MO_CorpRateCut.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "id": "EIBsVBIXc7Q8"
   },
   "source": [
    "# Notebook for replicating the analyses in \"What are the Effects of Missouri Corporate Income Tax Rate Cuts?\"\n",
    "### Richard W. Evans, March 2023\n",
    "This notebook replicates the analyses in the *Research in Focus* article by [Richard W. Evans](https://sites.google.com/site/rickecon) entitled \"What are the Effects of Missouri Corporate Income Tax Rate Cuts?\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction\n",
    "Before opening and running this notebook, make sure that you have downloaded or cloned the [`MO-CorpRateCut`](https://github.com/TheCGO/MO-CorpRateCut) repository (https://github.com/TheCGO/MO-CorpRateCut) and created and activated the associated conda environment `mo-corpratecut-dev` in the `environment.yml` file. You can also run this notebook easily in the cloud by going to this [Google Colab version](https://colab.research.google.com/drive/1y6P3qMIm9hQFPBzyhETwqRyoOpj43Si9?usp=sharing) of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from bokeh.io import output_file, output_notebook, export_png\n",
    "from bokeh.plotting import figure, show\n",
    "from bokeh.resources import CDN\n",
    "from bokeh.models import (ColumnDataSource, Title, Legend, HoverTool,\n",
    "                          NumeralTickFormatter)\n",
    "from bokeh.models.tickers import SingleIntervalTicker\n",
    "from bokeh.models.annotations import Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PEW state balances data: Rainy day fund balances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/3895132095.py:48: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_pct_df.rename(columns = {\n"
     ]
    }
   ],
   "source": [
    "# Read in the PEW data on rainy day funds by year and\n",
    "# state from worksheet\n",
    "rain_totbal_path = (\n",
    "    \"https://github.com/TheCGO/MO-CorpRateCut/blob/main/data/\" +\n",
    "    \"ReservesBalancesData.xlsx?raw=true\"\n",
    ")\n",
    "rain_df = pd.read_excel(\n",
    "    rain_totbal_path,\n",
    "    sheet_name=\"Rainy Day Fund Data\",\n",
    "    header=5,\n",
    "    index_col=0,\n",
    "    skipfooter=17\n",
    ")\n",
    "rain_df.replace(0, np.nan, inplace=True)\n",
    "\n",
    "# Create a DataFrame of just the rainy day funds in $millions\n",
    "# by state and by year\n",
    "rain_dol_df = rain_df.loc[:'Wyoming', 'FY 2000.1':'FY 2022 (estimated).1']\n",
    "rain_dol_df.rename(columns = {\n",
    "    'FY 2000.1': '2000',\n",
    "    'FY 2001.1': '2001',\n",
    "    'FY 2002.1': '2002',\n",
    "    'FY 2003.1': '2003',\n",
    "    'FY 2004.1': '2004',\n",
    "    'FY 2005.1': '2005',\n",
    "    'FY 2006.1': '2006',\n",
    "    'FY 2007.1': '2007',\n",
    "    'FY 2008.1': '2008',\n",
    "    'FY 2009.1': '2009',\n",
    "    'FY 2010.1': '2010',\n",
    "    'FY 2011.1': '2011',\n",
    "    'FY 2012.1': '2012',\n",
    "    'FY 2013.1': '2013',\n",
    "    'FY 2014.1': '2014',\n",
    "    'FY 2015.1': '2015',\n",
    "    ' FY 2016.1': '2016',\n",
    "    ' FY 2017.1': '2017',\n",
    "    ' FY 2018.1': '2018',\n",
    "    'FY 2019.1': '2019',\n",
    "    'FY 2020.1': '2020',\n",
    "    'FY 2021.1': '2021',\n",
    "    'FY 2022 (estimated).1': '2022',\n",
    "}, inplace = True)\n",
    "\n",
    "# Create a DataFrame of just the rainy day funds as a percent of\n",
    "# general fund expenditures by state and by year\n",
    "rain_pct_df = rain_df.loc[:, 'FY 2000.2':'FY 2022 (estimated).2']\n",
    "rain_pct_df.rename(columns = {\n",
    "    'FY 2000.2': '2000',\n",
    "    'FY 2001.2': '2001',\n",
    "    'FY 2002.2': '2002',\n",
    "    'FY 2003.2': '2003',\n",
    "    'FY 2004.2': '2004',\n",
    "    'FY 2005.2': '2005',\n",
    "    'FY 2006.2': '2006',\n",
    "    'FY 2007.2': '2007',\n",
    "    'FY 2008.2': '2008',\n",
    "    'FY 2009.2': '2009',\n",
    "    'FY 2010.2': '2010',\n",
    "    'FY 2011.2': '2011',\n",
    "    'FY 2012.2': '2012',\n",
    "    'FY 2013.2': '2013',\n",
    "    'FY 2014.2': '2014',\n",
    "    'FY 2015.2': '2015',\n",
    "    ' FY 2016.2': '2016',\n",
    "    ' FY 2017.2': '2017',\n",
    "    ' FY 2018.2': '2018',\n",
    "    'FY 2019.2': '2019',\n",
    "    'FY 2020.2': '2020',\n",
    "    'FY 2021.2': '2021',\n",
    "    'FY 2022 (estimated).2': '2022',\n",
    "}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PEW state balances data: Total balances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/3735137075.py:44: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_pct_df.rename(columns = {\n"
     ]
    }
   ],
   "source": [
    "# Read in the PEW data on total reserves and balances\n",
    "# by year and state from worksheet\n",
    "totbal_df = pd.read_excel(\n",
    "    rain_totbal_path,\n",
    "    sheet_name=\"Total Balances Data\",\n",
    "    header=5,\n",
    "    index_col=0,\n",
    "    skipfooter=17\n",
    ")\n",
    "totbal_df.replace(0, np.nan, inplace=True)\n",
    "\n",
    "# Create a DataFrame of just the total reserves and balances\n",
    "# funds in $millions by state and by year\n",
    "totbal_dol_df = totbal_df.loc[:'Wyoming', 'FY 2000.1':'FY 2022 (estimated).1']\n",
    "totbal_dol_df.rename(columns = {\n",
    "    'FY 2000.1': '2000',\n",
    "    'FY 2001.1': '2001',\n",
    "    'FY 2002.1': '2002',\n",
    "    'FY 2003.1': '2003',\n",
    "    'FY 2004.1': '2004',\n",
    "    'FY 2005.1': '2005',\n",
    "    'FY 2006.1': '2006',\n",
    "    'FY 2007.1': '2007',\n",
    "    'FY 2008.1': '2008',\n",
    "    'FY 2009.1': '2009',\n",
    "    'FY 2010.1': '2010',\n",
    "    'FY 2011.1': '2011',\n",
    "    'FY 2012.1': '2012',\n",
    "    'FY 2013.1': '2013',\n",
    "    'FY 2014.1': '2014',\n",
    "    'FY 2015.1': '2015',\n",
    "    ' FY 2016.1': '2016',\n",
    "    ' FY 2017.1': '2017',\n",
    "    ' FY 2018.1': '2018',\n",
    "    'FY 2019.1': '2019',\n",
    "    'FY 2020.1': '2020',\n",
    "    'FY 2021.1': '2021',\n",
    "    'FY 2022 (estimated).1': '2022',\n",
    "}, inplace = True)\n",
    "\n",
    "# Create a DataFrame of just the total reserves and balances funds\n",
    "# as a percent of general fund expenditures by state and by year\n",
    "totbal_pct_df = totbal_df.loc[:, 'FY 2000.2':'FY 2022 (estimated).2']\n",
    "totbal_pct_df.rename(columns = {\n",
    "    'FY 2000.2': '2000',\n",
    "    'FY 2001.2': '2001',\n",
    "    'FY 2002.2': '2002',\n",
    "    'FY 2003.2': '2003',\n",
    "    'FY 2004.2': '2004',\n",
    "    'FY 2005.2': '2005',\n",
    "    'FY 2006.2': '2006',\n",
    "    'FY 2007.2': '2007',\n",
    "    'FY 2008.2': '2008',\n",
    "    'FY 2009.2': '2009',\n",
    "    'FY 2010.2': '2010',\n",
    "    'FY 2011.2': '2011',\n",
    "    'FY 2012.2': '2012',\n",
    "    'FY 2013.2': '2013',\n",
    "    'FY 2014.2': '2014',\n",
    "    'FY 2015.2': '2015',\n",
    "    ' FY 2016.2': '2016',\n",
    "    ' FY 2017.2': '2017',\n",
    "    ' FY 2018.2': '2018',\n",
    "    'FY 2019.2': '2019',\n",
    "    'FY 2020.2': '2020',\n",
    "    'FY 2021.2': '2021',\n",
    "    'FY 2022 (estimated).2': '2022',\n",
    "}, inplace = True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figure 1. Plot time series of rainy day funds and total balances as percent of general fund expenditures for both the 50-state median and for Missouri\n",
    "Executing the cell below and the following cell will create the `rain_totbal_50_mo_timeseries.html` file in this notebook and in the `/images/` folder of this repository. The figure should also open in a browser window in your browser. I created the `.png` version of the file by just screenshotting the `.html` image."
