weather.ipynb

{
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   "metadata": {},
   "source": [
    "## Related Python projects\n",
    "\n",
    "- [a Kaggle notebook using BigQuery](https://www.kaggle.com/paultimothymooney/how-to-query-the-noaa-ghcn-daily-weather-data)\n",
    "- [Get_NOAA_GHCN_data](https://github.com/aaronpenne/get_noaa_ghcn_data) - a python3 library to make it easier to access GHCN data\n",
    "\n",
    "## Sources for historical weather data\n",
    "\n",
    "- [weather underground](https://www.wunderground.com/history/daily/KRIC/date/2021-1-23) - private, ad supported - not intended for bulk download\n",
    "- [national weather service](https://w2.weather.gov/climate/local_data.php?wfo=akq) - mostly aggregate PDFs\n",
    "- [national climate data center](https://www.ncdc.noaa.gov/cdo-web/) - prominently offers paid access\n",
    "    - [NCDC 2](https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00013740/detail) - RIC station\n",
    "- [NCEI](https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00861/html) - very helpful dataset resource - provided NCDC GCHN public links\n",
    "\n",
    "For one station, download from e.g. https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/all/USW00013740.dly\n",
    "where in this case, Richmond International Airport has code 'USW00013740'\n",
    "\n",
    "Station codes can be found in https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import urllib.request\n",
    "import os\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "station_id = 'USW00013740'\n",
    "output_dir = 'ghcn_data'\n",
    "os.environ[\"DLY_OUTPUT\"] = output_dir\n",
    "\n",
    "# download the daily file\n",
    "#urllib.request.urlretrieve(f'https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/all/{station_id}.dly', f'{output_dir}/{station_id}.dly')\n",
    "\n",
    "station_id_list = ['USW00013740','USC00440327','USC00448829','USC00444101','USC00445050']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stations\n",
    "\n",
    "Source: https://www.ncdc.noaa.gov/cdo-web/datatools/findstation\n",
    "\n",
    "- USW00013740 - RIC airport - 1939-03-01 to 2021-01-23\n",
    "- USC00440327 - Ashland - 1893-01-01 to 2021-01-23\n",
    "- USC00448829 - Walkerton 2 NW - 1932-07-01 to 2020-11-30\n",
    "- USC00444101 - Hopewell - 1916-10-01 to 2021-01-20\n",
    "- USC00445050 - Louisa - 1916-05-01 to 2021-01-23\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "230-****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      " ** You are accessing a U.S. Government information system, which includes:                         **\n",
      " ** 1) This computer, 2)This computer network, 3) All computers connected to this network, and      **\n",
      " ** 4) All devices and storage media attached to this network or to a computer on this network.     **\n",
      " ** You understand and consent to the following:                                                    **\n",
      " ** you may access this information system for authorized use only; you have no reasonable          **\n",
      " ** expectation of privacy regarding any communication of data transiting or stored on this         **\n",
      " ** information system; at any time and for any lawful Government purpose, the Government may       **\n",
      " ** monitor, intercept, and search and seize any communication or data transiting or stored on      **\n",
      " ** this information system; and any communications or data transiting or stored on this            **\n",
      " ** information system may be disclosed or used for any lawful Government purpose.                  **\n",
      " ****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      "230 Anonymous\n",
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      "\n",
      "Output CSV saved to: ghcn_data/USC00440327.csv\n",
      "230-****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      " ** You are accessing a U.S. Government information system, which includes:                         **\n",
      " ** 1) This computer, 2)This computer network, 3) All computers connected to this network, and      **\n",
      " ** 4) All devices and storage media attached to this network or to a computer on this network.     **\n",
      " ** You understand and consent to the following:                                                    **\n",
      " ** you may access this information system for authorized use only; you have no reasonable          **\n",
      " ** expectation of privacy regarding any communication of data transiting or stored on this         **\n",
      " ** information system; at any time and for any lawful Government purpose, the Government may       **\n",
      " ** monitor, intercept, and search and seize any communication or data transiting or stored on      **\n",
      " ** this information system; and any communications or data transiting or stored on this            **\n",
      " ** information system may be disclosed or used for any lawful Government purpose.                  **\n",
      " ****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      "230 Anonymous\n",
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      "\n",
      "Output CSV saved to: ghcn_data/USC00448829.csv\n",
      "230-****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      " ** You are accessing a U.S. Government information system, which includes:                         **\n",
      " ** 1) This computer, 2)This computer network, 3) All computers connected to this network, and      **\n",
      " ** 4) All devices and storage media attached to this network or to a computer on this network.     **\n",
      " ** You understand and consent to the following:                                                    **\n",
      " ** you may access this information system for authorized use only; you have no reasonable          **\n",
      " ** expectation of privacy regarding any communication of data transiting or stored on this         **\n",
      " ** information system; at any time and for any lawful Government purpose, the Government may       **\n",
      " ** monitor, intercept, and search and seize any communication or data transiting or stored on      **\n",
