diff --git a/README.md b/README.md
index a73fbb7..20bf665 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,2 @@
-# hs-training
-Jupyter Notebooks with training material for HS interns.
+# Materials for HS Internship 2020
+
diff --git a/notebooks/fit_us_data.ipynb b/notebooks/fit_us_data.ipynb
new file mode 100644
index 0000000..c17ff3b
--- /dev/null
+++ b/notebooks/fit_us_data.ipynb
@@ -0,0 +1,3415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# fit_us_data.ipynb\n",
+    "\n",
+    "Fit various functions to COVID-19 time series for U.S. counties using the Broyden–Fletcher–Goldfarb–Shanno solver from `sklearn.optimize`.\n",
+    "\n",
+    "Inputs:\n",
+    "* `outputs/us_counties_clean.csv`: The contents of `data/us_counties.csv` after data cleaning by [clean_us_data.ipynb](./clean_us_data.ipynb)\n",
+    "* `outputs/us_counties_clean_meta.json`: Column type metadata for reading `data/us_counties_clean.csv` with `pd.read_csv()`\n",
+    "\n",
+    "Outputs:\n",
+    "* `outputs/us_counties_curves.csv`: The curves that this notebook generated\n",
+    "* `outputs/us_counties_curves_meta.json`: Column type metadata for reading `data/us_counties_curves.csv` with `pd.read_csv()`\n",
+    "* `outputs/us_counties_curves_params.csv`: Model parameters corresponding to the curves in `data/us_counties_curves.csv`\n",
+    "\n",
+    "**Note:** You can redirect these input and output files by setting the environment variable `COVID_OUTPUTS_DIR` to a replacement for the prefix `outputs` in the above paths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialization boilerplate\n",
+    "import os\n",
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.optimize\n",
+    "from sklearn import metrics\n",
+    "from typing import *\n",
+    "\n",
+    "import text_extensions_for_pandas as tp\n",
+    "\n",
+    "# Local file of utility functions\n",
+    "import util\n",
+    "\n",
+    "# What precision of floating-point to use.\n",
+    "# Consider 32-bit if using GPU-accelerated solvers. Otherwise, 64-bit\n",
+    "# floating point is better because it reduces the chance of divergence.\n",
+    "fp_type = np.float64\n",
+    "\n",
+    "# Allow environment variables to override data file locations.\n",
+    "_OUTPUTS_DIR = os.getenv(\"COVID_OUTPUTS_DIR\", \"outputs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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></th>\n",
+       "      <th>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Confirmed</th>\n",
+       "      <th>Deaths</th>\n",
+       "      <th>Recovered</th>\n",
+       "      <th>Confirmed_Outlier</th>\n",
+       "      <th>Deaths_Outlier</th>\n",
+       "      <th>Recovered_Outlier</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</th>\n",
+       "      <th>Date</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 rowspan=\"5\" valign=\"top\">1001</th>\n",
+       "      <th>2020-01-22</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-23</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-24</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-25</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-26</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">56045</th>\n",
+       "      <th>2020-05-13</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-14</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-15</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-16</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-17</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>367614 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    State   County  Population  Confirmed  Deaths  Recovered  \\\n",
+       "FIPS  Date                                                                     \n",
+       "1001  2020-01-22  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-23  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-24  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-25  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-26  Alabama  Autauga       55869          0       0          0   \n",
+       "...                   ...      ...         ...        ...     ...        ...   \n",
+       "56045 2020-05-13  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-14  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-15  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-16  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-17  Wyoming   Weston        6927          0       0          0   \n",
+       "\n",
+       "                  Confirmed_Outlier  Deaths_Outlier  Recovered_Outlier  \n",
+       "FIPS  Date                                                              \n",
+       "1001  2020-01-22              False           False              False  \n",
+       "      2020-01-23              False           False              False  \n",
+       "      2020-01-24              False           False              False  \n",
+       "      2020-01-25              False           False              False  \n",
+       "      2020-01-26              False           False              False  \n",
+       "...                             ...             ...                ...  \n",
+       "56045 2020-05-13              False           False              False  \n",
+       "      2020-05-14              False           False              False  \n",
+       "      2020-05-15              False           False              False  \n",
+       "      2020-05-16              False           False              False  \n",
+       "      2020-05-17              False           False              False  \n",
+       "\n",
+       "[367614 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read in the CSV file and apply the saved type information\n",
+    "csv_file = os.path.join(_OUTPUTS_DIR, \"us_counties_clean.csv\")\n",
+    "meta_file = os.path.join(_OUTPUTS_DIR, \"us_counties_clean_meta.json\")\n",
+    "\n",
+    "# Read column type metadata\n",
+    "with open(meta_file) as f:\n",
+    "    cases_meta = json.load(f)\n",
+    "\n",
+    "# Pandas does not currently support parsing datetime64 from CSV files.\n",
+    "# As a workaround, read the \"Date\" column as objects and manually \n",
+    "# convert after.\n",
+    "cases_meta[\"Date\"] = \"object\"\n",
+    "\n",
+    "cases_vertical = (\n",
+    "    pd\n",
+    "    .read_csv(csv_file, dtype=cases_meta, parse_dates=[\"Date\"])   \n",
+    "    .set_index([\"FIPS\", \"Date\"], verify_integrity=True)\n",
+    ")\n",
+    "cases_vertical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Confirmed</th>\n",
+       "      <th>Deaths</th>\n",
+       "      <th>Recovered</th>\n",
+       "      <th>Confirmed_Outlier</th>\n",
+       "      <th>Deaths_Outlier</th>\n",
+       "      <th>Recovered_Outlier</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</th>\n",
+       "      <th>Date</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 rowspan=\"5\" valign=\"top\">1001</th>\n",
+       "      <th>2020-01-22</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-23</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-24</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-25</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-01-26</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">56045</th>\n",
+       "      <th>2020-05-13</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-14</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-15</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-16</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-17</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>367614 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    State   County  Population  Confirmed  Deaths  Recovered  \\\n",
+       "FIPS  Date                                                                     \n",
+       "1001  2020-01-22  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-23  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-24  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-25  Alabama  Autauga       55869          0       0          0   \n",
+       "      2020-01-26  Alabama  Autauga       55869          0       0          0   \n",
+       "...                   ...      ...         ...        ...     ...        ...   \n",
+       "56045 2020-05-13  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-14  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-15  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-16  Wyoming   Weston        6927          0       0          0   \n",
+       "      2020-05-17  Wyoming   Weston        6927          0       0          0   \n",
+       "\n",
+       "                  Confirmed_Outlier  Deaths_Outlier  Recovered_Outlier  \n",
+       "FIPS  Date                                                              \n",
+       "1001  2020-01-22                  0               0                  0  \n",
+       "      2020-01-23                  0               0                  0  \n",
+       "      2020-01-24                  0               0                  0  \n",
+       "      2020-01-25                  0               0                  0  \n",
+       "      2020-01-26                  0               0                  0  \n",
+       "...                             ...             ...                ...  \n",
+       "56045 2020-05-13                  0               0                  0  \n",
+       "      2020-05-14                  0               0                  0  \n",
+       "      2020-05-15                  0               0                  0  \n",
+       "      2020-05-16                  0               0                  0  \n",
+       "      2020-05-17                  0               0                  0  \n",
+       "\n",
+       "[367614 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# As a workaround for a bug in Pandas' extension types system,\n",
+    "# we need to cast the boolean columns to ints.\n",
+    "for col in [\"Confirmed_Outlier\", \"Deaths_Outlier\", \"Recovered_Outlier\"]:\n",
+    "    cases_vertical[col] = cases_vertical[col].astype(np.int8)\n",
+    "cases_vertical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Confirmed</th>\n",
+       "      <th>Deaths</th>\n",
+       "      <th>Recovered</th>\n",
+       "      <th>Confirmed_Outlier</th>\n",
+       "      <th>Deaths_Outlier</th>\n",
+       "      <th>Recovered_Outlier</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1005</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Barbour</td>\n",
+       "      <td>24686</td>\n",
+       "      <td>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1007</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Bibb</td>\n",
+       "      <td>22394</td>\n",
+       "      <td>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1009</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Blount</td>\n",
+       "      <td>57826</td>\n",
+       "      <td>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56037</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Sweetwater</td>\n",
+       "      <td>42343</td>\n",
+       "      <td>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56041</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Uinta</td>\n",
+       "      <td>20226</td>\n",
+       "      <td>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56043</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Washakie</td>\n",
+       "      <td>7805</td>\n",
+       "      <td>[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56045</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Weston</td>\n",
+       "      <td>6927</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3142 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         State      County  Population  \\\n",
+       "FIPS                                     \n",
+       "1001   Alabama     Autauga       55869   \n",
+       "1003   Alabama     Baldwin      223234   \n",
+       "1005   Alabama     Barbour       24686   \n",
+       "1007   Alabama        Bibb       22394   \n",
+       "1009   Alabama      Blount       57826   \n",
+       "...        ...         ...         ...   \n",
+       "56037  Wyoming  Sweetwater       42343   \n",
+       "56039  Wyoming       Teton       23464   \n",
+       "56041  Wyoming       Uinta       20226   \n",
+       "56043  Wyoming    Washakie        7805   \n",
+       "56045  Wyoming      Weston        6927   \n",
+       "\n",
+       "                                               Confirmed  \\\n",
+       "FIPS                                                       \n",
+       "1001  [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "1003  [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "1005  [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...   \n",
+       "1007  [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...   \n",
+       "1009  [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...   \n",
+       "...                                                  ...   \n",
+       "56037 [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...   \n",
+       "56039 [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "56041 [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...   \n",
+       "56043 [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  ...   \n",
+       "56045 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "\n",
+       "                                                  Deaths  \\\n",
+       "FIPS                                                       \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1005  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1007  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1009  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "...                                                  ...   \n",
+       "56037 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56041 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56043 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56045 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "\n",
+       "                                               Recovered  \\\n",