   ]
  },
  {
   "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>year</th>\n",
       "      <th>rain_50m_pct</th>\n",
       "      <th>rain_mo_pct</th>\n",
       "      <th>totbal_50m_pct</th>\n",
       "      <th>totbal_mo_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>4.127259</td>\n",
       "      <td>3.992000</td>\n",
       "      <td>8.766520</td>\n",
       "      <td>6.305019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>4.614270</td>\n",
       "      <td>5.847348</td>\n",
       "      <td>7.148315</td>\n",
       "      <td>7.257439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>1.655668</td>\n",
       "      <td>6.148270</td>\n",
       "      <td>2.887308</td>\n",
       "      <td>8.307165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.722015</td>\n",
       "      <td>7.245491</td>\n",
       "      <td>2.656212</td>\n",
       "      <td>10.630063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>1.844196</td>\n",
       "      <td>6.667667</td>\n",
       "      <td>5.246650</td>\n",
       "      <td>13.992795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005</td>\n",
       "      <td>2.459819</td>\n",
       "      <td>6.506291</td>\n",
       "      <td>9.016673</td>\n",
       "      <td>10.719301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006</td>\n",
       "      <td>4.553273</td>\n",
       "      <td>6.919037</td>\n",
       "      <td>11.801597</td>\n",
       "      <td>16.673054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007</td>\n",
       "      <td>4.738918</td>\n",
       "      <td>6.822401</td>\n",
       "      <td>11.327766</td>\n",
       "      <td>16.385208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>4.834502</td>\n",
       "      <td>6.902232</td>\n",
       "      <td>8.292193</td>\n",
       "      <td>17.256199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009</td>\n",
       "      <td>2.728263</td>\n",
       "      <td>6.628317</td>\n",
       "      <td>4.582794</td>\n",
       "      <td>9.741259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010</td>\n",
       "      <td>1.638317</td>\n",
       "      <td>6.967159</td>\n",
       "      <td>4.488299</td>\n",
       "      <td>9.411081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2011</td>\n",
       "      <td>1.761059</td>\n",
       "      <td>6.639760</td>\n",
       "      <td>6.368404</td>\n",
       "      <td>11.606148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012</td>\n",
       "      <td>2.436049</td>\n",
       "      <td>6.271338</td>\n",
       "      <td>7.907533</td>\n",
       "      <td>8.841352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>3.558071</td>\n",
       "      <td>6.287153</td>\n",
       "      <td>9.639226</td>\n",
       "      <td>11.857732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.524613</td>\n",
       "      <td>6.645200</td>\n",
       "      <td>7.292836</td>\n",
       "      <td>8.899225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>4.918311</td>\n",
       "      <td>6.207614</td>\n",
       "      <td>9.204278</td>\n",
       "      <td>9.386901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>5.339671</td>\n",
       "      <td>6.486127</td>\n",
       "      <td>9.739579</td>\n",
       "      <td>8.182976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017</td>\n",
       "      <td>5.613689</td>\n",
       "      <td>6.457997</td>\n",
       "      <td>8.315685</td>\n",
       "      <td>8.291837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018</td>\n",
       "      <td>6.547734</td>\n",
       "      <td>6.649616</td>\n",
       "      <td>11.724584</td>\n",
       "      <td>11.993482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019</td>\n",
       "      <td>7.929661</td>\n",
       "      <td>6.826615</td>\n",
       "      <td>13.993790</td>\n",
       "      <td>13.681529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020</td>\n",
       "      <td>8.439704</td>\n",
       "      <td>7.081366</td>\n",
       "      <td>12.711729</td>\n",
       "      <td>15.797644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021</td>\n",
       "      <td>9.890083</td>\n",
       "      <td>6.168917</td>\n",
       "      <td>27.125529</td>\n",
       "      <td>30.217378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022</td>\n",
       "      <td>11.642068</td>\n",
       "      <td>7.291539</td>\n",
       "      <td>24.368634</td>\n",
       "      <td>36.333085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  rain_50m_pct  rain_mo_pct  totbal_50m_pct  totbal_mo_pct\n",
       "0   2000      4.127259     3.992000        8.766520       6.305019\n",
       "1   2001      4.614270     5.847348        7.148315       7.257439\n",
       "2   2002      1.655668     6.148270        2.887308       8.307165\n",
       "3   2003      0.722015     7.245491        2.656212      10.630063\n",
       "4   2004      1.844196     6.667667        5.246650      13.992795\n",
       "5   2005      2.459819     6.506291        9.016673      10.719301\n",
       "6   2006      4.553273     6.919037       11.801597      16.673054\n",
       "7   2007      4.738918     6.822401       11.327766      16.385208\n",
       "8   2008      4.834502     6.902232        8.292193      17.256199\n",
       "9   2009      2.728263     6.628317        4.582794       9.741259\n",
       "10  2010      1.638317     6.967159        4.488299       9.411081\n",
       "11  2011      1.761059     6.639760        6.368404      11.606148\n",
       "12  2012      2.436049     6.271338        7.907533       8.841352\n",
       "13  2013      3.558071     6.287153        9.639226      11.857732\n",
       "14  2014      4.524613     6.645200        7.292836       8.899225\n",
       "15  2015      4.918311     6.207614        9.204278       9.386901\n",
       "16  2016      5.339671     6.486127        9.739579       8.182976\n",
       "17  2017      5.613689     6.457997        8.315685       8.291837\n",
       "18  2018      6.547734     6.649616       11.724584      11.993482\n",
       "19  2019      7.929661     6.826615       13.993790      13.681529\n",
       "20  2020      8.439704     7.081366       12.711729      15.797644\n",
       "21  2021      9.890083     6.168917       27.125529      30.217378\n",
       "22  2022     11.642068     7.291539       24.368634      36.333085"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create source DataFrame for Figure 1 and save it as .csv\n",
    "rain_pct_50_df = rain_pct_df.loc[\"50-state median\", :].to_frame().reset_index()\n",
    "rain_pct_50_df.rename(\n",
    "    columns = {\"index\":\"year\", \"50-state median\":\"fraction\"}, inplace = True\n",
    ")\n",
    "rain_pct_50_df[\"percent\"] = 100 * rain_pct_50_df[\"fraction\"]\n",
    "\n",
    "rain_pct_mo_df = rain_pct_df.loc[\"Missouri\", :].to_frame().reset_index()\n",
    "rain_pct_mo_df.rename(\n",
    "    columns = {\"index\":\"year\", \"Missouri\":\"fraction\"}, inplace = True\n",
    ")\n",
    "rain_pct_mo_df[\"percent\"] = 100 * rain_pct_mo_df[\"fraction\"]\n",
    "\n",
    "totbal_pct_50_df = \\\n",
    "    totbal_pct_df.loc[\"50-state median\", :].to_frame().reset_index()\n",
    "totbal_pct_50_df.rename(\n",
    "    columns = {\"index\":\"year\", \"50-state median\":\"fraction\"}, inplace = True\n",
    ")\n",
    "totbal_pct_50_df[\"percent\"] = 100 * totbal_pct_50_df[\"fraction\"]\n",
    "\n",
    "totbal_pct_mo_df = totbal_pct_df.loc[\"Missouri\", :].to_frame().reset_index()\n",
    "totbal_pct_mo_df.rename(\n",
    "    columns = {'index':'year', \"Missouri\":\"fraction\"}, inplace = True\n",
    ")\n",
    "totbal_pct_mo_df[\"percent\"] = 100 * totbal_pct_mo_df[\"fraction\"]\n",
    "\n",
    "# Merge the four DataFrames to save the joint dataframe as a .csv\n",
    "fig1_source_df = rain_pct_50_df[['year', 'percent']].rename(\n",
    "    columns={'percent':'rain_50m_pct'}\n",
    ")\n",
    "fig1_source_df = pd.merge(\n",
    "    fig1_source_df,\n",
    "    rain_pct_mo_df[['year', 'percent']].rename(\n",
    "        columns={'percent':'rain_mo_pct'}\n",
    "    ),\n",
    "    on='year', how='inner'\n",
    ")\n",
    "fig1_source_df = pd.merge(\n",
    "    fig1_source_df,\n",
    "    totbal_pct_50_df[['year', 'percent']].rename(\n",
    "        columns={'percent':'totbal_50m_pct'}\n",
    "    ),\n",
    "    on='year', how='inner'\n",
    ")\n",
    "fig1_source_df = pd.merge(\n",
    "    fig1_source_df,\n",
    "    totbal_pct_mo_df[['year', 'percent']].rename(\n",
    "        columns={'percent':'totbal_mo_pct'}\n",
    "    ),\n",
    "    on='year', how='inner'\n",
    ")\n",
    "fig1_source_df.to_csv('data/fig1_source.csv', index=False)\n",
    "fig1_source_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"1002\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
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   "source": [
    "# fig1_title = (\"Rainy Day fund and total reserves as a percentage of \" +\n",
    "#               \"general fund expenditures: 2000-2022\")\n",
    "fig1_title = \"\"\n",
    "output_file(\n",
    "    \"images/rain_totbal_50_mo_timeseries.html\", title=fig1_title, mode='inline'\n",
    ")\n",
    "output_notebook()\n",
    "min_year = 2000\n",
    "max_year = 2022\n",
    "min_pct = 2.0\n",
    "max_pct = 0.0\n",
    "pct_buffer = 0.05\n",
    "\n",
    "var_list = [rain_pct_50_df, rain_pct_mo_df, totbal_pct_50_df, totbal_pct_mo_df]\n",
    "color_list = [\"#3477A5\", \"#D5AB53\", \"#3477A5\", \"#D5AB53\"]\n",
    "marker_list = [\"circle\", \"circle\", \"square\", \"square\"]\n",
    "legend_label_list = [\n",
    "    \"Rainy Day Fund, 50-state median\", \"Rainy Day Fund, Missouri\",\n",
    "    \"Total balances, 50-state median\", \"Total balances, Missouri\"\n",
    "]\n",
    "cds_list = []\n",
    "\n",
    "for k, df in enumerate(var_list):\n",
    "    min_pct = np.minimum(min_pct, df[\"percent\"].min())\n",
    "    max_pct = np.maximum(max_pct, df[\"percent\"].max())\n",
    "    cds_list.append(ColumnDataSource(df))\n",
    "\n",
    "fig1 = figure(title=fig1_title,\n",
    "              height=420,\n",
    "              width=700,\n",
    "              x_axis_label='Year',\n",
    "              x_range=(min_year - 1, max_year + 1.5),\n",
    "              y_axis_label='Percent of general fund expenditures',\n",
    "              y_range=(min_pct - pct_buffer * (max_pct - min_pct),\n",
    "                       max_pct + pct_buffer * (max_pct - min_pct)),\n",
    "              toolbar_location=None)\n",
    "\n",
    "# Turn off any pan, drag, or scrolling ability\n",
    "fig1.toolbar.active_drag = None\n",
    "fig1.toolbar.active_scroll = None\n",
    "fig1.toolbar.active_tap = None\n",
    "\n",
    "# Set title font size and axes font sizes\n",
    "fig1.title.text_font_size = '0pt'  # Hide title by setting font size to 0\n",
    "fig1.title.text_color = '#434244'\n",
    "fig1.xaxis.axis_label_text_font_size = '12pt'\n",
    "fig1.xaxis.major_label_text_font_size = '12pt'\n",
    "fig1.xaxis.major_label_text_color = '#434244'\n",
    "fig1.yaxis.axis_label_text_font_size = '12pt'\n",
    "fig1.yaxis.major_label_text_font_size = '12pt'\n",
    "fig1.yaxis.major_label_text_color = '#434244'\n",
    "\n",
    "# Modify tick intervals for X-axis and Y-axis\n",
    "fig1.xaxis.ticker = SingleIntervalTicker(interval=2, num_minor_ticks=2)\n",
    "fig1.xaxis.axis_line_color = '#434244'\n",
    "fig1.xaxis.major_tick_line_color = '#434244'\n",
    "fig1.xaxis.minor_tick_line_color = '#434244'\n",
    "fig1.xgrid.ticker = SingleIntervalTicker(interval=2)\n",
    "fig1.yaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=5)\n",
    "fig1.yaxis.axis_line_color = '#434244'\n",
