+9v1CGZ4Q4K4UyepC2unG3fdB+pVQDUEcN9FNKzQXuAx5BRvgh\nYzporTfYbbgICYW1+7WRFOlgx1hnAicP8ZFUt99Ba92jlBoPHHBixXafOhX4nl87SZ8ejvN4mx1i\nQSm1AAmlXOHXRtKnwwDS5KFnvVDGVkq3faj9kLB+SqkbkOlV9wKXaK2dOHrqdVBKTbANeD9a60+A\n/wWOIf06zEUe1XcopT6mOJ7RYU+FTXv7+9Fa73MP/Nn9aCMSbsiCHo6cDc4GrbWFjAscSzZ0GECa\nDHrWC2WsxdV2m7Motn0tcJxSaopn/0fA6/E3T1BKLUbm+t+ktb7S7sAOWdDhc8CvlFJfcDYopY5A\nBrj+QPp1uBiYjgyanQJ8zd5+GXAT6W8/AEqpU5VS+5RSp7q21SM6bSQberyKeNJtzgZ75st0xEHI\ngg4DSM20RQCl1O2IBzOXYqGMT93ThtKCUuoF4C3XtMWTgP9GYtK/Av4eiS3+mdZ6k91R1iHxte8D\nE5DY3XKt9ZKE2nwy0olXAjd4dn+ExETTrkMOmUkwDpiHPDbfDvwpYkyOTbsObuxZOlspTltMfT+y\n251H+tJBYAHwMXANMotqmt2uLOhxi93+yxBP/XvA5UhfasyCDm7S5KED/DPwS2Qmye+ALcCFNW1R\nQOx47teR9r6ODBL9rTPf2/aEvw7sRAp7/BwZiPlxgs28CFnE8h0kD7379U9Z0MF+rL/Abt8zyEKQ\nfcAZWuuPs6BDKbLSfnuc61wkZPQ0sB5ZYDNba70rK3ogT0V3ImsbNgBfAuZoISs69JMqD91gMBgM\nlZM2D91gMBgMFWIMusFgMAwTjEE3GAyGYYIx6AaDwTBMMAbdYDAYhgnGoBsMBsMwwRh0g8FgGCYY\ng24wGAzDBGPQDQaDYZjw/7OwEpb4xREqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442b5f38d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for p1 in p1_array:\n",
+    "    system = run_simulation(p1=p1)\n",
+    "    print(p1, system.olin_empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can do the same thing, but plotting the results instead of printing them.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EE/B//o/6/x3vgIMHM9vqpk0qb7na6tNPqx4EqLq87rrc1736amaI6/bt889Dwe8YwzA0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\n/wegFnUa+pk1egAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442b65ffd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "for p1 in p1_array:\n",
+    "    system = run_simulation(p1=p1)\n",
+    "    plot(p1, system.olin_empty, 'rs', label='olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, we should decorate the figure.  This version of `decorate_bikeshare` takes `xlabel` as a parameter, for reasons you will see soon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def decorate_bikeshare(xlabel):\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel=xlabel, \n",
+    "             ylabel='Number of unhappy customers')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
+      "  warnings.warn(\"No labelled objects found. \"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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viZZe+1dYN4qY+6Xavru1L6UiItJR6tXa67t1Oo6IiDS53Mr7SsOt9CQ6NR4D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\nF9cT5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442a1d4208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "decorate_bikeshare(xlabel='Arrival rate at Olin (p1 in customers/min)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Wrap this code in a function named `parameter_sweep` that takes an array called `p1_array` as a parameter.  It should create a new figure, run a simulation for each value of `p1` in `p1_array`, and plot the results.\n",
+    "\n",
+    "Once you have the function working, modify it so it also plots the number of unhappy customers at Wellesley.  Looking at the plot, can you estimate a range of values for `p1` that minimizes the total number of unhappy customers?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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O4Fe5lJVlN1ZY/2WhlxXFuKeVlUYgGT87iZcvB377W/swv/z4Xr4ceOSRzMid+YaFc8IQ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442b609550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def parameter_sweep(p1_array):\n",
+    "    for p1 in p1_array:\n",
+    "        run_simulation(p1, p1, 10, 2, 60)\n",
+    "        \n",
+    "parameter_sweep(p1_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Exercise:** Write a function called `parameter_sweep2` that runs simulations with `p1=0.2` and a range of values for `p2`.\n",
+    "\n",
+    "Note: If you run `parameter_sweep2` a few times without calling `newfig`, you can plot multiple runs on the same axes, which will give you a sense of how much random variation there is from one run to the next. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cbbXGxeVSDQtdK/RYoRV6vVKuv7QYbgvdLt3l4l7q2FDBuSB/g9SrhR7k814L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\nrtFoNA3C/wf9BMPt+e0jnwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442b527d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def parameter_sweep2(p1_array):\n",
+    "    for p1 in p1_array:\n",
+    "        run_simulation(0.2, p1, 10, 2, 60)\n",
+    "        \n",
+    "parameter_sweep(p1_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Hold `p1=0.4` and `p2=0.2`, and sweep a range of values for `num_steps`.\n",
+    "\n",
+    "Hint: You will need a version of `run_simulation` that takes `num_steps` as a parameter.\n",
+    "\n",
+    "Hint: Because `num_steps` is supposed to be an integer use `range` rather than `linrange`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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//x6iWq7oal1D3/uBoYbMXEN1bmJnxrGufgr7/wKWUp2NBl7fjbMT8ONNM6TG5tNTT+1g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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1442bafbd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p2_array = range(1, 20, 2)\n",
+    "\n",
+    "def run_simulation(p1=0.4, p2=.02, olin=10, wellesley=2, num_steps=p2_array):\n",
+    "    bikeshare = System(olin=olin, wellesley=wellesley, \n",
+    "                  olin_empty=0, wellesley_empty=0)\n",
+    "    run_steps(bikeshare, num_steps, p1, p2, True)\n",
+    "    return bikeshare\n",
+    "\n",
+    "def parameter_sweep3(p2_array):\n",
+    "    for p2 in p2_array:\n",
+    "        run_simulation(0.4, 0.2, 10, 2, p2)\n",
+    "        \n",
+    "parameter_sweep(p2_array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Exercise:** The code below runs a simulation with the same parameters 10 times and computes the average number of unhappy customers.\n",
+    "\n",
+    "1.  Wrap this code in a function called `run_simulations` that takes `num_runs` as a parameter.\n",
+    "\n",
+    "2.  Test `run_simulations`, and increase `num_runs` until the results are reasonably consistent from one run to the next.\n",
+    "\n",
+    "3.  Generalize `run_simulations` so it also takes the initial value of `olin` as a parameter.\n",
+    "\n",
+    "4.  Run the generalized version with `olin=12`.  How much do the two extra bikes decrease the average number of unhappy customers.\n",
+    "\n",
+    "5.  Make a plot that shows the average number of unhappy customers as a function of the initial number of bikes at Olin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "num_runs = 10\n",
+    "total = 0\n",
+    "for i in range(num_runs):\n",
+    "    system = run_simulation(p1=0.4, p2=0.2, olin=10, wellesley=2, num_steps=60)\n",
+    "    total += system.olin_empty + system.wellesley_empty\n",
+    "total / num_runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#I don't understand the question, this seems impossible. I will fix during/before checkin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/ModSimProj3.ipynb b/code/ModSimProj3.ipynb
new file mode 100644
index 00000000..7121fa42
--- /dev/null
+++ b/code/ModSimProj3.ipynb
@@ -0,0 +1,1864 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.seconds\n",
+    "kg = UNITS.kilogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(height = 4000 * m,\n",
+    "                      v_init = 0 * m / s,\n",
+    "                      g = 9.8 * m/s**2,\n",
+    "                      mass = 85 * kg, #person avg weight with avg weight chute\n",
+    "                      surface_area_person = 1.75 * m**2,\n",
+    "                      surface_area_chute = 18 * m**2,\n",
+    "                      rho = 1.112 * kg/m**3,\n",
+    "                      v_term_person = 54.2 * m / s,\n",
+    "                      v_term_chute = 8 * m / s,\n",
+    "                      duration = 300 * s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition, starting_free_fall):\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    init = State(y=height, v=v_init)\n",
+    "    C_d_person = 2 * mass * g / (rho * surface_area_person * v_term_person**2)\n",
+    "    C_d_chute = 2 * mass * g / (rho * surface_area_chute * v_term_chute**2)\n",
+    "    ts = linspace(0, duration, 201)\n",
+    "    opening_duration = 20 * s\n",
+    "    \n",
+    "    return System(init=init, g=g, mass=mass, rho=rho, \n",
+    "                  C_d_person=C_d_person, C_d_chute = C_d_chute, \n",
+    "                  surface_area_person=surface_area_person, \n",
+    "                  surface_area_chute = surface_area_chute, \n",
+    "                  ts=ts, free_fall=starting_free_fall, opening_duration=opening_duration)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = make_system(condition, starting_free_fall = 40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    \n",
+    "    plot(system.results.y, label='y')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    y, v = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "   \n",
+    "    \n",
+    "    F_drag_person = 1/2 * rho * v**2 * surface_area_person * C_d_person\n",
+    "    \n",
+    "    F_drag_chute = 1/2 * rho * v**2 * surface_area_chute * C_d_chute\n",
+    "    \n",
+    "    a_drag_person = F_drag_person / mass\n",
+    "    \n",
+    "    a_drag_chute = F_drag_chute / mass\n",
+    "    \n",
+    "    delta_surface_area = (surface_area_chute / (opening_duration)) \n",
+    "    #change 90 to parmeter when sweeping\n",
+    "    \n",
+    "    surface_area_changing = delta_surface_area * (t - free_fall) + surface_area_person\n",
+    "    \n",
+    "    F_drag_chute_changing = 1/2 * rho * v**2 * surface_area_changing * C_d_chute\n",
+    "    \n",
+    "    a_drag_chute_changing = F_drag_chute_changing / mass\n",
+    "\n",
+    "    if t <= free_fall:\n",
+    "        dydt = v\n",
+    "        dvdt = -g + a_drag_person\n",
+    "    elif t <= (free_fall + opening_duration):\n",
+    "        dydt = v\n",
+    "        dvdt = -g + a_drag_person + a_drag_chute_changing\n",
+    "    else:\n",
+    "        dydt = v\n",
+    "        dvdt = -g + a_drag_person + a_drag_chute\n",
+    "\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def parameter_sweep():\n",
+    "    newfig()\n",
+    "    free_fall = linspace(10 * s, 70 * s, 7)\n",
+    "    \n",
+    "    for i in free_fall:\n",
+    "        system = make_system(condition, i)\n",
+    "        run_odeint(system, slope_func)\n",
+    "        plot_position(system.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "parameter_sweep()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                 y          v\n",
+      "0.0    4000.000000   0.000000\n",
+      "1.5    3989.107575 -14.349860\n",
+      "3.0    3957.906795 -26.819747\n",
+      "4.5    3910.110757 -36.400752\n",
+      "6.0    3850.160366 -43.088913\n",
+      "7.5    3782.008391 -47.451164\n",
+      "9.0    3708.625483 -50.171693\n",
+      "10.5   3632.025912 -51.821211\n",
+      "12.0   3553.492682 -52.804313\n",
+      "13.5   3473.812904 -53.384247\n",
+      "15.0   3393.458830 -53.724278\n",
+      "16.5   3312.710106 -53.922940\n",
+      "18.0   3231.731044 -54.038764\n",
+      "19.5   3150.617773 -54.106210\n",
+      "21.0   3069.426378 -54.145457\n",
+      "22.5   2988.189531 -54.168286\n",
+      "24.0   2906.926250 -54.181561\n",
+      "25.5   2825.647598 -54.189280\n",
+      "27.0   2744.360009 -54.193768\n",
+      "28.5   2663.067224 -54.196377\n",
+      "30.0   2581.771419 -54.197894\n",
+      "31.5   2500.473857 -54.198776\n",
+      "33.0   2419.175275 -54.199288\n",
+      "34.5   2337.876099 -54.199586\n",
+      "36.0   2256.576578 -54.199759\n",
+      "37.5   2175.276856 -54.199860\n",
+      "39.0   2093.977018 -54.199919\n",
+      "40.5   2017.070711 -38.356183\n",
+      "42.0   1975.393132 -21.251234\n",
+      "43.5   1947.861546 -16.243876\n",
+      "...            ...        ...\n",
+      "256.5   222.928358  -7.914254\n",
+      "258.0   211.056978  -7.914254\n",
+      "259.5   199.185597  -7.914254\n",
+      "261.0   187.314216  -7.914254\n",
+      "262.5   175.442836  -7.914254\n",
+      "264.0   163.571455  -7.914254\n",
+      "265.5   151.700074  -7.914254\n",
+      "267.0   139.828694  -7.914254\n",
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+      "\n",
+      "[201 rows x 2 columns]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = run_odeint(system, slope_func)\n",
+    "print(system.results)\n",
+    "plot_position(system.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/code/Project 2.ipynb b/code/Project 2.ipynb
new file mode 100644
index 00000000..c92c8a87
--- /dev/null
+++ b/code/Project 2.ipynb	
@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "init = State(temp=T1)\n",
+    "\n",
+    "coffee = System(init=init,\n",
+    "                volume=300,\n",
+    "                r=r_coffee,\n",
+    "                T_env=T_env,\n",
+    "                t0=0, \n",
+    "                t_end=30,\n",
+    "                dt=1)\n",
+    "\n",
+    "def update(state, system):\n",
+    "    unpack(system)\n",
+    "    T = state.temp\n",
+    "    T += -r * (T - T_env) * dt\n",
+    "\n",
+    "    return State(temp=T)\n",
+    "\n",
+    "\n",
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a TimeFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.loc[t0] = init\n",
+    "    ts = linrange(t0, t_end-dt, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.loc[t+dt] = update_func(frame.loc[t], system)\n",
+    "    \n",
+    "    system.results = frame\n",
+    "    \n",
+    "def final_temp(system):\n",
+    "    \"\"\"Final temperature.\n",
+    "    \n",
+    "    If system has no results, return initial temp.\n",
+    "    \n",
+    "    system: System object.\n",
+    "    \n",
+    "    returns: temperature (degC)\n",
+    "    \"\"\"    \n",
+    "    if hasattr(system, 'results'):\n",
+    "        return system.results.temp[system.t_end]\n",
+    "    else:\n",
+    "        return system.init.temp\n",
+    "    \n",
+    "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n",
+    "    \"\"\"Runs a simulation with the given parameters.\n",
+    "\n",
+    "    T_init: initial temperature in degC\n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    volume: volume of liquid in mL\n",
+    "    t_end: end time of simulation\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(temp=T_init)\n",
+    "    \n",
+    "    system = System(init=init,\n",
+    "                    volume=volume,\n",
+    "                    r=r,\n",
+    "                    T_env=22, \n",
+    "                    t0=0,\n",
+    "                    t_end=t_end,\n",
+    "                    dt=1)\n",
+    "    return system\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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@@ -0,0 +1,2307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 1: Modeling\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jupyter\n",
+    "\n",
+    "Welcome to Modeling and Simulation, welcome to Python, and welcome to Jupyter.\n",
+    "\n",
+    "This is a Jupyter notebook, which is a development environment where you can write and run Python code.  Each notebook is divided into cells.  Each cell contains either text (like this cell) or Python code (like the cell below this one).\n",
+    "\n",
+    "### Selecting and running cells\n",
+    "\n",
+    "To select a cell, click in the left margin next to the cell.  You should see a blue frame surrounding the selected cell.\n",
+    "\n",
+    "To edit a code cell, click inside the cell.  You should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
+    "\n",
+    "To edit a text cell, double-click inside the cell.  Again, you should see a green frame around the selected cell, and you should see a cursor inside the cell.\n",
+    "\n",
+    "To run a cell, hold down SHIFT and press ENTER.  If you run a text cell, it will typeset the text and display the result.\n",
+    "\n",
+    "If you run a code cell, it runs the Python code in the cell and displays the result, if any.\n",
+    "\n",
+    "To try it out, edit this cell, change some of the text, and then press SHIFT-ENTER to run it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding and removing cells\n",
+    "\n",
+    "You can add and remove cells from a notebook using the buttons in the toolbar and the items in the menu, both of which you should see at the top of this notebook.\n",
+    "\n",
+    "You might want to try the following exercises:\n",
+    "\n",
+    "1.  From the Insert menu select \"Insert cell below\" to add a cell below this one.  By default, you get a code cell, and you can see in the pulldown menu that says \"Code\".\n",
+    "\n",
+    "2.  In the new cell, add a print statement like `print('Hello')`, and run it.\n",
+    "\n",
+    "3.  Add another cell, select the new cell, and then click on the pulldown menu that says \"Code\" and select \"Markdown\".  This makes the new cell a text cell.\n",
+    "\n",
+    "4.  In the new cell, type some text, and then run it.\n",
+    "\n",
+    "5.  Use the arrow buttons in the toolbar to move cells up and down.\n",
+    "\n",
+    "6.  Use the cut, copy, and paste buttons to delete, add, and move cells.\n",
+    "\n",
+    "7.  As you make changes, Jupyter saves your notebook automatically, but if you want to make sure, you can press the save button, which looks like a floppy disk from the 1990s.\n",
+    "\n",
+    "8.  Finally, when you are done with a notebook, selection \"Close and Halt\" from the File menu."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the notebooks\n",
+    "\n",
+    "The notebooks for each chapter contain the code from the chapter along with addition examples, explanatory text, and exercises.  I recommend you read the chapter first to understand the concepts and vocabulary, then run the notebook to review what you learned and see it in action, and then attempt the exercises.\n",
+    "\n",
+    "The notebooks contain some explanatory text, but it is probably not enough to make sense if you have not read the book.  If you are working through a notebook and you get stuck, you might want to re-read (or read!) the corresponding section of the book.\n",
+    "\n",
+    "If you try to work through the notebooks without reading the book, you're gonna have a bad time.  If you have previous programming experience, you might get through the first few notebooks, but sooner or later, you will get to the end of your leash, and you won't like it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing modsim\n",
+    "\n",
+    "The following cell imports `modsim`, which is a collection of functions we will use throughout the book.  Whenever you start the notebook, you will have to run the following cell.  It does two things:\n",
+    "\n",
+    "1.  It uses a Jupyter \"magic command\" to specify whether figures should appear in the notebook, or pop up in a new window.\n",
+    "\n",
+    "2.  It imports everything defined in `modsim`.\n",
+    "\n",
+    "Select the following cell and press SHIFT-ENTER to run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If this cell runs successfully, it produces no output other than this message.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# To switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib qt5\n",
+    "\n",
+    "from modsim import *\n",
+    "\n",
+    "print('If this cell runs successfully, it produces no output other than this message.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The penny myth\n",
+    "\n",
+    "The following cells contain code from the beginning of Chapter 1.\n",
+    "\n",
+    "`modsim` defines `UNITS`, which contains variables representing pretty much every unit you've ever heard of.  The following to lines create new variables named `meter` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "meter = UNITS.meter\n",
+    "second = UNITS.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To find out what units are defined, type `UNITS.` in the next cell and then press TAB.  You should see a pop-up menu with a list of units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-276-cd42a0bb6380>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-276-cd42a0bb6380>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    UNITS.\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "UNITS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a variable named `a` and display its value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "9.8 meter/second<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$9.8 \\frac{meter}{second^{2}}$"
+      ],
+      "text/plain": [
+       "<Quantity(9.8, 'meter / second ** 2')>"
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 9.8 * meter / second**2\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Create `t` and display its value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "4 second"
+      ],
+      "text/latex": [
+       "$4 second$"
+      ],
+      "text/plain": [
+       "<Quantity(4, 'second')>"
+      ]
+     },
+     "execution_count": 278,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t = 4 * second\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "If you create a variable and don't display the value, you don't get any output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "156.8 meter"
+      ],
+      "text/latex": [
+       "$156.8 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(156.8, 'meter')>"
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = a * t**2\n",
+    "h"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Add a second line to the previous cell to display the value of `h`.\n",
+    "\n",
+    "Now let's solve the falling penny problem.  The following lines set `h` to the height of the Empire State Building and compute the time it would take a penny to fall, assuming constant acceleration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.817885349720552 second"
+      ],
+      "text/latex": [
+       "$8.817885349720552 second$"
+      ],
+      "text/plain": [
+       "<Quantity(8.817885349720552, 'second')>"
+      ]
+     },
+     "execution_count": 280,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = 381 * meter\n",
+    "t = sqrt(2 * h / a)\n",
+    "t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Given `t`, we can compute the velocity of the penny when it lands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "86.41527642726142 meter/second"
+      ],
+      "text/latex": [
+       "$86.41527642726142 \\frac{meter}{second}$"
+      ],
+      "text/plain": [
+       "<Quantity(86.41527642726142, 'meter / second')>"
+      ]
+     },
+     "execution_count": 281,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v = a * t\n",
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "We can convert from one set of units to another like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mile = UNITS.mile\n",
+    "hour= UNITS.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "193.30546802805438 mile/hour"
+      ],
+      "text/latex": [
+       "$193.30546802805438 \\frac{mile}{hour}$"
+      ],
+      "text/plain": [
+       "<Quantity(193.30546802805438, 'mile / hour')>"
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.to(mile/hour)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In reality, air resistance prevents the penny from reaching this velocity.  At about 20 meters per second, the force of air resistance equals the force of gravity and the penny stops accelerating.\n",
+    "\n",
+    "As a simplification, let's assume that the acceleration of the penny is `a` until the penny reaches 20 meters per second, and then 0 afterwards.  What is the total time for the penny to fall 381 meters?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "20.070408163265306 second"
+      ],
+      "text/latex": [
+       "$20.070408163265306 second$"
+      ],
+      "text/plain": [
+       "<Quantity(20.070408163265306, 'second')>"
+      ]
+     },
+     "execution_count": 284,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vmax = 20 * meter/second\n",
+    "t1 = vmax / a\n",
+    "h1 = a * t1**2 / 2\n",
+    "h2 = h - h1\n",
+    "t2 = h2 / vmax\n",
+    "ttotal = t1 + t2\n",
+    "ttotal\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling a bikeshare system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll start with a `System` object that represents the number of bikes at each station."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2, babson=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you display the value of a `System` object, it lists the system variables and their values (not necessarily in the order you defined them):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>babson</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "babson        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the system variables using dot notation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.wellesley"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** What happens if you spell the name of a system variable wrong?  Edit the previous cell, change the spelling of `wellesley`, and run the cell again.\n",
+    "\n",
+    "The error message uses the word \"attribute\", which is another name for what we are calling a system variable. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add a third attribute called `babson` with initial value 0, and print the state of `bikeshare` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting\n",
+    "\n",
+    "`newfig` creates a new figure, which should appear either in the notebook or in a new window, depending on which magic command you ran in the first code cell.\n",
+    "\n",
+    "`plot` adds a data point to the figure; in this example, you should see a red square and a blue circle representing the number of bikes at each station."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "newfig()\n",
+    "plot(bikeshare.olin, 'rs-')\n",
+    "plot(bikeshare.wellesley, 'bo-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the operators `+=` and `-=` to increase and decrease the system variables.  The following lines move a bike from Olin to Wellesley."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         9\n",
+       "wellesley    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin -= 1\n",
+    "bikeshare.wellesley += 1\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the following lines plot the updated state of the system.  You should see two new data points with lines connecting them to the old data points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plot(bikeshare.olin, 'rs-')\n",
+    "plot(bikeshare.wellesley, 'bo-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In the cell below, write a few lines of code to move a bike from Wellesley to Olin and plot the updated state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare.olin -= 1\n",
+    "bikeshare.wellesley += 1\n",
+    "\n",
+    "plot(bikeshare.olin, 'rs-')\n",
+    "plot(bikeshare.wellesley, 'bo-')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plot(bikeshare.olin, 'rs-')\n",
+    "plot(bikeshare.wellesley, 'bo-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions\n",
+    "\n",
+    "Now we can take the code we've written so far and encapsulate it in functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    bikeshare.olin -= 1\n",
+    "    bikeshare.wellesley += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you define a function, it doesn't run the statements inside the function, yet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_state():\n",
+    "    plot(bikeshare.olin, 'rs-', label='Olin')\n",
+    "    plot(bikeshare.wellesley, 'bo-', label='Wellesley')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now when we run the functions, it runs the statements inside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         7\n",
+       "wellesley    5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 315,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bike_to_wellesley()\n",
+    "plot_state()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should see two more data points that represent the current state of the system.  If the figure is embedded in the notebook, you might have to scroll up to see the change.\n",
+    "\n",
+    "One common error is to omit the parentheses, which has the effect of looking up the function, but not running it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bike_to_wellesley()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output indicates that `bike_to_wellesley` is a function defined in a \"namespace\" called `__main__`, but you don't have to understand what that means."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Define a function called `bike_to_olin` that moves a bike from Wellesley to Olin.  Run the new function and print or plot the results to confirm that it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         7\n",
+       "wellesley    5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def bike_to_olin():\n",
+    "    bikeshare.olin += 1\n",
+    "    bikeshare.wellesley -= 1\n",
+    "bike_to_olin()\n",
+    "plot(bikeshare.olin, 'rs-', label='Olin')\n",
+    "plot(bikeshare.wellesley, 'bo-', label='Wellesley')\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we go on, let's start with a new state object and a new plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we have two similar functions, we can create a new function, `move_bike` that takes a parameter `n`, which indicates how many bikes are moving, and in which direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def move_bike(n):\n",
+    "    bikeshare.olin -= n\n",
+    "    bikeshare.wellesley += n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `move_bike` to write simpler versions of the other functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def bike_to_wellesley():\n",
+    "    move_bike(1)\n",
+    "    \n",
+    "def bike_to_olin():\n",
+    "    move_bike(-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we define these functions, we replace the old definitions with the new ones.\n",
+    "\n",
+    "Now we can test them and update the figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         9\n",
+       "wellesley    3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bike_to_wellesley()\n",
+    "plot_state()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, each time you run `plot_state` you should see changes in the figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bike_to_olin()\n",
+    "plot_state()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point, `move_bike` is complicated enough that we should add some documentation.  The text in triple-quotation marks is in English, not Python.  It doesn't do anything when the program runs, but it helps people understand what this function does and how to use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def move_bike(n):\n",
+    "    \"\"\"Move bikes.\n",
+    "    \n",
+    "    n: number of bikes: positive moves from Olin to Wellesley;\n",
+    "                        negative moves from Wellesley to Olin\n",
+    "    \"\"\"\n",
+    "    bikeshare.olin -= n\n",
+    "    bikeshare.wellesley += n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever you make a figure, you should put labels on the axes to explain what they mean and what units they are measured in.  Here's how:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "label_axes(title='Olin-Wellesley Bikeshare',\n",
+    "           xlabel='Time step (min)', \n",
+    "           ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, you might have to scroll up to see the effect.\n",
+    "\n",
+    "And you can save figures as files; the suffix of the filename indicates the format you want.  This example saves the current figure in a PDF file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap01_fig01.pdf\n"
+     ]
+    }
+   ],
+   "source": [
+    "savefig('chap01_fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The following function definitions start with print statements so they display messages when they run.  Run each of these functions (with appropriate arguments) and confirm that they do what you expect.\n",
+    "\n",
+    "Adding print statements like this to functions is a useful debugging technique.  Keep it in mind!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def move_bike_debug(n):\n",
+    "    print('Running move_bike_debug with argument', n)\n",
+    "    bikeshare.olin -= n\n",
+    "    bikeshare.wellesley += n\n",
+    "    \n",
+    "def bike_to_wellesley_debug():\n",
+    "    print('Running bike_to_wellesley_debug')\n",
+    "    move_bike_debug(1)\n",
+    "    \n",
+    "def bike_to_olin_debug():\n",
+    "    print('Running bike_to_olin_debug')\n",
+    "    move_bike_debug(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running move_bike_debug with argument 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "move_bike_debug(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running bike_to_wellesley_debug\n",
+      "Running move_bike_debug with argument 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "bike_to_wellesley_debug()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running bike_to_olin_debug\n",
+      "Running move_bike_debug with argument -1\n"
+     ]
+    }
+   ],
+   "source": [
+    "bike_to_olin_debug()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conditionals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The function `flip` takes a probability and returns either `True` or `False`, which are special values defined by Python.\n",
+    "\n",
+    "In the following example, the probability is 0.7 or 70%.  If you run this cell several times, you should get `True` about 70% of the time and `False` about 30%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flip(0.7)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modify the argument in the previous cell and see what effect it has.\n",
+    "\n",
+    "In the following example, we use `flip` as part of an if statement.  If the result from `flip` is `True`, we print `heads`; otherwise we do nothing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "if flip(0.7):\n",
+    "    print('heads')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With an else clause, we can print heads or tails depending on whether `flip` returns `True` or `False`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heads\n"
+     ]
+    }
+   ],
+   "source": [
+    "if flip(0.7):\n",
+    "    print('heads')\n",
+    "else:\n",
+    "    print('tails')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's get back to the bikeshare system.  Again let's start with a new `System` object and a new plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose that in any given minute, there is a 70% chance that a student picks up a bike at Olin and rides to Wellesley.  We can simulate that like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 360,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if flip(0.7):\n",
+    "    bike_to_wellesley()\n",
+    "    print('Moving a bike to Wellesley')\n",
+    "\n",
+    "plot_state()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And maybe at the same time, there is also a 60% chance that a student at Wellesley rides to Olin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 361,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         10\n",
+       "wellesley     2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 361,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "if flip(0.6):\n",
+    "    bike_to_olin()\n",
+    "    print('Moving a bike to Olin')\n",
+    "\n",
+    "plot_state()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can wrap that code in a function called `step` that simulates one time step.  In any given minute, a student might ride from Olin to Wellesley, from Wellesley to Olin, or both, or neither, depending on the results of `flip`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def step():\n",
+    "    if flip(0.7):\n",
+    "        bike_to_wellesley()\n",
+    "        print('Moving a bike to Wellesley')\n",
+    "        \n",
+    "    if flip(0.6):\n",
+    "        bike_to_olin()\n",
+    "        print('Moving a bike to Olin')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you run `step` a few times, it should update the current figure.  In each time step, the number of bikes at each location might go up, down, or stay the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Olin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         11\n",
+       "wellesley     1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 362,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step()\n",
+    "plot_state()\n",
+    "bikeshare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function labels the axes and adds a legend to the figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def decorate():  \n",
+    "    legend(loc='best')\n",
+    "    label_axes(title='Olin-Wellesley Bikeshare',\n",
+    "               xlabel='Time step (min)', \n",
+    "               ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As always, when you define a function, it has no effect until you run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "decorate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Change the argument of `legend` to `'random string'` and run `decorate` again.  You should get an error message that lists the valid location where you can put the legend."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optional parameters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again let's start with a new `System` object and a new plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can make `step` more general by adding parameters.  Because these parameters have default values, they are optional."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def step(p1=0.5, p2=0.5):\n",
+    "    print('p1 ->', p1)\n",
+    "    print('p2 ->', p2)\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I added print statements, so each time we run `step` we can see the arguments.\n",
+    "\n",
+    "If you provide no arguments, you get the default values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p1 -> 0.5\n",
+      "p2 -> 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "step()\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you provide one argument, it overrides the first parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p1 -> 0.4\n",
+      "p2 -> 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "step(0.4)\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you provide two arguments, they override both."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p1 -> 0.4\n",
+      "p2 -> 0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "step(0.4, 0.2)\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can specify the names of the parameters you want to override."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p1 -> 0.4\n",
+      "p2 -> 0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "step(p1=0.4, p2=0.2)\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which means you can override the second parameter and use the default for the first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p1 -> 0.5\n",
+      "p2 -> 0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "step(p2=0.2)\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can combine both forms, but it is not very common:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p1 -> 0.4\n",
+      "p2 -> 0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "step(0.4, p2=0.2)\n",
+    "plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One reason it's not common is that it's error prone.  The following example causes an error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you remove the # at the beginning of the next line and run it, you get\n",
+    "# SyntaxError: positional argument follows keyword argument\n",
+    "\n",
+    "#step(p1=0.4, 0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the error message, you might infer that arguments like `step(0.4, 0.2)` are called \"positional\" and arguments like `step(p1=0.4, p2=0.2)` are called \"keyword arguments\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a version of `decorate` that takes an optional parameter named `loc` with default value `'best'`.  It should pass the value of `loc` along as an argument to `legend.`  Test your function with different values of `loc`.  [You can see the list of legal values here](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def decorate(loc = 'best'):  \n",
+    "    legend(loc = 'best')\n",
+    "    label_axes(title='Olin-Wellesley Bikeshare',\n",
+    "               xlabel='Time step (min)', \n",
+    "               ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "decorate(loc = 'best')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For loop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we go on, I'll redefine `step` without the print statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def step(p1=0.5, p2=0.5):\n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley()\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And let's start again with a new `System` object and a new figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_state()\n",
+    "decorate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use a for loop to move 4 bikes from Olin to Wellesley."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 380,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(4):\n",
+    "    bike_to_wellesley()\n",
+    "    plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can simulate 4 random time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(4):\n",
+    "    step()\n",
+    "    plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If each step corresponds to a minute, we can simulate the rest of the hour like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 382,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(52):\n",
+    "    step(p1=0.4, p2=0.2)\n",
+    "    plot_state()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Combine the examples from the previous two sections to write a function named `run_steps` that takes three parameters, named `num_steps`, `p1`, and `p2`.  It should use a for loop to run `step` the number of times specified by `num_steps`, passing along the specified values of `p1` and `p2`.  After each step, it should plot the updated state.\n",
+    "\n",
+    "Test your function by creating a new `System` object, creating a new figure, and running `run_steps`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plot_state' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-39-2c21015af0e4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mbikeshare\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSystem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0molin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwellesley\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mnewfig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mplot_state\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[0mdecorate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[0mrun_steps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'plot_state' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    " def run_steps(num_steps = 5, p1 = .5, p2 = .5):\n",
+    "    for i in range(num_steps):\n",
+    "        step(p1, p2)\n",
+    "        plot_state()\n",
+    "bikeshare = System(olin=10, wellesley=2)\n",
+    "newfig()\n",
+    "plot_state()\n",
+    "decorate()\n",
+    "run_steps()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heads\n",
+      "tails\n",
+      "heads\n",
+      "heads\n",
+      "heads\n",
+      "tails\n",
+      "heads\n",
+      "heads\n",
+      "heads    6\n",
+      "tails    2\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "penny = System(heads=0, tails=0)\n",
+    "def flip_count(n,p):\n",
+    "    for i in range (n):\n",
+    "        if flip(p):\n",
+    "            penny.heads +=1\n",
+    "            print ('heads')\n",
+    "        else:\n",
+    "            penny.tails +=1\n",
+    "            print ('tails')\n",
+    "flip_count(8,0.5)\n",
+    "print(penny)\n",
+    "8#just practice from class, not part of assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "penny = System(heads=0, tails=0)\n",
+    "def update_heads(penny, n):\n",
+    "    penny.heads += n\n",
+    "def flip_until_tails(system, p):\n",
+    "    print(p)\n",
+    "    for i in range (100):\n",
+    "        if flip(p):\n",
+    "            update_heads(system, 1)\n",
+    "            print ('heads')\n",
+    "        else:\n",
+    "            system.tails += 1\n",
+    "            print('tails')\n",
+    "            return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5\n",
+      "heads\n",
+      "heads\n",
+      "heads\n",
+      "tails\n"
+     ]
+    }
+   ],
+   "source": [
+    "flip_until_tails(penny, .5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>heads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tails</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "heads    3\n",
+       "tails    1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "penny"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/chap03-fig01.pdf b/code/chap03-fig01.pdf
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new file mode 100644
index 00000000..34f558fb
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diff --git a/code/chap03.ipynb b/code/chap03.ipynb
index f692fa8b..86efd7da 100644
--- a/code/chap03.ipynb
+++ b/code/chap03.ipynb
@@ -89,6 +89,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -108,6 +109,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -148,8 +150,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 8,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "table2"
@@ -164,8 +176,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 9,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "census = table2.census\n",
@@ -183,8 +205,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 10,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "census.values"
@@ -199,8 +231,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 11,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "census.index"
@@ -221,8 +263,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 12,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "type(table2)"
@@ -230,8 +282,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 13,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "type(census)"
@@ -239,8 +301,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 14,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "type(census.index)"
@@ -248,8 +320,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 15,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "type(census.values)"
@@ -298,6 +380,7 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -347,8 +430,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 20,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "max(abs(census - un) / un) * 100"
@@ -369,6 +462,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -380,6 +474,7 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -391,6 +486,7 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -400,8 +496,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 24,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -423,8 +529,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 25,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "census[1950]"
@@ -439,8 +555,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 26,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "census[2015]"
@@ -455,8 +581,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 27,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "first_year = census.index[0]\n",
@@ -475,8 +611,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 28,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "total_growth = census[last_year] - census[first_year]\n",
@@ -512,8 +658,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 30,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "results[1950] = census[1950]\n",
@@ -548,8 +704,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 32,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "newfig()\n",
@@ -579,8 +745,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 33,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -702,8 +878,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 38,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "run_simulation1(system)\n",
@@ -719,8 +905,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 39,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "help(decorate)"
@@ -730,6 +926,7 @@
    "cell_type": "code",
    "execution_count": 40,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -761,8 +958,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 42,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -834,8 +1041,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 45,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "run_simulation2(system)\n",
@@ -874,8 +1091,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 47,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -926,8 +1153,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 49,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "update_func1"
@@ -942,8 +1179,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 50,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "type(update_func1)"
@@ -1006,8 +1253,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 53,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "plot_results(system, title='Proportional model, factored')"
@@ -1064,8 +1321,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 55,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "system.alpha = system.birth_rate - system.death_rate\n",
@@ -1099,8 +1366,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 57,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -1150,8 +1427,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 59,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "system.alpha = 0.025\n",
@@ -1192,8 +1479,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 61,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "newfig()\n",
@@ -1232,8 +1529,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 63,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "-system.alpha / system.beta"
@@ -1259,8 +1566,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 64,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -1279,8 +1596,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 65,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 66,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -1302,8 +1629,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 66,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 67,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "table1 = tables[1]\n",
@@ -1319,8 +1656,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 68,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "table1.tail()"
@@ -1402,6 +1749,7 @@
    "cell_type": "code",
    "execution_count": 72,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -1422,8 +1770,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 73,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "newfig()\n",
@@ -1455,8 +1813,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 75,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
@@ -1464,8 +1832,18 @@
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+<<<<<<< HEAD
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": true
+   },
+=======
+=======
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "execution_count": 76,
    "metadata": {},
+>>>>>>> 545f93a6710b0dad19a3e391f04c72c290c12945
    "outputs": [],
    "source": [
     "# Solution goes here"
diff --git a/code/chap03mine.ipynb b/code/chap03mine.ipynb
new file mode 100644
index 00000000..20785bc4
--- /dev/null
+++ b/code/chap03mine.ipynb
@@ -0,0 +1,4337 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 3: Explain\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# To switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas is a module that provides tools for reading and processing data.  The `read_html` reads a web page from a file or the Internet and creates one DataFrame for each table on the page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from pandas import read_html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data directory contains a downloaded copy of https://en.wikipedia.org/wiki/World_population_estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`tables` is a sequence of DataFrame objects.  We can select the DataFrame we want using the bracket operator.  The tables are numbered from 0, so `table2` is actually the third table on the page.\n",
+    "\n",
+    "`head` selects the header and the first five rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>United States Census Bureau (2015)[18]</th>\n",
+       "      <th>Population Reference Bureau (1973–2015)[6]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[7]</th>\n",
+       "      <th>Maddison (2008)[8]</th>\n",
+       "      <th>HYDE (2007)[15]</th>\n",
+       "      <th>Tanton (1994)[9]</th>\n",
+       "      <th>Biraben (1980)[10]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[11]</th>\n",
+       "      <th>Thomlinson (1975)[12]</th>\n",
+       "      <th>Durand (1974)[13]</th>\n",
+       "      <th>Clark (1967)[14]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1950</th>\n",
+       "      <td>2557628654</td>\n",
+       "      <td>2.516000e+09</td>\n",
+       "      <td>2525149000</td>\n",
+       "      <td>2.544000e+09</td>\n",
+       "      <td>2.527960e+09</td>\n",
+       "      <td>2.400000e+09</td>\n",
+       "      <td>2.527000e+09</td>\n",
+       "      <td>2.500000e+09</td>\n",
+       "      <td>2.400000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.486000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1951</th>\n",
+       "      <td>2594939877</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2572850917</td>\n",
+       "      <td>2.571663e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1952</th>\n",
+       "      <td>2636772306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2619292068</td>\n",
+       "      <td>2.617949e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1953</th>\n",
+       "      <td>2682053389</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2665865392</td>\n",
+       "      <td>2.665959e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954</th>\n",
+       "      <td>2730228104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2713172027</td>\n",
+       "      <td>2.716927e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      United States Census Bureau (2015)[18]  \\\n",
+       "Year                                           \n",
+       "1950                              2557628654   \n",
+       "1951                              2594939877   \n",
+       "1952                              2636772306   \n",
+       "1953                              2682053389   \n",
+       "1954                              2730228104   \n",
+       "\n",
+       "      Population Reference Bureau (1973–2015)[6]  \\\n",
+       "Year                                               \n",
+       "1950                                2.516000e+09   \n",
+       "1951                                         NaN   \n",
+       "1952                                         NaN   \n",
+       "1953                                         NaN   \n",
+       "1954                                         NaN   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[7]  \\\n",
+       "Year                                                                       \n",
+       "1950                                         2525149000                    \n",
+       "1951                                         2572850917                    \n",
+       "1952                                         2619292068                    \n",
+       "1953                                         2665865392                    \n",
+       "1954                                         2713172027                    \n",
+       "\n",
+       "      Maddison (2008)[8]  HYDE (2007)[15]  Tanton (1994)[9]  \\\n",
+       "Year                                                          \n",
+       "1950        2.544000e+09     2.527960e+09      2.400000e+09   \n",
+       "1951        2.571663e+09              NaN               NaN   \n",
+       "1952        2.617949e+09              NaN               NaN   \n",
+       "1953        2.665959e+09              NaN               NaN   \n",
+       "1954        2.716927e+09              NaN               NaN   \n",
+       "\n",
+       "      Biraben (1980)[10]  McEvedy & Jones (1978)[11]  Thomlinson (1975)[12]  \\\n",
+       "Year                                                                          \n",
+       "1950        2.527000e+09                2.500000e+09           2.400000e+09   \n",
+       "1951                 NaN                         NaN                    NaN   \n",
+       "1952                 NaN                         NaN                    NaN   \n",
+       "1953                 NaN                         NaN                    NaN   \n",
+       "1954                 NaN                         NaN                    NaN   \n",
+       "\n",
+       "     Durand (1974)[13]  Clark (1967)[14]  \n",
+       "Year                                      \n",
+       "1950               NaN      2.486000e+09  \n",
+       "1951               NaN               NaN  \n",
+       "1952               NaN               NaN  \n",
+       "1953               NaN               NaN  \n",
+       "1954               NaN               NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table2 = tables[2]\n",
+    "table2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`tail` selects the last five rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>United States Census Bureau (2015)[18]</th>\n",
+       "      <th>Population Reference Bureau (1973–2015)[6]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[7]</th>\n",
+       "      <th>Maddison (2008)[8]</th>\n",
+       "      <th>HYDE (2007)[15]</th>\n",
+       "      <th>Tanton (1994)[9]</th>\n",
+       "      <th>Biraben (1980)[10]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[11]</th>\n",
+       "      <th>Thomlinson (1975)[12]</th>\n",
+       "      <th>Durand (1974)[13]</th>\n",
+       "      <th>Clark (1967)[14]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2011</th>\n",
+       "      <td>6944055583</td>\n",
+       "      <td>6.986951e+09</td>\n",
+       "      <td>6997998760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012</th>\n",
+       "      <td>7022349283</td>\n",
+       "      <td>7.057075e+09</td>\n",
+       "      <td>7080072417</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>7101027895</td>\n",
+       "      <td>7.136796e+09</td>\n",
+       "      <td>7162119434</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>7178722893</td>\n",
+       "      <td>7.238184e+09</td>\n",
+       "      <td>7243784000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015</th>\n",
+       "      <td>7256490011</td>\n",
+       "      <td>7.336435e+09</td>\n",
+       "      <td>7349472000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      United States Census Bureau (2015)[18]  \\\n",
+       "Year                                           \n",
+       "2011                              6944055583   \n",
+       "2012                              7022349283   \n",
+       "2013                              7101027895   \n",
+       "2014                              7178722893   \n",
+       "2015                              7256490011   \n",
+       "\n",
+       "      Population Reference Bureau (1973–2015)[6]  \\\n",
+       "Year                                               \n",
+       "2011                                6.986951e+09   \n",
+       "2012                                7.057075e+09   \n",
+       "2013                                7.136796e+09   \n",
+       "2014                                7.238184e+09   \n",
+       "2015                                7.336435e+09   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[7]  \\\n",
+       "Year                                                                       \n",
+       "2011                                         6997998760                    \n",
+       "2012                                         7080072417                    \n",
+       "2013                                         7162119434                    \n",
+       "2014                                         7243784000                    \n",
+       "2015                                         7349472000                    \n",
+       "\n",
+       "      Maddison (2008)[8]  HYDE (2007)[15]  Tanton (1994)[9]  \\\n",
+       "Year                                                          \n",
+       "2011                 NaN              NaN               NaN   \n",
+       "2012                 NaN              NaN               NaN   \n",
+       "2013                 NaN              NaN               NaN   \n",
+       "2014                 NaN              NaN               NaN   \n",
+       "2015                 NaN              NaN               NaN   \n",
+       "\n",
+       "      Biraben (1980)[10]  McEvedy & Jones (1978)[11]  Thomlinson (1975)[12]  \\\n",
+       "Year                                                                          \n",
+       "2011                 NaN                         NaN                    NaN   \n",
+       "2012                 NaN                         NaN                    NaN   \n",
+       "2013                 NaN                         NaN                    NaN   \n",
+       "2014                 NaN                         NaN                    NaN   \n",
+       "2015                 NaN                         NaN                    NaN   \n",
+       "\n",
+       "     Durand (1974)[13]  Clark (1967)[14]  \n",
+       "Year                                      \n",
+       "2011               NaN               NaN  \n",
+       "2012               NaN               NaN  \n",
+       "2013               NaN               NaN  \n",
+       "2014               NaN               NaN  \n",
+       "2015               NaN               NaN  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table2.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Long column names are awkard to work with, but we can replace them with abbreviated names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the DataFrame looks like now.  \n",
+    "\n",
+    "Some of the values use scientific notation; for example, `2.544000e+09` is shorthand for $2.544 \\cdot 10^9$ or 2.544 billion.\n",
+    "\n",
+    "`NaN` is a special value that indicates missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>census</th>\n",
+       "      <th>prb</th>\n",
+       "      <th>un</th>\n",
+       "      <th>maddison</th>\n",
+       "      <th>hyde</th>\n",
+       "      <th>tanton</th>\n",
+       "      <th>biraben</th>\n",
+       "      <th>mj</th>\n",
+       "      <th>thomlinson</th>\n",
+       "      <th>durand</th>\n",
+       "      <th>clark</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1950</th>\n",
+       "      <td>2557628654</td>\n",
+       "      <td>2.516000e+09</td>\n",
+       "      <td>2525149000</td>\n",
+       "      <td>2.544000e+09</td>\n",
+       "      <td>2.527960e+09</td>\n",
+       "      <td>2.400000e+09</td>\n",
+       "      <td>2.527000e+09</td>\n",
+       "      <td>2.500000e+09</td>\n",
+       "      <td>2.400000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.486000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1951</th>\n",
+       "      <td>2594939877</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2572850917</td>\n",
+       "      <td>2.571663e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1952</th>\n",
+       "      <td>2636772306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2619292068</td>\n",
+       "      <td>2.617949e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1953</th>\n",
+       "      <td>2682053389</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2665865392</td>\n",
+       "      <td>2.665959e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1954</th>\n",
+       "      <td>2730228104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2713172027</td>\n",
+       "      <td>2.716927e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1955</th>\n",
+       "      <td>2782098943</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2761650981</td>\n",
+       "      <td>2.769074e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1956</th>\n",
+       "      <td>2835299673</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2811572031</td>\n",
+       "      <td>2.822502e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957</th>\n",
+       "      <td>2891349717</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2863042795</td>\n",
+       "      <td>2.879934e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1958</th>\n",
+       "      <td>2948137248</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2916030167</td>\n",
+       "      <td>2.939254e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1959</th>\n",
+       "      <td>3000716593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2970395814</td>\n",
+       "      <td>2.995909e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1960</th>\n",
+       "      <td>3043001508</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3026002942</td>\n",
+       "      <td>3.041507e+09</td>\n",
+       "      <td>3.042000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1961</th>\n",
+       "      <td>3083966929</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3082830266</td>\n",
+       "      <td>3.082161e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>3140093217</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3141071531</td>\n",
+       "      <td>3.135787e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.036000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1963</th>\n",
+       "      <td>3209827882</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3201178277</td>\n",
+       "      <td>3.201354e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1964</th>\n",
+       "      <td>3281201306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3263738832</td>\n",
+       "      <td>3.266477e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1965</th>\n",
+       "      <td>3350425793</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3329122479</td>\n",
+       "      <td>3.333138e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1966</th>\n",
+       "      <td>3420677923</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3397475247</td>\n",
+       "      <td>3.402224e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.288000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>3490333715</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3468521724</td>\n",
+       "      <td>3.471464e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1968</th>\n",
+       "      <td>3562313822</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3541674891</td>\n",
+       "      <td>3.543086e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1969</th>\n",
+       "      <td>3637159050</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3616108749</td>\n",
+       "      <td>3.615743e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1970</th>\n",
+       "      <td>3712697742</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3691172616</td>\n",
+       "      <td>3.691157e+09</td>\n",
+       "      <td>3.710000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.637000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.600000e+09</td>\n",
+       "      <td>3,600,000,000– 3,700,000,000</td>\n",
+       "      <td>3.632000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1971</th>\n",
+       "      <td>3790326948</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3766754345</td>\n",
+       "      <td>3.769818e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>3866568653</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3842873611</td>\n",
+       "      <td>3.846499e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1973</th>\n",
+       "      <td>3942096442</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3919182332</td>\n",
+       "      <td>3.922793e+09</td>\n",
+       "      <td>3.923000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.860000e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1974</th>\n",
+       "      <td>4016608813</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3995304922</td>\n",
+       "      <td>3.997677e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1975</th>\n",
+       "      <td>4089083233</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4071020434</td>\n",
+       "      <td>4.070671e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.900000e+09</td>\n",
+       "      <td>4.000000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1976</th>\n",
+       "      <td>4160185010</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4146135850</td>\n",
+       "      <td>4.141445e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>4232084578</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4220816737</td>\n",
+       "      <td>4.213539e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1978</th>\n",
+       "      <td>4304105753</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4295664825</td>\n",
+       "      <td>4.286317e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1979</th>\n",
+       "      <td>4379013942</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4371527871</td>\n",
+       "      <td>4.363144e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1986</th>\n",
+       "      <td>4940571232</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4953376710</td>\n",
+       "      <td>4.920968e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>5027200492</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5045315871</td>\n",
+       "      <td>5.006672e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1988</th>\n",
+       "      <td>5114557167</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5138214688</td>\n",
+       "      <td>5.093306e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1989</th>\n",
+       "      <td>5201440110</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5230000000</td>\n",
+       "      <td>5.180540e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1990</th>\n",
+       "      <td>5288955934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5320816667</td>\n",
+       "      <td>5.269029e+09</td>\n",
+       "      <td>5.308000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1991</th>\n",
+       "      <td>5371585922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5408908724</td>\n",
+       "      <td>5.351922e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>5456136278</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5494899570</td>\n",
+       "      <td>5.435722e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1993</th>\n",
+       "      <td>5538268316</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5578865109</td>\n",
+       "      <td>5.518127e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1994</th>\n",
+       "      <td>5618682132</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5661086346</td>\n",
+       "      <td>5.599396e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1995</th>\n",
+       "      <td>5699202985</td>\n",
+       "      <td>5.760000e+09</td>\n",
+       "      <td>5741822412</td>\n",
+       "      <td>5.681575e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1996</th>\n",
+       "      <td>5779440593</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5821016750</td>\n",
+       "      <td>5.762212e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>5857972543</td>\n",
+       "      <td>5.840000e+09</td>\n",
+       "      <td>5898688337</td>\n",
+       "      <td>5.842122e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1998</th>\n",
+       "      <td>5935213248</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5975303657</td>\n",
+       "      <td>5.921366e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1999</th>\n",
+       "      <td>6012074922</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6051478010</td>\n",
+       "      <td>5.999622e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2000</th>\n",
+       "      <td>6088571383</td>\n",
+       "      <td>6.067000e+09</td>\n",
+       "      <td>6127700428</td>\n",
+       "      <td>6.076558e+09</td>\n",
+       "      <td>6.145000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.750000e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2001</th>\n",
+       "      <td>6165219247</td>\n",
+       "      <td>6.137000e+09</td>\n",
+       "      <td>6204147026</td>\n",
+       "      <td>6.154791e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>6242016348</td>\n",
+       "      <td>6.215000e+09</td>\n",
+       "      <td>6280853817</td>\n",
+       "      <td>6.231704e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2003</th>\n",
+       "      <td>6318590956</td>\n",
+       "      <td>6.314000e+09</td>\n",
+       "      <td>6357991749</td>\n",
+       "      <td>6.308364e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2004</th>\n",
+       "      <td>6395699509</td>\n",
+       "      <td>6.396000e+09</td>\n",
+       "      <td>6435705595</td>\n",
+       "      <td>6.374056e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2005</th>\n",
+       "      <td>6473044732</td>\n",
+       "      <td>6.477000e+09</td>\n",
+       "      <td>6514094605</td>\n",
+       "      <td>6.462987e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2006</th>\n",
+       "      <td>6551263534</td>\n",
+       "      <td>6.555000e+09</td>\n",
+       "      <td>6593227977</td>\n",
+       "      <td>6.540214e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>6629913759</td>\n",
+       "      <td>6.625000e+09</td>\n",
+       "      <td>6673105937</td>\n",
+       "      <td>6.616689e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2008</th>\n",
+       "      <td>6709049780</td>\n",
+       "      <td>6.705000e+09</td>\n",
+       "      <td>6753649228</td>\n",
+       "      <td>6.694832e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009</th>\n",
+       "      <td>6788214394</td>\n",
+       "      <td>6.809972e+09</td>\n",
+       "      <td>6834721933</td>\n",
+       "      <td>6.764086e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010</th>\n",
+       "      <td>6866332358</td>\n",
+       "      <td>6.892319e+09</td>\n",
+       "      <td>6916183482</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2011</th>\n",
+       "      <td>6944055583</td>\n",
+       "      <td>6.986951e+09</td>\n",
+       "      <td>6997998760</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012</th>\n",
+       "      <td>7022349283</td>\n",
+       "      <td>7.057075e+09</td>\n",
+       "      <td>7080072417</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>7101027895</td>\n",
+       "      <td>7.136796e+09</td>\n",
+       "      <td>7162119434</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>7178722893</td>\n",
+       "      <td>7.238184e+09</td>\n",
+       "      <td>7243784000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015</th>\n",
+       "      <td>7256490011</td>\n",
+       "      <td>7.336435e+09</td>\n",
+       "      <td>7349472000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>66 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          census           prb          un      maddison          hyde  \\\n",
+       "Year                                                                     \n",
+       "1950  2557628654  2.516000e+09  2525149000  2.544000e+09  2.527960e+09   \n",
+       "1951  2594939877           NaN  2572850917  2.571663e+09           NaN   \n",
+       "1952  2636772306           NaN  2619292068  2.617949e+09           NaN   \n",
+       "1953  2682053389           NaN  2665865392  2.665959e+09           NaN   \n",
+       "1954  2730228104           NaN  2713172027  2.716927e+09           NaN   \n",
+       "1955  2782098943           NaN  2761650981  2.769074e+09           NaN   \n",
+       "1956  2835299673           NaN  2811572031  2.822502e+09           NaN   \n",
+       "1957  2891349717           NaN  2863042795  2.879934e+09           NaN   \n",
+       "1958  2948137248           NaN  2916030167  2.939254e+09           NaN   \n",
+       "1959  3000716593           NaN  2970395814  2.995909e+09           NaN   \n",
+       "1960  3043001508           NaN  3026002942  3.041507e+09  3.042000e+09   \n",
+       "1961  3083966929           NaN  3082830266  3.082161e+09           NaN   \n",
+       "1962  3140093217           NaN  3141071531  3.135787e+09           NaN   \n",
+       "1963  3209827882           NaN  3201178277  3.201354e+09           NaN   \n",
+       "1964  3281201306           NaN  3263738832  3.266477e+09           NaN   \n",
+       "1965  3350425793           NaN  3329122479  3.333138e+09           NaN   \n",
+       "1966  3420677923           NaN  3397475247  3.402224e+09           NaN   \n",
+       "1967  3490333715           NaN  3468521724  3.471464e+09           NaN   \n",
+       "1968  3562313822           NaN  3541674891  3.543086e+09           NaN   \n",
+       "1969  3637159050           NaN  3616108749  3.615743e+09           NaN   \n",
+       "1970  3712697742           NaN  3691172616  3.691157e+09  3.710000e+09   \n",
+       "1971  3790326948           NaN  3766754345  3.769818e+09           NaN   \n",
+       "1972  3866568653           NaN  3842873611  3.846499e+09           NaN   \n",
+       "1973  3942096442           NaN  3919182332  3.922793e+09  3.923000e+09   \n",
+       "1974  4016608813           NaN  3995304922  3.997677e+09           NaN   \n",
+       "1975  4089083233           NaN  4071020434  4.070671e+09           NaN   \n",
+       "1976  4160185010           NaN  4146135850  4.141445e+09           NaN   \n",
+       "1977  4232084578           NaN  4220816737  4.213539e+09           NaN   \n",
+       "1978  4304105753           NaN  4295664825  4.286317e+09           NaN   \n",
+       "1979  4379013942           NaN  4371527871  4.363144e+09           NaN   \n",
+       "...          ...           ...         ...           ...           ...   \n",
+       "1986  4940571232           NaN  4953376710  4.920968e+09           NaN   \n",
+       "1987  5027200492           NaN  5045315871  5.006672e+09           NaN   \n",
+       "1988  5114557167           NaN  5138214688  5.093306e+09           NaN   \n",
+       "1989  5201440110           NaN  5230000000  5.180540e+09           NaN   \n",
+       "1990  5288955934           NaN  5320816667  5.269029e+09  5.308000e+09   \n",
+       "1991  5371585922           NaN  5408908724  5.351922e+09           NaN   \n",
+       "1992  5456136278           NaN  5494899570  5.435722e+09           NaN   \n",
+       "1993  5538268316           NaN  5578865109  5.518127e+09           NaN   \n",
+       "1994  5618682132           NaN  5661086346  5.599396e+09           NaN   \n",
+       "1995  5699202985  5.760000e+09  5741822412  5.681575e+09           NaN   \n",
+       "1996  5779440593           NaN  5821016750  5.762212e+09           NaN   \n",
+       "1997  5857972543  5.840000e+09  5898688337  5.842122e+09           NaN   \n",
+       "1998  5935213248           NaN  5975303657  5.921366e+09           NaN   \n",
+       "1999  6012074922           NaN  6051478010  5.999622e+09           NaN   \n",
+       "2000  6088571383  6.067000e+09  6127700428  6.076558e+09  6.145000e+09   \n",
+       "2001  6165219247  6.137000e+09  6204147026  6.154791e+09           NaN   \n",
+       "2002  6242016348  6.215000e+09  6280853817  6.231704e+09           NaN   \n",
+       "2003  6318590956  6.314000e+09  6357991749  6.308364e+09           NaN   \n",
+       "2004  6395699509  6.396000e+09  6435705595  6.374056e+09           NaN   \n",
+       "2005  6473044732  6.477000e+09  6514094605  6.462987e+09           NaN   \n",
+       "2006  6551263534  6.555000e+09  6593227977  6.540214e+09           NaN   \n",
+       "2007  6629913759  6.625000e+09  6673105937  6.616689e+09           NaN   \n",
+       "2008  6709049780  6.705000e+09  6753649228  6.694832e+09           NaN   \n",
+       "2009  6788214394  6.809972e+09  6834721933  6.764086e+09           NaN   \n",
+       "2010  6866332358  6.892319e+09  6916183482           NaN           NaN   \n",
+       "2011  6944055583  6.986951e+09  6997998760           NaN           NaN   \n",
+       "2012  7022349283  7.057075e+09  7080072417           NaN           NaN   \n",
+       "2013  7101027895  7.136796e+09  7162119434           NaN           NaN   \n",
+       "2014  7178722893  7.238184e+09  7243784000           NaN           NaN   \n",
+       "2015  7256490011  7.336435e+09  7349472000           NaN           NaN   \n",
+       "\n",
+       "            tanton       biraben            mj    thomlinson  \\\n",
+       "Year                                                           \n",
+       "1950  2.400000e+09  2.527000e+09  2.500000e+09  2.400000e+09   \n",
+       "1951           NaN           NaN           NaN           NaN   \n",
+       "1952           NaN           NaN           NaN           NaN   \n",
+       "1953           NaN           NaN           NaN           NaN   \n",
+       "1954           NaN           NaN           NaN           NaN   \n",
+       "1955           NaN           NaN           NaN           NaN   \n",
+       "1956           NaN           NaN           NaN           NaN   \n",
+       "1957           NaN           NaN           NaN           NaN   \n",
+       "1958           NaN           NaN           NaN           NaN   \n",
+       "1959           NaN           NaN           NaN           NaN   \n",
+       "1960           NaN           NaN           NaN           NaN   \n",
+       "1961           NaN           NaN           NaN           NaN   \n",
+       "1962           NaN           NaN           NaN           NaN   \n",
+       "1963           NaN           NaN           NaN           NaN   \n",
+       "1964           NaN           NaN           NaN           NaN   \n",
+       "1965           NaN           NaN           NaN           NaN   \n",
+       "1966           NaN           NaN           NaN           NaN   \n",
+       "1967           NaN           NaN           NaN           NaN   \n",
+       "1968           NaN           NaN           NaN           NaN   \n",
+       "1969           NaN           NaN           NaN           NaN   \n",
+       "1970           NaN  3.637000e+09           NaN  3.600000e+09   \n",
+       "1971           NaN           NaN           NaN           NaN   \n",
+       "1972           NaN           NaN           NaN           NaN   \n",
+       "1973           NaN           NaN           NaN           NaN   \n",
+       "1974           NaN           NaN           NaN           NaN   \n",
+       "1975           NaN           NaN  3.900000e+09  4.000000e+09   \n",
+       "1976           NaN           NaN           NaN           NaN   \n",
+       "1977           NaN           NaN           NaN           NaN   \n",
+       "1978           NaN           NaN           NaN           NaN   \n",
+       "1979           NaN           NaN           NaN           NaN   \n",
+       "...            ...           ...           ...           ...   \n",
+       "1986           NaN           NaN           NaN           NaN   \n",
+       "1987           NaN           NaN           NaN           NaN   \n",
+       "1988           NaN           NaN           NaN           NaN   \n",
+       "1989           NaN           NaN           NaN           NaN   \n",
+       "1990           NaN           NaN           NaN           NaN   \n",
+       "1991           NaN           NaN           NaN           NaN   \n",
+       "1992           NaN           NaN           NaN           NaN   \n",
+       "1993           NaN           NaN           NaN           NaN   \n",
+       "1994           NaN           NaN           NaN           NaN   \n",
+       "1995           NaN           NaN           NaN           NaN   \n",
+       "1996           NaN           NaN           NaN           NaN   \n",
+       "1997           NaN           NaN           NaN           NaN   \n",
+       "1998           NaN           NaN           NaN           NaN   \n",
+       "1999           NaN           NaN           NaN           NaN   \n",
+       "2000           NaN           NaN  5.750000e+09           NaN   \n",
+       "2001           NaN           NaN           NaN           NaN   \n",
+       "2002           NaN           NaN           NaN           NaN   \n",
+       "2003           NaN           NaN           NaN           NaN   \n",
+       "2004           NaN           NaN           NaN           NaN   \n",
+       "2005           NaN           NaN           NaN           NaN   \n",
+       "2006           NaN           NaN           NaN           NaN   \n",
+       "2007           NaN           NaN           NaN           NaN   \n",
+       "2008           NaN           NaN           NaN           NaN   \n",
+       "2009           NaN           NaN           NaN           NaN   \n",
+       "2010           NaN           NaN           NaN           NaN   \n",
+       "2011           NaN           NaN           NaN           NaN   \n",
+       "2012           NaN           NaN           NaN           NaN   \n",
+       "2013           NaN           NaN           NaN           NaN   \n",
+       "2014           NaN           NaN           NaN           NaN   \n",
+       "2015           NaN           NaN           NaN           NaN   \n",
+       "\n",
+       "                            durand         clark  \n",
+       "Year                                              \n",
+       "1950                           NaN  2.486000e+09  \n",
+       "1951                           NaN           NaN  \n",
+       "1952                           NaN           NaN  \n",
+       "1953                           NaN           NaN  \n",
+       "1954                           NaN           NaN  \n",
+       "1955                           NaN           NaN  \n",
+       "1956                           NaN           NaN  \n",
+       "1957                           NaN           NaN  \n",
+       "1958                           NaN           NaN  \n",
+       "1959                           NaN           NaN  \n",
+       "1960                           NaN           NaN  \n",
+       "1961                           NaN           NaN  \n",
+       "1962                           NaN  3.036000e+09  \n",
+       "1963                           NaN           NaN  \n",
+       "1964                           NaN           NaN  \n",
+       "1965                           NaN           NaN  \n",
+       "1966                           NaN  3.288000e+09  \n",
+       "1967                           NaN           NaN  \n",
+       "1968                           NaN           NaN  \n",
+       "1969                           NaN           NaN  \n",
+       "1970  3,600,000,000– 3,700,000,000  3.632000e+09  \n",
+       "1971                           NaN           NaN  \n",
+       "1972                           NaN           NaN  \n",
+       "1973                           NaN  3.860000e+09  \n",
+       "1974                           NaN           NaN  \n",
+       "1975                           NaN           NaN  \n",
+       "1976                           NaN           NaN  \n",
+       "1977                           NaN           NaN  \n",
+       "1978                           NaN           NaN  \n",
+       "1979                           NaN           NaN  \n",
+       "...                            ...           ...  \n",
+       "1986                           NaN           NaN  \n",
+       "1987                           NaN           NaN  \n",
+       "1988                           NaN           NaN  \n",
+       "1989                           NaN           NaN  \n",
+       "1990                           NaN           NaN  \n",
+       "1991                           NaN           NaN  \n",
+       "1992                           NaN           NaN  \n",
+       "1993                           NaN           NaN  \n",
+       "1994                           NaN           NaN  \n",
+       "1995                           NaN           NaN  \n",
+       "1996                           NaN           NaN  \n",
+       "1997                           NaN           NaN  \n",
+       "1998                           NaN           NaN  \n",
+       "1999                           NaN           NaN  \n",
+       "2000                           NaN           NaN  \n",
+       "2001                           NaN           NaN  \n",
+       "2002                           NaN           NaN  \n",
+       "2003                           NaN           NaN  \n",
+       "2004                           NaN           NaN  \n",
+       "2005                           NaN           NaN  \n",
+       "2006                           NaN           NaN  \n",
+       "2007                           NaN           NaN  \n",
+       "2008                           NaN           NaN  \n",
+       "2009                           NaN           NaN  \n",
+       "2010                           NaN           NaN  \n",
+       "2011                           NaN           NaN  \n",
+       "2012                           NaN           NaN  \n",
+       "2013                           NaN           NaN  \n",
+       "2014                           NaN           NaN  \n",
+       "2015                           NaN           NaN  \n",
+       "\n",
+       "[66 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use dot notatio to select a column from a DataFrame.  The result is a Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2557628654\n",
+       "1951    2594939877\n",
+       "1952    2636772306\n",
+       "1953    2682053389\n",
+       "1954    2730228104\n",
+       "1955    2782098943\n",
+       "1956    2835299673\n",
+       "1957    2891349717\n",
+       "1958    2948137248\n",
+       "1959    3000716593\n",
+       "1960    3043001508\n",
+       "1961    3083966929\n",
+       "1962    3140093217\n",
+       "1963    3209827882\n",
+       "1964    3281201306\n",
+       "1965    3350425793\n",
+       "1966    3420677923\n",
+       "1967    3490333715\n",
+       "1968    3562313822\n",
+       "1969    3637159050\n",
+       "1970    3712697742\n",
+       "1971    3790326948\n",
+       "1972    3866568653\n",
+       "1973    3942096442\n",
+       "1974    4016608813\n",
+       "1975    4089083233\n",
+       "1976    4160185010\n",
+       "1977    4232084578\n",
+       "1978    4304105753\n",
+       "1979    4379013942\n",
+       "           ...    \n",
+       "1986    4940571232\n",
+       "1987    5027200492\n",
+       "1988    5114557167\n",
+       "1989    5201440110\n",
+       "1990    5288955934\n",
+       "1991    5371585922\n",
+       "1992    5456136278\n",
+       "1993    5538268316\n",
+       "1994    5618682132\n",
+       "1995    5699202985\n",
+       "1996    5779440593\n",
+       "1997    5857972543\n",
+       "1998    5935213248\n",
+       "1999    6012074922\n",
+       "2000    6088571383\n",
+       "2001    6165219247\n",
+       "2002    6242016348\n",
+       "2003    6318590956\n",
+       "2004    6395699509\n",
+       "2005    6473044732\n",
+       "2006    6551263534\n",
+       "2007    6629913759\n",
+       "2008    6709049780\n",
+       "2009    6788214394\n",
+       "2010    6866332358\n",
+       "2011    6944055583\n",
+       "2012    7022349283\n",
+       "2013    7101027895\n",
+       "2014    7178722893\n",
+       "2015    7256490011\n",
+       "Name: census, Length: 66, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census = table2.census\n",
+    "census"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Series object has two parts, `values` and `index`.\n",
+    "\n",
+    "The `values` part is an array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2557628654, 2594939877, 2636772306, 2682053389, 2730228104,\n",
+       "       2782098943, 2835299673, 2891349717, 2948137248, 3000716593,\n",
+       "       3043001508, 3083966929, 3140093217, 3209827882, 3281201306,\n",
+       "       3350425793, 3420677923, 3490333715, 3562313822, 3637159050,\n",
+       "       3712697742, 3790326948, 3866568653, 3942096442, 4016608813,\n",
+       "       4089083233, 4160185010, 4232084578, 4304105753, 4379013942,\n",
+       "       4451362735, 4534410125, 4614566561, 4695736743, 4774569391,\n",
+       "       4856462699, 4940571232, 5027200492, 5114557167, 5201440110,\n",
+       "       5288955934, 5371585922, 5456136278, 5538268316, 5618682132,\n",
+       "       5699202985, 5779440593, 5857972543, 5935213248, 6012074922,\n",
+       "       6088571383, 6165219247, 6242016348, 6318590956, 6395699509,\n",
+       "       6473044732, 6551263534, 6629913759, 6709049780, 6788214394,\n",
+       "       6866332358, 6944055583, 7022349283, 7101027895, 7178722893,\n",
+       "       7256490011], dtype=int64)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `index` part is yet another kind of object, an `Int64Index`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960,\n",
+       "            1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n",
+       "            1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982,\n",
+       "            1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993,\n",
+       "            1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
+       "            2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015],\n",
+       "           dtype='int64', name='Year')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you ever wonder what kind of object a variable refers to, you can use the `type` function.\n",
+    "\n",
+    "The result indicates what type the object is, and the module where that type is defined.\n",
+    "\n",
+    "DataFrame, Series, and Int64Index are defined by Pandas.\n",
+    "\n",
+    "array is defined by NumPy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(table2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(census)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.indexes.numeric.Int64Index"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(census.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(census.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function plots the estimates generated by the US Censis and UN DESA, and labels the axes.\n",
+    "\n",
+    "`1e9` is scientific notation for $1 \\cdot 10^9$ or 1 billion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_estimates(table):\n",
+    "    \"\"\"Plot world population estimates.\n",
+    "    \n",
+    "    table: DataFrame with columns 'un' and 'census'\n",
+    "    \"\"\"\n",
+    "    un = table.un / 1e9\n",
+    "    census = table.census / 1e9\n",
+    "    \n",
+    "    plot(census, ':', color='darkblue', label='US Census')\n",
+    "    plot(un, '--', color='green', label='UN DESA')\n",
+    "    \n",
+    "    decorate(xlabel='Year',\n",
+    "             ylabel='World population (billion)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can plot the estimates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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sdF2WKMFT+/k7Ojoya9YsZs2axfXr14mJiSE9PR0bGxucnZ1p2LBhRdQphKhE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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22ea2023240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_estimates(table2)\n",
+    "savefig('chap03-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here on, we will work in units of billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "un = table2.un / 1e9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This expression computes the elementwise differences between the series, then divides through by the UN value to produce relative errors, then finds the largest element.\n",
+    "\n",
+    "So the largest relative error between the estimates is about 1.3%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2862470293832287"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(abs(census - un) / un) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Break down that expression into smaller steps and display the intermediate results, to make sure you understand how it works.\n",
+    "\n",
+    "Where in the series is the largest relative error between the two estimates, near the beginning or the end?\n",
+    "\n",
+    "When I computed relative errors, I used `un` as the denominator.  But that was an arbitraty choice.  What happens if we use `census` instead?  How much difference does it make."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    0.032480\n",
+       "1951    0.022089\n",
+       "1952    0.017480\n",
+       "1953    0.016188\n",
+       "1954    0.017056\n",
+       "1955    0.020448\n",
+       "1956    0.023728\n",
+       "1957    0.028307\n",
+       "1958    0.032107\n",
+       "1959    0.030321\n",
+       "1960    0.016999\n",
+       "1961    0.001137\n",
+       "1962    0.000978\n",
+       "1963    0.008650\n",
+       "1964    0.017462\n",
+       "1965    0.021303\n",
+       "1966    0.023203\n",
+       "1967    0.021812\n",
+       "1968    0.020639\n",
+       "1969    0.021050\n",
+       "1970    0.021525\n",
+       "1971    0.023573\n",
+       "1972    0.023695\n",
+       "1973    0.022914\n",
+       "1974    0.021304\n",
+       "1975    0.018063\n",
+       "1976    0.014049\n",
+       "1977    0.011268\n",
+       "1978    0.008441\n",
+       "1979    0.007486\n",
+       "          ...   \n",
+       "1986    0.012805\n",
+       "1987    0.018115\n",
+       "1988    0.023658\n",
+       "1989    0.028560\n",
+       "1990    0.031861\n",
+       "1991    0.037323\n",
+       "1992    0.038763\n",
+       "1993    0.040597\n",
+       "1994    0.042404\n",
+       "1995    0.042619\n",
+       "1996    0.041576\n",
+       "1997    0.040716\n",
+       "1998    0.040090\n",
+       "1999    0.039403\n",
+       "2000    0.039129\n",
+       "2001    0.038928\n",
+       "2002    0.038837\n",
+       "2003    0.039401\n",
+       "2004    0.040006\n",
+       "2005    0.041050\n",
+       "2006    0.041964\n",
+       "2007    0.043192\n",
+       "2008    0.044599\n",
+       "2009    0.046508\n",
+       "2010    0.049851\n",
+       "2011    0.053943\n",
+       "2012    0.057723\n",
+       "2013    0.061092\n",
+       "2014    0.065061\n",
+       "2015    0.092982\n",