      " ** this information system; and any communications or data transiting or stored on this            **\n",
      " ** information system may be disclosed or used for any lawful Government purpose.                  **\n",
      " ****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      "230 Anonymous\n",
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      "\n",
      "Output CSV saved to: ghcn_data/USC00444101.csv\n",
      "230-****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      " ** You are accessing a U.S. Government information system, which includes:                         **\n",
      " ** 1) This computer, 2)This computer network, 3) All computers connected to this network, and      **\n",
      " ** 4) All devices and storage media attached to this network or to a computer on this network.     **\n",
      " ** You understand and consent to the following:                                                    **\n",
      " ** you may access this information system for authorized use only; you have no reasonable          **\n",
      " ** expectation of privacy regarding any communication of data transiting or stored on this         **\n",
      " ** information system; at any time and for any lawful Government purpose, the Government may       **\n",
      " ** monitor, intercept, and search and seize any communication or data transiting or stored on      **\n",
      " ** this information system; and any communications or data transiting or stored on this            **\n",
      " ** information system may be disclosed or used for any lawful Government purpose.                  **\n",
      " ****** WARNING ** WARNING ** WARNING ** WARNING ** WARNING ** ** WARNING ** WARNING ** WARNING ******\n",
      "230 Anonymous\n",
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      "\n",
      "Output CSV saved to: ghcn_data/USC00445050.csv\n"
     ]
    }
   ],
   "source": [
    "# from https://github.com/aaronpenne/get_noaa_ghcn_data\n",
    "import get_dly \n",
    "#station_id = 'USW00013740'\n",
    "for s in station_id_list:\n",
    "    ftp = get_dly.connect_to_ftp()\n",
    "    get_dly.dly_to_csv(ftp, s)\n",
    "    ftp.quit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>MM/DD/YYYY</th>\n",
       "      <th>ID</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMAX_FLAGS</th>\n",
       "      <th>TMIN</th>\n",
       "      <th>TMIN_FLAGS</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>...</th>\n",
       "      <th>WT04</th>\n",
       "      <th>WT04_FLAGS</th>\n",
       "      <th>WT05</th>\n",
       "      <th>WT05_FLAGS</th>\n",
       "      <th>DAPR</th>\n",
       "      <th>DAPR_FLAGS</th>\n",
       "      <th>MDPR</th>\n",
       "      <th>MDPR_FLAGS</th>\n",
       "      <th>WT06</th>\n",
       "      <th>WT06_FLAGS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [MM/DD/YYYY, ID, YEAR, MONTH, DAY, TMAX, TMAX_FLAGS, TMIN, TMIN_FLAGS, PRCP, PRCP_FLAGS, SNOW, SNOW_FLAGS, WT18, WT18_FLAGS, WT01, WT01_FLAGS, SNWD, SNWD_FLAGS, TOBS, TOBS_FLAGS, WT03, WT03_FLAGS, WT11, WT11_FLAGS, WT14, WT14_FLAGS, WT08, WT08_FLAGS, WT04, WT04_FLAGS, WT05, WT05_FLAGS, DAPR, DAPR_FLAGS, MDPR, MDPR_FLAGS, WT06, WT06_FLAGS]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 39 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kfinity/anaconda3/envs/python38/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (14,24,26,48,50,52,56,58,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
      "/home/kfinity/anaconda3/envs/python38/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (14,26,28,34) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
      "/home/kfinity/anaconda3/envs/python38/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (22,34,38,40,42) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
      "/home/kfinity/anaconda3/envs/python38/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (18,20,22,24,26,28,30,32,34) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
      "/home/kfinity/anaconda3/envs/python38/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (24,28,30,34,36) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "#ric = pd.read_csv(station_id + '.csv')\n",
    "\n",
    "# create a blank df using the columns from the first csv\n",
    "sdf = pd.read_csv(output_dir + '/' + station_id_list[0] + '.csv', nrows=0)\n",
    "\n",
    "# load all the files into one df\n",
    "for s in station_id_list:\n",
    "    df = pd.read_csv(output_dir + '/' + s + '.csv')\n",
    "    sdf = sdf.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['USW00013740', 'USC00440327', 'USC00448829', 'USC00444101',\n",
       "       'USC00445050'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdf.ID.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix date column\n",
    "sdf['DAY'] = pd.to_datetime(sdf['MM/DD/YYYY'], format='%Y-%m-%d')\n",
    "\n",
    "# drop columns\n",
    "sdf = sdf.loc[:,['DAY','ID','YEAR','MONTH','TMIN','TMAX']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert TMIN from \"tenths of degrees C\" to F\n",
    "sdf['TMIN_F'] = (sdf['TMIN'] /10 * 9/5) +32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop dates outside of min date (1939-03-01) for RIC station\n",
    "sdf = sdf.loc[sdf['DAY'] >= datetime(1939, 3, 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first-last df\n",
    "# max date in first half of year with frost (last frost date)\n",
    "fl = sdf.loc[(sdf['MONTH']<7) & (sdf['TMIN_F']<=32.0),:].groupby(['YEAR','ID']).agg({'DAY': 'max'}).reset_index()\n",
    "fl['LAST'] = pd.to_numeric(fl['DAY'].apply(lambda d: d.strftime('%j')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# min date in second half of year with frost\n",
    "fl['FIRST'] = pd.to_numeric(sdf.loc[(sdf['MONTH']>6) & (sdf['TMIN_F']<=32.0), :]\\\n",
    "                            .groupby(['YEAR','ID']).agg({'DAY': 'min'}).reset_index()['DAY']\\\n",
    "                            .apply(lambda d: d.strftime('%j')))\n",
    "fl = fl.dropna() # probably drops the current year, if it doesn't have a first fall frost date yet.\n",
    "fl['FIRST'] = fl['FIRST'].astype('int16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "from datetime import datetime\n",
    "\n",
    "# convert day of year to month-day string labels\n",
