diff --git a/code/Project 3.ipynb b/code/Project 3.ipynb
new file mode 100644
index 00000000..94c52d58
--- /dev/null
+++ b/code/Project 3.ipynb	
@@ -0,0 +1,1552 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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",
+    "\n",
+    "\n",
+    "\n",
+    "from modsim import *"
+   ]
+  },
+  {
+   "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>t</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "init = State(t=0)\n",
+    "init"
+   ]
+  },
+  {
+   "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>t    0\n",
+       "dtype: int64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cod</th>\n",
+       "      <td>0.72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init    t    0\n",
+       "dtype: int64\n",
+       "cod                    0.72\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "downforce = System(init=init,\n",
+    "                   cod=.72\n",
+    "                  )\n",
+    "downforce"
+   ]
+  },
+  {
+   "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.t\n",
+    "    #T += 1\n",
+    "    df= (0.5*(T**2)*0.72)\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "update(init, downforce)"
+   ]
+  },
+  {
+   "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[0] = init\n",
+    "    ts = linrange(0, 130, 1)\n",
+    "    \n",
+    "    for t in ts:\n",
+    "        frame.loc[t+1] = df= (0.5*(t**2)*0.72)\n",
+    "    \n",
+    "    system.results = frame"
+   ]
+  },
+  {
+   "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>t</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>12.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>17.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>23.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>29.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>36.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>43.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>51.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>60.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>70.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>81.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>92.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>104.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>116.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>129.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>144.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>158.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>174.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>190.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>207.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>225.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>243.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>262.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>282.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>3672.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>3745.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>3819.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>3893.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>3969.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>4044.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>4121.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>4199.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>4277.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>4356.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>4435.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>4515.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>4596.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>4678.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>4761.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>4844.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>4928.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>5012.64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>5097.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>5184.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>5270.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>5358.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>5446.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>5535.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>5625.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>5715.36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>5806.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>5898.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>5990.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>6084.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>132 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           t\n",
+       "0       0.00\n",
+       "1       0.00\n",
+       "2       0.36\n",
+       "3       1.44\n",
+       "4       3.24\n",
+       "5       5.76\n",
+       "6       9.00\n",
+       "7      12.96\n",
+       "8      17.64\n",
+       "9      23.04\n",
+       "10     29.16\n",
+       "11     36.00\n",
+       "12     43.56\n",
+       "13     51.84\n",
+       "14     60.84\n",
+       "15     70.56\n",
+       "16     81.00\n",
+       "17     92.16\n",
+       "18    104.04\n",
+       "19    116.64\n",
+       "20    129.96\n",
+       "21    144.00\n",
+       "22    158.76\n",
+       "23    174.24\n",
+       "24    190.44\n",
+       "25    207.36\n",
+       "26    225.00\n",
+       "27    243.36\n",
+       "28    262.44\n",
+       "29    282.24\n",
+       "..       ...\n",
+       "102  3672.36\n",
+       "103  3745.44\n",
+       "104  3819.24\n",
+       "105  3893.76\n",
+       "106  3969.00\n",
+       "107  4044.96\n",
+       "108  4121.64\n",
+       "109  4199.04\n",
+       "110  4277.16\n",
+       "111  4356.00\n",
+       "112  4435.56\n",
+       "113  4515.84\n",
+       "114  4596.84\n",
+       "115  4678.56\n",
+       "116  4761.00\n",
+       "117  4844.16\n",
+       "118  4928.04\n",
+       "119  5012.64\n",
+       "120  5097.96\n",
+       "121  5184.00\n",
+       "122  5270.76\n",
+       "123  5358.24\n",
+       "124  5446.44\n",
+       "125  5535.36\n",
+       "126  5625.00\n",
+       "127  5715.36\n",
+       "128  5806.44\n",
+       "129  5898.24\n",
+       "130  5990.76\n",
+       "131  6084.00\n",
+       "\n",
+       "[132 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(downforce,update)\n",
+    "downforce.results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0Wu9qcPp8NTrVeq4sLMANTydMg9BhyEUYySRBoUDIJFBe145/KephNJm5siej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\n3zZikT0ip3lgZogHPF1p5lt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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c8369ac7f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(downforce.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Torque</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RPM</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8756.147541</th>\n",
+       "      <td>220.693752</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8807.377049</th>\n",
+       "      <td>223.237408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8838.114754</th>\n",
+       "      <td>224.509979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8868.852459</th>\n",
+       "      <td>224.514433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8899.590164</th>\n",
+       "      <td>227.055120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8909.836066</th>\n",
+       "      <td>227.056605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8971.311475</th>\n",
+       "      <td>228.333630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8971.311475</th>\n",
+       "      <td>228.333630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
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+       "    </tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16471.311480</th>\n",
+       "      <td>332.137978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16471.311480</th>\n",
+       "      <td>332.137978</td>\n",
+       "    </tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16532.786890</th>\n",
+       "      <td>332.146888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16532.786890</th>\n",
+       "      <td>332.146888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>329.618080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16594.262300</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16665.983610</th>\n",
+       "      <td>328.361844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16665.983610</th>\n",
+       "      <td>328.361844</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16727.459020</th>\n",
+       "      <td>328.370753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16727.459020</th>\n",
+       "      <td>328.370753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16788.934430</th>\n",
+       "      <td>328.379663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16819.672130</th>\n",
+       "      <td>328.384117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16850.409840</th>\n",
+       "      <td>328.388572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16911.885250</th>\n",
+       "      <td>325.861250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16963.114750</th>\n",
+       "      <td>323.332442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17014.344260</th>\n",
+       "      <td>322.071751</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>184 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Torque\n",
+       "RPM                     \n",
+       "8756.147541   220.693752\n",
+       "8807.377049   223.237408\n",
+       "8838.114754   224.509979\n",
+       "8868.852459   224.514433\n",
+       "8899.590164   227.055120\n",
+       "8909.836066   227.056605\n",
+       "8971.311475   228.333630\n",
+       "8971.311475   228.333630\n",
+       "9053.278689   232.149857\n",
+       "9073.770492   232.152827\n",
+       "9155.737705   232.164707\n",
+       "9186.475410   234.705393\n",
+       "9186.475410   235.973509\n",
+       "9278.688525   235.986873\n",
+       "9278.688525   235.986873\n",
+       "9391.393443   241.075671\n",
+       "9391.393443   241.075671\n",
+       "9534.836066   244.900808\n",
+       "9616.803279   246.180803\n",
+       "9668.032787   247.456344\n",
+       "9739.754098   248.734854\n",
+       "9831.967213   248.748218\n",
+       "9842.213115   248.749703\n",
+       "9913.934426   248.760097\n",
+       "9985.655738   248.770492\n",
+       "10057.377050  250.049002\n",
+       "10098.360660  250.054942\n",
+       "10180.327870  251.334937\n",
+       "10211.065570  252.607508\n",
+       "10231.557380  253.878593\n",
+       "...                  ...\n",
+       "16040.983610  337.148076\n",
+       "16051.229510  337.149561\n",
+       "16112.704920  337.158470\n",
+       "16122.950820  337.159955\n",
+       "16122.950820  337.159955\n",
+       "16194.672130  335.902233\n",
+       "16235.655740  333.371941\n",
+       "16266.393440  333.376396\n",
+       "16286.885250  333.379366\n",
+       "16338.114750  333.386790\n",
+       "16338.114750  333.386790\n",
+       "16409.836070  332.129069\n",
+       "16461.065570  332.136493\n",
+       "16471.311480  332.137978\n",
+       "16471.311480  332.137978\n",
+       "16532.786890  332.146888\n",
+       "16532.786890  332.146888\n",
+       "16532.786890  332.146888\n",
+       "16584.016390  329.618080\n",
+       "16594.262300  329.619565\n",
+       "16665.983610  328.361844\n",
+       "16665.983610  328.361844\n",
+       "16727.459020  328.370753\n",
+       "16727.459020  328.370753\n",
+       "16788.934430  328.379663\n",
+       "16819.672130  328.384117\n",
+       "16850.409840  328.388572\n",
+       "16911.885250  325.861250\n",
+       "16963.114750  323.332442\n",
+       "17014.344260  322.071751\n",
+       "\n",
+       "[184 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1 = pd.read_csv('Torque_curve.csv',index_col='RPM')\n",
+    "data1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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s3r0bR48ehUgkgiAIsLGxwfr166n/g7QJmn1OSNeicTLFmTNnYsaMGbh27RpK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\nGGZrAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x202676ccba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(data1, 'bo', label='Torque')\n",
+    "decorate(xlabel='RPM (min)',\n",
+    "         ylabel='Torque (mg/dL)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RXFxs7ushw3TmYq0g8EUch0fnToIvPcQlZFwxWdN3dXXlm2m6HT58GGq1GgkJ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\nQgixIhT6hBBiRf4fhvozeoTUqI0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c836ecf978>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "I1 = interpolate(data1.Torque, kind ='linear')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MAIfPAaB2TvDJPjSSRn0dopE0aCWt+lonHXkQoNVoqXHWAMG+dOP7jhfbjghh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\nhIgcJSNCCCEiR8mIEEKIyP0/jV7t2Imh+oAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x20267f38898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = linrange(8500, 18000, 2000)\n",
+    "\n",
+    "plot(data1.Torque, 'go', label='Torque data')\n",
+    "plot(ts, I1(ts), color='green', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='RPM (min)',\n",
+    "         ylabel='Torque ($\\mu$U/mL)')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "I = interpolate(downforce.results.t,kind='cubic')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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79u3DxcWF5s2bV1edhBCizmto29tUlkFB59tvv2XGjBlMmzatuuojhBB1XvG1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\nVlRUxNrb21lhYSHjOM5girWl6enpMZgybU05FBQ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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c83731d668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = linrange(1, 100, 1000)\n",
+    "\n",
+    "plot(downforce.results, 'go', label='downforce data')\n",
+    "plot(ts, I(ts), color='green', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Torque ($\\mu$U/mL)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>RPM</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Speed</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.122448</th>\n",
+       "      <td>6388.837401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>6452.548344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.929958</th>\n",
+       "      <td>6452.548344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.839329</th>\n",
+       "      <td>6563.843564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.841576</th>\n",
+       "      <td>6595.619463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.748701</th>\n",
+       "      <td>6675.138784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.954082</th>\n",
+       "      <td>6722.908729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.160586</th>\n",
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+       "    <tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.487460</th>\n",
+       "      <td>7088.729432</td>\n",
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+       "    <tr>\n",
+       "      <th>10.092959</th>\n",
+       "      <td>7152.334279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11.300587</th>\n",
+       "      <td>7231.880123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11.903839</th>\n",
+       "      <td>7263.709071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12.808717</th>\n",
+       "      <td>7311.452492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14.319095</th>\n",
+       "      <td>7422.800760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15.223973</th>\n",
+       "      <td>7470.544181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15.830595</th>\n",
+       "      <td>7550.036978</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17.645968</th>\n",
+       "      <td>7724.963568</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18.854720</th>\n",
+       "      <td>7820.397363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.457972</th>\n",
+       "      <td>7852.226310</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.664476</th>\n",
+       "      <td>7915.884205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.268852</th>\n",
+       "      <td>7963.601102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.477603</th>\n",
+       "      <td>8059.034896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23.685230</th>\n",
+       "      <td>8138.580741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24.893982</th>\n",
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+       "    <tr>\n",
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+       "    <tr>\n",
+       "      <th>27.311484</th>\n",
+       "      <td>8424.882124</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28.216362</th>\n",
+       "      <td>8472.625545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287.292948</th>\n",
+       "      <td>13357.175400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287.895077</th>\n",
+       "      <td>13373.116400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290.005336</th>\n",
+       "      <td>13468.629760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291.209594</th>\n",
+       "      <td>13500.511760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292.413851</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294.519617</th>\n",
+       "      <td>13564.355320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296.024377</th>\n",
+       "      <td>13596.263840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298.736765</th>\n",
+       "      <td>13707.718210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300.546522</th>\n",
+       "      <td>13803.205050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302.053529</th>\n",
+       "      <td>13866.889470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303.861039</th>\n",
+       "      <td>13930.600410</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305.970174</th>\n",
+       "      <td>14010.225830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308.378690</th>\n",
+       "      <td>14073.989820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>310.183953</th>\n",
+       "      <td>14105.924860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>311.690960</th>\n",
+       "      <td>14169.609280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314.404471</th>\n",
+       "      <td>14296.951600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>315.909231</th>\n",
+       "      <td>14328.860120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317.114612</th>\n",
+       "      <td>14376.630060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>318.318870</th>\n",
+       "      <td>14408.512060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>320.428006</th>\n",
+       "      <td>14488.137480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321.634510</th>\n",
+       "      <td>14551.795370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>322.839891</th>\n",
+       "      <td>14599.565320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>323.743646</th>\n",
+       "      <td>14631.420790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326.754290</th>\n",
+       "      <td>14711.125780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327.659168</th>\n",
+       "      <td>14758.869200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328.864549</th>\n",
+       "      <td>14806.639140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331.271941</th>\n",
+       "      <td>14854.515190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>332.476199</th>\n",
+       "      <td>14886.397180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>334.284832</th>\n",
+       "      <td>14965.996070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>335.788469</th>\n",
+       "      <td>14982.016640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>409 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     RPM\n",
+       "Speed                   \n",
+       "0.122448     6388.837401\n",
+       "1.627208     6420.745921\n",
+       "1.929958     6452.548344\n",
+       "1.929958     6452.548344\n",
+       "2.839329     6563.843564\n",
+       "2.841576     6595.619463\n",
+       "3.748701     6675.138784\n",
+       "4.954082     6722.908729\n",
+       "6.160586     6786.566624\n",
+       "7.370461     6897.888368\n",
+       "8.578089     6977.434212\n",
+       "9.487460     7088.729432\n",
+       "10.092959    7152.334279\n",
+       "11.300587    7231.880123\n",
+       "11.903839    7263.709071\n",
+       "12.808717    7311.452492\n",
+       "14.319095    7422.800760\n",
+       "15.223973    7470.544181\n",
+       "15.830595    7550.036978\n",
+       "17.645968    7724.963568\n",
+       "18.854720    7820.397363\n",
+       "19.457972    7852.226310\n",
+       "20.664476    7915.884205\n",
+       "21.268852    7963.601102\n",
+       "22.477603    8059.034896\n",
+       "23.685230    8138.580741\n",
+       "24.893982    8234.014535\n",
+       "26.403235    8329.474854\n",
+       "27.311484    8424.882124\n",
+       "28.216362    8472.625545\n",
+       "...                  ...\n",
+       "287.292948  13357.175400\n",
+       "287.895077  13373.116400\n",
+       "290.005336  13468.629760\n",
+       "291.209594  13500.511760\n",
+       "292.413851  13532.393760\n",
+       "294.519617  13564.355320\n",
+       "296.024377  13596.263840\n",
+       "298.736765  13707.718210\n",
+       "300.546522  13803.205050\n",
+       "302.053529  13866.889470\n",
+       "303.861039  13930.600410\n",
+       "305.970174  14010.225830\n",
+       "308.378690  14073.989820\n",
+       "310.183953  14105.924860\n",
+       "311.690960  14169.609280\n",
+       "314.404471  14296.951600\n",
+       "315.909231  14328.860120\n",
+       "317.114612  14376.630060\n",
+       "318.318870  14408.512060\n",
+       "320.428006  14488.137480\n",
+       "321.634510  14551.795370\n",
+       "322.839891  14599.565320\n",
+       "323.743646  14631.420790\n",
+       "326.754290  14711.125780\n",
+       "327.659168  14758.869200\n",
+       "328.864549  14806.639140\n",
+       "331.271941  14854.515190\n",
+       "332.476199  14886.397180\n",
+       "334.284832  14965.996070\n",
+       "335.788469  14982.016640\n",
+       "\n",
+       "[409 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('Gear_ratios.csv',index_col='Speed')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "I = interpolate(data.RPM,kind='linear')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WlaLj6fg2GoxI4dMtH7TggXRHOhnODH531e/iMs6RxiMxDwV60MepUjpp9rQz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\nP0DH/ykcJviuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x20268083da0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = linrange(1, 300, 1000)\n",
+    "\n",
+    "plot(data, 'go', label='speed/RPM')\n",
+    "plot(ts, I(ts), color='green', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Speed',\n",
+    "         ylabel='RPM')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(14973.6158648135)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I(335)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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nJOgI0Uw06TihJ598kpkzZ4ZsW7NmDYMGDSI1NZU777yTtWvXhuz/y1/+QkpK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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x20267d44588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ts = linrange(1, 300, 1000)\n",
+    "\n",
+    "plot(data, 'go', label='Torque')\n",
+    "plot(ts, I1(I(ts)), color='green', label='interpolated')\n",
+    "\n",
+    "decorate(xlabel='Speed',\n",
+    "         ylabel='Torque')\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
+}
diff --git a/code/chap01_fig01.pdf b/code/chap01_fig01.pdf
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diff --git a/code/chap01mine.ipynb b/code/chap01mine.ipynb
new file mode 100644
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--- /dev/null
+++ b/code/chap01mine.ipynb
@@ -0,0 +1,2199 @@
+{
+ "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": 1,
+   "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": 2,
+   "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": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-3-cd42a0bb6380>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-3-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": 4,
+   "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": 4,
+     "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": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "4 second"
+      ],
+      "text/latex": [
+       "$4 second$"
+      ],
+      "text/plain": [
+       "<Quantity(4, 'second')>"
+      ]
+     },
+     "execution_count": 5,
+     "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "156.8 meter"
+      ],
+      "text/latex": [
+       "$156.8 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(156.8, 'meter')>"
+      ]
+     },
+     "execution_count": 6,
+     "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": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.817885349720552 second"
+      ],
+      "text/latex": [
+       "$8.817885349720552 second$"
+      ],
+      "text/plain": [
+       "<Quantity(8.817885349720552, 'second')>"
+      ]
+     },
+     "execution_count": 7,
+     "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": 8,
+   "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": 8,
+     "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": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mile = UNITS.mile\n",
+    "hour= UNITS.hour"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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": 10,
+     "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "20.070408163265306 second"
+      ],
+      "text/latex": [
+       "$20.070408163265306 second$"
+      ],
+      "text/plain": [
+       "<Quantity(20.070408163265306, 'second')>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tv= 20 * meter/second\n",
+    "t1 = tv/a \n",
+    "h1 = a / 2 * t1**2\n",
+    "td= 381 * meter\n",
+    "t2 = (td - h1) / tv\n",
+    "\n",
+    "tf = t1 + t2\n",
+    "tf\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": 12,
+   "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": 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>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": 13,
+     "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": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 15,
+     "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": 16,
+   "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": 17,
+   "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",
+       "    <tr>\n",
+       "      <th>babson</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         9\n",
+       "wellesley    3\n",
+       "babson       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "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": 18,
+   "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": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare.olin += 1\n",
+    "bikeshare.wellesley -= 1\n",
+    "bikeshare\n",
+    "\n",
+    "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": 20,
+   "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": 21,
+   "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": 22,
+   "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",
+       "    <tr>\n",
+       "      <th>babson</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         9\n",
+       "wellesley    3\n",
+       "babson       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.bike_to_wellesley>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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": 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>olin</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>babson</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         11\n",
+       "wellesley     1\n",
+       "babson        0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def bike_to_olin():\n",
+    "    bikeshare.olin += 1\n",
+    "    bikeshare.wellesley -= 1\n",
+    "bike_to_olin()\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": 26,
+   "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": 27,
+   "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": 28,
+   "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": 29,
+   "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": 29,
+     "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": 30,
+   "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": 30,
+     "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": 31,
+   "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": 32,
+   "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": 33,
+   "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": 34,
+   "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": 35,
+   "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": 36,
+   "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": 37,
+   "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": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flip(0.7)"
+   ]
+  },
+  {
+   "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": 39,
+   "metadata": {},
+   "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": 40,
+   "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": 41,
+   "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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\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>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": 42,
+     "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": 43,
+   "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>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": 43,
+     "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": 44,
+   "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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Wellesley\n",
+      "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>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": 45,
+     "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": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def decorate():  \n",
+    "    legend(loc='random string')\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": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\ProgramData\\Miniconda3\\lib\\site-packages\\matplotlib\\legend.py:326: UserWarning: Unrecognized location \"random string\". Falling back on \"best\"; valid locations are\n",
+      "\tbest\n",
+      "\tupper right\n",
+      "\tupper left\n",
+      "\tlower left\n",
+      "\tlower right\n",
+      "\tright\n",
+      "\tcenter left\n",
+      "\tcenter right\n",
+      "\tlower center\n",
+      "\tupper center\n",
+      "\tcenter\n",
+      "\n",
+      "  six.iterkeys(self.codes))))\n"
+     ]
+    }
+   ],
+   "source": [
+    "plot_state()\n",
+    "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": 48,
+   "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": 49,
+   "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": 50,
+   "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": 51,
+   "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": 52,
+   "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": 53,
+   "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": 54,
+   "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": 55,
+   "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": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "positional argument follows keyword argument (<ipython-input-56-3f4de737e0bb>, line 4)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-56-3f4de737e0bb>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    step(p1=0.4, 0.2)\u001b[0m\n\u001b[1;37m                ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
+     ]
+    }
+   ],
+   "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": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def decorate(loc='best'):  \n",
+    "    legend(loc=loc)\n",
+    "    label_axes(title='Olin-Wellesley Bikeshare',\n",
+    "               xlabel='Time step (min)', \n",
+    "               ylabel='Number of bikes')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "plot_state()\n",
+    "decorate(0)"
+   ]
+  },
+  {
+   "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": 59,
+   "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": 60,
+   "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": 61,
+   "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": 63,
+   "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": 62,
+   "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": 64,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here\n",
+    "def run_steps(num_steps,p1,p2):\n",
+    "    for i in range(num_steps):\n",
+    "        step(p1,p2)\n",
+    "        plot_state()\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin = 10, wellesley = 2)\n",
+    "newfig()\n",
+    "run_steps(2,.7,.3)"
+   ]
+  },
+  {
+   "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/chap02mine.ipynb b/code/chap02mine.ipynb
new file mode 100644
index 00000000..85219a35
--- /dev/null
+++ b/code/chap02mine.ipynb
@@ -0,0 +1,9297 @@
+{
+ "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": 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",
+    "%matplotlib notebook\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": 2,
+   "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": 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>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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare1 = System(olin=10, wellesley=2)\n",
+    "bikeshare1"
+   ]
+  },
+  {
+   "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>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": 4,
+     "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": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bike_to_olin(bikeshare1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'bikeshare2' 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-5-061a630d6297>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbike_to_wellesley\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbikeshare2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'bikeshare2' is not defined"
+     ]
+    }
+   ],
+   "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": 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>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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'bikeshare2' 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-7-2fac1599c3a1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbikeshare2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'bikeshare2' is not defined"
+     ]
+    }
+   ],
+   "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": 9,
+   "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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\" 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+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub data rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_data_rate_limit`.\n"
+     ]
+    }
+   ],
+   "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": 10,
+   "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": 11,
+   "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": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub data rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_data_rate_limit`.\n"
+     ]
+    }
+   ],
+   "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": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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": 12,
+   "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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x == 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use `==` in an `if` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "yes, x is 5\n"
+     ]
+    }
+   ],
+   "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": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "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 5')"
+   ]
+  },
+  {
+   "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": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bikeshare = System(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0, clock=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we need a version of `move_bike` that updates the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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": 17,
+   "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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yesGxv4zOJkGAocvC7sh/wbuscg1l3MFxw66quvq6+u7X07S4Dc4/xOwP10Q8dqvgIhKsGKhLvif0xo4JKoNnMCZv93g//EQyo5fv379MLQNhyF8D8echZgvNsfa72BOwyiampo4y5cikw7Nn8Txz8FnHYH/NSdsjEGgBbaHwEuDgt/vQvQ1EoCiKsEPNscueT76EH89DesQLQ30DRd0711v46KAh8yn3YdOEHAtCf4CcY7BOMD96+rqgiXdErp/A1AeCMC98EDm7MtwWTc4Jv23sS5qJqUHLakQPYejnegzLtMxacT7DBAPJlhEUMOHPsf5Bf8zdvFOmjSJvg29dY7b2tpWok3uxMP/bASOcQtmqKZy3p3V5wdAYD+BaB7aavCgQYOORnyCX2N+Fx57mU/wGd1dt7jOtMUUtuWYvk60zzVIU5RRJNNK2f1MwP2wAJHX3b1ly5ak/3cIx/3QJlPRHp4rGLYL7oHJCLRAe92z+J/8C/fq1/F5++LzTmE5YJdtMIEnKyD22NVMK/d/cHx2k1/PsZ4LFy58nfUcx4goPGwjLYp0vwvRp0gAiqoFD48z8APPN/0929vb6WA26Y84fujpcy0YCP8AHjSB77eSo6Ojg0KEXZL7IXgzJnHukQJw9OjR3JbdxntgG69LC3HCBBACIUmXI5ztOQltEen2YvDgwedDNHAM4nPYho54M2LAgAGcScs1l4csX76cznl7AKHxMT7scY3zN2/e7HV54v94BcIzKP+xt1EiKO4KLKK9PqSz/fwwqAusgBR/9BvI9sxr928YfE5gve5CG1DIZQoFHrvKOYEioSsYInotyr1Z1biGc71CB4g/CuzHEOagPT7CMnyX3oftud70/VaAJYB7qfv+wjnnKpwCh+cc0hGMzewG5/EpBE+o47PSHlNbjPtdiL5GAlBULewSxQ/86UhyRuWn8TD7j52x2M0ee+wxCA/FM7Adx5bxgb+gvr7+U4h7rEZQKrT5rjw4OH9nPJiPRqAj3cjVMzhzEvWcDToMMVd54PJvvNYErJ85b9Ymtr0dD7zjmLZwNQyuckGXIeRndixiRtBtB6LvM43PuA6fQRHW/RuFz/ggHuK/sNk/4jNooeLDmsKc/49j8b/6CscyspxMmDBhFzykv26zfKgnJdvPDwPx81u0I9v0wzgGrVtceYWrUeSbGlwru1y9a8Pn3YXzCdaCzgi8zHA1Ea4PzetO6ApG/luIKKw+hs+7OrDyEfo8xOfej23oNoj3B/0Fsrv478i/gfJhCL/BPt0TrSCgh6ANA6G+tH///t5qPDkQrLN9Cs6v2z/guHHjBiD/RXx+dzc97oO0x/oW434Xoq+RABRVDR7OHCR+OJJczuswiIn/4sf9NTw8nkb84saNG1fhAXAjwnBs90c8vPZJ4rql1AhWBRmC8AStOV5pBKj3unwRD8I1PhjlToNs2rSJftHuw3Y74GH6F7TRMoSn0E5sDw76p+PfuyAovuHtkAX4f1CI8ME7EJ/xKxyf/wuuuMCZoVxCbDA+47G1a9d2O2KeP3/+LJR5n4n6H6GOkz6exT7z8f+kSOPYuAU4Hpcl65VsPj+MHRf6N27HPLbNyfqH++2n+OyEST+4tv+xzfEZdyPwfzYL19nr8nKpwOdcgeOw25NdwfRD6Vnm0B6Po+5zqONLxPkQuK/jHJ5BaEb5HJRRLL6Gzz8GIshzTcMJKmi7j2Cfzaj/KPZZxu8S9nke+3DGLSfG0Pnyqa7rmUzB516BaAOOtRPCC/icuQiz+/Xrx5VKODOa930w4avX2ccuxbjfhehLJABF1YMf8P/hwRRHkm/1gZuPPRE4tocC4heofxe2+2i5vOl3dHR0r/OK6+kxpi8Mrq27exgPtaTb0kEuBMHxSH4Ux+TSY+xa2xtpPggfRDiVbYSyHl1xGUCHy2fgnDhp4F4EilEKOLpy4SD/cxsaGqaHHf4SfO73ELHblVa+TdhuKq6FzohnI33JgAED9oQI9pz9piCrzw+Dzw0ceW/Ctp5VLFtwrN0REib9oJiWRYotrtxx2ujRow+ECM7IobkLxRuOzfufltQDIHK6u4JRRxHL7wPdnqzAdjynXZCeh8//EQTeHmFn5gRtza7RA1BPi+gKhBj24UQNWil/in0mYxtvDG4uUPxDBO6J49MKvBjxRAZ8Fr+338Pn7I7/5XXexsawuzztcZJFut+FEEIIUQnEYrFzIKC6EP/RFgkhRMkhC6AQQuQXWtJoeY1c6UIIIUqBnHwwCSFEtcOJQps2bZrQ3t6+oa6ujmsFc3b5C62trTm5OBFCiEIiC6AQQuTA5s2buSzbHIg/rlV7BgJ9853NOiGEKFUkAIUQIgfmz5//KgRgK8JmWv46Ozs/2Nra+qitFkIIIYQQQgghhBBCCCGEEEIIIYQQopBU5CzgWCz2h5qamoNbWlp6eH5vbGzcG3WXIBze1dW1HYroMZ5rU16O7Rd5G6VJPB5/C1F/HIfe7YUQQghRBuC5T0fxm/Dcpw6oSipOAEKUfRPR5QjLXAEI8Xc0/un3IcnJL1zW6WWIN64Y8B7EbyIc0tbWxkXM0wKfRbcPA0aN4n0khBBCiHJgxYoVXDGJq70MtEVVR8UIQC7+3a9fv59CzHlOWIErAOsg2BYiHgOhd2R4ll5TU9PpKPslko9gH64LmxaxWGzh2LFjx8+c6S27KoQQQogyYPr06Wbp0qWLoAW45GdVUhFuYBobG4+D+JtnxR/XAu0BxN+BiLh+5V9cFw3Nzc2/QkRxeOiECROGeoVCCCGESKCri2tvG/P0037MfCak2j+X4+eybzVSERbAWCz2J0SHQABe2tLS8guIPTpiTbAATpw4sbGuru7Erq6uFyEA/2aLu8E+7PqdjPqdUb/ML+0dWQCFEEJUC7NnG3P33casXGkLwIgRxpx4ojF7720LeiHV/rkcP9N9ZQGsEAHY1NR0BKJZzc3Na5mHmKPu7zEGMBmTJk3avbOz83mIv9W4GXZEEQVkSiQAhRBCVAMUWDfeaExHhzFvv23MNtsY07+/X1cDJXHmmb2LtGD/9nZj3nrLj8MEApDU1hqz3XbGNDT4+VTHD45Ni9+WLcashRIYMsTfP9m+EoAV0gUM4fdQIP4yZdq0aQ0dHR2/QLK2pqaGcVriTwghhKgGKKxoXaP4o9h64QVj/vc/Y1av3lp/zz3Ju1yD/SnOnnnGmJde4nN7a5g/35if/3xrft48//jr12/dP9nxg2Mz3rDB3+/FF/3P2bix932rnapeCo7ib+3atXdA+B2M7HObNm36rl8jhBBCCNLW5netMrzzji0EL79sE+D11/3togj2X77cmE2bbGEICkOWb95sC0BnpzGLF9sMSHb84NhkyRJ/P8Jjrlrlp3s7t2qmagVgU1PTkDVr1vwVyRkIbRCBxy5evBjvC0IIIYQIYJctefNNPw5Yty5RtLFrOIpk+wfQskgC8RbA/cKWu6jjB8fmdu7x2ZUckOzcqpmqFIATJ04c29nZ+ThEH/3/zamtrT2subkZ7yZCCCGECMPxeFECiwQCjAxN4kOD+1PkrVljCywcR8ixettu64/XYzoMrXgUmQFRx+exCbuLw2KUBHUk2blVM1UnAOPx+B4QfE9C/E1F9t8QgIfOnz9fK3kIIYQQETQ2GjNggC/IXAJROHKkv10ULKc1LmzN4+QMTsxgOPBAPpv9mKIwTCAwkx2fZZzt64pTnu9A6+K5t3OrZqpKAEL8TYLgmwnxNxrZ3wwZMuT9bW1tzjuJEEIIIQIo1iZN8mOXQKDNmBFdT1g+cWJiPa1+dXV+muLw7LP9OGy1IxR23C/Z8VlGVy9hSyTZfns/7m3faqdqBOCUKVP6IboL4m84RODNLS0tp82aNSvifUYIIYQQYdi9OnnyVqtaAEVbOn76OEM3vH8g0Gido5uWU07x4wmOUxZaHU8/vffjT51qzM47J54bhWRw7FTnVq1UpCaO8gMYi8XOgPi7EeJvC+KrEDujBXwaGhp+MnfuXDu5vXfkB1AIIUSlQxF2/vl+zG5cjuWjIOzXz7fknXyyMUcdZTeOgNa5iy7y08H+3Ge33fyu2bB1jmP5PvMZf1ZwcPzzzvO3TQZdx1x99dZj8zy/+U1j9tgjueVPfgCrqwv4ffwD8UfXkhcj/lZU2LRp0zBuJ4QQQgjfhUow/o+CihMqOO6OMfP029cb9PMXwO132smY44+nEaWnQOMYwL32Sjx+eP8ogs8Pzm3aNGP23LPnsUUiFSkAW1paatxVQKDyT7DlvYYFCxbIW5AQQghhcQVe2L0KoQUucOUSxVwutBqiqannMcKwqziMu7+Le35TptiE6JVqsgAKIYQQIkNcgXUwl04Iwe7aRYtsxoHdsu7+rsBzcetfeaWnC5kAOqYOO4wmnLAiUiMBKIQQQohI6Idv6VKbsey/vz/pIowr8gJWrOgp3lJZ6DgRJFhnOIBWxihYHnYvQ3+CcvmSHhKAQgghhIiE4+/CAosTMyjQ0u2mdcuHDfPH9/UG3cPQL2CYdI/P/errbUb0igSgEEIIISJxLXuBwHIFINcFpqsXl6ju33QmZ7jH53HCQjTAPb66f9NHAlAIIYQQPeht/B5n8IYtbVzHt6XFZizt7T3L0p2g4W5Hh9Cvv24zljfe8EMYTQBJHwlAIYQQQvRg5UpjVq2yGUsgsNgV7I61c7tjFy7suT5vuhY6uopxVwVxj+/muZbwmDE2I1IiASiEEEKIHrjWPzplHjXKZoAr5tzt3fzYscYMHmwzKWA3cabHT7d7WfhIAAohhBCiB6kEltvd+tprxqwOraPlWugy7Z51tw/7G2SXs+sg2h03KHpHAlAIIYQQCUQJLFeQ0aI3aJDNWALRyCXdXP98mQo0d/uNG/3JJmTJEv8zwkgAZoYEoBBCCCESoHhzZ/W6XbJczYOreoQJBGA+/POxy9kd0xcc37VOcszg9tvbjEgLCUAhhBBCJOAKLI79cydlENcqGPgNdLt/Kf4oAjPFteoFx3XPT9a/zJEAFEIIIUQC6Y7fc4XX2rX+0m35Gp/n7scl57iyyIIFtsAiAZg5EoBCCCGE6IZr+9KFS5hkAmv48J4rezz2WE+ffZlOAAmgv0GuDBLAsYl/+5vvYzAgqitapEYCUAghhBDdtLZunW1LKLAoxJLhisNHHrEJC12/uGsHpwvXBJ440WYs7vHHjzdmwACbEWkjASiEEEKIbtzuXwqw3gSWKwBppQvDySO5+OdLdXx1/2aHBKAQQgghusl0gkUqgZdt929Aqv1zPX61Ip/ZORCLxRaOHTt2/MyZM21JGpx5pk0kIbBtH3aYHyfjxhttQgghhMgPb71lzFln+eMA2f1KVyxf+5oxEybYDZLw/e/7Pvo4A5iTNML7/+AHxgwbZjfMAlr8LrzQ9/vnHn/kSGOuuSZxnGA6TJ8+3SxdunRRa2triiurXGQBFEIIIYSZPduY88835vnn/Vm8jJ97zpg337Qb9AKthG+8YcwzzyTuz+5k1yF0pgSTPKKO39JizJw5dkORERKAQgghRJVD8ceOJbpZCUMr2003+fWpYNex6zya+/O46ezfG/X10cenb8F8HL8akQDsS2jLXrHCmBde8F9lGLiQIkOQZ3jxxZ5z6oUQQog8wEfR3Xf7Xa2utY/On1l/zz1+HAXLn3rKt9S5cHWOVPungvvRkhi1fzrnJ6KRAOxLVq3y59vzG/f2237YvNkPQZ6BgpD2bg7OEEIIIfJIW5sxK1f6Y+z4+AkTLK9GGwS3i4LlfJwNHWoLLJwYEpT1tn8quN+6dT1nItO6OHCgn87l+NWKBGBf8uqrNpEmr71mE0IIIUR+CGwLro2BgissumiPiCLYz12Ld8gQv+s2INn+qeDxKSbd4zMfnn2c7fGrFQnAvoK29kwterQUysYthBAijwRr/EZ1/4YFlmvhCwj232mnRMG46642YUm2fyqC448du3W2L7ubmQ+T7fGrFQnAvoLz2MPeLPkt42rZnDPPQLfr7rx72uZpoxdCCCHyBB89O+zQ04IWtrjR3Qq3i4LlXA6O4mzaNGN2282Y/fffKtxIb/unIjg+xSWPS79/BxywtfuX5HL8akUCsK9wrX9cK2f0aGO22cYPo0YZM2ZMz0EPmVoNhRBCiF6g/YGCyl1hIxBwrJ8xI9EaGIblJ57oxxSBFJPhR1eq/VMRPj5n/XL9YcYBuR6/WpEA7CtcW7s7uIHwbnbL3f2EEEKIPEBffoFVjTYJiixa1rh+wd57++XJYD234/Zh0t0/FYU+fjUivZwDWa0EQt55x3drHh7Pd8EFvqdLl1mzjPm//7MZwG/ktdcmjqwVQgghcoCrddAHIB9LHKG0zz7GnHCC362aiWWN+3M2LruTOSYv0/1Tka/jayUQWQD7Brp0CYs/ijquth2Fu8jili3GLFhgM0IIIURucGg5l3EjfNxQWB13nD8UPVNxxe253777Zrd/Kgp9/GpCArAvcFfajseTW/QGDTJml11sxuLuL4QQQmRJc3NPm4QmVFQ+EoB9gSvgOPCiN9x6CUAhhBB5wn2kUPyFJ1mIykQCsNjQ3TpXtA6TqQDkytocRyiEEELkSKY2CVEZSAAWG/ebRlfpdPfSGxwfGH4do62eNnshhBAiB7iEm7vUPP3sicpHArDYRL1qpRrFSvHH0a5h1A0shBAiR9xHCd2/7LyzzYiKRgKwmNDLJmcAh0nX1u5uN3euTQghhBDZ4QpA1/GEqFwkAIvJkiU9l3LLVgByHKE7llAIIYRIE44mcgWgun+rBwnAYuJ+07hydtQKIFHQJs/xgmHUDSyEECJLli7tOZ+QFkBRHUgAFhO32zZd6x+hTd79ZqobWAghRJa4NgQuq8Z1fEV1IAFYLDZt6rmCR6a2dlcwcjyhu3q3EEIIkQbq/q1uJACLBRcv7OiwGVCLpucKIJngfjs5npA2fCGEECIDuKpoa6vNWDLplBLljwRgsXC7a8ePN2bAAJtJE44X3HFHm7GoG1gIIUSG0CbR3m4zgKOMMrVJiPJGArBY5MvW7r6iaSKIEEKIDImySWyzjc2IqkACsBisWWPMsmU2Y8nW1u4KR44r3LzZZoQQQojUuLYDdf9WHxKAxcD9prHrd9ddbSZDaKPn+MEA2vDdgRxCCCFEEtau7Tl8XAKw+qhIARiLxf4Qj8dfsdkEJk2atAPqrsM2CxFvQDwf8VcPP/zwertJ/nEFYFOTMXV1NpMhAwf2FI/qBhZCCJEm7oJU/fv7XcCiuqg4AQgx982ampqTbDaBcePGbdfZ2fkwkudgm2e7urquQ8ylOX64YsWKO7yN8k2Uq/VcX7XcbmAJQCGEEGniPjLYsVRfOBOIKFEqRgBC3A2IxWI3IXm5X9KTfv36fRPRbghnt7S0fLi1tfVriPdD/h6EE7H/DMT55dVXjXnrLZux5CoA3f1fecUfZyiEEEL0Am0SuaxJICqHiljyubGx8biampqfILBv9H6E9yMsg7jbmfVk5513HghWYpvVKOd23R6U4/E4jd8LER5A3TFeYRqwG3ns2LHjZ86caUssZ55pE4CTP8IOoGlr339/f879jTfawgzgsfkN/u9/E/0KcpUQunF/5BE/f9hhfuxS6vUB2bSNEHmCXzG6yeC723bb8TcmcYH8VPXlTjlff67nXuhrL/T+qeppk/jSl/y1Cfg42nZbYy67zJjRo+0GVcL06dPN0qVLF7W2tk6wRVVHBrdd6QIh9idEh0DcXQoB9wsIOoq7BAEIkXh4bW3tQ11dXb/FP/xTtrgbijnsPxr7DEI2pKySk5YAfOklY1atshnA9X8DZ0vZCkCS7LilLvBS1QdIAIo+YvZsY+6+25iVK20BGDHCmBNPNGbvvVPXlzvlfP25nnuhr73Q+6dTf/XVxjzzjK0EdC97/fXG7LOPLagSJACNyXImQmkxYsSIVyHeLoJ4e5z5HXbYAe8zZu2qVatwq/ug7BhsQ0vhPSjnOMAEUM+62HbbbffbN998c7Ut7hXsc97QoUO3P/XUU22J5b77/JivYpyhyzhg7FhjBlFjguOO8+NMCI5NN+5vvumnCfNcxJFTu/h5FIScIeyGUqznrObwzGaSTdsIkSN8QPLdgwvkc5XFjRv9W/Ttt4353/+Mee01Y+66y8+znHO5aF3hojyzZhkzZowxo0bZg5Uh4eundymGZNfP9gnGjZXC9YfPnT8xtHDxZzHZ/y4I2dYHP1vpXrt7b/EcM7l3wvuz84fXl835s0NqwwZ7UDB0qDGLF5f/vZspt956q1mzZs1bq1evvs4WVR0VYQF0icfj+GolWgBR9jVE3+/q6joHih/vO4mgHu9NZgZE4H7Nzc2h96PkpLQA8pv3/PN+OuBd7zKmocFP89ucKcGx+asRfo0LoH2fUGBFUYr1/BXlFDT+AgVk0zZC5AAfyJde6ltP+LBkN1p4lAXr2a1Gi0kABVAs5ltZyMiRxlxxRWKXW7kQXP/rr/sdDKud1+Co6x8yxJipU7cKwb66/vD/jj+NL77oi/eAqHMPk009f7Z22cUPpLdrD58f25f3FssmTNgqutLdn6OKFi1KXAY+m/MPoFMKLjBVzvduNsgCiHvYxtVAPxvjvSkSrxwCMcP12Xph3TqbsAwevFX85QrdwXAARyXAXzL+ovGVVog+gg9lPmBpNXHFH6E1ibdo2O86t6WRP3gYBw/3ciS4/hUreoo/EnX99Ce3ZInNgL66/uDcCS1cYfFHos49TDb1/J/TchZY03q79uD8eE8xzfuGaZ4rj03S2Z/nsHBhovgjuVxfIArL+d4V2VE1AhDCzvua1tTUBELQxVNT2M5RbTng+vpjF22+4Gva8OE2UwHwFy3cpS1EkaGFhNBw74o/EpS5D18+zMOT8Ll/ORJcf3hocZhk1++Kxb64/uDceY5BOkyycw/Itp6WtfD1J7v28L3F+yWAxwufb6r9+RPJz3TJ9vw5AaRf6IlYrveuyI6qEYAQft7XFAJvO6/AAeVDGWO7/H0FaFdnYP8IxRrH/+WTceOMGTascmz2Ub/cQhQJzpgkyd5Dgvc5d7gqCd+6HFNVjvD6KRCSiYBk188uV1qXAvri+oP/Hf8PUQKpt/8dyaU+nf99+PxcMtk/m3uTRNWz+57DF8KU670rsqMie/uTjAE8GNFjCL9G+eleYYhYLLYI0YjW1lZ+Bez7Uu+kNQuY8BcpSqTlMgYwDI8f/Oo9xksEhxzixy6lVM+BVuFl7Ng9fuCBfltpDKAoMvwKcZzV3//ui5oALryzM35JWE8HuvTDzq6/pUvtBoAPU86yLOdxVLy+z3/emIdDU+R4HQcc4L/DBtdPn3FPPbW165LQAQGH9fbV9Qf/uyef9MfIBVDQcIxi+H8XRSb1b7yRuJIGxRWHdvP6U43h+8c//EkcYbgy6H77+baC3vb/xjeM+etfE9udAo77ZXN9rljUGMDqI8n7QuWxadOmWV1dXWsRjkA24boh5CbU1NTsivAEsmmJv4wo9DeKx+e3OfyNDvJuCIiqYwiIqmMIiKpjCIiqYwhg2h2RzF8299dRiCLBr9G73504Q5LwNuXtShH0xS/6D3z31uVwX3btzZhRvg9QnjcFRfj8OWyZXYTh62ccWKQCaMXifn11/fxMujpxLWxR/zvm3ZBJvfu/Z/cq//+9XTvLjzkm8cUigOMVaUFNtf/BByd2H5Ncri8Mj1/O967IDuc2qFwWL168EQLv9wjj4/H4ubaY1KHsSpv+mY1FMeCrLyezhInqIxGiSNAITQtXcFsyTxFE6wiN76ec4sd0rht+iPJWnj49PV9upQyFSPj6A7HjXr+7biwnF5xxRt9eP8+Jo2LCPyk8f/fcmQ+TaT2dFfCeCODn0Uqa6to5Zy/ctgHMH3po6v0p3sL7M+Z9l6/rK/d7V2RORer9qC5ggvLhXV1dT9Pah+xfkZ6L9NFI74P0H1tbW09GmvumRdIuYJE+t9221Tk04S8c3dQL0QfcfLMxTz/td5lxYgfddJx2WvRqC+zS45JatJBxMD19m3/iE3aDMoTG9wsv9K8tuP4Pf9iYPffsef2ckXrOOb7wC66f7ZHvYc6Z8MQTxtxyy9Zzp0D/1rf87mn3f8fZrhzryC7iZCtp9Fb/s58Z85//bL32iRONuegiu0ESfv1rv4s6OL9w29EJ81ln2Q2TcO21fjdusP/uuxvz0Y9md/691VcL6gKuIgsggSB8o66u7iCIvV8iuz/E33mIByL/1YaGhk8ija+GKCruoBX+MoUHuQhRJPhg5AOW8IHIh+P73tezW5Qwf/jhvv8/bsd8sG+5wnFtbAPC6+G8tQ99KPr6ed0UDqV0/e7/joKcPu7cc2ee17Tvvsn/t6nqae0NXzu9WEV17wZE3Vvh/Zubk8/gJfxJDIZLB/sfe2z2599bvageKlIAQujVuNa/gPnz56+A4v8s6ndCGIgwBfkr586dm8SDkigo7i80f+koAoUoMq+8Ynq47qRBOhluHScHBL7oyhFXwNFyxrFjyXCvn9bQviIssAJ6+9/lCsVvuG34+RRxyaD/+2SzqwnFY9ifogt/EsPj//iTyZ9OIXKhqiyAogTZZht/4E6YvjYliKrEve1ooenN1SbHgnH2b5hyvnUzFVBufV8a75cv97tFwxRSAHJsKEVgmPDMYBdXHHNsorvsWm8C2v3fcGY6fzqFyAUJQNH3uN3AEoCiD3AfwKkEBK0w7jbleuvSckkLZphU1z9pUk/jPVe26AvcdqfPfQr4QuL+bGUi4Ni2mdw7md6bQqSDBKDoe9xfs6VLe/bFCVFAokYeJPOpFsa9dWkF6m0sV6niig9aNsNLc0cRZbzvTQQVEvdz+b8r9Ng293/PpdSiVlGhm5iWFpux8Pzc/Smeww61A7jcHn8Sw0gAinwgASj6Hk615HS4AA6o6a0/RYg8E16TlaQ7xsp9EKcay1WqRFmo0hFQ7vX3xdeWY+PC/uQJrZOFhjOeBw2yGUuUFY9r97rCjufHMZZhV0IUiu51ELdN6U6GP5lC5IoEoOh7OJqav4ZheusPESLPuBYkWrbSGWPFsVxcASJMud26tFi6IiNdC5NrJaX4LbYvdwosulQJiOqaLwRRnxP1v3fLuKoMLaz04ecKuXT258zd3ibnCJEuEoCiNIj6JQ18UghRYNyHbDrdvwHurdtX3aDZQtHmujBJ9/qjjPdRIqaQuO29yy49LXOFwv3fU0i7P1u93VupBGRUe7r7CJEtEoCiNHB/1TiYppx9aoiygcNNcxlj5YqlZGO5ShVXYHB2qrvUWzJKwXjflwLJ/Sz3XuKygi+/bDOWcPe0uz/XMQ7PZua4wtWrbcaSycuJEL0hAShKg9GjfZf4YYr9JBFViWu1oUUrkzFWUWO5ysmVpWtBy3T8nCti+LUtlvGelsvFi23GUkwByNnGXEotTPhny3XwTMHMLtwALl/HruAw4f3dn0D+RLruY4TIFglAURqkO6BGiDzj3mapHCC78AHuro1bLt3AtFS6rlsytTC5X9tiGu9d8U7/fFyWrZj09rPl3lv0HRjuMueLgzvZqLf9+VnpTM4RIh0kAEXp4P6S8te9HH1qiLIhX2OsXNHkHrNU4axTWiwDKEjcLt1U9KXx3v0cWtcoAouJe7+wTYMZ5encW24Z9+F9GTU5R92/Ip9IAIrSwf0l5AAat39HiDxCS5Xruy0bAeju447lKlVcgRLVJZmKvjTeu5bWbP53ueKuZkm3NBwCwLF7r71mCy1R5+eKurfe8peO49jBjRttoaUY7m1E9SABKEoHjjx3B7gU60kiqhL39qIlixatTOHSXL2N5SpV3HPM1sLkCptiGO+5ckmmq5cUAroL4v8/DNvVbVvOTKbvQBeOIaQ7oTBR+/O+THdyjhDpIAEoSgv3F7zcfGqIssJ9yPL2y2aMVV1d72O5ShFaKGmpDJOtgHL3K4bx3m1f+tajj72+IOpnyz0/Wu/Ck4UCkllQo+5NIfKJBKAoLdxfuSg3+kLkgXyPsYp6iBdrNmw2uAIjajJLuvSF8T5q9nJfTZBw7xu6gnnxRZux9Cbg3P05e9idnCMBKPKNBKAoLaJ8akStjyREjtBCRUtVmFzGWLkP6GAsV6niCjRaMKMsVOkSJYALBcU7RVKYvhRIFM5coi2Me2/1dn7ufcd33nAXejaTc4RIhQSgKC1ohnCdsKkbWBQAV6Bk4gA5ih13jB7LVYrka/ZzGHf/QjrEpoXNXXIuF+ttrrj+/VxGjDBm+HCbiSBV9zVd27gCU4hckQAUpUcxTQmianHfK3IVEH05GzZTaJmkhTJMrtdfTOO9+7+LEt/Fprf2S0dc57q/EJkiAShKizPPNOa224x55JGt4Y47jDntNL8uCELkAN1rPPusv9TW22/7FrF8uNgIHtQ8Ho/L29d1VkyYpzh6+mk/zrQ+VyigeH7B9dPy6a5okSmB8Z7nGhx75szMry2derZr+H9XCgLJ/d9nen657i9EpvTRkNnKIBaLLRw7duz4mfyVE/mB4o6/eE884TvUCuAAJb7mB9x4o00IkRmzZxvz858b8+ijtgDQlcfPfmbMgQfagixZu9aYT3/amEWLto4B23NPfwWIE080Zu+9/c+/++7E1TLYRZhufa7w+BdfnDhLl92Xl1+e+/Gvu86Y3/xm67XT9cl735u/a2f9H/9ozF/+snWM3MCBxlx0kTEf/aif7yv4s8X//QsvJI7/4/ldf70xBx1kC5KwebMxn/iE70MwvD+7h3m/TptmC0RemD59ulm6dOmi1tbWDBZ+rCwkAHNAArAABNa9l15K9NBL8Rf2syEBKLKAAoK3Dh+yYRcoQ4cas9de/u2Xiwji8c84w5h162wBoHWND3F2EbObj9Y3igWXqHqeF7s2WceQj/P7xS+MefzxxBVAaGGiBTCX4/PYV17px2E4to1dw5lee5hwPccVhh0ss47i6uyzc2ubXOF1X3BBz4k//N/vs0/qtuX+vIY337QFFo4d3G233P/3IhEJQHUBi1LFHdDDX8WoJ4MQacLbh9Ylxu74N95uLL/nnuxvs+D47q3Lz+KkBVrcbr/dmCVL/LwbourpSuSVV/zj5Ov82K0YFn+EIjWX4wfHHjy45zrKPP9srj1Zvbu6BgUW/TDm0ja5Elw/BbtLOvdWsH/UJKRc/zdCJEMCUJQm7lOU/SPr19uMEJlDqx+7FnkruTNIgwcvx11xu2wIjh/1ECdcH5bWK35+FMnqKYCCB38+zs8VvxRtwfq52R4/ODatcVEiKNtrD+itPvipyKVtciW4/qj/fTrnl+v+QmSDBKAoTTii3PV74D65hMiA4PZxu9hosaIVKYAWsmwIjk8BxLFvLoHVLdkSacnqORQ23KWc6/m51+++a2Vz/PBXc8wYmwiR7bUHJKun5S88NDjbtsmV4Pr5k8UxiwEU11xeMCDZ+QX7c/uwgB42LHGJwb66PlGZSACK0oSmBPfJ5D65hMiAwLoSFiuE5bzdAqIsWOkQHJ/j3Xbf3Zhx43xxEoSRI/0JARQI4fKo+n79/GMFhM85l/OjmORElTCu1Smb44ePwfTUqb5fxahrC19zLvUUmnvskSiQsm2bXAlfPyfU0G8f//8cu5fOvRXsz205HpOOpTmjmsOe83FvChGFBKAoXaJME8lMBEKkgDNxOaDefY8IP7wpNLhdNnA/ChRCSxAFAB/gQeAkEz7UGYfLo+pdp8HBOed6fhSn4XFkzIctVNkeP3zthF9dCqGoawtfcy71FFlhy20ubZMr4eunRZnilP//cCdGb+cX3p/if+xYf/JM0DVP+vL6RGUiAShKl/CTmbAfyDVfCJEmtKQcfrg/nixM8J7B+hkzEi0umcD96K4k2f4UW5zlyTiKcL377rNmjf/uk+v5UUSF96f4YzcqYXm2x+c++br2KFLV53Lu+SDV9ac6v1z3FyIbknydhCgB+PrLQTFh1A0scoBih11s7E4k7D5koHUlH242uD+Pw+OFCY5/yinp1dPSE37Y05L0nvfkfn70Lxe+/kBo5uP683Xt2dbn2ja5kur6U51frvsLkSl6n8gB+QEsAvSN8K9/2QygCYNeX4XIAjrkpaNedoPSqkYx9LGP9RRcucLjc8YmRy1w3JZ7/HTqeZtzG3YJ0lJ3zDHGfPjDdoMsWL3adwBNguv/+Mf9FVDyef35uPZc6vuaXM+v1K+vUpAfQNxXNu4TpkyZslNnZ+eYfv36zZ8zZ47jmKH0kQAsAvT8yuUFAvhLePXV/tINQmQAJ0DQUS/diQR89rPG7LefzZQYf/2rMffdZzOAY8IuvdRmsoDOn2+91WYAZypfdVXyblUhKhkJwCJ1ATc2Nk6EWPrZxIkT32WLKJ6u2LJly1IIwKc2btz4CvKftlVCbIUjycOeZfl63NxsM0KkD5dnC4s/ko/1fwsFrZNh6FCZVrtsmTfPJiy8dok/IaqXgn/94/H4+JqamqcRzqqrq9vLlh2M/DcQ6rq6upagaAjSvwwLRCE8OA6QfSBh3CeZEGlAY3IYzrQMzyItNegKxHWFOX++TWQI35vcr40rMIUQ1UUx3v++CnG3HYTebZ2dnX9lAdKn+1HXd1pbWykQ383y2tra8xkLkYD7pJIAFFngiqdSF0CcsEJ3J2GyvfVpPQw7kyYSgEJUN8UQgEcjLITQO7Wtrc1b1RKC71jGEHy/YNzc3PwQxOATSB7CvBAJuE8qrom0apXNCJEariLILuAwU6bYRAnj3vq0YtKalymucKTPOdfXoBCiuiiGABwNcTcbsfezFYvF2A08EmUtEH7LWWZhegc/KUSIXXbpubaWrIAiAzhsNCycOKzUHVlQirgCkCuCvPaazWSA2/0t658QohgCcG1NTU33lE2k32OTD9o4gCtIysuv6Aln/rpPLAlAkQHu7cK5ReFVFkqVnXbq6Q8901ufjq/pViRMOVg/hRCFpeACkJY+hEMnTJgwctq0aQ1If8wv7rrf34JL+kx8F4ThgUg+75cI4eBO1+SArmz6wkRV4oqmcrGARb37uNa8VCxYkLj6CY/pji0UQlQfxbAA/g7ibnB9ff2La9eunYv0VJQtGzNmzD9YSfcwtbW1DzANbrKxEIm4JguOaF+61GaESA6Hi3LYaJhy6gJ1z7WlxV8VMV1cwcg1auVGUwhRcAHY2tp6IyIGju+biPAGwscefvjhdsRkOkThwK6urm9g2ztsmRCJ7IDbx10jSd3AIg3c24TDSekCplxwBeDGjca8/LLNpIF7/er+FUKQYlgA8cba8nkIvF2QPGDTpk27IP+YX+P1BX+lrq5uAsTf922RENG43cASgCINorp/y2lpLS4DN4YjpEOk2w0cZSgvJ+unEKJwFEUAEgi8ZRB+Ty9evBjvr1tpa2v767x58xbbrBDJcU0Xra2Jg5uEcOAw0XLz/xeFe87pvvu4Q2W5rjCX0xZCiKIJwMbGxv5NTU2nx2Kx38Xj8UcQvs1y5L+IOk4AEaJ3OHI9bLrh4q7u9EYhQtD6VQkOkN13H/o0ZFdwKlyhGI8nrqwohKheiiIAIfz2ra2t5Wzgm2pqajgL+GCkx7EO+c+g7nEIwQuZFyIpHLm+6642Y8l0SqSoKlwBxGGkHE5abtBnYVi4dXamXhKblr+o7m8hhCAFF4ATJ04c29nZyRm/HHbN+GyE8Aicf0MMtkMI/ggi8FBbJkQ02faFiaqkUgQQ1wR2u25T3forV/ZcMEcCUAgRUHABWFdXdwnE3TCIvPNaWlqORfi5rfJA/iuITkGgKLyAZUIkxX2CsY9vrfyHi55weCiHiYYpZwHkdgOnEoBuPSeTjB5tM0KIqqfgAhDC732I5rW2tv7UL+kJ6u5B9BwCl4krCocffnh9PB7/WiwWm4ewCek3Ef6G9AF2E1GK0AzCkexh3FH+QgAOD+Uw0YByd4DsitdXXzXmzTdtJoIo62c5zX4WQhSWggvAmpqanSACX7LZ3ljIbW264CxfvvxORN/HZ3JkzQ04x/sQjkb60cbGxmO4jShBOBCKI9nDpDKFiKrEvS04fLScHSBzSWz3/JPd+hwjWAmzn4UQhaPg74PxeBzvqWZxS0tLt2UNZZ0QW79rbW39lC1i2SxEY7BdwUUgBN7RtbW1/8I5PLN58+ZDA9c0TU1NR6Ls30guwnmkXCo+FostHDt27PiZM2faElFwzjzTmGXL/PWtAgYMMGa//RLNGzfS93gW8PjpkO3xywROIKAF7a23/LVoOQkh3LzlUH/BBb7DZI6fY/fnscca88EP2g3KFN52zz7rX9+aNf51n3pqz+tfuNCYSy4xZtOmrdf/ox/5bSWEMGb69Olm6dKli6BDqtYxUugnozBA2LF79/iOjo5DFyxY8IQtSxCAEGSHQ5A9iLI/o+wElhUSCLdv1NTUfBufdx4+7ye22APnRmvllPb29h0XLlzoLCCViARgH0CB9s47xszi+0IICsCBA20GSABmzezZxtx9tz+JIGDECGNOPNGYvfcuj/rf/96Y++6zlYC3xre+Vf4C8JFHjLnuOt8NzIYN/miIA/BqzdnN4ev/4Q+NeT60sjrbh/uxXgghAUjqbFwwdthhh1cQnQ7BdcKIESPeGDly5GoIry+hbM5OO+30t+222+6DqPs1wiCIwLNXrVqVwSJH2TFs2LAp+LwPIDl/9erV//RLOch6Sr/Ozs5v4Pz6of5bqOt1xU1c23lDhw7d/lS+goviwKd6Q4M/ACq8ICr7xoYMsRlw3HE2kSFh1dAb2R6/xKF4oLalxg6zfr2vuTnfhuKr1Otfwa/OG1x00kKLGf3m7byzMaNG2cIyhIbvW2/d6v+cXwGKO+bD18/uX1r/Arbf3heNXFGknK9fiHxxK75Ia9aseQvPebwaVScFtwCSeDxOwfdjP+cDkcXh2bUQWhyHWIP8FVDil3mVBWbSpEk7QOjRgdxQfO6ZCPciv119fT3em83JCFfZ2cm9IgtgHxBY6OgE7bXX/DQZPjxxmmQ+LIA0MVFJ0MwycaLf1RxQgRZAiqRLL/Uvm2PIKDZWr/bLg3rmhw1L7G4MKKV6nn/4/YBlu+/uW8quuCJ6/1KH18f/z9/+lugEmsNia/ErGr5+Tn5hPmC33bYup12u1y9EPpEFsAiTQAjE1LUQWUci+Q/EGxBD99U0IHQg/zDCB4ol/sj8+fNXQfAdhM99GudwS21t7dsQf1yO7mSUfR3n+1V/S1GyuIOZOBgs/MTLFT5hKTJpUqEzNQ44q3B4iUG3Kr3rrFjhW5E2b/YDrW5BCMrCoZTqw+KP0AJGXn+9fP+Vwf8nuJYAij33+sNfBYq94OtSztcvhMgvRRGABALvEQirYxFvC/G145YtW0aBwcgfhXC/3awocFk6iL5LIf4OQvZZiL5rEd+KGE97c3EsFuuenCJKFFcA8ilIsZYvKPpoRgqgv40KX3eYGjogPL4uIBBV4WYJU8r1YdH09ts2UWYE/x9a+KJIdv1Dh9Ifq82Acr1+IUR+KbgAjMfjw20yoLOtrW3lokWLXnv44Ye7vXRRlEF4JXQTFwqIv6sQnQrB91OI0n0hQM9HfGpHR8fuEIVvIvwa572fv7UoSTi10fWJEVYwueIeiyaVfB6/BAk0Na1+HFPnEogIdjdGUar1HPcXvlUoiMqR4P9DARi1nF3U9bPMXUGkXK9fCJFfCj4SBKLu+QEDBhzxwgsvJHVZCrHFtYF/BeHVCCFmf8YKRi0+7x3Em+rr60fOnTt3s1/sg7pPIroV4f9wLr1OCdUYwD4gPEaPg9ToEiaAT7Y99/TTuYwBpNh74olEL8KEo+djMT9dwWMAX3wxcZ1ZjjHj8ErWs8npciSKUqznsM3w0M1KGAMYWGcp0tn1G+BeP6+R86LCglBjAIXw0RhA/DbYuGBA1E3dtGnTv8eNG+f02RmDsgEQUex+fRjbxSACOQ6voEyZMgU/gYaPhAWu+CM4hxdsPM4rEKWLOxiKjtGCfrBc4HFc8UfYDcynbIVCUUBXIq6hk81M6xMtT1/+8ta8G0qxPiz+eH0zZpSv+An+P8H506rZ2/XzfSgs/sr9+oUQ+aXgAhDcgbB3v379HpgwYUJ35wOE36EomwPhdw7EFrlm3bp1u9vqgrF582ZaIjfh88bT7YtfuhWcj7fMBOIVXoEoXfiECz/NKM7yMcApWVcvJ4aEp19WIHvtZczYsYkuFSkmaDmicfSUU/yY+TDlUl/ufvB4/tV8/UKI/FGMd8GaeDx+C2J2rT5VW1t7QkdHx9chsM5CngL0eZR9dv78+bOQLgo4n98jOgUi8Eetra0X+aXsOmkcgfN6zIrAd7e0tPTat6su4BLgqqsSV/yfPt2Yk06ymSzhkgnsS4vi4x835rDDbKbyoNebb3/b19I0hLKL8SJ8Q/bfv6fW5mxS6m3qcHY7llN9uVPt1y9ErqgLGL8JNi40NRBLN0FYnQ7R1Y6Y4/zopvRyiCxOyMhDv136jB8/fsf6+vpHcR7sdn4G8UOIh6GKTqk5vPrHOK8vexv3ggRgCUCnaH/5i80AjtO7LAePQlxegWuIJZtqShPKWXx3qUweeMCYu+6yGUD3it/9rs0IIUSFIAFYnC5g0oVG/iziGyGw6ikCOzs7D4bIouPlooo/whnIHR0d++E8+Pl0Bn0u4o/g3F5C+Eg64k+UCO4K93Rel8ts3ZaW5OKPcHZEb/Vlzrx5NmFxm1cIIURlkHcLYGNj47Y2GUltbS2XXeHaaY9BBJ6IOLRgEbst2tbYZMkjC2AJQDFGix0tdwGnnWbMgQfaTIbccYcxDz5oM4AWRYrKMBdfbMyuu9pM5cB5L1/6UqK7wzPOMGbaNJsRQogKQRbAAlgAIfDe7C1gEzpZ7kI4GPlXnfrVPIYQacNpjk1NNmOZy1X+ssTdlyvtu4un5nL8EobDHsPij2PGJk2yGSGEEBVFIbqAaVXMNhSrS1pUEm4/JfsxOQo+U+jmJby+MKEDvKjjVyDuZe2yizGDBtmMEEKIiiLvgqulpaU2l2API0T6UKSF4fRVt9s2HVzLHh2t0SeKKwBpKuNyGRWGe/luswohhKgcJLhE+TNihO8FN0w23bSuCYz9n+xijsf9OIDOpsOuZyqAd94xZskSm7Go+1cIISoXCUBR/nCwWq7dtOwynj/fZiyBCYzLSbgLqlZYNzAnN4d7zRsafN9xQgghKpO8C8BYLLYaYVVjY+PEUD7dsMo7iBCZ4vZX0kIXtZxbMugBee1am7GERaUrMCtsIoh7OTR6cg1gIYQQlUneBWBNTc12CNvX19fT2XOQTzds7x1EiExxZwJzjN7ChTaTBq5Fjx6QGQJcAbh8uT/WsEKI6v0WQghRueRdANbV1Y1nmD9/vreWVpBPN3gHESJThgzxJ2yEyaSb1t3WtSiOx63JruAwFdIN/MYbfgijCSBCCFHZ5F0Azps3bzEDkt4KH0E+3cB9hMgKV7WkK9Do/M6d1OFa/PLtb7CEcC+DWnrMGJsRQghRkeRdAPbG1KlTt29sbPxALBY7s6mp6XTE754yZcpgWy1Ebrii7eWXjVm/3mZ6IV0PyK7A5KSRbPwNlhiuTmYzsgmEEEJULkURgBB5w+Lx+C2bNm1aUVtb++eampoburq6bkL8zy1btryKuquwTT+7uRDZwWmr4ZkLFGec3poKVwGNG+f7AHRxBSbXHH71VZspT7iSnjv52b1MIYQQlUfBBSDXBm5vb38UyU9C9NUiPIH0HxD+iPA0RCAHVp0PIfj3ww8/XPMORfbQd0ksZjOWdLqBo0xgUYwcacz2zjylMu8Gpu8/10gqASiEEJVPwQVgbW3tRYj4SPlPXV3duNbW1kNaWlo+hnAKwoEQhHHUPQUheMTy5cvP4z5CZI2rXlIJwCgPyMlmQLBfNNtxhiWKe/o77dRT4wohhKg8Ci4AIfA+guj1AQMGfGj+/Pk91ueCIFwIkfgBbPc2ROBptliI7HAF4OuvG7OqF/eS7ji+fv16On0O4x6/pcVfGaRMSdf4KYQQorIouACEqBsLcffonDlz3rFFPYAwXIXtHkLScx4tRNbQFcxgZ15Rb1Y6t45dyL15QHYnh2Tqb7CE2LzZn/8SRgJQCCGqg2JMAlkOcbejTScFInFbRI43MiEyJGoGbzIBSMufO4YvlQO8XP0NlhDuYilRnm6EEEJUJgV39hCPx7+J6FudnZ3va2tr+5dfmsikSZOmdXR0PInk1a2trRwzWBbEYrGFY8eOHT9z5kxbIvqcM8/0Z+ayazaAk0MOPNAXh4884pcddpgxGzYY8/TTfj5g2jRjBg0y5sYbbYEDj79okTFLl9oCsC3eXfbay2YsyfYvEah9r7/emP/8x5j+/f1L4CTqr37VbiCEEBXM9OnT8TO+dBE0Ry9jfiqbvFsAIfj2CAcU/R1hHt2/QDBd3djYuPeECROGIu6P/ARsczbE3wPY5smamporeQwhcmK77WzCQh9/nOzhQjcuYTj+L8r9i4t7fK4hnMm6w33M7NnGXHqpMbfe6g+BfP55Y555xr98IYQQ1UEhuoCfQ8AjpjvQsseRRf0h8M6DEHymvr5+NeL1yHP5hZ8g5jrAByFd3k7VRGnAJdsGDrQZy4oV/mQQjtljYHrlSltp4fTXdDwgDx3q95cG0Jz29ts2U9pQ/NE4uWyZMevW2UJAY+hTT/n1QgghKp+8C8Curq4lScLiiLIgBHWhfjUhcsD1ZUIB+NJLxrz5ph+Ydi2ArmUvGRR/7DMNw2OWONSpd9/tx+6lc94L587cc49fL4QQorLJuwBsbW3dFWF8tsEeRojcyMaZXboCkLjHLwMB2Na21ejpCkAaNWn8pNccbieEEKKyKUQXsBB9D8VcJoPauD1nQ6SLKxbZh8qu5RImEH208Ll6Naxny6Q3WwghRA5IAIrKpK7Od2pHZcMxgUFgOUO4jEu8xbkgTQawv5Szi8OUuBUw0KxRWjWsZ2kNFEIIUdlIAIrKhUpm6lRj9t9/axgxwg/hMvoNpBDMBPaXulZAt1+1xKCbF166e5o0fAZzZqiFuZ0QQojKRgJQiGxxBSAtgCU8g4Ka9cQTo7t/WccwY4YfCyGEqGwkAIXIFnciSDJ/gyXEHnv4C5mEveRQx9LyRx/Xe+9tC4UQQlQ0etfPAa0EIjyPypw6G0AT2nveYzOlB2f4Xnmlb6hcs8ZfD5iXwIVMZPkTIn+8+uqr5tZbbzWPPfaYWbZsmens7DRjxowx06ZNMx/72MdMU8S6i/fcc4+5+OKLzVlnnWXOP/98r+xrX/uauffee82vf/1rc9BBdJcr8oFWAimABRCi6PF4PP4dmzUTJkzYZcqUKcNsVojKIt11h0uE4PQo9jhEkhY/Bok/IfLHXXfdZY455hjzy1/+0myzzTbmxBNPNB/96EfNqFGjzJ133mk++MEPmquvvtoThal497vfbb74xS+ase4a5ELkSCG6gPdEmOgn6WC2flF7e/s1NitEZTFlik1YaGJjV3CJ4upTTpQWQuSPP/3pT+brX/+6GT58uLnjjju8cMkll3iWvJtuusn885//xM/GFHPjjTea73yn21aSFArAc845RwJQ5J1CCMDOrq4uPhWDY9cgL/uCqEzYjRM2n1H8lagnZbp/WbTIZiwSgKLk4eDUdEIJsHr1anP55ZebwYMHm1tuucXsHTGolkLut7/9rdcdfNttt5knnnjC1ghRXAohAJ+vqanZPRaLLYvH48/asvcznUaYZbcXojzYZhtjdt3VZiwl2g3c0oK3s1CPE5d/i8VsRgiRM7T2rV+/3pxyyilml112saU9GTRokLngggu8NEVgb9ByyPGC//3vf20J3zuboHnPxLtmm/nCF75g9ttvP7Pnnnuak046ybMwCpEOeReAtbW1FyFaCxG4I+K9WIY0xwAynU4QorxwzWglKgDd05o4MbPFUoTIGL5xrF2bW6BVPZ0QtW8mIY3xeKl4/PHHvfiII47w4t446qijTP/+/c1DDz1kNmWxitDixYu9cYWcbMIxhkcffbR56aWXzLnnnmv+/e9/262ESE5BumbxdjKks7NzCsTgwK6urv+g6AHkv+fX9g7eaB62yZJHs4CFB01rP/6xzQB2CV91lb9aSAnxzW8a89prNgM+9CFj3vc+mxEi38yaZcztt/viKhceecQmUnDYYTaRJUOGGHPKKcZMm2YLMufAAw80b775pnnyySfN9q6bqAje+973mkWLFpkHHnjAsximOws4mEH8yU9+0htvWGOHodx9993eeENux+1FcjQLuDBdwKa5uXktGvV/iB9iHiLwNQq7dIJ3ACHKiQn4/Qib0uhjZf58mykN6Pw5LP6IO39FiLzy29/mLv6KCc+V55wD69at8+IhFJNpsJ11Js+xg9nA7t9A/BFOGCGvvPKKFwvRGwURgGFaWlpqIQY/ZbNCVB4cTOeuJVxi3cDu6XDooiYVCpFfhtqFtDdu3OjFqXjHOo5Px1roQvE4bFiih7Vtt93WizfTwacQKSi4AAyYNGnSDrFY7BKEf8fj8bkIs5H+B8JXpkyZspPdTIjyJGocYAktCzd3rk1Y6L6wtmjfflGVfPKTfrdqucBz5TnnQOCqZeHChV7cGxRpHMfX0NBgdtop80cgxw+6BNbArhJeklKUDgUZA+jS2Nh4OG7MuxH4muN+Ju7VrtWoO7mlpaWsBtNpDKDoZvlyYy6/3GYs3/62v8ZaH8NnwVe+ktgb94lPGHPooTYjRKHgxIpcl0c87zybSMF119lElgwalPNb0a9+9Svzwx/+0Hz2s5/Fdw5ful7gRI2zzz7bG4t2ww03eGWZjAHccccdzSMR4yN7qxNb0RjAIlgAJ0+ePK62tvbPEHjDIPTuQ9EpCAd0dnYehDK+bv0D8Q6o++PEiRPVKSXKk1Gj2P9iMxbX7NZHcDiQOxRL/v9EUaCgomUtl9DQkF6I2jeTkAeT+Ic+9CGva/b3v/+9WbBggS3tyYYNG7yVQAiXhROiLyi4AGxvb6dbGD4ZL4bS/mBLS8sfEJ5ua2t7srm5+Takj4X4+zqtgxCKX/J2EqLcYNeLq6pKZCKIO/5v+HA/CCHyC8fkXXbZZZ4vwNNOO808+2zgCncry5cvN6effronEE8++WRzyCGH2BohikvBBSB4LwReG4TeD22+BxCG30fE16UPeAVClCPutFoKwDz4FssVVwBq9q8oK268Mb1QIrzvfe8z1157rVm7dq3nEJoWvh/84AfmyiuvNJ///Oc91y+zZ8/2ROA3vvENu5cQxafgArCmpmY0otl+LjkQic9iW3UBi/KFMyvCcO21l1+2mb6B/nFbW23Gou5fIQoLReC//vUvbw1fzgjmGD52C3PSx4wZM7yxfhdddJHhBBAh+oqCTwKJxWKrIOxeaGlp6dU1OrZ7ENEera2tO/glpY8mgYgeXHaZMStW2Az44AeNef/7bab40Ah5zTU2A9hTTZ/VHO8uhBDViiaBFMcC+ExXV9dBEEt72qIe2LpDuK1fIkSZ4vav9vFEELf7d9w4iT8hhBDFGQN4A4RdPeL74/H4hxCHP7PWlt2PbWohFH/hFxcefO77IDzpk/BthLcQnkA4yVYLkR1u/yr9gWWxzme+cAWgun+FEEKQggvAlpaWPyO6CQJvFOK7IboouF5iYJplrIP4+1Vra+u93KfQ4LPpYImicyo+97cIv0d+IsIfcE4XchshsoIrgoTdSXR0+GsF9wF0v7Zkic1YNAFECCEEKYojaAJhdSYEFz1juv3tCzs7O69qa2srivWvqalpKj6PE05a29vbj1i4cOHrLB8/fvyODQ0Nz0MMDuvo6BiBcorTXsE1aQygSOTMM415/nlj3n7bc8DctmWceWuHiWa7CcNM47avm5pHrXPWww7z69eMNG9tGmi2678h7/XPvL6LuWrO0WZTR73pX9duhg94x1x70B9NfW1nSc2aFEKIYqMxgEUUgAEQTWMgvkZDaNUgLIfwK+qq1fh8WiM/i88+HP/4BFfpEIeno/yA2traH8+fPz+l2UYCUPSAAnDJEjN7/kBz99pjzMqO7Y2pb/Ac740YuM6c+ObNZu8B88zsKR83dy/ax6zcMNjuaPJef9msD5iWt3e0tcbsMni1+cH+95q9hy+VABRCVDUSgH0gAPsaiLbljPFPp3uanJAAFD2AAJy9eJi58X97mq7w14tLwtXWmZpXl5vDBz5tHt7ueIN3IFu5lXzVPzT0ePPUyvFmY8dWNxMTh6w0Ow9+05w5+VGz993yPyaEqF4kAKtMADY2No6ora1ll+8DiE/t6Oj4dk1NDZ1PD+3q6noB6e+1tLT8yds4DSQAhUvXGWeaS58+3qxcvN6sbR9oXu0YbrZ0QYTV1UEA1piu9RvNq50jzU6D1nguWVzyV7/WrGq3S9Pxs+vrzbThS8yg+k1m5MC15opZx0buL4QQ1YAEYBEmgZQS7Hq2yW0h/jgO8Eik70T4IwLnR94LUXcOYiGygmPyVm4cYjY1DDIvbIqbFe0jzBsd25k3Ng8xb2wcbFZ0jDCrOrc3KzaiDHk35K9+qD8BhWHzZtOva5PZBuKPvL5hiGlr85JCCCGqlGoTgMGAqQMQ5g4YMGCPlpaWcxBORd1+XV1dXDL/x5MnTx7nbSVEhnBCBnm9a6RpN3VeOkyHLevsiv7qFap++643E8z9b6ec4iSEEKKSqSoB2NnZ2WGTBmLvnDlz5rxjs3RXMx8i8KcIDe3t7R+2xUJkBGfjkjdrtucbh5cOUweJRmprotcILkR9rekyO9csszmfoUNtQgghRFVSVQIQ4s6ze0D8vdPW1ua4yPUE4rOMsV2jVyBEhtAVy7D+75g1mwcaM2CAMf36GdPQYHYavM7suu1qE2t42Yyuf83Eh77u5d2Qt3qcx64Ny8zEhqVm2oCXzKAavOts2eydI8cANuoOF0KIqqbgAjAej/8U4fM226c0NDQsgPhrh8DjyiQ9zDMox9PaE4jrvQIhMoRGv31HLDZdQaa+3tQ01JsJI9aaXYZvMLtu87r5xoj/M+NGrPfybshb/UjUD3zDjGl4zQys3eidm9m0GafUZWaMn+2dmhBCiOqlGBbATyB81k/2LXPnzt0MkfcEkv2bmpoO80sT2M/Gz9tYiKyYvN0KM7DOt7gNadhk6ms6Pcvbmdv90Zwy9H7PFQvzYfJePyTxPWZk56u+Cxj6ARRCCFHVFNwOEIvF1kF0/aulpWWGLepTcD4n43xuR3J2e3v7kcGKH/F4fA9ET9L6V1tbO765uTnx6RqB3MCIKL7zHWOWQmNxpY41a/BWgdeK44+nGyLfKBjgreTR5k/I4Ji8vNe/+JJp+84d5u3NA83QfhtM43ZvmJprr/G7poUQooqRG5giWAAhtu6CqDq6sbGxJFYhxT/7DkS3IOxdX1/PNYmvQ2D+CZxnPc73s+mIPyGiWIs7h+KPUIxRmFH8xWKJ4owwz/J99y1QfTxmYjus9rqkY0NfNzVdncY0N9taIUS+ufjii7milPnjH+lZrCe33HKLV8/wzDPP2NJEvvKVr3j1f//7321Jevzvf//z9vvyl79sS4z56U9/6pXdeSe9nRWWr33ta95n/fe//7UlotQpuADs7Oy8DdErtbW1z0Jo3YfwbYQvxWKxc6OCv1dhaWlpOR3nxWXfuCoIl4U7HuFhnOMRqEvbEbQQLvPn24Slf39jJvTV+yUnoEycaDOWeT3mPgkh8sS73vUuL372WW8+YQ8effRRg+dMdzoKCkNuc9BBB9kSIQpDwQUgbuR/1tTUxJHkBIv3I1yC8GOUXeOEaxmjrhh0tbW1/bq1tXV/CL5BiIchvL+5uVmvLiIn5s61CQteiL2FOPqMyfRvHsI9QSHKCA57aG015umn/Zj5UiIQbVECcOPGjTjvp83hhx9uttlmm0gB+Morr5jly5eb3Xff3QyVryZRYIoxCeRWG35jQ5B3Q1AnRFnCh5FrYJs0ySb6ClcAvvaaMW++aTNClA+zZxtz6aXGXHWVMTff7MfMs7xUGD58OMeTm8WLF5s33njDlvo89dRTZtOmTebQQw81+++/PyclmtWrV9tan6Bb+OCDD/ZiIQpJwQVgS0vLpxFOSzfY3YQoO15/vae2mtLXI1932cWYbbaxGYu6gUWR6Oz0x8XmGh57zJjrrzdm+XJjtmzZGphnOeuj9ssk8FzzwYEHHujFrhXwMZ4kOOSQQzyB14U3RtcKSAsh4TYBr+Gl7bLLLjNHHnmkZxlkHcca0lqYLWvWrIGAvsocc8wx3jF5zueee66Z745hAUuXLjVf/epXzbvf/W5vW577OeecY1588UW7Re+kc/7cZgp+LPkZUXAMI8cX/uY3tBOJfOEMGxeZENMsYBHiwQeNuYNTjCzbbWfMD37Qc3JG0bnxRj6NbAZwWvJnS8Izk6hgZs0y5vbbfXGVC7Ss0zC2wV9kJ5KBA/3JULl814YMMeaUU4yZNs0WZMmD+CE466yzzGmnneZNjAh43/veB9G6xfz73/82Cxcu9PLHHXecJ8QCKMhoOaS1sL6+3ixYsMB86lOfMqtWrfK6jhsbGz3h9MADD+B8h3iTSiZbKz8ngXDb8DE5CeR6KOTvfOc75iMf+YhXxuN//OMfNy+//LJniaQooyXyH//4hydKb7jhhm4BynIeby3+iTy30aNHm2XLlpl//vOf3jhFTnaZZLs5eK333nuv+fWvf93dFZ7J+Z9xxhnm4YcfNrfddhv+l/hnhvjYxz5m5syZYx555BEzbNgwW5obmgVcnC5gD6j7nSCYrkB4LB6PL0F8A8uR/g7CSd5GQpQxUd2/fS7+iNsNzLf8Uhs8JSqO3/42d/FH6EqpN/FHWM/tcoHnynPOlf3wgkXxNosK2ELRRNEXCKsJEyaYMWPGmMcff9wTXeT111/3uo4POOAAb3/CGcEUYb/4xS/wHnejl7/uuuvMr371K8+KR8tcsH+6XH755Z74+9a3voXr/a256KKLzA9/+ENz1113mbq6Ou8zNtgG50xkCsZLL73UE5UXXHCB+fGPf2y++93vet3ZFGu9kcn5n3jiiV78l7/8xYsDlixZ4rUlLYj5En/CpygCEGLv/e3t7Vxr9+sIfDUYgzCYdfjnfxDR7RCBP2FeiHKE3Ueuh5U+7/4NcE+ETzo8kIQoB6Az0mKz73e9zxk8eLDZY4898EI4z5v4QYKu3nDXLrtSKY6CrtRg/F+wDS1eL730kmepOuKII7yyAHbZsrylpcU899xztjQ1FHO0vrG7lVa1MHhOm5NOOsk7p6BXq9P2iz///POe9TLg/e9/v2fJpIhMRqbnf9RRR5ntt9/eE52bQ/9MWhXJjBkl4Uq4oii4AMRNNRmij74At0H4BcIHUNxtF6mtrb0ZEd8Tz25sbDzOKxSizFi0iLP8bMbiGt76jOHD/RBGs4FFgfnkJ/1u1VyhK6V0oNejXOC58pzzAd3BUDBRBBEKwIaGhu7xgSQQek8++aQXB+P/gu7TF154wYspyNiV64a36QEecDJJulCQ0eLW3t4eeUyO9wu2I+9973s9qxvH4FGwcpzgH/7wB09Ijh07tttSGUWm58/2Of744z3L4H/+8x+vjOf65z//2YwYMcKbPCPyS8E7qCAAfwcBeAr+kR9pbW29h2XxeLwT+d8h/ymb55SnR1D2AMrey7JyANemMYDC4777jPnrX20GjBljzDe/aTOlALtqHnnEZgCtguedZzNCFAYakN55x2ayhD2E3/62MatW2YII+H7zjW/kNuRi0CAaJGwmR2jN4zi7L33pS+Zzn/uc163LsW6/+93v7Bb+RAwKQgqrm266yRtr9w4aKxA/P//5z821117rpXuDouzss89Oawwgu1fZDZsKdsd+73vf89IrVqzwum9p8Vu5cqVXRnjunNwxfvx4L++OAcz0/Elzc7MnAtndy25jiuNTTz3VfOYzn/G6i/OJxgAWpwv4KAi72YH4i6KlpeVxbPMkhOJutkiIssId/1cy1r8A94ToRC3UpSNEIaCgomUtl7DttpwE4Fv4Ghp6BpZz8ga3i9o/3ZAv8Uf23HNPz9cfu04Z1q1bl9D9S7bFCbOrmLOFaSXDM9ITgwGDqEjBhRde6AmjZCEQT+kQHPPkk0+OPFYQAvFHRo0a5Qk9WjEpIDmDd+rUqZ4442QXPLvtlolkc/6c6ctJKZwxTQvhfXyzBsH4QJFfCi4AIeo4anORn0sOtnsV0Qg/J0T5wK7fhQttxlJyApAeqcPmEYq/BQtsRojSZu+9jTnzTGNGjrQFFuZZzvpSgt2ZnAzC8X3u2L4wLKM4pLCikAoLQI7TI0E3sgu7ZX/yk594k0vSJZhxG3TPunB84DXXXNP9mffff783zo+zgPGM9gTapz/9aa8beNddd/Umk3DyShTZnj/FHrvP2bPGGdUUyRPdFY1EXiiGBZDeMLkSSCo4l3yrfVmIMqGlJdGHGFf+4Nq8JQXfxseNsxmL/AGKMoIi74orjOFSt5/7nB8zX2riL4DjANll+re//c0bR7fbbj07uALBR3cqnIEbLCVH9tlnH2+2MEUZXbSEoYD79re/bW6++WazHf1NpQnduPAzOcaPM3HDcPwfxR67XvvbgZf0C3jHHXckdF0Tdl+/9dZbnpWPEzeiyPb8P/CBD3ifz65rjjXU5I/CUXABiLeaB/HmsDveHI63RT1obGykfXcytn3YLxGifHB1FF9W0x24XlRcs6QEoCgzaMTmyxXdxDEuCTdLSQjEHLs5OSaOFjQXWrfYFUx/eez6DC//Rj97V155pVd/3nnnmdNPP91z18IuVc7g5UxZunTJ1DUKhRe7dXksdgX/4Ac/MJdccon50Ic+5Pnr45g8WvoIx9/RXQ3H8tHy96Mf/cj7TI4zpACkW5h+SWbfZHv+3P7oo4/2XOdQCB577LG2RuSbggvAzs7OHyDaAnH3h1gsdgXE3uF+jemH9MR4PH4Bvhi3oL4d215t64QoG0p+/F+Ae2JLluQ+Ql8IEQlF1A477OClo7p/SdjqF+7+DaAo5MQKumdhVyn99tFJNLelVe6EE06wW6YPBd0999zjOaqm4ONxHnroIe+zOHEjPCaP5//73//em9DCySD0+8dxeXTozG0/8YlP2C2jyfb8KTAJhSAFoSgMRXl/whfhIxB4XMMl0i6Cug6IwC+0tLTQJUzZoFnAgku/hZz9e1x8sTG77mozpUR7uzHnn5/oMO2MM3Jf+kAIIfIIVyOhw2iuFBLuFs8nmgVcnDGANIHf2dHRsQeEHv0AtqCIHtM2I70Y8W8g/vYtN/EnBHGXzuSyu1x+tyShzy53cKK6gYUQJQTXBb799ts99zJhv4ki/xRFAJIFCxa0QWmfjTAZYm8QwkAqb8SnI0RPExKixHH1E4fO5NOdRN7ROEAhRAnC7mXOAOYqI5xZfP7550eOmxT5o+iPqqampiEITbFYbMK4ceMG2GIhyg66v3L1k7vqWsnhCsA33jAm5NxVCCH6Ak5M4UzkgQMHemsPv+c977E1olAUTV5D8H0a0Reh6PdC7H1uV1dXO/JPdnZ2XtnW1uZ7fCwjNAawtKFAa2sz5q23jKGngcbGxFmDqepTwVWT6FSfa5Vy1i/HKn/3u8aMKFVvlnSYxov+3/8SxwGyWxg/vt3ceKNNCCFEZaIxgMURgLUQSrdD6H0Y6RpO+EBMkwOKaviopBWSrsR/2NLScgniskECsHSZPduYu+9ONG5RmNGhPP2GpapPBffnakvPPmsLAL0ZXH996fol8wQgaW7mQBs/TXjhYcugBKAQosKRACxCFzBE0mcg9D6C5HLEJ23evHkIGnw0wqi1a9duC0F4Guq4yuNF8Xj8Q9xHiFygOKOGobijwYuTXxlWrDDmZz8z5pZb/Jh5lnMbwu25H/fvjeD4L79sCyy0Aqazf5/jOo7lVOagEYQQQlQFxRgD+DmEDR0dHUc2NzfftXjxYs4A9lixYsV6CMHfQAQeze5gFF3g1wiRHdQxtOwxppGLvZ3//e/W8NhjvtuWxx/fWsaVmtas2br/Pfck10PB8Ts6/K7jMNRVqfYvCVwBSBW8bp3NCCGEqAYKLgBramq4/s2DnAXsl/QEIvB5bPcQRGCpdp6JMoFj+mjJ4xC31tbEoW6ES+ByzF64fMMGfzm3QLRxaUseJ4rg+GvXJi7/xrGDga7qbf+SgKZK+qsJQyugEEKIqqEYFkCaFlLaQyD+8Bg2eDwLkT2BVY56JizQAmi5I27d+vW+EAx4+22bcAgfP8zgwVwA3mZAsv1LBnf9TtecKYQQoqIphgD8G8JRTU1NSddGaGxsHFFTU3MEkv/yS4TIjsAKl0zP1NX5cZSvvvA+oSU5EwiO7wpAt1c12f4lg3vC7AMP1LEQQoiKp+ACsL29nQtlvdrV1TUTIvB4v3QrEydO3A3i7+9Iru3s7PyKXypEdtCVy/DhPQXa+PHG0Kn8oYcaM2mSH48caSstwT4s53GiYDnFnTtkLmxQ623/koECMOzzhibRkjdbCiGEyBd5F4CxWGx1ONTV1XGxrJ0QdoUIvDcej7+FMBvhCYQlqJ8DAbg36rYgfZd3ECGyhJrm8MP9sX5hKAr79TNmwABjzj3Xj+m2JUygf2bMSNRGYVi+Fz1ZhqA1MVivnPW97V8y0BTqLrKubmAhhKga8i4AIea2cwOKB7LKBj519kQ4AGFnW4bNathFvC+CEDlBQUa3dgN51wGKPQZa5ugK75RT/NhdFpdj+I49NrUfP04WCR+fFkF+ZnD8kvUD6OJ2A7tmUyGEEBVL3u0UkydPHmeTWTFv3rzFNlnyxGJyBF2K/PSnxrz4oi/UOLSNYu1jH/O7ZcOWOdaff74xS5b41kEaxI47zg+98fWv+yuoBcc/5BBjuGqRe/ySZ8ECY370I5uxXHllT8ugEEJUGHIEXQABWE1IAJYedGlHURd28/K5zxmzbxLb8l13GfPAAzYDJk405qtftZkIKPwoAMNceqkxO9OWXW5w3B8ba2O3a05jTj/dmANonBdCiMpFArAAXcBC9CULFyaKP1rkOOkjGeEV0MiiRYl6yGXePJuwDBlizJgxNlNusN+6qclmLO4FCiGEqEiKIgCbmpreE4vF/hSPx+fSapYsoH6B3UWIrHD1y9ixvo++ZHAcYH29zQAaxbhUbjLmzrUJC8VlWXX7urgKmA0YeMQWQghRsRRcAELYvb+rq+v+mpoauoCZxMkeyQLqk/oKFCIdXIHm6hsXjv1jt2+YZEYwisP5nNMeYsoUmyhX3AvgTOBXX7UZIYQQlUrBBSCE3aWMEG5GejrE4D4IdPsSFfbxdhIiC7iax2JnClEqAUiijGBRLF3qf0aYdI5f0nDqsrsqiLqBhRCi4ilGFzBNDLNbWlrOaG5ufrC1tfU5rv2bLPi7CJE57LoN917SrUs6DpldEUcDWJRHFNe6uOOOPbVT2cH+63QVsBBCiIqhGAJwU1dX1xKbFqJguAKN4i+8Pm8ydtnFmG22sRlLlAZyy8q++zfAvRAqaS0LJ4QQFU0xBOADNTU1+40bN26AzQtREFyBlm73LCfDujOFXTHJmcV0mxem7Lt/A9yL37TJn04thBCiYim4AKyrq7ukq6urP/jdhAkTRtpiIfIK/fOtXGkzlkwsdK6Y42SPcHdya6vvYzCAojEet5lyh75sXEeG7mwXIYQQFUXBBSBX9qipqfkakjPq6+uXc/1fhGeThFn+XkJkhmv9o+uXTJwzu2Jx7Vpjli2zGeAef/z4rUvBVQRuA7gmUCGEEBVFwQWgdQPzC5vl5/GxzOX0kwUhMsYVaJn65xs+3A9hwhoo2+7lssG9oJdfNmbDBpsRQghRaRRcANbU1HwToQ4i8M+IP4pwVGdn55FJwlF2NyHShl21+fDP52qgQPRxvd9XXvHTARUnADljxvWI3dJiM0IIISqNggtACL/dEc1pbW09obm5+U6Eh9ra2h5OFvy9hEifJUuMeecdm7FkI9Bc0chxf1u29BSXAwb4XcAVBT1iuz5z1A0shBAVSzEsgHSdqyXeRMFwu2fp23jYMJvJAC6LG+42pvjjzF/3+Jz8UVdnM5VEMhOoEEKIiqPgAhA80NXV9a4pU6b0s3kh8oqrU7Lp/iWDBhkzbpzNWHhs1xCW7fFLHlcAvvaaMatX24wQQohKohgC8BsIA7Zs2XLnxIkTx/pFQuQH+udra7MZSy7j89x9//tff3ncMK7bvIphLL6eVMFhZAUUQoiKpBhjAL9SU1MzB+EDdXV1L8disWUIL0S4gOkzNzBNTU1H4rM7EW6xRaJMoPgL++djF24u/vlcAcgJIGG2286YnXaymUojyiO2BKAQQlQkxRgDeBaiw5j0szWjEHZDOsoFDENRaWxs3Lazs/PXSGbgNESUCq4+4eQMd1m3TJg4sffl49j9m4l7mbIjahxg2CO2EEKIiqDgAtBx85IqFN0NTG1t7bUQpM7IL1EuuAIwl+5fQk8ovVkQK7b7N8BtwHXrevrAEUIIUfZUtdWrsbHxOAjAv3R1df0FIvB4FP2mpaXl035tamKx2MKxY8eOnzlzpi0RxYTds2ec4S9d27+/Mdtua8xXvsL/i90gSx54wJi77vINX/yM8PGvusqPK5Izz/Tjp59OdAI9YYK/rMojj/j5w2jQj6DU6wNuvNEmyozg/5OMSr9+IfLI9OnTzdKlSxe1trbiB646KcYkkJIkHo8Ph/i7CcmHIQCv90tFuTB7tjFf+pIxzz/v++ljzDJ3wkY20AjGtYWfeSbx+M3NvluYiocDHcO8+aZNCCGEqBQKbgGE0PqVTaYEQqwLavwzNltQYrHYnYjeV1NTMxUfOxHxA8jLAlgGUOjRiEFB9uqrthDssIMxu+/uG0r23tsWZsGzzxpz2mn+DOMwNIJxjGCuxy9ZAgsT1W/Y9w0nh+y6qzEvvODnp071Y5dSrKfXbjqF5DUEVIIFkDOfVq3ynVUGuNfPwaoU8+7MblkAhZAFEBRDAHbaZDKCEeY8ly4IsIK72G1qavo4RN/vkDwbn3cDhNy7JQDLA3bLXnqpMa+/bsxTT/ndswEUZ2PG+I6gr7jCf/5lSnD8Rx/1PyMMn6vbb5/b8UuaQGBQXDzxhN8YYQK1nWwadKnWUwDuttvWf1i5C0Au08e3lPX0sR8i2fXz2vl2FCABKIQEICjGLODzowKqvozwXQgxDlypQfxrhIO4TyGBaBuDz7keYSbE3s9tsSgT6PZl5UpjNm5MFH8k6LmkcHN9A6ZLcHwKvTA0IAVj/3I5flnAmTCDB9tMBUBn1mvX2kwFwBvUFX+9sXixTQghxFYKLgCbm5uviwoQX1cjXAr1fSQ2+yJE4achyooxvP6X+Jy6+vp6djU7Jg5R6gRj/NxhaVzKNuz+5e23bSJDguPTYMJjBtDqF17+Ldvjlw2jR9tEhVBJ4xgzvRbO5HbHMwghqp6CC8B0YDcsomaIwIv9ksLQ2Nh4Fj7jPUh+ed68eXotLkMCK5/7DKTFLtwlO3SoTWRIcHwawTiecNQof/gbu5fDZHv8soGKl9OpqYTZhcrAqdAMQd4NpVTvOoPMx+ygUoDd8u610FrrXn94zCOp+DcWIUSmlIQAJF1dXS9BnE2z2YJQW1t7MmN8zo3xeLwrCMhz/B851ZZpRZASpbHRmOHDez7PwhNXqV24XTZwvxEj/DSfq9RAu+ySaP3L5fhlA9U01S/Hj1EJM1BlMwR5N5RSPV3XhKE/n44Omylj3nmnpzWP3snd62ccRjO5hRAOJSMAIcIKvsQ+ROYtCJdHhN/aTZ5nHvGf/KwoNahLDjig57M8eN6xfsYMP84G7nfiicn3z/X4okjQRBu2gkVZzsoR9xoGDvRnOruE34gIBaA7qUcIUdUUXAByqbVkYcqUKcMmTpy4WywWuxmbco2Fp7ydCkRra+stCJe5AVW3+luY55hvaWmRACxh+Fynrz4++wi9XHC8Hi1z+XDRwv15HB4vTL6OL4oATbaux+5KEICuJc8VegGuBZAzpsLOvYUQVU/B7RjxeDydfhfOAu7g+DyIr//YsqIhNzDlxZVX+rNwadBgz96eexrz4Q/73bL5tMzx+PwcdjfToJTv44sCc//9xvz5zzYD2KV9Gd/3yhT6/Dv//ETff3wj2WcfmwnBm/drX0sUvSefbMyRnHMnhJAbmOJ0AfORmSyQdQiPIXyoL8SfKC/o/mXhQj9NMUZhdtxx/li9fIszHo/H3XffwhxfFBh3XeMVK8rbCshlaMLijzdkssWpWedev7twthCiqim4AISoq+0l1CEMRTgMKvxvdpeig8/+N86hBiFt65/oG1pafD+4AezpozgTogfjxm0dJxBQziIovDoL4fW5s53DuAKQS+dUwkQYIUReKIYFUIi84T6/OdmTni+E6AEHi7oWsnIWgFyUOowr8FzceprPX37ZZoQQ1Y4EoCgr3Oc3PWAIkZSobtBynA1L9y9LltiMJdXNz0kwXBsxTDkLYCFEXsn7qKZ4PP4rm8yYLtDa2soVOsoCTQIpLpwAyXHtYZgfP95mhHDhun1c3DnMN7/ZUxiVOs88Y8xNN9kM4LT3a67xPZb3xp13GvPvf9sMoEfzr37VZoSoXjQJpDAWQI6jyyScGoSamhrmhYjENV5weBeHQQmRFHr15momYcrRCuaeMwe+phJ/xLUSLlrkdwULIaqevAtAiLjz0w1dXV18FV3u7+lZI/HrJEQ07hAoDu9yV7wSIoFKmA3LLmv3nNMd+0DfRWGhyBlUnEklhKh68v74bG5uvi6dAAE4E+EU7MJV5yn+bq6vr9+LxxDCJeoZmGoMvBAe7o1CAdTebjNlwMqVxqxaZTOWdG9+zpByl8VzZxMLIaqSPrGfxGKxr3R2dj6N5N5dXV2vIj62paXljLlz59InoBA9WLbMd/ocRgJQpAVNxWEnjlxLN3AmWQ64bz6c3DGa781p4loLy80CKoQoCEUVgPF4fDzCIzU1NT9Atj/E3x8HDBiwO8Tf3/0thIjG7f7lsC4O7xIiJYMHGzN2rM1YykkERZm+M/FK7r4pvYp3bndJOSFE1VE0ARiLxc6E4HseyUMQ89fnlNbW1pNfeOEF/RKJlLi9Vpk+A0WV44qgcukG5Zg9OnAOk6npe5ddejqMlhVQiKqn4AJw0qRJoyD+/l5TU3MDAl7Fzd8bGhpo9fuDv4UQvcPhWu649UyfgaLKcbtBFy82Zv16mylhos4z05u/0hxiCyHyQkEFYGNj48c6OjpehPB7T1dX1zsIZ0L4HTt37lyO+xMiLTJZAlWISOj/rqHBZgBnFbnjCkoRV6iNGmXMdtvZTAa4opHHLUeH2EKIvFEQAThlypRhsVjsztra2t9C/G2PokcR79na2hryZCpEerjPQA7n4rAuIdKG4o8uUcKUgxXMPcdsTd/ufmvX+jOrhBBVS94FYGNj43Fbtmx5CYJvBrKbEC5saWk5HEE+/kRW5OsZKKqccpsNuwk/nzR/h8n25ueMqeHDbcZSDgJYCFEw8i4Aa2tr/wzxN9Jm6bjjk/F4/Nk0wyx/NyF8uAQqh0GFSdcHrhAJuOKJ/vXeeMNmSpDWVmM6OmwGcCxfPG4zWeBevwSgEFVNocYAcn4mA4UgnTtnEoTohhMgw0OV2JPH4VxCZMzOOxszZIjNWEpZBLnnRofOAwbYTBa4b07l5hBbCJFX8i4AOzs7j8whHGUPI4SH+wzkMK7wWH4h0iZq9lApC8Ao30e50NSU6DuJM6vcLmYhRNUQ+jUQmRKLxRaOHTt2/MyZM22JyCe0/H3hC77fWq5oxQUQPvxhY445xm4gRCaceaZ/M4V9CvFt4sADE4XRjTfaRIbw+L3xyCN+fNhhfuwSrudqJU8+6ecD9trL/xLkcn6zZ/sTQALoI3DXXW3Gks3x83ntUaSqD8i2bXIl1fUHJDu/XPcXGTN9+nSzdOnSRa2trc5aidVDobqAhcgJPqcuvNCYhx7yvXU8/7wxzzyTOCRKiIzZnk4JQtAKtq4EV6B0V+qor+/ZfZ0N7vVrRRAhqhYJQFFyUPzxRbetzRZYKP7+9Ce/XoisoCnZXRXjrbdsooRwz2no0EQrZba4PgQpfsNONoUQVYMEoCgp2O17991+7D4Dg2fXPffIh63IAVcEFcIKtmaNMS+95Jutg8AZxwzhsnAI13OGchjXcpct7ELmbOIAfpHefttm8gSPuXSp/6YWdW1RIZt6dgu89pr90BJiwwa/24L//3B3e7pQlHP8J8enlsNqNaJskQAUJQWtfnz29SYAX3+9p3VQiLRxxRTFWj7HFnBmLR/gq1b5D/AgsJwhXBYO4XquARwmXwKQ4q/QAphf0EWLfPETdW1RIZt6CleO58xGZBUK/nDRdQHbgP9/3geZ3Fv8v3Mfil3+EFIE6m1XFAgJQFFSBKIvqmcq/AzMt9FCVBFudyofuhSB+YI3MSdx5Au6fsnF/YtLoQVgMa1yFEeutbQv2bgx8V6iM2/3TbY3uC+PEUBHqKU4RlVUBBKAoqQInk3ubyaHbXH4VgCf4UJkRdSEikwe0qnIp6CiUKX/v3yM/wtwrYkUHGHRkQu0duVTTKdDKU1kiTqXTO6tqG3zeW8KEUICUJQU9PPHVavc39Gw0WLkyJ7LugqREa4IyqeIcI81apTvhJk3MQPTUSGqfv/9ey7hlit8m+rXz2Ys+bp+ir9w9zWFK/0XZnPt4RCupyAOQytZPi2uuRDVjpm0ba77C5EBEoCipODz4vjjexoRguc162fMyK9BRFQhFBNh2M2WDxHBCQCuNW30aF/EBV25TEeFqPqw2Ttf8MvjXn++rEyuWKGllW902Vx7OITrx4zxrbhhSkEksTs6amwKxyuyKzgVyVwS8cdQ/q9EAZAAFCXHoEH+gg0DB/p5Pq/Y5UvLH/2l7r23Xy5E1nA2bF2dzVjyMbDUFVK0tLluZ0qBKAtoPiYbuNfvfk4+CH4QwpRCNykno3CiShTpnB/vv6j/Qb7HqAphkR0lB7QSSGG46y5jHnjA/y3k7x4NCOee63f7yvIn8sbPfmbMnDk2Aw46yJhTT7WZLKEDy2eftRnALtzPfMZmSggKvq99zWYsF1/cc1WQTOCX9StfsRkL84UYr0EP8bffbjOAgvCHP+zbH4j77jPmr3+1GYd07oPf/c6YRx+1GQcuf3TiiTYj8oFWApEFUJQgwfKswYs+f/tiMYk/kWfctXVzdblBSw39v4XJdf3eQkHL3E472Ywl13WR3Wtnl+348TaTZ9x2pfVsxQqb6SN6a7907q1U+wuRZyQARUlBI8Irr9iMpVSfoaLMcW8sWsXovy1blizxx3uFKeWb1z23XEWGu3883rObPV9wPMiwYTZj6UuRxHGfCxfaTATsHl62zGYiCBxdJ4OOtUvJ36GoCCQARUnhGhE4Br5QRgRR5dAC5k6GoBPebHEFCI9fiDFw+cIVgAsWZD8RhtYt9/oLKX7ZHZBvAZsLdEgdnv1M4ev+73s7P/e+4+QZdwKQ++MoRI5IAIqSwv2NbGoqnBFBVDn5FhHuQ7yQAigf8MsVXhaOExhaW20mQ+j82Z2JW+jrd49PEZZsEkahce+biRON2X13m7H0dm+5dbw2WlDD5HJvChGBBKAoGYptRBCixw3GZbyycblBNx+0oIWhz7pSJmqMXrYWUPeLS8uqO8Yw39BVQBj+D7gEXV8Q9cPl/v8pUN3ljUiysaPuvcn/TS5jVIVwkAAUJUNfGBFElePeYBzL9fLLNpMBXJw6LBxpWXMtOKWIe/3ZdjNGCaBCz9piN+nYsTZj6QsrGV28uBNQeP20sIbbgOIvapxgsrGj7v8m1zGqQjhIAIqSoS+MCKLKoT9AOhYOk42IcC1ntKzRwlbquFYqzsDK1OcchS8tp2Fc8VIo3M/pCwHofib9Po4b5zs03WUXW2iJOj+3LBg7yhVk8jlGVQgHCUBRMri/g/xtl+sXUXBcEZHNQ9a9eUu9+zeAfv9coRolUnqDFlN39ZNiCUC3ndkF7FrTCo3bXuGxle75Rd1bUT98hD9+bjd3pv8bIXpBAlCUBH1pRBBVjnujUUS4gqY3aDFzXXyUy83LGVZuV3Wm3cCuKKFFlZbVYkAn0+Fl4ThGjmPtikWqgcuugGN3L9cuDuCsa3fsaHh/9z7ij2R4trEQOSABKEqCvjQiiCqHXsbDIoIP2ExEhCsAaFHLZUWNYhNlpcpksoFr1SrmF7ehoedKI8W0ki1f3rPLPHz9PDeeYwDbNfymy1nX4ZnLtBzSghjgtiV/JPtqoouoOCQARUnQl0YEUeXQ39oEZzWoTLqB3Zu33HwXuSKDkxpefdVmUhAlSIrd/Z1ON2uhcP/3O+zgr10ZwBcL18IaPj93f3fsKJdCGj3aZizFFLiiopEAFCVBXxoRhOghItJ9yKbqAiwHdtwxM6fFYdwuSQqeQqz92xtue3Om7KpVNlNg3B8u3kfuwOXexvGlc+9ke28KkQIJQNHnRBkRJABFUXFvOFrAXJ9EUXA7WszClNvNS8HinnO6IsPdjpZUdwWLQkNXMJxxG6YYq2ZEOc52xR5xBVyw7FvUupfutsT939CVTCZjVIVIggSg6HPcVZRoROCwLCGKBt110H1HmHREkLsNLWm0qJUbrsjglzIdh9ju9UcJmEITJWCL0Q1MIRZeOi/qPAjHs9BnYRienytSk40d5Y9heEhBpmNUhUiCBKDoc9zf6r4wIogqh4Pvs3G54W5DAeB2AZYD7rWnM9mAFlJ3rGCUACoG7udSXGUykSUb3B8uvkS4lkjC+yHq3nLvHY4VjBo7yh9DLi0XJp17U4gUSACKPifqGSpE0XFvPN6YvYmISvJdxBlXO+9sM5ZUIsOtpwXVdXxcLNx2X7euZ/dqvnGvP6r7N8C1jFKgRo0fTEbUvSlEjlSlAJwyZcrgeDz+vVgsNh/xRsRrED+M+AS7iSgSUUaEvuhFEqLHQ3bt2p7+/cLQQsb1Z8OUqwAkmYqMKAEUOEAuNpx9O3KkzVgK2Q1MZ9OLF9uMJRMBx/0zGTvq1nHpOXd/ITKk6gRgU1PTkC1btjyO5MU1NTXvdHV13YD0nQh7In8PRODF3E4Uh1IyIogqh+47hg+3GUtvIsitowXNHetVTrgigwJ3wwabcSjF2c/u5/f2v8sVt4uZvv7cbtowqcaGcsm33uq5tFw2Y1SF6IWqE4AQfBdB6O2B5C9aWlr2bW1tvQDhMyjfDWEF6r49ceLEIvsxqF7c37C+NCIIkZGIyKQLrxzIxCE2LaO0kIbp6+t3/3ecobtli83kGfe+YNuFHT5H0Vv7sK63saOug2giAShypAxHK+dGPB7nvP2JmzZtGrZ48eIEGzrqLkV0BYTgeRCFP/FLkxOLxRaOHTt2/MyZM21JT/iS2NbmW+v5kkcXWfkaI57q2KVez+fL5z5nzOrV/jhnDkP6xCeMOewwu4EQxeTMM41ZuTLxwcoH70EH+fEjj/hlvEHpAuSJJ/ybPGDqVN/Sc+ONtqDM4PXPmeN/YQM4gzWwbIWvn+PrOAs2gDNY99/fT/fF9fPce/ufhM89ikzrn3460TrKmWu0ACe7dp4ffRO+9JItcOCbL7uwe9uf3b5htzP9+hlzwAH+j2qu1xdQrvduFkyfPt0sXbp0EZ71jhf46qEabS3XQuB9wxV/lmBAT176cWbPNuZSSMqrrjLm5pv9mHmW50qqY5dD/Ze+ZMx//+v3pjz/vDHPPJO4KpIQRYdvKu5birvUF3n77UShQYFYCUvXuA6hk/lCDItE4u7XF9B66XbBu+eZDzhD2u0a532TCq7qEb63wqSzv7sNXdBwLKEQWVJG6xXlh1WrVj29evXqR202TM0OO+xwNeIxEIjXYpuUjpaw/XlDhw7d/tRTT7UlW6HA4csUJ6Px94K/Tfzu8/s6a5b/Yj1qlN04Q4Jjc01xPoP4GRyLzt9qCqqlS425807fssbyIJRaPcdQh5+tbCM+V3NpGyGy5r77fDccvHHD/t0o7ti9xxuXgpDjBF97LXFRfz7cg5v2uOP8uNzg9fNaw7Oy2IVKUcU4fP388oYFcNiPYl9cP8+d8MeWPyIBnKk9eHDiuYd/lIKQST2dOIc/g5Y4LuHGH/hk1x60LX8EebwwPL9gBnZv+/MHkquchN+Seb8yZHN9PB7PKUy53rtZcOutt+L5s+YtPOuvs0VVR5LXkeojHo9/AdHPEBYOGTJk0qxZs1IOHknWBczfRVq7aLF/7jlf9LGHZK+9/N8KMnKkMVdckfyFMBnBsdlTxd86Hj/8rGI9X3qTvZCXcj2fnxxKk23bCJET7GYjnPzAB6ZLIIx22smPw1AAcEUKUq7daLx+fkHZjRplik92/fyiHnjg1jFwfXH9wf+OwozdCS69/e9ILvX8wQpcwCS79uD8KJzd2cMUf8Fa1Kn2ZxcwHywu2Zw/hSPHTQ4bZgtAud67WaAuYOh/G1c1EH8nIfpJV1cXf/VOTUf89QbHvVGg8WUtsNBTrC1Z4qcJ67hdpgTHJnxOhcUf4Ys6X+7c8oBSrg96OLJtGyHyQrK3l95IpwuvHKCYy/T6acFKNQGiWNBaGeVMuZBk8r+P2jaT9s7nfUbr6IIFvugXVUnVC0CIv88jut3PmU+1tLQ8ZtNZQwtXFOxZChPuRUiX4Nj8zrI3wSVYvYnW/ihKtZ69EeHfwWzaRoi8wLF8mSxFM3CgL4IqBbrDyYRMty8k7NJkN2exoNgMW9BSwXuL3UEB7BLKZOwofyT5Y5kvOJaR1glRlVSzAKyNxWI/Rkw/gFu6urpOam1tDYRgTgQvaRwWFIbfs/DYYbc+HYJj0wOD20vD3wX+nvAlni/kzLuhFOv57GQPCtMB2bSNEHmBIoI3JB/MwU3KwBuXIVzGG5XbsrxSoFNljumjCA5fq3v9/DJz3AYH7ZYS7E6lCOzt3N2QTT1/uOiahe2QLjxGcG/RWsl0JhZLfi734WeHzyWT82ccJsqSIKoC506oDqZMmdKvvb2dYm8GhN/qmpqaD2Zj+Us1BpBdmf/7X2J3J8e48Tcz1zGAnEjy8su2EHD89b77+vV0T5bMrVSp1xONARR9QjDOKhmV7kqjnK8/13MvtBuVVOcXkO3+mZw/ly/kJKYAimX+IBONAawqqtECWLdly5Y7EVP8LaqtrT0oH92+YShcTjzRNyS4QzbYhcv6GTOyEzjBsd1u5qD7lJ959tl+HEWp1+fSNkIIIVIQ9VDim7moOpI8hiuXWCz2tZqamuOR5JSMQ5uBV5Fn9t7bf2nj5MAwnABB58eszxa+rHHiGIceBfA7TcsZP/OUU/yY+TDlUp9L2wghhOgFd9IJxxK5q7qIqqCq7CxTpkwZtmXLliUQgIO6urr+jKLn/JpEUP9IS0vLf2w2KemsBMKJH1/4gt8NzKEiHPpxySXG7Lqr3SALXnzRmJ/+1H9pox89zqy9/HJjdtst0XLGes6m5YQKDlVKtlJHqdYLIYQoAHxgLF9uM+D444059libqQ7UBVxlFsD29vZDKf6Y5rg/hG9FBVQfxW3yASeIcZwwJ8pR5FDg5LqEI8fQER6Lx6QLrt137ymemOeYQ44NZFxu9UIIIQpAJmtei4qlqgRgS0vLnxFq0gjfsLvkBfe7Fgi4bHG/q+7xhRBCiKQEkz4C6A9Q7mCqjqobA9gXRH3XOBYwG9hdGrbcE/f4QgghRFLY5RJ2P0PHrC0pVz8VFYYEYBGIx9HQoZamM2SOfcsG1/pHn6K5jCcUQghRZdDHY7D8XMD8+TYhqgUJwCJAkebOBs62G9gVgBxfmMylihBCCBGJ23WU69gkUXZIOhQJ97uWzZhbzpp191P3rxBCiIxxB4+vWJF8HVNRkUgAFgn3u7Zsme/CJRP4/eQYwDCaACKEECJjxo1LdCZL1A1cVUgAFgmO0wuvAU4ytQK629Ofp+tMWQghhEgJxw5xXeEw6gauKiQAiwQnXHG8XphcBSC7f+U7TwghRFa4XUh8yHCskagKJACLSC7fNa7W487SV/evEEKIrHEfIhyX5PoZExWLBGARcb9rHG/76qs2k4JFi3r6DnSt90IIIUTacImqHXawGUumXVOibJEALCI77thzHe50v2vudmPHGjNkiM0IIYQQmcIxRFFdU6IqkAAsIrl819zt1P0rhBAiZ9yHCccaccyRqHgkAIuM67evudlfGaQ31q/3u4DDyP+fEEKInOFYovBsws2bjVm40GZEJSMBWGTccXsc1+eKOxe+kIUni9TXG9PYaDNCCCFEtgwe7I8pCqNu4KpAArDIcNzezjvbjCXVd82tp/hraLAZIYQQIhfcbmD5A6wKJAD7ALf7NtV3za1X968QQoi84T5UFi/2xx6JikYCsA9wu4FfftmYDRtsxmHVKmNef91mLHL/IoQQIm9MnJjYrcQxRxygLioarSORA7FYbOHYsWPHz5w505akB8fYnn++MVu2+H43OQ7wtNOMOe64xLG4/A7+4Q9+6N/fmG239Ydr/PjHidsJIYQQWXPmmca88IIxb75pC8CoUXzI2YzlxhttwoH7p0Oy/VNRgONPnz7dLF26dFFra+sEW1R1yALYB/Tr568L/Mwzxjz/vL/+9nXXGXPppcbMnu1vw5j5G27w67kdtx80SOJPCCFEnnGd1HKlAlHRSAD2ARR3FHXhbl9+11au9F9gbr/dj9n1G/4Ocnu+pAUiUQghhMgL221nExY+cDZutBlRiUgAFhl26959tzFDh9oCC8fb0t0Lh11861t+zMBu4jDc75570l9DWAghhEgJu5fYPRUm3CWcLu+84w9sf+ONwjyo3n7bP74slDkjAVhk2tp8Sx/H8rmuXLgu8JIlfj1jd/LHwIF+1zHLeRwhhBAiL3BskWsFzFRkcUA7xyvxAUb3Fa+9ZivyBAfNz5njH58xxaDIGgnAIhN8n6K+ayRYFaSz04/DhIdo6L4XQgiRV6IEYCZWPFoxwsvIvfKKTeSJZcsSz4d5kTUSgEUm/P2iQ2h3QkddnR/XOv8Z5kePthngdiELIYQQOeFOBOEYpHXrbCYN3C5jjm2iVTAfUPi5FslMBapIQAKwyHAVjxEj/DRXBdlrL2N22cWYMWP8sOuuxuy4ozHjx28tGzfOmL33Nmabbfz9Ro7UUnBCCCHyDP2NBQ+agHS7gdl9tXatzYRId/9UcGyhOyie1sZMBKpIQAKwyNDid+KJWy1/FIEUffTDyUC3S5wEQoEXlFEAcnwu4X4zZvS0HAohhBA543YDpzsRJJk1Ll8CMNl5ZDNRRXhIAPYBtObRryUteWGYZ/kpp/Rez/2FEEKIvON2A3PiRTA4vTeSCT0KtHx00yY7fr4EZhUiO1IOZLsSSAC/E5zNywkdHNNHq1/YspeqXgghhMgr9P93wQWJMxHPOy/1IvTsuuIkkChYFx7Enins+g2Wz3LhwPlrrvG7rzNAK4HIAtinUMyxy3ffff3YFXep6oUQQoi8Qn9jHJcUZt48m0gCrXzJxB+hS5hcoCUkSvwRWidbW21GZIIEoBBCCCG24lr7Ugm4VAIxVX0qCn38KkUCUAghhBBbmTTJJiz05xc1wzfAFWC0IobhMldh/4CZkur4EoBZIQEohBBCiK1MmNBzTB0XsI+Cg9VdAXbMMTZh2bzZmIULbSZD6OZl6VKbsbznPTZhoUNoTlYRGSEBKIQQQoitcGJFU5PNWJJ1A1N8udbB/fc3ZuxYm7Fka6Wj8AzPIuZ6xdOn++uihpEVMGMkAIUQQgiRyOTJNmGhwIpy5+IKr+HD/eCOI8xWoLn7xeO+CHQFqgRgxkgACiGEECIRVwBypu/rr9tMCFd4Bfu5+7/8sr80XCZEdS8nOz63ixKoIikSgEIIIYRIZKedeq4K4nYD0zULJ3iECSx/dFxbX++nCcVZc7PNpMnKlcasWmUzlkD4uRZGOoTuzRWN6IEEoBBCCCESoePZKCtbmAULEv3zcZ+ga7ahwXdgGyZTf4Du9ttuu9WhNJfGclctyfT4VY4EoBBCCCF64gpAWvDCK4S4M4N32WXrwvXE3T/ZTOJkuNvzeMGKCFECNdPjVzkSgEIIIYToiSuwNm40ZtEimwGuxc3tlnXzHEPodukmg0LTFXSpjk+Bms66xcJDAlAIIYQQPWGX65gxNmMJuoHfeceYJUv8dIArGHfe2ZjBg23Gkm43LSeNcF3iMK6Daje/aVP2/garEAlAIYQQQkTjirpAwNE6F551yzF/EyfajIXdtK5IS7eb1t1u1Kiek1KGDPFFZhh1A6eNBKAQQgghonEFILuA2RXsTgihf77wrN8At5s2XXctqbqXA9xyTQRJGwlAIYQQQkTDmbxhYcexeXT94gpAVygGuBbAqK5jl6iuXPc4Ae7nRnUdi0gkAIUQQggRDdcE5trAYR55xJg33rAZSzIBuMMOvsuWMK54dKHADE/mqIVUoYUxCtffIAVqpv4GqxQJQCGEEEIkxxV3L7xgExaOxXMni4SJ6gbuDbeeYwvdtX8DuCwcRWCYVMcXHlUtAGOx2Kfi8fgsxOsQv474d5MnTx5nq4UQQgiRbPxdAAVi4J8vCrf7tq0t0YG0iyvgknX/BrgCVQIwLapWAELwfbempuY3SPK14mcIM5E/ub29/RnUjec2QgghRNVDB8/bbGMzESTr/g3g6iBhgdjebkxrq804cEm35cttxpKOAA3z2mvGrF5tMyIZvUj2yiUWi+0JsfdcV1fXYw0NDdPnzp272ZafgPJ7kPxrS0vL8SzrDWy/cOzYseNnzpxpS4QQQogK48wz/dm17ri/gAMO8McK3nijLXDg/s89Z8yaNbYAjB1rzHhra+GYQnLYYb54C4/h4/i+d73LF5BRx+exOav4yScTrYocM8j1jAOcfadPn26WLl26qLW11RngWD1UqwXwXP6BALwiEH8EN8K9iHgnfgDirpcBDUIIIUQV4a67G0DLIMVfKlwffm++aRMOtACGGTq09+5lwnr3+O5xRA+qVQAeBfHXjti+diRAc14NONLPCiGEEFWOK7ACkglDF3e7det6jgOkJc8VhukeP0pgpuNvsIqpOgE4ZcqUfhB3nOixtK2tbZNfmkDgfCjFqFMhhBCiShg4MHombjJh6MKZwnV1NmN54gljHn3UmFdf9QPTm7s75XyyFZgUl/Q5KJJSjRbAYQi0J0eOEO3s7HybcVdXV5p3tRBCCFEFuCIrqus1GfTlx+5cl96sdOxaTub+xYXbUaSGedt7nIskVJ0A3LhxYz+bjLL+kaA8zbtOCCGEqAKG0X4SgoLOter1Bp1CZwI/L9X4vzDu+WWybxVSdQKwtrY2WCMmEIIuwWjWdTYWQgghBAVWsKoHHTDTQXMmcFZuul26nFyy6642kyZhdzXsct5xRz8tIqk6AdivXz/ahDtramoi7dYQiIGNWrZjIYQQIoAWNTplplsWun4ZNMhWpAn3nzrV33fatK1h+HA/BPn99vPjhga7Y5pwe+534IHG7L13ZtbJKqTqBKB1+7Kwq6trl2nTpkXdXd4rDQTiXC8nhBBCiK1QaOXSvcqxfRSPQaCvP4Ygz7F82R6f+9E6KVJSdQKQQPw9BIHXb926dQfbojDTEbo6Ozsf97NCCCGEEJVFDhK+fJk4ceK76urq/ovkE+vXr5/+yiuveOMCg5VAIBD/3Nra+iGW9YZWAhFCCCHKD60EUqUCkEC8XQ+xdzbEXiviP6FoZ4STEN5A2UG4KQJ/gEmJx+MbICQHjBo1ypYIIYQQotRZsWKF6ejo2NjS0uL4jqkeqlYAghqIwLMRnwkBGIPoW4X0gwjfTEf8EQhArjXTH/uu8EuEEEIIUerguU/LzSYIQPn8FUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBD5pZpXAikKsVjsUzU1Ned1dXU1IV6P+F/19fVfnzdv3mK7iQiB9voD2unglpYWLs2XwKRJk3bo7Oz8JtrwOHpxR7wY8a9GjRp19cMPP9xuN6sqpkyZMri9vf0StMUMtMWuiDcjno342tbW1nvtZh5qv0TQdsPQdhejHY5HO+yCotcR/ow2+nZbW9tKbyOL2q53mpqajkSbcFH0W/Hd/bRf6qO260ljY+NZtbW1P7fZKEagHd9gQu0XTTwefx/a4kK0xX7IdiHMQ7gG7fZH1geo/ZJTa2NRAHCDfhc32m+QHIDwM4SZyJ+Mh84zqBvPbcRW0CbfRPtwPeYejBs3bjt8iR9G8hxs8yy+xNchXo/8D1esWHGHt1GVgYfukC1btjyO5MVoi3fQJjcgfSfCnsjfAzF9Mbcjar9EJkyYMBTfw8eQ/DLCUoSfIsxBYPs8N3HixLFIe6jtegdiZlu0z6+R7GFQUNtFA/G3F2O0xzUIl7th7dq1bCO1XxLwrDgf0f1oi6lok98i/B75iQh/wO/ehdyGqP1En4CbcE/cpF2IH50yZUo/W8zyE1iO8BdbVPXgSzoA7XKTbReGV2xVN6i/2tZ93haROuTvZjnqZ9iyqgHX/R3bJrQkdD980RZjEJajvB1CptGWqf1ChNrjW7bIA+VftOUUNB5qu95BG/zKtg/DLbbYQ20XDa79SQQKkV6NMGq/nuDFdyquewuufy5e5EbaYjN+/PgdUfYq6jbzBY9lar/ekQWwcJzLP3jjuGLu3LmbvRJgu+UeQfgAbr4xXmEV09jYeFy/fv3m4a3ss8je75cmsvPOOw9EdAbC0paWlhu9Qp8OBFpwuLD3WYyrjI8idG3atImWPnaBeOAeW4b2oCisq62tfb/aLxJa4F9bu3btlX7Wp6Oj47eM8b19F2O1Xe/w+4voNLRXjxdatV1S+Nyl5epFxJ1eSQRqv2g6OzvPxXXXo/3OWrhwIYdteCxatOg1lF+C8Gs8U3ZU+6VGArBwHIUblOMLKPZcOFYG917NkX62ekEbfAbREIQv4Ev6Aa/QYcCAAftju0Foz4eQTfjBxD6LUL4IycMQ6rzC6uFaXPs3Fi9e/JbNh9lk4yFqv55AJJ+Aa99pxYoVXldbAATzZJtcwT9qu+TE4/HhaK+bkHwY7XC9X7oVtV00TU1NMUTbIDzvFSRB7ZeUY3HtK/Ad7vFsbW5u/hXa5sz58+e3qP1SIwFYANjlixtvHJJL29raggdxmIU2nmTjqgUPkGsRxuMLSYtVtxUrDNoybuM2r6AnbM/+EydOrKpxlWizn+FH8Hs2G6YG4UQm8CM3R+2XmqlTp27f2Nh4Iu7FP6DN2tFW32G52i45aKefIwxG8jS0T4/vrtouGrSZN/6PbRaLxe5AWAYxvQHxUwineBsBtV9P8B0dgfYYhfDipEmTRqG9bkbbvRq0H+IP2U3VfmkgAVgYhiHwIbzayzl0dna+zRg/BNt5BVUM3tgeQlhrs5HgC7wDY7RXZHui3mvP+vr6qm9Pgh9BjnfZH2Hh0KFD/6H26x08VM7atGnTaoi/u5DlsIxPQlzTSq97LwlNTU0fx7V/GOGrtKbY4gTUdknZ08afQ+AYtt8h3IMwGW3ye3x/vZc6tV9PcM2jbXLbjo6OZ5FnLxonvnHmL63390IInoNY7ZcGEoAFYOPGjcGkjyjrHwnKOTtYpCat9sQXverbEw8PzqL+CdqCww9OnTVr1hbEar/eWYlr/yHiWxFvwIPhNjxEvuJXqe1c0DZjcL3XI8y0lvtkqO0iwPXWICxG+HRra+tRCBehHSmo90AZhx5cjO/xIYjVfg5oI1qcyQEIcwcMGLAH2u4chFNRtx/agsaEH0+ePJk9cGq/FEgAFoDa2toNNhncgC79bbzOxqIX8AX12hNf8F7bE9tVdXtay9/tfs58Cj+KdHOi9ktBW1vb3XgIf40PEXx3p6JoJdrqR01NTfuq7SL5Ja63rr6+nuN3I4dtELVdNLjXLkbYFYEuwrrB/UdLajAr/ZNqv550dnZyAocHrvucOXPmvGOzbL/5aKufIjS0t7d/WO2XGgnAAtCvXz+aljtx40WalvGQ8aaoA88ELXoH7eiZ8PFFjWxPlHvtie2qtT1rY7HYjxHTD+AWtMdJeLgEQlDtlwHNzc0vI/oR02iXD6rtEmF3Oa71PUh+OZUze7VdVjzFP2ibiWq/ngTXimt/By9udPycAATis4yxXaPaLzUSgAXAun1ZiBtsl2nTpjX4pQnQYSVvvLleTqRiPv+gvbx2i2AifxDwBrjE5qsGTjiKx+N3om0uQBvwB+/dEH8cTxRG7RcCIqY/wtFot/faIhcODicjENR2IfDyejJjtMeNaL/A918X8g94Gxhzqi2jP0C1XU9qJ02aNA333+E2nwDadxBjtBmtV2o/h4aGhgW4Zk7SqkeW4+wTQLln7cM2nN2v9kuBBGCBwI31EG/GdevWHWyLwkxH6MLbCldxECnYtGnTLLTnWoQjkE24Z2Ox2AS0864ITyDb3T1QJdRt2bKFA6BnoG0W4eFxEH7MvG7fMGq/ROrq6vqhrf6O5B1hJ+0BaKd9bLJFbZcI2uEWhB4rVyB4/hPB88wj/pPaLpqOjo6HcP89GHZiHIC2olsS8pTaryc0rthr7t/U1BS0VRguC0eeV/ulJqFRRP6AuPsVY9x837MOKT1w452A6FCU/6Wtra3HiheiJ4sXL96IL+rvEcbH43HPwbalDmWBI18utVdV4F76Gq7/eCT5BntoM/AqHNR+idhZ539G4HJwl3mFFlpnENGauq6+vv73artEWltbb0G4zA2outXfwjzHPF5E/qS2i4RDg/jSVoP7i0MNup/B+D5zdjDXpl6HbX6p9osG7cOhLoyvDlb8IGijPdAuZ6F8FQT2vWq/1PQwoYr8gS/09bjRzsYN2Yr4TyjaGYGzNN9A2UH4oQy6moQFX1QOKl+GBwjbqhuUD0ebPY123BXZvyI9F+mjkd4H6T+iLdk1lXRAeqUxZcqUYVu2bFmCNqCjU4qZ5/yaRFD/CNryP2q/RBobG3fGQ4IW+F1w/Y+hLZ5EzJmDH0KaTmM/inZju+reSwP81r0bbcJu4N+g3T7tl6rtoqDlD+KP9x6XaZyNdngQ8Ri0y4eQplPij6JdvGEcar9o0C5cqpH32TKEuxEoBD+CNmlA+5zEFxDk1X4pkAWwgODmOgc3GX0Scbr5uUgfjnAHgsRfhuAL/UZdXd1BaLtfIksP7+chHoj8VxsaGj6JdFV9idvb2w9FGwTjhThZ4VtRAdVHcRu1XyK0vnd2du6LJGcNUgR+CTG7iu5F+oBA/BG1Xfao7XrC5cv69++/P9rgagROUOAz4t0I96OtDgzEH1H7RYN2OR3f39PRDsuR/Szi4xEexkvdEajzxB9R+wkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQRUbLYQohqhL9+AlRwcRisctq/CXh0qarq+vy1tbWy4J9kb8O+S/Z6org8MMPr1+xYsUXcW27luq1cb1itP9sJK/GOX7fL80f8Xj8IUSHI5wQXj4rU3Cf/AHR5M2bN+/PBfj9UiFEqaO1gIWobOZA5NzmhO6HfUTdbSie49dWLsuXL/88omsgsLgWaylSU1tby//F6oaGhh/7RaUJzvN8ROP79+9/pV8ihCgHZAEUospoamraFUJvEdMtLS1JfwPi8fhwRMPb29tXcwF7v7QywLXR6ncNwm/QBp/2CkuIWCx2JsTpLxA+2Nzc/BdbnFcmTJiwS319/TYIr8ydO3edLc4KtOc3EV3W0dFx8IIFC57wS4UQpYwsgEKISCCM3kCYX2nir9SBQB8C4fdtJF8qlPgj+L8u4f83V/FH8JJwHaINdXV1JW2tFEJsRRZAIaqMdC2AUWMAUfZplP0ayYs7Ozv/WVtbeznSh2KbOsSzEF/c1tb2ZDwe3wP579i6LsTPY/tLoqxDkydPHtfR0fE1bPZeZEcjrEF4HNv/MBNr0pQpU3aCEPk6kkch7IqwBWE+wh9wrBtwXpuQ5jW8jGsYx3SIBEvgpEmTdsc5XYQkj0VL6Grs8x8c57s4zlxuE4BrvQXRqTj/oxFGok2+inwTwhsIM3Gc7+A62pBOCxzvAkQ/xrHOQbtf75f62HPfaQhYu3bthdjmNHstryHchuJvDR48uGvFihXddYiXIr5r/fr1337llVc2eAcC+JweYwCDa0E4FPsMw75fRtgLeWS98Yg/xrZ/RtwDnNvN2OYzCEdBuD5oi4UQJYosgEKIjIEoOAIP+icRT0X2QaRfQzgC4udBikSU/Q91uyNQZKy0dQ9bYdgNtj0MAul5JM/CNu0If0O6BfHxdXV1j6H+DG/DFOC47Kp+CskvIvRH+Cc++7+IeX5X47PvRhxwL+pm2fRCpDn2kdt6QCB/BOc0C+fwCWQp/O5DWIH0x3CcWY2NjR/wNuzJ2ajnsbZBuA9hPcpOxXU8jevY398kLT6LQNF8r5dzwHFr1qxZ8xfE38Z5LUH8EMJOqLoY5T9fvnz53chfgTzP+T8IFIgXDxw4kMI9LbD/eQh/RtgRn/FvFL2McCjCn9A+H+c2LtjWE5GIef5CiBJHAlAIkTEQBe9B+E1ra2tjS0vLjPr6+t3w4H8GVQNQ/mukf4W6GMIJCFOQ/yfKG1DfLQ6mTp26PcruQnIowtk4TpzHwvYHY/sjUMauyZ9BcO2NuFew/ZmIxiK+DceI2eO8H6JwMsppHTsWIuwAbovy8/G5v2MaPIr8JxD+j5nJkyfHcIxbmUbMY0zFsU5E2Af7fARl9Yh/DxFES2UCKP8QoiuxzySEjyDws69C2A51v+bMY27XGxyXh2gyPqcF+y/zSxPBsfoh4rXsi/M6GtvRcnqyrfsMokM6Ozv3QfmRqD8WZdNt3UkUykynAtueiGN8Hsdosm05FefEbl6D8m94Gzn069ePYr8T2x2DWM8WIUocfUmFENmwfu3atewW7mBm7ty5myEa7mQaAmD1hg0bvhzUMa6trf0jE6iLeyVg48aNFIMjEH4PkXEDYlq9PCA4HkH0XRyTgutCv7RXxvAPtmXXdvdxOM4Nn3k6Ars1X/FLkwPByGuiiL0K55BggWtubqZYvQl1Q3C8L/ilW0HZM7gOdht3+iWm0+bZZTxl2bJl7E7ulbq6umCb52ycjJ/g/Gg59UCa1jdaHNkGP1qwYMFLTBOcw2OIliLUQLw1eoUpwLX8p62t7Rc269HR0XE1Yxy/adq0aRTzCXAsIfZbgPrhENu0vAohShgJQCFENsxZsWKFJzgCIC5W2uS88Fgzgro3GUMcDPAKfDzLFOrYxdgDlN9nkymFEwRmMObsEoiPP8bj8U9yTCALII7uR7gVIdKi5hBYyyLPCSQ9J+xzO6Ju8WmhGPS6RnGO72bcGzgGLYDEG6OZDBzLHRvJz+WYQ4q3oHu7G5R57Q+BGW7/3ujuEg8YO3bscpusefvttwfatMsC/sHnBdchhChRJACFENmw2sbdQLwE4scTImFCdd0EYgdi5lcQbF1uQLlnxcJ2o6IsTmGam5tpffweQhe2/wjiW9vb21fgOLMRvjl+/PgduV0qgnOiBSx8LkFAPccokh4CB4K1xSYTwLGW2KRnpewNbOudJz7nLa8gCdiuR/ujLKP27w1s7wnGMA8//HC7TZotW7Yke3a8zT/436XV3kKIvkMCUAiRMdAanGGbEziG9/uD+O8ICc6o3bB8+fJeBSBpaWn5uhVwXOGDkyQoYjiD9fKGhobmSZMmTeN2KQjO6c7gs6MCNgmEYDcQPUGXdwLYPphp3S2gkoHz964T+3BWdVJQn3P7pyAjwRgiOO+U/y8hRN8iASiE6Cs4S5Vi5ppWfyJG0uB2Nyejubl5OYTgz7DPBxGGQ1BxQgnHwA2lOxZ/q+RgW++c6uvrvx7+fDfgMzjpJAHsu7NNJoBzGM8Y9Yu9gl7ANqtskmMjyw5c60ibDK5DCFGiSAAKIfoEiAXOGmX8Qa/AIRaLzUCYH4/HU7ovwXY3YbtXEQ62RaQTgvC/tbW1lzKDz+nutkU60sIVnFN7e3vkOTU1NZ2Hz3gRgd3NLlHuYWgR4+xgdhH/nXEKmm2csru4RPFmR0PIBtchhChRJACFEH0ChNn/QSisheg6C4KKs2q7nVIjPwnlP0FowjaRY+vCYDvO8N0R2/6gsbFxW7/UoxZlH7Np+gn0gBjzJqmgbnuvwIL8tQj0R3g5xN6HbbEHxSXq6F+PLm9e8Eu3gn2OxzZhH3h1yF+L8ji2f2zBggVP2vKk1NfXP84Y+xzkFZQREyZMoPWPs4zfam1tfdErFEKULBKAQog+Yf78+exuPQVhM8LPYrHYAoQ/IXBGLwXEGAinv2y77bb0pdcrAwYM4DbPQTgdgvAyjnE/wr08Jso/h7Csvb39W9zWwhVCKLSOg0i7D9tdwjyEC12rUIz2x2ffibq5CPcgcFbsowjbovwGbMcZvy6c7EFL5LM43h8RWpHneMRFCHSOnXJc3bx587gP3caMoU9Cr7BMgHil70bC5esix0MKIUoHCUAhRJ8BIfW32travSCQbkaWv0fvgyij4+gnKJpGjx594qxZs1JOeJgzZ847/fv3Pwr7/BD7v4pwJAIdJHP5N4rDvegTkNsSfC79DF6G7V9FOBrb0nmxB+ro6+9AJG9H3VCEDyBwNY2ZCHRszdVGorgG252FmH4Ej0dAtutHdXV1+7W1tXnuUdIB+9zIGIL1JK+gfPDOF9ftnb8QorTRWsBCCJED8a3r557f0tJyrVeYA+PGjRsAMUtLYBeOxwkkJW9Ni8ViHLPI5eJmQiBTeAshShxZAIUQooRYvHjxxi5/rOFYiMsT/dKS54s1NTV14Os2L4QoaYz5f5QA7+mub4NeAAAAAElFTkSuQmCC\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub data rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_data_rate_limit`.\n"
+     ]
+    }
+   ],
+   "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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare.olin_empty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 19,
+     "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": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Here's a copy of step to get you started\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",
+    "    print(system.clock)\n",
+    "    system.clock+=1\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": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "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": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n",
+      "25\n",
+      "26\n",
+      "27\n",
+      "28\n",
+      "29\n",
+      "30\n",
+      "31\n",
+      "32\n",
+      "33\n",
+      "34\n",
+      "35\n",
+      "36\n",
+      "37\n",
+      "38\n",
+      "39\n",
+      "40\n",
+      "41\n",
+      "42\n",
+      "43\n",
+      "44\n",
+      "45\n",
+      "46\n",
+      "47\n",
+      "48\n",
+      "49\n",
+      "50\n",
+      "51\n",
+      "52\n",
+      "53\n",
+      "54\n",
+      "55\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub data rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_data_rate_limit`.\n"
+     ]
+    }
+   ],
+   "source": [
+    " newfig()\n",
+    "plot_system(bikeshare)\n",
+    "decorate_bikeshare()\n",
+    "bikeshare.clock=0\n",
+    "run_steps(bikeshare, 60, 0.4, 0.2)\n"
+   ]
+  },
+  {
+   "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": [
+    "After the simulation, check the final value of `clock`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "60\n"
+     ]
+    }
+   ],
+   "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": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'system' 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-34-398d1e7d24ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msystem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt_first_empty\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'system' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    " system.t_first_empty= -1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "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": 35,
+   "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-35-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": 36,
+   "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": 37,
+   "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": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 38,
+     "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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 39,
+     "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 40,
+     "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": 41,
+   "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": 42,
+   "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": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def make_system():\n",
+    "    system = System(olin=10, wellesley=2)\n",
+    "    return system\n",
+    "\n",
+    "system = make_system()\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "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": 43,
+   "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": 44,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But after the simulation, we can still read the metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 45,
+     "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": 46,
+   "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": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = run_simulation()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 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": 49,
+   "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": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 50,
+     "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": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 51,
+     "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": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_simulation(p1=0.4, p2=0.2, 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, plot_flag=False)\n",
+    "    return bikeshare\n",
+    "\n",
+    "system = run_simulation(p1=0.2, p2=0.4, olin=3, wellesley=9, num_steps=30)\n",
+    "print(system.olin_empty, system.wellesley_empty)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "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": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
+      ]
+     },
+     "execution_count": 55,
+     "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": 56,
+   "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": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linspace(1,10,10)"
+   ]
+  },
+  {
+   "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": 58,
+   "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": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.,   3.,   5.,   7.,   9.,  11.])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "linrange(1,11,2)"
+   ]
+  },
+  {
+   "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": 60,
+   "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": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p1_array = linspace(0, 1, 11)\n",
+    "p1_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0\n",
+      "0.1 0\n",
+      "0.2 0\n",
+      "0.3 0\n",
+      "0.4 9\n",
+      "0.5 9\n",
+      "0.6 18\n",
+      "0.7 22\n",
+      "0.8 20\n",
+      "0.9 35\n",
+      "1.0 38\n"
+     ]
+    }
+   ],
+   "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": 62,
+   "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": [
+    "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": 63,
+   "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": 64,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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": 65,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def parameter_sweep(p1_array):\n",
+    "    for p1 in p1_array:\n",
+    "        system = run_simulation(p1)\n",
+    "        plot(p1, system.olin_empty, 'rs', label='Olin')\n",
+    "        plot(p1, system.wellesley_empty, 'bo', label='Wellesley')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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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jj8naXAH6n8wtqcLIh77ZRBRCa1YWOd852wNHOF2DpKMM387wPfxMtIOWL169bo4NxyGcAXKnImQf2z2tX+DGQ2jqKqq4ixfikw6NH8L5f8C+9oF3zUnbGwMYwtsJ4GXBp5f70L0NRKAYlCCGzbHLhkffQgvSKN1iC0N9A3ndO/90Q5zAh4yxyU/dBzDsST4C0QdnXGA3wkEAs4r3RK6fx2Q7gjALfFA5uzL+LQOUCb9t3Gd20xKA1tSIXqm4zzRZ1xPx6QRsw8QciZYuODjQ5/j/JzvjF281dXV9G1o3nPc0NDwFc7JA3j4nwrjGDdnhmp3zrt7tX8HCOw3EczGuRo+bNiwPREeHFtj3RU/9jKbYB8dXbc4zrTFFPJyTF8E5+cPiFOUUSSzlbLjmYDrYS4C093d2tqa8nuHcNwG52QizodxBcPzgmtgAowt0KZ7Ft/Js7hWL8D+pmJ/RzIdsMvWmcDTKyD22NXMVu4XUT67yf/IsZ7z5s37kus5jhFB/LCNtMjR9S5EnyIBKAYteHj8BDd4/tOf3NbWRgezKW/iuNHT55ozEP45PGgc3295R3t7O4UIuyS3gZkZk6i7qwAsKytjXnYbT0Ie06WFMGECCIGQpMsRzvasxrlwdXsxfPjwMyAaOAbxA+ShI94eMWTIEM6k5TuXRyxatIjOeTsBoXEUH/Y4xjktLS2myxPf42Ww95D+e5MpESRHnRbRLh/Svd1/PFjntAJS/NFvIM9nVrt/48F+nNbrKM4BhVxPocBjVzknUCR0BUNENyLdzKrGMZxmEpOA+KPAfg32Ic7HD5iG39I+yM/3TT9lC7AEcC11XF+oc6bCyXF4ziEdztjMDlCPY2BGqGNfaY+pzcX1LkRfIwEoBi3sEsUN/gREOaPyODzMXrRnLHYwadKkYXgo/gT5OLaMD/y5BQUFxyDs9DaCfKEh5sqDg/PH4sG8J4yOdF3fnsGZk1jP2aDrIuRbHvj6Nx5rArafOTNrE3n/hQfe/ozb8G0YfMsFXYaQP9ljEXsE3XYg+B3j2McN2AdFWMc9Cvs4EA/xv9qL92MfbKHiw5rCnN/HvviuzuFYRqaT8ePHb4qH9AX2Ih/qKent/uOB+LkT55Hn9Psog61bfPMK30aRbXw4Vna5mmPD/h5EfZx3QfcI/Jnh20T4fmged0JXMJYvRkBhdRT2d53Tykfo8xD7fQp56DaI1wf9BbK7+GksL0X6urA7sE3HRCsI6BE4h45Q/6y4uNi8jScDnPdsH4n6dfgHLC8vH4Lln2P/Hd30uA7SHuubi+tdiL5GAlAMavBw5iDx6YjydV7TICbewM19CR4e7yL8uKmpaRkeADfD1kO++/Hw2jqF65Z8w3kryAjYm2zNMakuYL3p8kU4DMf4kps7DdLc3Ey/aE8g32g8TB/HOfoc9g7OE88HB/3T8e+DEBS/Nhv0AnwfFCJ88JZgH39H+fwu+MYFzgzlK8SGYx+vNTY2djhinjNnzkykmX1i/dVYx0kf72ObOfg+KdI4Nm4uyuNrybqkN/uPxx4X+iTzcRl5M2r9w/V2E/adMOkHx/Y2zzn28RCM39lMHGeXr5frDuznMpTDbk92BdMPpWmZw/l4HetOwjr+iTgDAvdL1OE9WB3SP0QaxeIS7H8viCDjmoYTVHDufoBtWrD+cGzzOX9L2Oa/2IYzbjkxhs6Xj012PdNTsN/LEKxBWRvBPsJ+amGzioqK+KYSzozmde9M+Opy9nEyubjehehLJADFoAc38LfxYAohyn/1jpuPyTCO7aGA+CvWb498h/eXf/rt7e0d73nF8XQa0xcPjq2jexgPtZR56SAXguAARA9HmXz1GLvWtkKcD8KXYMfyHCGtU1dcD6DD5Z+gTpw08AiMYpQCjq5cOMj/tMLCwt3jHf4S7PcKBOx2ZStfM/JNxLHQGfEsxH81ZMiQyRDBxtlvN/Rq//Fgv44j72bkNa1ivQVlbQFLmPSDZLYsUmzxzR3Hl5WVbQcR3COH5slQvKFsXv9sSd0WIqejKxjrKGL5e6Dbk8XIxzptivhs7P9qCLxJ8c7MCc41u0a3xXq2iC6GBbENJ2qwlfImbDMBecwY3Eyg+IcInIzy2Qq8AGEFDfvi7/YK7GcLfJc3mMyWxe7ytMdJ5uh6F0IIIcRAIBgM/gICKorwfjtJCCHyDrUACiFEdmFLGlteXd90IYQQ+UBGPpiEEGKww4lCzc3N49va2tYEAgG+K5izyz+qr6/PyMWJEEJ4iVoAhRAiA1paWvhatg8h/viu2p/A6JvvVK4TQoh8RQJQCCEyYM6cOV9AANbDWtjyF4lEDqyvr3/VXi2EEEIIIYQQQgghhBBCCCGEEEIIIYSXaBZwBoRCoRUIiqPRKL3bCyGEEKIf4Is5im8Oh8OjYimDDwnADIAApNuHIWPG8DoSQgghRH9g8eLFfGMS3/ZSYicNOiQAMyAYDM7bZJNNxr3wgnntqhBCCCH6Abvvvrv12Wefza+vr+crPwclEoAZIAEohBAibzn5ZDvSDTffbEc8Il/qEYcEoPwACiGEEEIMOiQAhRBCCCEGGRKAQgghhBCDDAlAIYQQQohBhgSgEEIIIcQgQwJQCCGEEGKQIQEohBBCCDHIkAAUQgghhBhkyBF0BsgRtBBCiLylLxwwu+3zlVfsSBzTptmROHpTj14eoxxBSwBmhASgEEIIEUeuRacEYK9RF7AQQgghxCBDAlAIIYQQYpAhASiEEEIIMciQABRCCCGEGGRIAAohhBBCDDIkAEWf88UXX1hXX321dcABB1hTpkyxttpqK2u//fazLr74Yquurs7OlcjDDz9sVVVVWX/4wx/sFMs677zzTNobb7xhpwghhBDCDQlA0ac8+OCD1l577WXddttt1tChQ61DDz3UOvzww60xY8ZYDzzwgHXggQda1113nRWJROwtUrPHHntYP//5z61NNtnEThFCCCGEG/IDmAHyA5gZjz76qPXLX/7S2njjja3f//73puUvns8++8w6/fTTrf/973/WD3/4Q+uiiy6y18RaAM8//3zrlFNOsc444ww7VQghRJ/SWz+AOd5OfgAlADMi7wRgrh1wZsDy5cvND9Dv91uPPPKItemmm9prEvn222+t/fff3/r888+t22+/3dp+++1NugSgEEIMIHL8/JIAVBew6CPuvfdea/Xq1daRRx6ZUvyRYcOGWWeeeaaJ33333SZMhdsYQC6fjBtLQ0OD9bOf/czaZpttrMmTJ1uHHXaY9Z///MfOJYQQQgwuJABFn/D666+bcJdddjFhV+y2225WcXGx9fLLL1vNzc12avosWLDAjCvkZBOOMdxzzz1Nt/Jpp51mPf/883YuIYQQYvAgASj6hLlz55qwoqLChF3BySFlZWVWa2urtWTJEjs1febPn28dfPDB1kMPPWRaCa+99lrrsssuM+u6a1UUQgghBiISgKJPWLVqlQlHjBhhwu4YNWqUCTl2sDew+9fnWzvklTOGycKFC00ohBBCDCYkAEWfMHLkSBM2NTWZsDs4GYSss846JuwJFI/rrruuvRSjtLTUhC0tLSYUQgghBhMSgKJPcHz1zZs3z4RdQZHGcXyFhYXWRhttZKemD8cPJuO0BkajURMKIYQQgwkJQNEn0PkzSWcm7iuvvGImf0ybNs1VzAkhhBCiZ+RMAI4bN27D8vLy2EAuUFFRsUkwGPxTKBT6N8JfV1VVpTcYTAwIDjroINM1e88993RMCHFjzZo15k0g5KijjjKhEEKILEIffOlYrsGf/06WD/UaIOREAELk3VRQULCwqKhoby5T7AUCgTd8Pt8pWNwX4aXRaPTVsWPHlnC9GPhwTN4ll1xifAEef/zx1vvvv2+vWcuiRYusE044wQjEI444wtppp53sNUIIIQYUdPCcbNOmdTaRNTwXgMFg8DgEp0LktcDMC10h9k5CsDHCWtjxMPYDTiwpKdErHTLB7QfkZnnCPvvsY11//fVWY2OjcQjNFr4rr7zSuuaaa6yf/vSn1t57723NmjXLiMBf//rX9lZCCCGEyJRctAAeC4HXinD7cDh8PxOwfCgDCML/q6+vv6OwsPAgpC3B8g+4XgweKAKfffZZ6xe/+IWZEczXwrFbmJM+DjnkEPPKN74vmBNAhBBCCJEdPH8XcDAYXAZhNxPiz4z65zjA4uLipRB8qyH+6NOj3c73GILdkNZvxgKizvn1LmAhhBCip6Q7ji7XPUge1kvvAs5BCyDEXwnE3gp7kS05uyPgfvkuMCP+HJC3wI4KIYQQQgiP8FwAQvx9iqAqtoQd+v37ImD373OxFMuqqakpQrAN8n4WSxFCCCGEEF6RixbAt2BbhEKhyysrK49H0pGxNdYj/Kiqqipra2u7A3k2hHXvFE4IIYQQQmSE5wIwEolcjqAR9iu/338rwuJoNHp7OByeb6//L4LD2fqH9VcwTQghhBBCeIfnArChoWFuIBBg9+7fsfgMwnPr6+t/EltrWgjfQXB7W1vbtnPmzFkcSxVCCCH6CfFOibsyIfKIXMwC3mPIkCEzP/roo6/tpAGDZgELIYRIW9zlkR/WwY5mAedmDOBtTU1Ns+xFIYQQQgjRx3guAAEnd8y040IIIYQQoo/JhQCcHY1GN0cYiC0KIYQQQoi+xHMB2N7efqLP51s3GAw+X1VV9f3KysoKvg0EYamb2ZsJIYQQQgiP8FwA+v3+exlCBE6LRqP3YTlcXFy8DOHXLrbcbCSEEEIIITwjF5NAKhGsx2galosuaSGEEEKIQQ1Fl+glcgMjhBBCbmD6H3IDIwGYERKAQgghBqQA9PKYMik7S/WSAMxxl2tlZWVFKBT6MYTTZYgfxTTEdx47dmyJySAGBeeffz7fAW3df//9dkoit99+u1lPe++99+zURM455xyz/umnn7ZT0uPtt98225199tl2imXddNNNJu2BBx6wU7zjvPPOM/t644037BQhRL+HYiMdEyKPyIkAnDhx4joQeg/4/f46LN7s8/kugO3NdQivHjp06AIKQS6Lgc/2229vwvfff9+Eybz66qucPNQRd4PCkHl22GEHO0UIIYQQ6eK5ABwzZszQ5ubmFyH0DsXismg0+iDC+K7nZhgniTzNFkKTIjImGrWs+nrLevfdWMjlfMERbW4CsKmpCXV+15o+fbqFPwauAnDhwoXWokWLrC222MIaOXKknSqEEEKIdPFcAA4fPvxMBJNh/2psbBxXX19/uFlhEw6Hd0FwLQTiUL/fv7ZfTvSaWbMs68ILcVKvtaxbb42FXGZ6PrDeeutZoVDIWrBggbV06VI7NcY777xj4Q+DtfPOO1vf+c53rNraWmv58kTvQE638I477mhCIYQQQvSMXHQBHx6NRpdEIpETFi9evNpOSwAi8Dzk+RRGMSgygCKPQ02++spOsOEy0/NFBG633XYmTG4FfO2110y40047GYGHa6JTKyBbCAnzOCxZssS65JJLrF133dW0DHIdxxqytbC3rFy5EuL5WmuvvfYyZbLOp512mjVnzhw7x1o+++wz69xzz7X22GMPk5d1/8UvfmF9/PHHdo6uSaf+zFNTU2P24QbHMHJ84R133GGnCCGEEO54LgB9Pl8F7PWGhgZ29aYigjwzYZvay6IXsJv3oYdSd/cy/eGH86M7OFU3MMXeJptsYpWXl3cIPDcBOGzYMGvLLbc0y3PnzrUOOeQQ69577zUti8cee6w1ZcoU67HHHrMOPfRQa/bs2SZfT2DL5A9+8APrlltusTbccEPr6KOPNt3SM2bMsA477LAOoUrYQnnEEUdYzzzzjKnTCSecYMTiSy+9ZB111FGugjGedOvPevCcUGy6TY555JFHrMLCQmv//fe3U4QQQgh3ctEC2BKNRte1412xPqwlFhW9oaGhc8tfMl9+GcvX12yzzTZWQUGBNXPmTDvFsj7//HNr3rx5HcJv/Pjx1sYbb2y9/vrrpiWQfIkDYNfxtttua7YnnBFMEfbXv/7Vuvnmm83yDTfcYP397383rXhsmXO2T5dLL73U+uSTT6yLL77YuvPOO61f/vKX1lVXXWU9+OCDViAQMPtYs2aNycuZyBSMF154oWkxPPPMM63f//731m9/+1vTnX333XebfKnoSf0pCMnjjz9uQodPP/3UnEu2IK67bjo/NyGEEIOZXAjA/8K2CQaDG8cWO1NVVbUZgqmwD02C6BUrVtiRbvjmGzvShwwfPtyaNGmSad3ixA/itPQ5ApCwK5XiyOlKdVq+nDwffvih9b///c/4dNpll8QRBGyFY3o4HLY++OADO7V7KOaee+45093KFrx4cB2bFkDWyfH/GIlETPjf//7Xam1tNXHyve99z3r++eeNiExFT+u/2267Weuss44RnS0ta/8vsfWPsCVRCCGE6I5cCMBbfT7fMNjDbrN88ZDdKBqN/gvRYtg/TaLoFaNG2ZFuyJeJs3QHQ8FEEUQoANmF6YwPJI7Qe+utt0zojP9zupA/+ugjE1KQ0Z9fsn1jq11OJkkXCjK2uLW1tbmWyS5YJx/Ze++9Tasbx+BRsHKc4H333WeEJLuznZZKN3paf56fAw44wLQMvvjiiyaNdWV38frrr28mzwghBhh0fpyOCdEDcvImEPoAhABk3xWeVdFP7bF+nyO+AHG2/BUj/mx9ff0+zAPrF+C48upNIOwl5GzfrrqBN9jAsi67jGMz7YQ+hK15P/zhD63/+7//s0466STTrTthwgTrrrvusnPEJmJQEFJYcTwex7d9++23HeLnL3/5i3X99debeFdQlJ166qnGEfQxxxxjymF3LaHQ+uMf/2j95je/MeP+2L3KbtjuYHfsFVdcYeKLFy823bds8fsq7gtg3Tm5Y9y4cWaZjqDZWvePf/zDiNie1p/U1dUZEcjuXnYbUxxz3OCJJ55ououFEAOMdMUdZ/qJtNCbQHLTAmjR9QsE3kWwFRB85Uii/BiL+I5Ia0f82sLCwgMQ9hvxl49Q1HGIWCpxx3T2EOaD+COTJ082vv7YdUpbtWpVQvcvKS0tNV3FnCzCVjJcS0YMOnAyCDnrrLOMMEpljnhKB6dMTuxwK8sxR/yRMWPGGKHHVkwKSM7gnThxohFnp5xySscYvmR6U3/O9OVMYU5EYQvhE088YdKd8YFCCCFEd+REAIIIHty/wcOc3b3bQ/jRF+CRiO+yZs2a9cPh8Lm1tbWaAJIFttoq9meRLX3xcJnpXJ8vsDuTk0E4vi95bF88TKM4pLCikIoXgBynR5xu5GTYLXvjjTeaySXpwlZI4nTPJsPxgX/4wx869vnUU0+ZcX6NjY0Q1z4j0I477jjTDbzZZpuZySScvOJGb+tPscfuc7Y+c7YxRXJFhfyoCyGESI9cCUDDzJkzWyEE366rq3sAou8+xF9ZuHBhbCqlyBoUeezm5etuTzopFnI5n8SfA8cBssv0ySefNOPoNt98c3vNWhzBx3cHcwau8yo5svXWW5vZwhRldMMSDwXc5Zdfbt16663WqHQHSIKysjKzT47x40zceDj+j2KPXa/FxRy2ahk3L3ThEt91Tdh9vWLFCtPKx4kbbvS2/vvtt5/ZP7uuOdZQkz+EEEL0hJwJwJqamnWDweB3YNO6Mju7yBB28waDljV1aizMl27fZBwxx25OjoljC1oybN1iVzD95bHrM/71b3wf8DXXXGPWn3766cYHH921sEuVM3g5U5YuXXrqGoXCi926LItdwVdeeaX1q1/9yjrooIOsZcuWmTF5bOkjHH9HdzUcy8eWv6uvvtrsk+MMKQDpFqaoqMjkTaa39Wf+Pffc07jOoRDcd9997TVCCCFE93guAMvLy4eEQqF72travsTD/U3YS11YbGS/GDRQRI0ePdrE3bp/SXyrX3z3rwNFISdW0D0Lu0rpt4+vlGNetsodfPDBds70oaB7+OGHreOPP94IPpbz8ssvm31x4kb8mDzW/5577jETWjgZhH7/OC6vsrLS5P3Rj35k53Snt/V3HD5TCFIQCiGEEOniebsQxN9vEZwfW7LoP+NrWMrJHuFwOA87Kt0J5tksYDG4+POf/2wcRt9+++0J3eJCiAGGZgFnHc0CzkELYDQa/T6CZp/PtxPEXTlsS4q8VBbbSgjRFXwv8L/+9S/jXibeb6IQQgiRDp4LQAi/TRA8X1dX90YsRQjRW9i9zBnAfMsIZxafccYZruMmhRBCiK7w/MkRDAb5JvtF9fX1u8dSBg7qAha5hg6w6Uyak0roX7C78YVCCCE6oy7g3LQA3g7bIRQKVdtJQohewncBc4IInUBL/AkhhOgtueg7CgSDwYcQcvrmVRCD7yNcyhVuhMNhd4+4eYhaAIUQQoj+h1oAc9ACaPMVhN9o2FWIPweblcIoDoUQQgghhId4LgBDodBlEH4nMB6NRtfAPoN9msLoJkYIIYQQQnhILiaBfAIBuCGiR4bD4UdjqQMDdQELIYQQ/Q91AedmEsiG0Wj0pYEm/oQQQggh+iueC0CIvwUQgQF7UQghhBBC9DE5cQMDEbhLdXX1FnaSEEIIIYToQzwfAzh9+vSCRYsWPY7odyAGb4hEIm8VFBQsR9gay5GI3MAIIYQYtOi9vzlBYwBz0AK4ePHilRB+u8LWxeIlfr//GYi/dxCXGxghhBBCiD4gF2MAv4R9wbGAMDfXL/EmNzBCCCGEEB6jt8hngLqAhRBCZBV1AecEdQHnoAVQCCGEEELkFzkTgJwMUllZeVQwGLwV9hTskVAo9FfYYeXl5UPsbEIIIYQQwmNyIgAh/GoWLVpU6/f77/T5fCfA9oYdiFU/gf2ruLj4IwjBSSZzL6mpqVkXZVwDYVmHcA1sAexG7Ht9O0sH1dXVo7HuBnbhMi/COQjPpUi1swghhBBCDFg8F4AQVutB+D0LwVeJRbp4OR/xw6PR6FGwC2H/Q1oF7AmKOIQ9Zvz48SPb2tpeQ/RsGCeS3ATjvn6BfX1QUVGxCeKG8vLyUZFIZAaiXPc+9n8DwtVYvmrx4sX3mkxCCCGEEAOYXLQAngsrg/0pHA5vDbuqrq7ugfr6+nthv4VNhgj7C9aPbW1tPZ0b9JRAIHAxggmwS1DeHtjHubD9US5FXhnWX2YygqKioosQbA47FXm+j/znIdwGyw/DDg0Gg4cgFEIIIYQYsHg+C5jdqxBhw8aMGTNuxowZbXZyAlOmTClsbGycD8H2NQTZRDs5bbCPR7CP7VHG+MWLF7M1z8CWwYKCghUotw7lVo8dO7YEfIW8yyH6NkOWSCynaakch2Ae7Dms28skdgO7kDULWAghRNbo7Szgvpg93I9nLGsWcA5aACG2yiHA3kol/sjMmTNbkedNRCnKegy+wIMh2jaKF3/E7/ezVZAs5seQIUP4NpJh2NfLWOwQfwTbU4DOR3QaTO8uFkIIkXsoltIxITIkF13AzRBdpXY8JcwDa7cXM2LixInrVFZWHgoBeB9EXRvK/Q3TEYbssIGhC2wBLK6oqGBroBBCCCHEgMRzAQgB9hGCnSdMmFAeS+mMvW6anTcjIPxOaW5uXg7x9yAWN4YdHQ6HTR8thN9ohtjPcobJYP03DAsKCkaZBCGEEEKIAUguWgBvgw1pb29/OhgMbhlLWgvTsO5JRIsg2v4RS82IryDwrkL4T4RrIOruxj7Oia2yiuyw2Q6TMenYTn4JhRBCCDFg8VwA1tfX347gKVg1xNhMiLHZoVDoSRrjTMO6GtgzdXV1GQvAhoaGh7BPzuw9FoKSE0o46ePqqqqqqRSEzINlRwgmU8wP5FtlloQQQgghBiC5aAG0RowYcTBb5WCrIb6qkLQPjXGmwa5GnoOQFmX+bAFB+QmCqxnHPg7E/kzXL+KuXbxIH8kQ+UxXsBBCCCHEQCQnApCzfOvr688vLCzkGLydIbAOR3gEbFpLS8v6bLFjHubtKZWVlcWwPUOh0N52UjKc2EH4RpA5jGD/dDztRgVE4LfhcPhTe1kIIYQQYsDhuQAMBoPHQJztyHhtbW0LxNXrdASN8H7YawsWLGjiuqqqqgOQ73LGe0IgEODYwacRvbempqZT1y4E3dZ2NNzc3DwTy42wXbCccOyo53gIw81gdEeTldnIQgghhBD5iOcCEIKKYwD5zt8ugSg7BnaGvZg2EJONCB6D8XVwl5hEm+rq6ikIzkS5qwoKCu6h2ER97oGNg9g8LZbLEEDaNXb8T3YohBAi36Ez4nRMCJFA1t8EUllZeRTEVIG9aAQgBNgbiP4tltIZv9+/DvIY8RYOh9cxiT0A+xyLMl5HdFOU8xr2+RZCupY5CHE6fD4c5VIkmncTY927SKfT6X8jXov4nohvjfj99fX17JpOayxiUG8CEUKIviVdcSfnySIOvQnEgxZAiKkdYP9wDElRhNvHpyUbhNd1yDcSId3B9JiGhoaFkUhkKqI3oTyKwP9DyG7eRxDf1hF/BPGlgUBgB6TTPQ3fDML3D5dg+dzCwsKjEc/qRBQhhBBCiHwj6y2AfAtHc3PzrRBURlxCYB2I4DMsv8/lZLCegovuWeYUFBT8oba2tt+4YFELoBBC9DFqARS9QC2AHgjAZEKhUATi7y6c5GPspAGDBKAQQvQxEoCiF0gA5mASSCAQGAfr8eQOIYQQQgjhDZ4LwNmzZy+YM2fOMkQD9NkXSzUTN0qDweA5oVDoJhjH3gVia4QQQgghhJd4LgAJhN5ZEHnLAoHA/lymvz6fz8fZuldi8VTY7VhPX34SgUIIIYQQHuO5AISw4yvY6GOvNGq/aq21tfVYpG2B6GLYpbAPYbsj7ykIhRBCCCGEh3g+CSQYDD4FsbdXJBLZp6Gh4TmmQegx3A12CF20sDsYeT6B1WF5e+bpD2gSiBBC9DG9nQSSD5NH+tMElgE22UaTQHLQAghRR/98rzvib8yYMUOj0eg0RJshCp9hGtatRD6+gm0Cl4UQQoi0oOBIx4QQCeRiDCC7fr+w49bw4cN3gdgrRNrbEH7NdjJhfEgsKoQQQgghvCIXAnAhBB9fu2ZAfB8EdP78vEmI4Ycg3BLGMYFCCCGEEMJDPBeAEHWzEEwNhUInVlVV7Yo4Xb7QP6B5PduUKVMKse5qCMNy2AymCSGEEEII7/BcAELo0dVLK+xvEINs9SuFPTlnzpyPEVqNjY3zEZyBdd9AAP6OaUIIIYQQwjs8F4AQejMh7vZC9EUuIv7ngoKCo8zKGHxPMFv+dqwDsSQhhBBCCOEVuRgDaNXX178SDof3hG2O+M9ra2tX2aus5ubmXZG2G2y2nSSEEEIIITwkJwKwKxYsWNBkR4UQQgghRA7w3BF0KBS6yI6mQzQcDl9ux/MeOYIWQog8I12HxemS7EMwmw6R5Qi6z5Aj6NwIwAgCun3pDtaFArDfvA9YAlAIIfIMr4XKABNCgxUJwNy4gbnRzbDqTwjvQchZwBR/d8LiJ4cIIYQQQggP8FwAQl3/H+yMZAuHw6chPBphEELwatgRPp9vrr2ZEEIIIYTwiD6fBAKiEILnI1wEEXhxLEkIIYQQQnhFPghAEvX5fPQXuKO9LIQQQgghPCJfBCDHCpYjKIwtCSGEEEIIr+hzAThp0qRhoVDoQp/PNwWLH8ZShRBCCCGEV3guAIPB4PIubGVTU9NKZLsEFvX7/debjYQQQgghhGfkyg9gd3wGuzIcDv8lttg/gICVH0AhhMgn8sUPoBuvvGJH4pg2zY7EIR+CniM/gDkQgBMmTODYPldaWloiQ4cOXfXRRx99bSf1KyQAhRBCuOImFCUA8wYJwBx0Ac+ePXtBKmtubl769ddf613AQgghhBA5JCeTQMrLy0cFg8E/hkIhvvmjg5KSkr2HDh3KsYC31dTUrGsnCyGEEEIID/FcAFL8FRcXv+7z+X6Gxe/EUmMgbTiCQoTHtba2zuCM4NgaIYQQQgjhFZ4LQIi/cxBMiEajj7W3t+8aS40RDofvLCgo2ADRRyACa5qams6LrRFCCCGEEF6Riy7gA2ELS0tLD5s7dy5n+yZQW1u7PBKJ/BDRxRCJh8VShRBCCCGEV3guACHqNoO9PXPmzFY7qRMNDQ3NCN7x+XwpZwwLIYQQQojskIsWwJUQduzm7RKIxFLYantRCCGEEEJ4RC4cQf8bwm7vQCCw3Zw5c2bayQlUVFRs7vf7Z0EozgiHw3vayXmP/AAKIUQO8drJczbJVz+A/ekceoj8AOZGAO6N4CmIwGUQeOdB6D0KIbiM6yZOnLhOU1PT/oheiXUbIjwUAvBRrusPSAAKIUQOkXjJHJ1DgwRgDrqAIeiegfi7BgJvNBb/FolEvoQo/BbiaRUdQSP9H7CNkOem/iT+hBBCCCH6K7kYA2hBYf8Swu8AiLznsNgCK4HoG4rlNsRfx7ofIM//Ma8QQgghhPCWnAhA0tDQ8ARE3t7hcLikoKBgvdbW1jFlZWXDsDwN6x6yswkhhBBCCI/JmQCMh77/5s+fv2TGjBlsARRCCCGEEDmkTwSgEEIIIYToOyQAhRBCCCEGGRKAQgghhBCDDM/9AA5k5AdQCCFySG992A1E33c6FxkhP4ASgBkhASiEEP0ACUCRhARgDrqAQ6FQtR0VQgghhBB5QC7GANYGg8FXYceOHTu2xE4TQgghhBB9RC4E4Gyfz7cj7O9Dhw5dHAqF/lJVVTXVXieEEEIIIXKM5wIwHA5vHo1Gt4f9HYscc3gy4m9DCM4KBoM/Ly8vH2UyCiGEEEKInJATNzD19fVvw05qbGwcA/F3AuwNJE/2+Xw3FhcXL4IQvKuqqmrXWG4hhBBCCOElORGADosXL14NIXg7bOdIJBKEELwA9j8IwSMRPh8Kheph59bU1KxrbyKEEEIIIbJMTgVgPAUFBasRNEL8MYzC2D1cAbuytbV1AYTgLxEXQgghhBBZJqcCsKampgjC7vBgMPh0e3v7pxB/NyB5p2g0+jLCI7A8FvFzbFF4BfL9mtsJIYQQQojskRNH0BBy30FwHOxwiDtO+vBB6C1BeDvslvr6+nkIO0D+LZHvfeRZjHUb28l5B+opR9BCCCFEP0OOoHPQAhgKhTjG703YKbCREHXPwg4tKyvbBCf+/GTxR5D2AYImWKlJEEIIIYQQWSMXXcATYIsg+n7T1tY2HuJuH9gjM2bMaIut7kx5efkQBHfCzjQJQgghhBAia3guACORyAHhcLgcou+iefPmfWond8mCBQuasM3J2OYWO0kIIYQQQmQJzwVgQ0PDEwgisSXLGjNmzNDKysqKCRMmlE+ZMqXQThZCCCGEEDkiZ7OAg8HgEbDXhg8f/o3f7w+3t7fPW7ly5SqkPRUKhXazswkhhBBCCI/JiQCEyPunz+e7G7YDlzkDGPYllgOwvZH0HETgxVwnhBBCCCG8xXMBCGH3Y4i8HyG6GHZES0vLiPr6+jLYmMbGRs7yPRa2FHZRZWXlXgiFEEIIIYSHeC4Ao9HoyQia/H7/buFw+H5O8Iitib0aDml3QiDugXxtyHOWvUoIIYQQQniE546gg8EgX/c2A0JvPzvJlVAo9B+IwKn19fWj7aS8B8cmR9BCiIHHyfzfngY332xHBjA6FwMSOYLOQQsgxN8aBrGl1ED88fVvOXkziRBCCCHEYCYXk0Cehu06YcKEYGyxM6FQaD0IxV1h/7GThBBCCCGER3guACORyNnRaPSz9vb2FyD09rGTOwgGg5Ox/hnYmkAgcJ6dLIQQQgghPMJzAej3+x9C0AwbC3sCgm857B2IwTdhn/l8vvdhW8FGtrW1zbLXO7aMZQghhBBCiOyRiy7gnSDutrDjiPpGwaYivi1sY6bZNsReF2/rIF0IIYQQQmQRzwVgIBAYl4nZxQghhBBCiCyhWbcZEJQbGCHEQESuT9aiczEgkRuY3HQBdzBp0qRhoVBob9iJEE/HVlVV7TJlypRCe7UQQgghhMgBOWkBnDhx4jrNzc2/Q5SvfSsyiWtZGY1GbywsLPxtbW1ti53WL1ALoBBiQDJYW73cjvuVV+xIHNOm2ZE41ALYr1ALYA4EYFVV1QgIPP6CJiFsR/iOz+f7FCH3PQ5pU7DsR/gffBH7Ii0K6xdIAAohxABC3b2DBgnAHHQBRyKRcxDQ198MhONwsncKh8NHwY6Ebef3+4NY9xZE4HdDodApZiMhhBBCCOEZuRgDeDjsq8LCwgMg/j6PJa2lrq7ukyFDhuwHEbgCiz+OpQohhBBCCK/wXAD6fL5NIO5eqa2tXWUndeKjjz76GvlmIF+VnSSEEEIIITzCcwEIUbcEQVlsqUtGQAR+bceFEEIIIYRH5KIL+A4Iu+1CodCB9nInqqurp0Ao7gK7x07qMTU1NcOxjyuCweAchE0IVyKcgfBgO0sH2N9orLuBkzgQrrG3OXf69OkFdhYhhBBCiAGL5wKwpaXlSgSPwe6D0LoKtuWYMWOGVlZWFsMqqqqqTm9vb38W6xdGIpFHIcQmxRvL6A7ONG5tbX0d0fMhNr+FkPwz4g/AJmP5YezzfOYj5eXlo7AfTkj5Bda9j7w3IFyN5asWL158r8kkhBBCCDGA8VwAFhcXf4uArX9FEFpnw2aOGDGi0e/3r4aFIcCuQ9o6sE0DgcBryDcrzt6HdQvK+CW2p1j8azgcnlpfX38m7ESkbw5bjHWXV1RUVDJvUVHRRQg2h52KvN9HvvMQboPlh2GHQiweglAIIYQQYsDiuR9ACKpPEPTatx8EWrfvAw6FQvUIKpqbm9ddsGABZxN3gHUXIrgMQvD0NWvW3FJSUvIVBOFyiL7NkB4xmQDycT/zYM9h3V4msRtwbPIDKITwFvmmyx0614MG+QHM0ZtAvAbi7VQIvJH4Iq+wkzrAunMRXIX1v4a95vf7X0Z4J/IeE8uxFgo6iMMyCMBhWKTT6i6RABRCeI5EiRBZRwIwN5NAPAeC7U9u4g9Q4B7KCETfhxB3IZPo8zUwdIEtgMUVFRXdtjoKIYQQQvRXcioAOfGDEzYQljrGSRk1NTUbBYPBLUOh0OV21qyA8n6K4DuweSNHjnwGwm800yEGlzNMBuu/YVhQUDDKJAghhBBCDEByIgAh7s6BLebED4ivFQi/dqy4uHhZW1vb5xBfM5H1V7EtMgfi7zAEN2J/bQiPnTlzZivCIq4DzXaYjEnHNkPMkhBCCCHEAMRzAQjhdyTE3VWwDbHILllXg+haArsR8YyxW/7+FVuyjgmHw5xdTGG3hiHq4gjBZIr5gXwp31oihBBCCNHfyUUL4EkwzgI+PxKJjIK4Og3xCETY2OLi4tEIj0baMoSB9vb233GDDPBDcP4eIf0AtqLcw+rr6x0hSOFnun6R7trFi/SRDJHPdAULIYQQQgxEciEAJ0NY1YfD4asaGhpWBgKBN5HmhxicxncA19XV3Q3BdTjS1i8oKDjbbNELampqikKh0AMo60zsj0JvD4g/+vaLZw4/kKfCLHWmAtt+i7p+ai8LIYQQQgw4ciEA+Y7f/9lx65tvvpltR7e0Q87ifRHC62NE942l9JhAa2sr3/xxCMqZ7/f7d3C6feNpbm6eifWNsF2wmHDswWBwPOq5GYwCtVsXMEIIIYQQ/ZVcCMBGWGEsalmLFy/ma9e+hFWbhLXUQZiV2/EeAfF2HoTbAYiy5W7nOmBWJLFgwYIm5LsHNi4UCrEr2iGAtGvs+J/sUAgh8pNXXuls9BeYbEIIkQJOwPAUiDO+o3ez+vr6TRGaljWIL9ytrPHhcHgslwny0ZvyNshXGktJj5qamnVbW1s/hYAbBgHJdw5/EFuTCNa/wpZG7Hs95HsXy3wTyL8Rr0V8T8S3Rvx+7P8IxNN6cwnqLEfQQojck664k3NoIVyRI+gctABCXD0OGwOx9GB1dbVxxAxehY2xZ+tSEO6IYBpsPpd7Qltb284on2/u4L4OhF3sZli9G/NABC4NBAI7QOzdhsXvYN3pCEuwfG5hYeHRiPf6tXVCCCGEEP0Bz1sAa2pqhkOkvYMou3yfggDbD2JwYwivuVhm1/BS2HowzsL9JdT4tYz3B9QCKIToE9QCKERGqAUwBy2AtbW1q4qLi9nCdx0E3ltMwwn/PBKJ/BBRjg9cH0Yh+khpaekNCIUQQgghhIfkYhKIRXcv4XD4bAi/39hJVkNDw0ONjY1lEIXbwSqx/vv22zqEEEIIIYSH5EQApoIzgiEK34HNs5OEEEIIIYTHeD4G0KGysrI0EAhswbeB+P3+Aju5E3V1dY/b0bxHYwCFEH2CxgAKkREaA5ibFkAfhNIffT7f0mg0+ipCul55JJXZ2wghhBBCCI/wvAUwFAr9H4LrYkvWZxB5dNbcFlvsDNT4rnY071ELoBADnHxtaRtoLYBq0RQ5Ri2AORCAEEkf+Xy+CYj+MBwO3xdLHRhIAAoxwJEwyQ06zyLHSADmxhF0BYI3Bpr4E0IIIYTor3guAKPR6LcI6OxZCCGEEELkAbmYBPIybIeampqi2KIQQgghhOhLPBeAkUjkEgQj2tra/jF27NiSWKoQQgghhOgrsj4JJBQKPWxHO4hGo1v6fL5ydgcjnIXwa4QRe3U80XA4fKgdz3s0CUSIAY4mJ+QGnWeRYzQJxJsWwIOSDWJvM4QIfMMR7ozwgPj1SSaEEEIIITwk6wIwGo0en4GdYBcjhBBCCCE8ImevghuIqAtYiAFONrsmB1o3p86N6MeoC1gCMCMkAIUQaSMBKETeIAGYIwE4ffr0gi+++OLgSCSyhc/nGxaNRlN1PUfxZZxlx/MeCUAhRNpIAAqRN0gA5sANTHV19ehFixa9D9F3L8Tfr5F0JsL/S2WxrYQQQgghhFfkwg/gRRB2W0AAroDdjqTfwi5NYZfBhBBCCCGEh3jeBRwMBj9BMNrv90+sq6tjfMCgLmAhRNqoC1iIvEFdwDloAfT5fBsheHGgiT8hhBBCiP6K5wIwGo1+ARE41F4UQgghhBB9TC5aAB+HCNyuurp6jJ0khBBCCCH6EM/HAE6cOHGdpqamtyEEl7e3t58xd+7cd5DcHlvbv9EYQCFE2riNmXvlFTsSx7RpdiSOfBxHl+7xuJHOMWqMofAQjQHMgQAMhULPIhgN25LL0Wg0AjG4CmGUy0nQDyDz9gskAIUQGTEYJob0VuRKAAoPkQDMQRcw2AO2FYxiE9rPF0A4EuEoF1sH64QQQgghhId4LgADgcC4npi9mRBCCCGE8AjPBeDs2bMX9MTszYQQQgghhEfkogtYCCGEEELkEZ4LwGAw+GIPTLMphBBCCCE8xnMB6PP5dknDpjtxezMhhBBCCOERuegCPtjNotHo9xGeBLsD8XbY31tbW8uwLIQQQgghPMRzP4DpEAqFDkTwMETgD+vr6++NpeY/QfkBFEJkQjZ93SWXlcopc7IPvmz60ZMfQNFPkB/APBGABGLqPZ/P1x4Oh7e1k/IeCUAhRN6QDwJQiH6CBGB+zQKeH41GN7fjQgghhBDCI/JFAPLtIFNgzWZJCCGEEEJ4hucCMBQKTUplFRUVWweDwX1hT/p8vnJkfzW2lRBCCCGE8IpctAB+AJvlZoFA4F0Iv8dhe2G5ORKJXIZQCCGEEEJ4iOcCMBqNfprKsPoThB/D7oL423Hu3Lnvx7YSQgghhBBekTezgPsjmgUshMgbNAtYiLTRLOD8mgUshBBCCCFygASgEEIIIcQgQ13AGaAuYCGEEKL/oS5gtQAKIYQQQgw6JACFEEIIIQYZEoBCCCGEEIOMrAvAYDD4eigU+o29aI0fP37Tmpqade1FIYQQQgjRx3jRAjgZVhGLWlZBQcH8tra2P9iLQgghhBCij/FCAEai0WgNQqdsH5Y121gIIYQQIk/IujALBoOv+ny+HSD6vkS4GElbIr4ccb76rTui4XB4ih3Pe+QGRgghhOh/yA2MBy2Afr//lwgaIfg2RLgl0xDnGEDG0zEhhBBCCOEhnnTNVlVVjYhEIjUQgyXRaPRFJD2H5Stia7umoaFhhh3Ne9JpAYxGeUyWtWKFZY0aZVmVlRTE9kohRN+Q/N7cVAyG9+TqXIhBiFoAPRKA8YRCIY4JvAsn+Rg7acDQnQCcNcuyHnrIsr76yk4A669vWYceallbbWUnCCFyj0TPWnQuxCBEAtCbSSAJhMNh/0AUf91B8cf7Zbz4I1xmOtcLIYQQQvQFngtAh+rq6tHBYPBXsOdDoVAtbBbiz8DOqamp2cjONiBgty9b/hi6wfSHH069XgghhBDCS3IiACsrK6e3t7fX+Xy+y2G7IakaNhnxvWBXtra2fgRBuLvJPADgmL/klr9kvvwylk8IIYQQItd4LgAnTJhQ7vf7H+NM4Gg0+gSSjoRtG4lEdkDa0Yg/g3A01t1fUVGxCbfp73DCRzp8840dEUIIIYTIIZ4LwLa2NrqFKYWdX19ff2A4HL4P9m5DQ8NbdXV1dyO+L8TfBRCB60Ao/p/ZqJ/D2b7pMHKkHRFCCCGEyCG56ALeGwKvAULvKnu5ExCGv0MwF7afSejn0NULZ/t2xQYbxPIJIYQQQuQazwWgz+crQ9DtnFeIxPeRd0B0AdPPH129pPL3x/RDDkm9XgghhBDCSzyXIMFgcBmE3UfhcHgXO8kV5HsJwaT6+vrRsZT8B3Xu1g8gZ/tywocDW/4o/uL9AMpZtBC9pLc+7LLp+66/+9FLt/7pkq/HKUQc8gOYAwEYCoX+E41Gd0V0G5zo/8ZSE4GQmozgPQjFFyEUvxtLzX+6E4DEEXec8MExf8niTs6ihciAfBBf/V0ApstgOU4xKJAAzM0YwD9D2BUgfApi8CCE8fv022lPIY8fQvGvseSBA8VeMGhZU6fGwmTxx3ulnEULIYQQIpd4LgDD4fBjCG6BwBuD8KFgMPgNRN//aIwzjesg/v4OJf4ItxkMsGVQzqKFEEII0RfkogWQIvBkCLyfIjofYm8Ywgk0Oz4/Eon8DOLvJMQHDXIWLYQQQoi+IicCkEDg3QwhWAkhyJm+2yLcDsJvU6Y1NDQMuK7f7pCzaCGEEEL0FTkTgA4Qgp9D9L2L8B0Iv4V28qBDzqKFEEII0VfkXACKGHIWLYQQQoi+QgKwj+BsYDmLFkIIIURfIAHYh9DPH11rsaUvHi4zXX4AhRBCCOEFal/KgHQcQadDd86ihRBCCJE95AhaLYB5AcVeKmfRQgghhBDZxnMBGAqFboLRB6AQQgghhMgDctEC+CPYj2NRIYQQQgjR13guAKPRaCGCBbElIYQQQgjR13guAH0+34MQgXtWVlbW2EmeEwwG7wuFQq5Opqurq0dj3Q2cwIFwDcI5CM+dPn16gZ1FCCGEEGJA4/l0Awi/PSECb4SNw+LzsFmwZRCFEa5Ppr6+/kY72isg5i5CcCmMbxwZaxJtysvLRxUXF7+GKMXow6hDA+q1F+J0uPIQ8n8fYdpkaxawEEIIIXKHZgHnoAXQ7/f/ByIrhGgR7HuwX8F+j7Q/JNn1DLGuV0DcDYEguwVRij9XioqKKA43h51KsYcv/jyE22D5Ydih2P4QhEIIIYQQAxrPWwBDodDtCKKxpe6BIDvejqZNZWXl/hCPbGXcDItPwSg0E1oAx44dWwK+Qp7lSGe+jhZI1JGtk/Ngz2EdWwTTQi2AQoisQg/w6XDzzXZECNEb1AKYAwGYCyDEHkWwE8TdhRBwf4Wgo7hLEIAQidP9fv/L0Wj0Tnzhx9jJHVDMYfsybDMMi+2x1K6RABRCZBUJQCFyggRgbtzAeA6E3fWwcRBvf8Gia2sjxB27oRk2mITOsAWwuKKigq2BQgghhBADlpwJwJqamo2CweBlsNdCodCnCP/MdMR/AzvMZOoldXV1L8Ma7UVXIPxGM4xGo8tNQhJY/w3DgoKCUSZBCCGEEGKAkhMBCLH3vba2tjkQWRfAdkDSxrDhXAdBdiCCf0EEZjT7Nw04CYU022EyJh31GWKWhBBCCCEGKJ4LQIi/CRB99AU4FPZX2H5I7hh76Pf7b0XA1rtTOZnDJHoA9ruGIeriCMFkivmBfKvMkhBCCCHEACUXLYAXwCiujqivrz8Vxlm6HdTV1d2AYF/GIc5OZegFKNt0/ULguXbxIn0kQ+QzXcFCCCGEEAOVXAjA3SCuZkH40deeK+Fw+HXkeQviiz76vGIOP7CPCrPUmQrU4VvU5VN7WQghhBBiQOK5AITgWhfB/NhSapDvCwTrx5ayT3Nz80wIvEbYLlhMOO5gMDge+98M9iYW03IBI4QQQgjRX8lFC+BSmHHB0g3VsK9i0eyzYMGCJgi8e2DjQqHQaXYyCSDtGjv+Jzv0jGiUr7uzrHffjYVcFkKIQQX9HaZjQgjP8NwRdDAYvBMC6yjYwXV1dY8zDQIsEo1G73IcMldWVh7q9/sfQNo9SPsR0zIB5VNWdXoXMNLXwz7eRV34JpB/I16L+J6Ib434/dj3EYinLclwbD1yBD1rlmU99BBUbpzMXX99yzr0UMvaim8jFkKIwUC64k4Or4VHyBF0DloAI5HIlQhaIbDug2C6jG/kiK2xihCvgCg7EyLsdqxvQ97r7HWeAEG4NBAI7IB93YbF72C/pyMswfK5hYWFRyPuWXscxR/vZfHij3CZ6VwvhBBCCJELcvIquKqqqh9AZN2BqHG1kgzWtUOM/QwCjS5h+g3ptgCym/fCCzuLv3g22MCyLruMYyHtBCGEGKioBVD0MWoBzM0YQLp6eaC9vX0ShB79AIaR1ARrQXwBwjsg/qb2N/HXExoauhZ/5MsvY/mEEEIIIbwmJwKQzJ07twFKm34AJ0DsDYOVUHkjPAH2oZ1tQLJihR3phm/kgVAIIYQQOSBnAtChqqpqBKyKrlfKy8sHxWvXRqX5duGRxhW1EEIIIYS35EwAQvAdB3svGo1+DePs2/qioqJvQqHQjMrKSr4ebsBSWRmb7dsVHAPIfEIIIYQQXpMLAeiH8LsPgu82GN2tkCWwL7EcwPqd/X7/YxCCV8SyDzw4sYOuXlJN8GD6IYdoAogQQgghcoPnAhDi70QIvR8gugjhYS0tLSPq6+vLYGMaGxtLIQSPx7plsF9CBB7EbQYi9PPHiW9s6YuHy0xPxw9gshPpSCQ9p9JyPi2EEEKIeDxvc4IAfAfCb/P29vbJnAhiJyeAPJMRMN/b4XB4Wiw1/0G9e+QImlB8cbYvJ3xwzB+7fdNp+Ut2Ir10qWUtWWJZG25oWeutF0tzcyot59NCCCFEInIDk4MWQIo/BC+lEn8EX8B/ke/laDQ64CUJxV4waFlTp8bCdMVfvBNpir/Zsy1r2bJYyGXC9fFOpZO3c0jOJ4QQQojBRS7GAK6CddvpCPG3BkFrbEk4sMWQLXhOty3D+fO7Xn744Vj3cPx2yTj5Uq0XQgghxMAlFwLwSdhuVVVVfP+uK5WVlev7fL5dEH02liIckp1Ir1xpWWsolePgMtMd6FSavdLJLX/JyPm0EEIIMTjxXAC2tbWdh+CLaDT6AkTgAbHUtVRUVGwO8fc0oo2RSOScWKpwSHYi3dxsR5JoabEjNosW2ZFukPNpIYQQYvCRdQEYDAaXx1sgEJiD5I1gm0EEPhIKhVbAZsHehH2K9R9CAG6Fda2IP2gKER0kO5Eudn2bsmUVFdkRm7IyO9INcj4thBBCDD6yLgAh5kYlG5JLuMq2Uhhn/W4LG2unIZuPXcRTYSKOZCfSpTh7JTybcXCZ6Q50LbP77nI+LYQQQgh3si4AA4HAuAxs0E7HTgVnCcc7kWY4blzXy3Qq7cc3K+fTQgghhHBDj/8M6I0fwN5Cly2ctcuJG4SuXxhnK57jB5Dxgw+2rBEjYmMH2X3c2GhZjzyydjvCfBR/3fkB5AxhThJxykrXZ6EQGUPv6OlAf0b5SH+vvxADHPkBlADMiFwKQOIIMseJdEWFZc2du3aZYo8iMX72L7uBKfYoCnvifJqCUw6kRZ8hASiE8BAJwBwJwKqqqu9GIpGf+ny+UDQaHWIndwLro+FwGLKmf5BrAdgVjtNnN79+FHvpvm6OZLMsIXqFBKAQwkMkAHPgBgYi6XsQfU9B3NEFTDUne6QyrE/pK1CkhkItW06fs1mWEEIIIfITzwUghN2FDGC3Ir47xODWMLp9cbOtzUaiRyQ7i3YjXafP2SxLCCGEEPmJ5wIQ1MBmhcPhn9TV1b1UX1//Ad/9m8pim4iekOwsOhXpOH3OZllCCCGEyE9yIQCbo9Hop3ZceECys+hUpOP0OZtlCSGEECI/yYUAfM7n821TXl6ecvKHyIxkZ9FupOv0OZtlCSGEECI/8VwABgKBX0Wj0WJw1/jx4yEdRLbhzNxsOX3OZllCCCGEyE9y8hgPhUInIrgFFoEtgi2FuUE3MFPseN7jpRsYzrLlRIueOGFOdhZN0nX6nEw2yxIDFC9dnWRSdm+3zebx5KsbmHytlxA5Rm5gciAA6QYGwWM+ny8QS+kSCsB08uUFXgnATJwwO8KxJ06fU5HNssQAZKCJnMEgjiQAhTBIAObGDcxFFH/RaJQi8HDYbpFIZNcUtpu92aDFccKc7IqFy0zn+q6gQAsGLWvq1FiYiWDLZllCCCGEyB88F4AQflsg+BAq++C6uroHYC83NDTMSGWxrQYnbHGTE2YhhBBCeE0uWgBXI5gbWxJdISfMQgghhMgFOXEDE41Gt6+pqSmyl0UK5IRZCCGEELkgFwLw17Ahra2tD1RUVGwSSxJuyAmzEEIIIXJBLsYAnuPz+T6E7RcIBD4JBoOfwz4KhULvu9hMe7NBiZwwCyGEECIX5GIM4CkIpjEaW/SNgW2O+JYpbNDCWbZywiyEEEIIr/FcALq4eunKBr0bGPr5o6sutvTFw2WmywmzEEIIITJFbUkZkIs3geTKCbOzv568eSRX5Gvd+qxe/cmZrxwPCyHyEDmClgDMCC8FYC7J5M0jXpOvdevTekkACiFERkgA5kAAhkKhv9vRbokCfBl8b3C/YCAIQOfNI27Opdma1Zfdzvlatz6vlwSgEEJkhARgbtzAHNeNHWvbcT6fj8siR1DA5OubR/K1bvlaLyGEEKIn5GIW8BluhlVnw34bjUZfYTaE/4DtwG1EbuD4tXx980i+1i2fz5kQQgiRLp4LwLq6uhvcLBwOXwe7sL6+fldk+zlE4XEQgKWxrUQuyOc3j+Rr3fL5nAkhhBDpkosu4G6BEPwzgjqIwPNjKSIX5PObR/K1bvl8zoQQQoh0yQsBSKLR6P8gAKfYiyIH0G1Jvr55JF/rls/nTAghhEiXvBGAEH81dlTkCM5Yzdc3j+Rr3fK1XkIIIURP8FwAVlZWlqaympqadSsqKjYPBoO3Ims17B2zkcgZdFdCTx35+OaRTOvGmbj19Zb17ruxMFszc/P5nAkhhBDp4Hk7RSgUarejXcFZwO0+n++74XD4RTst7xkojqAJxRFnrubqzSM9oTd1y4Wj5nw+Z0IIIVIjP4C56QLmIzGVkVWw12AH9SfxN9CgcAkGLWvq1FiYT0Kmp3VzHDUnu2vhMtO5Phvk8zkTQgghukKPrAwYSC2AAwW2yl14Yde++thVe9llEmxCCDFYUQtgHk0CESIbyFGzEEII0T0SgGJAIUfNQgghRPdkvRMsFAr93Y72mCior68/0V7Me9QFnH9wtu+119oLXXD22bFxe0IIIQYf6gL2RgBG7Gi6JDjnCIfDATua90gA5h8aAyiEEKI7JAA96AL2+XxnpGvRaPRcbLIotqURo/NjUSF6B0WdHDULIYQQXdNnj8Hq6uotIpHIHYhuCWM9bi0oKDiztraWbmH6Bf25BdDxYccxc3y/bSY+7NzKItkqvzfQ1cvDD8cmfDiw5Y/iL68dNdOTdDrQn40QQoheoRbAPhKAEE7n+Hy+yxAtjkajixH/cTgcfjq2tv/QXwVgNp0ku5XVbrv+DsR15mfbCXM69EtHzRKAQgjhORKAOZ4FHAqFxsFegeC7EosUf/cPGTJki/4o/vor2XSS7FbW0qWW9cYbMWPcoTflZwrFnhw1CyGEEJ3JmQAMBoMnQ/D9F9GdEH6N8Ego7yM++ugjxkUOYIsYW+sYusF0dpumWh+PW1mMz58fC+PjDoynW74QQgghvMNzAVhdXT0G4u9pn8/3Z9hwJD1dWFjIVr/7YjlErmB3aHLLXzLpOkl2K2vlSstas8ZeAIwzLR45YRZCCCH6Hk8FYGVl5VHt7e0fQ/h9NxqNfgs7GcJv39ra2i/sLCKHZNNJsltZzc12JI6WFjsSh5wwCyGEEH2LJwKwpqZm3WAw+IDf778T4m8dJL2KcHJ9ff0tsRyiL+Bs3HTghInucCuruNiOxFFUZEfiSKd8IYQQQnhH1gVgZWXl/q2trf+D4DsEi2wTOiscDk+HycdfH8NZsJyN2xV0leK4cekKt7JKSy2rpMReAIwzLZ50yxdCCCGEd2RdAPr9/scg/vCYN3AE2NGhUOj9NG1mbDPhBZwFmy0nyW5lMT5uXCyMjzswLifMQgghRN+T9UcxRFxPXwUXTzSsV8F5Dl2xZMtJsltZEfsK8Mf9vcimE2bOIuZEknxyPN0T3OrfUdcs+AHsKP/cK6xRxWusytIvuz4XXZSVM/qT/0P5ahSi3yM/gB4IwMrKyul2tFc0NDTMsKN5T38VgMQRCdlwkuxWFslW+fFQcOar4+l0cKt/NuuaUP4rr5i09UtWWYeOe9/aar3PzHInJKp6hgSgEP0eCUAPBOBgoj8LwP4IxQ2fqRScDnQ2PXt2LD5hgmWtt14sTig4+azOFxHoVn+HbNS1U/m2ACQ+X9Q6ecKr7iJQoqpnSAAK0e+RAPRoFrAQ2Yaipj87nmYdkusfT6Z17b58n/Xw/K16Xb4QQoiBhQSg6BewO7k/O552q38ymdQ1rfLXjLAaVjrzs4QQQgxmJABFv6C/O552q78bva1r2uW3xPnpEUIIMWiRABT9gv7ueNqt/m70tq5pl18U12QqhBBi0CIBKPoFnEXcnx1Pu9U/mUzqmlb5JY3GJYwQQgghASj6BZwl258dT7MO2XLC7Ub35UetQ8bN6nX5QgghBhZ6HGSA3MBkBmekcvJCT5w309VJrh1Pk3Tr2l0+t/pns64J5dtuYNjyR/HnuR/ATNyjyA2MECKHyA2MBGBGSAD2HgqV3jpEdkRWLhxPk3Trmm4+t/pns2XO6/JTImEkhOgnSACqC1j0ARRK1ADJbku4zHSu7wqKmWDQsqZOjYVcdkvLBunWtSfH5FVdHbwuXwghRP9HAlDkFLZOeekQOZukW1d2QfeXYxJCCCGIBKDIKeyaTG4lS6a/OW/mCID+ckxCCCEEkQAUOcVrh8jZJN26LlpkR7ohH45JCCGEIBKAIqd47RA5m6Rb17IyO9IN+XBMQgghBBnUAjAYDB4TCoVmIlyF8EuEd02YMKHcXi08gDNSvXSInE3Srevuu/efYxJCCCHIoBWAEHy/9fl8dyA6BPYn2AtYPqKtre09rBvHPCL7cEaqlw6Rs0m6daX/wf5yTEIIIQQZlI+kYDA4GWLvg2g0+lphYeHutbW1LXb6wUh/GNF/h8PhA5jWFcjv7gcw2R+a7ZA3gS++sKyNNrIXeoDbdjksizNZG1rLrRXtI6xRgUarsnBBamETV1bHdl+2WKM2KLIa24daj6za0/qyfV2znmwQWG4dPPw5a0Rgdefys3Tcph6Lhlor1h1vyq8oWGDNbVtbL+6PxB9jqroeMuI5a6shs+0Uy5rVNMF6uBH5Gkssa2jsHXXr+5db25R8bJV9M7ujfLfz1XF+Jk2zRhWv6XhlW8MLCzrSKkZ8ac1t3MBa0VySmGdlYlrK8t3KemtOx3kmyd9tl2lZqGuXaWddbrrhKyosa+5cy1px7hU92s5pcW048sKOfF3WK3m7hkSH3umkddS1h9v1p7K8Ll/HnV9leV2+12Wlej7JD+DgFYC3QeidEIlE9mpoaHjOTjaEQqEZCHaGONwEF8bnsVR3BpsApMB5qHEv66v2dewUCJzA19ahI55NEEId2GUlbLd6jRFH3O6Q4c8asfdN+3BrZGCVEVoPr0pR/oqXMj7ujno0DjF1WNo2ylrSvp61YWCZtV7LIpPWHo01igd89utFgFtduxRyEJjfrDvO+rxtQ+udNROtpZHE404+XwnnZ6MxJq2jHktwCSJtadMwa8makdaGJSut9YasMutc61qyyjp03PsJb/2YtXQT66H5W1tfzW/sXNaKepPHraxu05LryrRp06ylSy1ryRLL2nBDy1pvPbPKam+PhYEAPuzfQ7flJ5dVm/52pGOfr8e26/YcJm/Hutqkk9btcdv057KIl+XruHXc2SzLzRG/gwQgzp0dDipGjx59A4LhsJOXL19uX0YxsG5TBLtCIM5atmzZh7FUd5D39JEjR65z7LHH2ik2TzxhR2wWxFpNEliFB9BwVqGHuG2Xg7IoUm5ecZj1bTTWsuWwGsszmza3Ni740hpTgF9gPChrVsE2idu1tllWYaHZ7v3mza1JxWFrSkmt9WlrmfW3b7oov+1Ta0zpajvVpgfHnVB/1GGpb31rdkuFKX8phNewyCprtX+4Nau5xvqsbYw1zNdkDfU3mW2T6zo68I2V6l8l00ev/sxaUrKZdW/j98y2hrjjjj9fnc7r8BFGqMxatqn12beoV1ujtbpopDV7xRhrdXsR1g23hhW0WKvbCtfmwfJQGFndVmTNXLqptfGwFdaYoSuN+Lt59s7Wt63F5nwtLdgosSyWHxnS6bgpjrtNKy1IrCvrNXKMNRvadjW+Kt6Qhw2LxekI+zNoUi4P/WpBp+1Y/27LalrmetydyqqAyEX+jn3a23V7DpO3Y12HxvbdXRrr2O1x9/Oy+lNds1lWf6prNsvqT3V1K4v5uDxzpmVtvLFljYn9X+3gn//8p7Vy5coV0ADUA4OS2N/fQURNTU0RxB0nenzW0NDQHEtNYJ4dVtvhoIetWmyhiqZoMGY6uz6ZL550tzOOlLvL17p/p/LTJbkeXJ7fOnbtMsJ57eXWPDuNNr9144T9Mc3tGN1I3l8yTllux23q1ri+SaPNa9nYmmcvm/VMWzm6I402v3E0wrVEoyh//lax8udvbZZj6WvLNsssi+UnHTe3c85Pl2nJdWW98OtxzhFDLjtpZv/zrU7bsf5pleVy3K5lsa7Yj7PP+O1MWQhdy0raztQ1jbT4YzTlJ6XR+ntZbmnZLD9fy3JLy2b5+VqWW1o2y/e6LGe9syxH/O4MOgEIOJCLT4LlZimJSCRivLVFo9FRJkGY8V7x3bJucHwc88XTEBmf1nYvrNm++3zR9TuVny7J9V9pjbDWRDn3Zy1MWxkZYS9ZZn38MnE7RjcyOe6VrUOsNe2F9hKWo6hXS1JdW0sS0ta0F2E5seX0yzUjrBcWVVtfrVnbQroyMjyhbGLKTzruRe0bJpyflGmrRyXWlfVaaS/YcDk+bc0aq9N2rH9aZbkct2tZi2L7cUjejriWlbSdqWsaacnHSFyPux+X5ZaWzfLztSy3tGyWn69luaVls3yvy+L6+DQ54ndn0AnApqamIjvq1vpHnPTEJ8YghhMA0oHj4+JZEU3P8d2i1m58qNgkl58uyfVvjjqXwFrarYAVsVvKHFqiBXZsLenUIZPjbm5P3CfHqEXscWoObmktkc51pTiKx/W4TVmJx80u4WRc09qK7VgMUxb+ncfDcTrJacnbkbTKcjlu17JW2xEbt+1cy0rajqST5naMrsfdj8si/aWu2SyL9Je6ZrMs0l/q6rYdaYmNiulAjvg7k3gHHAT4/X7nv0Pnp2EM54myyg4HPZz9mQ6cHBHPKF96v7iywm7eo2aTXH66JNe/2Jd0ZwABSEC/L7GPoMjXZsfWkk4dMjnu4kDiPjk5wR832YG4pRX5O9e1bOgKOxbD9bhNWYnH7Yx9jMc1rSDxP5QpK+mOwgHayWnJ25G0ynI5bteyhtoRG7ftXMtK2o6kk+Z2jK7H3Y/LIv2lrtksi/SXumazLNJf6uq2HSlKesLLEX9nXE7bwKaoqIhP54jP53Pt4oVAdC4T/V+w4YxXzl7tCrpFcVyEOFT656W13e4lb3afz/dVp/LTJbn+pVajVeJLFDRMK/WvFYpcH79M3I7RjUyOu7SwySoJtNpLWPahXkVJdS1ck5BWEmjBclyfCNigpNHavWyOmRXsUOpflVA2MeUnHXdZYEnC+UmZBoGZUFfWq9ResOFyfFpJSUyYxm/H+qdVlstxu5ZVFtuPQ/J2xLWspO1MXdNISz5G4nrc/bgst7Rslp+vZbmlZbP8fC3LLS2b5XtdFtfHp20gR/yuDDoBaPv8mxeNRjedMmVK4oCoGBX8gECsNUvCzGyl6xIOl3eD6fSJlzwzNt3t+O+t23yF/+5Ufrok14PL4woXrl1GOD6wwBpvp9HGFX6esD+muR2jG8n7S8Ypy+24Td1GfGXSaOOLPrfG28tmPdNKl3Wk0caNWIZwLT4fyh83K1b+uPfNcix9bdlmmWWx/KTj5nbj0klLrivrNT62H1M+Qi47aWb/43DTSdqO9U+rLJfjdi2LdcV+nH3Gb2fKQuhaVtJ2pq5ppMUfoyk/KY3W38tyS8tm+flalltaNsvP17Lc0rJZvtdlOeudZTnid2dQnpJgMHgLBN6PYbvW1dW9bCcbQqEQnYbtFIlENm1oaFgYS3VnMPoBNI6Ou3GI3IFdVsJ2tj88t+26LD9LfgBN+bajZro1+bJ9tNmH4wfQGRcW3z3Y5TG6kclx2771nHF5/iWol+2778s1pRZb9hwfdh154rpwuZ7iL9kPIGcFfzkf2yWXZfsBdDvubtOS68p62L77OOia/7odf1zOGB3evJ3fg1v9uyzL9gOYznakY5+vrfUD2OU5TN4udqiGdNK6PW6b/lwW8bJ8HbeOO5tlMU7xJz+A7sTufoOMioqK7QOBwBuIvrl69erdFy5caPrPIOjMm0Ci0ehjuCgOYlpXpBSAAxhOpedsKg6o5ZiKrjytx5Pudr0tP12Sy3c8x8fvj2SrDpkcN8lWXXt73L1Ny2b5+VqW1+XruPOrLK/L13F7U1aqe7cEIM6NHQ46IN7+CLF3KsRePcJHkTQWdhhsKdJ2wEUxj/m6IhQKrYGQHDIm2cOkEEIIIfKWxYsXW+3t7U3hcDhuROHgYtAKQOCDCDwV4ckQgEGIvmWIvwS7KB3xRyAAOc2yGNsujqUIIYQQIt/Bc58tN80QgPL5K4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGyy2B+E0jGBIPBY3w+3+nRaLQK4WqEzxYUFFwwe/bsBXaWLhk/fvymyH8ZorvDRsPCKONP9fX1t3C9SE2m576qqmrXSCRyLqLbwobDFqGcfyPtsoaGhq+YR6Qm0/OfBN/K8wLK2RU2rq6u7hM7XbiQ6bmfOHHiOs3NzRdgu0OwfRlCXvvP+f3+S+bMmaO3GnVBpuce22+J4BLYzrARsE9Qzt2471yJ+04zlkUa4Dzeh/O2Yzgc5itc00LP28747VD0kFAo9FtcgHcgOgT2JxgfYEe0tbW9h3XjmKcrJkyYUB4IBN7EBXgUjK+g+yNsGMr4Gy7u35tMwpVMzz3O73E459xmGhafRHgjwoWwnyP+bk1NzUbMJ9zJ9PwnAzF+Grbf1V4UXZDpuR83btyGTU1NbyF6FrarR8hrn6++/El7e/sb1dXVfDAKFzI995WVldsh/5uw/bH4AuwmGEXfJUh7CmEAJroB5/oinC++tz9t9Lx1RwKwF+CCmYzgV7iQXsM/iq3wD+KX+CdyJJZ/gAtqPay7wWTsAtxs/4C8ZYgehO2Pwfbnrl69ehKW30L6GbgRTzEZRQKZnnu2fiC4EflXIf/W2P5obH82wp2QdjnSynFDvyqWWySTjWs/Hoi/Kmz7O3tRdEE2zj22uxF5Q9jmF9j2u7BzUc4eWEURshnuS2wVF0lk49wjH4XGkEgkchi2p51VVla2FdKew7rdIGyONBmFK+Xl5UPwPbC17tJYSvroeeuOBGDvOI0f+PFfVltb22JSAC6sRxC8AtsPF+rGJtEF/htBcBC2fwPb8J+fYeHChWtwMf4KUR9uEifHUkUSGZ37lpaW7+Ecs+vl1joQS41RWlp6OYJmlM1/6MKdjM5/EgGU80/Yl7AP7TSRmozOfWVl5Vhc+z/A9i9hG7aAdIA/Pdcj/U6sVxewO9m47r+D7b9uaGh4yF62ZsyY0YZzbrogsW4Hkyg6gWt3/6Kiotk4Vz/GYsczMx30vE2NBGDv2A