HealthKit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# heartparser.py\n",
    "#   Author: Kristy Yancey Spencer\n",
    "#\n",
    "#   This script parses the Apple Health export xml file for Heart Rate and\n",
    "#   Blood Pressure data and produces graphs of the data for given date ranges.\n",
    "\n",
    "import numpy as np\n",
    "from datetime import date, datetime, timedelta as td\n",
    "from matplotlib import pyplot, dates as mdates\n",
    "from matplotlib.ticker import FormatStrFormatter, MultipleLocator\n",
    "from openpyxl import load_workbook\n",
    "from pandas import DataFrame, ExcelWriter, Index, Timestamp, DatetimeIndex\n",
    "from xml.dom import minidom\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "def weeklyhr(datelist, df_hr):\n",
    "    # This module filters through df_hr and returns a dataframe containing\n",
    "    # weekly heart rate values.\n",
    "    list_weekhr = []\n",
    "    dict_weekday = {}\n",
    "    for di in range(len(datelist)):\n",
    "        year = int(datelist[di][:4])\n",
    "        month = int(datelist[di][5:7])\n",
    "        day = int(datelist[di][8:10])\n",
    "        weekj = date(year, month, day).isocalendar()[1]\n",
    "        if weekj not in dict_weekday:\n",
    "            dict_weekday[weekj] = mdates.date2num(datetime(year, month, day))\n",
    "        try:\n",
    "            i = df_hr.index.get_loc(datelist[di])\n",
    "            di_hr = df_hr.get_value(i, 1, takeable=True)\n",
    "            list_weekhr.append({\n",
    "                'Date': mdates.date2num(datetime(year, month, day)),\n",
    "                'PlotDate': dict_weekday[weekj], \n",
    "                'Week': weekj,\n",
    "                'Heart Rate (bpm)': float(di_hr)\n",
    "            })\n",
    "        except:\n",
    "            pass\n",
    "    df_weekhr = DataFrame(list_weekhr)\n",
    "    print(df_weekhr)\n",
    "    return df_weekhr\n",
    "\n",
    "\n",
    "def makehrplot(dates, df_week):\n",
    "    # This module plots the heart rate data between the dates given.\n",
    "    plotname = 'heartrate_{0}to{1}'.format(dates[0], dates[1])\n",
    "    fig, ax = pyplot.subplots()\n",
    "    fig.dpi = 2000\n",
    "    regplot(x='PlotDate', y='Heart Rate (bpm)', data=df_week,\n",
    "            x_estimator=np.mean, color='#D42426', ax=ax)\n",
    "    # assign formatter for the xaxis ticks.\n",
    "    ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y.%m.%d'))\n",
    "    pyplot.savefig(plotname + '.pdf', format='pdf')\n",
    "    # pyplot.savefig(plotname + '.jpg', format='jpg')\n",
    "    pyplot.close()\n",
    "\n",
    "\n",
    "def plotbp(dates, datelist, recordlist):\n",
    "    # This module parses the blood pressure data from the xml file and\n",
    "    # calls the plotting function.\n",
    "    cat1 = \"HKQuantityTypeIdentifierBloodPressureSystolic\"\n",
    "    df_bps = parse(cat1, recordlist)\n",
    "    df_bps.columns = ['Time', 'BP Systolic']\n",
    "    cat2 = \"HKQuantityTypeIdentifierBloodPressureDiastolic\"\n",
    "    df_bpd = parse(cat2, recordlist)\n",
    "    df_bpd.columns = ['Time', 'BP Diastolic']\n",
    "    df_bp = df_bps.merge(df_bpd, left_index=True, right_index=True, \n",
    "                         how='outer')\n",
    "    df_bp.drop_duplicates()\n",
    "    df_bloodpressure = select_bprange(datelist, df_bp)\n",
    "    makebpplot(dates, df_bloodpressure)\n",
    "\n",
    "\n",
    "def select_bprange(datelist, df_bp):\n",
    "    # This module filters df_bp with the given datelist and returns a usable\n",
    "    # dataframe.\n",
    "    list_bp = []\n",
    "    for di in range(len(datelist)):\n",
    "        year = int(datelist[di][:4])\n",
    "        month = int(datelist[di][5:7])\n",
    "        day = int(datelist[di][8:10])\n",
    "        try:\n",
    "            i = df_bp.index.get_loc(datelist[di])\n",
    "            di_bp_sys = df_bp.get_value(i, 1, takeable=True)\n",
    "            di_bp_dia = df_bp.get_value(i, 3, takeable=True)\n",
    "            list_bp.append({\n",
    "                    'Date': mdates.date2num(datetime(year, month, day)),\n",
    "                    'Blood Pressure': di_bp_sys[0],\n",
    "                    'Type': 'Systolic'\n",
    "                })\n",
    "            list_bp.append({\n",
    "                    'Date': mdates.date2num(datetime(year, month, day)),\n",
    "                    'Blood Pressure': di_bp_dia[0],\n",
    "                    'Type': 'Diastolic'\n",
    "                })\n",
    "        except:\n",
    "            pass\n",
    "    df_bloodpressure = DataFrame(list_bp)\n",
    "    print(df_bloodpressure)\n",
    "    return df_bloodpressure\n",
    "    \n",
    "    \n",
    "def makebpplot(dates, df_bp):\n",
    "    # This module plots the blood pressure data between the dates given.\n",
    "    plotname = 'bloodpressure_{0}to{1}'.format(dates[0], dates[1])\n",
    "    fig, ax = pyplot.subplots()\n",
    "    fig.dpi = 2000\n",
    "    kws = dict(linewidth=1, edgecolor=\"w\")\n",
    "    bpplot = lmplot(x='Date', y='Blood Pressure', data=df_bp, hue='Type',\n",
    "                    palette='Set1', aspect=1.5, markers=['^', 'v'],\n",
    "                    legend_out=False, fit_reg=False, scatter_kws=kws)\n",
    "    ax.legend(loc='upper right', frameon=True)\n",
    "    # assign formatter for the xaxis ticks.\n",
    "    axes = bpplot.axes\n",
    "    axes[0, 0].xaxis.set_major_locator(mdates.AutoDateLocator())\n",
    "    axes[0, 0].xaxis.set_major_formatter(mdates.DateFormatter('%Y.%m.%d'))\n",
    "    # put the labels at 45deg since they tend to be too long\n",
    "    fig.autofmt_xdate()\n",
    "    pyplot.savefig(plotname + '.pdf', format='pdf')\n",
    "    # pyplot.savefig(plotname + '.jpg', format='jpg')\n",
    "    pyplot.close()\n",
    "    \n",
    "    \n",
    "def makedatelist(dates):\n",
    "    # This module creates a list of days from dates[0] to dates[1].\n",
    "    startyr, startmn, startdy = int(dates[0][0:4]), int(dates[0][5:7]), int(dates[0][8:10])\n",
    "    endyr, endmn, enddy = int(dates[1][0:4]), int(dates[1][5:7]), int(dates[1][8:10])\n",
    "    # Make date list\n",
    "    d1 = date(startyr, startmn, startdy)\n",
    "    d2 = date(endyr, endmn, enddy)\n",
    "    delta = d2 - d1\n",
    "    datelist = []\n",
    "    for i in range(delta.days + 1):\n",
    "        di = d1 + td(days=i)\n",
    "        datelist.append(di.strftime(\"%Y-%m-%d\"))\n",
    "    return datelist\n",
    "\n",
    "\n",
    "def parse(category, recordlist):\n",
    "    # This module parses the xml file for category's data and\n",
    "    # returns a Pandas Dataframe\n",