+       "Length: 66, dtype: float64"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(census - un)\n",
+    "#this represents the annual difference (positive b/c the absolute value) between census and un."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    1.286247\n",
+       "1951    0.858540\n",
+       "1952    0.667365\n",
+       "1953    0.607232\n",
+       "1954    0.628640\n",
+       "1955    0.740425\n",
+       "1956    0.843928\n",
+       "1957    0.988701\n",
+       "1958    1.101054\n",
+       "1959    1.020766\n",
+       "1960    0.561750\n",
+       "1961    0.036871\n",
+       "1962    0.031146\n",
+       "1963    0.270201\n",
+       "1964    0.535045\n",
+       "1965    0.639908\n",
+       "1966    0.682939\n",
+       "1967    0.628856\n",
+       "1968    0.582745\n",
+       "1969    0.582126\n",
+       "1970    0.583151\n",
+       "1971    0.625807\n",
+       "1972    0.616597\n",
+       "1973    0.584666\n",
+       "1974    0.533223\n",
+       "1975    0.443692\n",
+       "1976    0.338849\n",
+       "1977    0.266959\n",
+       "1978    0.196499\n",
+       "1979    0.171246\n",
+       "          ...   \n",
+       "1986    0.258520\n",
+       "1987    0.359053\n",
+       "1988    0.460423\n",
+       "1989    0.546078\n",
+       "1990    0.598794\n",
+       "1991    0.690025\n",
+       "1992    0.705441\n",
+       "1993    0.727689\n",
+       "1994    0.749047\n",
+       "1995    0.742263\n",
+       "1996    0.714242\n",
+       "1997    0.690252\n",
+       "1998    0.670935\n",
+       "1999    0.651132\n",
+       "2000    0.638560\n",
+       "2001    0.627448\n",
+       "2002    0.618347\n",
+       "2003    0.619705\n",
+       "2004    0.621627\n",
+       "2005    0.630170\n",
+       "2006    0.636478\n",
+       "2007    0.647257\n",
+       "2008    0.660376\n",
+       "2009    0.680460\n",
+       "2010    0.720789\n",
+       "2011    0.770837\n",
+       "2012    0.815290\n",
+       "2013    0.852981\n",
+       "2014    0.898165\n",
+       "2015    1.265152\n",
+       "Length: 66, dtype: float64"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(census - un)/un *100\n",
+    "#This represents the annual margin of error in the data. \n",
+    "#Note that un being the denominator suggests that we assume it is the more accurate estimate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2862470293832287"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(abs(census - un)/un) *100\n",
+    "#This represents the highest percent error found for any one year in the table above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.2813631502151765"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(abs(un - census)/census) *100\n",
+    "#This makes a small difference in percent error, in this case negligeable. \n",
+    "#But we should always consider which is the denominator for the sake of accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Constant growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can select an element from a series using bracket notation and one of the elements from the index.  Here's the first element:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5576286540000002"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the last element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.2564900110000004"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census[2015]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we can get the first and last years from the index itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1950, 2015)"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_year = census.index[0]\n",
+    "last_year = census.index[-1]\n",
+    "first_year, last_year"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And use them to look up the first and last elements.\n",
+    "\n",
+    "Then we can compute the average annual growth in billions of people per year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07229017472307693"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_growth = census[last_year] - census[first_year]\n",
+    "elapsed_time = last_year - first_year\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's create a `TimeSeries` to contain values generated by a linear growth model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "results = TimeSeries()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initially the Series is empty, but we can initialize it so the starting value, in 1950, is the 1950 population estimated by the US Census."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1950</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "1950    2.557629\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results[1950] = census[1950]\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After that, the population in the model grows by a constant amount each year."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for t in linrange(1950, 2015):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results looks like, compared to the actual data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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X17O2NQylc3R0RKfTcWaVtoZC1mAusA7w9ttvExYWZnXc6b8MFMUWer2eoqIi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\nBM33y9YAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bb93208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_estimates(table2)\n",
+    "plot(results, '--', color='gray', label='model')\n",
+    "decorate(xlabel='Year', ylabel='World population (billion)')\n",
+    "savefig('chap03-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model fits the data pretty well after 1990, but not so well before."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Try fitting the model using data from 1965 to the present, and see if that does a better job.\n",
+    "\n",
+    "Hint: Copy the code from above and make a few changes.\n",
+    "\n",
+    "Make sure your model starts in 1950, even though the estimated annual growth is based on later data.  You might have to shift the first value in the series up or down to match the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8ABjcfTDdFoVgMOjx8TG2c9RdQ1ODvL6u86sOGAwcF0L8iLXSxwcYCzgC9i7k\n8htQCowEDtRcp7btb5sb/IUIIS6qKyYqKuq8rqDWZDDU/8+h0+kaHbFoNJ7/P0jtqmwGgwGdTse5\nq7Q5OjraXjs5WWujX3rpJYKDg+sdV/fDQFF+j9qa/bS0NCy9LXye+DlF5SWkphaxN+sLFgT3YcoU\nr3rndOum7vbbUlN3/k7UH9i1r+anI1B7O3q45qdd86VKKcuEEP8EVgkhcrB+A7gb6If14bFip379\n+nHo0KF62w4ePGjbV1hYaFtEvfbuKjY21nZscHAwjo6O5OTkMGHCBNv2f//735jNZu6///42eBfK\npaigoIDo6GgycjNIOpNE3ok8KnwqKCszkZVVSrfqMBJjyomLyyM8XH3LbC9NDfKa1ErXXAaUAS9g\nLRc9DEyTUspWut4l6c477+T6669nzZo1zJkzh4yMDJ566ikmTpxIv379CAgIYN26dfz1r39lyZIl\n5OTk8K9//ct2vouLC4sWLWLt2rW4ubkRGRnJ7t27WbduHatWrWrHd6Z0VrU1+8kpyaTkp5BVkgWA\nQTOABmE9exNVdgNpB4xERvoTEKDu9NtTU90+Y6WUPza3QSHEeCnl3sb2Sylrp39WU0BfhAEDBvDq\nq6/ywgsv8O677+Lt7c2MGTN44IEHAHB3d+ftt99mxYoVzJkzh+7du3PnnXfaHvgCPPDAAzg6OvLc\nc8+Rl5dH7969WbFiBTfccEN7vS2lE6pbs5+en87xM8epNFdh1iyYfKqw+Fi4bsB1TOs3jfKRFo5f\nXkBUVDc1CVs7053bL1xLCHEEOAaslFLGNnhQ/eNHYn34GyalbNGOciFECJBi77QEiqK0jZKSEmJi\nYsjLyyO/Ip/YU7FUVppJLywg26GQm6ZNYH7UfPxdVfdOW0tPT2fq1KkAoQ1NndNUn/8IYDlwoGZW\nz0+A/UAK1oe23lj7/scB12AdvfsSML/lwlcUpaNKSkpCSmmr2fcx+uDm7MGP6TGUVkO/islEVcxU\nib+DaqrPvxrr9A0vAw8Cd2Ltr6/7VUEHnAA+Bq6VUmac15CiKJcmPbbEX1uzP2z8MMo+30rej33x\ncnPDxcXxAo0o7eWCdf41Cf1h4GEhxECgL9aJ3fKANCmlfUNoFUW5JFSYKth2bBtJZ5IY5zEeR4OB\nyMhIW1XZqvn38rl7ElddFYKHh5puuaNq1vR4Usp4rFMyKIrSRdTW7Pv6+pJSnsL7Me9zpjyf9JPF\nJOS48Pq6n5LJAAAgAElEQVTj92M0nr3DNxj03HDDgCZaVDoCNTeqoiiNqq3Zzz2TS2plKgkuCWg6\njZiYPAoLKympPsn27cnMmTOwvUNVmkklf0VRzlNbs5+WlkZ2STbH849jsphw9HOk2q2avr26U5EV\ngX91GMePF2I2W9Ssm52MSv6KothomkZGRgZHjx6lsLSQpDNJ5Ffkgw4qvCqodq1mbJ+x3DDtBt4p\nS2TgQD8mTeqNXq9q9jsblfwVRQHq1+xnFmeSUpBCtclMdmkRzn01evp34+4hdzPQ39rF8+c/D1UD\ntToxlfwVpYszm80kJiaSnJxsK92sMldRXFFBdF4mp02ljMuexLLZ9+FscLadpxJ/52bvMo5G4DGs\na/i6cf4KYJqU8uJWMlEUpV2UlpaSlJRkmwVWp9Mxadgk5NF0yjOMDC2/Ft3RHuRmVxEU5HyB1pTO\nwt47/xexrrj1PRALWJo8WlGUTqNUV0r3Xt3JSc/Bx8eHqKgoPD09earvX/ms7CRpKaUsXDiYoCCP\n9g5VaUH2Jv8bgcellGtaMxhFUVqXpmkUFxfj6emJRbPwTfI3fC4/p5vWh+uC/sjQoQNs3Tl+rn7M\nv9kLg0GPwaAqeS419iZ/J6zz+iiK0knl5+cTExNDaWkp4SPDeT/+fRJPJ5GWWsTujFRyvP1YN2QA\ndbvyjUb1WPBSZe9/2a+xTt62uxVjURSlFVRXV3Ps2DFOnDhhnX65OJNdO3ZR7FtMRYWZzMwS3M3d\nKT7pxu7dJ5g6NfjCjSqdnr3J/z3gdSGEP/AT1sVY6qldkF1RlI6hbs1+ZWUllaZK5GlJYVUhVZ5V\noIGHmzNzom4gY08gEYO7M3Ro9/YOW2kj9ib/T2p+Lqr5cy4NUMlfUTqIujX7ADmlOSSfSabcqZzK\nnpVYDBaCPINYNHQRge69ODIol2HDuqvyzS7E3uQf2qpRKIrSIs6t2dfQOJZ7jFMVp0gszyU9p5AR\n3XswI2w6MwbMwKC3poDhwwPaOXKlrdmV/KWUabWvhRBugAdwumbOf0VROoj9+/fb7vYB9Do9Lt1c\n2PfbcYpKq3CxeBNZOIfrBl7ZjlEqHYHdj/KFEJOANcBlWBdxQQixH3hSSrmrVaJTFKVZQkNDbcm/\ntmZ/snEyv55JIPOQB6HlY9EX+WGxaGo+ni7O3hG+E7BW/MRjXc0rBwgE5gI7hRBTm1q0XVGUlqdp\n2nl99CZXEz179aSbXzf69Olj2//qvDV8aD5ORIQ/I0f2UH37it13/k8D3wIzpJS2ZRyFECuBHVjX\n+p3a4tEpitKg/Px8oqOjGThwIAEBAWiaxjfHv+Hj2K045/TnmXn31Uvwbk5u3H57ZDtGrHQ09ib/\nEcDcuokfQEqpCSHWAe+3eGSKopzn3Jr92NhYDO4G3o15l/8mH0bGn6Gi8iReb/Xm6Xvnqjt8pVH2\nJv98wL2RfR6AuWXCURSlIefW7NfKzM9k5zc7KXYoxmLWqKg042HuzomjOhIT8xkwwLcdo1Y6MnuT\n/3fAciHEXillZu1GIUQg1i6fb1shNkVRgOLiYmJiYjh9+rRtm0WzkKllcsThCJqD9Qu5r68L1/S/\nGkv8IBb+KUIlfqVJ9ib/x4ADQKIQYh+QDfQAxgFFwNLWCU9Ruq6G5tkHMDuYOWQ+zInqdJycHADw\nNnrzp+F/Iti9L+XlJry9je0VttJJ2FvnnyGEGAYsAcZjHfSVD6wD/iGlzG69EBWl6ykoKODgwYOU\nlZ2dSUWn01HhUcGO7G+IS8hBp9MxZEg3hvUYxq1DbsXNyQ0AZ2c1GZtyYXb/K6lJ8I+0YiyKotQw\nGo1UVVXZfvf19SU8Ipx///YKBw5nYDJr6HEgrHwq/ztCPdhVmq/R5C+EeBx4U0qZVfO6KZqUcnXL\nhqYoXZfRaEQIQWJiIoMGDaJ3797odDr+PGoxB+ITSE8yE1E1neF+o1XiV36Xpu78V2J9kJtV87op\nGmBX8hdCDAbiGtg1Xkq5z542FOVSkp+fT0FBAaGh9afQCg4JplevXjg7n1060c/Vj3/e9H/s3l7A\n1Vf1JzCwsSI8RWlao8lfSqlv6HULiATyan7WdbqBYxXlklVVVUV8fDxpaWnodDr8/Pzw9PQEoKCi\ngJd/fo2iZB9W3Hq77cEuQKhvCKG3tVfUyqXC3ukdlgEb6pZ51tkXDCyRUt5n5zUjgKPqIbHSVWma\nRnp6OkePHrX162uaRlxcHFdccQVHso/w/LevEH00A5NJo+cHIdx/61XtHLVyqbH3jv7/gF6N7LsC\nWNyMa0YAx5pxvKJcMoqLi/n55585fPhwvQe6AQEBDI4YzPsx7/Pyry9TXFFCtckCaHx74BA5OaXt\nF7RySWrqge8+rIkdrLN4/lcI0djhvzbjmhGAUQjxXyAEiMW6OLxaI1i5ZJnNZhISEkhOTkbTzs6S\n4uLiQkREBBY3Cy8cfIHMYuuX64AAV8rPOBKSfyUP/vlqAgLc2it05RLVVLfPHcBsrIl/BfAakH7O\nMWagAPjUnosJIVyAvkAu1rLRSuBe4AchxHAppfpGoFxycnJyiI2NPa9mv2/fvoSFhbE7ZQ+f/PIx\n6M8O5Breczgrxt6Ep9EdV1fH9ghbucQ19cA3HlgFIIRwwNrnn3ExF5NSlgshfIBKKWVlTduLsK4R\ncDfwl4tpX1E6Gk3TSEhIqJf4fX19iYyMBGdY/uXf2XHgZ9zdHRk82A8nByfmhs9lfJ/xqoRTaVX2\njvB9CkAI4Qc4UbOYC9ZnBm5YyzQ32NlW0Tm/W4QQcUBve4NWlM5Cp9MRFRXF3r17cXR0rFezv/w/\nq9n6o7W6ubLSjK7Yi8dnPkigR2A7R610BfZW+0QCm4DwRg7RgAsmfyHEZcBuYLKU8mDNNgdgKLDF\nnlgUpSMrKCjAy8ur3l27l5cXw4YNo1u3bjg5Odm2/2n0QvYdPUJGVjH9GMGfxJ0q8Sttxt7pHZ4H\n/ICHgWux9tVvB6YD1wCT7GznCJAKrBdC3AOUYJ0Uzh940d6gFaWjqaqqss2zHxkZSUhISL39vXqd\nXyzX26s3j177vxz6sYS7brgKT0/n845RlNZib6nnFcDfpJT/BD4E3KSUr0gpZ2J92GtXjb+U0oT1\nw0Ji/fDYj3V20AlSylPNDV5R2pumaZw8eZLdu3dz4sQJAI4dO1Zvzv1qczXPb9/Ig//YiMVSbz0k\nrgybxCOLrlWJX2lz9t75OwOJNa8TgCF19r0JvGrvBWseGi+w93hF6agammcfwN/f31bOebLwJPdu\neJaEnFQcNEc+2TmUOTOGt0e4ilKPvXf+J7BO4wzW5O9ZM7IXoAJQq0YoXYbJZOLYsWP88MMP9RK/\ni4sLI0eOZOTIkTg5O7EzcSer962m2jUfALOumg9/+RKz2dJY04rSZuy9898GPCuEKJZSbhNCxANP\nCyFWAw8Cya0WoaJ0II3V7Pfr14+wsDAMBgNZxVm8dfgtUgtSAQjq5c6Z3CqmBs7gyQULcXBoyamy\nFOX3sTf5PwWEAXdi/SB4sObnAqwDvW5ulegUpQM5efIkhw8frrfN19eXqKgoPDw8KCuv4un3N5Lt\n+Wu9AVuhPqH87S//R2+fxmZIUZS2Z2+dfxlwgxDCueb3/9SUfw4HfpNSqjt/5ZLXs2dPpJSUl5fj\n5ORUr2Z/96+xLP/sX5yqyiAw0I3+/Xww6A3MFDOZ1m8aep2621c6lmat91Y7KrfmdTKqu0e5hGma\nVq9e32AwEBERQU5ODoMGDbLV7GuaxqaENzhVZR0An5VZysiwQdw34S5Vt690WE1N7JaIdfCWPTQp\nZaOzvilKZ1Jbs28ymbjsssvq7evRowc9evSot02n0/HgVXcQf3IZJcUmbrtiLvddczMOegcUpaNq\n6s7/R+xP/orS6dXW7B87dsw23XLv3r3p3r17veMysgpxNTrj42O0bQvvHs5D0xcRFRBJ/4CQtgxb\nUX6XpiZ2W9SGcShKuyouLiY6OpozZ87U237q1Clb8jeZLLzx2S5e/+Vtruo1k2fuq79w+g1RM9s0\nZkW5GPbO7TPmQsdIKX+6+HAUpW2ZTCYSExPPm2ff1dWViIgIAgICACitKuXVPe/wxi9foAFfpn/C\njP0jGTeqbztFrigXx94Hvvu4cBeQ6uBUOpXs7GxiY2MpLy+3bTu3Zl/TNA5kHuDDuA8priymZ083\nMrNKcffUYXYraMfoFeXi2Jv8JzewzR0YD9yCddEXRekULBYLBw8eJDu7/jLSfn5+REZG4uHhAUBG\nfg5bEz4i9lSs7ZiQUC+G9hjGk9f9GR9X7zaNW1Fakr11/j80smuHEKIEeBLrbJ+K0uHp9fp6ffVO\nTk4MHjyYoKAgdDodBYXlLH/nLf5bsIuoob7oapav8DZ6Mz9yPkN6DGmsaUXpNJpV59+IvcCjLdCO\norSZ8PBwcnNzCQwMrFezfyI/nQVrl5FXlQNAenoJfXp7MilkEn8c+EeMBmNTzSpKp9ESyX8mUHTB\noxSlHVRVVZGUlMSAAQMwGM7+c3dxcWHKlCk4O9efStnb1ZNuvSGvZviiq8mPR8ctIcQ7pA2jVpTW\nZ2+1z9cNbHbAuvRiP2BNSwalKBeroZr9wYMH1zvG2dn5vFG8ns6e3P+HRSz/6GXmj7iRP02+QU3N\noFyS7L3zd+L8ah8NOAo8B2xsyaAU5WIUFRURExNTr2b/+PHjhISE4OrqCoDFovHprgN8uvdnXln6\nv7i5nV1ecULweLb+JRIfF582j11R2oq9D3wntXIcinLRTCYTCQkJHD9+vMGa/drEX2mq5J5/vcRP\n2d8DGhu3hvOXW6bajtfpdCrxK5e8ZvX5CyGuwVre6QPkAN9JKfe0RmCK0hyN1ez379+fsLAwHBwc\nbDX7Hx/9mHzfLLRs6wfEtsSPuatqIk5OLfEITFE6B3v7/P2AncAIrIu35wLdgb/VPA+4XkpZ0WpR\nKkojysrKiIuLa7JmX9M0Mosz+SD2A2SerNlvxN/PBdFtAMuuv1slfqXLsfdf/EtYl3GcKaXcUbtR\nCDELeAN4Fnig5cNTlIbVduucOnWqXuJ3dnZm8ODB9OplXTjll4Mn+Pv2t3ELT8PZeHYQupezF8/N\n/x9GB42u98BXUboKe5P/NcADdRM/gJTycyHEY8AqVPJX2phOpyM4OJiTJ09SUFBAcHAwAwcOxMnJ\nCU3TePa9LXwQvYUqXRl+yUbCw/3R6/RMDp3MzAEzcXF0ae+3oCjtxt7kbwIam8gkC2s1kKK0qsrK\nSkwmE25ubra7dZ1Ox5AhQzCbzfj41H9IW+aXRLXOutZuYVEVQa6h/OnyW9UCK4qC/cn/ZeAZIcSv\nUsrM2o1CCE+so3tfao3gFAXq1+y7uroybty4+rX5np6YTJZ65+h0Ov48fhE/yWjcDO48fM3tjO93\nheriUZQa9ib/wJo/yUKIfUAm4AeMBTyAyjoDwTQp5R9aPFKlSzq3Zr+qqooTJ04QHBwMWD8Yftmf\nybrPPmXpwhuJGBxgOzfQI5B/zHuMgf4DcTY4N9i+onRV9ib//sDhOuf0qXldu80BNaWz0oKaqtl3\ncTnbV//a1m95/ad3KXU4zXMfV/HWkw+g15+9u1eTsClKw+wd5NXQlM6K0uI0TSM7O5u4uLh6Nft6\nvd42z76DgwP55flsObqFn9lPheMZsEBs5V4ST85FBPdqx3egKJ1Dcwd5DQYmAl5Ya/33SSllawSm\ndD1lZWXExsaSk5NTb3ttzb7BYMRkMfOf4//hy8QvqTZX4+zkQJ9gT3RmA4snz6Vf74BGWlcUpS57\nB3npgfXA7UDdJ2aaEOJd4H+klGqxd+V30TSN6upqfvjhB0wmk217bc1+z56B7N2bzsYdX0P4EVz8\nquqdP3vUVGYPno23US2uoij2svfO/1Hg1pqfm7BO7dATmA+s4OwEb80ihBiNdYnIK6WU3zf3fKXz\nq+3Pd3Jyok+fPhw/fhygXs3+xzsP8s9vNnLGkIpTkp6R3j1wcNAT5BnEzRE3E+YX1p5vQVE6JXuT\n/5+AVVLK5+tsSweeE0IYa/Y3K/kLIdyAd1EPirukug9xa8svhRCUlpYSFhZWr2bfMTiTUteTUAV6\nnQ5MTswbOocJwRPUdMuK8jvZm/x7Aj82su8n4LHfce1/YP0A6f87zlU6KU3TOHHiBElJSYwdOxaj\n8ezKWAaDgfDwoRgM9RP6dHE1nw/aRVbhKeZecTU3ht+Au5N7W4euKJcUe5P/ceAKYFcD+67AOsrX\nbkKI6cAMrNNGRDfnXKXzKioqIjo6mvz8fADi4uK47LLLAOv8+nv2nGTz578yfmxvbpl9ue08RwdH\nHr36HpwcnAj2Dm6X2BXlUmNv8t8ArBZClAIfYO3zDwDmAY8Dz9h7QSGEP9bJ4P4HyG9WtEqnZDKZ\nkFKSkpJSr7unoKCA6upqHB0d+e+vJ3hm2xtkOB/i2M99uGZiBP7+rrZjVb++orSs5szqOQxYC/y9\nznYd8B7Wid3stR74XEr5lRAiqBnnKZ2MPTX7er2eA5kH+LToIwr8E7AUWyhxPcl/Uw9yrf/4doxe\nUS5t9g7yMgO3CSGew7qYiy/Wu/Y9Uso4ey8mhLgN64dI1O+IVelEysrKiImJ4dSpU/W2+/v7M2hQ\nOE5OLpwqO8UHsR8QnxcPQP9+3uQXVDIxcihDB4S2R9iK0mU0dwWLk1j7//OBUzWvm2MREARkCyHg\n7JiBnUKIt6WU/9vM9pQOKC0tjbi4OMxms21bbc1+QYEza9YeoKjHYSzBiVi0sxOy9fLvxn0TZjOq\n1yg1AZuitLLmDPJ6DrgXcORs0i4VQqySUj5r5/UWAnUnUe8B7AXuAL6xsw2lgzMajbbEr9Pp6NOn\nD4MGDSIrq4xlr77LceMeKtNKGeLdDS9PZ3Q6HVNCp6g59hWlDdl7578cuB94AfgE611/ADAHWCGE\nKJJSvnyhRqSUGXV/F0LULv2YIaU81cApSicUEBBAz549KSsrIzIy0lazr/cuJq/P91TmlWMw6Kiq\nMtPftz/zIucR5Kke/yhKW2rOIK8VUsqn62w7DvwshCgGHsQ657/ShdTW7Lu4uNC9e/d6+6wLrIDR\n6GjbFuQZxJwxV7Ht111EhgUxb+hc1cWjKO3E3uTvBexvZN8+4OHfc3EpZTr15wpSOom6Nfuurq5M\nnDgRg8H6zykvr4wPPojnTPkZ/vbw1HrJ/baR8wj09Wd62HTVxaMo7cje5P8F8L/AfxrYdzPwZYtF\npHRoDdXsl5WVkZKSQlhYGGVl1Ty6cgdx+l2UOeQx8edQJo7pZzvf09mT2YNnt1f4iqLUsDf57wFW\nCSGisQ7yysK6kte1wDjgH0KIx2uO1aSUq1s8UqVd1dbsx8bGUlFRYdteW7Pft29fKkwV7Ezdwcl+\nWyk4WQTA5/JzJo55sL3CVhSlEfYm/3/X/PQCVjawv263jwao5H8JaapmPyIiAnd3dw5kHuDjox9T\nUFFA7z7ulJZV0aePJ6OjgtA0TfXrK0oHY+8gLzV1YhdksVhITk4mMTHxvJr9gQMHkZBgZuW/duM7\nIZ7jBcm2/Q56PdeNG62qeBSlA2vuIC+lC6mqqiIpKalezX5wcDBCCP7+4i98l76TTOdo+kR7ENzH\nE7D26d84+EYu73W5uttXlA5MJX+lUUajkYEDBxIbG4uXlxdRUVF4e3ujaRpJ3T8lI9c6LcPp0+WE\nBHtxZd8ruXbAtRgNxgu0rChKe1PJXwGsD3Tz8/Px9fWttz0kJASDwUBQUJDtTl6n03H7pBs4mr4W\nP18jk6NGsCBqHj09erZH6Iqi/A4q+SsUFhYSHR1NYWEhEyZMwNPT07YvLi6PTz47zkP3BeDh4WTb\nPrbPGG6/Oo4RgSMY1mOY6uJRlE5GJf8urKGa/ejoaMaOHYtOp+P9D+J478ftnDD+gstHpfz1T9Nt\n5+p0OhZftri9QlcU5SI1mvyFEIHNaUhKmXnx4Shtoama/W7duqFpGvF58ezVv81xF+uM3Z8mfsyf\nS6bg4a768xXlUtDUnX861pp9e6mF2DuB0tJSYmNjG6zZj4yMpFxXzvqD6zmcfRhcwN/PBYOjnssH\n96RKXwqo5K8ol4Kmkv/tnE3+vsCzWNfw/YizI3xnYR3l+1Arxqi0gKZq9t3cgvjqmxxiTJ9ypOhn\nTBaTbf+wqECuHXAtU0KnYNCrXkJFuVQ0+n+zlPKt2tdCiG3AO1LKO885bLMQ4kVgLvBaq0SotIgj\nR46Qnp5u+722Zj8tzZl123eQavwJ171mIiP90dXMtTc6aDQ3DLoBL6NXe4WtKEorsfdWbhrwx0b2\nfQGc+6GgdDB9+/YlIyMDTdPq1ezvL95Mouu3aBqYi3RUVJgI7ym4KeImQrxD2jtsRVFaib3JPw+4\nnIZX25oEZDSwXWkntZU7dcsvvby8CAnpi6urkdDQUNu+WUOu4oP926k2mxgi+jB/6Fw1OldRugB7\nk//rwDIhhAvwOZDL2ZW87gMeaJ3wlOaqrdkPCgoiNNS6CLrForFvXzqfbE9l/k0R9O17NrF3c+vG\nPVffhIPOgWn9puFscG6v0BVFaUP2Jv9VgDfwCPBYne0VwN+klOtaOjCleUwmE/Hx8aSmpqJpGiUl\nJfTs2ROj0cj2LyXrv/6Ek84HyP78cjYPW4qDw9m5+v44sLEePUVRLlX2zuqpAQ8LIZ4GrgB8sHYF\n/SSlLG3F+JQL0DSNrKws4uLi6tXsWywWTp85zfHq4/yg/4yT7vGYTBqJlv0kpmcyMFjNtqkoXVmz\naveklIXAV60Ui9JMDdXsV1aa6dmzO/oeOtbL9eSU5AAQEuKFyWRh+IB+GL1MjTWpKEoX0dQI30Ts\nH+SlSSlFy4SkXIjFYiEpKYnExEQsFgsAJpOFjIxyDiafQRf5E45nCuudMzAkkFliFmN6j8FBr8bj\nKUpX19Sd/480b4Sv0gZKSkr49ddfKSkpsW3T6XTklFSz+fh35DtmYEjSc7lPDwwGPUaDkav7X82U\n0CnqYa6iKDZNDfJaVPtaCHEzsEtKmdsWQSmNMxqN9Uboent7ExkZSXRBLOWJ2VABLi4GNLOOKwdc\nyTVh1+Du5N6OESuK0hE1p9RzEfBJ64Wi2MNgMODt3YeysgQiI8MJCQlBp9MxzmsMo8LDOF1+musv\nm8b0sOl4G73bO1xFUTooe5N/BuDamoEo5yssLCQrK4uBAwcCkJdXxitbvmFH4hfMGXIts2rq+AH0\nOj1/nXYPXs5e+Ln6tVfIiqJ0EvYm/1eAF4UQo4EjQMm5B0gpN7dkYF3ZuTX73t7e5JDDm3s/5j8p\nv4EBtsR8wYLsqfTocbZLp69P33aMWlGUzsTe5P/Pmp9/bmS/Bqjkf5HOrdnX0MgtzWX9V+vJ9M1E\nM2p4eTlTWFiJc0Ah+dW59ED15yuK0nz2Jv/QCx+iXIzS0lJiYmLIzc3FZDYhM05QqJ2i0qWccp9y\n0IEOHSLMjyG+I7ltzGzVvaMoyu9m7wjftNrXQgg3wAM4LaWsbq3Auopza/aPZiQjM1MoM1dS5F5C\n7z5uoANngzMTgycyte9U9SBXUZSLZvcIXyHEJGANcBlYJ3wXQuwHnpRS7mqV6C5xeXl5xMTE1KvZ\n1xyqyag+Q4Y5H3O+RkCZJzcOn8GkkEm4Obm1Y7SKolxK7Er+QogJwNdAPLAMyAECsS7islMIMVVK\nudfOtoKwPkOYCuixThfxUFdaA9hi0UhKymfnru/o08MZg8E6yZq3tzc3DL+OHe/9gkexN9cPmc7i\nq2fh7aH69RVFaVn23vk/DXwLzKiZ5A0AIcRKYAewHGsyb5IQQldzfC4wuWbzv4DtWL9RXPIsmoVH\n//U+35/cRZVDPnc7zqV3L28GDRpEcHAwOp2OF+ctJzJwII4GtWyioiitw97sMgKYWzfxg3W2TyHE\nOuB9O9sJAI4Bj0opUwGEEP8APhVC+Egp8+1sp1OwWDRKS6vx8HAiKy+LI2eOsOfkHuKMqRQ7WOfe\niTmTzW233ICz89mpF4b3iWivkBVF6SLsTf750GhNoQdgbmRfPVLKbODm2t9ruoDuAn69lBJ/YWEl\n339/kn370ukWUo1X6HFSU1Mpcy+jwqcCfz8X0tOL6e7vzoAIv3qJX1EUpS3Ym/y/A5YLIfbW7ZsX\nQgRi7fL5trkXFkJ8ClyH9YNl8gUO71Ty8srY9PV3FLvE4Z1VSZDmgU6nw7nYmSq3Kvw9vXl64XVM\nCp2Ep7Nne4erKEoXZG/yfww4ACQKIfYB2UAPYBxQBCz9Hdf+G/AM8CTwjRBimJSy060FXFVlxtFR\nX2/N29P6ROj+K/7VTjjoDZhMGo6OOty83Lgm8hrG9RuHo4NjO0atKEpXZ2+df4YQYhiwBBiPddBX\nPrAO+EdNd06zSCljwDZj6EngNqwfBp1CQUEFu3efZM+edO69dxj9+nljNptJTk4mNzGXPl7+VJkr\nMRoN+Hv4M27EOEYPHI1er79w44qiKK2sqcVcJmJdprEabP31j1zMxYQQAcBkKeUHtduklGVCiGSg\n18W03dY++zyR7b/sI9M5Gtcvc3jg5qnExMRQWmpd1TKsewgVpgpGDB7BqKGjMKjKHUVROpCmMtJu\noFQIsQdrjf+3Usq4i7xeMPC+ECJJSnkAQAjhBQjg7Ytsu00UVRbx44kfOeD5LXFu8QDE5v6Xn35y\nRa8/2/UzuPdgoqKi8PLyaq9QFUVRGtVU8r8ea5/+eOB5wEEIkY314e43WD8MmtvdcwDYC2wQQiwG\nqoFnsdb9d7jkX1Fh4uefM4mOzmXaPDf2ndzL/7d379FVVXcCx7+EBBIgmEAQAwlP4QcGEBQHiQgo\nKoNWbKfqass4OlPHqS5rfUylM9ba+kBbLfVRZ3C1Tjtq1Wo7VqpisfioVHwWFKL8TAgoJGAgPDQk\nxBju/LH3TU4uITcacu893N9nrazA2eee7F9O7u/su88+e6/etprm/c3QE4qLc8nN7UXBgAZycrNp\nrK/pru8AABA5SURBVGskKyuLcePGtYzZN8aYVNTRSl5PAk8CiEgfYDruYjATWALkiEgZ7kLwnKrG\nXdhdVfeLyD8AdwBPAdnAn4BZqnrANNHJFIlE+MHNL1C25y229V7Ly8t6MnBAji8EesDEsUOZMWwG\nM4fPJLMpk8rKSo455hgbummMSXmdveFbD6zwX4hIJjALuAS4HLgS6NSq4Kq6A7cqWEprjjSzvvBh\nKj+tAWDb1mwG5uWQszuHQZmDOHXmqUwdMrXNqJ0pU6Ykq7rGGPO5fJ6J3bKB2cBpuHH5k3Bt4Ndx\n9wRCaffufaxcWUVWVgZz57bOXJ2ZkcncY0up3vE0hUf1ZXTekYzaO5Ijc4+kb1ZfCpsLycywm7jG\nmHDqMHuJyARgrv+ageum2YBL9jcCL6jqx91dye6yYWMt31v8GNWZ6+jXK5c5cxa1TLIGMK9kDrsb\naxjROIJ+n/Ujo0drWW1tLcOHD09GtY0xpss6Guq5BSjEjed/Ede1szw6J0+YVX1cxSubX2HVllVU\n5FWwr7GZ3ZGevPrmB8w40bX+m5ubaappYvK+yezfv99PYg3Z2dmUlJRQWFhoN3SNMaHVUct/CLAD\nuB93U/flMC7e0tTUzJo1NaxYWc6wE3dT2fQOm/dsbikvLOzLrl2NFA7pS07xbgBqampYt25dy5h9\ngB49ejBy5EhExMbsG2NCr6Msdhquu2cecC1QHxjzv1xV30tA/brsp79ZylNvP8/OrI0UvtqHo4/O\nb1M+ccwwSotLKS0upaBPAatXr2bLli1t9snLy7Mx+8aYw0pHQz2fx03ottA/mTsXOB03z8/PfLfQ\nc7iLwXOqujMB9T2oSCRCXZ2bPjmobuB6arMqAdi+vYHRo/PI6pnF5KMmU1pcyvhB49v05QeHaWZl\nZTF+/HiGDRtmXTzGmMNKZ4d6fgQ84L8Qkcm4C8FM4Nf+OEmZqayhoYllyzay8m/l1LOL/75pQZtE\nPX/KHFa88wZ5+b35uzElzBk7k6lDptInq0+7xxs7dizV1dUMHDjQxuwbYw5bn6vzWkTycA97lQLT\ncIu8ZAJvHfqqxdfQ1MDr297kztcfZvv+D+kV6UPlxjMZPWpAyz5TCqfwH+dexLSh0xjcb3DL9qam\nJt5//31GjRpFTk5Oy/bMzExmzZpFVpbNummMOXzFG+o5BpfoT/Lfx+HW3X0X98DXz4EXEzXc89FH\n13Pe+f2oy65i1ZZVrNm2hqbmJjIG7YSPYH9mA69Vvs3oUa3LA2T1zGK+zG/5fyQSobq6mrKyMhob\nG2loaGDq1Kltfo4lfmPM4a6joZ7bgQG4QY4f4pL9IuD5LzKF86HwjC5j1TOPUxAz/+fQIf0YVNCH\n0jFTmDZu2EFfX1dXx9q1a9mxY0fLtq1bt7Jr1y7y8/MP+jpjjDncxJvV88/AClXdkKD6dKi691rq\ntvehYGhr901R/yKml0znhCEncER2+6NxmpubqaiooKKiwo3Z97Kzs5kwYQJ5eXndXndjjEklHY32\nOT+RFemM4qJ+DB6RR27vXKYNncb04ukU9S/q8DU2Zt8YYw4Uqsx32rHTmHfcPEoGldAzo+N55Pbt\n20dZWRnV1dVttufn5zNx4kQbs2+MSWuhSv4LJi6gaHDHLf2oPXv2tEn8NmbfGGNahSr5fx6DBw+m\nsLCQrVu3UlRUZGP2jTEm4LBI/k1NTdTX1x/QlVNSUsKIESMoKChIUs2MMSY1hTr5RyIRqqqqePfd\nd8nIyGD27NltbuDm5OS0eYDLGGOME9rk396Y/fLycsaPH5/EWhljTDiELvk3NzdTXl7Ohg0bDhiz\nb+P1jTGmc0KV/Gtra1FV6uvrW7bZmH1jjPn8QpUt16xZw4ABrZO25efnM2nSJPr375/EWhljTPiE\nKvlH2Zh9Y4zpmtAlfxuzb4wxXReq5H/88cczYcKEZFfDGGNCLyP+LqnDRvMYY8yhEarkb4wx5tAI\nS7dPT4Bt25KyhowxxoROIF+2OwVyWJJ/IcCCBQuSXQ9jjAmbQuCABbnCkvzfAE4GtgLNSa6LMcaE\nQU9c4n+jvcIekUgksdUxxhiTdHbD1xhj0pAlf2OMSUOW/I0xJg1Z8jfGmDRkyd8YY9JQ0oZ6isgS\nIFNVLw5suwBYCIwE1gHfV9XnAuWXAffGHKpZVTMD+1wFXAkMAv4KXKaq5d0WCF84ll7ArcACoC/w\nF+ByVd2YzFi+SDwi8kPghoMc7gZVvdHvF5ZzMxK4C5gJNABPAd9V1d2BfUJxbnz5GB9PKVAH3A/c\npKqfJToeERkM/AQ4A8gBXgOuUdV1vvwMXy5AObBQVZcFXn8k8HP/+k+BXwHXhTGWwHF6A68Dt6vq\nQzFl3RZLwlv+ItJDRG4E/i1m+9eB/wV+A0wBHgCWisjswG4TgaW4savRr6GBY3wT+BFwDTAN98Z9\n1v9yUy2W+4DzgW8A03F/PEtFpEcyYuliPHfQ9pwUAkuAGlyiCc25EZFM4Bnc8yTTga8CM4BfBI4R\nmnMjIvnAy0A2cArwddzf3X2JjkdEMoAngLHAObiL0R5ghYgMFJFjcO/vx30sTwJ/EJGSwGF+DxwF\nzAIuAv7Z1z2MsSAiuf44k9r5Gd0aS0Jb/iIyCpcMJgAfxhQvBB5W1Vv9/98Xkcm4FuWLftsE4HlV\nPdg8D9cCi1X1d/7nfQP3YNhXgYcPVRz+2F84Fv/ai4A5qvq8P96lwHJgNFCRyFi6Go+q1uFalNFj\nTQcuAc5S1Sq/ORTnBhjnv85X1ff88e4BbgscIzTnBrgQ6AOcq6o7/fEuBlaKyE2quimB8RyLu6Ae\nE/jdXgDsBM4CTgJeVdVb/P7Xi8gM4DvAJf7vagYwyn9CfltEvgvcIyI3qmpjWGLx+5+Guwjvpn3d\nGkuiW/6lwGZcC35jTNkYXAslaDVQ6ltjACXAe+0d2H8cHEvrhQKflN7EPR18qHUlljOA7dHE7+uq\nqjpcVSuSEAt0/dwAroWK62L4vao+67eF6dzsBPbjkk22iBTgWspvQlJiga7FMwYoiyb+QDnAzATH\n8yHwJUAD26ILcef7n/dizGteDNTjZOCDYNeoL88FJocsFoCzcZ/USmMPnohYEtry9/1ZDwGISGxx\nNVAcs20E0AvI8x918oF5vo+5L/AScK2qVgNF/jVVMcdo77hd1pVYcCe10l/JF9Lan3eVqm4hwbFA\nl+PZEdg+HzgO150VFZpzo6rVIvJtXF/tZbgG0nu4bgYI37mpBs4WkQxV3R8oBziSBMajqrXA0zGb\nr8B1eS4HbopTj6KDlOP3afL/DkMsqOp3ov9u57x2+3lJpbl9HgSuFpEXcFe7mcA3fVkvXKsf3An+\nGlAALML1sR2H+2gLsC/muI24/s5EihdLf1zXwjXAVb6Ot+JiOZbUigXixxN0JfC4qlYEtqVSPB3G\n4vtyxwF/xnX19Mfd0/itiJxOasUC8c/NY8D1wI9F5AZcK/lu4DNfnrR4RGQ+7u9+saq+JyJ94tTj\ngHJVbRKRiN8nTLHE0+2xpFLyvw3XElmGm5CoDLgd9wvdo6rLRWSQqra0MkWkDHdlPBPY5DfH3gzp\nDezt3qofoMNYcBewI3D9sBsBRORcXH/emcAH/jipEAvEjwcAESkCZgOnxry+wX9PhXjixbIA96ll\nuKruBRCRL+NmRTyT1pZmKsQC8d831SJyHq5v+WrcvZkf4G4w7iFJ50ZELsLdRH8U17eNr0tH9Tig\nXESygB5+nzDFEk+3x5Iy4/xV9VNVvRzXMhmqqpOAeuCj6JswmPj9/7fiuhyKcX2i4Kd/DhjCgR+d\nulUnYqkC9gb7LlW1BqjFDddLmVigc+fGOwd3AXsp5hApE08nYjkRWB+MS1UrcX9nR5NCsUCn3zd/\nVNUhuK6EQbjhkYNwF7SExyMi1/k6LAH+KdAdtTlOPQ5Wjt8nTLHE0+2xpEzyF5GbRWShqjYGRvN8\nGdd/hohcISLV/koffc1w3B9xmU+e5bT2zSIi/YCpuDH0CRMvFtwNur4iMj7wmqNwXVkbUikW/7Pj\nxRN1MvBS4A0AtFzYUiKeTsSyBRgbHE4nIoXAQKA8lWLxPzve+2aGiKwQkZ6qulVVP/Xle4FXEh2P\niFwL3Az8QFW/rarBaYVXBuvhnRKox0pglIgUx5R/AqwJWSwdSkQsqdTtswn4qYisBdbj+o5PAC71\n5U8DtwD3i8gi3JvxLmCltj7Qshi4Q0QqcA+7LMK1RP8vUUF4m+g4lr/gLgCP+CGee4E7cSMHnvH7\npEosED+eqCm4MeftSZV4NtFxLA/gPro/KCI/wvW9/gxYAzzr90mVWCB+POtxN+B/LCL3ApOBe4BF\nqvqx3ych8YjIJH/s/wF+4Rs8UZ/4er3lf++P4LrfpgViWQW8irv/cjkQfchqsb+ohSmWzujWWFKm\n5a+qv8T1Vd4HvIMb1naqqqov3wCcjuvieR33AMU7uNEl0WMswV0gFuP+SHoBfx/4w0iITsQS8fV+\nE3dR+yuu//X0aF1TJRZflw7jCSjEDZVs7xgpEU8nzk0V7hNMLu4CvRSoBOaqf4o0VWLxdYkXzw7c\nkMKTab0fcIOqLgocI1HxfA13X+JfcEks+HWVqq4FvgKci7vYzgfOVj+O3r9vvgJ8hDs3vwJ+CdwY\ntlg6o7tjscVcjDEmDaVMy98YY0ziWPI3xpg0ZMnfGGPSkCV/Y4xJQ5b8jTEmDVnyN8aYNGTJ36Q1\nEVkiIhEROfMg5fN9+fcTXTdjupON8zdpTdxKSmVABCjxc6ZHy44A3sVN+VCqqs3JqaUxh561/E1a\nU9VPcCsrDcM9Ph90OzAAuNASvzncWMvfGEBEfg1cgGvhvyYiM3Hz41+tqncG9vsWbim+UbjZFZfg\nFt6OBPa5FPhX3LoAPXCfHm5W1Sd8+cW4uZwW4pZbzACmqltS0ZiEsJa/Mc5VuDlj7hGRXsB/4Sbg\nuyu6g4hcD9yLm4/pbNzcMrcQWN9XRK7GLZbyGG7+/3/ELe/3iJ8dNCoHN8nXhbi5YDZ1V2DGtCeV\nZvU0JmlUdZeIXAY8ATyH6wb6UrRFLyL5wH8Cd6vqv/uXLReReuA2EbnbTwo3ArhNVYMXhM3Aa7i1\nAp7wmzOAH6rqsu6PzpgDWfI3xlPVP4jIo7gZGy+JaY2fhFs+748xi9YvxS3zeArwkKpeAS0XC8Et\nADPH7xu75OWaQx6EMZ1kyd+Ytv6ES/6xLfKB/vuKg7xuCICIjMFNr3wKbr3V9bi52MH1/wfVYUyS\nWPI3pnOiaxWfR+t60UFVItITtxjPx8DxwDuq+plf+GNBQmppTCdZ8jemc1YBTcBRqvq76EYRmQFc\nD3wP17I/GviWqv4t8Np5/rsNsDApw5K/MZ2gqh+JyJ245RDzcauvjcA9G1CLG875KW7h7StFpAb3\nCWAecIU/TN9E19uYg7GWiDGdtxC4DteFswy3ePdTuGUTG/3IoHOAGuBB4Le49XTPAipwSykakxLs\nIS9jjElD1vI3xpg0ZMnfGGPSkCV/Y4xJQ5b8jTEmDVnyN8aYNGTJ3xhj0pAlf2OMSUOW/I0xJg39\nP6re9AM4WjhPAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bc2fcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "first_year = census.index[15]\n",
+    "last_year = census.index[-1]\n",
+    "first_year, last_year\n",
+    "\n",
+    "total_growth = census[last_year] - census[first_year]\n",
+    "elapsed_time = last_year - first_year\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "annual_growth\n",
+    "\n",
+    "results[1950] = census[1950] - .3\n",
+    "\n",
+    "for t in linrange(1950, 2015):\n",
+    "    results[t+1] = results[t] + annual_growth\n",
+    "    \n",
+    "newfig()\n",
+    "plot_estimates(table2)\n",
+    "plot(results, '--', color='gray', label='model')\n",
+    "decorate(xlim = [1950, 2015], xlabel='Year', ylabel='World population (billion)')\n",
+    "savefig('chap03-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now with system objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can rewrite the code from the previous section using system objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "t0 = census.index[0]\n",
+    "t_end = census.index[-1]\n",
+    "total_growth = census[t_end] - census[t0]\n",
+    "elapsed_time = t_end - t0\n",
+    "annual_growth = total_growth / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the system object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = System(t0=t0, \n",
+    "                t_end=t_end,\n",
+    "                p0=census[t0],\n",
+    "                annual_growth=annual_growth)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can encapsulate the code tha runs the model in a function that stores the resulting Series as a new system variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation1(system):\n",
+    "    \"\"\"Runs the constant growth model.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "    \n",
+    "    system: system object\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = results[t] + system.annual_growth\n",
+    "    system.results = results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also encapsulate the code that plots the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(system, title=None):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    system: System object with `results`\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot_estimates(table2)\n",
+    "    plot(system.results, '--', color='gray', label='model')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z5s0jKCiI2bNn2x4Yl5WVsWHDBkaOHGkbEObaa6/FaDTy2Wd6Bn9TlIaZzWYS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\nDf5KKTUE/S/ueJcybGcgtAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bc2f1d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_simulation1(system)\n",
+    "plot_results(system, title='Constant growth model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`plot_results` uses `decorate`, which takes parameters that specify the title of the figure, labels for the $x$ and $y$ axis, and limits for the axes.  To read the documentation of `decorate`, run the cells below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function decorate in module modsim:\n",
+      "\n",
+      "decorate(**kwargs)\n",
+      "    Decorate the current axes.\n",
+      "    \n",
+      "    Call decorate with keyword arguments like\n",
+      "    \n",
+      "    decorate(title='Title',\n",
+      "             xlabel='x',\n",
+      "             ylabel='y')\n",
+      "    \n",
+      "    The keyword arguments can be any of the axis properties\n",
+      "    defined by Matplotlib.  To see the list, run plt.getp(plt.gca())\n",
+      "    \n",
+      "    In addition, you can use `legend=False` to suppress the legend.\n",
+      "    \n",
+      "    And you can use `loc` to indicate the location of the legend\n",
+      "    (the default value is 'best')\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(decorate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    adjustable = box\n",
+      "    agg_filter = None\n",
+      "    alpha = None\n",
+      "    anchor = C\n",
+      "    animated = False\n",
+      "    aspect = auto\n",
+      "    autoscale_on = True\n",
+      "    autoscalex_on = True\n",
+      "    autoscaley_on = True\n",
+      "    axes = Axes(0.125,0.125;0.775x0.755)\n",
+      "    axes_locator = None\n",
+      "    axis_bgcolor = (1.0, 1.0, 1.0, 1.0)\n",
+      "    axisbelow = True\n",
+      "    children = [<matplotlib.spines.Spine object at 0x000001349BC3...\n",
+      "    clip_box = None\n",
+      "    clip_on = True\n",
+      "    clip_path = None\n",
+      "    contains = None\n",
+      "    cursor_props = (1, (0.0, 0.0, 0.0, 1))\n",
+      "    data_ratio = 1.0\n",
+      "    default_bbox_extra_artists = [<matplotlib.spines.Spine object at 0x000001349BC3...\n",
+      "    facecolor = (1.0, 1.0, 1.0, 1.0)\n",
+      "    fc = (1.0, 1.0, 1.0, 1.0)\n",
+      "    figure = Figure(432x288)\n",
+      "    frame_on = True\n",
+      "    geometry = (1, 1, 1)\n",
+      "    gid = None\n",
+      "    images = <a list of 0 AxesImage objects>\n",
+      "    label = \n",
+      "    legend = None\n",
+      "    legend_handles_labels = ([], [])\n",
+      "    lines = <a list of 0 Line2D objects>\n",
+      "    navigate = True\n",
+      "    navigate_mode = None\n",
+      "    path_effects = []\n",
+      "    picker = None\n",
+      "    position = Bbox(x0=0.125, y0=0.125, x1=0.9, y1=0.88)\n",
+      "    rasterization_zorder = None\n",
+      "    rasterized = None\n",
+      "    renderer_cache = None\n",
+      "    shared_x_axes = <matplotlib.cbook.Grouper object at 0x00000134941A...\n",
+      "    shared_y_axes = <matplotlib.cbook.Grouper object at 0x00000134941A...\n",
+      "    sketch_params = None\n",
+      "    snap = None\n",
+      "    subplotspec = <matplotlib.gridspec.SubplotSpec object at 0x00000...\n",
+      "    title = \n",
+      "    transform = IdentityTransform()\n",
+      "    transformed_clip_path_and_affine = (None, None)\n",
+      "    url = None\n",
+      "    visible = True\n",
+      "    window_extent = Bbox(x0=54.0, y0=36.0, x1=388.8, y1=253.44)\n",
+      "    xaxis = XAxis(54.000000,36.000000)\n",
+      "    xaxis_transform = BlendedGenericTransform(CompositeGenericTransform(...\n",
+      "    xbound = (0.0, 1.0)\n",
+      "    xgridlines = <a list of 6 Line2D xgridline objects>\n",
+      "    xlabel = \n",
+      "    xlim = (0.0, 1.0)\n",
+      "    xmajorticklabels = <a list of 6 Text xticklabel objects>\n",
+      "    xminorticklabels = <a list of 0 Text xticklabel objects>\n",
+      "    xscale = linear\n",
+      "    xticklabels = <a list of 6 Text xticklabel objects>\n",
+      "    xticklines = <a list of 12 Text xtickline objects>\n",
+      "    xticks = [ 0.   0.2  0.4  0.6  0.8  1. ]\n",
+      "    yaxis = YAxis(54.000000,36.000000)\n",
+      "    yaxis_transform = BlendedGenericTransform(BboxTransformTo(Transforme...\n",
+      "    ybound = (0.0, 1.0)\n",
+      "    ygridlines = <a list of 6 Line2D ygridline objects>\n",
+      "    ylabel = \n",
+      "    ylim = (0.0, 1.0)\n",
+      "    ymajorticklabels = <a list of 6 Text yticklabel objects>\n",
+      "    yminorticklabels = <a list of 0 Text yticklabel objects>\n",
+      "    yscale = linear\n",
+      "    yticklabels = <a list of 6 Text yticklabel objects>\n",
+      "    yticklines = <a list of 12 Line2D ytickline objects>\n",
+      "    yticks = [ 0.   0.2  0.4  0.6  0.8  1. ]\n",
+      "    zorder = 0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bc2f9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.getp(plt.gca())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The constant growth model doesn't make a lot of sense, because it seems like the number of deaths and births should depend on the size of the population.  As a small improvement, let's write a version of `run_simulation1` where the number of deaths is proportional to the size of the population, but the number of births is constant.  This model doesn't make a lot of sense, either, but it's a good exercise.\n",
+    "\n",
+    "Write a function called `run_simulation1b` that implements a model where the number of births is constant, but the number of deaths is proportional to the current size of the population.  Set the death rate to `0.01`, which means that 1% of the population dies each year; then choose the number of annual births to make the model fit the data as well as you can.\n",
+    "\n",
+    "Hint: It probably won't fit very well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zJtZuk/HFF1/w5JNPcujQoU7ondLdpJ5I5a1db1FWWEHNER0OejucfZ0RfgJP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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bd23e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def run_simulation1b(system):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        births = system.birthrate\n",
+    "        deaths = system.deathrate * results[t]\n",
+    "        results[t+1] = results[t] + births - deaths\n",
+    "    system.results = results\n",
+    "    plot_results(system)\n",
+    "    \n",
+    "system.birthrate = 0.115\n",
+    "system.deathrate = 0.01\n",
+    "\n",
+    "run_simulation1b(system)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Proportional death, proportional birth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's get to a more realistic model where the number of births and deaths is proportional to the current population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation2(system):\n",
+    "    \"\"\"Runs the constant growth model.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "    \n",
+    "    system: system object\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        births = system.birth_rate * results[t]\n",
+    "        deaths = system.death_rate * results[t]\n",
+    "        results[t+1] = results[t] + births - deaths\n",
+    "    system.results = results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I kept the death rate at 1% and chose the birth rate to fit the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system.death_rate = 0.01\n",
+    "system.birth_rate = 0.027"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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nL1LcejRoA/dLRW3grihKc/jr4F/8+MeP5JXlAXAyI49DRWl0KOnP1MHjuOuu\nHvXU0HJc8AbuQoiODTmRlPJUg1unKIrSAhgqDKz+dTUH4w9iNG9RXuFQgVuIO+E7rsHN6EtxsQGT\nydQqu3hqUle3TyoN6+qxqz+LoihKy5Kcncwnmz6hIPPcaDujs5FhVw3jZnEzq0mgXz8/wsO9L2Er\nG19dwf9+VD+/oiiXsXWH1rH9z+2Y8nXk5ZXh6elEWx9Ppt48lQAvrYt58uTwS9zKplFr8JdSftSM\n7VAURWlWBoOBMwlnKM0ykpdbBujQlXRgzvhHsbdr0Cj4VqmuPv+5DajHJKVsnBXGFEVRmoGdnR3R\nXaI5euI4JUYHHIpCKTrbmcyMUvz9r+DgDzzfgHpMgAr+iqK0WLHpsQR6BNLWpS2gLa3cN6Iv+cX5\nxB+oICfHlcmTw/H3d73ELW0edXX7XLotZhRFURpJXmkeqw+uZvep3bQzBDM9ajqBgR6Atk7V0KuG\nclWUtp2iXn95jOSxxeX/3UZRlCuSyWRix4kdfBH3BTlF+ZxJKCElN44XD33Jm3OnWgK9TqfD0fHK\nG6yolndQFOWyc6bwDCtiVyAzJQCOuY60L3TCRdcWMuzYuvUEw4c3dA+qy4ta3kFRlMtGhbGCTUmb\n+C7xO8orysEEzjnOeJZ54ubTgcxU6BjsRL9+l9eY/QtRV5//fVWeT22W1ihNKiYmhokTJ2LrMhlf\nffUVzzzzDHFxcc3QOkW5OMeyj/Fp7KecyEmluMSAq4sDbbLaEGwfTFCHIHToMQU5c/PNwyx7SlzJ\nbO7zF0LogVuAawBPtN28fpVS/tJEbVMURbFJWkEai39fTHZ2MYlHctAZdYzt3o9Qj+64OWr7RHTo\n0IHIyEirpcevZDYFfyGEH/ADEAGUAhmAL/BvIcRm4HYpZWGTtVJRFKUO/m7+9PXtxzt/bMCuzIH+\n+n54Zgfh1lYL/MHBwYSHh1826/I0BluHc76Ctu/uzVJKFyllZymlM3An0A/rrR2VCyCEYO3atdx9\n99307t2bUaNGsW/fPlatWsWwYcPo168fTzzxBGVlZZYyMTExTJo0icjISK6++mqef/55iouLLcfj\n4+OZNGkSERER3HLLLRw6ZL0Pj9Fo5N1332X48OH07duXO++8k61btzbbe1aUC2U0Vd/ydGLEPYwO\nH8IwRuBt54NrG20rxfDwcBX4a2Brt88Y4BEp5Y9VE6WUXwshfIDFwMON3biLJaUkISHBprxBQUHV\n9pGNjY29tal7AAAgAElEQVQlJSXFpvKhoaEIcXEDnv73v/+xcOFCunTpwpw5c3jwwQfp3bs3H3zw\nAceOHWPmzJlERUUxYcIE9u/fz9SpU5k8eTLPPvssqampzJ8/n9TUVN59911yc3OZOnUqgwYN4ssv\nvyQ5OZn//Oc/Vud75ZVX+Omnn1iwYAGdO3dm27ZtPPLIIyxdupSBAwde1HtRlKZgNBnZmryVrSlb\neTx6Fp6u57b+dHN044YO0RzOOY2fnyvOzvZERkbSsWODFii+Ytga/EuB3FqO2RYdlXqNHz+eESNG\nAHDrrbeyYMEC5s+fT2BgIKGhoSxdupTExEQAli9fTq9evZg9ezag7Yg1f/58HnzwQRITE/nrr78o\nLy9n4cKFuLq60q1bN9LT0y2bvBcWFvLJJ5/w5ptvMmTIEED7AIyPj+f9999XwV9pcU7knmBF7AqO\nnj3GidR8JmxczIo5/6ZtW22HLZ1OR1RUFCUl29Hr9URHR9OuXbtL3OqWy9bg/w7wnBDiTyllemWi\nEMIVmAN80BSNu9JU3ULRxcUFvV5vNSrH2dnZ0u2TmJjIsGHDrMpHRUVZjiUmJhIcHIyr67mp6n37\n9rU8T0pKoqysjMceewy9/lzvX3l5Od7eahic0nKUGkrZkLCBzUc3YzQZORyfRVZWCW2MyaxafYAZ\nD0db8rq7uzNgwACcnZ2tfveV6uqa5LWpyksdEAYcFUL8jjbSpy0wGHAAWuRGLkKIi+qK6dOnT7Wu\noKZkb2/936HT6Wrtp3R2dq6WVrkrm729PTqdjvN3aXNwcLA8rxzq9uabbxIUFGSVr+qHgaJcSgfS\nD7DqwCrOFp+1pAUFtsXtZGcCS/uTl1NASYkBZ+dzfzvt27e+zdQvhbqu/B2xnti13fzoAFReju4z\nP6pOtWYWEhLC3r17rdJ2795tOZabm2vZRN3T0xOAgwcPWvIGBQXh4OBAeno6Q4cOtaS/9dZbVFRU\n8NhjjzXDu1CUmuWU5LDm4Br2nN5jlS68BROHT2SLPp2SkmR8fUsoLS3E2dnzErW09aprkte1zdgO\npYEeeOABbr/9dhYvXsy4ceM4efIkzz77LMOGDSMkJAQ/Pz+WLFnCU089xcyZM0lPT+eNN96wlHdx\ncWHq1Km88soruLq60rt3b7Zs2cKSJUtYuHDhJXxnypXu9+O/8/mhz8nOz+dIUg7BXTzxa9eWcWHj\nGBQwiNzcXNq1S6O0VEdFhYFdu3YxfPjwat+clbrV1e0zWEr5e0MrFEIMkVJuu7hmKfUJDQ3l3Xff\n5bXXXuPTTz/Fy8uL0aNH869//QsANzc3Pv74YxYsWMC4cePw9fXlgQcesNzwBfjXv/6Fg4MDL730\nEpmZmQQGBrJgwQLuuOOOS/W2FAUTJk6cPotMyMZoNNGxIoD5tz+Fh7M7p0+fZu/evVRUVABaF2XP\nnj1V4L8AuvP7hSsJIfYDh4HnpZQHa8xknT8a7eZvdyllo3aUCyG6AMdsXZZAUZTWy2Qy8d9NC9m4\nOZ6uBSNoZwrk8cf7o9NlER8fb8nn6OhIVFSU6uOvRWpqKtdddx1AsJQy+fzjdX1cRgHzgRjzqp5f\nAruAY0Ah4IXW938NcDMggDeBCY3XfEVRLmf70/bj5exFkNe5QQc6nY4nhj1CtOEMB2OzmTAhlKys\nY5w8edKSx9XVlYEDB6oRPRehrj7/crTlG94GHgceAOZhfRNYBxwHvgBukVKerFZRDYQQ04CngEAg\nDnhSrRGkKFeO7OJsPjv4GfvS9kGeF4/0mUmf3n6W417OXoy6yYPrR5Sxe3cM2dnZlmPe3t70799f\nLc52kertKDMH9FnALCFED6Ar2sJumUCKlNK2KbRmQogpwBK0GcG/AdOBb4QQvWr6aqIoyuXDaDLy\ny7Ff+EZ+Q15hIVJmk5N7kvxDn/Fh90eshmyaTEZ27PidoqIiS1pQUBC9evVSw5EbQYPukkgp44H4\nejPWQgihA54FFkspl5vTZgEjgKuB5AutW1GUli05J5kVsSs4kXsCAL2djqKicvzLwnHJ68pPP6Uw\nZkyIJb+9vT2dO3cmPj4enU5HWFgYwcHBao2eRtLct8gFEASsqUyQUhqBvrWWUBSlVSsuL+br+K/Z\nmrLVauJhUNtARl13P5vXFnL9DUGMHBlUrWy3bt0oLi7G398fX1/f5mz2Za+5g3+o+dFLCPEL0Avt\nm8QcKeWOZm6LoihNyGQysef0HlYfXE1WYTYFBeW09XLGwc6BW0Jv4fqu12Ons2NIeBF+fq5UVFRQ\nVlZm1Zev0+madZb9laS5g7+H+fFjtJvH8cA04BchRKSU8nAzt0dRlCaSVZzFB3s+IC29gKNHczEa\nTUy+cQR/H3Av3m3OrR/l5+dKcXExf/31F3Z2dlx11VWqT78ZNPdPuNz8uFBKuUpKuQeYASTSApeE\nVhTlwnm38eaG4JGcOJGPrsyF0PybcY8bbhX4AbKysti2bRu5ubmcPXuWAwcOVFuXSml8zX3lXzkU\n9EBlgpTSJIQ4DAQ3c1sURWlEhWWFuDpaj7sf22MMZ4YVE7PaA5+2nlxzjfUkzZSUFKtgr9Pp8PLy\nUjd1m4Gt2zg6A0+j7eHrSvVvDCYppS3LZ+5BmyAWDcSY665cMfRnG9usKEoLUmIoYX38enac2MFj\nfWfTtcO5dR4d7Bx4ePhEYtzT6N3bGycnLeQYjUYOHjxotVmSk5MT/fv3VzN2m4mtV/6vo/XN/woc\nBKrvoWYDKWWREOJVYKEQIh3tG8B0IARtS0hFUVqR2PRYVh1YRWZBFsnJeUz5+QVWPLqQoCDrVTaj\novwtz0tLS4mJieHs2XPLNHt6ehIdHY2Li0uztf1KZ2vw/xswV0q5uBHOOQ8oAl5D2wR+HzBSSikb\noW5FUZpBXmkenx34zLLkcuKRHM6cKaKdqT0fr9jPM08PQa+v3nWTnZ1NTEwMJSUllrROnToRERGB\nnZ1ds7VfsT34O6Kt63PRpJQmYJH5n6IorYjJZOKP1D9Ye2gtReXnZt6Gd+uIz4nutCvpTttgN0pK\nDLRp42BVNisri507d2I0ah0HOp2OHj16EBISovr4LwFbg/8mtMXbtjRhWxRFacGyirL4NPZTDmdY\nj8i+OvBq/hb2N/7yysLV1YGoKP8ag7mXlxceHh7k5OTg4OBA//798fHxaa7mK+exNfivAD4QQngD\nO9C6baxIKVc1ZsMURWk5/jjxB6sOrCK/uJgjR7Lp0MGV0E6BTOoziZ4+PQG49tq6V9i0s7MjKiqK\n/fv306dPH9q0adMcTVdqYWvw/9L8ONX873wmQAV/RblMuTu5cyYrj7i4LCoqTHjn9uGpO2bh6VZ7\nwM/Pz8fNzc3qW4CLiwuDBg1qjiYr9bA1+Ksx+IpyBevl24vh3Ydw4vDvdCkYjnuFP4nx+URFVQ/+\nJpOJY8eOERcXR1hYGF27dr0ELVbqY1Pwl1JaBuMKIVwBdyDLvOa/oiiXkdP5pykoK6B7++5W6VOj\nJtFXfxPfbUjm3nvD6d69bbWyBoOB/fv3c+rUKQDi4uLw9PRUY/dbIJtn+AohrgUWA/3RNnFBCLEL\neEZKublJWqcoSrMxmoz8lPQT38hv0BmcuLfzvxgQ2dly3NnemYHRnYjq1xF7++orw+Tn57N7927y\n8/MtaV5eXqpvv4WydYbvULQRP/Fo4/TTgY7AeGCjEOI6tWm7orReaQVpfLTvI45mH+VkagHJKXkk\nbn+X1V3m0batsyWfTqfD3r76SJ6TJ08SGxuLwWCwpHXp0oXw8HC1SFsLZeuV/3Noyy+MNo/TB0AI\n8TzwHdpev9c1eusURWlSRpORn4/+zPr49RiMBkxGSEsrxLXcB5+i3qxadZgZMyJrL280EhcXx7Fj\nxyxpdnZ29O7dm8DAwOZ4C8oFsjX4RwHjqwZ+sCzKtgT4rNFbpihKk8oozODDfR+SdDbJkuZgb8+M\n6ybx5yoPAgM8GDs2pNbyxcXF7N6922p/XVdXV6KiovDw8Ki1nNIy2Br8swG3Wo65AxWN0xxFUZqa\nyWRi2/FtfBH3BQUlRTjYa8sqBHoGMrXvVAI8AhjknYkQ7bCzq7nLxmQyERMTQ05OjiWtQ4cORERE\n4ODgUGMZpWWxtTPuF2C+EKJj1UTz6/moFTkVpdV4f/f7fLp/BQlHM9j1ZxpFRQbGiDE8fc3TBHho\nSy6HhXnXGvhB6/vv3bs3er3esr9u//79VeBvRWy98n8abQnmRCHEdiAN8AeuAfKA2U3TPEVRGltP\nn56s2baZ06cLaWNsh68czU13jsKugTdmvby8LDN11VDO1sem/20p5UkgEngb8ASuAryAJUCklPJo\nk7VQUZRGNaTzEK7rPYAgQ3/65d+Dn3MARUV1T9nJzMwkPT29WnpgYKAK/K2UzeP8pZRpwJNN2BZF\nURpZQlYC7o7udHDvYEnT6XQ8fd0TDLZPxWAwMWJE5xqXXwatbz8hIYHExETs7e0ZMmQIrq51r+Gj\ntA61Bn8hxFzgQynlafPzupiklGqJZkVpIQxGA9/Ib/gx6UfKM915YsCT9ArzsxzX6/Rce23nOmqA\nkpIS9uzZQ1ZWFgDl5eUcPHiQgQMHNmnbleZR15X/82g3ck+bn9elco1+RVEusfSCdJbtXUbCmaMk\nJJwlOzuV+QnL+eSZWdXW2K9NRkYGe/fupbS01JLm7e1NREREUzVbaWa1Bn8ppb6m54qitEwmk4kd\nJ3aw+uBqyirK0OmgoKCctoZA3PIEmzYlc9tt3eusw2g0IqXkyJEjljSdTkdoaCjdu3dXm65cRmxd\n3mEesFRKeaqGY0HATCnlo43dOEVRbFNUXsSK2BXsPrXbktbGyYnpw6aw9ysPbhzZhVtuqX3CFkBR\nURF79uyxmrTl5OREv3798Pb2brK2K5eGrTd8/wtsBKoFf7SRPw8CKvgryiWQdDaJD/Z8wOnsDJyd\ntT/pDu4dmNZvGgEeAaRHFOLnV/dN2rS0NPbt20d5+blRPz4+PkRGRuLk5NSk7Vcujbpu+G5HC+yg\nreK5UwhRW/a/GrldiqLY4McjP7L24Jfmvv0S+vf344bQEYwLH4ejnSNAvYEftK6dysCv9ta9MtR1\n5T8NuBMt8C8A3gdSz8tTAeQAXzdJ6xRFqVN+WT4HDmaQl1eGvckJ75QRTLhzQoODtp+fH8HBwaSn\np9OvXz/atq2+Vr9yeanrhm88sBBACGGH1ud/srkapihK/W7rcRt/9j7Att9OIQpvpG9EGBUVphqX\nXa5kMpkoKSnBxcXFKj0sLAwhhFqi4Qph605ezwIIIdoDjpg3c0GbIewKDJFSLm2SFiqKAmjLL5dV\nlOFsf259fXu9PfNumsXvLmfo6O9Bnz4+ddZRWlrKvn37yMvLY9iwYTg6OlqO6fV6tfb+FcTW0T69\ngZVAeC1ZTIAK/orSRHJLclm6Zyknkot4cti/6NTJ3XLM3cmdm0a611Fac+bMGfbt22cZux8bG0v/\n/v1Vv/4VytbRPi8D7YFZwC1AKbABGAXcDFzbFI1TFAUOZxzmnT/fZ8+h42RllZB9ZBnL5j5a41aK\nNamoqODw4cNWG64AanvFK5yt3/GuAv4jpXwVWAO4SinfkVKOQbvZq4Z5KkojM5qMbJAbeP3P18ku\nzCU7uxQdcCazgM2bU2yqIzc3l23btlkFficnJwYNGkRYWJi66r+C2Xrl7wQkmp8nAFXneH8IvNuY\njVKUK11eaR7L9iwjPjMegDZtHOjdvROmPdHcMfQaRoyoe10ek8nE0aNHiY+Px2g0WtL9/f3p06eP\nGruv2Bz8jwPBwDa04O8hhAiSUqYAJUC7JmqfolxxErISeC/mfQrK8i1pPbx7cP8N93N2hIngYK86\nyxcXF7Nv3z4yMzMtaXZ2doSHh9O5c2d1ta8Atgf/dcCLQoh8KeU6IUQ88JwQYhHwOJBUd/FzhBBh\nwKEaDg2RUm63tR5FudyYTCY2Jm7knS0rOXWqgL6RvjjY2zG6+2hGh45Gr9PjGVx/PZmZmVaB38vL\ni8jISNzcatuJVbkS2Rr8nwW6Aw+gfRA8bn6ciDbR6+4GnLM3kGl+rCqrAXUoymXnx6QfWbx+OWcy\nigFIPVrGa5Pn0NOnZ4PqCQgIIC0tjfT0dLp160ZoaKgawqlUY+s4/yLgDiGEk/n1j+bhn/2APVJK\nm6/8gV5AnHlzGEVRzIYFDePzjhs5k3EET0NHogrHEexe9yqcAAaDAXv7c3/KOp2OPn36UFhYSLt2\nqkdWqZnNO3kBSClLqzxPogHdPVX0Ag5fQDlFuay5OLjw39FP8L+89VzjM5LbbwutczinwWAgLi6O\ns2fPMmTIEOzs7CzHnJyc1E1dpU51LeyWiDZ5yxYmKWWtq76dpxfgLITYCXQBDgJzpZS7bCyvKK1e\nfmk+G/dtY6QYgZfXuRm7QV5BvPaPf9Z7UzYrK4t9+/ZRVFQEQHx8POHhtc3BVJTq6rry/x3bg79N\nhBAuQFcgA20/4FLgEWCrEKKflFJ9I1Aue4fPxPOfdf8j7uhJ/vQ9y/89MdEq2NcV+CsqKpBScvTo\nUUymc3+excXFmEwmNZJHsVldC7tNbeyTSSmLhRBtgdLKLiQhxFSgPzAd+Gdjn1NRWorKSVuf711P\nXFI6JmBT2pd8/1MUo0f2qLd8dnY2+/bto6CgwJLm4OBAr1696NSpkwr8SoPYurbP1fXlkVLusKUu\nKWXeea+NQohDQKAt5RWlNTpbfJale5aSdDYJNzcHAgLdST9uYES7OxgQWfeELaPRSEJCAkeOHLG6\n2vfx8SEiIqLa6pyKYgtbb/hup/4uILt6jiOE6A9sAYZLKXeb0+yAvsBaG9uiKK3K7lO7WRG7gqLy\nIkvayH4DCAu/kZuuDUOvr/2KPTc3l71795Kff27Cl729PWFhYWrClnJRbA3+w2tIcwOGAJPRNn2x\nxX4gGXhPCDEDKABmA97A6zbWoSitQkl5Cc9//S4/ya306eODXqdDr9MzVozlxm43otfVP/Y+IyPD\nKvC3b9+evn37qkXZlItm6zj/rbUc+k4IUQA8g7baZ331GIQQNwMvoa0K6op2Y3molPKMbU1WlJYv\nNTeVBz94lmNntG2vj6fk0a9nMNP6TSOkXd0bqVcVEhLC6dOnyc/Pp2fPnnTp0kVd7SuNokHj/Gux\nDZhja2bzbmATG+G8itJiuTm54eplAvMljVNWF54e/G88XGpfYqGiooLy8nKcnc8N/dTpdERGRqLT\n6XB1rX8vXkWxVWPM+R4D5NWbS1GuIF7OXswdPQOftu5MDp/CF3OfrzPwnz17lt9++43du3db3dQF\ncHNzU4FfaXS2jvbZVEOyHdoInRBgcWM2SlFak4oKI2t+/IOxQ6Nxczu3LWJkh0i+e3wp7s61B32D\nwUB8fDzJycmWoJ+cnExwsA0ruCnKRbC128eR6qN9TEAcWv/98sZslKK0FoePnmT2ylc5kn+Y46kP\nM+dB61tfdQX+jIwMYmNjLbN0QRvJU3WZBkVpKrbe8L22iduhKK3O7lO7eeOPpSTmHwdgTcJKRsX1\no09YxzrLlZWVERcXx4kTJ6zSfX196dOnjxq3rzSLBt3wNY/UGQK0BdKBX6SUvzVFwxSlpSooK+Cz\nA58RcyoGZw/w9XEhM6uEm/pcTWi39rWWM5lMnDp1ikOHDlk2UQdwdHQkPDxczdJVmpWtff7tgY1A\nFNp6PBmAL/Af8/2A26WUJU3WSkVpAQwGI3+m7GZd0hryS8+Nve8fHswdXe/hmh79ai1rMpmIiYkh\nLc16JfOOHTvSq1cvtQKn0uxsvfJ/E20bxzFSyu8qE4UQY4FlwIvAvxq/eYrSMhxKOMncVW9xxlHS\nu7c3OrQr9MGdBzMubBwuDnV31eh0OquJWc7OzvTu3Rt/f/8mbbei1MbW4H8z8K+qgR9ASvmNEOJp\nYCEq+CuXqd3JB5m2dAElFEIRpKUV0rNLAJMjJtPLt5fN9QghSEtLw9fXl549e1ptwKIozc3W3z4D\nkFPLsdNoo4EU5bIU6OOLf6AjyScKsbPTEe4ZyX+unU4bh5qXWDAYDBw5coQuXbpYTdiyt7dn2LBh\nKugrLYKtk7zeBl4QQlgNYxBCeKDN7n2zsRumKC2Fr6svj904ma6d/Fgy9T8sGj+r1sB/5swZtm7d\nSmJiIocOHap2XAV+paWw9Texo/lfkhBiO3AKaA8MBtyB0ioTwUxSyhsbvaWK0gxiDh3ji5//4PkZ\nd1ttoTiy+w0MDR5Sa9AvLi7m0KFDnD592pJ26tQpgoOD1T66Sotka/DvBuyrUqZyAfLKNDtsWNJZ\nUVoqk8nECys/Y3XsWoxUELouiPvHDbYc1+v0NQZ+k8nEsWPHkFJiMBgs6Y6OjoSFhdG2bdtmab+i\nNJStk7xqWtJZUS4L6QXpfBr7KTsL92KgDICPdn/C3aOjadOm9ttZ2dnZHDhwgNzcXKv0wMBAwsLC\ncHRUt8KUlquhk7zCgGGAJ9pY/+1SStkUDVOUpmYwGtiUtInvEr7DYDTQoaMrmVnFeNq1Z+H4f9Ya\n+MvKyoiPj+f48eNWi7C5u7vTu3dv2revfaKXorQUtk7y0gPvAfcDVacgmoQQnwL3SSkbdbN3RWkq\nFRVGVm3czkG7TeQYMizpdjo7Hr9lIreFjcHRvvar9pycHFJSUs6Vs7Oje/fuhISEoNc3xkK5itL0\nbL3ynwPca35ciba0QwdgArCAcwu8KUqLdkieZt5n73K4KAYfHxd69NCu0oO8gpjcZzKBnvVvJe3r\n64u/vz9paWn4+fnRq1cvtbOW0urYGvz/DiyUUr5cJS0VeEkI4Ww+roK/0uKtkh8RVxQDwJmMYroE\nwr0DxjM8eHiN2yqWl5dTVFSEp6enVXp4eDiBgYFqhq7Satn6HbUD2naLNdnBudE/itKiTRtyN74+\nbbCz0zEifAAvjXqe67peVy3wm0wmjh8/zpYtW/jrr7+sRvIAtGnTRgV+pVWz9cr/KHAVsLmGY1eh\nzfJVlBYlMSkTVxcnOnZ0t6QFtw1m+g134+viz7WhV9e4imZOTg4HDx4kOzvbknbkyBF69OjRLO1W\nlOZga/BfCiwSQhQCq9H6/P2Ae4C5wAtN0zxFabi8vFKWrPmRL+QaBra/liVzplkF+bv63lljudLS\nUuLj4zlx4oTVKB4XF5dq3T6K0to1ZFXPSOAV4P+qpOuAFWgLuynKJZdXmseyvStYkfQ9Rr2J7Wd/\n4OffhnLDMFFrGaPRSHJyMgkJCZSXl1vS9Xo9ISEhdOvWTS3LoFx2bJ3kVQFMEUK8hLaZSzsgG/hN\nSll9ARNFaWZGk5GtyVv5Ov5rSgwldOrkxokT+bT3ccK1Q2Gt5TIyMjh06BD5+flW6X5+foSHh6uN\n05XLVkMvZ06g9f9nA2fMzxXlksnMLOLgyUR+z/2W47nHLemdO7szpOsgHr3uPjyda+6yMRgM7N69\n2+pq39XVlV69euHr69vkbVeUS6khk7xeAh4BHDg30atQCLFQSvliE7VPUWpUWmrg6+/i+GD7Ks66\nHaZ/fz/0eu3X0s/Njwm9J9DDu+4btPb29gghOHjwIPb29nTv3p2uXbuqiVrKFcHWK//5wGPAa8CX\naFf9fsA4YIEQIk9K+XaTtFBRanAkK4mXYp6nyL4QSuBEaj7dgtszqvsoRoaMxF5v/attMpk4e/Zs\ntaUXgoKCKC0trbb2vqJc7hoyyWuBlPK5KmlHgT+EEPnA42hr/itKswjxDUJ0b8/ew4W4uztyVdd+\nzBh2H95tvKvlzczMtPTrX3PNNXh5eVmO6fV6NYRTuSLZGvw9gV21HNsOzGqc5ihKdfn5ZRw/nkd4\n+LnA7mzvzGM33Me7jit4bMT9RPhHVBuzX1hYSFxcnNWm6XFxcVx11VU1ju9XlCuJrcH/W+AfwI81\nHLsb+P5CTi6EGIT24XG9lPLXC6lDuXwZjSZ++SWFZd//SK7+FKvnzcXDw8lyfECnaCLv7oujnfUi\nbGVlZSQmJpKcnIzRaLSk29nZ0b59e0wmkwr+yhXP1uD/G7BQCBGLNsnrNNpOXrcA1wD/E0LMNec1\nSSkX1VehEMIV+BS1CYxSi4yCTP637Q2O2Wurhi9Z8yNPPzDWclyn01kF/trG6wMEBATQo0cPXFxc\nmqfxitLC2Rr83zI/egLP13C8arePCag3+AP/Q1scrpuNbVCuEBXGCn4++jMbEjbg1i0fDkAbF3sy\n2+8BxlbLbzKZSEtL4/DhwxQWWo/pb9euHeHh4Vb9/Iqi2D7Jq1HHvgkhRgGjgZuB2MasW2mdDAYj\nR45kY++bzcrYlZzKPwVAWy9nwsO8Gdv3ev4WXvOyDDqdjpSUFKvA7+rqSs+ePfH391ddPIpSg2af\nsy6E8AaWAfehTRZTrnDx8Vl8/Nle/szbRMeBmbi6OliOBXgEMPuaiXRt27XOOsLCwvjtt9+wt7cn\nNDSULl26qPH6ilKHS7FgyXvAN1LKH4QQAZfg/EoLYjQaeWfDN2wp+pZyhxIKjzgSEeGDs70zY8VY\nRgSPsFpuubS0lKNHjxIaGoqd3bnbRR4eHkRGRuLj46P2zlUUGzRr8BdCTEFbIK5Pc55Xabl0Oh0+\nfbOo+LkEO70OHx8XIvwiuKf3PbRzaWfJZzAYSEpK4ujRoxgMBhwdHQkJCbGqq1OnTs3dfEVptZr7\nyn8qEACkCSHg3DIRG4UQH0sp/9HM7VGaWUZGET4+57Y81Ol0PDz4PmJPxtHRux33RU0mwj/Cctxo\nNJKSkkJiYiKlpaWW9MTERIKCgtRqm4pygZr7L2cSUHWsnT+wDZgG/NTMbVGaUXFxOevXJ/H1tj+Y\n/dCNRPbpaDnWvk17nrtlNl28uuBkr43jN5lMnDx5EiklRUVFVnV5eHjQs2dPq24fRVEaptbgL4To\nWNuxmkgpT9mQ5+R55ygxPz0ppTzTkPMprcvqdbF8tGsVZ1zjeeHzNFb2eApHx3PBW3hr6+2bTCbO\nnDj4gT4AABm8SURBVDlDfHw8eXl5VnW4uLjQo0cPOnXqpEbwKMpFquvKPxVtzL6t1GWYUo3JZGL7\n8e3EuK8lu80xMMBp1z2kZKXSvUNQtfwxMTFWyzEAODo60q1bN7p06aKu9hWlkdQV/O/nXPBvB7yI\ntofv55yb4TsWbZbvExdycillKuf6/ZXLhMFgRK/XcbrgFCsPrCTpbBIA3bprE61u6jMEv3Ztayzb\nrl07S/C3s7Oja9euhISE4ODgUGN+5f/bu/P4KqszgeO/m3uT3BASkhAS9kBC8kRwAUURCEgKyqB1\n71Cn2urMOHbqdFxr6Uyrbd1qpx1ba52x07222trFlgpadgvaYnEBRXhYwxK2AAJZSMhy54/zJlwC\nJBdI7kKe7+eTT8J933vuc7jvfd5zz3vec4w5PSdN/qr6k9a/ReQl4Geq+i/tdnteRJ4CZgL/1y0R\nmoSyadMBfvzcSlJGKTuC79ASOjq3zjlDh/KJ8z7BqLxRANTX1x83jfKwYcOoqKggPz+f4uJiUlNT\nMcZ0vUgv+F4BXHeSbS8D7U8KpgdataqKR/7vd2wILqbp7RrGju1PSrIff5KfK4qu4MriK0nxp1Bb\nW8u6deuorKxk8uTJZGZmtpXh9/spLy+3G7SM6WaRJv+9wCWceETOFKDyBI+bHqYhZysb+86l/nAT\n/hYfNTVHGDfifG4+72YGZAygrq6ONevXsG3bNkIh16Ooqlx88cXHlGOJ35juF2ny/z7wkIikAbOB\nKo6u5HUXcE/3hGcSyYUDRzPp/FGsWK+cXzqIWy78OBOGTKC+vp733nuPrVu3HjPFMrhx/M3NzXYh\n15goizT5PwZkAQ8A/xH2eD3woKo+09WBmfjV3NzCwoVbqatv4LprpO1xf5Kfuz9yO8tGLOOGc24g\n0BJg9erVbNmy5bikn5ubS2lpKdnZJ77wa4zpXpHO6hkCPicijwDjgWxcV9Abqlrb4ZPNWaW6+ghf\nf3Ipr3/4J+r9Bxh74TcZPPhon31hdiHDs4afNOnn5OQgIuTmHr/cojEmek7pDl9VPQi82k2xmDgX\nCoVYuf9Nlib/kD0pBwF4ds5sHv30Lcfs5/P5qKmpOSbxZ2dntyV9u0HLmNjr6A7f9UR+k1dIVaXz\n3Uyi2n5oO8+/9zwb929kaFGQ/SurGTokgxGXNNHS0nLcRdqSkhKqqqrIyspCROjXr58lfWPiSEct\n/9c5tTt8zVlm+/Zqlr+1jeYR77N48+K2Mfu9eiUzY8ooZsrHSD2UypIlS5gyZcoxJ4CcnBwmTpxI\ndna2JX1j4lBHN3nd1vq3iNwELFTVqmgEZWIrFArx4otreXHZIjYEX6PkYJCcbDcfnz/JT/mgcgpD\nhex6f1db1862bdsoKDh2uoacnJzjyjbGxIdIB1R/H5jcnYGY+BEixOw9P+WDtLkc8dWyadNBQoQo\nzihmZt+ZpG1PY8f2Hcf06VdVWbvAmEQS6QXfSqBXp3uZs0KSL4lpl57Lqh3v0zsjhYuKCijvVUZq\nTSrV1dXH7JuTk0NJSYmN3jEmwUSa/P8XeEpELgVWAjXtd1DV57syMBMd+/cfZvHibVx/fTFJSUf7\n5j8++gbe3fYOpUkjGOAbgL/OTyjsElBubi4lJSXk5ORYn74xCSjS5P8t7/dnTrI9BFjyTzCvvrqZ\n5+e+wYbAMlL73MlHp53Xtq1Xci8em/FVlr22jMbGxrbH8/LyKC4utv58YxJcpMl/eLdGYaLuUMMh\nFlT9jhXBRYSApxc8R/n4h0lPPzrLZu+03gwbNowNGzbQv39/iouL6dOnT+yCNsZ0mUjv8N3S+reI\npAMZwD5VbTz5s0w8amppYtHmRcxZN4e6zMMEg376+TOQnCbeeX8FZePKjtm/sLCQQYMGkZGREaOI\njTHdIeI7fEVkCvB14CK8BVhE5E3gS6q6sFuiM11i377DzJmzkcKyGl7Z/Ef21u2FFkirCXJZXjH5\nvfpRlF3EgaoD1NbWkp6e3vbclJQUUlJSYhi9MaY7RJT8RWQyMA9YCzwE7AYG4hZxeUVEpqrq0m6L\n0py2xYu38qOXlrAu+TX6HKilcEgWweogKTUppAfSKcwvJDvoJlcLBAJUV1cfk/yNMWenSFv+jwAL\ngKu8Sd4AEJFHgTnAV4CpXR6dOWNv1S5kRfA3BH3JJO/uQ7ovg9RAMgVZBQzIGIAPH8FgkMLCQgoK\nCggETmm6J2NMgor0kz4WmBme+MHN9ikizwAvdHlkpktMv3gsr72zhH4tmWRlBinIGsKQzCEEkgL0\n7t2bESNGMGjQIFtAxZgeJtLk/yHQ+yTbMoDmrgnHnK6qqjp++dtVfPyG88jLO9ptc27euVx6YSkp\newIMyxpGMBAkJyeHoqIi8vPzbYy+MT1UpMl/EfAVEVmqqjtaHxSRgbgunwXdEJuJ0IIl6/nWH35F\nZeBtdv3iSh65+5/aWvI+n48Hpt/P8r8sJy0tjaKiIltAxRgTcfL/D2AFsF5ElgG7gP5AGXAImNU9\n4ZmONDQ1sGjzIn5b+TJNwUOM8uezY+9qVq5cx5gxpW37pQRSmDhxonXtGGPaRDrOv1JExgD3A5Nw\nN319CDwDPKmqu7ovRNNeQ1MDizcvZuHqhTTtbyLncDpJvQM0NbWQn5POjr0bGR2SY7p0LPEbY8J1\ntJjLZbhlGhsBvAT/QLQCM8fbWXWA//rVLzicvo70xgD+Rj/JJAOQl5PJ8OxhFPYvpKiwKMaRGmPi\nXUct/8VArYj8GTfGf4Gqro5OWKa97859gZeX/YnspCBpyclk5voBCAaCDM0cyrnDz6WoqMiWSTTG\nRKSj5H89rk9/EvANwC8iu3AXd+fjTgbW3RMl6dkt9PWnkRRKorGxBZoClPQvZGzpWIoKi2z6BWPM\nKeloJa8/AH8AEJFewHjcyWAy8CyQJiKrcSeC+apqC7t3kQ1bdpGXnUFm5tEhmzdeeDUL/7KUtOo0\nzisoYfrEKQwrGGZTLxhjTkukF3zrgIXeDyISAC4D7gA+C9wD+CMpS0QG46aInopbSexV4L7wIaQ9\n1SvL3uTFxbOpr9/HVJnO7Z+6rm1bZmom/znzbnqHejPYbsoyxpyhU5nYLQhMAaYB5cD5uHn838Rd\nE4ikDB9uOogqrwyA7wB/xE0Y1+O0tLSwYuMKlry7hG1bd9HU0EDAl8S7G96htvYK0tOPLqA2cvA5\nMYzUGHM26TD5i8i5wHTvpwwIAhtxyf5hYLGqHjqF18sH1gBfUNUK7zWeBH4vItmq+uEp1yDBVFcf\n4e23d7N124fkle7kzdVvUl3jlkYMpvrx+YAQ+ILN7N178Jjkb4wxXaWjoZ7bgQG48fxLcF0781qT\n9unwLhDfFPYag4FPA3/rCYm/oaGJL3xxLtUpH0DqbvrtT8bvP9p94/P5KBo2hMsvLmf8eZfYqB1j\nTLfpqOU/ENgL/BB3UXdpVy7eIiK/B67FnVzKO9n9rFDdfIAD+fNIOuyS+uHDSfTunYQvycfwocOZ\nPnY6w/Nt0TRjTPfrKPlPw3X3zAA+D9SFjfmfp6przvC1HwQeB74EzBeRMapaeYZlxoW9e+uYP38L\nJSWZXHTRoLbH+6b1JWtABvXb60hLC9CrTwoXlJ7PjItmkJNua+IaY6Kno6Gei3ATus0SkXzcieBy\n3Dw/3/K6hebjTgbzVXX/qbywqr4HICI3AduAW3Eng4T2xhvbef6F5TSmVvD+pmRGj/43/H43EMrn\n83HLtOuY+8Zcxo8cz9SRU0kNpMY2YGNMjxTpUM/dwM+8H0RkNO5EMBn4iVdOcmfleCeRclX9ZVjZ\ndSKyERh08mfGv9raWtZvWs+7m5dT22ctTRxhR10qq1ZtZMyYkrb9xg0Zx7iPjyPJZ0M1jTGxc0rL\nNolIFu5mrwnAONwiLwHgrQiLKABeEJENqrrCK7MPIMBPTyWWWGtubuGtt3aSn9+Cbl7DB1s/YHfN\nbppDzSSnteBv8ZOe7qOqoQI4mvwt6Rtj4kFnQz2LcYl+ove7FHdj1ge4G76+Cyw5heGeK4ClwA9E\n5A6gEXgCN+4/YZL/kiUbmD//bQ41VdB7wGGak2uP2Z6Zk4wvy8clIy/hErkkRlEaY8zJdTTUswrI\nAXzAVlyyfxxYdLpz+qhqi4jcAHwTeBl338CfgMtUteZ0yoyF5RVLqWQlzcmN1Bzw0a+fG4vfmNZI\nVn4W5aPKuXTIpaT4beoFY0x86mxWzwXAQlXd2FUvqKp7gdu6qrzu1NLSwqZNOykqGnjMmPuhF6Tz\n3sYjBHxJBHr5OJxZjxSVMK1kGtJXbHy+MSbudTTaZ2Y0A4kntbV1zJu3kndXreVgfRWf+7dPMXhw\n/7bt00unsWDYIvpkBykrLeOyYZfRt1ffGEZsjDGn5pQu+J7Nmpub2b17N1u3bmXj9o0sX72OA437\nwB9i7sK/csetRydZy0nL4YFr76Eou4hkf6eDnIwxJu706OQfCoXYu3c/mzZtoWrfDioPVLKzZid1\njXWEejUROhgi5Aux9XDFcc8tzS09vkBjjEkQPTb5V1TsZM7cZWzesZXmtEOkZjXQQkvb9kBWiMZg\nPSUyhKtGjo9hpMYY0/V6bPJf9+F6/lb5Os2+RnyHoV9GL3wpIY70PoIv08fYgrFMKphEQZ8Cu4Br\njDnrnNXJPxQKsWvXHlasUCZNGk1WVmbbtqKCQTQE6/A1+KlOOkwoo4FzigqZVDCJiwddTDAQjGHk\nxhjTvc665B8KhTh06BDbt29n6dLVbNhWSXVoP02EuP7qy9r2K8wupG9xH6qba7jlgiuZVFDGwIyB\nMYzcGGOi56xJ/nV1dVRWVlJZWcm+A/vYU7sHrd7CPl81+GD5+yu47qOT27pwfD4fD139ADlpOQSS\nzpr/BmOMiUhCZ73q6sMsXbqaNWsqCFFD7mDYVbOL/fVugtGklBYaQ81U++tIztpNKBQ6pv8+Lz0v\nVqEbY0xMJXTy37lzPwteW0Zd0oc0BA6Qm5qKzwchX4jGtEYa0xtJGwAzSqZSNrTMFj03xhhPQif/\nhsz97Aquxd/sJxQKUU0Lgb4u8UueMGHIBMb0H2Nz5htjTDsJnfxL+5USyEuioame1DzIzO3LxCET\nmTh0Irm9cmMdnjHGxK2ETv7J/mSuKf8Ie+v2Uja0jJH9Rtp8+cYYE4GETv4AN55zo92EZYwxpyjh\nm8mW+I0x5tQlSsvfD7Br12mtIWOMMT1OWL70n2h7oiT/AQA333xzrOMwxphEMwA4bkGuREn+fwMm\nATuB5hjHYowxicCPS/x/O9FGXygUim44xhhjYi7hL/gaY4w5dZb8jTGmB7Lkb4wxPZAlf2OM6YEs\n+RtjTA8Ud0M9ReRZIKCqt4c99klgFjAceB/4kqrOD9t+J/BMu6KaVTUQts+9wD1AP+B14E5VXR9H\ndUgBvgbcDKQDfwY+q6qbE6EOIvIV4MsnKe7LqvpwNOtwmu/BcOApYDJwGHgZeEBVD4TtE7fvgbe9\n2KvDBKAG+CHwiKo2RasOIpIP/BdwBZAGLAfuV9X3ve1XeNsFWA/MUtVXwp6fB3zXe/4R4MfAF6NV\nhzONP6ycVOBN4Buq+vN226J2HJ1M3LT8RcQnIg8Dn273+D8APwV+AYwBfgbMFpEpYbudB8zGjWlt\n/RkUVsY/A18F7gfG4T7Yr3pvTrzU4XvATOATwHjcQTdbRHwJUodvcuz//wDgWWAPLgFFpQ6nG7+I\nBIC5uPtIxgM3AmXA98PKiOv3QESygaVAECgH/gF3TH0vWnUQkSTgJaAEuBZ3EjoILBSRviIyEvdZ\n/bVXhz8AvxeRUWHF/BboD1wG3Ab8oxdzt9ehi+JHRDK8cs4/wWtE5TjqTFy0/EWkEJcgzgW2tts8\nC3heVb/m/XudiIzGtTKXeI+dCyxS1ZPN//B54ElV/Y33ep/A3TB2I/B8rOvgPfc2YKqqLvLK+www\nDygCNsR7HVS1BtfSbC1rPHAHcJWqVnoPd2sdzvA4KvV+ZqrqGq+8p4EnwsqI6/cAuBXoBXxMVfd7\n5d0OLBORR1S1Igp1uAB38hwZ9v/4SWA/cBUwEfirqj7m7f+giJQBdwN3eMdNGVDofetdKSIPAE+L\nyMOq2tDNdTij+L39p+FOuAc4sW4/jiIRLy3/CcA2XAt+c7ttxbjWTLh3gAleaw1gFLDmRAV7XyFL\nOHqiwEtUK3B3DXeVM6nDFUBVa+L3YlRVLVDVDQlShzbet5WngN+q6qveY9Gow5nEvx9owSWgoIjk\n4lrNK6IY/5nWoRhY3Zr4w7YDTI5SHbYCHwU07LEW73e29zpL2j1nSdjrTwK2hHd3etszgNFRqMOZ\nxg9wNe5b2YT2hUfxOOpUXLT8vf6wnwOISPvNO4Ah7R4bBqQAWd5XpWxghtfvnA68BnxeVXcAg73n\nVLYr40TlnrYzqQPuYNjktQBmcbQf8F5V3U5i1GFv2OPXABfiurBadXsdziR+Vd0hIv+O68u9E9cw\nWoPreoDEeA92AFeLSJKqtoRtB8gjOu/BPmBOu4fvwnVjzgMe6eT1B59kO94+jd7f3VKHLogfVb27\n9e8TvIdROY4iERfJvxPPAfeJyGLc2XIy8M/ethRcqx/cQXETkAs8juujuxD3NRigvl25Dbi+0Wjo\nrA6ZuC6H+4F7vdi+hqvDBSRGHcLdA/xaVTeEPRbrOnQYv9fXWwoswHX1ZOKuY/xKRC4n9vFD5+/B\ni8CDwNdF5Mu41vJ3gCZve9TrICLX4I7lJ1V1jYj06uT1j9uuqo0iEvL2iWodTiP+zsTDcQQkRvJ/\nAtdqeQU3UdFq4Bu4N+Sgqs4TkX6q2tbyFJHVuDPrlUCF93D7iympQG33ht6mwzrgTlx9cH21mwFE\n5GO4fsArgS1hMYeLpzoAICKDgSnAR9o9/7D3O1Z16Cz+m3HfVApUtRZARK7DzYZ4JUdbn3H7Hnjf\nXv4e1998H+4azEO4i44HifJ7ICK34S6Y/xLXz40XQ0evf9x2EUkGfN4+UavDacbfmVh/DtrES5//\nSanqEVX9LK4VM0hVzwfqgN2tH9LwxO/9eyeuG2IIrv8UvGmhwwzk+K9e3SKCOlQCteH9nKq6B9iH\nG9KXCHVodS3upPVauyJiWocI4r8UWBteF1XdhDuORsQ6fi+eSD4Lf1TVgbjuhX64YZL9cCexqNVB\nRL7ovfazwKfCuqG2dfL6J9uOt09U6nAG8Xcm5sdRq7hP/iLyqIjMUtWGsNE81+H63xCRu0Rkh9c6\naH1OAe6AX+0l0fUc7btFRHoDY3Fj6WNeB9xFvHQROSfsOf1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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bd0cd68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_simulation2(system)\n",
+    "plot_results(system, title='Proportional model')\n",
+    "savefig('chap03-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model fits the data pretty well for the first 20 years, but not so well after that."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In this implementation, we compute the number of deaths and births separately, but since they are both proportional to the current population, we can combine them.\n",
+    "\n",
+    "Write a function called `run_simulation2b` that implements a model with a single parameter, `alpha`, that represents the net growth rate, which is the difference between the birth and death rates.  For example, if `alpha=0.01`, the population should grow by 1% per year.\n",
+    "\n",
+    "Choose the value of `alpha` that fits the data best."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XF3wjfXlzz5v8d+9/KS4pZ8eOHAoLKti8J5WkpNxWjrh12NvtMwG49+wi61rr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349bef5dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def run_simulation2b(system, n):\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    alpha = n\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = results[t] * (1 + alpha)\n",
+    "    system.results = results\n",
+    "    plot_results(system)\n",
+    "    \n",
+    "\n",
+    "run_simulation2b(system, .0175)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Factoring out the update function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The functions that run the model all look the same except the body of the loop.  So we can factor that part out into a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func1(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    births = system.birth_rate * pop\n",
+    "    deaths = system.death_rate * pop\n",
+    "    print(t)\n",
+    "    print(pop)\n",
+    "    return pop + births - deaths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the name `update_func1` refers to a function object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.update_func1>"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "update_func1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which we can confirm by checking its type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(update_func1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`run_simulation` takes the update function as a parameter and calls it just like any other function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Simulate the system using any update function.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "\n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "    system.results = results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1950.0\n",
+      "2.557628654\n",
+      "1951.0\n",
+      "2.60110834112\n",
+      "1952.0\n",
+      "2.64532718292\n",
+      "1953.0\n",
+      "2.69029774503\n",
+      "1954.0\n",
+      "2.73603280669\n",
+      "1955.0\n",
+      "2.78254536441\n",
+      "1956.0\n",
+      "2.8298486356\n",
+      "1957.0\n",
+      "2.87795606241\n",
+      "1958.0\n",
+      "2.92688131547\n",
+      "1959.0\n",
+      "2.97663829783\n",
+      "1960.0\n",
+      "3.02724114889\n",
+      "1961.0\n",
+      "3.07870424842\n",
+      "1962.0\n",
+      "3.13104222065\n",
+      "1963.0\n",
+      "3.1842699384\n",
+      "1964.0\n",
+      "3.23840252735\n",
+      "1965.0\n",
+      "3.29345537032\n",
+      "1966.0\n",
+      "3.34944411161\n",
+      "1967.0\n",
+      "3.40638466151\n",
+      "1968.0\n",
+      "3.46429320075\n",
+      "1969.0\n",
+      "3.52318618517\n",
+      "1970.0\n",
+      "3.58308035032\n",
+      "1971.0\n",
+      "3.64399271627\n",
+      "1972.0\n",
+      "3.70594059245\n",
+      "1973.0\n",
+      "3.76894158252\n",
+      "1974.0\n",
+      "3.83301358942\n",
+      "1975.0\n",
+      "3.89817482044\n",
+      "1976.0\n",
+      "3.96444379239\n",
+      "1977.0\n",
+      "4.03183933686\n",
+      "1978.0\n",
+      "4.10038060559\n",
+      "1979.0\n",
+      "4.17008707588\n",
+      "1980.0\n",
+      "4.24097855617\n",
+      "1981.0\n",
+      "4.31307519163\n",
+      "1982.0\n",
+      "4.38639746988\n",
+      "1983.0\n",
+      "4.46096622687\n",
+      "1984.0\n",
+      "4.53680265273\n",
+      "1985.0\n",
+      "4.61392829783\n",
+      "1986.0\n",
+      "4.69236507889\n",
+      "1987.0\n",
+      "4.77213528523\n",
+      "1988.0\n",
+      "4.85326158508\n",
+      "1989.0\n",
+      "4.93576703202\n",
+      "1990.0\n",
+      "5.01967507157\n",
+      "1991.0\n",
+      "5.10500954779\n",
+      "1992.0\n",
+      "5.1917947101\n",
+      "1993.0\n",
+      "5.28005522017\n",
+      "1994.0\n",
+      "5.36981615891\n",
+      "1995.0\n",
+      "5.46110303361\n",
+      "1996.0\n",
+      "5.55394178519\n",
+      "1997.0\n",
+      "5.64835879553\n",
+      "1998.0\n",
+      "5.74438089506\n",
+      "1999.0\n",
+      "5.84203537027\n",
+      "2000.0\n",
+      "5.94134997157\n",
+      "2001.0\n",
+      "6.04235292108\n",
+      "2002.0\n",
+      "6.14507292074\n",
+      "2003.0\n",
+      "6.2495391604\n",
+      "2004.0\n",
+      "6.35578132612\n",
+      "2005.0\n",
+      "6.46382960867\n",
+      "2006.0\n",
+      "6.57371471201\n",
+      "2007.0\n",
+      "6.68546786212\n",
+      "2008.0\n",
+      "6.79912081577\n",
+      "2009.0\n",
+      "6.91470586964\n",
+      "2010.0\n",
+      "7.03225586943\n",
+      "2011.0\n",
+      "7.15180421921\n",
+      "2012.0\n",
+      "7.27338489093\n",
+      "2013.0\n",
+      "7.39703243408\n",
+      "2014.0\n",
+      "7.52278198546\n",
+      "2015.0\n",
+      "7.65066927921\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_simulation(system, update_func1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember not to put parentheses after `update_func1`.  What happens if you try?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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v5cUXX2TUqFG89tprzJt3edslcnJyrGz8Dg4OfPfddxXalU/C3rx5c6ZPn870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\n/wAU/PgZNwB+ZwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349cfd1fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system, title='Proportional model, factored')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** When you run `run_simulation`, it runs `update_func1` once for each year between `t0` and `t_end`.  To see that for yourself, add a print statement at the beginning of `update_func1` that prints the values of `t` and `pop`, then run `run_simulation` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Combining birth and death"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since births and deaths get added up, we don't have to compute them separately.  We can combine the birth and death rates into a single net growth rate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func1b(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha  * pop\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how it works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ne5eWjBjRlpkz+zRKpQGO9zhmAA+YvlcAKWhG7jFow0yH0WwVVWEc8KGUsriK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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d05d198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system.alpha = system.birth_rate - system.death_rate\n",
+    "\n",
+    "run_simulation(system, update_func1b)\n",
+    "plot_results(system, title='Proportional model, combined birth and death')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Maybe the reason the proportional model doesn't work very well is that the growth rate, `alpha`, might be changing over time.  So let's try a model with different growth rates before and after 1980 (as an arbitrary choice).\n",
+    "\n",
+    "Write a function called `update_func1c` that takes `pop`, `t`, and `system` as parameters.  The system object, `system`, should contains two parameters: the growth rate before 1980, `alpha1`, and the growth rate after 1980, `alpha2`.  It should compute and return the simulated population one year later.\n",
+    "\n",
+    "Note: Don't forget the `return` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def update_func1c(pop, t, system):\n",
+    "    if t < 1980:\n",
+    "        net_growth = system.alpha1  * pop\n",
+    "    else:\n",
+    "        net_growth = system.alpha2  * pop\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TGDx4MF9//TUHDhzgiSeesLveiy++yM8//8ysWbMIDQ3lt99+Y8qUKbz//vsM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\nY0wSsuRvjDFJ6P8B6poAeoYZPBUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d528978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system.alpha1 = .018\n",
+    "system.alpha2 = .01575\n",
+    "\n",
+    "run_simulation(system, update_func1c)\n",
+    "plot_results(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quadratic growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the implementation of the quadratic growth model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func2(pop, t, system):\n",
+    "    \"\"\"Compute the population next year.\n",
+    "    \n",
+    "    pop: current population\n",
+    "    t: current year\n",
+    "    system: system object containing parameters of the model\n",
+    "    \n",
+    "    returns: population next year\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results.  Can you find values for the parameters that make the model fit better?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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39WN8/GomHuFWZs7sfVYmfmjgAu6uIgu4CyGcVVxSzIc/fIjer+kc0I0+3Xoz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d695748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system.alpha = 0.025\n",
+    "system.beta = -0.0018\n",
+    "\n",
+    "run_simulation(system, update_func2)\n",
+    "plot_results(system, title='Quadratic model')\n",
+    "savefig('chap03-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To understand the quadratic model better, let's plot net growth as a function of population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "pop_array = linspace(0.001, 15, 100)\n",
+    "net_growth_array = system.alpha * pop_array + system.beta * pop_array**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like.  Remember that the x axis is population now, not time.\n",
+    "\n",
+    "The function `sns.set` sets the style for the plots.  I added a grid to this one to make it easier to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig05.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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2zU2Gt5+KipH9wSTaPcWK3lfySIZ7yk3r5si5LgD+8FIjHU1V5GQO3yORDPc1\nGrG+r2grpR5sF1k4VTGKZUAW4DPG9IWJISNC+e4wZQEyRCQF+BHwDSe9/bJxqeGUlZWFPV5RURHx\nXDxUnm+l93Ql+fn28Zs3LWBu0Yjq34S7p1jR+0oeyXJPa3x+ntp6gvrmTgAu9GSxecPiiFuoJ8t9\njVSk+7qaiira7rt/Ax5xxpYCnLGkTwHfG3UEl+sC3E5lEiwduBShfOjKogOPLwGfBXzAN2IYY0Lp\n7fPy0p7qwONl83NHXCEppaLndru4dd2cQCVU39zJwZONcY5qYhhu8mxw5poLWA6cFpGd2My7GcBN\nQCq2uyxWBlYOLQ76GWAWl3fpDZQPTS+bBXRgW3fvdR63iggMVsSHReSrxpivxSbs+Nl1sJaOrsG1\n7W5aNaL9GZVSo5A3LZN1y2byqrOFevnhWhaUTNMtLq7ScC2lNGyFk4qtvHYArzqPS4ApwD7gNeyb\nfqzsx07avXnggIjMB+YD28OU3wFsDk5qALYAO53kh1uAa7AJFKuBgflXbwAej2HccVHbeImDpwY/\noW1eM5uM9Kgz/ZVSV6FMCsmdakcV+vp9vLSnGr9fs/GuxnCTZ28ZxziCf2+PiDwGfFNEGoEG7Dyl\nl4wx5U7KeC7QbIzpBZ7Apqc/LiLfAW4H3gnc7TzfkDEvESlyfqwyxjSPy02NEa/Xx9aKoVtSLCqZ\nHseIlJpcPB43W8rm8NTWEwCcqW3jZHULi+fMiHNkyStiS0lEbhrNE4pIuLTtkfoc8GPsfKOt2GSK\n+5xzG4Ba5zvOKg93Y1dz2At8GHhgjCb1JpS9xy8M7iSb4ubmtSW6JYVS46w4fworFuYHHr+873xg\nzUk1csP18zwmIkeBrxhjDl3piURkPTbpYTGw6mqCMsb0Aw87X6HntmHHuIKPlQPXRfncO0KvT0Yt\n7T28dqQu8PiGFcVka1+2UnFx48pizpxvpaOrj87uPnYdrGVLWbgZLOpKhquU1mGX+3ndWSX8KeyY\nUiU2q206dmxpI3aFBwEexXadqTHk9/t5aW91YCZ54YwsVgZ9UlNKja/0VA+bVs/mj7vOAHD4dBPL\n5udSlBezxW4mjYjdd8aYPmPMZ4GF2LXl/hL4HXAIWzHtdR6/E3gBWGyM+XtjTE+Ep1QxcuJcC+fq\n7QLuLpeLW9aWRJwfoZQaHwtmT6O0eHAqxrY91fh0CaIRu2KaljGmBruO3MdFZCmwALsgayM2WeD4\n2IaognW+zRGbAAAgAElEQVT39l+2ArguCKlU/LlcLjatKaG64Rh9Xh+NLV0cOHmB1UsK4x1aUhlR\n7rAx5hhwbIxiUVHYfaiOrp7Bjfuuv6boClcopcbL1ClprF9exCsH7dTN3YfrNCN2hHSHqiTScLGT\nQ6ebAo83rp5Nmm7cp1RCuXZJAXlBc5eCezbUlWmllCT8fv+QiXlzi3JYOHtanKNSSoXyuF3cXFYS\neHyqppWGltClPFUkWikliSOVzYHFHz1uF5tX65wkpRLVrPxsls3PDTw+VNWJ1+sb5go1QCulJNDV\n08+ug7WBx2ulkOk5oWvQKqUSyY0ri0l3utcv9fjYe/xCnCNKDlopJYHyQ7WBGeJTp6RRtmzmFa5Q\nSsVbVkYq168YTER6/Wg97Z29cYwoOUSVfScimcDngVuxk2ZDKzO/MUZiHJsCGpo7OVI5uETfptWz\nA7teKqUS24oF+RypbKaxEfq9Pnbsq+GeDaXxDiuhRZsS/m3gr7Ercu/G7k+kxpjf72f7vppAcsO8\noqmUztLkBqWShdvt4uY1JRw7aRdOPlXTyrn6dubMzIlzZIkr2krpz4DPG2O+OpbBqKHM2YvUNdl9\nDd1uFxtX6z5JSiWb4vwplOSlBbbHfnlfDW+/Q/DoKixhRdsPlA7sHMtA1FC9fV5eOTCY3LB6cQEz\ncsLtBq+USnTL5mSSmmLfbpvbujmku9RGFG2l9Axw71gGooZ67Wg9nd12bkN2Zirrl2tyg1LJKiPN\nzfplg0kPrx6pC/x9q6GG2w49eLXvV4GviEghdlzpUmh5Y8x/xz68yamlvYf9JwbTRzesmkVqiq7c\noFQyu3ZxPkcqm2jp6KGnz8vuw3W6vUUYw40pPRnm2Ludr1B+QCulGNl54HxgdeHivCksnqNrZymV\n7DweNxtXz+Z3O04DdkL8igX5FMzIjHNkiWW4SknzFuPgXH07ledbA483rp6tKzcoNUHML57KvKKp\nVNW14ff72bG/hrfevFD/xoNErJSMMVUDP4vIA8DvjTFNoeVEpAh4F/CtMYlwEvH5/OwIWrxx6bxc\nZuq2FEpNKBuvncW5+nZ8fj81Fzo4XdPKQl1JPCDaRIfvY/dRCmc1oKniMXD4dBNNbTZxNDXFzQ0r\ni+MckVIq1mZMzRiyU/TOA+d1XbwgwyU6/A5Y7jx0Ab8SkXC7ys4ETo1BbJNKd28/uw/XBR6XLZ1J\ndmZqHCNSSo2V9ctncuxsMz29Xtou9bL/ZCNrRTcDhOHHlL4CvN/5+f3Aa0DoioJeoAX4r9iHNrlU\nHGsYsr7d6iUFcY5IKTVWMtJTuP6aIrbvtd31rx+tZ+m8GWRl6AfR4caUyoFyABFJAb5sjKkcr8Am\nk9aOHg4EpYDfuLJY17dTaoK7ZkE+B082cbG9m94+L68eruMWTRGPekxpC/AxEblLRNLGMqDJqPxQ\nLV4nBbwob4pun6zUJOBxu9h47eDSYUcqm2lu6x7miskh2rXvngTuBj4IdIrIC8DvsBl5tcNeOQoi\n4sF2H74XyAGeBj5kjKmPUH4d8F1gDVADPGKM+WHQ+UXAN4GN2DlV24CHjTFnYx37SNU1XeLEuZbA\n45tWzdL0UKUmiblFOZQU5lDdYLPxdh04z70bI+WUTQ5RtZSMMZ8zxqwDioEPYVd0+DpQLSKvi8g/\nxDiuLwLvAR4ANgMlwFPhCopIAXYZpD3AWuCfgSdE5E7n/BTnvAe79cZdQD7wRxGJ6055dp7C+cDj\nRSXTKc6fEseIlFLjyeVyDfkgWlnbRnVDe5yjiq8RDVwYYxqcFsiHsVtZ7MJWBF+IVUBO9+BDwGeM\nMc8ZY/YA9wM3iciGMJc8CLQCDxljjhljHsW27D7unL8TmAu8yxhzwHm+B7CZhdfHKu7ROFXTGlgF\n3ON2caOmgCs16RTMyETmzgg83nngfGC7msko2k3+CrAtlpudr2uAfqAC+BqwNYYxrcZ22W0bOGCM\nOSMiZ4BNwCsh5TcB240xwYn+24DHRMSFXbfvDcaYtqDzA2VnECden5/yoC3OVy0qYFq2bnGu1GR0\nw4oiTla30O/1ceFiF8fPXkTm5cY7rLiIdkypHjsWsw/4DfAJYIcxpnMMYipxvteEHD8PhEtNKQH2\nhimbBeQZY2rCPNensF2QL19dqKN35LRdmBEgPc1D2VKdo6DUZJWdZaeBvH7UDpvvOljLwpLpkzIL\nN9pK6efALdhWjAuYAqSLyMvGmJbhLhyFLMBnjAld170HCLehUBYQmrIyMMn3svIi8gFs9+NHjDHN\noedDVVRUjOrccPq8fl7c30pvv22iL5uTyeFD+0f1XLE22ntKdHpfyWMi3hNEcV9eP20t9n2hEfjF\nH1tYWJz4e6jF+vWKqlIyxrwdQERWYpMFbsVmxk0VkQPAVmPMwzGKqQtwi0iKMaY/6Hg6YbbMcMqH\n9nsNPB5SXkQ+i83q+7ox5l+iCaasrCzs8YqKiojnrmT3oVqmTrf/9DlZafzp3UsT4hPR1dxTItP7\nSh4T8Z4g+vvKyr0QmFDb6vVwzcplZKRF23YYf5Hu62oqqpEmOhw0xnwXeBvwVmxX3mrgb0cdweXO\nOd9DR/1ncXk33ED5cGU7sAkQiIhbRB7HVkh/b4z5TOzCHZlLXX3sOz44Ufb6FUUJUSEppeLvmtK8\nwNhyT6+XPcca4hzR+Iv63VBEVonI3zlr4l3EJjfMxy7GelMMY9oPtGMTKgZ+93znd20PU34HsNlJ\nahiwBdgZlPzwL9gsvfcZY74Rw1hH7LUjdfQ5iy/mTx+adaOUmtw8Hjc3rBjcofbAyUY6OnvjGNH4\nizb7rgHIw3aHPQ/8HfCHsZg4a4zpEZHHgG+KSCPQADwGvGSMKXdSxnOBZmNML/AE8EngcRH5DnA7\n8E7sZF9E5F7gA8CXgKedrTYGtBhjxm0KdUt7D0cqB4exblxZrBNllVJDLCqZzt4ZF2i42Em/18er\nR+q4dd3ceIc1bqJtKT2Jne+TZ4x5mzHmibGokIJ8Dvix83u3AlXAfc65DUCt8x1nlYe7sas57MUm\nMTxgjHnRKf8u5/s/ONcFfw0857jYfbgWnzP/oKQwm7kzc8bz1yulkoDLNXTO4tEzFyfV8kPRJjp8\nDEBE7hGRW4BpQCM2LfzpWAflJDg87HyFntuGzQAMPlYOXBfhud6JbTnFVcPFziHLCd2wQltJSqnw\n5szMYW5RDmfr2vH7/ew+VMs9GybHZuDRdt9lYJMabgd6sVtYFAKfFpFtwL3j2Q2WjMoPDTYsF86e\nRlGeLieklIrshhXFnK2zSw6dqmmlvrlzUuxEHW333SPADcDbgUxjzBzsHKB3AGXYrjEVQXVDe+A/\nl8vl4voVupyQUmp4hTOyhuwYEPzBdiKLtlK6H/iCMebnxhg/gDHGb4z5GXbx1HeMUXxJz+/3syto\nOaFl82eQOzXxJ8QppeLv+hVFuJ1u/nP17Zyrn/iLtUZbKc0ADkU4dwi7JboK40xtG/XNdjUmj9vF\n+uVFV7hCKaWsGTkZLJ0/OG2k/FDthF+sNdpKyeCkWIfxBkB3pA3D7/ez+3Bd4PGKhfnkZOkeiUqp\n6F23vAiP27aW6ps7qTzfdoUrklu061d8B/iBiKQCPwXqgCJst90HgY+OTXjJ7WR1C40tXQCkety6\n6KpSasSys9JYuSg/sBLM7sN1lM6aOmGzd6Pd5O9H2CV6/gq7gsJJ5/tfAl8zxjw2ZhEmKZ9vaCtp\n1eICsjJS4xiRUipZrZVCUlPs23VTaxcnq2O9DnbiiKpSEpFSY8w/YNeUuxd4N/BGYJYxJmYb/E0k\npuoiLe3O1hSpHtZIQZwjUkolq6yMVFYtGnwP2X24Dp9vYo4tRdt996qI/J0x5kkg5pNlJxqv18dr\nRwdbSWukMKFX+lVKJb41Swo4eKqR3j4vLe09HD93kaUTcCPAaBMdvNgVHFQUjp5ppu2SXUQxMz2F\nVYvy4xyRUirZZaSnsHrJYGvp1cN1eCdgaynaj+9fwC6QmoNdxbsjtIAx5nwsA0tWXq8vsHsk2FZS\nWqonjhEppSaK1YsLOHCike7eftou9XK0sokVCyfWh95oW0rfBZZhM++OYvcwCv1SwJHKZjq67Ka5\nmekprFyYF+eIlFITRVqqh7UymMX7+tF6vF7fMFckn2hbSn8zplFMEP1eHxXHBltJZUsLSU3RVpJS\nKnZWLspj7/EGunr66ejq48iZZlZOoNZStKuE/9dYBzIRHD7VFGglZWWkTrhmtVIq/lJTbGtp5wE7\nYlJxtJ7l83PxTJAdrKNdJfyBYU77sGNMJ40xkZYimvD6+n1UmMGti9ctK9RtzpVSY2LFwnz2Hr9A\nZ3efbS1VNrNygiRURdt99wSD40/B04j9Qcf8IrIVeIsx5lKM4ksah0830tltW0nZmaksL9WxJKXU\n2EhNcbNWCtix32ktHatnWWnuhPggHO0d3INtDX0KmI/dtmIu8LfO8fcDbwIWYbe5mFT6vT72mAuB\nx2VLZ06I/xxKqcS1YmF+YJUY21pqinNEsRFtS+lb2OWE/inoWDXwqIikAB81xqwRkX8Avgx8LMZx\nJrTDp5qGtJKWlU68CW1KqcSS4nFTJoW8vL8GgD3HGlhempf0H4ijjX4xsDfCucPAUufnU9gdaScN\n20oaHEtau1THkpRS4+OahXlMCWotHT3THOeIrt5Itq54T4RzD2ArI4AF2BXEJ40jlU1cclpJUzJ0\nLEkpNX5SPO4h85YqJsC8pWi7774E/K+ILAR+AVzAtojeAlwHvF1EVgH/iJ1gOyn0e33sORbUShJt\nJSmlxtfyBXm8fqw+MG/pWNVFrlmQvB+Oo9264lfYTf56ga9is/G+DPQAtxljngLmAb8CPj02oSae\no0GrN2RlpHKNrt6glBpnqSlu1gS3lo7VJ/WaeFEvXW2MeR54XkTSgFygwRjjCzr/W+C3sQ8xMXl9\n/qFjSVKgrSSlVFysXJjHXmNXeWi71MvxqotJm3A14v0UjDG9TLJxo3Bqmnpp77T/fJnpKUndXFZK\nJbfUFA+rlxSw62AtAK8fq0fmzcDtTr7daRNykx8R8WB3un0vkIPdw+lDxpj6COXXYReNXQPUAI8Y\nY34YdD4Lu6X727D3/HPg74wxl612Hg2fz8+J891k5WQBsHpJga5xp5SKq5UL89lrLtDd209rRw8n\nzl1EknC/pUTtb/oiNtvvAWAzUAI8Fa6giBQAzwB7gLXAPwNPiMidQcX+HdiI3S33TcAtzrFROXHu\nIp09tucyPc0zoRZDVEolp7RUD6sWD74XVRxrwO9PvrGlhKuUnDGrh4DPGGOeM8bsAe4HbhKRDWEu\neRBoBR4yxhwzxjwKPAl83Hm+EuCdwAeNMeXGmJeda94hIrNHGp/f76ciKOPu2sUFul+SUiohrFqU\nH3g/am7r5nRNa5wjGrmoKiUReUBEwg6aiEiRiDwcw5hWY7vstg0cMMacAc4Am8KU3wRsD066cK69\nSURcwAbsorE7g87vxO6mu3GkwZ2qaaW5rRtwPploK0kplSAy0lJYETS+/fqx+qRrLUXbUvo+dmJs\nOKuxaeKxUuJ8rwk5fh6YE6F8uLJZQJ5zvsEY0zdw0hjTDzREeL5hnaoe/OSxcmEeGekJOSynlJqk\nVi8ZzAS+cLGLs/XtcY5oZCK+o4rI74DlzkMX8CsR6QlTdCaDKzrEQhbgC65EHD3YhWDDle8OUxan\nfLjzwz3fEBUVFUMeX7zQRWNjN1npbvyXaqioqL3SUySV0PudKPS+ksdEvCcY3/vKpJPKRvs2+Kvn\nWrhpWTYu19hk4sX6vob7mP8V7OrfON9fw67kEMwLtACx3ASwC3CLSIrTohmQDoTbEqPLOUdIWZzy\n4c4P93xDlJWVDXm8dq2f5rZujh87xI3Xr7/S5UmloqLisvudCPS+ksdEvCcY//uSZb388I9H8TmT\naIvnLWJ2QXbMf0+k+7qaiipipWSMKQfKAZyVwL9sjKkc9W+K3jnne3HQzwCzuLybbqB8ccixWdgt\nNVqd84Ui4jHGeCFwP4URnm9YLpeLvGmZpKUkXI6IUkoBkJ2VxtJ5uYHtLCqO1Y9JpTQWol1m6H3G\nmEoRyRCRzSJyv4jMcDLbYm0/0A7cPHBAROZj93HaHqb8DmCzk9QwYAuw00l+2ImtfG8MOr8Re+/B\nyQ9KKTVhrJXCQJfd2bp2LlzsinNE0Yn6476IfAibQLAN+DFQCvy7iDwvIlNiFZAxpgd4DPimiNwt\nImuxi7y+ZIwpF5E0J+MvzbnkCaAAeFxElonIR7Ap4N9wnq8G+Bl27tJNIrIR+B7wI+ecUkpNONNz\n0llUMi3weE/4tQcSTrQp4X+BnZT6A+A2BrdEfwJYj11FPJY+h634ngS2AlXAfc65DUCt8x1nlYe7\nsas57AU+DDxgjHkx6PkeBF4B/gD8GngR+ECMY1ZKqYSyVmYGfj5Z3UpLe7hctcQSbT7zJ4BvGWM+\n6SwBBIAx5hfOBNSHcSarxoKT4PCw8xV6bhuDleLAsXLsFhqRnq8DeJ/zpZRSk0LBjEzmFuVwtq4d\nv9/P3uMNbCkb8UyYcRVt910p8GyEcweBotiEo5RSKpbKlg62lo6dGdxuJ1FFWylVE7klssY5r5RS\nKsHMyp9CUZ4d9vf6/Ow/ETqzJ7FEWyn9J/B5EflbbKsJIFNE3gx8FvhhxCuVUkrFjcvlomzp4CaA\nh0830dPnjWNEw4u2Uvo6tuL5FmCcY9uBX2JX6I7lMkNKKaViaH7xVHKn2gVsevu8HD7VFOeIIosq\n0cEY4wf+WkS+BdyK3Xm2FbsQ6sExjE8ppdRVcrlcrFlSyAuvnwVg34kLXLs4H08C7pY9otVEjTHH\ngeNjFItSSqkxsmTudHYfrqWjq4/O7j7M2YssL028HbOHW5D1CyN4Hr8x5pEYxKOUUmoMeDxuVi0u\n4JUD5wHYYxpYNj93zBZqHa3hWkqfj+J6F4NzhrRSUkqpBHbNgjxeP1pPb5+XlvYeztS2UTpr2pUv\nHEfDLciaOtyFIvI3wD9iK6VPxzgupZRSMZae6mHFgjz2GLt79p5jDQlXKY14lEtESkXkBeBfgV3A\nCmPMv8Y8MqWUUjG3anEBbrft4KptukRd0xV38BlXI6qUnHlKB7ATZh80xtxtjDk7JpEppZSKuezM\nVGTujMDjvccTazJtVNl3IiLYCbQ3Ar8BPmCMmVhbriql1CSxekkBR880A3C6ppXWjh6mZYfbC3X8\nDdtSEhG3iHwa2AcsBt5hjHmrVkhKKZW88qbZhVoB/H4/+xKotRSxUhKRVcCr2G3RfwEsM8b8z3gF\nppRSauysWTK49NDRM8109/THMZpBw3XfvQ54sCs3FAA/sb14YfmNMXfFODallFJjpKQwm/zpmTS2\ndNHv9XHodBPrls288oVjbLjuu1ew69vtB1Kv8JUW4TmUUkolIJfLxeolBYHHB0420u/1xTEia7h5\nSreMYxxKKaXG2eKS6ZQfHFx66MTZFpaV5sY1psRbjU8ppdS48HjcrFo02Frad+ICfr8/jhFppaSU\nUpPa8gW5pDqrhTe1dlHd0BHXeLRSUkqpSSwjLYWl8we77OK9M61WSkopNcmtWpwfWC38TG0bF9u6\n4xaLVkpKKTXJzcjJYL4zmRbi21rSSkkppRSrZXAy7bGqi3GbTKuVklJKKWblT6FgeiYA/V4fhyub\n4hLHiLZDHw8iUgj8C3An0At8H/isMSZitS0i7wK+AMzFTvb9iDHmtaDzt2M3IVwBNAM/A75gjOka\nq/tQSqlk4nK5uHZJAc+/ajd+OHiykdVLCvG4x3dn2kRsKT0FFAE3A+8F3gd8KVJhp8L5T+BbwFrg\nIPCsiBQ4568Ffg8875z/K+Dt2IpPKaWUY3HJdLIy7P6uHV19nK5pGfcYEqpSEpEbgY3Ae4wx+40x\nfwA+AXxERCKtq/4J4CfGmP9njDkK/DW2NfSXzvm/APYZYz5vjDlhjHkG+Czwf0Rk2N11lVJqMvF4\n3KxYkBd4vP9E47jHkFCVErAJqDLGVAYd2wbkAKtDC4uIG7jJKQOAMcaHXbNvk3Poe8CHQi71Ydfr\nmxKjuJVSakJYsTAvsDNtXdMl6ps7x/X3J1qlVALUhBw773yfE6b8dGzFEu6aOQDGmEPGmNcHTjit\no48B5caY8W+bKqVUAsvKSGXJnOmBxwfGOT18XBMdRGQ+UBnhdA/wJDBk1pYxpk9E/EBGmGuynO+h\nM716wpUXEQ/wA+AabDfhFVVUVIzqXLKaiPcEel/JZCLeEyTXfbm6+2lsbAegqamRLF8DGWnh2zCx\nvq/xzr6rAZZFOOcDPgIMGTtyWjYu4FKYaway50LHm9JDy4tIFvAT4C7gvuDW03DKysrCHq+oqIh4\nLllNxHsCva9kMhHvCZLzvlr6T1DbZN9GU3JmUrai+LIyke7raiqqca2UjDF9wLFI50XkHPCGkMOz\nnO+hXXRgExouAaH/WrOCy4tIHjYDbzlwrzHmhZFFrpRSk8u1iwsCldLABoAez9iP+CTamNIOYIGI\nBI8fbQHagX2hhY0xfuxmhDcPHHOSHzZjkx0QkUzgGWABcItWSEopdWWls6eRnWkTlLt6+jlZPT5D\n8Ik2eXYXUA78j4h8GJgJfAP4tjGmF0BEsoFsY0ydc823gd+KyF7gRWwSwzTgP5zzX8Zm7r0ZOC8i\nRUG/r8HJ1lNKKRXE43axYmE+5YdqAbszrcwb+w0AE6ql5LR8/gSoB17GrubwH9iKZcDHgdqga57G\nToh9GNiD7aK70xgzkGD/LsCD7b6rDfmahVJKqbCWl+YGVnSob+4cl/TwRGsp4bSA/mSY818Evhhy\n7PvYCixcea14lFJqFLIyUlk8ZzrHqi4CNj38juvnjenvTKiWklJKqcQSvF36yeoWOrv7xvT3aaWk\nlFIqosLcLIry7OI3Xp+fI5XNY/r7tFJSSik1rJULB9fDO3SqEa/PP2a/SyslpZRSw1oUsnp45fnW\nMftdWikppZQalsfjZnnpYDr4oVNjt3q4VkpKKaWuaMWCPNwumx5e3dBBc1vokqOxoZWSUkqpK8rO\nSqN01tTA44Mnx6a1pJWSUkqpqKxYmB/42Zy9SJ839gkPWikppZSKSklhNjNy7K5AvX1eqht7Yv47\ntFJSSikVFZfLxcpFg+nhZ+p78Ptj21rSSkkppVTUZF4uqSm26ujo9uGL8ZwlrZSUUkpFLT3Vw81r\nS8hIS2FOfhpuZ8HWWEm4BVmVUkoltqXzclk6L5eKigpcrthWStpSUkoplTC0UlJKKZUwXLHOnJhI\nKioq9B9HKaVGoaysbFT9elopKaWUShjafaeUUiphaKWklFIqYWilpJRSKmFopaSUUiphaKWklFIq\nYWilpJRSKmHoMkNhiIgH+ArwXiAHeBr4kDGmPkL5dcB3gTVADfCIMeaH4xNtdERkJvAN4E4gE9gN\nPGyMORSh/M+APws5/IIx5vYxDXSERGQ5cDjMqU3GmB1hyifDa3ULsDXC6a3GmFvDXJPQr5eIPA6k\nGGMeDDp2J/b/pAAngL83xvxxmOfIAr4DvA373vVz4O+MMR1jGftwItzXh4EPA3OAKuDbxpj/GOY5\n3gD8PsypOcaY6hiHHJUI9/UqsD6k6BPBZUKeY1Svl7aUwvsi8B7gAWAzUAI8Fa6giBQAzwB7gLXA\nPwNPOH9wCUFE3MAvgSXAW4ANQCvwgojkRbhsJfApoDjoK/RNLxGsBBoZGmcxttIdIhleK8crXH4/\nDwA+4B8jXJOQr5eIuETky8BfhxxfDvwG+0a1Bvg18CsRuWaYp/t3YCPwRuBNwC3OsXE3zH19APi/\n2A+1q4BvA4+JyLuHebqVwF4uf83Pj0HowxrmvlzANcC7QmL82DBPN6rXS1tKIUQkDXgI+Kgx5jnn\n2P1ApYhsMMa8EnLJg9g3+IeMMT7gmIisBT4OPDuOoQ/nWuBGYLkx5iiA80fSDNwLDGkpiEg6sAh4\n1RhTN86xjtQK4EiUcSbDa4UxphcI3I+ITMO2KP7JGPNMaPlEfb1EZAHwBPY1Ohty+iGg3BjzVefx\n50Vko3P8r8I8VwnwTuA2Y0y5c+xBYKuIfNIYUzNGt3GZK9zX3wD/aox50nl8SkRuBN4H/CjCU64A\nDsb7tbvCfS0AsoBd0cR5Na+XtpQutxrbZbdt4IAx5gxwBtgUpvwmYLvzJjdgG3CT8+kiEZzFflox\nQccG4p0RpvxS7AeWo2McVyysIPo4k+G1CufzQA/w5QjnE/X12gCcw7YEKkPObSLob8yxjfB/YwPP\n5QN2Bh3bCXixn8bH03D39VHg8ZBjPsL/nQ0Yyf/hsTTcfa0AurDdkdE+16heL20pXa7E+R5ak5/H\n9hGHK783TNksIA/btRRXxpgmLu+z/ih2bClcC2EF0At8SUTuwf5n/DnwFWNM91jGOgorgAwRKQfm\nA4eAzxhjXg1TNuFfq1AiUogdn/iAMaYzQrGEfL2c1sKTACISerqE6P/GBso3GGP6gp6/X0Qahrlm\nTAx3X8aYl4Ifi8hc4B3Ao+Geyxm/XgqUich+oAB4DfikMcaEu2asXOH1WgG0AD8WkZuBJuD7wHdC\nPuQNGPXrpS2ly2UBvuB/TEcPkBGhfOgf/sDG9eHKx52IvBn4OnYANtwntGsAF3AM2733JWzXV1z6\n7yMRkUxst8I04BPAm7FvbC+JyLIwlyTdawV8AGjAebOIIClerxCRXotIr0O48le6Jq6cMczfY7ti\n/2+EYgux8acDfwn8ufPzy84HkkRxDZCNHZO9C/hX7P+zf4hQftSvl7aULtcFuEUkxRjTH3Q8HbgU\noXx6yLGBx+HKx5WIvBf4HvBT4JMRin0O+KYxptl5fFBEvMBPReRjTssr7owxXSIyA+gxxvRA4P7K\ngA8CHwm5JKleK8f/Ab4f5kNSsKR4vUJEei0ivQ7hyl/pmrhxxmf+iH1zvtkY0xqunDHmuJNs1DLQ\n4p2ujo0AAAo/SURBVBCRt2G73N8NfGucQr6SB4BsY0yL8/igM9b5WRH5ojEmdGXvUb9e2lK63Dnn\ne3HI8Vlc3t0wUD5c2Q7soHrCEJHPYpvcjwMPRGh2Y4zxBb3BDTjofB/XrpIrMca0DVRIzmMfNkU8\nXJxJ81oBOJloi7AfICJKptcrSKTXItIA+Dmg0OnuAkBEUoDCYa6JCyd5Zhd2TGWDMeb0cOWNMc3B\nf4tON+1pEui1M8b0B1VIAw5ix9+nhblk1K+XVkqX2w+0AzcPHBCR+djxiu1hyu8ANocMlG8BdkZ6\n048HEfkkNk31C8aYj4T5ZBNc9mci8suQw+uwTe+TYxjmiIhImYi0iUhZ0DEPNlkl3NylpHitgmwC\naiN0sQYky+sVYgdBf2OOLYT/GwM7SJ6CzSIdsBH7HrYz7BVxICJLgeewiVEbjTHnrlD+rSLS7nT1\nDRzLwU7fCPd/OC5EpFxEvhtyeB1wPkxlBVfxemn3XQhjTI+IPAZ8U0Qasf35jwEvGWPKnZTxXKDZ\nSd19AtsN9riIfAe4HZsKeXd87uByIrIK+Brwn8D3RKQo6HQ70MfQe/pfnK4f7PyRNcA3sV1EcZuo\nGMZ+7B//v4vIh7Atnr8H8oHvJuNrFWINNnFjiDD3lSyvV7BHgQoR+RLwE+zrcD12DA0IjMn0GmNa\njTE1zgThJ0TkL7BjaN8DfjSe6eBR+CF2LOXdQGrQ31q/MaYRht4X8BLQBvzI+eCYgv1bbSRyCnk8\n/AL4sohUYCuVW7B/aw8NFIjV66UtpfA+B/wYO7i8FZsGeZ9zbgNQ63zHWeXhbuwbwV5sptQDxpj/\n3975x9hRVXH8QxqgKBAEIoJaCEKP/Rcjppa2pECh2lJ+CRYRUiCkQE0oxRg1LdXKj4IIiTExpqhI\ngQIrwZQiCC1LwKBtLRTQeMxCS7OhKWhqqCHyo5Q/vne607tv3773drtvXns+STP73tyZuXdu3z1z\nzz1zvqtHuM71+CYwCrgc1b38bx792/QQymYxGw2Kd6AsCAtHuN51SWt+01Co+wpgDfAZYJK7v0Vn\n9lWZo9G7ZDkd2V9l3P0V4Fz0u3oJBanMyGaFa1E7Cq5ELxY/jozvakpGrN2Y2ViU8eAY9H+y/Dv7\nS6norna5+zb0cPQBConvRmsuUyoW6Xo78AM0Nv4dGaR5WaaKYemvUJ4NgiAIKkPMlIIgCILKEEYp\nCIIgqAxhlIIgCILKEEYpCIIgqAxhlIIgCILKEEYpCEaQimcjD4K2Ey/PBpXGzLrp/+b/+0AvEl5c\n2I73Oczst+iN/ROaOGYcsBSYUPpuJ7DA3X8y7JWsXYcDgHVISfm5dH8/rKdQm7e1XOeUa/A3JJXU\nVu7LcGBmJ6CM9ycNkGEg6BDCKAWdwFoktVEwGhmqhcAY9HJwJ3A+6YXXEuPpy7c4EtwI/NPdn2vi\nmMUox9lwlx023L3HzB5BasKXjvT1g+EjjFLQCbxTqFeW6E7qlleY2Tx339KOig2VGu3aY5jZZ5HK\n7snNHOfur+2JsnuA24BeM7vL3de3sR7BEAijFHQy61EqkzHAlpSFeC7SpTkepXe5G7jV3XfALndg\nD9Jdugb9Bv4IfKeUm6ybzKVlZqeilFMT3f35vCJm9kk0czsv1ec9lCn6u+7+spktImnPJPfXj9x9\nUe6+S4b2ZuA0pFa6Lu1/Nu0/DqmCno9kLaYid2YXcF0dIUBQSqked9+Qfb+fmf0YpYA5EKVsml/I\nXjfjkqvh6mu0Txyl87oaCd39DcnWr0tlDkLpk85O+zcCS939p8W13f0tM1sFfB/4xmB1DapJBDoE\nnczYtC2ezu8GlgAPocHrd8gQ/Co77gI0aF2FBuozgCfMbCi/h3uBy5BBmQpcj2Sl70/BDUvpE90b\nnz7vhpkdjVyVX0GihRcB7wJPm9mUrPhSJG8wE+UluxINxvW4GK3D5UxGeeiuQhpUpwNPJoMyVBrt\nk4uA6ciAzUI5DLtKfXIXynM4H4nM/QG43cwuy87TBcxMDwlBBxIzpaAT2C8bII9EA9Qc4GF3/3fS\nHroUzUyKp+enzOxdYImZ3enuRcbtg4CphayAmb2NZgfT6C8bPyhmNhqJuc1196709bNmdih6uj8y\nBQH0Ql2X3fXAYcDJpbqtRNnQl6BknwUr3P2G9PcqMzsDDeoLBqjjOJTgtZZM/AfAme7+Ziq7Fc0e\npwOPDtb+gWiyT0alOmxPxx4C3IMM+wZkOJ9y9wdT+W4z+x/9JezXAfujYJI/tVr3oH2EUQo6gSlo\n4CyzAw2YRdbhSWn7QFbuPjSgT6ZPBuL5TOdmJXK3TaQFo5Si/86CXes2Y9O/6anIAQ2ealJeN3f/\nyMyWA4vTQF2Qa9L0Ap+rc+7j03ZjjX1/LgxSuuYTZvYe0r9p2SjRXJ+8UhikRG/aFjOeZ4A5yb35\nOLDS3RfXuOamtD2u9WoH7SSMUtAJrAGuTX/vRFLLm7L1k8PTdmt2bPG5rI75ZrmAu+9Ms6VPtVpB\nMzsTuZi+iDSqNiB9J5CWTCMcDvyrxvdb0znKRilfO/qI+u74ov21pKjzewbwNrUVRZuhmT6p1R7o\na9N1yFBdgrSYfm5mLwBXZ2tkRfuGWvegTYRRCjqB7cWCdx22pe1R7C63XEhul908R5QPTGs+n0aC\njiDDN4rdOXigC5vZF9CM4hHg68DGZOiuoTkBwW1oLSWnaMN/6C8h3ihF+w+rsW83Y1zjfrRKM31S\nlyR5fxNwk5mNAWYgV+Uy5OIrKNrS8LmDahGBDsHeQiGjPSv7vvhcjpibYGblwXkGcrEVYn/vAJ/P\nznNKnWt/Cb07dbO7v16Smp+WtsXvbEedc4BUSE9JLioA0kL/hcDaNDC3yhtpW8vFNyFzDc5E96N7\nCNeD5vpkQMxstJm5mc0HcPfN7v4L5BbM+6lo3+YW6htUgJgpBXsF7v6qmS1DT9KfQOHY44EfAsvc\n/R+l4ocAK83sFjQzuRUtoj+T9j8GnG1md6AAiInUfyFzPfAhcJuZ3YkM1Gw0a4K+dZH/ApjZLOAF\nd9+Unedn6TqrUgj5dhS2Pg74WoO3oibu7ma2GRnXx7Ld+wMr0v0Yg+7HauDpIV6zmT6pd57/m9ka\n4EYzex94GTCkttuVFZ+AXHj5mlvQIcRMKdibmI1Csi9HA++3gUVo8CrTjRbOlwG3oCfuc0r7f40W\n4r+FFtW/isLIa+LuPejp/1hkxIrQ71ORK3Bi+vwoCvm+B73Emp9nCxpUXwV+CSxHkYKnu/twRJJ1\n0Td7K1PUazkKL/89cE5pxjcUGu2TwZiDwstvQFF1C1BY/Nys3DQUBFElKfGgCUIOPdinaCTX295K\ncgv2AJPd/a/trs9wk9aaXge+7O4vtrs+QWvETCkI9hHcvRdFrn2v3XXZQ8xH762FQepgwigFwb7F\nAmCsmeWZ1zsaMzsRBWhcO1jZoNqE+y4IgiCoDDFTCoIgCCpDGKUgCIKgMoRRCoIgCCpDGKUgCIKg\nMoRRCoIgCCrDxyOfzAVqkIdQAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d5d9e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "sns.set(style='whitegrid', font_scale=1.5)\n",
+    "plot(pop_array, net_growth_array, '-')\n",
+    "decorate(xlabel='Population (billions)',\n",
+    "         ylabel='Net growth (billions)',\n",
+    "         legend=False)\n",
+    "savefig('chap03-fig05.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using `sns.set` to reset the plot style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sns.set(style='white', font_scale=1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the book we found that the net growth is 0 when the population is $-\\alpha/\\beta$:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.88888888888889"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-system.alpha / system.beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is the equilibrium the population tends toward."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** In the book, I presented a different way to parameterize the quadratic model:\n",
+    "\n",
+    "$ \\Delta p = r p (1 - p / K) $\n",
+    "\n",
+    "where $r=\\alpha$ and $K=-\\alpha/\\beta$.  Write a version of `update_func2` that implements this version of the model.  Test it by computing system variables `r` and `K` equivalent to `alpha` and `beta`, and confirm that you get the same results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.025, 13.88888888888889)"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.r = system.alpha\n",
+    "system.K = -system.alpha / system.beta\n",
+    "\n",
+    "system.r, system.K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func2(pop, t, system):\n",
+    "    net_growth = system.r * pop * (1 - pop/system.K)\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wd3enR48eNZZ2FEKIutIb9PyQ+gPfxHyDVY4VVpVXiv54u7rhWu5FUaEfI+/p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\n/w+7gjH+B/QktwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d8350f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_simulation(system, update_func2)\n",
+    "plot_results(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** On the Wikipedia page about world population estimates, the first table contains estimates for prehistoric populations.  The following cells process this table and plot some of the results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select `table1`, which is the second table on the page."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Population Reference Bureau (1973–2015)[6]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[7]</th>\n",
+       "      <th>Maddison (2008)[8]</th>\n",
+       "      <th>HYDE (2010)[citation needed]</th>\n",
+       "      <th>Tanton (1994)[9]</th>\n",
+       "      <th>Biraben (1980)[10]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[11]</th>\n",
+       "      <th>Thomlinson (1975)[12]</th>\n",
+       "      <th>Durand (1974)[13]</th>\n",
+       "      <th>Clark (1967)[14]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>-10000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2M[15]</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1–10M</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-9000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-8000</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5–10M</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-7000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>-6000</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Population Reference Bureau (1973–2015)[6]  \\\n",
+       "Year                                                 \n",
+       "-10000                                         NaN   \n",
+       "-9000                                          NaN   \n",
+       "-8000                                          5.0   \n",
+       "-7000                                          NaN   \n",
+       "-6000                                          NaN   \n",
+       "\n",
+       "        United Nations Department of Economic and Social Affairs (2015)[7]  \\\n",
+       "Year                                                                         \n",
+       "-10000                                                NaN                    \n",
+       "-9000                                                 NaN                    \n",
+       "-8000                                                 NaN                    \n",
+       "-7000                                                 NaN                    \n",
+       "-6000                                                 NaN                    \n",
+       "\n",
+       "       Maddison (2008)[8] HYDE (2010)[citation needed]  Tanton (1994)[9]  \\\n",
+       "Year                                                                       \n",
+       "-10000                NaN                       2M[15]               NaN   \n",
+       "-9000                 NaN                           4.               NaN   \n",
+       "-8000                 NaN                           5.               NaN   \n",
+       "-7000                 NaN                           8.               NaN   \n",
+       "-6000                 NaN                          11.               NaN   \n",
+       "\n",
+       "        Biraben (1980)[10]  McEvedy & Jones (1978)[11] Thomlinson (1975)[12]  \\\n",
+       "Year                                                                           \n",
+       "-10000                 NaN                         4.0                 1–10M   \n",
+       "-9000                  NaN                         NaN                   NaN   \n",
+       "-8000                  NaN                         NaN                   NaN   \n",
+       "-7000                  NaN                         NaN                   NaN   \n",
+       "-6000                  NaN                         NaN                   NaN   \n",
+       "\n",
+       "       Durand (1974)[13] Clark (1967)[14]  \n",
+       "Year                                       \n",
+       "-10000               NaN              NaN  \n",
+       "-9000                NaN              NaN  \n",
+       "-8000              5–10M              NaN  \n",
+       "-7000                NaN              NaN  \n",
+       "-6000                NaN              NaN  "
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table1 = tables[1]\n",
+    "table1.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not all agencies and researchers provided estimates for the same dates.  Again `NaN` is the special value that indicates missing data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Population Reference Bureau (1973–2015)[6]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[7]</th>\n",
+       "      <th>Maddison (2008)[8]</th>\n",
+       "      <th>HYDE (2010)[citation needed]</th>\n",
+       "      <th>Tanton (1994)[9]</th>\n",
+       "      <th>Biraben (1980)[10]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[11]</th>\n",
+       "      <th>Thomlinson (1975)[12]</th>\n",
+       "      <th>Durand (1974)[13]</th>\n",
+       "      <th>Clark (1967)[14]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1913</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1793.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1920</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1860.0</td>\n",
+       "      <td>1863.</td>\n",
+       "      <td>1912.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1968.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1925</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1930</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2070.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2092.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2145.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1940</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2300.0</td>\n",
+       "      <td>2299.</td>\n",
+       "      <td>2307.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2340.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Population Reference Bureau (1973–2015)[6]  \\\n",
+       "Year                                               \n",
+       "1913                                         NaN   \n",
+       "1920                                         NaN   \n",
+       "1925                                         NaN   \n",
+       "1930                                         NaN   \n",
+       "1940                                         NaN   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[7]  \\\n",
+       "Year                                                                       \n",
+       "1913                                                NaN                    \n",
+       "1920                                             1860.0                    \n",
+       "1925                                                NaN                    \n",
+       "1930                                             2070.0                    \n",
+       "1940                                             2300.0                    \n",
+       "\n",
+       "     Maddison (2008)[8] HYDE (2010)[citation needed]  Tanton (1994)[9]  \\\n",
+       "Year                                                                     \n",
+       "1913              1793.                          NaN               NaN   \n",
+       "1920              1863.                        1912.               NaN   \n",
+       "1925                NaN                          NaN               NaN   \n",
+       "1930                NaN                        2092.               NaN   \n",
+       "1940              2299.                        2307.               NaN   \n",
+       "\n",
+       "      Biraben (1980)[10]  McEvedy & Jones (1978)[11] Thomlinson (1975)[12]  \\\n",
+       "Year                                                                         \n",
+       "1913                 NaN                         NaN                   NaN   \n",
+       "1920                 NaN                         NaN                   NaN   \n",
+       "1925                 NaN                      2000.0                   NaN   \n",
+       "1930                 NaN                         NaN                   NaN   \n",
+       "1940                 NaN                         NaN                   NaN   \n",
+       "\n",
+       "     Durand (1974)[13] Clark (1967)[14]  \n",
+       "Year                                     \n",
+       "1913               NaN              NaN  \n",
+       "1920               NaN            1968.  \n",
+       "1925               NaN              NaN  \n",
+       "1930               NaN            2145.  \n",
+       "1940               NaN            2340.  "
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table1.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some of the estimates are in a form we can't read as numbers.  We could clean them up by hand, but for simplicity I'll replace any value that has an `M` in it with `NaN`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "table1.replace('M', np.nan, regex=True, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we'll replace the long column names with more convenient abbreviations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "table1.columns = ['prb', 'un', 'maddison', 'hyde', 'tanton', \n",
+    "                  'biraben', 'mj', 'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function plots selected estimates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_prehistory(table):\n",
+    "    \"\"\"Plots population estimates.\n",
+    "    \n",
+    "    table: DataFrame\n",
+    "    \"\"\"\n",
+    "    plot(table.prb, 'ro', label='PRB')\n",
+    "    plot(table.un, 'co', label='UN')\n",
+    "    plot(table.hyde, 'yo', label='HYDE')\n",
+    "    plot(table.tanton, 'go', label='Tanton')\n",
+    "    plot(table.biraben, 'bo', label='Biraben')\n",
+    "    plot(table.mj, 'mo', label='McEvedy & Jones')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results.  Notice that we are working in millions now, not billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Qhg4dCkBoaCihoaEaxxUKRa4DUkhICJMmTaJ169aMGTMGyAokaWlpavlUKhXG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\ntxl+wTmyC6wuq+HAo8va1ORXSY7sSeXIYznys6Eu7YaRrAbTm5qiC8T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YunRpnby3336b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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d8d39b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_prehistory(table1)\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistorical population estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `xlim` to zoom in on everything after Year 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NGzYMgKCgIIKCgjS2KxSKbCepwMBAJk+eTKtWrRg7diyQnlySk5PV6qlUKgwN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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349d99ac88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_prehistory(table1)\n",
+    "decorate(xlim=[0, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistorical population estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See if you can find a model that fits these data well from Year -1000 to 1940, or from Year 0 to 1940.\n",
+    "\n",
+    "How well does your best model predict actual population growth from 1950 to the present?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "p0 = table1.mj[1]\n",
+    "\n",
+    "prehistory = System(t0=1, t_end=2016, alpha=0.0011, p0=p0)\n",
+    "\n",
+    "run_simulation(prehistory, update_func1b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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78dVKUuPGjQPAx8cHHx+fHNsVCkWBk9SBAweYNWsWHTt2ZMqUKUBWcklLS1PZ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\nMKxyt0ggxCoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349eb61780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_prehistory(table1)\n",
+    "plot(prehistory.results)\n",
+    "decorate(xlim=[0, 2000], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistorical population estimates')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jLS1NqSwrK4tly5aRlJSEtrY2VatW5c6dO9jY2FC5cmUqV66Mmpoa06ZN4/Hj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\nQgzQ/wc/OSgSa9IqkwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1349ee35e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_estimates(table2)\n",
+    "plot(prehistory.results/1000)\n",
+    "decorate(xlim=[1950, 2016], xlabel='Year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistorical population estimates')\n",
+    "#not a good fit :)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/chap04-fig01.pdf b/code/chap04-fig01.pdf
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diff --git a/code/chap04-fig02.pdf b/code/chap04-fig02.pdf
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diff --git a/code/chap04mine.ipynb b/code/chap04mine.ipynb
new file mode 100644
index 00000000..9ce8e587
--- /dev/null
+++ b/code/chap04mine.ipynb
@@ -0,0 +1,1125 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 4: Predict\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# To switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Functions from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_estimates(table):\n",
+    "    \"\"\"Plot world population estimates.\n",
+    "    \n",
+    "    table: DataFrame with columns `un` and `census`\n",
+    "    \"\"\"\n",
+    "    un = table.un / 1e9\n",
+    "    census = table.census / 1e9\n",
+    "    \n",
+    "    plot(census, ':', color='darkblue', label='US Census')\n",
+    "    plot(un, '--', color='green', label='UN DESA')\n",
+    "    \n",
+    "    decorate(xlabel='Year',\n",
+    "             ylabel='World population (billion)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(system):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    system: System object with `results`\n",
+    "    \"\"\"\n",
+    "    #new_fig()\n",
+    "    plot_estimates(table2)\n",
+    "    plot(system.results, '--', color='gray', label='model')\n",
+    "    decorate(xlabel='Year', \n",
+    "             ylabel='World population (billion)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Run a model.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "\n",
+    "    system: System object\n",
+    "    update_func: function that computes the population next year\n",
+    "    \"\"\"\n",
+    "    results = Series([])\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        results[t+1] = update_func(results[t], t, system)\n",
+    "    system.results = results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Reading the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# The data directory contains a downloaded copy of\n",
+    "# https://en.wikipedia.org/wiki/World_population_estimates\n",
+    "\n",
+    "from pandas import read_html\n",
+    "filename = 'data/World_population_estimates.html'\n",
+    "tables = read_html(filename, header=0, index_col=0, decimal='M')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "table2.columns = ['census', 'prb', 'un', 'maddison', \n",
+    "                  'hyde', 'tanton', 'biraben', 'mj', \n",
+    "                  'thomlinson', 'durand', 'clark']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P8y2LiYlBKcUzzzyT4745rZB13fVjs/5r1qwZ9957L3PmzMnWU+G///3vLftm/ffzzz9n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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eedfdf5518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_estimates(table2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Running the quadratic model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the update function for the quadratic growth model with parameters `alpha` and `beta`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func2(pop, t, system):\n",
+    "    \"\"\"Update population based on a quadratic model.\n",
+    "    \n",
+    "    pop: current population in billions\n",
+    "    t: what year it is\n",
+    "    system: system object with model parameters\n",
+    "    \"\"\"\n",
+    "    net_growth = system.alpha * pop + system.beta * pop**2\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select the estimates generated by the U.S. Census, and convert to billions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "census = table2.census / 1e9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the starting time and population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "t0 = census.index[0]\n",