    "fig1.yaxis.major_tick_line_color = '#434244'\n",
    "fig1.yaxis.minor_tick_line_color = '#434244'\n",
    "fig1.ygrid.ticker = SingleIntervalTicker(interval=10)\n",
    "\n",
    "# Create lines and markers for time series\n",
    "for k, yvar in enumerate(var_list):\n",
    "    fig1.line(x='year', y='percent', source=cds_list[k], color=color_list[k],\n",
    "              line_width=3, alpha=0.7)\n",
    "    fig1.scatter(x='year', y='percent', source=cds_list[k], size=8,\n",
    "                 line_width=1, line_color='black', fill_color=color_list[k],\n",
    "                 marker=marker_list[k], line_alpha=0.7, fill_alpha=0.7,\n",
    "                 legend_label=legend_label_list[k])\n",
    "\n",
    "fig1.segment(x0=2021.4, y0=min_pct - pct_buffer * (max_pct - min_pct),\n",
    "             x1=2021.4, y1=max_pct + pct_buffer * (max_pct - min_pct),\n",
    "             color='#434244', line_dash='6 2', line_width=2)\n",
    "    \n",
    "label_temp = Label(\n",
    "    x=2021.5, y=18.0, x_units='data', y_units='data', text='Projected',\n",
    "    text_font_size='9pt', text_color='#434244'\n",
    ")\n",
    "fig1.add_layout(label_temp)\n",
    "\n",
    "# Add information on hover\n",
    "tooltips = [('Year', '@year'),\n",
    "            ('Pct of gen. fund exps.','@percent{0.0}' + '%')]\n",
    "fig1.add_tools(HoverTool(tooltips=tooltips, toggleable=False))\n",
    "\n",
    "# Add legend\n",
    "fig1.legend.location = 'top_center'\n",
    "fig1.legend.border_line_width = 1\n",
    "fig1.legend.border_line_color = '#434244'\n",
    "fig1.legend.border_line_alpha = 1\n",
    "fig1.legend.label_text_font_size = '12pt'\n",
    "fig1.legend.label_text_color = '#434244'\n",
    "\n",
    "# Add notes below image\n",
    "note_text_list1 = [\n",
    "    (\n",
    "        'Source: Pew Charitable Trusts, \"Fiscal 50: State Trends and ' +\n",
    "        'Analysis,\" December 16, 2022,'\n",
    "    ),\n",
    "    ('        accessed December 31, 2022.')    \n",
    "]\n",
    "for note_text in note_text_list1:\n",
    "    caption = Title(\n",
    "        text=note_text, align='left', text_font_size='11pt',\n",
    "        text_font_style='normal',\n",
    "        text_color='#434244',\n",
    "        # text_font='Open Sans'\n",
    "    )\n",
    "    fig1.add_layout(caption, 'below')\n",
    "\n",
    "# # This export_png() function requires selenium package as well as firefox\n",
    "# # and geckodriver packages\n",
    "# # (see https://docs.bokeh.org/en/3.0.3/docs/user_guide/output/export.html)\n",
    "# export_png(fig, filename=\"/images/rain_totbal_50_mo_timeseries.png\")"
   ]
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Pew Charitable Trusts, \\\"Fiscal 50: State Trends and Analysis,\\\" December 16, 2022,\",\"text_color\":\"#434244\",\"text_font_size\":\"11pt\",\"text_font_style\":\"normal\"},\"id\":\"1155\",\"type\":\"Title\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"text\":\"        accessed December 31, 2022.\",\"text_color\":\"#434244\",\"text_font_size\":\"11pt\",\"text_font_style\":\"normal\"},\"id\":\"1156\",\"type\":\"Title\"},{\"attributes\":{\"callback\":null,\"toggleable\":false,\"tooltips\":[[\"Year\",\"@year\"],[\"Pct of gen. fund exps.\",\"@percent{0.0}%\"]]},\"id\":\"1153\",\"type\":\"HoverTool\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#434244\"},\"line_dash\":{\"value\":\"6 2\"},\"line_width\":{\"value\":2},\"x0\":{\"value\":2021.4},\"x1\":{\"value\":2021.4},\"y0\":{\"value\":-1.05853901},\"y1\":{\"value\":38.11363821}},\"id\":\"1148\",\"type\":\"Segment\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1146\"},\"glyph\":{\"id\":\"1147\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1149\"},\"nonselection_glyph\":{\"id\":\"1148\"},\"view\":{\"id\":\"1151\"}},\"id\":\"1150\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"#434244\"},\"line_dash\":{\"value\":\"6 2\"},\"line_width\":{\"value\":2},\"x0\":{\"value\":2021.4},\"x1\":{\"value\":2021.4},\"y0\":{\"value\":-1.05853901},\"y1\":{\"value\":38.11363821}},\"id\":\"1149\",\"type\":\"Segment\"},{\"attributes\":{},\"id\":\"1065\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1090\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#3477A5\",\"line_width\":3,\"x\":{\"field\":\"year\"},\"y\":{\"field\":\"percent\"}},\"id\":\"1051\",\"type\":\"Line\"},{\"attributes\":{\"interval\":10},\"id\":\"1046\",\"type\":\"SingleIntervalTicker\"}],\"root_ids\":[\"1007\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n  const render_items = [{\"docid\":\"14743f66-f3ba-403d-bc5b-dd711fb7853f\",\"root_ids\":[\"1007\"],\"roots\":{\"1007\":\"f630a33a-1fce-4896-808a-cddd9a5946c1\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1007"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(fig1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Figure 1 without the source text underneath (for web publication) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1446\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\nconst JS_MIME_TYPE = 'application/javascript';\n  const HTML_MIME_TYPE = 'text/html';\n  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n  const CLASS_NAME = 'output_bokeh rendered_html';\n\n  /**\n   * Render data to the DOM node\n   */\n  function render(props, node) {\n    const script = document.createElement(\"script\");\n    node.appendChild(script);\n  }\n\n  /**\n   * Handle when an output is cleared or removed\n   */\n  function handleClearOutput(event, handle) {\n    const cell = handle.cell;\n\n    const id = cell.output_area._bokeh_element_id;\n    const server_id = cell.output_area._bokeh_server_id;\n    // Clean up Bokeh references\n    if (id != null && id in Bokeh.index) {\n      Bokeh.index[id].model.document.clear();\n      delete Bokeh.index[id];\n    }\n\n    if (server_id !== undefined) {\n      // Clean up Bokeh references\n      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n      cell.notebook.kernel.execute(cmd_clean, {\n        iopub: {\n          output: function(msg) {\n            const id = msg.content.text.trim();\n            if (id in Bokeh.index) {\n              Bokeh.index[id].model.document.clear();\n              delete Bokeh.index[id];\n            }\n          }\n        }\n      });\n      // Destroy server and session\n      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n      cell.notebook.kernel.execute(cmd_destroy);\n    }\n  }\n\n  /**\n   * Handle when a new output is added\n   */\n  function handleAddOutput(event, handle) {\n    const output_area = handle.output_area;\n    const output = handle.output;\n\n    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n      return\n    }\n\n    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n      // store reference to embed id on output_area\n      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n    }\n    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n      const bk_div = document.createElement(\"div\");\n      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n      const script_attrs = bk_div.children[0].attributes;\n      for (let i = 0; i < script_attrs.length; i++) {\n        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n      }\n      // store reference to server id on output_area\n      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n    }\n  }\n\n  function register_renderer(events, OutputArea) {\n\n    function append_mime(data, metadata, element) {\n      // create a DOM node to render to\n      const toinsert = this.create_output_subarea(\n        metadata,\n        CLASS_NAME,\n        EXEC_MIME_TYPE\n      );\n      this.keyboard_manager.register_events(toinsert);\n      // Render to node\n      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n      render(props, toinsert[toinsert.length - 1]);\n      element.append(toinsert);\n      return toinsert\n    }\n\n    /* Handle when an output is cleared or removed */\n    events.on('clear_output.CodeCell', handleClearOutput);\n    events.on('delete.Cell', handleClearOutput);\n\n    /* Handle when a new output is added */\n    events.on('output_added.OutputArea', handleAddOutput);\n\n    /**\n     * Register the mime type and append_mime function with output_area\n     */\n    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n      /* Is output safe? */\n      safe: true,\n      /* Index of renderer in `output_area.display_order` */\n      index: 0\n    });\n  }\n\n  // register the mime type if in Jupyter Notebook environment and previously unregistered\n  if (root.Jupyter !== undefined) {\n    const events = require('base/js/events');\n    const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  }\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  const NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"1446\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"1446\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_file(\"images/rain_totbal_50_mo_timeseries_nosubtxt.html\",\n",
    "            title=fig1_title, mode='inline')\n",
    "output_notebook()\n",
    "fig1_nosubtxt = figure(title=fig1_title,\n",
    "                       height=420,\n",
    "                       width=700,\n",
    "                       x_axis_label='Year',\n",
    "                       x_range=(min_year - 1, max_year + 1.5),\n",
    "                       y_axis_label='Percent of general fund expenditures',\n",
    "                       y_range=(min_pct - pct_buffer * (max_pct - min_pct),\n",
    "                                max_pct + pct_buffer * (max_pct - min_pct)),\n",
    "                       toolbar_location=None)\n",
    "\n",
    "# Turn off any pan, drag, or scrolling ability\n",
    "fig1_nosubtxt.toolbar.active_drag = None\n",
    "fig1_nosubtxt.toolbar.active_scroll = None\n",
    "fig1_nosubtxt.toolbar.active_tap = None\n",
    "\n",
    "# Set title font size and axes font sizes\n",
    "fig1_nosubtxt.title.text_font_size = '0pt'  # Hide title by setting font size to 0\n",
    "fig1_nosubtxt.title.text_color = '#434244'\n",
    "fig1_nosubtxt.xaxis.axis_label_text_font_size = '12pt'\n",
    "fig1_nosubtxt.xaxis.major_label_text_font_size = '12pt'\n",
    "fig1_nosubtxt.xaxis.major_label_text_color = '#434244'\n",
    "fig1_nosubtxt.yaxis.axis_label_text_font_size = '12pt'\n",
    "fig1_nosubtxt.yaxis.major_label_text_font_size = '12pt'\n",
    "fig1_nosubtxt.yaxis.major_label_text_color = '#434244'\n",
    "\n",
    "# Modify tick intervals for X-axis and Y-axis\n",
    "fig1_nosubtxt.xaxis.ticker = SingleIntervalTicker(interval=2, num_minor_ticks=2)\n",
    "fig1_nosubtxt.xaxis.axis_line_color = '#434244'\n",
    "fig1_nosubtxt.xaxis.major_tick_line_color = '#434244'\n",
    "fig1_nosubtxt.xaxis.minor_tick_line_color = '#434244'\n",
    "fig1_nosubtxt.xgrid.ticker = SingleIntervalTicker(interval=2)\n",
    "fig1_nosubtxt.yaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=5)\n",
    "fig1_nosubtxt.yaxis.axis_line_color = '#434244'\n",
    "fig1_nosubtxt.yaxis.major_tick_line_color = '#434244'\n",
    "fig1_nosubtxt.yaxis.minor_tick_line_color = '#434244'\n",
    "fig1_nosubtxt.ygrid.ticker = SingleIntervalTicker(interval=10)\n",
    "\n",
    "# Create lines and markers for time series\n",
    "for k, yvar in enumerate(var_list):\n",
    "    fig1_nosubtxt.line(x='year', y='percent', source=cds_list[k], color=color_list[k],\n",
    "                       line_width=3, alpha=0.7)\n",
    "    fig1_nosubtxt.scatter(x='year', y='percent', source=cds_list[k], size=8,\n",
    "                          line_width=1, line_color='black', fill_color=color_list[k],\n",
    "                          marker=marker_list[k], line_alpha=0.7, fill_alpha=0.7,\n",
    "                          legend_label=legend_label_list[k])\n",
    "\n",
    "fig1_nosubtxt.segment(x0=2021.4, y0=min_pct - pct_buffer * (max_pct - min_pct),\n",