    "def j_to_md(x, pos):\n",
    "    return datetime.strptime(str(int(x)), '%j').strftime('%b %d')\n",
    "    \n",
    "jmd_formatter = FuncFormatter(j_to_md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f19da341940>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# with multiple stations, show the mean and confidence intervals\n",
    "sns.relplot(x=\"YEAR\",y=\"FIRST\", kind=\"line\", data=fl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.scatterplot(data=fl, x=\"YEAR\", y=\"LAST\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.scatterplot(data=fl, x=\"YEAR\", y=\"LAST\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average last frost date:\n",
      "1939: Apr 20\n",
      "2020: Apr 07\n",
      "Average first frost date:\n",
      "1939: Oct 18\n",
      "2020: Oct 31\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "# 2 plots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "# set figure size in inches\n",
    "fig.set_figheight(10)\n",
    "fig.set_figwidth(20)\n",
    "\n",
    "ax1.scatter(fl.YEAR, fl.LAST, s=5)\n",
    "\n",
    "z = np.polyfit(fl.YEAR, fl.LAST, 1)\n",
    "p = np.poly1d(z)\n",
    "ax1.plot(fl.YEAR, p(fl.YEAR), \"r\", linewidth=1)\n",
    "\n",
    "ax1.yaxis.set_major_locator(MultipleLocator(14))\n",
    "ax1.yaxis.set_major_formatter(jmd_formatter)\n",
    "ax1.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "ax1.set_title('Last spring frost date', fontsize=18)\n",
    "\n",
    "# first frost\n",
    "ax2.scatter(fl.YEAR, fl.FIRST, s=5)\n",
    "\n",
    "z2 = np.polyfit(fl.YEAR, fl.FIRST, 1)\n",
    "p2 = np.poly1d(z2)\n",
    "ax2.plot(fl.YEAR, p2(fl.YEAR), \"r\", linewidth=1)\n",
    "\n",
    "ax2.yaxis.set_major_locator(MultipleLocator(14))\n",
    "#ax2.yaxis.set_major_formatter(jmd_formatter)\n",
    "ax2.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "ax2.invert_yaxis()\n",
    "\n",
    "ax2.set_title('First fall frost date', fontsize=18)\n",
    "\n",
    "fig.suptitle('Central VA weather stations', fontsize=12)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('Average last frost date:')\n",
    "print('1939: {}'.format(jmd_formatter(p(1939),0)))\n",
    "print('2020: {}'.format(jmd_formatter(p(2020),0)))\n",
    "#print('2020 actual last frost date: {}'.format(jmd_formatter(fl.loc[fl['YEAR']==2020,'LAST'],0)))\n",
    "print('Average first frost date:')\n",
    "print('1939: {}'.format(jmd_formatter(p2(1939),0)))\n",
    "print('2020: {}'.format(jmd_formatter(p2(2020),0)))\n",
    "#print('2020 actual first frost date: {}'.format(jmd_formatter(fl.loc[fl['YEAR']==2020,'FIRST'],0)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# central va frosts\n",
    "fig.savefig(\"cva_frosts.png\", facecolor='white', bbox_inches='tight') # tight margins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot stations separately\n",
    "# 2 plots\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "# set figure size in inches\n",
    "fig.set_figheight(10)\n",
    "fig.set_figwidth(20)\n",
    "\n",
    "# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\n",
    "colormap = plt.cm.prism\n",
    "colors = [colormap(i) for i in np.linspace(0,1,len(station_id_list))]\n",
    "\n",
    "for i, s in enumerate(station_id_list):\n",
    "    df = fl.loc[fl.ID==s,:]\n",
    "    ax1.scatter(df.YEAR, df.LAST, s=5, color=colors[i])\n",
    "    z = np.polyfit(df.YEAR, df.LAST, 1)\n",
    "    p = np.poly1d(z)\n",
    "    ax1.plot(df.YEAR, p(df.YEAR), \"r\", linewidth=1)\n",
    "    \n",
    "for i,j in enumerate(ax1.lines):\n",
    "    j.set_color(colors[i])\n",
    "\n",
    "ax1.legend(station_id_list, loc='upper left')\n",
    "ax1.yaxis.set_major_locator(MultipleLocator(14))\n",
    "ax1.yaxis.set_major_formatter(jmd_formatter)\n",
    "ax1.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "ax1.set_title('Last spring frost date', fontsize=18)\n",
    "\n",
    "# first frost\n",
    "ax2.scatter(fl.YEAR, fl.FIRST, s=5)\n",
    "\n",
    "z2 = np.polyfit(fl.YEAR, fl.FIRST, 1)\n",
    "p2 = np.poly1d(z2)\n",
    "ax2.plot(fl.YEAR, p2(fl.YEAR), \"r\", linewidth=1)\n",
    "\n",
    "ax2.yaxis.set_major_locator(MultipleLocator(14))\n",
    "#ax2.yaxis.set_major_formatter(jmd_formatter)\n",
    "ax2.yaxis.set_minor_locator(MultipleLocator(1))\n",
    "ax2.invert_yaxis()\n",
    "\n",
    "ax2.set_title('First fall frost date', fontsize=18)\n",
    "\n",
    "fig.suptitle('Central VA weather stations', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>ID</th>\n",
       "      <th>DAY</th>\n",
       "      <th>LAST</th>\n",
       "      <th>FIRST</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1939</td>\n",
       "      <td>USC00444101</td>\n",
       "      <td>1939-04-14</td>\n",
       "      <td>104</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1939</td>\n",
       "      <td>USC00445050</td>\n",
       "      <td>1939-05-04</td>\n",
       "      <td>124</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1939</td>\n",
       "      <td>USC00448829</td>\n",
       "      <td>1939-04-14</td>\n",
       "      <td>104</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1939</td>\n",
       "      <td>USW00013740</td>\n",
       "      <td>1939-04-14</td>\n",
       "      <td>104</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1940</td>\n",
       "      <td>USC00444101</td>\n",
       "      <td>1940-04-14</td>\n",
       "      <td>105</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR           ID        DAY  LAST  FIRST\n",
       "0  1939  USC00444101 1939-04-14   104    289\n",
       "1  1939  USC00445050 1939-05-04   124    288\n",
       "2  1939  USC00448829 1939-04-14   104    288\n",
       "3  1939  USW00013740 1939-04-14   104    288\n",
       "4  1940  USC00444101 1940-04-14   105    295"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fl[['YEAR']]\n",
    "y = fl['FIRST']\n",
    "\n",
    "X_train = X[:-10]\n",
    "X_test = X[-10:]\n",
    "y_train = y[:-10]\n",
    "y_test = y[-10:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# without test/train split\n",
    "X_train = X\n",
    "X_test = X\n",
    "y_train = y\n",
    "y_test = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [0.15741462]\n",
      "Mean squared error: 168.25\n",