+    "p0 = census[t0]\n",
+    "t_end = census.index[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initialize the system object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2015.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha</th>\n",
+       "      <td>0.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta</th>\n",
+       "      <td>-0.001800</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t0       1950.000000\n",
+       "t_end    2015.000000\n",
+       "p0          2.557629\n",
+       "alpha       0.025000\n",
+       "beta       -0.001800\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = System(t0=t0, \n",
+    "                t_end=t_end,\n",
+    "                p0=p0,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)\n",
+    "\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the model and plot results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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39WN8/GomHuFWZs7sfVYmfmjgAu6uIgu4CyGcVVxSzIc/fIjer+kc0I0+3Xoz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\nhVaeXVPMlUvPbTsmKz2La86+JhbVNsaYEyIpk38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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eee001edd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_simulation(system, update_func2)\n",
+    "plot_results(system)\n",
+    "decorate(title='Quadratic model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To generate projections, all we have to do is change `t_end`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap04-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+ISkpid69e2tzXqXqILc+1caYhILnIhIKhAGnHXOOKFWiwMBABgwYQGpqqs4N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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eee00e9d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system.t_end = 2250\n",
+    "run_simulation(system, update_func2)\n",
+    "plot_results(system)\n",
+    "decorate(title='World population projection')\n",
+    "savefig('chap04-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The population in the model converges on the equilibrium population, `-alpha/beta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.856665141368708"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.888888888888889"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-system.alpha / system.beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  What happens if we start with an initial population above the carrying capacity, like 20 billion?  The the model with initial populations between 1 and 20 billion, and plot the results on the same axes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PnDghK1KkY1VKMQXE/0AgEMD+/ftly8liIEVLxXZa0Khe6foBxN/IarVWVG60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\nGAwGg8GT/w/HSnRJlPRdGgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eee12c2710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def run_system():\n",
+    "    for n in range (20):\n",
+    "        \n",
+    "        systemn = System(t0=t0, \n",
+    "                    t_end=t_end,\n",
+    "                    p0=n,\n",
+    "                    alpha=0.025,\n",
+    "                    beta=-0.0018)\n",
+    "    \n",
+    "        run_simulation(systemn, update_func2)\n",
+    "        plot_results(systemn)\n",
+    "    decorate(title='Quadratic model')\n",
+    "    \n",
+    "run_system()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparing projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compare the projection from our model with projections produced by people who know what they are doing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>United States Census Bureau (2015)[18]</th>\n",
+       "      <th>Population Reference Bureau (1973-2015)[6]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[7]</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016</th>\n",
+       "      <td>7.334772e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.432663e+09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017</th>\n",
+       "      <td>7.412779e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018</th>\n",
+       "      <td>7.490428e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019</th>\n",
+       "      <td>7.567403e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>7.643402e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.758157e+09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      United States Census Bureau (2015)[18]  \\\n",
+       "Year                                           \n",
+       "2016                            7.334772e+09   \n",
+       "2017                            7.412779e+09   \n",
+       "2018                            7.490428e+09   \n",
+       "2019                            7.567403e+09   \n",
+       "2020                            7.643402e+09   \n",
+       "\n",
+       "      Population Reference Bureau (1973-2015)[6]  \\\n",
+       "Year                                               \n",
+       "2016                                         NaN   \n",
+       "2017                                         NaN   \n",
+       "2018                                         NaN   \n",
+       "2019                                         NaN   \n",
+       "2020                                         NaN   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[7]  \n",
+       "Year                                                                      \n",
+       "2016                                       7.432663e+09                   \n",
+       "2017                                                NaN                   \n",
+       "2018                                                NaN                   \n",
+       "2019                                                NaN                   \n",
+       "2020                                       7.758157e+09                   "
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table3 = tables[3]\n",
+    "table3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`NaN` is a special value that represents missing data, in this case because some agencies did not publish projections for some years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "table3.columns = ['census', 'prb', 'un']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function plots projections from the UN DESA and U.S. Census.  It uses `dropna` to remove the `NaN` values from each series before plotting it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_projections(table):\n",
+    "    \"\"\"Plot world population projections.\n",
+    "    \n",
+    "    table: DataFrame with columns 'un' and 'census'\n",
+    "    \"\"\"\n",
+    "    census = table.census / 1e9\n",
+    "    un = table.un / 1e9\n",
+    "    \n",
+    "    plot(census.dropna(), ':', color='darkblue', label='US Census')\n",
+    "    plot(un.dropna(), '--', color='green', label='UN DESA')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the model until 2100, which is as far as the other projections go."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system.p0 = census[t0]\n",
+    "system.t_end = 2100\n",
+    "run_simulation(system, update_func2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap04-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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hWI3Jw8vJiwj/iCpPDDqdjnGh44jwj8DXxbfZJA+w/AkkHhgCVFdRNwRItVpE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\nC/UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1eee14ce0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system)\n",
+    "plot_projections(table3)\n",
+    "decorate(title='World population projections')\n",
+    "savefig('chap04-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "People who know what they are doing expect the growth rate to decline more sharply than our model projects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose there are two banks across the street from each other, The First Geometric Bank (FGB) and Exponential Savings and Loan (ESL).  They offer the same interest rate on checking accounts, 3%, but at FGB, they compute and pay interest at the end of each year, and at ESL they compound interest continuously.\n",
+    "\n",
+    "If you deposit $p_0$ dollars at FGB at the beginning of Year 0, the balanace of your account at the end of Year $n$ is\n",
+    "\n",
+    "$ x_n = p_0 (1 + \\alpha)^n $\n",
+    "\n",
+    "where $\\alpha = 0.03$.  At ESL, your balance at any time $t$ would be\n",
+    "\n",
+    "$ x(t) = p_0 \\exp(\\alpha t) $\n",
+    "\n",
+    "If you deposit \\$1000 at each back at the beginning of Year 0, how much would you have in each account after 10 years?\n",
+    "\n",
+    "Is there an interest rate FGB could pay so that your balance at the end of each year would be the same at both banks?  What is it?\n",
+    "\n",
+    "Hint: `modsim` provides a function called `exp`, which is a wrapper for the NumPy function `exp`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1343.9163793441223"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "n = 10\n",
+    "\n",
+    "x_n = p0 * (1 + alpha)**n\n",
+    "\n",
+    "x_n\n",
+    "#below is printed the monentary value at FGB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1349.8588075760031"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "t = 10\n",
+    "\n",
+    "x_t = p0 * (exp(alpha*t))\n",
+    "\n",
+    "x_t\n",
+    "#below is printed the monentary value at ESL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.030454533953516938"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "n = 10\n",
+    "t = 10\n",
+    "alpha1 = (exp(alpha*t))**(1/n) - 1\n",
+    "\n",
+    "alpha1\n",
+    "#below is printed thevalue of compounded interest needed at FGB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose a new bank opens called the Polynomial Credit Union (PCU).  In order to compete with First Geometric Bank and Exponential Savings and Loan, PCU offers a parabolic savings account where the balance is a polynomial function of time:\n",
+    "\n",
+    "$ x(t) = p_0 + \\beta_1 t + \\beta_2 t^2 $\n",
+    "\n",
+    "As a special deal, they offer an account with $\\beta_1 = 30$ and $\\beta_2 = 0.5$, with those parameters guaranteed for life.\n",
+    "\n",
+    "Suppose you deposit \\$1000 at all three banks at the beginning of Year 0.  How much would you have in each account at the end of Year 10?  How about Year 20?  And Year 100?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1343.9163793441223\n",
+      "FGB^\n",
+      "1349.85880758\n",
+      "ELS^\n",
+      "1350.0\n",
+      "PCU^\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Year 10:\n",
+    "\n",
+    "#FGB\n",
+    "\n",
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "n = 10\n",
+    "\n",
+    "x_n = p0 * (1 + alpha)**n\n",
+    "\n",
+    "\n",
+    "#ESL\n",
+    "\n",
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "t = 10\n",
+    "\n",
+    "x_t = p0 * (exp(alpha*t))\n",
+    "\n",
+    "\n",
+    "#PCU\n",
+    "\n",
+    "p0 = 1000\n",
+    "beta1 = 30\n",
+    "beta2 = 0.5\n",
+    "t2 = 10\n",
+    "\n",
+    "x_t2 = p0 + beta1 * t2 + beta2 *t2**2\n",
+    "\n",
+    "print(x_n)\n",
+    "print('FGB^')\n",
+    "print(x_t)\n",
+    "print('ELS^')\n",
+    "print(x_t2)\n",
+    "print('PCU^')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1806.111234669415\n",
+      "FGB^\n",
+      "1822.11880039\n",
+      "ELS^\n",
+      "1800.0\n",
+      "PCU^\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Year 20:\n",
+    "\n",
+    "#FGB\n",
+    "\n",
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "n = 20\n",
+    "\n",
+    "x_n = p0 * (1 + alpha)**n\n",
+    "\n",
+    "#ESL\n",
+    "\n",
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "t = 20\n",
+    "\n",
+    "x_t = p0 * (exp(alpha*t))\n",
+    "\n",
+    "\n",
+    "#PCU\n",
+    "\n",
+    "p0 = 1000\n",
+    "beta1 = 30\n",
+    "beta2 = 0.5\n",
+    "t2 = 20\n",
+    "\n",
+    "x_t2 = p0 + beta1 * t2 + beta2 *t2**2\n",
+    "\n",
+    "print(x_n)\n",
+    "print('FGB^')\n",
+    "print(x_t)\n",
+    "print('ELS^')\n",
+    "print(x_t2)\n",
+    "print('PCU^')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19218.6319808563\n",
+      "FGB^\n",
+      "20085.5369232\n",
+      "ELS^\n",
+      "9000.0\n",
+      "PCU^\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Year 100:\n",
+    "\n",
+    "#FGB\n",
+    "\n",
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "n = 100\n",
+    "\n",
+    "x_n = p0 * (1 + alpha)**n\n",
+    "\n",
+    "\n",
+    "#ESL\n",
+    "\n",
+    "p0 = 1000\n",
+    "alpha = 0.03\n",
+    "t = 100\n",
+    "\n",
+    "x_t = p0 * (exp(alpha*t))\n",
+    "\n",
+    "#PCU\n",
+    "\n",
+    "p0 = 1000\n",
+    "beta1 = 30\n",
+    "beta2 = 0.5\n",
+    "t2 = 100\n",
+    "\n",
+    "x_t2 = p0 + beta1 * t2 + beta2 *t2**2\n",
+    "\n",
+    "print(x_n)\n",
+    "print('FGB^')\n",
+    "print(x_t)\n",
+    "print('ELS^')\n",
+    "print(x_t2)\n",
+    "print('PCU^')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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@@ -0,0 +1,2042 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 5: Design\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# To switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SIR implementation\n",
+    "\n",
+    "We'll use a `State` object to represent the number or fraction of people in each compartment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "S    89\n",
+       "I     1\n",
+       "R     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(S=89, I=1, R=0)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To convert from number of people to fractions, we divide through by the total."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>0.988889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>0.011111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "S    0.988889\n",
+       "I    0.011111\n",
+       "R    0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init /= sum(init)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` creates a `System` object with the given parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example with hypothetical values for `beta` and `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The update function takes the state during the current time step and returns the state during the next time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update1(state, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State with variables S, I, R\n",
+    "    system: System with beta and gamma\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To run a single time step, we call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>0.985226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>0.011996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R</th>\n",
+       "      <td>0.002778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "S    0.985226\n",
+       "I    0.011996\n",
+       "R    0.002778\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state = update1(init, system)\n",
+    "state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run a simulation by calling the update function for each time step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \n",
+    "    returns: State object for final state\n",
+    "    \"\"\"\n",
+    "    state = system.init\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        state = update_func(state, system)\n",
+    "    return state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is the state of the system at `t_end`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>0.520453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>0.000615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>R</th>\n",
+       "      <td>0.478933</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "S    0.520453\n",
+       "I    0.000615\n",
+       "R    0.478933\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(system, update1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  After 14 weeks, how many students, total, have been infected?\n",
+    "\n",
+    "Hint: what is the change in `S` between the beginning and the end of the simulation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.37943042989926101"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc = 4      # time between contacts in days \n",
+    "tr = 5      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "S_0 = system.init.S\n",
+    "\n",
+    "final = run_simulation(system, update1)\n",
+    "S_end = final.S\n",
+    "\n",
+    "S_0 - S_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using Series objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to store the state of the system at each time step, we can use one `TimeSeries` object for each state variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add three Series objects to the System: S, I, R\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    S = TimeSeries()\n",
+    "    I = TimeSeries()\n",
+    "    R = TimeSeries()\n",
+    "\n",
+    "    state = system.init\n",
+    "    t0 = system.t0\n",
+    "    S[t0], I[t0], R[t0] = state\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        state = update_func(state, system)\n",
+    "        S[t+1], I[t+1], R[t+1] = state\n",
+    "    \n",
+    "    system.S = S\n",
+    "    system.I = I\n",
+    "    system.R = R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "run_simulation(system, update1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then we can plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(S, I, R):\n",
+    "    \"\"\"Plot the results of a SIR model.\n",
+    "    \n",
+    "    S: TimeSeries\n",
+    "    I: TimeSeries\n",
+    "    R: TimeSeries\n",
+    "    \"\"\"\n",
+    "    plot(S, '--', color='blue', label='Susceptible')\n",
+    "    plot(I, '-', color='red', label='Infected')\n",
+    "    plot(R, ':', color='green', label='Recovered')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what they look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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evJiQkBBSUkr7kW677TaWL1/Oc889R1paGmvWrOHBBx8kLCysZO96byeKOTIy\nkquuuop//OMffP755+zYsYPp06eTl5dXvX+4J6lS+/I5twN2Pd+JNHuJGnLeeWZU2KZN5vWRI7Bo\nEXTrZvZriY/3b3zCfxITE7nlllt49tlnGTNmDC+++CKPPfYY119/PTabjR49evDyyy8T7/xHct11\n15GXl8f8+fPJzMykQ4cOLFq0qCTpvPDCC8yfP5/LLruM8PBwhg4dyt///neP5qaRI0fyyCOPMGrU\nKILctjc955xzmDdvHs899xzPPPMMcXFxXHzxxUyaNKnc+MPCwk4Y85QpUwgLC2PGjBnk5+czduxY\nn36h2qKinR+3Y+agWOHQWpe72mRNk50f6yeHw9ROXn/dc4fJkBAYPdosue8cPCOEOAnVvfPjOqwn\nFSGqnc1m5rN07AjLlsGXX5rjBQVmU7BvvoFrrpGOfCH8qaIFJW+owTiEsCwiAsaNM0OQly6F/fvN\n8fR0+Pe/Ye5cKGcQjxCimlnqU1FK9T/RNVrrb049HCGsS0w0a4h9/jl89JFZM+ziiyWhCOFPVjvq\nv+bETWHSmi1qXGAgDB8OffrAmjVmuLG3nTuhfXvZs0WImmA1qQwp41gDIAUYj9nMSwi/adjQ1FK8\n7doF8+aZpHL55aZ2I4SoPlb3U1lbzqnlSqkc4H5gVJVFJUQVcDjgnXfMc1dySU42yadZM//GJkR9\nZXWV4op8hSx9L2qh4mJTM3GbRsCPP5qJk0uWlE6mFEJUnUpNfizHaCDrhFcJUcOCgsxyLoMGmSHI\nP/xgjjscsG6dWRU5JQVGjgTn6hhCiFNkdfTXZ2UcDgRaAYnA41UZlBBVqVEjuPlmMyt/2TLYutUc\nLyqCL76Ar7+GIUPg0kulM1+IU2W1phKC7+gvB2Yr4XnAC1UZlBDVoU0buPtuSE2F99+H334zxwsL\n4dAhSShCVAWrHfWDqzkOIWpMx44wZQr88otZqHL3bnBuh+Hh+HEz0VIIYV2l+lSUUiMxw4hjgQPA\n51rrL6sjMCGqk81mtjLu3NnUWLyXiMvLgxkzICnJNJslJUlNRggrrPapxAP/A3oD+cCfQBPgAWd/\nyyVa69q5DrMQFbDZzBwWb199ZWoqP/9sSqtWcO650Lu352gyIYQnq/89nsVsJzxaa73cdVApdRGw\nGHgMuNvKjZRSgcAc4AYgCvgEuENrfaCc61sC/wDOB3KBd4DJWuvjFmMXotKcG/SV+P13ePFFM+8l\nJcWUONlEjyzSAAAgAElEQVREWwgfVuepjMR8kS93P6i1/hCYBlxdic+cCVwPXAecA7QE3i3rQqVU\nKLASiAMGAFdiJlmWvYWaEFVk3DgznyUlxXMXyuxsWLECpk+HhQth82aw2/0WphC1jtWkUgQcKedc\nOmZ02AkppUKAu4DpWuuVWuuNmN0jB5SzaOU1QHPgMq31z1rrL4CHgLMsxi3ESWveHK69Fh57zMzC\nb9iw9JzDYTYMW7DAJBYhhGE1qSwEHlVKJbgfVEpFA1MxzWNW9MA0ea1xHXBuBJOGGQDg7Xxgpdb6\nsNv1L2qtJamIGtOggZkgOXcu3HqrGT3mEh1tOvzdFRWZ/hghTkdW+1QSnGWnUuprYD8Qj2mSigLy\n3SZIOrTW55dzH9cYm31ex/djJlJ6OwP4XCn1MHAtZm7Me8D9MjBA1LSAALN2WHIyHDhgJk02aOC7\n2+SmTfDCC9C1K5x1lnl0b0IToj6zmlSSgJ/c3tPa+dx1LBBrS99HAHatdaHX8XygrF0wooGbMCPP\nxgItgAWYkWfXWYxdiCrXtKlZAqYs335rais//mhKaKhJLD17mlpNaGjNxipETbI6+bGspe9PRi4Q\noJQK0loXuR0PBY6VcX0hkAmM11oXAz8opYKBt5VSk7TWh6ooLiGqRHGx6cx3l59v1h374QdTYznz\nTOjRA7p1g6go/8QpRHWp7OTHTsAgIAYzV+VrrbWuxC1+dz42d3sOpmnNu0kM57E8Z0Jx+dX52BaQ\npCJqlcBAmDbNNI99/z1s2GCeuxQWls59sdngnnvgjDP8F68QVc3q5McA4D/AjYD7vGKHUuoVYILW\n+kQ7QwJsArIxiWmp895tMQmirJn5XwE3K6WC3ZrMugDFmM59IWqlpk1h9GgYNQrS0+H//g82boT9\n+0uvCQw065G5c9VqzjxT5sGIuslqTWUqpg9jKvAqZomW5pghv7MpXViyQlrrfKXUQuAJpVQGcBAz\nsmyt1vo755DjOCBTa10ALAL+BixRSs3CdPTPB5ZI05eoC2w2SEgwZfRo+PNP05H/008QFubbv6K1\n2esFzOrKSpmaTIcOJsnIUjGitrOaVG4CHtFaz3c7theYp5QKc563OiHxfiAYU1MJxjmj3nmuP/AF\nZvviNVrrA0qpc4CngY1AjvN90yx+lhC1SuPGZrmXc88te9Kka1l+gIwMU9atM69jY80aZImJZmmZ\nli19R54J4W9Wk0pzYF05576hEl/yzg76e53F+9waPJvX0Fr/ipmvIkS9ElDGLLHWraFTJ9ixAwoK\nPM8dPmz6aDZsMK9TUszkTCFqE6tJZRfQD1hdxrl+mFn1QohT1K+fKUVFkJZmmsO2b4ddu0x/i7t2\n7Xzfv2gRZGWZBTBbtoQWLUyRYcyiplhNKs8Dc5VSx4A3MH0qTTFrfk0HHq2e8IQ4PQUFmaaupCTz\n2m43i1ru3GkSzK5dvqsrOxwmAeXkmOvcxcebZWcSEsxj06amViSTMkVVq8wqxcnAk8ATbsdtmD6O\nR6o4LiGEm4AAM1KsTRsYOrTsa44cMQmlLIcOmbJlS+mxWbOgWbPS10VFZpRa48amNGggAwNE5Vmd\n/FgMXK+UmodZoysOOAx8qbX+pRrjE0JYFBsL8+aZGs3evaXlwAHfQQEBASZxuMvIMMvLuISEmBpO\nfLwZeeYqsbGmeL9fCKjk5EfMhMVdmIRy0PlcCFFLxMSY4r7IZVGR2R9m/34zZ+aPP8wkTO+RYxkZ\nnq8LCsz16WX0mMbGmtWb3e3ZY+bYREeXlqgoUxo0KHtggqh/KjP5cR7wV8wwYFel+JhS6hGt9WPl\nvlkI4VdBQaVzZSoSHm4Wy8zIMPNp8ipYsjU21vdYWhp8+mn574mIMAkmMtJM7rzoIs/z6emQmWni\nCA8314eHm34faYarO6zWVGZi9kH5B2ZDrYOYjvqxwGylVJbWemG1RCiEqBGJiaaA6fTPzTX9MBkZ\nZjhzZqYpR46YkWXesrIqvv/x46VbAsTH+57/5hv47DPf4wEBJrmEhZU+9u0L55zjed2mTSbe0FDT\ndOd6dJXg4NLnoaGyLXR1qczkx9la64fdju0CvlVKZQOTMDPjhRD1gM1magoREWZ4shVnnmma1I4e\nNQkmO9uUrCyTTBxuCzk1aOD7/mNlLSmL6Q86dszzfFnrpa1bZxKLFVdfDYMHex5buNA0DQYFmRIc\n7Pk8MLD09ZAhvon1iy9KmxXLKgEBpc+TkkxydHE4TPOh6zr3YrP5HouI8Ky9ORym2Gz+r9VZTSox\nwPflnPsamFw14Qgh6ir3mo43u90kluxskxzKSirNm5uJn64aTW6uKUVFvteGlbFRhvc8noqElLFX\nbUaG5+KfFUlO9k0qH39c/ug7bw884Pl+ux0ercTEjCee8FzhOjPTbHHt4kpGroTk/jwoCObP971n\nVbGaVD4GbgXKajG9ClhRZREJIeqdgACTSMpKJi7Dh5virbDQ9O+4Sm5u2c1nyckmMeXnm0EGrkdX\nKSwsfV5WUin03uWpAmU1nRUX+x4rj/cgibKW7KmI96AHh9dyvhXdr7qb/aze/kvgEaXUz5jJj+mY\nnR9HAQOBp5RSrjzp0FrPrfJIhRCnpeBgU06094x3c1Zl3XOPSURFRSbBFBV5Pi8uLj3mPr/HZehQ\n8/7iYs9it3s+Fhf71rQcDtPMaLeXFofD872u1w5H2UnJZvNNLmWp7uYxq0llgfMxBphTxnn35i8H\nIElFCFGnlDWirTK8R7NVRkgI3H//yb+/SROzRA+U9q94JyJX7cVK4jkVVic/yghzIYSoA9z7T/xB\nkoUQQogqI0lFCCFElZGkIoQQosrInFIhhKjjHA4HDhzYHXbsDjshgZ5jpu0OO0fzjmJ3mN76+Igy\nxmRXkXKTilJqAfCU1nqXUqo1kK61rsRIbiGEqD9yCnLIL8qnoLiA+Ih4ny/unw/8THZ+NkX2Ivq0\n6ENEcITH+Y/0R2TlZ1FkL+LyTpcTGRLpcf5f3/+LY4XHKLYXM6nfJMKCPMcdT189ncLiQoodxcwb\nPo+ggNKv72JHMXcsN7uyBwYEsvBCzwVOcgtzmbpqKgARwRE8PeLpU/vDqEBFNZWJwJuY5Vh+A84G\nNlRbJEIIcYocDgf5xfkE2gIJDvTcgeyXg7+QnpNOXlEevZr3onlUc4/zr29+nR2ZO8gvzmdCjwkk\nxnkuD/Ds+mdJO5IGwJSBU2gf67lL2rKty9ifvR+ApLgkn6Ty7d5vOXT8EAAXnnGhT1LZdXgXOQVm\nSn5hcaFPUsnKz6KwuLDk53QXYCvtyXDVRtzZ3CanOKjeMcUVJZV04DGl1GeYVYlvVkqNLOdah9e6\nYEIIUaV2Zu7ktyO/kVOQQ9cmXX2+9Jf+vJSv93yNw+FgQvIEzm55tsf5r/d8zcb0jQA0a9DMJ6lk\nHM9gb9ZeAI4V+i5E5l4zcX25u3NPYkV237Vl3GsWZZ0PDCid0Vjs8J2e7544ih3FBFP6eTZs2Gw2\nbNgIDAjE4XB4JJJAWyANwxpis9kIDwr3uXdVqiip3Ac8A8zATGicUMG1DkCSihCiQnaHnaz8LAJs\nAUSHRnuc++b3b1ibtpbsgmyGthvKue3P9Ti/MX0jq3atAiA8KNwnqQTaAkt+gz9eeNzns91/888v\n8l0ozP18QXGBz/mYsBjiwuMIDgz2SAAuXZt0JSEqgeCAYJ9aCMCFHS4kryiPoIAgn58d4JZet2B3\n2AkMCKRBiO96NrOHzCbAFkCALYDQwFCPczabjUWjFvm8xyU0KJTHhz9e7vmqVG5S0Vq/DbwNoJSy\nAwO01uUtKimEEOQX5XMo9xA2bD41gTVpa3hzy5vYHXaGthvKlV2u9DifU5BT0ryUmZvpc2/3L9qy\nahKu5qaQwJAym4A6Ne5EeHA4oYGhtIz2Xbt/TMcxjEgaQWhQKDGhMT7nJ/acWMZPXGq0Gl3h+b4t\n+1Z43jtJemsY1rDC87WF1dFfQ4BfqzMQIUTdcLzwOLmFuT4jiDbs28DzG58HoGfzntzS+xaP85HB\nkSVf9kfyjvjc1/2396x8381Z2sW2Y2i7oUSGRNIhroPP+Qs6XMCoM0aVWYsA6NOiD31a9Cn352oS\n2aTcc8I6q8u0rFVKdVRKzQIGY9YAywC+AubIPvVC1H+7Du9i4YaFZOdn0yG+A5P7e+54ERteunhW\nWTUN12/aDUIa+IycAlOTuG/AfUSFRpVZU+jYqCMdG3UsNz7vjnnhH1a3E+4KrAOOAx8AB4DmwGhg\ntFLqbK31lmqLUghR7Y4VHGP59uXszdpLkb2I+wbc53E+OjSa7PxsAP7I+cPn/Y0iGhEYEEh8eDyN\nIxv7nG8f254FFywo98s/OjS6zL4GUbdYbf56HEgFhmitSxozlVKRwGrgEWBM1YcnhKhKdoedP3L+\n4Pejv3NWi7M8RggFBQTx+W+fl4wcKiwu9EgA8eHxJa+jQ6N9zseExvCvC/7lcU93gQGBBFJ205So\nP6wmlRRgvHtCAdBaH1NKzQMWV3lkQogq5XA4mLJySkl/xRnxZ3g0WYUGhdIksgkHcg7gcDhIz0mn\ndUzrkvM2m405Q+cQExpTZuIoL5mI04vVpHIcyp0x4wD59UOI2uBYwTG2HdrG9sztDG03lEYRjUrO\n2Ww2mjVoVpJU0o6keSQVgDFqDIEBgbSMbkl8uO9SHnVlBJLwH6tJ5VtgqlLqU611nuugUiocM5/l\nm+oITghROS/+9CKbD2wGoHmD5qS0SfE4nxiXyB85f9Auth3hwb6T4Hol9KqROEX9ZTWpTAO+B35T\nSn0I/AE0w3TUR2Oax4QQNeDgsYNsPrCZxpGN6da0m8e5jo06liSV7ZnbfZLKqDNGMUaNkaYqUW2s\nDineqpTqDzyI6ZCPAw4Da4HZlRn5pZQKxGxJfAMQBXwC3KG1PmDhvR8DDbTWg61+nhD1ybo961iy\naQkA3Zp2KzOptItth4pXdG7S2ef97kuFCFEdLP8L01pvBsZWwWfOBK4HrgMOAQuBd4GBFb1JKXUL\ncCEmkQlRrxXbi0nPSfeZ+d0hvnTSnz6kKbIXeSSKltEtmTpwao3FKYS3Gv21RSkVAtwF3Km1Xuk8\ndhWmWa2/1rrMvhmlVBLwKKZvR4h6K68ojze3vMlPf/yE3WHnifOe8Bi22ySyCa1iWhEXHkfXJl19\nVqsVwt9qui7cA9PktcZ1QGudppRKw/TL+CQVZ3PZEsxcmTOApBqIUwi/CA0MZduhbSULIv765690\nb9bd45oZKTOkT0TUWjW9nbCrLr/P6/h+oFU575mGGbb8RHUFJURNO5p3lM92fsbvR3/3OG6z2Tir\nxVmAWfYkv9h3NV1JKKI2q+maSgRgL2MHyXwgzPtipVQv4F6gj9barpSqgRCFqF6rdq3i3V/fxe6w\nM6jtIK7peo3H+XPanEPXpl1p17CdJBBR59R0TSUXCFBKeSezUMBjtr5SKgx4Bbhfa72jhuITotq1\nim5Vslrvhn0bfDZsig2PpX1se0kook6yuqCkDTMEeBQQiW8ycmitz7dwK1ddv7nbc4AEfJvE+gJn\nAo8rpVy7y4RiklIO0ElrvcdK/ELUNIfDwW9HfmPTH5u4uOPFHgnijPgzaBLZhJiwGAa0GuDHKIWo\nelabvx4FpmD2qt8L+O6AY80mIBsYBCwFUEq1BdoCX3pd+z3gvWnCo0AbYBymH0aIWsfhcPDkt0+y\n/dB2AHo060G72HYl5202GzPOmeGzB7kQ9YHVpHID8JTWevKJLqyI1jpfKbUQeEIplQEcxMxTWau1\n/s455DgOyNRa5wIezV5KqSwgV5rDRG1ms9loFNGoJKmsSVvjkVQASSii3rLapxINfFRFn3k/8Cqm\npvIFsBu43HmuP5DufBSi1svOz/YZwQUwrN0wggKCGNB6AMMTh/shMiH8w2pN5RtgAFUwm11rXYQZ\n0XVvGefWAOX2TmqtK94kWogacqzgGCu2r+DL3V/SKKIRDw560KPfpFVMK+afN79k33QhThdWk8oj\nwGvOUVvfYJbC91DebHgh6quv9nxFQXEB+7P3s+nAJno06+FxXhKKOB1ZTSqfOx9nOh/d14awIXuq\niNNMZEgkg9sO5tMdn9IyuiWhgaH+DkmIWsFqUhlSrVEIUUvlFuby2c7PaBzZmP6tPLv6zm1/Lklx\nSXRt0lXmlAjhZHXpe1kZWJx2dh3exYLvF3Cs4BgxYTH0TuhNSGBIyfno0GifpeeFON1ZXqZFKdUR\nmAUMBmKADOAr4GGt9a/VEp0QftQiqkXJsvJH847y3d7vOKfNOX6OSojazdKQYqVUV8xkxEHAB8B8\nzOZaQ4HvneeFqNO8l5EPDQpljBpDo4hG3Jh8IwNbV7jljxAC6zWVx4FUYIjWumSNLqVUJLAas5Pj\nmKoPT4jql5mbyXtb36NZg2aMOmOUx7l+rfrRt2Vf2TFRCIus/k9JAca7JxQArfUxpdQ8YHGVRyZE\nDdh9ZDfzv5lPYXEhwYHBDGg1gNjw2JLzAbYAAmw1ve6qEHWX1f8tx/EcRuxOhhOLOqtVTCuaNWgG\nQGFxIT/98ZOfIxKibrOaVL4FpjqXoy+hlAoH7qOMHRuFqAsCbAFc1eUqWse0ZnL/yQxpJ6PnhTgV\nVpu/pmE66n9TSn0I/AE0A0Zj1gVLqZ7whKgaRfYiPt3xKccKj3FF5ys8ziXFJTE9ZbrMNRGiClid\np7JVKdUfeBDTIR8HHMasBTZba72l+kIU4tTkFOQwb908DuQcwGaz0TuhN+1j23tcIwlFiKpheUiL\n1nozMLYaYxGiWkQGR9IksgkHcg7gcDj4cveXPklFCFE1yk0qSqlrgE+01pnO5xXSWr9WpZEJUUVs\nNhtXd7maPUf3MDJpJIPaDvJ3SELUWxXVVJYCZ2P6Upae4D4OQJKK8LsjeUdYv3c95yWe59GkFR8R\nz6PDHpX5JkJUs4r+h7XDbJjlei5ErfbN79/w1i9vkVuYS3xEPL0Tenucl4QiRPUr93+Z1nq328tB\nwHKt9SHv65RSzTB7xj9Z9eEJYd2uw7vILcwF4M0tb9K9aXeCA4P9HJUQpxer81ReBMrr2eyB2cRL\nCL+67MzLiA2PpXFkY27udbMkFCH8oKKO+o+BTs6XNuB9pVR+GZc2BXZWQ2xClCsrP4sGIQ08llAJ\nDw7nzr530iiikccS9UKImlNRI/Mc4Cbn85uADcCfXtcUA0eAl6s+NCF8ORwONuzfwOubX+fCMy7k\n3PbnepxPiErwU2RCCKi4T+U74DsA5970s7XWv9VUYEKU5Yf9P7B4o1m/dNnWZXRp0qVk7S4hhP9Z\n6lPRWk8AzlRKzXcdU0qdpZRaqZSSxZJEjUlunkyrmFYANAxrSF5Rnp8jEkK4s7pJ1xXAR5T2sQAc\nc77/M6XUiGqITQgfQQFB3Jh8I4PaDuKBQQ/QtmFbf4ckhHBjdfTXDOBfWusLXQe01r9orYcBi4DZ\n1RGcOL3tOryL1btW+xxPiErgmq7XEBYUVsa7hBD+ZHU2WBJwdznnlgETqiYcIcDusLNi+wqWb1uO\nAwetY1rTIb6Dv8MSQlhgtaZyAOhVzrluQGbVhCME2LCxI3MHdocdh8PBG1ve8Nk/XghRO1mtqbwK\nPKSUysHUTA4CjTH7qcwCFlZPeOJ0ZLPZuKHHDcxaM4sW0S2Y0GOCLE0vRB1hNanMBjpikse/3I7b\ngPcw+6wIcVLyi/IJDQr1ONYwrCH3DbiPpg2ayh7xQtQhVjfpKgTGKqW6AAMxm3QdBb7WWm+qxvhE\nPZeakcqLP77IVV2uIrl5sse55lHN/RSVEOJkVWrZVucOjz67PCqlGmitc6osKnFa2LBvA89vfB6A\nV35+hXax7WgY1tDPUQkhToWlpKKUCgHuxKxWHIJp9gLT0R+J6ayPtHivQMwSMDcAUcAnwB1a6wPl\nXH8lMA3ogFmK/3lgvta62Mrnidqrc5POxIXHkZmbiQ0bGcczJKkIUcdZrak8DtwFbAaaALmYdcC6\nYpLMzEp85kzgeuA64BCmn+ZdTLOaB6XUSMwggbuB/wHJwH+BYODhSnymqIUigiO4MflGPtnxCdf3\nuJ7o0Gh/hySEOEVWe0AvB57UWncHngV+0Fr3xdQe0qzex1njuQuYrrVeqbXeCFwFDFBK9S/jLbcC\n72qtF2itd2qt3wGeQubF1DnZ+dlsOejTckqH+A78re/fJKEIUU9YTSpNMTUFMLWVswC01vuAxzCJ\nwYoemCavNa4DWus0TGJKKeP6OZghy+7sQKzFzxO1wJaDW5i9djaLflhEenb6id8ghKizrCaVI5hm\nLoAdQCulVJTz9TagtcX7tHQ+7vM6vh9o5X2x1nqD1vpX12ulVDRwG6YfRtQBdoedZVuXkZWfRWFx\nIYt/XCwTGYWox6wmla+BvymlwoHtmMUkL3ae64sZXmxFBGB3DlF2lw9UuJCTUioCeB8IB6Za/Dzh\nZwG2AG5MvpGggCCiQ6O5pOMlMpFRiHrMalKZjelIX661LsJ0rj+nlFoPPIrpaLciFwhw7s/iLhST\nqMqklGoErAJ6AiO01rstfp6oYWXVQlpEt+CW3rfw4KAH6dyksx+iEkLUFKv7qfwEnIkZBQZmiO/D\nQAam32Oyxc/73fnoPastAd8mMQCUUm2Bb4B2wDla6w0WP0vUsMzcTP7x3T/Ymem7u3S3pt2ICo0q\n411CiPrE6jyVZ4GXtdafAmitHZgaSmVtArIx812WOu/dFmgLfFnG5zYBvsBsW9xfdp6svbb+uZVF\nPywiryiPjOMZPDDoAVmaXojTkNV5KjcBH57qh2mt85VSC4EnlFIZmIUpFwJrtdbfOYccxwGZWusC\nzDpjjYChQK5SyrVvrKO8yZLCP5o1aFayRteh3EOkZqTSo1kPP0clhKhpVvtUvqPsIb8n437MhMal\nmFrIbsw8GID+mFnz/Z2DAi4FGgDfO4+7SplNZcJ/YsNjuabrNTSObMx9A+6ThCLEacpqTWUjMEUp\ndTnwE+C9zpdDa32LlRs5O/rvdRbvc2soXQIGINBifKIG5RXlkZ6dTrvYdh7H+7ToQ49mPQgODPZT\nZEIIf7OaVC7DzCUJB/qVcV4mHpwmUjNSWbJpCXlFecwcPNNnJrwkFCFOb1aXvm934qtEfVdkL+KV\nTa9w6PghAF7Z9Aq397ld5p0IIUqU26eilBqqlGpQk8GI2i0oIIjx3ccDZjHIvi37SkIRQnioqKay\nEtPU9b3rgFLqL5gFHg9Vd2DC/+wOu8+uix0bdWR89/F0a9pNFoEUQvioaPSXx6+gzn1Q/g20qdaI\nRK2w5eAWHvziQfZn7/c5N7D1QEkoQogyVXbzb2nrOA38b/v/eHb9s/x57E9e/ull7A67v0MSQtQR\nlU0q4jTQo1kPggJMy+ih3EMcPHbQzxEJIeqKSu1RL04PzaOaM7LDSP489idjO4+lQYiM1xBCWHOi\npFLW/BOZk1JPFBQX8L/t/yMxLpEuTbp4nLuww4UysksIUWknSirvKKXyvY69X8Yxh9ZaVWFcoprt\nObqHRT8s4tDxQ8RHxDNz8ExCAkNKzktCEUKcjIqSystlHFtXXYGImhUfHk9eUR4Ah44fYt2edQxp\nN8TPUQkh6rpyk4rWekJNBiJqVmRIJJd0vIRlqcu49MxLGdBqgL9DEkLUA9JRX8/ZHXbW7VmHAwfn\ntDnH49zA1gPpldCLiOAIP0UnhKhvJKnUY5m5mTyz/hnSs9MJDQqlR7MeHpMWbTabJBQhRJWSeSr1\nWMOwhtic81Xzi/L5dMenfo5ICFHfSVKpRxwOz9HeAbYALut0GaFBoYzpOIYxHcf4KTIhxOlCmr/q\ngYzjGSzfthyA63tc73Guc+POzB02l8iQSH+EJoQ4zUhSqeMOHT/EA58/gN1hx2azMTxxOAlRCSXn\nbTabJBQhRI2R5q86Lj4ins5NOgOm+Wtj+kY/RySEOJ1JTaUOSTuSht1hp31se4/jo88YTUFxAaPP\nGE2H+A5+ik4IISSp1Anp2em8tvk1th3aRvvY9tw34D6PZVTaNGzDPf3u8WOEQghhSPNXHRARHMGu\nw7sA2HV4FzsP7/RzREIIUTZJKrVMenY6uYW5HsdiwmLo27IvAbYA+rbsS1RIlJ+iE0KIiknzVy2x\n9c+trNi+gm2HtnFF5ysY1n6Yx/nRZ4xm9BmjiQ2P9VOEQghxYpJUaonM3Ey2HdoGwNrdaxnabqhH\nv0mdSSZ2OxQXQ1GRKXZ7afGanInNZkpAgCmBgaYEBZlHWX5fiDpHkkoNO5J3hO2HttOnRR+P470S\nevHmL29SWFxIQlQCuUW5Nbcul8MB+fmQkwPHjsHx46WPubmmHD8OeXmlJT/flIICUwoLzaO9Cvez\nDwoyJSSktISFQWioKWFhpoSHl5aIiNISGWlKeLgkKCFqiCSVGmJ32Pnnd/9EH9IAdIjvQMOwhiXn\nw4LCmNhzIq2iW1VNrSQ/H7KySkt2dtnFlUiKi0/9M6uaq7aTl3dq97HZShNMgwaeJSqqtERHm8cG\nDUwyE0JUmvzPqSYOh8Oj+SrAFoDNZitZn+v7fd9zXuJ5Hu/p1rRbxTe1200iOHrUJIqjR8t+npVl\nkoo/2GylzVeuEhBQ2szlusbhKC2u5rHi4tKms6pMcg6HSZ45OXDggLX3RER4JproaM/ifiwk5MT3\nE+I0IUmlCh3IOcCG/Rv4+cDPpLROIaVNisf5Pgl9SM1IRcUrmjVoVnrCvVZRVqJwlexs336JqhIS\n4vnbvHszknvzkqvZydUEFRpq3hscbEpV9YU4HCa5uJrVXMXV7ObeFJebax6PH/csx46ZcjI1Hdc9\nrCSh0FDfpOOdeFzPQ0OlKU7Ua5JUqtDWjK18pD8CHEQSQkq4MonAmTB6H82k69HeRO8tgq8+gay3\nqq9WERRkvshiYjy/1FzNO9HRns1AwcFVH8OpsNlKE1XEKfYtFReb5OJq6nM1++XkeDYDujcTViZ5\n56tu7oUAAA+MSURBVOfDn3+aciKuvxfXn79785t7k5zrufQHiTpGksqJuDqxjx2jKPso+o8tpB7c\nyvHco4yP7F/65ZSTQ7esA7xesB4KC9nuWEf+0lRCCSy5VaiznJLISJMoXCU62vO1K5HIl1GpwMDS\nGoMVruYy1y8Ebr8YePRRuZ5XpqmuqAgOHzbFioAAzxqk67l7cQ1KcD26apUBMg1N1LwaTypKqUBg\nDnADEAV8AtyhtS6znUEp1Rv4J5AM7AMe1lovOekA9u0z/6Fdo5rcRze5Sm6u5ygo54imfFshz0R/\nC0AANq44etAjacQBI8Ka0Kq4AZ2KYj3OVSgoqPRL70QJQzqQq5/NVlp7SEio+FqHw/w78U405T0v\nLKxcLK5+tOzsyv8cYWG+zZcREaUj5twf3Yt3E2egxX/HQuCfmspM4HrgOuAQsBB4FxjofaFSqjHw\nKfAacBMwHFislPpDa/1ZpT/57bdh1aoKL/k65A900BF2BWYxOac7sY7SukWkI5gWxZHsCzyGHQfb\ng47SpSjO4/2X5LUzT0JDPZs5ympndxWpVdRd7iPLmjc/8fX5+WWPwPN+dJVTGfnm6nM6Va5h3d79\naK5H9xIc7Pm8vBIUVPro/jw4uHT+kqiTajSpKKVCgLuAO7XWK53HrgJ+U0r111p/4/WWicBR4C6t\ntR1IVUr1BCYDlU8qO3YAcMxWyN6AYzS2hxHnCPO4ZEPwQVKDjgCwKyiLXoWNzT90Z9PD2SEODoc6\n6BjdjqSGSRAd59seHhVV+/ooRO3g+lJu1Mja9UVFpQMOXInGVYvOyfGcU+Q+QKEqkol7DEVF5t41\nwX0EoSvpuE+K9X7uXdwn0rq/dp9k63q02TzPlVVcIxe9H8s67z6ht6zjFRX30ZHuidX7uoqucT0/\n0aMrrmpQ0zWVHpgmrzWuA1rrNKVUGpACeCeVFOBLZ0JxWQMsVErZtNaVGwp1xRW89eFcVudpCArk\nipj+DIvvU9osEB5OuyPfkZrxHQQFsbP9MHolX+vR5HReBbcXosoFBZU2f1aG3V46Is67qde9uCay\nuj/3fqyuEYflcThMM2FlmwqFdcHBMGoUjBhR5beu6aTS0vm4z+v4fqBVOdf/WMa1EUA8kFGpT09M\nJH7M1fDLWwDsbXUm9LjM45LkI82IOdyNtg3b0iqmFQRIH4aogwICSoeEnwrXF7xr9QT3VRTcn3uv\nrOD+vLCwdHi4q7heez8WF1ftqgyibIWFpivg/POrvMZS09+YEYBda+39K0g+EPb/7Z17sN3TFcc/\n95LQqgpS2lQmEcZXU1GPRhFBRko8IuJZqkRrKMVQFVQnXvVK1ShlGCoeqXeUNBpEuEGIZ71jjXe9\ngsQrQcUj/WPtX/Lzyzn33Nyeey/ntz4zZ37n7L1/57f3mnN+67f2XnutKu2Ldnzmf1upfU16r9Cb\nbkt1o9fyvej5zcWnIPr06EOfHn3a89VB0Hg0NS1aI+ksvvhi0ZRbfjNsvqwtr+Km2vz7bNNt9j7f\nvhivrtLnfHl+E29xU2+19/DlttVeWbviOcXzs8/FNtXKl1nGrZQOmALrbKXyMdAsaWkz+yxXvgzw\nYZX2RS/c7HOl9jVZc6U1OWfbc2huCnfLIPhK0tzc+YosqBudfWd9JR2LbjK9WHxKLGtfqe08fAF/\niWluag6FEgRB0EF0tqXyGDAX2AIYDyCpL9AXuKtC+3uA/QqL8kOA6YXF+yJLAcyaNas+vQ6CICgB\nuXtmuzcnNS3Iz711ApJOxzc+jgLewvep/NfMtkwuxysB75jZfEmrAgZcA5wNDAX+DAwzsztaucZm\nwN0dOY4gCIIGZrCZ3dOeE7vCtekPQDfcUulG2lGf6jYF7sStkRYze1PSMOAc3AvsZWCf1hRK4kHc\nHfkN4CsY0z0IguAryVL4ksOD7f2CTrdUgiAIgsYlVqyDIAiCuhFKJQiCIKgboVSCIAiCuhFKJQiC\nIKgbpQlstaR5XBqB5JI9Fo+D+Q3gfuBIM3sy1W+d6gU8CxxtZpO7qLsdjqSN8b1PQ82sJZWVQgaS\n9gdG4zH2ngaOyrwoyyADScsBpwO74OGf7sP/C0+n+oaWgaQLgKXNbP9cWatjlrQK8Ff8/jEfGAcc\nV4iGshhlslROYFEel83xYJUTurJDHYmkZuAfwFrACNxd+31gqqSVJfUHJgLX4QnQbgJulPTDLupy\nh5JuKleQ29RVFhlI2hc4D7+pDgCmARMl9S2LDPBEf0OB3YBN8JiCt0hatpFlIKlJ0knAgYXytox5\nAvBdfLP6KGA/4MRa1yyFS3HaVDkbz+NyaSrrC7wIDKqQx+Vrj6T1gUeA/mY2M5UtA7wDHAQMAmRm\nW+bOuRN41swO6PwedyySLsQV7JbAEDNrSWUNLQNJTfjv/HIzG5PKmvHfxlj8htHQMgCQNBs40czO\nTZ/7A08BG+I33IaTgaR+wN+AdYCPgCmZpVLrty9pEzwVST8zezHV7wucC3zHzD6hCmWxVCrmcQFe\nwjdJNiL/AXbAIxJkZKFtVsTH3VI4p4UGlIek7YDtgcMKVWWQgYA+eFQKAMzsCzNbz8yupBwyAHgb\n2EPSKukh81fAu8ALNK4MNsXjJw7AHyzy1BrzYODlTKHk6pfH76dVKcuaypLmcfnaY2ZzgJsLxYfh\nayu3ASdTAnlI6ok/re2H30TyrEbjy2CtdOwh6Q78qfUZ4JhkoZdBBgAH4FE83sSjbHwEbG1m70lq\nSBmY2XgWxVgsVtcac7V6Upv7q123LJbKkuZxaTgk7QicBpyVpsOq5appNHlcCEw0s1sq1JVBBt9O\nx8uAi4FhwJPAHZJ+QDlkALAmMAu3WAcBtwLXJ4VSFhnkqTXmxerT/XMBNeRSFktlSfO4NBSSRgEX\nAVfjHkBQPVdNw8gjzQGvD6xbpUnDywDIHqROSdNdSPoNPr1xECWQgaTV8d//ZmY2I5XtBcwEjqAE\nMqhArTEvVi+pG9BEDbmUxVJZ0jwuDYOk43BXwAvwYJzZukq1XDWNJI9RuBk/S9I8Fq0vTU4ulmWQ\nQTaWJ7KClEZiJrA65ZDBj3Gvv4eygvTU/W/cgimDDIrUGnO1eqghl7IolXweF6BmHpeGQNJofG/O\nGDM7NJeTBny/xhaFU4bQWPLYG+iPLyyuB2yTyvcHxlAOGTyCP1kOzAqSR1h/4HnKIYNX03GhxZqT\nwbOUQwZFao35HqCfpN6F+rnAo619cSlciqH1PC5d16uOQ9K6+A3lMuC4QvVcoB/wML7OchWwF3AU\nsEHmgtxopPnzV1jkUjyAEshA0sl4eon9cYvlYODXuKLtToPLIG18vgdYDh/7bOBw4Oe448K3aXwZ\ntADP5VyKW/3tJ6V7L76GcgiwKn4vOd/MTmjtWmWxVMDzuPwd94a4E8/NsmuX9qhj+Rlu8v8SzyuT\nfx1hZk8AI3EZPArsCAxvlD9RWyiRDMYAf8IT3T2Bb/7b2pyGl4GZfQ4Mxz2WrgZm4NNeg83s5TLI\noEitMadZjZG4t9zd+BT6xcBJtb67NJZKEARB0PGUyVIJgiAIOphQKkEQBEHdCKUSBEEQ1I1QKkEQ\nBEHdCKUSBEEQ1I1QKkEQBEHdKEvsr6DBkXQpnoStNaaZ2ZZpI9hnZja0wztWBUkr4ZtTh5rZc1Xa\njML3B/Q2s1crtemgvk0FLjSzazvrmkHjEEolaBROxuObZZwPfMaXc6h8kI4H4zuFu5JzgWurKZQu\n5ghgiqQWM3urqzsTfL0IpRI0BGb2PB7LCgBJH+DWyIwKbZ/uzL4VkTQQT2vbq1bbrsDMHpc0A49C\nUUxsFgStEkolKB3F6S9JC/CUspsDI/A8EufiYU3OBnbBQ4Ffhie3WpDOWxnP+z4Cz4j3MHC0mU2v\n0YWj8dSus3N9agZ+jyeT6oknUlssoKGkA1ObtfE10Zl4WPsJaUrtdWBsljo4nbMCnkvkd2Z2nqQ9\ngWPwBF5z07VGm9nruUtdCVwi6WQze7vGeIJgIbFQHwTOmXigwRHAJOBE4AE8Q+DOwA14LpqdASQt\nC0zFkz4di8dQeheYmiyRikj6Fh5naUKhaixwPB5faSQwB1dY+XMPA85L526PB0T8FLhK0vfN7B1g\nYirPsweeB+MqSYOAK9J3DAN+C2yFx8XLMwmPHbdTtbEEQSXCUgkC5xEzOxxA0mOkaNZmdkgquwO/\nWW+C35B/gYdS38jMHkptJuOK6FTgp1WuMxjoltqRzuuBTzOdaWZZwL5bJfXCb/wZq+NWyKm5c1/C\nLaRNgeuAS4DdJA3KWUz7AP80s3ckDcYV5Rlm9kn6jjnAQElNmRVmZh9KmomHO7+oTRIMAkKpBEHG\nwpzbZjZH0ueFsgWS3gV6pKKt8GRFj0rK/48mAcdK6m5m8ytcp186vpgr2xhXNDcV2l5LTqmY2RGw\nUAmtjUfaHZKqu6fjbXj+kL2B6ZLWwNPn7pDqpwGnAE9Kuh74F3CbmU2u0NeX8JxDQdBmYvorCJy5\nFcpaS5u6Mp5V8tPC63j8Bt+zynkrpONHubKV0rG4dvFG/oOkNSTdjk+zTcPzX3RL1U0AKbPn5cDu\nkrrjVsobwC2p/j5gO+AFfOrrLuA1SYdW6OuHuf4GQZsIpRIE7eN9fJF8YJXX7CrnZeUrVChbtdB2\n5exNWsi/GVdWA4HlzOxHFNZdEuNwRTUU2B0Yn3KKAGBmt5rZNsCKeJ6RJ4BzJG1Y+J4VWxlHEFQk\npr+CoH1MA7YFXs97TaUsi32ovhHz5XRcDXgvvb8X9y7bLb3PGJ573xMQcEi2hpPYNh0XPiCa2XOS\n7sIdC9bGvdey/p2BT5n9xMw+AiZJegVP1NQbX5/JWA14vMo4gqAioVSCoH2MAw4Fbpd0Kr6+sgM+\npXRituBdgbtxBbIZ8CSAmc1LyuiPkj4GWnDvroVKxczeSovyh0l6Hd/IuQ2eFhc8VW6xf+OABwv7\ncm7Hp80ulTQen6objVskLVmj5Ia8Dp4xMgjaTEx/BUE7MLN5uCfX/cBZ+IL3MODQ1nJ4J+tgMoss\njKz8NFxB7IG7BQ8AjiycvhO+PnI5cA2+wD8ceCb1Jc+kdBxXuM4UYE9cYdyA5yefBwwxs/dyTbcG\n5uNTbkHQZiKdcBB0MpI2AqYDfc3stQ66xj542Jrvmdn77Th/CvBU5mYdBG0lLJUg6GTM7AHgRha3\nRP5vJI1M03F/AS5qp0LZANiAyk4AQdAqoVSCoGs4GNhV0pp1/t6++DTafXjsrvZwFu4QMKtenQrK\nQ0x/BUEQBHUjLJUgCIKgboRSCYIgCOpGKJUgCIKgboRSCYIgCOpGKJUgCIKgboRSCYIgCOrG/wDK\ne236ks8bQQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f8234dd358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system.S, system.I, system.R)\n",
+    "savefig('chap05-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using a DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of making three `TimeSeries` objects, we can use one `DataFrame`.\n",
+    "\n",
+    "We have to use `loc` to indicate which row we want to assign the results to.  But then Pandas does the right thing, matching up the state variables with the columns of the `DataFrame`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a DataFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    frame = DataFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], system)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it, and what the result looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>S</th>\n",
+       "      <th>I</th>\n",
+       "      <th>R</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.011111</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.985226</td>\n",
+       "      <td>0.011996</td>\n",
+       "      <td>0.002778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.981287</td>\n",
+       "      <td>0.012936</td>\n",
+       "      <td>0.005777</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.977055</td>\n",
+       "      <td>0.013934</td>\n",
+       "      <td>0.009011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.972517</td>\n",
+       "      <td>0.014988</td>\n",
+       "      <td>0.012494</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          S         I         R\n",
+       "0  0.988889  0.011111  0.000000\n",
+       "1  0.985226  0.011996  0.002778\n",
+       "2  0.981287  0.012936  0.005777\n",
+       "3  0.977055  0.013934  0.009011\n",
+       "4  0.972517  0.014988  0.012494"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "sir = make_system(beta, gamma)\n",
+    "run_simulation(system, update1)\n",
+    "system.results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can extract the results and plot them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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evJiQkBBSUkr7kW677TaWL1/Oc889R1paGmvWrOHBBx8kLCysZO96byeKOTIy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\ne236ks8bQQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f823367a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frame = system.results\n",
+    "plot_results(frame.S, frame.I, frame.R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise**  Suppose the time between contacts is 4 days and the recovery time is 5 days.  Simulate this scenario for 14 days and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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evJiQkBBSUkr7kW677TaWL1/Oc889R1paGmvWrOHBBx8kLCysZO96byeKOTIy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\ne236ks8bQQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f82351d5f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tc = 4      # time between contacts in days \n",
+    "tr = 5      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "sir = make_system(beta, gamma)\n",
+    "run_simulation(system, update1)\n",
+    "system.results.head()\n",
+    "\n",
+    "frame = system.results\n",
+    "plot_results(frame.S, frame.I, frame.R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given the results, we can compute metrics that quantify whatever we are interested in, like the total number of sick students, for example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(system):\n",
+    "    \"\"\"Fraction of population infected during the simulation.\n",
+    "    \n",
+    "    system: System object with results.\n",
+    "    \n",
+    "    returns: fraction of population\n",
+    "    \"\"\"\n",
+    "    frame = system.results\n",
+    "    return frame.S[system.t0] - frame.S[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an example.|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.333 0.25 0.467162931836\n"
+     ]
+    }
+   ],
+   "source": [
+    "system.beta = 0.333\n",
+    "system.gamma = 0.25\n",
+    "run_simulation(system, update1)\n",
+    "print(system.beta, system.gamma, calc_total_infected(system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write functions that take a `System` object as a parameter, extract the `results` object from it, and compute the other metrics mentioned in the book:\n",
+    "\n",
+    "1.  The fraction of students who are sick at the peak of the outbreak.\n",
+    "\n",
+    "2.  The day the outbreak peaks.\n",
+    "\n",
+    "3.  The fraction of students who are sick at the end of the semester.\n",
+    "\n",
+    "Hint:  If you have a `TimeSeries` called `I`, you can compute the largest value of the series like this:\n",
+    "\n",
+    "    I.max()\n",
+    "\n",
+    "And the index of the largest value like this:\n",
+    "\n",
+    "    I.idxmax()\n",
+    "\n",
+    "You can read about these functions in the `Series` [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add three Series objects to the System: S, I, R\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    S = TimeSeries()\n",
+    "    I = TimeSeries()\n",
+    "    R = TimeSeries()\n",
+    "\n",
+    "    state = system.init\n",
+    "    t0 = system.t0\n",
+    "    S[t0], I[t0], R[t0] = state\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        state = update_func(state, system)\n",
+    "        S[t+1], I[t+1], R[t+1] = state\n",
+    "    \n",
+    "    system.S = S\n",
+    "    system.I = I\n",
+    "    system.R = R\n",
+    "\n",
+    "    print('Sick at peak:')\n",
+    "    print(I.max())\n",
+    "    print('Time of peak:')\n",
+    "    print(I.idxmax())\n",
+    "    \n",
+    "def sick_end(system):\n",
+    "    frame = system.results\n",
+    "    return frame.I[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sick at peak:\n",
+      "0.0435362026876\n",
+      "Time of peak:\n",
+      "30\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.00067419431560344738"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(system, update1)\n",
+    "sick_end(system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a DataFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    frame = DataFrame(columns=system.init.index)\n",
+    "    frame.loc[system.t0] = system.init\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        frame.loc[t+1] = update_func(frame.loc[t], system)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What if?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use this model to evaluate \"what if\" scenarios.  For example, this function models the effect of immunization by moving some fraction of the population from S to R before the simulation starts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def add_immunization(system, fraction):\n",
+    "    \"\"\"Immunize a fraction of the population.\n",
+    "    \n",
+    "    Moves the given fraction from S to R.\n",
+    "    \n",
+    "    system: System object\n",
+    "    fraction: number from 0 to 1\n",
+    "    \"\"\"\n",
+    "    system.init.S -= fraction\n",
+    "    system.init.R += fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start again with the system we used in the previous sections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3333333333333333, 0.25)"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "system = make_system(beta, gamma)\n",
+    "system.beta, system.gamma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And run the model without immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.46832081102878098"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(system, update1)\n",
+    "calc_total_infected(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now with 10% immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.30650802853979753"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system2 = make_system(beta, gamma)\n",
+    "add_immunization(system2, 0.1)\n",
+    "run_simulation(system2, update1)\n",
+    "calc_total_infected(system2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "10% immunization leads to a drop in infections of 16 percentage points.\n",
+    "\n",
+    "Here's what the time series looks like for S, with and without immunization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WVR7paWVlhaen500vhtkYmBS5EOIx4D3ga2AYULoK4hdAL2B+XQTXUtlYWzB+YAh2Nto+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\nQUUppdSk0aCilFJq0vw/g284ayXXOccAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f8237440b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(system.results.S, '-', label='No immunization')\n",
+    "plot(system2.results.S, 'g--', label='10% immunization')\n",
+    "\n",
+    "decorate(xlabel='Time (days)',\n",
+    "         ylabel='Fraction susceptible')\n",
+    "\n",
+    "savefig('chap05-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can sweep through a range of values for the fraction of the population who are immunized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.468320811029\n",
+      "0.1 0.30650802854\n",
+      "0.2 0.161365457006\n",
+      "0.3 0.0728155898425\n",
+      "0.4 0.035520216753\n",
+      "0.5 0.0196887157825\n",
+      "0.6 0.0116220579983\n",
+      "0.7 0.00683873780062\n",
+      "0.8 0.00369649625371\n",
+      "0.9 0.00148153267227\n",
+      "1.0 -0.000161212109412\n"
+     ]
+    }
+   ],
+   "source": [
+    "immunize_array = linspace(0, 1, 11)\n",
+    "for fraction in immunize_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_immunization(system, fraction)\n",
+    "    run_simulation(system, update1)\n",
+    "    print(fraction, calc_total_infected(system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function does the same thing and stores the results in a `Sweep` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_immunity(immunize_array):\n",
+    "    \"\"\"Sweeps a range of values for immunity.\n",
+    "    \n",
+    "    immunize_array: array of fraction immunized\n",
+    "    \n",
+    "    returns: Sweep object\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for fraction in immunize_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        run_simulation(system, update1)\n",
+    "        sweep[fraction] = calc_total_infected(system)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "immunize_array = linspace(0, 1, 21)\n",
+    "infected_sweep = sweep_immunity(immunize_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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T0y3G7zWQAZXr2Lq9kcamCO99aIMsGmPyS8oAIiJDgVpVjfi/O6QarE2qqrYA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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f8237f45f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Fraction immunized',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Fraction infected vs. immunization rate',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap05-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If 40% of the population is immunized, less than 4% of the population gets sick."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logistic function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To model the effect of a hand-washing campaign, I'll use a [generalized logistic function](https://en.wikipedia.org/wiki/Generalised_logistic_function), which is a convenient function for modeling curves that have a generally sigmoid shape.  The parameters of the GLF correspond to various features of the curve in a way that makes it easy to find a function that has the shape you want, based on data or background information about the scenario."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def logistic(x, A=0, B=1, C=1, M=0, K=1, Q=1, nu=1):\n",
+    "    \"\"\"Computes the generalize logistic function.\n",
+    "    \n",
+    "    A: controls the lower bound\n",
+    "    B: controls the steepness of the transition \n",
+    "    C: not all that useful, AFAIK\n",
+    "    M: controls the location of the transition\n",
+    "    K: controls the upper bound\n",
+    "    Q: shift the transition left or right\n",
+    "    nu: affects the symmetry of the transition\n",
+    "    \n",
+    "    returns: float or array\n",
+    "    \"\"\"\n",
+    "    exponent = -B * (x - M)\n",
+    "    denom = C + Q * exp(exponent)\n",
+    "    return A + (K-A) / denom ** (1/nu)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following array represents the range of possible spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([    0.,    60.,   120.,   180.,   240.,   300.,   360.,   420.,\n",
+       "         480.,   540.,   600.,   660.,   720.,   780.,   840.,   900.,\n",
+       "         960.,  1020.,  1080.,  1140.,  1200.])"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spending = linspace(0, 1200, 21)\n",
+    "spending"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`compute_factor` computes the reduction in `beta` for a given level of campaign spending.\n",
+    "\n",
+    "`M` is chosen so the transition happens around \\$500.\n",
+    "\n",
+    "`K` is the maximum reduction in `beta`, 20%.\n",
+    "\n",
+    "`B` is chosen by trial and error to yield a curve that seems feasible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def compute_factor(spending):\n",
+    "    \"\"\"Reduction factor as a function of spending.\n",
+    "    \n",
+    "    spending: dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: fractional reduction in beta\n",
+    "    \"\"\"\n",
+    "    return logistic(spending, M=500, K=0.2, B=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4LjCNpjqSK7H5269LUzyfAyao6h2qulBVbwGuBS4IKuKd61Hi8ThPv7qGrTur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\nQkJCr5AUSUJCQkJCr5AUSUJCQkJCr/Bfk3xdeHfqCS4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f8254a3630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "percent_reduction = compute_factor(spending) * 100\n",
+    "\n",
+    "plot(spending, percent_reduction)\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Percent reduction in infection rate',\n",
+    "         title='Effect of hand washing on infection rate',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap05-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Modify the parameters `M`, `K`, and `B`, and see what effect they have on the shape of the curve.  Read about the [generalized logistic function on Wikipedia](https://en.wikipedia.org/wiki/Generalised_logistic_function).  Modify the other parameters and see what effect they have."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hand washing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can model the effect of a hand-washing campaign by modifying `beta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def add_hand_washing(system, spending):\n",
+    "    \"\"\"Modifies system to model the effect of hand washing.\n",
+    "    \n",
+    "    system: System object\n",
+    "    spending: campaign spending in USD\n",
+    "    \"\"\"\n",
+    "    factor = compute_factor(spending)\n",
+    "    system.beta *= (1 - factor)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with the same values of `beta` and `gamma` we've been using."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3333333333333333, 0.25)"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc = 3      # time between contacts in days \n",
+    "tr = 4      # recovery time in days\n",
+    "\n",
+    "beta = 1 / tc      # contact rate in per day\n",
+    "gamma = 1 / tr     # recovery rate in per day\n",
+    "\n",
+    "beta, gamma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can sweep different levels of campaign spending."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.332887143272 0.466770231236\n",
+      "100.0 0.332134252669 0.464141650401\n",
+      "200.0 0.330171608455 0.457217006313\n",
+      "300.0 0.325386471865 0.439887202912\n",
+      "400.0 0.315403905242 0.401630646271\n",
+      "500.0 0.3 0.33703425949\n",
+      "600.0 0.284596094758 0.267317030568\n",
+      "700.0 0.274613528135 0.22184699046\n",
+      "800.0 0.269828391545 0.200791598416\n",
+      "900.0 0.267865747331 0.192392183393\n",
+      "1000.0 0.267112856728 0.189213207818\n",
+      "1100.0 0.26683150821 0.18803175228\n",
+      "1200.0 0.266727403413 0.187595503995\n"
+     ]
+    }
+   ],
+   "source": [
+    "spending_array = linspace(0, 1200, 13)\n",
+    "\n",
+    "for spending in spending_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_hand_washing(system, spending)\n",
+    "    run_simulation(system, update1)\n",
+    "    print(spending, system.beta, calc_total_infected(system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function that sweeps a range of spending and stores the results in a `Sweep` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_hand_washing(spending_array):\n",
+    "    \"\"\"Run simulations with a range of spending.\n",
+    "    \n",
+    "    spending_array: array of dollars from 0 to 1200\n",
+    "    \n",
+    "    returns: Sweep object\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for spending in spending_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        run_simulation(system, update1)\n",
+    "        sweep[spending] = calc_total_infected(system)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "spending_array = linspace(0, 1200, 20)\n",
+    "infected_sweep = sweep_hand_washing(spending_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig05.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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L0ReKqmpIVVcBtwFX+zzOfu92UszyyRzf/HYOsAC4S0QaRKQBlw13gfd4mne8\nQq8fCACvUCvs4XhmlCnIzWDpgq4s/vWvH+JoQ0sCIzLG+OG34MkEmnpZdxTI83mczUA9cFFkgYjM\nwF2QujZm2w24vqVFUX8PA6949w/iEgmS6T469nLc64pOODCj1BIppCDHtaq2dYRY9WqZTZ9gzDDn\ndwbSl4AbReRJr/MeABEJAjfgCoN+qWqLiNwD3C0iVbgkgHuANaq63ku3zgdqVLUZ6NbEJiJ1QHNU\n09sBb7y4X4nIJ3DZd/cC91kq9diQlBTk0rOm8eCzOwiHw5RV1PPmnhoWzixIdGjGmF74LXi+gUsu\n2C4ij+EKjIm4JripuL4ev27HXeh5v3f7BK7wApcevQq4hKiU6358Evgp8A+gHXgQ+PwA4jEjXFF+\nJovmTmTjdpdWvW7zQaYV55CVkZLgyIwxPQn4bZYQkaW4QuN8XK2kDnge+Laqbhi0CAeB17xX+swz\nz1BSUtLf5mYEaGsP8cBK5YjXxzNzci7vXDaDQMAuLDUmXsrKylixYgXATO9SmxPit8aDqr4CXHWi\nT2TMYEpJDnLJ0qk8vNq1wpYePMrOsiPMneq3+9EYM1R8FzzQOSDoZbistB8AAmxSVctjNQk3ZWIW\np84q4PXd1QCs3XiAksJsMtIG9DE3xgwyX1ltIpIiIn/AJRncAXwW18dzC7BJRGYNWoTGDMCy0yd3\n9u00t7Tz/CbLMTFmuPGbTv1t3MjQ7wPG47LHAD6Nu1DT7wWkxgyq1JQkLl7SNVqS7qtlz6G6BEZk\njInlt+D5Z+A2VX0YNxwNAKq6C5fxdskgxGbMCZkxKQeZ1tW3s/rV/bS2dSQwImNMNL8FTwGgvayr\nAvyO1WbMkFi+aEpn305DcxsvbDmY4IiMMRF+C55twAd7Wfd24M34hGNMfGSkJXPh4imdj1/fXc2B\nSpuU1pjhwG/B813gEyLyIK7ZLQycKyI/wE3s1tOUBcYk1JyS8cycnNv5+NlX9tPW3tMg58aYoeSr\n4FHVh3ADdJ4H/AaXXPAz4OPA51T1D4MWoTEnKBAIcNGZJaSluDFkjza0sGFbeYKjMsb0WvCIyBdE\npHMUaVW9Dzfx2mnAxcBiYJKq/nywgzTmRGVlpHD+GZM7H2/eXmkjWBuTYH1dWfdt3HU7h0SkFVjm\njV7wxpBEZkycLJiRz1t7ajhY1UgoHOaVNw+z4qxpiQ7LmDGrr4KnDvi8N65ZMvAOEZnX28aq+vs4\nx2ZMXAQKrSaGAAAgAElEQVQCAc49dRJ/8YbTeWtvLWfOLyQv2yapNSYR+ip47sIlDbwPl0zwzT62\nDQNW8Jhha/LELKYWZbP/cD3hcJgNbxzm7edOT3RYxoxJvfbxqOqPcRO8zcQlE1zj3e/pz4bMMcPe\nOacUd97fWXaE6qPNCYzGmLGrz9ETVbUeqBeRTwHPqWr10IRlTPwVF4xj5qQcSg/VebWect6xbGai\nwzJmzPE1bK+q/kpExonIO4Bx9FBTUtU/xTs4Y+Lt7FMmUeqN3bbrwFEqapsozMtMcFTGjC2+Ch4R\neRtuZs9sugYIjRYGrOAxw97EvAxml4xnV9kRADa8Uc6Vy62l2Jih5Heikh8Au4AvAWWAXf5tRqyz\nFxax+8BRwuEwew7VUV7dSHHBuESHZcyY4bfgWQhcpaqrBjMYY4ZCQW4G86aOR/fVArD+9XKuumh2\ngqMyZuzwO1bbfiBrMAMxZiidtbCYYMC1GpdV1NsAosYMIb8Fzw+Ar4vIlH63NGYEGJ+dxvwZXXP2\nvPT6IcLhcAIjMmbs8NvU9h5gKrBXRMqAppj1YVU9Ja6RGTPIli4o5q29tYRCYQ5WNbL/cD3Tim1q\nKWMGm9+C5wjw6GAGYsxQyxmXysKZBby+qwqAl94oZ2pRNoFAT4mbxph48Xsdz4cHOxBjEmHpgiLe\nLK2mIxTmcE0Tew7VdZvDxxgTf70WPCJSCFSraod3v0+qWhHXyIwZAlkZKZw2ZwKbtlcCrtYzY1KO\n1XqMGUR91XgO4SZ+2wCU4y4S7UuSnycUkSTgTuBjuAtSnwBuUNXDvWz/CeDLuDHhdgM/VNXfRK1/\nJ/BYD7tOVdUyPzGZse1MKeSNXdW0dYSoOtLMrrKjzJk6PtFhGTNq9VXw/AvuotHI/Xil/NyBm830\nI0A1cA/wELA8dkMReR/wc+BfgTXACuBeEalW1b95m50GbATeGbO71cCML5npKZw+dwKvvuU+Mhu2\nlTNrSi7BoNV6jBkMvRY8qvqrqPu/jMeTiUgq8DngRlVd6S37EFAqIstU9YWYXSYA31DV//Ue/1JE\nbsAVQJGC51Rgq6ranMbmhC2eV8jWXdW0tnVQU3eMHftrken5iQ7LmFHJ73U88bII17y2OrJAVfcA\ne4ALYjdW1V+o6vcBRCRZRK4FFgArozY7FXhz0CI2Y0J6WjKL5k7sfLxh22E6QnZdjzGDYagLnhLv\n9kDM8oO464R6JCJLgWO4gUjvx+vT8fqL5gNLRGSziBwUkUdEROIeuRn1zpg3kbRU11V5tKEF3VuT\n4IiMGZ2GuuDJBEKq2hazvAXoax7iUmAp8AngA7jkBIDZ3n5pwKe8dWnAc34y8YyJlpaSxOJ5XR+b\nl7cdpqPDxsM1Jt6GuuBpBoIiEtu3lAY09raTqlar6iYvm+07wE0ikqSq24EC3ACmG1T1edxMqUHA\nrj0yA3bG3AlkpLmPZ31TK9tKrdZjTLwNdcGz37udFLN8Msc3vyEiF4nIopjFW4EMIB9AVWtUtfNn\nqao24dKue226M6Y3KclJLJnfVet55c3DtFutx5i48jtkDiLyYeBKep6BNKyq7/JxmM1APXARrq8G\nEZkBzADW9rD9Lbi5f66MWnY2LlW6SkSuAu4DZqlqpXe8bGAecK+f12VMrFNnT2CjVtJ4rI3GY228\nvquKRfOs5daYePE7A+l3gFtxNZYTnghOVVtE5B7gbhGpwhUg9wBrVHW9l26dD9SoaivwY+AJEfkS\n8FdcgXUz8AVVDYvIGqAOuE9EbvZez3eBKlyBZMyAJScFWbqgiDUb3fXHr75VwSmzCkhJ9nWNtDGm\nH35rPB8HfqKqN8XhOW8HUnA1nhS8kQu8dcuAVcAlwGpVfUpE3g98A/g2ruD798g1Rqpa603L/QNc\ninYyLtX6UlU9FodYzRi1cGY+r2kF9U2tNLe0s3lHFUsXFCU6LGNGBb8Fz3hcjeOkqWo78EXvL3bd\naiAQs+wvwF/6ON6bwLvjEZsxEUlJQc5aWMSzr7huyY3bKzhtzgTSUqzWY8zJ8ptc8CJu3DZjxgyZ\nnk9uVhoALa0dbPYGEjXGnBy/NZ5vAn8QkSDwAsdPBIeqbohnYMYkWlIwwNkLi1i5YR8Am3ZUcvqc\nCaSn+c7JMcb0wO8ZtNq7vZPjBwsNeMusDcKMOnOn5vHqWxXU1B2jta2DjdsrOO+0yYkOy5gRzW/B\nc9mgRmHMMBUMBjh7YTFPrN8DwJYdVZwxdyKZ6SmJDcyYEczvDKTPDHYgxgxXs0tymTA+g6ojzbR1\nhHj1rQouWDQl0WEZM2IN5ALSubi5dC4GcnHXyjwH3KmqOhjBGTMcBAIBzjmlmMfWlQLwxu5qzpRC\nxmVYrceYE+Erq01ETgFeAS4HngR+iuv3uQJ4xVtvzKg1Y1IOhXmZALR3hHjNZno35oT5rfHcBewA\nLlHV+shCb3iaZ3EDd14V//CMGR4CgQBnLSzqVutZMr/Q+nqMOQF+r+O5EPhOdKED4D3+nrfemFHN\naj3GxIffgucY0NHLuhBu6BtjRrVIrSfi9V3VNB2LnVrKGNOfgYxccLOIpEUvFJF04Mu4i0qNGfVm\nTMph4vgMwNV6NqqNZmDMQPnt47kNeAnYJSKPAOVAMfBeIA9rajNjRCAQ4OyoDLetu6pYLHZdjzED\n4avGo6pvAOcDLwMfxKVVfwjYACxT1VcHK0Bjhpvjaj02hpsxA+L7Oh5V3QxcPYixGDMiBAIBli4o\n4vEX9wDw+s4qFs+zWo8xfvVa8IjIB4CV3pw3H+jvQKr6p7hGZswwNmtK99EMNm2vZNnpNoabMX70\nVeP5I3Aurjntj/0cJwxYwWPGjEAgwFlRtR7X11NIho1cbUy/+jpL5uJm/IzcN8ZEmTUll4LcDKqP\nNtPWHmKTjVxtjC+9Fjyquivq4TnAE6paE7udiBThEg1+Ev/wjBm+Itf1PPHiHgC27Kxi8bxCm6/H\nmH74vY7nPmB2L+sW4YbUMWbMmT0ll4KcdADa2i3DzRg/+kou+Bsw33sYAB4UkZYeNp0E7B6E2IwZ\n9gKBAEsXFvHk+r2A19czb6LVeozpQ19nx13AJ737c4CtQOzPuQ7gCPC/cY/MmBFiTsl4Xs453DlL\n6aYdlZx76qREh2XMsNVXH886YB2AiCQDX1fV0qEKzJiRItLXE6n1bNlZxaK5Vusxpjd+Ry74MDBX\nRL4XWSYiZ4nI4yJiw+WYMW/2lPHke309rW0dbN5hfT3G9MbvRHDvBx4HFkctbgYygadF5LJBiM2Y\nESMYdKMZRGzeWcWx1vYERmTM8OU3q+124L9V9YrIAlV9XVUvAn4J3DkYwRkzkswpGU9edletZ8uO\nqgRHZMzw5LfgmQs82Mu6BwGb+tqMecFg9/l6Nu+otFqPMT3w2/tZgWtmW9XDulOBWr9PKCJJuBrS\nx4Bs4AngBlU93Mv2n8DN+TMTl7b9Q1X9TdT6TODHwDW41/Nn4CZVbfAbkzHxMqdkPBu2lXOkvoWW\ntg627Kzi7IXFiQ7LmGHFb43n98AdIvJJESkAEJF8Efko8C1vvV93AB8FPoKbx6cEeKinDUXkfcDP\ncandC4D/AO4VkfdEbfYLYDlwJfBu4GJvmTFDLhh0Y7hFbN5RSUtbb5P3GjM2+S14vgk8A/wPUCEi\nbbhren4DrAa+5ucgIpIKfA64TVVXqupruOF2zheRZT3sMgH4hqr+r6qWquovcdcTrfCOVwL8E/BZ\nVV2vqs/hrj26TkSm+HxtxsTV3Kl5jM9yk/W2tHawxTLcjOnGV1ObqrYCV4vIIlztIh84Cjw/wEng\nFuGa11ZHHXuPiOwBLiBmCm1V7ay5eNcSXY2r+UQKumVACO96I8863IWty4EHBhCbMXERDLrRDJ7e\nsA+ATTsqOWPuRFJTkhIcmTHDw4CucFPVTcCm2OUikqmqTT4OUeLdHohZfhCY2ttOIrIUWA8kAb8C\nHos6XoWqtkXF2C4iFX0dz5jBNm9qHq9sO8yRhhZX69lZ1S3d2pixzFfBIyIpwA3ARUAqbuw2cE11\n4+iqyfQnEwhFFxSeFiC9j/1KgaW4BIefAIeBr3rHO9bD9v0dz5hBFbmu5+mXXa1n4/YKTp8zwWo9\nxuC/j+f7uI79ebgCYCFQhOvIPw+42+dxmoGg12wWLQ1o7G0nVa1W1U1eNtt3gJu87Lhmb99YfR7P\nmKEwb1oeudF9PTvtuh5jwH/Bcy3wY1U9BfhP4CVVXYIriPbh+lT8iEwsFzuC4mSOb35DRC7y+pWi\nbQUycP1M+4FCrxCK7JMMFPZ0PGOGUjAYYOn8rua1TdsrabUMN2N8FzzFwKPe/a3A2QCqug/4HnCd\nz+NsBupxTXYAiMgMYAawtoftb+H4URHOxl1XVIVLJEjG1boiluNe1zqMSbB50/PIGZcKwLHWdrbu\nslqPMX6TC47i+nYAdgDTRCTLu0hTgWl+DqKqLSJyD3C3iFThCpB7gDWqut5Lt84HarxMuh8DT4jI\nl4C/4gqsm4EvqGoYOCAifwJ+5V1oGgDuBe5TVavxmIRL8vp6nn3FVfY3aiWnz5lASrL19Zixy2+N\n53ngsyKShit4moDIRZxLcbUYv24HfgfcjxsJYS/wfm/dMuCQd4uqPuWt+zCupnUL8O+q+t9Rx/sk\nLg37H8AjwLPAZwYQjzGDSqbnd6/17KxOcETGJFYgHA73u5GInAmsAV5W1UtF5Ie4LLeNwFnA/6jq\nvw1qpHHkNe+VPvPMM5SUlPS3uTEn7Y3d1ax61dV6MtKS+cg7F1itx4w4ZWVlrFixAmCmqu450eP4\nnY/nNdyFm5HstZtxmW4NuOFsvnSiARgzFsyP6utpbmln6y6r9Zixy+91PD/C9Zv8A8DrX/nWYAZm\nzGiSlBRkyfyizlrPq28dZsGMfDJsllIzBvnt4/lXoGAwAzFmtIuu9bS0drD+9UMJjsiYxPBb8GzA\n6/A3xpyYpKQgFyzqGrt2W2kNh2v8jDRlzOjit56/AbjNm6ZgE65vJ1pYVW+Ia2TGjEIzJ+cyc1IO\npYfqCIfDrN1YxvsvnUsgEOh/Z2NGCb8Fz3W4a27ygEt6WB/GZbkZY/qxfNEU9h2upyMU5nBNE9tK\nazhllrVkm7Gj14JHRK4FnlbVWlW1kZ6NiZPcrDSWzC9iw7ZyAF7ceojZU3JJt0QDM0b01cfza9xg\noIjIdhE5fWhCMmb0O3N+YbeLSi3RwIwlff3EasXN5AkwBzhLRLJ621hVX+htnTGmu2Qv0eCxdaUA\nvFFaw8KZBRTmZyY4MmMGX18Fzy+BL+OGnwnjpr3uScBbb5dhGzMAMyblML04h73lXqLBpgO875I5\nlmhgRr1eCx5VvUVEfgtMwI2pdgOwbagCM2a0CwQCXLBoCmVPuUSD8upG3tpTy4KZ+YkOzZhB1Wdv\npqq+ASAi3wEeUdWDQxKVMWPE+Ow0Fs0r5NW3DgPwwtaDzJySQ3qqJRqY0cvvWG1fs0LHmMGxdEEh\nWRkpgBvHbcMb5QmOyJjB5XfkAmPMIElJTmJ51IgGW3dVU1nbnMCIjBlcVvAYMwzMnpLL1KJsgM4R\nDfxMWWLMSGQFjzHDQCAQ4MLFUwgGXUbboepGdF9tgqMyZnBYwWPMMJGXnc6iuRM7H7+w5RAtbR0J\njMiYwdHXkDn3DOA4NkioMXFw1sIitu+rpaG5jaZjbWx4o7zbiNbGjAZ95Wy+ewDHsUFCjYmDlOQk\nzj9jMk+u3wvA1p1VLJyZT0FuRoIjMyZ++rqA1AYGNSYB5pSM543CGsoq6gmFw6x57QBXXzzbRjQw\no8ZJ9/GISJKIXByHWIwxRCUaeAXNwaoGduw/kuCojIkfX5dHi0gJ8DPgIiAVNz4buIIrxbtvY7UZ\nEyf5OemcMXciG7dXALBu80FmTMohNcVOMzPy+a3x/Ai4FPg9oMBrwM+BHbj+nfcNSnTGjGFnLSxi\nXLr7Xdd4rI2Xtx1OcETGxIffgucS4HYvc+03QKOqfhFYDDwPvGuQ4jNmzEpNcYkGEZt3VFJTdyyB\nERkTH34Lnmxgk3f/LVyBg6q245rg3hb/0Iwxc6eOZ/IENw1WyEY0MKOE3yFwy4FC7/4OoEBEilW1\nHKgCivw+oYgkAXcCH8MVaE8AN6hqj+0IIvJB4FZgLnAIN0/QD1W1w1v/TuCxHnadqqplfuMyZjgK\nBAJcdOYUHli5nVA4TFlFAzvLjjB3al6iQzPmhPmt8TwOfFNElqrqHuAA8DkRSQX+2Xvs1x3AR4GP\nABcCJcBDPW0oIu8AfocrbE4HvgLcAtwWtdlpwEZgUsyfjaZtRoWC3AxOmzOh8/G6zQdpa7cRDczI\n5bfG83XgSeAuYAVwO66v58u4DLcb/RzEK6g+B9yoqiu9ZR8CSkVkWQ/TZ38aeEhVf+Y93iUiC4CP\nA9/2lp0KbPVqX8aMSmefUsyO/UdoOtZGQ7NLNFh2+uT+dzRmGPI7H08FcCaueQxV/S2uAPoacLmq\n/pfP51uEa15bHXXsPcAe4IIetr8T+GbMshAQ3c5wKvCmz+c3ZkRKS0li2emTOh9v2lFJbb0lGpiR\nyVfBIyK3AcWquj+yTFVXq+r3gJ0i8h8+n6/Eu41tmjsIHDdSgqq+rKqd022LSA7wGVy/UKS/aD6w\nREQ2i8hBEXlERMRnPMaMGDItj0kF4wAIhcI8vWEf7R2hBEdlzMD57eP5Nl2FRqyzgc/6PE4mEFLV\ntpjlLUB6XzuKSCbwVyAD19cDMNvbLw34FPAB7/5zIlLY03GMGalcokFJ59A5h2uaWLlhn2W5mRGn\nr9Gp1wLneg8DwLpeKhJJwKs+n68ZCIpIspeKHZEGNPYRywTgb8BC4DJV3QugqttFpAA4oqohb9tr\ngH3Ah4H/5zMuY0aECeMzWH7GZJ7b5BoNdpUd4cWtqdbfY0aUvpILPg1ciyt0vg78HxCbntwBHAH+\n4vP5Ik11k6LuA0yml8w4EZkBPIXrG7pQVbdEr1fVmpjHTSKymx6a7owZDc6YO5GjDS1s2VkFwGta\nQW5WGqfMKkhwZMb409fo1NvwOva9vpT/VtWBpE33ZDNQjxvz7X7v2DOAGcDa2I295rJVuAJumaqW\nxqy/CrgPmKWqld6ybGAecO9JxmrMsLX8jCnUN7ZSeqgOgDWvlZGVmcL04pwER2ZM/3ylU6vq1wBE\n5DLgYiAXd+Hoc6r6jN8nU9UWb4K5u0WkCqgA7gHWqOp6L906H6hR1Vbgv4AJuHHimkWk2DtU2Lvg\ndA1QB9wnIjd7r+e7Xmz3+Y3LmJEmGAxw+bnT+cvqnVTWNhMKh3ly/V6uuXgOE8bb3D1mePOb1ZYm\nIv/AXcvzZdygoF8FnhKRp0QkbQDPeTvuotD7cbWZvcD7vXXLcKMTLBORDOAaIAvY4C2P/B0AUNVa\n3HA9bbgU7dW4vqJLVdVyTc2olpKcxJXnzyIrww0k2trWwaPP76ahOTZ3x5jhxe8FpN/CXWdzPfCA\nqoa85rcPAv+N6wP6qp8DeUkFX/T+YtetpmvKBfAx1YKqvsnAZks1ZtQYl5HCuy+YxUOrdtLa1kFD\ncxuPPb+bay6ZQ0qyTaFghie/6dTXAV9X1T9EssdUtUNVfw98A/inwQrQGNO3gtwMrjh3eufEcZVH\nmnlq/V5CIUuzNsOT34KnANjay7qtuCw1Y0yCTCvO4aIzuy61Kz1Ux/ObD9g1PmZY8lvwKHB5L+uu\nAEp7WWeMGSKnzCpgyfyu66a37Kxiy46qBEZkTM/89vH8BPiV16/zR9w0CcW4Jrh/B74wOOEZYwbi\n3FMnUdfYyo79RwB4fstBsselMmtKboIjM6aL30FC/w83MvW/Aetxg3quB27AzY3z08EK0BjjXyAQ\nYMVZ0yj2xnQLh8M89dJeDtc0JTgyY7r4bWpDVb8KTAHei5uW4Cpgiqre1ueOxpghlZwU5J3LZpAz\nLhWA9o4Qj60rpa6xNcGRGeP0NVbbs8BnVfWtyDJVrQIeHYrAjDEnLjPdpVk/+OwOWlo7aDrWxqNe\nmnV6qt8WdmMGR181nosBG3/DmBEqLzuddy2bSVLQpVnX1B3jiRf30mFTKZgE893UZowZeSZPzOLS\npV3j5ZZV1LNmY5mlWZuE6q/gsU+nMSOcTM/nnFOKOx9vK63h1bcqEhiRGev6a+z9qYjU+ThOWFXf\nHo+AjDHxt3RBEXWNrby5x80isv71Q+SMS2XetLx+9jQm/voreFK8P2PMCBYIBLj4zBLqm9ooq6gH\n4JmX95GWmmRTKZgh11/B8xlV3TAkkRhjBlVSUpArzpvOX1btpKbuGB2hMH9/bjeL5xVy7qnFJCVZ\nl68ZGvZJM2YMSU9N5srlsxiX3tWQsXF7BX9+dgc1dTaTiBkaVvAYM8bkjEvlg5fN69bEVnWkmT89\nvZ2tO6ss480Mur4Knv8DKocqEGPM0MlMT+HK5TO5cPEUkr0mtvaOEGs2lvHo86U0HbPJ5Mzg6bXg\nUdWPq6qNOm3MKBUIBDh9zkSuXTG323TZe8vr+MNTyp5DfhJajRk4a2ozZowryM3g2kvnsmjexM5l\nzS3tPPr8bta8VkZbu410YOLLCh5jDElJQZafMYX3Xji7W+LB1l1V/PmZ7VTWNicwOjPaWMFjjOk0\ntSib6y4XZkfN31NTd4w/P7ud17TCEg9MXFjBY4zpJj0tmSvOm8GlS6eS4iUehEJhXthykEfW7qah\nyaZXMCfHCh5jzHECgQALZxbwwcuEovzMzuVlFfX8YaWys+xIAqMzI50VPMaYXo3PTuOaS+aydEER\ngYCbXqGltYMnXtzDs6/so629I7EBmhHJCh5jTJ+SggHOPXUSV180m+zM1M7l20pr+OPK7by1p4Z2\nm+PHDIAVPMYYXyZPzOKDl83rNqL10YYWnn55H7959A2e23SAWht2x/hgc+AaY3xLT03m8nOmM704\nmzUbD9Da5praWlo72Lyjks07KpkyMYtTZhUwe0quDTxqejTkBY+IJAF3Ah8DsoEngBtU9XAv238Q\nuBWYCxwCfgn8UFU7vPWZwI+Ba3Cv58/ATaraMLivxJixS6bnM7Uom7f21PL67irqGrsy3Q5UNnCg\nsoGMtGQWzMjnlFkF5GalJTBaM9wkosZzB/BR4CNANXAP8BCwPHZDEXkH8Dvg88DjwGLgXtwcQd/2\nNvsFsAS40lv+a2/Z9YP4GowZ8zLTUzhzfiGLZSL7D9fzxu5qSg/WEfKu9Wluaec1reA1rWBaUTan\nzCpg5uRcgsFAgiM3iTakBY+IpAKfA25U1ZXesg8BpSKyTFVfiNnl08BDqvoz7/EuEVkAfBz4toiU\nAP8ErFDV9d7xPgmsEpGbVfXAELwsY8a0QCDAtOIcphXn0NDcxrbSarbtrqahuWug0X2H69l3uJ6s\njBQWzixg4cx8sqISFczYMtQ1nkW45rXVkQWqukdE9gAXALEFz51AY8yyEBDp3VzmPV4XtX4d0IGr\nQT0Qn7CNMX5kZaRw9sJils4vYm95Ha/vqmbf4frOEQ8amtvYsK2cl988zIxJOZw6q4CpRdlWCxpj\nhrrgKfFuY2siB4GpsRur6svRj0UkB/gMrl8ocrwKVW2L2qddRCp6Op4xZmgEgwFmTs5l5uRc6hpb\neWN3NW/uqemcbiEcDlN68CilB4+SlZHClIlZFBeMo6ggkwm5GVYQjXJDXfBkAqHogsLTAqT3taOX\nRPBXIAP4StTxesrf7Pd4xpihkTMulfNOm8TZC4soPeRqQWUV9Z3rG5rb0H216L5aAFKSghTmZ1Jc\nMI7iAnebkWYJuKPJUP83m4GgiCSranvU8jSOb1LrJCITgL8BC4HLVHVv1PF6Spfp83jGmKGXlBRk\nTsl45pSM50h9C2+UVvNmaQ3HWtu7bdfWEerMjIsYn5XWWQgVF4wjPyfdakUj2FAXPPu920lR9wEm\nc3zzGwAiMgN4Ctc3dKGqbok5XqGIJEWlVycDhb0dzxiTeOOz0zj/9Mmce0oxlUeaOVzdRHlNI4eq\nGrslJUQcaWjhSEMLb+11taLUlCQK8zIpLshkUsE4JuZlkJGW3Dmsjxnehrrg2QzUAxcB90NnwTID\nWBu7sYgUAqtwyQLLepgRdR3uNZwHPO8tW44bkWEdxphhLSkp2FmLOQM3EV1DUyvlXkFUXt1ERW0T\noVD36Rha2zooq6jv1mSXkhQke1wqWZkp5GSmkpWZSs64VLIzU8nOTCEzPcVqScPEkBY8qtoiIvcA\nd4tIFVCBu45njaqu99Kt84EaVW0F/guYAFwKNItIsXeosKoeVtUDIvIn4Fci8gkggLvO5z5LpTZm\nZMrKTGVOZipzpo4HoL0jRGVtM+XVjZTXNFFe1UjjseNrRW0dIWrqjlHTy7A9wWCArIyUqMLI/WVl\numXjMlJItpEWhkQieuxux13oeb93+wRwg7duGa6Gc4mIvIQbjSAIbIg5RgddsX8S+CnwD6AdeBB3\nwakxZhRITgoyacI4Jk0YB7iMuPqmNsqrGzlc3cSh6kZq64/1O0V3KBSmrrG12ygLsZKCAdJSk0lP\nTSI1JYm0lKSu+6nucffbZHebmkRqctCa+nwKjMUZBb3mvdJnnnmGkpKS/jY3xgxz4XCYltYO6pva\nqG9qpb6xlfpm77apjbrG1uOSGOItEAiQkhwkOSlIclKA5KQgSUkBkoNBkpODJAcDJHVb17VtUlKQ\nFG/7pGCAYDBAMODdRt/vdkuP6wIBV4BCAHfj3XoxnoyysjJWrFgBMFNV95zocSxH0Rgz4gUCAdLT\nkklPS2ZiXkaP27S1ewVTY6srnJpaqWtso8G739TSflxf0kCEw2Fa2zo6B04djgIBVzAFgKB3p9ut\ntzwQgBmTc7lo8ZRBqcVZwWOMGRNSkpPIz0kiP6fnS/zC4TDtHWFa2jpoaW33bjuOvz1umdu2v6a+\n4SAcDhNp5Oqg70L29V1VLJyRT2HUDLTxYgWPMcYQaSpzzWVZGSkD3r8jFKatvYOOjjDtHSHaO0Ld\n76AE7WwAABWcSURBVIfCtLX3siwUor09RHtHmI5QmHA4TCgcJhTy/sJ4t+Hut92WdW0TKWBC4TBE\nbgdowvgM8nMH5zp8K3iMMSYOkoIBklKH71dqpDAKh8OEox7HFk6R1sZx6YN3XdTwfZeMMcbETaR/\nx/XwJNZYLXiSAMrLyxMdhzHGjBhR35lJJ3OcsVrwTAK4/nqbK84YY07AJGDXie48Vguel3Hz/xzC\nXYxqjDGmf0m4Qufl/jbsy5i8gNQYY0zi2MBExhhjhpQVPMYYY4aUFTzGGGOGlBU8xhhjhpQVPMYY\nY4bUWE2nPo6IJAF3Ah/DTbP9BHCDqh5OZFx9EZEi4AfA5UAG8BLwRVV93Vt/ubdegB3ALar6eNT+\nhcDPvP1bgd8AX1XVwR0/fgBE5Fzc7LJvU9XV3rIR+7pE5JPA/9/emcfbNZ19/Bs1K0oar2rU7Ef6\nUjG8JRIlrRBTq2hasw6GiNRQJSXmoEXwoUoNb6gWjRRpzCFDRaJVhJY+H5VSM9E2piRUvH/81r53\nZ+fcIbm55+a8Xd/P53zOOWuvvfZ69l57PWs9a3h+CKwNPA2cGBEPpmMNKZeklYDzgX2AFYGpuBw+\nnY43lFySrgSWjojvlsI6LIOk47CvsB7YQ/LgiHi28yVqun4tuYYAQ3B5fAEYGRHXlI53ily5x9PM\nGcAhwMHADkBPYExXZqg1JC0F3AZsDHwVO9GbBTwgqbukXsBYYDTQG7gDuF3S50vJjAHWxK7IDwUO\nA86slwxtkSq0X1BaJd3Ickk6BHvVPR/YDJgEjJW0biPLBVwKfAXYD7uhnwPcI2n5RpJLUjdJZwFH\nVMI7LIOk76T/JwBfBGbje7RcZ8lTunZLch2Fy+I5wObASOAKSQeVonWKXHkdD5Bcbs8EhkbEqBS2\nLvA3YPuIeLjrclcbSb2Bx4BeEfFMClsO+AdwFLA9oIjYsXTOBODZiDhc0nbAw8D6EfG3dPwQ7M21\nR0TMrac8tZB0FVasOwI7RcTEFNZwcknqhsvTDRFxWgpbCj/Dn+AXu+HkSvmYCZwZEZel/72APwNb\n4cpuiZdL0vrAtcB/A+8D9xc9g8VR5iQFcFNEnJGOfxIvYD8iIn7VRXJNB+6JiJNK8a/FTt76d6Zc\nucdjtsDmtYlFQPKu9zze4WBJ5O/AHkCUwgqHIKvhfE+snDORZnn6AS8UBap0fGV8P7oUSbsBuwND\nK4caVS4B6wC3FAERMS8itkgvaKPKBfAmMEjSGqkR9x3gn8AMGkeuPsCLuCf6t8qxDsmQzFUbM3/9\n8i7wKJ1fv7Qm11DgykrYPFx/QCfKlcd4TOH/+uVK+CvY9rnEERFvAXdWgofisZ77gLNpXZ6eLRwn\nxXlksWV2IZH0adxKOwxXYGVayveSLtfG6ftTkh7ELdC/ACenHnWjygVwOHAj8Dregup9YEBE/EtS\nQ8gVETdiGZBUPdxRGT5Mv+tev7QmV0RMKv+X9DngW7hHA50oV+7xmBWBeRHxYSV8LtA5npAWM5L2\nAs7Dg4PPYJnmVKKV5VngeJL/Y7pe5quAsRFxT41jjSrXKun7euAaYFfgT8CDkjalceUC2BB4DfdQ\ntwfuBW5NSqeR5SroqAyFC8/W0uhSJPXADdnX8LgPdKJcucdjZgNLSVq6MpNmOeC9LspTu5F0KHA1\ncDOeMQWWqTrAV5ZngeOSlsHOOrpM5mRD7o0HO2vRkHLR3DocUdi+JR2NTRJH0aBySVoPl72+ETEt\nhe0PPAMcR4PKVaGjMswundNSGl1GGge6GyuSL0XErHSo0+TKPR7zYvr+TCV8LRbsRi5RSDoFT3G8\nEjg4IopxnhdpXZ6WjkPXynwo7uK/Juldmsew7k7TQRtVruLaTxUBEfExrqDXo3Hl2hrPOny0CEit\n4sdxT6hR5SrTURmW2PpF0pZ4+vs8oE9EzCgd7jS5suIx04F38MwioGlW27rA5K7JUttI+iGeCnla\nRByTKrKChyjJk9iJZnkeAtaXtHbl+DvAE52U5fZwINALDyxvAeySwr8LnEbjyvUYbgVuUwSkmW69\nsF+TRpXrpfTd1EMtyfUsjStXmQ7JEBFv4HtRrl8+iZV2l9UvkjYB7seTqPpGxIuVKJ0mVza1AWla\n4BXAhWlq6BvAFcCkwnywpCFpc+Bc4Drgaklrlg6/gwcI/yjpTOAmYH88z/6oFGcqMA24JS0iKxaj\njoyID+ojxYJExHwtJUmF/fjliHhDUqPK9b6ki4ERkl7HPZ/BwAZ44eWyNKBcwO9TvkZJGoyXJRwL\nfA6XwVVoTLnKLI4yNxLXL3/FY3vn4mnHv6mbFAtyAx6fOQhYplSH/DsiZtKJcuUeTzOnAr/EM0Am\n4FW8+3Zpjlrnm9jE8W38oMuf4yLiKWBvLMMTwF7AnsWan9Q72hvPRPodNtddA5xVXzEWjgaX6zTg\nAuASrHi2w7O/olHlioiPgD3x7LObcUW1IdAvIl5oVLnKLA4ZIuJKYASuqKfhhsauXaVcJW2Me99r\nYXN2uf6YlvLcaXLlBaSZTCaTqSu5x5PJZDKZupIVTyaTyWTqSlY8mUwmk6krWfFkMplMpq5kxZPJ\nZDKZupIVTyaTyWTqSl5AuohIGoVX+27YwvHngfFlb3+dnJ+6Xq+9SNoRr4vqFxEPtRLvY2B4RJxT\nr7w1Mm2Vv/80JJ0BnBoRS6f/E/FCyK/UMQ8/B16MiLPT/on/C6wdES/ViHsN9qq7bins08ApeJ1Q\nT7zTxePA5RFxWylekXaZuXibmnHA2WkBaBH/NGDNiBi8GMRcLGTFk1lS2I7mvZ8ybXM29ouSqc1g\nvItyXZBdY++C/S4tyvkr4i1qwKv/nwM+BQwCfiPp2Ii4tHLaXtgXUjdgJWBL4GRgN0l9IuLNFO8C\nICSNiYgHFiV/i5useDJLBEvq1kRLKhHxXFfnYUkmIp6u17XS3nQj8VYyVRcB7WVfrLSavH0mbpe0\nAnCWpMvTThEFj1d6U+Ml3Yd3EDgfO+QjIman7ZpGAl9YxPwtVrLiqROSVsJbpnwd72M1F++FdGJE\nPJnijML+zUfjlsvn8O7FJ0XEvaW0Ngcuwr2Et4AfteP6F+MNONcoNhOVdAvwDdwNfz2FnQMcFBHr\nSPoEdrNwAN5TbB7u+p8aERNT/BVSXvYCemAvh9dExIWVLPSSdDrQF5iF95gbXrxIZVNbyTzXH5se\n+gBvA6OAU0rnrApcnK69DPbuORPYv2zCqHEvPgP8GBiIt3D/A34Oj6XjPfC2ILvhnXffTfk5PiJe\nSHEmAk/jff2OBD6JzRzfA44Gjklh44HDk+O+Qs4hwA7Yg+ws7PTujJJc7S0rTaa25PnzPLyP2CrY\nt8pUXBl2K+U58HZQR6Xn9Ufg+xHRtLt0jfu1Fd6ja2s8LvwILgOFG4RR2DT0G/y8VsGbRA4tK0hJ\nm6X73g+XpXvTPX0pHd+R9j335XGvYP90j3+dnkM5zxMpmdrSfT8S77G2N6777gaGpM0uCwVycoq3\nBt4m5kbsQ2m95JW4FrsDm1LyLrsI/Ff6rjXuPgL3hpbDTvZaJCKekHQrcKCkYyKiiH8z3lNt94io\nOpCsO3lyQQeRtHStT42ovwAOwS/MAOB47I72V6nAF2wLnAAMB74G/BsYkypZJH0Wv9SrYoUwHL/M\nn20jq3cCnya1eNI1d0rHdijFG0izZ9MLcAXwM+y47HspjdHJNADed2xgyvMuwB3ABcmvTplLsYvc\n3bFiHZbSa42bSuf8CjgJu00oGIuVzsk072p9fGsJpt1zpySZTwD2w+/BeEk90325G1d+J+FndQaw\nc7oPZQ7ElePBWFEMwkpsAN5Nexjw1XR+mRHYUda+2OndMHyvC9pbVspcjZXJBSnd5bAiqjIIK7wh\n2NvkmthpW826QNIqwD1Yoe+D9whcCbgnHSvYGpeVk3BLe1NgQlKixd5gU4DV8aaUhyeZJhdlu0Rb\nz/1GXHbOxc9vddp47okfY7PUN3CDak/caCo4A5swr8fv3ovAz9uR7gHAlIh4rR1xW+I+7L11kqTh\nkr6YfN8QEX+IiAtLSqQtxuM905p2Qo+IV4GHsbLucnKPp2NsQLODrxZJLbQVcevq1hQ8Kb24F+HK\nvLDHrgr0Lrrbkt4DJgE74kr9WLw56MBSKzpIG/u1wmTccv8y3uhws3TdJ/C25qNlH+q9sTIDbyA4\nLCJ+WpJlDjAG+DyuZL8E3B8RRWtvYvKj0zS4mbgoIkakNCbgCrk/C/p8L3NVabLBBElfw5XmtZL6\nk3oNRQtOdild9Stf5VDs7mLziPhTOm8adluwPa4c38Gt9YdLMm1IMl2U6AbsExHvAPdJOgxYB/hi\n4UxL0kDcMy3zMrB38p10t6SVgaGSzsK7Bbe3rJCusQGuzI8pnpWke4En8XMq8wlgl5Rn0rWvx+Vh\neo371Std89Lifkj6C1YcK+MeCbjc7hIRj6Q4z6T0DgV+CpyOy99XIuLdFGcSMAMrwRGla7b23D+P\nFeCREXFVSdanaHt8ZXpEHJZ+3y9pG9z7KXqZJwIXR8RpKc69qXc8sI10++PGwiITEdMlfRO4HPe2\nzwLelzQZuC4iRi9Ecq+n7zUr4Y/ihkeXkxVPx3iJVHBrMLb4key+u0JTj2Xj9NkjRVm2dN6rFRtv\nYcNdKX33w62rt0rpPyLp78X/ZCIrt4w/jogPJI3Hiuci/LI8CdyFew2kPM4GHkzpfjOl1wO/1Bvh\nVmI5zxOAI2U3x3cBd0bE2TXux+9K+f04zcL7VI14ZaZU/r9E833ojyvpu0rpvifpTpp7crXoCzxb\nKJ103izciCjYSVI32SfTRsAmWCmVnxPA00UFnngdmBPNHhzBptBNK+fdHM0O+8CK/ARg27C77/aW\nlab84uc9piTTPEmjWVDxPFXJc7V8VfkTVnTjJP0am8fui4iTKvFmFEonXf8pSc/i8vpTXO7GA3NK\nFoGZ2Gy3M/Mrntaee7/0fUdF1ltxj6s1Wkt3O2AFSvcwcQutKJ6ksNZgwQZPeyY2zBcnIm6VdAcu\n2zvjxuYAYNdkFv9WzO9za2F5HviMpGW7alfsgqx4Osbclmzjkj6o/N8Fm6U2wS3q6bgFCPMriWp3\nuqigClPI6tj5UpVXS78fYH7HVUWP6U7gktSF74/NGZOBYZK64wrvgWKAVNLW2C/RNilffwYKBVfk\n+Vj8Ah+I/ZZcJmkqcFRElFvQVVe482jb1FvrXhTn9ABm1ngR2zJ3dKcyHlBF0gHYTLU28A88rvU+\n8z8n8HOs0h5Xxq9U/hf5WS1dv71lpaBH+n6zEl7rXrRVvuYjIt6V1A+7DRkEHAHMlnQDHhua24JM\nYLlWS7+7Y5PUATXiVctza8999fRdlfVV2qat8lQr3bbKU2EmrD734n/VLXTBsjXOKby33ps+SFoL\nv1eD8FjXPW3kB5rN7lUvoMX1VmVBOetKHuOpA8kUcjs252wArBoR/YDfLkJyM2keiCzTvfT7CKws\nis8RKfwu3MLrg1uOE/Gg5b9pbl2NS3kubPtv41bzyhHxP3hSQBMRMTciRkTEptjMNARYH9vhO5OX\ngR41xjzWaOO8WTRXMk1I6idpI0l9sYOs0UDPiOieBqinLo5MJ7pX/hfP841FLCtFBVOVva170S7C\nHIRNbn1wBXgEftYFVZnAchVKdRYuE9vU+OyzENkpTLjVd6DW9ReGRb2HheWh2nsvFNZa1KZnKQ6S\npkqqrs0hIl6h2cTbq428FPTHSvaxSvhqWNn+o53pdBpZ8dSHrfBg8rkRMaPUSi+68AvzHB4A+qrk\ncVRSL1zZA00VxaOlT6TwV/CYzrG41TM5It7Dtt/j8ctbTCzYJP2/OCKeLpmGmvIsaXlJIemElP7f\n0xjDTbi30JlMxq3JwjU2kpYjmala4SFgI0lN4wFpwsFv8cB5H/w8To/kDTWZLndm8b0ve1T+74sr\nimksWlmZggemv1oJr/5faCTtLelNSWtGxEcRMTW8EPFfzP+MN5a0Uem8L2CHcA+moEm44nysKJe4\nJzmMtsdQyhTp7VcJ37MacSF5Ajeyqvfsa62dlHp8r2FFUuZR3MNYYEwlvbt9sJm64HlgP0nrV+PT\nPHb1VGt5SWlvhhX59TUmI/QEXon5p2R3CdnUVh8ew72Kn8jTmpcHDsOzdqBl+3otLsEtoPvk1drL\nYPt4e222d2Jb+PSIKFo+E/CU7Mej2fV04BdxeJqK+hGuIL9d5Dki5kj6PXB6Mi0+iV+SQ4FiYLxT\niIiJaZLC9ZJ+hF/+7+MB1RdaOfU6YCgeszgdt/5OxONF19D8kl8u6Xps2hmCZwN2k7RCRMzuYPb7\nSroOT3HdPuXn9DRGtdBlJSKeS6avC9NElmfwM+hNxxdRTsHK7nZJ5+MyMQhPmS67N14KGCvpVFwm\nz8PTzX+Zjp+FFetYeYX/h3jKea3Zgi0SEX9N55+fGhrT8azCzRdZQqf7tqSLcHmfjWeA7UWzIprX\n4smekda3kt4ceceAi9IzGYNl3gT4ATZPX1Y65RQ8Vvd7SZfiHvZHuEf4A2BcRNxfuW7vNLYKnla+\nNR4rfI7a413bk0x4XU3u8dSBiPgrnrq6Dm5ZX5UO7Ygrhn61z6yZ1lu4kD+PZyNdggdva81IqkXR\no5lYCitaXuNK15mFX7pPYCVyA27h7oDHHYo8H4nXofwAv4DDcQVeNsN0Fvula47EFdyzuDJ8t6UT\nIuJtLMMf8X27GSudnSLi1fD6pKOxfHentF/Aa2pgIZ5VK4zEPc478NjYccWMvw6UlaPxNiqn4Hvw\nMZ4x2OK9aA/hNS4DcA/nWlx+tsSz+SaXos7A44FXpOtOwff0g5TO9JT3pfGzugXPitstIsYvZLYG\n43VFQ4Hb8KSAEa2e0T7OSekcju/9BnjKNrR+H28FtkqzQpuIiJFYSW+EZR4HHIdNqdumsljEnYEb\nCr/E42B34Ht9AJb16yzIWKygpqa0D8bT6reLiH+WI6Ze1hYsOHmiS8iurzMNiaR18Jqn20sD3KQe\n2EsRUetF7XLUCXvSSVodmxjvioh/lcJ/DWwYEVsurmu1cP1RNPi+cWnCzbfwfoevlMJ/AnwnIloc\nQ0rjjE8CN0bEjzs9s4uApFOwxWLLDs6MWyxkU1umkbkBuCWZmYqFgVvjBaX/SczG6z+mSboM9+AG\nYFt/de1RpgYR8WEy2Q6WdB7u4W2LzYGtKpO0PGAY8DNJl9UYW+lS0pTvwViBdrnSgWxqyzQo4a1r\n9gDWw+aD2/B6mYER8WBr5/5/I405DcDv843YRDMAODgiRnVh1hqNPfDYy9V4LOQQvGvCmW2dGBHj\nsNn3xM7M4CJyIh4jas9U7LqQTW2ZTCaTqSu5x5PJZDKZupIVTyaTyWTqSlY8mUwmk6krWfFkMplM\npq5kxZPJZDKZuvJ/nn31/8jM9GoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f825478080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Hand-washing campaign spending (USD)',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Effect of hand washing on total infections',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap05-fig05.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's put it all together to make some public health spending decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose we have \\$1200 to spend on any combination of vaccines and a hand-washing campaign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "num_students = 90\n",
+    "budget = 1200\n",
+    "price_per_dose = 100\n",
+    "max_doses = int(budget / price_per_dose)\n",
+    "dose_array = linrange(max_doses)\n",
+    "max_doses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can sweep through a range of doses from, 0 to `max_doses`, model the effects of immunization and the hand-washing campaign, and run simulations.\n",
+    "\n",
+    "For each scenario, we compute the fraction of students who get sick."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.988888888889 0.266727403413 0.187595503995\n",
+      "1.0 0.977777777778 0.26683150821 0.174580718826\n",
+      "2.0 0.966666666667 0.267112856728 0.162909838349\n",
+      "3.0 0.955555555556 0.267865747331 0.153508349478\n",
+      "4.0 0.944444444444 0.269828391545 0.148565092315\n",
+      "5.0 0.933333333333 0.274613528135 0.152945950611\n",
+      "6.0 0.922222222222 0.284596094758 0.174964415024\n",
+      "7.0 0.911111111111 0.3 0.217343161684\n",
+      "8.0 0.9 0.315403905242 0.259071044488\n",
+      "9.0 0.888888888889 0.325386471865 0.278402884103\n",
+      "10.0 0.877777777778 0.330171608455 0.277914534623\n",
+      "11.0 0.866666666667 0.332134252669 0.267357496693\n",
+      "12.0 0.855555555556 0.332887143272 0.252796945636\n"
+     ]
+    }
+   ],
+   "source": [
+    "for doses in dose_array:\n",
+    "    fraction = doses / num_students\n",
+    "    spending = budget - doses * price_per_dose\n",
+    "    \n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_immunization(system, fraction)\n",
+    "    add_hand_washing(system, spending)\n",
+    "    \n",
+    "    run_simulation(system, update1)\n",
+    "    print(doses, system.init.S, system.beta, calc_total_infected(system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function wraps that loop and stores the results in a `Sweep` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_doses(dose_array):\n",
+    "    \"\"\"Runs simulations with different doses and campaign spending.\n",
+    "    \n",
+    "    dose_array: range of values for number of vaccinations\n",
+    "    \n",
+    "    return: Sweep object with total number of infections \n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for doses in dose_array:\n",
+    "        fraction = doses / num_students\n",
+    "        spending = budget - doses * price_per_dose\n",
+    "        \n",
+    "        system = make_system(beta, gamma)\n",
+    "        add_immunization(system, fraction)\n",
+    "        add_hand_washing(system, spending)\n",
+    "        \n",
+    "        run_simulation(system, update1)\n",
+    "        sweep[doses] = calc_total_infected(system)\n",
+    "\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can compute the number of infected students for each possible allocation of the budget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_doses(dose_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig06.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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7E9aTSSjfAj5+tE8oIom4pYxvADJx1WW3qGqnC4mJyFW4mZQLgZ3A74D7VbW1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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f8266a7d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Doses of vaccine',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Total infections vs. doses',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap05-fig06.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose the price of the vaccine drops to $50 per dose.  How does that affect the optimal allocation of the spending?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose we have the option to quarantine infected students.  For example, a student who feels ill might be moved to an infirmary, or a private dorm room, until they are no longer infectious.\n",
+    "\n",
+    "How might you incorporate the effect of quarantine in the SIR model?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0.988888888889 0.266727403413 0.187595503995\n",
+      "1.0 0.977777777778 0.266766745484 0.174324794892\n",
+      "2.0 0.966666666667 0.26683150821 0.16186489064\n",
+      "3.0 0.955555555556 0.266938009181 0.150272695439\n",
+      "4.0 0.944444444444 0.267112856728 0.13961230444\n",
+      "5.0 0.933333333333 0.267399129509 0.129963581944\n",
+      "6.0 0.922222222222 0.267865747331 0.121434809166\n",
+      "7.0 0.911111111111 0.268620815383 0.114180370856\n",
+      "8.0 0.9 0.269828391545 0.108423787107\n",
+      "9.0 0.888888888889 0.271723878668 0.104483374203\n",
+      "10.0 0.877777777778 0.274613528135 0.102788098227\n",
+      "11.0 0.866666666667 0.278828368254 0.103846179534\n",
+      "12.0 0.855555555556 0.284596094758 0.108083570148\n",
+      "13.0 0.844444444444 0.291836044587 0.115450119409\n",
+      "14.0 0.833333333333 0.3 0.124877480279\n",
+      "15.0 0.822222222222 0.308163955413 0.134149883099\n",
+      "16.0 0.811111111111 0.315403905242 0.140764543663\n",
+      "17.0 0.8 0.321171631746 0.143202490748\n",
+      "18.0 0.788888888889 0.325386471865 0.141392287173\n",
+      "19.0 0.777777777778 0.328276121332 0.136229534349\n",
+      "20.0 0.766666666667 0.330171608455 0.128888830991\n",
+      "21.0 0.755555555556 0.331379184617 0.120402986465\n",
+      "22.0 0.744444444444 0.332134252669 0.111526601711\n",
+      "23.0 0.733333333333 0.332600870491 0.102750907495\n",
+      "24.0 0.722222222222 0.332887143272 0.0943669224857\n",
+      "Saving figure to file chap05-fig07.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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jzHuB++l9xUaPiNw3lAVT/YuJCmNBXhIHz1QAsPt4GdMyJhMaovcISqnA8OvT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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f82693ebe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_students = 90\n",
+    "budget = 1200\n",
+    "price_per_dose = 50\n",
+    "max_doses = int(budget / price_per_dose)\n",
+    "dose_array = linrange(max_doses)\n",
+    "max_doses\n",
+    "\n",
+    "for doses in dose_array:\n",
+    "    fraction = doses / num_students\n",
+    "    spending = budget - doses * price_per_dose\n",
+    "    \n",
+    "    system = make_system(beta, gamma)\n",
+    "    add_immunization(system, fraction)\n",
+    "    add_hand_washing(system, spending)\n",
+    "    \n",
+    "    run_simulation(system, update1)\n",
+    "    print(doses, system.init.S, system.beta, calc_total_infected(system))\n",
+    "    \n",
+    "infected_sweep = sweep_doses(dose_array)\n",
+    "    \n",
+    "plot(infected_sweep)\n",
+    "\n",
+    "decorate(xlabel='Doses of vaccine',\n",
+    "         ylabel='Total fraction infected',\n",
+    "         title='Total infections vs. doses',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap05-fig07.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Suppose we have the option to quarantine infected students. \n",
+    "#For example, a student who feels ill might be moved to an infirmary, \n",
+    "#or a private dorm room, until they are no longer infectious.\n",
+    "#How might you incorporate the effect of quarantine in the SIR model?\n",
+    "\n",
+    "#We would decrease the infection rate, thereby increasing gamma.\n",
+    "def quarantine_update(system, n): \n",
+    "    min = 1\n",
+    "    max = 4\n",
+    "    tr = max - n * (max-min)\n",
+    "    system.gamma = 1 / tr\n",
+    "    \n",
+    "print(quarantine_update(system, 0.15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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diff --git a/code/chap06mine.ipynb b/code/chap06mine.ipynb
new file mode 100644
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--- /dev/null
+++ b/code/chap06mine.ipynb
@@ -0,0 +1,1271 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 6: Analysis\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# To switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system`, `plot_results`, and `calc_total_infected` are unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(beta, gamma):\n",
+    "    \"\"\"Make a system object for the SIR model.\n",
+    "    \n",
+    "    beta: contact rate in days\n",
+    "    gamma: recovery rate in days\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(S=89, I=1, R=0)\n",
+    "    init /= np.sum(init)\n",
+    "\n",
+    "    t0 = 0\n",
+    "    t_end = 7 * 14\n",
+    "\n",
+    "    return System(init=init, t0=t0, t_end=t_end,\n",
+    "                  beta=beta, gamma=gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(S, I, R):\n",
+    "    \"\"\"Plot the results of an SIR model.\n",
+    "    \n",
+    "    S: TimeSeries\n",
+    "    I: TimeSeries\n",
+    "    R: TimeSeries\n",
+    "    \"\"\"\n",
+    "    plot(S, '--', color='blue', label='Susceptible')\n",
+    "    plot(I, '-', color='red', label='Infected')\n",
+    "    plot(R, ':', color='green', label='Resistant')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def calc_total_infected(system):\n",
+    "    \"\"\"Fraction of population infected during the simulation.\n",
+    "    \n",
+    "    system: System object with results.\n",
+    "    \n",
+    "    returns: fraction of population\n",
+    "    \"\"\"\n",
+    "    frame = system.results\n",
+    "    return frame.S[system.t0] - frame.S[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's an updated version of `run_simulation` that uses `unpack`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a TimeFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.loc[t0] = init\n",
+    "    \n",
+    "    for i in linrange(t0, t_end):\n",
+    "        frame.loc[i+1] = update_func(frame.loc[i], system)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a version of `update1` that uses `unpack`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Original\n",
+    "\n",
+    "def update1_no_supdate(state, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State (s, i, r)\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (sir)\n",
+    "    \"\"\"\n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = system.beta * i * s    \n",
+    "    recovered = system.gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update1(state, system):\n",
+    "    \"\"\"Update the SIR model.\n",
+    "    \n",
+    "    state: State (s, i, r)\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (sir)\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    s, i, r = state\n",
+    "\n",
+    "    infected = beta * i * s    \n",
+    "    recovered = gamma * i\n",
+    "    \n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\n",
+    "    \n",
+    "    return State(S=s, I=i, R=r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test the updated code with this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>S</th>\n",
+       "      <th>I</th>\n",
+       "      <th>R</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.988889</td>\n",
+       "      <td>0.011111</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.985230</td>\n",
+       "      <td>0.011992</td>\n",
+       "      <td>0.002778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.981296</td>\n",
+       "      <td>0.012929</td>\n",
+       "      <td>0.005776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.977071</td>\n",
+       "      <td>0.013921</td>\n",
+       "      <td>0.009008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.972541</td>\n",
+       "      <td>0.014970</td>\n",
+       "      <td>0.012488</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          S         I         R\n",
+       "0  0.988889  0.011111  0.000000\n",
+       "1  0.985230  0.011992  0.002778\n",
+       "2  0.981296  0.012929  0.005776\n",
+       "3  0.977071  0.013921  0.009008\n",
+       "4  0.972541  0.014970  0.012488"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(0.333, 0.25)\n",
+    "run_simulation(system, update1)\n",
+    "system.results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G1z9y5AhvvPEGgYGBNGzYkF69enH9+nWjlpzMnSGzKw0d9Ho6EyQnJ+vGjh2r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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61c72f908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frame = system.results\n",
+    "plot_results(frame.S, frame.I, frame.R)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sweeping beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a range of values for `beta`, with constant `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "beta_array = linspace(0.1, 0.9, 11)\n",
+    "gamma = 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run the simulation once for each value of `beta` and print total infections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 0.00723090166498\n",
+      "0.18 0.0262722567457\n",
+      "0.26 0.160575485321\n",
+      "0.34 0.490862856866\n",
+      "0.42 0.689867847411\n",
+      "0.5 0.804506112463\n",
+      "0.58 0.873610307851\n",
+      "0.66 0.916554007142\n",
+      "0.74 0.943729262152\n",
+      "0.82 0.961060480958\n",
+      "0.9 0.972099315633\n"
+     ]
+    }
+   ],
+   "source": [
+    "for beta in beta_array:\n",
+    "    system = make_system(beta, gamma)\n",
+    "    run_simulation(system, update1)\n",
+    "    print(system.beta, calc_total_infected(system))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wrap that loop in a function and return a `SweepSeries` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_beta(beta_array, gamma):\n",
+    "    \"\"\"SweepSeriess a range of values for beta.\n",
+    "    \n",
+    "    beta_array: array of beta values\n",
+    "    gamma: recovery rate\n",
+    "    \n",
+    "    returns: SweepSeries that maps from beta to total infected\n",
+    "    \"\"\"\n",
+    "    sweep = SweepSeries()\n",
+    "    for beta in beta_array:\n",
+    "        system = make_system(beta, gamma)\n",
+    "        run_simulation(system, update1)\n",
+    "        sweep[system.beta] = calc_total_infected(system)\n",
+    "    return sweep"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SweepSeries `beta` and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_beta(beta_array, gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap06-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Os/3333+PtLQ0AEDHjh25W1nbtWuHrl27okOHDvVKFkDVWMWoUaOwcuVKnD59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\nwmCMMVYVThiMMcaq8hdPP4CA9+7K2gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61cce42b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "label = 'gamma = ' + str(gamma)\n",
+    "plot(infected_sweep, label=label)\n",
+    "decorate(xlabel='Contacts per day (beta)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "savefig('chap06-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sweeping gamma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the same array of values for `beta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.1 ,  0.18,  0.26,  0.34,  0.42,  0.5 ,  0.58,  0.66,  0.74,\n",
+       "        0.82,  0.9 ])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "beta_array = linspace(0.1, 0.9, 11)\n",
+    "beta_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now an array of values for `gamma`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.1,  0.3,  0.5,  0.7])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gamma_array = linspace(0.1, 0.7, 4)\n",