    "                      x1=2021.4, y1=max_pct + pct_buffer * (max_pct - min_pct),\n",
    "                      color='#434244', line_dash='6 2', line_width=2)\n",
    "    \n",
    "label_temp = Label(x=2021.5, y=18.0, x_units='data', y_units='data',\n",
    "                   text='Projected', text_font_size='9pt', text_color='#434244')\n",
    "fig1_nosubtxt.add_layout(label_temp)\n",
    "\n",
    "# Add information on hover\n",
    "tooltips = [('Year', '@year'),\n",
    "            ('Pct of gen. fund exps.','@percent{0.0}' + '%')]\n",
    "fig1_nosubtxt.add_tools(HoverTool(tooltips=tooltips, toggleable=False))\n",
    "\n",
    "# Add legend\n",
    "fig1_nosubtxt.legend.location = 'top_center'\n",
    "fig1_nosubtxt.legend.border_line_width = 1\n",
    "fig1_nosubtxt.legend.border_line_color = '#434244'\n",
    "fig1_nosubtxt.legend.border_line_alpha = 1\n",
    "fig1_nosubtxt.legend.label_text_font_size = '12pt'\n",
    "fig1_nosubtxt.legend.label_text_color = '#434244'\n",
    "\n",
    "# # Add notes below image\n",
    "# for note_text in note_text_list1:\n",
    "#     caption = Title(\n",
    "#         text=note_text, align='left', text_font_size='11pt',\n",
    "#         text_font_style='normal',\n",
    "#         text_color='#434244',\n",
    "#         # text_font='Open Sans'\n",
    "#     )\n",
    "#     fig1_nosubtxt.add_layout(caption, 'below')\n",
    "\n",
    "# # This export_png() function requires selenium package as well as firefox\n",
    "# # and geckodriver packages\n",
    "# # (see https://docs.bokeh.org/en/3.0.3/docs/user_guide/output/export.html)\n",
    "# export_png(fig, filename=\"/images/rain_totbal_50_tn_timeseries.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div class=\"bk-root\" id=\"611b38c7-1783-459d-b6fb-a9d2f50a3fbf\" data-root-id=\"1447\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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   "source": [
    "### Figure 2. Estimated 2022 rainy day fund and total balances as percent of general fund expenditure by state in order of rainy day fund balances, highlighting Missouri"
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      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/519929142.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_totbal_pct_dol_2022_df[\"rain_color\"][rain_totbal_pct_dol_2022_df[\"state\"]==\"Missouri\"] = \"#EED17E\"\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/519929142.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_totbal_pct_dol_2022_df[\"totbal_color\"][rain_totbal_pct_dol_2022_df[\"state\"]==\"Missouri\"] = \"#74AAC7\"\n"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>rain_pct</th>\n",
       "      <th>rain_dol</th>\n",
       "      <th>rain_color</th>\n",
       "      <th>totbal_pct</th>\n",
       "      <th>totbal_dol</th>\n",
       "      <th>totbal_color</th>\n",
       "      <th>totbal_rain_pct_dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>1.139188</td>\n",
       "      <td>305.300000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>23.588609</td>\n",
       "      <td>6321.700000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>22.449421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>1.348466</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>3.371165</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>2.022699</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New York</td>\n",
       "      <td>3.887742</td>\n",
       "      <td>3351.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>35.400376</td>\n",
       "      <td>30513.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>31.512634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Montana</td>\n",
       "      <td>4.395113</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>28.933476</td>\n",
       "      <td>776.805971</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>24.538363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>5.474238</td>\n",
       "      <td>1160.226358</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>23.045000</td>\n",
       "      <td>4884.230000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>17.570762</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>5.504065</td>\n",
       "      <td>280.300000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>44.594117</td>\n",
       "      <td>2271.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>39.090052</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>5.565183</td>\n",
       "      <td>253.260983</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>8.542891</td>\n",
       "      <td>388.770886</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>2.977708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Florida</td>\n",
       "      <td>6.344123</td>\n",
       "      <td>2729.800000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>32.600723</td>\n",
       "      <td>14027.700000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>26.256600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>6.430386</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>17.553881</td>\n",
       "      <td>1637.900000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>11.123496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>7.160229</td>\n",
       "      <td>720.816069</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>22.566079</td>\n",
       "      <td>2271.710000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>15.405850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>7.291539</td>\n",
       "      <td>772.400000</td>\n",
       "      <td>#EED17E</td>\n",
       "      <td>36.333085</td>\n",
       "      <td>3848.800000</td>\n",
       "      <td>#74AAC7</td>\n",
       "      <td>29.041546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>7.315709</td>\n",
       "      <td>340.100000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>20.721031</td>\n",
       "      <td>963.300000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>13.405322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>7.427179</td>\n",
       "      <td>2866.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>24.155178</td>\n",
       "      <td>9321.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>16.727998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>7.464062</td>\n",
       "      <td>979.800000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>22.565876</td>\n",
       "      <td>2962.200000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>15.101814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>8.810923</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>33.390557</td>\n",
       "      <td>5874.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>24.579634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>8.960241</td>\n",
       "      <td>1729.900000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>23.660548</td>\n",
       "      <td>4568.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>14.700307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>9.029197</td>\n",
       "      <td>1644.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>28.056965</td>\n",
       "      <td>5108.500000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>19.027769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>9.131794</td>\n",
       "      <td>2138.100000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>18.164928</td>\n",
       "      <td>4253.100000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>9.033134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>9.252584</td>\n",
       "      <td>4463.400000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>12.940225</td>\n",
       "      <td>6242.300000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>3.687642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>9.556625</td>\n",
       "      <td>556.100000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>9.556625</td>\n",
       "      <td>556.100000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>9.677676</td>\n",
       "      <td>2700.468452</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>19.538593</td>\n",
       "      <td>5452.070000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>9.860916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Utah</td>\n",
       "      <td>10.005132</td>\n",
       "      <td>1033.200000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>20.848858</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>10.843726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>10.059237</td>\n",
       "      <td>816.800000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>24.523701</td>\n",
       "      <td>1991.300000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>14.464464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>10.779302</td>\n",
       "      <td>1457.200000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>31.329659</td>\n",
       "      <td>4235.300000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>20.550357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>11.614768</td>\n",
       "      <td>1506.900000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>24.093572</td>\n",
       "      <td>3125.900000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>12.478804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>11.669368</td>\n",
       "      <td>1209.600000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>32.959018</td>\n",
       "      <td>3416.400000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>21.289650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>11.971631</td>\n",
       "      <td>3116.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>21.049093</td>\n",
       "      <td>5478.700000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>9.077462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>12.091131</td>\n",
       "      <td>3005.745000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>38.638292</td>\n",
       "      <td>9605.130000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>26.547161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Maine</td>\n",
       "      <td>12.466863</td>\n",
       "      <td>507.900000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>12.619048</td>\n",
       "      <td>514.100000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.152185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>12.725299</td>\n",
       "      <td>1597.522881</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>44.293832</td>\n",
       "      <td>5560.610000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>31.568533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.414786</td>\n",
       "      <td>1404.300000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>31.252448</td>\n",
       "      <td>3271.600000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>17.837662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>14.860685</td>\n",
       "      <td>4288.774541</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>14.998336</td>\n",
       "      <td>4328.500000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.137652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>15.099071</td>\n",
       "      <td>307.100000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>15.099071</td>\n",
       "      <td>307.100000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>15.285286</td>\n",
       "      <td>5902.971882</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>16.601881</td>\n",
       "      <td>6411.420000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>1.316595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Texas</td>\n",
       "      <td>15.561054</td>\n",