0XUxtC/vCTYdM+rqvUXVq42e6CgOMvmTeB4cOHv4ayeYPZLZYiksj03M/G9hf4/f4H7aQOZs6c2Y51rYhyTKBwJ6PzHw8emOcj2Ab5T0TYaBJFV2R07rHuewxg95mEOObNm/cNHo5sGbneThKJZHzdY/0yWKndCxEPW6aIxh6nAOeN9wj+cf8ZrtH9TGKa6HmbGgnAHlJTU1OEi5H/KD5LMWiX42lItR12AtuH7GiDHXYAEdKK9Z8hOo77iqUKko1zP3fu3PfxoLuirq7uDTupAwiSvVH+cNhHdpKIIxvn3wH/6Nn1dRFuvn/FDb3TjVkkkqVzz25M3n8+rqqq+iGu93dCodBq2Bewv8DYlSmSyOJ1z3GDgebm5vtwrqsnTZo0DOGBkUjkYvwOlre3t98WyyaSwR/262HjcK/4CxajsdT0wHen520KJAB7zrow/ptYbpaSwI/5G4b4QY8yCe6Ygdb80ZulzrAMP/65lMYWhU02zr0r48ePH4nAaf34sx2KRLJy/iH+inHT/SeiCwsLCzXmLD2yce5NSxPynA3j+f8c4V9hnyJ+CsI38BDkfkQiWbnuIV4uR55fwKZjcXZTU9MqhI/it7AYZWw7b948fg/CBfxhfxnW214CPW9TIAHYQ/Cjdf4luP0TJE46Z4qlIq0ycFPoqoxBR5bOfSeqqqpGBAIBzgYO4ibxNG7Uf7dXiTiydf7xT56TbTbHuT6+traWD0HRDdk49zjnZmgDwgNx7vevr68/GHYmjK6Qbub1j4fglcwj1pKt6x73Gbo5Og/WhvN/N+wPSH4bVoP7z20S356h520KJAB7CB5ea+yoc1ElU2yHKR9s+OGbMnAj6LKM1tZWPRzjyMa5TwY33Y3wfbyM72JHLL6NfRyOsEddDIOFbJz/UCi0E4KzYH9saGiYYRJFt2TpvtNuhw9C9MUPpI/i4ceW2CYY3WvouRBHNs49J+DgvD8JGwKxtyXO/48ovvFnczukcVbrNIhvtsqKLIPzq+dtCvRD7yFFRUVsLo7gYnJt7sfNgl2JxHQLuIFtTVM08qbqMmAZ0QULFqyMLQqSjXMfD/6RT8RN9x1Et8ZN4kWUu2cG3QwDnkzPP8c8IbgdNq+xsfE8kyjSIkvXvrPuXTvsAGJ8JX4DHCM1cty4cevHUgXJ0j3/aAQlCK+ZPXs2/S92ACFIAchzv291dfUYkyiyBs65nrcpkADsIbYLgHm4WW46ZcqUwlhqAhX8wEVXa5ZcwLo5DFGGyRsPy0T6JjC6KInEUgXJxrl3CIVCnNX3GqI813cVFhbuI/HXNZme/+bm5m0QME/liBEjvsV3EHUM27AFlr+J+VyGON+MyyJGlq59x+1RqpYQkw7Bs9osCUOW7vmcRMJuRrc8KDr6P0bwh9TkE9kD517P2xRIAPYCXCzsMixatWqVeWglwdfMsEvl9dhiZ9rb29n1xW7GTlPPGxsbd2bZiFKciCQyPfckGAzyNUxPwDjo9wr8Az/avsmLbsjw/H+C7S91M6zjTDyWfwOXm5qaVnBZrAXnJaNrH9u+bId7moQ4ILo5A3gc9jFff4Q6k+m5x/ZfMEQZVSYhCaQHGeKPqPwwZhk9b1MjAdgL8EM3kwTwo75i7NixJSYRQFgcjGBnpD/e0NDAV4u5wnXI8yyi03HjPSiWall2Wb+NLRmXASKJTM+9/aqr+2ElyHthOBy+wKwQaZHJ+Yew+ARi+xI3w3ZmBqTf77+eywsWLJAATCLTax/XOgUgW6B432GXpAOfA7/Hg5CtW3oPuQuZnnus5z2H3chn49wnvDYOZdDJdA3yvNbLd2mLLtDzNjWc2i56AX60f8SP+VRcWPUIH0USX0rNAdRLkbYDHmLGN1RVVdUuuHnsgjwf4AbMfAYIkRD+mfC9kBx/wJsDL9KDsMx/gtcgr9xjpCCTc48bwBUI6IB4BfKmfH0TyuBLw9UF70Km174bKPM15NsRNo5C0U4WSWR67ul/EWl8hy3HQ3EiyBxsx3xTEL5RWlq6C32jmcwigUzPPe49ZyL4PfLyNZQPI/4ljMMipiPti4KCgmnJ4wOFOziXbNH7HOeX30EHet72DLUA9hL82OnP6ReIcgr5aYjzR3wvrONGQOyL8WJEO/55kDlz5oTxg+cMsIdg30XSqcj3LcIf42L8pckkXMnw3O9jh6O4LpVNnz5dv40UZHrti96T6blvaGiYxXfZIno7ttka4c+RbyTil7a0tOwu8ZeaTM897uvXYd1eiLKr+ABsdzqMY/7+WFhYuJXEX+akOvd63gohhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcQAJJ9fu6hXQoq+QteeEH2EfnzCqqmpKWptbf3c5/Oth8X3w+HwlNia7BIKhfgy+umwg7GPjvc0ekkwGDwOx/WPaDT6WH19fZ+8kqyysnI71OEm7J/v/cwbKioqNvH7/dfg3PyloaFhhp2cNlVVVTtEIpHjEZ0GK4MVwRbhWPnOzTvq6ur+g+VO4Dq4HcGxsDNwHVzPNHxPl2Cbi1GXG3Ce/o9pXjF9+vSCRYsW8VpchX3tHUvNDjiOC3Acv8FxbIWyP7CTu6Uvfht9Ce456+KeczmvFRzvXXayiAPXxBsIRuH81MRSskO27omoH1+r+W+UsxvKeSWWKvoTet+psNra2g7GDYHirwm2NQWLWSEyBueyFCLrDZzfqXZS3hAIBB5FvQ5H2KM/gpMmTRqGhwjfgfo6tv8xkgphL8KegfEaOhLrnsED4vHy8vJRWM4rIP4uQrBNQUEB3+uaNXBODkFwSWxJdAXuObfg2vkZrpMCO0nEgd8O78fbwp4wCXkIhOnTCB7H93jP+PHjR8ZSRX9CAlAQPsQt3IyvYYgf9KkMsw1u+scgmIAH7/OxlIFPa2srf2P52tIesMO0gaAtbmpqep7CEddLHUL++x8PO9C2CUjng4utX/sXFRW9TRFsNu4ClPNHBBPa29uviKV4Q3V19RYIzoP9KZsv38cDm62W9+I4eiVoBttvA9dIj6+9wQTOz/cQ+BFmXQDiN/YIggmwjO/zkUjkHNRxA1y3V9pJoh8hATjIwYNrHILd8SP+pLCw8GqE3+Ih9gM8tNeP5cge8+bN+xT/GufU1tauspNEPwPXxu8QbIfr5GPYd+rq6l6KrVkLROA7jY2NOyL6NvKHYH+KrUkNroulvDZwjXxpJ3kCHn7mT04gEDBhplRVVU0NBoNs/fwDjnMFzkljbE3P0G9DJLEfrqWv8Vt63V7OGrjWvuG1hrI/t5N6TUNDw1wE98NOwu+AolL0IzQGcJADAfgbBBfgZnMVbgjnYdmMz8LyBVhOaI3Bw24XpL8Euw2Lr+CBx203gM1ra2vbBQ/VnyGN47g4xmQi0n+MOP/pv4iyDkTZCeOccMO4C/l+iDznYn2nB3JFRcXWKHMmog3IH4ylxsaeYZtTYTtg+42QFIUtwvJ/YL/DTWmhyQiwjx6Nd0H+T5B/LPJTuNyOkK1Zy2HnYfs7mCfd/aMsM66N8XhwLB2/u5qamuGtra1sPToMeSuxfRvi/0X8b8jHsVEsOy0mTJhQju/hDGy7B8opR1IxbCmW38DyNaj/28znfI+Mx4N8u0LQ8TtyBXXdCOXPR3QIhNQOc+fOfTO2xh38iajx+/0fI+rD9xhyWtycawzW5RjAuO/uwkgk8gjKuBTx6UgbgZBl3VFWVnb9jBkzeM66Je56egj7/X4sNUbcdb8nbAPU+1wsV8GWwl7A8f4Gx9uAeAL29cJz/STqeArir3EZZWQ8BjDuPO2MMtdFmWfDtsQyFn2zEP4eeR9DmDYok2Meee1ORRlsmf0E9lhxcfE1H3300dfMg+tjM6zn90yh0KkLH8d8PbY9HXkuxTF2dHlXV1dPwXk6D+u2xrqxSFoJew/LNzvHFFd2Akg7HmXxeIkP++Bxn4htJyEsxvoFiD+Oc3w1fl9fmVw29new0QiAPx5nsSws8ztZArsbyRcPHz48unjx4o51CD9D+ODq1asvX7hw4RpT0Fp8OE8/QsieEZ5vjm2l0LlvyJAh13344YffIt4B8vI3OhfXzOGo360ouwZlL0H8x6jrs7juKnHd/Rrp2yN9U4RrEH6I9XdgPY854Tc+ZcqUQhwHj/FpnLcjmYZjdH4fRyL8EuGvkczvsB3ha7BzkHcOfnP8fZghDgh5XO8gL++vsxE3oKxO90TnngC7DfW6FPW9HPHvYtW6yPspwntx7FcmHzvB8e+E4FXYP1EHfm+in6AWwMFNAD9yDuK3cPP6B0Ms/50hfvSnIEjVTcMHEm9cFDovYJvlSS035yPtNIS8MX2IvHUmtTPmho/1R5mlJFCnoxmiLCO8CG42P8Uyy+WNcDHiT8DewvLGWKYAfZsDzE3m3kOBxvFs41Hek7AW3BTf44oe7v9DrOO/YwPid9PsRT4wx0BQsZWMg+EpJCnKOF6Qk3D+iRs1jzutP2nIuy0evjzXp2PRj/A5hC8g5Hd4KOxV+0bNbpsldl3MAx88z2Wm28uuoK77IhiCvHXdiT+Ch1st8pp82NZ8l70Bx7AdHkjvIroT4jzXPGebw65ZtGjRX0ymNMD1ZIY64DgfNgnunIp8PDdDYfxuVyPtWO4f5/g7sSwJvADbAw++/Rzhn21QB4qtx2Ab4pjZRUzRtjPsUTy4f8g86YD6X4WA47bYvUgxy2ucY7fOb2pqeiOT3w3Knobrj9fu91FP/mF6HDYHyxQRj2D92cyHdatg/A045+pNe5kCy0xIw3X6b2z3DxgnTb0DexI2HHY20j6wu/ETQBm+lStXPo6Qv6VPEb4M42/qfKT/BdfJQ1i+DMuLYWyxpUA8v6SkxNz34gigrvzN/hPGcbsU2jxn7BG5DOfpdex/NOIJoOxROP5nEA7D4lMwX0FBwUzkDeHa4b2DwqgFxmN5H/l2xHX2d+zrJiwnAPHH73YkjqNT9y/SeK/kEIyNYLwWKMj2g81AWSejzBeRPgbG338z1yH+Jv+8IZ4OFSjjfYQHwj7AtjyPPFe/xrGz67gTuPY5WYX3/8OxH35Pop8gATiIwb/F7+EHXoYf+Gt1gGn4R/gKlucgugluxPszLRlsE0Keq/DD3wG2L7bhLNAOsL4KN5H9+VDEuh0RcsxVJ7CON+LPYFuytcgkroXC5QhYFDdW3oyt8ePHs7XxOuy7DTad+0cZP4Dthv2NRxpbCcogNg5n/gwwvwuUNQH7OAQ2DoLnfz3dP9IfbmlpOZlxguUf0exFdkfeiYDHfQf+XY9DeTyXe0Og8Px+jLKOxk395yZzNyDvnxGwRec8lFPDeqOsfVavXs0Wl8ewvhChmfSA9NmsB9L4z57b/pbLTOdyKrC9MzmID+W0QNmmRRFhJjPLKTzvxYNxPI6L19V01IWiluWe2IOHm5nxW1RUxOvOFZTHFhG2llbD+N2yW+ta2Cis+wdnEDOfA9afiPpQBHoG9nsoromfYl9V9vc6Ecd/A9chnS1B3YLfF4XAudhuOWx7lLEzymK5FVjm9VHd2trKFv1ege0vhBWhrJNR9jYwnjv+UeRMUXIRW7awz6VI/xHysSWW19TfuAxjCxLHzF6KYF+kh2G8jvdkPQsLC8dh+c8orwx1fpRCkfkduG8EbK2fym1QHr9r3j/MNYJgJ2y3NdJ3xfp9kba7ve4w3Oc44cKA39v5SKOInYnfMX//u8AO4e8Iq/8Fm4xy/mYyx4FtRsNml5aWbo59sBW3fM6cOcvwGz8Hqymyf4f0iUj/PsI9sExxy5bAn/KPIOIdYN8UdO0QjhSeyfCefDHKMb9x1IX3D4rpDVDWXxFeYK87FL+XapT1IdJG4lhc/2QngzJ2QfAuxGsF6rkPyqGA3wnltCLcE+eH5ziZCNbTi0Ax7ne7xpJEf0ACcBAD0XKSHWWXbjy38gM/6pTiAzcU03VnE7FDh9lJLkCS1zsw3Yg71CWhJaOqqmoP3Iw2Qh1e4vgopuGmxAc9/4Veh5uTeWA44GbLf/bmHyq24c06I7DvW3EMznguU/9s7h8PHXbRcOzlfNzET47vWrFbkvjQYj3YFdklY8aMYWvVB7DHEP+9nWxg9xbKMK0cCDM6L9jeCC2EXbYUxoM6fWGH7BLsFdi2EQ/gUxcvXszWOAPO/yNIZ1eiD9fi5rHU1LDrEfXmeNcltbW1pk5uoMz38ND7JaLONRuxl2thNZ9//vluJjWHoE4v4prgw70DCIvrGOKYqiisTGIXIJ/zWz4f565DwKPcZpR1mn0uO7Vs9YCN+YH9mJY8B94HUPZPkP7jJUuWdDlBBiKVQxbMnxSIn6NQz3mME3xnLVjmuv/CKvCdH2ZWJHIj8nC9AXF2O5trBvu/mn/iGCf4TtmKzz+fPvz+Kplmi8ozGef+nfsO4e8If9J4v2TX7METJkzoGJISxx9nzpxJoUSc68ecF5BwXlA3/l5PwL6Pxr2PLXXxcPzfmzhmtqQmwO8Jde8Q6vj+ViLt3/ZiLdaxlddg/16cIQIhO+wW3o/i94268nrh+eJ57NT6amOGO2B9zn8fovdIAA5S8EAsQ8CuoJWrVq3q6KYk+BHfgZtKC8LdIVSq7eQOsG7R/PnzuxIB7DZJCzx8TDcwyjRjXRxwEzItZaiDMy6IN+0PYXwwxLco+ujPDg8P/jOeHEsyY3YyAvvvdAxZ3r/TAvEqH8ImJQ77psuHzVh2I5nEFPBGj/wnwg6KHw/Hlg2O7UHU8XWX0XnBd+QMCWBXVlpgG+eB2Gtwjj5wGadFFvED69nt1iX4Pje1o53Gn8WDstjKkzzukg9zM4YND2u23uQadrElsMkmm5hjB75vvvmmxI6nAodlWnb4fXTqxqPQwbXDmdy9bjlHuc6Y0oeDweAfYfvSXRATUO4t+N3cn+I77ADnln+KhqGsMP5QmRbCJPg93BuLWp2EBrZPHpbA75FjOFm/TuUhzQyBgNgbwrClpWVr7H8dRD/F/sNMi4d/0rANW7T5p6NTSxfKcbvvOeflT/g9/gO/x+9PnDiR++B5uRe//XvixRZ/66hDEMau4k4gneN4k69PMyYSdeP+E9YhvzPMwxxjGnwGoUxhnIy53rAP198a9mMELkLndyb6ARKAgxQ8EDn2j2MA24cPH/44btjPO4Y03mQdUfIzO4xnmR2morv1HXBgPfZHf3LjcHPcgWn2g+NgpDeCh5gWhx830oNQzwdgsxFfgxvvp7j50x+VeciBjCc3Yd+pjiEr+0f5zo3yGJQRdTOsMzOxIZLTuqni/E3Fdn+DvY+6cQD+V9gPH0BON3RG5wXH5zwY0p4hjm1M9xbCXo+PwzE4D7EEUKYRu1jf7X0M38+GDJF3hUlIAX4XnR78BNs5rUFOi07OwHF2Ov54oW+7GkoJRAXH9rF1rQmCI2ECRbbA+eWfIoqWUtT3VNgTa9as+RrXIe8pP7Fb97qDf0pJR8ufC2Ydvg8nbwdIc2sxcwSREYLxoI4JYgnH4PzONo3/HcYbtvkBMyDs9JtEWqd7RllZ2R8QsAWex38cqvNAc3PzUpT1Bs7LWTgvCS6ScP2x+5d1cXX/4naMIOUxgoRj7A6U3+VvDWGqa+0bfmB78zsT/QMJwMEJfse+E+zIOrDdXWyEyWlZxyYP7MU6p3vDFdwEulyfDMozrXy4+Zlu4KamJjqmHgZ7ML7bb+zYsSW4aXKsCVsxDsJ6dn9wwD59UXEGJV2UZAXcgDsdQzb3j22d3x4HhJvJIakMAtP1phwPHijXIi8nSpyEsATl82F8Cc7pAQgPZp5MQblmIgzC7U1CGqAeTt60Z8S60KOHmBsQ0aabFPVJNbHJgO+dsyo7gWN2xHNaM46zTEbHj2NyuogzPo/E7RxyuEQ4HKZ4YSs4Z6HSS0AExnvJzbD30nAKns4fJ2ffnVrNsS6j1mZs7/wmP0fc9bfoGPJwnHQC3377bad7BoU6zgu7ejnDn+MBn0XIllDOCL4W300tZ++bzADr2P37yZw5czh73o2MW9S7AnXq1TWCOpvvBdt3OxxB5A8SgIMQiBh2P5pJC7g5+WE+F+MPmn6iStsymMGZDrg5sgt6NW4edM1B56dGCCLs6P4lJSUlZyEPZ7L+F9twQsC29bGuz2thfBWRI1o9Icv755hB8gy25SD4lJaiO6wDiD/W6SzYNzhn07ANB68fCbu0oaGBr2rqUvSkC0QUv6cmnIMtnNbarqD7CwROV7cZ69lXQEQ7rTNdtl7iXLmOVUT9OX6Q6xeYhH4ErttlqDeFQ0m1ywxWgnvCMbBD2Pre2tpqhAy2cR2zh3TThekGrjkOk7gcNn316tXrIO8hML4ecIvi4uKOCVFuMJ8dHW+HblTwA+WlPQ41XbB/85tEuCD5N5hsOL4evb4Ov8O52O5abPfd0tLSdXD/2AvJHFe6MX5XZkiJ/TYN/pZdu3/zGZwzTpBjmHbvj+h7JAAHJ8YdBm6i9yBI9Y+PDwHjfgU/ardu4KyBmyNb0tiqtgEeQnQ/wHFW83DDTJhoAehcmNySPE7Fnp1pxmfhuLy6rnu8fwgP1/OLB4CZHYvj5tjBTi0flZWVYyHs6nE+Xqrp3j2HqRfKes7lnBEzExPrk89Lj/7t04EsyjCDzFH/27pq0cG6IX6/n9cPBf0DdSC2ps9w9t9dF67pgkuCAtr4S8Nxu83MzGvsiQnGByTERqeZ/RSFuGbp/onjH60hQ4YYZ9RIGwahn/ynhi6GElqA2TKOa/VNXKufx3f1cswfrkf+rm/hMq6D+G7TTtfemjVr3kMeOqIP4c/D1nZyPLx+zeQP5Es5k7u3fPvtt2xBZ4/DVBx3py5mQP+AL8PewnGyxb9bkPdJnJel/D3bSeb7wD3vORyD4/vUnJeCgoLv4tgLYf1OAKLOzvnq69+56AESgIMMuwXAdAniYdblv1is50OB4162SPeGlwGOT8AbYBRTbDFKfkg4A7r3i3fHwS7qRYsWsa6OJ/p0Bzz3lB7vHw/TjoHvzuBvggfADJTzLo51Ih4Qt8R3s+NccxwVj78SYYvbbMAknLE/O9iuahz8KPsXKMPMKAbJ58XUDd9zyhadZMrKyn6Der+MMquLioreQvkJLoAI0iYXFxczD1sJ57W0tPwktqbvgP6k7zy2aK+fYganAXU+AA9t8wfJJoBlOj6ma57XIPzph7DfATFu3MbgOK7EPaBjJieFOr7/mxENYN2/ONHBvt7MHxwcs5kVa8Pric7hE1roKPSQjy3DZTAOg+hocbavZf6pIx2zj5Fmrj2EHdeePUnEvDUGf5zuggjrmLWO30cR9n0T8tMFzvxVq1bRz2BWsYeb0NUM3dk8jO/dtPra8Drgnx+6IAoOHTrUDIdIgy9RHgX273EuOsSxHXdmMpvzgnLZ/fttc3NzJyft/QDnz7HbH1CRp0gADjJwsz+GNzhEZ0GEsAsiJey2QMCuTd6oPXk/sEP9Wp+Am8AoOjucPzvwIYYbJGcn7w3B1YAb8kN4KDzT2tq6GGlHY50zbiZdv3A9ojf7x8O0Belm5mlTU9Mr3CZO7B2BdXyLwYko4xOWhfVPYj+fIo2zDOciNI66uwJ57kc5xgdhQUFBGGX8G2U9hpDl3Igs/J55TikO43/zzjimPyPvgxUVFd2O7eOYpsLCQvoG47iuKtgMbNuA/T2KkA9N+i/keD/6C+PbMbZbsGBBlxMvcgXOkXGJ0dbW5kzWcYOTPW7BcXAizf0wvnHk5/wOYcch3qNW03wBAvhBBHQ6vGF7e/ssHNcLsEcg4vkbp09F+oA0zpptnHe7XoJ87+F88FrnBAy+TaPTH0ek8f7wDb77M5CP1wOdPz+Fa5ld5lti/YulpaXscXBwrr2LWTbMiERcWxciLx1U853Sc5D+LOxB/D7mo+yfIY1v3Dk0fmxwNsH1Sr+KdKLM67cWx0AH6ryueewcw7cGx3So2xsx3MDv8XwEfOsI3/TzCcp5nMY4bB8cy8e4HulSi79LttS/gN9LE8L+BAU/fQV+i3PDcyf6CRKAgw+ndSPdMSyOj8CD8eBw6xbJFuxyNuPEcCOZYbfYJIA0dhHxPbT890/HxgfgJkrXDXybAJ3FspWS5ezBlgduk016u3+s5xhKiiK2Iu2Ch5kZxwTRO2/IkCF8bdblWKSIZGsa/0nz2C8qLi7eBvt0xkWlBHkasW/Wi2/FWIpwL5TFVztx27PwUKMTZuMQFvUyY/IIbtZ8BSC7NPlqNXY/OW5suoSiNhwOn4JtuM/bYO3Ydm+Ee2I17yn/wD7pQJdvx/Bk1mkvoQNfCmE3H3IOf8Bx8C04Q5CP3y8Wo1cHAoFt7D9E/RZ8H/T3R7HHCRq8JugQmULqdwi3xfqOWaSI07E4HSm/hXVs2eZ1wz8SvD47dVHyzyTK3h52J/LzGqDzbo5no0Pn0yH+9o7zkcffBGfHssuZwx8ofMxbVnht4XfBbnhOZpoJo/PxfZCffyJ+i3Ay9pW2m6megrKbcewUZj+B8TV2fBUdlynKbsF1MBm/t5SvS0wGx/MFtuV5Ycsi3cjwzxPPJd+ccVFJScl2HFpRVVW1HdbTIbXr7N98BvcUfj8jYPfwXmQnCyGEEPkDW5Ngkeok34pIux1GVx/mPcRCiPQIBoNPw5rHjx+flrsqkT+oBVAIMWiIRqMXIoi0t7dL6AmRIRB+E3w+H3sPbo5/c4roD1jW/wPOzw7XWSXQDQAAAABJRU5ErkJggg==\" width=\"640\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "p1_array = linspace(0, 1, 101)\n",
+    "parameter_sweep(p1_array)\n",
+    "decorate_bikeshare(xlabel='Arrival rate at Olin (p1 in customers/min)')"
+   ]
+  },
+  {
+   "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": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def parameter_sweep2(p2_array):\n",
+    "    for p2 in p2_array:\n",
+    "        system = run_simulation(p1=0.2, p2=p2)\n",
+    "        plot(p2, system.olin_empty, 'rs', label='Olin')\n",
+    "        plot(p2, system.wellesley_empty, 'bo', label='Wellesley')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "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": [
+    "newfig()\n",
+    "p2_array = linspace(0, 1, 101)\n",
+    "parameter_sweep2(p2_array)\n",
+    "decorate_bikeshare(xlabel='Arrival rate at Wellesley (p2 in customers/min)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "IOPub data rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_data_rate_limit`.\n",
+      "IOPub data rate exceeded.\n",
+      "The notebook server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--NotebookApp.iopub_data_rate_limit`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_sweep2(p2_array)"
+   ]
+  },
+  {
+   "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": 72,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation(p1=0.4, p2=0.2, 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, plot_flag=False)\n",
+    "    return bikeshare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def parameter_sweep3(num_steps_array):\n",
+    "    for num_steps in num_steps_array:\n",
+    "        system = run_simulation(num_steps=num_steps)\n",
+    "        plot(num_steps, system.olin_empty, 'rs', label='Olin')\n",
+    "        plot(num_steps, system.wellesley_empty, 'bo', label='Wellesley')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "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",
+    "num_steps_array = linrange(1, 101)\n",
+    "parameter_sweep3(num_steps_array)\n",
+    "decorate_bikeshare('Number of time steps (minutes)')"
+   ]
+  },
+  {
+   "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": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.3999999999999999"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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": 76,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulations(olin, num_runs=1000):\n",
+    "    total = 0\n",
+    "    for i in range(num_runs):\n",
+    "        system = run_simulation(p1=0.4, p2=0.2, olin=olin, wellesley=2, num_steps=60)\n",
+    "        total += system.olin_empty + system.wellesley_empty\n",
+    "    return total / num_runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8119999999999998"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulations(olin=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.4529999999999998"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulations(olin=12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 11.022\n",
+      "4.0 9.06\n",
+      "6.0 6.818\n",
+      "8.0 5.101\n",
+      "10.0 3.658\n",
+      "12.0 2.284\n",
+      "14.0 1.357\n",
+      "16.0 0.783\n",
+      "18.0 0.357\n"
+     ]
+    }
+   ],
+   "source": [
+    "for olin in linrange(2, 18, 2):\n",
+    "    avg = run_simulations(olin=olin)\n",
+    "    print(olin, avg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "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",
+    "for olin in linrange(2, 18, 2):\n",
+    "    avg = run_simulations(olin=olin)\n",
+    "    plot(olin, avg, 'bo-')\n",
+    "    \n",
+    "xlabel('Initial number of bikes at Olin')\n",
+    "ylabel('Avg number of unhappy customers')\n",
+    "ylim([0, 12]);"
+   ]
+  },
+  {
+   "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/chap02soln.ipynb b/code/chap02soln.ipynb
index 6463279a..7cd24ef7 100644
--- a/code/chap02soln.ipynb
+++ b/code/chap02soln.ipynb
@@ -5004,10 +5004,12 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
-    "# print(bikeshare.t_first_empty)"
+    " system.t_first_empty= -1"
    ]
   },
   {
@@ -6621,7 +6623,9 @@
   {
    "cell_type": "code",
    "execution_count": 84,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "decorate_bikeshare(xlabel='Arrival rate at Olin (p1 in customers/min)')"
diff --git a/code/chap03-fig01.pdf b/code/chap03-fig01.pdf
new file mode 100644
index 00000000..e1724c94
Binary files /dev/null and b/code/chap03-fig01.pdf differ
diff --git a/code/chap03-fig02.pdf b/code/chap03-fig02.pdf
new file mode 100644
index 00000000..149bd1a7
Binary files /dev/null and b/code/chap03-fig02.pdf differ
diff --git a/code/chap03-fig03.pdf b/code/chap03-fig03.pdf
new file mode 100644
index 00000000..230a6bbf
Binary files /dev/null and b/code/chap03-fig03.pdf differ
diff --git a/code/chap03-fig04.pdf b/code/chap03-fig04.pdf
new file mode 100644
index 00000000..d821fb94
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diff --git a/code/chap03-fig05.pdf b/code/chap03-fig05.pdf
new file mode 100644
index 00000000..47baa954
Binary files /dev/null and b/code/chap03-fig05.pdf differ
diff --git a/code/chap03mine.ipynb b/code/chap03mine.ipynb
new file mode 100644
index 00000000..31324713
--- /dev/null
+++ b/code/chap03mine.ipynb
@@ -0,0 +1,4203 @@
+{
+ "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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D9yF9NMFflZVqKLlr165x9uxZMjIysLGxwdvbW/NUqBBCVDd9Gvfh/O2LZEWkUTe9E0bG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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dcfce6a58>"
+      ]
+     },
+     "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": 20,
+   "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": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a=abs(census-un)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    0.012862\n",
+       "1951    0.008585\n",
+       "1952    0.006674\n",
+       "1953    0.006072\n",
+       "1954    0.006286\n",
+       "1955    0.007404\n",
+       "1956    0.008439\n",
+       "1957    0.009887\n",
+       "1958    0.011011\n",
+       "1959    0.010208\n",
+       "1960    0.005617\n",
+       "1961    0.000369\n",
+       "1962    0.000311\n",
+       "1963    0.002702\n",
+       "1964    0.005350\n",
+       "1965    0.006399\n",
+       "1966    0.006829\n",
+       "1967    0.006289\n",
+       "1968    0.005827\n",
+       "1969    0.005821\n",
+       "1970    0.005832\n",
+       "1971    0.006258\n",
+       "1972    0.006166\n",
+       "1973    0.005847\n",
+       "1974    0.005332\n",
+       "1975    0.004437\n",
+       "1976    0.003388\n",
+       "1977    0.002670\n",
+       "1978    0.001965\n",
+       "1979    0.001712\n",
+       "          ...   \n",
+       "1986    0.002585\n",
+       "1987    0.003591\n",
+       "1988    0.004604\n",
+       "1989    0.005461\n",
+       "1990    0.005988\n",
+       "1991    0.006900\n",
+       "1992    0.007054\n",
+       "1993    0.007277\n",
+       "1994    0.007490\n",
+       "1995    0.007423\n",
+       "1996    0.007142\n",
+       "1997    0.006903\n",
+       "1998    0.006709\n",
+       "1999    0.006511\n",
+       "2000    0.006386\n",
+       "2001    0.006274\n",
+       "2002    0.006183\n",
+       "2003    0.006197\n",
+       "2004    0.006216\n",
+       "2005    0.006302\n",
+       "2006    0.006365\n",
+       "2007    0.006473\n",
+       "2008    0.006604\n",
+       "2009    0.006805\n",
+       "2010    0.007208\n",
+       "2011    0.007708\n",
+       "2012    0.008153\n",
+       "2013    0.008530\n",
+       "2014    0.008982\n",
+       "2015    0.012652\n",
+       "Length: 66, dtype: float64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b=a/un\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.012862470293832286"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c=max(b)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.28624702938\n"
+     ]
+    }
+   ],
+   "source": [
+    "d= c*100\n",
+    "print (d)"
+   ]
+  },
+  {
+   "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5576286540000002"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census[1950]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the last element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.2564900110000004"
+      ]
+     },
+     "execution_count": 25,
+     "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": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1950, 2015)"
+      ]
+     },
+     "execution_count": 26,
+     "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07229017472307693"
+      ]
+     },
+     "execution_count": 27,
+     "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": 28,
+   "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": 29,
+   "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": 29,
+     "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": 30,
+   "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": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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lly5dQkdHh3bt2iGVSHm+6/Mkp18n56gX5vJ2lMukVFQoMDR8OJY+1+pV1DVfjCAIwoMu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\nH+Lizxhj/dD/Ax5g0dPfawoUAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd01f7278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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A7Nixg4CAABYvXgzcWhFr2bJlzJkzh9TUVM6dO0ddXR1vvvkmxsbGeHt7k5ub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\nQlz8GWPMCv0/HbkdSQLxDOIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd01dd6d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "first_year = 1965\n",
+    "last_year = census.index[-1]\n",
+    "total_growth = census[last_year] - census[first_year]\n",
+    "elapsed_time = last_year - first_year\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "results = TimeSeries()\n",
+    "results[1950] = census[1950] -.4\n",
+    "for t in linrange(1950, 2015):\n",
+    "    results[t+1] = results[t] + annual_growth\n",
+    "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": [
+    "### 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": 33,
+   "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": 34,
+   "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": 35,
+   "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": 36,
+   "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": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MkWSEpqYmNjY2GBgYYKZvxlf+/2U3URgb69CvXxM0NF6uFj1PUqWvsJs3b3L+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\nouJPCCEDEBV/QggZgP4fbOhbJX2WDCwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd01fc1d0>"
+      ]
+     },
+     "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": 38,
+   "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": 39,
+   "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 0x0000021DD03B...\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 0x0000021DD03B...\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 0x0000021DCC3F...\n",
+      "    shared_y_axes = <matplotlib.cbook.Grouper object at 0x0000021DCC3F...\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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okTwej5YuXSqPx6NJkyZp1apVkqTGxkbl5uaqurpaY8aM0SeffKKuri7Nnj3b6zXMZrNO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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd01fcc88>"
+      ]
+     },
+     "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MgoKCsLGxYfz48SQlJamOcc2aNSxYsIDBgweTn59Po0aN+Oabbyq86hCEZyE9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\nPQOKZRsjpAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd0467a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def run_simulation1b(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",
+    "        deaths = system.death_rate * results[t]\n",
+    "        results[t+1] = results[t] + system.annual_births - deaths\n",
+    "    system.results = results\n",
+    "    \n",
+    "system.death_rate = 0.01\n",
+    "system.annual_births = annual_growth * 1.6\n",
+    "\n",
+    "run_simulation1b(system)\n",
+    "plot_results(system, title='Constant birth, proportional death')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "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": 42,
+   "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": 43,
+   "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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ixURERIjVuYRGr7i4mKKiIkrKS4jPiueO3h2yNQuQFVbMvdPXsS8vub2ErpYu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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd042b908>"
+      ]
+     },
+     "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": 45,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def run_simulation2b(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",
+    "        net_growth = system.alpha * results[t]\n",
+    "        results[t+1] = results[t] + net_growth\n",
+    "    system.results = results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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97kG+CgIDA7ly5QqzZ88mIiKC27dv89dff/HBBx/QpUsXWrVqBRTdV/D09CQo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\npFf+H1sFbMS2u8i2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd047bcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system.alpha = 0.017\n",
+    "\n",
+    "run_simulation2b(system)\n",
+    "plot_results(system, title='Proportional model, combined birth and death')"
+   ]
+  },
+  {
+   "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": 47,
+   "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",
+    "    return pop + births - deaths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the name `update_func1` refers to a function object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.update_func1>"
+      ]
+     },
+     "execution_count": 48,
+     "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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 49,
+     "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": 50,
+   "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": 51,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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H/fv355tvviE3NxcfHx+Cg4NVEkRN6qtK//79OXDgALt37+bUqVNVJv9GjRrx448/snTp\nUnbs2EFRURGOjo7Mnz9f+YNTE8uWLWPBggX89NNPlJSU0LBhQ8aPH4+joyNvv/02J0+epHfv3jWu\nt6Y8PT3Zvn07y5cvZ+PGjSgUClq0aMGqVatU2u1dXV3ZvHkzS5YsYeXKlZiYmDBlyhQiIyM5c+ZM\npfpWrFjBd999h1wux8HBgYULFzJo0KBq47h8+TIffvghU6ZMqVHy19HRYe3atSxcuJBNmzYhSRKN\nGjVi1qxZyOVy5s+fT2RkJK6urjg6OrJz506++uor1q9fj4aGBm5ubixatEiZgNu1a0ffvn05dOgQ\nJ0+epFevXo/82Vdsv3z5cuWPjb29PTNnzlTeVAhQv359tm/fzuLFi1m/fj06OjoEBAQA5b3DXlQy\n6X5XXoTnUrdu3WjYsGG1PUeE58sPP/xAUVEREyZMeNqhCHWIaPMXhOdYxciwbm5uTzsUoY4RyV8Q\nnmOFhYViGG2hSiL5C8JzzMrKiiFDhjztMIQ66Jlo8y8qKiIyMhIrK6uHuplIEAThRVNWVkZaWhqu\nrq7o6elVWv5M9PaJjIxUjoIoCIIgqG/r1q1V9s57JpK/lZUVUP4iHvWuUkEQhBdBxcCMFfnzXs9E\n8q9o6rG1tVV77BtBEASh+nG3xAVfQRCEZ1ReXh7FxcUPta1I/oIgCM+ggoIC/v33X44fP/5Qc1WI\n5C8IgvCMKSoq4uTJkxQVFZGfn8+pU6dqPCexSP6CIAjPkJKSEk6ePKkcXE9DQwNXV9caz6kgkr8g\nCMIzJDY2ltzcXKB8Eh1vb2/lDIA1IZK/IAjCM6Rly5ZYW1sjk8nw9PR86O7vz0RXT0EQBKGcpqYm\nvr6+ZGRkVNuHXx3iyF8QBKEOq+pCroaGxiMlfhDJXxAEoc6SJImzZ89y+fLlx163aPYRBEGogyRJ\nIiIighs3bnDjxg3Kyspo3rx5jXv1VEcc+QuCINQxkiQRFRXFtWvXlGUPeydvdUTyFwRBqEMkSeLS\npUtcvXpVWWZvb/9QffnvRyT/OqJbt2588803ai0rLi5m+fLl9O7dG1dXV9q2bctbb71FZGTkA/eT\nl5fH0qVL6du3L+7u7nTu3Jng4GCVfzRBEJ4OSZKIiYlRaeNv0KAB7u7ujzXxg0j+z6RZs2bx+++/\n89FHH/H777+zYcMG9PX1CQwMvO+FofT0dF599VUOHz5McHAw+/fvZ9myZeTm5jJs2DDi4uKe4KsQ\nBOFecXFxKt9DW1tbPD09H3viB5H8nzl5eXn88ssvTJs2jc6dO2NnZ4erqyv/+9//qFevHj/++GO1\n286dOxdJktiyZQs9evTA3t4eT09PVq1ahY2NDYsWLXqCr0QQhLvFx8cTExOjfG5jY4O3tzcaGrWT\npkXyfwZpaGjwzz//UFZWpizT1NTkhx9+YPz48VVuk5aWxsGDBxk9ejRGRkYqy7S1tVmyZAmzZ89W\nlsXGxvLmm28qm4bmzJlDTk6Ocnm3bt3YuHEjEydOxN3dnQ4dOrBy5Url8vT0dKZMmUKbNm3w8PBg\nzJgxXLp0Sbnc2dmZPXv2qMRxd9mVK1d444038PLywtvbm0mTJpGUlPQQ75Yg1H1XrlxR+X5YWVnV\nauKH5zz579t3mQkTDjBhwgH27avcHBISEqNc/uefCZWWb9lyUbn82LHKiWf9+gjl8lOnbtXGS6jE\nyMiIESNGsHXrVjp37syHH35ISEgIN2/exM7Ojnr16lW53aVLl1AoFLi7u1e5vHnz5jRp0gSAlJQU\ngoKCcHJyIjQ0lOXLlxMfH8+UKVNUtlm2bBldu3Zl//79jBkzhhUrVhAWFgbAp59+ilwuZ/v27eze\nvRtDQ0PefvtttV/ntGnTaNCgAaGhoWzdupWsrCxmzZql9vaC8CwxNjZWTrpiaWmJr69vrc9XXqN+\n/kVFRaSkpJCbm4u5uTlWVlbo6OjUVmxCNWbPno2bmxu7du3i119/Zc+ePchkMnr16sX8+fMxNjau\ntE3FUbuJickD69+2bRt2dnZMnz5dWfb111/TuXNnzp49i6enJwBdu3Zl6NChAIwbN461a9dy7tw5\nfHx8SExMxNnZGTs7O3R1dfnss8+Ij49HoVCodTSTmJhIhw4daNiwIVpaWvzvf/8jPT1drfdHEJ41\nVlZW+Pr6cvnyZXx8fKpM/EXyInKKc7A2tH4s+3xg8i8pKWHXrl3s37+fiIiISk0Nbdq0oXfv3gwa\nNEj8EDwCLS0tFApFlcsUCgVaWqoflb+/P/7+/hQUFBAeHs5vv/1GaGgoGhoaLF26tFId5ubmAGRn\nZz8wlkuXLnHp0iVlkr/b5cuXleUVZwoVjI2NKS0tBWDSpElMnz6dAwcO4OvrS+fOnRkwYIDap7FT\np05l0aJFbNu2DT8/P1566SX69++v1raC8CyysrLC0tKyyou7WYVZrDi1giJ5ETM6zsBE98EHcQ9y\n3+S/e/dulixZQklJCV27dqVPnz40bNgQAwMDsrOzSU5O5syZM3z11VesXLmSd955h4CAgEcO6nEZ\nMMCRAQMcq10eEOBMQIBztcsDA1sRGNiq2uVjx7oxdqzbI8VYwcTEhLy8vCqXZWdnY2ZmBsB///3H\n4cOHlUflBgYGdOrUiU6dOmFpacnmzZurrMPV1RUtLS3OnTuHm1vlmPft28fBgwdZtGgR2tradOjQ\nQeUaQAULCwvl46p+7CvGIenTpw/t27fnyJEjnDhxgm+++YY1a9awZ8+eKoeflcvlKs9HjRpFv379\nOHToECdOnGDBggVs3LiRPXv2iIMM4ZmXlJREvXr10NfXVymvKvEn3k5k1elVZBeVH7itOrWK6R2n\noyF7tFb7apP/hAkTyMzM5NNPP6Vz587VfuHGjBlDSUkJv//+O9999x0HDhxg3bp1jxTUi8jFxYWz\nZ89WKo+OjqagoIDWrVsD5b19Nm7cyMsvv4yLi4vKusbGxtW2+ZuamtKzZ09++OEHBg8ejKGhoXJZ\ncXEx69atw8LCAl1dXZo1a8a+ffto0KAB2traAFy/fp158+YRHBxcZbPS3eRyOUuWLMHf358BAwYw\nYMAAMjIyaN++PadOnaJfv35oa2ur/NglJiYqH2dlZbFy5UrGjRtHQEAAAQEBREREEBAQQHR0dJU/\nXoLwrEhMTCQiIgIDAwPatWuHgYFBtetGpESwLnwdJWUlAGhqaNLVoesjJ364zwXfvn37EhISQo8e\nPR54pKWjo4O/vz+7d++mX79+jxzUiygoKIiLFy8yZ84coqOjuXbtGn/99RfvvfceXbt2pWXLlkB5\nO7uvry8TJkwgJCSExMREYmJi2LFjB2vWrGHy5MnV7mPGjBlIksTIkSP5+++/uX79OidPnmTs2LGk\npKQwZ84cAAIDA8nJyWHGjBnExMRw4cIF3n//fRISEio19VRFS0uLqKgo5syZw/nz57l+/To7d+5E\nW1tb+YPl4eHBjz/+SHR0NFFRUXzyySfK/zNTU1OOHj2qfC8SExPZvXs3JiYmODg4POI7LQhPT0JC\nAhEREUD5HLx39/C51/Xs63xz+htKykqQkMjJlHij1Vv42fk9lliqTf6vvPJKjSuTyWQMGjTokQJ6\nUTVr1oytW7eSnJzM6NGj6d+/P4sWLaJXr14sX75cuZ6GhgZr165lyJAhfP/99/j7+zN06FB+/vln\nvvjii/u+/7a2tuzcuRMfHx+++OIL+vfvz4wZM6hfvz4hISE0bdoUKG97/O6770hPT2fIkCGMHTuW\n+vXr891336nd5LJkyRLs7OyYMGEC/fr146+//mLVqlU0btwYKL/nwNjYmICAAN555x2GDBminJRC\nQ0ODNWvWAOU/iv7+/sTHx7Nhw4YHnnUIQl2VkJDAhQsXlM/NzMzuexZrZ2JH58adKSqWk3ixDK2T\nXQn7o6za9WtKJtVg1t+YmBgKCwurvDDp5eX12IK6V1JSEt27d+fgwYPY2dnV2n4EQRBqw9WrV1WG\nXzEzM8PPz0/ZrFodhaTgm783Ex5iio5U3jw0ebInbm4PHsv/QXlTra6ekZGRTJ06lZs3b1ZaJkkS\nMpnsvqcvgiAIL6orV64QFRWlfG5ubk7btm1VEn9GQQa7Lu5ieOvhKj15NGQaTOk+mk03ojhx4iYv\nvWSPk5P5Y4lLreQ/f/58NDQ0WLBgAba2trV615kgCMLzoqrE7+fnp+y6LUkSx64dY9fFXRTLiymR\nyxne7HUsLVUvAg8e3JyOHRvStKnZY4tNreQfFRXFV199RY8ePR7bjgVBEJ5n8fHxKi0iFhYWtG3b\nVpn4Mwsz2XR+E5fSytfJul3Ed6cPkKRnz4IZA9DQuNPt09BQh6ZNH28XZ7WSv4WFRa3faiwIgvC8\nkCSJ27dvK5/fnfglSeKfa/+w6+IuiuRFAJSUlnH1QhnOea9yu0yPQ4eu0b1741qNUa3kP3z4cNau\nXYufn1+lmxJq4r///mPUqFFVLmvbti2bNm166LoFQRDqCplMhpeXF2FhYZSVleHr64uWlhZZhVls\njthMVGqUyrovt+jLK9ou/Lr/GgYG2hgZ1f6NjGol/xs3bhAfH0/Hjh1xcnKq8q60DRs2PLAeT09P\n/vnnH5Wy48ePM3PmTMaNG1eDsAVBEOo2DQ0NvL29lY+PXzvOj1E/UiQvQkJChgwbIxtGu4/G0cIR\nuZMCeYkGvXo1wdi4jiT/q1ev0qJFC+XzivFbakpHRwcrqztdlHJzc/nyyy9588036dSp00PVKQiC\n8LRJkkRKSgo2NjYqQzRUNJfHZsSy6fwmJCRu3swnOTmf918ZRoDrYLQ1y3v9aGlpMHiw0xOLWa3k\nX914MY/qm2++QUdH5753pQqCINRlkiQRERHBtWvXcHJywtm58nhhTvWc8Gngww9//k5Buh5OBQPR\nivFA2/3+/fxrU42GdI6Pj+fUqVPk5eVhbm6Ot7e38q7QmsrIyGDLli3MnTv3ka4jCIIgPC0KhYJz\n585x48YNoHwSJFNT00pnAADDWw8nM0mTuF9t0ESb6OhM5HIFWlpPp+u8WntVKBTMnj2bAQMG8Nln\nn/HVV1/x8ccfK4cHqMFNwkrbt2+nXr16+Pv713jb55G6E7gnJSXh7OzMe++9V+W6Vc2QVaFi27v/\n3N3dGThwIFu3blX5HHfv3l1p3bv/fv/9d+W6ly9fZurUqfj5+eHq6krPnj1ZvHhxtaOUTpgwAWdn\nZ86fP6/WeyMIdZFCoeDMmTPKxA9gZ2fHlZIrfHHsC4rlxSrrG+kY8aH/63i6NWDAAEdmzfJ7aokf\n1DzyX7t2LT///DPBwcEMGDAAS0tL0tLS2LdvH8uXL8fR0bHGF2z37t3Lq6+++sDbm4Wq/frrr/Tv\n3/+h7r345ptvcHNzQ5IkcnNzOXToEAsXLiQpKUllAhdNTU2OHDlSZR2mpqZA+fSQI0aMoEePHnz3\n3XcYGxsTExPDggULiIyMrNSDKy0tjX/++YcmTZqwc+fOamcWE4S6rKysjLCwMFJTU5VlVg2s+Kfk\nHyLPRyKXK5i2YSWfD52MmZmech2ZTMakSR61MiF7TamV/Hft2sXEiRMZO3assszW1pZx48ZRXFzM\nrl27apT84+LiSExMFJNzPAJ7e3vmzp2Lr6+vMhGry9TUVHnh3draGkdHR7S0tFi0aBGDBw+mWbNm\nynXvvkBflYozgPnz5yvL7OzsMDQ0ZPTo0URHR6t0Fti7dy/W1taMHDmSr7/+mlmzZlWaU1gQ6jK5\nXM6pU6fIyMi4U2YqZ0fqDgrlhWRmFRIbm4VmUQGbt0fw9lttVLavC4kf1Gz2SUtLU3ZZupeXlxe3\nbtVs/tqwsDCsrKxwdKx+ohXh/j744ANKS0tZsGDBY6kvICAAHR0dfvvttxptp6GhQW5uLuHh4Srl\nvr6+7N+/v9IQzD///DN+fn707NmTwsJC9u7d+8ixC8KTUlJSwr///qtM/CVlJcQSy++3f6dQXgiA\npoYGlrmt8codTuS521y+fPt+VT41ah3529vbc/bsWdq1a1dp2dmzZx94dHivS5cu4eRU+12a9sXs\nY3/sfrXW7dS4E4FugSplWyK2cCzxmFrbv+z0MgOcB9Q4xodVr149Zs6cyfTp0+nXrx+dO3d+pPoM\nDQ2xs7N+3aONAAAgAElEQVQjNja2Rtv179+fDRs2MGLECFxcXGjbti1t27bFz8+P5s2bq6x74cIF\nYmNjCQ4Opn79+nh4eBASEsKIESMeKXZBeBKKioo4efIkubm5AKTkpRBNNLlGufD/B/OWBpYEtx/N\nSUUpUVHpDBvWAkfHxzcez+OkVvJ/7bXX+OqrrzAwMKBfv35YWlqSnp7OL7/8wpo1a5gwYUKNdpqa\nmlrjpgqhsldeeYXffvuNOXPmsH///kduPrl3KsmysrIq5/E1Nzfn77//BsqHpv3pp5/YuHEjBw4c\nYOPGjWzcuBEjIyOmTZvG8OHDlduFhoZiYmJC+/btgfIfjnnz5hERESFm5xLqvKKiIgoLCykuKyYu\nM46bejfJ0sxHVlA+9k43h24MdB6IrpYu9gGlBAQ4oa9fd69pqpX8g4KCuHTpEgsXLmTRokXKckmS\n8Pf356233qrRTlevXl2zKF8ANZ3AvcKnn35K//79Wbx4MZ999tkjxZCXl6dyFqepqcnPP/9cab17\nR3U1NzcnODiY4OBgbt68yYkTJ9i2bRtz586lQYMGdOnShZKSEn755Re6d++unBCmT58+fPHFF+zc\nuVMkf6HOMzMzo02bNuz+azdJeknEZ2RwPSkXWyMbvp34Ps5Wd1oz6nLSr6BW8tfU1GTRokWMHTuW\n06dPk5OTg4mJCb6+vpVO7euSAc4DHqkpJtAtsFJTUG1RdwL3e9na2jJ9+nTmzJnzSFNoFhYWcvXq\n1UoX4Stm3qrO2rVrady4Mb179wagQYMGvPbaa/j7+9OnTx+OHDlCly5d+Pvvv7l9+zZ79uxRaedX\nKBT8+uuvzJw5U1z4Feq8evXqMeqVUVw/nMrhiEQaFHnQ5HZ7kiJ0ce7+tKOrmRrd5NW8efM6neyf\nZepO4F6VIUOG8OuvvzJ79uyH3n9ISAgKhaLGPyARERH89ttv9OjRQ2XkVx0dHfT19ZUTyoeGhmJj\nY8P69etVtg8PD2fu3Lns27dPpYlIEJ62jIwMtLW1kenKMNa9M32orq4uUzqOo3F+e079UUqzZma0\nalXvKUb6cKpN/r1792bZsmW0aNGCXr16PbB70h9//PHYg3uRBAUFMWjQIObMmcOIESMwMDAgNjaW\nJUuWqEzgXp158+YxYIB6ZznZ2dmkpaUhSRI5OTkcPXqUpUuXMn78eBo1aqSyblpaWpV16OvrY2Rk\nxOTJkxkxYgTjx49n7NixNGrUiFu3bhEaGkp2djZDhw5V9u2fPHlypQv9jo6OrFu3jpCQEJH8hToj\nOTmZf/77h9jbsWAPb7d5H2OjO/31rQ2tGTPQktZ2Kfj62taZ7ps1UW3y9/LywtDQUPn4WXxxz5KK\nCdxXrlzJ6NGjKSgowNbWln79+qk19pGdnR3BwcF8/vnnD1x30qRJysdmZmY4Ojry+eefM3DgQJX1\nysrK6NixY5V1jBw5kjlz5tCyZUt27tzJt99+ywcffMDt27cxMTGhQ4cO7NixA0tLSzZs2IBMJmPI\nkCGV6tHU1GTUqFEsWLCACxcu3PcMRxCehGvXrvHb8d+4mnUVeVkZySdzGXdwFT/MmYqu7p2Uqamp\nQZs29Z9ipI+mRhO4Py1iAndBEJ6E05Gn+ePfP8gpyQHgRloOUQXJ1C/yZkyHAIYObfGAGuqOh57A\nPSUlpUY7srGxqXl0giAIdYC8TM6OwzuIjI5EQXmvuzLtMowcjXE50REjhTWFhXIkSXpuWkGqTf5d\nunSp0Yu8e65KQRCEZ0VCVgKbDmwiL/1ObzuFnoIu7brQ17kvO4jFy8sGFxfLpxjl41dt8v/iiy+e\nm184QRCEqoRGhfLPf/8g5crIySnB1FQXcytTxvQdg51ZeVNJUJDLU46ydlSb/F999dUnGYcgCMIT\nJZfLSY1NpThDQU52CSBDVlSfGUPeQUuzRr3gn0nVvsKa3IUrk8lqPMSDIAjC06SpqYlvE1+uXL9G\nkUIb7QInCjIbkZ5WjK3tC5z8ly5dqnYlIvkLglDXRaREYG9ij7m+OVCetzzcPcgtzCX6Qhm3bxsS\nFOSCra3hU470yag2+UdHRz/JOARBEGpFTnEOOyJ3EH4zHAu5A5N8JmFvbwKUj1PVuV1n2vmUT6eo\nofHiXOd8/s9tBEF4IUmSxInrJ9h1cRe3C3JJjS0iMfsiC6N+YsWsMcpEL5PJ0NHRfEBtzx8xvIMg\nCM+d1PxUtkRsISY9BgCdbB3q5euiLzOHNE2OHLlO166NHlDL800M7yAIwnOjTFHGgcsH+CXuF0rL\nSkECvdt6mJaYYmRVn/QkaOCgi5fX89Vn/2FUm/zvnh5w4cKFTyQYoXaFhYUxcuRItYfJ2L17N7Nn\nz+bixYtPIDpBeDRXs66yOWIz128nUVgkx1BfG4MMAxy0HGhcvzEyNJAa69G3bxflnBIvMrXb/BUK\nBYcOHSI8PJy8vDzq1atHmzZtqpzaURAE4UlKzktm0fFFZGUVEhd/G5lChn9zL5xMmmOkUz5PRP36\n9fH09FQZevxFplbyT09PZ+zYsURHR6Ojo4OFhQUZGRmsXr2adu3asXLlSgwMDGo7VkEQhCrZGtni\nYe3Ft//uQ7NEG28NL0yzGmNkXp74HRwccHFxEc3Xd9F48CrlzT5paWmsW7eOiIgIDh8+zIULF1ix\nYgVRUVEqUzsKD8fZ2ZmQkBCGDRtG69at6devH+fOnWPbtm106dIFLy8v3n//fUpKSpTbhIWFERgY\niKenJ+3bt2fevHkUFhYql0dHRxMYGIi7uzsvv/wyUVFRKvtUKBSsXr2arl274uHhweDBgzly5MgT\ne82C8LAUUuUpT0e6D6e/Sye60A1LTSsMDcqnUnRxcRGJvwpqHfkfOnSIjz/+mE6dOqmU9+jRg8zM\nTL788ks+/fTTWgnwUcTExBAbG6vWuo0bN640j2xERASJiYlqbe/k5ISzs3ONY7zbV199xfz582nS\npAkzZsxg/PjxtG7dmnXr1nH16lWCg4Px8fFhxIgRnD9/njFjxhAUFMSnn35KUlISc+fOJSkpidWr\nV5Odnc2YMWPw8/Pjp59+IiEhgY8//lhlf0uWLOHPP//ks88+o1GjRhw7dowpU6awfv162rZt+0iv\nRRBqg0JScCThCEcSj/Ce7zRMDe9M/WmkY0TP+r5cun0LGxtD9PS08PT0pEGDBk8x4rpLreSvo6OD\nsbFxlcvEG/v4DBkyhG7dugEwcOBAPvvsM+bOnYu9vT1OTk6sX7+euLg4ADZu3IirqyvTp08HymfE\nmjt3LuPHjycuLo7Tp09TWlrK/PnzMTQ0pFmzZqSkpCgnec/Pz2fTpk2sWLFC+aPeuHFjoqOjWbt2\nrUj+Qp1zPfs6WyK2cCXzKteTchnx2yK2zPgIc/PyGbZkMhk+Pj4UFf2DhoYGvr6+WFhYPOWo6y61\nkv/w4cNZtmwZ7u7uWFre6SJVUFDA2rVrCQgIqLUAXyR3T6Gor6+PhoaGSq8cPT09ZbNPXFwcXbp0\nUdnex8dHuSwuLg4HBwdld10ADw8P5ePLly9TUlLC1KlT0dC40/pXWlqq8hkLwtNWLC9mX+w+Dl45\niEJScCk6g4yMIgwUCWzbcYHJb/kq1zU2NqZNmzbo6emp/O8LlVWb/N944w3lY0mSuHz5Mj169MDL\ny4t69eqRk5PDmTNnkMvlWFtbP5Fga8rZ2fmRmmLc3NwqNQXVJi0t1Y9DJpNV206pp6dXqaxiUjYt\nLS1kMhn3TtKmra2tfFzR1W3FihU0btxYZb27fwwE4Wm6kHKBbRe2kVmYqSxrbG+O0Y1G2Bd7k3M7\nj6IiOXp6d7479eo9e5OpPw3VJv/S0lKV515eXsry5ORkAFq0KJ/SLDU1tbbiE6rh6OjI2bNnVcrC\nw8OVy7Kzs5WTqJuamgIQGRmpXLdx48Zoa2uTkpJC586dleUrV66krKyMqVOnPoFXIQhVu110m52R\nOzlz64xKubOlMyO7juSQRgpFRQlYWxdRXJyPnp7pU4r02VVt8t+8efOTjEOooXHjxjFo0CAWLVpE\nQEAAN27c4NNPP6VLly44OjpiY2PDqlWr+PDDDwkODiYlJYXly5crt9fX12fMmDEsWbIEQ0NDWrdu\nzaFDh1i1ahXz589/iq9MeNEdv3acH6N+JCs3l/jLt3FoYoqNhTkBrQLws/MjOzsbC4tkiotllJXJ\nOXXqFF27dq105izcX7XvVnh4ON7e3jWuMCwsTNn2LNQeJycnVq9ezdKlS9m8eTNmZmb079+fd999\nFwAjIyN++OEHPvvsMwICArC2tmbcuHHKC74A7777Ltra2ixevJj09HTs7e357LPPxEQ+wlMlIXH9\nViYxsVkoFBINyuyYO+hDTPSMuXXrFmfPnqWsrAwob6Js2bKlSPwPQSbd2zD8//z9/XF0dOStt97C\nycnpgRVFRESwbt06EhIS2Ldv32MN8kGz0AuC8PyQJIlPDsznt4PRNM3rhoVkz3vveSOTZagMNa+j\no4OPj49o46/Gg/JmtT+XP/30EytXrmTw4ME0adKEXr164ebmhp2dHfr6+uTk5JCSkkJ4eDhHjx7l\n6tWrBAYGsmTJklp9QYIgPD/OJ5/HTM+MxmZ3Oh3IZDLe7zIFX3kqkRFZjBjhREbGVW7cuKFcx9DQ\nkLZt24oePY+g2iP/CikpKXz//ffs37+ftLQ0ld4nkiTRoEEDevfuzZgxY7CxsVFrpyEhIaxfv55b\nt27RrFkzPvjgg/uOESSO/AXh+ZJVmMX2yO2cSz4HOWZMcQvGrbVq/igrU1BSUkJ4eBhZWVnKcktL\nS7y9vcXgbA/w0Ef+FWxsbJg+fTrTp0/n8uXLJCUlkZubi7m5OQ0aNMDBwaFGAYWGhvLpp58yd+5c\nfH192bZtG5MmTWLfvn0isQvCc04hKfj76t/sjdlLTn4+MTFZ3M6+QW7Udr5rPkWly6YkKThx4jgF\nBQXKssaNG+Pq6iq6Iz8GNbpK4ujoiKOj40PvTJIkVqxYwbhx43jttdcAmD59OidPnuTs2bMi+QvC\ncyzhdgJbIrZwPfs6ABqaMgoKSrEtcUE/pyl//pnIgAF38ouWlhaNGjUiOjoamUxGq1atcHBwEGP0\nPCZP9BL5lStXuHHjBv369VOWaWhosGfPnicZhiAIT1BhaSE/R//MkcQjKjceNja3p1/3NzgYkk+P\nno3p1atxpW2bNWtGYWEhtra2dfZm0mfVE03+CQkJAOTk5DBq1Cji4uJo2rQpwcHBypvIBEF4PkiS\nxJlbZ9gRuYOM/Czy8koxN9NDW1Obl51epkfTHmjKNOnkUoCNjSFlZWWUlJSotOXLZLInepf9i+SJ\nNpzl5eUBMGPGDAICAli/fj3Nmzdn9OjRXL58+UmGIghCLcsozGDdmXXEXb9FWFgKFy9m0NTYmbkv\nzaVPsz5oaZQPQ2JjY0hhYSHHjx/n9OnTKBSVh2sWHr8nmvwrxpaZOHEiAwYMwMXFhU8++YQmTZqw\nffv2JxmKIAi1zNLAkp4Ovbh+PRdZiT5OuX0xvtgVSwPVgQMzMjI4duwY2dnZZGZmcuHChUrjUgmP\n3xNt9qlos7v7pjGZTEbTpk1JSkp6kqEIgvCY5ZfkY6ij2u/ev8UAUrsUErbDBCtzUzp2VO3UkZiY\nqJLsZTIZZmZm4qLuE6BW8i8uLmbNmjUcPnyYgoKCKn+V//jjjwfW4+LigoGBARcuXKB169bAnRFD\nxVzAgvBsKpIXsSd6Dyeun2Cqx3Sa1r8zx4e2pjZvdR1JmHEyrVtboqtbnnIUCgWRkZEqkyXp6uri\n7e0t7th9QtRK/vPnzyckJIQ2bdrQvHnzh+5jq6+vz+jRo1m6dCmWlpY4OTmxbds2rl27pjLomCAI\nz4aIlAi2XdhGel4GCQk5jP7rC7a8M5/GjVVH2fTxsVU+Li4uJiwsjMzMO8M0m5qa4uvri76+/hOL\n/UWnVvL/448/eO+99xg/fvwj73Dq1Kno6+vzxRdfkJGRQcuWLdm4cSNNmzZ95LoFQXgycopz2H5h\nu3LI5bj426SmFmAh1eOHLeeZPbMTGhqVm26ysrIICwujqKhIWdawYUPc3d3R1NR8YvELaib/kpKS\nx9bdSiaTMWHCBCZMmPBY6hME4cmRJIl/k/4lJCqEgtI7d966NGuA1fXmWBQ1x9zBiKIiOQYG2irb\nZmRkcPLkSWVvHplMRosWLXB0dBRt/E+BWsm/Y8eOHD16FD8/v9qORxCEOiqjIIPNEZu5lHZJpby9\nfXtea/Uap80yMDTUxsfHtspkbmZmhomJCbdv30ZbWxtvb2+srKyeVPjCPdRK/v7+/syePZusrCy8\nvLyqnEJwwIABjz04QRDqhn+v/8u2C9vILSwkPj6L+vUNcWpoT6BbIC2tWgLw0kv3H2FTU1MTHx8f\nzp8/j5ubGwYGBk8idKEaaiX/t99+GygflC00NLTScplMJpK/IDzHjHWNSc3I4eLFDMrKJCyz3fjw\n1WmYGlWf8HNzczEyMlI5C9DX1xctCHWEWsn/4MGDtR2HIAh1mKu1K12bd+L6peM0yeuKcZktcdG5\n+PhUTv6SJHH16lUuXrxIq1atRGeOOkqt5N+wYUPl44KCAvLz8zEzM1PesSsIwvPjVu4t8kryaF6v\nuUr5GJ9APDT68Mu+BEaNcqF5c/NK28rlcs6fP8/NmzcBuHjxIqampqLvfh2k9h2+//33H19++SVR\nUVHKm7zc3Nx49913xQ1agvAcUEgK/rz8J3tj9iKT6zKq0bu08WykXK6npUdb34b4eDVAS6vyvT65\nubmEh4eTm5urLDMzMxNt+3WUWsn/9OnTvPnmmzg4OPDOO+9Qr149UlNT+f333xk3bhzff/+9mLRd\nEJ5hyXnJfH/ue65kXeFGUh4JiTnE/bOaHU3mYG5+p4OHTCZDS6tyT54bN24QERGBXC5XljVp0gQX\nFxcx8UodpVbyX7ZsGe3atWPt2rUqF28mTZrE+PHjWbFiBT/88EOtBSkIQu1QSAr+uvIXe6L3IFfI\nkRSQnJyPYakVVgWt2bbtEpMne1a/vULBxYsXuXr1qrJMU1OT1q1bY29v/yRegvCQ1Er+kZGRLF26\ntFLfXZlMxsiRI3n//fdrJThBEGpPWn4a3537jsuZd4ZT19bSYnL3QP7bZoK9nQn+/tXP3FdYWEh4\neLjK/LqGhob4+PhgYmJSq7ELj06t5G9iYqIyj+bd8vPzxW3ZgvAMkSSJY9eOseviLvKKCtDWKv/+\n2pvaM8ZjDHYmdvhZpuPsbIGmZtVNNpIkERYWxu3bt5Vl9evXx93dXXQEeUao1Rjn5+fHihUrSElJ\nUSlPSUlhxYoV4oKvIDxD1oavZfP5LcReSePUf8kUFMgZ4DyAmR1nYmdSPuRyq1aW1SZ+KD/rb926\nNRoaGsr5db29vUXif4aodeQfHBzM4MGD6d27N97e3lhaWpKenk54eDhGRkZ88MEHtR2nIAiPSUur\nluw8dpBbt/IxUFhgHdOfPoP7oVnDC7NmZmbKO3VFV85nj1qfto2NDaGhoQwfPpzc3FzOnTtHTk4O\nI0aMIDQ0VFzYEYRnSKdGnejeug2N5d545Q7HRs+OgoLS+26Tnp5e6cwfwN7eXiT+Z5Ta/fytrKyY\nPn16bcYiCMJjFpsRi7GOMfWN6yvLZDIZM7u/TwetJORyiW7dGlU5/DKUt+3HxsYSFxeHlpYWnTp1\nwtDw/mP4CM+GapP/6tWrefXVV7G2tmb16tX3raRimGZBEOoGuULO3pi9/HH5D0rTjXm/zQe4trJR\nLteQafDSS43uUwMUFRVx5swZMjIyACgtLSUyMpK2bdvWauzCk1Ft8l+6dCnt27fH2tqapUuX3rcS\nkfwFoe5IyUthw9kNxKZeITY2k6ysJObGbmTT7GmVxtivTlpaGmfPnqW4uFhZZmlpibu7e22FLTxh\n1Sb/6OjoKh8LglA3SZLEiesn2BG5g5KyEmQyyMsrxVxuj1GOMwcOJPDKK83vW4dCoSAmJob4+Hhl\nmUwmw8nJiebNm4tJV54jal3wXblyZZUXe6D8tu558+Y91qAEQaiZgtIC1p1Zx6bzmygpKwHAQFeX\nSV1G07rgVfx7ufLyy9XfsAXlgzaeOHFCJfHr6uri5+eHk5OTSPzPGbUu+K5atYrOnTtjY2NTadm5\nc+fYuXMns2fPfuzBCYLwYJczL7PuzDpuZaWhp1f+la5vXJ+xXmOxM7EjxT0fG5v7X6RNTk7m3Llz\nlJbe6fVjZWWFp6cnurq6tRq/8HRUm/yHDx/OuXPngPLTyaFDh1ZbSevWrR9/ZIIgPNAf8X8QEvnT\n/7ftF+HtbUNPp24EuASgo6kD8MDED+VNOxWJX8yt+2KoNvnPmzePAwcOIEkSy5cvZ8iQIdja2qqs\no6mpibGxMT169Kj1QAVBqCy3JJcLkWnk5JSgJelimdiNEYNH1Dhp29jY4ODgQEpKCl5eXpibVx6r\nX3i+VJv8HR0deeutt4Dyi0ABAQFVNvsIgvD0vNLiFf5rfYFjR2/inN8bD/dWlJVJVQ67XEGSJIqK\nitDX11cpb9WqFc7OzmKIhheEWm3+U6ZMASArK4vS0lLlZC6SJFFQUEB4eDgBAQG1F6UgCCgkBSVl\nJehp3RlfX0tDizl9pnFcP5UGtia4uVndt47i4mLlHfpdunRBR0dHuUxDQ0OMvf8CUSv5x8TEMG3a\nNJVeAHeTyWQi+QtCLcouymb9mfVcTyjggy7v0rChsXKZsa4xfXoZ32frcqmpqZw7d07Zdz8iIgJv\nb2/Rrv+CUiv5L168mNu3bzN9+nQOHTqEjo4OXbt25ejRoxw9epRNmzbVdpyC8MK6lHaJb/9by5mo\na2RkFJEVv4ENs96pcirFqpSVlXHp0iWVCVcAMb3iC06t/55z584xdepUxowZQ79+/SgsLGTEiBGs\nXr2aHj16sHnz5tqOUxBeOApJwb6YfSz7bxlZ+dlkZRUjA1LT8zh4MFGtOrKzszl27JhK4q/ou9+q\nVStx1P8CU+vIv6SkhCZNmgDl83Lefcfvq6++yieffFIrwQnCiyqnOIcNZzYQnV7+XTMw0KZ184ZI\nZ3x5tXNHunW7/7g8kiRx5coVoqOjUSgUynJbW1vc3NxE331BveTfoEEDkpKS8PHxoUmTJuTl5XHj\nxg0aNmyIrq4u2dnZtR2nILwwYjNiWRO2lrySXGVZC8sWvNHzDTK7STg4mN13+8LCQs6dO0d6erqy\nTFNTExcXFxo1aiSO9gVAzeTfo0cPvvzySwwNDenZsydNmzZl2bJlTJgwge+//75G4/nHx8fTv3//\nSuVbt27Fx8dH/cgF4TkjSRK/xf3Gt4e2cvNmHh6e1mhradK/eX/6O/VHQ6aBqcOD60lPT1dJ/GZm\nZnh6emJkZFSL0QvPGrW7eiYmJvLjjz/Ss2dPZs6cyZQpU9i3bx+ampp89dVXau8wNjYWc3Nz9u3b\np1JuZnb/oxlBeN79cfkPFu3ZSGpaIQBJV0pYGjSDllYta1SPnZ0dycnJpKSk0KxZM5ycnEQXTqES\ntZK/vr4+K1eupKSkfMCoTp06sW/fPqKiopSnkuqKjY2lWbNmWFndvz+yILxoujTuwo8NfiM1LR5T\neQN88gNwML7/KJwAcrkcLa07X2WZTIabmxv5+flYWFjUZsjCM0ztmbwAlRtCGjVqVKOkXyEuLo6m\nTZvWeDtBeN7pa+vzSf/3+SpnDx2tejHoFaf7dueUy+VcvHiRzMxMOnXqhKampnKZrq6uuKgr3Fe1\nyb9Xr141ujD0xx9/qLVeXFwcxcXFDBkyhBs3btC8eXPef/993Nzc1N6XIDzrcotz+e3cMXo5d8PM\n7M4du43NGrN04tsP/O5lZGRw7tw5CgoKgPI5N1xcXGo1ZuH5Um3y9/Lyeuy9AoqKirh+/ToWFhZ8\n+OGH6OjosGXLFgIDAwkNDcXR8f7jjQvC8+BSajQfh37FxSs3+M86ky/fH6nyXbvf966srIyYmBiu\nXLmiHGYFynv4SJIkevIIaqs2+S9cuPCx70xPT4/Tp0+jo6OjbEJauHAhUVFRbNu2jY8//vix71MQ\n6oqKm7Z+PLuHi5dTkIADyT/x658+9O/V4oHbZ2Vlce7cOfLy8pRl2trauLq60rBhQ5H4hRpRq83/\nzJkzD1zHy8tLrR3e291MQ0ODZs2acevWLbW2F4RnUWZhJuvPrOdy5mWMjLSxszcm5Zqcbhav0sbz\n/tfOFAoFsbGxxMfHqxztW1lZ4e7uXml0TkFQh1rJf8SIB48PfunSpQfWExkZyahRo9i0aROurq5A\n+WlsdHQ0ffr0UScUQXjmhN8MZ0vEFgpKC5Rlvbza0MqlN31eaoWGRvXfrezsbM6ePUtu7p0bvrS0\ntGjVqpW4YUt4JGol/6oGbisoKCAsLIw9e/awYsUKtXbWokULGjZsyJw5c/jkk08wMDBg3bp1ZGVl\nMWrUqJpFLgh1XFFpEfN+Xs2fMUdwc7NCQyZDQ6aBv7M/vZv1RkP24L73aWlpKom/Xr16eHh4iEHZ\nhEemVvJv06ZNleUvvfQSBgYGfPvtt6xZs+bBO9PSYv369SxevJiJEydSWFiIl5cXW7ZsoV69ejWL\nXBDqsKTsJMav+5SrqTcBuJaYg1dLB8Z6jcXRQv2ODY6Ojty6dYvc3FxatmxJkyZNxNG+8FjUqJ9/\nVXx8fFi3bp3a69vY2LBkyZJH3a0g1GlGukYYmkmQWv5cN6MJMzt8hIl+9UMslJWVUVpaip7ena6f\nMpkMT09PZDIZhoYPnotXENT1yPd8Hzp0SPxTCsI9zPTMmNV/MlbmxgS5jGbXrHn3TfyZmZkcPXqU\n8PBwlYu6UN5JQnzHhMdNrSP/N954o1JZWVkZycnJXLt2jXHjxj32wAThWVFWpmDnH//i39kXI6M7\nd8F71vfkl/fWY6xXfdKXy+VER0eTkJCgTPoJCQk4OKgxgpsgPAK1kn9paWmlMplMhqOjI2PHjmXw\n4EWq7ygAACAASURBVMGPPTBBeBZcunKD6Vu/Jj73EteS3mLG+JdVlt8v8aelpREREaG8SxfKr4vd\nPUyDINQWtZK/mKlLECoLvxnO8n/XE5d7DYCdsVvpd9ELt1YN7rtdSUkJFy9e5Pr16yrl1tbWuLm5\niX77whNRowu+R44cITw8nOzsbCwtLfHz88PX17e2YhOEOimvJI/tF7YTdjMMPROwttInPaOIPm7t\ncWpWfa81SZK4efMmUVFRyknUoXzARBcXF3GXrvBEqZX8s7KyGDduHJGRkejo6GBhYUFGRgbffPMN\nHTp0YNWqVWIEQeG5J5cr+C8xnNDLO8ktvtP33tvFgVebDqdji+rvcpckibCwMJKTk1XKGzRogKur\nq/j+CE+cWsl/3rx5JCUlsXr1al566SVl+cGDB/noo4/48ssv+eijj2orRkF46qJibzBr20pSdWJo\n3doSGeVH6B0adSCgVQD62vdvqpHJZCo3Zunp6dG6dWtsbW1rNW5BqI5ayf/o0aPMmjVLJfEDdO/e\nnczMTL7++muR/IXnVnhCJGPXf0YR+VAAycn5tGxiR5B7EK7WrmrX4+zsTHJyMtbW1rRs2VJlAhZB\neNLU+u/T1NTE2Ni4ymVWVlZV9gYShOeFvZU1tvY6JFzPR1NThoupJx+/NAkD7aqHWJDL5cTHx9Ok\nSROVG7a0tLTo0qWLSPpCnaDWTV4jRozg66+/JiUlRaU8Ly+PtWvXEhgYWCvBCUJdYG1ozdTeQTRt\naMOqMR+zYMi0ahN/amoqR44cIS4ujqioqErLReIX6gq1/hNTU1NJTU2lZ8+eeHt7Y21tze3btzlz\n5gz5+fno6OgobwSTyWRs2LChVoMWhNoSFnWVXX/9y7zJw1SmUOzVvCedHTpVm/QLCwuJiopSGZr8\n5s2bODg4iHl0hTpJreSfmJhIixblk03I5XJu3iwfrKqirKysjLKysloKURBqnyRJfLF1OzsiQlBQ\nhlNoY94I6KBcriHTqDLxS5LE1atXiYmJQS6XK8t1dHRo1aoV5ubmTyR+QagpcZOX8MJLyUthc8Rm\nTuafRU4JAN+Hb2JYf18MDHSq3S4rK4sLFy6QnZ2tUm5vb0+rVq2Us9UJQl1UowbI+Ph4Tp06RV5e\nHubm5v/X3p2HNXWt+wP/hoQwT2EIqAgSCCigjDJKnY6zOLRH69TqqSN9jvqr11at5dxftda2WkWr\nbfW21tah1dZaqa21ImBxQEDEggyCMogiBATCFEmy7h9ct6ZAjQMB5P08D88De+3svK9JXnfWXnst\n+Pv7w8XFpaNiI6RDKdVKnCw4ieN5x6FUK+HQywSyykZY8K3x3rR/t1v47927h5ycHBQXF2tMwmZm\nZgZvb2+anpx0C1oVf7VajejoaPzwww8ab3Yej4dJkybh/fffpzsTSbehUqlx4NckZPJPolpZwW3n\n8/j4fxNmYfKAiRAK2j9rr66uRlFR0YPH8flwc3ODRCKBnt5TT5RLiE5oVfx37dqFo0ePYsWKFZg4\ncSJsbGxQUVGB2NhYbNu2DRKJhGb2JN1CVu5tRB/8DNkNqbC1NYKHR8tZupOlE+YMnANHC8dHHsPO\nzg729vYoKyuDWCyGl5cXraxFuh2tiv/333+PxYsXY/78+dw2e3t7LFiwAAqFAt9//z0Vf9ItHMj9\nClcbUgEA5RWNcHYEXhk8DcP6DWtzWcXm5mY0NDTAwsJCY7unpyccHR3pDl3SbWn1HbWiogL+/v5t\ntvn5+WkMbyOkK5s/5GXY2RqDz+dhuOdgfDhuPUa4jGhV+BljKC4uRnx8PFJSUjRG8gCAsbExFX7S\nrWl15u/o6Ij09HSEhIS0aktPT4etre0zD4yQp3WtQAYTIwP06vXg7vR+Vv0Q9Y+XYWdkj6HS0Dav\nVVVXVyMzMxN3797ltuXn53NDmwl5HmhV/F966SV8/PHHMDY2xrhx42BjYwOZTIbjx4/j888/x6JF\nizo6TkK0VlurwI7vfsP3ud8hyHoodqyar1Hkp/u0vfiQQqFATk4OSkpKNAY2GBkZter2IaS706r4\nz5kzB9nZ2di4cSM++OADbjtjDJGRkViyZEmHBUjI46hV1OKL9H3YV/AL1HoMSVUncOpMBP7xgnu7\nj1Gr1SgsLEReXp7GPFV6enqQSCRwdXWlaRnIc0frid0++OADzJ8/H6mpqaipqYG5uTkCAwPh5ubW\n0TES8khqpkZiYSKO5hxFk7IJvXuboqREDmtbA5g41Lf7uIqKCmRlZUEul2tsF4vF8PT0pIXTyXPr\nsU5nHBwc4OjoCAsLC4hEIjg6PnpYHCEdSSZrQGbpNZyt+RnFNcXc9r59zTDEJRhLR8yDhWHbXTZK\npRJpaWkaZ/smJibw8vKCnZ1dh8dOSGfS+iavjz76CPv27YNSqeT6Q42MjLBkyRIsXLiwQ4Mk5K8U\nCiWOHr+K3UkHUGWaDX9/MfT0Wvr1xaZizPSeCQ+bv79AKxAI4O7ujszMTAgEAri5ucHFxYVu1CI9\nglbFf/v27fj666/xyiuvYPTo0bC2toZMJsOJEyewbds2mJiYYNasWR0dKyGc/MoCfJi6Hg2CeqAJ\nKLkph2s/a4xzG4dRklEQ6Gm+tRljqKqqajX1gpOTExQKRau59wl53ml9k1dUVBRef/11bpujoyN8\nfX1hYmKCvXv3UvEnOiWxc4K7mzXSs+thZiZEiIsfXn9hHmyMbVrtK5PJuH798PBwWFpacm16eno0\nhJP0SFp9v62rq8PAgQPbbPP390d5efkzDYqQh8nl95CVJdPYZigwxLJ/zEPwIAl2zf8Posf8V6vC\nX19fj5SUFJw/fx61tbVgjOHq1asawzgJ6am0Kv5Dhw7Ft99+22bb8ePHERER8URPfvnyZQwYMADJ\nyclP9HjyfFOrGU6dKsTC/+zByj3bUVur0Ggf3DsQn7+8CT4OPhrj+O/du4esrCwkJCSgrKyM287n\n82FtbU3FnxBo2e0TEBCArVu3YuLEiRg/fjxsbW1RXV2NhIQEpKWlYe7cufjss88AtMz0qc1NXw0N\nDXjzzTdpERjSroo6GT7+YxtuCHIBADu++w2rF0Ry7TweD0L+g9k32xuvDwB9+vSBh4cHjIyMdBM8\nIV2cVsV/3bp1AAC5XI6tW7e2av/yyy+537Ut/hs3boRYLNaYGpcQAFCpVTh1/RRi82Jh6ioH/gSM\njQSQWV8CENlqf8YYysrKkJ2djfp6zTH9IpEInp6eGv38hBAti39OTs4zfdLExEQkJCRg9+7diIxs\n/WEmPY9SqUZ+/l0I7O5i/5X9uCVvWSrUytIQngNsEOkzEi95tj0tA4/HQ1FRkUbhNzExQf/+/WFv\nb09rTRDSBp3fs15VVYW3334bGzZsoPlSCAAgJ6cSew+mI7n2JHoFyWBios+19THvg7fCZ8HF6u9X\njBswYADOnDkDgUAAqVQKZ2dnGq9PyN/QefH/z3/+g+HDhyMiIkLjYhzpmdRqNT6NPYb4hp/RrN+E\n+nwhBg2yhaHAEJHukRjeb7jGdMsKhQLXr1+HVCoFn8/ntpubm8PX1xe2tra0di4hWtBp8f/xxx9x\n9epVHDt2TJdPS7owHo8HW59KqE41ga/Hg62tEQaJB2GG9wyIjETcfkqlEgUFBbh+/TqUSiWEQiEk\nEonGsXr37q3r8AnptnRa/I8cOYI7d+4gPDwcALghdwsWLMDkyZPx7rvv6jIc0gkqKhpga/tgyUMe\nj4clYfNwpfQqetmIMC9gDgbZD+La1Wo1ioqKcO3aNSgUD4Z6Xrt2DU5OTjTbJiFPSKefnE2bNqGp\nqYn7u6KiArNmzcL69esRFhamy1CIjjU2NuOnnwpw9I/zeGvRaPgO7MW1WRtbY92Et+Bs6QwDgQGA\nlhOD0tJS5ObmoqGhQeNY5ubm6N+/v0a3DyHk8bRb/O/cufNYBxKLxY+9j4GBAbf9r3OukOfLtz9e\nwVcXD6DcJAcbDpVhv8ebEAofFG93m5b59hljKC8vR05ODmprazWOYWRkBA8PD/Tu3ZtG8BDylNot\n/i+88MJjfcCys7OfSUDk+cIYQ1JxElLNDuOu8Q1ACdw2uYSiyptwc3BqtX9qamqrgQBCoRCurq5w\ndnams31CnpF2i/+GDRu44l9TU4NNmzYhJCQEY8eO5e7wPX36NBISErBq1aonenJ7e3vk5uY+WeSk\ny1Iq1dDT4+F23S3s/3M/CqoKAACubi03Wo0ZOARikVWbjxWJRFzx5/P5cHFxgUQigb6+fpv7E0Ke\nTLvFf+rUqdzvr7/+OiZPnoz169dr7DNx4kSsX78ev/76K6ZPn95xUZJu4/r1auz5JgNCz1zcMkyH\nmqm5tv59+2Km90x42nkCAJqamlpNo+zs7IzCwkKIxWK4ublxXYOEkGdLqwu+Z8+exY4dO9psGzZs\nGA4fPvxMgyLd05UrFVi36wjyDeOhvFSHgAB7CPX54OvxMUoyCuPcxkHIF6K+vh55eXkoLS1FREQE\nzM3NuWPw+XwMGzaMbtAipINpVfytrKxw5cqVNkfkXLx4UauLveT5pxAVo8D6FzQ1KsFX81BXdw9B\nrgMxy3sWHMwc0NDQgOxr2SgpKeGG+ebm5iIwMFDjOFT4Cel4WhX/f/7zn9ixYweampowYsQIWFlZ\nobKyEidOnMA333yDNWvWdHScpBvw6+WDIQM9kXotFwM9emO233SEOoaiqakJf/75J4qLi6FWqzUe\no1aroVKp6EIuITqmVfFfsmQJ5HI5vvjiC+zatYvbbmBggGXLltEqXj2MSqVGXFwxGpoUmBzpzm3n\n6/GxbPh8JLkmYWr/qRCoBcjKykJRUVGrom9jYwMPDw9YWbV94ZcQ0rG0Kv48Hg9vvfUWoqKikJ6e\njtraWlhZWcHX1xfGxsaPPgB5bsjl9/DBx3/g7N3f0MSvRoDfJvTp86DP3sXKBf0s+7Vb9EUiEdzd\n3WFj03q5RUKI7jzWHb5mZmZPvGoX6f4YY8iouog/9L9AubAGAPDZ8WNYv2i2xn48Hg91dXUahd/K\nyoor+nSDFiGdr93iP2rUqMf6kP7222/PJCDSNd2svYkDfx5AQVUB+koMUZUhR19HM7gOVkKtVre6\nSCuVSlFRUQFLS0u4u7vD1taWij4hXUi7xd/Pz48+rD3czZtyJKeVQOWaifgb8dyYfWNjfYwd6olp\n7i/BoNYACQkJGDp0qMZ/ACKRCGFhYbCysqL3ESFdULvFf+PGjdzvx48fR0hICEQiUXu7k+cIYwyH\nDuXgUNJp5BsmQlpjCJFVy9q3fD0+hvUeBhfmgrLMMq5rp6SkBE5OmtM10PuFkK5LqwHVa9euRUpK\nSkfHQroIBoZj5Xtx1egX3OPV4/r1GjAwuJm5YZr1NBjdNMKtm7c0+vQrKio6MWJCyOPS6oKvWCxG\nY2NjR8dCugg9nh5GBnvhyq1MmJoJ4S9xwjDjcBjUGUAul2vsKxKJIJVKafQOId2MVsV/xowZ2LBh\nAzIyMuDh4dHm8M6JEyc+8+BIx6uqakR8fAmmTHGDnt6DvvnpPlNxuSQdHnqucOA5gN/ABwPj2m1s\nbCCVSiESiahPn5BuSKvi//777wMADh482GY7j8ej4t8NnThxAwd+OYd8QRIMLKIwYaQ312asb4z3\nxv5/JCUmobm5mdtuZ2cHNzc36s8npJvTqvjHxcV1dBxEx2oVtThVcQSphqfBAGw/9Q2GhbwLE5MH\ns2yaGpnC2dkZ+fn5sLe3h5ubGywsLDovaELIM6NV8X94YeyGhgbU19fD0tKS5ljvhpRqJU7fOI3j\necfRYN4IQ0M+bPlmcBcpkZ6ZivCgcI39XVxc0Lt3b5iZmXVSxISQjqD1Hb7JycnYtGkTsrKyuBkZ\nBw4ciOXLlyMkJKTDAiRPr7KyEcePF8AlvA6/3oiFrEEGqAGjOkO8YOcGsbEtJFYSVFdUo76+HiYm\nJtxjhUIhhEJhJ0ZPCOkIWhX/lJQUvPbaa+jXrx+WLl0Ka2trlJeX48SJE1iwYAG++uorBAQEdHSs\n5AnExxfjyx8TkKefCIvqerg4WsJQbghhnRAmAhO4iF1gZdgyuZpAIIBcLtco/oSQ55NWxT8mJgYh\nISHYtWuXxsiOqKgoLFy4ENu3b8fevXs7LEjy5NLq45Bq+D0MefrQv2MBE54ZDAT6cLJ0goOZA3jg\nwdDQEC4uLnBycoJA8FjTPRFCuimtPumZmZnYunVrqyF9PB4Ps2bNwhtvvNEhwZGnNzowAInpCbBV\nm8PS3BBOlo5wNHeEQE8AU1NTuLq6onfv3rSACiE9jFbF39zcHA0NDW221dfX00IcXUBFRQO+/eEK\npk/1hp3dg24bLzsvBPt5QFgugLOlMwwFhhCJRJBIJBCLxTRGn5AeSqviHxwcjO3bt8Pf319jycY7\nd+5g+/btdMG3k51KuIYtP32HUsEllO0fh3XL/sWdyfN4PKwcvQLJ55NhZGQEiURCC6gQQrQr/itW\nrMCLL76I0aNHw9/fHzY2NpDJZEhLS4OpqSlWrlzZ0XGSNiiUCpy+cRo/lP4MpWEtPPli3JJlISMj\nD76+Htx+QoEQYWFh1LVDCOFoPbfPjz/+iC+//BJpaWm4efMmzM3NMXPmTMybNw+2trYdHSd5iEKp\nQPyNeMRlxUFZpYSo0QR6pgIolWqIRSa4JSuAD3PX6NKhwk8IeVi7xf/ixYvw9fXlbuSytbXFW2+9\npbPASGu3K6rx4Xf70WiSB5NmAfjNfOij5fWxE5mjn5UzXOxdIHGRdHKkhJCurt3i/8orr8DIyAiB\ngYEICwtDaGgo3NzcdBkbecgnvxzEz0m/wUrPEEb6+jC3abnIbigwRF/zvvDq5wWJRELLJBJCtNJu\n8f/kk0+QlpaGtLQ0fPTRR1CpVLCxsUFoaCj3Q909umNipYY13wh6TA/NzWpAKYDU3gUBHgGQuEho\n+gVCyGNpt/iPHDkSI0eOBAA0Njbi8uXLSEtLQ0pKCv77v/8bTU1NcHV15b4V0MLuz05+URnsrMxg\nbv5gyOaLfhMRd/4PGMmN4O0kxeiwoXB2cqapFwghT0SrC75GRkYICQnhhnQqlUqkpKTgu+++w759\n+7B3715kZ2dr9YRlZWXYsGEDLly4ALVajSFDhmDVqlUaQ0h7ql+TLuJQ/DE0NVVihPtozH9lMtdm\nbmCONdOWwZSZog/dlEUIeUpa38uvUCiQnJyM8+fPIzk5Gbm5ueDxePD29kZYWJhWx2CMYeHChRCJ\nRPj6668BAOvXr8eSJUtw5MiRJ8ugm1Or1UgtSEXC5QSUFJdBqVBAwNPD5fx01NePgonJg4VzBvTp\n34mREkKeJ39b/PPy8pCUlISkpCSkpaVBoVCgb9++CAsLQ1RUFIKDg2Fqaqr1k8lkMkgkEqxYsQJ9\n+vQBAMydOxevv/46ampqesRc8XL5PVy6dAfFJXdh53EbF7MuQl7XsjSioQEfPB4ABvAMVZDJajSK\nPyGEPCvtFv+IiAhUVFTA3NwcQUFBWLNmDcLCwrii/SRsbW2xZcsW7u+ysjJ899138Pb27hGFX6FQ\nYtXbv0AuvAoY3IFtlT74/AfdNzweDxJnR/wjcBhCvAfTqB1CSIdpt/iXl5fDysoKL730EkJDQxEQ\nEPBMF2+JiopCXFwcLCwsuC6g551cVY1q8UnoNbYU9cZGPZia6oGnx0O/vv0wOmA0+on7dXKUhJCe\noN3iv2fPHiQlJeHMmTP4n//5HxgaGnJj/sPDwyGRPN2NRMuWLcPixYuxc+dOzJs3D0ePHn1uLvrK\nZA34/fciSKXm8Pd/sAqatZE1LB3M0HSzAUZGAhhbCDHIYyDG+o+FyITWxCWE6A6P3V+W62/IZDIk\nJSXh7NmzOHfuHCorK2Fvb4/Q0FCEh4cjNDQUlpaWTxRAY2Mjhg4dinnz5mHx4sVt7nPz5k2MGDEC\ncXFxT9XtpAvnzt3EgYPJaDYohI2VPt5d9brGrKfni8/jl3O/IGRACEYMGAEDgUHnBUsIeW49qm5q\nNdrHxsYGkydPxuTJLUMPs7OzcfbsWaSmpmLVqlVQqVTIysp65HFkMhmSk5Mxfvx4bpuRkREcHR1x\n584dbXPqkurr63Ht+jVcvpGMeoscKHEPtxoMcOVKAXx9pdx+QY5BCJoeBD0eDdUkhHSex1q2qba2\nFunp6UhPT8eVK1eQmZkJlUoFT09PrR5/69YtvPHGG+jbty+8vb0BAHK5HDdu3MCUKVMeP/pOpFKp\nkZZ2G2KxGrk3snG1+Cru1N2Biqmgb6QGX82HiQkPFYpCAA+KPxV9QkhX8LfFv7CwEOnp6bh06RLS\n09Nx/fp1qNVquLq6Ijg4GLNmzUJQUJDWwz29vLwQEBCAtWvXYt26dRAIBNi8eTNEIhH3raI7SEjI\nx++/X0KtshCmDo1Q6ddrtJuL9MGz5GHwgMEY7D64k6IkhJD2tVv8g4ODUVNTA8YYevXqheDgYCxa\ntAjBwcFPPKePnp4etm/fjg8//BCLFi2CQqFAeHg49u3b160WDU8u/AOlyIBKvxl11TzY2raMxW82\naoal2BLDPIch2DEYQj5NvUAI6ZraLf5BQUEIDQ1FSEgI+vbt+8yeUCQSYePGjc/seB1JrVbj+vXb\nkEh6aYy57zvIBH8W3IOApweBMQ+N5k1wl0gxUjoS7tbuND6fENLltVv8Y2JidBlHl1Jf34CTJzNw\n+UoOapoq8F+vv4I+fey59tEeI3HK+TQsrAwR7hGOF5xfgLWxdSdGTAghj+exLvg+z1QqFe7cuYPi\n4mIU3CxAclYeqpsrAT7DL3EXsPDVB9ckREYirJy0HBIrCfT5z+7GN0II0ZUeXfwZY5DJqnD9ehEq\nKm+htLoUt+tuo6G5AcxYCVbDwHgMxY2FrR7rYePR+oCEENJN9NjiX1h4G8d/ScKNW8VQGdXCwFIB\nNdRcu8CSodmwCVJ3R4wfENKJkRJCyLPXY4t/3t1rSCk9CxWvGbxGwNbMGDwhwz3Te+CZ8xDgFIAh\nTkPgZOFEF3AJIc+d57r4M8ZQVlaO1NRcDBniA0tLc65N4tQbCsMG8BR8yPUawcwU6C9xwRCnIQjs\nHQhDgWEnRk4IIR3ruSv+jDHU1tbi5s2b+OOPLOSXlELOqqAEw5SJL3D7uVi5wNrNAnJVHWYPGoch\nTuHoZdarEyMnhBDdeW6Kf0NDA0pLS1FaWorK6kqU15cjV16ESp4c4AHJmamYPCGC68Lh8XiInrgS\nIiMRBHrPzT8DIYRopVtXPbm8EX/8kYXs7EIw1MGmD1BWV4aqpioAgJ5QjWamgpzfAH3LO2CMafTf\n25nYdVbohBDSqbp18b99uwqnEpPQoHcXCkE1bAwMwOMBjMfQbNSMZpNmGDkAY6UjEN43nBY9J4SQ\n/9Oti7/CvAplhjngq/hgjEEONQTWLYXf3c4doY6h8LX3pTnzCSHkL7p18few9YDATg8KZRMM7ABz\nG2uEOYYhrG8YbIxtOjs8Qgjpsrp18dfn6yNy2HDIGmQI7xuOAbYDaL58QgjRQrcu/gDwYv8X6SYs\nQgh5TN3+NJkKPyGEPL5uceavUqkAAGVlZZ0cCSGEdA/36+X9+vlX3aL4V1RUAABmzZrVyZEQQkj3\nUlFRAScnp1bbeYwx1gnxPJampiZkZmbC1tYWfD6/s8MhhJAuT6VSoaKiAl5eXjA0bD1XWbco/oQQ\nQp6tbn/BlxBCyOOj4k8IIT0QFX9CCOmBqPgTQkgPRMWfEEJ6oC5X/KOjo/H2229rbDt69CgmTJgA\nHx8f/POf/8TZs2c12vfv3w93d3eNnwEDBmjs89VXX2HYsGEYNGgQ5s2bh8LCwi6Vw71797Bx40aE\nhYXB19cXCxcuRElJSbfJYfv27a1eg/s/n3zyic5zeJLXoKSkBIsXL0ZAQADCw8Oxdu1a1NbWauzT\nlV8DACgsLMSCBQsQEBCAiIgIbNu2DUqlUqc5yGQyvPXWWwgPD0dAQABee+015OXlce1JSUmYNGkS\nBg4ciIkTJyIxMVHj8ZWVlVi2bBkCAgIQEhKCjz76SKc5PG389927dw+RkZH46aefWrXp8n3ULtZF\nqNVqtnXrViaVStmaNWu47bGxsczd3Z199tln7Pr162zfvn3M29ubXbhwgdsnOjqaLV68mJWXl3M/\nFRUVXPuhQ4eYr68v+/XXX1lOTg5btGgRGzFiBFMoFF0mh1WrVrGIiAh27tw5lpuby+bMmcMmTJjA\n1Gp1t8ihrq5O49+/vLycRUdHs5CQEFZWVqazHJ40/ubmZjZmzBgWFRXF8vPzWVpaGhszZgz797//\nzR2jq78G1dXVLDQ0lM2ZM4dlZWWxlJQUNmbMGLZ69Wqd5aBSqdj06dPZtGnTWEZGBrt27RpbunQp\nCwkJYVVVVezatWvMy8uL7dy5k+Xn57MtW7YwT09PlpeXxx1jxowZbObMmSw7O5slJCSw4OBg9vHH\nH+skh2cRP2OMyeVyNn/+fCaVStnRo0c12nT1PnqULlH8i4uL2ezZs1lQUBAbOnSoxhs+MjKSrVix\nQmP/t99+m82ePZv7e8aMGSwmJqbd448aNYpt27aN+7uuro75+PiwY8eOdYkciouLmVQqZefOnePa\nCwoK2NChQ1lhYWG3yOGvLl26xDw8PFhiYiK3raNzeJr4c3NzmVQqZTk5OVz7vn37mK+vr87if9oc\n9uzZw3x9fdndu3e59tTUVCaVSllJSYlOcsjKymJSqZTl5+dz2xQKBRs0aBD78ccf2TvvvNPqPTN7\n9my2du1axljL+0YqlbLi4mKu/ciRI8zX15crjh2Zw9PGzxhjZ8+eZSNGjGBTpkxps/jr4n2kjS7R\n7XPp0iU4ODggNjYWffr00WgrKipCQECAxrb+/fsjPT2d+yqYn58PiUTS5rErKytRWFiIwYMHc9tM\nTEzg5eWF1NTULpFDUlISRCIRQkJCuHYXFxfEx8fDycmpW+TwMMYY3nvvPYwaNQoREREAdPM6IADr\njQAACrNJREFUPE38FhYW0NPTw6FDh6BQKFBVVYUTJ07Ay8tLZ/E/bQ5FRUVwc3ODpaUl136/+zM1\nNVUnOTg4OODzzz9Hv379uG33J1+sqalBamqqxvMDQFBQEPf8qamp6N27NxwdHbn2wYMHo76+HtnZ\n2R2ew9PGDwCnT5/G5MmT8e2337Y6vq7eR9roEnP7TJo0CZMmTWqzzc7ODrdv39bYVlpaiubmZtTW\n1qK5uRk1NTU4c+YMtm/fjsbGRgQGBmLlypUQi8Xc5EZisbjVcZ/lRHFPk0NhYSEcHR0RGxuL3bt3\no6qqCn5+flizZg3s7e27RQ4ikYjbHhcXh6tXr2Lz5s3cNl3k8DTxi8VirF27Fps2bcKBAwegVqsh\nkUiwb98+ncX/tDnY2dkhPj4earWaW7K0tLQUQEvR0UUOVlZWGDp0qMa2b775Bk1NTQgPD0dMTMzf\nPv+dO3dgZ2fXqh0Abt++DYFA0KE5PG38ALB27dp2j6+r95E2usSZ/9+JjIzE/v37cf78eahUKly4\ncAE//PADAKC5uRnXrl0DAAgEAmzZsgXvv/8+CgsLMXfuXDQ1NaGxsREAYGCguZSjUCiEQqHoEjnU\n1dXh+vXr2LNnD1avXo2YmBhUVlbi1VdfhUKh6BY5PGzv3r0YM2aMxmRSnZ3Do+JXq9W4ceMGQkJC\ncPDgQXzxxRfg8/lYvnw5VCpVp8evTQ5jx45FZWUlPvroIzQ2NkImk2H9+vUQCARobm7ulBzi4uLw\n8ccfY968eZBIJGhqaoJQKGz3+RsbG1vFp6+vDx6P1ymfhceN/1G6wvvovi5x5v93Fi5ciKqqKixY\nsAAqlQqurq547bXXsHnzZpiZmSE8PBznz5/XOPN0dXVFREQEEhMT0bt3bwAtV94fdu/ePRgZGXWJ\nHAQCAeRyOWJiYrivu9u2bUN4eDgSExPRq1evLp/DfWVlZbh48SL27t2r8fj7E0t1Vg6Piv/YsWOI\njY1FfHw8jI2NAQBOTk4YOXIkEhMTubPPrvwaiMVixMTEIDo6Gl999RWMjY2xdOlS5ObmwszMTOev\nwZEjR/DOO+9g3LhxWLlyJYCWovfXk4WHn9/Q0LBVfM3NzWCMwdjYWKc5PEn8j9LZn4OHdfkzf6FQ\niOjoaFy6dAlnzpxBbGwsDA0NYWNjw31IHy78QMtXKCsrK9y+fRsODg4AHkwLfV95eXmrr16dlYNY\nLIaxsbFGP6e1tTUsLS1x8+bNbpHDfXFxcbC1tW3VL9rZOTwq/oyMDLi4uGjk4ujoCCsrKxQXF3d6\n/NrkAADDhw9HUlISEhMTcf78ebz44ouoqqqCo6OjTnP49NNPsXr1arz88sv48MMPuW4oBwcHlJeX\nt/v89vb2bcYHtHSV6CqHJ43/UbrC++i+Ll/8t2zZgl27dkEoFMLW1hYAcOrUKYSFhQEAvv76a4SH\nh2v8b1xaWoqqqiq4ubnB2toazs7OuHjxItdeX1+PzMxMBAYGdokcAgIC0NDQgIKCAu4xFRUVuHv3\nLvr27dstcrjv/gWx+x+W+zo7h0fFb29vj8LCQo0zsvLyclRXV8PJyanT49cmh9TUVLz66qtQqVSw\ns7ODUCjEqVOnYGxsDD8/P53lsHv3bmzduhVLly7FO++8o7Hanr+/P1JSUjT2T05O5i5k+/v7o6Sk\nROPaRnJyMkxMTODh4aGTHJ4m/kfpCu8jjk7HFmlh9uzZGsPbDh06xPz8/FhCQgIrLi5m69atYz4+\nPqygoIAxxlhRURHz8fFhK1euZPn5+Sw1NZVNmTKFzZgxgzvGgQMHmI+PD/v5559Zbm4uW7RoERs1\nalSHjat93BzUajWbOXMmi4yMZJcuXWLZ2dlszpw5bMyYMVyMXT2H+0aNGsU+/fTTNo+pyxweN/6y\nsjIWEBDAli5dyvLy8lhGRgZ7+eWX2eTJk1lzc7PO43+SHCorK1lAQADbuHEjKy4uZidPnmR+fn4a\nr0dH55Cdnc369+/PVq9e3eq+j/r6epaTk8M8PT1ZTEwMy8/PZ1u3bmXe3t7c0Eq1Ws2mTZvGpk+f\nzjIzM7lx/g8PjezIHJ42/r9qa6inrt9H7enyxZ8xxnbs2MEiIiKYj48Pmz17NsvIyNBoT09PZ7Nn\nz2a+vr5s8ODBbNWqVay6ulpjn88++4yFhYUxHx8f9q9//UtjHHFXyKGmpoatWbOGBQYGMh8fHxYV\nFcVu377drXJgjDFfX1924MCBdo+rqxyeJP7c3Fz22muvscDAQBYWFsZWrlzJKisrOyX+J80hJSWF\nvfTSS2zgwIFs5MiRbM+ePa2O25E5bN68mUml0jZ/duzYwRhjLD4+no0bN455eXmxyMhIdvbsWY1j\nlJeXs6ioKDZo0CAWGhrKNm/ezFQqlU5yeBbxP6yt4t+R8T8OWsyFEEJ6oC7f508IIeTZo+JPCCE9\nEBV/Qgjpgaj4E0JID0TFnxBCeiAq/oQQ0gNR8Sc9WnR0NNzd3dtdjSkuLg7u7u7YuXOnjiMjpGPR\nOH/So9XV1WHChAng8Xj4+eefYWJiwrXJ5XKMGzcO9vb2+Pbbb8Hn8zsxUkKeLTrzJz2aqakp3n33\nXdy6dQtbtmzRaPvwww9RU1ODjRs3UuEnzx0q/qTHi4iIwJQpU7B//35kZGQAAFJSUnD48GG88cYb\nGqvEHTx4EGPHjoWXlxdGjBiB3bt3469fng8cOIApU6Zg0KBBGDhwIKZOnYrff/+daz98+DB8fX2x\nf/9+hISEICgoCDdv3tRNsoT8H+r2IQQtS/SNHz8e9vb2OHDgAKZOnQorKyt8/fXX3KyOO3bswCef\nfIK5c+ciLCwMGRkZ2LlzJ+bOncvN975nzx5s2rQJy5Ytw6BBg1BdXY1du3YhLy8PcXFxsLOzw+HD\nhxEdHQ2JRIKVK1fi7t27mDx5cmemT3oinc8mREgX9fvvvzOpVMpmzZrFfH19uUXPGWOsurqaeXt7\ns/fee0/jMV988QUbMGAAKysrY4wxtm7dOrZlyxaNfTIyMphUKmUnT55kjLXMzimVStmvv/7awRkR\n0j7q9iHk/4wcORLjx49HSkoKVq1apbGA+qVLl6BQKDBs2DAolUruZ/jw4VAqlbhw4QKAlvVbly9f\njpqaGly+fBk//fQTDh48CKD1cpf9+/fXXXKE/EWXX8aREF0KDw/H8ePHERERobG9uroaADB37tw2\nH3d/dafCwkJER0cjOTkZQqEQLi4ucHNzA4BW1wYeXjWMEF2j4k+IFu6vUxwTE8OtC/0wsVgMlUqF\nhQsXwtTUFEeOHIG7uzsEAgFycnIQGxur65AJ+VvU7UOIFnx8fKCvrw+ZTAZvb2/uR6FQYOvWrZDJ\nZJDJZCgqKsK0adPg6ekJgaDl3OrMmTMAALVa3ZkpEKKBzvwJ0YKNjQ1eeeUVbNq0CTU1NfDz80Np\naSm2bNkCS0tLuLq6Ql9fHw4ODti7dy+sra1hamqKM2fO4JtvvgEANDY2dnIWhDxAZ/6EaGnlypVY\nvnw5YmNjsWDBAmzduhVDhw7F3r17IRQKwePxsHPnTlhbW+PNN9/E8uXL8eeff+Lzzz+Hk5MTUlNT\nOzsFQjg0zp8QQnogOvMnhJAeiIo/IYT0QFT8CSGkB6LiTwghPRAVf0II6YGo+BNCSA9ExZ8QQnog\nKv6EENID/S9s8sixJDKW5AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd04c7f98>"
+      ]
+     },
+     "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": 53,