+    "gamma_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each value of `gamma`, SweepSeries `beta` and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap06-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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TwQ1bIQ53RWJohYWLb9y4wYgRI4iPjycgIKCph9Mg5Ofnc/r0aQYNGoRMZlwop1KpiIyM\nZNu2bSbHsKZKx874JApLjNFCzo62PD2iO/Yin5OghWMwGFCV5ZtMTSp1Xp19ZVIZ7Zx8CHT1J9Cl\nHU52ykYcacvhbvdOcddoochkMqKjo4mKimLy5Mmo1Wo+/PBDOnToQFhYGGD8Qf10MtUkFjYyKWMe\n6STEQtBi0Wg13CjOIrXIKBJ3SqmhtHMk0KUdgS7+tHPywUYmvvcPivgEWyguLi588sknxMbGsmXL\nFuRyOREREcTFxSGXG1eF/noxh6vpt2y3w/u2rzdntkDQGBgMBgorikktSie1MIOs0pw6gyUkEgk+\nSi8CXfwJdG2Hm71LqwtrbWqEYLRgBg4cyMCBAy3uS8kq5vi5LNN2WDcvuge6NdbQBIL7RqvXkVGc\nRWpRBmlFGZRUltbZ115uT3tnPwJd/Qlw9hNO6gZGCEYrpKi0kh+Op5iexPy9lDzSs10Tj0ogqJvS\nSjWpRRmkFqWTXpx1x7BXT0d3k6nJy9FDzCIaESEYrYwqrZ7vjl6nUmP8wSkd5Dwe0QFZI6UZFwis\nQW/Qk12ae9PUlE7BHcJe5TI5/s6+RlOTSzsUtsKs2lQIwWhFGAwG9v2aRm5hOQAyqYQnBnZEYd88\nMl0K2jYGg4GsUhUXVJdJLcpAo617HY6LvTOBrsZZhK/SC5m08VPmC2ojBKMVcTo5l0upBabtweEB\n+HqIRWqCpkWn13G1IJUz2RfJVedb7COVSo1hrzdNTc72Thb7CZoWIRithAxVKYd/v5WwL6STBw93\n9mjCEQnaOhVVFZxXJXNelUSZprzWfkdbBe1vCoS/s0+9VoYTNAxCMFoBpeVVfHf0uik/lI+7giHh\n95aPSSCoL/LLCzmbfYnLeddqOa9lUhndPDoR4tUND4WbcFi3MIRgtHB0Oj3fHblGeaUxu6uDnQ2j\nB3ZEJmv+xVgErQeDwUBaUQZnsi+SXpxVa7/C1oGHvbvzkGdX7Ntw8r6WjhCMFs7B39LJzi8DQCox\nOrmVirYZi/7VV1/x0UcfkZmZSXBwMAsXLqRnz5519v/1119ZuXIlFy5cwMnJifHjxxMdHW2xCqDA\nMlW6Ki7nXeNM9iWKKopr7fd0dKeHTzCd3QKF47oVIASjBXP+Wh5nr97Kn/NITz/8vdpmjpwjR47w\n3nvvsWjRIvr27cvmzZt56aWX+P777y1W8EtPT+fll1/m6aefJiYmhhs3bjBv3jy0Wi3vvvtuE7yD\nlkWpRs25nCQuqJJrRztJJHRybU8PnyB8lF7C7NSKEILRQsnOL2N/4q3iRt3auxHWzfoCTK2NTZs2\nMW7cOJ555hkAli5dyrFjx9i5c6fFYk3p6emMGjXKJA6BgYGMGTOGo0ePNuq4WxrZpSrOZF/iWkFq\nrRQdcpmcYK8uPOwdhLNI7tcqEYLRAimrqOK7I9coKS5g31ebuHH1DE5KR9KmTWPHjh289tprPPXU\nU1RWVrJq1Sp++OEHVCoVSqWSYcOGsXjxYhwcHNi9ezfr169n6tSpbNiwgYKCAoYOHcp7773H8uXL\niY+Px8XFhTlz5jBp0iQApk6dSlhYGJmZmcTHx6NUKnnzzTfp3LkzS5cuJSUlhZCQEGJiYkyFnY4f\nP86aNWs4d+4cVVVVdOnShbfffpvBgwdbfH/Dhw8nPT3d4r6tW7cyYMAAsza9Xk9iYiKLFi0ytUml\nUvr160dCQoLF8/Tv399Ugxzg3Llz/PTTTzz++OPW/yHaCHqDnmsFaZzJvkhOaW6t/c72SkK9g+nu\n2RlbEenUqml0wdDpdMTGxrJnzx7UajWDBg1i8eLFeHp6Wux/9OhRPvjgA5KTk/H09OSZZ57h5Zdf\nbpBp7umsCyRknEars1wetCGxkdnQt11Pevo+dMd+er2B74+lUKKuZO+/Y5DJZHy6IQ57uYT333+f\ntLQ0U9+YmBgOHTrEihUr8PX15fTp07zzzjsEBQURFRUFGNMZx8fHs379ejIzM5k1axbHjh1j1qxZ\nvP7668TFxbF48WKGDx+Oi4sLAJ999hlvvfUWc+bMYePGjSxdupROnTqxaNEiHBwciI6OZtWqVcTG\nxpKZmcmMGTOYNm0ay5YtQ61WExsby/z589m/f79Ff8GuXbvqrPddPYaaFBcXU1ZWVqscq7e3N2fO\nnLnj5wnQt29fSkpKCAkJYdasWXft31ao1Gq4oErmXM4l1JqyWvv9nHzo4RNMoGs7pBIRZNEWaHTB\nWLNmDXv27CEmJgZXV1eWLFnC7Nmz+eKLL2r1TUlJ4dVXX2XGjBmsXr2ac+fO8c4776BQKJgyZUq9\nj+109oUmEQsArU7L6ewLdxWMI2cySFeVkn79PDnpV9ny+R4i+oQAsGLFCp588klT37CwMMaOHUuf\nPn0ACAgI4PPPPycpKcnUp6qqisWLF9OxY0e6d+9OcHAwDg4OvPjiiwBMmzaNL7/8kpSUFJMDOTQ0\nlOnTpwPw/PPPs337dqKiokxP7KNHj2bfvn2m80dHRzN9+nSTyEdFRfHiiy+Sl5eHn59frfdoyedw\nJyoqjCmu7ezszNrlcjmVlZV3PFav1xMXF0dRURH/+Mc/eOWVV/j888/btN29sKKYs9kXScq9hlZv\n/nuQSqV0de9IqE8Qnop7+zsJWj6NKhgajYatW7eycOFCHn30UQBWrVrFiBEjSExMpHfv3mb9Dx48\niL29PW+88QYA7du357vvvuPgwYMNIhg9fR5q0hlGT587i0VSagG/JakAyMm4hrOLm0ksALp3746T\n060VshMmTODQoUMsX76c69evk5ycTGpqaq3CKNWmIwCFQmG2v/omXLOcaocOHUyvHRwcap3D3t7e\n1D8wMJCJEyeyZcsWLl26REpKChcuXACocxYxduxYMjIyLO7bsGGDqTjUncYIRrGqHl9dSKVSkxDG\nxMTw9NNPc+rUqVrfxdaOwWAgvSSLM1kXSSuq/dnby+0J8epGiHc3FHKRy6mt0qiCcfHiRdRqtZnt\nOCAgAH9/fxISEmr9SN3d3SksLOTrr79mzJgxJCcnk5CQwHPPPdcg4+vp+9Bdn/CbitzCcn5JuGVu\n8nJTcrelFgsWLCA+Pp5JkyYxatQo5s6da6r7XY1MJkMqNT/R7du3Y2NT+2tT1xN5UlISU6ZMISws\njIEDBzJmzBi0Wq1FR3Q169evR6u1LNq3m50AXF1dUSgU5OTkmLXn5ORY7A+QnJxMdna26cEFjIIL\nkJ2dXefYWhtavY7kvGucyb5oMQGgu8KNHj5BdHHviI0Ii23zNKpgZGUZF/RYsjVX76vJqFGjmDx5\nMn/+85+ZN28eOp2O0aNHtzk7c4VGy3dHr1Ol0wPg6mRH2MgB/O8Xn5Cammp6ur969SolJSUAFBQU\nsGvXLtasWcOoUaMA0Gq1pKWl0a5d46U637FjB35+fmzcuNHUtn37doA6C+H4+9/bKnWJREJ4eDgn\nT55k4sSJgNHUdPLkSZ5++mmLx/zyyy9s3LiRAwcOmGYop0+fBqBr1673dP2WSJmmnHOqJC7kXKZC\ne5vZTiIh0KUdPXyCaefk06bNcwJzGlUwysvLkUqlpopw1dja2lq0NRcXF5vi5ceMGUNSUhL//Oc/\nWbt2LW+++WZjDbtJMRgM/Hg8laJS4+cjtzGWWXV3fojQ0FDmzZvHwoUL0ev1ptmDRCJBqVSiVCqJ\nj48nODiY0tJSPv30UzIzM2uZbhoSX19f0tPTOXz4MB07diQhIYHVq1cDtU1ID0JUVBSvvfYaISEh\nREREsHnzZkpKSpg8ebKpj0qlQqFQ4OjoyMSJE9m4cSPvvfces2bNIisri7/+9a+MGTOGbt261du4\nmhvFFSUkZJzhan4KeoPebJ+NzIYgz86EegfhYu/cRCMUNGcaNbTB3t4evV5fy9yg0Wgs2ppXrlyJ\nTCbjz3/+MyEhIUycOJF58+axfv16CgoKavVvjZw8n01K1q0VtCP6BeLubEytsHbtWlxdXZkyZQqz\nZs1i/PjxSCQS5HI5crmc2NhYzp07x7hx45g1axYuLi5Mnz6ds2fPNtr4X3jhBUaOHMncuXMZP348\n27ZtY8mSJSgUCqsimKxl8ODBLF26lLi4OCZNmkRycjJxcXFmDvTIyEji4uIA8PLyYsuWLeTl5TF5\n8mTmzZvHyJEjiYmJqbcxNScMBgNnsy+x69y3JOddMxMLpZ0jEe17M6XnJB4N7CfEQlAnEkNddoEG\n4PTp0/zxj39k3759ZtExw4cP57nnnmPGjBlm/ceOHctjjz3G3LlzTW2XL19m3Lhx7Nmzh5CQECxx\n48YNRowYQXx8fC0Hb0viWkYR3xy+ZtruHeRtqpyXn5/P6dOnGTRoEDKZ0basUqmIjIxk27ZttRzD\ngrZLcWUp+68dI7PE3Dfjo/Sih08wHd0CRFisALj7vbNRTVLBwcE4Ojpy4sQJJkyYYBpgeno6/fr1\nq9Xf19eXS5cumbVdvnwZqVRqFpXTGikoqeDHE6mm7fY+TkSE3hJZmUxGdHQ0UVFRTJ48GbVazYcf\nfkiHDh0ICwtriiELmhkGg4ELqmSO3Ug0i/xzc3BhUMcB+CrbbmYAwf3RqIJha2vLn/70J5YvX46b\nmxseHh4sWbKE/v3706tXLzQaDUVFRbi4uGBra8sLL7zAzJkzWbduHU8++STJycksW7aMP/3pTyiV\nrTf1gKZKx3dHrqOpMoadOjva8viADkhrlFl1cXHhk08+ITY2li1btiCXy4mIiCAuLq6Wj0jQ9ijV\nqNl/7Zh55liJhF6+IfRp10MkAhTcF3UKRs00C9bwt7/9zap+c+bMQavV8pe//AWtVmta6Q1w6tQp\nXnjhBVP6hyFDhrB27VrWrVvHhg0bTCu9Z86ceU9ja0kYDAbiE9LILzYuRrORSXliYEfs7Wr/qQYO\nHMjAgQMbe4iCZozBYOBS7lWOpv1Kla7K1O5i78ywTgPxVlrOqCAQWEOdgnH48GGz7ZycHLRaLe3a\ntcPLy4vCwkLS0tKwtbUlODjY+gva2PDOO+/wzjvv1No3YMCAWiaoxx57jMcee8zq87d0Tl1SceVG\noWl7aJ8AvN0UTTgiQUtBrSnjwPXj5gvvJBJ6+gTT1z9MrKMQPDB1CsbPP/9ser13715WrlzJmjVr\nzOoLJCcnM2vWLEaPHt2wo2wjpGWXcPRspmm7Z1dPgjuI9AuCO2MwGLicd40jab+apRp3tndiaKeB\nwlchMKOquBhNfj4O/v5I79F8bZUPY/Xq1bz11lu1itF07dqVOXPmsGzZMlPuIcH9UazW8P2xFNNi\nNj8PRx7t2XgL7AQtk7Kqcg5eP0FK4Q2z9lCfIPr5h4k62QITlapcCk6dojT5KmDAsWNH/MY8cU/n\nsEowCgoKcHa2HJstl8spK6udyVJgPVqdnu+OXqNCY4xkcbSX84Qosyq4AwaDgSv5KRxOTaCyxkpt\nJzslQzpF0M7JckoUQdujPCODgsTfKEtNNWvX3UzaeS9YJRi9evXi448/pk+fPmbCkZeXx5o1a2rV\nJxBYj8FgYN+vN1AVlAMglRrLrDo6iCdDgWXKqyo4lHKSawXmN4AQ724MCAgXswoBBoOBspRUChJP\nUWEh7ZKifXu8hgy65/NaJRjz589n6tSpDBs2jN69e+Pu7k5ubi6JiYk4OTmxbt26e76wwMjZK3lc\nTMk3bQ8K88fP07EJRyRozlwrSONgygkqqm49HSrtHBnccQABzrVTxQvaFga9ntIrVylMPEVlXt5t\neyUou3TCrXc4dl7359eySjCCg4P5+uuv+eyzz0hMTCQ1NRU3NzdTXQNXV9f7unhbJzNXzcHfblWW\ne6ijO6FdPJpwRC2br776io8++ojMzEyCg4NZuHBhLb9bTbZt22Yxe+/58+cbeqj3TIW2ksMpJ7mS\nn2LWHuzVlYj2vUWluzaOXqul5NIlChN/o+pmAtJqJFIpTt2749q7F7YPeK+2euGej48P8+fPf6CL\nCW6hLq/iu6PX0d90cnu5OTCkd4DIDHqfHDlyhPfee49FixbRt29fNm/ezEsvvcT3339fZ0GmpKQk\nhg8fbiYazfHzv15wg4MpxymvMatQ2CoY0nEA7V1EYERbRq/RUHTuPIW//Y6uvNxsn9TGBueHQ3AN\n64lNPS1IL+UTAAAgAElEQVR0tlow9Ho93377LYcPH0alUrFw4UJ+++03QkND20Q66PpEp9Pz36PX\nKaswLqyyt7Vh9MBO2Agn932zadMmxo0bxzPPPAPA0qVLOXbsGDt37qyz9sbly5eJiIjA6z6n5w1N\npVbDkdQELuddM2vv7tmZge37YGdTu7ytoG2gLSun6MwZis6cQ68xz/QttbXDtWcoLj17ILO3r9fr\nWnWHKikp4bnnnuMvf/kLJ06c4PDhw6jVavbu3cvTTz/dLKfwzZlDv2eQmacGjE+0j0d0wNnx3n/8\nubm5zJ49m969exMZGcnGjRsZOXIku3fvBqCyspJly5YxbNgwQkNDiYiI4N1336X85pPI7t27eeKJ\nJ9i2bRtDhw4lLCyM6OhosrOzefvtt+nVqxdDhgxhz549pmtOnTqVlStXmvZHRkayc+dOEhISGD9+\nPGFhYTz33HOk1ojIOH78OM8//zzh4eGEhoYyYcIEDhw4UOf7Gj58OEFBQRb/HT9+vFZ/vV5PYmKi\nWWEuqVRKv379SEhIqPM6ycnJdOnSxfoPvBFJLUzny3PfmImFg9yeUV2HMLTTQCEWbZSqkhJUBw+T\n8u//UPBroplY2Dg64vnIQDq++Dzu/fvVu1iAlTOM5cuXk5GRwZ49e+jatSuhoaEAfPjhh7z00kvE\nxsayfv36eh9ca+Ti9XzOXMk1bQ/s4Ud7H6c7HGEZvV7PzJkzkclkbNmyBa1Wy/vvv09a2q2qfDEx\nMRw6dIgVK1bg6+vL6dOneeeddwgKCiIqKgowJn+Mj49n/fr1ZGZmMmvWLI4dO8asWbN4/fXXiYuL\nY/HixQwfPhwXFxcAPvvsM9566y3mzJnDxo0bWbp0KZ06dWLRokU4ODgQHR3NqlWriI2NJTMzkxkz\nZjBt2jSWLVuGWq0mNjaW+fPns3//fmxta9/4du3aVWf51uox1KS4uJiysjKLhbnqSqGenZ1NUVER\nBw4cYM2aNZSXl9OvXz/+8pe/1FmlrzHQ6Ko4mvorl3KvmLV39ejII4F9sbexq+NIQWtGU1BA4anf\nKLmUVKvwmNzFBbfwXjgFdUcia9jV/FYJxo8//sj8+fMJDg42+yErlUpmzJjBggULGmyAjUnhb7+T\nfyIBvbbq7p3vg/JKLdfSi2h/8+/trLTFOceJ5HiQ2shx798X117WZZo9ceIEZ8+e5aeffqJ9+/YA\nrFixgieffNLUJywsjLFjx9KnTx/AWA73888/JykpydSnqqqKxYsX07FjR7p3705wcDAODg6mhZjT\npk3jyy+/JCUlxeRADg0NZfr06QA8//zzbN++naioKNMT/ujRo9m3b5/p/NHR0UyfPt3kH6gOlsjL\nyzNLc19NXT6Huqi4GU9eXTmvGrlcbrEwFxjNUWBMVbN69WoKCgpYtWoVUVFR7NmzB/sGeDq7GzeK\nM9l/7Rhqza11TfZyewZ16E8nt/aNPh5B01ORk0PBr6dQX7sOmAuFnacnruG9UHbpjOQuZZXrC6sE\no6Kios4fsZ2dXaNWcGtICn/7vcHEAiA7v4zqhwM7Wxn+Xkq46WPVa6so/O13qwXj/PnzeHh4mMQC\njDWpnZxuzVYmTJjAoUOHWL58OdevXyc5OZnU1NRaee5rpopXKBRm+6tvwjX/xh06dDC9ri58VfMc\n9vb2pv6BgYFMnDiRLVu2cOnSJVJSUrhw4QJAnbOIsWPHkpGRYXHfhg0batX6sDRGMIqVpcJcYCym\ndPToUbPvddeuXRk8eDD79+/n8ccft3hcQ1Clq+JY2ikuqC6btXdyCySyQz8c5I0vXoKmw2AwUJ6e\nTmHib5TduFFrv4OfH259wnFo377RgzSsEozQ0FC++OILhgwZUmvft99+W2cho5aGa6+wBpthGPQG\nyspvnbe9j5NZunKpjdxqsQBj+Kder79jnwULFhAfH8+kSZMYNWoUc+fOtRhGKr3t6eT27duxsan9\ntanri5uUlMSUKVMICwtj4MCBjBkzBq1WW6cjGmD9+vW1qjJWY8lc5OrqikKhICcnx6w9Jyfnjual\n2x+CvL29cXNzIzMzs44j6p+Mkmz2XTtKaaXa1GZnY0dkh350ce9whyMFrQ2DwYD62nUKE09Rcdt3\nGcCxQwdce4fj4OfbBKMzYpVgREdHM23aNJ566imGDBmCRCLhu+++4+OPP+aXX35h48aNDT3ORsG1\nV9g93bTvhXRVKan7kgFwc7Ln4Sesz/BriaCgIAoKCkhNTTU93V+9epWSmzHYBQUF7Nq1izVr1jBq\n1CgAtFotaWlptGvXeKGYO3bswM/Pz+w7sn37doBatthq/P397+kaEomE8PBwTp48ycSJEwGjj+fk\nyZM8/fTTFo/ZunUr69ev55dffjHVD0lPTyc/P79RanpX6ao4kf4b57KTzNo7uAYwqGN/FHLLMyNB\n68Og01GafIWCxFNoapWeluDUrQuu4eHYeTb9Gi2rBKNfv35s3ryZDz74gE8//RSDwcCmTZt46KGH\n+Pjjj0VNBivIUJWaXrfzevCV3BEREYSGhjJv3jwWLlyIXq83zR4kEglKpRKlUkl8fDzBwcGUlpby\n6aefkpmZ2agmRF9fX9LT0zl8+DAdO3YkISGB1atXA7VNSA9CVFQUr732GiEhIURERLB582ZKSkqY\nPHmyqY9KpUKhUODo6MjQoUNZvXo1CxYsYObMmRQWFvKPf/yDPn368Oijj9bbuCyRVapi37WjFFfc\nWmBla2PLo4F96eresVmuBRHUP3qtluLzFyj6/bSFxXYynIKDcOvdC3kdefyaAqvXYfTr14/t27dT\nUVFBUVERSqUSR0fjjU+v19/VjNHWSVfdMjm0q6fUH2vXrmXJkiVMmTIFJycnXnnlFc6ePYtcLkcu\nlxMbG0tMTAzjxo3D3d2dwYMHM336dH766ad6ub41vPDCC1y5coW5c+ei0+no0qULS5Ys4d133+XM\nmTP1FtY6ePBgli5dyrp164iJiSEkJIS4uDgzs1NkZCRvvPEGs2fPJjAw0PQQ9Mc//hG5XM7w4cMt\n1mmpLwwGA4mZZ/k14wzUmF21d2nH4I4DcLQVdU/aArrKSorPnqPw9Jnai+3kclxCH8alZw9sHJtf\niiCJoS67QA1GjBjBRx99ZLFQ0unTp3nllVc4duxYgwzwfrhbIfPGRqfTs+H/zqLVGX0OUWNDUCoe\nLI4+Pz+f06dPM2jQIGQ3Q+lUKhWRkZFs27atlmNY0PQkZpwlIf1307ZcJueRwD509+gsZhVtAIPB\nQMnFS+QdOYrutug9mb09Lj174NIjFJld04VO3+3eWecM4+uvvzY5HtPT0/nxxx+5ePFirX5Hjx5t\nNVFSDYWqsNwkFs6Otg8sFmB0VkdHRxMVFcXkyZNRq9V8+OGHdOjQgbCwhvHDCO6fs9kXzcSinbMv\nQztFoLRtfk+RgvpHk1+Aav8Bym8LqLBxdMQ1vBfODwXfczGjpqBOwTh37hybN28GjDbxjz76yGI/\niURiiskXWCbDzBxVPzldXFxc+OSTT4iNjWXLli3I5XIiIiKIi4szOXEFzYNLuVc4kvqradvf2Zcn\nug1FJkqmtnr0VVUU/JpI4W+/Y6gR1Sh3csKtbx+cundr8MV29UmdgvHWW28RFRWFwWBg6NChfPzx\nx7XCZ6VSKUqlss5Yd4GR9BoOb3+v+hEMgIEDB4qAg2bO1fxU9l+/lc7ER+nFqK6DhVi0AcpSU1Ht\nP2jm0JZIJLj2CsOtb58WMaO4nToFQy6Xm2LY4+Pj8fb2Jisry7RQLD8/n2vXrplWEQsso9cbTHmj\noH4ipAQtg7SiDH6+etjk4PZQuPFEt6GiwFErR6tWk3v4CKXJ5uld7H198RoyCDuPpg+PvV+sipJy\ncHDg+eefp6CggB9++AGAM2fOMHPmTCIiIlizZo3ZCmPBLXKLytFUGVc0Kx3k95VkUNDyyCrJ4Yfk\nA+gNRjOEi70zY7oPF0kDWzEGvZ7ic+fJO3YcfdWtRbpSWzs8Bg7AOeShFh/cYFUsbExMDCqViiVL\nlpjaBg8ezH/+8x9u3LjBqlWrGmyALZ3MGv4LP09li//CCO5Orjqf7y7vQ6e/+aBg58jYoOEixUcr\nplKVS/ru/0V18JCZWDh170bgn57F5eGQVvHbt0owDh48yLx588zs5RKJhL59+zJ37txGjetvaaTn\n1vRfCHNUa6egvIhvk36mSme8aTjI7RnbfbiIhmql6DUacg8dJu3L/2eWzkPu4kK78ePweWwENorW\n4+O1yiRVWVlZKxNoNY6OjqZ0FAJzDAaDeYRUPTq8Bc2PkspSvkn6mQqtMcbe1saWMd2H42LffFbq\nCuoHY96na+QePIxWfes3LpHKcOsTjmt4L6QWcq61dKx6R2FhYWzdupVBgwaZJZ7T6XT85z//oUeP\nHg02wJZMfnEFFRrjWhYHOxvcnEQtg9ZKmaacb5LiKbuZmtxGZsPobkPxULg18cgE9U1VcQm5Bw+h\nTjGvr+7g74/XkEEPXDe7OWOVYLz55ptMnTqVkSNHMnjwYDw8PMjPz+fgwYOoVCq2bNnS0ONskWTc\nlg6kNdgwBbWp0FbyTVI8xRVG86NUKuXxrkPwUTbP0q+C+8Og01F4+gwFJxPQ18imLHNwwPORgSi7\nd2v1v3GrBKNXr17s2LGDTz75hPj4eAoLC1EqlfTp04d//etfPPzwww09zhZJRm7NhIPCHNXQfPXV\nV3z00UdkZmYSHBzMwoULTUWfbmfNmjWsXbvW4r7Zs2fzxhtvWHVNja6K75J+oaC8CDD69h7rPAh/\n56ZLQS2ofyqyssjZdwBNfr5Zu3PIQ3gMjGjSdB6NidVGtpCQEP71r3815FhaFQaD4baEg0IwGpIj\nR47w3nvvsWjRIvr27cvmzZt56aWX+P777y0W/5o+fTrPPvusWdvatWv58ccf+eMf/2jVNbV6Hd9f\n3o9KnWdskEgY1ukROro1ff4yQf2gq6wk7+gxis9fMGu3dXfHa8jgJq1N0RTck1fm1KlTHD58GJVK\nxcyZM7ly5QohISF4tOCFKA1FUamGsgpjpIydXIaHiwipbEg2bdrEuHHjeOaZZwBYunQpx44dY+fO\nnRaLNTk6OpqyLYPxu71z504+/fRTq2p66/V6frpykMySbFNbZGA/unp0fPA3I2hyDAYDpUmXyT1y\n1CyjrNTGBrd+fXHt2aNFpfSoL6wKq9VoNLz55ps899xzfPrpp+zcuZOCggI2bdrEhAkTSE1NtfqC\nOp2ODz74gMjISMLDw3nzzTfJzc2ts39WVhZvvvkm4eHhDBw4kPfff5/y21ICN0fMzFGejmbV9eqL\n3NxcZs+eTe/evYmMjGTjxo2MHDmS3bt3A8botmXLljFs2DBCQ0OJiIjg3XffNX1+u3fv5oknnmDb\ntm0MHTqUsLAwoqOjyc7O5u2336ZXr14MGTKEPXv2mK45depUVq5cadofGRnJzp07SUhIYPz48YSF\nhfHcc8+ZfSeOHz/O888/T3h4OKGhoUyYMIEDBw7U+b6GDx9OUFCQxX/Hjx+v1V+v15OYmGiqKQ5G\nP0K/fv1ISEi46+doMBj4xz/+wahRoxg8ePBd++sNen65doTUwnRTW/+AcEK8G77wkqDh0RQWkbn3\nG7LjfzYTC8cOHWj/7DO4hfdqk2IBVs4wYmNjOXz4MOvWrePRRx81ZUP9+9//zowZM1i9erWpKM7d\nWLNmDXv27CEmJgZXV1eWLFnC7Nmz+eKLL2r11Wg0TJs2DS8vL7744gsKCwt55513kEqlLF68+B7e\npnVcuaTi8vlstFrLtabvhfScUtSlxvBKVWElX2feOfTYxkZGtxAfugRZ5yjV6/XMnDkTmUzGli1b\n0Gq1vP/++6SlpZn6xMTEcOjQIVasWIGvry+nT5/mnXfeISgoiKioKMCYzjg+Pp7169eTmZnJrFmz\nOHbsGLNmzeL1118nLi6OxYsXM3z4cFxcXAD47LPPeOutt5gzZw4bN25k6dKldOrUiUWLFuHg4EB0\ndDSrVq0iNjaWzMxMZsyYwbRp01i2bBlqtZrY2Fjmz5/P/v37sbWtvfJ5165dddb7rh5DTYqLiykr\nK6s1M/D29ubMmTN3/Szj4+M5f/48H3zwwV37GgwGDqWc5Er+rQiZXn4P08uvdZQpbssYdDoKEk9R\n8OspDPpb3z8bR0c8Bz2KY6dOrd6pfTesEoy9e/fy1ltvMXz4cLMfckBAAG+88Qb//Oc/rbqYRqNh\n69atLFy40FTVbNWqVYwYMYLExER69+5d67oqlYrt27ebbhR1iUt9cDVJVS9iAaCuuBVFobC/+8es\n1eq4mqSyWjBOnDjB2bNn+emnn0z5vVasWMGTTz5p6hMWFsbYsWNN+b4CAgL4/PPPSUq6VRa0qqqK\nxYsX07FjR7p3705wcDAODg68+OKLAEybNo0vv/ySlJQUkwM5NDTUlKH4+eefZ/v27URFRZme8EeP\nHs2+fftM54+Ojmb69OmmH1tUVBQvvvgieXl5+Pn51XpvlnwOd6KiogKg1lohuVxO5W11ByyxZcsW\nnnjiCTp0uHMNbYPBwPEbp7ioSja1PezTnX7+Ip18S6fsRjqq/QeoKiqq0SrBtWco7v37IbXwYNMW\nsUowioqK6vwxubm5UVpaanHf7Vy8eBG1Wm1mOggICMDf35+EhIRagnHo0CEeeeQRs6fKP/zhD/zh\nD3+w6nr3SufuXvUyw9Bo9VTdPIdUKsHB7u4fs42NjM7drQ/DPH/+PB4eHiaxAOjevbtZTq8JEyZw\n6NAhli9fzvXr10lOTiY1NbVWYZTqmuAACoXCbH/1TbhmzZOa34XqTMU1z2Fvb2/qHxgYyMSJE9my\nZQuXLl0iJSWFCxeMDsS6ZhFjx44lIyPD4r4NGzbUKg5laYxgFKu7ZVLOysrixIkTVoWGn8o8x+ms\nW87Pbh6deKR93zb/1NmS0ZaVk3fkKCVJ5rXV7by88B46GDsvERpdE6sEo2vXrnzzzTdERkbW2nfg\nwAGry2xmZWUBWDQdVO+ryfXr14mIiCA2NpavvvoKiUTCqFGjmDNnTp0rzx+ELkFeVj/h34mLKflk\n2BhvIu19nHhycP2UIa2JTCZDXyO/viUWLFhAfHw8kyZNYtSoUcydO9dU97vmeW4vr3u3crs2Flaw\n1nXTTEpKYsqUKYSFhTFw4EDGjBmDVqu16IiuZv369abiXbdjySHt6uqKQqEgp0ZqBoCcnJy7OrDj\n4+Px8vIye4ixxO0FkDq6tWdIpwghFi0Ug8FA8fkL5B09jl5zaxYqlctxH9Afl9CHkYiy07WwSjBe\ne+01Zs+eTVFREcOGDUMikZCYmMhXX33Ftm3bWL58uVUXKy8vRyqV1irwY2tra9F0UFpayq5duxg8\neDAffvgh2dnZ/O1vfyMvL8/qazYFNRfs1Wf9i5oEBQVRUFBAamqq6en+6tWrpjQtBQUF7Nq1izVr\n1jBq1CgAtFotaWlptGvXrkHGZIkdO3bg5+fHxo0bTW3bt28HjD9aS/j7+9/TNSQSCeHh4Zw8eZKJ\nEycCRh/PyZMnefrpp+94bEJCAv3797+jSFoqgDSi86NIJeKG0hKpzMtHtf8AFbc9pCq7dsHzkUew\nUYq8X3VhlWCMHDmSFStW8MEHH/Dzzz8D8I9//AN3d3cWL17MmDFjrLqYvb09er0erVZr9pSq0Wgs\nmg5sbGxwcXFh+fLlyGQyevTogVarJTo6mnfffRc3t+aZduH2CKmGICIigtDQUObNm8fChQvR6/Wm\n2YNEIkGpVKJUKomPjyc4OJjS0lI+/fRTMjMzG7Wkrq+vL+np6Rw+fJiOHTuSkJBgCpCoz3FERUXx\n2muvERISQkREBJs3b6akpITJkyeb+qhUKhQKhVk47fnz55k0aVKd5xUFkFoXRefOk3vwUK3qd15D\nBqGoYVYVWKbOR6QNGzaQnX0rxvzJJ59k3759fPvtt3z++efs3buXQ4cO3fUJribVDk6VSmXWXpfp\nwMfHhy5duiCrEcLWtWtXwFhnvDmiLq+isMQ4W5JJJXi7KxrsWmvXrsXV1ZUpU6Ywa9Ysxo8fj0Qi\nQS6XI5fLiY2N5dy5c4wbN45Zs2bh4uLC9OnTOXv2bION6XZeeOEFRo4cydy5cxk/fjzbtm1jyZIl\nKBQKqyKYrGXw4MEsXbqUuLg4Jk2aRHJyMnFxcWYO9MjISOLi4syOU6lUFiOvoHYBJHdRAKnFYtDp\nUB04iGr/AZNYSCQS3HqH0/7Zp4VYWInEUIddoFevXmzatIk+ffrw0EMPsWPHjjrTLFiLRqMhIiKC\nv/71r0yYMAEwhnWOGDGCHTt20KtXL7P+a9euZefOncTHx5vMWN9++y1//vOfOXz4cJ0zjOpzxsfH\n13LwNjSX0wr4/pgx5NLfS8mkoV0b5Dr5+fmcPn2aQYMGmQRVpVIRGRnJtm3bajmGBfdGVkkO3yT9\nbKpp4WLvzPjgkaKmRQtEV1FB1vc/UJ5+K5DCztMT7xHDsfO4t4i81s7d7p11mqSUSiWbN28mNTUV\ng8HAvn37uHr1ap0XqrYd3wlbW1v+9Kc/sXz5ctzc3PDw8GDJkiX079+fXr16odFoKCoqwsXFBVtb\nW5599ln+/e9/M3/+fF5//XWys7NZsWIFEyZMaL7mqNsSDjYUMpmM6OhooqKimDx5Mmq1mg8//JAO\nHTqY1skI7g9RAKn1UJmXT9a335nV1VZ26Yz38GEtsqZ2U1OnYMycOZP/+Z//4aeffkIikbBu3bo6\nTyKRSKwSDIA5c+ag1Wr5y1/+glarZdCgQaZFeKdOneKFF15g69atDBgwAE9PT7Zt28ayZct46qmn\nUCgUjB8/nrfffvse32bjkaFqnISDLi4ufPLJJ8TGxrJlyxbkcjkRERHExcXVCioQWI8ogNR6UF+7\nTvaP8ei1tyrguffvh1uf3iK67T6p0yQFxqim4uJihgwZwieffMJDDz1U54msyb/TWDSVSaqiUsvG\nr4z+AalEwoyJochthHO0pVBSWcr/XfzRVNPC1saWJ4MeEzUtWhgGg4GCXxPJP3HS1Ca1keMzcgSO\nnTo22bhaAvdtkgLjoiwHBweWLVtGWFhYszUDNRcycm+Zo7zdFUIsWhCiAFLrQF9VRc7Pv1B65Zb5\nXO7khO+Y0cJfUQ9YFVY7adIkSktL+fnnnykvL7e4YKxmSoq2SmOE0wrqH1EAqXVQVVxC1n+/p7JG\nMlMH/3b4Pj4Kmb3wP9UHVgnGoUOHePPNNykvL7e42EoikQjBoHEW7AnqF1EAqXVQnpFB1vc/mmWX\ndQkNxfPRgW02s2xDYJVgrFy5kk6dOvHuu+/i4+Nz19QRbZHKKh2qQuOXVSKR4CtmGM0eUQCpdXD7\nYjyJVIrnoEhcHhYZhOsbqwTjypUrrFu3TsT234GsXLVp9uXpYo+dXDzVNGdEAaSWj0GnI/fwUYpq\nLESVOTjg+8QoHCxkQRY8OFYJRrt27azOSNtWEfW7Ww6iAFLLp67FeL6jH0deI2OzoH6xyrY0Y8YM\nPvroozpTTgu4rX63MEc1V0QBpJZPZV4+N778f2ZioezSGf9JE4RYNDBWzTD++9//kp2dzYgRI/Dy\n8rKYKPD777+v98G1FKq0enLyy0zbYobRfLm9AFKItyiA1JIQi/GaFqsEw8vLi8cee6yhx9JiycpT\no7/pv/BwtreqYJKg8UkryqhVAOnRQFEAqSVQ12I878eGo+zcqQlH1raw6s62bNmyhh5HiyazxoI9\nPzG7aJbo9DqzmhaBrv6iAFILQSzGaz7UKRjZ2dl4eHhgY2Njlua8LppTapDGJl0lFuw1d85kX6Ko\nohgAuUzO4I4DRAGkFoBYjNe8qFMwhg4dakppPmTIkLs+iVXXaW5r6HR6soX/olmj1pSRmHmr9kZf\n/54o5Heu9S1oesRivOZHnYLxz3/+k/bt25tei6m7ZbILytDqjAuGXJV2KB1EptjmxvEbp9DqjDXC\n3RxceNirexOPSHA3xGK85kmdglGzbOVTTz3VKINpiZjVv/AS5qjmRmZJDsl5103bjwb2FZkKmjFi\nMV7zRoTzPCCNVf9CcO/oDXoOpyaYtju7d6CdyBHVbBGL8Zo/QjAeAL3eQGZezQV7QjCaE+dzLpNf\nVgCAjdSGiPbhTTwiQV2IyngtAyEYD4CqsJwqrdHG6qSwxdnRtolHJKimvKqChIzTpu3wdg+LqnnN\nFLEYr+UgBOMBEOG0zZeT6b+j0WoAcLZ3oqdP3dUiBU2DWIzX8hCC8QBkCv9FsyRHncfF3Cum7Ufa\n90EmFWGYzQmxGK9lYpVgGAwGdu/ezb59+ygrK6tVREkikbBp06YGGWBzRa83kJEnIqSaGwaDgcMp\nJ+HmdzTQ1Z9AV/8mHpWgJmIxXsvFKsFYtWoVGzZsICAgAF9fX2FXBPKLK6jU6ABQ2MtxVdo18YgE\nAJdyr5oKIkmlUh5p36eJRySoSUVODpnffCcW47VQrBKMPXv2MG3aNObPn9/Q42kx3F6/W4ho01Op\n1XAi/TfTdphvCM72IhyzuVCZl0/m3m/QVVYCYjFeS8QqwSgtLWXYsGENPZYWRbqo393s+DXjNBVV\nFQA42iro5StuRM0FTWERGV/tNYmFzM4O39GP49CuXROPTHAvWLXkNTw8nMTExIYeS4vBYDDctmBP\n+C+amvyyQs7lJJm2B7bvg1wm4vebA9rSUjL3fm0yQ0nlcvyeHCvEogVi1Qzj1Vdf5e2330ar1dK7\nd2/sLTimevfuXe+Da64UllRSXmnMTWRva4O7s3DUNSUGg4HDqQmmYIx2zr50cmvfxKMSAGjLykn/\nv72mBXkSmQy/saOx9/Zu4pEJ7gerBOPFF18EYO3atQBm9nqDwYBEImlT2Wprrr/wE/6LJudKfgqZ\nJcYU/BKJRBRFaiboKivJ3Ps1VUVFgNFn4SfMUM0CrVaHjc29BxlYJRhbt2695xO3ZjJya/ovhDmq\nKWewCfwAACAASURBVKnSVXHsxinTdqhPEG4OLk04IgEY11lkfv0tlXl5N1sk+Ix8DEVgYJOOq61T\nXqbh1PE0CvLUdH3Im6CH7y23mlWC0b9///saXGuklv9C5I9qUk5lnqNMY6xH4iC3p49fjyYekUCv\n1ZL57X+pqFF4zXv4UJRdOjfdoASosks4dTwVzU1zetaNooYRDIArV66wZs0aTpw4QUlJCW5ubvTt\n25dZs2bRtWvXext5C6ZYraG03JjzxlYuw9NVFOJpKgoris1qdA8ICMfWRuTzakoMOh3ZP/xEeXq6\nqc0z8lGcg4OacFRtG4PBQPLFHJLOZZv8fBKJhG4h914l1SrBuHTpEs899xwODg6MGDECDw8PVCoV\nv/zyC7/88gvbt28nKKhtfCFq1r/w9VAglQpbeVNgMBg4mvoreoMx+aO30pNuHiL/UFNi0OvJ+fkX\n1Nevm9o8BvTHtaeY9TUVVVU6fjuRRnZGkanNzl5On4EdcL+P/HdWCcbKlSvp3LkzW7duRaFQmNrL\nysqIiooiNjaWjz/+2KoL6nQ6YmNj2bNnD2q1mkGDBrF48WI8PT3veuzMmTMpKyvj3//+t1XXaghq\nLtgT6y+ajtSidNKKbtZNkEiIDOwnHN1NiMFgQHXgICWXk01tbuHhuPVpO9GTzY3ionJ+PZKCurTS\n1ObuqaR3RCD291kZ1Kp1GAkJCbz66qtmYgGgUCh4+eWXSUhIqOPI2qxZs4Y9e/YQExPDf/7zH7Ky\nspg9e/Zdj9u+fTv79u2z+joNRbrwXzQ5Wr2OI6m/mrYf8uyKp6NIWNdUGAwG8o4eo/j8LfOgS+jD\nuEcI32dTkZ5awOH4ZDOx6Nzdi4ghne9bLMDKGYaDQ912eolEgk6ns+piGo2GrVu3snDhQh599FHA\nmKdqxIgRJCYm1rmWIyUlhdWrVxMe3rQFcErLNBSrjSmzbWRSvN2E/6IpOJ11npJKo3Db2djRLyCs\niUfUtin4NZHC3343bTt1747noEgx42sC9Do9509ncj35VmJHGxsZPfsG0K696wOf36oZRq9evdiw\nYQOVlZVm7RUVFWzcuNHqG/nFixdRq9VmUVcBAQH4+/vXOUvR6XTMnz+fl19+mS5dulh1nYaiZjit\nr4cjMpmoDd3YlFSWcirznGm7n39P7G1E4semovD302b1LBw7dcJ7+FAhFk1ARXkVR/dfNRMLRyc7\nHh3RtV7EAqycYbz99ttMnjyZESNGMHz4cDw9PcnNzeXnn39GrVazbds2qy6WlZUFgI+PuXfe29vb\ntO92Pv30UwBeeuklFi1aZNV1GgqRDqTpOZZ2Cp3eOKP1ULgR7NV2IvSaG8UXLpJ7+IhpWxEQgO+o\nx5BIxYNUY5OnKiXxWCqVFbeqFvr6u9CrX3ts5PWXBdgqwejSpQvbt2/no48+Ij4+nqKiIpydnenX\nrx+vv/463bt3t+pi5eXlSKVS5LfV6LW1ta01ewE4e/YsmzdvZteuXUibwZew5gxDVNhrfG4UZ3Kt\nINW0/WiHfkglTf+9aIuUXE4m55f9pm17X198Rz8uUpQ3MgaDgWuXc7lwOtMsZDa4hy+du3vV+0zP\n6nUYQUFB/Otf/3qgi9nb26PX69FqtdjY3Lq0RqOp5SeprKxk3rx5zJkzhw4dOjzQdeuDsooq8ouN\nmVClUgm+HkIwGhO9Xm/m6O7m0QlfpVcTjqjtok5JJeeneMB4g7Lz9MRv7GikcpHssTHRVun4PeEG\nmTcKTW22djb0HhCIp0/DpPWvUzD27t3LoEGDcHV1Ze/evXc90ZNPPnnXPn5+fgCoVCrTa4CcnJxa\nZqrff/+dK1eusHLlSlauXAkYhUWv1xMeHs4333xDu0bMSVNzduHjpsBG+C8albM5lygsN8aSy2Vy\nBgQ0bQBEW6U8PYOs/9/ee8dHVeX//687Lb33BBIUnPSQEBIIVQggKyAguCoCCvIFqeICCq4FllUX\nBZXy+MiKFfCHKArLUgQWDIoBQkLoKXTSEzLp0+89vz8muZlJcwamJJnzfDzymLnnnjv3PSfJed/z\nPu/yyxH+aVbi5YWg8eMgdKD7SNakvlaJzNN3Ud/4EAsAnt7OSEwJg5Oz5YJX21UYK1aswA8//ABP\nT0+sWLGiww9hGMYohREREQEXFxdkZGRg4sSJAIDCwkIUFRUhKSnJoG9cXByOHj1q0Pbxxx+juLgY\n69evh7+Vs12WVOiXY6XutNZErlYgq/gyf5wYHAtnCfVQszbKMl21PNLoFSl2c0PwhPEQOdPfhTUp\nKazGxXOF0GqbvVN79fZFVN8gCCz8INuuwjh+/Dj8/Pz49+ZAIpFg2rRp+PDDD+Hl5QUfHx+sWbMG\nycnJiI+Ph1qtRk1NDTw8PODo6NjKFOXq6tpmuzUouk83vG3F2cJsaFjdZp6nkwdi/O0jq0BnQlVZ\nieL/HgSn1f0eRC4uCJ44ASJX+r9gLQhHkHulBDfzKvg2oVCA2MQe6BHmZRUZ2lVHISEhkEh0S5tz\n587B2dkZISEhrX4kEgmOHDli9A2XLl2KCRMmYMWKFZg5cyaCg4OxceNGAEB2djaGDBmC7OzsP/kU\n66JUa1FZo1v6MQyDILp/YTVK6ytwvfI2fzwoNLFTOEDYE7pqeQfAqZur5QVPGA+xu7uNJbMfVEoN\nzvx2y0BZOLtIMHhkH6spC8DITe9Vq1Zh9+7d8PJqLVhOTg4++eQTzJ4927gbikRYuXIlVq5c2erc\ngAEDkJeX1+617733nlH3MDcl9xt4m62fpxMkZnRTo7QPRzj8cbfZx/8Rr1D0cA/q4AqKudHW1+tK\nq7aolifxtt4kZe9UVTYg6/RdKBXNLrMBQe6IT+4JscRovyWz0O7d5s2bhxs3dHlhCCFYuHAhv+LQ\np7KyEqHdPMe9Yf0Lun9hLXIrbqJSXgUAEAqEGNiTbnRbE61cjqL//Bfaep05lhEKETT+SVotz0oQ\nQnDnZiVyLhaD45pdZqXRAegT4W+T4Mh2Fcb8+fOxZ88eAMCePXsQGxsLb2/DfD0CgQDu7u6YPHmy\nZaW0MTRgz/ootSqcK2pON5EQFA03B6qsrYWuWt5BvWp5QgT9ZSycgugKzxqwWg6XsgpRdK+KbxNL\nREgYEAr/QMu4zBpDuwojPj4e8fHxAHTpORYsWICePe2vTrJaw6KiSrccZxgGQTRgzyqcK7wIlVZn\nM3dzcEVcYJSNJbIfOLUaJf89yFfLYxgGAWNS4Rxqf///tqChToWsM3dRW63g2zy8nJCY0gvOLrat\n92LU7uEHH3yAW7duYd26dXzbpUuXMGvWLJw5c8ZiwnUGSisbwDXuX/h4OMLRyjZDe+R+gww595vT\nZKf0TIRIQPeNrAGn1aLk8C9Qlpfzbf4jR8D1UVotzxqUFdfi1PHrBsqi5yPeGDSij82VBWCkwjh0\n6BBeeeUV3Lx5k29zcnICx3F4+eWX8dtvv1lMQFtD04FYF0IITt07BzQq6Z4ewQjzDLGxVPYBYVmU\nHTkGRVEx3+Y3dAjcwo1L/UN5cAhHkHelFOf+uA2NRhdfIRAIEJfYA3379+w0iU6NkmLr1q144YUX\n8Pnnn/Ntjz32GL799ls899xzD50ypDNjuH9BbeiW5nrlbZTX67JtChgBUkITaeZTK0A4DmXHf0XD\n3bt8m8/AAfCIjbGhVPaBWqXF2VO3cT2nuQa6k7MEg0b0RuijPjaUrDVGKYx79+5h1KhRbZ4bNWqU\nwcqjO6FlOZTJ5PwxXWFYFrVWjbOFzTE4cYGR8HSkvv6WhhCCipO/of6GXrW8fgnw6ke90ixNtUyO\n3/93HffL6vg2vwA3DB31GDy9nTu40jYYpTB8fHxw9erVNs/l5eXBw8PDrEJ1FspkcrCN7myebg5w\ndqTJ1SxJVsllKDS6AElniTMSgqJtLFH3hxCCyvTTqM3J5ds8YqLhPYBWy7M0925VIv3Xm1DI1Xzb\nY5EBSB7yCCQOnXOv1CipJkyYgC1btsDZ2RmjR4+Gj48PZDIZTpw4gc2bN2PatGmWltMm6JdjpfEX\nlkWmqMaVsuagzYE9EiAWUgVtaaoys1B98RJ/TKvlWR6W5XAluwgFt2V8m1gsRHxyKAKCO/eK2iiF\nsXDhQty6dQtr1qzBP/7xD76dEIIxY8ZgyZIlFhPQlhRX0A1va0AIQfq9LD6aPsgtAL29bZ/SvrtT\nfeEiZOeaK13SanmWR6nQ4Nwfd1BT1WzqdvNwQv+UMLi4df6Mv0YpDLFYjE2bNiE/Px9ZWVmoqamB\nm5sbEhMTERERYWkZbQLLciitpBHe1uB2VQGKa3UVFxmGwSC60W1xaq/l4H76af7YuWdPWi3PwtRU\nyXHujzsGKT5CQr0Ql9gDQlHXGHeTDGVSqbTN6noNDQ1wceleT+AV1QpoWQ4A4O4igasFc8zbMxpW\ng9MFzYWRov2l8HGmeYosSd31GyhPa3aFdwoKQuDYMbRangUpLapB9tl7YBvnFIZhEBUfjF69fbrU\nw5FRCkOtVmPHjh04d+4cNBoNbzrgOA4KhQJ5eXm4cOGCRQW1NobmKLq6sBQXSq+hQa1bnjuKHZEY\nHGdjibo3DXfuGlbL8/ND4JNjabU8C0EIwc28CuRdKeXnTbFYiH4pYfCzUFU8S2KUwli/fj22b98O\nqVQKmUwGBwcHeHt7Iz8/HxqNBosWLbK0nFaniOaPsji1yjpcLL3GHyeHxMNBRFdylkJZVobSI0cN\nquUFj3+SVsuzEBzL4XKLzW1nVwckD+4FV3dHG0r24BhlODty5AhmzZqF/fv3Y/r06YiJicGPP/6I\no0ePIiQkBBzHWVpOq8JxBCV0/8LipBdk8X87fi4+CPel6Scshbq6us1qeUInWi3PEqhVWpz9/baB\nsvD2dcWQkX26rLIAjFQYlZWVGDZsGADdPsbly7pymQEBAZg7dy4OHTpkOQltwP0aBdSN4fmuTmK4\nd4IcLt2Ne9VFuFddpDtgGAwOS+pSttyuhFYuR8l/D4JV6mJchI6OCJownlbLsxD1dSr8ceIGKvWs\nFD3CvDFwWOeNrzAWo6R3c3ODRqPb2Q8LC0NJSQnq6+vh6uqKXr16oaSkxKJCWhv9dCBBvq50IjMz\nLMciXW+jO9znUfi7dK4UCN0FTqNBycHD0NTpIokZoRBB4/4CiWf3DLa1NffL65F1+g406uZ62xGx\nQegd7tct5hGjVhiJiYnYuXMnlEolwsLC4OTkhP/9738AgIsXL8LVtXuZbAwLJtGnMHNzqSwHtUrd\nBCYRSZDcI97GEnVPCMui9JejUFU0lfVkEPjEaDgGBNhUru7KvdsynP3tFq8shEIBElPCbFbsyBIY\npTAWLlyIrKwszJ07FyKRCNOmTcM777yDZ555Bp988gmeeOIJS8tpNQghhh5SdP/CrNSrG5Bd3Jxm\npn9wHJzEXdem21lpyg8lLyjg2/yGD4VLr162E6qbQghBzqViXMos4B0KHBzFSHm8N4J6eNpYOvNi\nlEkqMjIShw4dQn5+PgBg2bJlcHV1xfnz5zF//nzMnTvXokJaE1mtEkq1FgDg5CCCVxeIvuxKnCnI\nhpbTja+3syei/B+zsUTdE1nGOdTmNqda8e6fCI9oWoTK3Gi1LC5kFKC0qIZvc/d0QtLgXnDqhrFb\nRimMtWvXYtKkSRg6dCgAXdDJK6+8YlHBbEXLdCDdZSnZGSioKcYtWXP67MGhSRAwXSPCtStRc/Uq\nqrLO88fuERHwSupvQ4m6Jwq5Guf+uGNQ7CggyB0JA0MhEnXPIEij/lv37NmD2tpaS8vSKSi+rxd/\nQQP2zIZCo0Ta7eZUFL29wxDk5m9DibonDbfvoOLkKf7YOTQUfsOH0gcfM1Mtk+OPEzcMlMWjUj/0\nH9Sr2yoLwEiF0bdvX2RmZv55xy4OIQRFdP/C7BBCcPLOGT51uaPYESmhiTaWqvuhLC1F6dFjMIji\nfmI0TflhZkoKa3A67SafE4phGMQm9kBU32Awgu6tmI0ySUVHR2Pbtm04cuQIIiMj4exsWNiDYRiD\nLLZdlZp6NeRK3R+Bg1gIHw+6GWsOrlVcb465APB4r4FwFtOAMXPSKjDP3R1B456kKT/MSFOaj9zL\nzWEEYokQiQPD4NsF03w8CEYpjCNHjsDf3x9KpRLZ2dmtzneX5W6RQfyFCwTd/GnBGsgU1ThT0GxP\njwkIRyit0W1W+MA8lQoAIHRyQtD4cRA5U6VsLjiWw+XzRSi4033SfDwIRimMEydOWFqOTkHJfVq/\n25xoORYnbqWD5XRPvd7OnkjuQct+mhNOrUbJgUN8YJ5AJELQkzQwz5yoVVpknb5rELnt7euK/oPC\nunzktqm0u4dx+vRpNDQ0tHe6W6IfsEcLJj08GYUXIJNXAQCEAiFGPjoYIgG1p5sLwrIoPXIUqvv3\nAehW+gFjRsMxgDoTmIu20nz07NU90nw8CO0qjNmzZ+PmzZsGbbt370ZVVZXFhbIFtQ1q1DboauuK\nRQL4eXW+AuxdiYKaYlwpa64TPbBnP3g7da8gJltCCEH5rychLyjk2/yGD4NLL1qp0FzcL6/HHyeu\no6FexbdFxAYhrn8PCIT26Q7e7rduilhsgmVZrF69GsXFxRYXyhbou9MG+rhASPcvHpiWLrShniGI\n8qMBeuZEdjYDdY2BtADgndQf7lGRNpSoe3HvVmUbaT56das0Hw+CSWuqlkqkO6EfsEfTmT84LV1o\nncSOGN5roF3/k5mbmitXUXW+2fnEPTICXv2pm7I5IBxB7pUS3Myr4NscHMVIGtwLnt7U6mB/Rrh2\n0M9QS/cvHpxWLrSPpNBcUWak/tZtVPzWHJjnEhYKv+HDqEI2A1oti+yzBSgrto80Hw+C1Q1xLMti\nw4YNGDJkCBISErBkyRLcb9y0a4tDhw5h4sSJiI+Px+jRo/H555+DZdl2+z8IDQoNqhvtlEIBA3/6\nJPFAtHahjUBPj2AbStS9UJSUouzY/9AUmOfo74+AMaPBCOzTnm5OFHI10n+9aaAsAoI9MGhEb6os\n9DD5L+1hn2Q2b96MvXv3Yt26ddi5cydKS0uxePHiNvuePHkSy5cvxzPPPIP9+/dj2bJl2LZtG7Zu\n3fpQMrREf/8iwNsFIjvd0HoYdC60f+i50HrRtOVmRF1VhdJDeoF5Hh4IfPIvNDDPDFTL5Dh13DDN\nR+9wP/RPCevWaT4ehA5NUq+++iokEkPtunDhwlZtgC64789Qq9XYvn073nrrLQwePBgA8PHHHyM1\nNRXnz59Hv379DPp///33GDNmDKZPnw4ACA0Nxc2bN/Hzzz9j4cKFf3o/YzHcv6DmqAdB50JbDUDn\nQptKXWjNhrahASUHDhkE5gWPf5IG5pmBksIaXMi4B5bVlQpmGAax/UIQ+igt6NUW7SqMyZMnt2pr\nOaGbSm5uLhoaGpCcnMy39ejRAyEhIcjMzGz1+fPnz2+VhkQgEJg9EaLB/gXd8DaZe9VFrVxovZxo\n4Jg54NRqg4p5ApEYQeP+ArEHHd+Hgab5eDDaVRgffPCB2W9WWloKQFcLXB9/f3/+nD5xcXEGx/X1\n9di1axefZt0cKFRaVNbqPHoEDINAH7p/YQpyjQJpd87wx9SF1nzwFfP0A/OeGA1HfxqY9zC0lebD\nxdUBSUMegSutf9MhVvWSUigUEAgEELewu0okEqhUqnauar52wYIFUKlUWLZsmdlkKtGL7vb3doaY\n2iyNhhCCk7fPQEldaM2OLjAvDfJCvcC8x4fDJSzUhlJ1fRRyNbLPFkCmt2/p4+eKxBT7S/PxIFh1\nhBwdHcFxHLRaLUSi5lur1Wo4ObVvj5XJZFiwYAFu3LiBr776CiEh5kteZ1j/gu5fmMLV8nwU1DQH\nco54dBB1oTUTsjMZqMu/zh97JyfBPTLChhJ1bQghuHdLhpzLJdBqmr0se/byRmy/ELuN3DYVqyqM\noKAgAEBFRQX/HgDKy8tbmamaKCwsxMsvv4yGhgbs3LkTERHm/acpovsXD4RMXo0zhc0utLGBEejh\nHtTBFRRjqbl8BVV6WaHdoyLhlfhw+4f2TEOdCpeyCg3yQTEMg/CYQPQO96MrYhOwqlqNiIiAi4sL\nMjIy+LbCwkIUFRUhKSmpVf/KykrMnDkTHMdh165dZlcWKg2L+9U6cwrDMAiiKwyj0HIsjt86BY7T\neZb4OHshOYS60JqD+lu3UPH7H/yxS1gY/IbRinkPAuF0G9u/Hcs3UBYubg5Ieby33af5eBCsusKQ\nSCSYNm0aPvzwQ3h5ecHHxwdr1qxBcnIy4uPjoVarUVNTAw8PD0gkEqxZswZVVVX49ttv4ejoiIoK\nXbg+wzDw9fV9aHlK7zfw6U58PRzhIKb7F8ZwtvA8qhS6AKemLLRC6kL70ChKSlB29DgMA/NG0cC8\nB6CuRomLmQWolsn5NoZh0DvcD49FBUBITVAPhNV3eZYuXQqtVosVK1ZAq9Vi6NCheOeddwAA2dnZ\nmDlzJrZv346+ffvi2LFj4DgOzzzzjMFnCIVCXLt27aFloeYo07lXXYSrZc1J71J6JlIXWjOgllWh\n5OAvIBwNzHsYOJbDjbwK3MgpA8c1575z93RC3/494EGzUD8UVlcYIpEIK1euxMqVK1udGzBgAPLy\n8vjjnJwci8pC61+YRksX2jDPHoj062NDiboH2voGFB84CE6tF5g3gVbMM5VqmRwXMwtRV9McsS0Q\nCPBYlD96h/vTCppmwG79yDRaFuV6y1W6f9ExLV1onSVOGN5rALUBPyS6wLxD0NbrVrt8YJ67u40l\n6zqwLIf8q2W4lV9hkFHby8cFcf17wM2OSqhaGrtVGKWVcnCNf1ze7o5wdqRL/464Wp5n4EL7+CMp\ncKQutA8FYVmUHD4CVWUlAJ2NPXAsDcwzhcqKelzKLDQociQUChAeE4hH+viCoasKs2K3CkM/YI/u\nX3SMzoW22c0zLjCSutA+JE2BeYqi5lTwfo8Ph3MoDcwzBq2GRe7lUty5aZjp2tffFXGJPeDsSiO2\nLYHdKowiWv/CKNpyoU0K6Wtjqbo+sjNnaWDeA1JeWofLWYVQyNV8m0gsRGRcEEIf8aZmUgtilwqD\nZTmUVtIVhjG0dKFNpS60D031pcuoyr7AH7tHRdHAPCNQq7S4drEEhXdlBu0BQe6I6RdC61ZYAbtU\nGGVVcrCNLneerg5wdaL7F21xt7rQwIV2UGgiPKkL7QNDCEF9/nXcP5XOt7n06gW/YUPoU/GfUFJY\ngyvZRVApNXybWCJCTEIwgnt60vGzEnapMPTrXwTT+hdtItcocPJ2swttL6+eiPClLrQPirK8HJXp\nZ6AobnYccAwIQMDoVBqY1wEqpQZXsotQUlhj0B7c0xPR8SFwcLTLKcxm2OVo0/oXHUMIQdrt01Bq\ndZ4nzhJnDKMutA+Etr4elWcyUJefb9Au9vBA0JNjaWBeOxBCUHSvGlcvFEOj1vLtDo5ixCaGIDCY\nrnRtgd0pDI4jKNHfv/ClCqMlV8rzUFjTWFiGYTDikRQ4iqjXiSlwajWqzmej+uIlvqwqoHOddY+O\nhndyfwgdqVtyWyjkalzOKkJ5qWGhtJ6PeCMqLghiid1NW50Guxv5imoFNFqdx4+rkxhuzvQJT59K\neRXO6rvQBkQgxD3QhhJ1LQjHoTYnB7KMTLAKhcE5l1694DNoICSenjaSrnPTXgpyJxcJ4hJ7wI9W\nwrM5dqcw9N1pQ/xcqZlFD50L7R+8C62vizd1oTUSQgjkd++h8vQZqKuqDM45+PnBd1AKnEKCbSRd\n56e9FOS9evsgPDYQIlrYrFNgdwqjhO5ftMuZgvOobnShFQlENAutkagq7uN++mmDIDwAELm4wGfg\nALhKH6MPJu1AOIJb1+8j/2opWJbj213cHNC3f0940xipToVdKQyOI4YJB6mHFM/d6kJcK2/hQutI\n8xl1hLa+AbKMDNTm5qMpJTkACMRiePVLgEffOAhEdvUvZhI0BXnXw67+mmW1SqgabaPOjmJ40vQB\nAAC5urULbbhvbxtK1LnhNBpUnb+A6gsXWm9oR0XBK6k/zTTbATQFedfFrhRGy3Qg1EzQ6EJ7h7rQ\nGoNuQzsXsoxzrTe0w8LgkzIQEm8vG0nX+WmoU6GspBYFt2Woq1Xy7TQFedfBrhQGNUe15nJZroEL\n7UjqQtsm8nv3cD/9DNQyw7QUDr6+8Bk0EM49ethIss4L4QhklQ0oL6lFWXEd6uuUrfrQFORdC7tS\nGGV68RchdMMb9+UyZBQ15zTqGxiJYOpCa4DqfiUqT5+GvKDQoJ1uaLeNRsOiorQOZcW1KC+tMwi6\n04emIO+a2JXCcHESo16hgY+7I7zt/IlGy2pxooULbf/gOBtL1XnQbWifQ21uHgw2tEViePaLh2ff\nOBql3UhDvUqnIEpqUVnRYFDESB+hUADfADcEBLkhINiDpvXogtjVb2zCkEdxq7gGoQFudv9UeLrg\nPKoVukha6kLbDKfRoPrCRVRnXwCn1X86ZuAeFQHv5CSInO17U5ZwBFUyOcqKa1FWUov62tampiYc\nncQICHKHf7A7fP1dqedTF8euFIajgwhRj/jYWgybc6eqEDkVzbUYBoX2t3sXWsJxqMvLh+xsBrRy\nucE559BQ+KQMhIOPt42ksz1aDYvy0jrdfkRJ+6YmAPDwckZAsDsCgtzg7ulk9w9n3Qm7UhgUoEEt\nx8k7zS60j3iFItz3URtKZHvkBYWoTD/Nl0ptwsHHBz6DUuDc0z43tOX1KpQ2mppk9xsMXGD1EQoF\n8PV3hX+wOwKC3OFIywV0W6jCsCNYjkXa7dNQNbrQukicMbRXst0+AaoqZahMPw15QYFBu8jZGd4D\nkuEWLrWr1OOmmpr8g3QKwtffFUKR/YyTPUMVRjeGIxzuy6tQVFuC4toylNZXgOUaA80YBiMeHWSX\nLrRauVy3oX0tF4Yb2iJ4JsTDM76v3WxoazUsKsqavZrUqo5MTU68kvDwoqYme4QqjG4EIQRVuZGh\nfAAAHhBJREFUihoU1ZWiqLYUJXXl0LCaNvvGB0Yh2C3AyhLaBq1cAbVMxv/U598Ap9UfFwbuEeHw\nHpAEkUv3js8hhEDeoEZ5o+urrKK+Q1OTj78rAoLcERBMTU0UqjC6NIQQ1KnqGxVEGYrryqDUtG9G\nAAAPR3dIfR9B38AoK0lpPVilslEpVBm8ssr2x8S5Zw/4pKTAwbd7OEMQQqBWsVDI1ZA36H4UDWrI\n5U3vNbwrdVs4OIoREOTGezXRLLEUfajC6GI0qOUoritDUW0piuvKUK9q6LC/i8QZIe6BCHYLQLB7\nAFwlXf8JmlWpWikFtUzWKl1HR0i8veE7aCCcQ0MtKKn5IYRAq2EblYFGpwTkzYpBIddAq2X//IP0\ncPd04lcR1NRE6QiqMDo5Sq0KJXXlvIJoSj/eHg4iB4S4ByDYLRAh7gFwd+i6MSecRmOgEJreaxs6\nVpItEYjEkHh7Nf54Q+LrA6fg4E67oc0rBLka8nqdEtBXDPrFhR4EsUQEL29n+Ae7ISDIHU7OEjNJ\nTunuUIXRydCwGpTWV/AK4r68CmgnchYAREKRbvXgFoAQ90B4O3l2OQXBaTRQV1VBU1UNtUwGVaUM\nmqoqaOrqTPocRiiExEtPMTS+itw6l9JktRzk8kZTkZ4iaFoxdBTjYAwisRDOLhI4OUsaX8VwdpXA\nufFYJKZmJsqDQRWGjWE5FuUNlbyCKK+/D460b2MWCAQIdPXjVxB+zj4QdNIn5SYIy4JVqcGplOBU\nKmhq6wxWDJraOuh7K/0ZjEAIsaeHgVKQeHtD7O5mlVVDk1lIo+GgUbPQaFiDV905tt1z+oWCHgSh\nUAAnF93k7+wsaXwv5pWEWCLsVAqS0n2gCsPKcIRDpbyqcZO6FKV1FdByHTxRMgz8XXz4FUSAqx9E\nNkjhQQgB0WjAKlXgVCqwKiU4lRqsUqcEdG0qcMoW55SqFh5JxsMwDMSengZKQeLtBbGHx0MpBkII\nOI60OaFrNI0TfgfntBqu3XxJ5kAgEMCpUQE0KwQJrxAkDlQhUGyD1RUGy7L49NNPsXfvXjQ0NGDo\n0KF455134Ovr22b/y5cv47333kNOTg4CAgKwYMECTJo0ycpSt4YQApZjoWRVUGs1ULIqqLRq3Y/B\nezVUWlXjqxoKrRJatmOTg7ezJ7+CCHL1h0RkHhszIQTgON3TvloFTqnUUwCNx40rAb5dqQSnVoNT\nKi04STIQe7jrVglenhB6eEHo4Qmhqxs4woBlObAsBzlLUNfAga2t0bVpOXAcAavl+D4cS/hzujbC\nn2O1RO+6h3vKf6hvyzA6M5FLszJoMh85u0jg4CiiCoHSKbG6wti8eTP27t2LdevWwdPTE2vWrMHi\nxYuxa9euVn1lMhnmzJmD8ePH47333kN6ejr+/ve/w9fXF0OGDDGLPBzhoGY1ukmdn+DVUGpVULNq\nKLXqxldVKwVglkmHELiLnRHs5IsgZ18EOHrDgRGB02pB6rTQVBdDpdGCaLUgLAui1erONR5z/Dkt\niJZtcU6jdx3bmEyP6N8ahH9lwBm8AoQwIAA4woBAyJ8nBOAaX5v68dc2fh7XeK3uMxhAJAJEYkCo\ne2UcnQAHRzASRxCxBBwBODkHrp4ABQoACgAlDz++FkIkFkIkEkLioHsVS4QQixtfJUKIRAJIHEQQ\niQXN7Y2vQqGAKgRKl8SqCkOtVmP79u146623MHjwYADAxx9/jNTUVJw/fx79+vUz6P/jjz/C1dUV\nf//73yEQCNC7d29cu3YNX3311QMpjNK6cly4dR5KeT1UGiXUajW0GjXAETAcAVjdDMiwBPysyREw\nLAEhAMNxYDhdCgUHjqB5doTuev4agCEEhCNgGo+h118EARwFjnAUiOEgkEAg0ICQGhTjFoqgN5G3\nmJR1bc0TfNN7tOxrcJ0ABMI2ryMwcdJiGDBCYTs/At2roO1z7UIAqB/O6+dBEAgEuom9jQm96VUk\nbt0mbmyjNRwo9ohVFUZubi4aGhqQnJzMt/Xo0QMhISHIzMxspTAyMzORlJRksKmbnJyMNWvWgBBi\n8lPa8f9vH+pua8FBoDdZMo0/psNAZ15gwOhe9d4LwIBhBI19BGAY8OdYCNDAAKY5h5oPRiAAIxZB\nINBN8hAKIWjzVQCBUNT4KgSs8FTMMAyEQgEEQgZCkQBCoQBCIdP4KoBQJIBAoH9O0Pi+6bqmNr1r\nWvRp+nz6lE+hmIZVFUZpaSkAICDAMCWFv78/f65l/6ioqFZ9FQoFqqqq4O1tWrppdakIbBveOK0n\n+o6UgN45MA+qa1oKoNvEbfUq0D3JtjwWNCqjxr6tj3X9GKatY8NbCwSMTnEImMb3zT+MgOHNJwJh\n6/NN1zZNvgJBc7+mid/gc4UMBI39hCJh8wSuN5kzAjqRUyidFasqDIVCoTMFtEjsJpFIoFKpWvVX\nKpWQSCSt+gI685ap9B+aiMsZ18GxpHGSFOgmvKYVAD+xQvcqEEDAMPx7pnESFQh0kyjfp+m1acLj\nP5vRmWr0zukmWCEEYhEEIiEYgW6y5JVT4/2bJt2m16aJl2HA922a1AVN8jJ6fZqO2/nMpmvp5Eyh\nUIzFqgrD0dERHMdBq9VCJGq+tVqthpOTU5v9WyqGpuO2+v8ZsSP7IvrxOKDJnNU4+VIoFArlz7Fq\nxFdQUBAAoKKiwqC9vLy8lZkKAAIDA9vs6+zsDDc3tweSQWcaoaYPCoVCMRWrrjAiIiLg4uKCjIwM\nTJw4EQBQWFiIoqIiJCUlteqfmJiIn3/+2WCD++zZs+jXr1+H0c0sq/O6aWtfhEKhUCht0zRnNs2h\nLbGqwpBIJJg2bRo+/PBDeHl5wcfHB2vWrEFycjLi4+OhVqtRU1MDDw8PSCQSTJ06FV988QXeffdd\nvPjii0hPT8eBAwewbdu2Du/TtCp54YUXrPG1KBQKpVtRUVGBsLCwVu0MsWSOgzbQarVYv3499u7d\nC61Wy0d6e3t74+zZs5g5cya2b9+OAQMGAAAuXLiAf/7zn8jLy0NwcDCWLFmCcePGdXgPpVKJK1eu\nwM/PD0IhTbRGoVAoxsCyLCoqKhATEwNHR8dW562uMCgUCoXSNencaU4pFAqF0mmgCoNCoVAoRkEV\nBoVCoVCMgioMCoVCoRgFVRhWhmVZbNiwAUOGDEFCQgKWLFmC+/fvt9v/0KFDmDhxIuLj4zF69Gh8\n/vnn7fpI2wOmjp8+8+bNw4wZMywsYefH1DEsLS3FkiVLkJCQgJSUFKxevRoKhcKKEncuTB2/06dP\nY+rUqYiPj8eoUaOwbds2ixbgsiiEYlU++eQTMnjwYHLq1Cly5coV8swzz5Dnnnuuzb5paWkkMjKS\n7Nixg9y9e5ccPnyY9O/fn2zZssXKUnceTBk/fXbt2kWkUimZPn26FaTs3JgyhiqViowdO5bMmDGD\n5OTkkNOnT5Phw4eTNWvWWFnqzoMp43fnzh0SFxdHNm/eTO7du0cOHz5M+vbtS3bu3Gllqc0DVRhW\nRKVSkYSEBPLTTz/xbQUFBUQqlZKsrKxW/V955RXy6quvGrRt2bKFjBw50uKydkZMHb8m7ty5Q5KT\nk8mzzz5r9wrD1DHcs2cPSUxMJNXV1QZtU6ZMsYq8nQ1Tx2/Hjh0kOTnZoG3JkiVk3rx5FpfVElCT\nlBX5s3ogLZk/fz4WLVpk0CYQCFBbW2txWTsjpo4foDMfvPHGG5gzZw569+5tLVE7LaaO4alTpzBo\n0CB4eHjwbVOmTMGePXusIm9nw9Tx8/b2RnV1NQ4cOACO45Cfn4/MzEzExMRYU2yzQRWGFTG1Hkhc\nXBz69OnDH9fX12PXrl0YOnSoZQXtpJg6fgDw73//GwDw8ssvW1a4LoKpY3jnzh2EhITg008/xciR\nI5Gamop169a1WY7AHjB1/MaMGYOpU6di+fLliImJwYQJE5CUlIQFCxZYRV5zQxWGFTG1HkjLaxcs\nWACVSoVly5ZZUsxOi6njd+XKFXz99ddYt25dh8kq7QlTx7C+vh579uxBQUEBNm7ciFWrVuHQoUN4\n++23rSVyp8LU8autrUVRURHmzJmDPXv2YN26dUhPT8eWLVusJbJZsWryQXvH1HogTchkMixYsAA3\nbtzAV199hZCQEGuI2+kwZfxUKhVef/11LF26tM0kavaKqX+DIpEIHh4e+PDDDyEUChEbGwutVotX\nX30Vq1atgpeXlzXFtzmmjt/69eshFAqxfPlyAEBUVBS0Wi1Wr16NGTNmdLnxo49dVsTUeiCALv37\n888/j8LCQuzcuRNxcXEWl7OzYsr4Xbx4ETdv3sT69euRkJCAhIQE7Nu3D5mZmUhISEBxcbHV5O5M\nmPo3GBAQgN69exsk8WwykxYVFVlQ0s6JqeN38eLFVvsVffv2hUajQUlJieUEtRBUYVgR/XogTXRU\nD6SyshIzZ84Ex3HYtWsXIiIirClup8OU8YuLi8PRo0exb98+/mfUqFGIiYnBvn374O/vb23xOwWm\n/g32798fOTk50Gg0fFt+fj6EQqFdrnRNHb/AwEDk5eUZtF2/fh0CgQChoaEWl9fcCFevXr3a1kLY\nC0KhEHV1dfjyyy/x2GOPob6+Hm+++SbCwsKwYMECqNVqyGQyiMViCIVCrFy5Enl5efjss8/g5eUF\nuVwOuVwOhUIBZ2dnW38dq2PK+Dk4OMDT09Pg59SpU2hoaMCsWbPsdk/D1L/BRx99FNu3b0deXh76\n9OmD3NxcrF27FqNGjcL48eNt/XWsjqnj5+npiS1btkAgECAwMBDnz5/H2rVrMWnSJIwePdrWX8d0\nbO3Xa29oNBrywQcfkOTkZNKvXz/y6quvksrKSkIIIWfOnCFSqZScOXOGKBQKEhERQaRSaaufyMhI\nG38L22Hs+LXFm2++afdxGISYPobXr18ns2fPJnFxcWTgwIHk/fffJyqVylbi2xxTx+/YsWNk8uTJ\nJD4+nowaNYps3ryZqNVqW4n/UNB6GBQKhUIxCvtcl1MoFArFZKjCoFAoFIpRUIVBoVAoFKOgCoNC\noVAoRkEVBoVCoVCMgioMSqfGUk581Dmw80F/J50fqjAoRnHx4kUsW7YMw4cPR1xcHEaPHo1//OMf\nKCsrs8j9ysrKMG/ePLOnn6irq8PKlSvbTYfe1ZgxYwZeeuklq91v69atePPNNwEAP//8M8LDw9vN\nFGwKP/30E9atW2fSNTU1NRg5ciQKCgoe+v4U46AKg/KnfPvtt3j++edRU1ODFStWYNu2bZg1axbS\n0tIwZcoU3Llzx+z3PHPmDNLS0sz+uXl5edi7dy84jjP7Z3d38vPz8fXXX1skW/LWrVtRXV1t0jUe\nHh6YPXs23nzzTbo6sRJUYVA6JCsrC//617/w4osv4osvvsD48eMxYMAATJs2Dbt27eIzb1K6P+vX\nr8fEiRPh4+Nja1F4/vrXv+LGjRs4duyYrUWxC6jCoHTIl19+CU9PTyxdurTVuYCAAKxcuRIpKSnQ\narUAAK1Wi2+++Qbjxo1DXFwcUlNT8dlnn4FlWf66GTNm4J133sHWrVsxfPhwxMbG4rnnnsPly5cB\n6Ewdr7/+OgAgNTUVK1euBADI5XJ89NFHGDNmDGJiYtCvXz+8/PLLyM3NNZDr5MmTeO655xAfH4+h\nQ4fin//8JxoaGnD27Fm88MILAICZM2dixowZAIB79+7hlVdewYABA9C3b188++yzOHnyZIfjMnLk\nSGzcuBGrV69Gv379MHDgQKxevRoKhcKg37Fjx/D0008jNjYWQ4YMwbp166BWq/nzmzdvxtixY7Fp\n0yYkJSVh2LBhaGhoaPOexcXFWLRoERITEzF48GB8/fXXrfrIZDK8++67GDFiBGJiYpCcnIzFixfz\npr3vvvsO4eHhrcw433//PWJjY9t9ys/Pz8fJkyfbzB+VkZGBCRMmIDY2Fk8//TR+++03g/NVVVV4\n6623kJKSgri4ODz//PPIysoyGMt79+5h7969CA8PR2FhIQDg7NmzmD17NpKSkhATE4PU1FRs2bLF\nYHUokUgwZswYvlAWxcLYMi8JpXPDcRyJjY0lr732mtHXvP766yQ6Opps3ryZnDp1imzatIlER0eT\nVatW8X2mT59OEhMTybPPPkuOHTtGjh49SlJTU8mIESMIy7KksrKSbN68mUilUnL06FFy9+5dQggh\nCxcuJIMGDSJ79uwhZ8+eJT/88AMZPHgwGTduHOE4jhBCyIkTJ0h4eDhZvHgxSUtLI3v27CFJSUlk\nyZIlpK6ujnz//fdEKpWSnTt3kuvXrxOWZcnYsWPJzJkzSVpaGjl16hSZO3cuiYyM5O/bFiNGjCCJ\niYnkueeeI8ePHyfffPMN6du3L1m4cCHfZ//+/UQqlZLXX3+dnDx5kuzYsYMkJCSQRYsW8X02bdpE\noqKiyF//+ldy6tQpcujQoTbv19DQQEaMGEFGjx5NDh48SA4fPkz+8pe/kOjoaPLiiy/yv6/JkyeT\nMWPGkAMHDpAzZ86Qb7/9liQkJJA5c+YQQgiprq4mMTExZPPmzQaf/+yzz5LFixe3+33Xr19PHn/8\ncYO2n376iUilUhIfH0+2bdtG0tLSyJw5c0hkZCRf31qpVJKnnnqKDB48mPzwww/k119/JfPmzSMx\nMTHk4sWLhBBCrl69SoYNG0b+3//7fyQ7O5uoVCpy5coVEhUVRZYvX05OnTpFfvvtN7JixQoilUrJ\nwYMHDeRIT08nUqmU3L59u135KeaBKgxKu1RWVhKpVEo++ugjo/rn5+cTqVRKvvjiC4P2zz//nEil\nUpKXl0cI0SmM+Ph4UldXx/fZu3cvkUqlJCcnhxBCyL59+4hUKiUFBQWEEN3EM3v2bHL48GGDz/7q\nq6+IVCrlk79NnjyZTJkyxaDPnj17yJNPPknq6urIuXPnDJLDlZeXE6lUSvbv38/3r62tJe+//z7J\nz89v97uOGDGCDBgwgNTX1/NtO3bsIFKplOTn5xOO48iwYcPIvHnzDK47duwYkUqlJDMzkxCiUxhS\nqZRkZ2e3ey9CCNm5cyeJiIggN27c4NuKi4sNFEZJSQmZPn06P1k3sXbtWtK3b1/+eOnSpWTMmDH8\n8e3bt4lUKiW//vpru/efOnVqK4XSpDC+/vprvk2tVpPhw4eT+fPnE0II2b17NwkPDyeXLl3i+7As\nSyZPnkxeeuklvm3UqFHkjTfe4I9//vlnMnfuXP5BoOm6xMREsnr1agM5amtriVQqJd9//3278lPM\nAzVJUdqlqWiOvjmpI86dOwcArcwWTz31lMF5AAgPD4erqyt/3FR8Ri6Xt/nZDg4O+PLLLzF27FiU\nlZXhzJkz+P777/Hrr78CADQaDZRKJa5du9YqbfSUKVNw8OBBg/s14evriz59+uDtt9/GG2+8gf/+\n97/gOA6rVq3CY4891uH3HTlyJFxcXPjjMWPGAAAyMzNx69YtlJaWYuTIkdBqtfzP0KFDIRaLkZ6e\nbvBZkZGRHd4rMzMTYWFh6N27N98WFBSE+Ph4/jgwMBA7duxAQkICCgsL8ccff2DHjh04f/68QT2L\nJkeFixcvAgD27dsHPz+/DmvFFxQUtFv/YuzYsfx7sViMYcOG8San06dPIyAgAJGRkfwYcByHESNG\n4Ny5cwbmOX0mT56Mf//731Cr1cjNzcXRo0exadMmsCxr8F0AwM3NDe7u7nZZ0Mna0BKtlHbx8PCA\ni4tLh9Xp6uvrAQCurq6oqakBgFabok3HdXV1fJujo6NBn6b6FB15L/3+++94//33cevWLbi4uCAi\nIoKvC0IIQU1NDQgh8Pb2NvYrgmEYfPXVV/jss89w7Ngx7Nu3D2KxGKNGjcKaNWvg4eHR7rUtizA1\n3be2tpbfC3j77bfbrH9dXl7OvxcKhXBwcOhQzpqamja/l5+fH6qqqvjj/fv34+OPP0ZJSQk8PT0R\nGRkJR0dHAy+iQYMGISgoCP/5z38QFxeH/fv346mnnjKoqteS+vr6dmuwtPx9e3t7838X1dXVKC0t\nRXR0dJvXVlVVtVmpTqlUYu3atfjPf/4DrVaLHj16ICEhASKRqE2PKCcnJ4O/L4ploAqD0iFDhgzB\n2bNnoVKp2pzUvvnmG/zf//0fDh8+DHd3dwC6SoH6k0BTOcuHqV987949LFy4EKNHj8bnn3+OHj16\ngGEYfPfdd/j9998BgF9B6E+ggG6yy87ONnga1ycgIACrV6/Gu+++i9zcXPzyyy/Ytm0bfHx82pzs\nm2i5QVxZWQlAN2G6ubkBAFatWoXExMRW15o6Fl5eXrhy5UqHMmRmZuKNN97Aiy++iFmzZvG/gw8/\n/BAXLlzg+wkEAkyaNAk//vgjJkyYgKKiIjz99NMd3t/T0xO1tbVtnqutrTVQGvfv3+eVm5ubG3r3\n7t1ujEV74/Dee+/h6NGj2LhxI1JSUnhllZKS0q4MXa0+dleEmqQoHTJr1ixUV1dj48aNrc4VFxfj\nu+++Q1xcHMLCwvgSlQcOHDDo13Tc1sTZHi2fdq9cuQKVSoVXXnkFPXv2BMMwAMArC47j+FXHiRMn\nDK793//+hzlz5qCurq7V5166dAmDBg3CpUuXwDAMIiMj8dprr0Eqlf5pzeXff/+d9w4DgCNHjoBh\nGAwcOBC9e/eGt7c3ioqKEBsby/94eXlh/fr1uHnzptFjAQADBw7E3bt3kZOTw7fJZDIDRZCdnQ2O\n47B48WJeWbAsi/T09FYrtylTpqCyshIff/wxYmNj+Trd7RESEtJukGbT7wDQrQzS0tKQnJwMAEhK\nSkJxcTH8/f0NxuH48ePYsWMHxGIxALSqgJiVlYWUlBSkpqbyyuLKlSuQyWStvktNTQ0UCgVfb5ti\nOegKg9IhCQkJWLhwIbZs2YJbt25h4sSJ8PT0RG5uLr788ksIBAJ89NFHAACpVIqnnnoKn3zyCRQK\nBRISEpCdnY2tW7fiqaee+tNJSZ+mJ/Rjx45h2LBhiI6OhkgkwkcffYSXXnoJKpUKP//8Mx/c1+TO\numTJEixcuBDLly/HxIkTUVpaig0bNmDSpEkIDg7m+6WlpcHDw4M3a73++utYvHgxfH19kZ6ejpyc\nHMyaNatDGYuKirBo0SJMmzYNN2/exKeffoqpU6eiZ8+eAIClS5dizZo1EAgEGDZsGKqrq7F582bU\n1dUhKirKpN/DxIkTsX37dsyfPx+vvfYaXFxc8NlnnxlMnnFxcQDAlwCtqanBzp07kZubC0IIlEol\nbwrs2bMnkpKSkJGRgXfeeedP7z948GD88MMPbZ7bsGEDtFot/Pz88OWXX6K+vh4LFiwAADz99NPY\nuXMnZs2ahXnz5iEgIABpaWn4+uuvsWjRIl7xu7u749q1a8jIyEBcXBzi4uLwyy+/YPfu3XjkkUeQ\nm5uLzz77DAzDtHJdPn/+PADdaphiYWy65U7pMhw/fpzMnj2bDB48mMTGxpIxY8aQtWvXkrKyMoN+\nGo2GbN68mYwYMYJER0eT0aNHk61btxKtVsv3mT59Ou/Z00RTactz584RQgiRy+Vk1qxZJDo6mvc0\nOnz4MBk3bhyJjY0lQ4YMIYsWLSIZGRkkPDzcwEPm+PHjZPLkySQmJoYMHz6cbNiwgSiVSkKIztPm\nb3/7G4mNjSXjx48nhBBy584dsnjxYpKSkkKio6PJuHHjyO7duzscjxEjRpDly5eTt956i8THx5Mh\nQ4aQjRs3GnxPQgg5cOAAmTRpEomJiSEDBw4kixcvNnD/3LRpk9EldysrK8myZctI//79SVJSEvno\no4/Iq6++ajCWO3fuJKmpqSQmJoY8/vjj5I033uA9s37//XeDz/viiy9ITEwMqa6u/tN75+TkEKlU\nSq5evcq3NXlJpaWlkSeeeIJER0eTZ599lneXbaKiooKsXLmSDBw4kMTGxpInn3yS7Nixw6DPwYMH\nSUpKComJiSFZWVmkqqqK/O1vfyPJyckkPj6ejB8/nnz77bfk7bffJsOGDSMsy/LXrl69mkydOtWo\nMaQ8HLREK4XyAIwcORIpKSl47733bC3KAzNz5kz4+flhw4YNRvWfO3cugoKCsGbNGgtLZjwKhQJD\nhw7Fv/71L4waNcrW4nR76B4GhWJnbNmyBa+99hoyMjL+1Oymz2uvvYZDhw4ZeHjZmt27d6NPnz5I\nTU21tSh2AVUYFIqdceLECZw6dQqrVq1CTEyM0ddFRkbipZdewieffGJB6Yynuroa33zzDdatW8fv\nhVAsCzVJUSgUCsUo6AqDQqFQKEZBFQaFQqFQjIIqDAqFQqEYBVUYFAqFQjEKqjAoFAqFYhRUYVAo\nFArFKP5/H1F/DqvcupYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61cd80470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    infected_sweep = sweep_beta(beta_array, gamma)\n",
+    "    label = 'gamma = ' + str(gamma)\n",
+    "    plot(infected_sweep, label=label)\n",
+    "    \n",
+    "decorate(xlabel='Contacts per day (beta)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         loc='upper left')\n",
+    "\n",
+    "savefig('chap06-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now wrap that loop in a function and store the results in a `SweepSeriesFrame`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_parameters(beta_array, gamma_array):\n",
+    "    \"\"\"SweepSeriess a range of values for beta and gamma.\n",
+    "    \n",
+    "    beta_array: array of infection rates\n",
+    "    gamma_array: array of recovery rates\n",
+    "    \n",
+    "    returns: SweepSeriesFrame with one row for each beta\n",
+    "             and one column for each gamma\n",
+    "    \"\"\"\n",
+    "    frame = SweepFrame(columns=gamma_array)\n",
+    "    for gamma in gamma_array:\n",
+    "        frame[gamma] = sweep_beta(beta_array, gamma)\n",
+    "    return frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0.1</th>\n",
+       "      <th>0.3</th>\n",
+       "      <th>0.5</th>\n",
+       "      <th>0.7</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.10</th>\n",
+       "      <td>0.084693</td>\n",
+       "      <td>0.005444</td>\n",
+       "      <td>0.002736</td>\n",
+       "      <td>0.001827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.18</th>\n",
+       "      <td>0.708623</td>\n",
+       "      <td>0.015914</td>\n",
+       "      <td>0.006118</td>\n",
+       "      <td>0.003783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.26</th>\n",
+       "      <td>0.900780</td>\n",
+       "      <td>0.055380</td>\n",
+       "      <td>0.011639</td>\n",
+       "      <td>0.006427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.34</th>\n",
+       "      <td>0.956888</td>\n",
+       "      <td>0.267864</td>\n",
+       "      <td>0.022115</td>\n",
+       "      <td>0.010191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.42</th>\n",
+       "      <td>0.977045</td>\n",
+       "      <td>0.524563</td>\n",
+       "      <td>0.047816</td>\n",
+       "      <td>0.015946</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           0.1       0.3       0.5       0.7\n",
+       "0.10  0.084693  0.005444  0.002736  0.001827\n",
+       "0.18  0.708623  0.015914  0.006118  0.003783\n",
+       "0.26  0.900780  0.055380  0.011639  0.006427\n",
+       "0.34  0.956888  0.267864  0.022115  0.010191\n",
+       "0.42  0.977045  0.524563  0.047816  0.015946"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frame = sweep_parameters(beta_array, gamma_array)\n",
+    "frame.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we can plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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TwQ1bIQ53RWJohYWLb9y4wYgRI4iPjycgIKCph9Mg5Ofnc/r0aQYNGoRMZlwop1KpiIyM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\nFArFKP5/H1F/DqvcupYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61cd90208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for gamma in gamma_array:\n",
+    "    label = 'gamma = ' + str(gamma)\n",
+    "    plot(frame[gamma], label=label)\n",
+    "    \n",
+    "decorate(xlabel='Contacts per day (beta)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         loc='upper left')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's often useful to separate the code that generates results from the code that plots the results, so we can run the simulations once, save the results, and then use them for different analysis, visualization, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contact number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After running the SweepSeriess, we have a `SweepSeriesFrame` with one row for each value of `beta` and one column for each value of `gamma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11, 4)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frame.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following loop shows how we can loop through the columns and rows of the `SweepSeriesFrame`.  With 11 rows and 4 columns, there are 44 elements.\n",
+    "\n",
+    "One implementation note: when we select a column from a `SweepSeriesFrame` we get a `Series` object, rather than a `SweepSeries` object, but they are almost the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 0.1 0.0846929424381\n",
+      "0.18 0.1 0.70862278537\n",
+      "0.26 0.1 0.900780251778\n",
+      "0.34 0.1 0.956887899544\n",
+      "0.42 0.1 0.977045257074\n",
+      "0.5 0.1 0.984595862826\n",
+      "0.58 0.1 0.987400345318\n",
+      "0.66 0.1 0.988404249064\n",
+      "0.74 0.1 0.988743421406\n",
+      "0.82 0.1 0.988849515052\n",
+      "0.9 0.1 0.988879570517\n",
+      "0.1 0.3 0.00544355912239\n",
+      "0.18 0.3 0.0159140691448\n",
+      "0.26 0.3 0.0553797621068\n",
+      "0.34 0.3 0.267864167733\n",
+      "0.42 0.3 0.524562935844\n",
+      "0.5 0.3 0.686050483916\n",
+      "0.58 0.3 0.788378556339\n",
+      "0.66 0.3 0.85506574641\n",
+      "0.74 0.3 0.89947913569\n",
+      "0.82 0.3 0.929469302619\n",
+      "0.9 0.3 0.949853310327\n",
+      "0.1 0.5 0.00273576554115\n",
+      "0.18 0.5 0.00611834135832\n",
+      "0.26 0.5 0.0116394693217\n",
+      "0.34 0.5 0.0221147665242\n",
+      "0.42 0.5 0.0478162266689\n",
+      "0.5 0.5 0.132438038458\n",
+      "0.58 0.5 0.303264192648\n",
+      "0.66 0.5 0.464110227319\n",
+      "0.74 0.5 0.588476972528\n",
+      "0.82 0.5 0.682749610978\n",
+      "0.9 0.5 0.754595298329\n",
+      "0.1 0.7 0.001826769347\n",
+      "0.18 0.7 0.00378256160842\n",
+      "0.26 0.7 0.00642667221076\n",
+      "0.34 0.7 0.0101905519335\n",
+      "0.42 0.7 0.0159458265615\n",
+      "0.5 0.7 0.0257079250464\n",
+      "0.58 0.7 0.0450077531168\n",
+      "0.66 0.7 0.0906940688294\n",
+      "0.74 0.7 0.189795211656\n",
+      "0.82 0.7 0.318343186735\n",
+      "0.9 0.7 0.436999374456\n"
+     ]
+    }
+   ],
+   "source": [
+    "for gamma in frame.columns:\n",
+    "    series = frame[gamma]\n",
+    "    for beta in series.index:\n",
+    "        frac_infected = series[beta]\n",
+    "        print(beta, gamma, frac_infected)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can wrap that loop in a function and plot the results.  For each element of the `SweepSeriesFrame`, we have `beta`, `gamma`, and `frac_infected`, and we plot `beta/gamma` on the x-axis and `frac_infected` on the y-axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_sweep_frame(frame):\n",
+    "    \"\"\"Plots the values from a parameter SweepSeries.\n",
+    "    \n",
+    "    For each (beta, gamma), computes the contact number,\n",
+    "    beta/gamma\n",
+    "    \n",
+    "    frame: SweepFrame with one row per beta, one column per gamma\n",
+    "    \"\"\"\n",
+    "    for gamma in frame.columns:\n",
+    "        series = frame[gamma]\n",
+    "        for beta in series.index:\n",
+    "            frac_infected = series[beta]\n",
+    "            plot(beta/gamma, frac_infected, 'ro',\n",
+    "                 label='Simulation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what it looks like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap06-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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LFi1CQEAAfHx8EBUVhRs3bpR7/M6dOxEaGgpvb2906dIFq1evRklJiQEjJiIi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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61cf58198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "\n",
+    "decorate(xlabel='Contact number (beta/gamma)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap06-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It turns out that the ratio `beta/gamma`, called the \"contact number\" is sufficient to predict the total number of infections; we don't have to know `beta` and `gamma` separately.\n",
+    "\n",
+    "We can see that in the previous plot: when we plot the fraction infected versus the contact number, the results fall close to a curve.\n",
+    "\n",
+    "But if we didn't know about the contact number, we might have explored other possibilities, like the difference between `beta` and `gamma`, rather than their ratio."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Write a version of `plot_sweep_frame`, called `plot_sweep_frame_difference`, that plots the fraction infected versus the difference `beta-gamma`.\n",
+    "\n",
+    "What do the results look like, and what does that imply? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_sweep_frame_difference(frame):\n",
+    "    \"\"\"Plots the values from a parameter SweepSeries.\n",
+    "    \n",
+    "    For each (beta, gamma), computes the contact number,\n",
+    "    beta/gamma\n",
+    "    \n",
+    "    frame: SweepFrame with one row per beta, one column per gamma\n",
+    "    \"\"\"\n",
+    "    for gamma in frame.columns:\n",
+    "        series = frame[gamma]\n",
+    "        for beta in series.index:\n",
+    "            frac_infected = series[beta]\n",
+    "            plot(beta-gamma, frac_infected, 'ro',\n",
+    "                 label='Simulation')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap06-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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UwN3dnUuztrZG06ZNkZCQoJT/7Nmz6NixI+rUqcOlDRw4ELGxsYL2RwghRHMENXq3atUK\nv//+Ozw9PZW2nT59Gi1bthS0s7S0NADgZsFVUMyEW9qjR4/Qvn17rF69Gr/99htEIhF69OiBqVOn\nqh15TggpA63FTd6DoIAxYcIEBAUFITc3F127doVIJEJiYiJ+++037Nq1C8uWLRO0s8LCQhgZGcHY\n2JiXbmJiguLiYqX8+fn5iI2NRefOnbFmzRqkp6fjm2++QVZWluB9EkL+h9biJu9JUMDo3r07li9f\njpUrV+LEiRMAgO+++w6WlpZYsGABPv30U0E7MzU1hVwuh1Qq5bWFSCQSlYswVa9eHXXq1MGyZctQ\nrVo1tG3bFlKpFFOmTMHcuXNhYWEhaL+EEJS9FjcFDCKA2oCxefNm9O3bl3t81KdPH/Tp0wcPHz5E\nTk4OateujZYtW8LISFAzCACgcePGAIDMzEzudwDIyMhQekwFlDy6qlGjBqpVq8altWrVCkDJOuMU\nMAgpB1qLm7wntVf79evXIyUlBQBga2vL9Uz66KOP4OLigo8//rhcwQIAbGxsUKtWLV6vqpSUFKSm\npsJNxTecdu3a4datW3j9+jWXdvfuXVSrVg1NmzYt174JMXhvfEnjobW4iUBq7zDMzc2xfft2PHny\nBIwxxMfH4+HDh2oL6tev31t3ZmJigqFDh2LZsmWwsLCAlZUVQkND4e7uDicnJ0gkEuTm5qJOnTow\nMTHB4MGDsXPnTsyePRuBgYFIT0/H8uXL4efnR3cXhJQXrcVN3pPagBEQEIClS5fi2LFjEIlEiIyM\nVFuISCQSFDAAYOrUqZBKpZg5cyakUim8vLywYMECACVTj4wcORLR0dHw8PBAvXr1sGvXLixZsgQD\nBgxAzZo10bdvX0yfPr2ch0kIobW4yfsSMcWwbRUKCwvx8uVLeHt7IyoqCra2tmoLUtUGUVFSUlLg\n6+uL48ePw9rauqKrQwghlcLbrp1l9pIyMzODmZkZlixZAkdHR3oMRAghBkxQt9r+/fsjPz8fJ06c\nQGFhIeRyuVKePn36aLxyhBBC9IeggHH27FlMnjwZhYWFUPUESyQSUcAghJAqTlDAWLFiBVq0aIG5\nc+eiYcOG5e5OSwghpPITFDAePHiAyMhItGvXTtv1IYQQoqcE3So0adJE8Iy0hBBCqiZBdxj//e9/\nsX79ejg6OqIJjQolRHNo9lhSiQgKGH/88QfS09Ph6+uL+vXrq5wo8PDhwxqvHCFVGs0eSyoZQQGj\nfv366Natm7brQohhodljSSUjKGAsWbJE2/UgxPDQ7LGkklEbMNLT02FlZYXq1asjPT39rQXp09Qg\nhFQKjRuXPIYqjdoJiZ5SGzC6dOmCvXv3wsHBAd7e3hCJRGUWdOvWLY1XjpAqjWaPJZWM2oDx/fff\no1mzZtzvbwsYhJByotljSSWjNmD079+f+33AgAE6qQwhBsfNjQIEqTRojg9CCCGCUMAghBAiCAUM\nQgghglDAIIQQIggFDEIIIYIIGunNGMO+ffsQHx+PV69eKS2iJBKJsHXrVq1UkBCDRxMUEj0hKGCs\nWrUKmzdvhrW1NRo1akRjMgjRFZqgkOgRQQEjLi4OY8aMwezZs7VdH0LIm2iCQqJHBLVh5Ofno2vX\nrtquCyGkNJqgkOgRQQHD2dkZiYmJ2q4LIaS0xo1Vp9MEhaQCCHokNX78eEyfPh1SqRQuLi4wNTVV\nyuPi4qLxyhFi8IROUEgN40QHBAWMUaNGAQDWrVsHALxGb8YYRCIRzVZLiDYImaCQGsaJjggKGNHR\n0dquByFEnbdNUEgN40RHBAUMd3d3bdeDEPKuqGGc6IiggAEADx48QEREBC5fvoy8vDxYWFigXbt2\nmDhxIlq1aqXNOhJCykIr9xEdERQw7ty5gyFDhsDMzAy+vr6wsrJCZmYmTp48iZMnT2LPnj1o3bq1\ntutKCFGFVu4jOiIoYKxYsQIfffQRoqOjUbNmTS791atXGD16NFavXo0NGzYI2qFMJsPq1asRFxeH\ngoICeHl5YcGCBahXr95b3xsQEIBXr15h586dgvZFiEGglfuIjggah5GQkIDx48fzggUA1KxZE/7+\n/khISBC8w4iICMTFxSEsLAwxMTFIS0tDUFDQW9+3Z88exMfHC94PIQbFzQ0ICQE2bCj5ScGCaIGg\ngGFmZqZ2m0gkgkwmE7QziUSC6OhoBAcHo1OnTrCzs8OqVauQmJhY5sDAx48fIzw8HM7OzoL2Qwgh\nRPMEBQwnJyds3rwZxcXFvPSioiJs2bJF8IX89u3bKCgo4PW6sra2RtOmTdXepchkMsyePRv+/v5o\n2bKloP0QQgjRPEFtGNOnT8egQYPg6+sLHx8f1KtXD8+fP8eJEydQUFCAXbt2CdpZWloaAKBhw4a8\n9AYNGnDbStu4cSMAYNy4cQgJCRG0H0IIIZonKGC0bNkSe/bswfr163H8+HHk5ubigw8+gJubGwID\nAyEWiwXtrLCwEEZGRjA2Nualm5iYKN29AEBycjK2b9+O2NhYGBnRWk+EEFKRBI/DaN26NdauXfte\nOzM1NYVcLodUKkX16v/uWiKRKLWTFBcXY9asWZg6dSqaN2/+XvslhBDy/tQGjP3798PLywt169bF\n/v3731pQnz593pqn8f9m3szMzOR+B4CMjAylx1TXrl3DgwcPsGLFCqxYsQJASWCRy+VwdnbG77//\njiY0MIkQQnRGbcCYOXMmfvrpJ9StWxczZ84ssxCRSCQoYNjY2KBWrVq4fPky/Pz8AAApKSlITU2F\nW6lugA4ODjhy5AgvbdWqVXj27BlWrFiBBg0avHV/hBBCNEdtwDh+/Djq16/P/a4JJiYmGDp0KJYt\nWwYLCwtYWVkhNDQU7u7ucHJygkQiQW5uLurUqQNTU1OlR1Hm5uYq0wkhhGif2pbkpk2bwsTEBABw\n5coV1KxZE02bNlX6Z2JigsOHDwve4dSpU9GnTx/MnDkTI0eORJMmTbBmzRoAQFJSEjw9PZGUlPSe\nh0UIIUTTRIwx9rZMtra22Lt3LxwcHJS2nT59GoGBgbhx44ZWKvguUlJS4Ovri+PHj8Pa2rqiq0MI\nIZXC266dah9JBQQE4P79+wBKFkkKDAzk7jjelJWVhQ8//FCDVSaEEKKP1AaMCRMmIDY2FgAQGxuL\ntm3bwtLSkpfHyMgIH3zwAfr376/dWhJCCKlwagOGk5MTnJycAJRMzzFx4kQ0a9ZMZxUjhBCiXwQN\nn16yZAkePnyIsLAwLu369esYM2YMLl68qLXKEUII0R+CAsbBgwcxfvx4PHjwgEszMzODXC7HuHHj\ncPr0aa1VkBBCiH4QFDCioqIwbNgwbNq0iUv7+OOP8cMPP2Dw4MHvPWUIIYQQ/ScoYDx58gTdunVT\nua1bt268Ow9CCCFVk6DJB62srPDXX3+hffv2Stvu3LmDOnXqaLxihBiEK1eAQ4eAf/4BGjcuWZ+b\nVssjekpQwOjTpw/WrVuHmjVronv37rCyskJ2djZOnDiBiIgIDB06VNv1JKTquXIF2LLl39epqf++\npqBB9JCggBEYGIiHDx8iNDQUixcv5tIZY+jRowcmT56stQoSUmUdOqQ6/Y8/KGAQvSQoYBgbG2Pt\n2rW4e/curl69itzcXNSuXRuurq6wsbHRdh0JqZr++Ud1+rNnuq0HIQIJXkAJAMRiscrV9QoKClCr\nVi2NVYqQKuFt7RONG5c8hiqN1nkhekpQwJBIJNi5cyeuXLmC169fQzFfoVwuR2FhIe7cuYM///xT\nqxUlpFIR0j7Rqxc/j0LPntqvHyHvQFDAWLFiBaKjoyEWi5GdnY0aNWrA0tISd+/exevXrzFp0iRt\n15OQykVI+4Ti5x9/lDyGatKkJFhQ+wXRU4ICxuHDhzFmzBjMnj0bUVFRuHXrFtasWYP09HQMHz4c\ncrlc2/UkpHIR2j7h5kYBglQaggbuZWVloXPnzgBK2jEUa180bNgQX331FQ4ePKi9GhJSGb2xZj0P\ntU+QSkxQwKhduzZev34NAGjevDn++ecf5OfnAwD+85//4B9136YIMVS9eqlOp/YJUokJChiurq6I\niYlBUVERmjdvDjMzMxw7dgwAcO3aNZibm2u1koRUOm5ugL8/YG0NGBmV/PT3p8dPpFITPHBv+PDh\n+OqrrxAdHY2hQ4diwYIF2LVrF27evIkhQ4Zou56E6BchU3pQ+wSpYgQFDFtbWxw8eBB3794FAEyf\nPh3m5uZITEzEhAkT8NVXX2m1koToFZrSgxgoQQHjm2++Qb9+/eDl5QUAEIlEGD9+vFYrRojeoik9\niIES1IYRGxuLly9farsuhFQONKUHMVCCAoajoyMSEhK0XRdCKgfqMksMlKBHUnZ2dti8eTMOHz4M\nW1tb1KxZk7ddJBLxZrElpEqjKT2IgRI80rtBgwYoKipCUlKS0naRSKTxihGit2hKD2KgBAWMEydO\naLsehFQu1GWWGCC1bRgXLlxAQUGBLutCCCFEj6kNGGPHjsWDBw94aXv37sWLFy+0XilCCCH6R23A\nUKx5oSCTybBo0SI8o66DhBBikAR1q1UoHUQIIYQYjnIFDEIIIYZL5wFDJpNh5cqV8PT0hLOzMyZP\nnoznz5+rzX/w4EH4+fnByckJ3bt3x6ZNmyCTyXRYY0IIIcA7BIz3HXMRERGBuLg4hIWFISYmBmlp\naQgKClKZ99SpU5gxYwY+//xz/Pbbb5g+fTo2b96MqKio96oDIYSQ8itzHMaUKVNgYmLCSwsMDFRK\nA0oG972NRCJBdHQ05s+fj06dOgEAVq1aBV9fXyQmJsLFxYWXf8+ePejRoweGDx8OAPjwww/x4MED\n7Nu3D4GBgW/dHyGEEM1RGzD69++vlFb6gl5et2/fRkFBAdzd3bk0a2trNG3aFAkJCUrlT5gwQWka\nEiMjI5oIkRBCKoDagLFkyRKN7ywtLQ1AyVrgb2rQoAG37U0ODg681/n5+di9ezc3zTohFUbIAkqE\nVDGCpgbRlMLCQhgZGcHY2JiXbmJiguLi4re+d+LEiSguLsb06dO1WU1CykYLKBEDpdNeUqamppDL\n5ZBKpbx0iUQCMzMzte/Lzs7GmDFjcPPmTWzevBlNmzbVdlUJUa+sBZQIqcJ0GjAa/28dgczMTF56\nRkaG0mMqhZSUFAwZMgQpKSmIiYlRekxFiM7RAkrEQOk0YNjY2KBWrVq4fPkyl5aSkoLU1FS4qbiV\nz8rKwsiRIyGXy7F7927Y2NjosrqEqEYLKBEDpdM2DBMTEwwdOhTLli2DhYUFrKysEBoaCnd3dzg5\nOUEikSA3Nxd16tSBiYkJQkND8eLFC/zwww8wNTXl7kxEIhHq1auny6oT8i9aQIkYKJ0GDACYOnUq\npFIpZs6cCalUCi8vLyxYsAAAkJSUhJEjRyI6OhqOjo44evQo5HI5Pv/8c14Z1apVw82bN3VddUJK\n0AJKxECJWBWcUTAlJQW+vr44fvw4rK2tK7o6hBBSKbzt2kmTDxJCCBGEAgYhhBBBKGAQQggRhAIG\nIYQQQXTeS4oQvUZzRBGiFgUMQhRojihCykSPpAhRoDmiCCkTBQxCFGiOKELKRAGDEAWaI4qQMlHA\nIEShVy/V6TRHFCEAqNGbkH/RHFGElIkCBiFvcnOjAEGIGvRIihBCiCB0h0EMBw3KI+S9UMAghkHo\noDwKKoSoRY+kiGEQMihPEVRSUwG5/N+gcuWKbupIiJ6jgEEMg5BBeTTSm5AyUcAghkHIoDwa6U1I\nmShgEMMgZFAejfQmpEwUMIhhcHMD/P0Ba2vAyKjkp78/v0GbRnoTUibqJUUMC2P8n2+ikd6ElIkC\nBjEMQrvV0khvQtSigEH0n5CxEW/LU1YPKAoQhAhCAYPoNyF3BkLyUA8oQt4bNXoT/SZkbISQPNQD\nipD3RgGD6DchdwZC8lAPKELeGz2SItqhqTmZGjcuecRU2pt3BkLyUA8oQt4bBQyieUJ7JCnylhVY\nevXil6Xw5p2BkDyKfVOAIOSdUcAgmie0R5KQwCLkzoDuHgjRCQoYhE8TXViF9kgqT1fXsgbcAXT3\nQIgO6DxgyGQyrF69GnFxcSgoKICXlxcWLFiAevXqqcx/48YNfPfdd7h16xYaNmyIiRMnol+/fjqu\ndSWgiTZ7G6vQAAAdJUlEQVQDTXVhFdKmAAgLLOV5vEUI0SqdB4yIiAjExcUhLCwMdevWRWhoKIKC\ngrB7926lvNnZ2fD390fv3r3x3Xff4fz585g3bx7q1asHT09PXVf9X5pq0BVaztvyaWpxICHf+IXk\n6dULWLIEePIEePUKqFkT+PDDkrmb3iQksNCAO0L0hk4DhkQiQXR0NObPn49OnToBAFatWgVfX18k\nJibCxcWFl//nn3+Gubk55s2bByMjI7Rs2RI3b97Etm3b3i1g6OpbuCbLEZJPyEVVU4PbhD5uYgwQ\niUp+F4lUP0oS0lhNA+4I0Rs6HYdx+/ZtFBQUwN3dnUuztrZG06ZNkZCQoJQ/ISEBbm5uMDL6t5ru\n7u5ITEwEU/csWx1NraamqUV2hJYjJJ+mFgcSMrhNSJ5Dh4AGDQAXF8DLq+RngwbKxyZkBlkacEeI\n3tBpwEhLSwMANGzYkJfeoEEDblvp/KryFhYW4sWLF+XbuaYu9Jr6xiu0HCH5NLU4kJDBbULylOcz\ncnMDQkKADRtKfpa+S6MBd4ToDZ0GjMLCQhgZGcHY2JiXbmJiguLiYqX8RUVFMDExUcoLlDzeKhdN\nXeg19Y1XaDlC8mlqcSAh3/h1fVcgZH+EEJ3QaRuGqakp5HI5pFIpqlf/d9cSiQRmZmYq85cODIrX\nqvKXSWjPnbcROkhMU+UIySdkHIImB7e9LY+mPqPy1IkQonU6DRiN//fNMzMzk/sdADIyMpQePQFA\no0aNkJmZyUvLyMhAzZo1Ubt27fLtXFMXMU0NEhNaTnnylVUHXQ5uo4F0hFRJOg0YNjY2qFWrFi5f\nvgw/Pz8AQEpKClJTU+Gm4mLi6uqKffv2gTEG0f963Fy6dAkuLi68hvDSZDIZAPDbRRo3Bvz8gFOn\ngPR0oGFDwNu7JD0lpXwH0rgxMGYMP628ZZSnHF3vTxN0uS9CiEYorpmKa2hpOg0YJiYmGDp0KJYt\nWwYLCwtYWVkhNDQU7u7ucHJygkQiQW5uLurUqQMTExMMGjQIW7ZswcKFCzFq1CicP38eBw4cwObN\nm8vcj+KuZNiwYWVX6NdfNXVohBBSZWRmZqJ58+ZK6SJW7v6p70cqlWLFihWIi4uDVCrlRnpbWlri\n0qVLGDlyJKKjo+Hh4QEA+PPPP/Htt9/izp07aNKkCSZPnozPPvuszH0UFRUhOTkZ9evXR7Vq1XRx\nWIQQUunJZDJkZmbC3t4epqamStt1HjAIIYRUTrSAEiGEEEEoYBBCCBGEAgYhhBBBKGAQQggRhAKG\nQFlZWZgyZQratWuHDh06YPny5ZBKpWW+p0OHDmjdujXvX2RkJLf98ePHGDduHJydneHt7Y0tqgYW\n6lh5j/P169dYt24dunXrBicnJ/Tv3x/Hjh3j5Vm2bJnS59C9e3dtHwpHJpNh5cqV8PT0hLOzMyZP\nnoznz5+rzX/jxg0MHjwYjo6O6NGjB34t1f26sLAQISEh8PDwQLt27TB//nwUFBRo+zDeqrzHefDg\nQfj5+cHJyQndu3fHpk2beP3vT506pXTeWrdurXLeN10p7zFOmTJFqf6jR4/mtleFczlixAiV56l1\n69a48r/JVTV2LhkRZMiQIWzo0KHs1q1bLD4+nrVv356tWrVKbf7MzEwmFovZlStXWEZGBvevoKCA\nMcZYcXEx69atGwsKCmL37t1jv/32G3N0dGR79+7V1SGpVN7jXLZsGevUqRM7fvw4e/ToEYuKimI2\nNjbs8uXLXJ5x48ax0NBQ3ueQlZWli8NhjDEWHh7OOnXqxM6ePcuSk5PZ559/zgYPHqwyb1ZWFnN3\nd2eLFy9m9+/fZ9HR0axNmzbszJkzXJ4ZM2awXr16saSkJHblyhXWvXt3FhwcrKvDUas8xxkfH89s\nbW3Zzp072ePHj9mhQ4dYu3bt2Lp167g8GzduZP369eOdt4yMDCaTyXR1SErKc4yMMdazZ0+2ceNG\nXv1zcnK47VXhXL548YJ3fGlpaaxfv35s+PDh7PXr14wxzZ1LChgCJCYmMrFYzJ48ecKl7du3jzk7\nO7Pi4mKV7zl//jxr06YNk0gkKrfv37+fOTk5sfz8fC4tIiKC9ejRQ7OVL4fyHqdMJmNubm5s165d\nvPSRI0eyOXPmcK87d+7MYmNjtVfxMhQXFzNnZ2f2yy+/cGlPnz5lYrGYXb16VSl/VFQU8/Hx4f1H\nmjNnDhszZgxjjLF//vmH2djYsIsXL3LbL126xFq3bs3S0tK0eCRlK+9xjh8/nk2ZMoWXtm7dOubj\n48O9njFjBps1a5b2Kl1O5T3G4uJi1qZNG3bhwgWV5VWVc1naxo0bmaurK8vIyODSNHUu6ZGUAAkJ\nCWjatCmaNWvGpbm7u6OgoAC3bt1S+Z67d++iWbNmSjPzvlmmvb09atWqxSvz0aNHZd5ia1N5j1Mu\nl2P16tXo0aMHL93IyAgvX74EAOTl5SEtLQ0tW7bUbuXV0PQaLImJiTAyMuIt9uXi4oJq1arh6tWr\n2j2YMpT3OCdMmIBJkybx0t48bwBw7969CjtvqpT3GB8+fAipVKr2GKrKuXxTZmYmNmzYgGnTpqF+\n/fpcuqbOJQUMAdLT09GgQQNemuL1P2qmTb937x6qV6+OgIAAdOrUCQMGDOA9C09LSyt3mdpW3uOs\nXr06OnbsyFuP/fr167h48SK8vLwAlAROANi3bx98fX3h6+uL0NBQ5OXlaesweDS9Bkt6ejosLS15\nXwSqV68OS0vLCjtvQPmP08HBAa1ateJe5+fnY/fu3dx5k8lkePjwIZKTk9G3b194enpiwoQJePjw\noRaPomzlPca7d+/C2NgYERER6NKlCz755BOEh4dzSylUlXP5ps2bN8PKygqDBw/m0jR5LnW+prc+\nSklJga+vr8ptJiYm6Nu3L2rUqMFLNzY2hkgkUrmOBwDcv38fOTk5mDJlCqZNm4bTp0/j66+/hkwm\nw8CBA1FUVARLS0ulfQFQW+b70sZxvunx48eYNGkSHBwcMHDgQAAlnwMA1K1bF5GRkUhJSUFYWBju\n37+P6OhoblJJbdH0GiyFhYVKn1FZ5elKeY+z9HsnTpyI4uJiTJ8+HQDw5MkTFBcXQyKR4Ntvv4VE\nIsGGDRswbNgwHDhwAFZWVlo7lrLqWZ5jVPztffTRRxg2bBju3r2LpUuXIi0tDWFhYVXuXObn5+OX\nX37BzJkzeVMiafJcUsBASSQ/ePCgym1GRkaIiYlRWpfj9evXYIyhZs2aKt8XHR0NiUQCc3NzACUz\n9aampmLHjh0YOHBgmWt9qCvzfWnjOBWSk5MREBAAS0tLREVFcX/sX3zxBbp3784Fx9atW6NevXr4\n4osv8Ndff8He3l4DR6aeptdgUbVdkUdb502I8h6nQnZ2NiZOnIj79+9j27ZtaNq0KQCgRYsWuHTp\nEj744APu8dy6devQpUsX/N///R/Gjh2r3QNSobzHOHXqVIwdOxZ169YFUPK3V61aNUybNg1z5syp\ncufy+PHjkMlk6Nu3Ly9dk+eSAgZKvkWX9XyvUaNGOHXqFC8tIyMDgPJto4KJiYnSN1WxWIzff/+d\nK/Pvv/8uV5nvSxvHCQBnz55FUFAQbGxsEBUVhTp16nDbRCKR0p2UWCwGUHLrre2Aoek1WBo1aoTs\n7GzIZDLuW5xUKkV2drbS4zxdKu9xAiV3nOPGjUNBQQFiYmJgY2PD26640CqYmZmhWbNmFfa4przH\naGRkpHQMb/7tVaVzCZQEjC5duqgMdpo6l9SGIYCrqyuePn3K+3AvXbqEWrVqKf0nA0r+6Ly9vbF9\n+3ZeenJyMvfc2NXVFcnJySgsLOSV2aJFiwq53VfUqTzHCZQ0Ek+YMAEeHh7Yvn07L1gAQFhYGAYM\nGMBLS05OBgCdNKi+uQaLwtvWYElISAB7Y07ON9dgcXV1hVQqRVJSErf96tWrkMvlcHV11e7BlKG8\nx5mVlYWRI0dCLpdj9+7dSuf32LFjcHZ2RnZ2NpeWn5+PR48e4eOPP9begZShvMc4ZcoUBAYG8tKS\nk5NhYmKCDz/8sMqcS4WrV6+iffv2SukaPZfv3c/KAMjlcvbFF1+wL7/8kiUnJ3PjE9auXcvlyc/P\n53VjW7hwIXN3d2fHjh1jjx49Ylu2bOH15y8sLGRdu3ZlEyZMYHfu3GH79+9njo6OvK50ulbe4ywu\nLmadO3dmvXv3Zs+ePVPZ1/3q1avM1taWhYWFsUePHrEzZ86wbt26senTp+vsuJYvX846duzITp06\nxfVpHz58OHcMGRkZXLfhzMxM5urqykJCQrhxGHZ2duz8+fNceVOnTmU9evRgCQkJXN/92bNn6+x4\n1CnPcQYFBTEnJyd27do13nnLzMxkjDGWk5PDPD092dixY9mtW7dYcnIyGzt2LOvWrRsrKiqqFMf4\n+++/s9atW7Nt27ZxY01KjyuqCueSMcbS09PVdrvV5LmkgCFQRkYGmzhxInN0dGQdO3ZkK1eu5PXV\nX7t2LROLxdzr4uJitmrVKta1a1dmZ2fH+vTpw44cOcIr88GDB2zEiBGsbdu2rEuXLmzHjh06Ox51\nynOcZ86cYWKxWOW/UaNGce+Jj49nAwcO5Mr8/vvvdXrRef36NVuyZAlzd3dnLi4ubMqUKdzAwYsX\nLzKxWMzri5+UlMQGDhzI7O3tWY8ePdiBAwd45eXn57M5c+YwFxcX5u7uzkJCQlhhYaHOjkcdocdZ\nWFjIbGxsVJ43W1tbrrz79++zgIAA5ubmxpydndmkSZNYampqRR0eY6z85zIuLo717t2b+z8WGRnJ\n+3uu7OdSITk5mYnFYnb//n2V5WnqXNJ6GIQQQgShNgxCCCGCUMAghBAiCAUMQgghglDAIIQQIggF\nDEIIIYJQwCDvRFud66jTXsWgz50IQQGjkrt27RqmT58Ob29vODg4oHv37li8eDHS09O1sr/09HQE\nBAQgNTVVo+Xm5eVhzpw5b52+uaKVXjVRV3JycuDj44PHjx8DAHx8fDBv3rz3Lvd9z+emTZuURlNX\nRaNGjVI7D5shoYBRif3www8YMmQIcnNzMXPmTGzevBljxoxBfHw8Bg4ciEePHml8nxcvXkR8fLzG\ny71z5w7i4uIgl8s1XnZV8M0336Bnz55o3ry5Rst93/N5+vRpdO7cWXMV0lNz587Ft99+i6ysrIqu\nSoWigFFJXb16FUuXLsWoUaOwZcsW9O7dGx4eHhg6dCh2794NqVSKRYsWVXQ1iQZcv34dhw8fhr+/\nf0VXhScvLw9JSUncGhpVmY2NDRwdHbFhw4aKrkqFooBRSW3duhV169bF1KlTlbY1bNgQc+bMQYcO\nHSCVSgGUTIi4Y8cOfPbZZ3BwcICvry82bNgAmUzGvW/EiBFYsGABoqKi4O3tjbZt22Lw4MG4ceMG\ngJJFkGbNmgUA8PX1xZw5cwAAr169wvLly9GjRw/Y29vDxcUF48aNw+3bt3n1OnXqFAYPHgwnJyd4\neXnh22+/RUFBAS5duoRhw4YBAEaOHIkRI0aoPOaUlBS0bt0aR44cwaRJk+Ds7Ax3d3eEhITwJnFU\n9dgoIiICbdq04R3rokWLEBERgU6dOsHZ2RnTpk1Dfn4+Nm3aBC8vL7i6uiIoKAgvXrzglZWXl4dp\n06bByckJnp6eCA8Px+vXr3l5fvrpJ3z66aewt7eHj48PNm3axGsnmDNnDsaOHYuQkBA4OzvDz89P\nbTvCli1b0LFjR6VZfyUSCRYuXAhXV1d4eHhg0aJFyM/P5+U5evQoBgwYgLZt28LT0xNhYWHclN7v\nez7Pnz+P//znP2jSpAn3ucydOxceHh5wdXVFSEgIVq1aBR8fH+49QsqeM2cOAgICsGvXLnTt2hWO\njo4YN24cMjMzERsbi27dusHZ2RmjR49GSkoK9z4fHx9ERkbim2++gbu7O1xdXbF48WIUFhYiLCwM\nHh4e8PDwwLx583jrSjx9+hQzZ86Ep6cn7Ozs0LFjR8yZMwe5ubm84+3Tpw9iY2N5k/gZnHJPJkIq\nnFwuZ23btmXTpk0T/J5Zs2YxOzs7FhERwc6ePcvWrl3L7Ozs2Ny5c7k8w4cPZ66uruzLL79kR48e\nZUeOHGG+vr6sa9euTCaTsaysLBYREcHEYjE7cuQIe/z4MWOMscDAQNaxY0cWGxvLLl26xH766SfW\nqVMn9tlnnzG5XM4YY+zEiROsdevWLCgoiMXHx7PY2Fjm5ubGJk+ezPLy8tiePXuYWCxmMTEx7N69\neyqPQbGusZubG1u6dCk7f/48i4qKYq1bt2bh4eFcPrFYzNavX89779q1a3nzJA0fPpw5OzuzMWPG\nsDNnzrBt27YxsVjMPvnkEzZixAgWHx/PYmJimK2tLVu8eDGvbFtbWzZp0iQWHx/P1q9fz9q0acMW\nLlzI5VHU6fvvv2dnzpxhkZGRzM7Oji1ZsoTLM3v2bNamTRsWEBDAzp8/z06cOKHymPPz85mdnR37\n+eefeeldu3Zltra2bPjw4ezEiRMsOjqaOTk5MX9/fy7Pb7/9xsRiMZs1axY7deoU27lzJzePEGPs\nvc4nY4x9/fXXbOnSpdzrYcOGMXd3d/bTTz+x48ePs8GDBzN7e3vWtWtXLo+QsmfPns2cnZ1Z//79\n2YkTJ1hsbCyzt7dnn3zyCevbty87evQo279/P3NycmIBAQG8z8TZ2ZkFBwez8+fPs2XLlnHndMqU\nKezMmTPc8W7dupUxxtirV69Y586d2aBBg9jRo0fZhQsXWGRkpNI5ZYyxgoICZm9vz/bu3avyXBkC\nChiVUFZWFhOLxWz58uWC8t+9e5eJxWK2ZcsWXvqmTZuYWCxmd+7cYYyVXESdnJxYXl4elycuLo6J\nxWJ269Ytxhhjv/76KxOLxezp06eMMcaKiorY2LFj2aFDh3hlKy7AignT+vfvzwYOHMjLExsbyz79\n9FOWl5fHrly5ojShWmmKgFF6MfsRI0YwPz8/7rXQgFH6WHv37s2cnZ3Zy5cvubSAgADWv39/Xtl9\n+/blXTiXL1/O2rRpw7KystjLly+Zg4MDL8gwxtiOHTuYra0tN+Hb7NmzmVgsZunp6WqPl7GSiRvf\nPEcKXbt2ZZ06deJNlPfjjz8ysVjMbt68yeRyOevcuTPvgsoYY0ePHmVisZglJCQwxt79fDLGmJeX\nFzt37hxjjLHz588zsVjMTp48yW0vKChg7du35wKG0LIVn82TJ0+4PAEBAUppixcvZu7u7rzPxMfH\nh5tcUCaTMVdXV+bj48Nev37N5evduzcLCgpijJVM2jdkyBDu+N/c36effspK8/PzY8HBwUrphoIe\nSVVCisVe3nycVJYrV64AAHr37s1LV6zMpdgOlDzOUawSCPy7cNKrV69Ull2jRg1s3boVPXv2RHp6\nOi5evIg9e/bg5MmTAEpW7CsqKsLNmzfRvXt33nsHDhyI33//nbc/IVxcXHivGzVqxHskJVSrVq14\n+7ayskKLFi1Qu3ZtLq1u3bpK649/8sknvKVlfX19IZVKce3aNSQlJaGoqAg+Pj6QSqXcPx8fH8hk\nMly8eJF7X7169d66UI/ikYu1tbXSNm9vb5iamvLqAZS0bz18+BBpaWlK9fDy8oKxsTHOnz+vcn9C\nzicA3L59G3l5eWjXrh2AksbzGjVqwNvbmyurZs2a6NKlS7nLBkrORbNmzXivLSwseGmqzk3btm25\nVeWMjIxgYWEBOzs73sp1devWxcuXLwEAdnZ2+PHHH9GkSRM8evQIp06dwtatW/Hw4UOlx4wA0LRp\nU433EKxMaMW9SqhOnTqoVasWnj17pjaP4lm2ubk59yy29MJMitdv/qd78wIEgPvPV1bvpTNnzuD7\n77/Hw4cPucWWFKt+McaQm5sLxpjSM/h3paqO79K7qlatWkppQpbmrFevHu+1qs9R3bKXihUMhe5L\nUaaqpTlL10Px+ebl5SEnJwcAEBISgpCQkDLrUdrbzidQ0jvKw8ODW1XyxYsXsLCwUFqjvXQdhZQN\nvPu5eZf3bd++HVFRUcjJyUG9evVgb28PMzMzlV+SzMzMlIKUIaGAUUl5enri0qVLKC4uVrmQ/Y4d\nOxAZGYlDhw7hgw8+AFCyytqbSzwqliK1sLB453o8efIEgYGB6N69OzZt2gRra2uIRCLs2rULZ86c\nAQDuW3zpxuP8/HwkJSXBycnpnfevTukAou4O6V2UbgxVfI5WVlZcg3J4eDjv27BCeZf+VJybvLw8\n7jyqq4eiy6eVlRV3lzR37lyVq8epO+dCzidQcuHv2bMn77iys7PBGOMFjTe7oQotW5f279+PpUuX\nYtasWejfvz8XdKdMmYKbN28q5X/58uV7/X+p7OiRVCU1ZswY5OTkYM2aNUrbnj17hl27dsHBwQHN\nmzfnlnU8cOAAL5/idXmWo1Q8DlNITk5GcXExxo8fj2bNmnEXC8UFQC6Xc98kT5w4wXvvsWPH4O/v\nj7y8PKVy34e5ubnSWsWJiYkaK7/0xe2PP/6AqakpHBwc4OjoCGNjY2RkZKBt27bcP6lUivDwcKX1\nwt9G0QMpLS1NaduFCxd4jyUPHToEAHBzc0PLli1haWmJ1NRUXj0sLCywYsUKPHjwAMC7nU9FoH9z\n/IWbmxskEgnvsyn9WkjZunb16lVYWFhg3LhxXLAoKCjglmotLS0tjbfOtqGhO4xKytnZGYGBgVi3\nbh0ePnwIPz8/1K1bF7dv38bWrVthZGSE5cuXAyhZ+L5v374IDw9HYWEhnJ2dkZSUhKioKPTt25db\nZ1wIxTfXo0ePonPnztzz4eXLl2P06NEoLi7Gvn37uMFgiraFyZMnIzAwEDNmzICfnx/S0tKwcuVK\n9OvXD02aNOHyxcfHo06dOmrXEBeiS5cu2L9/P9q2bYvmzZtj37593AhpTbh27RoWLlyInj174tKl\nS4iJiUFgYCD32YwdOxbh4eHIz8+Hq6srnj17hvDwcNSuXbvcayi3a9cOpqamuHr1KsRiMW9bWloa\npk2bhiFDhuDWrVtYvXo1BgwYgBYtWgAApk6ditDQUBgZGaFz587IyclBREQE8vLyuC7G73I+k5OT\nYW1tzbuDUnRZnTNnDqZNm4b69esjOjoaz58/54Ke0L8VXXJwcMDu3buxbNkydOnSBWlpadi2bRue\nP3+u9Ag1Ly8P9+7dw7hx43ReT31BAaMSCwoKgp2dHXbt2oXvvvsOL1++ROPGjdGzZ0989dVXvMcf\nS5Ys4S6eUVFRaNKkCYKCgso9GMzd3R2dOnXCypUrcenSJURFRWHlypVYt24dxo8fjzp16sDJyQk7\nd+7EiBEjkJCQgJYtW8LX1xeRkZFYt24dJk6cCCsrK3zxxRfctBItWrRA7969sWvXLpw9exb79+9/\n589l7ty5kEqlCAsLQ/Xq1fHpp59i+vTpWLhw4TuX+abAwED8+eefCAgIgIWFBWbMmMG7iCgumD/+\n+COioqJQt25deHl5ITg4WOXjw7KYmZmhc+fOOH36NIYMGcLbphjlP3HiRJiammLkyJGYMmUKt/3L\nL7+Eubk5tmzZgh9//BHm5uZwc3NDcHAw6tevD+DdzueNGzdUDtZbs2YNvv/+eyxduhQikQh9+vSB\nubk5/v77bwBA8+bNBf2t6FL//v2RkpKCX375BTExMWjYsCG8vb0xdOhQhISE4O+//+YC8NmzZ2Fs\nbMxryDc0tEQrIXru+vXrGDJkCE6cOMFrg9InqampuHbtGrp168Y1hAPAoEGD0KhRI6xbt64Ca6cZ\nY8aMQatWrTQyh1dlRW0YhOg5xcj8bdu2VXRVyjRr1izMnz8f58+fx7lz5zB//nwkJydzo/grs7/+\n+gs3b97EV199VdFVqVB0h0FIJZCdnY0BAwbghx9+0PgEhJpy7tw5rF+/Hnfu3IFcLoeNjQ0mTpxY\nJeaaGjFiBL788kulsUyGhgIGIYQQQeiRFCGEEEEoYBBCCBGEAgYhhBBBKGAQQggRhAIGIYQQQShg\nEEIIEeT/AQ7RkL0TPoTXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61cfe92e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sweep_frame_difference(frame)\n",
+    "\n",
+    "decorate(xlabel='Contact number (beta/gamma)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap06-fig03.pdf')\n",
+    "\n",
+    "#Scattered, so not such a direct corelation as the ratio."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the book we figured out the relationship between $c$ and $s_{\\infty}$ analytically.  Now we can compute it for a range of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "s_inf_array = linspace(0.0001, 0.9999, 101)\n",
+    "#s_inf_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "c_array = log(s_inf_array) / (s_inf_array - 1)\n",
+    "#c_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`total_infected` is the change in $s$ from the beginning to the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "frac_infected = 1 - s_inf_array\n",
+    "frac_infected_series = Series(frac_infected, index=c_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can plot the analytic results and compare them to the simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap06-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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o0i4WQsIZ/Qq1Hk4QO0n6L4QQFiNjNBuwNHd/iHQFwLdXW/B3tXBEQojmTBaF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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c61cfcb6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sweep_frame(frame)\n",
+    "plot(frac_infected_series, label='Analysis')\n",
+    "\n",
+    "decorate(xlabel='Contact number (c)',\n",
+    "         ylabel='Fraction infected')\n",
+    "\n",
+    "savefig('chap06-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The agreement is generally good, except for values of `c` less than 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Suppose you run a survey at the end of the semester and find that 26% of students had the Freshman Plague at some point.  What is your best estimate of `c`?\n",
+    "\n",
+    "Hint: if you print `frac_infected_series`, you can read off the answer. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.211261    0.999900\n",
+      "4.642296    0.989902\n",
+      "3.987365    0.979904\n",
+      "3.612133    0.969906\n",
+      "3.350924    0.959908\n",
+      "3.151808    0.949910\n",
+      "2.991711    0.939912\n",
+      "2.858363    0.929914\n",
+      "2.744467    0.919916\n",
+      "2.645332    0.909918\n",
+      "2.557767    0.899920\n",
+      "2.479505    0.889922\n",
+      "2.408879    0.879924\n",
+      "2.344627    0.869926\n",
+      "2.285771    0.859928\n",
+      "2.231541    0.849930\n",
+      "2.181315    0.839932\n",
+      "2.134590    0.829934\n",
+      "2.090947    0.819936\n",
+      "2.050040    0.809938\n",
+      "2.011573    0.799940\n",
+      "1.975299    0.789942\n",
+      "1.941002    0.779944\n",
+      "1.908499    0.769946\n",
+      "1.877628    0.759948\n",
+      "1.848249    0.749950\n",
+      "1.820238    0.739952\n",
+      "1.793487    0.729954\n",
+      "1.767898    0.719956\n",
+      "1.743384    0.709958\n",
+      "              ...   \n",
+      "1.181034    0.290042\n",
+      "1.173263    0.280044\n",
+      "1.165630    0.270046\n",
+      "1.158132    0.260048\n",
+      "1.150765    0.250050\n",
+      "1.143524    0.240052\n",
+      "1.136407    0.230054\n",
+      "1.129409    0.220056\n",
+      "1.122527    0.210058\n",
+      "1.115758    0.200060\n",
+      "1.109099    0.190062\n",
+      "1.102547    0.180064\n",
+      "1.096099    0.170066\n",
+      "1.089751    0.160068\n",
+      "1.083503    0.150070\n",
+      "1.077350    0.140072\n",
+      "1.071291    0.130074\n",
+      "1.065323    0.120076\n",
+      "1.059444    0.110078\n",
+      "1.053651    0.100080\n",
+      "1.047943    0.090082\n",
+      "1.042317    0.080084\n",
+      "1.036772    0.070086\n",
+      "1.031305    0.060088\n",
+      "1.025914    0.050090\n",
+      "1.020598    0.040092\n",
+      "1.015356    0.030094\n",
+      "1.010185    0.020096\n",
+      "1.005083    0.010098\n",
+      "1.000050    0.000100\n",
+      "Length: 101, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(frac_infected_series)\n",
+    "\n",
+    "#We see that at c = 1.158132, 26.00% are or were infected at the end of 14 weeks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.158096819542062"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Alternative solution\n",
+    "\n",
+    "\"\"\"We can use `np.interp` to look up `s_inf` and\n",
+    "estimate the corresponding value of `c`, but it only\n",
+    "works if the index of the series is sorted in ascending\n",
+    "order.  So we have to use `sort_index` first.\n",
+    "\"\"\"\n",
+    "\n",
+    "frac_infected_series.sort_index(inplace=True)\n",
+    "np.interp(0.26, frac_infected_series, frac_infected_series.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/chap07-fig01.pdf b/code/chap07-fig01.pdf
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diff --git a/code/chap07mine.ipynb b/code/chap07mine.ipynb
new file mode 100644
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+++ b/code/chap07mine.ipynb
@@ -0,0 +1,1933 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 7: Thermal systems\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# tempo switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The coffee cooling problem.\n",
+    "\n",
+    "I'll use a `State` object to store the initial temperature.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "temp    90\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(temp=90)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And a `System` object to contain the system parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>temp    90\n",
+       "dtype: int64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volume</th>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r</th>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>T_env</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t0</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dt</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init      temp    90\n",
+       "dtype: int64\n",
+       "volume                        300\n",
+       "r                            0.01\n",
+       "T_env                          22\n",
+       "t0                              0\n",
+       "t_end                          30\n",
+       "dt                              1\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = System(init=init,\n",
+    "                volume=300,\n",
+    "                r=0.01,\n",
+    "                T_env=22,\n",
+    "                t0=0, \n",
+    "                t_end=30,\n",
+    "                dt=1)\n",
+    "coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `update` function implements Newton's law of cooling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update(state, system):\n",
+    "    \"\"\"Update the thermal transfer model.\n",
+    "    \n",
+    "    state: State (temp)\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (temp)\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    T = state.temp\n",
+    "    T += -r * (T - T_env) * dt\n",
+    "\n",
+    "    return State(temp=T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>89.32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "temp    89.32\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "update(init, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run simulations using the same function from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a TimeFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.loc[t0] = init\n",
+    "    ts = linrange(t0, t_end-dt, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.loc[t+dt] = update_func(frame.loc[t], system)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>89.320000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>88.646800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>87.980332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>87.320529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>86.667323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>86.020650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>85.380444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>84.746639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>84.119173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>83.497981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>82.883001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>82.274171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>81.671430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>81.074715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>80.483968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>79.899128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>79.320137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>78.746936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>78.179466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>77.617672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>77.061495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>76.510880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>75.965771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>75.426114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>74.891852</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>74.362934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>73.839305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>73.320912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>72.807702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>72.299625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         temp\n",
+       "0   90.000000\n",
+       "1   89.320000\n",
+       "2   88.646800\n",
+       "3   87.980332\n",
+       "4   87.320529\n",
+       "5   86.667323\n",
+       "6   86.020650\n",
+       "7   85.380444\n",
+       "8   84.746639\n",
+       "9   84.119173\n",
+       "10  83.497981\n",
+       "11  82.883001\n",
+       "12  82.274171\n",
+       "13  81.671430\n",
+       "14  81.074715\n",
+       "15  80.483968\n",
+       "16  79.899128\n",
+       "17  79.320137\n",
+       "18  78.746936\n",
+       "19  78.179466\n",
+       "20  77.617672\n",
+       "21  77.061495\n",
+       "22  76.510880\n",
+       "23  75.965771\n",
+       "24  75.426114\n",
+       "25  74.891852\n",
+       "26  74.362934\n",
+       "27  73.839305\n",
+       "28  73.320912\n",
+       "29  72.807702\n",
+       "30  72.299625"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(coffee, update)\n",
+    "coffee.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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U2I1NQt8GNhljfuV67M9ddaPuwZYuV8qvhIeFcM3lqeQU2S1Tm8qQ7y88TuGR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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c46f0ef358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(coffee.results.temp, label='coffee')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After running the simulation, we can extract the final temperature from the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def final_temp(system):\n",
+    "    \"\"\"Final temperature.\n",
+    "    \n",
+    "    If system has no results, return initial temp.\n",
+    "    \n",
+    "    system: System object.\n",
+    "    \n",
+    "    returns: temperature (degC)\n",
+    "    \"\"\"    \n",
+    "    if hasattr(system, 'results'):\n",
+    "        return system.results.temp[system.t_end]\n",
+    "    else:\n",
+    "        return system.init.temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It will be convenient to wrap these steps in a function.  `kwargs` is a collection of whatever keyword arguments are provided; they are passed along as arguments to `System`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n",
+    "    \"\"\"Runs a simulation with the given parameters.\n",
+    "\n",
+    "    T_init: initial temperature in degC\n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    volume: volume of liquid in mL\n",
+    "    t_end: end time of simulation\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(temp=T_init)\n",
+    "    \n",
+    "    system = System(init=init,\n",
+    "                    volume=volume,\n",
+    "                    r=r,\n",
+    "                    T_env=22, \n",
+    "                    t0=0,\n",
+    "                    t_end=t_end,\n",
+    "                    dt=1)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "72.299625390403094"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system()\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.514978275278718"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(T_init=5, r=0.15, volume=50, t_end=15)\n",
+    "#picked new r because it seemed like the milk should get warmer faster. new value is arbitrary. \n",
+    "#but felt like after fifteen min it would barely be colder than room temp.\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tHGlsA1yyufSsKZxQlh/nyIwxiSzchPO6iNyoqr/rafCSwKuRDkhETsMlmxpg\nkapWDLS/qtYEPG4WkR24YdUmwuqb2nl6+Tbqm9w6rslJSVx21hRmWLIxxgwi3EtqPiDqy9qIyCzg\nRWAXsHiwZCMi7xeRBu/yW09bDjAT2BTNWEejI41t/ZNNchKXL5pqycYYE5Zwezh3At8TkTHAW0Bj\n4A6qeigC8TwCtOIuh6WJSInX3tmzjpuXXNq9HtZy3ITUR70y2Km4SaqHCT2M2hyDugaXbBpbXJXO\nlOQkrlg0jSkThzJA0RgzmoWbcL4LZAF/GmCf4ypE7w2zPt17GDjCbDtwgvf3tcBLwKdVtVZELgbu\n89pScT2kC1W19XjiMX1qG1p5Zvn2/snmnGlMKbFkY4wJX7gJ50vReHJVPd/v7+/QtyjoQMdMDXi8\nBbgq0rEZp7a+laeXb6ep1SWb1JRkrjxnmlXpNMYMWdirRUc7EJN4qo+08MyKHTR7ySYtJZkrF0+j\nrNiSjTFm6MIuuSgiScC/AZfg1iq7ETgDWGeTLEee6iMtPL18Oy1tnQCkpSZz1eLplBZlxzkyY8xw\nFdYoNRHJBVYAfwAuw60SnQt8EnjNq5NjRoiq2haeeqkv2aSnpfC+JTMs2Rhjjku4w6Lvw920X4Cb\n3d9zr+U/gHdwqzubEeBQbTNPr9hGa7t/spnOxPFj4xyZMWa4CzfhfBD4qqqux83JAXonf94LnB2F\n2EyM1Ta08syK7bS1dwGQkZ7C1efOoKTQko0x5viFew8nGzgYYlsLbsi0GcaaWzv468s7jko2xQVj\n4hyZMWakCLeHsw74bIhtHwbeiEw4Jh46u7p5/pVdvSsIpKUkc/USSzbGmMgKt4dzO/CCiKzBreDs\nAz4kIrcCHwCujFJ8Jsp8Ph8vrtlDZXUT4EoMXHrWFIrHWbIxxkRWWD0cVX0JNzrNB3wDN2jgq7g1\ny96vqi9EK0ATXa9sOMD2vX2LfS+eV8q00rw4RmSMGanCnoejqsuAM0UkGxgHHAlVNsAMDxu3H2a9\n3xJ4804sYt6JRQMcYYwxxy7shAMgIpcAS3Alnw+KyL9U9ZWoRGaiaveBelas39f7eFppHuecUhrH\niIwxI11YCUdExgHPAmcBnbjVmMcDd4rI34APqWpb1KI0EXW4roW/r95Ft8+NcC8uGMOlZ04mOXnQ\npeyMMeaYhTtK7ce4+zUfADJUtRRX2vnfcUno29EJz0RaY3M7z67cQUdnNwC5Y9N57+JppKUe12Lf\nxhgzqHCOUZffAAAabElEQVQvqV0BfFlVn+lp8Eo6/9kr8Xw3cFMU4jMR1N7RxbOrdvaWGchIS+HK\nc6YxJjMtzpEZY0aDcHs43UB1iG37gYzIhGOipbvbx99X7+JwXQvgSkO/5+ypFObZnF1jTGyEm3B+\nDtzjV4ETAG/E2s3AzyIdmIkcn8/HivV72VPZ0Nt2wYJyq2ljjImpcC+pFQHlwA4RWY7r1RTiRqzl\nAS0i8ry3r09VbSJoAln/ThUbd/R1UBeeNIGTpo2LY0TGmNEo3IQzG9jo/T0bN4AAYLP301Z3TFDb\n9tbxytv7ex/PnFzAmbNLBjjCGGOiI9yKn0uiHYiJvMrqJv65Zk/v49Lx2Vy0sJykJBv+bIyJvaFO\n/MzAXUI7iqrflHUTd0ca23hu1U46u9zw5/ycDK5YNJWUlHBv2xljTGSFO/FzLvAbYB59xdcC2USO\nBNHa1slfV+7ordiZlZHKVYunk5kxpO8XxhgTUeF+Aj0ITAK+Rujh0SYBdHmlBuoa3MIPKclJXLFo\nGnnZNnLdGBNf4SacecBHVPWvkXxyEfkFkKqq1/m1XYoraS3Au8Atqvq3Ac4xBvghrippKvBH4EZV\nbYxkrMOBz+fjX69XsP9w30u/5IwpVh7aGJMQwr2gvxO3lE1EiEiSiNwFfC6g/WTgL7ikMR94Bnha\nRGYPcLoHgcXAe4GrgPO9tlFnzaZKdE9t7+NFc0s5oTw/jhEZY0yfcBPObcDdInKOiKQfzxOKyHTg\nX8DngT0Bm28AVqvqt1R1q6reDrzitQc7VxnwMeALqrpaVV8GrgM+KiKTjifO4WbLzhrWbumrAj57\neiHzxUoNGGMSR7iX1DbhBgWsABCRroDtPlUN9ybBIqAC+CjweMC2JcATAW0vAR8Z4FzdwCq/tlVA\nF67X84cwYxrWKg42sGxdRe/jySU5nDe/zIY/G2MSSrgJ57e4omsPAgcH2XdAqvoY8BiAiARuLgP2\nBbTtx61yEEwZcEhVO/zO3ykihwY4ZkSpqW/l76/2lRoYn5/Fe86aaqUGjDEJJ9yEMx/4hKo+Gc1g\ngDFAa0BbG6HvHwXbf7BjRozm1g6eXbmDtg7X4czOSuO950wjPc1GqBtjEk+493D24C5dRVsLR688\nnQE0DWH/wY4ZETo6u3h25U7qm9oBSEtN5spzppM95rhusRljTNSEm3C+AXxLRM4WkWh+fa4AJga0\nlXL0ZTb//Yv9YxKRVKB4gGNGhOVv7OVQbTMASUlJvOesqRQVWKkBY0ziCveS2ldx90RWAohIYDlp\nn6pGYrLHSuA8XEG3HhfgDVYIYhXuNZzdExtusEAy/QcSjChbd9ewdXff8Odz509iysTcOEZkjDGD\nCzfhPBvVKPo8AKwTkTuB3+OGPJ+JG0INgIgUAe2qekRV94nIE8DDIvIZ3LI7DwGPquqI7OHUNrSy\n/I29vY9PmjqOuTPGxzEiY4wJT7irRd8e7UC859kgIh/ArTRwC7AVuEpVt/jtthY3VPrT3uPrcInq\neaAT+BPwpVjEG2udXd28sHo3HZ19C3KeO39UTTcyxgxjQ10t+nTgEtx9lp7lZ95U1cPH8uSqen6Q\ntueA5wY4ZmrA40bgWu/PiPbK2/up8kpEpyQncdmZU0lLtRFpxpjhIdzVotOAR4D/wPUiUoBf43oh\nJ4nIuaq6I2pRGnbuP8Lb2/ry+uJ5k2yQgDFmWAl3lNrdwJXAh4B8+koU/Bdu+PG3Ih+a6dHY3M4/\n1/atAjR9Uh5zZhTGMSJjjBm6cBPOx4GvqepTuEmVAKjqduCbuJFkJgq6u3288Npu2tr7JndeuMCq\ndhpjhp9wE04hoCG2HQZsTG6UrN1cyf7Dbg5rclISl5011QqpGWOGpXATzmbc/ZtgLgO2hNhmjsPe\nQw28vrWvcvcZs0usto0xZtgK96vyvcAfRSQf+CvgA84SkY/gSgd8KkrxjVrNrR28+NoefN6inGXF\nOZwmxXGOyhhjjl1YPRxv0c5P4Wb0/xo3aOAnuKHIN6jq76MW4Sjk8/lYuraCpla3CHZWRiqXnDHZ\nVoA2xgxr4V5SQ1UfxZUDmIurqjkfmKiqP49OaKPXW+9WsbuyvvfxxWdMZmxWWhwjMsaY4xfykpqI\n/AtXSXNrT5uq+nDF2EyUHKxp5pUNB3ofz5dippTYmAxjzPA3UA/nfGz0WUy1dXTxj9W76O52920m\njBvDWbNL4hyVMcZERtiX1Ex0+Xw+XlpX0VvfJj0thUvPnEJKir1FxpiRYbBPM19MojBs2VXDuxV1\nvY8vWFBGXnaw2nLGGDM8DTYs+gERqR9kH3D1cC6LRECjUU19KyvW91VTmD29kBPLC+IYkTHGRN5g\nCSfN+2OipLOrm3+8uovOLldyYFxuJovnWckBY8zIM1jC+byqrolJJKPUyjf3UV3fCkBqSjKXnTWF\ntFS7b2OMGXnsky2Otu2tY+OO6t7HS06dRGGelRwwxoxMlnDipL6pnWWvV/Q+PqEsn5OnjYtjRMYY\nE10DJZzfAlWxCmQ06eopOdDhSg7kjk3n/AVlVnLAGDOihbyHo6ojvmRzvKzZdIDK6r6SA5eeOYXM\ndCs5YIwZ2eySWoztqaxnnV/JgbPmTKSk0EoOGGNGPks4MdTc2sGLa/pKRU+ekMN8KYpjRMYYEzsJ\ndR1HRM4HloXYvExVLwxyzBPAvwc0L1XViyMc3nHx+Xy8uGYPLW2dAIzJTOPiMybbfRtjzKiRUAkH\neAWYGNB2CfAb4LshjpkL3Iob5NCjLeKRHaf1WkXFwQYAkpKSuOSMyYzJtDm1xpjRI6ESjqq2A5U9\nj0UkD7gP+J6q/iNwfxHJAE4A1qhqZeD2RFFZ3cTqjX0lB06TYson5MQxImOMib1Ev4dzO663cleI\n7bNwSXNLzCIaoo7OLl54bTfdXqnoiYVjOcNKDhhjRqGE6uH4E5Fi4Hrc8jrNIXabA7QDd4rI5UAL\n8EfgHlVtjU2kA1uz+WBvyYGM9BQuOXMKKVYq2hgzCiVswgE+DxwCHhtgn9lAErAV+Anufs79QDnw\nqWgHOJjDdS289U7f3NnFp0wid2x6HCMyxpj4SeSE83Hg16raMcA+Xwe+r6o13uMNItIFPC4iN6lq\n9QDHRlV3t49l6yp6L6VNKspm1lQrOWCMGb0SMuGIyGzcYIDHB9pPVbuBmoDmDd7PciBuCWfTjmoO\n1rgrgSnJSbZ0jTFm1EvUQQNLgAOqOuBgABF5QkSeCmheiBtosC1awQ2msaWDV/1GpS04aQIFOZnx\nCscYYxJCQvZwgPnAxsBGEUkHxgE13hDqP+FdPgOe8Y77Pu4yW2MM4+1n5Zv7aPcW5szPyWCBFMcr\nFGOMSRiJ2sOZyNGXygAWAQe8n6jqE8CngWtxCeoHwI+Ab8QkyiB2Hahn29663scXLCgnJSVR/5mN\nMSZ2ErKHo6rvC9H+Em5Umn/bI8AjMQhrUB2dXaxYv7f38UlTxzGpKDuOERljTOKwr94R5D/nJjM9\nlUWnlMY5ImOMSRyWcCIkcM7NOaeUkpWRkB1IY4yJC0s4EWBzbowxZnCWcCLgqDk3p9mcG2OMCWQJ\n5zg1Bc65mTWBglybc2OMMYEs4RynlW/1n3Nz2iybc2OMMcFYwjkOuw/U825F35yb808rI9Xm3Bhj\nTFD26XiMOjq7WO4352bWlHGUFVtRNWOMCcUSzjFaGzDn5px5NufGGGMGYgnnGByua+FNm3NjjDFD\nYglniHw+m3NjjDHHwhLOEG30m3OTbHNujDEmbJZwhqCppYNXN/TNuVloc26MMSZslnCGoN+cm2yb\nc2OMMUNhCSdMgXNuzrM5N8YYMyT2iRmGjs7ugDk3BZRPsDk3xhgzFJZwwrB2c6XVuTHGmONkCWcQ\n1UeOnnMzJjMtjhEZY8zwZAlnAG7Ozd7eOTel423OjTHGHCtLOAPYtKOayuomwM25uWCBzbkxxphj\nZQknhMA5Nwuk2ObcGGPMcbCEE8LKt/bR5jfnZsFJE+IckTHGDG8Jt+KkiJwMbAqyaYmqrgyy/0Lg\nR8B8YB9wt6o+cjwx7K60OTfGGBNpifgpOhc4DEwM+PNa4I4iUgT8A3gDOA34MfCwiFx6rE/e0dnN\n8jdszo0xxkRawvVwgDnAZlWtDGPf64AjwA2q2g1sFZHTgK8ALxzLk7++xebcGGNMNCRiD2cOsCXM\nfZcAK7xk0+Ml4BwRGfJwspr6VtZr35ybRadMtDk3xhgTIYnaw8kUkdXAVGAj8DVVXRNk3zJgfUDb\nfmAMUIi7NBe2vYca+s25OWnquKFFbowxJqSE6uGISBYwHcgD/g/wPlwCWS4iJwU5ZAzQGtDW5v0c\n8hjmaaV55GdnUJiXxUWnl9ucG2OMiaCE6uGoaouIFABtqtoGICKfBhYAXwD+J+CQFiAjoK3ncdNQ\nnz9nTDofvzxYXjPGGHO8EirhAKhqfcDjbhHZBJQH2b0CN4LNXynQiBtMYIwxJkEkVMIRkQXAMuAC\nVV3ntaUApwJ/DHLISuBaEUlSVZ/XdgGwKmAgQTApAJWV4QyGM8YY4/d5mXIsxyf5fL7B94oREUnF\nzalpB/4b11O5BXgvMAuoA8YBNaraLiITAAX+APwQuBj4AfAeVf3XIM+1GHg5Si/FGGNGsqAT8QeT\nUD0cVe0UkcuB+4C/AmOBVcC5qnpIRM7H6wEBL6nqQRF5D27C53pgN/DJwZKNZy1uWPUBoCviL8YY\nY0aeFNxtjLXHcnBC9XCMMcaMXAk1LNoYY8zIZQnHGGNMTFjCMcYYExOWcIwxxsSEJRxjjDExkVDD\nomPBm0h6D/BpIAf4O/DfqnowxP4RL/AWDd6cpPuAS4EsXP2gL6vqxhD7PwH8e0DzUlW9OKqBDkEi\nFOOLNL+h/cEsU9ULgxwzHN6rXwCpqnqdX9uluP+TArwL3KKqfxvgHGNw8+k+iPts+iNwo6o2RjP2\nAeIJ9pquB67HrXyyG7hfVX85wDmuAJ4LsqlcVfcGaY+6EK9rDXB6wK4P++8TcI5jeq9GYw/nDuBT\nwCeBc3ErTj8ZbMdoFHiLBhFJBp4CZgJXA4twS/ssFZHCEIfNBW6lf5G7wA+1eItrMb4oeYWjX88n\ngW7guyGOSdj3SkSSROQu4HMB7ScDf8F9EM0HngGeFpHZA5zuQWAxbqL3VcD5XltMDfCaPg98B/eF\n9RTgfuBnIvKJAU43FzdHMPA93x+F0Ac0wOtKAmYD1wTEeNMApzum92pU9XBEJB24Afiiqr7otX0E\n2Ckii1T1lYBDIl7gLUrmAWcDJ6vqFgDvl6AGuBLo9y1fRDKAE4A1YRa6i5e4FuOLBlVtB3pfj4jk\n4XoB31PVfwTun8jvlYhMBx7GvU97AjbfAKxW1W95j2/3Vve4AfhskHOVAR8DLlLV1V7bdcAyEblZ\nVfdF6WUExjHQa/ov4Keq+pj3eLuInA1cCzwa4pRzgA3xfu8GeV3TcSvvvxpOnMfzXo22Hs6puMto\nL/U0qOouYBdu1YFAES3wFkV7cN801K+tJ+aCIPvPwn3ZCLfQXbzErRhfDN2OK6lxV4jtifxeLcIt\noDsX2BmwbQl+v2eelwj+e9Zzrm7cyiI9VuFWAVl8nHEOxUCv6YvALwLaugn+O9ZjKP+Ho2mg1zUH\nt/L+7iGc65jeq1HVw8FdPgN3fd/ffoKvRh3RAm/RoqrVHH2d+Iu4eznBvt3Pwa1Xd6e3lFAL7tLH\nPaoaWF8onuJWjC8WRKQYdz/g86raHGK3hH2vvG/6jwGISODmMsL/PevZ/5Cqdvidv1NEDg1wTMQN\n9JpUdbn/YxGZDHwUeCDYubz7xbOABSLyFlCEWxLmZlXVYMdEyyDv1RzcOpW/E5HzgGrg18APQyyC\nfMzv1Wjr4YwBuv3/oTxtBC/YFtECb7EiIu8Dvo27oRns29VsIAnYirvkdifuklTMr5eHEu9ifDHy\neeAQ3gdBCAn/XoUQ6v0I9V4E23+wY+LGu2f4HO7y6HdC7DYDF3sG8J/Ah72/v+x92UgUs4Fs3D3Q\ny4Cf4v6ffTPE/sf8Xo22Hk4LkCwiqara6deeQfCCbREt8BYLXsG6h4DHgZtD7PZ14PuqWuM93iAi\nXcDjInKT12OKq3gX44uRjwO/DvIFyF/Cv1chhHo/Qr0XwfYf7Ji48O6H/A33wXueqgatvaWq73iD\ndup6egoi8kHcJfBP4Fa2TwSfBLJVtc57vMG7t3ibiNzhV/qlxzG/V6Oth1Ph/QxWtC3Yja5hVeBN\nRG7DdYV/gVs1O2hNIFXt9vsA67HB+xmzyxeDUdX6nmTjPe7GDZMe9sX4vNFaJ+C+GIQ0XN6rIEK9\nH6FuKFcAxd5lKKC3XEnxAMfEnDcQ5VXcPYxFqrpjoP1Vtcb/99C7dLqDBHrvVLXTL9n02IC7350X\n5JBjfq9GW8J5C2gAzutpEJGpuPsDK4LsvxI4N+Cmc7gF3mJKRG7GDdf8hqr+T5BvJf77PiEiTwU0\nL8R1ibdFMcywicgCEan3ivL1tPUU4ws2N2fYvFeeJcCBEJc8ew2H9yqElfj9nnkuIPjvGbibzqm4\n0ZY9FuM+o1YFPSLGRGQW8CJukNFiVa0YZP/3i0iDd/mtpy0HN30h2P/huBCR1SLyo4DmhcD+IIkI\njuO9GlWX1FS1TUR+BnxfRA7jrp//DFiuqqu9YdO9Bd5wwwhvBn4hIj0F3j4GvCc+ryA4ETkFuBf4\nFfCQiJT4bW4AOuj/uv6Ed0kGNz9iPvB93KWbuEyyC+It3C/2gyLiX4xvPPCj4fpe+ZmPGwTRT5DX\nNRzeq2AeANaJyJ3A73HvxZm4+1ZA732QdlU9oqr7vAmuD4vIZ3D3rR4CHo3VkOgwPIK7d/EJIM3v\n96xTVQ9D/9cELAfqgUe9L4SpuN/Tw4QeRh0PfwbuEpF1uIRxPu537YaeHSL1Xo22Hg64a+K/w92o\nXYYbCvhv3rZFuIJsiwC81Qfeg/slX48bURRugbdY+giuMNJncPH7/7mRo1/XE7iVFq7Ffej9ADdD\n/xsxjjsk7x7b5bih3n8F1gAleMX4GL7vVY+JuHlSgYbdexWMqm4APoD73XoTN+jjqoAe3Vrca+lx\nHW5i7PO45Pov/BJUPInITNxM/FLc/0n/37HVfrv2viZVrcV98enADQl/CXeP48J4jzAM8D3ga7jP\nxk24ZHNjwAoKEXmvrACbMcaYmBiNPRxjjDFxYAnHGGNMTFjCMcYYExOWcIwxxsSEJRxjjDExYQnH\nGNMrwVfWNsPcqJr4aUYWEfkNrpjeQJar6vki8hJugl7cqmSKyDhcgbiLVfWYVgnwVsbYCXzCry5L\nRIjItbiVg78c4fMuBR705hSZUcwSjhnO7qZ/fZKfAZ240gw96r2fXwDiPensAeCJY002ngO4JUWi\nsazNbbglaSLtRuBFEXnJm7RrRimb+GlGjEToxYQiIqfjlg0p7VkGJdGIyDZgpap+OgrnfgbYrapf\nHHRnM2JZD8eMCoHJSER8uNru5wJX49bIegD4offnQ7hl2H8L3NqzGKq33Px3vGNygHXALao62AKT\ntwAv+icbEdkF/BKYgKsnn4JbY+tmXD2Sa3HrVD0FXK+qrYGX1LySDb8ALgT+F1du/CDwY1X9gfc8\n5+OWcVqiqr09GP9/Ey+WKcAMEfkUME1Vd4nIFFwJ7EuBdOBl4CZV3ex3no8Ct+IWpWzAFf27WVX3\n+73+/x/4lYjcrapVg/xbmRHKBg2Y0ez7uIUUrwaexX3IrwGagQ/iFjW82fs7IpIJLMUVQvsqbp2w\nWmCp14MJSkSycWuJPRlk8824iqT/jksc/427zzMZt+Dlj4D/z2sPJQ1X5uB3uPXnVuIWqL1okNfv\n7wPAXtzaWGcDB0RkPK5XNg+3TtY1uCS70ktEiMg5uCT5JG4tu5uAi7xY/D2LS6jvH0JMZoSxhGNG\nszdU9UveAp9f8doOqer1qroU9yFfT98y7J8ATgHep6oPq+pzuGS1EbcKcChLcEkhWGnsw8DHvef7\nKq52Tzpwjaq+oKp3eec/O8ixPZKBb6rqj1V1GW4R11bgvYO8/l6quh5X8qBKVVd7dYhuxK1cfbGq\nPq6qT+MWo2zBLfTY89qage+q6nJvIMNngGX+I95UtQnYgitRYEYpSzhmNHut5y9e5cyugDYfrgeT\n7zVdhCsw9aaIpHpFp5Jx397P9UoLBDPd+7kzyLa1PfV6vJ+HgXUBFWmr/WIIpfeSnpcsqoCxgxwz\nmItwlwwr/V5vB64mzCXePsu959koIt8WkSXAC6p6V5CaTLtwtafMKGUJx4xmDUHaBiqRWwiU4T50\n/f98E9crGR/iuJ6qic0RiCGUwHN3c/y/34W4wlqBr/dTuGX6UdVXgStwVSxvwhVY2ycigSXAwb2u\nYBUkzShhgwaMCd8R3GWhT4bYHmr0WU97HhCsgmK09fQ0UgLasxk4niO4Oie3DHRyVf0H8A8RGYMb\nvHAD8GMReUVV1/ntWkDofyMzCljCMSZ8y3E35ff7j8ASkbtxI7xCTULd7f0sIz4Jp2cuUnlPg4gU\nACfjimj16Ao4bjnwH8AW7x5Mz7H/F9cze11Evou7L3OmqjYDz4pIBa7oWjnuklyPMuDtiLwiMyxZ\nwjEmfL8G/gf4p4jci7uf817cpaQ7g9yz6PEy7kb7YoKUlY6Bt4EK4E4RacT1eL7G0Zfu6oD5InIe\nboDD/bje3Isicr+3/ZO4wRPXesf8E/g/wG9E5DHcpcWbcT2Zl3pOLCJ5uFUMvheF12eGCbuHY0yY\nVLURNyrrNdyH8fO4ocD/o6p3DHBcM/A3XO8o5lS1CzevqBI3fPrHwO85epj2/bgy3v8A5nv16RcB\n+3E1658BZgMfVdXfeOd+EfgoLpn82TtvI3CBqvr35i4F2oHnIv8KzXBhKw0YEwMicgZuJNlU74N8\nVBGRF4FNqvqleMdi4sd6OMbEgKquAZ4mwgtjDgcichpwGm6FBjOKWcIxJna+APybiJwQ70Bi7H7c\n0jyV8Q7ExJddUjPGGBMT1sMxxhgTE5ZwjDHGxIQlHGOMMTFhCccYY0xMWMIxxhgTE/8PW4SPwyEM\nHqIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c46f0efa58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(milk.results.temp, label='milk')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using `fsolve`\n",
+    "\n",
+    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    return (x-1) * (x-2) * (x-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`modsim.py` provides `fsolve`, which does some error-checking and then runs `scipy.optimize.fsolve`.  The first argument is the function whose roots we want.  The second argument is an initial guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.])"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, x0=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Usually the root we get is the one that's closest to the initial guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.])"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, 1.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.])"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, 2.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But not always."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.])"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func1(r):\n",
+    "    \"\"\"Runs a simulation and returns the `error`.\n",
+    "    \n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    \n",
+    "    returns: difference between final temp and 70 C\n",
+    "    \"\"\"\n",
+    "    system = make_system(r=r)\n",
+    "    print(r)\n",
+    "    run_simulation(system, update)\n",
+    "    return final_temp(system) - 70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `r=0.01`, we end up a little too warm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2.2996253904030937"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func1(r=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The return value from `fsolve` is an array with a single element, the estimated value of `r`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01\n",
+      "[ 0.01]\n",
+      "[ 0.01]\n",
+      "[ 0.01]\n",
+      "[ 0.01]\n",
+      "[ 0.01150871]\n",
+      "[ 0.01154231]\n",
+      "[ 0.01154308]\n",
+      "[ 0.01154308]\n",
+      "[ 0.01154308]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.011543084583978345"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution = fsolve(error_func1, 0.01, xtol=1e-8)\n",
+    "r_coffee = solution[0]\n",
+    "r_coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system(r=r_coffee)\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  When you call `fsolve`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `fsolve` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
+    "\n",
+    "Before you use `fsolve`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.501444483698084"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_milk = 0.12\n",
+    "#picked this value because it seemed pretty close from my graph above.\n",
+    "milk = make_system(T_init=5, t_end=15, r=r_milk)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func2(r):\n",
+    "    system = make_system(r=r, T_init=5, t_end=15)\n",
+    "    print(r)\n",
+    "    run_simulation(system, update)\n",
+    "    return final_temp(system) - 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.12\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "-0.49855551630191641"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func2(r=0.12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.12\n",
+      "[ 0.12]\n",
+      "[ 0.12]\n",
+      "[ 0.12]\n",
+      "[ 0.12]\n",
+      "[ 0.1317062]\n",
+      "[ 0.13283515]\n",
+      "[ 0.13295952]\n",
+      "[ 0.13296079]\n",
+      "[ 0.13296079]\n",
+      "[ 0.13296079]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.13296078935466449"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution = fsolve(error_func2, 0.12, xtol=1e-8)\n",
+    "r_milk = solution[0]\n",
+    "r_milk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.999999999999996"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(r=r_milk, T_init=5, t_end=15)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mixing liquids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mix(s1, s2):\n",
+    "    \"\"\"Simulates the mixture of two liquids.\n",
+    "    \n",
+    "    s1: System representing coffee\n",
+    "    s2: System representing milk\n",
+    "    \n",
+    "    returns: System representing the mixture\n",
+    "    \"\"\"\n",
+    "    assert s1.t_end == s2.t_end\n",
+    "    \n",
+    "    volume = s1.volume + s2.volume\n",
+    "    \n",
+    "    temp = (s1.volume * final_temp(s1) + \n",
+    "            s2.volume * final_temp(s2)) / volume\n",
+    "    \n",
+    "    mixture = make_system(T_init=temp,\n",
+    "                          volume=volume,\n",
+    "                          r=s1.r)\n",
+    "    \n",
+    "    return mixture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system(T_init=90, t_end=30, r=r_coffee, volume=300)\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.764705882352942"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(T_init=5, t_end=30, r=r_milk, volume=50)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap07-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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OGz4JWCUijwCnG2PcdLD/N7J7Qk0FngK+CKzFjrdYDlydccyJ2IF8SimlRpjb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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c46f251cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(coffee.results.temp, label='coffee')\n",
+    "plot(milk.results.temp, '--', label='milk')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)',\n",
+    "         loc='center left')\n",
+    "\n",
+    "savefig('chap07-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what happens when we mix them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.109243697478988"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mix_last = mix(coffee, milk)\n",
+    "final_temp(mix_last)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what we get if we add the milk immediately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, r=r_coffee, volume=300)\n",
+    "milk = make_system(T_init=5, r=r_milk, volume=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.428571428571438"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mix_first = mix(coffee, milk)\n",
+    "mix_first.t_end = 30\n",
+    "run_simulation(mix_first, update)\n",
+    "final_temp(mix_first)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_and_mix(t_add, t_total=30):\n",
+    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
+    "    \n",
+    "    t_add: time in minutes\n",
+    "    t_total: total time to simulate, min\n",
+    "    \n",
+    "    returns: final temperature\n",
+    "    \"\"\"\n",
+    "    coffee = make_system(T_init=90, t_end=t_add, \n",
+    "                         r=r_coffee, volume=300)\n",
+    "    run_simulation(coffee, update)\n",
+    "\n",
+    "    milk = make_system(T_init=5, t_end=t_add, \n",
+    "                       r=r_milk, volume=50)\n",
+    "    run_simulation(milk, update)\n",
+    "    \n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    run_simulation(mixture, update)\n",
+    "\n",
+    "    return final_temp(mixture)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can try it out with a few values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.428571428571438"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "62.90280912845234"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.109243697478988"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then sweep a range of values for `t_add`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sweep = SweepSeries()\n",
+    "for t_add in linrange(0, 30, 2):\n",
+    "    temp = run_and_mix(t_add)\n",
+    "    sweep[t_add] = temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the result looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap07-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MU9UKP3G2R2V7y1jw2AICgQDgJv8cfqqVrjbGND+/iec24D0R+Ro3I3UA+KGI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\nJq4s8RhjjIkrSzzGGGPiyhKPMcaYuPr/YTfgUW0MWbwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2c46f368898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(sweep, color='purple')\n",
+    "decorate(xlabel='Time added (min)',\n",
+    "         ylabel='Final temperature (C)',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap07-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#Now you can only add the milk at two points, the beggining and the end. \n",
+    "#Assume that both refrigerators are equally cold, so the milk will be 5 deg C whenever you add it. \n",
+    "\n",
+    "def run_and_mix(t_add, t_total=30):\n",
+    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
+    "    \n",
+    "    t_add: time in minutes\n",
+    "    t_total: total time to simulate, min\n",
+    "    \n",
+    "    returns: final temperature\n",
+    "    \"\"\"\n",
+    "    coffee = make_system(T_init=90, t_end=t_add, \n",
+    "                         r=r_coffee, volume=300)\n",
+    "    run_simulation(coffee, update)\n",
+    "\n",
+    "    milk = make_system(T_init=5, t_end=t_add, \n",
+    "                       r=r_milk, volume=50)\n",
+    "    #remove the run_simulation becaause the milk is not warming up\n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    run_simulation(mixture, update)\n",
+    "\n",
+    "    return final_temp(mixture)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.428571428571438"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60.714285714285715"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#so better to add milk at beggining"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_analysis(system):\n",
+    "    \"\"\"Computes temperature using the analytic solution.\n",
+    "    \n",
+    "    Adds TimeFrame to `system` as `results`\n",
+    "    \n",
+    "    system: System object\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    T_init = init.temp    \n",
+    "    ts = linrange(t0, t_end, dt)\n",
+    "    \n",
+    "    temp_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
+    "    temp_series = TimeSeries(temp_array, index=ts)\n",
+    "    \n",
+    "    system.results = TimeFrame(temp_series, columns=['temp'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it.  From the analysis, we have the computed value of `r_coffee2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "r_coffee2 = 0.011610223142273859"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(temp=90)\n",
+    "coffee2 = System(init=init, T_env=22, r=r_coffee2, \n",
+    "                 t0=0, t_end=30)\n",
+    "run_analysis(coffee2)\n",
+    "final_temp(coffee2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can compare to the results from simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(temp=90)\n",
+    "coffee = System(init=init, T_env=22, r=r_coffee, \n",
+    "                t0=0, t_end=30, dt=1)\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They are identical except for small roundoff errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>-1.421085e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            temp\n",
+       "0   0.000000e+00\n",
+       "1   0.000000e+00\n",
+       "2   0.000000e+00\n",
+       "3   0.000000e+00\n",
+       "4   0.000000e+00\n",
+       "5   0.000000e+00\n",
+       "6   0.000000e+00\n",
+       "7   0.000000e+00\n",
+       "8   0.000000e+00\n",
+       "9   0.000000e+00\n",
+       "10  0.000000e+00\n",
+       "11  0.000000e+00\n",
+       "12  0.000000e+00\n",
+       "13 -1.421085e-14\n",
+       "14 -1.421085e-14\n",
+       "15  0.000000e+00\n",
+       "16 -1.421085e-14\n",
+       "17 -1.421085e-14\n",
+       "18 -1.421085e-14\n",
+       "19  0.000000e+00\n",
+       "20 -1.421085e-14\n",
+       "21  0.000000e+00\n",
+       "22  0.000000e+00\n",
+       "23  0.000000e+00\n",
+       "24  0.000000e+00\n",
+       "25  0.000000e+00\n",
+       "26 -1.421085e-14\n",
+       "27  0.000000e+00\n",
+       "28 -1.421085e-14\n",
+       "29  0.000000e+00\n",
+       "30  0.000000e+00"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee.results - coffee2.results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/chap07soln.ipynb b/code/chap07soln.ipynb
deleted file mode 100644
index e0aed3d1..00000000
--- a/code/chap07soln.ipynb
+++ /dev/null
@@ -1,1820 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Modeling and Simulation in Python\n",
-    "\n",
-    "Chapter 7: Thermal systems\n",
-    "\n",
-    "Copyright 2017 Allen Downey\n",
-    "\n",
-    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you want to interact with them, use\n",
-    "# %matplotlib notebook\n",
-    "\n",
-    "# If you want the figures to appear in the notebook, \n",
-    "# and you don't want to interact with them, use\n",
-    "# %matplotlib inline\n",
-    "\n",
-    "# If you want the figures to appear in separate windows, use\n",
-    "# %matplotlib qt5\n",
-    "\n",
-    "# tempo switch from one to another, you have to select Kernel->Restart\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "from modsim import *"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The coffee cooling problem.\n",
-    "\n",
-    "I'll use a `State` object to store the initial temperature.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>temp</th>\n",
-       "      <td>90</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "temp    90\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(temp=90)\n",
-    "init"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And a `System` object to contain the system parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>init</th>\n",
-       "      <td>temp    90\n",
-       "dtype: int64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>volume</th>\n",
-       "      <td>300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>r</th>\n",
-       "      <td>0.01</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>T_env</th>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t0</th>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>t_end</th>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>dt</th>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "init      temp    90\n",
-       "dtype: int64\n",
-       "volume                        300\n",
-       "r                            0.01\n",
-       "T_env                          22\n",
-       "t0                              0\n",
-       "t_end                          30\n",
-       "dt                              1\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = System(init=init,\n",
-    "                volume=300,\n",
-    "                r=0.01,\n",
-    "                T_env=22,\n",
-    "                t0=0, \n",
-    "                t_end=30,\n",
-    "                dt=1)\n",
-    "coffee"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `update` function implements Newton's law of cooling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def update(state, system):\n",
-    "    \"\"\"Update the thermal transfer model.\n",
-    "    \n",
-    "    state: State (temp)\n",
-    "    system: System object\n",
-    "    \n",
-    "    returns: State (temp)\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    T = state.temp\n",
-    "    T += -r * (T - T_env) * dt\n",
-    "\n",
-    "    return State(temp=T)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>temp</th>\n",
-       "      <td>89.32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "temp    89.32\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "update(init, coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can run simulations using the same function from the previous chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_simulation(system, update_func):\n",
-    "    \"\"\"Runs a simulation of the system.\n",
-    "    \n",
-    "    Add a TimeFrame to the System: results\n",
-    "    \n",
-    "    system: System object\n",
-    "    update_func: function that updates state\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    \n",
-    "    frame = TimeFrame(columns=init.index)\n",
-    "    frame.loc[t0] = init\n",
-    "    ts = linrange(t0, t_end-dt, dt)\n",
-    "    \n",
-    "    for t in ts:\n",
-    "        frame.loc[t+dt] = update_func(frame.loc[t], system)\n",
-    "    \n",
-    "    system.results = frame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's how it works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>temp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>90.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>89.320000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>88.646800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>87.980332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>87.320529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>86.667323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>86.020650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>85.380444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>84.746639</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>84.119173</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>83.497981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>82.883001</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>82.274171</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>81.671430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>81.074715</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>80.483968</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>79.899128</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>79.320137</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>78.746936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>78.179466</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>77.617672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>77.061495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>76.510880</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>75.965771</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>75.426114</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>74.891852</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>74.362934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>73.839305</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>73.320912</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>72.807702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>72.299625</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         temp\n",
-       "0   90.000000\n",
-       "1   89.320000\n",
-       "2   88.646800\n",
-       "3   87.980332\n",
-       "4   87.320529\n",
-       "5   86.667323\n",
-       "6   86.020650\n",
-       "7   85.380444\n",
-       "8   84.746639\n",
-       "9   84.119173\n",
-       "10  83.497981\n",
-       "11  82.883001\n",
-       "12  82.274171\n",
-       "13  81.671430\n",
-       "14  81.074715\n",
-       "15  80.483968\n",
-       "16  79.899128\n",
-       "17  79.320137\n",
-       "18  78.746936\n",
-       "19  78.179466\n",
-       "20  77.617672\n",
-       "21  77.061495\n",
-       "22  76.510880\n",
-       "23  75.965771\n",
-       "24  75.426114\n",
-       "25  74.891852\n",
-       "26  74.362934\n",
-       "27  73.839305\n",
-       "28  73.320912\n",
-       "29  72.807702\n",
-       "30  72.299625"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_simulation(coffee, update)\n",
-    "coffee.results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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EcePGITAwEF9++SU2bNgAOzu7do/7/vvvYWNjgxkzZtx2voCAAAQEBODnn3/GoUOH4Ofn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\n0qe1QlwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8f492c7828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(coffee.results.temp, label='coffee')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After running the simulation, we can extract the final temperature from the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def final_temp(system):\n",
-    "    \"\"\"Final temperature.\n",
-    "    \n",
-    "    If system has no results, return initial temp.\n",
-    "    \n",
-    "    system: System object.\n",
-    "    \n",
-    "    returns: temperature (degC)\n",
-    "    \"\"\"    \n",
-    "    if hasattr(system, 'results'):\n",
-    "        return system.results.temp[system.t_end]\n",
-    "    else:\n",
-    "        return system.init.temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It will be convenient to wrap these steps in a function.  `kwargs` is a collection of whatever keyword arguments are provided; they are passed along as arguments to `System`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n",
-    "    \"\"\"Runs a simulation with the given parameters.\n",