+       "FIPS                                                       \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1005  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1007  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1009  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "...                                                  ...   \n",
+       "56037 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56041 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56043 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56045 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "\n",
+       "                                       Confirmed_Outlier  \\\n",
+       "FIPS                                                       \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1005  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1007  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1009  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "...                                                  ...   \n",
+       "56037 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56041 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56043 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56045 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "\n",
+       "                                          Deaths_Outlier  \\\n",
+       "FIPS                                                       \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1005  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1007  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1009  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "...                                                  ...   \n",
+       "56037 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56041 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56043 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56045 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "\n",
+       "                                       Recovered_Outlier  \n",
+       "FIPS                                                      \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1005  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1007  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1009  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "...                                                  ...  \n",
+       "56037 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56041 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56043 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56045 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "\n",
+       "[3142 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use Text Extensions for Pandas to collapse each time series or mask down to a single cell\n",
+    "cases, dates = util.collapse_time_series(cases_vertical, [\n",
+    "    \"Confirmed\", \"Deaths\", \"Recovered\", \n",
+    "    \"Confirmed_Outlier\", \"Deaths_Outlier\", \"Recovered_Outlier\"])\n",
+    "cases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To simplify the code that follows, we only fit curves to one of\n",
+    "# the time series for each county. \n",
+    "# Change the following constant to use a different time series:\n",
+    "ts_col_name = \"Confirmed\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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t+Euq226eQTmpZOYFwHuB86K6neJHPPC69odSXam6kwfeyPdvm7UlNkNmXk/1i9n/o6rXC6j+fcpwZg5T/Rp3DFWdXkItScrMn1B9Efg61ZsKx/7fyTdRnUBvKGWfpnoeAaq3tl4ZEb+h2n/HZeYNmXk78HyqxPd2qi8Sz8/MX21G3X5PlaD9mOp5zrupHrzfjeqFA8OlvoeVun8EeFW56gnVm+OGqRq7s4BzprH6S6lezHRrRNRjv4Byu86YW1wXUSX5k9XnxjLNDlTbbDpezgP/EuY9VI3j76e5jLHx3AP8DdWFhTuBl21GXJI2k213Zb603Zn5C6o6vigi/oXqjpceqhfS3UmVqE902+vJwFlR3YL5F5ux7vOpvgO8hmpb3UZ1LH2pTHIGVeJ3OdWbd39H9R1g9OLIXwOfoLqD6LdMsj/H8S9UF07uioi/q40/m+ri7diLKIuo3nPQSr1+XuKu3wk14bFZ8y2qlyR9A3hfZq6cZDV/R9U+3kN1zNST0ymPo5LwH0u1zb5E9Z3q36l+FLiNahu0VF+1LjZ9nEzSTIqIM6ke/p/T/8OzSRHxM6rbtL5eG/cJ4HOZefEMxfAZ4MeZ+c4pJ5YkaZaJiFcBx2bmwbVxe1G9CPGpnYtMc4H/QFXSVisi/pzqVphL6+Mz87UNr3cJ1dX7n1Pd9noEcGqT65QkqQlRPdf71zzwTCsAmbmG6n95S1vEhFPSVikiLqN6HvWVY96MOxP+gOp26F2pbst5fWZ+f4ZjkCRpi0TEc6nas68z+dvapc3mLbWSJEmSpEb40iBJkiRJUiMauaV2t912y8WLFzexaEnSPHPVVVf9KjOn83/mNA7bZklSu0ynbW4k4Vy8eDGrVq1qYtGSpHkmIm7qdAxzgW2zJKldptM2e0utJEmSJKkRJpySJEmSpEaYcEqSJEmSGmHCKUmSJElqhAmnJEmSJKkRJpySJEmSpEZMmXBGxKMiYrDW3R0Rb56J4CRJmosiYlFEfDMiro2IayLiuHGmiYj4UESsjogfRMSBtbKjI+KnpTt6ZqOXJKl1U/4fzsy8HugDiIguYC1wQcNxSZJaMDQ4yLqBAXZfsgRgyv7evr5pz7Oly+rt65uJTbG12QC8NTOvjogdgasi4pLMvLY2zWHA/qV7EvBR4EkRsQvwTqAfyDLvhZl558xWQZK0uSZqP7e0nZ6NbfOUCecYzwJ+lpn+E25J6rChwUEuXb6cjcPDLFhYnc5HNmyYsL+rp4cDTziBq089teV5tnRZXT09LFuxwqRzjMy8Bbil9N8TEdcBewL1hPMI4OzMTOCKiNgpIvYAlgKXZOYdABFxCXAocO4MVkGStJkmar+3tJ2erW3zdJ/hPIoJGrSIODYiVkXEqqGhoS2PTJI0qXUDA2wcHoaREUbWr2dk/fop+29euXLa82zpstYNDHR6U81qEbEYeCJw5ZiiPYGba8NryriJxo+3bNtmSZplJmu/t7Sdno1tc8sJZ0T0AC8EPjdeeWaenpn9mdnf29vbrvgkSRPYfckSunp6iK4uFnR3s6C7e8r+RYccMu15tnRZo7fx6MEi4iHA54E3Z+bd7V6+bbMkzT6Ttd9b2k7PxrZ5OrfUHgZcnZm3NRWMJKl1vX19LFuxYtrPduy0//5te4azlWV5O+34IqKbKtk8JzO/MM4ka4FFteG9yri1VLfV1sdf1kyUkqR2m6z93tJ2eja2zVE9GtLChBHnARdn5n9NNW1/f3+uWrVqS2OTJImIuCoz+zsdRztFRABnAXdk5rhvfo+I5wFvBA6nemnQhzLzoPLSoKuA0bfWXg38yegznROxbZYktct02uaWfuGMiB2A5wCv25LAJEkSAE8DXgn8MCIGy7i3AXsDZObHgIuoks3VwL3Aq0vZHRHxbmD0AZxTpko2JUnqlJYSzsz8LbBrw7FIkjQvZOa3gZhimgTeMEHZGcAZDYQmSVJbTfcttZIkSZIktcSEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY1Y2OkAJEmabyLiDOD5wLrMfNw45X8PvLwMLgQeA/Rm5h0RcSNwD7AR2JCZ/TMTtSRJ0+cvnJIkzbwzgUMnKszMf8vMvszsA04EvpWZd9QmeWYpN9mUJM1qJpySJM2wzLwcuGPKCSsvBc5tMBxJkhpjwilJ0iwVEdtT/RL6+droBFZGxFURcewU8x8bEasiYtXQ0FCToUqSNK6WEs6I2Ckizo+IH0fEdRHxlKYDkyRJvAD43zG30x6cmQcChwFviIinTzRzZp6emf2Z2d/b29t0rJIkPUirLw36IPC1zHxRRPQA2zcYk6R5ZGhwkHUDA+y+ZAnA/f29fX0TlrXSv6Xzby2x9Pb1tW1faFY6ijG302bm2vJ3XURcABwEXN6B2CRJmtKUCWdEPAx4OnAMQGYOA8PNhiVpPhgaHOTS5cvZODzMgoXV6Whkwwa6eno48IQTuPrUUx9U1kr/ls6/tcTS1dPDshUrTDrnqNL+PgN4RW3cDsCCzLyn9B8CnNKhECVJmlIrv3DuCwwB/xURTwCuAo7LzN/WJyrPkRwLsPfee7c7Tklz0LqBATYOD8PICCPr11cjMxlZv56bV66csKyV/i2df2uJZd3AgAnnVigizgWWArtFxBrgnUA3QGZ+rEz2p8DKMe3tw4ELIgKqNvzTmfm1mYpbkqTpaiXhXAgcCLwpM6+MiA8CJwD/WJ8oM08HTgfo7+/Pdgcqae7ZfckSunp6GFm/nujqAiA3bmRBdzeLDjmEoauvflBZK/1bOv/WEsuC7u77b7nV1iUzX9rCNGdS/fuU+rgbgCc0E5UkSe3XSsK5BliTmVeW4fOpEk5J2iK9fX0sW7FiwmcVd9p//y161nFL5t9aYvHXTUmSNJtF5tQ/RkbE/wCvzczrI+JkYIfM/PuJpu/v789Vq1a1L0pJ0rwVEVdlZn+n49ja2TZLktplOm1zq2+pfRNwTnlD7Q3Aqzc3OEmSJEnS/NBSwpmZg4BXlyVJkiRJLVvQ6QAkSZIkSXOTCackSZIkqREmnJIkSZKkRphwSpIkSZIaYcIpSZIkSWqECackSZIkqREmnJIkSZKkRphwSpIkSZIaYcIpSZIkSWqECackSZIkqREmnJIkSZKkRphwSpIkSZIaYcIpSZIkSWqECackSZIkqREmnJIkSZKkRphwSpI0wyLijIhYFxE/mqB8aUT8OiIGS3dSrezQiLg+IlZHxAkzF7UkSdNnwilJ0sw7Ezh0imn+JzP7SncKQER0AacBhwGPBV4aEY9tNFJJkraACackSTMsMy8H7tiMWQ8CVmfmDZk5DJwHHNHW4CRJaiMTTkmSZqenRMT/RcRXI+KAMm5P4ObaNGvKuHFFxLERsSoiVg0NDTUZqyRJ4zLhlCRp9rka2CcznwD8P+CLm7OQzDw9M/szs7+3t7etAUqS1AoTTkmSZpnMvDszf1P6LwK6I2I3YC2wqDbpXmWcJEmzkgmnJEmzTET8QURE6T+Iqr2+HRgA9o+IfSOiBzgKuLBzkUqSNLmFnQ5AkqT5JiLOBZYCu0XEGuCdQDdAZn4MeBHw+ojYANwHHJWZCWyIiDcCFwNdwBmZeU0HqiBJUktaSjgj4kbgHmAjsCEz+5sMSpKkuSwzXzpF+YeBD09QdhFwURNxSZLUbtP5hfOZmfmrxiKRNG8MDQ6ybmCA3Zcsobevr9PhSJIkqSHeUitpRg0NDnLp8uVsHB6mq6eHZStWmHRKkiTNUa2+NCiBlRFxVUQcO94E/q8vSa1YNzDAxuFhGBlhZP161g0MdDokSZIkNaTVhPPgzDwQOAx4Q0Q8fewE/q8vSa3YfckSunp6iK4uFnR3s/uSJZ0OSZIkSQ1p6ZbazFxb/q6LiAuAg4DLmwxM0tzU29fHshUrfIZTkiRpHpgy4YyIHYAFmXlP6T8EOKXxyCTNWb19fSaakiRJ80Arv3A+HLig/P/phcCnM/NrjUYlSZIkSdrqTZlwZuYNwBNmIBZJkiRJ0hzS6kuDJEmSJEmaFhNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkaYZFxBkRsS4ifjRB+csj4gcR8cOI+E5EPKFWdmMZPxgRq2YuakmSps+EU5KkmXcmcOgk5T8HnpGZjwfeDZw+pvyZmdmXmf0NxSdJUlss7HQAkiTNN5l5eUQsnqT8O7XBK4C9mo5JkqQm+AunJEmz23Lgq7XhBFZGxFURcexkM0bEsRGxKiJWDQ0NNRqkJEnj8RdOSZJmqYh4JlXCeXBt9MGZuTYidgcuiYgfZ+bl482fmadTbsft7+/PxgOWJGkMf+GUJGkWiog/Bj4BHJGZt4+Oz8y15e864ALgoM5EKEnS1Ew4JUmaZSJib+ALwCsz8ye18TtExI6j/cAhwLhvupUkaTbwllpJkmZYRJwLLAV2i4g1wDuBboDM/BhwErAr8JGIANhQ3kj7cOCCMm4h8OnM/NqMV0CSpBaZcEqSNMMy86VTlL8WeO04428AnvDgOSRJmp28pVaSJEmS1AgTTkmSJElSI0w4JUmSJEmNaDnhjIiuiPh+RHylyYAkSZIkSXPDdF4adBxwHfDQhmKRNMcMDQ6ybmCA3ZcsAbi/v7evr8ORSZIkaSa0lHBGxF7A84B/At7SaESS5oShwUEuXb6cjcPDLFhYnWpGNmygq6eHZStWmHRKkiTNA63eUvsB4HhgZKIJIuLYiFgVEauGhobaEpykrde6gQE2Dg/DyAgj69czsn79/f3rBgY6HZ4kSZJmwJQJZ0Q8H1iXmVdNNl1mnp6Z/ZnZ39vb27YAJW2ddl+yhK6eHqKriwXd3Szo7r6/f/QWW0mSJM1trdxS+zTghRFxOLAt8NCI+FRmvqLZ0CRtzXr7+li2YoXPcEqSJM1jUyacmXkicCJARCwF/s5kU1Irevv6NkkuTTQlSZLmF/8PpyRJkiSpEdP5tyhk5mXAZY1EIkmSJEmaU/yFU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkqQMi4oyIWBcRP5qgPCLiQxGxOiJ+EBEH1sqOjoiflu7omYtakqTpWdjpACRJmqfOBD4MnD1B+WHA/qV7EvBR4EkRsQvwTqAfSOCqiLgwM+9sPGJJmseGBgdZNzDA7kuWANzf39vXN2HZRP29fX2dqUQHmHBKktQBmXl5RCyeZJIjgLMzM4ErImKniNgDWApckpl3AETEJcChwLnNRixJ89fQ4CCXLl/OxuFhFiysUqiRDRvo6unhwBNO4OpTT31Q2UT9XT09LFuxYt4knd5SK0nS7LQncHNteE0ZN9H4B4mIYyNiVUSsGhoaaixQSZrr1g0MsHF4GEZGGFm/npH16+/vv3nlygnLJupfNzDQ6SrNGBNOSZLmqMw8PTP7M7O/t7e30+FI0lZr9yVL6OrpIbq6WNDdzYLu7vv7Fx1yyIRlE/WP3mI7H3hLrSRJs9NaYFFteK8ybi3VbbX18ZfNWFSSNA/19vWxbMWKCZ/H3Gn//X2GcwImnJIkzU4XAm+MiPOoXhr068y8JSIuBv45InYu0x0CnNipICVpvujt69skURzbP1nZeP3zhQmnJEkdEBHnUv1SuVtErKF682w3QGZ+DLgIOBxYDdwLvLqU3RER7wZGHwA6ZfQFQpIkzTYmnJIkdUBmvnSK8gTeMEHZGcAZTcQlSVI7+dIgSZIkSVIjTDglSZIkSY0w4ZQkSZIkNcKEU5IkSZLUCBNOSZIkSVIjTDglSZIkSY0w4ZQkSZIkNWLKhDMito2I70XE/0XENRHxrpkITJIkSZK0dVvYwjS/B5Zl5m8iohv4dirTHdwAACAASURBVER8NTOvaDg2SR0wNDjIuoEBdl+yBGBa/b19fZvM39vX15lKSJIkaVaYMuHMzAR+Uwa7S5dNBiWpM4YGB7l0+XI2Dg+zYGF1ehjZsKGl/q6eHg484QSuPvVUNg4P09XTw7IVK0w6JUmS5rGWnuGMiK6IGATWAZdk5pXjTHNsRKyKiFVDQ0PtjlPSDFg3MMDG4WEYGWFk/XpG1q+fVv/NK1duMv+6gYFOV0mSJEkd1FLCmZkbM7MP2As4KCIeN840p2dmf2b29/b2tjtOSTNg9yVL6OrpIbq6WNDdzYLu7mn1LzrkkE3mH73lVpIkSfNTK89w3i8z74qIbwKHAj9qJiRJndLb18eyFSu26BnOnfbf32c4JUmSBEBUj2hOMkFEL7C+JJvbASuB92bmVyaap7+/P1etWtXeSCVJ81JEXJWZ/Z2OY2tn2yxJapfptM2t/MK5B3BWRHRR3YL72cmSTUmSJEmSoLW31P4AeOIMxCJJkiRJmkNaemmQJEmSJEnTZcIpSZIkSWqECackSZIkqREmnJIkdUBEHBoR10fE6og4YZzy90fEYOl+EhF31co21sounNnIJUlq3bT+D6ckSdpy5c3vpwHPAdYAAxFxYWZeOzpNZv5tbfo3sekL/O7LTP/RrSRp1vMXTkmSZt5BwOrMvCEzh4HzgCMmmf6lwLkzEpkkSW1kwilJ0szbE7i5NrymjHuQiNgH2Be4tDZ624hYFRFXRMSRE60kIo4t060aGhpqR9ySJE2LCackSbPbUcD5mbmxNm6fzOwHXgZ8ICIeOd6MmXl6ZvZnZn9vb+9MxCpJ0iZMOCVJmnlrgUW14b3KuPEcxZjbaTNzbfl7A3AZmz7fKUnSrGHCKUnSzBsA9o+IfSOihyqpfNDbZiPi0cDOwHdr43aOiG1K/27A04Brx84rSdJs4FtqJUmaYZm5ISLeCFwMdAFnZOY1EXEKsCozR5PPo4DzMjNrsz8G+HhEjFBdOD61/nZbSZJmExNOSZI6IDMvAi4aM+6kMcMnjzPfd4DHNxqcJElt4i21kiRJkqRGmHBKkiRJkhphwilJkiRJaoQJpyRJkiSpESackiRJkqRGmHBKkiRJkhphwilJkiRJaoQJpyRJkiSpESackiRJkqRGmHBKkiRJkhphwilJkiRJasSUCWdELIqIb0bEtRFxTUQcNxOBSZIkSZK2bgtbmGYD8NbMvDoidgSuiohLMvPahmOTtIWGBgdZNzDA7kuWAEzZ39vX17FYJUmSNPdMmXBm5i3ALaX/noi4DtgTMOGUZrGhwUEuXb6cjcPDLFhYfdRHNmyYsL+rp4dlK1aYdEqSJKltpvUMZ0QsBp4IXDlO2bERsSoiVg0NDbUnOkmbbd3AABuHh2FkhJH16xlZv37K/nUDA50OW5IkSXNIywlnRDwE+Dzw5sy8e2x5Zp6emf2Z2d/b29vOGCVtht2XLKGrp4fo6mJBdzcLurun7B+9xVaSJElqh1ae4SQiuqmSzXMy8wvNhiSpHXr7+li2YoXPcEqSJKljpkw4IyKAFcB1mfkfzYckqV16+/o2SSJb6ZckSZLapZVbap8GvBJYFhGDpTu84bgkSZIkSVu5KRPOzPx2ZkZm/nFm9pXuopkITpKkuSoiDo2I6yNidUScME75MRExVLvY+9pa2dER8dPSHT2zkUuS1LqWnuGUJEntExFdwGnAc4A1wEBEXDjO/7j+TGa+ccy8uwDvBPqBpPr/2Bdm5p0zELokSdMyrX+LIkmS2uIgYHVm3pCZw8B5wBEtzvtc4JLMvKMkmZcAhzYUpyRJW8SEU5KkmbcncHNteE0ZN9afR8QPIuL8iFg0zXn9H9mSpI4z4ZQkaXb6MrA4M/+Y6lfMs6a7AP9HtiSp00w4JUmaeWuBRbXhvcq4+2Xm7Zn5+zL4CeBPWp1XkqTZwoRTkqSZNwDsHxH7RkQPcBRwYX2CiNijNvhC4LrSfzFwSETsHBE7A4eUcZIkzTq+pVaSpBmWmRsi4o1UiWIXcEZmXhMRpwCrMvNC4G8i4oXABuAO4Jgy7x0R8W6qpBXglMy8Y8YrIUlSC0w4JUnqgPI/rS8aM+6kWv+JwIkTzHsGcEajAUqS1AbeUitJkiRJaoQJpyRJkiSpESackiRJkqRGmHBKkiRJkhphwilJkiRJaoQJpyRJkiSpESackiRJkqRGmHBKkiRJkhphwilJkiRJaoQJpyRJkiSpESackiRJkqRGmHBKkiRJkhphwilJkiRJaoQJpyRJkiSpESackiRJkqRGTJlwRsQZEbEuIn40EwFJkiRJkuaGhS1McybwYeDsZkORtLmGBgdZNzDA7kuWANzf39vX1+HIJEmSNJ9NmXBm5uURsbj5UCRtjqHBQS5dvpyNw8MsWFh9pEc2bKCrp4dlK1aYdEqSJKlj2vYMZ0QcGxGrImLV0NBQuxYraQrrBgbYODwMIyOMrF/PyPr19/evGxjodHiSJEmax9qWcGbm6ZnZn5n9vb297VqspCnsvmQJXT09RFcXC7q7WdDdfX//6C22kmafiDg0Iq6PiNURccI45W+JiGsj4gcR8Y2I2KdWtjEiBkt34cxGLklS61p5hlPSLNbb18eyFSt8hlPaikREF3Aa8BxgDTAQERdm5rW1yb4P9GfmvRHxeuBfgZeUsvsy0w+4JGnWM+GU5oDevr5NkksTTWnWOwhYnZk3AETEecARwP0JZ2Z+szb9FcArZjRCSZLaoJV/i3Iu8F3gURGxJiKWNx+WJElz2p7AzbXhNWXcRJYDX60Nb1vem3BFRBw50Uy+X0GS1GmtvKX2pTMRiCRJerCIeAXQDzyjNnqfzFwbEX8IXBoRP8zMn42dNzNPB04H6O/vzxkJWJKkmra9NEiSJLVsLbCoNrxXGbeJiHg28HbghZn5+9Hxmbm2/L0BuAx4YpPBSpK0uUw4JUmaeQPA/hGxb0T0AEcBm7xtNiKeCHycKtlcVxu/c0RsU/p3A55G7dlPSZJmE18aJEnSDMvMDRHxRuBioAs4IzOviYhTgFWZeSHwb8BDgM9FBMAvMvOFwGOAj0fECNWF41PHvN1WkqRZw4RTkqQOyMyLgIvGjDup1v/sCeb7DvD4ZqOTJKk9vKVWkiRJktQIE05JkiRJUiNMOCVJkiRJjTDhlCRJkiQ1woRTkiRJktQIE05JkiRJUiNMOCVJkiRJjTDhlCRJkiQ1woRTkiRJktQIE05JkiRJUiNMOCVJkiRJjTDhlCRJkiQ1woRTkiRJktQIE05JkiRJUiNMOCVJkiRJjTDhlCRJkiQ1woRTkiRJktQIE05JkiRJUiNMOCVJkiRJjWgp4YyIQyPi+ohYHREnNB2UJElz3VRta0RsExGfKeVXRsTiWtmJZfz1EfHcmYxbkqTpWDjVBBHRBZwGPAdYAwxExIWZeW2TgQ0NDrJuYIDdlywBGLe/t6+vpela6d/SZRnL7I9lrtart68PSVuXFtvW5cCdmblfRBwFvBd4SUQ8FjgKOAB4BPD1iPijzNzYdNy2zXMjlrlaL2Mxlqbn9zvX5pky4QQOAlZn5g0AEXEecATQWMI5NDjIpcuXs3F4mAULqxBHNmzYpL+rp4cDTziBq089ddLpWunf0mUZy+yPZa7Wq6unh2UrVngClLY+rbStRwAnl/7zgQ9HRJTx52Xm74GfR8TqsrzvNhmwbfPciGWu1stYjKXp+f3OtfkWtjDNnsDNteE1wJPGThQRxwLHAuy9995bFNS6gQE2Dg/DyAgj69dXIzMf1H/zypUtTddK/5Yuy1hmfyxztV7rBgY8+Ulbn1ba1vunycwNEfFrYNcy/oox8+453kpsm41lPtXLWIyl6fn9zrV52vbSoMw8PTP7M7O/t7d3i5a1+5IldPX0EF1dLOjuZkF397j9iw45pKXpWunf0mUZy+yPZa7Wa/R2D0kay7bZWOZTvYzFWJqe3+9cm6eVXzjXAotqw3uVcY3p7etj2YoVLd1HvdP++7ftPvAtWZaxzP5Y5mq9vNImbZVaaVtHp1kTEQuBhwG3tzhv29k2z51Y5mq9jMVYmp7f71ybJzJz8gmqRu4nwLOoGrQB4GWZec1E8/T39+eqVavaGackaZ6KiKsys7/TcbRTK21rRLwBeHxm/lV5adCfZeZfRMQBwKepntt8BPANYP+pXhpk2yxJapfptM1T/sJZnht5I3Ax0AWcMVmyKUmSJjdR2xoRpwCrMvNCYAXwyfJSoDuo3kxLme6zVC8Y2gC8YSbeUCtJ0uZo5ZZaMvMi4KKGY5Ekad4Yr23NzJNq/b8DXjzBvP8E/FOjAUqS1AZte2mQJEmSJEl1JpySJEmSpEaYcEqSJEmSGmHCKUmSJElqhAmnJEmSJKkRU/4fzs1aaMQQcFMbFrUb8Ks2LGdrNZ/rP5/rDvO7/vO57mD9x6v/PpnZ24lg5hLb5raZz/Wfz3WH+V3/+Vx3sP5b1DY3knC2S0Ssmmv/7Hs65nP953PdYX7Xfz7XHaz/fK//1mC+76P5XP/5XHeY3/Wfz3UH67+l9feWWkmSJElSI0w4JUmSJEmNmO0J5+mdDqDD5nP953PdYX7Xfz7XHaz/fK//1mC+76P5XP/5XHeY3/Wfz3UH679F9Z/Vz3BKkiRJkrZes/0XTkmSJEnSVsqEU5IkSZLUiFmbcEbEoRFxfUSsjogTOh1PkyJiUUR8MyKujYhrIuK4Mn6XiLgkIn5a/u7c6VibEhFdEfH9iPhKGd43Iq4s+/8zEdHT6RibEhE7RcT5EfHjiLguIp4yz/b935bj/kcRcW5EbDuX939EnBER6yLiR7Vx4+7vqHyobIcfRMSBnYt8y01Q938rx/4PIuKCiNipVnZiqfv1EfHczkStOttm2+a5em4ey7bZttm2uX1t86xMOCOiCzgNOAx4LPDSiHhsZ6Nq1AbgrZn5WODJwBtKfU8AvpGZ+wPfKMNz1XHAdbXh9wLvz8z9gDuB5R2JamZ8EPhaZj4aeALVdpgX+z4i9gT+BujPzMcBXcBRzO39fyZw6JhxE+3vw4D9S3cs8NEZirEpZ/Lgul8CPC4z/xj4CXAiQDkHHgUcUOb5SGkb1CG2zbbNzO1z81i2zbbNts1taptnZcIJHASszswbMnMYOA84osMxNSYzb8nMq0v/PVQntT2p6nxWmews4MjORNisiNgLeB7wiTIcwDLg/DLJXK77w4CnAysAMnM4M+9inuz7YiGwXUQsBLYHbmEO7//MvBy4Y8zoifb3EcDZWbkC2Cki9piZSNtvvLpn5srM3FAGrwD2Kv1HAOdl5u8z8+fAaqq2QZ1j22zbPGfPzXW2zYBtM9g2t61tnq0J557AzbXhNWXcnBcRi4EnAlcCD8/MW0rRrcDDOxRW0z4AHA+MlOFdgbtqB/pc3v/7AkPAf5Xblj4RETswT/Z9Zq4F3gf8gqox+zVwFfNn/4+aaH/Pt3Pha4Cvlv75VvetwbzdJ7bNgG2zbfP82f+jbJsrW9w2z9aEc16KiIcAnwfenJl318uy+v81c+5/2ETE84F1mXlVp2PpkIXAgcBHM/OJwG8Zc4vOXN33AOV5iCOoGvdHADvw4Ns65pW5vL8nExFvp7qF8ZxOxyLV2TbPS7bNts2bmMv7ezLtaptna8K5FlhUG96rjJuzIqKbqkE7JzO/UEbfNvoTffm7rlPxNehpwAsj4kaq27OWUT03sVO5jQPm9v5fA6zJzCvL8PlUjdx82PcAzwZ+nplDmbke+ALVMTFf9v+oifb3vDgXRsQxwPOBl+cD/xx6XtR9KzPv9olts21zGbZttm22ba5sVt1na8I5AOxf3obVQ/Vw6oUdjqkx5bmIFcB1mfkftaILgaNL/9HAl2Y6tqZl5omZuVdmLqbaz5dm5suBbwIvKpPNyboDZOatwM0R8agy6lnAtcyDfV/8AnhyRGxfPgej9Z8X+79mov19IfCq8ka8JwO/rt3eMydExKFUt+29MDPvrRVdCBwVEdtExL5UL2f4Xidi1P1smytz/vxs22zbjG0z2Da3r23OzFnZAYdTvRXpZ8DbOx1Pw3U9mOpn+h8Ag6U7nOp5iW8APwW+DuzS6Vgb3g5Lga+U/j8sB/Bq4HPANp2Or8F69wGryv7/IrDzfNr3wLuAHwM/Aj4JbDOX9z9wLtUzMeuprqIvn2h/A0H1VtCfAT+kemNgx+vQ5rqvpnoeZPTc97Ha9G8vdb8eOKzT8dvZNts2z91z8zj1tm22bbZtblPbHGVGSZIkSZLaarbeUitJkiRJ2sqZcEqSJEmSGmHCKUmSJElqhAmnJEmSJKkRJpySJEmSpEaYcEqSJEmSGmHCKUmSJElqhAmnJEmSJKkRJpySJEmSpEaYcEqSJEmSGmHCKUmSJElqhAmnJEmSJKkRJpySJEmSpEaYcEqSJEmSGmHCKUmSJElqhAmnJEmSJKkRJpySJEmSpEaYcEqSJEmSGmHCuZWJiO0i4ssR8euI+FxEvDwiVnYwnhsj4tmdWv+WiIivRsTRnY5Dc19EvC0iPtHpOCTNPNvt9rHd3jpExOKIyIhYOJ2ycaZdGhFrmolSM8mEsyER8bKIWBURv4mIW8pJ8uA2LPpFwMOBXTPzxZl5TmYe0oblNiIiDoqIiyLiroi4IyK+FxGv7nRcAJl5WGaetbnzR8QxEfHDiLg3Im6NiI9ExMPaGeM0YtkjIlaUY+2eiPhxRLwrInZoeL0nR8Sn2ri8LNt0QW3ceyLizHatY5L1rqs3gBHRXcblli4/M/85M1+7pcuR1Bzb7cpcbbdnQ6Ld4DE21XozIvZr4/IeGxEXloso90TENyPiqe1avuYeE84GRMRbgA8A/0zVyOwNfAQ4og2L3wf4SWZuaCGOKa8eNSkingJcCnwL2A/YFXg9cFgn42qHiHgr8F7g74GHAU8GFgMrI6J7hmPZBfgusB3wlMzcEXgOsBPwyJmMpU0eARzVgfXeyabH5mFlnKQ5znb7/vXP2Xa70xo+xmZMRDwS+F/gh8C+VG32BVTff57Sydg0i2WmXRs7quTjN8CLJ5lmG6qTzi9L9wFgm1K2FFgDvBVYB9wCvLqUvQsYBtaXdSwHjgG+XVt2Am8Afgr8vLa842vLOxI4HPgJcAfwttr8C4ATgJ8BtwOfBXaplb8SuKmUvR24EXj2BPX8NnDaJNthZ+ArwBDVF/uvAHvVyo8BbgDuKXV5ea3sNcB1Zb6LgX3K+ADeX+p6N9UJ8XETrP8y4LW1dX0beF9Z5s+BwyaY76Fl+//FmPEPKXU5ugyfCbynVr4UWFMbHt3O9wDXAn9axveU/fL42rS7A/cCvePE855SzwWTbOunAgPAr8vfp9bKNtmHwMnAp0r/4nJMHQ38AvgV8PZSdiibHo//B7wYuGrMut8CfKnFz08C/0B1/C6s1e/M2jRPBr4D3FXWubSMfybww9p0lwADteH/AY6cZL3vAD5XG3c+1TGeYz7fK6g+R2tLbF1lnw0CbyrTdVE1yCeN3aZl+OBaHW4Gjqkt/+xyHN1UYppwv9rZ2W15h+12vZ5zst0u049b7yn27XXA82vTLix1P7AMj9seNXCMbXLM1I6b/Ur/mcBpwH+XbX8l8MhSdnmZ9rclhpcAPwJeUFtWN1X7/sQWPi+fBC4aZ/xHgctL/+KyztF2/M/L9n/cOGWvLtv5nnLsvK62zKVM8Nmqbddx28yyzf63HFt3lWU/tYy/uSzv6Nqyngd8n+oYvBk4udPnprnUdTyAudZRfQnfMPpBmmCaU4ArqJKI3nKyencpW1rmP6WcAA6nSjR2LuUns+kX101OQuVDfAmwC9UvXqPLO6ks7y/LB/PTwI7AAcB9wL5l/uNKbHtRnfw+Dpxbyh5bTlZPL2X/UZY93gl8e2Aj8MxJtsOu5SS0fYnlc8AXS9kO5UP/qDK8B3BA6T8CWA08hurk/w7gO6XsucBVVL/uRZlmjwnWfxmbNlzry/bporqi+0sgprOPgbOAc0r/mUyecL6Y6srgAqoG4LejsVJd9XxvbdrjgC9PUI8rgHdNsp13oWqMX1m210vL8K6l/EamTjj/sxxPTwB+DzxmguNxG6ovQ4+pjfs+8Octfn4S2L/sw9F9c3/CCexJ9aXp8LLdnlOGe0t8vwN2ozrWb6NKCncsZfeN1nmC9T6uzLMT1Zeq28q4rE13AdVnYgeqz+/3KI1jmfbOcsy9veyXrnG26T5UDetLS5y7An2l7GzgSyXmxVRfLpd3+rxmZzeXO2y3R+OYs+12mf7GCeo92b49idKml+HnAdeV/gnbowaOsU2OmdpxU084bwcOKtv3HOC88aYtw8cDn6kNH0Htgu0Un5dbqSV9tfHPLMfPdtSSSqqEcnUt1vvLatv0kWXfP4PqszOa0C9l8s/WhG1m2WYbyvq7qL5L/IIqMd8GOISqLX5IbV2PL/vyj6m+A4x7kdpu+l3HA5hrHfBy4NYppvkZcHht+LnAjaV/KVVDsrBWvg54cuk/makbrmW14dHljX7x3bFM86TaNFeNfqiorjI9q1a2B9UJfSHVibd+AtuB6srteCfwPct6Hj2NbdcH3Flb9l1UDdt2Y6b7KrUv4eXkcC/VF/ll5YTzZKb4ZYgHN1yra2Xbl/j/YJz5XjHRPgZOBVaW/jOZJOEcZ95B4IjS/6RyYowyvIoxv6jW5vsp8FeTLPeVwPfGjPsuD/yqdiNTJ5z1K9jfA44a73gs4z4K/FPpP4AqCdumxWMgqW7jOpzqamUPmyac/wB8csw8F/PAr8r/A/xZ2f8rqa70H0rVEP6ghfV+Angd8FdUSfZ+lIST6hao39ePR6qk8Zu14bcC15c67z/BNj0RuGCcGLqoPk+PrY17HXBZq58hOzu76XfYbo+Wzdl2u5TfOEG9J9u3+1ElJduX4XN44M6VSdujNh9jmxwzteOmnnB+olZ2OPDj8aYtw48o9XpoGT4fOL7Ffb4BOHSc8Y8u69mTB747/B3VHVz17xCjZeMm38AXgeOm+mwxRZtZttlPa2WPL+t9eG3c7ZQLvuPE8QHg/a1+Fuwm73yGs/1uB3ab4jmMR1B9mR51Uxl3/zJy02c97qW6XbNVN4+NKTM3lv77yt/bauX31Za/D3BBeVnAXVQN2UaqL9uPqC87M39LVd/x3AmMUDV844qI7SPi4xFxU0TcTXXbx04R0VWW/RKqL/63RMR/R8SjazF+sBbjHVRXxvbMzEuBD1NdwVoXEadHxEMnimGMW2t1u7f0jrfdf8XE+3iPUj6liHhVRAzW6vE4ql/nyMwrqfb70lLv/YALJ1jU7UyynXnw8UYZ3rOVOItba/1THY9nAS+LiKBKdj+bmb+fxrrIzIuobqN53ZiifYAXj26zst0O5oH6f4uqgXp66b+M6orpM8rwVM4GXlW6s8dZdzfV8Ti67o9TXY0edVaZ7qLM/OkE61hE9cVirNFfZseeG6azn/T/t3fvUXLWdZ7HP5+udAMqmBEqyOZinDW6RxwpYnfQxZ1hM2MEZIjjeEFn8LLZzY4LI8ywumF0Udn1bJwZnRnFkcParIAIOoguy0ENCq66A0x1Mi1C8JJBNIkZuwG5eevu9Hf/+D0dKk13ujpdv6rqqvfrnDp5qup5nnx//VTXtz/1XAqYP/p20sl9+1Bm3bYRsUvp5/m7tp8m6WylPc1TYzpUP6rViNfYXOru0xHxY6XDTX/f9lKlc3SvrfP/eVAzj/EEpddP7bUP3ql0iPasV5q1fYbtO4sLVD2iFJaPq5lltt+tenrm9N8ZRcSMv0e2TykufjRq+1Gl13FtHVgAAmfj3aG0F+TVh5jnx0pvVFNWFY81Sixg2d1K50AsrbkdGRF7lY6dXzk1Y/Hme+yMBaQ3/juUPumczUWSXqD0qe0xSiFBSk1IEfHliHiF0pvYd5T2OE3V+B+n1XhURPx9sdxHIuIlSocSPV/pDa+Rprbxa2oftP0MpTftrxUP/UzpE9cpz66Z9znFeM5XOsxzqdI5Fa6Z/yqlvannSrohIn45Sz1fkfR7tVd2nWb6601Kr7m9c9VZh6e81iLiTqVPHf+NpDcpne9xON4t6c+m1bZb6RPl2m3/9IjYWjw/PXD+X80vcH5D6fV2vNK5QbV2K23342r+72Mi4sSaef5W6ZymVx7iyoO7NfPFnB5U2isx/b1h7wzzAmgc+rY6vm8fylzb9jqlo1k2StpZhFBp7n5Ua6GvsYP6tO359OnZTP2N8TpJdxSvl3p8pVhmutcX6/l5zWMbJL3H9oyvKdtHSPqc0nm4xxd/C92ig/8Wmk2je+anlT7YXxkRz5R0eZ11oA4EzgaLiEeVDmH5mO1XF58G9haf4Px5Mdt1Sr+AZdvHFfM37KslFuhySR8oApGKGqeuoHaDpLNsv9x2n9Ix9Yd6Db1L0lttv9P2scX6TrJ9ffH80UqfLj1SXGn1vVML2j7e9kanr/X4ldI5KJM1NV5s+8Ri3mfafl0xPVB8StWr9Ab9y5rlGqLYxu+X9FHbpxfbd7XS4ZsP6slPCYclnWn7WUVzuLBmNU9X+gNjtKj7bUp7OGt9StLvKTWE6Xvban1Y6UJGV9Vst+W2P2z7xUpv3s93uhz7EttvUGrqN9fUeU4xjn6lS/jX6yeSVs8Qdq9W+sR6PCIOBDenr5J5oJ4VR8TXlEL4W2oe/pTSJ82vtF2yfaTT93StKJ7/e6U/htYpHUZ8r1IzOkXpk/i5/s+Q9LuSzi6ma5/bp3SY7odsH2O7x/a/tP1bxdjOlfQSpcN43qG0PWb6hPlaSb9j+/XF9jjWdqXYm/FZpd+/o4tt+adqn/cGoCPRtw/SkX27Rm/RN6ZuSzT3tr1eKTi9XU/u3ZTm7kcHNOA19i1JJ9qu2D5S6TDt+fiJpF+f9tgXJK1VOgf4oL8xnL5C5q2zrOv9kv617Q8Uf98cbfuPlY4M+i/T5r1X6bSWGgp52QAAHyZJREFUj9k+e4Z19SmdTzkqacL2GUo/6zll6JlHS3o4In5pe53SB+ZoEAJnBhHxIaUX/XuUfol2K+3J+kIxy39XOifvbqWrse0oHmsHf6P0Cc82248rncB+iiQVf7yfp/SGu0/psIlZD5MoPrlcX9zut/2wpCuUApCUjo8/Simk3SnpSzWL9yj9DH+sdOjNbym92SsiPq/0lSTXOx3Sc4+evGT7MUqfqP5UT16V7y8O6ydxCBHx50p73/5ST16N72lK54f8rJjtGqUm8YBSUPlMzfI7JX1I6VPPnyidW/D/pv0fu5VeG6G05222Wh5WuvLauKS7iu32VaUr0u6KiIcknaX0yfRDSn9QnBURU4f+/lelPW4/VWokn1b9/q749yHbO2oev0YpQE9/4185fZxzeI/ShTQkHfiZbFT62U/9br1TxXtZ8bPfIeneiBgrFrtD0g8jYqSe/zAi7i1e6zN5s1KD3Kn087pB0gm2Vym9nt8cEU9ExKeVfsf/aob1/0jpkKGLlF7bw0oXY5KkP1b6g+t+pT2sn5Z0ZT11Azh89O2kk/t24RalwDx1e5/m2LbFh413KPXZ2j5+yH403UJeYxHxPaUPC76idN2G6UfgzOV9Sh+CPmL79cU6f6G0d/G5km6cmrH4YOJYpe070zi+r3To8ElKf9/sU9or/sqIeEp/j4hvKf0N8j+LQFn73ONKH9B+Vmn7v0mznz40k0b2zP8k6dLid+iSoiY0yNQFSQAsQLGH8lJJpxaBolHrvVLSjyPiPY1aZzPYPkrpxP61tecy2t6mdDGA+1pWHAAAkO1LJD0/Iv6w5rGXSzovIt7YusrQaQicQIMUh1SOR8T1c85c3/pWK+39OjkiftCIdTaL0xdcnxUR61tdCwAAOFhxSPQ/Sjo3IuY85QRYiENdLQvAPETE4V4c5yls/zdJfyLpfyzCsPmA0on2h7o4AgAAaAHb/0Hp8OhrCJtoBvZwAgAAAACy4KJBAAAAAIAsshxSe9xxx8Xq1atzrBoA0GW2b9/+YESUW13HYkdvBgA0ynx6c5bAuXr1ag0NDeVYNQCgy9j+Yatr6AT0ZgBAo8ynN3NILQAAAAAgCwInAAAAACALAicAAAAAIAsCJwAAAAAgCwInAAAAACALAicAAAAAIIu6vhbF9gOSHpe0X9JERPTnLAoAgG5l+0hJX5d0hFKfviEi3jttniMkXS3pJZIekvSGiHigyaUCADCn+XwP57+NiAezVQIAaBujw8MaqVa1bGBAkmacLlcqdc+HefmVpPUR8YTtXknftP3FiLizZp5Nkn4aEc+zfY6kD0p6QyuKBQDUp5G9daHLN7M3zydwAgC6wOjwsG7btEn7x8bUsyS1icmJiYOmS319Wrtli3Zs3TrnfOsHBwmd8xARIemJ4m5vcYtps22U9L5i+gZJl9l2sSwAoM00srcutt5c7zmcIWmb7e22N880g+3NtodsD42OjjauQgBAU41Uq9o/NiZNTmpyfFyT4+MzTu/etq2u+Uaq1VYPadGxXbI9LGlE0q0Rcde0WZZL2i1JETEh6VFJx86wHnozALSBRvfWxdSb6w2cL4+ItZLOkHSe7d+cPkNEXBER/RHRXy6XG1okAKB5lg0MqNTXJ5dK6untVU9v74zTKzdsqGu+qcN4UL+I2B8RFUkrJK2z/aLDXA+9GQDaQKN762LqzXUdUhsRe4t/R2x/XtI6pQsaAAA6TLlS0frBwbrO/1i6Zk1bnSfSaSLiEdu3Szpd0j01T+2VtFLSHttLJD1T6eJBAIA21OjeutDlm9mbPdfpHrafLqknIh4vpm+VdGlEfGm2Zfr7+2NoaKixlQIAupLt7d10dXTbZUnjRdg8StI2SR+MiJtr5jlP0m9ExB8VFw16TUS8/lDrpTcDABplPr25nj2cx0v6vO2p+T99qLAJAAAW5ARJV9kuKZ368tmIuNn2pZKGIuImSYOSrrG9S9LDks5pXbkAAMxuzsAZEfdLOqkJtQAA0PUi4m5JJ8/w+CU107+U9Lpm1gUAwOGo96JBAAAAAADMC4ETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAIA2YXul7dtt77R9r+0LZpjnNNuP2h4ubpe0olYAAOqxpNUFAACAAyYkXRQRO2wfLWm77VsjYue0+b4REWe1oD4AAOaFPZwAALSJiNgXETuK6ccl3SdpeWurAgDg8BE4AQBoQ7ZXSzpZ0l0zPP0y29+y/UXbJx5iHZttD9keGh0dzVQpAACzI3ACANBmbD9D0uckXRgRj017eoek50TESZI+KukLs60nIq6IiP6I6C+Xy/kKBgBgFgROAADaiO1epbB5bUTcOP35iHgsIp4opm+R1Gv7uCaXCQBAXQicAAC0CduWNCjpvoj48CzzPLuYT7bXKfXyh5pXJQAA9eMqtQAAtI9TJZ0r6du2h4vH/kzSKkmKiMslvVbS221PSPqFpHMiIlpRLAAAc6k7cNouSRqStJdLsQMA0HgR8U1JnmOeyyRd1pyKAABYmPns4bxA6fLsx2SqBQDa1ujwsEaqVS0bGJCkeU2XK5UFLd/IddW7fLlSyfJzBAAA3aWuwGl7haRXSfqApD/NWhEAtJnR4WHdtmmT9o+NqWdJetucnJioa7rU16e1W7Zox9ath7V8I9dV7/Klvj6tHxwkdAIAgAWrdw/nX0t6l6SjZ5vB9mZJmyVp1apVC68MANrESLWq/WNj0uSkJsfH04MRdU/v3rZtQcs3cl31Lj9SrRI4AQDAgs15lVrbZ0kaiYjth5qP7/oC0KmWDQyo1Ncnl0rq6e1VT2/vvKZXbtiwoOUbua56l586xBYAAGAh6tnDeaqks22fKelIScfY/lRE/GHe0gCgPZQrFa0fHFzQeZNL16xp2DmcC1lXvcuzdxMAADSC53MlddunSfrPc12ltr+/P4aGhhZYGgAAku3tEdHf6joWO3ozAKBR5tOb5zykFgAAAACAwzGfr0VRRHxN0teyVAIAAAAA6Cjs4QQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAoE3YXmn7dts7bd9r+4IZ5rHtj9jeZftu22tbUSsAAPVY0uoCAADAAROSLoqIHbaPlrTd9q0RsbNmnjMkrSlup0j6ePEvgC4wOjyskWpVywYGJGnO6XKlMu9lcq2r22spVyrz29gdgsAJAECbiIh9kvYV04/bvk/Sckm1gXOjpKsjIiTdaXup7ROKZQF0sNHhYd22aZP2j42pZ0n6M35yYmLW6VJfn9Zu2aIdW7fWvUyudXV7LaW+Pq0fHOzK0EngBACgDdleLelkSXdNe2q5pN019/cUjz0lcNreLGmzJK1atSpHmQCaaKRa1f6xMWlyUpPj4+nBiENO7962bd7L5FpXt9cyUq12ZeDkHE4AANqM7WdI+pykCyPiscNdT0RcERH9EdFfLpcbVyCAllg2MKBSX59cKqmnt1c9vb1zTq/csGHey+RaV7fXMnWIbbdhDycAAG3Edq9S2Lw2Im6cYZa9klbW3F9RPAagw5UrFa0fHJz3+YVL16xp2LmKC1lXt9fSjXs3JcnpFJDG6u/vj6GhoYavFwDQfWxvj4j+VtfRDLYt6SpJD0fEhbPM8ypJ50s6U+liQR+JiHVzrZveDABolPn0ZvZwAgDQPk6VdK6kb9seLh77M0mrJCkiLpd0i1LY3CXp55Le1oI6AQCoC4ETAIA2ERHflOQ55glJ5zWnIgAAFoaLBgEAAAAAsiBwAgAAAACyIHACAAAAALIgcAIAAAAAsiBwAgAAAACyIHACAAAAALKYM3DaPtL2P9j+lu17bb+/GYUBAAAAABa3er6H81eS1kfEE7Z7JX3T9hcj4s7MtQFAdqPDwxqpVrVsYECSZpwuVyqtLBEAAGDRmjNwFl8w/URxt7e4Rc6iAKAZRoeHddumTdo/NqaeJentcHJi4qDpUl+f1g8OEjoBAAAOQ13ncNou2R6WNCLp1oi4a4Z5Ntsesj00Ojra6DoBoOFGqlXtHxuTJic1OT6uyfHxGadHqtVWlwoAALAo1RU4I2J/RFQkrZC0zvaLZpjniojoj4j+crnc6DoBoOGWDQyo1Ncnl0rq6e1VT2/vjNNTh9gCAABgfuo5h/OAiHjE9u2STpd0T56SAKA5ypWK1g8Ocg4nAABAJnMGTttlSeNF2DxK0iskfTB7ZQDQBOVK5aBAOds0AAAA5q+ePZwnSLrKdknpENzPRsTNecsCAAAAACx29Vyl9m5JJzehFgAAAABAB6nrokEAAAAAAMwXgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAgDZi+0rbI7bvmeX502w/anu4uF3S7BoBAKjXklYXAAAADvJJSZdJuvoQ83wjIs5qTjkAABw+9nACANBGIuLrkh5udR0AADQCgRMAgMXnZba/ZfuLtk+cbSbbm20P2R4aHR1tZn0AAEgicAIAsNjskPSciDhJ0kclfWG2GSPiiojoj4j+crnctAIBAJhC4AQAYBGJiMci4oli+hZJvbaPa3FZAADMiMAJAMAiYvvZtl1Mr1Pq5Q+1tioAAGbGVWoBAGgjtq+TdJqk42zvkfReSb2SFBGXS3qtpLfbnpD0C0nnRES0qFwAAA6JwAkAQBuJiDfO8fxlSl+bAgBA2+OQWgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWcwZO2ytt3257p+17bV/QjMIAAAAAAIvbkjrmmZB0UUTssH20pO22b42InZlrA4CGGR0e1ki1qmUDA5J0YLpcqbS4MgAAgM41Z+CMiH2S9hXTj9u+T9JySQROAIvC6PCwbtu0SfvHxtSzJL3tTU5MqNTXp/WDg4ROAACATOZ1Dqft1ZJOlnTXDM9ttj1ke2h0dLQx1QFAA4xUq9o/NiZNTmpyfFyT4+MHpkeq1VaXBwAA0LHqDpy2nyHpc5IujIjHpj8fEVdERH9E9JfL5UbWCAALsmxgQKW+PrlUUk9vr3p6ew9MTx1iCwAAgMar5xxO2e5VCpvXRsSNeUsCgMYqVypaPzjIOZwAAABNNmfgtG1Jg5Lui4gP5y8JABqvXKkcFC4JmgAAAPnVc0jtqZLOlbTe9nBxOzNzXQAAAACARa6eq9R+U5KbUAsAAAAAoIPM6yq1AAAAAADUi8AJAAAAAMiCwAkAAAAAyILACQAAAADIgsAJAAAAAMiCwAkAAAAAyILACQBAG7F9pe0R2/fM8rxtf8T2Ltt3217b7BoBAKjXnN/DCQAAmuqTki6TdPUsz58haU1xO0XSx4t/ASxio8PDGqlWtWxgQJIOTJcrlYOeK1cqLa4UmB8CJwAAbSQivm579SFm2Sjp6ogISXfaXmr7hIjY15QCATTc6PCwbtu0SfvHxtSzJP15PjkxoVJfn9Zu2aIdW7dq/9iYSn19Wj84SOjEosIhtQAALC7LJe2uub+neOwpbG+2PWR7aHR0tCnFAZi/kWpV+8fGpMlJTY6Pa3J8/MD07m3bDnpupFptdbnAvBA4AQDoUBFxRUT0R0R/uVxudTkAZrFsYEClvj65VFJPb696ensPTK/csOGg56YOuQUWCw6pBQBgcdkraWXN/RXFYwAWqXKlovWDg7Oew7l0zRrO4cSiReAEAGBxuUnS+bavV7pY0KOcvwksfuVK5aAwOX2aoInFisAJAEAbsX2dpNMkHWd7j6T3SuqVpIi4XNItks6UtEvSzyW9rTWVAgAwNwInAABtJCLeOMfzIem8JpUDAMCCcNEgAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFgROAAAAAEAWBE4AAAAAQBYETgAAAABAFnMGTttX2h6xfU8zCgIAAAAAdIYldczzSUmXSbo6bykAcLDR4WGNVKtaNjAgSfOaLlcqBy1frlRaMwgAAIAuNmfgjIiv216dvxQAeNLo8LBu27RJ+8fG1LMkvVVNTkzUNV3q69PaLVu0Y+tW7R8bU6mvT+sHBwmdAAAATdawczhtb7Y9ZHtodHS0UasF0KVGqlXtHxuTJic1OT6uyfHxeU3v3rbtoOVHqtVWDwkAAKDrNCxwRsQVEdEfEf3lcrlRqwXQpZYNDKjU1yeXSurp7VVPb++8pldu2HDQ8lOH3AIAAKB56jmHEwCarlypaP3g4ILO4Vy6Zg3ncAIAALQQgRNA2ypXKgcFxcOZJmgCAAC0Tj1fi3KdpDskvcD2Htub8pcFAEB3sn267e/a3mV7ywzPv9X2qO3h4vbvW1EnAAD1qOcqtW9sRiEAAHQ72yVJH5P0Ckl7JFVt3xQRO6fN+pmIOL/pBQIAME8Nu2gQAABYsHWSdkXE/RExJul6SRtbXBMAAIeNwAkAQPtYLml3zf09xWPT/b7tu23fYHvlbCvjK8sAAK1G4AQAYHH5P5JWR8SLJd0q6arZZuQrywAArUbgBACgfeyVVLvHckXx2AER8VBE/Kq4+wlJL2lSbQAAzBuBEwCA9lGVtMb2c233STpH0k21M9g+oebu2ZLua2J9AADMC9/DCQBAm4iICdvnS/qypJKkKyPiXtuXShqKiJskvcP22ZImJD0s6a0tKxgAgDkQOAEAaCMRcYukW6Y9dknN9MWSLm52XQAAHA4OqQUAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkQeAEAAAAAGRB4AQAAAAAZEHgBAAAAABkUVfgtH267e/a3mV7S+6iAADoVnP1XNtH2P5M8fxdtlc3v0oAAOqzZK4ZbJckfUzSKyTtkVS1fVNE7MxZ2OjwsEaqVS0bGJCkGafLlUpd89UzvdB1UUv719Kp4+rkWoBuU2fP3STppxHxPNvnSPqgpDc0oz56c2fU0qnjWuy10PfQqeYMnJLWSdoVEfdLku3rJW2UlC1wjg4P67ZNm7R/bEw9S1KJkxMTB02X+vq0dssW7di69ZDz1TO90HVRS/vX0qnj6uRa1g8O0nzRjerpuRslva+YvkHSZbYdEZGzMHpzZ9TSqeNa7LXQ99DJ6gmcyyXtrrm/R9Ip02eyvVnSZklatWrVgooaqVa1f2xMmpzU5Ph4ejDiKdO7t22ra756phe6Lmpp/1o6dVydWstItUrjRTeqp+cemCciJmw/KulYSQ9OXxm9mVq6aVyLvRb6HjpVwy4aFBFXRER/RPSXy+UFrWvZwIBKfX1yqaSe3l719PbOOL1yw4a65qtneqHropb2r6VTx9WptUwdagTg8NGbqaWbxrXYa6HvoVPVs4dzr6SVNfdXFI9lU65UtH5wsK5j3ZeuWdOwY/UXsi5qaf9aOnVcnVwL0IXq6blT8+yxvUTSMyU9lLswenPn1NKp41rstdD30Kk81ykfRTP7nqTfVmpyVUlvioh7Z1umv78/hoaGGlknAKBL2d4eEf2trqMZ6um5ts+T9BsR8UfFRYNeExGvn2vd9GYAQKPMpzfPuYezOD/kfElfllSSdOWhwiYAADg8s/Vc25dKGoqImyQNSrrG9i5JD0s6p3UVAwBwaPUcUquIuEXSLZlrAQCg683UcyPikprpX0p6XbPrAgDgcDTsokEAAAAAANQicAIAAAAAsiBwAgAAAACyIHACAAAAALIgcAIAAAAAspjzezgPa6X2qKQfNmBVx0l6sAHrWay6efzdPHapu8ffzWOXGP9M439ORJRbUUwnoTc3TDePv5vHLnX3+Lt57BLjX1BvzhI4G8X2ULd82fdMunn83Tx2qbvH381jlxh/t49/Mej2bdTN4+/msUvdPf5uHrvE+Bc6fg6pBQAAAABkQeAEAAAAAGTR7oHzilYX0GLdPP5uHrvU3ePv5rFLjL/bx78YdPs26ubxd/PYpe4efzePXWL8Cxp/W5/DCQAAAABYvNp9DycAAAAAYJEicAIAAAAAsmjbwGn7dNvftb3L9pZW15OT7ZW2b7e90/a9ti8oHn+W7Vttf7/499daXWsutku2/9H2zcX959q+q9j+n7Hd1+oac7G91PYNtr9j+z7bL+uybf8nxev+HtvX2T6yk7e/7Sttj9i+p+axGbe3k48UP4e7ba9tXeULN8vY/6J47d9t+/O2l9Y8d3Ex9u/afmVrqkYtejO9uVPfm6ejN9Ob6c2N681tGThtlyR9TNIZkl4o6Y22X9jaqrKakHRRRLxQ0kslnVeMd4ukr0bEGklfLe53qgsk3Vdz/4OS/ioinifpp5I2taSq5vgbSV+KiH8l6SSln0NXbHvbyyW9Q1J/RLxIUknSOers7f9JSadPe2y27X2GpDXFbbOkjzepxlw+qaeO/VZJL4qIF0v6nqSLJal4DzxH0onFMn9b9Aa0CL2Z3qzOfm+ejt5Mb6Y3N6g3t2XglLRO0q6IuD8ixiRdL2lji2vKJiL2RcSOYvpxpTe15UpjvqqY7SpJr25NhXnZXiHpVZI+Udy3pPWSbihm6eSxP1PSb0oalKSIGIuIR9Ql276wRNJRtpdIepqkferg7R8RX5f08LSHZ9veGyVdHcmdkpbaPqE5lTbeTGOPiG0RMVHcvVPSimJ6o6TrI+JXEfEDSbuUegNah95Mb+7Y9+Za9GZJ9GaJ3tyw3tyugXO5pN019/cUj3U826slnSzpLknHR8S+4ql/lnR8i8rK7a8lvUvSZHH/WEmP1LzQO3n7P1fSqKT/VRy29AnbT1eXbPuI2CvpLyX9SKmZPSppu7pn+0+ZbXt323vhv5P0xWK628a+GHTtNqE3S6I305u7Z/tPoTcnC+7N7Ro4u5LtZ0j6nKQLI+Kx2ucifX9Nx32Hje2zJI1ExPZW19IiSyStlfTxiDhZ0s807RCdTt32klScD7FRqbn/C0lP11MP6+gqnby9D8X2u5UOYby21bUAtejNXYneTG8+SCdv70NpVG9u18C5V9LKmvsrisc6lu1epYZ2bUTcWDz8k6ld9MW/I62qL6NTJZ1t+wGlw7PWK503sbQ4jEPq7O2/R9KeiLiruH+DUpPrhm0vSb8j6QcRMRoR45JuVHpNdMv2nzLb9u6K90Lbb5V0lqQ/iCe/HLorxr7IdN02oTfTm4v79GZ6M705Oayxt2vgrEpaU1wNq0/p5NSbWlxTNsV5EYOS7ouID9c8dZOktxTTb5H0v5tdW24RcXFErIiI1Urb+baI+ANJt0t6bTFbR45dkiLinyXttv2C4qHflrRTXbDtCz+S9FLbTyt+D6bG3xXbv8Zs2/smSW8uroj3UkmP1hze0xFsn6502N7ZEfHzmqduknSO7SNsP1fp4gz/0IoacQC9Oen492d6M71Z9GaJ3ty43hwRbXmTdKbSVZH+SdK7W11P5rG+XGk3/d2ShovbmUrnS3xV0vclfUXSs1pda+afw2mSbi6mf714Ae+S9HeSjmh1fRnHXZE0VGz/L0j6tW7a9pLeL+k7ku6RdI2kIzp5+0u6TumcmHGlT9E3zba9JVnpqqD/JOnbSlcMbPkYGjz2XUrng0y9911eM/+7i7F/V9IZra6fG72Z3ty5780zjJveTG+mNzeoN7tYEAAAAACAhmrXQ2oBAAAAAIscgRMAAAAAkAWBEwAAAACQBYETAAAAAJAFgRMAAAAAkAWBEwAAAACQBYETAAAAAJDF/wdVqj8TU8kxfQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Because these time series are integer-valued, there can be\n",