    "    dates = []\n",
    "    datalist = []\n",
    "    for s in recordlist:\n",
    "        if s.attributes['type'].value == category:\n",
    "            date = s.attributes['startDate'].value[:10]\n",
    "            time = s.attributes['startDate'].value[11:16]\n",
    "            datum = s.attributes['value'].value\n",
    "            dates.append(date)\n",
    "            datalist.append([time, int(datum)])\n",
    "    dfindex = Index(dates, name='Date')\n",
    "    df = DataFrame(datalist, index=dfindex)\n",
    "    return df\n",
    "\n",
    "def parse_float(category, recordlist):\n",
    "    # This module parses the xml file for category's data and\n",
    "    # returns a Pandas Dataframe\n",
    "    dates = []\n",
    "    datalist = []\n",
    "    for s in recordlist:\n",
    "        if s.attributes['type'].value == category:\n",
    "            date = s.attributes['startDate'].value[:16] #scollis edit to get time into date\n",
    "            time = s.attributes['startDate'].value[11:16]\n",
    "            datum = s.attributes['value'].value\n",
    "            dates.append(date)\n",
    "            datalist.append([time, float(datum)])\n",
    "    dfindex = DatetimeIndex(dates, name='Date')\n",
    "    df = DataFrame(datalist, index=dfindex)\n",
    "    return df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting\n",
      "Parsed and now ordering\n"
     ]
    }
   ],
   "source": [
    "print('Starting')\n",
    "xmldoc = minidom.parse('/Users/scollis/Downloads/apple_health_export 2/export.xml')\n",
    "print('Parsed and now ordering')\n",
    "recordlist = xmldoc.getElementsByTagName('Record')\n",
    "del xmldoc\n",
    "#plotheartrate(dates, datelist, recordlist)\n",
    "#plotbp(dates, datelist, recordlist)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# This module parses the heart rate data from the xml file and calls\n",
    "# the plotting function.\n",
    "category = \"HKQuantityTypeIdentifierBodyMass\"\n",
    "df_mass = parse_float(category, recordlist)\n",
    "df_mass.columns = ['Time', 'Mass (kg)']\n",
    "#df_mass = df_mass.set_index('Time')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'Mass (kg)'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mass.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_mass.plot(marker='o',figsize=(15,4))\n",
    "#ax.set_xlim(Timestamp('2019-01-02'), Timestamp('2020-03-01'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates_array = df_mass.index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure( figsize=(18,8) )\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.scatter(dates_array, df_mass['Mass (kg)'])\n",
    "ax.set_xlim(Timestamp('2011-01-02'), Timestamp('2020-05-01'))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#two epochs\n",
    "start_of_year = df_mass['2020-1-1':'2020-3-31'].sort_values('Date')\n",
    "dry_april = df_mass['2020-4-1':'2020-4-30'].sort_values('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Time</th>\n",
       "      <th>Mass (kg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-02 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>236.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02 06:03:00</th>\n",
       "      <td>06:03</td>\n",
       "      <td>236.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02 07:57:00</th>\n",
       "      <td>07:57</td>\n",
       "      <td>236.556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>235.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03 06:03:00</th>\n",
       "      <td>06:03</td>\n",
       "      <td>235.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03 06:39:00</th>\n",
       "      <td>06:39</td>\n",
       "      <td>235.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03 06:49:00</th>\n",
       "      <td>06:49</td>\n",
       "      <td>235.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>232.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04 06:17:00</th>\n",
       "      <td>06:17</td>\n",
       "      <td>232.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04 06:38:00</th>\n",
       "      <td>06:38</td>\n",
       "      <td>232.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04 08:15:00</th>\n",
       "      <td>08:15</td>\n",
       "      <td>232.367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>232.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05 06:06:00</th>\n",
       "      <td>06:06</td>\n",
       "      <td>232.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05 06:16:00</th>\n",
       "      <td>06:16</td>\n",
       "      <td>232.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07 05:57:00</th>\n",
       "      <td>05:57</td>\n",
       "      <td>233.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07 12:20:00</th>\n",
       "      <td>12:20</td>\n",
       "      <td>233.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>231.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08 05:28:00</th>\n",
       "      <td>05:28</td>\n",
       "      <td>231.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-08 12:20:00</th>\n",
       "      <td>12:20</td>\n",
       "      <td>231.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>231.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09 04:57:00</th>\n",
       "      <td>04:57</td>\n",
       "      <td>232.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09 07:16:00</th>\n",
       "      <td>07:16</td>\n",
       "      <td>231.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-09 08:38:00</th>\n",
       "      <td>08:38</td>\n",
       "      <td>231.485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-10 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>230.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-10 04:33:00</th>\n",
       "      <td>04:33</td>\n",
       "      <td>232.147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-10 06:37:00</th>\n",
       "      <td>06:37</td>\n",
       "      <td>230.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-10 07:28:00</th>\n",
       "      <td>07:28</td>\n",
       "      <td>230.824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-11 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>229.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-11 05:16:00</th>\n",
       "      <td>05:16</td>\n",
       "      <td>229.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-11 06:44:00</th>\n",
       "      <td>06:44</td>\n",
       "      <td>229.722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-22 05:25:00</th>\n",
       "      <td>05:25</td>\n",
       "      <td>224.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-22 05:44:00</th>\n",