-    "\n",
-    "    T_init: initial temperature in degC\n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    volume: volume of liquid in mL\n",
-    "    t_end: end time of simulation\n",
-    "    \n",
-    "    returns: System object\n",
-    "    \"\"\"\n",
-    "    init = State(temp=T_init)\n",
-    "    \n",
-    "    system = System(init=init,\n",
-    "                    volume=volume,\n",
-    "                    r=r,\n",
-    "                    T_env=22, \n",
-    "                    t0=0,\n",
-    "                    t_end=t_end,\n",
-    "                    dt=1)\n",
-    "    return system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we use it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "72.299625390403094"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, r=0.01, volume=300, t_end=30)\n",
-    "run_simulation(coffee, update)\n",
-    "final_temp(coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.499850754390966"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, t_end=15, r=0.1, volume=50)\n",
-    "run_simulation(milk, update)\n",
-    "final_temp(milk)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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6+uG2+CBehicikzKo3GfNmnXDy/Lnzp0zSiCi3k7Z1o69xwqRV9agG3N0kGLs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\nCCGSzF8YBw0CtpUnZAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8f46b074e0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(milk.results.temp, label='milk')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using `fsolve`\n",
-    "\n",
-    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def func(x):\n",
-    "    return (x-1) * (x-2) * (x-3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`modsim.py` provides `fsolve`, which does some error-checking and then runs `scipy.optimize.fsolve`.  The first argument is the function whose roots we want.  The second argument is an initial guess."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fsolve(func, x0=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Usually the root we get is the one that's closest to the initial guess."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 2.])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fsolve(func, 1.9)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3.])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fsolve(func, 2.9)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But not always."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 3.])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fsolve(func, 1.5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def error_func1(r):\n",
-    "    \"\"\"Runs a simulation and returns the `error`.\n",
-    "    \n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    \n",
-    "    returns: difference between final temp and 70 C\n",
-    "    \"\"\"\n",
-    "    system = make_system(T_init=90, r=r, volume=300, t_end=30)\n",
-    "    run_simulation(system, update)\n",
-    "    return final_temp(system) - 70"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With `r=0.01`, we end up a little too warm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.2996253904030937"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "error_func1(r=0.01)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The return value from `fsolve` is an array with a single element, the estimated value of `r`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.011543084583973956"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solution = fsolve(error_func1, 0.01)\n",
-    "r_coffee = solution[0]\n",
-    "r_coffee"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "70.000000000006395"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300, t_end=30)\n",
-    "run_simulation(coffee, update)\n",
-    "final_temp(coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  When you call `fsolve`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `fsolve` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
-    "\n",
-    "Before you use `fsolve`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18.499850754390966"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "r_milk = 0.1\n",
-    "milk = make_system(T_init=5, t_end=15, r=r_milk, volume=50)\n",
-    "run_simulation(milk, update)\n",
-    "final_temp(milk)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "def error_func2(r):\n",
-    "    \"\"\"Runs a simulation and returns the `error`.\n",
-    "    \n",
-    "    r: heat transfer rate, in 1/min\n",
-    "    \n",
-    "    returns: difference between final temp and 20C\n",
-    "    \"\"\"\n",
-    "    system = make_system(T_init=5, t_end=15, r=r, volume=50)\n",
-    "    run_simulation(system, update)\n",
-    "    return final_temp(system) - 20"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1.500149245609034"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "error_func2(r=0.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.13296078935465339"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "solution = fsolve(error_func2, 0.1)\n",
-    "r_milk = solution[0]\n",
-    "r_milk"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "19.999999999999613"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Solution\n",
-    "\n",
-    "milk = make_system(T_init=5, t_end=15, r=r_milk, volume=50)\n",
-    "run_simulation(milk, update)\n",
-    "final_temp(milk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mixing liquids"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def mix(s1, s2):\n",
-    "    \"\"\"Simulates the mixture of two liquids.\n",
-    "    \n",
-    "    s1: System representing coffee\n",
-    "    s2: System representing milk\n",
-    "    \n",
-    "    returns: System representing the mixture\n",
-    "    \"\"\"\n",
-    "    assert s1.t_end == s2.t_end\n",
-    "    \n",
-    "    volume = s1.volume + s2.volume\n",
-    "    \n",
-    "    temp = (s1.volume * final_temp(s1) + \n",
-    "            s2.volume * final_temp(s2)) / volume\n",
-    "    \n",
-    "    mixture = make_system(T_init=temp,\n",
-    "                          volume=volume,\n",
-    "                          r=s1.r)\n",
-    "    \n",
-    "    return mixture"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "70.000000000006395"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee = make_system(T_init=90, t_end=30, r=r_coffee, volume=300)\n",
-    "run_simulation(coffee, update)\n",
-    "final_temp(coffee)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21.76470588235285"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "milk = make_system(T_init=5, t_end=30, r=r_milk, volume=50)\n",
-    "run_simulation(milk, update)\n",
-    "final_temp(milk)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the results look like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file chap07-fig01.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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gsssuAwAcPnwYX3/9NVatWoU77rijRytJRERE0Ysq3O+77z7U1dXhueeew3PP\nPQdBELBgwQKIxWLMnDmTY+5ERER9SFThLpfL8dRTT+H+++/Hrl27YLVaodFokJ2dHdyjnYiIiPqG\nqMJ96dKl+N3vfof09HSkp6f3dJ2IiIjoLEQ1oe7LL7/EyZMne7ouRERE1A2iCveHH34YK1aswL//\n/W9YLBZ4vd6wLyIiIuobouqWf+KJJ+B0OnHXXXd1eFwkEmHPnj3dWjEiIiI6M1GF+y233AKRSNTT\ndSEiIqJuEFW4P/DAAz1dDyIiIuomUYX7Dz/8cNpzJkyYcNaVoZ5TXl6O3/3ud6itrcWnn34KuVyO\ne++9F3v27METTzyBGTNm9HYViYiom0QV7rNmzTptt/zevXu7pULUM8rKyqDT6bB582bIZDK8+eab\n2LNnD/7xj39Ap9P1dvWIiKgbRRXur7zySlhZS0sLfvjhB2zbtg3Lli3r9opR92pqakJGRgZkMhkA\noLm5GcnJyUhNTe3lmhERUXeLKtyvuOKKDsuvvfZavPfee3j33XdxySWXdGvFKFx9fT2WL1+OL7/8\nEhKJBP/5n/+Jhx56CDqdDlu2bMFLL72EyspKyOVyXHnllVi2bBmSk5Mxffp0HD58GCKRCJ9++imu\nv/56fPjhh/B4PMjOzsZjjz2Gm266CevWrcM777yD6upqaDQazJgxAw888ACkUt+vyb59+/DMM89g\n9+7dcDqdmDhxIpYtW4YRI0b08k+GiIjaiyrcO3PppZfimWee6Y66xNT2/bXYtuckXO7Y36Mvk4px\nyQXpyMnq2tK9ixYtgl6vx2effQaRSIT7778fDz74IAoLC7F48WI8++yzuOaaa1BTU4P77rsP//3f\n/41XXnkFH3/8MfLz8zFgwAA8++yzAIBBgwbhvffew5dffgkAeP/997F69Wr8+c9/xoQJE3DgwAEs\nXLgQKpUKixYtgslkwty5czF79myUlpbC6XTiySefxG9+8xts3rwZEomk239ORER0ZqJaxKYz5eXl\nEIujf5mpU6fiwgsvRHZ2dshXZWUlAGDjxo2YOXMmcnJyYDQasXLlSng8nrOtZpiKA3W9EuwA4HJ7\nUXGgrkvX7Nu3D9u3b8d9992H5ORkaLVa/OEPf8CvfvUrvPXWW5gyZQqmT58OuVyOoUOHYuHChfjy\nyy9RX18f1euvXbsWt99+OyZOnAixWIzRo0ejoKAA7733HgDgo48+gkwmQ1FREZRKJTQaDZYtW4bq\n6mps27aAq/h3AAAgAElEQVStyz8DIiLqOVG13GfPnh1WJggCmpqacOTIEVx33XVdetPHHnsMN998\nc1j5tm3bUFxcjBUrViA3NxeVlZVYsGABZDIZFi1a1KX3OJ3xowy92nIfP8rQpWuqqqoAAEOGDAmW\nZWRkICMjA6Wlpbj88stDzj/vvPMAANXV1dDr9ad9/SNHjuDgwYMoKysLlgmCAABwOp04cuQI6uvr\nkZ2dHXKdWCzGsWPHuvRZiIioZ0UV7l6vN2y2vEgkwtChQ3Hddddh7ty53VKZtWvXYsqUKcjLywMA\nZGVlYd68eSgtLUVhYWGXeghOJycrrcvd4r0p0O0dCNz2HA5HWHlgSeBoFx9SKpUoLCzEvHnzIh4f\nNWoUNmzY0IVaExFRb4gq3N9+++1ufdPNmzfj1VdfRU1NDYYNG4bCwkJMmzYNFRUVmDVrVsi548aN\ng9lsRlVVFTIzM7u1HueS4cOHA/C1sMeNGwcA+Omnn/D5559j6NCh2L9/f8j5Bw8ehFgsxrBhw6J+\n/VOXEG5oaIBSqURiYiKGDx+Od999F1arFWq1GoDvD41jx45h6NChZ/npiIioO0XVFP7FL34Bs9nc\n4bG9e/eGdQl3ZtSoUcjMzMTatWvxxRdf4JprrsGiRYtQUVEBk8kErVYbcn5KSgoAwGQyRf0e8ej8\n88/HpEmTsHLlStTX18NisWD58uX44osvMGfOHPzrX//CRx99BLfbjcrKSpSWlsJoNAZ/fqczd+5c\nbNq0CZs3b4bL5UJ1dTXmz5+P5cuXAwBmzJgBlUqFxx57DI2NjbDZbFi1ahV++ctfwmq19uRHJyKi\nLuq05R5Yme7o0aOoqKiARqMJOS4IAv75z3+iubk56jd86aWXQr5fuHAhtmzZgr/+9a9Rv0Z/9cIL\nL+CRRx6B0WiETCbDf/zHf+Cxxx6DXq/H8uXL8eqrr+L3v/89dDodjEYjioqKon7t6dOno6GhAStX\nrsSSJUug0+lwzTXX4Le//S0AQK1W49VXX8XTTz+Nq6++GjKZDGPHjkVZWVmwJU9ERH2DSOhoENfv\nyiuvRE1NTafjtoIgYNq0aXjhhRfOuBKLFy+G1WrF4cOHcfvtt2PhwoXBY9u3b8cdd9yBLVu2ROxi\nPnbsGHJzc7F169aQCWdERETx6HS512nL/YsvvsDx48eRm5uLP/3pT2EtdwDQarVhM6gjqa6uxmuv\nvYYHHngg5LWOHDmCSZMmQaPRYMeOHSHXlJeXw2AwICMjI6r3ICIi6u9OO6Fu8ODBKCsrw6RJk4Ir\nlbXX2tqKjz/+GNdff/1p30yv12Pr1q1obm7Gww8/DIVCgddeew2VlZVYtWoVmpubMWfOHGzatAnT\npk3D/v37UVZWhoKCAm45S0REFKWoZstfdtllAHzrkTc1NQXLBUHA999/jz/84Q9RhbtKpUJZWRlW\nrFiBvLw82Gw2XHDBBVi7dm1wJnxJSQlWr16NJUuWQK/XIz8/HwUFBWfy2YiIiPqlqML9xIkTuP/+\n+7Fz584Oj1900UVRv+HIkSPDJtW1ZzQaYTQao349IiIiChXVrXBPP/00XC4XHn74YchkMtx///1Y\ntGgRhg8fjttvvx1vvfVWT9eTiIiIohRVuAe63mfPng2JRILrrrsO9957Lz766CNUV1fj448/7ul6\nEhERUZSiCnez2Yy0NN9SrXK5HDabDQAglUrxX//1X3jxxRd7roZERETUJVGFe1paGnbv3h18Xl5e\nHjwmk8lQU1PTM7UjIiKiLotqQt306dPxwAMPYMOGDZg6dSqeeeYZNDQ0IDk5GevXr+/Xa74TERH1\nNVG13BcvXoyCggJoNBrMnz8fkyZNwgsvvIDHH38cFosFjz76aA9Xk7qqtLQU11xzDQDfSkZZWVn4\n+uuvAQD5+fnBZWWJiCj+RNVyl0qlWLx4cfD7l19+GU1NTXC73UhNTe2xytGZKywsRGFhYW9Xg4iI\nekFULfdp06ahrq4upEyr1TLYiYiI+qCowl2hUGDfvn09XRfqRFZWFv72t7/h17/+NcaPH49rr70W\nO3fuxNtvv42rrroKF198MYqLi+HxeAAAzz//PKZMmRLVa3/88cfIyckJmShJRETnrqi65e+//36s\nXr0a27dvxwUXXIDExMSwcwJL1FLPWbNmDVasWIHMzEzce++9KCoqQl5eHj755BMcO3YMM2fOhNFo\nxNSpU6N+zW+++QYPP/wwnn/+eVx88cU9WHsiIoqVqML9vvvuAwDs2rULAEI2cREEASKRCHv37u2B\n6vWs74/vxA8/74rq3NGG8zBl+OSQsi+rvsW+ukNRXT9hUDYmDh7X5Tq2d/XVV2P06NEAgKuuugrf\nfPMN7r//figUCpx33nnIysrCoUOHog73ffv2YfHixXjqqadw+eWXn1XdiIio74gq3MvKynq6HhSF\nwYMHB5+rVCro9XooFIqQMofDEdVrnThxAnfffTemTJmCX/ziF91eVyIi6j1d2hWOepdYLO70+67Y\nvn07brnlFqxfvx533HEHJk6ceLbVIyKiPiKqcAeA2tparFu3Dnv27EF9fT1efPFFpKamYsuWLZg+\nfXpP1rHHTBw87qy6yqcMnxzWVX+uuPbaa/HYY49BrVbjwQcfxIcffoiUlJTerhYREXWDqJp++/bt\nw/XXX49169bB5XLh4MGDcLlcOH78OIqLi/HRRx/1dD2pmwVa/Q8++CAMBgOKi4shCEIv14qIiLpD\n1Fu+Xnzxxfi///s/vP7665DJZACA4cOHY8mSJXjttdd6tJLUc2QyGVauXInvvvuO/45ERHFCJETR\nXMvJycHbb78dnKmdk5ODDRs2YOjQoaiursb06dOxc+fOHq9sJMeOHUNubi62bt2KIUOG9Fo9iIiI\nYuF0uRdVy12pVEacvGW1WiGVRj10T0RERD0sqnAfM2YM/vSnP8Fut4eUe71erFmzBuPHj++RyhER\nEVHXRdXkXrRoEQoKCjBlyhSMHz8eLpcLjzzyCCorK2E2m/Hmm2/2dD2JiIgoSlG13CdMmIAPPvgA\nRqMRJ0+eRFpaGhobG3HllVdi/fr1yM7O7ul6EhERUZSiHiwfOXIkHn/88Z6sCxEREXWDqMO9vr4e\nn332Gaqrq9Hc3Izk5GScd955mDZtGtRqdU/WkYiIiLogqnD/+uuvce+998Jms0GlUiExMREtLS2w\n2WxITEzEiy++iEsuuaTLb15eXo45c+agsLAwuDnNxo0bsWbNGlRVVcFgMCAvLw9FRUWQSCRdfn0i\nIqL+KKox9+XLlyMnJweffPIJtm/fjq+++grbt2/Hxx9/jAsvvPCMuuvtdjuWLVsWsn3stm3bUFxc\njPnz5+Pbb7/F888/jw0bNuDFF1/s8usTERH1V1GFe1VVFZYsWYLhw4eHlI8cORJLly5FVVVVl9+4\npKQEI0aMwJgxY4Jla9euxZQpU5CXlwe5XI6srCzMmzcPb731Frxeb5ffg4iIqD+KKtwHDhwYsod7\neyKRCAMHDuzSm37//ff48MMP8Yc//CGkvKKiAuPGhW7kMm7cOJjN5jP6A4KIiKg/iirc7733Xqxa\ntQpNTU0h5SaTCc899xwKCwujfkObzYZly5bhd7/7HQYMGBD2elqtNqQssFOZyWSK+j2IiIj6s6gm\n1G3evBm7d+/G5ZdfjuHDh0OtVsNms+HIkSPQaDSwWCz461//CsDXkl+7dm3E1yopKcHw4cNx8803\nd88nICIiohBRhXtzczOGDh2KoUOHBssSEhIwduxYAAjZKrSzfWgC3fGRtojV6/Uwm80hZY2NjQAA\ng8EQTVWJiIj6vajC/X/+53+65c0++OADtLa24oYbbgiWWa1W7Ny5E3//+9+Rk5ODHTt2hFxTXl4O\ng8GAjIyMbqkDERFRvIvpdm7FxcVYvHhxSNnixYsxfvx43H333Th+/DjmzJmDTZs2Ydq0adi/fz/K\nyspQUFAQcUIfERERhYoq3A8dOoTly5djz549sFgsHZ7z448/nvZ1tFpt2IQ5uVwOtVoNg8EAg8GA\nkpISrF69GkuWLIFer0d+fj4KCgqiqSYREREhynBfunQpbDYbCgoKkJyc3K0VeOutt0K+NxqNMBqN\n3foeRERE/UlU4X7w4EG8++67yMrK6un6EBER0VmK6j73YcOGwel09nRdiIiIqBtEFe7FxcV45pln\nsHPnToY8ERFRHxdVt7xWq4XZbMbtt98OAB3u0BbNhDoiIiLqeVFPqAOABx98sNsn1BEREVH3iirc\nq6qq8N5772HUqFE9XR8iIiI6S1GNuWdmZsJut/d0XYiIiKgbRBXuv//977Fq1Sp89913sFqt8Hq9\nYV9ERETUN0TVLb9w4UI4nU7ceeedHR4XiUTYs2dPt1aMiIiIzkxU4X777bdzbXciIqJzRFTh/sAD\nD/R0PYiIiKibdGlXuK+//hq7d+9GXV0dFi5ciJSUFFRXV4fs805ERES9K6pwb2xsxMKFC1FRUQGZ\nTAaPx4P8/HyYTCb88pe/xBtvvIFx48b1dF2JiIgoClHNln/mmWfQ1NSEN998E9u3b4dCoQAAjBw5\nEjfccANWrVrVo5UkIiKi6EUV7v/4xz/w6KOP4pJLLoFUGtrYnz17NrZv394jlSMiIqKuiyrcnU4n\nBg4c2OExmUwGt9vdrZUiIiKiMxdVuI8YMQLvv/9+h8c+++wzZGZmdmuliIiI6MxFNaFu1qxZeOih\nh7B7925cdtll8Hq9+OCDD1BdXY1PP/0UTz31VE/Xk4iIiKIUVbjfcsstEIlE+Mtf/oJnn30WAPDS\nSy8hMzMTTzzxBK6//voerSQRERFFL+r73G+++WbcfPPNaGpqgtVqhVarhVqt7sm6ERER0RmIOOZ+\n5513orm5Oaxcq9Vi8ODBDHYiIqI+KmK4b9u2DS6XK5Z1ISIiom4Q1Wz57nTw4EEsWLAAkydPRnZ2\nNmbOnInPP/88eHzjxo2YOXMmcnJyYDQasXLlSng8nlhXk4iI6JzVabh3905wNpsNc+bMQUZGBrZu\n3Yry8nIYjUYUFRXh0KFD2LZtG4qLizF//nx8++23eP7557Fhwwa8+OKL3VoPIiKieNbphLp77rkH\nMpksqhd65513TnuOzWbDb3/7W1x//fVQqVQAgDlz5uC5557DgQMH8Mknn2DKlCnIy8sDAGRlZWHe\nvHkoLS1FYWEhxOKYdzQQERGdczpNS5lMFvVXNHQ6HW699dZgsDc2NqK0tBTp6em47LLLUFFREbYB\nzbhx42A2m1FVVXVmn5CIiKif6bTl/uc//xmpqak98sZjx46Fy+VCdnY2XnvtNaSkpMBkMkGr1Yac\nl5KSAgAwmUxcCY+IiCgKEVvu3T3efqoff/wR33zzDa688krMmjULlZWVPfp+RERE/UXEcBcEocff\nXKfT4b777sOAAQPwzjvvQK/Xw2w2h5zT2NgIADAYDD1eHyIiongQMdxnzpwZ3Le9u2zduhVTp06F\nw+EIKXc6nZBIJMjJycGOHTtCjpWXl8NgMCAjI6Nb60JERBSvIob78uXLu30VupycHNhsNvzxj3+E\n2WyGw+HAG2+8gZ9++glGoxFz587FV199hU2bNsHpdGLXrl0oKyvDXXfd1ePDBERERPEi6rXlu4NO\np8Obb76Jp59+GldffTXEYjEyMzPxwgsvYPz48QCAkpISrF69GkuWLIFer0d+fj4KCgpiWU0iIqJz\nWkzDHQDOP/98vPrqqxGPG41GGI3GGNaIiIgovnBVGCIiojjDcCciIoozDHciIqI4w3AnIiKKMwx3\nIiKiOMNwJyIiijMMdyIiojjDcCciIoozDHciIqI4w3AnIiKKMwx3IiKiOMNwJyIiijMMdyIiojjD\ncCciIoozDHciIqI4w3AnIiKKMwx3IiKiOMNwJyIiijMMdyIiojgj7e0KEBERxRNBECBAAATACwEQ\nBEjEEohEouA5XsELu9sB32n+/wkCBACC4IVELIFannjGdWC4ExHRGXF7PXB73RAEAV7BC6/gbXuO\nQJkAQfAiUZYAtSI0rE5aamF1tkLwn+u71v/cH3aB7zO0g6BP1IVcv+PkHlgcLcEwDbyG73kgLH11\nmDBwLNLU+pDrPz34BWxue7vrfMHqe28ErxUEAdeefxVSE1JCrn/9h7/CGwzltnCGIIT9rGZfNBOJ\n8oTg93aXA2t3rI/4s01PSsMNo6+J7h+iAwx3IqI+IhCMHq8HbsETfO4RvBCLxEhWakLOb7ZbUNNS\nD4/XC6/gO8/3vO37QOgmK7W4MG1UyPUH6o/gQEOl71xv27nedmEd+P781OH4j4yJIdd//dP32Fd3\nKKrPNmFQNiYOHhdSVnFyD34yH4/qeqVUERbuh01HUd9iiur60fqRYWV1rSa0Olujut7j9YSVOb3u\nDoO8I/7YbyPq+Lzg+VG+biQMdyKiDngFb7Bl6vZ64PF6kKLShpxjdztw1HwseDxwbuB7j9D2XCqW\nIHfk5SHXH2s+gX8c+botzL3eiPVJTUjBLRdeF1L2s6UGX1Z9G9XnGaIdGBbuVmcrfm4+GdX1To8r\nrEwsin7allcI/2yxvT48LMWi0yRsO2HhDF8+R4xgkQgiACKRCCKIwk4Ui8RQypT+c8TBcwP1at/K\nPxMxD/eGhgY8++yz+Oc//4nW1lacd955eOCBB3DZZZcBADZu3Ig1a9agqqoKBoMBeXl5KCoqgkQi\niXVViegc4PK44PK64fa44fL6vzwuuDxuuIPfu+HyunDRgDGQS+XBa+1uB7Yc+sJ/bls4u7yuDoP2\n7ot/BbG4LVBanK34ovLfUdVTIVV0WG5z2aO63tMj4XZ218skUsglMojFEoj9ISYWif1fIoj8j2KR\nGOoOwipdbQg9HyKIRKJ21/ueiyCC4ZRWOwBkDxgNm84ePFcEcbvnomAdIl1/zcgr4BW8IeEqhth/\nvT+Y/ccSZKqw6+8c/0v/NSIg+Ijg5+iMUqrAneNv6fScsxHzcC8sLIRarcb//u//QqPR4IUXXkBh\nYSE++eQTHD16FMXFxVixYgVyc3NRWVmJBQsWQCaTYdGiRbGuKhHFgKnVDLvHAafbBZfXBafHBZfH\n99j+ucvrwn9kTIROlRxy/Rvb3++wVdeRUakjQsJdDBFOWuqirqtb8EDe7iYjiTj6RkdH3boSUfj1\nYrEYEpEEErEEEpEYErEYYpEEGoU67FyNMgkjdcMgEUsg9p8rEQWe+wI38H1Hk7NG6jJgSNQFw1Qs\nClzfPqR9xzr6rJOH5GDykJyofwanGpc+5oyvBYCRumFndb0hMfWsrle0+13qa2Ia7haLBSNHjsSv\nf/1rGAwGAMA999yDl19+GTt37sRHH32EKVOmIC8vDwCQlZWFefPmobS0FIWFhSF/MRNR32CymdHq\ntMHhccLpccLhdsEZfO6E0+MKHps8JAfDkoeEXL/l8Jdotluiei+byw6c0oCSSqRwup1RXe8+JWCl\n4k7+EygSQSqWQCqWBh9P/SNCKVVglD4TUrEvjKViKSQiif/8QEC3PT9VWmIq5lx0s+88f5ifrsXX\nXrragHS1IerzT5WkUCOpgz8a6NwX03BPSkrCk08+GVJWXV0NAEhPT0dFRQVmzZoVcnzcuHEwm82o\nqqpCZmZmzOpK1F+4vR7YXDbY3Q443E7fo8cJh9sBuzvw6CsbrT8Pow2hE5O++akcx6Mct2112cLK\n5BJZ1HXtaNxXJVVCLBJDJpZCJpFCKpb6n8vaHiW+MuUpXeNisRgzRl8TDG9ZIMglsqiCVilV4KoR\nl0Vd/1NJxBIkyMO7e4nOVq9OqLNarVi6dClyc3ORnZ0Nk8kErTZ0wkpKiu/WA5PJxHAnioLb44bN\nbYfd7YDN5X9022Fz2ZGakILzU0eEnP/V0W04UH8kqtcelDQgrKwrXZMdhbM+QQep2Dd2K5fIIBPL\nIJf6HwNl/sdTu+QB4PbsGVG/f0cGJqWd1fVEfVGvhfvx48exYMEC6PV6PPvss71VDaJzgiAIsLsd\ncHvdYd2oh01Hsbt2P1pdNrS67HB73BFfZ6RuWFi4q2TKqOthdzvCynSqFNg1TigkcsglMiikcshD\nnst8x6RyJMrCJ1VNGT456vcnouj0Srjv3LkTCxYsgNFoxEMPPQSZzNctp9frYTabQ85tbGwEgOAY\nPVG8cXs9sDissDpb0OK0ocXVihZnqz+sfYFtc9kgCAIGqA24cYwx5HqH2xH1pDBbB+GcKEtAgjwB\nSqkCSqkCConc9yhte1RIFFDKFB3OeJ4waCwmYOyZfXgi6hExD/cDBw7gnnvuwcKFCzFv3ryQYzk5\nOdixY0dIWXl5OQwGAzIyMmJYS6Lu4fS40OL0hbXV2Qqnxxk2Q/h48wl8evCLqF6vozHrU2/RCdw/\nq/KHtUqmhFKqhEqmQLJSG3b92AFZGDsgqwufioj6upiGu8fjQXFxMW699dawYAeAuXPnYs6cOdi0\naROmTZuG/fv3o6ysDAUFBV2aQUoUS26PG8ctJ2FxtMDitPoeHVZYHNawMWaRSITsAaNDfp+jXaxC\nLpV3OL6drjbg+qxcJMhUUMlUkEtk/P8LUT8X03Dfvn07du/ejQMHDuCNN94IOXbjjTfi8ccfR0lJ\nCVavXo0lS5ZAr9cjPz8fBQUFsawmUZDX6w0GdrO/6/yi9AtCQtbpdUXd8hYEATaXPWSGtFqeCI0y\nCWp5AhJlCUiU+74SZCokyJTB0JZGuKdaKVNikCz97D4oEcWVmIb7xIkTsX///k7PMRqNMBqNnZ5D\n1N2szhY02S2+L0dz8HmzwxK2xvOIlKEwSNsWv1BJlZCKpXB7wyeyScQSJMoTQoL71Fa1UqrAHdk3\n9MwHI6J+iWvLU79hdzvQZG+GWp4Y1hW+Yd9nsDpaonodi8MasrKVSCTCSN0wiEQiJCkSkSRX+x4V\naqikSnaRE1HMMdwp7jjdTpjsTWi0mWGymdFoa4bJZobdv4b3fw6bFLaBhlaRFDHcE+QJ0CjUSJL7\nAjtZFT4p7coRl3b/ByEiOkMMd4oL++sP47DpKBptTWg5zRaOTfbmsDJDYipcXje0iiRolRokK32P\nGoUasi6soEZE1Bcw3KnPEwQBVmcL6lsbUd9qgkqqDLt1q9lhxbGmE52+jkQsgVap6XDRlkuGjO/W\nOhMR9SaGO/UpgiCg2WEJBnl9iwn1rY1wtFt8xZCYGhbuKe3u3xaLxUhWaqFTaZGi0iJFmQxdQjKS\n5Ikc/yaifoHhTn1CQ2sjvv6pHA2tpg7XH2+v0d4EQRBCgnpQ0gBMG3kFdCotNMqkLu1TTUQUbxju\nFDM2lx011nrUtTZg4qBxIeEsFUtwwlIT8Vq5VA59gg76hBSkJqSEhXuCXIVMHVcxJCICGO7UQwRB\nQKOtCSetdaix1qGmpT5kz+7zdcNDZp1rFEmQSWRweVxQSBUwJOr8Ya6DPlHHLnUioi5guFO3+dlS\ngxOWWl+YW+vh6qR7/aS1PiTcRSIRrj3/KiT570FnkBMRnTmGO52RU7vFAWDbsQrUWusjXiMWi2FI\nSMUAtR76hJSw49xXm4ioezDcKSpOjwsnLDU43lyDny0nMSIlAxcPyg45J11tCAl3lUyJAWoD0tUG\nf6DrIImwPjoREXUfhjt1yOP1oKalHsebT+J480nUtTSErLEul8jCwn2odhDcXjcGqA0YoDZwnJyI\nqJcw3CmoxdmKgw1VON58AietdfB4PRHPrW9phNvrCdmpbLAmHYM13J2MiKi3MdwpyOa2Y9ux7RGP\npyakYLAmHYOSBmBgUlrELUiJiKh3Mdz7EUEQYLY346j5OH5qOoZrRk4JWYo1VZUChVQRXA1Oo0zC\n4KR0DNYMwKCkAVB2sGwrERH1PQz3OOf1enHSWosqf6A3263BYz81HUeWfmTwe5FIhIsHZUMmkWJw\nUjrUisTeqDIREZ0lhnsccridqG76GUfNx1DdfAJOt7PD846aQ8MdQNia7UREdO5huMeZihN78N3x\nipCZ7e1JJVIM1QzCsOTBGKodFOPaERFRLDDcz2GnzlYHAK0yKSzYE+UJGJY8BMOSB2NQ0gDea05E\nFOcY7ucYp8eFqsZqHDYdRX2rCbPHzYRY3LYD2hBNOiRiCXSq5GCg61TJvN+ciKgfYbifA9xeD6qb\nfsahhir81HQ85P7zY80nkJE8OPi9TCLDnItuhkIq742qEhFRH8Bw76O8ghc/N9fgkKkKlY3VETdh\nqWs1hYQ7AAY7EVE/x3Dvg36s2YcfTuyG3WXv8LguIQXn6YZhpG4YkhTqGNeOiIj6upiHe3V1NZYt\nW4Zt27Zh69atGDJkSPDYxo0bsWbNGlRVVcFgMCAvLw9FRUWQSPrbBDBRWLBrlGqM1A3HebrhSGm3\nVSoREdGpYhrun332GR555BFcccUVYce2bduG4uJirFixArm5uaisrMSCBQsgk8mwaNGiWFYzZupb\nTKhu/hk5A8eGlGfqMvB1dTkSZEpkpgzDeanDYUjQcVIcERFFJabhbjabsW7dOpw4cQJ/+9vfQo6t\nXbsWU6ZMQV5eHgAgKysL8+bNQ2lpKQoLC0NmhJ/LXB4XDpmqsLfuEOpbTAB8u6npE3TBcxJkKtx8\nwbXQqZIhFsXH5yYiotiJabjfeuutAIATJ06EHauoqMCsWbNCysaNGwez2YyqqipkZmbGpI49pb7V\nhL21h3DIVBU2OW5v7SFcMfySkLL2YU9ERNQVfWZCnclkglYbOpackpISPHYuhrvL48Jh00/YW3cQ\ndS0NYcfFYjEyU4ZhlH5EL9SOiIjiVZ8J93hzoP4I/vXT9x3ewqZVajDGcB5G6TOhlCp6oXZERBTP\n+ky46/V6mM3mkLLGxkYAgMFg6I0qnRVdQnJIsIvFYoxIHooxaedjoDqNk+OIiKjH9Jlwz8nJwY4d\nO0LKysvLYTAYkJGR0Uu1Oj1BEPCzpQaGxFTIJbJguT5Bh8GadFidrb5WeuoI7odOREQx0WfCfe7c\nuRPzV/4AABA4SURBVJgzZw42bdqEadOmYf/+/SgrK0NBQUGfbOV6BS+qGqtRcXIP6ltMuHToBIxL\nHxNyzrSRV0AukfXJ+hMRUfyKabj/4he/wM8//xzctezaa6+FSCTCjTfeiMcffxwlJSVYvXo1lixZ\nAr1ej/z8fBQUFMSyiqfl9npwoP4IdtbsQbPdGizfWbMPF6aNCtlxjcvAEhFRb4hpuH/66aedHjca\njTAajTGqTdfY3Q7sqT2IH2v3h60eJxFLMDx5CNxeD7dTJSKiXtdnuuX7KquzBbtq9mFv3SG4Pe6Q\nY3KpHGPTRuHCtCyoOJ5ORER9BMO9Ex6vB+t3b4bd7QgpT5QnYFz6GIzWj4Ss3SQ6IiKivoDh3gmJ\nWILs9NH47phvFn+KSouL0i/AebrhcbMcLhERxR+Gu5/b68EJSw2GageFlI9Ny0Jdiwmj9SMxVDuI\nM9+JiKjP6/fh7hW8ONhQie+P70SLy4ZbL5wesqWqTCKD8bwpvVhDIiKirum34S4IAo6aj2Pb8QqY\nbU3B8m3HKvCL86/sxZoRERGdnX4Z7icstdh2rAI11rqQcqVMicGadAiCwO53IiI6Z/WrcG9obcR3\nx3fgJ/PxkHKpRIqL0i9A9oDRIUvIEhERnYv6Rbi7PW58eXQbDpmqAP/qeAAgFolxQdoo5Ay8kPep\nExFR3OgX4S4RS2B1WtuCXSTC+brhmDh4HJIU6t6tHBERUTfrF+EuEolwyZAcbNi7BRnJgzFp8EVI\nTUjp7WoRERH1iH4R7gCQrjbgl2OnQ6dK7u2qEBER9ah+tcwag52IiPqDfhXuRERE/QHDnYiIKM7E\nxZi7x+MBAJw8ebKXa0JERNTzAnkXyL9TxUW419X5VpqbPXt2L9eEiIgodurq6jBs2LCwcpEgtFvV\n5Rxlt9vx448/wmAwQCKR9HZ1iIiIepTH40FdXR3Gjh0LpTJ8Eba4CHciIiJqwwl1REREcYbhTkRE\nFGcY7kRERHGG4U5ERBRnGO5ERERxJu7D3Waz4dFHH8XUqVNx8cUX4/bbb8e//vWv3q5WnzF16lRc\neOGFyM7ODvmqrKzs7arFXHV1NfLz85GVlYVjx46FHNu4cSNmzpyJnJwcGI1GrFy5MuLiEfEq0s/n\n+eefx+jRo8N+h5577rlerG1sNTQ0YOnSpbj88ssxYcIE3Hbbbfjmm2+Cx/v7709nP5/+/vtz8OBB\nLFiwAJMnT0Z2djZmzpyJzz//PHj8jH93hDhXXFws3HDDDcKRI0cEu90uvP3228LYsWOFw4cP93bV\n+oSrr75a+OCDD3q7Gr1uy5YtwmWXXSYsWbJEGDVqlFBdXR089u233woXXnihsGnTJsHhcAj79u0T\nrrrqKuH555/vxRrHVmc/n9WrVwtz5szpxdr1vttuu00oKCgQamtrBbvdLjz77LPC+PHjhZMnT/L3\nR+j859Off39aW1uFSy65RHjiiScEi8UiOBwOobS0VBgzZoxw8ODBs/rdieuWe1NTEz766CPcd999\nGDFiBBQKBe644w6MHDkS77zzTm9Xj/oQs9mMdevW4cYb/3979x8Tdf0HcPwJeMivEznB2EIIGB4m\nkICITFfzSknKfsgoCBjQBkaTmWYGZhKKiuYfzR+pTaA6FiRJWGrOtNFGUf4KxdKphIvIpAKT05Pj\nR98/HPf15IdMlIPj9djYuPeHz+de7/de43X3/vx4P9ttW1FREY8++ihz587F1tYWtVpNcnIyWq2W\nzs5OM0Q7+Poan5GupaUFX19fli9fjpubG6NHjyY1NZXr169z6tSpEZ8/dxqfkUyv17N06VIWL16M\nk5MTtra2JCQk0NHRwblz5waUOxZd3H/++Wfa2toIDAw0aQ8KCuLkyZNmimro+eqrr4iKiiI0NJT5\n8+ebTAmNFDExMXh7e/e4rbq6mqCgIJO2oKAgrly5wsWLFwchOvPra3zg5nOuU1JSCA8PR6PRsH79\nem7cuDGIEZqPUqlk7dq1+Pr6Gtvq6+sBcHd3H/H5c6fxgZGbPyqVipiYGOzt7QFobm7m/fffx93d\nnYiIiAHljkUX96amJgDGjjVdx93FxYV//vnHHCENORMnTsTHx4eioiK+/fZbZs+ezcKFC6murjZ3\naENGU1MTzs7OJm0uLi7GbSPd+PHj8fT0ZMmSJVRWVrJ+/Xq+/PJL1q1bZ+7QzEKn05GVlcXjjz9O\nYGCg5M9tbh8fyZ+bAgICmD59OkePHqWgoAAXF5cB5Y5FF/e+WFlZmTuEIWH79u1kZWWhUqlwcnIi\nPT2dSZMmsWvXLnOHJoaJF198kfz8fAIDA1EoFISFhZGWlkZZWRnt7e3mDm9QNTQ0EBcXx7hx49i4\ncaO5wxlyehofyZ+bTp8+TVVVFY899hgvvfTSgC9qtujiPm7cOODm+cJbNTc34+rqao6QhgVPT08u\nX75s7jCGDFdX1x5zCMDNzc0cIQ15Xl5eGAwG4ziNBKdOnSImJobQ0FA++OADHBwcAMmfLr2NT09G\nYv7AzWn6jIwMHnjgAUpKSgaUOxZd3AMCArC1te02xXzixAmmTp1qpqiGjvr6enJycrh69apJ+6+/\n/trjEoIjVXBwcLdrNI4fP46bmxuenp5mimro2LZtGxUVFSZttbW1ODg4jJgP0efOnSM1NZW0tDTe\neecdFAqFcZvkT9/jM5Lz5/Dhw2g0GlpbW03aDQYDNjY2A8odiy7uSqWS6OhoNm/eTF1dHXq9nvz8\nfBoaGoiNjTV3eGbn6urK4cOHycnJobm5mevXr7Nlyxbq6upISEgwd3hDRlJSEpWVlezfvx+DwUBN\nTQ2FhYWkpKTI6R1uzoytXLmSmpoa2tvbOXr0KDt37hwx49PR0UFmZiYxMTEkJyd32z7S8+dO4zOS\n8yc4OBi9Xs+qVau4cuUKra2tfPTRR/z222/MmTNnQLlj8Uu+GgwGNmzYwL59+7h27RqTJk1i2bJl\nhIaGmju0IaG2tpZ3332X6upq9Ho9Dz/8MG+++SZTpkwxd2iDKjIykj/++IP//vuPtrY2FAoFVlZW\nPPvss+Tm5nLw4EE2bdrExYsXcXV1JTY2lgULFlj8P58ufY3PypUr2bp1K3v37qWxsRE3NzcSEhJI\nSkrCxsbG3KHfd8eOHSM+Pt44JreS/Lnz+Iz0/Dl//jzr16/n+PHjWFtb4+PjQ3p6OhqNBuCuc8fi\ni7sQQggx0lj0tLwQQggxEklxF0IIISyMFHchhBDCwkhxF0IIISyMFHchhBDCwkhxF0IIISyMFHch\nBkFmZiZqtbrPn8TERAASExN54YUXzBrvtWvXmDdvHnl5eXd9jN9//x21Wk1xcfE9jOz++eSTT5g5\nc6Y8ellYBLnPXYhB0NLSYrKEZUZGBgaDgR07dhjbFAoFY8eONT5L+vbVDAfTokWLuHz5MkVFRYwa\nNequjtHR0UFTUxNKpRI7O7t7Gt/u3bspLy9Hq9Xe0+MuWbKE+vp6iouL77rfQgwFkr1CDAKlUolS\nqTS+VigUdHZ29rj4gzmLOkBVVRUHDhzg008/HVCBs7GxuW8Lo/z000/35bjLli1j9uzZfPbZZ/KI\najGsybS8EEPM7dPyarWagoIC1q5dS3h4OKGhoeTm5nLjxg2ys7OZNm0aERERbNiwweQ4jY2NLF26\nFI1GQ1BQEPPmzWPv3r13fP8tW7Ywffp0k0cQazQa1qxZw44dO5g5cybBwcEsWbIEvV7Pe++9x4wZ\nMwgLCyMrKwuDwQB0n5YvKytDrVZz4cIFUlNTCQ4OZubMmeTm5tLR0dHjPl26+tE1PqWlpRw5cgS1\nWk1ZWVm/+/v1118THR1NSEgIISEhxMbG8v333xu3u7u789xzz7Ft2zZkUlMMZ1LchRgGSkpKUKlU\n7Nq1i0WLFqHVaklOTsbDw4PS0lIWLFhAfn4+R44cAW6uqZCcnEx1dTWrV69mz549REZG8vrrr3Po\n0KFe36epqYkTJ04wa9asbtsqKipobGzk448/Zu3atezfv5+UlBT0ej1FRUXk5OTw+eefs2/fvj77\n8vbbbxMdHc0XX3xBQkICWq22Xx86umzevJmgoCCCg4OprKwkKiqqX/2tq6vjtddeIzIykj179lBa\nWkpAQABpaWlcunTJeHyNRsOff/5JTU1Nv2MSYqiR4i7EMKBSqXjllVfw8vIiMTERR0dH7OzsSE1N\nxcvLi6SkJBwdHfnll18AOHToELW1taxZs4YZM2bg7e3NwoULiYiIYPv27b2+z7Fjx+js7CQkJKTb\ntra2Nt566y18fHyYO3cufn5+NDU1kZmZibe3N1FRUfj5+Rlj6M1TTz3Fk08+yYQJE0hLS8PBwaHb\nspZ9GTt2LKNGjUKhUODm5oadnV2/+nvmzBna29uZP38+EyZMwNfXl6ysLLRaLWPGjDEePywsDIAf\nf/yx3zEJMdRIcRdiGJg8ebLxdysrK5ydnZk0aVK3Np1OB8DJkydRKBTGQtUlIiKCs2fP9jrl/Ndf\nfwEwfvz4btv8/f2xtv7/vwxnZ2f8/f1NVqe6NYbePPLII8bfra2tTS4ivFv96W9ISAgqlYqEhAQK\nCws5e/ascc1sR0dH4z5OTk7Y29vT2Ng4oJiEMCe5oE6IYcDe3t7ktZWVFQ4ODt3auoq2Tqejra2t\n29LG7e3ttLW10dzcjEql6vY+V69eBW4WuIHG0Ju72edO+tNfd3d3SktLyc/P58MPPyQvL48HH3yQ\n9PR0YmJiTPZTKpX8+++/A4pJCHOS4i6EBRozZgx2dnaUl5f3ur2vdp1O12OBv9+6ZgFuL/bXrl3r\nc7/+9tfDw4Ps7Gyys7M5f/48Wq2WFStW4OHhQUREhPHvW1pacHZ2HkhXhDArmZYXwgJNmTKFGzdu\n0NraipeXl/Fn9OjRuLi49HqLW9eta+aaku4qwrd+azYYDJw+fbrb3976AaA//T1z5gxVVVXGffz8\n/Fi1ahVOTk4mF8/pdDr0en2PpyaEGC6kuAthgWbNmsXEiRN54403qKqqoqGhgW+++Ya4uDjWrVvX\n635Tp07F2tqa48ePD2K0/6dUKnnooYcoLy+npqaGCxcusGLFCpNz4nDz3P7Fixc5deoUly5d6ld/\nq6urefXVV9m9ezf19fXU19dTUFCAXq9n2rRpxmN33XEQHh4+eB0X4h6T4i6EBbK1taWwsBB/f38W\nL17MnDlzWL16Nc888ww5OTm97qdSqQgJCaGiomLwgr1NXl4e9vb2xMfHk5qayuTJk7vdmpeUlARA\nfHw8Bw4c6Fd/4+LiyMjIYOfOnTz99NM8//zzHDx4kE2bNpnc019RUYG7uzsBAQGD12kh7jF5/KwQ\nwsR3333Hyy+/TGlpKUFBQeYOZ1BdvnyZJ554guXLlxMXF2fucIS4a1LchRDdZGRk8Pfff1NUVISN\njY25wxk0S5cupa6ujpKSEhQKhbnDEeKuybS8EKKbvLw8dDodGzduNHcog6a4uJgffviBrVu3SmEX\nw558cxdCCCEsjHxzF0IIISyMFHchhBDCwkhxF0IIISyMFHchhBDCwkhxF0IIISyMFHchhBDCwvwP\n4f9vjH2cyvwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8f4696ae48>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(coffee.results.temp, label='coffee')\n",
-    "plot(milk.results.temp, '--', label='milk')\n",
-    "decorate(xlabel='Time (minutes)',\n",
-    "         ylabel='Temperature (C)',\n",
-    "         loc='center left')\n",
-    "\n",
-    "savefig('chap07-fig01.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what happens when we mix them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "63.109243697484459"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_last = mix(coffee, milk)\n",
-    "final_temp(mix_last)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And here's what we get if we add the milk immediately."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "coffee = make_system(T_init=90, r=r_coffee, volume=300)\n",
-    "milk = make_system(T_init=5, r=r_milk, volume=50)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "61.42857142857666"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mix_first = mix(coffee, milk)\n",
-    "mix_first.t_end = 30\n",
-    "run_simulation(mix_first, update)\n",
-    "final_temp(mix_first)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_and_mix(t_add, t_total=30):\n",
-    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
-    "    \n",
-    "    t_add: time in minutes\n",
-    "    t_total: total time to simulate, min\n",
-    "    \n",
-    "    returns: final temperature\n",
-    "    \"\"\"\n",
-    "    coffee = make_system(T_init=90, t_end=t_add, \n",
-    "                         r=r_coffee, volume=300)\n",
-    "    run_simulation(coffee, update)\n",
-    "\n",
-    "    milk = make_system(T_init=5, t_end=t_add, \n",
-    "                       r=r_milk, volume=50)\n",
-    "    run_simulation(milk, update)\n",
-    "    \n",
-    "    mixture = mix(coffee, milk)\n",
-    "    mixture.t_end = t_total - t_add\n",
-    "    run_simulation(mixture, update)\n",
-    "\n",
-    "    return final_temp(mixture)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can try it out with a few values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "61.42857142857666"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_and_mix(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "62.902809128457783"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_and_mix(15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "63.109243697484459"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "run_and_mix(30)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And then sweep a range of values for `t_add`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sweep = SweepSeries()\n",
-    "for t_add in linrange(0, 30, 2):\n",
-    "    temp = run_and_mix(t_add)\n",
-    "    sweep[t_add] = temp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's what the result looks like."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving figure to file chap07-fig02.pdf\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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UlBR910nUp7riOvy69VcI6q4w4ODugLCEMJhbG3y6DSKiAUenOwR33nkntm/f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\nhBBIQiCEEEIIJCEQQgghBPD/Np5/JdxwwqQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f8f46933cc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(sweep, color='purple')\n",
-    "decorate(xlabel='Time added (min)',\n",
-    "         ylabel='Final temperature (C)',\n",
-    "         legend=False)\n",
-    "\n",
-    "savefig('chap07-fig02.pdf')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
-    "\n",
-    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def run_analysis(system):\n",
-    "    \"\"\"Computes temperature using the analytic solution.\n",
-    "    \n",
-    "    Adds TimeFrame to `system` as `results`\n",
-    "    \n",
-    "    system: System object\n",
-    "    \"\"\"\n",
-    "    unpack(system)\n",
-    "    \n",
-    "    T_init = init.temp    \n",
-    "    ts = linrange(t0, t_end, dt)\n",
-    "    \n",
-    "    temp_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
-    "    temp_series = TimeSeries(temp_array, index=ts)\n",
-    "    \n",
-    "    system.results = TimeFrame(temp_series, columns=['temp'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here's how we run it.  From the analysis, we have the computed value of `r_coffee2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "r_coffee2 = 0.011610223142273859"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "70.0"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(temp=90)\n",
-    "coffee2 = System(init=init, T_env=22, r=r_coffee2, \n",
-    "                 t0=0, t_end=30)\n",
-    "run_analysis(coffee2)\n",
-    "final_temp(coffee2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can compare to the results from simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "70.000000000006395"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "init = State(temp=90)\n",
-    "coffee = System(init=init, T_env=22, r=r_coffee, \n",
-    "                t0=0, t_end=30, dt=1)\n",
-    "run_simulation(coffee, update)\n",
-    "final_temp(coffee)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "They are identical except for small roundoff errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>temp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000000e+00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.984279e-13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>5.968559e-13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>8.810730e-13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.165290e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.435296e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.691092e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.946887e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2.202682e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>2.458478e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>2.700062e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>2.941647e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>3.169021e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>3.382183e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>3.609557e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>3.836931e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>4.021672e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>4.234835e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>4.419576e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>4.632739e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>4.803269e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>5.002221e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>5.172751e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>5.343281e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>5.499601e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>5.670131e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>5.812240e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>5.968559e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>6.096457e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>6.252776e-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>6.394885e-12</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            temp\n",
-       "0   0.000000e+00\n",
-       "1   2.984279e-13\n",
-       "2   5.968559e-13\n",
-       "3   8.810730e-13\n",
-       "4   1.165290e-12\n",
-       "5   1.435296e-12\n",
-       "6   1.691092e-12\n",
-       "7   1.946887e-12\n",
-       "8   2.202682e-12\n",
-       "9   2.458478e-12\n",
-       "10  2.700062e-12\n",
-       "11  2.941647e-12\n",
-       "12  3.169021e-12\n",
-       "13  3.382183e-12\n",
-       "14  3.609557e-12\n",
-       "15  3.836931e-12\n",
-       "16  4.021672e-12\n",
-       "17  4.234835e-12\n",
-       "18  4.419576e-12\n",
-       "19  4.632739e-12\n",
-       "20  4.803269e-12\n",
-       "21  5.002221e-12\n",
-       "22  5.172751e-12\n",
-       "23  5.343281e-12\n",
-       "24  5.499601e-12\n",
-       "25  5.670131e-12\n",
-       "26  5.812240e-12\n",
-       "27  5.968559e-12\n",
-       "28  6.096457e-12\n",
-       "29  6.252776e-12\n",
-       "30  6.394885e-12"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coffee.results - coffee2.results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
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-  },
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-   "cell_type": "code",
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-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
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-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
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+++ b/code/chap08mine.ipynb
@@ -0,0 +1,3270 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 8: Pharmacokinetics\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# tempo switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data\n",
+    "\n",
+    "We have data from Pacini and Bergman (1986), \"MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test\", *Computer Methods and Programs in Biomedicine*, 23: 113-122.."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>glucose</th>\n",
+       "      <th>insulin</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>92</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>350</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>287</td>\n",
+       "      <td>130</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>251</td>\n",
+       "      <td>85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>240</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>216</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>211</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>205</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>196</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>192</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>172</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>163</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>142</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>124</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>105</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>92</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>84</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>77</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>82</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>81</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>82</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>82</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>85</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>90</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      glucose  insulin\n",
+       "time                  \n",
+       "0          92       11\n",
+       "2         350       26\n",
+       "4         287      130\n",
+       "6         251       85\n",
+       "8         240       51\n",
+       "10        216       49\n",
+       "12        211       45\n",
+       "14        205       41\n",
+       "16        196       35\n",
+       "19        192       30\n",
+       "22        172       30\n",
+       "27        163       27\n",
+       "32        142       30\n",
+       "42        124       22\n",
+       "52        105       15\n",
+       "62         92       15\n",
+       "72         84       11\n",
+       "82         77       10\n",
+       "92         82        8\n",
+       "102        81       11\n",
+       "122        82        7\n",
+       "142        82        8\n",
+       "162        85        8\n",
+       "182        90        7"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('glucose_insulin.csv', index_col='time')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the glucose time series looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GplU33p07dzJgwAASExP585//zPjx4/nxxx/ZsGEDJiYmvPjiiw06+ZEjR1i3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\nEbVbKAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe939dff60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(data.glucose, 'bo', label='glucose')\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the insulin time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ufp+RnyH9nsWFiBozhQrLuXPnsG/fPgDPZt2bmZmhY8eOaN++PR48eIBff/0VoaGhSgmq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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe94b8e630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(data.insulin, 'go', label='insulin')\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the book, I put them in a single figure, using `subplot`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ycli2bBn9+vUztbds2dL056lTp2IwGEhNTaWwsJA5c+bg7OzMzJkzrboPIiIi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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe94bc1ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot(2, 1, 1)\n",
+    "plot(data.glucose, 'bo', label='glucose')\n",
+    "decorate(ylabel='mg/dL')\n",
+    "\n",
+    "subplot(2, 1, 2)\n",
+    "plot(data.insulin, 'go', label='insulin')\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='$\\mu$U/mL')\n",
+    "\n",
+    "savefig('chap08-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpolation\n",
+    "\n",
+    "We have measurements of insulin concentration at discrete points in time, but we need to estimate it at intervening points.  We'll use `interpolate`, which is a wrapper for `scipy.interpolate.interp1d`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%psource interpolate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The return value from `interpolate` is a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "I = interpolate(data.insulin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the result, `I`, to estimate the insulin level at any point in time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(68.0)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`I` can also take an array of time and return an array of estimates, which we can plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ma6lYhBDiDiqWxvLee+/x5ptv8vDDD2NqaoqVlRWffvopzZo1IyMjg5YtW2pd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\nLEIIIdxKCosQQgi3ksIihBDCrf4FLBNQahkEzlsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe94f12438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = linrange(0, 182, 2)\n",
+    "\n",
+    "plot(data.insulin, 'go', label='insulin data')\n",
+    "plot(ts, I(ts), color='green', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')\n",
+    "\n",
+    "savefig('chap08-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** [Read the documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html) of `scipy.interpolate.interp1d`.  Pass a keyword argument to `interpolate` to specify one of the other kinds of interpolation, and run the code again to see what it looks like. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "G = interpolate(data.glucose)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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QtVB0MjPVz0OHJLEIIWo3kxLLjz/+SGpqKlFRUURHRxMVFUVUVBR79+7l9u3b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\nhYWIiCLqPxrYs1zevbe5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe93923400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = linrange(0, 182, 2)\n",
+    "\n",
+    "plot(data.glucose, 'bo', label='insulin data')\n",
+    "plot(ts, G(ts), color='blue', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')\n",
+    "\n",
+    "savefig('chap08-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The glucose minimal model\n",
+    "\n",
+    "I'll cheat by starting with parameters that fit the data roughly; then we'll see how to improve them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "k1 = 0.03\n",
+    "k2 = 0.02\n",
+    "k3 = 1e-05\n",
+    "G0 = 290"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To estimate basal levels, we'll use the concentrations at `t=0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "Gb = data.glucose[0]\n",
+    "Ib = data.insulin[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the initial conditions, `X(0)=0` and `G(0)=G0`, where `G0` is one of the parameters we'll choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "init = State(G=G0, X=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the system object with all parameters and the interpolation object `I`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = System(init=init, \n",
+    "                k1=k1, k2=k2, k3=k3,\n",
+    "                I=I, Gb=Gb, Ib=Ib,\n",
+    "                t0=0, t_end=182, dt=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the update function.  Using `unpack` to make the system variables accessible without using dot notation, which makes the translation of the differential equations more readable and checkable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func(state, t, system):\n",
+    "    \"\"\"Updates the glucose minimal model.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time in min\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State object\n",
+    "    \"\"\"\n",
+    "    G, X = state\n",
+    "    unpack(system)\n",
+    "        \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    G += dGdt * dt\n",
+    "    X += dXdt * dt\n",
+    "\n",
+    "    return State(G=G, X=X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before running the simulation, it is always a good idea to test the update function using the initial conditions.  In this case we can veryify that the results are at least qualitatively correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>G</th>\n",
+       "      <td>278.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>X</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "G    278.12\n",
+       "X      0.00\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "update_func(init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now run simulation is pretty much the same as it always is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Adds a TimeFrame to `system` as `results`\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.loc[t0] = init\n",
+    "    ts = linrange(t0, t_end-dt, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.loc[t+dt] = update_func(frame.loc[t], t, system)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we run it.  `%time` is a Jupyter magic command that runs the function and reports its run time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 154 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time run_simulation(system, update_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results are in a `TimeFrame object` with one column per state variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>G</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>278.120000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>266.952800</td>\n",
+       "      <td>0.000300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>256.295460</td>\n",
+       "      <td>0.002668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>245.070140</td>\n",
+       "      <td>0.004041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>233.905138</td>\n",
+       "      <td>0.004680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>223.201651</td>\n",
+       "      <td>0.005252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>212.984844</td>\n",
+       "      <td>0.005722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>203.288207</td>\n",
+       "      <td>0.006093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>194.133461</td>\n",
+       "      <td>0.006330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>185.547835</td>\n",
+       "      <td>0.006490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>177.526606</td>\n",
+       "      <td>0.006610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>170.048014</td>\n",
+       "      <td>0.006726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>163.077697</td>\n",
+       "      <td>0.006813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>156.590997</td>\n",
+       "      <td>0.006872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>150.563255</td>\n",
+       "      <td>0.006929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>144.962829</td>\n",
+       "      <td>0.007008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>139.753191</td>\n",
+       "      <td>0.007108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>134.901294</td>\n",
+       "      <td>0.007172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>130.392302</td>\n",
+       "      <td>0.007201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>126.210925</td>\n",
+       "      <td>0.007197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>122.341665</td>\n",
+       "      <td>0.007161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>118.769029</td>\n",
+       "      <td>0.007094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>115.477698</td>\n",
+       "      <td>0.007003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>112.451743</td>\n",
+       "      <td>0.006887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>109.675836</td>\n",
+       "      <td>0.006747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>107.135308</td>\n",
+       "      <td>0.006585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>104.816180</td>\n",
+       "      <td>0.006402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>102.705192</td>\n",
+       "      <td>0.006226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>100.784058</td>\n",
+       "      <td>0.006057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>86.390694</td>\n",
+       "      <td>0.001095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>86.538102</td>\n",
+       "      <td>0.000973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>86.697421</td>\n",
+       "      <td>0.000858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>86.866797</td>\n",
+       "      <td>0.000750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132</th>\n",
+       "      <td>87.044539</td>\n",
+       "      <td>0.000648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>87.229105</td>\n",
+       "      <td>0.000552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>136</th>\n",
+       "      <td>87.419090</td>\n",
+       "      <td>0.000462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>87.613215</td>\n",
+       "      <td>0.000377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>87.810314</td>\n",
+       "      <td>0.000298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>88.009328</td>\n",
+       "      <td>0.000224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>88.209296</td>\n",
+       "      <td>0.000155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>88.409343</td>\n",
+       "      <td>0.000089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>88.609033</td>\n",
+       "      <td>0.000026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>88.807970</td>\n",
+       "      <td>-0.000036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>89.005799</td>\n",
+       "      <td>-0.000094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>89.202200</td>\n",
+       "      <td>-0.000150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>89.396887</td>\n",
+       "      <td>-0.000204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>89.589604</td>\n",
+       "      <td>-0.000256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>160</th>\n",
+       "      <td>89.780123</td>\n",
+       "      <td>-0.000306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>89.968242</td>\n",
+       "      <td>-0.000354</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>90.153784</td>\n",
+       "      <td>-0.000400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>90.336592</td>\n",
+       "      <td>-0.000446</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>168</th>\n",
+       "      <td>90.516892</td>\n",
+       "      <td>-0.000492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>90.694895</td>\n",
+       "      <td>-0.000538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>90.870797</td>\n",
+       "      <td>-0.000585</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>91.044781</td>\n",
+       "      <td>-0.000631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>91.217018</td>\n",
+       "      <td>-0.000678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>91.387668</td>\n",
+       "      <td>-0.000725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>91.556880</td>\n",
+       "      <td>-0.000772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>91.724792</td>\n",
+       "      <td>-0.000819</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>92 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              G         X\n",
+       "0    290.000000  0.000000\n",
+       "2    278.120000  0.000000\n",
+       "4    266.952800  0.000300\n",
+       "6    256.295460  0.002668\n",
+       "8    245.070140  0.004041\n",
+       "10   233.905138  0.004680\n",
+       "12   223.201651  0.005252\n",
+       "14   212.984844  0.005722\n",
+       "16   203.288207  0.006093\n",
+       "18   194.133461  0.006330\n",
+       "20   185.547835  0.006490\n",
+       "22   177.526606  0.006610\n",
+       "24   170.048014  0.006726\n",
+       "26   163.077697  0.006813\n",
+       "28   156.590997  0.006872\n",
+       "30   150.563255  0.006929\n",
+       "32   144.962829  0.007008\n",
+       "34   139.753191  0.007108\n",
+       "36   134.901294  0.007172\n",
+       "38   130.392302  0.007201\n",
+       "40   126.210925  0.007197\n",
+       "42   122.341665  0.007161\n",
+       "44   118.769029  0.007094\n",
+       "46   115.477698  0.007003\n",
+       "48   112.451743  0.006887\n",
+       "50   109.675836  0.006747\n",
+       "52   107.135308  0.006585\n",
+       "54   104.816180  0.006402\n",
+       "56   102.705192  0.006226\n",
+       "58   100.784058  0.006057\n",
+       "..          ...       ...\n",
+       "124   86.390694  0.001095\n",
+       "126   86.538102  0.000973\n",
+       "128   86.697421  0.000858\n",
+       "130   86.866797  0.000750\n",
+       "132   87.044539  0.000648\n",
+       "134   87.229105  0.000552\n",
+       "136   87.419090  0.000462\n",
+       "138   87.613215  0.000377\n",
+       "140   87.810314  0.000298\n",
+       "142   88.009328  0.000224\n",
+       "144   88.209296  0.000155\n",
+       "146   88.409343  0.000089\n",
+       "148   88.609033  0.000026\n",
+       "150   88.807970 -0.000036\n",
+       "152   89.005799 -0.000094\n",
+       "154   89.202200 -0.000150\n",
+       "156   89.396887 -0.000204\n",
+       "158   89.589604 -0.000256\n",
+       "160   89.780123 -0.000306\n",
+       "162   89.968242 -0.000354\n",
+       "164   90.153784 -0.000400\n",
+       "166   90.336592 -0.000446\n",
+       "168   90.516892 -0.000492\n",
+       "170   90.694895 -0.000538\n",
+       "172   90.870797 -0.000585\n",
+       "174   91.044781 -0.000631\n",
+       "176   91.217018 -0.000678\n",
+       "178   91.387668 -0.000725\n",
+       "180   91.556880 -0.000772\n",
+       "182   91.724792 -0.000819\n",
+       "\n",
+       "[92 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following plot shows the results of the simulation along with the actual glucose data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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MYdeuXXz++edcuHCB+fPn3/O/2Z1YdJNEVlYWsbGx9O/f35BWp04dmjVrRnp6Oh4eHmRk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\nPrpOAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe95044ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot(2, 1, 1)\n",
+    "\n",
+    "plot(system.results.G, 'b-', label='simulation')\n",
+    "plot(data.glucose, style='bo', label='glucose data')\n",
+    "decorate(ylabel='mg/dL')\n",
+    "\n",
+    "subplot(2, 1, 2)\n",
+    "\n",
+    "plot(system.results.X, style='g-', label='remote insulin')\n",
+    "\n",
+    "decorate(xlabel='Time (min)', \n",
+    "         ylabel='Arbitrary units')\n",
+    "\n",
+    "savefig('chap08-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Numerical solution\n",
+    "\n",
+    "We can do the same thing using `odeint`.  Instead of an update function, we provide a slope function that just evaluates the right-hand side of the differential equations.  We don't have to do the update part; `odeint` does it for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time in min\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: derivatives of G and X\n",
+    "    \"\"\"\n",
+    "    G, X = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    return dGdt, dXdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-5.9399999999999995, 0.0)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `System` object we use with `run_odeint` is almost the same as the one we used with `run_simulation`, but instead of providing `t0`, `t_end`, and `dt`, we provide an array of times where we want to evaluate the solution.  In this case, we use `data.index`, so the results are evaluated at the same times as the measurements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system2 = System(init=init, \n",
+    "                k1=k1, k2=k2, k3=k3,\n",
+    "                I=I, Gb=Gb, Ib=Ib,\n",
+    "                ts=data.index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`run_odeint` is a wrapper for `scipy.integrate.odeint`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%psource run_odeint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 96.2 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time run_odeint(system2, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>G</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>278.441946</td>\n",
+       "      <td>0.000148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>267.246339</td>\n",
+       "      <td>0.001463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>255.791154</td>\n",
+       "      <td>0.003294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>244.385049</td>\n",
+       "      <td>0.004280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>233.385689</td>\n",
+       "      <td>0.004877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>222.875391</td>\n",
+       "      <td>0.005391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>212.883104</td>\n",
+       "      <td>0.005807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>203.432604</td>\n",
+       "      <td>0.006108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>190.311106</td>\n",
+       "      <td>0.006378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>178.430723</td>\n",
+       "      <td>0.006560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>161.141293</td>\n",
+       "      <td>0.006767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>146.627308</td>\n",
+       "      <td>0.006957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>124.272257</td>\n",
+       "      <td>0.007043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>109.125928</td>\n",
+       "      <td>0.006436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>99.310554</td>\n",
+       "      <td>0.005632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>93.102455</td>\n",
+       "      <td>0.004786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>89.434359</td>\n",
+       "      <td>0.003872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>87.498085</td>\n",
+       "      <td>0.002986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>86.712650</td>\n",
+       "      <td>0.002313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>86.844866</td>\n",
+       "      <td>0.001199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>88.356758</td>\n",
+       "      <td>0.000232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>90.136930</td>\n",
+       "      <td>-0.000339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>91.795663</td>\n",
+       "      <td>-0.000810</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               G         X\n",
+       "time                      \n",
+       "0     290.000000  0.000000\n",
+       "2     278.441946  0.000148\n",
+       "4     267.246339  0.001463\n",
+       "6     255.791154  0.003294\n",
+       "8     244.385049  0.004280\n",
+       "10    233.385689  0.004877\n",
+       "12    222.875391  0.005391\n",
+       "14    212.883104  0.005807\n",
+       "16    203.432604  0.006108\n",
+       "19    190.311106  0.006378\n",
+       "22    178.430723  0.006560\n",
+       "27    161.141293  0.006767\n",
+       "32    146.627308  0.006957\n",
+       "42    124.272257  0.007043\n",
+       "52    109.125928  0.006436\n",
+       "62     99.310554  0.005632\n",
+       "72     93.102455  0.004786\n",
+       "82     89.434359  0.003872\n",
+       "92     87.498085  0.002986\n",
+       "102    86.712650  0.002313\n",
+       "122    86.844866  0.001199\n",
+       "142    88.356758  0.000232\n",
+       "162    90.136930 -0.000339\n",
+       "182    91.795663 -0.000810"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system2.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the results from `run_simulation` and `run_odeint`, we can see that they are not very different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ez2zG775f8cuaF3nqy6mcbobGgkLW/iGRh5f7YTS6u0AhxEDS6z19vV7vDPwO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\n8y60TnfPudxlHc0WLkOLA3t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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe94fb3cc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(system.results.G, 'r-')\n",
+    "plot(system2.results.G, 'b-')\n",
+    "plot(data.glucose, 'bo')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The differences are usually less than 1% and always less than 2%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>G</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.115624</td>\n",
+       "      <td>-100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.109838</td>\n",
+       "      <td>-79.488129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.197156</td>\n",
+       "      <td>-19.008861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.280333</td>\n",
+       "      <td>-5.584132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.222571</td>\n",
+       "      <td>-4.045820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.146387</td>\n",
+       "      <td>-2.574882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.047791</td>\n",
+       "      <td>-1.457351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>-0.070980</td>\n",
+       "      <td>-0.241936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>-0.506705</td>\n",
+       "      <td>0.770903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>-1.135176</td>\n",
+       "      <td>0.739019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>-1.553518</td>\n",
+       "      <td>1.669890</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>-1.824149</td>\n",
+       "      <td>2.322664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>-1.877001</td>\n",
+       "      <td>1.900503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>-1.764022</td>\n",
+       "      <td>2.270604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>-1.574539</td>\n",
+       "      <td>2.091263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>-1.346686</td>\n",
+       "      <td>2.279411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>-1.093100</td>\n",
+       "      <td>0.658312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>-0.676648</td>\n",
+       "      <td>2.068862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>-0.393212</td>\n",
+       "      <td>-3.390112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>-0.187146</td>\n",
+       "      <td>4.346125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>-0.077205</td>\n",
+       "      <td>1.148839</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            G           X\n",
+       "2   -0.115624 -100.000000\n",
+       "4   -0.109838  -79.488129\n",
+       "6    0.197156  -19.008861\n",
+       "8    0.280333   -5.584132\n",
+       "10   0.222571   -4.045820\n",
+       "12   0.146387   -2.574882\n",
+       "14   0.047791   -1.457351\n",
+       "16  -0.070980   -0.241936\n",
+       "22  -0.506705    0.770903\n",
+       "32  -1.135176    0.739019\n",
+       "42  -1.553518    1.669890\n",
+       "52  -1.824149    2.322664\n",
+       "62  -1.877001    1.900503\n",
+       "72  -1.764022    2.270604\n",
+       "82  -1.574539    2.091263\n",
+       "92  -1.346686    2.279411\n",
+       "102 -1.093100    0.658312\n",
+       "122 -0.676648    2.068862\n",
+       "142 -0.393212   -3.390112\n",
+       "162 -0.187146    4.346125\n",
+       "182 -0.077205    1.148839"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff = system.results - system2.results\n",
+    "percent_diff = diff / system2.results * 100\n",
+    "percent_diff.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** What happens to these errors if you run the simulation with a smaller value of `dt`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 282 ms\n",
+      "Wall time: 78.1 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>G</th>\n",
+       "      <th>X</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.051625</td>\n",
+       "      <td>-49.331789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.014801</td>\n",
+       "      <td>-39.214438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.113775</td>\n",
+       "      <td>-9.385110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.139376</td>\n",
+       "      <td>-2.763359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.109220</td>\n",
+       "      <td>-2.004115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.069648</td>\n",
+       "      <td>-1.278311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.019481</td>\n",
+       "      <td>-0.726602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>-0.040906</td>\n",
+       "      <td>-0.126634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>-0.146794</td>\n",
+       "      <td>0.311986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>-0.254580</td>\n",
+       "      <td>0.312622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>-0.419990</td>\n",
+       "      <td>0.516914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>-0.557227</td>\n",
+       "      <td>0.273386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>-0.761257</td>\n",
+       "      <td>0.751189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>-0.894453</td>\n",
+       "      <td>1.082737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>-0.920389</td>\n",
+       "      <td>0.881158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>-0.867159</td>\n",
+       "      <td>1.068144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>-0.775162</td>\n",
+       "      <td>0.981869</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>-0.664337</td>\n",
+       "      <td>1.072775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>-0.539065</td>\n",
+       "      <td>0.271689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>-0.335484</td>\n",
+       "      <td>0.952389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>-0.194963</td>\n",
+       "      <td>-1.890950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>-0.093130</td>\n",
+       "      <td>2.248757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>-0.038741</td>\n",
+       "      <td>0.598199</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            G          X\n",
+       "2   -0.051625 -49.331789\n",
+       "4   -0.014801 -39.214438\n",
+       "6    0.113775  -9.385110\n",
+       "8    0.139376  -2.763359\n",
+       "10   0.109220  -2.004115\n",
+       "12   0.069648  -1.278311\n",
+       "14   0.019481  -0.726602\n",
+       "16  -0.040906  -0.126634\n",
+       "19  -0.146794   0.311986\n",
+       "22  -0.254580   0.312622\n",
+       "27  -0.419990   0.516914\n",
+       "32  -0.557227   0.273386\n",
+       "42  -0.761257   0.751189\n",
+       "52  -0.894453   1.082737\n",
+       "62  -0.920389   0.881158\n",
+       "72  -0.867159   1.068144\n",
+       "82  -0.775162   0.981869\n",
+       "92  -0.664337   1.072775\n",
+       "102 -0.539065   0.271689\n",
+       "122 -0.335484   0.952389\n",
+       "142 -0.194963  -1.890950\n",
+       "162 -0.093130   2.248757\n",
+       "182 -0.038741   0.598199"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system_flynn = System(init=init, \n",
+    "                k1=k1, k2=k2, k3=k3,\n",
+    "                I=I, Gb=Gb, Ib=Ib,\n",
+    "                t0=0, t_end=182, dt=1)\n",
+    "\n",
+    "update_func(init, 0, system_flynn)\n",
+    "\n",
+    "%time run_simulation(system_flynn, update_func)\n",
+    "\n",
+    "slope_func(init, 0, system_flynn)\n",
+    "\n",
+    "system2 = System(init=init, \n",
+    "                k1=k1, k2=k2, k3=k3,\n",
+    "                I=I, Gb=Gb, Ib=Ib,\n",
+    "                ts=data.index)\n",
+    "\n",
+    "%psource run_odeint\n",
+    "\n",
+    "%time run_odeint(system2, slope_func)\n",
+    "\n",
+    "diff = system_flynn.results - system2.results\n",
+    "percent_diff = diff / system2.results * 100\n",
+    "percent_diff.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#We can see that with a smaller dt, the error decreases.\n",
+    "#This is because the original equation assumes dt to be infinitesimal, in which case the error should be zero.\n",
+    "#As dt gets smaller, it approaches zero, and so does the error."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's find the parameters that yield the best fit for the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "k1 = 0.03\n",
+    "k2 = 0.02\n",
+    "k3 = 1e-05\n",
+    "G0 = 290"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we'll get basal levels from the initial values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "Gb = data.glucose[0]\n",
+    "Ib = data.insulin[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the slope function is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes derivatives of the glucose minimal model.\n",
+    "    \n",
+    "    state: State object\n",
+    "    t: time in min\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: derivatives of G and X\n",
+    "    \"\"\"\n",
+    "    G, X = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    dGdt = -k1 * (G - Gb) - X*G\n",
+    "    dXdt = k3 * (I(t) - Ib) - k2 * X\n",
+    "    \n",
+    "    return dGdt, dXdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` takes the parameters and `DataFrame` and returns a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(G0, k1, k2, k3, data):\n",
+    "    \"\"\"Makes a System object with the given parameters.\n",
+    "    \n",
+    "    G0: initial blood glucose\n",
+    "    k1: rate parameter\n",
+    "    k2: rate parameter\n",
+    "    k3: rate parameter\n",
+    "    data: DataFrame\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(G=G0, X=0)\n",
+    "    system = System(init=init, \n",
+    "                    k1=k1, k2=k2, k3=k3,\n",
+    "                    Gb=Gb, Ib=Ib, \n",
+    "                    I=interpolate(data.insulin),\n",
+    "                    ts=data.index)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`error_func` takes the parameters and actual data, makes a `System` object and runs it, then compares the results of the simulation to the data.  It returns an array of errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func(params, data):\n",
+    "    \"\"\"Computes an array of errors to be minimized.\n",
+    "    \n",
+    "    params: sequence of parameters\n",
+    "    data: DataFrame of values to be matched\n",
+    "    \n",
+    "    returns: array of errors\n",
+    "    \"\"\"\n",
+    "    print(params)\n",
+    "    \n",
+    "    # make a System with the given parameters\n",
+    "    system = make_system(*params, data)\n",
+    "    \n",
+    "    # solve the ODE\n",
+    "    run_odeint(system, slope_func)\n",
+    "    \n",
+    "    # compute the difference between the model\n",
+    "    # results and actual data\n",
+    "    error = system.results.G - data.glucose\n",
+    "    return error.loc[8:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we call `error_func`, we provide a sequence of parameters as a single object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(290, 0.03, 0.02, 1e-05)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = G0, k1, k2, k3\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how that works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(290, 0.03, 0.02, 1e-05)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "time\n",
+       "8       4.385049\n",
+       "10     17.385689\n",
+       "12     11.875391\n",
+       "14      7.883104\n",
+       "16      7.432604\n",
+       "19     -1.688894\n",
+       "22      6.430723\n",
+       "27     -1.858707\n",
+       "32      4.627308\n",
+       "42      0.272257\n",
+       "52      4.125928\n",
+       "62      7.310554\n",
+       "72      9.102455\n",
+       "82     12.434359\n",
+       "92      5.498085\n",
+       "102     5.712650\n",
+       "122     4.844866\n",
+       "142     6.356758\n",
+       "162     5.136930\n",
+       "182     1.795663\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func(params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`fit_leastsq` is a wrapper for `scipy.optimize.leastsq`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%psource fit_leastsq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  2.90000000e+02   3.00000000e-02   2.00000000e-02   1.00000000e-05]\n",
+      "[  2.90000000e+02   3.00000000e-02   2.00000000e-02   1.00000000e-05]\n",
+      "[  2.90000000e+02   3.00000000e-02   2.00000000e-02   1.00000000e-05]\n",
+      "[  2.90000004e+02   3.00000000e-02   2.00000000e-02   1.00000000e-05]\n",
+      "[  2.90000000e+02   3.00000004e-02   2.00000000e-02   1.00000000e-05]\n",
+      "[  2.90000000e+02   3.00000000e-02   2.00000003e-02   1.00000000e-05]\n",
+      "[  2.90000000e+02   3.00000000e-02   2.00000000e-02   1.00000001e-05]\n",
+      "[  2.78224727e+02   2.85801825e-02   1.61589429e-02   1.19289840e-05]\n",
+      "[  2.78224731e+02   2.85801825e-02   1.61589429e-02   1.19289840e-05]\n",
+      "[  2.78224727e+02   2.85801829e-02   1.61589429e-02   1.19289840e-05]\n",
+      "[  2.78224727e+02   2.85801825e-02   1.61589432e-02   1.19289840e-05]\n",
+      "[  2.78224727e+02   2.85801825e-02   1.61589429e-02   1.19289842e-05]\n",
+      "[  2.71951893e+02   2.68943464e-02   1.28042303e-02   1.03928810e-05]\n",
+      "[  2.71951897e+02   2.68943464e-02   1.28042303e-02   1.03928810e-05]\n",
+      "[  2.71951893e+02   2.68943468e-02   1.28042303e-02   1.03928810e-05]\n",
+      "[  2.71951893e+02   2.68943464e-02   1.28042305e-02   1.03928810e-05]\n",
+      "[  2.71951893e+02   2.68943464e-02   1.28042303e-02   1.03928811e-05]\n",
+      "[  2.71443394e+02   2.69030677e-02   1.26482637e-02   1.04193915e-05]\n",
+      "[  2.71443398e+02   2.69030677e-02   1.26482637e-02   1.04193915e-05]\n",
+      "[  2.71443394e+02   2.69030681e-02   1.26482637e-02   1.04193915e-05]\n",
+      "[  2.71443394e+02   2.69030677e-02   1.26482639e-02   1.04193915e-05]\n",
+      "[  2.71443394e+02   2.69030677e-02   1.26482637e-02   1.04193917e-05]\n",
+      "[  2.71712741e+02   2.69019185e-02   1.24302337e-02   1.05307739e-05]\n",
+      "[  2.71712745e+02   2.69019185e-02   1.24302337e-02   1.05307739e-05]\n",
+      "[  2.71712741e+02   2.69019189e-02   1.24302337e-02   1.05307739e-05]\n",
+      "[  2.71712741e+02   2.69019185e-02   1.24302339e-02   1.05307739e-05]\n",
+      "[  2.71712741e+02   2.69019185e-02   1.24302337e-02   1.05307740e-05]\n",
+      "[  2.71841622e+02   2.68015619e-02   1.21756047e-02   1.07056217e-05]\n",
+      "[  2.71841626e+02   2.68015619e-02   1.21756047e-02   1.07056217e-05]\n",
+      "[  2.71841622e+02   2.68015623e-02   1.21756047e-02   1.07056217e-05]\n",
+      "[  2.71841622e+02   2.68015619e-02   1.21756049e-02   1.07056217e-05]\n",
+      "[  2.71841622e+02   2.68015619e-02   1.21756047e-02   1.07056219e-05]\n",
+      "[  2.71722563e+02   2.67317277e-02   1.18108500e-02   1.06903270e-05]\n",
+      "[  2.71827386e+02   2.67803404e-02   1.19921543e-02   1.06990976e-05]\n",
+      "[  2.71859602e+02   2.67976625e-02   1.20801103e-02   1.07032846e-05]\n",
+      "[  2.71863092e+02   2.68026355e-02   1.21300274e-02   1.07056075e-05]\n",
+      "[  2.71857182e+02   2.68027413e-02   1.21534664e-02   1.07066721e-05]\n",
+      "[  2.71857186e+02   2.68027413e-02   1.21534664e-02   1.07066721e-05]\n",
+      "[  2.71857182e+02   2.68027417e-02   1.21534664e-02   1.07066721e-05]\n",
+      "[  2.71857182e+02   2.68027413e-02   1.21534666e-02   1.07066721e-05]\n",
+      "[  2.71857182e+02   2.68027413e-02   1.21534664e-02   1.07066723e-05]\n",
+      "[  2.71898905e+02   2.68013322e-02   1.21544446e-02   1.07084438e-05]\n",
+      "[  2.71898909e+02   2.68013322e-02   1.21544446e-02   1.07084438e-05]\n",
+      "[  2.71898905e+02   2.68013326e-02   1.21544446e-02   1.07084438e-05]\n",
+      "[  2.71898905e+02   2.68013322e-02   1.21544448e-02   1.07084438e-05]\n",
+      "[  2.71898905e+02   2.68013322e-02   1.21544446e-02   1.07084439e-05]\n",
+      "[  2.71898433e+02   2.67985448e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.71898437e+02   2.67985448e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.71898433e+02   2.67985452e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.71898433e+02   2.67985448e-02   1.21544444e-02   1.07084478e-05]\n",
+      "[  2.71898433e+02   2.67985448e-02   1.21544443e-02   1.07084480e-05]\n",
+      "[  2.71916930e+02   2.67987318e-02   1.21542838e-02   1.07088289e-05]\n",
+      "[  2.71907097e+02   2.67986221e-02   1.21543757e-02   1.07086105e-05]\n",
+      "[  2.71902239e+02   2.67985768e-02   1.21544154e-02   1.07085162e-05]\n",
+      "[  2.71900248e+02   2.67985597e-02   1.21544307e-02   1.07084799e-05]\n",
+      "[  2.71899292e+02   2.67985518e-02   1.21544379e-02   1.07084629e-05]\n",
+      "[  2.71898837e+02   2.67985481e-02   1.21544413e-02   1.07084549e-05]\n",
+      "[  2.71898624e+02   2.67985464e-02   1.21544429e-02   1.07084511e-05]\n",
+      "[  2.71898523e+02   2.67985456e-02   1.21544436e-02   1.07084494e-05]\n",
+      "[  2.71898476e+02   2.67985452e-02   1.21544440e-02   1.07084486e-05]\n",
+      "[  2.71898453e+02   2.67985450e-02   1.21544441e-02   1.07084482e-05]\n",
+      "modsim.py: scipy.optimize.leastsq ran successfully\n",
+      "                 and returned the following message:\n",
+      "The relative error between two consecutive iterates is at most 0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_params = fit_leastsq(error_func, params, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have `best_params`, we can use it to make a `System` object and run it.\n",
+    "\n",
+    "We have to use the scatter operator, `*`, to make `best_params` behave like four separate parameters, rather than a single object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = make_system(*best_params, data)\n",
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results, along with the data.  The first few points of the model don't fit the data, but we don't expect them to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ef/55kxLI3Zo2bRoHDhzA3t7e8ISRkpJCy5YtjfZzdnY2fHpITU3F2dm5QjlAcnKyJBAh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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe9531f0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(system.results.G, label='simulation')\n",
+    "plot(data.glucose, style='bo', label='glucose data')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')\n",
+    "\n",
+    "savefig('chap08-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Since we don't expect the first few points to agree, it's probably better not to make them part of the optimization process.  We can ignore them by leaving them out of the `Series` returned by `error_func`.  Modify the last line of `error_func` to return `errors.loc[8:]`, which includes only the elements of the `Series` from `t=8` and up.\n",
+    "\n",
+    "Does that improve the quality of the fit?  Does it change the best parameters by much?\n",
+    "\n",
+    "Note: You can read more about this use of `loc` [in the Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-integer)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func(params, data):\n",
+    "    \"\"\"Computes an array of errors to be minimized.\n",
+    "    \n",
+    "    params: sequence of parameters\n",
+    "    data: DataFrame of values to be matched\n",
+    "    \n",
+    "    returns: array of errors\n",
+    "    \"\"\"\n",
+    "    print(params)\n",
+    "    \n",
+    "    # make a System with the given parameters\n",
+    "    system = make_system(*params, data)\n",
+    "    \n",
+    "    # solve the ODE\n",
+    "    run_odeint(system, slope_func)\n",
+    "    \n",
+    "    # compute the difference between the model\n",
+    "    # results and actual data\n",
+    "    error = system.results.G - data.glucose\n",
+    "    return error.loc[8:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(290, 0.026798544833976808, 0.012154444266554253, 1.0708447822446748e-05)\n",
+      "[  2.90000000e+02   2.67985448e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.90000000e+02   2.67985448e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.90000000e+02   2.67985448e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.90000004e+02   2.67985448e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.90000000e+02   2.67985452e-02   1.21544443e-02   1.07084478e-05]\n",
+      "[  2.90000000e+02   2.67985448e-02   1.21544444e-02   1.07084478e-05]\n",
+      "[  2.90000000e+02   2.67985448e-02   1.21544443e-02   1.07084480e-05]\n",
+      "[  2.71475095e+02   2.60559800e-02   1.17121613e-02   1.18616762e-05]\n",
+      "[  2.71475099e+02   2.60559800e-02   1.17121613e-02   1.18616762e-05]\n",
+      "[  2.71475095e+02   2.60559804e-02   1.17121613e-02   1.18616762e-05]\n",
+      "[  2.71475095e+02   2.60559800e-02   1.17121614e-02   1.18616762e-05]\n",
+      "[  2.71475095e+02   2.60559800e-02   1.17121613e-02   1.18616764e-05]\n",
+      "[  2.71737577e+02   2.59844937e-02   1.17123461e-02   1.18850353e-05]\n",
+      "[  2.71737581e+02   2.59844937e-02   1.17123461e-02   1.18850353e-05]\n",
+      "[  2.71737577e+02   2.59844941e-02   1.17123461e-02   1.18850353e-05]\n",
+      "[  2.71737577e+02   2.59844937e-02   1.17123463e-02   1.18850353e-05]\n",
+      "[  2.71737577e+02   2.59844937e-02   1.17123461e-02   1.18850355e-05]\n",
+      "[  2.71785411e+02   2.59507650e-02   1.17151047e-02   1.18752386e-05]\n",
+      "[  2.71785415e+02   2.59507650e-02   1.17151047e-02   1.18752386e-05]\n",
+      "[  2.71785411e+02   2.59507653e-02   1.17151047e-02   1.18752386e-05]\n",
+      "[  2.71785411e+02   2.59507650e-02   1.17151048e-02   1.18752386e-05]\n",
+      "[  2.71785411e+02   2.59507650e-02   1.17151047e-02   1.18752388e-05]\n",
+      "[  2.72113260e+02   2.60186670e-02   1.17222048e-02   1.18654262e-05]\n",
+      "[  2.72113264e+02   2.60186670e-02   1.17222048e-02   1.18654262e-05]\n",
+      "[  2.72113260e+02   2.60186674e-02   1.17222048e-02   1.18654262e-05]\n",
+      "[  2.72113260e+02   2.60186670e-02   1.17222050e-02   1.18654262e-05]\n",
+      "[  2.72113260e+02   2.60186670e-02   1.17222048e-02   1.18654263e-05]\n",
+      "[  2.72144771e+02   2.60041951e-02   1.17244971e-02   1.18491964e-05]\n",
+      "[  2.72144775e+02   2.60041951e-02   1.17244971e-02   1.18491964e-05]\n",
+      "[  2.72144771e+02   2.60041955e-02   1.17244971e-02   1.18491964e-05]\n",
+      "[  2.72144771e+02   2.60041951e-02   1.17244973e-02   1.18491964e-05]\n",
+      "[  2.72144771e+02   2.60041951e-02   1.17244971e-02   1.18491965e-05]\n",
+      "[  2.72074649e+02   2.59815095e-02   1.17311059e-02   1.18197967e-05]\n",
+      "[  2.72074653e+02   2.59815095e-02   1.17311059e-02   1.18197967e-05]\n",
+      "[  2.72074649e+02   2.59815099e-02   1.17311059e-02   1.18197967e-05]\n",
+      "[  2.72074649e+02   2.59815095e-02   1.17311061e-02   1.18197967e-05]\n",
+      "[  2.72074649e+02   2.59815095e-02   1.17311059e-02   1.18197969e-05]\n",
+      "[  2.71464044e+02   2.59759460e-02   1.17259563e-02   1.18251342e-05]\n",
+      "[  2.71464044e+02   2.59759460e-02   1.17259563e-02   1.18251342e-05]\n",
+      "[  2.71767913e+02   2.59810708e-02   1.17337377e-02   1.18235256e-05]\n",
+      "[  2.71927053e+02   2.59836312e-02   1.17323934e-02   1.18226706e-05]\n",
+      "[  2.72014683e+02   2.59849671e-02   1.17316175e-02   1.18221520e-05]\n",
+      "[  2.72068214e+02   2.59845093e-02   1.17311455e-02   1.18212304e-05]\n",
+      "[  2.72072652e+02   2.59830116e-02   1.17311148e-02   1.18204970e-05]\n",
+      "[  2.72073768e+02   2.59822606e-02   1.17311093e-02   1.18201452e-05]\n",
+      "[  2.72074191e+02   2.59819180e-02   1.17311076e-02   1.18199859e-05]\n",
+      "[  2.72074445e+02   2.59816966e-02   1.17311066e-02   1.18198832e-05]\n",
+      "[  2.72074546e+02   2.59816048e-02   1.17311063e-02   1.18198408e-05]\n",
+      "[  2.72074598e+02   2.59815575e-02   1.17311061e-02   1.18198189e-05]\n",
+      "[  2.72074623e+02   2.59815335e-02   1.17311060e-02   1.18198078e-05]\n",
+      "[  2.72074637e+02   2.59815213e-02   1.17311060e-02   1.18198021e-05]\n",
+      "[  2.72074641e+02   2.59815213e-02   1.17311060e-02   1.18198021e-05]\n",
+      "[  2.72074637e+02   2.59815217e-02   1.17311060e-02   1.18198021e-05]\n",
+      "[  2.72074637e+02   2.59815213e-02   1.17311061e-02   1.18198021e-05]\n",
+      "[  2.72074637e+02   2.59815213e-02   1.17311060e-02   1.18198023e-05]\n",
+      "[  2.72074632e+02   2.59815199e-02   1.17311045e-02   1.18198016e-05]\n",
+      "[  2.72074636e+02   2.59815199e-02   1.17311045e-02   1.18198016e-05]\n",
+      "[  2.72074632e+02   2.59815203e-02   1.17311045e-02   1.18198016e-05]\n",
+      "[  2.72074632e+02   2.59815199e-02   1.17311047e-02   1.18198016e-05]\n",
+      "[  2.72074632e+02   2.59815199e-02   1.17311045e-02   1.18198018e-05]\n",
+      "[  2.72074620e+02   2.59815194e-02   1.17311049e-02   1.18198013e-05]\n",
+      "modsim.py: scipy.optimize.leastsq ran successfully\n",
+      "                 and returned the following message:\n",
+      "The relative error between two consecutive iterates is at most 0.000000\n",
+      "Saving figure to file chap08-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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uYGVlhZubW5XHHzt2DDc3N9zd3Q3bgoKCKCgoICYmBh8fnxqJE8DfuzVn47I4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\nrEIFhBBCiFWogBBCCLHK/wG3EzKShRnkHQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe94fe1ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "params = G0, k1, k2, k3\n",
+    "params\n",
+    "\n",
+    "error_func(params, data)\n",
+    "\n",
+    "%psource fit_leastsq\n",
+    "\n",
+    "best_params = fit_leastsq(error_func, params, data)\n",
+    "\n",
+    "system = make_system(*best_params, data)\n",
+    "run_odeint(system, slope_func)\n",
+    "\n",
+    "plot(system.results.G, label='simulation')\n",
+    "plot(data.glucose, style='bo', label='glucose data')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')\n",
+    "\n",
+    "savefig('chap08-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** How sensitive are the results to the starting guess for the parameters.  If you try different values for the starting guess, do we get the same values for the best parameters?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interpreting parameters\n",
+    "\n",
+    "Based on the parameters of the model, we can estimate glucose effectiveness and insulin sensitivity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def indices(G0, k1, k2, k3):\n",
+    "    \"\"\"Compute glucose effectiveness and insulin sensitivity.\n",
+    "    \n",
+    "    G0: initial blood glucose\n",
+    "    k1: rate parameter\n",
+    "    k2: rate parameter\n",
+    "    k3: rate parameter\n",
+    "    data: DataFrame\n",
+    "    \n",
+    "    returns: State object containing S_G and S_I\n",
+    "    \"\"\"\n",
+    "    return State(S_G=k1, S_I=k3/k2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>S_G</th>\n",
+       "      <td>0.025982</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S_I</th>\n",
+       "      <td>0.001008</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "S_G    0.025982\n",
+       "S_I    0.001008\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indices(*best_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The insulin minimal model\n",
+    "\n",
+    "In addition to the glucose minimal mode, Pacini and Bergman present an insulin minimal model, in which the concentration of insulin, $I$, is governed by this differential equation:\n",
+    "\n",
+    "$ \\frac{dI}{dt} = -k I(t) + \\gamma (G(t) - G_T) t $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Write a version of `make_system` that takes the parameters of this model, `I0`, `k`, `gamma`, and `G_T` as parameters, along with a `DataFrame` containing the measurements, and returns a `System` object suitable for use with `run_simulation` or `run_odeint`.\n",
+    "\n",
+    "Use it to make a `System` object with the following parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(I0, k, gamma, G_T, data):\n",
+    "    init = State(I=I0)\n",
+    "    system = System(init=init, \n",
+    "                    k=k, G_T=G_T, k3=k3,\n",
+    "                    Gb=Gb, Ib=Ib, \n",
+    "                    G=interpolate(data.glucose),\n",
+    "                    ts=data.index)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "I0 = 360\n",
+    "k = 0.25\n",
+    "gamma = 0.004\n",
+    "G_T = 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = make_system(I0, k, gamma, G_T, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Write a slope function that takes state, t, system as parameters and returns the derivative of `I` with respect to time.  Test your function with the initial condition $I(0)=360$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    I = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    dIdt = - k * I + gamma * (G(t) - G_T) * t\n",
+    "   \n",
+    "    \n",
+    "    return dIdt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I   -90.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(I=360)\n",
+    "slope_func(init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I   -90.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(I=I0)\n",
+    "slope_func(init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run `run_odeint` with your `System` object and slope function, and plot the results, along with the measured insulin levels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>I</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>360.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>219.652719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>137.739127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>89.505898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>61.636133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>45.765330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>37.053121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>32.989732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>31.406589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>31.811071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>32.581632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>34.014287</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33.758216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>30.932902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>24.554216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>15.767804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>7.782053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>-0.382598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>0.598298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1.943431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>3.415756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>4.413573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>11.170251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>25.702699</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               I\n",
+       "time            \n",
+       "0     360.000000\n",
+       "2     219.652719\n",
+       "4     137.739127\n",
+       "6      89.505898\n",
+       "8      61.636133\n",
+       "10     45.765330\n",
+       "12     37.053121\n",
+       "14     32.989732\n",
+       "16     31.406589\n",
+       "19     31.811071\n",
+       "22     32.581632\n",
+       "27     34.014287\n",
+       "32     33.758216\n",
+       "42     30.932902\n",
+       "52     24.554216\n",
+       "62     15.767804\n",
+       "72      7.782053\n",
+       "82     -0.382598\n",
+       "92      0.598298\n",
+       "102     1.943431\n",
+       "122     3.415756\n",
+       "142     4.413573\n",
+       "162    11.170251\n",
+       "182    25.702699"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_odeint(system, slope_func)\n",
+    "system.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SycRy9y4UFpouFiGEqMX0KrHMmzdPM6JerVazaNEinSWWy5cv07x5c+NGWFuY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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe959a34e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(system.results, 'r-', label='simulation')\n",
+    "plot(data.insulin, 'bo', label='insulin data')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Write an error function that takes a sequence of parameters as an argument, along with the `DataFrame` containing the measurements.  It should make a `System` object with the given parameters, run it, and compute the difference between the results of the simulation and the measured values.  Test your error function by calling it with the parameters from the previous exercise.\n",
+    "\n",
+    "Hint: As we did in a previous exercise, you might want to drop the errors for times prior to `t=8`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func(params, data):\n",
+    "    \"\"\"Computes an array of errors to be minimized.\n",
+    "    \n",
+    "    params: sequence of parameters\n",
+    "    data: DataFrame of values to be matched\n",
+    "    \n",
+    "    returns: array of errors\n",
+    "    \"\"\"\n",
+    "    print(params)\n",
+    "    \n",
+    "    # make a System with the given parameters\n",
+    "    system = make_system(*params, data)\n",
+    "    \n",
+    "    # solve the ODE\n",
+    "    run_odeint(system, slope_func)\n",
+    "    \n",
+    "    # compute the difference between the model\n",
+    "    # results and actual data\n",
+    "    error = system.results.I - data.insulin\n",
+    "    return error.loc[8:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[360, 0.25, 0.004, 80]"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = [I0, k, gamma, G_T]\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Use `fit_leastsq` to find the parameters that best fit the data.  Make a `System` object with those parameters, run it, and plot the results along with the measurements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[  3.60000000e+02   2.50000000e-01   4.00000000e-03   8.00000000e+01]\n",
+      "[  3.60000000e+02   2.50000000e-01   4.00000000e-03   8.00000000e+01]\n",
+      "[  3.60000000e+02   2.50000000e-01   4.00000000e-03   8.00000000e+01]\n",
+      "[  3.60000005e+02   2.50000000e-01   4.00000000e-03   8.00000000e+01]\n",
+      "[  3.60000000e+02   2.50000004e-01   4.00000000e-03   8.00000000e+01]\n",
+      "[  3.60000000e+02   2.50000000e-01   4.00000006e-03   8.00000000e+01]\n",
+      "[  3.60000000e+02   2.50000000e-01   4.00000000e-03   8.00000012e+01]\n",
+      "[  3.42251420e+02   2.48488980e-01   4.00000000e-03   8.05531158e+01]\n",
+      "[  3.42251426e+02   2.48488980e-01   4.00000000e-03   8.05531158e+01]\n",
+      "[  3.42251420e+02   2.48488984e-01   4.00000000e-03   8.05531158e+01]\n",
+      "[  3.42251420e+02   2.48488980e-01   4.00000006e-03   8.05531158e+01]\n",
+      "[  3.42251420e+02   2.48488980e-01   4.00000000e-03   8.05531170e+01]\n",
+      "[  3.34646649e+02   2.48068307e-01   4.00000000e-03   8.05020415e+01]\n",
+      "[  3.38562301e+02   2.48195694e-01   4.00000000e-03   8.05134952e+01]\n",
+      "[  3.40466123e+02   2.48324294e-01   4.00000000e-03   8.05306577e+01]\n",
+      "[  3.41333899e+02   2.48398864e-01   4.00000000e-03   8.05408251e+01]\n",
+      "[  3.41793354e+02   2.48442526e-01   4.00000000e-03   8.05467843e+01]\n",
+      "[  3.42048733e+02   2.48468063e-01   4.00000000e-03   8.05502665e+01]\n",
+      "[  3.42152590e+02   2.48478709e-01   4.00000000e-03   8.05517170e+01]\n",
+      "[  3.42203396e+02   2.48483972e-01   4.00000000e-03   8.05524339e+01]\n",
+      "[  3.42228124e+02   2.48486547e-01   4.00000000e-03   8.05527845e+01]\n",
+      "[  3.42240131e+02   2.48487800e-01   4.00000000e-03   8.05529551e+01]\n",
+      "[  3.42245950e+02   2.48488408e-01   4.00000000e-03   8.05530379e+01]\n",
+      "[  3.42248774e+02   2.48488704e-01   4.00000000e-03   8.05530781e+01]\n",
+      "[  3.42250139e+02   2.48488846e-01   4.00000000e-03   8.05530975e+01]\n",
+      "[  3.42250801e+02   2.48488916e-01   4.00000000e-03   8.05531070e+01]\n",
+      "[  3.42251123e+02   2.48488949e-01   4.00000000e-03   8.05531116e+01]\n",
+      "[  3.42251279e+02   2.48488966e-01   4.00000000e-03   8.05531138e+01]\n",
+      "[  3.42251353e+02   2.48488973e-01   4.00000000e-03   8.05531148e+01]\n",
+      "[  3.42251389e+02   2.48488977e-01   4.00000000e-03   8.05531154e+01]\n",
+      "modsim.py: scipy.optimize.leastsq ran successfully\n",
+      "                 and returned the following message:\n",
+      "The relative error between two consecutive iterates is at most 0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_params = fit_leastsq(error_func, params, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = make_system(*best_params, data)\n",
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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SAI8//jj/+c9/6N27Nx07dqRXr14MGjQIX19fp5/7WmfOnKFVq1a2lQrBugpiSSNGjGDb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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fe95a7b9b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(system.results.I, label='simulation')\n",
+    "plot(data.insulin, style='bo', label='insulin data')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration (mg/dL)')\n",
+    "\n",
+    "savefig('chap08-fig04.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Using the best parameters, estimate the sensitivity to glucose of the first and second phase pancreatic responsivity:\n",
+    "\n",
+    "$ \\phi_1 = \\frac{I_{max} - I_b}{k (G_0 - G_b)} $\n",
+    "\n",
+    "$ \\phi_2 = \\gamma \\times 10^4 $"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130, 11, 290, 92)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I_max = data.insulin.max()\n",
+    "Ib = data.insulin[0]\n",
+    "Gb = data.glucose[0]\n",
+    "\n",
+    "I_max, Ib, G0, Gb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.4186589683769704, 40.0)"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi_1 = (I_max - Ib) / k / (G0 - Gb)\n",
+    "phi_2 = gamma * 1e4\n",
+    "\n",
+    "phi_1, phi_2\n",
+    "# Still kinda confused abt the significance of phi"
+   ]
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 9: Projectiles\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# tempo switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Dropping pennies\n",
+    "\n",
+    "I'll start by getting the units we'll need from Pint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "kg = UNITS.kilogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "And defining the initial state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>381 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>v</th>\n",
+       "      <td>0.0 meter / second</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "y             381 meter\n",
+       "v    0.0 meter / second\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(y=381 * m, \n",
+    "             v=0 * m/s)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Acceleration due to gravity is about 9.8 m / s$^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "g = 9.8 * m/s**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "When we call `odeint`, we need an array of timestamps where we want to compute the solution.\n",
+    "\n",
+    "I'll start with a duration of 10 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "[  0.    0.5   1.    1.5   2.    2.5   3.    3.5   4.    4.5   5.    5.5   6.    6.5   7.    7.5   8.    8.5   9.    9.5  10. ] second"
+      ],
+      "text/latex": [
+       "$[  0.    0.5   1.    1.5   2.    2.5   3.    3.5   4.    4.5   5.    5.5   6.    6.5   7.    7.5   8.    8.5   9.    9.5  10. ] second$"
+      ],
+      "text/plain": [
+       "<Quantity([  0.    0.5   1.    1.5   2.    2.5   3.    3.5   4.    4.5   5.    5.5\n",
+       "   6.    6.5   7.    7.5   8.    8.5   9.    9.5  10. ], 'second')>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duration = 10 * s\n",
+    "ts = linspace(0, duration, 21)\n",
+    "ts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Now we make a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = System(init=init, g=g, ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "And define the slope function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing `g`\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    unpack(system)    \n",
+    "\n",
+    "    dydt = v\n",
+    "    dvdt = -g\n",
+    "    \n",
+    "    print(t)\n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "It's always a good idea to test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "0.0 meter / second\n",
+      "-9.8 meter / second ** 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "dydt, dvdt = slope_func(init, 0, system)\n",