+   "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": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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97kG+CgIDA7ly5QqzZ88mIiKC27dv89dff/HBBx/QpUsXWrVqBRTdV/D09CQo\nKIidO3cSExPDtWvX2LZtG6tWrWLixIllbmP69OlIksTw4cM5fPgwd+7c4dSpU4wZM4b4+Hhmz54N\nQEBAAOnp6UyfPp1r165x+fJlPvzwQ27dulWieqo0WlpahIeHM3v2bC5dusSdO3fYvn072traymTX\npk0bduzYQUREBOHh4Xz66afKz5mxsTHHjh1THouYmBj27NmDkZERTZs2fcYjLQg159atW4SGhgJF\nY4Y/3pLqSXfS7rDivxXkFeYhIZGeLPFO63fxsvGqrnDLVGbiePPNNyu9MplMRv/+/Z8poFdV8+bN\n2bx5M/fv32fkyJH4+vqyYMECevbsyZIlS5TzaWhosHr1agYNGsSPP/6In58fgwcP5ueff+bLL78s\n9/hbW1uzfft2PDw8+PLLL/H19WX69OnUr1+fnTt30qxZM6CorvWHH34gMTGRQYMGMWbMGOrXr88P\nP/ygdjXRwoULsbGxISgoiL59+/LXX3+xfPlybG1tgaJnSgwNDfH39+f9999n0KBBygFpNDQ0WLVq\nFVCUUP38/IiOjmbdunUVXu0IQm1169YtLl++rHxtYmJS7tWzjZENPrY+5OQWEHOlEK1TXTj7R2GZ\n81cnmVSJEc6vXbtGdnZ2qTdx3dzcnmtgj4uNjaVbt24cOnQIGxubKtuOIAhCVbh586ZKl0AmJiZ4\neXkpq4LLopAUrDi8kXM7jdGRiqq0Jk50xdm54rE4qvJ7U63muGFhYUyePJm7d++WmCZJEjKZrNxL\nLkEQhFfVjRs3CA8PV742NTWlXbt2KkkjKSuJXVd2MdRpqEqLKQ2ZBpO6jWRDXDgnT97ltdcaIZeb\nVmv8pVErccybNw8NDQ3mz5+PtbV1rX2aURAEoTYpLWl4eXkpm9dLksQ/t/9h15Vd5BbkkldQwNDm\nb2NhoXrDfODAFnTq1JBmzUyqNf6yqJU4wsPD+e677+jevXtVxyMIgvBSiI6OVqmJMTMzo127dsqk\nkZydzIZLG7iaUDRPSmoOP/x3kNi6jZg/vR8aGo+a5urr69CsWe1phq5W4jAzM6vVj78LgiDUJpIk\nkZqaqnz9eNKQJInjt4+z68oucgpyAMjLL+Tm5ULsMweQWliXI0du062bbU2FXyG1EsfQoUNZvXo1\nXl5eJR5YqYzTp08zYsSIUqe1a9eODRs2PPW6BUEQaguZTIabmxtnz56lsLAQT09PtLS0SMlOYWPo\nRsIfhKvh18feAAAgAElEQVTM+3rLPryp7cCvB26jp6eNgUHtuboojVqJIy4ujujoaDp16oRcLi/1\nacd169ZVuB5XV1eOHz+uUnbixAlmzJjB2LFjKxG2IAhC7aahoYG7u7vy/xO3T7AjfAc5BTlISMiQ\nYWVgxUiXkdiZ2VEgV1CQp0HPnk0wNHwJEsfNmzdp2bKl8nVxf0SVpaOjg6Xlo2ZkGRkZfPvtt4we\nPRpvb++nWqcgCEJNkySJ+Ph4rKysVLoNKa7ij0yKZMOlDUhI3L37kPv3H/Lhm0PwdxyItmZR6yot\nLQ0GDpTXSPyVpVbiKKv/o2e1YsUKdHR0yn3aWRAEoTaTJInQ0FBu376NXC7H3r5k/3dyczkeDTz4\n6c/fyUqsizzrDbSutUHbpfznOGqrSnWrHh0dzZkzZ8jMzMTU1BR3d3fl08aVlZSUxKZNm5gzZ84z\n3TcRBEGoKQqFgosXLxIXFwcUDYBmbGxc4soDYKjTUJJjNYn61QpNtImISKagQIGW1ov3eINaESsU\nCmbNmkW/fv34/PPP+e677/jkk0+UXVZU4uFzpa1bt2Jubo6fn1+ll30Zde3alRUrVlQ4LTY2Fnt7\nez744INS5y1tZL1ixcs+/ufi4sIbb7zB5s2bVd7HPXv2lJj38b/ff/9dOe/169eZPHkyXl5eODo6\n0qNHD77++usye/sNCgrC3t6eS5cuqXVsBKE2UigUnD9/Xpk0AGxsbLiRd4Mv//mS3IJclfkNdAz4\n2O9tXJ0b0K+fHTNner2QSQPUvOJYvXo1P//8M8HBwfTr1w8LCwsSEhLYv38/S5Yswc7OrtI3t/ft\n28eAAQMqfOReKN2vv/6Kr6/vUz1bs2LFCpydnZEkiYyMDI4cOcJXX31FbGysyuBNmpqaHD16tNR1\nGBsbA0VD0g4bNozu3bvzww8/YGhoyLVr15g/fz5hYWElWsolJCRw/PhxmjRpwvbt28sckVAQarPC\nwkLOnj3LgwcPlGWWDSw5nnecsEthFBQomLpuGV8MnoiJSV3lPDKZjAkT2pTaffqLRK3EsWvXLsaP\nH8+YMWOUZdbW1owdO5bc3Fx27dpVqcQRFRVFTEyMGJjnGTRq1Ig5c+bg6emp/BJXl7GxsbKRQr16\n9bCzs0NLS4sFCxYwcOBAmjdvrpz38cYMpSm+8pg3b56yzMbGBn19fUaOHElERIRKw4p9+/ZRr149\nhg8fzv/93/8xc+bMEmOgC0JtVlBQwJkzZ0hKSnpUZlzAtgfbyC7IJjklm8jIFDRzsti4NZT33m2r\nsvyLnjRAzaqqhIQEZbOyJ7m5uXHvXuXG2z579iyWlpbY2ZU9yJJQvo8++oj8/Hzmz5//XNbn7++P\njo4Ov/32W6WW09DQICMjg3PnzqmUe3p6cuDAgRLdoP/88894eXnRo0cPsrOz2bdv3zPHLgjVJS8v\nj3///VeZNPIK84gkkt9Tfye7IBsATQ0NLDKccMsYStjFVK5fTy1vlS8kta44GjVqxIULF2jfvn2J\naRcuXKjwV+mTrl69ilxe9c3O9l/bz4HIA2rN623rTYBzgErZptBN/BPzj1rLvy5/nX72/Sod49My\nNzdnxowZTJs2jb59++Lj4/NM69PX18fGxobIyMhKLefr68u6desYNmwYDg4OtGvXjnbt2uHl5UWL\nFi1U5r18+TKRkZEEBwdTv3592rRpw86dOxk2bNgzxS4I1SEnJ4dTp06RkZEBQHxmPBFEkGGQAf//\nIsJCz4LgDiM5pcgnPDyRIUNaYmdXO/qXep7UShxvvfUW3333HXp6evTt2xcLCwsSExP55ZdfWLVq\nFUFBQZXa6IMHDypdvSKU9Oabb/Lbb78xe/ZsDhw48MxVPk8OX1tYWFjquOOmpqYcPnwYKOoeevfu\n3axfv56DBw+yfv161q9fj4GBAVOnTmXo0KHK5UJCQjAyMqJDhw5AUdKZO3cuoaGhYlQ/odbLyckh\nOzub3MJcopKjuFv3LimaD5FlFfUl1bVpV96wf4M6WnVo5J+Pv78cXd2X8x6uWokjMDCQq1ev8tVX\nX7FgwQJluSRJ+Pn58e6771ZqoytXrqxclK8ALS2tUsc5gaLWG8Udoz3ps88+w9fXl6+//prPP//8\nmWLIzMxUuXrU1NTk559/LjHfk70jm5qaEhwcTHBwMHfv3uXkyZNs2bKFOXPm0KBBAzp37kxeXh6/\n/PIL3bp1Uw4G1bt3b7788ku2b98uEodQ65mYmNC2bVv2/LWH2LqxRCclcSc2A2sDK74f/yH2lo9q\nUV7WhFFMrcShqanJggULGDNmDP/99x/p6ekYGRnh6elZojqiNuln3++Zqo8CnANKVF9VlSd/7T8u\nLS0NE5PSL3etra2ZNm0as2fPfqZhe7Ozs7l582aJBgvFI/aVZfXq1dja2tKrVy8AGjRowFtvvYWf\nnx+9e/fm6NGjdO7cmcOHD5OamsrevXtV7msoFAp+/fVXZsyYIW6SC7Weubk5I94cwZ2/H/B3aAwN\nctrQJLUDsaF1sO9W09FVn0o9ANiiRYtanSheZA4ODly4cKFEeUREBFlZWTg5OZW57KBBg/j111+Z\nNWvWU29/586dKBSKSief0NBQfvvtN7p3767Sg7KOjg66urqYm5sDRdVUVlZWrF27VmX5c+fOMWfO\nHPbv369SrSUINS0pKQltbW1kdWQY1nk0ZHGdOnWY1Gkstg87cOaPfJo3N6F1a/MajLT6lZk4evXq\nxeLFi2nZsiU9e/assAnZH3/88dyDe5UEBgbSv39/Zs+ezbBhw9DT0yMyMpKFCxfSpUsXWrVqVe7y\nc+fOpV8/9a6u0tLSSEhIQJIk0tPTOXbsGIsWLWLcuHE0btxYZd6EhIRS16Grq4uBgQETJ05k2LBh\njBs3jjFjxtC4cWPu3btHSEgIaWlpDB48WPnsxsSJE0s0irCzs2PNmjXs3LlTJA6h1rh//z7HTx8n\nMjUSGsF7bT/E0ODR8xj19Osx6g0LnGzi8fS0fima2FZGmYnDzc0NfX195f+v2oGpbs2bN2fz5s0s\nW7aMkSNHkpWVhbW1NX379lWrLy8bGxuCg4P54osvKpx3woQJyv9NTEyws7Pjiy++4I033lCZr7Cw\nkE6dOpW6juHDhzN79mxatWrF9u3b+f777/noo49ITU3FyMiIjh07sm3bNiwsLFi3bh0ymYxBgwaV\nWI+mpiYjRoxg/vz5XL58udwrK0GoDrdv3+a3E79xM+UmBYWF3D+VwdhDy/lp9mTq1Hn0lampqUHb\ntvVrMNKaI5Oepr+QalaVg64LgiAU+y/sP/749w/S89IBiEtIJzzrPvVz3BnV0Z/Bg1tWsIbaoyq/\nN8u84oiPj6/UiqysrJ45GEEQhJpQUFjAtr+3ERYRhoKi1o2F2oUY2BnicLITBop6ZGcXIEmSqH2h\nnMTRuXPnSh2gx8fWFQRBeFHcSrnFhoMbyEx81KpRUVdB5/ad6WPfh21E4uZmhYODRQ1GWbuUmTi+\n/PJLkVkFQXiphYSHcPz0caQMGenpeRgb18HU0phRfUZhY1JUvRMY6FDDUdY+ZSaOAQMGVGccgiAI\n1aqgoIAHkQ/ITVKQnpYHyJDl1Gf6oPfR0qzUkwqvnDKPTmWe7pbJZJXudkQQBKEmaWpq4tnEkxt3\nbpOj0EY7S05WcmMSE3KxthaJozxlHp1FixapvRKROARBqO1C40NpZNQIU11ToOh7q41LGzKyM4i4\nXEhqqj6BgQ5YW+vXcKS1X5mJIyIiojrjEARBqBLpuelsC9vGubvnMCtoygSPCTRqZAQU9bvm096H\n9h5FQ7hqaIj7uuoQ12OCILyUJEni5J2T7Lqyi9SsDB5E5hCTdoWvwnezdOYoZZKQyWTo6GhWsDbh\ncaLLEUEQXjoPHj5gU+gmriVeA0AnTQfzh3XQlZlCgiZHj96hS5fGFaxFKIvockQQhJdGoaKQg9cP\n8kvUL+QX5oMEdVPrYpxnjIFlfRJjoUHTOri5iWcynkWZiePxIUm/+uqraglGqFpnz55l+PDhandB\nsGfPHmbNmsWVK1eqITpBeDY3U26yMXQjd1Jjyc4pQF9XG70kPZpqNcW2vi0yNJBs69KnT2flmDDC\n01H7HodCoeDIkSOcO3eOzMxMzM3Nadu2banDyQqCIFSn+5n3WXBiASkp2URFpyJTyPBr4YbcqAUG\nOkXjvNSvXx9XV1eV7v+Fp6NW4khMTGTMmDFERESgo6ODmZkZSUlJrFy5kvbt27Ns2TL09PSqOlZB\nEIRSWRtY06aeG9//ux/NPG3cNdwwTrHFwLQoaTRt2hQHBwdR5f6caFQ8S1FVVUJCAmvWrCE0NJS/\n//6by5cvs3TpUsLDw1WGkxWejr29PTt37mTIkCE4OTnRt29fLl68yJYtW+jcuTNubm58+OGH5OXl\nKZc5e/YsAQEBuLq60qFDB+bOnUt2drZyekREBAEBAbi4uPD6668THh6usk2FQsHKlSvp0qULbdq0\nYeDAgRw9erTa9lkQnpZCKjnM8nCXofg6eNOZrlhoWqKvVzR8q4ODg0gaz5laVxxHjhzhk08+wdvb\nW6W8e/fuJCcn8+233/LZZ59VSYDP4tq1a0RGRqo1r62tbYlxr0NDQ4mJiVFreblcjr29faVjfNx3\n333HvHnzaNKkCdOnT2fcuHE4OTmxZs0abt68SXBwMB4eHgwbNoxLly4xatQoAgMD+eyzz4iNjWXO\nnDnExsaycuVK0tLSGDVqFF5eXuzevZtbt27xySefqGxv4cKF/Pnnn3z++ec0btyYf/75h0mTJrF2\n7VratWv3TPsiCFVBISk4eusoR2OO8oHnVIz1Hw03bKBjQI/6nlxNvYeVlT5162rh6upKgwYNajDi\nl5NaiUNHRwdDQ8NSp4k35fkZNGgQXbt2BeCNN97g888/Z86cOTRq1Ai5XM7atWuJiooCYP369Tg6\nOjJt2jSgaCS9OXPmMG7cOKKiovjvv//Iz89n3rx56Ovr07x5c+Lj4/n8888BePjwIRs2bGDp0qXK\nHwS2trZERESwevVqkTiEWudO2h02hW7iRvJN7sRmMOy3BWya/j9MTYtG5pPJZHh4eJCTcxwNDQ08\nPT0xMzOr4ahfTmoljqFDh7J48WJcXFywsHjUjC0rK4vVq1fj7+9fZQG+Sh4ftlVXVxcNDQ2V1k91\n69ZVVlVFRUXRuXNnleU9PDyU06KiomjatKmySTVAmzZtlP9fv36dvLw8Jk+ejIbGoxrL/Px8lfdY\nEGpabkEu+yP3c+jGIRSSgqsRSSQl5aCnuMWWbZeZ+K6ncl5DQ0Patm1L3bp1VT77wvNVZuJ45513\nlP9LksT169fp3r07bm5umJubk56ezvnz5ykoKKBevXrVEmxl2dvbP1P1kbOzc4nqq6qkpaX6dshk\nsjLrZevWrVuirHgwRy0tLWQyGU8O7qitra38v7g54tKlS7G1tVWZ7/FEIgg16XL8ZbZc3kJydrKy\nzLaRKQZxjWmU6056aiY5OQXUrfvo3DE3N6+JUF8pZSaO/Px8lddubm7K8vv37wPQsmXRMIoPHjyo\nqviEMtjZ2XHhwgWVsnPnzimnpaWlERISQlpaGsbGxgCEhYUp57W1tUVbW5v4+Hh8fHyU5cuWLaOw\nsJDJkydXw14IQulSc1LZHrad8/fOq5TbW9gzvMtwjmjEk5Nzi3r1csjNfUjdusY1FOmrqczEsXHj\nxuqMQ6iksWPH0r9/fxYsWIC/vz9xcXF89tlndO7cGTs7O6ysrFi+fDkff/wxwcHBxMfHs2TJEuXy\nurq6jBo1ioULF6Kvr4+TkxNHjhxh+fLlzJs3rwb3THjVnbh9gh3hO0jJyCD6eipNmxhjZWaKf2t/\nvGy8SEtLw8zsPrm5MgoLCzhz5gxdunQpccUuVJ0yj/S5c+dwd3ev9ArPnj2rrGsXqo5cLmflypUs\nWrSIjRs3YmJigq+vL1OmTAHAwMCAn376ic8//xx/f3/q1avH2LFjlTfHAaZMmYK2tjZff/01iYmJ\nNGrUiM8//1wM4iXUKAmJO/eSuRaZgkIh0aDQhjn9P8aoriH37t3jwoULFBYWAkXVqq1atRJJo5rJ\npCcrwv8/Pz8/7OzsePfdd5HL5RWuKDQ0lDVr1nDr1i3279//XIOMjY2lW7duaneVIQjCi0uSJD49\nOI/fDkXQLLMrZlIjPvjAHZksSWW4Bx0dHTw8PMQ9jTJU5fdmmWl69+7dLFu2jIEDB9KkSRN69uyJ\ns7MzNjY26Orqkp6eTnx8POfOnePYsWPcvHmTgIAAFi5c+FwDFATh5XXp/iVM6ppga/KogYZMJuPD\nzpPwLHhAWGgKw4bJSUq6SVxcnHIefX192rVrJ1pO1ZAyrziKxcfH8+OPP3LgwAESEhJUWvlIkkSD\nBg3o1asXo0aNwsrKSq2N7ty5k7Vr13Lv3j2aN2/ORx99VG6fV+KKQxBeLinZKWwN28rF+xch3YRJ\nzsE4O6l+fxQWKsjLy+PcubOkpKQoyy0sLHB3dxcdFVagRq44illZWTFt2jSmTZvG9evXiY2NJSMj\nA1NTUxo0aEDTpk0rtcGQkBA+++wz5syZg6enJ1u2bGHChAns379fJAVBeMkpJAWHbx5m37V9pD98\nyLVrKaSmxZERvpUfWkxSaVYrSQpOnjxBVlaWsszW1hZHR0fRZLyGVeqOkp2dHXZ2dk+9MUmSWLp0\nKWPHjuWtt94CYNq0aZw6dYoLFy6IxCEIL7FbqbfYFLqJO2l3ANDQlJGVlY91ngO66c34888Y+vV7\n9P2ipaVF48aNiYiIQCaT0bp1a5o2bSr6nKoFqrUpwo0bN4iLi6Nv377KMg0NDfbu3VudYQiCUI2y\n87P5OeJnjsYcVXko1da0EX27vcOhnQ/p3sOWnj1tSyzbvHlzsrOzsba2rrUPGr+KqjVx3Lp1C4D0\n9HRGjBhBVFQUzZo1Izg4WPmAoSAILwdJkjh/7zzbwraR9DCFzMx8TE3qoq2pzevy1+nerDuaMk28\nHbKwstKnsLCQvLw8lXsXMpmsWntvENRTrRWFmZmZAEyfPh1/f3/Wrl1LixYtGDlyJNevX6/OUARB\nqGJJ2UmsOb+GqDv3OHs2nitXkmhmaM+c1+bQu3lvtDSKusaxstInOzubEydO8N9//6FQlOwyXahd\nqjVxFPeVNH78ePr164eDgwOffvopTZo0YevWrdUZiiAIVcxCz4IeTXty504Gsjxd5Bl9MLzSBQs9\n1U40k5KS+Oeff0hLSyM5OZnLly+X6GdNqF2qtaqquI7y8QcKZTIZzZo1IzY2tjpDEQThOXuY9xB9\nHdXnKvxa9uNB52zObjPC0tSYTp1UG8DExMSoJAqZTIaJiYm4AV7LqZU4cnNzWbVqFX///TdZWVml\n/hr4448/KlyPg4MDenp6XL58GScnJ+BRz7ti7HJBeDHlFOSwN2IvJ++cZHKbaTSr/2iMHm1Nbd7t\nMpyzhvdxcrKgTp2irxyFQkFYWJjKQGl16tTB3d1dPAn+AlArccybN4+dO3fStm1bWrRo8dRtqHV1\ndRk5ciSLFi3CwsICuVzOli1buH37tkoHfIIgvBhC40PZcnkLiZlJ3LqVzsi/vmTT+/OwtVXtrdbD\nw1r5f25uLmfPniU5+VFX6cbGxnh6eqKrq1ttsQtPT63E8ccff/DBBx8wbty4Z97g5MmT0dXV5csv\nvyQpKYlWrVqxfv16mjVr9szrFgSheqTnprP18lZlt+dR0ak8eJCFmWTOT5suMWuGNxoaJaubUlJS\nOHv2LDk5Ocqyhg0b4uLigqamZrXFLzwbtRJHXl7ec2sSJ5PJCAoKIigo6LmsTxCE6iNJEv/G/svO\n8J1k5T96otuheQMs77TALKcFpk0NyMkpQE9PW2XZpKQkTp06pWw1JZPJaNmyJXZ2duKexgtGrcTR\nqVMnjh07hpeXV1XHIwhCLZWUlcTG0I1cTbiqUt6hUQfeav0W/5kkoa+vjYeHdamJwMTEBCMjI1JT\nU9HW1sbd3R1LS8vqCl94jtRKHH5+fsyaNYuUlBTc3NxKHba0X79+zz04QRBqh3/v/MuWy1vIyM4m\nOjqF+vX1kTdsRIBzAK0sWwHw2mvl91SrqamJh4cHly5dwtnZGT09veoIXagCaiWO9957DyjqoDAk\nJKTEdJlMJhKHILzEDOsY8iApnStXkigslLBIc+bjAVMxNig7WWRkZGBgYKBy9aGrqytqLl4CaiWO\nQ4cOVXUcgiDUYo71HOnSwps7V0/QJLMLhoXWREVk4OFRMnFIksTNmze5cuUKrVu3Fg1fXkJqJY6G\nDRsq/8/KyuLhw4eYmJgonwQXBOHlcS/jHpl5mbQwb6FSPsojgDYavfll/y1GjHCgRQvTEssWFBRw\n6dIl7t69C8CVK1cwNjYWz2a8ZNR+cvz06dN8++23hIeHKx8AdHZ2ZsqUKeLhPUF4CSgkBX9e/5N9\n1/YhK6jDiMZTaOvaWDm9rlZd2nk2xMOtAVpaJZ/lysjI4Ny5c2RkZCjLTExMxL2Ml5BaieO///5j\n9OjRNG3alPfffx9zc3MePHjA77//ztixY/nxxx/x8PCo6lgFQagi9zPv8+PFH7mRcoO42ExuxaQT\ndXwl25rMxtT0UWMYmUyGllbJFlNxcXGEhoZSUFCgLGvSpAkODg5i0KWXkFqJY/HixbRv357Vq1er\n3OiaMGEC48aNY+nSpfz0009VFqQgCFVDISn468Zf7I3YS4GiAEkB9+8/RD/fEsssJ7ZsucrEia5l\nL69QcOXKFW7evKks09TUxMnJiUaNGlXHLgg1QK3EERYWxqJFi0q0zZbJZAwfPpwPP/ywSoITBKHq\nJDxM4IeLP3A9+dGQBtpaWkzsFsDpLUY0sjHCz6/sET+zs7M5d+6cynjg+vr6eHh4YGRkVKWxCzVL\nrcRhZGSkMu7v4x4+fCi6ChCEF4gkSfxz+x92XdlFZk4W2lpF528j40aMajMKGyMbvCwSsbc3Q1Oz\n9GomSZI4e/YsqampyrL69evj4uIiGs28AtSqfPTy8mLp0qXEx8erlMfHx7N06VJxc1wQXiCrz61m\n46VNRN5I4Mzp+2RlFdDPvh8zOs3Axqio2/PWrS3KTBpQVNvg5OSEhoaGcjxwd3d3kTReEWpdcQQH\nBzNw4EB69eqFu7s7FhYWJCYmcu7cOQwMDPjoo4+qOk5BEJ6TVpat2P7PIe7de4iewox613zpPbAv\nmpW8iW1iYqJ8Alw0t321qPVJsbKyIiQkhKFDh5KRkcHFixdJT09n2LBhhISEiJtggvAC8W7sTTen\nttgWuOOWMRSrujZkZeWXu0xiYmKJGgeARo0aiaTxClL7OQ5LS0umTZtWlbEIgvCcRSZFYqhjSH3D\n+soymUzGjG4f0lErloICia5dG5faBToU3cuIjIwkKioKLS0tvL290dcvv08q4eVXZuJYuXIlAwYM\noF69eqxcubLclRR3lS4IQu1QoChg37V9/HH9D/ITDfmw7Uc4trZSTteQafDaa43LWQPk5ORw/vx5\nkpKSAMjPzycsLIx27dpVaexC7Vdm4li0aBEdOnSgXr16LFq0qNyViMQhCLVHfGY86y6sI/LBDSIj\nk0lJiWVO5Ho2zJpaYoyMsiQkJHDhwgVyc3OVZRYWFri4uFRV2MILpMzEERERUer/giDUTpIkcfLO\nSbaFbSOvMA+ZDDIz8zEtaIRBuj0HD97izTdblLsOhULBtWvXiI6OVpbJZDLkcjktWrQQAy4JgJo3\nx5ctW1bqjTEo6mpg7ty5zzUoQRAqJys/izXn17Dh0gbyCvMA0KtThwmdR+KUNQC/no68/nrZD/NB\nUQemJ0+eVEkaderUwcvLC7lcLpKGoKTWzfHly5fj4+ODlZVViWkXL15k+/btzJo167kHJwhCxa4n\nX2fN+TXcS0mgbt2iU7q+YX3GuI3BxsiGeJeHWFmVf0P7/v37XLx4kfz8R62rLC0tcXV1pU6dOlUa\nv/DiKTNxDB06lIsXLwJFl8CDBw8ucyVOTk7PPzJBECr0R/Qf7Azb/f/vZeTg7m5FD3lX/B380dHU\nAagwaUBRdVRx0hBjgQsVKTNxzJ07l4MHDyJJEkuWLGHQoEFYW1urzKOpqYmhoSHdu3ev8kAFQSgp\nIy+Dy2EJpKfnoSXVwSKmK8MGDqv0F76VlRVNmzYlPj4eNzc3TE1LjrUhCMXKTBx2dna8++67QNEN\nM39//1KrqgRBqDlvtnyT006X+efYXewf9qKNS2sKC6VSuz4vJkkSOTk56OrqqpS3bt0ae3t70W2I\nUCG17nFMmjQJgJSUFPLz85UDOUmSRFZWFufOncPf37/qohQEAYWkIK8wj7paj8bH0NLQYnbvqZzQ\nfUADayOcnS3LXUdubq6y54fOnTujo6OjnKahoSHGzhDUolbiuHbtGlOnTlVpbfE4mUwmEocgVKG0\nnDTWnl/LnVtZfNR5Cg0bGiqnGdYxpHdPw3KWLvLgwQMuXryofDYjNDQUd3d3cR9DqDS1EsfXX39N\namoq06ZN48iRI+jo6NClSxeOHTvGsWPH2LBhQ1XHKQivrKsJV/n+9GrOh98mKSmHlOh1rJv5fqnD\nt5amsLCQq1evqgy2BIghXYWnptYn7+LFi0yePJlRo0bRt29fsrOzGTZsGCtXrqR79+5s3LixquMU\nhFeOQlKw/9p+Fp9eTMrDNFJScpEBDxIzOXQoRq11pKWl8c8//6gkjeJnM1q3bi2uNoSnotYVR15e\nHk2aNAGKxhF+/EnyAQMG8Omnn1ZJcILwqkrPTWfd+XVEJBada3p62ji1aIh03pMBPp3o2rX8fqYk\nSeLGjRtERESgUCiU5dbW1jg7O4tnM4RnolbiaNCgAbGxsXh4eNCkSRMyMzOJi4ujYcOG1KlTh7S0\ntKqOUxBeGZFJkaw6u5rMvAxlWUuLlrzT4x2Su0o0bWpS7vLZ2dlcvHiRxMREZZmmpiYODg40btxY\nXIdTCfUAACAASURBVGUIz0ytxNG9e3e+/fZb9PX16dGjB82aNWPx4sUEBQXx448/Vmo8jujoaHx9\nfUuUb968GQ8PD/UjF4SXjCRJ/Bb1G98f2czdu5m0ca2HtpYmvi188ZX7oiHTwLhpxetJTExUSRom\nJia4urpiYGBQhdELrxK1m+PGxMSwY8cOevTowYwZM5g0aRL79+9HU1OT7777Tu0NRkZGYmpqyv79\n+1XKTUzK/xUlCC+7P67/wYK963mQkA1A7I08FgVOp5Vlq0qtx8bGhvv37xMfH0/z5s2Ry+Wima3w\nXKmVOHR1dVm2bBl5eUWdp3l7e7N//37Cw8OVl7/qioyMpHnz5lhalt/eXBBeNZ1tO7OjwW88SIjG\nuKABHg/9aWpYfm+2AAUFBWhpPTqVZTIZzs7OPHz4EDMzs6oMWXhFqT0CIKDysFDjxo0rlTCKRUVF\n0axZs0ovJwgvO11tXT71/ZDv0vfSybIn/d+Ul9vktqCggCtXrpCcnIy3tzeamprKaXXq1BE3wIUq\nU2bi6NmzZ6Vuov3xxx9qzRcVFUVubi6DBg0iLi6OFi1a8OGHH+Ls7Kz2tgThRZeRm8FvF/+hp31X\nTEwePQlua2LLovHvVXjuJSUlcfHiRbKysoCiMXMcHByqNGZBKFZm4nBzc3vurS9ycnK4c+cOZmZm\nfPzxx+jo6LBp0yYCAgIICQnBzq788QIE4WVw9UEEn4R8x5UbcZyul8y3Hw5XOdfKO+8KCwu5du0a\nN27cUHb9A0UtqSRJEi2mhGpRZuL46quvnvvG6taty3///YeOjo6y2uurr74iPDycLVu28Mknnzz3\nbQpCbVH8QN+OC3u5cj0eCTh4fze//umBb8+WFS6fkpLCxYsXyczMVJZpa2vj6OhIw4YNRdIQqo1a\n9zjOnz9f4Txubm5qbfDJJoEaGho0b96ce/fuqbW8ILyIkrOTWXt+LdeTr2NgoI1NI0PibxfQ1WwA\nbV3Lv1eoUCiIjIwkOjpa5SrD0tISFxeXEr3cCkJVUytxDBtWcf/+V69erXA9YWFhjBgxgg0bNuDo\n6AgUXXpHRETQu3dvdUIRhBfOubvn2BS6iaz8LGVZT7e2tHboRe/XWqOhUfa5lZaWxoULF8jIePQw\noJaWFq1btxYP8wk1Rq3EUVonhllZWZw9e5a9e/eydOlStTbWsmVLGjZsyOzZs/n000/R09NjzZo1\npKSkMGLEiMpFLgi1XE5+DnN/Xsmf147i7GyJhkyGhkwDP3s/ejXvhYas4mcrEhISVJKGubk5bdq0\nER0UCjVKrcTRtm3bUstfe+019PT0+P7771m1alXFG9PSYu3atXz99deMHz+e7Oxs3Nzc2LRpE+bm\n5pWLXBBqsdi0WMat+YybD/5fe3ceFlXZ/w/8PcwwDAwgDLuIIAMDsiirskmYpqZp2mK5lZZbdj3q\nr74+mRrP95dWVhouaaVPmeXSapZZVhJimCEgYSCLoKzKMiAwbAMzc//+4OfRCchBmWHAz+u65rrk\nvs+c+dzOmfnMOedergIASksaETJyBBaHLIZUonsnEKlUimvXrkGhUGDkyJHw8PCgswzS73o1jqM7\nYWFh2Lt3r87bOzk5YevWrXf7soQYNUszS4htGFDd+bdZrQdejl4Pa/Oep/1Qq9Xo6OiASHSzey6P\nx0NwcDB4PB7E4tuvHU6IIdz1PARJSUl0QBPyNzYiG6yb9jwcbK2wwP9pfLVu0z8mjbq6Opw+fRoZ\nGRlaN8CBzg4l9BkjxkSnM45nnnmmS5larUZlZSVKS0uxZMmSPg+MkIFCrdbg85/OYkZsOCwtb86u\nEOwSjOP/57+wEvWcMFQqFfLy8lBcXMwljOLiYowYocNshoT0E50SR0dHR5cyHo8HqVSKxYsX49FH\nH+3zwAgZCHIvV+ClgwkoVOSitPw5rF36kFb9PyWNmpoaXLhwgRv9DXTeB7x16hBCjJFOiYNW+COk\nq4yrGdhx9r+4pCgFAHxecBBTL4ZglN/Qf3xee3s7Ll68iLKyMq1yR0dHjBo1isZlEKPXq5vjycnJ\nyMjIQENDA+zt7REREYHw8HB9xUaIUWpqb8Lhvw4j/Wo6RNaAo4M55LVtmDIqCjKvnnsHMsZw9epV\n5OTkQKlUcuVCoRD+/v40+psMGDoljuvXr2PJkiXIzs6GUCiERCJBbW0tdu/ejejoaOzatYtm4iSD\nnkqlQWpJBr4p+hwK5c2xFaH+I/CI5xzE+PY8ewJjDOnp6aisrNQqHzp0KAICAujzQwYUnRLHpk2b\nUF5ejvfffx9xcXFceWJiItavX48tW7Zg/fr1+oqRkH6XU1CBdYfeRbUwH4GB9uCh88wgeng0Hvd7\nHOam/3x5icfjaQ3aE4lECAwMhLOzs17jJkQfdEocp0+fxrp167SSBgBMmDABdXV1SEhIoMRBBq2M\n4mws/u+raEMz0AJUVjZjpMcwLBi9AAGOATrvx8fHB5WVlXB0dMTIkSO1Fl8iZCDR6cjl8/mwsrLq\nts7BwaHbXleEDBZuDo5wdhOiuKwZfD4P/kOC8UrcCliYdj/th0qlQmFhITw8PLQG8wkEAtx3332U\nMMiAp9MAwLlz5yIhIQFVVVVa5U1NTdizZw/mz5+vl+AIMQaOYkesmrwAnq5O2LXwFbwx+396TBrV\n1dVITk7GpUuXkJOT06WekgYZDHQ6iqurq1FdXY0HHngAoaGhcHR0RH19Pc6fP4/m5mYIhUJukCCP\nx8OHH36o16AJ0Zf0nCv46uRZbHr+Sa1lWyd5P4DYEeN6TBitra3IycnRWh7g6tWrGDFiBK37TQYd\nnRJHSUkJfH07F5pRqVS4erVz4rYbZWq1Gmq1Wk8hEqJ/jDG8fvAwPrvwJTRQQ/aNO555PJqrN+GZ\ndJs0GGO4cuUK8vPzoVKpuHKhUAg/Pz/Y2toaJH5CDIkGAJJ7XlVTFT698Cn+aM6ECu0AgI8zPsGT\n08JhYSHs8XnXr1/HX3/9hYaGBq1yNzc3+Pn5catcEjLY9OqCa2FhIc6dO4empibY2toiNDQUnp6e\n+oqNEL1SaVT4uehnHC84DpVGBZehYshrWzGEb4fXZv+rx6TR3t6OvLw8lJaWak1IaGVlhcDAQFoi\ngAx6OiUOjUaD+Ph4fP3111ofFB6Ph4cffhhvvPEGjXglA4ZarcGhH1OQzf8Z9aoarpzP4+P/PDQP\nM/2mQyjo+Wyhvr4eJSUlN5/H58Pb2xtSqRQmJnc94TQhRk+nxLFnzx4cPXoUL774IqZPnw57e3vU\n1NTg2LFj2LFjB6RSKc2QSwaEnPxriD/8PnJb0uHgYA5f386zA3cbdywYtQBuQ9xuuw9HR0c4Ozuj\nsrISTk5OCAgIoBX5yD1Fp8Tx1VdfYfny5Vi8eDFX5uzsjCVLlkCpVOKrr76ixEEGhEP5H+NiSzoA\noLqmFR5uwFNjZmP8iPHdLuXa0dGBlpYWDBkyRKvc398fbm5uNPKb3JN0Oq+uqalBaGhot3UhISFa\nXRAJMWaLxz0JRwcL8Pk83O8/Bm9N3YQJnhO6JA3GGEpLS5GUlIS0tDStHlMAYGFhQUmD3LN0OuNw\nc3NDZmYmIiMju9RlZmbCwcGhzwMj5G5dKpJDbG6GoUNvznowwnYEVjzwJBzNnREni+r23lx9fT2y\ns7Nx/fp1rqywsJDrfk7IvU6nxPHYY4/hnXfegYWFBaZOnQp7e3vI5XIcP34cH3zwAZYtW6bvOAnR\nWWOjErs+/wlf5X+OsXZx2LV2sVaCeCKo+4XHlEol8vLyUFZWptUJxNzcvMulKkLuZToljgULFiA3\nNxebN2/Gm2++yZUzxjBjxgw899xzeguQkN5oVDbiw8wDOFD0AzQmDCl1J3DydCweuM+nx+doNBoU\nFxejoKBAa941ExMTSKVSeHl50VQhhNxC50kO33zzTSxevBjp6eloaGiAtbU1wsPD4e3tre8YCbkt\nDdMguTgZR/OOok3VBldXS5SVKWDnYAaxS3OPz6upqUFOTg4UCoVWuZOTE/z9/SEWi/UdOiEDTq9+\nRrm4uMDNzQ1DhgyBRCKBm9vtuy4Sok9yeQuyKy7hTMP3KG0o5cqHD7fCOM8IrJywCENE3V9mUqlU\nyMjI0DrLEIvFCAgIgKOjo95jJ2Sg0nkA4Ntvv40DBw5ApVJx13/Nzc3x3HPPYenSpXoNkpC/UypV\nOHr8IvamHEKdZS5CQ51gYtJ5H8PJ0glzA+fC1/6fb2YLBAL4+PggOzsbAoEA3t7e8PT0pEF8hNyG\nTolj586d+OSTT/DUU09h8uTJsLOzg1wux4kTJ7Bjxw6IxWLMmzdP37ESwimsLcJb6ZvQImgG2oCy\ncgW8RthhqvdUTJJOgsBE+9BmjKGurq7LdCDu7u5QKpVd1s4ghPRM5wGAK1aswPPPP8+Vubm5ITg4\nGGKxGPv376fEQQxK6ugOH287ZOY2w8pKiEjPEDx/3yLYW9h32VYul3P3MWJiYmBjY8PVmZiYUDdb\nQnpJp3PypqYmjBo1qtu60NBQVFdX92lQhNxKoWhHTo5cq0wkEGHVA4sQMVqKPYv/g/gp/9MlaTQ3\nNyMtLQ1nz55FY2MjGGO4ePGiVldbQkjv6ZQ44uLi8Nlnn3Vbd/z4ccTGxt7Ri//555/w8/NDamrq\nHT2fDG4aDcPJk8VY+p99WLNvJxoblVr1Y1zD8cGTWxDkEqQ1TqO9vR05OTk4deoUKisruXI+nw87\nOztKHITcJZ0uVYWFhWHbtm2YPn06pk2bBgcHB9TX1+PUqVPIyMjAwoUL8f777wPonDFXlwGBLS0t\n+Pe//00LQJEe1TTJ8c5vO3BFkA8A2PX5T3h5yQyunsfjQci/OYttT+MxAGDYsGHw9fWFubm5YYIn\nZBDTKXFs3LgRAKBQKLBt27Yu9R999BH3b10Tx+bNm+Hk5KQ1PTUhAKDWqHHy8kkcKzgGSy8F8Bdg\nYS6A3O48gBldtmeMobKyErm5uWhu1h6zIZFI4O/vr3VfgxByd3RKHHl5eX36osnJyTh16hT27t2L\nGTO6fhGQe49KpUFh4XUIHK/j4IWDuKroXJ7Y1kYEfz97zAiaiMf8u58qhMfjoaSkRCtpiMVijBw5\nEs7OzrRWDCF9zODzKNTV1WH9+vV4/fXXaf4fAgDIy6vF/sOZSG38GUPHyiEWm3J1w6yH4aWYefC0\n/eeVJv38/HD69GkIBALIZDJ4eHjQeAxC9MTgieM///kP7r//fsTGxmrduCT3Jo1Gg/eOfYeklu/R\nYdqG5kIhRo92gEggwgyfGbh/xP1aU54rlUpcvnwZMpkMfD6fK7e2tkZwcDAcHBxorW9C9MygieOb\nb77BxYsX8d133xnyZYkR4/F4cAiqhfpkG/gmPDg4mGO002jMCZwDibmE206lUqGoqAiXL1+GSqWC\nUCiEVCrV2perq6uhwyfknmTQxHHkyBFUVVUhJiYGALhukUuWLMHMmTPx6quvGjIc0g9qalrg4HBz\nmVUej4fnohfhQsVFDLWXYFHYAox2Hs3VazQalJSU4NKlS1Aqb3bHvXTpEtzd3WnWWkL6gUE/dVu2\nbEFbWxv3d01NDebNm4dNmzYhOjrakKEQA2tt7cC33xbh6G9n8dKyyQgeNZSrs7Oww8aHXoKHjQfM\nBGYAOn9UVFRUID8/Hy0tLVr7sra2xsiRI7UuVRFCDKfHxFFVVdWrHTk5OfV6GzMzM67873MIkcHl\ns28u4ONzh1AtzsPrX1TioO+/IRTe/OL3se9cL4MxhurqauTl5aGxsVFrH+bm5vD19YWrqyv1lCKk\nH/WYOO67775efThzc3P7JCAyuDDGkFKagnSrL3Hd4gqgAq6Jz6OkthzeLu5dtk9PT+/SaUIoFMLL\nywseHh50lkGIEegxcbz++utc4mhoaMCWLVsQGRmJBx98kBs5/uuvv+LUqVNYu3btHb24s7Mz8vPz\n7yxyYrRUKg1MTHi41nQVB/86iKK6IgCAl3fnILwpo8bBSWLb7XMlEgmXOPh8Pjw9PSGVSmFqatrt\n9oQQw+sxcTzyyCPcv59//nnMnDkTmzZt0tpm+vTp2LRpE3788Uc88cQT+ouSDBiXL9dj36dZEPrn\n46ooExqm4epGDh+OuYFz4e/oDwBoa2vrMpW5h4cHiouL4eTkBG9vb+5yJiHEeOh0c/zMmTPYtWtX\nt3Xjx4/Hl19+2adBkYHpwoUabNxzBIWiJKjONyEszBlCUz74JnxMkk7CVO+pEPKFaG5uRkFBASoq\nKhAbGwtra2tuH3w+H+PHj6fBe4QYMZ0Sh62tLS5cuNBtz6dz587pdGOcDH5KSSmK7H5AW6sKfA0P\nTU3tGOs1CvMC58HFygUtLS3IvZSLsrIyrit2fn4+wsPDtfZDSYMQ46ZT4nj88cexa9cutLW1YcKE\nCbC1tUVtbS1OnDiBTz/9FOvWrdN3nGQACBkahHGj/JF+KR+jfF0xP+QJRLlFoa2tDX/99RdKS0uh\n0Wi0nqPRaKBWq+mmNyEDiE6J47nnnoNCocCHH36IPXv2cOVmZmZYtWoVrf53j1GrNUhMLEVLmxIz\nZ/hw5XwTPlbdvxgpXil4ZOQjEGgEyMnJQUlJSZeEYW9vD19fX9jadn+TnBBivHRKHDweDy+99BJW\nrFiBzMxMNDY2wtbWFsHBwbCwsLj9DsigoVC04813fsOZ6z+hjV+PsJAtGDbs5j0KT1tPjLAZ0WPC\nkEgk8PHxgb191yVeCSEDQ69GjltZWd3xan9k4GOMIavuHH4z/RDVwgYAwPvHv8OmZfO1tuPxeGhq\natJKGra2tlzCoMF7hAxsPSaOSZMm9eoD/tNPP/VJQMQ4lTeW49Bfh1BUV4ThUhHqshQY7mYFrzEq\naDSaLje0ZTIZampqYGNjAx8fHzg4OFDCIGSQ6DFxhISE0Af9HlderkBqRhnUXtlIupLEjcmwsDDF\ng3H+mO3zGMwazXDq1CnExcVpJQ+JRILo6GjY2trScUTIINNj4ti8eTP37+PHjyMyMhISiaSnzckg\nwhjDF1/k4YuUX1EoSoasQQSJbeda3XwTPsa7jocn80RldiV3OaqsrAzu7tpTiNDxQsjgpFOH+Q0b\nNiAtLU3fsRAjwcDwXfV+XDT/Ae28Zly+3AAGBm8rb8y2mw3zcnNcLb+qdQ+jpqamHyMmhBiSTjfH\nnZyc0Nraqu9YiJEw4ZlgYkQALlzNhqWVEKFSd4y3iIFZkxkUCoXWthKJBDKZjHpJEXIP0SlxzJkz\nB6+//jqysrLg6+vbbRfc6dOn93lwRP/q6lqRlFSGWbO8YWJy817EE0GP4M+yTPiaeMGF5wJ+Cx8M\njKu3t7eHTCaDRCKhexiE3GN0ShxvvPEGAODw4cPd1vN4PEocA9CJE1dw6IffUShIgdmQFXhoYiBX\nZ2Fqgdce/L9ISU5BR0cHV+7o6Ahvb2+6f0HIPUynxJGYmKjvOIiBNSobcbLmCNJFv4IB2HnyU4yP\nfBVi8c3Zai3NLeHh4YHCwkI4OzvD29sbQ4YM6b+gCSFGQafE4erqyv27paUFzc3NsLGxoTUSBiCV\nRoVfr/yK4wXH0WLdCpGIDwe+FXwkKmRmpyNmbIzW9p6ennB1dYWVlVU/RUwIMTY6jxxPTU3Fli1b\nkJOTw81sOmrUKKxevRqRkZF6C5DcvdraVhw/XgTPmCb8eOUY5C1yQAOYN4lwn6M3nCwcILWVor6m\nHs3NzRCLxdxzhUIhhEJhP0ZPCDE2OiWOtLQ0PPvssxgxYgRWrlwJOzs7VFdX48SJE1iyZAk+/vhj\nhIWF6TtWcgeSkkrx0TenUGCajCH1zfB0s4FIIYKwSQixQAxPJ0/YijonGhQIBFAoFFqJgxBC/k6n\nxLF9+3ZERkZiz549Wj1oVqxYgaVLl2Lnzp3Yv3+/3oIkdy6jORHpoq8g4pnCtGoIxDwrmAlM4W7j\nDhcrF/DAg0gkgqenJ9zd3SEQ9Gr6MkLIPUinb4ns7Gxs27atS7dLHo+HefPm4YUXXtBLcOTuTQ4P\nQ3LmKThorGFjLYK7jRvcrN0gMBHA0tISXl5ecHV1pcWTCCE60ylxWFtbo6Wlpdu65uZmWoTHCNTU\ntOCzry/giUcC4eh481JTgGMAIkJ8IawWwMPGAyKBCBKJBFKpFE5OTjQGgxDSazoljoiICOzcuROh\noaFay8RWVVVh586ddHO8n508dQkJ336OCsF5VB6cio2rnuHOIHg8HtZMfhGpZ1Nhbm4OqVRKiycR\nQu6KTonjxRdfxKOPPorJkycjNDQU9vb2kMvlyMjIgKWlJdasWaPvOEk3lColfr3yK76u+B4qUSP8\n+U64Ks9BVlYBgoN9ue2EAiGio6PpchQhpE/oPFfVN998g48++ggZGRkoLy+HtbU15s6di0WLFsHB\nwUHfcZJbKFVKJF1JQmJOIlR1KkhaxTCxFECl0sBJIsZVeRGCmI/WZShKGoSQvtJj4jh37hyCg4O5\nQX4ODg546aWXDBYY6epaTT3e+vwgWsUFEHcIwO/gwxSd74+jxBojbD3g6ewJqae0nyMlhAxmPSaO\np556Cubm5ggPD0d0dDSioqLg7e1tyNjILd794TC+T/kJtiYimJuawtq+s0OCSCDCcOvhCBgRAKlU\nSkuzEkL0rsfE8e677yIjIwMZGRl4++23oVarYW9vj6ioKO5Bl6gMR2yrgR3fHCbMBB0dGkAlgMzZ\nE2G+YZB6SmlKEEKIwfSYOCZOnIiJEycCAFpbW/Hnn38iIyMDaWlp+N///V+0tbXBy8uLOxuJjY01\nWNCDXWFJJRxtrWBtfbNb7aMh05F49jeYK8wR6C7D5Og4eLh70HQghBCD0+nmuLm5OSIjI7lutyqV\nCmlpafj8889x4MAB7N+/H7m5uTq9YGVlJV5//XX88ccf0Gg0GDduHNauXavVzfde9WPKOXyR9B3a\n2moxwWcyFj81k6uzNrPGutmrYMksMYwG7BFC+pHO80solUqkpqbi7NmzSE1NRX5+Png8HgIDAxEd\nHa3TPhhjWLp0KSQSCT755BMAwKZNm/Dcc8/hyJEjd9aCAU6j0SC9KB2n/jyFstJKqJRKCHgm+LMw\nE83NkyAW31w0y2/YyH6MlBBCOv1j4igoKEBKSgpSUlKQkZEBpVKJ4cOHIzo6GitWrEBERAQsLS11\nfjG5XA6pVIoXX3wRw4YNAwAsXLgQzz//PBoaGu6JtR4UinacP1+F0rLrcPS9hnM556Bo6lyOVWTG\nB48HgAE8kRpyeYNW4iCEEGPQY+KIjY1FTU0NrK2tMXbsWKxbtw7R0dHcF/6dcHBwQEJCAvd3ZWUl\nPv/8cwQGBt4TSUOpVGHt+h+gEF4EzKrgUGcKPv/mJScejwephxseCB+PyMAx1DuKEGKUekwc1dXV\nsLW1xWOPPYaoqCiEhYX16cJNK1asQGJiIoYMGcJdthrsFOp61Dv9DJPWzoTQ2moCS0sT8Ex4GDF8\nBCaHTcYIpxH9HCUhhPyzHhPHvn37kJKSgtOnT+O///0vRCIRN6YjJiYGUundDTJbtWoVli9fjt27\nd2PRokU4evTooLlBLpe34JdfSiCTWSM09ObqiXbmdrBxsUJbeQvMzQWwGCLEaN9ReDD0QUjEtIY3\nIWRg4LEby/n9A7lcjpSUFJw5cwa///47amtr4ezsjKioKMTExCAqKgo2NjZ3FEBrayvi4uKwaNEi\nLF++vNttysvLMWHCBCQmJt7VpTJD+P33chw6nIoOs2LY25ri1bXPa80efLb0LH74/QdE+kVigt8E\nmAnM+i9YQsigpc/vTZ16Vdnb22PmzJmYObOze2hubi7OnDmD9PR0rF27Fmq1Gjk5Obfdj1wuR2pq\nKqZNm8aVmZubw83NDVVVVXfYBOPQ3NyMS5cv4c8rqWgekgcV2nG1xQwXLhQhOFjGbTfWbSzGPjEW\nJjzqTksIGZh6tdxbY2MjMjMzkZmZiQsXLiA7OxtqtRr+/v46Pf/q1at44YUXMHz4cAQGBgIAFAoF\nrly5glmzZvU++n6kVmuQkXENTk4a5F/JxcXSi6hqqoKaqWFqrgFfw4dYzEONshjAzcRBCYMQMtD9\nY+IoLi5GZmYmzp8/j8zMTFy+fBkajQZeXl6IiIjAvHnzMHbsWJ275AYEBCAsLAwbNmzAxo0bIRAI\nsHXrVkgkEu5sZiA4daoQv/xyHo2qYli6tEJt2qxVby0xBc+GhzF+YzDGZ0w/RUkIIfrRY+KIiIhA\nQ0MDGGMYOnQoIiIisGzZMkRERNzxHFUmJibYuXMn3nrrLSxbtgxKpRIxMTE4cOAAxGLx7XdgJFKL\nf0MFsqA27UBTPQ8ODp1jLTrMO2DjZIPx/uMR4RYBIZ+mAyGEDD49Jo6xY8ciKioKkZGRGD58eJ+9\noEQiwebNm/tsf/qk0Whw+fI1SKVDtcZUDB8txl9F7RDwTCCw4KHVug0+UhkmyibCx86Hxl8QQga1\nHhPH9u3bDRmHUWlubsHPP2fhzwt5aGirwf88/xSGDXPm6if7TsRJj18xxFaEGN8Y3OdxH+ws7Pox\nYkIIMZxe3RwfzNRqNaqqqlBaWoqi8iKk5hSgvqMW4DP8kPgHlj598x6MxFyCNQ+vhtRWClN+3w2K\nJISQgeCeThyMMcjldbh8uQQ1tVdRUV+Ba03X0NLRAmahAmtgYDyG0tbiLs/1tfftukNCCLkH3LOJ\no7j4Go7/kIIrV0uhNm+EmY0SGmi4eoENQ4eoDTIfN0zzi+zHSAkhxLjcs4mj4PolpFWcgZrXAV4r\n4GBlAZ6Qod2yHTxrHsLcwzDOfRzch7jTzW5CCLnFoE4cjDFUVlYjPT0f48YFwcbGmquTurtCKWoB\nT8mHwqQVzEqJkVJPjHMfh3DXcIgEon6MnBBCjNegSxyMMTQ2NqK8vBy//ZaDwrIKKFgdVGCYNf0+\nbjtPW0/YeQ+BQt2E+aOnYpx7DIZaDe3HyAkhZGAYNImjpaUFFRUVqKioQG19Laqbq5GvKEEtUdOt\nvwAAEAdJREFUTwHwgNTsdMx8KJa77MTj8RA/fQ0k5hIITAbNfwMhhOjdgP7GVCha8dtvOcjNLQZD\nE+yHAZVNlahrqwMAmAg16GBqKPgtMLWpAmNM636Fo9ixv0InhJABa0AnjmvX6nAyOQUtJtehFNTD\n3swMPB7AeAwd5h3oEHfA3AV4UDYBMcNjYGJCEwwSQsjdGtCJQ2ldh0pRHvhqPhhjUEADgV1n0vBx\n9EGUWxSCnYNpzQtCCOlDAzpx+Dr4QuBoAqWqDWaOgLW9HaLdohE9PBr2Fvb9HR4hhAxKAzpxmPJN\nMWP8/ZC3yBEzPAZ+Dn603gUhhOjZgE4cAPDoyEdpgB4hhBjQgP95TkmDEEIMa0CccajVagBAZWVl\nP0dCCCEDw43vyxvfn31pQCSOmpoaAMC8efP6ORJCCBlYampq4O7u3qf75DHGWJ/uUQ/a2tqQnZ0N\nBwcH8Pn8/g6HEEKMnlqtRk1NDQICAiAS9e3cewMicRBCCDEeA/7mOCGEEMOixEEIIaRXKHEQQgjp\nFUochBBCeoUSByGEkF4xusQRHx+P9evXa5UdPXoUDz30EIKCgvD444/jzJkzWvUHDx6Ej4+P1sPP\nz09rm48//hjjx4/H6NGjsWjRIhQXFxtVG9rb27F582ZER0cjODgYS5cuRVlZ2YBpw86dO7u8Bzce\n7777rsHbcCfvQVlZGZYvX46wsDDExMRgw4YNaGxs1NrGmN8DACguLsaSJUsQFhaG2NhY7NixAyqV\nyqBtkMvleOmllxATE4OwsDA8++yzKCgo4OpTUlLw8MMPY9SoUZg+fTqSk5O1nl9bW4tVq1YhLCwM\nkZGRePvttw3ahruN/4b29nbMmDED3377bZc6Qx5HesGMhEajYdu2bWMymYytW7eOKz927Bjz8fFh\n77//Prt8+TI7cOAACwwMZH/88Qe3TXx8PFu+fDmrrq7mHjU1NVz9F198wYKDg9mPP/7I8vLy2LJl\ny9iECROYUqk0mjasXbuWxcbGst9//53l5+ezBQsWsIceeohpNJoB0Yampiat///q6moWHx/PIiMj\nWWVlpcHacKfxd3R0sClTprAVK1awwsJClpGRwaZMmcL+9a9/cfsw9vegvr6eRUVFsQULFrCcnByW\nlpbGpkyZwl5++WWDtUGtVrMnnniCzZ49m2VlZbFLly6xlStXssjISFZXV8cuXbrEAgIC2O7du1lh\nYSFLSEhg/v7+rKCggNvHnDlz2Ny5c1lubi47deoUi4iIYO+8845B2tAX8TPGmEKhYIsXL2YymYwd\nPXpUq85Qx5E+GUXiKC0tZfPnz2djx45lcXFxWh+WGTNmsBdffFFr+/Xr17P58+dzf8+ZM4dt3769\nx/1PmjSJ7dixg/u7qamJBQUFse+++84o2lBaWspkMhn7/fffufqioiIWFxfHiouLB0Qb/u78+fPM\n19eXJScnc2X6bsPdxJ+fn89kMhnLy8vj6g8cOMCCg4MNFv/dtmHfvn0sODiYXb9+natPT09nMpmM\nlZWVGaQNOTk5TCaTscLCQq5MqVSy0aNHs2+++Ya98sorXY6Z+fPnsw0bNjDGOo8bmUzGSktLufoj\nR46w4OBg7otVn2242/gZY+zMmTNswoQJbNasWd0mDkMcR/pmFJeqzp8/DxcXFxw7dgzDhg3Tqisp\nKUFYWJhW2ciRI5GZmcmdvhYWFkIqlXa779raWhQXF2PMmDFcmVgsRkBAANLT042iDSkpKZBIJIiM\njOTqPT09kZSUBHd39wHRhlsxxvDaa69h0qRJiI2NBWCY9+Fu4h8yZAhMTEzwxRdfQKlUoq6uDidO\nnEBAQIDB4r/bNpSUlMDb2xs2NjZc/Y1Ltunp6QZpg4uLCz744AOMGDGCK7sxEWlDQwPS09O1Xh8A\nxo4dy71+eno6XF1d4ebmxtWPGTMGzc3NyM3N1Xsb7jZ+APj1118xc+ZMfPbZZ132b6jjSN+MYq6q\nhx9+GA8//HC3dY6Ojrh27ZpWWUVFBTo6OtDY2IiOjg40NDTg9OnT2LlzJ1pbWxEeHo41a9bAycmJ\nm+jLycmpy377ctLEu2lDcXEx3NzccOzYMezduxd1dXUICQnBunXr4OzsPCDaIJFIuPLExERcvHgR\nW7du5coM0Ya7id/JyQkbNmzAli1bcOjQIWg0GkilUhw4cMBg8d9tGxwdHZGUlASNRsMtk1xRUQGg\n8wvLEG2wtbVFXFycVtmnn36KtrY2xMTEYPv27f/4+lVVVXB0dOxSDwDXrl2DQCDQaxvuNn4A2LBh\nQ4/7N9RxpG9GccbxT2bMmIGDBw/i7NmzUKvV+OOPP/D1118DADo6OnDp0iUAgEAgQEJCAt544w0U\nFxdj4cKFaGtrQ2trKwDAzEx7+VihUAilUmkUbWhqasLly5exb98+vPzyy9i+fTtqa2vx9NNPQ6lU\nDog23Gr//v2YMmWK1sRq/d2G28Wv0Whw5coVREZG4vDhw/jwww/B5/OxevVqqNXqfo9flzY8+OCD\nqK2txdtvv43W1lbI5XJs2rQJAoEAHR0d/dKGxMREvPPOO1i0aBGkUina2togFAp7fP3W1tYu8Zma\nmoLH4/XLZ6G38d+OMRxHfcEozjj+ydKlS1FXV4clS5ZArVbDy8sLzz77LLZu3QorKyvExMTg7Nmz\nWr94vby8EBsbi+TkZLi6ugLo7OFwq/b2dpibmxtFGwQCARQKBbZv386dou/YsQMxMTFITk7G0KFD\njb4NN1RWVuLcuXPYv3+/1vNvTLLWX224Xfzfffcdjh07hqSkJFhYWAAA3N3dMXHiRCQnJ3O/eo35\nPXBycsL27dsRHx+Pjz/+GBYWFli5ciXy8/NhZWVl8PfgyJEjeOWVVzB16lSsWbMGQOcX5t9/aNz6\n+iKRqEt8HR0dYIzBwsLCoG24k/hvp78/B33F6M84hEIh4uPjcf78eZw+fRrHjh2DSCSCvb099wG/\nNWkAnad9tra2uHbtGlxcXADcnJr9hurq6i6ni/3VBicnJ1hYWGhd17Wzs4ONjQ3Ky8sHRBtuSExM\nhIODQ5frwP3dhtvFn5WVBU9PT622uLm5wdbWFqWlpf0evy5tAID7778fKSkpSE5OxtmzZ/Hoo4+i\nrq4Obm5uBm3De++9h5dffhlPPvkk3nrrLe7SmYuLC6qrq3t8fWdn527jAzov7xiqDXca/+0Yw3HU\nF4w+cSQkJGDPnj0QCoVwcHAAAJw8eRLR0dEAgE8++QQxMTFavwIqKipQV1cHb29v2NnZwcPDA+fO\nnePqm5ubkZ2djfDwcKNoQ1hYGFpaWlBUVMQ9p6amBtevX8fw4cMHRBtuuHHz8MYH7Yb+bsPt4nd2\ndkZxcbHWL8Hq6mrU19fD3d293+PXpQ3p6el4+umnoVar4ejoCKFQiJMnT8LCwgIhISEGa8PevXux\nbds2rFy5Eq+88orWKp2hoaFIS0vT2j41NZW76R8aGoqysjKtezmpqakQi8Xw9fU1SBvuJv7bMYbj\nqE/0d7euv5s/f75WF8QvvviChYSEsFOnTrHS0lK2ceNGFhQUxIqKihhjjJWUlLCgoCC2Zs0aVlhY\nyNLT09msWbPYnDlzuH0cOnSIBQUFse+//57l5+ezZcuWsUmTJumt33Rv26DRaNjcuXPZjBkz2Pnz\n51lubi5bsGABmzJlChejsbfhhkmTJrH33nuv230asg29jb+yspKFhYWxlStXsoKCApaVlcWefPJJ\nNnPmTNbR0WHw+O+kDbW1tSwsLIxt3ryZlZaWsp9//pmFhIRovR/6bkNubi4bOXIke/nll7uM62lu\nbmZ5eXnM39+fbd++nRUWFrJt27axwMBArvurRqNhs2fPZk888QTLzs7mxnHc2n1Vn2242/j/rrvu\nuIY+jvTB6BMHY4zt2rWLxcbGsqCgIDZ//nyWlZWlVZ+Zmcnmz5/PgoOD2ZgxY9jatWtZfX291jbv\nv/8+i46OZkFBQeyZZ57R6iduDG1oaGhg69atY+Hh4SwoKIitWLGCXbt2bUC1gTHGgoOD2aFDh3rc\nr6HacCfx5+fns2effZaFh4ez6OhotmbNGlZbW9sv8d9pG9LS0thjjz3GRo0axSZOnMj27dvXZb/6\nbMPWrVuZTCbr9rFr1y7GGGNJSUls6tSpLCAggM2YMYOdOXNGax/V1dVsxYoVbPTo0SwqKopt3bqV\nqdVqg7ShL+K/VXeJQ5/xGwot5EQIIaRXjP4eByGEEONCiYMQQkivUOIghBDSK5Q4CCGE9AolDkII\nIb1CiYMQQkivUOIg97T4+Hj4+Pj0uIpbYmIifHx8sHv3bgNHRojxonEc5J7W1NSEhx56CDweD99/\n/z3EYjFXp1AoMHXqVDg7O+Ozzz4Dn8/vx0gJMR50xkHuaZaWlnj11Vdx9epVJCQkaNW99dZbaGho\nwObNmylpEHILShzknhcbG4tZs2bh4MGDyMrKAgCkpaXhyy+/xAsvvKC1uuThw4fx4IMPIiAgABMm\nTMDevXvx95P2Q4cOYdasWRg9ejRGjRqFRx55BL/88gtX/+WXXyI4OBgHDx5EZGQkxo4di/LycsM0\nlpA+QJeqCEHnsqDTpk2Ds7MzDh06hEceeQS2trb45JNPuNlRd+3ahXfffRcLFy5EdHQ0srKysHv3\nbixcuJBbr2Hfvn3YsmULVq1ahdGjR6O+vh579uxBQUEBEhMT4ejoiC+//BLx8fGQSqVYs2YNrl+/\njpkzZ/Zn8wnpnX6cJ4sQo/LLL78wmUzG5s2bx4KDg1lZWRlXV19fzwIDA9lrr72m9ZwPP/yQ+fn5\nscrKSsYYYxs3bmQJCQla22RlZTGZTMZ+/vlnxljnLLcymYz9+OOPem4RIfpBl6oI+f8mTpyIadOm\nIS0tDWvXrsWwYcO4uvPnz0OpVGL8+PFQqVTc4/7774dKpcIff/wBoHO96dWrV6OhoQF//vknvv32\nWxw+fBhA1yV2R44cabjGEdKHjH7pWEIMKSYmBsePH0dsbKxWeX19PQBg4cKF3T7vxqpwxcXFiI+P\nR2pqKoRCITw9PeHt7Q0AXe6F3LraICEDCSUOQnRwY1317du3c+vY38rJyQlqtRpLly6FpaUljhw5\nAh8fHwgEAuTl5eHYsWOGDpkQvaFLVYToICgoCKamppDL5QgMDOQeSqUS27Ztg1wuh1wuR0lJCWbP\nng1/f38IBJ2/y06fPg0A0Gg0/dkEQvoMnXEQogN7e3s89dRT2LJlCxoaGhASEoKKigokJCTAxsYG\nXl5eMDU1hYuLC/bv3w87OztYWlri9OnT+PTTTwEAra2t/dwKQvoGnXEQoqM1a9Zg9erVOHbsGJYs\nWYJt27YhLi4O+/fvh1AoBI/Hw+7du2FnZ4d///vfWL16Nf766y988MEHcHd3R3p6en83gZA+QeM4\nCCGE9AqdcRBCCOkVShyEEEJ6hRIHIYSQXqHEQQghpFcocRBCCOkVShyEEEJ6hRIHIYSQXqHEQQgh\npFf+H1sFbMS2u8i2AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd06f4e10>"
+      ]
+     },
+     "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": 55,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def update_func1c(pop, t, system):\n",
+    "    \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": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Sy65evcqYMWPk5rjZs2eTkZEhT/fw8GD9+vVMnDiRFi1a0KFDB1asWCFPT0pK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\nJYVHCCGESf1/pc/0zdo7l4sAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd0750ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system.alpha1 = 0.019\n",
+    "system.alpha2 = 0.015\n",
+    "\n",
+    "run_simulation(system, update_func1c)\n",
+    "plot_results(system, title='Proportional model, parameter changes over time')"
+   ]
+  },
+  {
+   "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": 57,
+   "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": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Pnj1Mnz6dnj178vnnn1c6f2hoKCYmJvTo0QO4/8Fx7do1IiMjHzt2QVBXWVkZ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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd1879da0>"
+      ]
+     },
+     "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": 59,
+   "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": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap03-fig05.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9TRQKBfPmzbNJQML2mppb2XuswLwdGeLNiEh5HCFEZ5ydVNyVOIAd+3IxGo0U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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd18c57b8>"
+      ]
+     },
+     "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": 61,
+   "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": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.88888888888889"
+      ]
+     },
+     "execution_count": 62,
+     "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": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.025, 13.88888888888889)"
+      ]
+     },
+     "execution_count": 63,
+     "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": 64,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def update_func2b(pop, t, system):\n",
+    "    \n",
+    "    net_growth = system.r * pop * (1 - pop / system.K)\n",
+    "    return pop + net_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "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 0x21dd1987eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "run_simulation(system, update_func2b)\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": 66,
+   "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": 66,
+     "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": 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>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": 67,
+     "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": 68,
+   "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": 69,
+   "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": 70,
+   "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": 71,
+   "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 0x21dd19d6390>"
+      ]
+     },
+     "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": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NGzYMgKCgIIKCgjS2KxSKbCepwMBAJk+eTKtWrRg7diyQnlySk5PV6qlUKgwN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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd1a30748>"
+      ]
+     },
+     "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": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "p0 = table1.biraben[1]\n",
+    "\n",
+    "prehistory = System(t0=1, \n",
+    "                    t_end=2016,\n",
+    "                    p0=p0,\n",
+    "                    alpha=0.0011)\n",
+    "run_simulation(prehistory, update_func1b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FCxYwZswYNm7ciLa2Nv369SMtLe2t4xcEQXjXnb4awd3wWHm5ibs9Vqb6tDIx\nIUqrGtfLDSao/ESulxtMlFY1WpZwxziVkpSnpyfHjx9/64PdvHmTS5cuMXXqVKpXr07lypWZNWsW\nSUlJHDt2jDVr1lCzZk0GDRqEk5MTI0eOpFatWqxZswaAS5cuceHCBaZPn46rqyuNGzfm22+/Ze3a\ntXISWrFiBb6+vrRs2RIXFxdmz55NZGSkaDMTBOG998+9SC7eeiIv16hiLk8B73ILvlgDTaYk4LYo\nAdeb0M/amrqGhiUVLqDi7b7PPvuMCRMmEB0djbu7e54jTLRt2/aV9VhbW7Ns2TIcHR3lspynmWNj\nYwkKCqJVq1ZK+3z00Ufs3r0bgKCgIGxtbbG3t5fX16tXj8TERG7cuIGdnR0hISHUq1dPXq+vr4+b\nmxtBQUEqxSgIglAWhT6O5+iF50MeOVobylNvxJ2PI2JFBGaAmb4BpAJ7wNoGKOFxB1RKUsOGDQNg\n69atbN26Ndd6hUKhUgIwNjamSZMmSmVr164lJSUFT09P5s+fj6WlpdJ6CwsLHj16BMDjx4+xsLDI\ntR4gIiJCbpcqqA5BEIT3TVRcCvtOh8g9+cyNdGnxcUXU1LIvEqL2RuW9374oDOu+A1dSgYGBRXLw\nwMBA5syZg6+vL05OTqSkpOSaeE9LS4vU1FQge16jl2cK1tTURKFQkJqaSnJydkPgy9u8WIcgCML7\nJCklnV0n/iU1PRMAA11N2jRwRFPj+QSmaRF5t9mnPSz5tnyV2qRsbW3l/4yNjdHS0sLCwkKp/HVt\n2bKF4cOH06pVK7755hsgO7mkp6crbZeWloauri4AOjo6uTpApKenI0kSenp68m3Il7d5sY73mZeX\nF4sXLy5wXVhYGC4uLvj5+eW5nYuLC9u3by/KMAVBKCQZmVnsPRVCXGL2d6KmuhqtGzhioPfSxYB1\n3rNya9kUPFt3cVD5gaezZ8/SuXNn6tSpQ6NGjahevTpdu3bl9OnTr33QJUuWMG7cOLp168bMmTPl\n566sra3l2X9zPHnyRL59Z2VllWsU7pztLS0tsba2Bshzm5dvAQoF27NnD4cOHSrpMARBeEOSJBF4\nPpSIyEQgu1mmxccVsTDWy7WtSau8e/CZtCz5Ie9USlLnz5/nyy+/JCUlheHDhzN58mSGDh1KUlIS\n/fv3JygoSOUDLl++nHnz5jF8+HC+//57pWHga9euzfnz55W2P3v2LHXq1JHXh4aGEvHC7LZnz55F\nX18fV1diRbfwAAAgAElEQVRXTE1NcXBw4Ny5c/L6xMRErl27Rt1SNOr4+bg4JoeEMOj2bSaHhHA+\nLq6kQ8rF3t6eiRMnEhsb++qNBUEodc5ef0RwaLS83KC6NY425fPc1rCuIdb9rNG200ahpkDbThvr\nftYl3h4FKrZJzZ8/Hw8PD3755RelpDJ48GAGDBhAQEAAq1evfmU9N2/eZO7cuXTs2JEuXbooXfHo\n6+vTs2dPOnbsyIIFC2jTpg27du3i77//lmd4rVWrFjVr1sTPz4/vv/9efjDY19dXbsvq06cPM2fO\npGLFilSpUoU5c+ZgYWGBt7f367wuReZ8XBwrXkiy4amp8nJJd/V80TfffIO/vz/Tpk1j+vTpJR2O\nIAiv4drdZwTdeCwvu1UyfeXkhYZ1DUtFUnqZSknq2rVrzJs3L9fkVwqFAh8fH5WnZdizZw+ZmZls\n3ryZzZs3K60bMWIEgwcPZuHChcyaNYvly5dTqVIlli5dipOTk3y8hQsXMnHiRHx8fNDX16dz584M\nGTJErqd79+7ExcUxbdo0EhMTcXd3Z8WKFbk6ZJSUvVF596LZFxVVqpKUqakp48aNY8yYMbRu3ZpG\njRqVdEiCIKjg3sNYjl0Kl5crWJWjUS27AicvjIs7T1TUXtLSItDSssbEpFWJTs/xIpWSlKGhIUlJ\nSXmuS0xMRF1dPc91L/v6669fmdCaNGmSq5v6i8zNzVm0aFGBdQwcOJCBAweqFFNxi8hn5IuHpXBE\njM8//5y9e/fi7+/Prl27xFTwglDKPYpMZP+Z+/Ig4BbGerTycJC7muclLu68PG8UQGpqeKmYRyqH\nSm1SH3/8MQEBATx+/Fip/PHjxwQEBODh4VEkwZVF1vlc0dkUw5WehoYGWVlZea7LyspSGv8wx6RJ\nk4iPj2fmzJlFHZ4gCG8hOj6FXSfuydO/G+pr8amnclfzvERF7c2nfF+hx/gmVLqSGjVqFB07duST\nTz6hdu3amJmZ8ezZMy5cuICBgYHchVx4tVYmJkptUjmKY3wsQ0NDEhIS8lwXGxuLkZFRrnIrKyvG\njBmDv78/rVu3LuoQBUF4A0kp6ez86195VHNdbQ0+a+iEno7mK/dNS8v9fZRd/rBQY3xTKiUpS0tL\ntm7dysqVK7lw4QJhYWEYGhrSo0cPfH19MTcvuEFOeC6n3WlfVBQP09Kw0dKipYlJsbRHffjhh1y6\ndClX+c2bN0lKSqJatWp57telSxf27NnDhAkTijpEQRBeU1p6Jjv/+lfpWahPPSthVE77FXtm09Ky\nJizhHqEpqSRlZSnNyFsaqDwzr7m5OWPGjCnKWN4bdQ0NS6STRK9evWjfvj3+/v706NEDPT09bt++\nzezZs2natClVq1YlLCwsz31//PFHMfahIJQymZlZ7D0dwtOY7NF21BQKWno4YGmS+1mo/DzSbsjN\nJ9fl5ZwZeTVMPUt0Rt4c+SappUuX0qFDBywsLFi6dGmBlSgUilLbUUF4rnLlyqxfv56FCxfSu3dv\nkpKSsLKyonXr1ko9JPNiZ2fHqFGjmDJlSjFFKwhCQbKyJA6dDyX0cbxc1qS2HRWtX+8H8IFUR5J1\nO+eaRyoi1ZGPCzvoN6CQ8poLHnB1dWXjxo1Ur14dV1fXgitRKLhx40aRBFhcwsLCaNasGYGBgdjZ\n2ZV0OIIgCPmSJIljF8O49m+kXFbvQyvqfWD12nUNun1bHnj2RWoKBUv+m4y2IEX93ZnvldTNmzfz\n/LcgCIJQss5ci1BKUNUrm1G36psN/WatpUV4HgNwF0ePY1WoPHafIAiCUPIu3nzChZvPxzh1qWBM\nw5q2BT6sW5BW+fQsLukZeXPkeyXVt29flStRKBT8+uuvhRKQIAiCkLfr/0Zy6urzruGO1oZ41a3w\nxgkKSrbHsSryTVIvT5khCIIglJzg0GiOXnze+9bW3IBPPBxQL2A0CVWVVI9jVeSbpNauXVuccQiC\nIAj5uP8ojoPnHigNd9SmgSMa6m/fYlOax+2DApLUy0MgvYqYr0kQBKHwPXyawN5TIWRlZSco43I6\ntG1YCS1N1cZMLUhpH7cPCkhSjRs3fq37nO96F3RBEITSJuJZIjtP/Ks0Hl+7RpXQ1VZ5HIYCRUXt\n5Wl6Wq7RJrSj9pX+JDV16tS3aowTBEEQ3tzjqCR2nviX9IzsBKWvo8lnDZ1yTf3+NsIS7nPzhRku\nckabgJBSMdoEFJCkOnToUJxxCIIgCP95Ep3Ejr/ukpaeCWQPGNuusZPK4/Gp6k5GeSD3oNN3Msrj\nWahHenNiWCRBEIRS5FlMMjuO/0tqWnaC0tHS4PPGTpgY6hT6sYI16uOYtinP8tIi3yQ1b9486tev\nj4WFBfPmzSuwEpGk3h29evWiQoUK/PTTT7nW9enTB3Nzc+7evUtsbCy7du1CV1dXaZs9e/bg5+fH\nkiVLcHZ2plmzZkrrdXR0cHBwoEuXLvTo0UO+ZbxlyxbGjRuXb1zz58+nZcuWhXCGgvDuiopLYfvx\nu/KUG9pa6rRr5IRped1X7PlmtA3qcFci17h9egZ1iuR4b0IMiyQoUVdXZ+rUqXTq1ImAgAC+/fZb\neV1sbCw//fQT7du3x8vLSx4xffHixVSvXh1JkoiPj+fIkSNMnz6dsLAwpZHz1dXVOXbsWJ7HLV++\nfNGemCCUctHxKWw7dpfk1OwEpaWpTruGTpgbF02Cgv/mt0utRpSW8jQ9HUrJaBPwGlN1CIWntD+X\n4OrqSv/+/fnll19o27YtVatWBWDmzJloaGgwfvx4pe3Lly8vzylmYWGBk5MTGhoazJgxg44dO1K5\ncmV5WzH3mCDkFhOfyvZjd0lKyR5EQVNDjc8aVsLiNabceBOlfbQJUDFJxcbGEhAQwOXLl4mPj89z\nm/379xdqYGXVu/BcAsCgQYM4cOAA/v7+bNy4kQsXLrB582aWL19OuXLlXrl/586dmTt3Lnv37mXY\nsGHFELEgvJui47KvoBJzEpS6Gm09K2Flql8sxy/No02Aiknq+++/JzAwkIYNG1KlSpWijqlMi4ra\nm0956XkuAUBLS4upU6fSrVs3/vzzT9auXUvnzp1p2LChSvvr6+tjZ2fH7du3izhSQXh3RcYmK93i\n01RXo42nIzbmBiUcWemhUpI6deoUEyZMoHv37kUdT5mXlhaRT/nDPMuLwrZt29izZ0+u8tTUVD77\n7DN5uUaNGvTu3ZuJEydiaWn52jMzGxoakpDwvHtrZmYmtWrVyrWdsbExhw8ffq26BeFd9ywmme3H\nX0hQGtlXUMWVoM6fOcPemzeJyMjAWkODVq6u1P24NExzqEylJKWnpycmAiwkWlrWpKaG51FuU2wx\nNG/enK+//jpXeV5JaOTIkfz2228MHDgQA4PX+/AkJCQotUGpq6uzbdu2XNupqYkZY4T3y5PoJHYc\n/1fuxaelqU5bz0pYmxXPLb7zZ86w4to1eTk8I0NeLm2JSqUk1bNnT3799Vfc3d3R1y+eF7GsMjFp\npdQm9by8+LpfGxgYULFixVzlOjq5n8PIKctrXUGSk5O5d+8ebdq0USrP67iC8D55HJX9oG7Oc1Da\nmuq0bVh8bVAAe2/eRHHSEuPTpujGqZNsmEm0RyT7NG6+m0nKx8eHrVu30rhxYxwdHXM9O6NQKFi9\nenWRBFjW5LQ7RUXtIy3tIVpaNpiYtCxV7VGFYdOmTWRlZdG6deuSDkUQSo1HkYns+OtfeSQJba3s\nbuZF3YvvZRHHTLHdZyEv68Wqo7fPgocAfYo1lFdSuePEvXv3qFKlymvf8hFyMzSsW6aSUmxsLE+f\nPkWSJOLi4jh+/Djz5s1jwIABVKhQQWnbp0+f5lmHrq6ueG8JZVrYk3h2n7wnj8Wno6VBu0ZF+xxU\nfizPmOVdfjbv8pKkUpI6cuQIY8eOpU+fPkUcjvAuGjx4sPxvIyMjnJycmDJlCu3atVPaLjMzE0/P\nvEcE8/Hxwd/fv0jjFISScu9hLPtOh5D533QbutrZCcrMqPgTFIBpqi6RUgLpUgpZZKGGGpoKHUxT\nSt8PRZWSlL6+Ps7OzkUdi1AMCprMctWqVXmW37p1K89yOzu7fNe9rEOHDmLQYuG9dOt+FIHnQ8n6\nb8JCA11NPmtUNGPxqUqyVoN/U0HK+q8kCxSpYFP6npdSqVtVt27d+PXXX0lOTi7qeARBEMqMq3ee\ncfDcAzlBlTfQpn2TyiWaoAAiGv2NuoY6Olpa6GlpoaOlhbqGOhEN/y7RuPKi0pVUZGQkly9fxtPT\nk8qVK+fq4adQKPj111+LJEBBEIR3jSRJXLj5hDPXnj8XaWqow2eNnNDX1SzByLLFNDhFdHJVjA87\noxujQ7JRCtFet1E0uAH0KunwlKiUpO7cucMHH3wgL6enpxdZQIIgCO8ySZI4dTWCS7eeyGWWJnq0\n9ayETiHNqPu2rMtZE97sOlHNriuV25Yrfc/DqvSKFdSOIQiCIGTLypI4dimM6/9GymV2FuVo08AB\nTQ31EoxMWavKrVhxMffzmi0rl77pcvJtk7pw4cIbVRgUFPTGwQiCILyr0jOy2Hs6RClBOdqU51NP\nx1KVoADq2taln3s/7AztUFOoYWdoRz/3ftS1LX2PxuR7JTVp0iScnJwYNGiQSj37rly5wvLlywkJ\nCWHnzp0qHdzf35/MzEylCfg6derE1atXlbbr1KmTvE1kZCSTJ0/m5MmTaGpq0qFDB/z8/NDQeH4q\nq1atYvXq1URFReHu7s4PP/yAg4ODSjEJgiC8rpTUDHadvMejyES5zLWiMV51KqCmpijByPJX17Zu\nqUxKL8s3SW3evJmFCxfSsWNHHBwcaNGiBdWrV8fOzg5dXV3i4uJ4/PgxFy5c4Pjx49y7d4+ePXsy\ne/bsVx5UkiQWLFjAhg0b6NSpk1L5nTt3+Pnnn/n4haE5XhzhYtiwYSgUCtatW8fjx48ZO3YsGhoa\n+Pn5AdkjHSxYsICpU6fi6OjI3Llz6devH3v27EFLS+uNXiRBEIT8xCWmseOvu8TEp8pltVwsqF/N\nWp6ZWnhz+SYpTU1N/Pz86NGjB6tWrWLjxo0sWrRI6UWXJAkbGxs++eQTli1bhqWl5SsPGBoaynff\nfUdwcDA2Nja51iUnJ1OzZs08J8e7dOkSFy5c4NChQ9jb2+Pq6sq3337LlClTGDJkCFpaWqxYsQJf\nX195KvLZs2fj6enJ/v37adu2rcovjCAIwqs8jU5m14l/5bmgFAoFntVtqOEsJvcsLK/sOJEzRcOY\nMWO4e/cuYWFhxMfHY2xsjI2NDY6Ojq91wIsXL2Jtbc2cOXNyjcR9+/ZtdHR0sLW1zXPfoKAgbG1t\nsbe3l8vq1atHYmIiN27cwM7OjpCQEOrVqyev19fXx83NjaCgIJGkBEEoNKGP49l7OkQeh09dTUHz\nehWoYm9csoGVMa/VH9LJyQknJ6e3OmC7du1yDZeTIzg4mHLlyjF69GjOnTuHsbExHTp0oHfv3qip\nqfH48WMsLCyU9slZjoiIkNulXr6is7Cw4NGjR28Vd1kwduxYtm7dmu96W1vbQpnX6fDhwzg4OFCp\nUqW3rksQSqPbD6I5dP4BWf8Nc6StqU7rBo7YiskKC13p6LT/nzt37pCUlISnpycDBw7k4sWLzJw5\nk/j4eIYPH05ycjLa2tpK+2hqaqJQKEhNTZVHxHh5Gy0tLVJTU3nfjR8/nlGjRgHZSb1z584sXryY\n6tWrA9nzPb2t8PBwBg0axPr160WSEsqcvB7SNdDVpG3DSpiWL5lx+Mq6UpWkZsyYQVJSEoaG2eNH\nubi4EB8fz9KlSxk2bBg6OjqkpaUp7ZOeno4kSejp6clzHr28TVpaWq7pRUrS+fDz7L2zl4j4CKzL\nWdOqcqti6WVTrlw5ypUrByAn7fLly+fZ/vempP+GfxGEsiYzM4ujF8O4ERIll5kY6vBZw0oY6IlO\nWUWlVCUpDQ0NOUHlcHFxITExkfj4eKysrDh27JjS+idPsp/qtrS0xNraGsieDuLFyfWePHny1rcp\nC8v58PNKD9GFx4XLy6WhO2hoaCizZs3i7NmzJCQkYGlpSa9evfD19QVg9OjR6Orqoqmpya5du0hP\nT6dZs2ZMmjQJbW1tmjVrBmSPap7z6EBYWJhcZ1paGvXr12fs2LHybM+NGjWib9++nDp1irNnz2Jg\nYICPj4/S6OqCUJJSUjPYezqE8KcJcpmdhQEtPRzQ0SpVX6NlTqmat7tLly78+OOPSmVXr17FwsIC\nQ0NDateuTWhoKBERzy+1z549i76+Pq6urpiamuLg4MC5c+fk9YmJiVy7do26dUs+AQDsvbM3z/J9\nd/YVcyS5SZLEgAEDyMjIYO3atezZs4e2bdsyffp0pdHOt27dipqaGhs2bGDOnDkcPHiQ9evXo6Gh\nwaZNmwBYvHgx48aNIy4uju7du5OQkMDKlStZvXo1MTEx9OrVi4SE5x/4efPm4e3tza5du/jiiy+Y\nP38+ly9fLvbXQBBeFhOfyp9HgpUSVFUHk+xhjkSCKnKlKkl5e3uzYcMGtm3bxoMHD9i0aRMrVqxg\n+PDhANSqVYuaNWvi5+fH9evXOXbsGLNmzcLX11d+BqpPnz4sX76c3bt3c/v2bUaNGoWFhQXe3t4l\neWqyiPiIPMsfxj8s5khyS05OplOnTkycOBFnZ2cqVqzIkCFDgOxOLTlMTU357rvvcHR0pGnTpnh4\neHDp0iUATExMgOzbiAYGBmzbto3ExETmzp3LBx98QLVq1Zg/fz5RUVHs2rVLrrNZs2Z07twZe3t7\nBgwYgL6+vkhSQol7+DSBPw8HKz0D5VHNGq869qirl6qvzzJLpZ8BqampLFu2jKNHj5KUlJRnu8P+\n/fvfOph+/fqhoaHBkiVLePjwITY2NowbN47OnTsD2c8gLFy4kIkTJ+Lj44O+vj6dO3eWv0gBunfv\nTlxcHNOmTSMxMRF3d3dWrFhRah7ktS5nTXhceK5ym3I2eWxdvPT09OjZsyd79uzhypUr3L9/n5s3\nbwLZExbmqFChAmpqzz+gBgYGxMTE5FlncHAwlStXVrqNa2pqiqOjo1Lie/FRBoVCgYGBQa62RUEo\nTjfvR3E4KFTuwaehrkbzuhWobG9UwpG9X1RKUj/99BObNm2iXr16VKlSRekL6m28PHCtQqHA19dX\nbv/Ii7m5OYsWLSqw3oEDBzJw4MBCibGwleaBHRMSEujevTsAn3zyCR4eHlSrVo0mTZoobZdXws+v\nw8TLPS1zZGZmKg1lVVp+RAhCVpbE2esRXLj5fBRzXW0N2jRwxMpUv4A9haKgUpLav38/fn5+DBgw\noKjjKfNyOkfsu7OPh/EPsSlnQ8vKLUtFp4njx48THBzM+fPn5V6AOVc7qvbae3kYmMqVK7Nlyxbi\n4uLkq6nIyEju37/PF198UYjRC8LbS03P5MCZ+9x/FCeXmRrq0MazEob64odUSVApSaWlpcnP0ghv\nr7QO7GhlZYUkSezYsYMmTZpw//59pk2bBqg+h1jOhJi3bt2icuXKtGvXjmXLlvH111/z9ddfk5WV\nxYwZMzAxMZGHrhKE0iA6PoXdJ+8ptT9VtDKkxccV0dYsXaOYv09Uum/n6enJ8ePHizoWoYS5u7sz\natQoli1bRuvWrZkyZQrt27enbt26uUamz4+RkRHdu3dn+vTp+Pv7o6ury8qVK1FXV8fHx4c+ffpg\nbGzM+vXr5as1QShp9yPi2BSo3EHC3cWCNg0cRYIqYQpJhfs4Bw8eZMKECXh5eeHu7i4/NPuid31c\nvLCwMJo1a0ZgYKD8/I4gCGWbJElcuvWU09ci5FvaGupqeNWxx7mCGINPFUX93anS7b5hw4YB2c/H\n5DX2m0KheOeTlCAI75f0jCwOB4USHBotlxnoatK6gSMWxnolGJnwIpWSVGBgYFHHIQiCUGxiE1LZ\ndzqEpzHJcpmNmQEtPSqip6NZcoEVobjzcUTtjSItIg0tay1MWplgWNfw1TuWMJWS1ItTZyQlJZGY\nmIiRkRGammXzjykIQtl172Esh849IDX9+bN/bpVMaVjTtsw+oBt3Po6IFc8HEkgNT5WXS3uiUnlM\nj7Nnz/Lzzz9z/fp1+d5t9erVGTlyJB4eHkUWoCAIQmHIfv7pERduPpbL1NUUNKxpi5uTWQlGVvSi\n9kblXb4vqmwkqfPnz/Pll1/i6OjI8OHDMTU15cmTJ+zbt4/+/fuzatUq6tSpU9SxCoIgvJGklHQO\nnH1A2JN4uaycnhYtPRywNCn77U9pEXmP3pL2sPSP6qJSkpo/fz4eHh788ssvSg9rDh48mAEDBhAQ\nEMDq1auLLEhBEIQ3FfEskf1nQkhIfv6sXwWrcrSoVxEd7fdjgFgtay1Sw3PPqadlU/ofUFbpBuy1\na9fw8fHJNZqAQqHAx8dH5WdoBEEQioskSfwd/JStR+/ICUqhUFDvQys+bVDpvUlQACatTPIub5l3\neWmi0l/J0NCQpKSkPNclJiYWyoyugiAIhSUlLYMjQaHcDY+Vy3S0NPD+qAIVrUp3G0xhOHNqJTcf\n7yRDLRqNLGNcLdvyQb9ORO2LIu1hGlo2Wpi0LEO9+z7++GMCAgKoXbs2lpaWcvnjx48JCAgQHScE\nQSg1Ip4lcuDsfeKTnre3WBjr0dLD4b0Yf+/MqZVce7pavk+WoRaVvWwOH3/ft2SDewMqJalRo0bR\nsWNHPvnkE2rXro2ZmRnPnj3jwoULGBgY8M033xR1nIIgCAWSJImLt55w9tojsl4YSKd6ZTPqV7dB\no4x2L3/Zzcc7uXfXlWsXPyI22pTyxpG4uZ9FI2sXH/PuJSmV/mqWlpZs3bqV7t27Ex8fz+XLl4mL\ni6NHjx5s3boVe3v7oo5TKAReXl64uLjI/1WrVo1PP/2UP//8U97GxcWF7du3v/ExtmzZwgcffFAY\n4QqCypJS0tnx17+cvhohJyhtLXVaeTjQqJbde5OgAILvWnDiYBtiIs2QshTERJpx4mAbgu+al3Ro\nb0TllkNzc3PGjBlTlLEIxaB///707t0byJ6J98SJE/j7+2NmZkaTJk04ceKE0gSFglDahT6O5+C5\nBySlPO+9Z22qj/dHFd+L23sv++dCkzzLb+RTXtrlm6SWLl1Khw4dsLCwYOnSpQVWolAoSu0kg6XR\n+fOwdy9ERIC1NbRqBXWLaeYOPT09zM2f/6Lq0aMHgYGBbNu2jSZNmiitE4TSLDMzi7PXH3Hp9lN5\ngAGFQoG7iwX1PrRCXU3xihrKJkVKTSA494qUmsUeS2HIN0nNmzeP+vXrY2Fhwbx58wqsRCQp1Z0/\nDytemJg3PPz5cnElqpfp6urKjxe4uLgwc+ZM2rVrx9ixY0lJSSEyMpJ//vlHbpucM2cOBw4c4OnT\npxgYGNC0aVN5Wo4c69evZ8mSJSQmJtK4cWP8/f0xMcnu7hobG8v06dM5fPgwkiRRo0YNxo0bR6VK\nlQAYO3Ysampq6OnpsXPnTtLS0vDy8mLSpEkYGBgU/wsklDpRcSkcPHtfaew9PR1NvOtVwN7y/Z4C\npmatqoQHZmAV9hiDtCwStNR4ZGeJnWfVkg7tjeSbpG7evJnnv4W3s3dv3uX79hV/kpIkidOnT3Py\n5EkWLlyY5zZ79+5l/PjxTJw4EUNDQ2bMmMGJEyeYNWsWVlZWXLlyhbFjx+Li4kKfPn2A7KnhN2/e\nzOLFi8nIyOD7779n3LhxLFu2jKysLAYMGICBgQErVqxAV1eXtWvX0qNHD/bu3Yuxcfb0CDt27KBz\n58788ccfPHjwgJEjR+Lk5MTgwYOL6+URSiFJkrh69xmnrkSQkZkll9tblsO7XoUyOzjs62hRMY5r\nT7RAyx60wBiwfwJuFeKAd+9WvkptUgsXLqRz585K3c9zhIeH89tvvzFhwoRCD64siojIu/zhw+I5\n/uLFi1m+fDmQPeNyRkYG3t7e1M0nQ5qbmytN816jRg3atGlD7dq1AbCzs+P333/n9u3bSvvNmjUL\nJycnAH744Qd69erF/fv3CQ8P5+rVq5w7d06+Kpo0aRJnzpxh48aN8hW5kZEREyZMQF1dHUdHR+rX\nr8/ly5cL98UQ3ikJyekcPv+AB4+fD22krqagfjUbqlcxyzXYwPvK6n4UGVUhNBSSEkFPH+ztwepB\nFGU2SS1atIhGjRrlmaQuX77Mhg0bRJJSkbV19i2+l9nYFM/xfXx86NGjB5CdpIKDg5k1axZDhgyR\nk9eLXp7ErF27dpw4cYKZM2cSEhLCnTt3ePDggdJ25cuXlxMUgJubGwDBwcGEhISQmZlJw4YNlepN\nTU3l7t278nKFChWUHhIvV64cjx8/Rng/3QmL4eiFMFLSMuQyMyNdvOtVwLS8bgF7vn/SItIwN4eX\nm5ffhXH68pJvkurevbv8y1WSJLp27ZpvJdWqVSv8yMqoVq2U26RytGxZPMcvX748FStWlJerVKlC\nRkYG33zzDcHBuRtbX56Fefz48QQGBtK+fXtatGiBn58fkydPVtpGTU25u29Oo7ampiaampoYGRmx\ncePGXMfS03s+0KeWVu5eWSpMIi2UManpmfx1KZyb95+P4q1QKKjpbM7HH1qV2ak13sa7PE5fXvJN\nUj/++CMHDhxAkiQWLFhAly5dsLKyUtpGXV2dcuXK0bx58yIPtKzIuau2b1/2LT4bm+wEVVKdJuD5\nl39WVlaB20VHR/Pnn38SEBBAixYtAMjIyCA0NBSbFy4FY2JiiIiIwNraGoCLFy+iUCioXLkympqa\nxMTEAMjJMjMzk9GjR+Pt7U3r1q0L/fyEd9P9R3EcCQpVGhjWQFcT748qYmsuOtDkx6SVidLcUXL5\nOzBOX17yTVJOTk4MGjQIyP7yyq9NSnh9deuWXFJKSkri6dOnQPbf9e7duwQEBFC1alWcnZ0L3NfA\nwAADAwMCAwNxdXUlISGBZcuWERERQVra81sJCoUCPz8/xo8fT1JSEpMnT6Zt27bY2tpiY2NDzZo1\nGT5DjoIAACAASURBVDlyJOPHj8fU1JRffvmFw4cPM2TIkCI9d+HdkJqeycm/w/nnnvIcSM4VjGlU\nyxYdrfdnYNg3kTMe37s4Tl9eVPprDx06FMj+JZ2eni7/8pYkiaSkJC5cuEDnzp2LLkqh0Cxfvlxu\ne1JXV8fExIT69eszatSoVzY8a2pqMm/ePGbMmMGnn36KiYkJjRo1om/fvhw6dEjeztzcHG9vb/r1\n60dGRgatWrXiu+++A7IT2KJFi5gxYwaDBw8mLS2NqlWr8uuvv1K5cuWiO3HhnZDX1ZOutgaNatlS\nxd64BCN7txjWNXxnk9LLFJIKN/pv3brF6NGjuXPnTt6VKBT8888/hR5ccQoLC6NZs2YEBgbm6iwg\nCELRyu/qqbKdEY1q2Yqu5aVYUX93qnQlNXPmTGJiYhgzZgxHjhxBS0uLpk2bcvz4cY4fP86aNWsK\nPTBBEN4P+V09Na5lR2V7oxKMTCgNVEpSly9fZty4cXTq1AldXV127txJjx496NGjB8OHD2ft2rVi\n+nhBEF5LUko6J/9+yK0H0UrlVeyNaFhTXD0J2VRKUmlpaTg4OADg4OCgNAJFhw4d+OGHH4okOEEQ\nyh5JkrgZEs3JKw+VnnvS1dagsbsdle3E1ZPwnEpJysbGhrCwMOrUqYODgwMJCQmEh4dja2uLtrY2\nsbGxr65EEIT3Xkx8KkcvhhL2JEGpvIq9MQ1r2oirJyEXlZJU8+bN+fnnn9HX18fb25tKlSoxf/58\nBg4cyKpVq8R8UoIgFCgzM4tLt59y/p9HZGY976tlqK9FY3e792JKd+HNqNwF/f79+2zcuBFvb2/G\njRvH0KFD2blzJ+rq6syZM6eo4xQE4R31KDKRI0GhRMalyGVqCgU1nM2p94ElmhrqBewtvO9USlK6\nurosXLhQfmCzYcOG7Ny5k+vXr/Phhx9SoUKFIg1SEIR3T3JqBqevRnAjJEppSCsLYz2a1rbH3FiM\nuSe82msNfPXieGoVKlSgVatWb5Wg/P39GT9+vFLZiRMnaNeuHdWrV6dt27YcO3ZMaX1kZCQjRoyg\nTp06eHh4MGvWLDIyMpS2WbVqFU2bNqVGjRr4+voSEhLyxjEKgvB6srIkrt19xrp9N/jnXuTzsRvV\n1fCsYUMnryoiQQkqy/dKqkWLFq819P3+/ftV3jZnPMANGzbQqVMnufzOnTsMGjSIwYMH06JFC3bu\n3MmQIUPYunUrVapUAWDYsGEoFArWrVvH48ePGTt2LBoaGvj5+QGwadMmFixYwNSpU3F0dGTu3Ln0\n69ePPXv25DloqSAIhedRZCLHL4XzJDpJqdzR2pCGtezey+nci0RJTu9dzPJNUu7u7kUyP0toaCjf\nffcdwcHBSoOSAqxZs4aaNWvKYwaOHDmSCxcusGbNGqZMmcKlS5e4cOEChw4dwt7eHldXV7799lum\nTJnCkCFD0NLSYsWKFfj6+tLyv2HFZ8+ejaenJ/v376dt27aFfj7vEi8vL8LDw/nhhx/k6Tpe1K9f\nP/766y95Zl5V68tLlSpV2LVr11vHnJft27fz7bffcuvWrTeu49y5c/z444+Ehobi7u7O1KlTX2ts\nyqCgIHx8fMQIJf/JubX3z71IpXJDfS0a1rTF0aZ8CUVWBpXG6b2LUL5Javr06UVywIsXL2Jtbc2c\nOXP4+uuvldYFBQXRqlUrpbKPPvqI3bt3y+ttbW2VehPWq1ePxMREbty4gZ2dHSEhIdSrV09er6+v\nj5ubG0FB/2/vzOOirNr//x6EYRnWYR0UN0RxBUEU1MrqyTVzSyu10swW+2Zpmy36mJVrLrllLi3W\nr/Upe1IfzFZNK0UgDQOFcgEc1pFtGGaY4f79QYyOAzrEyHrer9e8dM65zz3Xfbjn/sw55zrXdazN\nixRUx9/7+uuvrUSqqKiIX3/9td7nmz17Nvfff79VuaNj8w0CWlJSwpw5c7j//vsZPXo0zzzzDK+9\n9hrr169vatNaHFVVEifPFPJrihq9wWQud2znQFR4AFE9AnAU6TTsS3w8CUol8SoValdXVDodo9Rq\nYpoivXcjYNOTJCkp6ZrHREVF2fSB48aNq/NXek5OjtWv2YCAAHJycgDIzc0lICDAqh5ArVabH4xX\nO0dzoCShBE28BoPagFwlRzmq8SIUx8bG8vPPP6PRaFAqL4Xu/+abb4iIiODYsWP1Op+bmxv+V2ZX\na+ZkZWVRWlrKbbfdRmhoKIMHD7Za+xRcm8zcUg6fuEBBkc6ivEuwF0MjgvFyd24iy1o3CXo92y9L\nKprt5lb9/swZWp9E2eg4MXXqVKZNm3bVlz2oqKiwWjeSy+Xo9dUJvHQ6Hc7Olje+k5MTMpkMvV6P\nTlf9ZbnymMvP0dSUJJSg3q5Gn61HqpLQZ+tRb1dTklDSKJ/fv39//Pz8LKKWA8THx9eay+nAgQNM\nnjyZiIgIbrnlFrbXlrHxKtx7770sWLDA6rMiIiIoK6ve0Pnpp58yYsQIs7PMrl27LI7/5ZdfmDhx\nIv369eOuu+4iKyvLXPfuu+8ycOBAi1QhWq2WyMhIq2usoVu3bgQEBLB27VpOnTrFl19+2eBRttFo\nZNu2bQwfPpy+ffsyduxY/ve//5nrN2zYwKxZs9i0aRNDhw6lX79+PPTQQxbZhtVqNXPnziUqKorB\ngwczb948i/rffvuNu+++m8jISAYNGsQzzzxjzs3VmFwsrWDv4TP89+CfFgLlqZAzZkgXxgzpIgTq\nOhL/9/r8leyro7ylY5NI7dy5k/fee8/i9eabbzJr1iz8/Pz46KOP7GKMs7MzlZWVFmUGgwFX12pP\nIBcXF4uHEWBOHeLm5mbOInvlMZefo6nRxGtqL99Xe7m9kclkDB8+3MLRRaPRkJCQwIgRIyyOTU5O\n5pFHHmHIkCF8+eWXPP/882zatKnWrLp1MX78eL755huLHwm7d+/mX//6F+7u7nz44YesXbuWefPm\nsWfPHh588EFee+01s1CdO3eOhx56iKioKL788kvuvvtuizT3Y8eORavVWoyE9u/fj6urKzfddFOt\nNsnlcl566SV+/PFHJk2axPTp03nooYdsvqbaWL58OTt27GD+/Pl89dVXjBkzhvnz51v085EjRzh1\n6hTvvPMOb7/9Nn/88Yd5irG8vJx7770XZ2dnPv74Y3bs2EFlZSX3338/BoMBk8nEo48+SlxcHHv2\n7GHr1q38/vvvrFixokF214cKg5Gffsvmo69PcebCpSgzTu0cGNQ7iKkjwsXaUyOg7taNvDN+JO7u\nz08fDCFxd3/yzvhxoZWmurFpuu/yNZ7LGTZsGG5ubrz55pu89dZbDTZGpVKRl5dnUZaXl2eevgsK\nCrKalqk5PjAw0JwJNj8/3yJFel5eHqGXDY+bEoPaUHv5hdrLrwcjR45kxowZFBcX4+Xlxf79+4mK\nisLPz8/iuJrAwU8++SQAXbp04d///jft2l3afLl582YL0ahhwYIF3HXXXYwYMYJXXnmFAwcOMHz4\ncIqLizl48CBvvvkmAFu2bOH//u//zI4uHTt25MKFC2zZsoUJEybw6aefolKpeOGFF3BwcKBr166k\np6ezY8cOAHx9fbnxxhv56quvuO2224Bqx4rbb78dJ6faQ+x8++23LFq0iO7du3P69Gm6du0KQFlZ\nGe7u9c/4WlZWxkcffcSiRYvM1/HII4+QlpbG1q1bzeIvSRJLly41f8bo0aM5fPgwAHv37kWn07F8\n+XJz/65Zs4ZBgwaxf/9+hg4dysWLF/Hz86N9+/Z06NCBTZs2Wf2oux6YqiRO/lXA0ZO5FrH2AMI7\nKYntq8LdVYQzaiyqCruSlhIKZVqQjGjLvEhLCSZw4DWzLrVIGry6PWDAgFofUv+E6OhoEhISLMqO\nHDlijrAeHR3N66+/bpGa/MiRIygUCsLDw5HL5XTu3JmjR4+a22i1WlJSUrj77rvtYmNDkavk6LOt\npx7lwY3nmhsdHY2Pjw/fffcdEydOrHOq7/Tp09x4440WZePHj7d4P23atFo9BWvWu9zd3bntttvY\ns2cPw4cPZ9++fXh7ezN48GA0Gg25ubmsWLGC119/3dzWaDRiMpkwGAykp6fTs2dPHBwuDfojIyMt\nPmvixInMnz+fkpISdDodR44c4dlnn6312lNSUpg7dy5PPfUUs2bN4qmnnmLBggV07tyZe++9l/vv\nv585c+Zcowct+euvvzAajfTv39+iPCYmhu+//9783s/Pz0IEPTw8zCLzxx9/oNForLIJ6HQ6/vzz\nT26//XZmzpzJkiVL2LBhA0OGDOHmm2+2Gv3aE0mSOJdTyuHjF7hYWmFRF+ynYGhEewKUbtft8wV1\ncFQJLhXgcsXsUIILXNspt8XRYJH64YcfUCgU9rCF6dOnM2nSJNavX8+YMWPYs2cPx48fZ/HixUD1\nekpkZCTz5s1j4cKFFBQUsGrVKmbOnGley5oxYwYrV66kU6dOhIWFsWbNGgICAsy/spsa5Sgl6u1q\n6/KRylqOvj7IZDJGjBjB119/zbBhw0hKSmLt2rVWx9nioefl5WUxaq2NCRMm8Mgjj1BWVsaePXu4\n4447aNeunXmks3DhwlpH646OjshkMq7My3nlCGnYsGEoFAq+/vpriouLCQsLo1evXrXasnv3bjp3\n7sysWbMAeO2115g6dSrTp0+npKSEW265pdZ2arWaiooKunTpYmXjlWugNZhMJos+rG2fnnmjq5MT\n3bp1Y+PGjVbHeHh4APDcc88xbdo0Dhw4wKFDh3j++ef59NNPr0s+t5xCLT+fUHOhwDIQrKdCzuB+\nwYS297ouW1QE18ZB40JPNwcy9Xq0VSYUDu0IcXZGVtg696DZJFIPPPCAVZnJZCInJ4fz588ze/Zs\nuxjTo0cPNm7cyKpVq9i2bRtdu3Zly5Yt5qk6mUzGxo0bWbx4MdOmTUOhUDB58mQee+wx8znuuece\nSkpKWLZsGVqtlqioKLZv395sNvLWePFp9mkwXDAgD5ajHNl43n01jBw5kpkzZ/Lll18ycOBAC0+/\nGkJDQ0lJSbEoW7t2Lenp6WzevNnmz4qNjcXHx4fPP/+cY8eOsXDhQqD64RsYGEhWVhaTJ082H//R\nRx+RmprKkiVLCA8PZ/fu3RiNRvMD/0qbnJycuP322/n2228pLi5mwoQJddri6upKSUkJlZWVODk5\n4eLiwsqVKxkzZgwdOnSoM4X9smXLMBqN5usuLi7GwcEBLy8vvL29cXJyIikpie7du5vbJCYm1nm+\nKwkLC+Ozzz7D29sbL6/qdZ2ysjKefvppZsyYQXBwMDt27OCFF14wOyv973//Y968eRQWFuLr62vT\n51wLTUkFv6ao+SvbMrOB3KkdA8ID6RfmJ1zKmxiVCqRf1PQ4k41reSU6NycudmlP8OCr/1hsqdgk\nUrXNe8tkMkJDQ3nwwQeZNGnSP/rw999/36ps2LBhDBs2rM42/v7+bNq06arnffjhh3n44Yf/kU2N\ngWeMZ6OL0pVERUXh5eXFxo0brUJT1fDAAw9w5513snnzZsaMGUNaWho7d+60OL68vJz8/Pxa2/v5\n+SGTyXBwcGDcuHG88cYb9OzZ0+JB/uijj7J8+XKCg4OJi4vj+PHjLF++nAcffBCAu+++m/fff59F\nixYxa9YsTp06Vet9M3HiRO666y4kSbrq/TFp0iTeeecdXnzxRR5++GE0Gg0rV66ke/fuZGZm8vTT\nT7NixQqr0VFsbCwrV67k0KFDqFQq3n33XWJiYswOOTNnzmTdunV4e3sTHh7O/v372b9/v83Bl8eO\nHcubb77Jk08+yfz583F2dmb16tWcOHGCsLAw5HI58fHxGAwGc9/Ex8fTsWNHfHx8bPqMq1FWbuDo\nHzmknr1oMXJ1kMno3dWXmF6BIo1GMyHM4ziylEuJIt20BtxSztBteBEQ0XSGXSdsEqnaHgqClo2D\ngwMjRozgk08+qXMqtHfv3mzYsIH169ezefNmgoKCmDdvnkUoq23bttW5JvnLL7+YR2jjx4/nrbfe\nstojd88992AwGNixYwevvPIKgYGBzJkzx+xtVyMIS5cuZcKECXTu3JnZs2dbrGEB9OrVi86dO9O+\nffurjipCQkJ4++23zVE1vLy8GDVqFE8++SQpKSksXbqUoqIiq712U6ZMITMzkwULFlBWVsbAgQN5\n+eWXzfVz587FwcGBpUuXcvHiRUJDQ1mzZo3V5vS6cHFx4Z133mH58uXcf//9yGQyIiMjee+998zX\ns23bNlatWsWUKVOoqqpi4MCBbN261WK9rr5UGIwkpeVxIqMAo6nKoi4sxIdBvYPw9hDu5M0J/e/J\neHdwQVvgQ2WFM04uehR+F9GnnKU1ipRMunLC/yocOHCAxMREiouL8fPzIzY2lphWssM5KyuLW2+9\nVYS5aaEYjUaGDRvGokWLGD58+D8+jyRJbWKtxVBp4kRGAcmn8ywiRQCEBHoQ11dFgI9wimiObB+9\nHapqqXCAB//3YKPbc72fnTaNpC5evMjs2bNJSUlBLpejVCopLCxk8+bNDBkyhE2bNtW5eCwQXE8M\nBgPff/89P/30E3K5nJtvvrlB52vtAlVpNPF7RiFJp/Ks3MkDfNyI66siJNCjiawT2IJjkCPGC0br\nclXzDUXWEGy6qldffZWsrCy2bNlisV703Xff8eKLL/L666/Xua4hEFxPnJyceOWVV5DL5axatarO\nvVFtnUpjFSl/FpB0Kg+d3vIB5+3uzKA+QXTr4N3qRbo1ED4pnJQNKdblE8ObwJrrj00idfDgQV54\n4QUrh4Zbb70VjUbD2rVrhUgJmgSZTGbeECuwxmiq4uSfhSSeyqO8wtIBylMhJ6ZnED06+eDgIMSp\npRA7JhaAtC/SMKqNOKocCZ8Ybi5vbdgkUu3atTPv1bgSf3//Rtn1LhAIbMdoquKPM4UkpeVRprP8\nfnq4yRnQM5DwzkraCXFqkcSOiW21onQlNonU1KlTWbt2LX379rXweiorK2Pr1q1Mnz79uhkoEAhs\nx1BpIuXPQpJPW0/rubs6MaBnID07K2kn9jo1a0pKEtBo4jEY1MjlKpTKUXh6tg4ntfpik0jl5eWR\nl5fHbbfdRnR0NAEBARQVFZGUlIRWq0Uul5s3/MpkMnNcNYFA0Djo9EZOpOdz4s8CK289hYsT0T0D\n6NXFV2zEbQGUlCSgVl/KNqDXZ5vft0Whskmkzp07R3h49aKc0WjkwoULAOYyk8mEyWSqs71AILg+\nlOkq+e10Hif/KqTSaOmX7O7qRFS4EKeWhkYTT35eLplFRZRXSbg5yAjx9sbZeZ8QqboQm3kFguZF\ncZme5FN5pJ7VYKqy3Oro7e5MVHgAPTr6iGm9FkhW5m+kaS5FlNBWSX+/T6Zz5yYzq8mol2N9RkYG\nR48epaysDB8fH6Kjo81pDgQCwfUnp1BL8ul8/soutgq86+ftSnR4AKHtvYW3Xgsmo0DizJ/hpCQN\noviiL14+hfSJOoJjaD5Dm9q4JsAmkaqqqmLRokV8/vnnFl8MmUzGuHHjWLZsmdhfIRBcJ6qqJM6q\nS0g+lYe6UGtVH+SrIDo8gM4qT/E9bAUcOHEbf/18KXJDUaEfh74ZwwVtFjPqjp3carFpLmDr1q18\n+eWXPPXUUxw4cICTJ0/y448/Mn/+fPbu3VvvlOKC1suxY8fo0aOHRYr3q/HFF1/UmVajrVNprOL3\nPwv48Os0/vfzGSuB6hjowbgbQ5l0cze6BIvUGa2Fc7/FUVIZhFFyRgKMkjMllUGcP942XM6vxKaR\n1H/+8x8eeeQRc/RlqM6SO3v2bPR6Pf/5z3/slq5DIGjrlFdUkvJnIScyCqxCFzk4yOge4kNkd3/8\nvF3rOIOgJeMjC0Zt0lBhstyb6k3j5ZxrTtgkUvn5+URHR9daFxUVxdatW+1qlEDQFsnVlHMiPZ+M\nrCIrZwhneTv6dPWlbzd/kaq9lRMRGYiUBJlFRWilKhQyB0K8vYnsH3jtxq0Qm0QqJCSE5ORk4uLi\nrOqSk5Px9/e3u2EC+9OjRw9effVVPv/8c06ePElISAhLly7ljz/+4K233qK0tJRhw4axfPlyc5LI\nY8eOsW7dOk6ePImrqyujR4/mqaeeMudRSktL49VXX+X3338nJCTEInkhVK9nbt26lU8++cScwmLu\n3LncdNNNjX79zRGTqYqMrCJOZBSQqym3qvdUyIkI86dXFyVOju2awEJBYzNqFOiSXRlWpsO13IDO\nTY7ax5WRI5vasqbBJpG68847WbNmDW5ubowePRo/Pz8KCgrYu3cvb731VrNOMHg9ST6Vx9E/cqz2\npzQGTo4ODOwVRP8eAfVqt2bNGl577TU6d+7MggULeOihh+jbty/btm3jzJkzPPXUUwwYMICpU6dy\n/PhxZsyYwb333svLL79MVlYWixcvNgcbLi4uZsaMGcTGxvL5559z9uxZc9bdGlavXs0333zDkiVL\n6NixIz/99BP/93//x/bt2xk0aJA9u6RFUaar5OSfBaT8VWgVGQKqnSEiwvyEp14bpAcljJGpyZRB\nOeAv0xMlU9MDgKZNltoU2CRS9957L6mpqSxfvpwVK1aYyyVJ4o477uDRRx+9bgY2Z347nd8kAgXV\ni+q/nc6vt0hNmTKFW265BYBx48axZMkSFi9eTEhICN27d2f79u2kp6cD8Pbbb9OnTx+ee+45oDqd\n/OLFi3nooYdIT08nISGByspKXnvtNRQKBd26dSM3N5clS5YAoNVq2blzJxs2bOCGG24AoFOnTqSl\npbF169Y2J1KSJKEu1PJ7RgF/ZhVTdYULeTsHGWEhPvTr5keAUuRyaqto4jX4+8OVE1SafZomz+jd\nFNgcYHbFihU8+OCDHDt2jOLiYjw9PYmJiSEsLOx629hsiezu36Qjqcju9Z9m7dixo/n/rq6uODg4\nWCQqc3FxwWAwAJCenm41LTdgwABzXXp6Ol26dEGhUJjrIyMjzf//888/MRgMPPHEExbZYysrK/Hz\n86u37S2VCoORU+cu8sdfhRSWVFjVu7s60SfUj15dlCJFexsgIQHi40GtBpWqenrv8tyxBrWh1naG\nC7WXt3bqtZlXpVIREhKCl5cXSqWSkJCQ62VXi6B/j4B6j2SaGkdHyz+5TCar03XZxcXFqqxmn5yj\noyMymcxqQ+nl+Zxq1rU2bNhAp06dLI5rSMrzloAkSeQUlnPyrwIysoqtUrMDBPu50y/Mj67BXmJK\nr42QkACX79jJzr70vkao5Co5+my9VVt5sLwRLGx+2LyZd9WqVXzwwQcYjUbzg8nV1ZVHH32Uhx56\n6LoaKWgaQkNDSU5OtihLTEw01xUXF7Nr1y6Ki4vx8vICICXlUjK2Tp064eTkRG5uLjfeeKO5fOPG\njZhMJp544olGuIrGpUJfPWo6eaYQTS2jJidHB7p39KFvqJ9wIW+DxMcD+XlwPhPKy8HNDTqGsG9f\ngFmklKOUqLerrdoqRwoX9DrZsGEDO3fu5L777mPEiBH4+vpSUFDAvn37WL9+PQqFgmnTpl1vWwWN\nzOzZs5kwYQIrVqxg8uTJZGdn8/LLL3PTTTcRGhpKYGAgmzZt4tlnn+Wpp54iNzeX9evXm9u7uroy\nY8YMVq9ejUKhoG/fvvzwww9s2rSJ1157rQmvzL5IkoS6QMsfZwrrHDX5e7vSu6sv3Tv6IHcSXnpt\nFfXxPEhNu1Sg1UJqGhdkANWzMjXrTpp9GgwXDMiD5ShHKtvkehTUYzPvnDlzeOyxx8xlISEh9O/f\nH4VCwXvvvSdEqhXSvXt3tmzZwrp163j//ffx9vZmzJgxPPnkkwC4u7vz3nvvsWTJEiZPnkxAQACz\nZ882O04APPnkkzg5ObFy5UoKCgoICQlhyZIlTJw4sakuy26UaA2kndOQdlZDidZ6vaBm1NS7i69w\nhBAAoLr4B9m1BPoJLvqDGpGCaqFqq6J0JTLpykWFWujfvz/r1683e2hdzuHDh3nsscf47bffrouB\njUVWVha33nor3333nYUjgUBwOZVGE39mF5N2VkNWXlmtxwT4uNG7qy9hId5i1CSwIGHScrb/Mdiq\n/MFePxPz+YImsKjhXO9np00jqWHDhvHxxx/XKlJ79+61WG8QCFobNdN5qWc1ZGQV1erN6SxvR1gH\nb3qJUZPgKsREGDAV/sap9O5Ulrvg5FZBj7DTxERWNrVpzRabRGrAgAGsW7eOsWPHMmbMGPz9/Skq\nKuLHH38kMTGRGTNmsGXLFqDaW6ytbu4VtC4ullRw+vxFTp2/WOt0nkwmo2OgB+GdfegS7CUSCwqu\nSUmn4RjzE5D7JmP0MyKXHDHmu1PS8bY2uE3XNmwSqVdeeQWA0tJS1q1bZ1X/9ttvm/8vRErQkikr\nN3A6s4j08xfJL9LVeozS04XwTkq6d/IRcfQE9eL3302k+QNlMjCC1klGmjvIUkwMaWrjmik2iVRa\nWtq1DxIIWigVeiMZWUWkZxZxoUBrtfcL/p7OC/GhZ2clAT6uIi1GG+Zam3Gvxrn0c+DiWv26jPOn\nzzNEyFSt1Gszr0DQWqg0mjhzoYT08xc5l1NqFaIIqsMUdQ72IizEm84qTzGdJ7BpM+7VKHQvxFVv\nvT+uwKPATha2PoRICdoMhkoTZ9Ul/JlVxLmc0lr3M8lkMjoEuNM9xIeuHbxwFt55gsuwZTPu1ai8\nsRLXXdYiZbzBOsiwoBohUoJWTYXB+LcwFXM+p8QqT1MNQb4Kunf0plsHbxE/T1An6uN5KJNSUWnB\n1SihcyxDXZBqsRm3JKEETbwGg9qAXCVHOerSRtwbbr+BL7RfoEpU4apxRafUoY5WM/H2lr9v8Hoh\nRErQ6qjQGzlzoYSMrCIy80qpqkOYfD1dCOvoQ1iIN17uzo1spaAlEnbuDzyLL713M0qEFkPp+erN\nuCUJJRYhjfTZevN7zxhPYtrHwF2wL3ofqaWpBHsEM7HbxOpyQa0IkRK0CkrLDZy9UMJfF4rJbKKv\nRgAAIABJREFUziurdY0JqsMThXbwJrS9Fz6e1gF0BYKrMag8k1SsA2sP1GYC1Wk2auPyNBsx7WOE\nKNWDOkUqNze3XicKDLRPauOMjAzGjBljVf7//t//Y8CAARw6dIhVq1Zx5swZOnXqxNNPP22RTqKw\nsJAlS5Zw+PBhnJycmDhxIvPmzbOK/i1o2UiSRH6RjrMXSjhzobhOd3GojgDRrYM3Xdt74e0hRkyC\nf46/oh2Sdw6ZZT6UG51xc9QT4n4Rf/fq54tIs2F/6nxy33TTTfVys01NTbWLQadPn8bHx4fdu3db\nlHt7e5ORkcGjjz7KnDlzGD58OLt37+axxx5j165d5rxWjz/+ODKZjA8++IDc3FwWLFiAo6Mj8+bN\ns4t9gqbDZKoiK7/MLExlurp36Qf5Kght70VoB288FW0zxUGzoiF+2/Y049dfiU9LQ200onJ0ZFR4\nODGxsTa3l0d1wv9gKv4uZVeU96z+VyVH/1uWlWOFvL8ItfZPqVOkli5dahap4uJiXn/9deLi4hg1\napQ54sT333/Pjz/+yIIF9os5dfr0abp164b/lWkpgZ07dxIZGWnOBPzkk0+SmJjIzp07eeWVV0hO\nTiYxMZFvv/2WkJAQwsPDefbZZ3nllVd47LHHzPmNBC2H8opKzueWcvZCCedzSzFUmmo9zsGh2iuv\ni8qLLsGeuLuJv3WzoaF+2/Yy49df2X5ZKplso9H83lahUj7YF3W+BJmZoC0HhRuEhKCc1be6vlMO\n6o+so5wrxzkCne11KW2KOkXq8ijVjz32GOPHj+fVV1+1OGbs2LG8+uqrxMfHc9ddd9nFoPT0dLp2\n7Vpr3bFjxxg1apRF2aBBg9i7d6+5vn379hbJGAcOHIhWqyU1NZWIiAi72Ci4flRVSeRqyjmfU8K5\nnFLyi3S1bq6F6g22nYM86RLsRccgDxHMtbkSH197+b59jSpS8XUEJdiXlmazSHnGeMLz/dDs61Br\nGg3Pc/shXIcmS4VB64pcoUPZQY3neTVg+4hNcAmbFmoOHz7Mpk2baq27+eab+eyzz+xmUHp6Onq9\nnilTppCdnU1YWBjz58+nX79+5OTkWK19BQQEkJOTA1SvowUEBFjVA6jVaiFSzZTyikrO55RyLqd6\ntKQ31D5aAvBUyOna3osuwV6ofBUio21LQG2dwA+ACxca1wxj7XuRLtRRXhdXTaOhVuMZUIVnwBUO\nFBeK6vUZgkvYJFI+Pj6cOHGCIUOsw3YcPXrUbk4TFRUVZGZmolQqefbZZ5HL5XzwwQdMnz6dXbt2\nUVFRYTVlJ5fL0eurUy3rdDqcnS0Xxp2cnJDJZOZjBE2PqUoiV6M1C1P+xbqdHhxkMoJ8FXQM8qBr\ney98PJxFSKKWhkpVPcV3JcHBjWuGoyPJ5elkVqRQXlWMm4MXIS59iHILs+OHNI9rbU3YJFKTJ09m\n06ZNVFRUcOutt+Lj40NhYSH79u3j/fff54UXXrCLMS4uLiQkJCCXy81itHz5ck6ePMmHH36Is7Mz\nlZWWi+UGgwFXV1dze4PB0oumsrISSZJwcxPpE5oKSZLQlFSQlVtGZl4p2flltaa7qMHd1YmOQR50\nDPKkQ4A7LnLhmdnkNMTxYdQoyzWpGkaObPCp60MnVRUfnThkfq81FZGmPcT40FD7fcg1rlVQf2z6\n9j/66KOUlpayY8cOtm7dai53dnbmiSeesGtWXnd3d4v3Dg4OdOvWDbVajUqlIi8vz6I+Ly/PPJIL\nCgriwIEDVvVgPxd5gW2UlhvIzC0lK6+MrLwyyivq9sRzkMlQ+SnoFORJxyAPfL1cxGipOdFQx4eY\nGBL+UBD/Xh7qXAdUgVWMuj+AmJhe9Tt1A9XsnEsWPZU+ZBYVoa2SUDjICPH25rxLLSOff0qNPfv2\nVU9nBgdXC1QTeDK2FmwSKZlMxnPPPcecOXNITk6mpKQEHx8f+vfvb9cRSkpKCvfddx87d+6kT58+\nAJhMJtLS0hg5ciS+vr4kJCRYtDly5AgDBgwAIDo6mtdff90saDX1CoWC8PBwu9kpsKZCbyQrv4ys\nv4WpqOzq06ueCjkhgR50DPQgJFA4PTRrGuj4kJAA23/uBWG9IAyyge0/A73qcWo7eAiqS9VIxQOR\nkvqAxgtJWYwUncKF0kyb2ttMTIwQJTtSr3kUDw+P65qFNzw8nPbt27No0SL+/e9/4+bmxrZt27h4\n8SL33XcfBQUFTJo0ifXr1zNmzBj27NnD8ePHWbx4MVCd5j4yMpJ58+axcOFCCgoKWLVqFTNnzhTu\n53amvKKS7PwyLuRruZBfRmFJxVWPd5E70iHAnZBADzoEuOOpkIvRUkvBBseHqw1yriZENvtU2MFD\nsCo7mrT93c3vtYXepO0fSqDitE3tBU1DnSI1fPjwej1Evv7664Yb4+jI9u3bWblyJY888gg6nY6o\nqCg++OADfH198fX1ZePGjaxatYpt27bRtWtXtmzZQujfc8oymYyNGzeyePFipk2bhkKhYPLkyTz2\n2GMNtq2tU1pu4EJ+Gdn5Wi4UlFFUevWRkmM7B4L9FXQI8CAkwAM/bzGF949oDptgVSoSkh2Jz+yD\nutwLlVsxo0JSiIkymU1cttnAeb2e8ioTbsXt+O2MM8/PkRMTc3UhstnPwB4egumjgD+tyzPEelFz\npk6RioqKapKHSmBgIKtXr66zftiwYQwbNqzOen9//zrd5QW2IUkSRWV61AXVo6QLBdpa06dfjoNM\nRoDSzTxaClK60U7kX2oYzWUTbKc72bwrE72bliq3XIpNjpw5G82c8SHEANu/rCC1/NJIWmsykVpe\nzo7/VhET44JKBWfO5KHXZ1JVVY6DgxvOziF07RrAyJE2+hmoVJToktGoMjG4liPXuaFUh+DpFmXz\ndTho29PTz4nMkky0hnIUcjdCPEOQlQVcu7GgyahTpJYvX27+/969e4mLi0OpVDaKUYLGpdJYRd7F\nctQFWnILteRoytHpr753pJ1DtWt4sJ+CYH93gnzdcHIU60p2pZlsgv3ytBflHbOgDDCCyQXK/WT8\nN92TGCDpXO0/YJLOGgAXbrjhD06evJTUz2TSUl6extChBcTE9AKu7WdQMrwT+3f/zsHv7ie/2B9/\nr3xu7HOQ4bd1pI4dS1aoVFBVFYC/wlKUhHd488amNamXXnqJ5cuXM2LEiOttj+A6I0kSZbpK1AVa\ncgq15BSWU1CkqzNqeA1O7RwI8lPQ3t+dYD8FAUo3kan2etNMNsGeO6cGF5fq12WcPZsDdAClAXJr\neZT4VotXly7/YfJkR376qTd5ed4EBBRxww0n6dLFBPSyyc/gh9xKPkuZDWVlIBnJLWvPZymzcRp4\nnnE2XofwDm+Z2CRSgYGB6HR1b7gUNF8qjSbyi3TkacpRF5aTW6i9amDWGpzl7QhS/i1K/gr8fdxo\nJ6I71I+GrifZaWNoSUkCGk08BoMauVyFUjkKT0/b7VAq1VSletJRDYoKCa2LjPMqaNerWiyjbjVw\n8EPrR0nULdUiZTCo6YE3naVzVJGHg6TDmVIMBtujMHz3XWCtQvn99wGMs1GlhHd4y8QmkbrnnntY\nunQpx48fJzw8vFa387Fjx9rdOEH9MJmqKCyuIO9iOXkXy8nV6NCUVNQZ++5ylJ4uBPkqUPkqCPJ1\nw1tEdmgY9lhPssNP/5KSBNTqS+fQ67PN720VquEdizi318P8XqGT6PkXdBpRnf3vwZvdyau8SNZP\nCrR5jigCjHS4Qcusm30AkFLD0H12aVKuKtcN3WehyJ1KoTs2odGo8NdorYSy0Kl+gi28w1seNonU\nsmXLAPjoo49qrZfJZEKkGhlJkrhYqidPU06uplqUCop0daZHvxwnRweCfBUEKd0I8lMQqHQTUR1q\noyEjIXusJ9nhp79GU7sdGs0+m0Uq7GJX2nXNJCfHB53OGVdXPUFBF+laVB0IOsbTkxeGw74BGi4Y\nDATL5YxUKonx/FuYjg4CaknlkzAQW+fq+jr54P7XpfQYNUKpDfG27QSCFotNT6bvvvvuetshuAqm\nKomi0gryi3QUFOnIv6gj72L5VUML1SCTyfDxcCbAx41AXzdUvgqUni4iMOu1aOhIyF7rSQ386W8w\nqDH8rkR/UEVVvisO/jqcb1Qj62e7HTKNPyEdJPz9L3nnuTiHICu8lE4nxtPzkijV0t7NTaJCX3f7\nazFU3o4UR2+qTFokyYhM5ohDOwVDxI+rVo9Nf+H27dub/19eXo5Wq8Xb2xsnJ6frZlhbpdJooqCo\nolqM/halwmLbRkhQHckhUOmGv49b9b/erm0zmkND14MaOhKy03pSQkkJ8RoNaoMBlVzOqMtHKDZg\nj6k2uUqOlO2Pk9xSVOTBtm2Qb2h7AH8HA717u5KZ6Uq5FtwUEBIC/jKR8ba1Y/PPkCNHjvD6669z\n8uRJ8xpHv379ePLJJ4mLi7tuBjY26//zHybHxtYrWydQ74eiJEno9Ebyi3QUFlWQ/9sf5J84RXGZ\nAUlR8w28+i9NNxcnAn1cCVC6Vb/+SsX1268atvGzOWwebagN9lgPauhIyA7rSQklJWy/zI5svd78\n3mahOjqIQk0mOWolugo5ri4GglQavBJ62jzVphylRL3duj+UI23bkqIcpeTEGyfILMmk3FCO29/7\nk/qN7GebAVQLnX+V3uorUR+hE7RMbBKphIQEZs2aRZcuXZg7dy6+vr7k5eWxb98+Zs+ezbvvvmuO\nn9fSyTGZ6p2t81oPRUOlCU1JBYXFFRQW6ygsrkBTUnFpL1J+PqRdkc2z5v3f30oPNzl+3q74e7vi\n5+1KgI8rClenS84NCQnw3tt12mCP62gU7GGDPdaDGjoSssN6UrxGU2v5Po3GZpHKT/Pn/HkFVSYt\nSEYqKhScPx+Ah6cbPWy0oyZ3kmafptZEf9fiVPAp9kbtRZWowlXjSr5HPieiTuAc7EwMtvVHQ4VS\n0HKxSaTeeOMN4uLi2Lp1q4XH15w5c3jooYfYsGED77333nUzsimoT7bOmoeiCRkXZc4UypwplLmg\n+W8ChXkKSsuvMSWReSnApQzwkfT4V+nwy0nBb9KD+Hu74uJ8jT+VPR7M9jhHU0+zgX3Wg+yxqaaB\n60lqQ+33zYU6ymsj46Kcdg4S7RxcLcuL5Aythy1XTfR3DeIz4tGEadCEWYruvox9xLS3rX8aKpSC\nlotNIpWSksK6deusXJJlMhnTpk1j/vz518W4puRq2TorjSYuluopKtVzsaQCTb4jGnkYxTI5VVzW\nRyVVcBWBcnJ0QOnpgn9pFn6mcvylCnylChz5e/0p3wECPepsb4E9HswNPUdzmGYD+6wH2WEk1ND9\nSSq5HN2xUlQH9bjmV6Hzd0B9ozNuA2y8J4B0HyVdaunTdO/GG4GoS2v/m14orZ8TSUOEUtBysUmk\nPD09KS8vr7VOq9XSrl3rWZhP+bY3naIq6N+tiPKKSi7+LUSX/2s1MnIPqp6iuxJF9X4yh7897JRe\nrvh6ueDr5YLS0+VSJPDDkJDsyVuZg2sN4GkT9ngwN/QczWGaDewXWqABIyG77E8640zCW4WUqZ0o\nrnDA0aWK4FQDMb7O0Nk2O5wjPPmzClRZGly1BnQKOeoOStwiG+9hr/JQkV1i/TcN9hDxiATXxiaR\nio2NZcOGDURHR1skD8zNzWXDhg2tynHCoV0lORn+XHTz4u3dJ21rFBJiXkPylAz4VlWPiJQ3jcQ3\nrgfe7s5XDbaa0OlOtn90KbZZttab7WlDYbyfjTP22OfB3NBzNKdpNmhwaIGGjIQ0mvhaXb81zrbv\nTzJ9rIfzbshMJpAkZBXt4Lwc0yd6uNm2axg1CrZne6IJsBSliY0YCmhUt1FsT7L+m47sJuIRCa6N\nTSL11FNPMWnSJEaMGEF0dDR+fn4UFBSQmJiIu7s7zzzzzPW2s9FQKAx4ezhQXOZT5zEOMhme7nJ8\nPFyqR0gxHfHp6Y7yp+9wUmf//VC8w+aHYvy5XtAzr3ptSltePQILCWHf+QDbRcoeD+aGnqOZTLOZ\nz9OA9aCGjoTKjunRfXYpLXmN67eMMzaPgs4lGXB1cMDVwfIHzvkkA0NsO0WzCAVUs+60L2MfF0ov\nEOwRzMhuI21ejxK0bWyO3bdr1y7efvttEhMTycrKwtPTk6lTpzJz5kz8r+Eq3ZLw8fTA2bV6L4bc\nqR0+Hs74eDjj/bcg+Xi64KWQW4+MOsfBzf9sRKlWA/4B1a/LqHccUXvEfGnIOZrBNFsNDV0P0mji\nSd3VmwtfdkcqdEHmW0Hw+NM4323bSMj4cxiFGhcr12/5z2Fwp202FCLHFeu8XQXUz+26OYQCimkf\nI0RJ8I+oU6SOHj1K//79zRt2/f39ee655xrNsKbCxajCzdCZEJULs8c5Nkr8Ojvt+7QLDXq4N4Np\ntpr232/+hQtfDrxMYH7hljm2rwf99rEP2dv6mt9L+S5kb+sH/E7nBddufzGjJ3/9dSkoc7lOzl9/\nBeHi6nqVVpZURilxPWg9hWqMEm7XgrZDnSJ133334erqSkxMDEOGDGHw4MGEhYU1pm1Ngtzkg5Pk\nwe2jobHiq44aBZs3WyeFGzmyfsnY7PFw37//Gw4ejCQ//yb8/Yu58cZvGD7c9od7AjHESzGoAZUE\no8D2KUvsIzAHt6b9LSjV1AjMQcc0bn/atnPkfNW37nIbROpcWSCOjkVWYXzOaW2PNXfDg558kWft\n9DBxlvBwE7Qd6hSpjRs3kpiYSGJiIqtWrcJkMuHn58fgwYPNr9Y0zVeDSgV33ln/6ZGGCESPHgmM\n6/HLZVNL+QSPP0yPHnHY+oi3hzfZDz8c58e3htBRDT0qJLQuXvyYOgQnp+OMG3ftcyQkwBdLS1Bl\nalCVG9C5yfkiWQkveNrcn/YQmPP/qV3cz3/uD0/bZofsog9wsY7ya1Pt+q232p9UH9fvmBjgBU/2\n7fMk9e+B6USRWkLQxqhTpP71r3/xr3/9CwCdTsdvv/1GYmIiCQkJLF68mIqKCrp162YeZd14442N\nZvT15PHHoUOH+rVpqECof/iJ4GRPgjtlQae/C5PdUP9wCE8bxAHsE+066UMVPf+6FCOwJtJ00kdB\nNuXs+Wl7CaFpl6an3LR6QtPUHNoBMTbub7GLwGgUSFjvc5MVutt2AsBZ5QXZktVIyDnYy7b2dnL9\nbg7rSQJBU2KT44SrqytxcXFmV3Oj0UhCQgKffPIJH3zwAe+99x6pqbWE4m+BZK3PwnNy/TYNNlQg\nyr6rPVBv2feONsdXMxhqd/82GGz3vvBM8wGsNx97pNr2698pqfYwPo5JGrAxybc9BEYe5ElFtrUt\ncpXtf9Pw+5WkLLceCYXfZ1tfNAfXb4GgNWBzgFm9Xs+RI0f45ZdfOHLkCKdOnUImk9G3b1+GDLHV\nIbb5Y8gxmGOE2SpUDRYIjS9Qy2bpQj/b2gNyuQq93tr7Qi633fsioJ0rJbWIVJCjSy1HW+OLobar\nwK+Wc9aFPQSm96zOHF9aZTUK6v1AZ5vPEXt/9eel7dRgzDHgGCQn/D6lufxaNAfXb4GgNXBVkTp9\n+jSHDh3i0KFDJCYmotfr6dixI0OGDGHOnDnExsbi7m77L9yWQEp+Cg6+Djjvc7ZZpBoqEO6dOlGc\nYT0Sde/c0ab2AErlKIspx0vltv907xrjQ8qPMquHe5cY2xb7O0XJST1o7TLdMcp2l2n7CUzXfyww\nl5+nvm0uR0zVCQQNp06RuvHGG8nPz8fT05NBgwbxwgsvMGTIEDrUd8GmhaEz6EgrSAMH6GzjrsuG\nCoRqfF8qN1snhVONq93DrDZqphU1mn0YDBeQy4NRKkfWy7uv74NKpHy9Vc6evrNsm+Kqbq8mM5N/\n1B6aj8AIBILmQZ0ilZeXh4+PD3feeSeDBw9mwIABbSrJYYZTBkNtjBPdUIHwjPGk45x+aPZ1aFCE\nZ0/PmHqJUm129HseOvzDSNMNbV+DEBiBQFBDnSL1zjvvcOjQIQ4ePMj27dtxcXEx75kaOnQooaGh\ndTVtFaT3Tq/X8fYQiOYQ4bmhdjSX6xAIBK2DOkWqxpvvmWeeoaCggEOHDnH48GG2bt3KsmXLCAoK\nYvDgwQwdOpTBgwfj7W37JsXmjM5HR8HgAtyi3ZraFIFAIGjz2OTd5+fnx/jx4xk/fjwAqampHD58\nmGPHjrFgwQJMJhMnT9oYMbyZc3rsaVyVrkzsNrGpTREIBII2j80u6AAlJSUkJyeTnJzMiRMnSElJ\nwWQy0bt37+tlX6OjcldxZ9SdIhimQCAQNAOuKlJnz54lOTmZpKQkkpOT+euvv6iqqqJbt27ExsYy\nbdo0Bg0a1Krc0B8f9Dgd2rduD0aBQCBoKdQpUrGxsRQXFyNJEsHBwcTGxvLwww8TGxvbKmP2CQQC\ngaD5UadIDRo0iMGDBxMXF0fHjrZvKhUIBAKBwF7UKVJvvPFGY9ohEAgEAoEVDtc+pOVhMplYvXo1\nQ4cOpX///sydO5eCgoKmNksgEAgE9aRVitSGDRvYtWsXK1as4IMPPiAnJ4fHH3+8qc0SCAQCQT1p\ndSJlMBjYuXMn8+fPZ8iQIfTu3Zs1a9aQlJREUlJSU5snEAgEgnrQ6kQqLS0NrVbLwIEDzWUdOnSg\nffv2HDt2rAktEwgEAkF9aXUilZOTA0BgYKBFeUBAgLlOIBAIBC2DVidSOp0OBwcHq4jtcrkcvd46\n15FAIBAImi+tTqRcXFyoqqrCaLRMQW4wGHB1da2jlUAgEAiaI61OpFQqFQD5+fkW5Xl5eVZTgAKB\nQCBo3tQrwGxLIDw8HIVCwdGjRxk3bhwAWVlZZGdnE3OVXN4mkwlArFsJBAJBPah5ZtY8Q+1NqxMp\nuVzO1KlTWblyJT4+Pvj6+vLyyy8zcOBAIiMj62xXM/KaNm1aY5kqEAgErYb8/Hw6depk9/PKJEmS\n7H7WJsZoNPL666+za9cujEYjN9xwA4sWLUKpVNbZpqKigpSUFPz9/WnXrl0jWisQCAQtF5PJRH5+\nPn369MHFxcXu52+VIiUQCASC1kGrc5wQCAQCQetBiJRAIBAImi1CpAQCgUDQbBEiJRAIBIJmixAp\ngUAgEDRb2rxIiQSJ9SMjI4MePXpYvWoizB86dIhx48bRr18/xo4dy4EDByzaFxYW8sQTTzBgwADi\n4uJYtWqVVQirtsCiRYt48cUXLcrs0XfvvvsuN998MxEREcycOZOzZ89e70tpFtTWn3feeafVfXr5\nMaI/L1FQUMBzzz3H0KFDGTBgALNmzeL06dPm+ia9N6U2ztq1a6UhQ4ZIhw4dklJSUqTJkydLd999\nd1Ob1WzZu3evNGjQICkvL8/iZTAYpPT0dKlPnz7S5s2bpYyMDGnt2rVS7969pdOnT5vb33PPPdLU\nqVOl1NRU6ccff5RiY2OlNWvWNOEVNS5VVVXSunXrpO7du0svvPCCudwefffpp59K/fv3l+Lj46W0\ntDTp4Ycflm699VZJr9c36jU2JnX1Z1VVlRQRESF99dVXFvdpaWmp+RjRn9WYTCbprrvukqZMmSId\nP35cSk9Pl+bOnSvFxcVJGo2mye/NNi1Ser1e6t+/v/T555+byzIzM6Xu3btLiYmJTWhZ82Xt2rXS\ntGnTaq1buHChNH36dIuy6dOnSy+99JIkSZKUlJQkde/eXTp//ry5/osvvpD69+/f6r74tXH+/Hlp\n+vTp0qBBg6Rhw4ZZPFTt0XfDhw+X1q9fb64vKyuTIiMjpa+++up6XlaTcbX+PHfunFV/XY7oz0uc\nPHlS6t69u5SRkWEu0+v1UkREhLRr164mvzfb9HSfSJBYf9LT0+natWutdceOHbPoS4BBgwaZ+/LY\nsWO0b9+ekJAQc/3AgQPRarWkpqZeP6ObCUlJSahUKnbv3k2HDh0s6hrad4WFhZw9e9biHAqFgj59\n+rTae/lq/Xn69GlcXFxo3759rW1Ff15CpVLx1ltv0aVLF3OZTCYDoLi4uMnvzVYXu68+iASJ9Sc9\nPR29Xs+UKVPIzs4mLCyM+fPn069fP3Jycq7al7m5uQQEBFjVA6jVaiIiIhrnIpqIcePGmYMeX0lD\n+87Rsfqr3Jbu5av1Z3p6Oh4eHjz99NMcPXoUHx8fJk6cyP3334+Dg4Poz8vw8fFh2LBhFmXvv/8+\nFRUVDB06lDfeeKNJ7802PZISCRLrR0VFBZmZmZSVlfHss8/y5ptvEhAQwPTp0/nzzz+pqKhALpdb\ntLm8L3U6Hc7Ozhb1Tk5OyGSyNt/fDe07nU4HYHVMW72XMzIyKC8vZ+jQoezYsYOpU6eyfv16Nm7c\nCIj+vBrfffcda9asYebMmYSGhjb5vdmmR1KXJ0isUXsQCRLrwsXFhYSEBORyufmmXb58OSdPnuTD\nDz/E2dmZyspKizaX96WLiwsGg8GivrKyEkmScHNza5yLaKY0tO9qAnteeUxbvZdXrFhBeXk5np6e\nAPTo0YPS0lK2bNnC448/LvqzDr744gsWLlzI6NGjeeaZZ4Cmvzfb9EhKJEisP+7u7ha/qhwcHOjW\nrRtqtRqVSkVeXp7F8Zf3ZVBQUK19DdZTAW2NhvaduJctcXR0NAtUDT169ECr1VJaWir6sxbefPNN\nnn/+ee6++25WrlyJg0O1PDT1vdmmReryBIk12JIgsa2SkpJCVFQUKSkp5jKTyURaWhphYWFER0eT\nkJBg0ebIkSMMGDAAgOjoaDIzM1Gr1Rb1CoWC8PDwxrmIZkpD+87X15fOnTtb3MtarZaUlJQ2eS9P\nmTKFV1991aLs999/JyAgAE9PT9GfV7Bt2zbWrVvH3LlzWbhwodlxAprBvfmP/RZbCatWrZIGDx4s\nHThwwLxP6kp3S0E1lZWV0u233y5NmDBB+u2336TTp09LzzzzjBQTEyMVFBRIaWlpUu/evaU33nhD\nysjIkNatWyf17dvX7NpaVVUlTZkyRbrrrruklJQU836Ky11T2wrTp0+3cJm2R999+OHUHWgkAAAF\nG0lEQVSHUmRkpLRnzx7p1KlT0sMPPywNHz68Tbj3X9mfW7dulfr06SPt2rVLOnfunPTpp59KERER\n0qeffipJkujPy0lNTZV69uwpPf/881b7H7VabZPfm21epCorK6Vly5ZJAwcOlKKioqQnnnhCKiws\nbGqzmi05OTnS/PnzpdjYWCkiIkKaOXOmdOrUKXP9Dz/8II0ePVrq06ePdMcdd0iHDx+2aJ+XlyfN\nmTNHioiIkAYPHiytXr1aMplMjX0ZTc6VD1VJsk/fbdmyRRoyZIgUGRkpPfDAA3XuE2ptXNmfVVVV\n0ttvvy0NHz5c6tOnjzR8+HDp448/tmgj+rOa1atXS927d6/1tWnTJkmSmvbeFEkPBQKBQNBsadNr\nUgKBQCBo3giREggEAkGzRYiUQCAQCJotQqQEAoFA0GwRIiUQCASCZosQKYFAIBA0W4RICQQCgaDZ\nIkRKIBAIBM0WIVICgUAgaLYIkRIIGoEVK1YQGRmJVqu1KF+9ejVDhw7FaDSSkJDAtGnTiIiIYNCg\nQbz00kuUlJRYHH/kyBEeeOABYmJi6NOnD7feeisbN26kqqoKqA6Q3KNHD959911GjBhBZGQku3fv\nbrTrFAjsjRApgaARmDRpEjqdjm+++cZcVlVVxVdffcXYsWNJTk5m5syZKBQK3njjDZ599ll+/PFH\nZs2ahdFoBODkyZM88MAD+Pr6sm7dOt58802io6PZsGED+/bts/i8jRs38sgjj7Bs2TLi4uIa9VoF\nAnvSppMeCgSNRbdu3YiIiOC///0v48ePB+DXX38lJyeHiRMnsnDhQkJDQ9myZYs5j0+vXr2YMGEC\n//vf/7jjjjs4ffo0Q4cOZeXKleZUCkOGDOH7778nISGB0aNHmz9v9OjRTJgwofEvVCCwM2IkJRA0\nEpMmTeLXX38lNzcXgF27dtGnTx86dOjA8ePHGTZsmDlTtNFoJCwsjODgYH7++WcAJkyYwFtvvYXB\nYCAtLY39+/ezfv16TCaTVebUnj17Nvr1CQTXAyFSAkEjMWbMGORyOXv27EGr1fLtt98yceJESkpK\nqKqqYsuWLfTu3dvilZ2dbc5yWlFRwYsvvkh0dDTjx49n5cqVZGdn4+joyJXJDNzc3JriEgUCuyOm\n+wSCRsLd3Z3hw4cTHx+PSqXCaDRy++23065dO2QyGQ888ACjRo2yaqdQKAB47bXX2L9/P2+88QZx\ncXFmIRJrToLWjBApgaARmTRpEjNmzODDDz/klltuwcvLC6hefzp79ix9+/Y1H1tWVsYTTzzB7bff\nTteuXUlMTCQuLo5bb73VfExKSgoajcbs3ScQtDbEdJ9A0IgMGjSI9u3bk5CQwMSJE83lTzzxBD/8\n8AMLFizg4MGDfPvtt8yaNYukpCR69+4NQL9+/Th48CCffPIJR48eZefOncyePRuZTIZOp2uqSxII\nritiJCUQNCIymYybbrqJ/fv3M3ToUHP5TTfdxPbt29m4cSOPP/44zs7O9O3bl507d9K9e3cAFixY\nQGVlJWvWrMFgMNChQwceffRRMjIyOHDggBhNCVolIn28QNCIVFVVMXLkSEaNGsW8efOa2hyBoNkj\nRlICQSNQWlrKe++9R3JyMrm5uUydOrWpTRIIWgRCpASCRsDFxYWPP/4YSZJYtmwZgYGBTW2SQNAi\nENN9AoFAIGi2CO8+gUAgEDRbhEgJBAKBoNkiREogEAgEzRYhUgKBQCBotgiREggEAkGz5f8D1xTA\nx0/N1YEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd1c2c358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_prehistory(table1)\n",