+    "# problems with aliasing. \n",
+    "# Here are some examples of what aliasing looks like:\n",
+    "# (graph_examples() function defined in util.py)\n",
+    "util.graph_examples(cases, ts_col_name, {}, \n",
+    "                    mask=(np.max(cases[ts_col_name].values, axis=1) < 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dropping the first 48 elements of each time series.\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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Confirmed</th>\n",
+       "      <th>Confirmed_Outlier</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>[  0   0   0   0   0   1   1   1   1   1   2   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   1   1   1   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   1   2   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>[  0   0   1   1   3   3   3   4   5  12  15  2...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>[  0   0   0   0   1   1   3   3   5   5   5   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>[  0   0   0   0   1   1   1   8   8   8   9   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   1   3   4   4   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   1   2   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  \\\n",
+       "FIPS                                      \n",
+       "1001     Alabama    Autauga       55869   \n",
+       "1003     Alabama    Baldwin      223234   \n",
+       "1013     Alabama     Butler       19448   \n",
+       "1015     Alabama    Calhoun      113605   \n",
+       "1017     Alabama   Chambers       33254   \n",
+       "...          ...        ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198   \n",
+       "55139  Wisconsin  Winnebago      171907   \n",
+       "56013    Wyoming    Fremont       39261   \n",
+       "56021    Wyoming    Laramie       99500   \n",
+       "56039    Wyoming      Teton       23464   \n",
+       "\n",
+       "                                               Confirmed  \\\n",
+       "FIPS                                                       \n",
+       "1001  [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "1003  [  0   0   0   0   0   1   1   1   1   1   2   ...   \n",
+       "1013  [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "1015  [  0   0   0   0   0   0   0   0   1   1   1   ...   \n",
+       "1017  [  0   0   0   0   0   0   0   0   0   1   2   ...   \n",
+       "...                                                  ...   \n",
+       "55133 [  0   0   1   1   3   3   3   4   5  12  15  2...   \n",
+       "55139 [  0   0   0   0   1   1   3   3   5   5   5   ...   \n",
+       "56013 [  0   0   0   0   1   1   1   8   8   8   9   ...   \n",
+       "56021 [  0   0   0   0   0   0   0   1   3   4   4   ...   \n",
+       "56039 [  0   0   0   0   0   0   0   0   0   1   2   ...   \n",
+       "\n",
+       "                                       Confirmed_Outlier  \n",
+       "FIPS                                                      \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1013  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1015  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "1017  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "...                                                  ...  \n",
+       "55133 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "55139 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56013 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56021 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...  \n",
+       "\n",
+       "[917 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# To avoid problems due to aliasing, we will restrict the analysis\n",
+    "# in the rest of this notebook to time series whose maximum values\n",
+    "# go above a threshold:\n",
+    "alias_threshold = 100\n",
+    "\n",
+    "# We also cut off the sections at the beginning of the time \n",
+    "# series where every time series' value is below this threshold.\n",
+    "\n",
+    "# Find what point in the time series at least one county went above\n",
+    "# the threshold.\n",
+    "first_time_above_min = np.argmax(np.max(cases[ts_col_name].values, axis=0) >= alias_threshold)\n",
+    "print(f\"Dropping the first {first_time_above_min} elements of each time series.\")\n",
+    "\n",
+    "# Find which counties have at least one time series value above the \n",
+    "# threshold.\n",
+    "counties_mask = np.max(cases[ts_col_name].values, axis=1) >= alias_threshold\n",
+    "\n",
+    "# Filter rows\n",
+    "filtered = cases[counties_mask].copy(deep=True)\n",
+    "\n",
+    "# Truncate time series to just the times when at least one county\n",
+    "# was above our threshold.\n",
+    "filtered[ts_col_name] = filtered[ts_col_name].values[:,first_time_above_min:]\n",
+    "\n",
+    "# Also filter the outlier masks\n",
+    "outlier_col_name = ts_col_name + \"_Outlier\"\n",
+    "filtered[outlier_col_name] = filtered[outlier_col_name].values[:,first_time_above_min:]\n",
+    "\n",
+    "filtered_dates = dates[first_time_above_min:]\n",
+    "\n",
+    "# Drop time series columns other than the one we analyze\n",
+    "series_to_keep = [ts_col_name, outlier_col_name]\n",
+    "metadata_cols = []\n",
+    "to_drop = []\n",
+    "for colname in filtered.columns:\n",
+    "    if not isinstance(filtered[colname].dtype, tp.TensorType):\n",
+    "        metadata_cols.append(colname)\n",
+    "    elif colname not in series_to_keep:\n",
+    "        to_drop.append(colname)\n",
+    "        \n",
+    "filtered = filtered.drop(columns=to_drop)\n",
+    "\n",
+    "filtered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Result_Object</th>\n",
+       "      <th>Slope</th>\n",
+       "      <th>Intercept</th>\n",
+       "      <th>Curve</th>\n",
+       "      <th>R^2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>{'fun': 9787.73581780539, 'jac': [0.0001818989...</td>\n",
+       "      <td>1.412422</td>\n",
+       "      <td>-18.558592</td>\n",
+       "      <td>[-18.5585916  -17.14616925 -15.73374691 -14.321...</td>\n",
+       "      <td>0.847991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>{'fun': 24283.275703324885, 'jac': [0.00582076...</td>\n",
+       "      <td>4.216368</td>\n",
+       "      <td>-48.052173</td>\n",
+       "      <td>[-48.05217272 -43.83580444 -39.61943616 -35.403...</td>\n",
+       "      <td>0.952466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>{'fun': 161017.38024601561, 'jac': [-0.0029103...</td>\n",
+       "      <td>3.213226</td>\n",
+       "      <td>-59.307676</td>\n",
+       "      <td>[-59.30767574 -56.09444926 -52.88122278 -49.667...</td>\n",
+       "      <td>0.637028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>{'fun': 5900.674180976751, 'jac': [0.000181898...</td>\n",
+       "      <td>2.328498</td>\n",
+       "      <td>-22.545755</td>\n",
+       "      <td>[-22.54575489 -20.21725655 -17.88875822 -15.560...</td>\n",
+       "      <td>0.961758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>{'fun': 75499.45503592775, 'jac': [0.013096723...</td>\n",
+       "      <td>6.286920</td>\n",
+       "      <td>-46.682817</td>\n",
+       "      <td>[-46.68281742 -40.39589735 -34.10897727 -27.822...</td>\n",
+       "      <td>0.934763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>{'fun': 12548.548386314673, 'jac': [0.00109139...</td>\n",
+       "      <td>7.428863</td>\n",
+       "      <td>-43.436425</td>\n",
+       "      <td>[-43.43642457 -36.00756117 -28.57869777 -21.149...</td>\n",
+       "      <td>0.991761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>{'fun': 8152.435854342024, 'jac': [0.000272848...</td>\n",
+       "      <td>1.673438</td>\n",
+       "      <td>-17.737470</td>\n",
+       "      <td>[-17.73747009 -16.06403211 -14.39059414 -12.717...</td>\n",
+       "      <td>0.903862</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>{'fun': 43098.16865181122, 'jac': [0.000727595...</td>\n",
+       "      <td>3.160687</td>\n",
+       "      <td>-37.419888</td>\n",
+       "      <td>[-37.41988808 -34.25920094 -31.09851379 -27.937...</td>\n",
+       "      <td>0.863839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>{'fun': 11588.176263548901, 'jac': [-0.0001818...</td>\n",
+       "      <td>3.137632</td>\n",
+       "      <td>-29.751967</td>\n",
+       "      <td>[-29.75196698 -26.61433454 -23.4767021  -20.339...</td>\n",
+       "      <td>0.958767</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>{'fun': 6598.686786018984, 'jac': [-0.00891304...</td>\n",
+       "      <td>1.925320</td>\n",
+       "      <td>-12.127536</td>\n",
+       "      <td>[-12.12753577 -10.20221613  -8.2768965   -6.351...</td>\n",
+       "      <td>0.938932</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  \\\n",
+       "FIPS                                      \n",
+       "1001     Alabama    Autauga       55869   \n",
+       "1003     Alabama    Baldwin      223234   \n",
+       "1013     Alabama     Butler       19448   \n",
+       "1015     Alabama    Calhoun      113605   \n",
+       "1017     Alabama   Chambers       33254   \n",
+       "...          ...        ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198   \n",
+       "55139  Wisconsin  Winnebago      171907   \n",
+       "56013    Wyoming    Fremont       39261   \n",
+       "56021    Wyoming    Laramie       99500   \n",
+       "56039    Wyoming      Teton       23464   \n",
+       "\n",
+       "                                           Result_Object     Slope  Intercept  \\\n",
+       "FIPS                                                                            \n",
+       "1001   {'fun': 9787.73581780539, 'jac': [0.0001818989...  1.412422 -18.558592   \n",
+       "1003   {'fun': 24283.275703324885, 'jac': [0.00582076...  4.216368 -48.052173   \n",
+       "1013   {'fun': 161017.38024601561, 'jac': [-0.0029103...  3.213226 -59.307676   \n",
+       "1015   {'fun': 5900.674180976751, 'jac': [0.000181898...  2.328498 -22.545755   \n",
+       "1017   {'fun': 75499.45503592775, 'jac': [0.013096723...  6.286920 -46.682817   \n",
+       "...                                                  ...       ...        ...   \n",
+       "55133  {'fun': 12548.548386314673, 'jac': [0.00109139...  7.428863 -43.436425   \n",
+       "55139  {'fun': 8152.435854342024, 'jac': [0.000272848...  1.673438 -17.737470   \n",
+       "56013  {'fun': 43098.16865181122, 'jac': [0.000727595...  3.160687 -37.419888   \n",
+       "56021  {'fun': 11588.176263548901, 'jac': [-0.0001818...  3.137632 -29.751967   \n",
+       "56039  {'fun': 6598.686786018984, 'jac': [-0.00891304...  1.925320 -12.127536   \n",
+       "\n",
+       "                                                   Curve       R^2  \n",
+       "FIPS                                                                \n",
+       "1001  [-18.5585916  -17.14616925 -15.73374691 -14.321...  0.847991  \n",
+       "1003  [-48.05217272 -43.83580444 -39.61943616 -35.403...  0.952466  \n",
+       "1013  [-59.30767574 -56.09444926 -52.88122278 -49.667...  0.637028  \n",
+       "1015  [-22.54575489 -20.21725655 -17.88875822 -15.560...  0.961758  \n",
+       "1017  [-46.68281742 -40.39589735 -34.10897727 -27.822...  0.934763  \n",
+       "...                                                  ...       ...  \n",
+       "55133 [-43.43642457 -36.00756117 -28.57869777 -21.149...  0.991761  \n",
+       "55139 [-17.73747009 -16.06403211 -14.39059414 -12.717...  0.903862  \n",
+       "56013 [-37.41988808 -34.25920094 -31.09851379 -27.937...  0.863839  \n",
+       "56021 [-29.75196698 -26.61433454 -23.4767021  -20.339...  0.958767  \n",
+       "56039 [-12.12753577 -10.20221613  -8.2768965   -6.351...  0.938932  \n",
+       "\n",
+       "[917 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warm-up: Fit a straight line.\n",
+    "# We use sklearn's implementation of BGFS as the optimizer.\n",
+    "\n",
+    "# What precision of floating-point to use\n",
+    "# On a laptop, there's not much reason to use 32-bit.\n",
+    "fp_type = np.float64\n",
+    "series_values = filtered[ts_col_name].values.astype(fp_type)\n",
+    "series_len = series_values.shape[1]\n",
+    "x = np.linspace(0, series_len - 1, series_len, dtype=fp_type)\n",
+    "\n",
+    "def linear_curve(m, b):\n",
+    "    return m * x + b\n",
+    "\n",
+    "def linear_objective(var_values, y):\n",
+    "    m, b = var_values\n",
+    "    return np.sum((y - (linear_curve(m, b))) ** 2)\n",
+    "\n",
+    "linear_initial_guess = (0.1, 1.0)\n",
+    "linear_bounds = ((0.0, 1000.0), (float(-series_len), float(series_len)))\n",
+    "\n",
+    "linear = filtered[metadata_cols].copy()\n",
+    "linear[\"Result_Object\"] = [\n",
+    "    scipy.optimize.minimize(\n",
+    "        linear_objective, linear_initial_guess,\n",
+    "        args=(ts), bounds=linear_bounds)\n",
+    "    for ts in series_values\n",
+    "]\n",
+    "linear[\"Slope\"] = linear[\"Result_Object\"].apply(lambda r: r.x[0])\n",
+    "linear[\"Intercept\"] = linear[\"Result_Object\"].apply(lambda r: r.x[1])\n",
+    "\n",
+    "# Generate all the linear curves\n",
+    "M = linear[\"Slope\"].values.reshape([-1, 1])\n",
+    "X = x.reshape([1, -1])\n",
+    "B = linear[\"Intercept\"].values.reshape([-1, 1])\n",
+    "linear[\"Curve\"] = tp.TensorArray(M * X + B)\n",