       "      <td>05:44</td>\n",
       "      <td>224.431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>223.769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23 05:17:00</th>\n",
       "      <td>05:17</td>\n",
       "      <td>223.769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23 05:34:00</th>\n",
       "      <td>05:34</td>\n",
       "      <td>223.769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23 05:57:00</th>\n",
       "      <td>05:57</td>\n",
       "      <td>223.769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-23 12:49:00</th>\n",
       "      <td>12:49</td>\n",
       "      <td>221.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-24 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>221.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-24 05:34:00</th>\n",
       "      <td>05:34</td>\n",
       "      <td>221.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-24 06:00:00</th>\n",
       "      <td>06:00</td>\n",
       "      <td>221.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-24 06:35:00</th>\n",
       "      <td>06:35</td>\n",
       "      <td>221.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-25 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>223.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-25 05:56:00</th>\n",
       "      <td>05:56</td>\n",
       "      <td>223.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-25 07:44:00</th>\n",
       "      <td>07:44</td>\n",
       "      <td>222.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-25 13:43:00</th>\n",
       "      <td>13:43</td>\n",
       "      <td>223.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-25 15:16:00</th>\n",
       "      <td>15:16</td>\n",
       "      <td>222.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>222.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26 06:24:00</th>\n",
       "      <td>06:24</td>\n",
       "      <td>223.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26 07:36:00</th>\n",
       "      <td>07:36</td>\n",
       "      <td>222.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26 10:27:00</th>\n",
       "      <td>10:27</td>\n",
       "      <td>222.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-26 12:48:00</th>\n",
       "      <td>12:48</td>\n",
       "      <td>221.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>221.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 07:48:00</th>\n",
       "      <td>07:48</td>\n",
       "      <td>221.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-27 08:44:00</th>\n",
       "      <td>08:44</td>\n",
       "      <td>221.344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>220.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28 06:48:00</th>\n",
       "      <td>06:48</td>\n",
       "      <td>220.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-28 07:09:00</th>\n",
       "      <td>07:09</td>\n",
       "      <td>220.462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29 00:00:00</th>\n",
       "      <td>00:00</td>\n",
       "      <td>218.919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29 05:58:00</th>\n",
       "      <td>05:58</td>\n",
       "      <td>218.919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-29 07:09:00</th>\n",
       "      <td>07:09</td>\n",
       "      <td>218.919</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Time  Mass (kg)\n",
       "Date                                 \n",
       "2020-04-02 00:00:00  00:00    236.556\n",
       "2020-04-02 06:03:00  06:03    236.556\n",
       "2020-04-02 07:57:00  07:57    236.556\n",
       "2020-04-03 00:00:00  00:00    235.454\n",
       "2020-04-03 06:03:00  06:03    235.454\n",
       "2020-04-03 06:39:00  06:39    235.454\n",
       "2020-04-03 06:49:00  06:49    235.454\n",
       "2020-04-04 00:00:00  00:00    232.367\n",
       "2020-04-04 06:17:00  06:17    232.367\n",
       "2020-04-04 06:38:00  06:38    232.367\n",
       "2020-04-04 08:15:00  08:15    232.367\n",
       "2020-04-05 00:00:00  00:00    232.147\n",
       "2020-04-05 06:06:00  06:06    232.147\n",
       "2020-04-05 06:16:00  06:16    232.147\n",
       "2020-04-07 05:57:00  05:57    233.470\n",
       "2020-04-07 12:20:00  12:20    233.470\n",
       "2020-04-08 00:00:00  00:00    231.926\n",
       "2020-04-08 05:28:00  05:28    231.926\n",
       "2020-04-08 12:20:00  12:20    231.926\n",
       "2020-04-09 00:00:00  00:00    231.485\n",
       "2020-04-09 04:57:00  04:57    232.147\n",
       "2020-04-09 07:16:00  07:16    231.485\n",
       "2020-04-09 08:38:00  08:38    231.485\n",
       "2020-04-10 00:00:00  00:00    230.824\n",
       "2020-04-10 04:33:00  04:33    232.147\n",
       "2020-04-10 06:37:00  06:37    230.824\n",
       "2020-04-10 07:28:00  07:28    230.824\n",
       "2020-04-11 00:00:00  00:00    229.942\n",
       "2020-04-11 05:16:00  05:16    229.942\n",
       "2020-04-11 06:44:00  06:44    229.722\n",
       "...                    ...        ...\n",
       "2020-04-22 05:25:00  05:25    224.431\n",
       "2020-04-22 05:44:00  05:44    224.431\n",
       "2020-04-23 00:00:00  00:00    223.769\n",
       "2020-04-23 05:17:00  05:17    223.769\n",
       "2020-04-23 05:34:00  05:34    223.769\n",
       "2020-04-23 05:57:00  05:57    223.769\n",
       "2020-04-23 12:49:00  12:49    221.124\n",
       "2020-04-24 00:00:00  00:00    221.785\n",
       "2020-04-24 05:34:00  05:34    221.785\n",
       "2020-04-24 06:00:00  06:00    221.785\n",
       "2020-04-24 06:35:00  06:35    221.785\n",
       "2020-04-25 00:00:00  00:00    223.990\n",
       "2020-04-25 05:56:00  05:56    223.990\n",
       "2020-04-25 07:44:00  07:44    222.887\n",
       "2020-04-25 13:43:00  13:43    223.990\n",
       "2020-04-25 15:16:00  15:16    222.887\n",
       "2020-04-26 00:00:00  00:00    222.667\n",
       "2020-04-26 06:24:00  06:24    223.990\n",
       "2020-04-26 07:36:00  07:36    222.667\n",
       "2020-04-26 10:27:00  10:27    222.667\n",
       "2020-04-26 12:48:00  12:48    221.124\n",
       "2020-04-27 00:00:00  00:00    221.344\n",
       "2020-04-27 07:48:00  07:48    221.344\n",
       "2020-04-27 08:44:00  08:44    221.344\n",
       "2020-04-28 00:00:00  00:00    220.462\n",
       "2020-04-28 06:48:00  06:48    220.462\n",
       "2020-04-28 07:09:00  07:09    220.462\n",
       "2020-04-29 00:00:00  00:00    218.919\n",
       "2020-04-29 05:58:00  05:58    218.919\n",
       "2020-04-29 07:09:00  07:09    218.919\n",
       "\n",
       "[97 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dry_april "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit to start of year\n",