       "      <td>10057.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>27.770686</td>\n",
       "      <td>17948.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>12.209633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>15.850907</td>\n",
       "      <td>1180.100000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>45.351242</td>\n",
       "      <td>3376.400000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>29.500336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>16.136705</td>\n",
       "      <td>257.800000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>26.840260</td>\n",
       "      <td>428.800000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>10.703555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>16.963347</td>\n",
       "      <td>1324.700000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>27.864093</td>\n",
       "      <td>2175.960000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>10.900746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>19.632320</td>\n",
       "      <td>415.200000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>23.281699</td>\n",
       "      <td>492.380000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>3.649380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>19.693291</td>\n",
       "      <td>1151.900000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>24.213568</td>\n",
       "      <td>1416.300000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>4.520276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>20.714790</td>\n",
       "      <td>997.500000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>40.237571</td>\n",
       "      <td>1937.600000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>19.522781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>20.903366</td>\n",
       "      <td>883.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>25.974149</td>\n",
       "      <td>1097.200000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>5.070783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>21.422831</td>\n",
       "      <td>1013.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>35.695618</td>\n",
       "      <td>1687.900000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>14.272787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>22.388022</td>\n",
       "      <td>3510.800000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>22.388022</td>\n",
       "      <td>3510.800000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>22.540611</td>\n",
       "      <td>47342.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>24.052349</td>\n",
       "      <td>50517.100000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>1.511738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>27.407030</td>\n",
       "      <td>5591.500000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>27.407030</td>\n",
       "      <td>5591.500000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>27.607163</td>\n",
       "      <td>2557.500000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>30.575675</td>\n",
       "      <td>2832.500000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>2.968512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>30.721439</td>\n",
       "      <td>751.600000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>70.018394</td>\n",
       "      <td>1713.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>39.296955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>41.274547</td>\n",
       "      <td>1963.100000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>41.274547</td>\n",
       "      <td>1963.100000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>95.787427</td>\n",
       "      <td>1478.000000</td>\n",
       "      <td>#D5AB53</td>\n",
       "      <td>95.787427</td>\n",
       "      <td>1478.000000</td>\n",
       "      <td>#3477A5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state   rain_pct      rain_dol rain_color  totbal_pct  \\\n",
       "46      Washington   1.139188    305.300000    #D5AB53   23.588609   \n",
       "12        Illinois   1.348466    600.000000    #D5AB53    3.371165   \n",
       "31        New York   3.887742   3351.000000    #D5AB53   35.400376   \n",
       "25         Montana   4.395113    118.000000    #D5AB53   28.933476   \n",
       "19        Maryland   5.474238   1160.226358    #D5AB53   23.045000   \n",
       "7         Delaware   5.504065    280.300000    #D5AB53   44.594117   \n",
       "38    Rhode Island   5.565183    253.260983    #D5AB53    8.542891   \n",
       "8          Florida   6.344123   2729.800000    #D5AB53   32.600723   \n",
       "15          Kansas   6.430386    600.000000    #D5AB53   17.553881   \n",
       "17       Louisiana   7.160229    720.816069    #D5AB53   22.566079   \n",
       "24        Missouri   7.291539    772.400000    #EED17E   36.333085   \n",
       "27          Nevada   7.315709    340.100000    #D5AB53   20.721031   \n",
       "37    Pennsylvania   7.427179   2866.000000    #D5AB53   24.155178   \n",
       "2          Arizona   7.464062    979.800000    #D5AB53   22.565876   \n",
       "41       Tennessee   8.810923   1550.000000    #D5AB53   33.390557   \n",
       "48       Wisconsin   8.960241   1729.900000    #D5AB53   23.660548   \n",
       "13         Indiana   9.029197   1644.000000    #D5AB53   28.056965   \n",
       "45        Virginia   9.131794   2138.100000    #D5AB53   18.164928   \n",
       "29      New Jersey   9.252584   4463.400000    #D5AB53   12.940225   \n",
       "23     Mississippi   9.556625    556.100000    #D5AB53    9.556625   \n",
       "34            Ohio   9.677676   2700.468452    #D5AB53   19.538593   \n",
       "43            Utah  10.005132   1033.200000    #D5AB53   20.848858   \n",
       "14            Iowa  10.059237    816.800000    #D5AB53   24.523701   \n",
       "21        Michigan  10.779302   1457.200000    #D5AB53   31.329659   \n",
       "16        Kentucky  11.614768   1506.900000    #D5AB53   24.093572   \n",
       "39  South Carolina  11.669368   1209.600000    #D5AB53   32.959018   \n",
       "32  North Carolina  11.971631   3116.000000    #D5AB53   21.049093   \n",
       "22       Minnesota  12.091131   3005.745000    #D5AB53   38.638292   \n",
       "18           Maine  12.466863    507.900000    #D5AB53   12.619048   \n",
       "36          Oregon  12.725299   1597.522881    #D5AB53   44.293832   \n",
       "0          Alabama  13.414786   1404.300000    #D5AB53   31.252448   \n",
       "9          Georgia  14.860685   4288.774541    #D5AB53   14.998336   \n",
       "40    South Dakota  15.099071    307.100000    #D5AB53   15.099071   \n",
       "20   Massachusetts  15.285286   5902.971882    #D5AB53   16.601881   \n",
       "42           Texas  15.561054  10057.000000    #D5AB53   27.770686   \n",
       "35        Oklahoma  15.850907   1180.100000    #D5AB53   45.351242   \n",
       "28   New Hampshire  16.136705    257.800000    #D5AB53   26.840260   \n",
       "10          Hawaii  16.963347   1324.700000    #D5AB53   27.864093   \n",
       "44         Vermont  19.632320    415.200000    #D5AB53   23.281699   \n",
       "3         Arkansas  19.693291   1151.900000    #D5AB53   24.213568   \n",
       "26        Nebraska  20.714790    997.500000    #D5AB53   40.237571   \n",
       "11           Idaho  20.903366    883.000000    #D5AB53   25.974149   \n",
       "47   West Virginia  21.422831   1013.000000    #D5AB53   35.695618   \n",
       "5         Colorado  22.388022   3510.800000    #D5AB53   22.388022   \n",
       "4       California  22.540611  47342.000000    #D5AB53   24.052349   \n",
       "6      Connecticut  27.407030   5591.500000    #D5AB53   27.407030   \n",
       "30      New Mexico  27.607163   2557.500000    #D5AB53   30.575675   \n",
       "33    North Dakota  30.721439    751.600000    #D5AB53   70.018394   \n",
       "1           Alaska  41.274547   1963.100000    #D5AB53   41.274547   \n",
       "49         Wyoming  95.787427   1478.000000    #D5AB53   95.787427   \n",
       "\n",
       "      totbal_dol totbal_color  totbal_rain_pct_dif  \n",
       "46   6321.700000      #3477A5            22.449421  \n",
       "12   1500.000000      #3477A5             2.022699  \n",
       "31  30513.000000      #3477A5            31.512634  \n",
       "25    776.805971      #3477A5            24.538363  \n",
       "19   4884.230000      #3477A5            17.570762  \n",
       "7    2271.000000      #3477A5            39.090052  \n",
       "38    388.770886      #3477A5             2.977708  \n",
       "8   14027.700000      #3477A5            26.256600  \n",
       "15   1637.900000      #3477A5            11.123496  \n",
       "17   2271.710000      #3477A5            15.405850  \n",
       "24   3848.800000      #74AAC7            29.041546  \n",
       "27    963.300000      #3477A5            13.405322  \n",
       "37   9321.000000      #3477A5            16.727998  \n",
       "2    2962.200000      #3477A5            15.101814  \n",
       "41   5874.000000      #3477A5            24.579634  \n",
       "48   4568.000000      #3477A5            14.700307  \n",
       "13   5108.500000      #3477A5            19.027769  \n",
       "45   4253.100000      #3477A5             9.033134  \n",
       "29   6242.300000      #3477A5             3.687642  \n",
       "23    556.100000      #3477A5             0.000000  \n",
       "34   5452.070000      #3477A5             9.860916  \n",
       "43   2153.000000      #3477A5            10.843726  \n",
       "14   1991.300000      #3477A5            14.464464  \n",
       "21   4235.300000      #3477A5            20.550357  \n",
       "16   3125.900000      #3477A5            12.478804  \n",
       "39   3416.400000      #3477A5            21.289650  \n",
       "32   5478.700000      #3477A5             9.077462  \n",
       "22   9605.130000      #3477A5            26.547161  \n",
       "18    514.100000      #3477A5             0.152185  \n",
       "36   5560.610000      #3477A5            31.568533  \n",
       "0    3271.600000      #3477A5            17.837662  \n",
       "9    4328.500000      #3477A5             0.137652  \n",
       "40    307.100000      #3477A5             0.000000  \n",
       "20   6411.420000      #3477A5             1.316595  \n",
       "42  17948.000000      #3477A5            12.209633  \n",
       "35   3376.400000      #3477A5            29.500336  \n",
       "28    428.800000      #3477A5            10.703555  \n",
       "10   2175.960000      #3477A5            10.900746  \n",
       "44    492.380000      #3477A5             3.649380  \n",
       "3    1416.300000      #3477A5             4.520276  \n",
       "26   1937.600000      #3477A5            19.522781  \n",
       "11   1097.200000      #3477A5             5.070783  \n",
       "47   1687.900000      #3477A5            14.272787  \n",
       "5    3510.800000      #3477A5             0.000000  \n",
       "4   50517.100000      #3477A5             1.511738  \n",
       "6    5591.500000      #3477A5             0.000000  \n",
       "30   2832.500000      #3477A5             2.968512  \n",
       "33   1713.000000      #3477A5            39.296955  \n",
       "1    1963.100000      #3477A5             0.000000  \n",
       "49   1478.000000      #3477A5             0.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain_pct_2022_df = rain_pct_df.loc[:'Wyoming', '2022'].to_frame().reset_index()\n",
    "rain_pct_2022_df.rename(columns = {\"index\": \"state\", \"2022\": \"rain_frac\"}, inplace = True)\n",
    "rain_pct_2022_df[\"rain_pct\"] = 100 * rain_pct_2022_df[\"rain_frac\"]\n",
    "rain_pct_2022_df.drop('rain_frac', axis=1, inplace=True)\n",
    "\n",