+    "plot(prehistory.results, label='model')\n",
+    "decorate(xlim=[0, 2000], xlabel='year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistorical population estimates')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hoSE+Pj788ccfpKenK5VlZWWxbNkykpKS0NbWpk6dOty8eZMaNWpQq1YtatWq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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x21dd2c73a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "newfig()\n",
+    "plot_estimates(table2)\n",
+    "plot(prehistory.results / 1000, label='model')\n",
+    "decorate(xlim=[1950, 2016], xlabel='year', \n",
+    "         ylabel='World population (millions)',\n",
+    "         title='Prehistorical population estimates')"
+   ]
+  },
+  {
+   "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..ab33b01b
--- /dev/null
+++ b/code/chap04mine.ipynb
@@ -0,0 +1,1056 @@
+{
+ "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": 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": [
+    "### Functions from the previous chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 3,
+   "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",
+    "    newfig()\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": 4,
+   "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": 5,
+   "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": 6,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "table2 = tables[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D9yF9NMFflZVqKLlr165x9uxZMjIysLGxwdvbW/NUqBBCVDd9Gvfh/O2LZEWkUTe9E0bG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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f65207aba8>"
+      ]
+     },
+     "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": 9,
+   "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": 10,
+   "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": 11,
+   "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": 12,
+   "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": 12,
+     "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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Pnj1Mnz6dnj178vnnn1c6f2hoKCYmJvTo0QO4/8Fx7do1IiMjHzt2QVBXWVkZ\n5/53jq/PfU1UehRyhRxJQ6KsWQmtHdrjXRCMjc7TP3W8Wnf+EyZMICYmhjVr1rB27VpVuSRJDB8+\nnJkzZ9ZbgM+K2i7g/sCyZcsYMmQI69atY/ny5Y8VQ2FhYaVvcZqamtWuzPb3WV3Nzc2ZP38+8+fP\n5+7du5w7d47PPvuMpUuX0rx5c3r37o1cLufEiRP069dPtSDMoEGDWLVqFV9++aVYmlF4IrKysvj5\n3M9cvXuV0opSABQ6CmT2Mqb7TMfNtD1ffhnL8OEumJvrNXC09Uut5K+pqcnatWuZMmUKFy5cID8/\nHxMTE3x9fat8tW9MhrUZ9lhNMSGeIVWaguqLugu4/52dnR0LFixgyZIlDB48+JHrLykp4ebNm1XW\nWHiw8lZNdu3aRcuWLRk4cCAAzZs3Z/To0QwfPpxBgwZx5swZevfuzc8//0xubi7Hjx+v1M6vVCo5\nefIkb775pnjwK9SrckU5317/liu3r1BRrqSouBxdR4l2bdsR4hmCse79VeImTXJv4EifjFoN8nJ1\ndW3Uyb4pU3cB9+qMGTOGkydPsnjx4keu/9ChQyiVylp/gERGRvLNN98QEBBQaeZXHR0d9PX1VfOb\nh4aGYmtry4cffljp+PDwcJYuXUpYWBjjx49/5PgF4WEUkoJrxddIkeciZWmQVJ7HuHZjmdF5RIOs\nodvQakz+AwcOZPPmzbRt25YBAwY89M357rvv6jy4Z4m6C7jXZMWKFQwbpt63nLy8PDIyMpAkifz8\nfM6ePcumTZuYNm2aah3fBzIyMqo9h76+PkZGRsyePZugoCCmTZvGlClTaNGiBffu3SM0NJS8vDzG\njh2r6ts/e/bsKus4ODs7s3v3bg4dOiSSv1DnKioqVE2melp6TO40mVdj/ktRsSmuxaO5cdaUsucV\n6OnV6j74qVDjFXt7e2NoaKh6/Sx+Mj5J6i7gXhMHBwfmz5/PO++889B9Z82apXptZmaGs7Mz77zz\nDiNGjKi0n0Kh4Lnnnqv2HMHBwSxZsoR27drx5Zdf8sEHH/DGG2+Qm5uLiYkJPXv25IsvvsDKyoqP\nPvoImUzGmDFjqpxHU1OTiRMnsnr1aq5evfqP33AEQV2SJBERFUF2Wja9evVCV1cXAFdLVzaNXcbB\nHRlo22gyebL7M5n4oZYLuDcUsYC7IAjqKikt4YsfviDudhytjF3wdHGne/eulW5g8/LKMDbWqbTw\nytPmkRdiboQmAAAgAElEQVRwT0tLq1VFtra2tY9OEAShDl27dY0jPx+hoKSAosJy/ncvkvJCE3x9\nfdDW/nOAlqmpbgNG2TjUmPx79+5dq6aemJiYOglIEAShthRKBYfPHibiWgRKSUlpaQX5BXJyyiUu\nXpWRlFSIi4t5Q4fZqNSY/FetWiXa+QVBaPRuZ95m//f7ycvJU5XpGWphomyBfkJbWjiaoq//bLbr\n/5Ma3xExalcQhMZMKSkJuxTGuQvnkBR/Pro0MjNiwoAJmGk14/ff7zJwYCs0NdWazOCZUmPyr80i\n6zKZjOnTp9dJQIIgCA+TVZzF7nO7yY7LpqionPJyJeZmenh28GRsr7Foad5PbYMHi8WmalJj8t+0\naZPaJxHJXxCEJ0lbU5tUeSrJWQXoy/VAqcOAzoMZ3ad3Q4fWZNSY/GNjY59kHIIgCGoz0TUhuGMw\nb8ZsQC+/BQaFPiTHGMHAho6s6RANYYIgNGpKSUlsRiwJCQkoFApVeefmndkzeSMtdfsyfGg7XnnF\nqwGjbHrE9A6CIDRaqYWp7Du/j4yEDFoo29GnezEdO96fAVYmk9HCsjnLltmhpSXuY2tLTO8gCEKj\no5SU/JD4AycvnUSWqkV+jpxseTQ6kj4ODvaqCQMBkfgfUY3J/6/LA65Zs+aJBCPUr4sXLxIcHKz2\nNBlHjx5l8eLFXLt27QlEJwj33S24y75L+0i/mY5ukS6FJXIqyiUMlWbEXtdGoXi659l/UtQe+aBU\nKjl16hTh4eEUFhZiaWlJly5dql3aURAEobYUSgXfJ37PyaiT6GTooFN+f9EfW3MLzEttyM9zYPDQ\nDlhZGTRwpE8HtZJ/ZmYmU6ZMITY2Fh0dHSwsLMjKymLHjh10796dbdu2YWAgfiGCIDya5Pxk9kXs\nIzUlFf0cfWTI0ECDFmYt8G3ji62NK9raWjRrJhb8qStqNZatWbOGjIwMdu/eTWRkJKdPn+bq1ats\n3bqV6OjoSks7Co+mTZs2HDp0iHHjxuHh4cHgwYO5fPkyn332Gb1798bb25vXX38duVyuOubixYuE\nhITg5eVFjx49WLFiBSUlJartsbGxhISE0LFjR4YOHUp0dHSlOpVKJTt27MDf359OnToxatQozpw5\n88SuWRAAfr/zOytPryQlNo3ieBkF+eUY6xjjY+/D0F5D6ezTmRYtzETir2Nq3fmfOnWKt99+m169\nelUqDwgIIDs7m/Xr17Ns2TK1Kz106BAffvgh9+7dw8XFhTfeeKNemo/i4uK4fv26Wvu2bNmyyjqy\nkZGRJCUlqXW8m5sbbdq0qXWMf7Vx40ZWrlxJq1atWLhwIdOmTcPDw4Pdu3dz8+ZN5s+fT+fOnQkK\nCuLKlStMnjyZCRMmsGzZMpKTk1m6dCnJycns2LGDvLw8Jk+eTLdu3Thy5Ai3bt3i7bffrlTfhg0b\n+OGHH1i+fDktWrTgl19+Yc6cOXz44Yd07dr1sa5FENTV2rw1xcUV5N+WYyzTR7/UkvZuPgzo1wtj\nY+OGDu+ppdadv46OTo2/hObNm9eqwtDQUJYtW8bUqVMJCwvD19eXWbNmkZycXKvzPI3GjBlD3759\ncXJyYsSIEeTl5bF06VLc3NwYOHAg7dq1Iz4+HoA9e/bg7u7OggULcHZ2pnfv3ixdupRTp04RHx/P\niRMnKC8vZ+XKlbi4uBAQEMCcOXNUdRUVFfHJJ5+waNEievXqRcuWLQkJCWHEiBHs2rWrod4C4Rlk\nZ2THeJ9RYGKEpdwNPbkzdnYeIvHXM7Xu/MePH8/mzZvp2LEjVlZWqvLi4mJ27dpFYGCgWpVJksTW\nrVuZOnUqo0ePBmDBggX873//IyIi4plfqOWvSyjq6+ujoaFR6T3R09NTNfvEx8fTu3floeydO3dW\nbYuPj6d169aq7roAnTp1Ur1OTExELpczd+5cNDT+vAcoLy+v9DsWhLp0M+cmaUVp+Nj6oKWlpepC\nPsBlAL5T/fh0/1XGjXPH1tbwIWcSHleNyf+ll15SvZYkicTERAICAvD29sbS0pL8/HwuXbpERUUF\nNjY2alV248YNUlJSKi0SrqGhwfHjxx/jEmrWpk2bx2qK8fT0rNIUVJ8erDX6gEwmq3F8hZ5e1e5u\nDxZle/BH9fdF2v66mIWOzv2eFFu3bqVly5aV9vvrh4Eg1IVyRTnH447z440fkWfLcM+LY+jA7qo1\nnTVkGlhaGDB3rmhufFJqTP7l5eWVfvb29laVp6amAtC2bVsA0tPT1ars1q1bAOTn5zNx4kTi4+Nx\ncnJi/vz5qvML6nF2diYiIqJSWXh4uGpbXl6eahF1U1NTAKKiolT7tmzZEm1tbdLS0vDz81OVb9u2\nDYVCwdy5c5/AVQjPgoTsBD6+/DFphWnkxSvQydHjmvIa5r8ZYGVlhYWFRUOH+EyqMfnv37+/zisr\nLCwEYOHChbz66qs4OTlx6NAhJk2axLFjx3B2dq7zOp9WU6dOZeTIkaxdu5bAwEBSUlJYtmwZvXv3\nxtnZGVtbW7Zv385//vMf5s+fT1paGlu2bFEdr6+vz+TJk9mwYQOGhoZ4eHhw6tQptm/fzsqVKxvw\nyoSnRVlFGcdij3Hq1ikoB6MsI2RlChRKXUwr7LlxowC5vPzhJxLqRY3JPzw8HB8fn1qf8OLFi6q2\n57970OwwY8YMhg0bBkD79u0JDw/n888/Z/HixbWu71nl5ubGjh072LRpE/v378fMzIwhQ4Ywb948\nAIyMjPj4449Zvnw5gYGB2NjYMHXqVJYvX646x7x589DW1mbdunVkZmbi6OjI8uXLxUI+wmO7nnWd\njy9/TGZxJlolWhhkGaAladGmZWvuxIKRiSkhIf2xsxN3/Q1FJv29Yfj/DR8+HGdnZ2bOnKlql/sn\nkZGR7N69m1u3bhEWFlbtPg+mFzh8+DAeHh6q8rlz51JWVlbjAjIPW4VeEITGoayijNDYUE7dPEVR\nYTkW5UboFehhoWeBq6Urulq62Nm1wNvbXTxbqmcPy5s13vkfOXKEbdu2MWrUKFq1asWAAQPw9PTE\nwcEBfX198vPzSUtLIzw8nLNnz3Lz5k1CQkLYsGFDjcF06NABAwMDrl69qkr+Dx4mi2kiBKHp++Di\nB0SnXePurSL00vXBTIc2jm2wMbRBT08PLy8v0Zuskagx+Wtra/Paa68RFBTEvn37OHjwINu3b6/U\n+0SSJJo3b87AgQPZuXMntra2/1iZvr4+kyZNYtOmTVhZWeHm5sZnn33G7du3K7VHC4LQNA1xHcKP\nly+gm66PhWSFbpY9xi0tsLGxoVOnTujq6jZ0iML/e2g/f1tbWxYsWMCCBQtITEwkOTmZgoICzM3N\nad68Oa1bt65VhXPnzkVfX59Vq1aRlZVFu3bt2LNnD05OYq1NQWjqXC1dmdI7kO8OpWJUCOZmuri5\ntaVjx7ZiWvhGRu1ZPeF+F8LH7ZHzYL1fseavIDRd5YpyjsUew9nCGe9mlbtpv9DuBTq/VEBExHWe\ne84Jc3PzBopS+Ce1Sv6CIAhJuUnsvbyXuwV3+fzsdwzWH8OggPaVHio6OBjj4FD73oLCkyOSvyAI\nalEoFZyMP8nJ+JOUlZcTczUH61ITLkn/w1C3mOHDzStNJyI0biL5C4LwUPcK7rH38l6Scu/PcqtX\noUM7mT0GSlsMlBYkJ+cRHx9faf4ooXETyV8QhBpJksSpW6c4GnOUckU5SKCXp4dNmQ2t3JyJuZqP\nvb0RvXp1pH37dg0drlALIvkLglCt3NJc9l3eR0xGDPkFZZjp62OUZYSTvhMOtvfb93v2NMPHx1vt\nyR2FxkOt5F9WVsbOnTs5ffo0xcXFVWaLBPjuu+/qPDhBEBqGUlLy7m/vkpqfTmJiLuWZMhybOeDj\n6IGB9v0lW62trfHy8hJ995sotZL/ypUrOXToEF26dMHV1VUMyxaEp5yGTIMX2r7AosPr0cnSpZ3M\nGc20Zmg56CKTyWjXrh1OTk6i734Tplby/+6773jttdeYNm1afccjCEIj4Wvvy0u9X+TXQ8UYlpdi\nZauPsbEhXbr4YmZm1tDhCY9JreQvl8uf6KImgiA8OUpJyYnrJ/Cw9aCVWatK28Z3GktnwxxiY2Nw\ncNDHw8OjyqJDQtOk1m/xueee4+zZs3Tr1q2+4xEE4QnKLM7ko0sfkZiTyBe/fE9wixl0921eaVSu\nq6s5Li7dRRPPU0at5D98+HAWL15MTk4O3t7e1S4h+GB+fkEQmoYLKRf4NPJTCkqKuBaTjaxAg68T\nj1JW5M3gwQGVHuSKxP/0USv5v/LKKwCEhoYSGhpaZbtMJhPJXxCaiLKKMr6I+oJzd84BoKmpgV2F\nGc1lrTBSWHHjRhaRkZH4+vo2bKBCvVIr+f/000/1HYcgCE/A7bzb7A7fTXrR/XW3NeQa2OZb083Z\nhYToEhxaGuHqai1m2X0GqJX87e3tVa+Li4spKirCzMxMtSyjIAiNmyRJ/HzzZ47EHKG4VI6utiY6\nBTq0qmiFs6UzmjJNLLqY4uhoj6enJzo6Og0dslDP1H5s/8cff7B+/Xqio6NVg7w8PT2ZN2+eWIVL\nEBoxSZLYFb6L8LvhJCXlk5pchF8rVzqZd8DG5P7IXE1NTTw9PWnRooVo339GqDVa68KFC7z88suU\nlpby6quvsnz5cubMmUNxcTFTp07l4sWL9R2nIAiPSCaT0cK0BYk3cslLrqCjpitG95yw1Lu/nKKp\nqSl+fn60bNlSJP5niFp3/ps3b6Z79+7s2rWr0j+OWbNmMW3aNLZu3crHH39cb0EKgvB4BrkM4nLH\na0Sk5mFbYYexkT5KJbi5OdO2bVsxav8ZpFbyj4qKYtOmTVXuCmQyGcHBwbz++uv1EpwgCLVXJC9C\nISkw0TVRlclkMv7Tex6RZplERV3G1laGl5cX1tbWDRip0JDUSv4mJiYUFxdXu62oqAhNTc06DUoQ\nhEdzK/cWu8J3ISvTZ1yLabi5mqn662tqaOLlZYu7e18kSRIPdZ9xaiX/bt26sXXrVnx8fLC1tVWV\np6WlsXXrVvHAVxAamCRJnEk6w8HogyTdySXlZiGpmkoC/XrSv79/pRs00UtPADWT//z58xk1ahQD\nBw7Ex8cHKysrMjMzCQ8Px8jIiDfeeKO+4xQEoQZlFWXsj9zPhZQLVFQoKUxR0EHLEUOFHleuJGNv\nfw0PD4+GDlNoZNRK/ra2toSGhrJnzx7Cw8NJTk7GxMSEoKAg/vWvf4l2Q0FoIGmFaey4uIO7BXdB\nCcZ5hvS0aUZZsiVmRkY4OZmhpaWFJEmiJ49Qidr9/K2trVmwYEF9xiIIQi1cSb3Cnog9lFaUolmm\niUGWAfZ69ji1dCLPVI6d3f1VtiwtLRs6VKERqjH579ixgxdffBEbGxt27NjxjyeRyWRMnz69zoMT\nBKEqpaQkLC6M0KiviL+ei4edHSalxrhauGJjeH/Qlru7Mx4eHqJ9X6hRjcl/06ZN9OjRAxsbGzZt\n2vSPJxHJXxCenB9v/MhnF45yIyYfR5klyjt6eHboiLGuEVpaWnh4eODg4NDQYQqNXI3JPzY2ttrX\ngiA0rD6t+vBd9BkyuYmFZIlpsSOKEm0sm1vSqVMnDAwMGjpEoQlQa1jftm3bSEtLq3ZbSkoKK1as\nqNOgBEGomY6mDm8GvEY/7wBsZR3w7tSc7t070b17d5H4BbWplfy3b99eY/K/fPkyX375ZZ0GJQjC\nfQqlggspF5DLK1AqlapyKwMr3hz1L15/fQSDB/fDxcVF9OYRaqXGZp/x48dz+fJl4P4AkrFjx9Z4\nEtGHWBDqXkFZATsv7uTH8HCcszozpm9X/Px6qpK8TCbD0tK0gaMUmqoak/+KFSv4/vvvkSSJLVu2\nMGbMGOzs7Crto6mpibGxMQEBAfUeqCA8S27n3eb9C+9z8cItLApNKJDd5NfzRjRvbourq2tDhyc8\nBWpM/s7OzsycORMApVJJYGBgpakdBEGoH38k/8H+K/vRyNagnXZz8mRlGClskJXrkJubJwZsCXVC\nrUFec+bMASAnJ4fy8nLVYi6SJFFcXEx4eDiBgYFqVZiQkMCQIUOqlB84cIDOnTurG7cgPHWUkpIj\n147wc9zPGGQZoCnXRMtQCxtaY2Vow9ChPXBwcBCJX6gTaiX/uLg4/v3vf5OQkFDtdplMpnbyv379\nOubm5oSFhVUqNzMzU+t4QXgaFcoL2fLb+9xJTsK4wBgkMNA2oL11exxsHUQXTqHOqZX8161bR25u\nLgsWLODUqVPo6Ojg7+/P2bNnOXv2LJ988onaFV6/fh0XFxcxH5Ag/L/bebdZefI9MmLysdQxBHOw\n1LeknU07OrTrgJOTk7jbF+qcWl09L1++zNy5c5k8eTKDBw+mpKSEoKAgduzYQUBAAPv371e7wvj4\neJycnB45YEF4miiUCjac2crNyAwMlXqUliowrLChm3M3+vj1wdnZWSR+oV6olfzlcjmtWrUCoFWr\nVpVG/L744ouqLqHqiI+P5+7du4wZM4aePXsyefJkIiMjaxe1IDwlNDU0eeW56cjsFMglJTa0pnu7\nXvTq1QsTE5OHn0AQHpFayb958+YkJycD95N/YWEhKSkpAOjq6pKXl6dWZaWlpdy5c4fCwkL+85//\n8MEHH2BjY0NISAiJiYmPeAmC0PQ86DQB4GLhwrIX59Hb9UVemRLMgAFdxZq6Qr1Tq80/ICCA9evX\nY2hoSP/+/XFycmLz5s1Mnz6dffv24ejoqFZlenp6XLhwAR0dHdUScmvWrCE6OprPPvuMt99++9Gv\nRBCagOh71zj1/WW6tHPF17ezqkmnm2NXuk1u2NiEZ4vaXT2TkpI4ePAg/fv3580332TOnDmEhYWh\nqanJxo0b1a7QyMio0s8aGhq4uLhw79692kUuCE2IJEkc+OUQ3586jaxcC3leKfb2zbG3t2/o0IRn\nlFrJX19fn23btiGXywHo1asXYWFhREdH06FDB1q0aKFWZVFRUUycOJFPPvkEd3d3ABQKBbGxsQwa\nNOgRL0EQGreisiJ2f7eb+JgkpHIZEgpiUq8TF9dBJH+hwai9khegaqoBaNGihdpJ/4G2bdtib2/P\nkiVL+O9//4uBgQG7d+8mJyeHiRMn1upcgtAURN+O5uCPBykpKcHQSJsyuYKKUh16ew3C379LQ4cn\nPMNqTP4DBgyoVRez77777uGVaWnx4Ycfsm7dOmbMmEFJSQne3t58+umnYqk54amiUCg49usxLkRf\nQCn9ORunR1s3+nu8SPu2zRswOkH4h+Tv7e1dL/2LbW1t2bBhQ52fVxAai7SMdDYc2EVecQamproA\naGhq4O/rzwDv2t1UCUJ9qTH5r1mz5knGIQhPhRuZSbz9wQY05AoAdHU1sbQ1ZfLAybS0btnA0QnC\nn9Rq87906dJD9/H29n7sYAShqcstzyJVPw0buTkaaFCuNOfNcQvQ0dJ5+MGC8ASplfyDgoIe+lU1\nJiamTgIShKZEkiSUSiWampoAeDfzJsR/CB9/9S3D249k3pjRaGiIZh6h8VEr+Vc3cVtxcTEXL17k\n+PHjbN26tc4DE4TGLj8/n2+++4UO7Vqqui4DhHiNp79zAA7m4qGu0Hiplfy7dKm+S1qfPn0wMDDg\ngw8+YOfOnXUamCA0VpIkEXn1Gp+dCCO9MI3sbB+aNWum6rGmraktEr/Q6D32BCKdO3fm/PnzdRGL\nIDR6BQUFfH/qez757nPuFt2hQiYn4vY17tzJaOjQBKFWajXIqzqnTp3C0NCwLmIRhEZLkiQSEhL4\nNeJXrmdeR0NfgXaxBrnyErAtp7WrQ0OHKAi1olbyf+mll6qUKRQKUlNTuX37NlOnTq3zwAShsSgo\nKOBC+AUu3bpEelE6ADIZaDtI9HPsy8z+49CQiVk4haZFreRfXl5epUwmk+Hs7MyUKVMYNWpUnQcm\nCA1NkiSuX4/nq+9PcackEb3//4Kr0FGg76DPwm4zaW3eumGDFIRHpFbyr81KXYLwtMgrKGbrwYPk\nl92fcdZCRw+lVTleHbwY7zEePS29Bo5QEB5drdr8z5w5Q3h4OHl5eVhZWdGtWzd8fX3rKzZBaFDX\n86O5qhGJA1aUSHKyyot5p98reDcTAxqFpk+t5J+Tk8PUqVOJiopCR0cHCwsLsrKyeP/99+nZsyfb\nt29HV1e3vmMVhHqVn5+PsbGxakCjr70vg3t0I/TH3+jo3J7V417HwsC8gaMUhLqhVvJfsWIFycnJ\n7Nixgz59+qjKf/rpJ9566y3Wr1/PW2+9VV8xCkK9UigUXL9+natXY/H29sDFxQW4/1xrZo8p+Dh4\n0c+lj5iQTXiqqJX8z549y6JFiyolfoB+/fqRnZ3Ne++9J5K/0CRlZWUREXGZ3yIjuZ11l+KSCuzs\n7FQrzpnomhDg6t/AUQpC3VMr+WtqamJsbFztNmtr62p7AwlCY1ZRUUFMTAzXE6/zR2Ik93IyQQa/\nxl1jYEEAf1ttVBCeOmpP7Pbee+/h4eGBra2tqrywsJBdu3YREhJSbwEKQl1LT0/nypUrJGUlkZid\niFKnAjQkkuTZGJpVoKOn3dAhCkK9Uyv5p6enk56eTv/+/fHx8cHGxobc3FwuXbpEUVEROjo6qoFg\nMpmMjz76qF6DFoRHIZfLiYqK4ubtm8RnxZNdmg2AwqCCslYl9Dbw4+3RU8T0y8IzQa3kn5SURNu2\nbYH7X5fv3r0LoCpTKBQoFIp6ClEQHl9+fj6nT//K+egY8jTvoW+ogaQhUWJegrm1Oau8ZuNi4dLQ\nYQrCEyMGeQnPhJupmYSeP00FRchkIJlpUmEjp69rX15o+wI6muJuX3i21GqQV0JCAufPn6ewsBBz\nc3N8fHxwcnKqr9gEoc6kEMdN7SRs5WYklWfhom3Hkl6v42bp1tChCUKDUCv5K5VKlixZwpEjR5Ak\nSVUuk8kYMWIEq1evFn2ghUajoKCA5ORk2rZtq/p3OdBlAKe8znH60lWC/Yczs08wulpiYKLw7FIr\n+e/atYtjx44xf/58hg0bhpWVFRkZGYSFhbFlyxacnZ3FzJ5Cg1MqlcTHxxN+KZqsnAJMTEywt7cH\nQFNDk9f8ZzKtRzGuVqJtXxDUSv6HDx9mxowZTJkyRVVmZ2fH1KlTKSsr4/DhwyL5Cw0qOzuby5cv\ncz4invjseJBk2J2zZNSoZmho3J9uublxc6h+uIogPHPUmoQ8IyMDHx+fard5e3tz7969Og1KENRV\nXl5OZGQkv/72K9dSrpFYcg25rIRsWQ4/x98SzZGCUAO17vwdHR2JiIige/fuVbZFRERgbW1d54EJ\nwj+RJIl79+4RFRVFZkEm17OuU1xejLGpNgnF6SiMFUx4vrVI/oJQA7WS/+jRo9m4cSMGBgYMHjwY\nKysrMjMzOXHiBDt37mT69On1HacgqBQXFxMZeZWr0Tco0k7jbsH9cSfl+uWUmJfQy60DM7q/RHMT\nsYi6INREreQ/YcIEYmJiWLNmDWvXrlWVS5LE8OHDmTlzZr0FKAh/9+uvV/j53P9ILU/CxEIDXQNN\nii2K0TTWZGy7sfRpJWbgFISHUXtit7Vr1zJlyhQuXrxIXl4eJiYm+Pr64urqWt8xCoKKJEkcu3Ga\nnIoktGSaJOYW0qyVDp4OHgR7BGNpYNnQIQpCk1CrQV7NmjXD0dERU1NTLCwscHR0rK+4BAH4c/1o\nbe37k63JZDK6dnHkw+TLyJUVOLY25+UuL9PNoau42xeEWlB7kNe7777Lp59+SkVFhWqgl76+PjNn\nzmTatGn1GqTw7HnwQPePPyJo3dqeTp06qbaN9RzNucSLtLNz4+UuEzDWFf03BaG21Er+W7du5ZNP\nPmHixIkMHDgQS0tLMjMz+fbbb9myZQuGhoYEBwfXuvLLly8TFBTE3r176dq1a62PF55OxcXFRERc\nITw8kYR7d0i+l0WLFi2wsLAAQE9Ljw0jVmCkIybdF4RHpfYgr1mzZjF79mxVmaOjI15eXhgaGvLx\nxx/XOvkXFxfzn//8R8wGKqgolUoSExOJj4/nxu0MIlOvUaZZyJWUAgZk9FElf0AkfkF4TGoN8ios\nLMTT07PabT4+PqSnp9e64jVr1lRaGEZ4tmVmZnLmzBliY2NJzksmWRmDUreYdEU+1/TjSJPSGjpE\nQXiqqJX8+/TpwxdffFHtthMnTuDn51erSs+cOcPp06dZvHhxrY4Tnj5lZWVcunSJc+d+JzM3k8i0\nSBKyEyjXKqeidSn6zjD3hTF0dan+5kMQhEejVrNP586d2bRpE8OGDWPIkCFYW1uTm5vL6dOnCQ8P\nZ/LkyezYsQO43xvjnwZ9ZWdn89Zbb7Fq1SpMTU3r5iqEJqmoqIgffjhFTEwGJVpZyA0zUaCg1KwU\nubGc1sbNmdhxIs4Wzg0dqiA8ddRK/u+88w5wf6rcTZs2Vdm+Z88e1euHJf///ve/9O3bFz8/P1JT\nU2sbr/AUKSyUOH0umQLtBMplJRgYakCzcmTaMp53eZ4hrkPQ1hTr6QpCfVAr+cfGxtZJZaGhoVy7\ndo2vvvqqTs4nNC2SJFXqi6/Qz+eq+RnMC41ILs/GUqZLN4t2TOw4kZZmLRswUkF4+tVqkNfjOnr0\nKGlpaTz33HMAqvECU6dO5YUXXmD58uVPMhzhCZEkiTt37nDnzh26d++ummK5mXEzBnXvyuFfzuDi\nYs54nxcZ5DIILY0n+s9SEJ5JT/SvbP369ZSWlqp+zsjIIDg4mBUrVtCzZ88nGYrwhOTl5XH16lWS\nk9PJyCjG2toaN7f7SyfKZDKmdpuMlq5EYIdAHEwcGjhaQXh2PNHk//eunbq6uqpyS0sxJ8vTRC6X\nExcXR1JSEklJeSTeSadQIwOz88a4urqqmn/M9Mx4rftrDRytIDx7xPdroU49aOKJiYlBLpcjIZFS\nfId0rdukKvLIvKZPkHIYmppiHh5BaEgNmvzt7OyIi4tryBCEOpSbm8vVq1fJzc0FoFBeeH+RFbMc\nriShUFIAAB/xSURBVOenIjOUaNXuLiWKYow0xQhdQWhINSb/tLTajagUo3WfbVFRUdy8eZOionL0\n9TW5nX+b28W3KTYrpkK/gnbm5rS3c2Nyp8liagZBaARqTP69e/eu1RS5MTExdRKQ0DRpaelw61Y+\niSmpyKwyUdgWUWZXBhqgo6nDOI9xYpEVQWhEakz+q1atUv2h5uXlsX79erp3787zzz+vGuH7888/\nc/r0aRYuXPjEAhYap1vJEhEpsWRqJHMnMwd3RwsMNLRpa9WWCR0nYGVg1dAhCoLwFzUm/xdffFH1\nevbs2bzwwgusWLGi0j7Dhg1jxYoVfPPNN4wdO7b+ohQajeLiYmJiYnBzc8PY+M959LVaJ3NNP5q8\nojLMzHQx0NEn2HMsvVr0Enf7gtAIqfXA97fffmP79u3VbvP39+fQoUN1GpTQ+FRUVJCYmEhiYiIK\nhQK5XE63bt1Uib2vkz99Op8lOjmB/p26MsFzAub65g0ctSAINVEr+ZubmxMZGVntQKzz58+Lh71P\nMUmSuHv3LjExMZSUlKBUSty6lUdCYjbt2rXDzMwMAA2ZBq/0msad/Dt0tRdLKgpCY6dW8g8MDGT7\n9u2UlpbSr18/zM3NycrK4ttv/6+9O49q6sz/B/5OCPsiYUcElCWggiyyQykqda9Ca21VtDqOS+kZ\n9ehhqpZy5jvd+LVaRFvb6nQU69LlN2pL+22nLbVYrCKbOCKryCohhH0NkDzfPxyupkiNIgnI53VO\nzpHnubn5fMjl4829T57ne3z66afYvXv3SMdJNKClpQUFBQVoamoCAPT1KZBz5RaqeytR09+CRZKn\n8d/aDwCwM7GDnYmdhqIlhDwIlYr/Sy+9hPb2dnzyySc4dOgQ166rq4utW7c+1BKOZPSSyWQoKipC\ndXU1N/8SALSxZhTo56C4pw7gAynpX+NN0VrNBUoIeWgqFX8ej4dXXnkFsbGxyMvLQ1tbG4RCIXx8\nfGBgYDDSMRI1qq2txdWrV9Hf38+19Sp6Uc7KUSYog6kI0MvXgqODCTwCdTUYKSFkOB7oG77GxsYP\nvGoXGVsMDQ3R39+P3l4F6iUdMHZUIEuRBZmWDACgq6OFeU9Mx4veazDVcqqGoyWEPKwhi//cuXMf\n6Kbdv//970cSENEsU1NTdHUZIjOvEPnyqzDR7YKV5e1PdzweD7OnzMZSt6XQFdBZPyFj2ZDF39fX\nl0ZsPMZ6enpQWFgIoVCIyZMnc+2MMWS3X8f3iu/BeAwNN/gwN9eD/YRJWOO1BlOEUzQXNCHkkRmy\n+CcmJnL//vbbbxEcHAwzMzO1BEVGzu/H60skEtjZ2UFb+/ZyiTweD14zzfFjBR98LR7cRRaInhqF\neS7zaJEVQh4jKv01x8fHIzExEfPmzRvpeMgIGZhqubi4WGlBHbG4DdXVtXBymsy1Pef5DDIrszHJ\n3BprvFfD1thWAxETQkaSSsXf2toa3d3dIx0LGSENDQ24fv062trauLbu7n6Ul/cgV1wLfaErnJzu\nbK8r0MX/zHsVQj0hXfoj5DGlUvFfsWIF3nrrLeTn58Pd3f2ewzuffvrpRx4cGZ729nZcv34dEolE\nqV1PTw9NMjnONHyDNh0xGs7XYk6oG8zN9bltzPTpEh8hjzOViv/bb78NADh16tQ9+3k8HhX/Uaa+\nvh5ZWVlKX9LS0tKCwxQHXO+/jouyn6GY0AheB6BlfwuVXaUwN5+hwYgJIeqkUvFPS0sb6TjII2Zu\nbg5dXV309PRAJpPD3n4Sei26kXIjBW2y25d/RCIhtHhaeMZrMTwmums4YkKIOqlU/O3s7szX0tXV\nhc7OTpiamnIjRIhmKRQK9Pf3Q0dHh2sTCARwdnbBL79cR8bVJnQ6/QrTKR1Kz5vp6IkVHivohi4h\n45DKY/cyMzOxZ88eFBQUcJcSZsyYgW3btiE4OHjEAiRDY4xBLBajsLAQxsbG8Pf3V+pvbAeOX/0V\nYp3/gNUA3haWMDHWhameKZ6d9iz8J/rTDV1CximVin9WVhbWr1+PKVOmYMuWLTA3N4dEIsH333+P\nDRs24OjRo/Dz8xvpWMldmpqacP36dTQ3NwMAOjs70dTUpPRdDGM7GXptS8EaASMjbWhrCTDPZR4W\nuS6ib+gSMs6pVPyTk5MRHByMQ4cOKZ0pxsbGYuPGjThw4ABSUlJGLEhyR3t7OwoLC1FfX6/UrqUl\ngFTaplT83S3csSTgCWSUZmHOjAC84PECrI1o7QVCiIrF/9q1a9i3b9+gSwQ8Hg+rVq3C9u3bRyQ4\nckdXVxdKSkpQU1OjNIKHz+dDR8cCX/1aDoPCS/h/2x2V3qe1ASuxYNoceFh50CUeQghHpeJvYmKC\nrq6ue/Z1dnZCS0vrkQZF7pDJZCgrK0NFRQUUCgXXzuPxYGdnB2aoh60fHYRUUA7dLkNEXQ5GSKAj\nt52VoRWsDK00ETohZBTjq7JRUFAQDhw4MOhSQ319PQ4cOEA3fEdQV1cXysvLlQq/tbU1vAK8UKBV\ngEMlydB1vP0lLrl2FzIlv2oqVELIGKLSmf+OHTvw7LPPYt68eZg5cyYsLCwglUqRk5MDIyMjxMXF\njXSc45ZQKISNjQ3EYjGEQiEmOtohpy0bx7KPoU/eBwCY7GgCpmCICpiNFT7zNRwxIWQsUHlunzNn\nzuCf//wncnJyUFNTAxMTE6xcuRLr1q2DpaXlSMf52BuYeI3P52PSpElKfe7u7jCaIMThn7/DhZ+S\nMcNXCP5d1+89bKYjYdYzcJjgoO6wCSFj1JDF//Lly/Dx8eG+yGVpaYlXXnlFbYGNF4wx3Lp1C8XF\nxejs7ISuri5sbGwgENx5a+p7JXjx+G6093QCAGprtWE/yRiOpo6Ido+mFbUIIQ9syOK/Zs0a6Ovr\nw9/fH6GhoQgJCYGrq6s6Y3usMcZQX1+P4uJipdk2ZTIZqqqq4HTXNJuTJtjBwd4EBaW3i7+g2xSb\n/TbA28abRvAQQh7KkMX//fffR05ODnJycvDuu+9CLpfDwsICISEh3IMu9zw4xhikUimKiorQ0tKi\n1KetrQ0zCxuIm+RKUyzrCfSwPmIZ9rf8f8T4P4cVTz4FPk+le/WEEHJPQxb/yMhIREZGAgC6u7tx\n5coV5OTkICsrC3/729/Q09MDFxcX7lOBqgu7i8VivPXWW7h06RIUCgWeeOIJ7Ny5E9bWj/+Xjxob\nG1FcXIzGxkaldoFAgIn2djj47c+4KD0Mc+aAr0SJMDG58y3cSKc5mLvjKWjxaVgtIWT4VLrhq6+v\nj+DgYG5IZ39/P7KysvD555/j+PHjSElJQWFh4X33wxjDxo0bYWZmhmPHjgEA3njjDbz00ks4ffr0\nMNIY/WpqapCXl6fUxufzMdF+Iqq0qnC0+gj+o6hEH08GMa8En/3vZWx84QluW20tmkSPEPLoqDyx\nm0wmQ2ZmJi5evIjMzEwUFxeDx+PB09MToaGhKu1DKpXC2dkZO3bs4Ea0rF27Fi+//DJaW1sxYcKE\nh8tiDLCxsYG2tjYaGzshkXRj0hQb8B1bcbT2KHr6by+raGVpgJZmGezMrGE+UbPxEkIeb39Y/EtK\nSpCRkYGMjAzk5ORAJpPBwcEBoaGhiI2NRVBQEIyMjFR+MUtLSyQlJXE/i8VifP755/D09HysCn9L\nSwu0tbVhaGjItQkEAjQ1GeFidi1usJvo6EnFNC1Tpee5Ozrg5bA/Y7boCbq8QwgZUUMW//DwcDQ0\nNMDExASBgYHYvXs3QkNDB41Bf1ixsbFIS0vDhAkTuEtAY11LSwtKSkpQX18PW1vbQTOdGnt045cr\nX0EOOXjNgKzXGLo6WrA2ssZC14UIsAugG7mEELUYsvhLJBIIhUIsW7YMISEh8PPze6SLt2zduhWb\nN2/GwYMHsW7dOpw9e3bM3vS9u+gDgEwmx2+/FcHJyQVmZnfO7qdOmgIrWz2AAVbWBnCycMBC14Xw\ntfWlok8IUashi/+RI0eQkZGB8+fP4x//+Af09PS4Mf9hYWFwdnYe1gu7ubkBAJKSkhAREYEzZ85g\n8+bNw9qnuv2+6APAzZutqKntQFt/H65crcfsiDvF38XMBQsC/KFgCixwXQBPK08ap08I0Yghi//A\n6J64uDhIpVJkZGTgwoULOHToEN5++23Y2NggJCQEYWFhCAkJgamp6VC74kilUmRmZmLRokVcm76+\nPuzt7QdNGjeaNTc3o6SkBBKJRLmDB/To9uKSPBcNWrXoTdfCrCdFSgX+5YCXoS/Qp6JPCNEolUb7\nWFhYICoqClFRUQCAwsJCXLhwAdnZ2di5cyfkcjkKCgruu59bt25h+/btcHBwgKenJ4Dbi5PcvHkT\n0dHRw0hDfYqKilBaWgoA6O9XQCDgg4GhS68LBf0FuGUlRnNFHUyNdKHjWgYFU0CLd+fmrYG2gaZC\nJ4QQjspDPQGgra0NeXl5yMvLw9WrV3Ht2jXI5XJMnz5dped7eHjAz88P8fHxeP311yEQCLB3716Y\nmZlx/7GMdpaWlsjLK0BVVTuaWzvhHKyHYhSivacdACAQ8DHTzwaGeroItvdFr7wX+nx9DUdNCCHK\n/rD4V1RUIC8vD7m5ucjLy+PmlXdxcUFQUBBWrVqFwMBAlYd78vl8HDhwAO+88w42bdoEmUyGsLAw\nHD9+XGlY5GjAGENDQwMsLCzA59+5GWtmZoYrhU2o6ilHCa8YV2v0YD/JmOs30DbAAtcnMXvKbJjo\nmmgidEIIua8hi39QUBBaW1vBGMPEiRMRFBSETZs2ISgoaFhz+piZmSExMfGhnz/SGGOoq6tDaWkp\n2traMGPGDDg63lkZS87kqJycjfyiGoABPT23f4Vm+maIdIpEqEMo9AR6mgqfEEJUMmTxDwwMREhI\nCIKDg+Hg8PjPE69QKFBbW4uysjJ0dHRAoWAQiztRU5OFTZvsubN/AV+A5YHz0dB2Cra2RvCwE+Ep\np6fgY+tDwzUJIWPGkMU/OTlZnXFojFwuR1VVFW7cuIHu7m4AQF+fApeyK9HCGtAi78WyxvmwtLxz\naSvSZQ4ae6SY4zQHTkKnoXZNCCGj1gPd8H2c9PX1oaKiAuXl5ejt7eXaG7saUddVhyL9Ytxsb4Qc\nCvxvegFeXBbIbWOia4INMzdoImxCCHkkxmXxZ4whPT0dnZ1dkEq7wddWoIvfhLruOrTpt0FmJoMO\nXwHtcj4m25nAdJrk/jslhJAxZFwWfx6PB4XCBOcv56ONSdGr3wYTZz56rXuB/373ytxcH7OnBWP2\nlFlwM3fTbMCEEPKIPfbFv7m5Gc3NzUrLIgJAlWExKlCIekUbmto74Sewhj5PG8a6xnjC4QmEO4ZD\nqC/UUNSEEDKyHsviP7A+bllZGaqr69HQ0I3Vq80hFN6ZNjpiajCOZpxFZ2cfJttOgLuVCE+5zoGP\nrQ8E/Mfy10IIIZzHqsrJ5XLU1tbixo0b6OjoQEGhBNVNdejRaoF1mg2eXxbJbessdMY8/5lwspiM\nWVMiMNGYVk8hhIwfj0Xx7+3txc2bN1FZWQmZTIY2WRvEHWLUoBatgh40Kjrxa2kJnsed4s/j8RA/\naxdNsEYIGZfGdPGXSFrw44+5KC+vhLaOAmYOctS116GzrxOMx6Cw6MP15lqYWOjAwLlh0POp8BNC\nxqsxXfybm9uRfTUfXVpN6JG1wKpRD0zA0GvaC5mRDOADUU5+iJjyJALsAjQdLiGEjBpjuvj3TGiC\n2KAIgj4BulkvGvR7oWsJ6Ah0EGoXinDHcDhOcKQzfEII+Z0xXfynWU6Dtp0WZP1dmGAlgL2ZLZ50\nfBJBk4Kgr03TKBNCyFDGdPHX1tJGdOhTkHZJ8eTkJ+Fq5kpn+YQQooIxXfwBIMo9igo+IYQ8oDE/\nBzEVfkIIeXBj4sxfLpcDAMRisYYjIYSQsWGgXg7Uz98bE8W/oeH2GP1Vq1ZpOBJCCBlbGhoalFYj\nHMBjjDENxPNAenp6cO3aNVhaWkJLS0vT4RBCyKgnl8vR0NAADw8P6OkNXlp2TBR/Qgghj9aYv+FL\nCCHkwVHxJ4SQcYiKPyGEjENU/AkhZByi4k8IIePQqCv+CQkJePXVV5Xazp49i8WLF8Pb2xvPPfcc\nLly4oNR/4sQJuLm5KT2mTZumtM3Ro0cxa9YseHl5Yd26daioqBhVOfT29iIxMRGhoaHw8fHBxo0b\nUV1dPWZyOHDgwKD3YODx/vvvqz2Hh3kPqqursXnzZvj5+SEsLAzx8fFoa2tT2mY0vwcAUFFRgQ0b\nNsDPzw/h4eHYv38/+vv71ZqDVCrFK6+8grCwMPj5+WH9+vUoKSnh+jMyMrB06VLMmDEDTz/9NNLT\n05We39jYiK1bt8LPzw/BwcF499131ZrDcOMf0NvbiyVLluCrr74a1KfO42hIbJRQKBRs3759TCQS\nsd27d3PtqampzM3NjX300UesvLycHT9+nHl6erJLly5x2yQkJLDNmzcziUTCPRoaGrj+L774gvn4\n+LDvvvuOFRUVsU2bNrE5c+YwmUw2anLYuXMnCw8PZ7/99hsrLi5mq1evZosXL2YKhWJM5NDR0aH0\n+5dIJCwhIYEFBwczsVisthweNv6+vj42f/58Fhsby8rKylhOTg6bP38++8tf/sLtY7S/By0tLSwk\nJIStXr2aFRQUsKysLDZ//ny2a9cuteUgl8vZ888/z5YvX87y8/NZaWkp27JlCwsODmZNTU2stLSU\neXh4sIMHD7KysjKWlJTEpk+fzkpKSrh9rFixgq1cuZIVFhayX375hQUFBbH33ntPLTk8ivgZY6y9\nvZ39+c9/ZiKRiJ09e1apT13H0f2MiuJfVVXFYmJiWGBgIIuIiFA64JcsWcJ27NihtP2rr77KYmJi\nuJ9XrFjBkpOTh9z/3Llz2f79+7mfOzo6mLe3N/v6669HRQ5VVVVMJBKx3377jeu/ceMGi4iIYBUV\nFWMih9/Lzc1l7u7uLD09nWsb6RyGE39xcTETiUSsqKiI6z9+/Djz8fFRW/zDzeHIkSPMx8eHNTc3\nc/3Z2dlMJBKx6upqteRQUFDARCIRKysr49pkMhnz8vJiZ86cYa+99tqgYyYmJobFx8czxm4fNyKR\niFVVVXH9p0+fZj4+PlxxHMkchhs/Y4xduHCBzZkzh0VHR9+z+KvjOFLFqLjsk5ubC1tbW6SmpmLS\npElKfZWVlfDz81Nqmzp1KvLy8riPgmVlZXB2dr7nvhsbG1FRUYGAgDsreRkaGsLDwwPZ2dmjIoeM\njAyYmZkhODiY63dycsK5c+fg6Og4JnK4G2MMb775JubOnYvw8HAA6nkfhhP/hAkTwOfz8cUXX0Am\nk6GpqQnff/89PDw81Bb/cHOorKyEq6srTE1Nuf6By5/Z2dlqycHW1hYff/wxpkyZwrUNTL7Y2tqK\n7OxspdcHgMDAQO71s7OzYWdnB3t7e64/ICAAnZ2dKCwsHPEchhs/APz888+IiorCZ599Nmj/6jqO\nVDEq5vZZunQpli5des8+Kysr1NXVKbXV1tair68PbW1t6OvrQ2trK86fP48DBw6gu7sb/v7+iIuL\ng7W1NTe5kbW19aD9PsqJ4oaTQ0VFBezt7ZGamorDhw+jqakJvr6+2L17N2xsbMZEDmZmZlx7Wloa\nrl+/jr1793Jt6shhOPFbW1sjPj4ee/bswcmTJ6FQKODs7Izjx4+rLf7h5mBlZYVz585BoVCAz+dz\n/cDtoqOOHIRCISIiIpTaPv30U/T09CAsLAzJycl/+Pr19fWwsrIa1A8AdXV1EAgEI5rDcOMHgPj4\n+CH3r67jSBWj4sz/jyxZsgQnTpzAxYsXIZfLcenSJfzrX/8CAPT19aG0tBQAIBAIkJSUhLfffhsV\nFRVYu3Ytenp60N3dDQDQ1dVV2q+Ojg5kMtmoyKGjowPl5eU4cuQIdu3aheTkZDQ2NuLFF1+ETCYb\nEzncLSUlBfPnz1eaTErTOdwvfoVCgZs3byI4OBinTp3CJ598Ai0tLWzbtg1yuVzj8auSw4IFC9DY\n2Ih3330X3d3dkEqleOONNyAQCNDX16eRHNLS0vDee+9h3bp1cHZ2Rk9PD3R0dIZ8/e7u7kHxaWtr\ng8fjaeRv4UHjv5/RcBwNGBVn/n9k48aNaGpqwoYNGyCXy+Hi4oL169dj7969MDY2RlhYGC5evKh0\n5uni4oLw8HCkp6fDzs4OwO0773fr7e2Fvr56lnq8Xw4CgQDt7e1ITk7mPu7u378fYWFhSE9Px8SJ\nE0d9DgPEYjEuX76MlJQUpecPTCylqRzuF//XX3+N1NRUnDt3DgYGBgAAR0dHREZGIj09nTv7HM3v\ngbW1NZKTk5GQkICjR4/CwMAAW7ZsQXFxMYyNjdX+Hpw+fRqvvfYaFi5ciLi4OAC3i97vTxbufn09\nPb1B8fX19YExBgMDA7Xm8DDx34+m/w7uNurP/HV0dJCQkIDc3FycP38eqamp0NPTg4WFBfdHenfh\nB25/hBIKhairq4OtrS2AO9NCD5BIJIM+emkqB2traxgYGChd5zQ3N4epqSlqamrGRA4D0tLSYGlp\nOei6qKZzuF/8+fn5cHJyUsrF3t4eQqEQVVVVGo9flRwAYPbs2cjIyEB6ejouXryIZ599Fk1NTbC3\nt1drDh9++CF27dqFF154Ae+88w53GcrW1hYSiWTI17exsblnfMDtSyXqyuFh47+f0XAcDRj1xT8p\nKQmHDh2Cjo4OLC0tAQA//fQTQkNDAQDHjh1DWFiY0v/GtbW1aGpqgqurK8zNzTF58mRcvnyZ6+/s\n7MS1a9fg7+8/KnLw8/NDV1cXbty4wT2noaEBzc3NcHBwGBM5DBi4ITbwxzJA0zncL34bGxtUVFQo\nnZFJJBK0tLTA0dFR4/GrkkN2djZefPFFyOVyWFlZQUdHBz/99BMMDAzg6+urthwOHz6Mffv2YcuW\nLXjttdeUVtubOXMmsrKylLbPzMzkbmTPnDkT1dXVSvc2MjMzYWhoCHd3d7XkMJz472c0HEcctY4t\nUkFMTIzS8LYvvviC+fr6sl9++YVVVVWx119/nXl7e7MbN24wxhirrKxk3t7eLC4ujpWVlbHs7GwW\nHR3NVqxYwe3j5MmTzNvbm33zzTesuLiYbdq0ic2dO3fExtU+aA4KhYKtXLmSLVmyhOXm5rLCwkK2\nevVqNn/+fC7G0Z7DgLlz57IPP/zwnvtUZw4PGr9YLGZ+fn5sy5YtrKSkhOXn57MXXniBRUVFsb6+\nPrXH/zA5NDY2Mj8/P5aYmMiqqqrYDz/8wHx9fZXej5HOobCwkE2dOpXt2rVr0Pc+Ojs7WVFREZs+\nfTpLTk5mZWVlbN++fczT05MbWqlQKNjy5cvZ888/z65du8aN8797aORI5jDc+H/vXkM91X0cDWXU\nF3/GGPvggw9YeHg48/b2ZjExMSw/P1+pPy8vj8XExDAfHx8WEBDAdu7cyVpaWpS2+eijj1hoaCjz\n9vZmf/rTn5TGEY+GHFpbW9nu3buZv78/8/b2ZrGxsayurm5M5cAYYz4+PuzkyZND7lddOTxM/MXF\nxWz9+vXM39+fhYaGsri4ONbY2KiR+B82h6ysLLZs2TI2Y8YMFhkZyY4cOTJovyOZw969e5lIJLrn\n44MPPmCMMXbu3Dm2cOFC5uHhwZYsWcIuXLigtA+JRMJiY2OZl5cXCwkJYXv37mVyuVwtOTyK+O92\nr+I/kvE/CFrMhRBCxqFRf82fEELIo0fFnxBCxiEq/oQQMg5R8SeEkHGIij8hhIxDVPwJIWQcouJP\nxrWEhAS4ubkNuRpTWloa3NzccPDgQTVHRsjIonH+ZFzr6OjA4sWLwePx8M0338DQ0JDra29vx8KF\nC2FjY4PPPvsMWlpaGoyUkEeLzvzJuGZkZIS///3vuHXrFpKSkpT63nnnHbS2tiIxMZEKP3nsUPEn\n4154eDiio6Nx4sQJ5OfnAwCysrLw5ZdfYvv27UqrxJ06dQoLFiyAh4cH5syZg8OHD+P3H55PnjyJ\n6OhoeHl5YcaMGXjmmWfw448/cv1ffvklfHx8cOLECQQHByMwMBA1NTXqSZaQ/6LLPoTg9hJ9ixYt\ngo2NDU6ePIlnnnkGQqEQx44d42Z1/OCDD/D+++9j7dq1CA0NRX5+Pg4ePIi1a9dy870fOXIEe/bs\nwdatW+Hl5YWWlhYcOnQIJSUlSEtLg5WVFb788kskJCTA2dkZcXFxaG5uRlRUlCbTJ+OR2mcTImSU\n+vHHH5lIJGKrVq1iPj4+3KLnjDHW0tLCPD092Ztvvqn0nE8++YRNmzaNicVixhhjr7/+OktKSlLa\nJj8/n4lEIvbDDz8wxm7PzikSidh33303whkRMjS67EPIf0VGRmLRokXIysrCzp07lRZQz83NhUwm\nw6xZs9Df3889Zs+ejf7+fly6dAnA7fVbt23bhtbWVly5cgVfffUVTp06BWDwcpdTp05VX3KE/M6o\nX8aREHUKCwvDt99+i/DwcKX2lpYWAMDatWvv+byB1Z0qKiqQkJCAzMxM6OjowMnJCa6urgAw6N7A\n3auGEaJuVPwJUcHAOsXJycncutB3s7a2hlwux8aNG2FkZITTp0/Dzc0NAoEARUVFSE1NVXfIhPwh\nuuxDiAq8vb2hra0NqVQKT09P7iGTybBv3z5IpVJIpVJUVlZi+fLlmD59OgSC2+dW58+fBwAoFApN\npkCIEjrzJ0QFFhYWWLNmDfbs2YPW1lb4+vqitrYWSUlJMDU1hYuLC7S1tWFra4uUlBSYm5vDyMgI\n58+fx6effgoA6O7u1nAWhNxBZ/6EqCguLg7btm1DamoqNmzYgH379iEiIgIpKSnQ0dEBj8fDwYMH\nYW5ujr/+9a/Ytm0b/vOf/+Djjz+Go6MjsrOzNZ0CIRwa508IIeMQnfkTQsg4RMWfEELGISr+hBAy\nDlHxJ4SQcYiKPyGEjENU/AkhZByi4k8IIeMQFX9CCBmH/g+nTJMTtd29tQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f6525550b8>"
+      ]
+     },
+     "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": 14,
+   "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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GxsYYPXo0/v77b5w+fRqRkZFSk4iNjQ1CQkKwbds2HD9+HBUVFbCzs8PatWvZRCkve/fu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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f6525e3320>"
+      ]
+     },
+     "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.856665141368708"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system.results[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.888888888888889"
+      ]
+     },
+     "execution_count": 16,
+     "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap04-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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8Y0ZycnLw8OFDKUZESOsyePBg7Nmz54PbsrOzYWtri6+//lrgvoKWna1Xf+zbLwcHB4wc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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f653cf1ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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U4uHDh0hLS2PLDA0N4ePjA1VVVTlGRghpTmKTyKhRo5ozDtKCVFdX48GDB+w4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\nP0YEbzWX2JkFAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f653cd6dd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "system = System(t0=t0, \n",
+    "                t_end=t_end,\n",
+    "                p0=20,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)\n",
+    "\n",
+    "system\n",
+    "\n",
+    "system1 = System(t0=t0, \n",
+    "                t_end=t_end,\n",
+    "                p0=5,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)\n",
+    "\n",
+    "system1\n",
+    "\n",
+    "system.t_end = 2250\n",
+    "system1.t_end = 2250\n",
+    "\n",
+    "run_simulation(system, update_func2)\n",
+    "run_simulation(system1, update_func2)\n",
+    "\n",
+    "plot_results(system)\n",
+    "plot_results(system1)\n",
+    "\n",
+    "decorate(title='World population projection')\n",
+    "savefig('chap04-fig01.pdf')"
+   ]
+  },
+  {
+   "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": 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>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": 24,
+     "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": 25,
+   "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": 26,
+   "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": 27,
+   "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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap04-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ShEKhqHX3LEEQhCft6tWrxMbGVvvya2hoiJOTEy1btmxSS4w8LmolkCVLlqCh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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1f652c5f8d0>"
+      ]
+     },
+     "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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1343.9163793441223"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "firstBank = 1000 *  np.power(1.03,10) \n",
+    "\"\"\"after 10 years first bank\"\"\"\n",
+    "\n",
+    "firstBank  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1349.8588075760031"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ExpBank = 1000 * exp(10*0.03)\n",
+    "\"\"\"after 10 years Exponential bank\"\"\"\n",
+    "\n",
+    "ExpBank"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.030454533953516938"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interest = exp(0.03)-1\n",
+    "\"\"\"interest rate for first bank to equal exp bank\"\"\"\n",
+    "\n",
+    "interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1349.858807576004"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "firstBank = 1000 *  np.power(interest+1,10)\n",
+    "\"\"\"First bank using calculated interest\"\"\"\n",
+    "\n",
+    "firstBank"
+   ]
+  },
+  {
+   "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": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1350.0 1800.0 9000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "B1 = 30\n",
+    "B2 = 0.5\n",
+    "PCU = 1000 + (B1 * 10) + (B2*(10**2))\n",
+    "PCU2 = 1000 + (B1 * 20) + (B2*(20**2))\n",
+    "PCU3 = 1000 + (B1 * 100) + (B2*(100**2))\n",
+    "\n",
+    "print (PCU,PCU2, PCU3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1343.91637934 1806.11123467 19218.6319809\n"
+     ]
+    }
+   ],
+   "source": [
+    "firstBank1 = 1000 *  np.power(1.03,10) \n",
+    "firstBank2 = 1000 *  np.power(1.03,20) \n",
+    "firstBank3 = 1000 *  np.power(1.03,100) \n",
+    "\n",
+    "print (firstBank1,firstBank2,firstBank3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1349.85880758 1822.11880039 20085.5369232\n"
+     ]
+    }
+   ],
+   "source": [
+    "ExpBank1 = 1000 * exp(10*0.03)\n",
+    "ExpBank2 = 1000 * exp(20*0.03)\n",
+    "ExpBank3 = 1000 * exp(100*0.03)\n",
+    "\n",
+    "print(ExpBank1,ExpBank2,ExpBank3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Solution goes here"
+   ]
+  },
+  {
+   "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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+{
+ "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": 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": [
+    "### 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": 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>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": 2,
+     "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": 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>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": 3,
+     "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": 4,
+   "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": 5,
+   "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": 6,
+   "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": 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>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": 7,
+     "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": 8,
+   "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": 9,
+   "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": 9,
+     "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.37943042989926101"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tc = 4       \n",
+    "tr = 5      \n",
+    "beta = 1 / tc \n",
+    "gamma = 1 / tr\n",
+    "system = make_system(beta, gamma)\n",
+    "s0 = system.init.S\n",
+    "\n",
+    "final = run_simulation(system, update1)\n",
+    "s_end = final.S\n",
+    "s0 - 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": 11,
+   "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": 12,
+   "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": 13,
+   "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": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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tivt/HYP29vaEh4fTu3dvs3tUqlSJq1evAuCYbyTK7WLOHUatFJhFfLv3Yi0W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\niQpBEAShM0hUCIIgCJ1BokIQBEHojP8DRPD4ChRIKg0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90606b70>"
+      ]
+     },
+     "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": 15,
+   "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": 16,
+   "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": 16,
+     "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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tivt/HYP29vaEh4fTu3dvs3tUqlSJq1evAuCYbyTK7WLOHUatFJhFfLv3Yi0W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\niQpBEAShM0hUCIIgCJ1BokIQBEHojP8DRPD4ChRIKg0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90a33748>"
+      ]
+     },
+     "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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+/vj7+xMYGMjYsWM5V3iv2P/RFln/In/48a5du9i3bx8LFy7k448/5ptvvrnt\nKKz8Pwxef/31YlexLvzeotfR6/VYmLm5RtHz8jv2i/5hcjflEUKUnbvaNufChQt88cUXrF27ls2b\nN/P333/f1c3q/W8zhcTERKPjCQkJJk1ioDaT+fn5YVko1DZt2hSAuNutRlcD2NrasmXLFr4rpgPI\n2dkZjUaDu7s71tbW3Co01fjSpUuG56mpqbz11ltotVqGDRvGihUr+OSTT/j77785e/Ysfn5+pKSk\nGP1bx8TE0KVLF9LT06lTpw6xsbE0bNjQkPbt28dHH31kFAxOnTpleP7PP/+QlpbGAw88AHDHzd/S\n0tKIj483vD5y5Agajcbwc5DPycnJ7PIIIcqOWX++6fV65s2bx9dff200BFSj0TB48GAWLlx4xy8H\nUJvLHB0dOXjwoGE0UmxsLHFxccXOzA8MDOSrr74iLy/P8JdodHQ0lpaWNGjQwKwPWF3Vrl2bsWPH\nsmzZMm7dukX//v2xs7MjOjqalStX8vjjj1O/fn3at2/PV199RceOHdHpdCxcuNDQDOTi4sKePXu4\ncuUKU6dOxd7enq1bt+Ls7Ezjxo1xdHSkVatWzJgxg5kzZ2JlZcX8+fPx8/PD29ubiRMnsmjRIurX\nr0/Xrl05fvw4ixYtYty4cUZlDQ0NJTQ0FGtra+bPn0/btm0NtQlHR0fDz0Bx/6cajYZXXnmFOXPm\nkJmZyfz58xk0aFCx55pbHiFEGVLM8MEHHyitW7dW1q1bp1y7dk3RarVKfHy8snbtWqVNmzbK2rVr\nzbmMoiiKsmTJEqVbt27K7t27lVOnTinDhg1TRo4cqSiKouTk5CgJCQlKTk6OoiiKkpiYqAQFBSmv\nvPKKcuHCBeWPP/5QevfurcycOfO297hy5YrSvHlz5cqVK2aXq6r6+uuvlaeeekoJCAhQWrdurQwc\nOFBZu3atkpubqyiKopw/f14ZMWKE0qZNG6VPnz7KN998o/Tt21fZtm2boiiKEhMTo4wfP14JDAxU\n/P39lREjRijHjx83XD8+Pl6ZMmWK0r59eyUoKEiZOnWqkpSUZMj/5JNPlH79+imtW7dW+vTpo6xZ\ns0bR6/WKoijKn3/+qTRv3lxZv369EhwcrHTo0EGZOnWqkpycbHj/vn37lG7duin+/v5KQkKCMnLk\nSGX27NmGzxYcHKysX79eCQoKUgICApQ5c+YoGRkZhvc3b97c8FnuVB4hxO2VxnenWUElJCRECQ8P\nLzYvPDxpAfnGAAAgAElEQVRc6d+/v9k3zMvLUxYuXGj4knjppZcMXzL5X0J//vmn4fzz588rzz77\nrOLv76906dJFeeeddwxBpyQ1KahUZvn/n/Hx8RVdFCGEGUrju9Os5q/ExEQ6duxYbF5AQABr1641\nu2ZkZWXFzJkzmTlzpkle586dTTqXmzZtWuIQZSGEEJWLWb2XPj4+HD16tNi8o0eP3naOgRBCiJrD\nrJrK0KFDWb58OQ4ODjz88MN4eHiQlJTE999/z4cffsiECRPKupyiCiqu5imEqN7MCiqjRo0iKiqK\nRYsWERYWZjiuKAqPPfYYEydOLLMCCiGEqDrMXlAyLCyMcePGcejQIdLT03F2dqZTp05mLQUihBCi\nZriracb16tXDx8cHFxcXk+U7hBBCCLMnPy5ZsoQNGzag1WoNEyDt7e2ZOHEizz33XJkWUgghRNVg\nVlAJDw8nIiKC0aNHM2DAANzd3UlKSuKHH37gvffew9HRkREjRpR1WYUQQlRyZgWVLVu2MGnSJCZP\nnmw45uPjQ4cOHXB0dOTTTz+VoCKEEMK8eSq3bt3C39+/2LyOHTuSkJBQqoUSQghRNZkVVHr37s2m\nTZuKzfv+++/p2bNnqRZKmGfmzJk888wzZp+/YMECOnToQMeOHQ0bcd0rRVHYtm0bycnJ93WdVq1a\nsXXr1vu6hhCi8jCr+SswMJCVK1cyaNAgHnnkETw9PUlLS+O3337j8OHDPPPMM6xZswZQV5WVyZCV\nz4ULF/jss88IDQ2lR48eeHh43Nf1jhw5wowZM9i5c2cplVAIUR2YFVTy9/m+efMmK1euNMn/+OOP\nDc8lqFRO6enpAHTv3h1vb+/7vp5SaAsEIYTIZ1ZQOXv2bFmXQ9ynPn36MHLkSA4ePMj+/fupVasW\nTz/9NFOmTGHr1q3MmjULgL59+/L444+zaNEioqOjCQsL49ChQ7i4uNC7d2+mT5+Os7MzAHl5eaxa\ntYpt27aRnp5Oy5YtmTlzJh4eHoaBGSEhIUyZMoUXXnjhjtdLS0vjrbfe4rfffsPe3p5p06ZVzD+W\nEKLMyB6rhf38M2zfDjk55X9vW1t1H+N+/e75Eu+++y6zZ89mzpw5/PDDDyxdupQuXbrw8MMP4+Li\nwqRJk9i8eTONGzfm+vXrjBo1iieeeII5c+Zw48YNFi9ezJQpU4iIiADUPpidO3fy5ptv0qxZMz75\n5BPGjRvHDz/8wOrVqw3X8/PzM+t6L730EikpKaxfvx5LS0tCQ0PR6XSl8s8nhKgcJKgU9vPPFRNQ\nQL3vzz/fV1B58MEHeeqppwAYP348a9eu5dixYwQGBuLi4gKoO0Y6OTmxfv16vL29mTFjhuH9K1as\noGfPnhw9epRmzZrx9ddfM3/+fPr27QvAnDlzsLOz48aNG0bXc3R0ZO3atbe9nrOzM3/++Seff/45\nHTp0ACAsLIxHHnnknj+vEKLykaBSWL9+FVtTuY+AAtCoUSOj105OTuTl5RV7blRUFFFRUYYv+MJi\nYmKwsrIiLy/PaCi5lZWVIWikpKTc1fUcHR0BaN26teF406ZNDceFENWDBJXC+vW77y/2ipS/93xh\nJXWoW1tb0717d+bOnWuSV7t2beLi4u7q3ne63h9//FFseaytre/qPkKIyq3EeSrz58/nypUrAFy9\nerXEv3hF1dS0aVNiYmKoX78+DRs2pGHDhlhYWPDOO+8QHx+Pr68vVlZWnDp1yvAevV7PgAED+P77\n79FoNHd1vZYtWwIYbfYWGxtLWlpa+XxgIUS5KDGobN68mevXrwPqCJ+oqKhyK5QoeyNHjuTGjRvM\nnDmTc+fOcfLkSaZOncrFixdp1KgRDg4ODB8+nBUrVrB7924uXrzI/PnzSU9Pp3PnzoZmq6ioKG7e\nvHnH6zVq1IiQkBBCQ0M5ePAgUVFRzJgxAwsLs+bfCiGqiBKbvzw9PVm6dCk9evRAURQ2b97Mnj17\nij1Xo9EYrQsmKj9PT0/++9//snTpUv71r39hZ2dH586deffddw3NaK+++iqWlpbMnj2bjIwM2rZt\ny0cffYSHhweurq4MGDCAV155haeffpo5c+bc8XpLly5l4cKFTJ48GQsLC8aPH2+oDQshqgeNUkKj\n+//93//x9ttvk5aWhk6nu+1flBqNxqiZpKLFxsYSEhLCzp07S2WinxBC1ASl8d1ZYk1l4MCBDBw4\nEICWLVuycePGEheVFEIIIcDMBSUjIiLw8/Mr67IIIYSo4swaUhwUFERMTAzh4eEcPHiQmzdv4ubm\nRmBgIBMnTpR96oUQQgBmBpVz587x9NNPY29vT0hICO7u7iQmJrJr1y527drFl19+SfPmzcu6rEII\nISo5s4LK0qVLadKkCRERETg4OBiOZ2Zm8swzz7BixQo++OCDMiukEEKIqsGsPpVDhw7x/PPPGwUU\nAAcHB8aNG8ehQ4fKpHBCCCGqFrOCir29fYl5Go1GVpoVQggBmBlU2rdvz7p168gpstBidnY269ev\nL3YRQSGEEDWPWX0q06ZNY+jQoYSEhNCnTx88PDxISkri119/JSMjg88//7ysyymEEKIKMCuo+Pn5\nsWnTJt5//3127txJeno6zs7OdOrUicmTJ9/VyC+dTsfKlSuJjIwkIyOD4OBg5s2bZ9ae6RMmTCAz\nM5PPPvvM7PtVZ3369DFZTdjOzo769evz1FNP8cwzz1RMwSpIv379eOyxx3jhhRcquihC1FhmL33f\nokUL3nvvvfu+YXh4OJGRkYSFheHq6kpoaCgvvPACGzduvO37Nm3axG+//UZQUNB9l6E6GT9+PP/5\nz38Mr9PS0ti0aRMLFy7Ey8uLhx9+uAJLJ4Soacp1idjc3FwiIiKYOnUq3bt3p3Xr1ixfvpwjR45w\n5MiREt936dIlVqxYIX03xXBwcMDT09OQmjVrxuuvv46vry87duyo6OIJIWqYcg0qZ8+eJSMjw6i2\n4e3tTYMGDUoclqzT6ZgxYwbjxo2TpWLugrW1NZaWlgDEx8fz4osvEhAQQLdu3XjllVcM2xqAunHW\nJ598Qv/+/WnXrh2DBw9m9+7dhvzo6GjGjx9Pp06dCAoK4rXXXjPs/Dhz5kxGjRpldO8TJ07QokUL\nLl68CMAvv/zCY489Rtu2bXnooYf46KOP0Ov1gLqAXYsWLVizZg1du3Zl4MCB5Obm3rHMOTk5zJ8/\nn86dOxMUFMTatWvL5N9RCHF3yjWoXLt2DYA6deoYHffy8jLkFfXhhx8CMHbs2LIt3P9sP7edCdsn\nMGH7BLaf226Sv/n0ZkP+zzE/m+RvOLHBkL/30l6T/PVH1hvyD8YdLPXyZ2VlsX79emJiYnjsscfI\nzMxk1KhR2NrasmnTJj766CPy8vL4z3/+Q25uLgDr1q3jvffeY9KkSWzfvp2HHnqIyZMnc/78eWJj\nY3n66adxcXHh888/Z/Xq1Zw9e5Znn30WnU7HkCFDOHTokNEX/vbt2+nQoQONGjVi9+7dTJ8+ndGj\nR/P999/z6quvEhERwerVq43K/f3337NhwwaWLl2KVqu9Y5lDQ0PZuXMny5cv57PPPuPgwYNcvny5\n1P89hRB3p1y3E87KysLCwsJkC1kbGxuT4coAp06d4r///S9btmyRzZxKsHr1atatWweoNY6cnBxa\ntGjB8uXLCQkJYfPmzWRlZbFo0SJDzWX58uV07tyZn376iUceeYSIiAjGjBnDkCFDAJg4cSJarZbM\nzEwiIyNxdnZm4cKFhv+3FStW8PDDD7N371569epFvXr12LFjB2PGjEGn07Fjxw5DZ/maNWt4+umn\nGTp0KAC+vr5kZGTw+uuvM2nSJMPnGDFihKEmeqcy9+7dm2+//ZYFCxbQvXt3AJYsWULv3r3L+F9b\nCHEn5RpU7Ozs0Ov1aLVarKwKbp2bm2sywTInJ4fXXnuNl19+mYYNG5ZnMauUESNGMHz4cHQ6HTt3\n7mT16tU88cQTPPLIIwCcOXOGlJQUAgMDjd6XlZVFTEwMqampJCYmmmxrkB8UVq1aRdu2bY3+EPDz\n88PNzY3o6Gh69+7NY489xnfffceYMWPYv38/N27cMAwQiIqK4uTJk2zatMnwfr1eT3Z2NnFxcYZt\niX18fAz5dyqzr68veXl5tGnTxpDn5uaGr6/vPf87ClGd6BU9mXmZZOZlYmNpg6uda7nd26ygoigK\nW7du5bfffiMzM5Oi+3ppNBo++uijO16nXr16ACQmJhqeAyQkJJg0iR0/fpyYmBiWLl3K0qVLATX4\n6PV6OnTowPfff0/9+vXNKf5dGdRiEINaDCoxf1jrYQxrPazE/JH+IxnpP7LE/HEB4xgXMO6+yliY\ni4uLIeg2adIECwsL3n77bWrXrs2jjz6KtbU1TZs2ZdWqVSbvdXJyMqk1FmVnZ1fscb1eb3jvkCFD\n+OCDD7h48SLfffcdffr0wdnZGVD7dsaNG8egQab/pnXq1CEhIQEAW1tbw/E7lTl/GHXRn8M7fRYh\nqhpFUdApOqwsjL+qY2/EcujqITJyM/B29qZXo15G+bsv7mbTKfUPuV6NejG87fByK7NZbUrLly9n\nzpw5REVFkZOTQ15enlHKb+e+k5YtW+Lo6MjBgwV9CbGxscTFxdGpUyejc/39/fnpp5/Ytm2bIfXt\n25c2bdqwbds2vLy87uJj1hxjxoyhY8eOhIaGkpiYSLNmzYiNjcXV1ZWGDRvSsGFD3N3dWbhwIdHR\n0Tg5OeHp6cnJkyeNrjNq1CjWr1+Pn58fJ0+eJC8vz5B34cIF0tPTDc1VjRo1MgT6X375xdCMBtC0\naVMuXrxouHfDhg2Jjo5mxYoVJX6GO5W5SZMm2NjYcPToUcN7bt26ZRgYIERll6fL40r6FU4nnCYq\nMcok/1zSOWb8PIMpO6bwwV+mi/UmZCTwf+f/jz2X9nAm8YxJvoN1oYV/8zJLt/B3YFZNJTIykjFj\nxjBjxoz7upmNjQ3Dhw9n8eLFuLm54e7uTmhoKEFBQbRv357c3FzS09NxcXHBzs7OpNmrVq1axR4X\nBSwsLHjrrbcYMmQICxYsICwsjA8++ICXX36ZqVOnYmtry7Jlyzhx4oRhH5xx48axatUqGjduTNu2\nbfnuu+84fvw48+bNw83NjQ0bNjBr1iwmTJhAeno6CxYsoGXLlnTt2tVw38cff5ywsDDs7OwIDg42\nHJ84cSITJkygefPm9O/fn4sXLzJv3jx69epl2Lu+qEGDBt22zI6Ojvz73/9m5cqVeHh44Ovry3vv\nvUd2dnbZ/uMKYabMvEwOXT1EenY6Go2GR5s/apQfeyOWRb8vAsDHxYe5nnON8q0srEjLTgPgVu4t\nk+s7WjsanmfkZZjk17KphYO1Aw7WDtSyqXXfn+dumBVUbt26xYMPPlgqN3z55ZfRarW8+uqraLVa\nw4x6gKNHjzJ69GgiIiLo3LlzqdyvJvLz82PChAmEh4czePBg/vvf/7Jo0SL+85//oNFoaN++PZ9+\n+inu7u4AjB49muzsbJYsWUJKSgrNmjVjzZo1hqDz8ccfs2TJEp588kns7e3p06cPr776qlFz08CB\nA3n77bd59NFHjfrLevbsyeLFi1m7di3vvfcetWvXZsiQIbzyyisllt/Ozu6OZZ4xYwZ2dnbMmTOH\nnJwchg0bJttdi3KTo83hhws/kJKVQo4uh+cDnzfJ//yEunyVs62zSVBxtnU2PL+Zc9Pk+k62TgXX\n0pkOYvJy9GJQi0HUsqmFp4OnSX5rr9aseKjk1oCypFGKNkwXY8yYMXTu3Jnnn3/+TqdWCrGxsYSE\nhLBz5068vb0rujhCiCpGURQiz0aSnJlMclYyr3V/DQtNQW+BTq9j8o7JKIqCRqNh1cOrjPo9iua/\n//D7WFpYGvLzdHm8s/cdnG2dcbN345n2zxjdX6/oSc1KxcnWCRvL4mv0ZaE0vjvNqqk8//zzTJs2\nDa1WS0BAQLGdtwEBAfdUACGEqAh7L+0l9kYsCRkJjOkwxqj2oNFo2H9lPzdybgCQkpWCh0PB+oSW\nFpa42LqQlp2GoiikZaeZ5Pdp3AdbS1tc7VzRK3osKQgq1pbWvNH7jRLLZqGxwN3BvTQ/brkxK6jk\nry2VPxonfxgoYIjEUVGmnU1CCFFRrqRf4cqNK1y7dY0evj3wcjQe3LP38l4upV0C4Pqt60ZBBcDT\n0dMQVJIzk42CBsCjzR/FQmOBm70bTjZOFPWv1v8qzY9TZZgVVCIiIsq6HEIIcdeytdlcvXkVNzs3\n3OzdjPK+PfctJ66fAMDH2cckqHg5ehmCSmJmIs3cmxnl92vSj54Ne+Ju746Piw9FBTcMNjkmzAwq\nsjKwEKKy+frM1/wU8xOgzh/r26SvUX7dWnUNQeXaLdNloIIaBNHQpSFejl40cm1kkt+hnixgey/M\nnlEfExNDeHg4Bw8e5ObNm7i5uREYGMikSZNo2rRpWZZRCFEDXUy7yL4r+7iSfoVWnq1MJiUXrplc\nvXnV5P1NazclJSuFurXq0sqzlUm+fx1/qGNyWNwns4LKuXPnePrpp7G3tyckJAR3d3cSExPZtWsX\nu3btYtOmTbRo0aKsyyqEqGb0ip6rN6+Src2maW3jP06TMpPYfVFdLdvOynRwkLezN5YWltStVbfY\nZUja1W1Hu7rtyqbgokRmBZWlS5fSpEkTIiIicHAoNFMzM5NnnnmGlStX8sEHprM+hRCiJBfTLrJ0\n31LydHnqBMCexhMAfV0K1nK7cuOKyfub1m5K+MBwo6G6ouKZFVQOHTrEkiVLjAIKqBtEjRs3jjlz\n5pRJ4YQQVZde0XM5/TIXUi5w/dZ1RviPMMqv41gHrV4LQNyNOHJ1uUZzMjwdPHnigSfwdvYutqPc\nQmMBGpPDooKZFVSKriBcmEajQafTlVqBhBDVg17RG2oiAI80f8Somcre2p66teqSrc2mkWsjsvKy\njIKKRqNhQNMB5V5ucX/MCirt27dn3bp1BAcHG60mm52dzfr162WbXyFqqLNJZzmdcJro5Gieaf8M\n9ZwKVh+3srCisWtjopOjAfg79W8C6hlPkp7VYxa2VraI6sOsoDJt2jSGDh1KSEgIffr0wcPDg6Sk\nJH799VcyMjL4/PPPy7qcQohK6Nd/fuX4teMAnE85bxRUANrXbY+bvRt+bn40dm1s8n4JKNWPWUHF\nz8+PTZs28f7777Nz507S09NxdnamU6dOTJ48mebNm5d1OYUQFeD6resciT9CVFIUAfUC6N2ot1F+\nc/fmBUEl+Tw9G/Y0yg9pElJeRRWVhNnzVFq0aMF7771XlmURQlQy55LPse3sNgCsLaxNgkprz9ak\nNkmluXtzkxnpomYqMahs376d4OBgXF1d2b59+x0vVNzOfkKIyk2n1xGTGsPJ6yfJ1mabjNAqPGnw\nfMp5dHqd0RDeek71brsTqqh5Sgwqr776Kl999RWurq68+uqrt72IRqORoCJEFXQj5wbL9i0D1I71\noa2GGvVzeDh40LdJX3xcfGjp0VLmhIg7KjGo7Ny5E09PT8NzIUTVpCgK8bfiOX7tuLoce6Gg4Wbv\nRgPnBsTdiEOr1xKdHE3bOm2N3i81EXE3SgwqDRo0MDz/66+/6NWrF25ubibnJSYmsn37dp599tmy\nKaEQ4r6sOriKUwmnAKjvVN9k6ZJuPt1IyEigjVcbmrvLoBtxfyzufArMmjWLK1dMl0kAiIqKYsWK\nitm2UghRQKvXFrufeeHZ6MeuHTPJ79ukL8PbDse/jr8M8RX3rcSayoQJE7hw4QKgVp8nT56MjY3p\ntpbJycn4+vqaHBdClI8r6Vf4MeZHTl4/SacGnRjpP9Iov12dduz6ZxdtvNrIAouizJUYVCZOnMiW\nLVsA2LJlC23btqV27dpG51hYWODs7Mzjjz9etqUUQpQoW5vNX3F/AWpNZHjb4Ub7qTdybcSyAcuM\n9lAXoqyU+FPWvn172rdvD4BOp2PSpEn4+Jgu6iaEKHtavZbTCaeJSoriqdZPGW3p3bR2U1zsXEjP\nTsfW0pbUrFSj/c01Gg1WGgkoonyY9ZO2cOFCdu/ezRdffMGMGTMAOHHiBCtWrGDChAl06dKlTAsp\nRE2mKArzds0jOTMZgC7eXYx2KtRoNIz0H4mbnRvezt5GAUeI8mZWR/2OHTt4/vnniYmJMRyzt7dH\nr9czduxY9uzZU2YFFKImURTFsBx8Po1GQwv3gk3wDl09ZPI+/zr++Lj4SEARFc6soLJmzRpGjBjB\n2rVrDceaNWvGp59+yr///W9ZvkWI+5Scmcz2c9uZ++tcw77rhQXWD8TDwYOHmj5EN59uFVBCIcxj\nVlC5fPkyffv2LTavb9++RjUYIcTdi0mN4bvo70jKTOJA7AEURTHKb+XZigV9FvD4A49T36l+BZVS\niDszK6i4u7tz+vTpYvPOnTuHi4tLqRZKiOpKq9dyPvm8yfF2ddoZ5oik56STlp1mlK/RaKRpS1QJ\nZnXUDxo0iFWrVuHg4EC/fv1wd3cnJSWFX3/9lfDwcIYPH17W5RSiSlMUhS9Pf8nBuINk5mWyMGQh\nbvYFK1TYWtkypOUQXO1caevVFmtL6wosrRD3zqygMnnyZP7++29CQ0OZP3++4biiKPTv358XX3yx\nzAooRHWg0Wi4dusaGbkZAPwZ+ycDmw00OqdP4z4VUTQhSpVZQcXa2pr33nuP6OhoDh8+THp6Ok5O\nTnTs2JGWLVuWdRmFqDJ0eh0nE07iYO1gso5WV++uRCVGUdu+NvbW9hVUQiHK1l3NiGrevHmxuzxm\nZGTg6OhYaoUSoiqKSozio6MfcTPnJi09WtK8q/HvSod6HZhqN5Xm7s2lf0RUW2YFldzcXD777DP+\n+usv8vLyDCNT9Ho9WVlZnDt3jmPHTBeqK45Op2PlypVERkaSkZFBcHAw8+bNw8PDo9jzd+zYwYcf\nfsilS5fw9PRk2LBhjB07FktL2ddBVC51a9U1LOh4LvkcyZnJRjPbbSxtaOHRoqS3C1EtmDX6a+nS\npSxZsoSrV69y7tw5Ll26xM2bNzl69CgnT57kueeeM/uG4eHhREZGEhYWxoYNG7h27RovvPBCsefu\n3r2b6dOnM2zYML799lumTZvGunXrWLNmjdn3E6I0KYrCxbSLbDy5kVxdrlGem70brTxb4WrnykNN\nH5LOdlEjmVVT+fHHHxkzZgwzZsxgzZo1REVF8e6773L9+nVGjhyJXq8362a5ublEREQwd+5cunfv\nDsDy5csJCQnhyJEjBAQEGJ2/adMm+vfvz8iR6qqrvr6+xMTEsHXrViZPnnw3n1OIUvHh4Q85Gn8U\ngMZujenibbxE0TPtn6GWTS2jBR2FqEnM+slPTk6mZ8+egNqvcvLkSQDq1KnDc889x44dO8y62dmz\nZ8nIyCAoKMhwzNvbmwYNGnDokOnSExMnTmTKlCnGBbaw4MaNG2bdT4jS1ti1seH575d/N8l3tnWW\ngCJqNLN++p2cnMjLywOgYcOGxMfHc+uW2nbcqFEj4uPjzbrZtWvXADUYFebl5WXIK8zf35+mTZsa\nXt+6dYuNGzcSHBxs1v2EuBcZuRns/HsnO86b/rHU1acrdlZ2dPXpypCWQyqgdEJUbmY1f3Xs2JEN\nGzYQFBREw4YNsbe355dffmHIkCEcP36cWrVqmXWzrKwsLCwssLY2bmu2sbEhJyfnju+dNGkSOTk5\nTJs2zaz7CXG3EjISCP0tFK1ei62VLX0a98HOys6Q72zrzNL+S6W/RIgSmFVTmTx5MocPH+a5557D\nysqK4cOHM2/ePIYNG8aKFSsYMGCAWTezs7NDr9ej1Rqvwpqbm4u9fcnj9lNSUhgzZgxnzpxh3bp1\nNGjQwKz7CXG3PB088XL0AiBHm8PBuIMm50hAEaJkZtVUHnjgAXbs2EF0dDQA06ZNo1atWhw5coSJ\nEyeaPfqrXr16ACQmJhqeAyQkJJg0ieWLjY1l7NixZGRksGHDBplsKe6boijEpMaw59IeejXshV9t\nP0OeRqMhuGEwf8b+SbBvMEENgm5zJSFEUWYFlbfeeoshQ4YY+jI0Gg3PP//8Xd+sZcuWODo6cvDg\nQQYPHgyoQSMuLo5OnTqZnJ+cnMzo0aOxtLRk48aNsvOkKBXbo7fzffT3hteFgwpA70a9ZckUIe6R\nWc1fW7ZsKZURVzY2NgwfPpzFixezZ88eTp8+zdSpUwkKCqJ9+/bk5uaSmJhIbq46/j80NJTU1FSW\nLVuGnZ0diYmJJCYmkpSUdN9lETVX+7rtDc8PXz1MZl6mUb6M3hLi3plVU2nXrh2HDh0yzC25Hy+/\n/DJarZZXX30VrVZrmFEPcPToUUaPHk1ERATt2rXj559/Rq/XM2zYMKNrWFpacubMmfsui6i+MvMy\nORB7gNOJp5ncabLRsii+Lr608GiBl6MXwb7BOFg7VGBJhaheNErR3YCKERYWxmeffYavry8PPPAA\nDg7Gv4QajcZo9eKKFhsbS0hICDt37sTb27uiiyPKmaIozNo5i9SsVABe7Pwirb1am5wj628JYaw0\nvjvNnlHv5eVFdnY2R48eNcmXX05RmWg0GgLqBbDz750A7LuyzySoyM+sEGXDrKDy66+/lnU5hLgr\n+Wtw7b28F08HT5O9SXo27Mm5pHMENwymc4POFVRKIWqeEoPK/v378ff3lyXtRaUUlRTFu3++C4CL\nnQv9/fpjaVGwcnXdWnV5vdfrFVU8IWqsEoe5PPvss8TExBgd+/LLL0lNTS3zQglRWHHdfi3cW+Bs\n6wxAenY6Z5POlnexhBDFKLGmUvQXWafT8eabb9KmTRvc3NxKeJcQpedmzk0OxB3g98u/M6HjBOo5\nFUyYtbSw5MHGD5KYkUhww2CjhR6FEBXnrnZ+NGOgmBClZuOpjRy+ehhQVwQe1tp4aPnDzR6uiGIJ\nIW5DZnmJSkGr15oc6+5TMC/q2LVj8keNEFXAXdVUhChNWr2Wo/FH+ePKH2TmZTI7eLZR/gOeD9C+\nbpWbH2YAABkHSURBVHv86/gTWD9QhgELUQXcdVCRX2xRWvJ0eXx6/FPydOpePVfSr+DjUrC+m4XG\ngomdJlZU8YQQ9+C2QeWll17CxsbG6NjkyZNNjoE6QVKIkqRnp2NtaW20JIq9tT0d6nbgYNxBNBoN\nMakxRkFFCFH1lBhUHn/8cZNjRfeQF+JOziad5eeYnzmdeJqhrYbSt0lfo/zejXpTt1Zduvl0w81e\nRhUKUdWVGFQWLlxYnuUQ1VRyZjKnEk4B6nIpIY1DjJpQ/Wr7mSw9L4SoumT0lygVV29e5ffLv5sc\n71i/o2GnREdrR7K12eVdNCFEOZLRX+K+aPVaFv2+iCvpV9BoNLT1aouLnYsh387KjrEdxuLr4ou7\ng3sFllQIUR6kpiLui5WFFfZW9oA6Ofavq3+ZnNOhXgcJKELUEBJUxB1l5mXyx+U/ePfPdzkQe8Ak\nv7N3Z6wtrelYvyO+Lr4VUEIhRGUhzV/ijn6//Dtfn/kaUOcpdfY2Xko+qEEQgfUDsbOyq4jiCSEq\nEampCIObOTc5k2i6TXPHeh0Nz88mnSUrL8so38bSRgKKEAKQmooAcrQ5hB8M50LKBawsrFjWfxm2\nVraGfHcHd3r49qBurboE1g/E3tq+AksrhKjMJKjUMIqioKBgoSmopNpa2ZKRm4GiKOTp8jiVcIqO\n9TsavW9Uu1HlXVQhRBUkQaWGiEmJ4UDcAU5cP8FTrZ+iQ70ORvkB9QKIvxWPn5ufUS1FCCHuhgSV\nGuJkwkl2X9wNwInrJ0yCSu9GvenVqJdhN0UhhLgX0lFfTWj1Ws4mneXrM1/zzdlvTPL96/gbnsek\nxpjsTeJk6yQBRQhx36SmUk1cvXmVFftXAGqAeKzFY0ZrbDV2bczAZgNp5dkKPzc/2cJACFEmpKZS\nhaRkpbDvyj4+PfYpOr3OKM/H2QcnWydAHRp8Of2yUb5Go2FIyyE0d2+OpYVluZVZCFGzSE2lClm2\nbxlJmUkA9PDtYbS6r0ajoZtPN3K0ObTxakM9p3oVVUwhRA0mQaUSSc5M5mzSWc6nnKebTzeauzc3\nym/h0YKky2pQiUqKMlky/okHnii3sgohRHEkqFQQRVFM+jV+/edXfvn7FwCcbZ1NgkobrzakZqXS\n0qOlUcd7laEooNOBVlvwWPi5Tmea9PqCx6JJUQoei6b8+5VEoyk+WVgUPBZNlpYFj8UlK6uCx8LP\npf9K1CASVMrRtVvX2H1xN3+n/o2Piw8j/Uca5Tdzb2YIKueTz5u8P6BeAAH1SnH3TUVRv8xzciA3\nt+DxTikvT035z3Nz1evkv9ZqC84p/FqrLb2yVyWFA421dcFj4ZR/zMZGfW5jU/A6/xwbG+Pjt0sW\n0l0qKoYElVKmKApp2WmkZKWYNE9l5Gbw6z+/AhS7WVXT2k1pW6ctzWo3K6ilFP7iz0/5AaBoKnq8\nuNdFg8ft/poXpSO/1pWTU373zA9MxSVbW9PnxR3Lf174df4xS0upgYliSVApDXo95OVx82YSb/7+\nNrdybuCgsWW5/6toCv2F75uVgUVsHHqdluv6v8mK/S/2eYrhi75WTg5TcnIg93fI2VkQDKrTF7+F\nRcFf7UWbiUpqViquKapwM1X+Ixg3Zd1OcU1mhZvTimtuK9wcVzQVbr4r2qxXEfLLkJlZNte3sLh9\nYCouQJmbCtfYJHBVOeUeVHQ6HStXriQyMpKMjAyCg4OZN28eHh4exZ5/8uRJ3n77baKioqhTpw6T\nJk1iyJAh916Aq1chPd24eaZws05xTTyF0p6caC5pk4jVp/FKZjvscgq+OGqhoDj/CZo8MoHkXQvx\n0Bes3msNPGWjwUPvRGOdE/bKn/f+OUqLlVXxf4nm/3IXfl7cL37hJpyiTTdFm3vyA0RNUrgfqWhT\nYOHH/CbEws2KRZsYiz7eLpX1HyJ6PWRnq6msaDTGNa7imgNv93NY9HlxzY2FXxf+ea1pP6elqNyD\nSnh4OJGRkYSFheHq6kpoaCgvvPACGzduNDk3JSWFcePG8eijj/L222+zb98+5syZg4eHBz169Lj7\nm2/ZAj//fNtTbmpyibfI5JplFm3zauOmGK+DtbvWCWItbwEQl52In65gFroGDb66Wv/f3p0HRXHs\ncQD/csqlXIIgHomYVYTlEFjkUtBSFBUNQbzwNmgog5YxXoA+oyYqcmi8JWrWGE00iEeIJFExmigR\nKA88wRsiooiKyLnb7w/ccQcWUB7Hy+7vU7VVu90zOz0/y/0x3T3TuKNRjM4SfZSh9l+pPhUd373d\nsv9YbdoAOjr8bomafyXK1yv6XLM7g/7zNC/ZD6OmZvW/RUuQJTJFY2SKxs8Uva+rTPZeKm2Z85Al\n15KS5j+ePHV1frJ5l5f8xA1FV+I1t2vMS9HEkf+Tq7oWTSoVFRUQi8WIjIyEp6cnACA2NhYDBgxA\nZmYmevfmD0Lv378fBgYGiIiIgLq6OqytrXH16lXs2LGjcUklu3rwuwISFKiXwoBpwahG0hDrZuOS\nViEA4GNmA5dKM159R6kel1RyNV6+SSqvf6RD23hCV1sPatoK+qLr+4FXlARkL/rhJ+9CPpHp6zfP\nMRQlrbqSWHn5m6sr+feKPsu/b82JHVLpm7b/W8jPWqyZeGqW6+oCfn6Ak1PD3/uOWjSpXL9+HSUl\nJRCJRFxZp06dYGVlhfT09FpJJT09Ha6urlCX+1EViURYtmyZwim5DQoOxqHDa5BcegVQ10CgXm/4\nGTjyLp8tSkxw6eUlQF0dDzu4AVb9ed1AouJb6FJVhE7GXdHV1BrQM6yuf90WvcaHh5B/Dw0NQE+v\n+tVcXo9V1tsFWLMbu+YsRPlt6puVKF9WVfXvHMeUXaFKJNXn0pC9ewFHxya/wmnRpJKfnw8A6NCh\nA6/c3Nycq6u5fa9evWptW1paiqKiIpiYmLxbA6yt0XbkaCCr+mNBF1egxjohnfM6oevttrAwsEBn\ny96AhSOvXghrCN/tqISQxlBXf3O13pJkEzVkCUY+2dT3kp+kIV9W896suiZ4vO1LfrKI/Pt3JRQ2\nS5dZiyaV0tJSqKurQ0tLi1eura2NcgWXmWVlZdDW1q61LVDdldYY5vrmUFdTh6meKfS1a3cNiKxE\nEFmJFOxJCFEJampvxilaOqH9L+SvVOq7aVgiqe6dMTVtlma0aFLR0dGBVCpFVVUVNDXfHLqiogK6\nurWXqNXR0amVPGSfFW3/Nmza22CD/wZ6qCIhRLnIj6W1ohYdAba0rH7I4ePHj3nlBQUFtbrEAMDC\nwkLhtnp6emjbtm2j2qChrkEJhRBCmkmLprSePXtCX18ff//9N0aMGAEAyM3NRV5eHlxdXWtt7+zs\njMTERN6gfFpaGnr37s0bvK9J8rp/UdE4DSGEEMVkv5mSxozRvNaiSUVbWxvjxo3DmjVrYGxsDFNT\nUyxbtgwikQiOjo6oqKjA8+fPYWhoCG1tbQQFBSEhIQFLly7FpEmT8Ndff+Ho0aPYvn17vceRXd2M\nHz++JU6LEEKUyuPHj9G1a9dG7avGaq4r28yqqqqwdu1aHDx4EFVVVdwd9SYmJkhLS8PEiRMhFovh\n5uYGALhw4QJWrFiBGzduoGPHjggPD8fQoUPrPUZZWRmysrJgZmYGDQ3q6iKEkLchkUjw+PFj2NnZ\nQaeRN+u2eFIhhBCivOhWbUIIIU2GkgohhJAmQ0mFEEJIk6GkQgghpMmoTFKRSCSIiYmBl5cXnJyc\nEB4ejidPnrR2s5rVkydPsGDBAnh5ecHFxQXTpk3DzZs3ufozZ85gxIgRsLe3x/Dhw3Hq1KlWbG3z\nu3DhAnr16oW0tDSuTFVisH//fvj5+cHe3h6BgYE4e/YsV6cKMXj16hWWL1/O/V+YPn06cnJyuHpl\nj8GSJUsQERHBK2vonAsLCzF79my4uLjA3d0d0dHRqHqbJ0czFREXF8c8PT3ZmTNnWFZWFhs1ahQb\nM2ZMazer2UgkEjZ69GgWHBzMLl68yLKzs1l4eDhzd3dnT58+ZdnZ2czOzo5t2rSJ5eTksLi4OGZr\na8tu3rzZ2k1vFiUlJWzgwIFMIBCwc+fOMcaYysQgMTGR2drasv3797O7d++yL7/8kjk6OrIHDx6o\nTAwWL17MBg8ezNLT01lOTg4LCwtj/fr1Y2VlZUodA6lUyuLj45lAIGCLFy/myt/mnMeOHcvGjRvH\nrl27xlJTU1mfPn1YbGxsg8dUiaRSXl7OnJyc2E8//cSVPXjwgAkEApaRkdGKLWs+V65cYQKBgOXk\n5HBl5eXlzMHBgR08eJBFRUWxkJAQ3j4hISEsMjKypZvaImTnK59UVCEGUqmU+fr6svj4eK5MIpGw\ngIAAdvjwYZWIAWOMiUQiJhaLuc/Z2dlMIBCwrKwspY3B/fv3WUhICHNzc2M+Pj68pNLQOWdmZjKB\nQMDu37/P1ScmJjInJydWXl5e73FVovuroXVclJGlpSW2bt2K999/nyuTPerm+fPnSE9P58UDANzc\n3JQyHqdOnUJqaioiIyN55aoQg9u3byMvLw/+/v5cmbq6Og4dOoThw4erRAwAwMTEBMnJySgsLERF\nRQUOHDgAQ0NDdO7cWWljkJmZCUtLSxw5cgSdOnXi1TV0zunp6bCyskLnzp25epFIhJKSEly7dq3e\n46pEUnnXdVyUgbGxMXx8fHjPSNu9ezfKysrg5eWF/Px8lYjH06dPERERgRUrVsDQ0JBXpwoxuHv3\nLgDgxYsXmDhxItzd3TF+/HhkZmYCUI0YAMDy5cuRn58PDw8PODo64scff8S2bdvQrl07pY3BiBEj\nsGbNGpiZmdWqa+icHz16BHNz81r1APDw4cN6j6sSSeVd13FRRsePH0dsbCymTJkCa2vrOteqUbZ4\nLF26FP3790ffvn1r1alCDF6+rF76euHChRg1ahQSEhLwwQcfYNKkSbh165ZKxAAA7t27h/bt22Pb\ntm3Yu3cvvLy8EB4ejvz8fJWJgbyGzrm0tBRtaqwlo6WlBTU1tQbj0roP3m8h77qOi7JJTExEVFQU\n/P398fnnnwMA2rRpg8oaS44qWzwOHjyIq1ev4vDhwwrrVSEGsj+kZs6cieHDhwMAevXqhYyMDOzd\nu1clYvDgwQNERUXh+++/h6Nj9UquMTEx8Pf3x65du1QiBjU1dM6K1rKqrKwEYwx6DSwhrRJJRX4d\nF9l7oO51XJTJ5s2bER8fj5CQEERGRnLjKpaWligoKOBtq2zxSExMxKNHj+Dl5QUAYK8fc/fxxx9j\n5MiRKhEDWZeFQCDgytTU1NCtWzfk5uaqRAyysrIgkUhgZ2fHlWlpacHGxgb37t1TiRjU1NA5W1hY\n1JpiLNu+obioRPeX/DouMvWt46Istm/fjvj4eISHhyMqKopLKED1WjXnz5/nbZ+WlgYXF5eWbmaz\nWbt2LX7++WckJSUhKSkJCQkJAIAVK1Zg9uzZKhEDW1tb6Onp4fLly1wZYwy3bt1C586dVSIGFhYW\nAIAbN25wZbIYvPfeeyoRg5oaOmdnZ2c8ePCAN36SlpYGfX199OzZs/4vb6LZa//3oqOjmYeHBzt1\n6hR3n0rNKXXK5Nq1a8zGxoYtWrSIFRQU8F4lJSXs+vXrzNbWlq1bt47l5OSw+Ph4JhQKeVOQlc3D\nhw95U4pVJQZxcXHM1dWVpaSksDt37rCVK1cyoVDIbt26pRIxqKqqYsHBwWzYsGHs/PnzLCcnh0VF\nRTFHR0eWm5urEjEICQnhTSlu6JylUikLDg5mo0ePZllZWdx9KuvXr2/wWCqTVCorK9lXX33FRCIR\n6927N5s9ezYrLCxs7WY1m5iYGCYQCBS+Nm7cyBhj7OTJk8zf35/Z2dmxgIAA9ueff7Zyq5tXzaTC\nmGrEQCqVsi1btrB+/foxOzs7NmrUKHb+/HmuXhViUFhYyCIiIpi3tzdzdnZmkyZNYlevXuXqlT0G\nNZMKYw2fc0FBAQsLC2MODg7Mw8ODxcTEMIlE0uCxaD0VQgghTUYlxlQIIYS0DEoqhBBCmgwlFUII\nIU2GkgohhJAmQ0mFEEJIk6GkQgghpMlQUiFKYeHChejRo0e9rwkTJgAAJkyYgMmTJ7dqe589e4b+\n/fvj3r17dW6TmJiIHj16tPjTcidNmoTk5OQWPSZRHirx7C+i/MLCwjBmzBju87Jly6ChocFbQ8XA\nwABA9ZOL5R9Z0xqWL1+OwYMHo2vXrq3aDkUWLVqEqVOnws3NDaampq3dHPIvQ0mFKIUuXbqgS5cu\n3GcDAwNoaGhwT6WV171795ZsWi2XLl1CSkoK/vjjj1ZtR1169uwJBwcHbN68udbCZoQ0hLq/iMqp\n2f3Vo0cP/PDDD5g3bx6cnJzQp08fbNiwAS9fvsSiRYvg7OwMT09PREdHQ/4BFEVFRYiMjIS7uzvs\n7e0xduxYZGRkNHj8hIQEeHh4wMTEhCuTSqXYtGkTfHx84ODggLCwMDx//rzWvvv27UNgYCAcHR1h\nb2+PDz/8ECkpKQCqu9SEQiHWrVvH26e4uBj29vbYs2cPAODo0aMICAiAvb093N3dMW/ePDx69Ii3\nz/Dhw3HgwAE8ffq04YASIoeSCiEAVq9eDWNjY2zatAm+vr74+uuvERQUBF1dXWzYsAEDBw5EQkIC\nfv31VwBAeXk5Jk+ejNTUVMydOxfr16+HoaEhJk+ejEuXLtV5nJKSEpw4cQKDBg3ilUdHR2Pjxo0I\nCgrChg0bYGRkhJiYGN42YrEYX3zxBQYNGoStW7di7dq10NTUxGeffYZHjx7ByMgI/fv3x5EjR3j7\nJScngzGGoUOHIiMjA/Pnz8egQYOQkJCAhQsX4ty5c5g3bx5vHx8fH0gkEvz+++//S1iJCqLuL0JQ\n/Yj4iIgIANXdP4mJiTA1NcWSJUsAAH369MGRI0dw4cIF+Pn54dChQ7hx4wb2798PoVAIAOjbty+C\ngoIQFxeHnTt3KjxOeno6KisrYW9vz5W9ePECu3fvxtSpUzFr1iwAgLe3NwoKCnD69Gluu9zcXEyf\nPh0zZ87kyqysrBAYGIjMzEwMGTIEH330EY4dO4aMjAw4OzsDAJKSkuDr6wsjIyNkZGRAR0cHoaGh\n3Mp/RkZGuHz5Mhhj3FiTnp4erK2tkZaWhuDg4CaJMVENlFQIAXg/8sbGxtDQ0OCVqampwdDQEC9e\nvAAAnD17Fh06dICNjQ2qqqq47Xx9fbF161ZUVFTUWq4VqE4MANCpUyeu7MKFC6isrMSAAQN42w4Z\nMoSXVBYvXgygOgndvn0b9+7dQ1paGgBwq/h5eXnBwsIChw8fhrOzM+7fv4/MzExs3boVAODq6oq4\nuDgMGzYMfn5+6NevH7y8vNCvX79abbWyskJeXt7bhI8QDiUVQgDo6+vXKqtv2dRnz54hPz8ftra2\nCuuLiooUrpBXXFwMALylamVjJ/JjLABgZmbG+3z//n0sWbIEZ8+ehZaWFrp168YtmCQb61FXV8fI\nkSOxb98+REREICkpCWZmZvD29gYAODk5Ydu2bdi1axd27tyJbdu2oX379pg5cyY35VpGV1eXay8h\nb4uSCiGN0LZtW1hbW2P16tUK642NjestLy4uRrt27XhlT5484c1ge/bsGfdeKpUiNDQUbdq0wYED\nB2BjYwNNTU3k5OTg0KFDvGMEBgZiy5YtOHv2LH755RcEBARAQ0ODq/f29oa3tzdKS0tx7tw5iMVi\nrFixAk5OTrwld1+8eFHneRBSFxqoJ6QRXF1d8c8//8Dc3BxCoZB7HT9+HLt374aWlpbC/Tp27AgA\nvBsanZycoKOjg2PHjvG2PXnyJPe+qKgId+7cQXBwMIRCITQ1q/8elE1Llp+V1rVrV7i6uiIhIQG3\nb99GYGAgVxcdHY2goCAwxqCrqwtfX18sWLCgVptkny0tLd85NkS10ZUKIY0QGBiI7777DlOmTMGM\nGTPQoUMHpKamYufOnZg1a1adN1e6uLhAR0cHGRkZEAgEAKq73sLCwhAfHw8dHR2IRCKkpqbykoqp\nqSmsrKwgFothbm4OAwMDnD59GmKxGADw6tWrWu1btGgRhEIh774cDw8PfPPNN1i4cCECAgJQWVmJ\nhIQEGBsbQyQScdsVFxcjOzsb06ZNa7KYEdVAVyqENIK+vj727NkDBwcHrFq1CqGhoTh9+jSioqLw\n6aef1rmfrq4u+vbtW+vGxxkzZmDx4sVITk7GJ598gps3b3JXEDKbNm2Cubk55s+fjzlz5uDixYvY\nvHkzunXrVuv+GB8fHwDgXaUAgKenJ2JjY5GdnY1Zs2Zh7ty50NPTg1gs5rrjAODMmTPQ0tLivoeQ\nt0XLCRPSwi5duoSxY8fixIkTCgfzm0JSUhKWLl2KM2fOoG3btu+8/5QpU9C9e3dumjUhb4uuVAhp\nYfb29hgwYAB27NjR5N/922+/ITY2FitXrsSoUaMalVCuXLmCq1evIjQ0tMnbR5QfJRVCWsF//vMf\npKSk1PuU4sbIy8vDt99+C0dHR8yZM6dR37Fq1SpERUXVmtJMyNug7i9CCCFNhq5UCCGENBlKKoQQ\nQpoMJRVCCCFNhpIKIYSQJkNJhRBCSJOhpEIIIaTJ/BeYOX56mtSSXQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90a52588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tc = 4      \n",
+    "tr = 5      \n",
+    "\n",
+    "beta = 1 / tc     \n",
+    "gamma = 1 / tr  \n",
+    "system = make_system(beta, gamma)\n",
+    "final = run_simulation(system, update1)\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": 19,
+   "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": 20,
+   "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": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def fraction_sick_at_peak(system):\n",
+    "    frame = system.results\n",
+    "    return frame.I.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.043536202687592354"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraction_sick_at_peak(system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def time_of_peak(system):\n",
+    "    frame = system.results\n",
+    "    return frame.I.idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "time_of_peak(system)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sick_at_end(system):\n",
+    "    frame = system.results\n",
+    "    return frame.I[system.t_end]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.00067419431560344738"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sick_at_end(system)"
+   ]
+  },
+  {
+   "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": 28,
+   "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": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3333333333333333, 0.25)"
+      ]
+     },
+     "execution_count": 29,
+     "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.46832081102878098"
+      ]
+     },
+     "execution_count": 30,
+     "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": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.30650802853979753"
+      ]
+     },
+     "execution_count": 31,
+     "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Pvr6+//jVkrhZuLGg5wJmdJmh1qaSWZhJ+OlwsooavjpQR1uLgd3aoPXA+JXj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\nKoQQQroMBRVCCCFd5v8BEpAdEsdq/+EAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90caab70>"
+      ]
+     },
+     "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": 33,
+   "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": 34,
+   "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": 35,
+   "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig03.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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BWz2npQ0aNIjNmzdz8OBB7O3tSUhIYOrUqebtzs7OrFy5kiNHjrBt2zZze9OqVauYP38+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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90d69860>"
+      ]
+     },
+     "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": 37,
+   "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": 38,
+   "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": 38,
+     "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": 39,
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Atm7divj4eMTExKB9+/bc1UdsbCzy8vJw69YtjBo1qs7x+/v748yZM9xPdHQ0VqxYge3b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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90deca20>"
+      ]
+     },
+     "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": 41,
+   "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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.3333333333333333, 0.25)"
+      ]
+     },
+     "execution_count": 42,
+     "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": 43,
+   "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": 44,
+   "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": 45,