+    "print(dydt)\n",
+    "print(dvdt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Now we're ready to run `odeint`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 second\n",
+      "0.0\n",
+      "1.2204606467739427e-05\n",
+      "1.2204606467739427e-05\n",
+      "2.4409212935478853e-05\n",
+      "2.4409212935478853e-05\n",
+      "0.12207047389032974\n",
+      "0.244116538567724\n",
+      "0.3661626032451183\n",
+      "1.586623250019061\n",
+      "2.8070838967930034\n",
+      "4.027544543566946\n",
+      "16.23215101130637\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>381.000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>379.775</td>\n",
+       "      <td>-4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>376.100</td>\n",
+       "      <td>-9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.5</th>\n",
+       "      <td>369.975</td>\n",
+       "      <td>-14.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>361.400</td>\n",
+       "      <td>-19.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           y     v\n",
+       "0.0  381.000   0.0\n",
+       "0.5  379.775  -4.9\n",
+       "1.0  376.100  -9.8\n",
+       "1.5  369.975 -14.7\n",
+       "2.0  361.400 -19.6"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>67.400</td>\n",
+       "      <td>-78.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.5</th>\n",
+       "      <td>26.975</td>\n",
+       "      <td>-83.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>-15.900</td>\n",
+       "      <td>-88.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.5</th>\n",
+       "      <td>-61.225</td>\n",
+       "      <td>-93.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>-109.000</td>\n",
+       "      <td>-98.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            y     v\n",
+       "8.0    67.400 -78.4\n",
+       "8.5    26.975 -83.3\n",
+       "9.0   -15.900 -88.2\n",
+       "9.5   -61.225 -93.1\n",
+       "10.0 -109.000 -98.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The following function plots the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_position(results):\n",
+    "    \"\"\"Plot the results.\n",
+    "    \n",
+    "    results: DataFrame with position, `y`\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot(results.y, label='y')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Position (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Here's what it looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap09-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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YdC35eD5J+7MxynbTLVKTRjYnT57k/vvvx9nZmREjRuDt7U1hYSHff/8933//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\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8a1cc0b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_position(system.results)\n",
+    "savefig('chap09-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Exercise:** Add a print statement to `slope_func` to print the value of `t` each time it's called.  What can we infer about how `odeint` works, based on the results?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#odeint takes time values that are increasing exponentially"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "**Exercise:** Change the value of `dt` and run the solver again.  What effect does it have on the results?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#graph is more smoothe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Onto the sidewalk\n",
+    "\n",
+    "Here's the code again to set up the `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(duration, v_init=0):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    duration: time of simulation in seconds\n",
+    "    v_init: initial velocity, dimensionless\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(y=381 * m, v=v_init * m / s)\n",
+    "\n",
+    "    g = 9.8 * m/s**2\n",
+    "    ts = linspace(0, duration, 11)\n",
+    "    return System(init=init, g=g, ts=ts) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "0.0\n",
+      "1.2391823754412774e-05\n",
+      "1.2391823754412774e-05\n",
+      "2.478364750882555e-05\n",
+      "2.478364750882555e-05\n",
+      "0.12394302119163657\n",
+      "0.2478612587357643\n",
+      "0.37177949627989204\n",
+      "1.6109618717211696\n",
+      "2.850144247162447\n",
+      "4.089326622603725\n",
+      "16.481150377016498\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>381.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>376.1</td>\n",
+       "      <td>-9.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>361.4</td>\n",
+       "      <td>-19.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>336.9</td>\n",
+       "      <td>-29.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>302.6</td>\n",
+       "      <td>-39.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>258.5</td>\n",
+       "      <td>-49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.0</th>\n",
+       "      <td>204.6</td>\n",
+       "      <td>-58.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.0</th>\n",
+       "      <td>140.9</td>\n",
+       "      <td>-68.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>67.4</td>\n",
+       "      <td>-78.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>-15.9</td>\n",
+       "      <td>-88.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>-109.0</td>\n",
+       "      <td>-98.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          y     v\n",
+       "0.0   381.0   0.0\n",
+       "1.0   376.1  -9.8\n",
+       "2.0   361.4 -19.6\n",
+       "3.0   336.9 -29.4\n",
+       "4.0   302.6 -39.2\n",
+       "5.0   258.5 -49.0\n",
+       "6.0   204.6 -58.8\n",
+       "7.0   140.9 -68.6\n",
+       "8.0    67.4 -78.4\n",
+       "9.0   -15.9 -88.2\n",
+       "10.0 -109.0 -98.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(10)\n",
+    "system\n",
+    "run_odeint(system, slope_func)\n",
+    "system.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "To figure out when the penny hit the sidewalk, we use `interp_inverse`, which return a function that maps from height to time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = system.results.y\n",
+    "T = interp_inverse(y, kind='cubic')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "`T(0)` interpolates the time when the height was 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(8.81792826905006)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T_sidewalk = T(0)\n",
+    "T_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "We can compare that to the exact result.  Without air resistance, we have\n",
+    "\n",
+    "$v = -g t$\n",
+    "\n",
+    "and\n",
+    "\n",
+    "$y = 381 - g t^2 / 2$\n",
+    "\n",
+    "Setting $y=0$ and solving for $t$ yields\n",
+    "\n",
+    "$t = \\sqrt{\\frac{2 y_{init}}{g}}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.817885349720552 second"
+      ],
+      "text/latex": [
+       "$8.817885349720552 second$"
+      ],
+      "text/plain": [
+       "<Quantity(8.817885349720552, 'second')>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2 * init.y / g)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The estimate is accurate to 4 decimal places."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "We can double-check by running the simulation for the estimated flight time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n",
+      "0.0\n",
+      "1.2373598314419205e-05\n",
+      "1.2373598314419205e-05\n",
+      "2.474719662883841e-05\n",
+      "2.474719662883841e-05\n",
+      "0.1237607303408209\n",
+      "0.24749671348501295\n",
+      "0.371232696629205\n",
+      "1.6085925280711255\n",
+      "2.845952359513046\n",
+      "4.083312190954967\n",
+      "16.45691050537417\n"
+     ]
+    }
+   ],
+   "source": [
+    "system = make_system(duration=T_sidewalk)\n",
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "And checking the final state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def final_state(results):\n",
+    "    \"\"\"Returns the final position and velocity, with units.\n",
+    "    \n",
+    "    results: TimeFrame with y and v.\n",
+    "    \n",
+    "    returns: y, v at t_end\n",
+    "    \"\"\"\n",
+    "    t_end = results.index[-1]\n",
+    "    y, v = results.loc[t_end]\n",
+    "    return y*m, v*m/s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "As expected, the final height is close to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1.2732392917769175e-10 meter"
+      ],
+      "text/latex": [
+       "$1.2732392917769175e-10 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(1.2732392917769175e-10, 'meter')>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_final, v_final = final_state(system.results)\n",
+    "y_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "And we can check the final velocity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-18.00000000874157 meter/second"
+      ],
+      "text/latex": [
+       "$-18.00000000874157 \\frac{meter}{second}$"
+      ],
+      "text/plain": [
+       "<Quantity(-18.00000000874157, 'meter / second')>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "And convert to km/h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-64.80000003146965 kilometer/hour"
+      ],
+      "text/latex": [
+       "$-64.80000003146965 \\frac{kilometer}{hour}$"
+      ],
+      "text/plain": [
+       "<Quantity(-64.80000003146965, 'kilometer / hour')>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km = UNITS.kilometer\n",
+    "h = UNITS.hour\n",
+    "v_final.to(km / h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.\n",
+    "\n",
+    "So it's a good thing there is air resistance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Try changing the initial velocity and see what effect it has on the time to hot the sidewalk.  Sweep a range of values for the initial velocity, from 0 to 25 m/s, and plot `T_sidewalk` as a function of initial velocity.  You might find the following function useful.\n",
+    "\n",
+    "Things might go horribly wrong for the larger initial velocities.  What's going on?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def flight_time(system):\n",
+    "    \"\"\"Simulates the system and computes flight time.\n",
+    "    \n",
+    "    Uses cubic interpolation.\n",
+    "    \n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: flight time in seconds\n",
+    "    \"\"\"\n",
+    "    run_odeint(system, slope_func)\n",
+    "    y = system.results.y\n",
+    "    inverse = Series(y.index, index=y.values)\n",
+    "    T = interpolate(inverse, kind='cubic')\n",
+    "    T_sidewalk = T(0)\n",
+    "    return T_sidewalk * s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "make_system() takes 1 positional argument but 2 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-55-25a7d0fb743a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv_init\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0msystem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_system\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mduration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv_init\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[0mrun_odeint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslope_func\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msystem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: make_system() takes 1 positional argument but 2 were given"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "duration = 10\n",
+    "vs = linrange(0, 25, 1)\n",
+    "\n",
+    "for v_init in vs:\n",
+    "    system = make_system(duration, v_init)\n",
+    "    run_odeint(system, slope_func)\n",
+    "    y = system.results.y\n",
+    "    inverse = Series(y.index, index=y.values)\n",
+    "    T = interpolate(inverse, kind='cubic')\n",
+    "    T_sidewalk = T(0)\n",
+    "    plot(v_init, T_sidewalk, 'b-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### With air resistance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)\n",
+    "\n",
+    "First I'll create a `Condition` object to contain the quantities we'll need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(height = 381 * m,\n",
+    "                      v_init = 0 * m / s,\n",
+    "                      g = 9.8 * m/s**2,\n",
+    "                      mass = 2.5e-3 * kg,\n",
+    "                      diameter = 19e-3 * m,\n",
+    "                      rho = 1.2 * kg/m**3,\n",
+    "                      v_term = 18 * m / s,\n",
+    "                      duration = 30 * s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Now here's a version of `make_system` that takes a `Condition` object as a parameter.\n",
+    "\n",
+    "`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    condition: Condition with height, g, mass, diameter, \n",
+    "               rho, v_term, and duration\n",
+    "    \n",
+    "    returns: System with init, g, mass, rho, C_d, area, and ts\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    init = State(y=height, v=v_init)\n",
+    "    area = np.pi * (diameter/2)**2\n",
+    "    C_d = 2 * mass * g / (rho * area * v_term**2)\n",
+    "    ts = linspace(0, duration, 101)\n",
+    "    \n",
+    "    return System(init=init, g=g, mass=mass, rho=rho,\n",
+    "                  C_d=C_d, area=area, ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Let's make a `System`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>y             381 meter\n",
+       "v    0.0 meter / secon...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>g</th>\n",
+       "      <td>9.8 meter / second ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mass</th>\n",
+       "      <td>0.0025 kilogram</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rho</th>\n",
+       "      <td>1.2 kilogram / meter ** 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C_d</th>\n",
+       "      <td>0.4445009981135434 dimensionless</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area</th>\n",
+       "      <td>0.0002835287369864788 meter ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts</th>\n",
+       "      <td>[0.0 second, 0.3 second, 0.6 second, 0.8999999...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init    y             381 meter\n",
+       "v    0.0 meter / secon...\n",
+       "g                                 9.8 meter / second ** 2\n",
+       "mass                                      0.0025 kilogram\n",
+       "rho                             1.2 kilogram / meter ** 3\n",
+       "C_d                      0.4445009981135434 dimensionless\n",
+       "area                     0.0002835287369864788 meter ** 2\n",
+       "ts      [0.0 second, 0.3 second, 0.6 second, 0.8999999...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(condition)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Here's the slope function, including acceleration due to gravity and drag."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Compute derivatives of the state.\n",
+    "    \n",
+    "    state: position, velocity\n",
+    "    t: time\n",
+    "    system: System object containing g, rho,\n",
+    "            C_d, area, and mass\n",
+    "    \n",
+    "    returns: derivatives of y and v\n",
+    "    \"\"\"\n",
+    "    y, v = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    f_drag = rho * v**2 * C_d * area / 2\n",
+    "    a_drag = f_drag / mass\n",
+    "    \n",
+    "    dydt = v\n",
+    "    dvdt = -g + a_drag\n",
+    "    \n",
+    "    return dydt, dvdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "As always, let's test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(0.0, 'meter / second')>, <Quantity(-9.8, 'meter / second ** 2')>)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "And then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "First check that the simulation ran long enough for the penny to land."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(-136.083704279796, 'meter')>,\n",
+       " <Quantity(-18.00000000087864, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_state(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Then compute the flight time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(22.439794207078908)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = system.results.y\n",
+    "inverse = Series(y.index, index=y.values)\n",
+    "T = interpolate(inverse, kind='cubic')\n",
+    "T_sidewalk = T(0)\n",
+    "T_sidewalk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Setting the duration to the computed flight time, we can check the final conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "condition.set(duration=T_sidewalk)\n",
+    "system = make_system(condition)\n",
+    "run_odeint(system, slope_func)\n",
+    "y_final, v_final = final_state(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "The final height is close to 0, as expected.  And the final velocity is close to the given terminal velocity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(1.2732392917769175e-10, 'meter')>,\n",
+       " <Quantity(-18.00000000874157, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_final, v_final"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Here's the plot of position as a function of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap09-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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EiBBGoCgK0eGeBHo7sedYNoVXawDIL61m4+5Uhkf70S/UXVqhiE7RZoAkJibi\n6emp/2chRPfl6mTLjLERnEgp5Pj5QrQ6HY1NWvYl55KVV8G4+EAc7GQddnF3tXnJhr+/v/7u7uPH\nj2Nvb4+/v3+rH2tra3bu3NllBQshbs3CQmFwXx8eHBeB202tUC4XVLBhVyrpOWVGrE70RO265u+l\nl14iJyfnltsuXLjAypUr72pRQoiO83KzZ9aESGIiPfVTV3UNTew8cpmdRy5TV99k5ApFT9HmFNZj\njz2m77Sr0+lYvHjxLftNlZaWEhQU1HkVCiF+NkuVBSMG+hPi58KeY9lUXu/mm55TRn5JFePiAwny\ncTZylcLUtRkgixYt4osvvgDgiy++YMCAAbi5uRnsY2FhgbOzM9OmTevcKoUQHeLv6cjcSWoO/nCF\nC5euAlBV28j2gxcZEObBPdG+WFlKKxTRMW0GSExMDDExMQBoNBoef/xxg7XIhRCmwdpKxfjBQYT4\nufDtiRxqr09hncksIaewkgkJQfi4Oxi5SmGK2nUO5M0335TwEMLEhfq7MHeSmlB/F/1YeVU9m7/N\n4Psz+Wg0WiNWJ0xRm0cg/fv359NPPyU6Opp+/frd9jrys2fP3vXihBB3l72tFfcP603q5TIO/HCF\nhuutUE6kFJJd0HzzobuLtEIR7dNmgCxcuFC/TOzChQvlRiQheghFUYjq7YafpyN7k7LJLWru5ltc\nXstne9IY0t+XmAhPaYUibqvNAPn973+v/+cnnniiS4oRQnQdZwdrpowK43R6Cd+dyUOj1aHR6vju\ndB6X8ioYPzhQWqGIn9Tu3s85OTlkZmYCUFlZyRtvvMHvf/97vvrqq04rTgjRuRRFYWCkJ7MnqvFy\nbVlxNK+kio27UzmfVSrrsIs2tStA9u/fz/3336+/rHfZsmVs2LCBK1eu8Pzzz+vHhRCmyc3Zlhnj\nIhjcxxuL69PVjU1a9ibl8M3hLGrqGo1coeiO2hUga9euZcSIESxevJiKigp2797No48+ytatW3n0\n0Uf5v//7v86uUwjRyVQWCkP6+zJjXAS9nFqmrrLym1uh3FjESogb2hUgKSkpzJs3D0dHRw4cOIBG\no+Hee+8FYPjw4Vy+fLlTixRCdB1vN3tmT1ATHe6hH6utb+Lf319iz7HL1DVIKxTRrF0BYmNjg0aj\nAeDQoUO4u7sTFRUFQElJCc7O0hJBiJ7EytKCUbEBTBkVhuNNXXxTLpexcVeqfhErYd7aFSBxcXF8\n/PHHfP1f89RwAAAbJUlEQVT11+zcuZNJkyYBzfd+rFmzhkGDBnVqkUII4wj0dmLOJDXqIFf9WFVt\nI18eyOTgySs0yc2HZq1dAfLyyy9TUFDAs88+i7+/P4sWLQKaGy42NTXx3HPPdWqRQgjjsbW2ZOKQ\nYO4b1htb65Yr/09lFLNpd5p+ESthftq1JnpgYCDffPMNpaWleHi0zIuuXbuWPn36YGUlC9UI0dOF\nB/TCz8OBb5NyyMqvAKCsso7Ne9OJ7+PNoD7eqOTmQ7PSrgCB5uvFy8vL2bVrF1VVVbi6uhIXFyfh\nIYQZsbe14oHhIVy4dJWDP1yhsUmLVqfj2PkCLuU3t0K5eTEr0bO1K0C0Wi3Lli1j8+bNBjcVKYrC\nlClTePPNN6XViRBmQlEU+oa44+/pSOLxHPJKmluhFJXV8NmeNIb19yU6wkN+J5iBdp0D+Z//+R+2\nbdvGs88+y/79+zl37hz79u1jyZIlfP3113z00UedXacQoptxcbRh6ugw7on2009dNWm0HDx1hS8P\nZOoXsRI9V7sC5IsvvmDhwoU88sgjeHt7o1Kp8PHx4Xe/+x2PPfaY3IkuhJmysFCIU3sxa0IkHr1a\nuvjmFlWxYVcqKZeuSiuUHqxdAVJcXNzmpbpxcXHk5+ff1aKEEKbF3cWOmeMiGBTlrZ+6amjUsOd4\nNv/v+0vSCqWHaleABAYGcvLkyVtuO3nyJJ6enne1KCGE6VGpLBg2wJcZY8PpdVMX38wr19iwK5Ws\nvGtGrE50hnYFyIMPPsj777/PP//5T4qKitBqtRQVFfGPf/yDDz74gOnTp3d2nUIIE+Hj7sDsiZH0\nDzNshfL14SwSj2fT0KgxYnXibmrXVVi//vWvuXDhAsuXL+ett97Sj+t0On75y1/qbywUQggAK0sV\nY+ICCPFz5tukHKpqm6ewLly6ypXiKsYPDsLf09HIVYo71a4AUalUvPXWWzzyyCMkJSVx7do1nJ2d\nGTx4MBEREZ1doxDCRAX7ODNnopr9J6+QnlMGQEV1A9v2ZzIwwoOh/X2xVLV7WSLRzdw2QEpKSsjL\nyyMoKIiIiAgJDCHEz2JrY8m9Q4MJ8XNm/8lc6hua12H/Ia2Y7IJKJiQEGSxmJUxHm9Hf0NDAs88+\ny6hRo5g9ezbDhg1jyZIlXLsmJ8KEED9fZJArcydFEeTjpB+7WlHHF4npJF0oRKuVy31NTZtHIO+9\n9x7//ve/mTFjBn379iUrK4tNmzah1Wp59913u7JGIUQP4WhnxeQRoZy7WMrhU3k0appboRw5m09W\n3jUmJATh6iStUExFmwGya9cuFi9ezOLFi/VjarWa//qv/6K+vh4bG5u2HiqEEG1SFIX+YR4EeDmx\n53g2BaXVABRerWHT7jSGR/vRP8xdWqGYgDansAoKCkhISDAYGz16NE1NTeTm5nZ6YUKInq2Xkw3T\nx4QztL8vFje1Qtl/MpftBy9SJa1Qur02A6SxsbHVUYara/OiMvX19Z1blRDCLFhYKMT38WbmuEjc\nb+rim1NYyYbdqaRll0krlG6sQ9fPyX9QIcTd5Olqx6wJkcSqvfRTV/UNGnYdvczOI5epq5d12Luj\nDgWIzE0KIe42lcqC4dF+TBsdhrODtX48I7ecT3elcun6Ilai+/jJ+0DeeOMNHB1b7ha9ceTxxz/+\nEQcHB/24oih8/PHHnVSiEMKc+Hk6MmeimkOn8jifVQpATV0jXx26SL9Qd4ZH+2FtpTJylQJ+IkAG\nDx4MNJ8Lac+4EELcLdZWKsbFBxLq78LepBx9N99zF0vJKWy++dDPQ1qhGFubAbJ+/fpOf/Fly5ah\n0Wj405/+pB87dOgQK1asICsri+DgYJ577jlGjx6t315aWsrrr7/O4cOHsbKyYvr06TzzzDNYWrZ7\ndV4hhIno7evM3Elq9iXnkplbDjS3Qtm6L5PYSE+G9PNBJa1QjMYon7xOp+O9995j06ZNBuMZGRks\nWrSI++67j61btzJ+/HgWL15Menq6fp8nnniCkpISPvnkE5YvX86WLVtYvXp1V78FIUQXsbOx5L6h\nwUwaEoyNdfPUlU6nIzm1iM8S0ykprzVyhearywMkJyeH3/zmN2zYsAE/Pz+DbevWrSMmJoZFixYR\nFhbG008/TWxsLOvWrQOa1x45ceIEy5cvJyoqitGjR/PCCy+wfv16GhrkmnEheipFUZpboUxUE+jd\n0gql9FotnyWmcSJFWqEYQ5cHSHJyMr6+vuzYsYOAgACDbUlJSa1uXhwyZAhJSUn67f7+/gQGBuq3\nJyQkUF1dzYULFzq/eCGEUTnaW/PLkaGMivXXd/HVanV8fyafrfsyuFYl96h1pS4PkClTpvD222/f\nchXDgoICvL29Dca8vLwoKCgAoLCwEC8vr1bbAVlWVwgzoSgK0eGezJ4YibdbSxff/NJqNu5O5Wxm\nidyr1kW61dmnuro6rK2tDcasra31d77X1ta2ujveysoKRVHk7nghzIyrky0zxkYwpJ8PFtfvTWts\n0rIvOZcdhy7qF7ESnadbBYiNjU2ry4MbGhqws7MDwNbWttW5jsbGRnQ6Hfb2sp6AEObGwkJhcF8f\nHhwXgdtNrVCyCyrZsCtFv4iV6BzdKkB8fX0pKioyGCsqKtJPa/n4+FBcXNxqO9Bq6ksIYT683OyZ\nNSGSmEhPg1YoO49IK5TO1K0CZNCgQRw/ftxg7OjRo8THx+u35+TkGJzvOHr0KA4ODkRFRXVprUKI\n7sVSZcGIgf5MHR2Gk33LVHh6Thkbd6eSXSCtUO62bhUgDz/8MElJSaxatYrMzEzee+89Tp06xbx5\n8wCIjY0lJiaGZ555hnPnzrF//35WrFjBggULWp07EUKYJ39PR+ZOUtOnt5t+rKq2ke0HL7I/OZfG\nJo0Rq+tZulWAqNVq1qxZw86dO5k6dSp79+7l/fffJywsDGi++mLNmjW4u7vz0EMP8fLLLzNz5kyD\nRa+EEMLaSsX4wUE8cE8IdjYtXSrOZJawaXeafhErcWcUnRlc75abm8v48eNJTExsde+JEKJnq6lr\n5NsTuWTlXdOPKYpCnNqLhL7e0grlJ9zud6d8ckKIHs3e1ooH7unN+PggfRdfnU7HiZRCvtibTuk1\naYXSURIgQogeT1EU+oS4MWeiGn/Pli6+xeW1fLYnjeTUImmF0gESIEIIs+HsYM3U0WGMGOiH6vo6\n7Bqtju9O57Ftf6a0QvmZJECEEGZFURRiIr2YPVGNl2vLDch5JVVs3J3K+axSaYXSThIgQgiz5OZs\ny4xxEST0NWyFsjcph28OZ+kXsRJtkwARQpgtlYVCQj8fZoyLoJdTS5+9rPwKNuxK1S9iJW5NAkQI\nYfa83eyZPUHNwPCWLuG19U38+/tL7Dl2mboGaYVyKxIgQggBWFlaMDLWnymjwnC0s9KPp1wuY+Ou\nVHIKK41YXfckASKEEDcJ9HZiziQ16iBX/VhVbSNfHsjk4MkrNGm0Rqyue5EAEUKIH7G1tmTikGDu\nG9YbW+uWViinMorZtDuNwqs1Rqyu+5AAEUKINoQH9OJX96oJ8XXWj5VV1rF5bzrHzhWgMfObDyVA\nhBDiJ9jbWvHA8BDGxQdiZXl9HXadjmPnC9i8N52rFXVGrtB4JECEEOI2FEWhb4g7cyaq8fNw0I8X\nldXw2Z40TqUVm+XNhxIgQgjRTi6ONkwdHc490S2tUJo0Wg6eusKXBzKprGm4zTP0LBIgQgjxM1hY\nNLeCnzUhEo9edvrx3KIqNuxKJeXSVbM5GpEAEUKIDnB3sWPmuAgGRXnr12FvaNSw53g2//7+klm0\nQpEAEUKIDlKpLBg2wJfpY8JxcWxphXLxyjU27Eo1WMSqJ5IAEUKIO+Tr4cCciZH0D3XXj9XWN/H1\n4SwSj2fT0Ngz12GXABFCiLvAylLFmEGBTB4ZioNtSyuUC5eusnF3KleKq4xYXeeQABFCiLso2MeZ\nuZPURAS2tEKpqG5g2/5MDp3qWa1QJECEEOIus7Wx5N6hwUwaEoyNdcs67D+kFfPZnjSKynpGKxQJ\nECGE6CSRQa7MnRRFkI+TfuxqRR1fJKaTdKHQ5NdhlwARQohO5GhnxeQRoYyJC8BK1dIK5cjZfDZ/\nm05Zpem2QpEAEUKITqYoCv3DPJg9UY2ve0srlMKrNWzancaZjBKTvPlQAkQIIbpILycbpo0JZ2h/\nXyxuaoWy/2Qu2w9epMrEWqFIgAghRBeysFCI7+PNrPGRuLu0tELJKaxkw+5U0rLLTOZoRAJECCGM\nwKOXHbPGRxCn9tK3Qqlv0LDr6GV2HrlMXX33X4ddAkQIIYxEpbLgnmg/po0Jw9nBWj+ekVvOp7tS\nuZRfYcTqbk8CRAghjMzPw5E5E9X0DWlphVJT18hXhy7y7YkcGpu6ZysUCRAhhOgGrK1UjIsP5BfD\nQ7C/qRXKuYulbNiVSl5J92uFIgEihBDdSIifC3MnqQkL6KUfq6huYOu+TL47nYemG7VCkQARQohu\nxs7GkvuGBjMxIQgbq5ZWKMmpRXyWmE5Jea2RK2wmASKEEN2Qoiiog92YO0lNgFdLK5TSa7V8lpjG\niRTjt0KRABFCiG7M0d6aKaNCGRXrj+WNVihaHd+fyWfLvgzKK+uNVpsEiBBCdHOKohAd7snsiZF4\nu9nrxwtKq9m0J5WzmcZphSIBIoQQJsLVyZYZYyMY0s8Hi+s3HzY2admXnMtXh7Koqu3addglQIQQ\nwoRYWCgM7uvDg+MicHO21Y9fLqhgw64U0nPKuq6WLnslIYQQd42Xmz2zJkQSE+lp0Apl55Gua4Ui\nASKEECbKUmXBiIH+TB0dhpN9SyuU9JwyNu5OJbugc1uhSIAIIYSJ8/d0ZO4kNX16u+nHqmob2X7w\nIvuTczutFYoEiBBC9ADWVirGDw7iF8NDsLOx1I+fySxh0+40Ckqr7/prSoAIIUQPcqMVSqi/i36s\nvKqezd9mcORs/l1thSIBIoQQPYy9rRX3D+vNhMFBWN/UCiXpQiFf7E2n9NrdaYVikgGi0Wj461//\nyogRI4iNjeXJJ5+kpKTE2GUJIUS3oSgKUb3dmDNRTYCXo368uLyWz/akkZxadMetUEwyQFavXs3W\nrVt56623+OSTTygoKOCJJ54wdllCCNHtODtYM2VUGCMG+qG6vg67Rqvju9N5bNufybWqjrdCMbkA\naWhoYN26dSxZsoThw4fTr18/3nnnHZKTk0lOTjZ2eUII0e0oikJMpBezJ6rxcm1phZJXUsXG3amc\nzyrtUCsUkwuQlJQUqqurSUhI0I8FBATg7+9PUlKSESsTQojuzc3ZlhnjIkjoa9gKZW9SDonHs392\niJhcgBQUFADg7e1tMO7l5aXfJoQQ4tZUFgoJ/XyYMS6CXk42+vGUy2U/u7OvyQVIbW0tFhYWWFlZ\nGYxbW1tTX2+8tsZCCGFKvN3smT1BTXS4B5YqC7zd7HF2sL79A29ieftduhdbW1u0Wi1NTU1YWraU\n39DQgJ2dnRErE0II02JlacGo2ACGD/RHoblR489hckcgvr6+ABQXFxuMFxUVtZrWEkIIcXsqC+Vn\nhweY4BFIVFQUDg4OHDt2jClTpgCQm5vLlStXGDx48C0fo9E094GRcyRCCNF+N35n3vgd+mMmFyDW\n1tb86le/4u2338bV1RV3d3f++Mc/kpCQQExMzC0fc+No5aGHHurKUoUQokcoLi4mODi41biiM8Y6\niHeoqamJv/zlL2zdupWmpiZGjhzJsmXLcHNzu+X+dXV1nD17Fk9PT1QqVRdXK4QQpkmj0VBcXEz/\n/v2xtbVttd0kA0QIIYTxmdxJdCGEEN2DBIgQQogOkQARQgjRIRIgQgghOkQCRAghRIeYbYDIolQ/\nLSMjA7Va3epHOh7DsmXLeOWVVwzGDh06xJQpU4iOjmby5Mns37/fSNUZ160+mwcffLDV9+jH+/Rk\nJSUlvPjii4wYMYL4+Hh++9vfkpaWpt9u0t8dnZlauXKlbvjw4bpDhw7pzp49q5s5c6Zuzpw5xi6r\n2/j66691Q4YM0RUVFRn8NDQ0GLs0o9Fqtbp3331XFxkZqXv55Zf14+np6br+/fvr/v73v+syMjJ0\nK1eu1PXr10+XlpZmxGq7VlufjVar1Q0cOFC3fft2g+9RZWWlEavtOhqNRjd79mzdrFmzdKdOndKl\np6frnnzySd2wYcN0V69eNfnvjsndiX433FiU6tVXX2X48OEAvPPOO4wfP57k5GTi4uKMXKHxpaWl\nER4ejqenp7FL6RZycnJ4+eWXSU9Px8/Pz2DbunXriImJYdGiRQA8/fTTnDhxgnXr1vHf//3fxii3\nS/3UZ5OTk0NtbS0xMTFm+V1KSUnh5MmTfPPNN4SFhQGwYsUKEhIS2L9/P8nJySb93THLKSxZlOr2\n0tPTCQ0NNXYZ3UZycjK+vr7s2LGDgIAAg21JSUkG3yWAIUOGmM136ac+m7S0NGxtbfH39zdSdcbl\n6+vLBx98QEhIiH5Mub6Q07Vr10z+u2OWRyCyKNXtpaenU19fz6xZs7hy5QoREREsWbKE6OhoY5dm\nFFOmTNE37/yxgoICs/4u/dRnk56ejpOTE8899xzHjh3D1dWV6dOnM2/ePCwsev7fr66urowZM8Zg\nbP369dTV1TFixAjee+89k/7u9Pz/grcgi1L9tLq6OnJycqiqquKFF15g7dq1eHl58fDDD5OZmWns\n8rqduro6rK0NF+KR71KzjIwMampqGDFiBB9//DG/+tWvWLVqFWvWrDF2aUaRmJjIO++8w4IFCwgL\nCzP5745ZHoHIolQ/zdbWluPHj2Ntba3/ci9fvpxz587x6aefsnTpUiNX2L3Y2NjQ2NhoMCbfpWZv\nvfUWNTU1ODs7A6BWq6msrOT999/niSee0E/nmIMtW7awdOlSHnjgAZ5//nnA9L87ZnkEIotS3Z6j\no6PBX0YWFhaEh4eTn59vxKq6J19fX4qKigzG5LvUzNLSUh8eN6jVaqqrq6msrDRSVV1v7dq1vPTS\nS8yZM4e3335bP31n6t8dswyQmxeluuF2i1KZk7NnzxIXF8fZs2f1YxqNhpSUFCIiIoxYWfc0aNAg\njh8/bjB29OhR4uPjjVRR9zFr1izeeOMNg7EzZ87g5eXVKlh6qg8//JB3332XJ598kqVLlxocdZn6\nd8csA+TmRakOHDjAuXPnWLJkyU8uSmVOoqKi8Pf3Z9myZZw6dYr09HReeuklysrK+M1vfmPs8rqd\nhx9+mKSkJFatWkVmZibvvfcep06dYt68ecYuzegmTpzIpk2b2LZtG9nZ2Xz++ed89NFHPPnkk8Yu\nrUukpKSwcuVKZsyYwaxZsyguLtb/1NTUmPx3xyzPgUDz9dZNTU08//zzBotSieZph48++oi3336b\nhQsXUltbS1xcHJ988gnu7u7GLq/bUavVrFmzhhUrVvDhhx8SGhrK+++/r7/u35w98sgjWFpasnbt\nWvLy8vDz8+Oll15i5syZxi6tS3zzzTdoNBo2b97M5s2bDbY99dRTPP744yb93ZEFpYQQQnSIWU5h\nCSGEuHMSIEIIITpEAkQIIUSHSIAIIYToEAkQIYQQHSIBIoQQokPM9j4QIX7sD3/4A1u3bv3JfRIS\nEli/fj2//vWvUalU/POf/+ya4m6hvLyc6dOn849//IPg4ODb7r9mzRpKSkp47bXXOr84YRbkPhAh\nrsvOzubq1av6f//jH/+ISqXi1Vdf1Y85OjoSHh5ORkYGiqIY9YavZ599Fm9vb1544YV27V9XV8d9\n993Hm2++ybBhwzq5OmEO5AhEiOuCgoIICgrS/7ujoyMqleqW7W3Cw8O7srRWTp8+zc6dOzlw4EC7\nH2Nra8v8+fN588032b59eydWJ8yFnAMRogN+/etfM3/+fP2/q9VqNm3axHPPPUdsbCxDhw5lzZo1\nVFVV8dJLLzFo0CCGDx/OihUruPmgv6ysjFdffZVhw4YRHR3N3LlzOXHixG1f/6OPPuKee+7Bzc1N\nP3b27FnmzZvHoEGDiI2NZf78+fzwww8Gj3vggQdIT09n3759d/wZCCEBIsRd8tZbb+Hq6srf//53\nxo4dy+rVq3nwwQexs7NjzZo1TJw4kY8++ohdu3YBUF9fz/z589m3bx9Llixh1apVuLi4MH/+fE6f\nPt3m61RXV7N3714mTZqkH6uqquKRRx7B1dWV1atXs3LlSmpra3nkkUeoqqrS7+fl5UVsbCw7duzo\nvA9CmA2ZwhLiLunXrx+vvPIK0NzReMuWLbi7u+ubdA4dOpQdO3bwww8/cO+99/Lll1+SmprK559/\nzoABAwAYNWoUDz74ICtXruQf//jHLV8nKSmJxsZGg+WFMzIy9N2S4+LiAAgNDWXTpk1UV1fj6Oio\n37d///588803nfIZCPMiRyBC3CU3/0J3dXVFpVIZjCmKgouLCxUVFQB8//33eHt706dPH5qammhq\nakKr1TJ27FiOHz9OQ0PDLV8nNzcXgICAAP1YREQEbm5uLFy4kGXLlrF79248PDx4/vnnWy1O5O/v\nT3FxcZvPL0R7yRGIEHeJg4NDqzF7e/s29y8vL6egoIB+/frdcntZWdktV6a7sZLfzcueOjg48K9/\n/Yu1a9fy73//m02bNmFra8uUKVN49dVXDVaXvFFTVVWVwTkUIX4uCRAhjMTJyYmwsDDeeuutW253\ndXX9yfHKykqDVf1CQ0NZsWIFGo2G06dP8+WXX7JhwwZ69+7Nf/7nf+r3u3btGhYWFri4uNzFdyPM\nkUxhCWEkgwcPJi8vDy8vLwYMGKD/SUxMZP369VhZWd3ycX5+fgAUFBTox3bv3s3QoUMpLi5GpVIR\nGxvLa6+9hrOzc6t17AsKCvDy8kKlUnXemxNmQQJECCOZPn063t7eLFiwgC+//JIjR46wfPly1q5d\nS2BgoMHa2TeLj4/H1tbW4HLfuLg4dDodixcvZs+ePXz//fcsW7aMqqoqg6u1AJKTkxkxYkSnvjdh\nHiRAhDCSG+ctBg4cyPLly3n00Uc5ePAgS5cu5YknnmjzcXZ2dowaNcrgJkJ3d3c+/vhjnJyceOWV\nV3jsscc4d+4cq1evZvDgwfr9iouLSUlJaRUqQnSEtDIRwgSdPn2auXPnsnfv3lueaG/L2rVr2blz\nJ1u3bm3zCEeI9pIjECFMUHR0NOPHj+d///d/2/2YmpoaPv30U5YsWSLhIe4KCRAhTNRrr73Gzp07\nuXz5crv2//jjjxk7diyjRo3q5MqEuZApLCGEEB0iRyBCCCE6RAJECCFEh0iACCGE6BAJECGEEB0i\nASKEEKJD/j8VNerOzMcPPgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8a2347470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_position(system.results)\n",
+    "savefig('chap09-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And velocity as a function of time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_velocity(results):\n",
+    "    \"\"\"Plot the results.\n",
+    "    \n",
+    "    results: DataFrame with velocity, v\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot(results.v, label='v')\n",
+    "        \n",
+    "    decorate(xlabel='Time (s)',\n",
+    "             ylabel='Velocity (m/2)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8a2391710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_velocity(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity.  Hint: use `condition.set`.\n",
+    "\n",
+    "What do you expect to happen?  Plot velocity and position as a function of time, and see if they are consistent with your prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution goes here\n",
+    "\n",
+    "condition.set(v_init = -30 * m / s)\n",
+    "system = make_system(condition)\n",
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OHz7c5YXeCdmRsHdpatZw4lwBSeklBrPWBzlYMjXCBzcnKyNWJ4S47la/O9s9A/niiy+w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\nolP+P2wP/1EjEn+4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8a294f748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_position(system.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xAAAyMzMRHByMIUOGYMmSJcjNzUVERARCQ0Oxc+dOAEBCQgJmzZqF0NBQDBs2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\nkV7+H5Vb9XtGRlJ0AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8a29e3d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "plot_velocity(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dropping quarters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds.  We can use this measurement to estimate the coefficient of drag.\n",
+    "\n",
+    "Here's a `Condition` object with the relevant parameters from\n",
+    "https://en.wikipedia.org/wiki/Quarter_(United_States_coin)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(height = 381 * m,\n",
+    "                      v_init = 0 * m / s,\n",
+    "                      g = 9.8 * m/s**2,\n",
+    "                      mass = 5.67e-3 * kg,\n",
+    "                      diameter = 24.26e-3 * m,\n",
+    "                      rho = 1.2 * kg/m**3,\n",
+    "                      duration = 19.1 * s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's a modified version of `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition):\n",
+    "    \"\"\"Makes a System object for the given conditions.\n",
+    "    \n",
+    "    condition: Condition with height, v_init, g, mass, diameter, \n",
+    "               rho, C_d, and duration\n",
+    "    \n",
+    "    returns: System with init, g, mass, rho, C_d, area, and ts\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    init = State(y=height, v=v_init)\n",
+    "    area = np.pi * (diameter/2)**2\n",
+    "    ts = linspace(0, duration, 101)\n",
+    "    \n",
+    "    return System(init=init, g=g, mass=mass, rho=rho,\n",
+    "                  C_d=C_d, area=area, ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can run the simulation with an initial guess of `C_d=0.4`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zdu3adcsXmjNnTp8WRkS3x1YmwZwpI3C2oBo/nimHWqOFWqPFwaxSFJY3In68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\nr2LpAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8a25ffe48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "condition.set(C_d=0.4)\n",
+    "system = make_system(condition)\n",
+    "run_odeint(system, slope_func)\n",
+    "plot_position(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final height is -11 meters, which means our guess was too low (we need more drag to slow the quarter down)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(-11.034779626277231, 'meter')>,\n",
+       " <Quantity(-22.380158694375393, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_state(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`height_func` takes a hypothetical value of `C_d` and returns the height after 19.1 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def height_func(C_d, condition):\n",
+    "    \"\"\"Final height as a function of C_d.\n",
+    "    \n",
+    "    C_d: drag coefficient\n",
+    "    condition: Condition object\n",
+    "    \n",
+    "    returns: height in m\n",
+    "    \"\"\"\n",
+    "    condition.set(C_d=C_d)\n",
+    "    system = make_system(condition)\n",
+    "    run_odeint(system, slope_func)\n",
+    "    y, v = final_state(system.results)\n",
+    "    return y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we run it with `C_d=0.4`, we get -11 meters again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-11.034779626277231 meter"
+      ],
+      "text/latex": [
+       "$-11.034779626277231 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(-11.034779626277231, 'meter')>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "height_func(0.4, condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use `fsolve` to find the value of `C_d` that makes the final height 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.42587017])"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution = fsolve(height_func, 0.4, condition)\n",
+    "solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plugging in the estimated value, we can run the simulation again to get terminal velocity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(-4.725109192804666e-13, 'meter')>,\n",
+       " <Quantity(-21.689751051700213, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "condition.set(C_d=solution)\n",
+    "system = make_system(condition)\n",
+    "run_odeint(system, slope_func)\n",
+    "final_state(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, the terminal velocity of the quarter is higher than that of the penny, but we should not take this result seriously because the measurements we used are not real; I made them up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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@@ -0,0 +1,8119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 10: Vectors\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# tempo switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib notebook\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vectors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `Vector` object is like a combination of a NumPy array and a Pint Quantity.\n",
+    "\n",
+    "I'll start by grabbing the units we'll need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "m = UNITS.meter\n",
+    "s = UNITS.second\n",
+    "kg = UNITS.kilogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a two dimensional `Vector` in meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "A = Vector(3, 4) * m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can access the elements by name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "3.0 meter"
+      ],
+      "text/latex": [
+       "$3.0 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(3.0, 'meter')>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "4.0 meter"
+      ],
+      "text/latex": [
+       "$4.0 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(4.0, 'meter')>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The magnitude is the length of the vector."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "5.0 meter"
+      ],
+      "text/latex": [
+       "$5.0 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(5.0, 'meter')>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.mag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The angle is the number of radians between the vector and the positive x axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.9272952180016122 radian"
+      ],
+      "text/latex": [
+       "$0.9272952180016122 radian$"
+      ],
+      "text/plain": [
+       "<Quantity(0.9272952180016122, 'radian')>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we make another `Vector` with the same units,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "B = Vector(1, 2) * m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can add `Vector` objects like this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "[ 4.  6.] meter"
+      ],
+      "text/latex": [
+       "$[ 4.  6.] meter$"
+      ],
+      "text/plain": [
+       "<Quantity([ 4.  6.], 'meter')>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A + B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And substract like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "[ 2.  2.] meter"
+      ],
+      "text/latex": [
+       "$[ 2.  2.] meter$"
+      ],
+      "text/plain": [
+       "<Quantity([ 2.  2.], 'meter')>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A - B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can compute the Euclidean distance between two Vectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2.8284271247461903 meter"
+      ],
+      "text/latex": [
+       "$2.8284271247461903 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(2.8284271247461903, 'meter')>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.dist(B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the difference in angle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.17985349979247822 radian"
+      ],
+      "text/latex": [
+       "$-0.17985349979247822 radian$"
+      ],
+      "text/plain": [
+       "<Quantity(-0.17985349979247822, 'radian')>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A.diff_angle(B)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we are given the magnitude and angle of a vector, what we have is the representation of the vector in polar coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mag = A.mag\n",
+    "angle = A.angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `pol2cart` to convert from polar to Cartesian coordinates, and then use the Cartesian coordinates to make a `Vector` object.\n",
+    "\n",
+    "In this example, the `Vector` we get should have the same components as `A`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "[ 3.  4.] meter"
+      ],
+      "text/latex": [
+       "$[ 3.  4.] meter$"
+      ],
+      "text/plain": [
+       "<Quantity([ 3.  4.], 'meter')>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x, y = pol2cart(angle, mag)\n",
+    "Vector(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Create a `Vector` named `a_grav` that represents acceleration due to gravity, with x component 0 and y component $-9.8$ meters / second$^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a_grav= Vector(0, -9.8) * m/s/s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Degrees and radians\n",
+    "\n",
+    "Pint provides units to represent degree and radians."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "degree = UNITS.degree\n",
+    "radian = UNITS.radian"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have an angle in degrees,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "45 degree"
+      ],
+      "text/latex": [
+       "$45 degree$"
+      ],
+      "text/plain": [
+       "<Quantity(45, 'degree')>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "angle = 45 * degree\n",
+    "angle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can convert to radians."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.7853981633974483 radian"
+      ],
+      "text/latex": [
+       "$0.7853981633974483 radian$"
+      ],
+      "text/plain": [
+       "<Quantity(0.7853981633974483, 'radian')>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "angle_rad = angle.to(radian)\n",
+    "angle_rad"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If it's already in radians, `to` does the right thing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.7853981633974483 radian"
+      ],
+      "text/latex": [
+       "$0.7853981633974483 radian$"
+      ],
+      "text/plain": [
+       "<Quantity(0.7853981633974483, 'radian')>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "angle_rad.to(radian)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also convert from radians to degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "45.0 degree"
+      ],
+      "text/latex": [
+       "$45.0 degree$"
+      ],
+      "text/plain": [
+       "<Quantity(45.0, 'degree')>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "angle_rad.to(degree)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an alterative, you can use `np.deg2rad`, which works with Pint quantities, but it also works with simple numbers and NumPy arrays:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.7853981633974483 radian"
+      ],
+      "text/latex": [
+       "$0.7853981633974483 radian$"
+      ],
+      "text/plain": [
+       "<Quantity(0.7853981633974483, 'radian')>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.deg2rad(angle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Create a `Vector` named `a_force` that represents acceleration due to a force of 0.5 Newton applied to an object with mass 0.3 kilograms, in a direction 45 degrees up from the positive x-axis.\n",
+    "\n",
+    "Add `a_force` to `a_drag` from the previous exercise.  If that addition succeeds, that means that the units are compatible.  Confirm that the total acceleration seems to make sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mass2 = .3*kg\n",
+    "F_force = Vector((.5/sqrt(2)),(.5/sqrt(2)))*kg*m/s/s\n",
+    "#N = kg*m/s/s\n",
+    "a_force = F_force/mass2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "[ 1.1785113 -8.6214887] meter/second<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$[ 1.1785113 -8.6214887] \\frac{meter}{second^{2}}$"
+      ],
+      "text/plain": [
+       "<Quantity([ 1.1785113 -8.6214887], 'meter / second ** 2')>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_total = a_force + a_grav\n",
+    "a_total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Baseball"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a `Condition` object that contains the parameters for the Manny Ramirez problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(x = 0 * m, \n",
+    "                      y = 1 * m,\n",
+    "                      g = 9.8 * m/s**2,\n",
+    "                      mass = 145e-3 * kg,\n",
+    "                      diameter = 73e-3 * m,\n",
+    "                      rho = 1.2 * kg/m**3,\n",
+    "                      C_d = 0.3,\n",
+    "                      angle = 45 * degree,\n",
+    "                      velocity = 40 * m / s,\n",
+    "                      duration = 5.1 * s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the function that uses the `Condition` object to make a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    condition: Condition object with angle, velocity, x, y,\n",
+    "               diameter, duration, g, mass, rho, and C_d\n",
+    "               \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    # convert angle to degrees\n",
+    "    theta = np.deg2rad(angle)\n",
+    "    \n",
+    "    # compute x and y components of velocity\n",
+    "    vx, vy = pol2cart(theta, velocity)\n",
+    "    \n",
+    "    # make the initial state\n",
+    "    init = State(x=x, y=y, vx=vx, vy=vy)\n",
+    "    \n",
+    "    # compute area from diameter\n",
+    "    area = np.pi * (diameter/2)**2\n",
+    "    \n",
+    "    # compute timestamps\n",
+    "    ts = linspace(0, duration, 101)\n",
+    "    \n",
+    "    return System(init=init, g=g, mass=mass, \n",
+    "                  area=area, rho=rho, C_d=C_d, ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>x                               0 meter\n",
+       "y     ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>g</th>\n",
+       "      <td>9.8 meter / second ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mass</th>\n",
+       "      <td>0.145 kilogram</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area</th>\n",
+       "      <td>0.004185386812745002 meter ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rho</th>\n",
+       "      <td>1.2 kilogram / meter ** 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C_d</th>\n",
+       "      <td>0.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts</th>\n",
+       "      <td>[0.0 second, 0.051 second, 0.102 second, 0.153...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init    x                               0 meter\n",
+       "y     ...\n",
+       "g                                 9.8 meter / second ** 2\n",
+       "mass                                       0.145 kilogram\n",
+       "area                      0.004185386812745002 meter ** 2\n",
+       "rho                             1.2 kilogram / meter ** 3\n",
+       "C_d                                                   0.3\n",
+       "ts      [0.0 second, 0.051 second, 0.102 second, 0.153...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(condition)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the slope function that computes acceleration due to gravity and drag."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes derivatives of the state variables.\n",
+    "    \n",
+    "    state: State (x, y, x velocity, y velocity)\n",
+    "    t: time\n",
+    "    system: System object with g, rho, C_d, area, mass\n",
+    "    \n",
+    "    returns: sequence (vx, vy, ax, ay)\n",
+    "    \"\"\"\n",
+    "    x, y, vx, vy = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    a_grav = Vector(0, -g)\n",
+    "\n",
+    "    v = Vector(vx, vy)\n",
+    "    \n",
+    "    f_drag = -rho * v.mag * v * C_d * area / 2\n",
+    "    a_drag = f_drag / mass\n",
+    "    \n",
+    "    a = a_grav + a_drag\n",
+    "    \n",
+    "    return vx, vy, a.x, a.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Always test the slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(34.64101615137755, 'meter / second')>,\n",
+       " <Quantity(19.999999999999996, 'meter / second')>,\n",
+       " <Quantity(-7.199307418596192, 'meter / second ** 2')>,\n",
+       " <Quantity(-13.956522076105381, 'meter / second ** 2')>)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(system.init, 0, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run `odeint`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the first few time steps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.000</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>34.641016</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.051</th>\n",
+       "      <td>1.757414</td>\n",
+       "      <td>2.001943</td>\n",
+       "      <td>34.278822</td>\n",
+       "      <td>19.293697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.102</th>\n",
+       "      <td>3.496604</td>\n",
+       "      <td>2.968138</td>\n",
+       "      <td>33.926305</td>\n",
+       "      <td>18.598052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.153</th>\n",
+       "      <td>5.218055</td>\n",
+       "      <td>3.899118</td>\n",
+       "      <td>33.583080</td>\n",
+       "      <td>17.912625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.204</th>\n",
+       "      <td>6.922230</td>\n",
+       "      <td>4.795393</td>\n",
+       "      <td>33.248781</td>\n",
+       "      <td>17.237001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              x         y         vx         vy\n",
+       "0.000  0.000000  1.000000  34.641016  20.000000\n",
+       "0.051  1.757414  2.001943  34.278822  19.293697\n",
+       "0.102  3.496604  2.968138  33.926305  18.598052\n",
+       "0.153  5.218055  3.899118  33.583080  17.912625\n",
+       "0.204  6.922230  4.795393  33.248781  17.237001"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the last few.  The last value of `y` is negative, indicating that the ball hit the ground before the end of the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "      <th>vx</th>\n",
+       "      <th>vy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4.896</th>\n",
+       "      <td>119.051966</td>\n",
+       "      <td>-25.039907</td>\n",
+       "      <td>16.915531</td>\n",
+       "      <td>-24.925983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.947</th>\n",
+       "      <td>119.911217</td>\n",
+       "      <td>-26.318773</td>\n",
+       "      <td>16.780661</td>\n",
+       "      <td>-25.225049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.998</th>\n",
+       "      <td>120.763599</td>\n",
+       "      <td>-27.612802</td>\n",
+       "      <td>16.646105</td>\n",
+       "      <td>-25.520572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.049</th>\n",
+       "      <td>121.609126</td>\n",
+       "      <td>-28.921811</td>\n",
+       "      <td>16.511872</td>\n",
+       "      <td>-25.812556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.100</th>\n",
+       "      <td>122.447815</td>\n",
+       "      <td>-30.245622</td>\n",
+       "      <td>16.377974</td>\n",
+       "      <td>-26.101006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                x          y         vx         vy\n",
+       "4.896  119.051966 -25.039907  16.915531 -24.925983\n",
+       "4.947  119.911217 -26.318773  16.780661 -25.225049\n",
+       "4.998  120.763599 -27.612802  16.646105 -25.520572\n",
+       "5.049  121.609126 -28.921811  16.511872 -25.812556\n",
+       "5.100  122.447815 -30.245622  16.377974 -26.101006"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing the results\n",
+    "\n",
+    "We can extract the x and y components as `Series` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "xs = system.results.x\n",
+    "ys = system.results.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The simplest way to visualize the results is to plot x and y as functions of time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap10-fig01.pdf\n"
+     ]
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot(xs, label='x')\n",
+    "plot(ys, label='y')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Position (m)')\n",
+    "\n",
+    "savefig('chap10-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the velocities the same way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vxs = system.results.vx\n",
+    "vys = system.results.vy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The x velocity slows down due to drag.  The y velocity drops quickly while drag and gravity are in the same direction, then more slowly after the ball starts to fall."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot(vxs, label='vx')\n",
+    "plot(vys, label='vy')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Velocity (m/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way to visualize the results is to plot y versus x.  The result is the trajectory of the ball through its plane of motion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap10-fig02.pdf\n"
+     ]
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot(xs, ys, label='trajectory')\n",
+    "\n",
+    "decorate(xlabel='x position (m)',\n",
+    "         ylabel='y position (m)')\n",
+    "\n",
+    "savefig('chap10-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also animate the flight of the ball.  If there's an error in the simulation, we can sometimes spot it by looking at animations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "decorate(xlabel='x position (m)',\n",
+    "         ylabel='y position (m)',\n",
+    "         xlim=[0, 105],\n",
+    "         ylim=[-5, 35],\n",
+    "         legend=False)\n",
+    "\n",
+    "for x, y in zip(xs, ys):\n",
+    "    plot(x, y, 'bo', update=True)\n",
+    "    sleep(0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a function that encapsulates that code and runs the animation in (approximately) real time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def animate2d(xs, ys, speedup=1):\n",
+    "    \"\"\"Animate the results of a projectile simulation.\n",
+    "    \n",
+    "    xs: x position as a function of time\n",
+    "    ys: y position as a function of time\n",
+    "    \n",
+    "    speedup: how much to divide `dt` by\n",
+    "    \"\"\"\n",
+    "    # get the time intervals between elements\n",
+    "    ts = xs.index\n",
+    "    dts = np.diff(ts)\n",
+    "    dts = np.append(dts, 0)\n",
+    "\n",
+    "    # decorate the plot\n",
+    "    newfig()\n",
+    "    decorate(xlabel='x position (m)',\n",
+    "             ylabel='y position (m)',\n",
+    "             xlim=[xs.min(), xs.max()],\n",
+    "             ylim=[ys.min(), ys.max()],\n",
+    "             legend=False)\n",
+    "\n",
+    "    # loop through the values\n",
+    "    for x, y, dt in zip(xs, ys, dts):\n",
+    "        plot(x, y, 'bo', update=True)\n",
+    "        sleep(dt / speedup)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "animate2d(system.results.x, system.results.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Run the simulation for a few different launch angles and visualize the results.  Are they consistent with your expectations?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(x = 0 * m, \n",
+    "                      y = 1 * m,\n",
+    "                      g = 9.8 * m/s**2,\n",
+    "                      mass = 145e-3 * kg,\n",
+    "                      diameter = 73e-3 * m,\n",
+    "                      rho = 1.2 * kg/m**3,\n",
+    "                      C_d = 0.3,\n",
+    "                      angle = 45 * degree,\n",
+    "                      velocity = 40 * m / s,\n",
+    "                      duration = 5.1 * s)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "animate2d(system.results.x, system.results.y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finding the range"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we'll find the time and distance when the ball hits the ground."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition.set(duration=7*s)\n",
+    "system = make_system(condition)\n",
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have to interpolate y to find the landing time, then interpolate x to find the range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def interpolate_range(results):\n",
+    "    \"\"\"Computes the range of the ball when it lands.\n",
+    "    \n",
+    "    results: TimeFrame with x and y\n",
+    "    \n",
+    "    returns: distance in meters\n",
+    "    \"\"\"\n",
+    "    xs = results.x\n",
+    "    ys = results.y\n",
+    "    t_end = ys.index[-1]\n",
+    "    \n",
+    "    if ys[t_end] > 0:\n",
+    "        msg = \"\"\"The final value of y is still positive;\n",
+    "                 looks like the simulation didn't run\n",
+    "                 long enough.\"\"\"\n",
+    "        raise ValueError(msg)\n",
+    "        \n",
+    "    t_peak = ys.argmax()\n",
+    "    descent = ys.loc[t_peak:]\n",
+    "    T = interp_inverse(descent, kind='cubic')\n",
+    "    \n",
+    "    t_land = T(0)\n",
+    "    X = interpolate(xs, kind='cubic')\n",
+    "    return X(t_land)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(102.72237841710975)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interpolate_range(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** The baseball stadium in Denver, Colorado is 1,580 meters above sea level, where the density of air is about 1.0 kg / meter$^3$.  How much farther would a ball hit with the same velocity and launch angle travel?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Hint: rather than modify `condition`, make a copy\n",
+    "\n",
+    "condition2 = Condition(x = 0 * m, \n",
+    "                      y = 1 * m,\n",
+    "                      g = 9.8 * m/s**2,\n",
+    "                      mass = 145e-3 * kg,\n",
+    "                      diameter = 73e-3 * m,\n",
+    "                      rho = 1.0 * kg/m**3,\n",
+    "                      C_d = 0.3,\n",
+    "                      angle = 45 * degree,\n",
+    "                      velocity = 40 * m / s,\n",
+    "                      duration = 7 * s)\n",
+    "\n",
+    "condition2.set(duration=7*s)\n",
+    "system = make_system(condition2)\n",
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(109.07212101030548)"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "interpolate_range(system.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Optimal launch angle\n",
+    "\n",
+    "To find the launch angle that maximizes range, we need a function that takes launch angle and returns range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def range_func(angle, condition):  \n",
+    "    \"\"\"Computes range for a given launch angle.\n",
+    "    \n",
+    "    angle: launch angle in degrees\n",
+    "    condition: Condition object\n",
+    "    \n",
+    "    returns: distance in meters\n",
+    "    \"\"\"\n",
+    "    condition.set(angle=angle)\n",
+    "    print(angle)\n",
+    "    system = make_system(condition)\n",
+    "    run_odeint(system, slope_func)\n",
+    "    x_range = interpolate_range(system.results)\n",
+    "    return x_range"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's test `range_func`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45\n",
+      "Wall time: 314 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(102.72237841710975)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%time range_func(45, condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And sweep through a range of angles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0\n",
+      "30.0 97.09774794768241\n",
+      "33.0\n",
+      "33.0 100.08910744882512\n",
+      "36.0\n",
+      "36.0 102.12802725534989\n",
+      "39.0\n",
+      "39.0 103.23445991233197\n",
+      "42.0\n",
+      "42.0 103.42684240849546\n",
+      "45.0\n",
+      "45.0 102.72237841710975\n",
+      "48.0\n",
+      "48.0 101.13744083917668\n",
+      "51.0\n",
+      "51.0 98.68805053605158\n",
+      "54.0\n",
+      "54.0 95.39043242210352\n",
+      "57.0\n",
+      "57.0 91.2616536460809\n",
+      "60.0\n",
+      "60.0 86.32037231617414\n"
+     ]
+    }
+   ],
+   "source": [
+    "angles = linspace(30, 60, 11)\n",
+    "sweep = SweepSeries()\n",
+    "\n",
+    "for angle in angles:\n",
+    "    x_range = range_func(angle, condition)\n",
+    "    print(angle, x_range)\n",
+    "    sweep[angle] = x_range"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the `Sweep` object, it looks like the peak is between 40 and 45 degrees."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap10-fig03.pdf\n"
+     ]
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot(sweep)\n",
+    "decorate(xlabel='Launch angle (degree)',\n",
+    "         ylabel='Range (m)',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap10-fig03.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use `max_bounded` to search for the peak efficiently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "41.4051852323\n",
+      "41.2368620421\n",
+      "41.1390964616\n",
+      "41.1364127407\n",
+      "41.1367466842\n",
+      "41.1360787972\n",
+      "Wall time: 3.16 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time res = max_bounded(range_func, [0, 90], condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is an `OptimizeResult` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "scipy.optimize.optimize.OptimizeResult"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(res)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the following variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "     fun: 103.46412613060471\n",
+       " message: 'Solution found.'\n",
+       "    nfev: 9\n",
+       "  status: 0\n",
+       " success: True\n",
+       "       x: 41.136412740667161"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So the optimal angle is about 41 degrees, and the resulting range is 103 meters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Add a print statement to `range_func` that prints `angle`.  Then run `max_bounded` again so you can see how many times it calls `range_func` and what the arguments are."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Turning off units\n",
+    "\n",
+    "Each time `range_func` runs, it calls `odeint`, which runs `slope_func` many times.  And each time `slop_func` runs, it checks the units for all computations, which takes some time.  We can speed up the whole process by removing the units from the computation (now that we are satisfied that they are correct).\n",
+    "\n",
+    "Because of the way we organized the code, all units are in the `Condition` object, so we can \"turn off units\" by defining a new `Condition` object with no units:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(g = 9.8,\n",
+    "                      mass = 145e-3,\n",
+    "                      diameter = 73e-3,\n",
+    "                      rho = 1.2,\n",
+    "                      C_d = 0.3,\n",
+    "                      angle = 45,\n",
+    "                      velocity = 40,\n",
+    "                      duration = 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now `range_func` and `max_bounded` are substantially faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45\n",
+      "Wall time: 50.6 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(102.72237841710975)"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%time range_func(45, condition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "41.4051852323\n",
+      "41.2368620421\n",
+      "41.1390964616\n",
+      "41.1364127407\n",
+      "41.1367466842\n",
+      "41.1360787972\n",
+      "Wall time: 449 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time res = max_bounded(range_func, [0, 90], condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Manny Ramirez problem\n",
+    "\n",
+    "Finally, let's solve the Manny Ramirez problem:\n",
+    "\n",
+    "*What is the minimum effort required to hit a home run in Fenway Park?*\n",
+    "\n",
+    "Fenway Park is a baseball stadium in Boston, Massachusetts.  One of its most famous features is the \"Green Monster\", which is a wall in left field that is unusually close to home plate, only 310 feet along the left field line.  To compensate for the short distance, the wall is unusually high, at 37 feet.\n",
+    "\n",
+    "Although the problem asks for a minimum, it is not an optimization problem.  Rather, we want to solve for the initial velocity that just barely gets the ball to the top of the wall, given that it launches at the optimal angle.\n",
+    "\n",
+    "And we have to be careful about what we mean by \"optimal\".  For this problem, we don't want the longest range, we want the maximum height at the point where it reaches the wall.\n",
+    "\n",
+    "If you are ready to solve the problem on your own, go ahead.  Otherwise I will walk you through the process with an outline and some starter code.\n",
+    "\n",
+    "As a first step, write a function called `height_func` that takes a launch angle and a condition as parameters, simulates the flights of a baseball, and returns the height of the baseball when it reaches a point 94.5 meters (310 feet) from home plate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here\n",
+    "def height_func(angle, condition):\n",
+    "    system = make_system(condition)\n",
+    "    run_odeint(system, slope_func)\n",
+    "    condition.set(angle=angle)\n",
+    "    print(angle)\n",
+    "    T = interp_inverse(system.results.x)\n",
+    "    t_wall = T(94.5)\n",
+    "    Y = interpolate(system.results.y)\n",
+    "    return Y(t_wall)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your function with a launch angle of 45 degrees:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(10.360246887683163)"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "height_func(45, condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now use `max_bounded` to find the optimal angle.  Is it higher or lower than the angle that maximizes range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "41.9145567062\n",
+      "29.3614129124\n",
+      "35.2492069\n",
+      "32.4611797498\n",
+      "33.6451853245\n",
+      "34.7101169343\n",
+      "34.0974352111\n",
+      "34.5042028904\n",
+      "34.2701792964\n",
+      "34.4255507244\n",
+      "34.3361616657\n",
+      "34.3955082703\n",
+      "34.3613646881\n",
+      "34.3840330739\n",
+      "34.370991386\n",
+      "34.3796499389\n",
+      "34.3746684574\n",
+      "34.3779757303\n",
+      "34.3760729737\n",
+      "34.3773362395\n",
+      "34.3766071693\n",
+      "Wall time: 974 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "%time res = max_bounded(height_func, [0, 90], condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following lines compute the height of the ball at the wall, given that it's launched at the optimal angle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "34.3769410125\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(6.126792402276463)"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "angle = res.x\n",
+    "height = height_func(angle, condition)\n",
+    "height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we need to find the height of the ball at the wall, for a given velocity, given that it's launched at the optimal angle.\n",
+    "\n",
+    "Write a function called `best_height` that takes velocity and a `Condition` object as parameters.  It should use `max_bounded` to find the optimal launch angle, then compute and the highest possible height of the ball at the wall, for the given velocity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here\n",
+    "def best_height(velocity, condition):\n",
+    "    condition.set(velocity=velocity)\n",
+    "    res = max_bounded(height_func, [0, 90], condition)\n",
+    "    angle = res.x\n",
+    "    height = height_func(angle, condition)\n",
+    "    return height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use this code to test `best_height`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "41.9145567062\n",
+      "29.3614129124\n",
+      "35.2492069\n",
+      "32.4611797498\n",
+      "33.6451853245\n",
+      "34.7101169343\n",
+      "34.0974352111\n",
+      "34.5042028904\n",
+      "34.2701792964\n",
+      "34.4255507244\n",
+      "34.3361616657\n",
+      "34.3955082703\n",
+      "34.3613646881\n",
+      "34.3840330739\n",
+      "34.370991386\n",
+      "34.3796499389\n",
+      "34.3746684574\n",
+      "34.3779757303\n",
+      "34.3760729737\n",
+      "34.3773362395\n",
+      "34.3766071693\n",
+      "34.3769410125\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(6.126522189730487)"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_height(40, condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we want to use `fsolve` to find the initial velocity that makes the height of the ball exactly 11 meters when it reaches the wall.\n",
+    "\n",
+    "To use `fsolve`, we need an error function that returns 0 when we have the right velocity.  Write a function called `error_func` that takes a velocity and a `Condition` object, uses `best_height` to find the height of the ball at the wall, and returns the difference between the result and the target value (11 meters)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here\n",
+    "def error_func(velocity, condition):\n",
+    "    height = best_height(velocity, condition)\n",
+    "    return 11 - height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your error function like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "41.9145567062\n",
+      "29.3614129124\n",
+      "35.2492069\n",
+      "32.4611797498\n",
+      "33.6451853245\n",
+      "34.7101169343\n",
+      "34.0974352111\n",
+      "34.5042028904\n",
+      "34.2701792964\n",
+      "34.4255507244\n",
+      "34.3361616657\n",
+      "34.3955082703\n",
+      "34.3613646881\n",
+      "34.3840330739\n",
+      "34.370991386\n",
+      "34.3796499389\n",
+      "34.3746684574\n",
+      "34.3779757303\n",
+      "34.3760729737\n",
+      "34.3773362395\n",
+      "34.3766071693\n",
+      "34.3769410125\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4.8734778102695131"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func(40, condition)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then use `fsolve` to find the answer to the problem, the minimum velocity that gets the ball out of the park."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "41.9145567062\n",
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+      "45.0\n",
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+      "35.1483361893\n",
+      "34.7622996087\n",
+      "34.3772748557\n",
+      "34.3769410125\n",
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "39.6641544666\n",
+      "34.3766071693\n",
+      "36.3964768462\n",
+      "35.1483350595\n",
+      "34.7622990445\n",
+      "34.5241346829\n",
+      "34.4331639917\n",
+      "34.3984162796\n",
+      "34.3851438346\n",
+      "34.3800742118\n",
+      "34.3781377881\n",
+      "34.3773981401\n",
+      "34.3769410125\n",
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "39.6641614224\n",
+      "29.3614129124\n",
+      "36.3964795031\n",
+      "32.4611797498\n",
+      "35.1483360743\n",
+      "33.6451853245\n",
+      "34.6715877074\n",
+      "34.0974352111\n",
+      "34.4894860353\n",
+      "34.2701792964\n",
+      "34.4199293859\n",
+      "34.3361616657\n",
+      "34.39336111\n",
+      "34.3613646881\n",
+      "34.3832129317\n",
+      "34.370991386\n",
+      "34.3793366725\n",
+      "34.3746684574\n",
+      "34.3778560732\n",
+      "34.3760729737\n",
+      "34.3772905346\n",
+      "34.3766071693\n",
+      "34.3769410125\n",
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "39.6641612918\n",
+      "34.3766071693\n",
+      "36.3964794532\n",
+      "35.1483360553\n",
+      "34.7622995418\n",
+      "34.5241348728\n",
+      "34.4331640642\n",
+      "34.3984163073\n",
+      "34.3851438452\n",
+      "34.3800742158\n",
+      "34.3781377897\n",
+      "34.3773981407\n",
+      "34.3769410125\n",
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "39.6641621079\n",
+      "34.3766071693\n",
+      "36.3964797649\n",
+      "35.1483361743\n",
+      "34.7622996013\n",
+      "34.3772748557\n",
+      "34.3769410125\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_velocity = fsolve(error_func, 40, condition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 41.7798087])"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_velocity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And just to check, run `best_height` with the value you found.  The result should be 11 meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "45.0\n",
+      "34.3769410125\n",
+      "55.6230589875\n",
+      "21.246117975\n",
+      "39.6641621079\n",
+      "34.3766071693\n",
+      "36.3964797649\n",
+      "35.1483361743\n",
+      "34.7622996013\n",
+      "34.3772748557\n",
+      "34.3769410125\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(10.999986799374673)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_height(min_velocity, condition)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/chap11-fig01.pdf b/code/chap11-fig01.pdf
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diff --git a/code/chap11mine.ipynb b/code/chap11mine.ipynb
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+++ b/code/chap11mine.ipynb
@@ -0,0 +1,2344 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 11: Rotation\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# tempo switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rolling paper\n",
+    "\n",
+    "We'll start by loading the units we need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "radian = UNITS.radian\n",
+    "m = UNITS.meter\n",
+    "s = UNITS.second"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And creating a `Condition` object with the system parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(Rmin = 0.02 * m,\n",
+    "                      Rmax = 0.055 * m,\n",
+    "                      L = 47 * m,\n",
+    "                      duration = 130 * s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function estimates the parameter `k`, which is the increase in the radius of the roll for each radian of rotation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def estimate_k(condition):\n",
+    "    \"\"\"Estimates the parameter `k`.\n",
+    "    \n",
+    "    condition: Condition with Rmin, Rmax, and L\n",
+    "    \n",
+    "    returns: k in meters per radian\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    Ravg = (Rmax + Rmin) / 2\n",
+    "    Cavg = 2 * pi * Ravg\n",
+    "    revs = L / Cavg\n",
+    "    rads = 2 * pi * revs\n",
+    "    k = (Rmax - Rmin) / rads\n",
+    "    return k"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As usual, `make_system` takes a `Condition` object and returns a `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    condition: Condition with Rmin, Rmax, and L\n",
+    "    \n",
+    "    returns: System with init, k, and ts\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    init = State(theta = 0 * radian,\n",
+    "                 y = 0 * m,\n",
+    "                 r = Rmin)\n",
+    "    \n",
+    "    k = estimate_k(condition)\n",
+    "    ts = linspace(0, duration, 101)\n",
+    "    \n",
+    "    return System(init=init, k=k, ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>theta      0 radian\n",
+       "y           0 meter\n",
+       "r     ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k</th>\n",
+       "      <td>2.7925531914893616e-05 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts</th>\n",
+       "      <td>[0.0 second, 1.3 second, 2.6 second, 3.9000000...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init    theta      0 radian\n",
+       "y           0 meter\n",
+       "r     ...\n",
+       "k                            2.7925531914893616e-05 meter\n",
+       "ts      [0.0 second, 1.3 second, 2.6 second, 3.9000000...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(condition)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>theta</th>\n",
+       "      <td>0 radian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>0 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r</th>\n",
+       "      <td>0.02 meter</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "theta      0 radian\n",
+       "y           0 meter\n",
+       "r        0.02 meter\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can write a slope function based on the differential equations\n",
+    "\n",
+    "$\\omega = \\frac{d\\theta}{dt} = 10$\n",
+    "\n",
+    "$\\frac{dy}{dt} = r \\frac{d\\theta}{dt}$\n",
+    "\n",
+    "$\\frac{dr}{dt} = k \\frac{d\\theta}{dt}$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object with theta, y, r\n",
+    "    t: time\n",
+    "    system: System object with r, k\n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, y, r = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    omega = 10 * radian / s\n",
+    "    dydt = r * omega\n",
+    "    drdt = k * omega\n",
+    "    \n",
+    "    return omega, dydt, drdt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `slope_func`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(10.0, 'radian / second')>,\n",
+       " <Quantity(0.2, 'meter * radian / second')>,\n",
+       " <Quantity(0.0002792553191489362, 'meter * radian / second')>)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(system.init, 0*s, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And look at the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>theta</th>\n",
+       "      <th>y</th>\n",
+       "      <th>r</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>124.8</th>\n",
+       "      <td>1248.0</td>\n",
+       "      <td>46.707064</td>\n",
+       "      <td>0.054851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126.1</th>\n",
+       "      <td>1261.0</td>\n",
+       "      <td>47.422487</td>\n",
+       "      <td>0.055214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127.4</th>\n",
+       "      <td>1274.0</td>\n",
+       "      <td>48.142630</td>\n",
+       "      <td>0.055577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128.7</th>\n",
+       "      <td>1287.0</td>\n",
+       "      <td>48.867493</td>\n",
+       "      <td>0.055940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130.0</th>\n",
+       "      <td>1300.0</td>\n",
+       "      <td>49.597074</td>\n",
+       "      <td>0.056303</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        theta          y         r\n",
+       "124.8  1248.0  46.707064  0.054851\n",
+       "126.1  1261.0  47.422487  0.055214\n",
+       "127.4  1274.0  48.142630  0.055577\n",
+       "128.7  1287.0  48.867493  0.055940\n",
+       "130.0  1300.0  49.597074  0.056303"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extracting one time series per variable (and converting `r` to radians):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "thetas = system.results.theta\n",
+    "ys = system.results.y\n",
+    "rs = system.results.r * 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `theta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GF8f6wcOZr+MKSU/U5iGyf/3rX7h48SI+/PBDrF+/HoMGDUJwcDC8vLxgZWWF\n6upqyOVypKSkID09HT4+Pnj//fcfG+NCCNEulVqDyzeKkXqrBJoWMS8uDpaIDvOGi4OlDqsjPd1j\nz8FERkYiMjISSUlJOH36NL7++mvO3oyzszMiIyPx2muvISoqqtOLJYQ8UFTWHE5ZcY8bTjkk0AMi\nPxcYUTgl0bF2neSPiopiG0h9fT3u3bsHe3t7uh+GEB1QqtT4Na0IadkKTjilp7M1osRecLChcEqi\nHzp8EbylpSX7iGJCSNfKk1fjQkp+q3DKYSGeCPJxopgXolfoLitCDEBDowo/XytERg43nLKXuy1G\nh3nBxoqOJhD9Qw2GED2XlV+JH1MLUNcinNLCzAQjRJ7w86ZwSqK/qMEQoqdq65X4MTUf2QXcJ8b2\nE9pjhIjCKYn+owZDiJ5hGAY3cypw8VoBGpvU7DjfwhSjBnnBR2Cnw+oIab8ONRi5XI7ffvsNJSUl\n+Nvf/obS0lL07duXriYjREuqa5twQSpDXjE3nHJAH0cMC6FwSmJY2v2v9cMPP8T+/fuhUqnA4/Ew\nfPhwbN68GcXFxfi///s/ODk5dWadhHRrDMMgLbsMv6YVUTgl6TbajIppadeuXdi/fz+WL1+O77//\nnr32ftGiRaiqqsLHH3+s1aKOHz+O8ePHIyQkBJMnT8avv/7Kzl28eBGxsbEICQnBpEmTkJyczHmt\nQqHAkiVLIBaLERERgfj4eKhUqoe/BSF6o6K6ASeSsvBjagHbXHg8Hgb2c8HL4/ypuRCD1a4Gc/To\nUSxevBgzZ86Ep6cnOx4aGoqlS5fixx9/1FpBiYmJ+OCDDzBv3jycOnUKgwcPxoIFC5Cfn4+srCzM\nnz8fMTExSExMxJgxY7Bw4UJkZmayr1+8eDHKyspw4MABbNiwASdOnMD27du1Vh8h2qLWMJBkFOPI\n97dQpHgQTuloa4EpUX0xQkThlMSwtavBlJSUIDg4+JFzAoEAlZWVj5zrKIZhsH37dsybNw/PP/88\nevXqhRUrVsDb2xupqanYt28fRCIR5s+fD19fXyxduhShoaHYt28fACA1NRVSqRQbNmxAQEAARo0a\nheXLl2P//v0dToYmpDOVVNTh+Lnb+C2dG045uL8bXnzGD+5OFE5JDF+7Goy3tzd++umnR85JJBII\nhUKtFHPnzh0UFBRgwoQJDwo0MsLXX3+NSZMmQSKRIDw8nPOaIUOGQCKRsLUIBAJOPeHh4aitrUVG\nRoZWaiQOaWLKAAAgAElEQVTkaajUGvyaVogvz2WirLKeHXd1sMILz/hhSJAHjI3b9WNJiN5r10n+\nWbNm4f3334dKpUJ0dDR4PB5kMhmkUik+//xzvP3221opJicnBwBQXV2NmTNnIjMzEz4+Pnjrrbcw\naNAgyOVyuLm5cV7j6uoKuVwOACguLoarq2ureaD58QMDBw7USp2E/BmFpTU4L5Wh8l4jO2ZibITw\nQHeI+lE4Jel+2tVgXnjhBVRUVGDHjh04cOAAGIbB0qVLYWpqildffRXTpk3TSjE1NTUAgHfffRdv\nvPEGfHx8cPz4ccyaNQsnT55EQ0NDq0uizczM0NjY/ANbX18Pc3NzzrypqSl4PB67DSFdrUl5P5yy\njDMucLFGVJgQ9jbmbbySEMPW7suUX3vtNUybNg0pKSmoqqqCjY0NBg4cCAcHB60VY2rafGfy66+/\njkmTJgEABgwYAKlUisOHD8Pc3BxKpZLzmqamJjZ808LCotW5FqVSCYZhYGVlpbU6CWmv3KJqJEll\nqKl/8O/WzNQYw4I9EEjhlKSb69BdW9bW1hg5cmRn1cIezvLz82PHeDwefHx8kJ+fDw8PD5SUlHBe\nU1JSwh42c3d3b3XZ8v3tHz60RkhnamhU4eLVAtzMreCM9/awxehBXrCmcErSA7TZYMaNG9eh367O\nnj371MUEBgbCysoKaWlp7FVrDMMgOzsbERERcHFxweXLlzmvuXTpEsRiMQAgLCwMH330EYqKiuDh\n4cHO8/l8BAQEPHV9hDwJwzDIzq9Ccmo+6hsf3H9laW6CESIB+gntaa+F9BhtNphBgwZ1+Q+CpaUl\nZs2ahS1btsDZ2Rl+fn44dOgQ8vLysG3bNiiVSkyZMgXbtm3DxIkTcfr0aVy9ehVr1qwB0Hxfjkgk\nwrJlyxAXF4eysjLEx8djzpw5FGdDOl1tvRLJqfm481A4pZ+3AyIHelI4Jelx2mwwGzZs6Mo6WEuW\nLIGlpSX+85//QKFQoH///vjvf/8LHx8fAEBCQgLi4+Oxe/du+Pj4YOfOnfD19QXQfDgtISEBa9as\nwbRp08Dn8zF16lQsXLhQJ2shPQPDMMjIKcfPVwvRqHwQTmlt2RxO2ceTwilJz8RjWj5ztQ0PH5bi\nvAGPBz6fD6FQCGtra60W15Xy8/MxZswYnDt3Dl5eXrouhxiIqppGJEnzkV/CDacM9HHCsBBPmJvS\nnfike3vcZ2e7TvLPmDGDPVzWsh+1PIRmZGSE2NhYrF27FsbG9ENFujeNhkFaVhl+Sy+CUv0gnNLO\n2hxRYV7wcqX8MELa1WA+/fRTvPnmm/jb3/6GCRMmwNnZGQqFAj/88AMOHjyIt99+GyYmJti2bRsE\nAgEdkiLdWnl1A85LZJC3yA/j8XgQ+bkgfIA7TE3oTnxCgHY2mF27dmHGjBl466232LE+ffpALBaD\nz+fjf//7Hw4ePAgej4e9e/dSgyHdklqtQcqtEkgyitn8MABwsrNEtFgIN0e614qQltr1q1ZGRgaG\nDh36yLmwsDCkpaUBaL5/5X5sCyHdSUl5HY6fz8Sl6/IH4ZRGPIQHuuOFMf2ouRDyCO3ag/Hw8EBS\nUhKGDx/eai4pKYm9ibG0tBT29vbarZAQHVKqNPj9hhxXbpdyzj+6OVohWiyEk52lDqsjRL+1q8H8\n/e9/R1xcHBQKBcaOHQtHR0eUl5fj3Llz+PbbbxEXF4e8vDxs3boVkZGRnV0zIV2ioLQGSRIZKmu4\n4ZRDg9wR0pfCKQl5knY1mKlTp8LIyAiffPIJvvvuO3bcy8sL69evx3PPPYczZ87Ay8tLa8nKhOhK\nk1KNX64VIv2OgjPu5docTmlnTeGUhLRHu7PIpkyZgilTpiAvLw/l5eVwc3Nj41gAYOLEiZg4cWKn\nFElIV8kpqsaFR4RTDg/xxIA+jhTzQkgHdCjssqamBpaWlmxjKS4uZucoTJIYsvpGFX66UoDbedxw\nyj6edhg1yAvWlhTzQkhHtavB5OXl4Z///CekUmmb29ATI4khYhgGmbJK/HSloFU45chQAfp6UTgl\nIX9WuxrMv/71L2RlZWHRokVwd3eHkRHdSEYMX029EslSGe4WVXPGA3o5IHKgABbmHdrBJ4Q8pF0/\nQRKJBOvWrcNf//rXzq6HkE7HMAxu3C3Hz9cK0fRQOGVUmBC9PGx1WB0h3Ue7Ggyfz4edHSXCEsPX\nHE4pQ35JDWc86I9wSjMKpyREa9rVYJ599lkcPHgQkZGRdDyaGCSNhsG1rFL8li6HqkU4pb21OaLF\nQni6GG4SOCH6ql0NxtraGlKpFOPHj0dISAgsLVvfvbx27VqtF0eINiiq6nFeIkNxeR07xuPxEOrn\ngvBAd5gY0zlFQjpDuxrMV199BRsbG6hUKqSkpLSap70aoo/Uag2kN0sguVkMTYtwSmd7S0SHCeFK\n+WGEdKp2NZjz588/cvzevXv4+uuvcfToUa0WRcjTkitqkSSRQVHdwI4ZG/EweIA7Qv1dYUwxL4R0\nuj91Hea1a9dw5MgRfPfdd6ivr4eTk5O26yLkT1Gq1Lh0XY6rmWWccEp3Jz6ixUI42lrosDpCepZ2\nN5ja2lp88803OHr0KG7dugVTU1NERUXhueeew8iRIzuzRkLaRVZ8D0lSGaprm9gxU2MjDA32QLCv\nM4VTEtLFnthg0tPTcfToUZw5cwb19fUYMGAAAOCzzz5DREREpxdIyJM0/hFOef2hcEqhmw1GD/Ki\ncEpCdKTNBnPs2DEcOXIEN27cgKurK6ZNm4a//e1vcHZ2Rnh4OExM6C5nont3C6uQnJLPCac0NzNG\nZIgAAb0d6AIUQnSozeszV69eDbVajd27dyM5ORlvvfUWfHx8uvQH9sqVKxgwYAAuXbrEjl28eBGx\nsbEICQnBpEmTkJyczHmNQqHAkiVLIBaLERERgfj4eKhUqoffmhi4ugYlzv6WizM/3+U0F1+BHV4Z\nF4D+lHxMiM612WDGjRuHO3fu4M0338Sbb76JCxcuQKPRtLW51tXV1WH58uVQqx9EeWRlZWH+/PmI\niYlBYmIixowZg4ULFyIzM5PdZvHixSgrK8OBAwewYcMGnDhxAtu3b++yuknnYhgGt3LLcejsLWTK\nHiQfW5qbICaiN/4yrA/4lHxMiF5o8zjXtm3bUFlZiW+++QaJiYl4/fXX4ezsjLFjx4LH43X6b4cb\nNmyAm5sbcnNz2bF9+/ZBJBJh/vz5AIClS5dCKpVi3759WLt2LVJTUyGVSvHDDz9AKBQiICAAy5cv\nx9q1a7Fw4UKYmZl1as2kc9XUNeFCSj5yKJySEIPw2FuY7e3tMXPmTCQmJiIxMRExMTH47rvvwDAM\nVq1ahYSEBNy9e1frRSUnJ+PChQtYtWoVZ1wikSA8PJwzNmTIEEgkEnZeIBBAKBSy8+Hh4aitraXH\nCRgwhmGQnl2GQ/+7xWkuNlZmmDTCB8+E96LmQogeandGRv/+/bFq1Sr89NNP2Lp1K3r37o0dO3Zg\nwoQJmDx5stYKKi8vx8qVK7Fu3bpWAZtyubzVg81cXV0hl8sBND8AzdXVtdU8ABQVFWmtRtJ1Ku81\nIvFCNi6k5LPJxzweDyF9nfHyOH/0cqfkY0L0VYd/7TM1NcX48eMxfvx4lJaW4uTJk0hMTNRaQe+/\n/z6io6MxcuRItnHc19DQ0Oowl5mZGRobGwEA9fX1MDfnXpJqamoKHo/HbkMMg0bD4MrtUvx+46Fw\nSps/wimdKZySEH33VMcVXFxcMG/ePMybN08rxSQmJuLGjRv45ptvHjlvbm4OpVLJGWtqamLDNy0s\nLNDU1MSZVyqVYBgGVlaUO2UoyiqbwylLKh6EUxrxeAj1d8XgAW4UTkmIgdCrA9cnTpxAcXExIiMj\nAYCN+pg3bx6ee+45eHh4oKSkhPOakpIS9rCZu7t7q8uW72//8KE1on/Uag0uZxQj5WYJNC1iXlzs\nLREt9oaLQ+sUb0KI/tKrBvPRRx+hoeFBOGFpaSmmTZuGdevWYfjw4diyZQsuX77Mec2lS5cgFosB\nAGFhYfjoo49QVFQEDw8Pdp7P5yMgIKDrFkI6TK6oxXmJDOUPhVOGB7pD5EfhlIQYIr1qMA/vZdw/\nn+Lm5gYnJydMnz4dU6ZMwbZt2zBx4kScPn0aV69exZo1awAAoaGhEIlEWLZsGeLi4lBWVob4+HjM\nmTOHLlHWU0qVGr+ly3EtixtO6enMR5RYCAcbCqckxFDpVYN5En9/fyQkJCA+Ph67d++Gj48Pdu7c\nCV9fXwDNVxclJCRgzZo1mDZtGvh8PqZOnYqFCxfquHLyKI8MpzQxwrBgTwT5OtGd+IQYOL1uMO7u\n7rh16xZnbPTo0Rg9enSbr3FxccEnn3zSyZWRp9HQpMIv1wpx4245Z9zb3QajBwlhy6e9TUK6A71u\nMKT7yc6vRHJqAeoauOGUI0QC+HtTOCUh3Qk1GNIl6hqU+DG1AFn5lZxxXy97jAoVwMqC8sMI6W6o\nwZBOxTAMbuVV4KcrBWhsehBcamVhilGhAvh62euwOkJIZ6IGQzpNdW0TLqTIkCe/xxnv39sRwwd6\nwsKM/vkR0p3RTzjRuuZwSgV+SSuEUvUg5sWWb4aoMCGEbjY6rI4Q0lWowRCtqrjXgCSJDIVltewY\nj8dDiK8zhga7w9TEWIfVEUK6EjUYohVqDYMrt0vw+3U51JoHN0w62FggWiyEhzNfh9URQnSBGgx5\naqUV9TgvyUNpZT07ZsTjYVCAK8T9KZySkJ6KGgz501RqDS7fKEbqrYfCKR0sER1G4ZSE9HTUYMif\nUlTWHE5Zce9BOKWJsRHCB7hD5OcCIwqnJKTHowZDOqRJqcZv6UVIy1Y8FE5pjSixF4VTEkJY1GBI\nu+XJq5Ekzce9ugfhlGamxhgW7IFAHwqnJIRwUYMhT9TQqMLFq4W4mcsNp+zlbouoMC9YW1E4JSGk\nNWow5LEeFU5pYWaCESJP+FE4JSHkMajBkEeqa1AiObUA2Q+FU/YT2mOEiMIpCSFPRg2GcDAMg5s5\nFbh4jRtOybcwxegwL/TxtNNhdYQQQ0INhrCqa5uQJJVBVswNpxzQxwnDQjwonJIQ0iH0iUHAMAzS\nssvwa1oRhVMSQrSGGkwPV17dHE5ZpOCGUw7s54whgRROSQj586jB9FBqDYPUWyW4fIMbTulo2xxO\n6e5E4ZSEkKejdymEZWVlWLFiBSIjIyEWi/H3v/8dt2/fZucvXryI2NhYhISEYNKkSUhOTua8XqFQ\nYMmSJRCLxYiIiEB8fDxUKlVXL0OvlZTX4fi52/gtvYhtLkY8HsIHuOPFZ/youRBCtEKv9mA0Gg0W\nLVoEhmHw6aefwsrKCtu3b8fs2bNx5swZKBQKzJ8/HwsWLMC4ceNw6tQpLFy4EImJiejXrx8AYPHi\nxeDxeDhw4ACKi4vx7rvvwsTEBMuWLdPx6nRPpdbg9+tyXLldygmndHO0QrRYCCc7CqckhGiPXjWY\nmzdvIjU1Fd9++y18fX0BAPHx8QgPD0dycjJSUlIgEokwf/58AMDSpUshlUqxb98+rF27FqmpqZBK\npfjhhx8gFAoREBCA5cuXY+3atVi4cCHMzHruHeeFpTU4L5GhsqaRHTMxNkJ4oDtE/SickhCifXp1\niMzDwwOfffYZ+vTpw47dv1O8qqoKEokE4eHhnNcMGTIEEokEACCRSCAQCCAUCtn58PBw1NbWIiMj\nowtWoH+alGokp+TjxIUsTnMRuFjjpbH+GOTvSs2FENIp9KrBODg4YPTo0TAyelDW/v370dDQgMjI\nSMjlcri5uXFe4+rqCrlcDgAoLi6Gq6trq3kAKCoq6uTq9U9uUTUOnb2JtOwydszM1BhRYUI8N8oX\n9jbmOqyOENLd6dUhsoedO3cOmzdvxpw5c+Dr64uGhoZWh7nMzMzQ2Nj8m3l9fT3MzbkfmqampuDx\neOw2PUFzOGUBbuZWcMb7eNhi1CAKpySEdA29bTAnTpxAXFwcJkyYgHfeeQcAYG5uDqVSydmuqakJ\nlpbNJ6ctLCzQ1NTEmVcqlWAYBlZWVl1TuA4xDIOs/Er8mFqA+sYHV85ZmptghEiAfkJ7CqckhHQZ\nvWwwO3bswJYtWzB9+nSsWrWK/VD08PBASUkJZ9uSkhL2sJm7u3ury5bvb//wobXupqZeieSUfNwt\nrOKM+3k7IHKgJ4VTEkK6nF6dgwGA3bt3Y8uWLXjjjTcQFxfH+Y07LCwMly9f5mx/6dIliMVidl4m\nk3HOt1y6dAl8Ph8BAQFds4AuxjAMbtxV4PDZm5zmYm1pionD+2DckF7UXAghOqFXezA3b97Exx9/\njClTpuCFF15AaWkpO8fn8zF9+nRMmTIF27Ztw8SJE3H69GlcvXoVa9asAQCEhoZCJBJh2bJliIuL\nQ1lZGeLj4zFnzpxueYlyVU0jkqT5yC/hhlMG+TghIsQT5qYU80II0R29ajDffvst1Go1vvrqK3z1\n1VecuSVLlmDBggVISEhAfHw8du/eDR8fH+zcuZO9Z4bH4yEhIQFr1qzBtGnTwOfzMXXqVCxcuFAX\ny+k0Gg2DtKwy/JZeBKX6QTilnbU5osVCCFysdVgdIYQ04zFMi1u6e7D8/HyMGTMG586dg5eXl67L\naZOiqh7nJTIUl9exYzweD6J+LggPdIepid4d9SSEdGOP++zUqz0Y0ja1WoOUWyW4nFEMTYtwSic7\nS0SLhXBz7P5XyRFCDAs1GANQUl6H81IZyirr2TEjIx7E/d0Q5u8KY2PaayGE6B9qMHpMqdLg9xvN\n4ZQMhVMSQgwMNRg9VVBag6SHwilNjY0wNMgDwX2dKT+MEKL3qMHomUalGr9eK0T6HQVn3MvVBlFh\nXrCzpvwwQohhoAajR3KKqnFBKkNN/YM4HHNTYwwf6In+vR0p5oUQYlCoweiBugYlLl4txO28h8Ip\nPe2awykt6U58QojhoQajQwzDIFNWiZ+utA6nHBkqQF8vCqckhBguajA6UlOvRLJUhrtF1Zxxf28H\nRIoEsDSnvxpCiGGjT7Eu1hxOWY6frxWiSalmx60tTREVJkQvD1sdVkcIIdpDDaYLVdU04rxEhoLS\nGs54sK8zIoI9YEbhlISQboQaTBfQaBhczSzFpetyqFqEU9r/EU7pSeGUhJBuiBpMJ3tUOKURjweR\nX3M4pQnFvBBCuilqMJ1ErdZAerMEkpvccEpne0tEhwnhSuGUhJBujhpMJ5ArapEkkUFR3cCOGRvx\nMHiAO0L9XWFMMS+EkB6AGowWKVVqXLoux9XMMk44pbsTH9FiIRxtLXRYHSGEdC1qMFoiK76HJKkM\n1bVN7JipsRGGBnsg2JfCKQkhPQ81mKfU0KTCr2lFuP5QOKXQzQZRYULY8s10VBkhhOgWNZincLew\nCskp+dxwSjNjRIYIENDbgWJeCCE9GjWYP6GuQYmfrhQgU1bJGfcV2GFkqBf4FE5JCCHds8Go1Wps\n2bIFiYmJqK2txYgRI7B69Wo4Ozs/1fsyDIPbeRX46UohGpoehFNaWZiy4ZSEEEKadcu7/LZv347E\nxER8+OGHOHDgAORyORYvXvxU71lT14TTF+/i+9/zOM0loJcjXhnnT82FEEIe0u32YJqamrBv3z6s\nWrUKw4cPBwBs3rwZY8aMQUpKCgYNGtSh92MYBul3FPg1rYgTTmljZYbRYV7o5U7hlIQQ8ijdrsHc\nvHkTtbW1CA8PZ8e8vLwgEAggkUg61GA0Gganf76DPPk9dozH4yHY1wlDgyickhBCHqfbNRi5XA4A\ncHNz44y7urqyc+1VWFbDaS72NuYYI/aGhzP/6QslhJBurts1mPr6ehgZGcHUlHsll5mZGRobGzv0\nXk52lnC0tcC9uiaE9HXB4AFuFE5JCCHt1O0ajIWFBTQaDVQqFUxMHiyvqakJlpaWHXovS3MTvDjW\nH0Y80D0thBDSQd3u13EPDw8AQGlpKWe8pKSk1WGz9jA24lFzIYSQP6Hb7cEEBASAz+fj999/R2xs\nLAAgPz8fBQUFGDx4cJuvU6ubrxDr6HkaQgjpye5/Zt7/DG2p2zUYMzMzvPLKK9i4cSMcHBzg5OSE\nDz74AOHh4RCJRG2+7v4ez7Rp07qqVEII6TZKS0vRq1cvzhiPaZkr302oVCp89NFHSExMhEqlYu/k\nd3R0bPM1DQ0NSE9Ph4uLC4yN6fJjQghpD7VajdLSUgQFBcHCgvtIkm7ZYAghhOhetzvJTwghRD9Q\ngyGEENIpqMEQQgjpFNRgCCGEdApqMIQQQjoFNZjHUKvV2LRpEyIjIxEaGoo33ngDZWVlui7rscrK\nyrBixQpERkZCLBbj73//O27fvs3OX7x4EbGxsQgJCcGkSZOQnJysw2qf7MqVKxgwYAAuXbrEjhnS\nGo4fP47x48cjJCQEkydPxq+//srOGcI66urqsHbtWvbf09y5c5GVlcXOG8IaVq9ejZUrV3LGnlS3\nQqHAkiVLIBaLERERgfj4eKhUKujKo9Zw4MABxMTEQCQSYcKECTh+/DhnXi/WwJA2ffzxx8zw4cOZ\nixcvMunp6czUqVOZl156SddltUmtVjMvvvgi88ILLzBXr15lMjMzmTfeeIOJiIhgysvLmczMTCYo\nKIj59NNPmaysLObjjz9mAgMDmdu3b+u69Eeqra1lxo4dy/j5+TG//fYbwzCMQa3hxIkTTGBgIHP8\n+HEmJyeH+c9//sOIRCJGJpMZzDr++c9/MjExMYxEImGysrKYBQsWMKNGjWIaGhr0fg0ajYbZsmUL\n4+fnx/zzn/9kx9tT98svv8y88sorTEZGBnPhwgVm6NChzObNm/VmDQcPHmREIhFz8uRJJjc3lzl2\n7BgTGBjIJCYm6tUaqMG0obGxkQkNDWW++uordkwmkzF+fn6MVCrVYWVtu379OuPn58dkZWWxY42N\njczAgQOZxMREJi4ujpk+fTrnNdOnT2dWrVrV1aW2y/16WzYYQ1mDRqNhoqKimC1btrBjarWaefbZ\nZ5lvvvnGYNYRHh7O7Nu3j/06MzOT8fPzY9LT0/V6DXl5ecz06dOZIUOGMKNHj+Z8OD+p7pSUFMbP\nz4/Jy8tj50+cOMGEhoYyjY2NXbMA5vFrmDRpErNx40bO9u+99x4zY8YMhmH0Zw10iKwNT3pwmT7y\n8PDAZ599hj59+rBj94M6q6qqIJFIOOsBgCFDhujlepKTk3HhwgWsWrWKM24oa7hz5w4KCgowYcIE\ndszIyAhff/01Jk2aZDDrcHR0xLfffguFQoGmpiZ8+eWXsLOzg1Ao1Os1pKSkwMPDA6dOnYKXlxdn\n7kl1SyQSCAQCCIVCdj48PBy1tbXIyMjo/OL/8Lg1rFq1Ci+99BJnzMjICNXV1QD0Zw3UYNqgzQeX\ndRUHBweMHj0aRkYP/lr379+PhoYGREZGQi6XG8R6ysvLsXLlSqxbtw52dnacOUNZQ05ODgCguroa\nM2fOREREBKZNm4aUlBQAhrOOtWvXQi6XY9iwYRCJRDh27Bh27doFW1tbvV5DbGwsNm7cCBcXl1Zz\nT6q7uLgYrq6ureYBoKioqJMqbu1xawgPD+c0j8LCQpw5cwYjRowAoD9roAbTBm0+uExXzp07h82b\nN2POnDnw9fVFQ0MDzMzMONvo43ref/99REdHY+TIka3mDGUNNTU1AIB3330XU6dOxZ49e9CvXz/M\nmjUL2dnZBrOO3NxcODs7Y9euXTh8+DAiIyPxxhtvQC6XG8waHvakuuvr62Fubs6ZNzU1BY/H08u1\nlZeX47XXXoOzszP+8Y9/ANCfNXS7NGVt0eaDy3ThxIkTiIuLw4QJE/DOO+8AAMzNzaFUKjnb6dt6\nEhMTcePGDXzzzTePnDeENQBgfzF5/fXXMWnSJADAgAEDIJVKcfjwYYNYh0wmQ1xcHA4dOsQmkW/a\ntAkTJkzA3r17DWINj/Kkui0sLNDU1MSZVyqVYBgGVlZWXVZne8hkMsydOxcNDQ04cOAAbGxsAOjP\nGqjBtKHlg8vu/z/w5x9c1pV27NiBLVu2YPr06Vi1ahV7HsbDwwMlJSWcbfVtPSdOnEBxcTEiIyMB\nAMwfWazz5s3Dc889ZxBrAB4cjvDz82PHeDwefHx8kJ+fbxDrSE9Ph1qtRlBQEDtmamqK/v37Izc3\n1yDW8ChPqtvd3b3VZcv3t9entV2/fh3z5s2DnZ0djhw5wvmc0pc10CGyNrR8cNl97Xlwma7t3r0b\nW7ZswRtvvIG4uDjO0zjDwsJw+fJlzvaXLl2CWCzu6jLb9NFHH+HMmTM4efIkTp48iT179gAA1q1b\nhyVLlhjEGgAgMDAQVlZWSEtLY8cYhkF2djaEQqFBrMPd3R0AcOvWLXbs/hp69+5tEGt4lCfVHRYW\nBplMxjlXcenSJfD5fAQEBHRprW3Jzs7Gq6++CoFAgEOHDnGaC6BHa+iy69UMUHx8PDNs2DAmOTmZ\nvQ/m4csb9UlGRgbTv39/5r333mNKSko4/9XW1jI3b95kAgMDma1btzJZWVnMli1bmODgYM5lzfqm\nqKiIc5myIa3h448/ZgYPHsycPXuWuXv3LvPvf/+bCQ4OZrKzsw1iHSqVinnhhReYv/71r8zly5eZ\nrKwsJi4ujhGJREx+fr5BrIFhmi9BbnmJ75Pq1mg0zAsvvMC8+OKLTHp6OnsPybZt23S1hFZrmDJl\nChMZGcncuXOH83OuUCgYhtGfNVCDeQylUsmsX7+eCQ8PZwYNGsQsWbKE/QvUR5s2bWL8/Pwe+d8n\nn3zCMAzDJCUlMRMmTGCCgoKYZ599lvn55591XPXjPdxgGMZw1qDRaJidO3cyo0aNYoKCgpipU6cy\nly9fZucNYR0KhYJZuXIlM2LECCYsLIyZNWsWc+PGDXbeENbw8Iczwzy57pKSEmbBggXMwIEDmWHD\nhjGbNm1i1Gp1V5bN0XINd+7cafPn/JlnnmFfow9roAeOEUII6RR0DoYQQkinoAZDCCGkU1CDIYQQ\n0qSyXAQAAAUvSURBVCmowRBCCOkU1GAIIYR0CmowhBBCOgVFxRDSAe+++y4SExMfu014eDj279+P\nGTNmwNjYGHv37u2a4h6hsrISkydPxhdffIFevXo9cfuEhASUlZVhzZo1nV8c6fboPhhCOiAvLw/l\n5eXs1x988AGMjY05z62xtrZG3759kZWVBR6PB19fX12UCgB466234ObmhuXLl7dr+4aGBsTExGD9\n+vWIiIjo5OpId0d7MIR0gLe3N7y9vdmvra2tYWxszKYNt9S3b9+uLK2Va9eu4ezZs/jxxx/b/RoL\nCwvMnj0b69evbzPRmpD2onMwhHSSGTNmYPbs2ezX/v7+OHr0KN5++22EhoZi6NChSEhIQE1NDd57\n7z2EhYVh+PDhiI+PR8sDCxUVFVi1ahUiIiIQEhKCl19+GVKp9Inff8+ePRg2bBgcHR3ZsfT0dMya\nNQthYWEIDQ3F7NmzceXKFc7rJkyYgMzMTFy4cOGp/wxIz0YNhpAu9OGHH8LBwQGffvopoqKisH37\ndjz//POwtLREQkICxo4diz179uB///sfAKCxsRGzZ8/GhQsX8Oabb2Lbtm2ws7PD7Nmzce3atTa/\nT21tLc6fP49x48axYzU1NZg7dy4cHBywfft2fPzxx6ivr8fcuXPZB6QBzY8aCA0NxalTpzrvD4L0\nCHSIjJAuFBgYiJUrVwJofiTEiRMn4OTkhNWrVwMAhg4dilOnTuHKlSsYP348vv76a9y6dQvHjx9H\ncHAwAGDkyJF4/vnn8fHHH+OLL7545PeRSCRQKpUICQlhx7KyslBRUYGZM2di0KBBAAAfHx8cPXoU\ntbW1sLa2ZrcNCgrCt99+2yl/BqTnoD0YQrpQyw98BwcHGBsbc8Z4PB7s7OxQXV0NAPj111/h5uaG\n/v37Q6VSQaVSQaPRICoqCpcvX2711ML78vPzAQBeXl7sWL9+/eDo6IjXX38dq1evxvfffw9nZ2e8\n8847rR5CJRAIUFpa2ub7E9IetAdDSBfi8/mtxh73CNvKykrI5XIEBgY+cr6iouKRTyi8d+8eAHAe\nX8zn83Hw4EHs2LED3333HY4ePQoLCwvExsZi1apVnOfU36+ppqaGcw6HkI6gBkOIHrOxsYGvry8+\n/PDDR847ODg8dvzevXuwtbVlx318fBAfHw+1Wo1r167h66+/xuHDh9G7d2+8+uqr7HZVVVUwMjKC\nnZ2dFldDeho6REaIHhs8eDAKCwvh6uqK4OBg9r9z585h//79MDU1feTrPD09AQByuZwd+/777zF0\n6FCUlpbC2NgYoaGhWLNmDWxt/397d8iqMBSGcfyRlRm8RdDkV1AQbTIQv4HNaNEgFqs4jOuGpRmt\nYhyIxaDJIAhWs1Ws3uSFcR1MLgdk9//L54Wd9PBu7875ilyt+6wrFAqyLMvc5pB6BAzwwdrttorF\norrdrlarlfb7vTzPk+/7KpVKymQyL+tqtZps246MM1erVT0eDw0GA63Xa+12O7muq9vtFpk2k6TD\n4aBGo2F0b0g/Agb4YM/vJpVKRZ7nqdfrabvdajKZaDgcxtZls1k5jhP5yTKfzysIAuVyOY3HY/X7\nfZ1OJ81mM9Xr9Z911+tV5/P5V+gA7+KoGCCljsejOp2ONpvNy0GAOL7vKwxDLZfL2A4JSIIOBkip\ncrmsVqul+XyeuOZ+v2uxWGg0GhEu+DMCBkix6XSqMAx1uVwSrQ+CQM1mU47jGH4y/Ae8IgMAGEEH\nAwAwgoABABhBwAAAjCBgAABGEDAAACO+AQfS1f3oFUTeAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b61f9e898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(thetas, label='theta')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angle (rad)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `y`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NzMxaJJwIUQZiqRjH7h3D3xl/yxw1TOszDeYdzF/wakIUS65v+vDwcDx+/BhT\np04FAKirq8vsk5aW1uwPP3PmDH7++WdMnz4ddnZ2EAqFMsuXamlp0fQdpE14UPEAv9/4HQXVBWyN\njhqIMpMrIAIDA1v8g6OiorB06VIEBATgs88+A/B0XieRSMTZr6GhAbq6ui3++YQoSoOkAUfuHsHp\nrNOc5xrsTewxtc9UutZAlJZcAfHxxx+36Idu3rwZERERCAkJwZIlS9jrEJaWliguLubsW1xcLHPa\niRBVca/sHnal7EJx7T//XWtraOOdXu/Av7s/3aFElFqzLiZcv34dly5dQklJCWbNmoXMzEz07t0b\nJiby36O9detWREREYP78+Zg7dy5nm6enJxISEji1+Ph4eHl5NadNQnj3RPQEUXeicOHhBU7dydQJ\nIW4h6KzXmafOCJGfXAHR0NCATz/9FCdPnoSmpibEYjEmTpyI7du3IyMjA3v37pVrFbn09HSsWbMG\n48aNw8SJE1FSUsJu09fXR0hICMaNG4d169YhMDAQR48eRUpKCpYvX/7KAyRE0VKLUrEndQ8qhBVs\nTVdTFxN6T6AlQIlKkeuqWEREBC5duoRNmzYhMTGRPY+6cuVKGBgYYM2aNXJ92PHjxyGRSHDw4EH4\n+flx/uzcuROOjo7YsGEDTpw4gTFjxuDs2bPYsmUL+8wEIcqsqr4KW5O2YuO1jZxw6GPRB8v9l+ON\nrm9QOBCVItcRRExMDBYtWoShQ4dCIpGwdWtra3z88cf47rvv5PqwRYsWYdGiRS/cx9/fX+ZWWEKU\nGcMwuJx7GX/d/gt1ojq2bqBtgPdc3kNfy74UDEQlyb2iXLdu3RrdZmRkhJqamhZtihBVUVxbjN2p\nu3G39C6n7mvji/G9x9N6DUSlyRUQPXv2xLFjx+Dn5yez7cKFC3QKiLQ7YqkYpzJP4ei9oxBLxWy9\ns15nhLiFwMnUicfuCGkZcgVEWFgY5s2bh8rKSgwZMgQCgQDJyck4cuQI9uzZg1WrVrV2n4QojczH\nmdidupvzwJuaQA1v2r6JIMcgWhuatBlyBcTw4cMRHh6O1atX4+zZswCAb7/9FsbGxli2bBmtIkfa\nhTpRHaLvRMvcutqtUzdMcZsCG0MbnjojpHXI/RxEUFAQgoKCkJWVhYqKChgYGMDOzg5qajQ9AGnb\nGIZBYkEi/rz1J6rqq9i6toY2RjuOpmkySJvV7Fn3bG1tOT8nJCTg9OnT+Oqrr1qsKUKURWldKfbe\n3Itbxbf1gCy5AAAaAUlEQVQ4dTdzN0xymUQL+ZA27dWmZX3O7du3ERkZSQFB2hSxVIyTmSdx/P5x\niCT/zA9mqGOISS6T4GHhQbeukjbvtQOCkLbmbuld7Lm5B0U1RWxNIBDAv7s/gh2DoatJk0eS9oEC\ngpD/U1Vfhb9u/4X4vHhOvathV0x2m4zunbrz0xghPKGAIO2elJEi9kEsDqUfglAsZOs6GjoY02sM\nBncfTBehSbtEAUHatazyLOy9uRe5lbmculcXL0xwnoBOOp146owQ/jUZEDNmzJDrDQoKCl6+EyFK\nprq+GtHp0biUc4lTN+9gjvdc3qMnoQnBCwLi3yu7NcXU1BSmpqYt1hAhrUnKSHHh4QUcTj/MmVhP\nU10TAfYBGGE3AhpqdGBNCPCCgNi1a5ci+yCk1WU+zsS+tH0yp5PcLdwx0XkiPdNAyL/Qr0qkzasU\nViLqThSu5l3l1E31TfGu87twNXflqTNClBsFBGmzxFIxzmafxbF7xzh3Jz07nTTcdjg01TV57JAQ\n5UYBQdqkW8W3sP/Wfs7DbgDgYemBCb0n0OkkQuRAAUHalOLaYhy4dQCpRamcukUHC0xymUR3JxHS\nDBQQpE0QioU4du8YzmSfgUT6z7K4Oho6CHIMgn93f7o7iZBmon8xRKVJGSmu5F7BofRDnKm4BQIB\nfG18MabXGHTU7shjh4SoLgoIorLul93H/lv7ZW5btTWyxSSXSejWqfF11Akh8qGAICqntK4UB28f\nRPKjZE69k04njOs9Dv269KOpuAlpARQQRGU8ET3B8fvHcTb7LMRSMVvXVNfEW3ZvYYTdCGhraPPY\nISFtCwUEUXrPpseIuRuDmoYazrZ+Vv0w1mksjHWNeeqOkLaLAoIoLYZhkFachoN3DuJR9SPONlsj\nW0xwngBbI9smXk0IeV0UEEQp5VTm4ODtg0gvTefUjXWNMdZpLLy6eNF1BkJaGQUEUSqPnzzG4fTD\nMvMm6WjoYJT9KAzrMYymxyBEQSggiFKoE9Xh74y/cSbrDOcCtJpADX5d/TDacTQMtA147JCQ9ocC\ngvBKLBXj/IPzOH7/OGobajnb+lj0wVinsbDoYMFTd4S0bxQQhBcMw+Ba/jUcvnsYZXVlnG3dO3XH\nuN7j4GDiwFN3hBCA54BYtmwZJBIJvv32W7YWFxeH8PBwZGdno1u3bvj0008xePBgHrskLYlhGNwq\nuYXoO9HIq8rjbOus1xnvOL0DT0tPugBNiBLgJSAYhsG6deuwf/9+jB8/nq1nZGQgLCwMc+bMwYgR\nIxATE4O5c+ciOjoa9vb2fLRKWlBWeRai70TjXtk9Tr2DVgcEOgRiULdBNKEeIUpE4f8ac3Nz8d//\n/hf3799Hly5dONsiIyPh7u6OsLAwAMDChQuRlJSEyMhIrFixQtGtkhZSUF2AQ+mHkFKYwqlrqWvh\nTds38VbPt6CjocNTd4SQpig8IJKTk2FpaYmff/4ZixYt4mxLTEzEqFGjODUfHx8cO3ZMkS2SFlJa\nV4qYuzGIz48HwzBsXU2ghoHdBuJth7dpplVClJjCAyI4OBjBwcGNbissLIS5uTmnZmZmhsLCQkW0\nRlpIpbASx+8fx8Wci5y1GYCnU2OMdhwNM30znrojhMhLqU74CoVCaGlpcWpaWlqor6/nqSPSHDUN\nNTiRcQLnHpyDSCLibHMxc8GYXmNgY2jDU3eEkOZSqoDQ1taGSMT9YmloaICuri5PHRF51InqcDrr\nNE5nnUa9mBvmPY17YkyvMbA3oZsMCFE1ShUQlpaWKC4u5tSKi4tlTjsR5SAUC3E2+yxOZZ5CnaiO\ns62rYVeM6TUGvU170y2rhKgopQoIT09PJCQkcGrx8fHw8vLiqSPSmHpxPc4/OI8TmSdknn62NLBE\nsGMw3C3cKRgIUXFKFRAhISEYN24c1q1bh8DAQBw9ehQpKSlYvnw5360RPA2G2IexOJl5EtX11Zxt\nZvpmeNvhbfSz6gc1gRpPHRJCWpJSBYSjoyM2bNiA8PBwbN26Fba2ttiyZQvs7Oz4bq1de1EwmOiZ\n4G2Ht9Hfuj8FAyFtDK8BsWvXLpmav78//P39Fd8MkfGiYDDWNUaAfQAG2Aygp58JaaPoXzaRIRQL\ncf7BeZzKPCWzxKeRrhEC7APga+NLwUBIG0f/wgmrTlSHc9nncCb7jMzFZ2NdY4yyH0XBQEg7Qv/S\nCWoaanA66zTOZZ+DUCzkbDPRM8GonqPoVBIh7RD9i2/HKoQVOJl5EhcfXkSDpIGzzVTfFAH2AfCx\n8oG6mjpPHRJC+EQB0Q6V1JbgROYJXMm9wlneEwAsOlggwD6AblclhFBAtCd5VXn4O+NvJBYkcmZX\nBQDrjtYYZT8KfS37UjAQQgBQQLR5DMMg43EG/s74G2nFaTLbbY1sMcp+FFzNXOnJZ0IIBwVEG8Uw\nDFKKUnAi4wSyyrNktjuZOmFUz1FwMHGgYCCENIoCoo0RSUSIz4/HycyTKKop4mwTCATwsPDAyJ4j\n0a1TN546JISoCgqINqK2oRYXHl7A2eyzqKqv4mzTUNPAAJsBGG47HOYdaGZcQoh8KCBUXGldKU5n\nncbl3MsyazHoaOhgcPfBGNpjKDrpdOKpQ0KIqqKAUEEMwyCrPAunsk7hRuENmTuSOul0wjDbYRjU\nbRB0NHR46pIQouooIFSIRCpB8qNknM46jQcVD2S2W3W0wgi7EfDq4kVPPRNCXht9i6iA2oZaxOXE\n4dyDcyh/Ui6z3dnMGW/avgmnzk50RxIhpMVQQCixR9WPcDb7LK7kXYFIwl2rW0NNA95W3hhuNxxd\nDLrw1CEhpC2jgFAyDMPgZvFNnM0+izsld2S2G2gbYHC3wRjcfTA6anfkoUNCSHtBAaEk6kR1uJx7\nGeeyz6G0rlRmu3VHawyzHYZ+XfpBU12Thw4JIe0NBQTP8qrycP7BecTnxcvMqCoQCOBu4Y4h3YfQ\nE8+EEIWjgOCBWCrG9UfXce7BOWQ+zpTZrqepB7+ufvDv7g8TPRMeOiSEEAoIhSqrK8PFnIuIy4mT\nWeMZeHoaaUiPIfC28oaWuhYPHRJCyD8oIFqZlJEirTgNFx5eQFpxmsxDbWoCNXh28cTgboPR07gn\nnUYihCgNCohWUv6kHJdyLyEuJ67RZxeMdI0wsOtADOw2kO5GIoQoJQqIFvTsaCEuJw6pRakyRwsA\n0Nu0NwZ3Hww3czdamIcQotQoIFpAWV0Z4nLicDn3MiqEFTLbDbQN4Gvji4FdB8JU35SHDgkhpPko\nIF6RSCLCjcIbiMuJQ3ppeqP79OrcCwO7DYS7hTvNjUQIUTn0rdUMDMMgtyoXl3Iu4Vr+NdSJ6mT2\nMdA2wADrAfDr6kdrLxBCVBoFhByq66txLf8aLudeRl5Vnsx2gUAAZ1Nn+HX1g5u5G9TV1HnokhBC\nWhYFRBPEUjFuFt3ElbwruFl0E1JGKrOPiZ4J/Lr6YYD1ABjpGvHQJSGEtB4KiOcwDIMHFQ9wNe8q\nEgoSUNtQK7OPpromPC094WvjS9NfEELaNAoIACW1JYjPj0d8XjyKa4sb3cfO2A6+Nr7wtPSErqau\ngjskhBDFU7qAkEgkiIiIQHR0NGprazFw4EAsW7YMnTt3btHPqa6vRmJBIq7lX0NWeVaj+5jomcDH\nygcDbAbATN+sRT+fEEKUndIFxPr16xEdHY0ff/wRnTp1wjfffIN58+Zh3759r/3eQrEQNwpv4Fr+\nNdwpudPodQUdDR14dvFEf+v+sDe2p1NIhJB2S6kCoqGhAZGRkViyZAneeOMNAMDPP/+MYcOGITk5\nGX379m32e4okItwsvomE/ATcLL4pszIb8HQ+JBczF/hY+6CPeR9ab4EQQqBkAZGeno7a2lp4e3uz\nNWtra1hZWSExMbHZAXE59zL+SPsD9eL6RrfbGdvBx8oHnl080UGrw2v1TgghbY1SBURhYSEAwNyc\n+4CZmZkZu605Dt4+KBMO1h2t4W3lDa8uXrTWAiGEvIBSBcSTJ0+gpqYGTU3uKR4tLS3U1zd+FPAi\nfS374sLDCzDvYI5+XfrBq4sXLA0sW6pdQghp05QqIHR0dCCVSiEWi6Gh8U9rDQ0N0NVt/q2lk90m\n4z3X9yCAgC42E0JIMynVfNOWlk9/uy8pKeHUi4uLZU47yUtNoEbhQAghr0CpjiB69eoFfX19XLt2\nDcHBwQCAvLw85Ofno1+/fk2+TiKRAMArXacghJD26tl35rPv0H9TqoDQ0tLC+++/j1WrVsHIyAgm\nJib45ptv4O3tDXd39yZf9+yIY/LkyYpqlRBC2oySkhJ069ZNpi5gGlv2jEdisRg//fQToqOjIRaL\n2SepjY2Nm3yNUChEWloaTE1Noa5OM6kSQog8JBIJSkpK4OLiAh0dHZntShcQhBBClINSXaQmhBCi\nPCggCCGENIoCghBCSKMoIAghhDSKAoIQQkij2mxASCQSrF69Gn5+fvDw8MD8+fNRWlrKd1svVFpa\nii+++AJ+fn7w8vLCBx98gHv37rHb4+LiEBwcDDc3NwQFBSE2NpbHbl/uxo0b6N27N+Lj49maKo3h\nwIEDeOutt+Dm5oaxY8fiypUr7DZVGUddXR1WrFjB/jf14YcfIiMjg92u7ONYtmwZFi9ezKm9rOey\nsjIsWLAAXl5eGDBgAMLDwyEWixXZtozGxrF7926MHDkS7u7uCAgIwIEDBzjblWIcTBu1Zs0a5o03\n3mDi4uKYtLQ0ZsKECcykSZP4bqtJEomEeffdd5mJEycyKSkpzP3795n58+czAwYMYB4/fszcv3+f\ncXFxYTZt2sRkZGQwa9asYZydnZl79+7x3XqjamtrmeHDhzMODg7M1atXGYZhVGoMUVFRjLOzM3Pg\nwAHmwYMHzHfffce4u7szubm5KjWO//73v8zIkSOZxMREJiMjg5kzZw4zePBgRigUKvU4pFIpExER\nwTg4ODD//e9/2bo8Pb/33nvM+++/z9y5c4c5f/48079/f+bnn3/mYxhNjmPPnj2Mu7s7c+jQIebh\nw4fMn3/+yTg7OzPR0dHsPsowjjYZEPX19YyHhwdz8OBBtpabm8s4ODgwSUlJPHbWtFu3bjEODg5M\nRkYGW6uvr2f69OnDREdHM0uXLmVCQkI4rwkJCWGWLFmi6Fbl8qzf5wNCVcYglUqZIUOGMBEREWxN\nIpEwo0ePZo4cOaIy42AYhvH29mYiIyPZn+/fv884ODgwaWlpSjuOnJwcJiQkhPHx8WH8/f05X6wv\n6zk5OZlxcHBgcnJy2O1RUVGMh4cHU19fr5gB/J8XjSMoKIhZtWoVZ/+vvvqKmTJlCsMwyjOONnmK\n6WULDykjS0tL/PLLL+jRowdbezbJYGVlJRITEznjAQAfHx+lHE9sbCzOnz+PJUuWcOqqMoasrCzk\n5+cjICCArampqeHw4cMICgpSmXEAgLGxMY4fP46ysjI0NDTgr7/+gqGhIWxsbJR2HMnJybC0tERM\nTAysra05217Wc2JiIqysrGBjY8Nu9/b2Rm1tLe7cudP6zT/nReNYsmQJJk2axKmpqamhqqoKgPKM\no00GREsvPKQIRkZG8Pf3h5raP/+X7Nq1C0KhEH5+figsLFSJ8Tx+/BiLFy/GypUrYWhoyNmmKmN4\n8OABAKCqqgpTp07FgAEDMHnyZCQnJwNQnXEAwIoVK1BYWAhfX1+4u7vjzz//xK+//oqOHTsq7TiC\ng4OxatUqmJqaymx7Wc9FRUUwMzOT2Q4Ajx49aqWOG/eicXh7e3O+/AsKCnDs2DEMHDgQgPKMo00G\nREsvPMSHM2fO4Oeff8b06dNhZ2cHoVAILS0tzj7KOJ6vv/4aQ4cOxaBBg2S2qcoYampqAABffvkl\nJkyYgG3btsHe3h7Tpk1DZmamyowDAB4+fIjOnTvj119/xb59++Dn54f58+ejsLBQpcbxzMt6fvLk\nCbS1tTnbNTU1IRAIlHZcjx8/xqxZs9C5c2fMnDkTgPKMQ6lmc20pLb3wkKJFRUVh6dKlCAgIwGef\nfQYA0NbWhkgk4uynbOOJjo7G7du3ceTIkUa3q8IYALC/WMyePRtBQUEAgN69eyMpKQn79u1TmXHk\n5uZi6dKl2Lt3Lzsb8urVqxEQEICdO3eqzDie97KedXR00NDQwNkuEonAMAz09PQU1qe8cnNz8eGH\nH0IoFGL37t0wMDAAoDzjaJMB8fzCQ8/+N/B6Cw8pyubNmxEREYGQkBAsWbKEvQ5haWmJ4uJizr7K\nNp6oqCgUFRXBz88PAMD83zyQH330EcaMGaMSYwD+OZR3cHBgawKBALa2tsjLy1OZcaSlpUEikcDF\nxYWtaWpqwsnJCQ8fPlSZcTzvZT1bWFjI3Pb6bH9lG9etW7fw0UcfwdDQEH/88Qfnu0pZxtEmTzE9\nv/DQM/IsPMS3rVu3IiIiAvPnz8fSpUs5K+F5enoiISGBs398fDy8vLwU3WaTfvrpJxw7dgyHDh3C\noUOHsG3bNgDAypUrsWDBApUYAwA4OztDT08PN2/eZGsMwyAzMxM2NjYqMw4LCwsAwN27d9nas3F0\n795dZcbxvJf17OnpidzcXM55+vj4eOjr66NXr14K7fVFMjMzMWPGDFhZWWHv3r2ccACUaBwKu19K\nwcLDwxlfX18mNjaWfQ7i37fHKZM7d+4wTk5OzFdffcUUFxdz/tTW1jLp6emMs7Mzs3btWiYjI4OJ\niIhgXF1dObfFKptHjx5xbnNVpTGsWbOG6devH3PixAkmOzub+fbbbxlXV1cmMzNTZcYhFouZiRMn\nMm+//TaTkJDAZGRkMEuXLmXc3d2ZvLw8lRhHSEgI5/bQl/UslUqZiRMnMu+++y6TlpbGPj+wbt06\nvobAMIzsOMaNG8f4+fkxWVlZnH/rZWVlDMMozzjabECIRCLm+++/Z7y9vZm+ffsyCxYsYP/yldHq\n1asZBweHRv9s3LiRYRiGOXfuHBMQEMC4uLgwo0ePZi5dusRz1y/274BgGNUZg1QqZbZs2cIMHjyY\ncXFxYSZMmMAkJCSw21VlHGVlZczixYuZgQMHMp6ensy0adOY27dvs9uVfRz//mJlmJf3XFxczMyZ\nM4fp06cP4+vry6xevZqRSCSKbFvG8+PIyspq8t/6m2++yb5GGcZBCwYRQghpVJu8BkEIIeT1UUAQ\nQghpFAUEIYSQRlFAEEIIaRQFBCGEkEZRQBBCCGlUm5xqg5DGfPnll4iOjn7hPt7e3ti1axemTJkC\ndXV17Ny5UzHNNaKiogJjx47Fjh070K1bt5fuv2HDBpSWlmL58uWt3xxpF+g5CNJu5OTk4PHjx+zP\n33zzDdTV1TnrVnTo0AE9e/ZERkYGBAIB7Ozs+GgVAPDJJ5/A3Nwcn3/+uVz7C4VCjBw5Et9//z0G\nDBjQyt2R9oCOIEi70bVrV3Tt2pX9uUOHDlBXV2dnOn1ez549FdmajNTUVJw4cQIXLlyQ+zU6OjoI\nDQ3F999/3+SMuoQ0B12DIKQRU6ZMQWhoKPuzo6Mj9u/fj08//RQeHh7o378/NmzYgJqaGnz11Vfw\n9PTEG2+8gfDwcDx/UF5eXo4lS5ZgwIABcHNzw3vvvYekpKSXfv62bdvg6+sLY2NjtpaWloZp06bB\n09MTHh4eCA0NxY0bNzivCwgIwP3793H+/PnX/jsghAKCEDn9+OOPMDIywqZNmzBkyBCsX78e48eP\nh66uLjZs2IDhw4dj27ZtOHnyJACgvr4eoaGhOH/+PBYtWoR169bB0NAQoaGhSE1NbfJzamtrcfbs\nWYwYMYKt1dTU4MMPP4SRkRHWr1+PNWvW4MmTJ/jwww/ZBY6Ap1OVe3h4ICYmpvX+Iki7QaeYCJGT\ns7MzFi9eDODplPJRUVEwMTHBsmXLAAD9+/dHTEwMbty4gbfeeguHDx/G3bt3ceDAAbi6ugIABg0a\nhPHjx2PNmjXYsWNHo5+TmJgIkUgENzc3tpaRkYHy8nJMnToVffv2BQDY2tpi//79qK2tRYcOHdh9\nXVxccPz48Vb5OyDtCx1BECKn57+wjYyMoK6uzqkJBAIYGhqyC89fuXIF5ubmcHJyglgshlgshlQq\nxZAhQ5CQkCCzYtgzeXl5AMBZ6N7e3h7GxsaYPXs2li1bhlOnTqFz58747LPPZBaQsbKyQklJSZPv\nT4i86AiCEDnp6+vL1F60/GNFRQUKCwvh7Ozc6Pby8vJGVwerrq4GAM7Sn/r6+tizZw82b96M//3v\nf9i/fz90dHQQHByMJUuWcNZpftZTTU0N5xoGIc1FAUFIKzEwMICdnR1+/PHHRrcbGRm9sF5dXY2O\nHTuydVtbW4SHh0MikSA1NRWHDx/Gvn370L17d8yYMYPdr7KyEmpqajA0NGzB0ZD2iE4xEdJK+vXr\nh4KCApiZmcHV1ZX9c+bMGezatQuampqNvq5Lly4AgMLCQrZ26tQp9O/fHyUlJVBXV4eHhweWL1+O\njh07cpalfPY6MzMzqKurt97gSLtAAUFIKxk7dizMzc0xffp0HD58GFevXsUPP/yAzZs3w8bGhrPm\n+PO8vLygo6PDuR22b9++YBgGc+fOxenTp3HlyhUsW7YMNTU1nLudACA5ORl+fn6tOjbSPlBAENJK\nnl036NOnD3744QfMnDkTFy9exNKlSzFv3rwmX6erq4tBgwZxHpIzMTHB9u3bYWBggMWLF2PWrFm4\ndesW1q9fj379+rH7lZSUID09XSY0CHkVNNUGIUooNTUV7733Hs6ePdvoheymbN68GSdOnEB0dHST\nRyiEyIuOIAhRQm5ubhg2bBh+++03uV9TV1eHvXv3YtGiRRQOpEVQQBCipJYvX44TJ07g4cOHcu2/\nfft2DBkyBIMGDWrlzkh7QaeYCCGENIqOIAghhDSKAoIQQkijKCAIIYQ0igKCEEJIoyggCCGENOr/\nA6ef1MpfzGxDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b6245a0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(ys, color='green', label='y')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Length (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `r`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mPtX7kR5JkhgI8+bJB0Pt2sCECcCkSRwMREak0vcNPsvT0BkZGWjevDneffdd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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b624fc0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(rs, color='red', label='r')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Radius (mm)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also see the relationship between `y` and `r`, which I derive analytically in the book."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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6IwYEEXVXWWoZMj/OhLL15vMGv+l+8Jvpx+cNWtKpgPjPf/6Du+++G6+88gpsbW17uiYi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\nUbsYEERE1C4GBBERtYsBQURE7fr/Yjm+Jt2sBpsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b6257dba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(rs, ys, color='purple')\n",