    "rain_dol_2022_df = rain_dol_df.loc[:'Wyoming', '2022'].to_frame().reset_index()\n",
    "rain_dol_2022_df.rename(columns = {\"index\": \"state\", \"2022\": \"rain_dol\"}, inplace = True)\n",
    "\n",
    "totbal_pct_2022_df = totbal_pct_df.loc[:'Wyoming', '2022'].to_frame().reset_index()\n",
    "totbal_pct_2022_df.rename(columns = {\"index\": \"state\", \"2022\": \"totbal_frac\"}, inplace = True)\n",
    "totbal_pct_2022_df[\"totbal_pct\"] = 100 * totbal_pct_2022_df[\"totbal_frac\"]\n",
    "totbal_pct_2022_df.drop('totbal_frac', axis=1, inplace=True)\n",
    "\n",
    "totbal_dol_2022_df = totbal_dol_df.loc[:'Wyoming', '2022'].to_frame().reset_index()\n",
    "totbal_dol_2022_df.rename(columns = {\"index\": \"state\", \"2022\": \"totbal_dol\"}, inplace = True)\n",
    "\n",
    "# Merge the four DataFrames\n",
    "rain_totbal_pct_dol_2022_df = rain_pct_2022_df.copy()\n",
    "rain_totbal_pct_dol_2022_df = pd.merge(rain_totbal_pct_dol_2022_df, rain_dol_2022_df, on='state', how='inner')\n",
    "rain_totbal_pct_dol_2022_df[\"rain_color\"] = \"#D5AB53\"\n",
    "rain_totbal_pct_dol_2022_df[\"rain_color\"][rain_totbal_pct_dol_2022_df[\"state\"]==\"Missouri\"] = \"#EED17E\"\n",
    "rain_totbal_pct_dol_2022_df = pd.merge(rain_totbal_pct_dol_2022_df, totbal_pct_2022_df, on='state', how='inner')\n",
    "rain_totbal_pct_dol_2022_df = pd.merge(rain_totbal_pct_dol_2022_df, totbal_dol_2022_df, on='state', how='inner')\n",
    "rain_totbal_pct_dol_2022_df[\"totbal_color\"] = \"#3477A5\"\n",
    "rain_totbal_pct_dol_2022_df[\"totbal_color\"][rain_totbal_pct_dol_2022_df[\"state\"]==\"Missouri\"] = \"#74AAC7\"\n",
    "\n",
    "# Create a new variable that is totbal_pct - rain_pct\n",
    "rain_totbal_pct_dol_2022_df[\"totbal_rain_pct_dif\"] = (\n",
    "    rain_totbal_pct_dol_2022_df[\"totbal_pct\"] - rain_totbal_pct_dol_2022_df[\"rain_pct\"]\n",
    ")\n",
    "\n",
    "# Sort by rainy day fund percent\n",
    "rain_totbal_pct_dol_2022_sorted_df = rain_totbal_pct_dol_2022_df.sort_values(\n",
    "    by=['rain_pct', 'totbal_pct'], ascending=[True, True]\n",
    ")\n",
    "\n",
    "rain_totbal_pct_dol_2022_sorted_df.to_csv('data/fig2_source.csv', index=False)\n",
    "rain_totbal_pct_dol_2022_sorted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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   "source": [
    "# fig2_title = ('Estimated 2022 Rainy day Fund Balances and Total Fund ' +\n",
    "#               'Balances as Percent of General Fund Expenditures')\n",
    "fig2_title = \"\"\n",
    "output_file(\"images/rain_totbal_pct_2022.html\", title=fig2_title,\n",
    "            mode='inline')\n",
    "output_notebook()\n",
    "\n",
    "states = rain_totbal_pct_dol_2022_sorted_df['state'].tolist()\n",
    "legend_label_list = [\"Rainy day fund\", \"Total reserves and balances\"]\n",
    "rain_totbal_pct_dol_2022_cds = \\\n",
    "    ColumnDataSource(rain_totbal_pct_dol_2022_sorted_df)\n",
    "\n",
    "min_pct = rain_totbal_pct_dol_2022_sorted_df['rain_pct'].min()\n",
    "max_pct = rain_totbal_pct_dol_2022_sorted_df['totbal_pct'].max()\n",
    "pct_buffer = 0.05\n",
    "\n",
    "fig2 = figure(title=fig2_title,\n",
    "              height=900,\n",
    "              width=550,\n",
    "              y_range=states,\n",
    "              x_axis_label='Percent of general fund expenditures',\n",
    "              x_range=(min_pct - pct_buffer * (max_pct - min_pct),\n",
    "                       max_pct + pct_buffer * (max_pct - min_pct)),\n",
    "              toolbar_location=None)\n",
    "\n",
    "# Turn off any pan, drag, or scrolling ability\n",
    "fig2.toolbar.active_drag = None\n",
    "fig2.toolbar.active_scroll = None\n",
    "fig2.toolbar.active_tap = None\n",
    "\n",
    "# Set title font size and axes font sizes\n",
    "fig2.title.text_font_size = '0pt'  # Hide title by setting font size to 0\n",
    "fig2.title.text_color = '#434244'\n",
    "fig2.xaxis.axis_label_text_font_size = '11pt'\n",
    "fig2.xaxis.major_label_text_font_size = '11pt'\n",
    "fig2.xaxis.major_label_text_color = '#434244'\n",
    "fig2.yaxis.axis_label_text_font_size = '9pt'\n",
    "fig2.yaxis.major_label_text_font_size = '9pt'\n",
    "fig2.yaxis.major_label_text_color = '#434244'\n",
    "\n",
    "# Modify tick intervals for X-axis and Y-axis\n",
    "fig2.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=5)\n",
    "fig2.xaxis.axis_line_color = '#434244'\n",
    "fig2.xaxis.major_tick_line_color = '#434244'\n",
    "fig2.xaxis.minor_tick_line_color = '#434244'\n",
    "fig2.xgrid.ticker = SingleIntervalTicker(interval=10)\n",
    "fig2.yaxis.axis_line_color = '#434244'\n",
    "fig2.yaxis.major_tick_line_color = '#434244'\n",
    "fig2.yaxis.minor_tick_line_color = '#434244'\n",
    "\n",
    "fig2.hbar_stack(\n",
    "    ['rain_pct', 'totbal_rain_pct_dif'], y='state', height=0.9,\n",
    "    color=['rain_color', 'totbal_color'],\n",
    "    source=rain_totbal_pct_dol_2022_cds, legend_label=legend_label_list\n",
    ")\n",
    "\n",
    "# Add information on hover\n",
    "tooltips = [('State', '@state'),\n",
    "            ('Rainy day fund % exps.','@rain_pct{0.0}' + '%'),\n",
    "            ('Total balances % exps.','@totbal_pct{0.0}' + '%'),\n",
    "           ]\n",
    "fig2.add_tools(HoverTool(tooltips=tooltips, toggleable=False))\n",
    "\n",
    "# Add legend\n",
    "fig2.legend.location = 'center_right'\n",
    "fig2.legend.border_line_width = 1\n",
    "fig2.legend.border_line_color = '#434244'\n",
    "fig2.legend.border_line_alpha = 1\n",
    "fig2.legend.label_text_font_size = '12pt'\n",
    "fig2.legend.label_text_color = '#434244'\n",
    "fig2.y_range.range_padding = 0.02\n",
    "\n",
    "# Add notes below image\n",
    "note_text_list2 = [\n",
    "    (\n",
    "        'Source: Pew Charitable Trusts, \"Fiscal 50: State Trends and'\n",
    "    ),\n",
    "    (\n",
    "        '        Analysis,\" December 16, 2022, accessed December 31, 2022.'\n",
    "    ),\n",
    "    (\n",
    "        'Note: For states in which the blue bar is not visible for total ' +\n",
    "        'balances'\n",
    "    ),\n",
    "    (\n",
    "        '         and reserves, the value equals the rainy day fund balance'\n",
    "    ),\n",
    "    (\n",
    "        '         percentage.'\n",
    "    )\n",
    "]\n",
    "for note_text in note_text_list2:\n",
    "    caption = Title(text=note_text, align='left', text_font_size='11pt',\n",
    "                    text_font_style='normal', text_color='#434244')\n",
    "    fig2.add_layout(caption, 'below')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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For states in which the blue bar is not visible for total 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Dakota\",\"Alaska\",\"Wyoming\"],\"range_padding\":0.02},\"id\":\"1939\",\"type\":\"FactorRange\"},{\"attributes\":{},\"id\":\"1984\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"label\":{\"value\":\"Total reserves and balances\"},\"renderers\":[{\"id\":\"1997\"}]},\"id\":\"2008\",\"type\":\"LegendItem\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"text\":\"Source: Pew Charitable Trusts, \\\"Fiscal 50: State Trends and\",\"text_color\":\"#434244\",\"text_font_size\":\"11pt\",\"text_font_style\":\"normal\"},\"id\":\"2011\",\"type\":\"Title\"}],\"root_ids\":[\"1934\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n  const render_items = [{\"docid\":\"52627099-3eba-498c-b7f7-d4b61db3b099\",\"root_ids\":[\"1934\"],\"roots\":{\"1934\":\"c8731843-7864-4683-8a4b-d1920ae927ec\"}}];\n  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1934"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(fig2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Figure 2 without the source text underneath (for web publication) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"2229\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"2229\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"2229\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));",
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_file(\"images/rain_totbal_pct_2022_nosubtxt.html\", title=fig2_title,\n",
    "            mode='inline')\n",
    "output_notebook()\n",
    "\n",
    "fig2_nosubtxt = figure(title=fig2_title,\n",
    "                       height=900,\n",
    "                       width=550,\n",
    "                       y_range=states,\n",
    "                       x_axis_label='Percent of general fund expenditures',\n",
    "                       x_range=(min_pct - pct_buffer * (max_pct - min_pct),\n",
    "                                max_pct + pct_buffer * (max_pct - min_pct)),\n",
    "                       toolbar_location=None)\n",
    "\n",
    "# Turn off any pan, drag, or scrolling ability\n",
    "fig2_nosubtxt.toolbar.active_drag = None\n",
    "fig2_nosubtxt.toolbar.active_scroll = None\n",
    "fig2_nosubtxt.toolbar.active_tap = None\n",
    "\n",
    "fig2.toolbar.active_drag = None\n",
    "fig2.toolbar.active_scroll = None\n",
    "fig2.toolbar.active_tap = None\n",
    "\n",
    "# Set title font size and axes font sizes\n",
    "fig2_nosubtxt.title.text_font_size = '0pt'  # Hide title by setting font size to 0\n",
    "fig2_nosubtxt.title.text_color = '#434244'\n",
    "fig2_nosubtxt.xaxis.axis_label_text_font_size = '11pt'\n",
    "fig2_nosubtxt.xaxis.major_label_text_font_size = '11pt'\n",
    "fig2_nosubtxt.xaxis.major_label_text_color = '#434244'\n",
    "fig2_nosubtxt.yaxis.axis_label_text_font_size = '9pt'\n",
    "fig2_nosubtxt.yaxis.major_label_text_font_size = '9pt'\n",
    "fig2_nosubtxt.yaxis.major_label_text_color = '#434244'\n",
    "\n",
    "# Modify tick intervals for X-axis and Y-axis\n",
    "fig2_nosubtxt.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=5)\n",
    "fig2_nosubtxt.xaxis.axis_line_color = '#434244'\n",
    "fig2_nosubtxt.xaxis.major_tick_line_color = '#434244'\n",
    "fig2_nosubtxt.xaxis.minor_tick_line_color = '#434244'\n",
    "fig2_nosubtxt.xgrid.ticker = SingleIntervalTicker(interval=10)\n",
    "fig2_nosubtxt.yaxis.axis_line_color = '#434244'\n",
    "fig2_nosubtxt.yaxis.major_tick_line_color = '#434244'\n",
    "fig2_nosubtxt.yaxis.minor_tick_line_color = '#434244'\n",
    "\n",
    "fig2_nosubtxt.hbar_stack(\n",
    "    ['rain_pct', 'totbal_rain_pct_dif'], y='state', height=0.9,\n",
    "    color=['rain_color', 'totbal_color'],\n",
    "    source=rain_totbal_pct_dol_2022_cds, legend_label=legend_label_list\n",
    ")\n",
    "\n",
    "# Add information on hover\n",
    "tooltips = [('State', '@state'),\n",
    "            ('Rainy day fund % exps.','@rain_pct{0.0}' + '%'),\n",
    "            ('Total balances % exps.','@totbal_pct{0.0}' + '%'),\n",
    "           ]\n",
    "fig2_nosubtxt.add_tools(HoverTool(tooltips=tooltips, toggleable=False))\n",
    "\n",
    "# Add legend\n",
    "fig2_nosubtxt.legend.location = 'center_right'\n",
    "fig2_nosubtxt.legend.border_line_width = 1\n",
    "fig2_nosubtxt.legend.border_line_color = '#434244'\n",
    "fig2_nosubtxt.legend.border_line_alpha = 1\n",
    "fig2_nosubtxt.legend.label_text_font_size = '12pt'\n",
    "fig2_nosubtxt.legend.label_text_color = '#434244'\n",
    "fig2_nosubtxt.y_range.range_padding = 0.02\n",
    "\n",
    "# # Add notes below image\n",
    "# for note_text in note_text_list2:\n",
    "#     caption = Title(text=note_text, align='left', text_font_size='11pt',\n",
    "#                     text_font_style='normal', text_color='#434244')\n",