      "Coefficient of determination: 0.07\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X_train, y_train)\n",
    "y_pred = regr.predict(X_test)\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print('Mean squared error: %.2f'\n",
    "      % mean_squared_error(y_test, y_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination: %.2f'\n",
    "      % r2_score(y_test, y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_pred, color='blue', linewidth=2)\n",
    "plt.gca().yaxis.set_major_formatter(jmd_formatter)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='FIRST'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals vs fitted plot\n",
    "sns.residplot(x=y_pred.reshape(-1), y='FIRST', data=fl,lowess=True,\n",
    "                                  line_kws={'color': 'red', 'lw': 1, 'alpha': 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# QQ plot\n",
    "import scipy.stats as stats\n",
    "\n",
    "stats.probplot(fl['FIRST']-y_pred.reshape(-1), dist=\"norm\", plot=plt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f19d2b85370>]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EUVWVm5hFelgmCaLQzKqA9wE3ZDkekfxVVhamar3iiv3Xn3BCaAz/3vdCY/nf/hambt2wAY46Co48EmbO1JwZ0udkkiBuAhYCj7n7M2Y2FViT3bBE+pDUoc/bHHMM1NWFaqqvfQ2efTYkjj//OUywNGdOSCxq15A8pruYRHrLyy+Hnt/LloXHj38cbrG99tqQMN70pjC67YQJuY5U+omstUGY2Q+AdrOHu1/d1ZOKDEjTpu0/FDpAbW1o61i+HP7yl3B7bVVV6KMxY8a+pHHKKbmJWQa0jqqY9JNdJNsqKuCCC8KjTTIJv/hF6KexbFloFD/lFDjrrJBQZs8OVVhXXQVNTaHdQ/NpSBa0myDc/fbeDEREIrFYaNw+6qj91999N6xcGUobr78e1n3pS6GqatasMOLtN74Rxq6qqwvDkShxSDdk0g9iFHA9MAvY273U3c/Ibmj7qA1CpAM7doTEsWIFfOQjYSKmyy+HhoZw99S3vhWGU1+4MCSSSZM0vMgA0Rv9IH4L/B54B3AlcDmhs5yI5IORI0M7xamnhuWzz4bqati5E1atCu0etbXwwx+G5e3b4fOfhy9+EW67LVRzzZwZhlMvK8vt3yJ5JZMEMcLdf25mn3b3RcAiM1uU7cBEpJuGDw9zarRZsCA819fvmz9j585wZ9WqVSFRPP443HprGNBwxgw4/PDQ/lFU1PvxS85lkiBao+dqM3sHsBkYn72QRCSrhgwJDwij27Zpq24+5piQQJYuDWNTPfAA/P73oapqxoxQ0vjQh8JMf42N+44l/U4mCeIrZlYBXAP8ACgH/v1gO5nZL4DzgW3uPjta9yXg39hXRfUf7n5fF+IWkZ7W1qB94onhkertbw9tF6tXw0svQUtLGBl39uxQUpk+Pdyue+ml8PDDcMghMHUqFBf39l8hPShrHeXM7FSgHvj1AQmi3t1v7syx1EgtkqeSyTB3+OrVYfyqOXPgfe8LPcc3bAgz+917byiJbN0Khx0WHoceqqFHekHWG6nN7Jek6TDn7lek2Tz1/UfMbHJXAxORPiAWC1VNEyfuW3fXXeG5tXXf7H6lpaEn+YIF8Mor4VbdP/4R5s8PyeLQQ0NiqaoKpRM1lueFTKqY/pbyuhS4kNAO0VVXmdmHCB3xrnH3Xek2MrN5wDyAian/+USkbygqCqUKgAsvDI9Up54KgwfD2rUheTQ0hNdHHhnuzJo2DT7xCbjkErjvvjD/+NSpoUpL/Tt6RaermMwsBvwzk34QUQnibylVTGOAHYQSyf8Dqg5WEgFVMYkMKIlEqLZ6+WUYMyZ0ALz88lDqePnlMAzJ/feHDoLr14ekMXVqGIq9ULMop+rVGeUihwFd+knv7lvbXpvZT9m/dCIiEjrxTZoUHm1ujwZ2cN93i+7hh4cqrMWLw7SxZ5wBP/sZfOUrMGVKeFx3XXhesiQ8H3KI2j46IZM2iDrCL36LnrcQelZ3mplVuXt1tHghsLwrxxGRAcps3xDpp58eHqkuvzwkinXrQlvHkCGhcfyaa8K6urowPMn118ONN4ZjTZkSbt898she/3Py3UEThLsP7cqBzewO4K3ASDPbCNwIvNXMjiIkmvXAx7tybBGRtIqK9lU5pXryyfBcXx8azwHGjw+37D7zDAwbBj/9aWjzePjhfXOXf/vbUFMDr70WGuIHWAmko+G+j+loR3dfepD3L02z+ucZxiUi0vNSO/X927+98f1vfhNefTW0baxfH+6+euEF+K//Cutra8OkTyecAJ/97L6qsLlzQ5+QRKJfjXPVUQni29FzKTAXeJ5QzTQHeArQAPUi0r8MHRq+6GfP3rfunHPCA0L7RywWbsU96aSQNP73f6G5OewzZ04YvmTSpNCY/v3vh+FLtm7ddzvwyJF95i6sjob7Ph3AzO4E5rn7C9HybODa3glPRCSPtPXPKClJXwJZtgy2bAmJY8+esG7t2tCI/tpr4bFiRRj76itfCbMHTpgQbuWdNSuMyjt+fBgXKw9kMtz3c+5+1MHWZZNucxWRfmXnznBn1YYNIWm8612hs+AJJ4RbfM3C0CU33xzGwGpoCIlj0qQwcVQ8HqqyDlIS6Y3bXFeZ2c+A3xAaly8DVnX1hCIiA97w4eGL/kCrVoVbeXfvDtVWEJLC8uWhqmrRorDfTTfBd74Tksb48fDzn4c+IH/+c1geNy4knG7KpARRCnwCiAab5xHgx+7e1O2zZ0glCBGRA+zeHUobGzaEIdk3bQp3XW3aFNZ/+tPYFVd0qwSRtcH6epIShIhI52WtisnM7nL395nZC6QfrG9OV08qIiL5r6M2iE9Hz+f3RiAiIpJf2u0SmDIkxg5gg7u/CpQAR9K90VxFRKQPyKTP+CNAqZmNAx4EPgL8KptBiYhI7mWSIMzd9wAXAT9w9wuBWdkNS0REci2jBGFmJwEfBP4erdOg6yIi/VwmCeIzwBeAe919hZlNBR7KalQiIpJzmQz3vQhYZGaDo+V1wNXZDkxERHLroCUIMzvJzFYSDa9hZkea2a1Zj0xERHIqkyqm7wHnAK8DuPvz7Bt2Q0RE+qmMpkZy9w0HrEpkIRYREckjmdyNtMHM3gy4mRUT2h80mquISD+XSQniSuBTwDhgI3AU8MksxiQiInkgk7uYdhD6QABgZsMICeK/sxiXiIjkWLslCDObYGbzzexvZvZRMxtkZjcDLwGjey9EERHJhY5KEL8GFgH3AOcCTwIrgDnuvqUXYhMRkRzqKEEMd/cvRa8XmtlW4Dh3b85+WCIikmsdtkFE7Q1ts2JvAQal9KjemeXYREQkhzpKEBXAEvYlCICl0bMDU7MVlIiI5F67CcLdJ/diHCIikmcy6kktIiIDjxKEiIikpQQhIiJptdsGYWbDO9pRdzGJiPRvHd3FtIRwt5IBE4Fd0etK4DVgSraDExGR3Gm3isndp7j7VGAh8E53H+nuI4DzgT8e7MBm9gsz22Zmy1PWDTezB8xsTfQ8rCf+CBER6XmZtEEc5+73tS24+z+A0zLY71eEITpSfR540N0PAx6MlkVEJA9lkiB2mNl/mtlkM5tkZjcQzS7XEXd/BDiwneLdwO3R69uBCzoTrIiI9J5MEsSlwCjg3ugxKlrXFWPcvRogem53VFgzm2dmi81s8fbt27t4OhER6apM5oPYCXzazIa4e30vxNR23vnAfIC5c+d6b51XRESCg5YgzOzNZrYSWBktH2lmt3bxfFvNrCo6ThWwrYvHERGRLMukium7wDlE7Q7u/jxwahfP9xfg8uj15cCfu3gcERHJsox6Urv7hgNWJQ62j5ndATwBzDCzjWb2UeDrwFlmtgY4K1oWEZE8dNA2CGCDmb0ZcDMrBq4GVh1sJ3dvryH7zE7EJyIiOZJJCeJK4FPAOGAjcFS0LCIi/VgmJYgyd/9g6gozOyRL8YiISJ7IpATxipndYWZlKevua3drERHpFzJJEC8AjwKPmdm0aJ11sL2IiPQDmVQxubvfambPA381s+sJo7yKiEg/lkmCMAB3/5eZnQn8Hjg8q1GJiEjOZZIg3t72wt2rzewM4M3ZC0lERPJBRzPKXebuvwEuNUvb5PBI1qISEZGc66gEMTh6HtobgYiISH5pN0G4+23R85d7LxwREckXHVUxfb+jHd396p4PR0RE8kVH/SCWRI9S4BhgTfQ4igwG6xMRkb6toyqm2wHM7MPA6e7eGi3/BLi/V6ITEZGcyaQn9Vj2b6geEq0TEZF+LJN+EF8HnjWzh6Ll04AvZS0iERHJCx0mCDOLAS8BJ0QPgM+7+5ZsByYiIrnVYYJw96SZfdvdT0LTg4qIDCiZtEHcb2bvsXa6U4uISP+USRvEZwm9quNm1kQYvM/dvTyrkYmISE4dNEG4u4baEBEZgDIpQWBmw4DDCJ3mAHB3DdYnItKPHTRBmNnHgE8D44HngBOBJ4AzshqZiIjkVCaN1J8GjgNedffTgaOB7VmNSkREci6TBNHk7k0AZlbi7i8CM7IbloiI5FombRAbzawS+BPwgJntAjZnMygREcm9TO5iujB6+aVouI0KYEFWoxIRkZzraD6I4WlWvxA9DwF2ZiUiERHJCx2VIJYATugYNxHYFb2uBF4DpmQ7OBERyZ12G6ndfYq7TwUWAu9095HuPgI4H/hjbwUoIiK5kcldTMe5+31tC+7+D8KQ3yIi0o9lchfTDjP7T+A3hCqny4DXsxqViIjkXCYJ4lLgRuDeaPmRaF2Xmdl6oI4wt3Xc3ed253giItLzMrnNdSehN3VPO93dd2ThuCIi0gMyGYtpOnAtMDl1e3fXWEwiIv1YJlVMdwM/AX5GqBLqCU6YiMiB29x9/oEbmNk8YB7AxIkTe+i0IiKSqUwSRNzdf9zD5z3Z3Teb2WjC8B0vHjh8eJQ05gPMnTvXe/j8IiJyEJnc5vpXM/ukmVWZ2fC2R3dO6u6bo+dthMbv47tzPBER6XmZlCAuj56vS1nnwNSunNDMBgMxd6+LXp8N3NSVY4mISPZkchdTTw+pMQa418zazv87d9fgfyIieSbTKUdnA7PYf8rRX3flhO6+DjiyK/uKiEjvyeQ21xuBtxISxH3AecBjQJcShIiI9A2ZNFJfDJwJbHH3jxB+/ZdkNSoREcm5TBJEo7sngbiZlQPb6GIDtYiI9B2ZtEEsjqYc/Slhjoh64OlsBiUiIrmXyV1Mn4xe/sTMFgDl7r4su2GJiEiuHbSKycwebHvt7uvdfVnqOhER6Z86mpO6FBgEjDSzYYTpRgHKgbG9EJuIyICyqrqWBcu3sqmmkXGVZZw7ewwzqypyFk9HVUwfBz5DSAZL2JcgdgM/ym5YIiIDy6rqWuY/8goVZUVUVZRS29jK/EdeYd6pU9pNEtlOKB3NSX1L1Iv6WnefGs1RPcXdj3T3H/ZYBCIiwoLlW0kkkqys3s2Dq7axsno3iUSSBcu3pt2+LaHUNrbul1BWVdf2WEztJggzO87MDnH3H0TLHzKzP5vZ97s7WJ+IiOxvxeZaVm+tp7k1wZCSAppbE6zeWs+Kzem/8Bcs30pFWREVZUXEzPa+bi+hdEVHVUy3AW8DMLNTga8D/xc4ijAM98U9FoWIyAC3uykOBqVFBUB4bo4nw/o0NtU0UhiDldW7qW+KE4a3c+qbw7Q9584e0+2YOkoQBdF0owDvB+a7+z3APWb2XLfPLCIie1WUFVK7p4Wm1gQlhTGa40ncnYqy9F/TxQXGU+t2MqS0EMPZVNNES0uCwaWF/H1ZNQuXbyFWOrSyOzF1mCDMrNDd44ShNuZluJ+IiHTSrKoKBhUVsGV3M7ubWikvLWLyiEFMHjkk7fZGmHdhT0uc6tommloSJIHCeJLhg4vY3RSnYMiwbt1x2lE/iDuARWb2Z6AReBTAzA4Feq4VREREOHf2GOqbEzS1JnB3mloT1Dcn3lBVtKq6lu8+sJrHXn4dPMn2uhZa4knMoDBmNLUmaGxNUF5aCFHFU1e1WxJw9/+OOsRVAfe7e9u0nzFCW4SIiPSgZPQ1a1GvgqTvP9ty6q2wY4aW8PL2BgAqBxWxuzFOzMDM2NnQwsghJbgnWrsTT4dVRe7+ZJp1q7tzQhGR/mJVdS3/88SrPLuhBsM4ekIFl500qUv9FhYs38qkEYOZM75y7/a1ja0sWL51v23a7lY6dPQQXtpaR4EZyaTjQDzpVJQVsKclQXM8CYnuJYhMRnMVEZEDrKqu5ZsLXuKRl7azs76Z6tpG/v7CFm7884q0fREO1m9hU00jQ0v3/80+tLSQTTWNe5dTtxk1tJQJwwZRWGC0Jp3xlaUMKi6kOe6UFsWYMWYIcEARpJPU2Cwi0kmrqmu57u5lvLRlNwkPdxQNLS3E3Vi9rZ7v/3MN0w8p36+kkPrrH9j73FZCGFdZRm1j6971AHVNccZVlu1dTt1me10TDjS2JCgrLuD4qcNpaAp9JyaMKGPyyCEkGnZ1q1OEEoSISCe0lRxefX0PSQ93E7UmnN1NccpLi4i3Jli0Zjuba5toiSdZs7WOZRtrKCwwDj+kfL9jpZYQzp09hvmPvLJ3fV1TnNrGVt5/3Pi9591e18Rja1/HPfSP8Oj8ZUUF/GvtTt5y6Aj+75nTWL21gU01jVm9zVVERA6wYPlWdja0MKi4gMbWOHhoGE4mYU9LgngiiWPsbGhmR10zzYlQyzOkuIDWeJLG1uTe21gPKS/ZexvrzKoK3jZzFLc+vI6Nu/ZQVBDj+MnDgH3VU4lEkkJz1u8MpYfKskLKBxVT35xgUHEBz22sZcvuZuKJBGu2NRArK+9WbzklCBGRTthU00hLPMnIIcW83tBM3Nlb1d/akoi2cjbuaopeBbubEzz+8uuMG1bGyMHF1Da2srmmkbOPCN/hq6pr+cOSTTS1JJg8fBAOvLilnpsXruaQ8hISiSSrt9XT0JKkuDAGOA0tcRyjMGbEE0k27WpkW20je1qTlBUVgCcTdIMShIhIJ4yrLGPZhhq21DWRSLa/XVtisJTlZNJpiSdpaElSXlbE9NFDWL21gXewr2QypLSQRNLZtaeFPS0Jal5tJolREAv9HFoSTmEBJN1obXVaEwkGFRezq6GZ1mS4kwmIkkj3KEGIiHTC9DGDufPpFhpaEsRidJgkYP9EkXAoLyti0vAynt9Uy0tbdvP4uteZPmYwK6trWbO1jj2t+w5YVgjxZEgxZcUFFJjR0BwnnnDatmpoSdLQ0rT3HG127WnFYgXd+o5XghAR6YTVWxs4ceoIFq3eQV1T5t0M2hLFquo6VlXX7V1fHIOb/rqSuqZW9rTuf1dqYxwKzSkvLaQxnsSAlkT7d66+4R2zbhUj1A9CRKQTNtU0MnHEYKYfMpSRQ0q6fbyWJLxe3/KG5NAm7jB2WBmVZYW0HKy40sOUIEREOmFcZRl1TXEOHTWYsqJYj1TDxA/SnW3l5jpq9sRpiStBiIjkrXNnj2Hl5loWvFDNxppGunWbUIaSQGM8SQe1S1mhBCEi0gnrttezdlsdDS0JCsz69Zdof/7bRER63O1PvBZ6UJuTcO+VEkSuKEGIiHTCxp17qG9sZfSOLSR6u84nA+ZJ3lS9hn9/9DfdPlZObnM1s3OBW4AC4Gfu/vVcxCEi0mkGg5oa+OI/b6MomeC5quk8O3YGy6qms3NQ+mG+e8PIhl2cufZp3rb2KZoLS7j/sBNg3eJuHbPXE4SZFQA/As4CNgLPmNlf3H1lb8ciItJZZQXG5tIhfOw9X6SqbgdHVq/mxNdeYN7T97KzrJznxk7nubEzWD5mGk1FpdmNpaWJk15bxmmvLOGwHRt4dMrRfOO0D7N2xIQwmdzTf+rW8XNRgjgeWOvu6wDM7E7g3YAShIjkvbrmqNXBjOryUVSXj2LBjJMxT3Lojg0cVf0SFy1/iM8tup2Xh4/n+aqQMFaPnEgyVtDt8xfHWzl200pOW7eEo6tX88KYafzz0BP46ukfpbmw+ICt+958EOOADSnLG4ETDtzIzOYB8wAmTpzYO5GJiHRgVXUtO/ek7z3tFmPNqEmsGTWJu+ecTUlrM0dsW8dRm1fziSf/wJj613nhkENZMm4mS8bNZPuQ4RmftyjRypuq13LaK0s5fuNyXh4+nkemHMMP33wJ9SWDeurPe4NcJIh0k2i/Icu5+3xgPsDcuXPzryVIRAacr/x15d7B8A6muaiEpeNmsnTcTAAqGus4evNLHL35RT747D9oKipm1agpbC4fxbYhw9kxuJKWgiKSZpQ3NzCioZaJNVuYsmsT03ZuYn1lFf+afBS/OvZ8dmXQ1mGA493qWZeLBLERmJCyPB7YnIM4REQ6ZemGGmJAV751a8uG8vC0uTw8bS64M373Ng7ftp5DonaMUQ27KE7EMU+yu3QIu8rK2VAxhmcmHMHaERM6XVIoiBnJPbv73IxyzwCHmdkUYBNwCfCBHMQhItIp8aRTGAvjJ3WLGRsrxrCxolvz+XQokXSSTXU13TlGr/eDcPc4cBWwEFgF3OXuK3o7DhGRzhpWVkQ8mf6Ls8DC8NwQqncK0lWm9yIHCoaMGNudY+Sko5y73+fu0919mrv/dy5iEBHprI++ZTLWzhd/zPbNDeHQ6+MmpRMrLis/+FYd7N9TgYiI9HcfP+0wTpo2nKKUyvmiGBQXQGuyB6qeepzmgxAR6TU3vGMWU0YM5fBDhjJ1RBnFhTE87c2ZPa8wZhR35lu7m2EpQYiIdMLMqgomjCijwGDHnlZKCmIMLu5+B7iOGOHLOpH0Xi2lKEGIiHTSrKoKCgtijBlaQnPC2d0Yz+r5nHBrbW83ayhBiIh00rmzx7Ctrpn1OxpoaEl0qV9Er/DuDbWhBCEi0gUlhUYvzwDarpjtq4ZqUxAz3L1b01XkZLhvEZG+bMHyrcwZV8mrrzemfd+AScPLmDW2nPuWd6szc0YKzagaVkLloBKa40mOnVRJUUEBX/nh7m3dOa5KECIinbSpppFJIwdTXBhuE0q9WciA0qIYP/4/x3LrZXO54uSJWb/H6W0zR+PR1/nREysoKiigtrG17/WkFhHp68ZVllHXFGfk4GKKYqHXdAwoNBhUHOPI8RXMrAoD6n3xnW/iIydPZEjJG+90KoyFhNKdBGLAkRMruO3/HMNbZ4ymNQEVZUXMO3UK3tqcvoiT6bG72YbRK+bOneuLF3dvZiQRkZ6yqrqW+Y+8wrrt9exsaGZnQwstCWdYWRGzqoYydtggRg0tZVNNI8UFhgHNCWdcZRmDio35i9bx+p44sSixxKOv4bJCSLgRT3hGDd8GlBXHqBxUzOOfP/ON75stcfe5Xf07VYIQEemkmVUVzDt1CkeMLccsxqGjh3LxseN4+5yxWCzG1t3N1Da2UhiDp9bt5Il1OykqgNrGVl7c0sBNF8zm5KnDKSmMYTFjWFkhR40vZ0xFGTGDwgKjMGaUFRoFFkomJYX7lzMMqBxUREVpIbWN6eeo6C41UouIdMHMqgq+etEcVlXXsmD5VjbVNDJ6aBFjK0opKiygoqyIldW7GVIavmbXbd/DiVNHALB6awO/nXfSfsdrO85fnt9EQ3OclniSksJY6AORhJKiGNt2N5F0KC0qIGYwqLiAhuYEFWVFWfkblSBERLphZtW+9gaAa+9+nuFDwldrfVN8b9vD7qbwK39oaSGbavZvGmirsqooK6K0sIChJYXsbmzFzCgsMHY2tLCroYWYhTGf9rQkqCgNyaElkeATJ03Jyt+mKiYRkR7U1oANMKS0kOZ4kuZ4kvLS8Cu/rinOuMqy/fZZsHwrFWVFVJQVMbSsCDOjYlAxg0sKGTa4hAIzHBhdXsq48mJKi2LUtyQpLjA+feahfPy0w7Lyt6gEISLSg86dPYb5j7wCwNSRg3jmlV04MGvsUGobW6ltbOX9x43fb59NNY1UVZQCcOiowSx9rYaSAiOeTDKrqpzde1o5adoIJo8csnef2sZWKsqKspYcQCUIEZEe1daAXRFNLnTC1OGcNHX4frefplZJwf6ljlFDSzlmYiWYEbMYFWVFTBhRxsQRg/fbJ11VVU9TCUJEpIcd2C5xMKmljqGlhRQXFjB11JC9yeS7D6zeW2Jok66qqqepBCEikmOppY7q2qY3lDTOnT1mb/VU0n3v63NnZ29Oa1AJQkQkL3RU6mhLIG23046rLOP9x43vVCmlK5QgRET6gM5WW/UEVTGJiEhaShAiIpKWEoSIiKSlBCEiImkpQYiISFp9Yj4IM9sOvNqNQ4wEdvRQOD1NsXVNvsaWr3GBYuuqvhzbJHcf1dWD94kE0V1mtrg7k2Zkk2LrmnyNLV/jAsXWVQM5NlUxiYhIWkoQIiKS1kBJEPNzHUAHFFvX5Gts+RoXKLauGrCxDYg2CBER6byBUoIQEZFOUoIQEZH03D3vH8C5wEvAWuDzad434PvR+8uAYw62L/BeYAWQBOYecLwvRNu/BJyTL7EBZwFLgBei5zPyJbaU9ycC9cC1+RQbMAd4Inr/BaA013EBRcDtUTyrgC/k4DP7FvBitP29QGUeXQdpYyM/roN2P7c8uA46+jfN6DrYu31Hb+bDAygAXgamAsXA88CsA7Z5O/CP6MM8EXjqYPsCM4EZwMPsf9HOirYrAaZE+xfkSWxHA2Oj17OBTfnyuaUc8x7g7o4ujBx8boWEi+XIaHlEun/THMT1AeDO6PUgYD0wuZc/s7OBwuj1N4Bv5NF10F5s+XAdpI0tT66D9j63jK6D1EdfqGI6Hljr7uvcvQW4E3j3Adu8G/i1B08ClWZW1dG+7r7K3V9Kc753Ey7aZnd/hZCdj8+H2Nz9WXffHC2uAErNrCQfYgMwswuAdVFsHent2M4Glrn789F2r7t7Ig/icmCwmRUCZUALsLuXP7P73T0e7f8kMD7lWLm+DtLGlifXQXufWz5cB+3Flul1sFdfSBDjgA0pyxujdZlsk8m+XTlfrmJL9R7gWXdvzofYzGwwcD3w5YNG3vuf23TAzWyhmS01s8/lSVx/ABqAauA14GZ335nD2K4g/FrN9Hy5ii1VPlwHe2PLw+sg9XPL9DrYqy/MKGdp1nmG22Syb1fO15ltezK2cECzIwhFx7M72qyXY/sy8F13rzdLt3tOYysETgGOA/YAD5rZEnd/MMdxHQ8kgLHAMOBRM/unu69Ls21WYzOzG4A48NtOnC9XsbWtz/l1kCa2vLkO0sSW6XWwV19IEBuBCSnL44HNGW5TnMG+XTlfrmLDzMYTGp4+5O4vd7Bpb8d2AnCxmX0TqASSZtbk7j/Mg9g2AovcfQeAmd0HHAMceGH0dlwfABa4eyuwzcz+BcwlVE+k+xuyEpuZXQ6cD5zp7m1fMnlxHbQTW15cB+3ElhfXQQf/pplcB/t4Bw0U+fAgJLF1hIaytsaYIw7Y5h3s35DzdCf2fZj9Gw6PYP/GuXW03zjX27FVRtu9J98+twPe+xIdN8719uc2DFhKaAguBP4JvCMP4roe+GV0rMHASmBOb35mhDthVgKjDjhWzq+DDmKrJMfXQXux5cN10MHnltF1sN8+B/uA8+FBaMlfTWi1vyFadyVwZfTagB9F77/A/hfhG/aN1l9IyKjNwFZgYcp7N0TbvwScly+xAf9JqLN+LuUxOh9i68yFkaN/08sIjYbLgW/mQ1zAEMKdLisIF/R1OfjM1hLqstv+P/0kj66DtLGRH9dBu59bHlwHHf2bZnQdtD001IaIiKTVF+5iEhGRHFCCEBGRtJQgREQkLSUIERFJSwlCRETSUoKQAcHM6nMdg0hfowQh0oOigfdE+gUlCBmwzGyamS0wsyVm9qiZHW5mFWa23sxi0TaDzGyDmRWl2z7a5ldm9h0zewj4hpkdb2aPm9mz0fOMlGPdZWbLzOz3ZvaUmc2N3jvbzJ6IBlG728yG5OyDEYno144MZPMJPVbXmNkJwK3ufoaZPQ+cBjwEvJPQ87nVzN6wPXBGdKzpwNvcPWFm5cCp7h43s7cBXyWMOvpJYJe7zzGz2YRerpjZSELv4Le5e4OZXQ98Fripdz4GkfSUIGRAin6hvxm4O2XUzbY5BX4PvJ+QIC4Bbj3I9gB3+76x9SuA283sMMIIm0XR+lOAWwDcfbmZLYvWn0iYoOdf0bGLCbN+ieSUEoQMVDGgxt2PSvPeX4Cvmdlw4FjgfwmD6bW3PYSxgdr8P+Ahd7/QzCYTBumD9EM0t61/wN0v7cwfIJJtaoOQAcnddwOvmNl7ASw4MnqvHnia8Gv/b+6e6Gj7NCqATdHrD6esfwx4X7T/LOBN0fongZPN7NDovUFmNr1n/lKRrlOCkIFikJltTHl8Fvgg8NGozWEF+0/3+HvCyJe/T1nX0fapvkkogfyLMHdwm1uBUVHV0vWE+YFr3X07IZHcEb33JHB49/5cke7TaK4ivcTMCoAid28ys2mEiVqme5hTWCTvqA1CpPcMAh4ysyJCu8MnlBwkn6kEISIiaakNQkRE0lKCEBGRtJQgREQkLSUIERFJSwlCRETS+v/Amyr7OEwmUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "model = sm.OLS(fl['FIRST'], fl['YEAR'])\n",