+    "\n",
+    "# Compute coefficient of determination\n",
+    "linear[\"R^2\"] = [\n",
+    "    metrics.r2_score(\n",
+    "        filtered.loc[fips][ts_col_name], linear.loc[fips][\"Curve\"]) \n",
+    "        for fips in filtered.index]\n",
+    "\n",
+    "linear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the lines we just fit, using the graph_examples() function in util.py.\n",
+    "util.graph_examples(filtered, ts_col_name, {\"Linear\": linear})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now let's fit some more appropriate curves to this data.\n",
+    "# First, break out the repetitive parts of the curve-fitting process into\n",
+    "# a Python function.\n",
+    "def curve_fit_df(df: pd.DataFrame, ts_col_name: str,\n",
+    "                 curve_fn,\n",
+    "                 param_names: Tuple,\n",
+    "                 initial_guess: Tuple,\n",
+    "                 bounds: Tuple,     \n",
+    "                 regularize_fn = None):\n",
+    "    \"\"\"\n",
+    "    Fit a curve to a column full of time series.\n",
+    "    \n",
+    "    :param df: DataFrame containing one time series per cell\n",
+    "    :param ts_col_name: Column containing the time series to analyze\n",
+    "    :param curve_fn: Function that, if called with a vector of X values\n",
+    "     and a tuple of parameters, will compute the value of the curve \n",
+    "     being fit.\n",
+    "     If parameters are vectors, this function must compute multiple\n",
+    "     curves, one for each element of the vectors.\n",
+    "    \n",
+    "    :param param_names: Names of the parameters of `curve_fn`.\n",
+    "    :param initial_guess: Initial values for the paramters to start\n",
+    "     off the solver.\n",
+    "    :param bounds: Lower and upper bounds for the parameters.\n",
+    "    :param regularize_fn: Optional function that returns a \n",
+    "     regularization penalty, given a set of parameter values.\n",
+    "    \n",
+    "    :returns: A dataframe with one row per row of `df`, containing\n",
+    "     information about the \n",
+    "    \"\"\"\n",
+    "    series_values = df[ts_col_name].values.astype(fp_type)\n",
+    "    series_len = series_values.shape[1]\n",
+    "    x = np.linspace(0, series_len - 1, series_len, dtype=fp_type)\n",
+    "    \n",
+    "    def return_zero(var_values):\n",
+    "        return 0.0\n",
+    "    \n",
+    "    if regularize_fn is None:\n",
+    "        regularize_fn = return_zero\n",
+    "    \n",
+    "    def objective(var_values, y):\n",
+    "        squared_diffs = np.sum((y - (curve_fn(x, var_values))) ** 2)\n",
+    "        return squared_diffs + regularize_fn(var_values)\n",
+    "    \n",
+    "    result = pd.DataFrame(index=df.index)\n",
+    "    result[metadata_cols] = df[metadata_cols]\n",
+    "    result[\"Result_Object\"] = [\n",
+    "        scipy.optimize.minimize(\n",
+    "            objective, initial_guess,\n",
+    "            args=(ts), bounds=bounds)\n",
+    "        for ts in series_values\n",
+    "    ]\n",
+    "    \n",
+    "    # Add a column for each parameter\n",
+    "    for i in range(len(param_names)):\n",
+    "        name = param_names[i]\n",
+    "        result[name] = result[\"Result_Object\"].apply(lambda r: r.x[i])\n",
+    "\n",
+    "    # Generate all the curves\n",
+    "    param_vectors = [\n",
+    "        result[name].values.reshape([-1, 1])\n",
+    "        for name in param_names\n",
+    "    ]\n",
+    "    result[\"Curve\"] = tp.TensorArray(curve_fn(x, param_vectors))\n",
+    "\n",
+    "    # Compute coefficient of determination\n",
+    "    result[\"R^2\"] = [\n",
+    "        metrics.r2_score(\n",
+    "            df.loc[fips][ts_col_name], result.loc[fips][\"Curve\"]) \n",
+    "            for fips in df.index]\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define curve-fitting routines using the function from the previous\n",
+    "# cell.\n",
+    "\n",
+    "def fit_exponential(ts_col_name: str):\n",
+    "    \"\"\"\n",
+    "    Fit an exponential curve to each time series in the specified column\n",
+    "    of the `cases` dataframe.\n",
+    "    \"\"\"    \n",
+    "    def curve_fn(x, var_values):\n",
+    "        rate, offset = var_values\n",
+    "        # Y = e^(rate * (X - offset))\n",
+    "        return np.exp(rate * (x - offset))\n",
+    "    \n",
+    "    param_names = (\"Rate\", \"Offset\")\n",
+    "    initial_guess = (0.1, 1.0)\n",
+    "    bounds = ((0.0, 1.0), (0.0, float(len(filtered_dates))))\n",
+    "    \n",
+    "    return curve_fit_df(filtered, ts_col_name, curve_fn,\n",
+    "                        param_names, initial_guess, bounds)\n",
+    "\n",
+    "\n",
+    "def fit_logistic(ts_col_name: str):\n",
+    "    \"\"\"\n",
+    "    Fit a logistic curve to each time series in the specified column\n",
+    "    of the `cases` dataframe.\n",
+    "    \"\"\"    \n",
+    "    def curve_fn(x, var_values):\n",
+    "        max_, rate, offset = var_values\n",
+    "        # Y = max / (1 + e^(-rate *(X - offset))\n",
+    "        return max_ / (1.0 + np.exp(-rate * (x - offset)))\n",
+    "    \n",
+    "    param_names = (\"Max\", \"Rate\", \"Offset\")\n",
+    "    initial_guess = (1000.0, 0.1, 1.0)\n",
+    "    bounds = ((0.0, 1e6), (0.0, 1.0), (0.0, float(len(filtered_dates))))\n",
+    "    \n",
+    "    return curve_fit_df(filtered, ts_col_name, curve_fn,\n",
+    "                        param_names, initial_guess, bounds)\n",
+    "\n",
+    "\n",
+    "def fit_logistic2(ts_col_name: str):\n",
+    "    \"\"\"\n",
+    "    Fit a mixture of two logistic curves to each time series in the \n",
+    "    specified column of the `cases` dataframe.\n",
+    "    \"\"\"\n",
+    "    # Distance from X=0 at which the logistic function is close enough to 0 or 1\n",
+    "    # that we can consider it to have \"triggered\"\n",
+    "    logistic2_limit = np.array(6., dtype=fp_type)\n",
+    "    \n",
+    "    def curve_fn(x, var_values):\n",
+    "        (max1, rate1, offset1, \n",
+    "         max2, rate2, offset2, \n",
+    "         switch_begin, switch_end) = var_values\n",
+    "        \n",
+    "        # logistic curve is Y = max / (1 + e^(-rate *(X - offset))\n",
+    "        logistic1 = max1 / (1.0 + np.exp(-rate1 * (x - offset1)))\n",
+    "        logistic2 = max2 / (1.0 + np.exp(-rate2 * (x - offset2)))\n",
+    "\n",
+    "        # What fraction of the output comes from logistic2?\n",
+    "        def sigmoid(x_):\n",
+    "            return 1. / (1. + np.exp(-x_))\n",
+    "        \n",
+    "        mix_input = (x - switch_begin - logistic2_limit) / np.maximum(1.0, (switch_end - switch_begin))\n",
+    "        return logistic1 * (1.0 - sigmoid(mix_input)) + logistic2 * sigmoid(mix_input)\n",
+    "    \n",
+    "    def regularize_fn(var_values):\n",
+    "        (max1, rate1, offset1, \n",
+    "         max2, rate2, offset2, \n",
+    "         switch_begin, switch_end) = var_values\n",
+    "        reg_coeff = 1e7\n",
+    "        linear_terms = [\n",
+    "            max(0.0, switch_begin - switch_end - 1.0)  # switch_end >= switch_begin + 1\n",
+    "        ]\n",
+    "        return reg_coeff * sum([t for t in linear_terms])\n",
+    "    \n",
+    "    param_names = (\n",
+    "        \"Max1\", \"Rate1\", \"Offset1\",\n",
+    "        \"Max2\", \"Rate2\", \"Offset2\",\n",
+    "        \"Switch_Begin\", \"Switch_End\")\n",
+    "    initial_guess = (\n",
+    "        1000.0, 0.1, 1.0,  # First logistic function\n",
+    "        1000.0, 0.1, 1.0,  # Second logistic function\n",
+    "        10.0, 20.0  # Switchover\n",
+    "    )\n",
+    "    series_len = len(filtered_dates)\n",
+    "    bounds = (\n",
+    "        (0.0, 1e6), (0.0, 10.0), (0.0, float(series_len)),   # First logistic function\n",
+    "        (0.0, 1e6), (0.0, 10.0), (0.0, float(series_len)),   # Second logistic function\n",
+    "        (0.0, float(series_len)), (0.0, float(series_len))  # Switchover\n",
+    "    )\n",
+    "    \n",
+    "    return curve_fit_df(filtered, ts_col_name, curve_fn,\n",
+    "                        param_names, initial_guess, bounds,\n",
+    "                        regularize_fn=regularize_fn)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Result_Object</th>\n",
+       "      <th>Rate</th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Curve</th>\n",
+       "      <th>R^2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>{'fun': 120292.0, 'jac': [60163.994203321636, ...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-0.868203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>{'fun': 1124499.0, 'jac': [224685.99490821362,...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-1.201186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>{'fun': 608885.0, 'jac': [60497.99267202616, 0...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-0.372575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>{'fun': 367780.0, 'jac': [141197.97269813716, ...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-1.383566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>{'fun': 1127644.815696666, 'jac': [-0.76834112...</td>\n",
+       "      <td>0.092567</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[  1.           1.09698636   1.20337908   1.320...</td>\n",
+       "      <td>0.025632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>{'fun': 1404933.1524302743, 'jac': [10.9663233...</td>\n",
+       "      <td>0.096465</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>[  1.           1.10127071   1.21279717   1.335...</td>\n",
+       "      <td>0.077550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>{'fun': 185273.0, 'jac': [92705.98529838026, 0...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-1.184843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>{'fun': 645446.0, 'jac': [160463.98086473346, ...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-1.039177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>{'fun': 678823.0, 'jac': [194975.9665876627, 0...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-1.415408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>{'fun': 297031.0, 'jac': [147377.97901034355, ...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>-1.748879</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  \\\n",
+       "FIPS                                      \n",
+       "1001     Alabama    Autauga       55869   \n",
+       "1003     Alabama    Baldwin      223234   \n",
+       "1013     Alabama     Butler       19448   \n",
+       "1015     Alabama    Calhoun      113605   \n",
+       "1017     Alabama   Chambers       33254   \n",
+       "...          ...        ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198   \n",
+       "55139  Wisconsin  Winnebago      171907   \n",
+       "56013    Wyoming    Fremont       39261   \n",
+       "56021    Wyoming    Laramie       99500   \n",
+       "56039    Wyoming      Teton       23464   \n",
+       "\n",
+       "                                           Result_Object      Rate  Offset  \\\n",
+       "FIPS                                                                         \n",
+       "1001   {'fun': 120292.0, 'jac': [60163.994203321636, ...  0.000000    69.0   \n",
+       "1003   {'fun': 1124499.0, 'jac': [224685.99490821362,...  0.000000    69.0   \n",
+       "1013   {'fun': 608885.0, 'jac': [60497.99267202616, 0...  0.000000    69.0   \n",
+       "1015   {'fun': 367780.0, 'jac': [141197.97269813716, ...  0.000000    69.0   \n",
+       "1017   {'fun': 1127644.815696666, 'jac': [-0.76834112...  0.092567     0.0   \n",
+       "...                                                  ...       ...     ...   \n",
+       "55133  {'fun': 1404933.1524302743, 'jac': [10.9663233...  0.096465     0.0   \n",
+       "55139  {'fun': 185273.0, 'jac': [92705.98529838026, 0...  0.000000    69.0   \n",
+       "56013  {'fun': 645446.0, 'jac': [160463.98086473346, ...  0.000000    69.0   \n",
+       "56021  {'fun': 678823.0, 'jac': [194975.9665876627, 0...  0.000000    69.0   \n",
+       "56039  {'fun': 297031.0, 'jac': [147377.97901034355, ...  0.000000    69.0   \n",
+       "\n",
+       "                                                   Curve       R^2  \n",
+       "FIPS                                                                \n",
+       "1001  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -0.868203  \n",
+       "1003  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -1.201186  \n",
+       "1013  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -0.372575  \n",
+       "1015  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -1.383566  \n",
+       "1017  [  1.           1.09698636   1.20337908   1.320...  0.025632  \n",
+       "...                                                  ...       ...  \n",
+       "55133 [  1.           1.10127071   1.21279717   1.335...  0.077550  \n",
+       "55139 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -1.184843  \n",
+       "56013 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -1.039177  \n",
+       "56021 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -1.415408  \n",
+       "56039 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1... -1.748879  \n",
+       "\n",
+       "[917 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now we can fit those curves.\n",
+    "# First, the exponential curves.\n",
+    "exp_df = fit_exponential(ts_col_name)\n",
+    "exp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "util.graph_examples(filtered, ts_col_name, {\"Exponential\": exp_df})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Result_Object</th>\n",
+       "      <th>Max</th>\n",
+       "      <th>Rate</th>\n",
+       "      <th>Offset</th>\n",
+       "      <th>Curve</th>\n",
+       "      <th>R^2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>{'fun': 1120.7360763818122, 'jac': [6.82121026...</td>\n",
+       "      <td>220.507559</td>\n",
+       "      <td>0.072698</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>[  1.45236888   1.56110969   1.6779297    1.803...</td>\n",
+       "      <td>0.982594</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>{'fun': 3242.66468345069, 'jac': [0.0002728484...</td>\n",
+       "      <td>253.546021</td>\n",
+       "      <td>0.107391</td>\n",
