diff --git a/notebooks/chap01.ipynb b/notebooks/chap01.ipynb
index 4d73a4a05..070379445 100644
--- a/notebooks/chap01.ipynb
+++ b/notebooks/chap01.ipynb
@@ -136,7 +136,15 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.10.3\r\n"
+     ]
+    }
+   ],
    "source": [
     "!python --version"
    ]
@@ -145,7 +153,15 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.4.12\r\n"
+     ]
+    }
+   ],
    "source": [
     "!jupyter-notebook --version"
    ]
@@ -195,7 +211,24 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "meter"
+      ],
+      "text/latex": [
+       "$\\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "<Unit('meter')>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "meter = UNITS.meter"
    ]
@@ -204,7 +237,24 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "second"
+      ],
+      "text/latex": [
+       "$\\mathrm{second}$"
+      ],
+      "text/plain": [
+       "<Unit('second')>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "second = UNITS.second"
    ]
@@ -218,10 +268,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "milliarcsecond"
+      ],
+      "text/latex": [
+       "$\\mathrm{milliarcsecond}$"
+      ],
+      "text/plain": [
+       "<Unit('milliarcsecond')>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "UNITS.milliarcsecond"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -232,9 +301,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "9.8 meter/second<sup>2</sup>"
+      ],
+      "text/latex": [
+       "$9.8\\ \\frac{\\mathrm{meter}}{\\mathrm{second}^{2}}$"
+      ],
+      "text/plain": [
+       "9.8 <Unit('meter / second ** 2')>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a = 9.8 * meter / second**2"
    ]
@@ -248,9 +334,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "4 second"
+      ],
+      "text/latex": [
+       "$4\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "4 <Unit('second')>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t = 4 * second"
    ]
@@ -264,9 +367,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "78.4 meter"
+      ],
+      "text/latex": [
+       "$78.4\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "78.4 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a * t**2 / 2"
    ]
@@ -280,11 +400,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "39.2 meter/second"
+      ],
+      "text/latex": [
+       "$39.2\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
+      ],
+      "text/plain": [
+       "39.2 <Unit('meter / second')>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v = a*t"
    ]
   },
   {
@@ -296,11 +433,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# how can you add secnods to meters per second squared? Units don't match.\n",
+    "\n",
+    "#a + t"
    ]
   },
   {
@@ -331,9 +470,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "381 meter"
+      ],
+      "text/latex": [
+       "$381\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "381 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "h = 381 * meter"
    ]
@@ -351,9 +507,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "8.817885349720552 second"
+      ],
+      "text/latex": [
+       "$8.817885349720552\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "8.817885349720552 <Unit('second')>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t = sqrt(2 * h / a)"
    ]
@@ -369,9 +542,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "86.41527642726142 meter/second"
+      ],
+      "text/latex": [
+       "$86.41527642726142\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
+      ],
+      "text/plain": [
+       "86.41527642726142 <Unit('meter / second')>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "v = a * t"
    ]
@@ -385,9 +575,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "hour"
+      ],
+      "text/latex": [
+       "$\\mathrm{hour}$"
+      ],
+      "text/plain": [
+       "<Unit('hour')>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mile = UNITS.mile\n",
     "hour = UNITS.hour"
@@ -395,9 +602,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "193.30546802805432 mile/hour"
+      ],
+      "text/latex": [
+       "$193.30546802805432\\ \\frac{\\mathrm{mile}}{\\mathrm{hour}}$"
+      ],
+      "text/plain": [
+       "193.30546802805432 <Unit('mile / hour')>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "v.to(mile/hour)"
    ]
@@ -415,20 +639,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "10 foot"
+      ],
+      "text/latex": [
+       "$10\\ \\mathrm{foot}$"
+      ],
+      "text/plain": [
+       "10 <Unit('foot')>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foot = UNITS.foot\n",
+    "pole_height = 10 * foot"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "384.048 meter"
+      ],
+      "text/latex": [
+       "$384.048\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "384.048 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_h = h + pole_height"
    ]
   },
   {
@@ -450,47 +709,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "18.0 meter/second"
+      ],
+      "text/latex": [
+       "$18.0\\ \\frac{\\mathrm{meter}}{\\mathrm{second}}$"
+      ],
+      "text/plain": [
+       "18.0 <Unit('meter / second')>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "term_v = 18 * meter / second"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "1.8367346938775508 second"
+      ],
+      "text/latex": [
+       "$1.8367346938775508\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "1.8367346938775508 <Unit('second')>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "term_t = term_v / a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "16.530612244897956 meter"
+      ],
+      "text/latex": [
+       "$16.530612244897956\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "16.530612244897956 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "term_h = (a * (term_t**2))/2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "364.46938775510205 meter"
+      ],
+      "text/latex": [
+       "$364.46938775510205\\ \\mathrm{meter}$"
+      ],
+      "text/plain": [
+       "364.46938775510205 <Unit('meter')>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "remaining_h = h - term_h"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "22.085034013605444 second"
+      ],
+      "text/latex": [
+       "$22.085034013605444\\ \\mathrm{second}$"
+      ],
+      "text/plain": [
+       "22.085034013605444 <Unit('second')>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_t = remaining_h / term_v + term_t"
    ]
   },
   {
@@ -516,7 +860,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -530,7 +874,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap02.ipynb b/notebooks/chap02.ipynb
index 192035b5f..4dc5dd501 100644
--- a/notebooks/chap02.ipynb
+++ b/notebooks/chap02.ipynb
@@ -52,11 +52,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "bikeshare = State(olin=10, wellesley=2)"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bikeshare = State(olin=10, wellesley=2, babson=0)"
    ]
   },
   {
@@ -70,18 +123,40 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare.olin"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare.wellesley"
    ]
@@ -113,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,9 +204,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>8</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         8\n",
+       "wellesley    2\n",
+       "babson       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare"
    ]
@@ -145,9 +273,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>8</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         8\n",
+       "wellesley    3\n",
+       "babson       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare.wellesley += 1\n",
     "bikeshare"
@@ -164,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,9 +363,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>babson</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         7\n",
+       "wellesley    4\n",
+       "babson       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bike_to_wellesley()\n",
     "bikeshare"
@@ -200,9 +434,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.bike_to_wellesley()>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bike_to_wellesley"
    ]
@@ -223,20 +468,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def bike_to_olin():\n",
+    "    bikeshare.wellesley -= 1\n",
+    "    bikeshare.olin += 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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>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         9\n",
+       "wellesley    2\n",
+       "babson       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "bike_to_olin()\n",
+    "bikeshare"
    ]
   },
   {
@@ -257,9 +560,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function flip in module modsim.modsim:\n",
+      "\n",
+      "flip(p=0.5)\n",
+      "    Flips a coin with the given probability.\n",
+      "    \n",
+      "    p: float 0-1\n",
+      "    \n",
+      "    returns: boolean (True or False)\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "help(flip)"
    ]
@@ -273,9 +592,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "flip(0.7)"
    ]
@@ -289,9 +619,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heads\n"
+     ]
+    }
+   ],
    "source": [
     "if flip(0.7):\n",
     "    print('heads')"
@@ -306,9 +644,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heads\n"
+     ]
+    }
+   ],
    "source": [
     "if flip(0.7):\n",
     "    print('heads')\n",
@@ -327,9 +673,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2)"
    ]
@@ -343,9 +737,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         8\n",
+       "wellesley    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "if flip(0.5):\n",
     "    bike_to_wellesley()\n",
@@ -363,9 +805,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Moving a bike to Olin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "if flip(0.4):\n",
     "    bike_to_olin()\n",
@@ -383,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,9 +903,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "step()\n",
     "bikeshare"
@@ -427,11 +972,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
     "def step(p1, p2):\n",
+    "    print(p1, p2)\n",
+    "    \n",
     "    if flip(p1):\n",
     "        bike_to_wellesley()\n",
     "        print('Moving a bike to Wellesley')\n",
@@ -450,11 +997,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "step(0.5, 0.4)\n",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.5 0.1\n",
+      "Moving a bike to Wellesley\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         7\n",
+       "wellesley    5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step(0.5, 0.1)\n",
     "bikeshare"
    ]
   },
@@ -490,7 +1093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -511,9 +1114,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2)"
    ]
@@ -527,9 +1178,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         6\n",
+       "wellesley    6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "for i in range(4):\n",
     "    bike_to_wellesley()\n",
@@ -546,9 +1245,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         5\n",
+       "wellesley    7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "for i in range(4):\n",
     "    step(0.3, 0.2)\n",
@@ -565,9 +1312,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>-5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin         -5\n",
+       "wellesley    17\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "for i in range(60):\n",
     "    step(0.3, 0.2)\n",
@@ -597,9 +1392,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "TimeSeries([], dtype: float64)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results = TimeSeries()"
    ]
@@ -613,9 +1446,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "0   -5\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results[0] = bikeshare.olin\n",
     "results"
@@ -632,9 +1508,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2)"
    ]
@@ -648,7 +1572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -666,9 +1590,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "0    11\n",
+       "1    12\n",
+       "2    12\n",
+       "3    11\n",
+       "4    11\n",
+       "5    12\n",
+       "6    11\n",
+       "7    11\n",
+       "8    11\n",
+       "9    10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results"
    ]
@@ -682,18 +1694,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11.2"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results.mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    10.000000\n",
+       "mean     11.200000\n",
+       "std       0.632456\n",
+       "min      10.000000\n",
+       "25%      11.000000\n",
+       "50%      11.000000\n",
+       "75%      11.750000\n",
+       "max      12.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results.describe()"
    ]
@@ -716,9 +1758,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap02-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot(results, label='Olin')\n",
     "\n",
@@ -738,9 +1798,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function decorate in module modsim.modsim:\n",
+      "\n",
+      "decorate(**options)\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",
+      "    \n",
+      "    https://matplotlib.org/api/axes_api.html\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)"
    ]
@@ -754,9 +1841,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function savefig in module modsim.modsim:\n",
+      "\n",
+      "savefig(filename, **options)\n",
+      "    Save the current figure.\n",
+      "    \n",
+      "    Keyword arguments are passed along to plt.savefig\n",
+      "    \n",
+      "    https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html\n",
+      "    \n",
+      "    filename: string\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "help(savefig)"
    ]
@@ -795,33 +1900,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def run_simulation(p1, p2, num_steps):\n",
+    "    results_olin = TimeSeries()\n",
+    "    results_wellesley = TimeSeries()\n",
+    "    \n",
+    "    for i in range(num_steps):\n",
+    "        step(p1, p2)\n",
+    "        results_olin[i] = bikeshare.olin\n",
+    "        results_wellesley[i] = bikeshare.wellesley\n",
+    "    \n",
+    "    plot(results_olin, label='Olin')\n",
+    "    plot(results_wellesley, label='Wellesley')\n",
+    "    decorate(title='Olin-Wellesley Bikeshare',\n",
+    "             xlabel='Time step (min)', \n",
+    "             ylabel='Number of bikes')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Opening the hood\n",
-    "\n",
-    "The functions in `modsim.py` are built on top of several widely-used Python libraries, especially NumPy, SciPy, and Pandas.  These libraries are powerful but can be hard to use.  The intent of `modsim.py` is to give you the power of these libraries while making it easy to get started.\n",
-    "\n",
-    "In the future, you might want to use these libraries directly, rather than using `modsim.py`.  So we will pause occasionally to open the hood and let you see how `modsim.py` works.\n",
-    "\n",
-    "You don't need to know anything in these sections, so if you are already feeling overwhelmed, you might want to skip them.  But if you are curious, read on."
+    "# Solution goes here\n",
+    "bikeshare = State(olin=10, wellesley=2)\n",
+    "run_simulation(0.5, 0.5, 10)"
    ]
   },
   {
@@ -878,9 +1997,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "def flip(p=0.5):\n",
+      "    \"\"\"Flips a coin with the given probability.\n",
+      "\n",
+      "    p: float 0-1\n",
+      "\n",
+      "    returns: boolean (True or False)\n",
+      "    \"\"\"\n",
+      "    return np.random.random() < p\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "source_code(flip)"
    ]
@@ -895,7 +2030,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -909,7 +2044,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap03.ipynb b/notebooks/chap03.ipynb
index 82a9d53a8..550de8779 100644
--- a/notebooks/chap03.ipynb
+++ b/notebooks/chap03.ipynb
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,55 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": [
     "bikeshare1 = State(olin=10, wellesley=2)"
    ]
@@ -136,7 +184,55 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin          2\n",
+       "wellesley    10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare2 = State(olin=2, wellesley=10)"
    ]
@@ -177,7 +273,55 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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"
    ]
@@ -186,7 +330,55 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin          1\n",
+       "wellesley    11\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare2"
    ]
@@ -211,7 +403,18 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2)\n",
     "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
@@ -263,7 +466,18 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2)\n",
     "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
@@ -382,9 +596,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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>olin_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin               10\n",
+       "wellesley           2\n",
+       "olin_empty          0\n",
+       "wellesley_empty     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2, \n",
     "                  olin_empty=0, wellesley_empty=0)"
@@ -399,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -435,9 +707,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
     "decorate_bikeshare()"
@@ -485,9 +768,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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>olin_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>clock</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin               10\n",
+       "wellesley           2\n",
+       "olin_empty          0\n",
+       "wellesley_empty     0\n",
+       "clock               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bikeshare = State(olin=10, wellesley=2, \n",
     "                  olin_empty=0, wellesley_empty=0,\n",
@@ -496,20 +842,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one minute of time.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "    state.clock += 1\n",