+   "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": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig05.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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bt2/Hx8enssOpshKSb/P7wQSj0RDMzdREBHkQ6u+Omdx8KkS1UlafnTJMsfjL\nfD0c6P90IGEB7qj/vMSm1en53/HrrN5+1tAbTggh7iWJRzwSC3M1bUNq0aezPzVd7naJv5GRw887\nzrH76FXytUWnbRBCPLkk8YgyUdPFhj6d/GkbUgvzP+f7URSFI2dTWbX1jNGMqUKIJ5skHlFm1GoV\nYQHu9H86AF8PB0P5rWwN//ffi1y+fusBWwshnhSSeESZc7K3oke7enRpURtry4Ie+3pFYcsfl6Tm\nI4SQxCPKh0qlIrCOKy939TdMMqfTK2zec4lrN7IqOTohRGWSxCPKlb2tJT3bN8DepmBg2Hydnl92\nX5Ieb0I8wSTxiHLnaGdJVPv62FoXJB9Nvo6N/73AjYycSo5MCFEZShwy54MPPijVjkq7vniyuDhY\nE/VUPdbtvECuRkueRseGXRfo1aEBLo7WlR2eEKIClZh4fv/9d6PHaWlpaLVa3N3dqVmzJhkZGVy7\ndg0rKysaNGhQ7oGKqs/NyYYeT9VjQ8wF8vJ15ORp2bDrAi92aICTvQxxJMSTosTEExMTY/h78+bN\nfPzxx8ydO9doeurTp0/z1ltv0bNnz/KNUlQb7i62dG9Xjw27LpCv1ZOVk2+o+djbWlZ2eEKICmBS\nG8+cOXMYN26cUdKBgmkOxowZw1dffVUuwYnqydPNjhci6xluNL2VrWF9zAWyc2R6BSGeBCYlnvT0\n9CITrhWytrYmOzu7TIMS1Z93TXuebVMX9Z8DiWZk5bFh1wXDdAtCiOrLpMTTtGlTvvzyS7KyjO+/\nSE9PZ+HChUUmcRPCFHU8HXmmVV3DAKPpt3LZuKug84EQovoyaSK4iRMnMnDgQDp06EDz5s1xdXUl\nLS2N2NhY7OzsWLBgQXnHKaqpet5OdGlRm99ir6AoCqkZOfyy+xI92tXD0sKsssMTQpQDk2o8jRo1\n4pdffqFXr16kpqYSGxtLeno6gwYNYuPGjfj6+pZ3nKIa86/tQsfwu3N7JKVls2nPJfK1+kqMSghR\nXkye+trLy4tJkyaVZyziCRbk54ZOpxBzOBGAq6lZbPnfJZ5v44eZmdznLER1YvIZrSgKW7ZsYcqU\nKQwfPpz4+Hj+7//+j4sXL5ZnfOIJ0qRBDdqE1DI8vpJ0m1/3xaPTP3GT5ApRrZmUeLKyshgwYABj\nx45l9+7dxMTEkJWVxfr16+nbty+nT58u7zjFE6JZgDstgjwNjy9ezWRb7BX0knyEqDZMSjyzZs3i\nypUrrF2t9V0eAAAgAElEQVS7lt9++w1FKfgQmDt3LnXr1mXu3LnlGqR4skQEeRAW4G54fC7hJr8f\nTDC874QQVZtJiee3335j3LhxBAUFofqz6yuAg4MDw4cP5/Dhw+UWoHjyqFQq2jTxokn9GoayU5fT\n2X30WiVGJYQoKyYlnjt37uDm5lbsMisrK/Ly8so0KCFUKhVPhXnTqK6roezouVTik2QWUyGqOpMS\nT+PGjfnpp5+KXbZlyxaCgoLKNCghoCD5dAz3xa+Wk6Fs58FENPm6SoxKCPGoTEo8o0ePJiYmht69\ne/PFF1+gUqnYunUro0ePZsOGDYwcObK84xRPKLVaRcdwH8MU2rfvaPjf8euVHJUQ4lGYlHhatmzJ\nkiVLUKlULFq0CEVRWLx4MZcvX2bRokVERkaWd5ziCWZrbcFTYd6Gx8cv3OBaqkyfLURVZfINpK1a\ntWL16tVkZ2eTmZmJg4MDDg4O5RmbEAYNfZ05e+Uml68XtPHsOJDAy08HGEa4FkJUHSadtd26dTPc\nq2NnZ0etWrUMSefYsWO0adPG5APqdDrmzJlDZGQkYWFhvP3229y4ccOkbYcNG8agQYOMynJycpgy\nZQotW7akefPmTJ48WUbLroZUKhUdmvkYxm/LyMoj9kRSJUclhPgrSqzxbN68GZ2uoBE3Pj6eHTt2\ncO7cuSLr/fHHH+Tm5pp8wAULFrBu3TpmzpyJs7Mz06ZNY9SoUaxateqB2/3444/s3LmTFi1aGJVP\nnTqVEydOsHjxYrRaLZMmTWLq1KnMmTPH5JhE1WBva0mbJl7sPFQwrM6Rs6k08HHG3dW2kiMTQpRG\niYnnyJEjLF++HCj4tjl//vwSd/Lqq6+adDCNRsPy5cuZPHkybdu2BeDTTz+lc+fOHDp0qMhEc4Xi\n4+P57LPPCAsLMypPSkril19+YdmyZYSGhgIwY8YMoqOjmTBhAh4eHibFJaqOxvXcOJeQwdXULPSK\nwo6DCfTp7I+ZWvXwjYUQj4USE8+7777L4MGDURSFLl26sHDhQho1amS0jpmZGfb29tjb25t0sNOn\nT5OdnW1Ua/Hx8cHb25sDBw4Um3h0Oh0TJ05k6NChXL58mStXrhiWHTp0CLVabbRds2bNMDMz4+DB\ngzz33HMmxSWqjsIu1j/+dgatTs+NjBwOn0mheSP5kiFEVVFiG4+lpSXe3t74+PiwdetW2rdvj0ql\nwtvbG29vb2xtbUlOTjY56UBBDQUoUhNxd3c3LLvf4sWLARgyZEiRZcnJybi6umJhYWEoMzc3x9XV\nlevXpcttdeXsYEXLxnfHc9t/Mon0W6Zf7hVCVC6TOhfY29sTHR3Na6+9Zig7duwY/fr1Y8iQIUVm\nJi1JTk4OarXaKFFAQZIrbvSDuLg4li5dysyZM1Gri4aak5ODlZVVkfKS9ieqj6YNa+LxZ9uOTq+w\n40CCDCQqRBVhUuL55JNPSEpKYsqUKYaydu3asWzZMi5dusRnn31m0sGsra3R6/VotcZTG2s0Gmxs\nbIzK8vLymDBhAmPGjKFOnTol7k+j0RQp12g02NpKg3N1plar6NTcF/WfbTtJadkcP29a70ghROUy\nKfHs2rWLCRMmGN0oqlaradWqFePGjeO3334z6WBeXl4ApKamGpWnpKQUufx29OhRLly4wOzZswkL\nCyMsLIz169dz4MABwsLCuHbtGp6enqSnpxt63wFotVrS09Nxd3dHVG9uTjZGbTt7466TmSU1XSEe\ndyYlnpycnCI1kkIODg7cumXawI2BgYHY2dkRGxtrKEtMTOTq1atEREQYrRsSEsLWrVtZv3694adL\nly4EBwezfv163N3dCQ8PR6vVGo2OffDgQfR6PeHh4SbFJKq28AB33BytAcjX6fn9YKJMnyDEY86k\nxNO0aVO+//77IpfI9Ho9P/zwA8HBwSYdzNLSkldeeYVZs2axa9cuTpw4wbhx42jRogWhoaFoNBpS\nU1PRaDRYW1tTp04dox97e3tDubm5OR4eHjz77LP8/e9/5+DBgxw4cIApU6YQFRUlXamfEGZmajpF\n1DZM15GYcptTl9MrOSohxIOYNGTOqFGjiI6Oplu3bnTo0AFXV1du3rzJrl27uH79OsuWLTP5gGPG\njEGr1TJ+/Hi0Wi3t2rVj6tSpABw+fJjo6GiWL19Oy5YtTdrfjBkzmDFjBm+++Sbm5uZ069aNSZMm\nmRyPqPo8XG0JbViTw2dTANhz9Bq1PR2xt7F4yJZCiMqgUky8LnH8+HG++OILDh06RGZmJvb29oSH\nhzNy5EhCQkLKO84ylZiYSOfOndm+fTs+Pj6VHY4oA/laPf/+7QwZf7bx+NVy4rk2dY0mLhRCPJqy\n+uw0eZDQJk2a8Pnnn//lAwlRnizM1XRs7su6necBuHQtk/OJGTT0dankyIQQ9zM58UDBvTt//PEH\nqampDB06lEuXLhEYGIirq+vDNxainHnXtCe4nhtxF9MA2HX4Kj7uDthYleptLoQoZyadkfn5+Uyc\nOJHNmzdjbm6OTqejV69efP3111y4cIEffvgBX1/f8o5ViIdqE1KLy9dvkZWTT06elt1HrtK1ZfH3\ngQkhKodJvdrmzZvHzp07WbBgAfv37zd0V502bRo2NjYm30AqRHmztDCjQ/jdL0Fn7pnDRwjxeDAp\n8WzcuJFx48bRtWtXLC0tDeW1a9fm7bffZt++feUWoBClVdfLkYDad9t2dh5MQJOve8AWQoiKZFLi\nycjIwM/Pr9hlLi4uJo/VJkRFiQz1NrTtZOXk88exa5UckRCikEmJp0GDBmzevLnYZf/973+pX79+\nmQYlxKOysTLnqTBvw+O4i2lcTZUvSEI8DkzqXDBs2DBGjx7N7du36dixIyqViiNHjrBp0ya+//57\nPv744/KOU4hSa+DjzNlaGVy6lgnAjgMJvNw1AAtzk75vCSHKiUlnYLdu3fj44485fPgw77//Poqi\nMH36dNauXcukSZN44YUXyjtOIUpNpVLRvpkPVhZmAGRm5RF7svh5n4QQFafEGs/SpUt5/vnnDaM8\n9+zZk6ioKM6fP09GRgYODg40aNAAc3O5R0I8vuxtLGjbtBY7DiQAcPRsKsH13HCyLzqPkxCiYpRY\n45k3bx4JCQUna3BwMMePH0elUtGwYUMiIiIIDAyUpCOqhEZ1XalVww4AvaJw4FRyJUckxJOtxMxh\nb2/Pd999x9WrV9FqtezatYvLly+XuKPu3buXR3xCPDKVSkWrYC/W/jmczun4mzQLdMfFwbqSIxPi\nyVRi4hk6dCizZs1i69atqFQqFixYUOJOVCqVJB7xWKtV0x5fDwcSkm+jKAqxJ5Lp1kpGNBCiMpSY\neF599VV69+5NZmYmnTt3ZuHChTRq1KgiYxOiTLVs7ElC8m0Azidm0DzTHTen4ic4FEKUnwc20tjb\n22Nvb8/06dMJDw/HxUVG+hVVl6ebHX5ejly6fuvPWk8Sz7Yp/sZoIUT5Mal3QJ8+fbhz5w4xMTHk\n5OSg1+uLrPPcc8+VeXBClLUWjb249OfYbReuZpJy8w7uLraVHJUQTxaTEs8ff/zB22+/TXZ2drHz\n2atUKkk8okqo6WJDfR9nLiRmABB7IokXIutVclRCPFlMSjyffPIJtWvXZsKECXh6eqJWy53foupq\nEeTBxauZKIrC5eu3SErLxtPNrrLDEuKJYVLiOX/+PIsWLaJVq1blHY8Q5c7NyQZ/X2fOXLkJwN64\nJHq2l/EGhagoJlVdvLy8uHPnTnnHIkSFiQjyRK1SAZCYclsGEBWiApmUeIYOHcqiRYtITpY7vkX1\n4OxgRWDdu70098VdL7b9UghR9ky61LZjxw6SkpLo2LEjnp6eWFsb3/GtUqnYtGlTuQQoRHlp3siT\n0/E30esVrt3IJiH5NrU9HSs7LCGqPZMSj4ODAx06dCjnUISoWI52lgT5uRF34QYA+04k4evhgOrP\nS3BCiPJhcq82Iaqj5o08OHUpDZ1eITn9Dpev38KvllNlhyVEtVZi4klLS8PZ2RkzMzPS0tIeuiM3\nN7cyDUyIimBvY0GTBjU4cjYVKKj11PVylFqPEOWoxMQTGRnJv//9b0JCQmjbtu1DT8RTp06ZdECd\nTsfcuXNZt24d2dnZtGvXjqlTp1KjRo1i1//555/55ptvSExMxNfXlyFDhvDSSy8ZlsfExPDmm28W\n2S4mJgZPT0+TYhJPtmYB7py4kEa+Ts+NjBwuJGbSwNe5ssMSotoqMfH885//xNfXF4Dp06eX2QEX\nLFjAunXrmDlzJs7OzkybNo1Ro0axatWqIuv++uuvfPDBB/zzn/8kIiKCvXv3MmXKFJydnencuTMA\nZ86cISgoiK+++spoW6mBCVPZWlsQ0rAGB0+nABB7Mol63k6o1VLrEaI8lJh4+vTpU+zfj0Kj0bB8\n+XImT55M27ZtAfj000/p3Lkzhw4dolmzZkbr37x5k7fffptevXoB4Ovryw8//MD//vc/Q+I5d+4c\n/v7+1KxZs0xiFE+mMH93jl9IQ5OvI/1WLucSbhJQx7WywxKiWqrQsW9Onz5NdnY2LVq0MJT5+Pjg\n7e3NgQMHiqz/8ssvGy6jabVatmzZwoULFwxJCwoST/36cte5eDTWVuaENrz75SX2ZDI6vdzXI0R5\nqNDEk5SUBICHh4dRubu7u2FZcY4fP05ISAhjxoyhR48ehq7dOp2OixcvEhcXR48ePYiMjGTEiBFc\nvHix3J6DqL6a+tfEytIMgMysPM7Ep1dyREJUTxWaeHJyclCr1VhYWBiVW1pakpeXV+J2Pj4+rFmz\nhg8//JAtW7Ywd+5cAK5cuUJeXh4ajYYZM2Ywd+5cNBoNAwYMMKknnhD3srIwI8zf3fB4/8lkdLqi\nU4AIIR5NhSYea2tr9Ho9Wq3WqFyj0WBjU/JMkC4uLjRq1IiXXnqJ4cOHs2zZMnQ6HX5+fuzbt4/P\nP/+ckJAQmjdvzsKFC9Hr9WzYsKG8n46ohpo2rIGNVUHT5+07Gk5eklqPEGWtQhOPl5cXAKmpqUbl\nKSkpRS6/AcTGxhbpph0QEEBubi6ZmZkAODs7G03TYGNjg6+vL9evXy/r8MUTwMLcjPDAu7WeA6eS\n0UqtR4gyZdLIBQDr169n586dxc5AqlKpinRnLk5gYCB2dnbExsYSFRUFQGJiIlevXiUiIqLI+l9/\n/TVqtZrFixcbyo4dO4abmxsuLi5s27aN8ePHs337dlxdC3ogZWVlcfnyZfr27WvqUxPCSHD9Ghw+\nk0p2bj7ZufnEXbhB6D2X4IQQj8akGs9nn33Ge++9x9GjR8nMzCQ7O9voJyvLtCHlLS0teeWVV5g1\naxa7du3ixIkTjBs3jhYtWhAaGopGoyE1NRWNRgPA4MGDiYmJ4ZtvviE+Pp7Vq1ezZMkSRo0ahUql\nIiIiAnt7e8aPH8/p06c5ceIEo0ePxsXFxZDYhCgtczM1zRvdrYEfPJ1CvlZXiREJUb2YVONZs2YN\n0dHRTJo06ZEPOGbMGLRaLePHj0er1RpGLgA4fPgw0dHRLF++nJYtWxIZGcn8+fNZuHAh8+bNw8vL\ni8mTJxvuK3JycmLZsmV88sknREdHo9Vqadu2Ld999x1WVlaPHKt4cgX5uXLoTAq372jIydNy9NwN\no2QkhPjrVIoJk5A0bdqUr7/+2uj+m6osMTGRzp07s337dnx8fCo7HPGYOnkpjR0HEgCwsjQj+rkg\nrCzMKjkqISpPWX12mnSpLTQ0lCNHjvzlgwhRFQXUccXJvqDmnKfRcfRs6kO2EEKYwqRLbW+99Rbj\nxo1Dr9cTFhZWbNfnkJCQMg9OiMpkplbRIsiD32KvAHDkXCohDWpgbWVynxwhRDFMOoMGDRoEwNy5\nc4uMUq0oCiqVyuTRqYWoShr6unDwdArpt3LR5Os4fDaF1k1qVXZYQlRpJiWepUuXlnccQjyW1GoV\nLYI8+c/eywAcO3eDpg1rYmtt8eANhRAlMinxtG7durzjEOKxVd/HiRrONtzIyCFfp+fg6RTahXpX\ndlhCVFkmj1xw+fJl3nnnHdq1a0dYWBidOnVi/PjxMiCnqPZUKhUtG9+dVPDExTSyc/IrMSIhqjaT\najznzp3j5ZdfxsLCgo4dO1KjRg1SU1PZuXMn27Zt46effqJhw4blHasQlaaulyPuLrak3LyDVqfn\n0Bmp9QjxV5mUeGbPnk2dOnVYvnw59vb2hvKsrCwGDx7MZ599xueff15uQQpR2VQqFRFBHmzacwko\nqPWEB7pLW48Qf4FJl9r279/PiBEjjJIOgL29PcOGDSt2EjchqpvCWg9gqPUIIUrPpMRjZWVlNAK0\n0Q7UavLz5Xq3qP4Kaz2F4i6kcSdX3vtClJbJIxcsWbLEMHhnoby8PJYsWUKzZs3KJTghHjd1vRyp\n6VxwA7VWp+fwGRnNQIjSMqmNZ9y4cfTt25cuXbrQuXNnatSowY0bN9i+fTu3bt1ixYoV5R2nEI8F\nlUpFi8aehrae4xduEBYg9/UIURomJZ6GDRuyatUqFixYwObNm7l16xaOjo5ERETw1ltvERgYWN5x\nCvHYKKz1pGbkFNR6zqbSNkRGMxDCVCYPOhUYGMiiRYvKMxYhqgSVSkXzRh5s+d9lAOLO3yDMX2o9\nQpiqxMSzefNm2rZti5OTE5s3b37ojp577rkyDUyIx1k9b+PRDI6cTaWN1HqEMEmJiWfcuHH89NNP\nhISEMG7cuAfuRKVSSeIRTxSVSkXEPbWegrYed2xk5GohHqrEs2Tr1q14enoa/hZCGKvn7YSbkw1p\nmTnka/UckZGrhTBJid2pa9eujaWlJQBHjx7F0dGR2rVrF/mxtbXl999/r7CAhXhc3H9fz7HzN8jN\n01ZiREJUDSbdxzNhwgSuXLlS7LJTp04xe/bsMg1KiKqivrcTbo7WAORrC3q4CSEerMRLbcOHD+fS\npYJ7FRRFYfTo0YYa0L1SUlLw9fUtvwiFeIypVCqaB3nw69544M+2Hv+aMkupEA9Q4tnxxhtv8PPP\nPwMQHx+Pv78/rq6uRuuo1WocHR158cUXyzdKIR5jDXyc2e+YbJil9Mi5VFoFe1V2WEI8tkpMPOHh\n4YSHhwOg1Wp5++23pWYjRDEK23oKaz3Hzt8gtKHUeoQoiUltPJ988gnx8fHMmTPHUHbs2DGGDh3K\n/v37yy04IaqK+t7OuP7Z1qPJ13H0nLT1CFESkxLPf/7zH9544w1OnjxpKLO2tiY3N5fXXnuNPXv2\nlFuAQlQFanXBaAaFjp6/Qa5GergJURyTEs8XX3zByy+/zDfffGMo8/f3Z8WKFfTu3Zu5c+eWW4BC\nVBUNfJxxcbhb6zl27kYlRyTE48mkxBMfH0+3bt2KXdatWzfOnz9fpkEJURWp1cb39Rw9lyq1HiGK\nYVLicXV15dSpU8UuO3fuHI6OjiYfUKfTMWfOHCIjIwkLC+Ptt9/mxo2Svxn+/PPPPPvsszRp0oTn\nnnuONWvWGC3PyclhypQptGzZkubNmzN58mSys7NNjkeIstTAxxlnBysA8vJ1HDsvtR4h7mdS4une\nvTsLFixg9erV3Lx5E4CMjAzWrVvH/PnzeeGFF0w+4IIFC1i3bh0zZ85kxYoVJCUlMWrUqGLX/fXX\nX/nggw9444032Lx5M6+99hpTpkxh+/bthnWmTp3KwYMHWbx4MV9++SWxsbFMnTrV5HiEKEtqdcEY\nboWOnkslL19XiREJ8RhSTJCXl6eMHDlSCQgIUAIDA5WgoCAlMDBQCQgIUEaMGKHk5eWZshslLy9P\nCQsLU9asWWMoS0hIUPz9/ZWDBw8WWX/VqlXK4sWLjcqioqKU6dOnK4qiKNevX1cCAwOVvXv3Gpbv\n27dPCQgIUJKSkkqMo/CYCQkJJsUtRGnodHrl+80nlQU/HVYW/HRYiT1xvbJDEqJMlNVnp0k3Glha\nWrJo0SJOnTrFwYMHycjIwMHBgfDwcIKDg01OcqdPnyY7O5sWLVoYynx8fPD29ubAgQNFptB++eWX\nDX9rtVp+++03Lly4wOjRowE4dOgQarXaaLtmzZphZmbGwYMHZcRsUSnU6oLRDLbFFgwzdeRcKk0b\n1sTSwqySIxPi8VCqO9waNWpEo0aNipTn5ORgY2Pz0O2TkpIA8PDwMCp3d3c3LCvO8ePH6devHzqd\njt69e9OhQwcAkpOTcXV1xcLi7gRc5ubmuLq6cv36dVOekhDlwt/XhQMnk8nIyiNPU9DWc293ayGe\nZCYlnvz8fH744Qf2799Pfn4+iqIAoNfrycnJ4dSpUxw+fPih+8nJyUGtVhslCiioUeXl5ZW4nY+P\nD2vWrOHkyZP861//okaNGowdO5acnBysrKyKrP+w/QlR3grv69m2v6DWc/hsCiENakitRwhMTDxz\n5sxh2bJl1K9fn5s3b2JtbY2zszPnzp1Dp9MxcuRIkw5mbW2NXq9Hq9Vibn730BqN5oE1JhcXF1xc\nXGjUqBFpaWksWrSIt99+G2trazQaTZH1NRoNtra2JsUkRHnxr+3C/lPJZEqtRwgjJo9cMHjwYDZt\n2sSgQYMICQlh7dq1/Prrr3h5eaFWm7QbvLwKBk5MTTUeTiQlJaXI5TeA2NjYIt24AwICyM3NJTMz\nE09PT9LT09Hp7vYa0mq1pKen4+7ublJMQpQXtVpF88C77+sjZ1PRSA83IUxLPDdu3DC0q/j7+3Ps\n2DEAatWqxbBhw9i0aZNJBwsMDMTOzo7Y2FhDWWJiIlevXiUiIqLI+l9//XWRURGOHTuGm5sbLi4u\nhIeHo9VqjS7zHTx4EL1ebxjgVIjK5F/HBUe7gulEcjVajl+Q+3qEMCnx2Nvbk5+fD0DdunW5fv26\n4SZNPz8/rl27ZtLBLC0teeWVV5g1axa7du3ixIkTjBs3jhYtWhAaGopGoyE1NdVw+Wzw4MHExMTw\nzTffEB8fz+rVq1myZAmjRo1CpVLh4eHBs88+y9///ncOHjzIgQMHmDJlClFRUcXWoISoaGb3jeF2\n+Ewq+Vqp9Ygnm0mJJzw8nJUrV6LRaKhTpw7W1tbs2LEDgLi4OOzs7Ew+4JgxY+jevTvjx48nOjqa\nWrVqMW/ePAAOHz5MZGSkoQYTGRnJ/Pnz2bBhA927d2fJkiVMnjyZ/v37G/Y3Y8YMmjVrxptvvsnf\n/vY3WrVqxQcffGByPEKUt4A6rsa1nvNplRyREJVLpRR2UXuAEydOMHDgQJo0acLy5cuZOXMmK1eu\npFGjRsTFxdG3b98qNVpAYmIinTt3Zvv27fj4+FR2OOIJcOJiGr8fTADAxsqc6OcaYWEuPdxE1VJW\nn50m9Wpr3LgxW7Zs4cyZMwBMmDABOzs7Dh8+zNChQxkxYsRfDkCIJ0FgHRcOnk7mVraGnDwtxy+k\n0SxAOsCIJ5NJiefDDz8kKiqK9u3bAwUzLr711lvlGpgQ1YmZmZrwQA9Drefg6WQa1XXFRmYpFU8g\nk9p4/v3vf5ORkVHesQhRrQXe08MtT6Njb5yMriGeTCYlnpCQEJNGJhBClMzMTE27UG/D45OX0klO\nv1OJEQlROUyq5zdp0oQvv/ySX3/9lUaNGhUZFUClUvGPf/yjXAIUojrxq+WEn5cjl67fQlEUdh1O\npHenhqhUqsoOTYgKY1Li2bRpE25ubty6dYt9+/YVWS6JRwjTRYZ6cyX5Njq9QnL6HU5eSqdxPbfK\nDkuIClNi4tmyZQtt2rTBycmJmJiYioxJiGrNyd6K8EAPYk8WjMj+v+PXqe/thLV0NBBPiBLbeCZN\nmsT58+cB6NatG6dPn66woISo7poFuhvdVCodDcSTpMSvWJaWloYx2OLj4zl+/Dh37pTcEHr/JG5C\niJKZ/9nRYNOeSwCcuJROkJ8b7q4yqrqo/kpMPL179+abb75h1apVqFSqEkcmUBQFlUpVZBRpIcSD\n1fVypI6nI/FJf3Y0OHKVlzo2kI4GotorMfGMHz+enj17cvPmTaKjo5k6dSoNGjSoyNiEqNZUKhXt\nQr1J3FrQ0SApLZvTl2/SyM+1skMTolw9sDWzYcOGAAwfPpzOnTvLiM9ClDFnBytC/d05eDoZgD+O\nX8PP2xFrS+loIKovk24gHTNmjCQdIcpJ80bu2NsUTAefk6cl9kRSJUckRPkybepQIUS5sTA3I/Ke\nEQ2OX0gj9WZOJUYkRPmSxCPEY6C+txO+Hg4AhhENTJixRIgqSRKPEI8BlUrFU2HeqNUFPdqup2Vz\n5srNSo5KiPIhiUeIx4SLgzWhDWsaHv9x7Dp5+TJNtqh+Suw6U5rpo2WsNiHKRkSQB2ev3CQrJ587\nufnEnkgyGtFaiOqgxMTz+++/m7wTSTxClA0LczPaNq3Fr3vjATh+/gZBfq64OdlUcmRClJ0SE48M\nDCpE5Wjg48wJ93QSU26jVxRiDl3lxQ71ZUQDUW08chuPTqcrdqoEIcRfY+ho8GeiuXYji3MJMgOw\nqD5Muj06KSmJf/7zn+zfv5/8/HxDN0+9Xo9WqwWQsdqEKEOujtY0bViTw2dTANhz9Bp1vRyxtDCr\n5MiEeHQm1Xg+/PBD9u7dywsvvICfnx9BQUH079+funXrolKpWLBgQXnHKcQTJyLIAzvrghENsnPz\n2X8yuZIjEqJsmJR49u3bx5gxY/jHP/5Br169sLW15b333mPdunWEh4ezc+fOcg5TiCePpUVBR4NC\nR8+lkn4rtxIjEqJsmJR4srOzadSoEQD16tXj5MmTAJibmzNgwAD++OOP8otQiCdYQ19natWwB0Av\nIxqIasKkxFOzZk3S0tIAqFOnDhkZGaSmpgLg4uJiWGYKnU7HnDlziIyMJCwsjLfffpsbN26UuP7m\nzZuJiooiNDSUrl278tVXX6HT3b2pLiYmhoCAgCI/SUky0KKo+lQqFe2b3e1okJiSxflE6WggqjaT\nEk+7du1YsGABx48fx8fHBw8PD5YvX45Go2Hjxo2lGrl6wYIFrFu3jpkzZ7JixQqSkpIYNWpUsevG\nxK+zOAcAACAASURBVMTw7rvv0qdPHzZu3Mg777zD119/zZdffmlY58yZMwQFBbF7926jH3d3d5Nj\nEuJx5uZkQ5MGNQyP9xy9Rr5WRjQQVZdJiWf06NFYWFgwe/ZsoGCahCVLlhAaGsratWsZPHiwSQfT\naDQsX76ccePG0bZtWxo3bsynn37KoUOHOHToUJH1f/zxR55++mkGDhxI7dq1eeaZZ3j11VdZu3at\nYZ1z587h7+9PzZo1jX7UahkNSFQfLRp7YvtnR4OsHOloIKo2k7pTu7m5sW7dOsPlq549e+Ll5cWR\nI0cICQmhdevWJh3s9OnTZGdn06JFC0OZj48P3t7eHDhwgGbNmhmtP2LECGxtjeegV6vV3Lp1y/D4\n3LlzPPfccyYdX4iqysrCjDYhXmyLvQLAkXOpNPJzxcXBupIjE6L0TKoWfPnll6SmpuLl5WUoa9my\nJcOGDaN27dp89NFHJh2sMHHdf2nO3d292DaZkJAQo+m2s7KyWLVqFe3atQMK2osuXrxIXFwcPXr0\nIDIykhEjRnDx4kWT4hGiKgmo7YKXmx0Aer3CttgraHX6So5KiNIzKfHMmzevxMb6Y8eOsXLlSpMO\nlpOTg1qtxsLCwqjc0tKSvLy8h247cuRI8vLyeOeddwC4cuUKeXl5aDQaZsyYwdy5c9FoNAwYMKBU\nHR6EqAoKOhr4GIbOSU6/w2+xV6SXm6hySrzUNmDAAI4ePQoUTEz1yiuvFLueTqejcePGJh3M2tra\nMNqBufndQ2s0GmxsSh4EMT09nZEjR3L+/Hm+/fZbvL0LRuv18/Nj3759ODo6Gtp0Fi5cSIcOHdiw\nYQOvv/66SXEJUVXUcLYhsmkt/nvkKgAXEjP433FL2oTUesiWQjw+Skw806ZN4z//+Q+KorBo0SJ6\n9uyJp6en0TpqtRpHR0e6du1q0sEKL9Xdf9kuJSWlxJ5xiYmJDBkyhOzsbFasWEFgYKDRcmdnZ6PH\nNjY2+Pr6cv36dZNiEqKqadqwJplZeRw7X3AbwqEzKTjZW9G4nlslRyaEaUpMPA0aNOCtt94CCmo1\n/fv3L1W36eIEBgZiZ2dHbGwsUVFRQEFiuXr1KhEREUXWT0tLIzo6GjMzM1atWoWvr6/R8m3btjF+\n/Hi2b9+Oq6srUNAOdPnyZfr27ftIsQrxOIts6s3tbA2Xrhd0tIk5lIi9rQV1PB0rOTIhHs6kXm1j\nxowBYM+ePcTGxnL79m1cXFxo3ry5yT3aoKAt55VXXmHWrFm4uLjg5ubGtGnTaNGiBaGhoWg0GjIz\nM3FycsLS0pJp06Zx8+ZNvvvuO6ytrQ03rapUKmrUqEFERAT29vaMHz+e8ePHo9Pp+PTTT/+/vfsO\ni+L4/wD+vjs4qiIgiEFEUSmCcBQBBUTEQogGTawRwRZrAE3siuWHFewtoEGNYonmKwajiTFGjQ2U\nohEVlaKIEalSjnLc3fz+wFtZmihwSJzX8/AczO7tzWdv2c/t7NwMNDU1mcRGUf9FXC4Hgx0NcfJS\nMrLzSyElBOein+KL/t3Rvh2du4f6sDUo8YhEIsyePRtXrlyBgoIC2rVrh/z8fEilUvTp0wehoaHg\n8/kNesE5c+ZALBZj/vz5EIvFcHFxwfLlywEACQkJ8PHxwcGDB2FlZYXz589DKpVi1KhRrG3weDzc\nv38fGhoaOHDgAEJCQuDj4wOxWAwnJyf8+OOPUFJSesddQVGti6ICD0OdjHDiwiMUl1ZAVCHBr1dT\nMdLdGOoqim/fAEW1EA5pQJeYkJAQHDlyBEFBQfD09ASXy4VEIsHZs2exYsUKTJgwAXPnzpVHfZtE\nRkYG3N3dceHCBXTq1Kmlq0NRjZJbUIr/XUyGqKJyNAOddir4wq07FBXoFApU02qqc2eDulOfOXMG\nAQEBGDp0KNN7jMfjYdiwYfD398fp06ffuwIURTWOtoYKPBwNmfHcsl+V4o/op5BKaTdr6sPUoMTz\n6tUrGBsb17rM2NiYufdCUVTL6KzXFq42bz6Bpr0oxNU7z+l3fKgPUoMST9euXXH16tVal125coU2\nV1HUB8DcSBu2pm8Gx/0nOQf/PK575HeKaikN6lzg4+ODpUuXQiqVwtPTEzo6OsjOzsaZM2cQERGB\nRYsWNXc9KYpqAEeLjigUivD4WeXUCVf/+Rdt1Pgw0tdo4ZpR1BsNSjwjRozAkydPEB4ejh9//JEp\n5/F4mDJlCiZMmNBsFaQoquE4HA7ce3dGUUkFMnOFIITgj5inGNG/Ozpoqb59AxQlBw1KPAAwd+5c\n+Pr64s6dOygoKEDbtm0hEAiYL25SFPVhUOBx4dm3C37+6zEKhSKIJVKcuZaGkQN6oK1aw772QFHN\nqc57PD4+PkhJSWGVaWlpwc3NDcOHD8eAAQNo0qGoD5SqsiKGuRhBiV/ZpbqkrAK/Xk1FmUjcwjWj\nqHoSz82bNyEUCuVZF4qimpBmG2V81rcreNzKbtZ5hWX4/cZTSOhUClQLo9N0UtR/2Cc66hhg92aM\nw4ysIlxOyKDdrKkWRRMPRf3HmRhqwcH8zcjy99PyEJeU1YI1oj529XYuWL16NdTV1d+6EQ6Hg/Dw\n8CarFEVRTcvOrAMKhSI8eJIHAIhOfIG2anwYd9Zs4ZpRH6N6E49YLEZFRYW86kJRVDPhcDjob9MJ\nRSUVyMgqAgBcuJUOJT6PTqVAyV29iWflypWwtLSUV10oimpGPB4XHn0McfJiMvIKyyCREpy+kgpr\nY104WuiBx6Mt75R80CONoj4iynwFDHU2gprym2kTEh5l4cRfj5FXWNaCNaM+JjTxUNRHpq0aH2MG\nGbOa2HJeleL4n49wNzmH9nijml2diWfEiBHQ1KQ3Hinqv0hVWRFDnbuin7U+FF43sYklUlxOyMCv\nV9NQUkbv7VLNp87Es27dOhgYGNS1mKKoVo7D4cCyuw5GufdgTZf9NLMQR/94iCcvCluwdtR/GW1q\no6iPnLaGCkYN6AGBsQ5TVlouxq9XU3E5PgMVYjrSAdW0aOKhKAo8HhfOVvrw6teN1fHgbkoOTlx4\nhOz80hasHfVfQxMPRVEMgw5tMG6wCbpVmb8nr7AMJ/56hPiHWbTjAdUkaOKhKIpFWUkBHn26YICd\nARRfdzyQSgmu//Mvfvk7FcUlohauIdXa0cRDUVQNHA4HPbtqY8wgE9YEchlZRTh6/iGSM161YO2o\n1o4mHoqi6tSujRK+cOsBO7MO4HAqp1coF0nw+40n+Cs2HRViSctWkGqVaOKhKKpePC4HjhYdMcK1\nG9qovpnB9H5aHo6df4SkJ3kQ0zl+qHdAEw9FUQ3yiY46xgwyZo1oXVBcjj9vpWP/r/dw5fZz5NNh\nd6gGqHeQUIqiqKqU+QoY7GAIQ702uJzwHKKKyqa2cpEEdx5n487jbOjrqMPcSBvd9DXowKNUreR+\nVEgkEmzatAnOzs6wtraGv78/cnJy6lz/7Nmz8PLygkAgwKBBg7Bnzx5IJG/alUtLSxEYGAgHBwfY\n2dlh2bJldMpuimpmJoZa8PYwRd9en6CtGp+17Hl2Mf6IeYoDZ+7j+j//oqC4vIVqSX2o5J54duzY\ngcjISGzYsAERERHIzMyEn59fretevnwZ8+bNw6hRoxAVFYXvvvsOe/fuRWhoKLPO8uXLERcXh7Cw\nMISGhuLmzZtYvny5vMKhqI+WqrIibEx1MeFTM3zuYoRu+hrgvu6AAFSOfhD/MAuHfnuAqL9TkJLx\nClIp/R4QJeemNpFIhIMHD2LZsmVwcnICAGzevBnu7u6Ij4+HjY0Na/1jx45h8ODB8Pb2BgB07twZ\nKSkpOHnyJGbPno3MzEz8+uuvOHDgAAQCAYDKWVN9fHywYMECdOjQQZ7hUdRHicPhoLNeW3TWa4vi\n0grcT8vF/dRcFJe+GWg0/WUR0l8WQV1FET27aqNnVy2oq/Lr2Sr1XybXxJOUlAShUAh7e3umrFOn\nTtDX10dsbGyNxDNz5kyoqqqyyrhcLgoLKwcvjI+PB5fLZT3PxsYGPB4PcXFx8PT0bMZoKIqqTl1F\nEfY99WBn2gFPMwuRmJKL9JdFzIgHxaUVuHk/E7cevESXjm1hYaQNgw5twOVy3rJl6r9EroknMzMT\nAGpciejq6jLLqqo++2lxcTGOHj0KFxcXAMDLly+hpaUFRcU3Y0spKChAS0sLL168aOrqUxTVQFwu\nB10/0UDXTzRQKBThXmouHjzJY6ZbIIQg7d8CpP1bAHUVRejrqENPWw0dtFXRXkOFJqL/OLkmntLS\nUnC5XFaiAAA+n4/y8vpvQJaWlmLWrFkoLy/Hd999x5QpKSnVWLch26MoSj7aqvHRp1dH2PfsgLQX\nlVdBGVlFzPLi0go8TM/Hw/R8AIAijwtdLVXoaatBT7vyUUWJdsD9L5Hru6msrAypVAqxWAwFhTcv\nLRKJoKKiUufz8vLyMGvWLCQnJ2Pfvn3Q19dnticS1Rw3SiQS1WiioyiqZfF4XHTv1A7dO7XDq6Jy\n3EvLxYO0PJSJxKz1KiRSPM8uxvPsYqasnboSk4T0tNWg1VaZXhW1YnJNPB07dgQAZGdnM78DQFZW\nVp0dATIyMjBlyhQIhUJERETA1NSUWaanp4e8vDxIJBLweDwAgFgsRl5eHnR1dZsxEoqiGqNdGyU4\nWX4CR3M9ZL8qxcvcEmTmCfEiR8jqlCDzqrgcr4rLkfS08qqIr8iDrqYq9LRV0VFbDTqaKlBRUmCG\n9aE+bHJNPKamplBTU8PNmzfh5eUFoDKxPH/+HL17966xfm5uLnx8fMDj8XD06NEaM6La2tpCLBYj\nISEBdnZ2AIC4uDhIpVLY2to2f0AURTUKj8dlrmKsUDkRXXGJCJmvE1Fmbgmy8ktqdMMWVUiQkVXE\narJT5HHRRo0PdVVFtFXlQ12Vj7ZqfLRR5aONqiJUlRXpVdIHQq6Jh8/n46uvvkJwcDA0NTWhra2N\nVatWwd7eHgKBACKRCAUFBdDQ0ACfz8eqVauQn5+PH3/8EcrKysjOzgZQ2X2zffv26NChAz799FMs\nXboUa9euBSEEgYGB8PLyol2pKaqVUlflo7sqH90N2gEAxBIpsvNLkZkrRGZeCTJzhBCW1bwqqpBI\nkVdYhrw6hu3hcjlQV1Gskowqf9RVK8vUVBShQEdakAu537GbM2cOxGIx5s+fD7FYDBcXF+YLnwkJ\nCfDx8cHBgwdhZWWF8+fPQyqVYtSoUaxt8Hg83L9/H0Dl93ZWr16NadOmQUFBAUOGDMGSJUvkHRZF\nUc1EgcdFx/Zq6NheDUBlj7iikgpk5grxMrcEL3KFyC8qe+sU3VIpQaFQhEJh3fMJ8bgcKPEVoMzn\nga/Ig5Ii783v/Mq/2Y8KlY98HvgKXNrU10Ac8hFOKZiRkQF3d3dcuHABnTp1aunqUBTVSIQQlIsk\nKCqpQFGJCEVCEYpKXz+WVKBQKKrRiaGpcTgcKCpwocDjQoHHgQKPCx6PAwUuFwoKXChwOeCxlr1Z\nl8fjQvH1+jwuB1wuB1zO68eqv7MeUesyDqcygQIcVD68fnxdx8ZoqnMn7aNIUVSrx+FwoKykAGUl\nBeho1t5DtkL8OjEJRZXJqUSEQmEFil//XlIubtSQPoQQiCokzMCpHyIOpzIxcYDK4Y041R5fl3M4\nQJdPNOBqrd8sV3E08VAU9VFQVOBBqy0PWm2Va11OCIFYQlBeIUG5SPz6UVLzsUZZ5bpva+r7EBBC\nIGvjkqD+JJuYkoOeXbSgq9X0X02hiYeiKAqyprLK5jJ1FcW3P6EaiZSgQiyBREIglkghlkjZv0sJ\nKsR1lEmlEIulEEsIJFICQgikhEAqff1D8PqRsB9ZZW/WkSUYKSGA7PEdtW+nAi2N2pN0Y9HEQ1EU\n1QR4XA54/A/3lCpLRoQQkCp/V09OstZGNeXm+17Uh7uXKIqiqCYju79TeYenZX2UiUc2kVxtA5NS\nFEVRtZOdM6tOxvk+PsrEI/si6vjx41u4JhRFUa1PdnY2DA0N3/v5H+X3eMrKypCYmAgdHR1mjDeK\noiiqfhKJBNnZ2bCwsICy8vt3PPgoEw9FURTVcujARBRFUZRc0cRDURRFyRVNPBRFUZRc0cRDURRF\nyRVNPBRFUZRc0cTzmkQiwaZNm+Ds7Axra2v4+/sjJyenpatVr5ycHCxcuBDOzs6ws7PDlClT8OjR\nI2b51atX4eXlBUtLSwwbNgyXL19mPT83NxcBAQGws7NDnz59EBISArG4eYeOf1e3b99Gz549ERMT\nw5S15rhOnDiBIUOGwNLSEl988QVu3LjBLGutcZWUlCAoKIg5DqdOnYrk5GRmeWuLa/ny5Vi6dCmr\nrCliOHDgANzc3GBlZYVJkybhyZMnzR0KS21xRUREwMPDAwKBAJ6enjhx4gRrebPFRShCCCFbtmwh\nTk5O5OrVqyQxMZGMGjWKjB07tqWrVSeJRELGjBlDRo8eTe7cuUMeP35M/P39SZ8+fUheXh55/Pgx\nsbCwILt37ybJyclky5YtxNzcnDx69IjZxrhx48hXX31FHjx4QC5dukQcHR3J5s2bWzAqNqFQSAYN\nGkSMjY1JdHQ0IYS06rhOnjxJzM3NyYkTJ8iTJ0/I2rVriUAgIM+ePWvVcS1ZsoR4eHiQ2NhYkpyc\nTGbNmkVcXV1JWVlZq4pLKpWSrVu3EmNjY7JkyRKmvCliOH78OLG2tia//fYbSUpKItOnTyfu7u6k\nvLy8xeI6fPgwEQgE5NSpU+Tp06fk+PHjxNzcnERGRjZ7XDTxEELKy8uJtbU1+d///seUPXv2jBgb\nG5O4uLgWrFnd7t27R4yNjUlycjJTVl5eTqysrEhkZCQJDAwk3t7erOd4e3uTZcuWEUIIiY+PJ8bG\nxiQ9PZ1ZfvLkSWJtbS2Xf4aGkMVQNfG01rikUilxc3MjW7duZcokEgn5/PPPSVRUVKuNixBC7O3t\nycGDB5m/Hz9+TIyNjUliYmKriSs9PZ14e3sTBwcH0r9/f9YJuiliGDx4MNm+fTuzvLi4mAgEAhIV\nFdWcYdUb17Bhw0hwcDBr/cWLF5MJEyYQQpo3LtrUBiApKQlCoRD29vZMWadOnaCvr4/Y2NgWrFnd\nOnbsiLCwMHTt2pUpk40kW1BQgNjYWFY8AODg4MDEExsbC319fRgYGDDL7e3tIRQK8eDBAzlEUL/L\nly/j0qVLWLZsGau8tcaVmpqK58+fw9PTkynjcrn45ZdfMGzYsFYbFwBoaWnh7NmzyM3NhUgkws8/\n/wwNDQ0YGBi0mrji4+PRsWNHnD59usbMmo2NITc3F0+ePGFtQ01NDRYWFs1+fqkvrmXLlmHs2LGs\nMi6Xi8LCQgDNGxdNPHgz8F2HDh1Y5bq6uh/sQKKampro378/uNw3b+GhQ4dQVlYGZ2dnZGZm1hvP\ny5cvoaurW2M5ALx48aKZa1+/vLw8LF26FKtXr4aGhgZrWWuNS9buXVhYCB8fH/Tp0wfjx49HfHw8\ngNYbFwAEBQUhMzMTffv2hUAgwPHjx7Fnzx60bdu21cTl5eWF4OBg6Ojo1FjW2Bha8vxSX1z29vas\npPLvv//izJkzcHFxAdC8cdHEA6C0tBRcLheKiuzJn/h8PsrLy1uoVu/mwoUL2Lx5MyZNmoRu3bqh\nrKwMfD6ftU7VeEpLS6GkpMRarqioCA6H0+Ixr1ixAgMGDEC/fv1qLGutcRUXFwMAFi1ahFGjRuGH\nH35Ajx494Ovri5SUlFYbFwA8ffoU7du3x549e3D06FE4OzvD398fmZmZrToumcbGUFpaCgA11vmQ\nzi95eXmYPn062rdvj2nTpgFo3rg+ytGpq1NWVoZUKoVYLIaCwptdIhKJoKJS+/ztH5KTJ08iMDAQ\nnp6emD9/PoDKg6GiooK1XtV4lJWVIRKJWMsrKipACIGqatNPddtQkZGRuH//PqKiompd3lrjkn2o\nmTFjBoYNGwYA6NmzJ+Li4nD06NFWG9ezZ88QGBiII0eOQCAQAAA2bdoET09PHDhwoNXGVVVjY5AN\npll9nQ/l/PLs2TNMnToVZWVliIiIQJs2bQA0b1z0igeV90uAN9MlyGRlZdW4jPzQfP/991i8eDHG\njh2L4OBgpumtY8eOyMrKYq1bNR49Pb1a4wVqXjrL08mTJ/Hy5UumW7uHhwcA4Ouvv8by5ctbbVyy\nJgpjY2OmjMPhwMjICBkZGa02rsTEREgkElhYWDBlioqKMDMzw9OnT1ttXFU1NoYP+fxy7949jBkz\nBlwuF8eOHWM1vTVnXDTxADA1NYWamhpu3rzJlGVkZOD58+fo3bt3C9asfnv37sXWrVvh7++PwMBA\n1jS1tra2uHXrFmv9mJgY2NnZMcufPXvGakePiYmBmpoaTE1N5RNALTZu3IgzZ87g1KlTOHXqFH74\n4QcAwOrVqxEQENBq4zI3N4eqqiru3r3LlBFCkJKSAgMDg1Ybl56eHgDg4cOHTJksri5durTauKpq\nbAza2tro0qUL6/wiFAqRmJjYoueXlJQUTJ48Gfr6+jhy5AiTSGSaNa737qf3HxMSEkL69u1LLl++\nzHyPp3oXyg/JgwcPiJmZGVm8eDHJyspi/QiFQpKUlETMzc3Jtm3bSHJyMtm6dSvp1asX0/1aKpWS\n0aNHkzFjxpDExESmj37VrpEfghcvXrC6U7fmuLZs2UJ69+5Nzp07R9LS0siaNWtIr169SEpKSquN\nSywWk9GjR5OhQ4eSW7dukeTkZBIYGEgEAgHJyMholXF5e3uzuh03RQxHjhwhAoGA/Prrr+Thw4dk\n+vTpZPDgwXLtMl49ri+//JI4OzuT1NRU1vkjNze32eOiiee1iooKsm7dOmJvb09sbGxIQEAA8wZ8\niDZt2kSMjY1r/dm1axchhJCLFy8ST09PYmFhQT7//HNy7do11jaysrLIrFmziJWVFenbty/ZtGkT\nkUgkLRFOnaonHkJab1xSqZSEhoYSV1dXYmFhQUaNGkVu3brFLG+tceXm5pKlS5cSFxcXYmtrS3x9\nfcn9+/eZ5a0truonaEKaJobQ0FDi5OREBAIBmTx5Muv7MfJQNa7U1NQ6zx8DBw5s9rjoRHAURVGU\nXNF7PBRFUZRc0cRDURRFyRVNPBRFUZRc0cRDURRFyRVNPBRFUZRc0cRDURRFyRVNPO9p0aJFGDRo\nUJ3LBwwYUGO2v+Yk79drqJiYGJiYmLx1mHQTExPs3r1bTrVq/d52/H1sduzYgZ49ezJ/T5gwARMn\nTpRrHQIDA7Fr1y4AlUM/mZiY1DlK89KlSzFgwABWWV5eHtauXYuBAwfCwsIC9vb28PX1xfnz51nr\nybZd9adXr14YOHAgVq9ejby8PNb6O3fuxMqVK5su0CZABwmlPgg//fRTjSE7qLrNmjULQqGwpavx\nwVqxYgVrCKnmdvXqVVy9ehW///77ez2/tLQUX331FQBg+vTp6Ny5M4qKinD27Fl88803WLJkCXx9\nfVnP+f7776GlpQVCCEpLS3Hv3j3s3bsXf//9N44dOwYtLS0AwNSpU+Hh4YEbN26gT58+jQu0idDE\nQ30QZCMbUw3TuXPnlq7CB6179+5yey1CCNavX4+JEyfWmCKgoc6dO4e0tDT8+eefrIE6Bw4ciLKy\nMmzfvh3e3t7g8XjMsp49ezJj5QFA37594ezsjNGjR2Pjxo1Yu3YtgMpRpidOnIh169bVOeq7vNGm\nNjkpKSlBSEgIBg8eDAsLC9jY2GDKlClISkpi1lm0aBGmTJmCEydOMOt5eXnhypUrrG0lJSVh0qRJ\nsLa2hpubW4MOprVr18LR0RFVB6qYM2cOTExMkJOTw5Rt2bIFbm5uAACJRIKwsDAMHToUlpaWEAgE\nGDduHGJiYpj1y8rKsHLlSvTr1w8WFhbw8PBAeHh4jddPSUnBpEmTYGlpCScnJ2zevBkSiYRZXrWp\nTdY8Fx0djYkTJ8LKygpOTk7YuHEj6zlFRUVYvHgxHBwcYGtri8DAQGzevLlGE0Z1WVlZWLBgARwd\nHWFjYwNfX1/cu3ePWZ6Xl4cVK1bAzc2NafLw8/PD8+fPmXUmTJiAlStXYseOHXBycoK1tTXmzp2L\n4uJi7NmzBy4uLrC1tYWfnx/y8/NZcR4+fBhz5syBQCCAs7Mztm7dyoqrocdK1aY2kUiE9evXw9nZ\nGQKBAAEBAThw4ABMTExYdV6+fDlCQ0Ph6uqKXr16YezYsayBS2uTmJgIX19f2NrawtraGhMnTsTt\n27dZdZk4cSIOHz4MFxcXWFtbY9q0aUhPT2dt5+HDh/j6669hbW0NW1tbBAQEsJqiGvq+l5eXY926\ndcx+X7x4cY35X6o3tZmYmODYsWNYvHgxevfuDWtrawQEBCA3N5dZhxCCsLAwuLm5wdLSEpMnT8ap\nU6dgYmKCjIyMOvfPpUuXkJKSwppd9l3J/gdrG0hmxowZmDVrVo3pB2pjZmaGIUOGICoqipkvBwA8\nPT3x+PFjXLp06b3r2JRo4mkksVhc6091CxYswKlTpzB9+nTs27cPixcvxsOHDzFv3jzWwXbnzh3s\n378fAQEB2LVrF3g8Hvz9/VFUVASgclZAb29vFBUVISQkBAEBAdi4cSNevnxZbz379++P/Px85uRF\nCGESSNWRd69cuQJXV1cAQHBwMEJDQzFu3Dj88MMPCAoKQn5+PgICApiDeu3atfj777+xaNEihIeH\nw93dHcHBwYiMjGS9/po1a2Bvb4+wsDB4eHggLCwMx48fr7fO3333HfOcoUOHYu/evTh58iSzfObM\nmfjrr78wb948hISEIDk5Gfv37693m0KhEOPGjUNsbCwWLVqEbdu2QSqVYtKkScjMzAQhBFOnTkV0\ndDTmzZuH8PBwfPPNN7h27VqNdvKoqCgkJCRgw4YN8Pf3x9mzZzFy5EhcvXoVq1evxrfffosLFy5g\n586drOdt2bIF5eXl2LZtG8aOHYs9e/YgODiYWd7QY6WqwMBAHD16FFOmTMG2bdsgEomwadOmWJzg\n0gAAD7NJREFUGuudPXsWly5dYpJ0Tk4OAgICIJVKa91ucXExpk6dCk1NTezYsQNbtmxBaWkppk6d\nykxuB1Qmp9DQUMybNw9r1qxBSkoKfHx8UFJSAgBIS0vDuHHjUFBQgJCQEAQFBeHRo0cYP348c2zL\nvO19nz9/Po4fP47p06dj69atKCgowIEDB2qtf1UbN24EAGzduhXz58/HxYsXsX79emb5jh07sG3b\nNgwfPhy7du2Cnp4eAgMD37rd06dPw8bGptZZPhvK2dkZPB4P3t7e2LVrF+7cucPMAWRpaYkpU6Y0\neO6evn37oqKigvWBQldXF9bW1jh9+vR717Ep0aa2RkhPT4e5uflb15PN1hcYGMjML2Nvb4/i4mKs\nX78e+fn5THtsUVERIiMjmcttVVVVeHt7IyYmBgMHDsSBAwcgkUiwd+9eaGpqAgC6du2K0aNH11sH\nOzs7qKqq4saNGzAzM8PDhw+Rn58PMzMz3Lp1C59++ilyc3Nx//59BAQEAKi8Mvj2228xfvx4ZjtK\nSkrw8/PD48ePYWlpiZs3b8LJyYn5tOfg4ABVVVWmbjKTJk3CzJkzAQCOjo64cOECoqOjMW7cuDrr\nPGbMGMyaNYt5zp9//olLly5h1KhRuHHjBm7duoWwsDD079+fWcfd3b3e/RAZGYnnz58jKiqKmRtH\nIBBgxIgRiI+Ph42NDdTU1LBs2TLY2NgwMaWnp+Pnn39mbYsQgu3bt0NdXR3Ozs44efIknj9/jhMn\nTjCTaV25cgUJCQms53Xo0AG7du0Cl8uFq6srhEIhDh06hNmzZ0NJSanBx4pMeno6fvnlFwQGBjLv\nlYuLCz7//HM8fvyYta5EIsEPP/wAdXV1AJWJeOHChXj06FGt0xAkJycjPz8fPj4+zP4wMjLCTz/9\nBKFQyGynqKgI4eHhsLKyAgB069YNXl5eiIyMxPjx47Fz506oqqpi//79UFNTAwD07t0bAwcORERE\nBHNsAPW/748fP8a5c+ewatUqjB07lol12LBhSEtLq++th6mpKdatWwcAcHJywt27d/Hnn38CqLzK\nDA8Ph6+vL3P8u7i4IDs7G3///Xe9242OjoaXl1e967yNqakpNm/ejKCgIGzfvh3bt2+HiooK7Ozs\n8OWXX+LTTz9t8La0tbUBgNWSAQAWFhY4e/Zso+rZVGjiaQQ9Pb0an2Zlqv4jKSkpMc1PL1++RFpa\nGp48eYKLFy8CAGt2Qx0dnRqTMQFgrjDi4uJgY2PDOrFbWVnhk08+Yf6WSCSsT8YcDgd8Ph99+/bF\njRs3MHnyZERHR8PExASurq7466+/AFSeJJWVleHo6Aig8pM5UNn0lJqaiqdPn9aos4ODA44dO4bM\nzEy4urrC1dUVs2fPrrE/ZHOXyOqjr69f45NudbITXdV9IdsP0dHRUFJSYq7OgMok3b9/f1ZTYHVx\ncXEwNDRkTcjWpk0b5gQEAIcOHQIhBBkZGXj69ClSU1MRHx9fYxbK7t27MydeoPIfns/nM0kHANq1\na4eUlBTW8zw9PZkJ+wBg8ODB2LdvH27fvo1+/fo1+FiRiYmJASEEgwcPZsq4XC48PDxqJB4TExNW\nnWUTdsmuTKrr0aMHtLS0MGPGDHh4eMDFxQVOTk7MTLcyBgYGTNKRvY6hoSFiY2Mxfvx4REdHo0+f\nPlBSUmJaBDQ1NWFpaYnr16+z/l/qe99lvSOrfsDgcrkYMmQIQkNDa42hIdu9ffs2ysrKMGTIENY6\nnp6e9SaekpIS5ObmolOnTqzy9+nY4OHhAXd3d0RHR+P69euIiYnBtWvXcOXKFfzxxx/YvHlzozpM\n6OvrIzs7GyKRqMZU3vJGE08j8Pl89OrVq85lVV25cgVr165FamoqM5GSbGrfqkmi+uW07ECTNYUU\nFBTA0NCwxutVvcyfOHEia3Ime3t7HDp0CP3798fatWtRUVGB6Oho2Nvbw87ODmFhYcjPz8eVK1fg\n6OjI3CC9e/cuVq1ahbt370JFRQXdu3dnEpyszkuXLoWenh6ioqIQFBSEoKAgWFtbY+XKlaxP0NXj\n4nK5dTbvyMim1q3tOfn5+dDU1Kzxj9i+fft6t/nq1SvmE2FdoqKisHnzZrx48QLt2rWDmZkZlJWV\nazRzyT65V9WQ6Zpls5HKyOpTWFgIoOHHioys+2z1K6Ha9kVt+xRAne+FmpoaDh8+jO+//x6//fYb\nfvrpJygrK8PLywvLli1jjvPqMcniksX06tUrnD59utamni5dury1jlWP/9pibUgzV33bfZd9WJXs\nw1P141v2d133ZSoqKmo9VhQVFeHi4gIXFxcAlR8+Vq9ejbNnz2LEiBHo169fvfUB3swSWv09kb1e\ncXFxjTjljSYeOUhPT8fs2bMxaNAg7NmzB506dQKHw8Hhw4drdBx4G01NTdYNUZlXr14xv69atYrV\n1VZ2guzXrx+WLVuGhIQExMXFYeTIkbC1tYWCggJu3ryJq1ev4ttvvwXwpm3fzMwMZ86cgZGREbhc\nLi5fvoxz584x2+bz+Zg5cyZmzpyJf//9FxcvXsTu3bsxf/78Zm1P1tXVRV5eHgghrORT276pqk2b\nNqwZFWViY2PRvn175OTkYOHChfD19cWkSZOYK4Lg4GDWDfXGqPpeAW+aRLS1td/rWJHVMTc3l3Wy\nedu+aCgjIyOEhIRAIpHgn3/+wS+//IKjR4+iS5cumDx5cq0xyV7f0tISAKCuro5+/frBx8enxnrv\n8ulbdqWfk5PDml65ttd/F1X3YdUeg2/bh7L6yBKsjCxhZWVl1doDMTMzk5XUxowZAyMjI6YpsGq9\n1qxZgz/++AMpKSkNSjzR0dFQUVGpcRugoKAAXC4XGhoab91Gc6OdC+QgMTER5eXlmDFjBgwMDJgT\npexE8rZP/lU5OjoiLi6ONc95cnIynj17xvxtZGSEXr16MT9GRkYAKg9iMzMz/PjjjygqKmLu+1hY\nWGD//v149eoVc78kNTUVr169wsSJE9G9e3fmk7Gs2YEQgvLycgwZMgT79u0DAHzyyScYP348Pvvs\ns1pP7k2pd+/eEIlErJNx9b9rY2NjwzSfyQiFQsyYMQNnzpxBQkICpFIp/Pz8mJORRCLB9evX3+l9\nqk/1nkXnzp2DiooKrKys3utYsbGxAY/Hw4ULF1jl1f9+H+fPn4ejoyOys7PB4/GYq9m2bduy3uMn\nT57gyZMnzN9JSUl4+vQp02xrb2+PlJQUmJubM8dlz549sWfPnrfeQ6lKtr3q35eRNUW+LzMzM6ir\nq7/zPuTz+dDR0anRuadXr15QVVWt9Z5KdnY2EhIS4ODgwJTp6+vj999/Z/0fy8juXVVtHq7Lw4cP\nce7cOQwfPrzGVVhmZiZ0dXVZXbJbCr3ikQNzc3MoKCggJCQEEydORHl5OU6ePMmcgKp2e3wbX19f\n/Pzzz5g8eTL8/PwgFouxZcsWKCoqNuj5rq6uCA0NhampKdq1aweg8j5NaGgoevbsyZxsu3btCnV1\ndezevRscDgdcLhfnzp3D//73PwCVbdtKSkqwtLTEzp07oaioCBMTE6SlpSEyMrJGW3lTc3BwgIOD\nAxYtWoS5c+dCR0cHBw8eRE5ODut+V3UjR47EoUOHMGPGDPj5+UFDQwPh4eFQUlLCqFGjmH/yoKAg\nDB8+HAUFBYiIiEBSUhIIISgrK6vRZPOu4uLisHjxYnz22WeIj4/HoUOH4OfnB1VV1fc6Vjp37gwv\nLy8EBwejvLwc3bp1Q2RkJB48eNDoL1Ha2NiAEILZs2dj2rRpUFNTw2+//Ybi4mLWPSWpVIqZM2di\nzpw5EIvF2LRpE7p3745hw4YBAGbPno3Ro0dj5syZGD16NBQUFBAREYHr16/X28GkOkNDQ4wZMwab\nNm2CSCSCqakpTp06hYcPHzYqTnV1dUyaNAnff/89lJWVYW1tjb/++otJPFXvyVXn5OSEuLg4VpmS\nkhL8/f2xfv16lJeXY/DgwVBUVERqair27dsHPT09TJgwgVl/7ty5iImJwciRI+Hj4wNra2twuVzc\nvXsX+/btg5ubG5ycnFivcf/+faY7eklJCRITE7Fv3z507twZc+fOrVHP+Ph4ODs7v/c+ako08ciB\noaEhNm3ahJ07d2LGjBnQ0NCAQCDAoUOHMGHCBMTGxqJbt24N2pampiaOHj2KNWvWYOHChVBTU8PU\nqVMb3Fulf//+CA0Nhb29PVMmSzyyqx2gsklq9+7dCA4Ohr+/P9TU1GBmZoaIiAh8/fXXiIuLg6ur\nK1atWgVNTU3s27cP2dnZ0NbWxsiRIzFnzpx32kfvY9u2bVi7di3Wr18PDoeDYcOGQV1dvd7eTerq\n6jh8+DA2bNiA//u//wMhBDY2Njh48CB0dXWhq6uL5cuXY//+/Thz5gzat28PBwcH+Pr6Yvbs2YiN\njW30P++kSZOQkZGBWbNmQVdXF4sXL2ZOQu97rKxYsQKqqqoIDQ1FWVkZ3N3dMXbsWPzyyy+Nqqu2\ntjbCw8OxZcsWLF26FKWlpejRowd27NiB3r17M+sZGBjgq6++wqpVqyASidC/f38sWbKEaUYzNTXF\n4cOHsXXrVsybNw8cDgempqbYs2cP+vbt+051WrFiBdq3b49Dhw6hoKAALi4umDFjBnbs2NGoWGfN\nmgVCCH766SeEhYXB0dER06dPx+7du+u9dzdkyBD4+fkhNzeXdf9Q1lR75MgRzJ8/H6WlpejQoQPc\n3d0xc+ZMVicPAwMDREZGYs+ePTh9+jT27t0LQggMDQ0xderUWpsoq3bI4PP5MDAwwOjRo/H111+z\nOrgAlVdZSUlJTI+9Fvc+c3dTVEvLyMggZ86cIeXl5azyL7/8ksyePbuFavV2xsbGZNeuXU26zfz8\nfBIVFUUKCgpY5f7+/mT48OFN+lq1WbhwIRk4cGCzv05zEolEJDIykmRmZrLKN2zYQOzt7et9rlQq\nJUOHDiVhYWHNWcVG2b17N/Hy8iJSqbSlq0IIIYRe8VCt1oIFC+Dp6Ynhw4eDEILffvsNiYmJ+O67\n71q6anKlrKyMoKAgREVFwdvbG0pKSrh27Rr++OMPrFmzpqWr1yooKioiNDQUR44cwbRp09C2bVvc\nvn2bucKvD4fDwbfffouVK1diwoQJDf6ip7yUlJTgyJEjWLNmjVzHr6sP7VxAtUr6+voICwtDRkYG\n/Pz88M033yAlJQV79+79YAZClBdlZWWEh4dDKpViwYIFmD59Oq5du4YNGzbgiy++aOnqtRphYWHM\naAVTpkzBqVOnMG/ePHzzzTdvfa7sHkxtw0W1tPDwcLi5uTWoR5y8cAipYwwOiqIoimoG9IqHoiiK\nkiuaeCiKoii5oomHoiiKkiuaeCiKoii5oomHoiiKkqv/B4Kg2Mv8Sez4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90e506a0>"
+      ]
+     },
+     "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": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 47,
+     "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": 48,
+   "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": 49,
+   "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": 50,
+   "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": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig06.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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O/fr1gcJ2+piYGAYPHsyQIUNwcXFh/fr1JCUl8e9//xu4lQ9Zu3Yt169fp3fv3sXuN3Dg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\nhRBCiEr9P+AKxQwmJbnhAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90ef9390>"
+      ]
+     },
+     "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": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap05-fig06.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Z2YVdE/ft20d+fj5PP/20cb+9vT1bt241qz5CiMqhKAo79p/n/MWrxrKwNvVM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\nhRBCiEr9P+AKxQwmJbnhAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cd90f02358>"
+      ]
+     },
+     "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",
+    "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": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-53-b29d2fc3cb2c>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-53-b29d2fc3cb2c>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    add a quarentine module and move people from infected to there before recovered.\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "add a quarentine module and move people from infected to there before recovered."
+   ]
+  },
+  {
+   "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/chap05soln.ipynb b/code/chap05soln.ipynb
index b4ad9dcb..77b1590b 100644
--- a/code/chap05soln.ipynb
+++ b/code/chap05soln.ipynb
@@ -542,7 +542,9 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "tc = 3      # time between contacts in days \n",
diff --git a/code/chap06-fig01.pdf b/code/chap06-fig01.pdf
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diff --git a/code/chap06-fig02.pdf b/code/chap06-fig02.pdf
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diff --git a/code/chap06-fig03.pdf b/code/chap06-fig03.pdf
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diff --git a/code/chap06mine.ipynb b/code/chap06mine.ipynb
new file mode 100644
index 00000000..df45dbc5
--- /dev/null
+++ b/code/chap06mine.ipynb
@@ -0,0 +1,1255 @@
+{
+ "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": 3,
+   "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='Resistant')\n",
+    "    decorate(xlabel='Time (days)',\n",
+    "             ylabel='Fraction of population')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 5,
+   "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": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Original\n",
+    "\n",
+    "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",
+    "    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": 7,
+   "metadata": {},
+   "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",
+    "    s, i, r = state\n",
+    "    infected = beta * i * s    \n",
+    "    recovered = gamma * i\n",
+    "    s -= infected\n",
+    "    i += infected - recovered\n",
+    "    r += recovered\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": 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>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": 8,
+     "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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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G1z9y5AhvvPEGgYGBNGzYkF69enH9+nWjlpzMnSGzKw0d9Ho6EyQnJ+vGjh2r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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1715d6298d0>"
+      ]
+     },
+     "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": 10,
+   "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": 11,
+   "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": 12,
+   "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": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "infected_sweep = sweep_beta(beta_array, gamma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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 0x171632a9198>"
+      ]
+     },
+     "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": 15,
+   "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": 15,
+     "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": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.1,  0.3,  0.5,  0.7])"
+      ]
+     },
+     "execution_count": 16,
+     "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": 17,
+   "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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\nFArFKP5/H1F/DqvcupYAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x171632b4400>"
+      ]
+     },
+     "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": 18,
+   "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": 19,
+   "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": 19,
+     "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": 20,
+   "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 0x171634ca518>"
+      ]
+     },
+     "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(11, 4)"
+      ]
+     },
+     "execution_count": 21,
+     "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": 22,
+   "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": 23,
+   "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": 24,
+   "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 0x17163580d30>"
+      ]
+     },
+     "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": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_sweep_frame_difference(frame):\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')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Jf39/XL58WWLzGGOMlQWSklFGRgYaN26sdZmFhQUyMzMlbez69evIysqCl5eXUGZjY4OG\nDRsiNja2QP1Tp06hdevWqFmzplDWr18/REVFSdoeY4yxskFSB4amTZti//79aNOmTYFlv/76K5o0\naSJpY0lJSQAgjAauoRkRPL979+6hZcuWWLVqFfbs2QOZTIbOnTtjypQphY4IwRgrwoULeTO1Pn6c\nNy9Rt2484CkrFSQlo/Hjx8Pf3x8ZGRnw8fGBTCZDXFwc9uzZg23btmHp0qWSNpadnQ0jIyMYGxuL\nyk1MTJCTk1OgfmZmJqKiotCuXTusXr0aycnJWLRoEZ4+fSp5m4yx/7lwQTwPUWLi68eckFgJk5SM\nOnXqhG+++QbLly/H0aNHAQCLFy+GpaUl5s+fj+7du0vamKmpKdRqNVQqlejak1Kp1DpBX+XKlVGz\nZk0sXboUlSpVgrOzM1QqFQICAvD555/DwsJC0nYZY8g7ItLm0CFORqzEFZqMQkND0atXL+GUWs+e\nPdGzZ0/cvXsX6enpqF69Opo0aQIjI0mXnQAA9evXBwCkpqYK/weAlJSUAqfugLzTeVWqVEGlSpWE\nsqZNmwIAEhMTORkxVhyPH2svf/TIsHEwpkWhmWTdunVISEgAANjb2ws92N5//314eHigWbNmxUpE\nAGBnZwczMzNR77uEhAQkJiZCoeWXWfPmzXHt2jW8evVKKLt58yYqVaqEhg0bFmvbjFV4b/wAFGnQ\nwLBxMKZFoUdG5ubm+OGHH/DgwQMQEY4fP467d+8WuqI+ffq8dWMmJiYYPHgwli5dCgsLC1hZWSEo\nKAheXl5wc3ODUqlERkYGatasCRMTEwwcOBDh4eGYPXs2Jk6ciOTkZHzzzTfo3bs3HxUxVlzduomv\nGWl07Wr4WBjLp9BkNHbsWHz99dc4cuQIZDIZ1q9fX+hKZDKZpGQEAFOmTIFKpcLMmTOhUqnQtm1b\nzJ8/H0DecEPDhw9HWFgYWrRoAWtra2zbtg1LlixB3759Ua1aNfTq1QvTp08vZjMZY8J1oUOH8k7N\nNWiQl4j4ehErBWSkGU5Bi+zsbDx//hzt27fHxo0bYW9vX+iKtF3zKSkJCQnw8/NDTEwMbGxsSjoc\nxhgrE0ryu7PI3nRVq1ZF1apVsWTJEri6uvKpMcYYY3ohqWv3hx9+iMzMTBw9ehTZ2dlQq9UF6vTs\n2VPnwTHGGKsYJCWjU6dOYfLkycjOzoa2s3oymYyTEWOMsX9MUjJatmwZ3nvvPXz++eeoW7dusbt0\nM8YYY0WRlIzu3LmD9evXo3nz5vqOhzHGWAUk6RCnQYMGkkfmZowxxopL0pHRp59+inXr1sHV1RUN\n+G5txnSHR9FmDIDEZHTo0CEkJyfDz88PtWvX1jqo6c8//6zz4Bgr13gUbcYEkpJR7dq10bFjR33H\nwljFwqNoMyaQlIyWLFmi7zgYq3h4FG3GBIUmo+TkZFhZWaFy5cpITk5+64pK03BAjJUJ9evnnZrL\nj6/Lsgqo0GTUoUMHREZGwsXFBe3bt4dMJityRdeuXdN5cIyVazyKNmOCQpPRf/7zHzRq1Ej4/9uS\nEWOsmHgUbcYEhSajDz/8UPh/3759DRIMYxWOQsHJhzFIvOmVMcYY0ydORowxxkocJyPGGGMljpMR\nY4yxEsfJiDHGWImTNAIDEWHnzp04fvw4/v777wIT7MlkMnz33Xd6CZCxCo8HU2UVgKRktGLFCoSG\nhsLGxgb16tXje44YMxQeTJVVEJKSUXR0NEaOHInZs2frOx7G2Jt4MFVWQUi6ZpSZmQkfHx99x8IY\ny48HU2UVhKRk5O7ujri4OH3HwhjLr3597eU8mCorZySdphs3bhymT58OlUoFDw8PmJqaFqjj4eGh\n8+AYq/CkDqbKnRxYGScpGY0YMQIAsHbtWgAQdWAgIshkMh61mzF9kDKYKndyYOWApGQUFham7zgY\nY4V522Cq3MmBlQOSkpGXl5e+42CM/VPcyYGVA5KSEQDcuXMHISEhOH/+PF68eAELCws0b94cEyZM\nQNOmTfUZI2OsKDxjLCsHJCWjGzduYNCgQahatSr8/PxgZWWF1NRUHDt2DMeOHcOOHTtga2ur71gZ\nY9rwjLGsHJCUjJYtW4b3338fYWFhqFatmlD+999/45NPPsGqVauwYcMGSRvMzc3FqlWrEB0djays\nLLRt2xbz58+HtbX1W587duxY/P333wgPD5e0LcYqBJ4xlpUDku4zio2Nxbhx40SJCACqVauGMWPG\nIDY2VvIGQ0JCEB0djeDgYERERCApKQn+/v5vfd6OHTtw/PhxydthrEJRKIDAQGDDhry/nIhYGSMp\nGVWtWrXQZTKZDLm5uZI2plQqERYWhmnTpsHb2xuOjo5YsWIF4uLiiryp9v79+1i5ciXc3d0lbYcx\nxljZIikZubm5ITQ0FDk5OaLyly9fYvPmzZKTxPXr15GVlSXqnWdjY4OGDRsWenSVm5uL2bNnY8yY\nMWjSpImk7TDGGCtbJF0zmj59Ovr37w8/Pz/4+vrC2toaT548wdGjR5GVlYVt27ZJ2lhSUhIAoG7d\nuqLyOnXqCMvy+/bbbwEAo0ePRmBgoKTtMMYYK1skJaMmTZpgx44dWLduHWJiYpCRkYEaNWpAoVBg\n4sSJkMvlkjaWnZ0NIyMjGBsbi8pNTEwKHHUBwJUrV/DDDz8gKioKRkY8DyBjjJVXku8zsrW1xZo1\na/7VxkxNTaFWq6FSqVC58utNK5XKAtelcnJyMGvWLEyZMgWNGzf+V9tljDFWuhWajPbu3Yu2bdui\nVq1a2Lt371tX1LNnz7fWqf+/EYhTU1OF/wNASkpKgVN3ly5dwp07d7Bs2TIsW7YMQF7SUqvVcHd3\nx/79+9GAb+pjjLFyodBkNHPmTPzf//0fatWqhZkzZxa5EplMJikZ2dnZwczMDOfPn0fv3r0BAAkJ\nCUhMTIQiX1dUFxcX/PLLL6KyFStW4NGjR1i2bBnq1Knz1u0xxhgrGwpNRjExMahdu7bwf10wMTHB\n4MGDsXTpUlhYWMDKygpBQUHw8vKCm5sblEolMjIyULNmTZiamhY4PWdubq61nDHGWNlWaK+Ahg0b\nwsTEBABw4cIFVKtWDQ0bNizwz8TEBD///LPkDU6ZMgU9e/bEzJkzMXz4cDRo0ACrV68GAMTHx6NN\nmzaIj4//l81ijDFWlsiIiN5Wyd7eHpGRkXBxcSmw7Ndff8XEiRPxxx9/6CXAfyIhIQF+fn6IiYmB\njY1NSYfDGGNlQkl+dxZ6mm7s2LG4ffs2gLwJ9CZOnCgcKb3p6dOneOedd/QXIWOMsXKv0GQ0fvx4\nREVFAQCioqLg7OwMS0tLUR0jIyPUqFEDH374oX6jZIwxVq4Vmozc3Nzg5uYGIG9IngkTJqBRo0YG\nC4wxxljFIWlYgyVLluDu3bsIDg4Wyi5fvoyRI0fit99+01twjDHGKgZJyejAgQMYN24c7ty5I5RV\nrVoVarUao0ePxq+//qq3ABljjJV/kpLRxo0bMWTIEGzatEkoa9asGbZu3YqBAwf+62GCGGOMVWyS\nktGDBw/QsWNHrcs6duwoOmJijDHGikvSQKlWVlb4888/0bJlywLLbty4gZo1a+o8MMYqhAsXgIMH\ngcePgfr1gW7deJZWViFJSkY9e/bE2rVrUa1aNXTq1AlWVlZIS0vD0aNHERISgsGDB+s7TsbKnwsX\ngM2bXz9OTHz9mBMSq2AkJaOJEyfi7t27CAoKwpdffimUExE6d+6MyZMn6y1Axsqtgwe1lx86xMmI\nVTiSkpGxsTHWrFmDmzdv4uLFi8jIyED16tXh6ekJOzs7fcfIWPn0+LH28kePDBsHY6WA5Mn1AEAu\nl2ud1TUrKwtmZmY6C4qxcuFt14Pq1887NZcfz9PFKiBJyUipVCI8PBwXLlzAq1evoBlbVa1WIzs7\nGzdu3MDvv/+u10AZK1OkXA/q1k1cR6NrV/3Hx1gpIykZLVu2DGFhYZDL5UhLS0OVKlVgaWmJmzdv\n4tWrV5g0aZK+42SsbJFyPUjz99ChvFNzDRrkJSK+XsQqIEnJ6Oeff8bIkSMxe/ZsbNy4EdeuXcPq\n1auRnJyMoUOHQq1W6ztOxsoWqdeDFApOPoxB4k2vT58+Rbt27QDkXTfSzF1Ut25dfPbZZzhw4ID+\nImSsLKpfX3s5Xw9iTCtJyah69ep49eoVAKBx48Z4/PgxMjMzAQDvvvsuHhf2K5CxiqpbN+3lfD2I\nMa0kJSNPT09ERETg5cuXaNy4MapWrYojR44AAC5dugRzc3O9BslYmaNQAGPGADY2gJFR3t8xY/iU\nHGOFkHzT69ChQ/HZZ58hLCwMgwcPxvz587Ft2zZcvXoVgwYN0necjJUuUobx4etBjEkmKRnZ29vj\nwIEDuHnzJgBg+vTpMDc3R1xcHMaPH4/PPvtMr0EyVqrwMD6M6ZykZLRo0SL06dMHbdu2BQDIZDKM\nGzdOr4ExVmrxMD6M6Zyka0ZRUVF4/vy5vmNhrGzgYXwY0zlJycjV1RWxsbH6joWxsoG7bTOmc5JO\n0zk6OiI0NBQ///wz7O3tUa1aNdFymUwmGs2bsXKNh/FhTOckj8BQp04dvHz5EvHx8QWWy2QynQfG\nWKnFw/gwpnOSktHRo0f1HQdjZQt322ZMpwq9ZnT27FlkZWUZMhbGGGMVVKHJaNSoUbhz546oLDIy\nEs+ePdN7UIwxxiqWQpORZs4ijdzcXCxcuBCPuPsqY4wxHZPUtVsjf4JijDHGdKFYyYgxxhjTB4Mn\no9zcXCxfvhxt2rSBu7s7Jk+ejCdPnhRa/8CBA+jduzfc3NzQqVMnbNq0Cbm5uQaMmDHGmL4VOxn9\n23uKQkJCEB0djeDgYERERCApKQn+/v5a6544cQIzZszARx99hD179mD69OkIDQ3Fxo0b/1UMjDHG\nSpci7zMKCAiAiYmJqGzixIkFyoC8G2PfRqlUIiwsDPPmzYO3tzcAYMWKFfDz80NcXBw8PDxE9Xfs\n2IHOnTtj6NChAIB33nkHd+7cwc6dOzFx4sS3bo8xxljZUGgy+vDDDwuU5U8WxXX9+nVkZWXBy8tL\nKLOxsUHDhg0RGxtbYP3jx48vMPSQkZERD9rKGGPlTKHJaMmSJTrfWFJSEgCgbt26ovI6deoIy97k\n4uIiepyZmYnt27cLU1kwVmKkTK7HGJNM0nBAupKdnQ0jIyMYGxuLyk1MTJCTk/PW506YMAE5OTmY\nPn26PsNkrGg8uR5jOmfQ3nSmpqZQq9VQqVSicqVSiapVqxb6vLS0NIwcORJXr15FaGgoGjZsqO9Q\nGStcUZPrMcb+EYMmo/r/mwcmNTVVVJ6SklLg1J1GQkICBg0ahISEBERERBQ4dceYwfHkeozpnEGT\nkZ2dHczMzHD+/HmhLCEhAYmJiVBoOb3x9OlTDB8+HGq1Gtu3b4ednZ0hw2VMO55cjzGdM+g1IxMT\nEwwePBhLly6FhYUFrKysEBQUBC8vL7i5uUGpVCIjIwM1a9aEiYkJgoKC8OzZM2zduhWmpqbCEZVM\nJoO1tbUhQ2fsNZ5cjzGdM2gyAoApU6ZApVJh5syZUKlUaNu2LebPnw8AiI+Px/DhwxEWFgZXV1cc\nPnwYarUaH330kWgdlSpVwtWrVw0dOmN5eHI9xnRORuVw9NOEhAT4+fkhJiYGNjY2JR0OY4yVCSX5\n3ckDpTLGGCtxnIwYY4yVOE5GjDHGShwnI8YYYyXO4L3pGCvVeMw5xkoEJyPGNHjMOcZKDJ+mY0yD\nx5xjrMRwMmJMg8ecY6zEcDJiTIPHnGOsxHAyYkyjWzft5TzmHGN6xx0YGNPgMecYKzGcjBh7k0LB\nyYexEsCn6RhjjJU4PjJiFQff0MpYqcXJiFUMUm9o5YTFWIng03SsYpByQ6smYSUmAmr164R14YJh\nYmSsAuNkxCoGKTe08ggMjJUYTkasYpByQyuPwMBYieFkxCoGKTe08ggMjJUYTkasYlAogDFjABsb\nwMgo7++YMeLOCTwCA2MlhnvTsYqFSPz3TTwCA2MlhpMRqxikdu3mERgYKxGcjFjpJ+Xen7fVKaqn\nHCcfxkocJyNWukk5opFSh3vKMVaqcQcGVrpJufdHSh3uKcdYqcbJiJVuUo5opNThnnKMlWp8mo7p\nh67GeKsBS0mMAAAgAElEQVRfP++0W35vHtFIqcM95Rgr1TgZMd2T2nNNU7eopNWtm3hdGm8e0Uip\no9k2Jx/GSiVORkz3pPZck5K0pBzR8FEPY2UeJyMmpotu1FJ7rhWnu3VRN6sCfNTDWBln8GSUm5uL\nVatWITo6GllZWWjbti3mz58Pa2trrfX/+OMPLF68GNeuXUPdunUxYcIE9OnTx8BRlwG6uEajq27U\nUq7hANKSVnFO+THGyiyDJ6OQkBBER0cjODgYtWrVQlBQEPz9/bF9+/YCddPS0jBmzBj06NEDixcv\nxpkzZzB37lxYW1ujTZs2hg79NV1dnJe6nrfV09XEcVKOVKTU6dYNWLIEePAA+PtvoFo14J138saC\ne5OUpMU3qzJWIRg0GSmVSoSFhWHevHnw9vYGAKxYsQJ+fn6Ii4uDh4eHqP6PP/4Ic3NzzJ07F0ZG\nRmjSpAmuXr2K77///p8lI0MdPehyPVLqSfnC1tWNoVJPwREBMlne/2Uy7afXpHQ84JtVGasQDHqf\n0fXr15GVlQUvLy+hzMbGBg0bNkRsbGyB+rGxsVAoFDAyeh2ml5cX4uLiQIVdOyiMrmbx1NUEbFLX\nI6WeriaOk3JjqJQ6Bw8CdeoAHh5A27Z5f+vUKdg2KSNp882qjFUIBk1GSUlJAIC6deuKyuvUqSMs\ny19fW93s7Gw8e/aseBvXVRLR1S91qeuRUk9XE8dJuTFUSp3ivEYKBRAYCGzYkPc3/9El36zKWIVg\n0GSUnZ0NIyMjGBsbi8pNTEyQk5NToP7Lly9hYmJSoC6Qd8qvWHSVRHT1S13qeqTU09XEcVKOVAx9\nNCNle4yxMs+g14xMTU2hVquhUqlQufLrTSuVSlStWlVr/fxJR/NYW/0iSe3h9TZSb7DU1Xqk1JNy\nn40ubwx9Wx1dvUbFiYkxVqYZNBnV/98v5tTUVOH/AJCSklLgdBwA1KtXD6mpqaKylJQUVKtWDdWr\nVy/exnX1BamrGyylrqc49YqKwZA3hvJNqIyxYjJoMrKzs4OZmRnOnz+P3r17AwASEhKQmJgIhZYv\nKk9PT+zcuRNEBNn/emadO3cOHh4eok4N+eXm5gKA+DpU/fpA797AiRNAcjJQty7Qvn1eeUJC8RpS\nvz4wcqS4rLjrKM56DL09XTDkthhjOqH5ztR8hxqSQZORiYkJBg8ejKVLl8LCwgJWVlYICgqCl5cX\n3NzcoFQqkZGRgZo1a8LExAT9+/fH5s2bsWDBAowYMQJnzpzBvn37EBoaWuR2NEdTQ4YMKTqgXbt0\n1TTGGCs3UlNT0bhxY4NuU0bF7iP976hUKixbtgzR0dFQqVTCCAyWlpY4d+4chg8fjrCwMLRo0QIA\n8Pvvv+Orr77CjRs30KBBA0yePBkffPBBkdt4+fIlrly5gtq1a6NSpUqGaBZjjJV5ubm5SE1NhZOT\nE0xNTQ26bYMnI8YYYyw/nlyPMcZYieNkxBhjrMRxMmKMMVbiOBkxxhgrcZyMJHr69CkCAgLQvHlz\ntGrVCt988w1UKlWRz2nVqhVsbW1F/9avXy8sv3//PkaPHg13d3e0b98em7XdlGtgxW3nq1evsHbt\nWnTs2BFubm748MMPceTIEVGdpUuXFngdOnXqpO+mCHJzc7F8+XK0adMG7u7umDx5Mp48eVJo/T/+\n+AMDBw6Eq6srOnfujF35bgHIzs5GYGAgWrRogebNm2PevHnIysrSdzPeqrjtPHDgAHr37g03Nzd0\n6tQJmzZtEt1fcuLEiQL7zdbWVus4koZS3DYGBAQUiP+TTz4RlpeHfTls2DCt+8nW1hYX/jcQdGnc\nlwUQk2TQoEE0ePBgunbtGh0/fpxatmxJK1asKLR+amoqyeVyunDhAqWkpAj/srKyiIgoJyeHOnbs\nSP7+/nTr1i3as2cPubq6UmRkpKGapFVx27l06VLy9vammJgYunfvHm3cuJHs7Ozo/PnzQp3Ro0dT\nUFCQ6HV4+vSpIZpDREQrV64kb29vOnXqFF25coU++ugjGjhwoNa6T58+JS8vL/ryyy/p9u3bFBYW\nRg4ODnTy5EmhzowZM6hbt24UHx9PFy5coE6dOtG0adMM1ZxCFaedx48fJ3t7ewoPD6f79+/TwYMH\nqXnz5rR27Vqhzrfffkt9+vQR7beUlBTKzc01VJMKKE4biYi6du1K3377rSj+9PR0YXl52JfPnj0T\ntS8pKYn69OlDQ4cOpVevXhFR6dyX+XEykiAuLo7kcjk9ePBAKNu5cye5u7tTTk6O1uecOXOGHBwc\nSKlUal2+d+9ecnNzo8zMTKEsJCSEOnfurNvgi6G47czNzSWFQkHbtm0TlQ8fPpzmzJkjPG7Xrh1F\nRUXpL/Ai5OTkkLu7O/30009C2cOHD0kul9PFixcL1N+4cSP5+vqKPqRz5syhkSNHEhHR48ePyc7O\njn777Tdh+blz58jW1paSkpL02JKiFbed48aNo4CAAFHZ2rVrydfXV3g8Y8YMmjVrlv6CLqbitjEn\nJ4ccHBzo7NmzWtdXXvZlft9++y15enpSSkqKUFba9qU2fJpOgtjYWDRs2BCNGjUSyry8vJCVlYVr\n165pfc7NmzfRqFGjAiOUv7lOJycnmJmZidZ57969Ik876FNx26lWq7Fq1Sp07txZVG5kZITnz58D\nAF68eIGkpCQ0adJEv8EXQtdzaMXFxcHIyEg0EaSHhwcqVaqEixcv6rcxRShuO8ePH49JkyaJyt7c\nbwBw69atEttv2hS3jXfv3oVKpSq0DeVlX74pNTUVGzZswNSpU1G7dm2hvLTtS204GUmQnJyMOnXq\niMo0jx8XMjXFrVu3ULlyZYwdOxbe3t7o27ev6NpDUlJSsdepb8VtZ+XKldG6dWtYW1sLZZcvX8Zv\nv/2Gtm3bAshLygCwc+dO+Pn5wc/PD0FBQXjx4oW+miGi6zm0kpOTYWlpKfqRUblyZVhaWpbYfgOK\n304XFxc0bdpUeJyZmYnt27cL+y03Nxd3797FlStX0KtXL7Rp0wbjx4/H3bt39diKohW3jTdv3oSx\nsTFCQkLQoUMHdOnSBStXrhSmqykv+/JNoaGhsLKywsCBA4Wy0rgvtTHo2HSlVUJCAvz8/LQuMzEx\nQa9evVClShVRubGxMWQymdZ5mADg9u3bSE9PR0BAAKZOnYpff/0VX3zxBXJzc9GvXz+8fPkSlpaW\nBbYFoNB1/lv6aOeb7t+/j0mTJsHFxQX9+vUDkPc6AECtWrWwfv16JCQkIDg4GLdv30ZYWJgwAK6+\n6HoOrezs7AKvUVHrM5TitjP/cydMmICcnBxMnz4dAPDgwQPk5ORAqVTiq6++glKpxIYNGzBkyBDs\n27cPVlZWemtLUXEWp42a997777+PIUOG4ObNm/j666+RlJSE4ODgcrcvMzMz8dNPP2HmzJmiYdBK\n477UhpMR8n6BHDhwQOsyIyMjREREFJhX6dWrVyAiVKtWTevzwsLCoFQqYW5uDiBvxPLExERs2bIF\n/fr1K3KupsLW+W/po50aV65cwdixY2FpaYmNGzcKH6QBAwagU6dOQuK1tbWFtbU1BgwYgD///BNO\nTk46aFnhdD2Hlrblmjr62m9SFLedGmlpaZgwYQJu376N77//Hg0bNgQAvPfeezh37hxq1KghnLJc\nu3YtOnTogN27d2PUqFH6bZAWxW3jlClTMGrUKNSqVQtA3nuvUqVKmDp1KubMmVPu9mVMTAxyc3PR\nq1cvUXlp3JfacDJC3q//os6n1qtXDydOnBCVpaSkACh4KK1hYmJS4Be2XC7H/v37hXX+9ddfxVrn\nv6WPdgLAqVOn4O/vDzs7O2zcuBE1a9YUlslksgJHgHK5HEDe6Qh9JyNdz6FVr149pKWlITc3V/j1\nqVKpkJaWVuAUpyEVt51A3pHy6NGjkZWVhYiICNjZ2YmWa77ENapWrYpGjRqV2Cms4rbRyMioQBve\nfO+Vp30J5CWjDh06aE2kpW1fasPXjCTw9PTEw4cPRTvu3LlzMDMzK/ABBvLe0O3bt8cPP/wgKr9y\n5Ypwnt7T0xNXrlxBdna2aJ3vvfdeiR02F7edQN4F//Hjx6NFixb44YcfRIkIAIKDg9G3b19R2ZUr\nVwDAIBdU35xDS+Ntc2jFxsaC3hg/+M05tDw9PaFSqRAfHy8sv3jxItRqNTw9PfXbmCIUt51Pnz7F\n8OHDoVarsX379gL798iRI3B3d0daWppQlpmZiXv37qFZs2b6a0gRitvGgIAATJw4UVR25coVmJiY\n4J133ik3+1Lj4sWLaNmyZYHy0rgvtSrZznxlg1qtpgEDBtDHH39MV65cEe6/WbNmjVAnMzNT1JVy\nwYIF5OXlRUeOHKF79+7R5s2bRferZGdnk4+PD40fP55u3LhBe/fuJVdXV1F3TkMrbjtzcnKoXbt2\n1KNHD3r06JHWezkuXrxI9vb2FBwcTPfu3aOTJ09Sx44dafr06QZr1zfffEOtW7emEydOCPdsDB06\nVGhDSkqK0HU9NTWVPD09KTAwULjPyNHRkc6cOSOsb8qUKdS5c2eKjY0V7k2ZPXu2wdpTmOK009/f\nn9zc3OjSpUui/ZaamkpEROnp6dSmTRsaNWoUXbt2ja5cuUKjRo2ijh070suXL8tEG/fv30+2trb0\n/fffC/dS5b9vrjzsSyKi5OTkQrt+l9Z9mR8nI4lSUlJowoQJ5OrqSq1bt6bly5eL7kVZs2YNyeVy\n4XFOTg6tWLGCfHx8yNHRkXr27Em//PKLaJ137tyhYcOGkbOzM3Xo0IG2bNlisPYUpjjtPHnyJMnl\ncq3/RowYITzn+PHj1K9fP2Gd//nPfwz6IXj16hUtWbKEvLy8yMPDgwICAoSbbn/77TeSy+Wie03i\n4+OpX79+5OTkRJ07d6Z9+/aJ1peZmUlz5swhDw8P8vLyosDAQMrOzjZYewojtZ3Z2dlkZ2endb/Z\n29sL67t9+zaNHTuWFAoFubu706RJkygxMbGkmkdExd+X0dHR1KNHD+Eztn79etH7uazvS40rV66Q\nXC6n27dva11fadyX+fF8RowxxkocXzNijDFW4jgZMcYYK3GcjBhjjJU4TkaMMcZKHCcjxhhjJY6T\nUQVVVjtRltW4/4mK1FbGSjwZFTVLoa2tLUaPHl3SIf5rCQkJsLW1xe7du4v1vPPnz6Nr165wcnLC\n2LFjdRbPTz/9hODgYOHxzp07S9+sj1rEx8fr9HVQKpXo2bOnMCz/sGHDRLOASpGZmYkJEybA1dUV\nCoUCDx8+1Elsd+7cwaBBg0Rl+WcKZvrzT94LZZk+2rtr1y589tlnkuuXirHpnJ2dMW/ePK3Lqlev\nbuBodK9OnTqIjIzEO++8U6znLVu2DLm5udi0aZNomoZ/a+PGjaLhTjp06IDIyMgCY8iVNlFRUcJI\nzLqwbt06vP/++2jevDkAYMGCBcUeRXzv3r2IiYnB/Pnz0axZM2Gg0X/r559/Fg1TAwCRkZGiscqY\n/vyT9wIT6927N8LDwxEVFYX+/fu/tX6pSEbm5uZwc3Mr6TD0xsTE5B+1Lz09HR4eHmjdurUeonrN\n0tKy1CciXUtOTsZ3332HqKgooezN+X2kSk9PBwAMHjxY719e5fkzUtr8k/cCE5PJZBg3bhwWLFiA\nHj16wNTUtMj6JX6aTqqtW7fC1tYWe/bsEcqOHz8OW1tbhIWFCWW7du1Cnz594OrqCl9fX6xZswa5\nubnC8hs3buDTTz+Fu7s7PD09ERAQUOD01NatW9G1a1c4Ozujbdu2WLhwITIzM4Xlp0+fxoABA+Du\n7g6FQoEJEybgzp07hcae/zTdzp074ezsjLi4OHz00UdwdnaGj48Pvv/+e1H9+/fvY9euXbC1tcW5\nc+ckx5+SkoJZs2ahZcuW8PDwwIgRI/Dnn38CAHx9ffHgwQNER0fD1tYWCQkJWk/TnThxAgMHDoS7\nuztatWqFefPm4dmzZ8LykJAQdO3aFTExMejZsyecnJzQpUuXt56KnDNnDkaNGoXAwEC4u7ujd+/e\nICKkpaVhwYIF8PHxgZOTE7y8vODv74/ExETheVFRUUhMTIStrS127twJIG/+oeDgYLRr1w7Ozs7o\n06cPYmJiiowBALZs2YLGjRuLBgjNf6rC1tYWO3bswOeffw6FQgF3d3cEBATg6dOnQv1Vq1YByBvc\ncs6cOZJjIiJs2bIFXbt2hYuLC7p06YLw8HDhtV29erUQQ0hIiPD/N0/TJSUlYdasWWjbti1cXV0x\nZMiQAoNr2tra4pdffsGkSZPg7u4OLy8vBAYGigbovXLlCkaMGAFPT0+4u7vjk08+we+//17k66dZ\n95YtW9ClSxe4ublh7969AP79e1TzGq5evRpdunSBs7MzunfvjsjISGH5559/jrZt20KtVovWO3fu\nXPj6+grX2y5cuIAhQ4bA1dUVLVq0wLx580Sz2Wo+izt27EDr1q3RokULPHjwoMB7Qa1WY+PGjejY\nsSOcnJzQtWtX/Pjjj6JtP3jwAOPGjUOLFi3g6uqKjz/+uMAo+Pn5+vpi9erVWLhwITw8PNCyZUss\nXLhQtH8A4PDhw+jbty+cnZ3Rpk0bBAcHi6a/0Hwe16xZA4VCgXbt2iErK0vrNh89eoRJkybB09MT\n3t7eBQZ0BvDWz+O2bdtga2tb4LT0jh074OzsLPxIa9++PZRKJX766aciXwcAJT9Q6tChQ2n48OH0\n6tUrrf/UajUR5Q3iOXjwYGrVqhWlp6dTeno6eXt708iRI4U6ERERJJfLKTAwkE6ePElbt24lJycn\nCg4OJiKiu3fvkru7O3300Ud0+PBh2r9/P3Xt2pV8fX3p+fPnRES0d+9ecnR0pLCwMDp37hxt376d\n3NzcaM6cOURE9ODBA3JxcaGgoCA6e/YsHTp0iLp06UJ+fn5CHPlp5q/ftWsXERH99NNPZGdnR+3b\nt6etW7fS2bNnadq0aSSXy+nMmTOUk5ND8fHx1K5dO/r0008pPj6eXrx4ISn+zMxM8vX1JR8fH4qO\njqZff/2Vhg4dSgqFgh4/fkx//vmnaL05OTn0008/kVwup8ePHxMRUVRUFMnlcpo5cyadOHGCIiMj\nqXXr1tSjRw/6+++/iShvjDo3Nzfy8fGhH3/8kU6fPk2jRo0iW1tb+uuvvwrd37NnzyYHBwcaO3Ys\nnTlzho4ePUpqtZo+/PBDYRy43377jbZu3Uru7u40ZswYIiK6f/8+jRs3jry9vSk+Pp6ePn1KarWa\nRo8eTR4eHrRlyxY6ceIEzZkzh2xtbenw4cNFvu+8vb1p1apVBd6Lb46pJ5fLydPTk+bMmUOnTp2i\nbdu2kbOzM82YMYOIiG7dukWBgYEkl8spPj6e7t+/Lzmmr7/+muzt7Wn58uV0+vRpCgkJIVtbW4qI\niKDHjx+L1qvZL3K5nNatW0dEeQNjent7U+fOnWnPnj0UExNDo0aNIgcHB2FQV837TqFQ0Ndff01n\nzpyhjRs3kq2tLa1cuZKIiF68eEEtWrSggIAAOn36NB07dowGDBhAnp6e9OLFi0JfP826PT09aefO\nnXTgwAFKTU3VyXtUrVbT0KFDyd3dnX744Qc6efIkLVq0iGxtbWnt2rVERHT27FmSy+V07tw5Iaac\nnBxSKBTCQKjnz58nR0dH+vTTT+nYsWMUFRVF3t7e1L9/f3r16hUR5X0WbW1tqVu3bnT8+HGKjo7W\n+l4IDAwkR0dHWrt2LZ08eZK+/vprsrW1pbCwMCIiys3Npa5du9Lw4cPp+PHjdOrUKfrss8/I3t6e\n7t+/X+jr6OPjQ56enjRw4ECKiYmhLVu2kKurK02cOFGos2fPHpLL5TRr1iw6ceIEhYeHC+PLaaxZ\ns4YcHBxowIABdOrUKTpw4IDW7WVlZZGPjw916tSJ9u/fTwcPHqRu3bqRo6Oj0F4pn8f09HRycnKi\nkJAQ0fo//vhj8vf3F5XNmDGDPv7440JfA41SkYwKG2xTLpfTiRMnhLr37t0jV1dXWrBgAc2YMYMU\nCgUlJSURUd6boVWrVgVeiJCQEBo4cCCp1WqaNm0aeXt7U2ZmprA8JSWFXFxcaP369USU96br0qWL\naDDF3bt3C2+6ffv2kVwuF7ZLRHTp0iVasWJFoR9ebclILpdTVFSUUCcnJ4ecnZ1p8eLFQlnHjh1F\nIwhLiT88PJxsbW3pxo0bQp3nz5+Tn58f7d+/X+t630xGubm51Lp1a/rss89Ebfj9999JLpdTREQE\nEb0eMPXs2bNCncTERJLL5UUO+Dp79mySy+WUnJwslD1+/JiGDh1aYMThRYsWkaurq/D4iy++IB8f\nH+HxqVOnSC6X06FDh0TPmzBhAnXu3LnQGG7fvk1yuZyOHj0qKteWjIYMGSKqM2fOHGrevLnweN26\ndaIBcqXElJGRQQ4ODvT111+L6sydO5cCAgK0rlcTjyYZff311+Ts7EyPHj0Slufm5lKPHj2ob9++\nRPT6fTdr1izReoYNG0a9e/cmorxBYfOP9nz//n1aunSp6D2en2bdgYGBonJdvEePHTtGcrmcDh48\nKFr3ggULyMnJidLS0kitVlP79u1p/vz5wvKYmBjRYKEff/wx9erVS/RZvnr1Ktna2tLu3buJ6PV7\nP/9guG++F+7evUu2trb03XffieosXryYPD096e+//6aUlBSSy+W0Z88eUZv+85//0M2bNwt9HX18\nfKhFixai1ys8PJzkcjndvHmT1Go1tWvXjsaOHSt63uHDh0kul1NsbCwRvf48xsfHF7otorwf7HZ2\ndqIBVR89eiRKRlI/j5oRzzX++usvksvldOzYMdHztmzZQg4ODpSVlVVkbKXiNJ2LiwuioqK0/vPw\n8BDqNW7cGNOmTUNkZCT27NmDhQsXCpNN/fXXX3j69Ck6d+4sWvekSZOwfft2yGQy/Pbbb2jZsiWq\nVKkClUoFlUoFCwsLuLi44MyZMwCAli1b4q+//kLfvn2xdu1a/PHHH+jZsyeGDRsGAHB1dUWVKlXQ\nv39/LF68GCdPnoSdnR2mTp0qzOoq1ZttMzExgaWlZYHD8zdJif/ixYto3LixMIkYkNcJ5MiRI+je\nvftbY/rrr7/w5MkTfPDBB6JyV1dXNG7cWDhdqK0N9erVA4Ai2wAA1tbWosnL6tWrh/DwcLi7uyMh\nIQGnT59GeHg44uLi8OrVq0LXc/bsWVSqVAnt2rUTXg+VSgVfX1/cu3cPCQkJWp+nObVgY2NTZJz5\n26eJtaj2SYnp999/h0qlKvBe/eqrr4TTfm8TGxsLT09PUYcGIyMjdO/eHX/++afotHJRbWjWrBks\nLS0xbtw4zJ8/H4cPH4a1tTVmzpyJunXrgohE7VCpVKIu5/b29qJ16+I9euHCBRgbGxd4fXr27Aml\nUolLly5BJpOhZ8+e+OWXX6BSqQAA+/fvh6OjI5o0aYLs7GxcunQJHTp0EGZNValUaNasGRo0aCDE\nolHYfF2aNhERfHx8CuzTFy9e4PLly7C2tkbTpk0RGBiI2bNnY+/evVCr1fj888/fOmeQr68vzMzM\nhMeadsfGxuLu3btISkqCr6+vaNtt27aFsbFxgXbk3x/5xcbGonHjxqK5xOrXry+6Hin189ivXz/c\nu3cPly5dApB3iaR27dpo27ataJsNGzaESqVCcnJykbGVig4MZmZmcHZ2llT3gw8+wNKlS2Fqaiqa\nSEpzjrKoienS09Oxd+9e4dz2m959910AQPfu3aFWq/Hf//4X69evR0hICBo2bIgZM2age/fusLGx\nQUREBDZt2oSoqCiEhYWhRo0aGDx4MKZMmVKsi9j5pxA2MjIqcA68uPGnp6f/q8n5NK9j7dq1Cyyz\nsrISfclVqlRJNJutZkrjotoAaJ9Wfc+ePVixYgUeP36MWrVqwd7eHqampkXea5Oeno7c3NxCL+yn\npKRoTTgvXrwAUPD11yb/RVcp++htMUl5r75NRkaGsM/fZG1tDSISXS8oqg1mZmbYtm0bNmzYgIMH\nDyIyMhKmpqbo3bs35s2bh/j4eAwfPlz0/LCwMKHXYP59qYv3aEZGBqysrIT305ttA17vv969e2PT\npk04e/YsmjdvjqNHj2LKlCkAgOfPnwvXeTZu3FhgG5oZjDXeTAb5afZX165dtS5PSUmBTCbD999/\njw0bNuDw4cPYtWsXjI2N0bFjRwQFBRWYdPJN+WeV1XQmev78ubDtwMBABAYGFtmOSpUqoUqVKoVu\nB8h7bbV1Vqpdu7bomrCUz2Pr1q1Rv3597N69Gy4uLtizZw969eolzJqroXmPaPZbYUpFMiqORYsW\noVatWiAiLFy4EGvWrAHwugv4m7MZAsCTJ09w69YteHh4wNzcHO3atSvw4QIg+lLt0aMHevTogRcv\nXuDUqVMIDQ3FzJkzoVAoULt2bbi4uGDt2rVQKpW4ePEiIiMjsXHjRjg4OKBLly56a7uU+KtXr651\nKuHY2FhYW1tr/QJ7k+ZDk3/qbU2Zq6vrP4i8aLGxsZg9ezZGjBiBkSNHCke7S5cuLfJCevXq1VG9\nenWtF2AB4L333tNabmFhAeDtH45/QkpMGRkZAPLeq29299fMsqvpal6UGjVq4MmTJwXKNV9OFhYW\nBb5wC/P+++/jm2++QW5uLi5fvozdu3dj+/btePfddzFgwABRj0NNGzRfkvnp4j1ao0YNPH36FGq1\nWpSQNO9Jzf5r2rQpHB0dcejQIWRmZiInJwc9evQAkJdcZDIZRo0ahW7duhXYVlHJJz/Nd0tERITW\nHmGaHzx169bFwoULsWDBAly/fh2HDh1CaGgorKystCYSjfyvpaaDjKWlpbDtzz//XOvss5rXQioL\nCwthpuXCYpD6eTQyMkKfPn3w448/omfPnkhMTCwwqzMA4f3+tlhLxWk6qQ4dOoSDBw9i7ty5+OKL\nL/Dzzz/j4MGDAPI+ULVq1cLRo0dFz4mMjMSECRMAAF5eXrhz5w4cHR3h7OwMZ2dnODg4YNOmTfj1\n18MVBqwAAAfASURBVF8BANOnTxemKq5evTq6deuGCRMmQKVS4cmTJwgPD4evry+USiVMTEzQqlUr\nLFq0CAD0Pp+8lPg9PDxw//593L17V3heVlYWxo0bh/379wNAgV+cb3r//fdhbW0t1NW4fPkyHj58\nWOCUjy7Ex8dDrVbD399feOPn5ubizJkzoqOQ/L+4FAoFXrx4gcqVKwuvh7OzMy5fvowNGzYUepTa\noEEDANDLTb5SYnJ1dYWxsTGOHTsmeu6GDRvwxRdfwMjIqEBbtW3n4sWLojao1WocOnQIzs7Ooh9X\nRTl8+DBatmyJ1NRUVKpUCe7u7li4cCFq1KiBx48fw9zcXNQOZ2fnIk9H6+I96uXlhVevXuGXX34R\nrXvfvn0wNjaGi4uLUNarVy+cOHECBw8ehLe3t3DEZW5uDgcHB9y7d08U+3vvvYdVq1YJp5ak0Pw4\nyMjIEK3r8ePHWLNmDbKzs3H58mW0bt0aly9fhkwmg729PaZOnQq5XP7W74WTJ08KpxqBvHvMZDIZ\nWrZsiSZNmsDS0hKJiYmibVtYWGDZsmVF9uLVpmXLlrh//z6uXbsmlKWlpYmSjNTPI5B3qu7p06dY\nsWIFnJ2dtXaJT05ORuXKlQscAeZXKo6MMjMzC/0FrPnwpqWlISgoCD4+PsIvnZ07d+LLL79EixYt\nYGlpiUmTJmHx4sWwsLCAr68vbt68iU2bNmH06NGoUqUKJk6ciAEDBmD8+PEYMGAAKleujIiICJw5\nc0a4271ly5aYN2+e0DX3+fPnWLt2Ld577z3I5XJUrlwZS5cuxcSJEzF06FBUqlQJO3bsQJUqVeDj\n46PX10lK/P3790d4eDjGjRsHf39/1KxZE9999x2qVKmCjz76CEDer+qrV6/i/Pnzog82kJeopkyZ\ngnnz5mHWrFno0aMHkpOTsXr1arz33nv48MMPdd4uTQyLFi1Cnz59kJGRgYiICFy/fh1EhJcvX8LU\n1BTVq1fHkydPcOLECdjb26NDhw7w8PDAuHHjMGHCBLz77ruIi4vDunXr0KNHj0J//b7//vto0KAB\nLl68qPN9JiUmMzMzDB06FN999x0qV66M5s2b4+LFi4iOjsZXX30F4PWv8X379sHNza3A6caRI0di\n9+7dGDFiBPz9/WFmZob//ve/uHPnDjZt2iQ5Xg8PDxARJk6ciM8++wxmZmY4ePAgMjMzC1yzkUIX\n71Fra2soFArMnTsXSUlJaNasGU6cOIEdO3Zg3LhxqFGjhrC9Hj16YOnSpThy5IhoVBEACAgIwLhx\n4zBnzhx0794dSqUSoaGhuHnzJmbPni25TXZ2dujRowe++OILPHz4EPb29rh16xZWrlwJR0dHNGjQ\nANbW1qhWrRpmzZoFf39/WFtb48yZM7h27RpGjhxZ5PoTExMxadIkDB48GHfu3MGqVavQv39/NGrU\nCAAwZcoUBAUFwcjICO3atUN6ejpCQkLw4sULODg4SG4HkHdqMywsDOPHj8fUqVNhZmaGDRs2iJKM\n1M8jADRq1AgKhQLnz5/H/PnztW4zLi4OCoXiracQS31vOs00yJMnTyY3NzdR7yFNN+vJkycLZT/+\n+KPQVbFTp060efNmUW+ay5cv06hRo8jNzY3c3d1p0KBBdOrUKVFM4eHh1L17d3JxcSEvLy8KCAgQ\nTdF7+vRpGjhwIHl4eJCrqysNGTKELly4UGgbC+tNp+myq+Hj40NffPGF8Dh/rzep8T9+/JimTJlC\nzZs3J09PT/r0009FvWf2799PrVq1IicnJ7p48aLWePbt20e9e/cmR0dHat26NQUGBlJaWpqwfM2a\nNaIpqjXe7PGlzezZs6ljx44FyiMiIsjPz4+cnJyoQ4cONHv2bKHH0MmTJ4koryt1165dydHRkUJD\nQ4kor2vyV199RW3atCFHR0fq2LEjhYSEkFKpLDQGIqIlS5ZQz549RWXaetPlb0v+dmvr9SYlptzc\nXPr222+FNn/wwQe0c+dOYXlycjL169ePHB0dKSgoSGs89+7do0mTJpGHhwe5ubnR0KFDRVNR53/f\naeTfB3/88QeNGjWKvLy8yNnZmfr27fvWrvGFrZtIN+/RzMxM+uqrr8jb25ucnJyoR48etGPHDq2x\njBkzhtzc3ITbDt506tQpGjhwILm4uJBCoaBRo0bR5cuXheWFfRbzvxeUSiWtWbOGfH19ydHRkdq3\nb0+LFy8W9aC9d+8e+fv7U6tWrcjR0ZE++OADioyMLPJ19PHxoRkzZtC8efPIzc2N2rRpQ6tXryaV\nSiWqt2/fPurTpw85OTlRy5Ytyd/fX3QLRWGfR22ePn1K06dPp+bNm5NCoaBvvvmGAgICRO2V8nnU\n2Lx5Mzk5OVF6enqBbeXk5JCXlxdt27btrXHxtOOsQkpKSkKnTp0QERGhl+tgjEnh6+uLVq1aYfHi\nxSUdyj82fPhw1K5dG8uXLy+wbM+ePcKRa7kZgYExXapXrx6GDh2K0NDQkg6FsTJp7dq1mDp1Ks6f\nP6/1VKRarcb333+PSZMmvTURAZyMWAU2ZcoU3Lt3TzSEDmNMmqNHj+LUqVP4/PPP4eTkVGD5rl27\nYGVlhYEDB0paH5+mY//ffh3TAAAAAAjq39oYPlDCCbBzRgDsxAiAnRgBsBMjAHZiBMBOjADYBceP\nAg7QICzZAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1716364a160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_sweep_frame_difference(frame)\n",
+    "decorate(xlabel='Excess infection rate (infections-recoveries per day)',\n",
+    "         ylabel='Fraction infected',\n",
+    "         legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": 27,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "s_inf_array = linspace(0.0001, 0.9999, 101)\n",
+    "#s_inf_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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": 29,
+   "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": 30,
+   "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\naqA0Wh0Z2eUT9nzcK899EUKI+iQJo4G6mlOEWqNfRt7F0RZHexsLRySEaO4kYTRQV7IKDI/l7kII\n0RBIwmig0iskDG932f9CCGF5JnV663Q6tmzZwt69eykoKKi0iZJCoeD99983S4DNVbr0XwghGhiT\nEsayZctYs2YNQUFB+Pn5yZwMczpwAM2Ob8jIbAFOztCyJd7uYZaOSgghTEsYW7duZcKECbz44ovm\njqd5K11wMEthj8bWHfLzcTl+BIfDLWT+hRDC4kzqw8jLy+POO+80dyyidMHBDIW9ochLW6hfcFAI\nISzMpITRtWtXEhMTzR2LSEkBIF1ZnjC8dUUgG1cJIRoAk5qknnjiCZ577jnUajWRkZHY29tXqhMZ\nGVnnwTU7/v6QnExGhRVqvXWFEBBgwaCEEELPpIQxbtw4AN5++20Ao05vnU6HQqGQ1WrrQuvW6LZt\nI6OVK9gVgLMznsoiGPR/lo5MCCFMSxjx8fHmjkMcOAC//kp+m/YU2zpCSQl217JxiYmSDm8hRINg\nUsKIjo42dxyitMM73TsIbLwA8NTmo7h00ZJRCSGEgcmr1Z49e5a4uDj2799Pbm4u7u7udO/enSlT\nptC+fXtzxtg8lHZ4Z1YcIaUrgss33u1QCCHqg0kJ4+TJkzzyyCM4ODgwYMAAPD09SU9P54cffuCH\nH35g48aNBAcHmzvWpq20wztTYWco8tQWSYe3EKLBMClhLFmyhLZt2xIfH4+jY/m6RgUFBYwfP57l\ny5ezatUqswXZLMTEwNq1XK1wh+GpK4JBD1gwKCGEKGfSPIyEhASeeOIJo2QB4OjoyKRJk0hISDD5\nhBqNhqVLl9KrVy+6du3KtGnTyMjIMOm9kydPZsyYMSafq1GJikLz6ESyXDwBBTg54TF+pHR4CyEa\nDJMShoND9YvfKRQKNBqNySeMi4tj69atLFq0iPXr15OamsrUqVNv+L6NGzeyd+9ek8/TGGWHdEYb\nGQm9e+Ha+zZsb5PBBkKIhsOkhBEREcGaNWsoLi42Ki8qKmLt2rV07drVpJOpVCri4+OZMWMGPXv2\nJDQ0lGXLlpGYmFjjTPILFy7w5ptvmnyexurqtSLDYw/XypMjhRDCkkzqw3juued48MEHGTBgAP37\n98fLy4uMjAz27NlDfn4+n3zyiUknO3HiBPn5+UbDdIOCgggMDCQhIaHK2eIajYYXX3yRSZMmcf78\neS5ebLryiUHjAAAgAElEQVTDTDNzJGEIIRouk+4w2rVrx8aNG4mMjGT37t2sXr2a77//nm7durFp\n0yY6depk0slSU1MB8PX1NSr38fExvHa99957D4CJEyeadI7GLKviHYabJAwhRMNi8jyM4OBgVqxY\ncVMnKywsRKlUYmNjvD+1ra1tpeYugKNHj/Lhhx+yefNmlMqmvzlgZsWE4SIJQwjRsFSbMLZv307v\n3r1p0aIF27dvv+GBYmNjb1jH3t4erVaLWq3G2rr81CqVqlLHenFxMS+88ALTp0+ndevWNzx2Y6fR\naLmWpzI8d3e1q6G2EELUv2oTxsyZM/nss89o0aIFM2fOrPEgCoXCpITh7+8PQHp6uuExwJUrVyo1\nU/3111+cPXuWJUuWsGTJEkCfWLRaLV27duXrr78moAlNasvOK0ZbuvWtq5MtNtZWFo5ICCGMVZsw\ndu/ejbe3t+FxXQgJCcHJyYn9+/czZMgQAJKSkkhOTibquvkG4eHh7Nq1y6hs2bJlXL58mSVLluDj\n41MnMTUUWbnlTXItXOTuQgjR8FSbMAIDAw2PDxw4QN++fXF3d69ULz09ne3bt/Poo4/e8GS2traM\nHDmSxYsX4+7ujqenJ/PmzSM6OpqIiAhUKhU5OTm4ublhb29fqSnK2dm5yvKmILtCwpARUkKIhsik\nnuSXXnqJS5cuVfna8ePHefPNN00+4fTp04mNjWXmzJmMHTuWgIAA3nrrLQAOHjxIr169OHjwoMnH\nayoqjpBylw5vIUQDVO0dxuTJkzlz5gyg3yTpqaeewtbWtlK9zMxMWrVqZfoJra2ZNWsWs2bNqvRa\njx49OHnyZLXvXbBggcnnaWwqNkm5S5OUEKIBqjZhPPnkk2zevBmAzZs307lzZzw8PIzqKJVKXF1d\nGTZsmHmjbOJ0Oh1ZueV3GNKHIYRoiKpNGBEREURERAD62dZTpkyhZcuW9RZYc5JfpKZErQXAztYK\nBzuTp8cIIUS9MakPY+HChZw7d45FixYZyg4fPsyECRP4/fffzRZcc5Fd8e7C2c5oz3QhhGgoTEoY\nO3bs4IknnuDs2bOGMgcHB7RaLRMnTuSnn34yW4DNQcURUi2cpTlKCNEwmZQw3n33XUaNGsXq1asN\nZbfeeivr1q3j4YcfvuklQ5q77LwKHd4ypFYI0UCZlDAuXrzIXXfdVeVrd911l9Gdh6i9nAp3GG7O\nlUeiCSFEQ2BSwvD09OTvv/+u8rWTJ0/i5uZWp0E1N1l5FZuk5A5DCNEwmTQcJzY2lrfffhtHR0fu\nvvtuPD09uXr1Knv27CEuLo6RI0eaO86m6cABtDu+4VqmOzg5QcuWuDl3tnRUQghRJZMSxlNPPcW5\nc+eYN28er732mqFcp9MxcOBApk2bZrYAm6wDB2DtWnKxQWvnDvn5OB4/iu2hFrKPtxCiQTIpYdjY\n2LBixQpOnTrFn3/+SU5ODi4uLnTr1o2QkBBzx9g0ffMNANcU5X0WLXTF8O23kjCEEA1SrWaIdejQ\ngQ4dOlQqz8/Px8nJqc6CahZSUiA9neycdPCwB2tr3GwKQZNn6ciEEKJKJiUMlUrFxx9/zIEDBygp\nKUFXum+DVqulsLCQkydPcujQIbMG2uRotXD8ODl+pX0WajVuyaehk59l4xJCiGqYlDCWLFlCfHw8\nHTp04OrVq9jZ2eHh4cGpU6coKSnh6aefNnecTVaObfmdmasq34KRCCFEzUwaVrtz504mTJjAtm3b\nGD16NGFhYXz++efs2rWLwMBAtFqtueNsepRKCAnhmnPpHiM2Nri19ANZFkQI0UCZlDAyMzPp06cP\noO/HOHLkCAC+vr48/vjj7Nixw3wRNlX+/uh8fLgW0Br8/cHLCzcPF2hC284KIZoWkxKGi4sLJSUl\nALRu3ZqUlBTy8vSds23atCElJcV8ETZVMTEUYk2JQv9PYKfTYIcGBg2ycGBCCFE1kxJGt27dWL9+\nPUVFRbRu3RoHBwe+//57AP766y+cnZ3NGmSTFBXFtYdH6yfsocDV1QHFpEkypFYI0WCZPHFv9OjR\nPP7448THxzNy5EheeeUVPvnkE44dO8Yjjzxi7jibpJy2wRDpAIBrUAuIamPZgIQQogYmJYyOHTuy\nY8cOTp06BcBzzz2Hs7MziYmJPPnkkzz++ONmDbJJOnCAa9v2QY4NODniatUeaGPpqIQQolomJYzX\nX3+doUOH0rt3bwAUCgVPPPGEWQNr0kqXBblmHQhW+mVBXL/7Blo6SJOUEKLBMqkPY/PmzVy7ds3c\nsTQfVSwL4opKvyyIEEI0UCYljC5dupCQkGDuWJqP0lFluQobQ5GrTgWXL1sqIiGEuCGTmqRCQ0NZ\ns2YNO3fupGPHjjg6Ohq9rlAojFaxFTfg7482OZm8CgnDRVcCgYEWDEoIIWpmUsLYuXMnPj4+FBUV\ncfDgwUqvK2R2cu3ExJC3dh1a9L83J10J1uhkDoYQokEzKWHs2bPH3HE0L1FR5F5Tw/fHIb8AF1dH\nGCpzMIQQDVu1CeO3334jPDxcli03k9x2wXBVPwfDpaU7RLW2cERCCFGzaju9H330Uc6ePWtUtmnT\nJrKyssweVHNwrUBleOziaFNDTSGEaBiqTRhle16U0Wg0zJ07l8sykqdO5OZXSBhOtjXUFEKIhsGk\nYbVlrk8i4p/LLSgxPHZ1lIQhhGj4apUw6oJGo2Hp0qX06tWLrl27Mm3aNDIyMqqtv2PHDoYMGUJE\nRAR33303q1evRqPR1GPE5pFXoUnKWZqkhBCNQL0njLi4OLZu3cqiRYtYv349qampTJ06tcq6P/74\nI88//zwjRoxg27ZtPPfcc6xZs4Z33323nqOuWzqdjlyjPgy5wxBCNHy1Thg3M+dCpVIRHx/PjBkz\n6NmzJ6GhoSxbtozExEQSExMr1d+4cSMDBw5k9OjRtGrVikGDBjF+/Hi2bNnyj2NoCAqL1Wi0+uY9\nO1srbG2sLByREELcWI3zMJ555hlsbY2//T711FOVykA/ue9GTpw4QX5+PtHR0YayoKAgAgMDSUhI\nIDIy0qj+k08+WWlWuVKpbPTrWlXsv5C7CyHqzhdffMH69es5c+YMCoWC4OBgxo4dy+DBgwEIDg5m\n8eLFDBkyxCznnzVrFqmpqXz00Ucm1T9z5gxJSUn069cPgP79+/Pggw8yZcoUs8R3s6pNGMOGDatU\ndv0FvbZSU1MB/dauFfn4+Bheqyg8PNzoeV5eHhs2bDCsmttYGfVfOEj/hRB1YdOmTSxatIg5c+bQ\nrVs3SkpK+O6775gxYwbFxcUMGzaMffv24erqaulQDaZMmUJsbKwhYWzevBl7e3vLBlWDahPGwoUL\n6/xkhYWFKJVKbGyML5K2trYUFxff8L1TpkyhuLiY5557rs5jqxcHDsA335CXVgyu7aBlS5zbelo6\nKiHqTunfOCkp+r3qY2LqbQWDTZs28dBDDzF8+HBDWfv27Tl//jzx8fEMGzYMb2/veonFVNePPPXw\n8LBQJKap105ve3t7tFotarXaqFylUuHg4FDt+65evcqECRM4duwYa9asIbAxLtJXugcGycnk6qwh\nPx9OnMA56bylIxOiblT4G0er1f9cu1ZfXg+USiWJiYnk5uYalb/44ovExcUB+iapL7/8EtA3H730\n0kvMmzeP7t2706NHD9555x1Onz7Nww8/THh4OEOGDOHIkSOGY1V8f01lZXbu3MkDDzxAeHg4Xbp0\n4eGHH+bw4cMAjBkzhosXL/L222/Tv39/QN8ktXLlSsP7d+/ezfDhw+nSpQv9+vUjLi7OcP38448/\n6Ny5M99//z2DBg0iLCyMoUOHmnVl8XpNGP7+/gCkp6cblV+5cqVSM1WZpKQkHnnkEZKSkli/fn2l\nZqpGo3QPDMB4ldrEPywRjRB1r8LfuJF62udl4sSJHD58mN69e/PEE0/w/vvvc/z4cTw8PAgKCqry\nPdu3b8fe3p4tW7Ywbtw4VqxYwVNPPcXkyZP5/PPPsbGx+ccrcR8+fJjp06czfPhwduzYwccffwzA\nyy+/DOhHjAYGBvLoo4+yefPmSu/ftWsXU6dOJSYmhi+//JIXXniBjz/+2Kj1p6SkhLfffpv58+fz\n5Zdf4uLiwr/+9S+zzZmr14QREhKCk5MT+/fvN5QlJSWRnJxMVBW3rZmZmYwdOxatVsuGDRsICQmp\nz3DrVukeGGCcMJzTU6qqLUTjk1LN33I9rQ4RExPDp59+St++fUlISGDx4sUMHTqUYcOGcfr06Srf\n4+HhwQsvvECrVq0YP348APfddx933nknwcHBDB8+vNr33oiNjQ2vvvoqo0aNIigoiPDwcEaMGGHY\n6rpFixZYWVnh6OhYZVPU6tWriYmJ4bHHHqNNmzYMHjyY6dOns3HjRsNdlE6n49lnn6V79+60a9eO\ncePGceHCBbMt4WTSarV1xdbWlpEjR7J48WLc3d3x9PRk3rx5REdHExERgUqlIicnBzc3N2xtbZk3\nbx5ZWVmsW7cOe3t7w52JQqHAy8urPkO/ef7++lt0rksYvtKHIZqICn/jRgIC6i2EyMhIIiMj0Wg0\n/P333+zZs4f169fz2GOPsWvXrkr1W7VqZZgqUDYis1WrVobX7e3tUalUld5nio4dO+Li4sJ7773H\nmTNnuHDhAsePH0er1Zr0/tOnTzN06FCjsqioKNRqNefOnTOU3XLLLYbHLi4ugP7OwxzqfeLe9OnT\niY2NZebMmYwdO5aAgADeeustAA4ePEivXr04ePAgRUVFfPfddxQUFDBixAh69epl+K9Pnz71HfbN\ni4kBQAsUKMrztNOguywUkBB1rPRvvJJ62OclJSWFuXPnGr5UWllZER4ezvTp01m+fDkpKSmcPHmy\n0vusrSt/Z67NXLPr+2Mr+u2334iJieH48eN07tyZGTNmMHv2bJOPXdVoqbJVLirGXdU0B3M1SdXr\nHQboP+isWbOYNWtWpdd69Ohh9I96/Pjx+gzNvEqb3Ap27EKbqQQnRxxuaYV1j+gbvFGIRqKsWfnb\nb/XNUAEB+mRRD6Ok7Ozs2Lx5M61bt2bChAlGr7m6uqJQKPD0vPm7eRsbG/Ly8gzPL1y4UG3ddevW\n0bNnT5YvX24o++WXXwD9BV2hUNSYnNq1a0diYiKjR482lP3555/Y2NjQqlUrjh07djMf5R+p94TR\nrEVFkd8uFHbr2zCdW1Q/MkyIRikqyiIbgXl4eDBx4kSWLl1KXl4eAwcOxN7enlOnTrF8+XKGDRtG\nQB00jUVERPDZZ5/RrVs3NBoNCxcurPIbPoCfnx979+7l0KFDeHp6snfvXtatWwfoR4ba2dnh5OTE\n+fPnSUtLqzTw58knn+Txxx+nY8eO3H333Rw/fpwVK1YwYsQIQ9NTfZOEUc9k0p4Q5vHss8/SunVr\nPvvsMz766COKi4tp1aoVw4YNM3Ro36y5c+cyd+5cRowYgY+PD8888wxpaWlV1p02bRpXrlxh4sSJ\nWFlZERwczH/+8x+effZZjhw5Qvfu3Rk/fjzz589n3759/Pbbb0bv7927N4sWLeK9997jrbfewsfH\nh7FjxzJ58uQ6+Sz/hELXBNcsT0pKYsCAAezevbva4XSWcvhMOj8d1HcMhrX1pF+3lhaOSAgh9G50\n7az3Tu/mLq/COlJOcochhGhEJGHUs/xCSRhCiMZJ+jDqQ4X1dfI9OkPgreDtLQlDCNGoSMIwt7L1\ndUrlXSuA3BMAODsEWyoqIYSoNWmSMrfr1tfJp/Su4tIlucMQQjQqkjDMrcL6OiqUlCj0v3Lr/Dzs\nZKc9IUQjIgnD3EpX6AXIr7AkiKOz/U1tdyuEEPVNEoa5VVhfx9AcBTiFtLdENEII8Y9Jp7e5VVhf\nJz81H5yc9DvtdWhr2biEEKKW5A6jPkRFwcsvkz9tBkRGgrc3jvbS4S2EOaSnpxMaGsrgwYPr/Nhb\ntmyhU6dOdXKsmnbqa6jkDqMeFRRVmLQnCUMIs9i2bRtBQUGcPXuWhIQEunfvbumQqrRv3z5cXV0t\nHUatSMIwpwoT9vD3J79DH7BqAYCjg/zqhTCHL774gsGDB7N37142bdrUYBOGt7e3pUOoNWmSMpey\nCXvJyZCWBtu2URD/Kfz8M6Snyx2GEGZw5MgRTp06xR133MHAgQPZuXMnOTk5hteDg4PZvHkzo0aN\nonPnzvTr149NmzYZXi8uLmbhwoXceeedhIWFcdttt/HSSy9RWFhY6VwLFizg3nvvNSq7ePEiwcHB\nHD9+nIyMDJ5++mnDjqLjx4832uOnYpPUuXPnePTRR4mMjKRbt25MmTKFpKSkuv713DRJGOZSNmEv\nPR2OH4f8fPKt7SEjA06cwPHk35aNT4gmaOvWrXh5edGtWzdiYmIoLi7miy++MKqzZMkSRo0axY4d\nO7j77ruZO3cuyaVbyy5atIgffviBN954g2+//ZZXXnmFr7/+2iiplBk2bBhnzpwx2sho27ZthISE\n0LFjR+bNm4darWbDhg1s2bIFJycnpk6dWmXczz//PAEBAWzdupVPPvmErKws/vWvf9Xhb6ZuSLuI\nuZRN2Lt40VBUYG0PpXvtOu39HvrcZonIhLihgyevsP9YKiVq0/afrks21kqiO/nRNdinVu9TqVR8\n/fXXDB48GKVSSZs2bQgNDeWzzz5j3LhxhnoPPPCAoUN82rRpxMfHc/jwYQIDA+nSpQv33nsv3bp1\nAyAoKIhPP/2UU6dOVTpfp06dCA4OZtu2bYaO8G3btjFy5EhAvxtfcHAwQUFB2NnZ8dprr3HmzBm0\nWi1KpfF39QsXLtCzZ08CAwOxtrbmjTfeICMjo1afvz7IHYa5lE3YKygAoERhhcrKGmxsUKLDLiXZ\ngsEJUbNDp9ItkiwAStRaDp1Kr/X79uzZQ3Z2NoMq7CEeExPDmTNnSEhIMJS1adPG8Lhs57qS0i9y\nQ4YMobCwkMWLFzNlyhQGDhxIQkICWm3Vv4vhw4fz1VdfodVqOXjwIMnJycTGxgIwZcoUdu3aRXR0\nNJMmTeLrr7+mQ4cOlZIFwDPPPMMHH3xAjx49eOqpp/jtt9/o0KFDrX8H5iYJw1zKJuw5OgJQYG2n\nf+7khKNOjaIOtosUwlwiOnhjY22Zy4ONtZKIDrXvEN66dSsAEyZMoFOnTnTq1Illy5YB8Nlnnxnq\nVbWlatk+crNnz+b5559Hp9MxcOBA3nnnHaJq2HI2NjaWrKws/vjjD7Zt20afPn0Me4cPGjSIn3/+\nmfnz5+Pt7c3KlSu57777qrxzGDt2LD/++COzZs3C1taWhQsX8sADD6BSqSrVtSRpkjKXsj8yhQJ+\n+olCNw9o0QIcHHDUFkKFb0FCNDRdg31q3SRkSenp6ezbt4+RI0fyyCOPGL22aNEidu7cyezZs2s8\nRlZWFps3byYuLo6BAwcCoFaruXTpUrX7gXt6etKnTx927drF7t27mTNnjuF9S5cu5f777yc2NpbY\n2FgyMzO544472L9/v9EckaysLN5++20ee+wxRowYwYgRIzh8+DAjRozgxIkThIeH38yvpk5Jwqhr\n1w2lZeJEmDiR/K/2QpYDODniGNa5PKEIIW7atm3b0Gq1TJo0icDAQKPXJk2axL59+244Sc7Z2Rln\nZ2d2795NSEgIeXl5vPfee6SkpNT4TX/48OE8//zz2Nvb069fPwCsra35+++/SUhIYM6cOXh4eLB9\n+3ZsbGwIDQ01er+bmxs//fQTly5dYsaMGTg4OLBlyxZcXV255ZZb/tkvxEykSaouVRxKq9Xqf5bu\nhVEwdgL07gWRkTgGt7NwoEI0LV988QX9+vWrlCwAbr/9dkJCQoyapapiY2PD8uXL+fvvv7nvvvuY\nMmUKbm5uPProoxw9erTa9/Xr1w97e3vuu+8+o+aupUuXEhQUxOTJkxk8eDDff/8977zzDq1btzZ6\nv1Kp5L333gNgzJgx3H///Zw5c4b333/f0MfSUCh0ZY13TciNNjI3m2HD4I8/oLAQHBygQwcIDoag\nIPYPf4z9x1IB6N7Rl9vC/G9wMCFEY5CVlUXv3r3ZtGlTpbuHxuZG105pkqor69bB3r3lzwsL4a+/\n9I+VSqNlQRzt5dcuRGOXlZXF/v37+eKLLwgLC2v0ycIU0iRVV9atA+sqEsHp0xAQQH6R2lDkaCez\nvIVo7NRqNbNnz+bChQssWLDA0uHUC/mqW1fS0sDZGbKzjcsLCmDQIApzKyQMucMQotHz9vY2mt/R\nHMiV62ZUHBFVOkGPFi0gLw/Uav0dR5s2EBVFwY7y5QMcJGEIIRohuXL9U+vWQVycPlE4OoKHB5w7\np08YXl7l9Z5+GoDC4vI7DAc7+bULIRofuXLVxrp1+v/On9cvKujiAm5ukJ+vf71tW8jKAqUS/Pxg\n7FgYN44StcawzIKVUoGdjZXlPoMQQvxD9Z4wNBoNy5cvZ+vWreTn59O7d29eeeUVvCp+K6/gyJEj\nLFiwgOPHj+Pr68uUKVMYOnRo/QRbNq8iMVG/ymx2dnmTk0oFmZn6em5u+p9KJdxzD6xaZXSYgiLj\nuwuFQlE/8QshRB2q91FScXFxbN26lUWLFrF+/XpSU1OrXfL36tWrTJo0idDQULZs2cKYMWOYPXs2\n+/btq9ugDhyAyZOhUycIDNT/HDYMXngBfvpJnyCuXNE3P2VkQFGRPjkAVFhrn/x8qGIJgYrNUbI1\nqxCisarXOwyVSkV8fDxz5syhZ8+eACxbtowBAwaQmJhIZGSkUf3PP/8cZ2dnZs+ejVKppF27dhw7\ndowPPviAXr161T6Ask7qv/7SNx3pdJCbC5cv65OBRqPvqC4oKF+evEULsLfXd2KD/qdSqa+nUpWX\nAzg5VblGlPRfCCGagnq9wzhx4gT5+flER0cbyoKCgggMDKxyeFpCQgJRUVFGywFHR0eTmJhIrSeo\nlzUvHToEx47pO6gPHYKzZ/XJo7DQOAEUFen/y8vTPy+bY6HVlicMW1uws9MvMOjsrO/grmKNKEkY\nQoimoF6vXqmp+qUxfH19jcp9fHwMr11fv2xjkop1CwsLycrKwsPDw/STl+2AV7ahUVlHdVGRPglo\nNGBlVT4ctkxZAnFz0/dZKJX6Ow5nZ30yCQuD/v31dxbVLChYsQ9D5mAIIRqrer16FRYWolQqsbEx\nbse3tbWluLi4Uv2ioqJKa9eXPa/1OvFlTUxl8yUqNiUplfqEAfrkAeV3DmXJo6xju6REPzrqllsM\no6BuRFvhbsjJQfowhBCNU70mDHt7e7RaLWq1GusK3+JVKhUODg5V1r8+MZQ9r6p+jfz99avHOjrq\n7y6srfVJo6x/oixRlDV/ubvrJ925u0PZZux9++qXK6/l0uS3BrXg1IUsrK2VBLdyr13cQgjRQNRr\nwvAv3bY0PT3d8BjgypUrlZqpAPz8/EhPN96q8cqVKzg6OtZ+2d+YGH0fRqtWcPy4voM6J0ffqV0m\nO1ufSHx8oHfvf5QcquLuas+oQSEynFYI0ajVa8IICQnBycmJ/fv3M2TIEEC/nG5ycnKV2yB269aN\nLVu2oNPpDBfbP/74g8jIyCr3xa1R2fG//Vbf1JSdXd6B3aIFRETU2A9xsyRZCCEau3pNGLa2towc\nOZLFixfj7u6Op6cn8+bNIzo6moiICFQqFTk5Obi5uWFra8uDDz7I2rVrefXVVxk3bhy//vorX331\nFWvWrKnxPJrS/ohKHen+/jBhQs1BljU/CSFEM1N2zSy7hl6v3ofsTJ8+HbVazcyZM1Gr1YaZ3gAH\nDx5k7NixxMfH06NHD7y8vFi7di3z589n6NChBAQEsGjRIm6//fYaz1HWjDVq1Cizfx4hhGhq0tPT\nK+0MCE10x72ioiKOHj2Kt7c3VlaybpMQQphCo9GQnp5OWFgY9vb2lV5vkglDCCFE3ZMd94QQQphE\nEoYQQgiTSMIQQghhEkkYQgghTNIkE4ZGo2Hp0qX06tWLrl27Mm3aNDIyMiwdVoOQkZHBiy++SK9e\nvejevTsTJ07k1KlTlg6rwTl06BCdOnXijz/+sHQoDcbnn3/OPffcQ3h4OMOHD+e3336zdEgNQkFB\nAa+//rrh/6lJkyZx5swZS4dlFk0yYdRmk6bmRKvV8vTTT3P+/HlWrlzJxo0bcXZ2Zvz48WRlZVk6\nvAajoKCAF154odrJS83R1q1bmTdvHo899hjbt28nKiqKKVOmkCQTXVmwYAG//vorb731Fps2bcLO\nzo5JkyZVuaBqo6drYoqLi3Vdu3bV/fe//zWUXbp0SdehQwfdn3/+acHILO/vv//WdejQQXfmzBlD\nWXFxsa5Lly66rVu3WjCyhuXll1/WjR49WtehQwfd77//bulwLE6r1eruvPNO3fLlyw1lGo1Gd//9\n9+u2bdtmwcgahujoaF18fLzh+enTp3UdOnTQHT161IJRmUeTu8Oo7SZNzYm/vz/vvfcet9xyi6Gs\nbI2rnIpbzTZjP/74I3v37mXOnDmWDqXBOHfuHMnJyQwePNhQplQq+fLLL4mNjbVgZA2Dh4cHO3bs\nIDMzE5VKxebNm3Fzc6Nly5aWDq3ONbmEUdtNmpoTd3d3+vXrZ7Rw48cff0xRUdE/2/K2ibl69Sqz\nZ89m/vz5uJXtfyI4f/48ANeuXWPs2LHcfvvtjBo1isTERMsG1kC8/vrrpKamcscddxAREcFnn33G\n6tWrcXV1tXRoda7JJYzabtLUnO3evZtly5YxYcIE2rVrZ+lwLO7VV1+lf//+9OnTx9KhNCh5pdsU\nz5o1ixEjRrB27VpuvfVWxo0bx9mzZy0cneVduHABLy8vVq9ezYYNG+jVqxfTpk1rkl9Qm1zCqLhJ\nU0XVbdLUXG3ZsoVp06YRExPDzJkzLR2OxW3dupVjx47x4osvWjqUBqfsy9cTTzxBbGwsoaGhvPrq\nq7Rp04YNGzZYODrLunTpEi+//DKzZ8+mb9++dOnShaVLl2JnZ8dHH31k6fDqXJPbYLq2mzQ1R6tW\nrdsyz8MAAAyXSURBVGL58uWMHj2aOXPmyF4d6BNoWlqaoWlOV7rE2mOPPcbQoUN57bXXLBmeRfn4\n+ADQoUMHQ5lCoaBt27bNfpTU0aNH0Wg0hIWFGcpsbGzo2LEjFy5csGBk5tHkEkZtN2lqbtasWcPy\n5cuZNm0aTz31lKXDaTCWLFlCUVGR4Xl6ejqjRo1i/vz59OzZ04KRWV5oaCiOjo4cOXKEzp07A/qE\nevbs2RtuNdDU+fn5AXDy5ElCQ0OB8t9NU2zabHIJ40abNDVnJ06c4M033+SBBx7goYceMtr+1snJ\nCUdHRwtGZ1nX333a2dkZyj09PS0RUoPh4ODAuHHjWL58OV5eXnTo0IFPP/2UixcvsmLFCkuHZ1Hh\n4eFEREQwa9YsXn31Vdzd3Vm3bh2XL19m9OjRlg6vzjW5hAE1b9LUnO3YsQONRsN///tf/vvf/xq9\n9swzzzBlyhQLRSYaumeeeQYHBwf+/e9/k5mZSceOHfnggw9o27atpUOzKCsrK1atWsWyZcuYMWMG\nBQUFhIWF8emnnxIYGGjp8Oqc7IchhBDCJE1ulJQQQgjzkIQhhBDCJJIwhBBCmEQShhBCCJNIwhBC\nCGESSRiiSTPXIEAZXCiaI0kYol789ddfPPfcc/Tt25fw8HDuvvtuXnvtNdLS0sxyvrS0NCZPnkxy\ncnKdHjc3N5dZs2Y1+KXyg4ODWblyZb2fNzs7m/79+5u8LMbbb7/N3LlzzRuUqDOSMITZrVu3jkce\neYScnBxmzpzJmjVrmDBhAnv37uWBBx4wLJ9dl37//Xf27t1b58c9efIkW7duRavV1vmxm4LXX3+d\nQYMG0bp1a5PqT5o0ib1798p2r42EJAxhVn/++Sf/+c9/GDduHGvXruW+++6jR48ejBw5kg0bNqBW\nq+UbZhNx+PBhdu7cyaRJk0x+j729PePHj2fhwoVmjEzUFUkYwqzef/99WrRowfTp0yu95uvry6xZ\ns7j99tsNy9Gr1Wo++ugj7r33XsLDwxkwYACrVq0y2l97zJgxvPLKK7z77rv07duXzp078/DDD3Pk\nyBFAv/LsCy+8AMCAAQOYNWsWoN+r+4033mDgwIGEhYURGRnJxIkTOXHihFFcP/74Iw8//DARERH0\n7t2b+fPnk5+fzx9//MGoUaMAGDt2LGPGjKnyMyclJREcHMyuXbt4+umn6dq1K9HR0bz88ssUFhYa\n6lXVbBQXF0enTp2MPuvcuXOJi4ujZ8+edO3alWeffZa8vDxWr15N79696datG1OnTq20L3tubi7P\nPvssERER9OrVizfffJOSkhKjOp999hmDBw8mLCyM/v37s3r1aqP+mVmzZvHoo4/y8ssv07VrV4YM\nGVJt/83atWu544478PDwMJTpdDo++ugjBg0aRHh4OPfccw8ff/yx0fsGDx7M6dOnzXJHKOqYpfaG\nFU2fVqvVde7cWffss8+a/J4XXnhBFxoaqouLi9Pt27dPt2LFCl1oaKjupZdeMtQZPXq0rlu3brr/\n+7//03333f+3d7chTb19HMC/mZGh5mNRpolkR7ehudRepEkqppjMRAiEhLRIaZlkYlQvjPApxljl\n05AUIRUx0cDCtLCRJQ7zKaKsQChJU1moG6Y1/d0v4hw6ZbVM7///vrk+sBfn2vVwrjPY75zrnO13\nn9rb2ykyMpLCw8NpYWGBDAYDFRcXE8dx1N7eTm/fviUiIqVSSXv37qXGxkbS6/XU0NBAISEhdPDg\nQVpcXCQioo6ODvLx8aGMjAzS6XTU2NhIwcHBdPr0aTIajVRfX08cx1FNTQ29efNmyTnwOeSDg4Op\nqKiIurq6SKvVko+PD2k0GqEex3FUWloqanv9+nWSSCSiucrlckpJSaHOzk6qqqoijuMoOjqakpOT\nSafTUU1NDUkkErp8+bKob4lEQqdOnSKdTkelpaUklUopNzdXqMPvU0FBAXV2dlJZWRnJZDIqLCwU\n6pw7d46kUimlpaVRV1cXdXR0LDlnk8lEMpmMbt26JSovKioiiURCarWanjx5QsXFxeTj40M1NTWi\neklJSZSVlbVk38y/BwsYzKoxGAzEcRypVCqL6r9+/Zo4jqMbN26IyisqKojjOHr16hURff0SDQgI\nIKPRKNRpbm4mjuPo5cuXRER0+/Zt4jiORkZGiIhobm6OUlNTqbW1VdQ3/wVsMBiIiCghIYESExNF\ndRobGyk2NpaMRiP19PQQx3HU3d3903nwASMnJ0dUnpycTPHx8cK2pQHj+7nGxcWRXC6nmZkZoSwt\nLY0SEhJEfSsUCiEQEhGpVCqSSqVkMBhoZmaG/P39RUGGiKi6upokEgm9f/+eiL4GDI7jaHx8/Kfz\nJSLS6XSiz4iIaHp6mqRSKRUVFYnqXrx4kTIzM0Vl+fn5FBIS8ssxmH8eW5JiVs3atWsBQLSc9Cs9\nPT0AgLi4OFG5QqEQvQ98Xc6xs7MTtvm/J5+dnV2y7/Xr16OyshIxMTEYHx9Hd3c36uvr8fDhQwDA\nly9fMDc3hxcvXiAqKkrUNjExEXfv3hWNZ4ndu3eLtrds2SJakrKUt7e3aGwXFxd4eXnB3t5eKHN0\ndITRaBS1i46OFiXHioyMhNlsxuDgIPr7+zE3N4eIiAiYzWbhFRERgYWFBXR3dwvtXF1dhSRKP8Mn\nUnJ3dxfKBgYGYDabceDAAVHdvLw8XL16VVS2bds2TE5O4vPnz787HMw/6P/y782ZfwcHBwfY2tpi\ndHT0p3X4fNF2dnaYnp4GgB/yT/Db334h2tjYiOpYWX099/nV00udnZ0oKCjA8PAwbG1t4evrK+QA\nISJMT0+DiERr8H9jqX1cztNVtra2P5RZkrvE1dVVtL3UcUxNTV2y7cTExB+Nxff5bRrkqakp0bi/\nwo9hMplW7PgzK48FDGZVhYaGQq/XY35+XkhK9K3q6mqUlZWhtbUVGzduBAAYDAZRQiM+0ZOTk9Oy\n9+Pdu3dQKpWIiopCRUUF3N3dsWbNGtTW1qKzsxMAhLP4728em0wm9Pf3r0oCru8DyM+ukJaDD8A8\n/ji6uLgIZ/IajQYeHh4/tP3dFcX3+M/GaDQKnyN/BfTx40ds375dqDsyMoKxsTEEBQUJgX56ehpW\nVlZwcHD4o3GZ/y62JMWsqpSUFExNTeHatWs/vDc6Oora2lr4+/vD09NTSKF7584dUT1+OzAw0OJx\n+eUw3vPnzzE/P4/09HR4eHgISzV8sFhcXBSuOjo6OkRtHzx4gOPHj8NoNP7Q79+ws7PD2NiYqKyv\nr2/F+ufnxrt37x5sbGzg7++PXbt2Yd26dZiYmICfn5/wMpvN0Gg0omyMlnBzcwMAfPjwQSjjx+CX\n/Xjl5eW4cOGCECz4dps3b17R48usPHaFwawquVwOpVKJkpISDA8PIz4+Ho6OjhgaGkJlZSWsrKyg\nUqkAABzHQaFQQKPR4NOnT5DL5ejv74dWq4VCoYC3t7fF4/Jnt/fv30dYWBhkMhmsra2hUqlw9OhR\nzM/Po6mpSXiUk7+3wOc6z87ORnx8PD58+AC1Wo1Dhw7Bzc1NqKfT6eDg4ABfX99lH5v9+/ejpaUF\nfn5+8PT0RFNTk8W/kLbE4OAgcnNzERMTA71ej5qaGiiVSuHYpKamQqPRwGQyITAwEKOjo9BoNLC3\nt8fOnTv/aKygoCDY2Nigt7cXHMcBAJydnXHkyBFUVlbC2toaQUFB6O3tRXNzM/Ly8kTt+/r6EBoa\nujITZ1YNCxjMqsvIyIBMJkNtbS3y8/MxMzODrVu3IiYmBidOnBAtfxQWFgpfnlqtFm5ubsjIyPij\nH4MBwJ49exASEgK1Wg29Xg+tVgu1Wo2SkhKkp6fDwcEBAQEBuHnzJpKTk/H06VPs2LEDkZGRKCsr\nQ0lJCU6ePAkXFxccPnwYSqUSAODl5YW4uDjU1tbi8ePHaGlpWfZxOX/+PMxmM65cuQJra2vExsbi\n7NmzyM3NXXaf31IqlRgYGEBaWhqcnJyQnZ2NY8eOCe+fOXMGmzZtQl1dHbRaLRwdHbFv3z5kZWUt\nuXz4Kxs2bEBYWBgePXqEpKQkoTwnJwfOzs5oaGhARUUFPD09UVBQgISEBKHO5OQkhoaGkJmZ+feT\nZlYVS9HKMMyKePbsGZKSktDR0SG6B/U75eXlaGtrQ3Nzs+ipLubfh93DYBhmRfC/zK+qqrK4zezs\nLOrq6pCVlcWCxf8AFjAYhlkxly5dQltbm8X3YiorKxEeHo6wsLBV3jNmJbAlKYZhGMYi7AqDYRiG\nsQgLGAzDMIxFWMBgGIZhLMICBsMwDGMRFjAYhmEYi7CAwTAMw1jkP2J13+n+ST/BAAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x17163695438>"