    "#x = np.arange(start_of_year['Mass (kg)'].size) # = array([0, 1, 2, ..., 3598, 3599, 3600])\n",
    "days1 = (start_of_year.index.values - start_of_year.index.values[0]).astype('timedelta64[D]')\\\n",
    "       / np.timedelta64(1, 'D')\n",
    "fit = np.polyfit(days1, start_of_year['Mass (kg)'], 1)\n",
    "fit_fn = np.poly1d(fit)\n",
    "\n",
    "#fit to Dry april\n",
    "days2 = (dry_april.index.values - dry_april.index.values[0]).astype('timedelta64[D]')\\\n",
    "        / np.timedelta64(1, 'D')\n",
    "fit2 = np.polyfit(days2, dry_april['Mass (kg)'], 1)\n",
    "fit_fn2 = np.poly1d(fit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x442dd8828>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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czJBAqR9//FHTp0+X67pasmSJJOmSSy6R4zi68cYbddJJJ3mcEAAAAADqj2rpkCgpHrwiaU3ZYkTJsMtS10v6rOT2LEnDjDEnlFxJo6ukf5Y9pz1QBVkiaVDJQ46kmZUJjvrtpJNOUmpqqhYvXqx169Zp9OjR2rhxo4LBoAKBgILBoN5++20VFxd7HRUAAAAAcBgVucpGH0nvSfpUBy77KUm/lzRcB7ZrWEnrJaVZa/NKjnlE0i2SinRgi8dbJY/PkfRra22uMaaz/nfZz5WSkq21heVloUMC5bHW6oMPPlAoFNLkyZO1a9cuxcbGKjU1VampqTrttNO8jggAAAAAdVK1XPazJqEggYras2ePZsyYIdd1tXDhQllr9Ytf/EKO42jw4MFq1qyZ1xEBAAAAoM6o1qGWQG1y4okn6qabbtL8+fO1ceNG/fGPf9S3336rX//61woEAkpOTtaCBQsUDoe9jgoAAAAA9RIdEqg3rLVaunSpQqGQJk2apO+//17t2rVTSkqKHMdRt27dvI4IAAAAALUSWzaACiooKNCsWbMUCoU0d+5cFRcX64ILLlAwGNTQoUPVvHlzryMCAAAAQK3Blg2ggho1aqQhQ4YoJydHmzdv1p///Gft2rVLt99+uwKBgIYNG6a33npLRUVFXkcFAAAAgDqJDgmghLVWK1askOu6mjhxonbs2KHo6GglJyfLcRydddZZXkcEAAAAgBqJLRtAFSksLFROTo5CoZBycnIUDoeVmJioYDCoYcOGqVWrVl5HBAAAAIAagy0bQBU54YQTdMMNN2jmzJnKzc3VM888o/379+uuu+5SdHS0Bg0apDfffFP79+/3OioAAAAA1Ep0SACVsGrVKoVCIWVlZWnr1q065ZRTlJSUJMdx1KNHD6/jAQAAAIAn6JCoA9Jz0uUf7Vd6TrrXUXAYPXv21DPPPKMtW7Zo5syZ6tOnj8aMGaOePXuqV69eevbZZ/Xtt996HTOi0nPSZTKMojKiyv3c8tkGAAAAUBYdEjWMf7RfYRuWz/hUNJIrPNQG27dvV3Z2tkKhkJYtWya/368BAwbIcRwNGDBADRs29DpitSr9zEoq93PLZxsAAACou+iQqAPSEtLkMz6lJaR5HQUV1KpVK911113617/+pc8++0y/+93vtHTpUt1www1q27atfvvb32r58uWqTcW/yij9rBqZcj+3fLYBAAAAlEWHBFANioqKtGDBArmuq5kzZ6qwsFBxcXFyHEfJyckKBAJeRwQAAACAKkOHBFBD+P1+XXXVVZo8ebLy8vL04osv6qSTTtL//d//qV27dhowYIBef/11FRQUeB0VAAAAADxBQQKeiR8bL5NhFD82vtx1pUMTK7K2JmrRooVuv/12ffTRR1qzZo0eeOABrV69WkOGDFF0dLTuvPNOLV26tM5u6QAAAACAw2HLBjxjMszB23bUkT+HZYcmHm1tbREOh7V48WK5rqvp06eroKBAZ5xxhhzHUUpKimJiYryOCAAAAAAVxpYN1CpxbeJ+8v1Iyg5BPNra2sLn8+nyyy9XVlaW8vPz9fe//12tW7fWww8/rPbt26tfv36aOHGi9uzZ43VUAAAAAKgWdEgANchXX32lzMxMhUIhbdy4Uc2aNdPQoUPlOI4uuugiGWOOfhIAAAAAiLBj6ZCgIAHUQMXFxXrnnXfkuq6mTp2qPXv2qEuXLge3dHTs2NHriAAAAABwEFs2UO9VdFBmTRcVFaVLLrlEoVBI+fn5evXVV9WuXTs9+uijio2N1WWXXabMzEzt3r3b66gAAAAAcEwoSKBO+WzrZz/5Xhc0bdpUwWBQS5Ys0bp165SRkaH169fLcRwFAgHdfPPNeuedd1RcXOx1VAAAAACoMAoSqFMqOiiztoqNjdXIkSP11Vdf6d1339XQoUM1bdo09e3bV6eddpoee+wxrV271uuYAAAAAHBUzJAAark9e/bojTfekOu6WrRokay1uvjii+U4jgYPHqymTZt6HREAAABAHccMCaAeOvHEE5WUlKQFCxZow4YNevLJJ5Wfn69bb71VgUBAKSkpWrhwIVs6AAAAANQodEgAdZC1VkuXLpXrupo0aZJ27typ9u3bKzU1VY7jqGvXrl5HBAAAAFCHcNlPAD+zd+9ezZo1S67rav78+SouLtaFF16oYDCoIUOGqHnz5l5HBAAAAFDLsWUDwM80btxYQ4cO1VtvvaVNmzbpqaee0s6dO5WWlqZAIKDhw4dr7ty5CofDXkcFAAAAUI/QIQHUQ9ZaLV++XK7rKjs7Wzt27FDbtm2VnJwsx3F05plneh0RAAAAQC1ChwSACjHGKDExUWPGjFFubq6mTp2qhIQE/eUvf9FZZ52l8847T2PHjtWOHTuq7Gem56TLZBhFZUQpPSe9Ss4ZPzZeJsMofmx8lZwPAAAAQOTQIQHgoG+++UYTJ06U67r65JNP1LBhQ11zzTVyHEdXXnml/H7/MZ/bP9qvsD2wLcRnfCoaWXTceU2GOXjbjqo9/ywDAAAA6ho6JAAcl1NPPVW/+93vtHr1aq1cuVJ33HGH3n77bQ0cOFDt2rXTfffdp08++eSYzp2WkCZJMjIHbx+vuDZxP/kOAAAAoPagQwJAufbt26e33npLoVBIs2fP1v79+9WrVy8Fg0ENHz5cbdq08ToiAAAAAI/RIQGgyjVs2FDXXnutpk+frtzcXD3//PMyxmjEiBFq27atrrvuOs2YMUP79u3zOioAAACAWoSCBIAKa926te6++24tX75cn3zyiUaMGKGPP/5Y119/vWJiYjRixAitWLFCNa3zKj0nXf7R/oPDNA+9DwAAACDy2LIB4LgUFRVp/vz5cl1XM2fO1L59+xQfHy/HcZSUlKRAIOB1xIMDNUuHaR56HwAAAMDxYcsGgIjz+/3q37+/pkyZory8PI0dO1Ynnnii7r//frVr105XX321pk6dqsLCQs8ypiWkyWd8B4dpHnofAAAAQOTRIQGgWnzxxRcKhULKzMxUbm6uWrRooeHDh8txHJ177rkyxhz9JAAAAABqhWPpkKAgAaBahcNhLVq0SK7r6o033lBBQYG6d+8ux3GUnJysmJgYryMCAAAAOE5s2QBQ4/h8Pl1xxRWaOHGi8vPzNX78eLVs2VIPPfSQOnTooCuvvFKTJk3S3r17vY4KVLuyA1Xjx8bLZBjFj433OhYAAIAn6JAA4In//ve/yszMVCgU0qZNm3TyySdryJAhCgaDuvDCC9nSgTqp7EDVsA0ffNyOqj3/LgYAADgcOiQA1Bpdu3bV448/rvXr12vRokW65pprlJWVpd69e6tbt2568skntXHjRq9jAlWq7EDVuDZxknTwOwAAQH1DhwSAGmPXrl2aNm2aXNfVO++8I2OMLr30UjmOoxtuuEFNmjTxOiIAAACAw6BDAkCt1rRpUwWDQb399ttau3atRo0apbVr1yo1NVWBQEC33HKL3n33XRUXF3sdFQAAAMBxokMCQI1WXFys999/X6FQSFOmTNGPP/6oTp06yXEcpaamqlOnTl5HBAAAAOo9OiQA1DlRUVG6+OKL9corryg/P18TJkxQ586dlZGRoc6dO6tv37569dVXtWvXLq+jAgAAAKgEChIAao0mTZooOTlZCxcu1Pr16/XEE08oNzdXt9xyiwKBgFJTU7V48WK2dAAAAAC1AFs2ANRq1lp99NFHCoVCmjRpkn744Qd16NBBqampchxHXbp08ToiAAAAUOexZQNAvWOM0UUXXaRx48YpPz9f2dnZ6t69u/74xz+qa9eu6tOnj/7+979r586dXkcFAAAAUMZRCxLGmPbGmCXGmDXGmM+NMSMOef5+Y4w1xrQuuf9/xphVJV+fGWPCxpiWhzmva4xZV2Ztz6p7WQDqo8aNG2vYsGGaO3euNm3apKeeeko7duzQbbfdpkAgoJtuuknz5s1TOByOWKb0nHSZDKOojCil56Qf9nn/aL/ix8aXuw4AAACoa466ZcMYEy0p2lq7whjTVNJySddZa/9tjGkv6WVJZ0hKsNZuO+TYgZJ+Z6299DDndSXNttZOrWhYtmwAqCxrrZYtWybXdZWdna3vvvtOMTExSk5OluM46t69e7X+fP9ov8L2QAHEZ3wqGll0xOdLHW4dAAAAUJNVy5YNa22etXZFye1dktZIiil5+hlJD0g6UlVjuKTsygQCgKpkjNG5556rF154QXl5eXr99dfVq1cvPf300zrzzDN1/vnn68UXX9R3331XLT8/LSHtQA6Zg7cPfd5nfIprE1fuOgAAAKCuqdRQS2NMrKR3JcVJ6ivpMmvtCGPMekmJZTskjDEnStosqYu1dsdhzuVKulBSoaRFkh6y1hYeZt1tkm6TpA4dOiRs2LChwnkB4Ejy8/M1ceJEua6rTz/9VA0bNtS1114rx3HUr18/+f1+ryMCAAAAtcaxdEhUuCBhjDlJ0juSnpQ0V9ISSVdYa3ceoSAxVFKytXbgEc4XLSlfUkNJ4yV9ba0dXV4GtmwAqGrWWq1atUqu62rixInatm2bTj311INbOuLj472OCAAAANR41XaVDWNMA0nTJGVZa6dLOk1SJ0mrS4oR7SStMMYEyhw2TOVs1yjZCmJLuiJelXReZYIDqJuONgSyqhlj1KtXLz333HPasmWL3njjDV144YV67rnndPbZZyshIUF/+9vftG3btqOfrI4qHbyZnpNeqd9P6aDO+LEUdQAAAPBzFRlqaSSFJO2w1t5zhDXrVaZDwhhzsqR1ktpba3cf4Zhoa21eyfmfkVRgrX2ovCx0SAB139GGQEbK1q1blZ2dLdd1tXLlSjVo0EBXX321HMdR//791aBBA09yeaH0d+IzPkmq8O/HZJiDt+2oim8PBAAAQO1TXR0SvSWlSLq0zCU6+x/lmOslzT+0GGGMmWOMaVtyN8sY86mkTyW1lvREZYIDqJuONgQyUtq0aaPf/va3WrFihVavXq27775bH374oa677jq1bdtW99xzj1atWuVZvkgqHbyZlpBWqd9P6aDO0u8AAABAWZUaauk1OiQAeKmoqEjz5s2T67qaNWuW9u3bp7PPPluO4ygpKUmnnnqq1xEBAAAAT1TbDAkAgOT3+zVgwAC9/vrrysvL0wsvvKBGjRrpvvvuU0xMjAYOHKhp06apsPBnFwwCAAAAcAgKEgAOK9LDJWubli1b6s4779TSpUv173//W/fff79WrFihQYMGqW3btrrrrrv0r3/9S7WpCw0AAACIJLZsADismjJcsjYJh8NauHChXNfVjBkzVFBQoDPPPFOO4yg5OVlt27Y9+kkAAACAWogtGwCqTE0ZLlmb+Hw+9evXT9nZ2crLy9O4cePUvHlzPfjgg2rfvr2uuuoqTZ48WQUFBV5HBQAAADxHhwQAVLMvv/xSmZmZyszM1KZNm3TyySdr2LBhchxHF1xwgQ5c/RgAAACovY6lQ4KCBABESHFxsZYsWSLXdTVt2jTt3btXp59+uhzHUUpKitq3b+91RAAAAOCYsGUDAGqwqKgoXXbZZZowYYLy8/P1yiuvKBAI6JFHHlHHjh11+eWX67XXXtOePXu8jgrUKek56fKP9jOgFwCAGoYOCQDw2Nq1aw9u6Vi3bp2aNm2qwYMHKxgMqk+fPmzpAI5T6ZBeBvQCAFB96JAAgFqoc+fOeuyxx/TVV1/p7bff1qBBgzR58mRdfPHF6tKli0aPHq3169d7HROotdIS0uQzPgb0AgBQw9AhAQA10O7duzV9+nS5rqvFixdLkvr27SvHcTRo0CCddNJJHicEAAAA/ocOCQCoI5o0aaKUlBQtWrRI69ev1+OPP67Nmzfr5ptvViAQkOM4WrJkiYqLi72OCgAAABwTChIAUKLs4LvDDcE79HmTYWQyjOLHxldrro4dO+oPf/iDvvzyS73//vu66aabNGPGDF166aXq3LmzRo4cqa+++qpaM1S1o73XAAAAqPvYsgEAJcoOvpP0syF4h3