+    "    \n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "bikeshare = State(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0,\n",
+    "                  clock=0)\n",
+    "run_simulation(bikeshare, 0.4, 0.2, 60)\n",
+    "bikeshare.clock"
    ]
   },
   {
@@ -540,40 +926,97 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "bikeshare = State(olin=10, wellesley=2, \n",
+    "                  olin_empty=0, wellesley_empty=0,\n",
+    "                  clock=0, t_first_tmepty=-1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def step(state, p1, p2):\n",
+    "    \"\"\"Simulate one minute of time.\n",
+    "    \n",
+    "    state: bikeshare State object\n",
+    "    p1: probability of an Olin->Wellesley customer arrival\n",
+    "    p2: probability of a Wellesley->Olin customer arrival\n",
+    "    \"\"\"\n",
+    "    state.clock += 1\n",
+    "    \n",
+    "    empty = state.olin_empty or state.wellesley_empty\n",
+    "    if empty and state.t_first_empty == -1:\n",
+    "        state.t_first_empty = state.clock\n",
+    "    \n",
+    "    if flip(p1):\n",
+    "        bike_to_wellesley(state)\n",
+    "    \n",
+    "    if flip(p2):\n",
+    "        bike_to_olin(state)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "olin               12\n",
+      "wellesley           8\n",
+      "olin_empty          0\n",
+      "wellesley_empty     0\n",
+      "clock              60\n",
+      "t_first_empty      -1\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "bikeshare = State(olin=10, wellesley=10, \n",
+    "                  olin_empty=0, wellesley_empty=0,\n",
+    "                  clock=0, t_first_empty=-1)\n",
+    "run_simulation(bikeshare, 0.4, 0.4, 60)\n",
+    "print(bikeshare)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Solution goes here"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -587,7 +1030,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap04.ipynb b/notebooks/chap04.ipynb
index 1945f357b..6e763b7bf 100644
--- a/notebooks/chap04.ipynb
+++ b/notebooks/chap04.ipynb
@@ -64,7 +64,18 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y = add_five(3)"
    ]
@@ -80,7 +91,18 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "add_five(5)"
    ]
@@ -98,7 +120,18 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "add_five(3)\n",
     "add_five(5)"
@@ -115,7 +148,15 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 10\n"
+     ]
+    }
+   ],
    "source": [
     "y1 = add_five(3)\n",
     "y2 = add_five(5)\n",
@@ -138,16 +179,67 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def make_state():\n",
+    "    return State(olin=10, wellesley=2)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "init = make_state()"
    ]
   },
   {
@@ -252,7 +344,65 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>olin</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley</th>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>olin_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wellesley_empty</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "olin                1\n",
+       "wellesley          11\n",
+       "olin_empty          0\n",
+       "wellesley_empty     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "state = run_simulation(0.4, 0.2, 60)"
    ]
@@ -268,7 +418,18 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "state.olin_empty"
    ]
@@ -284,7 +445,18 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "state = run_simulation(0.2, 0.2, 60)\n",
     "state.olin_empty"
@@ -301,7 +473,18 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "state = run_simulation(0.6, 0.2, 60)\n",
     "state.olin_empty"
@@ -325,7 +508,18 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "p1_array = linspace(0, 1, 5)"
    ]
@@ -341,7 +535,19 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "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)"
@@ -360,7 +566,28 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function linspace in module modsim.modsim:\n",
+      "\n",
+      "linspace(start, stop, num=50, **options)\n",
+      "    Returns an array of evenly-spaced values in the interval [start, stop].\n",
+      "    \n",
+      "    start: first value\n",
+      "    stop: last value\n",
+      "    num: number of 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(linspace)"
    ]
@@ -384,9 +611,21 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "linspace(1, 10, num=10)"
    ]
   },
   {
@@ -400,7 +639,33 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function linrange in module modsim.modsim:\n",
+      "\n",
+      "linrange(start=0, stop=None, step=1, endpoint=False, **options)\n",
+      "    Returns an array of evenly-spaced values in an interval.\n",
+      "    \n",
+      "    By default, the last value in the array is `stop-step`\n",
+      "    (at least approximately).\n",
+      "    If you provide the keyword argument `endpoint=True`,\n",
+      "    the last value in the array is `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",
+      "    start: first value\n",
+      "    stop: last value\n",
+      "    step: space between values\n",
+      "    \n",
+      "    returns: NumPy array\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "help(linrange)"
    ]
@@ -414,11 +679,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  3,  5,  7,  9, 11])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "linrange(1, 11, step=2, endpoint=True)"
    ]
   },
   {
@@ -437,9 +714,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "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": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "p2 = 0.2\n",
     "num_steps = 60\n",
@@ -455,9 +743,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 0\n",
+      "0.1 0\n",
+      "0.2 0\n",
+      "0.30000000000000004 0\n",
+      "0.4 0\n",
+      "0.5 1\n",
+      "0.6000000000000001 13\n",
+      "0.7000000000000001 25\n",
+      "0.8 26\n",
+      "0.9 37\n",
+      "1.0 35\n"
+     ]
+    }
+   ],
    "source": [
     "for p1 in p1_array:\n",
     "    state = run_simulation(p1, p2, num_steps)\n",
@@ -474,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,9 +800,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot(sweep, label='Olin')\n",
     "\n",
@@ -518,20 +835,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def sweep_p1(p1_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    for p1 in p1_array:\n",
+    "        r = run_simulation(p1, p2, num_steps)\n",
+    "        sweep[p1] = r.olin_empty\n",
+    "    return sweep"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "plot(sweep_p1(p1_array), label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
    ]
   },
   {
@@ -543,20 +882,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def sweep_p2(p2_array):\n",
+    "    sweep = SweepSeries()\n",
+    "    for p2 in p2_array:\n",
+    "        r = run_simulation(0.5, p2, num_steps)\n",
+    "        sweep[p2] = r.wellesley_empty\n",
+    "    return sweep"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "p2_array = linspace(0, 1, 11)\n",
+    "plot(sweep_p2(p2_array), label='Wellesley')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Wellesley (p2 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
    ]
   },
   {
@@ -589,20 +951,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def run_multiple_simulations(p1, p2, num_steps, num_runs):\n",
+    "    ts = TimeSeries()\n",
+    "    for n in range(num_runs):\n",
+    "        r = run_simulation(p1, p2, num_steps)\n",
+    "        total_unhappy = r.olin_empty + r.wellesley_empty\n",
+    "        ts[n] = total_unhappy\n",
+    "    return ts"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    3\n",
+      "1    2\n",
+      "2    0\n",
+      "3    6\n",
+      "4    2\n",
+      "5    7\n",
+      "6    2\n",
+      "7    0\n",
+      "8    5\n",
+      "9    3\n",
+      "dtype: int64\n",
+      "3.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "ts = run_multiple_simulations(0.3, 0.3, 60, 10)\n",
+    "print(ts)\n",
+    "print(ts.mean())"
    ]
   },
   {
@@ -624,28 +1015,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 61,
    "metadata": {
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "p1_array = linspace(0, 1, 25)\n",
+    "p2 = 0.3\n",
+    "num_steps = 60\n",
+    "num_runs = 20\n",
+    "sweep = SweepSeries()\n",
+    "\n",
+    "for p1 in p1_array:\n",
+    "    r = run_multiple_simulations(p1, p2, num_steps, num_runs)\n",
+    "    sweep[p1] = r.mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 62,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Solution goes here"
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Solution goes here\n",
+    "plot(sweep, label='Olin')\n",
+    "\n",
+    "decorate(title='Olin-Wellesley Bikeshare',\n",
+    "         xlabel='Arrival rate at Olin (p1 in customers/min)', \n",
+    "         ylabel='Number of unhappy customers')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0</th>\n",
+       "      <td>15.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.1</th>\n",
+       "      <td>9.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.2</th>\n",
+       "      <td>4.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.3</th>\n",
+       "      <td>2.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.4</th>\n",
+       "      <td>1.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>3.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.6</th>\n",
+       "      <td>8.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.7</th>\n",
+       "      <td>13.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.8</th>\n",
+       "      <td>20.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.9</th>\n",
+       "      <td>26.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>33.60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "0.0    15.60\n",
+       "0.1     9.90\n",
+       "0.2     4.85\n",
+       "0.3     2.00\n",
+       "0.4     1.80\n",
+       "0.5     3.45\n",
+       "0.6     8.85\n",
+       "0.7    13.25\n",
+       "0.8    20.45\n",
+       "0.9    26.95\n",
+       "1.0    33.60\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sweep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -659,7 +1191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap05.ipynb b/notebooks/chap05.ipynb
index d624e63eb..7d4d5d492 100644
--- a/notebooks/chap05.ipynb
+++ b/notebooks/chap05.ipynb
@@ -60,7 +60,18 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "filename = 'data/World_population_estimates.html'\n",
     "tables = read_html(filename, header=0, index_col=0, decimal='M')\n",
@@ -82,7 +93,185 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\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 (2017)[28]</th>\n",
+       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
+       "      <th>Maddison (2008)[17]</th>\n",
+       "      <th>HYDE (2007)[24]</th>\n",
+       "      <th>Tanton (1994)[18]</th>\n",
+       "      <th>Biraben (1980)[19]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
+       "      <th>Thomlinson (1975)[21]</th>\n",
+       "      <th>Durand (1974)[22]</th>\n",
+       "      <th>Clark (1967)[23]</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>2.525149e+09</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>2.572851e+09</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>2.619292e+09</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>2.665865e+09</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>2.713172e+09</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 (2017)[28]  \\\n",
+       "Year                                           \n",
+       "1950                              2557628654   \n",
+       "1951                              2594939877   \n",
+       "1952                              2636772306   \n",
+       "1953                              2682053389   \n",
+       "1954                              2730228104   \n",
+       "\n",
+       "      Population Reference Bureau (1973–2016)[15]  \\\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)[16]  \\\n",
+       "Year                                                                        \n",
+       "1950                                       2.525149e+09                     \n",
+       "1951                                       2.572851e+09                     \n",
+       "1952                                       2.619292e+09                     \n",
+       "1953                                       2.665865e+09                     \n",
+       "1954                                       2.713172e+09                     \n",
+       "\n",
+       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\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)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\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)[22]  Clark (1967)[23]  \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()"
@@ -99,7 +288,185 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\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 (2017)[28]</th>\n",
+       "      <th>Population Reference Bureau (1973–2016)[15]</th>\n",
+       "      <th>United Nations Department of Economic and Social Affairs (2015)[16]</th>\n",
+       "      <th>Maddison (2008)[17]</th>\n",
+       "      <th>HYDE (2007)[24]</th>\n",
+       "      <th>Tanton (1994)[18]</th>\n",
+       "      <th>Biraben (1980)[19]</th>\n",
+       "      <th>McEvedy &amp; Jones (1978)[20]</th>\n",
+       "      <th>Thomlinson (1975)[21]</th>\n",
+       "      <th>Durand (1974)[22]</th>\n",
+       "      <th>Clark (1967)[23]</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>2012</th>\n",
+       "      <td>7013871313</td>\n",
+       "      <td>7.057075e+09</td>\n",
+       "      <td>7.080072e+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",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013</th>\n",
+       "      <td>7092128094</td>\n",
+       "      <td>7.136796e+09</td>\n",
+       "      <td>7.162119e+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",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014</th>\n",
+       "      <td>7169968185</td>\n",
+       "      <td>7.238184e+09</td>\n",
+       "      <td>7.243784e+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",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015</th>\n",
+       "      <td>7247892788</td>\n",
+       "      <td>7.336435e+09</td>\n",
+       "      <td>7.349472e+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",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016</th>\n",
+       "      <td>7325996709</td>\n",
+       "      <td>7.418152e+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",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      United States Census Bureau (2017)[28]  \\\n",
+       "Year                                           \n",
+       "2012                              7013871313   \n",
+       "2013                              7092128094   \n",
+       "2014                              7169968185   \n",
+       "2015                              7247892788   \n",
+       "2016                              7325996709   \n",
+       "\n",
+       "      Population Reference Bureau (1973–2016)[15]  \\\n",
+       "Year                                                \n",
+       "2012                                 7.057075e+09   \n",
+       "2013                                 7.136796e+09   \n",
+       "2014                                 7.238184e+09   \n",
+       "2015                                 7.336435e+09   \n",
+       "2016                                 7.418152e+09   \n",
+       "\n",
+       "      United Nations Department of Economic and Social Affairs (2015)[16]  \\\n",
+       "Year                                                                        \n",
+       "2012                                       7.080072e+09                     \n",
+       "2013                                       7.162119e+09                     \n",
+       "2014                                       7.243784e+09                     \n",
+       "2015                                       7.349472e+09                     \n",
+       "2016                                                NaN                     \n",
+       "\n",
+       "      Maddison (2008)[17]  HYDE (2007)[24]  Tanton (1994)[18]  \\\n",
+       "Year                                                            \n",
+       "2012                  NaN              NaN                NaN   \n",
+       "2013                  NaN              NaN                NaN   \n",
+       "2014                  NaN              NaN                NaN   \n",
+       "2015                  NaN              NaN                NaN   \n",
+       "2016                  NaN              NaN                NaN   \n",
+       "\n",
+       "      Biraben (1980)[19]  McEvedy & Jones (1978)[20]  Thomlinson (1975)[21]  \\\n",
+       "Year                                                                          \n",
+       "2012                 NaN                         NaN                    NaN   \n",
+       "2013                 NaN                         NaN                    NaN   \n",
+       "2014                 NaN                         NaN                    NaN   \n",
+       "2015                 NaN                         NaN                    NaN   \n",
+       "2016                 NaN                         NaN                    NaN   \n",
+       "\n",
+       "     Durand (1974)[22]  Clark (1967)[23]  \n",
+       "Year                                      \n",
+       "2012               NaN               NaN  \n",
+       "2013               NaN               NaN  \n",
+       "2014               NaN               NaN  \n",
+       "2015               NaN               NaN  \n",
+       "2016               NaN               NaN  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "table2.tail()"
    ]
@@ -133,7 +500,161 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\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>2.525149e+09</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>2.572851e+09</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>2.619292e+09</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>2.665865e+09</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>2.713172e+09</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": [
+       "          census           prb            un      maddison          hyde  \\\n",