+    "\n",
+    "decorate(xlabel='Radius (mm)',\n",
+    "         ylabel='Length (m)',\n",
+    "         legend=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's the figure from the book."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap11-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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QiC5JlwLC6dOnsXz5codjI0eOxJYtWwAAKpXK+mLdHXq9HiUlJQgPD8ejjz4K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b6258b6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot(3, 1, 1)\n",
+    "plot(thetas, label='theta')\n",
+    "decorate(ylabel='Angle (rad)')\n",
+    "\n",
+    "subplot(3, 1, 2)\n",
+    "plot(ys, color='green', label='y')\n",
+    "decorate(ylabel='Length (m)')\n",
+    "\n",
+    "subplot(3, 1, 3)\n",
+    "plot(rs, color='red', label='r')\n",
+    "\n",
+    "decorate(xlabel='Time(s)',\n",
+    "         ylabel='Radius (mm)')\n",
+    "\n",
+    "savefig('chap11-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use interpolation to find the time when `y` is 47 meters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(125.33333334940457)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T = interp_inverse(ys, kind='cubic')\n",
+    "t_end = T(47)\n",
+    "t_end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At that point `r` is 55 mm, which is `Rmax`, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(55.00000000448797)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "R = interpolate(rs, kind='cubic')\n",
+    "R(t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The total amount of rotation is 1253 rad."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(1253.3333334940455)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "THETA = interpolate(thetas, kind='cubic')\n",
+    "THETA(t_end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unrolling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For unrolling the paper, we need more units:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "kg = UNITS.kilogram\n",
+    "N = UNITS.newton"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And a few more parameters in the `Condition` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(Rmin = 0.02 * m,\n",
+    "                      Rmax = 0.055 * m,\n",
+    "                      Mcore = 15e-3 * kg,\n",
+    "                      Mroll = 215e-3 * kg,\n",
+    "                      L = 47 * m,\n",
+    "                      tension = 2e-4 * N,\n",
+    "                      duration = 180 * s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`make_system` computes `rho_h`, which we'll need to compute moment of inertia, and `k`, which we'll use to compute `r`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    condition: Condition with Rmin, Rmax, Mcore, Mroll,\n",
+    "                              L, tension, and duration\n",
+    "    \n",
+    "    returns: System with init, k, rho_h, Rmin, Rmax,\n",
+    "                         Mcore, Mroll, ts\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    init = State(theta = 0 * radian,\n",
+    "                 omega = 0 * radian/s,\n",
+    "                 y = L)\n",
+    "    \n",
+    "    area = pi * (Rmax**2 - Rmin**2)\n",
+    "    rho_h = Mroll / area\n",
+    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
+    "    ts = linspace(0, duration, 101)\n",
+    "    \n",
+    "    return System(init=init, k=k, rho_h=rho_h,\n",
+    "                  Rmin=Rmin, Rmax=Rmax,\n",
+    "                  Mcore=Mcore, Mroll=Mroll, \n",
+    "                  ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>theta               0 radian\n",
+       "omega    0.0 radi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k</th>\n",
+       "      <td>2.7925531914893616e-05 meter / radian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>rho_h</th>\n",
+       "      <td>26.07109543981524 kilogram / meter ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rmin</th>\n",
+       "      <td>0.02 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rmax</th>\n",
+       "      <td>0.055 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mcore</th>\n",
+       "      <td>0.015 kilogram</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mroll</th>\n",
+       "      <td>0.215 kilogram</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts</th>\n",
+       "      <td>[0.0 second, 1.8 second, 3.6 second, 5.4 secon...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init     theta               0 radian\n",
+       "omega    0.0 radi...\n",
+       "k                    2.7925531914893616e-05 meter / radian\n",
+       "rho_h              26.07109543981524 kilogram / meter ** 2\n",
+       "Rmin                                            0.02 meter\n",
+       "Rmax                                           0.055 meter\n",
+       "Mcore                                       0.015 kilogram\n",
+       "Mroll                                       0.215 kilogram\n",
+       "ts       [0.0 second, 1.8 second, 3.6 second, 5.4 secon...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(condition)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>theta</th>\n",
+       "      <td>0 radian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>omega</th>\n",
+       "      <td>0.0 radian / second</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>47 meter</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "theta               0 radian\n",
+       "omega    0.0 radian / second\n",
+       "y                   47 meter\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we compute `I` as a function of `r`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def moment_of_inertia(r, system):\n",
+    "    \"\"\"Moment of inertia for a roll of toilet paper.\n",
+    "    \n",
+    "    r: current radius of roll in meters\n",
+    "    system: System object with Mcore, rho, Rmin, Rmax\n",
+    "    \n",
+    "    returns: moment of inertia in kg m**2\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    Icore = Mcore * Rmin**2   \n",
+    "    Iroll = pi * rho_h / 2 * (r**4 - Rmin**4)\n",
+    "    return Icore + Iroll"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When `r` is `Rmin`, `I` is small."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "6e-06 kilogram meter<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$6e-06 kilogram \\cdot meter^{2}$"
+      ],
+      "text/plain": [
+       "<Quantity(6e-06, 'kilogram * meter ** 2')>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "moment_of_inertia(system.Rmin, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As `r` increases, so does `I`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "0.00037418750000000006 kilogram meter<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$0.00037418750000000006 kilogram \\cdot meter^{2}$"
+      ],
+      "text/plain": [
+       "<Quantity(0.00037418750000000006, 'kilogram * meter ** 2')>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "moment_of_inertia(system.Rmax, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the slope function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    \"\"\"Computes the derivatives of the state variables.\n",
+    "    \n",
+    "    state: State object with theta, omega, y\n",
+    "    t: time\n",
+    "    system: System object with Rmin, k, Mcore, rho_h, tension\n",
+    "    \n",
+    "    returns: sequence of derivatives\n",
+    "    \"\"\"\n",
+    "    theta, omega, y = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    r = sqrt(2*k*y + Rmin**2)\n",
+    "    I = moment_of_inertia(r, system)\n",
+    "    tau = r * tension\n",
+    "    alpha = tau / I\n",
+    "    dydt = -r * omega\n",
+    "    \n",
+    "    return omega, alpha, dydt      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `slope_func`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(0.0, 'radian / second')>,\n",
+       " <Quantity(0.02939702689159846, 'newton / kilogram / meter / radian ** 0.5')>,\n",
+       " <Quantity(-0.0, 'meter * radian ** 0.5 / second')>)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(system.init, 0*s, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And look at the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>theta</th>\n",
+       "      <th>omega</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>172.8</th>\n",
+       "      <td>503.376998</td>\n",
+       "      <td>6.830222</td>\n",
+       "      <td>22.852269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174.6</th>\n",
+       "      <td>515.788219</td>\n",
+       "      <td>6.960580</td>\n",
+       "      <td>22.346268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176.4</th>\n",
+       "      <td>528.437150</td>\n",
+       "      <td>7.094376</td>\n",
+       "      <td>21.835001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178.2</th>\n",
+       "      <td>541.330151</td>\n",
+       "      <td>7.231802</td>\n",
+       "      <td>21.318468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180.0</th>\n",
+       "      <td>554.473940</td>\n",
+       "      <td>7.373066</td>\n",
+       "      <td>20.796665</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            theta     omega          y\n",
+       "172.8  503.376998  6.830222  22.852269\n",
+       "174.6  515.788219  6.960580  22.346268\n",
+       "176.4  528.437150  7.094376  21.835001\n",
+       "178.2  541.330151  7.231802  21.318468\n",
+       "180.0  554.473940  7.373066  20.796665"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extrating the time series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "thetas = system.results.theta\n",
+    "omegas = system.results.omega\n",
+    "ys = system.results.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `theta`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ppZdaOk4hRAdWXa2w98gVTl28tVirp4stE6J6YGUhsw3aqgb9n3n88ccpKChg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\nokkkgQghhGgSSSBCCCGa5P8DlKJgzxDOsIIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b629edb00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(thetas, label='theta')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angle (rad)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `omega`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qkZCQUKxFCWFzbp5Sv9DNuGdfa89m6ty+k7tl6hJllqbw9/X1JTIykqeeeirf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b629d9208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(omegas, color='orange', label='omega')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angular velocity (rad/s)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting `y`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UFNq0acPChQt59tlnH+p4Qoi652rnymt9X+P9iPe5rb0NwI7YHWiLtYz0GSl9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\nQghRJQkIIYQQVZKAEEIIUaX/D/xvzc1wbORsAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b627a3ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(ys, color='green', label='y')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Length (m)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's the figure from the book."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap11-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CHEr+CxcuJCkpiZkzZxIcHIxafe0f8jVr1rBy5Uoef/zxKusB9OzZs8pSwr/9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RI8fPgQY8aMgUQiQWpqKk6ePIkDBw4gIiJCVrsiIqJuRE1FDX+2/DPczN1w/Ppx/HLnFwBAfWM9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\nkmHzJyIiUjJs/kREREqGzZ+IiEjJ/D8JI9JZOyzItwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b6267b128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subplot(3, 1, 1)\n",
+    "plot(thetas, label='theta')\n",
+    "decorate(ylabel='Angle (rad)')\n",
+    "\n",
+    "subplot(3, 1, 2)\n",
+    "plot(omegas, color='orange', label='omega')\n",
+    "decorate(ylabel='Angular velocity (rad/s)')\n",
+    "\n",
+    "subplot(3, 1, 3)\n",
+    "plot(ys, color='green', label='y')\n",
+    "\n",
+    "decorate(xlabel='Time(s)',\n",
+    "         ylabel='Length (m)')\n",
+    "\n",
+    "savefig('chap11-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Yo-yo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Simulate the descent of a yo-yo.  How long does it take to reach the end of the string.\n",
+    "\n",
+    "I provide a `Condition` object with the system parameters:\n",
+    "\n",
+    "* `Rmin` is the radius of the axle.  `Rmax` is the radius of the axle plus rolled string.\n",
+    "\n",
+    "* `Rout` is the radius of the yo-yo body.  `mass` is the total mass of the yo-yo, ignoring the string.  \n",
+    "\n",
+    "* `L` is the length of the string.\n",
+    "\n",
+    "* `g` is the acceleration of gravity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "condition = Condition(Rmin = 8e-3 * m,\n",
+    "                      Rmax = 16e-3 * m,\n",
+    "                      Rout = 35e-3 * m,\n",
+    "                      mass = 50e-3 * kg,\n",
+    "                      L = 1 * m,\n",
+    "                      g = 9.8 * m / s**2,\n",
+    "                      duration = 1 * s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a `make_system` function that computes `I` and `k` based on the system parameters.\n",
+    "\n",
+    "I estimated `I` by modeling the yo-yo as a solid cylinder with uniform density ([see here](https://en.wikipedia.org/wiki/List_of_moments_of_inertia)).  In reality, the distribution of weight in a yo-yo is often designed to achieve desired effects.  But we'll keep it simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(condition):\n",
+    "    \"\"\"Make a system object.\n",
+    "    \n",
+    "    condition: Condition with Rmin, Rmax, Rout, \n",
+    "                              mass, L, g, duration\n",
+    "    \n",
+    "    returns: System with init, k, Rmin, Rmax, mass,\n",
+    "                         I, g, ts\n",
+    "    \"\"\"\n",
+    "    unpack(condition)\n",
+    "    \n",
+    "    init = State(theta = 0 * radian,\n",
+    "                 omega = 0 * radian/s,\n",
+    "                 y = L,\n",
+    "                 v = 0 * m / s)\n",
+    "    \n",
+    "    I = mass * Rout**2 / 2\n",
+    "    k = (Rmax**2 - Rmin**2) / 2 / L / radian    \n",
+    "    ts = linspace(0, duration, 101)\n",
+    "    \n",
+    "    return System(init=init, k=k,\n",
+    "                  Rmin=Rmin, Rmax=Rmax,\n",
+    "                  mass=mass, I=I, g=g,\n",
+    "                  ts=ts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Testing `make_system`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>theta               0 radian\n",
+       "omega    0.0 radi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k</th>\n",
+       "      <td>9.6e-05 meter / radian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rmin</th>\n",
+       "      <td>0.008 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rmax</th>\n",
+       "      <td>0.016 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mass</th>\n",
+       "      <td>0.05 kilogram</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>I</th>\n",
+       "      <td>3.0625000000000006e-05 kilogram * meter ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>g</th>\n",
+       "      <td>9.8 meter / second ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts</th>\n",
+       "      <td>[0.0 second, 0.01 second, 0.02 second, 0.03 se...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init    theta               0 radian\n",
+       "omega    0.0 radi...\n",
+       "k                                  9.6e-05 meter / radian\n",
+       "Rmin                                          0.008 meter\n",
+       "Rmax                                          0.016 meter\n",
+       "mass                                        0.05 kilogram\n",
+       "I            3.0625000000000006e-05 kilogram * meter ** 2\n",
+       "g                                 9.8 meter / second ** 2\n",
+       "ts      [0.0 second, 0.01 second, 0.02 second, 0.03 se...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(condition)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>theta</th>\n",
+       "      <td>0 radian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>omega</th>\n",
+       "      <td>0.0 radian / second</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <td>1 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>v</th>\n",
+       "      <td>0.0 meter / second</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "theta               0 radian\n",
+       "omega    0.0 radian / second\n",
+       "y                    1 meter\n",
+       "v         0.0 meter / second\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a slope function for this system, using these results from the book:\n",
+    "\n",
+    "$ r = \\sqrt{2 k y + R_{min}^2} $ \n",
+    "\n",
+    "$ T      = m g I / I^*  $\n",
+    "\n",
+    "$ a      = -m g r^2 / I^* $\n",
+    "\n",
+    "$ \\alpha  = m g r / I^*  $\n",
+    "\n",
+    "where $I^*$ is the augmented moment of inertia, $I + m r^2$.\n",
+    "\n",
+    "Hint: If `y` is less than 0, it means you have reached the end of the string, so the equation for `r` is no longer valid.  In this case, the simplest thing to do it return the sequence of derivatives `0, 0, 0, 0`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def slope_func(state, t, system):\n",
+    "    theta, omega, y, v = state\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    r = sqrt(2*k*y + (Rmin)**2)\n",
+    "    print(r)\n",
+    "    print(I)\n",
+    "    print(m)\n",
+    "    alpha = mass*g*r/(I + mass*r**2)\n",
+    "    \n",
+    "    a = -mass*g*r**2/(I +mass*r**2)\n",
+    "    \n",
+    "    return omega, alpha, v, a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your slope function with the initial conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.016 meter / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(0.0, 'radian / second')>,\n",
+       " <Quantity(180.54116292458264, '1 / radian ** 0.5 / second ** 2')>,\n",
+       " <Quantity(0.0, 'meter / second')>,\n",
+       " <Quantity(-2.8886586067933218, 'meter / radian / second ** 2')>)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "slope_func(system.init, 0*s, system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then run the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.016 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
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+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.0072042973012789915 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.0072042973011530115 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.006612432825173692 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.006612432822629659 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.006721229172161827 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.006721229170843022 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.006197528929789657 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.006197528929883268 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.005622336516677678 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.005622336515619461 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.005738833506487188 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.005738833505975772 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.0052383634993652005 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.005238363499498994 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.004683061199060851 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.004683061198660553 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.004807680245054155 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.004807680244875659 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.004333468053109765 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.004333468053200891 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.0037996591001560965 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.003799659100015638 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.003932822522437801 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n",
+      "0.003932822522380633 meter ** 0.5 / radian ** 0.5\n",
+      "3.0625000000000006e-05 kilogram * meter ** 2\n",
+      "meter\n"
+     ]
+    }
+   ],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the final conditions.  If things have gone according to plan, the final value of `y` should be close to 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>theta</th>\n",
+       "      <th>omega</th>\n",
+       "      <th>y</th>\n",
+       "      <th>v</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.96</th>\n",
+       "      <td>79.148555</td>\n",
+       "      <td>152.914392</td>\n",
+       "      <td>-0.163705</td>\n",
+       "      <td>-2.052485</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.97</th>\n",
+       "      <td>80.681953</td>\n",
+       "      <td>153.757140</td>\n",
+       "      <td>-0.184254</td>\n",
+       "      <td>-2.057147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.98</th>\n",
+       "      <td>82.223526</td>\n",
+       "      <td>154.548429</td>\n",
+       "      <td>-0.204846</td>\n",
+       "      <td>-2.061233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.99</th>\n",
+       "      <td>83.772733</td>\n",
+       "      <td>155.283077</td>\n",
+       "      <td>-0.225476</td>\n",
+       "      <td>-2.064733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.00</th>\n",
+       "      <td>85.328977</td>\n",
+       "      <td>155.954552</td>\n",
+       "      <td>-0.246139</td>\n",
+       "      <td>-2.067640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          theta       omega         y         v\n",
+       "0.96  79.148555  152.914392 -0.163705 -2.052485\n",
+       "0.97  80.681953  153.757140 -0.184254 -2.057147\n",
+       "0.98  82.223526  154.548429 -0.204846 -2.061233\n",
+       "0.99  83.772733  155.283077 -0.225476 -2.064733\n",
+       "1.00  85.328977  155.954552 -0.246139 -2.067640"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "thetas = system.results.theta\n",
+    "ys = system.results.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`theta` should increase and accelerate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P0K1bNzz11FNITU2FVCqFhYWFxjEWFhaQyWSGLJOIOgCVSsDh8xlIy7031figPt7o09XN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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b62ce3358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(thetas, label='theta')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Angle (rad)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`y` should decrease and accelerate down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wq0lISGDmzJn06NEDgEWLFtG3b1+Sk5Pp2rVrpe+7ePEiixcvJjg42GTZhBC3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\nCCGEMAkpMEIIIUxCCowQQgiT+H/Mz56RRhqkbwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x19b62cc6940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(ys, color='green', label='y')\n",
+    "\n",
+    "decorate(xlabel='Time (s)',\n",
+    "         ylabel='Length (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/data/Book1.csv b/code/data/Book1.csv
new file mode 100644
index 00000000..de25a144
--- /dev/null
+++ b/code/data/Book1.csv
@@ -0,0 +1,45 @@
+year,number of colonies
+1974,4210
+1975,4206
+1976,4285
+1977,4346
+1978,4081
+1979,4155
+1980,4140
+1981,4213
+1982,
+1983,
+1984,
+1985,
+1986,
+1987,3205
+1988,
+1989,3190
+1990,3219
+1991,3443
+1992,3210
+1993,3181
+1994,3030
+1995,2876
+1996,2770
+1997,2648
+1998,2564
+1999,2631
+2000,2633
+2001,2688
+2002,2620
+2003,2524
+2004,2574
+2005,2599
+2006,2556
+2007,2413
+2008,2393
+2009,2443
+2010,2342
+2011,2498
+2012,2692
+2013,2491
+2014,2539
+2015,2640
+2016,2740
+2017,2660
diff --git a/code/glucose_insulin.csv b/code/glucose_insulin.csv
index 91688b17..d026a659 100644
--- a/code/glucose_insulin.csv
+++ b/code/glucose_insulin.csv
@@ -1,25 +1,25 @@
-time,glucose,insulin
-0,92,11
-2,350,26
-4,287,130
-6,251,85
-8,240,51
-10,216,49
-12,211,45
-14,205,41
-16,196,35
-19,192,30
-22,172,30
-27,163,27
-32,142,30
-42,124,22
-52,105,15
-62,92,15
-72,84,11
-82,77,10
-92,82,8
-102,81,11
-122,82,7
-142,82,8
-162,85,8
+time,glucose,insulin
+0,92,11
+2,350,26
+4,287,130
+6,251,85
+8,240,51
+10,216,49
+12,211,45
+14,205,41
+16,196,35
+19,192,30
+22,172,30
+27,163,27
+32,142,30
+42,124,22
+52,105,15
+62,92,15
+72,84,11
+82,77,10
+92,82,8
+102,81,11
+122,82,7
+142,82,8
+162,85,8
 182,90,7
\ No newline at end of file
diff --git a/code/proj2start.ipynb b/code/proj2start.ipynb
new file mode 100644
index 00000000..e64bcbd9
--- /dev/null
+++ b/code/proj2start.ipynb
@@ -0,0 +1,3301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Chapter 7: Thermal systems\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# tempo switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The coffee cooling problem.\n",
+    "\n",
+    "I'll use a `State` object to store the initial temperature.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "temp    87\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T1 = 87\n",
+    "init = State(temp=T1)\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "temp    87\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "T1 = 90\n",
+    "init"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And a `System` object to contain the system parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>temp    87\n",
+       "dtype: int64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>volume</th>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r</th>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>T_env</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t0</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dt</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init      temp    87\n",
+       "dtype: int64\n",
+       "volume                        300\n",
+       "r                            0.01\n",
+       "T_env                          22\n",
+       "t0                              0\n",
+       "t_end                          30\n",
+       "dt                              1\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = System(init=init,\n",
+    "                volume=300,\n",
+    "                r=0.01,\n",
+    "                T_env=22,\n",
+    "                t0=0, \n",
+    "                t_end=30,\n",
+    "                dt=1)\n",
+    "coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `update` function implements Newton's law of cooling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update(state, system):\n",
+    "    \"\"\"Update the thermal transfer model.\n",
+    "    \n",
+    "    state: State (temp)\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (temp)\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    T = state.temp\n",
+    "    T += -r * (T - T_env) * dt\n",
+    "\n",
+    "    return State(temp=T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>temp</th>\n",
+       "      <td>86.35</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "temp    86.35\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "update(init, coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run simulations using the same function from the previous chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system, update_func):\n",
+    "    \"\"\"Runs a simulation of the system.\n",
+    "    \n",
+    "    Add a TimeFrame to the System: results\n",
+    "    \n",
+    "    system: System object\n",
+    "    update_func: function that updates state\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    frame = TimeFrame(columns=init.index)\n",
+    "    frame.loc[t0] = init\n",
+    "    #print('run sim init' + str(init))\n",
+    "    ts = linrange(t0, t_end-dt, dt)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.loc[t+dt] = update_func(frame.loc[t], system)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>87.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>86.350000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>85.706500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>85.069435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>84.438741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>83.814353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>83.196210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>82.584248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>81.978405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>81.378621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>80.784835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>80.196987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>79.615017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>79.038866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>78.468478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>77.903793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>77.344755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>76.791308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>76.243394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>75.700961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>75.163951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>74.632311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>74.105988</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>73.584928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>73.069079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>72.558388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>72.052804</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>71.552276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>71.056754</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>70.566186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>70.080524</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         temp\n",
+       "0   87.000000\n",
+       "1   86.350000\n",
+       "2   85.706500\n",
+       "3   85.069435\n",
+       "4   84.438741\n",
+       "5   83.814353\n",
+       "6   83.196210\n",
+       "7   82.584248\n",
+       "8   81.978405\n",
+       "9   81.378621\n",
+       "10  80.784835\n",
+       "11  80.196987\n",
+       "12  79.615017\n",
+       "13  79.038866\n",
+       "14  78.468478\n",
+       "15  77.903793\n",
+       "16  77.344755\n",
+       "17  76.791308\n",
+       "18  76.243394\n",
+       "19  75.700961\n",
+       "20  75.163951\n",
+       "21  74.632311\n",
+       "22  74.105988\n",
+       "23  73.584928\n",
+       "24  73.069079\n",
+       "25  72.558388\n",
+       "26  72.052804\n",
+       "27  71.552276\n",
+       "28  71.056754\n",
+       "29  70.566186\n",
+       "30  70.080524"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(coffee, update)\n",
+    "coffee.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ubGwsSUJ0Kp7OFjjZduOPU9mcST0PQGV1LduPpJGYfoGRA9wwNZanCtH5qTUF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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d2bb24a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(coffee.results.temp, label='coffee')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After running the simulation, we can extract the final temperature from the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def final_temp(system):\n",
+    "    \"\"\"Final temperature.\n",
+    "    \n",
+    "    If system has no results, return initial temp.\n",
+    "    \n",
+    "    system: System object.\n",
+    "    \n",
+    "    returns: temperature (degC)\n",
+    "    \"\"\"    \n",
+    "    if hasattr(system, 'results'):\n",
+    "        return system.results.temp[system.t_end]\n",
+    "    else:\n",
+    "        return system.init.temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It will be convenient to wrap these steps in a function.  `kwargs` is a collection of whatever keyword arguments are provided; they are passed along as arguments to `System`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n",
+    "    \"\"\"Runs a simulation with the given parameters.\n",
+    "\n",
+    "    T_init: initial temperature in degC\n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    volume: volume of liquid in mL\n",
+    "    t_end: end time of simulation\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(temp=T_init)\n",
+    "    #print(init) \n",
+    "    \n",
+    "    system = System(init=init,\n",
+    "                    volume=volume,\n",
+    "                    r=r,\n",
+    "                    T_env=22, \n",
+    "                    t0=0,\n",
+    "                    t_end=t_end,\n",
+    "                    dt=1)\n",
+    "    #print(system.init)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_init_temp(system, update_func):\n",
+    "    T_array = linspace(80, 100, 5)\n",
+    "    for i in T_array:\n",
+    "        system = make_system(T_init=i)\n",
+    "        #print(i)\n",
+    "        run_simulation(system, update_func)\n",
+    "        #print(system.results.temp)\n",
+    "        plot(system.results.temp, label='coffee')\n",
+    "        decorate(xlabel='Time (minutes)',\n",
+    "                 ylabel='Temperature (C)')\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kv0hPcYkJY6ZqylfQS2TqZP/5oK8Xr7OZgKdjhM5XDJ+7DZ+7bbCC3oxaX4JU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nAAAD\nVElEQVT7sGrVKtxyyy2DJgMaDAZmJ0OY2pAdCaFqEIvFg44NNaPF7/fD5XKVnTHR19dXcnpcfkpe\nqRGlp7uGcgy8NpvNRjabPe3rFOL3+2GxWEreL4/HAwAsXLgQTz/9NH7zm9/g17/+NZ5++mnU1dXh\n+9//PlN6XbjGiTwxkFA5iJAQCGWQSqVobW0t22uhVCqHPB4KhcZlYl5+ZzBQWCKRyJDrkUgkWLFi\nxaB8x0C+8IUv4Atf+AJisRg++ugjPPfcc7j//vuxcOFCzJkzhzkvGAyW/RkRphYktEUglGHp0qVw\nOBxQq9WYO3cu8+/tt9/G888/z3xKH4herwcAuFyuM7lcBolEAgBwOp3MsUAggBMnThSdlw+x5Vm2\nbBnMZjNaW1uL7nfXrl3MrO8tW7bgqquuAkVREAqFOPfcc7Fx40YAg+/X5XJBp9NV/P4IEw8iJARC\nGa644gpoNBqsXbsWf/7zn/HRRx/hoYcewlNPPYWGhoZBOYE8S5YsgUAgGFaZ8FjQ3t4OnU6Hn/3s\nZ3jrrbfw1ltv4Tvf+c6gcJhMJsOhQ4fw8ccfIx6PY+3atUgmk/j2t7+Nv/71r/jggw9w5513Yteu\nXZg+fToA4KyzzkJHRwfuuusuvP/++/j3v/+N+++/H0qlEsuWLWOuHQqFcOzYMaxateqM3jthfCBC\nQiCUQSwW44UXXsD8+fPx0EMP4aabbsK7776LzZs349Zbby37fUKhEOecc86omxFHCofDwRNPPIG6\nujrcfvvteOCBB3DJJZcMKkdeu3YtfD4fbrzxRhw6dAgajQa///3voVarsXnzZtx88804fvw4tm3b\nhiuuuAIAcPbZZ2Pbtm04duwYbrnlFqxfvx4ikQjPPfdcUdjsvffeA4/Hw+rVq8/krRPGCWKRQiCM\nAQcOHMDXv/51/POf/yyZkJ/qrF27Fm1tbUy5NWFqQ3YkBMIYMG/ePJx//vlFHefVwsGDB3Ho0CHc\ndNNN470UwhmC7EgIhDGit7cXV1xxBX7729+isbFxvJdzxrj++uvxta99DV/+8pfHeymEMwQREgKB\nQCCMChLaIhAIBMKoIEJCIBAIhFFBhIRAIBAIo4IICYFAIBBGBRESAoFAIIwKIiQEAoFAGBX/HwY7\nOvCCrFzhAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d2c557f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep_init_temp(coffee, update)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we use it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "72.299625390403094"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system()\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Simulate the temperature of 50 mL of milk with a starting temperature of 5 degC, in a vessel with the same insulation, for 15 minutes, and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.514978275278718"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(T_init=5, r=0.15, volume=50, t_end=15)\n",
+    "#picked new r because it seemed like the milk should get warmer faster. new value is arbitrary. \n",
+    "#but felt like after fifteen min it would barely be colder than room temp.\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)\n",
+    "\n",
+    "#coffee = System(init=init,\n",
+    "#                volume=300,\n",
+    "#                r=0.01,\n",
+    "#                T_env=22,\n",
+    "#                t0=0, \n",
+    "#                t_end=30,\n",
+    "#                dt=1)\n",
+    "#coffee"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Fu7s7oqOj2+zn5+eH5ORk2Nrags1u/eHaunUrYmJicOTIEcyYMUMf4RKiF1W1Qly6wcft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\nRESMeAeTVRWKUduWOgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d3192128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(milk.results.temp, label='milk')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using `fsolve`\n",
+    "\n",
+    "As a simple example, let's find the roots of this function; that is, the values of `x` that make the result 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def func(x):\n",
+    "    return (x-1) * (x-2) * (x-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_init_temp2(system, update_func):\n",
+    "    T_array = linspace(5, 25, 5)\n",
+    "    for i in T_array:\n",
+    "        system = make_system(T_init=i)\n",
+    "        #print(i)\n",
+    "        run_simulation(system, update_func)\n",
+    "        #print(system.results.temp)\n",
+    "        plot(system.results.temp, label='milk')\n",
+    "        decorate(xlabel='Time (minutes)',\n",
+    "                 ylabel='Temperature (C)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BkDhz5gzmzp2L//u//8PUqVO56RUVFXj22WfRtWtXrFmzBnFxcW5t/NChQ8jLy0NmZiai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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d31f95f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep_init_temp2(milk, update)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`modsim.py` provides `fsolve`, which does some error-checking and then runs `scipy.optimize.fsolve`.  The first argument is the function whose roots we want.  The second argument is an initial guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, x0=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Usually the root we get is the one that's closest to the initial guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, 1.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, 2.9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But not always."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 3.])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fsolve(func, 1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to find the value of `r` that makes the final temperature 70, so we define an \"error function\" that takes `r` as a parameter and returns the difference between the final temperature and the goal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func1(r):\n",
+    "    \"\"\"Runs a simulation and returns the `error`.\n",
+    "    \n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    \n",
+    "    returns: difference between final temp and 70 C\n",
+    "    \"\"\"\n",
+    "    system = make_system(r=r)\n",
+    "    #print(r)\n",
+    "    run_simulation(system, update)\n",
+    "    return final_temp(system) - 70"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `r=0.01`, we end up a little too warm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.2996253904030937"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func1(r=0.01)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The return value from `fsolve` is an array with a single element, the estimated value of `r`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011543084583978345"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution = fsolve(error_func1, 0.01, xtol=1e-8)\n",
+    "r_coffee = solution[0]\n",
+    "r_coffee"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we run the simulation with the estimated value of `r`, the final temperature is 70 C, as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system(r=r_coffee)\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  When you call `fsolve`, it calls `error_func1` several times.  To see how this works, add a print statement to `error_func1` and run `fsolve` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:** Repeat this process to estimate `r_milk`, given that it starts at 5 C and reaches 20 C after 15 minutes.  \n",
+    "\n",
+    "Before you use `fsolve`, you might want to try a few values for `r_milk` and see how close you can get by trial and error.  Here's an initial guess to get you started:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.501444483698084"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_milk = 0.12\n",
+    "#picked this value because it seemed pretty close from my graph above.\n",
+    "milk = make_system(T_init=5, t_end=15, r=r_milk)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def error_func2(r):\n",
+    "    system = make_system(r=r, T_init=5, t_end=15)\n",
+    "    #print(r)\n",
+    "    run_simulation(system, update)\n",
+    "    return final_temp(system) - 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.49855551630191641"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func2(r=0.12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.13296078935466449"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution = fsolve(error_func2, 0.12, xtol=1e-8)\n",
+    "r_milk = solution[0]\n",
+    "r_milk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.999999999999996"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(r=r_milk, T_init=5, t_end=15)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mixing liquids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes `System` objects that represent two liquids, computes the temperature of the mixture, and returns a new `System` object that represents the mixture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mix(s1, s2):\n",
+    "    \"\"\"Simulates the mixture of two liquids.\n",
+    "    \n",
+    "    s1: System representing coffee\n",
+    "    s2: System representing milk\n",
+    "    \n",
+    "    returns: System representing the mixture\n",
+    "    \"\"\"\n",
+    "    assert s1.t_end == s2.t_end\n",
+    "    \n",
+    "    volume = s1.volume + s2.volume\n",
+    "    \n",
+    "    temp = (s1.volume * final_temp(s1) + \n",
+    "            s2.volume * final_temp(s2)) / volume\n",
+    "    \n",
+    "    mixture = make_system(T_init=temp,\n",
+    "                          volume=volume,\n",
+    "                          r=s1.r)\n",
+    "    \n",
+    "    return mixture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we'll see what happens if we add the milk at the end.  We'll simulate the coffee and the milk separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee = make_system(T_init=90, t_end=30, r=r_coffee, volume=300)\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.764705882352942"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(T_init=5, t_end=30, r=r_milk, volume=50)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the results look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap07-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5Yl3EsVevXsjOzq7vyyCENCFO1SQ+/fRT6HQ6TJo0yeE+Fy9erLOgGlL6nbM4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\nBCGEEIf+H0W9gvkl+IxjAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d3317d30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(coffee.results.temp, label='coffee')\n",
+    "plot(milk.results.temp, '--', label='milk')\n",
+    "decorate(xlabel='Time (minutes)',\n",
+    "         ylabel='Temperature (C)',\n",
+    "         loc='center left')\n",
+    "\n",
+    "savefig('chap07-fig01.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what happens when we mix them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.109243697478988"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mix_last = mix(coffee, milk)\n",
+    "final_temp(mix_last)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's what we get if we add the milk immediately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee = make_system(T_init=90, r=r_coffee, volume=300)\n",
+    "milk = make_system(T_init=5, r=r_milk, volume=50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.428571428571438"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mix_first = mix(coffee, milk)\n",
+    "mix_first.t_end = 30\n",
+    "run_simulation(mix_first, update)\n",
+    "final_temp(mix_first)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function takes `t_add`, which is the time when the milk is added, and returns the final temperature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(T_init=90, r=0.01, volume=300, t_end=30):\n",
+    "    \"\"\"Runs a simulation with the given parameters.\n",
+    "\n",
+    "    T_init: initial temperature in degC\n",
+    "    r: heat transfer rate, in 1/min\n",
+    "    volume: volume of liquid in mL\n",
+    "    t_end: end time of simulation\n",
+    "    \n",
+    "    returns: System object\n",
+    "    \"\"\"\n",
+    "    init = State(temp=T_init)\n",
+    "    #print(init) \n",
+    "    \n",
+    "    system = System(init=init,\n",
+    "                    volume=volume,\n",
+    "                    r=r,\n",
+    "                    T_env=22, \n",
+    "                    t0=0,\n",
+    "                    t_end=t_end,\n",
+    "                    dt=1)\n",
+    "    #print(system.init)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def mix(s1, s2):\n",
+    "    \"\"\"Simulates the mixture of two liquids.\n",
+    "    \n",
+    "    s1: System representing coffee\n",
+    "    s2: System representing milk\n",
+    "    \n",
+    "    returns: System representing the mixture\n",
+    "    \"\"\"\n",
+    "    assert s1.t_end == s2.t_end\n",
+    "    \n",
+    "    volume = s1.volume + s2.volume\n",
+    "    \n",
+    "    temp = (s1.volume * final_temp(s1) + \n",
+    "            s2.volume * final_temp(s2)) / volume\n",
+    "    \n",
+    "    mixture = make_system(T_init=temp,\n",
+    "                          volume=volume,\n",
+    "                          r=s1.r)\n",
+    "    \n",
+    "    return mixture"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update(state, system):\n",
+    "    \"\"\"Update the thermal transfer model.\n",
+    "    \n",
+    "    state: State (temp)\n",
+    "    system: System object\n",
+    "    \n",
+    "    returns: State (temp)\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    T = state.temp\n",
+    "    T += -r * (T - T_env) * dt\n",
+    "    \n",
+    "\n",
+    "    return State(temp=T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_and_mix(t_add, t_total=30, T_milk0=5, T_coffee0=85):\n",
+    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
+    "    \n",
+    "    t_add: time in minutes\n",
+    "    t_total: total time to simulate, min\n",
+    "    \n",
+    "    returns: final temperature\n",
+    "    \"\"\"\n",
+    "    coffee = make_system(T_init=T_coffee0, t_end=t_add, \n",
+    "                         r=r_coffee, volume=300)\n",
+    "    run_simulation(coffee, update)\n",
+    "\n",
+    "    milk = make_system(T_init=T_milk0, t_end=t_add, \n",
+    "                       r=r_milk, volume=50)\n",
+    "    run_simulation(milk, update)\n",
+    "    \n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    run_simulation(mixture, update)\n",
+    "    \n",
+    "    return mixture.results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_init_temp2(update_func):\n",
+    "    T_milk_array = linspace(5, 25, 5)\n",
+    "    T_coffee_array = linspace(70, 90, 5)\n",
+    "    for i1 in T_milk_array:\n",
+    "        for i2 in T_coffee_array:\n",
+    "            res = run_and_mix(t_add=0, t_total=120, T_milk0=i1, T_coffee0=i2)\n",
+    "            \n",
+    "            plot(res.temp, label='milk' + str(i1) + \"coffee\" + str(i2))\n",
+    "            decorate(xlabel='Time (minutes)',\n",
+    "                    ylabel='Temperature (C)')\n",
+    "    return res "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = run_and_mix(t_add=0, t_total=60, T_milk0=5, T_coffee0=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def find_t_opt(res):\n",
+    "    for t in res.temp.index:\n",
+    "        x = res.temp[t]\n",
+    "        if x < 67:\n",
+    "            return t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(res.temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "'return' outside function (<ipython-input-108-3de27817ab32>, line 4)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-108-3de27817ab32>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    return(t)\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m 'return' outside function\n"
+     ]
+    }
+   ],
+   "source": [
+    "for t in res.temp.index:\n",
+    "    x = res.temp[t]\n",
+    "    if x < 67:\n",
+    "        return(t)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>80.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>80.036542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>79.366621</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>78.704433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78.049889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>77.402901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>76.763380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>76.131242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>75.506400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>74.888771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>74.278272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>73.674819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>73.078333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>72.488731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>71.905935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>71.329867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>70.760448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>70.197602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>69.641253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>69.091326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>68.547747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>68.010442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>67.479340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>66.954368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>66.435456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>65.922534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>65.415532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>64.914383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>64.419019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>63.929372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>42.412608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>42.176984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>41.944079</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>41.713863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>41.486304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>41.261372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>41.039036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>40.819267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>40.602035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>40.387310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>40.175064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>39.965267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>39.757893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>39.552912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>39.350297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>39.150021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>38.952057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>38.756378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>38.562958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>38.371770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>38.182789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>37.995990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>37.811347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>37.628835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>37.448430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>37.270108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>37.093844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>36.919614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>36.747396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>36.577165</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>121 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          temp\n",
+       "0    80.714286\n",
+       "1    80.036542\n",
+       "2    79.366621\n",
+       "3    78.704433\n",
+       "4    78.049889\n",
+       "5    77.402901\n",
+       "6    76.763380\n",
+       "7    76.131242\n",
+       "8    75.506400\n",
+       "9    74.888771\n",
+       "10   74.278272\n",
+       "11   73.674819\n",
+       "12   73.078333\n",
+       "13   72.488731\n",
+       "14   71.905935\n",
+       "15   71.329867\n",
+       "16   70.760448\n",
+       "17   70.197602\n",
+       "18   69.641253\n",
+       "19   69.091326\n",
+       "20   68.547747\n",
+       "21   68.010442\n",
+       "22   67.479340\n",
+       "23   66.954368\n",
+       "24   66.435456\n",
+       "25   65.922534\n",
+       "26   65.415532\n",
+       "27   64.914383\n",
+       "28   64.419019\n",
+       "29   63.929372\n",
+       "..         ...\n",
+       "91   42.412608\n",
+       "92   42.176984\n",
+       "93   41.944079\n",
+       "94   41.713863\n",
+       "95   41.486304\n",
+       "96   41.261372\n",
+       "97   41.039036\n",
+       "98   40.819267\n",
+       "99   40.602035\n",
+       "100  40.387310\n",
+       "101  40.175064\n",
+       "102  39.965267\n",
+       "103  39.757893\n",
+       "104  39.552912\n",
+       "105  39.350297\n",
+       "106  39.150021\n",
+       "107  38.952057\n",
+       "108  38.756378\n",
+       "109  38.562958\n",
+       "110  38.371770\n",
+       "111  38.182789\n",
+       "112  37.995990\n",
+       "113  37.811347\n",
+       "114  37.628835\n",
+       "115  37.448430\n",
+       "116  37.270108\n",
+       "117  37.093844\n",
+       "118  36.919614\n",
+       "119  36.747396\n",
+       "120  36.577165\n",
+       "\n",
+       "[121 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LFStWABAVFYVCoSAoKIivvvqKv//97wQFBXH06FG8vb359NNPAUhJSSEsLIz09HRatWpF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\n+ecUNRAw2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Lq64u7urs8J/msYGRlhZ2enf/Xs2ZO3336bZ555Rv8sPpVKRUFBAUOHDjUo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HH5fTqL79+U/PX09HDzzTdz3XXX8e1vf5spU6bwzW9+c6+mAUhIeK+hKRLdExspxYMwYvtA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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d6acaf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep_init_temp2(update)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sweep_init_temp2(update_func):\n",
+    "    T_milk_array = linspace(10, 20, 2)\n",
+    "    T_coffee_array = linspace(70, 90, 3)\n",
+    "    for i1 in T_milk_array:\n",
+    "        for i2 in T_coffee_array:\n",
+    "            res = run_and_mix(t_add=0, t_total=200, T_milk0=i1, T_coffee0=i2)\n",
+    "            t_opt = find_t_opt(res)\n",
+    "            #print('t_opt =', t_opt)\n",
+    "        \n",
+    "            plot(res.temp, label='milk ' + str(i1) + '° coffee ' + str(i2) + '°')\n",
+    "            decorate(xlabel='Time (minutes)',\n",
+    "                    ylabel='Temperature (C)')\n",
+    "    return res "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>80.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>79.330501</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>78.668730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>78.014598</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>77.368017</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>76.728899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>76.097159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>75.472711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>74.855471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>74.245356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>73.642283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>73.046172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>72.456942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>71.874513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>71.298807</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>70.729747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>70.167255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>69.611257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>69.061676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>68.518439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>67.981473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>67.450705</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>66.926063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>66.407478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>65.894879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>65.388196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>64.887363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>64.392310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>63.902972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>63.419283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>29.965302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>29.873358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>29.782475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>29.692641</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>29.603844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>29.516073</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>29.429314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>29.343557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179</th>\n",
+       "      <td>29.258789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>29.175001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>181</th>\n",
+       "      <td>29.092179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>182</th>\n",
+       "      <td>29.010313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183</th>\n",
+       "      <td>28.929393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>28.849406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>28.770343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>28.692192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187</th>\n",
+       "      <td>28.614944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>188</th>\n",
+       "      <td>28.538587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>189</th>\n",
+       "      <td>28.463111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>190</th>\n",
+       "      <td>28.388507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>191</th>\n",
+       "      <td>28.314764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>192</th>\n",
+       "      <td>28.241872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>28.169822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>28.098603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>28.028206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>27.958622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>27.889841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>27.821854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>199</th>\n",
+       "      <td>27.754652</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>27.688226</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>201 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          temp\n",
+       "0    80.000000\n",
+       "1    79.330501\n",
+       "2    78.668730\n",
+       "3    78.014598\n",
+       "4    77.368017\n",
+       "5    76.728899\n",
+       "6    76.097159\n",
+       "7    75.472711\n",
+       "8    74.855471\n",
+       "9    74.245356\n",
+       "10   73.642283\n",
+       "11   73.046172\n",
+       "12   72.456942\n",
+       "13   71.874513\n",
+       "14   71.298807\n",
+       "15   70.729747\n",
+       "16   70.167255\n",
+       "17   69.611257\n",
+       "18   69.061676\n",
+       "19   68.518439\n",
+       "20   67.981473\n",
+       "21   67.450705\n",
+       "22   66.926063\n",
+       "23   66.407478\n",
+       "24   65.894879\n",
+       "25   65.388196\n",
+       "26   64.887363\n",
+       "27   64.392310\n",
+       "28   63.902972\n",
+       "29   63.419283\n",
+       "..         ...\n",
+       "171  29.965302\n",
+       "172  29.873358\n",
+       "173  29.782475\n",
+       "174  29.692641\n",
+       "175  29.603844\n",
+       "176  29.516073\n",
+       "177  29.429314\n",
+       "178  29.343557\n",
+       "179  29.258789\n",
+       "180  29.175001\n",
+       "181  29.092179\n",
+       "182  29.010313\n",
+       "183  28.929393\n",
+       "184  28.849406\n",
+       "185  28.770343\n",
+       "186  28.692192\n",
+       "187  28.614944\n",
+       "188  28.538587\n",
+       "189  28.463111\n",
+       "190  28.388507\n",
+       "191  28.314764\n",
+       "192  28.241872\n",
+       "193  28.169822\n",
+       "194  28.098603\n",
+       "195  28.028206\n",
+       "196  27.958622\n",
+       "197  27.889841\n",
+       "198  27.821854\n",
+       "199  27.754652\n",
+       "200  27.688226\n",
+       "\n",
+       "[201 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jU0VX0LZzFI5vFF2gpqqoTWXb9A5A3lAvU3U9LmnnhPmOrAolF0vEEOwij5+g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\nfjpPP/00r7zyCu985zv52c9+x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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d56f3588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep_init_temp2(update)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_init_temp2b(update_func):\n",
+    "    T_milk_array = linspace(5, 25, 5)\n",
+    "    T_coffee_array = linspace(70, 95, 26)\n",
+    "    for i1 in T_milk_array:\n",
+    "        sweep = SweepSeries()\n",
+    "        for i2 in T_coffee_array:\n",
+    "            res = run_and_mix(t_add=0, t_total=60, T_milk0=5, T_coffee0=i2)\n",
+    "            t_opt = find_t_opt(res)\n",
+    "            sweep[i2] = t_opt\n",
+    "    print(sweep)\n",
+    "    plot(sweep)\n",
+    "    decorate(xlabel='Starting Coffee Temperature (C)',\n",
+    "             ylabel='Optimal Drinking Time(min)')\n",
+    "           \n",
+    "    return res "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     0\n",
+      "75     0\n",
+      "76     0\n",
+      "77     0\n",
+      "78     2\n",
+      "79     3\n",
+      "80     5\n",
+      "81     6\n",
+      "82     8\n",
+      "83     9\n",
+      "84    11\n",
+      "85    12\n",
+      "86    14\n",
+      "87    15\n",
+      "88    16\n",
+      "89    18\n",
+      "90    19\n",
+      "91    20\n",
+      "92    22\n",
+      "93    23\n",
+      "94    24\n",
+      "95    25\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
+      "  warnings.warn(\"No labelled objects found. \"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>82.142857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>81.448623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>80.762403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>80.084103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>79.413633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>78.750903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>78.095823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>77.448304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>76.808259</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>76.175603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>75.550249</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>74.932114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>74.321114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>73.717167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>73.120192</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>72.530107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>71.946834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>71.370293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>70.800408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>70.237101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>69.680296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>69.129918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>68.585893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>68.048148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>67.516611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>66.991209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>66.471871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>65.958529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>65.451112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>64.949552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>63.963734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>63.479343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>63.000543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>62.527271</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>62.059461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>61.597051</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>61.139979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>60.688183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>60.241602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>59.800176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>59.363845</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>58.932551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>58.506236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>58.084841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>57.668311</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>57.256589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>56.849619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>56.447347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>56.049718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>55.656679</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>55.268177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>54.884160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>54.504575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>54.129372</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>53.758500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>53.391909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>53.029550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>52.671373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>52.317331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>51.967375</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>61 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         temp\n",
+       "0   82.142857\n",
+       "1   81.448623\n",
+       "2   80.762403\n",
+       "3   80.084103\n",
+       "4   79.413633\n",
+       "5   78.750903\n",
+       "6   78.095823\n",
+       "7   77.448304\n",
+       "8   76.808259\n",
+       "9   76.175603\n",
+       "10  75.550249\n",
+       "11  74.932114\n",
+       "12  74.321114\n",
+       "13  73.717167\n",
+       "14  73.120192\n",
+       "15  72.530107\n",
+       "16  71.946834\n",
+       "17  71.370293\n",
+       "18  70.800408\n",
+       "19  70.237101\n",
+       "20  69.680296\n",
+       "21  69.129918\n",
+       "22  68.585893\n",
+       "23  68.048148\n",
+       "24  67.516611\n",
+       "25  66.991209\n",
+       "26  66.471871\n",
+       "27  65.958529\n",
+       "28  65.451112\n",
+       "29  64.949552\n",
+       "..        ...\n",
+       "31  63.963734\n",
+       "32  63.479343\n",
+       "33  63.000543\n",
+       "34  62.527271\n",
+       "35  62.059461\n",
+       "36  61.597051\n",
+       "37  61.139979\n",
+       "38  60.688183\n",
+       "39  60.241602\n",
+       "40  59.800176\n",
+       "41  59.363845\n",
+       "42  58.932551\n",
+       "43  58.506236\n",
+       "44  58.084841\n",
+       "45  57.668311\n",
+       "46  57.256589\n",
+       "47  56.849619\n",
+       "48  56.447347\n",
+       "49  56.049718\n",
+       "50  55.656679\n",
+       "51  55.268177\n",
+       "52  54.884160\n",
+       "53  54.504575\n",
+       "54  54.129372\n",
+       "55  53.758500\n",
+       "56  53.391909\n",
+       "57  53.029550\n",
+       "58  52.671373\n",
+       "59  52.317331\n",
+       "60  51.967375\n",
+       "\n",
+       "[61 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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C6sxLT0+HhYUFr43NmjULs2bNqneZkJCQOo/JEkJIY0llDEm38nDhRi43mhwAWJoZINTf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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d5114358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sweep_init_temp2b(update)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_coffee(T_coffee_array, T_milk0): \n",
+    "    sweep = SweepSeries()\n",
+    "    for i2 in T_coffee_array:\n",
+    "        res = run_and_mix(t_add=0, t_total=60, T_milk0=T_milk0, T_coffee0=i2)\n",
+    "        t_opt = find_t_opt(res)\n",
+    "        sweep[i2] = t_opt\n",
+    "    print(sweep)\n",
+    "    plot(sweep, label='milk at ' + str(i1) + '°')\n",
+    "    decorate(xlabel='Starting Coffee Temperature (C)',\n",
+    "             ylabel='Optimal Drinking Time(min)')   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     0\n",
+      "75     0\n",
+      "76     0\n",
+      "77     0\n",
+      "78     2\n",
+      "79     3\n",
+      "80     5\n",
+      "81     6\n",
+      "82     8\n",
+      "83     9\n",
+      "84    11\n",
+      "85    12\n",
+      "86    14\n",
+      "87    15\n",
+      "88    16\n",
+      "89    18\n",
+      "90    19\n",
+      "91    20\n",
+      "92    22\n",
+      "93    23\n",
+      "94    24\n",
+      "95    25\n",
+      "dtype: int64\n",
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     0\n",
+      "75     0\n",
+      "76     0\n",
+      "77     1\n",
+      "78     3\n",
+      "79     5\n",
+      "80     6\n",
+      "81     8\n",
+      "82     9\n",
+      "83    11\n",
+      "84    12\n",
+      "85    13\n",
+      "86    15\n",
+      "87    16\n",
+      "88    18\n",
+      "89    19\n",
+      "90    20\n",
+      "91    22\n",
+      "92    23\n",
+      "93    24\n",
+      "94    25\n",
+      "95    27\n",
+      "dtype: int64\n",
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     0\n",
+      "75     0\n",
+      "76     1\n",
+      "77     3\n",
+      "78     4\n",
+      "79     6\n",
+      "80     7\n",
+      "81     9\n",
+      "82    10\n",
+      "83    12\n",
+      "84    13\n",
+      "85    15\n",
+      "86    16\n",
+      "87    17\n",
+      "88    19\n",
+      "89    20\n",
+      "90    21\n",
+      "91    23\n",
+      "92    24\n",
+      "93    25\n",
+      "94    26\n",
+      "95    28\n",
+      "dtype: int64\n",
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     0\n",
+      "75     1\n",
+      "76     2\n",
+      "77     4\n",
+      "78     6\n",
+      "79     7\n",
+      "80     9\n",
+      "81    10\n",
+      "82    12\n",
+      "83    13\n",
+      "84    14\n",
+      "85    16\n",
+      "86    17\n",
+      "87    18\n",
+      "88    20\n",
+      "89    21\n",
+      "90    22\n",
+      "91    24\n",
+      "92    25\n",
+      "93    26\n",
+      "94    27\n",
+      "95    28\n",
+      "dtype: int64\n",
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     1\n",
+      "75     2\n",
+      "76     4\n",
+      "77     5\n",
+      "78     7\n",
+      "79     8\n",
+      "80    10\n",
+      "81    11\n",
+      "82    13\n",
+      "83    14\n",
+      "84    16\n",
+      "85    17\n",
+      "86    18\n",
+      "87    20\n",
+      "88    21\n",
+      "89    22\n",
+      "90    23\n",
+      "91    25\n",
+      "92    26\n",
+      "93    27\n",
+      "94    28\n",
+      "95    29\n",
+      "dtype: int64\n",
+      "70     0\n",
+      "71     0\n",
+      "72     0\n",
+      "73     0\n",
+      "74     2\n",
+      "75     3\n",
+      "76     5\n",
+      "77     7\n",
+      "78     8\n",
+      "79    10\n",
+      "80    11\n",
+      "81    12\n",
+      "82    14\n",
+      "83    15\n",
+      "84    17\n",
+      "85    18\n",
+      "86    19\n",
+      "87    21\n",
+      "88    22\n",
+      "89    23\n",
+      "90    24\n",
+      "91    26\n",
+      "92    27\n",
+      "93    28\n",
+      "94    29\n",
+      "95    30\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
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4CxcuFBybNWsWaWlprFixwtNWUFCATqe74gIBkBY+4rrOJNLCB+/M2LMetK+v\nL1KpVLC0o1KpPCVFL0Z+fj779u0jMjISna7/Wt9arZbZs2ezbds2Tp48SXl5OUVFRQA4nc5+rxsM\n/Y3bZrP1+5L38fHBbrf3Ol8MmRbxxu12k28wcaa5O4R1ZGiXzYbNaqLu7A+exDi5Qo1uSLdAuF1u\nKiuaKT5hwGoR/r4Fa9Ukp+oFBnwALoeDloLjNOcfxmXz+miJjUU7fhw+ocLNZpvDxhFDIQXGIpwu\n4b+r4do4MqNGE+gj7KfDamf/CQOFZU24BIIiYVS8lptGhuOnurzEun5FIjo6munTp3PPPfdw1113\nMWrUqH5vUlRUxP/8z/+wc+dO5s6de8HzXn31VWbOnMmsWbOor+9eY1Or1fzyl7/kr3/9KykpKWRk\nZJCXl8c777zD8uXLL+3pLkKafsSgv+SvN961oyUSySUvw6nVat544w0WLVrEunXreOaZZ/o8z2g0\ncv/99xMeHs7kyZOZNGkSOp2OmTNnXlK/faHX62lqasLpdCKTdX3xOBwOmpqa+hWwiIgI6rw2/urq\n6gYURCHy8+J4fSsnG7o3locFq8nQa7Db2qir+A6Xq+sDRSbzQRd7K3KFL263G2NtK0UFBtq8DfgC\nfEhOiUAfFejl6OrCfKqYpv0HcLQLcyp8wsLQ3jwev2hh9KbT5aSwvoRDNcc9eQ/niQrUMy5mDKF+\nwiVcm93J4VN1HCmux+6VpJcQo2HcqAg0Ab1n5pdCvyLx7//+70yZMoVXXnmFmTNnEhkZSWpqKtHR\n0fj6+mI2mzEYDBw6dIiGhgZuu+02Nm7cSHJycr+d7dy5E6fTyccff8zHH38sOPbEE08wf/585HI5\nb7zxBjU1NURGRvLUU0/xb//2b1fkYUWEJCcnM2bMGJYvX87ixYuZPn06mZmZvc7bsWMH7e3tfPDB\nB54X+Lfffgt0RzVd7n7R2LFjcTgcHD582DOG/Px8XC4XY8eO7feaAwcOCNry8vL6fAaRny/FTWaO\nGLu9kmICfRkfFYLLYaWu4luczi4BkEoV6IbcisLH/5wBXy1NDV4vepWiy4BvaAgSLwO+joqzNO7L\n62HA14UiMJCQcVn4Dx/WK6eitKmcA9VHaesU9qP1C2ZczBiiAyME7U6nixNljRwoNGLxStKL1vlz\nc2ok4SFXdnn1gnsSiYmJvPXWWxQXF/Ppp5+Sl5fH/v37MZvNBAcHExUVxaxZs7j99ttJSrp4abzF\nixezePG7AZitAAAgAElEQVTiC57z8MMP8/DDDw/uKUQui5ycHLZu3cry5cv55JNPeu0N6PV62tra\n+Pzzzxk9ejRFRUUem5TzS0RqtRqj0UhlZSURERG9ZjwXIzw8nOnTp7N8+XLWrFmD2+3mmWeeYcaM\nGZ6ZgdVqxWw2ExISgkwm48EHH2TmzJn89a9/5c4772T79u0cPXqUlStXXv4PReQnT7vdwVFjC6d7\nLDHp/X3IjgkFlx3j2e88GdQSiYywmFtQqjScKa6n8KjQ0VWukDEsKYyhCX0Y8BmNXQZ8NcJrZL6+\nhGSOJXDkCCQy4TVVLbXkVR2msaNZ0O7voyYrKp1hIUN6C0qViX3HDbS0CWcboRpfbk6NIDY84KqU\nmR6wC+ySJUuueOciPx6effZZ7r77bnJzc1m6dKng2PTp0ykoKGD16tV0dHQQGxvLggULePvttyko\nKGDixInce++97Nq1i5ycHD744APS0tIGPYbVq1ezevVqHn30UeRyOdOmTRP4he3cuZOnnnrKE2Kb\nlJTEa6+9xtq1a9mwYQPx8fG8+eabfYbwivx86HS6OFHfQlFjG84eNahDfJVMig1DipO6s99j7zw/\nu5AQGj0OlTqMyvImgUBIpV0GfMNHhOOjEr4ubaYWmvbl0XbmjKBdKlegSU9Dkz4aqVdgRX17I/ur\njlDdahC0+8h9yIhMYWRYAjIv99ZKo5m9BbXUNQsTUAP8lIxL0ZMYEyywFb/SSNwDyYLqQU1NDXV1\ndSQmJiKRSH4Um4RVVVVMnTq13/h8ERGRGx+Hy82pRjPH61s9NhvniQxQMSFai48U6qv2Ymnrfklr\nI2/CXzMEQ00L+T9UeJZQg7Vq0rNiUPt7G/B10Hwgn9bCwl4GfIGjRhKcORa5V7BFa2cbB6qOcLpJ\nmB8hk8q6youGj0ApFwpKg8nCDwU1nPVK0vNRyshMDid1eChy2eUlGQ/k3TngNYGvvvqKl156iYqK\nCiQSCZs3b+b1118nKCiI5557zrNWLSIiInItcbndlJk6OGo00e4VmhriqyRDryHCX4Xb7aaxer9A\nIILDR+OvGUJjfRuH9p71vPQDNb5kZccJkuFcNluXAd+RY70M+PyHxRMybhxKjdA/yWK3cqi2gJN1\npR47b+gSlKTQYYyNTEWt9BKUdht5x2sp9krSk8ukpA0PJSNZh0o5uOXcy2FAPX311VcsXLiQqVOn\n8sgjj3iijW655Rb+/Oc/Ex0dzYIFC67qQEVERER64na7qTlXg9rk5U3kr5QzJjyIIUF+SCQS3G43\nzYYjtLdWes4JCh1BoDYBU1MHB74v9yTG+fn7MO7WoR6BcDudtBaepOlgfi8DPt/ISLQ3j0cVLozA\nszvtFBiLOGo4id0pHFtccAxZUaN7GfJZOx0cLDJSUNogWCaTSCSMiAsma6Qef68kvWvBgEQiNzeX\ne+65hzVr1uB0Oj0i8cADD9Da2sq2bdtEkRAREblmNHR0cshgEmROA6jkUtJ0QQwP9kfWY52+pf4k\n5ubu4j0BwcMIChtJm7mT/d+VeZLjfFQKxt86FB+VosuAr/Q0TXn7sbe2CvpRhoR0hbPGCl1gXS4X\nRQ2nya85hsUuDJsN9w9jXMwY9P7ChFO7w8Wx0noOFdXR6TUTGhoRyPjUCLRB129Zf0Aicfr06X43\nrseOHcsbb7xxRQclIiIi0hetnXaOGFuoaBFu4sqlEkaGBjAyNBCF1zp9a2MpLQ3d/mXqwBiC9elY\nLXby9pzx1HtQKGWMnxiPn78PHVXVNO7dR2e90C9JrlYTMi6LgMSEXgZ8Zc2VHKg+2m3Adw6NbxBZ\nUekM0UR5CYqbooom9p8w0OaVpKfXqrklNYJIryS968GARCI4OJjy8nKys7N7HSsvLx9UVraIiIjI\nYLHYnRyrb6Gkqa2nUwUSCSQE+5OmC8JXIdwXddjaMdUdFywx+ar1aCMzsduc5O0pw9JxPolOSlb2\nUAKCVDQfOkzjPqG/mFTpQ/DYMQSlpvQy4Ks115FXdZi6tgZBu5/Sj8zINBJDhyKVCAWlvLaVvQW1\nNHkl6WkCfLg5JYL4qKCrEs56KQxIJHJycli/fj16vd4jFBKJhKKiIl5//XXuuOOOqzpIERGRnyd2\np4vCBjOFDa2eynHniQ3yZUy4hkAfoe2E02GlpaGItqYzuOneLPbx1RIaMx6nE/Z/V0ab+XwSnYSx\nt8QRrFXTcqJQIBASqQzN6FQ0GWOQeeUPNVlM7K86wllTtaBdKVOQHjGKFF0ScpmXoDS0s7eghhqv\nJD0/lYKskeGMHKq9quGsl8KAROJ3v/sdJSUlLFq0yJMkNXfuXMxmM2PGjOGJJ564qoMUERH5eeFy\nuylpauNYXQtWhzCcVaf2IUOvIcxP+NJ2uRyYG0tobSzG5RIu3/gFRKGNHAtuKQd/KMPU1LVcJZFI\nSM+KRacPoK30NPXffOu5xjcykvBfTEHuL1zyabO1c7D6GMWNZUIDPqmUUbpExkSkoJILx9bcamXv\n8VrOVLcI2pUKGRlJOkYnhF6yAd/VZkAi4ePjwzvvvMP333/P3r17aWlpwd/fn6ysLCZNmvSjmRaJ\niIj8tHG73ZxttXDYaMLsZTsRpFIwVq8h0l/llY3soq25nJb6Qo/Fxnl8fLUEh6fi4xeK2+XmUF4F\nDXXdNtopY6KIjNHQUVmFcdduzrvA+oSFEZFzhyAZrtNh44jhBMeNp4QGfBIJCSFxZEalEeBlwNdm\nsXOg0MBJbwM+qYTU+FDGjtBdtgHf1WZQwbYTJkxgwoQJV2ssIiIiP2MM58JZGy1CN2A/hYzR4UHE\na9RIvawqLOYaTHXHsdu8q8IFoglPwdc/whMCe+xQFbVV3V/ySSl6hgzTYjUaMXz2Oe5zIbCKoCAi\n7szxCITD5eRE3SkO157A5hCOLToognHRY9D6CfdlO3sY8Dl6GfAFMz5FT5D/lTHgu9oMSCRsNht/\n//vfOXz4MGazuddxiUTCf/7nf17xwYlcHt6V6ZKSknjppZeYMWMGy5Ytw2Aw8N57712RvkwmE7t2\n7eK+++676Llnz55lxowZfPbZZ+h71O/95ptvePTRR3ud/8033wjO68l3333H2rVrKSsrY8iQISxd\nupTbbrvNc3zlypV88sknpKWlsX79eoKCrk6xeJFLp8li47DRRI1ZOAtQyqSkhAWSpA1A7rVOb+1o\nwGQsoNPSKGiXyX3RhI1ErRmCpMdmcVGBgcqybuO9+MQwhifr6Gxsonb7Tk9ynFytJmrG3cj9fHG5\nXZQ2lnOw5lgvA75QdQjjosf0Kh3qdLo4frqRAyeNWG3CmVBMeAA3p0agC/5p1TcZkEisWrWKjz76\niISEBDQazdUek8gVIicnh4kTJ16Tvl5++WUqKiouKhJlZWU88sgjdHR09Dp26tQpRo4cydtvvy1o\n12r7LvJeWlrK/PnzWbBgAbfffjuffvopCxcuZOvWrSQkJLB3714aGxvZtm0bO3fuZOPGjWIZ3B8R\n7TYHR+paBDUeoKvedLI2gBRdED5e4az2zlaajQVY2oTVCqVSBYHaJAK0w5FKha+106fqOH2q21I+\nekgII9IicJjbqN2+A+e5IlUylYrIX92F3N+fypYa8qoO09QhLOIT4ONPVnQ68cGxvQz4SipN7Dte\nS2u7cLYRdt6AT3/la+JcCwYkEl9++SWPP/64mDD3E0OlUqHyKol4tRiIBdjGjRtZv349cXFxfR4v\nKSkhMTFxwNXtNm3aRHp6OvPnzwe6Aizy8/PZtGkTzz33HHa7ncDAQMLCwtBqtVR41RUWuT50Opwc\nr2+lqNGMV8ASw4LVjA4PQq0Qvpoc9g5a6gtpM1UgKP8pkRIQPIzA0GRkXpvFbeZOigpqMfTYLA6P\nDGJ0ZjROi4WaT7d7aj5I5Qoi7spBGRxMfk0B+dXHBPdSKVRkRKQwImx4LwM+t9vNv/IrKSwTWoQH\nqpWMG6UnMTb4J71vOyB3KIlEQnp6+tUei8hFSEpKYvPmzdx///2kpqaSk5PDkSNH+Pvf/85tt91G\nRkYGixcv9th3b9myhZEjR170vm63m6effprs7GxOnz7d5zlFRUU88sgjZGZmkpKSwrRp09i2bRvQ\nlZH/0UcfsX//fpKSkqiqqurzHrt37+a5557jD3/4Q5/HS0pKBuXgevDgQbKysgRt48aN4+DBg0DX\nHlpLSwvp6els3LiROXPmDPjeIlceh8vN8fpWthXXUtggFIioABV3Jei5JVorEAiX00azsYCa0s9p\nM5XTLRAS1EGxRA6bRrB+tEAgrBY7x/Kr+ObzUwKBCAn1J2N8LC67jdrtO7G3dB2TSGXoc+5ApdNx\n3FgkEAi5TE5GZCr3p/6KlPCkPgXi+2M1AoFQKeXcOjqKB6YlkzQk5CctEDCIGtcfffQR48ePRyq9\nPNdBkctj3bp1PP/888TFxbFs2TIeffRRUlNT2bBhA2VlZSxZsoTMzExmz5494HuuWrWKPXv2sGnT\nJuLj43sd7+joYN68eUyePJkPP/wQt9vNu+++y4oVK8jOzmbevHmUl5dTXV1Nbm4uISEhffTS9eUP\nXYWBvHE6nZw5c4bjx4/zq1/9iqamJlJTU3nyySf7HBOAwWDoVYVOp9NhMHQZuMlkMl577TU6OjrE\nOtfXEZfbzRlTO0eNLXR42U5ozxnw6f2FM163y4m5+TQtDUW4nMLlG5U6nODwVJQq4dK3w+7k9Kl6\nzhTX4/TaLI6KDSY1IwqJ20Xtzv+ls+F84puE8Nt/gV90FCWNZfxwNr/7mkA9k+NvwU/RvyVGflHX\n5vR5kmKDmZgRjY/ixxnOeikMSCSeeOIJ7rnnHqZNm8aoUaN62YNLJJIL1rX+sWI6cpSm/Qd7OTpe\nC6RyBSFZmWjSRw/qulmzZjFlyhQAZsyYwapVq1i5ciUxMTEkJibyzjvvUFJSMuD7vfjii3z55Ze8\n//77DB06tM9zLBYLc+fO5aGHHvL83T/22GNs3ryZ8vJyMjMzUalUKBSKAS8VeXP27Fk6Ozux2Wys\nXr0am83GG2+8wQMPPMD27dv73JewWq0ovfz6lUolnZ1eNYVFgbguuN1uqs0WDhlbaPEy4AvwkTMm\nXENsoG+vtf32lrO01Bd6CgKdR6kKJjg8FZVaaKbncrooP91IaVGdx2LjPKE6f5JTI9CE+OF2OjH8\n75dYarv3M3STb8M/fihnTdV8Xba3u90/lNuHT0Qh6z889cSZRvYd777XsKggpt4U+6NLhrtcBiQS\nL7/8MmVlZQQEBHgiZXryU51OmY4cvS4CAeBy2DEdOTpokYiNjfX82dfXF6lUKvCBV6lUnuWmi5Gf\nn8++ffuIjIzst440dG0cz549m23btnHy5EnKy8spKioCumYAV4KhQ4eSl5dHYGCgZ7b62muvMWnS\nJP75z38yb968Xtf4+Phgtwv//mw224+ixsnPnfpzBnx1/RjwJYT49wpntbYbMRkLsHUKE87kCjUa\nXQp+gdG9BKWm0sSp4wY6vDaLAzW+JKdGEBbu7wmBrfvXN7T32JcKveVmAkckU2uu48vT33bXkfAN\n4o6ESRcUiNJKE18f6l5WjdYF8MtxQ244gYABisS2bdt45JFHWLx48U9WEPpCkz76us4kBisQQK+y\noBKJ5JL/TtRqNW+88QaLFi1i3bp1PPPMM32eZzQauf/++wkPD2fy5MlMmjQJnU7HzJkzL6nf/vCO\nnPP19SUmJoba2to+z4+IiKCurk7QVldX12sJSuTa0dpp57DRxNkWoaV2lwFfICNDA3oZ8HVamjHV\nFWBtF/5dSmU+aMJG4K8ZisRrL6DeaKaooJaWZi/rbrWSpFF6omI1nn8Xbrebxu9/wFxc7DkveMwY\nNOmjaexo5vOSrz3JcQE+/uQkTumVMd2TSqOZL/Z3FyfSBfuRc0vcZRcA+rEyIJGQyWRMmDDhhhII\n6BKJS3lR3ygkJyczZswYli9fzuLFi5k+fTqZmZm9ztuxYwft7e188MEHnuJS337bZV9w/h/K5f5u\n7Nq1iyeffJLdu3d79jTa2tooLy9n1qxZfV4zduxYDhw4IGjLy8vr8xlEri4Wu5OjdS2UNvc24EsM\n6TLgU3nZTthtbZjqTtDRw4Cv6xo5gdoEArWJSL2+5luaLRQV1FJv9E6ek5MwQkfcMC1Sr5d1c/4h\nTMcKPP8fOHIEIeOzaLG2srP4K2zn6j34KlTkJE7uVQSoJ4bGdnb+UIbr3K67JsCHu2+NR3kD7UF4\nMyCRuPvuuz0b1yI3Hjk5OWzdupXly5fzySef4ONlZKbX62lra+Pzzz9n9OjRFBUV8fzzzwN4lrbU\najVGo5HKykoiIiJ6zXguxk033YS/vz9PPvkkTz75JE6nk3Xr1hEcHMyMGTOArj0Is9lMSEgIMpmM\nBx98kJkzZ/LXv/6VO++8k+3bt3P06FFWrlx5+T8UkQFj7rTzeZkRi124WTwkyI/08KC+Dfjqi87V\nd+gZAyvBP3gomrCRyOTCjeyOtk5OnTBSfbZZ0C6TSRmaEMqwZB0Krxd1Z0Mjjfvy6Dh71tPmHx9P\n2MRb6bBb2FH8lafmg1KmICdxCkGq/nMZGlssbP+uDPs5Lyl/XwW/njgMX59rVyXuejCgp9NqtWzd\nupVf/vKXpKamolarBcclEgmrVq26KgMUuTY8++yz3H333eTm5rJ06VLBsenTp1NQUMDq1avp6Ogg\nNjaWBQsW8Pbbb1NQUMDEiRO599572bVrFzk5OXzwwQekpaUNqv+goCDee+891q5dy5w5c3A4HEyY\nMIGNGzd6RGvnzp089dRTnnq8SUlJvPbaa6xdu5YNGzYQHx/Pm2++OagwWpHLo8PuZFd5nUAgws8Z\n8IX2YcDX2lhMa2Mxbpdwg9kvIAqNLgWFT4Cg3dbpoORkHRWnGz2V46DrnRMTF0ziKD0qX6EI2c1m\nmvIOYC4uoacI+UVHE/7LqXS67Ows/sqTRS2TyrgjYVIva42etLbb+PTbM54sal8fOTMmDrsuleKu\nNRL3ALKgzkfT9HsTiYTdu3dfsUENloEU8xYREbmydDpdfHHG6CkdKpNKuDVGS3SAd8SSi7bmMlrq\nT/Y24PMLJViXio+fMHrN6XBxpqSe06fqPVXjzhMeGURyqp6AQOFsw2m10px/mJaC47h7GvAhITA5\nidBbJ+CUwo7irzy1HyQSCdOG30asJqrf5+yw2tnyr1JMbV2b8Aq5lHtuG44u5KcfNTeQd+eAa1xf\nKRoaGli7di3ff/89VquV0aNH84c//IHExETg4l48IiIi1x+708W/yus8AiGRwMQYLdGB3S/OLgO+\naprrjuOwtQmuV/gEotF1G/B5rnG5OVveRPEJI51eYbPBWjUj0iIICRWuZLgcDlqOFdB86DAur8g+\n9ZBYQsaPw0erxely8mXpN4LiQJOG3nxBgei0O/n02zMegZBJJdw5YegNIRAD5ZouprlcLhYtWoTb\n7eb111/Hz8+P3Nxc5s6dy44dO2hsbLygF4+IiMj1x+lys6eygfqO7hfyLdFCgbC219NsLMBmFVpV\nyOR+aHQjUQfFCgz43G43xppWigoMnmJA5/EPUJGcqic8MtBLUFyYTxXTtP+Ax17jPCqdDu3N4/GN\nigTA5Xbxr7K9VLV0R8rdEptJgrbv3CAAh9PFju/KqDd1RVBJJBKmjY8jWhfQ7zU3Iv2KxLRp01i/\nfj3JycncfvvtF41e+fzzzy/aWVFREYcPH2bnzp2edeO1a9eSlZXFN998w6FDhy7oxSMiInJ9cbvd\n/FDdKHBszYwIJl7T9XVvs7ZgqjvehwGfksDQJAJDhvcKZ21qaOfksVqaG4Uveh+VgsRR4cTGhSCR\nCvMjOioqaNy3H1uTUIQUQUFox49DHT9UEAL7fcVBzjR150hkRKaSEp7U73M6XW4+31tOTUP3DGjK\n2Bjio35+LsL9ikRGRoZngzojI+OKhL9GRETw1ltvCTJ7z9+3paWFgwcPMn36dME148aNY8eOHZfd\nt4iIyOXhdrvZX9tMuanbwTdVF8iI0AAcdgstdSdoa+nDgC9kOIHaZGRy4SZvm7mTk8dqMdZ4J8/J\nGJYUxtCEUOReYbNWg4HGvXmCrGkAma8vITdlEjgiGYms+xq3283BmmOcrO92IRgVnsjYyNQLPue/\nDp6lrLbV03ZLWiQjhvZtN3Oj069I/PnPf/b8+YUXXrginQUHBzNp0iRB2/vvv4/VaiU7O5v169df\n0ItHRETk+nGsroXixu4v60StP6N1Qdg7zRjLv8bp7Jld3WXAp9GNQq7ovX7f1NBO3p4zAo8lqVTC\nkGGhJIzQofQKK7WZTDTt20/bmTOCdqlcgWZMV76TVCGMcqprb2R/1WFqWo2etuHaOG6Jyez3o7fS\naOaHghrqeyTpjU3WkZHUvyPBjU6/IjF16lT+4z/+g+Tk5KvW+e7du1m3bh0PP/www4YNG7AXj4iI\nyLXlZIOZY3XdX9ZxGj+yIoJxOiwYK74VCISvvx6NLqWXAd95Wk0WDnxXJhCIqNhgkkaF4+dVrc3R\n3k7TgXzMJ08K7OglEgmBo0YSnDkWuZc3V4u1lQPVxwTLSwAxQZFMiru5T4Gob7bwQ0ENlV5JeqPi\ntYxPiejvx/KzoF+RqK6uHrAH0KWwZcsWnnnmGXJycnjyyScB0YtHROTHyBlTOwdru5PYIgNU3BKl\nxeW0UVfxLU5H1/KTRCInLGY8vv59VxEEaG/rJO/bMuznwlqVPnKysoei8YoWctlsNB8+SsvRo7gc\nwpwK/2HxhIwbh1Ij3B+w2K0cqi2gsK6kl6CMCBvO+OiMXi7WLW2d7D9h4JRXkp5cJiU9MYyskfob\nzmlisFyXVME33niDv/zlLzz44IOsWLHC85cgevGIiPy4qGq18ENVd4nQMD8lE2NCkbgdGM9+76kt\nLUF6UYGwWuzk7TnjCW2VK2SMuzWeoODuj0C300nLiUKa8w/htHj5MkVFoh0/HlW4cOnH7rRzzFjE\nUUMhDqdQUOKCY8iKTkfjlUlt6XRw8KSR46cbcLq8BCUumKyR+p9FotxAuOYisWHDBv7yl7/w+OOP\ns3DhQsEx0YtHROTHg7Hdyp7KBo8XU5BKweQ4HXKJm7rKvT3CWyVoo266oEDYbQ7yvi3zuLXKZFJu\nmhDnEQi3201b6Wma9uVhNwuXfHy0WrQ3j8M3JkbwVe9yuShqKCW/psBjr3EefUAY46LHEO4vtK63\nO1wcLann0Kk6bF5JekMjgxifokcbJK5c9OSCIvHEE0/02iPoj4GGwL766qvMnDmTWbNmUV/fXaxD\nrVaLXjwiIj8Smiw2/lVR7/nK9lfK+EWcDqUUGqr2CxxbQ/RjUAfF9Hsvh8PJ/u/KMbd05xtkjB+C\nNswfgI6qKhr35tHZ430AIPf3RzsuC/+E4UikwpyKsuZK9lcfodUqFBSNbxDjotOJDYryEhQ3J8ub\n2H/CQLtXkp5eq+aW1Agiz41HRMgFRSIxMbHfKmOXws6dO3E6nXz88cd8/PHHgmNPPPEECxYsEL14\nriBbtmxhxYoVnhogSUlJvPTSS8yYMYNly5ZhMBh47733rkhfJpOJXbt2cd999/V7zs6dO3nrrbeo\nqKggLCyMf/u3f+P//t//63GWtVgsrFmzhi+++AKn08kdd9zBU0891csrrCcXy9BfuXIln3zyCWlp\naaxfv56goJ9fnPtgae20s7u8DruzSyBUcim/iNPhK5fSVHuIDnO151xNWAoBIX1XDoSugkD5eysE\nORCjb4ohPDKQzsZGGn/YR0el0AVWqvQhJDODwJRRSL2MImvMRvIqD1Pf3iho91P6kRmZRmLoUKRe\nSXplNa3sLail2StJTxPgw80pEcRHBf3s9x0uxAVFYuHChYM2arsQixcvZvHixRc8Z9KkSb3CZEUu\njZycHCZOnHhN+nr55ZepqKjoVyS++eYbli5dytNPP83EiRMpLCzkmWeewW63e5Yd//jHP3LixAne\neustHA4HTz/9NH/84x955ZVX+rxnaWnpBTP09+7dS2NjI9u2bWPnzp1s3LiRxx9//Kr9DH7q2J0u\nChtaKWww4zg3g1DIJEyN0xHgo6DZWECbqcxzfmBIAoGh/Sekud1ujhyopN7Q/bU/Kj2K6CHBWKpr\nqPl0h8BjSSKToUlLQ5ORjszLibipw8T+6iOcNVUL2pUyBekRKaSEJyH3StKrbWjnh2M11Hol6alV\nCrJG6RkRF3JDFgm60tzYHrc/c1QqFSqV6uInXgEu5hP53//939x+++08+OCDQFeFvdOnT7NlyxYW\nLlyIwWBg+/btvPfee6SnpwOwevVq5syZw+9///s+gxc2bdp0wQx9u91OYGAgYWFhaLVaKioqet1D\npCu7uLS5jWN1LVgd3WGpMqmEKUPCCPFV0tpwitbGU55j6qAhaMLT+v0Cd7vdnDhcQ02lydOWMCKc\noQmhWOvqqN35WQ+B6DLgC8nKRO4vXPJp62znYM0xihvL6FmoQiqVkqJLIj1iVK8CQU2tVvYW1FLm\nlaSnVMjISNIxOiEUhfzGrf9wpbkxSyndoCQlJbF582buv/9+UlNTycnJ4ciRI/z973/ntttuIyMj\ng8WLF3tCl7ds2cLIkSMvel+3283TTz9NdnY2p0+f7vOcoqIiHnnkETIzM0lJSWHatGls27YNgNzc\nXD766CP2799PUlISVVVVva6fP38+ixYtErRJpVJaW7ti7w8dOoRUKiUjI8NzPCMjA5lMRn5+Pn1x\n8OBBsrKyBG3jxo3j4MGDAEyYMIGWlhbS09PZuHEjc+bMuejP4ueE2+2m3NTOJyW17K9pFgiERqXg\nF3E6dGoVbc1lNNd1F+3x9Y9EGzn2gks0xYVGyk93G+nFDQslcVQ4NpOJ2u07cZ0LdZer1cT8n/vQ\nTZkkEIhOh419lYf47+OfUNxwplsgJBIStEP5Pyl3Mz4mQyAQbRY7Xx2s5B9fnBIIhFQqYXRCGA9N\nH/eo7TAAACAASURBVEHmiHBRIAZJvzOJRYsW3fChp6dP1VNSaMThuDJ1mgeDXC4jYWQ4w5LCLn5y\nD9atW8fzzz9PXFwcy5Yt49FHHyU1NZUNGzZQVlbGkiVLyMzMZPbs2QO+56pVq9izZw+bNm0iPr73\n+nJHRwfz5s1j8uTJfPjhh7jdbt59911WrFhBdnY28+bNo7y8nOrqanJzc/vcx/Jetmxra+Mf//gH\nt956K9BVIjUkJARFj6xZuVxOSEhIv+VLDQbDBTP0ZTIZr732Gh0dHfj5/XxcOweCoc1KvsFEk0WY\nC+WnkJEermGoxg+pREJHazWNtYc8x1V+YYRFjxOY83lTVtJASWF3lnNkjIZRYyJxtrdT889PcVq7\n9gakSh8i7roTH23374vD5eRE3SkO157A5hCOLSYokqzo9F51HzrtTg4V1XG0pB6HU1j4KDE2mHGj\n9AT591+OVOTCXFAkbnTOFNdfF4GAroiPM8X1gxaJWbNmeep7zJgxg1WrVrFy5UpiYmJITEzknXfe\noaSk5CJ36ebFF1/kyy+/5P333xd4avXEYrEwd+5cHnroIU9i42OPPcbmzZspLy8nMzMTlUqFQqEg\nLOziz2OxWFiwYAGdnZ0sWbLE0+ZdEQ8unHE/0Ax9USC6abLYOGw0CQz6AJQyKSlhgSRpA5CfW6e3\nttfRUJ3HeS8mpUpDWMwtvQz6elJV0cyJI937BmH6ANJvisFltVLzyXaPW6tULifyrukegXC5XZQ0\nlnGw+hjttg7BPcPUWsZFpxMZKAyxdTpdFJxu4ODJOk8xoPPEhAdwS2okYcFiOOvl8rPek4hPDLuu\nM4n4xMEJBHSt5Z/H19cXqVQqKBaiUqkGnCmfn5/Pvn37iIyMRKfr35tGq9Uye/Zstm3bxsmTJykv\nL6eoqAgAp3NwP7umpiYWLFhAaWkpf/vb34iKirrguG02W78veTFDf+C02RwcNbZwxiTcxJVJJSRr\n/RkVFoRPj9rQnZZm6ip/wO3u+jKXK/3RxWb3qjndE2NtK0cPdEcqBWvVjL15CDgd1O74DJupa39C\nIpWiv2MaKr0et9tNZUsNeVWHabYI9xACVQH8f/bOPD6K+v7/r9l7s8neZ8LmIhfkDiFc4RI8CCL+\nvKrWo2orxaut1VYFz6LfVv3yrWIf3q1Haau2glwWQZRDSAIJgYTc5D42u9kre2Tv+f2RZDezmw1B\nAVHm+Y9mdmZ2lmzmNZ/P5/1+vUoS8pEiSQwLMSLR3GVGxSkdhhzU74xCzMf8vHhoVZeWnff55JIW\niemZirN+kv++Cc+OJgjiW5fvCQQCvP7663jggQewceNGPPnkkxPuNzAwgJtvvhkqlQpLly7FkiVL\noFQqcf3115/V+/X09OCee+6Bw+HA3//+d4ovmFqthslkgt/vD5bE+nw+mEymqAJGd+ifGbfPj1rD\nEJqMNgTCagumSwTIV4kgYFO/U173EPRdh4IRo0wWH6rEhRG50+MxGuyoPtIZLGCIE/Iwe0EymATQ\n9/luuIK/JwLKZZchJlELg8OI8u7j6LcNUM7FY/MwKz4XM+RpETYa3QM2fHOyD4MWaje2UMDB3BwN\n0rViupz1HHNJi8SlTlZWFgoLC7Fu3To8/PDDWLFixYTd7Tt37oTD4cDmzZuDN/CDBw8CCFU1nekP\n02g04o477gCTycQ///lPaLXU5qtZs2bB5/Ph+PHjwWuoqqpCIBDArFmzJjwn3aEfHV+ARKNxCHWG\noWC/wxjT4vgoVIsg5lGn6gJ+D6yDTbCZWkGSIyNEBpMDVdJCsDgT96q4hr1orh9Ad7sp+F2IEXAw\nZ1Eq2GwGdLv3YLh33PTT4lLEpaehx9qP/7Z8jQAZWkNgMVnIV89ErioLnAlGLI0dJuw92kXZxuey\nUDxDhZxUGZhMug7nfECLBA3KysqwZcsWrFu3Dtu2bYtYG1Cr1bDb7di9ezfy8/PR2NiI559/HgCC\nU0QCgQADAwPo7u6GRqOJGPE8++yzMJvNeP/998Hj8YLd9gRBQC6XQ6VSYcWKFVi3bh1eeOEFkCSJ\nJ598EqtXrw6ODFwuF2w2G6RSKZhMJt2hPwEBkkSb2YEavRXDYbYT8hgOitRiqATUEQEZ8MNmaoV1\nsAmBQGj6hmCwoEwsBZtL9T0CAK/Xj7YmA9qaDRQ3Vy6PjTkLU8HlsWD4aj8c7aG+CtmcEoiyszFg\nN+CL1gNBgRgx4EtHUXwOYtgTTxW291mx71hoKovNZCA/Q4GiTCU4bLpa6XwyJZEYK3WcCIIgIBAI\nkJiYGMyppvnh8fTTT2PVqlXYtGkTHnnkEcprK1asQG1tLTZs2ACn04nExETcd999eOutt1BbW4tF\nixbhuuuuw969e1FWVobNmzdTqplcLhf27NmDQCCAG2+8kXJuJpMZ7AjfsGEDNmzYgHvvvRcsFgtX\nXnklnnjiieC+u3btwuOPPx4Mbc/MzKQ79EchSRK9tmFU6yywuqmLuEIuC4UqMbRCftjcfgAOazes\nhlPweamLxRyeBFJNIbh8aqVawB9Ax2kjWhr08IYtFsuVscgpmgZBHBfGI+UYGl23AgBxfh7ERYUw\nDVvwecvX8I1OZQk4MViZuSzCgG88vQY7/nukA4HRkYpczMfVpamI5UdfH6E5dxDkmbqgAGRnZyMQ\nGFH9cAvesW0EQWDOnDnB7OoLSU9PD5YtWxa8edDQXEoYnG5U6yzQO6hVXTwWA/kqEdIksWCELfy6\n7AMw62vhdYenwsVCrMxGjHBaxGJxX7cFTXW6oEnfGEIxHzNyNVCoRxaLzcdrYDxSHnw9LjMDysuW\nwu5x4LPGPXCOVi/xWFxcM+OKSQVCb3Zi6/7TQTM+cSwX1y1NQwyPFohzwVTunVMaSbz99tt48MEH\nsXbtWpSVlUEul8NkMmHPnj3485//jCeeeAJyuRzPPvssXnnlFTz++OPn9IPQ0NBEMuT2olpnQfcQ\ndRGXzSSQLRciSxYHdtg8vXvYBMtALVxOqpkek8mFSDETseLkiBJXw4ANDSf7MRS2WMwXcJCZrUZC\nYmixeKihkSIQguRkKJcuwbDPhZ3N+4ICwWaysSJj6aQCYba5sP1gW1AgYvlsrFqYSgvEBWZKIvHH\nP/4Ra9aswc9//vPgNrVajdtvvx1+vx8ffvghtm7digcffBCvvvoqLRI0NOcRp9ePk3orWs328U4V\nYBBAhjQWuUoReGFdxV63HRZDHZxD1G54gsGCUJoBoSw9orzVanaioVaHwbC0NjaHhfQZSiRPl4Ex\nToTsbW3Qf7U/+DNfo4HqiuXwBnz4vPmroGMrg8HAlemLoRDIon5Gu9ODbQfaMDw6dcblMLFqYSrd\nFPc9MCWR6OzsjGrvkJaWhrbR3FmtVguj0TjhfjQ0NN8NX4BEncGK+kEbJSgHGIkTLVCKEMel3uj9\nPheshgbYzG0Ya4obgUCcJBUixYyI0lan3Y2mUwPoDUtrYzIZSEmXY3qWEuywxWJndw8Gvvgy+B5c\nuRzqsqsQYBD4b/N+GJ2j5yIILE8tRXxc9DLlYbcPnx1og805Mq3FZjKwqjSVznn4npiSSKSkpGDL\nli0oLS2NeG3r1q3BBq+enh7I5fJze4U0NDTwBUjs69BjIGzdQR3LRZFaDBmf+oQd8HsxZGzBkKk5\n2O8wRkxcAsTKHLC51IYzt8uH1kY9Ok8PIhCW1qZNkSJjpgq8sMVi75ANpsqjsDW3YEwg2CIRNFev\nBMFmY8/pA9DZQr0si5PnIFkSPXvC4/Vjx6G2oK03g0HgqvnJUMui28XTnF+mJBIPPPAAHnroIXR3\nd+Pyyy+HVCqF0WjEvn37cPLkSfzf//0fGhsb8fLLL6OsrOx8XzMNzSVFgCRxsHuQIhASHhuFajHi\nY3kRFUt2czushnr4/WEL2TEKiJW54MZQK5Z8Pj/aWwZxuskAX1jZrDpBhMwcNeKE1NGG3+WCueo4\nrLV1FLtvlkCA+FVXg8nn4ev2IxRr77naImTKo1ee+f0B7DrcgQHTWGY2gctLEpGkjr5uQXP+mZJI\nLF++HO+88w7+8pe/4M9//jP8fj9YLBYKCwvx3nvvoaSkBPv27cOyZcuCXjw0NDTfHZIkcaTXhJ5x\ni9MFKhFyFMKI6iOnrRcWfR18HjvlHGyuEBJlHnixKuoxARJdHSY0nxoI5k6PIZEJMCNPA6mc+gQf\n8PlgPVkLc/VxBMJsVARJSZAvXABWXCyOdFejxRjqkSjQZCNPPSPq5wwESHxR2YUefWj9Y3FhAtK1\nkqjH0FwYptxMN3/+fMyfPx8ejwdWqxUymYzSMn/ZZZcFjedoaGi+OyRJokpnQZs55LeUrYhDrpKa\nrudyGGAeqB2XOT0CkxUDsXImBKJEimsrSZLQ9Q6hsa4fDht1tBEbx0NWnhoqTZgIBQKwNTXDVHk0\naNI3Bk+phGzeXPAT4gEA1X11qBsI9UhkKdIwOyF/0s/5dXUPTveEsifm5miQM52eur4YOKuO66am\nJgwPDyMQCKA7LHJwfA4ADQ3Nd6fOMISGwdCT9XSJAIUqcfBnj8sCi74Ow3Yd5TgGgwORPBNx0rSI\nclbToAMNJ/spcaIAwOOzkTFTBW2yFAQjbITS2QnjkQp4zNSFbLZIBNncORCkpgQFpV7fjGO9J4L7\npEgSUZo0e1LbliO1/ahvDxW8FGQoMCsruuEkzYVlSiJRV1eHX/3qV+jr6wtuG2ugG/tvQ0PDebtI\nGppLjWaTDTUDoUY3rZCPuQlSEAQBn9cJi/4UHNYujK9YIggG4qRpEMmzwGBSfZlsQy401uow0Bfe\nPMfE9EwFUtMVYLKoPRUunQ7GIxUYDsvzYPL5kJYUQ5iVBYIZEqFWYwcOdR0L/pwgVOOy1PmUzOlw\nqpv0qG4KLWxnJUmxIC+eNum7iJiSSDz//PNgMBj4n//5H6jV6ghnRpqLk08//RTr168P2l5kZmbi\nxRdfxOrVq/HYY49Bp9PhvffeOyfvZbFYsHfv3qgZ18CIrcabb76Jzs5OKBQK3HjjjbjnnnuCpoH7\n9+/HvffeG3Hc/v37oVarI7YDwKFDh/DSSy+hvb0dSUlJeOSRR7B48eLg68888wy2bduGvLw8vPLK\nKxCJRBOe52Kiw+JARW/oqV0dy8VCrRwg/TDr60cN+MaH6xCIFSVBpJwJFpvqduB2+dBY14+eDjPF\nLYHBYCA5TYa0LCU4XOptwGOxwHikguK7BAAMFhviogKI8/PAYFOrnLosvfiq/XAwQU4hkOGKtEVg\nTpI9carNiMMnQw+eKfEiXFaspQXiImNKInHq1Cls3LgRy5cvP9/XQ3MOKSsrw6JFiy7Ie7388svo\n7OyMKhL79+/HI488gieeeAKLFi1CfX09nnzySXi9Xtx///0ARqYzZ86cibfeeotyrEw2cdNVa2sr\n1q5di/vuuw9XXHEFtm/fjvvvvx9btmxBeno6jhw5AqPRiK1bt2LXrl14//338dBDD53bD36O6bUN\n45ue0NSLjM/BkkQFCPih7zwI9zC1D4kfq4FYmQMOL1L83C4fDn/dSll3IAgC8VoxMnPUiBFQRxs+\nhwOmo1WwNTRQ7XcYDAhnzoSkeBZYMdReBatrCEd7T6DNFHJnFfNFWJGxFOwo2RN6sxNHavvRPa5J\nL14eiyvnJoHBoAXiYmNKIjHmuknzw4LH44HHi54BcC45kwXYv/71L1xxxRW47bbbAIyEJ50+fRqf\nfvppUCRaWlqQkZExpXQ7APjggw9QUFCAtWvXAgB+/etfo6qqCh988AH+8Ic/wOv1QigUQqFQQCaT\nobOz8zt8wvOPwenG/q7BYO6DkMvCZckKsAgShu5yikBweFJIVLngCSb+t/J5/ag81EYRCIU6Dlk5\nGojC0toCHg/Mx2tgPXESAR+1pyI2LQ2yObPBDhuBOb3DqO6rQ4OhhfK7j+UKUJaxlJI9PYbV7kZ5\nnQ4t3dS1DYWYj5WlKWDRVt8XJVP6rdxyyy146623MDw8fOadac4bmZmZ+OSTT3DzzTcjNzcXZWVl\nqKmpwT/+8Q8sXrwYRUVFePjhh4P23Z9++mnUTvnxkCSJJ554AqWlpTh9+vSE+zQ2NuIXv/gFiouL\nkZOTgyuvvDLoDrxp0yb8+9//RmVlJTIzM9HT0xNx/Nq1ayMicRkMBoaGhoI/t7S0nJWD67Fjx1BS\nUkLZNmfOHBw7NjIvvmDBAlitVhQUFOD999/HHXfcMeVzX2gsLg/2dRiCndQxbCaWpyjBZTJg7DuG\nYUdocVqizIU6ZWlUgfD7Azj6TQes5pG/V4IgUDgnCXMWplIEgvT7YTlZi86//wPmqmqKQPATEjDt\nhuugvmI5RSA8fi+q+mrxr9ptqNc3UwQiRZKIa7IuR2xY9oTT5cXB473YvLuRIhAEQWBmigyrF00H\nl7b7vmiZ0kiit7cXra2tKC0tRUZGRkQ8JEEQePfdd8/6zZ966in4/f5gNgEA3HDDDaitraXsd8MN\nN1D2uZTZuHEjnn/+eSQnJ+Oxxx7Dvffei9zcXLz99ttob2/Hb3/7WxQXF+PWW2+d8jmfe+45HDhw\nAB988AFSU1MjXnc6nbj77ruxdOlSfPzxxyBJEn/729+wfv16lJaW4u6770ZHRwd6e3uxadMmSKXS\niHOMtw4HALvdjn/+859YuHAhgJEY1La2NtTV1eGaa66ByWRCbm4uHn300QmvCQB0Ol1ECp1SqYRO\nN3JDZTKZeO211+B0Oi/qnGubx4e9HXp4RnMZuCwGLk9RIobFhFlXA8dQqJJQJJ8JoTwz6rnIAInq\n8i4YDaFeidxZ05CQGKqKIkkS9tbTMJVXwGuj+jJxZTLI5s0BX0tdGwgEAmgYbEVVXy1cXmo+tjpO\nibnTCqGMpZasen1+nGgZRHWTPmjSN0ZqgghzczSQCi/MSJfm2zMlkWhvb6dETYbnCp8tJEni1Vdf\nxUcffUSZwyZJEq2trXj55Zcxd+7c4PbzlVk8ZGyGxVAfYVtwISAYLIgVMyGUnV0Gx0033RTsR1m9\nejWee+45PPPMM9BqtcjIyMA777yDlpaWKZ/vT3/6E/bs2YMPP/wQKSkpE+4zPDyMn/3sZ7j99tuD\nv4s1a9bgk08+QUdHB4qLi8Hj8cBms6c0VTQ8PIz77rsPbrc72HzZ1dUFt9sNj8eDDRs2wOPx4PXX\nX8dPf/pT7NixY8J1CZfLBQ6HOq/O4XDgdlNr/y9mgRj2+vFlhx7D3hGBYDMJLEtWQMhlw6Kvh80c\nGtnFSaZDpIjekEaSJE4c66FUMGXlapCYEhJtZ3cPjEfK4R4cpBzLjouDtGQ2YjPSI5r02s1dqOw9\nETToG0PCF2HOtEJoRfFhgkKiocOEylM6OMKa9DQyAebnxUMjp202fihMSSQ+/PDDc/aG3d3deOKJ\nJ9DS0oL4+PiI14aHh1FQUDDleenvwpAx0tfmQkEGfBgyNp+1SIz5ZAEj4slgMCg+8DweLzjddCaq\nqqpQXl6O+Pj4qDnSwMjC8a233oqtW7eioaEBHR0daBwNlPH7/VGPmwiTyYT77rsPra2t+Otf/4qE\nhAQAI/5gFRUVEAqFweq51157DUuWLMFnn32Gu+++O+JcXC434oHF4/Gct4eKc43bH8CXHXrYRp1O\nGQSwJFEBGZ+LIWMrrIP1wX0FQi0k6oKolT8kSaLhZD96OkMNddMzlUgb7TdwGwZhLC+Hs5s6Fcjk\nciGZVQRRbg6lnBUA+mwDqOg+DoODulgu4MSgOCEP6bIUSnkrSZJo67XiSF0/LGFNepI4HubnaZAc\n1qRHc/FzweNLq6urodFosHHjRjz88MOU15qbm8Hj8YI3jvONUJbxvY4kzlYgAETEghIE8a3/6AQC\nAV5//XU88MAD2LhxI5588skJ9xsYGMDNN98MlUqFpUuXYsmSJVAqlbj++uvP6v16enpwzz33wOFw\n4O9//ztldAoAYrGY8jOfz4dWq0V/WJ3+GBqNBnq9nrJNr9dHTEFdjHj9AXzdaYB53JP2wkQ51LE8\nOCxdMA/UBLfzBWrI4osn/T23NurR1hzKiNCmSJGVq4bP4YDxSPmoAV8IgskcSYsrLAAzLK7W4hrC\nka4qdFv7KNs5LA4KNdnIVmaCFVba2jdox+GT/dCFNenF8tmYPVONGclSunLpB0pUkcjJycE//vEP\n5OXlITs7+4w3orq6uim94erVq7F69eoJX2tpaUFcXBweeeQRVFZWQiKR4LrrrsOdd955XnozhLKM\nb3Wj/rGQlZWFwsJCrFu3Dg8//DBWrFiB4uLiiP127twJh8OBzZs3B6vcDh48CCBU1XSm74fRaMQd\nd9wBJpOJf/7zn9BqqU6ge/fuxaOPPoovv/wyuKZht9vR0dGBm266acJzzpo1C0ePHqVsq6iomPAz\nXCz4AySaTXbUGqxw+0K9DvOmSZEojIHT1ofBvtBn4vJlkGvnRnROj6fztBFNdaGFbXWCCHlF0+B3\nOtH76dawdQcCwhmZkM6eDVZs5JSPediKbY174PaFRgIMBgM5ykwUaLIjqpZMQy4cqe1He1iTHofN\nRFGmEvnpCrBZdNXSD5moIvHLX/4y+ET2y1/+8oIMEVtbW+F0OlFaWoo1a9aguroaL774Imw220Vf\n3/5DpqysDFu2bMG6deuwbds2cMOeLNVqNex2O3bv3o38/Hw0NjYGCwnGprYEAgEGBgbQ3d0NjUYT\nMeJ59tlnYTab8f7774PH48FgGHnqJQgCcrkcs2fPRmxsLB599FE8+uij8Pv92LhxIyQSSfChwuVy\nwWazBUuyb7vtNlx//fV49dVXsXLlSuzYsQMnTpzAM888c57/xc4ekiTRYXWiZsACu4c6RTdLLUaa\nJBYuhwGDPRUIWm5zRVAmLgCDEX3A39dtQd3xkNOqXBmLojmJCHg96Nu+kyIQguRkyObOAUc6sWme\nzW3HzuZ9IYEgCGTIUlAcn4dYLlVQ7E4PKut1aAhr0mMyCOSmyTErSwU+94JPVNCcB6L+FseXKz74\n4IOTnuRclcb+6U9/gtPphFA4Yg2cmZkJm82GN954Aw8++CA9l3keefrpp7Fq1Sps2rQJjzzyCOW1\nFStWoLa2Fhs2bIDT6URiYiLuu+8+vPXWW6itrcWiRYtw3XXXYe/evSgrK8PmzZsp1Uwulwt79uxB\nIBDAjTfeSDk3k8lEfX09RCIR3nvvPbz00ku444474PP5sGDBArz//vtB0dq1axcef/zxYB5vZmYm\nXnvtNbz00kt4++23kZqaijfeeOOsymgvBP12F6p1FpiGw1xT2UwUqsVIEQvgcVlg6D4MkhwREBZb\nAFViaYS9xngMOhtqKruCN2mxNAbF85OBgB/9Oz+HxzSyPkEQBFRXXo7YKFViADDspcaLspgslGVc\nBnUsdW3Q5fHheJMeJ1oG4fOHRkIEQSBDK0ZJtppOj/uRQZBn6oLCyML17bffPuFr+/fvx7PPPot9\n+/ad9ZvffvvtSExMnLS8dcyq4ejRo0HxCGcqYd40NBca07AHxwcs6LNRS0Y5TAZylUJkSOPAYhDw\nuu0Y6PgqmP/AZPKgSlkCNic26rnNRgfK97fBP3qjjo3jYf7S6WCzCPTv+i+c4ww4VcsuQ1xm9GlV\nj9+L7Y17gulxDAYDK9KXIkEYskLx+wOoPT2IYw16uDzUNbxEdRzm5cRDIflhFAzQhJjKvXPK3k1+\nvx8/+9nPgttMJhM2bNiAXbt2ISPj3Mzr33TTTcjLy8P69euD22pra6FUKqMKBA3NxYbd40PNgAXt\nFidlO5NBIEsWh2yFENzR7mKfdxj6roNBgWAw2FAmlU4qEDarC5WH2oMCweNzMGdRCtgcJgb2fEkR\nCHnpgkkFwhfwY3cLNV50WWppUCBIkkRTlxmVp3QYclBHQgoJH/Nz46FVxYWfluZHxJREYv369Xj+\n+efh8Xhw77334j//+Q9efPFFeDwe/O53v8Odd955Ti7m8ssvx6uvvoqcnBwUFRWhoqIC77zzDtat\nW3dOzk9Dcz5x+/yoNQyhyWhDWAQ1pksEyFeJIGCH/uT8Pg/0XQfh845UBBEEE8rEBeDwqFVe43E6\nPKg42Abv6LoGh8vC3MWp4PHZGDx4CPbW1uC+klmzIM7LjXquABnAl6cPod82ENy2KGkOUiRakCSJ\nrgEbjtT2Y9BCnU4WCjiYm6NBulZMTwFfAkxJJG677TbExsZi3bp12Lp1K9rb27F8+XKsW7cuqjvn\nt+HnP/85WCwWXn/9dfT19SE+Ph6PP/54xDw2Dc3FRIAkUT9oQ53BCq+fqg7ThHwUqkQQ86hrC36f\nB4bub+B1j9mSEFBMmwduTPSgnWGnBxUH2uAaHimbZbGZKClNQWwcF6bKo7DWnQruK8rJhrQkepUX\nSZI40FGBTkuob2KOthBZiukwWodxsKaPkhIHAHwuC7NnqpCdIgOT9lm6ZJhy+cG1114LgUCAhx9+\nGIsXL8amTZu+85uHN+kRBIG77roLd91113c+Nw3NhYAkSZT3mnDaTO0PkMdwUKQWQyWg2k6QAT9s\nplZYB5sQCIxN3xCQJ8wGP27iBy6vx4fTTQa0twwGp5gYDAZmL0iGWBoDy8lamI5VBfePTUuDfGHp\npI135T3VaB5sC27L18xEvnomBi3D2PJ1K9zjbDTYTAYKMhQozFSCQ3ssXXJEFYlojVXJycnYv38/\n1q5dC7l85KmHIAg899xz5+cKaWguUkiSRLXOQhEIIZeFQpUYWiE/zN4iAIe1CxZ9Pfw+6lqFVJ0P\ngSgR4QT8AXScNqKlQQ/vuMVigiBQNC8RMkUsbE3NGDz0TfC1GK0WqmVLJ50GqtGdQq0uFC+aKZ+O\nkoQCWO1ubDvYFhQIBkFgZooUs2eqIeBPbPtN8+Mnqkh888030V6CRqNBU1MTmpqaAJy5kYqG5sfI\nqcEh1IfFi85NkIIR5n3ksutg1teOm1oagcWJhUSZixgh1WGAJEn0dlnQdEqH4bDFYpGEj5n5dFdH\nvQAAIABJREFU8ZApYuHo6IR+31fB13hqNdRXXRFhrzGeen0LjvaE4kWTJVosTC6B0+XDZwdOwzna\nAc5hM3HtoulQSi9e3yuaC0NUkfg2Ja00NJcKzSY7jusi40XHC4TbaYJFXwuX00A5lsnkQqSYiVhJ\nCogw7yPDgB2Ntf0YClssjhFwkJmjQbxWBIIgMNzXB93uL4I9EhypFJqVKyIS48Zz2tSJQ12hbu54\noRrLUhfA4w1g24HTweolFpOBqxek0AJBA+B78G6iofmh02l1oqI3ZKSnEnBRqpUHBcLrtsGir4PT\n1ks5bsyvSyjLiOiitpicaKztx6DeTtnO4bKQPkOFpFQpGKOLxW7DIPp3fg5y1FyRHReH+FUrIzyY\nxtNj7cdXbZHxooEAsPNQO4xDI70cDILAlXOTEK+IXoJLc2kxJZFwu91488038fXXX8PpdE6YQrZ7\n9+5zfnE0NBcb/fZhHOoO2WxL+RwsTVKAxSDg97lgNTTAZm7DmLXGCATiJKkQKWaAyaIuZDvsbjTV\n6dDXbaFsZzIZSM1QYHqmAqxxi8UeixV9O3YiMOp+y+TzEX/N1WAJoltvD9gN+KL1AAKjudhivhAr\n0peACSZ2HmlH/zhTvmWztUiJv/hzwGkuHFNupvvkk09QUlKC9PT082K2R0NzsWNwuvF1JzVedFmy\nAiwGYDHUT2g9HyPUQqzIBptLfTJ3u3xoaRhAV5sRgXFNFQRBIDFFivSZKvDCFou9Q0Po374D/lEb\nHAaHg/hVV0dEi1Ku2WHE5y1fwzd6XQJODMoyLgOXxcUXFV3o0oXWVBYWJCAzKTIwiubSZkoisXv3\nbvzmN7/Bvffee76vh+Yc8umnn2L9+vWorx/JJcjMzMSLL76I1atX47HHHoNOp8N77713Tt7LYrFg\n7969lBCpcP7973/j3XffRU9PD7RaLe655x6K3fjw8DBeeOEFfPHFF/D7/bjqqqvw+OOPQzDJU/Kh\nQ4fw0ksvob29HUlJSXjkkUewePHi4OvPPPMMtm3bhry8PLzyyisQTXJDnfTzjcaL+sbHiyaPxov2\nHoVjqIuyPy9GAbEqF1w+9abr8/nR1jyItiYDfD6q0Z86QYSsXA1i46jTRn6XC+aqalhrT4EMjBxD\nMJnQrCwDVx4ZxgQAdrcDx/pOotnYHpxi4rG4WJm5DAJ2DA4c76VEic6eoUJ++vnPcKH54TGlIYHH\n44mIn6S5+CkrK8OBAwcuyHu9/PLL+Oyzz6K+vnv3bjzzzDP4xS9+gV27duGuu+7Ck08+iS+//DK4\nz1NPPYWqqiq8+eabeOONN1BZWYmnnnoq6jlbW1uxdu1aXHXVVdiyZQuWLVuG+++/P5jMd+TIERiN\nRmzduhVz587F+++//60+m93jw94OQzBelMNkYHmKEjFsJsy6ExSBYHNFUGpLoUxaRBGIQIBE52kj\nvvq8Cc2ndBSBkMoFWHBZGornJ1MEIuD1wlxVjc4PN8Ny4mRIIAgC6quuBF8T2Vfh9nlQ3l2Nf9Vt\nG+mDGBUINpONFRlLIeYJcbR+ALWnQ1NmudPlKMk+d02xND8upjSSKC0txYEDByiRojQXPzweDzze\nhckQPpNPpNlsxkMPPYTrrrsOAKDVarF582YcOXIEy5Ytg06nw44dO/Dee++hoKAAALBhwwbccccd\n+N3vfjdhkNAHH3yAgoICrF27FgDw61//GlVVVfjggw/whz/8AV6vF0KhEAqFAjKZDJ2dnWf9uYa9\nfuzt0GN4tHeAxSCwPEUBEZcNi6EeNnPIBiNWkgqpuiCiYknXa0VjnQ6OsLS2WCEPM3I1UGriqD0V\ngQBsjU0wHT0Gn4PapMdTqSBfMA+8MKcDX8CPU/omHO8/BY+PWjarFcVjrrYIEr4IJ1oMqKwPZU+k\nayVYVJhAl7HTRGVKInHNNddg/fr1MJvNKCoqmvDGs2rVqnN+cTRUMjMzsWHDBvznP//BqVOnoNVq\n8cILL6C+vh5vvvkmbDYblixZgj/+8Y/gcDgR003RIEkS69atw4EDB/D+++9PaLXd2NiI//3f/8Xx\n48fhcrmQkJCAtWvX4tprr8WmTZvw73//O3iNEzlK3nzzzcH/9/l82LNnD06fPo1f/epXAEYSCxkM\nBoqKioL7FRUVgclkoqqqCmVlZRHXdOzYMaxYsYKybc6cOdi5cycAYMGCBfj4449RUFCA9PR0/PWv\nf5303yEcjz+ALzvD4kWTRuJFbaZWWA2hf9cYoTZCIIwGOxpO9sNiojbP8fhsZGSroU2SgGBQeyqc\nHZ0wllfAYzZTjmGLRJDNmwNBSgo1T5oMoMXYjmO9J+HwUN9HIZBhzrQCxI+a9TV1mnCwJlRxlaiO\nw/LZWlogaCZlSiIxliexZcsWbNmyJeJ1giB+kCJRbxjCCb01OM98IWExCOQrRZipODt3240bN+L5\n559HcnIyHnvsMdx7773Izc3F22+/jfb2dvz2t79FcXExbr311imf87nnnsOBAwfwwQcfIHWCzAGn\n04m7774bS5cuxccffwySJPG3v/0N69evR2lpKe6++250dHSgt7cXmzZtCibLTURtbS1+8pOfwO/3\n44YbbsCSJUsAjESkSqVSsMfV+bNYLEil0qjxpTqdLmKEoVQqodONPCkzmUy89tprcDqdiIk5u5p/\nX4DEV50GmIfHxYtq5dCMxouadNR4UXl8cVAgbFYXGmv7MdBPbZ5js5mYnqVESro8wvtouF8H45Fy\nuHQ6ynYmnw9pyWwIZ2SBYFBHKN3WPlT0HId5mJoKJ+TFoSQhHymSxKAAtPdZ8eXRkDusRibAinnJ\ntAcTzRmZkkiMnzf+MVFvHPpeBAIYuQnVG4fOWiRuuukmXHbZZQBGomCfe+45PPPMM9BqtcjIyMA7\n77wTnJOfCn/605+wZ88efPjhh0hJSZlwn+HhYfzsZz/D7bffDj5/JDNgzZo1+OSTT9DR0YHi4mLw\neDyw2WwoFJMvfk6bNg3/+c9/UF9fj+effx5yuRy/+c1vMDw8HJGIBwAcDgdut3uCM42EGXE4nDPu\nf7YCESBJHOwahN4ROs+8BCkSRTEYtumixosOOz1oPjWAnk5qWhuDwUBymgxpWUpwwtLaPGYzjOUV\ncLR3ULYz2GyICwsgzs+LaJDT2wdR0VNDcW8FAB6bh1nxuZghT6NUIPYZ7Nhd3onA6DXJRHysLE0B\nm0X7MNGcmSmJREJCwpl3+gEyUyb8XkcSM2Vnn5GRmBjy+OHz+WAwGJSpHR6PF4wUPRNVVVUoLy9H\nfHw8lEpl1P1kMhluvfVWbN26FQ0NDejo6EBj44j3j9/vj3rcREgkEkgkEsyYMQNGoxF/+ctf8NBD\nD0W9bo/HE/Umz+Vy4fV6I/YfE7Jvgy8QQHmvCT22UMdzkVqMNGksXM5BGHqOIBQvKoRCuwAgGWis\n7acY8AEjI+yERDEystWIEVDFLODxYPDwEQzVN2J8TwXBYECUkw1xURFYMdTPYXUNobLnBNrN1Eoq\nFpOFPNUM5KlngMOkCkr/oAM7vmkPpsgJBRxcszAVPA7dR0szNSY1+FuzZg2mTZsW1exvjB+qwd9M\nhfCsn+S/b8KzowmC+NZzygKBAK+//joeeOABbNy4MerveWBgADfffDNUKhWWLl2KJUuWQKlUUspX\nz0RlZSXi4uIwY8aM4LbMzEy4XC5YrVao1WqYTCb4/X4wR72HfD4fTCZTVAHTaDTQ6/WUbXq9fsJF\n7jMRIEm0mR2o0VuDi9QAkK0YCQnyuCwwdH1DiRdVJi4Eg8FGdUUn+nuoUz5KtRBZuWoIxZGCFfB6\n0bdjV8TUUlx6GqRzSsAOC9hyeodR3VeLBkMrZYRCEARmKNJRFJ+DGHaYoNjdKK/TUcpcY3hsrF40\nnTbrozkrJjX4++lPfxr8/8mgF75+mGRlZaGwsBDr1q3Dww8/jBUrVqC4ODKDYOfOnXA4HNi8eXPw\nBn7w4EEAoaqmM30H3n77bTAYDLz55pvBbSdPnoRMJoNEIsGsWbPg8/lw/Pjx4DVUVVUhEAhg1qxZ\nE55z1qxZOHr0KGVbRUXFhJ8hGiRJosc2jOM6C6xuaiNcmlSAQpUYXo8d+s5DCARGu5yZXCgTF4LJ\n4qG2upciECJJDGbkaSBXTmxrQfr90O3eQxGImGnTIJs3B9ywqTqP34uTugacHGiAz0+9tlRpImYn\n5EPECxMUlxfHGgZQF9akx+Uwcc3CVDp/muasmZLB344dO856Xpfmh0NZWRm2bNmCdevWYdu2bRFr\nA2q1Gna7Hbt370Z+fj4aGxuDueRjU0QCgQADAwPo7u6GRqOJGPHceeed+PnPf453330Xy5cvR2Vl\nJd555x089thjIAgCKpUKK1aswLp16/DCCy+AJEk8+eSTWL16dXBk4HK5YLPZIJVKwWQycdttt+H6\n66/Hq6++ipUrV2LHjh04ceIEnnnmmSl9br3DjeMDFsraAwDwWAzkq0RIl8Qi4HNB33kQfv+otxGD\nDWXSQrC5sWis7UdXmzF4XEq6AjPzNZPmOOj3fQVnV2i6SD5/HsQF+ZT9/AE/GgytqO6vg8tLzcfW\nxKkwR1sIpYDaROf1+VHTbEB1kx5eX4Dy2vQEEeblxkMcRwsEzdkzpdKGsrIy2pvpR87TTz8NvV4/\nYZjUihUrcOedd2LDhg1YuXIlXnnlFdx3331ISkpCbW0tAOC6666D3+9HWVnZhCW3paWlePXVV/HZ\nZ59h1apVeOedd7B+/XrccsstwX02bNiAoqIi3Hvvvbj//vsxd+5cyg1/165dKC0tDVY7ZWZm4rXX\nXsPu3btx7bXXYt++fXjjjTcmLOEdj9XtxdedBuxuG6AIBItBIF8lwrUZ8ciQxoEMeDEQFi+q0M4H\nhyfG6SY9WhtDU13TkiRnFIjBg9/A1jI+XrSIIhAkSeK0qROfnNqBw13HKAIhjRHjqvQluDpzGUUg\n/AESdacH8eHnjag4paMIhEYmwPVL07FifgotEDTfGoI8UxcUgJKSErzyyiuYN2/ehbims6anpwfL\nli2bsD6fhmYMp9ePk3oLWs0OjP/WMwggXRqLPKUIvNGKn0DAB33nQbiHx0YKBBTaeYiJi0d3uwkn\njoXKSVUaIWbNTwaDEX3KzXT0GExHjwV/Fs6cCcXihUFR6R3SoaLnOAYdJspxAk4MZifkI02WDEZY\nk97pXivK6/phCWvSkwp5mJerQbJGSE8F00zKVO6dUypxuP322/HKK69AIBAgKysrouyQhuZixusP\nBAOC/GGVbMniGBQoRYjjhhZzyYAfg93l4wQCkMfPRkxcPHS9VpysCuVCS+WxKJqXNKlAWE7WUgQi\nNm06FItG4kWNTjMqeo6jx0rtBeGwOCjUZCNbmQkWg1qq2mew43BtP3RGajd2LJ+Nkmw1spKkk14P\nDc3ZMCWR2LVrF7q7u/GTn/wEAIKLl+Opq6s7t1dGQ/Md8QdINJtsqDUMwR02T6+O5aJILYaMT52G\nCfi9MPZXY9gRWliWqAogECdiUG9HdXlXcLFeKOZjdunkDWm25paweNFpUC27DJ6AD4c7jqHF1AGE\n9VTkqrJQoM4Gl0V9GLM7Pdhf3YP2sCY9LpuJWVkq5KbJwWbRzXE055YpicTKlSvP93XQ0JwzSJJE\nh9WJmgEL7B5qH4eEx0ahWoz4WF5EBrXd3A6roR5+f2j6RiSfCaEsDRaTE8cOdyAQGBEbQSwXcxam\ngM2O3pDm6OyC/stQAQhPpYL6qivhQwC7mvfB4AiNVEAQyJCloDghD7GcSNdbp8uLT79uDabHAQCT\nQSAvTYFZWUrwuHTfA835YUrfrAceeOB8XwcNzTmh3z6MKp2FYqcBAAIOEwUqMVJEMWHiQMJp64VF\nXwefh5oKFyeZDpFiBuw2NyoPtcM32j/B47MxZ1EquLzo/QbD/Tro/rs7Il6UZDLwRcvXFIFIFCeg\nZFoBpHzxhOdyeXzYdrAtKBAEQSAzUYySbA2EAnrql+b8MiWR6O7uRlVVFQYHR+yFVSoVZs2ahfj4\n+PN6cTQ0U8U47MFxnQX9dmrJKIfJQK5SiExpHJhh8/QuhwHmgVp4XNTFYiYrBmLlTAhESXANe1G+\nvw2e0R4KNoeJOQtTIzqox+MeNEbGi169EgSXgy9Pf4PeodBU1oKk2chWZkQ9l9cXwM5D7Rgczbxm\nEARWzE+m0+NoLhiTikRXVxeefvpplJeXR1hBMxgMLFq0CE8++eS3tu146qmn4Pf7gzX3wJlDZGho\nxmPz+FAzYEGHheqAymQQmCEb6ZbmhK0ZeFwWWPR1GLZTO54ZDA5E8kzESdNAMJjwuH2oONAO1/DI\nEzyLxURJaQriRNHt171WK/q270DAMzJlxeTzoVl1NZiCGBzsrKRYahQn5E8qEP4Aif8e6aDEi15G\nx4vSXGCiikRfXx9+8pOfgCRJrFmzBvPnz4dMNlKfrdfrceTIEXz00Ue45ZZb8Omnn0Iul0/5TUmS\nxKuvvoqPPvqIkmQ2FiJz33334YorrsD27dtx//33Y8uWLUhPT/8OH5Pmx4bL50etYQjNRhvCrbfS\npALkK0WIYVO/3j6vExb9KTisXaD4JREMxEnTIJJngcHkjO7rR8XBdthtY010BGbNS4JEFj0lz+dw\noG/buHhRNhvxV68ERyxCZU8NGg2hHokcVRYKNdlRz0WSJL482oVOXWiRemF+ArLoeFGaC0xUkXjt\ntdfA5XLx0UcfRXjhTJ8+HfPmzcNPf/pT3HLLLXjzzTexbt26Kb1hd3c3nnjiCbS0tERMV50pRIaG\nxusPoNFow6nBIXj9VHWYJuSjUCWGOGytwO/zYGiwETZzK0hyfJUTgVhREkTKmWCxQ44CAX8Axw53\nwGoeGZ0QBIGCkkQo1HFRr8vvdqNv+054bSOZ0SPxoivAVchxUteAmv5TwX3TZSmYpy2atPHuYE0v\nmrtCvkvFM1TIz6DjRWkuPFHr5Q4fPow1a9ZMapamUqlw1113Yf/+/VN+w+rqamg0Gmzfvj2ieePY\nsWMoKSmhbJszZw6OHTsGGpoOiwOftfShZsBKEQhFDAdXpqqwNElBEQiSJGEztaKv9XMMmZopAsGP\n1UCTuhyyhGKKQHg9PlRXdGFQH1rEzilMQLx24kVlYGQE0b/zc3hMI2sbBEFAfeXl4MfHo2nwNMq7\nq4P7JooTsDh57qRNbkcbBnCyNRQvmpMqwxw6XpTmeyLqSMJoNE4YQBNORkZG1FCYiVi9ejVWr149\n4WtnCpGhuXQ5bXbgcI+Rsk3IZaFILca0OP6EN92hwUZYDKco27h8KcTKXPAE1Kdyvz+AztNGtDTo\n4fWEzPQyc9RImk71SRoj4PHAfLwG1hMnEfCFjlFethSC5GR0mLuxv6MiuF0dp8Ty6QspWQ/hnGw1\noPLU+HhRMRYVTqM7p2m+N6KKhNfrnZIvP4/Hg8/nO+N+U2GqITI0lxbdQ04c6Q0JBJ/NRL5ShOkS\nARhRbp4202mKQLA4sZAoc8CPo+Y5kwESvd0WNNXpMOyk5lmkZiiQlhVpU076/bCeqof5WBX8Lmo1\nlbx0AeIyM9BnG8DetkPBRjlZjARXpS2O6J4eT3OXGQeOj4sXVcVh+exEunua5nvlourAOR8hMjQ/\nbHR2Fw50DQabkiU8Ni5PVYE7SZezw9pNiRflCVRQaueDGHeDJkkShgE7Gmv7MWQZphwfI+AgM0eD\neK0ooqfC3noapvKK4NrDGFyZDLL58xCjnQaDw4jdLfuDjXdCXhxWZCwFhxW9bLazfwh7K0OVT2qZ\nACvm0/GiNN8/k4pEU1PTGUcJra2tk75+NpzLEBmaHz6mYQ++7jIEq5diOSwsS1ZOKhDDdh0Geysx\nVr3E5Uuh0M6jCITF5ERjbT9l3QEAOFwW0meqkJQiBSPsPZzdPTAeKYd7cJCynR0XB2nJbMRmpIMg\nCFhcQ/i8+St4/SMPOzGcGKzMuCwiFGg8fYN2fH6kIxQvKuTh6gV0vCjNxcGkIvHUU0+d8QQkSZ6z\n+dJzESJD8+NgyO3Flx364AI1n83A8hQl+JPYYLidgzB0j4sX5Qih0JaCwRj5mjvsbjTV6dDXbaEc\nx2QykJqhwPRMBVhh53cbBmEsL4ezu4d6DJcLyawiiHJzQIx6mdk9Duxs+hIu38j0KIfFQVnGUsRx\nJw4gAoBByzB2HqLGi65aNJ222aC5aIj6Tfzggw8u5HUAwHcOkaH5ceDw+rC3XQ/XqCkfh8nA8mQl\n4ibJZfa4rNB3HabGiyYtBJPFgdvlQ0vDADpPGyPiPxNTpEifqQIvLNLTOzQEU+VR2JpbKNsJJhPi\n/DyICwvAHBfO5PK6sKt5HxyekbJZFpOFFelLolptACMRo9sOtsE9avfB57JwzcLpiKXjRWkuIqL+\n1YWXol4IxkJkXnrpJbz99ttITU2dUogMzY8Ht8+PL9v1cIzeOJkMApclKyDmRZ/P93kc0HceRCAw\nsvA8Pl70dJMeLfV6+HxUoz/NNBEyczSIDQvjIf1+GCsqYT1ZBzIw/hgCwqxMSEtmgxVLbaizux3Y\nc/ogLMMjjW8MgoErpi+CKjZ6X4NpyIUdh9rgdI1MS3HYTFyzcDodDkRz0fG9jmk//PDDiG1LlizB\nkiVLLvzF0HzveP0BfNlhCGZNMwhgcaIcipjoN06/z4WBzgMTxos21enQ0jBA2V8qj8WMPPWEndMk\nSUL/1dcRowdBcjJkc+eAI5VQtrt8btT0n0Kdvim4SA2CwNLU+Zgm0kx4vXanB5X1OjR0mIOjGhaT\ngasXpEAhoQs0aC4+6IlPmosCf4DE/q5BGIdDZagLpsmQEBf9xhnwe6DvnDhetK3ZQBGIOCEPWbka\nKDVxE66hkSQJ4zeHKQLBU6kgmz8XfA31hu8L+FE30IQa3Sl4fNSy2dLE2ZguTYo4v8vjw/EmPU60\nDAbXH4ARw74r5yYhXhF93YKG5vuEFgma750ASeJQzyDFwXVOggTJ4ug+SYGAD/qub+BxW0e3EJBP\nmwOeQIHuDhPqT/QF91WqhSheMHm8qLmqGpaTtcGfhTNnQLF4EUVQAmQALcZ2HOs9GVx7GEMhkGGu\ntgiaOGpfhd8fQO3pQRxr0MPloVYKJqrjMD83HnIxPYKguXihRYLme4UkSVT2mdBlDfUqFKhEyJBG\n90kiyQAGe6jxorL44pF40T4rTh4bHy8qwKwzxIta607BVBmqqotNTYViUSh/miRJdFl7UdlTA/Ow\nlXKskBeHkoQCpEi0ET0VTV1mVJ7SUYKCAEAh4WN+bjy0quifkYbmYoEWCZrvlZoBK1pMISvsLHkc\nchTCqPuTJAlj7zGKzbdElY9YcRKMBjuqj4TFiy5IBnOSSE9bSysMBw4Ff46ZNg2qy5eBGLXOGLAb\nUNFTA52N2r/DZ/MwKz4XWfI0is0GSZLoGrDhSG1/MANiDKGAg7k5GqRrxbTNBs0Phqgicffdd0/5\nJARB4N133z0nF0Rz6VBvGEKdIWSFnSoWoFgd/QZKkiTMuhNwDIU6k0XyLAhl6bCanTj6TSheNGYs\nXnSSsllnVxf0e7/EWF8FT6mEesWVIJhMWFxDONpzgpL/AIyUtuarZyJPlQU2k1qqqjc5cbi2Hz16\najc2n8vC7JkqZKfI6A5qmh8ck3o30dCcDwIkiUajDVW6UFPbtDg+5k2TRheIgB8W/SnYzKEO/1hJ\nKkSKbNhtblQcDMWLcnlszF2YcsZ40f7Px8WLSiTQXF0GksnAN11HUa9vieipmKFIR1F8TkT3tNvr\nx9dVPWjpNlO2s5kMFGQoUJipBGeSJkAamouZqCIxUXkqDc13gSRJ9NiGcVxnCZa5AoBSwMXCRPmE\nZn0kGYDD2gWLvh5+X2ixOEaohVRdANewFxUHwuJFF6UgJjZ62azbOEG86KqrQXA52Nd2GG2mTsr+\nqdIkzE7Ig4gXOQ02Fi/aNxiy+GAQBGamSDF7phoCujGO5gfOWa1JmM1meL3e4BMWSZJwOp2oqqrC\njTfeeF4ukObHgd7hRrXODEOY06qUz8bSJAVYYQvLJEnCZdfBrK+F1z1EeY0vUEMeXwyvJ4CKg+1B\n91Ymk4GS0hQIRdGrhbxDQ+jfvjMsXnQlmIIYHOo8ShGIeKEKJdMKoRRMbBXuD5D4oryDIhDTE0SY\nm6uBJC56xCkNzQ+JKYlEU1MTHnnkkahmfgRB0CJBMyFWtxfHdRZ0D1EXcdlMAtlyIWbI48AKy1dw\nO02w6Gvhchoo25lMLkSKmYiVpMDvI1F5qB32oXHxovOTJ48XdTrRt20HfM6REUkoXlSMo70n0GAI\n9UhkqzIwX1s86frIvqNdaO8PCdiCvHgUZkZai9PQ/JCZkki8+OKLsFgs+P3vf4+vvvoKHA4HS5cu\nxYEDB3DgwIHvxeeJ5uLG6fXhpJ5auQSMdFFnyOKQqxCCF+Zy6nXbYNHXwWnrpWwnGCwIZRkQyjLA\nYLBG4kWPdMJiosaLKs8QL9q/fSe8QyM39fB40eN9dcF902TJZxSIQzV9aBoXLzorS0ULBM2PkimJ\nRE1NDR5//HHccMMN4PP52L59O2699VbceuuteOihh/Dhhx/STq00AACPP4BThiE0GG3wB6gZ1Mni\nGBSoxBFGfX6fC1ZDA2zmNoxVGo1AIE6SCpFiBpiskekbMkDieGUXBgdCFUTZhfGTxosGvF707/wc\nbuNIX8X4eNHmwTZKvKhWFI8lyfMmLVE91jCAE62hUU52qgxzc+h4UZofJ1MSCY/Hg+TkZABAcnIy\nGhsbg69dd911ePrpp8/LxdH8cPAHSDSbbDipH4JnnO0EAGhieShUiyHjU036An4vhowtI/nTAWo3\ncoxQC7EiG+xxNtskSaL2eC/6e0INbZk5aiRPl0e9LtLvh273HrjGReCG4kV7sL+jPLhdHafA5WeI\nF61tHUTFuHjR6dPEWEzHi9L8iJmSSMTHx6OnpwfFxcVITk6G3W5Hb28vEhISwOVyYbUuL/7dAAAg\nAElEQVRaz3wSmh8t/XYXjvQa4fBQnVYlfDZmqcXQxEYuJDus3TDrauD3U6NpeTEKiFW54PKllO0+\nnx+NtTp0tYW6rFPSJ44XHcPvcsHw9QE4u0K9DvIF88fFix4MFmFIYyS4Mm0JWMzofxLNXWYcqAlN\nhWlVcbiihI4XpflxMyWRWL58OV5++WUIBAJcfvnlSE1NxSuvvII1a9bgvffeg1arPd/XSXORMuBw\n4atOA2VqScBholAlRrIoZsInbIe1m5IeBwBsrggSZS54sSqqX1KARHe7Cc31A3C7Qr0705IkmJmv\nmfD8Aa8X1pO1MFcfR2Bcv49kVhHE+XkYdJrC4kVjUZaxFNzJ4kV1I/GiY6KiksagjI4XpbkEmJJI\nPPDAA+js7MTHH3+Myy+/HI8//jgeeOABbN++HUwmExs3bjzf10lzEWIa9lAEgsNkIE8pRIY0Dswo\nT9fDdh2MvUcxJhBMVgzEymwIRFoQBNXeQtdrRWOdDg4bdbShihchr1gbIRBkIABbYxNMR4/B56Au\nmItysiEtmQ2Lawi7KPGifJRlLJs0XrR/0IHPD4fiRaVCHlaVptLxojSXBFMSCT6fj9deew0ez0g9\n+sKFC7F9+3acOnUK2dnZSExMPK8XSXPxER4vymMxcNV09aTpcW6nEYbucpAYeYJnc4RQJS8Gk0Vt\nfDMa7Gg42R+sXhqDx2cjI1sNbZIEBINqpufs6ISxvAIec1jXs0gE2bw5EKSkwOF1YlfzPri8I2Wz\nHBYHZemXQThJvKjROowd37RR4kWvoeNFaS4hzuqbzuGEhuOJiYm0OFyiOL0+7O2gxosum1K86Dcg\nyZEFahY7BsqkUopA2KwuNNb2Y6Cf2jzHZjMxPUuJlDR5hFnfcL8OxiPllIVpYKRJTloyG8IZWSAY\nDLh8buxq/gp298gIg8UYjReNmTxe9LMDbXB7QvGiqxam0vGiNJcUUxKJvr4+/OEPf0BNTQ1sNtuE\n+9TV1U24nebHhdvnx952fXCRmskgsDRJASn/DPGiXZHxoix2DABg2OlB86kB9HSaKX5JDAYDyWky\npGUpwQl7cveYzTCWV8DR3kHZzmCzIS4sgDg/Dwz2yM3c6/fi8+avYBm1+WYQDFyetnDSeFGny4vP\nDpyOiBelO6lpLjWmJBLr1q1DTU0Nrr/+eojF0Z+8aH7ceP0B7OsMxYsSo/GiSsGZ4kUPwu8bFy+a\nWAo2Nw5ejw+tjQZ0tA7CP65sliAIJCSKkZGtRoyAKj4+uwOmY8cwVN+I8QvfBIMBUU42xEVFYMWE\n1hf8AT++aD0Ag8M4dnIsTZ0PrSg+6jW7PD5sO9gWzIFgMRlYSceL0lyiTLmZ7vnnn0dZWdn5vh6a\ni5SxeNFB57eJFx3xNiIIxki8KF+Cnk4zTtX0whtWNqtUC5GVq4YwLK2NJElYjtfAdPRY0JhvjLj0\nNEjnlIAtpBrwOb3DONhRid6h0FRUaWLxhPGiY1jtbuyp7ApmQYzFiybQ8aI0lyhTEgm5XA4ejx5m\nX6oESBLf9Bgp8aIl8RKknDFe9HBYvOhc8AQK9HSaUVNJzWkQSWIwI08DuXLim7Gl+jiMFZWUbTHT\npkE2bw64Cuq0kcfvxUldA04ONMDnDzXpFSfkY6YyY8LzO11eHGsYQF2bEYFx5byXzdYiJV4U9XPS\n0PzYmZJIrFmzBps2bUJmZiYSEhLO9zXRXESMxIua0WkNVRrlKUXIlJ0pXrQC7uHB4DZZ/CzExMVj\noG8IJ452B7fHCDjIytVAM00UtWvZeuoURSC4cjlk8+YiRjuNsp8/4EeDoRXV/XXBCqbgNatnoFCT\nHXFur8+PmmYDqpv08PqoneIL8xOQlSSNOIaG5lJiSiKxZMkSvPvuu1i+fDkkEgn4fOpUAEEQ2Lt3\n7zm5oNbWVqxcuTJi++bNm2l/qO+BkXjRkBV2liwWecozxIv2HcOwvT+4TaLKQ6w4eSRetLwzuDgd\nJ+Jj/pLUSdPjbC2tMOwPxYvyExIQf3UZCGaoR4EkSbSZu3C0twZDLjvleGmMGCUJBUgUUx9u/AES\nDe1GVNYPBBenx4iXCzA/Lx7qSRxlaWguFaYkEr///e/R29uLxYsXQy6P7pNzLmhuboZEIsH27dsp\n2+kF8wtP/SA1XjRFHINijeTM8aLW8HjRDFjNwzj6TUdwgTpGwJlavOiX+zA+XlQzGi86Ru+QDhU9\nxzHoMFGOjeUKUByfhzRZMhhhTXqne60or+uHJaxJTyrkYV6uBskaIe3FREMzypREoqqqCs8++yz+\n3//7f+f7etDc3Iy0tDQoFNHLE2nOP61mO6r6qfGi86fJJr15WgcbJowXddjcqDjYRo0XXZQK3iT9\nBi6dDrr/fgFy1DqDI5FAs3IFGKO9OkanGRU9x9Fj7accx2FxUKTJwUxlBlgMakd0n8GOw7X90Bmp\n3dixfDZKstXISpLSPkw0NGFMSSSkUimk0gszN9vS0oLU1NQL8l40E9M95ER5b+jJfLJ40TFsptOw\nGuqDP8cIp0GqLoDb5UP5wbB40YVnihc1oW/H5wj4RhvvYmMRv2olmHw+bG47jvWeRIupAxjXU8Fk\nMJGjykSBOjvCg8loHUZ5bT8lIAgAuGwmZmWpkPv/2zvzuKiq/o9/ZpgZdhj2TUVEhpFlQFBUFgEx\n9yXNfMrUMksz9XH5aZmllZnrk9mT8ZBbZeCSPmpq5pMpgoqiKKmgCIgi27AMDDsMM3N+fyAXLsMA\nGijieb9evl7Oueec+73nHu733rN8P30twefRGEwUSku0y0nMnj0b3377LVxcXGBvr319eUeQlpaG\n2tpaTJ06FTk5OXBxccHSpUshkUg69byUeqQVNYh9WMQ8f830+AhpQV60AaJWobzkHkrybzFpeoY2\nsLQfiDqFGpdjM1Bd2SgvOjDASWN5a1Pq5UVPsORF7SeMg46hIa7n3sL1vCQmMB8AgMOBq0Uf+Dp4\nwkjAnkNQqwnO/5WDpAwZa5OeDpcDSV8r+IqtaXgNCqUN2vUXcv78edy/fx9hYWGwsLCAoaHmhN7/\n/ve/v21MTU0NsrKyYG5ujg8++AACgQCRkZGYPn06jhw5Amdn5799Dop2ZNW1OPewEA0rQI11eQjr\nbQ3dFiKdEkJQVZYNeUEysw8CAHT1zWHVcwhUarDkRTkcDnyHOMLcsg150eO/NZMXHQOBUIiEnBu4\nnsve1d9L6AC/Ht4w19ecryKE4GzCQ6RkNsZy4nA4cO0lhJ+7HUwMte8Qp1AojbTLSZiZmWHEiBGd\nbQv09PRw9epVCAQCJk7Uhg0bkJycjL1792LVqlWdbsOLSn3AvkImYJ8+vz4ekz5fM9JpTWUBSvJv\nQVHDDqYn0DWFVc9AgHCREPdAU17UTvuqKEZe9JE2CYerA7sxo6FrZYVb+SksB2FlaIHBPX1gZ9yy\nlgQhBBdu5LIcRC9bY/h72sOyla8YCoWiSbucxPr16zvbDgYjI/ZmKi6Xi759+yIvL09LCcrfpbJO\niT/vF6C2ScC+4S0E7FPUyCEvSEJ1BTuYHpcrgKmlK4zN+wLg4np8M3lRb3s49GpdXlR68pSmvKhD\nvbzopYfXmLw9TO0wsm8wdLjaw3RfSynAjbRGeVE3JwuE+lL1OArlSdDqJPLz82FhYQEej4f8/Pw2\nK7KxsfnbxiQlJWHmzJnYs2cPPDw8AAAqlQopKSkYNWrU366foklNQ8C+usaAfcN6W0Go1zgco6yr\ngrwg+dHS1ibxkjhcGJu7wNTSFVwdAQghSLqeg7zsxlVRIndb9O7burxo/h9/orrJS4BVaAgMnXoj\nU86WF7UxssII56GtOoike0W4nNRYl7ODKUJ8qIOgUJ4UrU4iJCQEBw4cgEQiQXBwcJt/ZHfu3Pnb\nxojFYjg4OGD16tX49NNPYWBggB07dqCkpAQzZ8782/VT2NSp1Dj7oBBlj1YecR8F7LMyqF95pFIq\nUFaUgvKSdBDSdDcyB0amjjC1dmMiuQLA3SQpMpvKi/a1hEs/7fKihBAURJ9DZWYmk2bpPwQmYlfk\nlRfgz3sXmsiLCjHKpXV50bSsEsQkNsqL9rA2xohBjnRZK4XyN9D6F7du3TpGlnTdunVP5U2Mx+Nh\n586d2LRpE9577z1UV1fDx8cHkZGRsLCw6PTzv0io1ATnHhZCVq0ZsI+oVSgrTkdZUQrUavZuZH0j\nOwitPSDQY8czykgtRHpKAfPboZcZ3LztW914V3QhDuWpaUyamU9/CL29UFRVjFNp56BS13/dGOsa\nYYzLsDblRU9TeVEKpcPR6iSabpwbPHgwrKyswOdrbn6qra3tkK+IBmxsbPDVV191WH0UTdSE4EJ2\nEaQVjTuOBzmYobfQEDWVhSjKuQqVkq0Kp6tvDqG1J/QMNTc5Zj0oxu0bucxvazsTeA3UlBdlzq9Q\nQBZ/FaW3GpfNmri5wXyQH0qbyYvq8/Uw1jUMBgLtE85SWSVOxT1gAvOZm+hhXGAfCFqYdKdQKI9H\nu16zwsLCtDqCmzdv4s033+xQoyidByEE8bnFeFhazaR525hCZG6M2ioZCh5eZDkInsAIVj0Gw6Z3\naIsOQppbipsJ2cxvc0tD+A5ueYiHqFSQ37iJzMi9LAdh1NcZVkMDUVVXjd+ay4uK2pYXPX4hA3WP\nwn0YGwgwIagP9On+BwqlQ9D6l7Rx40bI5fUTkIQQhIeHw8zMTCPfnTt3YGysPSIopWuRmF+K9OLG\nsBRiS2N4WJnUy4tmNcqL6ujowtTKHUZmvcHhtPwuISuswPVLjUM8JkJ9DAzorSExSghBRVo6iuOv\noK6ZsqFBr16wCRuGWnUdfks9y8iL6nB1MMolBBYGmn2ugdKKWhxrJi86YWgfGBnQPRAUSkeh1Um4\nuLggIiICQP2SxJSUFJbGNVC/PNXExAQrV67sXCspHUJyYRmSmwTs6yM0xABbIVR1VSh4eAFqVf38\nBFdHFza9g8HX1b6vobSkClcvPmB2PxsY6bYYsK8qKxuyS5dRW1TESucbG8PcbyCMRC5QqpU4lXaO\nkRflcDh4yTkItm3Iix4/n4HKJvKi4wP7UHlRCqWD0eokJk+ejMmTJwMAhg0bhvDwcIjF4qdmGKVj\nSS+uwHUpO2DfkB7mUKtqkf/wPFTK+uEnDpf3SF5Uu4OoKK9F/Pn77IB9QU7Q1Wucs6otLITsUjyq\nsrNZZXV0dWHm6wNTTw9wdHQYedGCikdOhMNBqJO/RmjvpjTIi8ofzanocDkYG+AEa3MDrWUoFMqT\n0a6B27NnzzL/v3//PsrKymBubs6sfqJ0bR6WVuFSCwH7oFai4OEFKBWN8qLWPf2hq699iKe6SoH4\nWHbAvsFD+zAB++rKylAcfwXlaemschwdHQi9JBD294aObn1eNVEj+n4cS140oNcA9LXorfX8dUo1\nTl58wMiLcjgcjBzcm8qLUiidRLtn9yIjIxEREQGZrHEdvL29PZYuXdqiSBClayCtqMH5rMahHjN9\nPkIdraADNfKzLkJR0/B1wYGlwyDoGWrf16CoVSL+/H1UVzUG7PMLdIKxqR6UVdWQX7+O0qRkJrx3\nQ70m/VxhPnAgeEaNcZsIIbiYmYCM4kbtCV8HCdy1yIsC9ct2/7j8ALlFjbGihvn2RB8HKi9KoXQW\n7XISe/bswbp16zBixAiMHDkS5ubmkMlkOHXqFJYtWwYul4vRo0d3tq2Ux4AQggel9SG/WQH7HK3B\n5wKFWZdRW9VMXtRE+xCPsk7FCtjH5XLg698bZhaGKL+bisLY81DXsfdUGPbuDYvBgyAw1/wyuZpz\nA3cKG/dIuNuI4GPnofX8FVUKxP6Vwwr3HSCxRz8nKi9KoXQm7XYSM2bMwMcff8xKHzduHNasWYPw\n8HDqJLoQeRU1uC6Vo7jJRjl9vg6G97aGHo+rKS9q7QkjYW+t9alVaiRcytQM2GdrjIp7Gcg/E42m\n4Tr0bG1hMWQw9O1sW6zvpvQO/spLZn73tXCCf88BLe6rqFEokXi3ADfSiqBUNX6h+Ipt0N9V+1cP\nhULpGNrlJAoLCxEcHNzisbCwMBw+fLhDjaI8GcXVClyXypFXUcNK1+NxMby3FQz5OijJv4nK0sYw\nGCYWrjCxdNVaJ1ETJF5hB+zz6O8A+55CVGVlI//0n2hwEHxTU1gMGQxDp95aN9KlFmXgctZ15ncv\noQNCeg/WyK9UqZF0rwgJdwpQo1Cyjnk6W2KwR8sOiEKhdCztchIDBgzAqVOnEBgYqHEsLi4O3t7e\nHW4Ypf1UKJT4K1+O+3L2LmkdLgdiC2O4W5lAV4eL0sI7KC9uHOIxEjpBaK19iIcQgluJOcjLLmXS\nXD1s4ehsgZr8fEh//x8z/8A3NYXDpJfBM9C+M/pBCTtgn62xFYb3CQSX27ivQq0mSM0qQXySFOVV\nClZ5KzN9+Hvao6cN3ZdDoTwt2uUkXnnlFXz22WcoLCzE2LFjYW1tDblcjnPnzuHEiRNYtGgRjh8/\nzuQfP358pxlMaaRGqcKtwjKkysqZeYcGnM0M4WVjCkN+/S0uL86AvLBxiMfA2AHmdv1bjcl1N0mK\nh00D9rlYoa/YGrWyYuSdOAm1sn4OgmdoCIeJ41t1ELnl+fgz43yTgH1mGNm3MWAfIQQP88tx6VYe\ns3KpARNDAQZ72MGlp5BGc6VQnjLtchJLly4FAMTExCAmJkbjeNNYSxwOhzqJTkapVuNOUTmSi8oY\nkaAGepjoo7+NKSvUd2VpFoqlicxvPUNrWDr4ad1JDQD37rID9vVwNIOblx2U5RXIO/EbVLWP9ijo\n6cF+wjjwjLQvQS2qLMb/0mKYjXcmekYYIwplAvYVFFch7lYusgsqWOX0dXkY6GYDdycLGqiPQnlG\ntMtJnDlzprPtoLQDNSG4V1KJGwWlqH60ka0BSwMBfG2FsDZk7ziurpBClnMVDfMGAj1zWPUYAk4r\nmgxZ94tx52ZjwD4bOxNIBvSEqroaucdPQFlZHzqDy+PDbtwYCFoI19KAvKYMJ9MaA/YZCPQxRhQG\nA74+5OW1iE/OQ1qWnFWGr8OFt8gK/V2taZA+CuUZ0y4n4eCgfWkk5elQVluHcw+LUFrDXmZqostD\nf1shehrrs4ZiCFGjsvQhivP+AsGjeQOBCax7BYCroxnNF6hfxXQ/XYaUW40rn8wtjeAzxBGkToG8\nEydZ8qK2Y0ZCz1r7CqMKRSVOthCwz1hgiIQ7+biSLIWaNH4JcTkcuDmZw8/dFgZ6LdtIoVCeLm06\niaKiIkRGRiI6Ohq5ubkghMDOzg7Dhg3DtGnTOkSRjtI6VXVK/PmgAJWKxq8HfT4XEmsh+poZgsty\nDgQ1FVKUFNxCXW3jngIe3wDWjoHQ4elq1E8IQc5DOe4mS1Fd2ThZbCLUx8DA3uAQNfJOnmoSf4kD\nmxFhMOjRQ6vNNXU1OJkazQTs43F5GO0SAnN9Ia6l5LPU4wDAuYcQgz1saewlCqWL0aqTuHTpEpYs\nWQK5XI5+/fphyJAh4PF4yMrKws6dO7F//35s2bIFAQEBT8veF47aBnlRRaO8qKeVCcQWxuA3G6ev\nrSqGvOAWaqoKWek6PH1Y9wpiqcgB9c6hML8CKbfyUNZsstjYRA+DgpzA0+FA+vsfLHlR69BgGPXp\no9VmhaoOvzcJ2MflcPFS3yDYGFkhOUOGS02+VGwtDBHoZQ9bC0Nt1VEolGeIVieRk5ODf/7znxCL\nxVi7di0cHR1Zx7Ozs/Hxxx9j8eLFOHr0KB2S6gTqVGqczSxE6aM4SZxH8qIOxuxVRHW15ZAXJKGq\nPIeVzuHyYGIugomFi8YQk7y4Cim38lDUbLJYoMuDSz8bOPYxB4fLQcHZaJa8qMWQwTDppz3Qo0qt\nwun0WBRWPloVxeEgtI8/epraIz1bjnPXGwP+9bA2xrhAJ/DopDSF0mXR6iR++OEHWFlZYceOHdDT\n0xwC6NGjB3bs2IFXXnkFP/30Ew0X3sGo1AQxD4tQVKUpL8rkUdbU730oyUDTHc8AB8ZmfWBq1Q86\nPPa9q6qoRUqSFLnNJot1dLjoI7KCs6sVeHwdEEIgi7uE8rupTB6z/t4w6699T4yaqHE2gx2wL7DX\nADibOyIrvxx/xGcyS2CtzerlRamDoFC6NlqdRGxsLN5+++0WHUQDAoEAM2fOxM6dO6mT6EDUhOBi\ntoy1c9rP3gxOwvohGbWqDmWyNJQVp4Ko2buRDUx6QmjlDn4zNbfaGiXS7uTjYYaMkfkE6pcs93Iy\nh4ubDfT0G7825NcTIb9xk/lt0k8M88GDtNpMCMGFzKu4X9IYsG+AgxfcrEWQyipxMu4+c16hsS7G\nBTrRlUsUynOAVichlUrh7OzcZgVOTk6QSqVt5qO0D0IIruQWI7O0cfe0l40pXC2MQdQqVMjvo7Tw\nDlSqWlY5PQMrCG08oavPDninVKqQkVqEjNRCRv+hAVsHU4g97WBkzJ7MLk1Ohiz+CvPbqE8fWAUP\nbXUj29WcG0gpbAwP7mEjRn87d8hKq3Hiwn3UKetXWBnp8/HyUGe6eolCeU7Q6iSMjIxYYcG1IZPJ\nIBQKO9SoF5m/8kuR1lRe1MIIHpbGqCzNgrwgGco69hwCX9cUZtae0DOyYT3E1WqCrPvFSL2dj9pm\ny2bNLQ3RT2IHs2aTxeq6Oshv3ETxlQQmTd/BATYvhYHDbXlYqFapQELuDSTnNw5LuVg4YUhPH5RX\n1avHNcRe0tflYeJQZyovSqE8R2h1Et7e3vj1118xatSoVis4cuQIjd3UQdwuKkNSE3lRJ6EBPEzq\nkP8gGoqaElZeHt8AplbuMDTtydo5TQiBNKcMKUl5qCxnf20Ymeihn6cdrO2M2Xsq1GqUp9xF8dUE\nZqMcAOhZW8Nu9EhwdDSHhZRqFZIL7iIxLxkKZeO8SS+hA4J7D0Z1rRLHYu+horreQfF53Hp5URO6\nxJVCeZ7Q6iRmzpyJt956C9u3b8ecOXNazLN161bExsYiKiqq0wx8UUgvqcC1vMbJZFt9gr6qFBQ8\nZA/lcbkCmFqJYWzmrLFrWlZYgTs385iQ3g3o6fMhcrdFT0czcLjsPRVVDzIhuxwPRQnbCelaWsJu\n7Ghwm+maq4kaabL7SMi5iUoF+zw9TO0w3DkIdSqC483kRcf4U3lRCuV5RKuTGDx4MBYsWIAtW7bg\n2LFjCAkJgYODA3g8HnJycnD69GlkZGTg//7v/9C/f/8OM0ilUmHr1q04cuQIKisrERQUhNWrV8PS\n0rLDztHVyCqrFwcCALW6DsbqEjjXZqJW0XSCWQfG5n1haukKrg77wV1eWoOUW3nIbyLIAwB8vg6c\nxdZwcrHUiH1UnSeF7NJl1DSbT9LR14e530CY9BOzhpgIIcgqzUV8diJKqktZZUz0jOHn4AUns15Q\nqQl+u5CBwibyoiMGOdLIrRTKc0qrm+kWLFgAsViM8PBw7Ny5k3XM29sb27dvR1BQUIca9O233+LI\nkSPYuHEjhEIhPv/8cyxcuBD79u3r0PN0FaQVNYh9WAS1WgVFjRx6ymJITOTQ4TQ4CA6MhI4wtXLT\n2AxXXaVAanI+sjNLmKWlAMDlctG7rwX6iq0h0GXfYkVxCWSX41H54AErncvnQ9jfG0IvCbh89qRy\nQUURLmcnQlpewErX4+vB194T/Sz7gsvlQq0m+N8ltrxoqG8POPegc1YUyvNKm2E5hg8fjuHDh6Ok\npAQ5OTkghMDBwQHm5h0vG6lQKLBnzx588sknzC7uLVu2ICwsDNevX4ePj0+Hn/NZIquuRXRmPmqq\nS6GokcOAWwc/Uzn43PoHvr6RHYTWHhDosTWc6xRKpKcU4kF6EVRN1No4HA4cegkhcreFgSH7a0NZ\nUYnihASU3U5B0z0VHC4Xph7uEPr4aIT6lteU4Wr2DdayVgDg6fDgZesGTxsxBI826RFCcDbhIUte\n1F9iDzcniydvIAqF8sxpV4A/ADAzM4NZK9E+O4KUlBRUVlbCz8+PSevRowccHByQkJDQrZxEaU0t\nTqWko6yyGEStgh5XDT+TUuhyCXT1LSC09oCeoRWrjEqlxoP0IqSnFKBOwV7Oam1rArGnLUyE7Ae9\nqrYW8sS/IL9xE0TFLmPs0hfmg/zANzFhpVfVVeNazi2kFKWzvlA4HA76WbnAx94DBvzG8xBCcOFG\nLlIyG+c1fFyt4UPlRSmU5552O4mnQcN+i+ZBA62trZ9oL8bO/ftRyOFBrWX55rOk/tFbP4msQ1Sw\nqkzHnRolivINUFkuBXC7hTIcpkwDPE41DFAAGacKd45pnodDAA5ha06UmRoip5cVquV3gf/dbeE8\nao00Y44VrDi9kfRQF0nX0jSON9WfdnOywBBPO01jKBTKc0eXchLV1dXgcrngNxsTFwgEqK2t1VKq\nZRQKBQq4PKg5XXtXLxdqOJZlokzKR2mJMZo7AU3qH/g6UMAABRCQsvoSpK0SQJWBLrJ6WqLc9NH+\nCLVKa5kGDDimsEQf6MEYIICyBQfSFGcHU4T49KAKchRKN6FLOQk9PT2o1WoolUrweI2mKRQK6Otr\nl8ZsCYFAALM6BYoFeo/ewLseXLUKwpwC5OfqgZD22ciFEvoohB7k4LTmGZpQo8tHroMFii2M66ME\ntgM9jhEs0BsGMGv3A9/ZwRQjBjmCy+2a7U2hUB6fLuUk7OzqhygKCwuZ/wNAQUHBE+lWzHtjGsrL\ny6BQtP72+6zQ4XLBfcyhMK4O57Hf0rn8x73NHPBaUa5r8RwcUIlRCqUb0qWchFgshqGhIa5cuYKJ\nEycCqA9JnpOTg4EDBz5RncbGJm1nolAoFEqLdCknIRAIMG3aNGzatAlmZmawsLDA559/Dj8/v1ZD\nf6gerdqhgQYpFAql/TQ8M1Uq7fOTXcpJAMDixYuhVCqxfPlyKJVKZsd1axQW1iuxvfHGG0/DRAqF\nQulWFBYWagjLNcAhhLRv9rMLU1NTg6SkJFhZWUGnhWB0FAqFQtFEpVKhsLAQHmHosuQAABidSURB\nVB4eWrWDuoWToFAoFErnQJejUCgUCkUr1ElQKBQKRSvUSVAoFApFK9RJUCgUCkUr3cJJxMfHw9XV\ntcV/M2fOBABcuHABEydOhEQiwfjx4xETE/OMre442nP9U6ZM0Tj28ccfP2PLO46qqip88cUXCAwM\nxIABA/DOO+8gPT2dOd6d738DbbVBd+8DFRUVWL16NQIDA+Hn54dly5ZBJpMxx1+EPtBWGzxRHyDd\ngNraWlJQUMD6d+TIESIWi0lsbCxJS0sjHh4eJDw8nKSnp5Ovv/6auLu7k9TU1GdteofQ1vWr1Wri\n5eVFjh07xspTXl7+rE3vMFauXElGjRpFEhISSHp6Onn//fdJcHAwqamp6fb3v4HW2uBF6ANvv/02\nCQkJIbGxsSQ1NZXMmzePjBkzhtTW1r4wfaC1NnjSPtAtnERzysrKSEBAANm8eTMhhJBVq1aR6dOn\ns/JMnz6dfPLJJ8/CvE6n+fVnZmYSkUhEHj58+Iwt6zz8/PzInj17mN9paWlEJBKRpKSkF+b+t9YG\n3b0P3L59m4hEInLx4kUmraKiggwYMIAcPnz4hegDbbXBk/aBbjHc1Jzw8HAIBALMnz8fAJCQkMAS\nMgKAQYMGISEh4VmY1+k0v/7U1FTo6enBwcHhGVvWeZibm+PkyZOQyWRQKBQ4dOgQTE1N0bNnzxfm\n/rfWBt29Dzx4JMfr6+vLpBkaGsLR0RFXrlx5IfpAW23wpH2g2zkJmUyGyMhIzJ8/nwkvLpVKO0zI\nqKvT0vWnpaXB2NgYy5YtQ2BgIMaPH48ffvgBanXXjI77JHzxxReQSqXw9/eHt7c3fvnlF2zfvh0m\nJiYvzP1vrQ26ex+wtq5XQWx6T1UqFaRSKYqLi1+IPtBWGzxpH+h2TmLfvn2wsLDAhAkTmLSamhoI\nBGzN5ycRMnoeaOn609PTUVVVhcDAQOzatQvTpk3Dv//9b2zbtu0ZWtqxZGZmwtLSEtu3b8e+ffsQ\nGBiIf/7zn5BKpS/M/W+tDbp7H/D09ESfPn3w6aefoqCgADU1Nfjqq69QUlKCurq6F6IPtNUGT9oH\nulyAv7/LsWPHMHnyZJa6na6uLurq6lj5nkTI6HmgpevfuHEjqqqqYPJIy9rV1RXl5eWIiIjAwoUL\nn3sVuaysLKxatQp79+5logV/9dVXGDNmDH788ccX4v631QbdvQ8IBAJs27YNy5cvR1BQEPh8PsaP\nH4+hQ4eCz+e/EH2grTZ40j7QrZxEWloaMjMzMXbsWFa6nZ0dCgoKWGlPKmTUldF2/Twej+kYDbi6\nuqKyshLl5eUax543kpKSoFKp4OHhwaTx+Xz069cPmZmZL8T9b6sNunsfAABnZ2ccPnwYJSUl4PP5\nMDIywqRJkxAQEPBC9AGg9TZ40j7QrYabEhISYGVlBWdnZ1a6r68vrl69ykqLj4/HgAEDnqZ5nY62\n6586dSrWrl3LSrt16xasra27xcPB1tYWAHD37l0mjRCCe/fuoXfv3i/E/W+rDbp7H6ioqMD06dOR\nmpoKMzMzGBkZITs7GykpKQgICHgh+kBbbfCkfUDns88++6yTbX9qHDx4EHw+n1G1a8DBwQFbt26F\nUqmEpaUlfv75Z/z+++9Yv349zM3Nn5G1HY+265fL5di9ezfs7e1hYGCAP/74A9988w2WL18Od3f3\nZ2Rtx2FtbY2LFy/i999/h0gkQnV1NbZu3Ypr165hw4YNEIvF3f7+t9UGarW6W/cBgUCAgwcP4tKl\nS/Dw8EB2djaWLFkCsViMBQsWvBDPgLba4ImfAx27UvfZMnfuXLJ48eIWj0VHR5MxY8YQDw8PMmHC\nBNZa4u6CtutXq9Vk9+7dZMSIEcTDw4OMGDGC7N+//xlY2HnIZDLy8ccfk6CgIOLr60vefPNNcvv2\nbeb4i3D/W2uDF6EP5OXlkXnz5hEfHx/i7+9PPvvsM1JRUcEcfxH6QGtt8KR9gOpJUCgUCkUr3WpO\ngkKhUCgdC3USFAqFQtEKdRIUCoVC0Qp1EhQKhULRCnUSFAqFQtEKdRLPMR29MI0udKN0FrRvPb9Q\nJ9HFuHv3LpYsWYKAgAB4eHggMDAQixcvRkpKCitfYmIi5s6d2yHnVCgU2LBhA44fP86krVixAi+9\n9FKH1N9ebty4gf/7v/9DcHAwJBIJXnrpJaxZswb5+fmPXVdFRQXef/99eHl5YeDAgcjKysKVK1cw\natQoeHh4dFjbtUR2drZWpcCm/+Lj4zvNhq7EwYMHsXnz5mdtBgDg6NGjmD17NitNqVRi3759eO21\n1zBw4EAMGDAAU6ZMwcGDB6FSqZh8GRkZGD58OMrLy5+22c+WTtzXQXlM7ty5Q7y9vcnbb79Nfv/9\ndxIfH09+/fVX8sorrxBPT0+SmJjI5F25ciUJDQ3tkPPm5eURkUhE/vvf/zJpmZmZrM1onc2PP/5I\n+vXrR2bPnk2OHz9OLl++TKKiokhoaCgJCAgg9+/ff6z69u7dS0QiEYmMjCTx8fFEpVKRV199lQwf\nPpxcvHiR3L17t3MuhNQrBSYmJjL/IiMjiUgkIvv372eldydVuNYIDQ0lK1eufNZmEKlUSgYNGkTS\n09OZtIqKCjJt2jTi7e1NNm/eTKKjo0lMTAz58ssviZubG1m6dClRqVRM/rVr15IVK1Y8C/OfGd0q\nwN/zzk8//QQLCwts374dOjo6THpYWBhGjx6N8PBwbN++/anY0qtXr6dyHgBM6Ii33noLH374IZM+\naNAghIWFYeLEifjss8/w448/trtOuVwOAJg2bRoT3VIul8PHxwf+/v4dan9zBAIBE4kVABOO2tnZ\nmZVOebp899138PPzY8U2+/LLL5GcnIzIyEhWcMShQ4fC0dERa9aswbBhw5igmXPmzEFISAjefPNN\niMXip34NzwI63NSFkMlkIIRoiIAYGhpi5cqVGD16NID6oaBDhw4hJycHrq6uOHz4MID6cNHLly9H\nYGAg3N3d4e/vjxUrVqC0tJSpa9iwYdiwYQNmzJgBiUSCOXPmIDg4GADw0UcfYdiwYcw5mg43DRs2\nDNu2bcOGDRvg7+8PLy8vzJ49G5mZmSxbDx48iJEjR0IikWDq1Kk4c+ZMm0Mru3btglAoxOLFizWO\n2djYYMWKFRgyZAiUSiWA+uGBH3/8EWPHjoVEIkFYWBj+85//MEMDM2bMwNatWwEAYrEYK1asgKur\nKzIzM3H06FGWPXfv3sW7776L/v37w9fXF4sWLdIQoikpKcEnn3yCIUOGQCKR4PXXX8e1a9e0Xs/j\nUlxcjI8//pipf9q0aUhMTGSOK5VKuLq64uDBg1i6dCn69++PIUOG4D//+Q/Ky8uxYsUK+Pj4IDAw\nEFu2bGHKxcXFwdXVFRcvXsSUKVMgkUgwduxYnDx5knX+6upqbNiwAUFBQfD09MSkSZNw7tw5Vp6h\nQ4di48aNmD59OiQSCRMoLjk5Ge+//z4GDRoEd3d3BAcHY/369VAoFEy5nJwcHDp0CK6urpBKpfj6\n66/h6enJqr/hGhteghpsP3DgAIKDg+Hr64vr168DqA/M9/rrr0MikWDw4MFYvXp1m0NARUVFOHLk\nCMaPH8+kFRYW4ujRo/jHP/7BchANvPbaa5gxYwYr+J2VlRX8/Pye2stal+BZf8pQGvn555+JSCQi\nkydPJpGRkazP4qZkZmaS9957jwQEBJDExEQik8lIVVUVGTp0KJkyZQo5ffo0uXTpEgkPDydubm7k\n008/ZcqGhoYSNzc3smXLFhIbG0v++usvcubMGSISicjXX39NkpOTCSGEfPjhh2T48OGscr6+vmTO\nnDnk3Llz5NdffyV+fn7k9ddfZ/IcOnSIiEQi8vnnn5PY2FiyadMmIpFIiEgkIpcvX27xWtRqNfH0\n9CRLlixpdzt98MEHxN3dnXz77bfkwoUL5N///jdxd3cnH330ESGkXtt51apVRCQSkcTERHLv3j2S\nmJhIhg4dSt59911mqCcjI4P079+fvPrqq+T06dPkt99+I6NGjSLDhg0jZWVlhBBCampqyIQJE0hA\nQAD55ZdfSHR0NJk7dy7x8PAgN27caJe9ly9fJiKRiFy9elXjWHV1NRk3bhwJCgoihw4dImfPniXv\nvvsu8fT0JElJSYQQQurq6ohIJCI+Pj5k3bp1JC4ujixfvpyIRCIycuRI8sUXX5C4uDjmmk+fPk0I\nIeTixYtEJBKRgQMHkn/9618kJiaGLFq0iLi6upLo6Gim/d966y3i6+tLfvrpJxITE0M++OADIhaL\nydmzZxk7g4KCiJubG9m6dSuJjY0lN2/eJLm5ucTb25vpExcvXiRr164lIpGI7N69mxBCSHJyMgkI\nCCDvvfceSUxMJLW1tWTLli3Ew8OD1Q4N1/j999+zbA8ODiZ//PEHOXz4MKmtrSWXLl0ibm5uZO7c\nuSQ6OpocPHiQ+Pv7k3/84x9EqVRqvQeRkZHEy8uL1NTUMGlHjx4lIpGIxMXFtes+NrBv3z7i4eFB\nqqurH6vc8wp1El0ItVpNtmzZQjw9PYlIJCIikYgMHjyYLFu2TOOB1HxOIikpibz++uskKyuLlW/u\n3LlkzJgxzO/Q0FAycuRIVp6W5iRachJhYWGsP8Rvv/2WiEQi5oEaEhJCFi5cyKr7008/bdVJyGQy\nIhKJyObNm1ttmwZSU1OJSCQiO3fuZKVv376diEQiZq7hu+++IyKRiJVn+PDh5MMPP2R+L126lAQE\nBLCCwBUUFBCJRELCw8MJIYQcOHCAuLq6kps3bzJ5VCoVmTRpEnnrrbfaZXNrTiIqKoq4urqy5n+U\nSiWZMGECmT17NiGk8QH65ptvMnkKCwuJSCQiM2bMYNnVMLZOSOODds2aNUwetVpNJk6cSKZOnUoI\nIeTcuXMsx9LA3LlzyahRo5jfQUFBZOzYsaw80dHRZPr06aSqqoqVPnr0aPLee+8xv5vPSTyOk2h+\nn6dMmUImTZrEmie4desWEYlE5LfffiPaWLBgAXn11VdZaREREUQkEpEHDx5oLdcSDed7XOfyvEKH\nm7oQHA4HS5Yswfnz5/HVV19hypQpMDQ0xLFjxzB16lRERUVpLevu7o69e/fC3t4eDx48QExMDHbt\n2oWMjAwNRa5+/fo9kX1eXl6suZIGDYOqqipkZmYiNzcXI0eOZJUZM2ZMq3U21Nd0FUlrNGgCjBs3\njpXeINfaXDOgNS5fvozBgwdDV1cXSqUSSqUSZmZmkEgkiIuLAwBcunQJNjY26NevH5NHrVYjNDQU\nV69eZYZVnpTLly/D3t4eLi4uTP2EEISEhODKlSvMEBtQ3/4NWFpaaqRxuVwYGxujrKyMdY6mbcXh\ncPDSSy/h5s2bUCgUuHz5Mvh8PoKCgpjzK5VKhIWFISMjgzX01nwMPiQkBD///DN4PB7S09Px559/\nIjw8nJHL7Aia9tWKigrcunULISEhUKvVjK1isRh2dna4ePGi1nqysrLQo0cPVtrj9r0GHBwcAAA5\nOTmPVe55hU5cd0FMTU0xbtw45o/79u3bWL58OTZu3Ihx48bB1NS0xXI//PADIiIiIJfLYWlpCQ8P\nD+jr66OqqoqVz8DA4Ins0tPTY/3mcuvfMQghKC4uBgCN2PwNDzNtmJqawtDQELm5uVrzVFRUAACM\njIyY+RULCwtWnobfj7M8US6X4/jx46ylvw307t2bySOVSrXG2y8pKflb6mYlJSXIycnRWn9paSlz\nvw0NDTWOt0d+09ramvXbwsICarUaFRUVkMvlqKurg0QiabFsQUEB8zLQvN+o1Wps3rwZ+/fvR1VV\nFezt7eHp6Qk9Pb0O2xfR9JylpaUghOC7777Dd99916Kt2igvL9doK3t7ewBAbm4u+vTp02K5/Px8\nWFlZMX29qU0N/bK7Q51EF0EqlWLKlClYtGgRXn31VdYxNzc3LFmyBPPnz0d2dnaLTuL48ePYsGED\nPvjgA0yaNIl5WC9atAi3b9/udPsbHpQymYyV3uA8WiMwMBDx8fGora2Frq6uxvEff/wR4eHh+P33\n35lJRJlMxno4FxYWAgDMzMzabbORkRGGDh2KmTNnahwTCAQAAGNjYzg7O2Pjxo0t1vE452sJY2Nj\niEQirFu3rsXjHaEaJ5fLmbdfoH4St0HK0sjICEKhEDt37myxrLaHJwCEh4cjMjISX3zxBYYPHw4j\nIyMAwKRJk1q1h8PhaCzOaP4i0xLGxsYA6lcYjRgxQuN4w/lbwszMTOMFwt/fHzo6OoiJiUFgYKBG\nGUIIXnvtNTg6OrJW1jW8qAiFwjZt7g7Q4aYugpWVFXR0dLB3715myWRTMjIyoK+vzyxNbTrsA9Qv\nIzUzM8Ps2bMZB1FZWYlr165p/EE2p+lb0pNiZ2eHHj164MyZM6z0P//8s82ys2bNglwuxzfffKNx\nLDc3F1FRUZBIJHB0dMTAgQMBACdOnGDla/jt6+vbbpv9/Pxw7949uLu7w9PTE56ennBzc8P27dsR\nGxsLABg4cCByc3NhbW3N5PH09MSZM2fw888/g8/nt/t82mzIzs6Gra0tq/4///wTe/fuBY/399/j\noqOjmf8TQvDHH39gwIAB4PF48PPzg1wuh66uLuv8iYmJ+P7771vtG9evX4ebmxtefvll5gGdm5uL\ntLQ01pdE8zqMjIygVCoZxw6gXavFTExMmFVqTW11dHTE1q1bcfPmTa1l7e3tNVatCYVCTJo0CQcO\nHNDYrAoAkZGRyM3NZa2IAsDU0/Al0t2hXxJdBB0dHaxevRoLFy7EK6+8gjfeeAPOzs6orq7GxYsX\nERUVhaVLlzJvU8bGxigqKkJMTAz69esHiUSCffv2YdOmTQgJCYFUKsXu3btRVFTUpjyjkZEROBwO\nLl26BGdnZ9Y4d3vhcDhYuHAhPvzwQ1hYWCA0NBTXr19HZGQkgNYdUf/+/TF//nxs27YNGRkZmDhx\nIoRCIVJSUrBr1y5wuVxmx65IJMKECRPw9ddfo7q6Gv3790diYiIiIiIwYcIE9O3bt902z58/H1On\nTsW8efMwdepU8Hg8REZGIi4uDq+//joAYPLkyYiMjMSsWbMwd+5c2NjY4Ny5c/jhhx+wYMECZg/G\nkzJlyhRERUVh1qxZmDNnDqytrXHmzBns2bMHixYt+tv1A8COHTsgEAggFotx6NAh3Lt3D3v27AFQ\nv7TZ29sbc+fOxbx58+Do6IiEhASEh4fj5ZdfbnU4y9PTEzt27MDu3bvh4eGBBw8eICIiAkqlkvVl\nYGJigtu3b+PKlSvw8vJCSEgINm/ejJUrV+Ltt99GdnY2wsPD2zUMumTJErz//vv46KOPMHr0aCgU\nCkRERCAjIwMrV67UWi4wMBBr1qxBTU0Na9h02bJlSEpKwhtvvIHp06fDz88PNTU1OHv2LA4fPozx\n48dj8uTJrLquX78OQ0PDF2bPC3USXYiwsDD88ssv2LVrFyIiIiCTyaCrqws3Nzds3bqVtW9h0qRJ\nOHv2LObPn4/Fixdj9uzZyM7Oxn//+19ERkbCxsYGwcHBmDZtGlatWoX79+/DycmpxfMaGBhg1qxZ\n2L9/P2JjY1udAGyNl19+GZWVldi9ezf2798PT09PLFu2DOvXr2/zAbBw4UK4u7sjKioKX375JcrK\nymBnZ4dRo0YxD88G1q9fD0dHRxw+fBgRERGwt7fHwoUL8c477zyWvWKxGFFRUdi6dSuWLVsGDocD\nsViM7du3MxvuDA0NERUVha+++gobNmxAZWUlevbsiVWrVmH69OmP30jNMDIyYupft24dqqqq0KtX\nL6xevRpvvPHG364fAFauXIn9+/dj27ZtcHV1xc6dO5kvLh0dHezatQtbt27Ft99+i5KSEtjb22P+\n/PmYM2dOq/XOmzcPpaWl2LVrFyorK2FnZ4fJkyczetqVlZUwNDTErFmzsH79erzzzjuIjIyERCLB\nhg0bEBERgXfffRcuLi5Yt24dVq9e3ea1hIaG4vvvv0d4eDgWLFgAPT09eHl54eeff2ZtkmvOsGHD\nsGbNGsTFxTF7gYD6YaioqCj89NNPOHXqFCIjI8HlcuHk5IQvv/wSEydO1HDU58+fR2hoKDMk2d2h\n8qWUDuPEiRPM538DUVFRWLt2LeLj4ztkfJ3SfuLi4jBr1iwcOHDghXnrbY3Vq1ejoKAAERERT1xH\nVlYWRo4cicOHD9Md1xTK43LkyBHMmTMHJ0+exNWrV7F371588803mDhxInUQlGfOvHnzcO3aNaSm\npj5xHbt378bYsWNfGAcB0OEmSgeyadMm/Otf/8K6desgl8tha2uLGTNm4L333nvWplEosLOzw4oV\nK7Bhwwbs3r37scvfu3cPMTExOHLkSCdY13Whw00UCoVC0QodbqJQKBSKVqiToFAoFIpWqJOgUCgU\nilaok6BQKBSKVqiToFAoFIpWqJOgUCgUilb+H9DrQsxUUoV1AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x268d38a65c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i1 in linspace(5, 30, 6):\n",
+    "    sweep_coffee(T_coffee_array = linspace(70, 95, 26), T_milk0=i1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can try it out with a few values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then sweep a range of values for `t_add`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "sweep = SweepSeries()\n",
+    "for t_add in linrange(0, 30, 2):\n",
+    "    temp = run_and_mix(t_add)\n",
+    "    sweep[t_add] = temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's what the result looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot(sweep, color='purple')\n",
+    "decorate(xlabel='Time added (min)',\n",
+    "         ylabel='Final temperature (C)',\n",
+    "         legend=False)\n",
+    "\n",
+    "savefig('chap07-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exercise:**  Suppose the coffee shop won't let me take milk in a separate container, but I keep a bottle of milk in the refrigerator at my office.  In that case is it better to add the milk at the coffee shop, or wait until I get to the office?\n",
+    "\n",
+    "Hint: Think about the simplest way to represent the behavior of a refrigerator in this model.  The change you make to test this variation of the problem should be very small!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#Now you can only add the milk at two points, the beggining and the end. \n",
+    "#Assume that both refrigerators are equally cold, so the milk will be 5 deg C whenever you add it. \n",
+    "\n",
+    "def run_and_mix(t_add, t_total=30):\n",
+    "    \"\"\"Simulates two liquids and them mixes them at t_add.\n",
+    "    \n",
+    "    t_add: time in minutes\n",
+    "    t_total: total time to simulate, min\n",
+    "    \n",
+    "    returns: final temperature\n",
+    "    \"\"\"\n",
+    "    coffee = make_system(T_init=90, t_end=t_add, \n",
+    "                         r=r_coffee, volume=300)\n",
+    "    run_simulation(coffee, update)\n",
+    "\n",
+    "    milk = make_system(T_init=5, t_end=t_add, \n",
+    "                       r=r_milk, volume=50)\n",
+    "    #remove the run_simulation becaause the milk is not warming up\n",
+    "    mixture = mix(coffee, milk)\n",
+    "    mixture.t_end = t_total - t_add\n",
+    "    run_simulation(mixture, update)\n",
+    "\n",
+    "    return final_temp(mixture)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run_and_mix(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#so better to add milk at beggining"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the analytic result to compute temperature as a function of time.  The following function is similar to `run_simulation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_analysis(system):\n",
+    "    \"\"\"Computes temperature using the analytic solution.\n",
+    "    \n",
+    "    Adds TimeFrame to `system` as `results`\n",
+    "    \n",
+    "    system: System object\n",
+    "    \"\"\"\n",
+    "    unpack(system)\n",
+    "    \n",
+    "    T_init = init.temp    \n",
+    "    ts = linrange(t0, t_end, dt)\n",
+    "    \n",
+    "    temp_array = T_env + (T_init - T_env) * exp(-r * ts)\n",
+    "    temp_series = TimeSeries(temp_array, index=ts)\n",
+    "    \n",
+    "    system.results = TimeFrame(temp_series, columns=['temp'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it.  From the analysis, we have the computed value of `r_coffee2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "r_coffee2 = 0.011610223142273859"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(temp=90)\n",
+    "coffee2 = System(init=init, T_env=22, r=r_coffee2, \n",
+    "                 t0=0, t_end=30)\n",
+    "run_analysis(coffee2)\n",
+    "final_temp(coffee2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can compare to the results from simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "init = State(temp=90)\n",
+    "coffee = System(init=init, T_env=22, r=r_coffee, \n",
+    "                t0=0, t_end=30, dt=1)\n",
+    "run_simulation(coffee, update)\n",
+    "final_temp(coffee)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They are identical except for small roundoff errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "coffee.results - coffee2.results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/rabbits2.ipynb b/code/rabbits2.ipynb
index f77cb8cf..4e62303f 100644
--- a/code/rabbits2.ipynb
+++ b/code/rabbits2.ipynb
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -79,38 +79,48 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>t0</th>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>t_end</th>\n",
-       "      <td>10.0</td>\n",
+       "      <td>10.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>adult_pop0</th>\n",
-       "      <td>10.0</td>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>juvenile_pop0</th>\n",
+       "      <td>0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>birth_rate</th>\n",
-       "      <td>0.9</td>\n",
+       "      <td>0.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>death_rate</th>\n",
-       "      <td>0.5</td>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>maturation_rate</th>\n",
+       "      <td>0.33</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "t0             0.0\n",
-       "t_end         10.0\n",
-       "adult_pop0    10.0\n",
-       "birth_rate     0.9\n",
-       "death_rate     0.5\n",
+       "t0                  0.00\n",
+       "t_end              10.00\n",
+       "adult_pop0         10.00\n",
+       "juvenile_pop0       0.00\n",
+       "birth_rate          0.90\n",
+       "death_rate          0.50\n",
+       "maturation_rate     0.33\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,8 +129,10 @@
     "system = System(t0 = 0, \n",
     "                t_end = 10,\n",
     "                adult_pop0 = 10,\n",
+    "                juvenile_pop0 = 0,\n",
     "                birth_rate = 0.9,\n",
-    "                death_rate = 0.5)\n",
+    "                death_rate = 0.5,\n",
+    "                maturation_rate = .33)\n",
     "\n",
     "system"
    ]
@@ -144,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
@@ -161,13 +173,21 @@
     "    adults = TimeSeries()\n",
     "    adults[system.t0] = system.adult_pop0\n",
     "    \n",
+    "    juveniles = TimeSeries()\n",
+    "    juveniles[system.t0] = system.juvenile_pop0\n",
+    "    \n",
     "    for t in linrange(system.t0, system.t_end):\n",
     "        births = system.birth_rate * adults[t]\n",
     "        deaths = system.death_rate * adults[t]\n",
+    "        maturations = system.maturation_rate *juveniles[t]\n",
     "        \n",
-    "        adults[t+1] = adults[t] + births - deaths\n",
+    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
     "        \n",
-    "    system.adults = adults"
+    "        adults[t+1] = adults[t] + maturations - deaths\n",
+    "        \n",
+    "\n",
+    "    system.adults = adults\n",
+    "    system.juveniles = juveniles"
    ]
   },
   {
@@ -179,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -213,69 +233,69 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>14.000000</td>\n",
+       "      <td>5.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>19.600000</td>\n",
+       "      <td>5.470000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>27.440000</td>\n",
+       "      <td>6.209900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>38.416000</td>\n",
+       "      <td>7.057723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>53.782400</td>\n",
+       "      <td>8.021560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>75.295360</td>\n",
+       "      <td>9.117031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>105.413504</td>\n",
+       "      <td>10.362107</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>147.578906</td>\n",
+       "      <td>11.777219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>206.610468</td>\n",
+       "      <td>13.385586</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>289.254655</td>\n",
+       "      <td>15.213601</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>404.956517</td>\n",
+       "      <td>17.291261</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "0      10.000000\n",
-       "1      14.000000\n",
-       "2      19.600000\n",
-       "3      27.440000\n",
-       "4      38.416000\n",
-       "5      53.782400\n",
-       "6      75.295360\n",
-       "7     105.413504\n",
-       "8     147.578906\n",
-       "9     206.610468\n",
-       "10    289.254655\n",
-       "11    404.956517\n",
+       "0     10.000000\n",
+       "1      5.000000\n",
+       "2      5.470000\n",
+       "3      6.209900\n",
+       "4      7.057723\n",
+       "5      8.021560\n",
+       "6      9.117031\n",
+       "7     10.362107\n",
+       "8     11.777219\n",
+       "9     13.385586\n",
+       "10    15.213601\n",
+       "11    17.291261\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -294,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 27,
    "metadata": {
     "collapsed": true
    },
@@ -309,6 +329,11 @@
     "    plot(system.adults, 'bo-', label='adults')\n",
     "    decorate(xlabel='Season', \n",
     "             ylabel='Rabbit population',\n",
+    "             title=title)\n",
+    "    \n",
+    "    plot(system.juveniles, 'ro-', label='juveniles')\n",
+    "    decorate(xlabel='Season', \n",
+    "             ylabel='Rabbit population',\n",
     "             title=title)"
    ]
   },
@@ -321,14 +346,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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hHoJGoz9tb48eSi3/6kySvxBCCPEQfv8dLlxQlq2soHt3k4ZjEEn+QgghxEPY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\nNmyY3KUZjREjRuDOnTuQJAk1NTW632va32Wpqan48MMPkZ2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99FM0b94cS5culTteQEAATp48iX379sHMzAyTJ0/G+PHjP8rHUtcQcaV5iAhCAd26dYO5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\ntdlOpg4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f615e3c12e8>"
+       "<matplotlib.figure.Figure at 0x1bab9357390>"
       ]
      },
      "metadata": {},
diff --git a/code/rabbits3mine.ipynb b/code/rabbits3mine.ipynb
new file mode 100644
index 00000000..cd624913
--- /dev/null
+++ b/code/rabbits3mine.ipynb
@@ -0,0 +1,734 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Rabbit example\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rabbit is Rich\n",
+    "\n",
+    "This notebook starts with a version of the rabbit population growth model.  You will modify it using some of the tools in Chapter 5.  Before you attempt this diagnostic, you should have a good understanding of State objects, as presented in Section 5.4.  And you should understand the version of `run_simulation` in Section 5.7.\n",
+    "\n",
+    "### Separating the `State` from the `System`\n",
+    "\n",
+    "Here's the `System` object from the previous diagnostic.  Notice that it includes system parameters, which don't change while the simulation is running, and population variables, which do.  We're going to improve that by pulling the population variables into a `State` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t0</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>20.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>juvenile_pop0</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>adult_pop0</th>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>birth_rate</th>\n",
+       "      <td>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mature_rate</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>death_rate</th>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t0                0.00\n",
+       "t_end            20.00\n",
+       "juvenile_pop0     0.00\n",
+       "adult_pop0       10.00\n",
+       "birth_rate        0.90\n",
+       "mature_rate       0.33\n",
+       "death_rate        0.50\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = System(t0 = 0, \n",
+    "                t_end = 20,\n",
+    "                juvenile_pop0 = 0,\n",
+    "                adult_pop0 = 10,\n",
+    "                birth_rate = 0.9,\n",
+    "                mature_rate = 0.33,\n",
+    "                death_rate = 0.5)\n",
+    "\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following cells, define a `State` object named `init` that contains two state variables, `juveniles` and `adults`, with initial values `0` and `10`.  Make a version of the `System` object that does NOT contain `juvenile_pop0` and `adult_pop0`, but DOES contain `init`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "init = State(juveniles=0, adults=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t0</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>init</th>\n",
+       "      <td>juveniles     0\n",
+       "adults       10\n",
+       "dtype: int64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>birth_rate</th>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mature_rate</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>death_rate</th>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t0                                                        0\n",
+       "t_end                                                    20\n",
+       "init           juveniles     0\n",
+       "adults       10\n",
+       "dtype: int64\n",
+       "birth_rate                                              0.9\n",
+       "mature_rate                                            0.33\n",
+       "death_rate                                              0.5\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = System(t0 = 0, \n",
+    "                t_end = 20,\n",
+    "                init = init,\n",
+    "                birth_rate = 0.9,\n",
+    "                mature_rate = 0.33,\n",
+    "                death_rate = 0.5)\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Updating `run_simulation`\n",
+    "\n",
+    "Here's the version of `run_simulation` from last time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system):\n",
+    "    \"\"\"Runs a proportional growth model.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "    \n",
+    "    system: System object\n",
+    "    \"\"\"\n",
+    "    juveniles = TimeSeries()\n",
+    "    juveniles[system.t0] = system.juvenile_pop0\n",
+    "    \n",
+    "    adults = TimeSeries()\n",
+    "    adults[system.t0] = system.adult_pop0\n",
+    "    \n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        maturations = system.mature_rate * juveniles[t]\n",
+    "        births = system.birth_rate * adults[t]\n",
+    "        deaths = system.death_rate * adults[t]\n",
+    "        \n",
+    "        if adults[t] > 30:\n",
+    "            market = adults[t] - 30\n",
+    "        else:\n",
+    "            market = 0\n",
+    "            \n",
+    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
+    "        adults[t+1] = adults[t] + maturations - deaths - market\n",
+    "        \n",
+    "    system.adults = adults\n",
+    "    system.juveniles = juveniles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the cell below, write a version of `run_simulation` that works with the new `System` object (the one that contains a `State` object named `init`).\n",
+    "\n",
+    "Hint: you only have to change two lines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system):\n",
+    "    \"\"\"Runs a proportional growth model.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "    \n",
+    "    system: System object\n",
+    "    \"\"\"\n",
+    "    juveniles = TimeSeries()\n",
+    "    adults = TimeSeries()\n",
+    "    state = system.init\n",
+    "    t0 = system.t0\n",
+    "    juveniles[t0], adults[t0] = state\n",
+    "   \n",
+    "\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        maturations = system.mature_rate * juveniles[t]\n",
+    "        births = system.birth_rate * adults[t]\n",
+    "        deaths = system.death_rate * adults[t]\n",
+    "        \n",
+    "        if adults[t] > 30:\n",
+    "            market = adults[t] - 30\n",
+    "        else:\n",
+    "            market = 0\n",
+    "            \n",
+    "        juveniles[t+1] = juveniles[t] + births - maturations\n",
+    "        adults[t+1] = adults[t] + maturations - deaths - market\n",
+    "        \n",
+    "    system.adults = adults\n",
+    "    system.juveniles = juveniles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test your changes in `run_simulation`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.470000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6.209900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.057723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.021560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.117031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10.362107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11.777219</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>13.385586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>15.213601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>17.291261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>19.652658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>22.336542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>25.386953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>28.853947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>32.794414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>34.478600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>36.487431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>37.893339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>39.401924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>40.546917</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "0     10.000000\n",
+       "1      5.000000\n",
+       "2      5.470000\n",
+       "3      6.209900\n",
+       "4      7.057723\n",
+       "5      8.021560\n",
+       "6      9.117031\n",
+       "7     10.362107\n",
+       "8     11.777219\n",
+       "9     13.385586\n",
+       "10    15.213601\n",
+       "11    17.291261\n",
+       "12    19.652658\n",
+       "13    22.336542\n",
+       "14    25.386953\n",
+       "15    28.853947\n",
+       "16    32.794414\n",
+       "17    34.478600\n",
+       "18    36.487431\n",
+       "19    37.893339\n",
+       "20    39.401924\n",
+       "21    40.546917\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(system)\n",
+    "system.adults"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting the results\n",
+    "\n",
+    "Here's a version of `plot_results` that plots both the adult and juvenile `TimeSeries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(system, title=None):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    system: System object with `results`\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot(system.adults, 'bo-', label='adults')\n",
+    "    plot(system.juveniles, 'gs-', label='juveniles')\n",
+    "    decorate(xlabel='Season', \n",
+    "             ylabel='Rabbit population',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If your changes in the previous section were successful, you should be able to run this new version of `plot_results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LFuLj4/H39wfk6iQDAwPi4uJo1KiR8hUSEsKuXbueOrYmTZpw5coVnJyclHOr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1391fe11080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system, title='Proportional growth model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's the end of the diagnostic.  If you were able to get it done quickly, and you would like a challenge, here are two bonus questions:\n",
+    "\n",
+    "\n",
+    "### Bonus question #1\n",
+    "\n",
+    "Write a version of `run_simulation` that puts the results into a single `TimeFrame` named `results`, rather than two `TimeSeries` objects.\n",
+    "\n",
+    "Write a version of `plot_results` that can plot the results in this form.\n",
+    "\n",
+    "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5.  We don't expect most people to be able to do this exercise at this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_simulation(system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pohJYdOUeFRXF6tWr6dGjB+np6Rw5coSMjAx69erF6tWradeuXVXXKYQQopIV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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f44cf56abe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bonus question #2\n",
+    "\n",
+    "Factor out the update function.\n",
+    "\n",
+    "1.  Write a function called `update` that takes a `State` object and a `System` object and returns a new `State` object that represents the state of the system after one time step.\n",
+    "\n",
+    "2.  Write a version of `run_simulation` that takes an update function as a parameter and uses it to compute the update.\n",
+    "\n",
+    "3.  Run your new version of `run_simulation` and plot the results.\n",
+    "\n",
+    "WARNING: This question is substantially harder, and requires you to have a good understanding of everything in Chapter 5.  We don't expect most people to be able to do this exercise at this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'update' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-5d0666d579e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_simulation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'update' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "run_simulation(system, update)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plot_results(system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/rabbitsmine.ipynb b/code/rabbitsmine.ipynb
new file mode 100644
index 00000000..ede08efe
--- /dev/null
+++ b/code/rabbitsmine.ipynb
@@ -0,0 +1,773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modeling and Simulation in Python\n",
+    "\n",
+    "Rabbit example\n",
+    "\n",
+    "Copyright 2017 Allen Downey\n",
+    "\n",
+    "License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you want to interact with them, use\n",
+    "# %matplotlib notebook\n",
+    "\n",
+    "# If you want the figures to appear in the notebook, \n",
+    "# and you don't want to interact with them, use\n",
+    "# %matplotlib inline\n",
+    "\n",
+    "# If you want the figures to appear in separate windows, use\n",
+    "# %matplotlib qt5\n",
+    "\n",
+    "# To switch from one to another, you have to select Kernel->Restart\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook develops a simple growth model, like the ones in Chapter 3, and uses it to demonstrate a parameter sweep.\n",
+    "\n",
+    "The system we'll model is a rabbit farm.  Suppose you start with an initial population of rabbits and let them breed.  For simplicity, we'll assume that all rabbits are on the same breeding cycle, and we'll measure time in \"seasons\", where a season is the reproductive time of a rabbit.\n",
+    "\n",
+    "If we provide all the food, space and other resources a rabbit might need, we expect the number of new rabbits each season to be proportional to the current population, controlled by a parameter, `birth_rate`, that represents the number of new rabbits per existing rabbit, per season.  As a starting place, I'll assume `birth_rate = 0.9`.\n",
+    "\n",
+    "Sadly, during each season, some proportion of the rabbits die.  In a detailed model, we might keep track of each rabbit's age, because the chance of dying is probably highest for young and old rabbits, and lowest in between.  But for simplicity, we'll model the death process with a single parameter, `death_rate`, that represent the number\n",
+    "of deaths per rabbit per season.   As a starting place, I'll assume `death_rate = 0.5`.\n",
+    "\n",
+    "Here's a system object that contains these parameters as well as:\n",
+    "\n",
+    "* The initial population, `p0`,\n",
+    "* The initial time, `t0`, and\n",
+    "* The duration of the simulation, `t_end`, measured in seasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t0</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p0</th>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>birth_rate</th>\n",
+       "      <td>0.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>death_rate</th>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t0             0.0\n",
+       "t_end         10.0\n",
+       "p0            10.0\n",
+       "birth_rate     0.9\n",
+       "death_rate     0.5\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = System(t0 = 0, \n",
+    "                t_end = 10,\n",
+    "                p0 = 10,\n",
+    "                birth_rate = 0.9,\n",
+    "                death_rate = 0.5)\n",
+    "\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's a version of run_simulation, similar to the one in Chapter 3, with both births and deaths proportional to the current population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(system):\n",
+    "    \"\"\"Runs a proportional growth model.\n",
+    "    \n",
+    "    Adds TimeSeries to `system` as `results`.\n",
+    "    \n",
+    "    system: System object with t0, t_end, p0,\n",
+    "            birth_rate and death_rate\n",
+    "    \"\"\"\n",
+    "    results = TimeSeries()\n",
+    "    results[system.t0] = system.p0\n",
+    "    for t in linrange(system.t0, system.t_end):\n",
+    "        births = system.birth_rate * results[t]\n",
+    "        deaths = system.death_rate * results[t]\n",
+    "        results[t+1] = results[t] + births - deaths\n",
+    "    system.results = results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run the simulation and display the results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>27.440000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>38.416000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>53.782400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>75.295360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>105.413504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>147.578906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>206.610468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>289.254655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>404.956517</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "0      10.000000\n",
+       "1      14.000000\n",
+       "2      19.600000\n",
+       "3      27.440000\n",
+       "4      38.416000\n",
+       "5      53.782400\n",
+       "6      75.295360\n",
+       "7     105.413504\n",
+       "8     147.578906\n",
+       "9     206.610468\n",
+       "10    289.254655\n",
+       "11    404.956517\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(system)\n",
+    "system.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that the simulation actually runs one season past `t_end`.  That's an off-by-one error that I'll fix later, but for now we don't really care.\n",
+    "\n",
+    "The following function plots the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_results(system, title=None):\n",
+    "    \"\"\"Plot the estimates and the model.\n",
+    "    \n",
+    "    system: System object with `results`\n",
+    "    \"\"\"\n",
+    "    newfig()\n",
+    "    plot(system.results, 'bo', label='rabbits')\n",
+    "    decorate(xlabel='Season', \n",
+    "             ylabel='Rabbit population',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eVLly4ceSF7kmkKFDh6o/BwQEPPUkMTExBReREEIUMz4+2m0gWdq3L/xY8kKn\ncSB169blr7/+ynFbREQE7Yv6XQohRBHm6Qn9+kHVqmBklPl3v35Fu/0DnvIEsnLlSpKSkoDMWXS3\nbNnCoUOHsu138uRJ6QElhBDPydOz6CeMJ+WaQNLS0li8eDGQ2fspNDQ02z5GRkaUKVNGekEJIUQx\nlGsCGTBgAAMGDADAycmJjRs34uLiUmiBCSGEKNp0GgcSGRmp7ziEEEK8YHQeSLh3716OHTumTl4I\nkJGRwaNHjzh58iQHDx7UW5BCCCGKHp0SyMKFC1mwYAGlS5cmLS0NExMTSpQowZ07dzAyMqJbt276\njlMIIUQRo1M33m3bttGlSxeOHj1Kr169eP311/n999/57rvvKFu2rLpeuRBCiOJDpwQSHR2Nr68v\nGo0GZ2dnTp48CWROdzJo0CC2bNmi1yCFEEIUPTolEAsLC4yMMnetXr06N27cIDk5GcgcZHjjxg39\nRSiEEKJI0imBNGjQgO3btwOZqxMaGxtz5MgRAC5fviwDCYUQohjSqRF9wIABfPTRRyQkJLB48WI6\nderExx9/TNOmTQkLC6NNmzb6jlMIIUQRo1MCady4MZs3b+bChQsATJkyBSMjI06cOEH79u0ZN26c\nXoMUQghR9Og8DsTZ2RlnZ2cAzMzMmD59ut6CEkIIUfTlmkB27tyZpxP5+vo+dzBCCCFeHLkmkDFj\nxuh8Eo1GIwlECCGKmVwTyIEDBwozDiGEEC+YXBNIlSpVCjMOIYQQLxidGtH79u37zH1Wrlz53MEI\nIYR4ceiUQFJTU7OVJSUlcenSJSwsLGjbtm2BByaEEKJo0ymBrF27NsfyhIQE+vfvT+3atQs0KCGE\nEEWfTlOZ5MbKyooBAwawatWqAgpHCCHEi+K5EkiW+Pj4gjiNEEKIF4hOVVgnTpzIVpaRkUFUVBQL\nFixQR6gLIYQoPnRKIB9++CEajSZbuaIo2NnZMWHChAIPTAghRNGmUwJZs2ZNtjKNRkOpUqVwdHRU\n1woRQghRfOiUQLy8vNSfU1JSePDgAVZWVpiYmOgtMCGEEEWbzrPx/vzzzyxevJizZ8+iKArGxsY0\natSIYcOG4eHhoc8YhRBCFEE61T3t3r2bIUOGoCgKI0aMYPr06QwdOpTExER69+6trk4ohBCi+NDp\nCWTRokW8+eabzJkzR6t80KBBjBgxgi+//JJvv/1WLwEKIYQomnR6Arlx4wZdunTJcds777zDP//8\nU6BBCSHu0b0AAAAd/klEQVSEKPp0SiBOTk6Eh4fnuO3cuXMylYkQQhRDOlVhBQQEMGrUKB4+fEjH\njh2pUKEC9+7d45dffmHFihWMHz9ea7Bho0aN9BawEEKIokGnBNKvXz8ANmzYwMaNG9VyRVEAmDZt\nmvpao9Fw/vz5Ag5TCCFEUZPvgYRCCCGKtzwPJBRCCCEgDwMJL126xIIFCzh69CgPHjygXLlyeHh4\nMGTIEF555RV9xiiEEKII0imB/P3333zwwQeYm5vTunVrbGxsiI2N5eDBgxw8eJBNmzbh6Oio71iF\nEEIUITolkDlz5lC7dm3WrFmDhYWFWp6UlETv3r2ZN28eixcv1luQQghRmI4dgz17ICoK7OzAxwc8\nPQ0dVdGj0ziQiIgIBg0apJU8ACwsLOjXrx8RERE6XzAuLo6PP/6YZs2a4eHhwUcffaQ1EPHw4cN0\n7twZFxcXfH19CQsL0zo+Pj6e4cOH4+HhQdOmTQkKCiItLU3n6wshxNMcOwbLl8PNm5CRkfn38uWZ\n5UKbTgnE3Nw8120ajYb09HSdLpaRkcHQoUO5cuUKixYtYtOmTZQqVYrevXtz9+5dLl68yODBg2nf\nvj3btm2jdevW+Pv7c+HCBfUcAQEBxMXFsW7dOmbNmkVoaCgLFizQ6fpCCPEse/bkXL53b+HG8SLQ\nKYG4urqybNkyUlJStMqTk5NZvnw5bm5uOl0sMjKSkydPMnPmTFxcXHjllVcICgoiKSmJsLAw1qxZ\ng6urK4MHD8be3p4RI0bg5uamdiM+efIkx48fZ9asWTg5OdGiRQvGjh3L2rVrefz4cR5vXQghsouK\nyrn81q3CjeNFoFMbyKhRo3jnnXdo3bo1rVq1onz58sTFxfHzzz/z8OFD1q9fr9PF7Ozs+Prrr6lV\nq5ZalrXSYUJCAhEREfj4+Ggd07hxY3bv3g1kVqVVqVKFatWqqdu9vLx4+PAh58+fp2HDhjrFIYQQ\nubGzy6y2elLlyoUfS1Gn0xOIvb09mzZtolGjRhw4cIClS5fy008/4e7uzubNm6lXr55OFytXrhwt\nW7bUWsFw7dq1JCcn06xZM6Kjo6lYsaLWMRUqVCA6OhqAmJgYKlSokG07QFRuXxuEECIPnvgOq2rf\nvnDjeBHoPA7E0dGR4ODgAr34gQMH+PLLL+nTpw/29vYkJydjamqqtY+pqaladfbo0SPMzMy0tpuY\nmKDRaLJVrwkhRH5k9bbauzez2qpy5czkIb2wstM5gcTExLBmzRqOHz9OQkICNjY2NGnShB49emBl\nZZXnC4eGhjJ58mQ6dOjAmDFjADAzMyM1NVVrv8ePH6uN+CVLlszW1pGamoqiKNl6iAkhRH55ekrC\n0IVOVVhnz56lY8eObNiwAUtLSxo0aICZmRnLly/nzTff5Pr163m66OLFixk/fjzvv/8+X3zxhVql\nZWdnx+3bt7X2vX37tlqtValSJWJjY7NtB7JVfQkhhNAvnZ5AZs2aRfXq1Vm+fDnW1tZqeVxcHP37\n92fWrFksXLhQpwsuW7aMefPmMWzYMPz9/bW2ubu7c+yJztbh4eHqmuvu7u7MmTOHqKgo7Ozs1O2W\nlpY4OTnpdH0hhBAFQ6cnkNOnTxMQEKCVPADKly+Pv7+/zmuiR0ZG8tVXX9G1a1feffddYmNj1T9J\nSUn4+fkRERFBcHAwly5dYv78+Zw6dYpevXoB4ObmhqurK4GBgZw9e5awsDCCgoLo06dPtrYTIYQQ\n+qXTE4itrW22qqUsDx8+1LkN5IcffiA9PZ2tW7eydetWrW3Dhw9nyJAhhISEEBQUxLJly6hduzZL\nlizB3t4eyOzyGxISwrRp0+jevTuWlpZ069Yt25OMEEII/dMpgYwZM4apU6diZWVFu3bt1LEb4eHh\nfPXVV4wfP16ni40cOZKRI0c+dZ+WLVvSsmXLXLfb2trqXF0mhBBCf3SeTDE5OZnAwEBKlChB+fLl\nuXfvHsnJySiKwqhRoxg1apS6/5kzZ/QWsBBCiKJBpwTSqVMnfcchhBDiBaNTAhk6dKi+4xBCCPGC\n0akXlhBCCPEkSSBCCCHyRRKIEEKIfJEEIoQQIl90SiDff/89d+/ezXFbbGwsK1euLNCghBBCFH06\nJZDx48fnOmHi+fPn+eqrrwo0KCGEEEVfrt14Bw4cyMWLFwFQFAV/f/8c55uKj4+nevXq+otQCCFE\nkZRrAhk8eDDfffcdAN999x0NGjTINpmikZERZcqU4a233tJvlEIIIYqcXBOIq6srrq6uAKSnpzNk\nyBCttciFEEIUbzqNRP/888/1HYcQQogXTK4JpH79+mzYsAEXFxecnZ3VGXhzIxMoCiFE8ZJrAhk0\naJC6TOygQYOemUCEEEIUL7kmkP9OoBgQEFAowQghhHhx6NQGApCYmEhoaCjHjx8nMTERa2trGjdu\nTKdOnWQ5WSGEKIZ0SiBXr16lZ8+e3L59mxo1amBjY8Nff/3Fzp07Wb16NatWrcLGxkbfsQohhChC\ndO6FVbJkSXbt2qWuTw5w7tw5hg4dymeffcaXX36ptyCFEMXXsWOwZw9ERYGdHfj4gKenoaMSoONU\nJuHh4YwcOVIreQDUq1ePkSNH8ssvv+gjNiFEMXfsGCxfDjdvQkZG5t/Ll2eWC8PTKYGULl2a9PT0\nHLdZWlpibm5eoEEJIQRkPnnkZO/ewo1D5EynBDJw4EDmzJlDZGSkVvmtW7eYN28e/fr100twQoji\nLSoq5/Jbtwo3DpGzXNtA2rZtqzX2IyYmhrfeeovq1atjY2PD/fv3+ffffzE1NeWnn36iT58+hRKw\nEKL4sLPLrLZ6UuXKhR+LyC7XBNKoUSOtBNKoUaNs+zRo0EA/UQkhBJkN5suXZy9v377wYxHZ5ZpA\nZs2aVZhxCCFENlm9rfbuzay2qlw5M3lIL6yiQeeBhBkZGRw8eFAdSGhjY4OXlxdNmzbVZ3xCiGLO\n01MSRlGlUwKJi4ujX79+REZGYmpqirW1NfHx8SxZsoSmTZsSEhKChYWFvmMVQghRhOjUC2vWrFnE\nxsaybNky/vrrL3755RdOnz7NggULOHv2LLNnz9Z3nEIIIYoYnRLIwYMHGTNmDM2bN9cqb9OmDaNG\njWJPbp21hRBCvLR0SiCmpqaULl06x22VpT+dEEIUSzolkA8++ID58+cTFxenVZ6UlMTSpUvp1q2b\nXoITQghRdOXaiN63b1/1Z0VRuHTpEm3atKFRo0bqQMITJ06QlpZGhQoVCiVYIYQQRUeuCSQ1NVXr\nddZAwtTUVKKjowFwcnIC4Pbt2/qKTwghRBGVawJZu3ZtYcYhhBDiBaNTG8jTPH78mN9//70gYhFC\nCPEC0Wkg4a1bt/jkk084evSoVtVWRkYGiqIAcP78ef1EKIQQokjSKYHMmjWLiIgIunbtyokTJzA3\nN8fV1ZXffvuNf/75hwULFug7TiGEEEWMzisSBgYGMmnSJN5++23MzMwYM2YMW7duxcPDgwMHDug7\nTiGEEEWMTgnk4cOHODo6AlC7dm3OnTsHgLGxMd27d+fIkSP6i1AIIUSRpFMCqVChgjqIsEaNGiQk\nJBAbGwtA2bJliY+P11+EQgghiiSd2kCaN29OcHAwlStXpmHDhlSqVIlvvvmGgIAAvv/+eypWrKjv\nOIUQRcCxY5nrlEdFZa4W6OMjU60XZzo9gQwfPhxzc3O+/PJLAAIDA1m1ahWNGjXi+++/z/dytlOm\nTGHixIlaZYcPH6Zz5864uLjg6+tLWFiY1vb4+HiGDx+Oh4cHTZs2JSgoiLS0tHxdXwihu2PHMlcH\nvHkTMjIy/16+PLNcFE86PYFYW1sTGhpKTEwMAJ06daJy5cr8+eefuLi44OXllaeLKopCcHAwmzdv\n5p133lHLL168yODBgxkyZAht27Zl586d+Pv7s23bNurUqQNAQEAAGo2GdevWERMTw7hx4yhRogSB\ngYF5ikEIkTe5Tbq9d688hRRXeRpI+N+qKg8PD/r164enpyfr16/X+RzXr1+nZ8+ebNy4MdtMvmvW\nrMHV1ZXBgwdjb2/PiBEjcHNzY82aNQCcPHmS48ePM2vWLJycnGjRogVjx45l7dq1PH78OC+3IoTI\no6ionMtv3SrcOETR8dQEcujQIQIDAxk5cmS2qiSAiIgI3nrrLWbMmKHzBU+cOIGdnR07d+6katWq\n2c735NNM48aNiYiIULdXqVKFatWqqdu9vLx4+PChDGQUQs/s7HIulxUdiq9cq7B27NjB2LFjMTEx\nwdTUlD179hAcHMwbb7zBvXv3mDFjBrt378bY2DhPbSCdO3emc+fOOW6Ljo7O1iBfoUIFdfLGmJiY\nbDP/Zr2OioqiYcOGOschhMgbH5/MNo8ntW9f+LGIoiHXBLJ69WoaNmzIihUrMDU1Zfz48SxatIg6\nderQp08foqKiaN68ORMmTKBWrVoFEkxycjKmpqZaZaampqSkpADw6NEjzMzMtLabmJig0WjUfYQQ\n+pHVzrF3b2a1VeXKmclD2j+Kr1wTyJUrV5g+fTqlSpUCwN/fn44dO+Lv78/jx4+ZP38+7dq1K9Bg\nzMzMsk0j//jxY8zNzQEoWbJktraO1NRUFEXBwsKiQGMRQmTn6SkJQ/xPrgkkKSkJu/9UelatWhVF\nUTA2NmbHjh3Y2NgUeDB2dnbZ1ha5ffu2Wq1VqVKlbG0xWfvLWBQhhChcuTaiZyWLLFk/jxgxQi/J\nA8Dd3Z1jT3QqDw8Px8PDQ91+/fp1ov7THSQ8PBxLS0t1cSshhBCFI8/rgejzm76fnx8REREEBwdz\n6dIl5s+fz6lTp+jVqxcAbm5uuLq6EhgYyNmzZwkLCyMoKIg+ffpkazsRQgihX3lOIBqNRh9xAODo\n6EhISAg//vgjXbp04eeff2bJkiXY29ur1w4JCcHGxobu3bszYcIEunXrhr+/v95iEkIIkTONkrUi\n1BOcnJxwcXFRG9EVReGPP/6gYcOGWFpaap9Eo2HFihX6jzafbty4QevWrTlw4EC2sSdCCCFy9qzP\nzlwb0T3/v6vFf3tF5VQmhBCieMo1gaxdu7Yw4xBCCPGCyXMbiBBCCAE6zsYrhChaZF0OURRIAhHi\nBZO1LkeWrHU5QJKIKFxShSXEC+Zp63IIUZgkgQjxgpF1OURRIQlEiBeMrMshigpJIEK8YHx8ci6X\ndTlEYZNGdCFeMLIuhygqJIEI8QKSdTlEUSBVWEIIIfJFEogQQoh8kQQihBAiX6QNRIjnJNOKiOJK\nEogQz0GmFRHFmVRhCfEcZFoRUZxJAhHiOci0IqI4kwQixHOQaUVEcSYJRIjnINOKiOJMGtGFeA4y\nrYgoziSBCPGcZFoRUVxJFZYQQoh8kScQ8VKRQX1CFB5JIOKlIYP6hChcUoUlXhoyqE+IwiUJRLw0\nZFCfEIVLEoh4acigPiEKl7SBCL0p7AZtHx/tNpAsMqhPCP2QBCL0whAN2jKoT4jCJQlE6MXTGrT1\n+YEug/qEKDzSBiL0Qhq0hXj5SQIReiEN2kK8/KQKqxgwxOhsadAW4uUnCeQlZ6jR2dKgLcTLTxLI\nS85QjdkgDdpCvOwkgRSywq5OksZsIYS+SAIpRIaoTrKzy7zOk6QxWwjxvIp1AinspwFDVCdJY7YQ\nQl+KbQIxxNOAIaqTpDFbCKEvxTaBGOJpwFDVSdKYLYTQh2I7kNAQTwM+PjmXS3WSEOJFVGyfQAzx\nNCDVSUKIl0mxTSCGalyW6iQhxMuiWCSQ9PR0AKKjo9UyOzvo3BnCwiAmBipWhBYtMstv3DBUpEII\nUXRkfWZmfYY+qVgkkNjYWAC6d+/+1P2+/74wohFCiBdLbGwsNWrUyFauURRFMUA8hSo5OZkzZ85g\na2uLsbGxocMRQogXQnp6OrGxsdSvX5+SJUtm214sEogQQoiCV2y78QohhHg+kkCEEELkiyQQIYQQ\n+SIJRAghRL5IAhFCCJEvxTaBpKenM3fuXJo1a4abmxvDhg0jLi7O0GHpRVxcHB9//DHNmjXDw8OD\njz76iH/++cfQYenVn3/+Sb169QgPDzd0KHqzZcsW2rVrh4uLC2+//TZ//PGHoUMqcElJSUyfPl39\nt9uvXz8uXrxo6LAK3JQpU5g4caJW2eHDh+ncuTMuLi74+voSFhZmoOhyV2wTyIIFC9i2bRuzZ89m\n3bp1REdHExAQYOiwClxGRgZDhw7lypUrLFq0iE2bNlGqVCl69+7N3bt3DR2eXiQlJTF27NhcR8++\nDLZt28Ynn3xC//792blzJ56engwZMoQbL9k0Cp999hm///478+fPZ/PmzZiZmdGvXz9SUlIMHVqB\nUBRFvbf/unjxIoMHD6Z9+/Zs27aN1q1b4+/vz4ULFwwUaS6UYiglJUVxc3NTtm7dqpZdv35dcXBw\nUI4fP27AyAre2bNnFQcHB+XixYtqWUpKitKwYUNl27ZtBoxMfyZPnqz4+fkpDg4OypEjRwwdToHL\nyMhQXn/9dWXevHlqWXp6utKpUydlx44dBoys4Hl5eSlr1qxRX1+4cEFxcHBQzpw5Y8CoCsa1a9cU\nPz8/pXHjxkrLli2VCRMmqNuy/g3/l5+fnzJp0qTCDvOpiuUTSGRkJA8fPsTLy0stq1q1KlWqVCEi\nIsKAkRU8Ozs7vv76a2rVqqWWaTQaABISEgwVlt6EhYXxyy+/MGnSJEOHojf//vsvN2/epEOHDmqZ\nkZER27dvx9fX14CRFTxra2t++OEH4uPjefz4Md999x1WVlZUq1bN0KE9txMnTmBnZ8fOnTupWrWq\n1raIiAitzyeAxo0bF7nPp2IxF9aTsiYIq1ixolZ5hQoVtCZcfBmUK1eOli1bapWtXbuW5ORkmjVr\nZpig9OTOnTtMnDiRmTNnYmVlZehw9ObKlSsA3L9/n549e3LhwgVq167NqFGjaNSokWGDK2DTp09n\nzJgxvPrqqxgbG1OyZElWrlxJmTJlDB3ac+vcuTOdO3fOcVt0dPQL8flULJ9AHj16hJGRESYmJlrl\npqamL03dam4OHDjAl19+SZ8+fbC3tzd0OAVq6tSptGrVCm9vb0OHoleJiYkAjBs3jm7durF8+XLq\n1KlDr169uHTpkoGjK1hXr16lfPnyLF26lI0bN9KsWTOGDRtW5D5IC1pycjKmpqZaZUXx86lYJpCS\nJUuSkZFBWlqaVvnjx48xNzc3UFT6FxoayrBhw/Dx8WHMmDGGDqdAbdu2jXPnzvHxxx8bOhS9y/ri\nM2jQIHx9fXF2dmbq1KnUrFmTjRs3Gji6gnP9+nUmT57MxIkTadGiBQ0bNmTu3LmYmZmxatUqQ4en\nV2ZmZqSmpmqVFcXPp2JZhWVnZwdkTlGc9TPA7du3sz02viwWL17MvHnz8PPzY9KkSWo7yMsiNDSU\nmJgYtVpO+f85Qvv370+XLl349NNPDRlegapQoQIADg4OaplGo6F27dovVS+sM2fOkJ6eTv369dUy\nExMT6taty9WrVw0Ymf7Z2dlx+/ZtrbKi+PlULBOIk5MTlpaWHD16VK2DvHHjBjdv3sTzJVwucNmy\nZcybN49hw4bh7+9v6HD0Ys6cOSQnJ6uvY2Nj6d69OzNmzOC1114zYGQFz9nZGQsLC06fPk2DBg2A\nzIR56dIlmjZtauDoCk6lSpUA+Pvvv3F2dgb+d58vezWlu7s7x44d0yoLDw/Hw8PDQBHlrFgmEFNT\nUz788EO++OILypUrh42NDZ988gleXl64uroaOrwCFRkZyVdffUXXrl1599131cW1ACwtLbGwsDBg\ndAXnyW9mZmZmarmNjY0hQtIbc3NzevXqxbx58yhfvjwODg5s2LCBa9euERwcbOjwCoyLiwuurq6M\nGzeOqVOnUq5cOVavXs2tW7fw8/MzdHh65efnR9euXQkODqZjx47s2rWLU6dOMW3aNEOHpqVYJhCA\nESNGkJaWxpgxY0hLS6N58+ZMmTLF0GEVuB9++IH09HS2bt3K1q1btbYNHz6cIUOGGCgy8TyGDx+O\nubk5M2fOJD4+nrp167Jy5Upq165t6NAKjLGxMYsXL+bLL79k5MiRJCUlUb9+fTZs2ECVKlUMHZ5e\nOTo6EhISQlBQEMuWLaN27dosWbKkyHV8kQWlhBBC5Eux7IUlhBDi+UkCEUIIkS+SQIQQQuSLJBAh\nhBD5IglECCFEvkgCEUIIkS/FdhyIELr4+++/WbJkCUePHiUhIYGyZcvi4eHBoEGDcHJyMnR4QhiU\njAMRIheRkZF88MEHNGrUiG7dumFtbU10dDRr1qzhn3/+Yc2aNS/dzAVC5IUkECFyMX78eI4dO8aP\nP/6IsbGxWv7w4UN8fHxwcnJi6dKlBoxQCMOSKiwhchEfH4+iKGRkZGglEEtLSyZMmMCjR4/Usv37\n97N48WIuXLiAlZUVvr6+BAYGaq3p8OOPP7Jq1SoiIyNJTU2lWrVq9OjRgw8//FDdZ/Xq1WzcuJGb\nN29StmxZWrduzejRoylVqhSQuU7E119/zQ8//MCtW7eoVq0avXr14r333lPP0apVK95++20SExPZ\nsWMHDx8+xMPDgylTplCjRg19vmWimDGeVtRm5xKiiEhISGDXrl0cOnSItLQ0LC0tsba2BuCVV16h\nbt26AOzcuZMRI0bw2muvMWLECOrUqcPy5cs5f/68uuzsgQMHGDp0KO3atcPf35+2bdty9epV1qxZ\nQ4sWLahYsSK7du3i008/ZfDgwfTt25c6deqwYsUKoqKiaNOmDYqi0LdvX/bv30+/fv3o2bMn6enp\nBAcHo9Fo1CVQV69eTXh4OObm5gQGBvLqq6+ydetWTpw4QdeuXQ3zZoqXkjyBCJGL7t27Exsbyzff\nfKOuJ2JtbU2zZs3o0aMHLi4uKIrCnDlzeP3115k9e7Z6bKVKlfD39+f48eO4u7tz6dIl3n77bcaP\nH6/u4+bmRuPGjTl69CguLi4cPXqUqlWr0r17d4yMjPDy8sLCwkJduz4sLIyjR48yf/582rdvD0Cz\nZs1IS0tjyZIlfPjhh5QrVw6AsmXLsmjRIvXJ6dq1ayxYsIAHDx5QunTpQnn/xMtPEogQudBoNAQG\nBtK3b19+/fVX/vjjD8LDw9mxYwc7d+5k8uTJNGnShOjoaPz9/bVWuGzevDkmJib8/vvvuLu7M2DA\nACCz/eTy5ctcu3aN06dPA6grzzVp0oTNmzfz9ttv06ZNG1q0aIGvr6+6+NexY8cwMTGhbdu2WnH6\n+vqyceNGTp06RcuWLQFo2LChVrVb1toaSUlJkkBEgZEEIsQzWFlZ8eabb/Lmm28CcO7cOcaMGcPs\n2bP55ptvAJg8eTKTJ0/OdmzWqnJ37txh6tSp/PTTT2g0GmrUqKEuDpTVj6VDhw5kZGSwYcMGFi1a\nxIIFC6hSpQqjR4+mQ4cOJCQkYGNjg5GR9vCt8uXLA/DgwQO1rGTJklr7ZB0jfWZEQZIEIkQOoqOj\neeeddxg+fDjdunXT2lavXj0CAwPx9/cnIyMDyOyx5e7unu08WVVKo0eP5vLly6xatQo3NzdMTU15\n9OgR3377rdb+WYnqwYMHHD58mGXLljFmzBg8PT0pU6YM8fHxZGRkaCWRrEXCsq4lRGGRkehC5MDW\n1hZjY2M2bNhASkpKtu3//vsv5ubmODk5YW1tzc2bN2nQoIH6p1y5csyZM4dLly4BcPz4cdq3b0/j\nxo3VnlmHDh0CUJPQqFGj1CWHS5cujY+PD0OGDCEtLY24uDi8vLxITU1l3759WrHs2rULExMTXFxc\n9PZ+CJETeQIRIgfGxsZMmTKFgIAAunbtSvfu3bG3t+fRo0f89ttvrF+/npEjR1K6dGlGjBjBJ598\ngpGREd7e3ty7d09tsK5Xrx6QuTzrjh07qFu3LhUrVuTEiRMsXboUjUajdgdu0qQJkyZNYvbs2Xh7\ne3P//n1CQkKoVasWDg4OODo64unpycSJE4mOjqZOnTqEhYWxadMmBg0aRJkyZQz5loliSAYSCvEU\nZ86cYcWKFZw4cYL4+HjMzMyoV68ePXv25I033lD32717N8uXL+fixYuUKlUKT09PRo4cSc2aNQG4\nefMm06dPJyIiAoCaNWvSs2dPduzYQWJiIps2bQJg3bp1bNy4kRs3blCyZEmaNm3K2LFjqVy5MpDZ\nCD9v3jz27NlDQkICNWvWxM/PL9s4kKZNm/LZZ5+pZaGhoYwfP56wsDC1QV2I5yUJRAghRL5IG4gQ\nQoh8kQQihBAiXySBCCGEyBdJIEIIIfJFEogQQoh8kQQihBAiXySBCCGEyBdJIEIIIfLl/wBMnSwc\nyIIo+AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1d6c4d6beb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system, title='Proportional growth model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's suppose our goal is to maximize the number of rabbits, so the metric we care about is the final population.  We can extract it from the results like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def final_population(system):\n",
+    "    t_end = system.results.index[-1]\n",
+    "    return system.results[t_end]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And call it like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "404.95651696640027"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_population(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To explore the effect of the parameters on the results, we'll define `make_system`, which takes the system parameters as function parameters(!) and returns a `System` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def make_system(birth_rate=0.9, death_rate=0.5):\n",
+    "    \n",
+    "    system = System(t0 = 0, \n",
+    "                    t_end = 10,\n",
+    "                    p0 = 10,\n",
+    "                    birth_rate = birth_rate,\n",
+    "                    death_rate = death_rate)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can make a `System`, run a simulation, and extract a metric:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "404.95651696640027"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system()\n",
+    "run_simulation(system)\n",
+    "final_population(system)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To see the relationship between `birth_rate` and final population, we'll define `sweep_birth_rate`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_birth_rate(birth_rates, death_rate=0.5):\n",
+    "    \n",
+    "    for birth_rate in birth_rates:\n",
+    "        system = make_system(birth_rate=birth_rate,\n",
+    "                             death_rate=death_rate)\n",
+    "        run_simulation(system)\n",
+    "        p_end = final_population(system)\n",
+    "        plot(birth_rate, p_end, 'gs', label='rabbits')\n",
+    "        \n",
+    "    decorate(xlabel='Births per rabbit per season',\n",
+    "             ylabel='Final population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first parameter of `sweep_birth_rate` is supposed to be an array; we can use `linspace` to make one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.  ,  0.05,  0.1 ,  0.15,  0.2 ,  0.25,  0.3 ,  0.35,  0.4 ,\n",
+       "        0.45,  0.5 ,  0.55,  0.6 ,  0.65,  0.7 ,  0.75,  0.8 ,  0.85,\n",
+       "        0.9 ,  0.95,  1.  ])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "birth_rates = linspace(0, 1, 21)\n",
+    "birth_rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can call `sweep_birth_rate`.\n",
+    "\n",
+    "The resulting figure shows the final population for a range of values of `birth_rate`.\n",
+    "\n",
+    "Confusingly, the results from a parameter sweep sometimes resemble a time series.  It is very important to remember the difference.  One way to avoid confusion: LABEL THE AXES.\n",
+    "\n",
+    "In the following figure, the x-axis is `birth_rate`, NOT TIME."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FBSErKwthYWHw9vaudO2FiIhqVrWBMnToUNnfu3XrBkNDQ9k1kGcVFhYqdHRSISAgAPr6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\nKEREpBIMFCIiUgkGChERqcT/A+EfZU016W7KAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1d6c4de7048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "birth_rates = linspace(0, 1, 21)\n",
+    "sweep_birth_rate(birth_rates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code to sweep the death rate is similar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sweep_death_rate(death_rates, birth_rate=0.9):\n",
+    "    \n",
+    "    for death_rate in death_rates:\n",
+    "        system = make_system(birth_rate=birth_rate,\n",
+    "                             death_rate=death_rate)\n",
+    "        run_simulation(system)\n",
+    "        p_end = final_population(system)\n",
+    "        plot(death_rate, p_end, 'r^', label='rabbits')\n",
+    "        \n",
+    "    decorate(xlabel='Deaths per rabbit per season',\n",
+    "             ylabel='Final population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results.  Again, the x-axis is `death_rate`, NOT TIME."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SpUsxatQo2NjYwMXFBatWrcLNmzfl3pm1tTUcHBywZs0a/P7771yf/fr1wyuv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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1d6c4a226d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "death_rates = linspace(0.1, 1, 20)\n",
+    "sweep_death_rate(death_rates)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous sweeps, we hold one parameter constant and sweep the other.\n",
+    "\n",
+    "You can also sweep more than one variable at a time, and plot multiple lines on a single axis.\n",
+    "\n",
+    "To keep the figure from getting too cluttered, I'll reduce the number of values in `birth_rates`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.4,  0.6,  0.8,  1. ])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "birth_rates = linspace(0.4, 1, 4)\n",
+    "birth_rates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By putting one for loop inside another, we can enumerate all pairs of values.\n",
+    "\n",
+    "The results show 4 lines, one for each value of `birth_rate`.\n",
+    "\n",
+    "(I did not plot the lines between the data points because of a limitation in `plot`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D1taWK4MQQoj6KNTRYejQofjmm28QGhqK8+fPc8u7du2K1atXY8yYMSoLaPXq1fjiiy8w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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1d6c54487b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for birth_rate in birth_rates:\n",
+    "    for death_rate in death_rates:\n",
+    "        system = make_system(birth_rate=birth_rate,\n",
+    "                             death_rate=death_rate)\n",
+    "        run_simulation(system)\n",
+    "        p_end = final_population(system)\n",
+    "        plot(death_rate, p_end, 'c^', label='rabbits')\n",
+    "        \n",
+    "decorate(xlabel='Deaths per rabbit per season',\n",
+    "         ylabel='Final population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you suspect that the results depend on the difference between `birth_rate` and `death_rate`, you could run the same loop, plotting the \"net birth rate\" on the x axis.\n",
+    "\n",
+    "If you are right, the results will fall on a single curve, which means that knowing the difference is sufficient to predict the outcome; you don't actually have to know the two parameters separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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x3NzcoNFo4OzsrHN8WbKg0WiQlZVVaX/ZMZmZmdBoNACg9xxl+4iIiKhmBnVh9OnTB4cP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\nv37g+OqCKepYJi8vD9OnT8eJEyewZs0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3Zf2ot7fX44mT6OhoiouLCQgI4NmzZ1PmHX/O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\nQgjhM+lACCGEEMJn/wEDHeDjKnY/1AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb4b52fe710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for birth_rate in birth_rates:\n",
+    "    for death_rate in death_rates:\n",
+    "        system = make_system(birth_rate=birth_rate,\n",
+    "                             death_rate=death_rate)\n",
+    "        run_simulation(system)\n",
+    "        p_end = final_population(system)\n",
+    "        net_birth_rate = birth_rate - death_rate\n",
+    "        plot(net_birth_rate, p_end, 'mv', label='rabbits')\n",
+    "        \n",
+    "decorate(xlabel='Net births per rabbit per season',\n",
+    "         ylabel='Final population')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On the other hand, if you guess that the results depend on the ratio of the parameters, rather than the difference, you could check by plotting the ratio on the x axis.\n",
+    "\n",
+    "If the results don't fall on a single curve, that suggests that the ratio alone is not sufficient to predict the outcome. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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o9XpERUVZuxYAwMqVK5GUlISBAwdCkiR4e3tj+fLl8PHxgVqthqenp8n+dWFBrVajtLS0\nXnvdPiUlJVCr1QDQYB91bURERHRnZk1hDBkyBNnZ2dauBVqtFomJiQgMDERWVhYOHTqEOXPmIDk5\nGb/88kuT+pYkqUntRERE9BuzRiAmTZqEl19+GadOnUKfPn3QsWPHevsMHjy4ycX897//xbFjx/De\ne+/Bx8cHADBlyhRs2rQJW7ZsgUKhqLeIs7S0FACgVCrh4+PT4CLP0tJSKBQKKBQKAGiwj7rPIyIi\nojszK0BMnToVAHDs2DGT7ZIkQQgBSZJw/PjxJhdjMBgAwOSyzLrXQghERkYiLy/PpC0nJwdKpRIB\nAQGIjIzEO++8g5KSEmMguHLlCoqKihAVFQU/Pz8olUrk5eWhX79+Jn201BQNERFRW2BWgGipKxT6\n9u0LhUKBN998E6mpqejYsSO2b9+O06dP4+WXXwZwPczs3LkTw4cPx8mTJ7Fu3TrMmDEDkiRh0KBB\n6NWrF5YuXYpFixZBCIElS5YgKCgIMTExkCQJ06ZNw9q1a9G/f38EBQXhq6++wv79+/Hhhx+2yDES\nERG1BWYFiP79+1u7DgCAh4cH1qxZg7S0NDz00EMoKytDjx498NZbbyEiIgIAkJaWhhUrVuC5556D\nQqFAfHw8ZsyYAQCQyWRIT0/H4sWLMWzYMEiShJiYGKSnp0MmkwEAEhMTUVNTg9mzZ0OtViMwMBDL\nly+vd2UGERERNU4SQghzdjx8+DA+/PBDHD9+HBUVFXB3d0d4eDgSEhLQq1cva9dpc2fPnkVsbCx2\n794NPz8/W5dDRERkVXc675l1FcaePXswZcoUHDhwAN27d8e9996Lbt26Yc+ePXj00Udx+PDhZi+c\niIiI7JdZUxirV6/G2LFj8dJLL8HB4bfModfr8f/+3//DsmXLeCdHIiKidsSsEYiTJ09ixowZJuEB\nuL7m4Mknn0R+fr5ViiMiIiL7ZFaAkCQJtbW1DXfgYHcP9CQiIiIrM+vsHxoairfffrteiNDpdFi1\nahVCQ0OtUhwRERHZJ7PWQDz11FOYPn067rvvPoSGhsLNzQ1lZWUoKChAdXU11q5da+06iYiIyI6Y\nNQIRFRWFLVu2YPjw4SgpKcHRo0ehVqsxYsQIbNmyBX379rV2nURERGRHzBqBAICgoCC89NJL1qyF\niIiIWolGA0RWVhYGDBgAR0dHZGVl3bGj5niYFhEREbUOjQaIxMRE/PDDD/Dx8UFiYqLxwVkNaa6H\naREREVHr0GiAWL9+PTw9PY2/ExEREdVpNEDc/ACt8+fP48EHH4RcLq+338WLF/Hll1+22AO3iIiI\nyPbMugojNTUV5eXlDbZdvnwZy5Yta9aiiIiIyL7d9iqM+Ph449qHv/zlL3BycjJpF0KgsLAQHh4e\nVi2SiIiI7MttRyDGjh2L7t27A7j+4Kza2lqTH71ej5CQELz++ustUiwRERHZh9uOQIwbNw7jxo1D\nYWEhVq1axZEGIiIiAmDmGogNGzY0Gh7Onz+PuLi4Zi2KiIiI7JvZd6Lcs2cPvv/+e2g0GuM2IQR+\n+eUXXL582SrFERERkX0yK0B8/PHHeOGFF6BQKKBWq6FUKnH16lVUV1cjIiKCt7gmIiJqZ8yawli/\nfj0WLVqErKwsODs7Y+PGjTh8+DDefPNNODg4ICoqytp1EhERkR0xK0CoVCrcf//9AK7ftlqv10OS\nJDz88MN49NFH8eKLL1qzRiIiIrIzZgUIR0dHVFdXAwA8PT1x8eJFY9uAAQOQnZ1tneqIiIjILpkV\nICIiIpCWloaysjIEBwfj3XffNQaKb775Bs7OzlYtkojaPr2+AmfPrkBNzQVbl0JEZjBrEeXcuXOR\nmJgItVqNhIQEPPHEE+jfvz/kcjkqKiowbdo0a9dJRG2cVluMioqjqKw8Dk/PoVAoRkEmc7V1WUTU\nCLMCREREBPbs2QMXFxd0794dmzZtwhdffIHa2lpERETgoYcesnadRNTG6XTFAAAhDNBovkNZ2QH4\n+DwML68/QpLMGiwlohZk9n0g3NzcjL+HhYUhLCzMKgURUfuk1RabvNbrK1BcvBkazV4olePh5hZq\no8qIqCGNBoi0tDSzO5EkCfPnz2+WgoiofaobgbiZTOYOV9cQODp62aAiIrqdRgNEenq62Z0wQBBR\nU9WNQEiSDK6uYfD0jIGraygkSWbjyoioIY0GiBMnTrRkHUTUzjk4uMDXdwLc3aPh6Oh25zcQkU2Z\nvQaCiMia/P05iknUmpgVIB5//PE77rN+/fomF0NEREStg1kBQqfTQZIkk20VFRUoLCxEly5d8Ic/\n/MEqxREREZF9MitAfPTRRw1uLy0txYIFCzBy5MhmLYqIiIjsW5PuzuLt7Y2nn34aK1asaK56iIiI\nqBVo8u3dnJyccOEC711PRETUnpg1hZGVlVVvmxACV69eRUZGBu66665mL4yIiIjsl1kBIjExEZIk\nQQhRr83DwwOvv/56sxdGRERE9susANHQJZqSJMHd3R3du3dHhw4dmr0wIiIisl9mBYj+/ftbuw4T\nW7duRXp6Os6dOwdfX1/Ex8cjISEBAJCZmYk1a9agsLAQSqUScXFxmDdvHmSy67e7ValUWLp0KY4c\nOQIhBPr06YPnn38e/v7+AAC9Xo8VK1bgyy+/RHFxMbp3744nnngCo0aNatFjJCIias3MvhPl119/\njc8//xwqlQpXr16Fl5cXevbsiXHjxmHgwIHNVtAXX3yB1157DWlpabj33ntx+PBhvPjii4iKikJl\nZSVSUlLwxhtvIDY2FqdPn0ZycjKcnJwwZ84c6HQ6JCUlITw8HJmZmXB0dMQrr7yCxMREZGZmwsnJ\nCatXr8a2bdvw9ttv45577sG+ffvw9NNPw9fXF9HR0c12HERERG2ZWVdhrFmzBnPnzkVBQQHuuusu\n9OvXD126dMHBgwcxY8YMfPDBB81W0KpVq5CYmIhBgwZBLpcjOjoau3btQmhoKDZu3IghQ4YgLi4O\ncrkcwcHBSEhIwIYNG2AwGJCVlYUzZ84gNTUVnTp1goeHBxYsWACVSoW9e/dCCIGMjAxMnz4dISEh\nkMvlGD58OIYOHco7aRIREVnA7DUQSUlJeOaZZ+q1vfbaa1i7di2mTZvW5GKKi4vx66+/omPHjpg0\naRJOnjyJbt26YebMmRg1ahRyc3MxefJkk/eEh4dDo9GgsLAQubm5CAgIgLe3t7Hdy8sL/v7+yMvL\nwz333AO1Wo3w8PB6fWzYsKHJ9RMREbUXZo1AaDQa/PnPf26wbcKECdBoNM1SzMWLFwEAmzdvxosv\nvoisrCyMHz8ezz77LA4dOgS1Wg1PT0+T99SFBbVajdLS0nrtdfuUlJRArVYDQIN91LURERHRnZkV\nIIKDg40n91tdvHgRvXv3bpZi6i4TjY+PR3BwMDp27IjHH38coaGh2Lp1a5P6vvVZHpa2ExER0W/M\nmsJYvHgxli5dirKyMkRERMDd3R2VlZU4dOgQ3n//faSkpECr1Rr3l8vlv6sYX19fADCZggCAgIAA\nXLp0CQqFot5oR2lpKQBAqVTCx8enwdGQ0tJSKBQKKBQKAGiwDx8fn99VMxERUXtkVoB47LHHUFNT\ng0OHDtVrE0Jg0qRJxteSJOHYsWO/qxhfX194eXkhPz8fw4cPN24/c+YMQkND4eHhgby8PJP35OTk\nQKlUIiAgAJGRkXjnnXdQUlJiDARXrlxBUVERoqKi4OfnB6VSiby8PPTr18+kj6ioqN9VMxERUXtk\n0Z0orU0mk2H69Ol49913ER0djaioKHzyySc4fvw4li5dipqaGkydOhU7d+7E8OHDcfLkSaxbtw4z\nZsyAJEkYNGgQevXqhaVLl2LRokUQQmDJkiUICgpCTEwMJEnCtGnTsHbtWvTv3x9BQUH46quvsH//\nfnz44YdWPz4iIqK2wqwAMXfuXGvXYfTkk0+itrYWqampKCkpQWBgIN59913jOou0tDSsWLECzz33\nHBQKBeLj4zFjxgwA1wNIeno6Fi9ejGHDhkGSJMTExCA9Pd14o6nExETU1NRg9uzZUKvVCAwMxPLl\ny+tdmUFERESNk0RDD7hoQHl5OXbt2oXjx4+joqIC7u7uCA8Px8iRI+Hs7GztOm3u7NmziI2Nxe7d\nu+Hn52frcoiIiKzqTuc9s0Ygfv31V0ybNg1XrlyBu7s7XF1dUV5ejo0bN2LVqlVYv349Onfu3OzF\nExERkX0y6zLOf/7zn+jWrRt27dqFgwcPYs+ePTh06BB27NiBDh068GmcRERE7YxZAeLQoUN4/vnn\nERgYaLI9KCgIf/vb35CVlWWV4oiIiMg+mRUgqqqq4OHh0WCbr68vKisrm7UoIiIism9mBYju3btj\n165dDbZ98cUX6N69e7MWRURERPbNrEWUjz/+OF544QXk5+cjMjISbm5uKCsrw08//YS9e/diyZIl\n1q6TiIiI7IhZAWLChAkArj/W+9tvvzVuv/vuu7F06VKMGzfOOtURERGRXTIrQADXQ8SECRNQXl6O\niooKuLq6ws3NzZq1ERERkZ0yO0AAwM8//wyVSoVr167By8sLvXr1gr+/v7VqIyIiIjtlVoBQqVSY\nO3cuTp48iZtvXClJEiIjI/HGG2+gW7duViuSiIiI7ItZAeKFF17AtWvXsGTJEoSEhKBjx46oqKhA\nQUEB3n77bbzwwgtYs2aNtWslIiIiO2FWgPjpp5/w3nvv4d577zXZ3rt3b/j7+yM5OdkqxREREZF9\nMus+EG5ublAqlQ22de7cGa6urs1aFBEREdk3swLEuHHjsGXLlgbbPv30Uzz66KPNWhQRERHZN7Om\nMNzd3bFp0ybs3bsXkZGRcHd3R1VVFQ4ePIirV69i1KhRSEtLA3B9YeX8+fOtWjQRERHZllkBoi4c\nANcv5bzVe++9Z/ydAYLI9vT6Cly4sAZK5Xg4O3e1dTlE1AaZFSBOnDhh7TqIqBlptcWoqDiKysrj\n8PQcCoViFGQyrlUiouZj0Y2kiKh10OmKAQBCGKDRfIeysgPw8XkYXl5/hCSZtfSJiOi2+C8JURuk\n1RabvNbrK1BcvBmFhf9AeXmBjaoioraEAYKoDaobgbiZTOYOV9cQODp62aAiImprOIVB1AbVjUBI\nkgyurmHw9IyBq2soJElm48qIqK1ggCBqgxwcXODrOwHu7tFwdORTc4mo+TUaIE6fPm1RR4GBgU0u\nhoiah78/L6UmIutqNEDExcVBkiSzOzp+/HizFERERET2r9EA8corr7RkHURERNSKNBogxo4da1YH\nFRUV+Prrr5utICIiIrJ/Fi2iLC0thUajMb4WQiAnJwdLlizBmDFjmr04IiIisk9mBYhz585h3rx5\nOHbsWIPtkZGRzVoUERER2TezbiT1+uuvQ5Ik/P3vf4eTkxOeeeYZPP300+jZsycee+wxrF+/3tp1\nEhERkR0xK0Dk5OTgxRdfxMSJEyGTyTBy5Eg8+eST2LFjB86dO4cdO3ZYu04iIiKyI2YFCI1GA6VS\nCQCQy+Woqqq6/mYHB8yfPx///ve/rVchERER2R2zAkTnzp2Rn58PAPD19cXBgweNbY6Ojrh06ZJ1\nqiMiIiK7ZNYiyocffhh//etfsWPHDsTGxuKNN97AlStX4Onpic8++wy9evWydp1ERERkR8wKEPPm\nzYOTkxM8PT0xc+ZMnDx5Eu+88w6EEOjevTuWLl1q7TqJiIjIjpgVIGQyGebMmWN8vXr1apSXl6O2\nthZeXnw0MBERUXtj8dM4dTodhBCQy+WQy+XQarUAri+uJCIiovbBrABRWFiIxYsXIzc313gFxs0k\nSWr0JlNERETU9pgVIBYtWoRTp05h9OjR6NSpk0VP6SQiIqK2x6wAUVBQgHfffRdRUVHWrsdETk4O\npk6ditmzZ2Pu3LkAgMzMTKxZswaFhYVQKpWIi4vDvHnzIJPJAAAqlQpLly7FkSNHIIRAnz598Pzz\nz8Pf3x8AoNfrsWLFCnz55ZcoLi5G9+7d8cQTT2DUqFEtemxEREStmVn3gXB3d4dCobB2LSaqq6ux\ncOFCuLq6GrcdOHAAKSkpmDlzJrKzs7Fy5Urs2LEDq1evBnB9fUZSUhI8PDyQmZmJ//znP/D29kZi\nYiJ0Oh1c2mGcAAAgAElEQVSA6wtAt23bhrS0NGRnZ2POnDlITU1FdnZ2ix4fERFRa2ZWgBg/fjw+\n+eQTa9diIi0tDYGBgejdu7dx28aNGzFkyBDExcVBLpcjODgYCQkJ2LBhAwwGA7KysnDmzBmkpqai\nU6dO8PDwwIIFC6BSqbB3714IIZCRkYHp06cjJCQEcrkcw4cPx9ChQ/k8DyIiIguYNYXh5eWFjz76\nCNnZ2YiIiEDHjh1N2iVJwvz585utqEOHDmH79u3YsWMHnn32WeP23NxcTJ482WTf8PBwaDQaFBYW\nIjc3FwEBAfD29jap3d/fH3l5ebjnnnugVqsRHh5er48NGzY0W/1ERERtnVkB4uYbRRUUFNRrb84A\nUVVVhYULF2LBggXo3LmzSZtarYanp6fJtrqwoFarUVpaWq+9bp+SkhKo1WoAaLCPujYiIiK6M7MC\nxIkTJ6xdh1FaWhruvvtujBs3rln7vdOVI7yyhIiIyHwW30jKmuqmLj7//PMG2xUKBTQajcm20tJS\nAIBSqYSPj0+99rp9FAqFcSFoQ334+Pg0xyEQERG1C40GiIkTJyI9PR0eHh6YOHHiHTvatGlTk4vZ\nsmULKisr8cgjjxi3lZeX48iRI/j2228RGRmJvLw8k/fk5ORAqVQiICAAkZGReOedd1BSUmIMBFeu\nXEFRURGioqLg5+cHpVKJvLw89OvXz6SPlr5ElYiIqDVrNEA4OTk1+Ls1paSk4KmnnjLZ9tRTTyEi\nIgKJiYk4d+4cpk6dip07d2L48OE4efIk1q1bhxkzZkCSJAwaNAi9evXC0qVLsWjRIgghsGTJEgQF\nBSEmJgaSJGHatGlYu3Yt+vfvj6CgIHz11VfYv38/PvzwwxY5RiIiorag0QBx81UJLXWFgqenZ70F\njnK5HG5ublAqlVAqlUhLS8OKFSvw3HPPQaFQID4+HjNmzABw/aFf6enpWLx4MYYNGwZJkhATE4P0\n9HTjjaYSExNRU1OD2bNnQ61WIzAwEMuXL693ZQZRc9DrK3DhwhoolePh7NzV1uUQETUbSQghGmrI\nyMjAn//8Zzg7O5tsz8vLQ+/evdvdw7POnj2L2NhY7N69G35+frYuh1qJqqrTKCp6FZLkAE/PoVAo\nRkEmc73zG4mIbOxO571GbyS1ZMkSlJeX19s+ffp0XLp0qXmrJGqjdLpiAIAQBmg03+H06UUoLf0W\nQhhsXBkRUdM0GiAaGZhodDsR1afVFpu81usrUFy8GYWF/0B5ef17qhARtRZm3cqaiH6fuhGIm8lk\n7nB1DYGjo5cNKiIiah52dR8IorambgRCkmRwdQ2Dp2cMXF1DIUkyG1dGZF+44Lj1YYAgsiIHBxf4\n+k6Au3s0HB3dbF0Okd3SaotRUXEUlZXHueC4lWg0QEiSxNs7EzWRv3/zPWSOqC27dcFxWdkB+Pg8\nDC+vP0KSONtujxoNEEIIjBo1ql6IqK6uxmOPPQYHh9/+g0qShO+//956VRIRUZvW2IJjjWYvlMrx\ncHMLtVFl1JhGA8TYsWNbsg4iImrHuOC49Wk0QLzyyistWQcREbVjXHDc+nARJRER2RwXHLc+DBBE\nRGRzXHDc+nBpKxEREVmMAYKIiIgsxgBBREREFmOAICIiIosxQBAREZHFGCCIiIjIYgwQREREZDEG\nCCIiIrIYAwQRERFZjAGCiIiILMYAQURERBZjgCC6Qa+vwNmzK1BTc8HWpRAR2T0+TIvoBq22GBUV\nR1FZeRyenkOhUIyCTOZq67KIiOwSAwTRDTpdMQBACAM0mu9QVnYAPj4Pw8vrj5AkDtYREd2M/yoS\n3aDVFpu81usrUFy8GYWF/0B5eYGNqiIisk8MEEQ31I1A3Ewmc4erawgcHb1sUBHdCdetENkOpzCI\nbqgbgZAkGVxdw+DpGQNX11BIkszGlVFjuG6FyHYYIIhucHBwga/vBLi7R8PR0c3W5ZAZuG6FyHYY\nIIhu8Pefb+sSyEKNrVvRaPZCqRwPN7dQG1VG1PYxohNRq8V1K0S2wxEIImq1uG6FyHYYIIio1eK6\nFSLbYYAgolaL61aIbIdrIIiIiMhiDBBERERkMQYIIiIishgDBBEREVmMAYKIiIgsZncBoqSkBKmp\nqRg8eDD69u2LCRMm4McffzS2Z2ZmYuzYsYiMjMSIESOwbNky6PV6Y7tKpUJycjJiYmIwcOBAJCcn\nQ6VSGdv1ej2WLVuGkSNHIjIyEmPGjMHnn3/eosdIRETU2tldgJg9ezaKi4vx2Wef4ccff0R0dDRm\nz56NS5cu4cCBA0hJScHMmTORnZ2NlStXYseOHVi9ejUAQKfTISkpCR4eHsjMzMR//vMfeHt7IzEx\nETqdDgCwevVqbNu2DWlpacjOzsacOXOQmpqK7OxsWx42NRGfykhE1LLsKkCUlZWhZ8+eWLhwIZRK\nJZydnZGUlITKykocOXIEGzduxJAhQxAXFwe5XI7g4GAkJCRgw4YNMBgMyMrKwpkzZ5CamopOnTrB\nw8MDCxYsgEqlwt69eyGEQEZGBqZPn46QkBDI5XIMHz4cQ4cOxfr16219+NQEdU9lPHNmMS5d2gS9\nvsLWJRERtWl2FSDc3d3x8ssvo2fPnsZtddMPXbp0QW5uLsLDw03eEx4eDo1Gg8LCQuTm5iIgIADe\n3t7Gdi8vL/j7+yMvLw9FRUVQq9UN9pGXl2fFIyNru/WpjKdPL0Jp6bcQwmDjyoiI2ia7ChC3Ki8v\nR2pqKmJjYxEWFga1Wg1PT0+TferCglqtRmlpab32un1KSkqgVqsBoME+6tqodWrsqYyFhf9AeXmB\njaqyDU7nEFFLsNtbWZ87dw7JyclQKBR48803m9yfJElNaif7xqcy/qZuOqey8jg8PYdCoRgFmczV\n1mURURtjlwHiyJEjSE5OxogRI/D888/DyckJAKBQKKDRaEz2LS0tBQAolUr4+PjUa6/bR6FQQKFQ\nAECDffj4+FjjUKiF8KmMv7l1Oqes7AB8fB6Gl9cfIUl2PehIRK2I3f1r8vPPPyMpKQkzZ87Eiy++\naAwPABAZGVlvrUJOTg6USiUCAgIQGRkJlUqFkpISY/uVK1dQVFSEqKgo+Pn5QalUNthHVFSUdQ+M\nrKruqYw9eryObt1mwc2tT7sMDwCnc4ioZdhVgNDr9UhJScH48eORkJBQr33atGnIysrCzp07odVq\nkZ+fj3Xr1mH69OmQJAmDBg1Cr169sHTpUpSWlkKtVmPJkiUICgpCTEwMJEnCtGnTsHbtWhQUFECr\n1SIzMxP79+9v8POo9fD3nw9v71g+0hmcziGilmFXUxiHDx/G0aNH8fPPP+ODDz4waRs9ejSWLFmC\ntLQ0rFixAs899xwUCgXi4+MxY8YMAIBMJkN6ejoWL16MYcOGQZIkxMTEID09HTLZ9b9GExMTUVNT\ng9mzZ0OtViMwMBDLly+vd2UGUWvF6RwiagmSEELYuojW4OzZs4iNjcXu3bvh5+dn63KIGqVSLYOb\nWzjc3aM5IkNEv9udznt2NQJBRE3n7z/f1iUQUTtgV2sgiIiIqHVggCAiIiKLMUCQ3eGdFImI7B/X\nQJDd4Z0UiYjsHwME2R3eSZGIyP7xX2OyO63xToqcdiGi9oYjEGR3WuOdFDntQkTtDQME2Z3WeCdF\nTrsQUXvDAEF2p+7BWK3pToqNTbtoNHuhVI6Hm1uojSojIrIOBgiyO63xToqtcdqFiKgpGCCImkFr\nnHYhImoKBgiiZtAap12IiJqCAYJajF5fgQsX1kCpHA9n5662LqdZtcZpFyKipmCAoBbDSx2JiNoO\nXl9GLebWSx1Pn16E0tJvIYTBqp/LmzwRETU/jkBQi7HVpY4c+SAian4cgaAWY6tLHW018kFtB0ex\niOrjCAS1GFtd6sibPFFTcRSLqD4GCGoxtrrUkTd5oqbircqJ6mOAoGZzp8s0bXWpI2/yRE3FUSyi\n+hggqNnY6zAvb/JETcVRLKL6GCCo2VhjmLc5bj7FmzxRU3EUi6g+BghqNtYY5rXXUQ171Zbv9mlL\nHMUiqo8BgpqNNYZ5uXjNMgxc1sFRLKL6GCCo2VhjmJeL1yzDwEVELYUBgixyuyFyawzzcvGaZRi4\niKilMECQRW43RH6nYd7fMz/PxWuWYeAiopbCAEEWacoQ+e+Zn7f14rXWtiiRgYuIWgoDBFmkKUPk\nvyd82HrxWmtblGjrwEVE7QdXVVGDGnt4UEND5A4OLqiuLoLBUHPbPhsLH4WF/0B5eUHTi7aC1vYg\nLn//+fD2jmV4ICKrY4CgBtX95X3mzGJcurQJen2FcTtwfYjczS0C3brNRrduc+HgIMfFi++Z7Hur\n5pqfb8knI7bG0ENE1BI4hUENzvM3Nt0gSfJ6Q+TXrmU3uO+tUxPNNT/fktMKXJRIRNQwBghq8IRc\nd7IXQofKypNwcemB4uLNkMu7wMmps8kQuTnrIvT6CpSVHYCv7yR06vRgk4bYW/JeB1yUSETUMAaI\ndsbc0QbAAUIIGAzVqK1Vo7y8FC4uPeDpOajeX97m/JWu1RbDyckXGs13EMLQpFGDlrzXQWtYlNja\nrhQhoraBAaKNu/Xk0thow80jDQBQXn4YQujh6OgNR0cF5PLOcHT0Rnl5LpycfHD58qfw9X3M2Cdw\n+7/Sm3PUwNxphfbyIK7WdqUIEbUNDBBt0M0nToOh2uTkIpN5oqKiAC4uPUxGG/T6KtTWqlFWVgJA\nwNHRGz4+I+HkdBcqKnJN+r50KQOVlSdx7dqPUCjGQpIc7vhXenV1kfFzZbKOTRo1MHdaob2cWHn7\naiKyBQaIVkyvr8C5c2/DYKiGr+9kXLmyFQZDNby9R6C8PBeXLn0EmawjnJx84ejoAY3mO1RWnkB5\n+U+oqTmPDh16wsUlABUVBaitvQYhdHB07ITa2ivQ6ytvTGFUmpz4r5Pg6OgNSXKERvPdjZOydFN7\nfVVVvxqnQuTyu+DiEgBHx06/azGiudMK7eXEyttXE5EttMsAUVVVhddeew379u3D1atX0atXL8yb\nNw+DBg2yuK+6k3ht7TWT7Q4OcnTuPBklJV/Ay+t+nD79Nzg7+6O6+hT8/J7F+fOr4Of3LK5dyzK+\nV6+vgFr9JXr0eA1VVSeg1ZagsjIfAQF/g0bzLa5d24/u3f+GkpIvceXKp/D3T0F5+WGUlf2ECxfW\nwdHRA1rtBVy+vAUODi7Q6YpRUXH95NKhQ0+4ufVFTc15GAxa1NQUobZWDa32PCRJDlfXcAASAC0q\nK8sB6HDlyhbodFcBCNTWauDhMQBduyZBq70ItXqXyXdwpxNWTU3Rjd8kGAw1MBh06NRpJLy9Yy0+\nmZs7rdBeTqy8UoSIbKFdBojFixfj2LFjWLNmDe666y589tlnSE5Oxvbt29GjRw+L+tJqi2+cxA+i\ntrYMQggAgCQ5oKTkCzg4uECt/g/Kyg5Br98NIXS4ejUbQhhQVnYYDg5OMBi0MBgqYTBoodeX4dix\nCZDJPCBJjtDrNbh27RAcHDpAry9FQcE4AA7Q6S7j559nQiZzg4NDR9TWqlFTowJggE6nuXFSliBE\nLSRJQmXlSVRXFwHQ36hcuhEkLqBDh2AYDJU3PqPKeGySJIeDg+ONPpxQW3sNly9/jOu3DxG4Hjiu\nu9MJy2DQwsWlJ5ycfOHg4AQAuHz5U1y9mmW1k3l7ObHyShEisoV2FyCuXr2Kzz//HP/6178QGBgI\nAJg4cSI2bdqETZs2YeHChRb1p9MVw2CoghC1MBgqYDDU3LhLoYTa2qsAHODg4AyDoRJC1ALQQ68v\nBSBQU3MNgAzXT8YOAGoBGCCEAbW1l1F3kjYYtAAMAK5fFXG9HwOEqEVtbTWun8jrTuoGAFpczzES\nAAEhJEiSIwBxIyBIcHDoAAeH6//5q6v/h+rqU3B1/QMkSWayaLKmRgWt9gKcnBRwcJBDr68wLrDs\n0OEeeHkNNeuE5ek5ENeuHTDZZu2TeXs5sbaGK0WIqO1pdwHi6NGj0Ol0CAsLM9keHh6OvLw8i/vT\naouh11fBYKiFEOLGCMT1k/31E71046QtbvzczHDj51bilt9rf3sltA3sJ/DbyEJD/TjdCAUKVFcX\nwmDQQZIcIEnOkMk6QCbrBEdHNyiVE6DTXYZWe8F40nV27oaamnMm0wwODq7w9ByMzp2nwtU1+I7f\nEWCbk3l7ObG2hitFiKjtaXcBQq1WAwC8vEz/6vX29kZJSYnF/dWNQNSNHvx20q4b3r95fv/WAGEN\nEq6PashwfaTBGc7OXeHtPRxdu07H0aOTIEQVHBxc4eYWAaVyLBSKMXBwkAMAVKpl8PK6z3jSPXPm\nlRtho2knfluczHliJSKynnYXIG5HkqQ773QLrbZuCkMPQAZJkkMIAZmsIyTJGXK5L7TaCzfWOFRB\nkhxujFI4wcHB8caJWG+84uE6B0iS440RDMcb76m9MeXgcCOwXK9VJnOHi0t3COEAne4sHB194Or6\nBwghwcFBhs6dp8LLa5jxpO3q2huenoPQuXM8nJ271DueW0+6zXXi58mciKhtaXcBwsfHBwCg0WjQ\nuXNn4/bS0lIoFAqL+3NwcIGbWz907Bhusl2pHIOKigJ4eNwLlWoZJElCTc1FKBSjcfVqFry9Y6HX\nV0KrvQghdDdq+BpeXsMgl3dFbW0pysp+gkLxCISoRWnpf9Cp00MQohYXL66Dl9cwdO/+PDp27AmV\nahnc3MJx7dpBeHjce9uTfVjYNouOjyd+IiJqSLsLEKGhoZDL5cjNzcXIkSON23/66Sfcf//9Fvd3\nuxOsQvEwAMDbO9byQm8RGPj3Bn+/uYbm+BwiIiJztJ276ZjJ3d0djz76KFauXInTp0+jqqoKa9as\nwblz5zBx4kRbl0dERNQqtLsRCABYuHAhXn/9dUyePBkVFRXo3bs33nvvPXTr1q3R9+j1169yuHjx\nYkuVSUREZDN157u689+tJFF35yO6rUOHDmHKlCm2LoOIiKhFZWRkICoqqt52BggzVVdXo6CgAEql\nEjJZ27oRERER0a30ej0uX76M0NBQuLi41GtngCAiIiKLtbtFlERERNR0DBBERERkMQYIIiIishgD\nBBEREVmMAYKIiIgsxgBBVldSUoLU1FQMHjwYffv2xYQJE/Djjz/auqw2KycnB71798bKlSttXUqb\ntHXrVjzwwAMICwtDbGws3n//fVuX1KacOnUKs2bNwsCBAxEVFYUJEybgu+++s3VZrZ5KpUJ8fDyC\ng4Nx9uxZk7bMzEyMHTsWkZGRGDFiBJYtW9bozaNuxgBBVjd79mwUFxfjs88+w48//ojo6GjMnj0b\nly5dsnVpbU51dTUWLlwIV1dXW5fSJn3xxRd47bXXsGjRIuTk5ODll1/G5s2bUVBQYOvS2gSDwYDE\nxES4uLhg165d2L9/P+Li4jB37lycOnXK1uW1Wl9//TUee+wx3HXXXfXaDhw4gJSUFMycORPZ2dlY\nuXIlduzYgdWrV9+xXwYIsqqysjL07NkTCxcuhFKphLOzM5KSklBZWYkjR47Yurw2Jy0tDYGBgejd\nu7etS2mTVq1ahcTERAwaNAhyuRzR0dHYtWsXQkNDbV1am6BWq3Hu3DmMGTMGXl5ekMvlmDx5MnQ6\nHU6cOGHr8lotjUaDjIwMjB49ul7bxo0bMWTIEMTFxUEulyM4OBgJCQnYsGEDDAbDbftlgCCrcnd3\nx8svv4yePXsat6lUKgBAly5dbFVWm3To0CFs374d//jHP2xdSptUXFyMX3/9FR07dsSkSZPQt29f\njBo1Cp9//rmtS2szFAoF+vXrh08//RRqtRo6nQ4fffQRvL29ER0dbevyWq3x48cjMDCwwbbc3FyE\nh4ebbAsPD4dGo0FhYeFt+22XD9Mi2ykvL0dqaipiY2MRFhZm63LajKqqKixcuBALFixA586dbV1O\nm1T3YKHNmzfjjTfegL+/Pz799FM8++yz6Nq1a4PPCiDLrVy5EklJSRg4cCAkSYK3tzeWL18OHx8f\nW5fWJqnVanh6epps8/b2Nrb16NGj0fdyBIJazLlz5zBp0iT4+PjgzTfftHU5bUpaWhruvvtujBs3\nztaltFl1d/2vW4jWsWNHPP744wgNDcXWrVttXF3boNVqkZiYiMDAQGRlZeHQoUOYM2cOkpOT8csv\nv9i6PLoFAwS1iCNHjmD8+PHo168f0tPT0bFjR1uX1GbUTV289NJLti6lTfP19QXw219ndQICArgg\nuJn897//xbFjx4xrptzc3DBlyhT4+flhy5Ytti6vTVIoFNBoNCbbSktLAQBKpfK27+UUBlndzz//\njKSkJMyaNQsJCQm2LqfN2bJlCyorK/HII48Yt5WXl+PIkSP49ttv8dlnn9mwurbD19cXXl5eyM/P\nx/Dhw43bz5w5w0WUzaRu0d6tlxDq9XrwuY/WERkZiby8PJNtOTk5UCqVCAgIuO17OQJBVqXX65GS\nkoLx48czPFhJSkoKvvnmG2zfvt34ExoaiokTJyI9Pd3W5bUZMpkM06dPx8aNG7F//35otVpkZGTg\n+PHjmDRpkq3LaxP69u0LhUKBN998E6WlpaipqcHHH3+M06dP44EHHrB1eW3StGnTkJWVhZ07d0Kr\n1SI/Px/r1q3D9OnTIUnSbd/Lx3mTVR06dAhTpkyBk5NTvf8ZR48ejSVLltiosrYtPj4e/fv3x9y5\nc21dSpsihMCqVavwySefoKSkBIGBgViwYAEGDx5s69LajBMnTiAtLQ0FBQUoKytDjx49MG/ePMTG\nxtq6tFZr5MiROH/+PIQQ0Ol0xn+P6/4N/uqrr7BixQoUFhZCoVBg4sSJePLJJxkgiIiIqPlxCoOI\niIgsxgBBREREFmOAICIiIosxQBAREZHFGCCIiIjIYgwQREREZDEGCKJmkpKSguDg4Ho/9957L2bO\nnFnvbm+/17BhwzB//vxm6etOioqKMG7cOISGhjZ6U6r4+HhMmDDhtv2sXLkSwcHBqKmpsejzU1JS\nMGzYMIveYw9+7/E25bOIWhoDBFEz6tSpE7Kysow/+/btw+rVq1FbW4v4+HicOHHCov60Wi1CQ0Nx\n9uxZ47ZPP/0Uixcvbu7SG5SRkYGff/4ZH374YZPutjhjxgxkZWXB2dn5tvtNnTq1RR5M1dD32los\nWLAAK1eutHUZRAwQRM3JwcEBSqXS+NO5c2dERUVh5cqVcHJywsaNGy3qLz8/HzqdzmRbp06d4O7u\n3pxlN+ratWvw9PREeHh4kz7T1dX1jg/mqa2tRUFBwe/+DEs09L22FocPH7Z1CUQAGCCIWoSrqysC\nAgJw4cIF47aKigosXrwYgwcPRkhICIYMGYKFCxcan4S3detWTJ48GQAQGxuL+Ph4APWnMK5evYq/\n/e1vGDRoEEJDQzFs2DD885//hFarvW1N58+fx/z58xEdHY3Q0FCMHDkS7733nvGhRcOGDcPWrVtx\n5coVBAcH3/Gv3p07d2LkyJEIDQ3Fgw8+iL179xrbbh3Sj4+Px7x585CWlobIyEh88sknCAkJQVVV\nFVJTU+sNyR85cgSPPvoowsLCMGTIEJMHhGm1Wrz66qsYNmwYwsLCMGjQICxYsMD4Pd6qse/VYDDg\n3//+N/70pz8hNDQUAwYMwDPPPHPHJ21evHgRSUlJ6NOnDwYOHIg33nij3sOgAGDbtm149NFHERkZ\niejoaDz33HMoKSmpt8/YsWMRFhaGfv36YdKkSThw4ICxPTg4GGfOnMFbb72F4OBgkxGUoqIixMfH\nIzw8HAMHDjSZchJC4J133sHIkSMRHh6OAQMGYM6cOVCpVLc9NqLbEkTULBYsWCBiYmIabKuqqhJR\nUVFi0aJFxm0pKSmif//+4ocffhDnz58XBw4cEMOGDRNz5swxvmft2rUiKChI5OXlidLSUiGEEPff\nf794+umnjf1MnDhRDBkyROzevVsUFRWJzz77TERERJh8VkP1DB8+XDz00EPixx9/FIWFhWLdunWi\nd+/e4p133hFCCFFSUiKeeuopMWDAAFFcXCzKy8sb7Gvq1Kli4MCBIiEhQeTl5YkTJ06IGTNmiLCw\nMHHhwgUhhBArVqwQQUFBorq62vieP/3pT+KZZ54Rp06dEteuXROHDx8WQUFB4v333xfFxcXG77R/\n//4iMTFR5OTkiP/9738iOTlZ/N///Z+4ePGiEEKIZcuWicGDB4v9+/eL8+fPi4MHD4qHH35YPPHE\nE40ee0Pfa1pamggNDRUbNmwQhYWFYv/+/WLEiBHioYceEjqdrtHvcuLEieK+++4TP/zwg/jll1/E\nq6++KgYNGmRyvNu2bRNBQUHi1VdfFadPnxY//vijiIuLE2PHjhV6vV4IIcR///tfERQUJJYvXy6K\niorEr7/+KlJSUkRERITxWC9evGjsp7i4WNTW1hq/25kzZ4qsrCxx6tQpsWjRIhEUFCRyc3OFEEJ8\n/PHHIiIiQnz99dfi3LlzIi8vT8THx4u4uLhGj4voThggiJpJYwHi0qVL4q9//asICQkRx44dM9le\nVFRksu8bb7whIiIihMFgEEJc/4c/KChIqFQq4z43B4icnBwRFBQkvvzyS5N+3nzzTRESEtLoSX/H\njh0iKChI5Ofnm2yfP3++GDx48B2P6WZTp04VvXv3FleuXDFuu3DhgggKChLr1q0TQjQcIEJCQsS1\na9eM7yksLBRBQUFiy5YtJp8fFBQk/ve//xm31R3z119/LYQQIjExsV5YuHDhgjhx4kSjNd/6vdbU\n1IiIiAixePFik/327dsngoKCRFZWVoP91NX88ccfm2z/85//bHK8DzzwgJg6darJPgcPHhRBQUFi\nz549QgghysvLxc8//2wSVn755RcRFBQkdu7cKYQQQqfTiaCgILFixQrjPnXfbV0/QvwWNOq+/7//\n/e/1wkJJSYnIz883BhgiSznaegSEqC0pKSlBZGSk8bXBYEB1dTVCQ0Px73//G7179za2OTg4YMOG\nDbhwUFUAAAcISURBVNi3bx+uXLkCvV4PnU4HnU4HrVZ7xwWHAIxrBvr27WuyvU+fPtDpdPj1118R\nHh7e4PtcXFwQEhJisj08PBxffPEFSkpK4OPjY/Zx+/v7m+zfpUsXeHl54dSpU42+p3v37matq3B1\ndUWvXr2Mrzt16gQAKCsrA3B9GuLvf/875s2bhwceeADR0dHo0qULunTpYnb9p06dQmVlZYPfIwAc\nO3YMgwYNqve+//3vfwCAP/zhDybbIyIicOTIEQBAeXk5Tp06hUceecRkn759+8LFxQVHjx7F0KFD\n4erqitzcXCxatAhFRUWoqqoyTidpNJo7HkNdrcBv31F5eTkA4P7778fHH3+MhIQEjB49GgMGDEDX\nrl2N+xH9HgwQRM3Iy8sLmzdvNr4+fPgwFixYgFmzZpmcgIQQ/7+9uwtpsv0DOP591JzbamsQRCBs\n9jKijDEarZMoqegkEokgqMTKjGTWDoKyTSJnqWUHQbMXRpliVkKUR7EIHIVgaR70wjLEl2q9SIIu\nZ1rY/yC2v7otN57n+Z/8fx8QvG/v63df18Xg+t33dV2T/fv38+nTJ44fP87KlStRKBQ0NDTQ0NCQ\n8P3CA8TcuXOnnVer1cDvdRbxyqlUqqh/1zu1XDIJRKxEQKlUEgqF4pYJ32s2SqVy2nG4zuHBdefO\nnSxcuJCbN29SWlrKxMQEa9euxeFwTEs8/uTv9GOsOk5tW/gat9sdtRV2fHycwcFBAOrq6qisrGT3\n7t2cOHECrVbL58+fI2s0ZjO1DjP7aP369dTX11NfX8/p06cJBoOYTCaOHTvG6tWrE4ovxEySQAjx\nD0pNTUWv10eO9Xo9Dx484NSpU1it1shA293djd/vx+VykZeXF7l+toWPM4XjBYPBaQNI+Ok83hP+\nvHnzGB0d5devX9OSiPBgl+yOi7GxsahzoVAo4STh78rJySEnJ4eJiQna2to4f/48RUVFPHr0KCpJ\nimVqP04VPtZoNDHLqVQqILr9U+OEYxcUFLBjx46oGOGkpaWlBbPZTFlZWeRvQ0NDs9Y9URaLBYvF\nws+fP+ns7OTixYscOHCA1tbWuO0T4k9kF4YQ/zKn00kwGOTcuXORc+EthPPnz4+c+/btG16vF/jv\nk2PYzOOw7OxsADo7O6ed7+rqQqlUxn0Cz87OZnx8nBcvXkSVy8zMRKfTJdK0iP7+fr58+RI5fv/+\nPcPDwyxbtiypOBC/rbFMTk7i9XoJBAIApKens2HDBg4fPsyHDx+idjnEu1dWVhZqtTpmPwKsWrUq\nZvnFixcDRPXj8+fPI7+r1WqMRiMDAwPo9fppPxMTE5FphB8/fkz7PACR3SaJfh7iefz4cWS6JS0t\nDavVSmlpKaOjo/T29iYVS4gwSSCE+JdlZmZSXFzMnTt3ePbsGfB74NFqtTQ2NtLb20tXVxf79u1j\n06ZNADx9+pSxsbHIk6HP5+PNmzdRsc1mM2vWrKGqqgqfz8fAwAC3b9+msbGR/Px8MjIyYtZp8+bN\nGAwGHA4HHR0d9PX1ceXKFbxeL0VFRUm3UaPR4HA4eP36NX6/H6fTiUqlYsuWLUnFCLfd7/fz/fv3\nWcukpKTg8Xiw2+10dHTw8eNHXr16xa1btzAajXGnYWb2a3p6OgUFBTQ3N9PU1MS7d+/w+Xy4XC7M\nZjMWiyVmnKVLl7JixQquXr1Ke3s7PT09nDlzJurNwcGDB3n48CGXLl2ip6eHt2/fUl5ezvbt2+nr\n6wN+r5tob2+nra2N/v5+qqurmZycJDU1lZcvXzI0NERaWlpkrYTf72dkZCShvr179y42m40nT54Q\nCATo7u7m+vXrLFiwgCVLliQUQ4iZZApDiP+BvXv3cv/+fZxOJy0tLahUKmpqaqisrCQ3Nxe9Xo/d\nbsdsNtPV1UVJSQmXL19m3bp1WCwWqqqqMBqNMb+l0e12U11dTWlpKSMjIyxatAibzUZhYWHc+igU\nCm7cuEFlZSXFxcWEQiEMBgMulyvma/bZLF++nNzcXOx2O4FAAIPBQG1t7axfHjWVTqdjz549NDc3\n09rayr179xIqF27/kSNHGB4eRqfTYbVaKS8vjzt9EatfS0pKyMjIwOPxUFFRgVarZePGjRw9evSP\n0yAXLlygrKyMwsJC1Go1eXl55Ofnc/bs2cg1W7duBcDj8eB2u5kzZw4mk4m6ujqysrIAsNvtDA4O\nYrPZUCgUbNu2jZMnT6JSqWhqaiIlJYWKigoOHTpEbW0tu3btwuPxJNRHLpeLmpoaHA4HX79+RaPR\nYDKZuHbtWtS6DyES9devZN+FCSGEEOL/nkxhCCGEECJpkkAIIYQQImmSQAghhBAiaZJACCGEECJp\nkkAIIYQQImmSQAghhBAiaZJACCGEECJpkkAIIYQQImmSQAghhBAiaf8BkLEx9wK5cvMAAAAASUVO\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb4b5390860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for birth_rate in birth_rates:\n",
+    "    for death_rate in death_rates:\n",
+    "        system = make_system(birth_rate=birth_rate,\n",
+    "                             death_rate=death_rate)\n",
+    "        run_simulation(system)\n",
+    "        p_end = final_population(system)\n",
+    "        birth_ratio = birth_rate / death_rate\n",
+    "        plot(birth_ratio, p_end, 'y>', label='rabbits')\n",
+    "        \n",
+    "decorate(xlabel='Ratio of births to deaths',\n",
+    "         ylabel='Final population')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}