+      ]
+     },
+     "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": 31,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frac_infected_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.158096819542062"
+      ]
+     },
+     "execution_count": 32,
+     "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 +1,1811 @@
+{
+ "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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sbCxJQnQp3V0scbYz5ZfTOZxLuwxAdW09O4+kk5xRzPBgd8yM5alCdH0aTYFd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\nD/dBZUkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2147a4fdeb8>"
+      ]
+     },
+     "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": [
+       "7.3790079710980949"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "milk = make_system(T_init=5, r=0.01, volume=50, t_end=15)\n",
+    "run_simulation(milk, update)\n",
+    "final_temp(milk)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LJ89EKGRwLbsCv/x9C4XljaJ2EyMdvBzugqH+9lSvhJCnINccyj///INly5bB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\nCFEISiiEEEIUghIKIYQQhfh/8JrY1G86jmwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2147a588898>"
+      ]
+     },
+     "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.011543084583973956"
+      ]
+     },
+     "execution_count": 21,
+     "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": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.000000000006395"
+      ]
+     },
+     "execution_count": 22,
+     "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18.499850754390966"
+      ]
+     },
+     "execution_count": 23,
+     "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": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1.500149245609034"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func2(r=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.13296078935465339"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solution = fsolve(error_func2, 0.1)\n",
+    "r_milk = solution[0]\n",
+    "r_milk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.999999999999613"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\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": 28,
+   "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": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.000000000006395"
+      ]
+     },
+     "execution_count": 29,
+     "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.76470588235285"
+      ]
+     },
+     "execution_count": 30,
+     "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": 31,
+   "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 0x2147a831780>"
+      ]
+     },
+     "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.109243697484459"
+      ]
+     },
+     "execution_count": 32,
+     "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": 33,
+   "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": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.42857142857666"
+      ]
+     },
+     "execution_count": 34,
+     "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": 35,
+   "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "61.42857142857666"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "62.902809128457783"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_and_mix(15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.109243697484459"
+      ]
+     },
+     "execution_count": 38,
+     "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": 39,
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap07-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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5MtOnT8ff35/+/fvzySefUFBQoDbnFvWjvKicvdF7KThfNeCnRquhd0Rv3APc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\nyNNCiEbNpE+osrIyXnrpJb7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WyfmYKnNfJuVF5QDYu9nj0dOjQY8vhBCNhUapaeA1qt6b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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2147ac5f5f8>"
+      ]
+     },
+     "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": 41,
+   "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": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "r_coffee2 = 0.011610223142273859"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.0"
+      ]
+     },
+     "execution_count": 43,
+     "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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "70.000000000006395"
+      ]
+     },
+     "execution_count": 44,
+     "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": 45,
+   "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>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": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "coffee.results - coffee2.results"
+   ]
+  },
+  {
+   "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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+++ b/code/chap08mine.ipynb
@@ -0,0 +1,2907 @@
+{
+ "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": 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>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": 2,
+     "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": 3,
+   "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 0x1a1ec6606d8>"
+      ]
+     },
+     "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": 4,
+   "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 0x1a1ecac5550>"
+      ]
+     },
+     "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": 5,
+   "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 0x1a1ecb8bb70>"
+      ]
+     },
+     "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": 6,
+   "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": 7,
+   "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": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(68.0)"
+      ]
+     },
+     "execution_count": 8,
+     "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": 9,
+   "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 0x1a1ecf85ef0>"
+      ]
+     },
+     "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": "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": 10,
+   "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": 11,
+   "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": 12,
+   "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": 13,
+   "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": 14,
+   "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": 15,
+   "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": 15,
+     "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": 16,
+   "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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 172 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": 18,
+   "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": 18,
+     "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": 19,
+   "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\nZBi9JiMjw9BM4e7uXmaYpf7425syhBBCqGaI8qSkmPZz//e//3H06FF++OEHQ7+ziIgIBgwYcE/v\nd/36dTZu3MisWbN4+umnAfjggw9wdHTkypUrfPHFF7Rr147JkycD0Lp1a2bOnMkbb7xBYmIi586d\nw87ODk9PT7y8vAgJCcHb25tWrVoBsGzZMt5++2369u0LQPPmzUlJSWHZsmW88MILZcqzfft2CgsL\n+fDDD3F2dqZNmzakp6cza9YswzE9evSga9euPPTQQwCEhoayYsUKfvvtN55++mnq1q0LqP59AI0a\nNWLu3Ln069cPAC8vL/r378/WrVvv6d+sMhYdMKSkpPDuu+/SvHlz2rdvD0B2djZnzpzhhRdeoKio\nyBDt6cXGxhIQEABA586dWbhwIampqXh4eBjynZ2d8fX1rdmTEUIIK+DhoZohbufpadrPPX78OI0b\nNzbqpN62bVtcXFzu6f3OnDlDYWEhHTp0MKRptVpDgJCYmEjPnj2NXqO/dyQmJvLcc8/x7bff8uc/\n/5m2bdsSFBTEs88+S+PGjbl06RLp6enMnz+fhQsXGl5fVFREcXExBQUFZZrLExMTadmyJc7OzoY0\nPz8/o2OGDh3K7t272bBhA3/88Qe//fYbaWlplJSUlHuOgYGBnDp1iujoaH7//XfOnDnDqVOnTPaD\n2KKbJNq1a0dAQADTpk3j119/5fjx47zzzjs0atSI559/nuHDhxMXF8eSJUs4ffo0n3zyCYcPH+a1\n114DwN/fHz8/P8LDwzl27Bh79+4lIiKCkSNHlrmYQgghVAfH8tz8IW0ytra2Fd4Y71ZxcbHhuZ2d\n3R2PdXR0LJOm71So1Wpp1KgRW7duJSYmhqeeeoqff/6ZQYMGsWnTJsN7T58+nS1bthi27777jp07\nd6LVlv0trtFoynRavLWMJSUlvPHGG8ybN486deowcOBAYmJi8PLyqvActmzZwqBBg0hJSSEgIIDp\n06fz17/+9Y7nfT8sOmCwsbEhKiqKhx9+mDfffJPhw4fj7OxMTEwMzs7O+Pj4EB0dzb/+9S+ef/55\n9uzZw7Jly2jdujWgLlB0dDSNGzcmNDSU999/n5CQEMLCwu67bLt2wcSJ8O9/3/dbCSGExejSBUaN\nAm9vsLFRj6NGmX6UhI+PD5cvX+bcuXOGtN9//53s7Oxyj9ffbK9fv25I++OPPwzPmzdvjlar5ejR\no4a0kpISnnnmGbZv307r1q1JSEgwes/4+HhANU/s2LGDr776ii5duhAeHs6WLVt44okn2LlzJy4u\nLjRt2pTz58/TokULw/bf//6XlStXYmNT9tb68MMP8/vvv3P16lVD2q1lO378OPv37ycqKorw8HD6\n9+9Pw4YNyczMNAQaGo3G6D1XrlzJkCFDmDt3LsOGDaNTp06cO3fOZKMpLLpJAlQbzbx58yrM79Wr\nF7169aow39XVlU8//bTay7V7N1y7Bhs3Qps2cLNZy4gMTRJCWKMuXWr+b1W3bt1o164dkyZNYtq0\naZSUlBja92+/UYJqrnBycmLZsmWMGzeOP/74g1WrVhnynZycGDZsGJGRkTRs2JAWLVrw5ZdfcvXq\nVUM/gRdeeIH58+cTEhLChQsX+Nvf/kbPnj1p3bo1R44cYf78+bi4uNC5c2fOnTvH8ePHGTp0KACj\nR49m3rx5eHp60r17dw4fPsy8efMYNWpUuecXHBzMp59+yqRJk3jvvfdIT09nyZIlhnxXV1e0Wi07\nd+6kfv36ZGZmEhkZSUFBgaHzvr4548iRI7Rq1Qp3d3fi4+M5efIkjo6OfPfdd+zYsYPGjRtXz0W5\njcUHDJaqbVu4OT8U33wDkyfDrd9p/dAkPf3QJJCgQQghyhMdHc3f/vY3QkNDcXFx4Y033uDo0aPl\nNi/UrVuXiIgIFi5cSL9+/fD19WXy5MlGNcgTJ07E1taW999/n5ycHNq3b8/KlStp0qQJTZo0Ydmy\nZSxevJg1a9bQoEED+vfvzzvvvAPA888/z8WLF4mKiiI1NZXGjRszaNAg3nrrLUD1NygoKGDlypXM\nnj2bpk2bMmbMGN54441yz61u3br84x//YNasWYSEhODm5sZf//pXQ1DUtGlT5s6dS1RUFP/4xz9o\n2rQpwcHBNG3alCNHjgBqYsLAwECGDh3Ke++9x/Tp05k2bRpDhgyhTp06dOjQgVmzZjFjxgxSUlLw\nrOaOJxqduWeCsFDnz5+nd+/e7N692zDT5K2ysuD//g/061i9/jp07VqaP2tW+R2HvL1h+nQTFVoI\nIazUpUuX+PXXX+nRowe2trYAZGZmEhQUxNq1aw0dEoXpVHbfs+g+DJasSRO4OVIHgE2b4Na5oMw1\nNEkIIayRra0t48ePZ8mSJSQnJ3Py5ElmzJhBixYtZFZeCyEBw30IDoZ69dTzK1fg++9L826O4izD\n1EOThBDCGtWvX59ly5bxyy+/MGDAAF555RW0Wi1ffPFFpSMeRM2QPgz3wdERnn8ebi5twa5d8Pjj\n0LChCiZu7cOgZ+qhSUIIYa26d+9O9+7dzV0MUQGpYbhP3buDfp6RwkLQzwpqrqFJQgghhClIDcN9\nsrGBwYPh44/VfmwsPPkktGxpnqFJQgghhClIDUM1aNsW/P1L97/+Wq3yJoQQQtQWEjBUkxdfBP1s\noH/8Afv3m7U4QgghRLWSgKGauLoad2jcvBkqmNFUCCGEsDoSMFSjvn3V/AwAublqbgYhhBCiNpCA\noRrZ2cHNacYB+O9/4fRp85VHCCGEqC4SMFSzdu2MO0CuWycdIIUQQlg/CRhMYPBgsLdXz8+fh//3\n/8xbHiGEEOJ+ScBgAo0aQf/+pftbt6qpo4UQQghrJQGDiTz9NLi7q+c3bsD69eYtjxBCCHE/JGAw\nEa0WQkNL9w8ehMOHzVceIYQQ4n5IwGBCbduqxaj01q1TtQ1CCCGEtZGAwcRefBFcXNTzK1dKF6cS\nQgghrIkEDCbm7AxDhpTu790Lv/9uvvIIIYQQ90IChhrQubOanwFAp4M1a6CoyLxlEkIIIapCAoYa\noNGoDpAODmo/JQX+9S/zlkkIIYSoCgkYakijRjBwYOn+jh2QkWG+8gghhBBVYfEBQ1ZWFpMnTyYo\nKIiAgAD+8pe/cOrUKUP+Sy+9hI+Pj9H2wQcfGPIvXrzI+PHjCQgIoHv37kRERFBkpvaAJ5+EP/1J\nPS8qgq+/Vk0UQgghhKXTmrsAd1JSUsLbb7+NTqfjs88+w8nJiaioKEaMGMH27dtp0KABSUlJLFy4\nkG7duhleV6dOHcPzsWPHotFoiImJIT09nSlTpqDVagkPD6/x87GxgUcfVatY5uRAfLyqeRg+vMaL\nIoQQQlSJRQcMJ0+eJCEhgR07dtC6dWsAIiIiCAwMZO/evXTq1Im8vDz8/PxwdXUt8/qEhATi4+P5\n4YcfaNasGb6+vkyaNInZs2cTFhaGvX7Bhxpy4ABs3w716sH16ypoWLgQWrY0nq9BCCGEsDQW3STh\n4eHB559/TsuWLQ1pGo0GgKtXr3Lq1CkcHR3x8vIq9/VxcXF4eXnRrFkzQ1pgYCA5OTmcOHHCtIUv\nx86d6vFPf1JLYQPk58Onn9Z4UYQQQogqseiAoWHDhvTq1Qsbm9Jirlmzhhs3bhAUFERiYiIuLi5M\nmDCBoKAgBgwYwKpVqyi5uZ50eno6bm5uRu+p309NTa25E7lJ/5FaLbRqVZp++DCkpdV4cYQQQoi7\nZtEBw+12797NokWLGDlyJK1btyYpKYnc3FyCgoJYuXIlw4YNY8mSJURHRwOQl5eHg34s4012dnZo\nNBry8/NrvPweHqXP3dxU0wSAk5N0gBRCCGHZLLoPw602bdrE9OnT6devHxMnTgRg/vz55ObmUu/m\nndfHx4fs7GyWLVvG2LFjcXR0pKCgwOh9CgsL0el0ODk51fg5BAfDihXquUYDbdpAQgJ4e8OJE6oT\nZEBAjRdLCCGEqJRV1DAsXbqUqVOnMmTIEBYsWGBootBqtYZgQc/Hx4ecnByys7Nxd3cnMzPTKD/j\n5uQHTZs2rZnC36JLFxg1SgUINjbg66smdNK3mmzYIItTCSGEsEwWX8OwfPlyFi9ezLhx4wgLCzPK\nGzx4MB06dGDatGmGtCNHjuDm5ka9evXo3LkzCxcuJDU1FY+b7QGxsbE4Ozvj6+tbo+eh16WL2vT+\n8x+YMgUuX1ZNEw0bqn0hhBDCklh0DcPJkyeJjIzkxRdfZPDgwWRmZhq23Nxc+vTpw/r169myZQvn\nzp1jw4YNrFixgnHjxgHg7++Pn58f4eHhHDt2jL179xIREcHIkSNrfEhleQ4cgJgYVcOg06lhlqtX\nq1kghRBCCEti0TUMO3bsoLi4mI0bN7Jx40ajvPHjxzN69Gi0Wi1Lly4lJSUFT09Ppk6dSkhICKCG\nYEZHRzNz5kxCQ0NxdnYmJCSkTE2FueiHWbq6qlESV66owOGTT1R/h5sjSIUQQgiz0+h05umbn5yc\nzKlTp+jdu7c5Pr5S58+fp3fv3uzevRtvb2+TfMbo0XBzBCi5uarTo06nAoUvvoBbJq8UQgghTKqy\n+57ZmiT27NnD22+/ba6Ptwi3DrN0clKdIQGcneHbb1UQIYQQQlgCi+7DUNsFBxvvN2+ulsD29obs\nbPjnP81TLiGEEOJ2EjCY0e3DLFu0gPfeKx1muXcvnD1r3jIKIYQQYOGdHh8Etw+z1OnUEMtjx9Tz\nNWvg/fdVQCGEEEKYi9yGLIxGA0OGlC5OlZwMu3ebt0xCCCFEtdcwLFu27K6OS0hIqO6PrjXc3ODZ\nZ2HzZrW/dSt06gSNG5u3XEIIIR5c1R4wLF68+K6P1chEAxXq0wf+9z+4cAEKCmDtWhg7VuZmEEII\nYR7VHjCcPHmyut/ygWRrC6+8AvPnq74Mx45BXJxxfwchhBCipkgfBgvWsiX06lW6v369mj5aCCGE\nqGnVXsMwffr0Kh0/e/bs6i5CrfL883DokBo5kZ0NGzfCq6+au1RCCCEeNNUeMPz0009G+xkZGRQV\nFeHp6YmrqytXrlwhOTkZe3t7s60YaU0cHWHoUPjsM7X/008QGKiWxhZCCCFqSrUHDHv27DE837Zt\nGwsXLiQqKooOHToY0pOSkhgzZgzBt091KMrVsaMaJXHwoNpfvRr+7//UrJBCCCFETTBpH4bIyEje\nffddo2ABoE2bNrzzzjusWLHClB9fqwwdqtaYALh4ETZtMm95hBBCPFhMOtPj5cuXqVevXrl5dnZ2\n5MrqSuU6cEAtfZ2aqhaoCg5WoyNeflmtYgnw44/QuTO0bWvWogohhHhAmLSGwc/Pj6VLl3Lt2jWj\n9IsXLxIVFUXXrl1N+fFW6cABWLFCzb9QUqIeV6xQ6YGBqnlC7x//gPx885VVCCHEg8OkNQyTJ0/m\nlVde4cknn6RTp040atSIrKwsDh48iIuLC5/pe/IJg507y0/ftUvVMoSGQmKiWvo6K0vNBjlkSM2W\nUQghxIPHpDUMvr6+fPfddwwePJhr165x6NAhcnJyGDFiBFu3bsXb29uUH2+VUlPLTz90CGbNgilT\n4Pp1yMhQ6f/v/8GpUzVXPiGEEA8mk4yS6N69O3Xq1AGgadOmTJ48ubo/ptby8FDNELfKyIDz56FJ\nE7Wv0ZQGDG5uqmlixgwZNSGEEMJ0qr2GISIigq5duzJixAi++OILkpKSqvsjarXyRpomJ0OzZqX7\nGg089BCkpan9rCz45puaKZ8QQogHU7XXMOzcuZPz58+zb98+9u3bR1RUFA0aNKBHjx488cQTdO/e\nHWf9+EBRhn6tiF27ICUFPD1VQKCvXdBzcFC1EXr790OHDsadIoUQQojqYpJOj97e3gwbNoxhw4ZR\nUFBAXFwc+/btY9GiRZw7dw5/f3+eeOIJevToIbM9lqNLF+NFpmbNKttMAeDnB+7uEB+v9tesUetP\nVDCSVQghhLhnJl98yt7enscee4wpU6awY8cOdu3aRb9+/Th48CDDhg0z9cfXChVNiNmiBZw7p5bB\njo+H06fVLJA6Xc2WTwghRO1n0mGV5fH29mbo0KEMHTqUgoKCmv54q1ReM0Xz5vDf/6r0hx6CI0dA\nv7J4hw6RxoxwAAAgAElEQVTwxBPmKasQQojayaQBwyuvvIJGoyk3z8bGBicnJ1q0aEFISAitWrUq\n97isrCwiIiL46aefuHHjBh07dmTy5Mm0vTnF4f79+4mIiODMmTO0aNGCCRMm0LNnT8PrL168yKxZ\ns/jpp5+ws7Nj0KBBhIeHo9XWeKx0X8prptBr2BC8vFSzxfnzsGED+PhA06Y1X04hhBC1k0mbJLy9\nvTl06BAJCQkAuLq6YmNjw6+//sqBAwe4dOkS3333HYMGDeLYsWNlXl9SUsLbb7/NH3/8wWeffcbX\nX39N3bp1GTFiBJcvXyYpKYnRo0fTt29fNm/eTO/evQkLCyMxMdHwHmPHjiUrK4uYmBjmzZvHpk2b\niIqKMuVp14jb52to2RKcnCAnBwoKYOVKKCoyT9mEEELUPiYNGFxdXWnWrBnff/89q1ev5uOPP+bL\nL7/khx9+wMfHh6CgIH788Ucef/xxIiMjy7z+5MmTJCQkMHfuXDp06ECbNm2IiIggNzeXvXv3snr1\navz8/Bg9ejStW7fmnXfewd/fn9WrVwOQkJBAfHw88+bNw9fXl549ezJp0iTWrFlj9c0ht46QALCx\nUUte162r9s+elQWqhBBCVB+TBgzffvst48ePx93d3Si9SZMmjBkzhq+++gpbW1sGDx7M4cOHy7ze\nw8ODzz//nJYtWxrS9E0cV69eJS4ujsDAQKPXdO3albi4OADi4uLw8vKi2S2TGAQGBpKTk8OJEyeq\n7TzNobyOkHXrwmuvle7v3g3l/LMKIYQQVWbSgKGwsJCiCurFCwoKDKtVOjo6UlJSUuaYhg0b0qtX\nL2xsSou5Zs0abty4QVBQEGlpaTS9raHezc2NtJszGqWnp+Pm5lYmHyC1ojmYrUSXLjBqFHh7q9oF\nb2947DE1Z8Px42rUREaGmgXy8mVzl1YIIYS1M2nA0K1bNxYtWlRmtsfTp0+zePFiunfvDsCPP/5o\nVItQkd27d7No0SJGjhxJ69atuXHjBvb29kbH2Nvbk39zCce8vDwcbpsv2c7ODo1GYzjGmnXpAtOn\nw9Kl0LevGjWRkqJGTRQVqVETZ87A8uVq5UshhBDiXpk0YPjggw+wtbXlueeeo1+/fgwfPpzg4GCe\nffZZbGxsmDZtGj/88ANffvklr7/++h3fa9OmTYwbN47g4GAmTpwIgIODA4WFhUbHFRQUGNaxcHR0\nLNNXobCwEJ1Oh5OTUzWeqfndusqlnZ3qz6DRqFETp0/D1q3mK5sQQgjrZ9Kxhe7u7mzbto1t27bx\nyy+/cOnSJfz9/XnzzTcZMGAAtra25Obm8tVXX+Hn51fh+yxdupTFixczfPhwpk2bZujH4OHhQYZ+\nFaabMjIyDM0U7u7u7N27t0w+UKYpw9rd3sJSv76a2OnsWbW/ejX88AMUF6sOk8HBxsM0hRBCiDsx\nacBQUFDAunXrSEhIIDs7G4C0tDS2bt3K1q1b0Wg0rFy58o7vsXz5chYvXsy4ceMICwszyuvcuTMH\nDhwwSouNjSUgIMCQv3DhQlJTU/G4OawgNjYWZ2fnWjcldXmrXDZrVrqy5cmTkJgInTqp41asUMdI\n0CCEEOJumLRJYtasWcybN4/ff/+dwsLCMltlQxtPnjxJZGQkL774IoMHDyYzM9Ow5ebmMnz4cOLi\n4liyZAmnT5/mk08+4fDhw7x2c6iAv78/fn5+hIeHc+zYMfbu3UtERAQjR44s0/fB2pU3akKjgYkT\nITNT7RcVqQ6RxcVqf9eumiufEEII62bSGobvv/+ecePGMWbMmHt6/Y4dOyguLmbjxo1s3LjRKG/8\n+PGMGTOG6OhoIiIiWL58Oa1atWLZsmW0bt0aUEMwo6OjmTlzJqGhoTg7OxMSElKmpqI2KG/66L59\nVXqLFmqkREkJXL8OSUnQtq06TgghhLgbJg0YNBrNHfsmVObdd9/l3XffveMxvXr1olevXhXmu7q6\n8umnn95zGazJ7dNH6/n4QHa2apIASE8HFxe4bQoLIYQQokImbZJ44YUX+Pbbb8udY0HUnOBgtQz2\nrfNnnT4N7dqZr0xCCCGsi0lrGMaPH88LL7zAM888w6OPPmoY7qin0WiYO3euKYsgKK112L5dDb8s\nKVETPf33v9Crl1q8SgghhLgTkwYMCxcu5MyZM7i4uHD8+PEy+RWtZCmqn765Ytw4+PBD1Zfh2jWI\njoZJk+C2+a2EEEIIIyYNGLZs2cJf//pX3n33XQkOLESjRvDmmxAZqWoazp9XK1u+9ZaaYloIIYQo\nj0lvEba2tjz++OMSLFiYtm1h+PDS/cOHYfNm85VHCCGE5TNpwDBgwAC+/fZbU36EuEePPw5//nPp\n/r//Dfv3m688QgghLJtJmyQaN27M5s2b6dOnD+3bt8fZ2dkoX6PRMGvWLFMWQdzBCy+oIZb6JbDX\nroUmTdQ6FEIIIcStTBowbNiwgfr161NcXMyhQ4fK5EtThXnZ2MBf/gIREZCcrPo0LF2qZof09jZ3\n6YQQQlgSkwYMe/bsMeXbi2rg4ABhYTBvHly5AjduwCefwOTJqrZBCCGEABP3YRDWISlJrS/x888Q\nH6/2P/lEzQ4phBBCgAQMD7wDB9TKldnZ8MgjkJenVrY8ehSiolSNgxBCCCEBwwNu587S5/Xrqw6P\nGo2an+HsWVi2TK1yKYQQ4sEmAcMDLjXVeL9JE2jTBnJy1P6JE2piJ1kORAghHmwSMDzgPDzKT+vU\nqXT/4EH44gsJGoQQ4kEmAcMDLji4/PRx4+Dpp0v3DxyA1atBp6uZcgkhhLAsJh1WKSyffiXLXbsg\nJQU8PaFv39LFqoqK4Mcf1TE//wxarZpaetcu1Zzh4aGCDv37CCGEqJ0kYBCG4OB2Gg0MGaKCBv20\n0Rs3qhEVrVqp/AsX1CgL/fsIIYSonaRJQtyRRqMWqurWTe0nJ6sgISnJuHli1y7zlE8IIUTNkIBB\nVEqjgddeg4AAyM1VaampcOpUadCQkmK+8gkhhDA9CRjEXdGvO9G2bWlaerqa5KmkRPV9EEIIUXtJ\nwCDumo0NvP++8VDMzEw1V8OtIyqEEELUPhIwiCoJDFQLVXXooJoq6tYFV1f45ReZRloIIWozqwoY\nZsyYwQcffGCU9tJLL+Hj42O03XrMxYsXGT9+PAEBAXTv3p2IiAiKZK7j+xIYCF9/DXPnqgme3NxU\n00REhFrxUgghRO1jFcMqdTodS5YsYf369bz00ktG6UlJSSxcuJBu+m78QJ06dQzPx44di0ajISYm\nhvT0dKZMmYJWqyU8PLxGz6G20Wjg+efV8thbtqi08+dh/nw16VN5M0gKIYSwXhZfw5CcnMyrr77K\nV199hedtPeuSk5PJy8vDz88PV1dXw1a3bl0AEhISiI+PZ968efj6+tKzZ08mTZrEmjVrKCgoMMfp\n1DrBwWoEhc3Nb9KlS7BggRp2KYQQovaw+IDh4MGDeHh4sG3bNry9vY3yTp06haOjI15eXuW+Ni4u\nDi8vL5o1a2ZICwwMJCcnhxMnTpi03A+Sxx6Dt99WtQ2ghl5GRkJcnHnLJYQQovpYfMAwcOBAFixY\ngKura5m8xMREXFxcmDBhAkFBQQwYMIBVq1ZRcnOVpPT0dNzc3Ixeo99PvX2ZRnFfHn0U3nsPXFzU\nflERLF8O27bJ+hNCCFEbWHzAcCdJSUnk5uYSFBTEypUrGTZsGEuWLCE6OhqAvLw8HPQ/e2+ys7ND\no9GQn59vjiLXai1awJQp0LRpadp338Hf/w7yzy2EENbNKjo9VmT+/Pnk5uZSr149AHx8fMjOzmbZ\nsmWMHTsWR0fHMn0VCgsL0el0ODk5maPItV6TJipo+Pvf1fwMoJbHzsyEMWOgUSPzlk8IIcS9seoa\nBq1WawgW9Hx8fMjJySE7Oxt3d3cyMzON8jMyMgBoeuvPYFGtnJzUSInevUvTkpPVMMxTp8xXLiGE\nEPfOqgOGwYMHM2fOHKO0I0eO4ObmRr169ejcuTPJyclG/RViY2NxdnbG19e3pov7QLGxgcGD4ZVX\n4OJFiI+HHTsgNBSWLJF+DUIIYW2sOmDo06cP69evZ8uWLZw7d44NGzawYsUKxo0bB4C/vz9+fn6E\nh4dz7Ngx9u7dS0REBCNHjsTe3t7MpX8wODiAVguFhSpIuH4dli5VzRb6hayEEEJYPqvuwzBq1Ci0\nWi1Lly4lJSUFT09Ppk6dSkhICAAajYbo6GhmzpxJaGgozs7OhISEEBYWZuaSPzh27oT69cHfX/Vp\nuHZNpe/ZowKJt96CW0a9CiGEsFAanU4qh8tz/vx5evfuze7du8vM/yDu3ujRajVLUDUMZ86oGSE1\nGujRQwUNgwbBU0+pNCGEEOZR2X3PqpskhOW7dYpojQZatYJHHlG1DqDma/jmG4iKKq19EEIIYXkk\nYBAmFRxcNq1JE5g5E5o3L007dgxmzYIjR2qsaEIIIapAAgZhUl26wKhR4O2tRk54e6v9Z56ByZPV\no152NkRHw7p1MtGTEEJYGqvu9CisQ5cuarudvv/Cww/DqlVw9apK37sXjh6FV18FGf0qhBCWQWoY\nhNk9/DDMmAF+fqVpFy+qBazWroUbN8xXNiGEEIoEDMIi1K2rhli+/rqaKVJv3z7Vt+HoUfOVTQgh\nhAQMwoJoNNC1K/ztb9CxY2n6xYtqFMXnn8OVK+YrnxBCPMgkYBAWp149NX/DX/4Czs6l6QcPqqaL\n3btL53YQQghRM6TTo7BIGg0EBqr+DRs3ws8/q/T8fDVvw88/q7Uq2raFAwfUjJKpqWreh+Dg8jtZ\nCiGEuHcSMAiL5uICI0bAY4+pDpBpaSo9ORk+/lgtl52SAo6OKv3CBVixQj2XoEEIIaqPNEkIq9C2\nLUyfDs8/D3Z2penffw9xcWrK6eLi0vRdu2q+jEIIUZtJwCCshlarmhtmz1bNFaBWvCwpUTUO//uf\nqmEoKVG1DkIIIaqPBAzC6jRsqDpETp4MXl6l6YWFcPq06tNQWCgdI4UQojpJwCCsVqtW8OGHajZI\nfR8GUB0jL15U61XExkrgIIQQ1UECBmHVAgPh/fdV34aHHlKdIH19wc0N0tPhiy/UUMyfflIrYwoh\nhLg3MkpCWL1b16rIz1fzNPzrX6VTSmdmwurV8N13arGrxx4De3vzlVcIIayR1DCIWsXBAfr1g7lz\nYcAA42mmL12Cr76CKVPgn/+Ea9fMV04hhLA2UsMgaiVnZ3j2WXj6afjxRzX88vp1lZeTAzt2wL//\nrZo0evdWy24LIYSomAQMolZzdIS+feHJJ2H/ftVccfGiyisqgv/+V22tWkHPntC5s/E8D0IIIRQJ\nGMQDwcFB1SQ8+SQcOqRqHH7/vTT/99/V9s03qo9DUBC4u5uvvEIIYWkkYBAPFBsb6NRJbb//Dnv2\nqEWt9LNE5uSoYOL771WtQ/fuEBBg3BdCCCEeRBIwiAdWq1Zqu3ZNDbv8z39KmytABRS//KJmj3Ry\ngkcegVdeUUGEEEI8aCRgEA+8evXUlNPPPAPHjqng4ddf1eqXJ0+qY7Kz1bwO//kPDBqkVsr09QVb\nW/OWXQghaopVBQwzZsyguLiYDz/80JC2f/9+IiIiOHPmDC1atGDChAn07NnTkH/x4kVmzZrFTz/9\nhJ2dHYMGDSI8PByt1qpOXdQAGxto315t16/D2LFQt27p6ApQHSV37FCrZjo7q2P9/FTtg4OD+cou\nhBCmZhV3TZ1Ox5IlS1i/fj0vvfSSIT0pKYnRo0czZswY/vznP7Nt2zbCwsLYvHkzDz30EABjx45F\no9EQExNDeno6U6ZMQavVEh4ebq7TEVagbl01wqJTJ9WvITMTMjLUZFA5OeqYnBzVZPHLL2pkxcMP\nqwCiXTs146QQQtQmFh8wJCcn8/7775OYmIinp6dR3urVq/Hz82P06NEAvPPOO8THx7N69Wpmz55N\nQkIC8fHx/PDDDzRr1gxfX18mTZrE7NmzCQsLw16m+xN34OGh+i84O6utRQtV21BcrBbAuny59NjC\nQtWM8euvpa999FEVPLRpI0M1hRDWz+IDhoMHD+Lh4cGiRYt49913jfLi4uIIDg42SuvatSvbt283\n5Ht5edGsWTNDfmBgIDk5OZw4cYKOHTua/gSE1QoOhhUrSvc1GnBxgVGj1MiJc+fUEM1Dh8oup52a\nqrYfflDLcrdqpfo8+PrCn/4kfR+EENbH4gOGgQMHMnDgwHLz0tLSaNq0qVGam5sbaWlpAKSnp+Pm\n5lYmHyA1NVUCBnFH+vUpdu1SAYGnp5oESp/eooXaBg5UTRa//qo6Tf72m/FCV0VFcOqU2rZuVetY\ntGqlah7atIGWLY1X2xRCCEtk8QHDndy4caNMs4K9vT35+fkA5OXl4XBbTzQ7Ozs0Go3hGCHu5NaF\nre7E1VVNDNW7NxQUqODg6FE1yiI11fjYggKVrh+BodGoqan/9CcVPLRsqSaNspGVXoQQFsSqAwYH\nBwcKCwuN0goKCqhTpw4Ajo6OFBQUGOUXFhai0+lwkpl4hInY26u+C+3aqf0rV1Stw2+/qSDh1rke\nAHQ6SE5W23/+o9IcHKB5c2jWrPTRw0OaMoQQ5mPVAYOHhwcZGRlGaRkZGYZmCnd3d/bu3VsmHyjT\nlFEVBw7Azp3ql6OHh2rrvptfoeLB1KABdO2qNlCdJZOS1LZ3L8THqxEXTk4qMHBzU8t0JyaqTU+r\nVTUPXl6lm6en6oCp0Zjn3IQQDw6rDhg6d+7MgQMHjNJiY2MJCAgw5C9cuJDU1FQ8PDwM+c7Ozvj6\n+t7TZx44YNwR7sKF0n0JGsTdaNiw9Lvy44/QsaOaGCo7G7Ky1JDO8irAiorg/Hm13crBQQUS+q1p\nUxV0uLpK3wghRPWx6oBh+PDhvPjiiyxZsoT+/fvz3XffcfjwYWbOnAmAv78/fn5+hIeHM336dLKy\nsoiIiGDkyJH3PKRy587y03ftkoBBVI3+u2Rrq2ohGjRQ+15eMG6cGoVx7pxqqjh71ngY563y81X+\n2bNl8+rVU4FDkyZqa9y49LFBA1VrIYSofiUlagh2YaEK9ivaCgvVcXd6fuumf8/y8m495va0Cxfg\njz/UEPEnnri3mnGr/nPh4+NDdHQ0ERERLF++nFatWrFs2TJat24NgEajITo6mpkzZxIaGoqzszMh\nISGEhYXd82fe3oFN7/ZhdUJUpqLvUmpqaQDRoUNpem6u+k9/65aWVjqRVHmuXVPb6dPl59erp2o8\nGjVSn1e/vvGji4v6AyMdMIWl0+nK3qD1z+/28fYb+e15tx93p2CgpMTc/yKlMjJKO1lfuaLWybmX\nmnGrChjWrFlTJq1Xr1706tWrwte4urry6aefVlsZ9JP53O62OaWEqFRVv0tOTvDQQ2rT0+nUZFJp\naaVbRobasrKMh3eWRx9QlFc7oaeff6JevdIAom7d0k0/sZWTU+ljnTrSQfNBodPd3U32bh4rel7e\ne5WXL8qXnFz63NVVdcyGqteMW1XAYAlun8xHr2/fmi+LsG7V8V3S38xdXIwDCVC/cC5fVnNEXLyo\nAgj946VL6peGTlf5Z+h0pYFFVdjZqcBBvzk6qv4Wjo6lz+3tSx/1m51d6aN+02qNN1vb0q02dvgs\nKSmt0r71uX5f/7y8TV8lfet+RdXUt6fdXiVeURX67WkPiowMdfPNzTXupFwejabs97Y6NlvbyvPt\n7IyPmzhR/T+2sTEO5KtaMy4BQxVVNpmPEHfL1N+l+Piyo3lunQOtpEQFDZcvqwDi6lW1XblS+vza\nNfXH8V7ofwVWNdCoKv0fQVvb0uc2NmrTaIyfV7SB8fO7odOVBlz65+VtJSVl9/Xb7fv6TdybW2+S\ntweb+ucVPd5+3O3H/PYb/POfatI1/XfKxgZeeEHN/Hr7a/XfOUvQokX11IxLwHAP7nYyHyEqY6rv\n0t2M5rGxUX0XGjWCm91+ylVUpJo9rl1TIzmuX1dbTo7az81Vz3NzS5/n5d1d7UVFqvJLTn+TvW1K\nllqrKv82NaW8G+7tN147O/U9PHFCfW8aNVKLu/n6lh5nb393N/eKHk15g965s/xF5Q4csPwa5uqq\nGZeAQYhaqDpH82i1xqM47oZOp2a0zMsr3fLz1Wqftz7m56vj9I8FBWp+il9/Vb8WnZ1VMHD2bOmI\nD321ub56/l5Y4k33VhqNcU2JvuYkI0P1dNdoVDMPqGG2np6la5TcWm2tr3m5vSlHf5O99bjymn7u\nlKZ/vNtmIX0Qqx/+C+q69uljHT/ArLnDe3XVZkrAIEQtZO4/bhqN6pvg4FC1QANg1izj0SF63t4w\nfbpxmr5n/O1t/sXF5Vf3AyQkwLp16pftrU0K/fuX/7l3OkcoHUFSXjPHrc0i+vTymktuDQru1DQy\na1ZpoHArd3fVTm3JrH1IurV3eK+O2kwJGISohaz5j1tVgh19x7KqWL5c1Vzc7vBheO65qr1XTTN3\nIHg/rLnsIB3eQQIGIWqlmvjjZqop0k0d7FjzjcuaA0FrLjtIh3eQgEGIWsnUf9xMOUW6qYMda75x\nWfOvXGsuu96D3uFdAgYhailT/nEzZXu0qYMda75xWfOvXGsuu1AkYBBCVJmpq/VNGexY+43Lmn/l\nWnPZhQQMQoh7YM3V+iA3LiHuhSwpI4SosuDg8tOtoVpfCHFvpIahAsXFxQCkpaWZuSRCWB4PDzXN\n9N69kJ4OTZtCz54q/fx5c5dOCHEv9Pc7/f3vdhIwVCAzMxOA0NBQM5dECOuwZYu5SyCEqA6ZmZm0\naNGiTLpGp7ufGd9rrxs3bnD06FFcXV2xlXV6hRBC1HLFxcVkZmbSrl07HB0dy+RLwCCEEEKISkmn\nRyGEEEJUSgIGIYQQQlRKAgYhhBBCVEoCBiGEEEJUSgIGIYQQQlRKAoZ7UFxczMcff0xQUBD+/v6M\nGzeOrKwscxfrvmVlZTF58mSCgoIICAjgL3/5C6dOnTLkv/TSS/j4+BhtH3zwgRlLfH+SkpLKnI+P\njw9xcXEA7N+/n4EDB9KhQwcGDBjA3r17zVziexMbG1vuefr4+PDqq68CtePazpgxo0yZK7uGFy9e\nZPz48QQEBNC9e3ciIiIoKiqqyWLfk/LONSYmhr59++Ln50e/fv3YsGGDUf7atWvLXONHHnmkJot9\nT8o718q+r7Xluj711FMV/t9NublwS41eV52ossjISN3jjz+u279/v+7o0aO6kJAQ3ZAhQ8xdrPtS\nXFyse/nll3WDBw/WHT58WJeYmKgbN26crnv37rpLly7pSkpKdB07dtRt3bpVl5GRYdiys7PNXfR7\ntn37dl3Xrl2NzicjI0NXUFCgS0xM1LVr10732Wef6ZKSknSRkZG6Rx99VHfq1ClzF7vK8vPzy5zj\n5s2bdb6+vrp9+/ZZ/bUtKSnRLV68WNe2bVvd+++/b0i/m2s4dOhQ3bBhw3QnTpzQ/fjjj7pu3brp\nFi1aZI7TuCsVnevatWt1fn5+ui1btujOnj2r++abb3SPPvqobvPmzYZjZsyYoXvrrbeMrnFmZqY5\nTuOuVHSud/N9rS3X9eLFi0bnePbsWV3Pnj117733nuGYmryuEjBUUX5+vs7f31+3ceNGQ1pycrKu\nbdu2uvj4eDOW7P4cO3ZM17ZtW11SUpIhLT8/X9exY0fd5s2bdWfPntW1bdtWd+7cOTOWsnpFRkbq\nQkNDy82bPn26bvjw4UZpw4cP102bNq0mimZS165d0z3++OO6iIgInU6ns+pre+7cOd3w4cN1Xbt2\n1fXq1cvoj21l1/DgwYNlznvTpk06f39/XX5+fs2cQBXc6VwHDBigW7BggdHxU6dO1b3yyiuG/aFD\nh+o++eSTGivv/bjTuVb2fa1N1/V2M2bM0D311FO63NxcQ1pNXldpkqiikydPkpOTQ2BgoCHN29sb\nLy8vQ1W2NfLw8ODzzz+nZcuWhjSNRgPA1atXOXXqFI6Ojnh5eZmriNUuMTGRVq1alZsXFxdndI0B\nunbtatXXWO+zzz7D3t6esLAwAKu+tgcPHsTDw4Nt27bh7e1tlFfZNYyLi8PLy4tmzZoZ8gMDA8nJ\nyeHEiROmL3wV3elcp02bxpAhQ4zSbGxsuHbtmmE/KSmJ1q1b10hZ79edzrWy72ttuq63OnnyJN98\n8w0zZsygTp06hvSavK4SMFSRfnGOpk2bGqW7ublZ9UJVDRs2pFevXtjYlH4l1qxZw40bNwgKCiIx\nMREXFxcmTJhAUFAQAwYMYNWqVZSUlJix1PcnMTGRlJQUBg8ezOOPP86IESP49ddfAXWda9s1BtW2\nGxMTQ1hYmOGPjjVf24EDB7JgwQJcXV3L5FV2DdPT03FzcyuTD5CammqiEt+7O51rYGCg0Q0yJSWF\n7du306NHD0Cd69WrV9m3bx99+/alZ8+eTJgwgfT09Borf1Xc6Vwr+77Wput6q6ioKDp37kzPnj0N\naTV9XSVgqKK8vDxsbGyws7MzSre3tyc/P99Mpap+u3fvZtGiRYwcOZLWrVuTlJREbm4uQUFBrFy5\nkmHDhrFkyRKio6PNXdR7cuPGDZKTk7l+/TqTJk1i6dKluLm5MXz4cE6fPs2NGzewt7c3ek1tuMZf\nffUVjRs35rnnnjOk1bZrq1fZNczLy8PBwcEo387ODo1GY9XX+dKlS7z55ps0adKEN954A1A3WQCt\nVktkZCQfffQRf/zxByNGjODGjRvmLG6VVfZ9rY3XNTk5mT179vDmm28apdf0dZXVKqvI0dGRkpIS\nioqK0GpL//kKCgqMqoms2aZNm5g+fTr9+vVj4sSJAMyfP5/c3Fzq1asHgI+PD9nZ2SxbtoyxY8ca\nmi+shaOjIwcOHMDe3t5wU5k3bx7Hjh1j3bp1ODg4UFhYaPSa2nCNt27dyqBBg4wC3tp2bfUqu4aO\njo4UFBQY5RcWFqLT6XBycqqxclan5ORkRo0axY0bN4iJicHFxQWAoKAgfv75Zxo1amQ4tk2bNjzx\nxFLw5pcAACAASURBVBPs3buXZ555xlxFrrLKvq+18bpu27YNDw8PgoKCjNJr+rpKDUMVeXh4AKXL\nX+tlZGSUqf60RkuXLmXq1KkMGTKEBQsWGJootFqt4T+ono+PDzk5OWRnZ5ujqPetbt26Rr9AbWxs\naNOmDampqXh4eJCRkWF0vLVf48TERM6ePUv//v2N0mvjtQUqvYbu7u7l/j+Gsk2O1uDYsWO8/PLL\n2NjY8PXXXxs1UQBGNxVQ1fQNGza0yGr6O6ns+1rbriuoGt/g4OByg/eavK4SMFSRr68vzs7O/O9/\n/zOknT9/ngsXLtClSxczluz+LV++nMWLFzNu3DimT59u9OUcPHgwc+bMMTr+yJEjuLm5lfnPaw2O\nHj1Kp06dOHr0qCGtuLiYkydP8tBDD9G5c2cOHDhg9JrY2FgCAgJquqjVJi4uDldX1zIdpGrbtdWr\n7Bp27tyZ5ORkoz+ssbGxODs74+vrW6NlvV+nT5/m9ddfx8vLi3Xr1hl+2OitXr2aoKAgoxqXCxcu\ncOnSJR566KGaLu59qez7WpuuK0Bubi4nTpygW7duZfJq+rpKwFBF9vb2DBs2jAULFrBv3z6OHTvG\nu+++S2BgIH5+fuYu3j07efIkkZGRvPjiiwwePJjMzEzDlpubS58+fVi/fj1btmzh3LlzbNiwgRUr\nVjBu3DhzF/2e+Pr64uXlxYwZMzh8+DCJiYlMnTqVy5cv8+qrrzJ8+HDi4uJYsmQJp0+f5pNPPuHw\n4cO89tpr5i76PTtx4gRt27Ytk17brq1eZdfQ398fPz8/wsPDOXbsGHv37iUiIoKRI0eW6ftg6SZP\nnoy9vT0LFiygqKjI8H/30qVLAPTq1YucnBw++OADTp8+TXx8PGPHjqVz5848/vjjZi591VT2fa1N\n1xXgt99+o7i4uNz/uzV9XaUPwz145513KCoqYuLEiRQVFdGjRw9mzJhh7mLdlx07dlBcXMzGjRvZ\nuHGjUd748eMZPXo0Wq2WpUuXkpKSgqenJ1OnTiUkJMRMJb4/Wq2WFStWsGDBAt566y3y8vLo1KkT\nMTExNG7cmMaNGxMdHU1ERATLly+nVatWLFu2zGqGpZUnIyOD+vXrl0kfNWpUrbq2ej4+Pne8hhqN\nhujoaGbOnEloaCjOzs6EhIQYhptaizNnznDkyBEA+vbta5TXvHlzvv/+e5o3b86qVav4+OOPCQkJ\nwc7OjqeeeoopU6aYo8j3pbLva225rnr65pUGDRqUyavp66rR6XQ6k7yzEEIIIWoNaZIQQgghRKUk\nYBBCCCFEpSRgEEIIIUSlJGAQQgghRKUkYBBCCCFEpSRgEEIIIUSlJGAQQgghRKUkYBBCCCFEpSRg\nEEIIIUSlJGAQQgghRKUkYBBCCCFEpSRgEEIIIUSlJGAQQgghRKUkYBBCCCFEpSRgEEIIIUSlJGAQ\nQgghRKUkYBBCCCFEpSRgEEIIIUSltOYugKW6ceMGR48exdXVFVtbW3MXRwghhDCp4uJiMjMzadeu\nHY6OjmXyJWCowNGjRwkNDTV3MYQQQogatXbtWgICAsqkS8BQAVdXV0D9w7m7u5u5NEIIIYRppaWl\nERoaarj/3U4ChgromyHc3d3x9vY2c2keLLmFuWTkZJB+PZ2MnAxyC3OxtbHFVmOLrY0tWhst9R3q\n413PG08XT+xs7cxdZCGEqDUqaoaXgEGYXV5hHkcyjpCQmkDipUSy87Pv+rUajYamzk3xrufNI66P\n4Ofuh7O9swlLK4QQDyYJGIRZ5Bfl878L/yMhLYGTWScpLim+p/fR6XSkXU8j7XoacSlxxPwaw8Ou\nD9PZo7MED0IIUY0kYBA1Kr/o/7N352FRlusDx78z7CDKJiCiouICuLAIImJplktpLlm2Wpm/TFMz\nS0tRPJaWmpWm56SZ5bFVLbHUSjvuRIosKiIo4pIoq4AgIjDM/P7g8tUJtFGBYbk/1+V1Mc/7zLz3\nWM7cPMv9lLDn7B52pO7gSumVKvuYqk1xtnHGpYkLzjbONLVoilanpVxbTrmunLLyMrKKsrhQeIGs\noix0Op3yXK1OS2JWIolZiXx99GuCWgYxoP0AWjZtWVtvUQghGiRJGESt+KdEoY1dG/xc/eju2p0W\nTVqgUqkMft30K+mcyj1F7MVYTuedVq5pdVoOpB3gQNoBujh3YaDnQDo4dDD4tYUQQtwgCYOoURlX\nMoj8K5Ko81EUlRbpXXO0duSBtg/g5+qHo7XjXb2+hakFHnYeeNh58GC7B8krziM2PZZDFw5xNv+s\n0u9Y1jGOZR2jrX1bHvd+nPYO7e/lbQkhRKMjCYOodmXlZcSlx7H/r/2kXEqpdN3R2pHBnoPp1aoX\npurq/V/Q3sqeB9s9yIPtHuRM3hl2pO4gPiNembY4k3eGxX8spqd7T0Z6jcTO0q5a7y+EEA2VJAyi\nWuRczeFY1jESMhM4cekEZeVllfrUZKJQlbb2bRnfYzxZRVn8nvo7Ueej0Gg1ABxMO8jhjMMM9hzM\ng+0elK2ZQgjxDyRhEHelXFtOal4qRzKOkJCVQOaVzCr7qVVqurt2p0/rPng190Ktqv3jS5xtnHmm\n2zMM8hzED8d/IC49DqhY/7A5eTN/pv3JWL+xeNh51HpsQghRX0jCIAxWVl7G4YzDHM44TGJ2IsVl\nxbfs69rElWD3YEJahdDMslktRnlrjtaOjO8xnhM5J/j+2PdcLLwIQOaVTBZFLmJIxyEM7jDYKEmN\nEELUdZIwiH90+dpl9pzdw75z+265FdLMxIzOTp3p4tyFLs5dcLJ2quUoDdfJqRNz7p/DvnP7iEiK\n4JrmGlqdlp9P/MyxrGOM9RtLc5uqS6MKIURjJQmDuKXzl8/z++nfibkYU2VhJXsre7q7dKerS1c6\nOXaqV+sA1Co1fT360sW5C1/Ef0FqbioAp/NO8+6+d3mm6zP0dO9p5CiFEKLukIRBVJJ/LZ+IpAgO\npB2odM3eyp7Q1qH4uvrS0rZlva9p4GTtxJshb7L91HZ+PvEzWp2WEk0JX8R/wV+X/+Ix78dkikII\nIQD5JBSKsvIyfjv1G+G7wyslC+0d2jO+x3je6/8eQzoOwb2pe71PFq5Tq9QM7jCYt0PfxqWJi9L+\nv9P/45ODn1SqHyGEqL/y8/P54Ycf7vr5aWlpdOrUiZiYmGqM6taWL1/OQw89ZJR7/52MMAgAjmYe\nZf2x9eRczdFr93X1ZXCHwY1iB0EbuzbM6jOLL+K/4EjGEQCSspN4P/J9JgZOxM3WzcgRCiHu1ZIl\nSzh37hyjRo26q+e3aNGCyMhI7Oxqv4aLMe8NkjA0ejlXc1h/bD1HM4/qtbvZujG6y2g6O3U2UmTG\nYWlqyYQeE9h6citbT24FILsom4WRCxkfMB4fZx8jRyiEuBc3nz1zN0xMTGje3DiLoo15b5CEodEq\nKy9je+p2fjv1m16RJWszax7t9Cj3e9zfaOfuVSoVQzsNpWXTlqw9vJYSTQklmhJWRK9grN9YAlsG\nGjtEIfg99Xe2nNxCiaak1u9tYWrB0I5Deaj9QwY/p1OnTkycOJEff/wRgB9//BFzc3MWLlzIrl27\n0Ol0dO/enZkzZ9KuXTsA3n77bVQqFZaWlmzZsgUTExPGjBnDgAEDmDNnDsePH6dt27bMnz+frl27\nApCXl8fHH3/M7t27KSgowNfXl7feegtvb2+WL1+uTEd06tSJnTt34u7uzoYNG1izZg3p6em0adOG\nsWPHMmLEiCrfR1paGv379+ebb76hR48ePPfcc/j6+pKRkcHOnTsxNTVlyJAhzJo1C1NTU65evcq7\n777L3r17KSwsxMvLi9dff51evXoB8MADDzBq1CgmTpyo3KOqtru5d3VrnN8IjVzKpRTm7Z3HlhNb\nlGRBpVLRp00f3n3gXfq17ddok4Wb+bfwZ0bvGThYOQAVh1mtiV/DnrN7jBuYEMDvp383SrIAFUXP\nfj/9+x0/b+PGjaxatYoVK1bg6OjIyy+/TFZWFp9//jnffvstbm5uPP300+Tl5SnP2bJlC5aWlmza\ntInnn3+eTz75hFdffZXx48ezceNGzMzMeOeddwAoLy9n7NixJCQksHTpUjZs2IC9vT3PPvssaWlp\njB07liFDhuDn50dkZCQtWrTg22+/5eOPP+b1119n69atjBs3jgULFhAREWHw+/ryyy9p27Ytmzdv\nZtasWXz33Xds27YNgE8++YRTp06xZs0afvnlF7y8vJg0aRJXr16947+/O713dZNvhUZEp9Ox5+we\nPvrzI7KLspX21s1a83bo2zzb7VmamDcxYoR1j3tTd2b0nkEL2xZAxd/hdwnfsfXk1nse2hTiXjzU\n7iEsTC2Mcm8LUwseamf46MJ1I0aMwMvLi27dunHgwAESEhJYtmwZXbt2xdPTk3nz5tGsWTM2bNig\nPMfBwYEZM2bQunVrXnjhBQCGDBlCv3796NSpEyNHjiQlpeLMmsjISI4fP85HH31EQEAAnTp1YvHi\nxTRt2pRvvvkGGxsbLC0tMTMzo3nz5piYmLBy5UomTZrEoEGDaN26NcOGDeOll15i5cqVBr8vLy8v\nJk6cSOvWrRk+fDidOnXi8OHDAJw7dw4bGxvc3d1p1aoVb731FsuXL8fExOSO//7u9N7VTaYkGgmN\nVsO3Cd/yx19/KG3WZtYM6zyM+9rcJyMKt2FvZc/0kOksj17OmbwzAGw5sYUrpVcY7TO6wewWEfXL\nQ+0fuqMpgbqgVatWys/Hjx+nvLycPn366PUpKSkhNTVVedy6dWvl35i1tbXSdp2lpSWlpaUAnDx5\nEjs7O9q2batcNzc3p1u3bkpScbPc3FwyMzNZtGgRS5YsUdo1Gg3l5eWUlpZibm7+j+/Lw8ND77Gt\nrS1lZRWjty+99BITJ06kV69e+Pn50adPHx599FEsLKon2bvdvaubJAyNQP61fFbGrFS+7KBiR8CE\nHhOwt7I3YmT1h425Da8Hv86nMZ+SlJ0EwO4zu7l87TJj/cbWq6JVQhjLzV+SZmZm2NnZ6Y0mXHc9\nMQCqnIu/VZJuaWlZZbtWq63ydczMKv7dzpkzh6CgoErXDV0HUFVScX0EskePHuzdu5fIyEgiIyP5\n5ptv+PTTT9mwYQMdOnSo8vU0Go1B9/2ne1c3+bWygbtYeJEF+xboJQvB7sFMD5kuycIdsjC1YFLQ\nJHq49VDa4tLjWHpgqdRqEOIOdejQgfz8fADatGlDmzZtcHd3Z+nSpRw6dOiuXtPT05P8/HxOnz6t\ntJWWlpKQkICnpyegn2zY2tri4uJCWlqaEkObNm2IiopizZo1qNX3/hW5YsUK4uLieOihh5g3bx47\nduzAzMyMPXv2ABVJy5UrN0ruX7lyhUuXLt3zfWuCJAwNWEFJAcsPLqegpACoKFD0hM8TvOD7gvxG\nfJdM1aa85P8SD7R9QGk7lXuKD6I+4NLVuvmPXIi6qFevXvj6+jJ16lRiYmI4c+YMs2fPZteuXXTs\n2PGuXjM4OBg/Pz/efPNNYmNjOXnyJDNnzqSgoIDRo0cDYGNjQ2ZmJufPn0ej0TBhwgTWrl3L+vXr\n+euvv9iyZQsLFy6stu2LFy5cYN68eRw8eJALFy7w888/U1hYSPfu3QHw9fVl27ZtxMfHk5KSwttv\nv11t6xuqmyQMDVRZeRmfHvqU3OJcoKK+wGvBr9G/XX+Zc79H1xOvUd43Cr+kF6azMHIh5y+fN2Jk\nQtQfKpWKf//733h6ejJx4kRGjBjB2bNnWbNmjTIacDevuWLFCtq2bcv48eMZPXo0+fn5fPvtt8r6\niZEjR1JeXs7DDz/M8ePHeeqpp5g2bRpr1qzh4YcfZunSpUycOJFJkyZVy/ucPXs2wcHBvPHGGwwc\nOJC1a9fy/vvvK1Mg06ZNo3Pnzrzwwgu8+OKL+Pv74+/vXy33rm4qnSz1rtL1/a7X9+nWJzqdjjXx\nazh0oWJYT6VSMSloEl2cuxg5sobn0IVDrD28Fo22Ys7RwtSCcf7j6ObSzciRCSHEnfmn7z0ZYWiA\ntqVsU5IFgMe9H5dkoYYEtgzkteDXsDKzAir2p//n0H/YeXqnbLsUQjQotZ4wlJeX8+GHHxIaGoqf\nnx9TpkwhJyfnlv0TEhJ48skn6d69OwMGDGDz5s1614uLi5kzZw49e/akR48ezJ49m6Ii/QVosbGx\njB49mm7dutG/f3+++uqrGnlvdUHMxRi2nNiiPL6vzX168+2i+nV07MiM3jNwtHYEKkZ4NiRu4Ptj\n36PVaY0cnRBCVI9aTxiWL19OREQEixYt4uuvvyYjI4PJkydX2Tc3N5dx48bh4+PDpk2beO655wgL\nCyMyMlLpEx4eTmxsLKtWrWLlypVER0cTHh6uXE9NTWXs2LF069aNLVu28Oqrr7Jo0SJ+++23Gn+v\ntS2rKIu1h9cqj72ae/FklydlzUItcLN1Y2boTNrZt1Pa9pzdw4roFRSXFRsxMiGEqB61mjCUlpay\nbt06pk2bRu/evfHx8eGjjz4iLi6OuLi4Sv03btxIkyZNCAsLo3379jz33HM8+uijfPHFFwBkZGSw\ndetW5s6di6+vLz169GD+/Pls27aNzMxMAD777DO6du1KWFgYbdq0YeTIkYwYMcJox4PWpJ+Sf1JK\nPbs0ceHlgJcxUdfN1bYNka2FLdN6TdM7ayIxK5GFkQvJKsoyYmRCCHHvajVhSE5OpqioSK9Ahru7\nOy1btqzyCzwmJobAwEC9vbBBQUHExcWh0+mIi4tDrVbrrSj19/fHxMSE2NhYoKJU6ODBg/Ve9913\n32X27NnV/faM6lz+OWIu3vg7fNH3RazNrG/zDFETzEzMeMnvJYZ0HKK0ZVzJ4P3975OYlWjEyIQQ\n4t7UasKQkZEBgIuLi167s7Ozcu3v/avqW1xcTF5eHpmZmTg4OCjVuqCiMpeDgwPp6elcuXKFnJwc\nrK2tmT59OiEhIQwdOpSNGzfWwLszrojkGwel+LXwo61929v0FjXp+mmX4/zHKfUurpZdZXn0cn5P\n/V0WQwoh6qVaTRiKi4tRq9V6X/BQUdqypKTyqWvXrl2rVPby+uPS0lKKi4urrMd9/fWuV89auHAh\nnp6erFmzhtGjRzNv3jzliNOGICk7SSlXrFapGdG56mNZRe0KbBmoV1FTp9Pxw/EfWHt4rd6R4kII\nUR/UasJgaWmJVqutVCe7tLQUKyurKvtfP1Tk5r4AVlZWVV6/3sfa2lqpA963b1/Gjx+Pl5cXzz77\nLE888QT//e9/q+ttGZVOp2NT0iblce/WvXFp4nKbZ4ja1MauDbP6zKK9Q3ul7UDaARb9sYicq7fe\nHSSEEHVNrSYMLVpUHBGcnZ2t156VlVVp6gHA1dW1yr7W1tbY2tri6upKbm4u5eXlynWNRkNubi7O\nzs7Y2dlhbm5eqcxo+/btSUtLq663ZVSx6bH8dfkvoGL+/Oa5c1E3NLVoyrRe0+jdurfSdv7yeRbs\nWyDrGoQQ9UatJgydO3fGxsaG6OhopS0tLY0LFy4QGBhYqX9AQAAxMTF6c74HDx7E398ftVpNQEAA\nGo2G+Ph45XpsbCxarZaAgABMTU3x9fUlISFB73VTUlL0jketr8q15WxOvlGX4oG2D2BnaWfEiMSt\nmKpNea7bczzV9Sll58r1dQ3bTm6TdQ1CiDqvVhMGc3Nznn76aRYvXsy+fftITExk2rRpBAUF4evr\nS2lpKdnZ2co0w6hRo8jNzWXu3Lmkpqby1VdfsXXrVsaNGwdULJ4cPHgwYWFhxMbGEhMTw5w5cxg2\nbJgyYvHKK6+wY8cOPvvsM86fP88PP/zADz/8wIsvvlibb71G7P9rP9lFFSMw1mbWDPIcZOSIxO2o\nVCr6evTlzZA3lcROp9Px84mf+fehf8uJl0KIOq3Wz5LQaDQsWbKEiIgINBoNffr0ITw8HAcHBw4e\nPMiYMWNYt24dPXv2BODw4cPMnz+fEydO4ObmxpQpU3jkkUeU1ysqKmL+/Pns2LEDU1NTBg4cyKxZ\ns/TORd+5cyfLli3j9OnTuLm58dJLLyknl91KXT9LQqPVMGvnLC5fuwzAY96PMaD9ACNHJQxVUFLA\n6tjVnLx0UmlzsHLg/wL+T6/4kxBC1JZ/+t6Tw6duoa4nDNEXolkTtwaAZpbNWPDAAjmyup7R6rRE\nJEWwI3WH0qZWqRnpNZIH2z0oFTqFELVKDp9qoHaf2a383NejryQL9ZBapeYx78eYGDhRKbKl1Wn5\n4fgP/OfQf2SKQghRp0jCUA+dyz/H6bzTQMViutDWoUaOSNyL7q7dmX3fbDzsPJS2o5lHeW//e6QV\nNIzdPEKI+k8Shnpoz9k9ys8BbgE0tWhqvGBEtXC0dmR67+k82O5BpS3nag6LIhfplfwWQghjkYSh\nniksKST6wo1tqf08+hkxGlGdTNWmPO7zOK/0eAUL04oKpqXlpayOXc2mpE1yVLYQwqgkYahn/jj/\nBxptRaVMDzsPOTOiAfJr4cfM0Jk42zgrbdtPbWdF9Aqull01YmRCiMZMEoZ6RKvT6k1H9GsrowsN\nVQvbFszsM5OuLl2VNjkqWwhhTJIw1CNHMo6QV5wHgK2FLQEtAowckahJ1mbWvBr4Kg93eFhpy7yS\nycLIhaRcSjFiZEKIxkgShnpk99kbWylDW4fKVspGQKVSMazzMP4v4P+U/95FpUV8fOBjDqQdMHJ0\nQojGRBKGeuJi4UVO5JwAKvbv39/mfiNHJGpTD7cevNHrDWwtbIGKc0S+jP+Sn5J/knMohBC1QhKG\nemLv2b3Kz76uvthb2RsxGmEMbe3bMjN0Jm62bkrbLym/sPbwWsq15bd5phBC3DuDE4ZDhw5x+PBh\nAC5evMiECRMYMWIEq1atqrHgRIVybTmHLh5SHvf16Gu8YIRROVo7MqP3DHycfZS2A2kH+Pehf1Oi\nKTFiZEKIhs6ghGHz5s2MGTOG33//HYDw8HAOHDhAy5YtWbFiBatXr67RIBu7pJwkpUywvZU9HR07\nGjkiYUxWZlZMCpqkV+EzMSuRD//8kMKSQiNGJoRoyAxKGNauXcuIESOYPn062dnZREVFMWnSJFas\nWMHrr7/ODz/8UNNxNmo3F2oKdAuUQ4kEapWaZ7s9y5COQ5S2c/nnWPzHYnKu5hgxMiFEQ2VQwnDm\nzBmGDx8OwN69e9HpdPTv3x+Arl27kp6eXnMRNnKl5aUczjisPA5sGWjEaERdolKpGNppKM90e0ZJ\nIrOKslgUuUjOoBBCVDuDEgZbW1uuXLkCwP79+3Fzc8PDwwOAv/76C3t7WYBXUxIyE5S5aZcmLrRq\n2srIEYm65r429/FKj1eUbZcFJQV8GPUhZ/LOGDkyIURDYlDC0LNnT1asWMFnn33Gzp07efjhikIy\n27dvZ9myZYSGymmJNeXmxY4yHSFuxdfVl6nBU7E0tQTgatlVPj7wsbIVVwgh7pVBCUNYWBj29vas\nWLGCXr16MX78eADef/99WrVqxRtvvFGjQTZWxWXFJGQmKI+DWgYZMRpR13k6ePJGyBs0MW8CQImm\nhE8OfsLRzKNGjkwI0RCYGtLJwcGBNWvWVGpfv349Li4uaLVyil5NOJxxWDloqlWzVrg0cTFyRKKu\na92sNW+GvMnSA0vJv5aPRqvh00OfMtZvrKx/EULcE4NGGPr3709ycnKldhcXF44ePUpISEi1Byb0\npyNkdEEYqoVtC2b0nkFzm+ZAxaFla+LXcDDtoJEjE0LUZ7ccYdi6dSsaTcVvtxcuXGDHjh1VJg1/\n/vknpaWlNRdhI1VYUkhSdpLyuIdbDyNGI+obR2tHpodMZ+mBpVwsvIhOp+PLw19SrisnpJUk+EKI\nO3fLhCExMZEvv/wSqNi+9Z///KfKfiqVirFjx9ZMdI1YbHosWl3FVI+ngycOVg5GjkjUN80smzGt\n1zQ+PvAxFwouoNPpWHdkHVqdVq/okxBCGOKWCcO0adN44YUX0Ol09O3bl08//RRvb2+9Pmq1miZN\nmmBlZVXjgTY2hy7ctDtC5p7FXbK1sGVar2ksPbCU85fPo9Pp+OrIV2h1Wu5rc5+xwxNC1CO3TBjM\nzMxwcalYZLdz506cnZ0xM5PjlGtDbnEup3JPARUV/QJaBBg5IlGfNTFvwuvBr7Ps4DLO5Z8D4Juj\n36DRanig7QNGjk4IUV/cMmFYuXIlI0eOxNnZmS1bttz2RVQqlbLVUty7uPQ45efOTp2VI42FuFs2\n5jZMDZ7KsgPLOJt/FoD1x9ZTVl7GQM+Bxg1OCFEv3DJhWLp0KSEhITg7O7N06dLbvogkDNXr5lLQ\nAW4yuiCqh7WZNVODp7I8ejmpuakAbEraRJm2jEc6PCJFwYQQt3XLhOHmHRFV7Y4QNaOwpFCZjlCp\nVHR36W7kiERDYmVmxWs9X2NF9ApOXjoJwJYTWygtL2VE5xGSNAghbsmgOgyi9iRkJaDT6QBob99e\npiNEtbMwtWByz8l4N7+xiHn7qe1sSNyg/L8nhBB/Z1ClR51Ox6ZNm9izZw9Xr16t9KGiUqmqrAQp\n7tzN0xG+rr5GjEQ0ZOYm5rwa9CqfxX7GkYwjAOw6s4uS8hKe7fYsapX8LiGE0GfQp8JHH31EWFgY\nSUlJlJSUUFZWpvdHCjdVjxJNCcezjyuPu7vKdISoOaZqU8YHjNdbJ/PHX3+wJm6NUpJcCCGuM2iE\nISIighdffJG33nqrpuNp1I5nH6esvAwAN1s3nG2cjRyRaOhM1CaM8x+HhYkFUeejAIi5GENJeQnj\nA8YrR2YLIYRBIwxXrlyhX79+NR1LoyfTEcIY1Co1Y7qPoV/bG//GEzITWB69nGuaa0aMTAhRlxiU\nMPj5+REXF/fPHcVd0+q0JGTdOMpaEgZRm1QqFaN9RjO4w2Cl7UTOCT7+82OKSouMGJkQoq4waEri\nlVde4Y033kCj0eDv74+lpWWlPv7+/tUeXGNyKveU8sFsZ2lH62atjRyRaGxUKhXDOw/HytSK2TeG\nJAAAIABJREFUTUmbADibf5YPoj5gavBU7CztjByhEMKYDEoYnn/+eQBWrFgBoLdXW6fToVKpSEpK\nqvK5wjB/n46Q/fDCWAZ6DsTS1JLvjn2HTqcjvTCdxX8sZmrwVFlXI0QjZlDCsG7dupqOo1HT6XSy\nfkHUKfd73I+1mTVfxH+BVqfl0tVLfPDHB7wW/BruTd2NHZ4QwggMShiCgoKq7Ybl5eUsXbqUiIgI\nioqK6NOnD+Hh4Tg5OVXZPyEhgQULFpCUlISLiwsTJ05k+PDhyvXi4mLee+89duzYQXl5OYMGDWLm\nzJnY2NhUeq3Lly/z6KOPMmrUKCZPnlxt7+lepRWkcenqJaCiEl8Hxw5GjkiIilNSrcysWBmzkrLy\nMgpKClgStYRJQZPwdPA0dnhCiFpmUMKwcuXKf+zzyiuvGHTD5cuXExERwaJFi7Czs2PevHlMnjyZ\n7777rlLf3Nxcxo0bx5AhQ1iwYAFRUVGEhYXh5OREaGgoAOHh4SQmJrJq1So0Gg2zZs0iPDycDz/8\nsNLrzZs3j4yMDIPirE03jy50de6Kqdqg/yxC1Lguzl2YGjyVFdErKC4rprismKUHlvJywMt0c+lm\n7PCEELXIoG+m2x0+1aRJE5ydnQ1KGEpLS1m3bh2zZ8+md+/eQEVRqP79+xMXF1dp4eTGjRtp0qQJ\nYWFhqNVq2rdvz/Hjx/niiy8IDQ0lIyODrVu3snbtWnx9K4bx58+fz5gxY5gxY4ZyPDfA1q1bSUxM\n1GurK2Q6QtRlng6evNHrDZYdXEZhSSFl5WV8euhTnuv+HCGtQowdnhCilhi0rTI5ObnSn7i4OD77\n7DOaNm3KnDlzDLpZcnIyRUVFelMc7u7utGzZkpiYmEr9Y2JiCAwMRK2+EWZQUBBxcXHodDri4uJQ\nq9V6iYa/vz8mJibExsYqbZmZmcyfP5+FCxdiYWFhUKy1Jf9aPmkFaUBF5T0fZx8jRyREZa2atWJG\n7xk4WVdMHWp1Wv57+L9sP7Vdzp8QopG464Lx1tbW3Hfffbz66qssXrzYoOdcnw74+2/5zs7OVU4V\nZGRkVNm3uLiYvLw8MjMzcXBwwMzsRjU6U1NTHBwcSE9PByoWFM6cOZNRo0bh5+d3R++xNqQXpis/\nt27WGkvTyltWhagLnG2ceSv0Lb1Fj5uSNvHD8R8kaRCiEbjnE2bc3NxITU01qG9xcTFqtVrvCx7A\n3NyckpKSSv2vXbuGubl5pb5QMb1RXFxc5YjBza/31VdfkZ2dzZQpUwyKsbZlFWUpP7s0qXvTJULc\nrKlFU94MeZOOjh2Vtv+d/h9fxH8h508I0cDdU8KQmZnJ559/TsuWLQ3qb2lpiVarRaPR/2ApLS3F\nysqqyv5/P9jq+mMrK6sqr1/vY21tTWpqKsuWLWPx4sWVEo+64uaEQfa4i/rAysyKKT2n4Nfixohd\n9IVoVkSvkFLSQjRgBi169PHxqVRISKvVotPp0Ol0Bk9JtGjRAoDs7GzlZ4CsrKwqFyO6urqSnZ2t\n15aVlYW1tTW2tra4urqSm5tLeXk5JiYmAGg0GnJzc3F2dubXX3/l6tWrPP3008rzi4uLWbVqFb/9\n9hvbtm0zKO6aJAmDqI/MTMx4OeBlvkv4jn3n9gGQlJ3EkqglTA6aTDPLZkaOUAhR3QwuDV1V5cEm\nTZrQt29fPDw8DLpZ586dsbGxITo6mmHDhgGQlpbGhQsXCAwMrNQ/ICCATZs2KdUkAQ4ePIi/vz9q\ntZqAgAA0Gg3x8fH06NEDgNjYWLRaLQEBAYSEhDB06FC913zhhRfo378/L774okEx17TMokzlZxcb\nmZIQ9Ydapebprk9jb2XPT8k/AXD+8nkW/bGI13q+JlNsQjQwBiUM1VXkyNzcnKeffprFixdjb2+P\no6Mj8+bNIygoCF9fX0pLS7l8+TLNmjXD3NycUaNG8fnnnzN37lyef/55oqKi2Lp1K6tXrwYqFk8O\nHjyYsLAw3nvvPXQ6HXPmzGHYsGHKiIWdnX79e1NTU5o1a2bwNEpN0uq05FzNUR43t2luxGiEuHMq\nlYqHOzxMM4tmfH30a6Uq5KI/FjEpaBLt7NsZO0QhRDW550WPd2rq1KkMHTqU6dOnM2bMGNzc3Fi2\nbBkA8fHxhIaGEh8fD4CTkxOff/45x48fZ/jw4Xz99dcsWrSIXr16Ka83f/58/P39efnll3n11VcJ\nDg7mX//6V22/rbuSW5xLubYcqFhMJjskRH3Vu3VvJgZOxNykYq1QUWkRH/35EQmZCf/wTCFEfaHS\nyX6oKqWlpdG/f3927tyJu3vN1M5PzErkk4OfANDBsQNvhrxZI/cRoraczT/L8oPLuVJ6BaiYtpAC\nT0LUD//0vVfrIwziBlnwKBoaDzsPZvSegaO1I3CjwNOvKb9KrQYh6jlJGIxIEgbRELk0ceGt3m/R\nqlkrpW1z8mbWJ65Hq9MaMTIhxL0wKGH44IMPDC7OJAynV7RJdkiIBqSZZTPeDHmTzk6dlbbdZ3bz\nedznUuBJiHrKoIRhy5YtDBkyhMcff5zvvvuOwsLCmo6rUbh5S6WMMIiGxtLUksk9J9PDrYfSFnsx\nluUHl0uBJyHqIYMShr1797J69WratGnD4sWLCQ0N5fXXX2ffvn0yL3mXyrXlXLp6SXksWypFQ2Sq\nNmWc/zj6te2ntCXnJLMkagkFJQVGjEwIcacMShhUKhWhoaEsWbKEyMhIwsPDuXLlCpMnT+b+++/n\no48+4ty5czUda4OSczVHmc+1t7JXtqMJ0dCoVCpG+4xmeOfhStv5y+dZFLlIb1pOCFG33fGiRxsb\nG/r27Uu/fv3w8vIiKyuLb775hkGDBjFp0iSysuQDwBCy4FE0JiqVisEdBjOm+xjUqoqPnZyrOSz+\nYzHn8uWXDSHqA4MThpKSErZu3crLL7/M/fffzwcffICHhwfr1q0jNjaWdevWcezYMV577bWajLfB\nkIRBNEa9W/dmQuAEzEwqTqwtLCnkwz8/JCk7yciRCSH+iUEJw9tvv01ISAhvvvkmBQUFzJ07l8jI\nSBYuXEhQUBAAgYGBjBw5khMnTtRowA2FJAyiserm0o3Xg1/H2swagBJNCcujlxNzMcbIkQkhbseg\nsyQiIyN58skneeyxx2jX7ta14Xv27EnHjh2rLbiGTA6dEo1Ze4f2TO89nWUHlpF/LZ9ybTmfx31O\nYUmh3gJJIUTdYdAIw4ABAxg0aNBtkwWoSBgGDRpULYE1dDLCIBo7N1s33gp9ixa2FUfd63Q6vj/2\nPT8l/yS7r4SogwxKGDZt2kRBgWyBqi4arYbc4lygYjGYbKkUjZWDlQPTQ6brnWr5S8ovfJPwjVSF\nFKKOMShh6NatG4cOHarpWBqN7KJs5TcoBysHTNUGzQwJ0SDZmNswNXgqXZy7KG37z+3ns9jPKCsv\nM2JkQoibGfRN5ePjw+eff86OHTvw8vLC2tpa77pKpeKdd96pkQAbIpmOEEKfhakFEwMnsu7IOg6k\nHQAgPj2eT0o/YWLgRKzMrIwcoRDCoIRh+/btODs7c+3aNeLj4ytdV6lU1R5YQyYLHoWozERtwgu+\nL2BrYcvvqb8DcPLSSZZELeG14NdoatHUyBEK0bgZlDDs2rWrpuNoVGSEQYiqqVQqRnmPoqlFU348\n/iMAaQVpLP5jMVODp+Jk7WTkCIVovKrleGspC31nJGEQ4vYGtB/A877PK1Uhs4uyWRS5iLSCNCNH\nJkTjZdAIQ0FBAUuXLuXQoUOUlpYq7VqtluLiYi5dukRSklRqM5QkDEL8s5BWIdiY2bA6bjVl5WUU\nlBTwwR8fMCloEh0cOxg7PCEaHYNGGN5//302bNiAu7s7AFZWVnh5eXHt2jVyc3NlweMdKC0vJa84\nDwC1Si1DrELcRnfX7rzW8zVl0eM1zTWWHlhK7MVYI0cmRONjUMKwb98+Jk+ezKeffsro0aNxdXVl\n6dKl/Pbbb3Tq1IlTp07VdJwNRnZRtvKzo7UjJmoTI0YjRN3XwbEDb4a8qSx61Gg1fBb7GVtObJEC\nT0LUIoMShsuXL+Pn5wdA+/btOXbsGFBxcuWLL77Inj17aizAhubm6QjZISGEYdybuvNW6Ft6U3hb\nT25lVewqSjQlRoxMiMbDoITBzs6OK1euAODh4cGlS5fIz88HoEWLFmRmZt7u6eImsn5BiLvjZO3E\nzD4z8WrupbTFp8ez6I9F5FzNMWJkQjQOBiUMvXr1YtWqVaSnp9O6dWuaNWvG5s2bAdizZw/29vY1\nGmRDcnMNBkkYhLgz1mbWTOk5hQfbPai0XSi4wHv73+No5lEjRiZEw2dQwjBlyhQyMjKYPn06KpWK\n8ePHs3DhQkJCQvjiiy947LHHajrOBkNGGIS4N2qVmsd9Hud53+eVsupFpUX8O/rffH/seyknLUQN\nMWhbZatWrdi+fTunT58G4MUXX8TJyYm4uDi6devGiBEjajTIhuTS1UvKz3LolBB3L6RVCK5NXFkV\ns4r8axVTpLvP7CblUgrj/Mcpp2AKIaqHQSMMEyZM4MiRI3h7eyttQ4cOZe7cuZIs3AGdTkdhaaHy\nuJlFMyNGI0T9186+HeH3h+Pr6qu0pRWksWD/Avae3Su7KISoRgYlDH/++af8w6sGJeUlynCpmYkZ\n5ibmRo5IiPrPxtyGV3q8wtNdn8bMxAyAsvIyvk34lg///JCMKxlGjlCIhsGghCE0NJRt27ah0Whq\nOp4G7UrpFeVnW3NbObRLiGqiUqm43+N+ZvWZhZutm9KecimFd/e+y9aTW9Fo5fNLiHth0BqGJk2a\nEBERwa+//oqnp2eVx1uvWbOmRgJsSApKCpSfbS1sjRiJEA2Tm60bs/rMYlvKNraf2o5Wp0Wj1bDl\nxBZiLsbwVJen6OTUydhhClEvGZQwXLhwQSncBFBWJquQ78bfRxiEENXPzMSM4Z2H08OtB18d+Yqz\n+WcBSC9M56M/P6KrS1dGeo3UG4kQQvwzgxKGr776qqbjaBQKS24seJQRBiFq1vXqkHvO7mFz8mal\nImRCZgLHso7Ru1VvHu30KM0sZfGxEIYwaA3DmDFjSE1NrfJacnIyw4YNq9agGqqbd0jICIMQNU+t\nUvNA2weY13ceIa1ClHVDOp2OyL8iCdsVxvpj68ktzjVypELUfbccYYiJiVF2RkRHR3Po0CFycyv/\no9q9ezfnzp2ruQgbEBlhEMI47K3sed73efq368+Px3/kePZxoGI3xa4zu9hzdg9BLYMY6DlQpiqE\nuIVbJgw//vgjERERqFQqVCoV8+bNq9TnekIxdOjQmouwAbl5hKGJeRMjRiJE4+Te1J3Xgl/jePZx\nNiVt4vzl8wBodVoOpB3gQNoBurt255EOj9DGro2RoxWibrllwhAWFsaoUaPQ6XQ8++yzvPPOO7Rv\n316vj4mJCba2trRr167GA20Ibh5huH5UrxCi9nk398bLyYvj2cf57dRvnLx0Url2JOMIRzKO0MW5\nC490fIR29vL5JgTcJmFo0qQJAQEBAKxbtw4fHx9sbGxqLbCGSEYYhKg7VCoVPs4++Dj7cDrvNNtP\nbedwxmHl+rGsYxzLOkZnp84M6TiEDo4djBitEMZ3y4Rhy5Yt9OnTBzs7OzIzM//xCGtDpyXKy8tZ\nunQpERERFBUV0adPH8LDw3Fycqqyf0JCAgsWLCApKQkXFxcmTpzI8OHDlevFxcW899577Nixg/Ly\ncgYNGsTMmTOV5KasrIxVq1axefNmcnJyaNu2La+++ioPPvhglferSXprGGTRoxB1Rjv7dkwInMDF\nwov8kvILMRdvrOFKzkkmOScZr+ZePNrpURlxEI3WLROG6dOns2HDBuzs7Jg+ffptX0SlUhmcMCxf\nvpyIiAgWLVqEnZ0d8+bNY/LkyXz33XeV+ubm5jJu3DiGDBnCggULiIqKIiwsDCcnJ0JDQwEIDw8n\nMTGRVatWodFomDVrFuHh4Xz44YcALF26lJ9++kmZUvntt9+YPHky69atIzAw0KCYq4NOp9OvwyCL\nHoWoc9xs3RjnP46hHYfyS8ovRF+IRqvTApCUnURSdhJdnLvwaKdHZY2DaHRumTDs3LmT5s2bKz9X\nh9LSUtatW8fs2bPp3bs3AB999BH9+/cnLi4Of39/vf4bN26kSZMmhIWFoVarad++PcePH+eLL74g\nNDSUjIwMtm7dytq1a/H1rTh8Zv78+YwZM4YZM2bQvHlzNm7cyNSpU3nggQcAGD9+PFFRUWzatKlW\nE4ZrmmtKaVoLUws5R0KIOsyliQsv+r3IkI5D+CXlF/5Mu3GezvWpCr8WfgzrNExOxRSNxi3rMLRs\n2RJzc3Pl5+t/7O3tMTMzo3nz5nrthkhOTqaoqIigoCClzd3dnZYtWxITE1Opf0xMDIGBgajVN8IM\nCgoiLi4OnU5HXFwcarVaL9Hw9/fHxMSE2NhYtFotS5cuZcCAAfpvWq2moKCA2iTrF4Sof5rbNOd5\n3+eZ13cePd176p3/Ep8ez7y98/jv4f9KHQfRKBhUuAngt99+Y8SIEfTo0YP7778ff39/nnnmGaKj\now2+WUZGxalxLi4ueu3Ozs7Ktb/3r6pvcXExeXl5ZGZm4uDggJmZmXLd1NQUBwcH0tPTMTU1JSQk\nRG99xNGjRzlw4AB9+vQxOO7qIOsXhKi/XJq4MNZvLHPvn0uAW4DSrtPpiDofxZxdc9iYuJGi0iIj\nRilEzTIoYfj555+ZOnUq5ubmTJ06lXfffZdXX32VoqIixo4dS2RkpEE3Ky4uRq1W633BA5ibm1NS\nUlKp/7Vr15RRjpv7QsX0RnFxMRYWFpWed6vXO3fuHJMmTaJbt2489thjBsVcXWT9ghD1XwvbFrwc\n8DKz+szCu7m30q7Ravjf6f8RtiuM7ae2K8fYC9GQGHSWxKpVqxg+fDgLFy7Ua58wYQJTpkxhyZIl\nyiLE27G0tESr1aLRaDA1vXHr0tJSrKysquxfWlqq13b9sZWVVZXXr/f5+4max44dY/z48Tg4OLBy\n5cpKSUtN0zupUkYYhKjX2ti14bXg1ziRc4KI5AjO5J0BoLismE1Jm9h9djfDOw+nZ8uecoy9aDAM\nGmE4f/78LXdBjB49mtOnTxt0sxYtKhYHZWdn67VnZWVVmnoAcHV1rbKvtbU1tra2uLq6kpubS3l5\nuXJdo9GQm5uLs7Oz0hYZGclzzz1H69at+frrr7G3tzco3uqkd46EjDAI0SB0curEW73f4pUer+Bs\nc+MzJ684jy/jv2TB/gV6RaGEqM8MShi8vb05dOhQlddSUlLw9PQ06GadO3fGxsZGb91DWloaFy5c\nqHLHQkBAgN6ZFgAHDx7E398ftVpNQEAAGo2G+Ph45fr1xY7Xi07FxMQwYcIEevbsyZdffkmzZsY5\nmU6OthaiYVKpVPi18ONfff/FU12f0vuF4Pzl83wY9SErY1aSXZR9m1cRou675ZREXFyc8vOwYcN4\n7733KC4uZuDAgTg5OXH58mX279/Pf//73yrPmaiKubk5Tz/9NIsXL8be3h5HR0fmzZtHUFAQvr6+\nlJaWcvnyZZo1a4a5uTmjRo3i888/Z+7cuTz//PNERUWxdetWVq9eDVQsnhw8eDBhYWG899576HQ6\n5syZw7Bhw3BxcaG0tJQ33ngDDw8P5s6dS2FhIYWFhUostZk86E1JyAiDEA2OidqEvh59CXYPZvup\n7fx++ndlLUN8ejxHM4/yQNsHeKTDI1iZVZ6CFaKuU+lu/vX9Jp07d9Y7ClZ5wk3zcdfbVSoVSUlJ\nBt1Qo9GwZMkSIiIi0Gg0SqVHBwcHDh48yJgxY1i3bh09e/YE4PDhw8yfP58TJ07g5ubGlClTeOSR\nR5TXKyoqYv78+ezYsQNTU1MGDhzIrFmzsLS0JDIykpdeeqnKOHr16sXatWtvGWdaWhr9+/dn586d\nuLu7G/TebmfpgaUkZVf8HU3pOQUfZ597fk0hRN2VV5xHRHIEB9MO6rXbWtgyovMIveO2hagL/ul7\n75YJw51slwT0ais0BNWdMLy7913SCtIAmNVnllSJE6KROJt/lvXH1nM6T3+tVxu7NjzZ5UkpNS3q\njH/63rvllMTNCcC7777L8OHD6dq1a81E2QjcvOhRTqoUovHwsPNgRu8ZxFyM4cekH8krzgPgXP45\nFkUuItg9mMe8H5PPBVHnGbTo8Ycffqj1yogNiU6n0yvcJJUehWhcVCoVgS0DeaffOwzpOART9Y3f\n1Q6kHWDOrjnsPL1TObdCiLrIoIShe/fuVZZuFoYp1hQrHwSWppaYmdRuDQghRN1gbmLO0E5Dmddv\nHn4t/JT2a5prbEjcwPx980m5lGLECIW4NYMKN/n4+LB69Wq2b9+Ol5dXpaJIUDFtIaomowtCiJs5\nWTvxSo9XSMpO4rtj35F5JROACwUXWBK1hGD3YEZ5j5IdVaJOMShh2L59O87Ozly7dk2v5sF1stL3\n9mT9ghCiKl7NvQi/P5ydp3ey9eRWSssrKtceSDvA0cyjjPQaSWjrUPmMFXWCQQnDrl27qmwvLCzk\np59+Yv369dUaVEMjIwxCiFsxVZsy0HMgQS2D2JC4gbj0iho4V8uu8vXRr4k6H8Uz3Z7Bvem979YS\n4l4YlDD83dGjR/n+++/59ddfKS4uxtHRsbrjalCkLLQQ4p/YW9kzvsd4ErMS+TbhW3Ku5gBwOu80\nC/Yt4MF2DzKk4xAsTCsfuCdEbTA4YSgqKuLnn39m/fr1nDhxAjMzM/r168fw4cO57777ajLGek+O\nthZCGMrH2Yd/9f0Xv6T8wvbU7ZRry9HqtOxI3UFcehxPd31aCr8Jo/jHhOHYsWOsX7+ebdu2UVxc\njLd3xZGuq1atolevXjUeYEMgIwxCiDthZmLGsM7DCGoZxLcJ3yoHWOVczeGTg5/Q070nj3s/Lp8n\nolbdMmHYsGED33//PcePH8fZ2ZlnnnmGESNG4OTkRFBQkN7x1OL2ZIRBCHE3Wti2YFqvafyZ9icb\nEzdytewqAAfTDnIs6xhP+DwhR2iLWnPLb/3w8HA6derE6tWrCQ29sUr3+uFNwnB6J1XKbwRCiDug\nUqkIaRVCV+eubEjcQPSFirL9RaVFfBn/JdEXonmm6zM4WstaMlGzblm4acCAAZw+fZpp06Yxbdo0\n9uzZg1YrVcjuht5JlTLCIIS4C7YWtrzk/xJTek7RSw4SsxKZt3ceu8/s5hZHAwlRLW45wvDJJ5+Q\nn5/Pzz//TEREBK+88gpOTk489NBDqFQqGQK7A7KGQQhRXXycfZh7/1x+OvETu87sQqfTUaIp4ftj\n33Po4iGe6foMLZu2NHaYogG6bWloOzs7xowZQ0REBBEREQwaNIhff/0VnU7H7NmzWbFiBWfOnKmt\nWOslnU6nNyUhdRiEEPfKwtSCJ3yeYEbvGbSwbaG0p+amMn/ffH48/iMlmhIjRigaIoPOkgDw8vJi\n9uzZ7N+/n2XLluHh4cGnn37Kww8/zMiRI2syxnqtqKxIGSa0MrPSO3RGCCHuRTv7dsy+bzZDOg5B\nrar4OL++BXPunrnEp8fLNIWoNnf87WVmZsbAgQMZOHAg2dnZbN68mYiIiJqIrUGQHRJCiJpkqjZl\naKeh9HDrobcFM684j5UxK/Fx9uEJnydwbeJq5EhFfWfwCENVmjdvzv/93//xyy+/VFc8DY6sXxBC\n1IbrWzBf9HtR77MmMSuReXvm6W3LFOJu3FPCIP6ZrF8QQtQWlUpFsHsw7/R7h/va3KcsTtfqtPzv\n9P+Ys2sO+87tQ6uTHW/izknCUMNu3lIpJ1UKIWqDtZk1z3R7hrA+YXRw7KC0Xym9wjdHv+Hdve+S\nkJkg6xvEHZGEoYbJCIMQwlhaNWvFG73e4OWAl3GwclDaLxZeZEX0Cj768yPO5p81XoCiXpGEoYbJ\nokchhDGpVCoC3AJ4p987PNrpUb3TLk9eOsn7+99ndexqMq9kGjFKUR/IHr8aJosehRB1gZmJGY90\nfIQ+bfqw7eQ2vbUMMRdjiE2PJdg9mCEdh+Bk7WTkaEVdJAlDDZMRBiFEXdLUoilPdX2KB9o+wObk\nzcSlxwEVReb+PP8nB9MO0rt1bx7u8LDeNIYQkjDUMBlhEELURS5NXBjfYzxn88/yU/JPHM8+DlTs\nqNh/bj9R56MIdg9mkOcgnG2cjRytqAskYahhMsIghKjLPOw8eC34NVIupfDziZ+Vwk/l2nL++OsP\nos5HEegWyOAOg3GzdTNytMKYJGGoQVqdlqKyIuWx7JIQQtRVHRw7MK3XNE5cOsGWE1s4lXsKqJiq\niL4QTfSFaLq7duehdg/h6eApBxA2QpIw1KCi0hvnSFibWWOiNjFyREIIcWsqlYrOTp3p7NSZlEsp\n/JLyizJVAXAk4whHMo7gYefBgPYD8Gvhp5xhIRo+SRhqkKxfEELUVx0cO/Ca42uczT/LLym/cCTj\niHLtbP5ZPov9DCdrJ/q17UdIqxCszayNGK2oDZIw1CBZvyCEqO887DyYGDiR9MJ0/nf6fxxIO4BG\nqwEg52oOGxM38lPyT/R070k/j360bNrSyBGLmiIJQw2SEQYhREPRwrYFz3V/jmGdh7Hn7B72nN1D\nUWnFGq3S8lL2n9vP/nP76eDYgfva3Iefqx9mJmZGjlpUJ0kYapCMMAghGpqmFk15tNOjDPIcRPSF\naHaf2U1aQZpyPeVSCimXUrAxtyHYPZg+rfvQwraFESMW1UUShhp08wiD7JAQQjQk5ibmhLYOpXer\n3pzKPcXus7uJT49XqkcWlRax8/ROdp7eSTv7doS0CiHALUDWOtRjkjDUkNLyUqIvRCuP7a3sjRiN\nEELUDJVKRQfHDnRw7ED+tXyizkcR+Vckl65eUvqczjvN6bzTfH/se3xdfenVqhfezb1lh0U9IwlD\nDdmcvJnsomwArMys8HX1NXJEQghRs+ws7Xi4w8MM9hxMUk4S+8/t53DGYWXUQaPVEHPs518gAAAa\ntUlEQVQxhpiLMdha2BLQIoDAloG0t28vdR3qAUkYasCp3FPsOrNLefyEzxM0tWhqxIiEEKL2qFQq\nvJt7493cm8KSQqIvRPNn2p+cv3xe6VNYUqgsnrS3sifQLZAAtwDaNGsjyUMdVevjQeXl5Xz44YeE\nhobi5+fHlClTyMnJuWX/hIQEnnzySbp3786AAQPYvHmz3vXi4mLmzJlDz5496dGjB7Nnz6aoqEiv\nz88//8zAgQPp1q0bTzzxBEePHq2R9wYVUxH/PfxfpWCTj7MPvdx71dj9hBCiLrO1sKV/u/7Mvm82\n4feH81D7h2hm2UyvT15xHjtSd/D+/vcJ2xXGj8d/5Gz+WeVzVNQNtZ4wLF++nIiICBYtWsTXX39N\nRkYGkydPrrJvbm4u48aNw8fHh02bNvHcc88RFhZGZGSk0ic8PJzY2FhWrVrFypUriY6OJjw8XLke\nFRXFrFmzGDt2LBEREXTs2JGXXnqJ3NzcGnl/PyX/RFZRFgCWppY81+05yZaFEAJo2bQlo7xHsfDB\nhbwR8gb3tbkPG3MbvT6Xrl5SkodZO2exIXEDKZdSlGkNYTwqXS2mcKWlpQQHBzN79mxGjhwJQFpa\nGv379+e7777D399fr/+qVavYsGEDv//+O2p1RW4zc+ZMMjMz+eKLL8jIyKBfv36sXbuWnj17AhAd\nHc2YMWPYu3cvLi4uvPTSSzRv3pyFCxcCoNVqGTBgAKNGjeKVV165ZazX49q5cyfu7u4Gvb/U3FQ+\niPpAyYrHdB9D79a97+wvSQghGpFybTlJOUnEXozlcMZhrpZdrbKfrYUtvq6++Lr60smxk9R4qAH/\n9L1Xq2sYkpOTKSoqIigoSGlzd3enZcuWxMTEVEoYYmJiCAwMVJIFgKCgIObNm4dOpyMuLg61Wq33\nPH9/f0xMTIiNjWXQoEHExcUxZ84c5bparSYwMJCYmJh7ei9//PUHv6T8Qkl5idJ2TXNNSRa8m3sT\n0irknu4hhBANnYnahC7OXeji3IVntM+QnJNcZfJQWFKoFIeyMLXAu7k3vq6+dHHuItvWa0mtJgwZ\nGRkAuLi46LU7Ozsr1/7e39vbu1Lf4uJi8vLyyMzMxMHBATOzG5mmqakpDg4OpKenU1BQwNWrV6u8\nX0JCwl2/D51Ox+bkzRSUFFR53dLUkue6y1SEEELcCVO1qV7ycPLSSeLT4zmccVjv87ZEU0J8ejzx\n6fGoVCra2bejq3NXurp0paVtS/nsrSG1mjAUFxejVqv1vuABzM3NKSkpqdT/2rVrmJubV+oLFdMb\nxcXFWFhYVHre9de7du0aQKU+ZmZmVd7PUCqVil6terEjdUelRTkmahOe7fYsDlYOd/36QgjR2Jmq\nTZWdFk91fYozeWeIz4jnSMYRZZ0YVPwCl5qbSmpuKpuTN2NvZU9X5650ce5CJ6dOWJpaGvFdNCy1\nmjBYWlqi1WrRaDSYmt64dWlpKVZWVlX2Ly0t1Wu7/tjKyqrK69f7WFtbK4nC3/uUlZVVeb87MdJr\nJIM9B1OmLdNrtzK1krk1IYSoRmqVmvYO7Wnv0J7HvB4jsyiTIxlHOJxxmDP5Z/R+ccsrzmPfuX3s\nO7cPE7UJng6e+DT3wcfZR0Yf7lGtJgwtWlTUE8/OzlZ+BsjKyqo0bQDg6upKdna2XltWVhbW1tbY\n2tri6upKbm4u5eXlmJiYAKDRaMjNzcXZ2Rk7Ozusra3Jysqq9BpV3e9OWZlZYcW9JR5CCCEMp1Kp\ncG3iiqunKwM9B1JYUkhidiIJmQkkZidSXFas9C3XlnMi5wQnck6wKWkTTS2a4tXcC+/m3ng5eVXa\n3ilur1YThs6dO2NjY0N0dDTDhg0DKlZlXrhwgcDAwEr9AwIC2LRpEzqdTskKDx48iL+/P2q1moCA\nADQaDfHx8fTo0QOA2NhYtFotAQEBqFQq/Pz8OHToEMOHDwcqdkkcOnSIJ554opbetRBCiJpia2FL\nsHswwe7BlGvLSc1LJTErkWNZx/QOxQIoKCngYNpBDqYdBCpO4Ozs1JnOTp3p6NhRzrn4B7WaMJib\nm/P000+zePFi7O3tcXR0ZN68eQQFBeHr60tpaSmXL1+mWbNmmJubM2rUKD7//HPmzp3L888/T1RU\nFFu3bmX16tVAxeLJwYMHExYWxnvvvYdOp2POnDkMGzZMGUF44YUXmDBhAt7e3gQHB/Pll19SWFjI\nqFGjavOtCyGEqGEmahM6Onako2NHRniNIP9aPsezj5OYlUhSTpJyHPd16YXppBems/vMblQqFa2b\ntaaTYyc6OnbE08ETKzMZQb5ZrdZhgIopgyVLlhAREYFGo6FPnz6Eh4fj4ODAwYMHGTNmDOvWrVPq\nKhw+fJj58+dz4sQJ3NzcmDJlCo888ojyekVFRcyfP58dO3ZgamrKwIEDmTVrFpaWNxa6/Pjjj/zn\nP/8hOzsbb29v5syZg4+Pz23jvJs6DEIIIeomnU7HX5f/4nj2cZJykkjNTUWj1dyy//UEoqNjRzo4\ndMDTwbNSkamG5p++92o9YagvJGEQQoiGq7S8lNTcVJJzkknOSebc5XP/WIrazdaNDo4VyUN7+/Y4\nWDk0qEWUdapwkxBCCFEXmJuY49XcC6/mXgBcLbvKqdxTnMg5wclLJzlfcL5SAnGx8CIXCy+y9+xe\noOJ0Tk8Hz4odHPbtadWsVYM+slsSBiGEEI2etZk13Vy60c2lG3AjgUi5lEJKbgrn8s9VOs8i/1q+\nclw3gIWpBW3t2tLeoT2eDp60s2/XoOpASMIghBBC/M3fE4gSTQln8s+QcimF1LxUTuedpkSjXwCw\nRFOiTHHw/+3de0xT5xsH8G9LKSCCVhSGoEy5lA0BAQWcVRGnMhfmpoKA4HAz6mLUzTtTydyWjMsU\nLwQ0srkpuKETb3GJMzpl/uJUJIrgrWUCFVDQchNKoe35/UE4ritQAWmlPp+kib7vOT3Py3Pa8/Rc\n0XYehKO1I5wFbQWEyxAXCCwEeh/Ly0IFAyGEEKKDGc+MvQQTANSMGuX15ZDIJCiuKYZEJkGNvEZj\nHoZhIK2TQlonxYWSCwCAIRZD2MMYLkNcMNxqeL85jEEFAyGEENJNXA4XIwaNwIhBIzB11FQAbXeZ\nlMgkbBHxsP6h1nkQMrkMV8uv4mr5VQBtNwB0FjizBcSowaNe2bsFU8FACCGEvAQCCwHGO4zHeIe2\nGxE2K5vxoOYBW0Q8qH2gdRhD3ipHYVUhCqsKAbTdS8JpkBNbQDgLnGFlZqX3sXSECgZCCCGkD5jz\nzDWuxFAzakjrpOweCPFTsdZTj1VqFf6p+Qf/1PyDs8VnAQB2A+3YcyBch7hi6IChBrmckwoGQggh\nRA+4HC6cBjvBabATpmEaGIbBU/lTdg+ERCZBZUOl1nyPnz3G42eP8b+y/wEArM2snxcQNq5wtHbU\ny3kQVDAQQgghBsDhcDB0wFAMHTAUgY6BAIDGlkb2JEqJTIKS2hKo1CqN+eoV9civzEd+ZT6AthMy\n/30lxijBKPBN+C89XioYCCGEkFeEJd9S43LOVlUrSmpLNPZCNCubNeZRKBW4XX0bt6tvA3i+J6P9\nltYv67bWVDAQQgghryhTE1O42rjC1cYVQNt5EBUNFWzxIH4qRm1zrcY8akaNBzUP8KDmAf4o/gNA\n25M5XYa4IOjNIDha9+xxB1QwEEIIIf0El8OFo7UjHK0dEfRmEBiGgUwue15AyMQdngfR/mTOy9LL\n2D5zO8x4Zt1eNhUMhBBCSD/F4XBgM8AGNgNsEODY9pTnxpZGjUMYJbUl7G2teVwelGolzEAFAyGE\nEPJas+RbwvsNb3i/4Q2g7cmcJbUlqGyoxGjB6B6fz0AFAyGEEGLE+CZ8uNm4wc3GrVfvQwVDJ1Sq\ntstYHj16ZOBICCGEkL7Xvr1r3/79FxUMnaiurgYALFiwwMCREEIIIfpTXV0NJycnrXYO898nYxAA\nQHNzMwoLCzFs2DCYmJgYOhxCCCGkT6lUKlRXV2PMmDEwNzfX6qeCgRBCCCE69Y+HcBNCCCHEoKhg\nIIQQQohOVDAQQgghRCcqGAghhBCiExUMhBBCCNGJCoYeUKlU2LZtG0QiEXx8fLBy5Uo8efLE0GH1\n2pMnT7BhwwaIRCKMGzcOn376Ke7fv8/2z5s3D0KhUOO1adMmA0bcOxKJRGs8QqEQeXl5AIBLly5h\n9uzZ8PLyQmhoKC5evGjgiHvmypUrHY5TKBRi4cKFAIwjt/Hx8Vox68rh06dPsWrVKowbNw4TJkxA\ncnIylEqlPsPukY7GmpmZiZCQEIwdOxazZs3CkSNHNPqzsrK0cvz222/rM+we6WisutZXY8lrcHBw\np5/diooKAHrOK0O6LSUlhZk4cSJz6dIlprCwkAkLC2MiIiIMHVavqFQqZv78+Ux4eDhz8+ZNRiwW\nMytXrmQmTJjAyGQyRq1WM97e3szJkyeZqqoq9tXQ0GDo0Hvs9OnTTEBAgMZ4qqqqmJaWFkYsFjNj\nxoxh0tLSGIlEwqSkpDAeHh7M/fv3DR12tykUCq0xHjt2jHF3d2dyc3P7fW7VajWzY8cOxs3Njfny\nyy/Z9hfJYWRkJBMVFcXcuXOHuXDhAhMYGMhs377dEMN4IZ2NNSsrixk7dixz/PhxprS0lDl8+DDj\n4eHBHDt2jJ0mPj6eWbZsmUaOq6urDTGMF9LZWF9kfTWWvD59+lRjjKWlpcyUKVOYNWvWsNPoM69U\nMHSTQqFgfHx8mKNHj7JtUqmUcXNzY65fv27AyHqnqKiIcXNzYyQSCdumUCgYb29v5tixY0xpaSnj\n5ubGlJWVGTDKlyslJYVZsGBBh31btmxhoqOjNdqio6OZzZs36yO0PlVfX89MnDiRSU5OZhiG6de5\nLSsrY6Kjo5mAgAAmKChI48tWVw7z8/O1xp2Tk8P4+PgwCoVCPwPohq7GGhoayiQlJWlMHxcXx8TE\nxLD/j4yMZHbu3Km3eHujq7HqWl+NKa//FR8fzwQHBzNNTU1smz7zSockuunu3btobGyEv78/2+bo\n6AgHBwd2V3Z/ZG9vj71792LUqFFsG4fDAQDU1dXh/v37MDc3h4ODg6FCfOnEYjFGjx7dYV9eXp5G\njgEgICCgX+e4XVpaGvh8PpYvXw4A/Tq3+fn5sLe3x6lTp+Do6KjRpyuHeXl5cHBwwIgRI9h+f39/\nNDY24s6dO30ffDd1NdbNmzcjIiJCo43L5aK+vp79v0QigbOzs15i7a2uxqprfTWmvP7b3bt3cfjw\nYcTHx8PCwoJt12deqWDopvaHc9jZ2Wm029ra9usHVQkEAgQFBYHLfb5KHDx4EM3NzRCJRBCLxbCy\nssLatWshEokQGhqK/fv3Q61WGzDq3hGLxaioqEB4eDgmTpyI2NhYFBQUAGjLs7HlGGg7tpuZmYnl\ny5ezXzr9ObezZ89GUlIShg0bptWnK4ePHz+Gra2tVj8AVFZW9lHEPdfVWP39/TU2kBUVFTh9+jQm\nTZoEoG2sdXV1yM3NRUhICKZMmYK1a9fi8ePHeou/O7oaq6711Zjy+m+7d++Gn58fpkyZwrbpO69U\nMHSTXC4Hl8uFqampRjufz4dCoTBQVC/fuXPnsH37dixatAjOzs6QSCRoamqCSCTCDz/8gKioKOza\ntQupqamGDrVHmpubIZVK8ezZM6xfvx7p6emwtbVFdHQ0iouL0dzcDD6frzGPMeT4l19+gY2NDT74\n4AO2zdhy205XDuVyOczMzDT6TU1NweFw+nWeZTIZli5diqFDh2LJkiUA2jayAMDj8ZCSkoLvvvsO\nJSUliI2NRXNzsyHD7TZd66sx5lUqleL8+fNYunSpRru+80pPq+wmc3NzqNVqKJVK8HjP/3wtLS0a\nu4n6s5ycHGzZsgWzZs3CunXrAACJiYloamqCtbU1AEAoFKKhoQF79uzBihUr2MMX/YW5uTmuXbsG\nPp/PblQSEhJQVFSEQ4cOwczMDK2trRrzGEOOT548iTlz5mgUvMaW23a6cmhubo6WlhaN/tbWVjAM\ngwEDBugtzpdJKpVi8eLFaG5uRmZmJqysrAAAIpEIly9fxpAhQ9hpXVxcMHnyZFy8eBEzZ840VMjd\npmt9Nca8njp1Cvb29hCJRBrt+s4r7WHoJnt7ewDPH3/drqqqSmv3Z3+Unp6OuLg4REREICkpiT1E\nwePx2A9oO6FQiMbGRjQ0NBgi1F4bOHCgxi9QLpcLFxcXVFZWwt7eHlVVVRrT9/cci8VilJaW4v33\n39doN8bcAtCZwzfeeKPDzzGgfcixPygqKsL8+fPB5XLx66+/ahyiAKCxUQHadtMLBIJXcjd9V3St\nr8aWV6Btj+97773XYfGuz7xSwdBN7u7usLS0xNWrV9m2hw8fory8HOPHjzdgZL23b98+7NixAytX\nrsSWLVs0Vs7w8HB8++23GtPfunULtra2Wh/e/qCwsBC+vr4oLCxk21QqFe7evQtXV1f4+fnh2rVr\nGvNcuXIF48aN03eoL01eXh6GDRumdYKUseW2na4c+vn5QSqVanyxXrlyBZaWlnB3d9drrL1VXFyM\nTz75BA4ODjh06BD7w6bdgQMHIBKJNPa4lJeXQyaTwdXVVd/h9oqu9dWY8goATU1NuHPnDgIDA7X6\n9J1XKhi6ic/nIyoqCklJScjNzUVRURFWr14Nf39/jB071tDh9djdu3eRkpKCuXPnIjw8HNXV1eyr\nqakJ06dPR3Z2No4fP46ysjIcOXIEGRkZWLlypaFD7xF3d3c4ODggPj4eN2/ehFgsRlxcHGpqarBw\n4UJER0cjLy8Pu3btQnFxMXbu3ImbN2/i448/NnToPXbnzh24ublptRtbbtvpyqGPjw/Gjh2LL774\nAkVFRbh48SKSk5OxaNEirXMfXnUbNmwAn89HUlISlEol+9mVyWQAgKCgIDQ2NmLTpk0oLi7G9evX\nsWLFCvj5+WHixIkGjr57dK2vxpRXALh37x5UKlWHn11955XOYeiBzz//HEqlEuvWrYNSqcSkSZMQ\nHx9v6LB65ffff4dKpcLRo0dx9OhRjb5Vq1bhs88+A4/HQ3p6OioqKjB8+HDExcUhLCzMQBH3Do/H\nQ0ZGBpKSkrBs2TLI5XL4+voiMzMTNjY2sLGxQWpqKpKTk7Fv3z6MHj0ae/bs6TeXpXWkqqoKgwYN\n0mpfvHixUeW2nVAo7DKHHA4Hqamp+Oqrr7BgwQJYWloiLCyMvdy0v3jw4AFu3boFAAgJCdHoGzly\nJM6ePYuRI0di//792LZtG8LCwmBqaorg4GBs3LjRECH3iq711Vjy2q798MrgwYO1+vSdVw7DMEyf\nvDMhhBBCjAYdkiCEEEKITlQwEEIIIUQnKhgIIYQQohMVDIQQQgjRiQoGQgghhOhEBQMhhBBCdKKC\ngZDX0MaNGyEUCrt8xcTEAABiYmIQGxtr0Hhra2sRHByM0tLSHr/Hw4cPIRQKceLEiReep66uDsHB\nwZBKpT1eLiHGgu7DQMhrqKysjL0LIABs3boVJiYm2Lx5M9s2cOBAuLi4QCKRgMPhGPSmVWvWrIGd\nnR3Wr1/f4/doaWnB7du3MXLkSK3773clMzMTZ86cwYEDB/rtg7gIeRmoYCCEICYmBiYmJvjpp58M\nHYqWgoICREVFITc3t1sb+pelpaUFU6ZMwdatWzFjxgy9L5+QVwUdkiCEdOm/hySEQiGys7Oxdu1a\n+Pj4IDAwEKmpqXj27Bni4uLY+9gnJyfj379HampqsHnzZkyYMAFeXl6IjIzE9evXdS4/IyMD77zz\njkaxEBwcjLS0NHzzzTfw9/eHn58fvv76a8jlciQmJiIgIAABAQHYtGkTFAoFAO1DEjk5OfD09ER+\nfj7CwsLg6emJqVOn4scff9RYPp/Px4wZM7B3797e/BkJ6feoYCCEdFtiYiIEAgHS0tIwdepU7N69\nG/PmzYOFhQVSU1Mxffp0ZGRk4I8//gAAKBQKxMbG4sKFC1i9ejV27dqFQYMGITY2FgUFBZ0up7Gx\nEefPn+/wl31GRgZqa2uxc+dOREREICsrCx999BEqKyuxbds2xMTE4LfffkNWVlan769UKrF69WqE\nhoZi37598PX1RWJiIi5fvqwxXUhICAoLC1FSUtKzPxghRoAePkUI6TYPDw9s2rQJQNuTP3NycmBj\nY8M+hC0wMBCnTp3CjRs3MHPmTJw4cQL37t3DkSNH4OnpCQCYPHky5s2bh5SUFOzfv7/D5eTl5aG1\ntRVeXl5afQKBAMnJyeByuQgICEB2djZaW1vx/fffg8fjQSQS4cyZM7hx40an41Cr1VixYgXmzp0L\nAPD19cXZs2fx559/YsKECex0Y8aMAdD2mOQ333yz+38wQowA7WEghHTbvzfgAoEAJiYmGm0cDgeD\nBg1CfX09AODy5cuws7PDW2+9BaVSCaVSCbVajalTp+LatWtoaWnpcDkPHz4EADg6Omr1eXp6gstt\n+wrjcrkQCATw8PAAj/f8d9DgwYPZGDrj6+vL/pvP52PIkCGQy+Ua01hZWcHa2hrl5eVdvhchxoz2\nMBBCus3S0lKrbcCAAZ1OX1tbi0ePHsHDw6PD/pqaGtjZ2Wm1NzQ0AAAsLCx6HUNn/vveXC4XarW6\nw+na4yHkdUQFAyGkz1lZWcHZ2RmJiYkd9gsEgi7bGxoaYG1t3WfxvYj6+vpO4yTkdUCHJAghfW78\n+PGoqKiAra0tPD092de5c+dw8OBBmJqadjjf8OHDAQCPHj3SZ7ha6urqIJfLYW9vb9A4CDEkKhgI\nIX1uzpw5sLOzw6JFi3DixAn8/fffSEhIQHp6OkaMGNHpDZHGjRsHc3PzF7r8si/l5+cDAEQikUHj\nIMSQqGAghPQ5S0tLZGVlwdvbGwkJCViyZAn++usvbNmyBStWrOh0PgsLC0yePBm5ubl6jFZbbm4u\nvLy8aA8Dea3RnR4JIa+0goICREZG4vz58x2eGNnX5HI5Jk2ahISEBLz77rt6Xz4hrwraw0AIeaV5\neXlh2rRpWndg1Jfs7Gy4uLhg2rRpBlk+Ia8K2sNACHnlyWQyzJkzBz///DOcnJz0ttza2lp8+OGH\nel8uIa8iKhgIIYQQohMdkiCEEEKITlQwEEIIIUQnKhgIIYQQohMVDIQQQgjRiQoGQgghhOj0f6VH\nCxxIgk0HAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1ece3a3c8>"
+      ]
+     },
+     "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": 20,
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-5.9399999999999995, 0.0)"
+      ]
+     },
+     "execution_count": 21,
+     "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": 22,
+   "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": 23,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%psource run_odeint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we run it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wall time: 147 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "%time run_odeint(system2, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And here are the results."