+       "Year                                                                       \n",
+       "1950  2557628654  2.516000e+09  2.525149e+09  2.544000e+09  2.527960e+09   \n",
+       "1951  2594939877           NaN  2.572851e+09  2.571663e+09           NaN   \n",
+       "1952  2636772306           NaN  2.619292e+09  2.617949e+09           NaN   \n",
+       "1953  2682053389           NaN  2.665865e+09  2.665959e+09           NaN   \n",
+       "1954  2730228104           NaN  2.713172e+09  2.716927e+09           NaN   \n",
+       "\n",
+       "            tanton       biraben            mj    thomlinson durand  \\\n",
+       "Year                                                                  \n",
+       "1950  2.400000e+09  2.527000e+09  2.500000e+09  2.400000e+09    NaN   \n",
+       "1951           NaN           NaN           NaN           NaN    NaN   \n",
+       "1952           NaN           NaN           NaN           NaN    NaN   \n",
+       "1953           NaN           NaN           NaN           NaN    NaN   \n",
+       "1954           NaN           NaN           NaN           NaN    NaN   \n",
+       "\n",
+       "             clark  \n",
+       "Year                \n",
+       "1950  2.486000e+09  \n",
+       "1951           NaN  \n",
+       "1952           NaN  \n",
+       "1953           NaN  \n",
+       "1954           NaN  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "table2.head()"
    ]
@@ -162,7 +683,24 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2557628654\n",
+       "1951    2594939877\n",
+       "1952    2636772306\n",
+       "1953    2682053389\n",
+       "1954    2730228104\n",
+       "Name: census, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "census = table2.census\n",
     "census.head()"
@@ -195,9 +733,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.525149\n",
+       "1951    2.572851\n",
+       "1952    2.619292\n",
+       "1953    2.665865\n",
+       "1954    2.713172\n",
+       "Name: un, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "un = table2.un / 1e9\n",
     "un.head()"
@@ -207,7 +762,24 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.557629\n",
+       "1951    2.594940\n",
+       "1952    2.636772\n",
+       "1953    2.682053\n",
+       "1954    2.730228\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "census = table2.census / 1e9\n",
     "census.head()"
@@ -226,7 +798,25 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap05-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot(census, ':', label='US Census')\n",
     "plot(un, '--', label='UN DESA')\n",
@@ -250,7 +840,18 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.3821293828998855"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "max(abs(census - un) / un) * 100"
    ]
@@ -273,9 +874,33 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "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",
+       "          ...   \n",
+       "2012   -0.066201\n",
+       "2013   -0.069991\n",
+       "2014   -0.073816\n",
+       "2015   -0.101579\n",
+       "2016         NaN\n",
+       "Length: 67, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "census - un"
    ]
   },
   {
@@ -284,9 +909,33 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "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",
+       "          ...   \n",
+       "2012    0.066201\n",
+       "2013    0.069991\n",
+       "2014    0.073816\n",
+       "2015    0.101579\n",
+       "2016         NaN\n",
+       "Length: 67, dtype: float64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "abs(census - un)"
    ]
   },
   {
@@ -295,18 +944,66 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "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",
+       "          ...   \n",
+       "2012    0.009350\n",
+       "2013    0.009772\n",
+       "2014    0.010190\n",
+       "2015    0.013821\n",
+       "2016         NaN\n",
+       "Length: 67, dtype: float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "abs(census - un) / un"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    1.286247\n",
+       "1951    0.858540\n",
+       "1952    0.667365\n",
+       "1953    0.607232\n",
+       "1954    0.628640\n",
+       "          ...   \n",
+       "2012    0.935034\n",
+       "2013    0.977243\n",
+       "2014    1.019023\n",
+       "2015    1.382129\n",
+       "2016         NaN\n",
+       "Length: 67, dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "abs(census - un) / un *100"
    ]
   },
   {
@@ -334,7 +1031,18 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.557628654"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "census[1950]"
    ]
@@ -350,7 +1058,18 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.325996709"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "census[2016]"
    ]
@@ -366,7 +1085,18 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1950"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t_0 = get_first_label(census)"
    ]
@@ -375,7 +1105,18 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t_end = get_last_label(census)"
    ]
@@ -384,7 +1125,18 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "elapsed_time = t_end - t_0"
    ]
@@ -400,7 +1152,18 @@
    "cell_type": "code",
    "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.557628654"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "p_0 = get_first_value(census)"
    ]
@@ -409,7 +1172,18 @@
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.325996709"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "p_end = get_last_value(census)"
    ]
@@ -425,7 +1199,18 @@
    "cell_type": "code",
    "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.768368055"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "total_growth = p_end - p_0"
    ]
@@ -434,7 +1219,18 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07224800083333333"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "annual_growth = total_growth / elapsed_time"
    ]
@@ -457,7 +1253,45 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "TimeSeries([], dtype: float64)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results = TimeSeries()"
    ]
@@ -473,7 +1307,50 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</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": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results[t_0] = census[t_0]\n",
     "results"
@@ -507,7 +1384,25 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap05-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot(census, ':', label='US Census')\n",
     "plot(un, '--', label='UN DESA')\n",
@@ -555,11 +1450,158 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1970    3.712698\n",
+       "1971    3.790327\n",
+       "1972    3.866569\n",
+       "1973    3.942096\n",
+       "1974    4.016609\n",
+       "1975    4.089083\n",
+       "1976    4.160185\n",
+       "1977    4.232085\n",
+       "1978    4.304106\n",
+       "1979    4.379014\n",
+       "1980    4.451363\n",
+       "1981    4.534410\n",
+       "1982    4.614567\n",
+       "1983    4.695737\n",
+       "1984    4.774569\n",
+       "1985    4.856463\n",
+       "1986    4.940571\n",
+       "1987    5.027200\n",
+       "1988    5.114557\n",
+       "1989    5.201440\n",
+       "1990    5.288956\n",
+       "1991    5.371586\n",
+       "1992    5.456136\n",
+       "1993    5.538268\n",
+       "1994    5.618682\n",
+       "1995    5.699203\n",
+       "1996    5.779441\n",
+       "1997    5.857973\n",
+       "1998    5.935213\n",
+       "1999    6.012075\n",
+       "2000    6.088571\n",
+       "2001    6.165219\n",
+       "2002    6.242016\n",
+       "2003    6.318591\n",
+       "2004    6.395700\n",
+       "2005    6.473045\n",
+       "2006    6.551264\n",
+       "2007    6.629914\n",
+       "2008    6.709050\n",
+       "2009    6.788214\n",
+       "2010    6.858585\n",
+       "2011    6.935999\n",
+       "2012    7.013871\n",
+       "2013    7.092128\n",
+       "2014    7.169968\n",
+       "2015    7.247893\n",
+       "2016    7.325997\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subset = census.loc[1970:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1970 2016 3.712697742 7.325996709 46\n",
+      "3.613298967 0.07854997754347826\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_0 = get_first_label(subset)\n",
+    "t_end = get_last_label(subset)\n",
+    "p_0 = get_first_value(subset)\n",
+    "p_end = get_last_value(subset)\n",
+    "elapsed_time = t_end - t_0\n",
+    "print(t_0, t_end, p_0, p_end, elapsed_time)\n",
+    "\n",
+    "total_growth = p_end - p_0\n",
+    "annual_growth = total_growth / elapsed_time\n",
+    "print(total_growth, annual_growth)\n",
+    "\n",
+    "results = TimeSeries()\n",
+    "results[1950] = subset[t_0] - 1.4\n",
+    "\n",
+    "for t in linrange(1950, t_end):\n",
+    "    results[t+1] = results[t] + annual_growth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap05-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(subset, ':', label='US Census')\n",
+    "plot(un, '--', label='UN DESA')\n",
+    "plot(results, color='gray', label='model')\n",
+    "\n",
+    "decorate(xlabel='Year', \n",
+    "         ylabel='World population (billion)',\n",
+    "         title='Constant growth')\n",
+    "\n",
+    "savefig('figs/chap05-fig02.pdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1635403720389917"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "census.loc[1960:1970]"
+    "rms = (((results - census) ** 2).mean() ** 0.5)"
    ]
   },
   {
@@ -572,7 +1614,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -586,7 +1628,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap06.ipynb b/notebooks/chap06.ipynb
index aee7cc16b..d8b0887e3 100644
--- a/notebooks/chap06.ipynb
+++ b/notebooks/chap06.ipynb
@@ -57,7 +57,40 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.525149\n",
+       "1951    2.572851\n",
+       "1952    2.619292\n",
+       "1953    2.665865\n",
+       "1954    2.713172\n",
+       "Name: un, dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.525149\n",
+       "1951    2.572851\n",
+       "1952    2.619292\n",
+       "1953    2.665865\n",
+       "1954    2.713172\n",
+       "Name: un, dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "un = table2.un / 1e9\n",
     "un.head()"
@@ -67,7 +100,40 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.557629\n",
+       "1951    2.594940\n",
+       "1952    2.636772\n",
+       "1953    2.682053\n",
+       "1954    2.730228\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.557629\n",
+       "1951    2.594940\n",
+       "1952    2.636772\n",
+       "1953    2.682053\n",
+       "1954    2.730228\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "census = table2.census / 1e9\n",
     "census.head()"
@@ -77,7 +143,28 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.07224800083333333"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.07224800083333333"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t_0 = get_first_label(census)\n",
     "t_end = get_last_label(census)\n",
@@ -108,7 +195,122 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>annual_growth</th>\n",
+       "      <td>0.072248</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0              1950.000000\n",
+       "t_end            2016.000000\n",
+       "p_0                 2.557629\n",
+       "annual_growth       0.072248\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>annual_growth</th>\n",
+       "      <td>0.072248</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0              1950.000000\n",
+       "t_end            2016.000000\n",
+       "p_0                 2.557629\n",
+       "annual_growth       0.072248\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "system = System(t_0=t_0, \n",
     "                t_end=t_end,\n",
@@ -186,7 +388,28 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEPCAYAAACdhMnXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABc6UlEQVR4nO3dd1gUV/vw8e8usHQQsTcsCAiCoFKsVKOJ3cQUS0xijMaS3nwSkxhjYoxPTIxGjZqiqSZRY4pGKWLHhqhYAcWOKIjSYfe8f/C6v/AouihV7891ccnOzpy5j8vuvTNz5j4apZRCCCGEqELa6g5ACCHEvUeSjxBCiConyUcIIUSVk+QjhBCiyknyEUIIUeUk+QghhKhyknxEpcvOzmb27Nn07t0bHx8fwsLC+Oijj7h69WqV7L+oqIjvv/++xrZXkZRSLF++nIKCAgBWrFhBYGBgNUdV2kcffcTIkSNNWrcmxi8qhiQfUamuXr3KI488wvbt23nrrbf466+/ePfdd9m0aROjR482fkhWpj///JM5c+bU2PYq0s6dO5kyZQrFxcXVHYoQN2Ve3QGIu9usWbNQSvHNN99gbW0NQPPmzXF1deW+++5j5cqVPProo5UaQ0XfR12T78uuybEJ8W9y5CMqTWFhIX/88QcjRowwJp5rmjRpwrfffkufPn2Akg/N7777jt69e+Pt7c3AgQOJjY01rv/GG2/w7rvv8sYbb+Dn50eXLl2YN2+e8fmkpCRGjhyJn58fQUFBvPnmm+Tm5hIXF8fkyZO5fPky7u7uxMXFUVxczOzZswkLC8PLy4uuXbsyffp09Ho9AJ9//jkTJ05kxowZBAQE0LlzZ6ZNm4Zer79hezfy3XffERISgq+vL6+++iovv/wyn3/+ubEvr7zyCkOHDiUgIICtW7dSWFjI3LlzCQsLw9vbm8cee4yEhAQAPvjgA0aPHm1se+3atbi7u7Nv3z7jsvvvv5+5c+fy+OOPA9CxY0dWrFhhfH7JkiV0794dX19fnn/++TJPecbFxdGtWzf++usvevbsiZ+fH++99x6pqamMGDECHx8fHnroIVJSUozbJCYmMmrUKDp27Ej37t35+OOPKSoqMj6/ceNG+vfvj4+PD+PGjbtu3wkJCTz66KN4e3tz3333sWjRIgwGww3jE3cRJUQlSUpKUm5ubmr//v23XHfBggWqc+fO6s8//1QpKSlqzpw5ql27durQoUNKKaVef/115eXlpT777DOVmpqqFi5cqNzc3FRiYqJSSqkBAwao1157TaWmpqq9e/eq0NBQNXv2bFVQUKC++eYbFRAQoC5cuKAKCgrU/PnzVXBwsIqLi1OnTp1SK1euVO3atVPr1q1TSik1Z84c5eXlpd566y2VkpKiVq5cqTw8PNS6detu2N7/+vPPP5WPj49auXKlSkpKUm+88YZyd3dXc+bMMfbF3d1drVy5Uh08eFDl5uaqKVOmqJ49e6oNGzaopKQkNWXKFOXn56fS0tLUpk2blK+vryosLFRKKfXuu+8qd3d3tXjxYqWUUmfOnFEeHh4qPT1d/fPPP8rNzU2dPHlS5eXlqd9++025ubmpsWPHqqNHj6q4uDgVEBCgPvnkkxu+Dtu3b1eenp5q1KhR6ujRo2r16tXKzc1NhYaGqvXr16tDhw6p/v37qwkTJiillDp+/Ljy9fVVU6dOVUlJSSo6Olp169ZNzZgxQymlVEpKivLy8lKff/65SklJUQsXLlTu7u5qxIgRSimlLl68qDp16qTmz5+vTpw4oTZs2KBCQkLUwoULlVJK/fbbbyogIMCEvzZR28iRj6g0WVlZANjb2990PaUUX3/9NePGjaNv3760atWKSZMm0bVrVxYtWmRcr0WLFjz33HO0aNGCZ555hjp16rB//34ATp8+TZ06dWjSpAkdOnRg/vz5DBgwAJ1OZ9x//fr10el0uLq68uGHHxIQEECzZs0YNGgQbdq04ejRo8Z9WVlZMWXKFFq1asWgQYPw8PBg//79N2zvfy1btoxHH33U2O57771Hw4YNS61zrd127dpRVFTEr7/+yuTJkwkODqZNmza8++67NG7cmO+//56AgACUUsYjobi4OIKDg9m5cycAmzdvpn379tSrVw9HR0cA6tati5WVFQAajYYPPviAtm3bEhAQQEREBAcPHizz9SguLubVV1+lbdu29O/fH2dnZ8LDw4mIiMDDw4MBAwZw7NgxAH7++WeaNWvGlClTaNOmDaGhofznP/9h2bJl5Obm8uuvv+Lu7s7EiRNp1aoVzzzzDN26dTPu6/vvvzceEbm4uBAcHMxLL73EV199ddO/GVH7SfIRlcbJyQn4vyRUloyMDDIzM/H19S21vFOnTiQlJRkft2jRotTztra2xgvrkyZNYunSpXTp0oUXX3yR5ORkWrVqdcP9RUREYGZmxscff8z48eOJiIjg6NGjpU71NG7cuFRisbOzK3Uq6WaOHDmCt7e38bGFhQXt27cvtU7z5s2Nv584cQK9Xo+fn59xmVarxc/Pj2PHjqHT6QgMDGT79u2kp6dz+vRpRo0axZ49e1BKsXnzZoKDg8uMx97enrp16xofOzg4kJ+ff9M+/Ds+Kyur6x5f+79ISkqiQ4cOaDQa4/OdOnWiqKiI1NRUjh07hpeXV6m2fXx8jL8nJSURFxeHn5+f8eett94iMzOTzMzMm8YoajdJPqLStGjRotTRyf/68MMP+eabb7C0tLzh8waDoVRCuNFRhvr/F9ifeOIJYmJieOGFF8jLy+O1117jP//5zw3bnTt3LhMmTECv13PfffexcOFC2rVrV2odCwsLk/p4I+bm5re8ZnHtqARu3C8o3f+ePXsSFxfHjh076NChA507dyY/P59Dhw6xffv2myYfrbb8b3MzMzOT2rjRa3ctZr1ej0ajuW4QxL//b4uLi7nvvvtYtWqV8Wf16tWsW7fulkfMonaT5CMqjZmZGf369WPZsmXXfdNOTU3lxx9/xMLCAjs7Oxo0aEB8fHypdeLj42nduvUt91NQUMD06dMpKipi+PDhLFiwgLfffpu//voLoNS3cii5+P7666/zxhtvMGjQIJo1a8bZs2dNHin2v+39r7Zt25KYmGh8rNfrOXToUJnru7i4YGFhwZ49e4zLlFLs3bvX2P+ePXsSHx/P5s2b8ff3R6fT0aFDB5YsWVLqyOpWsVW0Nm3akJCQUOr/Lj4+HgsLC1q0aIGbm1upgRFAqVN+bdq04fjx47i4uBh/jh07xueff35bSVPUHvLqikp17QjjiSeeYMuWLZw6dYp169YxevRovLy8GDp0KADPPPMMCxcu5O+//+bEiRPMnTuXLVu2mHQzoqWlJXv27OG9997j6NGjJCcns379euPpHRsbG3Jzc0lKSqKgoICGDRsSGxtLamoqiYmJPP/882RlZVFYWGhSn/63vf/1xBNP8PPPP7N69WpSUlKYNm0aZ86cKTMxWFtbM2LECGbMmEFsbCzJyclMnTqVM2fO8PDDDwMlp8GaNm3KH3/8gb+/PwCBgYHGUWnX2raxsQFKRqDl5OSY1J87MWzYME6fPs37779PcnIysbGxzJgxg0GDBuHg4MAjjzxCamoqM2fO5Pjx43z33Xds2LDBuP3w4cM5ceIE77//PikpKWzevJl33nkHe3t7ST53ObnPR1SqunXr8uOPP/LFF18wZcoULl68SMOGDenTpw/jxo0znnIaMWIEubm5zJw5k0uXLuHm5saCBQvo3LmzSfv59NNPef/99xk2bBh6vZ6uXbsybdo0ALp06YKnpyeDBg3iv//9LzNmzGDq1Kn079+funXrEhERwUMPPVTqaOVm/re93r17l3r+vvvu49SpU8ycOZPs7Gzuv/9+/Pz8bnoq76WXXkKj0TB58mRycnLw9vZm6dKltGzZ0rhOz549+fHHH43Xhvz9/VFK0bNnT+M6bm5uhIaG8tRTT/Hyyy8bByBUloYNG7J48WI+/vhjBg4ciJOTE0OGDGHChAkANGvWjMWLF/PBBx+wbNkyfH19efTRRzly5AgAjRo1YvHixcyaNYuBAwfi6OjIgAEDePHFFys1blH9NMrUcw1CCJPExcXRtGlTmjVrZlzWt29fxowZw6BBg6ovMCFqEDnyEaKCRUdHs337dt5//33q1KnD6tWrSUtLo0ePHtUdmhA1hiQfISrYc889x5UrVxgzZgx5eXl4enqyZMkSnJ2dqzs0IWoMOe0mhBCiyslwEiGEEFWuVpx2y8/P58CBA9SvX/+6m9+EEELUPHq9nvT0dNq3b1/qpuprakXyOXDgAMOHD6/uMIQQQpTT999/f8NbJmpF8qlfvz5Q0olGjRpVczRCCCFu5fz58wwfPtz4+f2/akXyuXaqrVGjRqXunRBCCFGzlXWpRAYcCCGEqHKSfIQQQlQ5ST5CCCGqnCQfIYQQVU6SjxBCiConyUcIIe5RyqAnc+Nyiq9cuu45vV5fqfuuFUOthRBCVCx97hUurPyEvBP7yTuxj8Yjp6HRaNh/KIVdcZs5dfIEI0aMMGk24dshyUcIIe4xBeeSSft1JsVXLmJm64hTyGNkZWXxwy9/kH42BSiZIfjazLiVQZKPEELcQ5S+mLQVsyi+chHLpm7Y3z+BjXsT2blzJXq9HoPSYOPcmolPDcbW1rbS4pDkU8Xc3d359ddf8fb2LrV85MiRhISEMHr0aAD+/vtvFi1axIkTJ9BqtXh7ezNx4sSbTiu9detWFi9eTGJiIgaDAXd3dyZMmECXLl0qtU9CiNpDY2ZOg4HPk5kQS5zBhf2LlmLQFwHg4+ODb6cutGpR+WXMJPnUQDt27OCdd95h/vz5dOzYkeLiYn766Seeeuop/vrrL5o3b37dNitWrGDmzJlMmzaN4OBgAFavXs24ceNYsGCBJCAh7lHKoOfKnvUUX07DOWIUBoOBxLQcYg5nk5MdB0CjJi0Y2P/+Kq2dKaPdaqCEhARcXFzo3LkzWq0WnU7H448/zrBhw8jMzLxu/by8PKZPn860adPo1asXOp0OnU7HQw89xIQJE0hOTgZKRq8sWLCA8PBwAgMDee6557h48SIAcXFx9O3bl1mzZhEUFET37t356KOPjPv45ZdfiIiIwN/fnyFDhhAbG2vczs/Pr1Q8YWFhrF279qbbCSEqX8HZJM5+M5lL/yzi8vbV7Nu+ifnz5/Pnn3+Sk52NpY0T9V17MHLkiCov2nzXJp/+L/9O/5d/L7XsvSXb6f/y7+xIPG9ctnbbCfq//Dtzf9lrXHYpK4/+L//OqKlrS23/wuwN9H/5d5JOXa7M0AkNDSU5OZknn3yS7777joMHD6LX63njjTfw8fG5bv34+HiKiooICQm57rlnnnmGESNGALB06VJ+//13vv76a2JjY3F2duall14yrpuUlIRWq2XTpk3Mnj2bpUuXsnfvXjIyMpg6dSoLFy5k586dPPbYY0yZMoVbTYJ7u9sJIe6MPj+Hi2sXcebrNyg4l8xF68b8bduVlf9Ec/HiRZycnHjwwQd5/ZVJjB8eho2VRZXHeNcmn9rM1dWVVatW4erqyvfff8/gwYPp2rUrs2fPvuHY+4yMDBwdHbGwuPkf0PLlyxk/fjwtWrTAysqK1157jV27dnHixAnjOuPGjcPCwgJ/f3+aNWvGiRMn0Gg0aLVafvrpJxISEoxHMBqN5qb7u93thBC3LztxE6cXPMeV3WvJworNDoGsuVqfjCvZFBnM0Dq1Y8KECbRv375a34t37TWfP/478Lplb48Oum5Zny4t6dOlZallzo7WN9z+0xdD7jguCwuLGyaQ4uJidDqd8bGLiwtvvvkmAJmZmcTGxvLhhx9ia2vLM888U2rb+vXrk5WVRVFR0XUJKDs7G3Nzc6ysrDh79ixvv/02U6dONT5vbm7OmTNnMDc3x8bGptTQSgsLCwwGA05OTnz77bd8+eWXPPnkk+h0Op544gnGjh17077ebDtJQEJUjtyUvVzNzma/lSfHcsxRGXklXygDAskxa0rfHm6YmVX/cUeVJZ/Vq1fzzjvvlFqWn59Ply5d+Oqrr6oqjGrXuHFjzp49i6+vr3GZUopTp07RuHFjAIYNG0a3bt2YMGECUPIhPmjQII4cOcLBgweva9PPzw9LS0tiY2OJiIgo9dynn35KYmIiP/74Iw0aNODNN98sdXouOTmZ5s2bEx8fX2bMWVlZ6PV65s+fT3FxMVu3bmXChAn4+flhZmZGcXFxqb5kZWXdcrvAwMBy/98JIa5nKC5EfzUDC6dG5Ofnk2DRhjiViT5HoYBOHTsRGhqCnZ1dNUdaWpWlvwEDBhAfH2/8+eGHH3BwcOC1116rqhBqhL59+/LFF19w7NgxAK5cucKnn36KVqs1jkjr27cvS5cuZd26dRQWFlJYWMju3bv5+++/r0suADqdjldffZW3336bqKgoiouLycvLY9myZSxfvpyJEycCMHjwYL744gvOnTuHXq9n0aJFPPLII+Tn59805oyMDJ566im2b9+Oubm5cWbCOnXq0Lx5c4qLi1m/fj0Gg4Fvv/2WnJycW24nhLhzucnxnP7yRc78/CHbtm5hzpw5bI3bid6gyNM4k20XQNee4TUu8UA1nXYrKiri1VdfZeLEiXh4eFRHCNVmwoQJmJubM2HCBNLT09HpdHTu3Jlly5YZb+gaPnw4NjY2LFq0iMmTJ6PX62ndujUvv/wy/fr1u2G7Dz/8MPb29nz55ZdMnjwZpRQeHh4sWrTIeJQxZswYDAYDw4cP5/Lly7Rt25bFixfj4OBw05hbtWrFtGnTeOedd7hw4QJ169ZlypQpuLu7A/D666/zwQcf8NZbb9G/f386duxo0nZCiNtTfDWDS+u/JvvgVo6rOuylKTnrI4GSU/YRERFY2jrj7GiFVlszT3FrVDUMPfr2229ZsWIFK1euRKu99cHX6dOnCQ8PJyoqSqbRFkLcs5RBz5Xda7kU8yNnC8zYo5qQqawAyNNb4eYdwJMPhdSIa6q3+tyu8iOfwsJClixZwpQpU0xKPEIIIUqc/3k6p5OPssfQiPPKHgAHBwdae3RiV6oZvXr61IjEYwqTks+xY8eIjY1l//79ZGRkoNVqqVevHt7e3oSGhuLi4mLyDjdt2gRAeHj47UUshBD3oMzMTGJznDmibwuA1syCsNBgAgICsLCw4PrxuTXbTZPPrl27mDNnDnv27MHHxwdXV1dcXFzQ6/VkZmayevVqZs2ahb+/P+PHj8ff3/+WO4yKiuL++++Xox4hhLgJpRTZ+2PJvpLF3qs6du3ahcFgQKs142yeMzkWzXjVPxALi9p5x0yZUU+ePJnk5GSGDRvG3Llzy7wonZ2dzV9//cUHH3yAu7s7M2bMuOkOExISjKOvhBBCXK8w/STn/l5E/MmLHDTUpwgzADp06EBoaCibD2QQ1L4RVrramXjgJsknLCyMDz/88JYN2NnZ8cgjj/DII48Y63ndzJkzZ4xDboUQQvwfQ2EeFzf+wp7tW9inr08eJfXWsoodeGrEINq7twKgbzfH6gyzQpSZfHr16lXuxvr06XPLdfbu3VvudoUQ4m6mlCLncBy7//6RPdm2XKEJAE0aNyLLvBVJJw3oNTXvXp07YdIxm1KKqKgo9u/fT3Fx8XWFIe+1G0WFEKIipaamsub3tVwoqAuAva0tfR54gHbt2nE1twhzM021FP+sTCYln+nTp/PDDz/g4eFx3cx2tWVYnxBC1CSG4kLOnz5J7LadHD16FNBgodWSlNOYVs288PT0BMDBVnfzhmopk5LP77//zocffsjAgbVtMJ8QQtQ8afu2ErVmNUn51ihKSmR17dqVdu078lNkEg9HuFV3iJXOpPHOWq22VCFMcfvc3d3Zv3//dctHjhzJkiVLAPj888/x8fEhKSmp1Do3mrjtmhUrVtCuXTv8/Pzw8/OjQ4cO9OrVi88++4yioqJS+2nfvr1xvX//5ObmAnDp0iUmT55M165d8fX1JTw8nFmzZlFQUHDdfseOHYuPjw+XLl267f8TIe4VV9NOsWrudBat/Idj+daAQlk2ZOLEiQQHB9PA2Z7nHvGjkbPtLduq7UxKPkOGDOGrr74qVb1YVK6CggJeeeUVCgsLTd6mTZs2xsKtCQkJzJkzh5iYGN56661S67344oulirxe+7k2ncKLL74IwJo1a9i7dy+LFy9my5YtTJ8+vVQ7Z8+eZe/evYSEhPDTTz/dYY+FuHsVFeQR9cNC5i5YRMKlYvRoadPAkRMGb1LyW5JbZFbdIVY5k067nT59mpiYGNasWUPjxo2vmzPm119/rZTg7mWBgYGkpaXx6aef3vaAjnbt2vHZZ5/Rp08fxowZg6urq0nbJSQkMHr0aBwdS4ZztmrViv/85z/G6hTXLF++nB49evDggw/y6quv8swzz9xyQjsh7iUGg4F9CQlE/rWKHL0WMKOJrRm9BwyhhZsnASczadbA7q4bTGAKk5KPu7t7ratEnDL9wTKfq3f/WBw63gfAlT3ruLhmYZnrtn7zN+Pvp5e8SuH5lDKfr0jW1tZ8/PHHDBs2jODg4Nue/8bFxQUXFxd27NhhcvJ54IEHeOONNxgwYAABAQH4+fnh7+9fqoJFcXExv/76K5988gn+/v7Y2tqyZs0aBgwYcFtxCnE3UUqRlJREZGQkFy5cALQ4aAo5l1efln0G0cKtJQBuLZyqNc7qZFLykYoE1cPHx4dnn32WN954g99///2226lTp45xgjeAzz77jPnz55dap0ePHsyePRsoGd34+++/8/fff/PLL7+Qm5uLn58fb731Fl5eXgBER0fj6OhIQEAAUDIB3rJlyyT5iHve6dRU/vn9F05nlsxr5ejoSHDPHlwursOBdcnUsbeq5ghrBpNrM8TGxrJw4UKSkpLQ6/W0adOGUaNG0bdv38qM77aZekTi0PE+41HQrTQb/fGdhASYPo32NePGjWPTpk1MnTqVhx9++Lb2mZGRgZPT/33Dev755xk9enSZ62u1WgYPHszgwYPR6/UcPnyYRYsW8dRTTxETE4ONjQ0//fQTp0+fplu3bsb4L1++zN69e2VwirgnXbp0iXWrV3D05FkAdOhp4taZ4UMfwNzcHKUUXf1aYmlx713fuRGTks+qVat46623eOihh3jssccwGAzs2bOHN954A71eL992y8GUabT/zczMjJkzZzJw4EDjgIDySElJ4eTJkyafttu4cSOvvPIKsbGxWFtbY2ZmhpeXF9OnT6djx46cO3cOCwsL4uLiWLVqValZSadPn87SpUsl+Yh7SnZ2NjHr1rJ3fyIGwAwDbpZ5xGW6sP+gOUOLFebmJfdESuL5PyYln4ULFzJ58mSGDx9uXDZw4EDc3NxYtGiRJJ9yuDaNdtu2bWnbti1XrlxhyZIlpabR/l8tWrTgzTff5M033zQ5ASmlOHDgAFOmTGHo0KG0atXKpO38/f2xs7Pjrbfe4sUXX6RZs2ZcvHiRxYsX4+rqSsuWLfnkk08IDAykbdu2pbYdOnQoY8eOJS0tjYYNG5q0PyFqq4KCArZu3sy2bVso0is0KFzNrtCzWxBNewziwsqDDPNufE8OJjCFScnn7Nmz9OjR47rl3bt356OPPqrwoO5mpkyjfSMPPfQQGzZsYMuWLWWuk5ycbLwPyMzMjEaNGjFgwACefvrpUuvNnj2buXPnXrf9ggULCAwM5Pvvv2fOnDkMGzaMK1euYGNjQ8+ePfnqq6/Q6/WsWLGC119//brtu3TpQr169fjhhx+Mw7WFuNvo9Xp2797Nxo0byckpua7TTJOFrbLgvtGvUvf/n8GYONS3GqOs+UyaRnvgwIEMGzaMRx55pNTyn376ia+//pp//vmn0gIEmUZbCFH9lFIcPHiQyHX/cPnKVQCaNWtGD29XInemsSbFkuce9qVXoOmTa97NKmQa7bFjx/L6669z7Ngx4/n8+Ph4fvnlF955550KDVgIIWqa48ePs37dP5w7nwaAg05Lt/sG4N+xZNpqu9bZhF4tpF2rutUcae1hUvJ54IEHMBgMfP311/z6669YWlrSunVrPvvsM0JDQys7RiGEqBZpaWmsX7+e5ORkAKwpwkebxoW8emw+XERAp5LCyk3q2dGkXnVGWvuYPNS6X79+9OvXrzJjEUKIGuHy5cvExMSwb98+ACzQ46lNx7eZE7ouL/DC0uP0trdCKSWV/W9Tmcln5syZTJw4ERsbG2bOnHnTRmQ+HyHE3SA3N5fNmzezY8cO9Ho9Wgy4aS7hpsvFte/j2LUPRqPR8NVbbbGzuTunOqgqZSafaxPHXfu9LJL1hRC1XVFREXFxcWzevNlYvd3b25t2RafYclDL++levGfrjdf//7yTxHPnykw+y5Ytu+HvQghxtzAYDCQkJBATE8PVqyUj2Fo0qk+fAYNp3LgxSimObExhhFL3dB22ylBm8omNjTW5keDg4AoJRgghqoJSiqNHjxIVFUV6ejoAdcnDT3uOS2lXOXNZS+PGJWd2BgW3qeZo705lJp+xY8ea1IBGo+HQoUMVFpAQQlSmU6dOERkZycmTJwGwpRA/7XlaWxdxukk4X+12ICzhLJ3bSZWOylRm8jl8+HBVxiGEEJXq4sWLREdHG78sW6LHW3uettpM6na6D6eej9BMZ8dknzQCvRpVc7R3vzKTz/9O4VwWjUZDmzZyWFrd9u/fz0MPPcSRI0duue7IkSMJCQm5aWVrIe4WV69eJTY2lj179qCUwtzcnED/TjTd+xMn8+z4WvsgU8OHYmZhhhkQ1P76Ar+i4pWZfPr164dGo+FW1XfktJsQoiYqKChgy5YtbN++naKiIjSAn68voWFh2NvbU9DZjy+WJNK6WR0Ki/TopOJ0lSoz+URFRVVlHPeM06dP079/f15++WXmz59PUVER48ePx8LCgvnz51NYWMi4ceN46qmnOHjwIB999BGJiYk4OTnx2GOP8eSTT6LRaCgsLOSDDz7gzz//xMbGhkGDBpXaT1JSEu+//z6JiYk4Ozvz7LPPMnDgwOrptBBVqLi42Fj4Mzc3F4Dmmiv4ac+RcsIJC501AJZ1GzLrhXoyzUE1KTP51K1bF2vrkhcpLy+vygKqCD/88APHjh2rkn21bduWYcOGlWub3Nxcjh49SkxMDOvWreO1115j4MCBREdHs2nTJiZNmkTv3r0ZNWoUTz/9NIsWLSI1NZWxY8ei0+kYMWIEn3/+OQkJCaxZswYzM7NSs83m5OTw5JNPMmrUKBYtWsShQ4cYN24cjRs3Ns48KsTd5to0ItHR0Vy+fBmABmb5+HGaBpo8Dph78tupBhg2pzA03A1AEk81KjP5dOzYkc2bN+Ps7Iyfn98Nbya9VlpCTruV3xNPPIFOpyMoKAi9Xs/jjz+OTqejR48e6PV6NmzYgJOTk3HUYdu2bRkzZgzLly9nxIgR/Pnnn7z66qvUr18fKJmd9PHHHwdKhsnb2NgYp1Lw8fHhwQcf5Oeff5bkI+5KycnJREZGcv78eQDqmOvxNZyiGVewdvHCudeT5Oc50PdIOv27t67maAXcJPl8++23ODo6ArB06dIqC6gilPdIpDpcmwFUq9UCYG9vX+rx5cuXadKkSaltmjZtytmzJVP0pqenl5qwrWnTpsbfz5w5w+nTp+ncubNxmV6vx8vLq+I7IkQ1OnfuHJGRkaSkpAAl76Mu7s2oH/8zWRp7Els+Qv/hQ9FoNHgCnq2cqzdgYVRm8vn3N+R//56dnY25uTlWVlbl3tmFCxd49913iYuLw8rKiocffpjnn3++3O3cC5RSnDlzptSyU6dOGY90GjRowNmzZ+nUqRNQUn33mgYNGuDh4cFvv/1mXHbhwgUphSTuGpmZmcTExBhLf1lamNO9ZzCBgYGYm5uTbGPD+38W0taxPn0VmMmffo2jNWUlg8HAl19+SUhICP7+/vj5+REeHs7y5cvLtbPx48fToEEDtm3bxk8//cTKlSv5888/byvwu11QUBA5OTksXLiQwsJCjh07xpIlS4yDBoYMGcLChQs5e/Ysly9fZs6cOcZtQ0JCOHv2LL/88gvFxcWcOnWKkSNH8uOPP1ZXd4SoELm5uaxdu5a5c+eyf/9+zLQaPC2yGMgBDHprLCws0Gg0uIb24/0JwcyY0B0zrWSemsikKRU++ugj/vzzT5599lm8vLwwGAzEx8cza9YsMjIyGDdu3C3bSEhIIDU1lR9++AGdTkfz5s1ZtmwZlpaWd9yJu5GDgwOLFy/mww8/ZNGiRdja2vLII48Y780ZN24cOTk5DBo0CAsLCx5++GG2b98OgKOjI1999RUffPABM2fOxNLSkv79+zN+/Pjq7JIQt62oqIjt27ezZcsWY+HPNtYF+BSmYKeKSNE34ue/D+Li3haXRg4AeLWWU2w1mUnTaAcGBjJr1ix69OhRavn69et599132bJlyy139P333/P333/j5+fHqlWrsLS0ZPjw4Tz11FO33Fam0Rbi3nTti+6GDRvIzs4GoJmthg75R6iryce8TgOcw0exLNGayzmFPNXfiwZONtUctYAKmkYbKHVx+5rmzZsbv4XcSlZWFvHx8QQGBhIdHU1KSgqjR4+mfv369O/f39QwhBD3AKUUR44cISoqiosXLwLQuHFjOuku4XRmF4UaHYaOD9Ks10NozXWMcVNyeq2WMSn5PPXUU0ybNo3//ve/NGjQAIArV64wa9YsRo4cadKOdDod1tbWTJo0CY1Gg4eHB0OGDGH9+vWSfIQQRidPniQyMpJTp04BUMfRkfCICLy8vCjOPEfcz4ovklvjea4Vb5mXzKsjiaf2KTP5BAUFlRodlZmZSVhYGA0aNMDMzIzz58+j1+tJS0szacRa69atMRgMFBcXY2FhAWCcrE4IIdLT04mKijLWJ7Sx0uGju4yHYwFWTVqh0WiwqNsEvyde5YENSQwOca3miMWdKDP5vPbaaxU6NLdbt244ODgwe/ZsXnjhBVJSUlixYgXvvfdehe1DCFH7XLlyhQ0bNrB3716UUlhYmOPtqGibuQddsYFLufas+DqG917si1arwdbaghH3t6vusMUdKjP5DBw4EDOz8pWe0Ov1ZW5jaWnJd999x7Rp0+jRowc6nY5nnnmG3r17ly9iIcRdIT8/n82bNxMXF0dxcTEajQbvxg54XNqF1eV8NDor7IIGMX2DDZZ6KzKv5uPsaF3dYYsKUmbyeeihhxgzZgz333//LY+AiouL+euvv/j6669ZtWpVmes1b96cL7/88raDFULUfsXFxezcuZNNmzYZ60a2a9cOzwtbsElPAMDWqyfO4SMxt6/LVI+rNHK2xdzMpNsSRS1RZvL54osvmDZtGh988AFhYWH07NkTV1dXnJycMBgMZGZmcvjwYXbu3MnatWvp0KED8+bNq8rYhRC1iFKK/fv3Ex0dTVZWFgAuLi5ERETQrFkzLm8153Dser6/3Ik+9cLoa18XgGYN7KszbFFJykw+jRs35osvviAxMZHvv/+ed955h0uXLpU6CnJ2dqZnz558+eWXeHt7V0nAQojaRSllLPx5rQxU/XrOBNRVtHVrjOP/vwfEMXAAyi4A69jjeLSsW50hiypwy6HWXl5efPDBBwCcPXuWixcvotVqqV+//g3v/RFCiGvOnj1LZGQkx48fB0oqdwS1bkDDpPWQcpWzqTvZeNWF/iEeaMzMCfJuSpB3U6lDeA8w+SZTgCZNmlxXaVkIIf5XRkYGMTExHDhwAAArKyu6+LTD5cwW9Ac2A1BU343ZSe5kR6XQq6srVjpzSTr3kHIlHyGEuJmcnBw2btzIrl27MBgMmJmZEdC5E55FqRTuWYoehZldXZx7PYFtu66ErDtC9w5NsNLJR9G9Rl5xIcQdKywsZNu2bWzdupXCwkIAfH19CQkJwcHejtOLX8WAhs1FXvR97HnsGpRc0xnW26M6wxbVSJKPEOK26fV6Y+HPnJwcANzc3Ojh245GzV0wtyuZkLLBgIks+uMQ64/qaXkqh5AGMqDgXifJRwhRbkopDh06RHR0NJcuXQJKZtMND+mJw8k4slZ9wMmW/tj3mUh9J2ssG7Xm0aGN6Z9XhEtjh2qOXtQEJiWfEydO8PHHH7N//36Ki4v531kYtm3bVinBCSFqntTUVNavX2+cabdu3bqEh4fTwiybjDX/JevKRRQathzM5Ex+ApOfDAKgXh1r6tWRCgWihEnJ5z//+Q+ZmZmMGTMGOzu7yo5JCFEDXbhwgaioKI4ePQqAra0twcHB+Li6kBn1DReOxAGga9gKi55P8Pe3qYQ72aI3yHQH4nomJZ8DBw7w22+/0bZt28qORwhRw2RlZbFhwwYSEhJQSqHT6ejSpQtdu3bFrLiAU1+Mx1CQi16ro0H4cBw6349Ga8bit9yxs7ao7vBFDWVS8mnatClXr16t7FiEEDVIXl6esfCnXq9Hq9XSuXNngoODsbW1LVlJp8OqXXd27T7Mz5f9mWTXmQBtSXFhSTziZkxKPi+88AJTp07l2WefxcXFxTgfzzWurjKvhhB3i+LiYuLi4ti8eTP5+flASaWTsLAw6tjbcnnzL2hc2mPT2heAhn1Gg+NJuqZdxVPK4ggTmZR8rk0W98ILL1z3nEaj4dChQxUalBCi6hkMBvbt20dMTAxXrlwBoFWrVkRERNCkSRPyUhM58/N8ijLOcSUuhrwH3qOLT1M0Zub06966mqMXtY1JyScqKqqy4xBCVBOlFMeOHSMqKooLFy4A0LBhQyIiImjTpg2qIJf0vxdwNX49APk2DVlyviOGf44Q5N1ESuKI22LyNR8omVv92LFjGAwG2rRpQ+vW8m1HiNrszJkzrF+/ntTUVAAcHR0JDQ3Fx8cHjUZDztGdXFzzJfrsDNCaU6fbEByCBuMfmUTfbq0k8YjbZlLyycnJ4c0332Tt2rVYWFiglEKv19O1a1c+//xzbGxsKjtOIUQFunTpEtHR0Rw8eBAAa2trevTogb+/P+bmJR8LhsJ80v+ejyEni9M0pMPIV3BsVvKF8/EHPKstdnF3MCn5zJgxg6NHj7J8+XLjvD379u3jzTffZNasWbz99tuVGqQQomJkZ2cTGxvLnj17MBgMmJubExgYSPfu3bGysgJAKQMajRatzgrn+8exYvVWVqS58HqGFV2bVXMHxF3DpOSzbt065s2bh4+Pj3FZhw4deOedd3j++ecl+QhRwxUUFLB161a2bdtGUVERGo0GPz+/ksKfDiXlbvQ5WVz8ZxHFtg1wCn4MGysL7N0D6DncnS7FBtxaOFVzL8TdxKTko5TCyen6P7w6deqQm5tb4UEJISqGXq9n9+7dxMbGGt+r7u7uhIeHU79+feN6OYfjSF+zAEPuFfKUjnU5Hjw5JACAVk0cqyV2cXczKfn4+/szd+5cPvroI3Q6HVBSQn3evHl06tSpUgMUQpSfUorExESio6PJzMwEoFmzZkRERODi4mJcT5+XzaV1S8g+sLFku0YefHzEE/erYDAotFIWR1QSk5LP66+/zrBhwwgJCaFdu3YAHDp0CEtLSxYvXlypAQohyuf48eNERkZy9uxZAJydnQkPD8fDw6PU6LTc5HjS//wCfXYGGnMddcNG4tC5D++lZdOikVSeFpXLpOTTokUL/v77b1avXk1ycjKWlpb06dOH/v37Gy9SCiGqV1paGpGRkSQlJQFgZ2dHSEgIfn5+aLXaUusqpbgc9yf67AyOF9en+cAXcGxfMrGbJB5RFUyez8fBwYERI0ZUZixCiNtw+fJlYmJi2LdvHwA6nY5u3boRFBRkPE1+jTLo0WjN0Gg0NOg3nuhffuXX8y150bZBdYQu7mFlJp8uXbrw119/UbduXYKCgm56M5nM5yNE1cvNzWXTpk3s3LnTWPjT39+fnj17XnfvnSouImPjT2QmH0R7/2u0buaEuYMzwY8/TU+DwspS5pUUVavMv7jXXnvNOHfP66+/XmUBCSFurqioyFj4s6CgAABvb29CQ0NvOCq1IO0E6as/o/DCSVAafvnxb1556THMzLRYWphVdfhCADdJPoMHDzb+rtFoeOCBB647hM/NzWX58uWVF50QwshgMLB37142bNhgnOKkdevWRERE0Lhx4+vWVwY9WXF/kBH7I+iLMavTiKVZXXBt3xGDAkk7ojqVmXwKCwvR6/UopZg8eTL+/v7UrVu6XHpiYiKffPIJTzzxRGXHKcQ9SynF0aNHiYqKIj09HYBGjRrRq1evMusrFl2+wPlVn1F05jAA9h3vwzl8FP/RWKCTox1RA5SZfH7//XemTJlivNYTERFxw/WCg4NN3tny5cuZOnVqqSOot99+u9RRlhDi/5w6dYrIyEhOnjwJlNzYHRYWRvv27W96HTbn0FaKzhwmy2DNpfaPEnF/PwB0ZW4hRNUqM/kMHTqUli1bYjAYGDVqFJ9//jmOjv93p7NGo8HGxgY3NzeTd3bgwAGefvppXnzxxTuLWoi73MWLF4mKiuLw4ZIjFxsbG3r06EHnzp2NhT//17WabACOgf05feYiK8+0YEzXLlUWtxCmuukQF39/f6BkPp8mTe583o7ExETGjBlzR20IcTe7evUqGzZsID4+HqUUFhYWBAUF0a1bNywtLcvcLvvIDs6s+YYzfmMJC+6ARmtG+4eexkspmfZA1Egmja90dnZm6dKlHDt2DL1eb1xeWFhIYmIia9euvWUbRUVFHD16lBUrVvD+++9jbW3N0KFDGTNmjLw5xD0vPz+frVu3sn37dmPhz06dOhEcHIy9vX2Z2xkKcrm0/muuJkRjAaREr6KDrxvOjtYA8t4SNZZJyeedd94hMjISf39/Nm7cSGhoKKmpqSQnJ/PMM8+YtKOMjAx8fHwYMmQIc+fOJSkpifHjx2Ntbc3IkSPvqBNC1FbFxcXs2rWLjRs3kpeXB4CHhwfh4eHUq1fvptvmpSaS/sdcirMuoDGzILFuCC4+vanrIFVHRM1nUvLZsGEDs2bNIjQ0lL59+zJp0iQ8PDx48803OX/+vEk7atiwId9//73xsaenJyNHjmTdunWSfMQ9RynFgQMHiI6O5vLly0BJGauIiAiaN29+020NRQWc/OtrDIkl01rrGrWmwYDnaFX/5tsJUZOYPJOph0dJ3SdXV1cSExPx8PDgiSee4KmnnjJpR0eOHGH9+vVMnDjRuKygoOC6e4eEuNslJycTGRlp/OJWr149IiIicHNzM+k0WVH6KfSJkRiUhmNO3bj/iYlozCwqO2whKpRJyadp06YcPXqUxo0b07p1axITE3nwwQfRarVkZ2ebtCNbW1u+/PJLGjduzODBgzlw4ADfffcdU6ZMuaMOCFFbnDt3jsjISFJSUgCwt7cnNDSUDh06XFf4838pfTEGtCVVCZq4Yh38ODGplgwYHCqJR9RKJiWfxx57jFdeeYUPP/yQXr168dhjj+Hk5ERcXBzt27c3aUfNmjXjiy++4JNPPuH999/HycmJCRMmcP/9999RB4So6TIzM4mJiWH//v0AWFpa0r17dwIDA7GwuHXiyDi2n9MrP2e3XTBPPDsMjUZDk+4DGN69siMXovKYlHyeeOIJ6tWrh729PZ6enrzzzjt8/fXXNGzYkDfffNPknXXv3p3u3eUdI+4NOTk5xsKfBoMBMzMz/P396dGjx3WFP2/EUJhHRswPXNm1BjsUTdK3cSZ9AM0alD36TYjawuRStv369TP+PmTIEIYMGVIpAQlR2xUWFrJ9+3a2bNlCYWEhAD4+PoSGhlKnTh2T2jiTEId+0zcUZ10AjZactvfh2f0hSTzirlFm8nn++edNbuSzzz6rkGCEqM0MBgPx8fFs2LDBeC3U1dWV8PBwGjVqZFob+TnsWDqHBum7ANA1bEX9fhOwbNSq0uIWojqUmXxMOS0ghCgZNn348GGioqK4dOkSAE2aNCEiIoJWrcqZNLRa6lxJolhpOdcsgp4jR6Mxk7l2xN2nzL/qDz/8sCrjEKJWOnnyJOvXr+f06dMAODk5ER4ejqenp8nVBZIOJ5NtsMDXswVanTXNh75MWo6WYE+PygxdiGpl0leqf98ceiPDhw+vkGCEqC3S09OJjIzk6NGjQMmZguDgYDp16oSZmWlTFiiDniNrf0HtWclB5YF7mylYW5pj6+LJjSdKEOLuYVLyWbJkSanHer2eS5cuYW5uTseOHSX5iHvGlStXiImJISEhwVj4s2vXrnTp0uWmhT//V/7ZJC7+vQBd2nHQQKs6JVWphbhXmJR8oqOjr1uWk5PDlClTaNeuXYUHJURNk5+fz+bNm4mLi6O4uBitVmss/HltunlTpJw4x9FVX+GWEw8ozB3q4XTf07R296+84IWogW77SqatrS2TJk1ixIgRMk2CuGsVFxezc+dONm3aZCz86enpSVhYGM7OzuVqS5+TRc4Pr+GmclFoqRM0AKceD6PVSSFQce+5o2E0J0+epKCgoKJiEaLGUEqxb98+YmJiyMrKAsDFxYVevXrRtGnTcrVTrDdgYW6Gma0jNi3acfnCBVo/NIk6LdpUVvhC1HgmJZ8b3fOTk5PD7t27S918KkRtp5QyFv5MS0sDoEGDBkRERODq6lqu+XFOnbnIzp8WU9DYl8eG9QGg1dAX0egsjTOOCnGvMin53OieH0dHR3r37s3AgQMrPCghqsPZs2eJjIzk+PHjADg4OBAaGoqPj88tC3/+m1KK3CNxFK1dgm9+BqeTU8nJC8PWWofW0rqywheiVjEp+cg9P+JulpGRQXR0NImJiQBYWVnRo0cPAgICMDcv35npE4eOYBn/M3nHEwAocmyO9/3PYGstU4cI8W8mv7N27NjBTz/9xLFjx9BqtXh4eDBy5EiTq1oLUdPk5OQQGxvL7t27jYU/AwMD6d69O9bW5TtC0efn8s/CubS9ugODRqG1ssMp+DEcOvZCozXtvh8h7iUmJZ/Vq1fzxhtvEBoaSr9+/dDr9ezbt49HH32UefPmERwcXNlxClFhCgsL2bZtG1u3bqWwsBCNRoOvry8hISE4OjreVpuquADX3L1oUOS06Irng2Mws3Go4MiFuHuYlHzmzZvH66+/zqhRo0otX7JkCbNmzZLkI2oFvV7Pnj17iI2NJScnBwA3NzfCw8Np0KBBudoyGBTbNsbh1LwVnm0aYm7nRP0HnqXQ2glXNzkbIMStmJR80tLS6Nmz53XLw8PDmTNnToUHJURFUkpx8OBBoqOjycjIAEpm5+3VqxcuLi7lbq84+zL7li+k4dkdbLbsSbtXnkOj0eDUoUdFhy7EXcuk5BMREcGyZct4++23Sy3//fffCQ8Pr5TAhKgIJ06cIDIykjNnzgDg7OxMWFgY7dq1K9ewaYCC/ALy9v5D5uZfqFOQi16jpX0za5SCcjYlxD3P5KHWv/zyC1u3bsXPzw9zc3MSExM5dOgQ3bp1K3UfkMztI2qCtLQ0oqKiOHbsGFBSkSMkJAQ/Pz+TC39ek51byF8/rsD1/DqcKLnh1Ma1E3XDR6GrZ/oNp0KI/2NS8ikqKip1M2lxcTHu7u64u7tXWmBC3I6srCxj4U8AnU5nLPyp093ecGfDyb34n/+l5HeHRjS5/ylsXDtVWMxC3IvkPh9xV8jLy2PTpk3s2LEDvV6PVqulc+fO9OzZE1tb23K1pdcb2LjrBN06uqCzMMPevTNpjdph2boTzXv2l8ndhKgAJr+Ljhw5wuLFi0lKSkKv19OmTRtGjhxJx44dKzM+IW6qqKiIHTt2sHnzZvLz8wHw8vIiLCyMunXrlrs9Q1EBfy2YT8vLO1h/+UX69vZHo9Hi+tS0cl8jEkKUzaTkExsby/jx4+natSu9e/c2zlU/cuRIFi5cSPfu3Ss7TiFKMRgMxsKfV65cAaBVq1ZERETQpEmTcren1+vJPbiJjJgfaH/1Emihae4RoGSqA0k8QlQsk5LPp59+yrPPPsvEiRNLLZ87dy6fffaZJB9RZZRSHDt2jKioKC5cuABAw4YNiYiIoE2bNuVOEmfSs/nr1zUE5W/ELvcsALqGrXAMG0Xr1t4VHr8QooRJySc5OZlPP/30uuX9+/dn0aJFFR2TEDd0+vRpIiMjSU1NBUqK24aFheHt7X3bRybZ21cQkfE7AFo7J5xDh2PnHSxVp4WoZCYlnyZNmnDw4MHrbshLTEws94RaQpTXpUuXiI6O5uDBgwBYW1vTo0cP/P39y134Mzu3kIPHMwjwagRAq8AQUhP/wbZzXxr1HCITuwlRRUx6544YMYJ3332XtLQ0OnToAEB8fDwLFy5k9OjRlRqguHdlZ2cbC38qpTA3NycoKIhu3bphZVX+JHE1O49vZ82hKWmca/gejevZoWvQgjYvLUark6kOhKhKJiefnJwcFi5cSGZmJlAywda1abSFqEgFBQVs3bqVbdu2UVRUhEajwc/Pj5CQEBwcyles02BQaDSQeySOzOhlPGBxHoDCM0ehXslITUk8QlQ9k89ZjB07lrFjx5KRkYFOp8POzu62dpibm8uQIUMYOnSoHDWJUvR6Pbt37yY2Npbc3FwA3N3dCQ8Pp379+uVub9v+s/zzVyyP198P548AYF63KfV6jcK6jV+Fxi6EKB+Tk09qaiq//vqrcT6fdu3aMXToUBo1alSuHb7//vvGC8ZCQMkItsTERKKjo41H1s2bNyciIoIWLVrcdruFG7/hcf1uOA9aa3ucej6Kg1+E3CQqRA1g0rtw8+bNjBs3DldXV3x8fNDr9axbt46vv/6ab775Bh8fH5N2tmbNGk6cOCE3pgqjlJQUIiMjOXfuHAD16tUjPDwcd3f3co9gSz1/BRS4NC45NdfWrQV5O/fi4P8AdXsMxcyqfJUOhBCVx6TkM3PmTJ588klefvnlUss/+ugjpk+fzs8//3zLNs6ePcvHH3/M0qVLmTx58u1FK+4a58+fJzIykuTkZADs7OyMhT+12vIPc96ScJqon3+hYX1HnnnhSTQaDY2CH0TfOQILp/IdnQshKp9JySc1NZUhQ4Zct/yRRx7hxx9/vOX2er2eV199leeff55mzZqVP0px17h8+TIxMTHs27cPAEtLS7p160ZQUBAWFha31Wb+6SM037WEEbbJFBTYUZj3CJY2tmh11jKYQIgayqTk4+/vz9q1a3n22WdLLd+yZYtJp9Dmz59Pw4YNGThw4O1FKWq93NxcNm3axM6dO42FP/39/enZsyc2NjblastgUMTsPkXC3iMMr3eAnIObAdDa1qFZ6HB01pJwhKjpTEo+7du3Z/78+ezYsYNOnToZ5/OJjo6mb9++zJw507jua6+9dt32f/75JxcuXKBz585AyQdRQkICp06d4t13362YnogaqaioiLi4ODZv3kxBQQEA3t7ehIaG4uTkdFttFuTlcXLtMh7QJJBzQY/GXIdj4ADqdB0kRzpC1BImJZ/du3fToUMHiouLiYuLMy7v2LEj586dM14sLusC8dq1a0s9HjlyJCEhITLU+i5mMBjYu3cvGzZs4OrVqwC0adOG8PBwGjduXO72zl/KoX4da8zMtFhZ6ehmfxrzHD227bpRN3wEFo4NKroLQohKZFLyWbZsWWXHIe4SSimOHj1KVFQU6enpADRu3JiIiAhat259W22u3JDExn9i6dPbn94hPmjMzGk+ZBIajRar5h4VGb4QoopUyw0PkszuTqdOnWL9+vWcOnUKgDp16hAWFkb79u1vu/BncVY6bseX08FuN2cOpUFIybB+6xaeFRa3EKLqyd124o5dvHiRqKgoDh8+DICNjQ09e/akc+fOmJmZlbu9hKPpXM7MwjtvJ1lxq7EuLgQzC7y82qCUkrl1hLgLSPIRt+3q1ats2LCB+Ph4lFJYWFgYC39aWlreVptHUy/x+9fLGGCzh8vaPABsPbtRN0yu6whxN5HkI8otPz+fLVu2sH37doqLi9FoNHTq1Ing4GDs7e3L3Z7BoNBqS45mXCyvMMJuCwAWjdpQ/76n5LqOEHehMpPPzp07TW7E39+/QoIRNVtxcTG7du1i48aN5OWVHJV4eHgQHh5OvXr1yt2eUiX36/yxbi9vTeiFs6M1lo1a4RDYH8uGrbBr30MmdRPiLlVm8hk5cmSpxxqNBqUUWq0WrVZLcXExWq0WCwsLEhISKj1QUX2UUhw4cIDo6GguX74MQIsWLYiIiKB58+a3325hHnlbfuJZw062rTOn39AHAKgX8UQFRC2EqMnKTD579uwx/r5mzRqWLl3Ke++9R/v27TEzM+Pw4cO88847DB48uEoCFdUjOTmZyMhIzp8vmQenfv36hIeH4+bmdlsX/i9k5IIyYJW6lcyNP9EuJws00LVhdkWHLoSowcpMPv8ueTJnzhw+//zzUtWrPTw8ePvttxkzZgyPPvpo5UYpqty5c+eIjIwkJSUFAHt7e0JDQ+nQocNtFf4E2LLvLH/99DtDHffiWHwRAMtmHjhHPIFV07YVFrsQouYzacBBTk4OSqkbLtfr9RUelKg+mZmZxMTEsH//fqCk8GePHj0ICAi47cKf17S6soenbSOhGMwcG+AcPhJbjy4ydFqIe5BJyad3795MnjyZN954A09PT5RSJCQkMGPGDCkWepfIyclh48aN7Nq1C4PBgJmZGQEBAfTo0QPr2yzUGX/kArsOnmPM4A4ANOwUwqn4v6jj3wfHzg+gMb+zZCaEqL1MSj5Tpkzh3XffZfz48cYjHXNzc4YOHcqrr75aqQGKylVYWMj27dvZsmULhYWFAHTo0IGQkBDq1Klz2+1mX8lm+w+L8NamsNfjPXzbNcXM2g6XCXPRaMt/46kQ4u5iUvKxsrJixowZvPXWWxw/fhyNRkOrVq2wtZWZIWsrvV5PfHw8sbGxZGeXXOx3dXUlIiKChg0b3labmVfzcbS1IOfAJjI2/EBv3SUA6hYfB5oCSOIRQgA3ST6xsbE33fDSpUvG34ODgysuIlGplFIcPnyYqKgo42vYpEkTIiIiaNWq1W23u3JDEnHro3myUSIWV04DoGvYCueIUVi39K6Q2IUQd48yk8/YsWNNakCj0XDo0KEKC0hUnpMnT7J+/XpOny5JDnXr1iUsLAxPT887vujfImUlHWy2wxUws69L3ZBh2HkHy02iQogbKjP5XCsSCSX3erRp06ZKAhIVLz09ncjISI4ePQqAra0twcHBdOzY8bYKf+r1BqJ2ncLGypzuHUpOp7n6B3FxTQLO3Yfg4N8XrcXt1XYTQtwbTLrm8/jjj7NgwQK8veX0SW1y5coVNmzYwN69e42FP7t27UqXLl1uu/AnwI74ZE7+sRQrKwsCPN9EZ2GGQ/tu2LXxxcym/LXdhBD3HpOSj6Ojo/GitKj58vPz2bx5M3FxccYySNcKf9rZ2ZW7PaUU6Zl51LPTkLXjLxpt/52G1rkorTmagmywcESj0UriEUKYzKTkExgYyNixYwkKCqJ58+bXfWt+7bXXKiU4UT7FxcXs3LmTTZs2GQt/enp6EhYWhrOz8221mZ1XxPRFm2mcsYsBjocw5GYBYN3al7ohw7Gwc6yw+IUQ9w6Tkk9SUhIdOnQgLy/PeN3gGrk7vfoZDAb2799PTEwMWVklycHFxYVevXrRtGnTO2rbSuXzWPa32FlcxZALlk3aUjd0uIxgE0LcEZOSj0x7XTMppYyFP9PS0gBo0KABERERuLq63tYXg5Pnr7B8/RFGD/LGyd4Kcxt7HFu4osm9hHPwY9i4+csXDiHEHTN5MrkjR46wePFikpKS0Ov1uLq6MmLECDp27FiZ8YkynDlzhsjISE6cOAGAg4MDoaGh+Pj43HbhT6UvYvOvP9MjYwvr/3yEhx+7H4BmD76A1spGhk0LISqMScknNjaW8ePH07VrV3r37o3BYCA+Pp6RI0eycOFCunfvXtlxiv8vIyOD6OhoEhMTgZLqE9cKf5qbl39i2ouX88BQhO74Ni5vW0lQVjqYQRPdEaAk+ZhZl3+QghBC3IxJn1affvopzz77LBMnTiy1fO7cuXz22WeSfKpATk4OsbGx7N6921j4MzAwkO7du9924c/NO1PYufoX7rM9hLX+KgAW9ZpRp9uD2Hl2q8jwhRCiFJOST3JyMp9++ul1y/v378+iRYsqOibxLwUFBWzbto1t27ZRWFiIRqPB19eXkJAQHB3vbKRZi0ubaWK1A/RgUb8FTt0fwtYjSOqvCSEqnUnJp0mTJhw8eBAXF5dSyxMTE297CK+4Ob1ez549e4iNjSUnJwcANzc3wsPDadCgQbnbK9YbiNmYwIVTpxn+eH8AmnTvz5lzR6jXdSA2bp3lmo4QosqYlHxGjBjBu+++S1paGh06lMzNEh8fz8KFCxk9enSlBnivUUpx8OBBoqOjycjIAKBp06b06tXruuRvqoJzyVzYvJKWR7bjaLAlKbUrri7OmNs54fLk9IoMXwghTGJy8snJyWHhwoVkZmYCJUN6J02axIgRIyo1wHvJiRMnWL9+PWfPngXA2dmZsLAw2rVrV+7hzQaDnkObN+CUGkv+yZLBCVqNFpvm7rjUk0nchBDVq8zks2XLFjp37mysZjB27FjGjh1LRkYGOp3utsq0iBtLS0sjKiqKY8eOASWFP0NCQvDz87utwp9Fmec5sPAtHPWZ5AManTUOfr1w9H8Ac8f6FRy9EEKUX5nJZ/To0VhYWNChQweCgoIICgrC19eXunXrVmV8d7WsrCxiYmJISEgAQKfTGQt/6nS6crV15nQa9RvWQ2dhhrljfazNDGQV26Lx7IXPA0PQWsnEf0KImqPM5LN161b27NlDfHw8W7duZdGiRWi1Wvz8/IzJqH379uW6oTEmJoZPPvmE06dPU69ePUaPHs2jjz5aIR2pTfLy8ti0aRM7duxAr9ej1Wrp3LkzPXv2LNfssEop8k8eZN8fP+Fw+SjHQt8mpJsXGq0ZzUa8i865UbmTmBBCVIUyk0/dunWJiIggIiICgKKiIg4cOMDevXuJj4/nhx9+IDs7G39/f+bPn3/LHZ09e5bnnnuO+fPn0717dw4fPszDDz+Mp6cnPj4+FdejGqyoqIgdO3awefNm8vPzAWjfvj2hoaEmH1EeO5XJ9vhUQuqcRnMkisILJ6kP6NFQdPYI4AWAXeMWldQLIYS4cybfEm9hYYGLiwsXL17k/PnzXLlyhYSEBJKTk03avkmTJmzbtg07OzsMBgOZmZmYmZndE9eODAYDCQkJbNiwgStXrgDQqlUrIiIiaNKkyU23LdYb0Go0aLWaklpuqxbjd3kPRdpCAMxs62DtE46Vdzht6zes9L4IIURFuGnyyc3NZefOnWzbto2tW7dy7NgxGjZsSEBAAAMGDOCDDz645Yfnv9nZ2ZGdnU1AQAB6vZ6xY8fSunXrO+5ETaWU4tixY0RGRpKeng5Ao0aNiIiIoHXr