+       "      <td>42.650713</td>\n",
+       "      <td>[  2.57287919   2.86127469   3.18158681   3.537...</td>\n",
+       "      <td>0.993653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>{'fun': 1002.2235094202017, 'jac': [0.00076170...</td>\n",
+       "      <td>333.446715</td>\n",
+       "      <td>0.178081</td>\n",
+       "      <td>59.250087</td>\n",
+       "      <td>[8.72249124e-03 1.04226431e-02 1.24541692e-02 1...</td>\n",
+       "      <td>0.997741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>{'fun': 3198.5785612292007, 'jac': [0.00013642...</td>\n",
+       "      <td>128.728254</td>\n",
+       "      <td>0.112374</td>\n",
+       "      <td>37.675949</td>\n",
+       "      <td>[  1.83955484   2.05484255   2.29487023   2.562...</td>\n",
+       "      <td>0.979270</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>{'fun': 5904.371293544457, 'jac': [-0.00090949...</td>\n",
+       "      <td>311.776092</td>\n",
+       "      <td>0.176634</td>\n",
+       "      <td>31.194822</td>\n",
+       "      <td>[  1.2563206    1.49786823   1.78559018   2.128...</td>\n",
+       "      <td>0.994898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>{'fun': 24447.530507882264, 'jac': [0.00145519...</td>\n",
+       "      <td>456.757453</td>\n",
+       "      <td>0.086515</td>\n",
+       "      <td>36.690306</td>\n",
+       "      <td>[ 18.33674957  19.92153259  21.63652355  23.491...</td>\n",
+       "      <td>0.983948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>{'fun': 1120.8009751589568, 'jac': [-6.8212102...</td>\n",
+       "      <td>249.030866</td>\n",
+       "      <td>0.060721</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>[  3.71663238   3.9456146    4.18846356   4.445...</td>\n",
+       "      <td>0.986783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>{'fun': 3252.604543311751, 'jac': [0.000227373...</td>\n",
+       "      <td>413.991045</td>\n",
+       "      <td>0.071534</td>\n",
+       "      <td>64.638514</td>\n",
+       "      <td>[  4.02380176   4.3190724    4.63576529   4.975...</td>\n",
+       "      <td>0.989724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>{'fun': 2754.3677827760876, 'jac': [4.54747350...</td>\n",
+       "      <td>192.222411</td>\n",
+       "      <td>0.101038</td>\n",
+       "      <td>41.219324</td>\n",
+       "      <td>[  2.94033003   3.24766025   3.58650468   3.959...</td>\n",
+       "      <td>0.990199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>{'fun': 1801.069601709264, 'jac': [-2.27373675...</td>\n",
+       "      <td>101.659727</td>\n",
+       "      <td>0.135081</td>\n",
+       "      <td>31.929410</td>\n",
+       "      <td>[  1.34348757   1.53486193   1.75302042   2.001...</td>\n",
+       "      <td>0.983332</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  \\\n",
+       "FIPS                                      \n",
+       "1001     Alabama    Autauga       55869   \n",
+       "1003     Alabama    Baldwin      223234   \n",
+       "1013     Alabama     Butler       19448   \n",
+       "1015     Alabama    Calhoun      113605   \n",
+       "1017     Alabama   Chambers       33254   \n",
+       "...          ...        ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198   \n",
+       "55139  Wisconsin  Winnebago      171907   \n",
+       "56013    Wyoming    Fremont       39261   \n",
+       "56021    Wyoming    Laramie       99500   \n",
+       "56039    Wyoming      Teton       23464   \n",
+       "\n",
+       "                                           Result_Object         Max  \\\n",
+       "FIPS                                                                   \n",
+       "1001   {'fun': 1120.7360763818122, 'jac': [6.82121026...  220.507559   \n",
+       "1003   {'fun': 3242.66468345069, 'jac': [0.0002728484...  253.546021   \n",
+       "1013   {'fun': 1002.2235094202017, 'jac': [0.00076170...  333.446715   \n",
+       "1015   {'fun': 3198.5785612292007, 'jac': [0.00013642...  128.728254   \n",
+       "1017   {'fun': 5904.371293544457, 'jac': [-0.00090949...  311.776092   \n",
+       "...                                                  ...         ...   \n",
+       "55133  {'fun': 24447.530507882264, 'jac': [0.00145519...  456.757453   \n",
+       "55139  {'fun': 1120.8009751589568, 'jac': [-6.8212102...  249.030866   \n",
+       "56013  {'fun': 3252.604543311751, 'jac': [0.000227373...  413.991045   \n",
+       "56021  {'fun': 2754.3677827760876, 'jac': [4.54747350...  192.222411   \n",
+       "56039  {'fun': 1801.069601709264, 'jac': [-2.27373675...  101.659727   \n",
+       "\n",
+       "           Rate     Offset                                              Curve  \\\n",
+       "FIPS                                                                            \n",
+       "1001   0.072698  69.000000 [  1.45236888   1.56110969   1.6779297    1.803...   \n",
+       "1003   0.107391  42.650713 [  2.57287919   2.86127469   3.18158681   3.537...   \n",
+       "1013   0.178081  59.250087 [8.72249124e-03 1.04226431e-02 1.24541692e-02 1...   \n",
+       "1015   0.112374  37.675949 [  1.83955484   2.05484255   2.29487023   2.562...   \n",
+       "1017   0.176634  31.194822 [  1.2563206    1.49786823   1.78559018   2.128...   \n",
+       "...         ...        ...                                                ...   \n",
+       "55133  0.086515  36.690306 [ 18.33674957  19.92153259  21.63652355  23.491...   \n",
+       "55139  0.060721  69.000000 [  3.71663238   3.9456146    4.18846356   4.445...   \n",
+       "56013  0.071534  64.638514 [  4.02380176   4.3190724    4.63576529   4.975...   \n",
+       "56021  0.101038  41.219324 [  2.94033003   3.24766025   3.58650468   3.959...   \n",
+       "56039  0.135081  31.929410 [  1.34348757   1.53486193   1.75302042   2.001...   \n",
+       "\n",
+       "            R^2  \n",
+       "FIPS             \n",
+       "1001   0.982594  \n",
+       "1003   0.993653  \n",
+       "1013   0.997741  \n",
+       "1015   0.979270  \n",
+       "1017   0.994898  \n",
+       "...         ...  \n",
+       "55133  0.983948  \n",
+       "55139  0.986783  \n",
+       "56013  0.989724  \n",
+       "56021  0.990199  \n",
+       "56039  0.983332  \n",
+       "\n",
+       "[917 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Then fit the logistic function\n",
+    "log_df = fit_logistic(ts_col_name)\n",
+    "log_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "util.graph_examples(filtered, ts_col_name, {\"Logistic\": log_df})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Result_Object</th>\n",
+       "      <th>Max1</th>\n",
+       "      <th>Rate1</th>\n",
+       "      <th>Offset1</th>\n",
+       "      <th>Max2</th>\n",
+       "      <th>Rate2</th>\n",
+       "      <th>Offset2</th>\n",
+       "      <th>Switch_Begin</th>\n",
+       "      <th>Switch_End</th>\n",
+       "      <th>Curve</th>\n",
+       "      <th>R^2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>{'fun': 103.09367966627978, 'jac': [-4.9737991...</td>\n",
+       "      <td>1419.349992</td>\n",
+       "      <td>0.348968</td>\n",
+       "      <td>65.279688</td>\n",
+       "      <td>53.714540</td>\n",
+       "      <td>3.236958</td>\n",
+       "      <td>14.382468</td>\n",
+       "      <td>33.278405</td>\n",
+       "      <td>43.598479</td>\n",
+       "      <td>[1.77447803e-07 2.50995262e-07 3.54948362e-07 5...</td>\n",
+       "      <td>0.998399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>{'fun': 474.161936528702, 'jac': [-0.046082959...</td>\n",
+       "      <td>535.638267</td>\n",
+       "      <td>0.695515</td>\n",
+       "      <td>45.357924</td>\n",
+       "      <td>519.301303</td>\n",
+       "      <td>0.040293</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>21.466305</td>\n",
+       "      <td>27.074776</td>\n",
+       "      <td>[2.24782961e-01 2.78630288e-01 3.45248445e-01 4...</td>\n",
+       "      <td>0.999072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>{'fun': 936.4052334299788, 'jac': [0.007139533...</td>\n",
+       "      <td>323.151902</td>\n",
+       "      <td>0.119303</td>\n",
+       "      <td>58.410333</td>\n",
+       "      <td>324.666982</td>\n",
+       "      <td>0.187087</td>\n",
+       "      <td>58.923584</td>\n",
+       "      <td>9.651052</td>\n",
+       "      <td>20.775420</td>\n",
+       "      <td>[2.45092359e-01 2.71296116e-01 2.99929789e-01 3...</td>\n",
+       "      <td>0.997889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>{'fun': 1763.2077124518307, 'jac': [-0.0253521...</td>\n",
+       "      <td>132.831373</td>\n",
+       "      <td>0.429680</td>\n",
+       "      <td>31.692333</td>\n",
+       "      <td>129.950014</td>\n",
+       "      <td>0.100274</td>\n",
+       "      <td>37.259914</td>\n",
+       "      <td>16.734170</td>\n",
+       "      <td>15.736972</td>\n",
+       "      <td>[1.61907557e-04 2.48814446e-04 3.82370951e-04 5...</td>\n",
+       "      <td>0.988572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>{'fun': 1591.2480200914388, 'jac': [0.03351487...</td>\n",
+       "      <td>56.398120</td>\n",
+       "      <td>8.364028</td>\n",
+       "      <td>29.968907</td>\n",
+       "      <td>447.777401</td>\n",
+       "      <td>0.020263</td>\n",
+       "      <td>17.395100</td>\n",
+       "      <td>23.179009</td>\n",
+       "      <td>28.260747</td>\n",
+       "      <td>[  0.59115411   0.72779069   0.89578444   1.102...</td>\n",
+       "      <td>0.998625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>{'fun': 1876.3339325386778, 'jac': [0.00109139...</td>\n",
+       "      <td>932.476564</td>\n",
+       "      <td>0.351420</td>\n",
+       "      <td>64.329276</td>\n",
+       "      <td>940.493799</td>\n",
+       "      <td>0.032788</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>12.643473</td>\n",
+       "      <td>17.395936</td>\n",
+       "      <td>[  1.7202925    2.17717658   2.75229679   3.474...</td>\n",
+       "      <td>0.998768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>{'fun': 1094.4836088777304, 'jac': [0.0, 0.000...</td>\n",
+       "      <td>511.082241</td>\n",
+       "      <td>8.922810</td>\n",
+       "      <td>68.373833</td>\n",
+       "      <td>293.599386</td>\n",
+       "      <td>0.053269</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>37.173960</td>\n",
+       "      <td>[  3.33516736   3.56400346   3.80758097   4.066...</td>\n",
+       "      <td>0.987093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>{'fun': 3337.1254430272625, 'jac': [0.00027284...</td>\n",
+       "      <td>566.841775</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>571.117421</td>\n",
+       "      <td>0.066692</td>\n",
+       "      <td>63.593953</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>[  3.87501415   4.16950833   4.48585047   4.825...</td>\n",
+       "      <td>0.989457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>{'fun': 1311.4369143087672, 'jac': [0.15945715...</td>\n",
+       "      <td>183.440284</td>\n",
+       "      <td>0.174015</td>\n",
+       "      <td>31.299472</td>\n",
+       "      <td>177.537208</td>\n",
+       "      <td>0.151751</td>\n",
+       "      <td>41.160583</td>\n",
+       "      <td>23.552154</td>\n",
+       "      <td>26.605609</td>\n",
+       "      <td>[  0.78740397   0.93629068   1.11315152   1.323...</td>\n",
+       "      <td>0.995334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>{'fun': 377.1254386232161, 'jac': [-3.97903932...</td>\n",
+       "      <td>303398.658878</td>\n",
+       "      <td>0.460009</td>\n",
+       "      <td>38.777462</td>\n",
+       "      <td>97.339265</td>\n",
+       "      <td>0.651497</td>\n",
+       "      <td>41.041717</td>\n",
+       "      <td>12.315527</td>\n",
+       "      <td>14.805814</td>\n",
+       "      <td>[5.43001979e-03 8.59891345e-03 1.36150128e-02 2...</td>\n",
+       "      <td>0.996510</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  \\\n",
+       "FIPS                                      \n",
+       "1001     Alabama    Autauga       55869   \n",
+       "1003     Alabama    Baldwin      223234   \n",
+       "1013     Alabama     Butler       19448   \n",
+       "1015     Alabama    Calhoun      113605   \n",
+       "1017     Alabama   Chambers       33254   \n",
+       "...          ...        ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198   \n",
+       "55139  Wisconsin  Winnebago      171907   \n",
+       "56013    Wyoming    Fremont       39261   \n",
+       "56021    Wyoming    Laramie       99500   \n",
+       "56039    Wyoming      Teton       23464   \n",
+       "\n",
+       "                                           Result_Object           Max1  \\\n",
+       "FIPS                                                                      \n",
+       "1001   {'fun': 103.09367966627978, 'jac': [-4.9737991...    1419.349992   \n",
+       "1003   {'fun': 474.161936528702, 'jac': [-0.046082959...     535.638267   \n",
+       "1013   {'fun': 936.4052334299788, 'jac': [0.007139533...     323.151902   \n",
+       "1015   {'fun': 1763.2077124518307, 'jac': [-0.0253521...     132.831373   \n",
+       "1017   {'fun': 1591.2480200914388, 'jac': [0.03351487...      56.398120   \n",
+       "...                                                  ...            ...   \n",
+       "55133  {'fun': 1876.3339325386778, 'jac': [0.00109139...     932.476564   \n",
+       "55139  {'fun': 1094.4836088777304, 'jac': [0.0, 0.000...     511.082241   \n",
+       "56013  {'fun': 3337.1254430272625, 'jac': [0.00027284...     566.841775   \n",
+       "56021  {'fun': 1311.4369143087672, 'jac': [0.15945715...     183.440284   \n",
+       "56039  {'fun': 377.1254386232161, 'jac': [-3.97903932...  303398.658878   \n",
+       "\n",
+       "           Rate1    Offset1        Max2     Rate2    Offset2  Switch_Begin  \\\n",
+       "FIPS                                                                         \n",
+       "1001    0.348968  65.279688   53.714540  3.236958  14.382468     33.278405   \n",
+       "1003    0.695515  45.357924  519.301303  0.040293  69.000000     21.466305   \n",
+       "1013    0.119303  58.410333  324.666982  0.187087  58.923584      9.651052   \n",