+   ]
+  },
+  {
+   "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>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": 25,
+     "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": 26,
+   "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 0x1a1ed0a1a90>"
+      ]
+     },
+     "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": 27,
+   "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": 27,
+     "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": "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": 28,
+   "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": 29,
+   "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": 30,
+   "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": 31,
+   "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": 32,
+   "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(290, 0.03, 0.02, 1e-05)"
+      ]
+     },
+     "execution_count": 33,
+     "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": 34,
+   "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": 34,
+     "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": 35,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%psource fit_leastsq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here's how we call it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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": 37,
+   "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": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap08-fig04.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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lCudO2ZGXW0TzZtaYqf9sMoqKkgQihGjcqmxEnzdvHtevX7+jk2VkZPzlWBEH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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1ed1252b0>"
+      ]
+     },
+     "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": "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": 39,
+   "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": 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>S_G</th>\n",
+       "      <td>0.026799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S_I</th>\n",
+       "      <td>0.000881</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "S_G    0.026799\n",
+       "S_I    0.000881\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "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": 41,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "I0 = 360\n",
+    "k = 0.25\n",
+    "gamma = 0.004\n",
+    "G_T = 80"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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, gamma=gamma, G_T=G_T,\n",
+    "                    G=interpolate(data.glucose),\n",
+    "                    ts=data.index)\n",
+    "    return system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system = make_system(I0, k, gamma, G_T, data)"
+   ]
+  },
+  {
+   "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": 44,
+   "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",
+    "    return dIdt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "I   -90.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "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": 46,
+   "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": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_odeint(system, slope_func)\n",
+    "system.results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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h7Pi+uXPntvj6UVFRWLduHaZMmQI/Pz/MmjULV65c6T41lk2bNuHYsWNwdXVVWzVxyJAh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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1ed1b9a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(system.results, 'g-', label='simulation')\n",
+    "plot(data.insulin, 'go', 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": 48,
+   "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",
+    "    actual: array 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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[360, 0.25, 0.004, 80]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params = [I0, k, gamma, G_T]\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[360, 0.25, 0.004, 80]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "time\n",
+       "8      10.636133\n",
+       "10     -3.234670\n",
+       "12     -7.946879\n",
+       "14     -8.010268\n",
+       "16     -3.593411\n",
+       "19      1.811071\n",
+       "22      2.581632\n",
+       "27      7.014287\n",
+       "32      3.758216\n",
+       "42      8.932902\n",
+       "52      9.554216\n",
+       "62      0.767804\n",
+       "72     -3.217947\n",
+       "82    -10.382598\n",
+       "92     -7.401702\n",
+       "102    -9.056569\n",
+       "122    -3.584244\n",
+       "142    -3.586427\n",
+       "162     3.170251\n",
+       "182    18.702699\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_func(params, data)"
+   ]
+  },
+  {
+   "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": 51,
+   "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",
+      "[  3.41214716e+02   2.48476650e-01   4.01573092e-03   8.05611945e+01]\n",
+      "[  3.41214721e+02   2.48476650e-01   4.01573092e-03   8.05611945e+01]\n",
+      "[  3.41214716e+02   2.48476654e-01   4.01573092e-03   8.05611945e+01]\n",
+      "[  3.41214716e+02   2.48476650e-01   4.01573098e-03   8.05611945e+01]\n",
+      "[  3.41214716e+02   2.48476650e-01   4.01573092e-03   8.05611957e+01]\n",
+      "[  3.45910051e+02   2.47820998e-01   3.91185402e-03   8.05803504e+01]\n",
+      "[  3.45910056e+02   2.47820998e-01   3.91185402e-03   8.05803504e+01]\n",
+      "[  3.45910051e+02   2.47821001e-01   3.91185402e-03   8.05803504e+01]\n",
+      "[  3.45910051e+02   2.47820998e-01   3.91185408e-03   8.05803504e+01]\n",
+      "[  3.45910051e+02   2.47820998e-01   3.91185402e-03   8.05803516e+01]\n",
+      "[  3.45007554e+02   2.48467106e-01   3.84686996e-03   8.04490675e+01]\n",
+      "[  3.45007559e+02   2.48467106e-01   3.84686996e-03   8.04490675e+01]\n",
+      "[  3.45007554e+02   2.48467109e-01   3.84686996e-03   8.04490675e+01]\n",
+      "[  3.45007554e+02   2.48467106e-01   3.84687002e-03   8.04490675e+01]\n",
+      "[  3.45007554e+02   2.48467106e-01   3.84686996e-03   8.04490687e+01]\n",
+      "[  3.45051065e+02   2.48855798e-01   3.83011719e-03   8.03295605e+01]\n",
+      "[  3.45051070e+02   2.48855798e-01   3.83011719e-03   8.03295605e+01]\n",
+      "[  3.45051065e+02   2.48855802e-01   3.83011719e-03   8.03295605e+01]\n",
+      "[  3.45051065e+02   2.48855798e-01   3.83011724e-03   8.03295605e+01]\n",
+      "[  3.45051065e+02   2.48855798e-01   3.83011719e-03   8.03295617e+01]\n",
+      "[  3.44987120e+02   2.49050258e-01   3.91079996e-03   8.04365681e+01]\n",
+      "[  3.45068248e+02   2.49199738e-01   3.85963688e-03   8.03598764e+01]\n",
+      "[  3.45059632e+02   2.49011266e-01   3.84132605e-03   8.03405939e+01]\n",
+      "[  3.45054960e+02   2.48925498e-01   3.83491541e-03   8.03342302e+01]\n",
+      "[  3.45052537e+02   2.48882019e-01   3.83188685e-03   8.03312744e+01]\n",
+      "[  3.45051666e+02   2.48866487e-01   3.83083385e-03   8.03302535e+01]\n",
+      "[  3.45051308e+02   2.48860112e-01   3.83040566e-03   8.03298393e+01]\n",
+      "[  3.45051163e+02   2.48857532e-01   3.83023303e-03   8.03296724e+01]\n",
+      "[  3.45051104e+02   2.48856494e-01   3.83016363e-03   8.03296054e+01]\n",
+      "[  3.45051081e+02   2.48856077e-01   3.83013582e-03   8.03295785e+01]\n",
+      "[  3.45051071e+02   2.48855910e-01   3.83012466e-03   8.03295677e+01]\n",
+      "[  3.45051068e+02   2.48855843e-01   3.83012019e-03   8.03295634e+01]\n",
+      "[  3.45051066e+02   2.48855816e-01   3.83011840e-03   8.03295617e+01]\n",
+      "[  3.45051066e+02   2.48855806e-01   3.83011768e-03   8.03295610e+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": 52,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "system = make_system(*best_params, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "run_odeint(system, slope_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MGjQIvXr1wsCBAzF27FjeK7U3573rS0lJQdeuXbmdJYGaXSvrmjJlCi5cuIDDhw/j3r17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\niE79PxIEyQq1XdJMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1ed8151d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plot(system.results, 'g-', label='simulation')\n",
+    "plot(data.insulin, 'go', label='insulin data')\n",
+    "\n",
+    "decorate(xlabel='Time (min)',\n",
+    "         ylabel='Concentration ($\\mu$U/mL)')"
+   ]
+  },
+  {
+   "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": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(130, 11)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "I_max = data.insulin.max()\n",
+    "Ib = data.insulin[0]\n",
+    "I_max, Ib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(290, 92)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "G_max = data.glucose.max()\n",
+    "Gb = data.glucose[0]\n",
+    "G0, Gb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.4150938228741881"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi_1 = (I_max - Ib) / k / (G0 - Gb)\n",
+    "phi_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "38.301171870899175"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "phi_2 = gamma * 1e4\n",
+    "phi_2"
+   ]
+  },
+  {
+   "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/chap08soln.ipynb b/code/chap08soln.ipynb
index 24f4b3ed..7d433a0b 100644
--- a/code/chap08soln.ipynb
+++ b/code/chap08soln.ipynb
@@ -261,9 +261,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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yctPLjx9n+BMRkeZp9p7/sWPH8PnnnyMqKqrF4AcAKysrLF68GF988QWOHTum8CLVLT+/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\noiIsWrQI9vb28PLyUkPFT+9J79eOdF3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GplU33p07dzJgwAASExP585//zPjx4/nxxx/ZsGEDJiYmvPjiiw06+ZEjR1i3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\nEbVbKAAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3909d3e3c8>"
+       "<matplotlib.figure.Figure at 0x26ff44ea7b8>"
       ]
      },
      "metadata": {},
@@ -290,9 +290,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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LKc5R2X67+LaOKyEiIlJPvScmmjt3LpydneV/OnXqpNB/5coVtG7dulnfaWBrbgtxkVip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\n2Cc9+RpIyCQAAAAASUVORK5CYII=\n",
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Ufp+RnyH9nsWFiBozhQrLuXPnsG/fPgDPZt2bmZmhY8eOaN++PR48eIBff/0VoaGhSgmq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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f39074e4ef0>"
+       "<matplotlib.figure.Figure at 0x26ff49fe198>"
       ]
      },
      "metadata": {},
@@ -326,9 +326,9 @@
     },
     {
      "data": {
-      "image/png": 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w8908kydPxuTJk2t/DgoKwty5c7Fy5UosXry40cfa0jHmmcot7u7uAIxtqrfT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\nA3v37sXMmTNRVlaGxMRELF26FF9++SX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ycli2bBn9+vUztbds2dL056lTp2IwGEhNTaWwsJA5c+bg7OzMzJkzrboPIiIi\njsouAkFubi7PPvss2dnZ+F00cy03N5fy8nLCwsLw9vau89qsrCz27NnD9u3bCQwMJDQ0lFmzZvH8\n888TFxeHq6urtXZDRETEYdnFpMK9e/fi6+vL5s2bCQgIMOs7cuQI7u7u+Pv71/vazMxM/P39CQwM\nNLVFRkZSWlrKoUOHmrRuERGR5sIuAsGoUaNYunRpvWcAsrOz8fLy4umnnyYqKoqRI0eydu1aampq\nACgsLMTHx8fsNRce51/qgnoRERExYxdDBpeTk5NDWVkZUVFRTJ48mb1797J06VJKSkqYNm0a5eXl\nuLm5mb3GxcUFg8HAuXPnbFS1iIiIY7H7QPDyyy9TVlZGq1atAAgJCaGkpITk5GSmTp2Ku7s7lZWV\nZq+pqqrCaDTi4eFhi5JFREQcjl0MGVyOs7OzKQxcEBISQmlpKSUlJXTq1IlTp06Z9RcVFQHQsWNH\nq9UpIiLiyOw+EIwdO5YXXnjBrG3//v34+PjQqlUrevfuTW5urtl8gfT0dDw9PQkNDbV2udeljAxY\nvBhiY2t/z8iwdUUiItJQdh8Ihg0bxvvvv8+mTZs4fvw4H3zwAatXr2batGkAhIeHExYWxsyZMzl4\n8CBpaWnEx8cTExOjSw6t4MJSySdPQk3NL0slKxSIiDgWu59DMGnSJJydnVm1ahV5eXn4+fkxd+5c\noqOjATAYDCQlJbFo0SLGjx+Pp6cn0dHRxMXF2bjy68PllkrWnQ5FRByH3QWCdevWmT02GAzExMQQ\nExNzydd4e3vz2muvNXVpUg8tlSwi0jzY/ZCB2DctlSwi0jwoEEijaKlkEZHmwe6GDMSxaKlkEZHm\nQYFAGk1LJYuIOD4NGYiIiIgCgYiIiCgQiIiICAoEIiIigiYVigPIyKi9I2J+fu19D0aM0CRGERFL\nUyAQu3ZhrYQLLqyVAAoFIiKWpCEDsWuXWytBREQsR4FA7JrWShARsQ4FArFrWitBRMQ6FAjErmmt\nBBER69CkQrFrWitBRMQ6FAjE7mmtBBGRpqchAxEREVEgEBEREQUCERERQYFAREREUCAQERERdJWB\niBZPEhFBgUCuc1o8SUSklt0NGSxcuJB58+aZte3evZtRo0bRs2dPRo4cSVpamln/6dOnmT59OhER\nEfTv35/4+HjOnz9vzbLFQWnxJBGRWnYTCIxGIytWrOD99983a8/JySE2Npbhw4ezceNGhg4dSlxc\nHNnZ2aaYPgd5AAAgAElEQVTnTJ06leLiYlJTU1myZAkbNmwgMTHR2rsgDkiLJ4mI1LKLQJCbm8tD\nDz3Eu+++i99Fq9akpKQQFhZGbGwsQUFBzJgxg/DwcFJSUgDIyspiz549LFmyhNDQUAYPHsysWbNY\nt24dlZWVttgdcSBaPElEpJZdBIK9e/fi6+vL5s2bCQgIMOvLzMwkMjLSrK1v375kZmaa+v39/QkM\nDDT1R0ZGUlpayqFDh5q+eHFoWjxJRKSWXUwqHDVqFKNGjaq3r6CggI4dO5q1+fj4UFBQAEBhYSE+\nPj51+gHy8/O59dZbm6BiaS60eJKISC27CASXU1FRgaurq1mbq6sr586dA6C8vBw3NzezfhcXFwwG\ng+k5IpejxZNEROxkyOBy3NzcqKqqMmurrKykZcuWALi7u9eZK1BVVYXRaMTDw8NqdYqIiDgyuz9D\n4OvrS1FRkVlbUVGRaRihU6dOdS5DvPD8i4caGkI3qxFL0OdIRByF3Z8h6N27NxkZGWZt6enpRERE\nmPpzc3PJ/9X1Y+np6Xh6ehIaGnpN73nhZjUnT0JNzS83q7moDJHL0udIRByJ3QeCCRMmkJmZycqV\nKzl69CgrVqxg3759PPzwwwCEh4cTFhbGzJkzOXjwIGlpacTHxxMTE1Nn7sHV0s1qxBL0ORIRa8nI\ngMWLITa29vdr+cHD7gNBSEgISUlJfPrpp4wePZqdO3eSnJxMUFAQAAaDgaSkJNq3b8/48eN59tln\niY6OJi4u7prfUzerEUvQ50hErMFSZyPtbg7BunXr6rQNGTKEIUOGXPI13t7evPbaaxarwde39i/0\nYrpZjTSEPkciYg2XOxvZkDlLdn+GwBZ0sxqxBH2ORByLJU6724Klzkba3RkCe6Cb1YglWONzpKsY\nRCzDkVc+tdTZSAWCS9DNasQSmvJz5Mj/gYHCzOU48t+No9ZuqdPutjBihPn/BRc09GykAoGIg3Lk\n/8CaOsw46pcSOHbQc+TaHXkSsKXORioQiDgoR/4PrCnDjCN/KYFjBz1Hrt3RJwFb4mykJhWKOChH\nXrq5KcOMo9//wZGDniPXrknAOkMg4rAsNW54KU152r0pfxpz5C8lcOyfVB25dk0mVyAQcVhN+R9Y\nU592b8ow48hfStD0Qa8pOXLtoMnkCgQiDqyp/gNr6rHgpgwzzeFLCRzzJ1VHrl0UCESkHtY47d5U\nYaY5fCk58k+qjlz79U6BQETqcPTT7vpSEmk4XWUgInVoxrXI9ee6PUNQXV0NQEFBgY0rEbE/vr4w\nahSkpUFhIXTsCIMH17afOGHr6kTkWl34zrvwHfhr120gOHXqFADjx4+3cSUijmHTJltXICKWcurU\nKbp06WLWZjAajUYb1WNTFRUVHDhwAG9vb1q0aGHrckRERJpcdXU1p06dokePHri7u5v1XbeBQERE\nRH6hSYUiIiKiQCAiIiIKBCIiIoICgYiIiKBAICIiIigQ1Ku6uppXXnmFqKgowsPDmTZtGsXFxbYu\nyyKKi4uZPXs2UVFRRERE8Oijj3LkyBFT/5gxYwgJCTH7NW/ePBtWfO1ycnLq7EtISAiZmZkA7N69\nm1GjRtGzZ09GjhxJWlqajSu+Nunp6fXuZ0hICA899BDQfI7rwoUL69R9peN4+vRppk+fTkREBP37\n9yc+Pp7z589bs+xrUt++pqamMnz4cMLCwrjzzjv54IMPzPrXr19f5zj/9re/tWbZ16S+fb3SZ7a5\nHNfbb7/9kv9+8/6zeIjVjqtR6khISDAOGDDAuHv3buOBAweM0dHRxvvvv9/WZTVadXW18Q9/+INx\n7Nixxn379hmzs7ON06ZNM/bv39945swZY01NjfHWW281fvzxx8aioiLTr5KSEluXfk22bNli7Nu3\nr9m+FBUVGSsrK43Z2dnGHj16GF9//XVjTk6OMSEhwXjzzTcbjxw5YuuyG+zcuXN19nHjxo3G0NBQ\n465du5rFca2pqTEuX77c2L17d+Ozzz5rar+a4/jAAw8Yx40bZzx06JDx888/N/br18/46quv2mI3\nrsql9nX9+vXGsLAw46ZNm4w//PCD8a9//avx5ptvNm7cuNH0nIULFxoff/xxs+N86tQpW+zGVbnU\nvl7NZ7a5HNfTp0+b7eMPP/xgHDx4sPGpp54yPcdax1WB4CLnzp0zhoeHGz/66CNTW25urrF79+7G\nPXv22LCyxjt48KCxe/fuxpycHFPbuXPnjLfeeqtx48aNxh9++MHYvXt34/Hjx21YpeUkJCQYx48f\nX2/fggULjBMmTDBrmzBhgnH+/PnWKK1J/fTTT8YBAwYY4+PjjUaj0eGP6/Hjx40TJkww9u3b1zhk\nyBCz/0yvdBz37t1bZ983bNhgDA8PN547d846O9AAl9vXkSNHGpcuXWr2/Llz5xoffPBB0+MHHnjA\nuGLFCqvV2xiX29crfWab03G92MKFC4233367sayszNRmreOqIYOLHD58mNLSUiIjI01tAQEB+Pv7\nm041OypfX1/eeOMNbrrpJlObwWAA4OzZsxw5cgR3d3f8/f1tVaJFZWdn07Vr13r7MjMzzY4xQN++\nfR3+GAO8/vrruLq6EhcXB+Dwx3Xv3r34+vqyefNmAgICzPqudBwzMzPx9/cnMDDQ1B8ZGUlpaSmH\nDh1q+uIb6HL7On/+fO6//36zNicnJ3766SfT45ycHIKCgqxSa2Ndbl+v9JltTsf11w4fPsxf//pX\nFi5cSMuWLU3t1jquCgQXubDwQ8eOHc3afXx8HH4hpLZt2zJkyBCcnH457OvWraOiooKoqCiys7Px\n8vLi6aefJioqipEjR7J27VpqampsWPW1y87OJi8vj7FjxzJgwAAmTpzIN998A9Qe5+Z4jE+fPk1q\naipxcXGm/1Ac/biOGjWKpUuX4u3tXafvSsexsLAQHx+fOv0A+fn5TVTxtbvcvkZGRpp9Aebl5bFl\nyxYGDhwI1O7r2bNn2bVrF8OHD2fw4ME8/fTTFBYWWq3+hrjcvl7pM9ucjuuvJSYm0rt3bwYPHmxq\ns+ZxVSC4SHl5OU5OTri4uJi1u7q6cu7cORtV1TR27NjBq6++SkxMDEFBQeTk5FBWVkZUVBRr1qxh\n3LhxrFy5kqSkJFuX2mAVFRXk5uby888/M2vWLFatWoWPjw8TJkzg6NGjVFRU4Orqavaa5nCM3333\nXdq3b8/dd99tamtOx/ViVzqO5eXluLm5mfW7uLhgMBgc+lifOXOGyZMn06FDBx577DGg9ksUwNnZ\nmYSEBP785z/z/fffM3HiRCoqKmxZboNd6TPbHI9rbm4uO3fuZPLkyWbt1jyu1+1qh5fi7u5OTU0N\n58+fx9n5l7+eyspKs1M4jm7Dhg0sWLCAO++8k2eeeQaAl19+mbKyMlq1agVASEgIJSUlJCcnM3Xq\nVNPwgiNwd3cnIyMDV1dX0xfGkiVLOHjwIO+88w5ubm5UVVWZvaY5HOOPP/6Ye++91yzQNqfjerEr\nHUd3d3cqKyvN+quqqjAajXh4eFitTkvKzc1l0qRJVFRUkJqaipeXFwBRUVH885//pF27dqbnBgcH\nM2jQINLS0rjjjjtsVXKDXekz2xyP6+bNm/H19SUqKsqs3ZrHVWcILuLr6wv8sjzyBUVFRXVOTTqq\nVatWMXfuXO6//36WLl1qGkJwdnY2/QO8ICQkhNLSUkpKSmxRaqPccMMNZj89Ojk5ERwcTH5+Pr6+\nvhQVFZk939GPcXZ2Nj/88AN33XWXWXtzO66/dqXj2KlTp3r/LUPdYUFHcPDgQf7whz/g5OTEe++9\nZzaEAJh9aUDtafS2bdva5Wn0y7nSZ7a5HVeoPWM7YsSIegO6tY6rAsFFQkND8fT05F//+pep7cSJ\nE5w8eZI+ffrYsDLLePPNN1m+fDnTpk1jwYIFZh++sWPH8sILL5g9f//+/fj4+NT5x2nvDhw4QK9e\nvThw4ICprbq6msOHD9OtWzd69+5NRkaG2WvS09OJiIiwdqkWk5mZibe3d53JR83puF7sSsexd+/e\n5Obmmv3HmZ6ejqenJ6GhoVattbGOHj3KI488gr+/P++8847ph5cLUlJSiIqKMjtjcvLkSc6cOUO3\nbt2sXW6jXOkz25yOK0BZWRmHDh2iX79+dfqseVwVCC7i6urKuHHjWLp0Kbt27eLgwYM8+eSTREZG\nEhYWZuvyGuXw4cMkJCRw3333MXbsWE6dOmX6VVZWxrBhw3j//ffZtGkTx48f54MPPmD16tVMmzbN\n1qU3WGhoKP7+/ixcuJB9+/aRnZ3N3Llz+fHHH3nooYeYMGECmZmZrFy5kqNHj7JixQr27dvHww8/\nbOvSr9mhQ4fo3r17nfbmdFwvdqXjGB4eTlhYGDNnzuTgwYOkpaURHx9PTExMnbkH9m727Nm4urqy\ndOlSzp8/b/q3e+bMGQCGDBlCaWkp8+bN4+jRo+zZs4epU6fSu3dvBgwYYOPqG+ZKn9nmdFwBvvvu\nO6qrq+v992vN46o5BPWYMWMG58+f55lnnuH8+fMMHDiQhQsX2rqsRvvkk0+orq7mo48+4qOPPjLr\nmz59OrGxsTg7O7Nq1Sry8vLw8/Nj7ty5REdH26jia+fs7Mzq1atZunQpjz/+OOXl5fTq1YvU1FTa\nt29P+/btSUpKIj4+njfffJOuXbuSnJzsMJds1aeoqIjWrVvXaZ80aVKzOa4XCwkJuexxNBgMJCUl\nsWjRIsaPH4+npyfR0dGmSzIdxbFjx9i/fz8Aw4cPN+vr3Lkzn332GZ07d2bt2rW88sorREdH4+Li\nwu23386cOXNsUXKjXOkz21yO6wUXhj/atGlTp8+ax9VgNBqNFt+qiIiIOBQNGYiIiIgCgYiIiCgQ\niIiICAoEIiIiggKBiIiIoEAgIiIiKBCIiIgICgQiIiKCAoGIiIigQCAiIiIoEIiIiAgKBCIiIoIC\ngYiIiKBAICIiIigQiIiICAoEIiIiggKBiIiIoEAgIiIigLOtC7CViooKDhw4gLe3Ny1atLB1OSIi\nIk2uurqaU6dO0aNHD9zd3c36rttAcODAAcaPH2/rMkRERKxu/fr1REREmLVdt4HA29sbqP1L6dSp\n0zVt45vCb/j8+88pKi3Cx9OHITcOoWfHnpYsU0RExGIKCgoYP3686Tvw167bQHBhmKBTp04EBAQ0\n+PUZJzP4W97fwBXcXN04y1n+lvc3vDt508e/j6XLFRERsZj6hso1qfAabc3ZWm/7tpxtVq5ERESk\n8RQIrlF+SX697XkleVauREREpPEUCK6Rr5dvve1+Xn5WrkRERKTxFAiu0YjgEfW2Dw8ebuVKRERE\nGs/uJhUuXLiQ6upqXnzxRVNbamoqqampFBQU4OfnR0xMDNHR0ab+9evXs3jxYrPttGjRgm+//bbJ\n6rwwcXBbzjbySvLw8/JjePBwTSgUERGHZDeBwGg0snLlSt5//33GjBljan/nnXd45ZVXWLRoEeHh\n4aSnp/Pcc8/h4uLC6NGjAThy5Ai33367WSgwGAxNXnMf/z4KACIi0izYRSDIzc3l2WefJTs7Gz8/\n8zH49957j3HjxjFq1CgAOnfuTFZWFhs2bDAFguzsbPr161fvdZUiIiJyZXYxh2Dv3r34+vqyefPm\nOvcEmD9/Pvfff79Zm5OTEz/99JPpcU5ODkFBQVapVUREpDmyizMEo0aNMp0BuFhkZKTZ47y8PLZs\n2cKECRMAKCws5OzZs+zatYvExETKy8vp06cPzzzzDB07dmzy2kVERJoDuzhDcLXOnDnD5MmT6dCh\nA4899hhQO1wA4OzsTEJCAn/+85/5/vvvmThxIhUVFbYsV0RExGHYxRmCq5Gbm8ukSZOoqKggNTUV\nLy8vAKKiovjnP/9Ju3btTM8NDg5m0KBBpKWlcccdd9iqZBEREYfhEGcIDh48yB/+8AecnJx47733\nCAwMNOv/dRgA8PHxoW3btuTn1383QRERETFn94Hg6NGjPPLII/j7+/POO+/g62t+h8CUlBSioqKo\nqqoytZ08eZIzZ87QrVs3a5crIiIOLiQkhL/97W9Wea8NGzbw29/+1ibvfTG7HzKYPXs2rq6uLF26\nlPPnz3Pq1Cmg9sZD7dq1Y8iQISQkJDBv3jwmT57Mv//9b1588UV69+7NgAEDbFy9iIhcq4yTGWzN\n2Up+ST6+Xr6MCB5hlXu/7N69m1atWjX5+9jbe9t1IDh27Bj79+8HYPhw81sCd+7cmc8++4zOnTuz\ndu1aXnnlFaKjo3FxceH2229nzpw5tihZREQsIONkBqv3rjY9PvnTSdPjpg4FtrynjS3f2+4Cwbp1\n60x/vummm/juu++u+JqwsDCz14mIiGO73BLzTR0IQkJCWLp0KaNGjWLOnDk4OTnh4eHB5s2bqays\n5Pbbb+e5557jhhtuoLq6mmXLlvH3v/+dH3/8kZtuuokpU6YwYkTtejcPPvggnTt3Nrsdf31t1/Le\nlmb3cwhEROT6Y09LzH/88cdUV1fz3nvvsXz5cnbu3ElKSgpQe3v9zz77jMTERLZt28bw4cN56qmn\nyM3NbfL3tjS7O0MgIiLi6+XLyZ9O1mm3xRLzbdq0Yf78+bRo0YKbbrqJ2267ja+//hqAH374gZYt\nW+Lv74+3tzdTpkyhZ8+etGnTpsnf29J0hkBEROyOPS0x37lzZ1q0aGF67OXlZbqybdy4cfz0008M\nGjSI6OhoEhMTCQgIMN0rpynf29IUCERExO708e/DpF6TCGgVgJPBiYBWAUzqNckmK8y6urrWaTMa\njQB07dqV7du388Ybb9CrVy+2bNnC73//e/75z39ecnvnz5+3yHtbmoYMRETELjnCEvPr16+nTZs2\n3HXXXQwaNIjZs2dz99138+mnn9K/f39cXFz4+eefTc+vqakhNzeXrl272rDq+ikQiIiIXKMff/yR\nxMREPDw86N69O99++y0nTpzg0UcfBWqvgnv77bf54osvCAwMZO3atWar9doTBQIREZFr9Pjjj1NR\nUcFzzz1HcXExvr6+TJ06lXvuuQeARx55hOPHjzNt2jRcXV0ZM2YMd911l42rrp/B2FSDEXbuxIkT\nDB06lB07dhAQEGDrckRERJrc5b77NKlQREREFAhEREREgUBERERQIBAREREUCERERAQFAhEREUGB\nQERERFAgEBERERQIREREBAUCERERQYFAREREUCAQERERFAhEREQEBQIRERFBgUBERERQIBAREREU\nCERERAQFAhEREUGBQERERLDDQLBw4ULmzZtn1rZ7925GjRpFz549GTlyJGlpaWb9p0+fZvr06URE\nRNC/f3/i4+M5f/68NcsWERFxaHYTCIxGIytWrOD99983a8/JySE2Npbhw4ezceNGhg4dSlxcHNnZ\n2abnTJ06leLiYlJTU1myZAkbNmwgMTHR2rsgIiLisOwiEOTm5vLQQw/x7rvv4ufnZ9aXkpJCWFgY\nsbGxBAUFMWPGDMLDw0lJSQEgKyuLPXv2sGTJEkJDQxk8eDCzZs1i3bp1VFZW2mJ3REREHI5dBIK9\ne/fi6+vL5s2bCQgIMOvLzMwkMjLSrK1v375kZmaa+v39/QkMDDT1R0ZGUlpayqFDh5q+eBERkWbA\n2dYFAIwaNYpRo0bV21dQUEDHjh3N2nx8fCgoKACgsLAQHx+fOv0A+fn53HrrrU1QsYiISPNiF2cI\nLqeiogJXV1ezNldXV86dOwdAeXk5bm5uZv0uLi4YDAbTc0REROTy7D4QuLm5UVVVZdZWWVlJy5Yt\nAXB3d68zV6Cqqgqj0YiHh4fV6hQREXFkdh8IfH19KSoqMmsrKioyDSN06tSJU6dO1ekH6gw1iIiI\nSP3sPhD07t2bjIwMs7b09HQiIiJM/bm5ueTn55v1e3p6EhoaatVaRUREHJXdB4IJEyaQmZnJypUr\nOXr0KCtWrGDfvn08/PDDAISHhxMWFsbMmTM5ePAgaWlpxMfHExMTU2fugYiIiNTP7gNBSEgISUlJ\nfPrpp4wePZqdO3eSnJxMUFAQAAaDgaSkJNq3b8/48eN59tlniY6OJi4uzsaVi4iIOA67uOzw19at\nW1enbciQIQwZMuSSr/H29ua1115rwqpERESaN7s/QyAiIiJNT4FAREREFAhEREREgUBERERQIBAR\nEREUCERERAQFAhEREUGBQERERFAgEBERERQIREREBAUCERERQYFAREREsMPFjRxdxskMtuZsJb8k\nH18vX0YEj6CPfx9blyUiInJZCgQWlHEyg9V7V5sen/zppOmxQoGIiNgzDRlY0NacrfW2b8vZZuVK\nREREGkaBwILyS/Lrbc8rybNyJSIiIg2jQGBBvl6+9bb7eflZuRIREZGGUSCwoBHBI+ptHx483MqV\niIiINIwmFVrQhYmD23K2kVeSh5+XH8ODh2tCoYiI2D2LBYKMjAy2b9/O3LlzLbVJh9THv48CgIiI\nOByLDRl8++23pKSkWGpzIiIiYkWaQyAiIiIKBCIiIqJAICIiIigQiIiICFdxlcEjjzxyVRvKy9Pd\n+ERERBzVFQNBVVXVVW3I29sbb2/vRhd0sfT0dB566KF6+/r27UtKSgpjxoxh//79Zn1jxozhxRdf\ntHg9IiIizdEVA8G6deusUcclhYeHs3v3brO2L7/8krlz5/LHP/4Ro9FITk4Oy5Yto1+/fqbntGzZ\n0tqlioiIOKwG3ZiosrKSd955h6ysLEpKSur0GwwG1qxZY7HiAFxdXc3OPJSUlLBs2TIeffRRBg4c\nyPHjxykvLycsLKxJzlCIiIhcDxoUCBYvXsyHH35It27daNOmTVPVdFmvv/46rq6uxMXFAXDkyBHc\n3d3x9/e3ST0iIiLNQYMCwWeffca0adOYMmVKU9VzWadPnyY1NZVFixaZhgSys7Px8vLi6aef5l//\n+hdt27bl3nvv5eGHH8bJSRdRiIiIXI0GBQKDwUBYWFhT1XJF7777Lu3bt+fuu+82teXk5FBWVkZU\nVBSTJ09m7969LF26lJKSEqZNm2azWkVERBxJgwLBPffcw4cffki/fv1s8tP3xx9/zL333ouLi4up\n7eWXX6asrIxWrVoBEBISQklJCcnJyUydOhWDwWD1OkVERBxNgwLB9OnTueeee7jjjju4+eab68zk\nNxgMvPTSSxYt8ILs7Gx++OEH7rrrLrN2Z2dnUxi4ICQkhNLSUkpKSur0iYiISF0NCgTLli3j2LFj\neHl58e2339bpb8qfxjMzM/H29iYoKMisfezYsfTs2ZP58+eb2vbv34+Pj4/CgIiIyFVqUCDYtGkT\nf/zjH3nyySetfir+0KFDdO/evU77sGHDWLlyJT169KBXr16kp6ezevVq5s2bZ9X6REREHFmDAkGL\nFi0YMGCATcbli4qKaN26dZ32SZMm4ezszKpVq8jLy8PPz4+5c+cSHR1t9RpFREQcVYMCwciRI02T\nCq0tOTm53naDwUBMTAwxMTFWrkhERKT5aFAgaN++PRs3bmTYsGHccssteHp6mvUbDAYWL15s0QJF\nRESk6TUoEHzwwQe0bt2a6upqvv766zr9usRPRETEMTUoEOzcubOp6hAREREbuuLdhdatW8fx48et\nUYuIiIjYyBXPEKSlpbFs2TJ8fHwYNGgQgwYNom/fvri7u1ujPhEREbGCKwaC1atXc+7cOb766iu+\n+OILXnzxRQoLC4mIiGDgwIEMHDiwzs2CRERExLFc1RwCNzc3Bg8ezODBgwH4/vvv+eKLL9i1axcJ\nCQm0b9+eQYMGMXDgQIYOHdqkBYuIiIjlNWhS4QU33ngjN954Iw8++CDnzp0jPT2dXbt2sXTpUgUC\nERERB3RNgeDX3NzcTHMLRERExDFdMRAkJSXV224wGPDw8KBDhw706dOHTp06Wbw4ERERsY4rBoJV\nq1Zdsq+6uhqoXePgkUce4amnnrJcZSIiImI1VwwEBw8evGRfTU0NhYWFfPrppyxbtoygoCBGjx5t\n0QJFRESk6V3xxkSXfbGTE76+vkycOJH777+fd99911J1iYiIiBU1KhD8Wr9+/Th27JilNiciIiJW\nZLFA0KpVK6qqqiy1OREREbEiiwWCQ4cO6UoDERERB2WRQHDw4EH+8pe/MGzYMEtsTkRERKzsilcZ\nPPLII5fsq6yspKioiNzcXH7zm98QGxtr0eJERETEOq4YCC41L8BgMHDDDTdw44038sQTT3DnnXfi\n7NzoGx+KiIiIDVzxG3zdunXWqENERERs6Kp/pJ82bRqhoaF0796dkJAQAgMDzfq/++47WrZsSefO\nnS1epIiIiDStqw4Ex48f5/PPP6eyshKDwYC7uzvdunUjJCSEbt26kZWVxf79+9m+fXtT1isiIiJN\n4KoDwaZNm6iurubYsWMcOXKE7777jsOHD7NlyxbKy8sB8PX1bbJCRUREpOk0aBZgixYtCA4OJjg4\nmDvvvBOovdLgzTffJCUlhTfeeKNJihQREZGm1ej7ELi6uhIXF0e/fv149dVXLVGTiIiIWJnF7lTY\nu3dvvvrqK0ttTkRERKzoqocMFixYYHaVQatWrcz6jx8/Tvv27S1eoIiIiDS9qw4EX3zxBR988AFQ\ne1Oijh07Ehoayk033cTp06f5f//v/7Fs2bImKTInJ4e77rqrTvv69euJiIhg9+7dxMfHc+zYMbp0\n6cLTTz/N4MGDm6SWa5FxMoOtOVvJL8nH18uXEcEj6OPfx9ZliYiImFx1IPj888/5+eefOXLkCNnZ\n2Rw5coQjR46wadMmfvzxRwDi4uLo0qULQUFBdO3aleDgYH7/+983usgjR47Qtm1bNm/ebNbepk0b\ncnJyiI2NZcqUKfzud79j8+bNxMXFsXHjRrp169bo926sjJMZrN672vT45E8nTY8VCkRExF406CqD\nG264gV69etGrVy+z9uLiYlNAuBAWvvjiCyoqKiwWCIKDg/H29q7Tl5KSQlhYmGkdhRkzZrBnzx5S\nUhrjo3oAABiRSURBVFJ4/vnnG/3ejbU1Z2u97dtytikQiIiI3bDI4gMdOnSgQ4cO3HbbbWbtubm5\nltg82dnZdO3atd6+zMxMRowYYdbWt29ftmzZYpH3bqz8kvw6bUWlRezN30teSZ6GEERExC5Y7CqD\n+lx8e+NrlZ2dTV5eHmPHjmXAgAFMnDiRb775BoCCggI6duxo9nwfHx8KCgos8t6N5etlfrOmotIi\nDp8+DECNscY0hJBxMsMW5YmIiABNHAgsoaKigtzcXH7++WdmzZrFqlWr8PHxYcKECRw9epSKigpc\nXV3NXuPq6sq5c+dsVLG5EcHmZy9yf6o9axLY2jwsbcvZZrWaRERELmb36xW7u7uTkZGBq6ur6Yt/\nyZIlHDx4kHfeeQc3N7c6SzRXVlbSsmVLW5Rbx4WhgG0528grycPJ4MRvOvwGbw/z+RB5JXm2KE9E\nRARwgEAAtZMZf83JyYng4GDy8/Px9fWlqKjIrL+oqKjOMIIt9fHvYwoGi9MWc/Knk3We4+flZ+2y\nRERETOx+yODAgQP06tWLAwcOmNqqq6s5fPgw3bp1o3fv3mRkmI+/p6enExERYe1Sr8rFQwgXDA8e\nbuVKREREfmH3ZwhCQ0Px9/dn4cKF/OlPf8LDw4M333yTH3/8kYceeoji4mLuu+8+Vq5cyf9v796D\nojrPMIA/rCsBCSihQleMSYSwtghyUcC6CpKaqBlqq0C8QAqtE3VSsCUaQ7lMYtKpQBQvDOhAaxsh\nrbGgxmgnzWgDTcdakEaFSgSnIoIKigLhKuzpHw6ryy4gsLtnz/L8ZpjR7+zlPbxnOe8532VfffVV\nfPbZZ7hw4QLeffddsUPXa2AXwjT7aZpiYHvxdi5eREREojD7gkAulyMvLw/p6enYuHEjOjs74efn\nh/z8fDg5OcHJyQlZWVnIyMhAbm4uZs6cif3798PNzU3s0Af1eBcCwMWLiIhIfGZfEACAi4sLdu7c\nOej2kJAQhISEmC4gA+PiRUREJDazH0MwHuhbvAjgzAMiIjIdFgRmYODiRf0484CIiEyFBYEZ4MwD\nIiISmyTGEFg6zjwgIiKxsSAwE5x5QEREYmKXgZkaauYBERGRobEgMFOceUBERKbEgsBMceYBERGZ\nEgsCM8WZB0REZEocVGimBpt5wAGFRERkDCwIzNjAmQdERETGwi4DIiIiYkFARERE7DKQnNL6Uvy1\n5q9cvZCIiAyKBYGEcPVCIiIyFnYZSAhXLyQiImNhQSAhXL2QiIiMhQWBhHD1QiIiMhYWBBLC1QuJ\niMhYOKhQQrh6IRERGQsLAokZavXCoaYkGnO6IqdCEhFJHwsCCzHUlEQARpuuyKmQRESWgWMILMRQ\nUxKNOV2RUyGJiCwD7xBYiNFMSTTEdEVOhSQisgy8Q2AhhpqSaMzpipwKSURkGVgQWIihpiSOZrpi\naX0pthdvx6bPNmF78XaU1peO+H2JiEg62GVgIZ5kSuKTTlccyUBBToUkIrIMkigI7ty5g4yMDPzz\nn/9EV1cX5syZg23btsHDwwMAEB4ejkuXLmk9Jzw8HL/5zW8MGoe5T68bakriUNsGGmqgoL7XGMlr\nExGReTL7gkCtVuMXv/gFBEFAdnY2Jk2ahH379iEmJgYnT57ElClTUFNTgw8//BBBQUGa59na2ho0\njvE0vY4DBYmIxh+zLwiqqqrwn//8B6dOnYKbmxsAICMjAwEBASguLoafnx86Ozvh4+ODqVOnGi2O\nkV41S5nCXoH61nqddg4UJCKyXGY/qFChUODAgQN44YUXNG1WVlYAgJaWFly5cgU2NjZwdXU1ahzj\n6aqZAwWJiMYfsy8IHB0dERISApnsUaiHDh1CV1cXVCoVqqurYW9vjy1btkClUiEsLAwHDx6EWq02\naBzjaXrdPNd5WO+3HtMdpkNmJcN0h+lY77fe4u6EEBHRI2bfZTDQ6dOnsWvXLsTGxsLNzQ01NTXo\n6OiASqXChg0bUF5ejvT0dLS1tSE+Pt5g77vMfZnWGIJ+lnrVzIGCRETji6QKgqKiIqSkpGD58uXY\nunUrACAtLQ0dHR1wcHAAACiVSrS1tWH//v2Ii4vTdC+MFafXicfcZ3cQEVkCyRQEOTk52L17N6Ki\nopCcnKw50cvlck0x0E+pVKK9vR1tbW0628aCV82mN55mdxARicnsxxAAQG5uLnbv3o34+HikpKRo\nXfVHRkbigw8+0Hr8pUuX4OzsbNBigMTBL08iIjINs79DUFVVhczMTKxatQqRkZFoamrSbLOzs8OS\nJUuwd+9ezJ49G35+fjh37hzy8vKQlJQkYtRkKONpdgcRkZjMviA4deoU+vr6UFhYiMLCQq1tmzdv\nxqZNmyCXy5GTk4OGhgZMmzYNiYmJiIiIECliMiSuiUBEZBpmXxAkJCQgISFhyMfExsYiNjbWRBGR\nKY232R1ERGIx+4KAxjfO7iAiMg0WBGT2OLuDiMj4JDHLgIiIiIyLBQERERGxICAiIiKOISAy6tLI\nXHaZiKSCBQGNa8ZcGpnLLhORlLDLgMY1Yy6NzGWXiUhKWBDQuGbMpZG57DIRSQkLAhrXFPYKve2G\nWBrZmK9NRGRoLAhoXFvmvkxvuyGWRjbmaxMRGRoHFQ6Co8PHB2MujWyKZZd5nOrH3wvRyLEg0IOj\nw8cXYy6NbMzXNvZxKtWTKj+/RKPDLgM9ODqcpMCYx2n/SbW+tR5qQa05qZbWl475tY2Nn1+i0WFB\noAdHh5MUGPM4lfJJlZ9fotFhl4EeCnsF6lvrddo5OpzMiTGPU1OcVI3VJcHPr7ik2tUESDt2Q+Ad\nAj04OpykwJjHqbGnTBqzS4KfX/FIuatJyrEbCu8Q6GGK0eFEY2XM43SZ+zKtgXn9DHVSHapLYqzx\nW8LnV6pXqsbMq7FJOXbAMMcMC4JBGHN0OJGhGOs4NfZJ1dhdElL+/Ep5loSUx29IOXZDHTMsCIhI\nL2OeVNnPPzgpX6lKOa9Sjt1QxwzHEBCRybGff3BSvlKVcl6lHLuhjhneISAik7OEfn5jkfKVqpTz\nKuXYDXXMsCAgIlFIuZ/fmIw9oNPYpJxXqcZuqGNm3BYEfX19AIBbt26JHAkR0SMKKLBi2goUXyvG\n7fbbcLFzQfDzwVAICty4cUPs8MgMjeSY6T/n9Z8DHzduC4KmpiYAwLp160SOhIhoaMdwTOwQSGKG\nO2aamprw3HPPabVZCYIgGDMoc9XV1YWKigpMnToVEyZMEDscIiIio+vr60NTUxNmz54NGxsbrW3j\ntiAgIiKiRzjtkIiIiFgQEBEREQsCIiIiAgsCIiIiAgsCIiIiAgsCvfr6+rBz506oVCr4+voiPj4e\nd+7cETssg7hz5w62bdsGlUqFuXPn4uc//zmuXLmi2R4eHg6lUqn1k5SUJGLEo1dTU6OzL0qlEmVl\nZQCAr776CitWrIC3tzfCwsJQXFwscsSjc+7cOb37qVQq8frrrwOwnLympqbqxD1cHu/evYvNmzdj\n7ty5mD9/PjIyMtDb22vKsEdF377m5+dj6dKl8PHxwfLly3HkyBGt7QUFBTp5/v73v2/KsEdF374O\nd8xaSl5DQ0MH/fw2NDz8LgKT5VUgHZmZmcKCBQuEr776SqioqBAiIiKE1atXix3WmPX19Qmvvfaa\nEBkZKVy4cEGorq4W4uPjhfnz5wvNzc2CWq0W5syZI3z66adCY2Oj5qetrU3s0Efl5MmTQmBgoNa+\nNDY2Cj09PUJ1dbUwe/ZsITs7W6ipqREyMzMFT09P4cqVK2KHPWLd3d06+3j06FFh1qxZQklJiUXk\nVa1WC7t37xY8PDyEX//615r2J8njmjVrhLVr1wqXL18WvvzySyEoKEjYtWuXGLvxRAbb14KCAsHH\nx0c4duyYUFtbK3zyySeCp6encPToUc1jUlNThY0bN2rluampSYzdeCKD7euTHLOWkte7d+9q7WNt\nba0QHBwsvPXWW5rHmCqvLAgG6O7uFnx9fYXCwkJNW11dneDh4SGcP39exMjGrrKyUvDw8BBqamo0\nbd3d3cKcOXOEo0ePCrW1tYKHh4dw/fp1EaM0nMzMTGHdunV6t6WkpAhRUVFabVFRUUJycrIpQjOq\n1tZWYcGCBUJGRoYgCILk83r9+nUhKipKCAwMFEJCQrT+mA6Xx/Lycp19LyoqEnx9fYXu7m7T7MAI\nDLWvYWFhQnp6utbjExMThejoaM3/16xZI+zZs8dk8Y7FUPs63DFrSXkdKDU1VQgNDRU6Ojo0babK\nK7sMBqiqqkJ7ezsCAgI0bdOnT4erq6vmVrNUKRQKHDhwAC+88IKmzcrKCgDQ0tKCK1euwMbGBq6u\nrmKFaFDV1dWYOXOm3m1lZWVaOQaAwMBAyecYALKzs2FtbY0333wTACSf1/LycigUCpw4cQLTp0/X\n2jZcHsvKyuDq6opnn31Wsz0gIADt7e24fPmy8YMfoaH2NTk5GatXr9Zqk8lkaG1t1fy/pqYGbm5u\nJol1rIba1+GOWUvK6+OqqqrwySefIDU1Fba2tpp2U+WVBcEA/V/84OLiotXu7Ows+S9CcnR0REhI\nCGSyR2k/dOgQurq6oFKpUF1dDXt7e2zZsgUqlQphYWE4ePAg1Gq1iFGPXnV1NRoaGhAZGYkFCxYg\nJiYGFy9eBPAwz5aY47t37yI/Px9vvvmm5g+K1PO6YsUKpKenY+rUqTrbhsvj7du34ezsrLMdAG7e\n1P8d8mIaal8DAgK0ToANDQ04efIkFi5cCODhvra0tKCkpARLly5FcHAwtmzZgtu3b5ss/pEYal+H\nO2YtKa+P27dvH/z9/REcHKxpM2VeWRAM0NnZCZlMhokTJ2q1W1tbo7u7W6SojOP06dPYtWsXYmNj\n4ebmhpqaGnR0dEClUuF3v/sd1q5di7179yIrK0vsUEesq6sLdXV1+Pbbb/H2228jJycHzs7OiIqK\nwtWrV9HV1QVra2ut51hCjv/0pz/ByckJP/rRjzRtlpTXgYbLY2dnJ5566imt7RMnToSVlZWkc93c\n3IwNGzbgO9/5Dt544w0AD0+iACCXy5GZmYnf/va3uHbtGmJiYtDV1SVmuCM23DFriXmtq6vDmTNn\nsGHDBq12U+Z13H7b4WBsbGygVqvR29sLufzRr6enp0frFo7UFRUVISUlBcuXL8fWrVsBAGlpaejo\n6ICDgwMAQKlUoq2tDfv370dcXJyme0EKbGxsUFpaCmtra80JY8eOHaisrMTHH3+Mp556Cg8ePNB6\njiXk+NNPP8XKlSu1ClpLyutAw+XRxsYGPT09WtsfPHgAQRAwadIkk8VpSHV1dVi/fj26urqQn58P\ne3t7AIBKpcLZs2fxzDPPaB7r7u6ORYsWobi4GK+88opYIY/YcMesJeb1xIkTUCgUUKlUWu2mzCvv\nEAygUCgAPPp65H6NjY06tyalKicnB4mJiVi9ejXS09M1XQhyuVzzAeynVCrR3t6OtrY2MUIdk6ef\nflrr6lEmk8Hd3R03b96EQqFAY2Oj1uOlnuPq6mrU1tbi1Vdf1Wq3tLw+brg8fve739X7WQZ0uwWl\noLKyEq+99hpkMhn+/Oc/a3UhANA6aQAPb6M7Ojqa5W30oQx3zFpaXoGHd2yXLVumt0A3VV5ZEAww\na9Ys2NnZ4d///rem7caNG6ivr8e8efNEjMwwcnNzsXv3bsTHxyMlJUXr4IuMjMQHH3yg9fhLly7B\n2dlZ58Np7ioqKuDn54eKigpNW19fH6qqqvDiiy/C398fpaWlWs85d+4c5s6da+pQDaasrAxTp07V\nGXxkSXkdaLg8+vv7o66uTusP57lz52BnZ4dZs2aZNNaxunr1Kn72s5/B1dUVH3/8sebipd9HH30E\nlUqldcekvr4ezc3NePHFF00d7pgMd8xaUl4BoKOjA5cvX0ZQUJDONlPmlQXBANbW1li7di3S09NR\nUlKCyspKJCQkICAgAD4+PmKHNyZVVVXIzMzEqlWrEBkZiaamJs1PR0cHlixZgsOHD+PYsWO4fv06\njhw5gry8PMTHx4sd+ojNmjULrq6uSE1NxYULF1BdXY3ExETcu3cPr7/+OqKiolBWVoa9e/fi6tWr\n2LNnDy5cuICf/vSnYoc+apcvX4aHh4dOuyXldaDh8ujr6wsfHx/86le/QmVlJYqLi5GRkYHY2Fid\nsQfmbtu2bbC2tkZ6ejp6e3s1n93m5mYAQEhICNrb25GUlISrV6/i/PnziIuLg7+/PxYsWCBy9CMz\n3DFrSXkFgG+++QZ9fX16P7+mzCvHEOjxy1/+Er29vdi6dSt6e3uxcOFCpKamih3WmJ06dQp9fX0o\nLCxEYWGh1rbNmzdj06ZNkMvlyMnJQUNDA6ZNm4bExERERESIFPHoyeVy5OXlIT09HRs3bkRnZyf8\n/PyQn58PJycnODk5ISsrCxkZGcjNzcXMmTOxf/9+yUzZ0qexsRGTJ0/WaV+/fr3F5HUgpVI5ZB6t\nrKyQlZWFd999F+vWrYOdnR0iIiI0UzKl4n//+x8uXboEAFi6dKnWthkzZuCLL77AjBkzcPDgQezc\nuRMRERGYOHEiQkND8c4774gR8pgMd8xaSl779Xd/TJkyRWebKfNqJQiCYPBXJSIiIklhlwERERGx\nICAiIiIWBERERAQWBERERAQWBERERAQWBERERAQWBEQW65133oFSqRzyJzo6GgAQHR2NmJgYUeO9\nf/8+QkNDUVtbO+rXuHHjBpRKJY4fP/7Ez2lpaUFoaCjq6upG/b5EloDrEBBZqOvXr2tWsQOA9957\nDxMmTEBycrKm7emnn4a7uztqampgZWUl6sJMb731FlxcXPD222+P+jV6enrw3//+FzNmzNBZ/30o\n+fn5+Pzzz/HRRx9J+sueiMaCBQHROBEdHY0JEybgD3/4g9ih6Lh48SLWrl2LkpKSEZ3IDaWnpwfB\nwcF477338PLLL5v8/YnMAbsMiEiny0CpVOLw4cPYsmULfH19ERQUhKysLHz77bdITEzUrKOekZGB\nx68p7t27h+TkZMyfPx/e3t5Ys2YNzp8/P+z75+Xl4Qc/+IFWMRAaGors7Gy8//77CAgIgL+/P7Zv\n347Ozk6kpaUhMDAQgYGBSEpKQnd3NwDdLoOioiJ4eXmhvLwcERER8PLywuLFi/H73/9e6/2tra3x\n8ssv48CBA2P5NRJJGgsCItIrLS0Njo6OyM7OxuLFi7Fv3z6Eh4fD1tYWWVlZWLJkCfLy8vC3v/0N\nANDd3Y2YmBh8+eWXSEhIwN69ezF58mTExMTg4sWLg75Pe3s7zpw5o/fKPC8vD/fv38eePXuwevVq\nFBQU4Cc/+Qlu3ryJnTt3Ijo6Gn/5y19QUFAw6Ov39vYiISEBYWFhyM3NhZ+fH9LS0nD27Fmtxy1d\nuhQVFRW4du3a6H5hRBLHLzciIr08PT2RlJQE4OG3RxYVFcHJyUnzRV9BQUE4ceIEvv76a7zyyis4\nfvw4vvnmGxw5cgReXl4AgEWLFiE8PByZmZk4ePCg3vcpKyvDgwcP4O3trbPN0dERGRkZkMlkCAwM\nxOHDh/HgwQN8+OGHkMvlUKlU+Pzzz/H1118Puh9qtRpxcXFYtWoVAMDPzw9ffPEF/v73v2P+/Pma\nx82ePRvAw6/Rff7550f+CyOSON4hICK9Hj9BOzo6YsKECVptVlZWmDx5MlpbWwEAZ8+ehYuLC773\nve+ht7cXvb29UKvVWLx4MUpLS9HT06P3fW7cuAEAmD59us42Ly8vyGQP/0zJZDI4OjrC09MTcvmj\na5kpU6ZoYhiMn5+f5t/W1tZ45pln0NnZqfUYe3t7ODg4oL6+fsjXIrJUvENARHrZ2dnptE2aNGnQ\nx9+/fx+3bt2Cp6en3u337t2Di4uLTntbWxsAwNbWdswxDGbga8tkMqjVar2P64+HaLxhQUBEBmFv\nbw83NzekpaXp3e7o6Dhke1tbGxwcHIwW35NobW0dNE4iS8cuAyIyiHnz5qGhoQHOzs7w8vLS/Jw+\nfRqHDh3CxIkT9T5v2rRpAIBbt26ZMlwdLS0t6OzshEKhEDUOIrGwICAig1i5ciVcXFwQGxuL48eP\n41//+hd27NiBnJwcPPvss4Mu+DN37lzY2Ng80fREYyovLwcAqFQqUeMgEgsLAiIyCDs7OxQUFGDO\nnDnYsWMH3njjDfzjH/9ASkoK4uLiBn2era0tFi1ahJKSEhNGq6ukpATe3t68Q0DjFlcqJCLRXbx4\nEWvWrMGZM2f0Djw0ts7OTixcuBA7duzAD3/4Q5O/P5E54B0CIhKdt7c3XnrpJZ0VBE3l8OHDcHd3\nx0svvSTK+xOZA94hICKz0NzcjJUrV+KPf/wjnnvuOZO97/379/HjH//Y5O9LZG5YEBARERG7DIiI\niIgFAREREYEFAREREYEFAREREYEFAREREQH4P03Ltvy5AvD7AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f39071f1588>"
+       "<matplotlib.figure.Figure at 0x26ff49fea58>"
       ]
      },
      "metadata": {},
@@ -436,9 +436,9 @@
     },
     {
      "data": {
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6fUN3JRVL2h88eJABAwZYj6emptKhQ4crJhdpaWmUlZVZqwSpqal4e3vTqlUrOnfuzMaN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\n+ipCQkKcXDNL9upaXl6O+vp6q+117do1AMDZs2exb98+1NT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ma6lYhBDiDiqWxvLee+/x5ptv8vDDD2NqaoqVlRWffvopzZo1IyMjg5YtW2pd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\nLEIIIdxKCosQQgi3ksIihBDCrf4FLBNQahkEzlsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3907132a90>"
+       "<matplotlib.figure.Figure at 0x26ff4ec1f60>"
       ]
      },
      "metadata": {},
@@ -696,8 +696,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 276 ms, sys: 0 ns, total: 276 ms\n",
-      "Wall time: 276 ms\n"
+      "Wall time: 182 ms\n"
      ]
     }
    ],
@@ -1150,9 +1149,9 @@
     },
     {
      "data": {
-      "image/png": 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KnT17lp9++gmARYsWVZhsAH7//XdOnjzJmTNn+PTTT/WWDR8+nAULFrB06VKW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MYdeuXXz++edcuHCB+fPn3/O/2Z1YdJNEVlYWsbGx9O/f35BWp04dmjVrRnp6Oh4eHmRk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\nCxxIgk0HAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f39070c6be0>"
+       "<matplotlib.figure.Figure at 0x26ff505e6d8>"
       ]
      },
      "metadata": {},
@@ -1293,8 +1292,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 216 ms, sys: 8 ms, total: 224 ms\n",
-      "Wall time: 208 ms\n"
+      "Wall time: 149 ms\n"
      ]
     }
    ],
@@ -1521,9 +1519,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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wgkf+3YHLFZIyRUQkRLSH3ZkZBsa027n9skIiw3xQVcXBz9387W+hLkxERDqa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\nLFx9NWzdCu+/D5s3E+H0c03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ONL1eb6hW6ZROt66vvPKKecUVV5jr1683/X6/+fnnn5tZ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\nRD+lGWwAAAAOSURBVGxAgS0iImID/x9C6XEExn31CgAAAABJRU5ErkJggg==\n",
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3907146860>"
+       "<matplotlib.figure.Figure at 0x26ff50145c0>"
       ]
      },
      "metadata": {},
@@ -2017,42 +2015,45 @@
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-      "[  2.71893881e+02   2.67752423e-02   1.22393337e-02   1.07668754e-05]\n",
-      "[  2.71894140e+02   2.67752508e-02   1.22393367e-02   1.07668795e-05]\n",
-      "[  2.71894273e+02   2.67752552e-02   1.22393383e-02   1.07668816e-05]\n",
-      "[  2.71894342e+02   2.67752574e-02   1.22393390e-02   1.07668827e-05]\n",
-      "[  2.71894376e+02   2.67752585e-02   1.22393394e-02   1.07668832e-05]\n",
-      "[  2.71894393e+02   2.67752591e-02   1.22393396e-02   1.07668835e-05]\n",
-      "[  2.71894401e+02   2.67752593e-02   1.22393397e-02   1.07668836e-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"
@@ -2105,9 +2106,9 @@
     },
     {
      "data": {
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Xp9GgTx3d7eFsb4mC4gqoqjX4Zk8Rjic4a9dnZgKxsfjrOxgsBhER0RPT6VW/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\nDAaUlZXhgw8+wPz58z2uOk0sFuPw4cNYs2YNVCoV7ty5g3f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ef/55kxLI3Zo2bRoHDhzA3t7e8ISRkpJCy5YtjfZzdnY2fHpITU3F2dm5QjlAcnKyJBAh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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3906c245f8>"
+       "<matplotlib.figure.Figure at 0x26ff4ffac88>"
       ]
      },
      "metadata": {},
@@ -2212,19 +2213,19 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>S_G</th>\n",
-       "      <td>0.026775</td>\n",
+       "      <td>0.026799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>S_I</th>\n",
-       "      <td>0.000880</td>\n",
+       "      <td>0.000881</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "S_G    0.026775\n",
-       "S_I    0.000880\n",
+       "S_G    0.026799\n",
+       "S_I    0.000881\n",
        "dtype: float64"
       ]
      },
@@ -2547,9 +2548,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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zSk52TvrXnHpIRES2pNUJwSuvvILdu3cjPT0d5eXljdoLCgrg6+tr0uCsjYu9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\nAfD/iR4DQ0nnVqIAAAAASUVORK5CYII=\n",
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h7Pi+uXPntvj6UVFRWLduHaZMmQI/Pz/MmjULV65c6T41lk2bNuHYsWNwdXVVWzVxyJAh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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3906b71588>"
+       "<matplotlib.figure.Figure at 0x26ff4e4e9b0>"
       ]
      },
      "metadata": {},
@@ -2782,9 +2783,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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OO7j11lu57bbbACgpufYvnH//+9+ZMGEC/fv3JywsjHfffZfS0qZ5HrV4IPD19WXy5Mm4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\nbZcQbzCws44QMuSJxWKL29wKBAKYTCabkze5MxEAgK+//hr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MGjQIvXr1wsCBAzF27FjeK7U3573rS0lJQdeuXbmdJYGaXSvrmjJlCi5cuIDDhw/j3r17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\nh6R1U9iDBw/w+uuvc8OJPT0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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f3906ba4080>"
+       "<matplotlib.figure.Figure at 0x26ff5235668>"
       ]
      },
      "metadata": {},
diff --git a/code/chap09-fig01.pdf b/code/chap09-fig01.pdf
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diff --git a/code/chap09-fig02.pdf b/code/chap09-fig02.pdf
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diff --git a/code/chap09mine.ipynb b/code/chap09mine.ipynb
new file mode 100644
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+++ b/code/chap09mine.ipynb
@@ -0,0 +1,1845 @@
+{
+ "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$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "g = 9.8 * m/s**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "When we call `odeint`, we need an array of timestamps where we want to compute the solution.\n",
+    "\n",
+    "I'll start with a duration of 10 seconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.] second"
+      ],
+      "text/latex": [
+       "$[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.] second$"
+      ],
+      "text/plain": [
+       "<Quantity([  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.], 'second')>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duration = 10 * s\n",
+    "ts = linspace(0, duration, 11)\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",
+    "    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": [
+      "0.0 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": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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>381.0</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",
+       "  </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"
+      ]
+     },
+     "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>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",
+       "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": 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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UKnYeKgSs09us/T6bW27ohNZFAqc5alLYAMTGxrJw4UJ71iKEEE1yfrYBjVrN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\n6wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x27632bf0c18>"
+      ]
+     },
+     "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": "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": "markdown",
+   "metadata": {},
+   "source": [
+    "### Onto the sidewalk\n",
+    "\n",
+    "Here's the code again to set up the `System` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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": 15,
+   "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>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": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "system = make_system(10)\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": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(8.81792826905006)"
+      ]
+     },
+     "execution_count": 17,
+     "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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.817885349720552 second"
+      ],
+      "text/latex": [
+       "$8.817885349720552 second$"
+      ],
+      "text/plain": [
+       "<Quantity(8.817885349720552, 'second')>"
+      ]
+     },
+     "execution_count": 18,
+     "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": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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": 20,
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-0.003708896250259386 meter"
+      ],
+      "text/latex": [
+       "$-0.003708896250259386 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(-0.003708896250259386, 'meter')>"
+      ]
+     },
+     "execution_count": 21,
+     "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": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-86.41569703669059 meter/second"
+      ],
+      "text/latex": [
+       "$-86.41569703669059 \\frac{meter}{second}$"
+      ],
+      "text/plain": [
+       "<Quantity(-86.41569703669059, 'meter / second')>"
+      ]
+     },
+     "execution_count": 22,
+     "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": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-311.0965093320861 kilometer/hour"
+      ],
+      "text/latex": [
+       "$-311.0965093320861 \\frac{kilometer}{hour}$"
+      ],
+      "text/plain": [
+       "<Quantity(-311.0965093320861, 'kilometer / hour')>"
+      ]
+     },
+     "execution_count": 23,
+     "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": 24,
+   "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fS19B5S1kPEPMHqD2MPS27vnVNXg926HmD9bIxbPeT739x38AkyaJnT2bDXj7bXVN+8Ce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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x27632e71f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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": 26,
+   "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": 27,
+   "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": 28,
+   "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": 28,
+     "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": 29,
+   "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": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(0.0, 'meter / second')>, <Quantity(-9.8, 'meter / second ** 2')>)"
+      ]
+     },
+     "execution_count": 30,
+     "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": 31,
+   "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(-136.083704279796, 'meter')>,\n",
+       " <Quantity(-18.00000000087864, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 32,
+     "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(22.439794207078908)"
+      ]
+     },
+     "execution_count": 33,
+     "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": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(1.2732392917769175e-10, 'meter')>,\n",
+       " <Quantity(-18.00000000874157, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 35,
+     "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file chap09-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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QyYZdKaRllxmxOiEESICIbs7T1Y6ZEyKJVXuhXG/MWN+gYdfRy+w8com6+iYjVyiE+Wp3\nE6Ls7GyOHj1Kbm4uVVVVuLq64uvry4gRI2QtDtGpbrRCCfF1Zs/xbCqqm7v5pueUk1dczbj4QIJ9\nnY1cpRDm57YBsmfPHj744APOnj2LTqfD2dkZOzs7KioqqK2tRVEUoqOjeeyxxxg3blxX1CzM1I1W\nKIdO5XE+qxSA6rpGdhy6SP9Qd4YP9MPKUmXkKoUwH20GyJUrV/jDH/5AZmYmkyZNYsmSJQwYMABH\nR0f9PhUVFZw4cYIDBw7w4osvEh4ezttvv01gYGCXFC/Mj7WVinHxgYT4OfPtiVxq6ppPqJ+9WEp2\nYSUTE4Lx9XC4zbMIIe6GNtu5jxs3jt/+9rfMmjULKyurW+1ioK6ujo0bN7Ju3Tr27t171wu9E9LO\nvWeqrW9iX3Iumbnl+jFFUYiN9GRIPx9UKjnFJ8SduN3vzjaPQLZt24azc/vnlW1tbZk/fz7Tpk3r\nWKVC/Ex2NpbcNzSYtGxnDpy8Qn2jBp1OR3JqEZcLKpmYEIRHLztjlylEj9Xmn2g/Jzxu5uLi0uFi\nhPi5FEVBHezG3ElqArxuaoVyrZbPEtM4kSKtUIToLO26CquhoYFPP/2UkydPUllZ2Wq7oih8/PHH\nd704IdrL0d6aKaNCOZNZwnen82nSaNFqdXx/Jp9LeRVMSAjCxdHG2GUK0aO0a5L49ddfZ/ny5Vy8\neJHGxsZWPw0NDZ1dpxC3pSgK0eGezJ4YibebvX48v7SajbtTOSutUIS4q9p1BLJ7926efPJJHn/8\n8c6uR4g75upky4yxESSlFJJ0vhCtTkdjk5Z9yblk5VUwLj4QB7vbXxgihPhp7ToCURSFmJiYzq5F\niLvGwkIhoa8PD46LwO2mViiXCyrYsCuV9BxphSLEnWpXgEybNo0vvvgCrVZWiROmxcvNnlkTIomJ\n9NS3QqlraGLnkcvsPHJZWqEIcQfaNYX11FNPMW3aNO6991769euHnZ3hpZGKovDnP/+5UwoU4k5Z\nqiwYMdCfED8X9hzLprLmRiuUMvJLqhgXH0iQj7RCEeLnaleA/OUvfyErKwsnJyfOnz/favuNv+yE\n6M78PR2ZM0nNoR+ucOHSVQCqahvZfvAi/cM8GB7tK61QhPgZ2hUg27Zt43e/+x1LliyRsBAmzcZK\nxfjBQYT4ufDtiRxqr09hnc0sIbewkgkJQQaLWQkh2taucyAqlYrhw4dLeIgeI9TfhbmT1IT6t9z4\nWl5Vz+ZvMzhyNh+NRs73CXE77QqQyZMn88UXX3R2LUJ0KXtbK+4f1psJg4OwtmqeutLpdCRdKOSL\nvemUXqs1coVCdG/tmsJyd3dn69atTJw4kQEDBuDgYHiIrygKr7/+eqcUKERnUhSFqN5u+Hk6sjcp\nm9yiKgCKy2v5bE8aQ/r7EhPhqV9aVwjRol0B8vnnn+Pi4oJGo+GHH35otV2mtoSpc3awZsqoME6n\nl/DdmTw0Wh0arY7vTudxKa+C8YMDpRWKED/SrgDpbu3ZhegMiqIwMNKTQB8n9hzLpqisBoC8kio2\n7k5lZIw/fXq7yR9MQlzX5jmQnJycDj1hRx8nRHfh5mzLjHERJPT1weJ6WDQ2admblMM3h7P0i1gJ\nYe7aDJB58+bx17/+lfLy8rZ2MVBUVMTy5cuZN2/eXStOCGNRWSgk9PNhxrgIejm1TF1l5Te3Qrl5\nESshzFWbAbJlyxby8vIYOXIkCxcuZOvWrWRkZFBXVwdAVVUVGRkZbNq0icWLFzNu3DgKCgrkai3R\no3i72TN7gprocA/9WG19E//+/hJ7jl2mrkFaoQjz1eY5kF69evHXv/6V06dP89FHH7F06VI0Gk2r\n/WxsbBg1ahSffvop0dHRnVqsEMZgZWnBqNgAQvxcSDyeTVVt8xRWyuUycouqGD84iEBvp9s8ixA9\nz21PokdHR7Nq1SpqampISkoiJyeHqqoqXF1d8fPzIz4+Hltb29s9jRAmL9DbiTmT1Bw8eYXU7OZu\nvlW1jXx5IJPocA+GDfDDylLWYRfmo11XYQHY29szatSozqxFiG7P1tqSiUOCCfF3Yd+JXP0U1umM\nErILK5mYEGywmJUQPZn8uSREB4QH9OJX96oJ8W3p4lteWc/mvekcO1eARtZhF2ZAAkSIDrK3teKB\n4SGMiw/UT11pdTqOnS9g8950rlbUGblCITqXBIgQd0BRFPqGuDNnoho/D0f9eFFZDZ/tSeNUWrGs\nwy56LAkQIe4CF0cbpo4O455oP1TX+2Y1abQcPHWFLw9k6hexEqInkQAR4i6xsFCIU3sxa0IkHr1a\nVu3MLapiw65UUi5dlaMR0aO06yosnU7Hli1b2LdvHzU1Na3+J1AUhY8//rhTChTC1Li72DFzXATH\nzheSnFqETqejoVHDnuPZZOVdY3RcAPa2VsYuU4g71q4Aeeedd/jwww8JCAjAx8dHmskJcRsqlQXD\nBvgS4ufMnmPZlFfVA5B55Rp5JdWMiw8kxM/lNs8iRPfWrgDZunUrCxYs4MUXX7zjFywpKWHFihUc\nPnyYuro6Bg4cyIsvvkhkZCQAhw4dYsWKFWRlZREcHMxzzz3H6NGj9Y8vLS3l9ddf5/Dhw1hZWTF9\n+nSeeeYZLC3bfUuLEF3Gx92B2RMjOXw6n7OZJUBzK5SvD2fRp7cbI2P89YtZCWFq2nUOpKqqirFj\nx97xi2m1Wn7/+99z6dIl/v73v7Nx40YcHR2ZP38+ZWVlZGRksGjRIu677z62bt3K+PHjWbx4Menp\n6frneOKJJygpKeGTTz5h+fLlbNmyhdWrV99xbUJ0FitLFWPiApg8MhRHu5apqwuXrrJxdypXiquM\nWJ0QHdeuAImNjSU5OfmOXywlJYWTJ0/y5z//mejoaMLDw1mxYgU1NTXs37+fdevWERMTw6JFiwgL\nC+Ppp58mNjaWdevWAXDy5ElOnDjB8uXLiYqKYvTo0bzwwgusX7+ehga5ykV0b8E+zsyZqCYi0FU/\nVlHdwLb9mRw+lUeTrMMuTEy75n0WLlzIs88+S1NTE3FxcbfsfRUXF3fb5/H19eWDDz4gJCREP3bj\nfMq1a9dISkri/vvvN3jMkCFD+PrrrwFISkrC39+fwMBA/faEhASqq6u5cOECAwcObM/bEcJobG0s\nuXdoMCF+zuw/mUt9gwadTsfJtCIuF1QwISEIL1dphSJMQ7sC5MYaH2vWrAEMl7DV6XQoisKFCxdu\n+zyurq6MGTPGYGz9+vXU1dUxYsQI3nvvPby9vQ22e3l5UVBQAEBhYSFeXl6ttgPk5+dLgAiTERnk\nql+HPbugEoCrFXV8kZhOQj8f4tResg676PbaFSA3ppDutsTERN555x0WLFhAWFgYdXV1WFtbG+xj\nbW1NfX3zFSy1tbXY2BiuS21lZYWiKPp9hDAVjnZWTB4RyrmLpRw+lUejRotWp+PI2Xyy8q4xISEI\nVyfpdC26r3YFSEJCwl1/4S1btrB06VIeeOABnn/+eaB5bZHGRsPlQhsaGrCza74py9bWttW5jsbG\nRnQ6Hfb2ctgvTI+iKPQP8yDAy4k9x7MpKK0GoPBqDZt2p3FPtC8Dwjzk0nnRLbX7TvTMzEyefvpp\n7rnnHgYMGMCoUaNYsmQJGRkZP/tF165dy0svvcScOXN4++23sbBoLsPX15eioiKDfYuKivTTWj4+\nPhQXF7faDrSa+hLClPRysmH6mHCG9vfVT101abQcOHmF7QcvUiWtUEQ31K4ASU1NZebMmRw/fpzx\n48fz29/+lpEjR3LkyBFmzpxJampqu1/www8/5N133+XJJ59k6dKlBn9ZDRo0iOPHjxvsf/ToUeLj\n4/Xbc3JyyM/PN9ju4OBAVFRUu2sQojuysFCI7+PNzHGRuDu3TF3lFFayYVcqqZelFYroXto1hfWX\nv/yF0NBQ1q1bZzBVVFNTw/z583n33XdZu3btbZ8nJSWFlStXMmPGDGbNmmVwNOHg4MDDDz/MjBkz\nWLVqFb/4xS/46quvOHXqFK+99hrQfDlxTEwMzzzzDEuXLtXflLhgwYJW506EMFWernbMmhDJkXMF\n/HC9m299o4bdx7LJyqtgdFwAdjZy46wwvnYdgSQlJbFw4cJW5xns7e155JFHSEpKateLffPNN2g0\nGjZv3syIESMMfv75z3+iVqtZs2YNO3fuZOrUqezdu5f333+fsLAwoHm+eM2aNbi7u/PQQw/x8ssv\nM3PmTBYvXvwz37YQ3ZtKZcHwaD+mjQ7D2aHlj6OM3HI27ErlUn6FEasTolm7/oy5cRL7VhRFQaPR\ntOvFlixZwpIlS35ynzFjxrS61Pdmnp6e/O1vf2vX6wlh6vw8HZkzUc3h03mcu1gKQE1dI18duki/\nUHeGR/tJKxRhNO06AomJieHDDz9sdalsXV0dH330EbGxsZ1SnBACrK1UjB0UyH+MCDXo4nvuYikb\nd6eSVyKtUIRxtOsI5Nlnn+XBBx9k/PjxjBs3Dg8PD0pKSti7dy/V1dX861//6uw6hTB7vX2dmTtJ\nzb7kXDJzy4HmVihb92USG+nJkH4+qFSyxI/oOu0KkLCwMDZu3Mjf/vY3EhMTuXbtGs7OzgwePJjF\nixfrO+kKITqXnY0l9w0NJi3bmQMnr1Df2NwKJTm1iMsFlUxMCDJYzEqIztTuSznUajWrVq3qzFqE\nEO2gKArqYDf8PR1JTMohp7C5FUrptVo+S0xjSD8fYiOlFYrofG0GyI4dOxg5ciS9evVix44dt32i\nyZMn39XChBA/zdHeml+ODOVMZgnfnc6nSaNFq9Xx/Zl8LuU1N2Z0cbS5/RMJ0UFtBsjzzz/PZ599\nRq9evfStRtqiKIoEiBBGoCgK0eGeBHo7sedYNoVXawDIL61m4+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 0x276331a8198>"
+      ]
+     },
+     "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": 37,
+   "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": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x27633189f28>"
+      ]
+     },
+     "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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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y8sLixYsBNC24KJfL8eqrr+o0SCJ6eAKBAP37NpW1BvvdW9YqYlmL/jCt2nh9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\nTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x27632c1e470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "condition.set(v_init = -80 * m / s)\n",
+    "system = make_system(condition)\n",
+    "run_odeint(system, slope_func)\n",
+    "plot_position(system.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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jUlNTUVlZiaCgIKxZswZ9+vTRnZOcnIzt27fj5s2bCAgIwNq1a+Hl5dWq63IU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\nk/w/z6EMxJvxCDgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2763326c748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "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": 41,
+   "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": 42,
+   "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": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zdu3adcsXmjNnTp8WRkS3x1YmwZwpI3C2oBo/nimHWqOFWqPFwaxSFJY3In68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\nr2LpAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x2763336a080>"
+      ]
+     },
+     "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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(-11.034779626277231, 'meter')>,\n",
+       " <Quantity(-22.380158694375393, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 44,
+     "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": 45,
+   "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": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "-11.034779626277231 meter"
+      ],
+      "text/latex": [
+       "$-11.034779626277231 meter$"
+      ],
+      "text/plain": [
+       "<Quantity(-11.034779626277231, 'meter')>"
+      ]
+     },
+     "execution_count": 46,
+     "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": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.42587017])"
+      ]
+     },
+     "execution_count": 47,
+     "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": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(-4.725109192804666e-13, 'meter')>,\n",
+       " <Quantity(-21.689751051700213, 'meter / second')>)"
+      ]
+     },
+     "execution_count": 48,
+     "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
+}
diff --git a/code/rabbits-mine.ipynb b/code/rabbits-mine.ipynb
new file mode 100644
index 00000000..06352a3c
--- /dev/null
+++ b/code/rabbits-mine.ipynb
@@ -0,0 +1,774 @@
+{
+ "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": 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>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": 4,
+     "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": 5,
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eVLly4ceSF7kmkKFDh6o/BwQEPPUkMTExBReREEIUMz4+2m0gWdq3L/xY8kKn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\nyIIo+AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1b36338a860>"
+      ]
+     },
+     "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": 7,
+   "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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "404.95651696640027"
+      ]
+     },
+     "execution_count": 8,
+     "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": 9,
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "404.95651696640027"
+      ]
+     },
+     "execution_count": 10,
+     "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": 11,
+   "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": 12,
+   "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": 12,
+     "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 0x1b3639b5128>"
+      ]
+     },
+     "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": 14,
+   "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sweep_death_rate' 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-15-dc20c4537d0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdeath_rates\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msweep_death_rate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeath_rates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'sweep_death_rate' is not defined"
+     ]
+    }
+   ],
+   "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": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.4,  0.6,  0.8,  1. ])"
+      ]
+     },
+     "execution_count": 16,
+     "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": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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D1taWK4MQQoj6KNTRYejQofjmm28QGhqK8+fPc8u7du2K1atXY8yYMSoLaPXq1fjiiy8w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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1b363a17198>"
+      ]
+     },
+     "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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LqHPnzti7dy8qKipQW1uLzz//HE5OTujatStyc3OhUqkE9fv374/c3FwAQG5uLjw9PdG1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\noANqCmpQV1EHTaEGVaerUF9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JIYY+rGo0GsUPP/xwz/r+21DmOAUAa9asYcWK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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1b363badb00>"
+      ]
+     },
+     "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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ajTVabUecnR9Fqw1Ao5HlsIS4V2Ylo7lz59Z0HELUeVZWDri7P0fjxt2wsXGy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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1b363b92d68>"
+      ]
+     },
+     "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
+}
diff --git a/code/rabbits2mine.ipynb b/code/rabbits2mine.ipynb
new file mode 100644
index 00000000..de9fb4dc
--- /dev/null
+++ b/code/rabbits2mine.ipynb
@@ -0,0 +1,416 @@
+{
+ "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 Redux\n",
+    "\n",
+    "This notebook starts with a version of the rabbit population growth model and walks through some steps for extending it.\n",
+    "\n",
+    "In the original model, we treat all rabbits as adults; that is, we assume that a rabbit is able to breed in the season after it is born.  In this notebook, we extend the model to include both juvenile and adult rabbits.\n",
+    "\n",
+    "As an example, let's assume that rabbits take 3 seasons to mature.  We could model that process explicitly by counting the number of rabbits that are 1, 2, or 3 seasons old.  As an alternative, we can model just two stages, juvenile and adult.  In the simpler model, the maturation rate is 1/3 of the juveniles per season.\n",
+    "\n",
+    "To implement this model, make these changes in the System object:\n",
+    "\n",
+    "0.  Before you make any changes, run all cells and confirm your understand them.\n",
+    "\n",
+    "1.  Then, add a second initial populations: `juvenile_pop0`, with value `0`.\n",
+    "\n",
+    "2.  Add an additional variable, `mature_rate`, with the value `0.33`."
+   ]
+  },
+  {
+   "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>t0</th>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>10.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>adult_pop0</th>\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>0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>death_rate</th>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mature_rate</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "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",
+       "mature_rate       0.33\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "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",
+    "               mature_rate = 0.33)\n",
+    "\n",
+    "system"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now update `run_simulation` with the following changes:\n",
+    "\n",
+    "1.  Add a second TimeSeries, named `juveniles`, to keep track of the juvenile population, and initialize it with `juvenile_pop0`.\n",
+    "\n",
+    "2.  Inside the for loop, compute the number of juveniles that mature during each time step.\n",
+    "\n",
+    "3.  Also inside the for loop, add a line that stores the number of juveniles in the new `TimeSeries`.  For simplicity, let's assume that only adult rabbits die.\n",
+    "\n",
+    "4.  During each time step, subtract the number of maturations from the juvenile population and add it to the adult population.\n",
+    "\n",
+    "5.  After the for loop, store the `juveniles` `TimeSeries` as a variable in `System`."
+   ]
+  },
+  {
+   "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 with t0, t_end, p0,\n",
+    "            birth_rate and death_rate\n",
+    "    \"\"\"\n",
+    "    adults = TimeSeries()\n",
+    "    adults[system.t0] = system.adult_pop0\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",
+    "        mature = system.mature_rate * juveniles[t]\n",
+    "        juveniles[t+1] = juveniles[t] - mature + births\n",
+    "        adults[t+1] = adults[t] + mature - deaths\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",
+       "  </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",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "run_simulation(system)\n",
+    "system.adults"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, update `plot_results` to plot both the adult and juvenile `TimeSeries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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, 'rs-', label='juveniles')\n",
+    "    decorate(xlabel='Season', \n",
+    "             ylabel='Rabbit population',\n",
+    "             title=title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And test your updated version of `plot_results`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wfPnyMvsLCAjAmTNncPDgQZiYmGD69OmYPHnyB/lYGhsCriIPEUHIoW/fvjA1NcXevXsV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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1fd2b16c2e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_results(system, title='Proportional growth model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates the steps we recommend for starting your project:\n",
+    "\n",
+    "1.  Start with one of the examples from the book, either by copying a notebook or pasting code into a new notebook.  Get the code working before you make any changes.\n",
+    "\n",
+    "2.  Make one small change, and run the code again.\n",
+    "\n",
+    "3.  Repeat step 2 until you have a basic implementation of your model.\n",
+    "\n",
+    "If you start with working code that you understand and make small changes, you can avoid spending a lot of time debugging.\n",
+    "\n",
+    "One you have a basic model working, you can think about what metrics to measure, what parameters to sweep, and how to use the model to predict, explain, or design."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bonus question\n",
+    "\n",
+    "Suppose you only have room for 30 adult rabbits.  Whenever the adult population exceeds 30, you take any excess rabbits to market (as pets for kind children, of course).  Modify `run_simulation` to model this strategy.  What effect does it have on the behavior of the system?  You might have to run for more than 10 seasons to see what happens."
+   ]
+  },
+  {
+   "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/rabbits3mine.ipynb b/code/rabbits3mine.ipynb
new file mode 100644
index 00000000..3075456a
--- /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": 35,
+   "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": 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>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": 36,
+     "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": 42,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "init = State(juveniles=0, adults=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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": 43,
+     "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",
+    "\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": 44,
+   "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": 45,
+   "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.init.juveniles\n",
+    "    \n",
+    "    adults = TimeSeries()\n",
+    "    adults[system.t0] = system.init.adults\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": 46,
+   "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": 46,
+     "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": 47,
+   "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": 48,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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A2xFENLC1gX1bARmjCYLQJnk37V3yj+U7t1WsIiJQ8hy8wVuB0MBFDeybAuxu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\nFCBERCSTAoSIiGRSgBARkUz/D51CV+w5OlwRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c9f12cf5c0>"
+      ]
+     },
+     "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/spiderman.ipynb b/code/spiderman.ipynb
index 0c9352cf..a893963a 100644
--- a/code/spiderman.ipynb
+++ b/code/spiderman.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -93,8 +93,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "condition = Condition(height = 381 * m,\n",
@@ -122,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -167,9 +169,99 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "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     -70.71067811865477 meter\n",
+       "y     310.28932...</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>75 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>0.3402777777777778 dimensionless</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>area</th>\n",
+       "      <td>1 meter ** 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>length</th>\n",
+       "      <td>100 meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>[0 meter, 381 meter]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>k</th>\n",
+       "      <td>20.0 newton / meter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ts</th>\n",
+       "      <td>[0.0 second, 0.12 second, 0.24 second, 0.36 se...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "init      x     -70.71067811865477 meter\n",
+       "y     310.28932...\n",
+       "g                                   9.8 meter / second ** 2\n",
+       "mass                                            75 kilogram\n",
+       "rho                               1.2 kilogram / meter ** 3\n",
+       "C_d                        0.3402777777777778 dimensionless\n",
+       "area                                           1 meter ** 2\n",
+       "length                                            100 meter\n",
+       "A                                      [0 meter, 381 meter]\n",
+       "k                                       20.0 newton / meter\n",
+       "ts        [0.0 second, 0.12 second, 0.24 second, 0.36 se...\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "system = make_system(condition)\n",
     "system"
@@ -186,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
@@ -232,9 +324,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(<Quantity(0.0, 'meter / second')>,\n",
+       " <Quantity(0.0, 'meter / second')>,\n",
+       " <Quantity(2.6796244628203094e-15, 'meter / second ** 2')>,\n",
+       " <Quantity(-9.799999999999997, 'meter / second ** 2')>)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "slope_func(system.init, 0, system)"
    ]
@@ -250,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -270,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -289,9 +395,799 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "newfig()\n",
     "plot(xs, label='x')\n",
@@ -310,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
@@ -322,9 +1218,799 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "newfig()\n",
     "plot(vxs, label='vx')\n",
@@ -343,9 +2029,799 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "newfig()\n",
     "plot(xs, ys, label='trajectory')\n",
@@ -363,11 +2839,801 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "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",
@@ -390,7 +3656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {
     "collapsed": true
    },
@@ -425,11 +3691,801 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "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)"
    ]