1rccwbZk9QEid5zg3ae74N7SGY1GQyubq1hcKUTVa02Dbv2xa9cFjZmMXhNC1C5lJp9hw4axb98+HB0d8ff3Z9iwYQQFBdGyZcs72qG1tTV79+7l8OHDjBkzhpYtWzJkyJA7arMmOn36NJGRkaSmpgJQp04dQkND8fb2LjPpXM0txMbKAjOtBn1OFs3SNvGSbjsnEnS4t7wPgJYPPIFSBqyauFZZX4QQoqKVmXz27NlDgwYNePDBB+natSu+vr5YWNz5N2wzMzPMzMzw8fHhoYceYt26dXdV8rl48SLR0dEcOnQIKEm2PXr0wN/f/6aFP7/5M5HVm5J5u19dGl/cQc7hONoaisEMWlmmGtezbHz3HikKIe4dZX4arl+/3ni67ccff6SgoICOHTvSpUsXgoKC8PLyKteNj9u2bWP27NksX77cuKyoqAgHB4c760ENcfXqVWJjY9mzZw9KKczNzQkKCqJbt25YWVldt35RsR7QYGFeMlqwefY+XrFdj8PGLHIANFps2vrj0LEX1m38qrQvQghR2cpMPs2bN6d58+Y8/PDDABw8eNCYjObNm4dOpyMgIICgoCCGDx9+yx21a9eOkydPsnTpUoYPH058fDwrV65k3rx5FdebalBQUMDWrVvZtm0bRUVFaDQa/Pz8CAkJKTOx/rP9BMvWHOTJB9oRHtgKAK+6heSaZWFm54S9bzgOvhFyQ6gQ4q6lUUqp8mxgMBg4cOAAv/32G6tXryY/P994iulW9u/fz/Tp0zl27BhNmjRh0qRJ3Hfffbfc7vTp04SHhxMVFUWzZs3KE26l0ev17Nq1i40bN5KbmwuAu7s74eHh1K9fOmkopSjWKyzMtehzstj91yr0hzdwvn4gg58dB0BR1gUKziVj29YfjVn55+URQoia5Faf27f8lDt//jwJCQns3buXffv2cfDgQczMzPDz8+PZZ58lMDDQ5GC8vb356aefyteDGkYpRWJiItHR0cY6d82bNyciIoIWLa6/t2bXoTS+Xr2fQa55eKtD5B7bRT2DHszAxeG8cT0LxwZYOJa/WrUQQtRGZSafSZMmkZCQQHp6OjY2NnTq1InQ0FBef/11vLy8bqvmWG2XkpJCZGQk586dA6BevXqEh4fj7u5uvP6llKKo2IDOouT/R5e6ndGFv1DncB658P+v5XTGvkMYNm07V1NPhBCiepWZfIqKihg1ahQBAQF4eXmVq4zO3eb8+fNERkYab6i1t7cnJCQEX1/fUv8v+5Mu8u3KXfi0sufxh0pmAm3ZzJmLu/Mwd2qMg284dt4hmNs7VUs/hBCipigz+SxYsKAq46iRLl++THR0NPv37wfA0tKSbt26ERQUhIWFBQaDIjuvCFudhryUBGx2rGdMwW4OH3VDqa5oNBrsPAKwcJiGVfPyT4sghBB3K7myfQO5ubls3LiRXbt2GQt/+vv707NnT2xsbAA4lHKJ739cSw+7k7gbjmLIvYIGMNdoCHC1N7alNddh3cKzmnoihBA1kySffykqKmL79u1s2bKFgoICoGSQRGhoKBaWtly6WsD/zz04nIjmKVZANhgAC+em2HkHY9e+hwwcEEKIW5DkQ8nw8b1797JhwwauXr0KQOvWrYmIiKBx48Yk7Eti/fJFONSrxzMvPAVA/faBnNrzFw7ePXDwDkbXuI2cVhNCCBPd08lHKcWRI0eIiori4sWLADRs1IgmbTpSz6kudumHObdhMfbH9zHE2sDFgoYUFT+BhbkWXYMWtH5pCRrtvTfqTwgh7tQ9m3xOnjxJZGQkp06dAkoKf4aFhVF8+QrHI3+gnu4M6ehLVtaaoWvph2eHYMzN/u/oRhKPEELcnnsu+aSnpxMVFcWRI0cAUBoLnBq2YeLTD2FmZkbWvlgcdSdRaLBs7ol9+x7YenTBzMb+Fi0LIYQw1T2TfC5czOTPv9dx+sQRlFJYmJvhUccCl0v7SU3PRqstqWFn7xGAyh2FnWc3zB2cqzlqIYS4O90TyScrK4uFC+Zj0BcB4G6VS/uiE9hcLpmyoH5jg3Fdrc6aOkEDqitUIYS4J9wTyUer1eJgqcUh7zK+2jQciwswc6iLnVcP7Lx6oGvYUkaqCSFEFbonko+9vT3jHn+Us8umYOfRDbv2PbFq4SkDBoQQoprcE8kHQNfABZcXlqA111V3KEIIcc+7Z5KPRqNBI4lHCCFqhHu3VLUQQohqI8lHCCFElZPkI4QQospJ8hFCCFHlJPkIIYSocrVitJteX1Lg8/z589UciRBCCFNc+7y+9vn9v2pF8klPTwdg+PDh1RyJEEKI8khPT8fFxeW65RqllKqGeMolPz+fAwcOUL9+fczMpCqBEELUdHq9nvT0dNq3b4+VldV1z9eK5COEEOLuIgMOhBBCVDlJPkIIIaqcJB8hhBBVTpKPEEKIKifJRwghRJWT5COEEKLKSfIRQghR5Wp18tm3bx8BAQHGx5mZmbzyyisEBgbSvXt3Zs+eXaq0w8svv4y3tzd+fn7Gn1OnTgFw7tw5Ro8ejZ+fH2FhYfz22281ui979uzhwQcfxM/Pj969e7N27Vrjc9XZl/L04+233y71Wvj5+eHu7s6CBQuqvR/l7QvAqlWr6NWrFx07dmTIkCFs27bN+FxteU0Avv/+eyIiIujUqRNPP/208T1SXf3Yt28fw4YNo3PnzoSEhPD555+jlKKwsJB33nmHwMBAAgMD+fDDD0v1Y+3atdx33334+voyYsQITpw4Ua39uJO+XPPNN98wduzYUsuq+31y21Qt9ddff6lOnTopX19f47JnnnlGjRgxQqWnp6vMzEz15JNPqk8++cT4fO/evdWWLVtu2N7DDz+spk+frgoKCtTu3btV586dVXx8fGV3QylV/r6kpaWpTp06qZUrVyqDwaA2b96s2rdvr86cOVOtfbmd1+TffvjhB3X//ferq1evVms/lCp/Xw4fPqw6deqkjhw5ogwGg1q+fLny9fVV+fn51dqX8vZjzZo1ytfXV23btk0VFRWp77//XoWHh1dbP7Kzs1VQUJBatmyZKi4uVsePH1dhYWHqu+++U7NmzVLDhg1TmZmZ6ty5c2rgwIFq4cKFSimljh49qnx9fdXOnTtVQUGB+uSTT9QDDzyg9Hp9tfTjTvqilFIFBQXq008/Ve7u7uqZZ54p1W51vk/uRK1MPp988okaPHiwWrJkifFNlZubq9zd3VViYqJxvb1796qgoCBlMBhUdna28vDwUJcuXbquveTkZNWuXTvjh55SSk2dOlW99dZbNbIvCxcuVGPGjCnVzqFDh9TVq1errS+3049/O3nypPL19TWuW9tek7Vr1ypfX1916NAhpdfr1a+//qoCAgJUXl5erXpNnn/+eTVt2rRS7fTp00fFxMRUSz+OHTumxo8fX2rZhx9+qF566SXVrVs3tWHDBuPyNWvWqPvuu08ppdSsWbPUc889Z3yuuLhYderUSe3evbvaXo/b7YtSSj322GNq0qRJ6p133imVfKrzfXKnauVpt2HDhrFixQq8vLyMywwGAwDW1tbGZVqtloyMDK5cucKhQ4fQ6XS88cYbBAUFMXjwYDZs2ABASkoKjRo1ws7Ozrht69atOXbsWI3sS2JiIk2bNuW5554jMDCQQYMGcfHiRezs7KqtL7fTj3+bMWMGDz74IJ6enkDte026d++Om5sbAwcOpH379kydOpVPPvkEKyurWvWaGAwGbGxsSrVjZmbGiRMnqqUfrq6uzJs3z/i4sLCQjRs34uHhQXp6Om3atCkVS2pqKoWFhSQnJ+Pq6lqqDy1atCApKanaXo/b7QvA7NmzmTNnDs7OzqXarM73yZ2qlcmnYcOG1y2ztbWla9eu/Pe//+Xy5ctkZmYaX+j8/HxycnLo1KkTzz//PJs2bWLs2LE899xzHDx4kJycnOsK31lZWZGXl1cj+5KVlcWKFSsYPHgwmzdv5plnnmHixImcPHmy2vpyO/245ujRo8Z+XFPbXpOioiJat27NTz/9xN69e3n11Vd56aWXSEtLq1WvSa9evfjll1/Yt28fRUVFLF++nJSUFON7qLpeEyj5sH7ppZfQ6XQ88MADQOkkam1tjVKK/Px8cnNzr4vV2tqa3Nzcau8HlK8vcOPXEqr3fXKnamXyKcvMmTOxsLDg/vvvZ+TIkYSHhwPg4OBAcHAwX331FV5eXlhYWNCnTx+6dOlCVFQUNjY2pT4MoeSN+L/fAKvSzfqi0+no0qULoaGhWFhY8MADD+Du7s7GjRtrXF9u1o9rfvvtNyIiImjQoIFxWU3rB9y8L59//jlOTk74+fmh0+kYOXIkTZo0Yd26dTWuLzfrR//+/Rk7diwvvfQSISEhHD16lK5du+Lg4FCt/UhPT+fxxx/n4sWLfP3118Zv+v+O59oHro2NDdbW1hQUFJRqIy8vD1tb22p/Pcrbl5up7r7ciVoxn4+pLl68yLRp04wv5saNG2nTpg3W1tZERkaSnZ3NoEGDjOsXFBRgaWlJmzZtjN9QbW1tgZLD2X8ftle1m/XlRofVxcXFADWuLzfrxzVRUVFMnjy51HY1rR9w876cOXOGZs2alVrf3Nwcc3PzGteXm/XjwoULhIeH88QTTwAlZfFDQ0N57rnnsLOzq5Z+HD16lKeffpquXbvy3nvvodPpAKhfvz4pKSk0bdrUGEvLli0xNzfH1dWVlJQUYxt6vZ6TJ0/Spk0b6tSpU22vx+305WZq2t9WedxVRz4zZszgs88+o7i4mFOnTvHf//7XOAFdYWEh06dPZ//+/ej1elatWkVCQgJ9+/aldevWtGvXjlmzZlFQUEB8fDx//PEHAwcOrJF9GTRoEHFxcfzzzz8YDAb++OMPkpOTCQ8Pr3F9uVk/ADIyMjh16hR+fn6ltqtp/YCb9yU0NJRVq1axd+9e9Ho9K1euJCUlhZCQkBrXl5v1Y9u2bYwePZr09HTy8vKYPXs2DRs2xNvbu1r6kZmZyVNPPUW/fv2YMWOG8cMaYMCAAcybN49Lly6RlpbGggULjF8u+/XrR0xMDFu2bKGwsJA5c+ZQr149OnToUG2vx+325WZq2t9WuVT3iIc7sX379lJDSE+ePKlGjRql/Pz8VHBwsFq0aFGp9b/++msVFhamOnTooAYPHqy2b99ufO7s2bNqzJgxqlOnTio0NFT98ssvVdYPpcrfl61bt6rBgwcrPz8/1bdvX7Vx40bjc9XZl/L2IyEhQXl6et6wrdr2mixevFiFh4erjh07qocffljt2rXL+FxteU0MBoOaOXOm6tKli+rcubOaNGmSSk9Pr7Z+fPXVV8rNzU116NBB+fr6Gn8mTZqk8vPz1dSpU1XXrl1VQECAmj59uiouLjZu+88//6g+ffooX19fNXz4cJWSklJt/bjTvlwzZ86c64ZaV/f75HbJZHJCCCGq3F112k0IIUTtIMlHCCFElZPkI4QQospJ8hFCCFHlJPkIIYSocpJ8hBBCVDlJPkJUkBdffJGgoCAuX7583XNz587Fz8+P06dPV31gQtRAknyEqCD/+c9/0Ov1fPLJJ6WWnzp1ii+//JIXXnjhuhI8QtyrJPkIUUHq16/PSy+9ZKwKfc20adPw9PRk5MiR1RidEDWLJB8hKtCjjz5Khw4dePfdd1FKERkZydatW5k+fTq5ublMmTKFgIAAAgMDee6550hLSzNue+LECcaNG0fnzp1p3769sT7ZNe7u7nz66ad07dqVAQMG3HCaZSFqCymvI0QFO3LkCEOGDGHq1Kl8+eWXDBw4kAkTJvDSSy+Rnp7Oa6+9hqWlJfPmzSMlJYWVK1diZmbG/fffj5eXFxMmTEApxbx589i6dSsbN25Ep9Ph7u5OixYtmD9/PkVFRbRr1666uyrE7avOwnJC3K0+/vhj5enpqfr166cKCwvVyZMnlZubmzp//rxxnYKCAuXr66tiYmJUTk6OWrRokcrMzDQ+v3//fuXm5qbOnj2rlFLKzc1NLVy4sKq7IkSluKvm8xGippg4cSKLFi1i/PjxWFhYkJSUBECfPn1KrZeXl2ecemH48OH8+eef7N+/nxMnTnDw4EGAUqfXmjdvXnWdEKISSfIRohJcm9r42r96vR4LCwtWrVp13bqOjo7k5OTw6KOPotPp6NWrF6GhodjY2PD444/fsF0hajtJPkJUgdatW1NUVERubq7xWk1OTg4vv/wy48aNIy0tjePHj7N7924sLS0B+PvvvwFQcllW3IVktJsQVaB169aEhYXx2muvsWvXLpKTk3n11Vc5cOAAbdq0oWHDhhQVFfH3339z5swZ1q9fzwcffACUzMIrxN1Gko8QVeSjjz6iffv2TJgwgYceeoj8/Hy++eYb7O3t8fX15cUXX+S///0vffv2Zd68ebz++us4OjqSmJhY3aELUeFkqLUQQogqJ0c+QgghqpwkHyGEEFVOko8QQogqJ8lHCCFElZPkI4QQospJ8hFCCFHlJPkIIYSocpJ8hBBCVDlJPkIIIarc/wMHbEaj4NffdwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "results = run_simulation1(system)\n",
     "plot_results(census, un, results, 'Constant growth model')"
@@ -258,7 +481,36 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap06-fig01.pdf\n",
+      "Saving figure to file figs/chap06-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAEPCAYAAACdhMnXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABY8ElEQVR4nO3deVxUZfv48c/MwLAqoCJuiIoLKiIgyCLuW+5rVirmUm6VlaXpU2abZWWaW2loPmkuabll7hvuO6DiDqIiCiig7DAz5/cHX+cXj6LjAghe79fLl8xZr5thuDjn3Pd1qxRFURBCCCGKkLq4AxBCCPHikeQjhBCiyEnyEUIIUeQk+QghhChyknyEEEIUOUk+QgghipwkH/FcatOmDfXq1TP+a9CgAS1atGDq1Kmkp6cXd3j5JCUl8ffffxtfBwcH8+233xbJuSdMmMCYMWOK5FwPMmbMGCZMmGDStrNnz6Z3796FHJEoKcyKOwAhCjJ27FjjLyuDwUBUVBTjxo0jNTWVKVOmFHN0/9/3339Peno63bp1A/J+yZqZyUdLiIeRKx/x3LKxscHR0RFHR0ecnJwIDAxk0KBBbNmypbhDy+d/x2nb29tja2tbTNEIUTLIn2eiRDEzM0Or1QJ5VxgREREoikJ4eDiffPIJPXv2ZOnSpSxZsoS4uDhq1arF2LFjadmyJZB3m0qr1ZKens727dupWLEib7/9Nj169DCeY+PGjcybN4+YmBgqV67MyJEj6dWrl3F/nU7HlStXuHLlCq6urpw4cQKAevXqcf78eYKDg3F3d+ejjz565PFmz57N+fPnqVatGqtXr8ZgMNCjRw/+85//oNFo0Ol0zJ49m7///pv4+Hjs7Ozo0qULEyZMQKPRPPR7tXr1alasWEHnzp2ZN28eer2eYcOG0axZMz799FOio6Px9PTkhx9+oEKFCgAcPHiQGTNmcOHCBRwcHBgwYADDhg1DpVIBsHbtWubOnUtCQgKdO3cmJycHa2tr4zlDQ0OZPn06ly9fxtnZmaFDh9KnT5+nft9F6SNXPqJEMBgMnDx5kt9//5127doZl+/du5emTZuycuVKWrZsyS+//MLMmTMZM2YM69evp127dowaNYpz584Z91m9ejW2trasXr2aAQMGMGHCBA4dOgTAhg0bGD9+PK+++irr168nODiYSZMmsXv3buP+GzZsYMCAAfz222+EhITQqVMnWrduzb59++6L25Tj7d69m/T0dP744w8++eQTli1bxs6dOwFYsGAB69atY+rUqWzZsoXx48ezdOlS4/pHOXPmDBEREaxYsYIRI0bw448/MnHiRMaPH8/ixYu5fPkyISEhABw7dow33niDVq1asWbNGt5//31++uknli1bBsChQ4f4+OOPGTJkCGvWrKFMmTLs2rXLeK6LFy8yZswY+vfvz4YNG3jrrbf49ttv+eeff0yKVbxY5MpHPLemTp3KDz/8AEBOTg4qlYo2bdrw4YcfGrexsrJi+PDhqFQqFEVh0aJFjBw5ki5dugDwzjvvEBERQUhIiPFYVatWZfLkyajValxdXTly5AgrVqzA39+fX3/9lX79+tG/f38AatSowcWLF5k3bx6tWrUCoGbNmvTs2dMYg6WlJQaDAUdHx/vaYMrxLC0tmTRpElqtlpo1a/Lbb79x6tQp2rdvT+3atfnmm29o2rQpANWqVWPhwoVcuHCB9u3bP/J7mJuby6effoqDgwMDBw7k+++/p2/fvgQEBADQtm1bLl26BMDixYsJCgpi9OjRxnbevHmTefPmMWDAAFasWEHbtm2NbZk4cSJ79+41nmvBggV0796dV155BYDq1atz9epVfv31V+P7IcQ9knzEc2vEiBF0794dAHNzcypUqGC85XZP1apVjbeEkpKSSE5OxtPTM982TZo0YfPmzcbXXl5eqNX//6Lfw8PD2FstKiqKwYMH37f/v/96d3Z2NrkNphyvcuXK+dpla2tLbm4uAO3atePIkSN8//33XL58mQsXLnDt2jU6dOhg0vnLlCmDg4MDkJfk/jd+S0tLcnJyALh06ZKx08S/Y/3hhx+4e/cuFy9eNL4fACqVikaNGhlfX7x4kQsXLrBhwwbjMp1OJ50vxAPJT4V4bjk4OODi4vLQbSwsLB749b8ZDAYMBoPx9f8+K9Hr9cZk9KBjGAwG9Hq98fW9X+KmMOV45ubmBe4/Z84cfvvtN/r06UOHDh344IMP+OCDD0w+/4OeC/078T4q1nudKfR6vfHq8t/Mzc2NbdHr9QQHB/Pqq6+aHJ94cckzH1Fq2NraUrFiRcLCwvItDwsLo1atWsbXZ86cybf+5MmTuLm5AVCrVq0H7u/q6lrgee9deT3Ikxzv3xYuXMhHH33EhAkT6NmzJ9WqVSMuLu6+JPAsPCjWEydOUL58eezt7albty4RERH51v/7e+nq6sqVK1dwcXEx/jt48CC///77M49VlHySfESpMnz4cObPn8/GjRuJiYlhzpw57N+/n+DgYOM2Z86cYcaMGVy+fJmFCxeyd+9e4/rhw4ezatUqli9fTkxMDEuXLuWvv/5i0KBBBZ7T2tqa69evExsb+8B4Hvd4/+bk5ERoaChXrlwhMjKSd999lzt37hhvlT1Lb7zxBvv27eOnn37i8uXLbNiwgfnz5xMcHIxKpWLQoEHs2bOHRYsWcfnyZWbMmJGvI8fQoUPZvXs38+bN48qVK2zatIlvv/0WJyenZx6rKPnktpsoVQYOHEhGRgbfffcdt2/fpm7dusybNw8fHx/jNkFBQVy5coUePXpQvXp15s6di4eHB5BXWWHy5MmEhIQwZcoUXFxc+Oqrr/I96/hfvXr1Yvv27XTp0oXt27fnW/ckx/u3qVOn8vnnn9OtWzfKlStHu3bt6Nu3L5GRkU/w3Xm4+vXrM3v2bGbOnMlPP/1EpUqVePvtt43PrDw9PZk5cyY//PADM2bMoEWLFvk6Eri7uzNr1ixmzZrFnDlzcHR0ZOTIkQwbNuyZxypKPpXMZCpeJBMmTCAjI4NZs2YVdyhCvNDktpsQQogiJ8lHCCFEkSvS224nT57kq6++IioqCjs7O4YMGZLvQbAQQogXQ5Fd+RgMBkaNGkX//v05fvw4c+fOZfr06Rw7dqyoQhBCCPGcKLLebnfu3OHWrVvGAX8qlQq1Wv3QAXb3ZGVlcfr0aRwdHR9ZTFEIIUTx0+v1JCYm4u7u/sCB2UV62+2LL75g2bJlqNVq9Ho9Y8aM4a233nrkfseOHWPAgAFFEKEQQohnaenSpfmGOtxTZFc+BoMBKysrvv/+ezp27EhYWBhvv/027u7uxnL3BblXsHHp0qVUqlSpKMIVQgjxFG7evMmAAQMeWHAXijD5bNu2jcOHDzNu3DgA/Pz86N27N3/++ecjk8+9W22VKlWiWrVqhR6rEEKIZ6OgRyVF1uEgLi7uvpIgZmZmUvFWCCFeQEWWfJo1a0Z0dDTLly9HURROnTrFn3/+KfN8CCHEC6jILjvq1q3L3LlzmTlzJtOmTaNChQp8+OGH+WalFEII8WIo0nteLVu2fOTzHSGEEKWflNcRQghR5CT5CCGEMFIUhX379jFjxowHzlH1rEhXMyGEEEDeeMxNmzYZy579e7r3Z02SjxBCCHJzc1mzZg1nz55Fo9HQu3dvXFxcCu18knyEEOIFl5mZyYoVK7h69SoWFha89tprhZp4QJ75FLl69epx6tSp+5YHBwezcOFC4+uNGzfSq1cvvLy8aNKkCYMHD35kBfADBw4wdOhQ/Pz88PX1ZeDAgRw8ePCZt0EIUXrcuXOHRYsWcfXqVcqUKcPQoUMLPfGAJJ/n0pEjR5g8eTIff/wxx48f5+DBg7Rp04ahQ4dy7dq1B+6zevVqxo4dy2uvvcbevXvZv38/PXv2ZOTIkZKAhBAPdPPmTRYuXEhiYiKOjo4MGzaMihUrFsm5Jfk8hyIiInBxccHHxwe1Wo1Wq2XQoEH079+f5OTk+7bPzMxkypQpfPnll7Rv3x6tVotWq6Vv37689dZbREVFAXkPD+fNm0fbtm3x8/NjzJgx3Lp1C4DDhw/TpUsXpk2bhr+/P0FBQXz77bfGc6xatYp27drh6+tL7969CQ0NNe7n5eWVL542bdqwefPmh+4nhCheUVFRLFq0iNTUVFxcXBgyZAh2dnZFdv5Sm3y6fbCObh+sy7fsi4WH6PbBOo5E3jQu23wwhm4frGPOqnDjstt3Mun2wTpe/3xzvv3fm7Gbbh+s49K1lMIMndatWxMVFcWQIUP4/fffOXPmDHq9ngkTJuDh4XHf9mFhYeTm5tKqVav71g0fPpyBAwcCsHjxYtatW8eiRYsIDQ2lfPnyjB071rjtpUuXUKvV7N27lxkzZrB48WLCw8NJSkri888/Z/78+Rw9epTXXnuNSZMm8ajZOJ50PyFE4QoPD2fZsmXk5OTg7u7OwIEDsbKyKtIYSm3yKclq167N2rVrqV27NkuXLqVXr14EBgYyY8aMB3Z9TEpKws7O7pET861cuZLRo0dTvXp1LC0tGT9+PMeOHSMmJsa4zciRIzE3N8fX15dq1aoRExNjnPhvxYoVREREGK9gVCrVQ8/3pPsJIQqHoiiEhoaybt06DAYDgYGB9O7du1gKPJfa3m5//9DjvmWfDvO/b9lLATV4KaBGvmXl7aweuP+P77d66rjMzc0fmEB0Oh1ardb42sXFhY8//hiA5ORkQkND+eabb7CxsWH48OH59nV0dOTOnTvk5ubel4DS0tIwMzPD0tKSuLg4Pv30Uz7//HPjejMzM65fv46ZmRnW1tZYW1vni9VgMODg4MBvv/3GL7/8wpAhQ9BqtQwePJgRI0Y8tK0P208SkBBFS6/Xs2HDBsLDw1GpVLz00ks0bdq02OIptcnneVW5cmXi4uLw9PQ0LlMUhWvXrlG5cmUA+vfvT7NmzYyzvDo4ONCzZ0/Onz/PmTNn7juml5cXFhYWhIaG3leo9ccffyQyMpLly5dTsWJFPv7443y356KionB2diYsLKzAmO/cuYNer+fnn39Gp9Nx4MAB3nrrLby8vNBoNOh0unxtuXPnziP38/Pze+zvnRDiyWRlZbFq1Sqio6MxMzOjT58+uLm5FWtMctutiHXp0oWffvqJixcvAnD37l1+/PFH1Go1AQEBxm0WL17M1q1bycnJIScnh+PHj7Nx48YHVgHXarWMGzeOTz/9lB07dqDT6cjMzGTJkiWsXLmSt99+G4BevXrx008/cePGDfR6PSEhIbzyyitkZWU9NOakpCSGDh3KoUOHMDMzM85MaG9vj7OzMzqdjm3btmEwGPjtt99IT09/5H5CiKKRkpLCr7/+SnR0NDY2NgwePLjYEw/IlU+Re+uttzAzM+Ott94iMTERrVaLj48PS5YswcbGBoABAwZgbW1NSEgIEydORK/XU6tWLT744AO6du36wOP269ePMmXK8MsvvzBx4kQURcHNzY2QkBDjVcabb76JwWBgwIABpKSkUKdOHRYsWEDZsmUfGnPNmjX58ssvmTx5MgkJCZQrV45JkyZRr149AD766CO+/vprPvnkE7p164a3t7dJ+wkhCldcXBzLly8nLS2NChUqMGDAgOfmjz+VUgK6HsXGxtK2bVt27Ngh02gLIYQJzp8/z19//UVubi41a9akX79+WFpaFtn5H/V7W658hBCiFFEUhUOHDrF161YAGjduTLdu3dBoNMUcWX4mJZ+LFy8SGhrKqVOnSEpKQq1WU6FCBRo1akTr1q2LpBSDEEKIh9Pr9WzatInjx48D0KpVK1q0aFFg71JDThYqM3NU6vyJSa83oFarCrVX6kM7HBw7doxBgwbRq1cvdu7ciZ2dHV5eXri7u2NhYcH69evp0qULQ4YM4ejRo4UWpBBCiIfLyspi2bJlHD9+HI1GQ58+fWjZsmWBCST75mWu/zqO5D0r8y1PTs3inR92cfhfg/ELQ4FXPhMnTiQqKor+/fszZ86cAh9Kp6Wl8c8///D1119Tr149pk6dWmjBCiGEuF9ycjLLly8nMTERGxsbXn311QKfjyuKwt1jG7m9YzHodWRcPIJDUF9UZnljBO1tLbC10rLl0BX83SsXWswFJp82bdrwzTffPPIAtra2vPLKK7zyyivGel5CCCGKxtWrV/njjz/IyMjA0dGR/v37F9ijTZeWwq1/fiLjUt5tuTLeHUhz7813y8IZ2dsDO1sLVCoV44N9sLO1KNS4C0w+7du3f+yDvfTSS08VjBBCCNOFh4fz999/YzAYcHV1pW/fvg/s0aYoCncOriF5/2qUnEzUlrY4dhmNjZsfc0MOcvxcAo4O1gzt1hCACvaFX+fNpA4HiqKwY8cOTp06hU6nu68w5Pjx4wslOCGEEPczGAzs2LGDAwcOAODn50eHDh1Qqx/8GF+lUpEVdwklJxPzml5YtxqCTZWqAAzq3IDK5W3o3ap2kcUPJiafKVOmsGzZMtzc3IwDIe+RGl1CCFF0srOzWbNmDefPn0etVtOpUyd8fHzu2y4r9hwqMwssKtUEoHzbQVwv58u32zMI0iQw5pW85FOrqh0jet9fLb+wmZR81q1bxzfffEOPHvcX2xRCCFE0UlJSWL58OQkJCVhaWtKvXz9q1qyZb5vclASSdi4h/ewBLKrWo8rrU1CpVJg7VKJyI1tyNu0kOTUbvUFBoy6+iweTarup1ep8hTDFkzNlGu3Zs2fj4eHBpUuX8m3zoInb7lm9ejX169fHy8sLLy8vGjduTPv27Zk5cya5ubn5zuPu7m7c7t//MjIyALh9+zYTJ04kMDAQT09P2rZty7Rp08jOzr7vvCNGjMDDw4Pbt28/8fdECPFoMTExhISEkJCQQPny5XnjjTfyJR5Ddga3dy4hdt4Y0s8eQGWmJdHKhV/XRhi3qeJoy08ftWHyG/7FmnjAxCuf3r178+uvvzJp0qQnnvdh/fr1TJ48Od+yrKwsAgIC+PXXX5/omKVZdnY2H374IStXrsw31cLDuLq6smHDBuPrs2fPMnHiROLi4vLNSvr+++8zbNiwAo/z/vvvU7VqVTZt2oSdnR2XL19m7Nix3L17ly+++MK4XVxcHOHh4bRq1YoVK1YYq3ALIZ6tY8eOsWnTJgwGA7Vr16ZPnz7GjgWKQU9q+A6SQpdjyLgLgK17CzQ+fflwdhg5uVdo4eNCHWcHAKpUsC22dvybSZkkNjaWXbt2sWnTJipXrnzfnDF//vnnI4/RvXt3unfvbnx99uxZBg8eLJ0VCuDn50d8fDw//vjjE3+P6tevz8yZM3nppZd48803qV3btAeKERERDBs2zDilbs2aNfnPf/7D3r178223cuVKmjdvTp8+fRg3bhzDhw9/5IR2QgjT6fV6tmzZYhzEHxAQQLt27fJ1LDBkpnF7x2KUnExUTnWo3GkYllXrADDwpUysLc2pVaXopsc2lUnJp169es+0EnFubi7jxo3j7bffLrTS3tFT+hS4rkKnEZT17gDA3RNbubVpfoHb1vr4L+PXsQvHkXMzusD1z5KVlRXff/89/fv3p2XLlk88/42LiwsuLi4cOXLE5OTTuXNnJkyYQPfu3WnatCleXl74+vri6+tr3Ean0/Hnn38yffp0fH19sbGxYdOmTfn+wBBCPLmMjAxWrVpFTEwMGo2Grl27Gh9/5NyKxdyhEiqNGRobO8q3H8y2sET+e9qKL5rbce/mfK8i7sH2OExKPvfmg3lWli1bhkajYcCAAc/0uKWNh4cHo0aNYsKECaxbt+6Jj2Nvb2+c4A1g5syZ/Pzzz/m2ad68OTNmzADyejeuW7eOjRs3smrVKjIyMvDy8uKTTz6hYcO8cQD3yi3dmwmxf//+LFmyRJKPEM/AzZs3+eOPP0hJScHGxoZXXnkFZ2dn9Ol3SN7zB3fDtlG+/RDsfDsDUNazHYbbFzA/d564W+l4lYBZS0x+gBMaGsr8+fO5dOkSer0eV1dXXn/9dbp06fJYJ8zJyWHhwoVMmjSpwD7pz4KpVyRlvTsYr4Iepdqw758mJMD0abTvGTlyJHv37uXzzz+nX79+T3TOpKQkHBwcjK/ffffdhz7zUavV9OrVi169eqHX6zl37hwhISEMHTqUXbt2YW1tzYoVK4iNjaVZs2bG+FNSUggPD5fOKUI8hcjISNatW0dubi5VqlThlVdewdbakpSDa0ne/xdKdgaoVEREXERrdcNYAqdb81q08nbG0aHwB4g+CyYln7Vr1/LJJ5/Qt29fXnvtNQwGAydOnGDChAno9frH+mv33nODtm3bPlnEJZwp02j/m0aj4bvvvqNHjx5YW1s/9vmio6O5evWqybft9uzZw4cffkhoaChWVlZoNBoaNmzIlClT8Pb25saNG5ibm3P48GHWrl2br4zHlClTWLx4sSQfIZ6AwWBg165d7Nu3D8ibCqFLly5kXzpK7M4l6FISALBy9eJs+XbM2BxP1VuR+DaohEatwlJrhqW25MySY1Kk8+fPZ+LEifluk/Xo0YO6desSEhLyWMlnx44ddOrUqVCvep5n96bRrlOnDnXq1OHu3bssXLgw3zTa/6t69ep8/PHHfPzxxyYnIEVROH36NJMmTeLll1++byxAQXx9fbG1teWTTz7h/fffp1q1aty6dYsFCxZQu3ZtatSowfTp0/Hz86NOnTr59n355ZcZMWIE8fHxODk5mXQ+IQRkZmayZs0aLl68iEqlokOHDvj5+ZFx4SgJq38AQOVQFaeOQ7B29cJRbyAsMYzOATWLvcv0kzIp+cTFxdG8efP7lgcFBeXrwmuKiIiIZ/4MqSQxZRrtB+nbty+7d+9m//79BW4TFRVlHAek0WioVKkS3bt354033si33YwZM5gzZ859+8+bNw8/Pz+WLl3KrFmz6N+/P3fv3sXa2poWLVrw66+/otfrWb16NR999NF9+wcEBFChQgWWLVvG+++/b+q3RIgXWkJCAitWrCA5ORkrKyv69OyOa928jljWdZpgVr0RG6+X4/CtOsyrnleJwEyj5oP+TYoz7Kdm0jTaPXr0oH///rzyyiv5lq9YsYJFixaxZcsWk0/o6enJggULHlgOoiAyjbYQojT69/OdSk4V6VDdAtXZ3VR74wfM7BwBMBgU3p8RSlpWLp8O88Ol0oOnt3nePJNptEeMGMFHH33ExYsXjffzw8LCWLVq1X0DRx8lPDz8sbYXQojS5n8Lg9avWgHvu0dRwu6gAL8v+IO+o0dga2WOWq3iP0OaUq6sJeZmpedxhUnJp3PnzhgMBhYtWsSff/6JhYUFtWrVYubMmbRu3bqwYxRCiFIjPT2dv/76i8uXL6NSqfCzTaP2zZOoVGBRzY1Vd73YEWNO2UMx9G6d91zVqdzjdzZ63pncNaJr16507dq1MGMRQohS7fr166xcuZK7d+9iZa6hueECTpnpqO2dcGw3COt6frx0JRm3G3dp6+tc3OEWqgKTz3fffcfbb7+NtbU133333UMPIiVyhBCiYIqicOLECTZt2oRer6datWr0aOXP7T+nsvqOD7b1OjLSzRsAtxrlcKtRrpgjLnwFJp97E8fd+7ogMp+PEEIULDc3l3/+Xk/EqdNA3nCGjh07otFoSBvwIyfmH6GfY8noRPAsFZh8lixZ8sCvhRBCmCbpViIrlvyXxLsZaDDgr44l9lZTNBoNADWqlWfRpx2wMNcUc6RFr8DkExoaavJBWrZs+UyCEUKI0kBRFE7u3MDG/cfIUdSUIZtWFeG3q82IOZlD5+QMKjrkdSJ4ERMPPCT5jBgxwqQDqFQqzp49+8wCEkKIkkyv1/P33K+ISAZQU12bTfcunSnXqBmJh69Su5qdMfG8yApMPufOnSvKOIQQosRLS0vjzz//5EoyqFDQ6Mzx7T+C8nUqAdDR36WYI3x+FJh8/ncK54KoVCpcXV2fWUDiyZw6dYq+ffty/vz5R24bHBxMq1atHlrZWghhGl1aCin7VpFgVp5NEZdJS0vD1saGcs6e7LukkGMoPQNDn6UCk0/Xrl1RqVQ8qvqO3HYTQryIDDmZ3Dn0N8mH1nEyuywRBidARY0aNejTpw/mFlYMVIG52Yv5TOdRCkw+O3bsKMo4XhixsbF069aNDz74gJ9//pnc3FxGjx6Nubk5P//8Mzk5OYwcOZKhQ4dy5swZvv32WyIjI3FwcOC1115jyJAhqFQqcnJy+Prrr9mwYQPW1tb07Nkz33kuXbrEV199RWRkJOXLl2fUqFH06NGjeBotRCmi6HXcDdtOyr5VpKelss9QnRtKGRQFcixdCA4OfmGr9j+OApNPuXLlsLLKm5QoMzOzyAJ6FpYtW8bFixeL5Fx16tShf//+j7VPRkYGFy5cYNeuXWzdupXx48fTo0cPdu7cyd69e3nnnXfo2LEjr7/+Om+88QYhISFcuXKFESNGoNVqGThwILNnzyYiIoJNmzah0WjyVQpPT09nyJAhvP7664SEhHD27FlGjhxJ5cqVjTOPCiEeX27yTW4s/xJd8k0SFGv20oAMRYWVtTVlqnjTr2ugJB4TFZh8vL292bdvH+XLl8fLy+uBg0kVRZHbbk9o8ODBaLVa/P390ev1DBo0CK1WS/PmzdHr9ezevRsHBwdjr8M6derw5ptvsnLlSgYOHMiGDRsYN24cjo55lW/fffddBg0aBOR1k7e2tjZOpeDh4UGfPn34448/JPkI8RTMylYgVweH9c5cVPJmB3Z2dqZv376ULfviDRR9GgUmn99++w07OzsAFi9eXGQBPQuPeyVSHO7NAHrvr6QyZcrke52SkkKVKlXy7VO1alXi4uIASExMzDdhW9WqVY1fX79+ndjY2HzTVuj1eho2bPjsGyJEKZZ9M5rkvauo0Gk4ZrYOpGdmsc/Wm5jkWAC8mzSlc6cOxkGjwnQFJp9//4X876/T0tIwMzPD0tKycCN7wSmKwvXr1/Mtu3btmvFKp2LFisTFxdGkSd6EUvHx8cbtKlasiJubG3/99ZdxWUJCgpRCEsJEuck3Sdq9jPQzeZM3/hOTi1v3V9mwYR1paWlYWVnRs2dP6tatW8yRllwm3Zw0GAz88ssvtGrVCl9fX7y8vGjbti0rV64s7PheWP7+/qSnpzN//nxycnK4ePEiCxcuNHYa6N27N/PnzycuLo6UlBRmzZpl3LdVq1bExcWxatUqdDod165dIzg4mOXLlxdXc4QoEXSpydzaHMK1eWNIP7MflcacE2aebEu2Z8WKpaSlpeHi4sLIkSMl8Twlk6ZU+Pbbb9mwYQOjRo2iYcOGGAwGwsLCmDZtGklJSYwcObKw43zhlC1blgULFvDNN98QEhKCjY0Nr7zyinFszsiRI0lPT6dnz56Ym5vTr18/Dh06BICdnR2//vorX3/9Nd999x0WFhZ069aN0aNHF2eThHiupUbs5NbmEBRdDqhU2Hq0wcyrE/F/78AxK+92d4sWLWjZsqV0KngGTJpG28/Pj2nTptG8efN8y7dt28Znn33G/v37Cy1AkGm0hRCFL+v6BeL+O5GTOc5ku/egcaPKrF+/nszMTGxtbenduzc1a9Ys7jBLjGcyjTaQ7+H2Pc7OzmRnZz9dhEIIUcQUvY7UiJ1k34iiQueRqFQqLKvWRd/jK5YtvUizpBuc+2MPkNfTtEePHtjY2BRz1KWLScln6NChfPnll/zwww9UrFgRgLt37zJt2jSCg4MLNUAhhHhWFMVAeuR+kvasQJd8E4BtKTXoP6ATAPZOFWjlvJvEmwmo1Wrat2+Pn5+fdNYpBAUmH39//3zf8OTkZNq0aUPFihXRaDTcvHkTvV5PfHw87777bpEEK4QQT0JRFDIuHSd59zJyEq7kLbSrxH/j6nE6IpdO3bK4dP4UW7duRafTUa5cOfr27UvlypWLN/BSrMDkM378eMn2QogST1EM3Fz2BZkxeTMya8qUx6FFP8p4tCbg0FUGVrZmy8a1xqK8np6edOrUCa1WW5xhl3oFJp8ePXo89sApvV4vg62EEM8VlUqNmWN1MmIusiXdnR4D36CsS954uXqVFNas/p20tDQsLCzo2rUr7u7uxRzxi6HA/oJ9+/Zl48aNj6xqDaDT6Vi3bh19+vR5psEJIcTjyrkVS/xf07h7ao9xWbkWr3C84QfEVgxEUZuh0+nYunUrS5YsIS0tDWdnZ0aOHCmJpwgVeOXz008/8eWXX/L111/Tpk0bWrRoQe3atXFwcMBgMJCcnMy5c+c4evQomzdvpnHjxsydO7coYxdCCKPcOwkk71lF2qndoBi4cv48FhZuNK5bEY2lDf27NmaQRk1iYiILFiwgPj4elUpFixYtaNGihYzdKWIFJp/KlSvz008/ERkZydKlS5k8eTK3b9/O9xyofPnytGjRgl9++YVGjRo98mQJCQl89tlnHD58GEtLS/r16yedFYQQT0WXlkLKgb+4e2Ir6HWg1pDg2JQ5551peDCGxnXzeuiaadQcOXKE7du3o9PpcHBwoHfv3jJ2sJg8sqt1w4YN+frrrwGIi4vj1q1bqNVqHB0dHzj252FGjx6Nu7s7Bw8eJD4+nuDgYFxdXenateuTRS+EeKFlxZ7nxrLPUXKzUVBRxr0FDs37UdnGkf7Hr9G+aXUAUlNTWb9+vXGGZi8vLzp27IiFhUVxhv9CM3mQKUCVKlXuq7RsqoiICK5cucKyZcvQarU4OzuzZMkSefOFEI9FUQyoVHm3yCwq1UJnbsOZdEeOWzdjcreX0ahVmAOdA/OqEZw5c4YNGzaQmZmJlZUV3bp1o379+sXYAgEmFhZ9Fk6fPk3dunWZNWsWQUFBtG3blm3bthkHrQohxMMoulzuHN1I7Px3yUq9A4DKzJzqb05nq20PajVsiE5vMG6flZXFmjVrWLVqFZmZmbi6ujJq1ChJPM+Jx7ryeRp37twhLCwMPz8/du7cSXR0NMOGDcPR0ZFu3boVVRhCiBJGMehJOxVK8t6V6O4kArB49kIGj3sXrbkGC9syzPqgNRr1/38eHRMTw5o1a7h79y5mZmZ06NABHx8fGbv4HCmy5KPVarGysuKdd95BpVLh5uZG79692bZtmyQfIcR9FEUh4/xhkkKXk3srb/I28wrO/JXSiJ2J5fG+dAuf+nnPne8lntzcXHbs2MHhw4eBvEcFvXr1okKFCsXTCFGgIks+tWrVwmAwoNPpMDc3B/LGBwkhxIPc2jiP1PDtAGjKOlKu1avYNmzOS9fu0MNcQ80qdvm2v379OmvWrDH2ym3RogXNmzeXge/PKZOST0xMDN9//z2nTp1Cp9PdN/D04MGDjzxGs2bNKFu2LDNmzOC9994jOjqa1atX88UXXzxZ5EKIUkcx6FGp85KFrXtzEk4eZENqQ6p7d+G1RnkDQN1cyuXbR6/XExoayr59+1AUhQoVKtCrV68n7hwlioZJyec///kPycnJvPnmm9ja2j7RiSwsLPj999/58ssvad68OVqtluHDh9OxY8cnOp4QovTIuR1HcugyQEW57u9hbqbBysUd+n5H7LqztK/54I5J8fHxrF27lps38ypU+/v706ZNG+PdFfH8Min5nD59mr/++os6deo81cmcnZ355ZdfnuoYQojSQ5eWQsreldwN2waKgVzM2Gw4zOt9AwFoWKcSsz5wuq+jgMFgYN++fYSGhmIwGLC3t6dnz564uLgURzPEEzAp+VStWpXU1NTCjkUI8YIw5GRy59DfpBxah5KbBSo11AliylEn1GdSeS1Xj9Y87/bb/yaehIQE1q1bR1xc3tTWPj4+tGvXTsYMljAmJZ/33nuPzz//nFGjRuHi4nLfJW3t2rULJTghROljyM3m2rwx6FOTALCu40O51gPROjozol4cXvUcjYkn334GAwcOHGD37t3o9Xrs7Ozo3r07tWrVKuomiGfApORzr/7ae++9d986lUrF2bNnn2lQQojS5V4nJZVKhdrcAm1NLy6En2RtujfvNX8VrWNZAJo1fnAngf+92vH29qZDhw5ytVOCmZR8duzYUdhxCCFKqewb0dze8RtlfbtgW68pAE4vDWNdzjkcUjIxNy+40Iper2f//v3GZztly5alW7ducrelFDD5mQ/A1atXuXjxIgaDAVdXV7ncFUIUSHf3Fkm7l5F2KhSA6Ku3cBxQm3ou5VCbW/Bmz0ZoNAUnnps3b7Ju3TpjTza52ildTEo+6enpfPzxx2zevBlzc3MURUGv1xMYGMjs2bOxtrYu7DiFECWEISeTlINruXNoPYouBzRmxJbzY86FajQJjeKjQXnjdApKPDqdjr1797Jv3z5jT7Zu3brJH7uljEnJZ+rUqVy4cIGVK1ca5+05efIkH3/8MdOmTePTTz8t1CCFECVD9o1obq78Gn1aMgA2DZpRrvUAnCzK0f/IFWOl6YJcu3aN9evXc+vWLQB8fX1p164dWq220GMXRcuk5LN161bmzp2Lh4eHcVnjxo2ZPHky7777riQfIQQA5uUrk6szEKuvwCGrVozv8Srq/5vioGfLgp/T5OTksHPnTmNNtvLly9OtWzcZt1OKmZR8FEXBwcHhvuX29vZkZGQ886CEECVD7p0EUvavpnzb11FbWKHWWlEp+CumzDtJHcdyZGTrsLV6eLWBqKgoNmzYQEpKCiqVisDAQFq1aoWZWZGVnhTFwKR319fXlzlz5vDtt98aL39zcnKYO3cuTZo0KdQAhRDPH0NOFikH1xif6xy6mEant8diplFjW7EKsz6sQBnrh98qy8jIYMuWLZw8eRKASpUq0b17dypXrlwUTRDFzKTk89FHH9G/f39atWplnIjp7NmzWFhYsGDBgkINUAjx/FAUhfQz+7i9Y7FxkGgkdVgZWwXt0Wt09M+7TfawxKMoCqdOnWLLli1kZGRgZmZGy5YtCQgIkArULxCTkk/16tXZuHEj69evJyoqCgsLC1566SW6deuGpaVlYccohHgO5CRc5daWELKungFAW8mVCh2GcivVnl7xqbTxqfbIY6SkpPDPP/9w6dIlAGrUqEHXrl0pX758ocYunj8m31QtW7YsAwcOLMxYhBDPsdw7CWRdPUO6Yslt1860fPU1VCo1TYGmDSo9dF+9Xs+hQ4fYvXs3Op0OS0tLOnTogKenp8wu+oIqMPkEBATwzz//UK5cOfz9/R/6A2LKfD5CiJJFUQxkX7+IZbV6ANjU8SHD6zW+3GnAO6cmrVQFDxD9t+vXr/P3338THx8PgLu7Ox07dnzi6VlE6VBg8hk/frzxh+Ojjz4qsoCEEMUv+2Y0tzaHkB13kYSgcfi38APAvXNfvmiQRD2X+3u/3neM7Gx27NjB0aNHgbzesZ07d37qqVlE6VBg8unVq5fxa5VKRefOne8b6JWRkcHKlSsLLzohRJHSZ6WTHLqcu8e3gGLgjsGKtVvCqdWoERUd8iqZuNUo99BjKIrCmTNn2Lx5M2lpaahUKgICAmjVqpVM8iaMCkw+OTk56PV6FEVh4sSJ+Pr6Uq5c/h+6yMhIpk+fzuDBgws7TiFEIVIUhfTIfdze/l/06SmgUlO2aVfWXK+Dk4U15mam3WJLSkpi06ZNxg4F1apVo0uXLlSq9PBnQuLFU2DyWbduHZMmTTI+62nXrt0Dt2vZsmXhRCaEKDIp+/8iOXQ5AKpKdajSdSQWTjV4z6CgUT+6Q8C96tN79+41diho164d3t7e0qFAPFCByefll1+mRo0aGAwGXn/9dWbPno2dnZ1xvUqlwtramrp16xZJoEKIwlPGozWx+zeyPqUB9mXb8ZZTDQCTEk90dDQbN27k9u3bAHh4eNChQwdsbGwKM2RRwj20q7Wvry+QN59PlSpV5C8YIUqJrGtnSTm+Bcs2I7Ava4VZ2fJUHTGbusev06OFq0nHSE1NZcuWLURGRgJ59di6dOlCzZoPLx4qBJg4zqd8+fIsXryYixcvotfrjctzcnKIjIxk8+bNhRagEOLZ0Welk7RzCalh2wDYesWK4DHDUalUlLO34eW2j76TYTAYOHz4MLt37yYnJwczMzNatGhBYGCgVCgQJjMp+UyePJnt27fj6+vLnj17aN26NVeuXCEqKorhw4cXdoxCiKekKArpZw9we+uveR0K1Bp2ZjfieJoL3dNysC9j2gRtMTExbNq0iYSEBADc3Nzo2LEj9vb2hRe8KJVMSj67d+9m2rRptG7dmi5duvDOO+/g5ubGxx9/bJxlUAjxfNLdSSRh0y9kRZ0AwKKaG46dR9Iyy5ZBlcqiNX/01Upqaipbt27l9OnTQN6YnU6dOskzX/HETJ7J1M3NDYDatWsTGRmJm5sbgwcPZujQoYUaoBDi6aSdP0xW1AkyDOZke/TGu3tfVCo1pgz11Ov1HD58mNDQUOMttqCgIJo1ayZTHoinYlLn/apVq3LhwgUAatWqZXzAqFarSUtLM/lkK1eupGHDhnh5eRn/rVmz5gnCFkI8jCE70/i1nU8nbru05VfNa5g3bI3KxLI4UVFRzJs3j23btpGTk4ObmxujR4+mZcuWknjEUzPpJ+i1117jww8/5JtvvqF9+/a89tprODg4cPjwYdzd3U0+2enTp3njjTd4//33nzhgIUTBDLockvasIunoZhJbTKBZQENUag1N+o/C02DA3OzRt9iSk5PZsmUL58+fB6BcuXJ06tSJ2rULnolUiMdlUvIZPHgwFSpUoEyZMjRo0IDJkyezaNEinJyc+Pjjj00+WWRkJG+++eYTByuEKFjmldPc2jiP3KQbqBU4snUbTZrUw1JrhlqtQq1+eOLJyclh3759HDhwAL1ej1arpUWLFvj7+0svNvHMmXzt3LVrV+PXvXv3pnfv3o91otzcXC5cuMDq1av56quvsLKy4uWXX+bNN9+U8UNCPAV9Ziq3tv9G+sldAJiXr8YObWtaBgZiqX30R/ze5G7bt28nNTUVyBso2q5dO8qUKVOosYsXV4E/me+++67JB5k5c+Yjt0lKSsLDw4PevXszZ84cLl26xOjRo7GysiI4ONjkcwkh/r+0i8e5vnom5rp00Jjh0Kwv9gE9GWxmWgHP69evs3nzZmJjYwGoXLkynTp1wtnZuTDDFqLg5GNtbf1MT+Tk5MTSpUuNrxs0aEBwcDBbt26V5CPEEzKztUejy+BirhPagCHUau5r0n6pqans3LmT8PBwAGxsbGjbtq1M7iaKTIHJ55tvvnmmJzp//jzbtm3j7bffNi7Lzs6+b5oGIUTBFF0u147twc49CDtbCywru2LW/ROctJXxrOf0yP1zc3M5ePAg+/btIzc3F41Gg7+/P82bN8fCwrSBpkI8CyY98/n3FcuDDBgw4JHHsLGx4ZdffqFy5cr06tWL06dP8/vvvzNp0iTTIhXiBZcZc4ora35Cm5HA1nO3eXlwPwBqNPJ85L6KonD69Gl27NjBnTt3gLzqBO3bt79vqhQhioJJyWfhwoX5Xuv1em7fvo2ZmRne3t4mJZ9q1arx008/MX36dL766iscHBx466236NSp05NFLsQLQpeaTNLOxaSd3oMWSNCXJUdtiaIoJt0ii42NZcuWLcbnOk5OTnTs2FEKgIpiZVLy2blz533L0tPTmTRpEvXr1zf5ZEFBQQQFBZkenRAvMMWg58zGlZif+hszQzYqMy32zfpg17Aj/g6P7oWWnJzMjh07jIPCbWxsaNOmDZ6enqjVpg00FaKwPPEwZRsbG9555x0GDhwoY3eEKAQ396zFKuJPAJRqjXHuPhxzh0fPCJqVlcWePXs4cuQIer0eMzMz/P39CQoKkuc64rnxVDUyrl69SnZ29rOKRYgXXmpaJmVsrQBwCuzMyZMHSKjaklbdu2D+iAKger2eY8eOERoaSmZmXnkdDw8P2rRpk28iSCGeByYlnweN+UlPT+f48eP5Bp8KIZ6MQZfDgeX/xTzmINYvf0kjt2qotVZ4jvnhkfsqisKZM2fYsWMHycnJALi4uNChQweqVKlS2KEL8URMSj4PGvNjZ2dHx44d6dGjxzMPSogXhaIoZFw4wu3tv1ElJR7UcP7YHhq59Tdp/ytXrrBt2zauX78OQIUKFWjXrh1169aV8TriuWZS8nnWY36EeNEpisLRAyewP7sGs/izAJiVr0a6Rz86BTZ75P6JiYns2LHDWPzTxsaG1q1b4+XlJZ0JRIlg8jOfI0eOsGLFCi5evIharcbNzY3g4ODHqmothMgT9uciyp3fiFqloLawwaHlq5Rt0hHVI4p/3r17l927dxMeHo6iKJibmxMYGEhgYKAM2BYliknJZ/369UyYMIHWrVvTtWtX9Ho9J0+e5NVXX2Xu3Lm0bNmysOMUosTT6w1oNHlXJbUbuZNwYSNJVZrh0Xco2jL2D903MzOT/fv3c/jwYXQ6HWq1miZNmtCyZUtsbW2LIHohni2Tks/cuXP56KOPeP311/MtX7hwIdOmTZPkI8RDZOXo2L7yL5KvXeaVD8ehNddQpp4vlqPnUPsRXadzc3M5fPgw+/fvJysrC4CGDRvSunVrypcvXxThC1EoTEo+8fHxtGjR4r7lbdu2ZdasWc88KCFKi6zYc9ze/hsNrl/AoKg4eewkPgFeqFQqtA9JPHq9nrCwMEJDQ42zBdeoUYN27dpRtWrVogpfiEJjUvJp164dS5Ys4dNPP823fN26dbRt27ZQAhOipFIUhaOHT1L12hayLxzOW2ZZBqVxT5r4eTxy39OnT7N7926SkpKAvGkO2rZtS61ataQHmyg1TO5qvWrVKg4cOICXlxdmZmZERkZy9uxZmjVrlm8ckClz+whRWimKwu6fv6dq0lGyVQZUZlrs/LpjH9ATtYXVQ/e7cOECu3btIj4+HoDy5cvTpk0b6tevL0lHlDomJZ/c3Nx8g0l1Oh316tWjXr16hRaYECWRSqWiWlkFVZKBu5V9ce/7JmZlH/5sJjo6mp07dxrH6pQtW5YWLVpIt2lRqsk4HyGeQnpGFofW/kmGtgLd+r4EQM1uQ8lM7Uvtaq4P3ffatWvs2rWLy5cvA3ljdYKCgvDx8cHM7KkqXwnx3DP5J/z8+fMsWLCAS5cuodfrcXV1JTg4GG9v78KMT4jnkqIopJ8/RML236l55ybXdOW51a45FextMLNzpIydY4H7xsXFsWvXLi5dugSApaUlgYGB+Pn5yVgd8cIwKfmEhoYyevRoAgMD6dixIwaDgbCwMIKDg5k/f75MkyBeGBlZuZw5sI/KMZvJvpGXPLIsylHBryfl7Qp+pgNw8+ZNdu/ebaxKoNVq8fPzIyAgACurh+8rRGljUvL58ccfGTVqVL4psAHmzJnDzJkzJfmIF0L6rZvs//lraquvkw1obOxxaP4yZTzbotKYF7hffHw8oaGhnD37f2V0zMxo2rQpzZo1e2DdRCFeBCYln6ioKH788cf7lnfr1o2QkJBnHZMQzw2d3oDZ/1UlsLQtg4vZbXIMWrTeXXFu2we11rLAfRMSEggNDeXMmTMAaDQafHx8CAoKkqoE4oVnUvKpUqUKZ86cwcXFJd/yyMhIGWUtSq3Q0BPc2LMGz9fewq12JTSWNlR55SOsnGpgZlO2wP0SEhLYs2ePcQZRjUZDkyZNCAoKokyZR89AKsSLwKTkM3DgQD777DPi4+Np3LgxAGFhYcyfP59hw4YVaoBCFDXdnUSS9/1J1fCdOKsNnNu5BrfaowAoU6vgQaLx8fHs2bMn35WOt7c3QUFBlC1bcLIS4kVkcvJJT09n/vz5xsmqKlasaJxGW4iSLjtXz87QU9S4vReLy/tAr0OtUpFR1Y+23Xo/dN+bN2+yZ88e4zOde0mnWbNmMoOoEAUwuav1iBEjGDFiBElJSWi1WrlnLUqV46uXUvPiBrQqPaDCpmEQDs37oS1fcB21uLg49uzZY+y9du/2WrNmzeRKR4hHMDn5XLlyhT///NM4n0/9+vV5+eWXqVTp4VV5hXgeZWXriE/KwKVyXpKo36AmyZf0ZFVujGuX17Fwcilw36tXr7J3717jOB0zMzNj0pFnOkKYxqTks2/fPkaOHEnt2rXx8PBAr9ezdetWFi1axH//+188PB5eLFGI58nVawls+XUB1mYGqk34DxqNGnv3ZlhXdMbCqcYD91EUhcuXL7N3715iYmIAMDc3x9fXl4CAALkTIMRjMin5fPfddwwZMoQPPvgg3/Jvv/2WKVOm8McffxRKcEI8KwaDAvoc7h7bhOHgWtqapaJHze2bN6lYtQoqlfqBiedewc+9e/caa69ZWFjQtGlT/P39ZZyOEE/IpORz5coVeve+/6HrK6+8wvLly595UEI8Kzq9gZVbzpAWvo0OVqcwZNwBQFO5LpXa9Me6apUH7mcwGDhz5gx79+4lISEBACsrK/z9/WnatCmWlgWP7xFCPJpJycfX15fNmzczatSofMv379//2LXdMjIy6N27Ny+//LJ00xaFTpWTQYPwaZRVUjFkgEVlVxxavoZVLc8HTlOg0+kIDw/nwIEDxp6dZcqUISAggCZNmkjtNSGeEZOSj7u7Oz///DNHjhyhSZMmxvl8du7cSZcuXfjuu++M244fP/6hx/rqq6+4cuXK00UtRAHupuewPvQi3VrUxs7WAo2VLbZVXTGkJlC5/UCs6zZ9YNLJzs7m2LFjHDp0yDhzqIODA82aNaNx48ZSZVqIZ8ykT9Tx48dp3LgxOp2Ow4cPG5d7e3tz48YNbty4AfDICa82bdpETEyMVMIWhUJRFNYsXolrwi52pfWlZ79OANTs9z5qS2tUqvvnxklLS+Pw4cMcO3aMrKwsAJycnAgKCqJBgwYyn44QhcSk5LNkyZKnPlFcXBzff/89ixcvZuLEiU99PCEA0jJz0en0aBPPkxy6nOZJF8EMKuVEAHnJR2N1f0+027dvc+DAASIiItDr9QBUr16doKAgateuLTOHClHIiuRegl6vZ9y4cbz77rtUq1atKE4pXgCHTt9g9crNvGx/ivKZebdyNTb22DfrQ1mv9g/cJzY2lgMHDhirEQC4ubkRGBiIs7NzkcQthCii5PPzzz/j5OREjx49iuJ04gVROekYIy02QCaoLW2xD+hBWZ/O91Wavtdd+sCBA1y9ehXIq0bg4eFBYGAgFSpUKI7whXihFUny2bBhAwkJCfj4+AB5Pd4iIiK4du0an332WVGEIEq4XJ2BbUeuEBuXzPC+ec8MqzVpwZVja7D3boedfw80ljb59tHpdJw8eZKDBw9y69YtIG+Mjo+PD35+flKNQIhiVCTJZ/PmzfleBwcH06pVK+lqLUyWEh9H8pZfaKS5SUzsD9SoVg6NjR01x8xHZZZ/Irf09HSOHTvG0aNHSU9PB6Bs2bL4+/vj7e2NhYVFcTRBCPEvBSafo0ePmnwQX1/fZxKMEPcoisLpqNs0qGxOysE1pB3bTIA2FwUVjrnXgXIA+RLPrVu3OHjwICdPnkSn0wFQqVIlAgICaNiwIRqNpjiaIoR4gAKTT3BwcL7XKpUKRVFQq9Wo1Wp0Oh1qtRpzc3MiIiIe66TPovecKL0UReGLn3fhGLcX6zLnUemyAbCpH4hDy1fzVZpWFIXo6GgOHTpkLPQJULduXQICAnBxcZGea0I8hwpMPidOnDB+vWnTJhYvXswXX3yBu7s7Go2Gc+fOMXnyZHr16lUkgYoXS+/stdhYXQcdWLl6U67Va1hUqmVcf+95zuHDh43lb8zMzPDw8CAgIEA6EQjxnCsw+fy7YOKsWbOYPXt2vurVbm5ufPrpp7z55pu8+uqrhRulKNWu3rzL7/+cpm2TKvh51kClUlG1ZXcyTu2mQpuBWDq7GbdNTU3l6NGjHD9+nIyMDABsbW3x9fXFx8dHCn0KUUKY1OEgPT0dRVEeuPzeAD0hnoRi0HNl7ybaX99IzO16+Hn+BwAHr7Y4eLU13jK7fv06hw8fJjIyEoPBAOQ9z/H39zdejQshSg6Tkk/Hjh2ZOHEiEyZMoEGDBiiKQkREBFOnTpWxO+KxJadmEX8rjWqZ50nes4Kqt+NAAxUdklEUAyqVGpVKhV6v58yZMxw5coTY2Fgg79lj/fr18fPzo3r16vI8R4gSyqTkM2nSJD777DNGjx5tvNIxMzPj5ZdfZty4cYUaoChdoq+nsOjnZXSyDEerug2Amb0TDi36YduwOSqVmtTUVI4fP87x48eNRT4tLS3x9vbG19cXe3v7YmyBEOJZMCn5WFpaMnXqVD755BMuX76MSqWiZs2a2NjYPHpn8cIzGBTU6rwrlEpKIkOsdgCgsnGgfIt+lGncBtQaYmNjOXr0aL5ba46OjjRt2hQPDw+ZzkCIUqTA5BMaGvrQHW/fvm38umXLls8uIlFq6A0K60KjOHE4jP+81xNrS3Osq9XBsn4QNtXqUMa7A3pFRVjESY4ePcrNmzeBvFtrbm5uNG3alBo1asitNSFKoQKTz4gRI0w6gEqlylekUYh7cuIuUP7wPIbqr3JkTxVadWgGQJXe73P79m227dhFeHi4cSoDa2trvLy88PHxkVtrQpRyBSafc+fOGb+OiorC1dW1SAISJVeuTs/2o9fwccxEd2w1mVEncAYMZpY0qWLAYDBw4cIFjh49SnR0tHG/qlWr4uvrS8OGDWXSNiFeECZ90gcNGsS8efNo1KhRYccjSrCly7ZiH7WFVO01AFTmltj5dkbdsA0nzl7gxMyZ3L17F8jrsOLu7o6vry9VqlQpzrCFEMXApORjZ2dn7HUkxD3ZuXqysnXY2eYV6mxufQmV9hqK2hw7n5dIquLN9shznJsXYhwnVq5cOXx8fPD09MTKyqo4wxdCFCOTko+fnx8jRozA398fZ2fn+6oCjx8/vlCCE8+vsPMJLF2+jUYutrw+JG+sl0uH14izsuKKVQ3+OX2G5ANrgP8/NsfHx4eaNWtKBwIhhGnJ59KlSzRu3JjMzEwuXLiQb538InlxKIqCSqUiO+4S5Y79wWjzE9y+UY6cnE7ExsZy/Phxzp27gcFwHci7Yvb29sbLy0vmzhFC5GNS8pEq1C+21Iwc/txxEf3NC3SxjSQzOgyATLUlyZUaMu/nn0lOSQHy/hipW7cuTZo0oXbt2qjV6mKMXAjxvDK5a9H58+dZsGABly5dQq/XU7t2bQYOHIi3t3dhxieeA7l3blH1xE/UNrtJejzc0JQnpkwdYpKzMFy9A+RN1nbvKqds2bLFHLEQ4nlnUvIJDQ1l9OjRBAYG0rFjRwwGA2FhYQQHBzN//nyCgoIKO05RhOJupbEn7DqvtKuLSqXC3rECjpbZhOuqctnMibRsHdzOQKVSUa9ePZo0aYKrq6tc5QghTGZS8vnxxx8ZNWoUb7/9dr7lc+bMYebMmZJ8SpHcrEz+/CkED+Usxx0/wkKTQVhYGJeznPM20OlwcHDA29sbT09PbG1tizdgIUSJZFLyiYqK4scff7xvebdu3QgJCXnWMYkidv5KEjUdVKSFbeHO8S0EqHOJMjhw+Z9l5OjyaqyZmZlRv359vL29ZXZQIcRTMyn5VKlShTNnzuDi4pJveWRkJOXLly+UwETRWLh4KxYXd6CzusZlfRmiDE6k8H/jb3QGqlSpgqenJ40aNcLS0rJ4gxVClBomJZ+BAwfy2WefER8fT+PGjQEICwtj/vz5DBs2rFADFM+WoigYFNCo8+bLcb69i1jzXP7KrYtC3tWMlZUVjRo1wsvLi0qVKhVzxEKI0sjk5JOens78+fNJTk4GoGLFirzzzjsMHDiwUAMUz07kuWscW/8nZWvVw8pWzalTp8jIsAQs87pI16mDp6cndevWlZlBhRCFqsDks3//fnx8fIzVDEaMGMGIESNISkpCq9XKg+YSJPtmNDcObiQ68ix39GWIibxrXOfo6Ejjxo3x8PCQgaBCiCJTYPIZNmwY5ubmNG7cGH9/f/z9/fH09KRcuXJFGZ94QrnZWRz552/uRB/jeoaOG0oZFCoCYKk1x8PTi8aNG1O5cmXpPCCEKHIFJp8DBw5w4sQJwsLCOHDgACEhIajVary8vIzJyN3dXcZ2PGcMBgNXrlxh/4Y/uZqUSi7WAKhVUKemC54+fnJbTQhR7ApMPuXKlaNdu3a0a9cOgNzcXE6fPk14eDhhYWEsW7aMtLQ0fH19+fnnn4ssYHG/7MxMQtet5nLiHdJzs0hNTf2/NRrsrC3xaxZEY08vrK2tizVOIYS4x+TyOubm5ri4uHDr1i1u3rzJ3bt3iYiIICoqyuST7dq1i+nTpxMbG0uFChUYNmwYr7766hMF/iJTFIU7aTnkJlzixJ5tnItNJMWgNa63t7fHw8ODRo0aUaFChWKMVAghHuyhyScjI4OjR49y8OBBDhw4wMWLF3FycqJp06Z0796dr7/+2uSJwOLi4hgzZgw///wzQUFBnDt3jn79+tGgQQM8PDyeSWNeBGcvxLB++R9Yqu6SotxLOFrMMVCpnAMtOvfAtVYNeY4jhHiuFZh8+vfvz8mTJ7Gzs8PX15f+/fvj7+9PjRo1nuhEVapU4eDBg9ja2mIwGEhOTkaj0UivuYe4lZLJnrBYzFQGXByyOH36NNFRURhQyFK0aDBQq7wNHj5+1PcNkuc4QogSo8Dkc+LECSpWrEifPn0IDAzE09MTc3PzpzqZra0taWlpNG3aFL1ez4gRI6hVq9ZTHbO0uTdnTm5uLicOHeTcwb3o1BD2f+tVKhVVrKBRAzcat+qEla1UkBZClDwFJp9t27YZb7ctX76c7OxsvL29CQgIwN/fn4YNGz7RrR0rKyvCw8M5d+4cb775JjVq1KB3795P1YjSIPr6HZZsjMRenUwVdSwXY2+SawD+rzOhc7VqNPLwoEGDBtjY2BRrrEII8bQKTD7Ozs44OzvTr18/AM6cOWNMRnPnzkWr1dK0aVP8/f0ZMGCAySfUaDRoNBo8PDzo27cvW7dufWGTT06uHjONisuXL3P4wEHU1y+RolKR8n/ry6kyqetkR2P/Fjg18kellttqQojSweTebg0aNKBBgwYMGTKE06dP89dff7F+/Xq2b99uUvI5ePAgM2bMYOXKlcZlubm5L+TEY9HXU/hlxW5slEQsDMlkZmbmrVCpsCcTVzsNjbx8qda0LRorqToghCh9Hpl8bt68SUREBOHh4Zw8eZIzZ86g0Wjw8vJi1KhR+Pn5mXSi+vXrc/XqVRYvXsyAAQMICwtjzZo1zJ0796kb8bxTFIWU1CySEuOIPBnB2bNnscrVYQAygQoVKtCwYUOcdfE4Nw5E6+hc3CELIUShKjD5vPPOO0RERJCYmIi1tTVNmjShdevWfPTRRzRs2PCxe1bZ29sTEhLClClTmDlzJlWqVGHKlCk0bdr0qRvxvNLr9Rw5Ecn6zfuwMtxGwWBcV4Zsapil4t//XSrXcJWu0UKIF0qBySc3N5fXX3+dpk2b0rBhw2dSRqdRo0asWLHiqY/zPEtNzyLs5FmS4q9w/vx5srKysAQUoCxZuKjvUte5EtW9O2Dr5odaa1XcIQshRJErMPnMmzevKOMo0XJycrh06RLHTkQQfekiKpViXOdYoQKVk8/h6mhLda8gyjQMQmNjV4zRCiFE8TO5w4HILyMjg72Hwrl04Twpt2LR6fNuqalUYE8W9QPa4e3dmAoVKqBPvyMJRwgh/kWSz2O4e/cu586d49y5c8TExKAo/7rCIZ3q6jvUsNdSxSMQe/8A1BZ5hTwl8QghRH6SfB5CURQSExMJPxnJibCTZGekGNepVCoqqVKprrpDDTtLnBr5Y1M/EK2T1FUTQohHkeTzPwwGAzExVzh5+gxXYy6RnJxiXKfGgGvd+jRsUJ86deqQE7EVq5qNJeEIIcRjkuQDZGdnExUVxYULFzh37hzZ2dnGdRboqKa6i7PqLhXty1C7d0/MLSwBsA7oWUwRCyFEyfbCJp87d+5wIjyS4+GnybgTj6LkH4PjrLqLs/oOVStXoUz9lljX80NbvmoxRiyEEKXHC5N89Ho9J05d4vq1y9y8HkN8fPy/1io4O1enXr161KlZncytIdg37IRN3aaY2TkWW8xCCFFavRDJJy0tjdk/zScnM824zAw9VVRpVFXdpbKlQsPBk1DfK9w5+ItiilQIIV4ML0Ty0el0KDkZ2JBDNdVdqqnuUsk8lzJ1vLCp1wvr2t7/P/EIIYQodC9E8rG3t+f9Yf25sfhTbOo2waZ+ANa1vFBrLYs7NCGEeCG9EMkHwLJSLWqM/RW1uUVxhyKEEC+8Fyb5qFQqVJJ4hBDiufD0paqFEEKIxyTJRwghRJGT5COEEKLISfIRQghR5CT5CCGEKHIlorebXq8H4ObNm8UciRBCCFPc+3197/f3/yoRyScxMRGAAQMGFHMkQgghHkdiYiIuLi73LVcp/56O8zmVlZXF6dOncXR0RKORMjhCCPG80+v1JCYm4u7ujqXl/dVkSkTyEUIIUbpIhwMhhBBFTpKPEEKIIifJRwghRJGT5COEEKLISfIRQghR5CT5CCGEKHKSfIQQQhS5Ep18Tp48SdOmTY2vk5OT+fDDD/Hz8yMoKIgZM2bkK+3wwQcf0KhRI7y8vIz/rl27BsCNGzcYNmwYXl5etGnThr/++uu5bsuJEyfo06cPXl5edOzYkc2bNxvXFWdbHqcdn376ab73wsvLi3r16jFv3rxib8fjtgVg7dq1tG/fHm9vb3r37s3BgweN60rKewKwdOlS2rVrR5MmTXjjjTeMn5HiasfJkyfp378/Pj4+tGrVitmzZ6MoCjk5OUyePBk/Pz/8/Pz45ptv8rVj8+bNdOjQAU9PTwYOHEhMTEyxtuNp2nLPf//7X0aMGJFvWXF/Tp6YUkL9888/SpMmTRRPT0/jsuHDhysDBw5UEhMTleTkZGXIkCHK9OnTjes7duyo7N+//4HH69evnzJlyhQlOztbOX78uOLj46OEhYUVdjMURXn8tsTHxytNmjRR1qxZoxgMBmXfvn2Ku7u7cv369WJty5O8J/+2bNkypVOnTkpqamqxtkNRHr8t586dU5o0aaKcP39eMRgMysqVKxVPT08lKyurWNvyuO3YtGmT4unpqRw8eFDJzc1Vli5dqrRt27bY2pGWlqb4+/srS5YsUXQ6nXL58mWlTZs2yu+//65MmzZN6d+/v5KcnKzcuHFD6dGjhzJ//nxFURTlwoULiqenp3L06FElOztbmT59utK5c2dFr9cXSzuepi2KoijZ2dnKjz/+qNSrV08ZPnx4vuMW5+fkaZTI5DN9+nSlV69eysKFC40fqoyMDKVevXpKZGSkcbvw8HDF399fMRgMSlpamuLm5qbcvn37vuNFRUUp9evXN/7SUxRF+fzzz5VPPvnkuWzL/PnzlTfffDPfcc6ePaukpqYWW1uepB3/dvXqVcXT09O4bUl7TzZv3qx4enoqZ8+eVfR6vfLnn38qTZs2VTIzM0vUe/Luu+8qX375Zb7jvPTSS8quXbuKpR0XL15URo8enW/ZN998o4wdO1Zp1qyZsnv3buPyTZs2KR06dFAURVGmTZumjBkzxrhOp9MpTZo0UY4fP15s78eTtkVRFOW1115T3nnnHWXy5Mn5kk9xfk6eVom87da/f39Wr15Nw4YNjcsMBgMAVlZWxmVqtZqkpCTu3r3L2bNn0Wq1TJgwAX9/f3r16sXu3bsBiI6OplKlStja2hr3rVWrFhcvXnwu2xIZGUnVqlUZM2YMfn5+9OzZk1u3bmFra1tsbXmSdvzb1KlT6dOnDw0aNABK3nsSFBRE3bp16dGjB+7u7nz++edMnz4dS0vLEvWeGAwGrK2t8x1Ho9EQExNTLO2oXbs2c+fONb7Oyclhz549uLm5kZiYiKura75Yrly5Qk5ODlFRUdSuXTtfG6pXr86lS5eK7f140rYAzJgxg1mzZlG+fPl8xyzOz8nTKpHJx8nJ6b5lNjY2BAYG8sMPP5CSkkJycrLxjc7KyiI9PZ0mTZrw7rvvsnfvXkaMGMGYMWM4c+YM6enp9xW+s7S0JDMz87lsy507d1i9ejW9evVi3759DB8+nLfffpurV68WW1uepB33XLhwwdiOe0rae5Kbm0utWrVYsWIF4eHhjBs3jrFjxxIfH1+i3pP27duzatUqTp48SW5uLitXriQ6Otr4GSqu9wTyflmPHTsWrVZL586dgfxJ1MrKCkVRyMrKIiMj475YraysyMjIKPZ2wOO1BR78XkLxfk6eVolMPgX57rvvMDc3p1OnTgQHB9O2bVsAypYtS8uWLfn1119p2LAh5ubmvPTSSwQEBLBjxw6sra3z/TKEvA/i//4FWJQe1hatVktAQACtW7fG3Nyczp07U69ePfbs2fPcteVh7bjnr7/+ol27dlSsWNG47HlrBzy8LbNnz8bBwQEvLy+0Wi3BwcFUqVKFrVu3PndteVg7unXrxogRIxg7diytWrXiwoULBAYGUrZs2WJtR2JiIoMGDeLWrVssWrTI+Jf+v+O59wvX2toaKysrsrOz8x0jMzMTGxubYn8/HrctD1PcbXkaJWI+H1PdunWLL7/80vhm7tmzB1dXV6ysrNi+fTtpaWn07NnTuH12djYWFha4uroa/0K1sbEB8i5n/33ZXtQe1pYHXVbrdDqA564tD2vHPTt27GDixIn59nve2gEPb8v169epVq1avu3NzMwwMzN77trysHYkJCTQtm1bBg8eDOSVxW/dujVjxozB1ta2WNpx4cIF3njjDQIDA/niiy/QarUAODo6Eh0dTdWqVY2x1KhRAzMzM2rXrk10dLTxGHq9nqtXr+Lq6oq9vX2xvR9P0paHed5+th5HqbrymTp1KjNnzkSn03Ht2jV++OEH4wR0OTk5TJkyhVOnTqHX61m7di0RERF06dKFWrVqUb9+faZNm0Z2djZhYWH8/fff9OjR47lsS8+ePTl8+DBbtmzBYDDw999/ExUVRdu2bZ+7tjysHQBJSUlcu3YNLy+vfPs9b+2Ah7eldevWrF27lvDwcPR6PWvWrCE6OppWrVo9d215WDsOHjzIsGHDSExMJDMzkxkzZuDk5ESjRo2KpR3JyckMHTqUrl27MnXqVOMva4Du3bszd+5cbt++TXx8PPPmzTP+cdm1a1d27drF/v37ycnJYdasWVSoUIHGjRsX2/vxpG15mOftZ+uxFHePh6dx6NChfF1Ir169qrz++uuKl5eX0rJlSyUkJCTf9osWLVLatGmjNG7cWOnVq5dy6NAh47q4uDjlzTffVJo0aaK0bt1aWbVqVZG1Q1Eevy0HDhxQevXqpXh5eSldunRR9uzZY1xXnG153HZEREQoDRo0eOCxStp7smDBAqVt27aKt7e30q9fP+XYsWPGdSXlPTEYDMp3332nBAQEKD4+Pso777yjJCYmFls7fv31V6Vu3bpK48aNFU9PT+O/d955R8nKylI+//xzJTAwUGnatKkyZcoURafTGffdsmWL8tJLLymenp7KgAEDlOjo6GJrx9O25Z5Zs2bd19W6uD8nT0omkxNCCFHkStVtNyGEECWDJB8hhBBFTpKPEEKIIifJRwghRJGT5COEEKLISfIRQghR5CT5CPGMvP/++/j7+5OSknLfujlz5uDl5UVsbGzRBybEc0iSjxDPyH/+8x/0ej3Tp0/Pt/zatWv88ssvvPfee/eV4BHiRSXJR4hnxNHRkbFjxxqrQt/z5Zdf0qBBA4KDg4sxOiGeL5J8hHiGXn31VRo3bsxnn32Goihs376dAwcOMGXKFDIyMpg0aRJNmzbFz8+PMWPGEB8fb9w3JiaGkSNH4uPjg7u7u7E+2T316tXjxx9/JDAwkO7duz9wmmUhSgopryPEM3b+/Hl69+7N559/zi+//EKPHj146623GDt2LImJiYwfPx4LCwvmzp1LdHQ0a9asQaPR0KlTJxo2bMhbb72FoijMnTuXAwcOsGfPHrRaLfXq1aN69er8/PPP5ObmUr9+/eJuqhBPrjgLywlRWn3//fdKgwYNlK5duyo5OTnK1atXlbp16yo3b940bpOdna14enoqu3btUtLT05WQkBAlOTnZuP7UqVNK3bp1lbi4OEVRFKVu3brK/Pnzi7opQhSKUjWfjxDPi7fffpuQkBBGjx6Nubk5ly5dAuCll17Kt11mZqZx6oUBAwawYcMGTp06RUxMDGfOnAHId3vN2dm56BohRCGS5CNEIbg3tfG9//V6Pebm5qxdu/a+be3s7EhPT+fVV19Fq9XSvn17WrdujbW1NYMGDXrgcYUo6ST5CFEEatWqRW5uLhkZGcZnNenp6XzwwQeMHDmS+Ph4Ll++zPHjx7GwsABg48aNACjyWFaUQtLbTYgiUKtWLdq0acP48eM5duwYUVFRjBs3jtOnT+Pq6oqTkxO5ubls3LiR69evs23bNr7++msgbxZeIUobST5CFJFvv/0Wd3d33nrrLfr27UtWVhb//e9/KVOmDJ6enrz//vv88MMPdOnShblz5/LRRx9hZ2dHZGRkcYcuxDMnXa2FEEIUObnyEUIIUeQk+QghhChyknyEEEIUOUk+QgghipwkHyGEEEVOko8QQogiJ8lHCCFEkZPkI4QQoshJ8hFCCFHk/h/7FrJNQJxmWAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "results = run_simulation2(system)\n",