+       "1015    0.429680  31.692333  129.950014  0.100274  37.259914     16.734170   \n",
+       "1017    8.364028  29.968907  447.777401  0.020263  17.395100     23.179009   \n",
+       "...          ...        ...         ...       ...        ...           ...   \n",
+       "55133   0.351420  64.329276  940.493799  0.032788  69.000000     12.643473   \n",
+       "55139   8.922810  68.373833  293.599386  0.053269  69.000000      0.000001   \n",
+       "56013  10.000000  69.000000  571.117421  0.066692  63.593953      0.000000   \n",
+       "56021   0.174015  31.299472  177.537208  0.151751  41.160583     23.552154   \n",
+       "56039   0.460009  38.777462   97.339265  0.651497  41.041717     12.315527   \n",
+       "\n",
+       "       Switch_End                                              Curve       R^2  \n",
+       "FIPS                                                                            \n",
+       "1001    43.598479 [1.77447803e-07 2.50995262e-07 3.54948362e-07 5...  0.998399  \n",
+       "1003    27.074776 [2.24782961e-01 2.78630288e-01 3.45248445e-01 4...  0.999072  \n",
+       "1013    20.775420 [2.45092359e-01 2.71296116e-01 2.99929789e-01 3...  0.997889  \n",
+       "1015    15.736972 [1.61907557e-04 2.48814446e-04 3.82370951e-04 5...  0.988572  \n",
+       "1017    28.260747 [  0.59115411   0.72779069   0.89578444   1.102...  0.998625  \n",
+       "...           ...                                                ...       ...  \n",
+       "55133   17.395936 [  1.7202925    2.17717658   2.75229679   3.474...  0.998768  \n",
+       "55139   37.173960 [  3.33516736   3.56400346   3.80758097   4.066...  0.987093  \n",
+       "56013   69.000000 [  3.87501415   4.16950833   4.48585047   4.825...  0.989457  \n",
+       "56021   26.605609 [  0.78740397   0.93629068   1.11315152   1.323...  0.995334  \n",
+       "56039   14.805814 [5.43001979e-03 8.59891345e-03 1.36150128e-02 2...  0.996510  \n",
+       "\n",
+       "[917 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# And finally a mixture of two copies of the logistic function\n",
+    "log2_df = fit_logistic2(ts_col_name)\n",
+    "log2_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "util.graph_examples(filtered, ts_col_name, {\"Logistic2\": log2_df})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Confirmed</th>\n",
+       "      <th>Confirmed_Outlier</th>\n",
+       "      <th>Exponential</th>\n",
+       "      <th>Logistic</th>\n",
+       "      <th>Logistic2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  1.45236888   1.56110969   1.6779297    1.803...</td>\n",
+       "      <td>[1.77447803e-07 2.50995262e-07 3.54948362e-07 5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>[  0   0   0   0   0   1   1   1   1   1   2   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  2.57287919   2.86127469   3.18158681   3.537...</td>\n",
+       "      <td>[2.24782961e-01 2.78630288e-01 3.45248445e-01 4...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   0   0   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[8.72249124e-03 1.04226431e-02 1.24541692e-02 1...</td>\n",
+       "      <td>[2.45092359e-01 2.71296116e-01 2.99929789e-01 3...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   1   1   1   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  1.83955484   2.05484255   2.29487023   2.562...</td>\n",
+       "      <td>[1.61907557e-04 2.48814446e-04 3.82370951e-04 5...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   1   2   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[  1.           1.09698636   1.20337908   1.320...</td>\n",
+       "      <td>[  1.2563206    1.49786823   1.78559018   2.128...</td>\n",
+       "      <td>[  0.59115411   0.72779069   0.89578444   1.102...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>[  0   0   1   1   3   3   3   4   5  12  15  2...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[  1.           1.10127071   1.21279717   1.335...</td>\n",
+       "      <td>[ 18.33674957  19.92153259  21.63652355  23.491...</td>\n",
+       "      <td>[  1.7202925    2.17717658   2.75229679   3.474...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>[  0   0   0   0   1   1   3   3   5   5   5   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  3.71663238   3.9456146    4.18846356   4.445...</td>\n",
+       "      <td>[  3.33516736   3.56400346   3.80758097   4.066...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>[  0   0   0   0   1   1   1   8   8   8   9   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  4.02380176   4.3190724    4.63576529   4.975...</td>\n",
+       "      <td>[  3.87501415   4.16950833   4.48585047   4.825...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   1   3   4   4   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  2.94033003   3.24766025   3.58650468   3.959...</td>\n",
+       "      <td>[  0.78740397   0.93629068   1.11315152   1.323...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>[  0   0   0   0   0   0   0   0   0   1   2   ...</td>\n",
+       "      <td>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...</td>\n",
+       "      <td>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...</td>\n",
+       "      <td>[  1.34348757   1.53486193   1.75302042   2.001...</td>\n",
+       "      <td>[5.43001979e-03 8.59891345e-03 1.36150128e-02 2...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  \\\n",
+       "FIPS                                      \n",
+       "1001     Alabama    Autauga       55869   \n",
+       "1003     Alabama    Baldwin      223234   \n",
+       "1013     Alabama     Butler       19448   \n",
+       "1015     Alabama    Calhoun      113605   \n",
+       "1017     Alabama   Chambers       33254   \n",
+       "...          ...        ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198   \n",
+       "55139  Wisconsin  Winnebago      171907   \n",
+       "56013    Wyoming    Fremont       39261   \n",
+       "56021    Wyoming    Laramie       99500   \n",
+       "56039    Wyoming      Teton       23464   \n",
+       "\n",
+       "                                               Confirmed  \\\n",
+       "FIPS                                                       \n",
+       "1001  [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "1003  [  0   0   0   0   0   1   1   1   1   1   2   ...   \n",
+       "1013  [  0   0   0   0   0   0   0   0   0   0   0   ...   \n",
+       "1015  [  0   0   0   0   0   0   0   0   1   1   1   ...   \n",
+       "1017  [  0   0   0   0   0   0   0   0   0   1   2   ...   \n",
+       "...                                                  ...   \n",
+       "55133 [  0   0   1   1   3   3   3   4   5  12  15  2...   \n",
+       "55139 [  0   0   0   0   1   1   3   3   5   5   5   ...   \n",
+       "56013 [  0   0   0   0   1   1   1   8   8   8   9   ...   \n",
+       "56021 [  0   0   0   0   0   0   0   1   3   4   4   ...   \n",
+       "56039 [  0   0   0   0   0   0   0   0   0   1   2   ...   \n",
+       "\n",
+       "                                       Confirmed_Outlier  \\\n",
+       "FIPS                                                       \n",
+       "1001  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1003  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1013  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1015  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "1017  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "...                                                  ...   \n",
+       "55133 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "55139 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56013 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56021 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "56039 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...   \n",
+       "\n",
+       "                                             Exponential  \\\n",
+       "FIPS                                                       \n",
+       "1001  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "1003  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "1013  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "1015  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "1017  [  1.           1.09698636   1.20337908   1.320...   \n",
+       "...                                                  ...   \n",
+       "55133 [  1.           1.10127071   1.21279717   1.335...   \n",
+       "55139 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "56013 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "56021 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "56039 [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1...   \n",
+       "\n",
+       "                                                Logistic  \\\n",
+       "FIPS                                                       \n",
+       "1001  [  1.45236888   1.56110969   1.6779297    1.803...   \n",
+       "1003  [  2.57287919   2.86127469   3.18158681   3.537...   \n",
+       "1013  [8.72249124e-03 1.04226431e-02 1.24541692e-02 1...   \n",
+       "1015  [  1.83955484   2.05484255   2.29487023   2.562...   \n",
+       "1017  [  1.2563206    1.49786823   1.78559018   2.128...   \n",
+       "...                                                  ...   \n",
+       "55133 [ 18.33674957  19.92153259  21.63652355  23.491...   \n",
+       "55139 [  3.71663238   3.9456146    4.18846356   4.445...   \n",
+       "56013 [  4.02380176   4.3190724    4.63576529   4.975...   \n",
+       "56021 [  2.94033003   3.24766025   3.58650468   3.959...   \n",
+       "56039 [  1.34348757   1.53486193   1.75302042   2.001...   \n",
+       "\n",
+       "                                               Logistic2  \n",
+       "FIPS                                                      \n",
+       "1001  [1.77447803e-07 2.50995262e-07 3.54948362e-07 5...  \n",
+       "1003  [2.24782961e-01 2.78630288e-01 3.45248445e-01 4...  \n",
+       "1013  [2.45092359e-01 2.71296116e-01 2.99929789e-01 3...  \n",
+       "1015  [1.61907557e-04 2.48814446e-04 3.82370951e-04 5...  \n",
+       "1017  [  0.59115411   0.72779069   0.89578444   1.102...  \n",
+       "...                                                  ...  \n",
+       "55133 [  1.7202925    2.17717658   2.75229679   3.474...  \n",
+       "55139 [  3.33516736   3.56400346   3.80758097   4.066...  \n",
+       "56013 [  3.87501415   4.16950833   4.48585047   4.825...  \n",
+       "56021 [  0.78740397   0.93629068   1.11315152   1.323...  \n",
+       "56039 [5.43001979e-03 8.59891345e-03 1.36150128e-02 2...  \n",
+       "\n",
+       "[917 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Stuff all the curves that we have fit into a single dataframe,\n",
+    "# along with the original series' values\n",
+    "curves_df = filtered[metadata_cols + [ts_col_name, outlier_col_name]].copy()\n",
+    "curves_df[\"Exponential\"] = exp_df[\"Curve\"]\n",
+    "curves_df[\"Logistic\"] = log_df[\"Curve\"]\n",
+    "curves_df[\"Logistic2\"] = log2_df[\"Curve\"]\n",
+    "curves_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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></th>\n",
+       "      <th>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Confirmed</th>\n",
+       "      <th>Confirmed_Outlier</th>\n",
+       "      <th>Exponential</th>\n",
+       "      <th>Logistic</th>\n",
+       "      <th>Logistic2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</th>\n",
+       "      <th>Date</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 rowspan=\"5\" valign=\"top\">1001</th>\n",
+       "      <th>2020-03-10</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.452369</td>\n",
+       "      <td>1.774478e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-11</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.561110</td>\n",
+       "      <td>2.509953e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-12</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.677930</td>\n",
+       "      <td>3.549484e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-13</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.803420</td>\n",
+       "      <td>5.018344e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-03-14</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.938211</td>\n",
+       "      <td>7.093184e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">56039</th>\n",
+       "      <th>2020-05-13</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.341289</td>\n",
+       "      <td>9.734251e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-14</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.505990</td>\n",
+       "      <td>9.734144e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-15</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>99</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.650322</td>\n",
+       "      <td>9.734072e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-16</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>100</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.776758</td>\n",
+       "      <td>9.734024e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020-05-17</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>100</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.887478</td>\n",
+       "      <td>9.733992e+01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>63273 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    State   County  Population  Confirmed  Confirmed_Outlier  \\\n",
+       "FIPS  Date                                                                     \n",
+       "1001  2020-03-10  Alabama  Autauga       55869          0              False   \n",
+       "      2020-03-11  Alabama  Autauga       55869          0              False   \n",
+       "      2020-03-12  Alabama  Autauga       55869          0              False   \n",
+       "      2020-03-13  Alabama  Autauga       55869          0              False   \n",
+       "      2020-03-14  Alabama  Autauga       55869          0              False   \n",
+       "...                   ...      ...         ...        ...                ...   \n",
+       "56039 2020-05-13  Wyoming    Teton       23464         99              False   \n",
+       "      2020-05-14  Wyoming    Teton       23464         99              False   \n",