    "#     fig2_nosubtxt.add_layout(caption, 'below')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div class=\"bk-root\" id=\"0dc85903-606b-4259-9882-f78d154658c3\" data-root-id=\"2230\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "(function(root) {\n  function embed_document(root) {\n  const docs_json = {\"7e9ea6b3-2362-4011-bc73-676fc5b8cb67\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"2241\"}],\"center\":[{\"id\":\"2244\"},{\"id\":\"2247\"},{\"id\":\"2290\"}],\"height\":900,\"left\":[{\"id\":\"2245\"}],\"renderers\":[{\"id\":\"2279\"},{\"id\":\"2296\"}],\"title\":{\"id\":\"2231\"},\"toolbar\":{\"id\":\"2255\"},\"toolbar_location\":null,\"width\":550,\"x_range\":{\"id\":\"2233\"},\"x_scale\":{\"id\":\"2237\"},\"y_range\":{\"id\":\"2235\"},\"y_scale\":{\"id\":\"2239\"}},\"id\":\"2230\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"totbal_color\"},\"hatch_alpha\":{\"value\":0.1},\"hatch_color\":{\"field\":\"totbal_color\"},\"height\":{\"value\":0.9},\"left\":{\"expr\":{\"id\":\"2273\"}},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"field\":\"totbal_color\"},\"right\":{\"expr\":{\"id\":\"2274\"}},\"y\":{\"field\":\"state\"}},\"id\":\"2294\",\"type\":\"HBar\"},{\"attributes\":{\"axis\":{\"id\":\"2241\"},\"coordinates\":null,\"group\":null,\"ticker\":{\"id\":\"2270\"}},\"id\":\"2244\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"toggleable\":false,\"tooltips\":[[\"State\",\"@state\"],[\"Rainy day fund % exps.\",\"@rain_pct{0.0}%\"],[\"Total balances % 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     "output_type": "display_data"
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   "source": [
    "show(fig2_nosubtxt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table 1. Number of states for which estimated 2022 amounts represent 23-year high for select categories of rainy day fund and total balances and reserves statistics: 2000-2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of states for which 2022 rainy day fund balances\n",
      "are 23-year high is 36 states.\n",
      "Number of states for which 2021 rainy day fund balances\n",
      "are 22-year high is 29 states.\n",
      "Missouri 2022: True\n",
      "Missouri 2021: False\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/597043741.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_dol_df[\"max_2021\"][\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/597043741.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_dol_df[\"max_2022\"][\n"
     ]
    }
   ],
   "source": [
    "# Number of states for which estimated 2022 rainy day fund balances\n",
    "# represent a 23-year high\n",
    "rain_dol_df[\"max_2021\"] = False\n",
    "rain_dol_df[\"max_2022\"] = False\n",
    "rain_dol_df[\"max_2021\"][\n",
    "    rain_dol_df[\"2021\"] > rain_dol_df.loc[:, \"2000\":\"2020\"].max(axis=1)\n",
    "] = True\n",
    "rain_dol_df[\"max_2022\"][\n",
    "    rain_dol_df[\"2022\"] > rain_dol_df.loc[:, \"2000\":\"2021\"].max(axis=1)\n",
    "] = True\n",
    "rain_dol_22high = rain_dol_df[\"max_2021\"].sum()\n",
    "rain_dol_23high = rain_dol_df[\"max_2022\"].sum()\n",
    "print(\"Number of states for which 2022 rainy day fund balances\")\n",
    "print(\"are 23-year high is\", rain_dol_23high, \"states.\")\n",
    "print(\"Number of states for which 2021 rainy day fund balances\")\n",
    "print(\"are 22-year high is\", rain_dol_22high, \"states.\")\n",
    "print(\"Missouri 2022:\", rain_dol_df.loc[\"Missouri\",\"max_2022\"])\n",
    "print(\"Missouri 2021:\", rain_dol_df.loc[\"Missouri\",\"max_2021\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of states for which 2022 rainy day fund balances\n",
      "as percent of general fund expenditures are 23-year high\n",
      "is 20 states.\n",
      "Number of states for which 2021 rainy day fund balances\n",
      "as percent of general fund expenditures are 22-year high\n",
      "is 26 states.\n",
      "Missouri 2022: True\n",
      "Missouri 2021: False\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/3894985178.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_pct_df[\"max_2021\"] = False\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/3894985178.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_pct_df[\"max_2022\"] = False\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/3894985178.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_pct_df[\"max_2021\"][\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/3894985178.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  rain_pct_df[\"max_2022\"][\n"
     ]
    }
   ],
   "source": [
    "# Number of states for which estimated 2022 rainy day fund balances\n",
    "# as percent of general fund expenditures represent a 23-year high\n",
    "rain_pct_df[\"max_2021\"] = False\n",
    "rain_pct_df[\"max_2022\"] = False\n",
    "rain_pct_df[\"max_2021\"][\n",
    "    rain_pct_df[\"2021\"] > rain_pct_df.loc[:, \"2000\":\"2020\"].max(axis=1)\n",
    "] = True\n",
    "rain_pct_df[\"max_2022\"][\n",
    "    rain_pct_df[\"2022\"] > rain_pct_df.loc[:, \"2000\":\"2021\"].max(axis=1)\n",
    "] = True\n",
    "rain_pct_22high = rain_pct_df[\"max_2021\"].sum()\n",
    "rain_pct_23high = rain_pct_df[\"max_2022\"].sum()\n",
    "print(\"\")\n",
    "print(\"Number of states for which 2022 rainy day fund balances\")\n",
    "print(\"as percent of general fund expenditures are 23-year high\")\n",
    "print(\"is\", rain_pct_23high, \"states.\")\n",
    "print(\"Number of states for which 2021 rainy day fund balances\")\n",
    "print(\"as percent of general fund expenditures are 22-year high\")\n",
    "print(\"is\", rain_pct_22high, \"states.\")\n",
    "print(\"Missouri 2022:\", rain_pct_df.loc[\"Missouri\",\"max_2022\"])\n",
    "print(\"Missouri 2021:\", rain_pct_df.loc[\"Missouri\",\"max_2021\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of states for which 2022 total balances and reserves\n",
      "are 23-year high is 26 states.\n",
      "Number of states for which 2021 total balances and reserves\n",
      "are 22-year high is 42 states.\n",
      "Missouri 2022: True\n",
      "Missouri 2021: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/1861455261.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_dol_df[\"max_2021\"][\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/1861455261.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_dol_df[\"max_2022\"][\n"
     ]
    }
   ],
   "source": [
    "# Number of states for which estimated 2022 total balances and reserves\n",
    "# represent a 23-year high\n",
    "totbal_dol_df[\"max_2021\"] = False\n",
    "totbal_dol_df[\"max_2022\"] = False\n",
    "totbal_dol_df[\"max_2021\"][\n",
    "    totbal_dol_df[\"2021\"] > totbal_dol_df.loc[:, \"2000\":\"2020\"].max(axis=1)\n",
    "] = True\n",
    "totbal_dol_df[\"max_2022\"][\n",
    "    totbal_dol_df[\"2022\"] > totbal_dol_df.loc[:, \"2000\":\"2021\"].max(axis=1)\n",
    "] = True\n",
    "totbal_dol_22high = totbal_dol_df[\"max_2021\"].sum()\n",
    "totbal_dol_23high = totbal_dol_df[\"max_2022\"].sum()\n",
    "print(\"\")\n",
    "print(\"Number of states for which 2022 total balances and reserves\")\n",
    "print(\"are 23-year high is\", totbal_dol_23high, \"states.\")\n",
    "print(\"Number of states for which 2021 total balances and reserves\")\n",
    "print(\"are 22-year high is\", totbal_dol_22high, \"states.\")\n",
    "print(\"Missouri 2022:\", totbal_dol_df.loc[\"Missouri\",\"max_2022\"])\n",
    "print(\"Missouri 2021:\", totbal_dol_df.loc[\"Missouri\",\"max_2021\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of states for which 2022 total balances and reserves\n",
      "as percent of general fund expenditures are 23-year high\n",
      "is 20 states.\n",
      "Number of states for which 2021 total balances and reserves\n",
      "as percent of general fund expenditures are 22-year high\n",
      "is 32 states.\n",
      "Missouri 2022: True\n",
      "Missouri 2021: True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/2073359744.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_pct_df[\"max_2021\"] = False\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/2073359744.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_pct_df[\"max_2022\"] = False\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/2073359744.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_pct_df[\"max_2021\"][\n",
      "/var/folders/d4/trj3dssd6s3g8kxvjmczz11w0000gn/T/ipykernel_48620/2073359744.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  totbal_pct_df[\"max_2022\"][\n"
     ]
    }
   ],
   "source": [
    "# Number of states for which estimated 2022 total balances and reserves\n",
    "# as percent of general fund expenditures represent a 23-year high\n",
    "totbal_pct_df[\"max_2021\"] = False\n",
    "totbal_pct_df[\"max_2022\"] = False\n",
    "totbal_pct_df[\"max_2021\"][\n",
    "    totbal_pct_df[\"2021\"] > totbal_pct_df.loc[:, \"2000\":\"2020\"].max(axis=1)\n",
    "] = True\n",
    "totbal_pct_df[\"max_2022\"][\n",
    "    totbal_pct_df[\"2022\"] > totbal_pct_df.loc[:, \"2000\":\"2021\"].max(axis=1)\n",
    "] = True\n",
    "totbal_pct_22high = totbal_pct_df[\"max_2021\"].sum()\n",
    "totbal_pct_23high = totbal_pct_df[\"max_2022\"].sum()\n",
    "print(\"\")\n",
    "print(\"Number of states for which 2022 total balances and reserves\")\n",
    "print(\"as percent of general fund expenditures are 23-year high\")\n",
    "print(\"is\", totbal_pct_23high, \"states.\")\n",
    "print(\"Number of states for which 2021 total balances and reserves\")\n",
    "print(\"as percent of general fund expenditures are 22-year high\")\n",
    "print(\"is\", totbal_pct_22high, \"states.\")\n",
    "print(\"Missouri 2022:\", totbal_pct_df.loc[\"Missouri\",\"max_2022\"])\n",
    "print(\"Missouri 2021:\", totbal_pct_df.loc[\"Missouri\",\"max_2021\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Missouri Business Tax Landscape\n",
    "Important features of TN business tax system:\n",
    "* 3 main components:\n",
    "  1. Corporate income Tax\n",
    "  2. Financial Institutions Tax\n",
    "  3. State and Local Sales and Use Tax"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Investment Incentives using Cost of Capital Calculator\n",
    "Computing the cost-of-capital at the state-level:\n",
    "  * the statutory rate used will be the sum of the federal and state CIT\n",
    "    * But can deduct state taxes at federal level\n",
    "    * So maybe rate is Fed Rate + State rate - fed rate * state rate?\n",
    "  * franchise tax can use the wealth tax parameter in CCC\n",
    "  * The interaction of state and federal is not something I want to rush into CCC, so we'll do most of the calculations here by functions defined in this notebook (with a bit of assistance from a couple CCC functions.\n",
    "\n",
    "\n",
    "Equation for the cost of capital at when considering state taxes in MO:\n",
    "$$\n",
    "\\rho = \\left[\\frac{(r-\\pi+\\delta)(1-k-u^fz^f-u^sz^s + u^fu^sz^s)}{(1-u^f-u^s+u^fu^s)} + w \\right] / (1-\\tau^{GR}) - \\delta\n",
    "$$\n",
    "where:\n",
    "* $\\rho$ = cost of capital\n",
    "* $r$ = nominal discount rate\n",
    "* $\\pi$ = inflation rate\n",
    "* $k$ = investment tax credit rate\n",
    "* $u^f$ = statutory CIT rate at the federal level \n",
    "* $u^s$ = statutory CIT rate at the state level \n",
    "* $z^f$ = NPV of depreciation deduction under federal system\n",
    "* $z^s$ = NPV of depreciation deduction under state system\n",
    "* $w$ = property tax rate\n",
    "* $\\tau^{GR}$ = gross receipts tax rate\n",
    "\n",
    "Calibration:\n",
    "\n",
    "| Parameter     | Value | Source |\n",
    "| ----------- | ----------- |----------- |\n",
    "| $r$ | 0.06 | To give real return of 4\\%|\n",
    "| $\\pi$ | 0.02 | Fed target|\n",
    "| $k$  | 0.0 | No general invest tax credit at federal or state level|\n",
    "| $u^f$  | 0.21 | Federal IRC|\n",
    "| $u^s$  | 0.04 | Missouri corporate income tax rate |\n",
    "| $z^f$  | Varies | Federal IRC|\n",
    "| $z^s$  | Varies | Like Federal IRC, but no bonus deprec|\n",