     "plot_results(census, un, results, 'Proportional model')\n",
@@ -317,7 +569,28 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.update_func1(pop, t, system)>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.update_func1(pop, t, system)>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "update_func1"
    ]
@@ -333,7 +606,28 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(update_func1)"
    ]
@@ -379,7 +673,132 @@
    "cell_type": "code",
    "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>birth_rate</th>\n",
+       "      <td>0.027000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>death_rate</th>\n",
+       "      <td>0.010000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0           1950.000000\n",
+       "t_end         2016.000000\n",
+       "p_0              2.557629\n",
+       "birth_rate       0.027000\n",
+       "death_rate       0.010000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>birth_rate</th>\n",
+       "      <td>0.027000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>death_rate</th>\n",
+       "      <td>0.010000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0           1950.000000\n",
+       "t_end         2016.000000\n",
+       "p_0              2.557629\n",
+       "birth_rate       0.027000\n",
+       "death_rate       0.010000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t_0 = get_first_label(census)\n",
     "t_end = get_last_label(census)\n",
@@ -396,7 +815,28 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "results = run_simulation(system, update_func1)\n",
     "plot_results(census, un, results, 'Proportional model, factored')"
@@ -460,7 +900,28 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "system.alpha = system.birth_rate - system.death_rate\n",
     "\n",
@@ -490,22 +951,63 @@
    },
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def update_func3(pop, t, system):\n",
+    "    if t < 1980: \n",
+    "        net_growth = system.alpha1 * pop\n",
+    "    else:\n",
+    "        net_growth = system.alpha2 * pop\n",
+    "    return pop + net_growth"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                alpha1=0.019,\n",
+    "                alpha2=0.015)\n",
+    "results3 = run_simulation(system, update_func3)\n",
+    "plot_results(census, un, results3, 'Proportional model, combined birth and death')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -519,7 +1021,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap07.ipynb b/notebooks/chap07.ipynb
index fb1ed01bd..dbae1c8ff 100644
--- a/notebooks/chap07.ipynb
+++ b/notebooks/chap07.ipynb
@@ -56,7 +56,24 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.525149\n",
+       "1951    2.572851\n",
+       "1952    2.619292\n",
+       "1953    2.665865\n",
+       "1954    2.713172\n",
+       "Name: un, dtype: float64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "un = table2.un / 1e9\n",
     "un.head()"
@@ -66,7 +83,24 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "1950    2.557629\n",
+       "1951    2.594940\n",
+       "1952    2.636772\n",
+       "1953    2.682053\n",
+       "1954    2.730228\n",
+       "Name: census, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "census = table2.census / 1e9\n",
     "census.head()"
@@ -162,7 +196,70 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</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": [
+       "t_0      1950.000000\n",
+       "t_end    2016.000000\n",
+       "p_0         2.557629\n",
+       "alpha       0.025000\n",
+       "beta       -0.001800\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "t_0 = get_first_label(census)\n",
     "t_end = get_last_label(census)\n",
@@ -186,7 +283,25 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap07-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "results = run_simulation(system, update_func_quad)\n",
     "plot_results(census, un, results, 'Quadratic model')\n",
@@ -200,6 +315,115 @@
     "**Exercise:**  Can you find values for the parameters that make the model fit better?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha</th>\n",
+       "      <td>0.021000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta</th>\n",
+       "      <td>-0.001000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0      1950.000000\n",
+       "t_end    2016.000000\n",
+       "p_0         2.557629\n",
+       "alpha       0.021000\n",
+       "beta       -0.001000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "p_0 = census[t_0]\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                alpha=0.021,\n",
+    "                beta=-0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap07-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results = run_simulation(system, update_func_quad)\n",
+    "plot_results(census, un, results, 'Quadratic model')\n",
+    "savefig('figs/chap07-fig01.pdf')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -211,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,9 +453,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap07-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "sns.set_style('whitegrid')\n",
     "\n",
@@ -262,9 +504,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "-system.alpha / system.beta"
    ]
@@ -301,9 +554,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
    "source": [
     "def carrying_capacity(system):\n",
     "    K = -system.alpha / system.beta\n",
@@ -325,9 +586,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
    "source": [
     "def carrying_capacity():\n",
     "    K = -sys1.alpha / sys1.beta\n",
@@ -349,9 +618,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
    "source": [
     "def carrying_capacity(system):\n",
     "    system = System(alpha=0.025, beta=-0.0018)\n",
@@ -377,9 +654,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "None\n"
+     ]
+    }
+   ],
    "source": [
     "def carrying_capacity(system):\n",
     "    K = -system.alpha / system.beta\n",
@@ -400,9 +685,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.88888888888889"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "def carrying_capacity(system):\n",
     "    K = -system.alpha / system.beta\n",
@@ -427,9 +723,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13.88888888888889\n"
+     ]
+    }
+   ],
    "source": [
     "print(carrying_capacity(sys1))"
    ]
@@ -443,9 +747,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27.77777777777778"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "total = carrying_capacity(sys1) + carrying_capacity(sys2)\n",
     "total"
@@ -466,29 +781,134 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>r</th>\n",
+       "      <td>0.025000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>K</th>\n",
+       "      <td>13.888889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0      1950.000000\n",
+       "t_end    2016.000000\n",
+       "p_0         2.557629\n",
+       "r           0.025000\n",
+       "K          13.888889\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "p_0 = census[t_0]\n",
+    "alpha=0.025\n",
+    "beta=-0.0018\n",
+    "r=alpha\n",
+    "K=-alpha/beta\n",
+    "\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p_0,\n",
+    "                r=r,\n",
+    "                K=K)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "def update_func_quad(pop, t, system):\n",
+    "    \"\"\"Compute the population next year with a quadratic model.\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.r * pop * (1 - pop/system.K)\n",
+    "    return pop + net_growth"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap07-ex.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "results = run_simulation(system, update_func_quad)\n",
+    "plot_results(census, un, results, 'Quadratic model')\n",
+    "savefig('figs/chap07-ex.pdf')"
    ]
   },
   {
@@ -501,7 +921,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -515,7 +935,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap08.ipynb b/notebooks/chap08.ipynb
index f86908654..efb904c02 100644
--- a/notebooks/chap08.ipynb
+++ b/notebooks/chap08.ipynb
@@ -118,7 +118,168 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</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",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1950</th>\n",
+       "      <td>1950</td>\n",
+       "      <td>2557628654</td>\n",
+       "      <td>2.516000e+09</td>\n",
+       "      <td>2.525149e+09</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>1951</td>\n",
+       "      <td>2594939877</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.572851e+09</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>1952</td>\n",
+       "      <td>2636772306</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.619292e+09</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>1953</td>\n",
+       "      <td>2682053389</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.665865e+09</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>1954</td>\n",
+       "      <td>2730228104</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.713172e+09</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": [
+       "      Year      census           prb            un      maddison  \\\n",
+       "Year                                                               \n",
+       "1950  1950  2557628654  2.516000e+09  2.525149e+09  2.544000e+09   \n",
+       "1951  1951  2594939877           NaN  2.572851e+09  2.571663e+09   \n",
+       "1952  1952  2636772306           NaN  2.619292e+09  2.617949e+09   \n",
+       "1953  1953  2682053389           NaN  2.665865e+09  2.665959e+09   \n",
+       "1954  1954  2730228104           NaN  2.713172e+09  2.716927e+09   \n",
+       "\n",
+       "              hyde        tanton       biraben            mj    thomlinson  \\\n",
+       "Year                                                                         \n",
+       "1950  2.527960e+09  2.400000e+09  2.527000e+09  2.500000e+09  2.400000e+09   \n",
+       "1951           NaN           NaN           NaN           NaN           NaN   \n",
+       "1952           NaN           NaN           NaN           NaN           NaN   \n",
+       "1953           NaN           NaN           NaN           NaN           NaN   \n",
+       "1954           NaN           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  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "table2 = pd.read_csv('data/World_population_estimates2.csv')\n",
     "table2.index = table2.Year\n",
@@ -129,7 +290,18 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "un = table2.un / 1e9\n",
     "census = table2.census / 1e9\n",
@@ -183,11 +355,20 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.557628654\n"
+     ]
+    }
+   ],
    "source": [
     "t_0 = get_first_label(census)\n",
     "t_end = get_last_label(census)\n",
-    "p_0 = get_first_value(census)"
+    "p_0 = get_first_value(census)\n",
+    "print(p_0)"
    ]
   },
   {
@@ -203,7 +384,70 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2016.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</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": [
+       "t_0      1950.000000\n",
+       "t_end    2016.000000\n",
+       "p_0         2.557629\n",
+       "alpha       0.025000\n",
+       "beta       -0.001800\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "system = System(t_0=t_0, \n",
     "                t_end=t_end,\n",
@@ -223,7 +467,18 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "results = run_simulation(system, update_func_quad)\n",
     "plot_results(census, un, results, 'Quadratic model')"
@@ -249,7 +504,25 @@
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap08-fig01.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "system.t_end = 2250\n",
     "results = run_simulation(system, update_func_quad)\n",
@@ -268,7 +541,18 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.856665141368708"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "results[system.t_end]"
    ]
@@ -277,7 +561,18 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.88888888888889"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "-system.alpha / system.beta"
    ]
@@ -293,9 +588,42 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "t_0 = get_first_label(census)\n",
+    "t_end = get_last_label(census)\n",
+    "ps = linspace(1, 20, 20)\n",
+    "results = SweepSeries()\n",
+    "\n",
+    "for p in ps:\n",
+    "    system = System(t_0=t_0, \n",
+    "                t_end=t_end,\n",
+    "                p_0=p,\n",
+    "                alpha=0.025,\n",
+    "                beta=-0.0018)\n",
+    "    results[p] = run_simulation(system, update_func_quad)\n",
+    "\n",
+    "plot(census, ':', label='US Census')\n",
+    "plot(un, '--', label='UN DESA') \n",
+    "for r in results:\n",
+    "    plot(r)\n",
+    "\n",
+    "    \n",
+    "decorate(xlabel='Year', \n",
+    "        ylabel='World population (billion)',\n",
+    "        title='title')"
    ]
   },
   {
@@ -339,7 +667,96 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Year</th>\n",
+       "      <th>census</th>\n",
+       "      <th>prb</th>\n",
+       "      <th>un</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2016</th>\n",
+       "      <td>2016</td>\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>2017</td>\n",
+       "      <td>7.412779e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>7.490428e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>7.567403e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>2020</td>\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": [
+       "      Year        census  prb            un\n",
+       "Year                                       \n",
+       "2016  2016  7.334772e+09  NaN  7.432663e+09\n",
+       "2017  2017  7.412779e+09  NaN           NaN\n",
+       "2018  2018  7.490428e+09  NaN           NaN\n",
+       "2019  2019  7.567403e+09  NaN           NaN\n",
+       "2020  2020  7.643402e+09  NaN  7.758157e+09"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "table3 = pd.read_csv('data/World_population_estimates3.csv')\n",
     "table3.index = table3.Year\n",
@@ -389,7 +806,70 @@
    "cell_type": "code",
    "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</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": [
+       "t_0      1950.000000\n",
+       "t_end    2100.000000\n",
+       "p_0         2.557629\n",
+       "alpha       0.025000\n",
+       "beta       -0.001800\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "system = System(t_0=t_0, \n",
     "                t_end=2100,\n",
@@ -402,7 +882,25 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving figure to file figs/chap08-fig02.pdf\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "results = run_simulation(system, update_func_quad)\n",
     "\n",
@@ -436,7 +934,18 @@
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "alpha_census = compute_rel_diff(census)\n",
     "plot(alpha_census, label='US Census')\n",
@@ -470,65 +979,215 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 226,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def alpha_func(t):\n",
+    "    intercept = 0.254\n",
+    "    slope = -0.00012\n",
+    "    alpha = intercept + slope * t\n",
+    "    return alpha"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 227,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970,\n",
+       "       1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981,\n",
+       "       1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n",
+       "       1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
+       "       2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
+       "       2015, 2016, 2017, 2018, 2019, 2020, 2021])"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "ts = linrange(1960, 2022, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 233,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2910457e0>]"
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "results = TimeSeries()\n",
+    "for t in ts:\n",
+    "    results[t] = alpha_func(t)\n",
+    "\n",
+    "plot(alpha_census, label='US Census')\n",
+    "plot(alpha_un, label='UN DESA')\n",
+    "plot(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 234,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>t_0</th>\n",
+       "      <td>1950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>t_end</th>\n",
+       "      <td>2100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p_0</th>\n",
+       "      <td>2.557629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha_func</th>\n",
+       "      <td>&lt;function alpha_func at 0x290ea3ac0&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "t_0                                           1950\n",
+       "t_end                                         2100\n",
+       "p_0                                       2.557629\n",
+       "alpha_func    <function alpha_func at 0x290ea3ac0>\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "system = System(t_0=t_0, \n",
+    "                t_end=2100,\n",
+    "                p_0=p_0,\n",
+    "                alpha_func=alpha_func)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 235,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "def update_func(pop, t, system):\n",
+    "    net_growth = system.alpha_func(t)\n",