+       "      2020-05-15  Wyoming    Teton       23464         99              False   \n",
+       "      2020-05-16  Wyoming    Teton       23464        100              False   \n",
+       "      2020-05-17  Wyoming    Teton       23464        100              False   \n",
+       "\n",
+       "                  Exponential    Logistic     Logistic2  \n",
+       "FIPS  Date                                               \n",
+       "1001  2020-03-10          1.0    1.452369  1.774478e-07  \n",
+       "      2020-03-11          1.0    1.561110  2.509953e-07  \n",
+       "      2020-03-12          1.0    1.677930  3.549484e-07  \n",
+       "      2020-03-13          1.0    1.803420  5.018344e-07  \n",
+       "      2020-03-14          1.0    1.938211  7.093184e-07  \n",
+       "...                       ...         ...           ...  \n",
+       "56039 2020-05-13          1.0  100.341289  9.734251e+01  \n",
+       "      2020-05-14          1.0  100.505990  9.734144e+01  \n",
+       "      2020-05-15          1.0  100.650322  9.734072e+01  \n",
+       "      2020-05-16          1.0  100.776758  9.734024e+01  \n",
+       "      2020-05-17          1.0  100.887478  9.733992e+01  \n",
+       "\n",
+       "[63273 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Expand out the tensors to multiple rows for writing to a CSV file, and\n",
+    "# cast the boolean values back to np.bool\n",
+    "curves_vertical = util.explode_time_series(curves_df, filtered_dates)\n",
+    "curves_vertical[\"Confirmed_Outlier\"] = curves_vertical[\"Confirmed_Outlier\"].astype(np.bool)\n",
+    "curves_vertical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing curves to outputs/us_counties_curves_meta.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Write out the results to a CSV file plus a JSON file of type metadata.\n",
+    "counties_curves_csv_data_file = os.path.join(_OUTPUTS_DIR, \"us_counties_curves.csv\")\n",
+    "curves_vertical.to_csv(counties_curves_csv_data_file, index=True)\n",
+    "col_type_mapping = {\n",
+    "    key: str(value) for key, value in curves_vertical.dtypes.iteritems()\n",
+    "}\n",
+    "\n",
+    "counties_curves_json_data_file = os.path.join(_OUTPUTS_DIR, \"us_counties_curves_meta.json\")\n",
+    "print(f\"Writing curves to {counties_curves_json_data_file}\")\n",
+    "with open(counties_curves_json_data_file, \"w\") as f:\n",
+    "    json.dump(col_type_mapping, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>State</th>\n",
+       "      <th>County</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>Exp_Rate</th>\n",
+       "      <th>Exp_Offset</th>\n",
+       "      <th>Log_Max</th>\n",
+       "      <th>Log_Rate</th>\n",
+       "      <th>Log_Offset</th>\n",
+       "      <th>Log2_Max1</th>\n",
+       "      <th>Log2_Rate1</th>\n",
+       "      <th>Log2_Offset1</th>\n",
+       "      <th>Log2_Max2</th>\n",
+       "      <th>Log2_Rate2</th>\n",
+       "      <th>Log2_Offset2</th>\n",
+       "      <th>Log2_Switch_Begin</th>\n",
+       "      <th>Log2_Switch_End</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FIPS</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",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1001</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Autauga</td>\n",
+       "      <td>55869</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>220.507559</td>\n",
+       "      <td>0.072698</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>1419.349992</td>\n",
+       "      <td>0.348968</td>\n",
+       "      <td>65.279688</td>\n",
+       "      <td>53.714540</td>\n",
+       "      <td>3.236958</td>\n",
+       "      <td>14.382468</td>\n",
+       "      <td>33.278405</td>\n",
+       "      <td>43.598479</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1003</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>223234</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>253.546021</td>\n",
+       "      <td>0.107391</td>\n",
+       "      <td>42.650713</td>\n",
+       "      <td>535.638267</td>\n",
+       "      <td>0.695515</td>\n",
+       "      <td>45.357924</td>\n",
+       "      <td>519.301303</td>\n",
+       "      <td>0.040293</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>21.466305</td>\n",
+       "      <td>27.074776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1013</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Butler</td>\n",
+       "      <td>19448</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>333.446715</td>\n",
+       "      <td>0.178081</td>\n",
+       "      <td>59.250087</td>\n",
+       "      <td>323.151902</td>\n",
+       "      <td>0.119303</td>\n",
+       "      <td>58.410333</td>\n",
+       "      <td>324.666982</td>\n",
+       "      <td>0.187087</td>\n",
+       "      <td>58.923584</td>\n",
+       "      <td>9.651052</td>\n",
+       "      <td>20.775420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1015</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Calhoun</td>\n",
+       "      <td>113605</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>128.728254</td>\n",
+       "      <td>0.112374</td>\n",
+       "      <td>37.675949</td>\n",
+       "      <td>132.831373</td>\n",
+       "      <td>0.429680</td>\n",
+       "      <td>31.692333</td>\n",
+       "      <td>129.950014</td>\n",
+       "      <td>0.100274</td>\n",
+       "      <td>37.259914</td>\n",
+       "      <td>16.734170</td>\n",
+       "      <td>15.736972</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1017</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Chambers</td>\n",
+       "      <td>33254</td>\n",
+       "      <td>0.092567</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>311.776092</td>\n",
+       "      <td>0.176634</td>\n",
+       "      <td>31.194822</td>\n",
+       "      <td>56.398120</td>\n",
+       "      <td>8.364028</td>\n",
+       "      <td>29.968907</td>\n",
+       "      <td>447.777401</td>\n",
+       "      <td>0.020263</td>\n",
+       "      <td>17.395100</td>\n",
+       "      <td>23.179009</td>\n",
+       "      <td>28.260747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55133</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Waukesha</td>\n",
+       "      <td>404198</td>\n",
+       "      <td>0.096465</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>456.757453</td>\n",
+       "      <td>0.086515</td>\n",
+       "      <td>36.690306</td>\n",
+       "      <td>932.476564</td>\n",
+       "      <td>0.351420</td>\n",
+       "      <td>64.329276</td>\n",
+       "      <td>940.493799</td>\n",
+       "      <td>0.032788</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>12.643473</td>\n",
+       "      <td>17.395936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55139</th>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>Winnebago</td>\n",
+       "      <td>171907</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>249.030866</td>\n",
+       "      <td>0.060721</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>511.082241</td>\n",
+       "      <td>8.922810</td>\n",
+       "      <td>68.373833</td>\n",
+       "      <td>293.599386</td>\n",
+       "      <td>0.053269</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>37.173960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56013</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Fremont</td>\n",
+       "      <td>39261</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>413.991045</td>\n",
+       "      <td>0.071534</td>\n",
+       "      <td>64.638514</td>\n",
+       "      <td>566.841775</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "      <td>571.117421</td>\n",
+       "      <td>0.066692</td>\n",
+       "      <td>63.593953</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56021</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Laramie</td>\n",
+       "      <td>99500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>192.222411</td>\n",
+       "      <td>0.101038</td>\n",
+       "      <td>41.219324</td>\n",
+       "      <td>183.440284</td>\n",
+       "      <td>0.174015</td>\n",
+       "      <td>31.299472</td>\n",
+       "      <td>177.537208</td>\n",
+       "      <td>0.151751</td>\n",
+       "      <td>41.160583</td>\n",
+       "      <td>23.552154</td>\n",
+       "      <td>26.605609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56039</th>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Teton</td>\n",
+       "      <td>23464</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>101.659727</td>\n",
+       "      <td>0.135081</td>\n",
+       "      <td>31.929410</td>\n",
+       "      <td>303398.658878</td>\n",
+       "      <td>0.460009</td>\n",
+       "      <td>38.777462</td>\n",
+       "      <td>97.339265</td>\n",
+       "      <td>0.651497</td>\n",
+       "      <td>41.041717</td>\n",
+       "      <td>12.315527</td>\n",
+       "      <td>14.805814</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>917 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           State     County  Population  Exp_Rate  Exp_Offset     Log_Max  \\\n",
+       "FIPS                                                                        \n",
+       "1001     Alabama    Autauga       55869  0.000000        69.0  220.507559   \n",
+       "1003     Alabama    Baldwin      223234  0.000000        69.0  253.546021   \n",
+       "1013     Alabama     Butler       19448  0.000000        69.0  333.446715   \n",
+       "1015     Alabama    Calhoun      113605  0.000000        69.0  128.728254   \n",
+       "1017     Alabama   Chambers       33254  0.092567         0.0  311.776092   \n",
+       "...          ...        ...         ...       ...         ...         ...   \n",
+       "55133  Wisconsin   Waukesha      404198  0.096465         0.0  456.757453   \n",
+       "55139  Wisconsin  Winnebago      171907  0.000000        69.0  249.030866   \n",
+       "56013    Wyoming    Fremont       39261  0.000000        69.0  413.991045   \n",
+       "56021    Wyoming    Laramie       99500  0.000000        69.0  192.222411   \n",
+       "56039    Wyoming      Teton       23464  0.000000        69.0  101.659727   \n",
+       "\n",
+       "       Log_Rate  Log_Offset      Log2_Max1  Log2_Rate1  Log2_Offset1  \\\n",
+       "FIPS                                                                   \n",
+       "1001   0.072698   69.000000    1419.349992    0.348968     65.279688   \n",
+       "1003   0.107391   42.650713     535.638267    0.695515     45.357924   \n",
+       "1013   0.178081   59.250087     323.151902    0.119303     58.410333   \n",
+       "1015   0.112374   37.675949     132.831373    0.429680     31.692333   \n",
+       "1017   0.176634   31.194822      56.398120    8.364028     29.968907   \n",
+       "...         ...         ...            ...         ...           ...   \n",
+       "55133  0.086515   36.690306     932.476564    0.351420     64.329276   \n",
+       "55139  0.060721   69.000000     511.082241    8.922810     68.373833   \n",
+       "56013  0.071534   64.638514     566.841775   10.000000     69.000000   \n",
+       "56021  0.101038   41.219324     183.440284    0.174015     31.299472   \n",
+       "56039  0.135081   31.929410  303398.658878    0.460009     38.777462   \n",
+       "\n",
+       "        Log2_Max2  Log2_Rate2  Log2_Offset2  Log2_Switch_Begin  \\\n",
+       "FIPS                                                             \n",
+       "1001    53.714540    3.236958     14.382468          33.278405   \n",
+       "1003   519.301303    0.040293     69.000000          21.466305   \n",
+       "1013   324.666982    0.187087     58.923584           9.651052   \n",
+       "1015   129.950014    0.100274     37.259914          16.734170   \n",
+       "1017   447.777401    0.020263     17.395100          23.179009   \n",
+       "...           ...         ...           ...                ...   \n",
+       "55133  940.493799    0.032788     69.000000          12.643473   \n",
+       "55139  293.599386    0.053269     69.000000           0.000001   \n",
+       "56013  571.117421    0.066692     63.593953           0.000000   \n",
+       "56021  177.537208    0.151751     41.160583          23.552154   \n",
+       "56039   97.339265    0.651497     41.041717          12.315527   \n",
+       "\n",
+       "       Log2_Switch_End  \n",
+       "FIPS                    \n",
+       "1001         43.598479  \n",
+       "1003         27.074776  \n",
+       "1013         20.775420  \n",
+       "1015         15.736972  \n",
+       "1017         28.260747  \n",
+       "...                ...  \n",
+       "55133        17.395936  \n",
+       "55139        37.173960  \n",
+       "56013        69.000000  \n",
+       "56021        26.605609  \n",
+       "56039        14.805814  \n",
+       "\n",
+       "[917 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combine all of the parameters of the curve into another dataframe and\n",
+    "# write that dataframe to a second CSV file.\n",
+    "params = filtered[metadata_cols].copy()\n",
+    "params[\"Exp_Rate\"] = exp_df[\"Rate\"]\n",
+    "params[\"Exp_Offset\"] = exp_df[\"Offset\"]\n",
+    "\n",
+    "params[\"Log_Max\"] = log_df[\"Max\"]\n",
+    "params[\"Log_Rate\"] = log_df[\"Rate\"]\n",
+    "params[\"Log_Offset\"] = log_df[\"Offset\"]\n",
+    "\n",
+    "params[\"Log2_Max1\"] = log2_df[\"Max1\"]\n",
+    "params[\"Log2_Rate1\"] = log2_df[\"Rate1\"]\n",
+    "params[\"Log2_Offset1\"] = log2_df[\"Offset1\"]\n",
+    "params[\"Log2_Max2\"] = log2_df[\"Max2\"]\n",
+    "params[\"Log2_Rate2\"] = log2_df[\"Rate2\"]\n",
+    "params[\"Log2_Offset2\"] = log2_df[\"Offset2\"]\n",
+    "params[\"Log2_Switch_Begin\"] = log2_df[\"Switch_Begin\"]\n",
+    "params[\"Log2_Switch_End\"] = log2_df[\"Switch_End\"]\n",
+    "params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing parameters of curves to outputs/us_counties_curves_params.csv.\n"
+     ]
+    }
+   ],
+   "source": [
+    "counties_curves_params_csv_data_file = os.path.join(_OUTPUTS_DIR, \"us_counties_curves_params.csv\")\n",
+    "print(f\"Writing parameters of curves to {counties_curves_params_csv_data_file}.\")\n",
+    "params.to_csv(counties_curves_params_csv_data_file, index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}