u+lB0V2X+W7t27VzNmzJDrulqwYIGsterdu7eCwaAGDx6sk08+OaJ5Kuto7zUAAABqF7ZsAMBxKDv47nBD8A59vlRcm7iIZ23cuLGGDx+uefPmaePGjfrTn/6k7dv7IISxAAAgAElEQVS36ze/+Y2io6OVlJSkBQsWKBwOH/1kHjjaew0AAIC6jw4JAKgjrLX65z//qVAopOzsbH3//feKiYlRSkqKHMfRGWec4XVEAAAA1FHH0iFBQQIA6qCCggK9+eabCoVCmjt3rsLhsM4//3wFg0ENHTpULVq08DoiAAAA6hC2bAAAJEmNGjXS4MGDNXv2bG3evFlPP/20du/erTvuuEPR0dEaOnSo5syZo6IiZjbURGUHfcaPjY/I8FRUDsNYAQA4fnRIAEA9Ya3VypUr5bquJk6cqO3btysQCCg5OVmO4yguLvKzMHB4ZYd+ejk8FUdW9nfEMFYAAOiQAACUwxijc845R88//7xyc3M1ffp0nX/++Xr22WcVHx+vxMREjRkzRtu3b/c6ar1XdtBn6dBUL4an4sgYxgoAwPGjQwIA6rlvv/1W2dnZcl1Xq1atUoMGDXT11VcrGAzqqquuUoMGDbyOCAAAgBqOoZYAgOOyevVqhUIhZWVl6dtvv1WbNm2UlJSkYDCoHj16eB0PAAAANRQFCQBAldi/f7/mzp2rUCikWbNmaf/+/erRo4ccx1FSUpJOOeUUryMCAACgBmGGBACgSjRo0EADBw7U1KlTlZeXpzFjxqhBgwa69957FRMTo2uuuUbTp0/Xvn37vI4KAACAWooOCQBAhf373/9WKBTShAkTlJeXp5YtW+qmm26S4zhKSEiQMcbriAAAAPAAWzYAABFRVFSkhQsXynVdzZgxQ4WFhTrrrLPkOI6Sk5MVHR3tdUQAAABEEFs2AAAR4ff7deWVV2rSpEnKz8/XSy+9pGbNmumBBx5Qu3bt1L9/f02ZMkUFBQVeRwUAAEANRUECAHBcmjdvrrS0NH344Yf64osv9NBDD+nTTz/V0KFDFR0drTvuuEMff/yxalNHXl2UnpMu/2i/Wj3VSibDKH5svNeRAABAPceWDQBAlQuHw1qyZIlc19X06dO1d+9edevWTY7jKCUlRe3atfM6Yr3jH+1X2IZ/8pgdVXv+BgAAADUbWzYAADWCz+fTr371K7322mvKz8/Xyy+/rFNOOUW///3v1aFDB11xxRXKysrSnj17vI5ab6QlpMlnfGrZqKUkKa5NnMeJAABAfUeHBAAgYr7++mtlZmYqFAppw4YNatq0qYYMGaJgMKjevXtzlQ4AAIBaiqtsAABqheLiYr377rsKhUJ6/fXXtXv3bp122mlyHEepqanq2LGj1xEBAABQCWzZAIAIKx0UmJ6T7nWUWiUqKkp9+/bVq6++qvz8fIVCIXXo0EEjR45UbGysLr30UoVCIf34449eRwUAAEA1oUMCAI5D6aBAn/GpaGSR13FqvfXr12vChAkKhUL6+uuv1aRJEw0aNEjBYFAXX3yxoqKoowMAANREdEgAQISVDgpMS0jzOkqdEBsbq0cffVT//e9/9d5772n48OGaPn26LrnkEp122mkaNWqUvv76a69jAgAAoArQIQEAqNH27NmjGTNmyHVdLVy4UNZa/eIXv5DjOBo8eLCaNWvmdUQAAIB6jw4JAECdc+KJJ+qmm27S/PnztXHjRv3xj3/Ut99+q1//+tcKBAJKTk7WggULFA6HvY4KAACASqBDAgBQ61hrtXTpUoVCIU2aNEnff/+92rVrp5SUFDmOo27dunkdETVEek66xi0fp5NPOFk7CnYork2cPr3zU69jAQBQ53DZTwBAvVNQUKBZs2YpFApp7ty5Ki4u1gUXXKBgMKihQ4eqefPmXkeEh0oHz5ZlR9Wev30AAKgt2LIBAKh3GjVqpCFDhignJ0ebN2/Wn//8Z+3atUu33367AoGAhg0bprfeektFRVwFpT4qHTzbslFLSVJcmziPEwEAgFJ0SAAA6hxrrVasWCHXdTVx4kTt2LFD0dHRSk5OluM4Ouuss7yOCAAAUKewZQMAgEMUFhYqJydHoVBIOTk5CofDSkxMVDAY1LBhw9SqVSuvIwIAANR6bNkAANQJ6Tnp8o/2K35svPyj/UrPSVd6TrpMhlFURpTSc9IrfK4TTjhBN9xwg2bOnKnc3Fw988wz2r9/v+666y5FR0dr0KBBevPNN7V///5qfEXHr/Q9Kfvayz52rO9PVeSJHxsvk2EUPza+2n8uAACoO+iQAADUOIcOIvQZnyQdfMxnfCoaeXwzIVatWqVQKKSsrCxt3bpVp5xyipKSkuQ4jnr06HFc564Ope9J2dde9jGpat+fyuQp+7tiYCQAAPUTHRIAgDqhdBBhXJs4+YxPaQlpSktIkyQZmYO3j0fPnj31zDPPaMuWLZo5c6b69OmjMWPGqGfPnurVq5eeffZZffvtt8f9c6pK6XtS9rWXfayq35/K5CkdFMnASAAAUBl0SAAAUGL79u3Kzs5WKBTSsmXL5Pf7NWDAADmOowEDBqhhw4ZeRwQAAKiRGGoJAEAV+fzzzxUKhTRhwgTl5+erVatWuummm+Q4js455xwZY7yOCAAAUGOwZQMAgCpy1lln6f/9v/+nTZs2ac6cObrssss0fvx4JSYm6uyzz9bTTz+t/Px8r2OiijCkEwCAyDtqh4Qxpr2kTEkBScWSxltrnyvz/P2S/iypjbV2mzGmr6SZktaVLJlurR19mPN2kjRJUktJKySlWGv3lZeFDgkAgJe+++47TZ48WaFQSB9//LF8Pp/69eunYDCogQMHqlGjRl5HxDFiSCcAAMenujokiiTdZ63tLukCSenGmDNLfmB7SZdL2njIMe9Za3uWfP2sGFHiKUnPWGu7SvpO0q2VCQ4AQKS1aNFCt99+uz766COtWbNGDzzwgFavXq0hQ4YoOjpad955p5YuXaratB0SBzCkEwCAyKv0DAljzExJY6y1C4wxUyU9rgMdEYllOiTut9ZeXc45jKStkgLW2iJjzIWSHrPW9ivvZ9MhAQCoacLhsBYvXizXdTV9+nQVFBTojDPOkOM4SklJUUxMjNcRAQAAql21z5AwxsRK6iVpqTHmGklbrLWrD7P0QmPMamPMW8aYsw7zfCtJ31trSy+SvlnSYf9iM8bcZoxZZoxZtnXr1srEBQCg2vl8Pl1++eXKyspSfn6+/v73v6t169Z6+OGH1aFDB/Xr10/Z2dnau3ev11ErhFkK5eP9AQCg6lS4Q8IYc5KkdyQ9KWmupCWSrrDW7jTGrNf/OiSaSSq21v5ojOkv6bmSbRllz9VG0kfW2i4l99tLmmOtLfff6HRIAABqi6+++kqZmZkKhULauHGjmjVrpqFDh8pxHF100UU19iodzFIoH+8PAACHV20dEsaYBpKmScqy1k6XdJqkTpJWlxQj2klaYYwJWGt/sNb+KEnW2jmSGhhjWh9yym2Smhtj/CX320nKrUxwAABqsi5dumj06NFat26dFi9erOuuu05ZWVnq06ePTj/9dD3xxBPasGGD1zF/hlkK5eP9AQCg6lTkKhtGUkjSDmvtPUdYs17/65AISPrGWmuNMedJmiqpoz3kBxljXpc0zVo7yRjzkqRPrLVjy8tChwQAoDbbtWuXpk2bplAopLfffluSdOmll8pxHN14441q0qSJtwEBAACOUXV1SPSWlCLpUmPMqpKv/uWsHyTpM2PMaknPSxpWWowwxswxxrQtWfegpHuNMV/pwEyJVyoTHACA2qZp06YKBoNasmSJ1q1bp4yMDK1fv16O4ygQCOjmm2/WO++8o+LiYq+jAgAAVLtKX2XDS3RIAADqGmut3n//fYVCIU2ZMkW7du1SbGysHMdRamqqOnfu7HXE45aek65xy8epe+vuWrNtjdIS0iRJ45aPU1pCml4Y8MLPjokfG6/Ptn6muDZx+vTOTyMdGQAAVNKxdEhQkAAAoIbYs2eP3njjDbmuq0WLFslaq4svvliO42jw4MFq2rSp1xGPSekgyFI+45Okg8Mhi0YW/ewYk/G/oZ8MjAQAoOar9st+AgCA6nPiiScqKSlJCxYs0IYNG/Tkk08qPz9ft956qwKBgFJSUrRw4cJat6WjdBBkXJu4gwMhyw6HPBwGRgIAUPfRIQEAQA1mrdXSpUvluq4mTZqknTt3qn379kpNTZXjOOratevRTwIAAFDN2LIBAEAdtnfvXs2aNUuu62r+/PkqLi7WhRdeqGAwqCFDhqh58+ZeRwQAAPUUWzYAAKjDGjdurKFDh+qtt97Spk2b9NRTT2nnzp1KS0tTIBDQ8OHDNXfuXIXD4aOfDAAAwGN0SAAAUItZa7V8+XK5rqvs7Gzt2LFDbdu2VXJyshzH0Zlnnul1RAAAUA+wZQMAgHqssLBQs2fPVigU0pw5cxQOh3XuuecqGAxq2LBhatmypdcRAQBAHcWWDQAA6rETTjhBN954o2bNmqUtW7bor3/9qwoLC5Wenq7o6GgNHjxYs2fPVlHRzy+zCQAAEGl0SAAAUMetWrVKrusqKytL27Zt06mnnqqkpCQ5jqOzzz7b63gAAKAOoEMCAAD8TM+ePfXss89qy5YtmjFjhi666CL97W9/U48ePXTOOefo+eef19atW72OWWHpOenyj/YrPSdd6TnpMhlGURlRSs9J9zoaAACoBDokAACoh7Zt26bs7Gy5rqsVK1bI7/drwIABCgaD6t+/vxo2bOh1xCPyj/YrbMPyGZ8kKWwPXFXEZ3wqGsl2FAAAvECHBAAAqJDWrVvr7rvv1vLly/XJJ59oxIgR+vjjj3X99dcrJiZGI0aM0IoVK1QT/8dFWkKafMantIQ0pSWkSZKMzMHbAACgdqBDAgAASJKKioo0f/58ua6rmTNnat++fYqPj5fjOEpKSlIgEPA6IgAAqKHokAAAAMfM7/erf//+mjJlivLy8jR27FideOKJuv/++9WuXTtdffXVmjp1qgoLC72OCgAA6gA6JAAAQLm++OILhUIhZWZmKjc3Vy1atNDw4cPlOI7OPfdcGWO8jggAADx2LB0SFCQAAECFhMNhLVq0SK7r6o033lBBQYG6d+8ux3GUnJysmJgYryMCAACPsGUDAABUG5/PpyuuuEITJ05Ufn6+xo8fr5YtW+qhhx5Shw4ddOWVV2rSpEnau3ev11EBAEAtQIcEAAA4Lv/973+VmZmpUCikTZs26eSTT9bQoUPlOI4uvPBCtnQAAFAPsGUDAAB4pri4WG+//bZc19W0adO0Z88ede3aVY7jKCUlRR06dPA6IgAAqCZs2QAAAJ6JiorSpZdeqszMTOXn5+vVV19V27Zt9Yc//EGxsbH61a9+pQkTJmj37t1eRwUAADUABQkAAFDlmjZtqmAwqLfffltr167VqFGjtHbtWqWmpioQCOiWW27Ru+++q+LiYq+jAgAAj7BlAwAARERxcbHef/99hUIhTZkyRT/++KM6deokx3GUmpqqTp06eR0RAAAcI7ZsAACAGisqKkoXX3yxXnnlFeXn52vChAnq3LmzMjIy1LlzZ/Xt21evvvqqdu3a5XVUAAAQARQkAABAxDVp0kTJyclauHCh1q9fryeeeEK5ubm65ZZbFAgElJqaqsWLF7OlAwCAOoyCBAAA8FSHDh30yCOP6D//+Y8++OADJScna+bMmbrsssvUqVMnPfroo/rqq6/KPUd6Trr8o/1Kz0mPUGoAAHC8mCEBAABqnL1792rmzJlyXVcLFixQcXGxevfuLcdxNGTIEJ188sk/We8f7VfYhuUzPhWNLPIoNQAA9RczJAAAQJ3QuHFjDRs2THPnztWmTZv01FNPaceOHbrtttsUCAR00003ad68eQqHw5KktIQ0+YxPaQlpHicHAAAVRYcEAACoFay1WrZsmVzXVXZ2tr777jvFxMQoOTlZjuOoe/fuXkcEAKDeOpYOCQoSAACg1iksLNSbb76pUCikt956S+FwWOedd56CwaCGDRumFi1aeB0RAIB6hS0bAACg1jrcYMr4sfEyGUbxY+N/svaEE07QoEGD9Oabb2rz5s36y1/+or179+rOO+9UIBDQkCFDlJOTo6Ii7+ZJlL6eVk+1kskwMhmGoZsAAJRBhwQAAKgRDjeY0mSYg8/bUeX/zWKt1apVq+S6riZOnKht27YpEAgoKSlJjuMoPj6+3OOrWunrKYuhmwCAuooOCQAAUGsdbjBlXJu4n3wvjzFGvXr10nPPPactW7bojTfe0AUXXKDnnntOZ599thISEvS3v/1N27Ztq7bXUFbp62nZqOVPHgMAAAfQIQEAAOq0rVu3Kjs7W67rauXKlWrQoIGuvvpqOY6j/v37q0GDBl5HBACg1mOoJQAAQDk++eQThUIhZWVl6ZtvvlHr1q2VlJSkYDConj17eh0PAIBai4IEAABABRQVFWnevHlyXVezZs3Svn37dPbZZ8txHCUlJenUU0/1OiIAALUKMyQAAAAqwO/3a8CAAXr99deVl5enF154QY0aNdJ9992nmJgYDRw4UNOmTVNhYaHXUQEAqLPokAAAACixZs0ahUIhTZgwQbm5uWrZsqWGDx8ux3GUmJgoY8zRTwIAQD3Elg0AAIAqEA6HtXDhQrmuqxkzZqigoEBnnnmmHMdRcnKy2rZt63VEAABqFLZsAAAAVAGfz6d+/fopOztbeXl5GjdunJo3b64HH3xQ7du311VXXaXJkyeroKDA66gAANRaFCQAAADK0