+    "    return pop + net_growth"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 236,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x290fe26e0>]"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD9CAYAAAC1DKAUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAA0OklEQVR4nO3deVyU5f7/8RcMjgyiuCEuqAikgopsgaLm2mYqKmaZeiqXsE39WWmLndTUo9Ux+3ZMzdLSsjod0yzXEy2moom4ooiKsqsgKLIMM8xcvz/IORImhOAA83k+HjzOmfu6Zroub+7rPdzLddkppRRCCCFsjr21GyCEEMI6JACEEMJGSQAIIYSNkgAQQggbJQEghBA2ysHaDagovV7P8ePHcXV1RaPRWLs5QghR45lMJjIzM+natSuOjo5lymtNABw/fpyxY8dauxlCCFHrfP755wQHB5fZXmsCwNXVFSjpSMuWLa3cGiGEqPkuXLjA2LFjLePnH1U4AAoKChg5ciQPP/wwEydOLFN+9OhRFi1aREJCAs7OzkRERPDcc89hZ2eHXq8nKCgIrVZrqR8QEMDq1asr3JHrp31atmyJu7t7hd8nhBC27s9Om1c4AObPn09SUtJNy/Lz84mMjOTZZ59l3bp1pKSkMHHiRJo2bcrYsWOJj4+nWbNm7Nq1q3KtF0IIUeUqdBfQtm3bOH/+PIGBgTctz8jIIDAwkHHjxqHRaPDw8ODee+8lNjYWgLi4OHx8fKqu1UIIIW5buQGQnp7O22+/zVtvvYW9/c2re3t7s2zZMstrg8HArl276Ny5M1ASAJmZmQwdOpSwsDCmTp3KxYsXq6gLQgghKuOWAWAymXjppZeYNm1ahc+7GwwGZsyYgVartdy14+TkRFBQEJ9++inbtm2jfv36PP3007ffeiGEEJV2y2sAy5cvx83NjfDw8Ap9WGZmJs8//zwAa9aswcnJCYDZs2eXqvfKK6/Qs2dPUlNT5YKuEEJYyS0D4Pvvv+fSpUuW+0cLCgo4cuQIKSkpzJkzp1TdhIQEJk2aRFhYGPPmzbPc8aOUYunSpYSHh+Pp6QmU/JUAUL9+/arujxBC1BkFeiMXswtooKtHiyZOVf75twyA7du3l3o9fvx4+vXrV+Y20JycHCZMmMCwYcOYOXNmqTI7Ozvi4uI4deoUb7/9Nmazmfnz59O3b98/vTdVCCFsxbUCA2mX8ki9dI30rHwuXC7gYnbJ/+bml3xZ1jrYs3bOAzTQ1avS/3alHwRbsWIF3333HVu2bGHTpk1kZmayfv16vvjiC0udPn368H//938sXryY+fPnM2jQIEwmE3379mXBggVV0gEhhKjplFJk5hSSfPEaqb8P9qmX8ki7lMeVvKI/fZ/WwR63Zk509WyOk2PVP7drV1tWBEtNTWXgwIFERUXJdQMhRI1VZDSRfCGXc+m5nEu/yrn0XM5n5JJfaLxp/fpaDe4tnHF3bUhr1wa0bNaAls2ccGvqRJOGjtjb21W6LeWNm7VmKgghhKhpDEYTielXSUjKISH5ConpV0nLzMNsLvu9urFzfdq1bEhbt4a0cXUuGfRbNKSZy+0N8rdDAkAIISrAbFakZeaRkJxj+TmXnovpD4O9vR20dWtIh9aN6NDahQ6tG+HZ2oUmjcrOxmltEgBCCHETxmIzZ1OvEJd4meOJlzl5PrvMaRw7O2jfsiEd2zXhrnZN8HZ3oV3LRtSvVzumrJcAEEIIQG8o5lRSDnGJl4lLvEx8Ug4Go6lUneYujtzVrgmd2jWhY7smeLm74ORYtXfm3EkSAEIIm2QymTmdeoUjCZkcSsjkVFI2xabSp3PaujnTxbM5XTo0xdezWbXci29NEgBCCJuglCLjcj6HEzI5nJDJ0dOZ5OuLLeX2duDl7kIXz2Z09WyGb4dmuDjX7YdVJQCEEHWWwWji6JksDpy4QEz8JS5lF5Qqb9WsAf4dXfHv6Iqfd3OcnbR/8kl1kwSAEKJOuXy1kJiTFzlw4iKHT2dSZPjfefyGTvXwu8uVgI6udL/LlZbNGlixpdYnASCEqNWUUpxLz2Xf8QwOnLjAmdSrpco927hwt68bIb4t8XJvjMZK99zXRBIAQohaRynF6ZQr7DmSzt5j6Vy4/L9TO9p6GvzvcuVuXzfu9nWjmYvOii2t2SQAhBC1gtmsOHk+m71H09l7LIOsK4WWssbO9Qnt2pIeXVvRzbt5rbkP39okAIQQNZZSilPJOfwSm8qeI+nkXPvfxGnNXBzp2a0Vvfxa49OhmZzaqQQJACFEjZNy8Rq/xKbyy6HUUqd3WjR1IqxbK3p1b03Htk2sNodOXSEBIISoEbKuFLLrUBq/xKaSmP6/C7lNG9XnngB37glog7d7Y+zsZNCvKhIAQgir0RuK2Xcsg//+lsyxs1lcn5y+gaMDYX6t6RvoTlev5nJ6p5pIAAgh7qjrd/D897dkdh1KpeD3p3HrOdhzt68b/QLdCershlYu5FY7CQAhxB1x5VoRP8em8N/fkkm+cM2yvWO7xgy6ux19AtxxruIlD8WtSQAIIaqN2aw4cjqTbdHn+S3ugmXufBdnLf2D2jLo7na0b9XIyq20XRIAQogql5tvIOpAMtuiz5ORlQ+Avb0dIb4tGRTSjmAfN+o52Fu5laLCAVBQUMDIkSN5+OGHmThxYpnyU6dO8cYbbxAfH0/Lli155ZVX6Nu3LwAGg4EFCxawfft2AIYPH87MmTPRaOQcnxB1xfV79rftPc+vh9MwFpsBaN5YxwM92nNvaHua1sBVsWxZhQNg/vz5JCUl3bTMYDDw9NNPM378eNatW8euXbuYPn0633//PW3atOH999/nzJkz7NixA71ez5QpU/j444956qmnqqwjQgjr0BuK+SU2la17zltu37Szg8DOLRjc04NgHzc0Gvm2XxNVKAC2bdvG+fPnCQwMvGn5/v370ev1PPHEE9jZ2TFw4EBCQkL47rvvmDJlChs3bmTBggU0btwYgClTpvDuu+9KAAhRi12+WsiWPefYHn2eawUlSyU2aqDl3pB2PNDTw+Zn2qwNyg2A9PR03n77bdauXcsrr7xy0zpnz57Fy8ur1AManp6enD59mtzcXDIzM/Hy8ipVlpSUhMFgQKu1rfm3hajtEpJz2Lwrkd1H0iwXde9q25hhfTzp1b019Rzk1G5tccsAMJlMvPTSS0ybNg13d/c/rVdQUICjY+lze46OjhQWFlJQUPIYt073vxn5dDodSin0er0EgBC1gMlkJvp4Bpt3JXLyfDZQclG3d/fWhN/jRaf2TeQJ3VrolgGwfPly3NzcCA8Pv+WHODk5odfrS23T6/U4OTlZBv4bywsLCy3vE0LUXPqiYnbuT2LTrrNk5pQctw109bg/tD0P9e5Q59bItTW3DIDvv/+eS5cuERwcDJR80z9y5AgpKSnMmTPHUs/Ly4uPPvqo1HsTExMJCAjAxcUFV1dXEhMTadOmjaXMw8MDBwe5C1WImuhqXhHf7z7Hlj2JlvP7bVwbMLSPFwOC26KrL8duXXDLvXj9ts3rxo8fT79+/crcBhoaGopGo+HDDz/kiSeeYPfu3ezfv5/Zs2cDMGzYMJYtW4avry/FxcWsWLGC4cOHV21PhBC37VJ2ARt/OcPO/ckYjCVLKXZq34SI/ncR2qWlzL5Zx1Q6xlesWMF3333Hli1b0Gq1rFq1ijlz5rBixQrc3Nx49913adu2LQDTpk1j8eLFDBs2jOLiYsLDw+UOICFqkPMZuWz46TS7DqVh/v3CbrCPGxH9veni2UzO79dRdkpdn3+vZktNTWXgwIFERUXd8oK0EKLizqRe4cudp9gfdwEoubB7j38bRvb3pkNrFyu3Ttyu8sZNOZEnhA06nZLDlzsT+O1EycCvdbDnvtD2DO/njVtTubBrKyQAhLAhCck5fLHzFDEnLwIlC6gPDvNgZD9vmsg0DTZHAkAIGxB/Ppsvdp4i9tQlAOprNTwU1oHh/bxo0lAGflslASBEHXYm9QqfbTvJwfiSgd9Rq+GhXh0Y0c8bF+f6Vm6dsDYJACHqoJSL1/h8Rzx7jqQDoKuvYUhvT8Lv8ZKBX1hIAAhRh1zKLuCLnaf4MSYZsypZZvGhXh0YNeAuGfhFGRIAQtQBObl6/v1DAtv3nafYpNDY2/FAj/Y8MqgjzRvryv8AYZMkAISoxQr0Rjb8dIZNv5zFYDRhZwf9At0Zc38nWjd3tnbzRA0nASBELVRsMrNjXxJf7Iznap4BgB5dWzLuAR9ZY1dUmASAELWIUop9xy/w6ZY40jJL1tr18WjKhKFd6OzR1MqtE7WNBIAQtcSppGxWfxfHiXMl8/G3bt6Axx/ypWe3VjJXj6gUCQAhargLl/P5dMsJdv9+S2ejBlrG3NeJB3p64CBr7YrbIAEgRA1VWFTM11EJbPrlLMZiM1oHe8L7ehHR/y4a6OpZu3miDpAAEKKGMZsVP8em8umWOLJziwDoF+TO3x70xbWJ3NIpqo4EgBA1SEJyDh9uOsappBwAOrZrzOTh3ejcXi7wiqonASBEDZCdq+fTLSf4MSYFgCYN6/P4Q770D2orq3CJaiMBIIQVGYvNbN51lq9+OEVhkQkHjT3D+3rx8MC7cHKU8/yiekkACGElx85ksfybI6RczAMgtEtLJg7rSqvmDazcMmErJACEuMNycvWs/i6On2NTgZL7+SNH+BHYuYWVWyZsjQSAEHeIyWRm697zfLb9JAX6YrQO9owe1JGR/b2p56CxdvOEDapQAPz0008sWbKE1NRUmjdvzsSJE3n00UdL1YmJiWHy5MmlthkMBtzd3dmxYwcAffr04dq1a5anFlu0aGEpE6Iui0/KZvl/jpKYfhWAYB83Ikd0o2UzOd0jrKfcAEhPT2fq1KksX76c3r17Ex8fz+jRo/H19cXPz89SLzg4mEOHDlleX7hwgYiICGbPng1AZmYm2dnZxMbGUr++zEsubENegYFPtpxgx74kAFo00TF5eDdCu7SU6RuE1ZUbAK1btyY6OhpnZ2fMZjM5OTloNBqcnW891exrr73G0KFD6dOnDwBxcXF06NBBBn9hE5RS7D6SzoebjnHlWhEOGjtG9PNm9KCOOGrlzKuoGSr0m+js7ExeXh4hISGYTCYiIyPx9PT80/o//PADp06d4l//+pdlW1xcHEajkYiICNLS0ujSpQuvvvoqXl5et98LIWqQzJxCln9zhAMnLgLg26Epzz3sT1u3hlZumRClVfiriE6n4/Dhw8THxzN58mQ8PDwYOXLkTesuX76cSZMmodP977F1BwcHunfvzgsvvEDDhg1ZtmwZkyZNYsuWLTg5Od1+T4SwMpNZsXXPOdZtO0FhkQknRweeGNKF+0Pby8NcokaqcABoNBo0Gg1+fn6MGjWKnTt33jQA4uPjOX36NBEREaW2R0ZGlnr94osv8sUXX3Ds2DFCQ0Mr2XwhaobzGbn869+HOZVcMoVDmF8rnhrejWYuMnePqLnKnUs2Ojqa0aNHl9pmNBpp1Ojmqw5FRUVxzz330LBh6T93V69eTWxsrOV1cXExJpNJrgmIWs1YbGLt1hNMX/Izp5JzaObiyGtPhvDK4yEy+Isar9wA8PHxITk5mbVr12IymYiJiWHjxo2MGjXqpvWPHDmCv79/me1JSUksXLiQzMxMCgsLWbhwIe3ataNbt2633QkhrCEhOYdpS37h66jTmJXioV4d+GDmAHp0bWXtpglRIeUGQOPGjVm1ahVbt24lJCSEuXPnsmDBAkJCQti8eTMBAQGl6qelpeHq6lrmc2bNmoWvry/h4eGEhYWRnp7OypUr0WjkARhRuxiMJj75Po6X/m8XKRev0ca1AYue7c2UkX4yf4+oVeyUUsrajaiI1NRUBg4cSFRUFO7u7tZujrBR8UnZvPflIVIv5WFvB8P7evPYA52pX0++yIiap7xxU25IFqICiowm1m+PZ9MvZzArcG/hzLRHA2SeflGrSQAIUY7489ks/fIQaZkl3/oj+nvz2P2d0cq3flHLSQAI8SeMxWbW74jnm59OY1bQ1q0h0x8NoGO7JtZumhBVQgJAiJtIysjln+sPci49F3s7GDXgLsbc10m+9Ys6RQJAiBuYzIpvfznLum0nKTaZadWsAf9vTCA+HeRcv6h7JACE+N3F7ALe/SKWuMTLANzfoz0Th3VFV18OE1E3yW+2sHlKKaIOJPPhpuMUFhXTuGF9po72527fltZumhDVSgJA2LSreUX86+vD7Dt+ASiZw+eZiO64OMsUJaLukwAQNutIQiZLvjhIdm4RTo4ORI7wo3+QuyzUImyGBICwOcZiM59vP8k3P59BqZL5+l94LIgWTWVacmFbJACETUnPzOPtz2I4k3oVezsYc39nRg+8C42m3GmxhKhzJACETSi50JvCyo1H0RtMtGii44WxQfh2aGbtpglhNRIAos7LKzTywX+O8OvhNADu8W/D06O646yTmTuFbZMAEHVa/Pls3v4shks5hThqNUwZ6ceA4LZyoVcIJABEHWU2Kzb9cpa1W09gMiu82zbmpbFBtHZ1tnbThKgxJABEnZObb+DdL2KJOXkRgOF9vfjbYF/qOciFXiFuJAEg6pST57J5a90Bsq7qcdbVY/qjAYTKEo1C3JQEgKgTzGbFxp/PsHbbScxmRad2TZg5Plju7RfiFiQARK13Na+Id7+I5WD8JUBO+QhRURIAolY7eS6bxesOcPmqnoZO9Zg+JpAQmcRNiAqp0Fekn376iaFDhxIQEMC9997Ll19+edN6e/bswdfXl4CAAMvPsmXLgJIHcd577z3CwsIICgpi1qxZFBQUVF1PhE1RSrH517O88sFuLl/V07l9E5bO6CeDvxB/Qbl/AaSnpzN16lSWL19O7969iY+PZ/To0fj6+uLn51eqblxcHIMHD+add94p8zlfffUV27dvZ8OGDTg5OTFjxgwWL17M3Llzq643wiYUFhXzr38fZtfvD3aF3+PFE0N8cZDpHIT4S8o9Ylq3bk10dDS9e/fGbDaTk5ODRqPB2bns/dTHjx/Hx8fnpp+zceNGxo0bR6tWrXBxcWH69Ol8++23GI3G2++FsBkpF6/xwnu72HU4DUethpnjg5kU3lUGfyEqoULXAJydncnLyyMkJASTyURkZCSenp5l6p04cYJr167x2WefAfDggw8yffp0tFotZ8+exdvb21LX09OTwsJC0tLS8PDwqJreiDptz5F03vsqlsIiE+4tnHn1iRDaujW0drOEqLUqfBFYp9Nx+PBh4uPjmTx5Mh4eHowcOdJSbjAYaNWqFffddx/Dhw/n0qVLTJs2DbPZzMsvv0xBQQE6na7U5wEUFhZWYXdEXVRsMvPplhNs+uUsAL27t+b50f44OcpcPkLcjgr/3azRaNBqtfj5+TFq1Ch27txZqlyr1bJu3TrGjBmDTqejffv2TJkyxVJPp9Oh1+st9a8P/E5Ocp+2+HM5uXpmr9jLpl/OorG3Y1J4V2aOD5bBX4gqUG4AREdHM3r06FLbjEYjjRo1KrUtIyODxYsXU1xcbNlWVFSEVqsFwNvbm8TEREtZYmIiOp2ONm3a3FYHRN2VkJzD9Hd/IS7xMk0b1WfB070Iv8dLJnITooqUGwA+Pj4kJyezdu1aTCYTMTExbNy4kVGjRpWq5+LiwsaNG1mxYgXFxcWcO3eO5cuXW+oNGzaMNWvWkJKSwtWrV1m6dClDhgzBwUEeRRBlRR1I5uVlu8nO1ePboSlL/18/unjK3P1CVKVyA6Bx48asWrWKrVu3EhISwty5c1mwYAEhISFs3ryZgIAAoORUzqpVq4iOjiY0NJTx48czePBgJkyYAMCYMWMYMmQIY8eOZdCgQTRt2pRXX321ensnap1ik5kPNx1j6ZeHMBabebCnB/On9KJJI0drN02IOsdOKaWs3YiKSE1NZeDAgURFReHu7m7t5ohqcDWviLfWxXD0TBYOGjsiR/jxQE8PazdLiFqrvHFTzr+IGuFc+lXmr/mNS9kFNG5Yn1cev1uWaxSimkkACKv79XAa7311iCKDibvaNubVJ0Jo3lhX/huFELdFAkBYjcms+Hz7Sb6OOg3AgOC2PDuqO9p6Giu3TAjbIAEgrCKv0Mg/Pz9IzMmL2NvbMXFoF4b28ZRbPIW4gyQAxB2XnpXHmx/vJ/VSHg2d6jFr/N107+hq7WYJYXMkAMQddfxsFgs/+Y1rBUbat2zI7AmhtGzWwNrNEsImSQCIO+aH35JZ9p/DFJsUwT5uvDQuSKZ0EMKKJABEtTObFWu3nmDDT2cAGHaPJxOGdkVjL+f7hbAmCQBRrfRFxSz5IpboYxnY29sxZUQ3HgzrYO1mCSGQABDV6PLVQuZ9vJ/EtKs0cHTg5cfvxr9jC2s3SwjxOwkAUS3OpFzhzdX7yc7V06pZA16fGCqLtwhRw0gAiCq392g6/1wfi8FoootnM159IoRGDbTWbpYQ4g8kAESVUUqx4aczfLrlBACD7m7HM6O6U89B1usVoiaSABBVwmQys3LjMbZFn8fODh4f7MvI/t7yZK8QNZgEgLht+qJi3voshgMnLqJ1sGfG2CB6+bW2drOEEOWQABC3Jeeannkf7+dMyhUaOtXj9Qk98OnQ1NrNEkJUgASAqLTUS9eYs2ofF7MLcGvqxJzJPXBvIXf6CFFbSACISjlx7jLzV+/nWoGRu9o25vWJoTRpKMs2ClGbSACIv2zPkXT+uf4gxmIzIb4teWlcEI715VdJiNpGjlrxl2z65SyrvzuOUvBgmAeRw7uh0chtnkLURhUKgJ9++oklS5aQmppK8+bNmThxIo8++miZeufPn2f+/PkcPXoUrVbL/fffz8yZM6lfvz4Affr04dq1a5ZbA1u0aMGOHTuqsDuiupjMitWbj7P510QAHn/Ilwi5zVOIWq3cAEhPT2fq1KksX76c3r17Ex8fz+jRo/H19cXPz69U3WeeeYb+/fvzwQcfcOXKFZ555hnee+89Zs6cSWZmJtnZ2cTGxloCQdQOBqOJdz4/SPSxDBw0dkx7NJB+ge7WbpYQ4jaVGwCtW7cmOjoaZ2dnzGYzOTk5aDQanJ2dS9XLzs6mVatWPPPMM2i1Wlq0aEF4eDhbtmwBIC4ujg4dOsjgX8vkFxqZv2Y/x89epoGjA689GUo37+bWbpYQogpU6BSQs7MzeXl5hISEYDKZiIyMxNPTs1Sdpk2b8vHHH1teK6WIioqic+fOQEkAGI1GIiIiSEtLo0uXLrz66qt4eXlVYXdEVcrJ1fPGqmjOpefStJEj857qSftWjazdLCFEFanw1TudTsfhw4f5+uuv+eqrr/jmm2/+tK7ZbGb+/PmcP3+eZ599FgAHBwe6d+/OBx98wI8//kjnzp2ZNGkSBQUFt98LUeUysvKZ+a9fOZeeS+vmDXjr+T4y+AtRx1Q4ADQaDVqtFj8/P0aNGsXOnTtvWi8vL49nn32WvXv3sm7dOlxdSxb7joyM5K233sLNzQ0nJydefPFFrl69yrFjx6qmJ6LKJKZdZea/fuXC5QK83V1Y/Fwf3Jo6WbtZQogqVm4AREdHM3r06FLbjEYjjRqV/TZ48eJFHnnkEYxGI//+979p27atpWz16tXExsZaXhcXF2MymeSaQA1z7GwWr3ywmyvXiuh+V3MWPN2Lxg1lHwlRF5UbAD4+PiQnJ7N27VpMJhMxMTFs3LiRUaNGlapnMBiYNGkSnTp1YuXKlTRsWHpKgKSkJBYuXEhmZiaFhYUsXLiQdu3a0a1bt6rtkai06GMZvPFhNAX6Ynp1b80bk3rIou1C1GHlBkDjxo1ZtWoVW7duJSQkhLlz57JgwQJCQkLYvHkzAQEBAPz8888kJCQQFRVFcHAwAQEBBAQEWIJi1qxZ+Pr6Eh4eTlhYGOnp6axcuRKNRlO9PRQVsnN/Eos+/Q1jsZkHwzx4aVww9Rxk3whRl9kppZS1G1ERqampDBw4kKioKNzd5R70qqKU4j8/nmbt1pMAjLmvE2Pu6yQPeAlRB5Q3bspUEDbMbFas/i6Ob3edxc4OIod346HenuW/UQhRJ0gA2CiTWfGvfx/mhwPJOGjsmDEmiD4BbazdLCHEHSQBYIOMxWb+uf4ge46ko62n4bUnQwjs1MLazRJC3GESADamyGhi0acHiDl5ESdHB/4+sQddPJtZu1lCCCuQALAhBXojC9b8xtEzWTR00jLvqZ54t21s7WYJIaxEAsBG5BUYmPPRPk4l5dC0UX3mRYbRvqVM7SCELZMAsAFXrhXx9w/3ci49lxZNdLw5JYzWzZ3Lf6MQok6TAKjjsq4UMnvFXtIy82jj2oA3I3vh2kRn7WYJIWoACYA67MLlfF5bsZdL2QV4tGrEvMiesnC7EMJCAqCOSr6Qy+sr95KdW0Sndk14Y3IPGjpprd0sIUQNIgFQByWmXeX1lXvJzTfQzas5syeEyKRuQogyJADqmDOpV3h9xV7yCo0E+7jx8uN3U7+eTOomhChLAqAOSUjO4e8fRpNfaCS0S0tm/U1m9BRC/DkJgDoiPinbMpd/z26tfp/OucILvgkhbJAEQB1w4txl5qzaR2FRyUIuL44NwkEjg78Q4tYkAGq542ezmPvRPvQGE/cEtGHGmEA0MvgLISpAAqAWO3Ymi7kf76PIYKJ/kDvTHg1EYy8LuQghKkYCoJY6kpDJvNX7MRhNDLy7Lc+PDpDBXwjxl0gA1EKx8ZdYsGY/hmIz94W259lR3bGXwV8I8RdJANQyMScvsvCTksXbH+jpwdMj/WTwF0JUSoWuFv70008MHTqUgIAA7r33Xr788sub1svIyGDixIkEBAQwYMAANmzYYClTSvHee+8RFhZGUFAQs2bNoqCgoGp6YSMOnLjAgjUlg/9DvTrwTIQM/kKIyis3ANLT05k6dSqzZs3i0KFDvP/++yxcuJCjR4+WqTt9+nS8vLzYv38/77zzDosWLeLw4cMAfPXVV2zfvp0NGzbw448/kpWVxeLFi6u8Q3XVwfiLLPzkAMUmM0P7eBI5oht2djL4CyEqr9wAaN26NdHR0fTu3Ruz2UxOTg4ajQZn59LzyScmJnLs2DGmTp2KVqslMDCQoUOHWv4K2LhxI+PGjaNVq1a4uLgwffp0vv32W4xGY/X0rA45nHCJBWt+o9hkZkjvDkwO7yqDvxDitlXoGoCzszN5eXmEhIRgMpmIjIzE09OzVJ3ExERatmxZKhg8PT35/vvvATh79ize3t6lygoLC0lLS8PDw6MKulI3HTuTxZurS077PNjTg6eGyzd/IUTVqPATQzqdjsOHD/P111/z1Vdf8c0335Qqz8/Px9Gx9Fzzjo6OFBYWAlBQUIBOpyv1eYClXJQVl3iZuR/vw2A0cV9oe6aM9JPBXwhRZSocABqNBq1Wi5+fH6NGjWLnzp2lyp2cnNDr9aW26fV6nJycgJIB/8by6wP/9XJR2slz2cz9KJoig4kBwW3lVk8hRJUrNwCio6MZPXp0qW1Go5FGjUovKO7l5cXFixfJz8+3bEtMTLSc9vH29iYxMbFUmU6no02bNrfVgbooITmHN1ZFU1hkol+gO1MfCZDBXwhR5coNAB8fH5KTk1m7di0mk4mYmBg2btzIqFGjStXz9PTEx8eHd955h6KiIg4dOsR3331HeHg4AMOGDWPNmjWkpKRw9epVli5dypAhQ3BwkEcRbnQm5Qp/X7mXwqJi+vi3Yfqj8oSvEKJ6lBsAjRs3ZtWqVWzdupWQkBDmzp3LggULCAkJYfPmzQQEBFjqvv/++6SlpdGrVy9eeOEFZs2aRXBwMABjxoxhyJAhjB07lkGDBtG0aVNeffXV6utZLXR9Ja98fTFhfq2Y8ZhM7CaEqD52Sill7UZURGpqKgMHDiQqKgp3d3drN6fKnc/I5dUP9nCtwPD7Yi53y3z+QojbUt64KSNMDZB8IZfZK0oG/2Aft99X8pJdI4SoXjLKWFlGVj6vr9zL1TwDAR1deeXxu2UZRyHEHSEBYEVZVwqZvWIP2blFdPNqzmsTQtHKAu5CiDtEAsBKrlwrYvaKvVzKKaRTuybMnhBCfRn8hRB3kASAFeQVGPj7h3tJy8zDo1Uj5kzugZNjPWs3SwhhYyQA7rACvZE5q/ZxLj2XNq4NmBfZE2cnrbWbJYSwQRIAd1CR0cSCNb9xKjmHFk10vBnZiyYNHct/oxBCVAMJgDvEWGxm0acHOHomiyYN6/PmlDBcm+jKf6MQQlQTCYA7wGRWLFl/kJiTF2nopOXNKWG0bu5c/huFEKIaSQBUM7NZsezrw+w+ko6uvgPznupJ+5aNyn+jEEJUMwmAaqSU4qPNx/nvb8lo62l4Y1IPvNs2tnazhBACkACoVp9vj+e7XxNx0Njz2pMhdPFsZu0mCSGEhQRANdm86yxf/ZCAvb0dM8cHEdiphbWbJIQQpUgAVIOfD6aw6tvjADz/sD89u7W2couEEKIsCYAqFnPyIku/PATAk0O6MCiknZVbJIQQNycBUIVOnsvmH58ewGRWRPT3ZmR/b2s3SQgh/pQEQBVJyshl7sf7MBhN3BvSjscf8rV2k4QQ4pYkAKrAxewC/v5hNPmFRnp0bcmzo7pjZyfr+AohajYJgNt05VoRr6/cS3aunq5ezXhpXLCs4yuEqBUcKlLp6NGjLFq0iISEBJydnYmIiOC5554r9S03JiaGyZMnl3qfwWDA3d2dHTt2ANCnTx+uXbtmeV+LFi0sZbVRgd7IG6uiycjKx7O1C7OflAVdhBC1R7kBkJ+fT2RkJM8++yzr1q0jJSWFiRMn0rRpU8aOHWupFxwczKFDhyyvL1y4QEREBLNnzwYgMzOT7OxsYmNjqV+/fjV05c4y/D6zZ2LaVVo1b8Ccp3rQQCdz+gshao9yz1VkZGQQGBjIuHHj0Gg0eHh4cO+99xIbG3vL97322msMHTqUPn36ABAXF0eHDh3qxOBvMive+fwgR89k0bRRfeY91VOmdRZC1DrlBoC3tzfLli2zvDYYDOzatYvOnTv/6Xt++OEHTp06xbRp0yzb4uLiMBqNRERE0KNHDyZOnMjZs2dvs/l3nlKK5RuOEH0sgwa6esx9KoyWzRpYu1lCCPGX/aWrlQaDgRkzZqDVakud/vmj5cuXM2nSJHS6/8137+DgQPfu3fnggw/48ccf6dy5M5MmTaKgoKDyrbeCL3eeYse+JLQO9rw+IRSPVjKzpxCidqrQRWAoOYf//PPPA7BmzRqcnJxuWi8+Pp7Tp08TERFRantkZGSp1y+++CJffPEFx44dIzQ09K+22yp27k9i/c5T2NvBzPHBMrmbEKJWq9BfAAkJCURERODh4cHatWtp0qTJn9aNiorinnvuoWHDhqW2r169utR1g+LiYkwmU625JnDgxAWW/ecIAFMiuhPatZWVWySEELen3ADIyclhwoQJDBkyhEWLFqHV3noB8yNHjuDv719me1JSEgsXLiQzM5PCwkIWLlxIu3bt6NatW6Ubf6ckJOeweF0MZrNi9KCOPNjTw9pNEkKI21ZuAGzatInMzEzWr19PQECA5Wfq1Kls3ryZgICAUvXT0tJwdXUt8zmzZs3C19eX8PBwwsLCSE9PZ+XKlWg0Nfu++fSsPOZ9vI8ig4kBwW0Z98CfX/wWQojaxE4ppazdiIpITU1l4MCBREVF4e7ufkf+m1euFTHz/V/JuJxPYKcWvD4xFAd5ylcIUUuUN27KaPYn9EXFzPt4HxmX8/Fyd2HW34Jl8BdC1Ckyot2EyWRm8boYTqdcoUVTJ96Y2AMnR3nKVwhRt0gA/IFSig82HCXm5EUaOmmZO7kHTRrJU75CiLpHAuAPvtx5ip37Sx70+vvEUNxbNCz/TUIIUQtJANzgxge9XhofTGePptZukhBCVBsJgN8djL/4vwe9RvrRQx70EkLUcRIAwLn0qyxeW/Kg18MD7+LBsA7WbpIQQlQ7mw+Ay1cLmffRPgqLiunj34ZxD/hYu0lCCHFH2HQAFBYVM+/j/WRd1ePj0ZTpjwZgby9r+QohbIPNBoDJrHj7s5iSFb2aNeC1J0NkOUchhE2x2QD46NtjHDhxEWddPd6Y3AMX59oxK6kQQlQVmwyAzbvO8v3uczho7HntyRDauDpbu0lCCHHH2VwA7DuewUebjwMw7RF/uno1t3KLhBDCOmwqAM6kXOGdzw+iFIx9oDP9gtpau0lCCGE1NhMAl7ILSs3r/8igjtZukhBCWJVNBEB+oZF5H+8j51oRft7Nee5hf+zs5HZPIYRtq/MBoJRi8doDJF24hnsLZ155/G7qOdT5bgshRLnq/EiYnavnUEImLs5a3pjUA2enW69pLIQQtsLB2g2obs1cdMx9qifuLZxp0cTJ2s0RQogao84HAEBgpxbWboIQQtQ4FToFdPToUR577DGCg4Pp168f77//PjdbS37Pnj34+voSEBBg+Vm2bBlQci7+vffeIywsjKCgIGbNmkVBQUHV9kYIIUSFlRsA+fn5REZGMnjwYPbv388nn3zCpk2bWL9+fZm6cXFxDB48mEOHDll+nn32WQC++uortm/fzoYNG/jxxx/Jyspi8eLFVd8jIYQQFVJuAGRkZBAYGMi4cePQaDR4eHhw7733EhsbW6bu8ePH8fG5+XTKGzduZNy4cbRq1QoXFxemT5/Ot99+i9FovP1eCCGE+MvKDQBvb2/LaRwAg8HArl276Ny5c5m6J06cYPfu3fTv35/+/fvz1ltvYTAYADh79ize3t6Wup6enhQWFpKWllYV/RBCCPEX/aWLwAaDgRkzZqDVahk7dmyZslatWnHfffcxfPhwLl26xLRp0zCbzbz88ssUFBSg0+ks9a///8LCwirohhBCiL+qws8BZGZm8re//Y2srCzWrFmDk1PpWyq1Wi3r1q1jzJgx6HQ62rdvz5QpU9i5cydQMuDr9XpL/esD/x8/RwghxJ1Rob8AEhISmDRpEmFhYcybNw+ttuzDVBkZGaxdu5YXXngBB4eSjy0qKrLU9fb2JjExkZCQEAASExPR6XS0adOmQg01mUwAXLhwoUL1hRDC1l0fL6+Pn39UbgDk5OQwYcIEhg0bxsyZM/+0nouLCxs3bqRBgwZMmTKFlJQUli9fzqhRowAYNmwYa9asoVevXjRq1IilS5cyZMgQS1iUJzMzE6DMqSchhBC3lpmZSfv27ctst1M3u6H/BmvWrGHRokXodLpSE6j16dOHQYMG8cYbb3Do0CEAjh07xqJFi4iPj0en0zF69Giee+457O3tMZvNLFu2jK+//prCwkL69evH3LlzK3wKSK/Xc/z4cVxdXdFoZOlGIYQoj8lkIjMzk65du+Lo6FimvNwAEEIIUTfV+cnghBBC3JwEgBBC2CgJACGEsFESAEIIYaMkAIQQwkZJAAghhI2SABBCCBtVawPg6NGjlmkloOSJ5RdffJHQ0FB69+7Nu+++W+rx5xdeeIFu3bqVWqwmJSUFKJnGYuLEiQQEBDBgwAA2bNhQY/vy97//vVQfAgIC6NSpEytWrABuvShPdffhZosGGQwG3njjDUJDQwkNDeUf//hHqf2yfft27rvvPvz9/Rk3bhznz5+3lFlrv1S2L5s3b2bw4MEEBgYyZMgQduzYYSmzxn6pbD9q4rFSmb7UtmPluqKiIkaPHs327dtLva9ajhVVC23ZskUFBQUpf39/y7annnpKjRs3TmVmZqqcnBz15JNPqiVLlljK77//frVnz56bft7o0aPVggULVFFRkTp48KAKDg5Whw4dqu5uKKUq15cbrV+/Xj344IPq2rVrSimlVq5cqV544YU70vbr8vLyVI8ePdS6detUcXGxOnfunBowYID67LPP1DvvvKMee+wxlZOTozIyMlR4eLhauXKlUkqphIQE5e/vrw4cOKCKiorUkiVL1ODBg5XJZFJKWWe/VLYvhw4dUgEBASomJkaZzWb1888/Kz8/P3Xy5Eml1J3fL5Xth1I171i5nb7cqKYfK0oplZycrB599FHVsWNHtW3bNsv7qutYqXUBsGTJEjVixAj18ccfWwbNgoIC1alTJxUXF2epd/jwYdWjRw9lNptVXl6e6ty5s7p8+XKZzzt79qzy8fGx/FIopdTcuXPV7Nmza2RfbpScnKz8/f1L1X3++efVRx99VO1tv9Hp06fVM888U2rbP/7xDzVjxgzVq1cv9fPPP1u2b9u2Td13331KKaXeeecdNXXqVEtZcXGxCgoKUgcPHrTafqlsX7Zs2aKWLl1a6n0jRoxQX375pVLqzu+XyvajJh4rle3LjWrDsXLmzBnVo0cPtWbNGtWvX79SAVBdx0qtOwX02GOP8c0339ClSxfLNrPZDFBqvQF7e3uys7PJzc3l5MmTaLVaXn75ZXr06MGIESP4+eefgZJZSVu2bImzs7PlvZ6enpw+fbpG9uVGixYtIiIiAl9fX8u2Wy3KU11utWhQZmYmXl5eljJPT0+SkpIwGAxlFgnSaDS0a9eOM2fOWG2/VLYvgwcPZtq0aZaypKQkTp8+bVkh707vl8r2oyYeK5Xty41qw7Hi5ubGf//7X5544olS865B2QW1qupYqXUB4ObmVmZbgwYNCAsL45///CdXrlwhJyfH8o+s1+vJz88nKCiIadOm8euvvxIZGcnUqVM5ceIE+fn5ZSZJcnR0vCML1VSmL9clJCSwe/dunnrqKcu2Gxfl2bp1K5988gl79+5lyZIl1d6XG9twfdGgwYMHA5RZCEgphV6vp6CgoMy/vU6no6CgwKr75bq/0pcbZWRkEBkZyfDhw/Hz87P6fvkr/aipx0pl+nJdbThWxo4di7Ozc6lB/EbVdazUugD4M2+99Rb16tXjwQcfZPz48QwcOBCARo0a0bdvX1avXk2XLl2oV68eDzzwAD179iQqKgonJ6cyB7Ber7fqQjW36st1GzZsYNCgQbRo0cKyrbxFearbHxcNuv7L/GcLAel0OoqKikp9RmFhIQ0aNLD6fvmrfbnu8OHDPPzww/To0YO5c+cC1t0vf7UfNflYqew+qQ3HSnn/htV1rNSZAMjKyuLNN98kOjqa77//Hjc3N7y8vNDpdPzwww9s2rSpVP2ioiLq16+Pl5cXFy9eJD8/31KWmJhY6s+tO+1WfbkuKirK8g3ouoyMDBYvXkxxcbFl242L8lSnhIQEIiIi8PDwYO3atTRp0gQXFxdcXV1JTEy01EtMTMTDwwMHBwfLIkHXmUwmkpOT8fLysup+qUxfALZu3cqTTz7JlClTmDNnDvb2JYeXtfZLZfpRU4+Vyu4TqB3HSnmq7Vi5zWsaVrNv375Sd848/vjjav78+cpoNKrk5GQ1bNgwy5X1LVu2qODgYHX06FFVXFysNm7cqPz9/VVqaqpSSqmIiAg1Z84cpdfrVWxsrAoODlYHDhyokX1RSqnLly+rjh07lrlQl5+fr0JDQ9X777+vjEajSkxMVPfff79atWpVtbY/Oztb9erVSy1evLhM2eLFi9UjjzyisrKy1IULF1R4eLj64IMPlFJKxcfHK39/f7V7927LnQ3333+/Ki4uVkpZZ79Uti8HDhxQXbp0UVFRUWXeZ439Utl+1MRjpbJ9Uap2HSs36t+/f6mLwNV1rNSZAEhOTlaPP/64CggIUH379i2zI9esWaMGDBigunfvrkaMGKH27dtnKUtPT1eTJ09WQUFBqn///urrr7++Y/1Q6q/35ciRI8rX1/emn3X06FH12GOPqcDAQNWrVy/13nvvWW4Vqy6rV69WHTt2VN27d1f+/v6Wn+eff17p9Xo1d+5cFRYWpkJCQtSCBQssv7RKKbVjxw71wAMPKH9/fzV27FiVmJhoKbPGfqlsXyIjI1WnTp1Kvcff3199+umnSqk7v19uZ5/UtGPldvpSm46VG/0xAJSqnmNFFoQRQggbVWeuAQghhPhrJACEEMJGSQAIIYSNkgAQQggbJQEghBA2SgJACCFslASAEELYKAkAIYSwURIAQghho/4/m/U2A0TC+3UAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "# Solution goes here\n",
+    "results2 = run_simulation(system, update_func)\n",
+    "plot(results2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 237,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Solution goes here"
+    "plot_results(census, un, results2, 'World population projections')\n",
+    "plot_projections(table3)"
    ]
   },
   {
@@ -550,7 +1209,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -564,7 +1223,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/chap09.ipynb b/notebooks/chap09.ipynb
index 183f2e33b..98313ad76 100644
--- a/notebooks/chap09.ipynb
+++ b/notebooks/chap09.ipynb
@@ -602,7 +602,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -616,7 +616,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.3"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/figs/chap02-fig01.pdf b/notebooks/figs/chap02-fig01.pdf
new file mode 100644
index 000000000..1f24eee86
Binary files /dev/null and b/notebooks/figs/chap02-fig01.pdf differ
diff --git a/notebooks/figs/chap05-fig01.pdf b/notebooks/figs/chap05-fig01.pdf
new file mode 100644
index 000000000..429b9b31e
Binary files /dev/null and b/notebooks/figs/chap05-fig01.pdf differ
diff --git a/notebooks/figs/chap05-fig02.pdf b/notebooks/figs/chap05-fig02.pdf
new file mode 100644
index 000000000..2c9853977
Binary files /dev/null and b/notebooks/figs/chap05-fig02.pdf differ
diff --git a/notebooks/figs/chap06-fig01.pdf b/notebooks/figs/chap06-fig01.pdf
new file mode 100644
index 000000000..e40a2540b
Binary files /dev/null and b/notebooks/figs/chap06-fig01.pdf differ