    "model_fit = model.fit()\n",
    "\n",
    "# model values\n",
    "model_fitted_y = model_fit.fittedvalues\n",
    "# model residuals\n",
    "model_residuals = model_fit.resid\n",
    "# normalized residuals\n",
    "model_norm_residuals = model_fit.get_influence().resid_studentized_internal\n",
    "# absolute squared normalized residuals\n",
    "model_norm_residuals_abs_sqrt = np.sqrt(np.abs(model_norm_residuals))\n",
    "# absolute residuals\n",
    "model_abs_resid = np.abs(model_residuals)\n",
    "# leverage, from statsmodels internals\n",
    "model_leverage = model_fit.get_influence().hat_matrix_diag\n",
    "# cook's distance, from statsmodels internals\n",
    "model_cooks = model_fit.get_influence().cooks_distance[0]\n",
    "\n",
    "plot_lm_4 = plt.figure();\n",
    "plt.scatter(model_leverage, model_norm_residuals, alpha=0.5);\n",
    "sns.regplot(x=model_leverage, y=model_norm_residuals,\n",
    "          scatter=False,\n",
    "          ci=False,\n",
    "          lowess=True,\n",
    "          line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8});\n",
    "plot_lm_4.axes[0].set_title('Residuals vs Leverage')\n",
    "plot_lm_4.axes[0].set_xlabel('Leverage')\n",
    "plot_lm_4.axes[0].set_ylabel('Standardized Residuals');\n",
    "\n",
    "# add cook's distance as a dotted line\n",
    "p = len(model_fit.params)\n",
    "x_range = np.linspace(0.001, max(model_leverage), 50)\n",
    "y_range = (lambda x: np.sqrt((0.5 * p * (1 - x)) / x))(x_range)\n",
    "plt.plot(x_range, y_range, label='Cooks distance', lw=1, ls='--', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 2.32795057e+04 -2.33738371e+01  5.94212749e-03]\n",
      "Mean squared error: 160.06\n",
      "Coefficient of determination: 0.12\n"
     ]
    },
    {
     "data": {
      "image/png": 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kzA6b/35pxb1vHuVpysGIhDk+8yGK2aHVSRJBl0bUUPlZaHSrQyLl4P7h8zy/T8NdQzrJBkwHrgIeAn4H9HmO9QK3AH8A7vceq7YVTTkkCbHr7++fVNHmzp0b2uZY2ZKKa59s9ArvR9h5ifKUqXI692pb2nbnRiRJBF3SqCHvs5B3Q+q++1STVN2kyuFFnHWjZ2u6ymEY+Jb7/WjgKs+x24FD3e8zga6gtIqkHJIOzgmzVXs5+LWkkiiIZjIbFXEVtWoyVTo7s3ZINiphn5Vq4w+STL1R+SzkMfZm82bVG25QXbjQeYPPmKH61FPx0kqqHDYCZwAjWqEcgBLwc+Ae97MX2BoYBdrcc7qAR4HOinRvBg5wv3cATwEC9AO/qiWXdyuScgjbKikFDOuvtVUzK/i1pMwE4ZCVkzELmcx8V5s8eg5R0sqiDJ95RvUrX1HdeWfnzQ2qXV2qixerPvFEvDTTUA5buS/8rSuUw43A8e73DwLXu99/BLzV/X4U8J0q6d4LzPP8fhiYDbwb+DFwLbAKOBdor3L9ELACWNHb2xsvZzIgikMrSQumkjDnpmV2KiqV/2/u3LmR8rQok8fFdXZGIUm6eV3rJYzPwS/kNE6PPYr/Iu1e6AMPqJ58smp39xal0Nenet55qhs2JEs7sXJwP/8N+EyFcngKt0cAdAJPud+PYYvJ6DpcE1FFuvdVUQ49wCLgWWBnnB7FNcCHgmS0nkPtnkPaZqeiEXZu/FplUk9qtTqLOD9SXtdWIyhaqdZyvLX8glGiiLJQ4OPjqjfdpPr2t29RCKB6yCGq11/vmJbSIC3lMAun9/CvAcphvft9JjDmXrOO6i1/P7PSG4HbPee9H/h6kIxFUg5F9Tk0u9kp7gyXab50o1LrZZmVySJJunldG5VGNdk984zq176muttuWxTCjBmqJ56oes896d8vFeXgfj/HfdmXlcMNwPvd7ycA13nO/SFwBfANn3RPZrJD+gfu93ZgDbCd+/sy4OQgGYukHFTTjVbaZpttJv0Oil4KMhsFvRiryZHmizJJHHhYOaIogqJGK6Xt7KxM2+vcDsqboPwIMwbGbw2CoGvTLoegeyW9TxZjFVasUP3whx0fQlkp7Lij6he/uMXZnIVZOE3l8Dpgk0c59AG34XFIe85d5BbGwT7pznAVyEPAXcDOnmOHummuBb4LTAuSsWjKIS3C9izCVJKgnkOWdtMoaSeRI0rPoVFI0vpNGsvvl+9+KwiGqaNhzKhp1bta94p7nzRHOW/cqPqd76guWDDZdPTWt6r+8Ieqr7yy5dyszMKJlEMjbFkohyKEcob1SYQxDQVVriy74FHSTiJHWJ/DwMBA4v9UL5Ioy7j+rFr5Hlc5+DVCsqp3Ye4V5z5x8rXciym39teuVT3lFNWtttqiELbdVvUTn3Ccz9XIyixsyiEiRRkEFiWaKQx+3dIsY7WjpJ1UjlrRSo2kGMrEbaRktYxm0gg77/+Jct8keZfmfeL//+kKxyj88lWFAKpvepPq5ZerbtoUfN+kz35AuqYcolAUZ1aUVkoW92mknoMxmSi9zjTKKMx9wqaVdnmneZ/o/39PhfMV1nuUwrM6PKy6Zk34+5pyKIhyKMqKY1Hsxmnfp9F8DsZkovirwuS7txUep/VczTYe5b5JggjSrFfh8nVbhWGFFR6FoAorFT6s0B3pfrUUUhJMOUSkSC1Y78PhZ3dMy0ablY+lHtFKxlSCopUqo12C8r3aC7GsIKql6xetFEbGWveN+4JPs15Vi1bq7d1J4TCF7ym84FEIGxQuUtjvVbnD+gmy8pt4MeUQkaK2YIsqVzOQ5cuj0csnr8ZSGBNOWjLELbM//Ul1yRLVefO8PYRxhf9QOEodX0PtXlQ1av3/NJ59Uw4xKOoDXlS5GpmszQ6NrsDzMrOGMVelIUPUMnvmGdVLL1U9+GCvQnDmPPrsZ1WPPfbMSb38ymilsGQ5VqOMKQfDCKAeDstGdqrn9Z/CjF2pV9DEiy+qXned6qJFqtOnb1EIr3mN6nHHqd5+uzPlRZrUI9+DlEMbRtMyPDz86uL0HR0dDA8P5y1SYrwLv/f19bF8+fLEafot/D42NoaIICIsXLgwUVp++7P4P2kzMjJCV1fXpH1dXV2MjIxket/x8fHA40lk8Ob72NhY1XPGxh7ljjtgaAjmzIEjj4Srr4aXX1amT/8NcCKzZ+/BYYct5+CDoS3lt2le+f4qflqjkTbrOUylGSfay8pkE3ZgV5hxElFae41kgsrDnBlkc08iQ21H7x4KX9C2tkcnmY3mz1c9+uiVOmPGrnUrs6zzHTMrtR7NONFeVt3sKKN+axHlhd+MJqg0yUp5Vs/3nRQ+rbB6kkLo7VU9/XTVtWuDrm3cMjPl0IIkecGppjvJV1qtn6wco2mPRA/7f+s5EV1cGeudVlZpVx+Z/b8UTldn/IF6tqcVvqlwoELbpPEVQeXViJhyaEGS9BzSNEml2frza+H39PRETstLmJDJKMohzftmYbJoteisyTLuqnCmwqoKhfCswjKFIxQ6Q9cH6zkUfDPlMJUkL/igKJGoLbha3fAoLcOslEPY0cRpz81Uz4novMQxjfiVU9pmlmoDzJL2HObOPUTh/yqsqVAIzyhc7iqEqeMRwm5FU4ZRMOXQosQ1DaX5QASZgqK2OrOMt698KVWuq5HVpH31nIiuTFC5+snoV05plkla02GPj6v+7nfO4LTdd/cqA1X4i8J3Fd6pMK2qEgqrFJphrJEpByMSSRZvrySoZRm11dlszsBK6vX/opoc0yzDIMKa96ql/cILqj/5ierQkOqcOZUKYYPCpQqHK0yL/X+bsc6Zcmgg8hoB7e1lRNlqTYiWZquzEezb1ag1f1D5WE9Pj3Z0dEz6f9OmTcsifNF3q0ZQa7rWWs1RCNtqL9eP9etVv/td1SOPVO3unqwQdtxR9eSTVW++WTXIh5D1nE5Fx5RDg5DXy8/PPxF37vqgmTWT2qsbbfqQoDINY0bp7OxM/T9Gzffu7u6aMqax5Gq4nsPrdZttPqcHHqja1jZZIey7r+pZZ6nefbfqxES0dLOYDbYRMOXQIORlNqllZoiz+lcYmRu1JxCFOCaZrMs/ar63tbXVRcbqynIrhSMVvq0wNkkZdHaqvu1tql//uuq6dVHTrf9zVkRSUQ7APOBHwJ+Ah4GvUmNtZ+BjQJfPseXAg8C9wKVAp7v/U8Bqd7sXGAdmBd2nHhPvpRE1UYswi7dnMcI56J5h5Kq2hXVINlpPoBpBdSUof6KaUZLiNR2KiM6cOTNUvtdTxssvX6Z/8zeHK5yp06f/VkU2T1IIW221SY89VvUHP1B99ln//9fe3q4DAwNVW/9Z/4dGIrFyAAS4C/iA+7sduAQ4t8Z1o8Bsn2OHu+kK8D1gcZVzjgBuqyVfPabsrtyyaOGGbaGnrSBq9RzCtnC927Rp01KVsaiEbZVWbvXuOWQV2pyGjH/+s+ry5arvf7/q9ttvUQSg2t6uetBBqiMjqitX+k9uF2YN8fIz2+yBDVFIQzkMAL+s2LcV8DTQ5SqLLwNrgXuAU4GPAC+7+35RI/2PAyNV9l8JnFhLvnot9pN1ZQqrHNKeAsPvwSq3LKs5HcNsrUAcxRnF5xDWf1OLLAZF+slYi6efVr32WtVTT1XdY4/JygAcZ/LQkOo11zjTYyf5f9We2bTNmZVlErSoUtFIQzl8BPhKlf2rgPnAYuAaoMPdP0tr9Bw8aXQCdwMHVezvAjbgY1IChoAVwIre3t60MyzUlnY3NO1pHKJQaXKojJqpdDqWTSetrhySxsVHMV8meaklLae4JilV1eeec8JMP/lJ1X32URWZrAxe8xrVww5TPf981fvvn+xMDkvUZzbNaTnCLsdaRNJQDh8Fzq+yfzWwp6sYDq1yPIxyuBi4oMr+o4Abw8iXds8hzTj/KIRthWY9eV6UbnerK4d69jKTmEPqORHjc8+p3nKL6hlnqL7xjY5pyKsMpk1zFso56yzVX/7SWSshKa3+zMYlDeWwkGCz0rXAwirXBSoH4F+B64G2KseuA44JI19c5fDCC6pjY1NbKmEKu15z3qTZCkk6IVy1ntLAwEDNh6Koraa41BqbkFVdSTIaOcsp3MfGVK+80hlXsPfeU0NM29sdJXHmmapHHHGBtrXNTL1uRPE5pEmevf00SEM5CI4J5zj3dztOi/889/dJwNVMNSutBXbySfPDwJ3Aa6oc2xrHpNQdRr64yuFnP3NyYM4cZyDNl76kescdqjvu+Le+L7p6z5aZlv0yy6mkaymItF5CRSCMAm9ra8skLj6pIzWNmXZfecUZR3DhhapHHVW5drKzdXSo7r+/Y0b6yU+cnkT5/lnWjaBopaye2bz8hGmRWDk4abAjcCNbQlm/Bkx3j3UA5wP3A2uAU9z9pwIPUMUhDWx201ntbv/Pc+wE4PthZYurHK68UnWbbaZW7ra2cRW5W+EihWMVdtHXvKax4++jvFiS2LabcR0JL3kFK6jWf1zIxITqo486zuMzz1RduFB15sypz8vWW6u+4x2qn/uc6i9+ofr889XTa8a6kVeEYVqkohyKvCXxOYyPqz7wgDMM/6STVPfaa2q3GFRnznxBDz/csZPecIPqhRdeo729jROfH2eqirCtrjCTx5FBtzrNNSfCUu+xCZVkOS5k/XrVm25SPfts1VJpjcLjU54DUN1pJ9Vjj1X91recRXDCrp1cK7+i/p8ijJHJa2xSWphyiMhf/+q0gL7wBdV3vWtq7PWW7c8KP1UY0WnTBvW8866PFWlRD7KK7Y4S558meS2DmmfPIU3+/GfVW29VPecc1fe+V7Wvz6+Ob1C4ReHz+ra3fVsffzz+PcM4jcP2hIoyur7Rx0yYckjIxITqf/2XY4b65CdVZ8z4lfvQTH2Ytt7aicT4+MdVL7/cGbizaVOm4oVi2bJlU8YqJJnULWgwUT1a0nmZKPIaIBmXl15SXbPGqYunneaEjL7uddUVQVeX6oEHqop8ReFohV1i5W1lj26bbbYJXU/CvlizfimHnVupKEoqLqYcUmZLV7JPnXlfPqvwE4Unqj50Iqq77OL0Qs4801Eya9akE8IXlmXLlmln5+TZKeNO6hZ3VHCa1Os+1chjapVabN6s+sgjjlnonHMcs8+eezrO4Wp18rWvVf2Hf1AdHla99FLHPLR5s5NWkrwNEzWURkMi67U9oszKWgTzVlyClIM4xxubBQsW6IoVK+p2v76+PsbGxqbsL5VK/OY3o9x9N6xaBWvWwH33wR//COPjU9Npb4ddd4Xdd3e23XZzfu+6K2y7bf1kHh0dTSWtINrb29m8eXOka4Lo6OhgvEqmpn2fovH88/Dgg/DAA1s+H3jAqWMvvjj1fBHYZReYPx/22sv5nD8f+vqcY9VIkrd+10YhTJ1Msz6HTTvt+xQBEVmpqguqHvTTGo20pdFziOqAjdKVfPFF1XvuUf3e95zVqd79btVdd63u+C5vPT2qb3iD0wI8+2ynt/H736suXXpV6FDXNFcZC+t09tvS9gXk5XOoB8895/Qsr7vOGTV8yimqhx7qTCvhV19Addttn3dNnhfprFn/omed9VP961+j3z9J3sapG2GfIy9ZmnPC1PFGDDioBmZWCiZORUujEDdtcmLGr7jCGU26aJETLdXVFfwScBzhv1X4gcK5Cqcq/KPC3grb6kknLU51muIoZqQ0x2bUIo9opTR44QXVhx5yHMJLl6qefrozZmD//VVnzw4u+85O1f5+1fe8xzFRXn656l13qS5d+v1UX5Zx8zbOglFxxw9l9SIN40trhlBlVVMONYnj3MpSw09MqP73f6vefrvqxRerfvrTziC9zs77FZ6voThU4Tnt7HxQ4SaFbyosUfiQOuvm7qswV6EjdMUL63jOYi3nJPla70FRExPOpHJr1ji2/4svdkKfTzxR9fDDHcVf6+UPqjNmqP7d3znXnHyy6pe/7IRP//GPziC0aiR10FYOZOzv74+VV1F9DkV03ublc8gj8smUQw0aZblKR05R2EHhTQrvU/gXhW8o/FhhrcJzIZSHs732tS/oHns4g5uOPdaJZjn3XNVLLnFMGrff7pjDnPvNCPWwJyHNfE1jOoWJCdUNG1QffFD1179Wvf5654X/hS+ofuITzhTTb3+76oIFqqWS81IPk+8dHY6J6M1vVj3hBMdseMUVqr/6ldMoCDtuwEsSB22YEe5RyqFWtFJHR0fhnbd5RCtl6WT3I0g5mEOa6M6tLJ1hQYR3BG/NnDkH8MQTHUAJmAvMAf6Gzs5eenp258knYWIiqgQv4sxqsgH4i7v9FXge2IjIC3z+80vo7oaZM6G7m0nfp0+Hzs6pW0eH89nfvyvr1j005a7lfC2/XicmJn+Oj8NLLzkO2fK25577MzHRCcyosr0WZ4aWrZg5cy7vfOcxPPssPPecs3m/R308ttoKdthh6jZv3pbv228PbW1R8z6YJHVS/DzTMdJqNeoR6JFlvptDugbLli2bshRiW1tbKhPT+d2v3CqptNH29/cHXhfW9l+rRbN5s+r//I/qqlWqP/2p6mWXOS3ij31M9fjjVY84wol57+9X3XrrTQovhu6RJN9ece/3svt9vI73rtyeVXhIt9/+EX3nO52W/qc+5YSLXnqp6o03qv72t44PoTyHUCVJprsOqjveFm21NTeCWrBxAgyaaaW0tExBtYI9oqRtPocCKoeo0RlJbINhXvC1FISfYqmUI037/RVXLHMnJJync+Ycpvvu+3FtaztSYVBFTtIDDrhazz7b8Y8MDzsKZtEix+xy0EHO4u+77666227O9Avz5jmDsWbNUt1qK2dOf0cZ1HpZjyts1o4Oxzk7fbpz7bbbOhMo7rST6utfrworFe5UuE0d38u1ClcqXKbwVXXGpnxKYUjhaJ0+/Uj9zGdu1jVrVAcHlyhsozA5f+M4vNNcKKdW3alccyNIMcQZp9LT0xP5/xeRNF/CYfxxUcvYopUKpByijrZNUrnCOnfD0OijMyvZMolZh8J0hU73BT11gftaxBmMVVaqaY6+TnOJzbSiaMLWwWZVDmk6ftOMCswDUw61MyjySzquhg/bjQ9LI4/OrCTtufH9opX80iybTcLcf2BgIJQMYdIKa65JK/4+7liVZjErpWkKUs1uPFE153faz7ophxrUc56eNHsOzUaUFm0W96nVc4ijIMKkFbZFnnXPoXxtHiGV9SRtU1CYtOOanIPWG0/DSmDKoQb1HG2b1OfQzITtopdDIbu7u18NJAgzjiFoskDvJIRRTVJBSiJMWmGVQ9T4+yjpeK8tqrkyrZZzlqagoLSr1VHvvFxBjdQ0ZfRiyiEE9RxtGzdaqRWofAHMnTt3Uv5EMYnUeuF5t8pJCL31IQ0FUSutOPkTFH8fNZ/9FGlRzJVpK6w0TUF+aUdpYMTdkpr6gpSDjXMwGoY4E/6V3BjxJJOphR0HUOtZatXJAtMgj4n2kqadxiSEtUgqY9A4h9BDcURknoj8SET+JCIPi8hXRWRajWs+JiJdPsdOEZGHRERFZLZn/6dEZLW73Ssi4yIyK6yc9WL58uX09fXR1tbG7NmzmT17Nm1tbfT19bF8+fK8xWtK1q1bF/uaMNfGST8Kfi+K8fFxRISOjg6Gh4czlSELvM9CVvXfr2zSKLORkRG6uia/prq6uhgZGUmUbtaKIQ0ZA/HrUng3QIC7gA+4v9uBS4Bza1w3Csz2ObYP0FfjnCOA22rJV+/1HNKy/RrRiNNVL9vzw1zrZ/sPa16qRaOvN1yNevknsnaSZ2FGizMJIWyZiLCWr6EQ0UrAAPDLin1bAU8DXa6y+DKwFrgHOBX4CPCyu+8XAWkHKYcrgRNryVdv5ZBW1IgRjTiDt8ov/DDX+imHME7lMJFLYZVD1qvZpUm9IpuK6iQPIs5Ym3oHBqShHD4CfKXK/lXAfGAxcA3Q4e6fpTVe/J40qp7jKp0N5bSqHB8CVgArent7Y2VMlmMVmiUmvGh4y8wbrRSmHGo5CoPKLMipHHbMQ9rjOIrgNK7nZHFF+L9RqTUzcK1VBLP+z2koh48C51fZvxrY01UMh1Y5nkQ5HAXcGEa+OD2HrEc5N8to0kYhSgs2rzj+sGaxMD2HorSkm31MRLOThnJYSLBZ6VpgYZXrkiiH64BjwsgXRzmkPVjFlEO+RHlZZv1irWwt9vf3R7I/h/E5FOWlnDQvs2wZN2JPo96koRwEx4RznPu7HbgYOM/9fRJwNVPNSmuBnWqkPUU54MynvAHoDiNfHOUQ9HCGIYmJwsiGKC+DrF4cUezMpVL8VfPymPvfj7h5maWSLkrPqugkVg5OGuwI3Aj8CXgY+Bow3T3WAZwP3A+sAU5x958KPEAVhzSOH+MxYDPwOPAdz7ETgO+HlS0P5VCmKC04oxiE7SEkdTo3Q73L8j80Q/7Ug1SUQ5G3PJVDtRZK2KmTm5XKlmQtp1szEbbXELWeVRKmZVx0s0qWvZ8g53/SvCh6vkbBlEP1TEntoa2c0iDKoivNRhh/TDPnR716Dqq1Z/Asulklj55D0rxohHyNgimHKvitmxs2LNGPVu/Oho3Iadb8COtzyHqgWyPUw3r7HNLIi0bI1yiYcvChUkEkVQyqxXIU1iKtSdy8RF12slqLt9G77EHRSllP6limUephluVda8LDOHnRKPkaFlMOdaRRWhZZTQEStudQ7V7N1mXPk0aph1lhPYdwmHKIQRHD85ISNFV4WhU+zhQX5Xtl/eDVc1r2vClyPQwirZ5EUX0OResZm3KISLNVgLJMcV7ace/lF61Uy8yUVZe9ngs6FYUi1sMg0lRoRYxWKqLCDlIOtp5DFbKcOz4v4qyFkMU6A0FrGsybNy+zfLe1FIpPms9dEZ/hIsqUynoOrUSWc8fnRRzZs5iPPmhNg6zm1a91XyMaaa7f4E3Lr/ESp+5mWZfi0nDvFb8uRSNtaZuVms3ppBrPUZzF/62Vt1mZQoLW5zXCk6ZpJKypM249LJpZrYjvFcznEI0i2gaTEtXnkNX/zStvs/Q5FGVEeD3kSPMFF6bB0ujPnZcivldMOcSgaK2ONAh6eaQ1ziGOHPXK2yyilYoyIrxecqQZNBDkNG6m585L0d4rQcrBHNKGkYCwjv6snY71kqPZncathjmkjVypdGAODw9nviB9loRxolaShdMxDznSdPRWS6utrY1HH30UEaGjo4Ph4eFE8hoJ8OtSNNJWpBHSxmSKYnZJiySD/JpFjjRNI960uru7M/MLGdXBzEpGXhTF7JIWccaLdHV1sXTpUgYHB5tOjjSxsSj1x8xKLcLChQsRkVe3HXbYgY6Ojly76GHNGOXz0oyhz4IoZhkRoVQqZfJCTipHEfPZxqIUDL8uRSNtZlbyn4K8cqt3Fz3s+Iqenp5ChvpVEvb/ZD1+IokcRc1nG4tSf0hpmdB5wI/YskzoV4FpNa75GNDlc2wn4HdueleV0wI+Bax2t3uBcdw1qf22VlYOtdayjvugpWVXjmIbD3o5FCX0L+z/yVoJh5Vj5syZU/LOr7548zmPsRp+Y1HKIa/NPlliHiRWDoAAdwEfcH+3A5cA59a4bhSY7XPsB8DR7vdvAYurnHMEcFst+VpVOWQ1mV7aLUuvookqa+VWhBZupeIcGBjIZbbXIDlERDs6OqrmXZxyqFe+11qDoR6Kt5VIQzkMAL+s2LcV8DTQ5SqLLwNrgXuAU4GPAC+7+35Rca0ATwEd7u8DgJur3PdK4MRa8rWqcogzJUaYnkOWw/zjyJyFHM1OUBnGLYN657uZmbInSDmEdUjvDqz07lDV54B1wC7AEI6ZaB9VnQ8sV9ULgceBt6rqWyvS6wGeUdVyCMJjwA7eE0SkC3g7cE01gURkSERWiMiK9evXh/wb8SmiAy9OzPrQ0FDsdNOI1a8W2x6VsbGxQpVDEQkqw7hl4JdmVuNYzEGdM35aQye34D8KnF9l/2pgT5wX+KFVjo9SxawEbAc85Pm9I7C24pyjgBvDyJd1z6GoDrxaLcC5c+fGMndkPUFYLT9J2fYdZkGiIpRDEalVhlEXfvIr/yzHsVjPIXtIway0kGCz0rXAwirX+SmHmmYl4DrgmDDyZa0csn5ZxiUrpVUvZVjrPlnP2tnMRCnDJC/4sCaqOGXUigs01Zs0lIMAK4Dj3N/twMXAee7vk4CrPS/7We7nWmAnnzR/yGSH9LDn2NbABqA7jHxZK4ciLSperxlAs5wgLKjV2t/f73uu34unXA5Fm9Ss3iSpG3GvDevcjvustNLSrnmQWDnoFtPPjWwJZf0aMN091gGcD9wPrAFOcfefCjxAhUPaPbYzTgTUQ66imO45dgLw/bCytUrPoajmrSiEaaUODAxUvTaoHJohb5KQ1//PsudgZE8qyqHIW94+h3q1WIuipJIQ9mVSjaByiJo3ea3BUKuuRKlLYfwGWdeNZps7q9Uw5ZACfg9tPVtsRTJvxSWsGcIPv3KIkjd5vdDi+FiS+AnqVTeKstiRER1TDhlSz9Z8q/cc4qRbLW/yMoXUkrER/oPRXAQpB5t4LyH1XDS8iIumRyVMjP3AwEAq6frlTdTJANOiVl2JUpfCyNZodaMWw8PDkyaSXLhwYeHGHjUVflqjkbZW6TmoNkdEjvc/TJs2bVK++Tmjo6YblDdhW93d3d2xZYly3zR7DkWahypN/MJavZv5NqKDmZWyo9WjZBqRsPb6tra2zO+bps+hmetdkoF6hj+mHDImamu+GVr/jU7YyQCzvG+a0UpFrkdpyBlGMUBjBWcUAVMOBaLVWnyNgE3TkB1p1XfrOWRDkHIwh3Qd8E5Mdvzxx7Np06ZJxzdt2sSSJUtyks7wm4wwzCSFrULciSeXLFmSSn0PUxbN5oDPHT+t0UhbkXsOUdZcMPLDpmnwJ0nrP82xOZVlNDAw0BBmtSJDQM9BnOONzYIFC3TFihV5i1GVsAvB2yLqRlHxq8OlUonR0dHMrjWyR0RWquqCasfMrJQxYWPlbY56o6gkGcvTDGNzWhVTDhnT29sb6rxSqZSxJIYRD786HKZuDw4OsnTpUkqlEiJCqVRi6dKlDA4Opi2mkTKmHEIS1yEXZkSwtaSMIpO09T84OMjo6CgTExOMjo6aYmgU/JwRjbTlPStrmOttYjKjkWmUMRVGNDCHdDLMqWYYRjNiDumEpD25XlwTlWEkweqdEYWOvAVoBHp7e6v2HMI6m70sX76coaGhVwcGjY2NvTrAx2yxRlZYvTOiYj2HEKQZjpfWiFHDiILVOyMqoZSDiCwRkftE5B4RWS0ib0h6YxE5VERWisha9/MQd/9r3XuUt6dE5IKk90tCmuF49Vz/wTDKWL0zolLTrCQiBwD/G9hXVV8SkdnAtBTu/RRwhKo+LiJ7ADcDO6jqX4G9PfdfCVybwv0SMTg4mEr3O00TlWGExeqdEZUwPYc5wFOq+hKAqj6lqo8DiMh+InKH2/K/WUTmuPtvF5ELROROEblXRP6+MlFVXVVOB7gPmCEi073niMiuwPbAf8b/i8XCRowaeWD1zohKGOVwC7CjiPxRRL4hIgcDiEgn8DVgkaruB1wKeGtat6q+CRh2jwXxT8CqsgLy8D7gKq0SbysiQyKyQkRWrF+/PsTfKAY2YtTIA6t3RlRCjXMQkXbgIOCtwD8DpwMrgDuBR9zT2oEnVPUwEbkd+DdVvc29fh0wX1WfqZL27sANwGGq+nDFsfuB96vqyiD5ijzxnmEYRlEJGucQKpRVVceB24HbRWQtcDywErhPVQ/wu6zGb0RkHnAdcFwVxbAX0FFLMRiGYRjpU9OsJCJ/69r+y+wNjAEPAtu5DmtEpNPtBZQ5yt1/IPCsqj5bke42wE+AM1T111Vu/T7ge+H/imEYhpEWYXoOM4GvuS/zzcBDwJCqviwii4ALRWRrN60LcJzLAH8RkTuBrYAPVkn3FGAX4DMi8hl332Gq+qT7/f8Ah0f/S4ZhGEZSavYcVHWlqr5JVftVdb6qvkdVn3KPrVbVN6vqXqq6u6pe7Ln0Gve6PVT1rirpfk5Vu1V1b8/2pOf4zqr6QCr/0sgVm7bBKGN1oXGw6TOMTLFpG4wyVhcai0ymz1DVt6iqhQ8ZNm1DRJq5ZW11obGwnoORKTZtQ3iavWVtdaGxsIn3jExJssRkq9HsLWurC42FKYcWph4mDJu2ITzN3rK2utBg+C0R10hb1suENiNJlz6Nei9bYrI2pVJpUnmUt1KplLdoqWF1oVhgy4QaldjSp8Wj0ucATsva5kAyssKWCTWm0OwmjEbEJsczioT1HFoU6zkYhmE9B2MK5hw0DCMIUw4tipkwDMMIwsxKhmEYLYqZlQzDMIxImHIwDMMwpmDKwTAMw5iCKQfDMAxjCqYcDMMwjCk0RbSSiKzHWdc6DWYDT6WUVpqYXOEpokxQTLmKKBMUU64iygTJ5Cqp6nbVDjSFckgTEVnhF9qVJyZXeIooExRTriLKBMWUq4gyQXZymVnJMAzDmIIpB8MwDGMKphymsjRvAXwwucJTRJmgmHIVUSYoplxFlAkykst8DoZhGMYUrOdgGIZhTMGUg2EYhjGFllAOInKpiDwpIvd69u0lIr8RkbUicqOIbFVxTa+IbBSR0zz79nPPf0hELhQRqYdMItInIi+IyGp3+1YWMkWVyz023z12n3t8Rt5yicigJ69Wi8iEiOydtlwRZeoUkX939/9BRM7wXJNnXk0Tkcvc/WtE5C1ZyCUiO4rIL9z/fp+IfNTdP0tEbhWRP7mf23quOcO994Mi8ra05Yoqk4j0uOdvFJGLKtLKLa9E5FARWenef6WIHJKKXH6LSzfTBrwZ2Be417Pv98DB7vcPAp+tuOYa4IfAaZ59dwEHAAL8FHhHPWQC+rznVaSTmkwx5OoA7gH2cn/3AO15y1Vx3Z7AIwUow2OA77vfu4BRoC/vvAJOBi5zv28PrATaMsirOcC+7vfXAn8E+oFzgNPd/acDX3K/9wNrgOnATsDDadetGDJ1AwcCJwEXVaSVZ17tA8x1v+8B/HcacsWugI22UfGCBZ5ji0N+R+B+z7F3A+cCZ+EqB7fAHvCc8z7g2/WQqfK8ikqUqkwR5TocWFY0uSqu+TwwUoAyfB9wI45C7XEf+Fl55xXwdeBYz3k/B/4+K7k86f0IOBR4EJjjKZ8H3e9nAGd4zr8Z5yWXmVy1ZPKcdwIe5ZB3XlWcK8DTOEo1kVwtYVby4V7gH93v78V5YBCRbuBfgLMrzt8BeMzz+zF3X+YyuewkIqtE5A4ROaiOMgXJtRugInKziNwtIp8uiFxejgK+V0e5/GS6GngeeAJYB3xZVTfUSaYgudYA7xKRDhHZCdjPPZaZXCLSh9Pa/R3wOlV9AsD93N49bQfg0Sr3z0SukDL5kXdeefknYJWqvpRUrlZWDh8EThaRlThdt5fd/WcDX1HVjRXnV7PVpR0H7CfTE0Cvqu4DfAK40rUZ10OmILk6cLrZg+7nkSIyUAC5ABCRNwCbVLVse8+zDP8eGAfm4phJPikiO9dJpiC5LsV5aawALgDuBDZnJZeIzMQx2X5MVZ8LOtXn/qnLFUEm3yTSlgmiyyUiuwNfAv45Dbk6wp7YbKjqA8BhACKyG/BO99AbgEUicg6wDTAhIi/iFNI8TxLzgMfrIZPbCnjJ/b5SRB7GabU/lrVMQXK5979DVZ9yj92EY+telrNcZY5mS6+hLG8uZYjjc/gPVX0FeFJEfg0sAP4za5mC5FLVzcDHy+eJyJ3An4C/pC2XiHTiPEfLVfVad/efRWSOqj4hInOAJ939jzG5J1i+f6plGFEmP1KvV1HlEpF5wHXAcar6cBpytWzPQUS2dz/bgP8LfAtAVQ9S1T5V7cNpSX1eVS9yu3F/FZE3uh7/43BsgZnLJCLbiUi7+31nYFccJ2vmMgXJhWMHni8iXSLSARyMY8vOW67yvvcC3y/vy7MMcUxJh4hDN/BGHHtwrnnlll23+/1QYLOqpl6GbhqXAH9Q1fM9h24Ajne/H++5xw3A0SIy3TV37QrclaZcMWSqSt55JSLbAD/B8dH8OjW50nKaFHnDaT0+AbyCo00/BHwUxyn4R+CLuM66iuvOYnK00gIc2+3DwEXVrslCJhw74n049uG7gSOykClOXgHHurLdC5xTILneAvy2Sjp5leFMnOi3+4D7gU8VIa9wHNcPAn8AfoYzhXMWeXUgjknjHmC1ux2O45z/OU5v5efALM81S9x7P4gnyiYtuWLKNApsADa6edufd17hKPvnPeeuBrZPKpdNn2EYhmFMoWXNSoZhGIY/phwMwzCMKZhyMAzDMKZgysEwDMOYgikHwzAMYwqmHAzDMIwpmHIwDMMwpvD/AZobHZFml9T+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline\n",
    "model = Pipeline([('poly', PolynomialFeatures(degree=2)),\n",
    "    ('linear', linear_model.LinearRegression(fit_intercept=False))])\n",
    "model = model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', model.named_steps['linear'].coef_)\n",
    "# The mean squared error\n",
    "print('Mean squared error: %.2f'\n",
    "      % mean_squared_error(y_test, y_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination: %.2f'\n",
    "      % r2_score(y_test, y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_pred, color='blue', linewidth=2)\n",
    "plt.gca().yaxis.set_major_formatter(jmd_formatter)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [0.         0.15740705]\n",
      "Mean squared error: 168.25\n",
      "Coefficient of determination: 0.07\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# deal with outliers?\n",
    "model = Pipeline([('poly', PolynomialFeatures(degree=1)),\n",
    "    ('ridge', linear_model.RidgeCV(fit_intercept=True))])\n",
    "model = model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: \\n', model.named_steps['ridge'].coef_)\n",
    "# The mean squared error\n",
    "print('Mean squared error: %.2f'\n",
    "      % mean_squared_error(y_test, y_pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination: %.2f'\n",
    "      % r2_score(y_test, y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, y_pred, color='blue', linewidth=2)\n",
    "plt.gca().yaxis.set_major_formatter(jmd_formatter)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc3 as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try a pooled model first - \n",
    "with pm.Model() as model_pooled:\n",
    "\n",
    "    # One shared intercept for all districts\n",
    "    a = pm.Normal('a', mu=0, sigma=1)\n",
    "    # model error\n",
    "    sigma_y = pm.HalfCauchy('sigma_y',0.001)\n",
    "    \n",
    "    μ = a + sigma_y\n",
    "    θ = pm.Deterministic('θ', pm.math.sigmoid(μ))\n",
    "    \n",
    "    y_1 = pm.Bernoulli('y_1', p=θ, observed=df5['y'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}