bx5c91222364IMP9J///EcPP/ywPv/8cw0bNkyBQEC33367PvroIx1r12l6Trr8o/1q9VQrmQyj+LHxVfwKAAComdiyAQAAUEnFxcVasmSJXNfVtGnTtHfvXp1++ulyHEcpKSlq3759hc/lH+1X2IZ/8pgdVXv+PgMAQGLLBgAAQERERUXpsssu04QJE5Sfn69XXnlFgUBAjzzyiDp27KjLL79cWVlZ2rNnz1HPlZaQJp/xqWWjlpKkuDZx1R0fAIAagQ4JAACAKrJ27VplZmYqMzNT69atU9OmTTV48GAFg0H16dOHq3QAAOosrrIBAABQAxQXF+u9995TKBTSlClTtHv3bnXu3FmO4yg1NVWxsbFeRwQAoEqxZQMAAKAGiIqK0i9/+Uv94x//0DfffKPMzEzFxsZq1KhR6tSpky655BK5rqsff/zR66gAAHiGggQAAEA1atKkiVJSUrRo0SKtX79ejz/+uDZv3qybb75ZgUBAjuNoyZIlKi4u9joqAAARxZYNAACACLPW6sMPP1QoFNLkyZP1ww8/qGPHjkpNTVVqaqq6dOnidUQAACqlWrZsGGPaG2OWGGPWGGM+N8aMOOT5+40x1hjTuuR+kjHmk5KvD40xPY5wXtcYs84Ys6rkq2dlggMAANRWxhj17t1b48ePV35+viZOnKhu3brpiSeeUNeuXdWnTx+9/PLL2rlzp9dRAQCoNhXZslEk6T5rbXdJF0hKN8acKR0oVki6XNLGMuvXSfqltfZsSY9LGl/Ouf/PWtuz5GvVMb0CAACAWqxx48YaPny45s2bp40bN+pPf/qTtm/frt/85jeKjo5WUlKSFixYoHA47HVUAACq1FELEtbaPGvtipLbuyStkRRT8vQzkh6QZMus/9Ba+13J3Y8ltavSxAAAAHVUu3bt9NBDD+nf//63Pv74YwWDQc2ZM0dXXHGFOnbsqIcfflhffPFFxHOl56TLP9qv9Jx0xY+Nl8kwih8bH/EcAIC6pVIzJIwxsZLelRQnqa+ky6y1I4wx6yUlWmu3HbL+fklnWGt/fZhzuZIulFQoaZGkh6y1hYdZd5uk2ySpQ4cOCRs2bKhwXgAAgNquoKBAb775pkKhkObOnatwOKzzzz9fwWBQQ4cOVYsWLao9g3+0X2Ebls/4FLb/69Swo2rPLDIAQPWq1st+GmNOkjRN0j06sI3jEUkjy1l/iaRbJT14hCUPSzpD0rmSWh5pnbV2vLU20Vqb2KZNm4rGBQAAqBMaNWqkwYMHa/bs2dq8ebOefvpp7d69W3fccYeio6M1dOhQzZkzR0VFRdWWIS0hTT7jU1pCmuLaxEnSwe8AAByrCnVIGGMaSJotaZ619q/GmHgd6GrYU7KknaRcSedZa/ONMWdLekPSVdbaLytw/r6S7rfWXl3eOq6yAQAAcOAqHStXrpTrupo4caK2b9+uQCCg5ORkOY6juDiKBQCAyDqWDomjFiSMMUZSSNIOa+09R1izXiVbNowxHSQtlpRqrf2wnPNGW2vzSs7/jKQCa+1D5WWhIAEAAPBT+/btU05OjkKhkHJyclRUVKSEhAQFg0ENHz5crVq18joiAKAeqK4tG70lpUi6tMwlOvuXs36kpFaSxpasPVhBMMbMMca0LbmbZYz5VNKnklpLeqIywQEAACA1bNhQ119/vWbMmKEtW7bo2WefVTgc1t13363o6GjdeOONmjVrlvbv3+91VAAAfqJSQy299v/bu/P4qKr7/+Pvk4RFShHZTFhCFP26NAEU3CgqqChC1SpbIIGZn6BBY6vftu4FC611q3WtWjfmBgOiiGwBBFdEFAQFWWVRRJawyJeCECJJzu+Pe4NDmh2SO0lez8djHpncufeez0w+HO6cOeczzJAAAAAon+XLl8txHGVmZmrnzp1q2bKlUlJSFAwG1alTJ7/DAwDUMlWyZCOSMCABAABQMYcPH9acOXPkOM6RmRKdOnVSIBBQSkqKWrVq5XeIAIBaoEq/ZQMAAAA1T7169XTNNddo8uTJ2r59u5599lnVq1dPf/jDH9SmTRtde+21mjJlin766Se/QwUA1DHMkAAAAKiDVq9eLcdxNH78eG3fvl3NmjXTkCFDFAgE1KVLF7l1xwEAKB9mSAAAAKBczj77bD3yyCPavHmzZs+erV69eumll17Seeedp6SkJD322GPavn2732HCZ+lZ6YoZG6P0rPSj7gPA8cAMCQAAAEiS9u7dq0mTJslxHH366aeKiorSVVddpWAwqGuvvVYNGzb0O0RUs5ixMcq3+Yo20ZJ05H7e6DyfIwMQaZghAQAAgEpr2rSp0tLStHDhQq1du1b33HOPVqxYoUGDBikuLk633HKLPvvsM9WkD7RwbNK6pCnaRCutS9pR9wHgeGCGBAAAAEqUn5+vDz74QKFQSFOmTFFOTo7OOOMMBQIBDR06VG3btvU7RABABGCGBAAAAI6r6OhoXXHFFXrttdeUnZ2tl19+Wa1atdJ9992n+Ph4XXnllcrMzNTBgwf9DhUAUMMwIAEAAFBLJT2XJDPGKOm5pONyviZNmmj48OGaP3++NmzYoFGjRmndunVKTU1VbGysRowYoQULFrCkAwBQLizZAAAAqKXMmJ+/utM+UDXXfAUFBZo/f74cx9Gbb76pAwcOqEOHDgoEAho2bJjat29fJe0CACILSzYAAABwRGLLxKN+VoWoqCj16NFD48aNU3Z2thzHUXx8vEaPHq2EhARddtllchxHP/74Y5XFAAComZghAQAAgONu06ZNGj9+vBzH0caNG/WLX/xC/fv3VzAY1CWXXKKoKD4XA4DapDIzJBiQAAAAQJWx1uqTTz6R4ziaNGmS9u/fr4SEBA0bNkzDhg1Thw4d/A4RAHAcsGQDAAAAEcUYo+7du+ull15Sdna2MjMzdfrpp+uvf/2rTjvtNF1yySV65ZVXtG/fPr9DBQBUMwYkAAAAUC0aNWqkIUOGaO7cudq8ebP+/ve/a+fOnRoxYoRiY2OVmpqqefPmKT8/3+9QAQDVgCUbAAAA8I21VosWLZLjOHr99de1d+9etW3bVkOHDlUgENAZZ5zhd4gAgHKghgQAAABqrEOHDmn69OlyHEdz5sxRQUGBLrzwQgWDQQ0aNEhNmzb1O0QAQAmoIQEAAIAaq2HDhho4cKCysrK0ZcsWPfbYY9q/f79Gjhyp2NhYJScna/bs2crLy/M7VADAccCABAAAQIRIz0pXzNgYpWel+x2K7+Li4vSnP/1JK1as0JIlS3TTTTdp3rx56tOnj+Lj43XXXXdp1apVfocJoJolPZckM8Yo6bkkv0PBccCSDQAAgAgRMzZG+TZf0SZaeaOZBVBUbm6usrKy5DiOsrKylJ+fr65duyoYDCo5OVnNmzf3O0QAVcyMMUfu2wdqznvZuoAlGwAAADVYWpc0RZtopXVJ8zuUiNSgQQPdcMMNmjZtmrZt26YnnnhChw8f1m233aa4uDj1799fM2bM0OHDh/0OFUAVSWyZeNRP1GzMkAAAAECNtmzZMjmOo8zMTO3atUutWrVSSkqKgsGgOnbs6Hd4AFAnMEMCAAAAR9SVmhSdO3fWE088oa1bt2ratGnq3r27nn32WXXq1EnnnHOOnnrqKe3atcvvMAEARTBDAgAAoJaqyzUpfvjhB02cOFGO42jJkiWKiYlR3759FQgE1LdvX9WvX9/vEAGgVmGGBAAAAI6oyzUpmjdvrttuu02ff/65VqxYoTvuuEOLFi3SDTfcoNatW+v3v/+9li5dqpr04RwA1DbMkAAAAECdkJeXp7lz58pxHE2bNk25ublKTExUIBBQamqqYmNj/Q4RAGosZkgAAAAAJYiJiVGfPn00adIkbd++Xc8//7waN26sO++8U23btlXfvn315ptv6tChQ36HCgB1AgMSAAAAqHaFBTeTnkuSGWNkxhglPZdU6jGF+5a1X3mcdNJJGjlypD799FOtWbNGd911l5YvX66BAwcqLi5Ot956qxYtWlRlSzrSs9JlxhhFjYkqtuhoeEHSsvYFgJqKJRsAAACodoUFN4uyD5R8bWrGmHLtV1n5+fl6//33FQqFNGXKFB06dEhnnnmmAoGAhg4dqjZt2hy3tsKff3FFR8MLkkoqdV8AiAQs2QAAAECNUFhwM7Fl4pFt4feLU/h4WftVVnR0tHr16qXMzExlZ2frpZdeUosWLXTvvfcqPj5eV111lSZOnKicnJxjbquw0KiRKbboaHhB0rL2BYCaihkSAAAAQCk2bNigjIwMOY6jzZs3q0mTJho0aJACgYC6desmY0WislcAACAASURBVEzZJwGAWq4yMyQYkAAAAADKoaCgQB999JFCoZAmT56sgwcP6rTTTjuypKN9+/Z+hwgAvmHJBgAAAMqtaGFJiiaWLioqSj179pTjOMrOzta4cePUtm1bjRo1SgkJCbr88suVkZGhAwcO+B0qANQIzJAAAACoo4orLEnRxIrbtGnTkSUd33zzjRo3bqz+/fsrGAzq4osvVlQUnwECqP2YIQEAAIByK1pYkqKJlZOQkKDRo0drw4YNmj9/vgYNGqS33npLPXr0UIcOHfSXv/xF33zzjd9hAkDEYYYEAAAAcJwdPHhQb7/9tkKhkN577z1Za3XJJZcoEAhowIAB+uUvf+l3iABwXDFDAgAAAIgAjRo1UkpKiubNm6fvvvtODz74oLKzszV8+HDFxsZq6NChevfdd1VQUOB3qADgGwYkAAAAgCrUrl073XfffVq7dq0WLlyooUOHasaMGerVq5cSEhL05z//WevXr6/WmAoLmlLEFICfWLIBAAAAVLOcnBxNnz5doVBIc+fOVUFBgS666CIFg0ENHDhQTZs2rdL2CwuaUsQUwPHCkg0AAACgBjjhhBM0aNAgzZ49W99//70eeeQR/ec//1FaWppiY2M1ePBgzZkzR/n5+WWfrBIKC5pSxBSAn5ghAQAAAEQAa62WLl2qUCikiRMnas+ePWrdurVSU1MVCAR09tln+x0iAJSoMjMkGJAAAAAAIkxubq5mzpwpx3E0a9Ys5efn67zzzlMwGFRycrKaNWvmd4gAcBSWbAAAAAC1QIMGDdSvXz9Nnz5dW7du1T//+U/l5uYqPT1dcXFxGjBggGbOnKm8POo/+KmwOGjSc0kUCQUqgRkSAAAAQA2xbNkyhUIhZWZmavfu3Tr55JOVkpKiQCCgjh07+h1enVNYHLQQRUJRlzFDAgAAAKjFOnfurCeffFJbt27V1KlT1a1bNz3zzDPq1KmTzj33XD399NPatWuX32HWGYXFQRNbJlIkFKgEZkgAAAAANdju3bs1ceJEhUIhffHFF4qJidFvfvMbBQIB9enTR/Xr1/c7RAB1AEUtAQAAgDpsxYoVchxHr732mnbs2KEWLVpoyJAhCgQCOuecc2SM8TtEALVUlSzZMMa0M8Z8YIxZY4xZZYy5vcjjfzLGWGNMC+93Y4x52hizwRjzlTHm3BLO28UYs8Lb72lD7wgAAFCiwuJ56VnpSs9KlxljFDUmiiJ6OEpSUpL+8Y9/aMuWLcrKylLPnj31wgsvqEuXLurUqZMef/xxZWdn+x0mAEgqxwwJY0ycpDhr7RfGmF9KWirpt9ba1caYdpJelnSmpC7W2t3GmD6Sfiepj6QLJD1lrb2gmPMulnS7pM8kzZL0tLV2dmmxMEMCAADUVYXF86JNtCQdKaRHET2UZc+ePZo0aZJCoZAWL16s6Oho9e7dW8FgUNdcc40aNGjgd4gAaoEqmSFhrd1urf3Cu79f0hpJbbyHn5B0l6TwUY3rJGVY12eSmnqDGuGBxklqYq391LojIhmSfluRwAEAAOqSwuJ5aV3SjhTOMzIU0UOZmjVrpltuuUWLFi3S6tWrdeedd+rLL7/UgAEDFBcXp/T0dC1evFg1aSk3gNqhQjUkjDEJkuZLSpTUQ9Ll1trbjTGbJHX1ZkjMlPSwtXaBd8x7ku621i4JO09Xb58rvN8v9vb5TTFt3izpZkmKj4/v8t1331XiaQIAAAAolJ+fr/fee0+hUEhvv/22Dh06pLPOOkuBQECpqalq06ZN2ScBgDBV+rWfxpjGkt6SdIekPEn3Sxpd3K7FbCs66lGefdyN1r5ore1qre3asmXL8oYLAAAAoATR0dG68sorNWHCBGVnZ+vFF19Us2bNdM899yg+Pl69e/fW66+/rpycHL9DBVCLlWtAwhhTT+5gRKa1doqkDpJOkbTcmx3RVtIXxphYSVsktQs7vK2kbUVOucXbXto+AAAAAKrYiSeeqJtuukkLFizQunXrdN9992n16tUaPHiw4uLilJaWpoULF7KkA5USXpC3tH0o1Fs3laeopZHkSNpjrb2jhH026eclG30l3aafi1o+ba09v5hjPpdb/HKR3KKWz1hrZ5UWC0UtAQAAgKpXUFCgDz/8UKFQSG+99ZYOHjyo008/XYFAQEOHDlV8fLzfIaKGCC/IW1IB3sJ9JAr11mRVtWTj15KGSrrMGLPMu/UpZf9Zkr6RtEHSS5JuDQtwWdh+t8j9ho4NkjZKKvUbNgAAAABUj6ioKF122WXKyMhQdna2xo0bp9atW+vPf/6zEhISdMUVV2j8+PE6cOCA36EiwoUX5C1tH4lCvXVRhYpa+o0ZEgAAAIB/vv32W2VkZMhxHH377bdq3LixBgwYoGAwqO7duysqqtwl6gDUMpWZIcGABAAAAIAKKSgo0IIFC+Q4jt544w39+OOPOuWUUxQIBDRs2DCdcsopfocIoJpV6bdsAAAAIPKFF5ArvJ/0XFKZReWqou2aXqQu/Pk0f6S5zBijhn9rWOprWZ4CfrVBVFSULrnkEr3yyivKzs7W+PHjdeqpp2rMmDE69dRT1aNHD40bN0779+/3O1QAEYwZEgAAALVIeAE5SUcKxUlVXyyupLZrapG68OcT/jpKJT+n8hTwq802b96s8ePHy3EcrV+/Xo0aNVK/fv0UDAbVo0cPlnQAtRgzJAAAAOq48AJyhfcTWyaWWVSuKtqWanaRuvDn06xhM0lSg+gGpb6W5SngV5vFx8fr/vvv19dff61PPvlEqampmjZtmi6//HKdcsopGjVqlDZs2OB3mAAiBDMkAAAAAFSZnJwcTZs2TaFQSPPmzVNBQYF+/etfKxAIaODAgTrxxBP9DhHAccAMCQAAAAAR5YQTTlBycrLmzJmjzZs36+GHH9aePXt08803KzY2VkOGDNE777yj/Pz8sk8GoFZhQAIAAKCGqisFFFF7tGnTRnfffbdWrVqlxYsX68Ybb9ScOXPUu3dvtW/fXvfcc4/WrFnjd5ioBZKeS5IZY5T0XJLfoaAULNkAAACooep6AUXUDrm5uZoxY4Ycx9Hs2bOVn5+v888/X8FgUMnJyTrppJP8DhE1kBljjty3D9Sc97w1GUs2AAAA6pC6XkARtUODBg3Uv39/zZgxQ1u2bNHjjz+unJwc3XrrrYqNjdXAgQOVlZWlvDwG3VB+iS0Tj/qJyMQMCQAAAAARxVqrZcuWKRQKacKECdq9e7diY2OVkpKiQCCgpCSm4QORpjIzJBiQAAAAABCxfvrpJ82aNUuO42jmzJnKy8vTueeeq2AwqMGDB6tFixZ+hwhALNkAAAAA6pSihU3Ts9JlxhhFjYmqNcVO69evr9/+9rd6++23tW3bNj311FOy1ur3v/+9WrdurRtuuEHTpk3T4cOH/Q4VQAUxQwIAAACooYoWNi38XVKtL3b61VdfyXEcZWZmaseOHWrRooVSUlIUDAbVuXNnv8MD6hxmSAAAAAB1SNHCpoU/jUytL3basWNHPf7449qyZYtmzpypHj166Pnnn9c555yjTp066YknntCOHTv8DhNAKZghAQAAAKBW2LNnj15//XU5jqPFixcrOjpaV199tYLBoH7zm9+oQYMGfocI1FoUtQQAAAAASWvWrJHjOBo/fry2bdumZs2aafDgwQoEAuratauMMX6HCNQqLNkAAAAAUKziCmDGjI1R0nNJR22vLc466yw9/PDD2rx5s+bMmaMrr7xSr7zyis4//3wlJibq0Ucf1bZt2/wOs0Kquohp0nNJMmOMkp7ja1VRPZghAQAAANQBpRXAlGp/EUxJ2rt3r9544w05jqOFCxcqKipKV155pYLBoK677jo1bNjQ7xBLVdVFTM2Yn2eN2AdqzvtERAZmSAAAAAAoVnEFMKNNtBJbJh61vTZr2rSpbr75Zn3yySf6+uuvde+992rVqlVKTk5WbGysRo4cqU8//VSR+qFtVRcxTWyZeNRPoKoxQwIAAABAnVVQUKAPPvhAoVBIb731lnJycvQ///M/CgQCGjp0qNq1a+d3iECNwAwJAAAAAKiAqKgoXX755Ro/fryys7P1yiuvKDY2Vvfff7/at2+vXr16KTMzUwcPHvQ7VKDWYUACAAAANV54sb+Gf2soM8ao4d8iux4AIk+TJk1044036qOPPtLGjRs1evRobdy4UampqYqNjdXw4cP18ccfR+ySDqCmYckGAAAAarzwYn/hhRopzIdjVVBQoI8//liO4+iNN97QgQMHdOqppyoQCGjYsGFKSEjwO0QgIrBkAwAAAHVSeLG/BtENJOnIT+BYREVF6dJLL9Wrr76qHTt2KCMjQwkJCXrggQd0yimnqGfPngqFQvrxxx/9DhWocZghAQAAAAAV9N1332n8+PFyHEcbNmzQL37xC/Xr10/BYFCXXnqpoqL47Bd1S2VmSDAgAQAAAACVZK3VwoUL5TiOJk2apH379ql9+/YaNmyYhg0bptNOO83vEIFqwYAEAAAAAPgkJydHU6dOVSgU0rx582StVffu3RUIBDRgwACdeOKJfocIVBlqSAAAAACAT0444QQNHjxY77zzjjZv3qyHHnpIu3fv1k033aS4uDilpKRo3rx5ys/PL/tkQB3ADAkAAAAAqCLWWi1evFiO42jixInau3ev2rRpo6FDhyoQCOjMM8/0O0TguGDJBgAAAABEqEOHDmnGjBkKhUJ65513lJ+frwsuuEDBYFCDBg3SSSed5HeIQKWxZAMAAAAAIlTDhg01YMAAZWVlacuWLfrHP/6hAwcO6JZbblFcXJwGDRqkWbNmKS8vz+9QgWrBgAQAAABQBdKz0mXGGEWNiVJ6Vrrf4SDCxMbG6o9//KO++uorLV26VDfffLPee+899e3bV+3atdOdd96plStX+h3mMePfAUrDkg0AAACgCsSMjVG+dYsXRpto5Y3mU2+U7qefflJWVpYcx1FWVpby8vLUpUsXBYNBDR48WM2bN/c7xArj30HdwZINAAAAIEKkdUmTJBmZI/eB0tSvX1/XX3+9pk6dqq1bt+rJJ59Ufn6+fve73ykuLk79+vXT9OnTdfjwYb9DLTf+HaA0zJAAAAAAgAi2fPlyOY6jzMxM7dy5Uy1btlRKSoqCwaA6derkd3iAJL5lAwAAAABqrcOHD2vOnDlyHOfITIlOnTopEAgoJSVFrVq18jtE1GEs2QAAAACAWqpevXq65pprNHnyZG3fvl3PPvus6tWrpz/84Q9q06aNrr32Wk2ZMkU//fST36EC5cIMCQAAAACowVavXi3HcTR+/Hht375dzZo105AhQxQMBnXuuefKGON3iKgDWLIBAAAAAHVUXl6e3n33XYVCIU2dOlW5ubn61a9+pUAgoNTUVMXFxfkdImoxlmwAAAAAQB0VExOj3r176/XXX1d2drZeeOEFNWnSRHfddZfatm2rPn366I033tChQ4f8DhWQxAwJAAAAAKjVvv76a2VkZCgjI0NbtmxR06ZNlZycrEAgoAsuuIAlHTgumCEBAAAAADjKGWecoQcffFCbNm3SvHnz1LdvXzmOo4suukhnnXWWHnroIW3ZssXvMKtNela6YsbGKD0rXZKU9FySzBijpOeSfI6s7mGGBAAAAADUMfv27dObb74px3H08ccfyxijK664QoFAQNdff70aNWrkd4hVJmZsjPJtvqJNtPJG58mM+XmGiH2g5rw/jjTMkAAAAAAAlKlJkyYaPny45s+frw0bNmjUqFFat26dUlNTFRsbqxEjRmjBggWqSR9gl1dalzRFm2ildUmTJCW2TDzqJ6oPMyQAAAAAACooKND8+fPlOI7efPNNHThwQB06dFAgENCwYcPUvn17v0NEBGOGBAAAAACgUqKiotSjRw+NGzdO2dnZchxH8fHxGj16tBISEnTZZZfJcRz9+OOPfoeKWoIBCQAAAADAURo3bqxhw4bp/fff17fffquxY8dq8+bNCgaDio2NVTAY1IcffqiCggK/Q0UNVuaAhDGmnTHmA2PMGmPMKmPM7d72vxpjvjLGLDPGzDXGtPa23+ltW2aMWWmMyTfGNCvmvCFjzLdh+3Y+/k8PAAAAAHAsEhISNGrUKK1fv14ff/yxBg8erClTpqhnz57q0KGDHnjgAW3cuNHvMFEDlVlDwhgTJynOWvuFMeaXkpZK+q2kLdbafd4+v5d0trV2ZJFjr5H0v9bay4o5b0jSTGvt5PIGSw0JAAAAAPDfwYMHNXXqVIVCIb377ruy1uriiy9WIBDQgAED1KRJE79DRDWrkhoS1trt1tovvPv7Ja2R1KZwMMLzC0nFjWwMljSxIgEBAAAAACJbo0aNNGTIEM2dO1ebN2/W3//+d+3cuVMjRoxQbGysUlNT9e677yo/P9/vUBHBKlRDwhiTIOkcSYu83x80xnwvKUXS6CL7NpLUW9JbpZzyQW/ZxxPGmAYltHmzMWaJMWbJrl27KhIuAAAA6rj0rHTFjI1Rela636EAtVbbtm117733as2aNfr0008VCASUlZWlXr16KSEhQffff7/WrVvnd5iIQOX+2k9jTGNJH0l60Fo7pchj90pqaK19IGzbIEmp1tprSjhfnKRsSfUlvShpo7V2bGkxsGQDAAAAFREzNkb5Nl/RJlp5o/P8DgeoMw4dOqTp06fLcRzNmTNHBQUFuvDCCxUMBjVo0CA1bdrU7xBxnFXZ134aY+rJnemQWXQwwjNBUr8i25JVynINbymItdbmShon6fzyhQwAAACUT1qXNEWbaKV1SfM7FKBOadiwoQYOHKisrCxt2bJFjz76qPbv36+RI0cqNjZWycnJmj17tvLyGCisy8pT1NJIciTtsdbeEbb9dGvteu/+7yRdaq3t7/1+oqRvJbWz1h4o4bxx1trt3vmfkHTIWntPabEwQwIAAAAAaiZrrZYuXSrHcTRhwgTt2bNHcXFxSk1NVSAQ0K9+9Su/Q8QxqMwMifIMSHSX9LGkFZIKv2T2PknDJZ3hbftO0khr7VbvmKCk3tba5CLnmiVphLV2mzHmfUktJRlJy7zjfywtFgYkAAAAAKDmy83NVVZWlhzHUVZWlvLz89W1a1cFg0ElJyerefPmfoeICqqSAYlIwoAEAAAAANQuO3fu1IQJExQKhbR8+XLVq1dP1157rQKBgHr37q169er5HWKVSc9K17+X/ltpXdL0r77/KnFbTcCABAAAAACgxlq2bJkcx1FmZqZ27dqlVq1aKSUlRcFgUB07dvQ7vOOuuMK7NbUYb5UVtQQAAAAAoKp17txZTzzxhLZu3app06ape/fuevbZZ9WpUyedc845euqpp7Rr1y6/wzxuiiu8W5eK8TJDAgAAAAAQsX744QdNnDhRjuNoyZIliomJUd++fRUIBNS3b1/Vr1/f7xAhlmwAAAAAAGqxlStXynEcvfbaa8rOzlbz5s01ZMgQBQIBnXvuuXK/xBF+YEACAAAAAFDr5eXlae7cuXIcR9OmTVNubq4SExMVCASUmpqq2NhYv0Osc6ghAQAAAACo9WJiYtSnTx9NmjRJ27dv1/PPP6/GjRvrzjvvVNu2bdW3b1+9+eabOnTokN+hohTMkAAAAAAA1Apr165VRkaGMjIytHXrVjVt2lSDBw9WIBDQ+eefz5KOKsSSDQAAAABAnZefn6/3339foVBIU6ZM0aFDh3TmmWcqEAho6NChatOmjd8h1jos2QAAAAAA1HnR0dHq1auXMjMzlZ2drZdeekktWrTQvffeq/j4eF111VWaOHGicnJy/A61TmOGBAAAAOqU9Kx0/Xvpv3VWi7O0ctfKI9sTWyZqxa0rfIwMQFXbsGGDMjIy5DiONm/erCZNmmjQoEEKBALq1q0bSzqOAUs2AAAAgDLEjI1Rvs0v9jH7QM25NgZQeQUFBfroo48UCoU0efJkHTx4UKeddtqRJR3t27f3O8QahyUbAAAAQBnSuqQp2kQrsWXiUduL/g6g9oqKilLPnj3lOI6ys7M1btw4tW3bVqNGjVJCQoIuv/xyZWRk6MCBA36HWqsxQwIAAAAAAEmbNm06sqTjm2++UePGjdW/f38Fg0FdfPHFioriM/2SsGQDAAAAAIBjZK3VggUL5DiO3njjDe3fv18JCQkKBAIaNmyYTj31VL9DjDgs2QAAAAAA4BgZY3TxxRfr5ZdfVnZ2tl577TWddtppGjt2rDp06KBLL71Ur776qvbv3y/JLZYbMzZG6Vnp5fodLmZIAAAAAABQDt9//73Gjx8vx3G0bt06NWrUSDfccIMmnDZBBSpQtIlW3ui8I8VzS/q9NmKGBAAAAAAAVaRdu3a67777tHbtWi1cuFBDhw7VjBkzVLC4QCqQOud11vr1648Uz03rkiZJ//U7XMyQAAAAAACgknJycjR9+nSFQiHNnTtXBQUF6tatmwKBgAYOHKimTZv6HWK1YIYEAAAAAADV6IQTTtCgQYM0e/Zsff/993rkkUe0d+9epaWlKS4uToMHD9acOXOUn5/vd6gRhwEJAAAAAECpwosyJj2XJDPGKOm5JL/DijitW7fWXXfdpZUrV+rzzz/X8OHDNXfuXF199dWKj4/X3XffrdWrV/sdZsRgyQYAAAAAoFThRRnz7c+f9NsHas77Sb/k5uZq5syZchxHs2bNUn5+vs477zwFg0ElJyerWbNmfod4XLBkAwAAAABw3IUXZUxsmShJR36idA0aNFC/fv00ffp0bd26Vf/85z+Vm5ur9PR0xcXFacCAAZo5c6by8mrnt2+UhhkSAAAAAABUs2XLlikUCikzM1O7d+/WySefrJSUFAUCAXXs2NHv8CqMGRIAAAAAANQAnTt31pNPPqmtW7dq6tSp6tatm5555hl16tRJDz30kN/hVYsYvwMAAAAAAKCuql+/vq677jpdd9112r17tyZOnKiePXv6HVa1YEACAAAAAIAI0KJFC/3ud7/zO4xqw5INAAAAAABQ7RiQAAAAAAAA1Y4BCQAAAAAAUO0YkAAAAAAAANWOAQkAAAAAAFDtGJAAAAAAAADVjgEJAAAAAABQ7RiQAAAAAAAA1S7G7wAAAAAAAIhE6Vnp+vfSfyutS5okFXv/X33/5WeINZqx1vodQ7l17drVLlmyxO8wAAAAAAB1QMzYGOXbfEWbaEkq9n7e6Dw/Q4wYxpil1tquFTmGJRsAAAAAABQjrUuaok200rqklXgflccMCQAAAAAAcEyYIQEAAAAAAGoEBiQAAAAAAEC1Y0ACAAAAAABUOwYkAAAAAABAtWNAAgAAAAAAVDsGJAAAAAAAQLVjQAIAAAAAAFQ7BiQAAAAAAEC1Y0ACAAAAAABUuzIHJIwx7YwxHxhj1hhjVhljbve2/9UY85UxZpkxZq4xprW3vYcx5j/e9mXGmNElnPcUY8wiY8x6Y8wkY0z94/vUAAAAAABApCrPDIk8SX+01p4l6UJJ6caYsyU9Zq3taK3tLGmmpPCBh4+ttZ2929gSzvuIpCestadL+j9Jwyv/NAAAAAAAQE1S5oCEtXa7tfYL7/5+SWsktbHW7gvb7ReSbHkbNcYYSZdJmuxtciT9trzHAwAAAACAmq1CNSSMMQmSzpG0yPv9QWPM95JSdPQMiYuMMcuNMbONMb8q5lTNJe211uZ5v2+R1KaENm82xiwxxizZtWtXRcIFAAAAAAARqtwDEsaYxpLeknRH4ewIa+391tp2kjIl3ebt+oWk9tbaTpKekTS1uNMVs63YGRbW2hettV2ttV1btmxZ3nABAAAAAEAEK9eAhDGmntzBiExr7ZRidpkgqZ8kWWv3WWt/9O7PklTPGNOiyP67JTU1xsR4v7eVtK0S8QMAAAAAgBqoPN+yYSS9ImmNtfafYdtPD9vtWklrve2x3jEyxpzvtfFD+DmttVbSB5L6e5sCkqZV/mkAAAAAAICaJKbsXfRrSUMlrTDGLPO23SdpuDHmDEkFkr6TNNJ7rL+kW4wxeZJyJCV7AxAyxsySNMJau03S3ZJeN8b8TdKXcgc9AAAAAABAHWC8sYIawRizS+7gR3m0kLs0xA9+to2aj/zBsaL/Q01F/uBYlTeHyDUcK3IIx6K2Xqu1t9ZWqPBjjRqQqAhjzBJrbde61jZqPvIHx4r+DzUV+YNjVd4cItdwrMghHAuu1X5Woa/9BAAAAAAAOB4YkAAAAAAAANWuNg9IvFhH20bNR/7gWNH/oaYif3CsyptD5BqOFTmEY8G1mqfW1pAAAAAAAACRqzbPkAAAAAAAABGKAQkAAAAAAFDtqm1AwhjTzhjzgTFmjTFmlTHmdm97M2PMPGPMeu/nSd72M40xnxpjco0xfypyrtuNMSu989xRSpuvGmN2GmPWFmn7PW/bKu/xjUXaTjHGfOU9dtAYs84Y87Yxpqkxprcx5mtjzD5jTLYxZrkxpkcxbQe857TeGBMI2/6gMeZ7Y8yPZbxeXYwxK4wxG4wxTxtjjLd9gBd3gTEmYr6upTarRO4W5s9XxpiFxphOYecqzJ8Nxph7SmnzWPOn2P2MMUFjzC5jzDLvNqKyrwvKz+cc+tZrt7DtuYW5UUrbK7w+7qDXzsNhbW/0tm83xnxojGlbSttH8tcY08gYkxXW9z5cSuz0fxEiUvq/CuYP/V8EqUAOJXr7bTXGHPJ+LjTGXBy23xfezw3GmNdLybV/GGN+8m5veNv+K4dKybWdXgyHjDFfGmOivcfaG/ca8iuv/0stLqeNMbd526wxpkX1vuJ1V6T0V972OcZ9j7DKGPNCYQ4Vc3zhe5WVRbb/xfs3UNhf9TnW1wel8zl/il6rrfHyZ633/1lxba8x7rVc4bXa7WFtbzLG5Bj3veoMY0yTUto+KnfDHp9eNC+LPF7sc6xU/2etrZabpDhJ53r3fylpnaSzJT0q6R5v+z2SHvHut5J0nqQHJf0p7DyJklZKaiQpRtK7kk4voc1L7c3+4AAACo9JREFUJJ0raW2RtrdISvLa/lDSI0Xa7ibpJElXSuoraZG3z6OSNkoaLcmRtFxSd0lLJUWFtdtM0jfez5O8+yd5j13ovRY/lvF6LZZ0kSQjabakq73tZ0k6w4u7a3X9/eryrRK52y3s7321pEXe/Wgvf06VVN/Ln7OLae945E+x+0kKSnrW79e0rt18zqEzvT7vJK/tzXL7xp9Kabu1pJ5e24slfez1hRslzZJ0o9f2/5M0vjz5K7fP7untU98759UlvF70fxFyi5T+r4L5Q/8XQbcK5NCzcq/Zuklq5+2XJmmb93i0pB8kveDlwHpJFxaTay0kHZZ0jtxryVwvJ4rm0KeStpeQa33D9tst6SHv9zclBbz7V0jaX1xOe20nSNokqYXff4O6couU/sp7rIn300h6S1JyCTEXvldZWWT7XxT2/odbrc+fotdqG8LaXiEpuZi2z/Ry52pJn3vxJnptfyXpcq/t+yX9tby56z1+g6QJRfMy7PESn2Nl+r9qmyFhrd1urf3Cu79f0hpJbSRdJ/fNvbyfv/X22Wmt/VzufyrhzpL0mbX2oLU2T9JHkq4voc35kvZIyivS9lJJsV7bGZLaFml7obX2/6y1cyUt9B7/TFInuQlysqR5kl6XdLGkvZLCP627StI8a+0ea+3/efv29s79mbV2e2mvlTEmTm5H9ql1/7IZYbGtsdZ+XdrxOL4qkbsLvb+75OZN4SfI50vaYK39xlr7k9z8ua6YJo8pfyqyH6qHzzm0VlKWpN5e219KaiB3QLektrdZaz/w2m4j6Qu5g68bJLWXNNdru7AfLantI/nr9dkfeG385J2zuNkV9H8RJFL6v/Lmj/c4/V8EqUAO9bLWfuHl0PfefjsltfQeP1/uRXZPLwdelTtwKh2da7dKyrbWfmmt3SlpmaTbi8mhvZLWlZBrWYXhS9on96Jdct8gvOfdPyDphOJy2mt70zG/eKiQSOmvvHPv8/aJkfuGrdhvEQh7rwKfRdi12qqwtrfLzZ+iba/14v1M7gdJayRdJvdaLUHS+17bv5TUr5S2j8pdY0xjSX+Q9LdSXq4Sn2Nl+j9fakgYYxLkjp4sknRy4YWD97NVGYevlHSJMaa5MaaRpD5yR9Ir1bbcwYzZpbQ9XO4ndDfKnWnxvdxRoOvkjtqfKalLkRjaePsV2uJtK6823jGVPR5VpBK5W5g/Uvnz4ljzpyz9vOllk40x5f63g+PDzxwq0rYpZ9vvSbrGO19h/9fPO+fFkn5pjGleVtvhJzXGNPXO+Z7+G/1fhIqU/q+M/CkL/Z+PyptDYfudLanAe7yN3E/kCvcLz43wXOsgaUdYs9+ryHWil0PnS1oStvmoXDPGvCNpl9feY97mwv5Pcj9BjA7r/+irIkgk9FdeDu2UO5NmciWexm1ef/Vq4VR9VI8IuVa7W9LpcmdpTS6j7Y+9Y/7PO+dKSdd65+yu/36vXFqMf5X0uKSDxbRVnuMrrNoHJLxRl7ck3RE2elhu1to1cpdPzJM0R+5/DnmVbLuBd2xmCfv3lPtHzvb2W+A99KrcF36s3AvyhUViMMWFXp4Yj9PxqAIVzd2w/Lm7cFMxuxX3d63Kv/8MSQnW2o5ylzs5ZeyP48jnHKpXybZbS3pa7oW5JP1J0qVy/8NqLWmrKtD/GWNiJE2U9LS19ptyxk7/57NI6f/KkT+lof/zUXlzKGy/lyWlSjpU+FAxu9ticq24a9vicmiB3Iv3YvfTz0t2N0o6xdv2J0mXGmO+lDtYclBH93/0VREgUvora+1VcpcBNJD7yXVFPC93cK2z3E/IH6/g8aikCLpWu0LuDK0S88dre4TcD8jv0M/95Y2S0uW+V60nd5luWW1bY0xnSadZa98urr2yji/jmBJV64CEMabwRc601k7xNu/wpugWTtXdWdZ5rLWvWGvPtdZeInea03qvEElh4ZeRZbUdVrzjj9Za67Ud5R0/yxjTUe5/huPkrsFJkTsI0c5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      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fx = np.array([days1[0], days1[-1]])\n",
    "start_dates_array = start_of_year.index.values\n",
    "ft = np.array([start_dates_array[0], start_dates_array[-1]])\n",
    "\n",
    "fig= plt.figure( figsize=(18,8) )\n",
    "\n",
    "fff = fit[0]*fx + fit[1]\n",
    "\n",
    "plt.plot(ft, fff, 'k-')\n",
    "plt.plot(start_dates_array, start_of_year['Mass (kg)'], 'go', ms=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x442f3d4a8>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fx2 = np.array([days2[0], days2[-1]])\n",
    "april_dates_array = dry_april.index.values\n",
    "ft2 = np.array([april_dates_array[0], april_dates_array[-1]])\n",
    "\n",
    "fig= plt.figure( figsize=(18,8) )\n",
    "ax  = fig.add_subplot(111)\n",
    "fff2 = fit2[0]*fx2 + fit2[1]\n",
    "\n",
    "plt.plot(ft2, fff2, 'k-')\n",
    "plt.plot(april_dates_array, dry_april['Mass (kg)'], 'go', ms=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig= plt.figure( figsize=(18,8) )\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.scatter(dates_array, df_mass['Mass (kg)'])\n",
    "ax.set_xlim(Timestamp('2019-01-02'), Timestamp('2020-05-01'))\n",
    "plt.plot(ft, fff, 'k-')\n",
    "plt.plot(ft2, fff2, 'r-')\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig= plt.figure( figsize=(18,8) )\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.scatter(dates_array, df_mass['Mass (kg)'])\n",
    "ax.plot(ft, fff, 'k-', label=\"m={:.2f}lb/day\".format(fit[0]))\n",
    "ax.plot(ft2, fff2, 'r-', label=\"m={:.2f}lb/day\".format(fit2[0]))\n",
    "ax.xaxis.set_major_locator(MultipleLocator(5))\n",
    "#ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "myFmt = mdates.DateFormatter('%Y-%m-%d')\n",
    "ax.xaxis.set_major_formatter(myFmt)\n",
    "ax.set_xlim(Timestamp('2019-12-26'), Timestamp('2020-05-01'))\n",
    "ax.set_ylim(215,255)\n",
    "fig.autofmt_xdate()\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Weight (lb)')\n",
    "plt.savefig('/Users/scollis/notebooks/HKwloss.png')\n",
    "\n",
    "\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.72636150e-01  2.51371626e+02]\n",
      "[ -0.62969398 234.9387201 ]\n"
     ]
    }
   ],
   "source": [
    "print(fit)\n",
    "print(fit2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = 218\n",
    "xs = wt_df.date[st::] - pd.datetime(2018,11,1)\n",
    "x = np.array(xs.dt.days)\n",
    "idx = ~np.isnan(wt_df['awt'][st::])\n",
    "\n",
    "fit = np.polyfit(x[idx], wt_df['awt'][st::][idx], 1)\n",
    "fit_fn = np.poly1d(fit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd1 = pd.datetime(2018,11,10)\n",
    "ed1 = pd.datetime(2019, 3,12)\n",
    "end_map = pd.datetime(2019, 4,13)\n",
    "\n",
    "in_range1 = np.logical_and(wt_df.date > sd1, wt_df.date < ed1)\n",
    "in_mapril = np.logical_and(wt_df.date > ed1, wt_df.date < end_map) #wt_df.date > ed1"
   ]
  }
 ],
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