    "| $w$  | 0.0 | Franchise tax rate in MO law |\n",
    "| $\\tau^{GR}$  | 0.0 | Bus tax rate in MO law|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "_t-_pFeOZCDR"
   },
   "outputs": [],
   "source": [
    "!pip install cost-of-capital-calculator &> /dev/null\n",
    "!pip install taxcalc &> /dev/null \n",
    "!pip install requests &> /dev/null"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2qmfg6kv8WHN"
   },
   "source": [
    "Now we'll import the Python packages and modules we'll work with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Tas5ZFKxbgCA"
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import ccc\n",
    "import taxcalc as tc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "# set template for plotting\n",
    "pio.templates.default = \"plotly_white\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FPc-vbli8fsb"
   },
   "source": [
    "### Setup\n",
    "\n",
    "Create objects to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "g-V3V27Rc4Bo"
   },
   "outputs": [],
   "source": [
    "# Creating an instance of the Assets class and call it \"assets\"\n",
    "assets = ccc.data.Assets()\n",
    "# Create an instance of the DepreciationParams\n",
    "# class object and call it dp\n",
    "dp = ccc.parameters.DepreciationParams()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "7DDWmsRDowWK"
   },
   "outputs": [],
   "source": [
    "# define new cost of capital function to take into account state taxes\n",
    "def eq_coc_state(delta, z_f, z_s, w, u_f, u_s, tau_GR, inv_tax_credit, pi, r):\n",
    "    r'''\n",
    "    Compute the cost of capital\n",
    "    .. math::\n",
    "        \\rho = \\frac{\\frac{(r-\\pi+\\delta)}{1-u^f-u^s+u^fu^s}(1-u^fz^f-u^sz^s+u^fu^sz^s)+w}{(1-\\tau^GR)}-\\delta\n",
    "    Args:\n",
    "        delta (array_like): rate of economic depreciation\n",
    "        z_f (array_like): net present value of depreciation deductions for\n",
    "            $1 of investment under the federal system\n",
    "        z_s (array_like): net present value of depreciation deductions for\n",
    "            $1 of investment under the state system\n",
    "        w (scalar): property tax rate\n",
    "        u_f (scalar): statutory marginal tax rate for the first layer of\n",
    "            income taxes at the federal level\n",
    "        u_s (scalar): statutory marginal tax rate for the first layer of\n",
    "            income taxes at the state level\n",
    "        tau_GR (scalar): gross receipts tax\n",
    "        inv_tax_credit (scalar): investment tax credit rate\n",
    "        pi (scalar): inflation rate\n",
    "        r (scalar): discount rate\n",
    "    Returns:\n",
    "        rho (array_like): the cost of capital\n",
    "    '''\n",
    "    rho = (\n",
    "        ((((r - pi + delta) / (1 - u_f - u_s + u_f * u_s)) *\n",
    "          (1 - inv_tax_credit - u_f * z_f - u_s * z_s + u_f * u_s * z_s) + w) /\n",
    "          (1 - tau_GR))\n",
    "        - delta\n",
    "    )\n",
    "\n",
    "    return rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "gQahy5bAAuzv"
   },
   "outputs": [],
   "source": [
    "# Define model parameters\n",
    "inflation_rate = 0.02 # Inflation rate\n",
    "nominal_int_rate = 0.06\n",
    "fraction_financed_w_debt = 0.0\n",
    "federal_bonus_depreciation = {\n",
    "    \"machines\": 0.8, \"buildings\": 0.0, \"intangibles\": 0.8\n",
    "}\n",
    "depreciation_rates = {\n",
    "    \"machines\": 0.1031, \"buildings\": 0.0314, \"intangibles\": 0.33\n",
    "}\n",
    "# Example machine: EI40, example building: SI00, example intangible: ENS3\n",
    "depreciation_lives = {\"machines\": 7, \"buildings\": 39, \"intangibles\": 3}\n",
    "depreciation_methods = {\"machines\": \"dbsl\", \"buildings\": \"sl\", \"intangibles\": \"sl\"}\n",
    "E = 0.06  # Expected after-tax return on corporate equity\n",
    "profit_rate = 0.2 # 0.2 is a 20% profit rate\n",
    "int_haircut = 0.0\n",
    "u_f = 0.21\n",
    "u_s = 0.04\n",
    "franchise_tax_rate = 0.0\n",
    "tau_GR = 0.0  # Gross receipts tax\n",
    "inv_tax_credit = 0.0\n",
    "bonus_s = {\"machines\": 0.8, \"buildings\": 0.0, \"intangibles\": 0.8}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "O42yHQtvTU23"
   },
   "outputs": [],
   "source": [
    "# compute outputs\n",
    "def compute_outputs(u_s, bonus_s, franchise_tax_rate, tau_GR):\n",
    "    \"\"\"\n",
    "    This function computes the outputs of interest and allows one to change the\n",
    "    parameters of MO state law\n",
    "\n",
    "    Args:\n",
    "      u_s (scalar): statutory marginal tax rate for the first layer of\n",
    "            income taxes at the state level\n",
    "      bonus_s (dict): rates of bonus depreciation in MO by asset type\n",
    "      franchise_tax_rate (scalar): MO franchise tax\n",
    "      tau_GR (scalar): gross receipts tax\n",
    "    \"\"\"\n",
    "    out_dict = {\"machines\": {}, \"buildings\": {}, \"intangibles\": {}}\n",
    "    for k, v in depreciation_rates.items():\n",
    "        r = ccc.paramfunctions.calc_r(\n",
    "            u_f + u_s - u_f * u_s, nominal_int_rate, inflation_rate,\n",
    "            nominal_int_rate, fraction_financed_w_debt, int_haircut, E, 0.0)\n",
    "        r_prime = ccc.paramfunctions.calc_r_prime(\n",
    "            nominal_int_rate, inflation_rate, fraction_financed_w_debt, E)\n",
    "        if depreciation_methods[k] == \"dbsl\":\n",
    "            z_f = ccc.calcfunctions.dbsl(depreciation_lives[k], 2, federal_bonus_depreciation[k], r)\n",
    "            z_s = ccc.calcfunctions.dbsl(depreciation_lives[k], 2, bonus_s[k], r) \n",
    "        elif depreciation_methods[k] == \"sl\":\n",
    "            z_f = ccc.calcfunctions.sl(depreciation_lives[k], federal_bonus_depreciation[k], r)\n",
    "            z_s = ccc.calcfunctions.sl(depreciation_lives[k], bonus_s[k], r) \n",
    "        else:\n",
    "          print(\"Please enter one of: dbsl, sl\")\n",
    "          assert False\n",
    "        rho = eq_coc_state(\n",
    "                depreciation_rates[k], z_f, z_s, franchise_tax_rate,\n",
    "                u_f, u_s, tau_GR, inv_tax_credit, inflation_rate, r)\n",
    "        metr = ccc.calcfunctions.eq_metr(rho, r_prime, inflation_rate)\n",
    "        eatr = ccc.calcfunctions.eq_eatr(rho, metr, profit_rate, u_f + u_f - u_f * u_s)\n",
    "        out_dict[k][\"rho\"] = rho\n",
    "        out_dict[k][\"metr\"] = metr\n",
    "        out_dict[k][\"eatr\"] = eatr\n",
    "    return out_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "C37mzRCgUtav"
   },
   "outputs": [],
   "source": [
    "base_df = pd.DataFrame(compute_outputs(\n",
    "    u_s, bonus_s, franchise_tax_rate, tau_GR\n",
    "))\n",
    "cut_corp_tax_half_df = pd.DataFrame(compute_outputs(\n",
    "    0.02, bonus_s, franchise_tax_rate, tau_GR\n",
    "))\n",
    "cut_corp_tax_all_df = pd.DataFrame(compute_outputs(\n",
    "    0.00, bonus_s, franchise_tax_rate, tau_GR\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "S_sLTwn_XYZp"
   },
   "outputs": [],
   "source": [
    "# Create policy names and put in one dataframe\n",
    "base_df['Policy'] = \"Current Law\"\n",
    "cut_corp_tax_half_df['Policy'] = \"Cut CIT in half\"\n",
    "cut_corp_tax_all_df['Policy'] = \"Repeal CIT\"\n",
    "# append dataframes together\n",
    "df = pd.concat([base_df, cut_corp_tax_half_df, cut_corp_tax_all_df])\n",
    "df.reset_index(inplace=True)\n",
    "df.rename(columns={\"index\": \"output_var\"}, inplace=True)\n",
    "df = pd.melt(df, id_vars=[\"Policy\", \"output_var\"], var_name=\"asset_type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 417
    },
    "id": "7CoFvIwXcIS6",
    "outputId": "7d047ebd-24ee-470f-d50d-c4838cf860ec"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "hovertemplate": "Policy=%{x}<br>Marginal Effective Tax Rate=%{y}<extra></extra>",
         "legendgroup": "Current Law",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Current Law",
         "offsetgroup": "Current Law",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
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         "xaxis": "x",
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         "yaxis": "y"
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        {
         "alignmentgroup": "True",
         "hovertemplate": "Policy=%{x}<br>Marginal Effective Tax Rate=%{y}<extra></extra>",
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    "# Plot results\n",
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    "# Plot results\n",
    "fig = px.bar(df[(df[\"output_var\"]==\"rho\") & (df[\"asset_type\"]==\"machines\")], x=\"Policy\", y=\"value\",\n",
    "             color='Policy', labels={'value':'Cost of Capital'}, height=400)\n",
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    "id": "Msb8FNavdHVV",
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   ],
   "source": [
    "# Plot results\n",
    "fig = px.histogram(df[(df[\"output_var\"]==\"metr\") & (df['Policy'].isin([\"Current Law\", \"Cut CIT in half\", \"Repeal CIT\"]))], x=\"asset_type\", y=\"value\",\n",
    "             color='Policy', barmode='group', labels={'asset_type':'Asset Type', 'value': 'Marginal Effective Tax Rate'},\n",
    "             height=400)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "j9TO10LbryyS",
    "outputId": "6250b37c-da56-4e48-9467-617c4431cf73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machines: rho =  0.0622899645192464 , metr =  0.03676297677997306  and eatr =  0.2948570756156229\n"
     ]
    }
   ],
   "source": [
    "# compute example results for equipment\n",
    "r = ccc.paramfunctions.calc_r(\n",
    "        u_f + u_s - u_f * u_s, nominal_int_rate, inflation_rate,\n",
    "        nominal_int_rate, fraction_financed_w_debt, int_haircut, E, 0.0)\n",
    "r_prime = ccc.paramfunctions.calc_r_prime(\n",
    "    nominal_int_rate, inflation_rate, fraction_financed_w_debt, E)\n",
    "z_f = ccc.calcfunctions.dbsl(5, 2, 0.8, r)  # 5 year asset, double declining balance, 80% bonus\n",
    "z_s = ccc.calcfunctions.dbsl(5, 2, 0.0, r)  # 5 year asset, double declining balance, no bonus\n",
    "rho = eq_coc_state(\n",
    "        depreciation_rates['machines'], z_f, z_s, franchise_tax_rate,\n",
    "        u_f, u_s, tau_GR, inv_tax_credit, inflation_rate, r)\n",
    "metr = ccc.calcfunctions.eq_metr(rho, r_prime, inflation_rate)\n",
    "eatr = ccc.calcfunctions.eq_eatr(rho, metr, profit_rate, u_f + u_f - u_f * u_s)\n",
    "print('Machines: rho = ', rho, ', metr = ', metr, ' and eatr = ', eatr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "A1PPzapJwHqU",
    "outputId": "c7123b69-c781-4fc9-da51-42b5b039e6f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machines: rho =  0.06125450230184801 , metr =  0.020480164799415276  and eatr =  0.2918107457720368\n"
     ]
    }
   ],
   "source": [
    "# compare to federal\n",
    "r = ccc.paramfunctions.calc_r(\n",
    "        u_f + u_s - u_f * u_s, nominal_int_rate, inflation_rate,\n",
    "        nominal_int_rate, fraction_financed_w_debt, int_haircut, E, 0.0)\n",
    "r_prime = ccc.paramfunctions.calc_r_prime(\n",
    "    nominal_int_rate, inflation_rate, fraction_financed_w_debt, E)\n",
    "z_f = ccc.calcfunctions.dbsl(5, 2, 0.8, r)  # 5 year asset, double declining balance, 80% bonus\n",
    "rho = ccc.calcfunctions.eq_coc(\n",
    "        depreciation_rates['machines'], z_f, 0.0,\n",
    "        u_f, inv_tax_credit, inflation_rate, r)\n",
    "metr = ccc.calcfunctions.eq_metr(rho, r_prime, inflation_rate)\n",
    "eatr = ccc.calcfunctions.eq_eatr(rho, metr, profit_rate, u_f + u_f - u_f * u_s)\n",
    "print('Machines: rho = ', rho, ', metr = ', metr, ' and eatr = ', eatr)"
   ]
  }
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